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microsoft/agogosml | agogosml/agogosml/writer/output_writer.py | OutputWriter.stop_incoming_messages | python | def stop_incoming_messages(self):
self.listener.stop()
self.logger.event('output.lifecycle.stop') | Stop accepting messages. | https://github.com/microsoft/agogosml/blob/5e603394f037640b2fb7ddee60be47c569ab48c9/agogosml/agogosml/writer/output_writer.py#L31-L34 | from typing import Callable
from typing import Optional
from agogosml.common.abstract_streaming_client import AbstractStreamingClient
from agogosml.common.listener_client import ListenerClient
from agogosml.utils.logger import Logger
class OutputWriter:
def __init__(self, streaming_client: AbstractStreamingClient,
listener: ListenerClient):
self.messaging_client = streaming_client
self.listener = listener
self.logger = Logger()
def on_message_received(self, message: str) -> bool:
success = self.messaging_client.send(message)
self.logger.event('output.message.received', {'success': str(success)})
return success
def start_incoming_messages(self, callback: Optional[Callable[[str], bool]] = None):
self.logger.event('output.lifecycle.start')
self.listener.start(callback or self.on_message_received) | MIT License |
google/init2winit | init2winit/init_lib/sparse_init.py | sparse_init | python | def sparse_init(loss_fn,
model,
hps,
input_shape,
output_shape,
rng_key,
metrics_logger=None,
log_every=10):
del loss_fn, input_shape, output_shape, rng_key, metrics_logger, log_every
activation_functions = hps.activation_function
num_hidden_layers = len(hps.hid_sizes)
if isinstance(hps.activation_function, str):
activation_functions = [hps.activation_function] * num_hidden_layers
for i, key in enumerate(model.params):
num_units, num_weights = model.params[key]['kernel'].shape
mask = np.zeros((num_units, num_weights), dtype=bool)
for k in range(num_units):
if num_weights >= hps.non_zero_connection_weights:
sample = np.random.choice(
num_weights, hps.non_zero_connection_weights, replace=False)
else:
sample = np.random.choice(num_weights, hps.non_zero_connection_weights)
mask[k, sample] = True
model.params[key]['kernel'] = model.params[key]['kernel'].at[~mask].set(0.0)
if i < num_hidden_layers and activation_functions[i] == 'tanh':
model.params[key]['bias'] = model.params[key]['bias'].at[:].set(0.5)
else:
model.params[key]['bias'] = model.params[key]['bias'].at[:].set(0.0)
return model | Implements SparseInit initializer.
Args:
loss_fn: Loss function.
model: Flax Model class.
hps: HParam object. Required hparams are meta_learning_rate,
meta_batch_size, meta_steps, and epsilon.
input_shape: Must agree with batch[0].shape[1:].
output_shape: Must agree with batch[1].shape[1:].
rng_key: jax.PRNGKey, used to seed all randomness.
metrics_logger: Instance of utils.MetricsLogger
log_every: Print meta loss every k steps.
Returns:
A Flax model with sparse initialization. | https://github.com/google/init2winit/blob/d54661d82576204bfcc306fae8606b8e7c3838b6/init2winit/init_lib/sparse_init.py#L29-L75 | from ml_collections.config_dict import config_dict
import numpy as np
DEFAULT_HPARAMS = config_dict.ConfigDict(dict(non_zero_connection_weights=15,)) | Apache License 2.0 |
thomasgermain/pymultimatic | pymultimatic/systemmanager.py | SystemManager.set_ventilation_operating_mode | python | async def set_ventilation_operating_mode(
self, ventilation_id: str, mode: OperatingMode
) -> None:
await self._call_api(
urls.set_ventilation_operating_mode,
params={"id": ventilation_id},
payload=payloads.ventilation_operating_mode(mode.name),
) | Set ventilation at night level.
Compatible modes are listed here
:class:`~pymultimatic.model.Ventilation.MODES`
Args:
ventilation_id (str): id of the ventilation
mode (OperatingMode): Mode to set | https://github.com/thomasgermain/pymultimatic/blob/9a05d0f1e341bb59f72cd6294aa1e22651803b3f/pymultimatic/systemmanager.py#L749-L764 | import asyncio
import logging
from datetime import date, timedelta
from typing import Any, Callable, List, Optional, Tuple, Type
from aiohttp import ClientSession
from schema import Schema, SchemaError
from .api import ApiError, Connector, WrongResponseError, defaults, payloads, schemas, urls
from .model import (
Circulation,
Dhw,
FacilityDetail,
HolidayMode,
HotWater,
HvacStatus,
OperatingMode,
OperatingModes,
QuickMode,
QuickVeto,
Report,
Room,
System,
Ventilation,
Zone,
ZoneCooling,
ZoneHeating,
constants,
mapper,
EmfReport,
)
_LOGGER = logging.getLogger("SystemManager")
def ignore_http_409(return_value: Any = None) -> Callable[..., Any]:
def decorator(func: Callable[..., Any]) -> Any:
async def wrapper(*args: Any, **kwargs: Any) -> Any:
try:
return await func(*args, **kwargs)
except ApiError as ex:
if ex.status != 409:
raise
return return_value
return wrapper
return decorator
def retry_async(
num_tries: int = 5,
on_exceptions: Tuple[Type[BaseException], ...] = (Exception,),
on_status_codes: Tuple[int, ...] = (),
backoff_base: float = 0.5,
) -> Callable[..., Any]:
on_exceptions = on_exceptions + (ApiError,)
def decorator(func: Callable[..., Any]) -> Any:
async def wrapper(*args: Any, **kwargs: Any) -> Any:
_num_tries = num_tries
last_response: Optional[str] = None
while _num_tries > 0:
_num_tries -= 1
try:
return await func(*args, **kwargs)
except on_exceptions as ex:
if not _num_tries:
if isinstance(ex, ApiError):
raise ex
raise ApiError(
"Cannot get correct response",
response=last_response,
status=200,
) from ex
if isinstance(ex, ApiError):
last_response = ex.response
if ex.status not in on_status_codes:
raise
retry_in = backoff_base * (num_tries - _num_tries)
_LOGGER.debug("Error occurred, retrying in %s", retry_in, exc_info=True)
await asyncio.sleep(retry_in)
return wrapper
return decorator
class SystemManager:
def __init__(
self,
user: str,
password: str,
session: ClientSession,
smartphone_id: str = defaults.SMARTPHONE_ID,
serial: Optional[str] = None,
):
self._connector: Connector = Connector(user, password, session, smartphone_id)
self._serial = serial
self._fixed_serial = self._serial is not None
self._ensure_ready_lock = asyncio.Lock()
async def login(self, force_login: bool = False) -> bool:
return await self._connector.login(force_login)
async def logout(self) -> None:
if not self._fixed_serial:
self._serial = None
await self._connector.logout()
async def get_system(self) -> System:
(facilities, full_system, live_report, hvac_state, gateway_json,) = await asyncio.gather(
self._call_api(urls.facilities_list, schema=schemas.FACILITIES),
self._call_api(urls.system, schema=schemas.SYSTEM),
self._call_api(urls.live_report, schema=schemas.LIVE_REPORTS),
self._call_api(urls.hvac, schema=schemas.HVAC),
self._call_api(urls.gateway_type, schema=schemas.GATEWAY),
)
hvac_status = mapper.map_hvac_status(hvac_state)
holiday = mapper.map_holiday_mode_from_system(full_system)
zones = mapper.map_zones_from_system(full_system)
outdoor_temp = mapper.map_outdoor_temp(full_system)
quick_mode = mapper.map_quick_mode_from_system(full_system)
ventilation = mapper.map_ventilation_from_system(full_system)
dhw = mapper.map_dhw_from_system(full_system, live_report)
reports = mapper.map_reports(live_report)
facility_detail = mapper.map_facility_detail(facilities, self._serial)
gateway = mapper.map_gateway(gateway_json)
rooms: List[Room] = []
if [z for z in zones if z.rbr]:
rooms_raw = await self._call_api(urls.rooms, schema=schemas.ROOM_LIST)
rooms = mapper.map_rooms(rooms_raw)
return System(
holiday=holiday,
quick_mode=quick_mode,
zones=zones,
rooms=rooms,
dhw=dhw,
reports=reports,
outdoor_temperature=outdoor_temp,
hvac_status=hvac_status,
facility_detail=facility_detail,
ventilation=ventilation,
gateway=gateway,
)
@ignore_http_409(return_value=[])
async def get_emf_devices(self) -> List[EmfReport]:
return mapper.map_emf_reports(await self._call_api(urls.emf_devices))
async def get_gateway(self) -> str:
return mapper.map_gateway(await self._call_api(urls.gateway_type))
@ignore_http_409()
async def get_outdoor_temperature(self) -> Optional[float]:
return mapper.map_outdoor_temp(await self._call_api(urls.system_status))
@ignore_http_409()
async def get_hvac_status(self) -> HvacStatus:
return mapper.map_hvac_status(await self._call_api(urls.hvac))
async def get_facility_detail(self, serial: Optional[str] = None) -> FacilityDetail:
serial = serial if serial is not None else self._serial
return mapper.map_facility_detail(
await self._call_api(urls.facilities_list, schema=schemas.FACILITIES), serial
)
@ignore_http_409()
async def get_live_reports(self) -> List[Report]:
return mapper.map_reports(
await self._call_api(urls.live_report, schema=schemas.LIVE_REPORTS)
)
@ignore_http_409()
async def get_live_report(self, report_id: str, device_id: str) -> Optional[Report]:
json = await self._call_api(
urls.live_report_device,
params={"device_id": device_id, "report_id": report_id},
schema=schemas.LIVE_REPORT,
)
return mapper.map_report(json)
@ignore_http_409()
async def get_ventilation(self) -> Optional[Ventilation]:
return mapper.map_ventilation(
await self._call_api(urls.system_ventilation, schema=schemas.VENTILATION_LIST)
)
@ignore_http_409()
async def get_holiday_mode(self) -> HolidayMode:
raw = await self._call_api(urls.system_holiday_mode)
return mapper.map_holiday_mode(raw)
@ignore_http_409()
async def get_quick_mode(self) -> Optional[QuickMode]:
return mapper.map_quick_mode(await self._call_api(urls.system_quickmode))
@ignore_http_409()
async def get_hot_water(self, dhw_id: str) -> Optional[HotWater]:
dhw = await self._call_api(urls.hot_water, params={"id": dhw_id}, schema=schemas.FUNCTION)
return mapper.map_hot_water(dhw, dhw_id)
@ignore_http_409()
async def get_dhw(self) -> Optional[Dhw]:
dhw = await self._call_api(urls.dhws, schema=schemas.DHWS)
return mapper.map_dhw(dhw)
@ignore_http_409(return_value=[])
async def get_rooms(self) -> Optional[List[Room]]:
rooms = await self._call_api(urls.rooms, schema=schemas.ROOM_LIST)
return mapper.map_rooms(rooms)
@ignore_http_409()
async def get_room(self, room_id: str) -> Optional[Room]:
new_room = await self._call_api(urls.room, params={"id": room_id}, schema=schemas.ROOM)
return mapper.map_room(new_room)
@ignore_http_409(return_value=[])
async def get_zones(self) -> Optional[List[Zone]]:
rooms = await self._call_api(urls.zones, schema=schemas.ZONE_LIST)
return mapper.map_zones(rooms)
@ignore_http_409()
async def get_zone(self, zone_id: str) -> Optional[Zone]:
new_zone = await self._call_api(urls.zone, params={"id": zone_id}, schema=schemas.ZONE)
return mapper.map_zone(new_zone)
@ignore_http_409()
async def get_circulation(self, dhw_id: str) -> Optional[Circulation]:
new_circulation = await self._call_api(
urls.circulation, params={"id": dhw_id}, schema=schemas.FUNCTION
)
return mapper.map_circulation_alone(new_circulation, dhw_id)
async def set_quick_mode(self, quick_mode: QuickMode) -> None:
await self._call_api(
urls.system_quickmode, payload=payloads.quickmode(quick_mode.name, quick_mode.duration)
)
@ignore_http_409(return_value=False)
async def remove_quick_mode(self) -> bool:
await self._call_api(urls.system_quickmode, "delete")
return True
async def set_holiday_mode(self, start_date: date, end_date: date, temperature: float) -> None:
payload = payloads.holiday_mode(True, start_date, end_date, self._round(temperature))
await self._call_api(urls.system_holiday_mode, payload=payload)
async def remove_holiday_mode(self) -> None:
payload = payloads.holiday_mode(
False,
date.today() - timedelta(days=2),
date.today() - timedelta(days=1),
constants.FROST_PROTECTION_TEMP,
)
await self._call_api(urls.system_holiday_mode, payload=payload)
async def set_hot_water_setpoint_temperature(self, dhw_id: str, temperature: float) -> None:
_LOGGER.debug("Will set dhw target temperature to %s", temperature)
payload = payloads.hotwater_temperature_setpoint(self._round(temperature))
await self._call_api(
urls.hot_water_temperature_setpoint, params={"id": dhw_id}, payload=payload
)
async def set_hot_water_operating_mode(self, dhw_id: str, new_mode: OperatingMode) -> None:
_LOGGER.debug("Will try to set hot water mode to %s", new_mode)
if new_mode in HotWater.MODES:
_LOGGER.debug("New mode is %s", new_mode)
await self._call_api(
urls.hot_water_operating_mode,
params={"id": dhw_id},
payload=payloads.hot_water_operating_mode(new_mode.name),
)
else:
_LOGGER.debug("New mode is not available for hot water %s", new_mode)
async def set_room_operating_mode(self, room_id: str, new_mode: OperatingMode) -> None:
if new_mode in Room.MODES and new_mode != OperatingModes.QUICK_VETO:
_LOGGER.debug("New mode is %s", new_mode)
await self._call_api(
urls.room_operating_mode,
params={"id": room_id},
payload=payloads.room_operating_mode(new_mode.name),
)
else:
_LOGGER.debug("mode is not available for room %s", new_mode)
async def set_room_quick_veto(self, room_id: str, quick_veto: QuickVeto) -> None:
payload = payloads.room_quick_veto(self._round(quick_veto.target), quick_veto.duration)
await self._call_api(urls.room_quick_veto, params={"id": room_id}, payload=payload)
async def remove_room_quick_veto(self, room_id: str) -> None:
await self._call_api(urls.room_quick_veto, "delete", params={"id": room_id})
async def set_room_setpoint_temperature(self, room_id: str, temperature: float) -> None:
_LOGGER.debug("Will try to set room target temperature to %s", temperature)
await self._call_api(
urls.room_temperature_setpoint,
params={"id": room_id},
payload=payloads.room_temperature_setpoint(self._round(temperature)),
)
async def set_zone_quick_veto(self, zone_id: str, quick_veto: QuickVeto) -> None:
payload = payloads.zone_quick_veto(self._round(quick_veto.target))
await self._call_api(urls.zone_quick_veto, params={"id": zone_id}, payload=payload)
async def set_zone_heating_operating_mode(self, zone_id: str, new_mode: OperatingMode) -> None:
if new_mode in ZoneHeating.MODES and new_mode != OperatingModes.QUICK_VETO:
_LOGGER.debug("New mode is %s", new_mode)
await self._call_api(
urls.zone_heating_mode,
params={"id": zone_id},
payload=payloads.zone_operating_mode(new_mode.name),
)
else:
_LOGGER.debug("mode is not available for zone %s", new_mode)
async def set_zone_cooling_operating_mode(self, zone_id: str, new_mode: OperatingMode) -> None:
if new_mode in ZoneCooling.MODES and new_mode != OperatingModes.QUICK_VETO:
_LOGGER.debug("New mode is %s", new_mode)
await self._call_api(
urls.zone_cooling_mode,
params={"id": zone_id},
payload=payloads.zone_operating_mode(new_mode.name),
)
else:
_LOGGER.debug("mode is not available for zone %s", new_mode)
async def remove_zone_quick_veto(self, zone_id: str) -> None:
await self._call_api(urls.zone_quick_veto, "delete", params={"id": zone_id})
async def set_zone_heating_setpoint_temperature(self, zone_id: str, temperature: float) -> None:
_LOGGER.debug("Will try to set zone target temperature to %s", temperature)
payload = payloads.zone_temperature_setpoint(self._round(temperature))
await self._call_api(
urls.zone_heating_setpoint_temperature,
params={"id": zone_id},
payload=payload,
)
async def set_zone_cooling_setpoint_temperature(self, zone_id: str, temperature: float) -> None:
_LOGGER.debug("Will try to set zone target temperature to %s", temperature)
payload = payloads.zone_temperature_setpoint(self._round(temperature))
await self._call_api(
urls.zone_cooling_setpoint_temperature,
params={"id": zone_id},
payload=payload,
)
async def set_zone_heating_setback_temperature(self, zone_id: str, temperature: float) -> None:
_LOGGER.debug("Will try to set zone setback temperature to %s", temperature)
await self._call_api(
urls.zone_heating_setback_temperature,
params={"id": zone_id},
payload=payloads.zone_temperature_setback(self._round(temperature)),
) | MIT License |
truckersmp-cli/truckersmp-cli | truckersmp_cli/gamestarter.py | StarterProton.setup_game_env | python | def setup_game_env(env, steamdir):
if not Args.disable_proton_overlay:
overlayrenderer = os.path.join(steamdir, File.overlayrenderer_inner)
if "LD_PRELOAD" in env:
env["LD_PRELOAD"] += ":" + overlayrenderer
else:
env["LD_PRELOAD"] = overlayrenderer
env.update(
SteamAppId=Args.steamid,
SteamGameId=Args.steamid,
PROTON_USE_WINED3D="1" if Args.use_wined3d else "0",
) | Set up environment variables for running the game with Proton.
env: A dict of environment variables
steamdir: Path to Steam installation | https://github.com/truckersmp-cli/truckersmp-cli/blob/98b0828ca2edca4b5d6cd7788bfb621e00a53882/truckersmp_cli/gamestarter.py#L280-L298 | import logging
import os
import shutil
import subprocess as subproc
import sys
import tempfile
import time
from .utils import (
activate_native_d3dcompiler_47, find_discord_ipc_sockets,
get_proton_version, get_steam_library_dirs, is_d3dcompiler_setup_skippable,
log_info_formatted_envars_and_args, print_child_output,
set_wine_desktop_registry, setup_wine_discord_ipc_bridge, wait_for_steam,
)
from .variables import Args, Dir, File
class GameStarterInterface:
def run(self):
raise NotImplementedError
@property
def runner_name(self):
raise NotImplementedError
class StarterProton(GameStarterInterface):
def __init__(self, cfg):
self._cfg = cfg
self._steamruntime_usr_tempdir = None
self._discord_sockets = find_discord_ipc_sockets()
major, minor = get_proton_version(Args.protondir)
logging.info("Proton version is (major=%d, minor=%d)", major, minor)
self._use_steam_runtime = (
not Args.without_steam_runtime
and (major >= 6 or (major == 5 and minor >= 13)))
logging.info(
"Using Steam Runtime container" if self._use_steam_runtime
else "Not using Steam Runtime container")
def _cleanup(self):
if self._steamruntime_usr_tempdir is not None:
with self._steamruntime_usr_tempdir:
pass
def _determine_steamruntime_shared_paths(self, steamdir):
if not self._use_steam_runtime:
return []
shared_paths = [Args.gamedir, Args.protondir, Args.prefixdir]
if Args.singleplayer:
shared_paths += get_steam_library_dirs(steamdir)
else:
shared_paths += Args.moddir, Dir.truckersmp_cli_data
if Dir.scriptdir.startswith("/usr/"):
logging.info("System-wide installation detected: %s", Dir.scriptdir)
self._steamruntime_usr_tempdir = tempfile.TemporaryDirectory(
prefix="truckersmp-cli-steamruntime-sharing-workaround-")
logging.debug(
"Copying Steam Runtime helper to %s",
self._steamruntime_usr_tempdir.name)
shutil.copy(
File.steamruntime_helper, self._steamruntime_usr_tempdir.name)
if not Args.singleplayer:
logging.debug(
"Copying inject program to %s", self._steamruntime_usr_tempdir.name)
shutil.copy(File.inject_exe, self._steamruntime_usr_tempdir.name)
shared_paths.append(self._steamruntime_usr_tempdir.name)
else:
shared_paths.append(Dir.scriptdir)
if len(self._discord_sockets) > 0:
shared_paths += self._discord_sockets
logging.debug("Shared paths: %s", shared_paths)
return shared_paths
def _init_args(self, args, steamdir):
if self._use_steam_runtime:
python = "python3"
args["steamrt"].append(os.path.join(Args.steamruntimedir, "run"))
shared_paths = self._determine_steamruntime_shared_paths(steamdir)
for shared_path in shared_paths:
args["steamrt"] += "--filesystem", shared_path
args["steamrt"].append("--")
else:
python = sys.executable
args["wine"] = args["steamrt"].copy()
args["steamrt"].append(python)
args["proton"].append(python)
args["proton"] += os.path.join(Args.protondir, "proton"), "run"
def _setup_helper_args(self, args):
args.append(
File.steamruntime_helper if self._steamruntime_usr_tempdir is None
else os.path.join(
self._steamruntime_usr_tempdir.name,
os.path.basename(File.steamruntime_helper)))
if (not Args.without_wine_discord_ipc_bridge
and len(self._discord_sockets) > 0):
args += "--early-executable", File.ipcbridge, "--early-wait-before-start", "5"
for executable in self._cfg.thirdparty_executables:
args += "--executable", executable
args += "--wait-before-start", str(self._cfg.thirdparty_wait)
if Args.verbose:
args.append("-v" if Args.verbose == 1 else "-vv")
return args
def _setup_proton_args(self, proton_args):
if Args.singleplayer:
exename = "eurotrucks2.exe" if Args.ets2 else "amtrucks.exe"
gamepath = os.path.join(Args.gamedir, "bin/win_x64", exename)
proton_args.append(gamepath)
else:
proton_args.append(
File.inject_exe if self._steamruntime_usr_tempdir is None
else os.path.join(
self._steamruntime_usr_tempdir.name,
os.path.basename(File.inject_exe))
)
proton_args += Args.gamedir, Args.moddir
for opt in f"-rdevice {Args.rendering_backend} {Args.game_options}".split(" "):
if opt != "":
proton_args.append(opt)
@staticmethod
def determine_env_print(env):
env_print = ["SteamAppId", "SteamGameId"]
if "LD_PRELOAD" in env:
env_print.append("LD_PRELOAD")
env_print += [
"PROTON_USE_WINED3D",
"STEAM_COMPAT_CLIENT_INSTALL_PATH",
"STEAM_COMPAT_DATA_PATH",
]
return env_print
def run(self):
args = dict(wine=[], proton=[], steamrt=[])
prefix = os.path.join(Args.prefixdir, "pfx")
env = os.environ.copy()
steamdir = wait_for_steam(use_proton=True, loginvdf_paths=File.loginusers_paths)
logging.info("Steam installation directory: %s", steamdir)
logging.debug("Creating directory %s if it doesn't exist", Args.prefixdir)
os.makedirs(Args.prefixdir, exist_ok=True)
env.update(
STEAM_COMPAT_DATA_PATH=Args.prefixdir,
STEAM_COMPAT_CLIENT_INSTALL_PATH=steamdir,
)
self._init_args(args, steamdir)
do_d3dcompiler_setup = (Args.activate_native_d3dcompiler_47
or (not Args.singleplayer
and Args.rendering_backend == "dx11"
and not is_d3dcompiler_setup_skippable()))
logging.debug("Whether to setup native d3dcompiler_47: %s", do_d3dcompiler_setup)
wine_command = os.path.join(
Args.protondir,
"files" if os.access(
os.path.join(Args.protondir, "files/bin/wine"),
os.R_OK | os.X_OK,
) else "dist",
"bin/wine")
args["wine"].append(wine_command)
if (not os.access(wine_command, os.R_OK)
or do_d3dcompiler_setup):
try:
subproc.check_output(
args["proton"] + ["wineboot", ], env=env, stderr=subproc.STDOUT)
except OSError as ex:
sys.exit(f"Failed to run wineboot: {ex}")
except subproc.CalledProcessError as ex:
sys.exit(f"wineboot failed:\n{ex.output.decode('utf-8')}")
if do_d3dcompiler_setup:
activate_native_d3dcompiler_47(prefix, args["wine"])
if Args.wine_desktop:
set_wine_desktop_registry(prefix, args["wine"], True)
StarterProton.setup_game_env(env, steamdir)
self._setup_proton_args(args["proton"])
log_info_formatted_envars_and_args(
runner="Steam Runtime helper",
env_print=StarterProton.determine_env_print(env),
env=env,
args=args["proton"])
try:
with subproc.Popen(
self._setup_helper_args(args["steamrt"])
+ ["--", ] + args["proton"],
env=env, stdout=subproc.PIPE, stderr=subproc.STDOUT) as proc:
if Args.verbose:
print_child_output(proc)
except subproc.CalledProcessError as ex:
logging.error(
"Steam Runtime helper exited abnormally:\n%s", ex.output.decode("utf-8"))
if Args.wine_desktop:
set_wine_desktop_registry(prefix, args["wine"], False)
self._cleanup()
@staticmethod | MIT License |
databand-ai/dbnd | modules/dbnd/src/dbnd/_vendor/cloudpickle/cloudpickle_fast.py | CloudPickler._dynamic_function_reduce | python | def _dynamic_function_reduce(self, func):
newargs = self._function_getnewargs(func)
state = _function_getstate(func)
return (types.FunctionType, newargs, state, None, None,
_function_setstate) | Reduce a function that is not pickleable via attribute lookup. | https://github.com/databand-ai/dbnd/blob/ec0076f9a142b20e2f7afd886ed1a18683c553ec/modules/dbnd/src/dbnd/_vendor/cloudpickle/cloudpickle_fast.py#L504-L509 | import _collections_abc
import abc
import copyreg
import io
import itertools
import logging
import sys
import struct
import types
import weakref
import typing
from enum import Enum
from collections import ChainMap
from .compat import pickle, Pickler
from .cloudpickle import (
_extract_code_globals, _BUILTIN_TYPE_NAMES, DEFAULT_PROTOCOL,
_find_imported_submodules, _get_cell_contents, _is_importable,
_builtin_type, _get_or_create_tracker_id, _make_skeleton_class,
_make_skeleton_enum, _extract_class_dict, dynamic_subimport, subimport,
_typevar_reduce, _get_bases, _make_cell, _make_empty_cell, CellType,
_is_parametrized_type_hint, PYPY, cell_set,
parametrized_type_hint_getinitargs, _create_parametrized_type_hint,
builtin_code_type,
_make_dict_keys, _make_dict_values, _make_dict_items,
)
if pickle.HIGHEST_PROTOCOL >= 5 and not PYPY:
def dump(obj, file, protocol=None, buffer_callback=None):
CloudPickler(
file, protocol=protocol, buffer_callback=buffer_callback
).dump(obj)
def dumps(obj, protocol=None, buffer_callback=None):
with io.BytesIO() as file:
cp = CloudPickler(
file, protocol=protocol, buffer_callback=buffer_callback
)
cp.dump(obj)
return file.getvalue()
else:
def dump(obj, file, protocol=None):
CloudPickler(file, protocol=protocol).dump(obj)
def dumps(obj, protocol=None):
with io.BytesIO() as file:
cp = CloudPickler(file, protocol=protocol)
cp.dump(obj)
return file.getvalue()
load, loads = pickle.load, pickle.loads
def _class_getnewargs(obj):
type_kwargs = {}
if "__slots__" in obj.__dict__:
type_kwargs["__slots__"] = obj.__slots__
__dict__ = obj.__dict__.get('__dict__', None)
if isinstance(__dict__, property):
type_kwargs['__dict__'] = __dict__
return (type(obj), obj.__name__, _get_bases(obj), type_kwargs,
_get_or_create_tracker_id(obj), None)
def _enum_getnewargs(obj):
members = dict((e.name, e.value) for e in obj)
return (obj.__bases__, obj.__name__, obj.__qualname__, members,
obj.__module__, _get_or_create_tracker_id(obj), None)
def _file_reconstructor(retval):
return retval
def _function_getstate(func):
slotstate = {
"__name__": func.__name__,
"__qualname__": func.__qualname__,
"__annotations__": func.__annotations__,
"__kwdefaults__": func.__kwdefaults__,
"__defaults__": func.__defaults__,
"__module__": func.__module__,
"__doc__": func.__doc__,
"__closure__": func.__closure__,
}
f_globals_ref = _extract_code_globals(func.__code__)
f_globals = {k: func.__globals__[k] for k in f_globals_ref if k in
func.__globals__}
closure_values = (
list(map(_get_cell_contents, func.__closure__))
if func.__closure__ is not None else ()
)
slotstate["_cloudpickle_submodules"] = _find_imported_submodules(
func.__code__, itertools.chain(f_globals.values(), closure_values))
slotstate["__globals__"] = f_globals
state = func.__dict__
return state, slotstate
def _class_getstate(obj):
clsdict = _extract_class_dict(obj)
clsdict.pop('__weakref__', None)
if issubclass(type(obj), abc.ABCMeta):
clsdict.pop('_abc_cache', None)
clsdict.pop('_abc_negative_cache', None)
clsdict.pop('_abc_negative_cache_version', None)
registry = clsdict.pop('_abc_registry', None)
if registry is None:
clsdict.pop('_abc_impl', None)
(registry, _, _, _) = abc._get_dump(obj)
clsdict["_abc_impl"] = [subclass_weakref()
for subclass_weakref in registry]
else:
clsdict["_abc_impl"] = [type_ for type_ in registry]
if "__slots__" in clsdict:
if isinstance(obj.__slots__, str):
clsdict.pop(obj.__slots__)
else:
for k in obj.__slots__:
clsdict.pop(k, None)
clsdict.pop('__dict__', None)
return (clsdict, {})
def _enum_getstate(obj):
clsdict, slotstate = _class_getstate(obj)
members = dict((e.name, e.value) for e in obj)
for attrname in ["_generate_next_value_", "_member_names_",
"_member_map_", "_member_type_",
"_value2member_map_"]:
clsdict.pop(attrname, None)
for member in members:
clsdict.pop(member)
return clsdict, slotstate
def _code_reduce(obj):
if hasattr(obj, "co_posonlyargcount"):
args = (
obj.co_argcount, obj.co_posonlyargcount,
obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,
obj.co_flags, obj.co_code, obj.co_consts, obj.co_names,
obj.co_varnames, obj.co_filename, obj.co_name,
obj.co_firstlineno, obj.co_lnotab, obj.co_freevars,
obj.co_cellvars
)
else:
args = (
obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals,
obj.co_stacksize, obj.co_flags, obj.co_code, obj.co_consts,
obj.co_names, obj.co_varnames, obj.co_filename,
obj.co_name, obj.co_firstlineno, obj.co_lnotab,
obj.co_freevars, obj.co_cellvars
)
return types.CodeType, args
def _cell_reduce(obj):
try:
obj.cell_contents
except ValueError:
return _make_empty_cell, ()
else:
return _make_cell, (obj.cell_contents, )
def _classmethod_reduce(obj):
orig_func = obj.__func__
return type(obj), (orig_func,)
def _file_reduce(obj):
import io
if not hasattr(obj, "name") or not hasattr(obj, "mode"):
raise pickle.PicklingError(
"Cannot pickle files that do not map to an actual file"
)
if obj is sys.stdout:
return getattr, (sys, "stdout")
if obj is sys.stderr:
return getattr, (sys, "stderr")
if obj is sys.stdin:
raise pickle.PicklingError("Cannot pickle standard input")
if obj.closed:
raise pickle.PicklingError("Cannot pickle closed files")
if hasattr(obj, "isatty") and obj.isatty():
raise pickle.PicklingError(
"Cannot pickle files that map to tty objects"
)
if "r" not in obj.mode and "+" not in obj.mode:
raise pickle.PicklingError(
"Cannot pickle files that are not opened for reading: %s"
% obj.mode
)
name = obj.name
retval = io.StringIO()
try:
curloc = obj.tell()
obj.seek(0)
contents = obj.read()
obj.seek(curloc)
except IOError as e:
raise pickle.PicklingError(
"Cannot pickle file %s as it cannot be read" % name
) from e
retval.write(contents)
retval.seek(curloc)
retval.name = name
return _file_reconstructor, (retval,)
def _getset_descriptor_reduce(obj):
return getattr, (obj.__objclass__, obj.__name__)
def _mappingproxy_reduce(obj):
return types.MappingProxyType, (dict(obj),)
def _memoryview_reduce(obj):
return bytes, (obj.tobytes(),)
def _module_reduce(obj):
if _is_importable(obj):
return subimport, (obj.__name__,)
else:
obj.__dict__.pop('__builtins__', None)
return dynamic_subimport, (obj.__name__, vars(obj))
def _method_reduce(obj):
return (types.MethodType, (obj.__func__, obj.__self__))
def _logger_reduce(obj):
return logging.getLogger, (obj.name,)
def _root_logger_reduce(obj):
return logging.getLogger, ()
def _property_reduce(obj):
return property, (obj.fget, obj.fset, obj.fdel, obj.__doc__)
def _weakset_reduce(obj):
return weakref.WeakSet, (list(obj),)
def _dynamic_class_reduce(obj):
if Enum is not None and issubclass(obj, Enum):
return (
_make_skeleton_enum, _enum_getnewargs(obj), _enum_getstate(obj),
None, None, _class_setstate
)
else:
return (
_make_skeleton_class, _class_getnewargs(obj), _class_getstate(obj),
None, None, _class_setstate
)
def _class_reduce(obj):
if obj is type(None):
return type, (None,)
elif obj is type(Ellipsis):
return type, (Ellipsis,)
elif obj is type(NotImplemented):
return type, (NotImplemented,)
elif obj in _BUILTIN_TYPE_NAMES:
return _builtin_type, (_BUILTIN_TYPE_NAMES[obj],)
elif not _is_importable(obj):
return _dynamic_class_reduce(obj)
return NotImplemented
def _dict_keys_reduce(obj):
return _make_dict_keys, (list(obj), )
def _dict_values_reduce(obj):
return _make_dict_values, (list(obj), )
def _dict_items_reduce(obj):
return _make_dict_items, (dict(obj), )
def _function_setstate(obj, state):
state, slotstate = state
obj.__dict__.update(state)
obj_globals = slotstate.pop("__globals__")
obj_closure = slotstate.pop("__closure__")
slotstate.pop("_cloudpickle_submodules")
obj.__globals__.update(obj_globals)
obj.__globals__["__builtins__"] = __builtins__
if obj_closure is not None:
for i, cell in enumerate(obj_closure):
try:
value = cell.cell_contents
except ValueError:
continue
cell_set(obj.__closure__[i], value)
for k, v in slotstate.items():
setattr(obj, k, v)
def _class_setstate(obj, state):
state, slotstate = state
registry = None
for attrname, attr in state.items():
if attrname == "_abc_impl":
registry = attr
else:
setattr(obj, attrname, attr)
if registry is not None:
for subclass in registry:
obj.register(subclass)
return obj
class CloudPickler(Pickler):
_dispatch_table = {}
_dispatch_table[classmethod] = _classmethod_reduce
_dispatch_table[io.TextIOWrapper] = _file_reduce
_dispatch_table[logging.Logger] = _logger_reduce
_dispatch_table[logging.RootLogger] = _root_logger_reduce
_dispatch_table[memoryview] = _memoryview_reduce
_dispatch_table[property] = _property_reduce
_dispatch_table[staticmethod] = _classmethod_reduce
_dispatch_table[CellType] = _cell_reduce
_dispatch_table[types.CodeType] = _code_reduce
_dispatch_table[types.GetSetDescriptorType] = _getset_descriptor_reduce
_dispatch_table[types.ModuleType] = _module_reduce
_dispatch_table[types.MethodType] = _method_reduce
_dispatch_table[types.MappingProxyType] = _mappingproxy_reduce
_dispatch_table[weakref.WeakSet] = _weakset_reduce
_dispatch_table[typing.TypeVar] = _typevar_reduce
_dispatch_table[_collections_abc.dict_keys] = _dict_keys_reduce
_dispatch_table[_collections_abc.dict_values] = _dict_values_reduce
_dispatch_table[_collections_abc.dict_items] = _dict_items_reduce
dispatch_table = ChainMap(_dispatch_table, copyreg.dispatch_table) | Apache License 2.0 |
bfreskura/kindle_note_parser | export.py | choose_export | python | def choose_export(export_index, template_dir):
author = input(
"Enter your name (this will appear on the top of the document): ")
if export_index == 0:
return exporter.ExportTex(author_name=author,
template_path=choose_template(
template_dir=template_dir,
extension=EXPORT_EXTENSIONS['tex']))
elif export_index == 1:
return exporter.ExportMarkdown(author_name=author,
template_path=choose_template(
template_dir=template_dir,
extension=EXPORT_EXTENSIONS[
'markdown']))
else:
return exporter.ExportPlain(author_name=author) | Choose export object based on the user input
:param template_dir: Templates directory path
:param export_index: Index which was input by user
:return: Export object | https://github.com/bfreskura/kindle_note_parser/blob/f560a3146a9199a39a21f1b9261b5e8ab07dd9a7/export.py#L37-L61 | import argparse
import collections
import sys
from constants import *
from export import exporter
from raw_parser import raw_parser
def choose_template(template_dir, extension):
print("Available templates for the specified format: ")
available = {id: name for id, name in enumerate(os.listdir(template_dir)) if
name.endswith(extension)}
if not available:
print("No supported template files in this folder. Try again with"
"different templates folder :)")
sys.exit()
[print("{}) {}".format(id, name)) for id, name in available.items()
if name.endswith(extension)]
selection = ""
while selection not in list(available.keys()):
try:
selection = int(input("Choose format index from the list: "))
except ValueError:
print("Please enter valid integer format from the list.")
return os.path.join(template_dir, available[selection]) | MIT License |
skamithi/ansible-tower-ldap-settings | library/tower_ldap_settings.py | transform_ldap_group_type | python | def transform_ldap_group_type(group_type):
transformed_group_type = ''
if group_type == 'NestedActiveDirectoryGroupType':
transformed_group_type = 'active_directory'
elif group_type == 'NestedGroupOfNamesType':
transformed_group_type = 'open_ldap'
elif group_type == 'active_directory':
transformed_group_type = 'NestedActiveDirectoryGroupType'
elif group_type == 'open_ldap':
transformed_group_type = "NestedGroupOfNamesType"
else:
transformed_group_type = 'MemberDNGroupType'
return transformed_group_type | This transformation function takes a group type name. If the group type name matches this module group_type
options then it outputs the Tower API equivalent output. And the reverse is true. | https://github.com/skamithi/ansible-tower-ldap-settings/blob/24c59dbcb935177803a3720c5c00d91fe54fa4b9/library/tower_ldap_settings.py#L402-L422 | from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = '''
---
module: tower_ldap_settings
author: "Stanley Karunditu (@linuxsimba)"
short_description: Set Ansible Tower LDAP Credentials
description:
- Set Ansible Tower LDAP Credentials. These actions can
only be performed by a Tower superuser. See
U(https://www.ansible.com/tower) for details.
options:
ldap_server_protocol:
description:
- LDAP connection protocol
required: False
default: ldaps
ldap_server_name:
description:
- LDAP Server FQDN
required: False
ldap_server_port:
description:
- LDAP Server Connection Port
default: 636
required: False
ldap_bind_dn:
description:
- LDAP Bind User DN
required: False
ldap_bind_password:
description:
- LDAP Bind User password. If set, this module will never be idempotent
- because bind password is encrypted and difficult to confirm if the password
- has changed.
required: False
ldap_start_tls:
description:
- Set LDAP Start TLS
default: false
required: False
ldap_user_search:
description:
- List of DNs to search through to find users. Performs recursive LDAP search
required: False
ldap_group_search:
description:
- single group DN to find User Groups. Performs recursive LDAP search.
- Multiple DNs cannot be specified
required: False
ldap_group_type:
description:
- Specify the type of LDAP database to be used.
- The C(active_directory) option will set the I(AUTH_LDAP_GROUP_TYPE) Tower setting attribute to I(NestedActiveDirectoryGroupType)
- The C(open_ldap) option will set the I(AUTH_LDAP_GROUP_TYPE) Tower setting attribute to I(NestedGroupOfNamesType)
- The default setting is C(active_directory)
choices: ['"active_directory"', "open_ldap"]
default: "active_directory"
required: False
ldap_superuser:
description:
- Specify DN that can have superuser privileges on Tower. Could be a Group or User.
- Multiple DNs cannot be specified
required: False
ldap_organization_map:
description:
- Provide a list of Tower organization maps dictionaries.
- Each dictionary contains the following
- C(organization) - Tower Organization name. This value is case sensitive.
- C(users) - List of DNs associated with the organization
- C(admins) list - List of DNs that have admin organization privileges
required: False
ldap_team_map:
description:
- Provide a list of Tower teams map directories
- Each directory contains the following
- C(organization) - Tower organization the team belongs to. This value is case sensitive.
- C(team) - Tower team name
- C(users) - List of User LDAP DNs that should belong to the team.
required: False
state:
description:
- When set to absent all LDAP configuration is removed.
required: True
default: "present"
choices: ["present", "absent"]
extends_documentation_fragment: tower
'''
EXAMPLES = '''
- name: Remove all LDAP configuration
tower_ldap_settings:
state: absent
tower_host: tower.example.com
tower_username: "{{ vault_tower_user }}"
tower_password: "{{ vault_tower_pass }}"
- name: |
Update Ldap org and team map with a dbapp org and admin team. Previously only
defined a webapp organization and webapp-admin team
tower_ldap_settings:
state: present
ldap_organization_map:
- organization: webapp
users: "cn=webapp,ou=groups,dc=example,dc=local"
admins: "cn=webadmins,ou=groups,dc=example,dc=local"
- organization: dbapp
users: "cn=dbapp,ou=groups,dc=example,dc=local"
admins: "cn=dbadmins,ou=groups,dc=example,dc=local"
ldap_team_map:
- team: webapp_admins
organization: webapp
users: "cn=webadmins,ou=groups,dc=example,dc=local"
- team: dbapp_admins
organization: dbapp
users: "cn=dbadmins, ou=groups,dc=example,dc=local"
tower_host: tower.example.com
tower_username: "{{ vault_tower_user }}"
tower_password: "{{ vault_tower_pass }}"
- name: | use multiple user search DNs to find users. Modify existing settings.
tower_ldap_settings:
state: present
ldap_user_search:
- "ou=hrdept,ou=users,dc=example,dc=local"
- "ou=engdept,ou=users,dc=example,dc=local"
tower_host: tower.example.com
tower_username: "{{ vault_tower_user }}"
tower_password: "{{ vault_tower_pass }}"
- name: |
set all available tower ldap module settings.
NOTE - it is not idempotent because ldap_bind_password is defined
tower_ldap_settings:
state: present
ldap_server_name: adserver.example.local
ldap_bind_dn: "cn=towerbinduser,ou=users,dc=example,dc=local"
ldap_bind_password: "{{ vault_bind_pass }}"
ldap_user_search:
- "ou=users,dc=example,dc=local"
ldap_group_search: "ou=groups,dc=example,dc=local"
ldap_superuser:
- cn=toweradmins, ou=groups,dc=example,dc=local
ldap_organization_map:
- organization: webapp
users: "cn=webapp,ou=groups,dc=example,dc=local"
admins: "cn=webadmins,ou=groups,dc=example,dc=local"
ldap_team_map:
- team: webapp_admins
organization: webapp
users: "cn=webadmins,ou=groups,dc=example,dc=local"
tower_host: tower.example.com
tower_username: "{{ vault_tower_user }}"
tower_password: "{{ vault_tower_pass }}"
'''
from ansible.module_utils.ansible_tower import tower_argument_spec, tower_auth_config, tower_check_mode,HAS_TOWER_CLI
try:
import tower_cli
import tower_cli.utils.exceptions as exc
import json
from tower_cli.conf import settings
import re
except ImportError:
pass
class FixBoolValuesEncoder(json.JSONEncoder):
def change_bool_to_str(self, obj):
if isinstance(obj, bool):
return str(obj)
elif isinstance(obj, dict):
for _key, _value in obj.items():
obj[_key] = self.change_bool_to_str(_value)
return obj
def iterencode(self, obj, _one_shot=True):
obj = self.change_bool_to_str(obj)
return json.JSONEncoder.iterencode(self, obj)
def empty_module_params(module):
module.params = {
'ldap_server_name': '',
'ldap_bind_dn': '',
'ldap_bind_password': '',
'ldap_start_tls': False,
'ldap_user_search': [],
'ldap_group_search': [],
'ldap_organization_map': [],
'ldap_team_map': [],
'ldap_group_type': '',
'ldap_user_attr_map': {},
'ldap_superuser': '',
'state': 'absent'
}
def clear_all_ldap_config(module, check_mode=False):
empty_module_params(module)
modify_ldap_config(module, check_mode)
def modify_ldap_config(module, check_mode=False):
module.changed_values = []
modified_server_uri = False
for _ldap_attr, _value in module.current_settings.items():
if _ldap_attr == 'ldap_server_uri' or _ldap_attr == 'ldap_user_attr_map':
continue
elif _ldap_attr == 'ldap_bind_password':
module.changed_values.append(_ldap_attr)
module.tower_settings.modify('AUTH_LDAP_BIND_PASSWORD',
module.params.get('ldap_bind_password'))
elif _value != module.params.get(_ldap_attr):
if _ldap_attr == 'ldap_bind_dn':
if not check_mode:
module.tower_settings.modify('AUTH_LDAP_BIND_DN',
module.params.get(_ldap_attr))
module.changed_values.append(_ldap_attr)
elif _ldap_attr == 'ldap_start_tls':
if not check_mode:
module.tower_settings.modify('AUTH_LDAP_START_TLS',
str(module.params.get(_ldap_attr)))
elif _ldap_attr == 'ldap_server_name' or _ldap_attr == 'ldap_server_port' or _ldap_attr == 'ldap_server_protocol':
if modified_server_uri == False:
if not check_mode:
module.tower_settings.modify('AUTH_LDAP_SERVER_URI',
transform_ldap_server_uri(
ldap_server_name=module.params.get('ldap_server_name'),
ldap_server_port=module.params.get('ldap_server_port'),
ldap_server_protocol=module.params.get('ldap_server_protocol')).get('ldap_server_uri'))
modified_server_uri = True
module.changed_values = module.changed_values + [
'ldap_server_name', 'ldap_server_protocol',
'ldap_server_port', 'ldap_server_uri']
del module.current_settings['ldap_server_name']
del module.current_settings['ldap_server_protocol']
del module.current_settings['ldap_server_uri']
del module.current_settings['ldap_server_port']
elif _ldap_attr == 'ldap_superuser':
if not check_mode:
module.tower_settings.modify('AUTH_LDAP_USER_FLAGS_BY_GROUP',
json.dumps(transform_ldap_user_flags_by_group(module.params.get(_ldap_attr)),
cls=FixBoolValuesEncoder))
module.changed_values.append(_ldap_attr)
elif _ldap_attr == 'ldap_user_search':
if not check_mode:
module.tower_settings.modify('AUTH_LDAP_USER_SEARCH',
json.dumps(transform_ldap_user_search(
module.params.get(_ldap_attr),
module.params.get('ldap_group_type')
), cls=FixBoolValuesEncoder))
module.changed_values.append(_ldap_attr)
else:
real_ldap_attr = "AUTH_%s" % _ldap_attr.upper()
if not check_mode:
_value = globals()["transform_%s" % (_ldap_attr)](module.params.get(_ldap_attr))
if isinstance(_value, str) or isinstance(_value, bool):
_value = str(_value)
else:
_value = json.dumps(_value, cls=FixBoolValuesEncoder)
try:
module.tower_settings.modify(real_ldap_attr, _value)
except:
module.fail_json(msg="Real ldap attr %s with value %s" % (real_ldap_attr, _value))
module.changed_values.append(_ldap_attr)
def get_ldap_values(module):
tower_settings = tower_cli.get_resource('setting')
current_settings = {
'ldap_server_protocol': None,
'ldap_server_name': None,
'ldap_server_port': None
}
current_ldap_server_uri = tower_settings.get('AUTH_LDAP_SERVER_URI').get('value')
ldap_server_uri_settings = transform_ldap_server_uri(ldap_server_uri=current_ldap_server_uri)
for _key, _value in ldap_server_uri_settings.items():
current_settings[_key] = _value
if module.params.get('ldap_bind_password'):
current_settings['ldap_bind_password'] = tower_settings.get('AUTH_LDAP_BIND_PASSWORD').get('value')
current_settings['ldap_bind_dn'] = tower_settings.get('AUTH_LDAP_BIND_DN').get('value')
current_settings['ldap_start_tls'] = tower_settings.get('AUTH_LDAP_START_TLS').get('value')
current_settings['ldap_user_attr_map'] = tower_settings.get('AUTH_LDAP_USER_ATTR_MAP').get('value')
group_search = tower_settings.get('AUTH_LDAP_GROUP_SEARCH').get('value')
current_settings['ldap_group_search'] = transform_ldap_group_search(group_search)
group_type_from_tower = tower_settings.get('AUTH_LDAP_GROUP_TYPE').get('value')
group_type = transform_ldap_group_type(group_type_from_tower)
current_settings['ldap_group_type'] = group_type
user_search = tower_settings.get('AUTH_LDAP_USER_SEARCH').get('value')
current_settings['ldap_user_search'] = transform_ldap_user_search(user_search, group_type)
ldap_user_flags = tower_settings.get('AUTH_LDAP_USER_FLAGS_BY_GROUP').get('value')
current_settings['ldap_superuser']= transform_ldap_user_flags_by_group(ldap_user_flags)
org_map = tower_settings.get('AUTH_LDAP_ORGANIZATION_MAP').get('value')
current_settings['ldap_organization_map'] = transform_ldap_organization_map(org_map)
team_map = tower_settings.get('AUTH_LDAP_TEAM_MAP').get('value')
current_settings['ldap_team_map'] = transform_ldap_team_map(team_map)
current_settings['ldap_user_attr_map'] = tower_settings.get('AUTH_LDAP_USER_ATTR_MAP').get('value')
module.tower_settings = tower_settings
module.current_settings = current_settings
def transform_ldap_user_attr_map(_value=None):
attr_map_assignment = {
"first_name": "givenName",
"last_name": "sn",
"email": "mail"
}
return attr_map_assignment
def transform_ldap_server_uri(**kwargs):
server_protocol = kwargs.get('ldap_server_protocol')
server_name = kwargs.get('ldap_server_name')
server_port = kwargs.get('ldap_server_port')
server_uri = kwargs.get('ldap_server_uri')
if server_uri:
uri_match = re.match('(\w+)://(.+):(\d+)', server_uri)
if uri_match:
server_protocol = uri_match.group(1)
server_name = uri_match.group(2)
server_port = int(uri_match.group(3))
else:
server_uri = ''
if server_protocol and server_name and server_port:
server_uri = "%s://%s:%s" % (server_protocol, server_name, server_port)
result = {
'ldap_server_protocol': server_protocol,
'ldap_server_name': server_name,
'ldap_server_port': server_port,
'ldap_server_uri': server_uri
}
return result | MIT License |
muyeby/amr-dialogue | DialogRG/nn_utils.py | PositionalEncoding.forward | python | def forward(self, x):
x = x + self.pe[: x.size(0), :]
return self.dropout(x) | r"""Inputs of forward function
Args:
x: the sequence fed to the positional encoder model (required).
Shape:
x: [sequence length, batch size, embed dim]
output: [sequence length, batch size, embed dim]
Examples:
>>> output = pos_encoder(x) | https://github.com/muyeby/amr-dialogue/blob/261535c407be6c166016e4759bc81176b1c99957/DialogRG/nn_utils.py#L79-L91 | import numpy as np
import copy
import math
import torch
import torch.nn as nn
from torch.nn import ModuleList
import torch.nn.functional as F
def has_nan(tensor):
return torch.isnan(tensor).any().item()
def _get_activation_fn(activation):
if activation == "relu":
return F.relu
elif activation == "gelu":
return F.gelu
raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
def _get_clones(module, N):
return ModuleList([copy.deepcopy(module) for i in range(N)])
def universal_sentence_embedding(sentences, mask, length, sqrt=True):
sentence_sum = torch.bmm(sentences.permute(0, 2, 1), mask.unsqueeze(2).float()).squeeze(
-1
)
divisor = length.float().unsqueeze(dim=1)
assert (divisor == 0.0).any() is False
if sqrt:
divisor = divisor.sqrt()
sentence_sum /= divisor
return sentence_sum
def clip_and_normalize(word_probs, epsilon):
word_probs = torch.clamp(word_probs, epsilon, 1.0 - epsilon)
return word_probs / word_probs.sum(dim=-1, keepdim=True)
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer("pe", pe) | MIT License |
sorsnce/red-team | 1. Information Gathering/recon-ng/recon/mixins/threads.py | ThreadingMixin._thread_wrapper | python | def _thread_wrapper(self, *args):
thread_name = threading.current_thread().name
self.debug(f"THREAD => {thread_name} started.")
while not self.stopped.is_set():
try:
obj = self.q.get_nowait()
except Empty:
continue
try:
self.module_thread(obj, *args)
except:
self.print_exception(f"(thread={thread_name}, object={repr(obj)})")
finally:
self.q.task_done()
self.debug(f"THREAD => {thread_name} exited.") | Wrapper for the worker method defined in the module. Handles calling the actual worker, cleanly exiting upon
interrupt, and passing exceptions back to the main process. | https://github.com/sorsnce/red-team/blob/5cd1932ccafcd2c1b92b8642e9a64fa0d2e99324/1. Information Gathering/recon-ng/recon/mixins/threads.py#L7-L27 | from queue import Queue, Empty
import threading
import time
class ThreadingMixin(object): | MIT License |
ngsutils/ngsutils | ngsutils/bam/count/count.py | _calc_read_regions | python | def _calc_read_regions(read):
regions = []
start = read.pos
end = read.pos
for op, length in read.cigar:
if op == 0:
end += length
elif op == 1:
pass
elif op == 2:
end += length
elif op == 3:
regions.append((start, end))
end += length
start = end
regions.append((start, end))
return regions | Find regions of reference the read covers - breaking on long gaps (N) | https://github.com/ngsutils/ngsutils/blob/417e90dc1918fb553dd84990f2c54bd8cea8f44d/ngsutils/bam/count/count.py#L196-L215 | import ngsutils.support.stats
import sys
import tempfile
import ngsutils
from ngsutils.bam.t import MockBam
assert(MockBam)
class TmpCountFile(object):
def __init__(self):
self.tmpfile = tempfile.TemporaryFile()
def write(self, count, coding_len, cols):
self.tmpfile.write('%s\t%s\t%s\n' % (count, coding_len, '\t'.join([str(x) for x in cols])))
def fetch(self):
self.tmpfile.flush()
self.tmpfile.seek(0)
for line in self.tmpfile:
cols = line.strip('\n').split('\t')
yield (int(cols[0]), int(cols[1]), cols[2:])
def close(self):
self.tmpfile.close()
class Model(object):
def __init__(self):
pass
def get_source(self):
raise NotImplemented
def get_name(self):
raise NotImplemented
def get_headers(self):
raise NotImplemented
def get_regions(self):
raise NotImplemented
def get_postheaders(self):
return None
def count(self, bam, library_type='FR', coverage=False, uniq_only=False, fpkm=False, norm='', multiple='complete', whitelist=None, blacklist=None, out=sys.stdout, quiet=False, start_only=False):
tmpcounts = TmpCountFile()
counts_tally = {}
total_count = 0.0
if library_type in ['FR', 'RF']:
stranded = True
else:
stranded = False
for chrom, starts, ends, strand, cols, callback in self.get_regions():
outcols = cols[:]
coding_len = 0
for s, e in zip(starts, ends):
coding_len += e - s
outcols.append(coding_len)
count, reads = _fetch_reads(bam, chrom, strand if stranded else None, starts, ends, multiple, False, whitelist, blacklist, uniq_only, library_type, start_only)
outcols.append('')
total_count += count
if coverage:
mean, stdev, median = calc_coverage(bam, chrom, strand if stranded else None, starts, ends, whitelist, blacklist, library_type=library_type)
outcols.append(mean)
outcols.append(stdev)
outcols.append(median)
if count > 0:
if not count in counts_tally:
counts_tally[count] = 1
else:
counts_tally[count] += 1
if callback:
for callback_cols in callback(bam, count, reads, outcols):
tmpcounts.write(count, coding_len, callback_cols)
else:
tmpcounts.write(count, coding_len, outcols)
if not quiet:
sys.stderr.write('Calculating normalization...')
norm_val = None
norm_val_orig = None
if norm == 'all':
norm_val_orig = _find_mapped_count(bam, whitelist, blacklist, quiet)
elif norm == 'mapped':
norm_val_orig = total_count
elif norm == 'median':
norm_val_orig = ngsutils.support.stats.count_median(counts_tally)
if norm_val_orig:
norm_val = float(norm_val_orig) / 1000000
if not quiet:
sys.stderr.write('\n')
out.write('## %s\n' % (ngsutils.version()))
out.write('## input%s%s\n' % (' ' if bam.filename else '', bam.filename))
out.write('## model %s %s\n' % (self.get_name(), self.get_source()))
out.write('## library_type %s\n' % library_type)
out.write('## multiple %s\n' % multiple)
if start_only:
out.write('## start_only\n')
if norm_val:
out.write('## norm %s %s\n' % (norm, float(norm_val_orig)))
out.write('## CPM-factor %s\n' % norm_val)
out.write('\t'.join(self.get_headers()))
out.write('\tlength\tcount')
if norm_val:
out.write('\tcount (CPM)')
if fpkm:
out.write('\tRPKM')
if coverage:
out.write('\tcoverage mean\tcoverage stdev\tcoverage median')
if self.get_postheaders():
out.write('\t')
out.write('\t'.join(self.get_postheaders()))
out.write('\n')
for count, coding_len, outcols in tmpcounts.fetch():
first = True
for col in outcols:
if not first:
out.write('\t')
first = False
if col == '' or col is None:
out.write('%s' % count)
if norm_val:
out.write('\t')
out.write(str(count / norm_val))
if fpkm:
out.write('\t')
out.write(str(count / (coding_len / 1000.0) / norm_val))
else:
out.write(str(col))
out.write('\n')
tmpcounts.close() | BSD 3-Clause New or Revised License |
sfanous/pyecobee | pyecobee/objects/report_job.py | ReportJob.status | python | def status(self):
return self._status | Gets the status attribute of this ReportJob instance.
:return: The value of the status attribute of this ReportJob
instance.
:rtype: six.text_type | https://github.com/sfanous/pyecobee/blob/3d6b4aec3c6bc9b796aa3d3fd6626909ffdbac13/pyecobee/objects/report_job.py#L64-L73 | from pyecobee.ecobee_object import EcobeeObject
class ReportJob(EcobeeObject):
__slots__ = ['_job_id', '_status', '_message', '_files']
attribute_name_map = {
'job_id': 'jobId',
'jobId': 'job_id',
'status': 'status',
'message': 'message',
'files': 'files',
}
attribute_type_map = {
'job_id': 'six.text_type',
'status': 'six.text_type',
'message': 'six.text_type',
'files': 'List[six.text_type]',
}
def __init__(self, job_id=None, status=None, message=None, files=None):
self._job_id = job_id
self._status = status
self._message = message
self._files = files
@property
def job_id(self):
return self._job_id
@property | MIT License |
jamescooke/flake8-aaa | tests/command_line/test_do_command_line.py | example_file | python | def example_file(tmpdir):
f = tmpdir.join('example_file.py')
f.write("""
def test():
do_stuff()
def test_other():
do_other_stuff()
""")
f.name = 'example_file.py'
return f | Returns:
file: Test file like argparse returns which has a 'name' property. This
is deliberately named to not look like a test file - which means that
the command line functionality of running files regardless of if
they're a test file or not can be tested. | https://github.com/jamescooke/flake8-aaa/blob/bc7970d925e43c1fb558dd22533edaabf283f39e/tests/command_line/test_do_command_line.py#L10-L27 | import ast
import pytest
from flake8_aaa.command_line import do_command_line
from flake8_aaa.helpers import find_test_functions, is_test_file
@pytest.fixture | MIT License |
dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | transformer_base | python | def transformer_base():
cfg = CN()
cfg.MODEL = CN()
cfg.MODEL.src_vocab_size = -1
cfg.MODEL.tgt_vocab_size = -1
cfg.MODEL.max_src_length = -1
cfg.MODEL.max_tgt_length = -1
cfg.MODEL.scale_embed = True
cfg.MODEL.pos_embed_type = "sinusoidal"
cfg.MODEL.shared_embed = True
cfg.MODEL.tie_weights = True
cfg.MODEL.attention_dropout = 0.0
cfg.MODEL.activation_dropout = 0.0
cfg.MODEL.dropout = 0.1
cfg.MODEL.layout = 'NT'
cfg.MODEL.dtype = 'float32'
cfg.MODEL.ENCODER = CN()
cfg.MODEL.ENCODER.num_layers = 6
cfg.MODEL.ENCODER.units = 512
cfg.MODEL.ENCODER.num_heads = 8
cfg.MODEL.ENCODER.hidden_size = 2048
cfg.MODEL.ENCODER.recurrent = False
cfg.MODEL.ENCODER.activation = 'relu'
cfg.MODEL.ENCODER.pre_norm = False
cfg.MODEL.ENCODER.use_qkv_bias = True
cfg.MODEL.DECODER = CN()
cfg.MODEL.DECODER.num_layers = 6
cfg.MODEL.DECODER.units = 512
cfg.MODEL.DECODER.num_heads = 8
cfg.MODEL.DECODER.hidden_size = 2048
cfg.MODEL.DECODER.recurrent = False
cfg.MODEL.DECODER.activation = 'relu'
cfg.MODEL.DECODER.pre_norm = False
cfg.MODEL.DECODER.use_qkv_bias = False
cfg.INITIALIZER = CN()
cfg.INITIALIZER.embed = ['xavier', 'gaussian', 'in', 1.0]
cfg.INITIALIZER.weight = ['xavier', 'uniform', 'avg', 3.0]
cfg.INITIALIZER.bias = ['zeros']
cfg.VERSION = 1
cfg.freeze()
return cfg | Configuration of Transformer WMT EN-DE Base | https://github.com/dmlc/gluon-nlp/blob/5d4bc9eba7226ea9f9aabbbd39e3b1e886547e48/src/gluonnlp/models/transformer.py#L27-L74 | __all__ = ['transformer_cfg_reg', 'transformer_base',
'transformer_base_prenorm', 'transformer_iwslt_de_en',
'transformer_wmt_en_de_big', 'transformer_wmt_en_de_big_t2t',
'TransformerEncoderLayer',
'TransformerDecoderLayer',
'TransformerEncoder', 'TransformerDecoder',
'TransformerModel', 'TransformerInference',
'TransformerNMTInference']
import numpy as _np
import mxnet as mx
from mxnet import np, npx
from mxnet import use_np
from mxnet.gluon import nn, HybridBlock
from typing import Optional, Tuple, List
from ..utils.registry import Registry
from ..attention_cell import MultiHeadAttentionCell, gen_self_attn_mask, gen_mem_attn_mask
from ..layers import PositionalEmbedding, PositionwiseFFN, InitializerType
from ..utils.config import CfgNode as CN
from ..sequence_sampler import BaseStepDecoder
transformer_cfg_reg = Registry('transformer_cfg')
@transformer_cfg_reg.register() | Apache License 2.0 |
quantaxis/quantaxis | QUANTAXIS/QASU/save_binance.py | QA_SU_save_binance | python | def QA_SU_save_binance(frequency):
if (frequency not in ["1d", "1day", "day"]):
return QA_SU_save_binance_min(frequency)
else:
return QA_SU_save_binance_day(frequency) | Save binance kline "smart" | https://github.com/quantaxis/quantaxis/blob/910cecae70ede6825f5ff58bb1d2186b6fb3dd1d/QUANTAXIS/QASU/save_binance.py#L66-L73 | import datetime
import time
from dateutil.tz import tzutc
from dateutil.relativedelta import relativedelta
import pandas as pd
from QUANTAXIS.QAUtil import (
DATABASE,
QASETTING,
QA_util_log_info,
QA_util_log_expection,
QA_util_to_json_from_pandas
)
from QUANTAXIS.QAUtil.QADate_Adv import (
QA_util_timestamp_to_str,
QA_util_datetime_to_Unix_timestamp,
QA_util_print_timestamp
)
from QUANTAXIS.QAFetch.QAbinance import (
QA_fetch_binance_symbols,
QA_fetch_binance_kline,
QA_fetch_binance_kline_min,
Binance2QA_FREQUENCY_DICT
)
from QUANTAXIS.QAUtil.QAcrypto import (
QA_util_save_raw_symbols,
QA_util_find_missing_kline
)
from QUANTAXIS.QAFetch.QAQuery import (QA_fetch_cryptocurrency_list)
import pymongo
BINANCE_MIN_DATE = datetime.datetime(2017, 7, 1, tzinfo=tzutc())
Binance_EXCHANGE = 'BINANCE'
Binance_SYMBOL = 'BINANCE.{}' | MIT License |
neccam/slt | signjoey/helpers.py | log_data_info | python | def log_data_info(
train_data: Dataset,
valid_data: Dataset,
test_data: Dataset,
gls_vocab: GlossVocabulary,
txt_vocab: TextVocabulary,
logging_function: Callable[[str], None],
):
logging_function(
"Data set sizes: \n\ttrain {:d},\n\tvalid {:d},\n\ttest {:d}".format(
len(train_data),
len(valid_data),
len(test_data) if test_data is not None else 0,
)
)
logging_function(
"First training example:\n\t[GLS] {}\n\t[TXT] {}".format(
" ".join(vars(train_data[0])["gls"]), " ".join(vars(train_data[0])["txt"])
)
)
logging_function(
"First 10 words (gls): {}".format(
" ".join("(%d) %s" % (i, t) for i, t in enumerate(gls_vocab.itos[:10]))
)
)
logging_function(
"First 10 words (txt): {}".format(
" ".join("(%d) %s" % (i, t) for i, t in enumerate(txt_vocab.itos[:10]))
)
)
logging_function("Number of unique glosses (types): {}".format(len(gls_vocab)))
logging_function("Number of unique words (types): {}".format(len(txt_vocab))) | Log statistics of data and vocabulary.
:param train_data:
:param valid_data:
:param test_data:
:param gls_vocab:
:param txt_vocab:
:param logging_function: | https://github.com/neccam/slt/blob/90588825f6229474bc19ac7a6b30ea3116635ba3/signjoey/helpers.py#L118-L162 | import copy
import glob
import os
import os.path
import errno
import shutil
import random
import logging
from sys import platform
from logging import Logger
from typing import Callable, Optional
import numpy as np
import torch
from torch import nn, Tensor
from torchtext.data import Dataset
import yaml
from signjoey.vocabulary import GlossVocabulary, TextVocabulary
def make_model_dir(model_dir: str, overwrite: bool = False) -> str:
if os.path.isdir(model_dir):
if not overwrite:
raise FileExistsError("Model directory exists and overwriting is disabled.")
shutil.rmtree(model_dir)
os.makedirs(model_dir)
return model_dir
def make_logger(model_dir: str, log_file: str = "train.log") -> Logger:
logger = logging.getLogger(__name__)
if not logger.handlers:
logger.setLevel(level=logging.DEBUG)
fh = logging.FileHandler("{}/{}".format(model_dir, log_file))
fh.setLevel(level=logging.DEBUG)
logger.addHandler(fh)
formatter = logging.Formatter("%(asctime)s %(message)s")
fh.setFormatter(formatter)
if platform == "linux":
sh = logging.StreamHandler()
sh.setLevel(logging.INFO)
sh.setFormatter(formatter)
logging.getLogger("").addHandler(sh)
logger.info("Hello! This is Joey-NMT.")
return logger
def log_cfg(cfg: dict, logger: Logger, prefix: str = "cfg"):
for k, v in cfg.items():
if isinstance(v, dict):
p = ".".join([prefix, k])
log_cfg(v, logger, prefix=p)
else:
p = ".".join([prefix, k])
logger.info("{:34s} : {}".format(p, v))
def clones(module: nn.Module, n: int) -> nn.ModuleList:
return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
def subsequent_mask(size: int) -> Tensor:
mask = np.triu(np.ones((1, size, size)), k=1).astype("uint8")
return torch.from_numpy(mask) == 0
def set_seed(seed: int):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed) | Apache License 2.0 |
hpac/elaps | elaps/backends/lsf.py | Backend.__init__ | python | def __init__(self, header="#!/bin/bash -l\n#BSUB -o /dev/null\n"):
self.jobs = []
self.header = header | Initialize the backend. | https://github.com/hpac/elaps/blob/390bbe8cbeb056ef57adbc91cdf5bcd1f7cbe187/elaps/backends/lsf.py#L13-L16 | import subprocess
import re
class Backend(object):
name = "lsf" | BSD 3-Clause New or Revised License |
webkom/lego | lego/apps/ical/viewsets.py | ICalViewset.list | python | def list(self, request):
token = ICalToken.objects.get_or_create(user=request.user)[0]
path = request.get_full_path()
data = {
"result": {
"calendars": [
{
"name": "events",
"description": "Calendar with all events on Abakus.no.",
"path": f"{path}events/",
},
{
"name": "personal",
"description": "Calendar with your favorite events & meetings.",
"path": f"{path}personal/",
},
{
"name": "registration",
"description": "Calendar with all event registration times.",
"path": f"{path}registrations/",
},
],
"token": ICalTokenSerializer(token).data,
}
}
return Response(data=data) | List all the different icals. | https://github.com/webkom/lego/blob/90204aca73fe1f22df4e356e35baf12e943f9fc7/lego/apps/ical/viewsets.py#L55-L81 | from datetime import timedelta
from django.utils import timezone
from rest_framework import decorators, permissions, viewsets
from rest_framework.permissions import IsAuthenticated
from rest_framework.response import Response
from rest_framework.settings import api_settings
from lego.apps.events.models import Event
from lego.apps.ical import constants, utils
from lego.apps.ical.authentication import ICalTokenAuthentication
from lego.apps.ical.models import ICalToken
from lego.apps.ical.serializers import ICalTokenSerializer
from lego.apps.meetings.models import Meeting
from lego.apps.permissions.utils import get_permission_handler
class ICalTokenViewset(viewsets.ViewSet):
permission_classes = (IsAuthenticated,)
@decorators.action(detail=False, methods=["PATCH"])
def regenerate(self, request, *args, **kwargs):
token, created = ICalToken.objects.get_or_create(user=request.user)
if not created:
token.regenerate()
serializer = ICalTokenSerializer(token)
return Response(serializer.data)
def list(self, request):
token = ICalToken.objects.get_or_create(user=request.user)[0]
serializer = ICalTokenSerializer(token)
return Response(serializer.data)
class ICalViewset(viewsets.ViewSet):
permission_classes = (permissions.IsAuthenticated,)
authentication_classes = api_settings.DEFAULT_AUTHENTICATION_CLASSES + [
ICalTokenAuthentication
] | MIT License |
bluemirrors/cvu | cvu/detector/yolov5/backends/yolov5_tensorflow.py | Yolov5.__init__ | python | def __init__(self, weight: str = "yolov5s", device='auto') -> None:
self._model = None
self._device = None
self._loaded = None
logging.disable(logging.WARNING)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
self._set_device(device)
self._load_model(weight) | Initiate Model
Args:
weight (str, optional): path to SavedModel weight files. Alternatively,
it also accepts identifiers (such as yolvo5s, yolov5m, etc.) to load
pretrained models. Defaults to "yolov5s".
device (str, optional): name of the device to be used. Valid devices can be
"cpu", "gpu", "cuda", "auto", "tpu". Defaults to "auto" which tries to use the
device best suited for selected backend and the hardware avaibility. | https://github.com/bluemirrors/cvu/blob/2eb10c5844d7cde2a54d2334d9fb8642bccf2b66/cvu/detector/yolov5/backends/yolov5_tensorflow.py#L32-L57 | import logging
import os
from typing import List
import numpy as np
import tensorflow as tf
from tensorflow.keras import mixed_precision
from cvu.interface.model import IModel
from cvu.utils.general import get_path
from cvu.detector.yolov5.backends.common import download_weights
from cvu.postprocess.bbox import denormalize
from cvu.postprocess.backend_tf.nms.yolov5 import non_max_suppression_tf
class Yolov5(IModel): | Apache License 2.0 |
saevon/webdnd | player/modifier_obj.py | ModField.get | python | def get(self):
return self._value['value'] | Returns the value of this field | https://github.com/saevon/webdnd/blob/4dd5d30ae105ede51bbd92bf5281a6965b7d55f4/player/modifier_obj.py#L58-L62 | from collections import defaultdict
from itertools import chain
from webdnd.shared.utils.decorators import cascade, dirty_cache
class StatVal(dict):
def __init__(self, value, stats=None):
super(StatVal, self).__init__(stats or {})
self['value'] = value
class ModField(object):
def __init__(self, value):
super(ModField, self).__init__()
self._label = ''
self._kind = ''
obj = self.StatVal(value, {'change_type': 'start'})
self._hist = [obj]
self._value = obj
def __repr__(self):
return '%s' % (unicode(self))
def __unicode__(self):
return u'"%s"' % self.get()
def StatVal(self, value='', stats=None):
defaults = {
'label': self._label,
'kind': self._kind,
}
defaults.update(stats or {})
return StatVal(value, defaults)
@cascade
def set(self, value):
self._get_dirty = True
val = self.StatVal(value)
val['change_type'] = 'set'
self._value = val
self._hist.append(val) | MIT License |
ndrplz/computer_vision_utils | io_helper.py | write_image | python | def write_image(img_path, img, channels_first=False, color_mode='BGR', resize_dim=None, to_normalize=False):
color = True if img.ndim == 3 else False
if color and channels_first:
img = img.transpose(1, 2, 0)
if color and color_mode == 'RGB':
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
if resize_dim is not None:
img = cv2.resize(img, resize_dim[::-1])
if to_normalize:
normalize(img)
cv2.imwrite(img_path, img) | Writes an image (numpy array) on file
Parameters
----------
img_path : string
Path where to save image
img : ndarray
Image that has to be saved
channels_first: bool
Set this True if shape is (c, h, w)
color_mode: "RGB", "BGR", optional
Whether the image is in RGB or BGR format
resize_dim: tuple, optional
Resize size following convention (new_h, new_w) - interpolation is linear
to_normalize: bool
Whether or not to normalize the image between 0 and 255.
Returns
---------- | https://github.com/ndrplz/computer_vision_utils/blob/869ca8d5dcd6a95392d67127aa2a43042b33993c/io_helper.py#L49-L85 | import cv2
import numpy as np
import os.path as path
def read_image(img_path, channels_first, color=True, color_mode='BGR', dtype=np.float32, resize_dim=None):
if not path.exists(img_path):
raise ValueError('Provided path "{}" does NOT exist.'.format(img_path))
image = cv2.imread(img_path, cv2.IMREAD_COLOR if color else cv2.IMREAD_GRAYSCALE)
if color and color_mode == 'RGB':
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if resize_dim is not None:
image = cv2.resize(image, dsize=resize_dim[::-1], interpolation=cv2.INTER_LINEAR)
if color and channels_first:
image = np.transpose(image, (2, 0, 1))
return image.astype(dtype) | MIT License |
quay/quay | data/logs_model/document_logs_model.py | _date_range_in_single_index | python | def _date_range_in_single_index(dt1, dt2):
assert isinstance(dt1, date) and isinstance(dt2, date)
dt = dt2 - dt1
if not isinstance(dt1, datetime) and not isinstance(dt2, datetime):
return dt == timedelta(days=1)
if dt < timedelta(days=1) and dt >= timedelta(days=0):
return dt2.day == dt1.day
if dt == timedelta(days=1):
return dt1.hour == 0 and dt1.minute == 0 and dt1.second == 0 and dt1.microsecond == 0
return False | Determine whether a single index can be searched given a range of dates or datetimes. If date
instances are given, difference should be 1 day.
NOTE: dt2 is exclusive to the search result set.
i.e. The date range is larger or equal to dt1 and strictly smaller than dt2 | https://github.com/quay/quay/blob/f50f37a393fa2273234f8ac0aa9f34a03a77a731/data/logs_model/document_logs_model.py#L72-L95 | import json
import logging
import uuid
from time import time
from datetime import timedelta, datetime, date
from dateutil.parser import parse as parse_datetime
from abc import ABCMeta, abstractmethod
from six import add_metaclass
from elasticsearch.exceptions import ConnectionTimeout, NotFoundError
from data import model
from data.database import CloseForLongOperation
from data.model import config
from data.model.log import (
_json_serialize,
ACTIONS_ALLOWED_WITHOUT_AUDIT_LOGGING,
DataModelException,
)
from data.logs_model.elastic_logs import LogEntry, configure_es
from data.logs_model.datatypes import Log, AggregatedLogCount, LogEntriesPage
from data.logs_model.interface import (
ActionLogsDataInterface,
LogRotationContextInterface,
LogsIterationTimeout,
)
from data.logs_model.shared import SharedModel, epoch_ms
from data.logs_model.logs_producer import LogProducerProxy, LogSendException
from data.logs_model.logs_producer.kafka_logs_producer import KafkaLogsProducer
from data.logs_model.logs_producer.elasticsearch_logs_producer import ElasticsearchLogsProducer
from data.logs_model.logs_producer.kinesis_stream_logs_producer import KinesisStreamLogsProducer
logger = logging.getLogger(__name__)
PAGE_SIZE = 20
DEFAULT_RESULT_WINDOW = 5000
MAX_RESULT_WINDOW = 10000
DATE_RANGE_LIMIT = 32
COUNT_REPOSITORY_ACTION_TIMEOUT = 30
def _date_range_descending(start_datetime, end_datetime, includes_end_datetime=False):
assert end_datetime >= start_datetime
start_date = start_datetime.date()
if includes_end_datetime:
current_date = end_datetime.date()
else:
current_date = (end_datetime - timedelta(seconds=1)).date()
while current_date >= start_date:
yield current_date
current_date = current_date - timedelta(days=1) | Apache License 2.0 |
2ndwatch/cloudendure-python | cloudendure/cloudendure_api/models/cloud_endure_account_request.py | CloudEndureAccountRequest.email | python | def email(self):
return self._email | Gets the email of this CloudEndureAccountRequest. # noqa: E501
:return: The email of this CloudEndureAccountRequest. # noqa: E501
:rtype: str | https://github.com/2ndwatch/cloudendure-python/blob/f81d1be1422b7c19adedb06c584803eaaa811919/cloudendure/cloudendure_api/models/cloud_endure_account_request.py#L124-L131 | import pprint
import re
import six
class CloudEndureAccountRequest:
"""
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",
"request_type": "str",
"email": "str",
"request_link": "str",
"registration_token": "str",
}
attribute_map = {
"id": "id",
"request_type": "request_type",
"email": "email",
"request_link": "request_link",
"registration_token": "registration_token",
}
def __init__(
self,
id=None,
request_type=None,
email=None,
request_link=None,
registration_token=None,
):
self._id = None
self._request_type = None
self._email = None
self._request_link = None
self._registration_token = None
self.discriminator = None
if id is not None:
self.id = id
if request_type is not None:
self.request_type = request_type
if email is not None:
self.email = email
if request_link is not None:
self.request_link = request_link
if registration_token is not None:
self.registration_token = registration_token
@property
def id(self):
return self._id
@id.setter
def id(self, id):
self._id = id
@property
def request_type(self):
return self._request_type
@request_type.setter
def request_type(self, request_type):
allowed_values = ["REGISTER", "RESET_PASSWORD"]
if request_type not in allowed_values:
raise ValueError(
"Invalid value for `request_type` ({0}), must be one of {1}".format(
request_type, allowed_values
)
)
self._request_type = request_type
@property | MIT License |
restran/fomalhaut | fomalhaut/tests/api_client.py | HMACHandler.response_headers_to_sign | python | def response_headers_to_sign(self, headers):
headers_to_sign = {}
for name, value in iteritems(headers):
if name.startswith(HEADER_X_PREFIX):
headers_to_sign[name] = value
return headers_to_sign | Select the headers from the request that need to be included
in the StringToSign. | https://github.com/restran/fomalhaut/blob/df6762f3aa64c0c0ca50dd8bfd6f2a70b0bced7b/fomalhaut/tests/api_client.py#L80-L90 | from __future__ import unicode_literals, absolute_import
import hmac
import json as json_util
import logging
import random
import time
import traceback
from base64 import urlsafe_b64encode
from hashlib import sha1
import requests
from future.moves.urllib.parse import urlparse, urlunparse, urlencode
from future.utils import iteritems
from fomalhaut.settings import SIGNATURE_EXPIRE_SECONDS, GATEWAY_ERROR_STATUS_CODE, HEADER_X_SIGNATURE, HEADER_X_TIMESTAMP, HEADER_X_APP_ID, HEADER_X_ENCRYPT_TYPE, HEADER_X_NONCE, HEADER_X_SIGN_RESPONSE, HEADER_X_ACCESS_TOKEN, HEADER_X_ENCRYPTED_HEADERS, HEADER_X_ENCRYPTED_URI, HEADER_X_PREFIX
from fomalhaut.utils import utf8, utf8_encoded_dict, text_type, AESCipher, unicode_encoded_dict, to_unicode
logger = logging.getLogger(__name__)
class RequestObject(object):
def __init__(self, method=None, uri=None, headers=None, body=None):
self.method = method
self.uri = uri
self.headers = headers
self.body = body
class APIClient(object):
def __init__(self, access_key, secret_key, api_server, *args, **kwargs):
self.access_key = access_key
self.secret_key = secret_key
self.api_server = api_server
self.gateway_error_status_code = kwargs.get(
'gateway_error_status_code', GATEWAY_ERROR_STATUS_CODE)
self.signature_expire_seconds = kwargs.get(
'signature_expire_seconds', SIGNATURE_EXPIRE_SECONDS)
class HMACHandler(object):
def __init__(self, client, algorithm=sha1):
self.client = client
self.algorithm = algorithm
def sign_string(self, string_to_sign):
logger.debug(string_to_sign)
new_hmac = hmac.new(
utf8(self.client.secret_key), utf8(string_to_sign),
digestmod=self.algorithm)
return to_unicode(urlsafe_b64encode(new_hmac.digest()).rstrip(b'='))
def string_to_sign(self, request):
headers_to_sign = self.headers_to_sign(request.headers)
canonical_headers = self.canonical_headers(headers_to_sign)
string_to_sign = b'\n'.join([utf8(request.method.upper()),
utf8(request.uri),
utf8(canonical_headers),
utf8(request.body)])
return string_to_sign | MIT License |
olitheolix/aiokubernetes | aiokubernetes/models/v1_ip_block.py | V1IPBlock.__init__ | python | def __init__(self, cidr=None, _except=None):
self._cidr = None
self.__except = None
self.discriminator = None
self.cidr = cidr
if _except is not None:
self._except = _except | V1IPBlock - a model defined in Swagger | https://github.com/olitheolix/aiokubernetes/blob/266718b210dff2a9b2212183261ea89adf89115e/aiokubernetes/models/v1_ip_block.py#L42-L51 | import pprint
import re
class V1IPBlock(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'cidr': 'str',
'_except': 'list[str]'
}
attribute_map = {
'cidr': 'cidr',
'_except': 'except'
} | Apache License 2.0 |
hibou57/postiats-utilities | postiats/lexemes.py | get_e | python | def get_e(source):
return get_char_of_category(source, d.E) | Try to read `E`?. | https://github.com/hibou57/postiats-utilities/blob/2148016083490ba1aeac04fe0f4a3983cd18c707/postiats/lexemes.py#L133-L135 | from . import lexemes_defs as d
from .lexemes_defs import (Fin, NonFin, Start)
class Input:
__slots__ = ["source", "length", "pos"]
def __init__(self, source):
self.source = source
self.length = len(source)
self.pos = 0
def char(self, offset=0):
i = self.pos + offset
if i < self.length:
return self.source[i]
if i == self.length:
return d.EOF
raise ValueError
def consume(self, count=1):
assert self.pos + count <= self.length
self.pos += count
def string(self, start, end=None):
if end is None:
end = self.pos
return self.source[start:self.pos]
def at(self, text):
i = self.pos
j = i + len(text)
return self.source[i:j] == text
def file_input(path):
source_file = open(path, encoding="iso-8859-15")
source_text = source_file.read()
source_file.close()
return Input(source_text)
def get_ident(source):
result = None
c = source.char()
if c in d.IDENTFST:
result = c
while True:
c = source.char(len(result))
if c not in d.IDENTRST:
break
result += c
if result is not None:
source.consume(len(result))
return result
def get_chars_of_category(source, category, count=-1):
result = ""
count_down = count
c = source.char()
while c in category and count_down != 0:
count_down -= 1
result += c
c = source.char(len(result))
source.consume(len(result))
return result if result else None
def get_char_of_category(source, category):
result = ""
c = source.char()
if c in category:
result = c
source.consume(len(result))
return result if result else None
def get_char(source, c):
result = ""
if source.char() == c:
result = c
source.consume(len(result))
return result if result else None
def get_symbol(source):
return get_chars_of_category(source, d.SYMBOLIC)
def get_oct(source):
return get_chars_of_category(source, d.OCTAL)
def get_dec(source):
return get_chars_of_category(source, d.DIGIT)
def get_hex(source):
return get_chars_of_category(source, d.XDIGIT)
def get_p(source):
return get_char_of_category(source, d.P) | BSD 2-Clause Simplified License |
ieeerobotics/bot | bot/driver/mec_driver.py | MecDriver.rough_rotate_90 | python | def rough_rotate_90(self, direction, r_speed=50, r_time=1):
if direction == "right":
r_speed = -r_speed
self.rotate(r_speed)
sleep(r_time)
self.rotate(0) | rotates 90 degrees by blindly turning. | https://github.com/ieeerobotics/bot/blob/9228b00f55ec949f3c39a0020a1e0f61dc64d601/bot/driver/mec_driver.py#L314-L324 | from math import sin, cos, pi, fabs, hypot, atan2, degrees
from time import sleep
import bot.lib.lib as lib
import bot.driver.driver as driver
from bot.hardware.dmcc_motor import DMCCMotorSet
class MecDriver(driver.Driver):
min_speed = 0
max_speed = 100
min_angle = -360
max_angle = 360
min_angular_rate = -100
max_angular_rate = 100
def __init__(self, mode='power'):
super(MecDriver, self).__init__()
motor_config = self.config['dmcc_drive_motors']
self.motors = DMCCMotorSet(motor_config)
self.mode = mode
def __str__(self):
return "fr: {}, fl: {} br: {}, bl: {}".format(
self.motors["front_right"],
self.motors["front_left"],
self.motors["back_right"],
self.motors["back_left"])
@lib.api_call
def get_motor(self, name):
if self.mode == 'power':
return self.motors[name].power
else:
return self.motors[name].velocity
@lib.api_call
def set_motor(self, name, value):
if self.mode == 'power':
self.motors[name].power = value
else:
self.motors[name].velocity = value
@property
def speed(self):
v_forward_right = self.get_motor("front_left") + self.get_motor("back_right")
v_forward_left = self.get_motor("front_right") + self.get_motor("back_left")
return int(round(hypot(v_forward_right / 2, v_forward_left / 2)))
@property
def angle(self):
v_forward_right = self.get_motor("front_left") + self.get_motor("back_right")
v_forward_left = self.get_motor("front_right") + self.get_motor("back_left")
return int(round(
degrees(atan2(v_forward_right, v_forward_left) - pi / 4))) % 360
@lib.api_call
def get_rotation(self):
v_left = self.get_motor("front_left") + self.get_motor("back_left")
v_right = self.get_motor("front_right") + self.get_motor("back_right")
rotation = int(round((v_right - v_left) / 4))
self.logger.debug(
"Rotation: (left {}: right: {})".format(
rotation,
v_left,
v_right))
return rotation
rotation_rate = property(get_rotation)
@lib.api_call
def rotate(self, rate):
self.logger.debug("Rotating with angular rate: {}".format(rate))
try:
assert MecDriver.min_angular_rate <= rate <= MecDriver.max_angular_rate
except AssertionError:
raise AssertionError("Angular rate is out of bounds")
self.set_motor("front_left", -rate)
self.set_motor("front_right", rate)
self.set_motor("back_left", -rate)
self.set_motor("back_right", rate)
@lib.api_call
def move(self, speed, angle=0):
self.logger.debug("speed: {}, angle: {}".format(speed, angle))
try:
assert MecDriver.min_speed <= speed <= MecDriver.max_speed
except AssertionError:
raise AssertionError("Speed is out of bounds")
try:
assert MecDriver.min_angle <= angle <= MecDriver.max_angle
except AssertionError:
raise AssertionError("Angle is out of bounds")
if speed == 0:
self.logger.debug("Special case for speed == 0")
self.set_motor("front_left", 0)
self.set_motor("front_right", 0)
self.set_motor("back_left", 0)
self.set_motor("back_right", 0)
return
front_left = speed * sin(angle * pi / 180 + pi / 4)
front_right = speed * cos(angle * pi / 180 + pi / 4)
back_left = speed * cos(angle * pi / 180 + pi / 4)
back_right = speed * sin(angle * pi / 180 + pi / 4)
self.logger.debug((
"pre-scale : front_left: {:6.2f}, front_right: {:6.2f},"
" back_left: {:6.2f}, back_right: {:6.2f}").format(
front_left, front_right, back_left, back_right))
max_wheel_speed = max(
[fabs(front_left), fabs(front_right),
fabs(back_left), fabs(back_right)]
)
front_left = front_left * speed / max_wheel_speed
front_right = front_right * speed / max_wheel_speed
back_left = back_left * speed / max_wheel_speed
back_right = back_right * speed / max_wheel_speed
self.logger.debug(
("post-scale: front_left: {:6.2f}, front_right: {:6.2f},"
" back_left: {:6.2f}, back_right: {:6.2f}").format(
front_left, front_right, back_left, back_right))
self.set_motor("front_left", front_left)
self.set_motor("front_right", front_right)
self.set_motor("back_left", back_left)
self.set_motor("back_right", back_right)
@lib.api_call
def hard_stop(self, current_speed, current_angle=0):
self.move(100, (current_angle + 180) % 180)
sleep(0.1)
self.move(0, 0)
@lib.api_call
def move_forward_strafe(self, forward, strafe):
speed = hypot(forward, strafe)
if speed < MecDriver.min_speed:
speed = MecDriver.min_speed
elif speed > MecDriver.max_speed:
speed = MecDriver.max_speed
angle = degrees(atan2(strafe, forward)) % 360
self.move(speed, angle)
@lib.api_call
def compound_move(self, translate_speed, translate_angle, angular_rate):
total_speed = translate_speed + angular_rate
if total_speed > MecDriver.max_speed:
self.logger.warn("Total speed of move exceeds max: {}/{}".format(
total_speed, MecDriver.max_speed))
self.logger.debug("translate_speed: {}, " +
"translate_angle: {}, " +
"angular_rate: {}".format(translate_speed,
translate_angle,
angular_rate))
front_left = translate_speed * sin(translate_angle * pi / 180 + pi / 4) + angular_rate
front_right = translate_speed * cos(translate_angle * pi / 180 + pi / 4) - angular_rate
back_left = translate_speed * cos(translate_angle * pi / 180 + pi / 4) + angular_rate
back_right = translate_speed * sin(translate_angle * pi / 180 + pi / 4) - angular_rate
max_wheel_speed = max(
[fabs(front_left), fabs(front_right),
fabs(back_left), fabs(back_right)]
)
total_speed = translate_speed + angular_rate
front_left = front_left * total_speed / max_wheel_speed
front_right = front_right * total_speed / max_wheel_speed
back_left = back_left * total_speed / max_wheel_speed
back_right = back_right * total_speed / max_wheel_speed
self.logger.debug(
("post-scale: front_left: {:6.2f}, front_right: {:6.2f},"
" back_left: {:6.2f}, back_right: {:6.2f}").format(
front_left, front_right, back_left, back_right))
self.set_motor("front_left", front_left)
self.set_motor("front_right", front_right)
self.set_motor("back_left", back_left)
self.set_motor("back_right", back_right)
@lib.api_call
def drive(self, speed=50, angle=0, duration=1):
self.move(speed, angle)
sleep(duration)
self.move(0, 0)
@lib.api_call
def rotate_t(self, r_speed, r_time=999):
self.rotate(r_speed)
sleep(r_time)
self.move(0, 0)
@lib.api_call | BSD 2-Clause Simplified License |
contextlab/hypertools | hypertools/_externals/srm.py | SRM._srm | python | def _srm(self, data):
samples = data[0].shape[1]
subjects = len(data)
np.random.seed(self.rand_seed)
w, voxels = _init_w_transforms(data, self.features)
x, mu, rho2, trace_xtx = self._init_structures(data, subjects)
shared_response = np.zeros((self.features, samples))
sigma_s = np.identity(self.features)
for iteration in range(self.n_iter):
logger.info('Iteration %d' % (iteration + 1))
rho0 = (1 / rho2).sum()
(chol_sigma_s, lower_sigma_s) = scipy.linalg.cho_factor(
sigma_s, check_finite=False)
inv_sigma_s = scipy.linalg.cho_solve(
(chol_sigma_s, lower_sigma_s), np.identity(self.features),
check_finite=False)
sigma_s_rhos = inv_sigma_s + np.identity(self.features) * rho0
(chol_sigma_s_rhos, lower_sigma_s_rhos) = scipy.linalg.cho_factor(
sigma_s_rhos, check_finite=False)
inv_sigma_s_rhos = scipy.linalg.cho_solve(
(chol_sigma_s_rhos, lower_sigma_s_rhos),
np.identity(self.features), check_finite=False)
wt_invpsi_x = np.zeros((self.features, samples))
trace_xt_invsigma2_x = 0.0
for subject in range(subjects):
wt_invpsi_x += (w[subject].T.dot(x[subject])) / rho2[subject]
trace_xt_invsigma2_x += trace_xtx[subject] / rho2[subject]
log_det_psi = np.sum(np.log(rho2) * voxels)
shared_response = sigma_s.dot(
np.identity(self.features) - rho0 * inv_sigma_s_rhos).dot(
wt_invpsi_x)
sigma_s = (inv_sigma_s_rhos
+ shared_response.dot(shared_response.T) / samples)
trace_sigma_s = samples * np.trace(sigma_s)
for subject in range(subjects):
a_subject = x[subject].dot(shared_response.T)
perturbation = np.zeros(a_subject.shape)
np.fill_diagonal(perturbation, 0.001)
u_subject, s_subject, v_subject = np.linalg.svd(
a_subject + perturbation, full_matrices=False)
w[subject] = u_subject.dot(v_subject)
rho2[subject] = trace_xtx[subject]
rho2[subject] += -2 * np.sum(w[subject] * a_subject).sum()
rho2[subject] += trace_sigma_s
rho2[subject] /= samples * voxels[subject]
if logger.isEnabledFor(logging.INFO):
loglike = self._likelihood(
chol_sigma_s_rhos, log_det_psi, chol_sigma_s,
trace_xt_invsigma2_x, inv_sigma_s_rhos, wt_invpsi_x,
samples)
logger.info('Objective function %f' % loglike)
return sigma_s, w, mu, rho2, shared_response | Expectation-Maximization algorithm for fitting the probabilistic SRM.
Parameters
----------
data : list of 2D arrays, element i has shape=[voxels_i, samples]
Each element in the list contains the fMRI data of one subject.
Returns
-------
sigma_s : array, shape=[features, features]
The covariance :math:`\\Sigma_s` of the shared response Normal
distribution.
w : list of array, element i has shape=[voxels_i, features]
The orthogonal transforms (mappings) :math:`W_i` for each subject.
mu : list of array, element i has shape=[voxels_i]
The voxel means :math:`\\mu_i` over the samples for each subject.
rho2 : array, shape=[subjects]
The estimated noise variance :math:`\\rho_i^2` for each subject
s : array, shape=[features, samples]
The shared response. | https://github.com/contextlab/hypertools/blob/948050a22b345c7dcccf729672c76f49609b1ac8/hypertools/_externals/srm.py#L319-L431 | from __future__ import division
import logging
import numpy as np
import scipy
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import assert_all_finite
from sklearn.utils.validation import NotFittedError
__all__ = [
"SRM", "DetSRM"
]
logger = logging.getLogger(__name__)
def _init_w_transforms(data, features):
w = []
subjects = len(data)
voxels = np.empty(subjects, dtype=int)
for subject in range(subjects):
voxels[subject] = data[subject].shape[0]
rnd_matrix = np.random.random((voxels[subject], features))
q, r = np.linalg.qr(rnd_matrix)
w.append(q)
return w, voxels
class SRM(BaseEstimator, TransformerMixin):
def __init__(self, n_iter=10, features=50, rand_seed=0):
self.n_iter = n_iter
self.features = features
self.rand_seed = rand_seed
return
def fit(self, X, y=None):
logger.info('Starting Probabilistic SRM')
if len(X) <= 1:
raise ValueError("There are not enough subjects "
"({0:d}) to train the model.".format(len(X)))
if X[0].shape[1] < self.features:
raise ValueError(
"There are not enough samples to train the model with "
"{0:d} features.".format(self.features))
number_trs = X[0].shape[1]
number_subjects = len(X)
for subject in range(number_subjects):
assert_all_finite(X[subject])
if X[subject].shape[1] != number_trs:
raise ValueError("Different number of samples between subjects"
".")
self.sigma_s_, self.w_, self.mu_, self.rho2_, self.s_ = self._srm(X)
return self
def transform(self, X, y=None):
if hasattr(self, 'w_') is False:
raise NotFittedError("The model fit has not been run yet.")
if len(X) != len(self.w_):
raise ValueError("The number of subjects does not match the one"
" in the model.")
s = [None] * len(X)
for subject in range(len(X)):
s[subject] = self.w_[subject].T.dot(X[subject])
return s
def _init_structures(self, data, subjects):
x = []
mu = []
rho2 = np.zeros(subjects)
trace_xtx = np.zeros(subjects)
for subject in range(subjects):
mu.append(np.mean(data[subject], 1))
rho2[subject] = 1
trace_xtx[subject] = np.sum(data[subject] ** 2)
x.append(data[subject] - mu[subject][:, np.newaxis])
return x, mu, rho2, trace_xtx
def _likelihood(self, chol_sigma_s_rhos, log_det_psi, chol_sigma_s,
trace_xt_invsigma2_x, inv_sigma_s_rhos, wt_invpsi_x,
samples):
log_det = (np.log(np.diag(chol_sigma_s_rhos) ** 2).sum() + log_det_psi
+ np.log(np.diag(chol_sigma_s) ** 2).sum())
loglikehood = -0.5 * samples * log_det - 0.5 * trace_xt_invsigma2_x
loglikehood += 0.5 * np.trace(
wt_invpsi_x.T.dot(inv_sigma_s_rhos).dot(wt_invpsi_x))
return loglikehood | MIT License |
mavensdc/cdflib | cdflib/cdfwrite.py | CDF.write_variableattrs | python | def write_variableattrs(self, variableAttrs):
if not (isinstance(variableAttrs, dict)):
raise ValueError('Variable attribute(s) not in dictionary form')
dataType = None
numElems = None
with self.path.open('rb+') as f:
f.seek(0, 2)
for attr, attrs in variableAttrs.items():
if not (isinstance(attr, str)):
raise ValueError('Attribute name must be a string')
return
if (attr in self.gattrs):
raise ValueError(f'Variable attribute: {attr}' +
' is already a global variable')
return
if (attr in self.vattrs):
attrNum = self.vattrs.index(attr)
offsetA = self.attrsinfo[attrNum][2]
else:
attrNum, offsetA = self._write_adr(f, False, attr)
entries = 0
if (attrs is None):
continue
if not (isinstance(attrs, dict)):
raise ValueError('An attribute''s attribute(s) not in dictionary form')
entryNumX = -1
poffset = -1
for entryID, value in attrs.items():
if (isinstance(entryID, str) and (not (entryID in self.zvars) and
not (entryID in self.rvars))):
raise KeyError(f'{entryID} not found in the CDF')
if (isinstance(entryID, numbers.Number) and
(len(self.zvars) > 0 and len(self.rvars) > 0)):
raise ValueError(f'{entryID} can not be used as the CDF has both zVariables and rVariables')
if (isinstance(entryID, str)):
try:
entryNum = self.zvars.index(entryID)
zVar = True
except Exception:
try:
entryNum = self.rvars.index(entryID)
zVar = False
except Exception:
raise KeyError(f'{entryID} not found')
else:
entryNum = int(entryID)
if (len(self.zvars) > 0 and len(self.rvars) > 0):
raise ValueError('Can not use integer form for variable id as there ',
'are both zVariables and rVaribales')
if (len(self.zvars) > 0):
if (entryNum >= len(self.zvars)):
raise ValueError('Variable id: ', entryID, ' not found')
else:
zVar = True
else:
if (entryNum >= len(self.rvars)):
raise ValueError('Variable id: ', entryID, ' not found')
else:
zVar = False
if (entryNum > entryNumX):
entryNumX = entryNum
if (isinstance(value, list) or isinstance(value, tuple)):
if (len(value) == 2):
value2 = value[1]
dataType = self._datatype_token(value2)
if (dataType > 0):
data = value[0]
if (dataType == self.CDF_CHAR or
dataType == self.CDF_UCHAR):
if (isinstance(data, list) or
isinstance(data, tuple)):
raise ValueError('Invalid variable attribute value')
numElems = len(data)
elif (dataType == self.CDF_EPOCH or
dataType == self.CDF_EPOCH16
or dataType == self.CDF_TIME_TT2000):
cvalue = []
if (isinstance(data, list) or
isinstance(data, tuple)):
numElems = len(data)
for x in range(0, numElems):
if (isinstance(data[x], str)):
avalue = cdfepoch.CDFepoch.parse(data[x])
else:
avalue = data[x]
if (dataType == self.CDF_EPOCH16):
cvalue.append(avalue.real)
cvalue.append(avalue.imag)
else:
cvalue.append(avalue)
data = cvalue
else:
if (isinstance(data, str)):
data = cdfepoch.CDFepoch.parse(data)
numElems = 1
else:
if (isinstance(data, list) or isinstance(data, tuple)):
numElems = len(data)
else:
numElems = 1
else:
data = value
numElems, dataType = self._datatype_define(value[0])
numElems = len(value)
else:
data = value
numElems, dataType = self._datatype_define(value[0])
numElems = len(value)
else:
data = value
numElems, dataType = self._datatype_define(value)
if (numElems is None):
warnings.warn('Unknown data')
return
offset = self._write_aedr(f, False, attrNum, entryNum, data,
dataType, numElems, zVar)
if (entries == 0):
if zVar:
self._update_offset_value(f, offsetA+48, 8, offset)
else:
self._update_offset_value(f, offsetA+20, 8, offset)
else:
self._update_offset_value(f, poffset+12, 8, offset)
poffset = offset
entries = entries + 1
if zVar:
self._update_offset_value(f, offsetA+56, 4, entries)
self._update_offset_value(f, offsetA+60, 4, entryNumX)
else:
self._update_offset_value(f, offsetA+36, 4, entries)
self._update_offset_value(f, offsetA+40, 4, entryNumX) | Writes a variable's attributes, provided the variable already exists.
Parameters
----------
variableAttrs : dict
Variable attribute name and its entry value pair(s).
The entry value is also a dictionary of variable id and value
pair(s). Variable id can be the variable name or its id number
in the file. Use write_var function if the variable does not exist.
For example::
variableAttrs={}
entries_1={}
entries_1['var_name_1'] = 'abcd'
entries_1['var_name_2'] = [12, 'cdf_int4']
....
variableAttrs['attr_name_1']=entries_1
entries_2={}
entries_2['var_name_1'] = 'xyz'
entries_2['var_name_2'] = [[12, 34], 'cdf_int4']
....
variableAttrs['attr_name_2']=entries_2
....
....
f.write_variableattrs(variableAttrs) | https://github.com/mavensdc/cdflib/blob/e0b57ed32ab74197d2c9aa4ff948bb92593da5fd/cdflib/cdfwrite.py#L456-L622 | from typing import Tuple
import logging
import numpy as np
import sys
import struct
import gzip
import hashlib
import platform as pf
import binascii
import cdflib.epochs as cdfepoch
import numbers
import math
import pathlib
import warnings
def is_open(func):
def ensure_open(self, *args, **kwargs):
if self.is_closed:
raise OSError("This file is already closed, and can no longer be modified.")
else:
return func(self, *args, **kwargs)
return ensure_open
class CDF:
version = 3
release = 7
increment = 0
CDF_VAR_NAME_LEN256 = 256
CDF_ATTR_NAME_LEN256 = 256
CDF_COPYRIGHT_LEN = 256
CDF_PATHNAME_LEN = 512
CDF_INT1 = 1
CDF_INT2 = 2
CDF_INT4 = 4
CDF_INT8 = 8
CDF_UINT1 = 11
CDF_UINT2 = 12
CDF_UINT4 = 14
CDF_REAL4 = 21
CDF_REAL8 = 22
CDF_EPOCH = 31
CDF_EPOCH16 = 32
CDF_TIME_TT2000 = 33
CDF_BYTE = 41
CDF_FLOAT = 44
CDF_DOUBLE = 45
CDF_CHAR = 51
CDF_UCHAR = 52
NETWORK_ENCODING = 1
SUN_ENCODING = 2
VAX_ENCODING = 3
DECSTATION_ENCODING = 4
SGi_ENCODING = 5
IBMPC_ENCODING = 6
IBMRS_ENCODING = 7
HOST_ENCODING = 8
PPC_ENCODING = 9
HP_ENCODING = 11
NeXT_ENCODING = 12
ALPHAOSF1_ENCODING = 13
ALPHAVMSd_ENCODING = 14
ALPHAVMSg_ENCODING = 15
ALPHAVMSi_ENCODING = 16
ARM_LITTLE_ENCODING = 17
ARM_BIG_ENCODING = 18
VARY = -1
NOVARY = 0
ROW_MAJOR = 1
COLUMN_MAJOR = 2
NO_CHECKSUM = 0
MD5_CHECKSUM = 1
OTHER_CHECKSUM = 2
GLOBAL_SCOPE = 1
VARIABLE_SCOPE = 2
GZIP_COMPRESSION = 5
NO_SPARSERECORDS = 0
PAD_SPARSERECORDS = 1
PREV_SPARSERECORDS = 2
V3magicNUMBER_1 = 'cdf30001'
V3magicNUMBER_2 = '0000ffff'
V3magicNUMBER_2c = 'cccc0001'
CDR_ = 1
GDR_ = 2
rVDR_ = 3
ADR_ = 4
AgrEDR_ = 5
VXR_ = 6
VVR_ = 7
zVDR_ = 8
AzEDR_ = 9
CCR_ = 10
CPR_ = 11
SPR_ = 12
CVVR_ = 13
NUM_VXR_ENTRIES = 7
NUM_VXRlvl_ENTRIES = 3
UUIR_BASE_SIZE64 = 12
UIR_BASE_SIZE64 = 28
CDR_BASE_SIZE64 = 56
GDR_BASE_SIZE64 = 84
zVDR_BASE_SIZE64 = 88 + CDF_VAR_NAME_LEN256
rVDR_BASE_SIZE64 = 84 + CDF_VAR_NAME_LEN256
VXR_BASE_SIZE64 = 28
VVR_BASE_SIZE64 = 12
ADR_BASE_SIZE64 = 68 + CDF_ATTR_NAME_LEN256
AEDR_BASE_SIZE64 = 56
CCR_BASE_SIZE64 = 32
CPR_BASE_SIZE64 = 24
SPR_BASE_SIZE64 = 24
CVVR_BASE_SIZE64 = 24
BLOCKING_BYTES = 65536
level = 0
def __init__(self, path, cdf_spec=None, delete=False):
path = pathlib.Path(path).expanduser()
major = 1
if cdf_spec is not None:
if (isinstance(major, str)):
major = self._majority_token(major)
encoding = cdf_spec.get('Encoding', 8)
if (isinstance(encoding, str)):
encoding = self._encoding_token(encoding)
checksum = cdf_spec.get('Checksum', False)
cdf_compression = cdf_spec.get('Compressed', 0)
if (isinstance(cdf_compression, int)):
if not 0 <= cdf_compression <= 9:
cdf_compression = 0
else:
cdf_compression = 6 if cdf_compression else 0
rdim_sizes = cdf_spec.get('rDim_sizes', None)
num_rdim = len(rdim_sizes) if rdim_sizes is not None else 0
else:
encoding = 8
checksum = False
cdf_compression = 0
num_rdim = 0
rdim_sizes = None
if (major < 1 or major > 2):
raise OSError('Bad major.')
osSystem = pf.system()
osMachine = pf.uname()[5]
if (encoding == 8):
if osSystem != 'SunOS' or osMachine != 'sparc':
self._encoding = self.IBMPC_ENCODING
else:
self._encoding = self.SUN_ENCODING
else:
self._encoding = encoding
if (self._encoding == -1):
raise OSError('Bad encoding.')
if not isinstance(checksum, bool):
raise ValueError('Bad checksum.')
if path.suffix != '.cdf':
path = path.with_suffix('.cdf')
if len(str(path)) > self.CDF_PATHNAME_LEN:
raise OSError('CDF:', path, ' longer than allowed length.')
if path.is_file():
if not delete:
raise OSError('file: ', path, ' already exists....\n',
'Delete it or specify the \'delete=False\' option.')
else:
path.unlink()
self.path = path
self.compressed_file = path.with_suffix('.tmp') if cdf_compression > 0 else None
self.zvarsinfo = {}
self.rvarsinfo = {}
self.attrsinfo = {}
self.gattrs = []
self.vattrs = []
self.attrs = []
self.zvars = []
self.rvars = []
self.checksum = checksum
self.compression = cdf_compression
self.num_rdim = num_rdim
self.rdim_sizes = rdim_sizes
self.majority = major
with path.open('wb') as f:
f.write(binascii.unhexlify(self.V3magicNUMBER_1))
f.write(binascii.unhexlify(self.V3magicNUMBER_2))
self.cdr_head = self._write_cdr(f, major, self._encoding, checksum)
self.gdr_head = self._write_gdr(f)
self.offset = f.tell()
self.is_closed = False
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
return
def close(self):
if self.is_closed:
return
if self.compressed_file is None:
with self.path.open('rb+') as f:
f.seek(0, 2)
eof = f.tell()
self._update_offset_value(f, self.gdr_head+36, 8, eof)
if self.checksum:
f.write(self._md5_compute(f))
self.is_closed = True
return
with self.path.open('rb+') as f:
f.seek(0, 2)
eof = f.tell()
self._update_offset_value(f, self.gdr_head+36, 8, eof)
with self.compressed_file.open('wb+') as g:
g.write(bytearray.fromhex(self.V3magicNUMBER_1))
g.write(bytearray.fromhex(self.V3magicNUMBER_2c))
self._write_ccr(f, g, self.compression)
if self.checksum:
g.seek(0, 2)
g.write(self._md5_compute(g))
self.path.unlink()
self.compressed_file.rename(self.path)
self.is_closed = True
@is_open
def write_globalattrs(self, globalAttrs):
if not (isinstance(globalAttrs, dict)):
raise ValueError('Global attribute(s) not in dictionary form')
dataType = None
numElems = None
with self.path.open('rb+') as f:
f.seek(0, 2)
for attr, entry in globalAttrs.items():
if (attr in self.gattrs):
raise ValueError(f'Global attribute: {attr} already exists.')
if (attr in self.vattrs):
logging.warning(f'Attribute: {attr} already defined as a variable attribute.')
continue
attrNum, offsetADR = self._write_adr(f, True, attr)
entries = 0
if entry is None:
continue
entryNumMaX = -1
poffset = -1
for entryNum, value in entry.items():
if (entryNumMaX < entryNum):
entryNumMaX = entryNum
if (isinstance(value, list) or isinstance(value, tuple)):
if (len(value) == 2):
value2 = value[1]
dataType = self._datatype_token(value2)
if (dataType > 0):
data = value[0]
if (dataType == self.CDF_CHAR or
dataType == self.CDF_UCHAR):
if (isinstance(data, list) or
isinstance(data, tuple)):
warnings.warn('Invalid global attribute value')
return
numElems = len(data)
elif (dataType == self.CDF_EPOCH or
dataType == self.CDF_EPOCH16
or dataType == self.CDF_TIME_TT2000):
cvalue = []
if (isinstance(data, list) or
isinstance(data, tuple)):
numElems = len(data)
for x in range(0, numElems):
if (isinstance(data[x], str)):
cvalue.append(cdfepoch.CDFepoch.parse(data[x]))
else:
cvalue.append(data[x])
data = cvalue
else:
if (isinstance(data, str)):
data = cdfepoch.CDFepoch.parse(data)
numElems = 1
else:
if (isinstance(data, list) or
isinstance(data, tuple)):
numElems = len(data)
else:
numElems = 1
else:
data = value
numElems, dataType = self._datatype_define(value[0])
numElems = len(value)
else:
data = value
numElems, dataType = self._datatype_define(value[0])
numElems = len(value)
else:
data = value
numElems, dataType = self._datatype_define(value)
if (numElems is None):
warnings.warn('Unknown data')
return
offset = self._write_aedr(f, True, attrNum, entryNum, data,
dataType, numElems, None)
if (entries == 0):
self._update_offset_value(f, offsetADR+20, 8, offset)
else:
self._update_offset_value(f, poffset+12, 8, offset)
poffset = offset
entries = entries + 1
self._update_offset_value(f, offsetADR+36, 4, entries)
self._update_offset_value(f, offsetADR+40, 4, entryNumMaX)
@is_open | MIT License |
fusionauth/fusionauth-python-client | src/main/python/fusionauth/fusionauth_client.py | FusionAuthClient.action_user | python | def action_user(self, request):
return self.start().uri('/api/user/action') .body_handler(JSONBodyHandler(request)) .post() .go() | Takes an action on a user. The user being actioned is called the "actionee" and the user taking the action is called the
"actioner". Both user ids are required in the request object.
Attributes:
request: The action request that includes all of the information about the action being taken including
the id of the action, any options and the duration (if applicable). | https://github.com/fusionauth/fusionauth-python-client/blob/20bf313710eb0af6bfb9c07b7864b52fe5853eb0/src/main/python/fusionauth/fusionauth_client.py#L39-L51 | from deprecated import deprecated
from fusionauth.rest_client import RESTClient, JSONBodyHandler, FormDataBodyHandler
class FusionAuthClient:
def __init__(self, api_key, base_url):
self.api_key = api_key
self.base_url = base_url
self.tenant_id = None
def set_tenant_id(self, tenant_id):
self.tenant_id = tenant_id | Apache License 2.0 |
sberbank-ai-lab/lightautoml | lightautoml/reader/base.py | Reader.fit_read | python | def fit_read(
self,
train_data: Any,
features_names: Optional[List[str]] = None,
roles: UserRolesDefinition = None,
**kwargs: Any
):
raise NotImplementedError | Abstract function to get dataset with initial feature selection. | https://github.com/sberbank-ai-lab/lightautoml/blob/51a4e2bd0ebffbe0817fb50434280f8e7c40fa4c/lightautoml/reader/base.py#L100-L108 | import logging
from copy import deepcopy
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Sequence
from typing import TypeVar
from typing import Union
from typing import cast
import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas import Series
from ..dataset.base import array_attr_roles
from ..dataset.base import valid_array_attributes
from ..dataset.np_pd_dataset import PandasDataset
from ..dataset.roles import CategoryRole
from ..dataset.roles import ColumnRole
from ..dataset.roles import DatetimeRole
from ..dataset.roles import DropRole
from ..dataset.roles import NumericRole
from ..dataset.utils import roles_parser
from ..tasks import Task
from .guess_roles import calc_category_rules
from .guess_roles import calc_encoding_rules
from .guess_roles import get_category_roles_stat
from .guess_roles import get_null_scores
from .guess_roles import get_numeric_roles_stat
from .guess_roles import rule_based_cat_handler_guess
from .guess_roles import rule_based_roles_guess
from .utils import set_sklearn_folds
logger = logging.getLogger(__name__)
RoleType = TypeVar("RoleType", bound=ColumnRole)
RolesDict = Dict[str, RoleType]
UserDefinedRole = Optional[Union[str, RoleType]]
UserDefinedRolesDict = Dict[UserDefinedRole, Sequence[str]]
UserDefinedRolesSequence = Sequence[UserDefinedRole]
UserRolesDefinition = Optional[Union[UserDefinedRole, UserDefinedRolesDict, UserDefinedRolesSequence]]
class Reader:
def __init__(self, task: Task, *args: Any, **kwargs: Any):
self.task = task
self._roles = {}
self._dropped_features = []
self._used_array_attrs = {}
self._used_features = []
@property
def roles(self) -> RolesDict:
return self._roles
@property
def dropped_features(self) -> List[str]:
return self._dropped_features
@property
def used_features(self) -> List[str]:
return self._used_features
@property
def used_array_attrs(self) -> Dict[str, str]:
return self._used_array_attrs | Apache License 2.0 |
tcalmant/ipopo | pelix/utilities.py | EventData.data | python | def data(self):
return self.__data | Returns the associated value | https://github.com/tcalmant/ipopo/blob/1d4b81207e67890dfccc8f562336c7104f194c17/pelix/utilities.py#L596-L601 | import collections
import contextlib
import functools
import inspect
import logging
import sys
import threading
import traceback
try:
from typing import Any, Optional, Union
except ImportError:
pass
import pelix.constants
__version_info__ = (1, 0, 1)
__version__ = ".".join(str(x) for x in __version_info__)
__docformat__ = "restructuredtext en"
PYTHON_3 = sys.version_info[0] == 3
@contextlib.contextmanager
def use_service(bundle_context, svc_reference):
if svc_reference is None:
raise TypeError("Invalid ServiceReference")
try:
yield bundle_context.get_service(svc_reference)
finally:
try:
bundle_context.unget_service(svc_reference)
except pelix.constants.BundleException:
pass
ArgSpec = collections.namedtuple("ArgSpec", "args varargs keywords defaults")
if hasattr(inspect, "signature"):
def get_method_arguments(method):
signature = inspect.signature(method)
args = []
varargs = None
keywords = None
defaults = []
for param in signature.parameters.values():
kind = param.kind
if kind == inspect.Parameter.VAR_POSITIONAL:
varargs = param.name
elif kind == inspect.Parameter.VAR_KEYWORD:
keywords = param.name
else:
args.append(param.name)
if param.default is not param.empty:
defaults.append(param.default)
return ArgSpec(args, varargs, keywords, defaults or None)
else:
import types
def get_method_arguments(method):
arg_spec = inspect.getargspec(method)
if not isinstance(method, types.FunctionType):
args = arg_spec.args[1:]
else:
args = arg_spec.args
return ArgSpec(
args, arg_spec.varargs, arg_spec.keywords, arg_spec.defaults
)
class Deprecated(object):
def __init__(self, message=None, logger=None):
self.__message = message or "Deprecated method"
self.__logger = logger or None
self.__already_logged = False
def __log(self, method_name):
if not self.__already_logged:
stack = "\n\t".join(traceback.format_stack())
logging.getLogger(self.__logger).warning(
"%s: %s\n%s", method_name, self.__message, stack
)
self.__already_logged = True
def __call__(self, method):
@functools.wraps(method)
def wrapped(*args, **kwargs):
self.__log(method.__name__)
return method(*args, **kwargs)
return wrapped
class Synchronized(object):
def __init__(self, lock=None):
if not is_lock(lock):
self.__lock = threading.RLock()
else:
self.__lock = lock
def __call__(self, method):
@functools.wraps(method)
def wrapped(*args, **kwargs):
with self.__lock:
return method(*args, **kwargs)
return wrapped
def SynchronizedClassMethod(*locks_attr_names, **kwargs):
locks_attr_names = [
lock_name for lock_name in locks_attr_names if lock_name
]
if not locks_attr_names:
raise ValueError("The lock names list can't be empty")
if "sorted" not in kwargs or kwargs["sorted"]:
locks_attr_names = list(locks_attr_names)
locks_attr_names.sort()
def wrapped(method):
@functools.wraps(method)
def synchronized(self, *args, **kwargs):
locks = [getattr(self, attr_name) for attr_name in locks_attr_names]
locked = collections.deque()
i = 0
try:
for lock in locks:
if lock is None:
raise AttributeError(
"Lock '{0}' can't be None in class {1}".format(
locks_attr_names[i], type(self).__name__
)
)
i += 1
lock.acquire()
locked.appendleft(lock)
return method(self, *args, **kwargs)
finally:
for lock in locked:
lock.release()
locked.clear()
del locks[:]
return synchronized
return wrapped
def is_lock(lock):
if lock is None:
return False
for attr in "acquire", "release", "__enter__", "__exit__":
if not hasattr(lock, attr):
return False
return True
def read_only_property(value):
return property(lambda cls: value)
def remove_all_occurrences(sequence, item):
if sequence is None:
return
while item in sequence:
sequence.remove(item)
def remove_duplicates(items):
if items is None:
return items
new_list = []
for item in items:
if item not in new_list:
new_list.append(item)
return new_list
def add_listener(registry, listener):
if listener is None or listener in registry:
return False
registry.append(listener)
return True
def remove_listener(registry, listener):
if listener is not None and listener in registry:
registry.remove(listener)
return True
return False
if PYTHON_3:
def is_bytes(string):
return isinstance(string, bytes)
def is_string(string):
return isinstance(string, str)
def to_bytes(data, encoding="UTF-8"):
if isinstance(data, bytes):
return data
return data.encode(encoding)
def to_str(data, encoding="UTF-8"):
if isinstance(data, str):
return data
return str(data, encoding)
to_unicode = to_str
else:
def is_bytes(string):
return isinstance(string, str)
def is_string(string):
return isinstance(string, (str, unicode))
def to_str(data, encoding="UTF-8"):
if type(data) is str:
return data
return data.encode(encoding)
to_bytes = to_str
def to_unicode(data, encoding="UTF-8"):
if type(data) is unicode:
return data
return data.decode(encoding)
def to_iterable(value, allow_none=True):
if value is None:
if allow_none:
return None
return []
elif isinstance(value, (list, tuple, set, frozenset)):
return value
return [value]
class EventData(object):
__slots__ = ("__event", "__data", "__exception")
def __init__(self):
self.__event = threading.Event()
self.__data = None
self.__exception = None
@property | Apache License 2.0 |
readthedocs/readthedocs.org | readthedocs/oauth/migrations/0006_move_oauth_source.py | forwards_move_org_source | python | def forwards_move_org_source(apps, schema_editor):
RemoteOrganization = apps.get_model('oauth', 'RemoteOrganization')
SocialAccount = apps.get_model('socialaccount', 'SocialAccount')
for account in SocialAccount.objects.all():
rows = (RemoteOrganization.objects
.filter(users=account.user, source=account.provider)
.update(account=account)) | Use source field to set organization account. | https://github.com/readthedocs/readthedocs.org/blob/2cff8376f0ef8f25ae6d8763bdbec86f47e33ab9/readthedocs/oauth/migrations/0006_move_oauth_source.py#L23-L30 | from django.db import migrations
def forwards_move_repo_source(apps, schema_editor):
RemoteRepository = apps.get_model('oauth', 'RemoteRepository')
SocialAccount = apps.get_model('socialaccount', 'SocialAccount')
for account in SocialAccount.objects.all():
rows = (RemoteRepository.objects
.filter(users=account.user, source=account.provider)
.update(account=account))
def backwards_move_repo_source(apps, schema_editor):
apps.get_model('oauth', 'RemoteRepository')
SocialAccount = apps.get_model('socialaccount', 'SocialAccount')
for account in SocialAccount.objects.all():
rows = (account.remote_repositories
.update(account=None, source=account.provider)) | MIT License |
dnandha/mopac | softlearning/scripts/console_scripts.py | launch_example_ec2_cmd | python | def launch_example_ec2_cmd(*args, **kwargs):
return launch_example_ec2(*args, **kwargs) | Forwards call to `launch_example_cluster` after adding ec2 defaults.
This optionally sets the ray autoscaler configuration file to the default
ec2 configuration file, and then calls `launch_example_cluster` to
execute the original command on autoscaled ec2 cluster by parsing the args.
See `launch_example_cluster` for further details. | https://github.com/dnandha/mopac/blob/058128183d16b7f8dcdaf2758a38b10f348566aa/softlearning/scripts/console_scripts.py#L174-L183 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import click
from mopac.examples.instrument import (
run_example_dry,
run_example_local,
run_example_debug,
run_example_cluster,
launch_example_cluster,
launch_example_gce,
launch_example_ec2)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def add_options(options):
def decorator(f):
for option in options[::-1]:
click.decorators._param_memo(f, option)
return f
return decorator
@click.group()
def cli():
pass
@cli.command(
name='run_example_dry',
context_settings={'ignore_unknown_options': True})
@click.argument("example_module_name", required=True, type=str)
@click.argument('example_argv', nargs=-1, type=click.UNPROCESSED)
def run_example_dry_cmd(example_module_name, example_argv):
return run_example_dry(example_module_name, example_argv)
@cli.command(
name='run_local',
context_settings={'ignore_unknown_options': True})
@click.argument("example_module_name", required=True, type=str)
@click.argument('example_argv', nargs=-1, type=click.UNPROCESSED)
def run_example_local_cmd(example_module_name, example_argv):
return run_example_local(example_module_name, example_argv)
@cli.command(
name='run_example_debug',
context_settings={'ignore_unknown_options': True})
@click.argument("example_module_name", required=True, type=str)
@click.argument('example_argv', nargs=-1, type=click.UNPROCESSED)
def run_example_debug_cmd(example_module_name, example_argv):
return run_example_debug(example_module_name, example_argv)
@cli.command(
name='run_example_cluster',
context_settings={'ignore_unknown_options': True})
@click.argument("example_module_name", required=True, type=str)
@click.argument('example_argv', nargs=-1, type=click.UNPROCESSED)
def run_example_cluster_cmd(example_module_name, example_argv):
run_example_cluster(example_module_name, example_argv)
@cli.command(
name='launch_example_cluster',
context_settings={
'allow_extra_args': True,
'ignore_unknown_options': True
})
@click.argument("example_module_name", required=True, type=str)
@click.argument('example_argv', nargs=-1, type=click.UNPROCESSED)
@click.option(
"--config_file",
required=False,
type=str)
@click.option(
"--stop/--no-stop",
is_flag=True,
default=True,
help="Stop the cluster after the command finishes running.")
@click.option(
"--start/--no-start",
is_flag=True,
default=True,
help="Start the cluster if needed.")
@click.option(
"--screen/--no-screen",
is_flag=True,
default=False,
help="Run the command in a screen.")
@click.option(
"--tmux/--no-tmux",
is_flag=True,
default=True,
help="Run the command in tmux.")
@click.option(
"--override-cluster-name",
required=False,
type=str,
help="Override the configured cluster name.")
@click.option(
"--port-forward", required=False, type=int, help="Port to forward.")
def launch_example_cluster_cmd(*args, **kwargs):
return launch_example_cluster(*args, **kwargs)
@cli.command(
name='launch_example_gce',
context_settings={
'allow_extra_args': True,
'ignore_unknown_options': True
})
@add_options(launch_example_cluster_cmd.params)
def launch_example_gce_cmd(*args, **kwargs):
return launch_example_gce(*args, **kwargs)
@cli.command(
name='launch_example_ec2',
context_settings={
'allow_extra_args': True,
'ignore_unknown_options': True
})
@add_options(launch_example_cluster_cmd.params) | MIT License |
coarse-graining/cgnet | cgnet/feature/utils.py | ShiftedSoftplus.forward | python | def forward(self, input_tensor):
return nn.functional.softplus(input_tensor) - np.log(2.0) | Applies the shifted softplus function element-wise
Parameters
----------
input_tensor: torch.Tensor
Input tensor of size (n_examples, *) where `*` means, any number of
additional dimensions.
Returns
-------
Output: torch.Tensor
Same size (n_examples, *) as the input. | https://github.com/coarse-graining/cgnet/blob/ce7dadb1f8e66771032275ef87b8193ad234d495/cgnet/feature/utils.py#L39-L53 | import numpy as np
import torch
import torch.nn as nn
class ShiftedSoftplus(nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__() | BSD 3-Clause New or Revised License |
labd/commercetools-python-sdk | src/commercetools/services/reviews.py | ReviewService.create | python | def create(self, draft: ReviewDraft, *, expand: OptionalListStr = None) -> Review:
params = self._serialize_params({"expand": expand}, traits.ExpandableSchema)
return self._client._post(
endpoint="reviews", params=params, data_object=draft, response_class=Review
) | Reviews are used to evaluate products and channels. | https://github.com/labd/commercetools-python-sdk/blob/d8ec285f08d56ede2e4cad45c74833f5b609ab5c/src/commercetools/services/reviews.py#L79-L84 | import typing
from commercetools.helpers import RemoveEmptyValuesMixin
from commercetools.platform.models.review import (
Review,
ReviewDraft,
ReviewPagedQueryResponse,
ReviewUpdate,
ReviewUpdateAction,
)
from commercetools.typing import OptionalListStr
from . import abstract, traits
class _ReviewQuerySchema(
traits.ExpandableSchema,
traits.SortableSchema,
traits.PagingSchema,
traits.QuerySchema,
):
pass
class _ReviewUpdateSchema(traits.ExpandableSchema, traits.VersionedSchema):
pass
class _ReviewDeleteSchema(
traits.VersionedSchema, traits.ExpandableSchema, traits.DataErasureSchema
):
pass
class ReviewService(abstract.AbstractService):
def get_by_id(self, id: str, *, expand: OptionalListStr = None) -> Review:
params = self._serialize_params({"expand": expand}, traits.ExpandableSchema)
return self._client._get(
endpoint=f"reviews/{id}", params=params, response_class=Review
)
def get_by_key(self, key: str, *, expand: OptionalListStr = None) -> Review:
params = self._serialize_params({"expand": expand}, traits.ExpandableSchema)
return self._client._get(
endpoint=f"reviews/key={key}", params=params, response_class=Review
)
def query(
self,
*,
expand: OptionalListStr = None,
sort: OptionalListStr = None,
limit: int = None,
offset: int = None,
with_total: bool = None,
where: OptionalListStr = None,
predicate_var: typing.Dict[str, str] = None,
) -> ReviewPagedQueryResponse:
params = self._serialize_params(
{
"expand": expand,
"sort": sort,
"limit": limit,
"offset": offset,
"with_total": with_total,
"where": where,
"predicate_var": predicate_var,
},
_ReviewQuerySchema,
)
return self._client._get(
endpoint="reviews", params=params, response_class=ReviewPagedQueryResponse
) | MIT License |
nrel/rdtools | rdtools/analysis_chains.py | TrendAnalysis.set_clearsky | python | def set_clearsky(self, pvlib_location=None, pv_azimuth=None, pv_tilt=None,
poa_global_clearsky=None, temperature_cell_clearsky=None,
temperature_ambient_clearsky=None, albedo=0.25,
solar_position_method='nrel_numpy'):
max_timedelta = self.max_timedelta
if poa_global_clearsky is not None:
poa_global_clearsky = normalization.interpolate(
poa_global_clearsky, self.pv_energy.index, max_timedelta)
if temperature_cell_clearsky is not None:
temperature_cell_clearsky = normalization.interpolate(
temperature_cell_clearsky, self.pv_energy.index, max_timedelta)
if temperature_ambient_clearsky is not None:
temperature_ambient_clearsky = normalization.interpolate(
temperature_ambient_clearsky, self.pv_energy.index, max_timedelta)
if isinstance(pv_azimuth, (pd.Series, pd.DataFrame)):
pv_azimuth = normalization.interpolate(
pv_azimuth, self.pv_energy.index, max_timedelta)
if isinstance(pv_tilt, (pd.Series, pd.DataFrame)):
pv_tilt = normalization.interpolate(
pv_tilt, self.pv_energy.index, max_timedelta)
self.pvlib_location = pvlib_location
self.pv_azimuth = pv_azimuth
self.pv_tilt = pv_tilt
self.poa_global_clearsky = poa_global_clearsky
self.temperature_cell_clearsky = temperature_cell_clearsky
self.temperature_ambient_clearsky = temperature_ambient_clearsky
self.albedo = albedo
self.solar_position_method = solar_position_method | Initialize values for a clearsky analysis which requires configuration
of location and orientation details. If optional parameters `poa_global_clearsky`,
`temperature_ambient_clearsky` are not passed, they will be modeled
based on location and orientation.
Parameters
----------
pvlib_location : pvlib.location.Location
Used for calculating clearsky temperature and irradiance
pv_azimuth : numeric
Azimuth of PV array in degrees from north. Can be right-labeled
Pandas Time Series or single numeric value.
pv_tilt : numeric
Tilt of PV array in degrees from horizontal. Can be right-labeled
Pandas Time Series or single numeric value.
poa_global_clearsky : pandas.Series
Right-labeled time Series of clear-sky plane of array irradiance
temperature_cell_clearsky : pandas.Series
Right-labeled time series of cell temperature in clear-sky conditions
in Celsius. In practice, back of module temperature works as a good
approximation.
temperature_ambient_clearsky : pandas.Series
Right-label time series of ambient temperature in clear sky conditions
in Celsius
albedo : numeric
Albedo to be used in irradiance transposition calculations. Can be right-labeled
Pandas Time Series or single numeric value.
solar_position_method : str, default 'nrel_numpy'
Optional method name to pass to :py:func:`pvlib.solarposition.get_solarposition`.
Switching methods may improve calculation time. | https://github.com/nrel/rdtools/blob/4ca70e3e2cec85fead10cb8e6ef5e098eeb6f686/rdtools/analysis_chains.py#L140-L201 | import pvlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from rdtools import normalization, filtering, aggregation, degradation
from rdtools import clearsky_temperature, plotting
import warnings
class TrendAnalysis():
def __init__(self, pv, poa_global=None, temperature_cell=None, temperature_ambient=None,
gamma_pdc=None, aggregation_freq='D', pv_input='power',
windspeed=0, power_expected=None, temperature_model=None,
power_dc_rated=None, interp_freq=None, max_timedelta=None):
if interp_freq is not None:
pv = normalization.interpolate(pv, interp_freq, max_timedelta)
if poa_global is not None:
poa_global = normalization.interpolate(
poa_global, pv.index, max_timedelta)
if temperature_cell is not None:
temperature_cell = normalization.interpolate(
temperature_cell, pv.index, max_timedelta)
if temperature_ambient is not None:
temperature_ambient = normalization.interpolate(
temperature_ambient, pv.index, max_timedelta)
if power_expected is not None:
power_expected = normalization.interpolate(
power_expected, pv.index, max_timedelta)
if isinstance(windspeed, pd.Series):
windspeed = normalization.interpolate(
windspeed, pv.index, max_timedelta)
if pv_input == 'power':
self.pv_power = pv
self.pv_energy = normalization.energy_from_power(
pv, max_timedelta=max_timedelta)
elif pv_input == 'energy':
self.pv_power = None
self.pv_energy = pv
self.temperature_cell = temperature_cell
self.temperature_ambient = temperature_ambient
self.poa_global = poa_global
self.gamma_pdc = gamma_pdc
self.aggregation_freq = aggregation_freq
self.windspeed = windspeed
self.power_expected = power_expected
self.temperature_model = temperature_model
self.power_dc_rated = power_dc_rated
self.interp_freq = interp_freq
self.max_timedelta = max_timedelta
self.results = {}
self.filter_params = {
'normalized_filter': {},
'poa_filter': {},
'tcell_filter': {},
'clip_filter': {},
'csi_filter': {},
'ad_hoc_filter': None
}
if power_expected is not None and temperature_cell is None:
del self.filter_params['tcell_filter'] | MIT License |
mrknow/filmkodi | plugin.video.mrknow/mylib/pydevd_attach_to_process/winappdbg/breakpoint.py | Breakpoint.set_action | python | def set_action(self, action = None):
self.__action = action | Sets a new action callback for the breakpoint.
@type action: function
@param action: (Optional) Action callback function. | https://github.com/mrknow/filmkodi/blob/0162cde9ae25ddbf4a69330948714833ff2f78c9/plugin.video.mrknow/mylib/pydevd_attach_to_process/winappdbg/breakpoint.py#L382-L389 | __revision__ = "$Id$"
__all__ = [
'Breakpoint',
'CodeBreakpoint',
'PageBreakpoint',
'HardwareBreakpoint',
'Hook',
'ApiHook',
'BufferWatch',
'BreakpointWarning',
'BreakpointCallbackWarning',
]
from winappdbg import win32
from winappdbg import compat
import sys
from winappdbg.process import Process, Thread
from winappdbg.util import DebugRegister, MemoryAddresses
from winappdbg.textio import HexDump
import ctypes
import warnings
import traceback
class BreakpointWarning (UserWarning):
class BreakpointCallbackWarning (RuntimeWarning):
class Breakpoint (object):
DISABLED = 0
ENABLED = 1
ONESHOT = 2
RUNNING = 3
typeName = 'breakpoint'
stateNames = {
DISABLED : 'disabled',
ENABLED : 'enabled',
ONESHOT : 'one shot',
RUNNING : 'running',
}
def __init__(self, address, size = 1, condition = True, action = None):
self.__address = address
self.__size = size
self.__state = self.DISABLED
self.set_condition(condition)
self.set_action(action)
def __repr__(self):
if self.is_disabled():
state = 'Disabled'
else:
state = 'Active (%s)' % self.get_state_name()
if self.is_conditional():
condition = 'conditional'
else:
condition = 'unconditional'
name = self.typeName
size = self.get_size()
if size == 1:
address = HexDump.address( self.get_address() )
else:
begin = self.get_address()
end = begin + size
begin = HexDump.address(begin)
end = HexDump.address(end)
address = "range %s-%s" % (begin, end)
msg = "<%s %s %s at remote address %s>"
msg = msg % (state, condition, name, address)
return msg
def is_disabled(self):
return self.get_state() == self.DISABLED
def is_enabled(self):
return self.get_state() == self.ENABLED
def is_one_shot(self):
return self.get_state() == self.ONESHOT
def is_running(self):
return self.get_state() == self.RUNNING
def is_here(self, address):
begin = self.get_address()
end = begin + self.get_size()
return begin <= address < end
def get_address(self):
return self.__address
def get_size(self):
return self.__size
def get_span(self):
address = self.get_address()
size = self.get_size()
return ( address, address + size )
def get_state(self):
return self.__state
def get_state_name(self):
return self.stateNames[ self.get_state() ]
def is_conditional(self):
return self.__condition is not True
def is_unconditional(self):
return self.__condition is True
def get_condition(self):
return self.__condition
def set_condition(self, condition = True):
if condition is None:
self.__condition = True
else:
self.__condition = condition
def eval_condition(self, event):
condition = self.get_condition()
if condition is True:
return True
if callable(condition):
try:
return bool( condition(event) )
except Exception:
e = sys.exc_info()[1]
msg = ("Breakpoint condition callback %r"
" raised an exception: %s")
msg = msg % (condition, traceback.format_exc(e))
warnings.warn(msg, BreakpointCallbackWarning)
return False
return bool( condition )
def is_automatic(self):
return self.__action is not None
def is_interactive(self):
return self.__action is None
def get_action(self):
return self.__action | Apache License 2.0 |
rustychris/stompy | stompy/model/fish_ptm/ptm_tools.py | PtmBin.dt_seconds | python | def dt_seconds(self):
dnum1,data = self.read_timestep(0)
dnum2,data = self.read_timestep(1)
return (dnum2-dnum1).total_seconds() | Return the bin file output interval in decimal seconds. | https://github.com/rustychris/stompy/blob/ef04d8b3ee9c9af827c87c72c7b50d365e5e567d/stompy/model/fish_ptm/ptm_tools.py#L131-L137 | import os
import time
import numpy as np
import xarray as xr
from datetime import datetime
import matplotlib.pyplot as plt
from ...spatial import wkb2shp
from ... import memoize, utils
import pandas as pd
class PtmBin(object):
use_memmap=True
fp=None
def __init__(self,fn,release_name=None,idx_fn='auto'):
self.fn = fn
if release_name is None:
release_name = os.path.basename(fn)
release_name = release_name.replace("_bin.out","")
self.release = release_name
self.fp = open(self.fn,'rb')
self.fn_bytes = os.stat(self.fn).st_size
self.read_bin_header()
self.offsets = {}
self.offsets[0] = self.fp.tell()
if idx_fn=='auto':
idx_fn=fn.replace('_bin.out','_bin.idx')
if os.path.exists(idx_fn):
self.idx_fn=idx_fn
self.read_index()
else:
self.getTime()
def __del__(self):
if self.fp is not None:
self.fp.close()
self.fp=None
def read_index(self):
df=pd.read_csv(self.idx_fn,sep='\s+',
names=['year','month','day','hour','minute','offset','count'])
df['time']=pd.to_datetime(df[['year','month','day','hour','minute']])
self.time=df.time.dt.to_pydatetime()
self.offsets=dict(zip(df.index.values,df.offset.values))
def read_bin_header(self):
self.Nattr = int( np.fromstring(self.fp.read(4),np.int32) )
atts = []
for i in range(self.Nattr):
idx = int( np.fromstring( self.fp.read(4), np.int32) )
type_str = self.fp.read(80).strip()
name_str = self.fp.read(80).strip()
atts.append( (idx,type_str,name_str) )
self.atts=atts
def scan_to_timestep(self,ts):
if ts<0:
nsteps=self.count_timesteps()
ts=nsteps+ts
assert ts>=0
if ts not in self.offsets:
for ts_scan in range(1,ts+1):
if ts_scan not in self.offsets:
self.fp.seek( self.offsets[ts_scan-1])
tstep_header = np.fromstring( self.fp.read( 6*4 ), np.int32 )
Npart = tstep_header[5]
frame = 6*4 + Npart * (2*4 + 3*8)
self.offsets[ts_scan] = self.offsets[ts_scan-1] + frame
if self.offsets[ts_scan] >= self.fn_bytes:
return False
if self.offsets[ts] >= self.fn_bytes:
return False
self.fp.seek(self.offsets[ts])
return True
def count_timesteps(self):
saved_pos = self.fp.tell()
valid_ts = -1
while 1:
if self.scan_to_timestep(valid_ts+1):
valid_ts += 1
else:
break
self.fp.seek(saved_pos)
return valid_ts + 1 | MIT License |
digital-concrete/light-sync | phue_lib.py | Sensor.state | python | def state(self):
data = self._get('state')
self._state.clear()
self._state.update(data)
return self._state | A dictionary of sensor state. Some values can be updated, some are read-only. [dict] | https://github.com/digital-concrete/light-sync/blob/b2f8405971b6204f4d43f5a63ae91381462913f2/phue_lib.py#L412-L417 | import json
import logging
import os
import platform
import sys
import socket
if sys.version_info[0] > 2:
PY3K = True
else:
PY3K = False
if PY3K:
import http.client as httplib
else:
import httplib
logger = logging.getLogger('phue')
if platform.system() == 'Windows':
USER_HOME = 'USERPROFILE'
else:
USER_HOME = 'HOME'
__version__ = '1.1'
def is_string(data):
if PY3K:
return isinstance(data, str)
else:
return isinstance(data, str) or isinstance(data, unicode)
class PhueException(Exception):
def __init__(self, id, message):
self.id = id
self.message = message
class PhueRegistrationException(PhueException):
pass
class PhueRequestTimeout(PhueException):
pass
class Light(object):
def __init__(self, bridge, light_id):
self.bridge = bridge
self.light_id = light_id
self._name = None
self._on = None
self._brightness = None
self._colormode = None
self._hue = None
self._saturation = None
self._xy = None
self._colortemp = None
self._effect = None
self._alert = None
self.transitiontime = None
self._reset_bri_after_on = None
self._reachable = None
self._type = None
def __repr__(self):
return '<{0}.{1} object "{2}" at {3}>'.format(
self.__class__.__module__,
self.__class__.__name__,
self.name,
hex(id(self)))
def _get(self, *args, **kwargs):
return self.bridge.get_light(self.light_id, *args, **kwargs)
def _set(self, *args, **kwargs):
if self.transitiontime is not None:
kwargs['transitiontime'] = self.transitiontime
logger.debug("Setting with transitiontime = {0} ds = {1} s".format(
self.transitiontime, float(self.transitiontime) / 10))
if (args[0] == 'on' and args[1] is False) or (
kwargs.get('on', True) is False):
self._reset_bri_after_on = True
return self.bridge.set_light(self.light_id, *args, **kwargs)
@property
def name(self):
if PY3K:
self._name = self._get('name')
else:
self._name = self._get('name').encode('utf-8')
return self._name
@name.setter
def name(self, value):
old_name = self.name
self._name = value
self._set('name', self._name)
logger.debug("Renaming light from '{0}' to '{1}'".format(
old_name, value))
self.bridge.lights_by_name[self.name] = self
del self.bridge.lights_by_name[old_name]
@property
def on(self):
self._on = self._get('on')
return self._on
@on.setter
def on(self, value):
if self._on and value is False:
self._reset_bri_after_on = self.transitiontime is not None
if self._reset_bri_after_on:
logger.warning(
'Turned off light with transitiontime specified, brightness will be reset on power on')
self._set('on', value)
if self._on is False and value is True:
if self._reset_bri_after_on:
logger.warning(
'Light was turned off with transitiontime specified, brightness needs to be reset now.')
self.brightness = self._brightness
self._reset_bri_after_on = False
self._on = value
@property
def colormode(self):
self._colormode = self._get('colormode')
return self._colormode
@property
def brightness(self):
self._brightness = self._get('bri')
return self._brightness
@brightness.setter
def brightness(self, value):
self._brightness = value
self._set('bri', self._brightness)
@property
def hue(self):
self._hue = self._get('hue')
return self._hue
@hue.setter
def hue(self, value):
self._hue = int(value)
self._set('hue', self._hue)
@property
def saturation(self):
self._saturation = self._get('sat')
return self._saturation
@saturation.setter
def saturation(self, value):
self._saturation = value
self._set('sat', self._saturation)
@property
def xy(self):
self._xy = self._get('xy')
return self._xy
@xy.setter
def xy(self, value):
self._xy = value
self._set('xy', self._xy)
@property
def colortemp(self):
self._colortemp = self._get('ct')
return self._colortemp
@colortemp.setter
def colortemp(self, value):
if value < 154:
logger.warn('154 mireds is coolest allowed color temp')
elif value > 500:
logger.warn('500 mireds is warmest allowed color temp')
self._colortemp = value
self._set('ct', self._colortemp)
@property
def colortemp_k(self):
self._colortemp = self._get('ct')
return int(round(1e6 / self._colortemp))
@colortemp_k.setter
def colortemp_k(self, value):
if value > 6500:
logger.warn('6500 K is max allowed color temp')
value = 6500
elif value < 2000:
logger.warn('2000 K is min allowed color temp')
value = 2000
colortemp_mireds = int(round(1e6 / value))
logger.debug("{0:d} K is {1} mireds".format(value, colortemp_mireds))
self.colortemp = colortemp_mireds
@property
def effect(self):
self._effect = self._get('effect')
return self._effect
@effect.setter
def effect(self, value):
self._effect = value
self._set('effect', self._effect)
@property
def alert(self):
self._alert = self._get('alert')
return self._alert
@alert.setter
def alert(self, value):
if value is None:
value = 'none'
self._alert = value
self._set('alert', self._alert)
@property
def reachable(self):
self._reachable = self._get('reachable')
return self._reachable
@property
def type(self):
self._type = self._get('type')
return self._type
class SensorState(dict):
def __init__(self, bridge, sensor_id):
self._bridge = bridge
self._sensor_id = sensor_id
def __setitem__(self, key, value):
dict.__setitem__(self, key, value)
self._bridge.set_sensor_state(self._sensor_id, self)
class SensorConfig(dict):
def __init__(self, bridge, sensor_id):
self._bridge = bridge
self._sensor_id = sensor_id
def __setitem__(self, key, value):
dict.__setitem__(self, key, value)
self._bridge.set_sensor_config(self._sensor_id, self)
class Sensor(object):
def __init__(self, bridge, sensor_id):
self.bridge = bridge
self.sensor_id = sensor_id
self._name = None
self._model = None
self._swversion = None
self._type = None
self._uniqueid = None
self._manufacturername = None
self._state = SensorState(bridge, sensor_id)
self._config = {}
self._recycle = None
def __repr__(self):
return '<{0}.{1} object "{2}" at {3}>'.format(
self.__class__.__module__,
self.__class__.__name__,
self.name,
hex(id(self)))
def _get(self, *args, **kwargs):
return self.bridge.get_sensor(self.sensor_id, *args, **kwargs)
def _set(self, *args, **kwargs):
return self.bridge.set_sensor(self.sensor_id, *args, **kwargs)
@property
def name(self):
if PY3K:
self._name = self._get('name')
else:
self._name = self._get('name').encode('utf-8')
return self._name
@name.setter
def name(self, value):
old_name = self.name
self._name = value
self._set('name', self._name)
logger.debug("Renaming sensor from '{0}' to '{1}'".format(
old_name, value))
self.bridge.sensors_by_name[self.name] = self
del self.bridge.sensors_by_name[old_name]
@property
def modelid(self):
self._modelid = self._get('modelid')
return self._modelid
@property
def swversion(self):
self._swversion = self._get('swversion')
return self._swversion
@property
def type(self):
self._type = self._get('type')
return self._type
@property
def uniqueid(self):
self._uniqueid = self._get('uniqueid')
return self._uniqueid
@property
def manufacturername(self):
self._manufacturername = self._get('manufacturername')
return self._manufacturername
@property | MIT License |
berkeley-reclab/reclab | reclab/environments/latent_factors.py | LatentFactorBehavior._get_rating | python | def _get_rating(self, user_id, item_id):
raw_rating = (self._user_factors[user_id] @ self._item_factors[item_id]
+ self._user_biases[user_id] + self._item_biases[item_id] + self._offset)
boredom_penalty = 0
for item_id_hist in self._user_histories[user_id]:
item_factor = self._item_factors[item_id_hist]
if item_factor is not None:
similarity = ((self._item_factors[item_id] @ item_factor)
/ np.linalg.norm(item_factor)
/ np.linalg.norm(self._item_factors[item_id]))
if similarity > self._boredom_threshold:
boredom_penalty += (similarity - self._boredom_threshold)
boredom_penalty *= self._boredom_penalty
rating = np.clip(raw_rating - boredom_penalty + self._dynamics_random.randn() *
self._noise, 1, 5)
return rating | Compute user's rating of item based on model.
Parameters
----------
user_id : int
The id of the user making the rating.
item_id : int
The id of the item being rated.
Returns
-------
rating : int
The rating the item was given by the user. | https://github.com/berkeley-reclab/reclab/blob/09d5b1639e9b7f6cbd230f181130b681e31cf4f0/reclab/environments/latent_factors.py#L100-L133 | import collections
import json
import os
import numpy as np
from . import environment
from .. import data_utils
class LatentFactorBehavior(environment.DictEnvironment):
def __init__(self, latent_dim, num_users, num_items,
rating_frequency=0.02, num_init_ratings=0,
noise=0.0, memory_length=0, affinity_change=0.0,
boredom_threshold=0, boredom_penalty=0.0, user_dist_choice='uniform'):
super().__init__(rating_frequency, num_init_ratings, memory_length, user_dist_choice)
self._latent_dim = latent_dim
self._num_users = num_users
self._num_items = num_items
self._noise = noise
self._affinity_change = affinity_change
self._boredom_threshold = boredom_threshold
self._boredom_penalty = boredom_penalty
if self._memory_length > 0:
self._boredom_penalty /= self._memory_length
self._user_factors = None
self._user_biases = None
self._item_factors = None
self._item_biases = None
self._offset = None
@property
def name(self):
return 'latent'
def _get_dense_ratings(self):
ratings = (self._user_factors @ self._item_factors.T + self._user_biases[:, np.newaxis] +
self._item_biases[np.newaxis, :] + self._offset)
item_norms = np.linalg.norm(self._item_factors, axis=1)
normalized_items = self._item_factors / item_norms[:, np.newaxis]
similarities = normalized_items @ normalized_items.T
similarities -= self._boredom_threshold
similarities[similarities < 0] = 0
penalties = self._boredom_penalty * similarities
for user_id in range(self._num_users):
for item_id in self._user_histories[user_id]:
if item_id is not None:
ratings[user_id] -= penalties[item_id]
return ratings | MIT License |
thunlp-mt/pr4nmt | thumt/nmt.py | RNNsearch.get_attention | python | def get_attention(self, x, xmask, y, ymask):
if not hasattr(self, "get_attentioner"):
self.get_attentioner = theano.function(inputs = [self.x, self.xmask, self.y, self.ymask],
outputs = [self.attention])
return self.get_attentioner(x, xmask, y, ymask) | Get the attention weight of parallel sentences. | https://github.com/thunlp-mt/pr4nmt/blob/104766db729f2babe1db69c5b10a1aa45f578bf3/thumt/nmt.py#L508-L515 | import numpy
import theano
import theano.tensor as tensor
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import tools
from layer import LayerFactory
import json
import traceback
import cPickle
import logging
class model(object):
def __init__(self):
pass
def sample(self, x, length, n_samples = 1):
sample, probs = self.get_sample(x.reshape((x.shape[0],1)), length, n_samples)
return numpy.asarray(sample, dtype = 'int64').transpose(), probs
def translate(self, x, beam_size = 10, return_array = False, rerank = False):
result = [[]]
loss = [0.]
result_eos = []
loss_eos = []
beam = beam_size
c, state = self.get_context_and_init(x)
emb_y = numpy.zeros((1, self.config['dim_emb_trg']), dtype = 'float32')
for l in range(x.shape[0] * 3):
energy, ctx = self.get_probs(numpy.repeat(c, len(result), axis = 1), state, emb_y)
probs = tools.softmax(energy)
losses = -numpy.log(probs)
if l < x.shape[0] / 2:
losses[:, self.config['index_eos_trg']] = numpy.inf
for i in range(len(loss)):
losses[i] += loss[i]
best_index_flatten = numpy.argpartition(losses.flatten(), beam)[:beam]
best_index = [(index / self.config['num_vocab_trg'], index % self.config['num_vocab_trg']) for index in best_index_flatten]
new_ctx = numpy.zeros((beam, 2 * self.config['dim_rec_enc']), dtype = 'float32')
new_y = []
new_state = numpy.zeros((beam, self.config['dim_rec_dec']), dtype = 'float32')
new_result = []
new_loss = []
for i in range(beam):
index = best_index[i]
new_result.append(result[index[0]] + [index[1]])
new_loss.append(losses[index[0], index[1]])
new_ctx[i] = ctx[index[0]]
new_y.append(index[1])
new_state[i] = state[index[0]]
new_emby = self.get_trg_embedding(numpy.asarray(new_y, dtype = 'int64'))[0]
new_state = self.get_next(new_ctx, new_state, new_emby)
state = []
emb_y = []
result = []
loss = []
for i in range(beam):
if new_result[i][-1] == self.config['index_eos_trg']:
result_eos.append(new_result[i])
loss_eos.append(new_loss[i])
beam -= 1
else:
result.append(new_result[i])
loss.append(new_loss[i])
state.append(new_state[i])
emb_y.append(new_emby[i])
if beam <= 0:
break
state = numpy.asarray(state, dtype = 'float32')
emb_y = numpy.asarray(emb_y, dtype = 'float32')
if return_array:
if len(result_eos) > 0:
return result_eos
else:
return [result[-1][:1]]
if len(result_eos) > 0:
if rerank:
for i in range(len(result_eos)):
feature_value = numpy.asarray([],dtype = 'float32')
for j in range(len(self.fls)):
fl = self.fls[j]
if isinstance(fl,featureListAttn):
fe =fl.getFeatures(xf, result_eos[i], [attentions_eos[i]])
else:
fe =fl.getFeatures(xf, result_eos[i])
feature_value = numpy.concatenate((feature_value, fe))
loss_eos[i] -= (feature_value * self.feature_weight.get_value()).sum()
return result_eos[numpy.argmin(loss_eos)]
elif beam_size > 100:
logging.warning('cannot find translation in beam size %d' % beam_size)
return []
else:
logging.info('cannot find translation in beam size %d, try %d' % (beam_size, beam_size * 2))
return self.translate(x, beam_size = beam_size * 2)
def translate_rerank(self, x, beam_size = 10, return_array = False):
return self.translate(x, beam_size, return_array, rerank = True)
def save(self, path, data = None, mapping = None):
values = {}
for p in self.creater.params:
values[p.name] = p.get_value()
values['config'] = json.dumps(self.config)
if data:
values['vocab_src'] = json.dumps(data.vocab_src)
values['ivocab_src'] = json.dumps(data.ivocab_src)
values['vocab_trg'] = json.dumps(data.vocab_trg)
values['ivocab_trg'] = json.dumps(data.ivocab_trg)
if mapping:
values['mapping'] = json.dumps(mapping)
numpy.savez(path, **values)
def load(self, path, decode = False):
try:
values = numpy.load(path)
for p in self.creater.params:
if p.name in values:
if values[p.name].shape != p.get_value().shape:
logging.warning(p.name + ' needs ' + str(p.get_value().shape) + ', given ' + str(values[p.name].shape) + ' , initializing')
else:
p.set_value(values[p.name])
logging.debug(p.name + ' loaded ' + str(values[p.name].shape))
else:
logging.warning('No parameter ' + p.name + ', initializing')
if decode:
return values
except:
if self.config['MRT'] or self.config['semi_learning'] or self.config['PR']:
logging.info('Initializing the model from ' + str(self.config['init_model']))
self.load(self.config['init_model'])
else:
logging.info('No model file. Starting from scratch.')
class RNNsearch(model):
def __init__(self, config, name = '', fls = None):
self.config = config
self.name = name
self.creater = LayerFactory()
self.fls = fls
self.trng = RandomStreams(numpy.random.randint(int(10e6)))
def sampling_step(self, state, prev, context):
emb = self.emb_trg.forward(prev)
energy, c = self.decoderGRU.decode_probs(context, state, emb)
probs = tensor.nnet.softmax(energy)
sample = self.trng.multinomial(pvals = probs, dtype = 'int64').argmax(axis = -1)
newemb = self.emb_trg.forward(sample)
newstate = self.decoderGRU.decode_next(c, state, newemb)
return newstate, sample, probs
def decode_sample(self, state_init, c, length, n_samples):
state = tensor.repeat(state_init, n_samples, axis = 0)
sample = tensor.zeros((n_samples,), dtype = 'int64')
c = tensor.repeat(c, n_samples, axis = 1)
result, updates = theano.scan(self.sampling_step,
outputs_info = [state, sample, None],
non_sequences = [c],
n_steps = length)
samples = result[1]
probs = result[2]
y_idx = tensor.arange(samples.flatten().shape[0]) * self.config['num_vocab_trg'] + samples.flatten()
return samples, probs, updates
def build(self, verbose = False):
config = self.config
logging.info('Initializing layers')
self.emb_src = self.creater.createLookupTable(self.name + 'emb_src',
config['num_vocab_src'], config['dim_emb_src'], offset = True)
self.emb_trg = self.creater.createLookupTable(self.name + 'emb_trg',
config['num_vocab_trg'], config['dim_emb_trg'], offset = True)
self.encoderGRU = self.creater.createGRU(self.name + 'GRU_enc',
config['dim_emb_src'], config['dim_rec_enc'], verbose = verbose)
self.encoderGRU_back = self.creater.createGRU(self.name + 'GRU_enc_back',
config['dim_emb_src'], config['dim_rec_enc'], verbose = verbose)
self.decoderGRU = self.creater.createGRU_attention(self.name + 'GRU_dec',
config['dim_emb_trg'], 2 * config['dim_rec_enc'],
config['dim_rec_dec'], config['num_vocab_trg'], verbose = verbose)
self.initer = self.creater.createFeedForwardLayer(self.name + 'initer',
config['dim_rec_enc'], config['dim_rec_dec'], offset = True)
if self.fls:
fl_weight = []
for fl in self.fls:
fl_weight.append(fl.feature_weight)
print fl.feature_weight
fl_weight = numpy.concatenate(fl_weight)
self.feature_weight = theano.shared(fl_weight.astype('float32'), name = "feature_weight")
self.creater.params += [self.feature_weight]
self.feature_weight_dim = self.feature_weight.dimshuffle('x', 0)
self.x = tensor.matrix('x', dtype = 'int64')
self.xmask = tensor.matrix('x_mask', dtype = 'float32')
self.y = tensor.matrix('y', dtype = 'int64')
self.ymask = tensor.matrix('y_mask', dtype = 'float32')
if 'MRT' in config and config['MRT'] is True:
self.MRTLoss = tensor.vector('MRTLoss')
self.inputs = [self.x, self.xmask, self.y, self.ymask, self.MRTLoss]
else:
self.MRTLoss = None
self.inputs = [self.x, self.xmask, self.y, self.ymask]
if config['PR']:
self.ans = tensor.scalar('ans', dtype = 'int64')
self.features = tensor.matrix('features', dtype = 'float32')
self.inputs += [self.features, self.ans]
logging.info('Building computational graph')
emb = self.emb_src.forward(self.x.flatten())
back_emb = self.emb_src.forward(self.x[::-1].flatten())
self.encode_forward = self.encoderGRU.forward(emb, self.x.shape[0], batch_size = self.x.shape[1], mask = self.xmask)
self.encode_backward = self.encoderGRU_back.forward(back_emb, self.x.shape[0], batch_size = self.x.shape[1], mask = self.xmask[::-1])
context_forward = self.encode_forward[0]
context_backward = self.encode_backward[0][::-1]
self.context = tensor.concatenate((context_forward, context_backward), axis=2)
self.init_c = context_backward[0]
self.state_init = self.initer.forward(context_backward[0])
emb = self.emb_trg.forward(self.y.flatten())
self.decode = self.decoderGRU.forward(emb, self.y.shape[0],
self.context, state_init = self.state_init,
batch_size = self.y.shape[1], mask = self.ymask,
cmask = self.xmask)
energy = self.decode[1]
self.attention = self.decode[2]
self.softmax = tensor.nnet.softmax(energy)
y_idx = tensor.arange(self.y.flatten().shape[0]) * self.config['num_vocab_trg'] + self.y.flatten()
cost = self.softmax.flatten()[y_idx]
cost = -tensor.log(cost)
self.cost = cost.reshape((self.y.shape[0], self.y.shape[1])) * self.ymask
self.cost_per_sample = self.cost.sum(axis = 0)
if 'MRT' in config and config['MRT'] is True:
self.cost_per_sample = self.cost.sum(axis = 0)
tmp = self.cost_per_sample
tmp *= config['MRT_alpha']
tmp -= tmp.min()
tmp = tensor.exp(-tmp)
tmp /= tmp.sum()
tmp *= self.MRTLoss
tmp = -tmp.sum()
self.cost = tmp
elif config['PR'] and self.fls:
self.cost_per_sample = self.cost.sum(axis = 0)
self.cost_per_sample *= config['alpha_PR']
cost_min = self.cost_per_sample - self.cost_per_sample.min()
probs = tensor.exp(-cost_min)
log_probs = -cost_min - tensor.log(probs.sum())
probs /= probs.sum()
self.probs = log_probs
energy_q = self.features * self.feature_weight_dim
energy_q = energy_q.sum(axis = 1)
self.energy_q = energy_q
energy_q_min = energy_q - energy_q.max()
probs_q = tensor.exp(energy_q_min)
log_probs_q = energy_q_min - tensor.log(probs_q.sum())
probs_q /= probs_q.sum()
self.probs_q = log_probs_q
cost_KL = tensor.exp(log_probs_q) * (log_probs_q - log_probs)
self.cost_KLs = cost_KL
self.cost_KL = cost_KL.sum()
self.cost_NMT = self.cost_per_sample[self.ans]
self.cost = config['lambda_PR'] * self.cost_KL + config['lambda_MLE'] * self.cost_NMT
else:
self.cost = self.cost.sum()
self.x_sample = tensor.matrix('x_sample', dtype = 'int64')
self.n_samples = tensor.scalar('n_samples', dtype = 'int64')
self.length_sample = tensor.scalar('length', dtype = 'int64')
emb_sample = self.emb_src.forward(self.x_sample.flatten())
back_emb_sample = self.emb_src.forward(self.x_sample[::-1].flatten())
encode_forward_sample = self.encoderGRU.forward(emb_sample, self.x_sample.shape[0], batch_size = self.x_sample.shape[1])
encode_backward_sample = self.encoderGRU_back.forward(back_emb_sample, self.x_sample.shape[0], batch_size = self.x_sample.shape[1])
context_sample = tensor.concatenate((encode_forward_sample[0], encode_backward_sample[0][::-1]), axis = 2)
state_init_sample =self.initer.forward(encode_backward_sample[0][::-1][0])
self.state_init_sample = state_init_sample
self.context_sample = context_sample
self.samples, self.probs_sample, self.updates_sample = self.decode_sample(state_init_sample, context_sample,
self.length_sample, self.n_samples)
self.y_decode = tensor.vector('y_decode', dtype = 'int64')
self.context_decode = tensor.tensor3('context_decode', dtype = 'float32')
self.c_decode = tensor.matrix('c_decode', dtype = 'float32')
self.state_decode = tensor.matrix('state_decode', dtype = 'float32')
self.emb_decode = tensor.matrix('emb_decode', dtype = 'float32')
def encode(self, x):
if not hasattr(self, "encoder"):
self.encoder = theano.function(inputs = [self.x,self.xmask],
outputs = [self.context])
x = numpy.reshape(x, (x.shape[0], 1))
xmask = numpy.ones(x.shape, dtype = 'float32')
return self.encoder(x, xmask)
def get_trg_embedding(self, y):
if not hasattr(self, "get_trg_embeddinger"):
self.get_trg_embeddinger = theano.function(inputs = [self.y_decode],
outputs = [self.emb_trg.forward(self.y_decode)])
return self.get_trg_embeddinger(y)
def get_init(self, c):
if not hasattr(self, "get_initer"):
self.get_initer = theano.function(inputs = [self.context],
outputs = [self.initer.forward(context_backward[0])])
return self.get_initer(c)
def get_context_and_init(self, x):
if not hasattr(self, "get_context_and_initer"):
self.get_context_and_initer = theano.function(inputs = [self.x,self.xmask],
outputs = [self.context, self.state_init])
x = numpy.reshape(x, (x.shape[0], 1))
xmask = numpy.ones(x.shape, dtype = 'float32')
return self.get_context_and_initer(x, xmask)
def get_probs(self, c, state, emb):
if not hasattr(self, "get_probser"):
self.get_probser = theano.function(inputs = [self.context_decode, self.state_decode, self.emb_decode], outputs = self.decoderGRU.decode_probs(self.context_decode, self.state_decode, self.emb_decode))
return self.get_probser(c, state, emb)
def get_next(self, c, state, emb):
if not hasattr(self, "get_nexter"):
self.get_nexter = theano.function(inputs = [self.c_decode, self.state_decode, self.emb_decode],
outputs = self.decoderGRU.decode_next(self.c_decode, self.state_decode, self.emb_decode))
return self.get_nexter(c, state, emb)
def get_cost(self, x, xmask, y, ymask):
if not hasattr(self, "get_coster"):
self.get_coster = theano.function(inputs = [self.x, self.xmask, self.y, self.ymask],
outputs = [self.cost])
return self.get_coster(x, xmask, y, ymask)
def get_sample(self, x, length, n_samples):
if not hasattr(self, "get_sampler"):
self.get_sampler = theano.function(inputs = [self.x_sample, self.length_sample, self.n_samples],
outputs = [self.samples, self.probs_sample],
updates = self.updates_sample)
return self.get_sampler(x, length, n_samples) | BSD 3-Clause New or Revised License |
rapid7/vm-console-client-python | rapid7vmconsole/models/scan_template_vulnerability_checks.py | ScanTemplateVulnerabilityChecks.unsafe | python | def unsafe(self, unsafe):
self._unsafe = unsafe | Sets the unsafe of this ScanTemplateVulnerabilityChecks.
Whether checks considered \"unsafe\" are assessed during a scan. # noqa: E501
:param unsafe: The unsafe of this ScanTemplateVulnerabilityChecks. # noqa: E501
:type: bool | https://github.com/rapid7/vm-console-client-python/blob/55e1f573967bce27cc9a2d10c12a949b1142c2b3/rapid7vmconsole/models/scan_template_vulnerability_checks.py#L228-L237 | import pprint
import re
import six
class ScanTemplateVulnerabilityChecks(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'categories': 'ScanTemplateVulnerabilityCheckCategories',
'correlate': 'bool',
'individual': 'ScanTemplateVulnerabilityCheckIndividual',
'links': 'list[Link]',
'potential': 'bool',
'types': 'VulnerabilityCheckType',
'unsafe': 'bool'
}
attribute_map = {
'categories': 'categories',
'correlate': 'correlate',
'individual': 'individual',
'links': 'links',
'potential': 'potential',
'types': 'types',
'unsafe': 'unsafe'
}
def __init__(self, categories=None, correlate=None, individual=None, links=None, potential=None, types=None, unsafe=None):
self._categories = None
self._correlate = None
self._individual = None
self._links = None
self._potential = None
self._types = None
self._unsafe = None
self.discriminator = None
if categories is not None:
self.categories = categories
if correlate is not None:
self.correlate = correlate
if individual is not None:
self.individual = individual
if links is not None:
self.links = links
if potential is not None:
self.potential = potential
if types is not None:
self.types = types
if unsafe is not None:
self.unsafe = unsafe
@property
def categories(self):
return self._categories
@categories.setter
def categories(self, categories):
self._categories = categories
@property
def correlate(self):
return self._correlate
@correlate.setter
def correlate(self, correlate):
self._correlate = correlate
@property
def individual(self):
return self._individual
@individual.setter
def individual(self, individual):
self._individual = individual
@property
def links(self):
return self._links
@links.setter
def links(self, links):
self._links = links
@property
def potential(self):
return self._potential
@potential.setter
def potential(self, potential):
self._potential = potential
@property
def types(self):
return self._types
@types.setter
def types(self, types):
self._types = types
@property
def unsafe(self):
return self._unsafe
@unsafe.setter | MIT License |
missionpinball/mpf | mpf/core/platform.py | DriverPlatform.__init__ | python | def __init__(self, machine):
super().__init__(machine)
self.features['has_drivers'] = True
self.features['max_pulse'] = 255 | Add driver feature and default max_pulse length. | https://github.com/missionpinball/mpf/blob/1eda6ba6892b8f7cc6dedf6cb6472ff92293b8ef/mpf/core/platform.py#L513-L520 | import abc
import asyncio
from collections import namedtuple
from enum import Enum
from typing import Optional, Dict, List
from mpf.core.logging import LogMixin
from mpf.core.utility_functions import Util
MYPY = False
if MYPY:
from mpf.devices.switch import Switch
from mpf.devices.stepper import Stepper
from mpf.platforms.interfaces.driver_platform_interface import DriverPlatformInterface
from mpf.platforms.interfaces.switch_platform_interface import SwitchPlatformInterface
from mpf.platforms.interfaces.light_platform_interface import LightPlatformInterface
from mpf.platforms.interfaces.servo_platform_interface import ServoPlatformInterface
from mpf.platforms.interfaces.segment_display_platform_interface import SegmentDisplayPlatformInterface
from mpf.platforms.interfaces.hardware_sound_platform_interface import HardwareSoundPlatformInterface
from mpf.platforms.interfaces.stepper_platform_interface import StepperPlatformInterface
from mpf.platforms.interfaces.accelerometer_platform_interface import AccelerometerPlatformInterface
from mpf.platforms.interfaces.i2c_platform_interface import I2cPlatformInterface
from mpf.platforms.interfaces.dmd_platform import DmdPlatformInterface
from mpf.core.machine import MachineController
class BasePlatform(LogMixin, metaclass=abc.ABCMeta):
__slots__ = ["machine", "features", "debug"]
def __init__(self, machine):
self.machine = machine
self.features = {}
super().__init__()
self.debug = False
self.features['has_dmds'] = False
self.features['has_rgb_dmds'] = False
self.features['has_accelerometers'] = False
self.features['has_i2c'] = False
self.features['has_servos'] = False
self.features['has_lights'] = False
self.features['has_switches'] = False
self.features['has_drivers'] = False
self.features['tickless'] = False
self.features['has_segment_displays'] = False
self.features['has_hardware_sound_systems'] = False
self.features['has_steppers'] = False
self.features['allow_empty_numbers'] = False
self.features['hardware_eos_repulse'] = False
def assert_has_feature(self, feature_name):
if not self.features.get("has_{}".format(feature_name), False):
self.raise_config_error("Platform {} does not support to configure {feature_name}. "
"Please make sure the platform "
"you configured for {feature_name} actually supports that type "
"of devices.".format(self.__class__, feature_name=feature_name), 99)
def _configure_device_logging_and_debug(self, logger_name, config):
if config['debug']:
self.debug = True
config['console_log'] = 'full'
config['file_log'] = 'full'
self.configure_logging(logger_name,
config['console_log'],
config['file_log'])
@classmethod
def get_config_spec(cls):
return False
def get_info_string(self) -> str:
return "Not implemented"
def update_firmware(self) -> str:
async def initialize(self):
async def start(self):
def tick(self):
def stop(self):
class DmdPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_dmds'] = True
@abc.abstractmethod
def configure_dmd(self) -> "DmdPlatformInterface":
raise NotImplementedError
class HardwareSoundPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_hardware_sound_systems'] = True
@abc.abstractmethod
def configure_hardware_sound_system(self) -> "HardwareSoundPlatformInterface":
raise NotImplementedError
class RgbDmdPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_rgb_dmds'] = True
@abc.abstractmethod
def configure_rgb_dmd(self, name: str) -> "DmdPlatformInterface":
raise NotImplementedError
class SegmentDisplayPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_segment_displays'] = True
@classmethod
def get_segment_display_config_section(cls) -> Optional[str]:
return None
def validate_segment_display_section(self, segment_display, config) -> dict:
if self.get_segment_display_config_section():
spec = self.get_segment_display_config_section()
config = segment_display.machine.config_validator.validate_config(spec, config, segment_display.name)
elif config:
raise AssertionError("No platform_config supported but not empty {} for segment display {}".
format(config, segment_display.name))
return config
@abc.abstractmethod
async def configure_segment_display(self, number: str, display_size: int,
platform_settings) -> "SegmentDisplayPlatformInterface":
raise NotImplementedError
class SegmentDisplaySoftwareFlashPlatform(SegmentDisplayPlatform, metaclass=abc.ABCMeta):
def __init__(self, machine):
super().__init__(machine)
self._displays = set()
self._display_flash_task = None
async def initialize(self):
await super().initialize()
self._display_flash_task = self.machine.clock.loop.create_task(self._display_flash())
self._display_flash_task.add_done_callback(Util.raise_exceptions)
async def _display_flash(self):
wait_time = 1 / (self.config['display_flash_frequency'] * 2)
while True:
await asyncio.sleep(wait_time)
for display in self._displays:
display.set_software_flash(True)
await asyncio.sleep(wait_time)
for display in self._displays:
display.set_software_flash(False)
def stop(self):
super().stop()
if self._display_flash_task:
self._display_flash_task.cancel()
def _handle_software_flash(self, display):
self._displays.add(display)
class AccelerometerPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_accelerometers'] = True
@abc.abstractmethod
def configure_accelerometer(self, number: str, config: dict, callback) -> "AccelerometerPlatformInterface":
raise NotImplementedError
class I2cPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_i2c'] = True
async def configure_i2c(self, number: str) -> "I2cPlatformInterface":
raise NotImplementedError
class ServoPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_servos'] = True
@abc.abstractmethod
async def configure_servo(self, number: str) -> "ServoPlatformInterface":
raise NotImplementedError
class StepperPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_steppers'] = True
@classmethod
def get_stepper_config_section(cls) -> Optional[str]:
return None
def validate_stepper_section(self, stepper: "Stepper", config: dict) -> dict:
if self.get_stepper_config_section():
spec = self.get_stepper_config_section()
config = stepper.machine.config_validator.validate_config(spec, config, stepper.name)
elif config:
raise AssertionError("No platform_config supported but not empty {} for stepper {}".
format(config, stepper.name))
return config
@abc.abstractmethod
async def configure_stepper(self, number: str, config: dict) -> "StepperPlatformInterface":
raise NotImplementedError
class LightConfigColors(Enum):
RED = 1
GREEN = 2
BLUE = 3
WHITE = 4
NONE = 5
LightConfig = namedtuple("LightConfig", ["name", "color"])
class LightsPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_lights'] = True
@abc.abstractmethod
def parse_light_number_to_channels(self, number: str, subtype: str):
raise NotImplementedError
def light_sync(self):
@abc.abstractmethod
def configure_light(self, number: str, subtype: str, config: LightConfig,
platform_settings: dict) -> "LightPlatformInterface":
raise NotImplementedError
SwitchConfig = namedtuple("SwitchConfig", ["name", "invert", "debounce"])
class SwitchPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = []
def __init__(self, machine):
super().__init__(machine)
self.features['has_switches'] = True
@abc.abstractmethod
def configure_switch(self, number: str, config: SwitchConfig, platform_config: dict) -> "SwitchPlatformInterface":
raise NotImplementedError
@classmethod
def get_switch_config_section(cls) -> Optional[str]:
return None
def validate_switch_section(self, switch: "Switch", config: dict) -> dict:
if self.get_switch_config_section():
spec = self.get_switch_config_section()
config = switch.machine.config_validator.validate_config(spec, config, switch.name)
elif config:
raise AssertionError("No platform_config supported but not empty {} for switch {}".
format(config, switch.name))
return config
@abc.abstractmethod
async def get_hw_switch_states(self) -> Dict[str, bool]:
raise NotImplementedError
SwitchSettings = namedtuple("SwitchSettings", ["hw_switch", "invert", "debounce"])
DriverSettings = namedtuple("DriverSettings", ["hw_driver", "pulse_settings", "hold_settings", "recycle"])
DriverConfig = namedtuple("DriverConfig", ["name", "default_pulse_ms", "default_pulse_power", "default_hold_power",
"default_recycle", "max_pulse_ms", "max_pulse_power", "max_hold_power"])
RepulseSettings = namedtuple("RepulseSettings", ["enable_repulse", "debounce_ms"])
class DriverPlatform(BasePlatform, metaclass=abc.ABCMeta):
__slots__ = [] | MIT License |
opennetworkingfoundation/tapi | RI/flask_server/tapi_server/models/tapi_connectivity_context_augmentation4.py | TapiConnectivityContextAugmentation4.__init__ | python | def __init__(self, connectivity_context=None):
self.openapi_types = {
'connectivity_context': TapiConnectivityConnectivityContext
}
self.attribute_map = {
'connectivity_context': 'connectivity-context'
}
self._connectivity_context = connectivity_context | TapiConnectivityContextAugmentation4 - a model defined in OpenAPI
:param connectivity_context: The connectivity_context of this TapiConnectivityContextAugmentation4. # noqa: E501
:type connectivity_context: TapiConnectivityConnectivityContext | https://github.com/opennetworkingfoundation/tapi/blob/1f3fd9483d5674552c5a31206c97399c8c151897/RI/flask_server/tapi_server/models/tapi_connectivity_context_augmentation4.py#L19-L33 | from __future__ import absolute_import
from datetime import date, datetime
from typing import List, Dict
from tapi_server.models.base_model_ import Model
from tapi_server.models.tapi_connectivity_connectivity_context import TapiConnectivityConnectivityContext
from tapi_server import util
class TapiConnectivityContextAugmentation4(Model): | Apache License 2.0 |
datadotworld/data.world-py | datadotworld/client/_swagger/models/oauth_token_reference.py | OauthTokenReference.owner | python | def owner(self, owner):
if owner is None:
raise ValueError("Invalid value for `owner`, must not be `None`")
if owner is not None and len(owner) > 31:
raise ValueError("Invalid value for `owner`, length must be less than or equal to `31`")
if owner is not None and len(owner) < 3:
raise ValueError("Invalid value for `owner`, length must be greater than or equal to `3`")
if owner is not None and not re.search('[a-z0-9](?:-(?!-)|[a-z0-9])+[a-z0-9]', owner):
raise ValueError("Invalid value for `owner`, must be a follow pattern or equal to `/[a-z0-9](?:-(?!-)|[a-z0-9])+[a-z0-9]/`")
self._owner = owner | Sets the owner of this OauthTokenReference.
User name of the owner of the OAuth token within data.world.
:param owner: The owner of this OauthTokenReference.
:type: str | https://github.com/datadotworld/data.world-py/blob/7e5f474b655f4f0c88cc6862353e4d52c0e0bb31/datadotworld/client/_swagger/models/oauth_token_reference.py#L93-L110 | from pprint import pformat
from six import iteritems
import re
class OauthTokenReference(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'id': 'str',
'owner': 'str',
'site': 'str'
}
attribute_map = {
'id': 'id',
'owner': 'owner',
'site': 'site'
}
def __init__(self, id=None, owner=None, site=None):
self._id = None
self._owner = None
self._site = None
self.id = id
self.owner = owner
self.site = site
@property
def id(self):
return self._id
@id.setter
def id(self, id):
if id is None:
raise ValueError("Invalid value for `id`, must not be `None`")
self._id = id
@property
def owner(self):
return self._owner
@owner.setter | Apache License 2.0 |
geoscienceaustralia/agdc | tests/test_landsat_tiler.py | TestLandsatTiler.get_tile_pathnames | python | def get_tile_pathnames(expected_conn, output_conn):
sql = """-- Retrieve list of tile.tile_pathname from each database
select tile_type_id, tile_pathname from tile where
tile_class_id = 1
"""
db_cursor = expected_conn.cursor()
db_cursor.execute(sql)
expected_dict = {}
for record in db_cursor:
expected_dict[os.path.basename(record[1])] = (record[0], record[1])
db_cursor = output_conn.cursor()
db_cursor.execute(sql)
output_dict = {}
for record in db_cursor:
output_dict[os.path.basename(record[1])] = (record[0], record[1])
return (expected_dict, output_dict) | From two different databases, get the tile pathnames from the tile
table. Return each as a dictionary of
{basename: (tile_type_id, full path)} | https://github.com/geoscienceaustralia/agdc/blob/2e22c6bdd9305555db3615305ff6a5df6219cd51/tests/test_landsat_tiler.py#L503-L521 | import sys
import os
import subprocess
import unittest
import dbutil
import dbcompare
from osgeo import gdal
import numpy
import re
class TestLandsatTiler(unittest.TestCase):
def process_args(self):
MODULE = 'new_ingest_benchmark'
SUITE = 'benchmark'
self.INPUT_DIR = dbutil.input_directory(MODULE, SUITE)
self.OUTPUT_DIR = dbutil.output_directory(MODULE, SUITE)
self.EXPECTED_DIR = dbutil.expected_directory(MODULE, SUITE)
mode_desc_dict = {0: 'Initialise benchmark data in the expected directory',
1: 'Do not do ingestion. Compare existing ingestion ' 'in\n %s\n with benchmark\n %s\n' %(self.OUTPUT_DIR, self.EXPECTED_DIR),
2: 'Compare from this run with ' 'expected benchmark database exiting if ' 'they are different',
3: 'Compare databases and also compare tiles, even if ' 'the databases are different'}
if len(sys.argv) < 2:
mode = -1
else:
try:
mode = int(sys.argv[1])
except ValueError:
mode = -1
msg = ''
if mode not in [0, 1, 2, 3]:
msg = 'Please specify a mode as follows:\n'
for mode_num, desc in mode_desc_dict.items():
msg = msg + 'python test_landsat_tiler.py %d:\t%s\n' %(mode_num, desc)
return mode, msg
def setUp(self):
self.PQA_CONTIGUITY_BIT = 8
self.test_dbname = None
self.expected_dbname = None
self.test_conn = None
self.expected_conn = None
self.logfile = None
self.bands_expected = None
self.bands_output = None
self.mode, self.msg = self.process_args()
if self.mode == 0:
self.OUTPUT_DIR = self.EXPECTED_DIR
print 'OUTPUT_DIR =%s' %self.OUTPUT_DIR
print self.mode
print self.msg
def test_landsat_tiler(self):
if self.mode not in [0, 1, 2, 3]:
self.skipTest('Skipping test_landsat_tiler since flag is not in [0, 1, 2, 3]')
logfile_path = os.path.join(self.OUTPUT_DIR, "test_landsat_tiler.log")
self.logfile = open(logfile_path, "w")
self.test_dbname = dbutil.random_name("test_tiler")
print 'About to create dbase from %s' %(os.path.join(self.INPUT_DIR, "hypercube_empty.sql"))
if self.mode != 1:
dbutil.TESTSERVER.create(self.test_dbname,
self.INPUT_DIR, "hypercube_empty.sql")
tile_root = os.path.join(self.OUTPUT_DIR, "tiles")
configuration_dict = {'dbname': self.test_dbname,
'tile_root': tile_root}
config_file_path = dbutil.update_config_file2(configuration_dict,
self.INPUT_DIR,
self.OUTPUT_DIR,
"test_datacube.conf")
ingest_dir = os.path.join(self.INPUT_DIR, 'tiler_testing')
dbupdater_cmd = ["python",
"dbupdater.py",
"--debug",
"--config=%s" % config_file_path,
"--source=%s" % ingest_dir,
"--removedblist",
"--followsymlinks"]
if self.mode != 1:
subprocess.check_call(dbupdater_cmd, stdout=self.logfile,
stderr=subprocess.STDOUT)
landsat_tiler_cmd = ["python",
"landsat_tiler.py",
"--config=%s" % config_file_path]
if self.mode != 1:
subprocess.check_call(landsat_tiler_cmd, stdout=self.logfile,
stderr=subprocess.STDOUT)
if self.mode != 1:
dbutil.TESTSERVER.save(self.test_dbname, self.OUTPUT_DIR,
"tiler_testing.sql")
if self.mode > 0 and os.path.isfile(os.path.join(self.EXPECTED_DIR,
"tiler_testing.sql")):
print 'starting to check differences'
if self.mode == 1:
self.test_dbname = dbutil.random_name("tiler_testing")
dbutil.TESTSERVER.create(self.test_dbname,
self.OUTPUT_DIR, "tiler_testing.sql")
self.expected_dbname = dbutil.random_name("expected_tiler_testing")
dbutil.TESTSERVER.create(self.expected_dbname,
self.EXPECTED_DIR, "tiler_testing.sql")
self.test_conn = dbutil.TESTSERVER.connect(self.test_dbname)
self.expected_conn = dbutil.TESTSERVER.connect(self.expected_dbname)
dbases_agree = dbcompare.compare_databases(self.test_conn,
self.expected_conn,
output=self.logfile,
verbosity=3)
if self.mode == 2:
assert dbases_agree, "Databases do not match."
expected_tile_dict, output_tile_dict = self.get_tile_pathnames(self.expected_conn, self.test_conn)
tiles_expected = set(expected_tile_dict.keys())
tiles_output = set(output_tile_dict.keys())
tiles_expected_or_output = tiles_expected | tiles_output
self.bands_expected = self.construct_bands_source_dict(self.expected_conn)
self.bands_output = self.construct_bands_source_dict(self.test_conn)
file_pattern = [r'(?P<sat>\w+)_(?P<sensor>\w+)_',
r'(?P<processing_level>\w+)_',
r'(?P<xindex>-*\d+)_(?P<yindex>-*\d+)_'
r'(?P<year>\d+)-(?P<month>\d+)-'
r'(?P<day>\d+)T(?P<hour>\d+)-(?P<minute>\d+)-',
r'(?P<second_whole>\d+)\.(?P<second_fraction>\d+)'
r'\.(?P<file_extension>.+)']
pattern = re.compile(''.join(file_pattern))
pixel_count_dict = {}
difference_count_dict = {}
all_levels_info_dict = {}
for tile_name in tiles_expected_or_output:
print 'processing tile %s' %tile_name
tile_type_id_expected = None
tile_type_id_output = None
fname_expected = None
fname_output = None
if tile_name in tiles_expected:
tile_type_id_expected, fname_expected = expected_tile_dict[tile_name]
if tile_name in tiles_output:
tile_type_id_output, fname_output = output_tile_dict[tile_name]
matchobj = re.match(pattern, tile_name)
tile_name_dict = matchobj.groupdict()
full_key_expected = self.get_tiletype_sat_sens_level(tile_type_id_expected,
tile_name_dict)
full_key_output = self.get_tiletype_sat_sens_level(tile_type_id_output,
tile_name_dict)
full_key = self.check_equal_or_null(full_key_expected,
full_key_output)
level_dict_expected = {}
level_dict_output = {}
if full_key in all_levels_info_dict:
(level_dict_expected, level_dict_output) = all_levels_info_dict[full_key]
if level_dict_expected == {} and full_key_expected != None:
level_dict_expected = self.collect_source_bands(self.bands_expected,
full_key)
if level_dict_output == {} and full_key_output != None:
level_dict_output = self.collect_source_bands(self.bands_output,
full_key)
if full_key not in all_levels_info_dict:
all_levels_info_dict[full_key] = [level_dict_expected,
level_dict_output]
if all_levels_info_dict[full_key][0] == {} and level_dict_expected != {}:
all_levels_info_dict[full_key][0] = level_dict_expected
if all_levels_info_dict[full_key][1] == {} and level_dict_output != {}:
all_levels_info_dict[full_key][1] = level_dict_output
([data_expected, data_output], number_layers) = self.load_and_check(fname_expected, fname_output,
level_dict_expected,
level_dict_output)
assert bool(fname_expected) == (data_expected != None) and bool(fname_output) == (data_output != None), "data array should exist if and only if fname exists"
for ilayer in range(number_layers):
band_expected, dtype_expected = self.get_band_data(data_expected, ilayer)
band_output, dtype_output = self.get_band_data(data_output, ilayer)
assert (band_expected == None) == (dtype_expected == None) and (band_output == None) == (dtype_output == None), "band data should exist if and only if dtype exists"
dtype_this = self.check_equal_or_null(dtype_expected,
dtype_output)
if tile_name_dict['processing_level'] == 'PQA':
bin_count = 16 + 1
else:
bin_count = numpy.iinfo(dtype_this).max - numpy.iinfo(dtype_this).min + 1
assert bin_count < 66000, "datatype is more than 16" "bits, need to add code to coarsen the" "histogram bins or use apriori max and" "min values of the data"
result_key = (full_key[3], ilayer + 1)
if result_key not in pixel_count_dict:
pixel_count_dict[result_key] = numpy.zeros(shape=(5),
dtype=numpy.uint64)
difference_count_dict[result_key] = numpy.zeros(shape=(bin_count), dtype=numpy.uint64)
pixel_count = pixel_count_dict[result_key]
difference_count = difference_count_dict[result_key]
if tile_name_dict['processing_level'] == 'PQA':
if band_expected is None:
band_expected = 0
if band_output is None:
band_output = 0
index_expected = numpy.bitwise_and(band_expected,
1 << self.PQA_CONTIGUITY_BIT) > 0
index_output = numpy.bitwise_and(band_output,
1 << self.PQA_CONTIGUITY_BIT) > 0
else:
nodata_value = level_dict_output[ilayer + 1]['nodata_value']
if band_expected is None:
band_expected = nodata_value
if band_output is None:
band_output = nodata_value
index_expected = band_expected != nodata_value
index_output = band_output != nodata_value
pixel_count[0] += numpy.count_nonzero(index_expected)
pixel_count[1] += numpy.count_nonzero(index_output)
pixel_count[2] += numpy.count_nonzero(numpy.logical_and(index_expected,
index_output))
pixel_count[3] += numpy.count_nonzero(numpy.logical_and
(index_expected, ~index_output))
pixel_count[4] += numpy.count_nonzero(numpy.logical_and
(~index_expected, index_output))
index_both = numpy.logical_and(index_expected,
index_output)
if numpy.count_nonzero(index_both) == 0:
continue
valid_data_expected = band_expected[index_both].ravel()
valid_data_output = band_output[index_both].ravel()
if tile_name_dict['processing_level'] == 'PQA':
difference = self.count_bitwise_diffs(valid_data_expected,
valid_data_output)
else:
difference = abs(valid_data_output.astype(numpy.int64)
- valid_data_expected.astype(numpy.int64))
hist, dummy_bin_edges = numpy.histogram(difference,
numpy.array(range(bin_count + 1),
dtype=numpy.uint64))
difference_count += hist
band_expected = None
band_output = None
difference = None
data_expected = None
data_output = None
fp = open(os.path.join(self.OUTPUT_DIR,
'Histogram_output.txt'), 'w')
fp.writelines('##### COMPARISON OF TILED DATA IN FOLLOWING ' 'DIRECTORES\n%s\n%s\n' %(self.EXPECTED_DIR,
self.OUTPUT_DIR))
result_keys_processed = []
for full_key in all_levels_info_dict.keys():
dummy, dummy, dummy, processing_level = full_key
top_layer_result_key = (processing_level, 1)
if top_layer_result_key in result_keys_processed:
continue
fp.writelines('#### Processing Level: %s\n' %processing_level)
level_dict_expected, level_dict_output = all_levels_info_dict[full_key]
assert set(level_dict_expected.keys()) == set(level_dict_output.keys()), "different key sets"
number_layers = len(level_dict_output.keys())
for this_layer in range(1, number_layers + 1):
result_key = (processing_level, this_layer)
result_keys_processed.append(result_key)
fp.writelines('### tile_layer = %d\n' %this_layer)
for key, val in level_dict_expected[this_layer].items():
if key == 'tile_layer' or key == 'level_name':
continue
outline = '# %s = %s' %(key, val)
if str(level_dict_output[this_layer][key]) != str(val):
outline = '%s (%s in output database)' %(outline, level_dict_output[this_layer][key])
fp.writelines('%s\n' %outline)
fp.writelines('#Valid data counts\n')
pixel_count = pixel_count_dict[result_key]
count_desc = ['Expected\t', 'Output\t\t', 'Common\t\t',
'Missing\t\t', 'Extra\t\t']
for desc, num in zip(count_desc, pixel_count):
fp.writelines('\t\t%s%d\n' %(desc, num))
fp.writelines('#Histogram of differences in valid data\n')
difference_count = difference_count_dict[result_key]
index_nonzero_bins = difference_count > 0
for bin_no in range(len(difference_count)):
if index_nonzero_bins[bin_no]:
fp.writelines('\t\tDifference of %d: %d\n'
%(bin_no, difference_count[bin_no]))
fp.close()
else:
if self.mode > 0:
self.skipTest("Expected database save file not found.")
def tearDown(self):
if self.test_conn:
self.test_conn.close()
if self.expected_conn:
self.expected_conn.close()
if self.test_dbname:
dbutil.TESTSERVER.drop(self.test_dbname)
if self.expected_dbname:
dbutil.TESTSERVER.drop(self.expected_dbname)
if self.logfile:
self.logfile.close()
@staticmethod | BSD 3-Clause New or Revised License |
faucetsdn/ryu | ryu/lib/bfdlib.py | ARPPacket.arp_packet | python | def arp_packet(opcode, src_mac, src_ip, dst_mac, dst_ip):
pkt = packet.Packet()
eth_pkt = ethernet.ethernet(dst_mac, src_mac, ETH_TYPE_ARP)
pkt.add_protocol(eth_pkt)
arp_pkt = arp.arp_ip(opcode, src_mac, src_ip, dst_mac, dst_ip)
pkt.add_protocol(arp_pkt)
pkt.serialize()
return pkt.data | Generate ARP packet with ethernet encapsulated. | https://github.com/faucetsdn/ryu/blob/537f35f4b2bc634ef05e3f28373eb5e24609f989/ryu/lib/bfdlib.py#L596-L610 | import logging
import time
import random
import six
from ryu.base import app_manager
from ryu.controller import event
from ryu.controller import ofp_event
from ryu.controller.handler import CONFIG_DISPATCHER, MAIN_DISPATCHER
from ryu.controller.handler import set_ev_cls
from ryu.exception import RyuException
from ryu.ofproto.ether import ETH_TYPE_IP, ETH_TYPE_ARP
from ryu.ofproto import ofproto_v1_3
from ryu.ofproto import inet
from ryu.lib import hub
from ryu.lib.packet import packet
from ryu.lib.packet import ethernet
from ryu.lib.packet import ipv4
from ryu.lib.packet import udp
from ryu.lib.packet import bfd
from ryu.lib.packet import arp
from ryu.lib.packet.arp import ARP_REQUEST, ARP_REPLY
LOG = logging.getLogger(__name__)
UINT16_MAX = (1 << 16) - 1
UINT32_MAX = (1 << 32) - 1
BFD_CONTROL_UDP_PORT = 3784
BFD_ECHO_UDP_PORT = 3785
class BFDSession(object):
def __init__(self, app, my_discr, dpid, ofport,
src_mac, src_ip, src_port,
dst_mac="FF:FF:FF:FF:FF:FF", dst_ip="255.255.255.255",
detect_mult=3,
desired_min_tx_interval=1000000,
required_min_rx_interval=1000000,
auth_type=0, auth_keys=None):
auth_keys = auth_keys if auth_keys else {}
assert not (auth_type and len(auth_keys) == 0)
self.app = app
self._session_state = bfd.BFD_STATE_DOWN
self._remote_session_state = bfd.BFD_STATE_DOWN
self._local_discr = my_discr
self._remote_discr = 0
self._local_diag = 0
self._desired_min_tx_interval = 1000000
self._required_min_rx_interval = required_min_rx_interval
self._remote_min_rx_interval = -1
self._demand_mode = 0
self._remote_demand_mode = 0
self._detect_mult = detect_mult
self._auth_type = auth_type
self._auth_keys = auth_keys
if self._auth_type in [bfd.BFD_AUTH_KEYED_MD5,
bfd.BFD_AUTH_METICULOUS_KEYED_MD5,
bfd.BFD_AUTH_KEYED_SHA1,
bfd.BFD_AUTH_METICULOUS_KEYED_SHA1]:
self._rcv_auth_seq = 0
self._xmit_auth_seq = random.randint(0, UINT32_MAX)
self._auth_seq_known = 0
self._cfg_desired_min_tx_interval = desired_min_tx_interval
self._cfg_required_min_echo_rx_interval = 0
self._active_role = True
self._detect_time = 0
self._xmit_period = None
self._update_xmit_period()
self._is_polling = True
self._pending_final = False
self._enable_send = True
self._lock = None
self.src_mac = src_mac
self.dst_mac = dst_mac
self.src_ip = src_ip
self.dst_ip = dst_ip
self.ipv4_id = random.randint(0, UINT16_MAX)
self.src_port = src_port
self.dst_port = BFD_CONTROL_UDP_PORT
if dst_mac == "FF:FF:FF:FF:FF:FF" or dst_ip == "255.255.255.255":
self._remote_addr_config = False
else:
self._remote_addr_config = True
self.dpid = dpid
self.datapath = None
self.ofport = ofport
hub.spawn(self._send_loop)
LOG.info("[BFD][%s][INIT] BFD Session initialized.",
hex(self._local_discr))
@property
def my_discr(self):
return self._local_discr
@property
def your_discr(self):
return self._remote_discr
def set_remote_addr(self, dst_mac, dst_ip):
self.dst_mac = dst_mac
self.dst_ip = dst_ip
if not (dst_mac == "FF:FF:FF:FF:FF:FF" or dst_ip == "255.255.255.255"):
self._remote_addr_config = True
LOG.info("[BFD][%s][REMOTE] Remote address configured: %s, %s.",
hex(self._local_discr), self.dst_ip, self.dst_mac)
def recv(self, bfd_pkt):
LOG.debug("[BFD][%s][RECV] BFD Control received: %s",
hex(self._local_discr), six.binary_type(bfd_pkt))
self._remote_discr = bfd_pkt.my_discr
self._remote_state = bfd_pkt.state
self._remote_demand_mode = bfd_pkt.flags & bfd.BFD_FLAG_DEMAND
if self._remote_min_rx_interval != bfd_pkt.required_min_rx_interval:
self._remote_min_rx_interval = bfd_pkt.required_min_rx_interval
self._update_xmit_period()
if bfd_pkt.flags & bfd.BFD_FLAG_FINAL and self._is_polling:
self._is_polling = False
if self._session_state == bfd.BFD_STATE_ADMIN_DOWN:
return
if bfd_pkt.state == bfd.BFD_STATE_ADMIN_DOWN:
if self._session_state != bfd.BFD_STATE_DOWN:
self._set_state(bfd.BFD_STATE_DOWN,
bfd.BFD_DIAG_NEIG_SIG_SESS_DOWN)
else:
if self._session_state == bfd.BFD_STATE_DOWN:
if bfd_pkt.state == bfd.BFD_STATE_DOWN:
self._set_state(bfd.BFD_STATE_INIT)
elif bfd_pkt.state == bfd.BFD_STATE_INIT:
self._set_state(bfd.BFD_STATE_UP)
elif self._session_state == bfd.BFD_STATE_INIT:
if bfd_pkt.state in [bfd.BFD_STATE_INIT, bfd.BFD_STATE_UP]:
self._set_state(bfd.BFD_STATE_UP)
else:
if bfd_pkt.state == bfd.BFD_STATE_DOWN:
self._set_state(bfd.BFD_STATE_DOWN,
bfd.BFD_DIAG_NEIG_SIG_SESS_DOWN)
if self._remote_demand_mode and self._session_state == bfd.BFD_STATE_UP and self._remote_session_state == bfd.BFD_STATE_UP:
self._enable_send = False
if not self._remote_demand_mode or self._session_state != bfd.BFD_STATE_UP or self._remote_session_state != bfd.BFD_STATE_UP:
if not self._enable_send:
self._enable_send = True
hub.spawn(self._send_loop)
if self._detect_time == 0:
self._detect_time = bfd_pkt.desired_min_tx_interval * bfd_pkt.detect_mult / 1000000.0
hub.spawn(self._recv_timeout_loop)
if bfd_pkt.flags & bfd.BFD_FLAG_POLL:
self._pending_final = True
self._detect_time = bfd_pkt.desired_min_tx_interval * bfd_pkt.detect_mult / 1000000.0
if self._auth_type in [bfd.BFD_AUTH_KEYED_MD5,
bfd.BFD_AUTH_METICULOUS_KEYED_MD5,
bfd.BFD_AUTH_KEYED_SHA1,
bfd.BFD_AUTH_METICULOUS_KEYED_SHA1]:
self._rcv_auth_seq = bfd_pkt.auth_cls.seq
self._auth_seq_known = 1
if self._lock is not None:
self._lock.set()
def _set_state(self, new_state, diag=None):
old_state = self._session_state
LOG.info("[BFD][%s][STATE] State changed from %s to %s.",
hex(self._local_discr),
bfd.BFD_STATE_NAME[old_state],
bfd.BFD_STATE_NAME[new_state])
self._session_state = new_state
if new_state == bfd.BFD_STATE_DOWN:
if diag is not None:
self._local_diag = diag
self._desired_min_tx_interval = 1000000
self._is_polling = True
self._update_xmit_period()
elif new_state == bfd.BFD_STATE_UP:
self._desired_min_tx_interval = self._cfg_desired_min_tx_interval
self._is_polling = True
self._update_xmit_period()
self.app.send_event_to_observers(
EventBFDSessionStateChanged(self, old_state, new_state))
def _recv_timeout_loop(self):
while self._detect_time:
last_wait = time.time()
self._lock = hub.Event()
self._lock.wait(timeout=self._detect_time)
if self._lock.is_set():
if getattr(self, "_auth_seq_known", 0):
if last_wait > time.time() + 2 * self._detect_time:
self._auth_seq_known = 0
else:
LOG.info("[BFD][%s][RECV] BFD Session timed out.",
hex(self._local_discr))
if self._session_state not in [bfd.BFD_STATE_DOWN,
bfd.BFD_STATE_ADMIN_DOWN]:
self._set_state(bfd.BFD_STATE_DOWN,
bfd.BFD_DIAG_CTRL_DETECT_TIME_EXPIRED)
if getattr(self, "_auth_seq_known", 0):
self._auth_seq_known = 0
def _update_xmit_period(self):
if self._desired_min_tx_interval > self._remote_min_rx_interval:
xmit_period = self._desired_min_tx_interval
else:
xmit_period = self._remote_min_rx_interval
if self._detect_mult == 1:
xmit_period *= random.randint(75, 90) / 100.0
else:
xmit_period *= random.randint(75, 100) / 100.0
self._xmit_period = xmit_period / 1000000.0
LOG.info("[BFD][%s][XMIT] Transmission period changed to %f",
hex(self._local_discr), self._xmit_period)
def _send_loop(self):
while self._enable_send:
hub.sleep(self._xmit_period)
if self._remote_discr == 0 and not self._active_role:
continue
if self._remote_min_rx_interval == 0:
continue
if self._remote_demand_mode and self._session_state == bfd.BFD_STATE_UP and self._remote_session_state == bfd.BFD_STATE_UP and not self._is_polling:
continue
self._send()
def _send(self):
if self.datapath is None:
return
flags = 0
if self._pending_final:
flags |= bfd.BFD_FLAG_FINAL
self._pending_final = False
self._is_polling = False
if self._is_polling:
flags |= bfd.BFD_FLAG_POLL
auth_cls = None
if self._auth_type:
auth_key_id = list(self._auth_keys.keys())[
random.randint(0, len(list(self._auth_keys.keys())) - 1)]
auth_key = self._auth_keys[auth_key_id]
if self._auth_type == bfd.BFD_AUTH_SIMPLE_PASS:
auth_cls = bfd.SimplePassword(auth_key_id=auth_key_id,
password=auth_key)
if self._auth_type in [bfd.BFD_AUTH_KEYED_MD5,
bfd.BFD_AUTH_METICULOUS_KEYED_MD5,
bfd.BFD_AUTH_KEYED_SHA1,
bfd.BFD_AUTH_METICULOUS_KEYED_SHA1]:
if self._auth_type in [bfd.BFD_AUTH_KEYED_MD5,
bfd.BFD_AUTH_KEYED_SHA1]:
if random.randint(0, 1):
self._xmit_auth_seq = (self._xmit_auth_seq + 1) & UINT32_MAX
else:
self._xmit_auth_seq = (self._xmit_auth_seq + 1) & UINT32_MAX
auth_cls = bfd.bfd._auth_parsers[self._auth_type](
auth_key_id=auth_key_id,
seq=self._xmit_auth_seq,
auth_key=auth_key)
if auth_cls is not None:
flags |= bfd.BFD_FLAG_AUTH_PRESENT
if self._demand_mode and self._session_state == bfd.BFD_STATE_UP and self._remote_session_state == bfd.BFD_STATE_UP:
flags |= bfd.BFD_FLAG_DEMAND
diag = self._local_diag
state = self._session_state
detect_mult = self._detect_mult
my_discr = self._local_discr
your_discr = self._remote_discr
desired_min_tx_interval = self._desired_min_tx_interval
required_min_rx_interval = self._required_min_rx_interval
required_min_echo_rx_interval = self._cfg_required_min_echo_rx_interval
src_mac = self.src_mac
dst_mac = self.dst_mac
src_ip = self.src_ip
dst_ip = self.dst_ip
self.ipv4_id = (self.ipv4_id + 1) & UINT16_MAX
ipv4_id = self.ipv4_id
src_port = self.src_port
dst_port = self.dst_port
data = BFDPacket.bfd_packet(
src_mac=src_mac, dst_mac=dst_mac,
src_ip=src_ip, dst_ip=dst_ip, ipv4_id=ipv4_id,
src_port=src_port, dst_port=dst_port,
diag=diag, state=state, flags=flags, detect_mult=detect_mult,
my_discr=my_discr, your_discr=your_discr,
desired_min_tx_interval=desired_min_tx_interval,
required_min_rx_interval=required_min_rx_interval,
required_min_echo_rx_interval=required_min_echo_rx_interval,
auth_cls=auth_cls)
datapath = self.datapath
ofproto = datapath.ofproto
parser = datapath.ofproto_parser
actions = [parser.OFPActionOutput(self.ofport)]
out = parser.OFPPacketOut(datapath=datapath,
buffer_id=ofproto.OFP_NO_BUFFER,
in_port=ofproto.OFPP_CONTROLLER,
actions=actions,
data=data)
datapath.send_msg(out)
LOG.debug("[BFD][%s][SEND] BFD Control sent.", hex(self._local_discr))
class BFDPacket(object):
class BFDUnknownFormat(RyuException):
message = '%(msg)s'
@staticmethod
def bfd_packet(src_mac, dst_mac, src_ip, dst_ip, ipv4_id,
src_port, dst_port,
diag=0, state=0, flags=0, detect_mult=0,
my_discr=0, your_discr=0, desired_min_tx_interval=0,
required_min_rx_interval=0,
required_min_echo_rx_interval=0,
auth_cls=None):
pkt = packet.Packet()
eth_pkt = ethernet.ethernet(dst_mac, src_mac, ETH_TYPE_IP)
pkt.add_protocol(eth_pkt)
ipv4_pkt = ipv4.ipv4(proto=inet.IPPROTO_UDP, src=src_ip, dst=dst_ip,
tos=192, identification=ipv4_id, ttl=255)
pkt.add_protocol(ipv4_pkt)
udp_pkt = udp.udp(src_port=src_port, dst_port=dst_port)
pkt.add_protocol(udp_pkt)
bfd_pkt = bfd.bfd(
ver=1, diag=diag, state=state, flags=flags,
detect_mult=detect_mult,
my_discr=my_discr, your_discr=your_discr,
desired_min_tx_interval=desired_min_tx_interval,
required_min_rx_interval=required_min_rx_interval,
required_min_echo_rx_interval=required_min_echo_rx_interval,
auth_cls=auth_cls)
pkt.add_protocol(bfd_pkt)
pkt.serialize()
return pkt.data
@staticmethod
def bfd_parse(data):
pkt = packet.Packet(data)
i = iter(pkt)
eth_pkt = next(i)
assert isinstance(eth_pkt, ethernet.ethernet)
ipv4_pkt = next(i)
assert isinstance(ipv4_pkt, ipv4.ipv4)
udp_pkt = next(i)
assert isinstance(udp_pkt, udp.udp)
udp_payload = next(i)
return bfd.bfd.parser(udp_payload)[0]
class ARPPacket(object):
class ARPUnknownFormat(RyuException):
message = '%(msg)s'
@staticmethod | Apache License 2.0 |
hewlettpackard/python-ilorest-library-old | src/redfish/ris/rmc_helper.py | RmcClient.get_cache_dirname | python | def get_cache_dirname(self):
parts = urlparse2.urlparse(self.get_base_url())
pathstr = '%s/%s' % (parts.netloc, parts.path)
return pathstr.replace('//', '/') | The rest client's current base URL converted to path | https://github.com/hewlettpackard/python-ilorest-library-old/blob/b00fd417024485a77c4f71f913135831d674a177/src/redfish/ris/rmc_helper.py#L224-L228 | import os
import json
import errno
import logging
import hashlib
import urlparse2
import redfish.rest
from .ris import (RisMonolith)
from .sharedtypes import (JSONEncoder)
from .config import (AutoConfigParser)
LOGGER = logging.getLogger(__name__)
class RdmcError(Exception):
errcode = 1
def __init__(self, message):
Exception.__init__(self, message)
class InvalidCommandLineError(RdmcError):
pass
class FailureDuringCommitError(RdmcError):
pass
class UserNotAdminError(RdmcError):
pass
class UndefinedClientError(Exception):
pass
class InstanceNotFoundError(Exception):
pass
class CurrentlyLoggedInError(Exception):
pass
class NothingSelectedError(Exception):
pass
class NothingSelectedFilterError(Exception):
pass
class NothingSelectedSetError(Exception):
pass
class InvalidSelectionError(Exception):
pass
class IdTokenError(Exception):
pass
class SessionExpired(Exception):
pass
class ValueChangedError(Exception):
pass
class LoadSkipSettingError(Exception):
pass
class InvalidPathError(Exception):
pass
class UnableToObtainIloVersionError(Exception):
pass
class ValidationError(Exception):
def __init__(self, errlist):
super(ValidationError, self).__init__(errlist)
self._errlist = errlist
def get_errors(self):
return self._errlist
class IloResponseError(Exception):
pass
class RmcClient(object):
def __init__(self, url=None, username=None, password=None, sessionkey=None, typepath=None, biospassword=None, is_redfish=False):
if is_redfish:
self._rest_client = redfish.rest.v1.redfish_client(base_url=url, username=username, password=password, sessionkey=sessionkey, biospassword=biospassword, is_redfish=is_redfish)
else:
self._rest_client = redfish.rest.v1.rest_client(base_url=url, username=username, password=password, sessionkey=sessionkey, biospassword=biospassword, is_redfish=is_redfish)
self.typepath = typepath
self._get_cache = dict()
self._monolith = RisMonolith(self)
self._selector = None
self._filter_attr = None
self._filter_value = None
def get_username(self):
return self._rest_client.get_username()
def set_username(self, username):
self._rest_client.set_username(username)
def get_password(self):
return self._rest_client.get_password()
def set_password(self, password):
self._rest_client.set_password(password)
def get_bios_password(self):
return self._rest_client.get_biospassword()
def set_bios_password(self, biospasswordword):
self._rest_client.set_biospassword(biospasswordword)
bios_password = property(get_bios_password, set_bios_password)
def get_session_key(self):
return self._rest_client.get_session_key()
def get_session_location(self):
return self._rest_client.get_session_location()
def get_authorization_key(self):
return self._rest_client.get_authorization_key()
def get_base_url(self):
return self._rest_client.get_base_url()
base_url = property(get_base_url, None) | Apache License 2.0 |
sohamtriveous/dbdump | dbdump.py | del_folder | python | def del_folder(devicename, path):
cmd = add_adb_device(devicename)
cmd = cmd + 'shell rm -r '
new_cmd = cmd + path
(status, output) = commands.getstatusoutput(new_cmd)
if status:
print 'Could not delete', path, sys.stderr
return False
else:
return True | delete files at a particular path
:param devicename: device
:param path: path to be deleted
:return: | https://github.com/sohamtriveous/dbdump/blob/f800723c40285df8dd22203990d902c4105ace7d/dbdump.py#L74-L91 | __author__ = 'sohammondal'
import sys
import commands
import re
def exec_cmd(cmd):
(status, output) = commands.getstatusoutput(cmd)
if status:
print 'Could not execute', cmd, sys.stderr
return False
return True
def find_all(pat, string):
matches = re.findall(pat, string)
if matches:
return matches
else:
return None
def find(pat, string):
match = re.search(pat, string)
if match:
return match.group(1)
else:
return None
def find_basic(pat, string):
match = re.search(pat, string)
if match:
return match
else:
return None
def add_adb_device(devicename=None):
cmd = 'adb '
if devicename:
cmd = cmd + '-s ' + devicename + ' '
return cmd | Apache License 2.0 |
microsoft/dowhy | dowhy/causal_estimator.py | CausalEstimator._generate_bootstrap_estimates | python | def _generate_bootstrap_estimates(self, num_bootstrap_simulations,
sample_size_fraction):
simulation_results = np.zeros(num_bootstrap_simulations)
sample_size = int(sample_size_fraction * len(self._data))
if sample_size > len(self._data):
self.logger.warning("WARN: The sample size is greater than the data being sampled")
self.logger.info("INFO: The sample size: {}".format(sample_size))
self.logger.info("INFO: The number of simulations: {}".format(num_bootstrap_simulations))
for index in range(num_bootstrap_simulations):
new_data = resample(self._data, n_samples=sample_size)
new_estimator = type(self)(
new_data,
self._target_estimand,
self._target_estimand.treatment_variable, self._target_estimand.outcome_variable,
treatment_value=self._treatment_value,
control_value=self._control_value,
test_significance=False,
evaluate_effect_strength=False,
confidence_intervals=False,
target_units=self._target_units,
effect_modifiers=self._effect_modifier_names,
params=self.method_params
)
new_effect = new_estimator.estimate_effect()
simulation_results[index] = new_effect.value
estimates = CausalEstimator.BootstrapEstimates(simulation_results,
{'num_simulations': num_bootstrap_simulations,
'sample_size_fraction': sample_size_fraction})
return estimates | Helper function to generate causal estimates over bootstrapped samples.
:param num_bootstrap_simulations: Number of simulations for the bootstrap method.
:param sample_size_fraction: Fraction of the dataset to be resampled.
:returns: A collections.namedtuple containing a list of bootstrapped estimates and a dictionary containing parameters used for the bootstrap. | https://github.com/microsoft/dowhy/blob/9c1371efc580fde142cd2017bf7789e1a8e53814/dowhy/causal_estimator.py#L269-L313 | import logging
import numpy as np
import pandas as pd
import sympy as sp
from collections import namedtuple
from sklearn.utils import resample
import dowhy.interpreters as interpreters
from dowhy.utils.api import parse_state
class CausalEstimator:
DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST = 1000
DEFAULT_NUMBER_OF_SIMULATIONS_CI = 100
DEFAULT_SAMPLE_SIZE_FRACTION = 1
DEFAULT_CONFIDENCE_LEVEL = 0.95
NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS = 5
TEMP_CAT_COLUMN_PREFIX = "__categorical__"
DEFAULT_NOTIMPLEMENTEDERROR_MSG = "not yet implemented for {0}. If you would this to be implemented in the next version, please raise an issue at https://github.com/microsoft/dowhy/issues"
BootstrapEstimates = namedtuple('BootstrapEstimates', ['estimates', 'params'])
DEFAULT_INTERPRET_METHOD = ["textual_effect_interpreter"]
def __init__(self, data, identified_estimand, treatment, outcome,
control_value=0, treatment_value=1,
test_significance=False, evaluate_effect_strength=False,
confidence_intervals=False,
target_units=None, effect_modifiers=None,
params=None):
self._data = data
self._target_estimand = identified_estimand
self._treatment_name = treatment
self._outcome_name = outcome[0]
self._control_value = control_value
self._treatment_value = treatment_value
self._significance_test = test_significance
self._effect_strength_eval = evaluate_effect_strength
self._target_units = target_units
self._effect_modifier_names = effect_modifiers
self._confidence_intervals = confidence_intervals
self._bootstrap_estimates = None
self._bootstrap_null_estimates = None
self._effect_modifiers = None
self.method_params = params
self.interpret_method = CausalEstimator.DEFAULT_INTERPRET_METHOD
if params is not None:
for key, value in params.items():
setattr(self, key, value)
self.logger = logging.getLogger(__name__)
if not hasattr(self, 'num_null_simulations'):
self.num_null_simulations = CausalEstimator.DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST
if not hasattr(self, 'num_simulations'):
self.num_simulations = CausalEstimator.DEFAULT_NUMBER_OF_SIMULATIONS_CI
if not hasattr(self, 'sample_size_fraction'):
self.sample_size_fraction = CausalEstimator.DEFAULT_SAMPLE_SIZE_FRACTION
if not hasattr(self, 'confidence_level'):
self.confidence_level = CausalEstimator.DEFAULT_CONFIDENCE_LEVEL
if not hasattr(self, 'num_quantiles_to_discretize_cont_cols'):
self.num_quantiles_to_discretize_cont_cols = CausalEstimator.NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS
if not hasattr(self, 'need_conditional_estimates'):
self.need_conditional_estimates = bool(self._effect_modifier_names)
if self._data is not None:
self._treatment = self._data[self._treatment_name]
self._outcome = self._data[self._outcome_name]
if self._effect_modifier_names:
self._effect_modifiers = self._data[self._effect_modifier_names]
self._effect_modifiers = pd.get_dummies(self._effect_modifiers, drop_first=True)
self.logger.debug("Effect modifiers: " +
",".join(self._effect_modifier_names))
@staticmethod
def get_estimator_object(new_data, identified_estimand, estimate):
estimator_class = estimate.params['estimator_class']
new_estimator = estimator_class(
new_data,
identified_estimand,
identified_estimand.treatment_variable, identified_estimand.outcome_variable,
control_value=estimate.control_value,
treatment_value=estimate.treatment_value,
test_significance=False,
evaluate_effect_strength=False,
confidence_intervals=estimate.params["confidence_intervals"],
target_units=estimate.params["target_units"],
effect_modifiers=estimate.params["effect_modifiers"],
params=estimate.params["method_params"]
)
return new_estimator
def _estimate_effect(self):
raise NotImplementedError(
("Main estimation method is " + CausalEstimator.DEFAULT_NOTIMPLEMENTEDERROR_MSG).format(self.__class__))
def estimate_effect(self):
est = self._estimate_effect()
est.add_estimator(self)
if self._significance_test:
self.test_significance(est.value, method=self._significance_test)
if self._confidence_intervals:
self.estimate_confidence_intervals(est.value, confidence_level=self.confidence_level,
method=self._confidence_intervals)
if self._effect_strength_eval:
effect_strength_dict = self.evaluate_effect_strength(est)
est.add_effect_strength(effect_strength_dict)
return est
def estimate_effect_naive(self):
df_withtreatment = self._data.loc[self._data[self._treatment_name] == 1]
df_notreatment = self._data.loc[self._data[self._treatment_name] == 0]
est = np.mean(df_withtreatment[self._outcome_name]) - np.mean(df_notreatment[self._outcome_name])
return CausalEstimate(est, None, None, control_value=0, treatment_value=1)
def _estimate_effect_fn(self, data_df):
raise NotImplementedError(
("Conditional treatment effects are " + CausalEstimator.DEFAULT_NOTIMPLEMENTEDERROR_MSG).format(
self.__class__))
def _estimate_conditional_effects(self, estimate_effect_fn,
effect_modifier_names=None,
num_quantiles=None):
if effect_modifier_names is None:
effect_modifier_names = self._effect_modifier_names
if num_quantiles is None:
num_quantiles = self.num_quantiles_to_discretize_cont_cols
if not effect_modifier_names:
raise ValueError("At least one effect modifier should be specified to compute conditional effects.")
effect_modifier_names = parse_state(effect_modifier_names)
if not all(em in self._effect_modifier_names for em in effect_modifier_names):
self.logger.warn(
"At least one of the provided effect modifiers was not included while fitting the estimator. You may get incorrect results. To resolve, fit the estimator again by providing the updated effect modifiers in estimate_effect().")
effect_modifier_names = effect_modifier_names.copy()
prefix = CausalEstimator.TEMP_CAT_COLUMN_PREFIX
for i in range(len(effect_modifier_names)):
em = effect_modifier_names[i]
if pd.api.types.is_numeric_dtype(self._data[em].dtypes):
self._data[prefix + str(em)] = pd.qcut(self._data[em],
num_quantiles, duplicates="drop")
effect_modifier_names[i] = prefix + str(em)
by_effect_mods = self._data.groupby(effect_modifier_names)
cond_est_fn = lambda x: self._do(self._treatment_value, x) - self._do(self._control_value, x)
conditional_estimates = by_effect_mods.apply(estimate_effect_fn)
for em in effect_modifier_names:
if em.startswith(prefix):
self._data.pop(em)
return conditional_estimates
def _do(self, x, data_df=None):
raise NotImplementedError(
("Do-operator is " + CausalEstimator.DEFAULT_NOTIMPLEMENTEDERROR_MSG).format(self.__class__))
def do(self, x, data_df=None):
est = self._do(x, data_df)
return est
def construct_symbolic_estimator(self, estimand):
raise NotImplementedError(("Symbolic estimator string is ").format(self.__class__)) | MIT License |
onshape-public/onshape-clients | python/onshape_client/oas/models/btp_statement_loop_for_in279.py | BTPStatementLoopForIn279.__init__ | python | def __init__(
self,
_check_type=True,
_from_server=False,
_path_to_item=(),
_configuration=None,
**kwargs
):
self._data_store = {}
self._check_type = _check_type
self._from_server = _from_server
self._path_to_item = _path_to_item
self._configuration = _configuration
constant_args = {
"_check_type": _check_type,
"_path_to_item": _path_to_item,
"_from_server": _from_server,
"_configuration": _configuration,
}
required_args = {}
required_arg_names = list(required_args.keys())
for required_arg_name in required_arg_names:
if required_args[required_arg_name] is nulltype.Null:
del required_args[required_arg_name]
model_args = {}
model_args.update(required_args)
model_args.update(kwargs)
composed_info = validate_get_composed_info(constant_args, model_args, self)
self._composed_instances = composed_info[0]
self._var_name_to_model_instances = composed_info[1]
self._additional_properties_model_instances = composed_info[2]
unused_args = composed_info[3]
for var_name, var_value in required_args.items():
setattr(self, var_name, var_value)
for var_name, var_value in six.iteritems(kwargs):
if (
var_name in unused_args
and self._configuration is not None
and self._configuration.discard_unknown_keys
and not self._additional_properties_model_instances
):
continue
setattr(self, var_name, var_value) | btp_statement_loop_for_in279.BTPStatementLoopForIn279 - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_from_server (bool): True if the data is from the server
False if the data is from the client (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
bt_type (str): [optional] # noqa: E501
container (btp_expression9.BTPExpression9): [optional] # noqa: E501
is_var_declared_here (bool): [optional] # noqa: E501
name (btp_identifier8.BTPIdentifier8): [optional] # noqa: E501
space_before_var (btp_space10.BTPSpace10): [optional] # noqa: E501
standard_type (str): [optional] # noqa: E501
type_name (str): [optional] # noqa: E501
var (btp_identifier8.BTPIdentifier8): [optional] # noqa: E501
atomic (bool): [optional] # noqa: E501
documentation_type (str): [optional] # noqa: E501
end_source_location (int): [optional] # noqa: E501
node_id (str): [optional] # noqa: E501
short_descriptor (str): [optional] # noqa: E501
space_after (btp_space10.BTPSpace10): [optional] # noqa: E501
space_before (btp_space10.BTPSpace10): [optional] # noqa: E501
space_default (bool): [optional] # noqa: E501
start_source_location (int): [optional] # noqa: E501
annotation (btp_annotation231.BTPAnnotation231): [optional] # noqa: E501
body (btp_statement269.BTPStatement269): [optional] # noqa: E501
space_after_loop_type (btp_space10.BTPSpace10): [optional] # noqa: E501 | https://github.com/onshape-public/onshape-clients/blob/20843a00c628e516e7219e17a23ec4ef2bf9f16f/python/onshape_client/oas/models/btp_statement_loop_for_in279.py#L195-L279 | from __future__ import absolute_import
import re
import sys
import six
import nulltype
from onshape_client.oas.model_utils import (
ModelComposed,
ModelNormal,
ModelSimple,
date,
datetime,
file_type,
int,
none_type,
str,
validate_get_composed_info,
)
try:
from onshape_client.oas.models import btp_annotation231
except ImportError:
btp_annotation231 = sys.modules["onshape_client.oas.models.btp_annotation231"]
try:
from onshape_client.oas.models import btp_expression9
except ImportError:
btp_expression9 = sys.modules["onshape_client.oas.models.btp_expression9"]
try:
from onshape_client.oas.models import btp_identifier8
except ImportError:
btp_identifier8 = sys.modules["onshape_client.oas.models.btp_identifier8"]
try:
from onshape_client.oas.models import btp_space10
except ImportError:
btp_space10 = sys.modules["onshape_client.oas.models.btp_space10"]
try:
from onshape_client.oas.models import btp_statement269
except ImportError:
btp_statement269 = sys.modules["onshape_client.oas.models.btp_statement269"]
try:
from onshape_client.oas.models import btp_statement_loop277
except ImportError:
btp_statement_loop277 = sys.modules[
"onshape_client.oas.models.btp_statement_loop277"
]
try:
from onshape_client.oas.models import btp_statement_loop_for_in279_all_of
except ImportError:
btp_statement_loop_for_in279_all_of = sys.modules[
"onshape_client.oas.models.btp_statement_loop_for_in279_all_of"
]
class BTPStatementLoopForIn279(ModelComposed):
allowed_values = {
("standard_type",): {
"UNDEFINED": "UNDEFINED",
"BOOLEAN": "BOOLEAN",
"NUMBER": "NUMBER",
"STRING": "STRING",
"ARRAY": "ARRAY",
"MAP": "MAP",
"BOX": "BOX",
"BUILTIN": "BUILTIN",
"FUNCTION": "FUNCTION",
"UNKNOWN": "UNKNOWN",
},
("documentation_type",): {
"FUNCTION": "FUNCTION",
"PREDICATE": "PREDICATE",
"CONSTANT": "CONSTANT",
"ENUM": "ENUM",
"USER_TYPE": "USER_TYPE",
"FEATURE_DEFINITION": "FEATURE_DEFINITION",
"FILE_HEADER": "FILE_HEADER",
"UNDOCUMENTABLE": "UNDOCUMENTABLE",
"UNKNOWN": "UNKNOWN",
},
}
validations = {}
additional_properties_type = None
@staticmethod
def openapi_types():
return {
"bt_type": (str,),
"container": (btp_expression9.BTPExpression9,),
"is_var_declared_here": (bool,),
"name": (btp_identifier8.BTPIdentifier8,),
"space_before_var": (btp_space10.BTPSpace10,),
"standard_type": (str,),
"type_name": (str,),
"var": (btp_identifier8.BTPIdentifier8,),
"atomic": (bool,),
"documentation_type": (str,),
"end_source_location": (int,),
"node_id": (str,),
"short_descriptor": (str,),
"space_after": (btp_space10.BTPSpace10,),
"space_before": (btp_space10.BTPSpace10,),
"space_default": (bool,),
"start_source_location": (int,),
"annotation": (btp_annotation231.BTPAnnotation231,),
"body": (btp_statement269.BTPStatement269,),
"space_after_loop_type": (btp_space10.BTPSpace10,),
}
@staticmethod
def discriminator():
return None
attribute_map = {
"bt_type": "btType",
"container": "container",
"is_var_declared_here": "isVarDeclaredHere",
"name": "name",
"space_before_var": "spaceBeforeVar",
"standard_type": "standardType",
"type_name": "typeName",
"var": "var",
"atomic": "atomic",
"documentation_type": "documentationType",
"end_source_location": "endSourceLocation",
"node_id": "nodeId",
"short_descriptor": "shortDescriptor",
"space_after": "spaceAfter",
"space_before": "spaceBefore",
"space_default": "spaceDefault",
"start_source_location": "startSourceLocation",
"annotation": "annotation",
"body": "body",
"space_after_loop_type": "spaceAfterLoopType",
}
required_properties = set(
[
"_data_store",
"_check_type",
"_from_server",
"_path_to_item",
"_configuration",
"_composed_instances",
"_var_name_to_model_instances",
"_additional_properties_model_instances",
]
) | MIT License |
pelioniot/mbed-cloud-sdk-python | src/mbed_cloud/_backends/iam/models/account_info.py | AccountInfo.idle_timeout | python | def idle_timeout(self):
return self._idle_timeout | Gets the idle_timeout of this AccountInfo.
The reference token expiration time in minutes for this account.
:return: The idle_timeout of this AccountInfo.
:rtype: str | https://github.com/pelioniot/mbed-cloud-sdk-python/blob/71dc67fc2a8d1aff31e35ec781fb328e6a60639c/src/mbed_cloud/_backends/iam/models/account_info.py#L557-L565 | from pprint import pformat
from six import iteritems
import re
class AccountInfo(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'address_line1': 'str',
'address_line2': 'str',
'aliases': 'list[str]',
'city': 'str',
'company': 'str',
'contact': 'str',
'contract_number': 'str',
'country': 'str',
'created_at': 'datetime',
'custom_fields': 'dict(str, str)',
'customer_number': 'str',
'display_name': 'str',
'email': 'str',
'end_market': 'str',
'etag': 'str',
'expiration_warning_threshold': 'str',
'id': 'str',
'idle_timeout': 'str',
'limits': 'dict(str, str)',
'mfa_status': 'str',
'notification_emails': 'list[str]',
'object': 'str',
'parent_id': 'str',
'password_policy': 'PasswordPolicy',
'phone_number': 'str',
'policies': 'list[FeaturePolicy]',
'postal_code': 'str',
'reason': 'str',
'reference_note': 'str',
'sales_contact': 'str',
'state': 'str',
'status': 'str',
'sub_accounts': 'list[AccountInfo]',
'template_id': 'str',
'tier': 'str',
'updated_at': 'datetime',
'upgraded_at': 'datetime'
}
attribute_map = {
'address_line1': 'address_line1',
'address_line2': 'address_line2',
'aliases': 'aliases',
'city': 'city',
'company': 'company',
'contact': 'contact',
'contract_number': 'contract_number',
'country': 'country',
'created_at': 'created_at',
'custom_fields': 'custom_fields',
'customer_number': 'customer_number',
'display_name': 'display_name',
'email': 'email',
'end_market': 'end_market',
'etag': 'etag',
'expiration_warning_threshold': 'expiration_warning_threshold',
'id': 'id',
'idle_timeout': 'idle_timeout',
'limits': 'limits',
'mfa_status': 'mfa_status',
'notification_emails': 'notification_emails',
'object': 'object',
'parent_id': 'parent_id',
'password_policy': 'password_policy',
'phone_number': 'phone_number',
'policies': 'policies',
'postal_code': 'postal_code',
'reason': 'reason',
'reference_note': 'reference_note',
'sales_contact': 'sales_contact',
'state': 'state',
'status': 'status',
'sub_accounts': 'sub_accounts',
'template_id': 'template_id',
'tier': 'tier',
'updated_at': 'updated_at',
'upgraded_at': 'upgraded_at'
}
def __init__(self, address_line1=None, address_line2=None, aliases=None, city=None, company=None, contact=None, contract_number=None, country=None, created_at=None, custom_fields=None, customer_number=None, display_name=None, email=None, end_market=None, etag=None, expiration_warning_threshold=None, id=None, idle_timeout=None, limits=None, mfa_status=None, notification_emails=None, object=None, parent_id=None, password_policy=None, phone_number=None, policies=None, postal_code=None, reason=None, reference_note=None, sales_contact=None, state=None, status=None, sub_accounts=None, template_id=None, tier=None, updated_at=None, upgraded_at=None):
self._address_line1 = address_line1
self._address_line2 = address_line2
self._aliases = aliases
self._city = city
self._company = company
self._contact = contact
self._contract_number = contract_number
self._country = country
self._created_at = created_at
self._custom_fields = custom_fields
self._customer_number = customer_number
self._display_name = display_name
self._email = email
self._end_market = end_market
self._etag = etag
self._expiration_warning_threshold = expiration_warning_threshold
self._id = id
self._idle_timeout = idle_timeout
self._limits = limits
self._mfa_status = mfa_status
self._notification_emails = notification_emails
self._object = object
self._parent_id = parent_id
self._password_policy = password_policy
self._phone_number = phone_number
self._policies = policies
self._postal_code = postal_code
self._reason = reason
self._reference_note = reference_note
self._sales_contact = sales_contact
self._state = state
self._status = status
self._sub_accounts = sub_accounts
self._template_id = template_id
self._tier = tier
self._updated_at = updated_at
self._upgraded_at = upgraded_at
self.discriminator = None
@property
def address_line1(self):
return self._address_line1
@address_line1.setter
def address_line1(self, address_line1):
self._address_line1 = address_line1
@property
def address_line2(self):
return self._address_line2
@address_line2.setter
def address_line2(self, address_line2):
self._address_line2 = address_line2
@property
def aliases(self):
return self._aliases
@aliases.setter
def aliases(self, aliases):
if aliases is None:
raise ValueError("Invalid value for `aliases`, must not be `None`")
self._aliases = aliases
@property
def city(self):
return self._city
@city.setter
def city(self, city):
self._city = city
@property
def company(self):
return self._company
@company.setter
def company(self, company):
self._company = company
@property
def contact(self):
return self._contact
@contact.setter
def contact(self, contact):
self._contact = contact
@property
def contract_number(self):
return self._contract_number
@contract_number.setter
def contract_number(self, contract_number):
self._contract_number = contract_number
@property
def country(self):
return self._country
@country.setter
def country(self, country):
self._country = country
@property
def created_at(self):
return self._created_at
@created_at.setter
def created_at(self, created_at):
self._created_at = created_at
@property
def custom_fields(self):
return self._custom_fields
@custom_fields.setter
def custom_fields(self, custom_fields):
self._custom_fields = custom_fields
@property
def customer_number(self):
return self._customer_number
@customer_number.setter
def customer_number(self, customer_number):
self._customer_number = customer_number
@property
def display_name(self):
return self._display_name
@display_name.setter
def display_name(self, display_name):
self._display_name = display_name
@property
def email(self):
return self._email
@email.setter
def email(self, email):
self._email = email
@property
def end_market(self):
return self._end_market
@end_market.setter
def end_market(self, end_market):
if end_market is None:
raise ValueError("Invalid value for `end_market`, must not be `None`")
self._end_market = end_market
@property
def etag(self):
return self._etag
@etag.setter
def etag(self, etag):
if etag is None:
raise ValueError("Invalid value for `etag`, must not be `None`")
self._etag = etag
@property
def expiration_warning_threshold(self):
return self._expiration_warning_threshold
@expiration_warning_threshold.setter
def expiration_warning_threshold(self, expiration_warning_threshold):
self._expiration_warning_threshold = expiration_warning_threshold
@property
def id(self):
return self._id
@id.setter
def id(self, id):
if id is None:
raise ValueError("Invalid value for `id`, must not be `None`")
self._id = id
@property | Apache License 2.0 |
paulgilmartin/graph_wrap | graph_wrap/tastypie/api_transformer.py | field_transformer | python | def field_transformer(tastypie_field):
try:
transformer_class = FieldTransformerMeta.registry[
(tastypie_field.dehydrated_type, tastypie_field.is_m2m)]
except KeyError:
raise KeyError('Dehydrated type not recognized')
return transformer_class(tastypie_field) | Instantiate the appropriate FieldTransformer class.
This acts as a factory-type function, which, given
a tastypie field as input, instantiates the appropriate
concrete FieldTransformer class for that field. | https://github.com/paulgilmartin/graph_wrap/blob/7fff829dc7d2818c57de00d3055fc6c50fae7484/graph_wrap/tastypie/api_transformer.py#L42-L54 | from __future__ import unicode_literals
import json
from abc import abstractmethod
from decimal import Decimal as _Decimal
import six
from graphene import (
String,
Int,
Float,
Boolean,
Decimal,
List,
Field,
Scalar,
ObjectType,
)
from graphene.types.generic import GenericScalar
from graph_wrap.shared.query_resolver import JSONResolver
def transform_api(tastypie_resource):
class_attrs = dict()
graphene_type_name = tastypie_resource._meta.resource_name + '_type'
for field_name, field in tastypie_resource.fields.items():
transformer = field_transformer(field)
class_attrs[field_name] = transformer.graphene_field()
resolver_method_name = 'resolve_{}'.format(field_name)
class_attrs[resolver_method_name] = (
transformer.graphene_field_resolver_method())
graphene_type = type(
str(graphene_type_name),
(ObjectType,),
class_attrs,
)
return graphene_type | MIT License |
ox-it/humfrey | humfrey/streaming/base.py | StreamingParser.get | python | def get(self):
if self._cached_get is None:
sparql_results_type = self.get_sparql_results_type()
if sparql_results_type == 'resultset':
self._cached_get = SparqlResultList(self.get_fields(), self.get_bindings())
elif sparql_results_type == 'boolean':
self._cached_get = self.get_boolean()
elif sparql_results_type == 'graph':
graph = rdflib.ConjunctiveGraph()
for prefix, namespace_uri in NS.iteritems():
graph.namespace_manager.bind(prefix, namespace_uri)
graph += self.get_triples()
self._cached_get = graph
else:
raise AssertionError("Unexpected results type: {0}".format(sparql_results_type))
for name in ('query', 'duration'):
if hasattr(self, name):
setattr(self._cached_get, name, getattr(self, name))
return self._cached_get | Returns an in-memory object representing the stream.
You will either get a SparqlResultsList, a bool, or a ConjunctiveGraph. | https://github.com/ox-it/humfrey/blob/c92e46a24a9bf28aa9638a612f166d209315e76b/humfrey/streaming/base.py#L109-L132 | import abc
import types
import rdflib
from humfrey.sparql.results import SparqlResultList
from humfrey.utils.namespaces import NS
from humfrey.utils.statsd import statsd
class ModeError(Exception):
pass
class StreamingParser(object):
__metaclass__ = abc.ABCMeta
def __init__(self, stream, encoding='utf-8'):
self._stream, self._encoding = stream, encoding
self._mode, self._cached_get = None, None
@property
def mode(self):
return self._mode
@mode.setter
def mode(self, mode):
if self._mode == mode:
return
elif self._mode is not None:
raise ModeError()
else:
self._mode = mode
@abc.abstractproperty
def media_type(self):
@abc.abstractproperty
def format_type(self):
@abc.abstractmethod
def get_sparql_results_type(self):
@abc.abstractmethod
def get_fields(self):
@abc.abstractmethod
def get_bindings(self):
@abc.abstractmethod
def get_boolean(self):
@abc.abstractmethod
def get_triples(self):
def read(self, num=None):
self.mode = 'stream'
return self._stream.read(num)
def readline(self):
self.mode = 'stream'
return self._stream.readline()
def __iter__(self):
self.mode = 'stream'
return iter(self._stream) | BSD 3-Clause New or Revised License |
retr0h/gilt | gilt/shell.py | overlay | python | def overlay(ctx):
args = ctx.obj.get("args")
filename = args.get("config")
debug = args.get("debug")
_setup(filename)
for c in config.config(filename):
with fasteners.InterProcessLock(c.lock_file):
util.print_info("{}:".format(c.name))
if not os.path.exists(c.src):
git.clone(c.name, c.git, c.src, debug=debug)
if c.dst:
git.extract(c.src, c.dst, c.version, debug=debug)
post_commands = {c.dst: c.post_commands}
else:
git.overlay(c.src, c.files, c.version, debug=debug)
post_commands = {
conf.dst: conf.post_commands for conf in c.files
}
for dst, commands in post_commands.items():
for command in commands:
msg = " - running `{}` in {}".format(command, dst)
util.print_info(msg)
cmd = util.build_sh_cmd(command, cwd=dst)
util.run_command(cmd, debug=debug) | Install gilt dependencies | https://github.com/retr0h/gilt/blob/afb6e22f74d2c8a4d064a684964dc5fba0414f7a/gilt/shell.py#L80-L106 | import os
import click
import click_completion
import fasteners
import gilt
from gilt import config
from gilt import git
from gilt import util
click_completion.init()
class NotFoundError(Exception):
pass
@click.group()
@click.option(
"--config",
default="gilt.yml",
help="Path to config file. Default gilt.yml",
type=click.File("r"),
)
@click.option(
"--debug/--no-debug",
default=False,
help="Enable or disable debug mode. Default is disabled.",
)
@click.version_option(version=gilt.__version__)
@click.pass_context
def main(ctx, config, debug):
ctx.obj = {}
ctx.obj["args"] = {}
ctx.obj["args"]["debug"] = debug
ctx.obj["args"]["config"] = config.name
@click.command()
@click.pass_context | MIT License |
kuri65536/python-for-android | python-modules/twisted/twisted/lore/tree.py | addMtime | python | def addMtime(document, fullpath):
for node in domhelpers.findElementsWithAttribute(document, "class","mtime"):
txt = dom.Text()
txt.data = time.ctime(os.path.getmtime(fullpath))
node.appendChild(txt) | Set the last modified time of the given document.
@type document: A DOM Node or Document
@param document: The output template which defines the presentation of the
last modified time.
@type fullpath: C{str}
@param fullpath: The file name from which to take the last modified time.
@return: C{None} | https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-modules/twisted/twisted/lore/tree.py#L63-L79 | from itertools import count
import re, os, cStringIO, time, cgi, string, urlparse
from xml.dom import minidom as dom
from xml.sax.handler import ErrorHandler, feature_validation
from xml.dom.pulldom import SAX2DOM
from xml.sax import make_parser
from xml.sax.xmlreader import InputSource
from twisted.python import htmlizer, text
from twisted.python.filepath import FilePath
from twisted.python.deprecate import deprecated
from twisted.python.versions import Version
from twisted.web import domhelpers
import process, latex, indexer, numberer, htmlbook
def fixLinks(document, ext):
supported_schemes=['http', 'https', 'ftp', 'mailto']
for node in domhelpers.findElementsWithAttribute(document, 'href'):
href = node.getAttribute("href")
if urlparse.urlparse(href)[0] in supported_schemes:
continue
if node.getAttribute("class") == "absolute":
continue
if node.getAttribute("class").find('listing') != -1:
continue
if href.endswith('html') or href[:href.rfind('#')].endswith('html'):
fname, fext = os.path.splitext(href)
if '#' in fext:
fext = ext+'#'+fext.split('#', 1)[1]
else:
fext = ext
node.setAttribute("href", fname + fext) | Apache License 2.0 |
autofitcloud/isitfit | isitfit/tags/tagsCsvDiff.py | TagsCsvDiff.droppedTags | python | def droppedTags(self):
if len(self.old_minus_new)==0:
return
logger.info("There are %i deleted tag(s)"%len(self.old_minus_new))
logger.info("")
old_processed = set()
for ni in self.old_minus_new:
confirm_msg = colored('Did you completely delete the tag "%s"? yes/[no] '%ni, 'cyan')
confirm_del = input(confirm_msg)
if confirm_del.lower() in ['y', 'yes']:
mig_i = ('rm', ni, None)
self.migrations.append(mig_i)
old_processed.add(ni)
self.old_minus_new -= old_processed | Identify if some tags are completely dropped.
Ask the user if indeed dropped, or accident.
Follows the idea of django/db/migrations/questioner.py
where django asks if fields are renamed or dropped
https://github.com/django/django/blob/e90af8bad44341cf8ebd469dac57b61a95667c1d/django/db/migrations/questioner.py | https://github.com/autofitcloud/isitfit/blob/6ffc0c67c00140120f5d5ad8dfe11c8f0f7dacc1/isitfit/tags/tagsCsvDiff.py#L121-L152 | from isitfit.cli.click_descendents import IsitfitCliError
from isitfit.utils import logger
from termcolor import colored
class TagsCsvDiff:
def __init__(self, df_old, df_new):
self.df_old = df_old
self.df_new = df_new
self.old_minus_new = set()
self.new_minus_old = set()
self.migrations = []
def noChanges(self):
to_json = lambda x: x.sort_values('instance_id', ascending=True)[sorted(list(x.columns))].to_json(orient='records')
json_old = to_json(self.df_old)
json_new = to_json(self.df_new)
if json_old == json_new:
raise IsitfitCliError("Aborting `tags push` due to no changes detected.")
def noNewInstances(self):
inst_old = set(self.df_old.instance_id)
inst_new = set(self.df_new.instance_id)
inst_created = inst_new - inst_old
inst_created = sorted(list(inst_created))
if len(inst_created)>0:
msg_1 = "Found new instances IDs: %s%s"
msg_2 = ", ".join(inst_created[:5])
msg_3 ="..." if len(inst_created)>5 else ""
msg_4 = msg_1%(msg_2,msg_3)
raise IsitfitCliError(msg_4)
def getDiffCols(self):
old_cols = set(self.df_old.columns)
new_cols = set(self.df_new.columns)
self.old_minus_new = old_cols - new_cols
self.new_minus_old = new_cols - old_cols
def renamedTags(self):
import difflib
corr_method = lambda s1, s2: 1 if s1 in s2 else (difflib.SequenceMatcher(None, s1, s2).ratio())
import pandas as pd
import numpy as np
col_dist = pd.DataFrame(
np.zeros(shape=(
len(self.old_minus_new),
len(self.new_minus_old)
)),
index=self.old_minus_new,
columns=self.new_minus_old
)
new_processed = set()
old_processed = set()
for c1 in self.old_minus_new:
for c2 in self.new_minus_old:
d12 = corr_method(c1, c2)
col_dist.loc[c1, c2] = d12
d1_maxD = col_dist.loc[c1].max()
if d1_maxD > 0.7:
d1_maxV = col_dist.loc[c1].idxmax()
confirm_msg = colored('Did you rename the tag "%s" to "%s"? yes/[no] '%(c1, d1_maxV), 'cyan')
confirm_mv = input(confirm_msg)
if confirm_mv.lower() in ['y', 'yes']:
mig_i = ('mv', c1, d1_maxV)
self.migrations.append(mig_i)
old_processed.add(c1)
new_processed.add(d1_maxV)
self.new_minus_old -= new_processed
self.old_minus_new -= old_processed
def newTags(self):
if len(self.new_minus_old)==0:
return
logger.info("Found %i new tag(s)"%len(self.new_minus_old))
logger.info("")
new_processed = set()
for ni in self.new_minus_old:
confirm_msg = colored('Did you add the tag "%s"? yes/[no] '%ni, 'cyan')
confirm_new = input(confirm_msg)
if confirm_new.lower() in ['y', 'yes']:
mig_i = ('touch', None, ni)
self.migrations.append(mig_i)
new_processed.add(ni)
self.new_minus_old -= new_processed | Apache License 2.0 |
azure/autorest.python | test/vanilla/legacy/Expected/AcceptanceTests/ModelFlattening/modelflattening/operations/_auto_rest_resource_flattening_test_service_operations.py | AutoRestResourceFlatteningTestServiceOperationsMixin.put_dictionary | python | def put_dictionary(
self,
resource_dictionary=None,
**kwargs
):
cls = kwargs.pop("cls", None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop("error_map", {}))
content_type = kwargs.pop("content_type", "application/json")
if resource_dictionary is not None:
json = self._serialize.body(resource_dictionary, "{FlattenedProduct}")
else:
json = None
request = build_put_dictionary_request(
content_type=content_type,
json=json,
template_url=self.put_dictionary.metadata["url"],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error)
if cls:
return cls(pipeline_response, None, {}) | Put External Resource as a Dictionary.
:param resource_dictionary: External Resource as a Dictionary to put.
:type resource_dictionary: dict[str, ~modelflattening.models.FlattenedProduct]
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError | https://github.com/azure/autorest.python/blob/90d60a965788e3b4c0809e6686bdc3525acac89c/test/vanilla/legacy/Expected/AcceptanceTests/ModelFlattening/modelflattening/operations/_auto_rest_resource_flattening_test_service_operations.py#L470-L513 | import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import (
ClientAuthenticationError,
HttpResponseError,
ResourceExistsError,
ResourceNotFoundError,
map_error,
)
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
T = TypeVar("T")
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
def build_put_array_request(
**kwargs
):
content_type = kwargs.pop('content_type', None)
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/array')
header_parameters = kwargs.pop("headers", {})
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_array_request(
**kwargs
):
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/array')
header_parameters = kwargs.pop("headers", {})
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_put_wrapped_array_request(
**kwargs
):
content_type = kwargs.pop('content_type', None)
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/wrappedarray')
header_parameters = kwargs.pop("headers", {})
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_wrapped_array_request(
**kwargs
):
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/wrappedarray')
header_parameters = kwargs.pop("headers", {})
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_put_dictionary_request(
**kwargs
):
content_type = kwargs.pop('content_type', None)
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/dictionary')
header_parameters = kwargs.pop("headers", {})
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_dictionary_request(
**kwargs
):
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/dictionary')
header_parameters = kwargs.pop("headers", {})
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_put_resource_collection_request(
**kwargs
):
content_type = kwargs.pop('content_type', None)
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/resourcecollection')
header_parameters = kwargs.pop("headers", {})
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_resource_collection_request(
**kwargs
):
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/resourcecollection')
header_parameters = kwargs.pop("headers", {})
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_put_simple_product_request(
**kwargs
):
content_type = kwargs.pop('content_type', None)
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/customFlattening')
header_parameters = kwargs.pop("headers", {})
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_post_flattened_simple_product_request(
**kwargs
):
content_type = kwargs.pop('content_type', None)
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/customFlattening')
header_parameters = kwargs.pop("headers", {})
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_put_simple_product_with_grouping_request(
name,
**kwargs
):
content_type = kwargs.pop('content_type', None)
accept = "application/json"
url = kwargs.pop("template_url", '/model-flatten/customFlattening/parametergrouping/{name}/')
path_format_arguments = {
"name": _SERIALIZER.url("name", name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
header_parameters = kwargs.pop("headers", {})
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
class AutoRestResourceFlatteningTestServiceOperationsMixin(object):
@distributed_trace
def put_array(
self,
resource_array=None,
**kwargs
):
cls = kwargs.pop("cls", None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop("error_map", {}))
content_type = kwargs.pop("content_type", "application/json")
if resource_array is not None:
json = self._serialize.body(resource_array, "[Resource]")
else:
json = None
request = build_put_array_request(
content_type=content_type,
json=json,
template_url=self.put_array.metadata["url"],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error)
if cls:
return cls(pipeline_response, None, {})
put_array.metadata = {"url": "/model-flatten/array"}
@distributed_trace
def get_array(
self, **kwargs
):
cls = kwargs.pop("cls", None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop("error_map", {}))
request = build_get_array_request(
template_url=self.get_array.metadata["url"],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize("[FlattenedProduct]", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_array.metadata = {"url": "/model-flatten/array"}
@distributed_trace
def put_wrapped_array(
self,
resource_array=None,
**kwargs
):
cls = kwargs.pop("cls", None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop("error_map", {}))
content_type = kwargs.pop("content_type", "application/json")
if resource_array is not None:
json = self._serialize.body(resource_array, "[WrappedProduct]")
else:
json = None
request = build_put_wrapped_array_request(
content_type=content_type,
json=json,
template_url=self.put_wrapped_array.metadata["url"],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error)
if cls:
return cls(pipeline_response, None, {})
put_wrapped_array.metadata = {"url": "/model-flatten/wrappedarray"}
@distributed_trace
def get_wrapped_array(
self, **kwargs
):
cls = kwargs.pop("cls", None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop("error_map", {}))
request = build_get_wrapped_array_request(
template_url=self.get_wrapped_array.metadata["url"],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize("[ProductWrapper]", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_wrapped_array.metadata = {"url": "/model-flatten/wrappedarray"}
@distributed_trace | MIT License |
berkeleyautomation/autolab_core | autolab_core/image.py | Image._preprocess_data | python | def _preprocess_data(self, data):
original_type = data.dtype
if len(data.shape) == 1:
data = data[:, np.newaxis, np.newaxis]
elif len(data.shape) == 2:
data = data[:, :, np.newaxis]
elif len(data.shape) == 0 or len(data.shape) > 3:
raise ValueError(
"Illegal data array passed to image. "
"Must be 1, 2, or 3 dimensional numpy array"
)
return data.astype(original_type) | Converts a data array to the preferred 3D structure.
Parameters
----------
data : :obj:`numpy.ndarray`
The data to process.
Returns
-------
:obj:`numpy.ndarray`
The data re-formatted (if needed) as a 3D matrix
Raises
------
ValueError
If the data is not 1, 2, or 3D to begin with. | https://github.com/berkeleyautomation/autolab_core/blob/cda081d2e07e3fe6cc9f3e8c86eea92330910d20/autolab_core/image.py#L137-L165 | from abc import ABCMeta, abstractmethod
import logging
import os
import cv2
import numpy as np
import PIL.Image as PImage
import matplotlib.pyplot as plt
import scipy.signal as ssg
import scipy.ndimage.filters as sf
import scipy.ndimage.interpolation as sni
import scipy.ndimage.morphology as snm
import scipy.spatial.distance as ssd
import sklearn.cluster as sc
import skimage.morphology as morph
import skimage.transform as skt
from .constants import MAX_DEPTH, MIN_DEPTH, MAX_IR, COLOR_IMAGE_EXTS
from .points import PointCloud, NormalCloud, PointNormalCloud
from .primitives import Contour
BINARY_IM_MAX_VAL = np.iinfo(np.uint8).max
BINARY_IM_DEFAULT_THRESH = BINARY_IM_MAX_VAL / 2
def imresize(image, size, interp="nearest"):
skt_interp_map = {
"nearest": 0,
"bilinear": 1,
"biquadratic": 2,
"bicubic": 3,
"biquartic": 4,
"biquintic": 5,
}
if interp in ("lanczos", "cubic"):
raise ValueError(
'"lanczos" and "cubic"' " interpolation are no longer supported."
)
assert (
interp in skt_interp_map
), 'Interpolation "{}" not' " supported.".format(interp)
if isinstance(size, (tuple, list)):
output_shape = size
elif isinstance(size, (float)):
np_shape = np.asarray(image.shape).astype(np.float32)
np_shape[0:2] *= size
output_shape = tuple(np_shape.astype(int))
elif isinstance(size, (int)):
np_shape = np.asarray(image.shape).astype(np.float32)
np_shape[0:2] *= size / 100.0
output_shape = tuple(np_shape.astype(int))
else:
raise ValueError('Invalid type for size "{}".'.format(type(size)))
return skt.resize(
image.astype(np.float),
output_shape,
order=skt_interp_map[interp],
anti_aliasing=False,
mode="constant",
)
class Image(object):
__metaclass__ = ABCMeta
def __init__(self, data, frame="unspecified"):
if not isinstance(data, np.ndarray):
raise ValueError("Must initialize image with a numpy ndarray")
if not isinstance(frame, str):
raise ValueError("Must provide string name of frame of data")
self._check_valid_data(data)
self._data = self._preprocess_data(data)
self._frame = frame
self._encoding = "passthrough" | Apache License 2.0 |
spyder-ide/spyder-unittest | spyder_unittest/widgets/configdialog.py | ConfigDialog.__init__ | python | def __init__(self, frameworks, config, parent=None):
super(ConfigDialog, self).__init__(parent)
self.setWindowTitle(_('Configure tests'))
layout = QVBoxLayout(self)
framework_layout = QHBoxLayout()
framework_label = QLabel(_('Test framework'))
framework_layout.addWidget(framework_label)
self.framework_combobox = QComboBox(self)
for ix, (name, runner) in enumerate(sorted(frameworks.items())):
installed = runner.is_installed()
if installed:
label = name
else:
label = '{} ({})'.format(name, _('not available'))
self.framework_combobox.addItem(label)
self.framework_combobox.model().item(ix).setEnabled(installed)
framework_layout.addWidget(self.framework_combobox)
layout.addLayout(framework_layout)
layout.addSpacing(10)
wdir_label = QLabel(_('Directory from which to run tests'))
layout.addWidget(wdir_label)
wdir_layout = QHBoxLayout()
self.wdir_lineedit = QLineEdit(self)
wdir_layout.addWidget(self.wdir_lineedit)
self.wdir_button = QPushButton(ima.icon('DirOpenIcon'), '', self)
self.wdir_button.setToolTip(_("Select directory"))
self.wdir_button.clicked.connect(lambda: self.select_directory())
wdir_layout.addWidget(self.wdir_button)
layout.addLayout(wdir_layout)
layout.addSpacing(20)
self.buttons = QDialogButtonBox(QDialogButtonBox.Ok |
QDialogButtonBox.Cancel)
layout.addWidget(self.buttons)
self.buttons.accepted.connect(self.accept)
self.buttons.rejected.connect(self.reject)
self.ok_button = self.buttons.button(QDialogButtonBox.Ok)
self.ok_button.setEnabled(False)
self.framework_combobox.currentIndexChanged.connect(
self.framework_changed)
self.framework_combobox.setCurrentIndex(-1)
if config.framework:
index = self.framework_combobox.findText(config.framework)
if index != -1:
self.framework_combobox.setCurrentIndex(index)
self.wdir_lineedit.setText(config.wdir) | Construct a dialog window.
Parameters
----------
frameworks : dict of (str, type)
Names of all supported frameworks with their associated class
(assumed to be a subclass of RunnerBase)
config : Config
Initial configuration
parent : QWidget | https://github.com/spyder-ide/spyder-unittest/blob/fc29baa9edd8614a341bbcfde93aa6fea5c4afb5/spyder_unittest/widgets/configdialog.py#L47-L112 | from collections import namedtuple
import os.path as osp
from qtpy.compat import getexistingdirectory
from qtpy.QtCore import Slot
from qtpy.QtWidgets import (QApplication, QComboBox, QDialog, QDialogButtonBox,
QHBoxLayout, QLabel, QLineEdit, QPushButton,
QVBoxLayout)
from spyder.config.base import get_translation
from spyder.py3compat import getcwd, to_text_string
from spyder.utils import icon_manager as ima
try:
_ = get_translation("unittest", dirname="spyder_unittest")
except KeyError:
import gettext
_ = gettext.gettext
Config = namedtuple('Config', ['framework', 'wdir'])
Config.__new__.__defaults__ = (None, '')
class ConfigDialog(QDialog): | MIT License |
crespo-otero-group/fromage | fromage/utils/mol/_geom.py | coord_array | python | def coord_array(self):
if self.geom.ignore_kinds:
self.set_connectivity
list_coord = []
for atom in self:
add_atom = True
if self.geom.ignore_hydrogens:
if atom.elem == 'H':
add_atom = False
if self.geom.ignore_kinds:
if atom.kind in self.geom.ignore_kinds:
add_atom = False
if add_atom:
new_row = [atom.x,atom.y,atom.z]
list_coord.append(new_row)
coord_arr = np.array(list_coord)
return coord_arr | Return a numpy array of the coordinates
Returns
-------
coord_arr : Nat x 3 numpy array
Array of the form [[x1,y1,z1],[x2,y2,z2],...] | https://github.com/crespo-otero-group/fromage/blob/9b4a80698ed1672268dde292d5512c72a23cb00a/fromage/utils/mol/_geom.py#L53-L81 | import numpy as np
import fromage.utils.array_operations as ao
class GeomInfo(object):
def __init__(self):
self.coord_array = np.array([0])
self.plane_coeffs = np.array([0])
self.prin_ax = np.array([0])
self.sec_ax = np.array([0])
self.perp_ax = np.array([0])
self.ignore_kinds = []
self.ignore_hydrogens = False
self.linear = False
def __str__(self):
out_str = "Coordinate array:\n" + str(self.coord_array) + "\nPlane coefficients:\n" + str(
self.plane_coeffs) + "\nPrincipal axis:\n" + str(self.prin_ax) + "\nSecondary axis:\n" + str(
self.sec_ax) + "\nPerpendicular axis:\n" + str(self.perp_ax)
return out_str
def __repr__(self):
return self.__str__() | MIT License |
kriaga/health-checker | HealthChecker/venv/Lib/site-packages/nltk/classify/maxent.py | MaxentFeatureEncodingI.labels | python | def labels(self):
raise NotImplementedError() | :return: A list of the \"known labels\" -- i.e., all labels
``l`` such that ``self.encode(fs,l)`` can be a nonzero
joint-feature vector for some value of ``fs``.
:rtype: list | https://github.com/kriaga/health-checker/blob/3d9ce933f131bcbb897103b0f509cc45393cae4a/HealthChecker/venv/Lib/site-packages/nltk/classify/maxent.py#L377-L384 | from __future__ import print_function, unicode_literals
try:
import numpy
except ImportError:
pass
import tempfile
import os
import re
from collections import defaultdict
from six import integer_types
from nltk import compat
from nltk.data import gzip_open_unicode
from nltk.util import OrderedDict
from nltk.probability import DictionaryProbDist
from nltk.classify.api import ClassifierI
from nltk.classify.util import CutoffChecker, accuracy, log_likelihood
from nltk.classify.megam import (call_megam,
write_megam_file, parse_megam_weights)
from nltk.classify.tadm import call_tadm, write_tadm_file, parse_tadm_weights
__docformat__ = 'epytext en'
@compat.python_2_unicode_compatible
class MaxentClassifier(ClassifierI):
def __init__(self, encoding, weights, logarithmic=True):
self._encoding = encoding
self._weights = weights
self._logarithmic = logarithmic
assert encoding.length() == len(weights)
def labels(self):
return self._encoding.labels()
def set_weights(self, new_weights):
self._weights = new_weights
assert self._encoding.length() == len(new_weights)
def weights(self):
return self._weights
def classify(self, featureset):
return self.prob_classify(featureset).max()
def prob_classify(self, featureset):
prob_dict = {}
for label in self._encoding.labels():
feature_vector = self._encoding.encode(featureset, label)
if self._logarithmic:
total = 0.0
for (f_id, f_val) in feature_vector:
total += self._weights[f_id] * f_val
prob_dict[label] = total
else:
prod = 1.0
for (f_id, f_val) in feature_vector:
prod *= self._weights[f_id] ** f_val
prob_dict[label] = prod
return DictionaryProbDist(prob_dict, log=self._logarithmic,
normalize=True)
def explain(self, featureset, columns=4):
descr_width = 50
TEMPLATE = ' %-'+str(descr_width-2)+'s%s%8.3f'
pdist = self.prob_classify(featureset)
labels = sorted(pdist.samples(), key=pdist.prob, reverse=True)
labels = labels[:columns]
print(' Feature'.ljust(descr_width)+''.join(
'%8s' % (("%s" % l)[:7]) for l in labels))
print(' '+'-'*(descr_width-2+8*len(labels)))
sums = defaultdict(int)
for i, label in enumerate(labels):
feature_vector = self._encoding.encode(featureset, label)
feature_vector.sort(key=lambda fid__: abs(self._weights[fid__[0]]),
reverse=True)
for (f_id, f_val) in feature_vector:
if self._logarithmic:
score = self._weights[f_id] * f_val
else: score = self._weights[f_id] ** f_val
descr = self._encoding.describe(f_id)
descr = descr.split(' and label is ')[0]
descr += ' (%s)' % f_val
if len(descr) > 47:
descr = descr[:44]+'...'
print(TEMPLATE % (descr, i*8*' ', score))
sums[label] += score
print(' '+'-'*(descr_width-1+8*len(labels)))
print(' TOTAL:'.ljust(descr_width)+''.join(
'%8.3f' % sums[l] for l in labels))
print(' PROBS:'.ljust(descr_width)+''.join(
'%8.3f' % pdist.prob(l) for l in labels))
def most_informative_features(self, n=10):
if hasattr(self, '_most_informative_features'):
return self._most_informative_features[:n]
else:
self._most_informative_features = sorted(list(range(len(self._weights))),
key=lambda fid: abs(self._weights[fid]),
reverse=True)
return self._most_informative_features[:n]
def show_most_informative_features(self, n=10, show='all'):
fids = self.most_informative_features(None)
if show == 'pos':
fids = [fid for fid in fids if self._weights[fid] > 0]
elif show == 'neg':
fids = [fid for fid in fids if self._weights[fid] < 0]
for fid in fids[:n]:
print('%8.3f %s' % (self._weights[fid],
self._encoding.describe(fid)))
def __repr__(self):
return ('<ConditionalExponentialClassifier: %d labels, %d features>' %
(len(self._encoding.labels()), self._encoding.length()))
ALGORITHMS = ['GIS', 'IIS', 'MEGAM', 'TADM']
@classmethod
def train(cls, train_toks, algorithm=None, trace=3, encoding=None,
labels=None, gaussian_prior_sigma=0, **cutoffs):
if algorithm is None:
algorithm = 'iis'
for key in cutoffs:
if key not in ('max_iter', 'min_ll', 'min_lldelta',
'max_acc', 'min_accdelta', 'count_cutoff',
'norm', 'explicit', 'bernoulli'):
raise TypeError('Unexpected keyword arg %r' % key)
algorithm = algorithm.lower()
if algorithm == 'iis':
return train_maxent_classifier_with_iis(
train_toks, trace, encoding, labels, **cutoffs)
elif algorithm == 'gis':
return train_maxent_classifier_with_gis(
train_toks, trace, encoding, labels, **cutoffs)
elif algorithm == 'megam':
return train_maxent_classifier_with_megam(
train_toks, trace, encoding, labels,
gaussian_prior_sigma, **cutoffs)
elif algorithm == 'tadm':
kwargs = cutoffs
kwargs['trace'] = trace
kwargs['encoding'] = encoding
kwargs['labels'] = labels
kwargs['gaussian_prior_sigma'] = gaussian_prior_sigma
return TadmMaxentClassifier.train(train_toks, **kwargs)
else:
raise ValueError('Unknown algorithm %s' % algorithm)
ConditionalExponentialClassifier = MaxentClassifier
class MaxentFeatureEncodingI(object):
def encode(self, featureset, label):
raise NotImplementedError()
def length(self):
raise NotImplementedError() | MIT License |
ryry013/rai | cogs/owner.py | Owner.database | python | async def database(self, ctx, depth, *, args):
config = self.bot.db
if '=' in args:
args = f"{depth} {args}"
depth = 1
split = args.split(' = ') + ['']
path = split[0]
set_to = split[1]
def process_arg(arg):
if arg.startswith('ctx'):
obj = ctx
for attr in arg.split('.'):
if attr == 'ctx':
continue
obj = getattr(obj, attr)
return str(obj)
else:
return arg
for arg in path.split()[:-1]:
try:
config = config[process_arg(arg)]
except KeyError:
await hf.safe_send(ctx, f"Invalid arg: `{arg}`")
return
if set_to:
config[path.split()[-1]] = literal_eval(set_to)
await hf.safe_send(ctx, f"```\n{config[path.split()[-1]]}"[:1997]+"```")
return
try:
config = config[process_arg(path.split()[-1])]
except KeyError:
await ctx.send(f"Couldn't find {path.split()[-1]} in the database.")
return
msg = ''
for key in config:
if type(key) == str:
key_str = f'\"{key}\"'
else:
key_str = key
msg += f"{key_str}\n"
if int(depth) >= 2:
if isinstance(config[key], dict):
for key_2 in config[key]:
if type(key_2) == str:
key_2_str = f'\"{key_2}\"'
else:
key_2_str = key_2
msg += f"\t{key_2_str}\n"
if int(depth) >= 3:
if isinstance(config[key][key_2], dict):
for key_3 in config[key][key_2]:
if type(key_3) == str:
key_3_str = f'\"{key_3}\"'
else:
key_3_str = key_3
msg += f"\t\t{key_3_str}\n"
if int(depth) >= 4:
if isinstance(config[key][key_2][key_3], dict):
for key_4 in config[key][key_2][key_3]:
if type(key_4) == str:
key_4_str = f'\"{key_4}\"'
else:
key_4_str = key_4
msg += f"\t\t\t{key_4_str}\n"
else:
if type(config[key][key_2][key_3]) == str:
s = f"\"{config[key][key_2][key_3]}\""
else:
s = config[key][key_2][key_3]
msg = msg[:-1] + f": {s}\n"
else:
if type(config[key][key_2]) == str:
s = f"\"{config[key][key_2]}\""
else:
s = config[key][key_2]
msg = msg[:-1] + f": {s}\n"
else:
if type(config[key]) == str:
s = f"\"{config[key]}\""
else:
s = config[key]
msg = msg[:-1] + f": {s}\n"
await hf.safe_send(ctx, f'```\n{msg[:1993]}```') | Shows or edits database | https://github.com/ryry013/rai/blob/8a8ce07c78a67967a8fc9cb5dabc68329bf85f9b/cogs/owner.py#L163-L255 | from discord.ext import commands
import asyncio
import traceback
import discord
import textwrap
from contextlib import redirect_stdout
import io
import sys
import codecs
import json
from .utils import helper_functions as hf
import re
from ast import literal_eval
import importlib
import datetime
from datetime import datetime, timedelta
import os
dir_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
RYRY_SPAM_CHAN = 275879535977955330
class Owner(commands.Cog):
def __init__(self, bot):
self.bot = bot
self._last_result = None
self.sessions = set()
async def cog_check(self, ctx):
return ctx.author.id in [202995638860906496, 414873201349361664, 528770932613971988]
def get_syntax_error(self, e):
if e.text is None:
return f'```py\n{e.__class__.__name__}: {e}\n```'
return f'```py\n{e.text}{"^":>{e.offset}}\n{e.__class__.__name__}: {e}```'
@commands.Cog.listener()
async def on_command(self, ctx):
if not ctx.guild:
return
if str(ctx.guild.id) not in self.bot.db['guildstats']:
self.bot.db['guildstats'][str(ctx.guild.id)] = {'messages': {}, 'commands': {}}
config: dict = self.bot.db['guildstats'][str(ctx.guild.id)]['commands']
date_str = datetime.utcnow().strftime("%Y%m%d")
config[date_str] = config.setdefault(date_str, 0) + 1
@commands.command()
async def guildstats(self, ctx):
config = self.bot.db['guildstats']
guild_info = {}
id_to_guild = {str(g.id): g for g in self.bot.guilds}
for guild_id in config:
message_count = 0
for day in config[guild_id]['messages']:
days_ago = (datetime.utcnow() - datetime.strptime(day, "%Y%m%d")).days
if days_ago > 30:
del(config[guild_id]['messages'][day])
else:
message_count += config[guild_id]['messages'][day]
command_count = 0
for day in config[guild_id]['commands']:
days_ago = (datetime.utcnow() - datetime.strptime(day, "%Y%m%d")).days
if days_ago > 30:
del(config[guild_id]['commands'][day])
else:
command_count += config[guild_id]['commands'][day]
guild = id_to_guild[guild_id]
bot_num = len([m for m in guild.members if m.bot])
human_num = len([m for m in guild.members if not m.bot])
guild_info[guild] = {"messages": message_count,
"member_count": guild.member_count,
"bots": bot_num,
"humans": human_num,
"commands": command_count}
msg = ''
for guild in guild_info:
info = guild_info[guild]
msg_addition = f"**{guild.name}: ({guild.id})**" f"\n{info['messages']} messages" f"\n{info['member_count']} members " f"({info['humans']} humans, {info['bots']} bots, " f"{round(info['humans']/info['member_count'], 2)})" f"\n{info['commands']} commands\n"
if len(msg + msg_addition) < 2000:
msg += msg_addition
else:
await hf.safe_send(ctx, msg)
msg = msg_addition
if msg:
await hf.safe_send(ctx, msg)
dead_guilds = []
for guild in self.bot.guilds:
if guild not in guild_info:
dead_guilds.append(guild)
msg = ''
for guild in dead_guilds:
bots = len([m for m in guild.members if m.bot])
msg_addition = f"{guild.name} -- {guild.id} -- {bots}/{guild.member_count}\n"
if len(msg + msg_addition) < 2000:
msg += msg_addition
else:
await hf.safe_send(ctx, msg)
msg = msg_addition
if msg:
await hf.safe_send(ctx, msg)
@commands.command()
async def edit(self, ctx, message_id, *, content):
try:
msg = await ctx.channel.fetch_message(int(message_id))
except discord.NotFound:
await hf.safe_send(ctx, "Message not found")
return
await msg.edit(content=content)
try:
await ctx.message.delete()
except discord.NotFound:
pass
@commands.command(aliases=['repl'])
async def reply(self, ctx, index, *, reply=''):
channel = self.bot.get_channel(RYRY_SPAM_CHAN)
index_re = re.search("^(;repl|;reply) (\d) ", ctx.message.content)
if not index_re:
reply = f"{index} {reply}"
index = 1
else:
index = int(index_re.group(2))
if not reply:
await hf.safe_send(ctx, "Include reply message")
async for msg in channel.history():
result_channel_id = re.search('^(\d{17,22}) <@202995638860906496>$', msg.content)
if not result_channel_id:
continue
else:
result_channel_id = result_channel_id.group(1)
if result_channel_id:
if (datetime.utcnow() - msg.created_at).seconds > 1:
if index > 1:
index -= 1
else:
send_channel = self.bot.get_channel(int(result_channel_id))
try:
await send_channel.send(reply)
except discord.Forbidden as e:
await hf.safe_send(ctx, e)
return
@commands.command(aliases=['db']) | MIT License |
uwdata/termite-visualizations | web2py/gluon/dal.py | ConnectionPool.after_connection_hook | python | def after_connection_hook(self):
if callable(self._after_connection):
self._after_connection(self)
self.after_connection() | hook for the after_connection parameter | https://github.com/uwdata/termite-visualizations/blob/79da58bc607893bbd5db703f7d87a89b5e97c311/web2py/gluon/dal.py#L619-L623 | __all__ = ['DAL', 'Field']
DEFAULTLENGTH = {'string':512,
'password':512,
'upload':512,
'text':2**15,
'blob':2**31}
TIMINGSSIZE = 100
SPATIALLIBS = {
'Windows':'libspatialite',
'Linux':'libspatialite.so',
'Darwin':'libspatialite.dylib'
}
DEFAULT_URI = 'sqlite://dummy.db'
import re
import sys
import locale
import os
import types
import datetime
import threading
import time
import csv
import cgi
import copy
import socket
import logging
import base64
import shutil
import marshal
import decimal
import struct
import urllib
import hashlib
import uuid
import glob
import traceback
import platform
PYTHON_VERSION = sys.version_info[:3]
if PYTHON_VERSION[0] == 2:
import cPickle as pickle
import cStringIO as StringIO
import copy_reg as copyreg
hashlib_md5 = hashlib.md5
bytes, unicode = str, unicode
else:
import pickle
from io import StringIO as StringIO
import copyreg
long = int
hashlib_md5 = lambda s: hashlib.md5(bytes(s,'utf8'))
bytes, unicode = bytes, str
if PYTHON_VERSION[:2] < (2, 7):
from gluon.contrib.ordereddict import OrderedDict
else:
from collections import OrderedDict
CALLABLETYPES = (types.LambdaType, types.FunctionType,
types.BuiltinFunctionType,
types.MethodType, types.BuiltinMethodType)
TABLE_ARGS = set(
('migrate','primarykey','fake_migrate','format','redefine',
'singular','plural','trigger_name','sequence_name','fields',
'common_filter','polymodel','table_class','on_define','rname'))
SELECT_ARGS = set(
('orderby', 'groupby', 'limitby','required', 'cache', 'left',
'distinct', 'having', 'join','for_update', 'processor','cacheable', 'orderby_on_limitby'))
ogetattr = object.__getattribute__
osetattr = object.__setattr__
exists = os.path.exists
pjoin = os.path.join
try:
from gluon.utils import web2py_uuid
except (ImportError, SystemError):
import uuid
def web2py_uuid(): return str(uuid.uuid4())
try:
import portalocker
have_portalocker = True
except ImportError:
have_portalocker = False
try:
from gluon import serializers
have_serializers = True
except ImportError:
have_serializers = False
try:
import json as simplejson
except ImportError:
try:
import gluon.contrib.simplejson as simplejson
except ImportError:
simplejson = None
LOGGER = logging.getLogger("web2py.dal")
DEFAULT = lambda:0
GLOBAL_LOCKER = threading.RLock()
THREAD_LOCAL = threading.local()
REGEX_TYPE = re.compile('^([\w\_\:]+)')
REGEX_DBNAME = re.compile('^(\w+)(\:\w+)*')
REGEX_W = re.compile('^\w+$')
REGEX_TABLE_DOT_FIELD = re.compile('^(\w+)\.(\w+)$')
REGEX_UPLOAD_PATTERN = re.compile('(?P<table>[\w\-]+)\.(?P<field>[\w\-]+)\.(?P<uuidkey>[\w\-]+)(\.(?P<name>\w+))?\.\w+$')
REGEX_CLEANUP_FN = re.compile('[\'"\s;]+')
REGEX_UNPACK = re.compile('(?<!\|)\|(?!\|)')
REGEX_PYTHON_KEYWORDS = re.compile('^(and|del|from|not|while|as|elif|global|or|with|assert|else|if|pass|yield|break|except|import|print|class|exec|in|raise|continue|finally|is|return|def|for|lambda|try)$')
REGEX_SELECT_AS_PARSER = re.compile("\s+AS\s+(\S+)")
REGEX_CONST_STRING = re.compile('(\"[^\"]*?\")|(\'[^\']*?\')')
REGEX_SEARCH_PATTERN = re.compile('^{[^\.]+\.[^\.]+(\.(lt|gt|le|ge|eq|ne|contains|startswith|year|month|day|hour|minute|second))?(\.not)?}$')
REGEX_SQUARE_BRACKETS = re.compile('^.+\[.+\]$')
REGEX_STORE_PATTERN = re.compile('\.(?P<e>\w{1,5})$')
REGEX_QUOTES = re.compile("'[^']*'")
REGEX_ALPHANUMERIC = re.compile('^[0-9a-zA-Z]\w*$')
REGEX_PASSWORD = re.compile('\://([^:@]*)\:')
REGEX_NOPASSWD = re.compile('\/\/[\w\.\-]+[\:\/](.+)(?=@)')
DRIVERS = []
try:
from new import classobj
from google.appengine.ext import db as gae
from google.appengine.ext import ndb
from google.appengine.api import namespace_manager, rdbms
from google.appengine.api.datastore_types import Key
from google.appengine.ext.db.polymodel import PolyModel
from google.appengine.ext.ndb.polymodel import PolyModel as NDBPolyModel
DRIVERS.append('google')
except ImportError:
pass
if not 'google' in DRIVERS:
try:
from pysqlite2 import dbapi2 as sqlite2
DRIVERS.append('SQLite(sqlite2)')
except ImportError:
LOGGER.debug('no SQLite drivers pysqlite2.dbapi2')
try:
from sqlite3 import dbapi2 as sqlite3
DRIVERS.append('SQLite(sqlite3)')
except ImportError:
LOGGER.debug('no SQLite drivers sqlite3')
try:
try:
import gluon.contrib.pymysql as pymysql
pymysql.ESCAPE_REGEX = re.compile("'")
pymysql.ESCAPE_MAP = {"'": "''"}
except ImportError:
import pymysql
DRIVERS.append('MySQL(pymysql)')
except ImportError:
LOGGER.debug('no MySQL driver pymysql')
try:
import MySQLdb
DRIVERS.append('MySQL(MySQLdb)')
except ImportError:
LOGGER.debug('no MySQL driver MySQLDB')
try:
import mysql.connector as mysqlconnector
DRIVERS.append("MySQL(mysqlconnector)")
except ImportError:
LOGGER.debug("no driver mysql.connector")
try:
import psycopg2
from psycopg2.extensions import adapt as psycopg2_adapt
DRIVERS.append('PostgreSQL(psycopg2)')
except ImportError:
LOGGER.debug('no PostgreSQL driver psycopg2')
try:
try:
import gluon.contrib.pg8000.dbapi as pg8000
except ImportError:
import pg8000.dbapi as pg8000
DRIVERS.append('PostgreSQL(pg8000)')
except ImportError:
LOGGER.debug('no PostgreSQL driver pg8000')
try:
import cx_Oracle
DRIVERS.append('Oracle(cx_Oracle)')
except ImportError:
LOGGER.debug('no Oracle driver cx_Oracle')
try:
try:
import pyodbc
except ImportError:
try:
import gluon.contrib.pypyodbc as pyodbc
except Exception, e:
raise ImportError(str(e))
DRIVERS.append('MSSQL(pyodbc)')
DRIVERS.append('DB2(pyodbc)')
DRIVERS.append('Teradata(pyodbc)')
DRIVERS.append('Ingres(pyodbc)')
except ImportError:
LOGGER.debug('no MSSQL/DB2/Teradata/Ingres driver pyodbc')
try:
import Sybase
DRIVERS.append('Sybase(Sybase)')
except ImportError:
LOGGER.debug('no Sybase driver')
try:
import kinterbasdb
DRIVERS.append('Interbase(kinterbasdb)')
DRIVERS.append('Firebird(kinterbasdb)')
except ImportError:
LOGGER.debug('no Firebird/Interbase driver kinterbasdb')
try:
import fdb
DRIVERS.append('Firebird(fdb)')
except ImportError:
LOGGER.debug('no Firebird driver fdb')
try:
import firebirdsql
DRIVERS.append('Firebird(firebirdsql)')
except ImportError:
LOGGER.debug('no Firebird driver firebirdsql')
try:
import informixdb
DRIVERS.append('Informix(informixdb)')
LOGGER.warning('Informix support is experimental')
except ImportError:
LOGGER.debug('no Informix driver informixdb')
try:
import sapdb
DRIVERS.append('SQL(sapdb)')
LOGGER.warning('SAPDB support is experimental')
except ImportError:
LOGGER.debug('no SAP driver sapdb')
try:
import cubriddb
DRIVERS.append('Cubrid(cubriddb)')
LOGGER.warning('Cubrid support is experimental')
except ImportError:
LOGGER.debug('no Cubrid driver cubriddb')
try:
from com.ziclix.python.sql import zxJDBC
import java.sql
from org.sqlite import JDBC
zxJDBC_sqlite = java.sql.DriverManager
DRIVERS.append('PostgreSQL(zxJDBC)')
DRIVERS.append('SQLite(zxJDBC)')
LOGGER.warning('zxJDBC support is experimental')
is_jdbc = True
except ImportError:
LOGGER.debug('no SQLite/PostgreSQL driver zxJDBC')
is_jdbc = False
try:
import couchdb
DRIVERS.append('CouchDB(couchdb)')
except ImportError:
LOGGER.debug('no Couchdb driver couchdb')
try:
import pymongo
DRIVERS.append('MongoDB(pymongo)')
except:
LOGGER.debug('no MongoDB driver pymongo')
try:
import imaplib
DRIVERS.append('IMAP(imaplib)')
except:
LOGGER.debug('no IMAP driver imaplib')
PLURALIZE_RULES = [
(re.compile('child$'), re.compile('child$'), 'children'),
(re.compile('oot$'), re.compile('oot$'), 'eet'),
(re.compile('ooth$'), re.compile('ooth$'), 'eeth'),
(re.compile('l[eo]af$'), re.compile('l([eo])af$'), 'l\\1aves'),
(re.compile('sis$'), re.compile('sis$'), 'ses'),
(re.compile('man$'), re.compile('man$'), 'men'),
(re.compile('ife$'), re.compile('ife$'), 'ives'),
(re.compile('eau$'), re.compile('eau$'), 'eaux'),
(re.compile('lf$'), re.compile('lf$'), 'lves'),
(re.compile('[sxz]$'), re.compile('$'), 'es'),
(re.compile('[^aeioudgkprt]h$'), re.compile('$'), 'es'),
(re.compile('(qu|[^aeiou])y$'), re.compile('y$'), 'ies'),
(re.compile('$'), re.compile('$'), 's'),
]
def pluralize(singular, rules=PLURALIZE_RULES):
for line in rules:
re_search, re_sub, replace = line
plural = re_search.search(singular) and re_sub.sub(replace, singular)
if plural: return plural
def hide_password(uri):
if isinstance(uri,(list,tuple)):
return [hide_password(item) for item in uri]
return REGEX_NOPASSWD.sub('******',uri)
def OR(a,b):
return a|b
def AND(a,b):
return a&b
def IDENTITY(x): return x
def varquote_aux(name,quotestr='%s'):
return name if REGEX_W.match(name) else quotestr % name
def quote_keyword(a,keyword='timestamp'):
regex = re.compile('\.keyword(?=\w)')
a = regex.sub('."%s"' % keyword,a)
return a
if 'google' in DRIVERS:
is_jdbc = False
class GAEDecimalProperty(gae.Property):
data_type = decimal.Decimal
def __init__(self, precision, scale, **kwargs):
super(GAEDecimalProperty, self).__init__(self, **kwargs)
d = '1.'
for x in range(scale):
d += '0'
self.round = decimal.Decimal(d)
def get_value_for_datastore(self, model_instance):
value = super(GAEDecimalProperty, self) .get_value_for_datastore(model_instance)
if value is None or value == '':
return None
else:
return str(value)
def make_value_from_datastore(self, value):
if value is None or value == '':
return None
else:
return decimal.Decimal(value).quantize(self.round)
def validate(self, value):
value = super(GAEDecimalProperty, self).validate(value)
if value is None or isinstance(value, decimal.Decimal):
return value
elif isinstance(value, basestring):
return decimal.Decimal(value)
raise gae.BadValueError("Property %s must be a Decimal or string." % self.name)
class NDBDecimalProperty(ndb.StringProperty):
data_type = decimal.Decimal
def __init__(self, precision, scale, **kwargs):
d = '1.'
for x in range(scale):
d += '0'
self.round = decimal.Decimal(d)
def _to_base_type(self, value):
if value is None or value == '':
return None
else:
return str(value)
def _from_base_type(self, value):
if value is None or value == '':
return None
else:
return decimal.Decimal(value).quantize(self.round)
def _validate(self, value):
if value is None or isinstance(value, decimal.Decimal):
return value
elif isinstance(value, basestring):
return decimal.Decimal(value)
raise TypeError("Property %s must be a Decimal or string." % self._name)
class ConnectionPool(object):
POOLS = {}
check_active_connection = True
@staticmethod
def set_folder(folder):
THREAD_LOCAL.folder = folder
def close(self,action='commit',really=True):
if action:
if callable(action):
action(self)
else:
getattr(self, action)()
if self.pool_size:
GLOBAL_LOCKER.acquire()
pool = ConnectionPool.POOLS[self.uri]
if len(pool) < self.pool_size:
pool.append(self.connection)
really = False
GLOBAL_LOCKER.release()
if really:
self.close_connection()
self.connection = None
@staticmethod
def close_all_instances(action):
dbs = getattr(THREAD_LOCAL,'db_instances',{}).items()
for db_uid, db_group in dbs:
for db in db_group:
if hasattr(db,'_adapter'):
db._adapter.close(action)
getattr(THREAD_LOCAL,'db_instances',{}).clear()
getattr(THREAD_LOCAL,'db_instances_zombie',{}).clear()
if callable(action):
action(None)
return
def find_or_make_work_folder(self):
self.folder = getattr(THREAD_LOCAL,'folder','')
if (os.path.isabs(self.folder) and
isinstance(self, UseDatabaseStoredFile) and
self.folder.startswith(os.getcwd())):
self.folder = os.path.relpath(self.folder, os.getcwd())
if False and self.folder and not exists(self.folder):
os.mkdir(self.folder) | BSD 3-Clause New or Revised License |
yeti-platform/yeti | core/web/api/file.py | File.get_id | python | def get_id(self, id):
try:
fileobj = self.objectmanager.objects.get(id=id)
return Response(fileobj.body.stream_contents(), mimetype=fileobj.mime_type)
except DoesNotExist:
abort(404) | Retrieves a file's content.
:<id ObjectId corresponding to the file ObjectId | https://github.com/yeti-platform/yeti/blob/fcd3ee3d3d064df772d0392c20c22aad2bc4c8e6/core/web/api/file.py#L57-L66 | from __future__ import unicode_literals
import zipfile
import magic
from flask import request, Response, abort
from flask_classy import route
from mongoengine import DoesNotExist
from core import observables
from core.database import AttachedFile
from core.helpers import stream_sha256
from core.web.api.api import render
from core.web.api.crud import CrudApi
from core.web.helpers import requires_permissions
def save_file(uploaded_file, filename=None):
value = "FILE:{}".format(stream_sha256(uploaded_file))
mime_type = magic.from_buffer(uploaded_file.read(100), mime=True)
uploaded_file.seek(0)
body = AttachedFile.from_upload(uploaded_file, force_mime=mime_type)
f = observables.File.get_or_create(value=value, body=body, mime_type=mime_type)
if not filename:
filename = uploaded_file.filename
if filename not in f.filenames:
f.filenames.append(filename)
return f.save()
def save_uploaded_files():
files = []
unzip = bool(request.form.get("unzip") in ["true", "on"])
for uploaded_file in request.files.getlist("files"):
if unzip and zipfile.is_zipfile(uploaded_file):
with zipfile.ZipFile(uploaded_file, "r") as zf:
for info in zf.infolist():
name = info.filename
size = info.file_size
data = zf.read(name)
if size > 0:
files.append(save_file(data, filename=name.split("/")[-1]))
else:
files.append(save_file(uploaded_file))
return files
class File(CrudApi):
objectmanager = observables.File
@route("/get/id/<id>", methods=["GET"])
@requires_permissions("read") | Apache License 2.0 |
thriftrw/thriftrw-python | thriftrw/compile/scope.py | Scope.resolve_service_spec | python | def resolve_service_spec(self, name, lineno):
if name in self.service_specs:
return self.service_specs[name].link(self)
if '.' in name:
include_name, component = name.split('.', 2)
if include_name in self.included_scopes:
return self.included_scopes[
include_name
].resolve_service_spec(component, lineno)
raise ThriftCompilerError(
'Unknown service "%s" referenced at line %d%s' % (
name, lineno, self.__in_path()
)
) | Finds and links the ServiceSpec with the given name. | https://github.com/thriftrw/thriftrw-python/blob/22f6ab645f5af48cae2fee0dc1431dfacb971348/thriftrw/compile/scope.py#L114-L131 | from __future__ import absolute_import, unicode_literals, print_function
import types
from ..errors import ThriftCompilerError
__all__ = ['Scope']
class Scope(object):
__slots__ = (
'const_specs', 'type_specs', 'module', 'service_specs',
'included_scopes', 'path'
)
def __init__(self, name, path=None):
self.path = path
self.type_specs = {}
self.const_specs = {}
self.service_specs = {}
self.included_scopes = {}
self.module = types.ModuleType(str(name))
def __str__(self):
return "Scope(%r)" % {
'const_specs': self.const_specs,
'type_specs': self.type_specs,
'service_specs': self.service_specs,
'module': self.module,
}
__repr__ = __str__
def __in_path(self):
if self.path:
return ' in "%s"' % self.path
else:
return ''
def resolve_const_spec(self, name, lineno):
if name in self.const_specs:
return self.const_specs[name].link(self)
if '.' in name:
include_name, component = name.split('.', 1)
if include_name in self.included_scopes:
return self.included_scopes[include_name].resolve_const_spec(
component, lineno
)
raise ThriftCompilerError(
'Unknown constant "%s" referenced at line %d%s' % (
name, lineno, self.__in_path()
)
)
def resolve_type_spec(self, name, lineno):
if name in self.type_specs:
return self.type_specs[name].link(self)
if '.' in name:
include_name, component = name.split('.', 1)
if include_name in self.included_scopes:
return self.included_scopes[include_name].resolve_type_spec(
component, lineno
)
raise ThriftCompilerError(
'Unknown type "%s" referenced at line %d%s' % (
name, lineno, self.__in_path()
)
) | MIT License |
nastools/homeassistant | homeassistant/components/light/isy994.py | ISYLightDevice.is_on | python | def is_on(self) -> bool:
return self.state == STATE_ON | Get whether the ISY994 light is on. | https://github.com/nastools/homeassistant/blob/7ca1180bd42713f2d77bbc3f0b27b231ba8784aa/homeassistant/components/light/isy994.py#L53-L55 | import logging
from typing import Callable
from homeassistant.components.light import (
Light, SUPPORT_BRIGHTNESS, ATTR_BRIGHTNESS)
import homeassistant.components.isy994 as isy
from homeassistant.const import STATE_ON, STATE_OFF, STATE_UNKNOWN
from homeassistant.helpers.typing import ConfigType
_LOGGER = logging.getLogger(__name__)
VALUE_TO_STATE = {
False: STATE_OFF,
True: STATE_ON,
}
UOM = ['2', '51', '78']
STATES = [STATE_OFF, STATE_ON, 'true', 'false', '%']
def setup_platform(hass, config: ConfigType,
add_devices: Callable[[list], None], discovery_info=None):
if isy.ISY is None or not isy.ISY.connected:
_LOGGER.error('A connection has not been made to the ISY controller.')
return False
devices = []
for node in isy.filter_nodes(isy.NODES, units=UOM,
states=STATES):
if node.dimmable or '51' in node.uom:
devices.append(ISYLightDevice(node))
add_devices(devices)
class ISYLightDevice(isy.ISYDevice, Light):
def __init__(self, node: object) -> None:
isy.ISYDevice.__init__(self, node)
@property | MIT License |
placeware/thisplace | thisplace.py | WordHasher.to_bytes | python | def to_bytes(self, integer):
bytes = [integer & 0b11111111]
for n in range(1,6):
div = 2**(n*8)
bytes.append((integer//div) & 0b11111111)
bytes.reverse()
return bytes | Convert a 48bit `integer` to a list of 6bytes | https://github.com/placeware/thisplace/blob/8c69fbc494a7fa4261398f0fdc0b3821a4e9f89a/thisplace.py#L168-L176 | import random
import geohash
def get_words(fname):
lines = open(fname)
words = []
for word in lines:
words.append(word.strip())
lines.close()
random.seed(634634)
random.shuffle(words)
words = words[:2**15]
assert len(words) == len(set(words))
return words
GOOGLE_WORDLIST = get_words("words/google-ngram-list")
GOOGLE_4096WORDS = get_words("words/google-ngram-list-4096")
WORDNET_LEMMAS = get_words("words/wordnet-list")
HUMAN_WORDLIST = (
'ack', 'alabama', 'alanine', 'alaska', 'alpha', 'angel', 'apart', 'april',
'arizona', 'arkansas', 'artist', 'asparagus', 'aspen', 'august', 'autumn',
'avocado', 'bacon', 'bakerloo', 'batman', 'beer', 'berlin', 'beryllium',
'black', 'blossom', 'blue', 'bluebird', 'bravo', 'bulldog', 'burger',
'butter', 'california', 'carbon', 'cardinal', 'carolina', 'carpet', 'cat',
'ceiling', 'charlie', 'chicken', 'coffee', 'cola', 'cold', 'colorado',
'comet', 'connecticut', 'crazy', 'cup', 'dakota', 'december', 'delaware',
'delta', 'diet', 'don', 'double', 'early', 'earth', 'east', 'echo',
'edward', 'eight', 'eighteen', 'eleven', 'emma', 'enemy', 'equal',
'failed', 'fanta', 'fifteen', 'fillet', 'finch', 'fish', 'five', 'fix',
'floor', 'florida', 'football', 'four', 'fourteen', 'foxtrot', 'freddie',
'friend', 'fruit', 'gee', 'georgia', 'glucose', 'golf', 'green', 'grey',
'hamper', 'happy', 'harry', 'hawaii', 'helium', 'high', 'hot', 'hotel',
'hydrogen', 'idaho', 'illinois', 'india', 'indigo', 'ink', 'iowa',
'island', 'item', 'jersey', 'jig', 'johnny', 'juliet', 'july', 'jupiter',
'kansas', 'kentucky', 'kilo', 'king', 'kitten', 'lactose', 'lake', 'lamp',
'lemon', 'leopard', 'lima', 'lion', 'lithium', 'london', 'louisiana',
'low', 'magazine', 'magnesium', 'maine', 'mango', 'march', 'mars',
'maryland', 'massachusetts', 'may', 'mexico', 'michigan', 'mike',
'minnesota', 'mirror', 'mississippi', 'missouri', 'mobile', 'mockingbird',
'monkey', 'montana', 'moon', 'mountain', 'muppet', 'music', 'nebraska',
'neptune', 'network', 'nevada', 'nine', 'nineteen', 'nitrogen', 'north',
'november', 'nuts', 'october', 'ohio', 'oklahoma', 'one', 'orange',
'oranges', 'oregon', 'oscar', 'oven', 'oxygen', 'papa', 'paris', 'pasta',
'pennsylvania', 'pip', 'pizza', 'pluto', 'potato', 'princess', 'purple',
'quebec', 'queen', 'quiet', 'red', 'river', 'robert', 'robin', 'romeo',
'rugby', 'sad', 'salami', 'saturn', 'september', 'seven', 'seventeen',
'shade', 'sierra', 'single', 'sink', 'six', 'sixteen', 'skylark', 'snake',
'social', 'sodium', 'solar', 'south', 'spaghetti', 'speaker', 'spring',
'stairway', 'steak', 'stream', 'summer', 'sweet', 'table', 'tango', 'ten',
'tennessee', 'tennis', 'texas', 'thirteen', 'three', 'timing', 'triple',
'twelve', 'twenty', 'two', 'uncle', 'undress', 'uniform', 'uranus', 'utah',
'vegan', 'venus', 'vermont', 'victor', 'video', 'violet', 'virginia',
'washington', 'west', 'whiskey', 'white', 'william', 'winner', 'winter',
'wisconsin', 'wolfram', 'wyoming', 'xray', 'yankee', 'yellow', 'zebra',
'zulu')
class WordHasher(object):
def __init__(self):
self._symbols = "0123456789bcdefghjkmnpqrstuvwxyz"
self._decode_symbols = dict((ch, i) for (i, ch) in enumerate(self._symbols))
self._encode_symbols = dict((i, ch) for (i, ch) in enumerate(self._symbols))
self.six_wordlist = HUMAN_WORDLIST
self.four_wordlist = GOOGLE_4096WORDS
self.three_wordlist = GOOGLE_WORDLIST
def three_words(self, lat_long):
lat, lon = lat_long
gh = geohash.encode(lat, lon, 9)
words = "-".join(self.three_wordlist[p] for p in self.to_rugbits(self.geo_to_int(gh)))
return words
def four_words(self, lat_long):
lat, lon = lat_long
gh = geohash.encode(lat, lon, 9)
words = "-".join(self.four_wordlist[p] for p in self.to_quads(self.pad(gh)))
return words
def six_words(self, lat_long):
lat, lon = lat_long
gh = geohash.encode(lat, lon, 9)
words = "-".join(self.six_wordlist[p] for p in self.to_bytes(self.pad(gh)))
return words
def decode(self, words):
words = words.split("-")
if len(words) == 3:
i = self.rugbits_to_int([self.three_wordlist.index(w) for w in words])
elif len(words) == 4:
i = self.quads_to_int([self.four_wordlist.index(w) for w in words])
i = self.unpad(i)
elif len(words) == 6:
i = self.bytes_to_int([self.six_wordlist.index(w) for w in words])
i = self.unpad(i)
else:
raise RuntimeError("Do not know how to decode a set of %i words."%(len(words)))
geo_hash = self.int_to_geo(i)
return geohash.decode(geo_hash)
def geo_to_int(self, geo_hash):
base = len(self._symbols)
number = 0
for symbol in geo_hash:
number = number*base + self._decode_symbols[symbol]
return number
def int_to_geo(self, integer):
base = len(self._symbols)
symbols = []
while integer > 0:
remainder = integer % base
integer //= base
symbols.append(self._encode_symbols[remainder])
return ''.join(reversed(symbols))
def pad(self, geo_hash):
assert len(geo_hash) == 9
return self.geo_to_int(geo_hash) * 8
def unpad(self, integer):
return integer>>3 | MIT License |
quantmind/lux | lux/utils/files.py | Filehandler.open | python | def open(self, name, mode='rb'):
raise NotImplementedError() | Retrieves the specified file from storage, using the optional mixin
class to customize what features are available on the File returned. | https://github.com/quantmind/lux/blob/7318fcd86c77616aada41d8182a04339680a554c/lux/utils/files.py#L54-L58 | import os
import re
import itertools
__all__ = ['Filehandler']
def skipfile(name):
return name.startswith('.') or name.startswith('_')
def directory(dir):
bd, fname = os.path.split(dir)
return dir if fname else bd
def get_rel_dir(dir, base, res=''):
dir = directory(dir)
base = directory(base)
if len(base) > len(dir):
raise RuntimeError('Base directory not in path')
if dir == base:
return res
dir, fname = os.path.split(dir)
if res:
fname = os.path.join(fname, res)
return get_rel_dir(dir, base, fname)
def get_valid_filename(s):
s = s.strip().replace(' ', '_')
return re.sub(r'(?u)[^-\w.]', '', s)
class Filehandler: | BSD 3-Clause New or Revised License |
square/mimicandrephrase | src/utils/token_mapper.py | TokenMapper.map_token | python | def map_token(self, token: str) -> int:
offset = sum([mapping.output_size() for mapping in self.unk_mappings])
for mapping in self.mappings:
if mapping.match(token):
return offset + mapping.map(token)
offset += mapping.output_size()
return -1 | This attempts to map a token to one of the special mappings we have in this TokenMapper. The first mapping that
triggers wins ties. If no mappings fire, this returns -1.
:param token: the token to map
:return: an offset into the output matrix, or -1 if no match | https://github.com/square/mimicandrephrase/blob/bd29a995b211cb4f7933fa990b0bba1564c22450/src/utils/token_mapper.py#L175-L188 | from typing import List, Sequence, Dict
from abc import ABC, abstractmethod
import re
import TensorflowModel_pb2 as proto
def simple_hash(token: str, output_size: int) -> int:
encoded = token.encode("utf-8")
hash_sum = 0
for letter in encoded:
hash_sum = ((31 * hash_sum) + letter) % output_size
return hash_sum
class TokenMapping(ABC):
@abstractmethod
def match(self, token: str) -> bool:
pass
@abstractmethod
def map(self, token: str) -> int:
pass
@abstractmethod
def output_size(self) -> int:
pass
@abstractmethod
def debug_token(self, offset: int) -> str:
pass
@abstractmethod
def serialize(self, serialized: proto.TokenMapping) -> proto.TokenMapping:
pass
class RegexTokenMapping(TokenMapping):
def __init__(self, regex: str, num_hash: int, debug_base: str):
self.regex = re.compile(regex)
self.num_hash = num_hash
self.debug_base = debug_base
def match(self, token: str):
return self.regex.match(token) is not None
def map(self, token: str) -> int:
return simple_hash(token, self.num_hash)
def output_size(self) -> int:
return self.num_hash
def debug_token(self, offset: int):
return self.debug_base+'$'+str(offset)
def serialize(self, serialized: proto.TokenMapping):
serialized.type = proto.REGEX
serialized.regex = self.regex
serialized.num_hash = self.num_hash
serialized.debug_base = self.debug_base
return serialized
class HashTokenMapping(TokenMapping):
def __init__(self, num_hash: int):
self.num_hash = num_hash
def match(self, token: str):
return True
def map(self, token: str):
return simple_hash(token.lower(), self.num_hash)
def output_size(self):
return self.num_hash
def debug_token(self, offset: int):
return "HASH$"+str(offset)
def serialize(self, serialized: proto.TokenMapping):
serialized.type = proto.HASH
serialized.num_hash = self.num_hash
return serialized
class ExactTokenMapping(TokenMapping):
def __init__(self, vocab: Sequence[str]):
self.tokens = list(vocab)
self.token_to_idx = {token: idx for idx, token in enumerate(self.tokens)}
def match(self, token: str):
return token in self.token_to_idx
def map(self, token: str):
return self.token_to_idx[token]
def output_size(self):
return len(self.tokens)
def debug_token(self, offset: int):
return self.tokens[offset]
def serialize(self, serialized: proto.TokenMapping):
serialized.type = proto.TOKEN
serialized.tokens.extend(self.tokens)
class TokenMapper:
def __init__(self, mappings: List[TokenMapping], unk_mappings: List[TokenMapping]):
self.mappings = mappings
self.unk_mappings = unk_mappings
def mapped_output_size(self) -> int:
return sum([mapping.output_size() for mapping in self.unk_mappings + self.mappings]) | MIT License |
craylabs/smartsim | smartsim/launcher/local/local.py | LocalLauncher.run | python | def run(self, step):
if not self.task_manager.actively_monitoring:
self.task_manager.start()
out, err = step.get_output_files()
output = open(out, "w+")
error = open(err, "w+")
cmd = step.get_launch_cmd()
task_id = self.task_manager.start_task(
cmd, step.cwd, env=step.env, out=output, err=error
)
self.step_mapping.add(step.name, task_id=task_id, managed=False)
return task_id | Run a local step created by this launcher. Utilize the shell
library to execute the command with a Popen. Output and error
files will be written to the entity path.
:param step: LocalStep instance to run
:type step: LocalStep | https://github.com/craylabs/smartsim/blob/0c4b198650a026d7bd960f38b1866fb3b8c59a96/smartsim/launcher/local/local.py#L84-L103 | from ...error import LauncherError
from ...settings import RunSettings
from ...utils import get_logger
from ..step import LocalStep
from ..stepInfo import UnmanagedStepInfo
from ..stepMapping import StepMapping
from ..taskManager import TaskManager
logger = get_logger(__name__)
class LocalLauncher:
def __init__(self):
self.task_manager = TaskManager()
self.step_mapping = StepMapping()
def create_step(self, name, cwd, step_settings):
if not isinstance(step_settings, RunSettings):
raise TypeError(
f"Local Launcher only supports entities with RunSettings, not {type(step_settings)}"
)
step = LocalStep(name, cwd, step_settings)
return step
def get_step_update(self, step_names):
updates = []
s_names, s_ids = self.step_mapping.get_ids(step_names, managed=False)
for step_name, step_id in zip(s_names, s_ids):
status, rc, out, err = self.task_manager.get_task_update(step_id)
step_info = UnmanagedStepInfo(status, rc, out, err)
update = (step_name, step_info)
updates.append(update)
return updates
def get_step_nodes(self, step_names):
return [["127.0.0.1"] * len(step_names)] | BSD 2-Clause Simplified License |
wolph/python-progressbar | progressbar/bar.py | ProgressBar.__init__ | python | def __init__(self, min_value=0, max_value=None, widgets=None,
left_justify=True, initial_value=0, poll_interval=None,
widget_kwargs=None, custom_len=utils.len_color,
max_error=True, prefix=None, suffix=None, variables=None,
min_poll_interval=None, **kwargs):
StdRedirectMixin.__init__(self, **kwargs)
ResizableMixin.__init__(self, **kwargs)
ProgressBarBase.__init__(self, **kwargs)
if not max_value and kwargs.get('maxval') is not None:
warnings.warn('The usage of `maxval` is deprecated, please use '
'`max_value` instead', DeprecationWarning)
max_value = kwargs.get('maxval')
if not poll_interval and kwargs.get('poll'):
warnings.warn('The usage of `poll` is deprecated, please use '
'`poll_interval` instead', DeprecationWarning)
poll_interval = kwargs.get('poll')
if max_value:
if min_value > max_value:
raise ValueError('Max value needs to be bigger than the min '
'value')
self.min_value = min_value
self.max_value = max_value
self.max_error = max_error
if widgets is None:
self.widgets = widgets
else:
self.widgets = []
for widget in widgets:
if getattr(widget, 'copy', True):
widget = deepcopy(widget)
self.widgets.append(widget)
self.widgets = widgets
self.prefix = prefix
self.suffix = suffix
self.widget_kwargs = widget_kwargs or {}
self.left_justify = left_justify
self.value = initial_value
self._iterable = None
self.custom_len = custom_len
self.initial_start_time = kwargs.get('start_time')
self.init()
poll_interval = utils.deltas_to_seconds(poll_interval, default=None)
min_poll_interval = utils.deltas_to_seconds(min_poll_interval,
default=None)
self._MINIMUM_UPDATE_INTERVAL = utils.deltas_to_seconds(
self._MINIMUM_UPDATE_INTERVAL)
self.poll_interval = poll_interval
self.min_poll_interval = max(
min_poll_interval or self._MINIMUM_UPDATE_INTERVAL,
self._MINIMUM_UPDATE_INTERVAL,
float(os.environ.get('PROGRESSBAR_MINIMUM_UPDATE_INTERVAL', 0)),
)
self.variables = utils.AttributeDict(variables or {})
for widget in (self.widgets or []):
if isinstance(widget, widgets_module.VariableMixin):
if widget.name not in self.variables:
self.variables[widget.name] = None | Initializes a progress bar with sane defaults | https://github.com/wolph/python-progressbar/blob/8eb963c6cc97949bc7ac3fc57e645506a2c9ae0c/progressbar/bar.py#L283-L361 | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import with_statement
import sys
import math
import os
import time
import timeit
import logging
import warnings
from datetime import datetime
from copy import deepcopy
try:
from collections import abc
except ImportError:
import collections as abc
from python_utils import converters
import six
from . import widgets
from . import widgets as widgets_module
from . import base
from . import utils
logger = logging.getLogger(__name__)
class ProgressBarMixinBase(object):
def __init__(self, **kwargs):
self._finished = False
def start(self, **kwargs):
pass
def update(self, value=None):
pass
def finish(self):
self._finished = True
def __del__(self):
if not self._finished:
try:
self.finish()
except Exception:
pass
class ProgressBarBase(abc.Iterable, ProgressBarMixinBase):
pass
class DefaultFdMixin(ProgressBarMixinBase):
def __init__(self, fd=sys.stderr, is_terminal=None, line_breaks=None,
enable_colors=None, **kwargs):
if fd is sys.stdout:
fd = utils.streams.original_stdout
elif fd is sys.stderr:
fd = utils.streams.original_stderr
self.fd = fd
self.is_ansi_terminal = utils.is_ansi_terminal(fd)
self.is_terminal = utils.is_terminal(
fd, is_terminal or self.is_ansi_terminal)
if line_breaks is None:
line_breaks = utils.env_flag('PROGRESSBAR_LINE_BREAKS', not
self.is_terminal)
self.line_breaks = line_breaks
if enable_colors is None:
enable_colors = utils.env_flag('PROGRESSBAR_ENABLE_COLORS',
self.is_ansi_terminal)
self.enable_colors = enable_colors
ProgressBarMixinBase.__init__(self, **kwargs)
def update(self, *args, **kwargs):
ProgressBarMixinBase.update(self, *args, **kwargs)
line = converters.to_unicode(self._format_line())
if not self.enable_colors:
line = utils.no_color(line)
if self.line_breaks:
line = line.rstrip() + '\n'
else:
line = '\r' + line
try:
self.fd.write(line)
except UnicodeEncodeError:
self.fd.write(line.encode('ascii', 'replace'))
def finish(self, *args, **kwargs):
if self._finished:
return
end = kwargs.pop('end', '\n')
ProgressBarMixinBase.finish(self, *args, **kwargs)
if end and not self.line_breaks:
self.fd.write(end)
self.fd.flush()
class ResizableMixin(ProgressBarMixinBase):
def __init__(self, term_width=None, **kwargs):
ProgressBarMixinBase.__init__(self, **kwargs)
self.signal_set = False
if term_width:
self.term_width = term_width
else:
try:
self._handle_resize()
import signal
self._prev_handle = signal.getsignal(signal.SIGWINCH)
signal.signal(signal.SIGWINCH, self._handle_resize)
self.signal_set = True
except Exception:
pass
def _handle_resize(self, signum=None, frame=None):
w, h = utils.get_terminal_size()
self.term_width = w
def finish(self):
ProgressBarMixinBase.finish(self)
if self.signal_set:
try:
import signal
signal.signal(signal.SIGWINCH, self._prev_handle)
except Exception:
pass
class StdRedirectMixin(DefaultFdMixin):
def __init__(self, redirect_stderr=False, redirect_stdout=False, **kwargs):
DefaultFdMixin.__init__(self, **kwargs)
self.redirect_stderr = redirect_stderr
self.redirect_stdout = redirect_stdout
self._stdout = self.stdout = sys.stdout
self._stderr = self.stderr = sys.stderr
def start(self, *args, **kwargs):
if self.redirect_stdout:
utils.streams.wrap_stdout()
if self.redirect_stderr:
utils.streams.wrap_stderr()
self._stdout = utils.streams.original_stdout
self._stderr = utils.streams.original_stderr
self.stdout = utils.streams.stdout
self.stderr = utils.streams.stderr
utils.streams.start_capturing(self)
DefaultFdMixin.start(self, *args, **kwargs)
def update(self, value=None):
if not self.line_breaks and utils.streams.needs_clear():
self.fd.write('\r' + ' ' * self.term_width + '\r')
utils.streams.flush()
DefaultFdMixin.update(self, value=value)
def finish(self, end='\n'):
DefaultFdMixin.finish(self, end=end)
utils.streams.stop_capturing(self)
if self.redirect_stdout:
utils.streams.unwrap_stdout()
if self.redirect_stderr:
utils.streams.unwrap_stderr()
class ProgressBar(StdRedirectMixin, ResizableMixin, ProgressBarBase):
_DEFAULT_MAXVAL = base.UnknownLength
_MINIMUM_UPDATE_INTERVAL = 0.050 | BSD 3-Clause New or Revised License |
tzhangwps/turbulence-and-systemic-risk | src/main.py | MainProcess.append_prices_and_returns | python | def append_prices_and_returns(self):
print('\nRequesting data from Yahoo Finance...')
self.prices = pd.read_pickle(path.prices_path_historical)
self.prices = get.GetPrices().update_weekly_prices(self.prices)
self.prices.to_pickle(path.prices_path_current)
self.prices = get.CalculateReturns().add_curve_slope(self.prices)
self.returns = get.CalculateReturns().calculate_returns(self.prices) | Appends new data to the prices dataset and the returns dataset. | https://github.com/tzhangwps/turbulence-and-systemic-risk/blob/ceb7d1c6a1914da5a2316603f289238a4bb6a826/src/main.py#L26-L35 | import pandas as pd
import os
import TurbulenceSuite_paths as path
import src.get_data as get
import src.calculate as calc
class MainProcess:
def __init__(self):
self.prices = pd.DataFrame()
self.returns = pd.DataFrame()
self.turbulence = pd.DataFrame()
self.systemic_risk = pd.DataFrame() | MIT License |
brython-dev/brython | www/speed/benchmarks/util.py | run_benchmark | python | def run_benchmark(options, num_runs, bench_func, *args):
if options.profile:
import cProfile
prof = cProfile.Profile()
prof.runcall(bench_func, num_runs, *args)
prof.print_stats(sort=options.profile_sort)
else:
data = bench_func(num_runs, *args)
if options.take_geo_mean:
product = reduce(operator.mul, data, 1)
print(math.pow(product, 1.0 / len(data)))
else:
for x in data:
print(x) | Run the given benchmark, print results to stdout.
Args:
options: optparse.Values instance.
num_runs: number of times to run the benchmark
bench_func: benchmark function. `num_runs, *args` will be passed to this
function. This should return a list of floats (benchmark execution
times). | https://github.com/brython-dev/brython/blob/33aeaab551f1b73209326c5a0aecf98642d4c126/www/speed/benchmarks/util.py#L10-L32 | __author__ = "collinwinter@google.com (Collin Winter)"
import math
import operator
from functools import reduce | BSD 3-Clause New or Revised License |
openmined/pyariesfl | aries_cloudagent/admin/server.py | WebhookTarget.__init__ | python | def __init__(
self, endpoint: str, topic_filter: Sequence[str] = None, retries: int = None
):
self.endpoint = endpoint
self._topic_filter = None
self.retries = retries
self.topic_filter = topic_filter | Initialize the webhook target. | https://github.com/openmined/pyariesfl/blob/dd78dcebc771971abfee301b80cdd5d246c14840/aries_cloudagent/admin/server.py#L81-L89 | import asyncio
import logging
from typing import Coroutine, Sequence, Set
import uuid
from aiohttp import web, ClientSession
from aiohttp_apispec import docs, response_schema, setup_aiohttp_apispec
import aiohttp_cors
from marshmallow import fields, Schema
from ..classloader import ClassLoader
from ..config.base import ConfigError
from ..config.injection_context import InjectionContext
from ..messaging.outbound_message import OutboundMessage
from ..messaging.responder import BaseResponder
from ..stats import Collector
from ..task_processor import TaskProcessor
from ..transport.outbound.queue.base import BaseOutboundMessageQueue
from .base_server import BaseAdminServer
from .error import AdminSetupError
from .routes import register_module_routes
LOGGER = logging.getLogger(__name__)
class AdminModulesSchema(Schema):
result = fields.List(
fields.Str(description="admin module"),
description="List of admin modules",
)
class AdminStatusSchema(Schema):
class AdminResponder(BaseResponder):
def __init__(self, send: Coroutine, webhook: Coroutine, **kwargs):
super().__init__(**kwargs)
self._send = send
self._webhook = webhook
async def send_outbound(self, message: OutboundMessage):
await self._send(message)
async def send_webhook(self, topic: str, payload: dict):
await self._webhook(topic, payload)
class WebhookTarget: | Apache License 2.0 |
ifding/wavenet-speech-to-text | model/networks.py | ResidualBlock.__init__ | python | def __init__(self, res_channels, skip_channels, dilation):
super(ResidualBlock, self).__init__()
self.dilated = DilatedCausalConv1d(res_channels, dilation=dilation)
self.conv_res = torch.nn.Conv1d(res_channels, res_channels, 1)
self.conv_skip = torch.nn.Conv1d(res_channels, skip_channels, 1)
self.gate_tanh = torch.nn.Tanh()
self.gate_sigmoid = torch.nn.Sigmoid() | Residual block
:param res_channels: number of residual channel for input, output
:param skip_channels: number of skip channel for output
:param dilation: | https://github.com/ifding/wavenet-speech-to-text/blob/4d786c2280527ff38ba615974dd227c4f44c93b2/model/networks.py#L61-L75 | import torch
import numpy as np
from utils.exceptions import InputSizeError
class DilatedCausalConv1d(torch.nn.Module):
def __init__(self, channels, dilation=1):
super(DilatedCausalConv1d, self).__init__()
self.conv = torch.nn.Conv1d(channels, channels,
kernel_size=2, stride=1,
dilation=dilation,
padding=0,
bias=False)
def init_weights_for_test(self):
for m in self.modules():
if isinstance(m, torch.nn.Conv1d):
m.weight.data.fill_(1)
def forward(self, x):
output = self.conv(x)
return output
class CausalConv1d(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super(CausalConv1d, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_channels,
kernel_size=2, stride=1, padding=1,
bias=False)
def init_weights_for_test(self):
for m in self.modules():
if isinstance(m, torch.nn.Conv1d):
m.weight.data.fill_(1)
def forward(self, x):
output = self.conv(x)
return output[:, :, :-1]
class ResidualBlock(torch.nn.Module): | MIT License |
adafruit/adafruit_python_gpio | Adafruit_GPIO/SPI.py | BitBang.set_clock_hz | python | def set_clock_hz(self, hz):
pass | Set the speed of the SPI clock. This is unsupported with the bit
bang SPI class and will be ignored. | https://github.com/adafruit/adafruit_python_gpio/blob/a12fee39839665966bd124fd22588b2c87ced9d2/Adafruit_GPIO/SPI.py#L173-L177 | import operator
import time
import Adafruit_GPIO as GPIO
MSBFIRST = 0
LSBFIRST = 1
class SpiDev(object):
def __init__(self, port, device, max_speed_hz=500000):
import spidev
self._device = spidev.SpiDev()
self._device.open(port, device)
self._device.max_speed_hz=max_speed_hz
self._device.mode = 0
self._device.cshigh = False
def set_clock_hz(self, hz):
self._device.max_speed_hz=hz
def set_mode(self, mode):
if mode < 0 or mode > 3:
raise ValueError('Mode must be a value 0, 1, 2, or 3.')
self._device.mode = mode
def set_bit_order(self, order):
if order == MSBFIRST:
self._device.lsbfirst = False
elif order == LSBFIRST:
self._device.lsbfirst = True
else:
raise ValueError('Order must be MSBFIRST or LSBFIRST.')
def close(self):
self._device.close()
def write(self, data):
self._device.writebytes(data)
def read(self, length):
return bytearray(self._device.readbytes(length))
def transfer(self, data):
return bytearray(self._device.xfer2(data))
class SpiDevMraa(object):
def __init__(self, port, device, max_speed_hz=500000):
import mraa
self._device = mraa.Spi(0)
self._device.mode(0)
def set_clock_hz(self, hz):
self._device.frequency(hz)
def set_mode(self,mode):
if mode < 0 or mode > 3:
raise ValueError('Mode must be a value 0, 1, 2, or 3.')
self._device.mode(mode)
def set_bit_order(self, order):
if order == MSBFIRST:
self._device.lsbmode(False)
elif order == LSBFIRST:
self._device.lsbmode(True)
else:
raise ValueError('Order must be MSBFIRST or LSBFIRST.')
def close(self):
self._device.Spi()
def write(self, data):
self._device.write(bytearray(data))
class BitBang(object):
def __init__(self, gpio, sclk, mosi=None, miso=None, ss=None):
self._gpio = gpio
self._sclk = sclk
self._mosi = mosi
self._miso = miso
self._ss = ss
gpio.setup(sclk, GPIO.OUT)
if mosi is not None:
gpio.setup(mosi, GPIO.OUT)
if miso is not None:
gpio.setup(miso, GPIO.IN)
if ss is not None:
gpio.setup(ss, GPIO.OUT)
gpio.set_high(ss)
self.set_mode(0)
self.set_bit_order(MSBFIRST) | MIT License |
datadotworld/data.world-py | datadotworld/client/_swagger/models/file_summary_response.py | FileSummaryResponse.created | python | def created(self, created):
if created is None:
raise ValueError("Invalid value for `created`, must not be `None`")
self._created = created | Sets the created of this FileSummaryResponse.
Date and time when file was created.
:param created: The created of this FileSummaryResponse.
:type: str | https://github.com/datadotworld/data.world-py/blob/7e5f474b655f4f0c88cc6862353e4d52c0e0bb31/datadotworld/client/_swagger/models/file_summary_response.py#L90-L101 | from pprint import pformat
from six import iteritems
import re
class FileSummaryResponse(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'created': 'str',
'description': 'str',
'labels': 'list[str]',
'name': 'str',
'size_in_bytes': 'int',
'source': 'FileSourceSummaryResponse',
'updated': 'str'
}
attribute_map = {
'created': 'created',
'description': 'description',
'labels': 'labels',
'name': 'name',
'size_in_bytes': 'sizeInBytes',
'source': 'source',
'updated': 'updated'
}
def __init__(self, created=None, description=None, labels=None, name=None, size_in_bytes=None, source=None, updated=None):
self._created = None
self._description = None
self._labels = None
self._name = None
self._size_in_bytes = None
self._source = None
self._updated = None
self.created = created
if description is not None:
self.description = description
if labels is not None:
self.labels = labels
self.name = name
if size_in_bytes is not None:
self.size_in_bytes = size_in_bytes
if source is not None:
self.source = source
self.updated = updated
@property
def created(self):
return self._created
@created.setter | Apache License 2.0 |
twisted/axiom | axiom/item.py | Empowered._getPowerupInterfaces | python | def _getPowerupInterfaces(self):
powerupInterfaces = getattr(self.__class__, "powerupInterfaces", ())
pifs = []
for x in powerupInterfaces:
if isinstance(x, type(Interface)):
pifs.append((x, 0))
else:
pifs.append(x)
m = getattr(self, "__getPowerupInterfaces__", None)
if m is not None:
pifs = m(pifs)
try:
pifs = [(i, p) for (i, p) in pifs]
except ValueError:
raise ValueError("return value from %r.__getPowerupInterfaces__"
" not an iterable of 2-tuples" % (self,))
return pifs | Collect powerup interfaces this object declares that it can be
installed on. | https://github.com/twisted/axiom/blob/28191ede99287e9a87c1ff561b831f7d80aaa2fe/axiom/item.py#L379-L402 | __metaclass__ = type
import gc
from zope.interface import implementer, Interface
from inspect import getabsfile
from weakref import WeakValueDictionary
from twisted.python import log
from twisted.python.reflect import qual, namedAny
from twisted.python.util import mergeFunctionMetadata
from twisted.application.service import (
IService, IServiceCollection, MultiService)
from axiom import slotmachine, _schema, iaxiom
from axiom.errors import ChangeRejected, DeletionDisallowed
from axiom.iaxiom import IColumn, IPowerupIndirector
from axiom.attributes import (
SQLAttribute, _ComparisonOperatorMuxer, _MatchingOperationMuxer,
_OrderingMixin, _ContainableMixin, Comparable, compare, inmemory,
reference, text, integer, AND, _cascadingDeletes, _disallows)
import six
from six.moves import zip
_typeNameToMostRecentClass = WeakValueDictionary()
def normalize(qualName):
return qualName.lower().replace('.', '_')
class NoInheritance(RuntimeError):
class NotInStore(RuntimeError):
class CantInstantiateItem(RuntimeError):
class MetaItem(slotmachine.SchemaMetaMachine):
def __new__(meta, name, bases, dictionary):
T = slotmachine.SchemaMetaMachine.__new__(meta, name, bases, dictionary)
if T.__name__ == 'Item' and T.__module__ == __name__:
return T
T.__already_inherited__ += 1
if T.__already_inherited__ >= 2:
raise NoInheritance("already inherited from item once: "
"in-database inheritance not yet supported")
if T.typeName is None:
T.typeName = normalize(qual(T))
if T.schemaVersion is None:
T.schemaVersion = 1
if not T.__legacy__ and T.typeName in _typeNameToMostRecentClass:
gc.collect()
if T.typeName in _typeNameToMostRecentClass:
if T.__legacy__:
return T
otherT = _typeNameToMostRecentClass[T.typeName]
if (otherT.__name__ == T.__name__
and getabsfile(T) == getabsfile(otherT)
and T.__module__ != otherT.__module__):
if len(T.__module__) < len(otherT.__module__):
relmod = T.__module__
else:
relmod = otherT.__module__
raise RuntimeError(
"Use absolute imports; relative import"
" detected for type %r (imported from %r)" % (
T.typeName, relmod))
raise RuntimeError("2 definitions of axiom typename {!r}: {!r} {!r}".format(
T.typeName, T, _typeNameToMostRecentClass[T.typeName]))
_typeNameToMostRecentClass[T.typeName] = T
return T
def __cmp__(self, other):
if isinstance(other, MetaItem):
return cmp((self.typeName, self.schemaVersion),
(other.typeName, other.schemaVersion))
return NotImplemented
def noop():
pass
@implementer(IColumn)
class _StoreIDComparer(Comparable):
def __init__(self, type):
self.type = type
def __repr__(self):
return '<storeID ' + qual(self.type) + '.storeID>'
def fullyQualifiedName(self):
return qual(self.type)+'.storeID'
def infilter(self, pyval, oself, store):
return pyval
def outfilter(self, dbval, oself):
return dbval
def getShortColumnName(self, store):
return store.getShortColumnName(self)
def getColumnName(self, store):
return store.getColumnName(self)
def __get__(self, item, type=None):
if item is None:
return self
else:
return getattr(item, 'storeID')
class _SpecialStoreIDAttribute(slotmachine.SetOnce):
def __get__(self, oself, type=None):
if type is not None and oself is None:
if type._storeIDComparer is None:
type._storeIDComparer = _StoreIDComparer(type)
return type._storeIDComparer
return super(_SpecialStoreIDAttribute, self).__get__(oself, type)
def serviceSpecialCase(item, pups):
if item._axiom_service is not None:
return item._axiom_service
svc = MultiService()
for subsvc in pups:
subsvc.setServiceParent(svc)
item._axiom_service = svc
return svc
class Empowered(object):
aggregateInterfaces = {
IService: serviceSpecialCase,
IServiceCollection: serviceSpecialCase}
def inMemoryPowerUp(self, powerup, interface):
self._inMemoryPowerups[interface] = powerup
def powerUp(self, powerup, interface=None, priority=0):
if interface is None:
for iface, priority in powerup._getPowerupInterfaces():
self.powerUp(powerup, iface, priority)
elif interface is IPowerupIndirector:
raise TypeError(
"You cannot install a powerup for IPowerupIndirector: " +
powerup)
else:
forc = self.store.findOrCreate(_PowerupConnector,
item=self,
interface=unicode(qual(interface)),
powerup=powerup)
forc.priority = priority
def powerDown(self, powerup, interface=None):
if interface is None:
for interface, priority in powerup._getPowerupInterfaces():
self.powerDown(powerup, interface)
else:
for cable in self.store.query(_PowerupConnector,
AND(_PowerupConnector.item == self,
_PowerupConnector.interface == unicode(qual(interface)),
_PowerupConnector.powerup == powerup)):
cable.deleteFromStore()
return
raise ValueError("Not powered up for {!r} with {!r}".format(interface,
powerup))
def __conform__(self, interface):
if interface is IPowerupIndirector:
return
pups = self.powerupsFor(interface)
aggregator = self.aggregateInterfaces.get(interface, None)
if aggregator is not None:
return aggregator(self, pups)
for pup in pups:
return pup
def powerupsFor(self, interface):
inMemoryPowerup = self._inMemoryPowerups.get(interface, None)
if inMemoryPowerup is not None:
yield inMemoryPowerup
if self.store is None:
return
name = unicode(qual(interface), 'ascii')
for cable in self.store.query(
_PowerupConnector,
AND(_PowerupConnector.interface == name,
_PowerupConnector.item == self),
sort=_PowerupConnector.priority.descending):
pup = cable.powerup
if pup is None:
cable.deleteFromStore()
else:
indirector = IPowerupIndirector(pup, None)
if indirector is not None:
yield indirector.indirect(interface)
else:
yield pup
def interfacesFor(self, powerup):
pc = _PowerupConnector
for iface in self.store.query(pc,
AND(pc.item == self,
pc.powerup == powerup)).getColumn('interface'):
yield namedAny(iface) | MIT License |
user-cont/conu | conu/backend/nspawn/container.py | NspawnContainer.inspect | python | def inspect(self, refresh=True):
return self.get_metadata(refresh=refresh) | return cached metadata by default (a convenience method)
:param refresh: bool, returns up to date metadata if set to True
:return: dict | https://github.com/user-cont/conu/blob/0d8962560f6f7f17fe1be0d434a4809e2a0ea51d/conu/backend/nspawn/container.py#L108-L116 | import logging
import subprocess
import time
from copy import deepcopy
from conu.apidefs.container import Container
from conu.exceptions import ConuException
from conu.utils import run_cmd, random_str, convert_kv_to_dict, command_exists
from conu.backend.nspawn import constants
logger = logging.getLogger(__name__)
class NspawnContainer(Container):
def __init__(self, image, container_id, name=None,
popen_instance=None, start_process=None, start_action=None):
self.systemd_wait_support = None
self.system_requirements()
super(NspawnContainer, self).__init__(image, container_id, name)
self.popen_instance = popen_instance
self.start_process = start_process
self.start_action = start_action
@staticmethod
def machined_restart():
logger.debug("restart systemd-machined")
return run_cmd("systemctl restart systemd-machined", ignore_status=True)
@staticmethod
def system_requirements():
command_exists("systemd-run",
["systemd-run", "--help"],
"Command systemd-run does not seems to be present on your system. "
"Do you have system with systemd?")
command_exists(
"machinectl",
["machinectl", "--no-pager", "--help"],
"Command machinectl does not seems to be present on your system. "
"Do you have system with systemd?")
def __repr__(self):
return "%s(image=%s, name=%s)" % (
self.__class__, self.image, self.name)
def __str__(self):
return self.name
def start(self):
self.start_process = NspawnContainer.internal_run_container(
name=self.name, callback_method=self.start_action)
return self.start_process
def get_id(self):
return self._id
def get_image_name(self):
return None | MIT License |
facebookresearch/mephisto | mephisto/operations/utils.py | get_extra_argument_dicts | python | def get_extra_argument_dicts(customizable_class: Any) -> List[Dict[str, Any]]:
dict_fields = fields(customizable_class.ArgsClass)
usable_fields = []
group_field = None
for f in dict_fields:
if not f.name.startswith("_"):
usable_fields.append(f)
elif f.name == "_group":
group_field = f
parsed_fields = [get_dict_from_field(f) for f in usable_fields]
help_text = ""
if group_field is not None:
help_text = group_field.metadata.get("help", "")
return [{"desc": help_text, "args": {f["dest"]: f for f in parsed_fields}}] | Produce the argument dicts for the given customizable class
(Blueprint, Architect, etc) | https://github.com/facebookresearch/mephisto/blob/ff81d7c8ef1f90ef400fc102cc0312a83d848764/mephisto/operations/utils.py#L218-L235 | import os
import sys, glob, importlib
import shlex
from distutils.dir_util import copy_tree
import functools
from mephisto.data_model.constants import NO_PROJECT_NAME
from mephisto.operations.config_handler import (
add_config_arg,
get_config_arg,
CORE_SECTION,
DATA_STORAGE_KEY,
DEFAULT_CONFIG_FILE,
)
from omegaconf import OmegaConf, MISSING, DictConfig
from dataclasses import fields, Field
from typing import Optional, Dict, Any, List, Type, TYPE_CHECKING
if TYPE_CHECKING:
from mephisto.abstractions.crowd_provider import CrowdProvider
from mephisto.data_model.task_runner import TaskRunner
from mephisto.abstractions.architect import Architect
from mephisto.data_model.task_run import TaskRun
from mephisto.data_model.requester import Requester
loaded_data_dir = None
def ensure_user_confirm(display_text, skip_input=False) -> None:
if skip_input:
return
res = input(f'{display_text}\nEnter "n" to exit and anything else to continue:')
if res == "n":
raise SystemExit(0)
return
def get_root_dir() -> str:
return os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
def get_mock_requester(db) -> "Requester":
mock_requesters = db.find_requesters(provider_type="mock")
if len(mock_requesters) == 0:
db.new_requester("MOCK_REQUESTER", "mock")
mock_requesters = db.find_requesters(provider_type="mock")
return mock_requesters[0]
def get_provider_dir() -> str:
return os.path.join(get_root_dir(), "mephisto/abstractions/providers")
def get_gallery_dir() -> str:
return os.path.join(get_root_dir(), "gallery")
def get_dir_for_task(task_name: str, not_exists_ok: bool = False) -> Optional[str]:
dir_path = os.path.join(get_tasks_dir(), task_name)
if os.path.exists(dir_path) or not_exists_ok:
return dir_path
dir_path = os.path.join(get_gallery_dir(), task_name)
if os.path.exists(dir_path) or not_exists_ok:
return dir_path
return None
def get_tasks_dir() -> str:
return os.path.join(get_root_dir(), "mephisto/tasks")
def get_root_data_dir() -> str:
global loaded_data_dir
if loaded_data_dir is None:
default_data_dir = os.path.join(get_root_dir(), "data")
actual_data_dir = get_config_arg(CORE_SECTION, DATA_STORAGE_KEY)
if actual_data_dir is None:
data_dir_location = input(
"Please enter the full path to a location to store Mephisto run data. By default this "
f"would be at '{default_data_dir}'. This dir should NOT be on a distributed file "
"store. Press enter to use the default: "
).strip()
if len(data_dir_location) == 0:
data_dir_location = default_data_dir
data_dir_location = os.path.expanduser(data_dir_location)
os.makedirs(data_dir_location, exist_ok=True)
database_loc = os.path.join(default_data_dir, "database.db")
if os.path.exists(database_loc) and data_dir_location != default_data_dir:
should_migrate = (
input(
"We have found an existing database in the default data directory, do you want to "
f"copy any existing data from the default location to {data_dir_location}? (y)es/no: "
)
.lower()
.strip()
)
if len(should_migrate) == 0 or should_migrate[0] == "y":
copy_tree(default_data_dir, data_dir_location)
print(
"Mephisto data successfully copied, once you've confirmed the migration worked, "
"feel free to remove all of the contents in "
f"{default_data_dir} EXCEPT for `README.md`."
)
add_config_arg(CORE_SECTION, DATA_STORAGE_KEY, data_dir_location)
loaded_data_dir = get_config_arg(CORE_SECTION, DATA_STORAGE_KEY)
if not os.path.isdir(loaded_data_dir):
raise NotADirectoryError(
f"The provided Mephisto data directory {loaded_data_dir} as set in "
f"{DEFAULT_CONFIG_FILE} is not a directory! Please locate your Mephisto "
f"data directory and update {DEFAULT_CONFIG_FILE} to point to it."
)
return loaded_data_dir
def get_data_dir(root_dir: Optional[str] = None) -> str:
if root_dir is None:
return get_root_data_dir()
return os.path.join(root_dir, "data")
def get_mephisto_tmp_dir() -> str:
return os.path.join(get_root_dir(), "tmp")
def get_dir_for_run(task_run: "TaskRun", project_name: str = NO_PROJECT_NAME) -> str:
run_id = task_run.db_id
root_dir = task_run.db.db_root
return os.path.join(get_data_dir(root_dir), "runs", project_name, run_id)
def build_arg_list_from_dict(in_dict: Dict[str, Any]) -> List[str]:
arg_list = []
for key, val in in_dict.items():
arg_list.append(f"--{key.replace('_', '-')}")
arg_list.append(str(val))
return arg_list
def find_or_create_qualification(db, qualification_name) -> str:
found_qualifications = db.find_qualifications(qualification_name)
if len(found_qualifications) == 0:
return db.make_qualification(qualification_name)
else:
return found_qualifications[0].db_id
def get_dict_from_field(in_field: Field) -> Dict[str, Any]:
found_type = "str"
try:
found_type = in_field.type.__name__
except AttributeError:
found_type = "unknown"
return {
"dest": in_field.name,
"type": found_type,
"default": in_field.default,
"help": in_field.metadata.get("help"),
"choices": in_field.metadata.get("choices"),
"required": in_field.metadata.get("required", False),
} | MIT License |
danielnyga/pracmln | python2/pracmln/logic/common.py | Logic.templ_atoms | python | def templ_atoms(self):
templ_atoms = []
for literal in self.literals():
for templ in literal.template_variants():
templ_atoms.append(templ)
return templ_atoms | Returns a list of template variants of all atoms
that can be generated from this formula and the given mln.
:Example:
foo(?x, +?y) ^ bar(?x, +?z) --> [foo(?x, X1), foo(?x, X2), ...,
bar(?x, Z1), bar(?x, Z2), ...] | https://github.com/danielnyga/pracmln/blob/bbda65696fb8753b11ff007e991280ebe42d78f9/python2/pracmln/logic/common.py#L234-L248 | import sys
from dnutils import logs, ifnone
from pracmln.mln.util import fstr, dict_union, colorize
from pracmln.mln.errors import NoSuchDomainError, NoSuchPredicateError
from collections import defaultdict
import itertools
from pracmln.mln.constants import HARD, auto, predicate_color, inherit
from grammar import StandardGrammar, PRACGrammar
logger = logs.getlogger(__name__)
def latexsym(sym):
return r'\textit{%s}' % str(sym)
class Logic(object):
def __init__(self, grammar, mln):
if grammar not in ('StandardGrammar', 'PRACGrammar'):
raise Exception('Invalid grammar: %s' % grammar)
self.grammar = eval(grammar)(self)
self.mln = mln
def __getstate__(self):
d = self.__dict__.copy()
d['grammar'] = type(self.grammar).__name__
return d
def __setstate__(self, d):
self.__dict__ = d
self.grammar = eval(d['grammar'])(self)
class Constraint(object):
def template_variants(self, mln):
raise Exception("%s does not implement getTemplateVariants" % str(type(self)))
def truth(self, world):
raise Exception("%s does not implement truth" % str(type(self)))
def islogical(self):
raise Exception("%s does not implement islogical" % str(type(self)))
def itergroundings(self, mrf, simplify=False, domains=None):
raise Exception("%s does not implement itergroundings" % str(type(self)))
def idx_gndatoms(self, l=None):
raise Exception("%s does not implement idxgndatoms" % str(type(self)))
def gndatoms(self, l=None):
raise Exception("%s does not implement gndatoms" % str(type(self)))
class Formula(Constraint):
def __init__(self, mln=None, idx=None):
self.mln = mln
if idx == auto and mln is not None:
self.idx = len(mln.formulas)
else:
self.idx = idx
@property
def idx(self):
return self._idx
@idx.setter
def idx(self, idx):
self._idx = idx
@property
def mln(self):
return self._mln
@mln.setter
def mln(self, mln):
if hasattr(self, 'children'):
for child in self.children:
child.mln = mln
self._mln = mln
@property
def weight(self):
return self.mln.weight(self.idx)
@weight.setter
def weight(self, w):
if self.idx is None:
raise Exception('%s does not have an index' % str(self))
self.mln.weight(self.idx, w)
@property
def ishard(self):
return self.weight == HARD
def contains_gndatom(self, gndatomidx):
if not hasattr(self, "children"):
return False
for child in self.children:
if child.contains_gndatom(gndatomidx):
return True
return False
def gndatom_indices(self, l=None):
if l == None: l = []
if not hasattr(self, "children"):
return l
for child in self.children:
child.gndatom_indices(l)
return l
def gndatoms(self, l=None):
if l is None: l = []
if not hasattr(self, "children"):
return l
for child in self.children:
child.gndatoms(l)
return l | BSD 2-Clause Simplified License |
netzkolchose/django-computedfields | computedfields/graph.py | Graph.get_cycles | python | def get_cycles(self):
left_edges = OrderedDict()
cycles = {}
for edge in self.edges:
left_edges.setdefault(edge.left, []).append(edge)
for edge in self.edges:
self._get_cycles(edge, left_edges, cycles)
return cycles | Gets all cycles in graph.
This is not optimised by any means, it simply walks the whole graph
recursively and aborts as soon a seen edge gets entered again.
Therefore use this and all dependent properties
(``edge_cycles`` and ``node_cycles``) for in-depth cycle inspection
only.
As a start node any node on the left side of an edge will be tested.
Returns a mapping of
.. code:: python
{frozenset(<cycle edges>): {
'entries': set(edges leading to the cycle),
'path': list(cycle edges in last seen order)
}}
An edge in ``entries`` is not necessarily part of the cycle itself,
but once entered it will lead to the cycle. | https://github.com/netzkolchose/django-computedfields/blob/ffa7c963cb0e70d2afe5954f2fdca241c0407b3f/computedfields/graph.py#L268-L298 | from collections import OrderedDict
from django.core.exceptions import FieldDoesNotExist
from django.db.models import ForeignKey
from computedfields.helper import pairwise, is_sublist, modelname, parent_to_inherited_path, skip_equal_segments
class ComputedFieldsException(Exception):
class CycleException(ComputedFieldsException):
class CycleEdgeException(CycleException):
class CycleNodeException(CycleException):
class Edge:
instances = {}
def __new__(cls, *args):
key = (args[0], args[1])
if key in cls.instances:
return cls.instances[key]
instance = super(Edge, cls).__new__(cls)
cls.instances[key] = instance
return instance
def __init__(self, left, right, data=None):
self.left = left
self.right = right
self.data = data
def __str__(self):
return 'Edge %s-%s' % (self.left, self.right)
def __repr__(self):
return str(self)
def __eq__(self, other):
return self is other
def __ne__(self, other):
return self is not other
def __hash__(self):
return id(self)
class Node:
instances = {}
def __new__(cls, *args):
if args[0] in cls.instances:
return cls.instances[args[0]]
instance = super(Node, cls).__new__(cls)
cls.instances[args[0]] = instance
return instance
def __init__(self, data):
self.data = data
def __str__(self):
return self.data if isinstance(self.data, str) else '.'.join(self.data)
def __repr__(self):
return str(self)
def __eq__(self, other):
return self is other
def __ne__(self, other):
return self is not other
def __hash__(self):
return id(self)
class Graph:
def __init__(self):
self.nodes = set()
self.edges = set()
self._removed = set()
def add_node(self, node):
self.nodes.add(node)
def remove_node(self, node):
self.nodes.remove(node)
def add_edge(self, edge):
self.edges.add(edge)
self.nodes.add(edge.left)
self.nodes.add(edge.right)
def remove_edge(self, edge):
self.edges.remove(edge)
def get_dot(self, format='pdf', mark_edges=None, mark_nodes=None):
from graphviz import Digraph
if not mark_edges:
mark_edges = {}
if not mark_nodes:
mark_nodes = {}
dot = Digraph(format=format)
for node in self.nodes:
dot.node(str(node), str(node), **mark_nodes.get(node, {}))
for edge in self.edges:
dot.edge(str(edge.left), str(edge.right), **mark_edges.get(edge, {}))
return dot
def render(self, filename=None, format='pdf', mark_edges=None, mark_nodes=None):
self.get_dot(format, mark_edges, mark_nodes).render(
filename=filename, cleanup=True)
def view(self, format='pdf', mark_edges=None, mark_nodes=None):
self.get_dot(format, mark_edges, mark_nodes).view(cleanup=True)
def edgepath_to_nodepath(self, path):
return [edge.left for edge in path] + [path[-1].right]
def nodepath_to_edgepath(self, path):
return [Edge(*pair) for pair in pairwise(path)]
def _get_edge_paths(self, edge, left_edges, paths, seen=None):
if not seen:
seen = []
if edge in seen:
raise CycleEdgeException(seen[seen.index(edge):])
seen.append(edge)
if edge.right in left_edges:
for new_edge in left_edges[edge.right]:
self._get_edge_paths(new_edge, left_edges, paths, seen[:])
paths.append(seen)
def get_edgepaths(self):
left_edges = OrderedDict()
paths = []
for edge in self.edges:
left_edges.setdefault(edge.left, []).append(edge)
for edge in self.edges:
self._get_edge_paths(edge, left_edges, paths)
return paths
def get_nodepaths(self):
try:
paths = self.get_edgepaths()
except CycleEdgeException as exc:
raise CycleNodeException(self.edgepath_to_nodepath(exc.args[0]))
node_paths = []
for path in paths:
node_paths.append(self.edgepath_to_nodepath(path))
return node_paths
def _get_cycles(self, edge, left_edges, cycles, seen=None):
if not seen:
seen = []
if edge in seen:
cycle = frozenset(seen[seen.index(edge):])
data = cycles.setdefault(cycle, {'entries': set(), 'path': []})
if seen:
data['entries'].add(seen[0])
data['path'] = seen[seen.index(edge):]
return
seen.append(edge)
if edge.right in left_edges:
for new_edge in left_edges[edge.right]:
self._get_cycles(new_edge, left_edges, cycles, seen[:]) | MIT License |
wayneweiqiang/gmma | gmma/utils/validation.py | check_memory | python | def check_memory(memory):
if memory is None or isinstance(memory, str):
if parse_version(joblib.__version__) < parse_version('0.12'):
memory = joblib.Memory(cachedir=memory, verbose=0)
else:
memory = joblib.Memory(location=memory, verbose=0)
elif not hasattr(memory, 'cache'):
raise ValueError("'memory' should be None, a string or have the same"
" interface as joblib.Memory."
" Got memory='{}' instead.".format(memory))
return memory | Check that ``memory`` is joblib.Memory-like.
joblib.Memory-like means that ``memory`` can be converted into a
joblib.Memory instance (typically a str denoting the ``location``)
or has the same interface (has a ``cache`` method).
Parameters
----------
memory : None, str or object with the joblib.Memory interface
Returns
-------
memory : object with the joblib.Memory interface
Raises
------
ValueError
If ``memory`` is not joblib.Memory-like. | https://github.com/wayneweiqiang/gmma/blob/30b116edb83f495341fef8e9ad4baa50e4e1f76a/gmma/utils/validation.py#L208-L238 | from functools import wraps
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from inspect import signature, isclass, Parameter
from numpy.core.numeric import ComplexWarning
import joblib
from contextlib import suppress
from .fixes import _object_dtype_isnan, parse_version
from .. import get_config as _get_config
from ..exceptions import NonBLASDotWarning, PositiveSpectrumWarning
from ..exceptions import NotFittedError
from ..exceptions import DataConversionWarning
FLOAT_DTYPES = (np.float64, np.float32, np.float16)
warnings.simplefilter('ignore', NonBLASDotWarning)
def _deprecate_positional_args(f):
sig = signature(f)
kwonly_args = []
all_args = []
for name, param in sig.parameters.items():
if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
all_args.append(name)
elif param.kind == Parameter.KEYWORD_ONLY:
kwonly_args.append(name)
@wraps(f)
def inner_f(*args, **kwargs):
extra_args = len(args) - len(all_args)
if extra_args > 0:
args_msg = ['{}={}'.format(name, arg)
for name, arg in zip(kwonly_args[:extra_args],
args[-extra_args:])]
warnings.warn("Pass {} as keyword args. From version 0.25 "
"passing these as positional arguments will "
"result in an error".format(", ".join(args_msg)),
FutureWarning)
kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
return f(**kwargs)
return inner_f
def _assert_all_finite(X, allow_nan=False, msg_dtype=None):
from .extmath import _safe_accumulator_op
if _get_config()['assume_finite']:
return
X = np.asanyarray(X)
is_float = X.dtype.kind in 'fc'
if is_float and (np.isfinite(_safe_accumulator_op(np.sum, X))):
pass
elif is_float:
msg_err = "Input contains {} or a value too large for {!r}."
if (allow_nan and np.isinf(X).any() or
not allow_nan and not np.isfinite(X).all()):
type_err = 'infinity' if allow_nan else 'NaN, infinity'
raise ValueError(
msg_err.format
(type_err,
msg_dtype if msg_dtype is not None else X.dtype)
)
elif X.dtype == np.dtype('object') and not allow_nan:
if _object_dtype_isnan(X).any():
raise ValueError("Input contains NaN")
@_deprecate_positional_args
def assert_all_finite(X, *, allow_nan=False):
_assert_all_finite(X.data if sp.issparse(X) else X, allow_nan)
@_deprecate_positional_args
def as_float_array(X, *, copy=True, force_all_finite=True):
if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
and not sp.issparse(X)):
return check_array(X, accept_sparse=['csr', 'csc', 'coo'],
dtype=np.float64, copy=copy,
force_all_finite=force_all_finite, ensure_2d=False)
elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
return X.copy() if copy else X
elif X.dtype in [np.float32, np.float64]:
return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
else:
if X.dtype.kind in 'uib' and X.dtype.itemsize <= 4:
return_dtype = np.float32
else:
return_dtype = np.float64
return X.astype(return_dtype)
def _is_arraylike(x):
return (hasattr(x, '__len__') or
hasattr(x, 'shape') or
hasattr(x, '__array__'))
def _num_samples(x):
message = 'Expected sequence or array-like, got %s' % type(x)
if hasattr(x, 'fit') and callable(x.fit):
raise TypeError(message)
if not hasattr(x, '__len__') and not hasattr(x, 'shape'):
if hasattr(x, '__array__'):
x = np.asarray(x)
else:
raise TypeError(message)
if hasattr(x, 'shape') and x.shape is not None:
if len(x.shape) == 0:
raise TypeError("Singleton array %r cannot be considered"
" a valid collection." % x)
if isinstance(x.shape[0], numbers.Integral):
return x.shape[0]
try:
return len(x)
except TypeError:
raise TypeError(message) | MIT License |
ethereum/trinity | p2p/kademlia.py | KademliaRoutingTable.get_least_recently_updated_log_distance | python | def get_least_recently_updated_log_distance(self) -> int:
try:
bucket_index = self.bucket_update_order[-1]
except IndexError:
raise ValueError("Routing table is empty")
else:
return bucket_index + 1 | Get the log distance whose corresponding bucket was updated least recently.
Only non-empty buckets are considered. If all buckets are empty, a `ValueError` is raised. | https://github.com/ethereum/trinity/blob/6383280c5044feb06695ac2f7bc1100b7bcf4fe0/p2p/kademlia.py#L443-L453 | import collections
import functools
import ipaddress
import itertools
import operator
import random
import struct
from typing import (
Any,
Dict,
Iterable,
List,
Type,
TypeVar,
Tuple, Deque, Iterator)
from urllib import parse as urlparse
from cached_property import cached_property
from eth_utils import (
big_endian_to_int,
decode_hex,
remove_0x_prefix,
encode_hex)
from eth_keys import (
datatypes,
keys,
)
from eth_enr import ENR, ENRAPI, V4CompatIdentityScheme
from eth_enr.constants import (
IP_V4_ADDRESS_ENR_KEY,
UDP_PORT_ENR_KEY,
TCP_PORT_ENR_KEY,
IDENTITY_SCHEME_ENR_KEY,
)
from eth_hash.auto import keccak
from eth_typing import NodeID
from p2p.abc import AddressAPI, NodeAPI
from p2p.constants import NUM_ROUTING_TABLE_BUCKETS
from p2p._utils import get_logger
from p2p.validation import validate_enode_uri
def int_to_big_endian4(integer: int) -> bytes:
return struct.pack('>I', integer)
def enc_port(p: int) -> bytes:
return int_to_big_endian4(p)[-2:]
TAddress = TypeVar('TAddress', bound=AddressAPI)
class Address(AddressAPI):
def __init__(self, ip: str, udp_port: int, tcp_port: int) -> None:
self.udp_port = udp_port
self.tcp_port = tcp_port
self._ip = ipaddress.ip_address(ip)
@property
def is_loopback(self) -> bool:
return self._ip.is_loopback
@property
def is_unspecified(self) -> bool:
return self._ip.is_unspecified
@property
def is_reserved(self) -> bool:
return self._ip.is_reserved
@property
def is_private(self) -> bool:
return self._ip.is_private
@property
def ip(self) -> str:
return str(self._ip)
@cached_property
def ip_packed(self) -> str:
return self._ip.packed
def __eq__(self, other: Any) -> bool:
return (self.ip, self.udp_port) == (other.ip, other.udp_port)
def __repr__(self) -> str:
return 'Address(%s:udp:%s|tcp:%s)' % (self.ip, self.udp_port, self.tcp_port)
def to_endpoint(self) -> List[bytes]:
return [self._ip.packed, enc_port(self.udp_port), enc_port(self.tcp_port)]
@classmethod
def from_endpoint(cls: Type[TAddress],
ip: str,
udp_port: bytes,
tcp_port: bytes = b'\x00\x00') -> TAddress:
return cls(ip, big_endian_to_int(udp_port), big_endian_to_int(tcp_port))
TNode = TypeVar('TNode', bound=NodeAPI)
@functools.total_ordering
class Node(NodeAPI):
def __init__(self, enr: ENRAPI) -> None:
self._init(enr)
def _init(self, enr: ENRAPI) -> None:
try:
ip = enr[IP_V4_ADDRESS_ENR_KEY]
udp_port = enr[UDP_PORT_ENR_KEY]
except KeyError:
self._address = None
else:
tcp_port = enr.get(TCP_PORT_ENR_KEY, udp_port)
self._address = Address(ip, udp_port, tcp_port)
self._pubkey = keys.PublicKey.from_compressed_bytes(enr.public_key)
self._id = NodeID(keccak(self.pubkey.to_bytes()))
self._id_int = big_endian_to_int(self.id)
self._enr = enr
@property
def id(self) -> NodeID:
return self._id
@property
def pubkey(self) -> keys.PublicKey:
return self._pubkey
@property
def address(self) -> Address:
return self._address
@classmethod
def from_pubkey_and_addr(
cls: Type[TNode], pubkey: datatypes.PublicKey, address: AddressAPI) -> TNode:
return cls(create_stub_enr(pubkey, address))
@classmethod
def from_uri(cls: Type[TNode], uri: str) -> TNode:
if uri.startswith("enr:"):
return cls.from_enr_repr(uri)
else:
return cls.from_enode_uri(uri)
@classmethod
def from_enr_repr(cls: Type[TNode], uri: str) -> TNode:
return cls(ENR.from_repr(uri))
@classmethod
def from_enode_uri(cls: Type[TNode], uri: str) -> TNode:
validate_enode_uri(uri)
parsed = urlparse.urlparse(uri)
pubkey = keys.PublicKey(decode_hex(parsed.username))
return cls.from_pubkey_and_addr(pubkey, Address(parsed.hostname, parsed.port, parsed.port))
@property
def enr(self) -> ENRAPI:
return self._enr
def uri(self) -> str:
hexstring = self.pubkey.to_hex()
hexstring = remove_0x_prefix(hexstring)
if self.address is not None:
ip = self.address.ip
tcp_port = self.address.tcp_port
else:
ip = None
tcp_port = None
return f'enode://{hexstring}@{ip}:{tcp_port}'
def __str__(self) -> str:
if self.address is not None:
ip = self.address.ip
else:
ip = None
return f"<Node({self.pubkey.to_hex()[:8]}@{ip})>"
def __repr__(self) -> str:
if self.address is not None:
ip = self.address.ip
tcp_port = self.address.tcp_port
else:
ip = None
tcp_port = None
return f"<Node({self.pubkey.to_hex()}@{ip}:{tcp_port})>"
def distance_to(self, id: int) -> int:
return self._id_int ^ id
def __lt__(self, other: Any) -> bool:
if not isinstance(other, self.__class__):
return super().__lt__(other)
return self._id_int < other._id_int
def __eq__(self, other: Any) -> bool:
if not isinstance(other, self.__class__):
return super().__eq__(other)
return self.pubkey == other.pubkey
def __ne__(self, other: Any) -> bool:
return not self == other
def __hash__(self) -> int:
return hash(self.pubkey)
def __getstate__(self) -> Dict[Any, Any]:
return {'enr': repr(self.enr)}
def __setstate__(self, state: Dict[Any, Any]) -> None:
self._init(ENR.from_repr(state.pop('enr')))
def check_relayed_addr(sender: AddressAPI, addr: AddressAPI) -> bool:
if addr.is_unspecified or addr.is_reserved:
return False
if addr.is_private and not sender.is_private:
return False
if addr.is_loopback and not sender.is_loopback:
return False
return True
def sort_by_distance(nodes: Iterable[NodeAPI], target_id: NodeID) -> List[NodeAPI]:
target_id_int = big_endian_to_int(target_id)
return sorted(nodes, key=operator.methodcaller('distance_to', target_id_int))
def create_stub_enr(pubkey: datatypes.PublicKey, address: AddressAPI) -> ENRAPI:
return ENR(
0,
{
IDENTITY_SCHEME_ENR_KEY: V4CompatIdentityScheme.id,
V4CompatIdentityScheme.public_key_enr_key: pubkey.to_compressed_bytes(),
IP_V4_ADDRESS_ENR_KEY: address.ip_packed,
UDP_PORT_ENR_KEY: address.udp_port,
TCP_PORT_ENR_KEY: address.tcp_port,
},
signature=b''
)
def compute_distance(left_node_id: NodeID, right_node_id: NodeID) -> int:
left_int = big_endian_to_int(left_node_id)
right_int = big_endian_to_int(right_node_id)
return left_int ^ right_int
def compute_log_distance(left_node_id: NodeID, right_node_id: NodeID) -> int:
if left_node_id == right_node_id:
raise ValueError("Cannot compute log distance between identical nodes")
distance = compute_distance(left_node_id, right_node_id)
return distance.bit_length()
class KademliaRoutingTable:
def __init__(self, center_node_id: NodeID, bucket_size: int) -> None:
self.logger = get_logger("p2p.kademlia.KademliaRoutingTable")
self.center_node_id = center_node_id
self.bucket_size = bucket_size
self.buckets: Tuple[Deque[NodeID], ...] = tuple(
collections.deque(maxlen=bucket_size) for _ in range(NUM_ROUTING_TABLE_BUCKETS)
)
self.replacement_caches: Tuple[Deque[NodeID], ...] = tuple(
collections.deque() for _ in range(NUM_ROUTING_TABLE_BUCKETS)
)
self.bucket_update_order: Deque[int] = collections.deque()
def _contains(self, node_id: NodeID, include_replacement_cache: bool) -> bool:
_, bucket, replacement_cache = self.get_index_bucket_and_replacement_cache(node_id)
if include_replacement_cache:
nodes = bucket + replacement_cache
else:
nodes = bucket
return node_id in nodes
def get_index_bucket_and_replacement_cache(self,
node_id: NodeID,
) -> Tuple[int, Deque[NodeID], Deque[NodeID]]:
index = compute_log_distance(self.center_node_id, node_id) - 1
bucket = self.buckets[index]
replacement_cache = self.replacement_caches[index]
return index, bucket, replacement_cache
def update(self, node_id: NodeID) -> NodeID:
if node_id == self.center_node_id:
raise ValueError("Cannot insert center node into routing table")
bucket_index, bucket, replacement_cache = self.get_index_bucket_and_replacement_cache(
node_id,
)
is_bucket_full = len(bucket) >= self.bucket_size
is_node_in_bucket = node_id in bucket
if not is_node_in_bucket and not is_bucket_full:
self.logger.debug2("Adding %s to bucket %d", encode_hex(node_id), bucket_index)
self.update_bucket_unchecked(node_id)
eviction_candidate = None
elif is_node_in_bucket:
self.logger.debug2("Updating %s in bucket %d", encode_hex(node_id), bucket_index)
self.update_bucket_unchecked(node_id)
eviction_candidate = None
elif not is_node_in_bucket and is_bucket_full:
if node_id not in replacement_cache:
self.logger.debug2(
"Adding %s to replacement cache of bucket %d",
encode_hex(node_id),
bucket_index,
)
else:
self.logger.debug2(
"Updating %s in replacement cache of bucket %d",
encode_hex(node_id),
bucket_index,
)
replacement_cache.remove(node_id)
replacement_cache.appendleft(node_id)
eviction_candidate = bucket[-1]
else:
raise Exception("unreachable")
return eviction_candidate
def update_bucket_unchecked(self, node_id: NodeID) -> None:
bucket_index, bucket, replacement_cache = self.get_index_bucket_and_replacement_cache(
node_id,
)
for container in (bucket, replacement_cache):
try:
container.remove(node_id)
except ValueError:
pass
bucket.appendleft(node_id)
try:
self.bucket_update_order.remove(bucket_index)
except ValueError:
pass
self.bucket_update_order.appendleft(bucket_index)
def remove(self, node_id: NodeID) -> None:
bucket_index, bucket, replacement_cache = self.get_index_bucket_and_replacement_cache(
node_id,
)
in_bucket = node_id in bucket
in_replacement_cache = node_id in replacement_cache
if in_bucket:
bucket.remove(node_id)
if replacement_cache:
replacement_node_id = replacement_cache.popleft()
self.logger.debug(
"Replacing %s from bucket %d with %s from replacement cache",
encode_hex(node_id),
bucket_index,
encode_hex(replacement_node_id),
)
bucket.append(replacement_node_id)
else:
self.logger.debug(
"Removing %s from bucket %d without replacement",
encode_hex(node_id),
bucket_index,
)
if in_replacement_cache:
self.logger.debug(
"Removing %s from replacement cache of bucket %d",
encode_hex(node_id),
bucket_index,
)
replacement_cache.remove(node_id)
if not in_bucket and not in_replacement_cache:
self.logger.debug(
"Not removing %s as it is neither present in the bucket nor the replacement cache",
encode_hex(node_id),
bucket_index,
)
if not bucket:
try:
self.bucket_update_order.remove(bucket_index)
except ValueError:
pass
def get_nodes_at_log_distance(self, log_distance: int) -> Tuple[NodeID, ...]:
if log_distance <= 0:
raise ValueError(f"Log distance must be positive, got {log_distance}")
elif log_distance > len(self.buckets):
raise ValueError(
f"Log distance must not be greater than {len(self.buckets)}, got {log_distance}"
)
return tuple(self.buckets[log_distance - 1])
@property
def is_empty(self) -> bool:
return all(len(bucket) == 0 for bucket in self.buckets) | MIT License |
slicermorph/slicermorph | IDAVLMConverter/IDAVLMConverter.py | IDAVLMConverterLogic.isValidInputOutputData | python | def isValidInputOutputData(self, inputVolumeNode, outputVolumeNode):
if not inputVolumeNode:
logging.debug('isValidInputOutputData failed: no input volume node defined')
return False
if not outputVolumeNode:
logging.debug('isValidInputOutputData failed: no output volume node defined')
return False
if inputVolumeNode.GetID()==outputVolumeNode.GetID():
logging.debug('isValidInputOutputData failed: input and output volume is the same. Create a new volume for output to avoid this error.')
return False
return True | Validates if the output is not the same as input | https://github.com/slicermorph/slicermorph/blob/67c622c4ab15f0a1dee2bb00dffde8dbcd5a91be/IDAVLMConverter/IDAVLMConverter.py#L148-L160 | import os
import unittest
import vtk, qt, ctk, slicer
from slicer.ScriptedLoadableModule import *
import logging
class IDAVLMConverter(ScriptedLoadableModule):
def __init__(self, parent):
ScriptedLoadableModule.__init__(self, parent)
self.parent.title = "IDAVLMConverter"
self.parent.categories = ["SlicerMorph.SlicerMorph Utilities"]
self.parent.dependencies = []
self.parent.contributors = ["Murat Maga (UW), Sara Rolfe (UW)"]
self.parent.helpText = """
This module converts raw landmark coordinates (.pts format) exported from the IDAV Landmark Editor into fcsv format.
It does not accept the Landmark Editor's project files (.land format).
<p>For more information about usage and potential issues, please see <a href="https://github.com/SlicerMorph/SlicerMorph/tree/master/Docs/IDAVLMConverter">online documentation.</A>
"""
self.parent.acknowledgementText = """
This module was developed by Sara Rolfe for SlicerMorph. SlicerMorph was originally supported by an NSF/DBI grant, "An Integrated Platform for Retrieval, Visualization and Analysis of 3D Morphology From Digital Biological Collections"
awarded to Murat Maga (1759883), Adam Summers (1759637), and Douglas Boyer (1759839).
https://nsf.gov/awardsearch/showAward?AWD_ID=1759883&HistoricalAwards=false
"""
class IDAVLMConverterWidget(ScriptedLoadableModuleWidget):
def setup(self):
ScriptedLoadableModuleWidget.setup(self)
parametersCollapsibleButton = ctk.ctkCollapsibleButton()
parametersCollapsibleButton.text = "Parameters"
self.layout.addWidget(parametersCollapsibleButton)
parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)
self.inputFileSelector = ctk.ctkPathLineEdit()
self.inputFileSelector.filters = ctk.ctkPathLineEdit().Files
self.inputFileSelector.setToolTip( "Select landmark file for import" )
parametersFormLayout.addRow("Select file containing landmark names and coordinates to load:", self.inputFileSelector)
self.outputDirectory = ctk.ctkDirectoryButton()
self.outputDirectory.directory = slicer.mrmlScene.GetCacheManager().GetRemoteCacheDirectory()
parametersFormLayout.addRow("Output Directory:", self.outputDirectory)
self.headerLengthWidget = ctk.ctkDoubleSpinBox()
self.headerLengthWidget.value = 2
self.headerLengthWidget.minimum = 0
self.headerLengthWidget.singleStep = 1
self.headerLengthWidget.setToolTip("Input the number of lines in header")
parametersFormLayout.addRow("Header length:", self.headerLengthWidget)
self.loadLandmarkNode = qt.QCheckBox()
self.loadLandmarkNode.checked = 0
self.loadLandmarkNode.setToolTip("After conversion, load landmarks into the scene.")
parametersFormLayout.addRow("Load landmarks into scene", self.loadLandmarkNode)
self.applyButton = qt.QPushButton("Apply")
self.applyButton.toolTip = "Run the algorithm."
self.applyButton.enabled = False
parametersFormLayout.addRow(self.applyButton)
self.applyButton.connect('clicked(bool)', self.onApplyButton)
self.inputFileSelector.connect('validInputChanged(bool)', self.onSelect)
self.layout.addStretch(1)
self.onSelect()
def cleanup(self):
pass
def onSelect(self):
self.applyButton.enabled = bool(self.inputFileSelector.currentPath)
def onApplyButton(self):
logic = IDAVLMConverterLogic()
loadFileOption = self.loadLandmarkNode.checked
logic.run(self.inputFileSelector.currentPath, self.outputDirectory.directory, self.headerLengthWidget.value, loadFileOption)
class IDAVLMConverterLogic(ScriptedLoadableModuleLogic):
def hasImageData(self,volumeNode):
if not volumeNode:
logging.debug('hasImageData failed: no volume node')
return False
if volumeNode.GetImageData() is None:
logging.debug('hasImageData failed: no image data in volume node')
return False
return True | BSD 2-Clause Simplified License |
geertj/python-ad | lib/ad/protocol/asn1.py | Decoder.leave | python | def leave(self):
if self.m_stack is None:
raise Error, 'No input selected. Call start() first.'
if len(self.m_stack) == 1:
raise Error, 'Tag stack is empty.'
del self.m_stack[-1]
self.m_tag = None | Leave the last entered constructed tag. | https://github.com/geertj/python-ad/blob/3089eae072bd2e871c11251961ec35a09b83dd38/lib/ad/protocol/asn1.py#L283-L290 | Boolean = 0x01
Integer = 0x02
OctetString = 0x04
Null = 0x05
ObjectIdentifier = 0x06
Enumerated = 0x0a
Sequence = 0x10
Set = 0x11
TypeConstructed = 0x20
TypePrimitive = 0x00
ClassUniversal = 0x00
ClassApplication = 0x40
ClassContext = 0x80
ClassPrivate = 0xc0
import re
class Error(Exception):
class Encoder(object):
def __init__(self):
self.m_stack = None
def start(self):
self.m_stack = [[]]
def enter(self, nr, cls=None):
if self.m_stack is None:
raise Error, 'Encoder not initialized. Call start() first.'
if cls is None:
cls = ClassUniversal
self._emit_tag(nr, TypeConstructed, cls)
self.m_stack.append([])
def leave(self):
if self.m_stack is None:
raise Error, 'Encoder not initialized. Call start() first.'
if len(self.m_stack) == 1:
raise Error, 'Tag stack is empty.'
value = ''.join(self.m_stack[-1])
del self.m_stack[-1]
self._emit_length(len(value))
self._emit(value)
def write(self, value, nr=None, typ=None, cls=None):
if self.m_stack is None:
raise Error, 'Encoder not initialized. Call start() first.'
if nr is None:
if isinstance(value, int) or isinstance(value, long):
nr = Integer
elif isinstance(value, str) or isinstance(value, unicode):
nr = OctetString
elif value is None:
nr = Null
if typ is None:
typ = TypePrimitive
if cls is None:
cls = ClassUniversal
value = self._encode_value(nr, value)
self._emit_tag(nr, typ, cls)
self._emit_length(len(value))
self._emit(value)
def output(self):
if self.m_stack is None:
raise Error, 'Encoder not initialized. Call start() first.'
if len(self.m_stack) != 1:
raise Error, 'Stack is not empty.'
output = ''.join(self.m_stack[0])
return output
def _emit_tag(self, nr, typ, cls):
if nr < 31:
self._emit_tag_short(nr, typ, cls)
else:
self._emit_tag_long(nr, typ, cls)
def _emit_tag_short(self, nr, typ, cls):
assert nr < 31
self._emit(chr(nr | typ | cls))
def _emit_tag_long(self, nr, typ, cls):
head = chr(typ | cls | 0x1f)
self._emit(head)
values = []
values.append((nr & 0x7f))
nr >>= 7
while nr:
values.append((nr & 0x7f) | 0x80)
nr >>= 7
values.reverse()
values = map(chr, values)
for val in values:
self._emit(val)
def _emit_length(self, length):
if length < 128:
self._emit_length_short(length)
else:
self._emit_length_long(length)
def _emit_length_short(self, length):
assert length < 128
self._emit(chr(length))
def _emit_length_long(self, length):
values = []
while length:
values.append(length & 0xff)
length >>= 8
values.reverse()
values = map(chr, values)
assert len(values) < 127
head = chr(0x80 | len(values))
self._emit(head)
for val in values:
self._emit(val)
def _emit(self, s):
assert isinstance(s, str)
self.m_stack[-1].append(s)
def _encode_value(self, nr, value):
if nr in (Integer, Enumerated):
value = self._encode_integer(value)
elif nr == OctetString:
value = self._encode_octet_string(value)
elif nr == Boolean:
value = self._encode_boolean(value)
elif nr == Null:
value = self._encode_null()
elif nr == ObjectIdentifier:
value = self._encode_object_identifier(value)
return value
def _encode_boolean(self, value):
return value and '\xff' or '\x00'
def _encode_integer(self, value):
if value < 0:
value = -value
negative = True
limit = 0x80
else:
negative = False
limit = 0x7f
values = []
while value > limit:
values.append(value & 0xff)
value >>= 8
values.append(value & 0xff)
if negative:
for i in range(len(values)):
values[i] = 0xff - values[i]
for i in range(len(values)):
values[i] += 1
if values[i] <= 0xff:
break
assert i != len(values)-1
values[i] = 0x00
values.reverse()
values = map(chr, values)
return ''.join(values)
def _encode_octet_string(self, value):
return value
def _encode_null(self):
return ''
_re_oid = re.compile('^[0-9]+(\.[0-9]+)+$')
def _encode_object_identifier(self, oid):
if not self._re_oid.match(oid):
raise Error, 'Illegal object identifier'
cmps = map(int, oid.split('.'))
if cmps[0] > 39 or cmps[1] > 39:
raise Error, 'Illegal object identifier'
cmps = [40 * cmps[0] + cmps[1]] + cmps[2:]
cmps.reverse()
result = []
for cmp in cmps:
result.append(cmp & 0x7f)
while cmp > 0x7f:
cmp >>= 7
result.append(0x80 | (cmp & 0x7f))
result.reverse()
result = map(chr, result)
return ''.join(result)
class Decoder(object):
def __init__(self):
self.m_stack = None
self.m_tag = None
def start(self, data):
if not isinstance(data, str):
raise Error, 'Expecting string instance.'
self.m_stack = [[0, data]]
self.m_tag = None
def peek(self):
if self.m_stack is None:
raise Error, 'No input selected. Call start() first.'
if self._end_of_input():
return None
if self.m_tag is None:
self.m_tag = self._read_tag()
return self.m_tag
def read(self):
if self.m_stack is None:
raise Error, 'No input selected. Call start() first.'
if self._end_of_input():
return None
tag = self.peek()
length = self._read_length()
value = self._read_value(tag[0], length)
self.m_tag = None
return (tag, value)
def eof(self):
return self._end_of_input()
def enter(self):
if self.m_stack is None:
raise Error, 'No input selected. Call start() first.'
nr, typ, cls = self.peek()
if typ != TypeConstructed:
raise Error, 'Cannot enter a non-constructed tag.'
length = self._read_length()
bytes = self._read_bytes(length)
self.m_stack.append([0, bytes])
self.m_tag = None | MIT License |
ucam-smt/sgnmt | cam/sgnmt/predictors/tf_t2t.py | T2TPredictor.predict_next | python | def predict_next(self):
log_probs = self.mon_sess.run(self._log_probs,
{self._inputs_var: self.src_sentence,
self._targets_var: utils.oov_to_unk(
self.consumed + [text_encoder.PAD_ID],
self.trg_vocab_size,
self._t2t_unk_id)})
log_probs[text_encoder.PAD_ID] = utils.NEG_INF
return log_probs | Call the T2T model in self.mon_sess. | https://github.com/ucam-smt/sgnmt/blob/c663ec7b251552e36b6b4f992f0ac21aad87cb7b/cam/sgnmt/predictors/tf_t2t.py#L332-L341 | import logging
import os
from cam.sgnmt import utils, tf_utils
from cam.sgnmt.predictors.core import Predictor
from cam.sgnmt.misc.trie import SimpleTrie
POP = "##POP##"
try:
from tensor2tensor import models
from tensor2tensor import problems as problems_lib
from tensor2tensor.utils import usr_dir
from tensor2tensor.utils import registry
from tensor2tensor.utils import devices
from tensor2tensor.utils import trainer_lib
from tensor2tensor.data_generators.text_encoder import TextEncoder
from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_encoder
import tensorflow as tf
import numpy as np
class DummyTextEncoder(TextEncoder):
def __init__(self, vocab_size, pop_id=None):
super(DummyTextEncoder, self).__init__(num_reserved_ids=None)
self._vocab_size = vocab_size
def encode(self, s):
raise NotImplementedError("Dummy encoder cannot be used to encode.")
def decode(self, ids):
raise NotImplementedError("Dummy encoder cannot be used to decode.")
@property
def vocab_size(self):
return self._vocab_size
flags = tf.flags
FLAGS = flags.FLAGS
flags.DEFINE_string("schedule", "train_and_evaluate",
"Method of tf.contrib.learn.Experiment to run.")
except ImportError:
pass
T2T_INITIALIZED = False
def _initialize_t2t(t2t_usr_dir):
global T2T_INITIALIZED
if not T2T_INITIALIZED:
logging.info("Setting up tensor2tensor library...")
tf.logging.set_verbosity(tf.logging.INFO)
usr_dir.import_usr_dir(t2t_usr_dir)
T2T_INITIALIZED = True
def log_prob_from_logits(logits):
return logits - tf.reduce_logsumexp(logits, keepdims=True, axis=-1)
def expand_input_dims_for_t2t(t, batched=False):
if not batched:
t = tf.expand_dims(t, 0)
t = tf.expand_dims(t, -1)
t = tf.expand_dims(t, -1)
return t
def gather_2d(params, indices):
batch_size = tf.shape(params)[0]
num_indices = tf.shape(indices)[1]
batch_indices = tf.tile(tf.expand_dims(tf.range(batch_size), 1),
[1, num_indices])
gather_nd_indices = tf.stack([batch_indices, indices], axis=2)
return tf.gather_nd(params, gather_nd_indices)
class _BaseTensor2TensorPredictor(Predictor):
def __init__(self,
t2t_usr_dir,
checkpoint_dir,
src_vocab_size,
trg_vocab_size,
t2t_unk_id,
n_cpu_threads,
max_terminal_id=-1,
pop_id=-1):
super(_BaseTensor2TensorPredictor, self).__init__()
self._n_cpu_threads = n_cpu_threads
self._t2t_unk_id = utils.UNK_ID if t2t_unk_id < 0 else t2t_unk_id
self._checkpoint_dir = checkpoint_dir
try:
self.pop_id = int(pop_id)
except ValueError:
logging.warn("t2t predictor only supports single POP IDs. "
"Reset to -1")
self.pop_id = -1
self.max_terminal_id = max_terminal_id
self.src_vocab_size = src_vocab_size
self.trg_vocab_size = trg_vocab_size
_initialize_t2t(t2t_usr_dir)
def _add_problem_hparams(self, hparams, problem_name):
if self.pop_id >= 0:
try:
hparams.add_hparam("pop_id", self.pop_id)
except:
if hparams.pop_id != self.pop_id:
logging.warn("T2T pop_id does not match (%d!=%d)"
% (hparams.pop_id, self.pop_id))
try:
hparams.add_hparam("max_terminal_id", self.max_terminal_id)
except:
if hparams.max_terminal_id != self.max_terminal_id:
logging.warn("T2T max_terminal_id does not match (%d!=%d)"
% (hparams.max_terminal_id, self.max_terminal_id))
try:
hparams.add_hparam("closing_bracket_id", self.pop_id)
except:
if hparams.closing_bracket_id != self.pop_id:
logging.warn("T2T closing_bracket_id does not match (%d!=%d)"
% (hparams.closing_bracket_id, self.pop_id))
problem = registry.problem(problem_name)
problem._encoders = {
"inputs": DummyTextEncoder(vocab_size=self.src_vocab_size),
"targets": DummyTextEncoder(vocab_size=self.trg_vocab_size)
}
p_hparams = problem.get_hparams(hparams)
hparams.problem = problem
hparams.problem_hparams = p_hparams
return hparams
def create_session(self):
return tf_utils.create_session(self._checkpoint_dir,
self._n_cpu_threads)
def get_unk_probability(self, posterior):
return utils.common_get(posterior, self._t2t_unk_id, utils.NEG_INF)
class T2TPredictor(_BaseTensor2TensorPredictor):
def __init__(self,
src_vocab_size,
trg_vocab_size,
model_name,
problem_name,
hparams_set_name,
t2t_usr_dir,
checkpoint_dir,
t2t_unk_id=None,
n_cpu_threads=-1,
max_terminal_id=-1,
pop_id=-1):
super(T2TPredictor, self).__init__(t2t_usr_dir,
checkpoint_dir,
src_vocab_size,
trg_vocab_size,
t2t_unk_id,
n_cpu_threads,
max_terminal_id,
pop_id)
if not model_name or not problem_name or not hparams_set_name:
logging.fatal(
"Please specify t2t_model, t2t_problem, and t2t_hparams_set!")
raise AttributeError
self.consumed = []
self.src_sentence = []
predictor_graph = tf.Graph()
with predictor_graph.as_default() as g:
hparams = trainer_lib.create_hparams(hparams_set_name)
self._add_problem_hparams(hparams, problem_name)
translate_model = registry.model(model_name)(
hparams, tf.estimator.ModeKeys.PREDICT)
self._inputs_var = tf.placeholder(dtype=tf.int32, shape=[None],
name="sgnmt_inputs")
self._targets_var = tf.placeholder(dtype=tf.int32, shape=[None],
name="sgnmt_targets")
features = {"inputs": expand_input_dims_for_t2t(self._inputs_var),
"targets": expand_input_dims_for_t2t(self._targets_var)}
translate_model.prepare_features_for_infer(features)
translate_model._fill_problem_hparams_features(features)
logits, _ = translate_model(features)
logits = tf.squeeze(logits, [0, 1, 2, 3])
self._log_probs = log_prob_from_logits(logits)
self.mon_sess = self.create_session() | Apache License 2.0 |
sphinx-toolbox/sphinx-toolbox | sphinx_toolbox/testing.py | Sphinx.add_enumerable_node | python | def add_enumerable_node(
self,
node: Type[nodes.Element],
figtype: str,
title_getter: Optional[TitleGetter] = None,
override: bool = False,
**kwargs: Tuple[Callable, Callable],
) -> None:
self.registry.add_enumerable_node(
node,
figtype,
title_getter,
override=override,
)
self.add_node(node, override=override, **kwargs) | Register a Docutils node class as a numfig target. | https://github.com/sphinx-toolbox/sphinx-toolbox/blob/cee88c6bceac20a9ae0e381ada2fb2453ca3fc0b/sphinx_toolbox/testing.py#L236-L255 | import copy
import sys
import tempfile
from functools import partial
from types import SimpleNamespace
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Set, Tuple, Type, Union, cast
import pytest
import sphinx.application
from bs4 import BeautifulSoup
from coincidence.regressions import check_file_output, check_file_regression
from docutils import nodes
from docutils.parsers.rst import Directive, roles
from docutils.transforms import Transform
from domdf_python_tools.doctools import prettify_docstrings
from domdf_python_tools.paths import PathPlus
from domdf_python_tools.stringlist import StringList
from domdf_python_tools.typing import PathLike
from jinja2 import Template
from pygments.lexer import Lexer
from pytest_regressions.common import check_text_files
from pytest_regressions.file_regression import FileRegressionFixture
from sphinx.builders import Builder
from sphinx.config import Config
from sphinx.domains import Domain, Index
from sphinx.domains.python import PythonDomain
from sphinx.environment.collectors import EnvironmentCollector
from sphinx.events import EventListener
from sphinx.events import EventManager as BaseEventManager
from sphinx.ext.autodoc.directive import AutodocDirective
from sphinx.highlighting import lexer_classes
from sphinx.registry import SphinxComponentRegistry
from sphinx.roles import XRefRole
from sphinx.util import docutils
from sphinx.util.typing import RoleFunction, TitleGetter
from sphinx_toolbox.utils import SphinxExtMetadata
__all__ = [
"Sphinx",
"run_setup",
"RunSetupOutput",
"remove_html_footer",
"check_html_regression",
"remove_html_link_tags",
"check_asset_copy",
"HTMLRegressionFixture",
"html_regression",
]
class FakeBuilder(Builder):
pass
class EventManager(BaseEventManager):
def connect(self, name: str, callback: Callable, priority: int) -> int:
listener_id = self.next_listener_id
self.next_listener_id += 1
self.listeners[name].append(EventListener(listener_id, callback, priority))
return listener_id
class Sphinx:
registry: SphinxComponentRegistry
config: Config
events: EventManager
html_themes: Dict[str, str]
def __init__(self):
self.registry = SphinxComponentRegistry()
self.config = Config({}, {})
self.events = EventManager(self)
self.html_themes: Dict[str, str] = {}
def add_builder(self, builder: Type[Builder], override: bool = False) -> None:
self.registry.add_builder(builder, override=override)
def add_config_value(
self,
name: str,
default: Any,
rebuild: Union[bool, str],
types: Any = (),
) -> None:
if rebuild in {False, True}:
rebuild = "env" if rebuild else ''
self.config.add(name, default, rebuild, types)
def add_event(self, name: str) -> None:
self.events.add(name)
def set_translator(
self,
name: str,
translator_class: Type[nodes.NodeVisitor],
override: bool = False,
) -> None:
self.registry.add_translator(name, translator_class, override=override)
def add_node(
self,
node: Type[nodes.Element],
override: bool = False,
**kwargs: Tuple[Callable, Callable],
) -> None:
if not override and docutils.is_node_registered(node):
raise ValueError(
f"node class {node.__name__!r} is already registered, its visitors will be overridden"
)
docutils.register_node(node)
self.registry.add_translation_handlers(node, **kwargs) | MIT License |
thingsboard/python_tb_rest_client | tb_rest_client/models/models_pe/dashboard.py | Dashboard.owner_id | python | def owner_id(self, owner_id):
self._owner_id = owner_id | Sets the owner_id of this Dashboard.
:param owner_id: The owner_id of this Dashboard. # noqa: E501
:type: EntityId | https://github.com/thingsboard/python_tb_rest_client/blob/87c6a3703974fc8a86e4c72c444168ee2b758ecb/tb_rest_client/models/models_pe/dashboard.py#L300-L308 | import pprint
import re
import six
class Dashboard(object):
swagger_types = {
'assigned_customers': 'list[ShortCustomerInfo]',
'configuration': 'str',
'created_time': 'int',
'customer_id': 'CustomerId',
'id': 'DashboardId',
'image': 'str',
'mobile_hide': 'bool',
'mobile_order': 'int',
'name': 'str',
'owner_id': 'EntityId',
'tenant_id': 'TenantId',
'title': 'str'
}
attribute_map = {
'assigned_customers': 'assignedCustomers',
'configuration': 'configuration',
'created_time': 'createdTime',
'customer_id': 'customerId',
'id': 'id',
'image': 'image',
'mobile_hide': 'mobileHide',
'mobile_order': 'mobileOrder',
'name': 'name',
'owner_id': 'ownerId',
'tenant_id': 'tenantId',
'title': 'title'
}
def __init__(self, assigned_customers=None, configuration=None, created_time=None, customer_id=None, id=None, image=None, mobile_hide=None, mobile_order=None, name=None, owner_id=None, tenant_id=None, title=None):
self._assigned_customers = None
self._configuration = None
self._created_time = None
self._customer_id = None
self._id = None
self._image = None
self._mobile_hide = None
self._mobile_order = None
self._name = None
self._owner_id = None
self._tenant_id = None
self._title = None
self.discriminator = None
if assigned_customers is not None:
self.assigned_customers = assigned_customers
if configuration is not None:
self.configuration = configuration
if created_time is not None:
self.created_time = created_time
if customer_id is not None:
self.customer_id = customer_id
if id is not None:
self.id = id
if image is not None:
self.image = image
if mobile_hide is not None:
self.mobile_hide = mobile_hide
if mobile_order is not None:
self.mobile_order = mobile_order
if name is not None:
self.name = name
if owner_id is not None:
self.owner_id = owner_id
if tenant_id is not None:
self.tenant_id = tenant_id
if title is not None:
self.title = title
@property
def assigned_customers(self):
return self._assigned_customers
@assigned_customers.setter
def assigned_customers(self, assigned_customers):
self._assigned_customers = assigned_customers
@property
def configuration(self):
return self._configuration
@configuration.setter
def configuration(self, configuration):
self._configuration = configuration
@property
def created_time(self):
return self._created_time
@created_time.setter
def created_time(self, created_time):
self._created_time = created_time
@property
def customer_id(self):
return self._customer_id
@customer_id.setter
def customer_id(self, customer_id):
self._customer_id = customer_id
@property
def id(self):
return self._id
@id.setter
def id(self, id):
self._id = id
@property
def image(self):
return self._image
@image.setter
def image(self, image):
self._image = image
@property
def mobile_hide(self):
return self._mobile_hide
@mobile_hide.setter
def mobile_hide(self, mobile_hide):
self._mobile_hide = mobile_hide
@property
def mobile_order(self):
return self._mobile_order
@mobile_order.setter
def mobile_order(self, mobile_order):
self._mobile_order = mobile_order
@property
def name(self):
return self._name
@name.setter
def name(self, name):
self._name = name
@property
def owner_id(self):
return self._owner_id
@owner_id.setter | Apache License 2.0 |
nrel/floris | floris/tools/power_rose.py | PowerRose.__init__ | python | def __init__(self,): | Instantiate a PowerRose object. No explicit arguments required, and an
additional method will need to be called to populate the PowerRose
object with data. | https://github.com/nrel/floris/blob/ef4934ec7feb7afd2615772d364a1eaa28db93e9/floris/tools/power_rose.py#L43-L48 | import os
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from floris.utilities import wrap_180
class PowerRose: | Apache License 2.0 |
georgebrock/git-browse | gitbrowse/git.py | GitFileHistory.line_mapping | python | def line_mapping(self, start, finish):
key = start + '/' + finish
if key in self._line_mappings:
return self._line_mappings[key]
forward, backward = self._build_line_mappings(start, finish)
self._line_mappings[start + '/' + finish] = forward
self._line_mappings[finish + '/' + start] = backward
return forward | Returns a dict that represents how lines have moved between versions
of a file. The keys are the line numbers in the version of the file
at start, the values are where those lines have ended up in the version
at finish.
For example if at start the file is two lines, and at
finish a new line has been inserted between the two the mapping
would be:
{1:1, 2:3}
Deleted lines are represented by None. For example, if at start the
file were two lines, and the first had been deleted by finish:
{1:None, 2:1} | https://github.com/georgebrock/git-browse/blob/a77031683f08bfded5959bed9f836503b3a1219a/gitbrowse/git.py#L130-L155 | import os
class GitCommit(object):
def __init__(self, sha, author, message):
self.sha = sha
self.author = author
self.message = message
class GitBlameLine(object):
def __init__(self, sha, line, current, original_line, final_line):
self.sha = sha
self.line = line
self.current = current
self.original_line = original_line
self.final_line = final_line
class GitFileHistory(object):
def __init__(self, path, start_commit):
if not verify_revision(start_commit):
raise ValueError('%s is not a valid commit, branch, tag, etc.' % (
start_commit,
))
if not verify_file(path):
raise ValueError('"%s" is not tracked by git' % (path, ))
self.path = path
p = os.popen('git log %s --follow --pretty="%s" -- %s' % (
start_commit,
'%H%n%an%n%s%n',
self.path,
))
output = p.read().split('\n\n')
self.commits = [GitCommit(*c.split('\n', 2)) for c in output if c]
self._index = 0
self._blame = None
self._line_mappings = {}
@property
def current_commit(self):
return self.commits[self._index]
def next(self):
if self._index <= 0:
return False
self._index -= 1
self._blame = None
return True
def prev(self):
if self._index >= len(self.commits) - 1:
return False
self._index += 1
self._blame = None
return True
def blame(self):
if self._blame:
return self._blame
lines = []
p = os.popen('git blame -p %s %s' % (
self.path,
self.current_commit.sha,
))
while True:
header = p.readline()
if not header:
break
sha, original_line, final_line = header.split(' ')[:3]
line = p.readline()
while not line.startswith('\t'):
line = p.readline()
lines.append(GitBlameLine(
sha=sha,
line=line[1:],
current=(sha == self.current_commit.sha),
original_line=original_line,
final_line=final_line,
))
self._blame = lines
return self._blame | MIT License |
pyansys/pymapdl | ansys/mapdl/core/_commands/solution/nonlinear_options.py | NonLinearOptions.arclen | python | def arclen(self, key="", maxarc="", minarc="", **kwargs):
command = f"ARCLEN,{key},{maxarc},{minarc}"
return self.run(command, **kwargs) | Activates the arc-length method.
APDL Command: ARCLEN
Parameters
----------
key
Arc-length key:
OFF - Do not use the arc-length method (default).
ON - Use the arc-length method.
maxarc
Maximum multiplier of the reference arc-length radius (default =
25).
minarc
Minimum multiplier of the reference arc-length radius (default =
1/1000).
Notes
-----
Activates the arc-length method and sets the minimum and maximum
multipliers for controlling the arc-length radius based on the initial
arc-length radius.
The initial arc-length radius, t0, is proportional (in absolute value)
to the initial load factor. The initial load factor is given by:
Initial Load Factor = TIME / NSBSTP
where TIME is the time specified by the TIME command for the arc-length
load step, and NSBSTP is the number of substeps specified by the NSUBST
command.
The factors MAXARC and MINARC are used to define the range for the arc-
length radius to expand and shrink during the substep solution:
In each substep, the arc-length radius is kept constant throughout the
equilibrium iterations. After each converged substep, the arc-length
radius for the next substep is modified depending on the convergence
behavior. If the substep converges and the program heuristic predicts
an easy convergence, the arc-length radius is enlarged. If the enlarged
value is greater than tMAX, the arc-length radius is reset to tMAX. If
the substep does not converge, bisection will take place until the arc-
length radius is reduced to tMIN. If further nonconvergence is
encountered, the solution terminates.
The arc-length method predicts the next time increment (that is, load
factor increment). Therefore, the AUTOTS and PRED commands are ignored
when the arc-length method is used.
The STABILIZE and LNSRCH commands are also ignored.
The arc-length method cannot be used in a multiframe restart.
For difficult problems, one suggestion is to increase the initial
number of substeps (NSUBST), and to prevent the arc-length radius from
increasing too rapidly (MAXARC = 1).
ARCLEN cannot be used for any load step that has no applied load or
displacement.
The arc-length method does not support tabular loads. In order to use
the arc-length method, you must replace tabular loads by other load
types and then run the analysis again. | https://github.com/pyansys/pymapdl/blob/e5cc21471c3a8fcef1f7b88359e38aa89cd63f73/ansys/mapdl/core/_commands/solution/nonlinear_options.py#L2-L72 | class NonLinearOptions: | MIT License |
komuw/sewer | sewer/dns_providers/aliyundns.py | AliyunDns.delete_dns_record | python | def delete_dns_record(self, domain_name, domain_dns_value):
self.logger.info("delete_dns_record start: %s", (domain_name, domain_dns_value))
root, _, acme_txt = self.extract_zone(domain_name)
record_id = self.query_recored_id(root, acme_txt)
if not record_id:
msg = "failed to find record_id of domain: %s, value: %s", domain_name, domain_dns_value
self.logger.warning(msg)
return
self.logger.info("start to delete dns record, id: %s", record_id)
request = DeleteDomainRecordRequest.DeleteDomainRecordRequest()
request.set_RecordId(record_id)
resp = self._send_reqeust(request)
self.logger.info("delete_dns_record end: %s", (domain_name, domain_dns_value, resp.json()))
return resp | delete a txt record we created just now.
:param str domain_name: the value sewer client passed in, like *.menduo.example.com
:param str domain_dns_value: the value sewer client passed in. we do not use this.
:return _ResponseForAliyun:
:return: | https://github.com/komuw/sewer/blob/056ac64fe294fb284ec5b920ec1a9425dd254e92/sewer/dns_providers/aliyundns.py#L180-L205 | import json
from aliyunsdkcore import client
import aliyunsdkalidns.request.v20150109
from aliyunsdkalidns.request.v20150109 import (
DescribeDomainRecordsRequest,
AddDomainRecordRequest,
DeleteDomainRecordRequest,
)
from . import common
class _ResponseForAliyun(object):
def __init__(self, status_code=200, content=None, headers=None):
self.status_code = status_code
self.headers = headers or {}
self.content = content or {}
self.content = json.dumps(content)
super(_ResponseForAliyun, self).__init__()
def json(self):
return json.loads(self.content)
class AliyunDns(common.BaseDns):
def __init__(self, key, secret, endpoint="cn-beijing", debug=False, **kwargs):
super().__init__(**kwargs)
self._key = key
self._secret = secret
self._endpoint = endpoint
self._debug = debug
self.clt = client.AcsClient(self._key, self._secret, self._endpoint, debug=self._debug)
def _send_reqeust(self, request):
request.set_accept_format("json")
try:
status, headers, result = self.clt.implementation_of_do_action(request)
if isinstance(result, bytes):
result = result.decode()
result = json.loads(result)
if "Message" in result or "Code" in result:
result["Success"] = False
self.logger.warning("aliyundns resp error: %s", result)
except Exception as exc:
self.logger.warning("aliyundns failed to send request: %s, %s", str(exc), request)
status, headers, result = 502, {}, b'{"Success": false}'
result = json.loads(result)
if self._debug:
self.logger.info("aliyundns request name: %s", request.__class__.__name__)
self.logger.info("aliyundns request query: %s", request.get_query_params())
return _ResponseForAliyun(status, result, headers)
def query_recored_items(self, host, zone=None, tipe=None, page=1, psize=200):
request = DescribeDomainRecordsRequest.DescribeDomainRecordsRequest()
request.get_action_name()
request.set_DomainName(host)
request.set_PageNumber(page)
request.set_PageSize(psize)
if zone:
request.set_RRKeyWord(zone)
if tipe:
request.set_TypeKeyWord(tipe)
resp = self._send_reqeust(request)
body = resp.json()
return body
def query_recored_id(self, root, zone, tipe="TXT"):
record_id = None
recoreds = self.query_recored_items(root, zone, tipe=tipe)
recored_list = recoreds.get("DomainRecords", {}).get("Record", [])
recored_item_list = [i for i in recored_list if i["RR"] == zone]
if len(recored_item_list):
record_id = recored_item_list[0]["RecordId"]
return record_id
@staticmethod
def extract_zone(domain_name):
if domain_name.count(".") > 1:
zone, middle, last = str(domain_name).rsplit(".", 2)
root = ".".join([middle, last])
acme_txt = "_acme-challenge.%s" % zone
else:
zone = ""
root = domain_name
acme_txt = "_acme-challenge"
return root, zone, acme_txt
def create_dns_record(self, domain_name, domain_dns_value):
self.logger.info("create_dns_record start: %s", (domain_name, domain_dns_value))
root, _, acme_txt = self.extract_zone(domain_name)
request = AddDomainRecordRequest.AddDomainRecordRequest()
request.set_DomainName(root)
request.set_TTL(600)
request.set_RR(acme_txt)
request.set_Type("TXT")
request.set_Value(domain_dns_value)
resp = self._send_reqeust(request)
self.logger.info("create_dns_record end: %s", (domain_name, domain_dns_value, resp.json()))
return resp | MIT License |
kuri65536/python-for-android | python-build/python-libs/gdata/build/lib/gdata/Crypto/Util/RFC1751.py | _key2bin | python | def _key2bin(s):
kl=map(lambda x: ord(x), s)
kl=map(lambda x: binary[x/16]+binary[x&15], kl)
return ''.join(kl) | Convert a key into a string of binary digits | https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-build/python-libs/gdata/build/lib/gdata/Crypto/Util/RFC1751.py#L15-L19 | __revision__ = "$Id: RFC1751.py,v 1.6 2003/04/04 15:15:10 akuchling Exp $"
import string, binascii
binary={0:'0000', 1:'0001', 2:'0010', 3:'0011', 4:'0100', 5:'0101',
6:'0110', 7:'0111', 8:'1000', 9:'1001', 10:'1010', 11:'1011',
12:'1100', 13:'1101', 14:'1110', 15:'1111'} | Apache License 2.0 |
jhuapl-boss/boss | django/bosscore/test/setup_db.py | SetupTestDB.insert_downsample_data | python | def insert_downsample_data(self):
self.add_coordinate_frame('cf_ds_aniso', 'Description for cf2', 0, 4096, 0, 4096, 0, 128, 4, 4, 35)
self.add_experiment('col1', 'exp_ds_aniso', 'cf_ds_aniso', 5, 500, 1)
aniso_chan = self.add_channel('col1', 'exp_ds_aniso', 'channel1', 0, 0, 'uint8', 'image')
self.add_coordinate_frame('cf_ds_iso', 'Description for cf2', 0, 4096, 0, 4096, 0, 128, 6, 6, 6)
self.add_experiment('col1', 'exp_ds_iso', 'cf_ds_iso', 3, 500, 1, hierarchy_method="isotropic")
iso_chan = self.add_channel('col1', 'exp_ds_iso', 'channel1', 0, 0, 'uint8', 'image')
return (aniso_chan, iso_chan) | Some resources for small downsample tests
Returns:
(Tuple[Channel, Channel]): The channels created for the downsample test. | https://github.com/jhuapl-boss/boss/blob/c2e26d272bd7b8d54abdc2948193163537e31291/django/bosscore/test/setup_db.py#L219-L232 | from django.contrib.auth.models import User
from django.contrib.auth.models import Group
from django.contrib.contenttypes.models import ContentType
from guardian.shortcuts import assign_perm
from ..models import Collection, Experiment, CoordinateFrame, Channel, BossLookup, BossRole, BossGroup
from ..views.views_resource import ChannelDetail
from ..constants import ADMIN_USER, ADMIN_GRP, PUBLIC_GRP
from ..permissions import BossPermissionManager
from spdb.spatialdb.test.setup import AWSSetupLayer
test_user = 'testuser'
test_group = 'testuser-primary'
BASE_RESOLUTION = 2
NUM_HIERARCHY_LEVELS = 7
EXP1 = 'exp1'
EXP22 = 'exp22'
EXP_BASE_RES = 'exp-base-res-test'
TEST_DATA_EXPERIMENTS = [EXP1, EXP22, EXP_BASE_RES]
CHAN_BASE_RES = 'chan-with-base-res'
class SetupTestDB:
def __init__(self, super_user=None):
self.super_user = super_user
self.user = super_user
def create_user(self, username=None):
if self.super_user is None:
self.create_super_user()
if not username:
username = test_user
self.user = User.objects.create_user(username=username, email=username+'@test.com', password=username)
user_primary_group, created = Group.objects.get_or_create(name=username + '-primary')
public_group, created = Group.objects.get_or_create(name=PUBLIC_GRP)
self.user.groups.add(user_primary_group)
public_group.user_set.add(self.user)
return self.user
def add_role(self, role_name, user=None):
if user is None:
user = self.user
BossRole.objects.create(user=user, role=role_name)
def create_super_user(self, username=ADMIN_USER, email=None, password=ADMIN_USER):
if self.super_user is not None:
return
if email is None:
full_email = username + '@boss.io'
else:
full_email = email
self.super_user = User.objects.create_superuser(username=username, email=full_email,
password=password)
user_primary_group, created = Group.objects.get_or_create(name=ADMIN_USER+'-primary')
public_group, created = Group.objects.get_or_create(name=PUBLIC_GRP)
admin_group, created = Group.objects.get_or_create(name=ADMIN_GRP)
self.super_user.groups.add(user_primary_group)
self.super_user.groups.add(public_group)
self.super_user.groups.add(admin_group)
self.add_role('admin', self.super_user)
self.user = self.super_user
return self.super_user
def get_user(self):
return self.user
def set_user(self, user):
self.user = user
def create_group(self, group_name):
group, created = Group.objects.get_or_create(name=group_name)
if created:
self.user.groups.add(group)
bgrp, created = BossGroup.objects.get_or_create(group=group, creator=self.user)
group_name = self.user.username + "-primary"
user_primary_group = Group.objects.get(name=group_name)
assign_perm('maintain_group', user_primary_group, bgrp)
return created
def insert_test_data(self):
self.add_collection('col1', 'Description for collection1')
self.add_collection('col1-22', 'Description for collection1-22')
self.add_collection('col2', 'Description for collection2')
self.add_coordinate_frame('cf1', 'Description for cf1', 0, 1000, 0, 1000, 0, 1000, 4, 4, 4)
self.add_experiment('col1', EXP1, 'cf1', NUM_HIERARCHY_LEVELS, 10, 1)
self.add_experiment('col1', EXP22, 'cf1', NUM_HIERARCHY_LEVELS, 500, 1)
self.add_experiment('col1', EXP_BASE_RES, 'cf1', NUM_HIERARCHY_LEVELS, 10, 1)
self.add_channel('col1', EXP1, 'channel1', 0, 0, 'uint8', 'image')
self.add_channel('col1', EXP1, 'channel2', 0, 0, 'uint8', 'image')
self.add_channel('col1', EXP1, 'channel3', 0, 0, 'uint64', 'annotation', ['channel1'])
self.add_channel('col1', EXP_BASE_RES, CHAN_BASE_RES, 0, BASE_RESOLUTION, 'uint8', 'image')
self.add_channel('col1', EXP1, 'layer1', 0, 0, 'uint64', 'annotation', ['channel1'])
def insert_lookup_test_data(self):
self.add_collection('col1', 'Description for collection1')
self.add_collection('col2', 'Description for collection2')
self.add_coordinate_frame('cf1', 'Description for cf1', 0, 1000, 0, 1000, 0, 1000, 4, 4, 4)
self.add_experiment('col1', 'exp1', 'cf1', 10, 10, 1)
self.add_experiment('col2', 'exp1', 'cf1', 10, 500, 1)
self.add_channel('col1', 'exp1', 'channel1', 0, 0, 'uint8', 'image')
self.add_channel('col1', 'exp1', 'channel2', 0, 0, 'uint8', 'image')
self.add_channel('col1', 'exp1', 'channel3', 0, 0, 'uint64', 'annotation', ['channel1'])
self.add_channel('col1', 'exp1', 'layer1', 0, 0, 'uint64', 'annotation', ['channel1'])
self.add_channel('col2', 'exp1', 'channel1', 0, 0, 'uinit8', 'image')
def insert_spatialdb_test_data(self):
self.add_collection('col1', 'Description for collection1')
self.add_coordinate_frame('cf1', 'Description for cf1', 0, 100000, 0, 100000, 0, 100000, 4, 4, 4)
self.add_experiment('col1', 'exp1', 'cf1', 10, 500, 1)
self.add_channel('col1', 'exp1', 'channel1', 0, 0, 'uint8', 'image')
self.add_channel('col1', 'exp1', 'channel2', 0, 0, 'uint16', 'image')
self.add_channel('col1', 'exp1', 'layer1', 0, 0, 'uint64', 'annotation', ['channel1'])
self.add_channel('col1', 'exp1', 'bbchan1', 0, 0, 'uint64', 'annotation', ['channel1'])
def insert_cloudvolume_test_data(self):
self.add_collection('col1', 'Description for collection1')
self.add_coordinate_frame('cf1', 'Description for cf1', 0, 100000, 0, 100000, 0, 100000, 4, 4, 4)
self.add_experiment('col1', 'exp1', 'cf1', 10, 500, 1)
self.add_channel('col1', 'exp1', 'chan1', 0, 0, 'uint8', 'image',
storage_type='cloudvol',
bucket='bossdb-test-data',
cv_path='col1/exp1/chan1')
self.add_channel('col1', 'exp1', 'chan2', 0, 0, 'uint16', 'image',
storage_type='cloudvol',
bucket='bossdb-test-data',
cv_path='col1/exp1/chan2')
self.add_channel('col1', 'exp1', 'anno1', 0, 0, 'uint64', 'annotation',
storage_type='cloudvol',
bucket='bossdb-test-data',
cv_path='col1/exp1/anno1')
def insert_ingest_test_data(self):
self.add_collection('my_col_1', 'Description for collection1')
self.add_coordinate_frame('cf1', 'Description for cf1', 0, 100000, 0, 100000, 0, 100000, 4, 4, 4)
self.add_experiment('my_col_1', 'my_exp_1', 'cf1', 10, 500, 1)
self.add_channel('my_col_1', 'my_exp_1', 'my_ch_1', 0, 0, 'uint8', 'image')
def insert_iso_data(self):
self.add_coordinate_frame('cf2aniso', 'Description for cf2', 0, 2000, 0, 5000, 0, 200, 4, 4, 35)
self.add_experiment('col1', 'exp_aniso', 'cf2aniso', 8, 500, 1)
self.add_channel('col1', 'exp_aniso', 'channel1', 0, 0, 'uint8', 'image')
self.add_coordinate_frame('cf2iso', 'Description for cf2', 0, 2000, 0, 5000, 0, 200, 6, 6, 6)
self.add_experiment('col1', 'exp_iso', 'cf2iso', 8, 500, 1, hierarchy_method="isotropic")
self.add_channel('col1', 'exp_iso', 'channel1', 0, 0, 'uint8', 'image') | Apache License 2.0 |
purestorage-openconnect/py-pure-client | pypureclient/pure1/Pure1_1_0/models/drive_get_response.py | DriveGetResponse.to_dict | python | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
if hasattr(self, attr):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(DriveGetResponse, dict):
for key, value in self.items():
result[key] = value
return result | Returns the model properties as a dict | https://github.com/purestorage-openconnect/py-pure-client/blob/2d9fdef0b73321cea9613e7d1eb881b42845099b/pypureclient/pure1/Pure1_1_0/models/drive_get_response.py#L78-L104 | import pprint
import re
import six
import typing
from ....properties import Property
if typing.TYPE_CHECKING:
from pypureclient.pure1.Pure1_1_0 import models
class DriveGetResponse(object):
swagger_types = {
'continuation_token': 'str',
'total_item_count': 'int',
'items': 'list[Drive]'
}
attribute_map = {
'continuation_token': 'continuation_token',
'total_item_count': 'total_item_count',
'items': 'items'
}
required_args = {
}
def __init__(
self,
continuation_token=None,
total_item_count=None,
items=None,
):
if continuation_token is not None:
self.continuation_token = continuation_token
if total_item_count is not None:
self.total_item_count = total_item_count
if items is not None:
self.items = items
def __setattr__(self, key, value):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `DriveGetResponse`".format(key))
self.__dict__[key] = value
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if isinstance(value, Property):
raise AttributeError
else:
return value | BSD 2-Clause Simplified License |
superkogito/pydiogment | pydiogment/utils/filters.py | butter_bandpass | python | def butter_bandpass(low_cut, high_cut, fs, order=5):
nyq = 0.5 * fs
low = low_cut / nyq
high = high_cut / nyq
b, a = butter(order, [low, high], btype='band')
return b, a | Design band pass filter.
Args:
- low_cut (float) : the low cutoff frequency of the filter.
- high_cut (float) : the high cutoff frequency of the filter.
- fs (float) : the sampling rate.
- order (int) : order of the filter, by default defined to 5. | https://github.com/superkogito/pydiogment/blob/000a07b2ad8d3480535e7d900aed1ed3358a5d4a/pydiogment/utils/filters.py#L49-L68 | from scipy.signal import butter, lfilter
def butter_lowpass(cutoff, fs, order=5):
nyq = 0.5 * fs
low = cutoff / nyq
b, a = butter(order, low, btype='low', analog=False)
return b, a
def butter_highpass(cutoff, fs, order=5):
nyq = 0.5 * fs
high = cutoff / nyq
b, a = butter(order, high, btype='high', analog=False)
return b, a | BSD 3-Clause New or Revised License |
voxel51/eta | eta/core/module.py | ModuleMetadata.get_input | python | def get_input(self, name):
return self.inputs[name] | Returns the ModuleInput instance for input `name`. | https://github.com/voxel51/eta/blob/e51510fda0722ac7cadb17b109bad413a6602ed3/eta/core/module.py#L629-L631 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from builtins import *
from future.utils import iteritems
from collections import OrderedDict
from glob import glob
import logging
import os
import eta
from eta.core.config import Config, ConfigError, Configurable
from eta.core.diagram import HasBlockDiagram, BlockdiagModule
import eta.core.logging as etal
import eta.core.serial as etas
import eta.core.types as etat
import eta.core.utils as etau
logger = logging.getLogger(__name__)
def run(module_name, module_config_or_path):
if etau.is_str(module_config_or_path):
return _run(module_name, module_config_or_path)
with etau.TempDir() as d:
module_config_path = os.path.join(d, "config.json")
etas.write_json(module_config_or_path, module_config_path)
return _run(module_name, module_config_path)
def _run(module_name, module_config_path):
module_exe = find_exe(module_name)
args = ["python", module_exe, module_config_path]
return etau.call(args)
def load_all_metadata():
return {k: _load_metadata(v) for k, v in iteritems(find_all_metadata())}
def load_metadata(module_name):
return _load_metadata(find_metadata(module_name))
def _load_metadata(config):
metadata = ModuleMetadata.from_json(config)
name = os.path.splitext(os.path.basename(config))[0]
if metadata.info.name != name:
raise ModuleMetadataError(
"Name '%s' from ModuleMetadata must match module name '%s'"
% (metadata.info.name, name)
)
return metadata
def find_all_metadata():
d = {}
mdirs = etau.make_search_path(eta.config.module_dirs)
for mdir in mdirs:
for path in glob(os.path.join(mdir, "*.json")):
name = os.path.splitext(os.path.basename(path))[0]
if name not in d:
d[name] = path
else:
logger.debug(
"Module '%s' already exists; ignoring %s", name, path
)
return d
def find_metadata(module_name):
try:
return find_all_metadata()[module_name]
except KeyError:
raise ModuleMetadataError("Could not find module '%s'" % module_name)
def find_exe(module_name=None, module_metadata=None):
if module_metadata is None:
module_metadata = load_metadata(module_name)
meta_path = find_metadata(module_metadata.info.name)
exe_path = os.path.join(
os.path.dirname(meta_path), module_metadata.info.exe
)
if not os.path.isfile(exe_path):
raise ModuleMetadataError(
"Could not find module executable '%s'" % exe_path
)
return exe_path
def setup(module_config, pipeline_config_path=None):
if pipeline_config_path:
from eta.core.pipeline import PipelineConfig
pipeline_config = PipelineConfig.from_json(pipeline_config_path)
module_config.base.eta_config.update(pipeline_config.eta_config)
module_config.base.logging_config = pipeline_config.logging_config
etal.custom_setup(module_config.base.logging_config)
eta.set_config_settings(**module_config.base.eta_config)
class BaseModuleConfig(Config):
def __init__(self, d):
self.base = self.parse_object(
d, "base", BaseModuleConfigSettings, default=None
)
if self.base is None:
self.base = BaseModuleConfigSettings.default()
class BaseModuleConfigSettings(Config):
def __init__(self, d):
self.eta_config = self.parse_dict(d, "eta_config", default={})
self.logging_config = self.parse_object(
d,
"logging_config",
etal.LoggingConfig,
default=etal.LoggingConfig.default(),
)
class ModuleMetadataConfig(Config):
def __init__(self, d):
self.info = self.parse_object(d, "info", ModuleInfoConfig)
self.inputs = self.parse_object_array(d, "inputs", ModuleInputConfig)
self.outputs = self.parse_object_array(
d, "outputs", ModuleOutputConfig
)
self.parameters = self.parse_object_array(
d, "parameters", ModuleParameterConfig
)
def attributes(self):
return ["info", "inputs", "outputs", "parameters"]
class ModuleInfoConfig(Config):
def __init__(self, d):
self.name = self.parse_string(d, "name")
self.type = self.parse_string(d, "type")
self.version = self.parse_string(d, "version")
self.description = self.parse_string(d, "description")
self.exe = self.parse_string(d, "exe")
def attributes(self):
return ["name", "type", "version", "description", "exe"]
class ModuleInputConfig(Config):
def __init__(self, d):
self.name = self.parse_string(d, "name")
self.type = self.parse_string(d, "type")
self.description = self.parse_string(d, "description")
self.required = self.parse_bool(d, "required", default=True)
def attributes(self):
return ["name", "type", "description", "required"]
class ModuleOutputConfig(Config):
def __init__(self, d):
self.name = self.parse_string(d, "name")
self.type = self.parse_string(d, "type")
self.description = self.parse_string(d, "description")
self.required = self.parse_bool(d, "required", default=True)
def attributes(self):
return ["name", "type", "description", "required"]
class ModuleParameterConfig(Config):
def __init__(self, d):
self.name = self.parse_string(d, "name")
self.type = self.parse_string(d, "type")
self.description = self.parse_string(d, "description")
self.required = self.parse_bool(d, "required", default=True)
if not self.required:
self.default = self.parse_raw(d, "default")
elif "default" in d:
raise ConfigError(
"Module parameter '%s' is required, so it should not have a "
"default value" % self.name
)
def attributes(self):
attrs = ["name", "type", "description", "required"]
if not self.required:
attrs.append("default")
return attrs
class ModuleInfo(Configurable):
def __init__(self, config):
self.validate(config)
self.name = config.name
self.type = self._parse_type(config.type)
self.version = config.version
self.description = config.description
self.exe = config.exe
@staticmethod
def _parse_type(type_str):
type_ = etat.parse_type(type_str)
if not etat.is_module(type_):
raise ModuleMetadataError(
"'%s' is not a valid module type" % type_
)
return type_
class ModuleInput(Configurable):
def __init__(self, config):
self.validate(config)
self.name = config.name
self.type = self._parse_type(config.type)
self.description = config.description
self.required = config.required
def is_valid_path(self, path):
return self.type.is_valid_path(path)
@property
def is_required(self):
return self.required
def _parse_type(self, type_str):
type_ = etat.parse_type(type_str)
if not etat.is_data(type_):
raise ModuleMetadataError(
(
"Module input '%s' has type '%s' but must be a subclass "
"of Data"
)
% (self.name, type_)
)
return type_
class ModuleOutput(Configurable):
def __init__(self, config):
self.validate(config)
self.name = config.name
self.type = self._parse_type(config.type)
self.description = config.description
self.required = config.required
def is_valid_path(self, path):
return self.type.is_valid_path(path)
@property
def is_required(self):
return self.required
def _parse_type(self, type_str):
type_ = etat.parse_type(type_str)
if not etat.is_concrete_data(type_):
raise ModuleMetadataError(
(
"Module output '%s' has type '%s' but must be a subclass "
"of ConcreteData"
)
% (self.name, type_)
)
return type_
class ModuleParameter(Configurable):
def __init__(self, config):
self.validate(config)
self.name = config.name
self.type = self._parse_type(config.name, config.type)
self.description = config.description
self.required = config.required
if not self.required:
self._default = config.default
self._validate_default()
def is_valid_value(self, val):
if self.is_builtin:
return self.type.is_valid_value(val)
return self.type.is_valid_path(val)
@property
def is_required(self):
return self.required
@property
def is_builtin(self):
return etat.is_builtin(self.type)
@property
def is_data(self):
return etat.is_data(self.type)
@property
def default_value(self):
if self.is_required:
raise ModuleMetadataError(
"Module parameter '%s' is required, so it has no default "
"value" % self.name
)
return self._default
@staticmethod
def _parse_type(name, type_str):
type_ = etat.parse_type(type_str)
if not etat.is_builtin(type_) and not etat.is_concrete_data(type_):
raise ModuleMetadataError(
"Module parameter '%s' has type '%s' but must be a subclass "
"of Builtin or ConcreteData" % (name, type_)
)
return type_
def _validate_default(self):
if self._default is None:
is_valid = True
elif self.is_builtin:
is_valid = self.type.is_valid_value(self._default)
else:
is_valid = self.type.is_valid_path(self._default)
if not is_valid:
raise ModuleMetadataError(
"Default value '%s' is invalid for module parameter '%s' of "
"'%s'" % (self._default, self.name, self.type)
)
class ModuleMetadata(Configurable, HasBlockDiagram):
def __init__(self, config):
self.validate(config)
self.config = config
self.info = None
self.inputs = OrderedDict()
self.outputs = OrderedDict()
self.parameters = OrderedDict()
self._parse_metadata(config)
def has_input(self, name):
return name in self.inputs
def has_output(self, name):
return name in self.outputs
def has_parameter(self, name):
return name in self.parameters
def is_valid_input(self, name, path):
return self.get_input(name).is_valid_path(path)
def is_valid_output(self, name, path):
return self.get_output(name).is_valid_path(path)
def is_valid_parameter(self, name, val):
return self.get_parameter(name).is_valid_value(val) | Apache License 2.0 |
nikcub/cexbot | cexbot/appdirs.py | site_config_dir | python | def site_config_dir(appname=None, appauthor=None, version=None, multipath=False):
if sys.platform in [ "win32", "darwin" ]:
path = site_data_dir(appname, appauthor)
if appname and version:
path = os.path.join(path, version)
else:
path = os.getenv('XDG_CONFIG_DIRS', '/etc/xdg')
pathlist = [ os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep) ]
if appname:
if version:
appname = os.path.join(appname, version)
pathlist = [ os.sep.join([x, appname]) for x in pathlist ]
if multipath:
path = os.pathsep.join(pathlist)
else:
path = pathlist[0]
return path | Return full path to the user-shared data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only required and used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname.
"version" is an optional version path element to append to the
path. You might want to use this if you want multiple versions
of your app to be able to run independently. If used, this
would typically be "<major>.<minor>".
Only applied when appname is present.
"multipath" is an optional parameter only applicable to *nix
which indicates that the entire list of config dirs should be
returned. By default, the first item from XDG_CONFIG_DIRS is
returned, or '/etc/xdg/<AppName>', if XDG_CONFIG_DIRS is not set
Typical user data directories are:
Mac OS X: same as site_data_dir
Unix: /etc/xdg/<AppName> or $XDG_CONFIG_DIRS[i]/<AppName> for each value in
$XDG_CONFIG_DIRS
Win *: same as site_data_dir
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
For Unix, this is using the $XDG_CONFIG_DIRS[0] default, if multipath=False
WARNING: Do not use this on Windows. See the Vista-Fail note above for why. | https://github.com/nikcub/cexbot/blob/0dd0b60415afd9c1feb959186d32b1a683887975/cexbot/appdirs.py#L182-L229 | __version_info__ = (1, 3, 0)
__version__ = '.'.join(map(str, __version_info__))
import sys
import os
PY3 = sys.version_info[0] == 3
if PY3:
unicode = str
def user_data_dir(appname=None, appauthor=None, version=None, roaming=False):
if sys.platform == "win32":
if appauthor is None:
appauthor = appname
const = roaming and "CSIDL_APPDATA" or "CSIDL_LOCAL_APPDATA"
path = os.path.normpath(_get_win_folder(const))
if appname:
path = os.path.join(path, appauthor, appname)
elif sys.platform == 'darwin':
path = os.path.expanduser('~/Library/Application Support/')
if appname:
path = os.path.join(path, appname)
else:
path = os.getenv('XDG_DATA_HOME', os.path.expanduser("~/.local/share"))
if appname:
path = os.path.join(path, appname)
if appname and version:
path = os.path.join(path, version)
return path
def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
if sys.platform == "win32":
if appauthor is None:
appauthor = appname
path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA"))
if appname:
path = os.path.join(path, appauthor, appname)
elif sys.platform == 'darwin':
path = os.path.expanduser('/Library/Application Support')
if appname:
path = os.path.join(path, appname)
else:
path = os.getenv('XDG_DATA_DIRS',
os.pathsep.join(['/usr/local/share', '/usr/share']))
pathlist = [ os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep) ]
if appname:
if version:
appname = os.path.join(appname, version)
pathlist = [ os.sep.join([x, appname]) for x in pathlist ]
if multipath:
path = os.pathsep.join(pathlist)
else:
path = pathlist[0]
return path
if appname and version:
path = os.path.join(path, version)
return path
def user_config_dir(appname=None, appauthor=None, version=None, roaming=False):
if sys.platform in [ "win32", "darwin" ]:
path = user_data_dir(appname, appauthor, None, roaming)
else:
path = os.getenv('XDG_CONFIG_HOME', os.path.expanduser("~/.config"))
if appname:
path = os.path.join(path, appname)
if appname and version:
path = os.path.join(path, version)
return path | MIT License |