PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

4d014c40a741be997af0b27dac58c60e95f4bc4a

5,539

py

Python

sdk/containerservice/azure-mgmt-containerservice/azure/mgmt/containerservice/v2020_11_01/aio/operations/_resolve_private_link_service_id_operations.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/containerservice/azure-mgmt-containerservice/azure/mgmt/containerservice/v2020_11_01/aio/operations/_resolve_private_link_service_id_operations.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/containerservice/azure-mgmt-containerservice/azure/mgmt/containerservice/v2020_11_01/aio/operations/_resolve_private_link_service_id_operations.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Callable, Dict, Generic, Optional, TypeVar import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.mgmt.core.exceptions import ARMErrorFormat from ... import models as _models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class ResolvePrivateLinkServiceIdOperations: """ResolvePrivateLinkServiceIdOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.containerservice.v2020_11_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def post( self, resource_group_name: str, resource_name: str, parameters: "_models.PrivateLinkResource", **kwargs: Any ) -> "_models.PrivateLinkResource": """Gets the private link service ID for the specified managed cluster. Gets the private link service ID the specified managed cluster. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param resource_name: The name of the managed cluster resource. :type resource_name: str :param parameters: Parameters (name, groupId) supplied in order to resolve a private link service ID. :type parameters: ~azure.mgmt.containerservice.v2020_11_01.models.PrivateLinkResource :keyword callable cls: A custom type or function that will be passed the direct response :return: PrivateLinkResource, or the result of cls(response) :rtype: ~azure.mgmt.containerservice.v2020_11_01.models.PrivateLinkResource :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.PrivateLinkResource"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-11-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.post.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str', min_length=1), 'resourceName': self._serialize.url("resource_name", resource_name, 'str', max_length=63, min_length=1, pattern=r'^[a-zA-Z0-9]$|^[a-zA-Z0-9][-_a-zA-Z0-9]{0,61}[a-zA-Z0-9]$'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'PrivateLinkResource') body_content_kwargs['content'] = body_content request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('PrivateLinkResource', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized post.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerService/managedClusters/{resourceName}/resolvePrivateLinkServiceId'} # type: ignore

49.900901

209

0.692724

0a58effa481f098a540f23a2040b5c2ffcf08aba

4,291

py

Python

homeassistant/components/roomba/__init__.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

3

2021-11-22T22:37:43.000Z

2022-03-17T00:55:28.000Z

homeassistant/components/roomba/__init__.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

25

2021-11-24T06:24:10.000Z

2022-03-31T06:23:06.000Z

homeassistant/components/roomba/__init__.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

3

2022-01-02T18:49:54.000Z

2022-01-25T02:03:54.000Z

"""The roomba component.""" import asyncio import logging import async_timeout from roombapy import RoombaConnectionError, RoombaFactory from homeassistant import exceptions from homeassistant.config_entries import ConfigEntry from homeassistant.const import ( CONF_DELAY, CONF_HOST, CONF_NAME, CONF_PASSWORD, EVENT_HOMEASSISTANT_STOP, ) from homeassistant.core import HomeAssistant from .const import ( BLID, CANCEL_STOP, CONF_BLID, CONF_CONTINUOUS, DOMAIN, PLATFORMS, ROOMBA_SESSION, ) _LOGGER = logging.getLogger(__name__) async def async_setup_entry(hass: HomeAssistant, config_entry: ConfigEntry) -> bool: """Set the config entry up.""" # Set up roomba platforms with config entry if not config_entry.options: hass.config_entries.async_update_entry( config_entry, options={ CONF_CONTINUOUS: config_entry.data[CONF_CONTINUOUS], CONF_DELAY: config_entry.data[CONF_DELAY], }, ) roomba = RoombaFactory.create_roomba( address=config_entry.data[CONF_HOST], blid=config_entry.data[CONF_BLID], password=config_entry.data[CONF_PASSWORD], continuous=config_entry.options[CONF_CONTINUOUS], delay=config_entry.options[CONF_DELAY], ) try: if not await async_connect_or_timeout(hass, roomba): return False except CannotConnect as err: raise exceptions.ConfigEntryNotReady from err async def _async_disconnect_roomba(event): await async_disconnect_or_timeout(hass, roomba) cancel_stop = hass.bus.async_listen_once( EVENT_HOMEASSISTANT_STOP, _async_disconnect_roomba ) hass.data.setdefault(DOMAIN, {}) hass.data[DOMAIN][config_entry.entry_id] = { ROOMBA_SESSION: roomba, BLID: config_entry.data[CONF_BLID], CANCEL_STOP: cancel_stop, } hass.config_entries.async_setup_platforms(config_entry, PLATFORMS) if not config_entry.update_listeners: config_entry.add_update_listener(async_update_options) return True async def async_connect_or_timeout(hass, roomba): """Connect to vacuum.""" try: name = None async with async_timeout.timeout(10): _LOGGER.debug("Initialize connection to vacuum") await hass.async_add_executor_job(roomba.connect) while not roomba.roomba_connected or name is None: # Waiting for connection and check datas ready name = roomba_reported_state(roomba).get("name", None) if name: break await asyncio.sleep(1) except RoombaConnectionError as err: _LOGGER.debug("Error to connect to vacuum: %s", err) raise CannotConnect from err except asyncio.TimeoutError as err: # api looping if user or password incorrect and roomba exist await async_disconnect_or_timeout(hass, roomba) _LOGGER.debug("Timeout expired: %s", err) raise CannotConnect from err return {ROOMBA_SESSION: roomba, CONF_NAME: name} async def async_disconnect_or_timeout(hass, roomba): """Disconnect to vacuum.""" _LOGGER.debug("Disconnect vacuum") async with async_timeout.timeout(3): await hass.async_add_executor_job(roomba.disconnect) return True async def async_update_options(hass: HomeAssistant, config_entry: ConfigEntry) -> None: """Update options.""" await hass.config_entries.async_reload(config_entry.entry_id) async def async_unload_entry(hass: HomeAssistant, config_entry: ConfigEntry) -> bool: """Unload a config entry.""" unload_ok = await hass.config_entries.async_unload_platforms( config_entry, PLATFORMS ) if unload_ok: domain_data = hass.data[DOMAIN][config_entry.entry_id] domain_data[CANCEL_STOP]() await async_disconnect_or_timeout(hass, roomba=domain_data[ROOMBA_SESSION]) hass.data[DOMAIN].pop(config_entry.entry_id) return unload_ok def roomba_reported_state(roomba): """Roomba report.""" return roomba.master_state.get("state", {}).get("reported", {}) class CannotConnect(exceptions.HomeAssistantError): """Error to indicate we cannot connect."""

30.65

87

0.695875

ffe27740365a711d837d971ddb7768cb9db0a542

17,520

py

Python

lingvo/tasks/asr/frontend.py

zhoudoufu/lingvo

bd0f89809942fd0508ff43bd4b6bca1b598220cb

[ "Apache-2.0" ]

null

lingvo/tasks/asr/frontend.py

zhoudoufu/lingvo

bd0f89809942fd0508ff43bd4b6bca1b598220cb

[ "Apache-2.0" ]

null

lingvo/tasks/asr/frontend.py

zhoudoufu/lingvo

bd0f89809942fd0508ff43bd4b6bca1b598220cb

[ "Apache-2.0" ]

null

# Lint as: python2, python3 # Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Layers to construct an ASR frontend.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import math import tensorflow as tf from lingvo.core import base_layer from lingvo.core import py_utils # AsrFrontendConfig which defines characteristics of the frontend that may # be relevant to interfacing code which needs to reason about inputs and # outputs. # Fields: # is_null: Whether this is the NullAsrFrontend. # src_type: Interpretation of the src_inputs. Can be one of 'none' or 'pcm'. # src_pcm_scale: If src_type is 'pcm', then this is the scale of each sample. # If normalized, this should be 1.0. If unnormalized from int16, then it # should be 32768.0. # src_pcm_sample_rate: Sample rate of the expected src PCM frames. # output_dim: Dimension of the output. Typically the number of mel bands # or equiv. May be -1 for unknown. # input_frame_ratio: Approximate ratio of the number of # input_frames / output_frames. Intended to be multiplied by output frames # (i.e. as part of bucket_bounds to arrive at input frames to the frontend). AsrFrontendConfig = collections.namedtuple('AsrFrontendConfig', [ 'is_null', 'src_type', 'src_pcm_scale', 'src_pcm_sample_rate', 'output_dim', 'input_frame_ratio', ]) def _NextPowerOfTwo(i): return math.pow(2, math.ceil(math.log(i, 2))) class BaseAsrFrontend(base_layer.BaseLayer): """Base class for ASR frontends. An ASR frontend is responsible for performing feature extraction from the input in the cases where features are not precomputed as part of the dataset. In such cases, it would be typical for the input to consist of waveform data in some form. """ @property def config(self): """Returns the AsrFrontendConfig namedtuple for this instance.""" return self.GetConfigFromParams(self.params) @staticmethod def GetConfigFromParams(params): """Returns an AsrFrontendConfig namedtuple with vital config settings.""" raise NotImplementedError() def FProp(self, theta, input_batch): """Generates ASR features for a batch. Shapes of the input_batch and output are dependent on the implementation and should be paired with the model's input format and encoder expectations. Args: theta: A NestedMap object containing weights' values of this layer and its children layers. input_batch: A NestedMap with fields: - 'src_inputs' - The inputs tensor, compatible with model input. Expected to be of shape [batch, time, ...]. - 'paddings' - The paddings tensor. It is expected to be of shape [batch, time]. Returns: NestedMap of encoder inputs which can be passed directly to a compatible encoder and contains: - 'src_inputs': inputs to the encoder, minimally of shape [batch, time, ...]. - 'paddings': a 0/1 tensor of shape [batch, time]. """ raise NotImplementedError() class NullAsrFrontend(BaseAsrFrontend): """ASR frontend that just returns its input as FProp output.""" @staticmethod def GetConfigFromParams(params): """Returns an AsrFrontendConfig namedtuple with vital config settings.""" return AsrFrontendConfig( is_null=True, src_type='none', src_pcm_sample_rate=-1, src_pcm_scale=1.0, output_dim=-1, input_frame_ratio=1.0) def FProp(self, theta, input_batch): return input_batch.DeepCopy() class MelAsrFrontend(BaseAsrFrontend): """An AsrFrontend that implements mel feature extraction from PCM frames. This is expressed in pure TensorFlow and without reference to external resources. The frontend implements the following stages: `Framer -> Window -> FFT -> FilterBank -> MeanStdDev` Also, if stack_left_context > 0, this will further apply: `FrameStack -> SubSample(stack_left_context + 1)` The FProp input to this layer can either have rank 3 or rank 4 shape: [batch_size, timestep, packet_size, channel_count] [batch_size, timestep * packet_size, channel_count] For compatibility with existing code, 2D [batch_size, timestep] mono shapes are also supported. In the common case, the packet_size is 1. The 4D variant is accepted for glueless interface to input generators that frame their input samples in some way. The external framing choice does not influence the operation of this instance, but it is accepted. TODO(laurenzo): Refactor call sites to uniformly use the 4D variant and eliminate fallback logic in this class. Only 1 channel is currently supported. TODO(laurenzo): Refactor this class to operate on multi-channel inputs. """ @classmethod def Params(cls): p = super(MelAsrFrontend, cls).Params() p.name = 'frontend' p.Define('sample_rate', 16000.0, 'Sample rate in Hz') p.Define('channel_count', 1, 'Number of channels.') p.Define('frame_size_ms', 25.0, 'Amount of data grabbed for each frame during analysis') p.Define('frame_step_ms', 10.0, 'Number of ms to jump between frames') p.Define('num_bins', 80, 'Number of bins in the mel-spectrogram output') p.Define('lower_edge_hertz', 125.0, 'The lowest frequency of the mel-spectrogram analsis') p.Define('upper_edge_hertz', 7600.0, 'The highest frequency of the mel-spectrogram analsis') p.Define('preemph', 0.97, 'The first-order filter coefficient used for preemphasis') p.Define('noise_scale', 8.0, 'The amount of noise (in 16-bit LSB units) to add') p.Define( 'window_fn', 'HANNING', 'Window function to apply (valid values are "HANNING", ' 'and None)') p.Define( 'pad_end', False, 'Whether to pad the end of `signals` with zeros when the provided ' 'frame length and step produces a frame that lies partially past ' 'its end.') p.Define( 'per_bin_mean', None, 'Per-bin (num_bins) means for normalizing the spectrograms. ' 'Defaults to zeros.') p.Define('per_bin_stddev', None, 'Per-bin (num_bins) standard deviations. ' 'Defaults to ones.') p.Define('stack_left_context', 0, 'Number of left context frames to stack.') return p @staticmethod def GetConfigFromParams(params): """Returns an AsrFrontendConfig namedtuple with vital config settings.""" subsample_factor = params.num_bins * (params.stack_left_context + 1) frame_step = round(params.sample_rate * params.frame_step_ms / 1000.0) return AsrFrontendConfig( is_null=False, src_type='pcm', src_pcm_scale=32768.0, src_pcm_sample_rate=16000.0, output_dim=subsample_factor, input_frame_ratio=frame_step * subsample_factor) @base_layer.initializer def __init__(self, params): super(MelAsrFrontend, self).__init__(params) p = self.params assert p.channel_count == 1, 'Only 1 channel currently supported.' # Make sure key params are in floating point. p.sample_rate = float(p.sample_rate) p.frame_step_ms = float(p.frame_step_ms) p.frame_size_ms = float(p.frame_size_ms) p.lower_edge_hertz = float(p.lower_edge_hertz) p.upper_edge_hertz = float(p.upper_edge_hertz) self._frame_step = int(round(p.sample_rate * p.frame_step_ms / 1000.0)) self._frame_size = (int(round(p.sample_rate * p.frame_size_ms / 1000.0)) + 1 ) # +1 for the preemph # Overdrive means double FFT size. # Note: 2* because of overdrive self._fft_size = 2 * int(max(512, _NextPowerOfTwo(self._frame_size))) self._CreateWindowFunction() # Mean/stddev. if p.per_bin_mean is None: p.per_bin_mean = [0.0] * p.num_bins if p.per_bin_stddev is None: p.per_bin_stddev = [1.0] * p.num_bins assert len(p.per_bin_mean) == p.num_bins assert len(p.per_bin_stddev) == p.num_bins def _CreateWindowFunction(self): p = self.params if p.window_fn is None: self._window_fn = None elif p.window_fn == 'HANNING': def _HanningWindow(frame_size, dtype): return tf.signal.hann_window(frame_size, dtype=dtype) self._window_fn = _HanningWindow else: raise ValueError('Illegal value %r for window_fn param' % (p.window_fn,)) @property def window_frame_size(self): return self._frame_size @property def window_frame_step(self): return self._frame_step def _RemoveChannelDim(self, pcm_audio_data): if pcm_audio_data.shape.rank == 3: pcm_audio_data = tf.squeeze(pcm_audio_data, 2) assert pcm_audio_data.shape.rank == 2, ( 'MelAsrFrontend only supports one channel') return pcm_audio_data def _ReshapeToMono2D(self, pcm_audio_data, paddings): """Reshapes a 3D or 4D input to 2D. Since the input to FProp can be 3D or 4D (see class comments), this will collapse it back to a 2D, mono shape for internal processing. Args: pcm_audio_data: 2D, 3D or 4D audio input. See class comments. Must have a rank. paddings: Original paddings shaped to the first two dims of pcm_audio_data. Returns: Tuple of 2D [batch_size, timestep] mono audio data, new paddings. """ shape = py_utils.GetShape(pcm_audio_data) rank = len(shape) if rank == 2: return pcm_audio_data, paddings elif rank == 3: # [batch, time, channel] with tf.control_dependencies([tf.assert_equal(shape[2], 1)]): return tf.squeeze(pcm_audio_data, axis=2), paddings elif rank == 4: # [batch, time, packet, channel] batch_size, orig_time, orig_packet_size, channel = shape time = orig_time * orig_packet_size with tf.control_dependencies([tf.assert_equal(channel, 1)]): pcm_audio_data = tf.reshape(pcm_audio_data, (batch_size, time)) # Transform paddings into the new time base with a padding per time # step vs per packet by duplicating each packet. paddings = tf.reshape( tf.tile(tf.expand_dims(paddings, axis=2), [1, 1, orig_packet_size]), (batch_size, time)) return pcm_audio_data, paddings else: raise ValueError('Illegal pcm_audio_data shape') def FProp(self, theta, input_batch): """Perform signal processing on a sequence of PCM data. NOTE: This implementation does not currently support paddings, and they are accepted for compatibility with the super-class. TODO(laurenzo): Rework this to support paddings. Args: theta: Layer theta. input_batch: PCM input map: - 'src_inputs': int16 or float32 tensor of PCM audio data, scaled to +/-32768 (versus [-1..1)!). See class comments for supported input shapes. - 'paddings': per frame 0/1 paddings. Shaped: [batch, frame]. Returns: NestedMap of encoder inputs which can be passed directly to a compatible encoder and contains: - 'src_inputs': inputs to the encoder, minimally of shape [batch, time, ...]. - 'paddings': a 0/1 tensor of shape [batch, time]. """ pcm_audio_data, pcm_audio_paddings = self._ReshapeToMono2D( input_batch.src_inputs, input_batch.paddings) mel_spectrogram, mel_spectrogram_paddings = self._FPropChunk( theta, pcm_audio_data, pcm_audio_paddings) mel_spectrogram, mel_spectrogram_paddings = self._PadAndReshapeSpec( mel_spectrogram, mel_spectrogram_paddings) return py_utils.NestedMap( src_inputs=mel_spectrogram, paddings=mel_spectrogram_paddings) def _PadAndReshapeSpec(self, mel_spectrogram, mel_spectrogram_paddings): p = self.params batch_size = py_utils.GetShape(mel_spectrogram)[0] # Stack and sub-sample. Only subsampling with a stride of the stack size # is supported. if p.stack_left_context > 0: # Since left context is leading, pad the left by duplicating the first # frame. stack_size = 1 + p.stack_left_context mel_spectrogram = tf.concat( [mel_spectrogram[:, 0:1, :]] * p.stack_left_context + [mel_spectrogram], axis=1) mel_spectrogram_paddings = tf.concat( [mel_spectrogram_paddings[:, 0:1]] * p.stack_left_context + [mel_spectrogram_paddings], axis=1) # Note that this is the maximum number of frames. Actual frame count # depends on padding. stacked_frame_dim = tf.shape(mel_spectrogram)[1] // stack_size mel_spectrogram = tf.reshape( mel_spectrogram[:, 0:(stack_size) * stacked_frame_dim, :], [batch_size, stacked_frame_dim, stack_size * p.num_bins]) # After stacking paddings, pad if any source frame was padded. # Stacks into [batch_size, stacked_frame_dim, stack_size] like the # spectrogram stacking above, and then reduces the stack_size dim # to the max (effectively, making padding = 1.0 if any of the pre-stacked # frames were 1.0). Final shape is [batch_size, stacked_frame_dim]. mel_spectrogram_paddings = tf.reshape( mel_spectrogram_paddings[:, 0:(stack_size) * stacked_frame_dim], [batch_size, stacked_frame_dim, stack_size]) mel_spectrogram_paddings = tf.reduce_max(mel_spectrogram_paddings, axis=2) # Add feature dim. Shape = [batch, time, features, 1] mel_spectrogram = tf.expand_dims(mel_spectrogram, -1) return mel_spectrogram, mel_spectrogram_paddings def _ApplyPreemphasis(self, framed_signal): p = self.params preemphasized = ( framed_signal[:, :, 1:] - p.preemph * framed_signal[:, :, 0:-1]) return preemphasized def _GetMelPadding(self, pcm_audio_paddings): p = self.params # shape: [batch, time, _frame_size] framed_paddings = tf.signal.frame(pcm_audio_paddings, self._frame_size, self._frame_step, p.pad_end) # Pad spectrograms that have any padded frames. mel_spectrogram_paddings = tf.reduce_max(framed_paddings, axis=2) return mel_spectrogram_paddings def _FPropChunk(self, theta, pcm_audio_chunk, pcm_audio_paddings): p = self.params pcm_audio_chunk = tf.cast(pcm_audio_chunk, tf.float32) # shape: [batch, time, _frame_size] framed_signal = tf.signal.frame(pcm_audio_chunk, self._frame_size, self._frame_step, p.pad_end) # Pre-emphasis. if p.preemph != 1.0: preemphasized = self._ApplyPreemphasis(framed_signal) else: preemphasized = framed_signal[:-1] # Noise. if p.noise_scale > 0.0: noise_signal = tf.random_normal( tf.shape(preemphasized), stddev=p.noise_scale, mean=0.0, seed=p.random_seed) else: noise_signal = 0.0 # Apply window fn. windowed_signal = preemphasized + noise_signal if self._window_fn is not None: window = self._window_fn(self._frame_size - 1, framed_signal.dtype) windowed_signal *= window mel_spectrogram = self._MelSpectrogram(windowed_signal) output_floor = 1.0 mel_spectrogram_log = tf.log( tf.maximum(float(output_floor), mel_spectrogram)) # Mean and stddev. mel_spectrogram_norm = ( (mel_spectrogram_log - tf.convert_to_tensor(p.per_bin_mean)) / tf.convert_to_tensor(p.per_bin_stddev)) return mel_spectrogram_norm, self._GetMelPadding(pcm_audio_paddings) def _MelSpectrogram(self, signal): """Computes the mel spectrogram from a waveform signal. Args: signal: f32 Tensor, shaped [batch_size, num_samples] Returns: features: f32 Tensor, shaped [batch_size, num_frames, mel_channels] """ p = self.params # FFT. real_frequency_spectrogram = tf.signal.rfft(signal, [self._fft_size]) magnitude_spectrogram = tf.abs(real_frequency_spectrogram) # Shape of magnitude_spectrogram is num_frames x (fft_size/2+1) # Mel_weight is [num_spectrogram_bins, num_mel_bins] mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix( num_mel_bins=p.num_bins, num_spectrogram_bins=self._fft_size // 2 + 1, sample_rate=p.sample_rate, lower_edge_hertz=p.lower_edge_hertz, upper_edge_hertz=p.upper_edge_hertz, dtype=tf.float32) # Weight matrix implemented in the magnitude domain. batch_size, num_frames, fft_channels = py_utils.GetShape( magnitude_spectrogram, 3) mel_spectrogram = tf.matmul( tf.reshape(magnitude_spectrogram, [batch_size * num_frames, fft_channels]), mel_weight_matrix) mel_spectrogram = tf.reshape(mel_spectrogram, [batch_size, num_frames, p.num_bins]) return mel_spectrogram

37.758621

80

0.689612

5e0569092e60e3bafd57c4c728e3b494e24f8edb

1,388

py

Python

fixture/session.py

Dimans10/Autotest_traning

9dbd8646a845fc743396cb78681b0aca111077f4

[ "Apache-2.0" ]

null

fixture/session.py

Dimans10/Autotest_traning

9dbd8646a845fc743396cb78681b0aca111077f4

[ "Apache-2.0" ]

null

fixture/session.py

Dimans10/Autotest_traning

9dbd8646a845fc743396cb78681b0aca111077f4

[ "Apache-2.0" ]

null

class SessionHelper: def __init__(self, app): self.app = app def login(self, username, password): wd = self.app.wd self.app.open_home_page() wd.find_element_by_name("user").click() wd.find_element_by_name("user").clear() wd.find_element_by_name("user").send_keys(username) wd.find_element_by_name("pass").clear() wd.find_element_by_name("pass").send_keys(password) wd.find_element_by_xpath("//input[@value='Login']").click() def logout(self): wd = self.app.wd wd.find_element_by_link_text("Logout").click() wd.find_element_by_name("user") def check_logout(self): wd = self.app.wd return len(wd.find_elements_by_link_text("Logout")) > 0 def get_logged_user(self): wd = self.app.wd return wd.find_element_by_xpath("//div[@id='top']/form/b").text[1:-1] def is_login_as(self, username): wd = self.app.wd return self.get_logged_user() == username def ensure_logout(self): wd = self.app.wd if self.check_logout(): self.logout() def ensure_login(self, username, password): wd = self.app.wd if self.check_logout(): if self.is_login_as(username): return else: self.logout() self.login(username,password)

29.531915

77

0.600865

5678a9cc27ca37fe7bd4af711d29aac99f718aad

71

py

Python

Chapter 05/Chap05_Example5.42.py

bpbpublications/Programming-Techniques-using-Python

49b785f37e95a3aad1d36cef51e219ac56e5e9f0

[ "MIT" ]

null

Chapter 05/Chap05_Example5.42.py

bpbpublications/Programming-Techniques-using-Python

49b785f37e95a3aad1d36cef51e219ac56e5e9f0

[ "MIT" ]

null

Chapter 05/Chap05_Example5.42.py

bpbpublications/Programming-Techniques-using-Python

49b785f37e95a3aad1d36cef51e219ac56e5e9f0

[ "MIT" ]

null

#games module from Kabaddi import defender defender.name_defender()

17.75

29

0.802817

282307ad25d854b6b6c0e8d7929d46d3bf30bbea

1,029

py

Python

blueking_api/urls.py

ganlvtech/blueking-django-startup-project

042aa36b0757c0d3929d88bc23534f54963d333e

[ "MIT" ]

1

2018-11-22T21:13:25.000Z

blueking_api/urls.py

ganlvtech/blueking-django-startup-project

042aa36b0757c0d3929d88bc23534f54963d333e

[ "MIT" ]

null

blueking_api/urls.py

ganlvtech/blueking-django-startup-project

042aa36b0757c0d3929d88bc23534f54963d333e

[ "MIT" ]

null

from django.conf.urls import url from . import views urlpatterns = [ url(r'^$', views.index, name='index'), url(r'^permission_test/', views.permission_test, name='permission_test'), url(r'^get_app_access_token/$', views.get_app_access_token, name='get_app_access_token'), url(r'^get_user_access_token/$', views.get_user_access_token, name='get_user_access_token'), url(r'^refresh_user_access_token/$', views.refresh_user_access_token, name='refresh_user_access_token'), url(r'^get_permissions/$', views.get_permissions, name='get_permissions'), url(r'^get_user_info/$', views.get_user_info, name='get_user_info'), url(r'^get_openid_openkey/$', views.get_openid_openkey, name='get_openid_openkey'), url(r'^verify_openid_openkey/$', views.verify_openid_openkey, name='verify_openid_openkey'), url(r'^get_auth_token/$', views.get_auth_token, name='get_auth_token'), url(r'^send_email/$', views.send_email, name='send_email'), url(r'^send_sms/$', views.send_sms, name='send_sms'), ]

54.157895

108

0.737609

43712f1330778894c23c8558d6868bb7003ee373

1,068

py

Python

examples/hddmcp.py

markito3/hddm

887b72c0d27869696e243d277c37fcf2fabd2207

[ "Apache-2.0" ]

null

examples/hddmcp.py

markito3/hddm

887b72c0d27869696e243d277c37fcf2fabd2207

[ "Apache-2.0" ]

2

2021-04-04T04:06:25.000Z

2021-10-18T00:59:12.000Z

examples/hddmcp.py

markito3/hddm

887b72c0d27869696e243d277c37fcf2fabd2207

[ "Apache-2.0" ]

2

2021-04-14T20:58:51.000Z

2021-04-14T21:13:58.000Z

#!/bin/env python # # hddmcp.py - python alternative to the old hddmcp, multithreaded. # # author: richard.t.jones at uconn.edu # version: march 23, 2018 import threading import hddm_s import sys def usage(): print "Usage: hddmcp.py [-n #threads] <file1.hddm> <file2.hddm> ... <outfile.hddm>" sys.exit(1) def copy(fin): for r in fin: fout.write(r) nthreads = 1 infiles = [] iarg = 1 while iarg < len(sys.argv): arg = sys.argv[iarg] iarg += 1 if arg == '-n': nthreads = int(sys.argv[iarg]) iarg += 1 continue elif arg[0:2] == '-n': nthreads = int(arg[2:]) continue infiles.append(arg) outfile = infiles.pop() if len(infiles) < 1: usage() fout = hddm_s.ostream(outfile) fout.compression = hddm_s.k_bz2_compression for infile in infiles: print "starting", infile fin = hddm_s.istream(infile) threads = [] for i in range(0, nthreads): t = threading.Thread(target=copy, args=(fin,)) t.start() threads.append(t) for t in threads: t.join() print "finished", infile

20.941176

86

0.626404

49928382b9f0968592c953c45ebc0fd0b5307b95

473

py

Python

project/apps/core/views.py

abdesslem/drf

e6010a771dc31a5f5ce7f5f0720302bd84a25a5f

[ "MIT" ]

null

project/apps/core/views.py

abdesslem/drf

e6010a771dc31a5f5ce7f5f0720302bd84a25a5f

[ "MIT" ]

null

project/apps/core/views.py

abdesslem/drf

e6010a771dc31a5f5ce7f5f0720302bd84a25a5f

[ "MIT" ]

null

from rest_framework import viewsets from .models import University, Student from .serializers import UniversitySerializer, StudentSerializer # ViewSets define the view behavior. class StudentViewSet(viewsets.ModelViewSet): queryset = Student.objects.all() serializer_class = StudentSerializer # ViewSets define the view behavior. class UniversityViewSet(viewsets.ModelViewSet): queryset = University.objects.all() serializer_class = UniversitySerializer

31.533333

64

0.811839

e4ce7a9b1cb89d935dc4de3e9f981a631d014696

42,277

py

Python

koalixcrm/crm/migrations/0003_auto_20171009_1949.py

Cataldir/koalixcrm

87d125379845d6ab990c19500d63cbed4051040a

[ "BSD-3-Clause" ]

290

2015-01-11T03:01:05.000Z

2019-12-17T03:56:17.000Z

koalixcrm/crm/migrations/0003_auto_20171009_1949.py

Cataldir/koalixcrm

87d125379845d6ab990c19500d63cbed4051040a

[ "BSD-3-Clause" ]

178

2016-02-26T14:41:49.000Z

2019-12-29T08:34:21.000Z

koalixcrm/crm/migrations/0003_auto_20171009_1949.py

Cataldir/koalixcrm

87d125379845d6ab990c19500d63cbed4051040a

[ "BSD-3-Clause" ]

124

2015-02-28T20:56:37.000Z

2019-12-13T18:15:35.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2017-10-09 19:49 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('crm', '0002_auto_20170927_2042'), ] operations = [ migrations.AlterModelOptions( name='contact', options={'verbose_name': 'Kontakt', 'verbose_name_plural': 'Kontakt'}, ), migrations.AlterModelOptions( name='contract', options={'verbose_name': 'Auftrag', 'verbose_name_plural': 'Aufträge'}, ), migrations.AlterModelOptions( name='currency', options={'verbose_name': 'Währung', 'verbose_name_plural': 'Währung'}, ), migrations.AlterModelOptions( name='customer', options={'verbose_name': 'Kunde', 'verbose_name_plural': 'Kunden'}, ), migrations.AlterModelOptions( name='customerbillingcycle', options={'verbose_name': 'Zahlungsbedingungen', 'verbose_name_plural': 'Zahlungsbedingungen'}, ), migrations.AlterModelOptions( name='customergroup', options={'verbose_name': 'Kundengruppe', 'verbose_name_plural': 'Kundengruppen'}, ), migrations.AlterModelOptions( name='customergrouptransform', options={'verbose_name': 'Kundengruppe Preisumrechnung', 'verbose_name_plural': 'Kundengruppen Preisumrechnungen'}, ), migrations.AlterModelOptions( name='emailaddress', options={'verbose_name': 'Email Adresse', 'verbose_name_plural': 'Email Adresse'}, ), migrations.AlterModelOptions( name='emailaddressforcontact', options={'verbose_name': 'Email Adresse für Kontakte', 'verbose_name_plural': 'Email Adresse für Kontakte'}, ), migrations.AlterModelOptions( name='emailaddressforcontract', options={'verbose_name': 'Email Adresse für Aufträge', 'verbose_name_plural': 'Email Adresse für Aufträge'}, ), migrations.AlterModelOptions( name='emailaddressforpurchaseorder', options={'verbose_name': 'Email Adresse für Aufträge', 'verbose_name_plural': 'Email Adresse für Aufträge'}, ), migrations.AlterModelOptions( name='emailaddressforsalescontract', options={'verbose_name': 'Email Adresse für Aufträge', 'verbose_name_plural': 'Email Adresse für Aufträge'}, ), migrations.AlterModelOptions( name='invoice', options={'verbose_name': 'Rechnung', 'verbose_name_plural': 'Rechnungen'}, ), migrations.AlterModelOptions( name='phoneaddress', options={'verbose_name': 'Telefonnummer', 'verbose_name_plural': 'Telefonnummer'}, ), migrations.AlterModelOptions( name='phoneaddressforcontact', options={'verbose_name': 'Telefonnummer für Kontakte', 'verbose_name_plural': 'Telefonnummer für Kontakte'}, ), migrations.AlterModelOptions( name='phoneaddressforcontract', options={'verbose_name': 'Telefonnummer für Aufträge', 'verbose_name_plural': 'Telefonnummer für Aufträge'}, ), migrations.AlterModelOptions( name='phoneaddressforpurchaseorder', options={'verbose_name': 'Telefonnummer für Aufträge', 'verbose_name_plural': 'Telefonnummer für Aufträge'}, ), migrations.AlterModelOptions( name='phoneaddressforsalescontract', options={'verbose_name': 'Telefonnummer für Aufträge', 'verbose_name_plural': 'Telefonnummer für Aufträge'}, ), migrations.AlterModelOptions( name='position', options={'verbose_name': 'Position', 'verbose_name_plural': 'Positionen'}, ), migrations.AlterModelOptions( name='postaladdress', options={'verbose_name': 'Postadresse', 'verbose_name_plural': 'Postadresse'}, ), migrations.AlterModelOptions( name='postaladdressforcontact', options={'verbose_name': 'Postadresse für Kontakte', 'verbose_name_plural': 'Postadresse für Kontakte'}, ), migrations.AlterModelOptions( name='postaladdressforcontract', options={'verbose_name': 'Postadresse für Aufträge', 'verbose_name_plural': 'Postadresse für Aufträge'}, ), migrations.AlterModelOptions( name='postaladdressforpurchaseorder', options={'verbose_name': 'Postadresse für Aufträge', 'verbose_name_plural': 'Postadresse für Aufträge'}, ), migrations.AlterModelOptions( name='postaladdressforsalescontract', options={'verbose_name': 'Postadresse für Aufträge', 'verbose_name_plural': 'Postadresse für Aufträge'}, ), migrations.AlterModelOptions( name='price', options={'verbose_name': 'Preis', 'verbose_name_plural': 'Preise'}, ), migrations.AlterModelOptions( name='product', options={'verbose_name': 'Produkt', 'verbose_name_plural': 'Produkte'}, ), migrations.AlterModelOptions( name='purchaseorder', options={'verbose_name': 'Bestellung', 'verbose_name_plural': 'Bestellung'}, ), migrations.AlterModelOptions( name='purchaseorderposition', options={'verbose_name': 'Bestellung Position', 'verbose_name_plural': 'Bestellung Positionen'}, ), migrations.AlterModelOptions( name='quote', options={'verbose_name': 'Offerte', 'verbose_name_plural': 'Offerten'}, ), migrations.AlterModelOptions( name='salescontract', options={'verbose_name': 'Verkaufsauftrag', 'verbose_name_plural': 'Verkaufsaufträge'}, ), migrations.AlterModelOptions( name='salescontractposition', options={'verbose_name': 'Verkaufsaufträg Position', 'verbose_name_plural': 'Verkaufsaufträge Positionen'}, ), migrations.AlterModelOptions( name='supplier', options={'verbose_name': 'Lieferant', 'verbose_name_plural': 'Lieferant'}, ), migrations.AlterModelOptions( name='tax', options={'verbose_name': 'Steuer', 'verbose_name_plural': 'Steuern'}, ), migrations.AlterModelOptions( name='unit', options={'verbose_name': 'Masseinheit', 'verbose_name_plural': 'Masseinheiten'}, ), migrations.AlterModelOptions( name='unittransform', options={'verbose_name': 'Einheiten Umrechnung', 'verbose_name_plural': 'Einheiten Umrechnungen'}, ), migrations.AddField( model_name='purchaseorder', name='last_print_date', field=models.DateTimeField(blank=True, null=True, verbose_name='Last printed'), ), migrations.AddField( model_name='salescontract', name='last_print_date', field=models.DateTimeField(blank=True, null=True, verbose_name='Last printed'), ), migrations.AlterField( model_name='contact', name='dateofcreation', field=models.DateTimeField(auto_now=True, verbose_name='Erstelldatum'), ), migrations.AlterField( model_name='contact', name='lastmodification', field=models.DateTimeField(auto_now_add=True, verbose_name='Zuletzt geändert'), ), migrations.AlterField( model_name='contact', name='lastmodifiedby', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='Zuletzt geändert durch'), ), migrations.AlterField( model_name='contact', name='name', field=models.CharField(max_length=300, verbose_name='Dateiname'), ), migrations.AlterField( model_name='contract', name='dateofcreation', field=models.DateTimeField(auto_now=True, verbose_name='Erstelldatum'), ), migrations.AlterField( model_name='contract', name='defaultSupplier', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='crm.Supplier', verbose_name='Standard Lieferant'), ), migrations.AlterField( model_name='contract', name='defaultcurrency', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Currency', verbose_name='Standard Währung'), ), migrations.AlterField( model_name='contract', name='defaultcustomer', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='crm.Customer', verbose_name='Standard Kunde'), ), migrations.AlterField( model_name='contract', name='description', field=models.TextField(verbose_name='Beschreibung'), ), migrations.AlterField( model_name='contract', name='lastmodification', field=models.DateTimeField(auto_now_add=True, verbose_name='Zuletzt geändert'), ), migrations.AlterField( model_name='contract', name='lastmodifiedby', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='db_contractlstmodified', to=settings.AUTH_USER_MODEL, verbose_name='Zuletzt geändert durch'), ), migrations.AlterField( model_name='contract', name='staff', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='db_relcontractstaff', to=settings.AUTH_USER_MODEL, verbose_name='Mitarbeiter'), ), migrations.AlterField( model_name='currency', name='description', field=models.CharField(max_length=100, verbose_name='Beschreibung'), ), migrations.AlterField( model_name='currency', name='rounding', field=models.DecimalField(blank=True, decimal_places=2, max_digits=5, null=True, verbose_name='Rundung'), ), migrations.AlterField( model_name='currency', name='shortName', field=models.CharField(max_length=3, verbose_name='Angezeigt nach dem Preis in der Position'), ), migrations.AlterField( model_name='customer', name='defaultCustomerBillingCycle', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.CustomerBillingCycle', verbose_name='Standard Zahlungsbedingung'), ), migrations.AlterField( model_name='customer', name='ismemberof', field=models.ManyToManyField(blank=True, to='crm.CustomerGroup', verbose_name='Ist Mitglied von'), ), migrations.AlterField( model_name='customerbillingcycle', name='name', field=models.CharField(max_length=300, verbose_name='Dateiname'), ), migrations.AlterField( model_name='customerbillingcycle', name='timeToPaymentDate', field=models.IntegerField(verbose_name='Tage bis zur Fälligkeit'), ), migrations.AlterField( model_name='customergrouptransform', name='factor', field=models.IntegerField(blank=True, null=True, verbose_name='Prismultiplikator zwischen der einen und der anderen Kundengruppe'), ), migrations.AlterField( model_name='customergrouptransform', name='fromCustomerGroup', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='db_reltransfromfromcustomergroup', to='crm.CustomerGroup', verbose_name='Von Masseinheit'), ), migrations.AlterField( model_name='customergrouptransform', name='product', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Product', verbose_name='Produkt'), ), migrations.AlterField( model_name='customergrouptransform', name='toCustomerGroup', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='db_reltransfromtocustomergroup', to='crm.CustomerGroup', verbose_name='Zu Masseinheit'), ), migrations.AlterField( model_name='emailaddress', name='email', field=models.EmailField(max_length=200, verbose_name='Email Adresse'), ), migrations.AlterField( model_name='emailaddressforcontact', name='purpose', field=models.CharField(choices=[('H', 'Privat'), ('O', 'Geschäftlich'), ('P', 'Mobil Privat'), ('B', 'Mobil Geschäftlich')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='emailaddressforcontract', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='emailaddressforpurchaseorder', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='emailaddressforsalescontract', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='invoice', name='payableuntil', field=models.DateField(verbose_name='Zahlbar bis'), ), migrations.AlterField( model_name='invoice', name='paymentBankReference', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Zahlungsreferenz der Bank'), ), migrations.AlterField( model_name='invoice', name='status', field=models.CharField(choices=[('P', 'Bezahlt'), ('C', 'Rechnung erstellt'), ('I', 'Rechung geschickt'), ('F', 'Erste Erinnerung geschickt'), ('R', 'Zweite Erinnerung geschickt'), ('U', 'Kunde kann nicht zahlen'), ('D', 'Gelöscht')], max_length=1), ), migrations.AlterField( model_name='phoneaddress', name='phone', field=models.CharField(max_length=20, verbose_name='Telefonnummer'), ), migrations.AlterField( model_name='phoneaddressforcontact', name='purpose', field=models.CharField(choices=[('H', 'Privat'), ('O', 'Geschäftlich'), ('P', 'Mobil Privat'), ('B', 'Mobil Geschäftlich')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='phoneaddressforcontract', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='phoneaddressforpurchaseorder', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='phoneaddressforsalescontract', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='position', name='description', field=models.TextField(blank=True, null=True, verbose_name='Beschreibung'), ), migrations.AlterField( model_name='position', name='discount', field=models.DecimalField(blank=True, decimal_places=2, max_digits=5, null=True, verbose_name='Rabatt'), ), migrations.AlterField( model_name='position', name='lastCalculatedPrice', field=models.DecimalField(blank=True, decimal_places=2, max_digits=17, null=True, verbose_name='Zuletzt berechneter Preis'), ), migrations.AlterField( model_name='position', name='lastCalculatedTax', field=models.DecimalField(blank=True, decimal_places=2, max_digits=17, null=True, verbose_name='Zuletzt berechnete Steuer'), ), migrations.AlterField( model_name='position', name='lastPricingDate', field=models.DateField(blank=True, null=True, verbose_name='Datum der letze Preisberechnung'), ), migrations.AlterField( model_name='position', name='overwriteProductPrice', field=models.BooleanField(verbose_name='Überschreie Produkt Preis'), ), migrations.AlterField( model_name='position', name='positionNumber', field=models.IntegerField(verbose_name='Positionsnummer'), ), migrations.AlterField( model_name='position', name='positionPricePerUnit', field=models.DecimalField(blank=True, decimal_places=2, max_digits=17, null=True, verbose_name='Preis pro Einheit'), ), migrations.AlterField( model_name='position', name='product', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='crm.Product', verbose_name='Produkt'), ), migrations.AlterField( model_name='position', name='quantity', field=models.DecimalField(decimal_places=3, max_digits=10, verbose_name='Anzahl'), ), migrations.AlterField( model_name='position', name='sentOn', field=models.DateField(blank=True, null=True, verbose_name='Verschickt am'), ), migrations.AlterField( model_name='position', name='shipmentID', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Lieferungs ID'), ), migrations.AlterField( model_name='position', name='supplier', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='crm.Supplier', verbose_name='Versandunternehmen'), ), migrations.AlterField( model_name='position', name='unit', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='crm.Unit', verbose_name='Masseinheit'), ), migrations.AlterField( model_name='postaladdress', name='addressline1', field=models.CharField(blank=True, max_length=200, null=True, verbose_name='Adresslinie 1'), ), migrations.AlterField( model_name='postaladdress', name='addressline2', field=models.CharField(blank=True, max_length=200, null=True, verbose_name='Adresslinie 2'), ), migrations.AlterField( model_name='postaladdress', name='addressline3', field=models.CharField(blank=True, max_length=200, null=True, verbose_name='Adresslinie 3'), ), migrations.AlterField( model_name='postaladdress', name='addressline4', field=models.CharField(blank=True, max_length=200, null=True, verbose_name='Adresslinie 4'), ), migrations.AlterField( model_name='postaladdress', name='country', field=models.CharField(blank=True, choices=[('AF', 'Afghanistan'), ('AX', 'Aland Islands'), ('AL', 'Albanien'), ('DZ', 'Algerien'), ('AS', 'Amerikanisch-Samoa'), ('AD', 'Andorra'), ('AO', 'Angola'), ('AI', 'Anguilla'), ('AQ', 'Antarktis'), ('AG', 'Antigua und Barbuda'), ('AR', 'Argentinien'), ('AM', 'Armenien'), ('AW', 'Aruba'), ('AU', 'Australien'), ('AT', 'Österreich'), ('AZ', 'Aserbaidschan'), ('BS', 'Bahamas'), ('BH', 'Bahrain\t'), ('BD', 'Bangladesch'), ('BB', 'Barbados'), ('BY', 'Belarus, (Weißrußland)'), ('BE', 'Belgien'), ('BZ', 'Belize'), ('BJ', 'Benin'), ('BM', 'Bermuda'), ('BT', 'Bhutan'), ('BO', 'Bolivien'), ('BA', 'Bosnien und Herzegovina'), ('BW', 'Botsuana'), ('BV', 'Bouvetinsel'), ('BR', 'Brasilien'), ('IO', 'Britisches Territorium im Indischen Ozean'), ('BN', 'Brunei Darussalam'), ('BG', 'Bulgarien'), ('BF', 'Burkina Faso (Obervolta - 1984)'), ('BI', 'Burundi'), ('KH', 'Kambodscha'), ('CM', 'Kamerun'), ('CA', 'Kanada'), ('CV', 'Kap Verde'), ('KY', 'Kaimaninseln'), ('CF', 'Zentralafrikanische Republik'), ('TD', 'Tschad'), ('CL', 'Chile'), ('CN', 'China'), ('CX', 'Weihnachtsinsel'), ('CC', 'Kokosinseln (Keeling)'), ('CO', 'Kolumbien'), ('KM', 'Komoren'), ('CG', 'Kongo'), ('CD', 'Kongo, Demokratische Republik'), ('CK', 'Cookinseln'), ('CR', 'Costa Rica'), ('CI', 'Elfenbeinküste'), ('HR', 'Kroatien'), ('CU', 'Kuba'), ('CY', 'Zypern'), ('CZ', 'Tschechische Republik'), ('DK', 'Dänemark'), ('DJ', 'Dschibuti'), ('DM', 'Dominica'), ('DO', 'Dominikanische Republik (Dom Rep)'), ('EC', 'Ecuador'), ('EG', 'Ägypten'), ('SV', 'El Salvador'), ('GQ', 'Äquatorialguinea'), ('ER', 'Eritrea'), ('EE', 'Estland'), ('ET', 'Äthiopien'), ('FK', 'Falklandinseln (Malvinen)'), ('FO', 'Färöer'), ('FJ', 'Fidschi'), ('FI', 'Finnland'), ('FR', 'Frankreich'), ('GF', 'Französisch-Guayana'), ('PF', 'Französisch-Polynesien'), ('TF', 'Französische Süd- und Antarktisgebiete'), ('GA', 'Gabun'), ('GM', 'Gambia'), ('GE', 'Georgien'), ('DE', 'Deutschland'), ('GH', 'Ghana'), ('GI', 'Gibraltar'), ('GR', 'Griechenland'), ('GL', 'Grönland'), ('GD', 'Grenada'), ('GP', 'Guadeloupe'), ('GU', 'Guam'), ('GT', 'Guatemala'), ('GG', 'Guernsey'), ('GN', 'Guinea'), ('GW', 'Guinea-Bissau'), ('GY', 'Guyana'), ('HT', 'Haiti'), ('HM', 'Heard und die Mcdonaldinseln'), ('VA', 'Heiliger Stuhl (Vatikanstadt)'), ('HN', 'Honduras'), ('HK', 'Hongkong'), ('HU', 'Ungarn'), ('IS', 'Island'), ('IN', 'Indien'), ('ID', 'Indonesien'), ('IR', 'Iran, Islamische Republik'), ('IQ', 'Irak'), ('IE', 'Irland'), ('IM', 'Insel Man'), ('IL', 'Israel'), ('IT', 'Italien'), ('JM', 'Jamaika'), ('JP', 'Japan'), ('JE', 'Jersey'), ('JO', 'Jordanien'), ('KZ', 'Kasachstan'), ('KE', 'Kenya'), ('KI', 'Kiribati'), ('KP', 'Korea, Demokratische Volksrepublik (Nord Korea)'), ('KR', 'Korea, Republik (Süd Korea)'), ('KW', 'Kuwait'), ('KG', 'Kirgisistan'), ('LA', 'Laos, Demokratische Volksrepublik'), ('LV', 'Lettland'), ('LB', 'Libanon'), ('LS', 'Lesotho'), ('LR', 'Liberia'), ('LY', 'Libyen, Libysch-Arabische Dschamahirija'), ('LI', 'Liechtenstein'), ('LT', 'Litauen'), ('LU', 'Luxemburg'), ('MO', 'Macau'), ('MK', 'Mazedonien'), ('MG', 'Madagaskar'), ('MW', 'Malawi'), ('MY', 'Malaysia'), ('MV', 'Malediven'), ('ML', 'Mali'), ('MT', 'Malta'), ('MH', 'Marshallinseln'), ('MQ', 'Martinique'), ('MR', 'Mauretanien'), ('MU', 'Mauritius'), ('YT', 'Mayotte'), ('MX', 'Mexiko'), ('FM', 'Mikronesien, Föderierte Staaten von'), ('MD', 'Moldau, Republik'), ('MC', 'Monaco'), ('MN', 'Mongolei'), ('ME', 'Montenegro'), ('MS', 'Montenegro'), ('MA', 'Marokko'), ('MZ', 'Mosambik'), ('MM', 'Myanmar, Birma'), ('NA', 'Namibia'), ('NR', 'Nauru'), ('NP', 'Nepal'), ('NL', 'Niederlande'), ('AN', 'Niederländische Antillen'), ('NC', 'Neukaledonien'), ('NZ', 'Neuseeland'), ('NI', 'Nicaragua'), ('NE', 'Niger'), ('NG', 'Nigeria'), ('NU', 'Niue'), ('NF', 'Norfolkinsel'), ('MP', 'Marianen, Nördliche'), ('NO', 'Norwegen'), ('OM', 'Oman'), ('PK', 'Pakistan'), ('PW', 'Palau'), ('PS', 'Palästinensische Autonomiegebiete'), ('PA', 'Panama'), ('PG', 'Papua-Neuguinea'), ('PY', 'Paraguay'), ('PE', 'Peru'), ('PH', 'Philippinen'), ('PN', 'Pitcairninseln'), ('PL', 'Polen'), ('PT', 'Portugal'), ('PR', 'Puerto Rico'), ('QA', 'Katar'), ('RE', 'Reunion'), ('RO', 'Rumänien'), ('RU', 'Russische Föderation (Russland)'), ('RW', 'Ruanda'), ('SH', 'St. Helena'), ('KN', 'St. Kitts und Nevis'), ('LC', 'St. Lucia'), ('PM', 'St. Pierre und Miquelon'), ('VC', 'St. Vincent und die Grenadinen'), ('WS', 'Samoa'), ('SM', 'San Marino'), ('ST', 'São Tomé und Príncipe'), ('SA', 'Saudi-Arabien'), ('SN', 'Senegal'), ('RS', 'Serbien'), ('SC', 'Seychellen'), ('SL', 'Sierra Leone'), ('SG', 'Singapur'), ('SK', 'Slowakei'), ('SI', 'Slowenien'), ('SB', 'Salomonen'), ('SO', 'Somalia'), ('ZA', 'Südafrika'), ('GS', 'Südgeorgien und die südlichen Sandwichinseln'), ('ES', 'Spanien'), ('LK', 'Sri Lanka'), ('SD', 'Sudan'), ('SR', 'Suriname'), ('SJ', 'Svalbard und Jan Mayen'), ('SZ', 'Swasiland'), ('SE', 'Schweden'), ('CH', 'Schweiz'), ('SY', 'Syrien, Arabische Republik'), ('TW', 'Taiwan'), ('TJ', 'Tadschikistan'), ('TZ', 'Tansania, Vereinigte Republik'), ('TH', 'Thailand'), ('TL', 'Timor-Leste (Osttimor)'), ('TG', 'Togo'), ('TK', 'Tokelau'), ('TO', 'Tonga'), ('TT', 'Trinidad und Tobago'), ('TN', 'Tunesien'), ('TR', 'Türkei'), ('TM', 'Turkmenistan'), ('TC', 'Turks- und Caicosinseln'), ('TV', 'Tuvalu'), ('UG', 'Uganda'), ('UA', 'Ukraine'), ('AE', 'Vereinigte Arabische Emirate'), ('GB', 'Vereinigtes Königreich'), ('US', 'Vereinigte Staaten'), ('UM', 'Amerikanische Überseeinseln, Kleinere'), ('UY', 'Uruguay'), ('UZ', 'Usbekistan'), ('VU', 'Vanuatu'), ('VE', 'Venezuela'), ('VN', 'Vietnam'), ('VG', 'Britische Jungferninseln'), ('VI', 'Amerikanische Jungferninseln'), ('WF', 'Wallis und Futuna'), ('EH', 'Westsahara'), ('YE', 'Jemen'), ('ZM', 'Sambia'), ('ZW', 'Simbabwe')], max_length=2, null=True, verbose_name='Land'), ), migrations.AlterField( model_name='postaladdress', name='name', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Dateiname'), ), migrations.AlterField( model_name='postaladdress', name='prefix', field=models.CharField(blank=True, choices=[('F', 'Firma'), ('W', 'Frau'), ('H', 'Herr'), ('G', 'Frau')], max_length=1, null=True, verbose_name='Anrede'), ), migrations.AlterField( model_name='postaladdress', name='prename', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Vorname'), ), migrations.AlterField( model_name='postaladdress', name='state', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Bundestaat/Kanton'), ), migrations.AlterField( model_name='postaladdress', name='town', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Ortschaft'), ), migrations.AlterField( model_name='postaladdress', name='zipcode', field=models.IntegerField(blank=True, null=True, verbose_name='PLZ'), ), migrations.AlterField( model_name='postaladdressforcontact', name='purpose', field=models.CharField(choices=[('H', 'Privat'), ('O', 'Geschäftlich'), ('P', 'Mobil Privat'), ('B', 'Mobil Geschäftlich')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='postaladdressforcontract', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='postaladdressforpurchaseorder', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='postaladdressforsalescontract', name='purpose', field=models.CharField(choices=[('D', 'Versand Addresse'), ('B', 'Rechnungs Adresse'), ('C', 'Kontakt Adresse')], max_length=1, verbose_name='Verwendungszweck'), ), migrations.AlterField( model_name='price', name='customerGroup', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='crm.CustomerGroup', verbose_name='Kundengruppe'), ), migrations.AlterField( model_name='price', name='price', field=models.DecimalField(decimal_places=2, max_digits=17, verbose_name='Preis pro Einheit'), ), migrations.AlterField( model_name='price', name='product', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Product', verbose_name='Produkt'), ), migrations.AlterField( model_name='price', name='unit', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Unit', verbose_name='Masseinheit'), ), migrations.AlterField( model_name='price', name='validfrom', field=models.DateField(blank=True, null=True, verbose_name='Gültig vom'), ), migrations.AlterField( model_name='price', name='validuntil', field=models.DateField(blank=True, null=True, verbose_name='Gültig bis'), ), migrations.AlterField( model_name='product', name='accoutingProductCategorie', field=models.ForeignKey(blank='True', null=True, on_delete=django.db.models.deletion.CASCADE, to='accounting.ProductCategorie', verbose_name='Buchhaltung Produktkategorie'), ), migrations.AlterField( model_name='product', name='dateofcreation', field=models.DateTimeField(auto_now=True, verbose_name='Erstelldatum'), ), migrations.AlterField( model_name='product', name='defaultunit', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Unit', verbose_name='Masseinheit'), ), migrations.AlterField( model_name='product', name='description', field=models.TextField(blank=True, null=True, verbose_name='Beschreibung'), ), migrations.AlterField( model_name='product', name='lastmodification', field=models.DateTimeField(auto_now_add=True, verbose_name='Zuletzt geändert'), ), migrations.AlterField( model_name='product', name='lastmodifiedby', field=models.ForeignKey(blank='True', null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='Zuletzt geändert durch'), ), migrations.AlterField( model_name='product', name='productNumber', field=models.IntegerField(verbose_name='Produktnummer'), ), migrations.AlterField( model_name='product', name='title', field=models.CharField(max_length=200, verbose_name='Titel'), ), migrations.AlterField( model_name='purchaseorder', name='contract', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Contract', verbose_name='Auftrag'), ), migrations.AlterField( model_name='purchaseorder', name='currency', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Currency', verbose_name='Währung'), ), migrations.AlterField( model_name='purchaseorder', name='dateofcreation', field=models.DateTimeField(auto_now=True, verbose_name='Erstelldatum'), ), migrations.AlterField( model_name='purchaseorder', name='description', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Beschreibung'), ), migrations.AlterField( model_name='purchaseorder', name='externalReference', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Externe Referenz'), ), migrations.AlterField( model_name='purchaseorder', name='lastCalculatedPrice', field=models.DecimalField(blank=True, decimal_places=2, max_digits=17, null=True, verbose_name='Zuletzt berechneter Preis mit Steuer'), ), migrations.AlterField( model_name='purchaseorder', name='lastCalculatedTax', field=models.DecimalField(blank=True, decimal_places=2, max_digits=17, null=True, verbose_name='Zuletzt berechnete Steuer'), ), migrations.AlterField( model_name='purchaseorder', name='lastPricingDate', field=models.DateField(blank=True, null=True, verbose_name='Datum der letze Preisberechnung'), ), migrations.AlterField( model_name='purchaseorder', name='lastmodification', field=models.DateTimeField(auto_now_add=True, verbose_name='Zuletzt geändert'), ), migrations.AlterField( model_name='purchaseorder', name='lastmodifiedby', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='db_polstmodified', to=settings.AUTH_USER_MODEL, verbose_name='Zuletzt geändert durch'), ), migrations.AlterField( model_name='purchaseorder', name='staff', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='db_relpostaff', to=settings.AUTH_USER_MODEL, verbose_name='Mitarbeiter'), ), migrations.AlterField( model_name='purchaseorder', name='status', field=models.CharField(choices=[('O', 'Bestellt'), ('D', 'Verspätet'), ('Y', 'Ausgeliefert'), ('I', 'Rechnung registriert'), ('P', 'Rechnung bezahlt')], max_length=1), ), migrations.AlterField( model_name='purchaseorder', name='supplier', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Supplier', verbose_name='Lieferant'), ), migrations.AlterField( model_name='purchaseorderposition', name='contract', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.PurchaseOrder', verbose_name='Auftrag'), ), migrations.AlterField( model_name='quote', name='status', field=models.CharField(choices=[('S', 'Erfolg'), ('I', 'Offerte erstellt'), ('Q', 'Offerte verschickt'), ('F', 'Erste Erinnerung geschickt'), ('R', 'Zweite Erinnerung geschickt'), ('D', 'Gelöscht')], max_length=1, verbose_name='Status'), ), migrations.AlterField( model_name='quote', name='validuntil', field=models.DateField(verbose_name='Gültig bis'), ), migrations.AlterField( model_name='salescontract', name='contract', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Contract', verbose_name='Auftrag'), ), migrations.AlterField( model_name='salescontract', name='currency', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Currency', verbose_name='Währung'), ), migrations.AlterField( model_name='salescontract', name='customer', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Customer', verbose_name='Kunde'), ), migrations.AlterField( model_name='salescontract', name='dateofcreation', field=models.DateTimeField(auto_now=True, verbose_name='Erstelldatum'), ), migrations.AlterField( model_name='salescontract', name='description', field=models.CharField(blank=True, max_length=100, null=True, verbose_name='Beschreibung'), ), migrations.AlterField( model_name='salescontract', name='discount', field=models.DecimalField(blank=True, decimal_places=2, max_digits=5, null=True, verbose_name='Rabatt'), ), migrations.AlterField( model_name='salescontract', name='externalReference', field=models.CharField(blank=True, max_length=100, verbose_name='Externe Referenz'), ), migrations.AlterField( model_name='salescontract', name='lastCalculatedPrice', field=models.DecimalField(blank=True, decimal_places=2, max_digits=17, null=True, verbose_name='Preis ohne Steuer'), ), migrations.AlterField( model_name='salescontract', name='lastCalculatedTax', field=models.DecimalField(blank=True, decimal_places=2, max_digits=17, null=True, verbose_name='Steuer'), ), migrations.AlterField( model_name='salescontract', name='lastPricingDate', field=models.DateField(blank=True, null=True, verbose_name='Letzte Preisberechnung'), ), migrations.AlterField( model_name='salescontract', name='lastmodification', field=models.DateTimeField(auto_now_add=True, verbose_name='Zuletzt geändert'), ), migrations.AlterField( model_name='salescontract', name='lastmodifiedby', field=models.ForeignKey(blank='True', null=True, on_delete=django.db.models.deletion.CASCADE, related_name='db_lstscmodified', to=settings.AUTH_USER_MODEL, verbose_name='Zuletzt geändert durch'), ), migrations.AlterField( model_name='salescontract', name='staff', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='db_relscstaff', to=settings.AUTH_USER_MODEL, verbose_name='Mitarbeiter'), ), migrations.AlterField( model_name='salescontractposition', name='contract', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.SalesContract', verbose_name='Auftrag'), ), migrations.AlterField( model_name='tax', name='accountActiva', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='db_relaccountactiva', to='accounting.Account', verbose_name='Aktiv Konto'), ), migrations.AlterField( model_name='tax', name='accountPassiva', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='db_relaccountpassiva', to='accounting.Account', verbose_name='Passiv Konto'), ), migrations.AlterField( model_name='tax', name='name', field=models.CharField(max_length=100, verbose_name='Name der Steuer'), ), migrations.AlterField( model_name='tax', name='taxrate', field=models.DecimalField(decimal_places=2, max_digits=5, verbose_name='Steuersatz in Prozent'), ), migrations.AlterField( model_name='unit', name='description', field=models.CharField(max_length=100, verbose_name='Beschreibung'), ), migrations.AlterField( model_name='unit', name='fractionFactorToNextHigherUnit', field=models.IntegerField(blank=True, null=True, verbose_name='Multiplikator zwischen dieser und der nächst höheren Einheit'), ), migrations.AlterField( model_name='unit', name='isAFractionOf', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='crm.Unit', verbose_name='Ist eine Untereinheit von'), ), migrations.AlterField( model_name='unit', name='shortName', field=models.CharField(max_length=3, verbose_name='Angezeigt nach der Anzahl in der Position'), ), migrations.AlterField( model_name='unittransform', name='factor', field=models.IntegerField(blank=True, null=True, verbose_name='Multiplikator zwischen der einen und der anderen Einheit'), ), migrations.AlterField( model_name='unittransform', name='fromUnit', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='db_reltransfromfromunit', to='crm.Unit', verbose_name='Von Masseinheit'), ), migrations.AlterField( model_name='unittransform', name='product', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='crm.Product', verbose_name='Produkt'), ), migrations.AlterField( model_name='unittransform', name='toUnit', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='db_reltransfromtounit', to='crm.Unit', verbose_name='Zu Masseinheit'), ), ]

54.692109

5,863

0.60721

f6091ab0704798a46eeae817671b3cd48beb2926

6,927

py

Python

sdk/python/pulumi_azure_native/securityinsights/v20210301preview/get_incident_relation.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/securityinsights/v20210301preview/get_incident_relation.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/securityinsights/v20210301preview/get_incident_relation.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = [ 'GetIncidentRelationResult', 'AwaitableGetIncidentRelationResult', 'get_incident_relation', ] @pulumi.output_type class GetIncidentRelationResult: """ Represents a relation between two resources """ def __init__(__self__, etag=None, id=None, name=None, related_resource_id=None, related_resource_kind=None, related_resource_name=None, related_resource_type=None, system_data=None, type=None): if etag and not isinstance(etag, str): raise TypeError("Expected argument 'etag' to be a str") pulumi.set(__self__, "etag", etag) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if related_resource_id and not isinstance(related_resource_id, str): raise TypeError("Expected argument 'related_resource_id' to be a str") pulumi.set(__self__, "related_resource_id", related_resource_id) if related_resource_kind and not isinstance(related_resource_kind, str): raise TypeError("Expected argument 'related_resource_kind' to be a str") pulumi.set(__self__, "related_resource_kind", related_resource_kind) if related_resource_name and not isinstance(related_resource_name, str): raise TypeError("Expected argument 'related_resource_name' to be a str") pulumi.set(__self__, "related_resource_name", related_resource_name) if related_resource_type and not isinstance(related_resource_type, str): raise TypeError("Expected argument 'related_resource_type' to be a str") pulumi.set(__self__, "related_resource_type", related_resource_type) if system_data and not isinstance(system_data, dict): raise TypeError("Expected argument 'system_data' to be a dict") pulumi.set(__self__, "system_data", system_data) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter def etag(self) -> Optional[str]: """ Etag of the azure resource """ return pulumi.get(self, "etag") @property @pulumi.getter def id(self) -> str: """ Azure resource Id """ return pulumi.get(self, "id") @property @pulumi.getter def name(self) -> str: """ Azure resource name """ return pulumi.get(self, "name") @property @pulumi.getter(name="relatedResourceId") def related_resource_id(self) -> str: """ The resource ID of the related resource """ return pulumi.get(self, "related_resource_id") @property @pulumi.getter(name="relatedResourceKind") def related_resource_kind(self) -> str: """ The resource kind of the related resource """ return pulumi.get(self, "related_resource_kind") @property @pulumi.getter(name="relatedResourceName") def related_resource_name(self) -> str: """ The name of the related resource """ return pulumi.get(self, "related_resource_name") @property @pulumi.getter(name="relatedResourceType") def related_resource_type(self) -> str: """ The resource type of the related resource """ return pulumi.get(self, "related_resource_type") @property @pulumi.getter(name="systemData") def system_data(self) -> 'outputs.SystemDataResponse': """ Azure Resource Manager metadata containing createdBy and modifiedBy information. """ return pulumi.get(self, "system_data") @property @pulumi.getter def type(self) -> str: """ Azure resource type """ return pulumi.get(self, "type") class AwaitableGetIncidentRelationResult(GetIncidentRelationResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetIncidentRelationResult( etag=self.etag, id=self.id, name=self.name, related_resource_id=self.related_resource_id, related_resource_kind=self.related_resource_kind, related_resource_name=self.related_resource_name, related_resource_type=self.related_resource_type, system_data=self.system_data, type=self.type) def get_incident_relation(incident_id: Optional[str] = None, operational_insights_resource_provider: Optional[str] = None, relation_name: Optional[str] = None, resource_group_name: Optional[str] = None, workspace_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetIncidentRelationResult: """ Represents a relation between two resources :param str incident_id: Incident ID :param str operational_insights_resource_provider: The namespace of workspaces resource provider- Microsoft.OperationalInsights. :param str relation_name: Relation Name :param str resource_group_name: The name of the resource group. The name is case insensitive. :param str workspace_name: The name of the workspace. """ __args__ = dict() __args__['incidentId'] = incident_id __args__['operationalInsightsResourceProvider'] = operational_insights_resource_provider __args__['relationName'] = relation_name __args__['resourceGroupName'] = resource_group_name __args__['workspaceName'] = workspace_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:securityinsights/v20210301preview:getIncidentRelation', __args__, opts=opts, typ=GetIncidentRelationResult).value return AwaitableGetIncidentRelationResult( etag=__ret__.etag, id=__ret__.id, name=__ret__.name, related_resource_id=__ret__.related_resource_id, related_resource_kind=__ret__.related_resource_kind, related_resource_name=__ret__.related_resource_name, related_resource_type=__ret__.related_resource_type, system_data=__ret__.system_data, type=__ret__.type)

38.483333

197

0.670564

8b51637a8cb5a75c5fbb87193caff0f623f202e6

3,165

py

Python

ginga/util/wcsmod/__init__.py

finagle29/ginga

161952a4016d3f014003db3d09dd78c61be85cb7

[ "BSD-3-Clause" ]

null

ginga/util/wcsmod/__init__.py

finagle29/ginga

161952a4016d3f014003db3d09dd78c61be85cb7

[ "BSD-3-Clause" ]

null

ginga/util/wcsmod/__init__.py

finagle29/ginga

161952a4016d3f014003db3d09dd78c61be85cb7

[ "BSD-3-Clause" ]

null

# # wcsmod -- module wrapper for WCS calculations. # # This is open-source software licensed under a BSD license. # Please see the file LICENSE.txt for details. # """ We are fortunate to have several possible choices for a python WCS package compatible with Ginga: astlib, kapteyn, starlink and astropy. kapteyn and astropy wrap Mark Calabretta's "WCSLIB", astLib wraps Jessica Mink's "wcstools", and I'm not sure what starlink uses (their own?). Note that astlib requires pyfits (or astropy) in order to create a WCS object from a FITS header. To force the use of one, do: .. code-block:: python from ginga.util import wcsmod wcsmod.use('kapteyn') before you load any images. Otherwise Ginga will try to pick one for you. Note that you can register custom WCS types using: .. code-block:: python from ginga.util.wcsmod.common import register_wcs register_wcs('mywcs', MyWCSClass, list_of_coord_types) Look at the implemented WCS wrappers for details. """ import sys import os.path import glob from ginga.misc.ModuleManager import my_import from . import common __all__ = ['get_wcs_class'] # Module variables that get configured at module load time # or when use() is called wcs_configured = False WCS = None """Alias to the chosen WCS system.""" # Holds names of coordinate types coord_types = [] display_types = ['sexagesimal', 'degrees'] # try to load them in this order until we find one that works. # If none can be loaded, we default to the BareBones dummy WCS wcs_try_order = ('astropy', 'astropy_ape14', 'kapteyn', 'starlink', 'astlib', 'barebones') wcs_home = os.path.split(sys.modules[__name__].__file__)[0] def use(wcspkg, raise_err=True): """Choose WCS package.""" global coord_types, wcs_configured, WCS if wcspkg not in common.custom_wcs: # Try to dynamically load WCS modname = 'wcs_%s' % (wcspkg) path = os.path.join(wcs_home, '%s.py' % (modname)) try: my_import(modname, path) except ImportError as e: if raise_err: raise e return False if wcspkg in common.custom_wcs: bnch = common.custom_wcs[wcspkg] WCS = bnch.wrapper_class coord_types = bnch.coord_types wcs_configured = True return True return False # configure at least one WCS wrapper if not wcs_configured: # Try some preconfigured names for name in wcs_try_order: if use(name, raise_err=False): break if not wcs_configured: wcs_path = os.path.join(wcs_home, 'wcs_*.py') # look up WCS wrappers we have in this directory for path in glob.glob(wcs_path): dirname, filename = os.path.split(path) modname, ext = os.path.splitext(filename) modname = modname[4:] # strip off "wcs_" if use(name, raise_err=False): break def get_wcs_wrappers(): return list(common.custom_wcs.keys()) def get_wcs_class(name): """Get a WCS class corresponding to the registered name. Will raise a KeyError if a class of the given name does not exist. """ return common.custom_wcs[name] # END

26.157025

77

0.685308

74ec1cf50568e21a728f034a4769adc74510092d

1,459

py

Python

TimeSeries/sarima.py

lauramurphy12/MachineLearningProjects

c6ea2c2644815de480c72728dd2e0b6a380be2ea

[ "CC0-1.0" ]

null

TimeSeries/sarima.py

lauramurphy12/MachineLearningProjects

c6ea2c2644815de480c72728dd2e0b6a380be2ea

[ "CC0-1.0" ]

null

TimeSeries/sarima.py

lauramurphy12/MachineLearningProjects

c6ea2c2644815de480c72728dd2e0b6a380be2ea

[ "CC0-1.0" ]

null

import itertools from statsmodels.tsa.statespace.sarimax import SARIMAX import pandas as pd import forecasting import warnings import numpy as np from sklearn.metrics import mean_squared_error, mean_absolute_error from math import sqrt def sarima_model(train, test, trend_order, seasonal_order, forecast_period): model = SARIMAX(train, order=trend_order, seasonal_order=seasonal_order) model_fit = model.fit() yhat = model_fit.forecast(forecast_period) return yhat, model_fit.summary() # Walk Forward Validation with expanding window approach. Model is fit with training data with initial starting size. Test set is the next time step in the sequence. A one-step forecast is made. # In each iteration the test value of the previous iteration is added to the training set. def sarimaWalkForwardVal(series, window_size, trend_order, seasonal_order, forecast_period): forecasted_values = list() seriesLen = len(series) for i in range(window_size, seriesLen): # Split data into train and test. train, test = series[0:i],series[i:i+1] # fit sarima model with current SARIMA configuration and forecast 1 time step ahead yhat, model = sarima_model(train, test, trend_order, seasonal_order, forecast_period) forecasted_values.append(yhat[0]) test = series[window_size:seriesLen] mape = np.mean(np.abs(forecasted_values - test)/np.abs(test)) rmse = sqrt(mean_squared_error(test, forecasted_values)) return forecasted_values, mape, rmse

42.911765

196

0.793009

0284e99cf49e7c16b07e773ffd6af23feeedcc77

1,430

py

Python

streams/transform_data.py

BogdanFloris/detecting-and-addressing-change

b11082ebd0fc421eca31c5ba82ddb52446fa56f5

[ "MIT" ]

2

2020-01-24T15:50:47.000Z

2021-04-16T21:21:25.000Z

streams/transform_data.py

BogdanFloris/detecting-and-addressing-change

b11082ebd0fc421eca31c5ba82ddb52446fa56f5

[ "MIT" ]

null

streams/transform_data.py

BogdanFloris/detecting-and-addressing-change

b11082ebd0fc421eca31c5ba82ddb52446fa56f5

[ "MIT" ]

null

""" Transforms a dataset using a transformer and saves it """ import os import torch from pathlib import Path from tqdm import tqdm from streams.loaders import load_wos from constants.transformers import TransformerModel, Transformer SAVE_DIR = os.path.join(Path(__file__).parents[1], "assets/datasets") def transform_wos(version=1, transformer_model=TransformerModel.BERT, hidden_state=0): """ Transformers the given version of the Web of Science dataset using the given transformer model and saves it as a tuple (x, y, no_classes). Args: version (int): the WOS version transformer_model (TransformerModel): the transformer model to use hidden_state (int): which hidden state to get from the transformer """ print("Transforming dataset...") transformer = Transformer(transformer_model) x, y, no_classes = load_wos(version) transformed_x = [] for i in tqdm(range(len(x))): transformed_x.append(transformer.transform(x[i], hidden_state)) print("Saving dataset...") f = open( os.path.join( SAVE_DIR, "wos_v_{}_transformed_{}_hidden_{}.pt".format( version, transformer_model.name, hidden_state ), ), "wb", ) torch.save((transformed_x, y, no_classes), f) f.close() if __name__ == "__main__": transform_wos(transformer_model=TransformerModel.DISTILBERT)

29.183673

86

0.681119

c79a6bcea0d5b63123fa418bf0c7c0d279f8de7e

897

py

Python

Live/main.py

Pawel095/PJ_Pygame_Prezentacja

f6ace78c2df504a262eac6b1ae46d458939dd0c0

[ "MIT" ]

null

Live/main.py

Pawel095/PJ_Pygame_Prezentacja

f6ace78c2df504a262eac6b1ae46d458939dd0c0

[ "MIT" ]

null

Live/main.py

Pawel095/PJ_Pygame_Prezentacja

f6ace78c2df504a262eac6b1ae46d458939dd0c0

[ "MIT" ]

null

import pygame import global_vars import events import loader import sprites.enemy from sprites.Player import Player from sprites.enemy import Controller import sprites.bullet as bullets from sprites.background import Background pygame.init() loader.load() pygame.display.set_mode(global_vars.SCREEN_SIZE) pygame.display.set_caption("Hell Of Bullets") player = Player() controller = Controller() clk = pygame.time.Clock() bg = Background() WHITE = (255,255,255) running=True while running: deltaT = clk.tick(60)/1000 events.process_events() if events.QUIT: running = False # Fizyka player.update(deltaT) controller.update(deltaT) bullets.update(deltaT) bg.update(deltaT) # RENDER srf = pygame.display.get_surface() srf.fill((0,0,0)) player.draw() controller.draw() bullets.draw() bg.draw() pygame.display.update()

17.94

48

0.714604

3dbd1501558382b66d980fa06d53e2a6b2e20175

1,864

py

Python

sovrin/test/upgrade/test_pool_upgrade_lower_version.py

sovrin-foundation/old-sovrin

d4e705054b7252c62fea00114060035c6eb314a4

[ "Apache-2.0" ]

3

2017-07-19T14:26:31.000Z

2020-05-16T16:09:37.000Z

sovrin/test/upgrade/test_pool_upgrade_lower_version.py

sovrin-foundation/old-sovrin

d4e705054b7252c62fea00114060035c6eb314a4

[ "Apache-2.0" ]

null

sovrin/test/upgrade/test_pool_upgrade_lower_version.py

sovrin-foundation/old-sovrin

d4e705054b7252c62fea00114060035c6eb314a4

[ "Apache-2.0" ]

3

2017-10-28T08:19:00.000Z

2021-06-06T10:48:55.000Z

from copy import deepcopy import pytest from plenum.common.eventually import eventually from plenum.common.txn import VERSION, NAME from plenum.common.util import randomString from sovrin.common.txn import SHA256, ACTION, CANCEL from sovrin.test.upgrade.helper import bumpVersion, checkUpgradeScheduled, \ ensureUpgradeSent def testDoNotScheduleUpgradeForALowerVersion(looper, tconf, nodeSet, validUpgrade, trustee, trusteeWallet): """ A node starts at version 1.2 running has scheduled upgrade for version 1.5 but get a txn for upgrade 1.4, it will not schedule it. To upgrade to 1.4, send cancel for 1.5 """ upgr1 = deepcopy(validUpgrade) upgr2 = deepcopy(upgr1) upgr2[VERSION] = bumpVersion(upgr1[VERSION]) upgr2[NAME] += randomString(3) upgr2[SHA256] = randomString(32) # An upgrade for higher version scheduled, it should pass ensureUpgradeSent(looper, trustee, trusteeWallet, upgr2) looper.run(eventually(checkUpgradeScheduled, nodeSet, upgr2[VERSION], retryWait=1, timeout=5)) # An upgrade for lower version scheduled, the transaction should pass but # the upgrade should not be scheduled ensureUpgradeSent(looper, trustee, trusteeWallet, upgr1) with pytest.raises(AssertionError): looper.run(eventually(checkUpgradeScheduled, nodeSet, upgr1[VERSION], retryWait=1, timeout=5)) # Cancel the upgrade with higher version upgr3 = deepcopy(upgr2) upgr3[ACTION] = CANCEL ensureUpgradeSent(looper, trustee, trusteeWallet, upgr3) # Now the upgrade for lower version should be scheduled looper.run(eventually(checkUpgradeScheduled, nodeSet, upgr1[VERSION], retryWait=1, timeout=5))

39.659574

78

0.684549

e47968452cbbbe2ff2138411716de565c75d2d32

138

py

Python

accounts/urls.py

haTrang061299/blog_project

5afc17b97c43aa300280dd97106562bf58b60e58

[ "MIT" ]

null

accounts/urls.py

haTrang061299/blog_project

5afc17b97c43aa300280dd97106562bf58b60e58

[ "MIT" ]

null

accounts/urls.py

haTrang061299/blog_project

5afc17b97c43aa300280dd97106562bf58b60e58

[ "MIT" ]

null

from django.urls import path from .views import SignUpView urlpatterns = [ path('signup/', SignUpView.as_view(), name='signup'), ]

15.333333

57

0.702899

9a78dffd0818d53bb16bb0d715c9ed860b41d225

2,498

py

Python

pfl/core/client.py

huyifan233/PFL

136bdaea6e62ff4ff0f585bf9b061598806ce1b8

[ "Apache-2.0" ]

1

2022-03-30T07:22:14.000Z

pfl/core/client.py

wuchaozju/PFL

39d6d24088714cd38ca8007ede0dfc98a48955ad

[ "Apache-2.0" ]

null

pfl/core/client.py

wuchaozju/PFL

39d6d24088714cd38ca8007ede0dfc98a48955ad

[ "Apache-2.0" ]

null

# Copyright (c) 2019 GalaxyLearning Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import importlib from pfl.utils.utils import JobUtils from pfl.entity.model import Model JOB_PATH = os.path.join(os.path.abspath("."), "res", "jobs_client") BASE_MODEL_PATH = os.path.join(os.path.abspath("."), "res", "models") class FLClient(object): def __init__(self): super(FLClient, self).__init__() self.job_path = JOB_PATH self.base_model_path = BASE_MODEL_PATH def get_remote_pfl_models(self, server_url=None): if server_url is None: return self._get_models_from_local() else: return self._get_models_from_remote(server_url) def _get_models_from_local(self): model_list = [] JobUtils.get_job_from_remote(None, self.job_path) job_list = JobUtils.list_all_jobs(self.job_path) for job in job_list: model = self._get_model_from_job(job) pfl_model = Model() pfl_model.set_model(model) pfl_model.set_job_id(job.get_job_id()) model_list.append(pfl_model) return model_list def _get_models_from_remote(self, server_url): model_list = [] JobUtils.get_job_from_remote(server_url, self.job_path) job_list = JobUtils.list_all_jobs(self.job_path) for job in job_list: model = self._get_model_from_job(job) pfl_model = Model() pfl_model.set_model(model) pfl_model.set_job_id(job.get_job_id()) model_list.append(pfl_model) return model_list def _get_model_from_job(self, job): job_id = job.get_job_id() module = importlib.import_module("res.models.models_{}.init_model_{}".format(job_id, job_id), "init_model_{}".format(job_id)) model_class = getattr(module, job.get_train_model_class_name()) return model_class()

34.219178

101

0.67494

5ec201bc901122c29ef9cda8bb077625050297ce

1,357

py

Python

stage_check/stage_check/OutputLogs.py

128technology/stage_check

2c9cdc491bafbcc6ed1a308093fe606dfd37da67

[ "MIT" ]

2

2020-05-26T15:13:47.000Z

2021-04-29T18:14:21.000Z

stage_check/stage_check/OutputLogs.py

128technology/stage_check

2c9cdc491bafbcc6ed1a308093fe606dfd37da67

[ "MIT" ]

null

stage_check/stage_check/OutputLogs.py

128technology/stage_check

2c9cdc491bafbcc6ed1a308093fe606dfd37da67

[ "MIT" ]

null

""" """ try: from stage_check import Output except ImportError: import Output class Base(Output.Base): def __init__(self): super().__init__() self.__full_name = 'OutputLogs.Base' self.status = Output.Status.OK def proc_pattern_matched(self, params, pattern): try: config = params["patterns"][pattern["pindex"]] for key in config: if key not in pattern: pattern[key] = config[key] except KeyError: pass try: threshold = pattern["threshold"] if int(pattern["matches"]) > threshold: self.status = Output.Status.FAIL self.amend_pattern_matched( params, pattern ) except (KeyError, ValueError) as e: self.status = Output.Status.FAIL return self.status def amend_pattern_matched( self, params, pattern ): return True """ amend_test_result """ def proc_test_result( self, params, stats ): self.amend_test_result( params, stats ) return self.status def amend_test_result( self, params, stats ): return True

20.253731

56

0.506264

8b7c1067a6be9679c356aa2e1505886751f2b875

7,079

py

Python

research/cv/CascadeRCNN/src/CascadeRcnn/roi_align_1.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

77

2021-10-15T08:32:37.000Z

2022-03-30T13:09:11.000Z

research/cv/CascadeRCNN/src/CascadeRcnn/roi_align_1.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

3

2021-10-30T14:44:57.000Z

2022-02-14T06:57:57.000Z

research/cv/CascadeRCNN/src/CascadeRcnn/roi_align_1.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

24

2021-10-15T08:32:45.000Z

2022-03-24T18:45:20.000Z

# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """CascadeRcnn ROIAlign module.""" import numpy as np import mindspore.nn as nn import mindspore.common.dtype as mstype from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.nn import layer as L from mindspore.common.tensor import Tensor class ROIAlign(nn.Cell): """ Extract RoI features from multiple feature map. Args: out_size_h (int) - RoI height. out_size_w (int) - RoI width. spatial_scale (int) - RoI spatial scale. sample_num (int) - RoI sample number. """ def __init__(self, out_size_h, out_size_w, spatial_scale, sample_num=0): super(ROIAlign, self).__init__() self.out_size = (out_size_h, out_size_w) self.spatial_scale = float(spatial_scale) self.sample_num = int(sample_num) self.align_op = P.ROIAlign(self.out_size[0], self.out_size[1], self.spatial_scale, self.sample_num) def construct(self, features, rois): return self.align_op(features, rois) def __repr__(self): format_str = self.__class__.__name__ format_str += '(out_size={}, spatial_scale={}, sample_num={}'.format( self.out_size, self.spatial_scale, self.sample_num) return format_str class SingleRoIExtractor_1(nn.Cell): """ Extract RoI features from a single level feature map. If there are multiple input feature levels, each RoI is mapped to a level according to its scale. Args: config (dict): Config roi_layer (dict): Specify RoI layer type and arguments. out_channels (int): Output channels of RoI layers. featmap_strides (int): Strides of input feature maps. batch_size (int)： Batchsize. finest_scale (int): Scale threshold of mapping to level 0. """ def __init__(self, config, roi_layer, out_channels, featmap_strides, batch_size=1, finest_scale=56): super(SingleRoIExtractor_1, self).__init__() cfg = config self.train_batch_size = batch_size self.out_channels = out_channels self.featmap_strides = featmap_strides self.num_levels = len(self.featmap_strides) self.out_size = roi_layer['out_size'] self.sample_num = roi_layer['sample_num'] self.roi_layers = self.build_roi_layers(self.featmap_strides) self.roi_layers = L.CellList(self.roi_layers) self.sqrt = P.Sqrt() self.log = P.Log() self.finest_scale_ = finest_scale self.clamp = C.clip_by_value self.cast = P.Cast() self.equal = P.Equal() self.select = P.Select() _mode_16 = False self.dtype = np.float16 if _mode_16 else np.float32 self.ms_dtype = mstype.float16 if _mode_16 else mstype.float32 self.set_train_local(cfg, training=True) def set_train_local(self, config, training=True): """Set training flag.""" self.training_local = training cfg = config # Init tensor self.batch_size = cfg.roi_sample_num if self.training_local else cfg.rpn_max_num self.batch_size = self.train_batch_size*self.batch_size \ if self.training_local else cfg.test_batch_size*self.batch_size self.ones = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype)) finest_scale = np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * self.finest_scale_ self.finest_scale = Tensor(finest_scale) self.epslion = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype)*self.dtype(1e-6)) self.zeros = Tensor(np.array(np.zeros((self.batch_size, 1)), dtype=np.int32)) self.max_levels = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=np.int32)*(self.num_levels-1)) self.twos = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * 2) self.res_ = Tensor(np.array(np.zeros((self.batch_size, self.out_channels, self.out_size, self.out_size)), dtype=self.dtype)) def num_inputs(self): return len(self.featmap_strides) def init_weights(self): pass def log2(self, value): return self.log(value) / self.log(self.twos) def build_roi_layers(self, featmap_strides): roi_layers = [] for s in featmap_strides: layer_cls = ROIAlign(self.out_size, self.out_size, spatial_scale=1 / s, sample_num=self.sample_num) roi_layers.append(layer_cls) return roi_layers def _c_map_roi_levels(self, rois): """Map rois to corresponding feature levels by scales. - scale < finest_scale * 2: level 0 - finest_scale * 2 <= scale < finest_scale * 4: level 1 - finest_scale * 4 <= scale < finest_scale * 8: level 2 - scale >= finest_scale * 8: level 3 Args: rois (Tensor): Input RoIs, shape (k, 5). num_levels (int): Total level number. Returns: Tensor: Level index (0-based) of each RoI, shape (k, ) """ scale = self.sqrt(rois[::, 3:4:1] - rois[::, 1:2:1] + self.ones) * \ self.sqrt(rois[::, 4:5:1] - rois[::, 2:3:1] + self.ones) target_lvls = self.log2(scale / self.finest_scale + self.epslion) target_lvls = P.Floor()(target_lvls) target_lvls = self.cast(target_lvls, mstype.int32) target_lvls = self.clamp(target_lvls, self.zeros, self.max_levels) return target_lvls def construct(self, rois, feat1, feat2, feat3, feat4): "SingleRoIExtractor_1" feats = (feat1, feat2, feat3, feat4) res = self.res_ target_lvls = self._c_map_roi_levels(rois) for i in range(self.num_levels): mask = self.equal(target_lvls, P.ScalarToArray()(i)) mask = P.Reshape()(mask, (-1, 1, 1, 1)) roi_feats_t = self.roi_layers[i](feats[i], rois) mask = self.cast(P.Tile()(self.cast(mask, mstype.int32),\ (1, 256, self.out_size, self.out_size)), mstype.bool_) res = self.select(mask, roi_feats_t, res) return res

39.110497

109

0.617178

cfc1359a4ea2cc557cc1bb339ed87a6b8d1bc820

2,547

py

Python

Utilities/ReleaseScripts/python/commentSkipper/cppCommentSkipper.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2019-03-09T19:47:49.000Z

Utilities/ReleaseScripts/python/commentSkipper/cppCommentSkipper.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

null

Utilities/ReleaseScripts/python/commentSkipper/cppCommentSkipper.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2019-04-03T19:23:27.000Z

__author__="Aurelija" __date__ ="$2010-07-13 12.17.20$" import string def filterFiles(fileList): files = [] for file in fileList: files.append((file, filterFile(file))) return files def filterFile(file): #ifstream& input) lines = open(file).readlines() commentStage = False for i in range(len(lines)): #for j in range(len(lines[i])): j = 0 while lines[i][j] != '\n': char = lines[i][j] #char / if char == '/': #comment /* if lines[i][j+1] == '*' and not commentStage: #why !commentStage? because it could be /*...../*.....*/ commentStage = True commentStartLine, commentStartColumn = i, j j += 1 #comment // why !commentStage? because, can be a variant of this example: /*....//.....*/ elif not commentStage and (lines[i][j+1] == '/'): lines[i] = string.replace(lines[i], lines[i][j:],'\n', 1) break #char " elif char == '"': if not commentStage: next = string.find(lines[i][j+1:], '"') #next " lines[i] = string.replace(lines[i], lines[i][j:j+next+2], '', 1) # clear string in "" #char ' elif char == '\'': if not commentStage: next = string.find(lines[i][j+1:], '\'') #next ' lines[i] = string.replace(lines[i], lines[i][j:j+next+2], '', 1) # clear string in '' #char * elif char == '*': if (commentStage and (lines[i][j+1] == '/')): commentStage = False; if commentStartLine != i: lines[i] = string.replace(lines[i], lines[i][:j+2],'', 1) # comment */ [..code] j = -1 #because of j+=1 at the end else: lines[i] = string.replace(lines[i], lines[i][commentStartColumn:j+2], '', 1) # [code..] /*comment*/ [.. code] j = commentStartColumn - 1 #because of j+=1 at the ends if j != len(lines[i]) - 1: j += 1 else: j = 0 break if commentStage: if i == commentStartLine: lines[i] = lines[i].replace(lines[i][commentStartColumn:],'\n', 1) else: lines[i] = lines[i].replace(lines[i][:], '\n') return lines

39.796875

133

0.444837

1da5366997b5b05f8fb23296f4b0f72da44934e6

40

py

Python

simple/management/commands/parse_government_bill_pdf/__init__.py

navotsil/Open-Knesset

d6cc6457f87e67a3dbeaec9c1ccbc8d8b7c60c04

[ "BSD-3-Clause" ]

7

2015-03-05T11:14:23.000Z

2017-02-11T18:06:25.000Z

simple/management/commands/parse_government_bill_pdf/__init__.py

navotsil/Open-Knesset

d6cc6457f87e67a3dbeaec9c1ccbc8d8b7c60c04

[ "BSD-3-Clause" ]

7

2020-02-12T01:09:15.000Z

2022-03-11T23:25:12.000Z

simple/management/commands/parse_government_bill_pdf/__init__.py

navotsil/Open-Knesset

d6cc6457f87e67a3dbeaec9c1ccbc8d8b7c60c04

[ "BSD-3-Clause" ]

11

2015-02-21T09:45:06.000Z

2021-05-05T06:01:24.000Z

from parse_government_bill_pdf import *

20

39

0.875

8f62d0a066663cb66be97dd4ed4dda3b7ee31a0f

1,986

py

Python

info_state.py

ProgrammingIncluded/PyAN

b5628891c2f0f7b4deb97d92c7b6ab1163a13b54

[ "MIT" ]

null

info_state.py

ProgrammingIncluded/PyAN

b5628891c2f0f7b4deb97d92c7b6ab1163a13b54

[ "MIT" ]

null

info_state.py

ProgrammingIncluded/PyAN

b5628891c2f0f7b4deb97d92c7b6ab1163a13b54

[ "MIT" ]

null

############################################ # Project: PyAN # File: info_state.py # By: ProgrammingIncluded # Website: ProgrammingIncluded.com # Desc: File to hold info state that # displays song information. ############################################ import chat_state as cs import connect as cnt import command as cmd from datetime import datetime INFO = {} LAST_FETCH = datetime.now() LAST_PRINT = datetime.now() PREV_PERC = 0.0 # Call function to setup state. def set_state(): # Call first to get info. update_state() def update_state(): # Prepare the info state by getting necessary info. # For now do a simple grab from website. Later figure out smart way of doing things. global LAST_FETCH global INFO delta = datetime.now() - LAST_FETCH if "nowPlayingSong" not in INFO or float(INFO["timeLeft"]) <= delta.total_seconds(): # Use manual sending because we only want to get info when we need it. INFO = cnt.send_get("now-playing") LAST_FETCH = datetime.now() cmd.CALLED = True # Call function to draw def display_state(): global LAST_PRINT # We need to check CALLED because other states may have drawn if cmd.CALLED or ((datetime.now() - LAST_PRINT).total_seconds() > 1): cur = INFO["nowPlayingSong"] print_song_data(cur) delta = datetime.now() - LAST_FETCH perc = 100.0 - ((float(INFO["timeLeft"]) - delta.total_seconds()) * 100.0 / float(cur["totalseconds"])) if perc > 100.0: perc = 100.0 elif perc < 0.0: perc = 0.0 cmd.print_buf("Percent: " + str(int(perc)) + "%\n\n") LAST_PRINT = datetime.now() def print_song_data(song_info): res = "Title: " + str(song_info["title"]) + "\n" res += "Album: " + str(song_info["album"]) + "\n" res += "Artist: " + str(song_info["artist"]) + "\n" res += "Comp: " + str(song_info["composers"]) + "\n" cmd.print_buf(res)

29.205882

111

0.602216

042d003827bdef5764e71d1de38b4638eedfd623

9,202

py

Python

src/einsteinpy/integrators/fantasy.py

iamhardikat11/einsteinpy

7bf0ca0020b273e616b6e7c19aed7a5e13925444

[ "MIT" ]

485

2019-02-04T09:15:22.000Z

2022-03-19T13:50:17.000Z

src/einsteinpy/integrators/fantasy.py

iamhardikat11/einsteinpy

7bf0ca0020b273e616b6e7c19aed7a5e13925444

[ "MIT" ]

570

2019-02-02T10:57:27.000Z

2022-02-26T16:37:05.000Z

src/einsteinpy/integrators/fantasy.py

iamhardikat11/einsteinpy

7bf0ca0020b273e616b6e7c19aed7a5e13925444

[ "MIT" ]

250

2019-01-30T14:14:14.000Z

2022-02-28T21:18:18.000Z

import warnings import numpy as np from .utils import _Z, _flow_A, _flow_B, _flow_mixed class GeodesicIntegrator: """ Geodesic Integrator, based on [1]_. This module uses Forward Mode Automatic Differentiation to calculate metric derivatives to machine precision leading to stable simulations. References ---------- .. [1] Christian, Pierre and Chan, Chi-Kwan; "FANTASY: User-Friendly Symplectic Geodesic Integrator for Arbitrary Metrics with Automatic Differentiation"; `2021 ApJ 909 67 <https://doi.org/10.3847/1538-4357/abdc28>`__ """ # TODO: Update arXiv attributions to ApJ (See #572) def __init__( self, metric, metric_params, q0, p0, time_like=True, steps=100, delta=0.5, rtol=1e-2, atol=1e-2, order=2, omega=1.0, suppress_warnings=False, ): """ Constructor Parameters ---------- metric : callable Metric Function. Currently, these metrics are supported: 1. Schwarzschild 2. Kerr 3. KerrNewman metric_params : array_like Tuple of parameters to pass to the metric E.g., ``(a,)`` for Kerr q0 : array_like Initial 4-Position p0 : array_like Initial 4-Momentum time_like : bool, optional Determines type of Geodesic ``True`` for Time-like geodesics ``False`` for Null-like geodesics Defaults to ``True`` steps : int Number of integration steps Defaults to ``50`` delta : float Initial integration step-size Defaults to ``0.5`` rtol : float Relative Tolerance Defaults to ``1e-2`` atol : float Absolute Tolerance Defaults to ``1e-2`` order : int Integration Order Defaults to ``2`` omega : float Coupling between Hamiltonian Flows Smaller values imply smaller integration error, but too small values can make the equation of motion non-integrable. For non-capture trajectories, ``omega = 1.0`` is recommended. For trajectories, that either lead to a capture or a grazing geodesic, a decreased value of ``0.01`` or less is recommended. Defaults to ``1.0`` suppress_warnings : bool Whether to suppress warnings during simulation Warnings are shown for every step, where numerical errors exceed specified tolerance (controlled by ``rtol`` and ``atol``) Defaults to ``False`` Raises ------ NotImplementedError If ``order`` is not in [2, 4, 6, 8] """ ORDERS = { 2: self._ord_2, 4: self._ord_4, 6: self._ord_6, 8: self._ord_8, } self.metric = metric self.metric_params = metric_params self.q0 = q0 self.p0 = p0 self.time_like = time_like self.steps = steps self.delta = delta self.omega = omega if order not in ORDERS: raise NotImplementedError( f"Order {order} integrator has not been implemented." ) self.order = order self.integrator = ORDERS[order] self.rtol = rtol self.atol = atol self.suppress_warnings = suppress_warnings self.step_num = 0 self.res_list = [q0, p0, q0, p0] self.results = list() def __str__(self): return f"""{self.__class__.__name__}(\n\ metric : {self.metric}\n\ metric_params : {self.metric_params}\n\ q0 : {self.q0},\n\ p0 : {self.p0},\n\ time_like : {self.time_like},\n\ steps : {self.steps},\n\ delta : {self.delta},\n\ omega : {self.omega},\n\ order : {self.order},\n\ rtol : {self.rtol},\n\ atol : {self.atol}\n\ suppress_warnings : {self.suppress_warnings} )""" def __repr__(self): return self.__str__() def _ord_2(self, q1, p1, q2, p2, delta): """ Order 2 Integration Scheme References ---------- .. [1] Christian, Pierre and Chan, Chi-Kwan; "FANTASY : User-Friendly Symplectic Geodesic Integrator for Arbitrary Metrics with Automatic Differentiation"; `2021 ApJ 909 67 <https://doi.org/10.3847/1538-4357/abdc28>`__ """ dl, omg = delta, self.omega g = self.metric g_prms = self.metric_params HA1 = np.array( [ q1, _flow_A(g, g_prms, q1, p1, q2, p2, 0.5 * dl)[1], _flow_A(g, g_prms, q1, p1, q2, p2, 0.5 * dl)[0], p2, ] ) HB1 = np.array( [ _flow_B(g, g_prms, HA1[0], HA1[1], HA1[2], HA1[3], 0.5 * dl)[0], HA1[1], HA1[2], _flow_B(g, g_prms, HA1[0], HA1[1], HA1[2], HA1[3], 0.5 * dl)[1], ] ) HC = _flow_mixed(HB1[0], HB1[1], HB1[2], HB1[3], dl, omg) HB2 = np.array( [ _flow_B(g, g_prms, HC[0], HC[1], HC[2], HC[3], 0.5 * dl)[0], HC[1], HC[2], _flow_B(g, g_prms, HC[0], HC[1], HC[2], HC[3], 0.5 * dl)[1], ] ) HA2 = np.array( [ HB2[0], _flow_A(g, g_prms, HB2[0], HB2[1], HB2[2], HB2[3], 0.5 * dl)[1], _flow_A(g, g_prms, HB2[0], HB2[1], HB2[2], HB2[3], 0.5 * dl)[0], HB2[3], ] ) return HA2 def _ord_4(self, q1, p1, q2, p2, delta): """ Order 4 Integration Scheme References ---------- .. [1] Yoshida, Haruo, "Construction of higher order symplectic integrators"; Physics Letters A, vol. 150, no. 5-7, pp. 262-268, 1990. `DOI: <https://doi.org/10.1016/0375-9601(90)90092-3>`__ """ dl = delta Z0, Z1 = _Z(self.order) step1 = self._ord_2(q1, p1, q2, p2, dl * Z1) step2 = self._ord_2(step1[0], step1[1], step1[2], step1[3], dl * Z0) step3 = self._ord_2(step2[0], step2[1], step2[2], step2[3], dl * Z1) return step3 def _ord_6(self, q1, p1, q2, p2, delta): """ Order 6 Integration Scheme References ---------- .. [1] Yoshida, Haruo, "Construction of higher order symplectic integrators"; Physics Letters A, vol. 150, no. 5-7, pp. 262-268, 1990. `DOI: <https://doi.org/10.1016/0375-9601(90)90092-3>`__ """ dl = delta Z0, Z1 = _Z(self.order) step1 = self._ord_4(q1, p1, q2, p2, dl * Z1) step2 = self._ord_4(step1[0], step1[1], step1[2], step1[3], dl * Z0) step3 = self._ord_4(step2[0], step2[1], step2[2], step2[3], dl * Z1) return step3 def _ord_8(self, q1, p1, q2, p2, delta): """ Order 8 Integration Scheme References ---------- .. [1] Yoshida, Haruo, "Construction of higher order symplectic integrators"; Physics Letters A, vol. 150, no. 5-7, pp. 262-268, 1990. `DOI: <https://doi.org/10.1016/0375-9601(90)90092-3>`__ """ dl = delta Z0, Z1 = _Z(self.order) step1 = self._ord_6(q1, p1, q2, p2, dl * Z1) step2 = self._ord_6(step1[0], step1[1], step1[2], step1[3], dl * Z0) step3 = self._ord_6(step2[0], step2[1], step2[2], step2[3], dl * Z1) return step3 def step(self): """ Advances integration by one step """ rl = self.res_list arr = self.integrator(rl[0], rl[1], rl[2], rl[3], self.delta) self.res_list = arr self.step_num += 1 # Stability check if not self.suppress_warnings: g = self.metric g_prms = self.metric_params q1 = arr[0] p1 = arr[1] # Ignoring # q_2 = arr[2] # p_2 = arr[3] const = -int(self.time_like) # g.p.p ~ -1 or 0 (const) if not np.allclose( g(q1, *g_prms) @ p1 @ p1, const, rtol=self.rtol, atol=self.atol ): warnings.warn( f"Numerical error has exceeded specified tolerance at step = {self.step_num}.", RuntimeWarning, ) self.results.append(self.res_list)

31.406143

100

0.480548

629f2aa8c250c5429b92f9b163e66ccfeed5d657

10,397

py

Python

koku/reporting/models.py

Vasyka/koku

b5aa9ec41c3b0821e74afe9ff3a5ffaedb910614

[ "Apache-2.0" ]

2

2022-01-12T03:42:39.000Z

2022-01-12T03:42:40.000Z

koku/reporting/models.py

Vasyka/koku

b5aa9ec41c3b0821e74afe9ff3a5ffaedb910614

[ "Apache-2.0" ]

null

koku/reporting/models.py

Vasyka/koku

b5aa9ec41c3b0821e74afe9ff3a5ffaedb910614

[ "Apache-2.0" ]

1

2021-07-21T09:33:59.000Z

# # Copyright 2021 Red Hat Inc. # SPDX-License-Identifier: Apache-2.0 # """Models for cost entry tables.""" # flake8: noqa from reporting.partition.models import PartitionedTable from reporting.provider.all.openshift.models import OCPAllComputeSummary from reporting.provider.all.openshift.models import OCPAllCostLineItemDailySummary from reporting.provider.all.openshift.models import OCPAllCostLineItemProjectDailySummary from reporting.provider.all.openshift.models import OCPAllCostSummary from reporting.provider.all.openshift.models import OCPAllCostSummaryByAccount from reporting.provider.all.openshift.models import OCPAllCostSummaryByRegion from reporting.provider.all.openshift.models import OCPAllCostSummaryByService from reporting.provider.all.openshift.models import OCPAllDatabaseSummary from reporting.provider.all.openshift.models import OCPAllNetworkSummary from reporting.provider.all.openshift.models import OCPAllStorageSummary from reporting.provider.aws.models import AWSAccountAlias from reporting.provider.aws.models import AWSComputeSummary from reporting.provider.aws.models import AWSComputeSummaryByAccount from reporting.provider.aws.models import AWSComputeSummaryByRegion from reporting.provider.aws.models import AWSComputeSummaryByService from reporting.provider.aws.models import AWSCostEntry from reporting.provider.aws.models import AWSCostEntryBill from reporting.provider.aws.models import AWSCostEntryLineItem from reporting.provider.aws.models import AWSCostEntryLineItemDaily from reporting.provider.aws.models import AWSCostEntryLineItemDailySummary from reporting.provider.aws.models import AWSCostEntryPricing from reporting.provider.aws.models import AWSCostEntryProduct from reporting.provider.aws.models import AWSCostEntryReservation from reporting.provider.aws.models import AWSCostSummary from reporting.provider.aws.models import AWSCostSummaryByAccount from reporting.provider.aws.models import AWSCostSummaryByRegion from reporting.provider.aws.models import AWSCostSummaryByService from reporting.provider.aws.models import AWSDatabaseSummary from reporting.provider.aws.models import AWSEnabledTagKeys from reporting.provider.aws.models import AWSNetworkSummary from reporting.provider.aws.models import AWSOrganizationalUnit from reporting.provider.aws.models import AWSStorageSummary from reporting.provider.aws.models import AWSStorageSummaryByAccount from reporting.provider.aws.models import AWSStorageSummaryByRegion from reporting.provider.aws.models import AWSStorageSummaryByService from reporting.provider.aws.models import AWSTagsSummary from reporting.provider.aws.openshift.models import OCPAWSComputeSummary from reporting.provider.aws.openshift.models import OCPAWSCostLineItemDailySummary from reporting.provider.aws.openshift.models import OCPAWSCostLineItemProjectDailySummary from reporting.provider.aws.openshift.models import OCPAWSCostSummary from reporting.provider.aws.openshift.models import OCPAWSCostSummaryByAccount from reporting.provider.aws.openshift.models import OCPAWSCostSummaryByRegion from reporting.provider.aws.openshift.models import OCPAWSCostSummaryByService from reporting.provider.aws.openshift.models import OCPAWSDatabaseSummary from reporting.provider.aws.openshift.models import OCPAWSNetworkSummary from reporting.provider.aws.openshift.models import OCPAWSStorageSummary from reporting.provider.aws.openshift.models import OCPAWSTagsSummary from reporting.provider.azure.models import AzureComputeSummary from reporting.provider.azure.models import AzureCostEntryBill from reporting.provider.azure.models import AzureCostEntryLineItemDaily from reporting.provider.azure.models import AzureCostEntryLineItemDailySummary from reporting.provider.azure.models import AzureCostEntryProductService from reporting.provider.azure.models import AzureCostSummary from reporting.provider.azure.models import AzureCostSummaryByAccount from reporting.provider.azure.models import AzureCostSummaryByLocation from reporting.provider.azure.models import AzureCostSummaryByService from reporting.provider.azure.models import AzureDatabaseSummary from reporting.provider.azure.models import AzureEnabledTagKeys from reporting.provider.azure.models import AzureMeter from reporting.provider.azure.models import AzureNetworkSummary from reporting.provider.azure.models import AzureStorageSummary from reporting.provider.azure.models import AzureTagsSummary from reporting.provider.azure.openshift.models import OCPAzureComputeSummary from reporting.provider.azure.openshift.models import OCPAzureCostLineItemDailySummary from reporting.provider.azure.openshift.models import OCPAzureCostLineItemProjectDailySummary from reporting.provider.azure.openshift.models import OCPAzureCostSummary from reporting.provider.azure.openshift.models import OCPAzureCostSummaryByAccount from reporting.provider.azure.openshift.models import OCPAzureCostSummaryByLocation from reporting.provider.azure.openshift.models import OCPAzureCostSummaryByService from reporting.provider.azure.openshift.models import OCPAzureDatabaseSummary from reporting.provider.azure.openshift.models import OCPAzureNetworkSummary from reporting.provider.azure.openshift.models import OCPAzureStorageSummary from reporting.provider.azure.openshift.models import OCPAzureTagsSummary from reporting.provider.gcp.models import GCPComputeSummary from reporting.provider.gcp.models import GCPComputeSummaryByAccount from reporting.provider.gcp.models import GCPComputeSummaryByProject from reporting.provider.gcp.models import GCPComputeSummaryByRegion from reporting.provider.gcp.models import GCPComputeSummaryByService from reporting.provider.gcp.models import GCPCostEntryBill from reporting.provider.gcp.models import GCPCostEntryLineItemDailySummary from reporting.provider.gcp.models import GCPCostEntryProductService from reporting.provider.gcp.models import GCPCostSummary from reporting.provider.gcp.models import GCPCostSummaryByAccount from reporting.provider.gcp.models import GCPCostSummaryByProject from reporting.provider.gcp.models import GCPCostSummaryByRegion from reporting.provider.gcp.models import GCPCostSummaryByService from reporting.provider.gcp.models import GCPDatabaseSummary from reporting.provider.gcp.models import GCPEnabledTagKeys from reporting.provider.gcp.models import GCPNetworkSummary from reporting.provider.gcp.models import GCPStorageSummary from reporting.provider.gcp.models import GCPStorageSummaryByAccount from reporting.provider.gcp.models import GCPStorageSummaryByProject from reporting.provider.gcp.models import GCPStorageSummaryByRegion from reporting.provider.gcp.models import GCPStorageSummaryByService from reporting.provider.gcp.models import GCPTagsSummary from reporting.provider.ocp.costs.models import CostSummary from reporting.provider.ocp.models import OCPCostSummary from reporting.provider.ocp.models import OCPCostSummaryByNode from reporting.provider.ocp.models import OCPCostSummaryByProject from reporting.provider.ocp.models import OCPEnabledTagKeys from reporting.provider.ocp.models import OCPNodeLabelLineItem from reporting.provider.ocp.models import OCPNodeLabelLineItemDaily from reporting.provider.ocp.models import OCPPodSummary from reporting.provider.ocp.models import OCPPodSummaryByProject from reporting.provider.ocp.models import OCPStorageLineItem from reporting.provider.ocp.models import OCPStorageLineItemDaily from reporting.provider.ocp.models import OCPStorageVolumeLabelSummary from reporting.provider.ocp.models import OCPUsageLineItem from reporting.provider.ocp.models import OCPUsageLineItemDaily from reporting.provider.ocp.models import OCPUsageLineItemDailySummary from reporting.provider.ocp.models import OCPUsagePodLabelSummary from reporting.provider.ocp.models import OCPUsageReport from reporting.provider.ocp.models import OCPUsageReportPeriod from reporting.provider.ocp.models import OCPVolumeSummary from reporting.provider.ocp.models import OCPVolumeSummaryByProject AWS_MATERIALIZED_VIEWS = ( AWSComputeSummary, AWSComputeSummaryByAccount, AWSComputeSummaryByRegion, AWSComputeSummaryByService, AWSCostSummary, AWSCostSummaryByAccount, AWSCostSummaryByRegion, AWSCostSummaryByService, AWSDatabaseSummary, AWSNetworkSummary, AWSStorageSummary, AWSStorageSummaryByAccount, AWSStorageSummaryByRegion, AWSStorageSummaryByService, ) AZURE_MATERIALIZED_VIEWS = ( AzureCostSummary, AzureCostSummaryByAccount, AzureCostSummaryByLocation, AzureCostSummaryByService, AzureComputeSummary, AzureStorageSummary, AzureNetworkSummary, AzureDatabaseSummary, ) OCP_MATERIALIZED_VIEWS = ( OCPPodSummary, OCPPodSummaryByProject, OCPVolumeSummary, OCPVolumeSummaryByProject, OCPCostSummary, OCPCostSummaryByProject, OCPCostSummaryByNode, ) OCP_ON_AWS_MATERIALIZED_VIEWS = ( OCPAWSCostSummary, OCPAWSCostSummaryByAccount, OCPAWSCostSummaryByService, OCPAWSCostSummaryByRegion, OCPAWSComputeSummary, OCPAWSStorageSummary, OCPAWSNetworkSummary, OCPAWSDatabaseSummary, ) OCP_ON_AZURE_MATERIALIZED_VIEWS = ( OCPAzureCostSummary, OCPAzureCostSummaryByAccount, OCPAzureCostSummaryByService, OCPAzureCostSummaryByLocation, OCPAzureComputeSummary, OCPAzureStorageSummary, OCPAzureNetworkSummary, OCPAzureDatabaseSummary, ) OCP_ON_INFRASTRUCTURE_MATERIALIZED_VIEWS = ( # OCPAllCostLineItemDailySummary, # OCPAllCostSummary, # OCPAllCostSummaryByAccount, # OCPAllCostSummaryByService, # OCPAllCostSummaryByRegion, # OCPAllComputeSummary, # OCPAllDatabaseSummary, # OCPAllNetworkSummary, # OCPAllStorageSummary, # OCPAllCostLineItemProjectDailySummary, OCPCostSummary, OCPCostSummaryByProject, OCPCostSummaryByNode, ) GCP_MATERIALIZED_VIEWS = ( GCPCostSummary, GCPCostSummaryByAccount, GCPCostSummaryByProject, GCPCostSummaryByRegion, GCPCostSummaryByService, GCPComputeSummary, GCPComputeSummaryByProject, GCPComputeSummaryByAccount, GCPComputeSummaryByService, GCPComputeSummaryByRegion, GCPStorageSummary, GCPStorageSummaryByProject, GCPStorageSummaryByService, GCPStorageSummaryByAccount, GCPStorageSummaryByRegion, GCPNetworkSummary, GCPDatabaseSummary, )

47.474886

93

0.864576

696f429ae60040e78d3a38bfc8fda8b41117b048

316

py

Python

constants.py

StevePirateX/recurring-chores

a682ac8b9d9bc050e6f4c19941524999b1d5951f

[ "MIT" ]

null

constants.py

StevePirateX/recurring-chores

a682ac8b9d9bc050e6f4c19941524999b1d5951f

[ "MIT" ]

null

constants.py

StevePirateX/recurring-chores

a682ac8b9d9bc050e6f4c19941524999b1d5951f

[ "MIT" ]

null

VERSION = "0.3" PRINT_INPUT_CHORE_LIST = False TEST_MODE = False TEST_ITERATIONS = 10000 # Default values for config import CHORE_LIST_CSV_NAME = "chore_list.csv" NUM_CHORES = 1 # Default CSV File CSV_HEADERS = "Chore,Frequency (every x days),Description" CSV_DEFAULT_ROW = "Demo Chore, 7, Demo chore description"

24.307692

58

0.778481

43f11f15a49bd36c2028ac01e96f9ab77af58c94

3,221

py

Python

tests/workflow_tests/test_storage.py

acidburn0zzz/cloudify-manager

ee2224c52347f7461a95976179ab61aee74a49dd

[ "Apache-2.0" ]

1

2015-11-03T14:27:11.000Z

tests/workflow_tests/test_storage.py

acidburn0zzz/cloudify-manager

ee2224c52347f7461a95976179ab61aee74a49dd

[ "Apache-2.0" ]

2

2021-03-20T05:33:19.000Z

2021-03-26T00:38:21.000Z

tests/workflow_tests/test_storage.py

acidburn0zzz/cloudify-manager

ee2224c52347f7461a95976179ab61aee74a49dd

[ "Apache-2.0" ]

1

2019-10-29T06:15:31.000Z

######## # Copyright (c) 2013 GigaSpaces Technologies Ltd. All rights reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # * See the License for the specific language governing permissions and # * limitations under the License. import uuid from testenv import TestCase from testenv.utils import get_resource as resource from testenv.utils import deploy_application as deploy from testenv.utils import create_rest_client from cloudify_rest_client.exceptions import CloudifyClientError class TestStorage(TestCase): def test_update_node_bad_version(self): deploy(resource("dsl/basic.yaml")) client = create_rest_client() instance = client.node_instances.list()[0] instance = client.node_instances.get(instance.id) # need the version props = {'key': 'value'} result = client.node_instances.update(instance.id, state='started', runtime_properties=props, version=instance.version,) self.assertEquals(instance.version+1, result.version) self.assertEquals(instance.id, result.id) self.assertDictContainsSubset(props, result.runtime_properties) self.assertEquals('started', result.state) # making another call with a bad version self.assertRaises( CloudifyClientError, client.node_instances.update, instance.id, version=1) def test_deployment_inputs(self): blueprint_id = str(uuid.uuid4()) blueprint = self.client.blueprints.upload(resource("dsl/basic.yaml"), blueprint_id) inputs = blueprint.plan['inputs'] self.assertEqual(1, len(inputs)) self.assertTrue('install_agent' in inputs) self.assertFalse(inputs['install_agent']['default']) self.assertTrue( len(inputs['install_agent']['description']) > 0) deployment_id = str(uuid.uuid4()) deployment = self.client.deployments.create(blueprint.id, deployment_id) self.assertEqual(1, len(deployment.inputs)) self.assertTrue('install_agent' in deployment.inputs) self.assertFalse(deployment.inputs['install_agent']) def test_node_operation_different_inputs(self): """ Tests storing different nodes with different structured inputs for the same operation. """ blueprint_id = str(uuid.uuid4()) blueprint = self.client.blueprints.upload( resource("dsl/two_nodes_different_inputs.yaml"), blueprint_id) deployment_id = str(uuid.uuid4()) self.client.deployments.create(blueprint.id, deployment_id)

42.381579

79

0.65104

99db158c01249c0f524e1c952f392023bb0633d8

4,095

py

Python

scrapy/resolver.py

gaelleboy/scrapy

ed95f41ce5e75404442a8f96bddeec382df56d34

[ "BSD-3-Clause" ]

null

scrapy/resolver.py

gaelleboy/scrapy

ed95f41ce5e75404442a8f96bddeec382df56d34

[ "BSD-3-Clause" ]

null

scrapy/resolver.py

gaelleboy/scrapy

ed95f41ce5e75404442a8f96bddeec382df56d34

[ "BSD-3-Clause" ]

null

from twisted.internet import defer from twisted.internet.base import ThreadedResolver from twisted.internet.interfaces import IHostResolution, IHostnameResolver, IResolutionReceiver, IResolverSimple from zope.interface.declarations import implementer, provider from scrapy.utils.datatypes import LocalCache # TODO: cache misses dnscache = LocalCache(10000) @implementer(IResolverSimple) class CachingThreadedResolver(ThreadedResolver): """ Default caching resolver. IPv4 only, supports setting a timeout value for DNS requests. """ def __init__(self, reactor, cache_size, timeout): super().__init__(reactor) dnscache.limit = cache_size self.timeout = timeout @classmethod def from_crawler(cls, crawler, reactor): cache_size = 0 if crawler.settings.getbool('DNSCACHE_ENABLED'): cache_size = crawler.settings.getint('DNSCACHE_SIZE') return cls(reactor, cache_size, crawler.settings.getfloat('DNS_TIMEOUT')) def install_on_reactor(self): self.reactor.installResolver(self) def getHostByName(self, name, timeout=None): if name in dnscache: return defer.succeed(dnscache[name]) # in Twisted<=16.6, getHostByName() is always called with # a default timeout of 60s (actually passed as (1, 3, 11, 45) tuple), # so the input argument above is simply overridden # to enforce Scrapy's DNS_TIMEOUT setting's value timeout = (self.timeout,) d = super().getHostByName(name, timeout) if dnscache.limit: d.addCallback(self._cache_result, name) return d def _cache_result(self, result, name): dnscache[name] = result return result @implementer(IHostResolution) class HostResolution: def __init__(self, name): self.name = name def cancel(self): raise NotImplementedError() @provider(IResolutionReceiver) class _CachingResolutionReceiver: def __init__(self, resolutionReceiver, hostName): self.resolutionReceiver = resolutionReceiver self.hostName = hostName self.addresses = [] def resolutionBegan(self, resolution): self.resolutionReceiver.resolutionBegan(resolution) self.resolution = resolution def addressResolved(self, address): self.resolutionReceiver.addressResolved(address) self.addresses.append(address) def resolutionComplete(self): self.resolutionReceiver.resolutionComplete() if self.addresses: dnscache[self.hostName] = self.addresses @implementer(IHostnameResolver) class CachingHostnameResolver: """ Experimental caching resolver. Resolves IPv4 and IPv6 addresses, does not support setting a timeout value for DNS requests. """ def __init__(self, reactor, cache_size): self.reactor = reactor self.original_resolver = reactor.nameResolver dnscache.limit = cache_size @classmethod def from_crawler(cls, crawler, reactor): if crawler.settings.getbool('DNSCACHE_ENABLED'): cache_size = crawler.settings.getint('DNSCACHE_SIZE') else: cache_size = 0 return cls(reactor, cache_size) def install_on_reactor(self): self.reactor.installNameResolver(self) def resolveHostName( self, resolutionReceiver, hostName, portNumber=0, addressTypes=None, transportSemantics="TCP" ): try: addresses = dnscache[hostName] except KeyError: return self.original_resolver.resolveHostName( _CachingResolutionReceiver(resolutionReceiver, hostName), hostName, portNumber, addressTypes, transportSemantics, ) else: resolutionReceiver.resolutionBegan(HostResolution(hostName)) for addr in addresses: resolutionReceiver.addressResolved(addr) resolutionReceiver.resolutionComplete() return resolutionReceiver

32.5

112

0.676679

2cf841133d613f2bb6b9eb042542be8d77b2c2c4

3,512

py

Python

nr.fs/src/nr/fs/_tempfile.py

NiklasRosenstein/nr-python

dc5b31ae5773ea4522a6f35112792dde9e872bef

[ "MIT" ]

3

2018-11-20T22:19:35.000Z

2020-10-31T09:23:53.000Z

nr.fs/src/nr/fs/_tempfile.py

NiklasRosenstein/python-nr

dc5b31ae5773ea4522a6f35112792dde9e872bef

[ "MIT" ]

3

2021-08-09T00:14:26.000Z

2021-08-09T00:28:27.000Z

nr.fs/src/nr/fs/_tempfile.py

NiklasRosenstein/nr-python

dc5b31ae5773ea4522a6f35112792dde9e872bef

[ "MIT" ]

3

2019-03-22T06:15:17.000Z

2020-10-31T09:23:53.000Z

# -*- coding: utf8 -*- # Copyright (c) 2019 Niklas Rosenstein # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. """ A better temporary file class. """ __all__ = ['tempfile', 'tempdir'] import codecs import errno import io import os import shutil import tempfile as _tempfile class tempfile(object): """ A proper named temporary file that is only deleted when the context-manager exits, not when the file is closed. Note that the file is also only opened via the context-manager. """ def __init__(self, suffix='', prefix='tmp', dir=None, text=False, encoding=None): self._text = text self._encoding = encoding self._mktemp = lambda: _tempfile.mkstemp(suffix, prefix, dir, text) self._fp = None self._name = None def __repr__(self): return '<tempfile name={!r}>'.format(self._name) @property def name(self): return self._name def __enter__(self): fd, self._name = self._mktemp() self._fp = os.fdopen(fd, 'w' if (self._text and not self._encoding) else 'wb') if self._encoding: self._fp = codecs.getwriter(self._encoding)(self._fp) if self._text and not self._encoding: self._encoding = self._fp.encoding return self def __exit__(self, *args): self._fp.close() try: os.remove(self._name) except OSError as e: if e.errno != errno.ENOENT: raise @property def encoding(self): return self._encoding def writable(self): return True def write(self, data): return self._fp.write(data) @property def closed(self): return self._fp.closed def close(self): self._fp.close() def tell(self): return self._fp.tell() def seekable(self): return False def readable(self): return False class tempdir(object): """ A context manager for a temporary directory. """ def __init__(self, suffix='', prefix='', dir=None): self._mkdtemp = lambda: _tempfile.mkdtemp(suffix, prefix, dir) self._name = None def __repr__(self): return '<tempdir name={!r}>'.format(self._name) def __enter__(self): self._name = self._mkdtemp() return self def __exit__(self, *args): self.close() @property def name(self): return self._name def close(self): if not self._name: return try: shutil.rmtree(self._name) except OSError as e: if e.errno != errno.ENOENT: raise def open(self, path, *args, **kwargs): return io.open(os.path.join(self._name, path), *args, **kwargs)

26.014815

83

0.692198

e7342a67a3b97193b26edf0bf0b927a1975fdfcb

248

py

Python

dice_classes.py

Winter259/easy-dice-roll

b5eba26932946ab3b2ebe0fdbaeaca74e55d40ad

[ "MIT" ]

null

dice_classes.py

Winter259/easy-dice-roll

b5eba26932946ab3b2ebe0fdbaeaca74e55d40ad

[ "MIT" ]

null

dice_classes.py

Winter259/easy-dice-roll

b5eba26932946ab3b2ebe0fdbaeaca74e55d40ad

[ "MIT" ]

null

__author__ = 'Simon' from dice_fnc import * class Dice(object): def __init__(self): self.type = choose_dice_type() def change_type(self): self.type = choose_dice_type() def roll_this_dice(self): roll_dice(self)

22.545455

38

0.657258

2c2272295b0c1c49d9672f2ff0655ac38d4dbcdb

3,460

py

Python

code.py

AbhishekKumarRoy/olympics-data-analysis

e5c97f90a96d7bd392c2b2a4ac4f6b9c122236ea

[ "MIT" ]

null

code.py

AbhishekKumarRoy/olympics-data-analysis

e5c97f90a96d7bd392c2b2a4ac4f6b9c122236ea

[ "MIT" ]

null

code.py

AbhishekKumarRoy/olympics-data-analysis

e5c97f90a96d7bd392c2b2a4ac4f6b9c122236ea

[ "MIT" ]

null

# -------------- #Importing header files import pandas as pd import numpy as np import matplotlib.pyplot as plt #Path of the file is stored in the variable path #Code starts here # Data Loading (Step 1) data = pd.read_csv(path) data.rename(columns = {"Total": "Total_Medals"}, inplace = True) data.head(10) # Summer or Winter (Step 2) data["Better_Event"] = np.where(data["Total_Summer"] > data["Total_Winter"], "Summer", "Winter") data["Better_Event"] = np.where(data["Total_Summer"] == data["Total_Winter"], "Both", data["Better_Event"]) better_event = data["Better_Event"].value_counts().index.values[0] print(better_event) # Top 10 (Step 3) top_countries = data[["Country_Name","Total_Summer", "Total_Winter","Total_Medals"]] top_countries = top_countries[: -1] # Defining function def top_ten(data, col): country_list = [] country_list = list((data.nlargest(10, col)["Country_Name"])) return country_list # Calling the function and creating a variable common top_10_summer = top_ten(top_countries, "Total_Summer") top_10_winter = top_ten(top_countries, "Total_Winter") top_10 = top_ten(top_countries, "Total_Medals") common = list(set(top_10_summer) & set(top_10_winter) & set(top_10)) print(common) # Plotting top 10 (Step 4) summer_df = data[data["Country_Name"].isin(top_10_summer)] winter_df = data[data["Country_Name"].isin(top_10_winter)] top_df = data[data["Country_Name"].isin(top_10)] # Bar plot # For summer plt.figure(figsize=(12, 5)) plt.bar(summer_df["Country_Name"], summer_df["Total_Summer"]) plt.xlabel("Countries") plt.ylabel("Total Summer Medals") plt.title("Top 10") plt.show() # For Winter plt.figure(figsize=(12, 5)) plt.bar(winter_df["Country_Name"], summer_df["Total_Winter"]) plt.xlabel("Countries") plt.ylabel("Total Winter Medals") plt.title("Top 10") plt.show() # For both events plt.figure(figsize=(12, 5)) plt.bar(top_df["Country_Name"], summer_df["Total_Medals"]) plt.xlabel("Countries") plt.ylabel("Total Medals") plt.title("Top 10") plt.show() # Top Performing Countries (Step 5) # For summer summer_df["Golden_Ratio"] = summer_df["Gold_Summer"] / summer_df["Total_Summer"] summer_max_ratio = summer_df["Golden_Ratio"].max() summer_country_gold = summer_df.loc[summer_df['Golden_Ratio'].idxmax(),'Country_Name'] print(summer_country_gold) # For winter winter_df["Golden_Ratio"] = winter_df["Gold_Winter"] / winter_df["Total_Winter"] winter_max_ratio = winter_df["Golden_Ratio"].max() winter_country_gold = winter_df.loc[winter_df['Golden_Ratio'].idxmax(),'Country_Name'] print(winter_country_gold) # For top 10 top_df["Golden_Ratio"] = top_df["Gold_Total"] / top_df["Total_Medals"] top_max_ratio = top_df["Golden_Ratio"].max() top_country_gold = top_df.loc[top_df['Golden_Ratio'].idxmax(),'Country_Name'] print(top_country_gold) # Best in the world (Step 6) data_1 = data[: -1] data_1["Total_Points"] = data_1["Gold_Total"]*3 + data_1["Silver_Total"]*2 + data_1["Bronze_Total"] most_points = data_1["Total_Points"].max() best_country = data_1.loc[data_1["Total_Points"].idxmax(), "Country_Name"] print(best_country) # Plotting the best (Step 7) best = data[data["Country_Name"] == best_country] best.reset_index(drop = True, inplace = True) best = best[["Gold_Total", "Silver_Total", "Bronze_Total"]] # Stacked bar plot plt.figure(figsize = (12, 5)) best.plot.bar(stacked = True) plt.xlabel("United States") plt.ylabel("Medals Tally") plt.xticks(rotation = 45) plt.show()

27.460317

107

0.732948

19b7ec977847d5372f44ee37118ba4d4284922d3

640

py

Python

tests/xlfunctions_vs_excel/irr_test.py

ckp95/xlcalculator

8f3f04a2a0a2ecb12c3d5837a5b13519137ae427

[ "MIT" ]

54

2020-04-26T09:18:29.000Z

2022-03-30T08:47:45.000Z

tests/xlfunctions_vs_excel/irr_test.py

st-lo/xlcalculator

19190ec90e948b7ab1eceb06448b96cc1728bc51

[ "MIT" ]

41

2020-05-04T04:12:36.000Z

2022-01-31T02:41:05.000Z

tests/xlfunctions_vs_excel/irr_test.py

st-lo/xlcalculator

19190ec90e948b7ab1eceb06448b96cc1728bc51

[ "MIT" ]

21

2020-05-21T20:49:23.000Z

2022-02-20T14:15:56.000Z

from .. import testing class IRRTest(testing.FunctionalTestCase): filename = "IRR.xlsx" def test_evaluation_A1(self): self.assertAlmostEqual( self.evaluator.get_cell_value('Sheet1!A1'), self.evaluator.evaluate('Sheet1!A1') ) def test_evaluation_B1(self): self.assertAlmostEqual( self.evaluator.get_cell_value('Sheet1!B1'), self.evaluator.evaluate('Sheet1!B1') ) def test_evaluation_C1(self): self.assertAlmostEqual( self.evaluator.get_cell_value('Sheet1!C1'), self.evaluator.evaluate('Sheet1!C1') )

26.666667

55

0.623438

334392537e3668a6ce009c931757759827158118

9,741

py

Python

pennylane/gradients/__init__.py

therooler/pennylane

88a8a5960a2ffd218a12f85ace632021eef2abf5

[ "Apache-2.0" ]

1

2022-01-21T15:20:20.000Z

pennylane/gradients/__init__.py

therooler/pennylane

88a8a5960a2ffd218a12f85ace632021eef2abf5

[ "Apache-2.0" ]

null

pennylane/gradients/__init__.py

therooler/pennylane

88a8a5960a2ffd218a12f85ace632021eef2abf5

[ "Apache-2.0" ]

null

# Copyright 2018-2021 Xanadu Quantum Technologies Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Quantum gradient transforms are strategies for computing the gradient of a quantum circuit that work by **transforming** the quantum circuit into one or more gradient circuits. These gradient circuits, once executed and post-processed, return the gradient of the original circuit. Examples of quantum gradient transforms include finite-differences and parameter-shift rules. This module provides a selection of device-independent, differentiable quantum gradient transforms. As such, these quantum gradient transforms can be used to compute the gradients of quantum circuits on both simulators and hardware. In addition, it also includes an API for writing your own quantum gradient transforms. These quantum gradient transforms can be used in two ways: - Transforming quantum circuits directly - Registering a quantum gradient strategy for use when performing autodifferentiation with a :class:`QNode <pennylane.QNode>`. Overview -------- Gradient transforms ^^^^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: api finite_diff param_shift param_shift_cv param_shift_hessian Custom gradients ^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: api gradient_transform hessian_transform Utility functions ^^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: api finite_diff_coeffs generate_shifted_tapes generate_multishifted_tapes generate_shift_rule generate_multi_shift_rule eigvals_to_frequencies compute_vjp batch_vjp vjp Registering autodifferentiation gradients ----------------------------------------- All PennyLane QNodes are automatically differentiable, and can be included seamlessly within an autodiff pipeline. When creating a :class:`QNode <pennylane.QNode>`, the strategy for determining the optimal differentiation strategy is *automated*, and takes into account the circuit, device, autodiff framework, and metadata (such as whether a finite number of shots are used). .. code-block:: python dev = qml.device("default.qubit", wires=2, shots=1000) @qml.qnode(dev, interface="tf") def circuit(weights): ... In particular: - When using a simulator device with exact measurement statistics, backpropagation is preferred due to performance and memory improvements. - When using a hardware device, or a simulator with a finite number of shots, a quantum gradient transform---such as the parameter-shift rule---is preferred. If you would like to specify a particular quantum gradient transform to use when differentiating your quantum circuit, this can be passed when creating the QNode: .. code-block:: python @qml.qnode(dev, gradient_fn=qml.gradients.param_shift) def circuit(weights): ... When using your preferred autodiff framework to compute the gradient of your hybrid quantum-classical cost function, the specified gradient transform for each QNode will be used. .. note:: A single cost function may include multiple QNodes, each with their own quantum gradient transform registered. Transforming QNodes ------------------- Alternatively, quantum gradient transforms can be applied manually to QNodes. .. code-block:: python dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(weights): qml.RX(weights[0], wires=0) qml.RY(weights[1], wires=1) qml.CNOT(wires=[0, 1]) qml.RX(weights[2], wires=1) return qml.probs(wires=1) >>> weights = np.array([0.1, 0.2, 0.3], requires_grad=True) >>> circuit(weights) tensor([0.9658079, 0.0341921], requires_grad=True) >>> qml.gradients.param_shift(circuit)(weights) tensor([[-0.04673668, -0.09442394, -0.14409127], [ 0.04673668, 0.09442394, 0.14409127]], requires_grad=True) Comparing this to autodifferentiation: >>> qml.grad(circuit)(weights) array([[-0.04673668, -0.09442394, -0.14409127], [ 0.04673668, 0.09442394, 0.14409127]]) Quantum gradient transforms can also be applied as decorators to QNodes, if *only* gradient information is needed. Evaluating the QNode will then automatically return the gradient: .. code-block:: python dev = qml.device("default.qubit", wires=2) @qml.gradients.param_shift @qml.qnode(dev) def decorated_circuit(weights): qml.RX(weights[0], wires=0) qml.RY(weights[1], wires=1) qml.CNOT(wires=[0, 1]) qml.RX(weights[2], wires=1) return qml.probs(wires=1) >>> decorated_circuit(weights) tensor([[-0.04673668, -0.09442394, -0.14409127], [ 0.04673668, 0.09442394, 0.14409127]], requires_grad=True) .. note:: If your circuit contains any operations not supported by the gradient transform, the transform will attempt to automatically decompose the circuit into only operations that support gradients. .. note:: If you wish to only return the purely **quantum** component of the gradient---that is, the gradient of the output with respect to **gate** arguments, not QNode arguments---pass ``hybrid=False`` when applying the transform: >>> qml.gradients.param_shift(circuit, hybrid=False)(weights) Differentiating gradient transforms ----------------------------------- Gradient transforms are themselves differentiable, allowing higher-order gradients to be computed: .. code-block:: python dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit(weights): qml.RX(weights[0], wires=0) qml.RY(weights[1], wires=1) qml.CNOT(wires=[0, 1]) qml.RX(weights[2], wires=1) return qml.expval(qml.PauliZ(1)) >>> weights = np.array([0.1, 0.2, 0.3], requires_grad=True) >>> circuit(weights) tensor(0.9316158, requires_grad=True) >>> qml.gradients.param_shift(circuit)(weights) # gradient array([[-0.09347337, -0.18884787, -0.28818254]]) >>> qml.jacobian(qml.gradients.param_shift(circuit))(weights) # hessian array([[[-0.9316158 , 0.01894799, 0.0289147 ], [ 0.01894799, -0.9316158 , 0.05841749], [ 0.0289147 , 0.05841749, -0.9316158 ]]]) Transforming tapes ------------------ Gradient transforms can be applied to low-level :class:`~.QuantumTape` objects, a datastructure representing variational quantum algorithms: .. code-block:: python weights = np.array([0.1, 0.2, 0.3], requires_grad=True) with qml.tape.JacobianTape() as tape: qml.RX(weights[0], wires=0) qml.RY(weights[1], wires=1) qml.CNOT(wires=[0, 1]) qml.RX(weights[2], wires=1) qml.expval(qml.PauliZ(1)) Unlike when transforming a QNode, transforming a tape directly will perform no implicit quantum device evaluation. Instead, it returns the processed tapes, and a post-processing function, which together define the gradient: >>> gradient_tapes, fn = qml.gradients.param_shift(tape) >>> gradient_tapes [<QuantumTape: wires=[0, 1], params=3>, <QuantumTape: wires=[0, 1], params=3>, <QuantumTape: wires=[0, 1], params=3>, <QuantumTape: wires=[0, 1], params=3>, <QuantumTape: wires=[0, 1], params=3>, <QuantumTape: wires=[0, 1], params=3>] This can be useful if the underlying circuits representing the gradient computation need to be analyzed. The output tapes can then be evaluated and post-processed to retrieve the gradient: >>> dev = qml.device("default.qubit", wires=2) >>> fn(qml.execute(gradient_tapes, dev, None)) [[-0.09347337 -0.18884787 -0.28818254]] Note that the post-processing function ``fn`` returned by the gradient transform is applied to the flat list of results returned from executing the gradient tapes. Custom gradient transforms -------------------------- Using the :class:`~.gradient_transform` decorator, custom gradient transforms can be created: .. code-block:: python @gradient_transform def my_custom_gradient(tape, **kwargs): ... return gradient_tapes, processing_fn Once created, a custom gradient transform can be applied directly to QNodes, or registered as the quantum gradient transform to use during autodifferentiation. For more details, please see the :class:`~.gradient_transform` documentation. """ import pennylane as qml from . import parameter_shift from . import parameter_shift_cv from . import param_shift_hessian from . import finite_difference from .gradient_transform import gradient_transform from .hessian_transform import hessian_transform from .finite_difference import finite_diff, finite_diff_coeffs from .parameter_shift import param_shift from .parameter_shift_cv import param_shift_cv from .param_shift_hessian import param_shift_hessian from .vjp import compute_vjp, batch_vjp, vjp from .hamiltonian_grad import hamiltonian_grad from .general_shift_rules import ( eigvals_to_frequencies, generate_shift_rule, generate_multi_shift_rule, generate_shifted_tapes, generate_multishifted_tapes, )

31.833333

94

0.700339

013eb778840cf462632b6d8a22936cae9ac822c8

1,682

py

Python

pygame_menu/examples/other/widget_positioning.py

dcartman/pygame-menu

52a823b9121aadd6893fb878150d613ab516fcac

[ "MIT" ]

null

pygame_menu/examples/other/widget_positioning.py

dcartman/pygame-menu

52a823b9121aadd6893fb878150d613ab516fcac

[ "MIT" ]

null

pygame_menu/examples/other/widget_positioning.py

dcartman/pygame-menu

52a823b9121aadd6893fb878150d613ab516fcac

[ "MIT" ]

null

""" pygame-menu https://github.com/ppizarror/pygame-menu EXAMPLE - WIDGET POSITIONING Test widget positioning example. """ import pygame_menu from pygame_menu.examples import create_example_window # Create the surface surface = create_example_window('Example - Widget Positioning', (640, 480)) # Create a custom theme my_theme = pygame_menu.themes.THEME_DARK.copy() my_theme.title = False # Hide the menu title menu = pygame_menu.Menu( height=480, # Use full-screen theme=my_theme, title='', center_content=False, width=640 ) menu.add.label( 'My App', background_color='#333', background_inflate=(30, 0), float=True # Widget does not add size to the menu ).translate(0, 10) label = menu.add.label( 'Lorem ipsum', float=True, font_name=pygame_menu.font.FONT_OPEN_SANS_ITALIC, font_size=25) label.rotate(90) label.translate(300, 160) # Button options b1 = menu.add.button( 'Main Menu', lambda: print(f'My method'), align=pygame_menu.locals.ALIGN_LEFT, float=True, selection_color='#fff' ) b1.translate(10, 170) b2 = menu.add.button( 'Exit', pygame_menu.events.EXIT, align=pygame_menu.locals.ALIGN_LEFT, float=True, selection_color='#fff' ) b2.translate(10, 235) # Bottom scrollable text f = menu.add.frame_v( background_color='#6b6e5e', border_color='#36372f', border_width=1, float=True, height=480, max_height=100, width=200 ) f.translate(220, 390) labels = [menu.add.label(f' Lorem ipsum #{i}', font_size=15, font_color='#000000', padding=0) for i in range(20)] for j in labels: f.pack(j) if __name__ == '__main__': menu.mainloop(surface)

21.844156

114

0.693817

e13f86275ac53900079f33d7b42eae045c7e908c

31,190

py

Python

analysis.py

aschelle/Thing2Vec

7b5d68cf47109b8d67eba492f8f119672721e4e1

[ "MIT" ]

null

analysis.py

aschelle/Thing2Vec

7b5d68cf47109b8d67eba492f8f119672721e4e1

[ "MIT" ]

null

analysis.py

aschelle/Thing2Vec

7b5d68cf47109b8d67eba492f8f119672721e4e1

[ "MIT" ]

null

import wordvectors.physicaldata.tools as tools import wordvectors.physicaldata.creation as creation import wordvectors.thing2vec as t2v from enum import Enum import numpy as np import collections import random class Question_Mode (Enum): """ Specifies how the answer given by the neural net is interpreted. Exact : The question is answered correctly only if the first answer of the neural net is the correct one. Near : The question is answered correctly if the first n answers of the neural net contain the correct one. n is specified in the analyzer class by _number_of_near. """ Exact = 1 Near = 2 class Translational_Mode (Enum): """ Specifies how the translational questions are generated. Collective : For all translational operations all particles of the system are moved. So from the start state all particles are moved randomly for one positive state and one negative state. Then the translations are added in the way positive_movement - negative_movement. The resulting movement is added to the start state for the correct answer state. Individual : Some translational operations only move single particles that were randomly chosen. First all particles are moved from a start state (original state is negative and moved is positive). Then a single particle is moved from the moved state and from the original (for both states the same)(first one is the correct answer, second is a positive state). """ Collective = 1 Individual = 2 class Question (): """ Class that bundles a question that is asked to the neural net. Objects of that class are used by the ReportClass and its child classes. """ def __init__(self, answer, positive, negative=[], vectors = False): """ Attention --------- The string of the parameters has to be a member of the dictionary in the thing2vec neural net! Answer has to be a string parameter always. positive and negative members can be vectors in the word vector space, too. Parameters ---------- answer : object that will be the correct answer of that question. positive : list of objects that will be positively (+) contribute to the question that is asked. negative : list of objects that will be negatively (-) contribute to the question that is asked. vectors : determines if the positive and negative objects are already vectors (True) or words that have to be translated into vectors (False) """ self.answer = answer self.positive = positive self.negative = negative self.vectors = vectors def set_answer(self, correct, accuracy=None, infos=None): """ Adds information about the answer that was given by the neural net Parameters ---------- correct : Set to True if question was answered correctly, False if not accuracy : Ranking of the right answer in the answer given by the neural net infos : Further infos about the answer that was given by the neural net """ self.correct = correct self.accuracy = accuracy self.infos = infos class ReportClass(): """ Class that bundles all information of a test that was performed on a neural net. """ def __init__(self, questions): """ Parameters ---------- questions : list of Question objects that were asked to the neural net. """ self.questions = questions def get_correct(self): """ Returns a list of all questions that were answered correctly. """ correct = [] for q in self.questions: if q.correct: correct.append(q) return correct def get_incorrect(self): """ Returns a list of all questions that were answered wrongly. """ incorrect = [] for q in self.questions: if not q.correct: incorrect.append(q) return incorrect def get_accuracy(self): """ Returns the accuracy of the neural net on that particular test. """ accuracy = 0 for q in self.questions: if q.correct: accuracy += 1 accuracy /= len(self.questions) return accuracy class base_analyzer (): """ Base class for analyzing a Thing2Vec neural net. Introduces general functions an variables to generate and ask questions to the neural net. """ #When using Question_Mode.Near this variable specifies the maximal distance (number of wrong candidates #before the right one) of the right answer and the answer the neural net has given. _number_of_near = 10 #Number of attempts when generating questions to find a allowed state before skipping that particular base state _max_it = 1000 def __init__(self, creator: creation.base_DataCreator, thing2vec: t2v.base_Thing2vec): """ Parameters ---------- creator : base_DataCreator Object of class base_DataCreator that was used for generating the data that was used by the neural net. thing2vec : base_Thing2vec The trained neural net which used the data created by the creator object. """ self.creator = creator self.thing2vec = thing2vec def get_translational_questions(self): raise NotImplementedError() def get_rotational_questions(self): raise NotImplementedError() def get_inversion_questions(self, number_of_questions=100): raise NotImplementedError() #Idea of enivronmental question: Take a state and its environment (nearest states), then translate the original state #and its environment by a vector and look at the nearest states of the translated original state. Now compare the shifted #environment of the original state and the nearest states of the shifted original state def get_environmental_questions(self): raise NotImplementedError() def shift_state(self, particles, particle_idx, movement): raise NotImplementedError() def visualize_translational_categories(self, labeling=False,number_of_categories=5): raise NotImplementedError() def get_translational_error(self): raise NotImplementedError() def test_translational_quality(self, iterations=100, question_mode = Question_Mode.Near): raise NotImplementedError() def ask_question(self, question, question_mode = Question_Mode.Exact, plot = False): """ Asks question to the neural net and returns wheater the question was anwered correctly or not. Parameters ---------- question : Question of type Question that shall be asked. question_mode : How the answer of the question by the neural net shall be interpreted. plot : If =True the answer by the neural net will be visualized and plotted Returns ------- Array of following form: [Question was answered right: bool, ranking of the right answer: int, result of neural net: list] """ plotsettings=t2v.PlotMode.noPlotting if plot and not question.vectors: self.visualize_question(question) plotsettings = t2v.PlotMode.answerOnly if not question.vectors: result = self.thing2vec.most_similar(positive= [str(q) for q in question.positive], negative=[str(q) for q in question.negative],number = self._number_of_near, plot=plotsettings) else: if question.negative != []: result = self.thing2vec.similar_by_vector(vector= np.sum(question.positive,axis=0) - np.sum(question.negative,axis=1), number=self._number_of_near, plot=plot) else: result = self.thing2vec.similar_by_vector(vector= np.sum(question.positive,axis=0), number=self._number_of_near, plot=plot) if question_mode == Question_Mode.Exact: if result[0][0] == str(question.answer): return [True,0,result] return [False,-1,result] elif question_mode == Question_Mode.Near: for i,r in enumerate(result): if r[0] == str(question.answer): return [True,i,result] return [False,-1,result] print("No suitable question mode selected!") return [False,-1,result] def test_neural_net (self, questions, question_mode = Question_Mode.Exact): """ Tests the neural net with the given bunch of questions. Parameters ---------- questions : List of questions that will be asked to the neural net. question_mode : How the answer of the question by the neural net shall be interpreted. """ report = ReportClass(questions) for q in questions: answer = self.ask_question(q, question_mode=question_mode) q.set_answer(answer[0],answer[1],answer[2]) return report def visualize_question (self, question): """ Visualizes the question by plotting all contributing states. Parameters ---------- question : Question that shall be plotted. """ titles = ["positive"] * len(question.positive) + ["negative"] * len(question.negative) + ["answer"] self.creator.plot_states(question.positive + question.negative + [question.answer], titles=titles) class Analyzer2DGrid (base_analyzer): """ Child class of base_analyzer that is used for analyzing neural nets that were trained with elements of the DataCreator2DGrid """ def __init__(self, creator: creation.DataCreator2DGrid, thing2vec: t2v.base_Thing2vec): """ Parameters ---------- creator : DataCreator2DGrid Object of class DataCreator2DGrid that was used for generating the data that was used by the neural net. thing2vec : base_Thing2vec The trained neural net which used the data created by the creator object. """ super().__init__(creator=creator, thing2vec=thing2vec) def _get_number_of_particles_in_state(self, particles): return self.creator.number_of_particles def get_translational_questions(self, number_of_questions=100, translational_mode = Translational_Mode.Individual): """ Get a randomly generated list of questions that have translational variances, meaning the particles have been only translational moved. Parameters ---------- number_of_questions : The maximal number of questions that will be generated translational_mode : Settings how the translational questions are created """ #TODO: Here one is limited by the number of states in the dictionary. One could implement a non-uniform random number generator #which takes more frequently more frequently occuring states. starting_states, translated_states, movement = [], [], [] changed_states, changed_translated_states, movement_change, particle_change = [],[],[],[] #TODO: Only maximal number_of_questions will be generated. Change for to while loop to be sure #to generate number_of_questions questions for n in range(number_of_questions): starting = self.creator.str_to_particles(self.thing2vec.reverse_dictionary[n+1]) if translational_mode == Translational_Mode.Individual: p_c = [random.randint(0,self._get_number_of_particles_in_state(starting)-1)] if translational_mode == Translational_Mode.Collective: p_c = [k for k in range(self._get_number_of_particles_in_state(starting))] it = 0 found = False while True: it +=1 if it > self._max_it: break #if maximal number of iterations is reached, break while loop [start, stop] = self._get_movement_range(starting) mov = random.randint(start, stop) shifted = self.shift_state(starting, [k for k in range(self._get_number_of_particles_in_state(starting))],mov) shifted_not_sorted = self.shift_state(starting, [k for k in range(self._get_number_of_particles_in_state(starting))],mov, sorted_p=False) if shifted != starting: found = True break if not found: continue #if while loop ended without finding a shifted state, this combination will be skipped it = 0 while True: it +=1 if it > self._max_it: break #if maximal number of iterations is reached, break while loop [start, stop] = self._get_movement_range(starting[p_c[0]:(p_c[-1]+1)]) mov_c = random.randint(start,stop) #mov_c = random.randint(-starting[p_c[0]], self.creator.size_of_system**2 - starting[len(p_c)-1]) changed_starting = self.shift_state(starting,p_c,mov_c) changed_translated = self.shift_state(shifted_not_sorted,p_c,mov_c) #The not sorted particles are used so that the same particle is moved as before #when suitable states for the question were found, add them to the list. if changed_starting != starting and changed_translated != shifted: if ((self.thing2vec.is_in_dictionary(str(shifted))) and (self.thing2vec.is_in_dictionary(str(starting))) and (self.thing2vec.is_in_dictionary(str(changed_starting))) and (self.thing2vec.is_in_dictionary(str(changed_translated)))): translated_states.append(shifted) starting_states.append(starting) movement.append(mov) changed_states.append(changed_starting) changed_translated_states.append(changed_translated) movement_change.append(mov_c) particle_change.append(p_c) break #Now sum up results for questions questions = [Question(changed_translated_states[k], [changed_states[k], translated_states[k]],[starting_states[k]]) for k in range(len(starting_states))] return questions def _get_movement_range(self, particles): """ Get maximal possible movement range of the given particles. Parameters ---------- particles : bunch of particles in particles format that shall be moved Returns ------- [minmal movement, maximal movement] """ return [-particles[0], self.creator.size_of_system**2 - particles[-1]] def get_rotational_questions(self, number_of_questions=100): """ Get randomly generated list of questions that have rotational variances. Two semirandom start configurations are rotated with the same angle. The rotation of of two states is taken as "difference" (by substracting) and is added to the other start state. Parameters ---------- number_of_questions : The maximal number of questions that will be generated """ first_states = [self.creator.str_to_particles(self.thing2vec.reverse_dictionary[n+1]) for n in range(number_of_questions)] second_states = [self.creator.str_to_particles(self.thing2vec.reverse_dictionary[random.randint(1,number_of_questions)]) for n in range(number_of_questions)] rotation_steps = [random.randint(1,3) for n in range(number_of_questions)] rotated_first_states = [self._rotate_particles(first_states[k],rotation_steps=rotation_steps[k]) for k in range(number_of_questions)] rotated_second_states = [self._rotate_particles(second_states[k], rotation_steps=rotation_steps[k]) for k in range(number_of_questions)] questions = [Question(rotated_second_states[k],[rotated_first_states[k], second_states[k]], [first_states[k]]) for k in range(number_of_questions) if self.thing2vec.is_in_dictionary(str(rotated_second_states[k])) and self.thing2vec.is_in_dictionary(str(rotated_first_states[k])) and self.thing2vec.is_in_dictionary(str(second_states[k])) and self.thing2vec.is_in_dictionary(str(first_states[k]))] return questions def _rotate_particles(self, particles,direction=+1, rotation_steps =1): """ Rotates the given particles state clockwise in 90° steps around the center of the system. Parameters ---------- particles : state in particles format that will be rotated direction : direction of the rotation. +1 is clockwise, -1 is anticlockwise rotation_steps : number of 90° rotations in the given direction """ xypositions = [self.creator.getxy_position(p) for p in particles] middle = (self.creator.size_of_system-1) / 2 for t in range(rotation_steps): for k in range(len(xypositions)): xypositions[k] = [int((-1)*direction*(xypositions[k][1]-middle)+middle), int(direction*(xypositions[k][0]-middle)+middle)] particles = [self.creator.getparticle_position(p) for p in xypositions] particles.sort() return particles #TODO: Implement function to show context of the given categories too. Meaning, plotting the most common states, too def visualize_translational_categories(self, labeling=False,number_of_categories=7, method=t2v.Dimension_Reduction_Method.tsne, hide_plot=False, starting=None, movement=None): """ Visualizes random states that were translated in x or y direction to build a translation line with different colours using the tsne method. Parameters ---------- labeling : if set to True all states will be labeled by their state name. number_of_categories : number of translation lines/categories that will be generated and visualized. method : Method that is used for dimension reduction hide_plot : if True the plot will not be shown (can be useful if the plot shall be saved directly) starting_state : starting state of creating the translational categories. If set to None the starting state will be chosen randomly. movement : array of the two movements that will be used for creating all starts for the categories (index 0) and the direction of movement of one category (index 1). If set to None the starting state will be chosen randomly. """ categories = [] if starting == None: starting = self.creator.str_to_particles(self.thing2vec.reverse_dictionary[random.randint(1,100)]) if movement == None: movement = sorted(self.creator.get_possibilities(), reverse=True)[:2] random.shuffle(movement) starts_for_categories = self._generate_translational_category(starting,movement[0]) #random.shuffle(starts_for_categories) for k in range(min(number_of_categories,len(starts_for_categories))): categories.append(self._generate_translational_category(starts_for_categories[k],movement[1])) categories = [[str(c) for c in ies] for ies in categories] return self.thing2vec.visualize_categories(categories, [str(c[0]) for c in categories], labeling=labeling, method=method, hide_plot=hide_plot) def _generate_translational_category(self,start,movement): """ Generates starting from the start state a translational line/category by shifting the start state in movement and -movement direction. Parameters ---------- start : starting state that will be shifted movement : direction of the shift of the start state """ category = [start] while True: new = self.shift_state(category[-1], [k for k in range(len(start))], movement) if new == category [-1]: break category.append(new) while True: new = self.shift_state(category[0], [k for k in range(len(start))], -movement) if new == category [0]: break category.insert(0,new) return category def shift_state(self, particles, particle_idx, movement, sorted_p=True): """ Shifts all specified particles in the given state by the given movement direction. Parameters ---------- particles : The state that shall be shifted in the particles format particle_idx : A list of indexes of the particles in the state that shall be moved. movement : Movement that shall be applied to all particles that were specified in particle_idx. sorted_p : If True, the particles are sorted due their indices (needed for the dictionary) """ shifted_particles = particles[:] #copy particles for idx in particle_idx: if self.creator.Is_move_possible([shifted_particles[idx]], movement, 0): #With these parameters only borders will be checked #shifted_particles[idx] = shifted_particles[idx] + movement shifted_particles[idx] = self.creator.Apply_movement(shifted_particles[idx], movement) else: #print("Not possible to move state!") return particles counter = collections.Counter(shifted_particles) if counter.most_common(n=1)[0][1] > 1: #print("Not possible to move state!") return particles if sorted_p: shifted_particles.sort() return shifted_particles def test_translational_quality(self, iterations=100, question_mode = Question_Mode.Near): """ Tests the quality of a translational motion. For that a random state is translated in x or y direction to get a "line of states". Now the normalized distance between the first and the last state is taken to get a difference vector. Parts of that vector are now added to the first state vector. The results are compared to the real word vector of the translated state given in the "line of states". Additionaly a test is performed if the true word vector is comparable near to the calculated vectors in contrast to all other vectors. Parameters ---------- iterations : How much iterations of the above procedure are performed to calculate a mean error question_mode : question mode that will be used for the test if the true word vector is near the calculated vector Returns ------- [errors, accuracies] : errors : distance between the real word vector and the calculated vector divided by the length of the added vector. accuracies : ratio of correct answered tests """ errors, accuracies = [], [] key_errors = 0 for k in range(iterations): try: result = self._get_translational_error(question_mode=question_mode) errors.append(result[0]) accuracies.append(result[1]) except KeyError: #for the case that some states do not exist in dictionary key_errors += 1 errors, accuracies = np.mean(errors), np.mean(accuracies) return [errors, accuracies] def _get_translational_error(self, question_mode = Question_Mode.Near): """ Is used by the test_translational_quality method to perform the translational test as described in the documentation of that function. Parameters ---------- question_mode : question mode that will be used for the test if the true word vector is near the calculated vector """ translation_line = [] #Find random translation line that has a length of minimum 3 to be able to perform the test while len(translation_line) < 3: starting = self.creator.str_to_particles(self.thing2vec.reverse_dictionary[random.randint(1,100)]) movement = sorted(self.creator.get_possibilities(), reverse=True)[:2] random.shuffle(movement) translation_line = self._generate_translational_category(starting,movement[0]) #Use normalized vectors because for most_similar methods normalized vectors are used too. start_vec, stop_vec = self.thing2vec.get_word_vector(str(translation_line[0]), normalized=True), self.thing2vec.get_word_vector(str(translation_line[-1]), normalized=True) diff_vec = stop_vec-start_vec error = 0 questions = [] for k in range(1,len(translation_line)-1): #Calculate vector starting from the start word vector and normalize it vector = start_vec + diff_vec * (k/(len(translation_line)-1)) vector /= np.linalg.norm(vector) questions.append(Question(answer=translation_line[k], positive=[vector],vectors=True)) dist_btw_pnts = np.linalg.norm(self.thing2vec.get_word_vector(str(translation_line[0]), normalized=True) - self.thing2vec.get_word_vector(str(translation_line[k]), normalized=True)) translational_error = np.linalg.norm(vector - self.thing2vec.get_word_vector(str(translation_line[k]), normalized=True)) error += translational_error/dist_btw_pnts #print("dist: %f error: %f"%(dist_btw_pnts, translational_error)) report = self.test_neural_net(questions,question_mode=question_mode) error /= len(translation_line)-2 return [error, report.get_accuracy()] class Analyzer2DGridPeriodic (Analyzer2DGrid): """ Child class of Analyzer2DGrid that is used for analyzing neural nets that were trained with elements of the DataCreator2DGridPeriodic """ def __init__(self, creator: creation.DataCreator2DGridPeriodic, thing2vec: t2v.base_Thing2vec): """ Parameters ---------- creator : DataCreator2DGrid Object of class DataCreator2DGrid that was used for generating the data that was used by the neural net. thing2vec : base_Thing2vec The trained neural net which used the data created by the creator object. """ super().__init__(creator=creator, thing2vec=thing2vec) def _get_movement_range(self,particles): """ Get maximal possible movement range of the given particles. Parameters ---------- particles : bunch of particles in particles format that shall be moved Returns ------- [minmal movement, maximal movement] """ return [0, self.creator.size_of_system**2] def _generate_translational_category(self,start,movement): """ Generates starting from the start state a translational line/category by shifting the start state in movement direction until it reaches start again. Parameters ---------- start : starting state that will be shifted movement : direction of the shift of the start state """ category = [start] while True: new = self.shift_state(category[-1], [k for k in range(len(start))], movement) if new == category [0]: break category.append(new) return category class Analyzer2DIsingModel(Analyzer2DGridPeriodic): def __init__(self, creator: creation.DataCreator2DIsingModel, thing2vec: t2v.base_Thing2vec): """ Parameters ---------- creator : DataCreator2DIsingModel Object of class DataCreator2DIsingModel that was used for generating the data that was used by the neural net. thing2vec : base_Thing2vec The trained neural net which used the data created by the creator object. """ super().__init__(creator=creator, thing2vec=thing2vec) def _get_number_of_particles_in_state(self, particles): return len(particles) def _invert_particles(self, particles): all_places = [k for k in range(self.creator.size_of_system**2)] for p in particles: all_places.remove(p) return all_places #TODO: Add option to only invert a certain area of the grid def get_inversion_questions(self, number_of_questions=100): idx_1 = [k for k in range(1,number_of_questions*2)] idx_2 = idx_1[:] random.shuffle(idx_1) random.shuffle(idx_2) first_states = [self.creator.str_to_particles(self.thing2vec.reverse_dictionary[idx_1[n]]) for n in range(number_of_questions)] second_states = [self.creator.str_to_particles(self.thing2vec.reverse_dictionary[idx_2[n]]) for n in range(number_of_questions)] first_states_inv = [self._invert_particles(s) for s in first_states] second_states_inv = [self._invert_particles(s) for s in second_states] questions = [Question(second_states_inv[k],[second_states[k],first_states_inv[k]],[first_states[k]]) for k in range(number_of_questions) if self.thing2vec.is_in_dictionary(str(first_states[k])) and self.thing2vec.is_in_dictionary(str(second_states[k])) and self.thing2vec.is_in_dictionary(str(first_states_inv[k])) and self.thing2vec.is_in_dictionary(str(second_states_inv[k]))] return questions

48.058552

191

0.629144

c86f0e98b79cc7e0aa44bfbbc6897f10181c5c30

6,781

py

Python

src/models/GravNet.py

InProgress18/GNN_code

339a0773b85d2e65e198446f54c15a48ad125674

[ "BSD-3-Clause" ]

null

src/models/GravNet.py

InProgress18/GNN_code

339a0773b85d2e65e198446f54c15a48ad125674

[ "BSD-3-Clause" ]

1

2022-02-11T19:15:55.000Z

src/models/GravNet.py

InProgress18/GNN_code

339a0773b85d2e65e198446f54c15a48ad125674

[ "BSD-3-Clause" ]

2

2020-07-27T22:44:29.000Z

2021-01-22T05:21:18.000Z

# torch geometric implementation of network introduced in arXiv:1902.07987 import torch import torch.nn as nn import numpy as np # this network has two outputs with different numbers of features # the two outputs are called "spatial" and "learned" based on usage in GravNet class DoubleOutputNetwork(nn.Module): def __init__(self, spatial, learned): super(DoubleOutputNetwork, self).__init__() self.spatial = spatial self.learned = learned def forward(self, x): return self.spatial(x), self.learned(x) # performs message passing with input vector of weights that modify the features from torch_geometric.nn import MessagePassing class WeightedMessagePassing(MessagePassing): def message(self, x_j, weights): return np.multiply(x_j, weights) # potential functions for GravNet # (essentially kernel functions of distance in latent space) def gaussian_kernel(d_ij): return np.exp(-d_ij**2) def exponential_kernel(d_ij): return np.exp(-np.abs(d_ij)) _allowed_kernels = { 'gaussian': gaussian_kernel, 'exponential': exponential_kernel, } # the full GravNet layer # this is a sandwich of dense NN + neighbor assignment & message passing + dense NN # the first dense NN should be a DoubleOutputNetwork or similar (or a sequence that ends in such) from torch_geometric.nn import knn_graph from torch import cdist, index_select class GravNetLayer(nn.Module): def __init__(self,first_dense,n_neighbors,aggrs,second_dense,kernel='gaussian'): self.first_dense = first_dense self.n_neighbors = n_neighbors self.second_dense = second_dense if kernel not in _allowed_kernels: raise ValueError("Unrecognized kernel "+kernel+" (allowed values: "+', '.join(allowed_kernels)+")") self.kernel = _allowed_kernels[kernel] self.messengers = [] for aggr in aggrs: self.messengers.append(WeightedMessagePassing(aggr=aggr,flow="target_to_source")) def forward(self, x, batch=None): # apply first dense NN to derive spatial and learned features spatial, learned = self.first_dense(x) # use spatial to generate edge index edge_index = knn_graph(spatial, self.n_neighbors, batch, loop=False) # make the vector of distance weights using kernel neighbors = index_select(spatial,0,edge_index[1]) distances = cdist(spatial,neighbors,metric='euclidean') weights = self.kernel(distances) # use learned for message passing messages = [x] for messenger in self.messengers: messages.append(messenger(learned,weights)) # concatenate features, keep input all_features = torch.cat(messages, dim=1) # apply second dense to get final set of features final = self.second_dense(all_features) return final # a single block for the network class GravBlock(nn.Module): def __init__(self, input_dim = 10, dense_dim = 64, spatial_dim = 4, learned_dim = 22, out_dim = 48, n_neighbors = 40, aggrs = ['add','mean','max']): self.layers = nn.Sequential( # first section: 3 dense layers w/ 64 nodes, tanh activation # add 1 to input_dim b/c concatenation of mean nn.Linear(in_features=input_dim+1,out_features=dense_dim), nn.Tanh(), nn.Linear(in_features=dense_dim,out_features=dense_dim), nn.Tanh(), nn.Linear(in_features=dense_dim,out_features=dense_dim), nn.Tanh(), # second section: GravNetLayer GravNetLayer( first_dense = DoubleOutputNetwork( spatial = nn.Linear(in_features=dense_dim,out_features=spatial_dim), learned = nn.Linear(in_features=dense_dim,out_features=learned_dim), ), n_neighbors = n_neighbors, aggrs = aggrs, second_dense = nn.Sequential( nn.Linear(in_features=learned_dim,out_features=out_dim), nn.Tanh(), ), ), nn.BatchNorm1d(out_dim) ) # keep track of this in order to chain blocks together self.out_dim = out_dim def forward(self, x): # concatenate mean of features x = torch.cat([x, np.mean(x)], dim=1) # apply layers x = self.layers(x) return x # the full network, with multiple blocks class GravNet(nn.Module): # kwargs passed to GravBlocks def __init__(self, n_blocks = 4, final_dim = 128, n_clusters = 2, **kwargs): #batch norm for the input data self.inputnorm = nn.BatchNorm1d(kwargs['input_dim']) # first block just takes kwargs self.blocks = [GravBlock(**kwargs)] # subsequent blocks need to know the first block's output self.blocks.extend([GravBlock(input_dim = self.blocks[0].out_dim, **kwargs) for n in range(1, n_blocks)]) # final set of layers: dense ReLU, input from all blocks -> small dense ReLU -> small dense softmax self.final = nn.Sequential( nn.Linear(in_features=n_blocks*self.blocks[0].out_dim,out_features=final_dim), nn.ReLU(), nn.Linear(in_features=final_dim,out_features=n_clusters+1), nn.ReLU(), nn.Linear(in_features=n_clusters+1,out_features=n_clusters), nn.Softmax(), ) def forward(self, x): # apply batch norm to input (and then to all block outputs) x = self.inputnorm(x) # feed each block's output to the next all_output = [x] for block in self.blocks: block_output = block(all_output[-1]) all_output.append(block_output) # concatenate output from all blocks all_output = torch.cat(all_output[1:], dim=1) return self.final(all_output) # define the loss function # expects features in target: BxVx[energy, truth fraction, truth fraction, ...] def energy_fraction_loss(pred, target, weight=None): energy = target[:,0] truth = target[:,1:] # used for per-sensor energy weighting w/in cluster total_energy_cluster = torch.sqrt(energy*truth) # get numer and denom terms for each shower numers = torch.sum(total_energy_cluster*(pred-truth)**2,axis=1) denoms = torch.sum(total_energy_cluster,axis=1) # sum of weighted differences loss = torch.sum(numers/denoms) return loss # make a module class EnergyFractionLoss(nn.Module): def forward(self, energy, pred, truth): return energy_fraction_loss(energy, pred, truth)

40.849398

152

0.645038

4bcafa6a3c037cba9fc6f3d39372e879ed27b198

21,229

py

Python

curator/interfaces/vnv_components_interface.py

juanlucruz/tng-vnv-curator

0ce5ff79cae406e64ce257fa746ecc3590e17e7d

[ "Apache-2.0" ]

1

2022-03-04T16:02:22.000Z

curator/interfaces/vnv_components_interface.py

juanlucruz/tng-vnv-curator

0ce5ff79cae406e64ce257fa746ecc3590e17e7d

[ "Apache-2.0" ]

15

2019-03-05T22:22:25.000Z

2019-12-12T13:52:46.000Z

curator/interfaces/vnv_components_interface.py

juanlucruz/tng-vnv-curator

0ce5ff79cae406e64ce257fa746ecc3590e17e7d

[ "Apache-2.0" ]

2

2019-02-18T13:20:48.000Z

2019-03-05T21:27:10.000Z

# Copyright (c) 2019 5GTANGO # ALL RIGHTS RESERVED. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Neither the name of the SONATA-NFV, 5GTANGO # nor the names of its contributors may be used to endorse or promote # products derived from this software without specific prior written # permission. # # # This work has been performed in the framework of the 5GTANGO project, # funded by the European Commission under Grant number 761493 through # the Horizon 2020 and 5G-PPP programmes. The authors would like to # acknowledge the contributions of their colleagues of the 5GTANGO # partner consortium (www.5gtango.eu). import os import requests import logging import json from curator.interfaces.interface import Interface from curator.database import context from curator.logger import TangoLogger _LOG = TangoLogger.getLogger('curator:interfaces', log_level=logging.DEBUG, log_json=True) # _LOG = logging.getLogger('flask.app') # States: STARTING, COMPLETED, CANCELLING, CANCELLED, ERROR class PlannerInterface(Interface): """ This is a Interface for Planner Callbacks: [ { eventActor: 'Curator', url: '/test-plans/on-change/completed', status:TEST_PLAN_STATUS.COMPLETED, }, { eventActor: 'Curator', url: '/test-plans/on-change’, } ] """ def __init__(self, cu_api_root, cu_api_version): Interface.__init__(self, cu_api_root, cu_api_version) self.__base_url = os.getenv('PLANNER_BASE') self.__running_test_plans = [] def add_new_test_plan(self, test_plan_uuid): self.__running_test_plans.append(test_plan_uuid) def send_callback(self, suffix, test_plan_uuid, result_list, status='UNKNOWN', event_actor='Curator', exception=None): url = self.__base_url + suffix payload = { 'event_actor': event_actor, 'test_plan_uuid': test_plan_uuid, 'exception': exception, 'status': status, 'test_results': result_list } headers = {"Content-type": "application/json"} _LOG.debug(f'Accesing {url}') _LOG.debug(f'Payload {payload}') try: r = requests.post(url, headers=headers, json=payload) _LOG.debug(f'ResContent {r.text}'.replace('\n', ' ')) _LOG.debug(f'ResHeaders {r.headers}') # resp = r.json() # Response should be None if r.status_code == 200: return r.status_code else: return r except Exception as e: resp = {'error': str(e), 'content': None} return resp class PlatformAdapterInterface(Interface): """ This is a Interface for Platform Adapter (PA) """ def __init__(self, cu_api_root, cu_api_version): Interface.__init__(self, cu_api_root, cu_api_version) self.base_url = os.getenv('PLATFORM_ADAPTER_BASE') # self.running_instances = [] self.events = [] self.osm_sp_usage_count = dict() self.sonata_sp_usage_count = dict() def available_platforms(self): url = '/'.join([self.base_url, 'service_platforms']) headers = {"Content-type": "application/json"} try: response = requests.get(url, headers=headers) if response.status_code == 200: return response.json() elif response.status_code == 404: raise FileNotFoundError except Exception as e: _LOG.exception(e) raise e def available_platforms_by_type(self, sp_type): url = '/'.join([self.base_url, 'service_platforms']) headers = {"Content-type": "application/json"} try: _LOG.debug(f'Getting {url}') response = requests.get(url, headers=headers) _LOG.debug(f'Response {response.json()}') if response.status_code == 200: return list(filter(lambda x: x['type'] == sp_type, response.json())) elif response.status_code == 404: raise FileNotFoundError except Exception as e: _LOG.exception(e) raise e def remote_download_package(self, package_id): """ Command the PA to download the package on his volume ref=https://github.com/sonata-nfv/tng-vnv-platform-adapter/wiki/curator# tng-vnv-platform-mngradapterspackagespackage_iddownload :param package_id: :return: """ url = '/'.join([self.base_url, 'adapters', 'packages', package_id, 'download']) try: response = requests.get(url) if response.status_code == 200: return response.text # FIXME: Should be a json elif response.status_code == 404: raise FileNotFoundError except Exception as e: _LOG.exception(e) raise e def transfer_package_sonata(self, package_info, service_platform): _LOG.debug(f'Start transfer of {package_info["uuid"]}@VnV(self) TO {service_platform}') self.remote_download_package(package_info["uuid"]) package_process_uuid = self.upload_package(service_platform, package_info["package_file_uuid"]) _LOG.debug(f'Transfer complete, running {package_process_uuid}@SP_({service_platform})') return package_process_uuid def transfer_package_osm(self): """ Understanding package as the bundle of nsd + related vnfds :return: """ pass def get_service_uuid_sonata(self, service_platform, name, vendor, version): """ /adapters/<service_platform>/services/<:name>/<:vendor>/<:version>/id :param service_platform: :param name: :param vendor: :param version: :return: """ url = '/'.join([self.base_url, 'adapters', service_platform, 'services', name, vendor, version, 'id']) headers = {"Content-type": "application/json"} try: response = requests.post(url, headers=headers) if response.status_code == 200: return response.text # return response.json() # FIXME: Every request should respond a JSON elif response.status_code == 404: raise FileNotFoundError except Exception as e: _LOG.exception(e) raise e def get_service_instantiations_inventory(self, service_platform): """ Returns a list of instantiated services { "id":"4537e905-5183-4d96-bb31-3860121e21df", "created_at":"2019-03-20T20:31:57.564Z", "updated_at":"2019-03-20T20:40:48.515Z", "status":"READY", "request_type":"CREATE_SERVICE", "instance_uuid":"143f50a9-ea11-4292-bd25-c0d4cbdabe6f", "ingresses":"[]", "egresses":"[]", "callback":"", "blacklist":"[]", "customer_uuid":null, "sla_id":null, "name":null, "error":null, "description":null, "service":{ "uuid":"c8dfa216-c1bd-46da-ac8a-fa3fb444cc16", "vendor":"eu.5gtango", "name":"ns-squid-haproxy", "version":"0.2" } } :param service_platform: :return: """ url = '/'.join([self.base_url, 'adapters', service_platform, 'instantiations']) headers = {"Content-type": "application/json"} try: response = requests.get(url, headers=headers) if response.status_code == 200: return response.json() elif response.status_code == 404: raise FileNotFoundError except Exception as e: _LOG.exception(e) raise e def get_service_instantiation(self, service_platform, service_uuid): """ Returns a instantiated service { "id":"4537e905-5183-4d96-bb31-3860121e21df", "created_at":"2019-03-20T20:31:57.564Z", "updated_at":"2019-03-20T20:40:48.515Z", "status":"READY", "request_type":"CREATE_SERVICE", "instance_uuid":"143f50a9-ea11-4292-bd25-c0d4cbdabe6f", "ingresses":"[]", "egresses":"[]", "callback":"", "blacklist":"[]", "customer_uuid":null, "sla_id":null, "name":null, "error":null, "description":null, "service":{ "uuid":"c8dfa216-c1bd-46da-ac8a-fa3fb444cc16", "vendor":"eu.5gtango", "name":"ns-squid-haproxy", "version":"0.2" } } :param service_platform: :param service_uuid: :return: """ url = '/'.join([self.base_url, 'adapters', service_platform, 'instantiations', service_uuid]) headers = {"Content-type": "application/json"} _LOG.debug(f'Accesing {url}') try: response = requests.get(url, headers=headers) _LOG.debug(f'ResContent {response.text}') _LOG.debug(f'ResHeaders {response.headers}') if response.status_code == 200: return response.json() elif response.status_code == 404: raise FileNotFoundError except Exception as e: _LOG.exception(e) raise e def automated_instantiation_sonata(self, service_platform, service_name, service_vendor, service_version, instance_name, test_plan_uuid, policy_id=None): """ Simpler version to instantiate a service, working for sonata :param service_platform: :param service_name: :param service_vendor: :param service_version: :param instance_name: :param callback: :return: network_service """ data = { "service_name": service_name, "service_vendor": service_vendor, "service_version": service_version, "service_platform": service_platform, "instance_name": instance_name, "callback": '/'.join([ 'http:/', context['host'], self.own_api_root, self.own_api_version, 'test-preparations', test_plan_uuid, 'service-instances', instance_name, 'sp-ready']) } if policy_id: data['policy_id'] = policy_id _LOG.debug(f'Instantiation payload: {data}') url = '/'.join([self.base_url, 'adapters', 'instantiate_service']) _LOG.debug(f'Accesing {url}') headers = {"Content-type": "application/json"} try: response = requests.post(url, headers=headers, json=data) _LOG.debug(f'Response {response.text}'.replace('\n', ' ')) if response.status_code == 200 and not response.json()['error']: return response.json() elif response.json()['error']: return response.json() elif response.status_code == 404: raise FileNotFoundError(response.json) else: raise Exception(response.json()) except json.decoder.JSONDecodeError as e: msg = f'Wrong JSON from PA: {response.raw}, exception: {e}' _LOG.error(msg) return {'error': msg} except Exception as e: _LOG.exception(e) return {'error': e} def automated_instantiation_osm(self, service_platform, service_name, service_vendor, service_version, instance_name, test_plan_uuid): """ Simpler version to instantiate a service, working for sonata :param service_platform: :param service_name: :param service_vendor: :param service_version: :param instance_name: :param callback: :return: network_service """ data = { "service_name": service_name, "service_vendor": service_vendor, "service_version": service_version, "service_platform": service_platform, "instance_name": instance_name, "callback": '/'.join([ 'http:/', context['host'], self.own_api_root, self.own_api_version, 'test-preparations', test_plan_uuid, 'service-instances', instance_name, 'sp-ready']) } _LOG.debug(f'Instantiation payload: {data}') url = '/'.join([self.base_url, 'adapters', 'instantiate_service']) _LOG.debug(f'Accesing {url}') headers = {"Content-type": "application/json"} try: response = requests.post(url, headers=headers, json=data) _LOG.debug(f'Response {response.text}'.replace('\n', ' ')) if response.status_code == 200: return response.json() elif response.json()['error']: return response.json() elif response.status_code == 404: raise FileNotFoundError(response.json) else: raise Exception(response.json()) except json.decoder.JSONDecodeError as e: msg = f'Wrong JSON from PA: {response.raw}, exception: {e}' _LOG.error(msg) return {'error': msg} except Exception as e: _LOG.exception(e) return {'error': e} def shutdown_package(self, service_platform, instance_uuid, package_uploaded): """ Shutdowns the instance and removes the package from the SP :param service_platform: :param instance_uuid: :return: """ # url = '/'.join([self.base_url, 'adapters', service_platform, 'instantiations']) url = '/'.join([self.base_url, 'adapters', service_platform, 'instantiations', 'terminate']) data = {"instance_uuid": instance_uuid, "request_type": "TERMINATE_SERVICE", "package_uploaded": package_uploaded} headers = {"Content-type": "application/json"} try: _LOG.debug(f'Accesing {url}') _LOG.debug(f'Payload {data}') response = requests.post(url, headers=headers, json=data) _LOG.debug(f'ResContent {response.text}'.replace('\n', ' ')) _LOG.debug(f'ResHeaders {response.headers}') if response.status_code == 200: return response.json() elif response.status_code == 404: raise FileNotFoundError except json.decoder.JSONDecodeError as e: msg = f'Wrong JSON from PA: {response.raw}, exception: {e}' _LOG.error(msg) return {'error': msg} except Exception as e: _LOG.exception(e) return {'error': e} def upload_package(self, platform, package_file_uuid): """ {"package_process_uuid":"8f3a26b6-34e4-45e4-b7a3-17f6c5a55820","status":"running","error_msg":null} :param platform: :param package_uuid: :return: """ url = '/'.join([self.base_url, 'adapters', platform, 'packages']) data = {"package": f"/app/packages/{package_file_uuid}.tgo"} headers = {"Content-type": "application/json"} try: response = requests.post(url, headers=headers, json=data) if response.status_code == 200: return response.json() elif response.status_code == 404: raise FileNotFoundError except Exception as e: _LOG.exception(e) raise e def delete_package_sonata(self, service_platform, package_uuid, name=None, vendor=None, version=None): # if not (name and vendor and version): # # Get packages and filter by uuid offline, asign to vars # package_inventory_url = '/'.join([self.base_url, 'adapters', service_platform, 'packages']) # response = requests.get() # # name = str() # vendor = str() # version = str() url = '/'.join([self.base_url, 'adapters', service_platform, 'packages', name, vendor, version]) response = requests.delete(url) def get_inventory(self, platform): url = '/'.join([self.base_url, 'adapters', platform, 'packages']) class ExecutorInterface(Interface): """ This is a Interface class for V&V Executor """ def __init__(self, cu_api_root, cu_api_version): Interface.__init__(self, cu_api_root, cu_api_version) self.base_url = os.getenv('EXECUTOR_BASE') self.version = 'v1' self.api = 'api' self.events = [] def execution_request(self, tdi, test_plan_uuid, service_instantiation_time=None, docker_host=None): """ :param tdi: :param test_plan_uuid: :param service_instantiation_time: :param docker_host: :return: """ # TODO: Specify content in the callbacks? data = { 'test': tdi, 'service_instantiation_time': service_instantiation_time, "callbacks": [ { 'name': 'running', 'path': '/'.join( ['http:/', context['host'], self.own_api_root, self.own_api_version, 'test-preparations', test_plan_uuid, 'change']) }, { 'name': 'cancel', 'path': '/'.join( ['http:/', context['host'], self.own_api_root, self.own_api_version, 'test-preparations', test_plan_uuid, 'tests', '<test_uuid>', 'cancel']) }, { 'name': 'finish', 'path': '/'.join( ['http:/', context['host'], self.own_api_root, self.own_api_version, 'test-preparations', test_plan_uuid, 'tests', '<test_uuid>', 'finish']) } ] } if docker_host: data['execution_host'] = docker_host url = '/'.join([self.base_url, self.api, self.version, 'test-executions']) headers = {"Content-type": "application/json"} _LOG.debug(f'Sending to executor {url} with payload {json.dumps(data)}') try: response = requests.post(url, headers=headers, json=data) _LOG.debug(f'Rstatus: {response.status_code}') _LOG.debug(f'Rdata: {response.content}') _LOG.debug(f'RESPONSE decoded: {response.json()}') if response.status_code == 202: # and not response.json()['error']: return response.json() elif response.status_code == 400: return response.json() elif response.status_code == 404: raise FileNotFoundError(response.json()) else: raise ValueError(f'Code not expected, {response.content}, status={response.status_code}') except Exception as e: _LOG.exception(e) raise e def execution_cancel(self, test_plan_uuid, test_uuid): data = { "callbacks": [ { 'name': 'cancel', 'path': '/'.join( ['http:/', context['host'], self.own_api_root, self.own_api_version, 'test-preparations', test_plan_uuid, 'tests', '<test_uuid>', 'cancel']) } ] } url = '/'.join([self.base_url, self.api, self.version, 'test-executions', test_uuid, 'cancel']) headers = {"Content-type": "application/json"} try: response = requests.delete(url, headers=headers, json=data) _LOG.debug(f'Rstatus: {response.status_code}') _LOG.debug(f'Rdata: {response.raw}') if response.status_code == 200: # and not response.json()['error']: return response.json() elif response.status_code == 404: raise FileNotFoundError(response.json()) elif response.status_code == 500: raise RuntimeError('Server error ', response.content) else: raise ValueError(f'Code not expected, {response.content}, status={response.status_code}') except Exception as e: _LOG.exception(e) raise e

39.532588

122

0.56875

fb13b3eceb65208514d8d90465afdd7d8e44c9c9

935

py

Python

task_inventory/order_31_to_60/order_33_different_charts.py

flyingSprite/spinelle

57834368abb5fb4eb73a8fb29226a30014254e40

[ "MIT" ]

1

2016-07-22T01:58:53.000Z

task_inventory/order_31_to_60/order_33_different_charts.py

flyingSprite/spinelle

57834368abb5fb4eb73a8fb29226a30014254e40

[ "MIT" ]

5

2016-07-26T02:02:47.000Z

2018-05-16T08:08:42.000Z

task_inventory/order_31_to_60/order_33_different_charts.py

flyingSprite/spinelle

57834368abb5fb4eb73a8fb29226a30014254e40

[ "MIT" ]

null

# plot a sine wave from 0 to 4pi import pylab as lib class DifferentCharts(object): @staticmethod def draw_sin_chart(): x_values = lib.arange(0.0, lib.math.pi * 4, 0.01) y_values = lib.sin(x_values) lib.plot(x_values, y_values, linewidth=1.0) lib.xlabel('x') lib.ylabel('sin(x)') lib.title('Simple plot') # If show grid lines in chart lib.grid(True) lib.savefig("sin.png") lib.show() @staticmethod def draw_cos_chart(): x_values = lib.arange(0.0, lib.math.pi * 4, 0.01) y_values = lib.cos(x_values) lib.plot(x_values, y_values, linewidth=1.0) lib.xlabel('x') lib.ylabel('sin(x)') lib.title('Simple plot') # If show grid lines in chart lib.grid(True) lib.savefig("sin.png") lib.show() DifferentCharts.draw_sin_chart() DifferentCharts.draw_cos_chart()

23.974359

57

0.589305

05e0952bf4c50b9107fdeb10263b6b9e068932c9

2,433

py

Python

Jun_14.py

keiraaaaa/Leetcode

90bf174f5613c79119aec58a1f0d1d2c1c3496bb

[ "MIT" ]

null

Jun_14.py

keiraaaaa/Leetcode

90bf174f5613c79119aec58a1f0d1d2c1c3496bb

[ "MIT" ]

null

Jun_14.py

keiraaaaa/Leetcode

90bf174f5613c79119aec58a1f0d1d2c1c3496bb

[ "MIT" ]

null

''' ############################ # 142. Linked List Cycle II ############################ # Definition for singly-linked list. class ListNode(object): def __init__(self, x): self.val = x self.next = None class Solution(object): def detectCycle(self, head): """ :type head: ListNode :rtype: ListNode """ if not head or not head.next: return None slow, fast = head, head while fast.next!=None and fast.next.next!=None: slow = slow.next fast = fast.next.next if fast == slow: cycle = True break; if fast.next==None or fast.next.next==None: return None else: # return True slow_new = head while slow_new != slow: slow_new = slow_new.next slow = slow.next return slow x = [] x = ListNode(1) # x.next = ListNode(2) # x.next.next = ListNode(3) # x.next.next.next = x.next solu = Solution() # print (x.next.next.next.val) print (solu.detectCycle(x)) ''' ################### # 148. Sort List ################### # Definition for singly-linked list. class ListNode: def __init__(self, x): self.val = x self.next = None class Solution: def merge(self, l1, l2): merged = ListNode(0) tail = merged while l1!=None and l2!=None: if l1.val>l2.val: tail.next = l2 l2 = l2.next else: tail.next = l1 l1 = l1.next tail = tail.next if l1==None: tail.next = l2 if l2==None: tail.next = l1 return merged.next def sortList(self, head): """ :type head: ListNode :rtype: ListNode """ if head==None or head.next==None: return head slow, fast = head, head while slow.next != None and fast.next != None and fast.next.next != None: slow = slow.next fast = fast.next.next prev = slow.next slow.next = None # print (head.val, prev.val) return self.merge(self.sortList(head), self.sortList(prev)) x = ListNode(4) x.next = ListNode(2) x.next.next = ListNode(1) x.next.next.next = ListNode(3) solu = Solution() s = solu.sortList(x) print (s.val)

22.321101

81

0.491985

48d78d82fd9441b33703a650065438cfabaa4b9b

4,101

py

Python

configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py

pallgeuer/mmpose

d3c17d5e6bdb9dbaca19f3bf53aa2802105355fd

[ "Apache-2.0" ]

null

configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py

pallgeuer/mmpose

d3c17d5e6bdb9dbaca19f3bf53aa2802105355fd

[ "Apache-2.0" ]

null

configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py

pallgeuer/mmpose

d3c17d5e6bdb9dbaca19f3bf53aa2802105355fd

[ "Apache-2.0" ]

null

_base_ = [ '../../../../_base_/default_runtime.py', '../../../../_base_/datasets/atrw.py' ] evaluation = dict(interval=10, metric='mAP', save_best='AP') optimizer = dict( type='Adam', lr=5e-4, ) optimizer_config = dict(grad_clip=None) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[170, 200]) total_epochs = 210 log_config = dict( interval=1, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ]) channel_cfg = dict( num_output_channels=15, dataset_joints=15, dataset_channel=[ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], ], inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) # model settings model = dict( type='TopDown', pretrained='torchvision://resnet152', backbone=dict(type='ResNet', depth=152), keypoint_head=dict( type='TopdownHeatmapSimpleHead', in_channels=2048, out_channels=channel_cfg['num_output_channels'], loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), train_cfg=dict(), test_cfg=dict( flip_test=True, post_process='default', shift_heatmap=True, modulate_kernel=11)) data_cfg = dict( image_size=[256, 256], heatmap_size=[64, 64], num_output_channels=channel_cfg['num_output_channels'], num_joints=channel_cfg['dataset_joints'], dataset_channel=channel_cfg['dataset_channel'], inference_channel=channel_cfg['inference_channel'], soft_nms=False, nms_thr=1.0, oks_thr=0.9, vis_thr=0.2, use_gt_bbox=True, det_bbox_thr=0.0, bbox_file='', ) train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='TopDownGetBboxCenterScale', padding=1.0, shift_prob=0.3, shift_factor=0.2), dict(type='TopDownRandomFlip', flip_prob=0.5), dict( type='TopDownHalfBodyTransform', num_joints_half_body=8, prob_half_body=0.3), dict( type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), dict(type='TopDownAffine'), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict(type='TopDownGenerateTarget', sigma=2), dict( type='Collect', keys=['img', 'target', 'target_weight'], meta_keys=[ 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ] val_pipeline = [ dict(type='LoadImageFromFile'), dict(type='TopDownGetBboxCenterScale', padding=1.0), dict(type='TopDownAffine'), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict( type='Collect', keys=['img'], meta_keys=[ 'image_file', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ] test_pipeline = val_pipeline data_root = 'data/atrw' data = dict( samples_per_gpu=64, workers_per_gpu=2, val_dataloader=dict(samples_per_gpu=32), test_dataloader=dict(samples_per_gpu=32), train=dict( type='AnimalATRWDataset', ann_file=f'{data_root}/annotations/keypoint_train.json', img_prefix=f'{data_root}/images/train/', data_cfg=data_cfg, pipeline=train_pipeline, dataset_info={{_base_.dataset_info}}), val=dict( type='AnimalATRWDataset', ann_file=f'{data_root}/annotations/keypoint_val.json', img_prefix=f'{data_root}/images/val/', data_cfg=data_cfg, pipeline=val_pipeline, dataset_info={{_base_.dataset_info}}), test=dict( type='AnimalATRWDataset', ann_file=f'{data_root}/annotations/keypoint_val.json', img_prefix=f'{data_root}/images/val/', data_cfg=data_cfg, pipeline=test_pipeline, dataset_info={{_base_.dataset_info}}), )

28.089041

79

0.61863

d78a78e190bd7b35bc01af17c5af134bb59f332d

4,085

py

Python

extractor/network/ResNet34.py

Mingqi-Yuan/ConvolutionBox

446fa8eeac7852fee3fcd0b4391fdcb82ce25552

[ "MIT" ]

13

2019-03-28T02:15:56.000Z

2020-03-09T11:26:56.000Z

extractor/network/ResNet34.py

Mingqi-Yuan/ConvolutionBox

446fa8eeac7852fee3fcd0b4391fdcb82ce25552

[ "MIT" ]

null

extractor/network/ResNet34.py

Mingqi-Yuan/ConvolutionBox

446fa8eeac7852fee3fcd0b4391fdcb82ce25552

[ "MIT" ]

3

2019-04-02T02:57:11.000Z

2020-05-04T04:59:26.000Z

""" MIT License Copyright (c) 2019 Mingqi Yuan Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ """ Encoding = UTF-8 By Mingqi, Yuan, 2019/3/31 Usage: Network---ResNet50 """ from utils.generate_unique_id import generate_unique_id import tensorflow as tf import numpy as np import cv2 tf.enable_eager_execution() def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same', name=None): if name is not None: bn_name = name + '_bn' conv_name = name + '_conv' else: bn_name = None conv_name = None x = tf.layers.Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x) x = tf.layers.BatchNormalization(axis=3, name=bn_name)(x) return x def Conv_Block(inpt, nb_filter, kernel_size, strides=(1, 1), with_conv_shortcut=False): x = Conv2d_BN(inpt, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, padding='same') x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, padding='same') if with_conv_shortcut: shortcut = Conv2d_BN(inpt, nb_filter=nb_filter, strides=strides, kernel_size=kernel_size) x = tf.add(x, shortcut) return x else: x = tf.add(x, inpt) return x def ResNet34(path_to_image): img_dir = path_to_image img_data = np.array(cv2.imread(img_dir, 1)).astype('float32') img_data = cv2.resize(img_data, (224, 224)) img_data = tf.reshape(img_data, [1, img_data.shape[0], img_data.shape[1], 3]) x = Conv2d_BN(img_data, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='valid') x = tf.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x) # (56,56,64) x = Conv_Block(x, nb_filter=64, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=64, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=64, kernel_size=(3, 3)) # (28,28,128) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3)) # (14,14,256) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) # (7,7,512) x = Conv_Block(x, nb_filter=512, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True) x = Conv_Block(x, nb_filter=512, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=512, kernel_size=(3, 3)) x = tf.layers.AveragePooling2D(pool_size=(7, 7), strides=2)(x) feature = x.numpy() feature = feature.astype('uint8') feature = np.reshape(feature, [32, 16]) result_dir = 'static/images/extractor/' + 'ResNet34_' + generate_unique_id() cv2.imwrite(result_dir, feature) return result_dir

40.04902

120

0.701591

1e67d5a86a0361bf479204dab1b08bb67eb94217

7,308

py

Python

test/python/pulse/test_cmd_def.py

diego-plan9/qiskit-terra

a4120d70bd631ad2add228fdb1f86706bc5f2339

[ "Apache-2.0" ]

1

2018-05-29T03:58:03.000Z

test/python/pulse/test_cmd_def.py

diego-plan9/qiskit-terra

a4120d70bd631ad2add228fdb1f86706bc5f2339

[ "Apache-2.0" ]

3

2018-11-13T17:33:37.000Z

2018-12-03T09:35:00.000Z

test/python/pulse/test_cmd_def.py

diego-plan9/qiskit-terra

a4120d70bd631ad2add228fdb1f86706bc5f2339

[ "Apache-2.0" ]

2

2017-12-03T15:48:14.000Z

2018-03-11T13:08:03.000Z

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2017, 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Test for the CmdDef object.""" import numpy as np from qiskit.test import QiskitTestCase from qiskit.test.mock import FakeProvider from qiskit.qobj.converters import QobjToInstructionConverter from qiskit.qobj import PulseQobjInstruction from qiskit.pulse import (CmdDef, SamplePulse, Schedule, PulseError, PersistentValue, ShiftPhase) from qiskit.pulse.schedule import ParameterizedSchedule class TestCmdDef(QiskitTestCase): """Test CmdDef methods. Deprecated.""" def setUp(self): self.provider = FakeProvider() self.backend = self.provider.get_backend('fake_openpulse_2q') self.config = self.backend.configuration() def test_get_backend(self): """Test that backend is fetchable with cmd def present.""" def test_init(self): """Test `init`, `has`.""" sched = Schedule() sched.append(SamplePulse(np.ones(5))(self.config.drive(0))) cmd_def = CmdDef({('tmp', 0): sched}) self.assertTrue(cmd_def.has('tmp', 0)) def test_add(self): """Test `add`, `has`, `get`, `cmdss`.""" sched = Schedule() sched.append(SamplePulse(np.ones(5))(self.config.drive(0))) cmd_def = CmdDef() cmd_def.add('tmp', 1, sched) cmd_def.add('tmp', 0, sched) self.assertEqual(sched.instructions, cmd_def.get('tmp', (0,)).instructions) self.assertIn('tmp', cmd_def.cmds()) self.assertEqual(cmd_def.cmd_qubits('tmp'), [(0,), (1,)]) def test_pop(self): """Test pop with default.""" sched = Schedule() sched.append(SamplePulse(np.ones(5))(self.config.drive(0))) cmd_def = CmdDef() cmd_def.add('tmp', 0, sched) cmd_def.pop('tmp', 0) self.assertFalse(cmd_def.has('tmp', 0)) with self.assertRaises(PulseError): cmd_def.pop('not_there', (0,)) def test_repr(self): """Test repr.""" sched = Schedule() sched.append(SamplePulse(np.ones(5))(self.config.drive(0))) cmd_def = CmdDef({('tmp', 0): sched}) repr(cmd_def) def test_parameterized_schedule(self): """Test building parameterized schedule.""" cmd_def = CmdDef() converter = QobjToInstructionConverter([]) qobj = PulseQobjInstruction(name='pv', ch='u1', t0=10, val='P2*cos(np.pi*P1)') converted_instruction = converter(qobj) cmd_def.add('pv_test', 0, converted_instruction) self.assertEqual(cmd_def.get_parameters('pv_test', 0), ('P1', 'P2')) sched = cmd_def.get('pv_test', 0, 0, P2=-1) self.assertEqual(sched.instructions[0][-1].command.value, -1) with self.assertRaises(PulseError): cmd_def.get('pv_test', 0, 0, P1=-1) with self.assertRaises(PulseError): cmd_def.get('pv_test', 0, P1=1, P2=2, P3=3) sched = cmd_def.pop('pv_test', 0, 0, P2=-1) self.assertEqual(sched.instructions[0][-1].command.value, -1) self.assertFalse(cmd_def.has('pv_test', 0)) def test_sequenced_parameterized_schedule(self): """Test parametrized schedule consist of multiple instruction. """ cmd_def = CmdDef() converter = QobjToInstructionConverter([]) qobjs = [PulseQobjInstruction(name='fc', ch='d0', t0=10, phase='P1'), PulseQobjInstruction(name='fc', ch='d0', t0=20, phase='P2'), PulseQobjInstruction(name='fc', ch='d0', t0=30, phase='P3')] converted_instruction = [converter(qobj) for qobj in qobjs] cmd_def.add('inst_seq', 0, ParameterizedSchedule(*converted_instruction, name='inst_seq')) with self.assertRaises(PulseError): cmd_def.get('inst_seq', 0, P1=1, P2=2, P3=3, P4=4, P5=5) with self.assertRaises(PulseError): cmd_def.get('inst_seq', 0, P1=1) with self.assertRaises(PulseError): cmd_def.get('inst_seq', 0, 1, 2, 3, P1=1) sched = cmd_def.get('inst_seq', 0, 1, 2, 3) self.assertEqual(sched.instructions[0][-1].phase, 1) self.assertEqual(sched.instructions[1][-1].phase, 2) self.assertEqual(sched.instructions[2][-1].phase, 3) sched = cmd_def.get('inst_seq', 0, P1=1, P2=2, P3=3) self.assertEqual(sched.instructions[0][-1].phase, 1) self.assertEqual(sched.instructions[1][-1].phase, 2) self.assertEqual(sched.instructions[2][-1].phase, 3) sched = cmd_def.get('inst_seq', 0, 1, 2, P3=3) self.assertEqual(sched.instructions[0][-1].phase, 1) self.assertEqual(sched.instructions[1][-1].phase, 2) self.assertEqual(sched.instructions[2][-1].phase, 3) def test_negative_phases(self): """Test bind parameters with negative values.""" cmd_def = CmdDef() converter = QobjToInstructionConverter([]) qobjs = [PulseQobjInstruction(name='fc', ch='d0', t0=10, phase='P1'), PulseQobjInstruction(name='fc', ch='d0', t0=20, phase='-(P2)')] converted_instruction = [converter(qobj) for qobj in qobjs] cmd_def.add('inst_seq', 0, ParameterizedSchedule(*converted_instruction, name='inst_seq')) sched = cmd_def.get('inst_seq', 0, -1, 2) self.assertEqual(sched.instructions[0][-1].phase, -1) self.assertEqual(sched.instructions[1][-1].phase, -2) def test_build_cmd_def(self): """Test building of parameterized cmd_def from defaults.""" defaults = self.backend.defaults() cmd_def = defaults.build_cmd_def() cx_pv = cmd_def.get('ParametrizedGate', (0, 1), P2=0) pv_found = False for _, instr in cx_pv.instructions: cmd = instr.command if isinstance(cmd, PersistentValue): self.assertEqual(cmd.value, 1) pv_found = True self.assertTrue(pv_found) self.assertEqual(cmd_def.get_parameters('u1', 0), ('P0',)) u1_minus_pi = cmd_def.get('u1', 0, P0=np.pi) shift_phase = u1_minus_pi.instructions[0][-1] self.assertEqual(shift_phase.phase, -np.pi) def test_default_phases_parameters(self): """Test parameters for phases.""" defaults = self.backend.defaults() cmd_def = defaults.build_cmd_def() for i in range(2): u1_phases = [] for _, instr in cmd_def.get('u1', i, P0=np.pi).instructions: if isinstance(instr, ShiftPhase): u1_phases.append(instr.phase) self.assertEqual(u1_phases, [-np.pi]) u2_phases = [] for _, instr in cmd_def.get('u2', i, P0=0, P1=np.pi).instructions: if isinstance(instr, ShiftPhase): u2_phases.append(instr.phase) self.assertEqual(u2_phases, [-np.pi, 0])

38.87234

98

0.6211

95e27a45ad193d012d8b4fea3c7658abd0698045

273

py

Python

graystruct/tests/__init__.py

kondalrao/graystruct

dc068b69c35fa625664982d6b044547c4a82e134

[ "BSD-3-Clause" ]

3

2015-08-14T08:12:24.000Z

2018-06-15T09:52:34.000Z

graystruct/tests/__init__.py

kondalrao/graystruct

dc068b69c35fa625664982d6b044547c4a82e134

[ "BSD-3-Clause" ]

2

2015-03-01T21:42:26.000Z

2015-03-01T21:56:16.000Z

graystruct/tests/__init__.py

kondalrao/graystruct

dc068b69c35fa625664982d6b044547c4a82e134

[ "BSD-3-Clause" ]

2

2019-05-09T21:38:19.000Z

2019-11-24T20:47:41.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2015 Simon Jagoe and Enthought Ltd # All rights reserved. # # This software may be modified and distributed under the terms # of the 3-clause BSD license. See the LICENSE.txt file for details. from __future__ import absolute_import

34.125

69

0.750916

969b037978a813623d884e203c360c428e57b08f

331

py

Python

CodeSignal/Arcade/The_Core/Level_07_Book_Market/055_Find_Email_Domain.py

Zubieta/CPP

fb4a3cbf2e4edcc590df15663cd28fb9ecab679c

[ "MIT" ]

8

2017-03-02T07:56:45.000Z

2021-08-07T20:20:19.000Z

CodeSignal/Arcade/The_Core/Level_07_Book_Market/055_Find_Email_Domain.py

zubie7a/Algorithms

fb4a3cbf2e4edcc590df15663cd28fb9ecab679c

[ "MIT" ]

null

CodeSignal/Arcade/The_Core/Level_07_Book_Market/055_Find_Email_Domain.py

zubie7a/Algorithms

fb4a3cbf2e4edcc590df15663cd28fb9ecab679c

[ "MIT" ]

1

2021-08-07T20:20:20.000Z

# https://app.codesignal.com/arcade/code-arcade/book-market/TXFLopNcCNbJLQivP def findEmailDomain(address): # Find the domain of a given email address, be careful # with @ signs previous to the actual one, the address # may not be valid but we still want the domain! parts = address.split("@") return parts[-1]

41.375

77

0.716012

71d5460ea4a4105ba87772bbe94df08ade0aed0a

287

py

Python

enterprise_course_api/users/apps.py

Wetrain/mmu-course-api

b304a5a26459c8fecdba4c5b10bfdf93a4b2ac54

[ "MIT" ]

null

enterprise_course_api/users/apps.py

Wetrain/mmu-course-api

b304a5a26459c8fecdba4c5b10bfdf93a4b2ac54

[ "MIT" ]

null

enterprise_course_api/users/apps.py

Wetrain/mmu-course-api

b304a5a26459c8fecdba4c5b10bfdf93a4b2ac54

[ "MIT" ]

null

from django.apps import AppConfig class UsersConfig(AppConfig): name = 'enterprise_course_api.users' verbose_name = "Users" def ready(self): """Override this to put in: Users system checks Users signal registration """ pass

20.5

40

0.61324

8e87737751c12ec3b09caa575521e5cafab4c0f0

991

py

Python

data/train/python/8e87737751c12ec3b09caa575521e5cafab4c0f0setup_controllers.py

harshp8l/deep-learning-lang-detection

2a54293181c1c2b1a2b840ddee4d4d80177efb33

[ "MIT" ]

84

2017-10-25T15:49:21.000Z

2021-11-28T21:25:54.000Z

data/train/python/8e87737751c12ec3b09caa575521e5cafab4c0f0setup_controllers.py

vassalos/deep-learning-lang-detection

cbb00b3e81bed3a64553f9c6aa6138b2511e544e

[ "MIT" ]

5

2018-03-29T11:50:46.000Z

2021-04-26T13:33:18.000Z

data/train/python/8e87737751c12ec3b09caa575521e5cafab4c0f0setup_controllers.py

vassalos/deep-learning-lang-detection

cbb00b3e81bed3a64553f9c6aa6138b2511e544e

[ "MIT" ]

24

2017-11-22T08:31:00.000Z

2022-03-27T01:22:31.000Z

#! /usr/bin/env python import os, roslib, time roslib.load_manifest('openrave_actionlib') roslib.load_manifest('pr2_controller_manager') import rospy, sys import rosparam import yaml from pr2_controller_manager import pr2_controller_manager_interface from pr2_mechanism_msgs.msg import MechanismStatistics from pr2_mechanism_msgs.srv import * pr2_controller_manager_interface.stop_controller('l_arm_controller') pr2_controller_manager_interface.stop_controller('r_arm_controller') pr2_controller_manager_interface.stop_controller('torso_controller') name_yaml = yaml.load(open(os.path.join(roslib.packages.get_pkg_dir('openrave_actionlib'),'pr2_midbody_controller.yaml'))) rosparam._set_param("",name_yaml) time.sleep(0.5) # wait for parameter server rospy.wait_for_service('pr2_controller_manager/load_controller') rospy.ServiceProxy('pr2_controller_manager/load_controller', LoadController)('midbody_controller') pr2_controller_manager_interface.start_controller('midbody_controller')

41.291667

122

0.860747

f179c830e41c62afcfce6bf18e1c6486b1d3813c

7,016

py

Python

bot/src/tests/yellowbot/storage/_test_datastorestorageservice.py

rainbowbreeze/yellowbutler

1dac7d312fce78127cac45f17da526c6a66be36a

[ "Apache-2.0" ]

null

bot/src/tests/yellowbot/storage/_test_datastorestorageservice.py

rainbowbreeze/yellowbutler

1dac7d312fce78127cac45f17da526c6a66be36a

[ "Apache-2.0" ]

null

bot/src/tests/yellowbot/storage/_test_datastorestorageservice.py

rainbowbreeze/yellowbutler

1dac7d312fce78127cac45f17da526c6a66be36a

[ "Apache-2.0" ]

null

"""Test Firestore in Datastore more storage service """ from typing import Any, Dict from unittest import TestCase import os import datetime from yellowbot.storage.datastorestorageservice import DatastoreStorageService, BaseEntity class DummyEntity(BaseEntity): """Entity to store information during this test """ url: str last_check: datetime.date def __init__(self) -> None: """ """ super().__init__() # Sets fundamental entity properties self.url: None self.last_check: None @staticmethod def get_entity_name() -> str: return "DummyEntity" def to_dict(self) -> Dict[str, Any]: fields: Dict[str, Any] = {} if hasattr(self, "url"): fields["url"] = self.url if hasattr(self, "last_check"): fields["last_check"] = self.last_check return fields def from_dict(self, source_dict: dict): if "url" in source_dict: self.url = source_dict["url"] if "last_check" in source_dict: self.last_check = source_dict["last_check"] return self class TestDatastoreStorageService(TestCase): def setUp(self): self._datastore = DatastoreStorageService() pass def tearDown(self): self._datastore.delete_all(DummyEntity) def test_delete_all(self): # Check for errors if no entities are stored self._datastore.delete_all(DummyEntity) self._datastore.delete_all(DummyEntity) def test_name(self): nameclass = DummyEntity self.assertEqual(DummyEntity.get_entity_name(), nameclass.get_entity_name()) def test_write_and_read(self): # ---- Tests for adding new entities to the datastore ---- # Add entity 1 to the datastore test_entity1 = self._create_dummy_entity() entity_id1 = self._datastore.put(test_entity1) self.assertIsNotNone(entity_id1) self.assertEqual(entity_id1, test_entity1.id) read_entities = self._datastore.get_all(DummyEntity) self.assertEqual(1, len(read_entities)) self.assertEqual(test_entity1.id, read_entities[0].id) self.assertEqual(test_entity1.url, read_entities[0].url) # Checks timestamp() because the returned object from Datastore is a DatetimeWithNanoseconds # self.assertEqual(test_entity1.last_check.timestamp(), test_items[0].last_check.timestamp()) # Add entity 2 to the datastore test_entity2 = self._create_dummy_entity() entity_id2 = self._datastore.put(test_entity2) self.assertIsNotNone(entity_id2) self.assertEqual(entity_id2, test_entity2.id) # And check total database elements read_entities = self._datastore.get_all(DummyEntity) self.assertEqual(2, len(read_entities)) # Add entity 3 to the datastore test_entity3 = self._create_dummy_entity() entity_id3 = self._datastore.put(test_entity3) self.assertIsNotNone(entity_id3) self.assertEqual(entity_id3, test_entity3.id) # And check total database elements read_entities = self._datastore.get_all(DummyEntity) self.assertEqual(3, len(read_entities)) # ---- Tests for retrieving entities from the datastore using keys ---- # Get entity 2 by id read_entity = self._datastore.get_by_id(DummyEntity, entity_id2) self.assertIsNotNone(read_entity) self.assertEqual(test_entity2.id, read_entity.id) self.assertEqual(test_entity2.url, read_entity.url) #self.assertEqual(test_entity2.last_check.timestamp(), read_entity.last_check.timestamp()) # Get entity 3 by id read_entity = self._datastore.get_by_id(DummyEntity, entity_id3) self.assertIsNotNone(read_entity) self.assertEqual(test_entity3.id, read_entity.id) self.assertEqual(test_entity3.url, read_entity.url) #self.assertEqual(test_entity3.last_check.timestamp(), read_entity.last_check.timestamp()) # Get entity 1 by id read_entity = self._datastore.get_by_id(DummyEntity, entity_id1) self.assertIsNotNone(read_entity) self.assertEqual(test_entity1.id, read_entity.id) self.assertEqual(test_entity1.url, read_entity.url) #self.assertEqual(test_entity3.last_check.timestamp(), read_entity.last_check.timestamp()) # Unable to read an entity by wrong id read_entity = self._datastore.get_by_id(DummyEntity, 123123812381) self.assertIsNone(read_entity) # Get entity 2 by url read_entities = self._datastore.get_by_property(DummyEntity, "url", "=", test_entity2.url) self.assertEqual(1, len(read_entities)) read_entity = read_entities[0] self.assertIsNotNone(read_entity) self.assertEqual(test_entity2.id, read_entity.id) self.assertEqual(test_entity2.url, read_entity.url) #self.assertEqual(test_entity2.last_check.timestamp(), read_entity.last_check.timestamp()) # Unable to read an entity by wrong url read_entities = self._datastore.get_by_property(DummyEntity, "url", "=", "RANDOM_URL_NOT_EXISTING") self.assertEqual(0, len(read_entities)) # ---- Tests for updating entities from the datastore ---- test_entity2_new = self._create_dummy_entity() test_entity2.url = test_entity2_new.url test_entity2.last_check = test_entity2_new.last_check entity_id2_new = self._datastore.put(test_entity2) self.assertEqual(entity_id2, entity_id2_new) # Check is the same id read_entity = self._datastore.get_by_id(DummyEntity, entity_id2) self.assertIsNotNone(read_entity) self.assertEqual(test_entity2.id, read_entity.id) self.assertEqual(test_entity2.url, read_entity.url) #self.assertEqual(test_entity4.last_check.timestamp(), read_entity.last_check.timestamp()) # ---- Tests for deleting entities from the datastore ---- # Delete 1 entity self._datastore.delete_by_id(DummyEntity, entity_id1) # It should still work, despite the entity is not present anymore self._datastore.delete_by_id(DummyEntity, entity_id1) # To be sure, check database for entity 1 read_entity = self._datastore.get_by_id(DummyEntity, entity_id1) self.assertIsNone(read_entity) read_entities = self._datastore.get_all(DummyEntity) self.assertEqual(2, len(read_entities)) # Delete all the entities self._datastore.delete_all(DummyEntity) read_entities = self._datastore.get_all(DummyEntity) self.assertEqual(0, len(read_entities)) def _create_dummy_entity(self) -> DummyEntity: """Created an entity with some test data """ test_item = DummyEntity() timenow = datetime.datetime.now() test_item.url = "testing_url_{}".format(timenow) test_item.last_check = timenow return test_item

39.863636

107

0.687286

4b7c99422eab207e3c74c8468e14e0431940461b

424

py

Python

importer/tests/test_cc_egov.py

cyroxx/meine-stadt-transparent

d5a3f03a29a1bb97ce50ac5257d8bbd5208d9218

[ "MIT" ]

null

importer/tests/test_cc_egov.py

cyroxx/meine-stadt-transparent

d5a3f03a29a1bb97ce50ac5257d8bbd5208d9218

[ "MIT" ]

null

importer/tests/test_cc_egov.py

cyroxx/meine-stadt-transparent

d5a3f03a29a1bb97ce50ac5257d8bbd5208d9218

[ "MIT" ]

null

import logging from importer.loader import CCEgovLoader from importer.tests.utils import spurious_500 logger = logging.getLogger(__name__) def test_spurious_500(caplog): spurious_500(CCEgovLoader) assert caplog.messages == [ "Got an 500 for a CC e-gov request, retrying: 500 Server Error: " "Internal Server Error for url: https://ratsinfo.leipzig.de/bi/oparl/1.0/papers.asp?body=2387&p=2" ]

28.266667

106

0.733491

0a988914165f88a2f87a8ac731581677d575c24b

16,296

py

Python

det3d/core/sampler/sample_ops.py

reinforcementdriving/CIA-SSD

f7b4a9ed4a2b852845303efc6c972125438817a6

[ "Apache-2.0" ]

382

2020-12-05T06:46:28.000Z

2022-03-29T17:40:58.000Z

det3d/core/sampler/sample_ops.py

reinforcementdriving/CIA-SSD

f7b4a9ed4a2b852845303efc6c972125438817a6

[ "Apache-2.0" ]

28

2020-12-08T07:50:57.000Z

2022-03-20T03:54:43.000Z

det3d/core/sampler/sample_ops.py

reinforcementdriving/CIA-SSD

f7b4a9ed4a2b852845303efc6c972125438817a6

[ "Apache-2.0" ]

57

2020-12-10T02:19:03.000Z

2022-03-19T09:49:38.000Z

import copy import pathlib import pickle import time from functools import partial, reduce import numpy as np from det3d.core.bbox import box_np_ops from det3d.core.sampler import preprocess as prep from det3d.utils.check import shape_mergeable class DataBaseSamplerV2: def __init__( self, db_infos, # object/dbinfos_train.pkl groups, # [dict(Car=15,),], db_prepor=None, # filter_by_min_num_points, filter_by_difficulty rate=1.0, # rate=1.0 global_rot_range=None, # [0, 0] logger=None, # logging.getLogger("build_dbsampler") ): # load all gt database here. for k, v in db_infos.items(): logger.info(f"load {len(v)} {k} database infos") # preprocess: filter_by_min_num_points/difficulty. if db_prepor is not None: db_infos = db_prepor(db_infos) logger.info("After filter database:") for k, v in db_infos.items(): logger.info(f"load {len(v)} {k} database infos") self.db_infos = db_infos self._rate = rate self._groups = groups self._group_db_infos = {} self._group_name_to_names = [] self._sample_classes = [] self._sample_max_nums = [] self._use_group_sampling = False # slower if any([len(g) > 1 for g in groups]): # False self._use_group_sampling = True # get group_name: Car and group_max_num: 15 if not self._use_group_sampling: # True self._group_db_infos = self.db_infos # just use db_infos for group_info in groups: group_names = list(group_info.keys()) self._sample_classes += group_names # ['Car'] self._sample_max_nums += list(group_info.values()) # [15] else: # False for group_info in groups: group_dict = {} group_names = list(group_info.keys()) group_name = ", ".join(group_names) self._sample_classes += group_names self._sample_max_nums += list(group_info.values()) self._group_name_to_names.append((group_name, group_names)) # self._group_name_to_names[group_name] = group_names for name in group_names: for item in db_infos[name]: gid = item["group_id"] if gid not in group_dict: group_dict[gid] = [item] else: group_dict[gid] += [item] if group_name in self._group_db_infos: raise ValueError("group must be unique") group_data = list(group_dict.values()) self._group_db_infos[group_name] = group_data info_dict = {} if len(group_info) > 1: for group in group_data: names = [item["name"] for item in group] names = sorted(names) group_name = ", ".join(names) if group_name in info_dict: info_dict[group_name] += 1 else: info_dict[group_name] = 1 print(info_dict) # get sampler dict for each class like Car, Cyclist, Pedestrian... # this sampler can ensure batch samples selected randomly. self._sampler_dict = {} for k, v in self._group_db_infos.items(): self._sampler_dict[k] = prep.BatchSampler(v, k) # get rotation range self._enable_global_rot = False if global_rot_range is not None: if not isinstance(global_rot_range, (list, tuple, np.ndarray)): global_rot_range = [-global_rot_range, global_rot_range] else: # True assert shape_mergeable(global_rot_range, [2]) # True if np.abs(global_rot_range[0] - global_rot_range[1]) >= 1e-3: # False self._enable_global_rot = True self._global_rot_range = global_rot_range # [0, 0] @property def use_group_sampling(self): return self._use_group_sampling def sample_all( self, root_path, gt_boxes, gt_names, num_point_features, random_crop=False, gt_group_ids=None, calib=None, ): # record the num of gt-aug samples with a dict and a list sampled_num_dict = {} sample_num_per_class = [] for class_name, max_sample_num in zip(self._sample_classes, self._sample_max_nums): # actual only once for ['Car': 15] sampled_num = int(max_sample_num - np.sum([n == class_name for n in gt_names])) sampled_num = np.round(self._rate * sampled_num).astype(np.int64) sampled_num_dict[class_name] = sampled_num sample_num_per_class.append(sampled_num) sampled_groups = self._sample_classes if self._use_group_sampling: # False assert gt_group_ids is not None sampled_groups = [] sample_num_per_class = [] for group_name, class_names in self._group_name_to_names: sampled_nums_group = [sampled_num_dict[n] for n in class_names] sampled_num = np.max(sampled_nums_group) sample_num_per_class.append(sampled_num) sampled_groups.append(group_name) total_group_ids = gt_group_ids sampled = [] sampled_gt_boxes = [] avoid_coll_boxes = gt_boxes # gt-augmentation: sample gt boxes and add them to current gt_boxes. # todo: we may sample box one by one to ensure num of gt-boxes is fulfilled. for class_name, sampled_num in zip(sampled_groups, sample_num_per_class): if sampled_num > 0: if self._use_group_sampling: # False sampled_cls = self.sample_group(class_name, sampled_num, avoid_coll_boxes, total_group_ids) else: sampled_cls = self.sample_class_v2(class_name, sampled_num, avoid_coll_boxes) sampled += sampled_cls if len(sampled_cls) > 0: if len(sampled_cls) == 1: # True sampled_gt_box = sampled_cls[0]["box3d_lidar"][np.newaxis, ...] else: sampled_gt_box = np.stack([s["box3d_lidar"] for s in sampled_cls], axis=0) sampled_gt_boxes += [sampled_gt_box] avoid_coll_boxes = np.concatenate([avoid_coll_boxes, sampled_gt_box], axis=0) if self._use_group_sampling: # False if len(sampled_cls) == 1: sampled_group_ids = np.array(sampled_cls[0]["group_id"])[np.newaxis, ...] else: sampled_group_ids = np.stack([s["group_id"] for s in sampled_cls], axis=0) total_group_ids = np.concatenate([total_group_ids, sampled_group_ids], axis=0) if len(sampled) > 0: ''' get points in sampled gt_boxes ''' sampled_gt_boxes = np.concatenate(sampled_gt_boxes, axis=0) num_sampled = len(sampled) s_points_list = [] # get points in sampled gt-boxes. for info in sampled: # info.keys: ['name', 'path', 'image_idx', 'gt_idx', 'box3d_lidar', 'num_points_in_gt', 'difficulty', 'group_id'] try: # TODO fix point read error s_points = np.fromfile(str(pathlib.Path(root_path) / info["path"]), dtype=np.float32).reshape(-1, num_point_features) if "rot_transform" in info: # False rot = info["rot_transform"] s_points[:, :3] = box_np_ops.rotation_points_single_angle(s_points[:, :4], rot, axis=2) # gt_points are saved with relative distance; so need to recover by adding box center. s_points[:, :3] += info["box3d_lidar"][:3] s_points_list.append(s_points) except Exception: print(info["path"]) continue '''todo: do something about random crop''' if random_crop: # False s_points_list_new = [] assert calib is not None rect = calib["rect"] Trv2c = calib["Trv2c"] P2 = calib["P2"] gt_bboxes = box_np_ops.box3d_to_bbox(sampled_gt_boxes, rect, Trv2c, P2) crop_frustums = prep.random_crop_frustum(gt_bboxes, rect, Trv2c, P2) for i in range(crop_frustums.shape[0]): s_points = s_points_list[i] mask = prep.mask_points_in_corners(s_points, crop_frustums[i : i + 1]).reshape(-1) num_remove = np.sum(mask) if num_remove > 0 and (s_points.shape[0] - num_remove) > 15: s_points = s_points[np.logical_not(mask)] s_points_list_new.append(s_points) s_points_list = s_points_list_new ret = { "gt_names": np.array([s["name"] for s in sampled]), "difficulty": np.array([s["difficulty"] for s in sampled]), "gt_boxes": sampled_gt_boxes, "points": np.concatenate(s_points_list, axis=0), "gt_masks": np.ones((num_sampled,), dtype=np.bool_), } if self._use_group_sampling: ret["group_ids"] = np.array([s["group_id"] for s in sampled]) else: ret["group_ids"] = np.arange(gt_boxes.shape[0], gt_boxes.shape[0] + len(sampled)) else: ret = None return ret def sample(self, name, num): if self._use_group_sampling: group_name = name ret = self._sampler_dict[group_name].sample(num) groups_num = [len(l) for l in ret] return reduce(lambda x, y: x + y, ret), groups_num else: ret = self._sampler_dict[name].sample(num) return ret, np.ones((len(ret),), dtype=np.int64) def sample_v1(self, name, num): if isinstance(name, (list, tuple)): group_name = ", ".join(name) ret = self._sampler_dict[group_name].sample(num) groups_num = [len(l) for l in ret] return reduce(lambda x, y: x + y, ret), groups_num else: ret = self._sampler_dict[name].sample(num) return ret, np.ones((len(ret),), dtype=np.int64) def sample_class_v2(self, name, num, gt_boxes): # sample num gt_boxes from gt_database sampled = self._sampler_dict[name].sample(num) sampled = copy.deepcopy(sampled) num_sampled = len(sampled) # get all boxes: gt_boxes + sp_boxes num_gt = gt_boxes.shape[0] gt_boxes_bv = box_np_ops.center_to_corner_box2d(gt_boxes[:, 0:2], gt_boxes[:, 3:5], gt_boxes[:, -1]) sp_boxes = np.stack([i["box3d_lidar"] for i in sampled], axis=0) # todo: need modification here boxes = np.concatenate([gt_boxes, sp_boxes], axis=0).copy() # unused: get mask valid_mask = np.zeros([gt_boxes.shape[0]], dtype=np.bool_) valid_mask = np.concatenate([valid_mask, np.ones([sp_boxes.shape[0]], dtype=np.bool_)], axis=0) if self._enable_global_rot: # False prep.noise_per_object_v3_(boxes, None, valid_mask, 0, 0, self._global_rot_range, num_try=100) # get all boxes_bev: gt_boxes_bev + sampled_boxes_bev sp_boxes_new = boxes[gt_boxes.shape[0] : ] sp_boxes_bv = box_np_ops.center_to_corner_box2d(sp_boxes_new[:, 0:2], sp_boxes_new[:, 3:5], sp_boxes_new[:, -1]) total_bv = np.concatenate([gt_boxes_bv, sp_boxes_bv], axis=0) # collision test on bev (stricter than 3d) coll_mat = prep.box_collision_test(total_bv, total_bv) # todo: too slow here diag = np.arange(total_bv.shape[0]) coll_mat[diag, diag] = False # get valid samples valid_samples = [] for i in range(num_gt, num_gt + num_sampled): # todo: without multiple try times, sometimes, the overall box num may not meet the requirement if coll_mat[i].any(): # i-th sampled box is not considered into gt-boxes. coll_mat[i] = False coll_mat[:, i] = False else: if self._enable_global_rot: # False sampled[i - num_gt]["box3d_lidar"][:2] = boxes[i, :2] # boxes has got noise sampled[i - num_gt]["box3d_lidar"][-1] = boxes[i, -1] sampled[i - num_gt]["rot_transform"] = (boxes[i, -1] - sp_boxes[i - num_gt, -1]) # i-th sampled box is considered into gt-boxes. valid_samples.append(sampled[i - num_gt]) return valid_samples def sample_group(self, name, num, gt_boxes, gt_group_ids): sampled, group_num = self.sample(name, num) sampled = copy.deepcopy(sampled) # rewrite sampled group id to avoid duplicated with gt group ids gid_map = {} max_gt_gid = np.max(gt_group_ids) sampled_gid = max_gt_gid + 1 for s in sampled: gid = s["group_id"] if gid in gid_map: s["group_id"] = gid_map[gid] else: gid_map[gid] = sampled_gid s["group_id"] = sampled_gid sampled_gid += 1 num_gt = gt_boxes.shape[0] gt_boxes_bv = box_np_ops.center_to_corner_box2d( gt_boxes[:, 0:2], gt_boxes[:, 3:5], gt_boxes[:, -1] ) sp_boxes = np.stack([i["box3d_lidar"] for i in sampled], axis=0) sp_group_ids = np.stack([i["group_id"] for i in sampled], axis=0) valid_mask = np.zeros([gt_boxes.shape[0]], dtype=np.bool_) valid_mask = np.concatenate( [valid_mask, np.ones([sp_boxes.shape[0]], dtype=np.bool_)], axis=0 ) boxes = np.concatenate([gt_boxes, sp_boxes], axis=0).copy() group_ids = np.concatenate([gt_group_ids, sp_group_ids], axis=0) if self._enable_global_rot: # place samples to any place in a circle. prep.noise_per_object_v3_( boxes, None, valid_mask, 0, 0, self._global_rot_range, group_ids=group_ids, num_try=100, ) sp_boxes_new = boxes[gt_boxes.shape[0] :] sp_boxes_bv = box_np_ops.center_to_corner_box2d( sp_boxes_new[:, 0:2], sp_boxes_new[:, 3:5], sp_boxes_new[:, -1] ) total_bv = np.concatenate([gt_boxes_bv, sp_boxes_bv], axis=0) # coll_mat = collision_test_allbox(total_bv) coll_mat = prep.box_collision_test(total_bv, total_bv) diag = np.arange(total_bv.shape[0]) coll_mat[diag, diag] = False valid_samples = [] idx = num_gt for num in group_num: if coll_mat[idx : idx + num].any(): coll_mat[idx : idx + num] = False coll_mat[:, idx : idx + num] = False else: for i in range(num): if self._enable_global_rot: sampled[idx - num_gt + i]["box3d_lidar"][:2] = boxes[ idx + i, :2 ] sampled[idx - num_gt + i]["box3d_lidar"][-1] = boxes[ idx + i, -1 ] sampled[idx - num_gt + i]["rot_transform"] = ( boxes[idx + i, -1] - sp_boxes[idx + i - num_gt, -1] ) valid_samples.append(sampled[idx - num_gt + i]) idx += num return valid_samples

45.016575

150

0.548908

ad413a69f52cdb2848f81da89ad1884177903cc9

3,839

py

Python

services/core-api/app/api/incidents/response_models.py

areyeslo/mds

e8c38e593e09b78e2a57009c0d003d6c4bfa32e6

[ "Apache-2.0" ]

1

2021-09-17T20:41:45.000Z

services/core-api/app/api/incidents/response_models.py

areyeslo/mds

e8c38e593e09b78e2a57009c0d003d6c4bfa32e6

[ "Apache-2.0" ]

null

services/core-api/app/api/incidents/response_models.py

areyeslo/mds

e8c38e593e09b78e2a57009c0d003d6c4bfa32e6

[ "Apache-2.0" ]

null

from app.extensions import api from flask_restplus import fields MINE_INCIDENT_CATEGORY_MODEL = api.model( 'Mine Incident Category', { 'mine_incident_category_code': fields.String, 'description': fields.String, 'display_order': fields.Integer, 'active_ind': fields.Boolean }) MINE_INCIDENT_DETERMINATION_TYPE_MODEL = api.model('Mine Incident Determination Type', { 'mine_incident_determination_type_code': fields.String, 'description': fields.String, 'active_ind': fields.Boolean }) MINE_INCIDENT_FOLLOWUP_INVESTIGATION_TYPE_MODEL = api.model( 'Mine Incident Followup Investigation Type', { 'mine_incident_followup_investigation_type_code': fields.String, 'description': fields.String, 'active_ind': fields.Boolean }) MINE_INCIDENT_STATUS_CODE_MODEL = api.model('Mine Incident Status Codes', { 'mine_incident_status_code': fields.String, 'description': fields.String, 'active_ind' : fields.Boolean }) MINE_INCIDENT_DOCUMENT_TYPE_CODE_MODEL = api.model('Mine Incident Document Type Codes', { 'mine_incident_document_type_code': fields.String, 'description': fields.String, 'active_ind': fields.Boolean }) MINE_INCIDENT_DOCUMENT_MODEL = api.model( 'Mine Incident Document', { 'mine_document_guid': fields.String, 'document_manager_guid': fields.String, 'document_name': fields.String, 'mine_incident_document_type_code': fields.String, 'upload_date': fields.DateTime, }) MINE_INCIDENT_RECOMMENDATION_MODEL = api.model('Mine Incident Recommendation', { 'recommendation': fields.String, 'mine_incident_recommendation_guid': fields.String }) MINE_INCIDENT_MODEL = api.model( 'Mine Incident', { 'mine_incident_id': fields.Integer, 'mine_incident_id_year': fields.Integer, 'mine_incident_guid': fields.String, 'mine_incident_report_no': fields.String, 'mine_guid': fields.String, 'mine_name': fields.String, 'mine_region': fields.String, 'major_mine_ind': fields.Boolean, 'incident_timestamp': fields.DateTime, 'incident_description': fields.String, 'reported_timestamp': fields.DateTime, 'reported_by_name': fields.String, 'reported_by_email': fields.String, 'reported_by_phone_no': fields.String, 'reported_by_phone_ext': fields.String, 'emergency_services_called': fields.Boolean, 'number_of_injuries': fields.Integer, 'number_of_fatalities': fields.Integer, 'reported_to_inspector_party_guid': fields.String, 'responsible_inspector_party_guid': fields.String, 'determination_type_code': fields.String, 'status_code': fields.String, 'followup_investigation_type_code': fields.String, 'followup_inspection': fields.Boolean, 'followup_inspection_date': fields.Date, 'determination_inspector_party_guid': fields.String, 'mms_inspector_initials': fields.String(attribute='mms_insp_cd'), 'dangerous_occurrence_subparagraph_ids': fields.List(fields.Integer), 'proponent_incident_no': fields.String, 'mine_incident_no': fields.String, 'documents': fields.List(fields.Nested(MINE_INCIDENT_DOCUMENT_MODEL)), 'recommendations': fields.List(fields.Nested(MINE_INCIDENT_RECOMMENDATION_MODEL)), 'categories': fields.List(fields.Nested(MINE_INCIDENT_CATEGORY_MODEL)) }) PAGINATED_LIST = api.model( 'List', { 'current_page': fields.Integer, 'total_pages': fields.Integer, 'items_per_page': fields.Integer, 'total': fields.Integer, }) PAGINATED_INCIDENT_LIST = api.inherit('IncidentList', PAGINATED_LIST, { 'records': fields.List(fields.Nested(MINE_INCIDENT_MODEL)), })

39.173469

90

0.70956

b09d5473bb0f3be24e685a8e3541256dabe53694

71

py

Python

chapter-supplement/Ex_01.py

yuetsin/CS-902

ae945f55944830ed3c6e4d71cfff2798cdf571ac

[ "MIT" ]

1

2019-05-16T11:00:40.000Z

chapter-supplement/Ex_01.py

yuetsin/CS-902

ae945f55944830ed3c6e4d71cfff2798cdf571ac

[ "MIT" ]

null

chapter-supplement/Ex_01.py

yuetsin/CS-902

ae945f55944830ed3c6e4d71cfff2798cdf571ac

[ "MIT" ]

null

# Exercise 1 print raw_input("Please input a string... >>>")[::-1]

17.75

54

0.591549

1f50c6dc528b445d374d1861814ac6679546b769

690

py

Python

sarikasama/0008/0008.py

saurabh896/python-1

f8d3aedf4c0fe6e24dfa3269ea7e642c9f7dd9b7

[ "MIT" ]

3,976

2015-01-01T15:49:39.000Z

2022-03-31T03:47:56.000Z

sarikasama/0008/0008.py

dwh65416396/python

1a7e3edd1cd3422cc0eaa55471a0b42e004a9a1a

[ "MIT" ]

97

2015-01-11T02:59:46.000Z

2022-03-16T14:01:56.000Z

sarikasama/0008/0008.py

dwh65416396/python

1a7e3edd1cd3422cc0eaa55471a0b42e004a9a1a

[ "MIT" ]

3,533

2015-01-01T06:19:30.000Z

2022-03-28T13:14:54.000Z

#!/usr/bin/env pyhton3 #get the text in a html file import re, urllib.request def get_text(url): content = url.read() try: content = content.decode('utf-8') except: content = content.decode('gbk') content = re.sub(r'<script.+?</script>', '', content, flags = re.DOTALL) content = re.sub(r'', '', content, flags = re.DOTALL) content = re.sub(r'<style.+?</style>', '', content, flags = re.DOTALL) content = re.sub(r'<[^>]*>', '', content) content = re.sub(r'\n', '', content) print(content) def main(): with urllib.request.urlopen('http://thwiki.cc/') as url: get_text(url) if __name__ == '__main__': main()

25.555556

76

0.575362

0ab0328f0c909ba6cebd7e2ba10d5894d9ecbd75

7,204

py

Python

tests/test_jsondict.py

ac-optimus/signac

43a5519531cc8cc44199c9251d0fd4e6208b5bdb

[ "BSD-3-Clause" ]

1

2020-12-28T18:00:24.000Z

tests/test_jsondict.py

ac-optimus/signac

43a5519531cc8cc44199c9251d0fd4e6208b5bdb

[ "BSD-3-Clause" ]

81

2020-12-28T20:23:57.000Z

2022-03-01T06:03:40.000Z

tests/test_jsondict.py

admdev8/signac

d639e682ca7ebaff781d68621a2d86cc26de04b9

[ "BSD-3-Clause" ]

null

# Copyright (c) 2018 The Regents of the University of Michigan # All rights reserved. # This software is licensed under the BSD 3-Clause License. import os import pytest import uuid from tempfile import TemporaryDirectory from signac.core.jsondict import JSONDict from signac.errors import InvalidKeyError from signac.errors import KeyTypeError FN_DICT = 'jsondict.json' def testdata(): return str(uuid.uuid4()) class TestJSONDictBase(): @pytest.fixture(autouse=True) def setUp(self, request): self._tmp_dir = TemporaryDirectory(prefix='jsondict_') request.addfinalizer(self._tmp_dir.cleanup) self._fn_dict = os.path.join(self._tmp_dir.name, FN_DICT) class TestJSONDict(TestJSONDictBase): def get_json_dict(self): return JSONDict(filename=self._fn_dict) def get_testdata(self): return str(uuid.uuid4()) def test_init(self): self.get_json_dict() def test_set_get(self): jsd = self.get_json_dict() key = 'setget' d = self.get_testdata() jsd.clear() assert not bool(jsd) assert len(jsd) == 0 assert key not in jsd jsd[key] = d assert bool(jsd) assert len(jsd) == 1 assert key in jsd assert jsd[key] == d assert jsd.get(key) == d def test_set_get_explicit_nested(self): jsd = self.get_json_dict() key = 'setgetexplicitnested' d = self.get_testdata() jsd.setdefault('a', dict()) child1 = jsd['a'] child2 = jsd['a'] assert child1 == child2 assert isinstance(child1, type(child2)) assert child1._parent == child2._parent assert id(child1._parent) == id(child2._parent) assert id(child1) == id(child2) assert not child1 assert not child2 child1[key] = d assert child1 assert child2 assert key in child1 assert key in child2 assert child1 == child2 assert child1[key] == d assert child2[key] == d def test_copy_value(self): jsd = self.get_json_dict() key = 'copy_value' key2 = 'copy_value2' d = self.get_testdata() assert key not in jsd assert key2 not in jsd jsd[key] = d assert key in jsd assert jsd[key] == d assert key2 not in jsd jsd[key2] = jsd[key] assert key in jsd assert jsd[key] == d assert key2 in jsd assert jsd[key2] == d def test_iter(self): jsd = self.get_json_dict() key1 = 'iter1' key2 = 'iter2' d1 = self.get_testdata() d2 = self.get_testdata() d = {key1: d1, key2: d2} jsd.update(d) assert key1 in jsd assert key2 in jsd for i, key in enumerate(jsd): assert key in d assert d[key] == jsd[key] assert i == 1 def test_delete(self): jsd = self.get_json_dict() key = 'delete' d = self.get_testdata() jsd[key] = d assert len(jsd) == 1 assert jsd[key] == d del jsd[key] assert len(jsd) == 0 with pytest.raises(KeyError): jsd[key] jsd[key] = d assert len(jsd) == 1 assert jsd[key] == d del jsd.delete assert len(jsd) == 0 with pytest.raises(KeyError): jsd[key] def test_update(self): jsd = self.get_json_dict() key = 'update' d = {key: self.get_testdata()} jsd.update(d) assert len(jsd) == 1 assert jsd[key] == d[key] def test_clear(self): jsd = self.get_json_dict() key = 'clear' d = self.get_testdata() jsd[key] = d assert len(jsd) == 1 assert jsd[key] == d jsd.clear() assert len(jsd) == 0 def test_reopen(self): jsd = self.get_json_dict() key = 'reopen' d = self.get_testdata() jsd[key] = d jsd.save() del jsd # possibly unsafe jsd2 = self.get_json_dict() jsd2.load() assert len(jsd2) == 1 assert jsd2[key] == d def test_copy_as_dict(self): jsd = self.get_json_dict() key = 'copy' d = self.get_testdata() jsd[key] = d copy = dict(jsd) del jsd assert key in copy assert copy[key] == d def test_reopen2(self): jsd = self.get_json_dict() key = 'reopen' d = self.get_testdata() jsd[key] = d del jsd # possibly unsafe jsd2 = self.get_json_dict() assert len(jsd2) == 1 assert jsd2[key] == d def test_write_invalid_type(self): class Foo(object): pass jsd = self.get_json_dict() key = 'write_invalid_type' d = self.get_testdata() jsd[key] = d assert len(jsd) == 1 assert jsd[key] == d d2 = Foo() with pytest.raises(TypeError): jsd[key + '2'] = d2 assert len(jsd) == 1 assert jsd[key] == d def test_buffered_read_write(self): jsd = self.get_json_dict() jsd2 = self.get_json_dict() assert jsd == jsd2 key = 'buffered_read_write' d = self.get_testdata() d2 = self.get_testdata() assert len(jsd) == 0 assert len(jsd2) == 0 with jsd.buffered() as b: b[key] = d assert b[key] == d assert len(b) == 1 assert len(jsd2) == 0 assert len(jsd) == 1 assert len(jsd2) == 1 with jsd2.buffered() as b2: b2[key] = d2 assert len(jsd) == 1 assert len(b2) == 1 assert jsd[key] == d assert b2[key] == d2 assert jsd[key] == d2 assert jsd2[key] == d2 with jsd.buffered() as b: del b[key] assert key not in b assert key not in jsd def test_keys_with_dots(self): jsd = self.get_json_dict() with pytest.raises(InvalidKeyError): jsd['a.b'] = None def test_keys_valid_type(self): jsd = self.get_json_dict() class MyStr(str): pass for key in ('key', MyStr('key'), 0, None, True): d = jsd[key] = self.get_testdata() assert str(key) in jsd assert jsd[str(key)] == d def test_keys_invalid_type(self): jsd = self.get_json_dict() class A: pass for key in (0.0, A(), (1, 2, 3)): with pytest.raises(KeyTypeError): jsd[key] = self.get_testdata() for key in ([], {}, dict()): with pytest.raises(TypeError): jsd[key] = self.get_testdata() class TestJSONDictWriteConcern(TestJSONDict): def get_json_dict(self): return JSONDict(filename=self._fn_dict, write_concern=True) class TestJSONDictNestedData(TestJSONDict): def get_testdata(self): return dict(a=super(TestJSONDictNestedData, self).get_testdata()) class TestJSONDictNestedDataWriteConcern(TestJSONDictNestedData, TestJSONDictWriteConcern): pass

26.880597

91

0.551083

bab7dd50b346050ad2a124737006d382bbb602e7

152

py

Python

4_loop/1_Zweierreihe.py

Coding-for-the-Arts/drawbot-samples-solutions

7191610d6efd7d788056070e7826d255b7ef496b

[ "CC0-1.0" ]

null

4_loop/1_Zweierreihe.py

Coding-for-the-Arts/drawbot-samples-solutions

7191610d6efd7d788056070e7826d255b7ef496b

[ "CC0-1.0" ]

null

4_loop/1_Zweierreihe.py

Coding-for-the-Arts/drawbot-samples-solutions

7191610d6efd7d788056070e7826d255b7ef496b

[ "CC0-1.0" ]

null

""" Zweierreihe Wie kannst du die Zweierreihe bis 10 auf der Konsole ausgeben? """ for i in range(1, 11): print(i, 'Element:') print(2*i)

13.818182

62

0.631579

14d6a86ad7cc650979e3962389fbb01b31890997

2,066

py

Python

xd/build/core/data/namespace.py

XD-embedded/xd-build-core

357e4d78d35456d6906aa30151ddc989781227ab

[ "MIT" ]

1

2020-11-27T23:34:53.000Z

xd/build/core/data/namespace.py

XD-embedded/xd-build-core

357e4d78d35456d6906aa30151ddc989781227ab

[ "MIT" ]

6

2015-10-30T12:22:56.000Z

2016-08-25T09:38:48.000Z

xd/build/core/data/namespace.py

XD-embedded/xd-build-core

357e4d78d35456d6906aa30151ddc989781227ab

[ "MIT" ]

null

import logging log = logging.getLogger(__name__) log.setLevel(logging.INFO) from .var import * from .expr import * from .wrap import * __all__ = ['Namespace', 'MultiBinding'] class MultiBinding(Exception): '''Variable binding to multiple names not allowed. For setting one (named) variable to reference the value of another (named) variable, you should use Expression() instead. Example: ns['FOO'] = 'foo' ns['ALSO_FOO'] = Expression('FOO') ''' pass class Namespace(dict): """XD-build data namespaces. Class for holding a namespace of XD-build data variables. It is implemented as a mapping so it can be used as a locals to eval(). """ def __init__(self): self.eval_wrapper = EvalWrapper(self) super(Namespace, self).__init__(self) def __setitem__(self, key, value): if key in self: self[key].set(value) return value = wrap(value) if isinstance(value, Variable): old_name = getattr(value, 'name', None) if old_name is not None and old_name != key: raise MultiBinding('rename of Variable %s to %s not allowed'%( value.name, key)) value.name = key elif isinstance(value, Expression): pass else: raise TypeError('unsupported type for Variable %s: %s'%( key, type(value))) value.set_scope(self) super(Namespace, self).__setitem__(key, value) def __delitem__(self, key): var = super(Namespace, self).__getitem__(key) var.name = None super(Namespace, self).__delitem__(key) def eval(self, expr, g=None): if isinstance(expr, Expression): expr = expr.code or expr.source if g is None: g = {} return eval(expr, g, self.eval_wrapper) class EvalWrapper(object): def __init__(self, namespace): self.namespace = namespace def __getitem__(self, key): return self.namespace[key].get()

26.831169

78

0.60455

1f2d722aca0fccb6ad64a2cf053ec83c4b707fc9

8,903

py

Python

python-package/xgboost/training.py

saurav111/xgboost

a13a3d1552610df734566df9a4e283560eef8981

[ "Apache-2.0" ]

null

python-package/xgboost/training.py

saurav111/xgboost

a13a3d1552610df734566df9a4e283560eef8981

[ "Apache-2.0" ]

null

python-package/xgboost/training.py

saurav111/xgboost

a13a3d1552610df734566df9a4e283560eef8981

[ "Apache-2.0" ]

null

# coding: utf-8 # pylint: disable=too-many-locals, too-many-arguments, invalid-name """Training Library containing training routines.""" from __future__ import absolute_import import sys import re import numpy as np from .core import Booster, STRING_TYPES def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, early_stopping_rounds=None, evals_result=None, verbose_eval=True): # pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init """Train a booster with given parameters. Parameters ---------- params : dict Booster params. dtrain : DMatrix Data to be trained. num_boost_round: int Number of boosting iterations. watchlist (evals): list of pairs (DMatrix, string) List of items to be evaluated during training, this allows user to watch performance on the validation set. obj : function Customized objective function. feval : function Customized evaluation function. early_stopping_rounds: int Activates early stopping. Validation error needs to decrease at least every <early_stopping_rounds> round(s) to continue training. Requires at least one item in evals. If there's more than one, will use the last. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have two additional fields: bst.best_score and bst.best_iteration. evals_result: dict This dictionary stores the evaluation results of all the items in watchlist verbose_eval : bool If `verbose_eval` then the evaluation metric on the validation set, if given, is printed at each boosting stage. Returns ------- booster : a trained booster model """ evals = list(evals) bst = Booster(params, [dtrain] + [d[0] for d in evals]) if evals_result is not None: if not isinstance(evals_result, dict): raise TypeError('evals_result has to be a dictionary') else: evals_name = [d[1] for d in evals] evals_result.clear() evals_result.update({key: [] for key in evals_name}) if not early_stopping_rounds: for i in range(num_boost_round): bst.update(dtrain, i, obj) if len(evals) != 0: bst_eval_set = bst.eval_set(evals, i, feval) if isinstance(bst_eval_set, STRING_TYPES): msg = bst_eval_set else: msg = bst_eval_set.decode() if verbose_eval: sys.stderr.write(msg + '\n') if evals_result is not None: res = re.findall(":-?([0-9.]+).", msg) for key, val in zip(evals_name, res): evals_result[key].append(val) return bst else: # early stopping if len(evals) < 1: raise ValueError('For early stopping you need at least one set in evals.') sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(\ evals[-1][1], early_stopping_rounds)) # is params a list of tuples? are we using multiple eval metrics? if isinstance(params, list): if len(params) != len(dict(params).items()): raise ValueError('Check your params.'\ 'Early stopping works with single eval metric only.') params = dict(params) # either minimize loss or maximize AUC/MAP/NDCG maximize_score = False if 'eval_metric' in params: maximize_metrics = ('auc', 'map', 'ndcg') if any(params['eval_metric'].startswith(x) for x in maximize_metrics): maximize_score = True if maximize_score: best_score = 0.0 else: best_score = float('inf') best_msg = '' best_score_i = 0 for i in range(num_boost_round): bst.update(dtrain, i, obj) bst_eval_set = bst.eval_set(evals, i, feval) if isinstance(bst_eval_set, STRING_TYPES): msg = bst_eval_set else: msg = bst_eval_set.decode() if verbose_eval: sys.stderr.write(msg + '\n') if evals_result is not None: res = re.findall(":-([0-9.]+).", msg) for key, val in zip(evals_name, res): evals_result[key].append(val) score = float(msg.rsplit(':', 1)[1]) if (maximize_score and score > best_score) or \ (not maximize_score and score < best_score): best_score = score best_score_i = i best_msg = msg elif i - best_score_i >= early_stopping_rounds: sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg)) bst.best_score = best_score bst.best_iteration = best_score_i break bst.best_score = best_score bst.best_iteration = best_score_i return bst class CVPack(object): """"Auxiliary datastruct to hold one fold of CV.""" def __init__(self, dtrain, dtest, param): """"Initialize the CVPack""" self.dtrain = dtrain self.dtest = dtest self.watchlist = [(dtrain, 'train'), (dtest, 'test')] self.bst = Booster(param, [dtrain, dtest]) def update(self, iteration, fobj): """"Update the boosters for one iteration""" self.bst.update(self.dtrain, iteration, fobj) def eval(self, iteration, feval): """"Evaluate the CVPack for one iteration.""" return self.bst.eval_set(self.watchlist, iteration, feval) def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None): """ Make an n-fold list of CVPack from random indices. """ evals = list(evals) np.random.seed(seed) randidx = np.random.permutation(dall.num_row()) kstep = len(randidx) / nfold idset = [randidx[(i * kstep): min(len(randidx), (i + 1) * kstep)] for i in range(nfold)] ret = [] for k in range(nfold): dtrain = dall.slice(np.concatenate([idset[i] for i in range(nfold) if k != i])) dtest = dall.slice(idset[k]) # run preprocessing on the data set if needed if fpreproc is not None: dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy()) else: tparam = param plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals] ret.append(CVPack(dtrain, dtest, plst)) return ret def aggcv(rlist, show_stdv=True): # pylint: disable=invalid-name """ Aggregate cross-validation results. """ cvmap = {} ret = rlist[0].split()[0] for line in rlist: arr = line.split() assert ret == arr[0] for it in arr[1:]: if not isinstance(it, STRING_TYPES): it = it.decode() k, v = it.split(':') if k not in cvmap: cvmap[k] = [] cvmap[k].append(float(v)) for k, v in sorted(cvmap.items(), key=lambda x: x[0]): v = np.array(v) if not isinstance(ret, STRING_TYPES): ret = ret.decode() if show_stdv: ret += '\tcv-%s:%f+%f' % (k, np.mean(v), np.std(v)) else: ret += '\tcv-%s:%f' % (k, np.mean(v)) return ret def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(), obj=None, feval=None, fpreproc=None, show_stdv=True, seed=0): # pylint: disable = invalid-name """Cross-validation with given paramaters. Parameters ---------- params : dict Booster params. dtrain : DMatrix Data to be trained. num_boost_round : int Number of boosting iterations. nfold : int Number of folds in CV. metrics : list of strings Evaluation metrics to be watched in CV. obj : function Custom objective function. feval : function Custom evaluation function. fpreproc : function Preprocessing function that takes (dtrain, dtest, param) and returns transformed versions of those. show_stdv : bool Whether to display the standard deviation. seed : int Seed used to generate the folds (passed to numpy.random.seed). Returns ------- evaluation history : list(string) """ results = [] cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc) for i in range(num_boost_round): for fold in cvfolds: fold.update(i, obj) res = aggcv([f.eval(i, feval) for f in cvfolds], show_stdv) sys.stderr.write(res + '\n') results.append(res) return results

35.189723

94

0.585196

fb31fbda193654d2e07af5a01959069a1a8349b6

2,340

py

Python

tests/base/test_sampling_method.py

ajrox090/VaRA-Tool-Suite

1550d36a4049e0615afb0bacfb96b1d506a29c98

[ "BSD-2-Clause" ]

8

2019-10-30T08:07:44.000Z

2020-11-13T08:02:36.000Z

tests/base/test_sampling_method.py

ajrox090/VaRA-Tool-Suite

1550d36a4049e0615afb0bacfb96b1d506a29c98

[ "BSD-2-Clause" ]

342

2019-02-14T15:53:31.000Z

2020-11-03T18:11:27.000Z

tests/base/test_sampling_method.py

ajrox090/VaRA-Tool-Suite

1550d36a4049e0615afb0bacfb96b1d506a29c98

[ "BSD-2-Clause" ]

5

2021-04-28T17:05:16.000Z

2022-03-31T23:11:22.000Z

"""Test module for sampling method tests.""" import typing as tp import unittest from varats.base.sampling_method import ( NormalSamplingMethod, SamplingMethodBase, UniformSamplingMethod, ) class TestSamplingMethodBase(unittest.TestCase): """Test if SamplingMethodBase is working.""" def test_registering_new_sampling_methods(self) -> None: """Tests if new sampling classes get automatically added to the registry.""" self.assertFalse( 'NewTestSamplingMethod' in SamplingMethodBase[ tp.Any].sampling_method_names() ) class NewTestSamplingMethod(SamplingMethodBase): # pylint: disable=W0612 pass self.assertTrue( 'NewTestSamplingMethod' in SamplingMethodBase[ tp.Any].sampling_method_names() ) def test_sampling_method_lookup(self) -> None: """Test if the type look-up for sampling methods works.""" self.assertEqual( SamplingMethodBase. get_sampling_method_type("UniformSamplingMethod"), UniformSamplingMethod ) def test_dump_sampling_method(self) -> None: """Test if we can dump the sampling method configuration into a string.""" expected_usm_dump = "{'sampling_method': 'UniformSamplingMethod'}" self.assertEqual( UniformSamplingMethod().dump_to_string(), expected_usm_dump ) def test_create_sampling_method_from_string(self) -> None: """Test if we can reload the sampling method from a configuration and create the correct type.""" dumped_usm = "{'sampling_method': 'UniformSamplingMethod'}" self.assertEqual( type( SamplingMethodBase[ tp.Any].create_sampling_method_from_config_str(dumped_usm) ), UniformSamplingMethod ) class TestNormalSamplingMethod(unittest.TestCase): """Test if NormalSamplingMethod is working.""" def test_if_all_sampling_methods_are_detected(self) -> None: """Checks of the automatic registration of NormalSamplingMethod works.""" print(NormalSamplingMethod.normal_sampling_method_types()) self.assertEqual( len(NormalSamplingMethod.normal_sampling_method_types()), 2 )

33.428571

81

0.663248

a08627aad093200c27139eefc568235c5fac2b3c

33,944

py

Python

fbgemm_gpu/bench/split_table_batched_embeddings_benchmark.py

caogao/FBGEMM

ffc2e1a91e2ae283bccb6448b5e6020d871af7cc

[ "BSD-3-Clause" ]

null

fbgemm_gpu/bench/split_table_batched_embeddings_benchmark.py

caogao/FBGEMM

ffc2e1a91e2ae283bccb6448b5e6020d871af7cc

[ "BSD-3-Clause" ]

null

fbgemm_gpu/bench/split_table_batched_embeddings_benchmark.py

caogao/FBGEMM

ffc2e1a91e2ae283bccb6448b5e6020d871af7cc

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. import logging import time from typing import Callable, Dict, List, Optional, Tuple import click import numpy as np import torch from fbgemm_gpu.split_table_batched_embeddings_ops import ( CacheAlgorithm, ComputeDevice, EmbeddingLocation, OptimType, SparseType, SplitTableBatchedEmbeddingBagsCodegen, Int4TableBatchedEmbeddingBagsCodegen, ) from torch import Tensor logging.basicConfig(level=logging.DEBUG) PRECISION_SIZE_MULTIPLIER: Dict[SparseType, int] = { SparseType.FP32: 4, SparseType.FP16: 2, SparseType.INT8: 1, } def div_round_up(a: int, b: int) -> int: return int((a + b - 1) // b) * b def get_device() -> torch.device: return ( torch.cuda.current_device() if torch.cuda.is_available() else torch.device("cpu") ) # Merged indices with shape (T, B, L) -> (flattened indices with shape # (T * B * L), offsets with shape (T * B + 1)) def get_table_batched_offsets_from_dense( merged_indices: Tensor, ) -> Tuple[Tensor, Tensor]: (T, B, L) = merged_indices.size() lengths = np.ones((T, B)) * L flat_lengths = lengths.flatten() return ( merged_indices.long().contiguous().view(-1).to(get_device()), torch.tensor(([0] + np.cumsum(flat_lengths).tolist())).long().to(get_device()), ) def generate_requests( iters: int, B: int, T: int, L: int, E: int, # inter-batch indices reuse rate reuse: float = 0.0, # alpha <= 1.0: use uniform distribution # alpha > 1.0: use zjpf distribution alpha: float = 1.0, weights_precision: SparseType = SparseType.FP32, weighted: bool = False, ) -> List[Tuple[Tensor, Tensor, Optional[Tensor]]]: if alpha <= 1.0: all_indices = torch.randint( low=0, high=E, size=(iters, T, B * L), device=get_device(), dtype=torch.int32, ) else: all_indices = ( torch.as_tensor(np.random.zipf(a=alpha, size=(iters, T, B * L))) .to(get_device()) .int() % E ) for it in range(iters - 1): for t in range(T): reused_indices = torch.randperm(B * L, device=get_device())[ : int(B * L * reuse) ] all_indices[it + 1, t, reused_indices] = all_indices[it, t, reused_indices] rs = [] for it in range(iters): weights_tensor = ( None if not weighted else torch.randn(T * B * L, device=get_device()) ) rs.append( get_table_batched_offsets_from_dense(all_indices[it].view(T, B, L)) + (weights_tensor,) ) return rs def benchmark_requests( requests: List[Tuple[Tensor, Tensor, Optional[Tensor]]], func: Callable[[Tensor, Tensor, Optional[Tensor]], Tensor], flush_gpu_cache_size_mb: int = 0, ) -> float: total_time = 0.0 if torch.cuda.is_available(): torch.cuda.synchronize() start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) else: start_time = time.time() for (indices, offsets, weights) in requests: if torch.cuda.is_available(): if flush_gpu_cache_size_mb: _ = torch.rand(flush_gpu_cache_size_mb * 1024 * 1024 // 4, dtype=torch.float) torch.cuda.synchronize() start_event.record() else: start_time = time.time() func(indices, offsets, weights) if torch.cuda.is_available(): end_event.record() torch.cuda.synchronize() total_time += start_event.elapsed_time(end_event) * 1.0e-3 else: total_time += time.time() - start_time return total_time / len(requests) def benchmark_pipelined_requests( requests: List[Tuple[Tensor, Tensor, Optional[Tensor]]], func1: Callable[[Tensor, Tensor, Optional[Tensor]], None], func2: Callable[[Tensor, Tensor, Optional[Tensor]], None], flush_gpu_cache_size_mb: int = 0, ) -> Tuple[float, float]: torch.cuda.synchronize() start_events = [ (torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)) for _ in requests ] end_events = [ (torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)) for _ in requests ] for ((indices, offsets, indices_weights), start_event, end_event) in zip( requests, start_events, end_events ): if flush_gpu_cache_size_mb: _ = torch.rand(flush_gpu_cache_size_mb * 1024 * 1024 // 4, dtype=torch.float) torch.cuda.synchronize() start_event[0].record() func1(indices, offsets, indices_weights) end_event[0].record() start_event[1].record() func2(indices, offsets, indices_weights) end_event[1].record() torch.cuda.synchronize() return ( sum( start_event[0].elapsed_time(end_event[0]) * 1.0e-3 for start_event, end_event in zip(start_events, end_events) ) / len(requests), sum( start_event[1].elapsed_time(end_event[1]) * 1.0e-3 for start_event, end_event in zip(start_events, end_events) ) / len(requests), ) @click.group() def cli() -> None: pass @cli.command() @click.option("--alpha", default=1.0) @click.option("--bag-size", default=20) @click.option("--batch-size", default=512) @click.option("--embedding-dim", default=128) @click.option("--weights-precision", type=SparseType, default=SparseType.FP32) @click.option("--stoc", is_flag=True, default=False) @click.option("--iters", default=100) @click.option("--managed", default="device") @click.option("--mixed", is_flag=True, default=False) @click.option("--num-embeddings", default=int(1e5)) @click.option("--num-tables", default=32) @click.option("--reuse", default=0.0) @click.option("--row-wise/--no-row-wise", default=True) @click.option("--weighted", is_flag=True, default=False) @click.option("--weighted-num-requires-grad", type=int, default=None) @click.option("--flush-gpu-cache-size-mb", default=0) def device( # noqa C901 alpha: float, bag_size: int, batch_size: int, embedding_dim: int, weights_precision: SparseType, stoc: bool, iters: int, managed: str, mixed: bool, num_embeddings: int, num_tables: int, reuse: float, row_wise: bool, weighted: bool, weighted_num_requires_grad: Optional[int], flush_gpu_cache_size_mb: int, ) -> None: np.random.seed(42) B = batch_size D = embedding_dim L = bag_size E = num_embeddings T = num_tables if weighted_num_requires_grad: assert weighted_num_requires_grad <= T weighted_requires_grad_tables = np.random.choice( T, replace=False, size=(weighted_num_requires_grad,) ).tolist() feature_requires_grad = ( torch.tensor( [1 if t in weighted_requires_grad_tables else 0 for t in range(T)] ) .to(get_device()) .int() ) else: feature_requires_grad = None if mixed: Ds = [ div_round_up(np.random.randint(low=int(0.5 * D), high=int(1.5 * D)), 4) for _ in range(T) ] D = np.average(Ds) else: Ds = [D] * T optimizer = OptimType.EXACT_ROWWISE_ADAGRAD if row_wise else OptimType.EXACT_ADAGRAD if managed == "device": managed_option = ( EmbeddingLocation.DEVICE if torch.cuda.is_available() else EmbeddingLocation.HOST ) else: managed_option = EmbeddingLocation.MANAGED emb = SplitTableBatchedEmbeddingBagsCodegen( [ ( E, d, managed_option, ComputeDevice.CUDA if torch.cuda.is_available() else ComputeDevice.CPU, ) for d in Ds ], optimizer=optimizer, learning_rate=0.1, eps=0.1, weights_precision=weights_precision, stochastic_rounding=stoc, ).to(get_device()) if weights_precision == SparseType.INT8: emb.init_embedding_weights_uniform(-0.0003, 0.0003) nparams = sum(w.numel() for w in emb.split_embedding_weights()) param_size_multiplier = PRECISION_SIZE_MULTIPLIER[weights_precision] logging.info( f"Embedding parameters: {nparams / 1.0e9: .2f} GParam, " f"{nparams * param_size_multiplier / 1.0e9: .2f}GB" ) logging.info( f"Accessed weights per batch: {B * sum(Ds) * L * param_size_multiplier / 1.0e6: .2f}MB" ) requests = generate_requests( iters, B, T, L, E, reuse=reuse, alpha=alpha, weights_precision=weights_precision, weighted=weighted, ) # forward time_per_iter = benchmark_requests( requests, lambda indices, offsets, per_sample_weights: emb.forward( indices.long(), offsets.long(), per_sample_weights, feature_requires_grad=feature_requires_grad, ), flush_gpu_cache_size_mb=flush_gpu_cache_size_mb, ) logging.info( f"Forward, B: {B}, " f"E: {E}, T: {T}, D: {D}, L: {L}, W: {weighted}, " f"BW: {param_size_multiplier * B * sum(Ds) * L / time_per_iter / 1.0e9: .2f}GB/s, " # noqa: B950 f"T: {time_per_iter * 1.0e6:.0f}us" ) grad_output = torch.randn(B, sum(Ds)).to(get_device()) # backward time_per_iter = benchmark_requests( requests, lambda indices, offsets, per_sample_weights: emb( indices.long(), offsets.long(), per_sample_weights, feature_requires_grad=feature_requires_grad, ).backward(grad_output), flush_gpu_cache_size_mb=flush_gpu_cache_size_mb, ) logging.info( f"ForwardBackward, B: {B}, E: {E}, T: {T}, D: {D}, L: {L}, " f"BW: {3 * param_size_multiplier * B * sum(Ds) * L / time_per_iter / 1.0e9: .2f}GB/s, " f"T: {time_per_iter * 1.0e6:.0f}us" ) @cli.command() @click.option("--alpha", default=1.0) @click.option("--bag-size", default=20) @click.option("--batch-size", default=512) @click.option("--embedding-dim", default=128) @click.option("--weights-precision", type=SparseType, default=SparseType.FP32) @click.option("--stoc", is_flag=True, default=False) @click.option("--iters", default=100) @click.option("--mixed", is_flag=True, default=False) @click.option("--num-embeddings", default=int(1e5)) @click.option("--num-tables", default=32) @click.option("--reuse", default=0.0) @click.option("--uvm-tables", default=1) @click.option("--uvm-bag-size", default=1) @click.option("--weighted", is_flag=True, default=False) @click.option("--flush-gpu-cache-size-mb", default=0) def uvm( alpha: bool, bag_size: int, batch_size: int, embedding_dim: int, weights_precision: SparseType, stoc: bool, iters: int, mixed: bool, num_embeddings: int, num_tables: int, reuse: float, uvm_tables: int, uvm_bag_size: int, weighted: bool, flush_gpu_cache_size_mb: int, ) -> None: np.random.seed(42) B = batch_size D = embedding_dim L = bag_size E = num_embeddings T = num_tables T_uvm = uvm_tables assert T_uvm <= T assert ( T_uvm > 0 ), f"T_uvm specified {T_uvm} <= 0. If not testing UVM, please use device benchmark." T_gpu = T - T_uvm L_uvm = uvm_bag_size if mixed: Ds = [ div_round_up(np.random.randint(low=int(0.5 * D), high=int(1.5 * D)), 4) for _ in range(T) ] D = np.average(Ds) else: Ds = [D] * T emb_uvm = SplitTableBatchedEmbeddingBagsCodegen( [ ( E, d, EmbeddingLocation.MANAGED, ComputeDevice.CUDA, ) for d in Ds[:T_uvm] ], weights_precision=weights_precision, stochastic_rounding=stoc, ).cuda() if weights_precision == SparseType.INT8: emb_uvm.init_embedding_weights_uniform(-0.0003, 0.0003) if T_gpu > 0: emb_gpu = SplitTableBatchedEmbeddingBagsCodegen( [ ( E, d, EmbeddingLocation.DEVICE, ComputeDevice.CUDA, ) for d in Ds[T_uvm:] ], weights_precision=weights_precision, stochastic_rounding=stoc, ).cuda() if weights_precision == SparseType.INT8: emb_gpu.init_embedding_weights_uniform(-0.0003, 0.0003) emb_mixed = SplitTableBatchedEmbeddingBagsCodegen( [ ( E, d, managed_option, ComputeDevice.CUDA, ) for (d, managed_option) in zip( Ds, [EmbeddingLocation.MANAGED] * T_uvm + [EmbeddingLocation.DEVICE] * T_gpu, ) ], weights_precision=weights_precision, stochastic_rounding=stoc, ).cuda() if weights_precision == SparseType.INT8: emb_mixed.init_embedding_weights_uniform(-0.0003, 0.0003) requests_uvm = generate_requests( iters, B, T_uvm, L_uvm, E, reuse=reuse, alpha=alpha, weights_precision=weights_precision, weighted=weighted, ) if T_gpu > 0: requests_gpu = generate_requests( iters, B, T_gpu, L, E, reuse=reuse, alpha=alpha, weights_precision=weights_precision, weighted=False, ) param_size_multiplier = PRECISION_SIZE_MULTIPLIER[weights_precision] time_per_iter = benchmark_requests( requests_uvm, lambda indices, offsets, per_sample_weights: emb_uvm.forward( indices.long(), offsets.long(), per_sample_weights, ), flush_gpu_cache_size_mb=flush_gpu_cache_size_mb, ) logging.info( f"UVM Forward, B: {B}, " f"E: {E}, T: {T_uvm}, D: {D}, L: {L_uvm}, W: {weighted}, " f"BW: {param_size_multiplier * B * sum(Ds[:T_uvm]) * L_uvm / time_per_iter / 1.0e9: .2f}GB/s, " # noqa: B950 f"T: {time_per_iter * 1.0e6:.0f}us" ) if T_gpu > 0: requests = [] for rs_uvm, rs_gpu in zip(requests_uvm, requests_gpu): indices = torch.cat([rs_uvm[0], rs_gpu[0]]) lengths = [L_uvm] * (T_uvm * B) + [L] * (T_gpu * B) offsets = torch.tensor(([0] + np.cumsum(lengths).tolist())).int().cuda() per_sample_weights = None if weighted: assert (this_rs_uvm_weights := rs_uvm[2]) is not None assert (this_rs_gpu_weights := rs_gpu[2]) is not None per_sample_weights = torch.cat( [this_rs_uvm_weights, this_rs_gpu_weights] ) requests.append((indices, offsets, per_sample_weights)) # forward time_per_iter = benchmark_requests( requests_gpu, lambda indices, offsets, per_sample_weights: emb_gpu.forward( indices.long(), offsets.long(), per_sample_weights, ), flush_gpu_cache_size_mb=flush_gpu_cache_size_mb, ) logging.info( f"GPU Forward, B: {B}, " f"E: {E}, T: {T_gpu}, D: {D}, L: {L}, W: {weighted}, " f"BW: {param_size_multiplier * B * sum(Ds[T_uvm:]) * L / time_per_iter / 1.0e9: .2f}GB/s, " # noqa: B950 f"T: {time_per_iter * 1.0e6:.0f}us" ) time_per_iter = benchmark_requests( requests, lambda indices, offsets, per_sample_weights: emb_mixed.forward( indices.long(), offsets.long(), per_sample_weights, ), flush_gpu_cache_size_mb=flush_gpu_cache_size_mb, ) logging.info( f"Mixed Forward, B: {B}, " f"E: {E}, T: {T}, D: {D}, L: {L}, W: {weighted}, " f"BW: {param_size_multiplier * B * sum(Ds) * L / time_per_iter / 1.0e9: .2f}GB/s, " # noqa: B950 f"T: {time_per_iter * 1.0e6:.0f}us" ) @cli.command() @click.option("--alpha", default=1.0) @click.option("--bag-size", default=20) @click.option("--batch-size", default=512) @click.option("--cache-algorithm", default="lru") @click.option("--cache-sets", default=1024) @click.option("--embedding-dim", default=128) @click.option("--weights-precision", type=SparseType, default=SparseType.FP32) @click.option("--stoc", is_flag=True, default=False) @click.option("--long-index", is_flag=True, default=False) @click.option("--iters", default=100) @click.option("--mixed", is_flag=True, default=False) @click.option("--num-embeddings", default=int(1e5)) @click.option("--num-tables", default=32) @click.option("--reuse", default=0.1) @click.option("--weighted", is_flag=True, default=False) @click.option("--flush-gpu-cache-size-mb", default=0) def cache( # noqa C901 alpha: float, bag_size: int, batch_size: int, cache_algorithm: str, cache_sets: int, embedding_dim: int, weights_precision: SparseType, stoc: bool, iters: int, long_index: bool, mixed: bool, num_embeddings: int, num_tables: int, reuse: float, weighted: bool, flush_gpu_cache_size_mb: int, ) -> None: np.random.seed(42) optimizer = OptimType.EXACT_ROWWISE_ADAGRAD B = batch_size D = embedding_dim L = bag_size E = num_embeddings T = num_tables cache_alg = CacheAlgorithm.LRU if cache_algorithm == "lru" else CacheAlgorithm.LFU if mixed: Ds = [ div_round_up(np.random.randint(low=int(0.5 * D), high=int(1.5 * D)), 4) for _ in range(T) ] D = np.average(Ds) else: Ds = [D] * T emb_nc = SplitTableBatchedEmbeddingBagsCodegen( [ ( E, d, EmbeddingLocation.MANAGED, ComputeDevice.CUDA, ) for d in Ds ], optimizer=optimizer, weights_precision=weights_precision, stochastic_rounding=stoc, ).cuda() if weights_precision == SparseType.INT8: emb_nc.init_embedding_weights_uniform(-0.0003, 0.0003) emb = SplitTableBatchedEmbeddingBagsCodegen( [ ( E, d, EmbeddingLocation.MANAGED_CACHING, ComputeDevice.CUDA, ) for d in Ds ], optimizer=optimizer, weights_precision=weights_precision, stochastic_rounding=stoc, cache_sets=cache_sets, cache_algorithm=cache_alg, ).cuda() if weights_precision == SparseType.INT8: emb.init_embedding_weights_uniform(-0.0003, 0.0003) nparams = sum(w.numel() for w in emb.split_embedding_weights()) param_size_multiplier = PRECISION_SIZE_MULTIPLIER[weights_precision] logging.info( f"Embedding tables: {E * T} rows, {nparams / 1.0e9: .2f} GParam, " f"{nparams * param_size_multiplier / 1.0e6: .2f}MB" ) logging.info( f"Accessed weights per batch: {B * T * L} rows, " f"{B * T * L * D * param_size_multiplier / 1.0e6: .2f}MB" ) requests = generate_requests( 2 * iters, B, T, L, E, reuse=reuse, alpha=alpha, weighted=weighted ) warmup_requests, requests = requests[:iters], requests[iters:] grad_output = torch.randn(B, sum(Ds)).cuda() time_per_iter = benchmark_requests( requests, lambda indices, offsets, per_sample_weights: emb_nc( indices.long(), offsets.long(), per_sample_weights ).backward(grad_output), flush_gpu_cache_size_mb=flush_gpu_cache_size_mb, ) logging.info( f"ForwardBackward (UVM), B: {B}, E: {E}, T: {T}, D: {D}, L: {L}, " f"BW: {3 * param_size_multiplier * B * sum(Ds) * L / time_per_iter / 1.0e9: .2f}GB/s, " f"T: {time_per_iter * 1.0e6:.0f}us" ) # warm up for indices, offsets, _ in warmup_requests: emb.forward(indices.long(), offsets.long()) # get cache miss rate (forward and backward) and exchanged cache lines (prefetch) cache_misses = [] exchanged_cache_lines = [] NOT_FOUND = -1 for indices, offsets, _ in requests: # pyre-fixme[29]: # `Union[BoundMethod[typing.Callable(Tensor.clone)[[Named(self, # Variable[torch._TTensor (bound to Tensor)])], Variable[torch._TTensor (bound # to Tensor)]], Tensor], Tensor, torch.nn.Module]` is not a function. old_lxu_cache_state = emb.lxu_cache_state.clone() emb.prefetch(indices.long(), offsets.long()) exchanged_cache_lines.append( # pyre-fixme[16]: `bool` has no attribute `sum`. (emb.lxu_cache_state != old_lxu_cache_state).sum().item() ) cache_misses.append((emb.lxu_cache_locations_list[0] == NOT_FOUND).sum().item()) emb.forward(indices.long(), offsets.long()) logging.info( f"Exchanged cache lines -- mean: {sum(exchanged_cache_lines)/len(requests): .2f}, " f"max: {max(exchanged_cache_lines)}, min: {min(exchanged_cache_lines)}" ) logging.info( f"Cache miss -- mean: {sum(cache_misses)/len(requests)}, " f"max: {max(cache_misses)}, min: {min(cache_misses)}" ) # benchmark prefetch emb.reset_cache_states() for indices, offsets, _ in warmup_requests: emb.forward(indices, offsets) prefetch_time, forward_backward_time = benchmark_pipelined_requests( requests, lambda indices, offsets, indices_weights: emb.prefetch(indices, offsets), lambda indices, offsets, indices_weights: emb.forward( indices, offsets, indices_weights ).backward(grad_output), flush_gpu_cache_size_mb=flush_gpu_cache_size_mb, ) e2e_time = prefetch_time + forward_backward_time logging.info( f"ForwardBackward (LXU), reuse: {reuse}, alpha: {alpha}, B: {B}, " f"E: {E}, T: {T}, D: {D}, L: {L}, " f"BW: {3 * param_size_multiplier * B * sum(Ds) * L / e2e_time / 1.0e9: .2f}GB/s, " f"Tprefetch: {prefetch_time * 1.0e6:.0f}us, " f"{2 * sum(exchanged_cache_lines) * param_size_multiplier * D / prefetch_time / len(requests) / 1.0e9: .2f} GB/s, " f"Tfwdbwd: {forward_backward_time * 1.0e6:.0f}us, " f"{3 * param_size_multiplier * B * sum(Ds) * L / forward_backward_time / 1.0e9: .2f} GB/s, " f"Te2e: {e2e_time * 1.0e6:.0f}us, " ) def benchmark_cpu_requests( requests: List[Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]], func: Callable[[Tensor, Tensor, Optional[Tensor]], Tensor], ) -> float: import time start_time = time.perf_counter() for (indices, offsets, weights) in requests: func(indices, offsets, weights) end_time = time.perf_counter() return (end_time - start_time) / len(requests) @cli.command() @click.option("--alpha", default=1.0) @click.option("--bag-size", default=20) @click.option("--batch-size", default=512) @click.option("--embedding-dim", default=128) @click.option("--weights-precision", type=SparseType, default=SparseType.FP32) @click.option("--stoc", is_flag=True, default=False) @click.option("--iters", default=100) @click.option("--managed", default="device") @click.option("--mixed", is_flag=True, default=False) @click.option("--num-embeddings", default=int(1e5)) @click.option("--num-tables", default=32) @click.option("--reuse", default=0.0) @click.option("--row-wise/--no-row-wise", default=True) @click.option("--weighted", is_flag=True, default=False) @click.option("--weighted-num-requires-grad", type=int, default=None) @click.option("--int4", is_flag=True, default=False) @click.option("--index-remapping", is_flag=True, default=False) def cpu( # noqa C901 alpha: float, bag_size: int, batch_size: int, embedding_dim: int, weights_precision: SparseType, stoc: bool, iters: int, managed: str, mixed: bool, num_embeddings: int, num_tables: int, reuse: float, row_wise: bool, weighted: bool, weighted_num_requires_grad: Optional[int], int4: bool, index_remapping: bool, ) -> None: assert int4 np.random.seed(42) B = batch_size D = embedding_dim L = bag_size E = num_embeddings T = num_tables if weighted_num_requires_grad: assert weighted_num_requires_grad <= T weighted_requires_grad_tables = np.random.choice( T, replace=False, size=(weighted_num_requires_grad,) ).tolist() feature_requires_grad = ( torch.tensor( [1 if t in weighted_requires_grad_tables else 0 for t in range(T)] ) .cuda() .int() ) else: feature_requires_grad = None if mixed: Ds = [ # int4 table batched emb op can only handle mixed D where D is multiple of 8 div_round_up(np.random.randint(low=int(0.5 * D), high=int(1.5 * D)), 8) for _ in range(T) ] D = np.average(Ds) else: Ds = [D] * T emb = Int4TableBatchedEmbeddingBagsCodegen( [(E, d) for d in Ds], use_cpu=True, index_remapping=[torch.arange(E) for _ in Ds] if index_remapping else None, ).cpu() nparams = sum(w.numel() for (w, _) in emb.split_embedding_weights()) logging.info( f"Int4 Embedding parameters: {nparams * 2 / 1.0e9: .2f} GParam, " f"{nparams / 1.0e9: .2f}GB" ) logging.info(f"Accessed weights per batch: {B * T * L * D * 0.5 / 1.0e6: .2f}MB") requests = generate_requests( iters, B, T, L, E, reuse=reuse, alpha=alpha, weights_precision=weights_precision, weighted=weighted, ) requests = [ (a.cpu().int(), b.cpu().int(), c.cpu() if c else None) for (a, b, c) in requests ] # forward time_per_iter = benchmark_cpu_requests( # pyre-fixme[6]: Expected `List[Tuple[Tensor, Tensor, Optional[Tensor]]]` # for 1st param but got `List[Tuple[torch.IntTensor, torch.IntTensor, # Optional[Tensor]]]`. requests, lambda indices, offsets, per_sample_weights: emb.forward( indices, offsets, per_sample_weights, feature_requires_grad=feature_requires_grad, ), ) logging.info( f"Int4 Forward, B: {B}, " f"E: {E}, T: {T}, D: {D}, L: {L}, W: {weighted}, " f"BW: {(0.5) * B * T * L * D / time_per_iter / 1.0e9: .2f}GB/s, " # noqa: B950 f"T: {time_per_iter * 1.0e6:.0f}us" ) @cli.command() @click.option("--alpha", default=1.0) @click.option("--bag-size", default=20) @click.option("--batch-size", default=512) @click.option("--embedding-dim", default=128) @click.option("--weights-precision", type=SparseType, default=SparseType.FP32) @click.option("--stoc", is_flag=True, default=False) @click.option("--iters", default=100) @click.option("--managed", default="device") @click.option("--mixed", is_flag=True, default=False) @click.option("--num-embeddings", default=int(1e5)) @click.option("--num-tables", default=32) @click.option("--reuse", default=0.0) @click.option("--row-wise/--no-row-wise", default=True) @click.option("--weighted", is_flag=True, default=False) @click.option("--weighted-num-requires-grad", type=int, default=None) @click.option("--index-remapping", is_flag=True, default=False) def int4_device( # noqa C901 alpha: float, bag_size: int, batch_size: int, embedding_dim: int, weights_precision: SparseType, stoc: bool, iters: int, managed: str, mixed: bool, num_embeddings: int, num_tables: int, reuse: float, row_wise: bool, weighted: bool, weighted_num_requires_grad: Optional[int], index_remapping: bool, ) -> None: np.random.seed(42) B = batch_size D = embedding_dim L = bag_size E = num_embeddings T = num_tables if weighted_num_requires_grad: assert weighted_num_requires_grad <= T weighted_requires_grad_tables = np.random.choice( T, replace=False, size=(weighted_num_requires_grad,) ).tolist() feature_requires_grad = ( torch.tensor( [1 if t in weighted_requires_grad_tables else 0 for t in range(T)] ) .cuda() .int() ) else: feature_requires_grad = None if mixed: # int4 table batched emb op can only handle mixed D where D is multiple of 8 Ds = [ div_round_up(np.random.randint(low=int(0.5 * D), high=int(1.5 * D)), 8) for _ in range(T) ] D = np.average(Ds) else: Ds = [D] * T emb = Int4TableBatchedEmbeddingBagsCodegen( [(E, d) for d in Ds], index_remapping=[torch.arange(E) for _ in Ds] if index_remapping else None, ).cuda() nparams = sum(w.numel() for (w, _) in emb.split_embedding_weights()) logging.info( f"Int4 Embedding parameters: {nparams * 2 / 1.0e9: .2f} GParam, " f"{nparams / 1.0e9: .2f}GB" ) logging.info(f"Accessed weights per batch: {B * T * L * D * 0.5 / 1.0e6: .2f}MB") requests = generate_requests( iters, B, T, L, E, reuse=reuse, alpha=alpha, weights_precision=weights_precision, weighted=weighted, ) requests = [(a.int(), b.int(), c if c else None) for (a, b, c) in requests] # TODO: multiple or single? requests = [requests[0] for _ in requests] # forward time_per_iter = benchmark_requests( # pyre-fixme[6]: Expected `List[Tuple[Tensor, Tensor, Optional[Tensor]]]` # for 1st param but got `List[Tuple[torch.IntTensor, torch.IntTensor, # Optional[Tensor]]]`. requests, lambda indices, offsets, per_sample_weights: emb.forward( indices.int(), offsets.int(), per_sample_weights, feature_requires_grad=feature_requires_grad, ), ) logging.info( f"Int4 Forward, B: {B}, " f"E: {E}, T: {T}, D: {D}, L: {L}, W: {weighted}, " f"BW: {(0.5) * B * T * L * D / time_per_iter / 1.0e9: .2f}GB/s, " # noqa: B950 f"T: {time_per_iter * 1.0e6:.0f}us" ) @cli.command() @click.option("--bag-size", default=20) @click.option("--batch-size", default=2048) @click.option("--iters", default=10) @click.option("--num-embeddings", default=int(1e5)) @click.option("--num-tables", default=100) @click.option("--load-factor", default=0.75) @click.option("--hit-rate", default=0.9) def hashtable( # noqa C901 bag_size: int, batch_size: int, iters: int, num_embeddings: int, num_tables: int, load_factor: float, hit_rate: float, ) -> None: B = batch_size T = num_tables L = bag_size E = num_embeddings np.random.seed(42) if hit_rate == 1.0: chosen_indices = torch.cat( [torch.arange(E) for _ in range(T)], dim=0 ).int() else: chosen_indices = torch.randint(low=0, high=int(E * 1.0 / hit_rate), size=(E * T,)).view(-1).int() dense_indices = torch.cat( [torch.arange(E) for _ in range(T)], dim=0 ).int() offsets = torch.tensor([E * t for t in range(T + 1)]).int() assert offsets[-1] == chosen_indices.numel() assert offsets.numel() == T + 1 assert (offsets.numel() - 1) // T == 1 capacity = div_round_up(int(dense_indices.numel() * 1.0 / load_factor), 32) hash_table = torch.zeros( (capacity, 3), dtype=torch.int32, ) assert hash_table.numel() * 4 < 2 ** 32 # initialize hash_table[:, :] = -1 torch.ops.fb.pruned_hashmap_insert(chosen_indices, dense_indices, offsets, hash_table, T) hash_table = hash_table.cuda() requests = generate_requests( iters, B, T, L, E, ) requests = [(a.cuda().int(), b.cuda().int(), c) for (a, b, c) in requests] empirical_hit_rate = np.mean([torch.ops.fb.pruned_hashmap_lookup( indices.cuda().int(), offsets.cuda().int(), hash_table.cuda().contiguous(), T ).ne(-1).sum().item() / indices.numel() for indices, offsets, _ in requests]) time_per_iter = benchmark_requests( # pyre-fixme[6]: Expected `List[Tuple[Tensor, Tensor, Optional[Tensor]]]` # for 1st param but got `List[Tuple[torch.IntTensor, torch.IntTensor, # Optional[Tensor]]]`. requests, lambda indices, offsets, _: torch.ops.fb.pruned_hashmap_lookup( indices.cuda().int(), offsets.cuda().int().contiguous(), hash_table.cuda().int().contiguous(), T ), ) logging.info( f"100% hit: B: {B}, T: {T}, L: {L}, E: {E}, QPS: {B * T * L / time_per_iter / 1.0e9:.2f}B QPS/s, " f"T: {time_per_iter * 1.0e6:.0f}us, load factor: {E * T / capacity * 100:.1f}%, hit rate: {empirical_hit_rate * 100:.2f}%, Table size: {hash_table.numel() * 4 / 1.0e6:.0f}MB" ) if __name__ == "__main__": cli()

32.451243

182

0.593802

2b6fbb2b88349e82aaf5ffc1cdf1bf2bd30bc470

1,421

py

Python

scikit-learn/tree_dtc.py

kingreatwill/penter

2d027fd2ae639ac45149659a410042fe76b9dab0

[ "MIT" ]

13

2020-01-04T07:37:38.000Z

2021-08-31T05:19:58.000Z

scikit-learn/tree_dtc.py

kingreatwill/penter

2d027fd2ae639ac45149659a410042fe76b9dab0

[ "MIT" ]

3

2020-06-05T22:42:53.000Z

2020-08-24T07:18:54.000Z

scikit-learn/tree_dtc.py

kingreatwill/penter

2d027fd2ae639ac45149659a410042fe76b9dab0

[ "MIT" ]

9

2020-10-19T04:53:06.000Z

2021-08-31T05:20:01.000Z

import graphviz from sklearn import tree, datasets from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt wine = datasets.load_wine() Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3) clf = tree.DecisionTreeClassifier(criterion="entropy" ,random_state=30 ,splitter="random" ,max_depth=3 ,min_samples_leaf=10 ,min_samples_split=10 ) clf = clf.fit(Xtrain, Ytrain) feature_name = ['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸'] # dot_data = tree.export_graphviz(clf # ,feature_names= feature_name # ,class_names=["琴酒","雪莉","贝尔摩德"] # ,filled=True # ,rounded=True # ) # graph = graphviz.Source(dot_data) # graph.save() plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签 plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 #有中文出现的情况，需要u'内容' plt.figure() tree.plot_tree(clf , feature_names=feature_name , class_names=["琴酒", "雪莉", "贝尔摩德"] , filled=True , rounded=True ) plt.show()

32.295455

105

0.513723

cc7ec806a6713a9a141c960a59d187fa76bb3400

6,984

py

Python

scripts/test/run_test_suite.py

zys-contrib/devtools-frontend

49ae10d02a40b48418788461bb50a19927f7b06a

[ "BSD-3-Clause" ]

null

scripts/test/run_test_suite.py

zys-contrib/devtools-frontend

49ae10d02a40b48418788461bb50a19927f7b06a

[ "BSD-3-Clause" ]

null

scripts/test/run_test_suite.py

zys-contrib/devtools-frontend

49ae10d02a40b48418788461bb50a19927f7b06a

[ "BSD-3-Clause" ]

null

#!/usr/bin/env vpython3 # # Copyright 2020 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """ Run tests on a pinned version of chrome. DEPRECATED: please use run_test_suite.js instead. """ import argparse import os import sys ROOT_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..') scripts_path = os.path.join(ROOT_DIRECTORY, 'scripts') sys.path.append(scripts_path) import devtools_paths import test_helpers def parse_options(cli_args): parser = argparse.ArgumentParser(description='Run tests') parser.add_argument('--chrome-binary', dest='chrome_binary', help='path to Chromium binary') parser.add_argument( '--test-suite', dest='test_suite', help= 'test suite name. DEPRECATED: please use --test-suite-path instead.') parser.add_argument( '--test-suite-path', dest='test_suite_path', help= 'path to test suite, starting from the out/TARGET directory. Should use Linux path separators.' ) parser.add_argument('--test-file', dest='test_file', help='an absolute path for the file to test') parser.add_argument( '--target', '-t', default='Default', dest='target', help='The name of the Ninja output directory. Defaults to "Default"') parser.add_argument( '--chrome-features', dest='chrome_features', help= 'comma separated list of strings passed to --enable-features on the chromium commandline' ) parser.add_argument( '--jobs', default='1', dest='jobs', help= 'The number of parallel runners to use (if supported). Defaults to 1') parser.add_argument('--cwd', dest='cwd', help='Path to the directory containing the out dir', default=devtools_paths.devtools_root_path()) parser.add_argument( '--node_modules-path', dest='node_modules_path', help= 'Path to the node_modules directory for Node to use. Will use Node defaults if not set.', default=None) parser.add_argument('test_patterns', nargs='*') return parser.parse_args(cli_args) def run_tests(chrome_binary, chrome_features, test_suite_path, test_suite, jobs, target, cwd=None, node_modules_path=None, test_patterns=None): env = os.environ.copy() env['CHROME_BIN'] = chrome_binary if chrome_features: env['CHROME_FEATURES'] = chrome_features if test_patterns: env['TEST_PATTERNS'] = ';'.join(test_patterns) if jobs: env['JOBS'] = jobs if target: env['TARGET'] = target if node_modules_path is not None: # Node requires the path to be absolute env['NODE_PATH'] = os.path.abspath(node_modules_path) if not cwd: cwd = devtools_paths.devtools_root_path() exec_command = [devtools_paths.node_path()] if 'DEBUG_TEST' in env: exec_command.append('--inspect') exec_command = exec_command + [ devtools_paths.mocha_path(), '--config', os.path.join(test_suite_path, '.mocharc.js'), ] exit_code = test_helpers.popen(exec_command, cwd=cwd, env=env) if exit_code != 0: return True return False def run_test(): print( "DEPRECATED: run_test_suite.py is deprecated and will be removed in the future.\nPlease use run_test_suite.js which is newer and more robust with handling paths." ) OPTIONS = parse_options(sys.argv[1:]) is_cygwin = sys.platform == 'cygwin' chrome_binary = None test_suite = None chrome_features = None # Default to the downloaded / pinned Chromium binary downloaded_chrome_binary = devtools_paths.downloaded_chrome_binary_path() if test_helpers.check_chrome_binary(downloaded_chrome_binary): chrome_binary = downloaded_chrome_binary # Override with the arg value if provided. if OPTIONS.chrome_binary: chrome_binary = OPTIONS.chrome_binary if not test_helpers.check_chrome_binary(chrome_binary): print('Unable to find a Chrome binary at \'%s\'' % chrome_binary) sys.exit(1) if OPTIONS.chrome_features: chrome_features = OPTIONS.chrome_features if (chrome_binary is None): print('Unable to run, no Chrome binary provided') sys.exit(1) if OPTIONS.jobs: jobs = OPTIONS.jobs test_file = OPTIONS.test_file test_patterns = OPTIONS.test_patterns if test_file: test_patterns.append(test_file) print('Using Chromium binary ({}{})\n'.format( chrome_binary, ' ' + chrome_features if chrome_features else '')) print('Using target (%s)\n' % OPTIONS.target) if test_file is not None: print( 'The test_file argument is obsolete, just pass the filename as positional argument' ) if test_patterns: print('Testing file(s) (%s)' % ', '.join(test_patterns)) cwd = OPTIONS.cwd target = OPTIONS.target node_modules_path = OPTIONS.node_modules_path print('Running tests from %s\n' % cwd) test_suite_path_input = OPTIONS.test_suite_path test_suite = OPTIONS.test_suite test_suite_parts = None if test_suite: # test-suite is deprecated and will be removed, but we support it for now to not break the bots until their recipes are updated. test_suite_parts = ['gen', 'test', test_suite] elif test_suite_path_input: # We take the input with Linux path separators, but need to split and join to make sure this works on Windows. test_suite_parts = test_suite_path_input.split('/') else: print( 'Unable to run, require one of --test-suite or --test-suite-path to be provided.' ) sys.exit(1) print('Using Test Suite (%s)\n' % os.path.join(*test_suite_parts)) test_suite_path = os.path.join(os.path.abspath(cwd), 'out', OPTIONS.target, *test_suite_parts) errors_found = False try: errors_found = run_tests(chrome_binary, chrome_features, test_suite_path, test_suite, jobs, target, cwd, node_modules_path, test_patterns=test_patterns) except Exception as err: print(err) if errors_found: print('ERRORS DETECTED') sys.exit(1) if __name__ == '__main__': run_test()

31.890411

170

0.610968

93ccc3c024e0ecb51443c1fc27e96d6281c10516

18,358

py

Python

gdal/swig/python/osgeo/gdal_array.py

mihadyuk/gdal

d4627981715b82ff368547ef00ef26e0b9207048

[ "MIT" ]

null

gdal/swig/python/osgeo/gdal_array.py

mihadyuk/gdal

d4627981715b82ff368547ef00ef26e0b9207048

[ "MIT" ]

null

gdal/swig/python/osgeo/gdal_array.py

mihadyuk/gdal

d4627981715b82ff368547ef00ef26e0b9207048

[ "MIT" ]

null

# This file was automatically generated by SWIG (http://www.swig.org). # Version 1.3.40 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. # This file is compatible with both classic and new-style classes. from sys import version_info if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_gdal_array', [dirname(__file__)]) except ImportError: import _gdal_array return _gdal_array if fp is not None: try: _mod = imp.load_module('_gdal_array', fp, pathname, description) finally: fp.close() return _mod _gdal_array = swig_import_helper() del swig_import_helper else: import _gdal_array del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static) or hasattr(self,name): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 import gdal class VirtualMem(_object): """Proxy of C++ CPLVirtualMemShadow class""" __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, VirtualMem, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, VirtualMem, name) def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr __swig_destroy__ = _gdal_array.delete_VirtualMem __del__ = lambda self : None; def GetAddr(self): """GetAddr(self)""" return _gdal_array.VirtualMem_GetAddr(self) def Pin(self, start_offset = 0, nsize = 0, bWriteOp = 0): """ Pin(self, size_t start_offset = 0, size_t nsize = 0, int bWriteOp = 0) Pin(self, size_t start_offset = 0, size_t nsize = 0) Pin(self, size_t start_offset = 0) Pin(self) """ return _gdal_array.VirtualMem_Pin(self, start_offset, nsize, bWriteOp) VirtualMem_swigregister = _gdal_array.VirtualMem_swigregister VirtualMem_swigregister(VirtualMem) def TermProgress_nocb(*args, **kwargs): """TermProgress_nocb(double dfProgress, char pszMessage = None, void pData = None) -> int""" return _gdal_array.TermProgress_nocb(*args, **kwargs) TermProgress = _gdal_array.TermProgress def GetArrayFilename(*args): """GetArrayFilename(PyArrayObject psArray) -> retStringAndCPLFree""" return _gdal_array.GetArrayFilename(*args) GetArrayFilename = _gdal_array.GetArrayFilename def BandRasterIONumPy(*args, **kwargs): """ BandRasterIONumPy(Band band, int bWrite, int xoff, int yoff, int xsize, int ysize, PyArrayObject psArray, int buf_type, GDALRIOResampleAlg resample_alg, GDALProgressFunc callback = None, void callback_data = None) -> CPLErr """ return _gdal_array.BandRasterIONumPy(*args, **kwargs) BandRasterIONumPy = _gdal_array.BandRasterIONumPy def DatasetIONumPy(*args, **kwargs): """ DatasetIONumPy(Dataset ds, int bWrite, int xoff, int yoff, int xsize, int ysize, PyArrayObject psArray, int buf_type, GDALRIOResampleAlg resample_alg, GDALProgressFunc callback = None, void callback_data = None) -> CPLErr """ return _gdal_array.DatasetIONumPy(*args, **kwargs) DatasetIONumPy = _gdal_array.DatasetIONumPy def VirtualMemGetArray(*args): """VirtualMemGetArray(VirtualMem virtualmem)""" return _gdal_array.VirtualMemGetArray(*args) VirtualMemGetArray = _gdal_array.VirtualMemGetArray def RATValuesIONumPyWrite(*args, **kwargs): """ RATValuesIONumPyWrite(RasterAttributeTable poRAT, int nField, int nStart, PyArrayObject psArray) -> CPLErr """ return _gdal_array.RATValuesIONumPyWrite(*args, **kwargs) RATValuesIONumPyWrite = _gdal_array.RATValuesIONumPyWrite def RATValuesIONumPyRead(*args, **kwargs): """ RATValuesIONumPyRead(RasterAttributeTable poRAT, int nField, int nStart, int nLength) -> PyObject """ return _gdal_array.RATValuesIONumPyRead(*args, **kwargs) RATValuesIONumPyRead = _gdal_array.RATValuesIONumPyRead import numpy import _gdal_array import gdalconst import gdal gdal.AllRegister() codes = { gdalconst.GDT_Byte : numpy.uint8, gdalconst.GDT_UInt16 : numpy.uint16, gdalconst.GDT_Int16 : numpy.int16, gdalconst.GDT_UInt32 : numpy.uint32, gdalconst.GDT_Int32 : numpy.int32, gdalconst.GDT_Float32 : numpy.float32, gdalconst.GDT_Float64 : numpy.float64, gdalconst.GDT_CInt16 : numpy.complex64, gdalconst.GDT_CInt32 : numpy.complex64, gdalconst.GDT_CFloat32 : numpy.complex64, gdalconst.GDT_CFloat64 : numpy.complex128 } def OpenArray( array, prototype_ds = None ): ds = gdal.Open( GetArrayFilename(array) ) if ds is not None and prototype_ds is not None: if type(prototype_ds).__name__ == 'str': prototype_ds = gdal.Open( prototype_ds ) if prototype_ds is not None: CopyDatasetInfo( prototype_ds, ds ) return ds def flip_code(code): if isinstance(code, (numpy.dtype,type)): # since several things map to complex64 we must carefully select # the opposite that is an exact match (ticket 1518) if code == numpy.int8: return gdalconst.GDT_Byte if code == numpy.complex64: return gdalconst.GDT_CFloat32 for key, value in codes.items(): if value == code: return key return None else: try: return codes[code] except KeyError: return None def NumericTypeCodeToGDALTypeCode(numeric_type): if not isinstance(numeric_type, (numpy.dtype,type)): raise TypeError("Input must be a type") return flip_code(numeric_type) def GDALTypeCodeToNumericTypeCode(gdal_code): return flip_code(gdal_code) def LoadFile( filename, xoff=0, yoff=0, xsize=None, ysize=None, buf_xsize=None, buf_ysize=None, buf_type=None, resample_alg = gdal.GRIORA_NearestNeighbour, callback=None, callback_data=None ): ds = gdal.Open( filename ) if ds is None: raise ValueError("Can't open "+filename+"\n\n"+gdal.GetLastErrorMsg()) return DatasetReadAsArray( ds, xoff, yoff, xsize, ysize, buf_xsize=buf_xsize, buf_ysize=buf_ysize, buf_type=buf_type, resample_alg=resample_alg, callback = callback, callback_data = callback_data ) def SaveArray( src_array, filename, format = "GTiff", prototype = None ): driver = gdal.GetDriverByName( format ) if driver is None: raise ValueError("Can't find driver "+format) return driver.CreateCopy( filename, OpenArray(src_array,prototype) ) def DatasetReadAsArray( ds, xoff=0, yoff=0, win_xsize=None, win_ysize=None, buf_obj=None, buf_xsize = None, buf_ysize = None, buf_type = None, resample_alg = gdal.GRIORA_NearestNeighbour, callback=None, callback_data=None ): """Pure python implementation of reading a chunk of a GDAL file into a numpy array. Used by the gdal.Dataset.ReadAsArray method.""" if win_xsize is None: win_xsize = ds.RasterXSize if win_ysize is None: win_ysize = ds.RasterYSize if ds.RasterCount == 0: return None if ds.RasterCount == 1: return BandReadAsArray( ds.GetRasterBand(1), xoff, yoff, win_xsize, win_ysize, buf_xsize = buf_xsize, buf_ysize = buf_ysize, buf_type = buf_type, buf_obj = buf_obj, resample_alg = resample_alg, callback = callback, callback_data = callback_data ) if buf_obj is None: if buf_xsize is None: buf_xsize = win_xsize if buf_ysize is None: buf_ysize = win_ysize if buf_type is None: buf_type = ds.GetRasterBand(1).DataType for band_index in range(2,ds.RasterCount+1): if buf_type != ds.GetRasterBand(band_index).DataType: buf_type = gdalconst.GDT_Float32 typecode = GDALTypeCodeToNumericTypeCode( buf_type ) if typecode == None: buf_type = gdalconst.GDT_Float32 typecode = numpy.float32 if buf_type == gdalconst.GDT_Byte and ds.GetRasterBand(1).GetMetadataItem('PIXELTYPE', 'IMAGE_STRUCTURE') == 'SIGNEDBYTE': typecode = numpy.int8 buf_obj = numpy.empty([ds.RasterCount, buf_ysize,buf_xsize], dtype = typecode) else: if len(buf_obj.shape) != 3: raise ValueError('Array should have 3 dimensions') shape_buf_xsize = buf_obj.shape[2] shape_buf_ysize = buf_obj.shape[1] if buf_xsize is not None and buf_xsize != shape_buf_xsize: raise ValueError('Specified buf_xsize not consistant with array shape') if buf_ysize is not None and buf_ysize != shape_buf_ysize: raise ValueError('Specified buf_ysize not consistant with array shape') if buf_obj.shape[0] != ds.RasterCount: raise ValueError('Array should have space for %d bands' % ds.RasterCount) datatype = NumericTypeCodeToGDALTypeCode( buf_obj.dtype.type ) if not datatype: raise ValueError("array does not have corresponding GDAL data type") if buf_type is not None and buf_type != datatype: raise ValueError("Specified buf_type not consistant with array type") buf_type = datatype if DatasetIONumPy( ds, 0, xoff, yoff, win_xsize, win_ysize, buf_obj, buf_type, resample_alg, callback, callback_data ) != 0: return None return buf_obj def BandReadAsArray( band, xoff = 0, yoff = 0, win_xsize = None, win_ysize = None, buf_xsize=None, buf_ysize=None, buf_type=None, buf_obj=None, resample_alg = gdal.GRIORA_NearestNeighbour, callback=None, callback_data=None): """Pure python implementation of reading a chunk of a GDAL file into a numpy array. Used by the gdal.Band.ReadAsArray method.""" if win_xsize is None: win_xsize = band.XSize if win_ysize is None: win_ysize = band.YSize if buf_obj is None: if buf_xsize is None: buf_xsize = win_xsize if buf_ysize is None: buf_ysize = win_ysize if buf_type is None: buf_type = band.DataType typecode = GDALTypeCodeToNumericTypeCode( buf_type ) if typecode == None: buf_type = gdalconst.GDT_Float32 typecode = numpy.float32 else: buf_type = NumericTypeCodeToGDALTypeCode( typecode ) if buf_type == gdalconst.GDT_Byte and band.GetMetadataItem('PIXELTYPE', 'IMAGE_STRUCTURE') == 'SIGNEDBYTE': typecode = numpy.int8 buf_obj = numpy.empty([buf_ysize,buf_xsize], dtype = typecode) else: if len(buf_obj.shape) == 2: shape_buf_xsize = buf_obj.shape[1] shape_buf_ysize = buf_obj.shape[0] else: shape_buf_xsize = buf_obj.shape[2] shape_buf_ysize = buf_obj.shape[1] if buf_xsize is not None and buf_xsize != shape_buf_xsize: raise ValueError('Specified buf_xsize not consistant with array shape') if buf_ysize is not None and buf_ysize != shape_buf_ysize: raise ValueError('Specified buf_ysize not consistant with array shape') datatype = NumericTypeCodeToGDALTypeCode( buf_obj.dtype.type ) if not datatype: raise ValueError("array does not have corresponding GDAL data type") if buf_type is not None and buf_type != datatype: raise ValueError("Specified buf_type not consistant with array type") buf_type = datatype if BandRasterIONumPy( band, 0, xoff, yoff, win_xsize, win_ysize, buf_obj, buf_type, resample_alg, callback, callback_data ) != 0: return None return buf_obj def BandWriteArray( band, array, xoff=0, yoff=0, resample_alg = gdal.GRIORA_NearestNeighbour, callback=None, callback_data=None ): """Pure python implementation of writing a chunk of a GDAL file from a numpy array. Used by the gdal.Band.WriteArray method.""" if array is None or len(array.shape) != 2: raise ValueError("expected array of dim 2") xsize = array.shape[1] ysize = array.shape[0] if xsize + xoff > band.XSize or ysize + yoff > band.YSize: raise ValueError("array larger than output file, or offset off edge") datatype = NumericTypeCodeToGDALTypeCode( array.dtype.type ) # if we receive some odd type, like int64, try casting to a very # generic type we do support (#2285) if not datatype: gdal.Debug( 'gdal_array', 'force array to float64' ) array = array.astype( numpy.float64 ) datatype = NumericTypeCodeToGDALTypeCode( array.dtype.type ) if not datatype: raise ValueError("array does not have corresponding GDAL data type") return BandRasterIONumPy( band, 1, xoff, yoff, xsize, ysize, array, datatype, resample_alg, callback, callback_data ) def RATWriteArray(rat, array, field, start=0): """ Pure Python implementation of writing a chunk of the RAT from a numpy array. Type of array is coerced to one of the types (int, double, string) supported. Called from RasterAttributeTable.WriteArray """ if array is None: raise ValueError("Expected array of dim 1") # if not the array type convert it to handle lists etc if not isinstance(array, numpy.ndarray): array = numpy.array(array) if array.ndim != 1: raise ValueError("Expected array of dim 1") if (start + array.size) > rat.GetRowCount(): raise ValueError("Array too big to fit into RAT from start position") if numpy.issubdtype(array.dtype, numpy.integer): # is some type of integer - coerce to standard int # TODO: must check this is fine on all platforms # confusingly numpy.int 64 bit even if native type 32 bit array = array.astype(numpy.int32) elif numpy.issubdtype(array.dtype, numpy.floating): # is some type of floating point - coerce to double array = array.astype(numpy.double) elif numpy.issubdtype(array.dtype, numpy.character): # cast away any kind of Unicode etc array = array.astype(numpy.character) else: raise ValueError("Array not of a supported type (integer, double or string)") return RATValuesIONumPyWrite(rat, field, start, array) def RATReadArray(rat, field, start=0, length=None): """ Pure Python implementation of reading a chunk of the RAT into a numpy array. Called from RasterAttributeTable.ReadAsArray """ if length is None: length = rat.GetRowCount() - start return RATValuesIONumPyRead(rat, field, start, length) def CopyDatasetInfo( src, dst, xoff=0, yoff=0 ): """ Copy georeferencing information and metadata from one dataset to another. src: input dataset dst: output dataset - It can be a ROI - xoff, yoff: dst's offset with respect to src in pixel/line. Notes: Destination dataset must have update access. Certain formats do not support creation of geotransforms and/or gcps. """ dst.SetMetadata( src.GetMetadata() ) #Check for geo transform gt = src.GetGeoTransform() if gt != (0,1,0,0,0,1): dst.SetProjection( src.GetProjectionRef() ) if (xoff == 0) and (yoff == 0): dst.SetGeoTransform( gt ) else: ngt = [gt[0],gt[1],gt[2],gt[3],gt[4],gt[5]] ngt[0] = gt[0] + xoff*gt[1] + yoff*gt[2]; ngt[3] = gt[3] + xoff*gt[4] + yoff*gt[5]; dst.SetGeoTransform( ( ngt[0], ngt[1], ngt[2], ngt[3], ngt[4], ngt[5] ) ) #Check for GCPs elif src.GetGCPCount() > 0: if (xoff == 0) and (yoff == 0): dst.SetGCPs( src.GetGCPs(), src.GetGCPProjection() ) else: gcps = src.GetGCPs() #Shift gcps new_gcps = [] for gcp in gcps: ngcp = gdal.GCP() ngcp.GCPX = gcp.GCPX ngcp.GCPY = gcp.GCPY ngcp.GCPZ = gcp.GCPZ ngcp.GCPPixel = gcp.GCPPixel - xoff ngcp.GCPLine = gcp.GCPLine - yoff ngcp.Info = gcp.Info ngcp.Id = gcp.Id new_gcps.append(ngcp) try: dst.SetGCPs( new_gcps , src.GetGCPProjection() ) except: print ("Failed to set GCPs") return return

37.773663

130

0.638087

5146692ca1929874c44fdf7a95bb18c5cabb70f2

115

py

Python

turbopy/__version__.py

AOstenfeld/turbopy

dc58f6482bd99385574a229739a32bcf3fb18fb2

[ "CC0-1.0" ]

12

2020-06-01T14:01:50.000Z

2021-11-29T01:09:55.000Z

turbopy/__version__.py

AOstenfeld/turbopy

dc58f6482bd99385574a229739a32bcf3fb18fb2

[ "CC0-1.0" ]

143

2020-05-30T13:18:34.000Z

2021-08-25T13:27:10.000Z

turbopy/__version__.py

AOstenfeld/turbopy

dc58f6482bd99385574a229739a32bcf3fb18fb2

[ "CC0-1.0" ]

34

2020-05-30T12:06:02.000Z

2021-07-23T14:22:37.000Z

"""Define a version number for turboPy""" VERSION = ('2020', '10', '14') __version__ = '.'.join(map(str, VERSION))

28.75

41

0.634783

015622962824126bfdda5307d81c61cbe7cbc909

6,228

py

Python

LogAnalysis/flowprocess.py

WenPeiyu/LianjiaSpider

9d4fd31df0120c756fbbd625b66f2fee4fe17891

[ "MIT" ]

null

LogAnalysis/flowprocess.py

WenPeiyu/LianjiaSpider

9d4fd31df0120c756fbbd625b66f2fee4fe17891

[ "MIT" ]

null

LogAnalysis/flowprocess.py

WenPeiyu/LianjiaSpider

9d4fd31df0120c756fbbd625b66f2fee4fe17891

[ "MIT" ]

null

import re import pandas as pd import pickle from log import logger import matplotlib import matplotlib.pyplot as plt import numpy as np class LogFlow: """ 日志编码流 """ def __init__(self, filepath): """ 初始化对象 :param filepath: 欲处理的日志文件地址 """ self.filepath = filepath self.logger = logger self.read_file() self.logger.info("模块{0}初始化成功".format(self.__class__.__name__)) def read_file(self): """ 读入日志文件 :return: """ self.df_log = pd.read_csv(self.filepath) self.logger.info("日志文件 - {file} 载入成功".format(file=self.filepath)) self.df_log_RA_MSG = self.df_log[self.df_log.MSG + self.df_log.RA > 0] def collect_message(self, crnti=None, RA=True): """ 提取日志的信息列 :param crnti: 欲提取的crnti编号，默认为None不具体到具体crnti :param RA: 是否开启随机接入过滤，默认开启，否则读取全部日志字段 :return: """ self.lst_message = [] if crnti is None: if RA: self.lst_message = list(self.df_log_RA_MSG["Message"]) else: self.lst_message = list(self.df_log["Message"]) else: if RA: df_temp = self.df_log_RA_MSG[self.df_log_RA_MSG.CRNTI == crnti] self.lst_message = list(df_temp["Message"]) else: df_temp = self.df_log[self.df_log.CRNTI == crnti] self.lst_message = list(df_temp["Message"]) def extract_information(self): """ 提取消息中的字段，格式化为格式数据 :return: """ self.lst_randomaccess = [] for i in self.lst_message: stra = i re_sign = re.compile(r"\[[a-zA-Z]+[a-zA-Z\s0-9]*?\]") re_allocate = re.compile(r"[ 0-9a-zA-Z_]+\[[,\:\-a-zA-Z\s0-9]+?\][a-zA-Z]*") re_measure = re.compile(r"[0-9a-zA-Z_]+=[-0-9]*") sign = re_sign.findall(stra) sign = {"sign": [i.strip() for i in sign if i.strip()]} stra = re_sign.sub("", stra) allocate = re_allocate.findall(stra) allocate = [i.strip() for i in allocate if i.strip()] allocate = {"allocate": {i.split('[')[0]: i.split('[')[1].replace(']', '') for i in allocate}} stra = re_allocate.sub("", stra) measure = re_measure.findall(stra) measure = [i.strip() for i in measure if i.strip()] measure = {"measure": {i.split('=')[0]: i.split('=')[1] for i in measure}} stra = re_measure.sub("", stra) describe = stra.replace(".", "").split(",") describe = {"describe": [ re.sub("\d+ msg3gid", "msg3gid", re.sub("SFN\[\d+\]", "", re.sub("\| \@\[\d+\]", "", i.strip()))) for i in describe if i.strip()]} self.lst_randomaccess.append({**sign, **allocate, **measure, **describe}) def generate_tagset(self): """ 生成标签集合 :return: """ sign = [] allocate = [] measure = [] describe = [] for i in self.lst_randomaccess: sign = sign + i['sign'] sign = list(set(sign)) measure = measure + list(i["measure"].keys()) measure = list(set(measure)) allocate = allocate + list(i["allocate"].keys()) allocate = list(set(allocate)) describe = describe + i['describe'] describe = list(set(describe)) self.set_tags = (sign, measure, allocate, describe) def init_mapping(self): """ 生成标签到编码的映射表 :return: """ tags = [] attrs = [] no = [] sign = self.set_tags[0] measure = self.set_tags[1] allocate = self.set_tags[2] describe = self.set_tags[3] tags = tags + list(sign) attrs = attrs + len(sign) * ["sign"] no1 = [hex(i)[2:].zfill(4) for i in range(len(sign))] tags = tags + list(measure) attrs = attrs + len(measure) * ["measure"] no2 = [hex(i+256)[2:].zfill(4) for i in range(len(measure))] tags = tags + list(allocate) attrs = attrs + len(allocate) * ["allocate"] no3 = [hex(i+1280)[2:].zfill(4) for i in range(len(allocate))] tags = tags + list(describe) attrs = attrs + len(describe) * ["describe"] no4 = [hex(i+2304)[2:].zfill(4) for i in range(len(describe))] no = [hex(i)[2:].zfill(4) for i in range(len(sign) + len(measure) + len(allocate) + len(describe))] self.df_mapping = pd.DataFrame({ "tag": tags, "attr": attrs, "no_order": no, "no": no1+no2+no3+no4}) def dump_mapping(self, path): """ 导出映射表 :param path: :return: """ pickle.dump(self.df_mapping, open(path, "wb")) def load_mapping(self, path): """ 导入映射表 :param path: :return: """ self.df_mapping = pickle.load(open(path, 'rb')) def generate_code(self): """ 根据映射表将日志消息编码化 :return: """ for num, i in enumerate(self.lst_randomaccess): str_code = "" str_code_color = "" for j in i: for m in i[j]: str_code = str_code + dict(zip(self.df_mapping["tag"], self.df_mapping["no"]))[m] str_code_color = str_code_color + dict(zip(self.df_mapping["tag"], self.df_mapping["no_order"]))[m] self.lst_randomaccess[num]["code"] = str_code self.lst_randomaccess[num]["code_color"] = str_code_color def plot_message_spectrum(self): """ 绘制消息流图谱 :return: """ length = len(self.lst_randomaccess) matplotlib.rcParams['figure.figsize'] = [10, length * 0.5] plt.figure() plt.subplots_adjust(hspace=0) for num, i in enumerate(self.lst_randomaccess): s = i["code_color"] plt.subplot(length, 1, num + 1) lst_color = [eval("0x" + s[i:i + 4]) for i in range(0, len(s), 4)] arr_color = np.array([lst_color]) plt.pcolor(arr_color) plt.axis('off')

34.40884

119

0.514451

7a51766bba3a8d47f51c606f8453e79c7e0d727f

363

py

Python

src/gym_rotors/envs/bebop_reach_position.py

prateeksamaiya/mythesis_copy

67334fe0e206d8cc9625d8356a7035baa3cdc9b8

[ "MIT" ]

null

src/gym_rotors/envs/bebop_reach_position.py

prateeksamaiya/mythesis_copy

67334fe0e206d8cc9625d8356a7035baa3cdc9b8

[ "MIT" ]

null

src/gym_rotors/envs/bebop_reach_position.py

prateeksamaiya/mythesis_copy

67334fe0e206d8cc9625d8356a7035baa3cdc9b8

[ "MIT" ]

1

2019-06-03T17:16:23.000Z

from gym import utils from gym_rotors.envs import bebop_env class BebopReachPositionEnv(bebop_env.BebopEnv, utils.EzPickle): def __init__(self, reward_type='sparse'): bebop_env.BebopEnv.__init__(self, launchfile='bebop_reach_position.launch', init_drone_pos=[0,0,0.5],n_actions=3,reward_type=reward_type) utils.EzPickle.__init__(self)

45.375

83

0.768595

d7d3528a8e7dbefb75c561a8bb704dd1e009675a

20,129

py

Python

CondCore/Utilities/python/CondDBFW/querying.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

13

2015-11-30T15:49:45.000Z

2022-02-08T16:11:30.000Z

CondCore/Utilities/python/CondDBFW/querying.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

640

2015-02-11T18:55:47.000Z

2022-03-31T14:12:23.000Z

CondCore/Utilities/python/CondDBFW/querying.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

51

2015-08-11T21:01:40.000Z

2022-03-30T07:31:34.000Z

""" connection class translates either a connection string for sqlite, oracle of frontier into a connection object. Also sets up ORM with SQLAlchemy. connection class can also take a pre-constructed engine - useful for web services. """ from __future__ import print_function from __future__ import absolute_import import sqlalchemy from sqlalchemy import create_engine, text, or_ from sqlalchemy.orm import sessionmaker from sqlalchemy.pool import NullPool import datetime from .data_sources import json_data_node from copy import deepcopy from . import models import traceback import os import netrc import sys class connection(object): engine = None connection = None session = None connection_data = None netrc_authenticators = None secrets = None """ Given a connection string, parses the connection string and connects. """ def __init__(self, connection_data, mode=None, map_blobs=False, secrets=None, pooling=False): self._pooling = pooling # add querying utility properties # these must belong to the connection since the way in which their values are handled # depends on the database being connected to. self.range = models.Range self.radius = models.Radius self.regexp = models.RegExp self.regexp.connection_object = self if type(connection_data) in [str, unicode]: # if we've been given a connection string, process it self.connection_data = new_connection_dictionary(connection_data, secrets=secrets, mode=mode) self.schema = self.connection_data.get("schema") if self.connection_data.get("schema") != None else "" self.range.database_type = self.connection_data["host"] self.radius.database_type = self.connection_data["host"] self.regexp.database_type = self.connection_data["host"] else: self.connection_data = connection_data # assume we have an engine # we need to take the string representation so we know which type of db we're aiming at engine_string = str(connection_data) db_type = None if "oracle" in engine_string: db_type = "oracle" elif "frontier" in engine_string: db_type = "frontier" elif "sqlite" in engine_string: db_type = "sqlite" self.range.database_type = db_type self.radius.database_type = db_type self.regexp.database_type = db_type from . import models as ms self.models = ms.generate(map_blobs) #self.base = self.models["Base"] def setup(self): """ Setup engine with given credentials from netrc file, and make a session maker. """ if isinstance(self.connection_data, dict): self.engine = engine_from_dictionary(self.connection_data, pooling=self._pooling) else: # we've been given an engine by the user # use it as the engine self.engine = self.connection_data self.sessionmaker = sessionmaker(bind=self.engine) self.session = self.sessionmaker() self.factory = factory(self) # assign correct schema for database name to each model tmp_models_dict = {} for key in self.models: if self.models[key].__class__ == sqlalchemy.ext.declarative.api.DeclarativeMeta\ and str(self.models[key].__name__) != "Base": if isinstance(self.connection_data, dict): # we can only extract the secrets and schema individuall # if we were given a dictionary... if we were given an engine # we can't do this without parsing the connection string from the engine # - a wide range of which it will be difficult to support! self.models[key].__table__.schema = self.connection_data["schema"] self.models[key].secrets = self.connection_data["secrets"] self.models[key].session = self.session # isn't used anywhere - comment it out for now #self.models[key].authentication = self.netrc_authenticators self.models[key].connection = self tmp_models_dict[key.lower()] = self.models[key] tmp_models_dict[key.lower()].empty = False self.models = tmp_models_dict return self @staticmethod def _get_CMS_frontier_connection_string(database): try: import subprocess return subprocess.Popen(['cmsGetFnConnect', 'frontier://%s' % database], stdout = subprocess.PIPE).communicate()[0].strip() except: raise Exception("Frontier connections can only be constructed when inside a CMSSW environment.") @staticmethod def _cms_frontier_string(database, schema="cms_conditions"): """ Get database string for frontier. """ import urllib return 'oracle+frontier://@%s/%s' % (urllib.quote_plus(connection._get_CMS_frontier_connection_string(database)), schema) @staticmethod def _cms_oracle_string(user, pwd, db_name): """ Get database string for oracle. """ return 'oracle://%s:%s@%s' % (user, pwd, db_name) @staticmethod def build_oracle_url(user, pwd, db_name): """ Build the connection url, and get credentials from self.secrets dictionary. """ database_url = connection._cms_oracle_string(user, pwd, db_name) try: url = sqlalchemy.engine.url.make_url(database_url) if url.password is None: url.password = pwd except sqlalchemy.exc.ArgumentError: url = sqlalchemy.engine.url.make_url('sqlite:///%s' % db_name) return url @staticmethod def build_frontier_url(db_name, schema): database_url = connection._cms_frontier_string(db_name, schema) try: url = sqlalchemy.engine.url.make_url(database_url) except sqlalchemy.exc.ArgumentError: """ Is this needed for a use case? """ url = sqlalchemy.engine.url.make_url('sqlite:///%s' % db_name) return url # currently just commits and closes the current session (ends transaction, closes connection) # may do other things later def tear_down(self): try: self.session.commit() self.close_session() except: return "Couldn't tear down connection on engine %s." % str(self.engine) def close_session(self): self.session.close() return True def hard_close(self): self.engine.dispose() return True # get model based on given model name def model(self, model_name): if model_name.__class__ == sqlalchemy.ext.declarative.api.DeclarativeMeta: model_name = model_name.__name__ model_name = model_name.replace("_", "") return self.models[model_name] # model should be the class the developer wants to be instantiated # pk_to_value maps primary keys to values def object(self, model, pk_to_value): if self.session == None: return None model_data = self.session.query(model) for pk in pk_to_value: model_data = model_data.filter(model.__dict__[pk] == pk_to_value[pk]) return model_data.first() def global_tag(self, **pkargs): return self.factory.object("globaltag", **pkargs) def global_tag_map(self, **pkargs): return self.factory.object("globaltagmap", **pkargs) """def global_tag_map_request(self, **pkargs): return self.factory.object("globaltagmaprequest", **pkargs)""" def tag(self, **pkargs): return self.factory.object("tag", **pkargs) def iov(self, **pkargs): return self.factory.object("iov", **pkargs) def payload(self, **pkargs): return self.factory.object("payload", **pkargs) """def record(self, **pkargs): return self.factory.object("record", **pkargs)""" # adds %% at the beginning and end so LIKE in SQL searches all of the string def _oracle_match_format(self, string): return "%%%s%%" % string # returns dictionary mapping object type to a list of all objects found in the search def search_everything(self, string, amount=10): string = self._oracle_match_format(string) gt = self.model("globaltag") global_tags = self.session.query(gt).filter(or_( gt.name.ilike(string), gt.description.ilike(string), gt.release.ilike(string) )).limit(amount) tag = self.model("tag") tags = self.session.query(tag).filter(or_( tag.name.ilike(string), tag.object_type.ilike(string), tag.description.ilike(string)) ).limit(amount) iov = self.model("iov") iovs = self.session.query(iov).filter(or_( iov.tag_name.ilike(string), iov.since.ilike(string), iov.payload_hash.ilike(string), iov.insertion_time.ilike(string) )).limit(amount) payload = self.model("payload") payloads = self.session.query(payload).filter(or_( payload.hash.ilike(string), payload.object_type.ilike(string), payload.insertion_time.ilike(string) )).limit(amount) return json_data_node.make({ "global_tags" : global_tags.all(), "tags" : tags.all(), "iovs" : iovs.all(), "payloads" : payloads.all() }) def write(self, object): new_object = models.session_independent_object(object, schema=self.schema) self.session.add(new_object) return new_object def commit(self): try: self.session.commit() except: traceback.print_exc() self.session.rollback() def write_and_commit(self, object): if isinstance(object, list): for item in object: self.write_and_commit(item) else: # should be changed to deal with errors - add them to exception handling if they appear self.write(object) self.commit() def rollback(self): try: self.session.rollback() except: traceback.print_exc() print("Session couldn't be rolled back.") class factory(): """ Contains methods for creating objects. """ def __init__(self, connection): self.connection = connection # class_name is the class name of the model to be used # pkargs is a dictionary of keyword arguments used as primary key values # this dictionary will be used to populate the object of type name class_name def object(self, class_name, **pkargs): from .data_sources import json_list from .models import apply_filters # get the class that self.connection holds from the class name model = self.connection.model(class_name) if self.connection.session == None: return None # query for the ORM object, and return the appropriate object (None, CondDBFW object, or json_list) model_data = self.connection.session.query(model) if len(pkargs.items()) != 0: # apply the filters defined in **kwargs model_data = apply_filters(model_data, model, **pkargs) amount = pkargs["amount"] if "amount" in pkargs.keys() else None model_data = model_data.limit(amount) if model_data.count() > 1: # if we have multiple objects, return a json_list return json_list(model_data.all()) elif model_data.count() == 1: # if we have a single object, return that object return model_data.first() else: # if we have no objects returned, return None return None else: # no column arguments were given, so return an empty object new_object = model() new_object.empty = True return new_object def _get_netrc_data(netrc_file, key): """ Returns a dictionary {login : ..., account : ..., password : ...} """ try: headers = ["login", "account", "password"] authenticator_tuple = netrc.netrc(netrc_file).authenticators(key) if authenticator_tuple == None: raise Exception("netrc file must contain key '%s'." % key) except: raise Exception("Couldn't get credentials from netrc file.") return dict(zip(headers, authenticator_tuple)) def new_connection_dictionary(connection_data, secrets=None, mode="r"): """ Function used to construct connection data dictionaries - internal to framework. """ frontier_str_length = len("frontier://") sqlite_str_length = len("sqlite://") #sqlite_file_str_length = len("sqlite_file://") oracle_str_length = len("oracle://") if type(connection_data) in [str, unicode] and connection_data[0:frontier_str_length] == "frontier://": """ frontier://database_name/schema """ db_name = connection_data[frontier_str_length:].split("/")[0] schema = connection_data[frontier_str_length:].split("/")[1] connection_data = {} connection_data["database_name"] = db_name connection_data["schema"] = schema connection_data["host"] = "frontier" connection_data["secrets"] = None elif type(connection_data) in [str, unicode] and connection_data[0:sqlite_str_length] == "sqlite://": """ sqlite://database_file_name """ # for now, just support "sqlite://" format for sqlite connection strings db_name = connection_data[sqlite_str_length:] schema = "" connection_data = {} connection_data["database_name"] = os.path.abspath(db_name) connection_data["schema"] = schema connection_data["host"] = "sqlite" connection_data["secrets"] = None elif type(connection_data) in [str, unicode] and connection_data[0:oracle_str_length] == "oracle://": """ oracle://account:password@database_name or oracle://database_name/schema (requires a separate method of authentication - either dictionary or netrc) """ new_connection_string = connection_data[oracle_str_length:] if ":" in new_connection_string: # the user has given a password - usually in the case of the db upload service database_name = new_connection_string[new_connection_string.index("@")+1:] schema_name = new_connection_string[0:new_connection_string.index(":")] # set username based on connection string username = new_connection_string[0:new_connection_string.index(":")] password = new_connection_string[new_connection_string.index(":")+1:new_connection_string.index("@")] else: mode_to_netrc_key_suffix = {"r" : "read", "w" : "write"} database_name = new_connection_string[0:new_connection_string.index("/")] schema_name = new_connection_string[new_connection_string.index("/")+1:] if secrets == None: username = str(raw_input("Enter the username you want to connect to the schema '%s' with: " % (schema_name))) password = str(raw_input("Enter the password for the user '%s' in database '%s': " % (username, database_name))) else: if isinstance(secrets, str): netrc_key = "%s/%s/%s" % (database_name, schema_name, mode_to_netrc_key_suffix[mode]) netrc_data = _get_netrc_data(secrets, key=netrc_key) # take the username from the netrc entry corresponding to the mode the database is opened in # eg, if the user has given mode="read", the database_name/schema_name/read entry will be taken username = netrc_data["login"] password = netrc_data["password"] elif isinstance(secrets, dict): username = secrets["user"] password = secrets["password"] else: raise Exception("Invalid type given for secrets. Either an str or a dict must be given.") #print("Connected to database %s, schema %s, with username %s." % (database_name, schema_name, username)) connection_data = {} connection_data["database_name"] = database_name connection_data["schema"] = schema_name connection_data["password"] = password connection_data["host"] = "oracle" connection_data["secrets"] = {"login" : username, "password" : password} return connection_data def engine_from_dictionary(dictionary, pooling=True): if dictionary["host"] != "sqlite": if dictionary["host"] != "frontier": # probably oracle # if not frontier, we have to authenticate user = dictionary["secrets"]["login"] pwd = dictionary["secrets"]["password"] # set max label length for oracle if pooling: return create_engine(connection.build_oracle_url(user, pwd, dictionary["database_name"]), label_length=6) else: return create_engine(connection.build_oracle_url(user, pwd, dictionary["database_name"]), label_length=6, poolclass=NullPool) else: # if frontier, no need to authenticate # set max label length for frontier if pooling: return create_engine(connection.build_frontier_url(dictionary["database_name"], dictionary["schema"]), label_length=6) else: return create_engine(connection.build_frontier_url(dictionary["database_name"], dictionary["schema"]), label_length=6, poolclass=NullPool) else: # if host is sqlite, making the url is easy - no authentication return create_engine("sqlite:///%s" % dictionary["database_name"]) def connect(connection_data, mode="r", map_blobs=False, secrets=None, pooling=True): """ Utility method for user - set up a connection object. """ con = connection(connection_data=connection_data, mode=mode, map_blobs=map_blobs, secrets=secrets, pooling=pooling) con = con.setup() return con

43.854031

154

0.57986

06f10e10522621bb75568c8c8f8cbbd37e2a177d

1,702

py

Python

lib/surface/bigtable/clusters/describe.py

bshaffer/google-cloud-sdk

f587382fd112f238c0d6d5ca3dab8f52d2b5c5f9

[ "Apache-2.0" ]

null

lib/surface/bigtable/clusters/describe.py

bshaffer/google-cloud-sdk

f587382fd112f238c0d6d5ca3dab8f52d2b5c5f9

[ "Apache-2.0" ]

null

lib/surface/bigtable/clusters/describe.py

bshaffer/google-cloud-sdk

f587382fd112f238c0d6d5ca3dab8f52d2b5c5f9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2016 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """bigtable clusters describe command.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.api_lib.bigtable import util from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.bigtable import arguments class DescribeCluster(base.DescribeCommand): """Describe an existing Bigtable cluster.""" @staticmethod def Args(parser): """Register flags for this command.""" arguments.AddClusterResourceArg(parser, 'to describe') def Run(self, args): """This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: Some value that we want to have printed later. """ cli = util.GetAdminClient() cluster_ref = args.CONCEPTS.cluster.Parse() msg = util.GetAdminMessages( ).BigtableadminProjectsInstancesClustersGetRequest( name=cluster_ref.RelativeName()) return cli.projects_instances_clusters.Get(msg)

34.04

79

0.750294

7b1cd0efcdf27e33841a290beeb721c9e29ae060

1,792

py

Python

python/paddle/fluid/tests/unittests/test_parallel_dygraph_sparse_embedding_over_height.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

11

2016-08-29T07:43:26.000Z

2016-08-29T07:51:24.000Z

python/paddle/fluid/tests/unittests/test_parallel_dygraph_sparse_embedding_over_height.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

null

python/paddle/fluid/tests/unittests/test_parallel_dygraph_sparse_embedding_over_height.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

1

2021-09-24T11:23:36.000Z

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import os import sys import unittest import paddle.fluid as fluid from test_dist_base import TestDistBase from spawn_runner_base import TestDistSpawnRunner from parallel_dygraph_sparse_embedding_over_height import TestSparseEmbeddingOverHeight flag_name = os.path.splitext(__file__)[0] class TestParallelDygraphSparseEmdeddingOverHeight(TestDistBase): def _setup_config(self): self._sync_mode = False self._nccl2_mode = True self._dygraph = True def test_sparse_embedding(self): if fluid.core.is_compiled_with_cuda(): self.check_with_place( "parallel_dygraph_sparse_embedding_over_height.py", delta=1e-5, check_error_log=True, log_name=flag_name) class TestParallelDygraphSparseEmdeddingOverHeightSpawn(TestDistSpawnRunner): def test_sparse_embedding_with_spawn(self): if fluid.core.is_compiled_with_cuda() and sys.version_info >= (3, 4): self.check_dist_result_with_spawn( test_class=TestSparseEmbeddingOverHeight, delta=1e-5) if __name__ == "__main__": unittest.main()

32.581818

87

0.743304

6c3749bda6728cc42982627bb7dd8b124ef77461

494

py

Python

donkeycar/__init__.py

MonikaFJ/donkeycar

1cf227027951da476865b3f69e26ea749a8c0eec

[ "MIT" ]

1

2022-01-03T13:09:42.000Z

donkeycar/__init__.py

JAHOON-JOO/donkeycar

e545c09d314ede51303657ad6d4c931c85307d09

[ "MIT" ]

null

donkeycar/__init__.py

JAHOON-JOO/donkeycar

e545c09d314ede51303657ad6d4c931c85307d09

[ "MIT" ]

null

__version__ = '4.0.1-dev' print('using donkey v{} ...'.format(__version__)) import sys if sys.version_info.major < 3: msg = 'Donkey Requires Python 3.6 or greater. You are using {}'.format(sys.version) raise ValueError(msg) # The default recursion limits in CPython are too small. sys.setrecursionlimit(10**5) from . import parts from .vehicle import Vehicle from .memory import Memory from . import utils from . import config from . import contrib from .config import load_config

23.52381

87

0.740891

89a9fc996c5d067f7663eed86064c33c03d2eec9

5,196

py

Python

src/preprocess.py

devanshb26/i3d_keras

511637275b90ff711d14233af23453ddb0070547

[ "MIT" ]

null

src/preprocess.py

devanshb26/i3d_keras

511637275b90ff711d14233af23453ddb0070547

[ "MIT" ]

null

src/preprocess.py

devanshb26/i3d_keras

511637275b90ff711d14233af23453ddb0070547

[ "MIT" ]

null

import cv2 import os import numpy as np import argparse SMALLEST_DIM = 256 IMAGE_CROP_SIZE = 224 FRAME_RATE = 25 # sample frames at 25 frames per second def sample_video(video_path, path_output): # for filename in os.listdir(video_path): if video_path.endswith((".mp4", ".avi")): # filename = video_path + filename os.system("ffmpeg -r {1} -i {0} -q:v 2 {2}/frame_%05d.jpg".format(video_path, FRAME_RATE, path_output)) else: raise ValueError("Video path is not the name of video file (.mp4 or .avi)") # the videos are resized preserving aspect ratio so that the smallest dimension is 256 pixels, with bilinear interpolation def resize(img): # print('Original Dimensions : ', img.shape) original_width = int(img.shape[1]) original_height = int(img.shape[0]) aspect_ratio = original_width / original_height if original_height < original_width: new_height = SMALLEST_DIM new_width = int(new_height * aspect_ratio) else: new_width = SMALLEST_DIM new_height = int(original_width / aspect_ratio) dim = (new_width, new_height) # resize image resized = cv2.resize(img, dim, interpolation=cv2.INTER_LINEAR) # print('Resized Dimensions : ', resized.shape) return resized def crop_center(img, new_size): y, x, c = img.shape (cropx, cropy) = new_size startx = x // 2 - (cropx // 2) starty = y // 2 - (cropy // 2) return img[starty:starty + cropy, startx:startx + cropx] def rescale_pixel_values(img): # print('Data Type: %s' % img.dtype) # print('Min: %.3f, Max: %.3f' % (img.min(), img.max())) img = img.astype('float32') # normalize to the range 0:1 # img /= 255.0 # normalize to the range -1:1 img = (img / 255.0) * 2 - 1 # confirm the normalization # print('Min: %.3f, Max: %.3f' % (img.min(), img.max())) return img # The provided .npy file thus has shape (1, num_frames, 224, 224, 3) for RGB, corresponding to a batch size of 1 def run_rgb(sorted_list_frames): result = np.zeros((1, IMAGE_CROP_SIZE, IMAGE_CROP_SIZE, 3)) for full_file_path in sorted_list_frames: img = cv2.imread(full_file_path, cv2.IMREAD_UNCHANGED) img = pre_process_rgb(img) new_img = np.reshape(img, (1, IMAGE_CROP_SIZE, IMAGE_CROP_SIZE, 3)) result = np.append(result, new_img, axis=0) result = result[1:, :, :, :] result = np.expand_dims(result, axis=0) return result def pre_process_rgb(img): resized = resize(img) img_cropped = crop_center(resized, (IMAGE_CROP_SIZE, IMAGE_CROP_SIZE)) img = rescale_pixel_values(img_cropped) return img def read_frames(video_path): list_frames = [] for file in os.listdir(video_path): if file.endswith(".jpg"): full_file_path = video_path + file list_frames.append(full_file_path) sorted_list_frames = sorted(list_frames) return sorted_list_frames def run_flow(sorted_list_frames): sorted_list_img = [] for frame in sorted_list_frames: img = cv2.imread(frame, cv2.IMREAD_UNCHANGED) img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) sorted_list_img.append(img_gray) result = np.zeros((1, IMAGE_CROP_SIZE, IMAGE_CROP_SIZE, 2)) prev = sorted_list_img[0] for curr in sorted_list_img[1:]: flow = compute_optical_flow(prev, curr) flow = pre_process_flow(flow) prev = curr result = np.append(result, flow, axis=0) result = result[1:, :, :, :] result = np.expand_dims(result, axis=0) return result def pre_process_flow(flow_frame): resized = resize(flow_frame) img_cropped = crop_center(resized, (IMAGE_CROP_SIZE, IMAGE_CROP_SIZE)) new_img = np.reshape(img_cropped, (1, IMAGE_CROP_SIZE, IMAGE_CROP_SIZE, 2)) return new_img # Pixel values are truncated to the range [-20, 20], then rescaled between -1 and 1 def compute_optical_flow(prev, curr): optical_flow = cv2.optflow.createOptFlow_DualTVL1() flow_frame = optical_flow.calc(prev, curr, None) flow_frame = np.clip(flow_frame, -20, 20) flow_frame = flow_frame / 20.0 return flow_frame def main(args): if not os.path.exists(args.path_output): os.makedirs(args.path_output) # sample all video from video_path at specified frame rate (FRAME_RATE param) sample_video(args.video_path, args.path_output) # make sure the frames are processed in order sorted_list_frames = read_frames(args.path_output) video_name = args.video_path.split("/")[-1][:-4] rgb = run_rgb(sorted_list_frames) npy_rgb_output = '../data/' + video_name + '_rgb.npy' np.save(npy_rgb_output, rgb) flow = run_flow(sorted_list_frames) npy_flow_output = '../data/' + video_name + '_flow.npy' np.save(npy_flow_output, flow) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--path_output', type=str, default="../data/frames/") parser.add_argument('--video_path', type=str, default="../data/input_videos/cricket.avi") args = parser.parse_args() main(args)

31.682927

122

0.665897

fd50c99d9d3035881f68d702db3207b9d33ec8e9

88

py

Python

ex024.py

victorccard/RepoPython

30e602c671b889cfcc7726cd2d7c90d647001fac

[ "MIT" ]

null

ex024.py

victorccard/RepoPython

30e602c671b889cfcc7726cd2d7c90d647001fac

[ "MIT" ]

null

ex024.py

victorccard/RepoPython

30e602c671b889cfcc7726cd2d7c90d647001fac

[ "MIT" ]

null

cid = str(input('Em que cidade você nasceu?')).strip() print(cid[:5].upper() == 'SANTO')

44

54

0.636364

81c4cc3ff9bf05df6f8faf2f0c2264a4d43e5cd5

24,576

py

Python

test/functional/p2p_sendheaders.py

redecoinproject/redecoin

9a27f0d4976fcc719a04218e0779fdc54d2dbaf2

[ "MIT" ]

2

2022-03-21T15:08:57.000Z

2022-03-26T17:22:27.000Z

test/functional/p2p_sendheaders.py

redecoinproject/redecoin

9a27f0d4976fcc719a04218e0779fdc54d2dbaf2

[ "MIT" ]

null

test/functional/p2p_sendheaders.py

redecoinproject/redecoin

9a27f0d4976fcc719a04218e0779fdc54d2dbaf2

[ "MIT" ]

1

2022-03-09T08:32:17.000Z

#!/usr/bin/env python3 # Copyright (c) 2014-2016 The Bitcoin Core developers # Copyright (c) 2017-2019 The Raven Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """ Test behavior of headers messages to announce blocks. Setup: - Two nodes, two p2p connections to node0. One p2p connection should only ever receive inv's (omitted from testing description below, this is our control). Second node is used for creating reorgs. Part 1: No headers announcements before "sendheaders" a. node mines a block [expect: inv] send getdata for the block [expect: block] b. node mines another block [expect: inv] send getheaders and getdata [expect: headers, then block] c. node mines another block [expect: inv] peer mines a block, announces with header [expect: getdata] d. node mines another block [expect: inv] Part 2: After "sendheaders", headers announcements should generally work. a. peer sends sendheaders [expect: no response] peer sends getheaders with current tip [expect: no response] b. node mines a block [expect: tip header] c. for N in 1, ..., 10: * for announce-type in {inv, header} - peer mines N blocks, announces with announce-type [ expect: getheaders/getdata or getdata, deliver block(s) ] - node mines a block [ expect: 1 header ] Part 3: Headers announcements stop after large reorg and resume after getheaders or inv from peer. - For response-type in {inv, getheaders} * node mines a 7 block reorg [ expect: headers announcement of 8 blocks ] * node mines an 8-block reorg [ expect: inv at tip ] * peer responds with getblocks/getdata [expect: inv, blocks ] * node mines another block [ expect: inv at tip, peer sends getdata, expect: block ] * node mines another block at tip [ expect: inv ] * peer responds with getheaders with an old hashstop more than 8 blocks back [expect: headers] * peer requests block [ expect: block ] * node mines another block at tip [ expect: inv, peer sends getdata, expect: block ] * peer sends response-type [expect headers if getheaders, getheaders/getdata if mining new block] * node mines 1 block [expect: 1 header, peer responds with getdata] Part 4: Test direct fetch behavior a. Announce 2 old block headers. Expect: no getdata requests. b. Announce 3 new blocks via 1 headers message. Expect: one getdata request for all 3 blocks. (Send blocks.) c. Announce 1 header that forks off the last two blocks. Expect: no response. d. Announce 1 more header that builds on that fork. Expect: one getdata request for two blocks. e. Announce 16 more headers that build on that fork. Expect: getdata request for 14 more blocks. f. Announce 1 more header that builds on that fork. Expect: no response. Part 5: Test handling of headers that don't connect. a. Repeat 10 times: 1. Announce a header that doesn't connect. Expect: getheaders message 2. Send headers chain. Expect: getdata for the missing blocks, tip update. b. Then send 9 more headers that don't connect. Expect: getheaders message each time. c. Announce a header that does connect. Expect: no response. d. Announce 49 headers that don't connect. Expect: getheaders message each time. e. Announce one more that doesn't connect. Expect: disconnect. """ from test_framework.mininode import NodeConnCB, mininode_lock, MsgGetdata, MsgGetHeaders, MsgHeaders, NodeConn, NetworkThread, MsgBlock, CInv, MsgInv, CBlockHeader, MsgGetBlocks, MsgSendHeaders from test_framework.test_framework import redecoinTestFramework from test_framework.util import wait_until, sync_blocks, p2p_port, assert_equal from test_framework.blocktools import create_block, create_coinbase direct_fetch_response_time = 0.05 class TestNode(NodeConnCB): def __init__(self): super().__init__() self.block_announced = False self.last_blockhash_announced = None def clear_last_announcement(self): with mininode_lock: self.block_announced = False self.last_message.pop("inv", None) self.last_message.pop("headers", None) # Request data for a list of block hashes def get_data(self, block_hashes): msg = MsgGetdata() for x in block_hashes: msg.inv.append(CInv(2, x)) self.connection.send_message(msg) def get_headers(self, locator, hashstop): msg = MsgGetHeaders() msg.locator.vHave = locator msg.hashstop = hashstop self.connection.send_message(msg) def send_block_inv(self, blockhash): msg = MsgInv() msg.inv = [CInv(2, blockhash)] self.connection.send_message(msg) def on_inv(self, conn, message): self.block_announced = True self.last_blockhash_announced = message.inv[-1].hash def on_headers(self, conn, message): if len(message.headers): self.block_announced = True message.headers[-1].calc_x16r() self.last_blockhash_announced = message.headers[-1].sha256 # Test whether the last announcement we received had the # right header or the right inv # inv and headers should be lists of block hashes def check_last_announcement(self, headers=None, inv=None): expect_headers = headers if headers is not None else [] expect_inv = inv if inv is not None else [] test_function = lambda: self.block_announced wait_until(test_function, timeout=60, lock=mininode_lock, err_msg="Waiting for last announcement") with mininode_lock: self.block_announced = False success = True compare_inv = [] if "inv" in self.last_message: compare_inv = [x.hash for x in self.last_message["inv"].inv] if compare_inv != expect_inv: success = False hash_headers = [] if "headers" in self.last_message: # treat headers as a list of block hashes hash_headers = [ x.sha256 for x in self.last_message["headers"].headers ] if hash_headers != expect_headers: success = False self.last_message.pop("inv", None) self.last_message.pop("headers", None) return success # noinspection PyMethodOverriding def wait_for_getdata(self, hash_list, timeout=60): if not hash_list: return test_function = lambda: "getdata" in self.last_message and [x.hash for x in self.last_message["getdata"].inv] == hash_list wait_until(test_function, timeout=timeout, lock=mininode_lock, err_msg="waiting for getData()") return def wait_for_block_announcement(self, block_hash, timeout=60): test_function = lambda: self.last_blockhash_announced == block_hash wait_until(test_function, timeout=timeout, lock=mininode_lock, err_msg="waiting for block announcement") return def send_header_for_blocks(self, new_blocks): headers_message = MsgHeaders() headers_message.headers = [ CBlockHeader(b) for b in new_blocks ] self.send_message(headers_message) def send_getblocks(self, locator): getblocks_message = MsgGetBlocks() getblocks_message.locator.vHave = locator self.send_message(getblocks_message) class SendHeadersTest(redecoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 2 # mine count blocks and return the new tip def mine_blocks(self, count): # Clear out last block announcement from each p2p listener [ x.clear_last_announcement() for x in self.p2p_connections ] self.nodes[0].generate(count) return int(self.nodes[0].getbestblockhash(), 16) # mine a reorg that invalidates length blocks (replacing them with # length+1 blocks). # Note: we clear the state of our p2p connections after the # to-be-reorged-out blocks are mined, so that we don't break later tests. # return the list of block hashes newly mined def mine_reorg(self, length): self.nodes[0].generate(length) # make sure all invalidated blocks are node0's sync_blocks(self.nodes, wait=0.1) for x in self.p2p_connections: x.wait_for_block_announcement(int(self.nodes[0].getbestblockhash(), 16)) x.clear_last_announcement() tip_height = self.nodes[1].getblockcount() hash_to_invalidate = self.nodes[1].getblockhash(tip_height-(length-1)) self.nodes[1].invalidateblock(hash_to_invalidate) all_hashes = self.nodes[1].generate(length+1) # Must be longer than the orig chain sync_blocks(self.nodes, wait=0.1) return [int(x, 16) for x in all_hashes] def run_test(self): # Setup the p2p connections and start up the network thread. inv_node = TestNode() test_node = TestNode() self.p2p_connections = [inv_node, test_node] connections = [NodeConn('127.0.0.1', p2p_port(0), self.nodes[0], inv_node), NodeConn('127.0.0.1', p2p_port(0), self.nodes[0], test_node, services=0)] # Set nServices to 0 for test_node, so no block download will occur outside of # direct fetching inv_node.add_connection(connections[0]) test_node.add_connection(connections[1]) NetworkThread().start() # Start up network handling in another thread # Test logic begins here inv_node.wait_for_verack() test_node.wait_for_verack() # Ensure verack's have been processed by our peer inv_node.sync_with_ping() test_node.sync_with_ping() tip = int(self.nodes[0].getbestblockhash(), 16) # PART 1 # 1. Mine a block; expect inv announcements each time self.log.info("Part 1: headers don't start before sendheaders message...") block_time = 0 for i in range(4): old_tip = tip tip = self.mine_blocks(1) assert_equal(inv_node.check_last_announcement(inv=[tip]), True) assert_equal(test_node.check_last_announcement(inv=[tip]), True) # Try a few different responses; none should affect next announcement if i == 0: # first request the block test_node.get_data([tip]) test_node.wait_for_block(tip) elif i == 1: # next try requesting header and block test_node.get_headers(locator=[old_tip], hashstop=tip) test_node.get_data([tip]) test_node.wait_for_block(tip) test_node.clear_last_announcement() # since we requested headers... elif i == 2: # this time announce own block via headers height = self.nodes[0].getblockcount() last_time = self.nodes[0].getblock(self.nodes[0].getbestblockhash())['time'] block_time = last_time + 1 new_block = create_block(tip, create_coinbase(height+1), block_time) new_block.solve() test_node.send_header_for_blocks([new_block]) test_node.wait_for_getdata([new_block.sha256]) test_node.send_message(MsgBlock(new_block)) test_node.sync_with_ping() # make sure this block is processed inv_node.clear_last_announcement() test_node.clear_last_announcement() self.log.info("Part 1: success!") self.log.info("Part 2: announce blocks with headers after sendheaders message...") # PART 2 # 2. Send a sendheaders message and test that headers announcements # commence and keep working. test_node.send_message(MsgSendHeaders()) prev_tip = int(self.nodes[0].getbestblockhash(), 16) test_node.get_headers(locator=[prev_tip], hashstop=0) test_node.sync_with_ping() # Now that we've synced headers, headers announcements should work tip = self.mine_blocks(1) assert_equal(inv_node.check_last_announcement(inv=[tip]), True) assert_equal(test_node.check_last_announcement(headers=[tip]), True) height = self.nodes[0].getblockcount()+1 block_time += 10 # Advance far enough ahead for i in range(10): # Mine i blocks, and alternate announcing either via # inv (of tip) or via headers. After each, new blocks # mined by the node should successfully be announced # with block header, even though the blocks are never requested for j in range(2): blocks = [] for _ in range(i+1): blocks.append(create_block(tip, create_coinbase(height), block_time)) blocks[-1].solve() tip = blocks[-1].sha256 block_time += 1 height += 1 if j == 0: # Announce via inv test_node.send_block_inv(tip) test_node.wait_for_getheaders() # Should have received a getheaders now test_node.send_header_for_blocks(blocks) # Test that duplicate inv's won't result in duplicate # getdata requests, or duplicate headers announcements [ inv_node.send_block_inv(x.sha256) for x in blocks ] test_node.wait_for_getdata([x.sha256 for x in blocks]) inv_node.sync_with_ping() else: # Announce via headers test_node.send_header_for_blocks(blocks) test_node.wait_for_getdata([x.sha256 for x in blocks]) # Test that duplicate headers won't result in duplicate # getdata requests (the check is further down) inv_node.send_header_for_blocks(blocks) inv_node.sync_with_ping() [test_node.send_message(MsgBlock(x)) for x in blocks] test_node.sync_with_ping() inv_node.sync_with_ping() # This block should not be announced to the inv node (since it also # broadcast it) assert "inv" not in inv_node.last_message assert "headers" not in inv_node.last_message tip = self.mine_blocks(1) assert_equal(inv_node.check_last_announcement(inv=[tip]), True) assert_equal(test_node.check_last_announcement(headers=[tip]), True) height += 1 block_time += 1 self.log.info("Part 2: success!") self.log.info("Part 3: headers announcements can stop after large reorg, and resume after headers/inv from peer...") # PART 3. Headers announcements can stop after large reorg, and resume after # getheaders or inv from peer. for j in range(2): # First try mining a reorg that can propagate with header announcement new_block_hashes = self.mine_reorg(length=7) tip = new_block_hashes[-1] assert_equal(inv_node.check_last_announcement(inv=[tip]), True) assert_equal(test_node.check_last_announcement(headers=new_block_hashes), True) block_time += 8 # Mine a too-large reorg, which should be announced with a single inv new_block_hashes = self.mine_reorg(length=8) tip = new_block_hashes[-1] assert_equal(inv_node.check_last_announcement(inv=[tip]), True) assert_equal(test_node.check_last_announcement(inv=[tip]), True) block_time += 9 fork_point = self.nodes[0].getblock("%02x" % new_block_hashes[0])["previousblockhash"] fork_point = int(fork_point, 16) # Use getblocks/getdata test_node.send_getblocks(locator = [fork_point]) assert_equal(test_node.check_last_announcement(inv=new_block_hashes), True) test_node.get_data(new_block_hashes) test_node.wait_for_block(new_block_hashes[-1]) for i in range(3): # Mine another block, still should get only an inv tip = self.mine_blocks(1) assert_equal(inv_node.check_last_announcement(inv=[tip]), True) assert_equal(test_node.check_last_announcement(inv=[tip]), True) if i == 0: # Just get the data -- shouldn't cause headers announcements to resume test_node.get_data([tip]) test_node.wait_for_block(tip) elif i == 1: # Send a getheaders message that shouldn't trigger headers announcements # to resume (best header sent will be too old) test_node.get_headers(locator=[fork_point], hashstop=new_block_hashes[1]) test_node.get_data([tip]) test_node.wait_for_block(tip) elif i == 2: test_node.get_data([tip]) test_node.wait_for_block(tip) # This time, try sending either a getheaders to trigger resumption # of headers announcements, or mine a new block and inv it, also # triggering resumption of headers announcements. if j == 0: test_node.get_headers(locator=[tip], hashstop=0) test_node.sync_with_ping() else: test_node.send_block_inv(tip) test_node.sync_with_ping() # New blocks should now be announced with header tip = self.mine_blocks(1) assert_equal(inv_node.check_last_announcement(inv=[tip]), True) assert_equal(test_node.check_last_announcement(headers=[tip]), True) self.log.info("Part 3: success!") self.log.info("Part 4: Testing direct fetch behavior...") tip = self.mine_blocks(1) height = self.nodes[0].getblockcount() + 1 last_time = self.nodes[0].getblock(self.nodes[0].getbestblockhash())['time'] block_time = last_time + 1 # Create 2 blocks. Send the blocks, then send the headers. blocks = [] for _ in range(2): blocks.append(create_block(tip, create_coinbase(height), block_time)) blocks[-1].solve() tip = blocks[-1].sha256 block_time += 1 height += 1 inv_node.send_message(MsgBlock(blocks[-1])) inv_node.sync_with_ping() # Make sure blocks are processed test_node.last_message.pop("getdata", None) test_node.send_header_for_blocks(blocks) test_node.sync_with_ping() # should not have received any getdata messages with mininode_lock: assert "getdata" not in test_node.last_message # This time, direct fetch should work blocks = [] for _ in range(3): blocks.append(create_block(tip, create_coinbase(height), block_time)) blocks[-1].solve() tip = blocks[-1].sha256 block_time += 1 height += 1 test_node.send_header_for_blocks(blocks) test_node.sync_with_ping() test_node.wait_for_getdata([x.sha256 for x in blocks], timeout=int(direct_fetch_response_time)) [test_node.send_message(MsgBlock(x)) for x in blocks] test_node.sync_with_ping() # Now announce a header that forks the last two blocks tip = blocks[0].sha256 height -= 1 blocks = [] # Create extra blocks for later for _ in range(20): blocks.append(create_block(tip, create_coinbase(height), block_time)) blocks[-1].solve() tip = blocks[-1].sha256 block_time += 1 height += 1 # Announcing one block on fork should not trigger direct fetch # (less work than tip) test_node.last_message.pop("getdata", None) test_node.send_header_for_blocks(blocks[0:1]) test_node.sync_with_ping() with mininode_lock: assert "getdata" not in test_node.last_message # Announcing one more block on fork should trigger direct fetch for # both blocks (same work as tip) test_node.send_header_for_blocks(blocks[1:2]) test_node.sync_with_ping() test_node.wait_for_getdata([x.sha256 for x in blocks[0:2]], timeout=int(direct_fetch_response_time)) # Announcing 16 more headers should trigger direct fetch for 14 more # blocks test_node.send_header_for_blocks(blocks[2:18]) test_node.sync_with_ping() test_node.wait_for_getdata([x.sha256 for x in blocks[2:16]], timeout=int(direct_fetch_response_time)) # Announcing 1 more header should not trigger any response test_node.last_message.pop("getdata", None) test_node.send_header_for_blocks(blocks[18:19]) test_node.sync_with_ping() with mininode_lock: assert "getdata" not in test_node.last_message self.log.info("Part 4: success!") # Now deliver all those blocks we announced. [test_node.send_message(MsgBlock(x)) for x in blocks] self.log.info("Part 5: Testing handling of unconnecting headers") # First we test that receipt of an unconnecting header doesn't prevent # chain sync. for i in range(10): test_node.last_message.pop("getdata", None) blocks = [] # Create two more blocks. for j in range(2): blocks.append(create_block(tip, create_coinbase(height), block_time)) blocks[-1].solve() tip = blocks[-1].sha256 block_time += 1 height += 1 # Send the header of the second block -> this won't connect. with mininode_lock: test_node.last_message.pop("getheaders", None) test_node.send_header_for_blocks([blocks[1]]) test_node.wait_for_getheaders() test_node.send_header_for_blocks(blocks) test_node.wait_for_getdata([x.sha256 for x in blocks]) [test_node.send_message(MsgBlock(x)) for x in blocks] test_node.sync_with_ping() assert_equal(int(self.nodes[0].getbestblockhash(), 16), blocks[1].sha256) blocks = [] # Now we test that if we repeatedly don't send connecting headers, we # don't go into an infinite loop trying to get them to connect. MAX_UNCONNECTING_HEADERS = 10 for j in range(MAX_UNCONNECTING_HEADERS+1): blocks.append(create_block(tip, create_coinbase(height), block_time)) blocks[-1].solve() tip = blocks[-1].sha256 block_time += 1 height += 1 for i in range(1, MAX_UNCONNECTING_HEADERS): # Send a header that doesn't connect, check that we get a getheaders. with mininode_lock: test_node.last_message.pop("getheaders", None) test_node.send_header_for_blocks([blocks[i]]) test_node.wait_for_getheaders() # Next header will connect, should re-set our count: test_node.send_header_for_blocks([blocks[0]]) # Remove the first two entries (blocks[1] would connect): blocks = blocks[2:] # Now try to see how many unconnecting headers we can send # before we get disconnected. Should be 5*MAX_UNCONNECTING_HEADERS for i in range(5*MAX_UNCONNECTING_HEADERS - 1): # Send a header that doesn't connect, check that we get a getheaders. with mininode_lock: test_node.last_message.pop("getheaders", None) test_node.send_header_for_blocks([blocks[i%len(blocks)]]) test_node.wait_for_getheaders() # Eventually this stops working. test_node.send_header_for_blocks([blocks[-1]]) # Should get disconnected test_node.wait_for_disconnect() self.log.info("Part 5: success!") # Finally, check that the inv node never received a getdata request, # throughout the test assert "getdata" not in inv_node.last_message if __name__ == '__main__': SendHeadersTest().main()

44.281081

193

0.636108

b2630593eb70dcc16f9fcc35bb50c6e989fb3c07

11,517

py

Python

convert-log.py

perotom/nfc-nci-communication-parser

058a4afd2d2167011343beac6401cd062370ed02

[ "MIT" ]

null

convert-log.py

perotom/nfc-nci-communication-parser

058a4afd2d2167011343beac6401cd062370ed02

[ "MIT" ]

null

convert-log.py

perotom/nfc-nci-communication-parser

058a4afd2d2167011343beac6401cd062370ed02

[ "MIT" ]

null

#!/usr/bin/python from argparse import ArgumentParser def parse(line): bytes = bytearray.fromhex(line) mt = (bytes[0] & 0b11100000) >> 5 if mt == 0: file1.write(" MT: Data packet\n") parseDataPacket(mt, bytes) if mt == 1: file1.write(" MT: Control packet - CMD\n") parseControlPacket(mt, bytes) if mt == 2: file1.write(" MT: Control packet - RES\n") parseControlPacket(mt, bytes) if mt == 3: file1.write(" MT: Control packet - NTF\n") parseControlPacket(mt, bytes) def parseDataPacket(mt, bytes): connId = (bytes[0] & 0b00001111) file1.write(" ConnId: " + str(connId) + "\n") file1.write(" Payload length: " + str(bytes[2]) + " bytes\n") def parseControlPacket(mt, bytes): gid = (bytes[0] & 0b00001111) oid = (bytes[1] & 0b00111111) if gid == 0: file1.write(" GID: NCI Core\n") if oid == 0: file1.write(" OID: CORE_RESET\n") if oid == 1: file1.write(" OID: CORE_INIT\n") if oid == 2: file1.write(" OID: CORE_SET_CONFIG\n") if oid == 3: file1.write(" OID: CORE_GET_CONFIG\n") if oid == 4: file1.write(" OID: CORE_CONN_CREATE\n") if oid == 5: file1.write(" OID: CORE_CONN_CLOSE\n") if oid == 6: file1.write(" OID: CORE_CONN_CREDITS\n") if oid == 7: file1.write(" OID: CORE_GENERIC_ERROR\n") if oid == 8: file1.write(" OID: CORE_INTERFACE_ERROR\n") if gid == 1: file1.write(" GID: RF Management\n") if oid == 0: file1.write(" OID: RF_DISCOVER_MAP\n") if oid == 1: file1.write(" OID: RF_SET_LISTEN_MODE_ROUTING\n") if oid == 2: file1.write(" OID: RF_GET_LISTEN_MODE_ROUTING\n") if oid == 3: file1.write(" OID: RF_DISCOVER\n") if oid == 4: file1.write(" OID: RF_DISCOVER_SELECT\n") if oid == 5: file1.write(" OID: RF_INTF_ACTIVATED\n") if oid == 6: file1.write(" OID: RF_DEACTIVATE\n") if oid == 7: file1.write(" OID: RF_FIELD_INFO\n") if oid == 8: file1.write(" OID: RF_T3T_POLLING\n") if oid == 9: file1.write(" OID: RF_NFCEE_ACTION\n") if oid == 10: file1.write(" OID: RF_NFCEE_DISCOVERY_REQ\n") if oid == 11: file1.write(" OID: RF_PARAMETER_UPDATE\n") if gid == 2: file1.write(" GID: NFCEE Management\n") if oid == 0: file1.write(" OID: NFCEE_DISCOVER\n") if oid == 1: file1.write(" OID: NFCEE_MODE_SET\n") if gid == 3: file1.write(" GID: Proprietary\n") file1.write(" Payload length: " + str(bytes[2]) + " bytes\n") # further parsing if mt == 1: # commands if gid == 0 and oid == 2: parseCoreConfig(bytes, 3) elif gid == 1 and oid == 0: parseRfDiscoverMap(bytes) elif gid == 1 and oid == 1: parseRfSetListenModeRouting(bytes) elif gid == 1 and oid == 3: parseRfDiscover(bytes) elif mt == 2: # responses if gid == 0 and oid == 3: parseCoreConfig(bytes, 4) else: if bytes[3] == 0: file1.write(" Status: OK\n") elif bytes[3] == 1: file1.write(" Status: REJECTED\n") else: file1.write(" Status: OK\n") def parseCoreConfig(bytes, startIndex): params = bytes[startIndex] file1.write(" Params: " + str(params) + "\n") if params <= 0: return i = startIndex + 1 while i < len(bytes): paramId = bytes[i] i += 1 paramLen = bytes[i] i += 1 file1.write(" ParamId: " + getCoreConfigParamName(paramId) + " (" + '0x{:02x}'.format(paramId) + ") length: " + str(paramLen) + "\n") file1.write(" ParamVal: " + " ".join('0x{:02x}'.format(x) for x in bytes[i:i+paramLen]) + "\n") i += paramLen def parseRfDiscoverMap(bytes): mappings = bytes[3] file1.write(" Mappings: " + str(mappings) + "\n") i = 4 while i < len(bytes): file1.write(" RF Protocol: " + getRfProtocol(bytes[i]) + " (" + '0x{:02x}'.format(bytes[i]) + ")" + "\n") if bytes[i + 1] == 1: file1.write(" Mode: Poll\n") elif bytes[i + 1] == 2: file1.write(" Mode: Listen\n") elif bytes[i + 1] == 3: file1.write(" Mode: Poll + Listen\n") else: file1.write(" Mode: Unkown\n") file1.write(" RF Interface: " + getRfInterface(bytes[i + 2]) + " (" + '0x{:02x}'.format(bytes[i + 2]) + ")" + "\n") i += 3 def parseRfDiscover(bytes): configs = bytes[3] file1.write(" Configs: " + str(configs) + "\n") i = 4 while i < len(bytes): file1.write(" RF Tech & Mode: " + getRfTechAndMode(bytes[i]) + " (" + '0x{:02x}'.format(bytes[i]) + ")" + "\n") file1.write(" Discover frequency: " + str(bytes[i + 1]) + "\n") i += 2 def parseRfSetListenModeRouting(bytes): routings = bytes[4] file1.write(" Routings: " + str(routings) + "\n") i = 5 while i < len(bytes): typ = bytes[i] i += 1 length = bytes[i] i += 1 if typ == 0: typeName = "Technology-based" elif typ == 1: typeName = "Protocol-based" elif typ == 2: typeName = "AID-based" file1.write(" Type: " + typeName + " (" + '0x{:02x}'.format(typ) + ") length: " + str(length) + "\n") file1.write(" Value: " + " ".join('0x{:02x}'.format(x) for x in bytes[i:i+length]) + "\n") if typ == 0: file1.write(" Technology: " + getRfTechnology(bytes[i + 2]) + "\n") elif typ == 1: file1.write(" Protocol: " + getRfProtocol(bytes[i + 2]) + "\n") elif typ == 2: file1.write(" AID: " +bytes[i+2:i+length].decode("utf-8") + "\n") getRfTechnology i += length def getRfTechAndMode(tech): if tech == 0: return "NFC_A_PASSIVE_POLL_MODE" if tech == 1: return "NFC_B_PASSIVE_POLL_MODE" if tech == 2: return "NFC_F_PASSIVE_POLL_MODE" if tech == 3: return "NFC_A_ACTIVE_POLL_MODE" if tech == 5: return "NFC_F_ACTIVE_POLL_MODE" if tech == 6: return "NFC_15693_PASSIVE_POLL_MODE" if tech == 0x80: return "NFC_A_PASSIVE_LISTEN_MODE" if tech == 0x81: return "NFC_B_PASSIVE_LISTEN_MODE" if tech == 0x82: return "NFC_F_PASSIVE_LISTEN_MODE" if tech == 0x83: return "NFC_A_ACTIVE_LISTEN_MODE" if tech == 0x85: return "NFC_F_ACTIVE_LISTEN_MODE" if tech == 0x86: return "NFC_15693_PASSIVE_LISTEN_MODE" return "RFU" def getRfTechnology(tech): if tech == 0: return "NFC_RF_TECHNOLOGY_A" if tech == 1: return "NFC_RF_TECHNOLOGY_B" if tech == 2: return "NFC_RF_TECHNOLOGY_F" if tech == 3: return "NFC_RF_TECHNOLOGY_15693" return "RFU" def getRfProtocol(proto): if proto == 0: return "PROTOCOL_UNDETERMINED" if proto == 1: return "PROTOCOL_T1T" if proto == 2: return "PROTOCOL_T2T" if proto == 3: return "PROTOCOL_T3T" if proto == 4: return "PROTOCOL_ISO_DEP" if proto == 5: return "PROTOCOL_NFC_DEP" if proto == 6: return "PROTOCOL_15693" if proto == 0x80: return "PROTOCOL_MIFARE_CLASSIC" if proto == 0x8A: return "PROTOCOL_KOVIO" return "UNKOWN" def getRfInterface(proto): if proto == 0: return "NFCEE Direct RF Interface" if proto == 1: return "Frame RF Interface" if proto == 2: return "ISO-DEP RF Interface" if proto == 3: return "NFC-DEP RF Interface" return "UNKOWN" def getCoreConfigParamName(paramId): if paramId == 0: return "TOTAL_DURATION" if paramId == 1: return "CON_DEVICES_LIMIT" if paramId == 8: return "PA_BAIL_OUT" if paramId == 0x10: return "PB_AFI" if paramId == 0x11: return "PB_BAIL_OUT" if paramId == 0x12: return "PB_ATTRIB_PARAM1" if paramId == 0x13: return "PB_SENSB_REQ_PARAM" if paramId == 0x18: return "PF_BIT_RATE" if paramId == 0x19: return "PF_RC_CODE" if paramId == 0x20: return "PB_H_INFO" if paramId == 0x21: return "PI_BIT_RATE" if paramId == 0x22: return "PA_ADV_FEAT" if paramId == 0x28: return "PN_NFC_DEP_SPEED" if paramId == 0x29: return "PN_ATR_REQ_GEN_BYTES" if paramId == 0x2A: return "PN_ATR_REQ_CONFIG" if paramId == 0x30: return "LA_BIT_FRAME_SDD" if paramId == 0x31: return "LA_PLATFORM_CONFIG" if paramId == 0x32: return "LA_SEL_INFO" if paramId == 0x33: return "LA_NFCID1" if paramId == 0x38: return "LB_SENSB_INFO" if paramId == 0x39: return "LB_NFCID0" if paramId == 0x3A: return "LB_APPLICATION_DATA" if paramId == 0x3B: return "LB_SFGI" if paramId == 0x3C: return "LB_ADC_FO" if paramId == 0x40 or paramId == 0x41 or paramId == 0x42 or paramId == 0x43 or paramId == 0x44 or paramId == 0x45 or paramId == 0x46 or paramId == 0x47 or paramId == 0x48 or paramId == 0x49 or paramId == 0x4A or paramId == 0x4B or paramId == 0x4C or paramId == 0x4D or paramId == 0x4E or paramId == 0x4F: return "LF_T3T_IDENTIFIERS" if paramId == 0x50: return "LF_PROTOCOL_TYPE" if paramId == 0x51: return "LF_T3T_PMM" if paramId == 0x52: return "LF_T3T_MAX" if paramId == 0x53: return "LF_T3T_FLAGS" if paramId == 0x54: return "LF_CON_BITR_F" if paramId == 0x55: return "LF_ADV_FEAT" if paramId == 0x58: return "LI_FWI" if paramId == 0x59: return "LA_HIST_BY" if paramId == 0x5A: return "LB_H_INFO_RESP" if paramId == 0x5B: return "LI_BIT_RATE" if paramId == 0x60: return "LN_WT" if paramId == 0x61: return "LN_ATR_RES_GEN_BYTES" if paramId == 0x62: return "LN_ATR_RES_CONFIG" if paramId == 0x80: return "RF_FIELD_INFO" if paramId == 0x81: return "RF_NFCEE_ACTION" if paramId == 0x82: return "NFCDEP_OP" return "UNKOWN" parser = ArgumentParser() parser.add_argument("input", type=str, help="Read binary logs") parser.add_argument("output", type=str, help="Write converted logs") args = parser.parse_args() file1 = open(args.output, 'w') file2 = open(args.input, 'r') for line in file2.readlines() : if "NxpNciX" in line: hexStr = line.split(">", 1)[1].strip() file1.write("> " + " ".join("0x" + hexStr[i:i+2] for i in range(0, len(hexStr), 2)) + '\n') parse(hexStr) file1.write("\n") if "NxpNciR" in line: hexStr = line.split(">", 1)[1].strip() file1.write("< " + " ".join("0x" + hexStr[i:i+2] for i in range(0, len(hexStr), 2)) + '\n') parse(hexStr) file1.write("\n") if not line.startswith("2020"): file1.write(line.strip() + '\n') file2.close() file1.close()

32.08078

308

0.541547

558e2fb6e75d5162dd7b22c1783e3d5e0db52437

740,065

py

Python

xml_rc.py

youufis/visioninfer

45ce4c8ed47751d329c8eb3fb97fc686119cdca3

[ "MIT" ]

null

xml_rc.py

youufis/visioninfer

45ce4c8ed47751d329c8eb3fb97fc686119cdca3

[ "MIT" ]

null

xml_rc.py

youufis/visioninfer

45ce4c8ed47751d329c8eb3fb97fc686119cdca3

[ "MIT" ]

null

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\xad\x4a\x3b\xe8\x04\x52\x79\x71\x10\x28\xa3\xba\xd8\x50\xc4\xd2\ \xa2\xe7\xf3\xd2\xa5\x8f\x7f\x3c\xde\xf1\x2c\x6f\x72\x56\xd7\x7c\ \x8b\xeb\xa4\xb5\x69\x1c\x74\x60\x94\xd2\xe5\x15\x46\x18\xf8\xba\ \xcc\xbd\xb6\x27\x0e\x41\x13\xc0\xb5\x40\xda\xfa\xb6\xb2\xb0\x03\ \x93\x4c\x40\xee\x3b\x23\xd8\x16\x32\x30\x8d\x50\x28\x88\xaf\xa6\ \x4d\x35\x13\x92\xa9\xea\x7b\x06\xd5\xbf\x3d\xe8\x01\x9c\x0c\x77\ \x3c\xb0\xd4\x4c\x82\xa5\x36\xdf\xc7\x35\xd2\xc7\x7d\xb8\x0b\x14\ \x93\x3a\x14\x39\x42\x46\xe0\xbc\x65\xae\xe5\x75\xd2\xf2\xe6\xee\ \x1c\x7b\x18\x61\x8c\x59\xc5\x9e\xdb\xd9\xf8\xde\xca\x50\xbc\x68\ \x92\x61\x58\xe7\xb2\x69\x3f\xec\x23\x4b\x93\x9d\xfc\xd8\xda\xb6\ \x50\x07\x61\x71\x53\xc1\xe0\x0e\x1c\x78\xb5\xd4\x43\xea\x65\x9d\ \x74\xde\x82\xdf\x6e\x6c\xe6\xd5\x0c\x77\x4d\xbc\xdf\xff\x6b\x95\ \xd1\xea\x8b\xbb\xd9\x67\x0d\x47\x76\xb3\xef\xf4\x45\xf4\x7c\x58\ \xe1\x7c\xd8\xbc\x79\xa4\x3c\xe6\x8d\xa3\x7c\x9f\x34\x5f\xf7\x94\ \x47\x34\x28\xa0\x27\xec\x52\x56\x57\x7d\x8b\xab\x2c\xac\x67\xb8\ \x69\x4e\x64\x5e\xdf\xed\x39\x7e\x9c\x84\x7a\xb2\x42\x9d\x45\x12\ \xf6\x52\x1b\xde\x56\x94\x0f\x92\xa9\x11\x52\x50\x1b\xd9\x5e\x7e\ \xcb\xf9\xfd\xa7\xff\xf2\x5f\xff\xf1\xcf\xff\xf6\x3f\xfe\xf5\x1f\ \xf8\xbf\xfe\xcb\x3f\xff\xf7\x7f\xf9\xe7\xff\xeb\x1f\xff\xc7\xff\ \xf6\x4f\xff\xe5\xbf\xfd\xbf\xff\xf8\x7f\xfe\xe5\x7f\xfe\x9f\xff\ \xfd\xdf\xfe\xdb\xbf\xfe\xf3\xff\xbd\xfe\x93\xff\x0f\xfc\xe3\xd1\ \xd6\ " qt_resource_name = b"\ \x00\x04\ \x00\x06\xe8\x92\ \x00\x68\ \x00\x61\x00\x72\x00\x72\ \x00\x03\ \x00\x00\x70\x37\ \x00\x69\ \x00\x6d\x00\x67\ \x00\x09\ \x08\x97\xa4\x27\ \x00\x73\ \x00\x74\x00\x61\x00\x72\x00\x74\x00\x2e\x00\x6a\x00\x70\x00\x67\ \x00\x1f\ \x00\xd4\xcf\x1c\ \x00\x68\ \x00\x61\x00\x61\x00\x72\x00\x63\x00\x61\x00\x73\x00\x63\x00\x61\x00\x64\x00\x65\x00\x5f\x00\x66\x00\x72\x00\x6f\x00\x6e\x00\x74\ \x00\x61\x00\x6c\x00\x66\x00\x61\x00\x63\x00\x65\x00\x5f\x00\x61\x00\x6c\x00\x74\x00\x2e\x00\x78\x00\x6d\x00\x6c\ " qt_resource_struct_v1 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x01\ \x00\x00\x00\x0e\x00\x02\x00\x00\x00\x01\x00\x00\x00\x04\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\ \x00\x00\x00\x32\x00\x01\x00\x00\x00\x01\x00\x01\x5b\x55\ \x00\x00\x00\x1a\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ " qt_resource_struct_v2 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x01\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x0e\x00\x02\x00\x00\x00\x01\x00\x00\x00\x04\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x32\x00\x01\x00\x00\x00\x01\x00\x01\x5b\x55\ \x00\x00\x01\x7d\x88\x3e\xb1\x3e\ \x00\x00\x00\x1a\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ \x00\x00\x01\x7d\x8e\xab\x77\xa1\ " qt_version = [int(v) for v in QtCore.qVersion().split('.')] if qt_version < [5, 8, 0]: rcc_version = 1 qt_resource_struct = qt_resource_struct_v1 else: rcc_version = 2 qt_resource_struct = qt_resource_struct_v2 def qInitResources(): QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) def qCleanupResources(): QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) qInitResources()

66.73866

130

0.716357

bddb1013f49a89c0d62c0f9f5af54cb609725fcd

463

py

Python

data/scripts/templates/object/mobile/shared_dressed_rebel_corporal_bothan_male_01.py

obi-two/GameServer

7d37024e2291a97d49522610cd8f1dbe5666afc2

[ "MIT" ]

20

2015-02-23T15:11:56.000Z

2022-03-18T20:56:48.000Z

data/scripts/templates/object/mobile/shared_dressed_rebel_corporal_bothan_male_01.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

null

data/scripts/templates/object/mobile/shared_dressed_rebel_corporal_bothan_male_01.py

apathyboy/swganh

665128efe9154611dec4cb5efc61d246dd095984

[ "MIT" ]

20

2015-04-04T16:35:59.000Z

2022-03-24T14:54:37.000Z

#### NOTICE: THIS FILE IS AUTOGENERATED #### MODIFICATIONS MAY BE LOST IF DONE IMPROPERLY #### PLEASE SEE THE ONLINE DOCUMENTATION FOR EXAMPLES from swgpy.object import * def create(kernel): result = Creature() result.template = "object/mobile/shared_dressed_rebel_corporal_bothan_male_01.iff" result.attribute_template_id = 9 result.stfName("npc_name","bothan_base_male") #### BEGIN MODIFICATIONS #### #### END MODIFICATIONS #### return result

27.235294

83

0.740821

4be7b74974234e2aa19099b4602f8e83c3d7bd1b

148

py

Python

metaci/plan/apps.py

sfdc-qbranch/MetaCI

78ac0d2bccd2db381998321ebd71029dd5d9ab39

[ "BSD-3-Clause" ]

48

2018-10-24T14:52:06.000Z

2022-03-25T21:14:50.000Z

metaci/plan/apps.py

sfdc-qbranch/MetaCI

78ac0d2bccd2db381998321ebd71029dd5d9ab39

[ "BSD-3-Clause" ]

2,034

2018-10-31T20:59:16.000Z

2022-03-22T21:38:03.000Z

metaci/plan/apps.py

sfdc-qbranch/MetaCI

78ac0d2bccd2db381998321ebd71029dd5d9ab39

[ "BSD-3-Clause" ]

27

2018-12-24T18:16:23.000Z

2021-12-15T17:57:27.000Z

from django.apps import AppConfig class PlanConfig(AppConfig): name = "metaci.plan" def ready(self): import metaci.plan.handlers

16.444444

35

0.695946

a077c5bbafc42837839088c4e006809dd0c7ef5c

1,547

py

Python

examples/Grafana-Demo-FOREX-master/backgroundForex.py

ambitus/containers

72b8eee2de8e484739cc45be0c2c50d7cabd6d0c

[ "Apache-2.0" ]

4

2019-10-17T20:51:32.000Z

2021-03-05T17:45:44.000Z

examples/Grafana-Demo-FOREX-master/backgroundForex.py

ambitus/containers

72b8eee2de8e484739cc45be0c2c50d7cabd6d0c

[ "Apache-2.0" ]

6

2020-07-14T14:25:13.000Z

2021-02-20T10:19:32.000Z

examples/Grafana-Demo-FOREX-master/backgroundForex.py

ambitus/containers

72b8eee2de8e484739cc45be0c2c50d7cabd6d0c

[ "Apache-2.0" ]

6

2020-03-12T08:39:48.000Z

2020-10-09T18:08:44.000Z

#Copyright Contributors to the Ambitus Project. # #SPDX-License-Identifier: Apache-2.0 import requests, json, os, logging, time from elasticsearch import Elasticsearch es = Elasticsearch([{'host': '### YOUR APPLIANCE URL HERE ###', 'port': '9200'}]) es.indices.create(index='forex') mapping={ "properties":{ "From_Currency Code":{"type": "text"}, "From_Currency Name":{"type": "text"}, "To_Currency Code":{"type": "text"}, "To_Currency Name":{"type": "text"}, "Exchange Rate":{"type": "float"}, "Last Refreshed":{"type": "date"}, "Time Zone":{"type": "text"}, "Bid Price":{"type": "float"}, "Ask Price":{"type": "float"}, } } es.indices.put_mapping(index='forex', body=mapping) API_URL = "https://www.alphavantage.co/query" data = { "function": "CURRENCY_EXCHANGE_RATE", "from_currency": "USD", "to_currency": "EUR", "apikey": "### YOUR API KEY HERE ###", "datatype":"csv", } idNum = 1 while True: response = requests.get(API_URL, params=data) info = str(json.dumps(response.json())) info = info[36:339] for i in range(1,10): Numeral = str(i) + ". " info = info.replace(Numeral, "") info = info[:200]+"Z"+info[200:] info = info[:191]+"T"+info[192:] es.index(index='forex', ignore=400, id=idNum, body=info) idNum += 1 time.sleep(200)

34.377778

82

0.529412

61087215ab6a3283110e64b32c880db99b1143cd

3,191

py

Python

examples/three_link_arm_block_pushing/run_3link_arm_pushing_2d.py

pangtao22/quasistatic_simulator

7c6f99cc7237dd922f6eb0b54c580303e86b5223

[ "MIT" ]

5

2021-07-15T03:58:55.000Z

2021-12-23T17:26:16.000Z

examples/three_link_arm_block_pushing/run_3link_arm_pushing_2d.py

pangtao22/quasistatic_simulator

7c6f99cc7237dd922f6eb0b54c580303e86b5223

[ "MIT" ]

5

2021-08-16T22:27:54.000Z

2022-02-07T18:06:07.000Z

examples/three_link_arm_block_pushing/run_3link_arm_pushing_2d.py

pangtao22/quasistatic_simulator

7c6f99cc7237dd922f6eb0b54c580303e86b5223

[ "MIT" ]

null

import matplotlib.pyplot as plt from examples.log_comparison import * from examples.setup_simulations import shift_q_traj_to_start_at_minus_h from examples.three_link_arm_block_pushing.utils import * # box initial state. q_u0 = np.array([1.7, 0.5, 0]) q0_dict_str = {robot_name: qa_knots[0], box_name: q_u0} def calc_integral_errors(q_robot_log_mbp, q_box_log_mbp, t_mbp, q_robot_log_quasistatic, q_box_log_quasistatic, t_quasistatic): shift_q_traj_to_start_at_minus_h(q_robot_traj, 0) qa_mbp_traj = PiecewisePolynomial.ZeroOrderHold(t_mbp, q_robot_log_mbp.T) # Quasistatic vs MBP, robot. e_robot, e_vec_robot, t_e_robot = calc_error_integral( q_knots=q_robot_log_quasistatic, t=t_quasistatic, q_gt_traj=qa_mbp_traj) # Quasistatic vs MBP, object angle. angle_mbp = q_box_log_mbp[:, 2].T angle_mbp = angle_mbp.reshape((1, angle_mbp.size)) angle_box_mbp_traj = PiecewisePolynomial.FirstOrderHold( t_mbp, angle_mbp) e_angle_box, e_vec_angle_box, t_angle_box = calc_error_integral( q_knots=q_box_log_quasistatic[:, 2], t=t_quasistatic, q_gt_traj=angle_box_mbp_traj) # Quasistatic vs MBP, object position. xyz_box_mbp_traj = PiecewisePolynomial.FirstOrderHold( t_mbp, q_box_log_mbp[:, :2].T) e_xyz_box, e_vec_xyz_box, t_xyz_box = calc_error_integral( q_knots=q_box_log_quasistatic[:, :2], t=t_quasistatic, q_gt_traj=xyz_box_mbp_traj) return (e_robot, e_vec_robot, t_e_robot, e_angle_box, e_vec_angle_box, t_angle_box, e_xyz_box, e_vec_xyz_box, t_xyz_box) if __name__ == "__main__": (q_robot_log_mbp, q_box_log_mbp, t_mbp, q_robot_log_quasistatic, q_box_log_quasistatic, t_quasistatic, q_sys) = \ run_mbp_quasistatic_comparison(q_model_path_2d, q0_dict_str, is_visualizing=True, real_time_rate=0.0) #%% figure, axes = plt.subplots(nq_a, 1, figsize=(4, 10), dpi=200) axes[0].set_title("Joint angles") for i, ax in enumerate(axes): ax.plot(t_mbp, q_robot_log_mbp[:, i], label="mbp") ax.plot(t_quasistatic, q_robot_log_quasistatic[:, i], label="quasistatic") ax.set_ylabel("[rad]") ax.legend() plt.xlabel("t [s]") plt.show() (e_robot, e_vec_robot, t_e_robot, e_angle_box, e_vec_angle_box, t_angle_box, e_xyz_box, e_vec_xyz_box, t_xyz_box) = calc_integral_errors( q_robot_log_mbp, q_box_log_mbp, t_mbp, q_robot_log_quasistatic, q_box_log_quasistatic, t_quasistatic) # %% print("Quasistatic vs MBP, robot", e_robot) # %% Orientation (angle). print("Quasistatic vs MBP, object angle", e_angle_box) plt.plot(t_angle_box, e_vec_angle_box) plt.title("Box angle difference, mbp vs. quasistatic [rad]") plt.xlabel("t [s]") plt.show() # %% Position. print("Quasistatic vs MBP, object position", e_xyz_box) plt.plot(t_xyz_box, e_vec_xyz_box) plt.title("Box position difference, mbp vs. quasistatic [m]") plt.xlabel("t [s]") plt.show()

37.541176

78

0.678784

ceb88de38c7114e4ca531c211fc0289fa522bc58

412

py

Python

coding/learn_re/demo_datetime.py

yatao91/learning_road

e88dc43de98e35922bfc71c222ec71766851e618

[ "MIT" ]

3

2021-05-25T16:58:52.000Z

2022-02-05T09:37:17.000Z

coding/learn_re/demo_datetime.py

yataosu/learning_road

e88dc43de98e35922bfc71c222ec71766851e618

[ "MIT" ]

null

coding/learn_re/demo_datetime.py

yataosu/learning_road

e88dc43de98e35922bfc71c222ec71766851e618

[ "MIT" ]

null

# -*- coding: utf-8 -*- import re def time_re(start_time, stop_time): if not re.match('^\d{4}-\d{1,2}-\d{1,2} \d{1,2}:\d{1,2}:\d{1,2}$', start_time) \ or not re.match('^\d{4}-\d{1,2}-\d{1,2} \d{1,2}:\d{1,2}:\d{1,2}$', stop_time): print('error') if __name__ == '__main__': start_time = '2019-05-15 15:38:38' stop_time = '2019-08-13 15:38:38' time_re(start_time, stop_time)

27.466667

90

0.546117

a0130ae029719485e9d06466f2ea9412543a3d7b

1,035

py

Python

wagtail/tests/snippets/migrations/0005_multisectionrichtextsnippet_richtextsection.py

brownaa/wagtail

c97bc56c6822eb1b6589d5c33e07f71acfc48845

[ "BSD-3-Clause" ]

8,851

2016-12-09T19:01:45.000Z

2022-03-31T04:45:06.000Z

wagtail/tests/snippets/migrations/0005_multisectionrichtextsnippet_richtextsection.py

brownaa/wagtail

c97bc56c6822eb1b6589d5c33e07f71acfc48845

[ "BSD-3-Clause" ]

5,197

2016-12-09T19:24:37.000Z

2022-03-31T22:17:55.000Z

wagtail/tests/snippets/migrations/0005_multisectionrichtextsnippet_richtextsection.py

brownaa/wagtail

c97bc56c6822eb1b6589d5c33e07f71acfc48845

[ "BSD-3-Clause" ]

2,548

2016-12-09T18:16:55.000Z

2022-03-31T21:34:38.000Z

# -*- coding: utf-8 -*- from django.db import migrations, models import wagtail.core.fields import modelcluster.fields class Migration(migrations.Migration): dependencies = [ ('snippetstests', '0004_fileuploadsnippet'), ] operations = [ migrations.CreateModel( name='MultiSectionRichTextSnippet', fields=[ ('id', models.AutoField(primary_key=True, serialize=False, verbose_name='ID', auto_created=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='RichTextSection', fields=[ ('id', models.AutoField(primary_key=True, serialize=False, verbose_name='ID', auto_created=True)), ('body', wagtail.core.fields.RichTextField()), ('snippet', modelcluster.fields.ParentalKey(to='snippetstests.MultiSectionRichTextSnippet', related_name='sections', on_delete=models.CASCADE)), ], ), ]

32.34375

160

0.590338

7e6e76b66630962810c81ee341a64f3a07e56c33

11,870

py

Python

tests/unit/test_simulator_interface.py

nordicneurolab/vunit

c69c99512d7cebe915f77653b473853b495886d2

[ "Artistic-2.0" ]

3

2021-03-09T15:01:40.000Z

2022-02-05T12:11:55.000Z

tests/unit/test_simulator_interface.py

noasic/vunit

4d65f1283ede458f8941910cd40787edb7a9271c

[ "Artistic-2.0", "Apache-2.0" ]

4

2022-02-04T08:24:33.000Z

2022-03-14T17:06:48.000Z

tests/unit/test_simulator_interface.py

noasic/vunit

4d65f1283ede458f8941910cd40787edb7a9271c

[ "Artistic-2.0", "Apache-2.0" ]

4

2020-03-25T16:49:30.000Z

2022-02-01T11:18:12.000Z

# This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this file, # You can obtain one at http://mozilla.org/MPL/2.0/. # # Copyright (c) 2014-2020, Lars Asplund lars.anders.asplund@gmail.com """ Test the SimulatorInterface class """ import unittest from pathlib import Path from os import chdir, getcwd import subprocess from shutil import rmtree from unittest import mock from vunit.project import Project from vunit.sim_if import ( SimulatorInterface, BooleanOption, ListOfStringOption, StringOption, VHDLAssertLevelOption, ) from vunit.exceptions import CompileError from vunit.ostools import renew_path, write_file class TestSimulatorInterface(unittest.TestCase): """ Test the SimulatorInterface class """ def test_compile_source_files(self): simif = create_simulator_interface() simif.compile_source_file_command.side_effect = iter( [["command1"], ["command2"]] ) project = Project() project.add_library("lib", "lib_path") write_file("file1.vhd", "") file1 = project.add_source_file("file1.vhd", "lib", file_type="vhdl") write_file("file2.vhd", "") file2 = project.add_source_file("file2.vhd", "lib", file_type="vhdl") project.add_manual_dependency(file2, depends_on=file1) with mock.patch("vunit.sim_if.check_output", autospec=True) as check_output: check_output.side_effect = iter(["", ""]) printer = MockPrinter() simif.compile_source_files(project, printer=printer) check_output.assert_has_calls( [ mock.call(["command1"], env=simif.get_env()), mock.call(["command2"], env=simif.get_env()), ] ) self.assertEqual( printer.output, """\ Compiling into lib: file1.vhd passed Compiling into lib: file2.vhd passed Compile passed """, ) self.assertEqual(project.get_files_in_compile_order(incremental=True), []) def test_compile_source_files_minimal_subset(self): simif = create_simulator_interface() project = Project() project.add_library("lib", "lib_path") write_file("file1.vhd", "") file1 = project.add_source_file("file1.vhd", "lib", file_type="vhdl") with mock.patch( "vunit.project.Project.get_minimal_file_set_in_compile_order", autospec=True ) as target_function: target_function.return_value = [] printer = MockPrinter() simif.compile_source_files(project, printer=printer, target_files=[file1]) simif.compile_source_files(project, printer=printer, target_files=[file1]) self.assertTrue(target_function.called) def test_compile_source_files_continue_on_error(self): simif = create_simulator_interface() project = Project() project.add_library("lib", "lib_path") write_file("file1.vhd", "") file1 = project.add_source_file("file1.vhd", "lib", file_type="vhdl") write_file("file2.vhd", "") file2 = project.add_source_file("file2.vhd", "lib", file_type="vhdl") write_file("file3.vhd", "") file3 = project.add_source_file("file3.vhd", "lib", file_type="vhdl") project.add_manual_dependency(file2, depends_on=file1) def compile_source_file_command(source_file): """ Dummy compile command """ if source_file == file1: return ["command1"] if source_file == file2: return ["command2"] if source_file == file3: return ["command3"] raise AssertionError def check_output_side_effect( command, env=None ): # pylint: disable=missing-docstring, unused-argument if command == ["command1"]: raise subprocess.CalledProcessError( returncode=-1, cmd=command, output="bad stuff" ) return "" simif.compile_source_file_command.side_effect = compile_source_file_command with mock.patch("vunit.sim_if.check_output", autospec=True) as check_output: check_output.side_effect = check_output_side_effect printer = MockPrinter() self.assertRaises( CompileError, simif.compile_source_files, project, printer=printer, continue_on_error=True, ) self.assertEqual( printer.output, """\ Compiling into lib: file3.vhd passed Compiling into lib: file1.vhd failed === Command used: === command1 === Command output: === bad stuff Compiling into lib: file2.vhd skipped Compile failed """, ) self.assertEqual(len(check_output.mock_calls), 2) check_output.assert_has_calls( [ mock.call(["command1"], env=simif.get_env()), mock.call(["command3"], env=simif.get_env()), ], any_order=True, ) self.assertEqual( project.get_files_in_compile_order(incremental=True), [file1, file2] ) def test_compile_source_files_check_output_error(self): simif = create_simulator_interface() simif.compile_source_file_command.return_value = ["command"] project = Project() project.add_library("lib", "lib_path") write_file("file.vhd", "") source_file = project.add_source_file("file.vhd", "lib", file_type="vhdl") with mock.patch("vunit.sim_if.check_output", autospec=True) as check_output: def check_output_side_effect( command, env=None ): # pylint: disable=missing-docstring, unused-argument raise subprocess.CalledProcessError( returncode=-1, cmd=command, output="bad stuff" ) check_output.side_effect = check_output_side_effect printer = MockPrinter() self.assertRaises( CompileError, simif.compile_source_files, project, printer=printer ) self.assertEqual( printer.output, """\ Compiling into lib: file.vhd failed === Command used: === command === Command output: === bad stuff Compile failed """, ) check_output.assert_called_once_with(["command"], env=simif.get_env()) self.assertEqual( project.get_files_in_compile_order(incremental=True), [source_file] ) def test_compile_source_files_create_command_error(self): simif = create_simulator_interface() project = Project() project.add_library("lib", "lib_path") write_file("file.vhd", "") source_file = project.add_source_file("file.vhd", "lib", file_type="vhdl") with mock.patch("vunit.sim_if.check_output", autospec=True) as check_output: check_output.return_value = "" def raise_compile_error(source_file): # pylint: disable=unused-argument raise CompileError simif.compile_source_file_command.side_effect = raise_compile_error self.assertRaises(CompileError, simif.compile_source_files, project) self.assertEqual( project.get_files_in_compile_order(incremental=True), [source_file] ) @mock.patch("os.environ", autospec=True) def test_find_prefix(self, environ): class MySimulatorInterface( SimulatorInterface ): # pylint: disable=abstract-method """ Dummy simulator interface for testing """ name = "simname" prefix_from_path = None @classmethod def find_prefix_from_path(cls): return cls.prefix_from_path simif = MySimulatorInterface(output_path="output_path", gui=False) simif.name = "simname" environ.get.return_value = None self.assertEqual(simif.find_prefix(), None) environ.get.assert_called_once_with("VUNIT_SIMNAME_PATH", None) environ.reset_mock() environ.get.return_value = "simname/bin" self.assertEqual(simif.find_prefix(), "simname/bin") environ.get.assert_called_once_with("VUNIT_SIMNAME_PATH", None) environ.reset_mock() environ.get.return_value = None MySimulatorInterface.prefix_from_path = "prefix_from_path" self.assertEqual(simif.find_prefix(), "prefix_from_path") environ.get.assert_called_once_with("VUNIT_SIMNAME_PATH", None) def setUp(self): self.output_path = str(Path(__file__).parent / "test_simulator_interface__out") renew_path(self.output_path) self.project = Project() self.cwd = getcwd() chdir(self.output_path) def tearDown(self): chdir(self.cwd) if Path(self.output_path).exists(): rmtree(self.output_path) class TestOptions(unittest.TestCase): """ The the compile and simulation options validators """ def test_boolean_option(self): option = BooleanOption("optname") self._test_ok(option, True) self._test_ok(option, False) self._test_not_ok(option, None, "Option 'optname' must be a boolean. Got None") def test_string_option(self): option = StringOption("optname") self._test_ok(option, "hello") self._test_ok(option, u"hello") self._test_not_ok(option, False, "Option 'optname' must be a string. Got False") self._test_not_ok( option, ["foo"], "Option 'optname' must be a string. Got ['foo']" ) def test_list_of_string_option(self): option = ListOfStringOption("optname") self._test_ok(option, ["hello", "foo"]) self._test_ok(option, [u"hello"]) self._test_not_ok( option, [True], "Option 'optname' must be a list of strings. " "Got [True]" ) self._test_not_ok( option, [["foo"]], "Option 'optname' must be a list of strings. " "Got [['foo']]", ) self._test_not_ok( option, "foo", "Option 'optname' must be a list of strings. " "Got 'foo'" ) def test_vhdl_assert_level(self): option = VHDLAssertLevelOption() self._test_ok(option, "warning") self._test_ok(option, "error") self._test_ok(option, "failure") self._test_not_ok( option, "foo", "Option 'vhdl_assert_stop_level' must be one of " "('warning', 'error', 'failure'). Got 'foo'", ) @staticmethod def _test_ok(option, value): option.validate(value) def _test_not_ok(self, option, value, message): """ Test taht setting option to value is not OK with message """ try: option.validate(value) except ValueError as err: self.assertEqual(str(err), message) else: assert False def create_simulator_interface(): """ Create a simulator interface with fake method """ simif = SimulatorInterface(output_path="output_path", gui=False) simif.compile_source_file_command = mock.create_autospec( simif.compile_source_file_command ) return simif class MockPrinter(object): """ Mock printer that accumulates the calls as a string """ def __init__(self): self.output = "" def write( self, text, output_file=None, fg=None, bg=None ): # pylint: disable=unused-argument self.output += text

33.817664

88

0.614659

dcd031dfc281dd4f019c626bd991843f8dd077b5

6,579

py

Python

tests/python/pants_test/backend/jvm/tasks/test_consolidate_classpath.py

mcguigan/pants

e085d45669b72d0c51ab8a54602306fc76e07256

[ "Apache-2.0" ]

null

tests/python/pants_test/backend/jvm/tasks/test_consolidate_classpath.py

mcguigan/pants

e085d45669b72d0c51ab8a54602306fc76e07256

[ "Apache-2.0" ]

null

tests/python/pants_test/backend/jvm/tasks/test_consolidate_classpath.py

mcguigan/pants

e085d45669b72d0c51ab8a54602306fc76e07256

[ "Apache-2.0" ]

null

# Copyright 2016 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). import os import re from typing import List from pants.backend.jvm.targets.jar_library import JarLibrary from pants.backend.jvm.targets.java_library import JavaLibrary from pants.backend.jvm.targets.jvm_app import JvmApp from pants.backend.jvm.targets.jvm_binary import JvmBinary from pants.backend.jvm.tasks.consolidate_classpath import ConsolidateClasspath from pants.build_graph.resources import Resources from pants.java.jar.jar_dependency import JarDependency from pants.util.dirutil import safe_file_dump from pants_test.backend.jvm.tasks.jvm_binary_task_test_base import JvmBinaryTaskTestBase class TestConsolidateClasspath(JvmBinaryTaskTestBase): @classmethod def task_type(cls): return ConsolidateClasspath def setUp(self): """Prepare targets, context, runtime classpath. """ super().setUp() self.task = self.prepare_execute(self.context()) self.jar_artifact = self.create_artifact(org='org.example', name='foo', rev='1.0.0') self.zip_artifact = self.create_artifact(org='org.pantsbuild', name='bar', rev='2.0.0', ext='zip') self.bundle_artifact = self.create_artifact(org='org.apache', name='baz', rev='3.0.0', classifier='tests') self.tar_gz_artifact = self.create_artifact(org='org.gnu', name='gary', rev='4.0.0', ext='tar.gz') self.jar_lib = self.make_target(spec='3rdparty/jvm/org/example:foo', target_type=JarLibrary, jars=[JarDependency(org='org.example', name='foo', rev='1.0.0'), JarDependency(org='org.pantsbuild', name='bar', rev='2.0.0', ext='zip'), JarDependency(org='org.apache', name='baz', rev='3.0.0', classifier='tests'), JarDependency(org='org.gnu', name='gary', rev='4.0.0', ext='tar.gz')]) safe_file_dump(os.path.join(self.build_root, 'resources/foo/file'), '// dummy content') self.resources_target = self.make_target('//resources:foo-resources', Resources, sources=['foo/file']) # This is so that payload fingerprint can be computed. safe_file_dump(os.path.join(self.build_root, 'foo/Foo.java'), '// dummy content') self.java_lib_target = self.make_target('//foo:foo-library', JavaLibrary, sources=['Foo.java']) self.binary_target = self.make_target(spec='//foo:foo-binary', target_type=JvmBinary, dependencies=[ self.java_lib_target, self.jar_lib, self.resources_target ]) self.dist_root = os.path.join(self.build_root, 'dist') def _setup_classpath(self, task_context): """As a separate prep step because to test different option settings, this needs to rerun after context is re-created. """ self.ensure_classpath_products(task_context) self.add_to_runtime_classpath(task_context, self.binary_target, {'Foo.class': '', 'foo.txt': '', 'foo/file': ''}) def test_remove_raw_deps(self): """Test default setting outputs bundle products using `target.id`.""" self.app_target = self.make_target(spec='//foo:foo-app', target_type=JvmApp, basename='FooApp', dependencies=[self.binary_target]) self.task_context = self.context(target_roots=[self.app_target]) self._setup_classpath(self.task_context) self.execute(self.task_context) task_dir = os.path.join( self.pants_workdir, 'pants_backend_jvm_tasks_consolidate_classpath_ConsolidateClasspath' ) found_files = [os.path.basename(f) for f in self.iter_files(task_dir)] consolidate_classpath_jar = self.find_consolidate_classpath_jar(found_files) self.assertEqual( sorted([consolidate_classpath_jar, 'Foo.class', 'foo.txt', 'file']), sorted(found_files) ) # Confirm that we haven't destroyed deps. expected_non_deps = {consolidate_classpath_jar, 'Foo.class', 'foo.txt', 'file'} found = set(os.listdir(self.pants_workdir)) print(expected_non_deps - found) self.assertTrue(expected_non_deps - found == expected_non_deps) def test_consolidate_classpath(self): """Test default setting outputs bundle products using `target.id`.""" self.app_target = self.make_target(spec='//foo:foo-app', target_type=JvmApp, basename='FooApp', dependencies=[self.binary_target]) self.task_context = self.context(target_roots=[self.app_target]) self._setup_classpath(self.task_context) self.execute(self.task_context) task_dir = os.path.join( self.pants_workdir, 'pants_backend_jvm_tasks_consolidate_classpath_ConsolidateClasspath' ) found_files = [os.path.basename(f) for f in self.iter_files(task_dir)] consolidate_classpath_jar = self.find_consolidate_classpath_jar(found_files) self.assertEqual( sorted([consolidate_classpath_jar, 'Foo.class', 'foo.txt', 'file']), sorted(found_files) ) # Confirm that we haven't destroyed deps. expected_deps = {'org.apache-baz-3.0.0-tests.jar', 'org.example-foo-1.0.0.jar', 'org.gnu-gary-4.0.0.tar.gz', 'org.pantsbuild-bar-2.0.0.zip'} found = set(os.listdir(self.pants_workdir)) self.assertTrue(expected_deps - found == set()) @staticmethod def find_consolidate_classpath_jar(files: List[str]) -> str: matching = [f for f in files if re.match("output-[0-9a-f]{6}\.jar", f)] if len(matching) == 1: return matching[0] raise ValueError(f"Could not find single consolidate classpath jar in {files}.")

48.375

100

0.595379

22f466e60dba83cc00b9204221d9b903bef636c8

2,307

py

Python

__main__.py

christopherkopic/PictureToBytes

95b93876bffdc4337056c5dcba6fc0c9322f6826

[ "MIT" ]

null

__main__.py

christopherkopic/PictureToBytes

95b93876bffdc4337056c5dcba6fc0c9322f6826

[ "MIT" ]

null

__main__.py

christopherkopic/PictureToBytes

95b93876bffdc4337056c5dcba6fc0c9322f6826

[ "MIT" ]

null

from PIL import Image from pathlib import Path from itertools import zip_longest import os file_txt = "Icons.h" def writeImg(file, pixels, sizeX, sizeY, name): file.write("const unsigned int {0}_X = {1};\n".format(name, sizeX)) file.write("const unsigned int {0}_Y = {1};\n".format(name, sizeY)) file.write("const unsigned char {0}[] PROGMEM =\n".format(name)) file.write("{\n") file.write("\t0X{:02X}".format(pixels.pop(0))) for pixel in pixels: file.write(", 0X{:02X}".format(pixel)) file.write("\n") file.write("};\n") file.write("\n") def writeFileHead(file, filename): name = Path(filename).stem file.write("#ifndef _{0}_H_\n".format(name)) file.write("#define _{0}_H_\n".format(name)) file.write("\n") file.write("#if defined(ESP8266) || defined(ESP32)\n") file.write("#include <pgmspace.h>\n") file.write("#else\n") file.write("#include <avr/pgmspace.h>\n") file.write("#endif\n") file.write("\n") def writeFileFoot(file): file.write("#endif\n") def grouper(n, iterable, padvalue=None): "grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')" return list(zip_longest(*[iter(iterable)]*n, fillvalue=padvalue)) def encodePixels(pixels, sizeX): rows = grouper(sizeX, pixels, 0); data = [] for row in rows: data.append(grouper(8, row, 0)) data_encoded = [] for row in data: for byte in row: binary = "" for value in byte: if value > 125: binary += "1" else: binary += "0" data_encoded.append(int(binary, 2)) return data_encoded def main(): #Load Textfile txt = open(file_txt, "w+") writeFileHead(txt, file_txt) #All bmp files as list files = os.listdir() files_bmp = [i for i in files if i.endswith('.bmp')] for bmp in files_bmp: #Load Image img = Image.open(bmp) pixels = list(img.getdata()) sizeX = img.size[0]; sizeY = img.size[1]; name = Path(bmp).stem pixels_encoded = encodePixels(pixels, sizeX) writeImg(txt, pixels_encoded, sizeX, sizeY, name) writeFileFoot(txt) txt.close() if __name__ == '__main__': main()

26.517241

80

0.577807

ed831c5f5160a0095dff4fd636c4e000f7349572

1,450

py

Python

app/core/tests/test_models.py

datilp/recipe-app-api

c62e3c3429b10b354b741d491d90b8297b393a95

[ "MIT" ]

null

app/core/tests/test_models.py

datilp/recipe-app-api

c62e3c3429b10b354b741d491d90b8297b393a95

[ "MIT" ]

null

app/core/tests/test_models.py

datilp/recipe-app-api

c62e3c3429b10b354b741d491d90b8297b393a95

[ "MIT" ]

null

from django.test import TestCase from django.contrib.auth import get_user_model ''' we use get_user_model instead of importing the user model directly later on if we change the user model by using get_user_model we only have to change the configuration ''' class ModelTest(TestCase): def test_create_user_with_email_sucessful(self): """Test creating a new user with an email is successful""" email = 'isuarezsola@gmail.com' password = 'Testpass123' user = get_user_model().objects.create_user( email=email, password=password ) self.assertEqual(user.email, email) self.assertTrue(user.check_password(password)) def test_new_user_email_normalized(self): """Test email is normalized""" email = 'isuarezsola@GMAIL.COM' user = get_user_model().objects.create_user(email, "test123") self.assertEqual(user.email, email.lower()) def test_new_user_invalid_email(self): """Test created user without email raises error""" with self.assertRaises(ValueError): get_user_model().objects.create_user(None, "test123") def test_create_new_super_user(self): """Test create super user""" user = get_user_model().objects.create_superuser( 'test@gmail.com', 'test123' ) self.assertTrue(user.is_superuser) self.assertTrue(user.is_staff)

33.72093

72

0.668966

6c459df916356d23b053d07735ab3e0d79116975

6,843

py

Python

exstracs/exstracs_prediction.py

bnanita/trafficapi

ba22faa609ce0f79b3791dca2c403591a26ee2e2

[ "MIT" ]

null

exstracs/exstracs_prediction.py

bnanita/trafficapi

ba22faa609ce0f79b3791dca2c403591a26ee2e2

[ "MIT" ]

null

exstracs/exstracs_prediction.py

bnanita/trafficapi

ba22faa609ce0f79b3791dca2c403591a26ee2e2

[ "MIT" ]

null

""" Name: ExSTraCS_Prediction.py Authors: Ryan Urbanowicz - Written at Dartmouth College, Hanover, NH, USA Contact: ryan.j.urbanowicz@darmouth.edu Created: April 25, 2014 Modified: August 25,2014 Description: Based on a given match set, this module uses a voting scheme to select the phenotype prediction for ExSTraCS. --------------------------------------------------------------------------------------------------------------------------------------------------------- ExSTraCS V2.0: Extended Supervised Tracking and Classifying System - An advanced LCS designed specifically for complex, noisy classification/data mining tasks, such as biomedical/bioinformatics/epidemiological problem domains. This algorithm should be well suited to any supervised learning problem involving classification, prediction, data mining, and knowledge discovery. This algorithm would NOT be suited to function approximation, behavioral modeling, or other multi-step problems. This LCS algorithm is most closely based on the "UCS" algorithm, an LCS introduced by Ester Bernado-Mansilla and Josep Garrell-Guiu (2003) which in turn is based heavily on "XCS", an LCS introduced by Stewart Wilson (1995). Copyright (C) 2014 Ryan Urbanowicz This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABLILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA --------------------------------------------------------------------------------------------------------------------------------------------------------- """ #Import Required Modules------------------------------- from exstracs.exstracs_constants import * import random #------------------------------------------------------ class Prediction: def __init__(self, population): #now takes in population ( have to reference the match set to do prediction) pop.matchSet """ Constructs the voting array and determines the prediction decision. """ self.decision = None #------------------------------------------------------- # DISCRETE PHENOTYPE #------------------------------------------------------- if cons.env.formatData.discretePhenotype: self.vote = {} self.tieBreak_Numerosity = {} self.tieBreak_TimeStamp = {} for eachClass in cons.env.formatData.phenotypeList: self.vote[eachClass] = 0.0 self.tieBreak_Numerosity[eachClass] = 0.0 self.tieBreak_TimeStamp[eachClass] = 0.0 for ref in population.matchSet: cl = population.popSet[ref] self.vote[cl.phenotype] += cl.fitness * cl.numerosity * cons.env.formatData.classPredictionWeights[cl.phenotype] self.tieBreak_Numerosity[cl.phenotype] += cl.numerosity self.tieBreak_TimeStamp[cl.phenotype] += cl.initTimeStamp highVal = 0.0 bestClass = [] #Prediction is set up to handle best class ties for problems with more than 2 classes for thisClass in cons.env.formatData.phenotypeList: if self.vote[thisClass] >= highVal: highVal = self.vote[thisClass] for thisClass in cons.env.formatData.phenotypeList: if self.vote[thisClass] == highVal: #Tie for best class bestClass.append(thisClass) #--------------------------- if highVal == 0.0: self.decision = None #----------------------------------------------------------------------- elif len(bestClass) > 1: #Randomly choose between the best tied classes bestNum = 0 newBestClass = [] for thisClass in bestClass: if self.tieBreak_Numerosity[thisClass] >= bestNum: bestNum = self.tieBreak_Numerosity[thisClass] for thisClass in bestClass: if self.tieBreak_Numerosity[thisClass] == bestNum: newBestClass.append(thisClass) #----------------------------------------------------------------------- if len(newBestClass) > 1: #still a tie bestStamp = 0 newestBestClass = [] for thisClass in newBestClass: if self.tieBreak_TimeStamp[thisClass] >= bestStamp: bestStamp = self.tieBreak_TimeStamp[thisClass] for thisClass in newBestClass: if self.tieBreak_TimeStamp[thisClass] == bestStamp: newestBestClass.append(thisClass) #----------------------------------------------------------------------- if len(newestBestClass) > 1: # Prediction is completely tied - ExSTraCS has no useful information for making a prediction self.decision = 'Tie' else: self.decision = newBestClass[0] #---------------------------------------------------------------------- else: #One best class determined by fitness vote self.decision = bestClass[0] #------------------------------------------------------- # CONTINUOUS PHENOTYPE #------------------------------------------------------- else: print("Prediction - Error: ExSTraCS 2.0 can not handle continuous endpoints.") def getFitnessSum(self,population,low,high): """ Get the fitness Sum of rules in the rule-set. For continuous phenotype prediction. """ fitSum = 0 for ref in population.matchSet: cl = population.popSet[ref] if cl.phenotype[0] <= low and cl.phenotype[1] >= high: #if classifier range subsumes segment range. fitSum += cl.fitness return fitSum def getDecision(self): """ Returns prediction decision. """ return self.decision def getSet(self): """ Returns prediction decision. """ return self.vote

56.090164

160

0.531346

807b0dc333f181bc98a2221cd5970c74e0c71fef

46,938

py

Python

droidlet/tools/hitl/vision_retrain/vision_annotation_jobs.py

colesbury/fairo

9e50a3aa7369c68c80e84d80abd5fcdee8a9277a

[ "MIT" ]

73

2021-09-14T19:24:45.000Z

2022-03-27T06:43:26.000Z

droidlet/tools/hitl/vision_retrain/vision_annotation_jobs.py

colesbury/fairo

9e50a3aa7369c68c80e84d80abd5fcdee8a9277a

[ "MIT" ]

268

2021-09-14T22:40:23.000Z

2022-03-31T23:01:54.000Z

droidlet/tools/hitl/vision_retrain/vision_annotation_jobs.py

colesbury/fairo

9e50a3aa7369c68c80e84d80abd5fcdee8a9277a

[ "MIT" ]

20

2021-09-14T19:24:47.000Z

2022-03-30T19:03:44.000Z

""" Copyright (c) Facebook, Inc. and its affiliates. """ import logging import os import signal import subprocess import time import csv import yaml import json import copy import boto3 from droidlet.tools.hitl.data_generator import DataGenerator from mephisto.abstractions.databases.local_database import LocalMephistoDB from mephisto.tools.data_browser import DataBrowser as MephistoDataBrowser db = LocalMephistoDB() mephisto_data_browser = MephistoDataBrowser(db=db) HITL_TMP_DIR = ( os.environ["HITL_TMP_DIR"] if os.getenv("HITL_TMP_DIR") else f"{os.path.expanduser('~')}/.hitl" ) ANNOTATION_JOB_POLL_TIME = 30 ANNOTATION_PROCESS_TIMEOUT_DEFAULT = 300 S3_BUCKET_NAME = "droidlet-hitl" S3_ROOT = "s3://droidlet-hitl" AWS_ACCESS_KEY_ID = os.environ["AWS_ACCESS_KEY_ID"] AWS_SECRET_ACCESS_KEY = os.environ["AWS_SECRET_ACCESS_KEY"] AWS_DEFAULT_REGION = os.environ["AWS_DEFAULT_REGION"] MEPHISTO_REQUESTER = os.environ["MEPHISTO_REQUESTER"] s3 = boto3.client( "s3", aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY, region_name=AWS_DEFAULT_REGION, ) logging.basicConfig(level="INFO") class VisionAnnotationJob(DataGenerator): """ This Data Generator is responsible for spinning up Vision Annotation Jobs. Each Vision Annotation Job is a single HIT where turkers are asked to annotate the visual scene captured at the time a command was issued and labeled as containing a perception error. On a high level: - The inputs of this data generator are a list of scenes and corresponding timestamps at which they were captured (used as the unique scene identifier) - The output of this data generator is the same visual scene and label, with a new field representing the instance segmentation mask. """ def __init__( self, batch_id: int, timestamp: str, scenes: list, timeout: float = ANNOTATION_PROCESS_TIMEOUT_DEFAULT, ) -> None: super(VisionAnnotationJob, self).__init__(timeout) self._batch_id = batch_id self._timestamp = timestamp self._scenes = scenes def run(self) -> None: try: # Put the scene in extra_refs logging.info("Saving scene list to extra_refs") scene_ref_filepath = os.path.join( os.getcwd(), "../../crowdsourcing/vision_annotation_task/server_files/extra_refs/scene_list.json", ) with open(scene_ref_filepath, "w") as f: json.dump(self._scenes, f) # Write scene indeces and labels to a new data.csv for Mephisto to read logging.info(f"Writing HIT data to {self._timestamp}data.csv") data_csv_path = os.path.join( os.getcwd(), f"../../crowdsourcing/vision_annotation_task/{self._timestamp}data.csv", ) with open(data_csv_path, "w") as f: csv_writer = csv.writer(f, delimiter=",") csv_writer.writerow(["batch_id", "scene_idx", "label"]) for i in range(len(self._scenes)): csv_writer.writerow([str(self._batch_id), str(i), self._scenes[i]["obj_ref"]]) # Edit Mephisto config file to have the right task name and data csv file with open( "../../crowdsourcing/vision_annotation_task/conf/annotation.yaml", "r" ) as stream: config = yaml.safe_load(stream) task_name = "ca-vis-anno" + str(self._batch_id) config["mephisto"]["blueprint"][ "data_csv" ] = f"${{task_dir}}/{self._timestamp}data.csv" config["mephisto"]["task"]["task_name"] = task_name logging.info(f"Updating Mephisto config file to have task_name: {task_name}") with open( "../../crowdsourcing/vision_annotation_task/conf/annotation.yaml", "w" ) as stream: stream.write("#@package _global_\n") yaml.dump(config, stream) # Launch the batch of HITs anno_job_path = os.path.join( os.getcwd(), "../../crowdsourcing/vision_annotation_task/run_annotation_with_qual.py", ) anno_cmd = ( "echo -ne '\n' | python3 " + anno_job_path + " mephisto.provider.requester_name=" + MEPHISTO_REQUESTER + " mephisto.architect.profile_name=mephisto-router-iam" ) p = subprocess.Popen(anno_cmd, shell=True, preexec_fn=os.setsid) # Keep running Mephisto until timeout or job finished while not self.check_is_timeout() and p.poll() is None: logging.info( f"Vision Annotation Job [{self._batch_id}] still running...Remaining time: {self.get_remaining_time()}" ) time.sleep(ANNOTATION_JOB_POLL_TIME) if p.poll() is None: # If mturk job is still running after timeout, terminate it logging.info(f"Manually terminate turk job after timeout...") os.killpg(os.getpgid(p.pid), signal.SIGINT) time.sleep(300) os.killpg(os.getpgid(p.pid), signal.SIGINT) time.sleep(300) os.killpg(os.getpgid(p.pid), signal.SIGKILL) # Load annotated scene data into output format logging.info(f"Retrieving data from Mephisto") units = mephisto_data_browser.get_units_for_task_name(task_name) output_scene = copy.deepcopy(self._scenes) for i in range(len(units)): data = mephisto_data_browser.get_data_from_unit(units[i]) output_scene[i]["inst_seg_tags"] = json.loads( data["data"]["outputs"]["inst_seg_tags"] ) # Upload vision annotation results to S3 and save locally logging.info( f"Uploading scene to S3 {self._batch_id}/annotated_scenes/{self._timestamp}.json" ) os.makedirs( os.path.join(HITL_TMP_DIR, f"{self._batch_id}/annotated_scenes"), exist_ok=True ) results_json = os.path.join( HITL_TMP_DIR, f"{self._batch_id}/annotated_scenes/{self._timestamp}.json" ) with open(results_json, "w") as f: json.dump(output_scene, f) with open(results_json, "rb") as f: s3.upload_fileobj( f, f"{S3_BUCKET_NAME}", f"{self._batch_id}/annotated_scenes/{self._timestamp}.json", ) logging.info(f"Uploading completed") # Delete the scene file from extra_refs and the bespoke data csv os.remove(scene_ref_filepath) os.remove(data_csv_path) except: logging.info(f"Annotation Job [{self._batch_id}] terminated unexpectedly...") self.set_finished() if __name__ == "__main__": example_scene = [ { "obj_ref": "floating blue hollow sqaure", "avatarInfo": {"pos": [1, 3, 1], "look": [0.0, 0.0]}, "agentInfo": {"pos": [3, 3, 0], "look": [0.0, 0.0]}, "inst_seg_tags": [ { "tags": ["HOLLOW_RECTANGLE"], "locs": [ [-6, 7, -2], [-6, 7, -1], [-6, 7, 0], [-6, 7, 1], [-5, 7, -2], [-5, 7, 1], [-4, 7, -2], [-4, 7, 1], [-3, 7, -2], [-3, 7, -1], [-3, 7, 0], [-3, 7, 1], ], } ], "blocks": [ [-9, 0, -9, 46], [-9, 0, -8, 46], [-9, 0, -7, 46], [-9, 0, -6, 46], [-9, 0, -5, 46], [-9, 0, -4, 46], [-9, 0, -3, 46], [-9, 0, -2, 46], [-9, 0, -1, 46], [-9, 0, 0, 46], [-9, 0, 1, 46], [-9, 0, 2, 46], [-9, 0, 3, 46], [-9, 0, 4, 46], [-9, 0, 5, 46], [-9, 0, 6, 46], [-9, 0, 7, 46], [-9, 0, 8, 46], [-9, 0, 9, 46], [-9, 0, 10, 46], [-9, 1, -9, 46], [-9, 1, -8, 46], [-9, 1, -7, 46], [-9, 1, -6, 46], [-9, 1, -5, 46], [-9, 1, -4, 46], [-9, 1, -3, 46], [-9, 1, -2, 46], [-9, 1, -1, 46], [-9, 1, 0, 46], [-9, 1, 1, 46], [-9, 1, 2, 46], [-9, 1, 3, 46], [-9, 1, 4, 46], [-9, 1, 5, 46], [-9, 1, 6, 46], [-9, 1, 7, 46], [-9, 1, 8, 46], [-9, 1, 9, 46], [-9, 1, 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-4, 46], [10, 1, -3, 46], [10, 1, -2, 46], [10, 1, -1, 46], [10, 1, 0, 46], [10, 1, 1, 46], [10, 1, 2, 46], [10, 1, 3, 46], [10, 1, 4, 46], [10, 1, 5, 46], [10, 1, 6, 46], [10, 1, 7, 46], [10, 1, 8, 46], [10, 1, 9, 46], [10, 1, 10, 46], [10, 2, -9, 46], [10, 2, -8, 46], [10, 2, -7, 46], [10, 2, -6, 46], [10, 2, -5, 46], [10, 2, -4, 46], [10, 2, -3, 46], [10, 2, -2, 46], [10, 2, -1, 46], [10, 2, 0, 46], [10, 2, 1, 46], [10, 2, 2, 46], [10, 2, 3, 46], [10, 2, 4, 46], [10, 2, 5, 46], [10, 2, 6, 46], [10, 2, 7, 46], [10, 2, 8, 46], [10, 2, 9, 46], [10, 2, 10, 46], [-6, 7, -2, 57], [-6, 7, -1, 57], [-6, 7, 0, 57], [-6, 7, 1, 57], [-5, 7, -2, 57], [-5, 7, 1, 57], [-4, 7, -2, 57], [-4, 7, 1, 57], [-3, 7, -2, 57], [-3, 7, -1, 57], [-3, 7, 0, 57], [-3, 7, 1, 57], ], } ] aj = VisionAnnotationJob(202201242254123, "000", example_scene, 300) aj.run()

32.892782

123

0.229729

7ed0b6e3a765c75a6f2595e28e43b8f25bbf87ee

9,141

py

Python

lib/models/dran_att.py

hongrui16/naic2020_B

9321bdd19e7d2d47ac9c711eb8437cd364e25f44

[ "MIT" ]

null

lib/models/dran_att.py

hongrui16/naic2020_B

9321bdd19e7d2d47ac9c711eb8437cd364e25f44

[ "MIT" ]

null

lib/models/dran_att.py

hongrui16/naic2020_B

9321bdd19e7d2d47ac9c711eb8437cd364e25f44

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import torch import torch.nn as nn import math # from network.utils import BNReLU from torch.nn.functional import upsample from torch.nn import Module, Sequential, Conv2d, ReLU,AdaptiveMaxPool2d, AdaptiveAvgPool2d, \ AvgPool2d, MaxPool2d, Parameter, Linear, Sigmoid, Softmax from torch.nn import functional as F from torch.autograd import Variable up_kwargs = {'mode': 'bilinear', 'align_corners': True} __all__ = ['CPAMEnc', 'CPAMDec','CCAMDec', 'CLGD'] class CPAMEnc(Module): """ CPAM encoding module """ def __init__(self, in_channels, norm_layer): super(CPAMEnc, self).__init__() self.pool1 = AdaptiveAvgPool2d(1) self.pool2 = AdaptiveAvgPool2d(2) self.pool3 = AdaptiveAvgPool2d(3) self.pool4 = AdaptiveAvgPool2d(6) self.conv1 = Sequential(Conv2d(in_channels, in_channels, 1, bias=False), norm_layer(in_channels), ReLU(True)) self.conv2 = Sequential(Conv2d(in_channels, in_channels, 1, bias=False), norm_layer(in_channels), ReLU(True)) self.conv3 = Sequential(Conv2d(in_channels, in_channels, 1, bias=False), norm_layer(in_channels), ReLU(True)) self.conv4 = Sequential(Conv2d(in_channels, in_channels, 1, bias=False), norm_layer(in_channels), ReLU(True)) def forward(self, x): b, c, h, w = x.size() feat1 = self.conv1(self.pool1(x)).view(b,c,-1) feat2 = self.conv2(self.pool2(x)).view(b,c,-1) feat3 = self.conv3(self.pool3(x)).view(b,c,-1) feat4 = self.conv4(self.pool4(x)).view(b,c,-1) return torch.cat((feat1, feat2, feat3, feat4), 2) class CPAMDec(Module): """ CPAM decoding module """ def __init__(self,in_channels): super(CPAMDec,self).__init__() self.softmax = Softmax(dim=-1) self.scale = Parameter(torch.zeros(1)) self.conv_query = Conv2d(in_channels = in_channels , out_channels = in_channels//4, kernel_size= 1) # query_conv2 self.conv_key = Linear(in_channels, in_channels//4) # key_conv2 self.conv_value = Linear(in_channels, in_channels) # value2 def forward(self, x,y): """ inputs : x : input feature(N,C,H,W) y:gathering centers(N,K,M) returns : out : compact position attention feature attention map: (H*W)*M """ m_batchsize,C,width ,height = x.size() m_batchsize,K,M = y.size() proj_query = self.conv_query(x).view(m_batchsize,-1,width*height).permute(0,2,1)#BxNxd proj_key = self.conv_key(y).view(m_batchsize,K,-1).permute(0,2,1)#BxdxK energy = torch.bmm(proj_query,proj_key)#BxNxK attention = self.softmax(energy) #BxNxk proj_value = self.conv_value(y).permute(0,2,1) #BxCxK out = torch.bmm(proj_value,attention.permute(0,2,1))#BxCxN out = out.view(m_batchsize,C,width,height) out = self.scale*out + x return out class CCAMDec(Module): """ CCAM decoding module """ def __init__(self): super(CCAMDec,self).__init__() self.softmax = Softmax(dim=-1) self.scale = Parameter(torch.zeros(1)) def forward(self, x,y): """ inputs : x : input feature(N,C,H,W) y:gathering centers(N,K,H,W) returns : out : compact channel attention feature attention map: K*C """ m_batchsize,C,width ,height = x.size() x_reshape =x.view(m_batchsize,C,-1) B,K,W,H = y.size() y_reshape =y.view(B,K,-1) proj_query = x_reshape #BXC1XN proj_key = y_reshape.permute(0,2,1) #BX(N)XC energy = torch.bmm(proj_query,proj_key) #BXC1XC energy_new = torch.max(energy,-1,keepdim=True)[0].expand_as(energy)-energy attention = self.softmax(energy_new) proj_value = y.view(B,K,-1) #BCN out = torch.bmm(attention,proj_value) #BC1N out = out.view(m_batchsize,C,width ,height) out = x + self.scale*out return out class CLGD(Module): """ Cross-level Gating Decoder """ def __init__(self, in_channels, out_channels, norm_layer): super(CLGD, self).__init__() inter_channels= 32 self.conv_low = nn.Sequential(nn.Conv2d(48, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) #skipconv self.conv_cat = nn.Sequential(nn.Conv2d(in_channels+inter_channels, in_channels, 3, padding=1, bias=False), norm_layer(in_channels), nn.ReLU()) # fusion1 self.conv_att = nn.Sequential(nn.Conv2d(in_channels+inter_channels, 1, 1), nn.Sigmoid()) # att self.conv_out = nn.Sequential(nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=False), norm_layer(out_channels), nn.ReLU()) # fusion2 self.gamma = nn.Parameter(torch.ones(1)) def forward(self, x,y): """ inputs : x : low level feature(N,C,H,W) y:high level feature(N,C,H,W) returns : out : cross-level gating decoder feature """ low_lvl_feat = self.conv_low(x) high_lvl_feat = y feat_cat = torch.cat([low_lvl_feat,high_lvl_feat],1) low_lvl_feat_refine = self.gamma*self.conv_att(feat_cat)*low_lvl_feat low_high_feat = torch.cat([low_lvl_feat_refine,high_lvl_feat],1) low_high_feat = self.conv_cat(low_high_feat) low_high_feat = self.conv_out(low_high_feat) return low_high_feat class DranHead(nn.Module): def __init__(self, in_channels, norm_layer): super(DranHead, self).__init__() inter_channels = in_channels // 4 ## Convs or modules for CPAM self.conv_cpam_b = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) # conv5_s self.cpam_enc = CPAMEnc(inter_channels, norm_layer) # en_s self.cpam_dec = CPAMDec(inter_channels) # de_s self.conv_cpam_e = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) # conv52 ## Convs or modules for CCAM self.conv_ccam_b = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) # conv5_c self.ccam_enc = nn.Sequential(nn.Conv2d(inter_channels, inter_channels//16, 1, bias=False), norm_layer(inter_channels//16), nn.ReLU()) # conv51_c self.ccam_dec = CCAMDec() # de_c self.conv_ccam_e = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) # conv51 ## Fusion conv self.conv_cat = nn.Sequential(nn.Conv2d(inter_channels*2, inter_channels//2, 3, padding=1, bias=False), norm_layer(inter_channels//2), nn.ReLU()) # conv_f ## Cross-level Gating Decoder(CLGD) self.clgd = CLGD(inter_channels//2,inter_channels//2,norm_layer) self.output_channels = inter_channels//2 def forward(self, feature,high_resolution_feature): ## Compact Channel Attention Module(CCAM) ccam_b = self.conv_ccam_b(feature) ccam_f = self.ccam_enc(ccam_b) ccam_feat = self.ccam_dec(ccam_b,ccam_f) ## Compact Spatial Attention Module(CPAM) cpam_b = self.conv_cpam_b(feature) cpam_f = self.cpam_enc(cpam_b).permute(0,2,1)#BKD cpam_feat = self.cpam_dec(cpam_b,cpam_f) ## Fuse two modules ccam_feat = self.conv_ccam_e(ccam_feat) cpam_feat = self.conv_cpam_e(cpam_feat) feat_sum = self.conv_cat(torch.cat([cpam_feat,ccam_feat],1)) ## Cross-level Gating Decoder(CLGD) final_feat = self.clgd(high_resolution_feature, feat_sum) return final_feat

40.991031

122

0.565146

95e7ca09954789b9c13b23e600b2407359792321

18,340

py

Python

nbgrader/apps/dbapp.py

aliniknejad/nbgrader

124095e48a840ac2af6e3178eab7ed32089f3cd2

[ "BSD-3-Clause" ]

1

2019-10-02T11:06:32.000Z

nbgrader/apps/dbapp.py

aliniknejad/nbgrader

124095e48a840ac2af6e3178eab7ed32089f3cd2

[ "BSD-3-Clause" ]

4

2019-03-02T11:49:46.000Z

2020-09-07T10:17:52.000Z

nbgrader/apps/dbapp.py

aliniknejad/nbgrader

124095e48a840ac2af6e3178eab7ed32089f3cd2

[ "BSD-3-Clause" ]

2

2019-05-31T08:53:48.000Z

2019-05-31T09:42:26.000Z

#!/usr/bin/env python # coding: utf-8 import csv import os import sys import shutil from textwrap import dedent from traitlets import default, Unicode, Bool, List from datetime import datetime from . import NbGrader from ..api import Gradebook, MissingEntry, Student, Assignment from ..exchange import ExchangeList from .. import dbutil aliases = { 'log-level': 'Application.log_level', 'course-dir': 'CourseDirectory.root', 'db': 'CourseDirectory.db_url' } flags = {} student_add_aliases = {} student_add_aliases.update(aliases) student_add_aliases.update({ 'last-name': 'DbStudentAddApp.last_name', 'first-name': 'DbStudentAddApp.first_name', 'email': 'DbStudentAddApp.email', 'lms-user-id': 'DbStudentAddApp.lms_user_id', }) class DbBaseApp(NbGrader): def start(self): if sys.platform != 'win32': lister = ExchangeList(coursedir=self.coursedir, parent=self) self.course_id = self.coursedir.course_id else: self.course_id = '' super(DbBaseApp, self).start() class DbStudentAddApp(DbBaseApp): name = u'nbgrader-db-student-add' description = u'Add a student to the nbgrader database' aliases = student_add_aliases flags = flags last_name = Unicode( None, allow_none=True, help="The last name of the student" ).tag(config=True) first_name = Unicode( None, allow_none=True, help="The first name of the student" ).tag(config=True) email = Unicode( None, allow_none=True, help="The email of the student" ).tag(config=True) lms_user_id = Unicode( None, allow_none=True, help="The LMS user id of the student" ).tag(config=True) def start(self): super(DbStudentAddApp, self).start() if len(self.extra_args) != 1: self.fail("No student id provided.") student_id = self.extra_args[0] student = { "last_name": self.last_name, "first_name": self.first_name, "email": self.email, "lms_user_id": self.lms_user_id } self.log.info("Creating/updating student with ID '%s': %s", student_id, student) with Gradebook(self.coursedir.db_url, self.course_id, self.authenticator) as gb: gb.update_or_create_student(student_id, **student) student_remove_flags = {} student_remove_flags.update(flags) student_remove_flags.update({ 'force': ( {'DbStudentRemoveApp': {'force': True}}, "Complete the operation, even if it means grades will be deleted." ), 'f': ( {'DbStudentRemoveApp': {'force': True}}, "Complete the operation, even if it means grades will be deleted." ), }) class DbStudentRemoveApp(DbBaseApp): name = u'nbgrader-db-student-remove' description = u'Remove a student from the nbgrader database' aliases = aliases flags = student_remove_flags force = Bool(False, help="Confirm operation if it means grades will be deleted.").tag(config=True) def start(self): super(DbStudentRemoveApp, self).start() if len(self.extra_args) != 1: self.fail("No student id provided.") student_id = self.extra_args[0] with Gradebook(self.coursedir.db_url, self.course_id, self.authenticator) as gb: try: student = gb.find_student(student_id) except MissingEntry: self.fail("No such student: '%s'", student_id) if len(student.submissions) > 0: if self.force: self.log.warning("Removing associated grades") else: self.log.warning("!!! There are grades in the database for student '%s'.", student_id) self.log.warning("!!! Removing this student will also remove these grades.") self.fail("!!! If you are SURE this is what you want to do, rerun with --force.") self.log.info("Removing student with ID '%s'", student_id) gb.remove_student(student_id) class DbGenericImportApp(DbBaseApp): aliases = aliases flags = flags expected_keys = List(help="These are the keys expected by the database") excluded_keys = List([], help=dedent( """ These are the column names in database table that should not be imported via a csv file. """).strip()) def db_update_method_name(self): """ Name of the update method used on the Gradebook for this import app. Arguments --------- instance_id: string Identifies which instance you are updating based on self.primary_key instance: dictionary Contents for the update from the parsed csv rows; unpacked as kwargs """ raise NotImplementedError name = u"" description = u"" @default('examples') def examples_default(self): keys = [x for x in self.expected_keys if not(x == self.primary_key or x in self.excluded_keys)] example_string = dedent( """ This command imports a CSV file into the database. The columns of the CSV file must match the names of the columns in the database. All columns are optional, except the columns corresponding to the unique identifier of the {}. The keys/column names that are expected are the following: - {} (required) """.format(self.table_class.__name__, self.primary_key)).strip() for key in keys: example_string += "\n - {}".format(key) return example_string @property def table_class(self): raise NotImplementedError @property def primary_key_default(self): """ The key for the instance_id passed to the get_db_update_method. """ raise NotImplementedError @default("expected_keys") def expected_keys_default(self): return self.table_class.__table__.c.keys() def start(self): super(DbGenericImportApp, self).start() if len(self.extra_args) != 1: self.fail("Path to CSV file not provided.") path = self.extra_args[0] if not os.path.exists(path): self.fail("No such file: '%s'", path) self.log.info("Importing from: '%s'", path) with Gradebook(self.coursedir.db_url, self.course_id, self.authenticator) as gb: with open(path, 'r') as fh: reader = csv.DictReader(fh) reader.fieldnames = self._preprocess_keys(reader.fieldnames) for row in reader: if self.primary_key not in row: self.fail("Malformatted CSV file: must contain a column for '%s'" % self.primary_key) # make sure all the keys are actually allowed in the database, # and that any empty strings are parsed as None instance = {} for key, val in row.items(): if key not in self.expected_keys: continue if val == '': instance[key] = None else: instance[key] = val instance_primary_key = instance.pop(self.primary_key) self.log.info("Creating/updating %s with %s '%s': %s", self.table_class.__name__, self.primary_key, instance_primary_key, instance) db_update_method = getattr(gb, self.db_update_method_name) db_update_method(instance_primary_key, **instance) def _preprocess_keys(self, keys): """ Helper function for preprocessing keys """ proposed_keys = [key.strip() for key in keys] unknown_keys = [k for k in proposed_keys if k not in self.expected_keys] if unknown_keys: self.log.info("Unknown keys in csv: '%s'", (', '.join(unknown_keys[:-1]) + 'and ' + unknown_keys[-1])) return proposed_keys class DbStudentImportApp(DbGenericImportApp): name = u'nbgrader-db-student-import' description = u'Import students into the nbgrader database from a CSV file' @property def table_class(self): return Student @property def primary_key(self): return "id" @property def db_update_method_name(self): return "update_or_create_student" class DbStudentListApp(DbBaseApp): name = u'nbgrader-db-student-list' description = u'List students in the nbgrader database' aliases = aliases flags = flags def start(self): super(DbStudentListApp, self).start() with Gradebook(self.coursedir.db_url, self.course_id, self.authenticator) as gb: print("There are %d students in the database:" % len(gb.students)) for student in gb.students: print("%s (%s, %s) -- %s, %s" % (student.id, student.last_name, student.first_name, student.email, student.lms_user_id)) assignment_add_aliases = {} assignment_add_aliases.update(aliases) assignment_add_aliases.update({ 'duedate': 'DbAssignmentAddApp.duedate', }) class DbAssignmentAddApp(DbBaseApp): name = u'nbgrader-db-assignment-add' description = u'Add an assignment to the nbgrader database' aliases = assignment_add_aliases flags = flags duedate = Unicode( None, allow_none=True, help="The due date of the assignment" ).tag(config=True) def start(self): super(DbAssignmentAddApp, self).start() if len(self.extra_args) != 1: self.fail("No assignment id provided.") assignment_id = self.extra_args[0] assignment = { "duedate": self.duedate } self.log.info("Creating/updating assignment with ID '%s': %s", assignment_id, assignment) with Gradebook(self.coursedir.db_url, self.course_id, self.authenticator) as gb: gb.update_or_create_assignment(assignment_id, **assignment) assignment_remove_flags = {} assignment_remove_flags.update(flags) assignment_remove_flags.update({ 'force': ( {'DbAssignmentRemoveApp': {'force': True}}, "Complete the operation, even if it means grades will be deleted." ), 'f': ( {'DbAssignmentRemoveApp': {'force': True}}, "Complete the operation, even if it means grades will be deleted." ), }) class DbAssignmentRemoveApp(DbBaseApp): name = u'nbgrader-db-assignment-remove' description = u'Remove an assignment from the nbgrader database' aliases = aliases flags = assignment_remove_flags force = Bool(False, help="Confirm operation if it means grades will be deleted.").tag(config=True) def start(self): super(DbAssignmentRemoveApp, self).start() if len(self.extra_args) != 1: self.fail("No assignment id provided.") assignment_id = self.extra_args[0] with Gradebook(self.coursedir.db_url, self.course_id, self.authenticator) as gb: try: assignment = gb.find_assignment(assignment_id) except MissingEntry: self.fail("No such assignment: '%s'", assignment_id) if len(assignment.submissions) > 0: if self.force: self.log.warning("Removing associated grades") else: self.log.warning("!!! There are grades in the database for assignment '%s'.", assignment_id) self.log.warning("!!! Removing this assignment will also remove these grades.") self.fail("!!! If you are SURE this is what you want to do, rerun with --force.") self.log.info("Removing assignment with ID '%s'", assignment_id) gb.remove_assignment(assignment_id) class DbAssignmentImportApp(DbGenericImportApp): name = u'nbgrader-db-assignment-import' description = u'Import assignments into the nbgrader database from a CSV file' def __init__(self, *args, **kwargs): super(DbAssignmentImportApp, self).__init__(*args, **kwargs) self.excluded_keys = ["id"] @property def table_class(self): return Assignment @property def primary_key(self): return "name" @property def db_update_method_name(self): return "update_or_create_assignment" class DbAssignmentListApp(DbBaseApp): name = u'nbgrader-db-assignment-list' description = u'List assignments int the nbgrader database' aliases = aliases flags = flags def start(self): super(DbAssignmentListApp, self).start() with Gradebook(self.coursedir.db_url, self.course_id, self.authenticator) as gb: print("There are %d assignments in the database:" % len(gb.assignments)) for assignment in gb.assignments: print("%s (due: %s)" % (assignment.name, assignment.duedate)) for notebook in assignment.notebooks: print(" - %s" % notebook.name) class DbStudentApp(DbBaseApp): name = u'nbgrader-db-student' description = u'Modify or list students in the nbgrader database' subcommands = { "add": (DbStudentAddApp, "Add a student to the database"), "remove": (DbStudentRemoveApp, "Remove a student from the database"), "list": (DbStudentListApp, "List students in the database"), "import": (DbStudentImportApp, "Import students into the database from a file") } @default("classes") def _classes_default(self): classes = super(DbStudentApp, self)._classes_default() # include all the apps that have configurable options for _, (app, _) in self.subcommands.items(): if len(app.class_traits(config=True)) > 0: classes.append(app) return classes def start(self): # check: is there a subapp given? if self.subapp is None: print("No db command given (run with --help for options). List of subcommands:\n") self.print_subcommands() # This starts subapps super(DbStudentApp, self).start() class DbAssignmentApp(DbBaseApp): name = u'nbgrader-db-assignment' description = u'Modify or list assignments in the nbgrader database' subcommands = { "add": (DbAssignmentAddApp, "Add an assignment to the database"), "remove": (DbAssignmentRemoveApp, "Remove an assignment from the database"), "list": (DbAssignmentListApp, "List assignments in the database"), "import": (DbAssignmentImportApp, "Import assignments into the database from a file") } @default("classes") def _classes_default(self): classes = super(DbAssignmentApp, self)._classes_default() # include all the apps that have configurable options for _, (app, _) in self.subcommands.items(): if len(app.class_traits(config=True)) > 0: classes.append(app) return classes def start(self): # check: is there a subapp given? if self.subapp is None: print("No assignment command given. List of subcommands:\n") for key, (app, desc) in self.subcommands.items(): print(" {}\n{}\n".format(key, desc)) # This starts subapps super(DbAssignmentApp, self).start() class DbUpgradeApp(DbBaseApp): """Based on the `jupyterhub upgrade-db` command found in jupyterhub.app.UpgradeDB""" name = u'nbgrader-db-upgrade' description = u'Upgrade the database schema to the latest version' def _backup_db_file(self, db_file): """Backup a database file""" if not os.path.exists(db_file): with Gradebook("sqlite:///{}".format(db_file), self.course_id, self.authenticator): pass timestamp = datetime.now().strftime('.%Y-%m-%d-%H%M%S.%f') backup_db_file = db_file + timestamp if os.path.exists(backup_db_file): self.fail("backup db file already exists: %s" % backup_db_file) self.log.info("Backing up %s => %s", db_file, backup_db_file) shutil.copy(db_file, backup_db_file) def start(self): super(DbUpgradeApp, self).start() if (self.coursedir.db_url.startswith('sqlite:///')): db_file = self.coursedir.db_url.split(':///', 1)[1] self._backup_db_file(db_file) self.log.info("Upgrading %s", self.coursedir.db_url) dbutil.upgrade(self.coursedir.db_url) class DbApp(DbBaseApp): name = u'nbgrader-db' description = u'Perform operations on the nbgrader database' subcommands = dict( student=( DbStudentApp, dedent( """ Add, remove, or list students in the nbgrader database. """ ).strip() ), assignment=( DbAssignmentApp, dedent( """ Add, remove, or list assignments in the nbgrader database. """ ).strip() ), upgrade=( DbUpgradeApp, dedent( """ Upgrade database schema to latest version. """ ).strip() ), ) @default("classes") def _classes_default(self): classes = super(DbApp, self)._classes_default() # include all the apps that have configurable options for _, (app, _) in self.subcommands.items(): if len(app.class_traits(config=True)) > 0: classes.append(app) return classes def start(self): # check: is there a subapp given? if self.subapp is None: print("No db command given (run with --help for options). List of subcommands:\n") self.print_subcommands() # This starts subapps super(DbApp, self).start()

32.006981

136

0.604198

80040f2f694d2daa705c586a68289f3df217d4b4

5,055

py

Python

fsdet/utils/video_visualizer.py

gist-ailab/FSCE

94ba3f4737d5e40af795db49b8c6914526c912b6

[ "Apache-2.0" ]

189

2021-03-13T11:42:42.000Z

2022-03-31T13:11:52.000Z

fsdet/utils/video_visualizer.py

gist-ailab/FSCE

94ba3f4737d5e40af795db49b8c6914526c912b6

[ "Apache-2.0" ]

56

2021-03-18T12:02:28.000Z

2022-03-24T15:12:57.000Z

fsdet/utils/video_visualizer.py

gist-ailab/FSCE

94ba3f4737d5e40af795db49b8c6914526c912b6

[ "Apache-2.0" ]

35

2021-03-15T08:40:19.000Z

2022-03-17T07:56:45.000Z

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import numpy as np import pycocotools.mask as mask_util from fsdet.utils.visualizer import ( ColorMode, Visualizer, _create_text_labels, ) from .colormap import random_color class _DetectedInstance: """ Used to store data about detected objects in video frame, in order to transfer color to objects in the future frames. Attributes: label (int): bbox (tuple[float]): color (tuple[float]): RGB colors in range (0, 1) ttl (int): time-to-live for the instance. For example, if ttl=2, the instance color can be transferred to objects in the next two frames. """ __slots__ = ["label", "bbox", "color", "ttl"] def __init__(self, label, bbox, color, ttl): self.label = label self.bbox = bbox self.color = color self.ttl = ttl class VideoVisualizer: def __init__(self, metadata, instance_mode=ColorMode.IMAGE): """ Args: metadata (MetadataCatalog): image metadata. """ self.metadata = metadata self._old_instances = [] assert instance_mode in [ ColorMode.IMAGE, ColorMode.IMAGE_BW, ], "Other mode not supported yet." self._instance_mode = instance_mode def draw_instance_predictions(self, frame, predictions): """ Draw instance-level prediction results on an image. Args: frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255]. predictions (Instances): the output of an instance detection model. Following fields will be used to draw: "pred_boxes", "pred_classes", "scores". Returns: output (VisImage): image object with visualizations. """ frame_visualizer = Visualizer(frame, self.metadata) num_instances = len(predictions) if num_instances == 0: return frame_visualizer.output boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None scores = predictions.scores if predictions.has("scores") else None classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None detected = [ _DetectedInstance(classes[i], boxes[i], color=None, ttl=8) for i in range(num_instances) ] colors = self._assign_colors(detected) labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) if self._instance_mode == ColorMode.IMAGE_BW: # any() returns uint8 tensor frame_visualizer.output.img = frame_visualizer._create_grayscale_image() alpha = 0.3 else: alpha = 0.5 frame_visualizer.overlay_instances( boxes=boxes, # boxes are a bit distracting labels=labels, assigned_colors=colors, alpha=alpha, ) return frame_visualizer.output def _assign_colors(self, instances): """ Naive tracking heuristics to assign same color to the same instance, will update the internal state of tracked instances. Returns: list[tuple[float]]: list of colors. """ # Compute iou with boxes: is_crowd = np.zeros((len(instances),), dtype=np.bool) boxes_old = [x.bbox for x in self._old_instances] boxes_new = [x.bbox for x in instances] ious = mask_util.iou(boxes_old, boxes_new, is_crowd) threshold = 0.6 if len(ious) == 0: ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32") # Only allow matching instances of the same label: for old_idx, old in enumerate(self._old_instances): for new_idx, new in enumerate(instances): if old.label != new.label: ious[old_idx, new_idx] = 0 matched_new_per_old = np.asarray(ious).argmax(axis=1) max_iou_per_old = np.asarray(ious).max(axis=1) # Try to find match for each old instance: extra_instances = [] for idx, inst in enumerate(self._old_instances): if max_iou_per_old[idx] > threshold: newidx = matched_new_per_old[idx] if instances[newidx].color is None: instances[newidx].color = inst.color continue # If an old instance does not match any new instances, # keep it for the next frame in case it is just missed by the detector inst.ttl -= 1 if inst.ttl > 0: extra_instances.append(inst) # Assign random color to newly-detected instances: for inst in instances: if inst.color is None: inst.color = random_color(rgb=True, maximum=1) self._old_instances = instances[:] + extra_instances return [d.color for d in instances]

35.34965

96

0.610089

7fad70f14ffd581db93dce8c488d7dc965890fdf

4,242

py

Python

tests/plugins/test_check_and_set_platforms.py

sosiouxme/atomic-reactor

91414bb8ffd988321cfa7120167636757e265a39

[ "BSD-3-Clause" ]

null

tests/plugins/test_check_and_set_platforms.py

sosiouxme/atomic-reactor

91414bb8ffd988321cfa7120167636757e265a39

[ "BSD-3-Clause" ]

1

2018-04-25T12:42:14.000Z

2018-04-29T20:31:00.000Z

tests/plugins/test_check_and_set_platforms.py

sosiouxme/atomic-reactor

91414bb8ffd988321cfa7120167636757e265a39

[ "BSD-3-Clause" ]

null

""" Copyright (c) 2017 Red Hat, Inc All rights reserved. This software may be modified and distributed under the terms of the BSD license. See the LICENSE file for details. """ from __future__ import unicode_literals import os import yaml from atomic_reactor.constants import PLUGIN_CHECK_AND_SET_PLATFORMS_KEY, REPO_CONTAINER_CONFIG import atomic_reactor.plugins.pre_reactor_config as reactor_config import atomic_reactor.koji_util as koji_util from atomic_reactor.core import DockerTasker from atomic_reactor.inner import DockerBuildWorkflow from atomic_reactor.plugin import PreBuildPluginsRunner from atomic_reactor.util import ImageName from flexmock import flexmock import pytest from tests.constants import SOURCE, MOCK if MOCK: from tests.docker_mock import mock_docker class X(object): pass KOJI_TARGET = "target" # ClientSession is xmlrpc instance, we need to mock it explicitly def mock_session(platforms): last_event_id = 456 build_target = { 'build_tag': 'build-tag', 'name': 'target-name', 'dest_tag_name': 'dest-tag' } session = flexmock() (session .should_receive('getLastEvent') .and_return({'id': last_event_id})) (session .should_receive('getBuildTarget') .with_args('target', event=last_event_id) .and_return(build_target)) (session .should_receive('getBuildConfig') .with_args('build-tag', event=last_event_id) .and_return({'arches': platforms})) return session class MockSource(object): def __init__(self, tmpdir): self.path = str(tmpdir) self.dockerfile_path = str(tmpdir) def get_build_file_path(self): return self.path, self.path def prepare(tmpdir): if MOCK: mock_docker() tasker = DockerTasker() workflow = DockerBuildWorkflow(SOURCE, "test-image") setattr(workflow, 'builder', X()) source = MockSource(tmpdir) setattr(workflow.builder, 'image_id', "asd123") setattr(workflow.builder, 'base_image', ImageName(repo='Fedora', tag='21')) setattr(workflow.builder, 'source', source) setattr(workflow, 'source', source) return tasker, workflow @pytest.mark.parametrize(('platforms', 'platform_exclude', 'platform_only', 'result'), [ (None, '', 'ppc64le', None), ('x86_64 ppc64le', '', 'ppc64le', ['ppc64le']), ('x86_64 spam bacon toast ppc64le', ['spam', 'bacon', 'eggs', 'toast'], '', ['x86_64', 'ppc64le']), ('ppc64le spam bacon toast', ['spam', 'bacon', 'eggs', 'toast'], 'ppc64le', ['ppc64le']), ('x86_64 bacon toast', 'toast', ['x86_64', 'ppc64le'], ['x86_64']), ('x86_64 toast', 'toast', 'x86_64', ['x86_64']), ('x86_64 spam bacon toast', ['spam', 'bacon', 'eggs', 'toast'], ['x86_64', 'ppc64le'], ['x86_64']), ('x86_64 ppc64le', '', '', ['x86_64', 'ppc64le']) ]) def test_check_and_set_platforms(tmpdir, platforms, platform_exclude, platform_only, result): platforms_dict = {} if platform_exclude != '': platforms_dict['platforms'] = {} platforms_dict['platforms']['not'] = platform_exclude if platform_only != '': if 'platforms' not in platforms_dict: platforms_dict['platforms'] = {} platforms_dict['platforms']['only'] = platform_only container_path = os.path.join(str(tmpdir), REPO_CONTAINER_CONFIG) with open(container_path, 'w') as f: f.write(yaml.safe_dump(platforms_dict)) f.flush() tasker, workflow = prepare(tmpdir) session = mock_session(platforms) mock_koji_config = { 'auth': {}, 'hub_url': 'test', } flexmock(reactor_config).should_receive('get_koji').and_return(mock_koji_config) flexmock(koji_util).should_receive('create_koji_session').and_return(session) runner = PreBuildPluginsRunner(tasker, workflow, [{ 'name': PLUGIN_CHECK_AND_SET_PLATFORMS_KEY, 'args': {'koji_target': KOJI_TARGET}, }]) plugin_result = runner.run() if platforms: assert plugin_result[PLUGIN_CHECK_AND_SET_PLATFORMS_KEY] assert plugin_result[PLUGIN_CHECK_AND_SET_PLATFORMS_KEY] == set(result) else: assert plugin_result[PLUGIN_CHECK_AND_SET_PLATFORMS_KEY] is None

32.136364

94

0.680575

891191b0261f777adf969c477a11318a4085ef21

9,278

py

Python

plugins/_Post_Process/_XAI/lime_batch.py

sony/nnc-plugin

579cc51ec2c85fcae058f1aaf1f6036f71734dbb

[ "Apache-2.0" ]

7

2021-09-04T13:10:07.000Z

2022-03-21T08:51:45.000Z

plugins/_Post_Process/_XAI/lime_batch.py

sony/nnc-plugin

579cc51ec2c85fcae058f1aaf1f6036f71734dbb

[ "Apache-2.0" ]

1

2021-11-15T04:39:34.000Z

2021-11-19T08:09:42.000Z

plugins/_Post_Process/_XAI/lime_batch.py

sony/nnc-plugin

579cc51ec2c85fcae058f1aaf1f6036f71734dbb

[ "Apache-2.0" ]

1

2022-03-25T16:52:05.000Z

# Copyright 2021 Sony Group Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import argparse import csv import math import numpy as np import tqdm import skimage.segmentation from nnabla import logger import nnabla.utils.load as load from nnabla.utils.image_utils import imsave from nnabla.utils.data_iterator import data_iterator_csv_dataset from nnabla.utils.cli.utility import let_data_to_variable import nnabla as nn import nnabla.functions as F import nnabla.parametric_functions as PF import nnabla.solvers as S from utils.file import save_info_to_csv def ridge(dataset): import nnabla_ext.cpu ctx = nnabla_ext.cpu.context() with nn.context_scope(ctx): dataset = np.array(dataset) nn.clear_parameters() x = nn.Variable((int(math.sqrt(dataset.shape[0])), dataset[0][0].size)) t = nn.Variable((x.shape[0], 1)) y = PF.affine(x, 1, name='affine') loss = F.squared_error(y, t) mean_loss = F.mean(loss) solver = S.Momentum() solver.set_parameters(nn.get_parameters()) for iter in range(100 * int(math.sqrt(dataset.shape[0]))): # 100 epoch np.random.shuffle(dataset) x.d = np.stack(dataset[:x.shape[0], 0]).reshape(x.shape) t.d = np.stack(dataset[:x.shape[0], 1]).reshape(t.shape) solver.zero_grad() mean_loss.forward() mean_loss.backward() solver.weight_decay(0.01) solver.update() return nn.get_parameters()['affine/affine/W'].d.flatten() def func(args): class ForwardConfig: pass # Load model info = load.load([args.model], prepare_data_iterator=False, batch_size=1) config = ForwardConfig config.global_config = info.global_config config.executors = info.executors.values() config.networks = [] if len(config.executors) < 1: logger.critical('Executor is not found in {}.'.format(args.model)) return executor = list(config.executors)[0] if len(config.executors) > 1: logger.log(99, 'Only the first executor {} is used in the LIME calculation.'.format( executor.name)) if executor.network.name in info.networks.keys(): config.networks.append(info.networks[executor.network.name]) else: logger.critical('Network {} is not found in {}.'.format( executor.network.name, args.model)) return # Prepare variable input_variable, data_name = list(executor.dataset_assign.items())[0] output_variable = list(executor.output_assign.keys())[0] # check csv_file = np.loadtxt(args.input, delimiter=",", dtype=str) header = [item.split(":")[0] for item in csv_file[0]] classes = csv_file[1:, header.index(args.label_variable)] num_classes = np.unique(classes).size output_size = output_variable.variable_instance.d.shape[1] is_binary_classification = num_classes != output_size # Load dataset data_iterator = (lambda: data_iterator_csv_dataset( uri=args.input, batch_size=1, shuffle=False, normalize=not executor.no_image_normalization, with_memory_cache=False, with_file_cache=False)) # Prepare output data_output_dir = os.path.splitext(args.output)[0] # Data loop with data_iterator() as di: index = 0 file_names = [] while index < di.size: # Load data data = di.next() im = data[di.variables.index(args.input_variable)] im = im.reshape((im.shape[1], im.shape[2], im.shape[3])) slic = skimage.segmentation.slic( im[0], n_segments=args.num_segments) if is_binary_classification: label = 0 else: label = data[di.variables.index(args.label_variable)] label = label.reshape((label.size,)) if label.size > 1: label = np.argmax(label) else: label = label[0] mask_and_result = [] # Sampling np.random.seed(0) while len(mask_and_result) < args.num_samples: x = input_variable.variable_instance mask = np.random.uniform( size=(x.shape[0], args.num_segments)) >= 0.5 im_mask = np.array([mask1[slic] for mask1 in mask]).astype(np.uint8) data = im_mask.reshape( x.shape[0], 1, x.shape[2], x.shape[3]) * im.reshape(1, x.shape[1], x.shape[2], x.shape[3]) # input data let_data_to_variable(input_variable.variable_instance, data, data_name=data_name, variable_name=input_variable.name) # Generate data for v, generator in executor.generator_assign.items(): v.variable_instance.d = generator(v.shape) # Forward executor.forward_target.forward(clear_buffer=True) for m, probability in zip(mask, output_variable.variable_instance.d): try: mask_and_result.append([m, probability[int(label)]]) except IndexError as e: if index != 0: pbar.close() logger.critical(e) return weight = ridge(mask_and_result) max_indices = np.argpartition(-weight, args.num_segments_2)[:args.num_segments_2] result = np.zeros(weight.shape) result[max_indices] = 1 # Generate output image result_mask = result[slic].astype( np.uint8).reshape(1, im.shape[1], im.shape[2]) if not executor.no_image_normalization: im = im * 255.0 im = im * result_mask + \ np.ones(im.shape, np.uint8) * 192 * np.logical_not(result_mask) result = im.transpose(1, 2, 0).astype(np.uint8) if result.shape[-1] == 1 and len(result.shape) == 3: result = result.reshape(result.shape[0:2]) # Output result image file_name = os.path.join(data_output_dir, '{:04d}'.format( index // 1000), '{}.png'.format(index)) directory = os.path.dirname(file_name) try: os.makedirs(directory) except OSError: pass # python2 does not support exists_ok arg imsave(file_name, result) file_names.append(file_name) if index == 0: pbar = tqdm.tqdm(total=di.size) index += 1 pbar.update(1) pbar.close() save_info_to_csv(args.input, args.output, file_names, column_name='lime') logger.log(99, 'LIME (image batch) completed successfully.') def main(): parser = argparse.ArgumentParser( description='LIME (image batch)\n' '\n' '"Why Should I Trust You?": Explaining the Predictions of Any Classifier' 'Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin' 'https://arxiv.org/abs/1602.04938\n' '', formatter_class=argparse.RawTextHelpFormatter) parser.add_argument( '-i', '--input', help='path to input csv file (csv) default=output_result.csv', required=True, default='output_result.csv') parser.add_argument( '-m', '--model', help='path to model nnp file (model) default=results.nnp', required=True, default='results.nnp') parser.add_argument( '-v1', '--input_variable', help='Variable to be processed (variable), default=x', required=True, default='x') parser.add_argument( '-v2', '--label_variable', help='Variable representing class index to visualize (variable) default=y', required=True, default='y') parser.add_argument( '-n', '--num_samples', help='number of samples N (int), default=1000', required=True, type=int, default=1000) parser.add_argument( '-s', '--num_segments', help='number of segments (int), default=10', required=True, type=int, default=10) parser.add_argument( '-s2', '--num_segments_2', help='number of segments to highlight (int), default=3', required=True, type=int, default=3) parser.add_argument( '-o', '--output', help='path to output image file (image) default=lime.csv', required=True, default='lime.csv') parser.set_defaults(func=func) args = parser.parse_args() args.func(args) if __name__ == '__main__': main()

38.338843

138

0.599698

0c3b314c8b04b8723984900fd30ff605ddb9cfd3

11,489

py

Python

torch/optim/swa_utils.py

wenhaopeter/read_pytorch_code

491f989cd918cf08874dd4f671fb7f0142a0bc4f

[ "Intel", "X11" ]

null

torch/optim/swa_utils.py

wenhaopeter/read_pytorch_code

491f989cd918cf08874dd4f671fb7f0142a0bc4f

[ "Intel", "X11" ]

null

torch/optim/swa_utils.py

wenhaopeter/read_pytorch_code

491f989cd918cf08874dd4f671fb7f0142a0bc4f

[ "Intel", "X11" ]

null

import torch import math from torch.nn import Module from copy import deepcopy from torch.optim.lr_scheduler import _LRScheduler class AveragedModel(Module): r"""Implements averaged model for Stochastic Weight Averaging (SWA). Stochastic Weight Averaging was proposed in `Averaging Weights Leads to Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson (UAI 2018). AveragedModel class creates a copy of the provided module :attr:`model` on the device :attr:`device` and allows to compute running averages of the parameters of the :attr:`model`. Arguments: model (torch.nn.Module): model to use with SWA device (torch.device, optional): if provided, the averaged model will be stored on the :attr:`device` avg_fn (function, optional): the averaging function used to update parameters; the function must take in the current value of the :class:`AveragedModel` parameter, the current value of :attr:`model` parameter and the number of models already averaged; if None, equally weighted average is used (default: None) Example: >>> loader, optimizer, model, loss_fn = ... >>> swa_model = torch.optim.swa_utils.AveragedModel(model) >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, >>> T_max=300) >>> swa_start = 160 >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) >>> for i in range(300): >>> for input, target in loader: >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() >>> if i > swa_start: >>> swa_model.update_parameters(model) >>> swa_scheduler.step() >>> else: >>> scheduler.step() >>> >>> # Update bn statistics for the swa_model at the end >>> torch.optim.swa_utils.update_bn(loader, swa_model) You can also use custom averaging functions with `avg_fn` parameter. If no averaging function is provided, the default is to compute equally-weighted average of the weights. Example: >>> # Compute exponential moving averages of the weights >>> ema_avg = lambda averaged_model_parameter, model_parameter, num_averaged:\ 0.1 * averaged_model_parameter + 0.9 * model_parameter >>> swa_model = torch.optim.swa_utils.AveragedModel(model, avg_fn=ema_avg) .. note:: When using SWA with models containing Batch Normalization you may need to update the activation statistics for Batch Normalization. You can do so by using :meth:`torch.optim.swa_utils.update_bn` utility. .. note:: :attr:`avg_fn` is not saved in the :meth:`state_dict` of the model. .. note:: When :meth:`update_parameters` is called for the first time (i.e. :attr:`n_averaged` is `0`) the parameters of `model` are copied to the parameters of :class:`AveragedModel`. For every subsequent call of :meth:`update_parameters` the function `avg_fn` is used to update the parameters. .. _Averaging Weights Leads to Wider Optima and Better Generalization: https://arxiv.org/abs/1803.05407 .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average: https://arxiv.org/abs/1806.05594 .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: https://arxiv.org/abs/1904.11943 .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That Generalizes Well: https://arxiv.org/abs/2001.02312 """ def __init__(self, model, device=None, avg_fn=None): super(AveragedModel, self).__init__() self.module = deepcopy(model) if device is not None: self.module = self.module.to(device) self.register_buffer('n_averaged', torch.tensor(0, dtype=torch.long, device=device)) if avg_fn is None: def avg_fn(averaged_model_parameter, model_parameter, num_averaged): return averaged_model_parameter + \ (model_parameter - averaged_model_parameter) / (num_averaged + 1) self.avg_fn = avg_fn def forward(self, *args, **kwargs): return self.module(*args, **kwargs) def update_parameters(self, model): for p_swa, p_model in zip(self.parameters(), model.parameters()): device = p_swa.device p_model_ = p_model.detach().to(device) if self.n_averaged == 0: p_swa.detach().copy_(p_model_) else: p_swa.detach().copy_(self.avg_fn(p_swa.detach(), p_model_, self.n_averaged.to(device))) self.n_averaged += 1 def update_bn(loader, model, device=None): r"""Updates BatchNorm running_mean, running_var buffers in the model. It performs one pass over data in `loader` to estimate the activation statistics for BatchNorm layers in the model. Arguments: loader (torch.utils.data.DataLoader): dataset loader to compute the activation statistics on. Each data batch should be either a tensor, or a list/tuple whose first element is a tensor containing data. model (torch.nn.Module): model for which we seek to update BatchNorm statistics. device (torch.device, optional): If set, data will be trasferred to :attr:`device` before being passed into :attr:`model`. Example: >>> loader, model = ... >>> torch.optim.swa_utils.update_bn(loader, model) .. note:: The `update_bn` utility assumes that each data batch in :attr:`loader` is either a tensor or a list or tuple of tensors; in the latter case it is assumed that :meth:`model.forward()` should be called on the first element of the list or tuple corresponding to the data batch. """ momenta = {} for module in model.modules(): if isinstance(module, torch.nn.modules.batchnorm._BatchNorm): module.running_mean = torch.zeros_like(module.running_mean) module.running_var = torch.ones_like(module.running_var) momenta[module] = module.momentum if not momenta: return was_training = model.training model.train() for module in momenta.keys(): module.momentum = None module.num_batches_tracked *= 0 for input in loader: if isinstance(input, (list, tuple)): input = input[0] if device is not None: input = input.to(device) model(input) for bn_module in momenta.keys(): bn_module.momentum = momenta[bn_module] model.train(was_training) class SWALR(_LRScheduler): r"""Anneals the learning rate in each parameter group to a fixed value. This learning rate scheduler is meant to be used with Stochastic Weight Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). Arguments: optimizer (torch.optim.Optimizer): wrapped optimizer swa_lrs (float or list): the learning rate value for all param groups together or separately for each group. annealing_epochs (int): number of epochs in the annealing phase (default: 10) annealing_strategy (str): "cos" or "linear"; specifies the annealing strategy: "cos" for cosine annealing, "linear" for linear annealing (default: "cos") last_epoch (int): the index of the last epoch (default: 'cos') The :class:`SWALR` scheduler is can be used together with other schedulers to switch to a constant learning rate late in the training as in the example below. Example: >>> loader, optimizer, model = ... >>> lr_lambda = lambda epoch: 0.9 >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, >>> lr_lambda=lr_lambda) >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) >>> swa_start = 160 >>> for i in range(300): >>> for input, target in loader: >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() >>> if i > swa_start: >>> swa_scheduler.step() >>> else: >>> scheduler.step() .. _Averaging Weights Leads to Wider Optima and Better Generalization: https://arxiv.org/abs/1803.05407 """ def __init__(self, optimizer, swa_lr, anneal_epochs=10, anneal_strategy='cos', last_epoch=-1): swa_lrs = self._format_param(optimizer, swa_lr) for swa_lr, group in zip(swa_lrs, optimizer.param_groups): group['swa_lr'] = swa_lr if anneal_strategy not in ['cos', 'linear']: raise ValueError("anneal_strategy must by one of 'cos' or 'linear', " "instead got {}".format(anneal_strategy)) elif anneal_strategy == 'cos': self.anneal_func = self._cosine_anneal elif anneal_strategy == 'linear': self.anneal_func = self._linear_anneal if not isinstance(anneal_epochs, int) or anneal_epochs < 1: raise ValueError("anneal_epochs must be a positive integer, got {}".format( anneal_epochs)) self.anneal_epochs = anneal_epochs super(SWALR, self).__init__(optimizer, last_epoch) @staticmethod def _format_param(optimizer, swa_lrs): if isinstance(swa_lrs, (list, tuple)): if len(swa_lrs) != len(optimizer.param_groups): raise ValueError("swa_lr must have the same length as " "optimizer.param_groups: swa_lr has {}, " "optimizer.param_groups has {}".format( len(swa_lrs), len(optimizer.param_groups))) return swa_lrs else: return [swa_lrs] * len(optimizer.param_groups) @staticmethod def _linear_anneal(t): return t @staticmethod def _cosine_anneal(t): return (1 - math.cos(math.pi * t)) / 2 @staticmethod def _get_initial_lr(lr, swa_lr, alpha): if alpha == 1: return swa_lr return (lr - alpha * swa_lr) / (1 - alpha) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.", UserWarning) step = self._step_count - 1 prev_t = max(0, min(1, (step - 1) / self.anneal_epochs)) prev_alpha = self.anneal_func(prev_t) prev_lrs = [self._get_initial_lr(group['lr'], group['swa_lr'], prev_alpha) for group in self.optimizer.param_groups] t = max(0, min(1, step / self.anneal_epochs)) alpha = self.anneal_func(t) return [group['swa_lr'] * alpha + lr * (1 - alpha) for group, lr in zip(self.optimizer.param_groups, prev_lrs)]

42.869403

98

0.618679

ed3372dfa30a1ee787d7ab1752a449c5ca00f91d

1,444

py

Python

tcfcli/common/tcsam/glob.py

alfredhuang211/scfcli

f5e086ff4fcee8d645682e85cd1486b28a224d08

[ "Apache-2.0" ]

14

2019-03-01T09:47:36.000Z

2019-11-28T01:58:54.000Z

tcfcli/common/tcsam/glob.py

alfredhuang211/scfcli

f5e086ff4fcee8d645682e85cd1486b28a224d08

[ "Apache-2.0" ]

8

2019-03-12T10:22:53.000Z

2020-08-20T08:15:51.000Z

tcfcli/common/tcsam/glob.py

alfredhuang211/scfcli

f5e086ff4fcee8d645682e85cd1486b28a224d08

[ "Apache-2.0" ]

7

2019-03-01T09:47:52.000Z

2020-06-13T12:14:48.000Z

from .tcsam_macro import TcSamMacro as macro glob_schema = { "$schema": "http://json-schema.org/draft-07/schema/global#", "$id": "tcsam.global", "type": "object", "properties": { macro.Function: { "type": "object", "properties": { macro.CodeUri: {"type": "string"}, macro.Desc: {"type": "string"}, macro.Envi: { "type": "object", "properties": { macro.Vari: { "type": "object", "properties": {}, "additionalProperties": { "type": "string" } } }, "additionalProperties": False }, macro.Handler: {"type": "string"}, macro.MemSize: {"type": "integer", "exclusiveMinimum": 0}, macro.Runtime: { "type": "string", "enum": ["Python2.7", "Python3.6", "Nodejs6.10", "Nodejs8.9", "Php5", "Php7", "Go1", "Java8", "python2.7", "python3.6", "nodejs6.10", "nodejs8.9", "php5", "php7", "go1", "java8"] }, macro.Timeout: {"type": "integer", "exclusiveMinimum": 0}, }, "additionalProperties": False } }, "additionalProperties": False }

38

81

0.40651

10b579dbf103e2daf42142c2b9771cf9d4e45861

3,040

py

Python

python/pyspark/sql/tests/test_utils.py

neilagupta/spark

1c61d90777204077fcd475fa6e0eb035122d532f

[ "BSD-2-Clause", "Apache-2.0", "CC0-1.0", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

1

2021-10-14T06:18:00.000Z

python/pyspark/sql/tests/test_utils.py

Talador12/spark

838a9d9398d2faf8885c9a84fd8f1d589f3c5539

[ "Apache-2.0" ]

null

python/pyspark/sql/tests/test_utils.py

Talador12/spark

838a9d9398d2faf8885c9a84fd8f1d589f3c5539

[ "Apache-2.0" ]

null

# -*- encoding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from pyspark.sql.functions import sha2 from pyspark.sql.utils import AnalysisException, ParseException, IllegalArgumentException from pyspark.testing.sqlutils import ReusedSQLTestCase class UtilsTests(ReusedSQLTestCase): def test_capture_analysis_exception(self): self.assertRaises(AnalysisException, lambda: self.spark.sql("select abc")) self.assertRaises(AnalysisException, lambda: self.df.selectExpr("a + b")) def test_capture_user_friendly_exception(self): try: self.spark.sql("select `中文字段`") except AnalysisException as e: self.assertRegex(str(e), "Column '`中文字段`' does not exist") def test_capture_parse_exception(self): self.assertRaises(ParseException, lambda: self.spark.sql("abc")) def test_capture_illegalargument_exception(self): self.assertRaisesRegex(IllegalArgumentException, "Setting negative mapred.reduce.tasks", lambda: self.spark.sql("SET mapred.reduce.tasks=-1")) df = self.spark.createDataFrame([(1, 2)], ["a", "b"]) self.assertRaisesRegex(IllegalArgumentException, "1024 is not in the permitted values", lambda: df.select(sha2(df.a, 1024)).collect()) try: df.select(sha2(df.a, 1024)).collect() except IllegalArgumentException as e: self.assertRegex(e.desc, "1024 is not in the permitted values") self.assertRegex(e.stackTrace, "org.apache.spark.sql.functions") def test_get_error_class_state(self): # SPARK-36953: test CapturedException.getErrorClass and getSqlState (from SparkThrowable) try: self.spark.sql("""SELECT a""") except AnalysisException as e: self.assertEquals(e.getErrorClass(), "MISSING_COLUMN") self.assertEquals(e.getSqlState(), "42000") if __name__ == "__main__": import unittest from pyspark.sql.tests.test_utils import * # noqa: F401 try: import xmlrunner # type: ignore[import] testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)

43.428571

97

0.700658

c3ee03475f1f518cd523c5e34a2560d3ba093344

24,825

py

Python

usr/lib/python2.7/dist-packages/rosdep2/sources_list.py

Roboy/roboy_controlled_node_fpga

dbba4eff19ed04469a6196ba368cea231cad539d

[ "BSD-3-Clause" ]

2

2018-12-11T16:35:20.000Z

2019-01-23T16:42:17.000Z

usr/lib/python2.7/dist-packages/rosdep2/sources_list.py

Roboy/roboy_managing_node_fpga

64ffe5aec2f2c98a051bb1a881849c195b8d052c

[ "BSD-3-Clause" ]

1

2018-12-28T21:11:50.000Z

usr/lib/python2.7/dist-packages/rosdep2/sources_list.py

Roboy/roboy_managing_node_fpga

64ffe5aec2f2c98a051bb1a881849c195b8d052c

[ "BSD-3-Clause" ]

3

2018-01-21T17:53:17.000Z

2021-09-08T10:22:05.000Z

# Copyright (c) 2012, Willow Garage, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the Willow Garage, Inc. nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Author Ken Conley/kwc@willowgarage.com from __future__ import print_function import os import sys import tempfile import yaml import hashlib try: from urllib.request import urlopen from urllib.error import URLError except ImportError: from urllib2 import urlopen from urllib2 import URLError try: import cPickle as pickle except ImportError: import pickle from .core import InvalidData, DownloadFailure, CachePermissionError from .gbpdistro_support import get_gbprepo_as_rosdep_data, download_gbpdistro_as_rosdep_data try: import urlparse except ImportError: import urllib.parse as urlparse #py3k try: import httplib except ImportError: import http.client as httplib # py3k import rospkg import rospkg.distro from .loader import RosdepLoader from .rosdistrohelper import get_index, get_index_url # default file to download with 'init' command in order to bootstrap # rosdep DEFAULT_SOURCES_LIST_URL = 'https://raw.githubusercontent.com/ros/rosdistro/master/rosdep/sources.list.d/20-default.list' #seconds to wait before aborting download of rosdep data DOWNLOAD_TIMEOUT = 15.0 SOURCES_LIST_DIR = 'sources.list.d' SOURCES_CACHE_DIR = 'sources.cache' # name of index file for sources cache CACHE_INDEX = 'index' # extension for binary cache PICKLE_CACHE_EXT = '.pickle' SOURCE_PATH_ENV = 'ROSDEP_SOURCE_PATH' def get_sources_list_dirs(source_list_dir): if SOURCE_PATH_ENV in os.environ: sdirs = os.environ[SOURCE_PATH_ENV].split(os.pathsep) else: sdirs = [source_list_dir] for p in list(sdirs): if not os.path.exists(p): sdirs.remove(p) return sdirs def get_sources_list_dir(): # base of where we read config files from # TODO: windows if 0: # we can't use etc/ros because environment config does not carry over under sudo etc_ros = rospkg.get_etc_ros_dir() else: etc_ros = '/etc/ros' # compute default system wide sources directory sys_sources_list_dir = os.path.join(etc_ros, 'rosdep', SOURCES_LIST_DIR) sources_list_dirs = get_sources_list_dirs(sys_sources_list_dir) if sources_list_dirs: return sources_list_dirs[0] else: return sys_sources_list_dir def get_default_sources_list_file(): return os.path.join(get_sources_list_dir(), '20-default.list') def get_sources_cache_dir(): ros_home = rospkg.get_ros_home() return os.path.join(ros_home, 'rosdep', SOURCES_CACHE_DIR) # Default rosdep.yaml format. For now this is the only valid type and # is specified for future compatibility. TYPE_YAML = 'yaml' # git-buildpackage repo list TYPE_GBPDISTRO = 'gbpdistro' VALID_TYPES = [TYPE_YAML, TYPE_GBPDISTRO] class DataSource(object): def __init__(self, type_, url, tags, origin=None): """ :param type_: data source type, e.g. TYPE_YAML, TYPE_GBPDISTRO :param url: URL of data location. For file resources, must start with the file:// scheme. For remote resources, URL must include a path. :param tags: tags for matching data source to configurations :param origin: filename or other indicator of where data came from for debugging. :raises: :exc:`ValueError` if parameters do not validate """ # validate inputs if not type_ in VALID_TYPES: raise ValueError("type must be one of [%s]"%(','.join(VALID_TYPES))) parsed = urlparse.urlparse(url) if not parsed.scheme or (parsed.scheme != 'file' and not parsed.netloc) or parsed.path in ('', '/'): raise ValueError("url must be a fully-specified URL with scheme, hostname, and path: %s"%(str(url))) if not type(tags) == list: raise ValueError("tags must be a list: %s"%(str(tags))) self.type = type_ self.tags = tags self.url = url self.origin = origin def __eq__(self, other): return isinstance(other, DataSource) and \ self.type == other.type and \ self.tags == other.tags and \ self.url == other.url and \ self.origin == other.origin def __str__(self): if self.origin: return "[%s]:\n%s %s %s"%(self.origin, self.type, self.url, ' '.join(self.tags)) else: return "%s %s %s"%(self.type, self.url, ' '.join(self.tags)) def __repr__(self): return repr((self.type, self.url, self.tags, self.origin)) class RosDistroSource(DataSource): def __init__(self, distro): self.type = TYPE_GBPDISTRO self.tags = [distro] # In this case self.url is a list if REP-143 is being used self.url = get_index().distributions[distro]['distribution'] self.origin = None # create function we can pass in as model to parse_source_data. The # function emulates the CachedDataSource constructor but does the # necessary full filepath calculation and loading of data. def cache_data_source_loader(sources_cache_dir, verbose=False): def create_model(type_, uri, tags, origin=None): # compute the filename has from the URL filename = compute_filename_hash(uri) filepath = os.path.join(sources_cache_dir, filename) pickle_filepath = filepath + PICKLE_CACHE_EXT if os.path.exists(pickle_filepath): if verbose: print("loading cached data source:\n\t%s\n\t%s"%(uri, pickle_filepath), file=sys.stderr) with open(pickle_filepath, 'rb') as f: rosdep_data = pickle.loads(f.read()) elif os.path.exists(filepath): if verbose: print("loading cached data source:\n\t%s\n\t%s"%(uri, filepath), file=sys.stderr) with open(filepath) as f: rosdep_data = yaml.load(f.read()) else: rosdep_data = {} return CachedDataSource(type_, uri, tags, rosdep_data, origin=filepath) return create_model class CachedDataSource(object): def __init__(self, type_, url, tags, rosdep_data, origin=None): """ Stores data source and loaded rosdep data for that source. NOTE: this is not a subclass of DataSource, though it's API is duck-type compatible with the DataSource API. """ self.source = DataSource(type_, url, tags, origin=origin) self.rosdep_data = rosdep_data def __eq__(self, other): try: return self.source == other.source and \ self.rosdep_data == other.rosdep_data except AttributeError: return False def __str__(self): return "%s\n%s"%(self.source, self.rosdep_data) def __repr__(self): return repr((self.type, self.url, self.tags, self.rosdep_data, self.origin)) @property def type(self): """ :returns: data source type """ return self.source.type @property def url(self): """ :returns: data source URL """ return self.source.url @property def tags(self): """ :returns: data source tags """ return self.source.tags @property def origin(self): """ :returns: data source origin, if set, or ``None`` """ return self.source.origin class DataSourceMatcher(object): def __init__(self, tags): self.tags = tags def matches(self, rosdep_data_source): """ Check if the datasource matches this configuration. :param rosdep_data_source: :class:`DataSource` """ # all of the rosdep_data_source tags must be in our matcher tags return not any(set(rosdep_data_source.tags)-set(self.tags)) @staticmethod def create_default(os_override=None): """ Create a :class:`DataSourceMatcher` to match the current configuration. :param os_override: (os_name, os_codename) tuple to override OS detection :returns: :class:`DataSourceMatcher` """ distro_name = rospkg.distro.current_distro_codename() if os_override is None: os_detect = rospkg.os_detect.OsDetect() os_name, os_version, os_codename = os_detect.detect_os() else: os_name, os_codename = os_override tags = [t for t in (distro_name, os_name, os_codename) if t] return DataSourceMatcher(tags) def download_rosdep_data(url): """ :raises: :exc:`DownloadFailure` If data cannot be retrieved (e.g. 404, bad YAML format, server down). """ try: f = urlopen(url, timeout=DOWNLOAD_TIMEOUT) text = f.read() f.close() data = yaml.safe_load(text) if type(data) != dict: raise DownloadFailure('rosdep data from [%s] is not a YAML dictionary'%(url)) return data except (URLError, httplib.HTTPException) as e: raise DownloadFailure(str(e) + ' (%s)' % url) except yaml.YAMLError as e: raise DownloadFailure(str(e)) def download_default_sources_list(url=DEFAULT_SOURCES_LIST_URL): """ Download (and validate) contents of default sources list. :param url: override URL of default sources list file :return: raw sources list data, ``str`` :raises: :exc:`InvalidData` :raises: :exc:`urllib2.URLError` If data cannot be retrieved (e.g. 404, server down). """ try: f = urlopen(url, timeout=DOWNLOAD_TIMEOUT) except (URLError, httplib.HTTPException) as e: raise URLError(str(e) + ' (%s)' % url) data = f.read().decode() f.close() if not data: raise RuntimeError("cannot download defaults file: empty contents") # parse just for validation parse_sources_data(data) return data def parse_sources_data(data, origin='<string>', model=None): """ Parse sources file format (tags optional):: # comments and empty lines allowed <type> <uri> [tags] e.g.:: yaml http://foo/rosdep.yaml fuerte lucid ubuntu If tags are specified, *all* tags must match the current configuration for the sources data to be used. :param data: data in sources file format :param model: model to load data into. Defaults to :class:`DataSource` :returns: List of data sources, [:class:`DataSource`] :raises: :exc:`InvalidData` """ if model is None: model = DataSource sources = [] for line in data.split('\n'): line = line.strip() # ignore empty lines or comments if not line or line.startswith('#'): continue splits = line.split(' ') if len(splits) < 2: raise InvalidData("invalid line:\n%s"%(line), origin=origin) type_ = splits[0] url = splits[1] tags = splits[2:] try: sources.append(model(type_, url, tags, origin=origin)) except ValueError as e: raise InvalidData("line:\n\t%s\n%s"%(line, e), origin=origin) return sources def parse_sources_file(filepath): """ Parse file on disk :returns: List of data sources, [:class:`DataSource`] :raises: :exc:`InvalidData` If any error occurs reading file, so an I/O error, non-existent file, or invalid format. """ try: with open(filepath, 'r') as f: return parse_sources_data(f.read(), origin=filepath) except IOError as e: raise InvalidData("I/O error reading sources file: %s"%(str(e)), origin=filepath) def parse_sources_list(sources_list_dir=None): """ Parse data stored in on-disk sources list directory into a list of :class:`DataSource` for processing. :returns: List of data sources, [:class:`DataSource`]. If there is no sources list dir, this returns an empty list. :raises: :exc:`InvalidData` :raises: :exc:`OSError` if *sources_list_dir* cannot be read. :raises: :exc:`IOError` if *sources_list_dir* cannot be read. """ if sources_list_dir is None: sources_list_dir = get_sources_list_dir() sources_list_dirs = get_sources_list_dirs(sources_list_dir) filelist = [] for sdir in sources_list_dirs: filelist += sorted([os.path.join(sdir, f) for f in os.listdir(sdir) if f.endswith('.list')]) sources_list = [] for f in filelist: sources_list.extend(parse_sources_file(f)) return sources_list def _generate_key_from_urls(urls): # urls may be a list of urls or a single string try: assert isinstance(urls, (list, basestring)) except NameError: assert isinstance(urls, (list, str)) # We join the urls by the '^' character because it is not allowed in urls return '^'.join(urls if isinstance(urls, list) else [urls]) def update_sources_list(sources_list_dir=None, sources_cache_dir=None, success_handler=None, error_handler=None): """ Re-downloaded data from remote sources and store in cache. Also update the cache index based on current sources. :param sources_list_dir: override source list directory :param sources_cache_dir: override sources cache directory :param success_handler: fn(DataSource) to call if a particular source loads successfully. This hook is mainly for printing errors to console. :param error_handler: fn(DataSource, DownloadFailure) to call if a particular source fails. This hook is mainly for printing errors to console. :returns: list of (`DataSource`, cache_file_path) pairs for cache files that were updated, ``[str]`` :raises: :exc:`InvalidData` If any of the sources list files is invalid :raises: :exc:`OSError` if *sources_list_dir* cannot be read. :raises: :exc:`IOError` If *sources_list_dir* cannot be read or cache data cannot be written """ if sources_cache_dir is None: sources_cache_dir = get_sources_cache_dir() sources = parse_sources_list(sources_list_dir=sources_list_dir) retval = [] for source in list(sources): try: if source.type == TYPE_YAML: rosdep_data = download_rosdep_data(source.url) elif source.type == TYPE_GBPDISTRO: # DEPRECATED, do not use this file. See REP137 if not source.tags[0] in ['electric', 'fuerte']: print('Ignore legacy gbpdistro "%s"' % source.tags[0]) sources.remove(source) continue # do not store this entry in the cache rosdep_data = download_gbpdistro_as_rosdep_data(source.url) retval.append((source, write_cache_file(sources_cache_dir, source.url, rosdep_data))) if success_handler is not None: success_handler(source) except DownloadFailure as e: if error_handler is not None: error_handler(source, e) # Additional sources for ros distros # In compliance with REP137 and REP143 print('Query rosdistro index %s' % get_index_url()) for dist_name in sorted(get_index().distributions.keys()): print('Add distro "%s"' % dist_name) rds = RosDistroSource(dist_name) rosdep_data = get_gbprepo_as_rosdep_data(dist_name) # dist_files can either be a string (single filename) or a list (list of filenames) dist_files = get_index().distributions[dist_name]['distribution'] key = _generate_key_from_urls(dist_files) retval.append((rds, write_cache_file(sources_cache_dir, key, rosdep_data))) sources.append(rds) # Create a combined index of *all* the sources. We do all the # sources regardless of failures because a cache from a previous # attempt may still exist. We have to do this cache index so that # loads() see consistent data. if not os.path.exists(sources_cache_dir): os.makedirs(sources_cache_dir) cache_index = os.path.join(sources_cache_dir, CACHE_INDEX) data = "#autogenerated by rosdep, do not edit. use 'rosdep update' instead\n" for source in sources: url = _generate_key_from_urls(source.url) data += "yaml %s %s\n" % (url, ' '.join(source.tags)) write_atomic(cache_index, data) # mainly for debugging and testing return retval def load_cached_sources_list(sources_cache_dir=None, verbose=False): """ Load cached data based on the sources list. :returns: list of :class:`CachedDataSource` instance with raw rosdep data loaded. :raises: :exc:`OSError` if cache cannot be read :raises: :exc:`IOError` if cache cannot be read """ if sources_cache_dir is None: sources_cache_dir = get_sources_cache_dir() cache_index = os.path.join(sources_cache_dir, 'index') if not os.path.exists(cache_index): if verbose: print("no cache index present, not loading cached sources", file=sys.stderr) return [] with open(cache_index, 'r') as f: cache_data = f.read() # the loader does all the work model = cache_data_source_loader(sources_cache_dir, verbose=verbose) return parse_sources_data(cache_data, origin=cache_index, model=model) def compute_filename_hash(key_filenames): sha_hash = hashlib.sha1() if isinstance(key_filenames, list): for key in key_filenames: sha_hash.update(key.encode()) else: sha_hash.update(key_filenames.encode()) return sha_hash.hexdigest() def write_cache_file(source_cache_d, key_filenames, rosdep_data): """ :param source_cache_d: directory to write cache file to :param key_filenames: filename (or list of filenames) to be used in hashing :param rosdep_data: dictionary of data to serialize as YAML :returns: name of file where cache is stored :raises: :exc:`OSError` if cannot write to cache file/directory :raises: :exc:`IOError` if cannot write to cache file/directory """ if not os.path.exists(source_cache_d): os.makedirs(source_cache_d) key_hash = compute_filename_hash(key_filenames) filepath = os.path.join(source_cache_d, key_hash) try: write_atomic(filepath + PICKLE_CACHE_EXT, pickle.dumps(rosdep_data, -1), True) except OSError as e: raise CachePermissionError("Failed to write cache file: " + str(e)) try: os.unlink(filepath) except OSError: pass return filepath def write_atomic(filepath, data, binary=False): # write data to new file fd, filepath_tmp = tempfile.mkstemp(prefix=os.path.basename(filepath) + '.tmp.', dir=os.path.dirname(filepath)) if (binary): fmode = 'wb' else: fmode = 'w' with os.fdopen(fd, fmode) as f: f.write(data) f.close() try: # switch file atomically (if supported) os.rename(filepath_tmp, filepath) except OSError: # fall back to non-atomic operation try: os.unlink(filepath) except OSError: pass try: os.rename(filepath_tmp, filepath) except OSError: os.unlink(filepath_tmp) class SourcesListLoader(RosdepLoader): """ SourcesList loader implements the general RosdepLoader API. This implementation is fairly simple as there is only one view the source list loader can create. It is also a bit degenerate as it is not capable of mapping resource names to views, thus any resource-name-based API fails or returns nothing interesting. This loader should not be used directly; instead, it is more useful composed with other higher-level implementations, like the :class:`rosdep2.rospkg_loader.RospkgLoader`. The general intent is to compose it with another loader by making all of the other loader's views depends on all the views in this loader. """ ALL_VIEW_KEY = 'sources.list' def __init__(self, sources): """ :param sources: cached sources list entries, [:class:`CachedDataSource`] """ self.sources = sources @staticmethod def create_default(matcher=None, sources_cache_dir=None, os_override=None, verbose=False): """ :param matcher: override DataSourceMatcher. Defaults to DataSourceMatcher.create_default(). :param sources_cache_dir: override location of sources cache """ if matcher is None: matcher = DataSourceMatcher.create_default(os_override=os_override) if verbose: print("using matcher with tags [%s]"%(', '.join(matcher.tags)), file=sys.stderr) sources = load_cached_sources_list(sources_cache_dir=sources_cache_dir, verbose=verbose) if verbose: print("loaded %s sources"%(len(sources)), file=sys.stderr) sources = [x for x in sources if matcher.matches(x)] if verbose: print("%s sources match current tags"%(len(sources)), file=sys.stderr) return SourcesListLoader(sources) def load_view(self, view_name, rosdep_db, verbose=False): """ Load view data into rosdep_db. If the view has already been loaded into rosdep_db, this method does nothing. :param view_name: name of ROS stack to load, ``str`` :param rosdep_db: database to load stack data into, :class:`RosdepDatabase` :raises: :exc:`InvalidData` """ if rosdep_db.is_loaded(view_name): return source = self.get_source(view_name) if verbose: print("loading view [%s] with sources.list loader"%(view_name), file=sys.stderr) view_dependencies = self.get_view_dependencies(view_name) rosdep_db.set_view_data(view_name, source.rosdep_data, view_dependencies, view_name) def get_loadable_resources(self): return [] def get_loadable_views(self): return [x.url for x in self.sources] def get_view_dependencies(self, view_name): # use dependencies to implement precedence if view_name != SourcesListLoader.ALL_VIEW_KEY: # if the view_name matches one of our sources, return # empty list as none of our sources has deps. if any([x for x in self.sources if view_name == x.url]): return [] # not one of our views, so it depends on everything we provide return [x.url for x in self.sources] def get_source(self, view_name): matches = [x for x in self.sources if x.url == view_name] if matches: return matches[0] else: raise rospkg.ResourceNotFound(view_name) def get_rosdeps(self, resource_name, implicit=True): """ Always raises as SourceListLoader defines no concrete resources with rosdeps. :raises: :exc:`rospkg.ResourceNotFound` """ raise rospkg.ResourceNotFound(resource_name) def get_view_key(self, resource_name): """ Always raises as SourceListLoader defines no concrete resources with rosdeps. :returns: Name of view that *resource_name* is in, ``None`` if no associated view. :raises: :exc:`rospkg.ResourceNotFound` if *resource_name* cannot be found. """ raise rospkg.ResourceNotFound(resource_name)

36.887073

121

0.664491

1b8a3f63d514903ea341d282532c429c06620e1e

3,395

py

Python

dmb/modeling/stereo/cost_processors/utils/cost_norm.py

jiaw-z/DenseMatchingBenchmark

177c56ca1952f54d28e6073afa2c16981113a2af

[ "MIT" ]

160

2019-11-16T13:59:21.000Z

2022-03-28T07:52:59.000Z

dmb/modeling/stereo/cost_processors/utils/cost_norm.py

jiaw-z/DenseMatchingBenchmark

177c56ca1952f54d28e6073afa2c16981113a2af

[ "MIT" ]

22

2019-11-22T02:14:18.000Z

2022-01-24T10:16:14.000Z

dmb/modeling/stereo/cost_processors/utils/cost_norm.py

jiaw-z/DenseMatchingBenchmark

177c56ca1952f54d28e6073afa2c16981113a2af

[ "MIT" ]

38

2019-12-27T14:01:01.000Z

2022-03-12T11:40:11.000Z

import torch import torch.nn as nn eps = 1e-5 class _CostVolumeNorm(nn.Module): """ Normalize Cost Volume Args: dim (int): which dim to apply normalization operation, default dim is for the cost dim. affine (bool): whether the parameters are learnable, default is True weight (float): weight for cost re-range bias (float): bias for cost Shape: - Input: :math:`(N, *)` - Output: :math:`(N, *)` (same shape as input) """ def __init__(self, dim=1, affine=True, weight=1, bias=0): super(_CostVolumeNorm, self).__init__() self.dim = dim self.affine = affine if self.affine: self.weight = nn.Parameter(data=torch.Tensor(1), requires_grad=True) self.bias = nn.Parameter(data=torch.Tensor(1), requires_grad=True) else: self.weight = nn.Parameter(data=torch.Tensor(1), requires_grad=False) self.bias = nn.Parameter(data=torch.Tensor(1), requires_grad=False) # init weight and bias self.weight.data.fill_(weight) self.bias.data.fill_(bias) def forward(self, input): raise NotImplementedError class RangeNorm(_CostVolumeNorm): def __init__(self, dim=1, affine=True, weight=1, bias=0): super(RangeNorm, self).__init__(dim=dim, affine=affine, weight=weight, bias=bias) def forward(self, input): # compute mean value mean = input.min(dim=self.dim, keepdim=True)[0] # compute margin var = input.max(dim=self.dim, keepdim=True)[0] - input.min(dim=self.dim, keepdim=True)[0] # normalize normalized_input = (input - mean) / (var + eps) # apply weight and bias output = normalized_input * self.weight + self.bias return output class VarNorm(_CostVolumeNorm): def __init__(self, dim=1, affine=True, weight=1, bias=0): super(VarNorm, self).__init__(dim=dim, affine=affine, weight=weight, bias=bias) def forward(self, input): # compute mean value mean = input.mean(dim=self.dim, keepdim=True) # compute var value var = input.var(dim=self.dim, keepdim=True) # normalize normalized_input = (input - mean).abs() / (var + eps) # apply weight and bias output = normalized_input * self.weight + self.bias return output class StdNorm(_CostVolumeNorm): def __init__(self, dim=1, affine=True, weight=1, bias=0): super(StdNorm, self).__init__(dim=dim, affine=affine, weight=weight, bias=bias) def forward(self, input): # compute mean value mean = input.mean(dim=self.dim, keepdim=True) # compute var value var = input.std(dim=self.dim, keepdim=True) # normalize normalized_input = (input - mean).abs() / (var + eps) # apply weight and bias output = normalized_input * self.weight + self.bias return output class SigmoidNorm(_CostVolumeNorm): def __init__(self, dim=1, affine=True, weight=1, bias=0): super(SigmoidNorm, self).__init__(dim=dim, affine=affine, weight=weight, bias=bias) def forward(self, input): # normalize normalized_input = torch.sigmoid(input) # apply weight and bias output = normalized_input * self.weight + self.bias return output

33.613861

99

0.622091

a5c8eb063f0c1ee701686daf770c8e676474c6e9

368

py

Python

app/app/tests.py

ppleventis/recipe-app-api

6ba3004540a136f22429adb42283f154a1b447c3

[ "MIT" ]

null

app/app/tests.py

ppleventis/recipe-app-api

6ba3004540a136f22429adb42283f154a1b447c3

[ "MIT" ]

null

app/app/tests.py

ppleventis/recipe-app-api

6ba3004540a136f22429adb42283f154a1b447c3

[ "MIT" ]

null

from django.test import TestCase from app.calc import add, subtract class calcTests(TestCase): def test_add_numbers(self): """Test that two numbers are added together""" self.assertEqual(add(3, 8), 11) def test_subtract_numbers(self): """Test that values are subtracted and returned""" self.assertEqual(subtract(5, 11), 6)

24.533333

58

0.679348

f377b6fcda8590183f8bbbb2f56c90398b47b627

8,535

py

Python

var/spack/repos/builtin/packages/py-horovod/package.py

genric/spack

e35a99e5d6da729f197954802d697d75e02aefe0

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

9

2018-04-18T07:51:40.000Z

2021-09-10T03:56:57.000Z

var/spack/repos/builtin/packages/py-horovod/package.py

BlueBrain/spack

047fa4b21b90abfd227dd70a77689bb473cd1a57

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

907

2018-04-18T11:17:57.000Z

2022-03-31T13:20:25.000Z

var/spack/repos/builtin/packages/py-horovod/package.py

genric/spack

e35a99e5d6da729f197954802d697d75e02aefe0

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

29

2018-11-05T16:14:23.000Z

2022-02-03T16:07:09.000Z

# Copyright 2013-2019 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) class PyHorovod(PythonPackage, CudaPackage): """Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.""" homepage = "https://github.com/horovod" git = "https://github.com/horovod/horovod.git" maintainers = ['adamjstewart', 'aweits', 'tgaddair'] version('master', branch='master', submodules=True) version('0.20.3', tag='v0.20.3', submodules=True) version('0.20.2', tag='v0.20.2', submodules=True) version('0.20.1', tag='v0.20.1', submodules=True) version('0.20.0', tag='v0.20.0', submodules=True) version('0.19.5', tag='v0.19.5', submodules=True) version('0.19.4', tag='v0.19.4', submodules=True) version('0.19.3', tag='v0.19.3', submodules=True) version('0.19.2', tag='v0.19.2', submodules=True) version('0.19.1', tag='v0.19.1', submodules=True) version('0.19.0', tag='v0.19.0', submodules=True) version('0.18.2', tag='v0.18.2', submodules=True) version('0.18.1', tag='v0.18.1', submodules=True) version('0.18.0', tag='v0.18.0', submodules=True) version('0.17.1', tag='v0.17.1', submodules=True) version('0.17.0', tag='v0.17.0', submodules=True) version('0.16.4', tag='v0.16.4', submodules=True) version('0.16.3', tag='v0.16.3', submodules=True) version('0.16.2', tag='v0.16.2', submodules=True) # https://github.com/horovod/horovod/blob/master/docs/install.rst variant('frameworks', default='pytorch', description='Deep learning frameworks to build support for', values=('tensorflow', 'pytorch', 'mxnet', 'keras', 'spark', 'ray'), multi=True) variant('controllers', default='mpi', description='Controllers to coordinate work between processes', values=('mpi', 'gloo'), multi=True) variant('tensor_ops', default='nccl', description='Framework to use for GPU/CPU operations', values=('nccl', 'mpi', 'gloo', 'ccl'), multi=False) variant('cuda', default=True, description='Build with CUDA') variant('rocm', default=False, description='Build with ROCm') # Required dependencies depends_on('python@3.6:', type=('build', 'run'), when='@0.20:') depends_on('py-setuptools', type='build') depends_on('py-cloudpickle', type=('build', 'run')) depends_on('py-psutil', type=('build', 'run')) depends_on('py-pyyaml', type=('build', 'run')) depends_on('py-six', type=('build', 'run'), when='@:0.19') depends_on('py-dataclasses', type=('build', 'run'), when='@0.20: ^python@:3.6') # Framework dependencies depends_on('py-tensorflow@1.1.0:', type=('build', 'link', 'run'), when='frameworks=tensorflow') depends_on('py-tensorflow@1.15:', type=('build', 'link', 'run'), when='frameworks=tensorflow @0.20:') depends_on('py-torch@0.4.0:', type=('build', 'link', 'run'), when='frameworks=pytorch') depends_on('py-torch@1.2:', type=('build', 'link', 'run'), when='frameworks=pytorch @0.20:') depends_on('py-torchvision', type=('build', 'run'), when='frameworks=pytorch @:0.19.1') depends_on('py-cffi@1.4.0:', type=('build', 'run'), when='frameworks=pytorch') depends_on('mxnet@1.4.1:+python', type=('build', 'link', 'run'), when='frameworks=mxnet') depends_on('py-keras@2.0.8,2.1.2:', type=('build', 'run'), when='frameworks=keras') depends_on('py-h5py@2.9:', type=('build', 'run'), when='frameworks=spark') depends_on('py-numpy', type=('build', 'run'), when='frameworks=spark') depends_on('py-petastorm@0.8.2', type=('build', 'run'), when='frameworks=spark @:0.19.1') depends_on('py-petastorm@0.9.0:', type=('build', 'run'), when='frameworks=spark @0.19.2:') depends_on('py-pyarrow@0.15.0:', type=('build', 'run'), when='frameworks=spark') depends_on('py-pyspark@2.3.2:', type=('build', 'run'), when='frameworks=spark') depends_on('py-ray', type=('build', 'run'), when='frameworks=ray') # Build dependencies depends_on('cmake@2.8.12:', type='build', when='@0.20:') depends_on('pkgconfig', type='build') # Controller dependencies depends_on('mpi', when='controllers=mpi') # There does not appear to be a way to use an external Gloo installation depends_on('cmake', type='build', when='controllers=gloo') depends_on('libuv@1.26:', when='controllers=gloo platform=darwin') # Tensor Operations dependencies depends_on('nccl@2:', when='tensor_ops=nccl') depends_on('mpi', when='tensor_ops=mpi') # There does not appear to be a way to use an external Gloo installation depends_on('cmake', type='build', when='tensor_ops=gloo') # Test dependencies depends_on('py-mock', type='test') depends_on('py-pytest', type='test') depends_on('py-pytest-forked', type='test') depends_on('py-parameterized', type='test', when='@0.20:') conflicts('cuda_arch=none', when='+cuda', msg='Must specify CUDA compute capabilities of your GPU, see ' 'https://developer.nvidia.com/cuda-gpus') conflicts('tensor_ops=nccl', when='~cuda~rocm', msg='NCCL requires either CUDA or ROCm support') conflicts('frameworks=ray', when='@:0.19', msg='Ray integration was added in 0.20.X') conflicts('controllers=gloo', when='@:0.20.0 platform=darwin', msg='Gloo cannot be compiled on MacOS') # https://github.com/horovod/horovod/pull/1835 patch('fma.patch', when='@0.19.0:0.19.1') def setup_build_environment(self, env): # https://github.com/horovod/horovod/blob/master/docs/install.rst#environment-variables # Build system env.set('PKG_CONFIG_EXECUTABLE', self.spec['pkgconfig'].prefix.bin.join('pkg-config')) if '^cmake' in self.spec: env.set('HOROVOD_CMAKE', self.spec['cmake'].command.path) env.set('MAKEFLAGS', '-j{0}'.format(make_jobs)) # Frameworks if 'frameworks=tensorflow' in self.spec: env.set('HOROVOD_WITH_TENSORFLOW', 1) else: env.set('HOROVOD_WITHOUT_TENSORFLOW', 1) if 'frameworks=pytorch' in self.spec: env.set('HOROVOD_WITH_PYTORCH', 1) else: env.set('HOROVOD_WITHOUT_PYTORCH', 1) if 'frameworks=mxnet' in self.spec: env.set('HOROVOD_WITH_MXNET', 1) else: env.set('HOROVOD_WITHOUT_MXNET', 1) # Controllers if 'controllers=mpi' in self.spec: env.set('HOROVOD_WITH_MPI', 1) else: env.set('HOROVOD_WITHOUT_MPI', 1) if 'controllers=gloo' in self.spec: env.set('HOROVOD_WITH_GLOO', 1) else: env.set('HOROVOD_WITHOUT_GLOO', 1) # Tensor Operations if 'tensor_ops=nccl' in self.spec: env.set('HOROVOD_GPU_ALLREDUCE', 'NCCL') env.set('HOROVOD_GPU_ALLGATHER', 'NCCL') env.set('HOROVOD_GPU_BROADCAST', 'NCCL') env.set('HOROVOD_NCCL_HOME', self.spec['nccl'].prefix) env.set('HOROVOD_NCCL_INCLUDE', self.spec['nccl'].headers.directories[0]) env.set('HOROVOD_NCCL_LIB', self.spec['nccl'].libs.directories[0]) if '+cuda' in self.spec: env.set('HOROVOD_GPU', 'CUDA') env.set('HOROVOD_CUDA_HOME', self.spec['cuda'].prefix) cuda_cc_list = ','.join(self.spec.variants['cuda_arch'].value) env.set('HOROVOD_BUILD_CUDA_CC_LIST', cuda_cc_list) env.set('HOROVOD_CUDA_INCLUDE', self.spec['cuda'].headers.directories[0]) env.set('HOROVOD_CUDA_LIB', self.spec['cuda'].libs.directories[0]) elif '+rocm' in self.spec: env.set('HOROVOD_GPU', 'ROCM') # env.set('HOROVOD_ROCM_HOME', self.spec['rocm'].prefix) else: env.set('HOROVOD_CPU_OPERATIONS', self.spec.variants['tensor_ops'].value.upper()) @run_after('install') @on_package_attributes(run_tests=True) def install_test(self): horovodrun = Executable(self.prefix.bin.horovodrun) horovodrun('--check-build')

48.771429

107

0.607264

8afd6bc097e9f19cf40204df2731a62a61475884

3,504

py

Python

airflow/contrib/sensors/bash_sensor.py

shrutimantri/airflow

61eaaacd20ab0f743786df895cf8f232b3b2a48c

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

15

2017-04-06T09:01:50.000Z

2021-10-02T13:54:31.000Z

airflow/contrib/sensors/bash_sensor.py

shrutimantri/airflow

61eaaacd20ab0f743786df895cf8f232b3b2a48c

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

26

2019-08-05T13:44:11.000Z

2022-03-30T10:06:18.000Z

airflow/contrib/sensors/bash_sensor.py

shrutimantri/airflow

61eaaacd20ab0f743786df895cf8f232b3b2a48c

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

21

2017-08-20T03:01:05.000Z

2021-09-07T06:47:51.000Z

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from builtins import bytes import os from subprocess import Popen, STDOUT, PIPE from tempfile import gettempdir, NamedTemporaryFile from airflow.utils.decorators import apply_defaults from airflow.sensors.base_sensor_operator import BaseSensorOperator from airflow.utils.file import TemporaryDirectory class BashSensor(BaseSensorOperator): """ Executes a bash command/script and returns True if and only if the return code is 0. :param bash_command: The command, set of commands or reference to a bash script (must be '.sh') to be executed. :type bash_command: str :param env: If env is not None, it must be a mapping that defines the environment variables for the new process; these are used instead of inheriting the current process environment, which is the default behavior. (templated) :type env: dict :param output_encoding: output encoding of bash command. :type output_encoding: str """ template_fields = ('bash_command', 'env') @apply_defaults def __init__(self, bash_command, env=None, output_encoding='utf-8', *args, **kwargs): super(BashSensor, self).__init__(*args, **kwargs) self.bash_command = bash_command self.env = env self.output_encoding = output_encoding def poke(self, context): """ Execute the bash command in a temporary directory which will be cleaned afterwards """ bash_command = self.bash_command self.log.info("Tmp dir root location: \n %s", gettempdir()) with TemporaryDirectory(prefix='airflowtmp') as tmp_dir: with NamedTemporaryFile(dir=tmp_dir, prefix=self.task_id) as f: f.write(bytes(bash_command, 'utf_8')) f.flush() fname = f.name script_location = tmp_dir + "/" + fname self.log.info("Temporary script location: %s", script_location) self.log.info("Running command: %s", bash_command) sp = Popen( ['bash', fname], stdout=PIPE, stderr=STDOUT, close_fds=True, cwd=tmp_dir, env=self.env, preexec_fn=os.setsid) self.sp = sp self.log.info("Output:") for line in iter(sp.stdout.readline, b''): line = line.decode(self.output_encoding).strip() self.log.info(line) sp.wait() self.log.info("Command exited with return code %s", sp.returncode) return not sp.returncode

38.505495

82

0.642409

0c0f7ac9a8193aa57d2942234da43ed0cb651bce

948

py

Python

statssite/home/www-data/stats/stats.py

tigran-a/ACDCStats

45daaba3149535face316e6c6e117409ae2f9cbf

[ "Apache-2.0" ]

null

statssite/home/www-data/stats/stats.py

tigran-a/ACDCStats

45daaba3149535face316e6c6e117409ae2f9cbf

[ "Apache-2.0" ]

null

statssite/home/www-data/stats/stats.py

tigran-a/ACDCStats

45daaba3149535face316e6c6e117409ae2f9cbf

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 """ (c) Copyright 2015 Tigran Avanesov, SnT, University of Luxembourg Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from flask import Flask, Blueprint import flask import ddata import frontpage app = Flask(__name__) app.register_blueprint(ddata.databp, url_prefix='/data') app.register_blueprint(frontpage.fpbp, url_prefix='/stats') if __name__ =='__main__': print(app.url_map) app.run(debug=True)

30.580645

75

0.748945

dd21a974c05172c9ba3cce52ff49f8ea3d160c6d

2,393

py

Python

app/storage/dynamodb_api.py

ONSdigital/census-survey-runner

9f8cd3d664db5c5b49d348bdf48c58d1a3492aab

[ "MIT" ]

null

app/storage/dynamodb_api.py

ONSdigital/census-survey-runner

9f8cd3d664db5c5b49d348bdf48c58d1a3492aab

[ "MIT" ]

3

2018-10-10T08:19:07.000Z

2018-10-29T11:43:08.000Z

app/storage/dynamodb_api.py

ONSdigital/census-survey-runner

9f8cd3d664db5c5b49d348bdf48c58d1a3492aab

[ "MIT" ]

1

2021-04-11T08:04:22.000Z

from botocore.exceptions import ClientError from flask import current_app from app.data_model import app_models from app.storage.errors import ItemAlreadyExistsError TABLE_CONFIG = { app_models.SubmittedResponse: { 'key_field': 'tx_id', 'table_name_key': 'EQ_SUBMITTED_RESPONSES_TABLE_NAME', 'schema': app_models.SubmittedResponseSchema, }, app_models.QuestionnaireState: { 'key_field': 'user_id', 'table_name_key': 'EQ_QUESTIONNAIRE_STATE_TABLE_NAME', 'schema': app_models.QuestionnaireStateSchema, }, app_models.EQSession: { 'key_field': 'eq_session_id', 'table_name_key': 'EQ_SESSION_TABLE_NAME', 'schema': app_models.EQSessionSchema, }, app_models.UsedJtiClaim: { 'key_field': 'jti_claim', 'table_name_key': 'EQ_USED_JTI_CLAIM_TABLE_NAME', 'schema': app_models.UsedJtiClaimSchema, }, } def put(model, overwrite): config = TABLE_CONFIG[type(model)] schema = config['schema'](strict=True) table = get_table(config) key_field = config['key_field'] item, _ = schema.dump(model) put_kwargs = {'Item': item} if not overwrite: put_kwargs['ConditionExpression'] = 'attribute_not_exists({key_field})'.format(key_field=key_field) try: response = table.put_item(**put_kwargs)['ResponseMetadata']['HTTPStatusCode'] return response == 200 except ClientError as e: if e.response['Error']['Code'] == 'ConditionalCheckFailedException': raise ItemAlreadyExistsError() from e raise # pragma: no cover def get_by_key(model_type, key_value): config = TABLE_CONFIG[model_type] schema = config['schema'](strict=True) table = get_table(config) key = {config['key_field']: key_value} response = table.get_item(Key=key, ConsistentRead=True) item = response.get('Item', None) if item: model, _ = schema.load(item) return model def delete(model): config = TABLE_CONFIG[type(model)] table = get_table(config) key_field = config['key_field'] key = {key_field: getattr(model, key_field)} response = table.delete_item(Key=key) item = response.get('Item', None) return item def get_table(config): table_name = current_app.config[config['table_name_key']] return current_app.eq['dynamodb'].Table(table_name)

29.54321

107

0.67781

f9d355acd71860e07be80e51cd42d1446caf66da

2,304

py

Python

setup.py

rfk/playitagainsam

75833222438638b7d7860032a928a6575c48e0b5

[ "MIT" ]

113

2015-01-09T05:41:43.000Z

2021-07-18T19:34:26.000Z

setup.py

rfk/playitagainsam

75833222438638b7d7860032a928a6575c48e0b5

[ "MIT" ]

14

2015-02-11T20:24:34.000Z

2022-01-03T10:34:59.000Z

setup.py

rfk/playitagainsam

75833222438638b7d7860032a928a6575c48e0b5

[ "MIT" ]

8

2015-02-11T19:26:32.000Z

2019-08-29T06:55:40.000Z

# # This is the setuptools script for "playitagainsam". # Originally developed by Ryan Kelly, 2012. # # This script is placed in the public domain. # import os import sys setup_kwds = {} # Use setuptools is available, so we have `python setup.py test`. # We also need it for 2to3 integration on python3. # Otherwise, fall back to plain old distutils. try: from setuptools import setup except ImportError: if sys.version_info > (3,): raise RuntimeError("python3 support requires setuptools") from distutils.core import setup else: setup_kwds["test_suite"] = "playitagainsam.tests" HERE = os.path.abspath(os.path.dirname(__file__)) # Extract the docstring and version declaration from the module. # To avoid errors due to missing dependencies or bad python versions, # we explicitly read the file contents up to the end of the version # delcaration, then exec it ourselves. src = open(os.path.join(HERE, "playitagainsam", "__init__.py")) info = {} lines = [] for ln in src: lines.append(ln) if "__version__" in ln: for ln in src: if "__version__" not in ln: break lines.append(ln) break exec("".join(lines),info) NAME = "playitagainsam" VERSION = info["__version__"] DESCRIPTION = "record and replay interactive terminal sessions" LONG_DESC = info["__doc__"] AUTHOR = "Ryan Kelly" AUTHOR_EMAIL = "ryan@rfk.id.au" URL="https://github.com/rfk/playitagainsam" LICENSE = "MIT" KEYWORDS = "shell record presentation tutorial" CLASSIFIERS = [ "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "License :: OSI Approved", "License :: OSI Approved :: MIT License", "Development Status :: 4 - Beta", "Intended Audience :: Developers", ] setup(name=NAME, version=VERSION, author=AUTHOR, author_email=AUTHOR_EMAIL, url=URL, description=DESCRIPTION, long_description=LONG_DESC, license=LICENSE, keywords=KEYWORDS, packages=["playitagainsam"], scripts=["scripts/pias"], install_requires=["psutil>=2.0", "six"], classifiers=CLASSIFIERS, **setup_kwds )

28.444444

70

0.674479