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paulo037
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setup.py
jawahar273/practNLPTools-lite
29
172233
<gh_stars>10-100 #!/usr/bin/env python # -*- coding: utf-8 -*- """The setup script.""" # from distutils.command.install import INSTALL_SCHEMES from setuptools import setup, find_packages with open("README.rst") as readme_file: readme = readme_file.read() with open("CHANGELOG.rst") as history_file: history = history_file.read() setup_requirements = [ "pytest-runner", # TODO(jawahar273): put setup requirements # (distutils extensions, etc.) here ] def parse_requirements(filename): """ load requirements from a pip requirements file refer: `link <https://stackoverflow.com/questions/25192794/no-module-named-pip-req/>`_ """ lineiter = (line.strip() for line in open(filename)) return [line for line in lineiter if line and not line.startswith("#")] # read the `extras_require.json` for getting # extras_require depencencys. extras_require = { "ujson": parse_requirements("requirements_ujson.txt"), "huey": parse_requirements("requirements_huey.txt"), "xxhash": parse_requirements("requirements_xxhash.txt"), } setup( name="pntl", version="0.3.6", description=( "used to interface with Senna and" " stanford-parser.jar for dependency parsing" ), long_description="\n\n" + readme + "\n\n" + history, author="<NAME>", author_email="<EMAIL>", url="https://github.com/jawahar273/practNLPTools-lite", packages=find_packages(include=["pntl.*"]), entry_points={"console_scripts": ["pntl=pntl.cli:user_test"]}, include_package_data=True, install_requires=parse_requirements("requirements.txt"), license="MIT license", zip_safe=False, keywords="practnlptools-lite senna python pntl".split(), classifiers=[ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Information Analysis", ], test_suite="tests", tests_require=parse_requirements("requirements_dev.txt"), setup_requires=setup_requirements, extras_require=extras_require, )
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pymongo/timestamp.py
ixc/mongo-python-driver
0
1608899
# Copyright 2010 10gen, 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. """Tools for representing MongoDB internal Timestamps. """ import calendar import datetime from pymongo.tz_util import utc class Timestamp(object): """MongoDB internal timestamps used in the opLog. """ def __init__(self, time, inc): """Create a new :class:`Timestamp`. This class is only for use with the MongoDB opLog. If you need to store a regular timestamp, please use a :class:`~datetime.datetime`. Raises :class:`TypeError` if `time` is not an instance of :class: `int` or :class:`~datetime.datetime`, or `inc` is not an instance of :class:`int`. Raises :class:`ValueError` if `time` or `inc` is not in [0, 2**32). :Parameters: - `time`: time in seconds since epoch UTC, or a naive UTC :class:`~datetime.datetime`, or an aware :class:`~datetime.datetime` - `inc`: the incrementing counter .. versionchanged:: 1.7 `time` can now be a :class:`~datetime.datetime` instance. """ if isinstance(time, datetime.datetime): if time.utcoffset() is not None: time = time - time.utcoffset() time = int(calendar.timegm(time.timetuple())) if not isinstance(time, (int, long)): raise TypeError("time must be an instance of int") if not isinstance(inc, (int, long)): raise TypeError("inc must be an instance of int") if not 0 <= time < 2 ** 32: raise ValueError("time must be contained in [0, 2**32)") if not 0 <= inc < 2 ** 32: raise ValueError("inc must be contained in [0, 2**32)") self.__time = time self.__inc = inc @property def time(self): """Get the time portion of this :class:`Timestamp`. """ return self.__time @property def inc(self): """Get the inc portion of this :class:`Timestamp`. """ return self.__inc def __eq__(self, other): if isinstance(other, Timestamp): return (self.__time == other.time and self.__inc == other.inc) else: return NotImplemented def __ne__(self, other): return not self == other def __repr__(self): return "Timestamp(%s, %s)" % (self.__time, self.__inc) def as_datetime(self): """Return a :class:`~datetime.datetime` instance corresponding to the time portion of this :class:`Timestamp`. .. versionchanged:: 1.8 The returned datetime is now timezone aware. """ return datetime.datetime.fromtimestamp(self.__time, utc)
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raspi_components/light/rgb_light.py
builderdev212/raspi_components
1
42649
<filename>raspi_components/light/rgb_light.py import RPi.GPIO as GPIO from .light_errors import RGBLedError class RGBLed: """ This class is used to control a RGB LED via the GPIO. Please make sure you have 220 Ohm resistors between the 3 gpio pins and the LED. """ def __init__(self, red_pin, green_pin, blue_pin): """ This initiates the pins needed to work with the RGB LED. """ try: self.red_pin = int(red_pin) self.green_pin = int(green_pin) self.blue_pin = int(blue_pin) GPIO.setmode(GPIO.BOARD) GPIO.setup(self.red_pin, GPIO.OUT) GPIO.setup(self.green_pin, GPIO.OUT) GPIO.setup(self.blue_pin, GPIO.OUT) GPIO.output(self.red_pin, GPIO.OUT) GPIO.output(self.green_pin, GPIO.OUT) GPIO.output(self.blue_pin, GPIO.OUT) self.pwm_red = GPIO.PWM(self.red_pin, 2000) self.pwm_green = GPIO.PWM(self.green_pin, 2000) self.pwm_blue = GPIO.PWM(self.blue_pin, 2000) except: raise RGBLedError("Error during the initiation of the RGB_LED class.") def on(self, red_val=0, green_val=0, blue_val=0): """ This will turn on the RGB LED. To change the default color, change red_val, green_val, and blue_val when calling the function. """ try: self.pwm_red.start(red_val) self.pwm_green.start(green_val) self.pwm_blue.start(blue_val) except: raise RGBLedError("Error while turning the RGB LED on.") def set_color(self, red_val, green_val, blue_val): """ This will set the color of the RGB LED. Keep in mind that this uses the RGB color scale. """ try: self.pwm_red.ChangeDutyCycle(red_val) self.pwm_green.ChangeDutyCycle(green_val) self.pwm_blue.ChangeDutyCycle(blue_val) except: raise RGBLedError("Error while changing the color of the RGB LED.") def off(self): """ This will turn the RGB LED off. """ try: self.pwm_red.stop() self.pwm_green.stop() self.pwm_blue.stop() except: raise RGBLedError("Error while turning the RGB LED off.")
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api/db/base.py
p0lygun/astounding-arapaimas
0
156419
<filename>api/db/base.py # Import all the models, so that Base has them before being imported by Alembic from api.db.base_class import Base # noqa: F401 from api.models.user import User # noqa: F401
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easy_gold/pann_utils.py
wdy06/kaggle-birdsong-recognition
1
63434
import logging import os import random import time import warnings from collections import OrderedDict from contextlib import contextmanager from pathlib import Path from typing import List, Optional import cv2 import numpy as np import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.utils.data as data from IPython.display import Audio from sklearn.metrics import average_precision_score, f1_score from sklearn.model_selection import StratifiedKFold import librosa import librosa.display as display import soundfile as sf import utils from catalyst.dl import Callback, CallbackOrder, State class DFTBase(nn.Module): def __init__(self): """Base class for DFT and IDFT matrix""" super(DFTBase, self).__init__() def dft_matrix(self, n): (x, y) = np.meshgrid(np.arange(n), np.arange(n)) omega = np.exp(-2 * np.pi * 1j / n) W = np.power(omega, x * y) return W def idft_matrix(self, n): (x, y) = np.meshgrid(np.arange(n), np.arange(n)) omega = np.exp(2 * np.pi * 1j / n) W = np.power(omega, x * y) return W class STFT(DFTBase): def __init__( self, n_fft=2048, hop_length=None, win_length=None, window="hann", center=True, pad_mode="reflect", freeze_parameters=True, ): """Implementation of STFT with Conv1d. The function has the same output of librosa.core.stft """ super(STFT, self).__init__() assert pad_mode in ["constant", "reflect"] self.n_fft = n_fft self.center = center self.pad_mode = pad_mode # By default, use the entire frame if win_length is None: win_length = n_fft # Set the default hop, if it's not already specified if hop_length is None: hop_length = int(win_length // 4) fft_window = librosa.filters.get_window(window, win_length, fftbins=True) # Pad the window out to n_fft size fft_window = librosa.util.pad_center(fft_window, n_fft) # DFT & IDFT matrix self.W = self.dft_matrix(n_fft) out_channels = n_fft // 2 + 1 self.conv_real = nn.Conv1d( in_channels=1, out_channels=out_channels, kernel_size=n_fft, stride=hop_length, padding=0, dilation=1, groups=1, bias=False, ) self.conv_imag = nn.Conv1d( in_channels=1, out_channels=out_channels, kernel_size=n_fft, stride=hop_length, padding=0, dilation=1, groups=1, bias=False, ) self.conv_real.weight.data = torch.Tensor( np.real(self.W[:, 0:out_channels] * fft_window[:, None]).T )[:, None, :] # (n_fft // 2 + 1, 1, n_fft) self.conv_imag.weight.data = torch.Tensor( np.imag(self.W[:, 0:out_channels] * fft_window[:, None]).T )[:, None, :] # (n_fft // 2 + 1, 1, n_fft) if freeze_parameters: for param in self.parameters(): param.requires_grad = False def forward(self, input): """input: (batch_size, data_length) Returns: real: (batch_size, n_fft // 2 + 1, time_steps) imag: (batch_size, n_fft // 2 + 1, time_steps) """ x = input[:, None, :] # (batch_size, channels_num, data_length) if self.center: x = F.pad(x, pad=(self.n_fft // 2, self.n_fft // 2), mode=self.pad_mode) real = self.conv_real(x) imag = self.conv_imag(x) # (batch_size, n_fft // 2 + 1, time_steps) real = real[:, None, :, :].transpose(2, 3) imag = imag[:, None, :, :].transpose(2, 3) # (batch_size, 1, time_steps, n_fft // 2 + 1) return real, imag class Spectrogram(nn.Module): def __init__( self, n_fft=2048, hop_length=None, win_length=None, window="hann", center=True, pad_mode="reflect", power=2.0, freeze_parameters=True, ): """Calculate spectrogram using pytorch. The STFT is implemented with Conv1d. The function has the same output of librosa.core.stft """ super(Spectrogram, self).__init__() self.power = power self.stft = STFT( n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True, ) def forward(self, input): """input: (batch_size, 1, time_steps, n_fft // 2 + 1) Returns: spectrogram: (batch_size, 1, time_steps, n_fft // 2 + 1) """ (real, imag) = self.stft.forward(input) # (batch_size, n_fft // 2 + 1, time_steps) spectrogram = real ** 2 + imag ** 2 if self.power == 2.0: pass else: spectrogram = spectrogram ** (power / 2.0) return spectrogram class LogmelFilterBank(nn.Module): def __init__( self, sr=32000, n_fft=2048, n_mels=64, fmin=50, fmax=14000, is_log=True, ref=1.0, amin=1e-10, top_db=80.0, freeze_parameters=True, ): """Calculate logmel spectrogram using pytorch. The mel filter bank is the pytorch implementation of as librosa.filters.mel """ super(LogmelFilterBank, self).__init__() self.is_log = is_log self.ref = ref self.amin = amin self.top_db = top_db self.melW = librosa.filters.mel( sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax ).T # (n_fft // 2 + 1, mel_bins) self.melW = nn.Parameter(torch.Tensor(self.melW)) if freeze_parameters: for param in self.parameters(): param.requires_grad = False def forward(self, input): """input: (batch_size, channels, time_steps) Output: (batch_size, time_steps, mel_bins) """ # Mel spectrogram mel_spectrogram = torch.matmul(input, self.melW) # Logmel spectrogram if self.is_log: output = self.power_to_db(mel_spectrogram) else: output = mel_spectrogram return output def power_to_db(self, input): """Power to db, this function is the pytorch implementation of librosa.core.power_to_lb """ ref_value = self.ref log_spec = 10.0 * torch.log10(torch.clamp(input, min=self.amin, max=np.inf)) log_spec -= 10.0 * np.log10(np.maximum(self.amin, ref_value)) if self.top_db is not None: if self.top_db < 0: raise ParameterError("top_db must be non-negative") log_spec = torch.clamp( log_spec, min=log_spec.max().item() - self.top_db, max=np.inf ) return log_spec class DropStripes(nn.Module): def __init__(self, dim, drop_width, stripes_num): """Drop stripes. Args: dim: int, dimension along which to drop drop_width: int, maximum width of stripes to drop stripes_num: int, how many stripes to drop """ super(DropStripes, self).__init__() assert dim in [2, 3] # dim 2: time; dim 3: frequency self.dim = dim self.drop_width = drop_width self.stripes_num = stripes_num def forward(self, input): """input: (batch_size, channels, time_steps, freq_bins)""" assert input.ndimension() == 4 if self.training is False: return input else: batch_size = input.shape[0] total_width = input.shape[self.dim] for n in range(batch_size): self.transform_slice(input[n], total_width) return input def transform_slice(self, e, total_width): """e: (channels, time_steps, freq_bins)""" for _ in range(self.stripes_num): distance = torch.randint(low=0, high=self.drop_width, size=(1,))[0] bgn = torch.randint(low=0, high=total_width - distance, size=(1,))[0] if self.dim == 2: e[:, bgn : bgn + distance, :] = 0 elif self.dim == 3: e[:, :, bgn : bgn + distance] = 0 class SpecAugmentation(nn.Module): def __init__( self, time_drop_width, time_stripes_num, freq_drop_width, freq_stripes_num ): """Spec augmetation. [ref] <NAME>., <NAME>., <NAME>., <NAME>., <NAME>., <NAME>. and <NAME>., 2019. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779. Args: time_drop_width: int time_stripes_num: int freq_drop_width: int freq_stripes_num: int """ super(SpecAugmentation, self).__init__() self.time_dropper = DropStripes( dim=2, drop_width=time_drop_width, stripes_num=time_stripes_num ) self.freq_dropper = DropStripes( dim=3, drop_width=freq_drop_width, stripes_num=freq_stripes_num ) def forward(self, input): x = self.time_dropper(input) x = self.freq_dropper(x) return x def init_layer(layer): nn.init.xavier_uniform_(layer.weight) if hasattr(layer, "bias"): if layer.bias is not None: layer.bias.data.fill_(0.0) def init_bn(bn): bn.bias.data.fill_(0.0) bn.weight.data.fill_(1.0) def do_mixup(x, mixup_lambda): """Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes (1, 3, 5, ...). Args: x: (batch_size * 2, ...) mixup_lambda: (batch_size * 2,) Returns: out: (batch_size, ...) """ out = ( x[0::2].transpose(0, -1) * mixup_lambda[0::2] + x[1::2].transpose(0, -1) * mixup_lambda[1::2] ).transpose(0, -1) return out def interpolate(x: torch.Tensor, ratio: int): """Interpolate data in time domain. This is used to compensate the resolution reduction in downsampling of a CNN. Args: x: (batch_size, time_steps, classes_num) ratio: int, ratio to interpolate Returns: upsampled: (batch_size, time_steps * ratio, classes_num) """ (batch_size, time_steps, classes_num) = x.shape upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1) upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num) return upsampled def pad_framewise_output(framewise_output: torch.Tensor, frames_num: int): """Pad framewise_output to the same length as input frames. The pad value is the same as the value of the last frame. Args: framewise_output: (batch_size, frames_num, classes_num) frames_num: int, number of frames to pad Outputs: output: (batch_size, frames_num, classes_num) """ pad = framewise_output[:, -1:, :].repeat( 1, frames_num - framewise_output.shape[1], 1 ) """tensor for padding""" output = torch.cat((framewise_output, pad), dim=1) """(batch_size, frames_num, classes_num)""" return output class ConvBlock(nn.Module): def __init__(self, in_channels: int, out_channels: int): super().__init__() self.conv1 = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False, ) self.conv2 = nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False, ) self.bn1 = nn.BatchNorm2d(out_channels) self.bn2 = nn.BatchNorm2d(out_channels) self.init_weight() def init_weight(self): init_layer(self.conv1) init_layer(self.conv2) init_bn(self.bn1) init_bn(self.bn2) def forward(self, input, pool_size=(2, 2), pool_type="avg"): x = input x = F.relu_(self.bn1(self.conv1(x))) x = F.relu_(self.bn2(self.conv2(x))) if pool_type == "max": x = F.max_pool2d(x, kernel_size=pool_size) elif pool_type == "avg": x = F.avg_pool2d(x, kernel_size=pool_size) elif pool_type == "avg+max": x1 = F.avg_pool2d(x, kernel_size=pool_size) x2 = F.max_pool2d(x, kernel_size=pool_size) x = x1 + x2 else: raise Exception("Incorrect argument!") return x class AttBlock(nn.Module): def __init__( self, in_features: int, out_features: int, activation="linear", temperature=1.0 ): super().__init__() self.activation = activation self.temperature = temperature self.att = nn.Conv1d( in_channels=in_features, out_channels=out_features, kernel_size=1, stride=1, padding=0, bias=True, ) self.cla = nn.Conv1d( in_channels=in_features, out_channels=out_features, kernel_size=1, stride=1, padding=0, bias=True, ) self.bn_att = nn.BatchNorm1d(out_features) self.init_weights() def init_weights(self): init_layer(self.att) init_layer(self.cla) init_bn(self.bn_att) def forward(self, x): # x: (n_samples, n_in, n_time) norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) cla = self.nonlinear_transform(self.cla(x)) x = torch.sum(norm_att * cla, dim=2) return x, norm_att, cla def nonlinear_transform(self, x): if self.activation == "linear": return x elif self.activation == "sigmoid": return torch.sigmoid(x) class PANNsCNN14Att(nn.Module): def __init__( self, sample_rate: int, window_size: int, hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int, ): super().__init__() window = "hann" center = True pad_mode = "reflect" ref = 1.0 amin = 1e-10 top_db = None self.interpolate_ratio = 32 # Downsampled ratio # Spectrogram extractor self.spectrogram_extractor = Spectrogram( n_fft=window_size, hop_length=hop_size, win_length=window_size, window=window, center=center, pad_mode=pad_mode, freeze_parameters=True, ) # Logmel feature extractor self.logmel_extractor = LogmelFilterBank( sr=sample_rate, n_fft=window_size, n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, freeze_parameters=True, ) # Spec augmenter self.spec_augmenter = SpecAugmentation( time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2, ) self.bn0 = nn.BatchNorm2d(mel_bins) self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) self.fc1 = nn.Linear(2048, 2048, bias=True) self.att_block = AttBlock(2048, classes_num, activation="sigmoid") self.init_weight() def init_weight(self): init_bn(self.bn0) init_layer(self.fc1) def cnn_feature_extractor(self, x): x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg") x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg") x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg") x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg") x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg") x = F.dropout(x, p=0.2, training=self.training) x = self.conv_block6(x, pool_size=(1, 1), pool_type="avg") x = F.dropout(x, p=0.2, training=self.training) return x def preprocess(self, input, mixup_lambda=None): # t1 = time.time() x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) frames_num = x.shape[2] x = x.transpose(1, 3) x = self.bn0(x) x = x.transpose(1, 3) if self.training: x = self.spec_augmenter(x) # Mixup on spectrogram if self.training and mixup_lambda is not None: x = do_mixup(x, mixup_lambda) return x, frames_num def forward(self, input, mixup_lambda=None): """ Input: (batch_size, data_length)""" x, frames_num = self.preprocess(input, mixup_lambda=mixup_lambda) # Output shape (batch size, channels, time, frequency) x = self.cnn_feature_extractor(x) # Aggregate in frequency axis x = torch.mean(x, dim=3) x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) x = x1 + x2 x = F.dropout(x, p=0.5, training=self.training) x = x.transpose(1, 2) x = F.relu_(self.fc1(x)) x = x.transpose(1, 2) x = F.dropout(x, p=0.5, training=self.training) (clipwise_output, norm_att, segmentwise_output) = self.att_block(x) segmentwise_output = segmentwise_output.transpose(1, 2) # Get framewise output framewise_output = interpolate(segmentwise_output, self.interpolate_ratio) framewise_output = pad_framewise_output(framewise_output, frames_num) output_dict = { "framewise_output": framewise_output, "clipwise_output": clipwise_output, } return output_dict class PANNsDataset(data.Dataset): # def __init__(self, file_list: List[List[str]], waveform_transforms=None, period=5): def __init__( self, df, datadir, waveform_transforms=None, period=5, sample_rate=32000 ): # self.file_list = file_list # list of list: [file_path, ebird_code] self.df = df self.datadir = datadir self.waveform_transforms = waveform_transforms self.period = period self.sample_rate = sample_rate def __len__(self): return len(self.df) def __getitem__(self, idx: int): sample = self.df.iloc[idx, :] # wav_name = sample["resampled_filename"] wav_name = sample["filename"] # wav_name = wav_name.replace("mp3", "wav") wav_name = wav_name.replace("mp3", "npy") wav_name = wav_name.replace("wav", "npy") ebird_code = sample["ebird_code"] duration = sample["duration"] wav_path = self.datadir / ebird_code / wav_name # y, sr = sf.read(self.datadir / ebird_code / wav_name) effective_length = self.sample_rate * self.period # wav_path, ebird_code = self.file_list[idx] # y, sr = sf.read(wav_path) y = np.load(wav_path) if self.waveform_transforms: y = self.waveform_transforms(y) else: len_y = len(y) effective_length = self.sample_rate * self.period if len_y < effective_length: new_y = np.zeros(effective_length, dtype=y.dtype) start = np.random.randint(effective_length - len_y) new_y[start : start + len_y] = y y = new_y.astype(np.float32) elif len_y > effective_length: start = np.random.randint(len_y - effective_length) y = y[start : start + effective_length].astype(np.float32) else: y = y.astype(np.float32) labels = np.zeros(len(utils.BIRD_CODE), dtype="f") labels[utils.BIRD_CODE[ebird_code]] = 1 return {"waveform": y, "targets": labels} class PANNsLoss(nn.Module): def __init__(self): super().__init__() self.bce = nn.BCELoss() def forward(self, input, target): input_ = input["clipwise_output"] input_ = torch.where(torch.isnan(input_), torch.zeros_like(input_), input_) input_ = torch.where(torch.isinf(input_), torch.zeros_like(input_), input_) target = target.float() return self.bce(input_, target) class F1Callback(Callback): def __init__( self, input_key: str = "targets", output_key: str = "logits", model_output_key: str = "clipwise_output", prefix: str = "f1", ): super().__init__(CallbackOrder.Metric) self.input_key = input_key self.output_key = output_key self.model_output_key = model_output_key self.prefix = prefix def on_loader_start(self, state: State): self.prediction: List[np.ndarray] = [] self.target: List[np.ndarray] = [] def on_batch_end(self, state: State): targ = state.input[self.input_key].detach().cpu().numpy() out = state.output[self.output_key] clipwise_output = out[self.model_output_key].detach().cpu().numpy() self.prediction.append(clipwise_output) self.target.append(targ) y_pred = clipwise_output.argmax(axis=1) y_true = targ.argmax(axis=1) score = f1_score(y_true, y_pred, average="macro") state.batch_metrics[self.prefix] = score def on_loader_end(self, state: State): y_pred = np.concatenate(self.prediction, axis=0).argmax(axis=1) y_true = np.concatenate(self.target, axis=0).argmax(axis=1) score = f1_score(y_true, y_pred, average="macro") state.loader_metrics[self.prefix] = score if state.is_valid_loader: state.epoch_metrics[state.valid_loader + "_epoch_" + self.prefix] = score else: state.epoch_metrics["train_epoch_" + self.prefix] = score class mAPCallback(Callback): def __init__( self, input_key: str = "targets", output_key: str = "logits", model_output_key: str = "clipwise_output", prefix: str = "mAP", ): super().__init__(CallbackOrder.Metric) self.input_key = input_key self.output_key = output_key self.model_output_key = model_output_key self.prefix = prefix def on_loader_start(self, state: State): self.prediction: List[np.ndarray] = [] self.target: List[np.ndarray] = [] def on_batch_end(self, state: State): targ = state.input[self.input_key].detach().cpu().numpy() out = state.output[self.output_key] clipwise_output = out[self.model_output_key].detach().cpu().numpy() self.prediction.append(clipwise_output) self.target.append(targ) score = average_precision_score(targ, clipwise_output, average=None) score = np.nan_to_num(score).mean() state.batch_metrics[self.prefix] = score def on_loader_end(self, state: State): y_pred = np.concatenate(self.prediction, axis=0) y_true = np.concatenate(self.target, axis=0) score = average_precision_score(y_true, y_pred, average=None) score = np.nan_to_num(score).mean() state.loader_metrics[self.prefix] = score if state.is_valid_loader: state.epoch_metrics[state.valid_loader + "_epoch_" + self.prefix] = score else: state.epoch_metrics["train_epoch_" + self.prefix] = score def get_model(config: dict, weights_path=None): model = PANNsCNN14Att(**config) model.att_block = AttBlock(2048, 264, activation="sigmoid") if weights_path: checkpoint = torch.load(weights_path) state_dict = checkpoint["model_state_dict"] new_state_dict = OrderedDict() for k, v in state_dict.items(): name = k if k[:7] == "module.": name = k[7:] # 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src/pykeen/datasets/nations/__init__.py
cthoyt/pykeen
1
133774
<gh_stars>1-10 # -*- coding: utf-8 -*- """Get triples from the Nations dataset.""" import os from ..base import PathDataset from ..literal_base import NumericPathDataset __all__ = [ 'NATIONS_TRAIN_PATH', 'NATIONS_TEST_PATH', 'NATIONS_VALIDATE_PATH', 'NATIONS_LITERALS_PATH', 'Nations', 'NationsLiteral', ] HERE = os.path.abspath(os.path.dirname(__file__)) NATIONS_TRAIN_PATH = os.path.join(HERE, 'train.txt') NATIONS_TEST_PATH = os.path.join(HERE, 'test.txt') NATIONS_VALIDATE_PATH = os.path.join(HERE, 'valid.txt') NATIONS_LITERALS_PATH = os.path.join(HERE, 'literals.txt') class Nations(PathDataset): """The Nations dataset.""" def __init__(self, **kwargs): super().__init__( training_path=NATIONS_TRAIN_PATH, testing_path=NATIONS_TEST_PATH, validation_path=NATIONS_VALIDATE_PATH, **kwargs, ) class NationsLiteral(NumericPathDataset): """The Nations dataset with literals.""" def __init__(self, **kwargs): super().__init__( training_path=NATIONS_TRAIN_PATH, testing_path=NATIONS_TEST_PATH, validation_path=NATIONS_VALIDATE_PATH, literals_path=NATIONS_LITERALS_PATH, **kwargs, )
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script_makevoc.py
skiteskopes/datasetCulling
1
174687
# -*- coding: utf-8 -*- """ script for setting up pascal-like datasets copyright:kentaroy47 10/2/2018 """ import os import subprocess import argparse parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') parser.add_argument('--dataset', dest='dataset', help='training dataset', type=str) parser.add_argument('--numobj', dest='numobj', help='training dataset', type=str, default='0') parser.add_argument('--sample', dest='sample', help='training dataset', type=str, default='0') parser.add_argument('--filterpix', dest='filterpix', help='filter pixel size', type=str, default=20) parser.add_argument('--topx', dest='topx', help='filter pixel size', type=str, default="64") parser.add_argument('--net', dest='net', help='target network', type=str, default="res101") parser.add_argument('--dsm', dest='dsm', help='filter pixel size', type=bool, default=False) args = parser.parse_args() target = args.topx + "-" +args.dataset filterpix = str(args.filterpix) # make dir in datasets datasetname = "VOCdevkit"+target command="mkdir data/"+datasetname subprocess.call(command, shell=True) command="mkdir data/"+datasetname+"/VOC2007/" subprocess.call(command, shell=True) command="mkdir data/"+datasetname+"/VOC2007/JPEGImages" subprocess.call(command, shell=True) command="mkdir data/"+datasetname+"/VOC2007/Annotations" subprocess.call(command, shell=True) command="mkdir data/"+datasetname+"/VOC2007/ImageSets" subprocess.call(command, shell=True) command="mkdir data/"+datasetname+"/VOC2007/ImageSets/Main" subprocess.call(command, shell=True) # generate xml args.numobj = str(args.numobj) if args.dsm: command="python xml_makelabels_domain.py --dataset "+target+" --numobj "+args.numobj + \ " --sample "+args.sample+" --filterpix "+filterpix+" --net "+args.net+" --dsm "+str(args.dsm) else: command="python xml_makelabels_domain.py --topx "+args.topx+" --dataset "+target+" --numobj "+args.numobj + \ " --sample "+args.sample+" --filterpix "+filterpix+" --net "+args.net print("com:", command) subprocess.call(command, shell=True) # make link command="cp -rf output/"+target+"-train-labels-"+args.net+"/* data/"+datasetname+"/VOC2007/Annotations/" subprocess.call(command, shell=True) command="cp -rf output/"+target+"-test-labels-"+args.net+"/* data/"+datasetname+"/VOC2007/Annotations/" subprocess.call(command, shell=True) command="cp -rf images/"+target+"_train/* data/"+datasetname+"/VOC2007/JPEGImages/" subprocess.call(command, shell=True) command="cp -rf /data2/lost+found/img/"+target+"_val/* data/"+datasetname+"/VOC2007/JPEGImages/" subprocess.call(command, shell=True) # copy text file to Main command="cp trainval/trainval_"+target+".txt data/"+datasetname+"/VOC2007/ImageSets/Main/" subprocess.call(command, shell=True) # make models and copy command="mkdir models/res18/pascal_voc_"+target subprocess.call(command, shell=True) command="cp models/res18/pascal_voc/faster_rcnn_500_40_625.pth models/res18/pascal_voc_"+target subprocess.call(command, shell=True) #command="mkdir models/squeeze/pascal_voc_"+target #subprocess.call(command, shell=True) #command="cp models/squeeze/faster_rcnn_500_40_625.pth models/squeeze/pascal_voc_"+target #subprocess.call(command, shell=True)
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plugins/jsmin.py
magentix/magentix.github.io
0
164740
""" Copyright (c) 2022, Magentix This code is licensed under simplified BSD license (see LICENSE for details) StaPy JsMin Plugin - Version 1.0.0 Requirements: - jsmin """ from pathlib import Path import jsmin import os def file_content_opened(content, args: dict) -> str: if _get_file_extension(args['path']) != 'js': return content return jsmin.jsmin(content.decode()).encode() def file_copy_before(source: str, args: dict) -> str: if _get_file_extension(source) != 'js': return source return _get_min_file(source, jsmin.jsmin(_get_file_content(source))) def _get_min_file(source: str, content: str) -> str: file = open(_get_tmp_file_path(source), 'w', encoding='utf-8') file.write(content) file.close() return _get_tmp_file_path(source) def _get_tmp_file_path(source: str) -> str: name = os.path.normpath(_get_current_directory() + '/../tmp/' + os.path.basename(source) + '.min') _create_directory(name) return name def _create_directory(path) -> None: if _get_file_extension(path): path = os.path.dirname(path) Path(os.path.normpath(path)).mkdir(parents=True, exist_ok=True) def _get_current_directory() -> str: return os.path.dirname(os.path.realpath(__file__)) def _get_file_content(source: str) -> str: file = open(os.path.normpath(source), 'r', encoding="utf-8") content = file.read() file.close() return content def _get_file_extension(file: str) -> str: name, extension = os.path.splitext(file) if not extension: extension = '' return extension.replace('.', '')
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dags/utils/voting/fetch_polls.py
makerdao-data/airflow-docker-image
0
187961
from dags.utils.bq_adapters import extract_calls_gas def _fetch_polls(**setup): calls = extract_calls_gas( setup['start_block'] + 1, setup['end_block'], setup['start_time'], setup['end_time'], (setup['polls_address'],), setup['polls_abi'], ) records = [ [ setup['load_id'], call.block_number, call.timestamp.__str__()[:19], call.breadcrumb, call.tx_hash, call.tx_index, call.type, str(call.value), call.from_address, call.to_address, call.function, call.arguments, call.outputs, call.error, call.status, call.gas_used, call.gas_price, ] for call in calls if call.function != '' ] calls_num = len(calls) calls = extract_calls_gas( setup['start_block'] + 1, setup['end_block'], setup['start_time'], setup['end_time'], (setup['new_polls_address'],), setup['new_polls_abi'], ) for call in calls: if call.function == 'voteMany': for i, poll in enumerate(call.arguments[0]['value']): option = call.arguments[1]['value'][i] if i < len(call.arguments[1]['value']) else None arguments = [ dict(name='pollId', type='uint256', value=poll), dict(name='optionId', type='uint256', value=option), ] record = [ setup['load_id'], call.block_number, call.timestamp.__str__()[:19], call.breadcrumb, call.tx_hash, call.tx_index, call.type, str(call.value), call.from_address, call.to_address, 'vote', arguments, [], call.error, call.status, call.gas_used / len(call.arguments[0]['value']), call.gas_price, ] records.append(record) elif call.function == 'withdrawPolls': for poll in call.arguments[0]['value']: arguments = [dict(name='pollId', type='uint256', value=poll)] record = [ setup['load_id'], call.block_number, call.timestamp.__str__()[:19], call.breadcrumb, call.tx_hash, call.tx_index, call.type, str(call.value), call.from_address, call.to_address, 'withdrawPoll', arguments, [], call.error, call.status, call.gas_used, call.gas_price, ] records.append(record) else: record = [ setup['load_id'], call.block_number, call.timestamp.__str__()[:19], call.breadcrumb, call.tx_hash, call.tx_index, call.type, str(call.value), call.from_address, call.to_address, call.function, call.arguments, call.outputs, call.error, call.status, call.gas_used, call.gas_price, ] records.append(record) print(f"POLLS: {len(calls) + calls_num} read, {len(records)} written") return records
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django_socketio_chat/admin.py
leukeleu/django-socketio-chat
6
140003
<filename>django_socketio_chat/admin.py from django.contrib import admin from .models import Chat, UserChatStatus, Message class ChatAdmin(admin.ModelAdmin): pass class UserChatStatusAdmin(admin.ModelAdmin): pass class MessageAdmin(admin.ModelAdmin): pass admin.site.register(Chat, ChatAdmin) admin.site.register(UserChatStatus, UserChatStatusAdmin) admin.site.register(Message, MessageAdmin)
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qcenginerecords/tests/test_validate.py
eljost/QCEngineRecords
1
119080
<gh_stars>1-10 import pytest import qcenginerecords as qcev import qcelemental as qcel def test_list_programs(): assert "molpro" in qcev.list_programs() assert "entos" in qcev.list_programs() assert "terachem" in qcev.list_programs() # Build out a list of all tests and validate validation_tests = [] for program in qcev.list_programs(): info = qcev.get_info(program) for case in qcev.list_test_cases(program): validation_tests.append(pytest.param(info, case, id="{}-{}".format(program, case))) @pytest.mark.parametrize("info, test_case", validation_tests) def test_validate_test_case(info, test_case): filenames = qcev.get_test_case_filenames(info.program, test_case) # Check all required files are present assert filenames.keys() >= info.required_files, "Missing required files for testing." assert filenames.keys() <= (info.required_files | info.optional_files), "Unknown files found." # Ensure the `input.json` is valid qcel.models.ResultInput.parse_file(filenames["input.json"]) # Ensure the `output.json` is valid # qcel.models.Result.parse_file(filenames["output.json"])
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ANSIBLE/library/eos_routemap.py
ayosef/pynet_test
0
18305
#!/usr/bin/python # # Copyright (c) 2015, Arista Networks, 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 Arista Networks 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 ARISTA NETWORKS # 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. # DOCUMENTATION = """ --- module: eos_routemap short_description: Manage EOS routemap resources description: - This module will manage routemap entries on EOS nodes version_added: 1.2.0 category: Route Policy author: Arista EOS+ requirements: - Arista EOS 4.13.7M or later with command API enabled - Python Client for eAPI 0.4.0 or later notes: - All configuration is idempotent unless otherwise specified - Supports eos metaparameters for using the eAPI transport - Supports stateful resource configuration. options: name: description: - The name of the routemap to manage. required: true default: null choices: [] aliases: [] version_added: 1.2.0 action: description: - The action associated with the routemap name. required: true default: 'permit' choices: ['permit','deny'] aliases: [] version_added: 1.2.0 seqno: description: - The sequence number of the rule that this entry corresponds to. required: true default: null choices: [] aliases: [] version_added: 1.2.0 description: description: - The description for this routemap entry. required: false default: null choices: [] aliases: [] version_added: 1.2.0 match: description: - The list of match statements that define the routemap entry. The match statements should be a comma separated list of match statements without the word match at the beginning of the string. See the example below for more information. required: false default: null choices: [] aliases: [] version_added: 1.2.0 set: description: - The list of set statements that define the routemap entry. The set statements should be a comma separated list of set statements without the word set at the beginning of the string. See the example below for more information. required: false default: null choices: [] aliases: [] version_added: 1.2.0 continue: description: - The statement defines the next routemap clause to evaluate. required: false default: null choices: [] aliases: [] version_added: 1.2.0 """ EXAMPLES = """ - eos_routemap: name=rm1 action=permit seqno=10 description='this is a great routemap' match='as 50,interface Ethernet2' set='tag 100,weight 1000' continue=20 """ #<<EOS_COMMON_MODULE_START>> import syslog import collections from ansible.module_utils.basic import * try: import pyeapi PYEAPI_AVAILABLE = True except ImportError: PYEAPI_AVAILABLE = False DEFAULT_SYSLOG_PRIORITY = syslog.LOG_NOTICE DEFAULT_CONNECTION = 'localhost' TRANSPORTS = ['socket', 'http', 'https', 'http_local'] class EosConnection(object): __attributes__ = ['username', 'password', 'host', 'transport', 'port'] def __init__(self, **kwargs): self.connection = kwargs['connection'] self.transport = kwargs.get('transport') self.username = kwargs.get('username') self.password = kwargs.get('password') self.host = kwargs.get('host') self.port = kwargs.get('port') self.config = kwargs.get('config') def connect(self): if self.config is not None: pyeapi.load_config(self.config) config = dict() if self.connection is not None: config = pyeapi.config_for(self.connection) if not config: msg = 'Connection name "{}" not found'.format(self.connection) for key in self.__attributes__: if getattr(self, key) is not None: config[key] = getattr(self, key) if 'transport' not in config: raise ValueError('Connection must define a transport') connection = pyeapi.client.make_connection(**config) node = pyeapi.client.Node(connection, **config) try: node.enable('show version') except (pyeapi.eapilib.ConnectionError, pyeapi.eapilib.CommandError): raise ValueError('unable to connect to {}'.format(node)) return node class EosAnsibleModule(AnsibleModule): meta_args = { 'config': dict(), 'username': dict(), 'password': dict(), 'host': dict(), 'connection': dict(default=DEFAULT_CONNECTION), 'transport': dict(choices=TRANSPORTS), 'port': dict(), 'debug': dict(type='bool', default='false'), 'logging': dict(type='bool', default='true') } stateful_args = { 'state': dict(default='present', choices=['present', 'absent']), } def __init__(self, stateful=True, autorefresh=False, *args, **kwargs): kwargs['argument_spec'].update(self.meta_args) self._stateful = stateful if stateful: kwargs['argument_spec'].update(self.stateful_args) ## Ok, so in Ansible 2.0, ## AnsibleModule.__init__() sets self.params and then ## calls self.log() ## (through self._log_invocation()) ## ## However, self.log() (overridden in EosAnsibleModule) ## references self._logging ## and self._logging (defined in EosAnsibleModule) ## references self.params. ## ## So ... I'm defining self._logging without "or self.params['logging']" ## *before* AnsibleModule.__init__() to avoid a "ref before def". ## ## I verified that this works with Ansible 1.9.4 and 2.0.0.2. ## The only caveat is that the first log message in ## AnsibleModule.__init__() won't be subject to the value of ## self.params['logging']. self._logging = kwargs.get('logging') super(EosAnsibleModule, self).__init__(*args, **kwargs) self.result = dict(changed=False, changes=dict()) self._debug = kwargs.get('debug') or self.boolean(self.params['debug']) self._logging = kwargs.get('logging') or self.params['logging'] self.log('DEBUG flag is %s' % self._debug) self.debug('pyeapi_version', self.check_pyeapi()) self.debug('stateful', self._stateful) self.debug('params', self.params) self._attributes = self.map_argument_spec() self.validate() self._autorefresh = autorefresh self._node = EosConnection(**self.params) self._node.connect() self._node = self.connect() self._instance = None self.desired_state = self.params['state'] if self._stateful else None self.exit_after_flush = kwargs.get('exit_after_flush') @property def instance(self): if self._instance: return self._instance func = self.func('instance') if not func: self.fail('Module does not support "instance"') try: self._instance = func(self) except Exception as exc: self.fail('instance[error]: %s' % exc.message) self.log("called instance: %s" % self._instance) return self._instance @property def attributes(self): return self._attributes @property def node(self): return self._node def check_pyeapi(self): if not PYEAPI_AVAILABLE: self.fail('Unable to import pyeapi, is it installed?') return pyeapi.__version__ def map_argument_spec(self): """map_argument_spec maps only the module argument spec to attrs This method will map the argumentspec minus the meta_args to attrs and return the attrs. This returns a dict object that includes only the original argspec plus the stateful_args (if self._stateful=True) Returns: dict: Returns a dict object that includes the original argument_spec plus stateful_args with values minus meta_args """ keys = set(self.params).difference(self.meta_args) attrs = dict() attrs = dict([(k, self.params[k]) for k in self.params if k in keys]) if 'CHECKMODE' in attrs: del attrs['CHECKMODE'] return attrs def validate(self): for key, value in self.attributes.iteritems(): func = self.func('validate_%s' % key) if func: self.attributes[key] = func(value) def create(self): if not self.check_mode: func = self.func('create') if not func: self.fail('Module must define "create" function') return self.invoke(func, self) def remove(self): if not self.check_mode: func = self.func('remove') if not func: self.fail('Module most define "remove" function') return self.invoke(func, self) def flush(self, exit_after_flush=False): self.exit_after_flush = exit_after_flush if self.desired_state == 'present' or not self._stateful: if self.instance.get('state') == 'absent': changed = self.create() self.result['changed'] = changed or True self.refresh() # After a create command, flush the running-config # so we get the latest for any other attributes self._node._running_config = None changeset = self.attributes.viewitems() - self.instance.viewitems() if self._debug: self.debug('desired_state', self.attributes) self.debug('current_state', self.instance) changes = self.update(changeset) if changes: self.result['changes'] = changes self.result['changed'] = True self._attributes.update(changes) flush = self.func('flush') if flush: self.invoke(flush, self) elif self.desired_state == 'absent' and self._stateful: if self.instance.get('state') == 'present': changed = self.remove() self.result['changed'] = changed or True elif self._stateful: if self.desired_state != self.instance.get('state'): func = self.func(self.desired_state) changed = self.invoke(func, self) self.result['changed'] = changed or True self.refresh() # By calling self.instance here we trigger another show running-config # all which causes delay. Only if debug is enabled do we call this # since it will display the latest state of the object. if self._debug: self.result['instance'] = self.instance if self.exit_after_flush: self.exit() def update(self, changeset): changes = dict() for key, value in changeset: if value is not None: changes[key] = value func = self.func('set_%s' % key) if func and not self.check_mode: try: self.invoke(func, self) except Exception as exc: self.fail(exc.message) return changes def connect(self): if self.params['config']: pyeapi.load_config(self.params['config']) config = dict() if self.params['connection']: config = pyeapi.config_for(self.params['connection']) if not config: msg = 'Connection name "%s" not found' % self.params['connection'] self.fail(msg) if self.params['username']: config['username'] = self.params['username'] if self.params['password']: config['password'] = self.params['password'] if self.params['transport']: config['transport'] = self.params['transport'] if self.params['port']: config['port'] = self.params['port'] if self.params['host']: config['host'] = self.params['host'] if 'transport' not in config: self.fail('Connection must define a transport') connection = pyeapi.client.make_connection(**config) self.log('Creating connection with autorefresh=%s' % self._autorefresh) node = pyeapi.client.Node(connection, autorefresh=self._autorefresh, **config) try: resp = node.enable('show version') self.debug('eos_version', resp[0]['result']['version']) self.debug('eos_model', resp[0]['result']['modelName']) except (pyeapi.eapilib.ConnectionError, pyeapi.eapilib.CommandError): self.fail('unable to connect to %s' % node) else: self.log('Connected to node %s' % node) self.debug('node', str(node)) return node def config(self, commands): self.result['changed'] = True if not self.check_mode: self.node.config(commands) def api(self, module): return self.node.api(module) def func(self, name): return globals().get(name) def invoke(self, func, *args, **kwargs): try: return func(*args, **kwargs) except Exception as exc: self.fail(exc.message) def invoke_function(self, name, *args, **kwargs): func = self.func(name) if func: return self.invoke(func, *args, **kwargs) def fail(self, msg): self.invoke_function('on_fail', self) self.log('ERROR: %s' % msg, syslog.LOG_ERR) self.fail_json(msg=msg) def exit(self): self.invoke_function('on_exit', self) self.log('Module completed successfully') self.exit_json(**self.result) def refresh(self): self._instance = None def debug(self, key, value): if self._debug: if 'debug' not in self.result: self.result['debug'] = dict() self.result['debug'][key] = value def log(self, message, log_args=None, priority=None): if self._logging: syslog.openlog('ansible-eos') priority = priority or DEFAULT_SYSLOG_PRIORITY syslog.syslog(priority, str(message)) @classmethod def add_state(cls, name): cls.stateful_args['state']['choices'].append(name) #<<EOS_COMMON_MODULE_END>> def instance(module): """ Returns an instance of Routemaps based on name, action and sequence number. """ name = module.attributes['name'] action = module.attributes['action'] seqno = int(module.attributes['seqno']) _instance = dict(name=name, action=action, seqno=seqno, state='absent') try: result = module.api('routemaps').get(name)[action][seqno] except: result = None if result: _instance['state'] = 'present' _instance['seqno'] = str(seqno) _instance['set'] = ','.join(result['set']) desc = result['description'] _instance['description'] = desc if desc else '' _instance['match'] = ','.join(result['match']) cont = result['continue'] _instance['continue'] = str(cont) if cont else '' return _instance def create(module): name = module.attributes['name'] action = module.attributes['action'] seqno = int(module.attributes['seqno']) module.log('Invoked create for eos_routemap[%s %s %s]' % (name, action, seqno)) module.api('routemaps').create(name, action, seqno) def remove(module): name = module.attributes['name'] action = module.attributes['action'] seqno = int(module.attributes['seqno']) module.log('Invoked remove for eos_routemap[%s %s %s]' % (name, action, seqno)) module.api('routemaps').delete(name, action, seqno) def set_description(module): """ Configures the description for the routemap """ name = module.attributes['name'] action = module.attributes['action'] seqno = int(module.attributes['seqno']) value = module.attributes['description'] module.log('Invoked set_description with %s for eos_routemap[%s %s %s]' % (value, name, action, seqno)) if value == '': module.node.api('routemaps').set_description(name, action, seqno, disable=True) else: module.node.api('routemaps').set_description(name, action, seqno, value) def set_continue(module): """ Configures the continue value for the routemap """ name = module.attributes['name'] action = module.attributes['action'] seqno = int(module.attributes['seqno']) try: value = int(module.attributes['continue']) except: value = None module.log('Invoked set_continue for eos_routemap[%s %s %s]' % (name, action, seqno)) if value is None: module.node.api('routemaps').set_continue(name, action, seqno, disable=True) else: module.node.api('routemaps').set_continue(name, action, seqno, value) def set_match(module): """ Configures the match statements for the routemap """ name = module.attributes['name'] action = module.attributes['action'] seqno = int(module.attributes['seqno']) statements = module.attributes['match'].split(',') module.log('Invoked set_match for eos_routemap[%s %s %s]' % (name, action, seqno)) module.node.api('routemaps').set_match_statements(name, action, seqno, statements) def set_set(module): """ Configures the set statements for the routemap """ name = module.attributes['name'] action = module.attributes['action'] seqno = int(module.attributes['seqno']) statements = module.attributes['set'].split(',') module.log('Invoked set_set for eos_routemap[%s %s %s]' % (name, action, seqno)) module.node.api('routemaps').set_set_statements(name, action, seqno, statements) def main(): """ The main module routine called when the module is run by Ansible """ argument_spec = dict( name=dict(required=True), action=dict(default='permit', choices=['permit', 'deny']), seqno=dict(required=True), description=dict(), match=dict(), set=dict() ) argument_spec['continue'] = dict() module = EosAnsibleModule(argument_spec=argument_spec, supports_check_mode=True) module.flush(True) main()
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emo/templatetags/emoji_dict.py
richardcornish/django-emobar
1
1604260
emoji_dict = { ':smile:': 'smile.png', ':smiley:': 'smiley.png', ':grinning:': 'grinning.png', ':blush:': 'blush.png', ':relaxed:': 'relaxed.png', ':wink:': 'wink.png', ':heart_eyes:': 'heart_eyes.png', ':kissing_heart:': 'kissing_heart.png', ':kissing_closed_eyes:': 'kissing_closed_eyes.png', ':kissing:': 'kissing.png', ':kissing_smiling_eyes:': 'kissing_smiling_eyes.png', ':wink2:': 'wink2.png', ':stuck_out_tongue_closed_eyes:': 'stuck_out_tongue_closed_eyes.png', ':stuck_out_tongue:': 'stuck_out_tongue.png', ':flushed:': 'flushed.png', ':grin:': 'grin.png', ':pensive:': 'pensive.png', ':satisfied:': 'satisfied.png', ':unamused:': 'unamused.png', ':disappointed:': 'disappointed.png', ':persevere:': 'persevere.png', ':cry:': 'cry.png', ':joy:': 'joy.png', ':sob:': 'sob.png', ':sleepy:': 'sleepy.png', ':relieved:': 'relieved.png', ':cold_sweat:': 'cold_sweat.png', ':sweat_smile:': 'sweat_smile.png', ':sweat:': 'sweat.png', ':weary:': 'weary.png', ':tired_face:': 'tired_face.png', ':fearful:': 'fearful.png', ':scream:': 'scream.png', ':angry:': 'angry.png', ':rage:': 'rage.png', ':triumph:': 'triumph.png', ':confounded:': 'confounded.png', ':laughing:': 'laughing.png', ':yum:': 'yum.png', ':mask:': 'mask.png', ':sunglasses:': 'sunglasses.png', ':sleeping:': 'sleeping.png', ':dizzy_face:': 'dizzy_face.png', ':astonished:': 'astonished.png', ':worried:': 'worried.png', ':frowning:': 'frowning.png', ':anguished:': 'anguished.png', ':smiling_imp:': 'smiling_imp.png', ':imp:': 'imp.png', ':open_mouth:': 'open_mouth.png', ':grimacing:': 'grimacing.png', ':neutral_face:': 'neutral_face.png', ':confused:': 'confused.png', ':hushed:': 'hushed.png', ':no_mouth:': 'no_mouth.png', ':innocent:': 'innocent.png', ':smirk:': 'smirk.png', ':expressionless:': 'expressionless.png', ':man_with_gua_pi_mao:': 'man_with_gua_pi_mao.png', ':man_with_turban:': 'man_with_turban.png', ':cop:': 'cop.png', ':construction_worker:': 'construction_worker.png', ':guardsman:': 'guardsman.png', ':baby:': 'baby.png', ':boy:': 'boy.png', ':girl:': 'girl.png', ':man:': 'man.png', ':woman:': 'woman.png', ':older_man:': 'older_man.png', ':older_woman:': 'older_woman.png', ':person_with_blond_hair:': 'person_with_blond_hair.png', ':angel:': 'angel.png', ':princess:': 'princess.png', ':smiley_cat:': 'smiley_cat.png', ':smile_cat:': 'smile_cat.png', ':heart_eyes_cat:': 'heart_eyes_cat.png', ':kissing_cat:': 'kissing_cat.png', ':smirk_cat:': 'smirk_cat.png', ':scream_cat:': 'scream_cat.png', ':crying_cat_face:': 'crying_cat_face.png', ':joy_cat:': 'joy_cat.png', ':pouting_cat:': 'pouting_cat.png', ':japanese_ogre:': 'japanese_ogre.png', ':japanese_goblin:': 'japanese_goblin.png', ':see_no_evil:': 'see_no_evil.png', ':hear_no_evil:': 'hear_no_evil.png', ':speak_no_evil:': 'speak_no_evil.png', ':skull:': 'skull.png', ':alien:': 'alien.png', ':poop:': 'poop.png', ':fire:': 'fire.png', ':sparkles:': 'sparkles.png', ':star2:': 'star2.png', ':dizzy:': 'dizzy.png', ':boom:': 'boom.png', ':anger:': 'anger.png', ':sweat_drops:': 'sweat_drops.png', ':droplet:': 'droplet.png', ':zzz:': 'zzz.png', ':dash:': 'dash.png', ':ear:': 'ear.png', ':eyes:': 'eyes.png', ':nose:': 'nose.png', ':tongue:': 'tongue.png', ':lips:': 'lips.png', ':thumbsup:': 'thumbsup.png', ':thumbsdown:': 'thumbsdown.png', ':ok_hand:': 'ok_hand.png', ':punch:': 'punch.png', ':fist:': 'fist.png', ':v:': 'v.png', ':wave:': 'wave.png', ':hand:': 'hand.png', ':open_hands:': 'open_hands.png', ':point_up_2:': 'point_up_2.png', ':point_down:': 'point_down.png', ':point_right:': 'point_right.png', ':point_left:': 'point_left.png', ':raised_hands:': 'raised_hands.png', ':pray:': 'pray.png', ':point_up:': 'point_up.png', ':clap:': 'clap.png', ':muscle:': 'muscle.png', ':walking:': 'walking.png', ':running:': 'running.png', ':dancer:': 'dancer.png', ':couple:': 'couple.png', ':family:': 'family.png', ':two_men_holding_hands:': 'two_men_holding_hands.png', ':two_women_holding_hands:': 'two_women_holding_hands.png', ':couplekiss:': 'couplekiss.png', ':couple_with_heart:': 'couple_with_heart.png', ':dancers:': 'dancers.png', ':ok_woman:': 'ok_woman.png', ':no_good:': 'no_good.png', ':information_desk_person:': 'information_desk_person.png', ':raised_hand:': 'raised_hand.png', ':massage:': 'massage.png', ':haircut:': 'haircut.png', ':nail_care:': 'nail_care.png', ':bride_with_veil:': 'bride_with_veil.png', ':person_with_pouting_face:': 'person_with_pouting_face.png', ':person_frowning:': 'person_frowning.png', ':bow:': 'bow.png', ':tophat:': 'tophat.png', ':crown:': 'crown.png', ':womans_hat:': 'womans_hat.png', ':shoe:': 'shoe.png', ':mans_shoe:': 'mans_shoe.png', ':sandal:': 'sandal.png', ':high_heel:': 'high_heel.png', ':boot:': 'boot.png', ':shirt:': 'shirt.png', ':necktie:': 'necktie.png', ':womans_clothes:': 'womans_clothes.png', ':dress:': 'dress.png', ':running_shirt_with_sash:': 'running_shirt_with_sash.png', ':jeans:': 'jeans.png', ':kimono:': 'kimono.png', ':bikini:': 'bikini.png', 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':ear_of_rice:': 'ear_of_rice.png', ':mushroom:': 'mushroom.png', ':cactus:': 'cactus.png', ':palm_tree:': 'palm_tree.png', ':evergreen_tree:': 'evergreen_tree.png', ':deciduous_tree:': 'deciduous_tree.png', ':chestnut:': 'chestnut.png', ':seedling:': 'seedling.png', ':blossom:': 'blossom.png', ':globe_with_meridians:': 'globe_with_meridians.png', ':sun_with_face:': 'sun_with_face.png', ':full_moon_with_face:': 'full_moon_with_face.png', ':new_moon_with_face:': 'new_moon_with_face.png', ':new_moon:': 'new_moon.png', ':waxing_crescent_moon:': 'waxing_crescent_moon.png', ':first_quarter_moon:': 'first_quarter_moon.png', ':waxing_gibbous_moon:': 'waxing_gibbous_moon.png', ':full_moon:': 'full_moon.png', ':waning_gibbous_moon:': 'waning_gibbous_moon.png', ':last_quarter_moon:': 'last_quarter_moon.png', ':waning_crescent_moon:': 'waning_crescent_moon.png', ':last_quarter_moon_with_face:': 'last_quarter_moon_with_face.png', ':first_quarter_moon_with_face:': 'first_quarter_moon_with_face.png', ':moon:': 'moon.png', ':earth_africa:': 'earth_africa.png', ':earth_americas:': 'earth_americas.png', ':earth_asia:': 'earth_asia.png', ':volcano:': 'volcano.png', ':milky_way:': 'milky_way.png', ':stars:': 'stars.png', ':star:': 'star.png', ':sunny:': 'sunny.png', ':partly_sunny:': 'partly_sunny.png', ':cloud:': 'cloud.png', ':zap:': 'zap.png', ':umbrella:': 'umbrella.png', ':snowflake:': 'snowflake.png', ':snowman:': 'snowman.png', ':cyclone:': 'cyclone.png', ':foggy:': 'foggy.png', ':rainbow:': 'rainbow.png', ':ocean:': 'ocean.png', ':bamboo:': 'bamboo.png', ':gift_heart:': 'gift_heart.png', ':dolls:': 'dolls.png', ':school_satchel:': 'school_satchel.png', ':mortar_board:': 'mortar_board.png', ':flags:': 'flags.png', ':fireworks:': 'fireworks.png', ':sparkler:': 'sparkler.png', ':wind_chime:': 'wind_chime.png', ':rice_scene:': 'rice_scene.png', ':jack_o_lantern:': 'jack_o_lantern.png', ':ghost:': 'ghost.png', ':santa:': 'santa.png', ':christmas_tree:': 'christmas_tree.png', ':gift:': 'gift.png', ':tanabata_tree:': 'tanabata_tree.png', ':tada:': 'tada.png', ':confetti_ball:': 'confetti_ball.png', ':balloon:': 'balloon.png', ':crossed_flags:': 'crossed_flags.png', ':crystal_ball:': 'crystal_ball.png', ':movie_camera:': 'movie_camera.png', ':camera:': 'camera.png', ':video_camera:': 'video_camera.png', ':vhs:': 'vhs.png', ':cd:': 'cd.png', ':dvd:': 'dvd.png', ':minidisc:': 'minidisc.png', ':floppy_disk:': 'floppy_disk.png', ':computer:': 'computer.png', ':iphone:': 'iphone.png', ':phone:': 'phone.png', ':telephone_receiver:': 'telephone_receiver.png', ':pager:': 'pager.png', ':fax:': 'fax.png', ':satellite:': 'satellite.png', ':tv:': 'tv.png', ':radio:': 'radio.png', ':sound_loud:': 'sound_loud.png', ':sound:': 'sound.png', ':speaker:': 'speaker.png', ':mute:': 'mute.png', ':bell:': 'bell.png', ':no_bell:': 'no_bell.png', ':loudspeaker:': 'loudspeaker.png', ':mega:': 'mega.png', ':hourglass_flowing_sand:': 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':calendar:': 'calendar.png', ':card_index:': 'card_index.png', ':file_folder:': 'file_folder.png', ':open_file_folder:': 'open_file_folder.png', ':scissors:': 'scissors.png', ':pushpin:': 'pushpin.png', ':paperclip:': 'paperclip.png', ':black_nib:': 'black_nib.png', ':pencil2:': 'pencil2.png', ':straight_ruler:': 'straight_ruler.png', ':triangular_ruler:': 'triangular_ruler.png', ':closed_book:': 'closed_book.png', ':green_book:': 'green_book.png', ':blue_book:': 'blue_book.png', ':orange_book:': 'orange_book.png', ':notebook:': 'notebook.png', ':notebook_with_decorative_cover:': 'notebook_with_decorative_cover.png', ':ledger:': 'ledger.png', ':books:': 'books.png', ':book:': 'book.png', ':bookmark:': 'bookmark.png', ':name_badge:': 'name_badge.png', ':microscope:': 'microscope.png', ':telescope:': 'telescope.png', ':newspaper:': 'newspaper.png', ':art:': 'art.png', ':clapper:': 'clapper.png', ':microphone:': 'microphone.png', ':headphones:': 'headphones.png', ':musical_score:': 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eval_eth.py
codyly/PatchmatchNet
1
195454
<filename>eval_eth.py import argparse import os import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim as optim from torch.utils.data import DataLoader from torch.autograd import Variable import torch.nn.functional as F import numpy as np import time from datasets import find_dataset_def from models import * from utils import * import sys from datasets.data_io import read_pfm, save_pfm import cv2 from plyfile import PlyData, PlyElement from PIL import Image import math # import scipy.signal as signal cudnn.benchmark = True parser = argparse.ArgumentParser(description='Predict depth, filter, and fuse') parser.add_argument('--model', default='PatchmatchNet', help='select model') parser.add_argument('--dataset', default='eth3d', help='select dataset') parser.add_argument('--testpath', help='testing data path') parser.add_argument('--testlist', help='testing scan list') parser.add_argument('--split', default='test', help='select data') parser.add_argument('--batch_size', type=int, default=1, help='testing batch size') parser.add_argument('--n_views', type=int, default=5, help='num of view') parser.add_argument('--loadckpt', default=None, help='load a specific checkpoint') parser.add_argument('--outdir', default='./outputs', help='output dir') parser.add_argument('--display', action='store_true', help='display depth images and masks') parser.add_argument('--patchmatch_iteration', nargs='+', type=int, default=[1,2,2], help='num of iteration of patchmatch on stages 1,2,3') parser.add_argument('--patchmatch_num_sample', nargs='+', type=int, default=[8,8,16], help='num of generated samples in local perturbation on stages 1,2,3') parser.add_argument('--patchmatch_interval_scale', nargs='+', type=float, default=[0.005, 0.0125, 0.025], help='normalized interval in inverse depth range to generate samples in local perturbation') parser.add_argument('--patchmatch_range', nargs='+', type=int, default=[6,4,2], help='fixed offset of sampling points for propogation of patchmatch on stages 1,2,3') parser.add_argument('--propagate_neighbors', nargs='+', type=int, default=[0,8,16], help='num of neighbors for adaptive propagation on stages 1,2,3') parser.add_argument('--evaluate_neighbors', nargs='+', type=int, default=[9,9,9], help='num of neighbors for adaptive matching cost aggregation of adaptive evaluation on stages 1,2,3') parser.add_argument('--geo_pixel_thres', type=float, default=1, help='pixel threshold for geometric consistency filtering') parser.add_argument('--geo_depth_thres', type=float, default=0.01, help='depth threshold for geometric consistency filtering') parser.add_argument('--photo_thres', type=float, default=0.8, help='threshold for photometric consistency filtering') # parse arguments and check args = parser.parse_args() print("argv:", sys.argv[1:]) print_args(args) # read an image def read_img(filename, img_wh): img = Image.open(filename) # scale 0~255 to 0~1 np_img = np.array(img, dtype=np.float32) / 255. original_h, original_w, _ = np_img.shape np_img = cv2.resize(np_img, img_wh, interpolation=cv2.INTER_LINEAR) return np_img, original_h, original_w def read_cam_file(filename): with open(filename) as f: lines = [line.rstrip() for line in f.readlines()] # extrinsics: line [1,5), 4x4 matrix extrinsics = np.fromstring(' '.join(lines[1:5]), dtype=np.float32, sep=' ') extrinsics = extrinsics.reshape((4, 4)) # intrinsics: line [7-10), 3x3 matrix intrinsics = np.fromstring(' '.join(lines[7:10]), dtype=np.float32, sep=' ') intrinsics = intrinsics.reshape((3, 3)) depth_min = float(lines[11].split()[0]) depth_max = float(lines[11].split()[1]) return intrinsics, extrinsics, depth_min, depth_max # save a binary mask def save_mask(filename, mask): assert mask.dtype == np.bool mask = mask.astype(np.uint8) * 255 Image.fromarray(mask).save(filename) def save_depth_img(filename, depth): # assert mask.dtype == np.bool depth = depth * 255 depth = depth.astype(np.uint8) Image.fromarray(depth).save(filename) def read_pair_file(filename): data = [] with open(filename) as f: num_viewpoint = int(f.readline()) # 49 viewpoints for view_idx in range(num_viewpoint): ref_view = int(f.readline().rstrip()) src_views = [int(x) for x in f.readline().rstrip().split()[1::2]] if len(src_views) != 0: data.append((ref_view, src_views)) return data # run MVS model to save depth maps def save_depth(): # dataset, dataloader MVSDataset = find_dataset_def(args.dataset) test_dataset = MVSDataset(args.testpath, args.split, args.n_views) TestImgLoader = DataLoader(test_dataset, args.batch_size, shuffle=False, num_workers=4, drop_last=False) # model model = PatchmatchNet(patchmatch_interval_scale=args.patchmatch_interval_scale, propagation_range = args.patchmatch_range, patchmatch_iteration=args.patchmatch_iteration, patchmatch_num_sample = args.patchmatch_num_sample, propagate_neighbors=args.propagate_neighbors, evaluate_neighbors=args.evaluate_neighbors) model = nn.DataParallel(model) model.cuda() # load checkpoint file specified by args.loadckpt print("loading model {}".format(args.loadckpt)) state_dict = torch.load(args.loadckpt) model.load_state_dict(state_dict['model']) model.eval() with torch.no_grad(): for batch_idx, sample in enumerate(TestImgLoader): start_time = time.time() sample_cuda = tocuda(sample) outputs = model(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_min"], sample_cuda["depth_max"]) outputs = tensor2numpy(outputs) del sample_cuda print('Iter {}/{}, time = {:.3f}'.format(batch_idx, len(TestImgLoader), time.time() - start_time)) filenames = sample["filename"] # save depth maps and confidence maps for filename, depth_est, photometric_confidence in zip(filenames, outputs["refined_depth"]['stage_0'], outputs["photometric_confidence"]): depth_filename = os.path.join(args.outdir, filename.format('depth_est', '.pfm')) confidence_filename = os.path.join(args.outdir, filename.format('confidence', '.pfm')) os.makedirs(depth_filename.rsplit('/', 1)[0], exist_ok=True) os.makedirs(confidence_filename.rsplit('/', 1)[0], exist_ok=True) # save depth maps depth_est = np.squeeze(depth_est, 0) save_pfm(depth_filename, depth_est) # save confidence maps save_pfm(confidence_filename, photometric_confidence) # project the reference point cloud into the source view, then project back def reproject_with_depth(depth_ref, intrinsics_ref, extrinsics_ref, depth_src, intrinsics_src, extrinsics_src): width, height = depth_ref.shape[1], depth_ref.shape[0] ## step1. project reference pixels to the source view # reference view x, y x_ref, y_ref = np.meshgrid(np.arange(0, width), np.arange(0, height)) x_ref, y_ref = x_ref.reshape([-1]), y_ref.reshape([-1]) # reference 3D space xyz_ref = np.matmul(np.linalg.inv(intrinsics_ref), np.vstack((x_ref, y_ref, np.ones_like(x_ref))) * depth_ref.reshape([-1])) # source 3D space xyz_src = np.matmul(np.matmul(extrinsics_src, np.linalg.inv(extrinsics_ref)), np.vstack((xyz_ref, np.ones_like(x_ref))))[:3] # source view x, y K_xyz_src = np.matmul(intrinsics_src, xyz_src) xy_src = K_xyz_src[:2] / K_xyz_src[2:3] ## step2. reproject the source view points with source view depth estimation # find the depth estimation of the source view x_src = xy_src[0].reshape([height, width]).astype(np.float32) y_src = xy_src[1].reshape([height, width]).astype(np.float32) sampled_depth_src = cv2.remap(depth_src, x_src, y_src, interpolation=cv2.INTER_LINEAR) # mask = sampled_depth_src > 0 # source 3D space # NOTE that we should use sampled source-view depth_here to project back xyz_src = np.matmul(np.linalg.inv(intrinsics_src), np.vstack((xy_src, np.ones_like(x_ref))) * sampled_depth_src.reshape([-1])) # reference 3D space xyz_reprojected = np.matmul(np.matmul(extrinsics_ref, np.linalg.inv(extrinsics_src)), np.vstack((xyz_src, np.ones_like(x_ref))))[:3] # source view x, y, depth depth_reprojected = xyz_reprojected[2].reshape([height, width]).astype(np.float32) K_xyz_reprojected = np.matmul(intrinsics_ref, xyz_reprojected) xy_reprojected = K_xyz_reprojected[:2] / K_xyz_reprojected[2:3] x_reprojected = xy_reprojected[0].reshape([height, width]).astype(np.float32) y_reprojected = xy_reprojected[1].reshape([height, width]).astype(np.float32) return depth_reprojected, x_reprojected, y_reprojected, x_src, y_src def check_geometric_consistency(depth_ref, intrinsics_ref, extrinsics_ref, depth_src, intrinsics_src, extrinsics_src, geo_pixel_thres, geo_depth_thres): width, height = depth_ref.shape[1], depth_ref.shape[0] x_ref, y_ref = np.meshgrid(np.arange(0, width), np.arange(0, height)) depth_reprojected, x2d_reprojected, y2d_reprojected, x2d_src, y2d_src = reproject_with_depth(depth_ref, intrinsics_ref, extrinsics_ref, depth_src, intrinsics_src, extrinsics_src) # print(depth_ref.shape) # print(depth_reprojected.shape) # check |p_reproj-p_1| < 1 dist = np.sqrt((x2d_reprojected - x_ref) ** 2 + (y2d_reprojected - y_ref) ** 2) # check |d_reproj-d_1| / d_1 < 0.01 # depth_ref = np.squeeze(depth_ref, 2) depth_diff = np.abs(depth_reprojected - depth_ref) relative_depth_diff = depth_diff / depth_ref mask = np.logical_and(dist < geo_pixel_thres, relative_depth_diff < geo_depth_thres) depth_reprojected[~mask] = 0 return mask, depth_reprojected, x2d_src, y2d_src def filter_depth(scan_folder, out_folder, plyfilename, geo_pixel_thres, geo_depth_thres, photo_thres, img_wh, geo_mask_thres): # the pair file pair_file = os.path.join(scan_folder, "pair.txt") # for the final point cloud vertexs = [] vertex_colors = [] pair_data = read_pair_file(pair_file) nviews = len(pair_data) # for each reference view and the corresponding source views for ref_view, src_views in pair_data: # load the reference image ref_img, original_h, original_w = read_img(os.path.join(scan_folder, 'images/{:0>8}.jpg'.format(ref_view)),img_wh) ref_intrinsics, ref_extrinsics = read_cam_file( os.path.join(scan_folder, 'cams_1/{:0>8}_cam.txt'.format(ref_view)))[0:2] ref_intrinsics[0] *= img_wh[0]/original_w ref_intrinsics[1] *= img_wh[1]/original_h # load the estimated depth of the reference view ref_depth_est = read_pfm(os.path.join(out_folder, 'depth_est/{:0>8}.pfm'.format(ref_view)))[0] ref_depth_est = np.squeeze(ref_depth_est, 2) # load the photometric mask of the reference view confidence = read_pfm(os.path.join(out_folder, 'confidence/{:0>8}.pfm'.format(ref_view)))[0] photo_mask = confidence > photo_thres photo_mask = np.squeeze(photo_mask, 2) all_srcview_depth_ests = [] # compute the geometric mask geo_mask_sum = 0 for src_view in src_views: # camera parameters of the source view _, original_h, original_w = read_img(os.path.join(scan_folder, 'images/{:0>8}.jpg'.format(src_view)), img_wh) src_intrinsics, src_extrinsics = read_cam_file( os.path.join(scan_folder, 'cams_1/{:0>8}_cam.txt'.format(src_view)))[0:2] src_intrinsics[0] *= img_wh[0]/original_w src_intrinsics[1] *= img_wh[1]/original_h # the estimated depth of the source view src_depth_est = read_pfm(os.path.join(out_folder, 'depth_est/{:0>8}.pfm'.format(src_view)))[0] geo_mask, depth_reprojected, x2d_src, y2d_src = check_geometric_consistency(ref_depth_est, ref_intrinsics, ref_extrinsics, src_depth_est, src_intrinsics, src_extrinsics, geo_pixel_thres, geo_depth_thres) geo_mask_sum += geo_mask.astype(np.int32) all_srcview_depth_ests.append(depth_reprojected) depth_est_averaged = (sum(all_srcview_depth_ests) + ref_depth_est) / (geo_mask_sum + 1) geo_mask = geo_mask_sum >= geo_mask_thres final_mask = np.logical_and(photo_mask, geo_mask) os.makedirs(os.path.join(out_folder, "mask"), exist_ok=True) save_mask(os.path.join(out_folder, "mask/{:0>8}_photo.png".format(ref_view)), photo_mask) save_mask(os.path.join(out_folder, "mask/{:0>8}_geo.png".format(ref_view)), geo_mask) save_mask(os.path.join(out_folder, "mask/{:0>8}_final.png".format(ref_view)), final_mask) if args.display: import cv2 cv2.imshow('ref_img', ref_img[:, :, ::-1]) cv2.imshow('ref_depth', ref_depth_est ) cv2.imshow('ref_depth * photo_mask', ref_depth_est * photo_mask.astype(np.float32)) cv2.imshow('ref_depth * geo_mask', ref_depth_est * geo_mask.astype(np.float32) ) cv2.imshow('ref_depth * mask', ref_depth_est * final_mask.astype(np.float32)) cv2.waitKey(1) height, width = depth_est_averaged.shape[:2] x, y = np.meshgrid(np.arange(0, width), np.arange(0, height)) valid_points = final_mask x, y, depth = x[valid_points], y[valid_points], depth_est_averaged[valid_points] color = ref_img[valid_points] xyz_ref = np.matmul( np.linalg.inv(ref_intrinsics), np.vstack((x, y, np.ones_like(x))) * depth) xyz_world = np.matmul(np.linalg.inv(ref_extrinsics), np.vstack((xyz_ref, np.ones_like(x))))[:3] vertexs.append(xyz_world.transpose((1, 0))) vertex_colors.append((color * 255).astype(np.uint8)) vertexs = np.concatenate(vertexs, axis=0) vertex_colors = np.concatenate(vertex_colors, axis=0) vertexs = np.array([tuple(v) for v in vertexs], dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4')]) vertex_colors = np.array([tuple(v) for v in vertex_colors], dtype=[('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]) vertex_all = np.empty(len(vertexs), vertexs.dtype.descr + vertex_colors.dtype.descr) for prop in vertexs.dtype.names: vertex_all[prop] = vertexs[prop] for prop in vertex_colors.dtype.names: vertex_all[prop] = vertex_colors[prop] el = PlyElement.describe(vertex_all, 'vertex') PlyData([el]).write(plyfilename) print("saving the final model to", plyfilename) if __name__ == '__main__': # step1. save all the depth maps and the masks in outputs directory save_depth() img_wh = (2688,1792) if args.split == "test": scans = ['botanical_garden', 'boulders', 'bridge', 'door', 'exhibition_hall', 'lecture_room', 'living_room', 'lounge', 'observatory', 'old_computer', 'statue', 'terrace_2'] geo_mask_thres = {'botanical_garden':1, # 30 images, outdoor 'boulders':1, # 26 images, outdoor 'bridge':4, # 110 images, outdoor 'door':1, # 6 images, indoor 'exhibition_hall':1, # 68 images, indoor 'lecture_room':1, # 23 images, indoor 'living_room':2, # 65 images, indoor 'lounge':1,# 10 images, indoor 'observatory':1, # 27 images, outdoor 'old_computer':1, # 54 images, indoor 'statue':1, # 10 images, indoor 'terrace_2':1 # 13 images, outdoor } num_images = {'botanical_garden':30, # 30 images, outdoor 'boulders':26, # 26 images, outdoor 'bridge':110, # 110 images, outdoor 'door':6, # 6 images, indoor 'exhibition_hall':68, # 68 images, indoor 'lecture_room':23, # 23 images, indoor 'living_room':65, # 65 images, indoor 'lounge':10,# 10 images, indoor 'observatory':27, # 27 images, outdoor 'old_computer':54, # 54 images, indoor 'statue':10, # 10 images, indoor 'terrace_2':13 # 13 images, outdoor } for scan in scans: start_time = time.time() scan_folder = os.path.join(args.testpath, scan) out_folder = os.path.join(args.outdir, scan) # step2. filter saved depth maps with geometric constraints filter_depth(scan_folder, out_folder, os.path.join(args.outdir, scan + '.ply'), args.geo_pixel_thres, args.geo_depth_thres, args.photo_thres, img_wh, geo_mask_thres[scan]) print('scan: '+scan+' time = {:3f}'.format(time.time() - start_time)) elif args.split == "train": scans = ['courtyard', 'delivery_area', 'electro', 'facade', 'kicker', 'meadow', 'office', 'pipes', 'playground', 'relief', 'relief_2', 'terrace', 'terrains'] geo_mask_thres = {'courtyard':1, # 38 images, outdoor 'delivery_area':1, # 44 images, indoor 'electro':1, # 45 images, outdoor 'facade':4, # 76 images, outdoor 'kicker':1, # 31 images, indoor 'meadow':1, # 15 images, outdoor 'office':1, # 26 images, indoor 'pipes':1,# 14 images, indoor 'playground':1, # 38 images, outdoor 'relief':1, # 31 images, indoor 'relief_2':1, # 31 images, indoor 'terrace':1, # 23 images, outdoor 'terrains':1 # 42 images, indoor } num_images = {'courtyard':38, # 38 images, outdoor 'delivery_area':44, # 44 images, indoor 'electro':45, # 45 images, outdoor 'facade':76, # 76 images, outdoor 'kicker':31, # 31 images, indoor 'meadow':15, # 15 images, outdoor 'office':26, # 26 images, indoor 'pipes':14,# 14 images, indoor 'playground':38, # 38 images, outdoor 'relief':31, # 31 images, indoor 'relief_2':31, # 31 images, indoor 'terrace':23, # 23 images, outdoor 'terrains':42 # 42 images, indoor } for scan in scans: start_time = time.time() scan_folder = os.path.join(args.testpath, scan) out_folder = os.path.join(args.outdir, scan) # step2. filter saved depth maps with geometric constraints filter_depth(scan_folder, out_folder, os.path.join(args.outdir, scan + '.ply'), args.geo_pixel_thres, args.geo_depth_thres, args.photo_thres, img_wh, geo_mask_thres[scan]) print('scan: '+scan+' time = {:3f}'.format(time.time() - start_time))
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Program_Python_code/IOT_02/sql_03.py
skyhigh8591/Learning_Test_Program
0
86178
<filename>Program_Python_code/IOT_02/sql_03.py # -*- coding: utf-8 -*- """ Created on Mon Jun 15 10:42:03 2020 @author: skyhigh """ from flask import Flask,request,render_template from flask_mysqldb import MySQL import datetime app = Flask (__name__) app.config['MYSQL_HOST'] = 'localhost' app.config['MYSQL_USER'] = 'root' app.config['MYSQL_PASSWORD'] = '<PASSWORD>' app.config['MYSQL_DB'] = 'iot' mysql = MySQL(app) @app.route('/', methods=['GET','POST']) def index(): if request.method == 'GET': mycursor = mysql.connection.cursor() mycursor.execute("SELECT * FROM temperature_db") myresult = mycursor.fetchall() mycursor.close() rows=[] for row in myresult: rows.append(row) print(row) return str(rows) @app.route('/data', methods=['GET','POST']) def data(): if request.method == 'GET': mycursor = mysql.connection.cursor() mycursor.execute("SELECT * FROM temperature_db") myresult = mycursor.fetchall() mycursor.close() return render_template('index_5.html',data = myresult) @app.route('/insert', methods=['GET','POST']) def insert_data(): if request.method == 'POST': userid = request.values['userid'] temperature=request.values['temperature'] mycursor = mysql.connection.cursor() time = datetime.datetime.now() print(time) currenttime = time.strftime('%Y-%m-%d %H:%M:%S') print(currenttime) sql = "INSERT INTO temperature_db(userid,temperature,datetime)VALUES( %s,%s,%s)" value = (userid,temperature,currenttime) mycursor.execute(sql,value) mysql.connection.commit() count=mycursor.rowcount print(count) mycursor.close() if count > 0: result = str(count)+"insert data ok" return render_template('index_6.html', insert_result=result) else: return "insert data ok" if request.method == 'GET': return render_template('index_6.html') @app.route('/update', methods=['GET','POST']) def update_data(): if request.method == 'POST': userid = request.values['userid_update'] temperature = request.values['temperature_update'] mycursor = mysql.connection.cursor() sql = "UPDATE temperature_db SET temperature = %s WHERE userid = %s" value = (temperature,userid) mycursor.execute(sql,value) mysql.connection.commit() count=mycursor.rowcount print(count) mycursor.close() if count > 0: result = str(count) +"update data ok" return render_template('index_7.html', update_result = result) else: return "update data fail" if request.method == 'GET': return render_template('index_7.html') @app.route('/delete', methods=['GET','POST']) def delete_data(): if request.method == 'POST': userid = request.values['userid_delete'] mycursor = mysql.connection.cursor() sql = "DELETE FROM temperature_db WHERE userid = %s " value = (userid,) mycursor.execute(sql,value) mysql.connection.commit() count=mycursor.rowcount print(count) mycursor.close() if count > 0: result = str(count) +"delete data ok" return render_template('index_8.html', delete_result = result) else: return "delete data fail" if request.method == 'GET': return render_template('index_8.html') if __name__ == '__main__': app.run()
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models/train_classifier.py
munnyraht/Disaster-Response-Message-Classification
0
1605981
import sys import pandas as pd from sqlalchemy import create_engine import nltk from nltk.tokenize import word_tokenize from nltk.stem import WordNetLemmatizer from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.pipeline import Pipeline from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score,precision_score,recall_score,recall_score,f1_score from sklearn.preprocessing import label_binarize from sklearn.multioutput import MultiOutputClassifier from sklearn.neighbors import KNeighborsClassifier import numpy as np import pickle def load_data(database_filepath): ''' INPUT - database_filepath : filepath where data will be read from OUTPUT - X :independent variables ( mesaages) Y: dependant variables or categories category name:categories names from the dataframe ''' engine = create_engine('sqlite:///{}'.format(database_filepath)) conn = engine.connect() df = pd.read_sql_table('DisasterResponse',conn) X = df['message'] Y = df.iloc[:,4:] category_names = Y.columns.tolist() return X,Y,category_names def tokenize(text): ''' INPUT - text OUTPUT - clean_token : tokenized text ''' tokenized = word_tokenize(text.lower()) lemmatizer = WordNetLemmatizer() clean_token = [] for token in tokenized: clean_token.append(lemmatizer.lemmatize(token)) return clean_token def build_model(): ''' OUTPUT - a model pipeline ''' pipeline = Pipeline([ ('vect', CountVectorizer(tokenizer=tokenize)), ('tfidf', TfidfTransformer()), ('clf', MultiOutputClassifier(KNeighborsClassifier())) ]) params={'clf__estimator__n_neighbors':(4, 5) } cv = GridSearchCV(pipeline, param_grid=params) return cv def evaluate_model(model, X_test, Y_test, category_names): ''' INPUT - model : a defined model pipeline X_test: test set of the independent variables (messages) Y_test: test set of the dependent variables (categories) category_names : categories names from the dataframe ''' y_pred = model.predict(X_test) for i,column in enumerate(Y_test.columns): print('report for {}'.format(column)) print('recall score:',recall_score(Y_test[column].values.astype(str),y_pred[:,i].astype(int).astype(str),average='micro')) print('precision_score:',precision_score(Y_test[column].values.astype(int),y_pred[:,i].astype(int),average='micro')) print('f1 score:',f1_score(Y_test[column].values.astype(int),y_pred[:,i].astype(int),average='micro'), '\n') return def save_model(model, model_filepath): ''' INPUT - model: a trained model model_filepath: filepath where model is to be saved ''' pickle.dump(model, open(model_filepath, 'wb')) return def main(): ''' A function that puts the above functions altogether, loads data from db splits data, build a model, trains a model, evaluates the trained model and saves the model ''' if len(sys.argv) == 3: database_filepath, model_filepath = sys.argv[1:] print('Loading data...\n DATABASE: {}'.format(database_filepath)) X, Y, category_names = load_data(database_filepath) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2) print('Building model...') model = build_model() print('Training model...') model.fit(X_train, Y_train) print('Evaluating model...') evaluate_model(model, X_test, Y_test, category_names) print('Saving model...\n MODEL: {}'.format(model_filepath)) save_model(model, model_filepath) print('Trained model saved!') else: print('Please provide the filepath of the disaster messages database '\ 'as the first argument and the filepath of the pickle file to '\ 'save the model to as the second argument. \n\nExample: python '\ 'train_classifier.py ../data/DisasterResponse.db classifier.pkl') if __name__ == '__main__': main()
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src/sphinx_theme_builder/_internal/nodejs.py
jorisvandenbossche/sphinx-theme-builder
1
164552
"""NodeJS tooling orchestration. Broadly, it has a `.nodeenv` created using the nodeenv package; and ensures that the `node_modules` directory is kept in sync with the `package.json` file of the project. """ import os import shlex import shutil import subprocess import sys import textwrap from pathlib import Path from typing import Any, List, Optional from unittest.mock import patch from .errors import DiagnosticError from .passthrough import passthrough_run from .project import Project from .ui import log _NODEENV_DIR = ".nodeenv" _BIN_DIR = "Scripts" if os.name == "nt" else "bin" def run_in( nodeenv: Path, args: List[str], *, production: bool = False, **kwargs: Any ) -> "Optional[subprocess.CompletedProcess[bytes]]": """Run a command, using a binary from `nodeenv`.""" assert nodeenv.name == _NODEENV_DIR log(f"[magenta](nodeenv) $[/] [blue]{' '.join(args)}[/]") env = { "NPM_CONFIG_PREFIX": os.fsdecode(nodeenv), "npm_config_prefix": os.fsdecode(nodeenv), "NODE_PATH": os.fsdecode(nodeenv / "lib" / "node_modules"), "PATH": os.pathsep.join([os.fsdecode(nodeenv / "bin"), os.environ["PATH"]]), "NODE_ENV": "production" if production else "development", } # Fully qualify the first argument. resolved_name = shutil.which(args[0], path=env["PATH"]) if not resolved_name: raise FileNotFoundError(resolved_name) args[0] = resolved_name with patch.dict("os.environ", env): if not kwargs: returncode = passthrough_run(args) if returncode: cmd = " ".join(shlex.quote(arg) for arg in args) raise subprocess.CalledProcessError(returncode=returncode, cmd=cmd) return None else: return subprocess.run(args, check=True, **kwargs) def _run_python_nodeenv(*args: str) -> None: presentation = ["python", "-m", "nodeenv", *args] log(f"[magenta]$[/] [blue]{' '.join(presentation)}[/]") command = [ sys.executable, "-c", textwrap.dedent( """ import runpy import rich import rich.traceback import urllib.request rich.traceback.install( width=rich.get_console().width, show_locals=True, suppress=[runpy, urllib.request], ) runpy.run_module("nodeenv", run_name="__main__", alter_sys=True) """ ), "-v", *args, ] try: subprocess.run(command, check=True) except subprocess.CalledProcessError as error: raise DiagnosticError( reference="nodeenv-creation-failed", message="Failed to create a `nodeenv`", context="See above for failure output from the underlying tooling.", hint_stmt=( "A `urllib.error.HTTPError` would indicate mean that the issue is " "related to the network, or the NodeJS servers, or the node version " "that this tool is trying to fetch is no longer available." ), ) from error def _should_use_system_node(node_version: str) -> bool: try: process = subprocess.run(["node", "--version"], capture_output=True, check=True) except FileNotFoundError: log("[yellow]#[/] [cyan]Could not find a `node` executable.[/]") return False except subprocess.CalledProcessError as error: log( "[yellow]#[/] [cyan]`node` executable did not exit cleanly.[/]\n" f"{error.stderr.decode()}" ) return False if process.stdout.decode().strip() != f"v{node_version}": log("[yellow]#[/] [cyan]The system `node` has a different version:[/] {}") return False return True def create_nodeenv(nodeenv: Path, node_version: str) -> None: log( "[yellow]#[/] [cyan]Generating new [magenta]nodeenv[/] with " f"NodeJS [green]{node_version}[/]!" ) if _should_use_system_node(node_version=node_version): log("[yellow]#[/] Will use system nodeJS, since version matches.") node_version = "system" _run_python_nodeenv( f"--node={node_version}", os.fsdecode(nodeenv), ) def run_npm_build(nodeenv: Path, *, production: bool) -> None: try: run_in(nodeenv, ["npm", "run-script", "build"], production=production) except subprocess.CalledProcessError as error: raise DiagnosticError( reference="js-build-failed", message="The Javascript-based build pipeline failed.", context="See above for failure output from the underlying tooling.", hint_stmt=None, ) from error def populate_npm_packages(nodeenv: Path, node_modules: Path) -> None: try: run_in(nodeenv, ["npm", "install", "--include=dev", "--no-save"]) except FileNotFoundError as error: raise DiagnosticError( reference="nodeenv-unhealthy-npm-not-found", message="The `nodeenv` for this project is unhealthy.", context=str(error), hint_stmt=( f"Deleting the {_NODEENV_DIR} directory and trying again may work." ), ) from error except subprocess.CalledProcessError as error: raise DiagnosticError( reference="js-install-failed", message="Javascript dependency installation failed.", context="See above for failure output from the underlying tooling.", hint_stmt=None, ) from error if node_modules.is_dir(): node_modules.touch() def generate_assets(project: Project, *, production: bool) -> None: package_json = project.location / "package.json" nodeenv = project.location / _NODEENV_DIR node_modules = project.location / "node_modules" assert package_json.exists() created_new_nodeenv = False if not nodeenv.exists(): log("[yellow]#[/] [magenta]nodeenv[cyan] does not exist.[/]") create_nodeenv(nodeenv, project.node_version) created_new_nodeenv = True # Checking the node version is a sanity check, and ensures that the environment is # "healthy". try: process = run_in(nodeenv, ["node", "--version"], stdout=subprocess.PIPE) except FileNotFoundError as error: raise DiagnosticError( reference="nodeenv-unhealthy-file-not-found", message="The `nodeenv` for this project is unhealthy.", context=str(error), hint_stmt=( f"Deleting the {_NODEENV_DIR} directory and trying again may work." ), ) from error except subprocess.CalledProcessError as error: raise DiagnosticError( reference="nodeenv-unhealthy-subprocess-failure", message="The `nodeenv` for this project is unhealthy.", context="See above for failure output from the underlying tooling.", hint_stmt=( f"Deleting the {_NODEENV_DIR} directory and trying again may work." ), ) from error # Present the `node --version` output to the user. assert process got = process.stdout.decode().strip() print(got) # Sanity-check the node version. expected = f"v{project.node_version}" if got != expected: raise DiagnosticError( reference="nodeenv-version-mismatch", message="The `nodeenv` for this project is unhealthy.", context=( "There is a mismatch between what is present in the environment " f"({got}) and the expected version of NodeJS ({expected})." ), hint_stmt=( f"Deleting the {_NODEENV_DIR} directory and trying again may work." ), ) need_to_populate = False if created_new_nodeenv: need_to_populate = True elif not node_modules.exists(): need_to_populate = True elif node_modules.stat().st_mtime < package_json.stat().st_mtime: log("[yellow]#[/] [cyan]Detected changes in [magenta]package.json[cyan].[/]") need_to_populate = True if need_to_populate: if node_modules.exists(): log("[yellow]#[/] [cyan]Cleaning up [magenta]node_modules[cyan].[/]") try: shutil.rmtree(node_modules) except OSError as error: raise DiagnosticError( reference="unable-to-cleanup-node-modules", message="Could not remove node_modules directory.", context=str(error), hint_stmt=f"Deleting {node_modules} and trying again may work.", ) from error log("[yellow]#[/] [cyan]Installing NodeJS packages.[/]") populate_npm_packages(nodeenv, node_modules) run_npm_build(nodeenv=nodeenv, production=production) log("[green]Done![/]")
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pipen/cli/__init__.py
sthagen/pwwang-pipen
41
73064
<filename>pipen/cli/__init__.py<gh_stars>10-100 """Provide CLI for pipen""" from ._hooks import CLIPlugin from ._main import main
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itsnp/__init__.py
CaffeineDuck/itsnp-discord-bot
0
18301
<filename>itsnp/__init__.py from .bot import ItsnpBot
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tests/test_app/models.py
Javex/pyramid_crud
4
1603192
from sqlalchemy import ( Column, Integer, Unicode, DateTime, Boolean, ) from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import ( scoped_session, sessionmaker, relationship, backref, ) from sqlalchemy.schema import ( ForeignKey, ) from datetime import datetime from pyramid.events import NewRequest, subscriber DBSession = scoped_session(sessionmaker()) Base = declarative_base() class Poll(Base): __tablename__ = 'poll' id = Column(Integer, primary_key=True) question = Column(Unicode, nullable=False, info={'label': 'Question'}) pub_date = Column(DateTime, nullable=False, default=datetime.utcnow, info={'label': 'Date Published'}) published = Column(Boolean, default=False, info={'label': 'Published?'}) class Choice(Base): __tablename__ = 'choice' id = Column(Integer, primary_key=True) poll_id = Column(ForeignKey(Poll.id), nullable=False) choice_text = Column(Unicode, nullable=False, unique=True, info={'label': 'Choice Text'}) votes = Column(Integer, default=0, info={'label': 'Votes'}) poll = relationship("Poll", backref=backref("choices", cascade="all, delete-orphan")) class Test(Base): __tablename__ = 'test' poll_id = Column(ForeignKey(Poll.id), primary_key=True) choice_id = Column(ForeignKey(Choice.id), primary_key=True) @subscriber(NewRequest) def create_session(event): event.request.dbsession = DBSession
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Python/snake_gameTurtle.py
Rohitshailesh43/hacktoberfest2021
0
129805
<gh_stars>0 from turtle import * from random import randrange from freegames import square, vector food = vector(0, 0) snake = [vector(10, 0)] aim = vector(0, -10) def change(x, y): "Change snake direction." aim.x = x aim.y = y def inside(head): "Return True if head inside boundaries." return -200 < head.x < 190 and -200 < head.y < 190 def move(): "Move snake forward one segment." head = snake[-1].copy() head.move(aim) if not inside(head) or head in snake: square(head.x, head.y, 9, 'red') update() return snake.append(head) if head == food: print('Snake:', len(snake)) food.x = randrange(-15, 15) * 10 food.y = randrange(-15, 15) * 10 else: snake.pop(0) clear() for body in snake: square(body.x, body.y, 9, 'green') square(food.x, food.y, 9, 'red') update() ontimer(move, 100) hideturtle() tracer(False) listen() onkey(lambda: change(10, 0), 'Right') onkey(lambda: change(-10, 0), 'Left') onkey(lambda: change(0, 10), 'Up') onkey(lambda: change(0, -10), 'Down') move() done()
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solutions/python3/245.py
sm2774us/amazon_interview_prep_2021
42
157680
class Solution: def shortestWordDistance(self, words, word1, word2): i1 = i2 = -1 res, same = float("inf"), word1 == word2 for i, w in enumerate(words): if w == word1: if same: i2 = i1 i1 = i if i2 >= 0: res = min(res, i1 - i2) elif w == word2: i2 = i if i1 >= 0: res = min(res, i2 - i1) return res
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tests/integration/test_between_tags.py
liorbass/pydriller
583
20081
# Copyright 2018 <NAME> # # 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 pydriller.repository import Repository import logging logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO) def test_between_revisions(): from_tag = 'tag1' to_tag = 'tag3' lc = list(Repository('test-repos/tags', from_tag=from_tag, to_tag=to_tag).traverse_commits()) assert len(lc) == 5 assert '6bb9e2c6a8080e6b5b34e6e316c894b2ddbf7fcd' == lc[0].hash assert 'f1a90b8d7b151ceefd3e3dfc0dc1d0e12b5f48d0' == lc[1].hash assert '4638730126d40716e230c2040751a13153fb1556' == lc[2].hash assert 'a26f1438bd85d6b22497c0e5dae003812becd0bc' == lc[3].hash assert '627e1ad917a188a861c9fedf6e5858b79edbe439' == lc[4].hash def test_multiple_repos_with_tags(): from_tag = 'tag2' to_tag = 'tag3' repos = [ 'test-repos/tags', 'test-repos/tags', 'test-repos/tags' ] lc = list(Repository(path_to_repo=repos, from_tag=from_tag, to_tag=to_tag).traverse_commits()) assert len(lc) == 9
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Random/cpp_compare.py
SuperV1234/Experiments
12
115321
<reponame>SuperV1234/Experiments<filename>Random/cpp_compare.py import subprocess import sys import itertools def run_cmd(cmd): return subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True) def main(): flags = "-std=c++17 -Ofast" time = "/usr/bin/time -f %e" cpp0 = sys.argv[1] cpp1 = sys.argv[2] cmdbase = "{} {}".format("g++", flags) cmd0 = "{} {}".format(cmdbase, "-o /tmp/x0.x") run_cmd(cmd0) et0 = float(run_cmd("{} /tmp/x0.x".format(time)).stderr) cmd1 = "{} {}".format(cmdbase, "-o /tmp/x1.x") run_cmd(cmd1) et1 = float(run_cmd("{} /tmp/x1.x".format(time)).stderr) if __name__ == "__main__": sys.exit(main())
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clld/tests/test_web_datatables_value.py
Woseseltops/clld
1
72835
<gh_stars>1-10 from clld.tests.util import TestWithEnv from clld.db.models import common class Tests(TestWithEnv): def _run(self, **kw): from clld.web.datatables.value import Values return self.handle_dt(Values, common.Value, **kw) def test_Values(self): dt = self._run() self.assertTrue(isinstance(dt.options, dict)) self.set_request_properties(params={ 'sSearch_0': '> 1', 'sSearch_1': 's', 'iSortingCols': '3', 'iSortCol_0': '0', 'sSortDir_0': 'desc', 'iSortCol_1': '1', 'sSortDir_1': 'desc', 'iSortCol_2': '2', 'sSortDir_2': 'desc', }) dt = self._run() def test_Values_with_language(self): self._run(language=common.Language.first()) def test_Values_with_contribution(self): self._run(contribution=common.Contribution.first()) def test_Values_with_parameter(self): self.set_request_properties(params={'parameter': 'parameter'}) self._run() self.set_request_properties(params={'parameter': 'parameter', 'sSearch_2': 's'}) self._run() self.set_request_properties(params={'parameter': 'no-domain'}) self._run()
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Robot Car/Old Scripts/VisionController.py
ATPHHe/Raspberry-Pi-3-Robot-Car
0
155366
<reponame>ATPHHe/Raspberry-Pi-3-Robot-Car<gh_stars>0 ###################################################################### ### Description ####### ### ###### ### Created by: <NAME> ##### ### Date Created: 3/03/2017 #### ### ### ###################################################################### # imports import sys import time import atexit import numpy import cv2 ###################################################################### ### Platform/OS compatibility Checks ### ####################################### # Create platform variables/constants _OS_ = sys.platform RASPBERRY_PI_3 = "linux" # Check for OS compatibility (ONLY WORKS ON RASPBERRY PI). if _OS_ == RASPBERRY_PI_3: # Import Raspberry PI packages. print("This OS platform \"" + _OS_ + "\" is OK.") import RPi.GPIO as GPIO import pigpio import picamera else: # Exit this program if the OS is not compatible. print("This OS platform \"" + _OS_ + "\" is not supported.") sys.exit() ################################################################## class VisionController(object): # Constants UP = 1 DOWN = -1 LEFT = -1 RIGHT = 1 ###################################################################### ### Initialization ### ##################### def __init__(self): #print("init test") ### User Configuration ### # Camera Setup self.camera = picamera.PiCamera() self.camera.rotation = 180 ### End of User Configuration ### ### GPIO Setup ### GPIO.setmode(GPIO.BOARD) #GPIO.setup(self.topServoPin, GPIO.OUT) #GPIO.setup(self.botServoPin, GPIO.OUT) ### Connect to local Pi. ### self.pi = pigpio.pi() if not self.pi.connected: print("pigpio.pi was unable to connect to the pi.") sys.exit() ### Software PWM Setup ### #self.topServo = GPIO.PWM(self.topServoPin, self.topServoPwmFreq) #self.botServo = GPIO.PWM(self.botServoPin, self.botServoPwmFreq) ###################################################################### ### Computer Vision Functions ### ################################ # Track a face. def trackFace(self): return ###################################################################### ### Camera/Servo Functions ### ############################# # Rotate top camera servo def rotateTopCameraServo(self, angle): print("Top Servo Rotating") #self.topServo.stop() self.pi.set_servo_pulsewidth(self.topServoGPIO, 0); #self.topServoPwmDutyCycle = (2.5 + (angle*(19.0/360.0))) self.topServoMs = (500 + (angle*(100.0/9.0))) #if self.topServoPwmDutyCycle >= self.topServoMinDutyCycle: if self.topServoMs >= self.topServoMsMin: pass else: #self.topServoPwmDutyCycle = self.topServoMinDutyCycle self.topServoMs = self.topServoMsMin #if self.topServoPwmDutyCycle <= self.topServoMaxDutyCycle: if self.topServoMs <= self.topServoMsMax: pass else: #self.topServoPwmDutyCycle = self.topServoMaxDutyCycle self.topServoMs = self.topServoMsMax #self.topServo.start(self.topServoPwmDutyCycle) self.pi.set_servo_pulsewidth(self.topServoGPIO, self.topServoMs) return # Rotate bottom camera servo def rotateBottomCameraServo(self, angle): print("Bottom Servo Rotating") #self.botServo.stop() self.pi.set_servo_pulsewidth(self.botServoGPIO, 0); #self.botServoPwmDutyCycle = (2.5 + (angle*(19.0/360.0))) self.botServoMs = (500 + (angle*(100.0/9.0))) #if self.botServoPwmDutyCycle >= self.botServoMinDutyCycle: if self.botServoMs >= self.botServoMsMin: pass else: #self.botServoPwmDutyCycle = self.botServoMinDutyCycle self.botServoMs = self.botServoMsMin #if self.botServoPwmDutyCycle <= self.botServoMaxDutyCycle: if self.botServoMs <= self.botServoMsMax: pass else: #self.botServoPwmDutyCycle = self.botServoMaxDutyCycle self.botServoMs = self.botServoMsMax #self.botServo.start(self.botServoPwmDutyCycle) self.pi.set_servo_pulsewidth(self.botServoGPIO, self.botServoMs) return ###################################################################### ### Stop Functions ### ##################### # Stop All def stop(self): print("Stopping All Parts") self.stopServos() return # Stop Servos def stopServos(self): print("Stopping Servos") #self.topServo.stop() #self.botServo.stop() self.pi.set_servo_pulsewidth(self.topServoGPIO, 0) self.pi.set_servo_pulsewidth(self.botServoGPIO, 0) return # Reset Servos position to face the front of the robot. def resetServos(self): print("Reseting Servos") #self.rotateTopCameraServo(180) #self.rotateBottomCameraServo(90) self.pi.set_servo_pulsewidth(self.topServoGPIO, 2500) self.pi.set_servo_pulsewidth(self.botServoGPIO, 1500) time.sleep(2) # Cleanup Handler def cleanUp(self): print("CLEANING GPIO") GPIO.cleanup() self.pi.stop() print("DONE CLEANING") # atexit cleaner if _OS_ == RASPBERRY_PI_3: atexit.register(cleanUp) ### End of class ### #print("Done") ##################################################################
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102785/D.py
julianferres/Codeforces
4
102074
from math import inf def solve(i2, i3, i5, i7, i11, i13, i17, i19): return pow(2, i2) * pow(3, i3) * pow(5, i5) * pow(7, i7) * pow(11, i11) * pow(13, i13) * pow(17, i17) * pow(19, i19) def main(): ans = [inf]*1005 for i1 in range(1000): print(i1) for i2 in range(32): for i3 in range(20): for i4 in range(6): for i5 in range(4): for i6 in range(4): for i7 in range(3): for i8 in range(3): k = (i1+1)*(i2+1)*(i3+1)*(i4+1)*(i5+1)*(i6+1)*(i7+1)*(i8+1) aux = solve(i1, i2, i3, i4, i5, i6, i7, i8) if(aux != -1 and k <= 1002): ans[k] = min(ans[k],aux) print(ans) main()
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src/constants.py
matheus3301/neps-academy-scraper
0
93433
NEPS_URL = 'https://neps.academy' ENGLISH_BUTTON = '/html/body/div/div/div/div[2]/div/div/a/div[2]/div' LOGIN_PAGE_BUTTON = '//*[@id="app"]/div/div/div/div[1]/div/header/div/div/div[3]/div/div/nav/ul/li[6]/button' EMAIL_INPUT = '/html/body/div/div/div/div[3]/div/div/div/form/div[1]/div/div[1]/div/input' PASSWORD_INPUT = '/html/body/div/div/div/div[3]/div/div/div/form/div[2]/div/div[1]/div[1]/input' LOGIN_MODAL_BUTTON = '//*[@id="app"]/div[3]/div/div/div/form/div[3]/button'
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api/gsn-api/gsndb/migrations/0004_auto_20190327_1847.py
DonovanDarko/gsn
2
120433
<reponame>DonovanDarko/gsn # Generated by Django 2.1.5 on 2019-03-27 18:47 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('gsndb', '0003_auto_20190326_2144'), ] operations = [ migrations.RenameField( model_name='student', old_name='birth_date', new_name='birthdate', ), migrations.RenameField( model_name='student', old_name='program', new_name='current_program', ), ]
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MENU and SUBMENU .py
HorridHanu/Notepad-Python
1
96271
######################################################################################## ######################################################################################## ## # CODE LANGUAGE IS PYHTON! ## ## ## ## # DATE: 1-JULY-2021 ## ## ######## ## ## ## ## ## # CODE BY HANU! ########## ## ######### ## ## ## ## # ONLY FOR EDUCATIONAL PURPOSE! ########## ####### ## ## ## ## ## ## # MENU AND SUBMENU! ## ## ## ## ## ## ## ## ## ## # ITS ONLY DEMO! ## ## ####### ## ## ######## ## ######################################################################################## ######################################################################################## from tkinter import * root = Tk() root.geometry("733x566") root.title("Menu and submenu!") root.bell() #used to bell on openin def file(): print("yes menu work!") # create for non drop command # mymenu= Menu(root) # mymenu.add_command(label="file",command=file) # mymenu.add_command(label="exit",command=quit) # root.config(menu=mymenu) # its menu! filemenu = Menu(root, tearoff = 00) # these are submenu! m1=Menu(filemenu) m1.add_command(label="new project",command=file) m1.add_separator() m1.add_command(label="new",command=file) m1.add_command(label="open",command=file) m1.add_command(label="rename",command=file) m1.add_command(label="close",command=file) m1.add_command(label="save project",command=file) m1.add_command(label="setting",command=file) m1.add_command(label="save all",command=file) m1.add_command(label="print",command=file) m1.add_command(label="exit",command=exit) filemenu.add_cascade(label="file",menu=m1) m2=Menu(filemenu) m2.add_command(label="new project",command=file) m2.add_separator() m2.add_command(label="new",command=file) m2.add_command(label="open",command=file) m2.add_command(label="rename",command=file) m2.add_command(label="close",command=file) m2.add_command(label="save project",command=file) m2.add_command(label="setting",command=file) m2.add_command(label="save all",command=file) m2.add_command(label="print",command=file) m2.add_command(label="exit",command=exit) #exit has pre-function to exit! filemenu.add_cascade(label="edit",menu=m1) root.config(menu=filemenu) root.mainloop()
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simsalabim/io/excel_loader.py
simklein/simsalabim
0
1605937
""" Excel Loader ============ Provides 1. IO stuff to access data from messy spreadsheets. """ import pandas as pd from openpyxl import load_workbook def load_xl_range(io, sheet_name=0, start=None, stop=None): """Load data from a certain range of an Excel sheet and transforms it to an dataframe. Parameters ---------- io : str path of the excel file sheet_name : str sheet name where the data lies Returns ------- DataFrame See Also -------- openpyxl.load_workbook() : Open the given filename and return the workbook. """ # Load sheet of workbook wb = load_workbook(filename=io, read_only=True) sheets = wb.sheetnames if sheet_name in sheets: ws = wb[sheet_name] else: try: ws = wb[sheets[sheet_name]] except: raise ValueError( "sheet_name '{}' not existing in Workbook!".format(sheet_name) ) # Read the cell values into a list of lists data_rows = [] for row in ws[start:stop]: data_cols = [] for cell in row: data_cols.append(cell.value) data_rows.append(data_cols) # Transform into dataframe df_out = pd.DataFrame(data_rows) return df_out
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Utilities/vtkTclTest2Py/expr.py
jasper-yeh/VtkDotNet
3
132571
"""Evalues a tcl expr expression.""" import math def expr(caller_globals_dict, caller_localvars_dict, arguments): """the arguments to expr in tcl are passed as individual arguments to this method. eg [expr sin(20)*$a] becomes expr.expr("sin","(","20",")","*", a)""" eval_str = "eval(\"\"\"" for arg in arguments: if arg in caller_localvars_dict.keys(): #arg is a local variable. First, get its value arg = caller_localvars_dict[arg] if type(arg) == type("string"): #this will obtain value in the variable. if arg in dir(math): eval_str += "math.%s" % arg else: if arg == "(": eval_str += arg else: if arg.strip() == "double": arg = "float" #tcl double is python float eval_str += " " + arg continue eval_str += " " + `arg` eval_str += "\"\"\")" #print "eval_str : %s" % eval_str return eval(eval_str, caller_globals_dict, caller_localvars_dict)
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mocksh.py
blyxxyz/mocksh
1
58146
<gh_stars>1-10 # Copyright 2018 <NAME> <<EMAIL>> # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. """A wrapper around subprocess for simple function-like process calling. The main access point is the mocksh.sh object. Examples: >>> from mocksh import sh >>> sh.echo("Hello, world") Hello, world <mocksh.Process: echo Hello, world> >>> str(sh.echo.capture_("Hello, world")) 'Hello, world\n' >>> list(sh.seq.capture_(3)) ['1', '2', '3'] >>> sh.fortune.pipe_().cowsay() ______________________________________ / Are you making all this up as you go \ \ along? / -------------------------------------- \ ^__^ \ (oo)\_______ (__)\ )\/\ ||----w | || || <mocksh.Process: <mocksh.Process: fortune> | cowsay> For more, see README.rst. """ import errno import os import signal import sys try: from time import monotonic as _time # type: ignore except ImportError: from time import time as _time import subprocess from subprocess import PIPE, STDOUT if sys.version_info >= (3, 3): from subprocess import DEVNULL PY3 = sys.version_info >= (3,) MYPY = False if MYPY: from types import TracebackType # noqa: F401 from typing import (Any, Dict, IO, Iterator, List, Mapping, # noqa: F401 NoReturn, Optional, Sequence, Text, TextIO, Tuple, Type, Union) if sys.version_info >= (3, 6): StrTypes = Union[Text, bytes, os.PathLike] else: StrTypes = Union[Text, bytes] if PY3: StreamType = TextIO else: StreamType = IO[str] __all__ = ['CommandError', 'Process', 'Command', 'sh', 'PIPE', 'STDOUT', 'DEVNULL'] _STR_TYPES = (str, bytes) # type: Tuple[Type, ...] if not PY3: _STR_TYPES = (str, unicode) # noqa: F821 elif sys.version_info >= (3, 6): _STR_TYPES = (str, bytes, os.PathLike) def _to_strlike(obj): # type: (Any) -> StrTypes """Convert an object to a string-like type.""" if isinstance(obj, _STR_TYPES): # Object is already suitable for subprocess return obj # type: ignore # Only convert objects that have an explicit string representation # e.g. ints are fine, tuples are not # Implicitly converting to bytes is not a good idea, argv can't even # contain null bytes if getattr(type(obj), '__str__', object.__str__) is object.__str__: # Note: this doesn't work on PyPy 2, but it doesn't break anything raise TypeError("Object of type '{}' can't be converted to string" .format(type(obj).__name__)) return str(obj) def _parse(args, opts): # type: (Sequence[Any], Mapping[str, Any]) -> Iterator[StrTypes] """Convert Python function arguments to GNU getopt-style command arguments. opts are converted using the following procedure: - Underscores in keys are replaced by dashes, because dashes are common in options but not supported by Python. - If a key does not start with a dash, dashes are prepended based on the key's length. - A single character is assumed to be a short option, and gets one dash. - Other keys are assumed to be long options, and get two dashes. - If the value is the False object, the option is discarded. - If the value is the True object, only the key is yielded. - Otherwise, the key is yielded first, and then the value, converted to a string unless it's a bytes object. args (that are not bytes) are converted to strings and yielded otherwise unchanged. >>> list(_parse(['foo', b'bar'], dict(x=True, y=False, baz=23))) ['-x', '--baz', '23', 'foo', b'bar'] Args: args: A sequence of positional arguments, like *args. opts: A dictionary of keyword arguments, like **kwargs. Yields: strings or bytes that can be used as command arguments. """ for key, value in opts.items(): key = key.replace('_', '-') if not key.startswith('-'): if len(key) == 1: key = '-' + key else: key = '--' + key if value is False: continue if value is True: yield key continue yield key yield _to_strlike(value) for arg in args: yield _to_strlike(arg) def _is_reserved(name): # type: (str) -> bool """Return whether a name is reserved by mocksh (or Python itself).""" return name.endswith('_') and len(name) > 2 if sys.version_info >= (3, 5): _SIGNAL_CODES = { signal.name: -signal for signal in signal.Signals } # type: Dict[str, int] else: _SIGNAL_CODES = { name: -getattr(signal, name) for name in dir(signal) if name.startswith('SIG') and '_' not in name } # type: Dict[str, int] _SIGNAL_NAMES = { value: name for name, value in _SIGNAL_CODES.items() } # type: Dict[int, str] class CommandError(Exception): """Raised when a command fails. This class is automatically subclassed for different return codes. "except CommandError:" works, but more specific handling can be done with e.g. "except CommandError.code(10)". """ _subclasses = {} # type: Dict[int, Type[CommandError]] returncode = None def __new__(cls, returncode, process): # type: (Type[CommandError], int, Process) -> CommandError subclass = CommandError.code(returncode) return super(CommandError, cls).__new__(subclass) # type: ignore def __init__(self, returncode, process): # type: (int, Process) -> None super(CommandError, self).__init__(returncode, process) self.returncode = returncode self.process = process def __str__(self): # type: () -> str if isinstance(self.process.args, _STR_TYPES): command = self.process.args else: command = ' '.join(str(arg) for arg in self.process.args) if self.returncode in _SIGNAL_NAMES: cause = _SIGNAL_NAMES[self.returncode] else: cause = "status code {}".format(self.returncode) return "Command '{}' failed with {}".format(command, cause) @classmethod def code(cls, returncode): # type: (Union[int, str]) -> Type[CommandError] """Get a subclass for an error code.""" returncode, signame = cls.normalize_code(returncode) if returncode not in cls._subclasses: def __new__(*_, **__): # type: (*Any, **Any) -> NoReturn raise TypeError("Cannot instantiate CommandError subclasses") index = signame if signame else returncode name = "{}[{!r}]".format(cls.__name__, index) members = {'__new__': __new__, 'returncode': returncode} subclass = type(name, (cls,), members) # type: Type[CommandError] cls._subclasses.setdefault(returncode, subclass) return cls._subclasses[returncode] __class_getitem__ = code @staticmethod def normalize_code(returncode): # type: (Union[int, str]) -> Tuple[int, Optional[str]] """Normalize an exit code or signal name.""" if isinstance(returncode, str): if returncode in _SIGNAL_CODES: return _SIGNAL_CODES[returncode], returncode raise ValueError("Unknown signal {}".format(returncode)) elif isinstance(returncode, int): if returncode in _SIGNAL_NAMES: return returncode, _SIGNAL_NAMES[returncode] return returncode, None raise TypeError class Process(subprocess.Popen): """A subclass of subprocess.Popen with easier behavior. Designed to be used through mocksh.Command. Arguments: args: A string, or a sequence of program arguments. check: Whether to raise an exception if the program exits with failure. tail: A Process instance to read the output of and wait for, as in a pipeline. input: Data to write into the standard input of the process. Can be either text or bytes. wait: Whether to wait until the process is finished when instantiating. timeout: How long to wait before raising a TimeoutExpired exception. capture_stdout: Whether to capture standard output. Alias for stdout=PIPE. capture_stderr: Whether to capture standard error. Alias for stderr=PIPE. kwargs: Arguments to be passed to subprocess.Popen's initializer. """ def __init__(self, args, # type: subprocess._CMD check=True, # type: bool tail=None, # type: Optional[Process] input=None, # type: Optional[Union[bytes, Text]] wait=False, # type: bool timeout=None, # type: Optional[float] capture_stdout=False, # type: bool capture_stderr=False, # type: bool **kwargs # type: Any ): # type: (...) -> None if kwargs.get('shell') and not isinstance(args, _STR_TYPES): # This is weird and not very useful, but just in case args = ' '.join(str(arg) for arg in args) # Strictly speaking, this breaks compatibility with Popen, which # just executes ["/bin/sh", "-c"] + args after normalizing args # to a list. # On my system, subprocess.Popen(['echo', 'foo'], shell=True) # effectively executes "echo", without the foo. But that # particular "feature" is never desirable for this module. if PY3: # TODO: it would be nice to remove this restriction # Maybe re-open the files in text mode in __str__ and __iter__? # A new attribute? A new property? if kwargs.get('universal_newlines', True) is not True: raise ValueError("Can't override universal_newlines") kwargs['universal_newlines'] = True if tail is not None: if kwargs.get('stdin') is not None: raise ValueError("Can't pipe and set stdin at the same time") kwargs['stdin'] = tail.stdout if input is not None: if kwargs.get('stdin') is not None: raise ValueError("Can't input and set stdin at the same time") kwargs['stdin'] = PIPE if capture_stdout: if kwargs.get('stdout') is not None: raise ValueError("Can't capture stdout if redirecting it") kwargs['stdout'] = PIPE if capture_stderr: if kwargs.get('stderr') is not None: raise ValueError("Can't capture stderr if redirecting it") kwargs['stderr'] = PIPE super(Process, self).__init__(args, **kwargs) self.check = check self.tail = tail if self.tail is not None: self.stdin = self.tail.stdin if not hasattr(self, 'args'): self.args = args if input is not None: if self.stdin is None: raise TypeError("Couldn't find stdin") try: if PY3 and isinstance(input, bytes): self.stdin.buffer.write(input) # type: ignore else: self.stdin.write(input) except IOError as exc: if exc.errno not in {errno.EINVAL, errno.EPIPE}: raise finally: self.stdin.close() if wait or timeout is not None: if timeout is not None: self.wait(timeout=timeout) else: self.wait() if self.check: self.check_returncode() @property def captured(self): # type: () -> StreamType """Get the output stream that's being captured.""" if self.stdout and self.stderr: # People might (reasonably) expect captured to access both if this # is possible raise AttributeError("Capturing both stdout and stderr, use " ".stdout and .stderr directly instead") if self.stdout: return self.stdout # type: ignore if self.stderr: return self.stderr # type: ignore raise AttributeError("Not capturing any output") def __repr__(self): # type: () -> str argv = ' '.join(str(arg) for arg in self.args) name = type(self).__module__ + '.' + type(self).__name__ if self.tail is None: return "<{}: {}>".format(name, argv) return "<{}: {!r} | {}>".format(name, self.tail, argv) def wait(self, timeout=None): # type: (Optional[float]) -> int """Wait for this process and its tail (if any) to finish. Arguments: timeout: How many seconds to wait before raising subprocess.TimeoutExpired. Only supported in Python 3.3+. """ if timeout is None: if self.tail is not None: self.tail.wait() return super(Process, self).wait() elif sys.version_info >= (3, 3): orig_time = _time() if self.tail is not None: self.tail.wait(timeout=timeout) rest = orig_time + timeout - _time() return super(Process, self).wait(timeout=rest) else: raise TypeError("Waiting with a timeout requires Python 3.3+") def check_returncode(self): # type: () -> None """Raise a CommandError if the exit code is non-zero.""" returncode = self.wait() if returncode != 0: raise CommandError(returncode, self) def __enter__(self): # type: () -> Process return self def __exit__(self, type, # type: Optional[Type[BaseException]] value, # type: Optional[BaseException] traceback # type: Optional[TracebackType] ): # type: (...) -> bool """Close all streams and wait for the process to finish.""" try: if self.tail is not None: self.tail.__exit__(type, value, traceback) for stream in self.stdout, self.stderr, self.stdin: if stream: stream.close() finally: self.wait() if self.check: self.check_returncode() return False def __iter__(self): # type: () -> Iterator[str] """Loop through the lines of command output, without newlines.""" with self: for line in self.captured: yield line.rstrip('\r\n') def __str__(self): # type: () -> str """Read the output of the command as a string.""" with self: return self.captured.read() if PY3: def __bytes__(self): # type: () -> bytes """Read the output of the command as bytes.""" with self: return self.captured.buffer.read() def __bool__(self): # type: () -> bool """Check whether the process exited successfully.""" return self.wait() == 0 __nonzero__ = __bool__ class Command(object): """A launcher for Process instances that mimics function calls. Arguments: args: A tuple of program arguments. opts: Keyword arguments to pass to mocksh.Process. kwargs: Alternative syntax for opts. In most cases you would use this through the pre-built mockh.sh object. """ # for the benefit of 'from mocksh import sh' PIPE_ = PIPE STDOUT_ = STDOUT if sys.version_info >= (3, 3): DEVNULL_ = DEVNULL CommandError_ = CommandError def __init__(self, args=(), opts=None, **kwargs): # type: (Tuple[StrTypes, ...], Optional[Dict[str, Any]], **Any) -> None self.args_ = args self.opts_ = {} # type: Dict[str, Any] if opts: self.opts_.update(opts) self.opts_.update(kwargs) def __repr__(self): # type: () -> str name = type(self).__module__ + '.' + type(self).__name__ return "{}({!r}, {!r})".format(name, self.args_, self.opts_) def __eq__(self, other): # type: (Any) -> bool return (type(self) is type(other) and self.args_ == other.args_ and self.opts_ == other.opts_) def __getattr__(self, item): # type: (str) -> Command """Return a new instance with `item` appended to the argument list.""" if _is_reserved(item): raise AttributeError(item) return type(self)(self.args_ + (item.replace('_', '-'),), self.opts_) def __getitem__(self, index): # type: (Union[Any, Tuple[Any]]) -> Command """Return a new instance with index appended to the argument list. index will be cast to a suitable type if necessary. Both `command[arg]` and `command[arg1, arg2, arg3]` are supported. """ if not isinstance(index, tuple): index = (index,) index = tuple(_to_strlike(item) for item in index) return type(self)(self.args_ + index, self.opts_) def __call__(self, *args, **opts): # type: (*Any, **Any) -> Process """Launch a new instance of Process. args and opts will be converted to (GNU) getopt/argparse-style command arguments. However, keyword arguments ending in an underscore will be passed (without the underscore) to the initializer of Process instead. By default, this method waits until the process is finished. To return immediately, pass wait_=False. sh.foo('bar', baz=3, cwd_='/') is equivalent to Process(('foo', '--baz', '3', 'bar'), cwd='/') """ process_kwargs = self.opts_.copy() cmd_opts = {} for key, value in opts.items(): if _is_reserved(key): process_kwargs[key[:-1]] = value else: cmd_opts[key] = value argv = self.args_ + tuple(_parse(args, cmd_opts)) # Wait by default when using this interface process_kwargs.setdefault('wait', True) return Process(argv, **process_kwargs) def pipe_(self, *args, **opts): # type: (*Any, **Any) -> Command """Launch an instance of Process as part of a pipeline. Returns a new Command object to launch a process to continue. """ # wait_=False and check_=False are defaults, stdout_=PIPE is mandatory opts.setdefault('wait_', False) opts.setdefault('check_', False) process = self(*args, stdout_=PIPE, **opts) return type(self)(opts=self.opts_, tail=process) def capture_(self, *args, **opts): # type: (*Any, **Any) -> Process """Launch an instance of Process to capture its output.""" opts.setdefault('wait_', False) return self(*args, stdout_=PIPE, **opts) def test_(self, *args, **opts): # type: (*Any, **Any) -> Process """Launch an instance of Process to test whether it succeeds.""" opts.setdefault('check_', False) return self(*args, **opts) if sys.version_info >= (3, 3): # older versions can't do super().__dir__ def __dir__(self): # type: () -> List[str] """Provide PATH autocompletion. Loosely based on shutil.which.""" listing = list(super(Command, self).__dir__()) if self.args_: return listing mode = os.F_OK | os.X_OK path = os.environ.get('PATH', os.defpath).split(os.pathsep) for directory in filter(os.path.isdir, path): for filename in os.listdir(directory): fpath = os.path.join(directory, filename) if os.path.isfile(fpath) and os.access(fpath, mode): listing.append(filename.replace('-', '_')) return listing sh = Command()
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legocollector/inventory/management/commands/import_ldraw_processed_parts.py
ericziethen/legocollector
1
98060
import json import logging import os from django.core.management.base import BaseCommand from django.db import transaction from inventory.models import Part, PartExternalId logger = logging.getLogger(__name__) # pylint: disable=invalid-name class Command(BaseCommand): def add_arguments(self, parser): parser.add_argument('json_file_path', type=str) def handle(self, *args, **options): json_file_path = options['json_file_path'] json_dic = {} if os.path.exists(json_file_path): with open(json_file_path, 'r', encoding='utf-8') as file_ptr: json_dic = json.load(file_ptr) else: logger.error(F'ERROR - Json file "{json_file_path}" does not exist') self.import_ldraw_data(json_dic) @staticmethod def import_ldraw_data(data_dic): logger.info('Importing Ldraw Data') parts_processed_counts = 0 part_list = Part.objects.values_list('part_num', flat=True) ldraw_external_ids = [ e.external_id for e in PartExternalId.objects.filter(provider=PartExternalId.LDRAW)] parts_not_found_list = [] with transaction.atomic(): for part_num, part_dic in data_dic.items(): # pylint: disable=too-many-nested-blocks part_list = [] if part_num in ldraw_external_ids: part_list = [e.part for e in PartExternalId.objects.filter( provider=PartExternalId.LDRAW, external_id=part_num)] else: part = Part.objects.filter(part_num=part_num).first() if part: part_list.append(part) else: parts_not_found_list.append(part_num) for part in part_list: if "processing_errors" not in part_dic: part.top_studs = part_dic['top_top_studs'] part.bottom_studs = part_dic['bottom_studs'] part.stud_rings = part_dic['stud_ring_count'] part.save() parts_processed_counts += 1 if (parts_processed_counts % 1000) == 0: logger.info(F' {parts_processed_counts} Parts Processed') logger.info(F'Parts not Found:\n{sorted(parts_not_found_list)}') logger.info(F'Total of {parts_processed_counts} DB Parts Processed')
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rango/views.py
farschmessivo/ranco
0
91043
from django.shortcuts import render from django.http import HttpResponseRedirect, HttpResponse from rango.models import Category, Page from rango.forms import CategoryForm, PageForm from rango.forms import UserForm, UserProfileForm from django.core.urlresolvers import reverse from django.contrib.auth import authenticate, login, logout from django.contrib.auth.decorators import login_required from django.contrib.auth.models import User from datetime import datetime from rango.webhose_search import run_query def index(request): # request.session.set_test_cookie() # Query the database for a list of ALL categories currently stored. # Order the categories by no. likes in descending order. # Retrieve the top 5 only - or all if less than 5. # Place the list in our context_dict dictionary # that will be passed to the template engine. category_list = Category.objects.order_by('-likes')[:5] pages_list = Page.objects.order_by('-views')[:5] context_dict = {'categories': category_list, 'pages': pages_list} print("context_dict:", context_dict) # Render the response and send it back! # Call the helper function to handle the cookies p = visitor_cookie_handler(request) print("visitor_cookie_handler:", p) # Return response back to the user, updating any cookies that need changed. context_dict['visits'] = request.session['visits'] print("request_session_visits:", request.session['visits']) print("context_dict_visits:", context_dict['visits']) response = render(request, 'rango/index.html', context=context_dict) print("response:", response) return response # return HttpResponse("Rango says hey there partner! <br/> <a href='/rango/about/'>About</a>") def about(request): context_dict = {'name': 'Roman'} print(request.method) print(request.user) visitor_cookie_handler(request) # return HttpResponse("Rango says here is the about page. <br/> <a href='/rango/'>Index</a>") context_dict['visits'] = request.session['visits'] print(context_dict['visits']) response = render(request, 'rango/about.html', context=context_dict) print("response:", response) return response # return render(request, reverse('about'), context=context_dict) def show_category(request, category_name_slug): # Create a context dictionary which we can pass # to the template rendering engine. context_dict = {} try: # Can we find a category name slug with the given name? # If we can't, the .get() method raises a DoesNotExist exception. # So the .get() method returns one model instance or raises an exception. category = Category.objects.get(slug=category_name_slug) # Retrieve all of the associated pages. # Note that filter() will return a list of page objects or an empty list pages = Page.objects.filter(category=category) # Adds our results list to the template context under name pages. context_dict['pages'] = pages # We also add the category object from # the database to the context dictionary. # We'll use this in the template to verify that the category exists. context_dict['category'] = category except Category.DoesNotExist: # We get here if we didn't find the specified category. # Don't do anything - # the template will display the "no category" message for us. context_dict['category'] = None context_dict['pages'] = None # Go render the response and return it to the client. return render(request, 'rango/category.html', context_dict) @login_required def add_category(request): form = CategoryForm() # A HTTP POST? if request.method == 'POST': form = CategoryForm(request.POST) # Have we been provided with a valid form? if form.is_valid(): # Save the new category to the database. cat = form.save(commit=True) print("Category name: {0} Slug: {1}".format(cat, cat.slug)) # Now that the category is saved # We could give a confirmation message # But since the most recent category added is on the index page # Then we can direct the user back to the index page. return index(request) else: # The supplied form contained errors - # just print them to the terminal. print(form.errors) # Will handle the bad form, new form, or no form supplied cases. # Render the form with error messages (if any). return render(request, 'rango/add_category.html', {'form': form}) @login_required def add_page(request, category_name_slug): try: category = Category.objects.get(slug=category_name_slug) except Category.DoesNotExist: category = None form = PageForm() # A HTTP POST? if request.method == 'POST': form = PageForm(request.POST) # Have we been provided with a valid form? if form.is_valid(): if category: page = form.save(commit=False) page.category = category page.views = 0 page.save() return show_category(request, category_name_slug) else: print(form.errors) context_dict = {'form':form, 'category': category} return render(request, 'rango/add_page.html', context_dict) # def register(request): # # A boolean value for telling the template # # whether the registration was successful. # # Set to False initially. Code changes value to # True when registration succeeds. # registered = False # # # If it's a HTTP POST, we're interested in processing form data. # if request.method == 'POST': # # Attempt to grab information from the raw form information. # # Note that we make use of both UserForm and UserProfileForm. # user_form = UserForm(data=request.POST) # profile_form = UserProfileForm(data=request.POST) # # # If the two forms are valid... # if user_form.is_valid() and profile_form.is_valid(): # # Save the user's form data to the database. # user = user_form.save() # # # Now we hash the password with the set_password method. # # Once hashed, we can update the user object. # user.set_password(<PASSWORD>) # user.save() # # # Now sort out the UserProfile instance. # # Since we need to set the user attribute ourselves, # # we set commit=False. This delays saving the model # # until we're ready to avoid integrity problems. # profile = profile_form.save(commit=False) # profile.user = user # # Did the user provide a profile picture? # # If so, we need to get it from the input form and #put it in the UserProfile model. # if 'picture' in request.FILES: # profile.picture = request.FILES['picture'] # # # Now we save the UserProfile model instance. # profile.save() # # Update our variable to indicate that the template # # registration was successful. # registered = True # else: # # Invalid form or forms - mistakes or something else? # # Print problems to the terminal. # print(user_form.errors, profile_form.errors) # else: # # Not a HTTP POST, so we render our form using two ModelForm instances. # # These forms will be blank, ready for user input. # user_form = UserForm() # profile_form = UserProfileForm() # # # Render the template depending on the context. # return render(request, 'rango/register.html', # {'user_form': user_form, # 'profile_form': profile_form, # 'registered': registered}) # def user_login(request): # # If the request is a HTTP POST, try to pull out the relevant information. # if request.method == 'POST': # # Gather the username and password provided by the user. # # This information is obtained from the login form. # # We use request.POST.get('<variable>') as opposed # # to request.POST['<variable>'], because the # # request.POST.get('<variable>') returns None if the # # value does not exist, while request.POST['<variable>'] # # will raise a KeyError exception. # username = request.POST.get('username') # password = request.POST.get('password') # # Use Django's machinery to attempt to see if the username/password # # combination is valid - a User object is returned if it is. # user = authenticate(username=username, password=password) # # # If we have a User object, the details are correct. # # If None (Python's way of representing the absence of a value), no user # # with matching credentials was found. # if user: # # Is the account active? It could have been disabled. # if user.is_active: # # If the account is valid and active, we can log the user in. # # We'll send the user back to the homepage. # login(request, user) # return HttpResponseRedirect(reverse('index')) # else: # # An inactive account was used - no logging in! # return HttpResponse("Your Rango account is disabled.") # else: # # Does the username exist in DB? # if User.objects.filter(username=username).exists(): # return HttpResponse("Your password is incorrect") # else: # return HttpResponse("{0} doesn't exist".format(username)) # # The request is not a HTTP POST, so display the login form. # # This scenario would most likely be a HTTP GET. # else: # # No context variables to pass to the template system, hence the # # blank dictionary object... # return render(request, 'rango/login.html', {}) @login_required def restricted(request): return render(request, 'rango/restricted.html', {}) # return render(request, reverse('about'), context=context_dict) # Use the login_required() decorator to ensure only those logged in can # access the view. # @login_required # def user_logout(request): # # Since we know the user is logged in, we can now just log them out. # logout(request) # # Take the user back to the homepage. # return HttpResponseRedirect(reverse('index')) def get_server_side_cookie(request, cookie, default_val=None): val = request.session.get(cookie) print(val) if not val: val = default_val return val def visitor_cookie_handler(request): # Get the number of visits to the site. # We use the COOKIES.get() function to obtain the visits cookie. # If the cookie exists, the value returned is casted to an integer. # If the cookie doesn't exist, then the default value of 1 is used. visits = int(get_server_side_cookie(request, 'visits', '1')) print("visits:", visits) last_visit_cookie = get_server_side_cookie(request, 'last_visit', str(datetime.now())) last_visit_time = datetime.strptime(last_visit_cookie[:-7], '%Y-%m-%d %H:%M:%S') # If it's been more than a day since the last visit... if (datetime.now() - last_visit_time).seconds > 0: visits = visits + 1 # Update the last visit cookie now that we have updated the count request.session['last_visit'] = str(datetime.now()) else: visits = 1 # Set the last visit cookie request.session['last_visit'] = last_visit_cookie # Update/set the visits cookie request.session['visits'] = visits def search(request): query = '' result_list = [] if request.method == 'POST': query = request.POST['query'].strip() if query: print(query) print(type(query)) # Run our Webhose search function to get the results list! result_list = run_query(query) context_dict = {'result_list': result_list, 'query': query} return render(request, 'rango/search.html', context_dict)
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fvp-core.py
duck-master/FVP
0
103472
#code written by duck_master since 2 january 2021 #licensed under MIT license #based on Final Version Perfected, invented by willbradshaw on 27 november 2020 #as described in https://www.lesswrong.com/posts/xfcKYznQ6B9yuxB28/final-version-perfected-an-underused-execution-algorithm #TODO: make into PyPI package or shell script #libraries import random #for randomizing reminder list #functions for reading/normalizing input #TODO: implement already having stars #TODO: implement try-catch in case of bad filepath def read_reminders_from_file(reminders_pathname): '''Reads in a list of reminders from a file. Notes: * indicates important reminders; filtered by no. of stars. ! indicates completed reminders; these are ignored. str -> [str]''' with open(reminders_pathname, mode = 'r') as f: rlines = f.readlines() #read from reminders_pathname and normalize rlines = [rr.strip('\n') for rr in reminders] return reminder def read_reminders_from_console(): '''Reads in a list of reminders from text input. (To finish the list, the user should type nothing and enter.) None, str input -> [str]''' print('Enter your reminders here. (Type nothing and enter to stop.)') reminders = [] #type in reminders while True: newreminder = input('What is one of your reminders? ') if newreminder != '': reminders.append(newreminder) else: break return reminders #function to add stars to reminder file lines def add_stars_to_reminderlines(reminderlines, items_to_star): '''This function should add stars to the marked items given by the indices in the second column. [str], [(str, int)] -> [str] ''' result = reminderlines for (item, index) in items_to_star: result[index] = '*' + result[index] return result #function to normalize reminders def normalize_reminderlines(reminderlines): '''Normalizes reminderlines. Deletes lines starting with -. Filters lines not starting with *, if any exists, and eliminates beginning *. [str] -> [str].''' result = reminderlines result = list(filter((lambda s: s[0] != '-'), result)) #detes lines starting with - imp = list(filter((lambda s: s[0] = '*'), result)) #selects lines starting with * if len(imp) > 0: result = list(map((lambda s: s[1:]), imp)) return result #main code #read in reminders if input('Do you want to read in your reminders from a file? ') in ['yes', 'Yes']: reminders_pathname = input('What is the file path? ') reminders = read_reminders_from_file(reminders_pathname) else: reminders = read_reminders_from_console() #normalize reminders reminders = normalize_reminderlines(reminders) #running FVP on idea list print('\nNow, let\'s sort your reminders!') #keep track of indices (to make file-writing easy) reminders_shuffled = [(reminders[index], index) for index in range(len(reminders))] random.shuffle(reminders_shuffled) #to remove bias from idea order; TODO: allow option to keep original order bestideas = [reminders_shuffled[0]] for i in range(1, len(reminders_shuffled)): response = input(f'Would you rather do "{reminders_shuffled[i][0]}" rather than "{bestideas[-1][0]}"? ') if response in ['yes', 'Yes']: bestideas.append(reminders_shuffled[i]) #output print(f'\nYou should do {list(bestideas.__reversed__())[0][0]}.') #for file writes if reminders_pathname != '': if input('Write to file? ') in ['yes', 'Yes']: with open(reminders_pathname, 'r') as f: reminderfile_contents = f.readlines() with open(reminders_pathname, 'w') as f: f.writelines(add_stars_to_reminderlines(reminderfile_contents, bestideas)) if input('\nQuit? ') in ['yes', 'Yes']: exit()
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mneia_admin_backend/migrations/0005_person_works.py
mneia-gr/mneia-admin-backend
0
1600679
<gh_stars>0 # Generated by Django 3.2.4 on 2021-06-25 07:34 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("mneia_admin_backend", "0004_personworkrelationship"), ] operations = [ migrations.AddField( model_name="person", name="works", field=models.ManyToManyField( related_name="people", through="mneia_admin_backend.PersonWorkRelationship", to="mneia_admin_backend.Work", ), ), ]
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math/rsa.py
javyxu/algorithms-python
2
103911
""" RSA encryption algorithm a method for encrypting a number that uses seperate encryption and decryption keys this file only implements the key generation algorithm there are three important numbers in RSA called n, e, and d e is called the encryption exponent d is called the decryption exponent n is called the modulus these three numbers satisfy ((x ** e) ** d) % n == x % n to use this system for encryption, n and e are made publicly available, and d is kept secret a number x can be encrypted by computing (x ** e) % n the original number can then be recovered by computing (E ** d) % n, where E is the encrypted number fortunately, python provides a three argument version of pow() that can compute powers modulo a number very quickly: (a ** b) % c == pow(a,b,c) """ import random from rabin_miller import * # is_prime from extended_gcd import * # extended_gcd """ generate a prime with k bits """ def genprime(k): while True: n = random.randrange(2 ** (k - 1),2 ** k) if is_prime(n,128): return n """ calculate the inverse of a mod m that is, find b such that (a * b) % m == 1 """ def modinv(a, m): x, y, g = extended_gcd(a,m) return x % m """ the RSA key generating algorithm k is the number of bits in n """ def generate_key(k): # size in bits of p and q need to add up to the size of n p_size = k / 2 q_size = k - p_size e = genprime(k) # in many cases, e is also chosen to be a small constant while True: p = genprime(k / 2) if p % e != 1: break while True: q = genprime(k - k / 2) if q % e != 1: break n = p * q l = (p - 1) * (q - 1) # calculate totient function d = modinv(e,l) return n, e, d """ sample usage: n,e,d = generate_key(1024) data = 1337 encrypted = pow(data,e,n) decrypted = pow(encrypted,d,n) assert decrypted == data """
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config.py
gve-sw/GVE_DevNet_Jabber_MigrateRooms-ChatToWebex
1
142599
<filename>config.py """Copyright (c) 2020 Cisco and/or its affiliates. This software is licensed to you under the terms of the Cisco Sample Code License, Version 1.1 (the "License"). You may obtain a copy of the License at https://developer.cisco.com/docs/licenses All use of the material herein must be in accordance with the terms of the License. All rights not expressly granted by the License are reserved. Unless required by applicable law or agreed to separately 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. """ import os # dCloud environment can be used for testing: # 'Evolution of Cisco Jabber and Migration of Jabber to Webex Lab v3' ''' DB variables to be used by SQLAlchemy to connect to DB. Check the DB types supported by SQLALchemy engine in its references. Following is a sample: - Postgres: postgres - Microsoft SQL: mssql+pymssql - Oracle: oracle - etc... ''' # Persistent Chat DB TC_DB_TYPE = "<db_type>" TC_DB_HOST = "<hostname_or_ip_address_here>" TC_DB_NAME = "tcmadb" TC_DB_USER = "<username_here>" TC_DB_PASSWORD = "<<PASSWORD>>" # This boolean variable is to choose to migrate the message attachments & file transfer activity (True), # or just handle text-chat only, and display 'Your chat application does not support downloading this file' # for messages with attachments (False) INCLUDE_FILE_TRANSFER = False # Managed file transfer DB # Only if INCLUDE_FILE_TRANSFER was set to True MFT_DB_TYPE = "<db_type>" MFT_DB_HOST = "<hostname_or_ip_address_here>" MFT_DB_NAME = "mftadb" MFT_DB_USER = "<username_here>" MFT_DB_PASSWORD = "<<PASSWORD>>" # File-transfer server details, to be able to download the files to a local folder, then forward it to Webex # Only if INCLUDE_FILE_TRANSFER was set to True FILE_SERVER_HOST = '<hostname_or_ip_address_here>' FILE_SERVER_USER = '<username_here>' FILE_SERVER_PASSWORD = '<<PASSWORD>>' # This boolean variable is to choose to migrate the detected Jabber rooms to Webex (True), # or just use this script to read the existing Jabber rooms and their details and save them in a log (False) CREATE_WEBEX_ROOMS = False # This boolean variable is to choose to check for already-existing Webex rooms that have the same title as the detected Jabber room, # then ask the user if they want to skip it (to avoid duplicates) or migrate it to new room anyway (True) # or not checking the existing Webex rooms' titles and just create new ones anyway (False) CHECK_WEBEX_EXISTING_ROOMS = False # Webex Authorization # Only if CREATE_WEBEX_ROOMS was set to True WEBEX_AUTH = 'Bearer <webex_user_token>' # These 2 variables are only needed if Jabber IM messaging uses a different domain than Webex domain. # Example: In dCloud environment, Jabber IM uses @dcloud.cisco.com while Webex environment uses @cbXXX.dc-YY.com JABBER_DOMAIN = "" WEBEX_DOMAIN = "" # Folder to store the script logs and its run results # If not changed: it will create a sub-folder of the current location called 'Logs' LOGS_FOLDER = os.path.dirname(os.path.realpath(__file__)) + '\\Logs\\' # Folder to store the files being transferred from Jabber's external file-server to Webex as attachments # If not changed: it will create a sub-folder of the current location called 'FileTransfer' LOCAL_FILE_TRANSFER_FOLDER = os.path.dirname(os.path.realpath(__file__)) + '\\FileTransfer\\'
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src/test.py
Times125/Graduation_Project
0
1614822
<reponame>Times125/Graduation_Project<filename>src/test.py #! /usr/bin/env python # -*- coding: utf-8 -*- """ @Author:lichunhui @Time: 2018/4/10 13:01 @Description: """ import nltk import re from src import log from sklearn.naive_bayes import MultinomialNB, BernoulliNB from nltk.classify.scikitlearn import SklearnClassifier class Testme: # 去除类似soooo 保留soo rpt_regex = re.compile(r"(.)\1{1,}", re.IGNORECASE) # twitter中@***这种将被替换成__HAND_ hndl_regex = re.compile(r"@(\w+)") # twitter中话题#***这种将被替换成__HASH_ hash_regex = re.compile(r"#(\w+)") # twitter中话题#***这种将被替换成__URL_ url_regex = re.compile(r"(http|https|ftp)://[a-zA-Z0-9\./]+") # twitter中存在的一些表情 emoticons = [('__EMOT_SMILEY', [':-)', ':)', '(:', '(-:', '^_^', '>y<', '>o<', '>O<', '^.^']), ('__EMOT_LAUGH', [':-D', ':D', 'X-D', 'XD', 'xD', ]), ('__EMOT_LOVE', ['<3', ':\*', ]), ('__EMOT_WINK', [';-)', ';)', ';-D', ';D', '(;', '(-;', '←_←', '→_→', '<_<', '>_>']), ('__EMOT_FROWN', [':-(', ':(', '(:', '(-:', '=_=', 'D:<','orz']), ('__EMOT_CRY', [':,(', ':\'(', ':"(', ':((', 't_t', 'T_T', '→_←', 'T^T', '>_<']), ] @classmethod def url_repl(cls, match): return '__URL_' @classmethod def hash_repl(cls, match): return '__HASH_' + match.group(1).upper() @classmethod def hndl_repl(cls, match): return '__HAND_' @classmethod def rpt_repl(cls, match): return match.group(1) + match.group(1) @classmethod def regex_union(cls, arr): return '(' + '|'.join(arr) + ')' @classmethod def escape_paren(cls, arr): return [text.replace(')', '[)}\]]').replace('(', '[({\[]') for text in arr] negtn_regex = re.compile(r"""(?:^(?:never|no|nothing|nowhere|noone|none|not|havent|hasnt|hadnt|cant|couldnt|shouldnt| wont|wouldnt|dont|doesnt|didnt|isnt|aret|aint)$)|n't""", re.X) @classmethod def get_negation_features(cls, words): INF = 0.0 negtn = [bool(cls.negtn_regex.search(w)) for w in words] left = [0.0] * len(words) prev = 0.0 for i in range(0, len(words)): if (negtn[i]): prev = 1.0 left[i] = prev prev = max(0.0, prev - 0.1) right = [0.0] * len(words) prev = 0.0 for i in reversed(range(0, len(words))): if (negtn[i]): prev = 1.0 right[i] = prev prev = max(0.0, prev - 0.1) return dict(zip(['neg_l(' + w + ')' for w in words] + ['neg_r(' + w + ')' for w in words], left + right)) @classmethod # 使用正则分词（没有做停用词处理） def text_parse(cls, x): try: sentence = x.strip().lower() except: sentence = x sentence = re.sub(cls.hndl_regex, cls.hndl_repl, sentence) # 匹配替换@*** sentence = re.sub(cls.hash_regex, cls.hash_repl, sentence) # 匹配替换#*** sentence = re.sub(cls.url_regex, cls.url_repl, sentence) # 匹配替换URL sentence = re.sub(cls.rpt_regex, cls.rpt_repl, sentence) # 匹配替换类似yoooooooo为yoo emoticons_regex = [(repl, re.compile(cls.regex_union(cls.escape_paren(regx)))) for (repl, regx) in cls.emoticons] # 匹配替换表情 for (repl, regx) in emoticons_regex: sentence = re.sub(regx, ' ' + repl + ' ', sentence) pattern = r""" (?x)(?:[a-z]\.)+ | \d+(?:\.\d+)?%?\w+ | \w+(?:[-']\w+)* | (?:[-.!?]{2,}) | [][.,;"'?():$-_*`]""" word_list = nltk.regexp_tokenize(sentence, pattern) return word_list if __name__ == '__main__': #log.console_out('1.txt',(1,2,(4,5))) #log.console_out('2.txt','22222') # x = "@times doesn't miss him and worries like it's her job or something #HMS.. ????? ------ >_< >_> >< *_* :D ;0) Work's fine? an interesting coworker, ha! o_O Lots of typing, what fun! <3<3" x = '@MrsFox Yeah...doesn\'t taste like #bacon at all :-( http://tinyurl.com/c2wwgp' # feat = {} # x = "@times still doesn't miss him and worries like it's ? .her job or something ??? .... ---------" y = Testme.text_parse(x) print(y) # print('\n') # negation_features = Testme.get_negation_features(y) # print("negation_features-->", negation_features) # print('\n') # words_bi = ['has(%s)' % ','.join(map(str, bg)) for bg in nltk.bigrams(y)] # print(words_bi) # print('\n') # bag = {} # for f in words_bi: # bag[f] = 1 # feat.update(bag) # feat.update(negation_features) # print(feat) # classifier = SklearnClassifier(BernoulliNB()) # classifier.train([[feat, 'neg']]) # print(classifier.classify(feat))
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anyfft/reikna/_version.py
tlambert03/anyfft
0
156851
<reponame>tlambert03/anyfft<filename>anyfft/reikna/_version.py # file generated by setuptools_scm # don't change, don't track in version control version = "0.1.dev1+ga7b326d.d20210618" version_tuple = (0, 1, "dev1+ga7b326d", "d20210618")
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asn1tools/version.py
eerimoq/asn1tools
198
27689
__version__ = '0.159.0'
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paddle/trainer/tests/gen_proto_data.py
lzhao4ever/Paddle-master
1
72587
# Copyright (c) 2016 Baidu, 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. from cStringIO import StringIO import paddle.proto.DataFormat_pb2 as DataFormat from google.protobuf.internal.encoder import _EncodeVarint import logging import pprint logging.basicConfig( format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s', ) logger = logging.getLogger('paddle') logger.setLevel(logging.INFO) OOV_POLICY_IGNORE = 0 OOV_POLICY_USE = 1 OOV_POLICY_ERROR = 2 num_original_columns = 3 # Feature combination patterns. # [[-1,0], [0,0]] means previous token at column 0 and current token at # column 0 are combined as one feature. patterns = [ [[-2, 0]], [[-1, 0]], [[0, 0]], [[1, 0]], [[2, 0]], [[-1, 0], [0, 0]], [[0, 0], [1, 0]], [[-2, 1]], [[-1, 1]], [[0, 1]], [[1, 1]], [[2, 1]], [[-2, 1], [-1, 1]], [[-1, 1], [0, 1]], [[0, 1], [1, 1]], [[1, 1], [2, 1]], [[-2, 1], [-1, 1], [0, 1]], [[-1, 1], [0, 1], [1, 1]], [[0, 1], [1, 1], [2, 1]], ] def make_features(sequence): length = len(sequence) num_features = len(sequence[0]) def get_features(pos): if pos < 0: return ['#B%s' % -pos] * num_features if pos >= length: return ['#E%s' % (pos - length + 1)] * num_features return sequence[pos] for i in xrange(length): for pattern in patterns: fname = '/'.join([get_features(i + pos)[f] for pos, f in pattern]) sequence[i].append(fname) ''' Source file format: Each line is for one timestep. The features are separated by space. An empty line indicates end of a sequence. cutoff: a list of numbers. If count of a feature is smaller than this, it will be ignored. if oov_policy[i] is OOV_POLICY_USE, id 0 is reserved for OOV features of i-th column. return a list of dict for each column ''' def create_dictionaries(filename, cutoff, oov_policy): def add_to_dict(sequence, dicts): num_features = len(dicts) for features in sequence: l = len(features) assert l == num_features, "Wrong number of features " + line for i in xrange(l): if features[i] in dicts[i]: dicts[i][features[i]] += 1 else: dicts[i][features[i]] = 1 num_features = len(cutoff) dicts = [] for i in xrange(num_features): dicts.append(dict()) f = open(filename, 'rb') sequence = [] for line in f: line = line.strip() if not line: make_features(sequence) add_to_dict(sequence, dicts) sequence = [] continue features = line.split(' ') sequence.append(features) for i in xrange(num_features): dct = dicts[i] n = 1 if oov_policy[i] == OOV_POLICY_USE else 0 todo = [] for k, v in dct.iteritems(): if v < cutoff[i]: todo.append(k) else: dct[k] = n n += 1 if oov_policy[i] == OOV_POLICY_USE: # placeholder so that len(dct) will be the number of features # including OOV dct['#OOV#'] = 0 logger.info('column %d dict size=%d, ignored %d' % (i, n, len(todo))) for k in todo: del dct[k] f.close() return dicts def encode_varint(v): out = StringIO() _EncodeVarint(out.write, v) return out.getvalue() def write_proto(file, message): s = message.SerializeToString() packed_len = encode_varint(len(s)) file.write(packed_len + s) ''' if oov_policy[i] == OOV_POLICY_USE, features in i-th column which are not existed in dicts[i] will be assigned to id 0. if oov_policy[i] == OOV_POLICY_ERROR, all features in i-th column MUST exist in dicts[i]. ''' def gen_proto_file(input_file, dicts, oov_policy, output_file): def write_sequence(out, sequence): num_features = len(dicts) is_beginning = True for features in sequence: assert len(features) == num_features, \ "Wrong number of features: " + line sample = DataFormat.DataSample() for i in xrange(num_original_columns): id = dicts[i].get(features[i], -1) if id != -1: sample.id_slots.append(id) elif oov_policy[i] == OOV_POLICY_IGNORE: sample.id_slots.append(0xffffffff) elif oov_policy[i] == OOV_POLICY_ERROR: logger.fatal("Unknown token: %s" % features[i]) else: sample.id_slots.append(0) if patterns: dim = 0 vec = sample.vector_slots.add() for i in xrange(num_original_columns, num_features): id = dicts[i].get(features[i], -1) if id != -1: vec.ids.append(dim + id) elif oov_policy[i] == OOV_POLICY_IGNORE: pass elif oov_policy[i] == OOV_POLICY_ERROR: logger.fatal("Unknown token: %s" % features[i]) else: vec.ids.append(dim + 0) dim += len(dicts[i]) sample.is_beginning = is_beginning is_beginning = False write_proto(out, sample) num_features = len(dicts) f = open(input_file, 'rb') out = open(output_file, 'wb') header = DataFormat.DataHeader() if patterns: slot_def = header.slot_defs.add() slot_def.type = DataFormat.SlotDef.VECTOR_SPARSE_NON_VALUE slot_def.dim = sum( [len(dicts[i]) for i in xrange(num_original_columns, len(dicts))]) logger.info("feature_dim=%s" % slot_def.dim) for i in xrange(num_original_columns): slot_def = header.slot_defs.add() slot_def.type = DataFormat.SlotDef.INDEX slot_def.dim = len(dicts[i]) write_proto(out, header) num_sequences = 0 sequence = [] for line in f: line = line.strip() if not line: make_features(sequence) write_sequence(out, sequence) sequence = [] num_sequences += 1 continue features = line.split(' ') sequence.append(features) f.close() out.close() logger.info("num_sequences=%s" % num_sequences) dict2 = { 'B-ADJP': 0, 'I-ADJP': 1, 'B-ADVP': 2, 'I-ADVP': 3, 'B-CONJP': 4, 'I-CONJP': 5, 'B-INTJ': 6, 'I-INTJ': 7, 'B-LST': 8, 'I-LST': 9, 'B-NP': 10, 'I-NP': 11, 'B-PP': 12, 'I-PP': 13, 'B-PRT': 14, 'I-PRT': 15, 'B-SBAR': 16, 'I-SBAR': 17, 'B-UCP': 18, 'I-UCP': 19, 'B-VP': 20, 'I-VP': 21, 'O': 22 } if __name__ == '__main__': cutoff = [3, 1, 0] cutoff += [3] * len(patterns) oov_policy = [OOV_POLICY_IGNORE, OOV_POLICY_ERROR, OOV_POLICY_ERROR] oov_policy += [OOV_POLICY_IGNORE] * len(patterns) dicts = create_dictionaries('trainer/tests/train.txt', cutoff, oov_policy) dicts[2] = dict2 gen_proto_file('trainer/tests/train.txt', dicts, oov_policy, 'trainer/tests/train_proto.bin') gen_proto_file('trainer/tests/test.txt', dicts, oov_policy, 'trainer/tests/test_proto.bin')
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Wk06/06-storing-input/site.py
beyondvalence/biof509_wtl
0
135567
from flask import Flask, render_template app = Flask(__name__) app.config['SECRET_KEY'] = 'important to keep unknown in production' from flask_wtf import Form from wtforms import StringField from wtforms.validators import DataRequired class MyForm(Form): name = StringField('first name', validators=[DataRequired()]) name1 = StringField('last name', validators=[DataRequired()]) from flask_sqlalchemy import SQLAlchemy app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///test.db' db = SQLAlchemy(app) class Visitor(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(80)) name1 = db.Column(db.String(80)) def __init__(self, name, name1): self.name = name self.name1 = name1 def __repr__(self): return '<Visitor %r, %r>' % self.name, self.name1 db.create_all() @app.route('/', methods=('GET', 'POST')) def index(): form = MyForm(text='first name') previous_visitors = Visitor.query.order_by('id DESC').all() if form.validate_on_submit(): name = form.name.data name1 = form.name1.data db.session.add(Visitor(name, name1)) db.session.commit() else: name = 'Doctor' name1 = 'Strange' return render_template('index.html', form=form, name=name, name1=name1, previous_visitors=previous_visitors) @app.route('/custom/<custom>/') def custom(custom): return render_template('custom.html', custom=custom) if __name__ == '__main__': app.run(debug=True)
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cupid/actions.py
iinnovations/iicontrollibs
11
85458
#!/usr/bin/python3 __author__ = "<NAME>" __copyright__ = "Copyright 2016, Interface Innovations" __credits__ = ["<NAME>"] __license__ = "Apache 2.0" __version__ = "1.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Development" import os import sys import inspect try: import simplejson as json except: import json top_folder = \ os.path.split(os.path.realpath(os.path.abspath(os.path.split(inspect.getfile(inspect.currentframe()))[0])))[0] if top_folder not in sys.path: sys.path.insert(0, top_folder) """ This class defines actions taken on stuff. We are going to restructure this. An action is something that happens, for example: action.type == 'setvalue' : value is sent actiondetail contains dbvn reference to value to be set. By default this is binary : action active sets to 1, active reset will set to 0 Active value will be set in actiondata, evaluating formula, etc. This can replace channels, in effect. action.type == 'email' : email is sent actiondetail contains email address (es) action.type == 'incvalue' : value is incremented actiondetail contains dbvn reference to value. By default this is add 1 action.conditiontype == 'logical' action.conditiondbname == friendly dbname action.conditiontablename == tablename action.conditionlogic == equation or equations, using x as variable, separated by ';' e.g. 'x > 42,x < 89' Can also code in other database variables using syntax: [dbname:tablename:valuename] Not yet implemented, but that's the idea. action.conditiontype == 'time' action.conditionvalue == date time string in UTC seconds action. Some value have fields. These are universal values: All other data is contained in the actiondata field. These are values that are typically actiontype or conditiontype specific These are dumped and loaded in and out to/from dictionaries """ class action: def __init__(self, actiondict): """ Currently we are overloading this with settings that are not standard row items. These should either all get columns, or all go into a settings field. The considerations at the moment have to do with what the UI is pulling. If we move all of this into a dict, we will need to change the UI to pull it out of a dict. """ settings = { 'ondelay':0, 'offdelay':0, 'activereset':True, 'actionfrequency': 0.0, # 0 means all the time. 'action_window': 60.0, 'log_path':None } settings.update(actiondict) for key, value in settings.items(): setattr(self, key, value) """ Instantiate the action class The dict that we get from the database contains a number of fixed fields, and also an 'actiondata' field that contains dynamic properties that may or may not exist for different action types. We parse it into a dictionary, and send it in as a property. We could append the dictionary, but then we would not be able to easily keep track of where to write back modified entries. """ self.actiondatadict = json.loads(actiondict['actiondata']) """ TODO: Set default values as a condition of alarm type to make alarm work when values are not specified. Publish will then insert these values into the database Also, when values are pulled in, use the schema to change value type, e.g. self.ondelay should come in as a float (or decimal), not string. """ self.statusmsg = '' def log(self, message, reqloglevel=1, currloglevel=1): from iiutilities.utility import log if self.log_path: log(self.log_path, message, reqloglevel, currloglevel) def determine_status(self): from iiutilities import dblib, datalib if self.conditiontype == 'logical': currstatus = datalib.evaldbvnformula(self.actiondatadict['condition']) # print('CURRSTATUS',currstatus) if currstatus == None: self.statusmsg += 'None returned by evaldbvn. Setting status to False' self.value = 0 try: self.status = int(currstatus) except: print('error parsing ' + str(currstatus)) self.status = 0 elif self.conditiontype == 'value': self.value = dblib.dbvntovalue(self.actiondatadict['dbvn']) # print(self.actiondatadict['dbvn']) # print(self.value) # self.value = datalib.evaldbvnformula(self.actiondatadict['dbvn']) # self.operator = self.actiondatadict['operator'] # self.criterion = self.actiondatadict['criterion'] try: self.status = int(datalib.calcastevalformula(str(self.value) + self.actiondatadict['operator'] + self.actiondatadict['criterion'])) # Should really throw an error here. except: self.log('ERROR in asteval') self.log(str(self.value) + self.actiondatadict['operator'] + self.actiondatadict['criterion']) self.status = 0 elif self.conditiontype == 'channel': from cupid import pilib control_channel_name = self.actiondatadict['channel_name'] control_db = pilib.cupidDatabase(pilib.dirs.dbs.control) self.value = control_db.get_single_value('channels','controlvalue',condition='"name"=\'' + control_channel_name + "'") self.status = 0 if 'PV_low' in self.actiondatadict and self.value < float(self.actiondatadict['PV_low']): self.status = 1 if 'PV_high' in self.actiondatadict and self.value > float(self.actiondatadict['PV_high']): self.status = 1 elif self.conditiontype == 'temporal': """ This will be done in a cron-style with a default dictionary """ settings = { 'window':60.0 } settings.update(self.actiondatadict) import datetime current_time = datetime.datetime.now() event_time_dict = { 'year':None, 'month':None, 'dow':None, 'day':None, 'hour':0, 'minute':0, 'second':0 } # TODO: integrate day of week # TODO: handle lists of items event_time_dict.update(self.actiondatadict['event_time']) for item in ['year', 'month', 'day']: if not event_time_dict[item] or event_time_dict[item] == '*': event_time_dict[item] = getattr(current_time, item) for item in ['hour','minute','second']: if event_time_dict[item] == '*': event_time_dict[item] = getattr(current_time, item) # print(event_time_dict) event_time = datetime.datetime(year=int(event_time_dict['year']), month=int(event_time_dict['month']), day=event_time_dict['day'], hour=int(event_time_dict['hour']), minute=int(event_time_dict['minute']), second=int(event_time_dict['second'])) current_event_delta = (current_time - event_time).total_seconds() time_to_act = False if current_event_delta > 0 and current_event_delta < self.action_window: time_to_act = True # try: # last_action = datetime.datetime.strptime(self.actiontime, datalib.time_format_string) # except: # print('Cannot convert last_action ({})'.format(self.actiontime)) # time_to_act = True # else: # time_since_last_ # Test event time against current time # Has time passed, and then is time within action window (don't want to act on all past actions if we miss them) # timediff = float( # datalib.timestringtoseconds(datalib.gettimestring()) - datalib.timestringtoseconds(self.actiontime)) # self.value = timediff if time_to_act: # We act self.statusmsg += 'Time to act. ' self.lastactiontime = datetime.date.strftime(current_time, datalib.time_format_string) self.status = 1 else: self.statusmsg += 'Not time yet for action scheduled for ' + str(event_time) + '. Current time is ' + str(current_time) + '. ' print(self.statusmsg) # print(self.conditiontype) # print(self.status) def test(self): from iiutilities import utility if self.actiontype == 'email': # process email action self.statusmsg += 'Processing email alert. ' email = self.actiondetail message = 'Test message for action ' + self.name subject = 'CuPID Test Alert : ' + self.name actionmail = utility.gmail(message=message, subject=subject, recipient=email) actionmail.send() def onact(self): from iiutilities import dblib, datalib, utility from cupid import pilib if self.actiontype == 'email': # process email action self.statusmsg += 'Processing email alert. ' email = self.actiondetail # Special messages if self.conditiontype == 'channel': message = 'Channel alarm for ' + self.name + ' is active with value of ' + str(self.value) + '. ' if 'PV_low' in self.actiondatadict: message += 'Low alarm: ' + str(self.actiondatadict['PV_low'] + '. ') if 'PV_high' in self.actiondatadict: message += 'High alarm: ' + str(self.actiondatadict['PV_high'] + '. ') elif self.conditiontype == 'value': # message = 'Alert is active for ' + self.name + '. Criterion ' + self.variablename + ' in ' + self.tablename + ' has value ' + str(self.variablevalue) + ' with a criterion of ' + str(self.criterion) + ' with an operator of ' + self.operator + '. This alarm status has been on since ' + self.ontime + '.' message = 'Alert for alarm ' + self.name + ' . On time of ' + self.ontime + '. Current time of ' \ + datalib.gettimestring() message += ' Value: ' + str(self.value) + self.actiondatadict['operator'] + str(self.actiondatadict['criterion']) else: # message = 'Alert is active for ' + self.name + '. Criterion ' + self.variablename + ' in ' + self.tablename + ' has value ' + str(self.variablevalue) + ' with a criterion of ' + str(self.criterion) + ' with an operator of ' + self.operator + '. This alarm status has been on since ' + self.ontime + '.' message = 'Alert for alarm ' + self.name + ' . On time of ' + self.ontime + '. Current time of ' \ + datalib.gettimestring() import socket hostname = socket.gethostname() subject = 'CuPID ' + hostname + ' Alert : Alarm On - ' + self.name try: actionmail = utility.gmail(message=message, subject=subject, recipient=email) actionmail.send() except: self.statusmsg += 'Error sending email. ' else: self.statusmsg += 'Mail sent. ' elif self.actiontype == 'indicator': # process indicator action self.statusmsg += 'Processing indicator on action. ' indicatorname = self.actiondetail dblib.sqlitequery(pilib.dirs.dbs.control, 'update indicators set status=1 where name = \'' + indicatorname + '\'') elif self.actiontype == 'output': self.statusmsg += 'Processing output on action. ' dblib.setsinglevalue(pilib.dirs.dbs.control, 'outputs', 'value', '1', condition='"id"=\'' + self.actiondetail + "'") elif self.actiontype == 'mote_command': settings = { 'no_duplicates':True, 'retries':5 } settings.update(self.actiondatadict) self.statusmsg += 'Processing command on action. ' destination = self.actiondatadict['destination'] message = self.actiondatadict['message'] command_id = '{}_{}'.format(destination, message) command = {'queuedtime': datalib.gettimestring(), 'destination': destination, 'status': 'new', 'message': message, 'commandid':command_id, } command['options'] = json.dumps({'retries':settings['retries']}) insert = True if settings['no_duplicates']: # Check to see if commands exist with our command id. condition = "commandid='{}'".format(command_id) matching_commands = pilib.dbs.motes.read_table('commands',condition=condition) if matching_commands: self.statusmsg += '{} matching commands are already queued. Not inserting. '.format(len(matching_commands)) insert = False if insert: self.statusmsg += 'Inserting command. ' pilib.dbs.motes.settings['quiet'] = False pilib.dbs.motes.insert('commands',command) # This should be the generic handler that we migrate to elif self.actiontype == 'setvalue': # to set a value, we need at minimum: # dbname, tablename, valuename, setmethod and either: # setmethod = increment, incrementvalue=1 # setmethod = value dbvndict = datalib.parsedbvn(self.actiondetail) dbpath = pilib.dbnametopath(dbvndict['dbname']) # Special set formula? if 'setvalueformula' in self.actiondatadict: # Stuff that we don't know yet. dblib.setsinglevalue(dbpath, dbvndict['tablename'], dbvndict['valuename'], 'formulastuff here', dbvndict['condition']) else: """ TODO: Fix this hack. We cannot currently single quote in the database entry because it breaks the reinsert. So for now, we have to add quotes on either side of the string literal before executing the sqlite query. """ if dbvndict['condition']: querycondition = dbvndict['condition'].split('=')[0] + "='" + dbvndict['condition'].split('=')[1] + "'" # print('FIXED CONDITION') # print(querycondition) else: querycondition = None dblib.setsinglevalue(dbpath, dbvndict['tablename'], dbvndict['valuename'], '1', querycondition) def offact(self): from iiutilities import dblib, datalib, utility from cupid import pilib if self.actiontype == 'email': # process email action # TODO: This really needs to queue the email action in case we are not online. self.statusmsg +='Processing email alert.' email = self.actiondetail # message = 'Alert has gone inactive for ' + self.name + '. Criterion ' + self.variablename + ' in ' + self.tablename + ' has value ' + str(self.variablevalue) + ' with a criterion of ' + str(self.criterion) + ' with an operator of ' + self.operator + '. This alarm status has been of since ' + self.offtime + '.' message = 'Alert for alarm ' + self.name + ' . Off time of ' + self.offtime + '. Current time of ' \ + datalib.gettimestring() if self.conditiontype == 'value': message += ' Value: ' + str(self.value) + self.actiondatadict['operator'] + str( self.actiondatadict['criterion']) import socket hostname = socket.gethostname() subject = 'CuPID ' + hostname + ' Alert : Alarm Off - ' + self.name try: actionmail = utility.gmail(message=message, subject=subject, recipient=email) actionmail.send() except: self.statusmsg += 'Error sending email. ' else: self.statusmsg += 'Mail sent. ' elif self.actiontype == 'indicator': # process indicator action self.statusmsg +='Processing indicator off action.' indicatorname = self.actiondetail dblib.setsinglevalue(pilib.dirs.dbs.control, 'indicators', 'status', 0, 'name=\'' + indicatorname+ '\'') print('INDICATORNAME = "' + indicatorname + '"') # dblib.sqlitequery(pilib.dirs.dbs.control, 'update indicators set status=0 where name = ' + indicatorname) elif self.actiontype == 'output': self.statusmsg += 'Processing output off action. ' dblib.setsinglevalue(pilib.dirs.dbs.control, 'outputs', 'value', '0', condition='"id"=\'' + self.actiondetail + "'") # This should be the generic handler that we migrate to elif self.actiontype == 'setvalue': # to set a value, we need at minimum: # dbname, tablename, valuename, setmethod and either: # setmethod = increment, incrementvalue=1 # setmethod = value dbvndict = datalib.parsedbvn(self.actiondetail) dbpath = pilib.dbnametopath(dbvndict['dbname']) # Special set formula? if 'setvalueformula' in self.actiondatadict: # Stuff that we don't know yet. dblib.setsinglevalue(dbpath, dbvndict['tablename'], dbvndict['valuename'], 'formulastuff here', dbvndict['condition']) else: """ TODO: Fix this hack. We cannot currently single quote in the database entry because it breaks the reinsert. So for now, we have to add quotes on either side of the string literal before executing the sqlite query. """ if dbvndict['condition']: querycondition = dbvndict['condition'].split('=')[0] + "='" + dbvndict['condition'].split('=')[1] + "'" # print('FIXED CONDITION') # print(querycondition) else: querycondition = None dblib.setsinglevalue(dbpath, dbvndict['tablename'], dbvndict['valuename'], '0', querycondition) def printvalues(self): for attr,value in self.__dict__.items(): print(str(attr) + ' : ' + str(value)) # @profile def publish(self): from cupid import pilib from iiutilities import dblib from iiutilities.datalib import dicttojson # reinsert updated action back into database valuelist=[] valuenames=[] control_db = pilib.cupidDatabase(pilib.dirs.dbs.control) # We convert our actiondatadict back into a string self.actiondata = json.dumps(self.actiondatadict) for attr in self.__dict__: if attr not in ['actiondatadict', 'actionindex', 'actiondata']: control_db.set_single_value('actions', attr, getattr(self, attr), condition='"actionindex"=\'' + str(self.actionindex) +"'", queue=True) # print(len(control_db.queued_queries)) control_db.execute_queue() # print(valuenames) # print(valuelist) # dblib.sqliteinsertsingle(pilib.dirs.dbs.control, 'actions', valuelist, valuenames) # setsinglevalue(dirs.dbs.control, 'actions', 'ontime', gettimestring(), 'rowid=' + str(self.rowid)) # @profile def process(self): from iiutilities import datalib # TODO: Always determine status. This loads the value into the indicators, etc. if self.enabled: act = False self.statusmsg = datalib.gettimestring() + ' : Enabled and processing. ' last_status = bool(self.status) # Update status. self.determine_status() # retrofit. i lazy. currstatus = bool(self.status) if last_status: self.statusmsg += 'Last status is ' + str(last_status) + '. Currstatus is ' + str(currstatus) + '. ' else: self.statusmsg += 'Last status is ' + str(last_status) + '. Currstatus is ' + str(currstatus) + '. ' currenttime = datalib.gettimestring() # if status is true and current status is false, set ontime (or if on/off time field is empty) if currstatus and (not last_status or not self.ontime): # print(str(curstatus) + ' ' + str(self.status)) self.statusmsg += 'Setting status ontime. ' self.ontime = datalib.gettimestring() elif not currstatus and (last_status or not self.offtime): self.statusmsg += 'Setting status offtime. ' self.offtime = datalib.gettimestring() # print('CURR STATUS',currstatus) # print('SELF.ACTIVE',self.active) # if status is true and alarm isn't yet active, see if ondelay exceeded if currstatus and not self.active: # print(pilib.timestringtoseconds(currenttime)) statusontime = datalib.timestringtoseconds(currenttime) - datalib.timestringtoseconds(self.ontime, defaulttozero=True) # print(statusontime) if statusontime >= float(self.ondelay): self.statusmsg += 'Setting action active. ' self.active = 1 else: self.statusmsg += 'On delay not reached. ' # print('on',self.ontime) # print('now',currenttime) # if status is not true and alarm is active, see if offdelay exceeded if not currstatus and self.active: statusofftime = datalib.timestringtoseconds(currenttime) - datalib.timestringtoseconds(self.offtime, defaulttozero=True) if statusofftime >= float(self.offdelay): self.statusmsg += 'Setting action inactive. ' self.active = 0 # act on inactive transition # Send an alert / reset indicator if activereset is on if self.activereset: time_since_last_action = datalib.timestringtoseconds(currenttime) - datalib.timestringtoseconds( self.lastactiontime, defaulttozero=True) if time_since_last_action >= float(self.actionfrequency): act = True self.statusmsg += "Time to act on activereset. " + str( time_since_last_action) + ' since last action, with action frequency of ' + str( self.actionfrequency) + '. ' else: act = False self.statusmsg += "Not yet time to act." else: self.statusmsg += 'Off delay not reached. ' # test to see if it is time to alert, based on delay ond alert time # print(self.statusmsg) if self.active: # check to see if it is time to alert # For things like outputs, actionfrequency should be zero to always enforce that action is on. # print(pilib.timestringtoseconds(currenttime)) # print(pilib.timestringtoseconds(self.lastactiontime)) # print(float(self.actionfrequency)) # print(pilib.timestringtoseconds(currenttime)-pilib.timestringtoseconds(self.lastactiontime)) time_since_last_action = datalib.timestringtoseconds(currenttime) - datalib.timestringtoseconds(self.lastactiontime, defaulttozero=True) if time_since_last_action >= float(self.actionfrequency): act = True self.statusmsg += "Time to act. " + str(time_since_last_action) + ' since last action, with action frequency of ' + str(self.actionfrequency) + '. ' else: act = False self.statusmsg += "Not yet time to act." else: # Active reset only happens on the transition. pass if act: # We're ready to alert or alert again. self.lastactiontime = currenttime if currstatus: self.onact() else: self.offact() else: self.statusmsg += 'Action disabled.' self.status = 0 def processactions(**kwargs): # Read database to get our actions from cupid import pilib settings = { 'debug':False } settings.update(kwargs) if settings['debug']: pilib.set_debug() actiondicts = pilib.dbs.control.read_table('actions') for actiondict in actiondicts: # if we only want to process one action, skip others. if 'name' in kwargs: if actiondict['name'] != kwargs['name']: continue actiondict['log_path'] = pilib.dirs.logs.actions thisaction = action(actiondict) thisaction.process() thisaction.publish() if __name__ == '__main__': debug = False if 'debug' in sys.argv: debug = True processactions(debug=debug)
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htdfsdk/web3/_utils/txpool.py
youngqqcn/htdfsdk
2
164876
<reponame>youngqqcn/htdfsdk<gh_stars>1-10 from typing import ( Callable, ) from htdfsdk.web3._utils.rpc_abi import ( RPC, ) from htdfsdk.web3.method import ( Method, ) from htdfsdk.web3.types import ( TxPoolContent, TxPoolInspect, TxPoolStatus, ) content: Method[Callable[[], TxPoolContent]] = Method( RPC.txpool_content, mungers=None, ) inspect: Method[Callable[[], TxPoolInspect]] = Method( RPC.txpool_inspect, mungers=None, ) status: Method[Callable[[], TxPoolStatus]] = Method( RPC.txpool_status, mungers=None, )
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sql_judge/control/exam_controller.py
r4ulill0/gui_dbjudge
1
127675
from model.exam import ExamData from dbjudge.connection_manager.manager import Manager from dbjudge import squema_recollector, exceptions from PyQt5.QtCore import pyqtSlot, QItemSelectionModel class Exam_controller(): def __init__(self, selection_view, exam_view, results_view): self.selection_view = selection_view self.exam_view = exam_view self.results_view = results_view self.model = ExamData() self.manager = Manager.singleton_instance self.selection_view.scenario_selection.currentTextChanged.connect( self.update_scenario_data) self.selection_view.access_button.clicked.connect( self.update_scenario_data) self.exam_view.test_button.clicked.connect(self.try_answer) self.exam_view.question_list.setModel(self.model.questions) self.exam_view.question_list.selectionModel().currentRowChanged.connect( self.update_current_exam_question) self.exam_view.generate_report_button.clicked.connect( self.finish_exam) self.results_view.question_list.setModel(self.model.questions) self.results_view.question_list.selectionModel().currentRowChanged.connect( self.update_current_results_question) def load_avaiable_scenarios(self): self.model.scenarios = self.manager.get_databases() self.selection_view.load_scenarios(self.model.scenarios) def update_scenario_data(self, scenario): scenario = self.selection_view.get_current_scenario() if scenario: self.manager.select_database(scenario) self.model.questions = self.manager.get_questions() tables = self.manager.get_tables() tuples = self.manager.get_total_data_count() total_tables = len(tables) total_questions = len(self.model.questions) total_rows = tuples self.selection_view.update_scenario_data( total_tables, total_questions, total_rows) self.selection_view.access_button.setEnabled(total_questions > 0) preselected_index = self.model.questions.index(0, 0) self.exam_view.question_list.selectionModel().setCurrentIndex( preselected_index, QItemSelectionModel.SelectionFlag.ClearAndSelect) def update_current_exam_question(self, index): self.model.answers[self.model.questions.question_list[self.model.current_question] ] = self.exam_view.get_answer_text() self.exam_view.update_current_question( self.model.questions.question_list[index.row()]) self.exam_view.set_answer_text( self.model.answers[self.model.questions.question_list[index.row()]]) self.model.current_question = index.row() def try_answer(self): answer = self.exam_view.get_answer_text() try: response = self.manager.execute_in_readonly(answer) for row in response: complete_row = '' for element in row: complete_row += ' '+str(element) self.exam_view.set_console_output(complete_row) except exceptions.ReadOnlyError: error_message = 'Error sintáctico detectado, revisa tu consulta' self.exam_view.set_console_output(error_message) def finish_exam(self): self.model.answers[self.model.questions.question_list[self.model.current_question] ] = self.exam_view.get_answer_text() self.exam_view.clear_ui() self.model.generate_report() preselected_index = self.model.questions.index(0, 0) self.results_view.question_list.selectionModel().setCurrentIndex( preselected_index, QItemSelectionModel.SelectionFlag.ClearAndSelect) def update_current_results_question(self, index): correct_answers_count = 0 total_count = 0 for question, analysis in self.model.report.items(): if analysis.is_correct(): correct_answers_count += 1 total_count += 1 question = self.model.questions.question_list[index.row()] self.results_view.update_total_count( correct_answers_count, total_count) self.results_view.update_current_question(question) self.results_view.update_correct_result( self.model.report[question].correct_result) self.results_view.update_correct_answer( self.model.report[question].is_correct()) self.results_view.update_excess_tables( self.model.report[question].excess_tables_used) self.results_view.update_keywords( self.model.report[question].expected_keywords, self.model.report[question].used_keywords) self.model.current_question = index.row()
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chat/forms.py
Firexd2/social-network
2
47407
<reponame>Firexd2/social-network from django import forms from chat.models import Message, Room class NewRoomForm(forms.ModelForm): ids_users = forms.CharField(label='', required=False, widget=forms.TextInput(attrs={'hidden': 'true'})) first_message = forms.CharField(label='Приветственное сообщение', required=False, widget=forms.Textarea(attrs={'class': 'form-control', 'cols': '52', 'rows': '1'})) type = forms.CharField(label='', widget=forms.TextInput(attrs={'style': 'display:none', 'value': 'conversation'})) class Meta: model = Room fields = ['name', 'ids_users', 'first_message', 'type'] widgets = {'name': forms.TextInput(attrs={'class': 'form-control'})} class EditRoomLogoForm(forms.ModelForm): class Meta: model = Room fields = ['logo'] widgets = {'logo': forms.FileInput(attrs={'class': 'form-control'})} class EditRoomNameForm(forms.ModelForm): class Meta: model = Room fields = ['name'] widgets = {'name': forms.TextInput(attrs={'class': 'form-control'})} class OutRoomForm(forms.ModelForm): id = forms.IntegerField(label='', widget=forms.TextInput()) class Meta: model = Room fields = ['id'] class SendMessageForm(forms.ModelForm): id = forms.CharField() destination = forms.CharField() class Meta: model = Message fields = ['text', 'id', 'destination']
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apps/fyle/migrations/0002_auto_20201221_0814.py
fylein/fyle-xero-api
0
67304
<filename>apps/fyle/migrations/0002_auto_20201221_0814.py # Generated by Django 3.0.3 on 2020-12-21 08:14 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('fyle', '0001_initial'), ] operations = [ migrations.AlterModelTable( name='expensegroupsettings', table='expense_group_settings', ), ]
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events/vizier.py
bonus-tm/euforia
1
1609001
<gh_stars>1-10 # Визирь from event import Event class Vizier(Event): """ Визирь Армия: довольствие, дезертиры Население: рождаемость, смертность, недовольство, голод Урожай: засухи-наводнения, урожайность, крысы Визирь: украл золото и скрылся """ def __init__(self, data, say, ask): self.data = data self.say = say self.ask = ask # def start(self): """ Запуск события — пересчёт параметров и вывод результатов """ self.check_army() self.check_population() self.check_crop() self.check_rob_caravan() self.check_rob_church() self.check_vizier() if self.let_him_in(): self.report() # довольствие армии def check_army(self): # дезертиры self.data.deserters = 0 # денежное довольствие армии self.data.allowance = self.data.resources['soldier'] * 10 # считаем дезертировавших из-за невыплаты довольствия if self.data.allowance > self.data.money: shortage = self.data.allowance - self.data.money self.data.allowance = self.data.money self.data.deserters = min(round(shortage / 10 * self.ask.frand(0, 1.5)), self.data.resources['soldier']) self.data.money -= self.data.allowance self.data.resources['soldier'] -= self.data.deserters # население # настроения народа def check_population(self): # родилось self.data.births = 0 # померло от голода self.data.famishes = 0 # сбежало self.data.fugitives = 0 sldr = self.data.resources['soldier'] psnt = self.data.resources['peasant'] # считаем скольким хватило зерна на прокорм, а сколько рабочих померло с голодухи if psnt + sldr > self.data.corn_for_food: shortage = psnt + sldr - self.data.corn_for_food self.data.famishes = round(shortage * self.ask.frand(0.3, 1)) self.data.resources['peasant'] -= self.data.famishes # считаем рабочих, сбежавших от нехватки земли if psnt > min(self.data.corn_for_seed, self.data.resources['land']): shortage = psnt - min(self.data.corn_for_seed, self.data.resources['land']) self.data.fugitives = min(round(shortage * self.ask.frand(0, 1.2)), self.data.resources['peasant'],) self.data.resources['peasant'] -= self.data.fugitives # эпидемия чумы # остальные размножаются self.data.births = round(self.data.resources['peasant'] * self.ask.frand(0.05, 0.4)) self.data.resources['peasant'] += self.data.births # надо посчитать заговорщиков # урожай def check_crop(self): self.data.rats_eat = 0 # крысы пожрали зерно if self.ask.dice(50): self.data.rats_eat = round(self.data.resources['corn'] * self.ask.frand(0.1, 0.9)) # расчёт урожайности self.data.resources['corn'] += round(self.data.corn_for_seed * self.data.crop_rate) # стихийные бедствия - засухи, пожары, наводнения # визирь украл и скрылся def check_vizier(self): pass # ограблен храм # def report(self): self.say.clear_screen() self.say.line("Жалованье солдат: {:>10n} руб.\n".format(self.data.allowance)) self.say.line("-----------------------------------------------------------------------") self.debug("на еду {:n}, на посев {:n} × {:.3} = урожай {:n} тонн, итого {:n}" .format(self.data.corn_for_food, self.data.corn_for_seed, self.data.crop_rate, round(self.data.corn_for_seed * self.data.crop_rate), self.data.resources['corn'])) if self.data.rats_eat: self.say.line(" - Крысы сожрали {:>10n} тонн зерна.".format(self.data.rats_eat)) if self.data.deserters: self.say.line(" - Гвардия не получила денежного довольствия.\n" + " {:>10n} солдат покинули казармы и ушли за кордон.".format(self.data.deserters)) if self.data.fugitives: self.say.line(" - Рабочим не хватает земли. Сбежало {:>10n} человек.".format(self.data.fugitives)) if self.data.famishes: self.say.line(" - Вы заморили голодом {:>10n} ваших верноподданных!".format(self.data.famishes)) if self.data.births: self.say.line(" - В государстве родилось {:>10n} детей.".format(self.data.births)) # if self.data.: # self.say.line(" - {:>10n} \n".format(self.data.)) # self.say.line() # def let_him_in(self): return self.ask.yesno("Ваше высочество! Прибыл визирь, впустить?")
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JasonKe-v1/DDPG/DDPG.py
jasonke1225/AI-introduction
0
119518
<gh_stars>0 import matplotlib.pyplot as plt import gym import torch import math import numpy as np from torch._C import dtype import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import os import DDPG.replaymemory as replaymemory from DDPG.OUNoise import OUNoise import time ''' 紀錄q直 try ''' # replaymemory state, next_state修改 in discrete criterion = nn.MSELoss() def fanin_init(size, fanin=None): fanin = fanin or size[0] v = 1. / np.sqrt(fanin) return torch.Tensor(size).uniform_(-v, v) class policyNetwork(nn.Module): def __init__(self, num_state, num_action, init_w = 3e-3): nn.Module.__init__(self) self.num_state = num_state self.num_action = num_action hidden_size = 256 self.input_layer = nn.Linear(num_state, hidden_size) self.linear_layer = nn.Linear(hidden_size, hidden_size) self.layer_output = nn.Linear(hidden_size, num_action) self.init_weights(init_w) def init_weights(self, init_w): self.input_layer.weight.data = fanin_init(self.input_layer.weight.data.size()) self.linear_layer.weight.data = fanin_init(self.linear_layer.weight.data.size()) self.layer_output.weight.data.uniform_(-init_w, init_w) def forward(self, state): #print(state) if torch.cuda.is_available(): state = state.cuda() state = self.input_layer(state) state = F.relu(state) state = self.linear_layer(state) state = F.relu(state) state = self.layer_output(state) ### 為甚麼 state = F.tanh(state) return state.view(-1, self.num_action) class valueNetwork(nn.Module): def __init__(self, num_state, num_action, init_w = 3e-4): nn.Module.__init__(self) self.num_state = num_state self.num_action = num_action hidden_size = 256 self.input_layer = nn.Linear(num_state, hidden_size) self.linear_layer = nn.Linear(hidden_size + num_action, hidden_size) self.layer_output = nn.Linear(hidden_size, 1) self.init_weights(init_w) def init_weights(self, init_w): self.input_layer.weight.data = fanin_init(self.input_layer.weight.data.size()) self.linear_layer.weight.data = fanin_init(self.linear_layer.weight.data.size()) self.layer_output.weight.data.uniform_(-init_w, init_w) def forward(self, state, ac): if torch.cuda.is_available(): state = state.cuda() state = self.input_layer(state) state = F.relu(state) state = self.linear_layer(torch.cat([state, ac], 1)) state = F.relu(state) state = self.layer_output(state) return state.view(-1, 1) class DDPG(): def __init__(self, env, learning_rate): self.num_action = env.action_space.shape[0] self.observation_space = env.observation_space.shape[0] self.pZ = policyNetwork(num_state=self.observation_space, num_action=self.num_action) self.pZ_target = policyNetwork(num_state=self.observation_space, num_action=self.num_action) self.policyoptimizer = optim.Adam(self.pZ.parameters(), 1e-4) self.vZ = valueNetwork(num_state=self.observation_space, num_action=self.num_action) self.vZ_target = valueNetwork(num_state=self.observation_space, num_action=self.num_action) self.valueoptimizer = optim.Adam(self.vZ.parameters(), 1e-3) if torch.cuda.is_available(): self.pZ.cuda() self.pZ_target.cuda() self.vZ.cuda() self.vZ_target.cuda() self.tau = 0.001 self.batch_size = 256 self.eps = 1.0 self.discount = 0.99 self.replaymemory = replaymemory.replaymemory(self.observation_space,self.num_action,1000000) self.n_iter = 5000 self.noise = OUNoise(self.num_action) # self.memory_enough = False self.action_low, self.action_high = env.action_space.low, env.action_space.high def get_action(self, state): with torch.no_grad(): action = self.pZ.forward(state)[0].cpu() + self.eps*torch.Tensor([self.noise.noise()]) action.clamp(min = self.action_low[0], max= self.action_high[0]) return action def soft_update(self): for target_param, param in zip(self.pZ_target.parameters(),self.pZ.parameters()): target_param.data.copy_(self.tau*param + (1.0-self.tau)*target_param) for target_param, param in zip(self.vZ_target.parameters(),self.vZ.parameters()): target_param.data.copy_(self.tau*param + (1.0-self.tau)*target_param) def load_model(self,name,episode): self.pZ.load_state_dict(torch.load('./preTrained/{}/DDPG_Actor_{}.pth'.format(name,episode))) self.pZ_target.load_state_dict(torch.load('./preTrained/{}/DDPG_TargetActor_{}.pth'.format(name,episode))) self.vZ.load_state_dict(torch.load('./preTrained/{}/DDPG_Critic_{}.pth'.format(name,episode))) self.vZ_target.load_state_dict(torch.load('./preTrained/{}/DDPG_TargetCritic_{}.pth'.format(name,episode))) def save_model(self,name,episode): torch.save(self.pZ.state_dict(), './preTrained/{}/DDPG_Actor_{}.pth'.format(name,episode)) torch.save(self.pZ_target.state_dict(), './preTrained/{}/DDPG_TargetActor_{}.pth'.format(name,episode)) torch.save(self.vZ.state_dict(), './preTrained/{}/DDPG_Critic_{}.pth'.format(name,episode)) torch.save(self.vZ_target.state_dict(), './preTrained/{}/DDPG_TargetCritic_{}.pth'.format(name,episode)) def calculate_loss(self, samples): theta = self.vZ(samples['states'],samples['actions']) with torch.no_grad(): Z_next = self.vZ_target(samples['next_states'],self.pZ_target(samples['next_states'])) dones = samples['dones'] target_theta = samples['rewards'] + self.discount *(1-dones)* Z_next loss = criterion(theta, target_theta) return loss def learn(self, env, th): state = env.reset() self.noise.reset() ### 使動作noise收斂 # self.eps = self.eps - 0.001 if self.eps > 0.001 else self.eps score = 0 for i in range(self.n_iter): #self.eps_annealing() action = self.get_action(torch.Tensor([state])) #print(action) env.render() next_state, reward, done, _ = env.step(action) score += reward self.replaymemory.push( state, action, next_state, reward, done) state = next_state if done: state = env.reset() while len(self.replaymemory) < self.batch_size: action = self.get_action(torch.Tensor([state])) next_state, reward, done, _ = env.step(action) self.replaymemory.push(state, action, next_state, reward, done) state = next_state samples = self.replaymemory.get_samples(self.batch_size) #print(samples) ### critic update self.valueoptimizer.zero_grad() loss = self.calculate_loss(samples) loss.backward() self.valueoptimizer.step() ### actor update policyloss = -self.vZ(samples['states'],self.pZ(samples['states'])).mean() #print(policyloss) self.policyoptimizer.zero_grad() policyloss.backward() self.policyoptimizer.step() #if self.memory_enough: #L.write(str(float(loss))+"\n") self.soft_update() if done: # with torch.no_grad(): # s = env.reset() # a = self.pZ(torch.Tensor([s])) # print(i,' ',a) # print(' ',self.vZ(torch.Tensor([s]), a)) break return float(score)
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tests/test_env_runner.py
pistarlab/simpleland
4
26443
<reponame>pistarlab/simpleland import pytest from landia.env import LandiaEnv, LandiaEnvSingle import time from landia.clock import clock def test_env(): agent_map = {str(i):{} for i in range(4)} env = LandiaEnv(agent_map=agent_map,dry_run=False) start_time = time.time() max_steps = 2000 dones = {"__all__":True} episode_count = 0 actions = {} all_rewards = [] for i in range(0,max_steps): if dones.get('__all__'): obs = env.reset() rewards, dones, infos = {}, {'__all__':False},{} episode_count+=1 else: obs, rewards, dones, infos = env.step(actions) all_rewards.extend(rewards.values()) actions = {agent_id:env.action_spaces[agent_id].sample() for agent_id in obs.keys()} steps_per_sec = max_steps/(time.time()-start_time) print(f"total_rewards {sum(all_rewards)}") print(f"steps_per_sec {steps_per_sec}") assert True def single_run(config_filename=None): print(f"Running config {config_filename}") env = LandiaEnvSingle(config_filename=config_filename) start_time = time.time() max_steps = 2000 all_rewards = [] done=True action=None eps = 0 for i in range(0,max_steps): if done: ob = env.reset() reward, done, info = None, False, {} eps+=1 else: ob, reward, done, info = env.step(action) action = env.action_space.sample() steps_per_sec = max_steps/(time.time()-start_time) print(f"eps {eps}") print(f"total_rewards {sum(all_rewards)}") print(f"steps_per_sec {steps_per_sec}") def test_gym_env(): single_run(config_filename="base_config.json") single_run(config_filename="ctf.json") single_run(config_filename="infection.json") single_run(config_filename="forager.json") assert True
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wsgi.py
javicacheiro/salt-git-synchronizer-proxy
0
2167
#!/usr/bin/env python import logging import sys from app import app as application def setup_flask_logging(): # Log to stdout handler = logging.StreamHandler(sys.stdout) # Log to a file #handler = logging.FileHandler('./application.log') handler.setLevel(logging.INFO) handler.setFormatter(logging.Formatter( '%(asctime)s [%(funcName)s] %(levelname)s: %(message)s ' )) application.logger.addHandler(handler) # Set default log level for the general logger # each handler can then restrict the messages logged application.logger.setLevel(logging.INFO) setup_flask_logging() if __name__ == '__main__': application.run()
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plsa_example.py
dingwanggmail/Topic_model
0
152704
<filename>plsa_example.py from plsa import Corpus, Pipeline, Visualize from plsa.pipeline import DEFAULT_PIPELINE from plsa.algorithms import PLSA csv_file = '/disk2/wangding/plsa_data/Full-Economic-News-DFE-839861.csv' pipeline = Pipeline(*DEFAULT_PIPELINE) corpus = Corpus.from_csv(csv_file, pipeline) n_topics = 5 plsa = PLSA(corpus, n_topics, True) result = plsa.fit() result = plsa.best_of(5) visualize = Visualize(result)
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app/crawlzero/apps.py
rputh055/crawlzerotest
0
38896
from django.apps import AppConfig class crawlzeroConfig(AppConfig): name = 'crawlzero'
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seraphim/util/extended_euclidean.py
kluhan/seraphim
0
101594
<gh_stars>0 # Python Module extended_euclidean import logging class Error(Exception): """Base class for exceptions in this module.""" pass class InversionError(Error): """Exception raised for errors in the input. Attributes: expression -- input expression in which the error occurred message -- explanation of the error """ def __init__(self, expression, message): self.expression = expression self.message = message def get_inverse(base, value_to_inverse, current_value): """Searches the Inverse of an given value_to_inverse. Args: base: Mod base, for the inverse to check. value_to_inverse: Value to check for inverse Returns: Returns the inverse to value_to_inverse """ return __extended_euclid(base, value_to_inverse, current_value) def __extended_euclid(base, value_to_inverse, current_value): """ Calculate Inverse Element to value_to_inverse or Raises Error, if not Possible Iterate the extended Euclidean Algyrthm to find an Inverse element of the given Value Args: base: Mod base, for the inverse to check. value_to_inverse: Value to check for inverse current_value: Current Value given in teh division, only used to Improve errors Returns: Returns the inverse to value_to_inverse """ value_to_inverse = value_to_inverse % base # init to get first step in Euclid from the vlaues q = int(base / value_to_inverse) a = value_to_inverse rest = base - (q * a) result = base # standard init for reverse euclid x0 = 0 x1 = 1 if rest == 0: logging.error( "ERROR - CANNOT DO %s/%s mod %s | UNDEFINED INVERSE FOR mod: %s, Value: %s", current_value, value_to_inverse, base, base, value_to_inverse, ) raise InversionError( "r = base-(q*a)", ( "ERROR - UNDEFINED INVERSE Base: %s, Value: %s", base, value_to_inverse, ), ) # Eclid and reverse Euclid iterations while rest != 0: # Reverse Euclid Calculation invers = (x0 - x1 * q) % base # Calculate new Values to use in Reverse Euclid result = a a = rest q = int(result / a) rest = result - (q * a) # New Values for Reverse Euclid x0 = x1 x1 = invers if rest == invers: logging.error( "ERROR - CANNOT DO %s/%s mod %s | UNDEFINED INVERSE 234124 FOR mod: %s, Value: %s", current_value, value_to_inverse, base, base, value_to_inverse, ) raise InversionError( "r = base-(q*a)", "ERROR - UNDEFINED INVERSE Base: {base}, Value: {value_to_inverse}", ) return invers
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13, 4706, 263, 353, 1791, 13, 4706, 3855, 353, 938, 29898, 2914, 847, 263, 29897, 13, 4706, 1791, 353, 1121, 448, 313, 29939, 334, 263, 29897, 13, 13, 4706, 396, 1570, 2630, 1041, 363, 830, 3901, 16430, 695, 333, 13, 4706, 921, 29900, 353, 921, 29896, 13, 4706, 921, 29896, 353, 297, 874, 13, 13, 1678, 565, 1791, 1275, 297, 874, 29901, 13, 4706, 12183, 29889, 2704, 29898, 13, 9651, 376, 11432, 448, 315, 2190, 12256, 11662, 1273, 29879, 22584, 29879, 878, 1273, 29879, 891, 8291, 24405, 1177, 3352, 2672, 5348, 1660, 29871, 29906, 29941, 29946, 29896, 29906, 29946, 15842, 878, 29901, 1273, 29879, 29892, 7865, 29901, 1273, 29879, 613, 13, 9651, 1857, 29918, 1767, 29892, 13, 9651, 995, 29918, 517, 29918, 262, 3901, 29892, 13, 9651, 2967, 29892, 13, 9651, 2967, 29892, 13, 9651, 995, 29918, 517, 29918, 262, 3901, 29892, 13, 4706, 1723, 13, 4706, 12020, 512, 3259, 2392, 29898, 13, 9651, 376, 29878, 353, 2967, 17722, 29939, 29930, 29874, 19123, 13, 9651, 376, 11432, 448, 8291, 24405, 1177, 3352, 2672, 5348, 1660, 7399, 29901, 426, 3188, 1118, 7865, 29901, 426, 1767, 29918, 517, 29918, 262, 3901, 17671, 13, 4706, 1723, 13, 13, 1678, 736, 297, 874, 2 ]
tests/test_bestip.py
po2xel/mootdx
88
193828
<gh_stars>10-100 import logging from pathlib import Path import pytest from mootdx import config class TestBestIP: config = '' @staticmethod def setup_class(cls): logging.info('setup_class') cls.config = Path.home() / '.mootdx' / 'config.json' cls.config.exists() and cls.config.unlink() @staticmethod def teardown_class(cls): logging.info('teardown_class') cls.config.exists() and cls.config.unlink() @pytest.mark.skip('skip') def test_config(self, caplog): caplog.set_level(logging.WARNING) self.config.exists() and self.config.unlink() config.setup() assert self.config.exists(), self.config def test_quotes(self): config.setup() assert self.config.exists(), self.config
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keras/downstream_tasks/BraTS/DataSet.py
joeranbosma/ModelsGenesis
574
99510
#!/usr/bin/env python """ File: DataSet Date: 5/1/18 Author: <NAME> (<EMAIL>) This file provides loading of the BraTS datasets for ease of use in TensorFlow models. """ import os import pandas as pd import numpy as np import nibabel as nib from tqdm import tqdm from BraTS.Patient import * from BraTS.structure import * from BraTS.modalities import * from BraTS.load_utils import * survival_df_cache = {} # Prevents loading CSVs more than once class DataSubSet: def __init__(self, directory_map, survival_csv, data_set_type=None): self.directory_map = directory_map self._patient_ids = sorted(list(directory_map.keys())) self._survival_csv = survival_csv self._num_patients = len(self._patient_ids) self.type = data_set_type # Data caches self._mris = None self._segs = None self._patients = {} self._survival_df_cached = None self._patients_fully_loaded = False self._id_indexer = {patient_id: i for i, patient_id in enumerate(self._patient_ids)} def subset(self, patient_ids): """ Split this data subset into a small subset by patient ID :param n: The number of elements in the smaller training set :return: A new data subset with only the specified number of items """ dir_map = {id: self.directory_map[id] for id in patient_ids} return DataSubSet(dir_map, self._survival_csv) @property def ids(self): """ List of all patient IDs in this dataset Will copy the ids... so modify them all you want :return: Copy of the patient IDs """ return list(self._patient_ids) @property def mris(self): if self._mris is not None: return self._mris self._load_images() return self._mris @property def segs(self): if self._segs is None: self._load_images() return self._segs def _load_images(self): mris_shape = (self._num_patients,) + mri_shape segs_shape = (self._num_patients,) + image_shape self._mris = np.empty(shape=mris_shape) self._segs = np.empty(shape=segs_shape) if self._patients_fully_loaded: # All the patients were already loaded for i, patient in enumerate(tqdm(self._patients.values())): self._mris[i] = patient.mri_data self._segs[i] = patient.seg else: # Load it from scratch for i, patient_id in enumerate(self._patient_ids): patient_dir = self.directory_map[patient_id] load_patient_data_inplace(patient_dir, self._mris, self._segs, i) @property def patients(self): """ Loads ALL of the patients from disk into patient objects :return: A dictionary containing ALL patients """ for patient_id in self.ids: yield self.patient(patient_id) self._patients_fully_loaded = True def patient(self, patient_id): """ Loads only a single patient from disk :param patient_id: The patient ID :return: A Patient object loaded from disk """ if patient_id not in self._patient_ids: raise ValueError("Patient id \"%s\" not present." % patient_id) # Return cached value if present if patient_id in self._patients: return self._patients[patient_id] # Load patient data into memory patient = Patient(patient_id) patient_dir = self.directory_map[patient_id] df = self._survival_df if patient_id in df.id.values: patient.age = float(df.loc[df.id == patient_id].age) patient.survival = int(df.loc[df.id == patient_id].survival) if self._mris is not None and self._segs is not None: # Load from _mris and _segs if possible index = self._id_indexer[patient_id] patient.mri = self._mris[index] patient.seg = self._segs[index] else: # Load the mri and segmentation data from disk patient.mri, patient.seg = load_patient_data(patient_dir) self._patients[patient_id] = patient # cache the value for later return patient def drop_cache(self): self._patients.clear() self._mris = None self._segs = None @property def _survival_df(self): if self._survival_csv in survival_df_cache: return survival_df_cache[self._survival_csv] df = load_survival(self._survival_csv) survival_df_cache[self._survival_csv] = df return df class DataSet(object): def __init__(self, data_set_dir=None, brats_root=None, year=None): if data_set_dir is not None: # The data-set directory was specified explicitly assert isinstance(data_set_dir, str) self._data_set_dir = data_set_dir elif brats_root is not None and isinstance(year, int): # Find the directory by specifying the year assert isinstance(brats_root, str) year_dir = find_file_containing(brats_root, str(year % 100)) self._data_set_dir = os.path.join(brats_root, year_dir) self._brats_root = brats_root self._year = year else: # BraTS data-set location was not improperly specified raise Exception("Specify BraTS location with \"data_set_dir\" or with \"brats_root\" and \"year\"") self._validation = None self._train = None self._hgg = None self._lgg = None self._dir_map_cache = None self._val_dir = None self._train_dir_cached = None self._hgg_dir = os.path.join(self._train_dir, "HGG") self._lgg_dir = os.path.join(self._train_dir, "LGG") self._train_survival_csv_cached = None self._validation_survival_csv_cached = None self._train_ids = None self._hgg_ids_cached = None self._lgg_ids_cached = None self._train_dir_map_cache = None self._validation_dir_map_cache = None self._hgg_dir_map_cache = None self._lgg_dir_map_cache = None def set(self, data_set_type): """ Get a data subset by type :param data_set_type: The DataSubsetType to get :return: The data sub-set of interest """ assert isinstance(data_set_type, DataSubsetType) if data_set_type == DataSubsetType.train: return self.train if data_set_type == DataSubsetType.hgg: return self.hgg if data_set_type == DataSubsetType.lgg: return self.lgg if data_set_type == DataSubsetType.validation: return self.validation @property def train(self): """ Training data Loads the training data from disk, utilizing caching :return: A tf.data.Dataset object containing the training data """ if self._train is None: try: self._train = DataSubSet(self._train_dir_map, self._train_survival_csv, data_set_type=DataSubsetType.train) except FileNotFoundError: return None return self._train @property def validation(self): """ Validation data :return: Validation data """ if self._validation is None: try: self._validation = DataSubSet(self._validation_dir_map, self._validation_survival_csv, data_set_type=DataSubsetType.validation) except FileNotFoundError: return None return self._validation @property def hgg(self): if self._hgg is None: try: self._hgg = DataSubSet(self._hgg_dir_map, self._train_survival_csv, data_set_type=DataSubsetType.hgg) except FileNotFoundError: return None return self._hgg @property def lgg(self): if self._lgg is None: try: self._lgg = DataSubSet(self._lgg_dir_map, self._train_survival_csv, data_set_type=DataSubsetType.lgg) except FileNotFoundError: return None return self._lgg def drop_cache(self): """ Drops the cached values in the object :return: None """ self._validation = None self._train = None self._hgg = None self._lgg = None self._dir_map_cache = None self._val_dir = None self._train_dir_cached = None self._train_survival_csv_cached = None self._validation_survival_csv_cached = None self._train_ids = None self._hgg_ids_cached = None self._lgg_ids_cached = None self._train_dir_map_cache = None self._validation_dir_map_cache = None self._hgg_dir_map_cache = None self._lgg_dir_map_cache = None @property def _train_survival_csv(self): if self._train_survival_csv_cached is None: self._train_survival_csv_cached = find_file_containing(self._train_dir, "survival") if self._train_survival_csv_cached is None: raise FileNotFoundError("Could not find survival CSV in %s" % self._train_dir) return self._train_survival_csv_cached @property def _validation_survival_csv(self): if self._validation_survival_csv_cached is None: self._validation_survival_csv_cached = find_file_containing(self._validation_dir, "survival") if self._validation_survival_csv_cached is None: raise FileNotFoundError("Could not find survival CSV in %s" % self._validation_dir) return self._validation_survival_csv_cached @property def _train_dir(self): if self._train_dir_cached is not None: return self._train_dir_cached self._train_dir_cached = find_file_containing(self._data_set_dir, "training") if self._train_dir_cached is None: raise FileNotFoundError("Could not find training directory in %s" % self._data_set_dir) return self._train_dir_cached @property def _validation_dir(self): if self._val_dir is not None: return self._val_dir self._val_dir = find_file_containing(self._data_set_dir, "validation") if self._val_dir is None: raise FileNotFoundError("Could not find validation directory in %s" % self._data_set_dir) return self._val_dir @property def _train_dir_map(self): if self._train_dir_map_cache is None: self._train_dir_map_cache = dict(self._hgg_dir_map) self._train_dir_map_cache.update(self._lgg_dir_map) return self._train_dir_map_cache @property def _validation_dir_map(self): if self._validation_dir_map_cache is None: self._validation_dir_map_cache = self._directory_map(self._validation_dir) return self._validation_dir_map_cache @property def _hgg_dir_map(self): if self._hgg_dir_map_cache is None: self._hgg_dir_map_cache = self._directory_map(self._hgg_dir) return self._hgg_dir_map_cache @property def _lgg_dir_map(self): if self._lgg_dir_map_cache is None: self._lgg_dir_map_cache = self._directory_map(self._lgg_dir) return self._lgg_dir_map_cache @property def _hgg_ids(self): if self._hgg_ids_cached is None: self._hgg_ids_cached = os.listdir(self._hgg_dir) return self._hgg_ids_cached @property def _lgg_ids(self): if self._lgg_ids_cached is None: self._lgg_ids_cached = os.listdir(self._lgg_dir) return self._lgg_ids_cached @classmethod def _directory_map(cls, dir): return {file: os.path.join(dir, file) for file in os.listdir(dir) if os.path.isdir(os.path.join(dir, file))}
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config/usb_device_cdc.py
newbs/usb
0
17954
"""***************************************************************************** * Copyright (C) 2019 Microchip Technology Inc. and its subsidiaries. * * Subject to your compliance with these terms, you may use Microchip software * and any derivatives exclusively with Microchip products. It is your * responsibility to comply with third party license terms applicable to your * use of third party software (including open source software) that may * accompany Microchip software. * * THIS SOFTWARE IS SUPPLIED BY MICROCHIP "AS IS". NO WARRANTIES, WHETHER * EXPRESS, IMPLIED OR STATUTORY, APPLY TO THIS SOFTWARE, INCLUDING ANY IMPLIED * WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A * PARTICULAR PURPOSE. * * IN NO EVENT WILL MICROCHIP BE LIABLE FOR ANY INDIRECT, SPECIAL, PUNITIVE, * INCIDENTAL OR CONSEQUENTIAL LOSS, DAMAGE, COST OR EXPENSE OF ANY KIND * WHATSOEVER RELATED TO THE SOFTWARE, HOWEVER CAUSED, EVEN IF MICROCHIP HAS * BEEN ADVISED OF THE POSSIBILITY OR THE DAMAGES ARE FORESEEABLE. TO THE * FULLEST EXTENT ALLOWED BY LAW, MICROCHIP'S TOTAL LIABILITY ON ALL CLAIMS IN * ANY WAY RELATED TO THIS SOFTWARE WILL NOT EXCEED THE AMOUNT OF FEES, IF ANY, * THAT YOU HAVE PAID DIRECTLY TO MICROCHIP FOR THIS SOFTWARE. *****************************************************************************""" currentQSizeRead = 1 currentQSizeWrite = 1 currentQSizeSerialStateNotification = 1 cdcInterfacesNumber = 2 cdcDescriptorSize = 58 cdcEndpointsPic32 = 2 cdcEndpointsSAM = 3 indexFunction = None configValue = None startInterfaceNumber = None numberOfInterfaces = None useIad = None epNumberInterrupt = None epNumberBulkOut = None epNumberBulkIn = None cdcEndpointNumber = None def handleMessage(messageID, args): global useIad if (messageID == "UPDATE_CDC_IAD_ENABLE"): useIad.setValue(args["iadEnable"]) return args def onAttachmentConnected(source, target): global cdcInterfacesNumber global cdcDescriptorSize global configValue global startInterfaceNumber global numberOfInterfaces global useIad global epNumberInterrupt global epNumberBulkOut global epNumberBulkIn global cdcEndpointsPic32 global cdcEndpointsSAM global currentQSizeRead global currentQSizeWrite global currentQSizeSerialStateNotification print ("CDC Function Driver: Attached") remoteComponent = target["component"] remoteComponentId = remoteComponent.getID() if (remoteComponentId == "usb_device"): dependencyID = source["id"] ownerComponent = source["component"] # Read number of functions from USB Device Layer nFunctions = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_FUNCTIONS_NUMBER") if nFunctions != None: #Log.writeDebugMessage ("USB Device CDC Function Driver: Attachment connected") # Update Number of Functions in USB Device, Increment the value by One. args = {"nFunction":nFunctions + 1} res = Database.sendMessage("usb_device", "UPDATE_FUNCTIONS_NUMBER", args) # If we have CDC function driver plus any function driver (no matter what Class), we enable IAD. if nFunctions > 0: args = {"nFunction":True} res = Database.sendMessage("usb_device", "UPDATE_IAD_ENABLE", args) iadEnableSymbol = ownerComponent.getSymbolByID("CONFIG_USB_DEVICE_FUNCTION_USE_IAD") iadEnableSymbol.clearValue() iadEnableSymbol.setValue(True, 1) isIadEnabled = Database.getSymbolValue("usb_device_cdc_0", "CONFIG_USB_DEVICE_FUNCTION_USE_IAD") if isIadEnabled == False: args = {"iadEnable":True} res = Database.sendMessage("usb_device_cdc_0", "UPDATE_CDC_IAD_ENABLE", args) nCDCInstances = Database.getSymbolValue("usb_device_cdc", "CONFIG_USB_DEVICE_CDC_INSTANCES") if nCDCInstances == 2: configDescriptorSize = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_CONFIG_DESCRPTR_SIZE") if configDescriptorSize != None: args = {"nFunction": configDescriptorSize + 8} res = Database.sendMessage("usb_device", "UPDATE_CONFIG_DESCRPTR_SIZE", args) configDescriptorSize = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_CONFIG_DESCRPTR_SIZE") if configDescriptorSize != None: iadEnableSymbol = ownerComponent.getSymbolByID("CONFIG_USB_DEVICE_FUNCTION_USE_IAD") if iadEnableSymbol.getValue() == True: descriptorSize = cdcDescriptorSize + 8 else: descriptorSize = cdcDescriptorSize args = {"nFunction": configDescriptorSize + descriptorSize} res = Database.sendMessage("usb_device", "UPDATE_CONFIG_DESCRPTR_SIZE", args) nInterfaces = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_INTERFACES_NUMBER") if nInterfaces != None: args = {"nFunction": nInterfaces + cdcInterfacesNumber} res = Database.sendMessage("usb_device", "UPDATE_INTERFACES_NUMBER", args) startInterfaceNumber.setValue(nInterfaces, 1) nEndpoints = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_ENDPOINTS_NUMBER") if nEndpoints != None: epNumberInterrupt.setValue(nEndpoints + 1, 1) epNumberBulkOut.setValue(nEndpoints + 2, 1) if any(x in Variables.get("__PROCESSOR") for x in ["PIC32MZ", "PIC32MX", "PIC32MK", "SAMD21", "SAMDA1","SAMD51", "SAME51", "SAME53", "SAME54", "SAML21", "SAML22", "SAMD11"]): epNumberBulkIn.setValue(nEndpoints + 2, 1) args = {"nFunction": nEndpoints + cdcEndpointsPic32} res = Database.sendMessage("usb_device", "UPDATE_ENDPOINTS_NUMBER", args) else: epNumberBulkIn.setValue(nEndpoints + 3, 1) args = {"nFunction": nEndpoints + cdcEndpointsSAM} res = Database.sendMessage("usb_device", "UPDATE_ENDPOINTS_NUMBER", args) def onAttachmentDisconnected(source, target): print ("CDC Function Driver: Detached") global cdcInterfacesNumber global cdcDescriptorSize global configValue global startInterfaceNumber global numberOfInterfaces global useIad global epNumberInterrupt global epNumberBulkOut global epNumberBulkIn global cdcEndpointsPic32 global cdcEndpointsSAM global cdcInstancesCount global currentQSizeRead global currentQSizeWrite global currentQSizeSerialStateNotification dependencyID = source["id"] ownerComponent = source["component"] remoteComponent = target["component"] remoteComponentId = remoteComponent.getID() if (remoteComponentId == "usb_device"): nFunctions = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_FUNCTIONS_NUMBER") if nFunctions != None: nFunctions = nFunctions - 1 args = {"nFunction":nFunctions} res = Database.sendMessage("usb_device", "UPDATE_FUNCTIONS_NUMBER", args) endpointNumber = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_ENDPOINTS_NUMBER") if endpointNumber != None: if any(x in Variables.get("__PROCESSOR") for x in ["PIC32MZ"]): args = {"nFunction":endpointNumber - cdcEndpointsPic32 } res = Database.sendMessage("usb_device", "UPDATE_ENDPOINTS_NUMBER", args) else: args = {"nFunction":endpointNumber - cdcEndpointsSAM } res = Database.sendMessage("usb_device", "UPDATE_ENDPOINTS_NUMBER", args) interfaceNumber = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_INTERFACES_NUMBER") if interfaceNumber != None: args = {"nFunction": interfaceNumber - 2} res = Database.sendMessage("usb_device", "UPDATE_INTERFACES_NUMBER", args) nCDCInstances = Database.getSymbolValue("usb_device_cdc", "CONFIG_USB_DEVICE_CDC_INSTANCES") if nCDCInstances != None: nCDCInstances = nCDCInstances - 1 args = {"cdcInstanceCount": nCDCInstances} res = Database.sendMessage("usb_device_cdc", "UPDATE_CDC_INSTANCES", args) if nCDCInstances == 1 and nFunctions != None and nFunctions == 1: args = {"iadEnable":False} res = Database.sendMessage("usb_device_cdc_0", "UPDATE_CDC_IAD_ENABLE", args) args = {"nFunction":False} res = Database.sendMessage("usb_device", "UPDATE_IAD_ENABLE", args) configDescriptorSize = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_CONFIG_DESCRPTR_SIZE") if configDescriptorSize != None: args = {"nFunction": configDescriptorSize - 8} res = Database.sendMessage("usb_device", "UPDATE_CONFIG_DESCRPTR_SIZE", args) configDescriptorSize = Database.getSymbolValue("usb_device", "CONFIG_USB_DEVICE_CONFIG_DESCRPTR_SIZE") if configDescriptorSize != None: if useIad.getValue() == True: descriptorSize = cdcDescriptorSize + 8 else: descriptorSize = cdcDescriptorSize args = {"nFunction": configDescriptorSize - descriptorSize} res = Database.sendMessage("usb_device", "UPDATE_CONFIG_DESCRPTR_SIZE", args) def destroyComponent(component): print ("CDC Function Driver: Destroyed") # This function is called when user modifies the CDC Queue Size. def usbDeviceCdcBufferQueueSize(usbSymbolSource, event): global currentQSizeRead global currentQSizeWrite global currentQSizeSerialStateNotification queueDepthCombined = Database.getSymbolValue("usb_device_cdc", "CONFIG_USB_DEVICE_CDC_QUEUE_DEPTH_COMBINED") if (event["id"] == "CONFIG_USB_DEVICE_FUNCTION_READ_Q_SIZE"): queueDepthCombined = queueDepthCombined - currentQSizeRead + event["value"] currentQSizeRead = event["value"] if (event["id"] == "CONFIG_USB_DEVICE_FUNCTION_WRITE_Q_SIZE"): queueDepthCombined = queueDepthCombined - currentQSizeWrite + event["value"] currentQSizeWrite = event["value"] if (event["id"] == "CONFIG_USB_DEVICE_FUNCTION_SERIAL_NOTIFIACATION_Q_SIZE"): queueDepthCombined = queueDepthCombined - currentQSizeSerialStateNotification + event["value"] currentQSizeSerialStateNotification = event["value"] # We have updated queueDepthCombined variable with current combined queue length. # Now send a message to USB_DEVICE_CDC_COMMON.PY to modify the Combined queue length. args = {"cdcQueueDepth": queueDepthCombined} res = Database.sendMessage("usb_device_cdc", "UPDATE_CDC_QUEUE_DEPTH_COMBINED", args) def instantiateComponent(usbDeviceCdcComponent, index): global cdcDescriptorSize global cdcInterfacesNumber global cdcDescriptorSize global configValue global startInterfaceNumber global numberOfInterfaces global useIad global currentQSizeRead global currentQSizeWrite global currentQSizeSerialStateNotification global epNumberInterrupt global epNumberBulkOut global epNumberBulkIn res = Database.activateComponents(["usb_device"]) if any(x in Variables.get("__PROCESSOR") for x in ["PIC32MZ"]): MaxEpNumber = 7 BulkInDefaultEpNumber = 2 elif any(x in Variables.get("__PROCESSOR") for x in ["PIC32MX", "PIC32MK"]): MaxEpNumber = 15 BulkInDefaultEpNumber = 2 elif any(x in Variables.get("__PROCESSOR") for x in ["SAMD21", "SAMDA1", "SAMD51", "SAME51", "SAME53", "SAME54", "SAML21", "SAML22", "SAMD11"]): MaxEpNumber = 7 BulkInDefaultEpNumber = 2 elif any(x in Variables.get("__PROCESSOR") for x in ["SAMA5D2", "SAM9X60"]): MaxEpNumber = 15 BulkInDefaultEpNumber = 3 elif any(x in Variables.get("__PROCESSOR") for x in ["SAME70", "SAMS70", "SAMV70", "SAMV71"]): MaxEpNumber = 9 BulkInDefaultEpNumber = 3 elif any(x in Variables.get("__PROCESSOR") for x in ["SAMG55"]): MaxEpNumber = 5 BulkInDefaultEpNumber = 3 # Index of this function indexFunction = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_INDEX", None) indexFunction.setVisible(False) indexFunction.setMin(0) indexFunction.setMax(16) indexFunction.setDefaultValue(index) # Config name: Configuration number configValue = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_CONFIG_VALUE", None) configValue.setLabel("Configuration Value") configValue.setVisible(False) configValue.setMin(1) configValue.setMax(16) configValue.setDefaultValue(1) configValue.setReadOnly(True) # Adding Start Interface number startInterfaceNumber = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_INTERFACE_NUMBER", None) startInterfaceNumber.setLabel("Start Interface Number") helpText = '''Indicates the Interface Number of the first interfaces in the Communication Device Interface Group. This is provided here for indication purposes only and is automatically updated based on the function driver selection.''' startInterfaceNumber.setDescription(helpText) startInterfaceNumber.setVisible(True) startInterfaceNumber.setMin(0) startInterfaceNumber.setDefaultValue(0) startInterfaceNumber.setReadOnly(True) # Adding Number of Interfaces numberOfInterfaces = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_NUMBER_OF_INTERFACES", None) numberOfInterfaces.setLabel("Number of Interfaces") helpText = '''Indicates the interfaces in the Communication Device Interface Group. This is provided here for indication purposes only.''' numberOfInterfaces.setDescription(helpText) numberOfInterfaces.setVisible(True) numberOfInterfaces.setMin(1) numberOfInterfaces.setMax(16) numberOfInterfaces.setDefaultValue(2) numberOfInterfaces.setReadOnly(True) # Use IAD useIad = usbDeviceCdcComponent.createBooleanSymbol("CONFIG_USB_DEVICE_FUNCTION_USE_IAD", None) useIad.setLabel("Use Interface Association Descriptor") helpText = '''Enable this option to generate a Interface Association Descriptor (IAD). This option should be enabled in case multiple CDC interfaces are included in the Device. Enabling the option will update the Class, Sublass fields in the Device Descriptor to indicate that that device uses IAD.''' useIad.setDescription(helpText) useIad.setVisible(True) useIad.setDefaultValue(False) useIad.setUseSingleDynamicValue(True) # CDC Function driver Read Queue Size queueSizeRead = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_READ_Q_SIZE", None) queueSizeRead.setLabel("CDC Read Queue Size") helpText = '''Configure the size of the Read Queue. This configures the maximum number of Read Requests that can be queued before the Function Driver returns a queue full response. Using a queue increases memory consumption but also increases throughput. The driver will queue requests if the transfer request is currently being processed.''' queueSizeRead.setDescription(helpText) queueSizeRead.setVisible(True) queueSizeRead.setMin(1) queueSizeRead.setMax(32767) queueSizeRead.setDefaultValue(1) currentQSizeRead = queueSizeRead.getValue() # CDC Function driver Write Queue Size queueSizeWrite = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_WRITE_Q_SIZE", None) helpText = '''Configure the size of the Write Queue. This configures the maximum number of Write Requests that can be queued before the Function Driver returns a queue full response. Using a queue increases memory consumption but also increases throughput. The driver will queue requests if the transfer request is currently being processed.''' queueSizeWrite.setDescription(helpText) queueSizeWrite.setLabel("CDC Write Queue Size") queueSizeWrite.setVisible(True) queueSizeWrite.setMin(1) queueSizeWrite.setMax(32767) queueSizeWrite.setDefaultValue(1) currentQSizeWrite = queueSizeWrite.getValue() # CDC Function driver Serial state notification Queue Size queueSizeSerialStateNotification = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_SERIAL_NOTIFIACATION_Q_SIZE", None) queueSizeSerialStateNotification.setLabel("CDC Serial Notification Queue Size") helpText = '''Configure the size of the Serial State Notification Queue. This configures the maximum number of Serial State Notification Requests that can be queued before the Function Driver returns a queue full response. Using a queue increases memory consumption but also increases throughput. The driver will queue requests if the transfer request is currently being processed.''' queueSizeSerialStateNotification.setDescription(helpText) queueSizeSerialStateNotification.setVisible(True) queueSizeSerialStateNotification.setMin(1) queueSizeSerialStateNotification.setMax(32767) queueSizeSerialStateNotification.setDefaultValue(1) currentQSizeSerialStateNotification = queueSizeSerialStateNotification.getValue() # CDC Function driver Notification Endpoint Number epNumberInterrupt = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_INT_ENDPOINT_NUMBER", None) helpText = '''Specify the endpoint number of Interrupt IN Endpoint to be used for this instance of the CDC Interface. Refer to Device Datasheet for details on available endpoints and limitations.''' epNumberInterrupt.setDescription(helpText) epNumberInterrupt.setLabel("Interrupt Endpoint Number") epNumberInterrupt.setVisible(True) epNumberInterrupt.setMin(1) epNumberInterrupt.setDefaultValue(1) epNumberInterrupt.setMax(MaxEpNumber) # CDC Function driver Data OUT Endpoint Number epNumberBulkOut = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_BULK_OUT_ENDPOINT_NUMBER", None) helpText = '''Specify the endpoint number of Bulk Out Endpoint to be used for this instance of the CDC Interface. Refer to Device Datasheet for details on available endpoints and limitations.''' epNumberBulkOut.setDescription(helpText) epNumberBulkOut.setLabel("Bulk OUT Endpoint Number") epNumberBulkOut.setVisible(True) epNumberBulkOut.setMin(1) epNumberBulkOut.setDefaultValue(2) epNumberBulkOut.setMax(MaxEpNumber) # CDC Function driver Data IN Endpoint Number epNumberBulkIn = usbDeviceCdcComponent.createIntegerSymbol("CONFIG_USB_DEVICE_FUNCTION_BULK_IN_ENDPOINT_NUMBER", None) helpText = '''Specify the endpoint number of Bulk IN Endpoint to be used for this instance of the CDC Interface. Refer to Device Datasheet for details on available endpoints and limitations.''' epNumberBulkIn.setDescription(helpText) epNumberBulkIn.setLabel("Bulk IN Endpoint Number") epNumberBulkIn.setVisible(True) epNumberBulkIn.setMin(1) epNumberBulkIn.setMax(MaxEpNumber) epNumberBulkIn.setDefaultValue(BulkInDefaultEpNumber) usbDeviceCdcBufPool = usbDeviceCdcComponent.createBooleanSymbol("CONFIG_USB_DEVICE_CDC_BUFFER_POOL", None) usbDeviceCdcBufPool.setLabel("**** Buffer Pool Update ****") usbDeviceCdcBufPool.setDependencies(usbDeviceCdcBufferQueueSize, ["CONFIG_USB_DEVICE_FUNCTION_READ_Q_SIZE", "CONFIG_USB_DEVICE_FUNCTION_WRITE_Q_SIZE", "CONFIG_USB_DEVICE_FUNCTION_SERIAL_NOTIFIACATION_Q_SIZE"]) usbDeviceCdcBufPool.setVisible(False) ############################################################################ #### Dependency #### ############################################################################ # USB DEVICE CDC Common Dependency Log.writeDebugMessage ("Dependency Started") numInstances = Database.getSymbolValue("usb_device_cdc", "CONFIG_USB_DEVICE_CDC_INSTANCES") if (numInstances == None): numInstances = 0 args = {"cdcInstanceCount": index+1} res = Database.sendMessage("usb_device_cdc", "UPDATE_CDC_INSTANCES", args) ############################################################# # Function Init Entry for CDC ############################################################# usbDeviceCdcFunInitFile = usbDeviceCdcComponent.createFileSymbol(None, None) usbDeviceCdcFunInitFile.setType("STRING") usbDeviceCdcFunInitFile.setOutputName("usb_device.LIST_USB_DEVICE_FUNCTION_INIT_ENTRY") usbDeviceCdcFunInitFile.setSourcePath("templates/device/cdc/system_init_c_device_data_cdc_function_init.ftl") usbDeviceCdcFunInitFile.setMarkup(True) ############################################################# # Function Registration table for CDC ############################################################# usbDeviceCdcFunRegTableFile = usbDeviceCdcComponent.createFileSymbol(None, None) usbDeviceCdcFunRegTableFile.setType("STRING") usbDeviceCdcFunRegTableFile.setOutputName("usb_device.LIST_USB_DEVICE_FUNCTION_ENTRY") usbDeviceCdcFunRegTableFile.setSourcePath("templates/device/cdc/system_init_c_device_data_cdc_function.ftl") usbDeviceCdcFunRegTableFile.setMarkup(True) ############################################################# # HS Descriptors for CDC Function ############################################################# usbDeviceCdcDescriptorHsFile = usbDeviceCdcComponent.createFileSymbol(None, None) usbDeviceCdcDescriptorHsFile.setType("STRING") usbDeviceCdcDescriptorHsFile.setOutputName("usb_device.LIST_USB_DEVICE_FUNCTION_DESCRIPTOR_HS_ENTRY") usbDeviceCdcDescriptorHsFile.setSourcePath("templates/device/cdc/system_init_c_device_data_cdc_function_descrptr_hs.ftl") usbDeviceCdcDescriptorHsFile.setMarkup(True) ############################################################# # FS Descriptors for CDC Function ############################################################# usbDeviceCdcDescriptorFsFile = usbDeviceCdcComponent.createFileSymbol(None, None) usbDeviceCdcDescriptorFsFile.setType("STRING") usbDeviceCdcDescriptorFsFile.setOutputName("usb_device.LIST_USB_DEVICE_FUNCTION_DESCRIPTOR_FS_ENTRY") usbDeviceCdcDescriptorFsFile.setSourcePath("templates/device/cdc/system_init_c_device_data_cdc_function_descrptr_fs.ftl") usbDeviceCdcDescriptorFsFile.setMarkup(True) ############################################################# # Class code Entry for CDC Function ############################################################# usbDeviceCdcDescriptorClassCodeFile = usbDeviceCdcComponent.createFileSymbol(None, None) usbDeviceCdcDescriptorClassCodeFile.setType("STRING") usbDeviceCdcDescriptorClassCodeFile.setOutputName("usb_device.LIST_USB_DEVICE_DESCRIPTOR_CLASS_CODE_ENTRY") usbDeviceCdcDescriptorClassCodeFile.setSourcePath("templates/device/cdc/system_init_c_device_data_cdc_function_class_codes.ftl") usbDeviceCdcDescriptorClassCodeFile.setMarkup(True) ################################################ # USB CDC Function driver Files ################################################ usbDeviceCdcHeaderFile = usbDeviceCdcComponent.createFileSymbol(None, None) addFileName('usb_device_cdc.h', usbDeviceCdcComponent, usbDeviceCdcHeaderFile, "middleware/", "/usb/", True, None) usbCdcHeaderFile = usbDeviceCdcComponent.createFileSymbol(None, None) addFileName('usb_cdc.h', usbDeviceCdcComponent, usbCdcHeaderFile, "middleware/", "/usb/", True, None) usbDeviceCdcSourceFile = usbDeviceCdcComponent.createFileSymbol(None, None) addFileName('usb_device_cdc.c', usbDeviceCdcComponent, usbDeviceCdcSourceFile, "middleware/src/", "/usb/src", True, None) usbDeviceCdcAcmSourceFile = usbDeviceCdcComponent.createFileSymbol(None, None) addFileName('usb_device_cdc_acm.c', usbDeviceCdcComponent, usbDeviceCdcAcmSourceFile, "middleware/src/", "/usb/src", True, None) usbDeviceCdcLocalHeaderFile = usbDeviceCdcComponent.createFileSymbol(None, None) addFileName('usb_device_cdc_local.h', usbDeviceCdcComponent, usbDeviceCdcLocalHeaderFile, "middleware/src/", "/usb/src", True, None) # all files go into src/ def addFileName(fileName, component, symbol, srcPath, destPath, enabled, callback): configName1 = Variables.get("__CONFIGURATION_NAME") #filename = component.createFileSymbol(None, None) symbol.setProjectPath("config/" + configName1 + destPath) symbol.setSourcePath(srcPath + fileName) symbol.setOutputName(fileName) symbol.setDestPath(destPath) if fileName[-2:] == '.h': symbol.setType("HEADER") else: symbol.setType("SOURCE") symbol.setEnabled(enabled)
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setup.py
ZebinYang/pyUniDOE
3
184962
<reponame>ZebinYang/pyUniDOE<gh_stars>1-10 from setuptools import setup, Extension pyUniDOE_module = Extension('_pyunidoe_swig', sources=['pyunidoe/pyunidoe_swig_wrap.cxx', 'pyunidoe/wrapper.cpp', 'pyunidoe/doe_optimizer.cpp', 'pyunidoe/doe_criteria.cpp', 'pyunidoe/doe_CD2.cpp', 'pyunidoe/doe_MD2.cpp', 'pyunidoe/doe_WD2.cpp', 'pyunidoe/doe_maximin.cpp', 'pyunidoe/doe_MC.cpp', 'pyunidoe/doe_A2.cpp']) setup(name='pyunidoe', version='0.1', description='Efficient procedures for constructing uniform design of experiments under various space-filling criteria. It is based on a stochastic and adaptive threshold accepting algorithm with flexible initialization, adaptive threshold, and stochastic evolution. The package may also construct the augmented uniform designs in a sequential manner.', author='<NAME> and <NAME>', author_email='<EMAIL>, <EMAIL>', license='BSD', packages=['pyunidoe'], ext_modules=[pyUniDOE_module], package_dir={'pyunidoe': 'pyunidoe'}, package_data={'pyunidoe': ['data/*.json']}, install_requires=['matplotlib', 'numpy', 'pandas', 'seaborn', 'joblib'], classifiers=['Development Status :: 4 - Beta', 'Environment :: Console', 'License :: OSI Approved :: BSD License', 'Operating System :: MacOS :: MacOS X', 'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX', 'Programming Language :: Python'], zip_safe=False)
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tests/test_ad_sid.py
morian/bonsai
59
108441
<reponame>morian/bonsai import pytest from bonsai.active_directory import SID, sid def test_create_from_bytes_le(): """ Test create SID from binary. """ with pytest.raises(ValueError): _ = SID(bytes_le=b"NOT-A-SID") with pytest.raises(ValueError): _ = SID( bytes_le=b"\x01\x05\x00\x00\x00\x05\x15\x00\x00\x00\xdc\xf4\xdc;\x83=+F\x82\xa6(\x00\x02\x00\x00" ) with pytest.raises(TypeError): _ = SID(bytes_le=True) with pytest.raises(TypeError): _ = SID(bytes_le="S-1-1-1") input_data = b"\x01\x05\x00\x00\x00\x00\x00\x05\x15\x00\x00\x00\xdc\xf4\xdc;\x83=+F\x82\x8b\xa6(\x00\x02\x00\x00" expected_sid = "S-1-5-21-1004336348-1177238915-682003330-512" sid = SID(bytes_le=input_data) assert sid.revision == 1 assert sid.identifier_authority == 5 assert sid.subauthorities == (21, 1004336348, 1177238915, 682003330, 512) assert sid == expected_sid def test_create_from_str_rep(): """ Test create SID from string. """ with pytest.raises(ValueError): _ = SID("NOT-A-SID") with pytest.raises(ValueError): _ = SID("S--") with pytest.raises(ValueError): _ = SID(str_rep="S-1") with pytest.raises(ValueError): _ = SID("S-1-NOT-SID") with pytest.raises(ValueError): _ = SID("NOT") with pytest.raises(TypeError): _ = SID(str_rep=0) with pytest.raises(TypeError): _ = SID(b"S-1-1-1") sid = SID("S-1-5-21-3623811015-3361044348-30300820-1013") assert sid.revision == 1 assert sid.identifier_authority == 5 assert sid.subauthorities == (21, 3623811015, 3361044348, 30300820, 1013) everyone_sid = SID("S-1-1-0") assert everyone_sid.identifier_authority == 1 assert everyone_sid.subauthorities == (0,) def test_bytes_le(): """ Test bytes_le property. """ expected_bytes = b"\x01\x05\x00\x00\x00\x00\x00\x05\x15\x00\x00\x00\xdc\xf4\xdc;\x83=+F\x82\x8b\xa6(\x00\x02\x00\x00" test_sid = SID("S-1-5-21-1004336348-1177238915-682003330-512") assert test_sid.bytes_le == expected_bytes assert ( SID(bytes_le=test_sid.bytes_le).identifier_authority == test_sid.identifier_authority ) assert SID(bytes_le=test_sid.bytes_le).subauthorities == test_sid.subauthorities def test_str(): """ Test __str__ method. """ test_sid = "S-1-5-21-3623811015-3361044348-30300820-1013" assert str(SID(test_sid)) == test_sid test_sid = "S-1-1-0" assert str(SID(test_sid)) == test_sid def test_repr(): """ Test __repr__ method. """ sid = SID("S-1-1-0") assert SID.__name__ in repr(sid) assert "S-1-1-0" in repr(sid) def test_eq(): """ Test __eq__ method. """ sid0 = SID("S-1-1-0") sid1 = SID("S-1-1-0") assert sid0 == sid1 assert sid0 != True assert sid0 == "S-1-1-0" def test_hash(): """ Test __hash__ method. """ test_sid = "S-1-5-21-3623811015-3361044348-30300820-1013" sid0 = SID(test_sid) assert hash(sid0) == hash(test_sid) assert set((sid0, SID(test_sid))) == {sid0} @pytest.mark.parametrize( "sid, alias", [ (SID("S-1-1-0"), "WD"), (SID("S-1-5-32-559"), "LU"), (SID("S-1-5-21-3623811015-3361044348-30300820-500"), "LA"), (SID("S-1-5-21-3623811015-3361044348-30300820-520"), "PA"), (SID("S-1-5-21-3623811015-3361044348-30300820-1013"), None), (SID("S-1-5-34-3623811015-3361044348-30300820-500"), None), ], ) def test_sddl_alias(sid, alias): """ Test sddl_alias property. """ assert sid.sddl_alias == alias @pytest.mark.parametrize( "sid", [ SID("S-1-1-0"), SID("S-1-5-32-559"), SID("S-1-5-34-3623811015-3361044348-30300820-500"), ], ) def test_size(sid): """ Test size property. """ assert sid.size == len(sid.bytes_le)
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src/util/perf/u_trace.py
SoftReaper/Mesa-Renoir-deb
0
140675
# # Copyright (C) 2020 Google, Inc. # # 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 (including the next # paragraph) 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. # from mako.template import Template from collections import namedtuple from enum import IntEnum import os TRACEPOINTS = {} class Tracepoint(object): """Class that represents all the information about a tracepoint """ def __init__(self, name, args=[], tp_struct=None, tp_print=None, tp_perfetto=None, end_of_pipe=False): """Parameters: - name: the tracepoint name, a tracepoint function with the given name (prefixed by 'trace_') will be generated with the specied args (following a u_trace ptr). Calling this tracepoint will emit a trace, if tracing is enabled. - args: the tracepoint func args, an array of TracepointArg - tp_print: (optional) array of format string followed by expressions - tp_perfetto: (optional) driver provided callback which can generate perfetto events """ assert isinstance(name, str) assert isinstance(args, list) assert name not in TRACEPOINTS self.name = name self.args = args if tp_struct is None: tp_struct = args self.tp_struct = tp_struct self.tp_print = tp_print self.tp_perfetto = tp_perfetto self.end_of_pipe = end_of_pipe TRACEPOINTS[name] = self def can_generate_print(self): return self.args is not None and len(self.args) > 0 class TracepointArgStruct(): """Represents struct that is being passed as an argument """ def __init__(self, type, var): """Parameters: - type: argument's C type. - var: name of the argument """ assert isinstance(type, str) assert isinstance(var, str) self.type = type self.var = var class TracepointArg(object): """Class that represents either an argument being passed or a field in a struct """ def __init__(self, type, var, c_format, name=None, to_prim_type=None): """Parameters: - type: argument's C type. - var: either an argument name or a field in the struct - c_format: printf format to print the value. - name: (optional) name that will be used in intermidiate structs and will be displayed in output or perfetto, otherwise var will be used. - to_prim_type: (optional) C function to convert from arg's type to a type compatible with c_format. """ assert isinstance(type, str) assert isinstance(var, str) assert isinstance(c_format, str) self.type = type self.var = var self.c_format = c_format if name is None: name = var self.name = name self.to_prim_type = to_prim_type HEADERS = [] class HeaderScope(IntEnum): HEADER = (1 << 0) SOURCE = (1 << 1) class Header(object): """Class that represents a header file dependency of generated tracepoints """ def __init__(self, hdr, scope=HeaderScope.HEADER|HeaderScope.SOURCE): """Parameters: - hdr: the required header path """ assert isinstance(hdr, str) self.hdr = hdr self.scope = scope HEADERS.append(self) FORWARD_DECLS = [] class ForwardDecl(object): """Class that represents a forward declaration """ def __init__(self, decl): assert isinstance(decl, str) self.decl = decl FORWARD_DECLS.append(self) hdr_template = """\ /* Copyright (C) 2020 Google, Inc. * * 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 (including the next * paragraph) 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. */ <% guard_name = '_' + hdrname + '_H' %> #ifndef ${guard_name} #define ${guard_name} % for header in HEADERS: #include "${header.hdr}" % endfor #include "util/perf/u_trace.h" #ifdef __cplusplus extern "C" { #endif % for declaration in FORWARD_DECLS: ${declaration.decl}; % endfor % for trace_name, trace in TRACEPOINTS.items(): /* * ${trace_name} */ struct trace_${trace_name} { % for arg in trace.tp_struct: ${arg.type} ${arg.name}; % endfor % if len(trace.args) == 0: #ifdef __cplusplus /* avoid warnings about empty struct size mis-match in C vs C++.. * the size mis-match is harmless because (a) nothing will deref * the empty struct, and (b) the code that cares about allocating * sizeof(struct trace_${trace_name}) (and wants this to be zero * if there is no payload) is C */ uint8_t dummy; #endif % endif }; % if trace.tp_perfetto is not None: #ifdef HAVE_PERFETTO void ${trace.tp_perfetto}( ${ctx_param}, uint64_t ts_ns, const void *flush_data, const struct trace_${trace_name} *payload); #endif % endif void __trace_${trace_name}( struct u_trace *ut, void *cs % for arg in trace.args: , ${arg.type} ${arg.var} % endfor ); static inline void trace_${trace_name}( struct u_trace *ut, void *cs % for arg in trace.args: , ${arg.type} ${arg.var} % endfor ) { % if trace.tp_perfetto is not None: if (!unlikely(ut->enabled || ut_trace_instrument || ut_perfetto_enabled)) % else: if (!unlikely(ut->enabled || ut_trace_instrument)) % endif return; __trace_${trace_name}( ut, cs % for arg in trace.args: , ${arg.var} % endfor ); } % endfor #ifdef __cplusplus } #endif #endif /* ${guard_name} */ """ src_template = """\ /* Copyright (C) 2020 Google, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a * copy of this software and associated documentation files (the "Software"), * to deal in the Software without restriction, including without limitation * the rights to use, copy, modify, merge, publish, distribute, sublicense, * and/or sell copies of the Software, and to permit persons to whom the * Software is furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice (including the next * paragraph) shall be included in all copies or substantial portions of the * Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS * IN THE SOFTWARE. */ % for header in HEADERS: #include "${header.hdr}" % endfor #include "${hdr}" #define __NEEDS_TRACE_PRIV #include "util/perf/u_trace_priv.h" % for trace_name, trace in TRACEPOINTS.items(): /* * ${trace_name} */ % if trace.can_generate_print(): static void __print_${trace_name}(FILE *out, const void *arg) { const struct trace_${trace_name} *__entry = (const struct trace_${trace_name} *)arg; % if trace.tp_print is not None: fprintf(out, "${trace.tp_print[0]}\\n" % for arg in trace.tp_print[1:]: , ${arg} % endfor % else: fprintf(out, "" % for arg in trace.tp_struct: "${arg.name}=${arg.c_format}, " % endfor "\\n" % for arg in trace.tp_struct: % if arg.to_prim_type: ,${arg.to_prim_type.format('__entry->' + arg.name)} % else: ,__entry->${arg.name} % endif % endfor % endif ); } % else: #define __print_${trace_name} NULL % endif static const struct u_tracepoint __tp_${trace_name} = { ALIGN_POT(sizeof(struct trace_${trace_name}), 8), /* keep size 64b aligned */ "${trace_name}", ${"true" if trace.end_of_pipe else "false"}, __print_${trace_name}, % if trace.tp_perfetto is not None: #ifdef HAVE_PERFETTO (void (*)(void *pctx, uint64_t, const void *, const void *))${trace.tp_perfetto}, #endif % endif }; void __trace_${trace_name}( struct u_trace *ut, void *cs % for arg in trace.args: , ${arg.type} ${arg.var} % endfor ) { struct trace_${trace_name} *__entry = (struct trace_${trace_name} *)u_trace_append(ut, cs, &__tp_${trace_name}); % if len(trace.tp_struct) == 0: (void)__entry; % endif % for arg in trace.tp_struct: __entry->${arg.name} = ${arg.var}; % endfor } % endfor """ def utrace_generate(cpath, hpath, ctx_param): if cpath is not None: hdr = os.path.basename(cpath).rsplit('.', 1)[0] + '.h' with open(cpath, 'w') as f: f.write(Template(src_template).render( hdr=hdr, ctx_param=ctx_param, HEADERS=[h for h in HEADERS if h.scope & HeaderScope.SOURCE], TRACEPOINTS=TRACEPOINTS)) if hpath is not None: hdr = os.path.basename(hpath) with open(hpath, 'w') as f: f.write(Template(hdr_template).render( hdrname=hdr.rstrip('.h').upper(), ctx_param=ctx_param, HEADERS=[h for h in HEADERS if h.scope & HeaderScope.HEADER], FORWARD_DECLS=FORWARD_DECLS, TRACEPOINTS=TRACEPOINTS)) perfetto_utils_hdr_template = """\ /* * Copyright © 2021 Igalia S.L. * * 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 (including the next * paragraph) 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. */ <% guard_name = '_' + hdrname + '_H' %> #ifndef ${guard_name} #define ${guard_name} #include <perfetto.h> % for trace_name, trace in TRACEPOINTS.items(): static void UNUSED trace_payload_as_extra_${trace_name}(perfetto::protos::pbzero::GpuRenderStageEvent *event, const struct trace_${trace_name} *payload) { % if all([trace.tp_perfetto, trace.tp_struct]) and len(trace.tp_struct) > 0: char buf[128]; % for arg in trace.tp_struct: { auto data = event->add_extra_data(); data->set_name("${arg.name}"); % if arg.to_prim_type: sprintf(buf, "${arg.c_format}", ${arg.to_prim_type.format('payload->' + arg.name)}); % else: sprintf(buf, "${arg.c_format}", payload->${arg.name}); % endif data->set_value(buf); } % endfor % endif } % endfor #endif /* ${guard_name} */ """ def utrace_generate_perfetto_utils(hpath): if hpath is not None: hdr = os.path.basename(hpath) with open(hpath, 'wb') as f: f.write(Template(perfetto_utils_hdr_template, output_encoding='utf-8').render( hdrname=hdr.rstrip('.h').upper(), TRACEPOINTS=TRACEPOINTS))
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src/certGenerator.py
m-lab/wehe_desktop
6
1611438
import argparse import os import sys from urllib2 import urlopen # https://gist.github.com/fntlnz/cf14feb5a46b2eda428e000157447309 default_root_cert_location = './ssl/ca.crt' default_root_key_location = './ssl/ca.key' parser = argparse.ArgumentParser( description='Generate a new certificate and sign it with root CA') parser.add_argument('--root_cert', metavar='ca.crt', type=str, help='path to root certificate', default=default_root_cert_location) parser.add_argument('--root_key', metavar='ca.key', type=str, help='path to root CA key', default=default_root_key_location) parser.add_argument('--root_pass', metavar='<password>', type=str, help='root certificate password', default='') parser.add_argument('--destination', metavar='./ssl', type=str, help='certificate output directory', default='./ssl/') parser.add_argument('--file_names', metavar='server', type=str, help='what to name the certificate and key files', default='server') parser.add_argument('--domain_name', metavar='127.0.0.1', type=str, help='the domain for which to generate the cert, by default we use the ip of the current server', default='') parser.add_argument('--days', metavar='500', type=str, help='Number of days until the signed certificate expires', default='500') args = parser.parse_args() # Check if root ca cert is there exists = os.path.isfile(args.root_cert) if not exists: print("-- Root certificate not found at path %s --" % args.root_cert) if args.root_cert == default_root_cert_location: print("-- Default location was used, you may want to specify the location using the '--root_cert' flag --") sys.exit(1) # Check if root ca key is there exists = os.path.isfile(args.root_key) if not exists: print("-- Root certificate key not found at path %s --" % args.root_key) if args.root_key == default_root_key_location: print("-- Default location was used, you may want to specify the location using the '--root_key' flag --") sys.exit(1) exists = os.path.isdir(args.destination) if not exists: print("-- Destination folder %s not found, attempting to create it --" % args.destination) os.makedirs(args.destination) # Generate a new key print("-- Generating private key for our new server certificate") key_name = "%s.key" % args.file_names key_path = os.path.join(args.destination, key_name) retvalue = os.system("openssl genrsa -out %s 2048" % key_path) if retvalue == 0: print("-- Created private key %s --" % key_path) else: print("-- Error creaing private key, openssl returned non-zero exit code %d --" % retvalue) sys.exit(1) # Generate signing request for root CA to sign print("-- Generating a certificate signing request for our root CA to sign") if args.domain_name == '': print("-- No domain name was provided, attemtping to figure out server ip via ip.42.pl --") my_ip = urlopen('http://ip.42.pl/raw').read() print("-- ip.42.pl reported the servers ip as %s --" % my_ip) args.domain_name = my_ip csr_name = "%s.csr" % args.file_names csr_path = os.path.join(args.destination, csr_name) retvalue = os.system("openssl req -new -sha256 -key %s -subj '/C=US/ST=MA/O=Northeastern/CN=%s' -out %s" % (key_path, args.domain_name, csr_path)) if retvalue == 0: print("-- Created certificate signing request %s --" % csr_path) else: print("-- Error creaing certificate signing request, openssl returned non-zero exit code %d --" % retvalue) sys.exit(1) # openssl x509 -req -in mydomain.com.csr -CA rootCA.crt -CAkey rootCA.key -CAcreateserial -out mydomain.com.crt -days 500 -sha256 # Sign the certificate request print("-- Signing the certificate signing request --") crt_name = "%s.crt" % args.file_names crt_path = os.path.join(args.destination, crt_name) retvalue = os.system("openssl x509 -req -in %s -CA %s -CAkey %s -passin pass:%s -CAcreateserial -out %s -days %s -sha256" % (csr_path, args.root_cert, args.root_key, args.root_pass, crt_path, args.days)) if retvalue == 0: print("-- Signed certificate %s --" % crt_path) else: print("-- Error signing certificate, openssl returned non-zero exit code %d --" % retvalue) if retvalue == 256: print("-- It looks like the root password was incorrect --") print("-- If you did not specify a root password but one is required, please use the '--root_pass' flag to specify one") sys.exit(1) print("-- Cleaning up --") os.remove(csr_path) print("-- Success --")
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babel_python/babel_python/rabbitmq.py
threathunterX/python_lib
2
136552
#!/usr/bin/env python # -*- encoding: utf-8 -*# # referencing code in pyspider from __future__ import absolute_import import time, logging import threading, Queue try: from urllib import parse as urlparse except ImportError: import urlparse import pika import pika.exceptions from threathunter_common.rabbitmq.rmqctx import RabbitmqCtx from threathunter_common.util import run_in_thread __author__ = "nebula" catch_error = 0 def catch_error(func): """Catch errors of rabbitmq then reconnect""" def wrap(self, *args, **kwargs): global catch_error try: func(self, *args, **kwargs) except Exception as e: catch_error += 1 if catch_error <= 10 or catch_error % 1000 == 0: logging.error('RabbitMQ error: %r, reconnect.', e) self.reconnect() return func(self, *args, **kwargs) else: catch_error = 0 return wrap class PikaQueue(object): """ A Queue like rabbitmq connector """ Empty = Queue.Empty Full = Queue.Full def __init__(self, dc, exchange, exchange_type, queue, binding_key, **kwargs): """ Constructor for a PikaQueue. Not works with python 3. Default for python 2. amqp_url: https://www.rabbitmq.com/uri-spec.html self_delete: remove the queue while stopped """ if not isinstance(dc, list): dc = [dc] self.dc = dc self.exchange = exchange self.exchange_type = exchange_type self.exchange_durable = kwargs.get("exchange_durable", True) self.queue = queue self.binding_key = binding_key self.queue_durable = kwargs.get("queue_durable", False) self.amqp_url = kwargs.get("amqp_url") if not self.amqp_url: self.amqp_url = RabbitmqCtx.get_instance().amqp_url self.self_delete = kwargs.get("self_delete", False) if not self.exchange: raise RuntimeError("exchange is required") self.lock = threading.RLock() self.consume_task = None self.reconnect() def _reconnect(self): """Reconnect to rabbitmq server""" self.connection = pika.BlockingConnection(pika.URLParameters(self.amqp_url)) self.channel = self.connection.channel() if self.queue: self.channel.queue_declare(queue=self.queue, durable=self.queue_durable, auto_delete=(not self.queue_durable)) for dc in self.dc: full_ex = "{}.{}".format(dc, self.exchange) self.channel.exchange_declare(exchange=full_ex, type=self.exchange_type, durable=self.exchange_durable, auto_delete=(not self.exchange_durable)) self.channel.queue_bind(queue=self.queue, exchange=full_ex, routing_key=self.binding_key) # reconnect successfully def reconnect(self): with self.lock: try: self._reconnect() except pika.exceptions.ChannelClosed: # try once more self._reconnect() @catch_error def qsize(self): with self.lock: if self.queue: ret = self.channel.queue_declare(self.queue, passive=True) else: return 0 return ret.method.message_count def empty(self): if self.qsize() == 0: return True else: return False def full(self): if self.queue and self.max_queue_size and self.qsize() >= self.max_queue_size: return True else: return False @catch_error def close(self): with self.lock: if self.self_delete and self.queue: self.channel.queue_delete(queue=self.queue) self.queue = None if self.channel: self.channel.close() self.channel = None class PikaQueueSender(PikaQueue): def __init__(self, dc, exchange, exchange_type, **kwargs): PikaQueue.__init__(self, dc, exchange, exchange_type, None, None, **kwargs) self.max_queue_size = kwargs.get("max_queue_size") self.lazy_limit = kwargs.get("lazy_limit") if self.queue and self.lazy_limit and self.max_queue_size: self.qsize_diff_limit = int(self.max_queue_size * 0.1) else: self.qsize_diff_limit = 0 self.qsize_diff = 0 self.local2server = Queue.Queue() #add by wxt 2015-12-16 解耦 run_in_thread(self.put_to_server) # add by wxt 2015-12-16 @catch_error def put(self, obj, routing_key, durable=False, block=True, timeout=None): if not block: return self.put_nowait(obj, routing_key) start_time = time.time() while True: try: return self.put_nowait(obj, routing_key, durable) except Queue.Full: if timeout: lasted = time.time() - start_time if timeout > lasted: time.sleep(min(self.max_timeout, timeout - lasted)) else: raise else: time.sleep(self.max_timeout) @catch_error def put_nowait(self, obj, routing_key, durable=False): qsize = self.local2server.qsize() if qsize > 10000: #add by wxt 2015-12-16 if qsize % 500 == 1: logging.error('babel local2server queue size too large %s',qsize) else: self.local2server.put((obj,routing_key,durable,)) def put_to_server(self): while True: try: data = self.local2server.get(timeout=1) obj = data[0] routing_key = data[1] durable = data[2] with self.lock: self.qsize_diff += 1 for dc in self.dc: full_ex = "{}.{}".format(dc, self.exchange) if durable: self.channel.basic_publish(exchange=full_ex, routing_key=routing_key, body=obj, properties=pika.BasicProperties(delivery_mode=2)) # make message persistent) else: self.channel.basic_publish(exchange=full_ex, routing_key=routing_key, body=obj) except Queue.Empty: continue except Exception,e: while 1: try: self.reconnect() break except Exception,e: time.sleep(5) class PikaQueueReceiver(PikaQueue): def __init__(self, dc, exchange, exchange_type, queue, binding_key, **kwargs): PikaQueue.__init__(self, dc, exchange, exchange_type, queue, binding_key, **kwargs) self.max_cache_size = kwargs.get("max_cache_size", 1000) self.cache = Queue.Queue(maxsize=self.max_cache_size) self.full_errors = 0 self.consumer_tag = None self.consume_task = None if not self.queue: raise RuntimeError("queue is required") self.running = False def backend_consuming(self): while self.running: try: while len(self.channel._consumers) and self.running: self.channel.connection.process_data_events() except Exception as ignore: print ignore if self.running: self._restore_consuming_during_server_outage() print 111 def _restore_consuming_during_server_outage(self): while self.running: try: self._reconnect() if self.connection and not self.connection.is_closed: self.consumer_tag = self.channel.basic_consume(self.on_message, queue=self.queue) return except Exception as err: pass finally: time.sleep(1) @catch_error def start_consuming(self): """This method sets up the consumer by first calling add_on_cancel_callback so that the object is notified if RabbitMQ cancels the consumer. It then issues the Basic.Consume RPC command which returns the consumer tag that is used to uniquely identify the consumer with RabbitMQ. We keep the value to use it when we want to cancel consuming. The on_message method is passed in as a callback pika will invoke when a message is fully received """ # self.channel.add_on_cancel_callback(self.on_consumer_cancelled) self.running = True self.consumer_tag = self.channel.basic_consume(self.on_message, queue=self.queue) self.consume_task = run_in_thread(self.backend_consuming) def stop_consuming(self): """Tell RabbitMQ that you would like to stop consuming by sending the Basic.Cancel RPC command. """ with self.lock: self.running = False if self.consume_task: self.consume_task.join(timeout=2) self.consume_task = None if self.channel and self.consumer_tag: self.channel.basic_cancel(self.consumer_tag) self.consumer_tag = None def on_consumer_cancelled(self, method_frame): """Invoked by pika when RabbitMQ sends a Basic.Cancel for a consumer receiving messages. :param pika.frame.Method method_frame: The Basic.Cancel frame """ pass # self.close() def on_message(self, unused_channel, basic_deliver, properties, body): """Invoked by pika when a message is delivered from RabbitMQ. The channel is passed for your convenience. The basic_deliver object that is passed in carries the exchange, routing key, delivery tag and a redelivered flag for the message. The properties passed in is an instance of BasicProperties with the message properties and the body is the message that was sent. :param pika.channel.Channel unused_channel: The channel object :param pika.Spec.Basic.Deliver: basic_deliver method :param pika.Spec.BasicProperties: properties :param str|unicode body: The message body """ try: self.cache.put(body) self.channel.basic_ack(basic_deliver.delivery_tag) except Queue.Full: self.full_errors += 1 def get_errors_due_to_queue_full(self): return self.full_errors def get_nowait(self): return self.cache.get_nowait() def get(self, block=True, timeout=None): return self.cache.get(block, timeout) def get_cached_number(self): return self.cache.qsize() def dump_cache(self): old_cache = self.cache self.cache = Queue.Queue(maxsize=self.max_cache_size) result = list() for _ in range(old_cache.qsize()): try: result.append(old_cache.get_nowait()) except Queue.Empty: continue return result def close(self): self.stop_consuming() PikaQueue.close(self) class PikaTwoLayerQueueReceiver(PikaQueueReceiver): """ For the exchange to exchange relaying routing """ def __init__(self, dc, first_exchange, first_exchange_type, first_binding_key, second_exchange, second_exchange_type, queue, binding_key, **kwargs): if not isinstance(dc, list): dc = [dc] self.first_exchange_dc = dc self.first_exchange = first_exchange self.first_exchange_type = first_exchange_type self.first_binding_key = first_binding_key self.first_exchange_durable = kwargs.get("first_exchange_durable", True) PikaQueueReceiver.__init__(self, [], second_exchange, second_exchange_type, queue, binding_key, **kwargs) def _reconnect(self): PikaQueue._reconnect(self) # ugly here, if self.queue: self.channel.queue_declare(queue=self.queue, durable=self.queue_durable, auto_delete=(not self.queue_durable)) self.channel.exchange_declare(exchange=self.exchange, type=self.exchange_type, durable=self.exchange_durable, auto_delete=(not self.exchange_durable)) self.channel.queue_bind(queue=self.queue, exchange=self.exchange, routing_key=self.binding_key) for dc in self.first_exchange_dc: full_ex = "{}.{}".format(dc, self.first_exchange) self.channel.exchange_declare(exchange=full_ex, type=self.first_exchange_type, durable=self.first_exchange_durable, auto_delete=(not self.first_exchange_durable)) self.channel.exchange_bind(source=full_ex, destination=self.exchange, routing_key=self.first_binding_key)
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Dataset/Utils.py
Armanfard-Lab/DASVDD
3
105197
import numpy as np import torch import torch.nn as nn import torchvision import pandas as pd import matplotlib.pyplot as plt import torch.nn.functional as F from sklearn import metrics import torchvision.transforms as transforms def get_target_label_idx(labels, targets): return np.argwhere(np.isin(labels, targets)).flatten().tolist() def global_contrast_normalization(x: torch.tensor, scale='l2'): n_features = int(np.prod(x.shape)) mean = torch.mean(x) x -= mean if scale == 'l1': x_scale = torch.mean(torch.abs(x)) if scale == 'l2': x_scale = torch.sqrt(torch.sum(x ** 2)) / n_features x /= x_scale return x def OneClass(train_dataset,test_dataset,Class): Samples = [] for i in train_dataset: x,y = i if y == Class: Samples.append(x) len(Samples) labels = [] test_points = [] bp = 0 counter = 0 for i in test_dataset: x,y = i if y == Class: bp+=1 LBL = 0 labels.append(LBL) test_points.append(x) elif y!=Class: counter+=1 LBL = 1 labels.append(LBL) test_points.append(x) return Samples,test_points,labels
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tests/operators01/__init__.py
Imperatorn/ohmygentool
26
1616823
import os import sys import subprocess import platform import helpers from io import open def run(): test_name = 'optest' config = 'Release' libExt = helpers.static_lib_extensions() dc = helpers.detect_compiler() extra_args = list() helpers.setup_args(extra_args, config=config) if (helpers.is_windows()): extra_args.append(f'-L{test_name}{libExt}') else: extra_args.append(f'-L-l{test_name}') prev_cwd = os.getcwd() rootPath = os.path.dirname(sys.argv[0]) path = (rootPath + '/' + __package__) os.chdir(path) try: os.mkdir('temp') except FileExistsError: pass os.chdir('temp') with open('out.txt', 'w') as out, open('err.txt', 'w') as err: kwargs = dict(stdout=out, stderr=err) try: try: helpers.cmake_run('../cpp', config=config, **kwargs) except subprocess.CalledProcessError: raise ValueError("C++ compilation error") try: subprocess.run(f'../../../build/gentool ../{test_name}.json'.split(), **kwargs) except subprocess.CalledProcessError: raise ValueError("Binding generation failed") try: subprocess.run(f'{dc} ../d/{test_name}.d generated.d {" ".join(extra_args)} -unittest -main -g'.split(), check=True) except subprocess.CalledProcessError: raise ValueError("D compilation error") subprocess.run(f'./{test_name}', check=True) finally: os.chdir(prev_cwd)
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examples/mnist_test_pipe.py
vfdev-5/fairscale
6
60905
<filename>examples/mnist_test_pipe.py # adapted from https://github.com/pytorch/examples/blob/master/mnist/main.py from __future__ import print_function import argparse import time import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import StepLR from torchvision import datasets, transforms from fairscale.nn import Pipe net = nn.Sequential( nn.Conv2d(1, 32, 3, 1), nn.ReLU(), nn.Conv2d(32, 64, 3, 1), nn.ReLU(), nn.MaxPool2d(kernel_size=2), nn.Dropout2d(0.25), nn.Flatten(1), nn.Linear(9216, 128), nn.ReLU(), nn.Dropout2d(0.5), nn.Linear(128, 10), nn.LogSoftmax(dim=1), ) def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output.to(device), target.to(device)) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print( "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( epoch, batch_idx * len(data), len(train_loader.dataset), 100.0 * batch_idx / len(train_loader), loss.item(), ) ) if args.dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output.to(device), target.to(device), reduction="sum").item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True).to(device) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print( "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset) ) ) def main(): # Training settings parser = argparse.ArgumentParser(description="PyTorch MNIST Example") parser.add_argument( "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)" ) parser.add_argument( "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)" ) parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)") parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)") parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)") parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass") parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)") parser.add_argument( "--log-interval", type=int, default=10, metavar="N", help="how many batches to wait before logging training status", ) parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model") args = parser.parse_args() torch.manual_seed(args.seed) kwargs = {"batch_size": args.batch_size} kwargs.update({"num_workers": 1, "pin_memory": True, "shuffle": True},) transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform) dataset2 = datasets.MNIST("../data", train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) model = net model = Pipe(model, balance=[6, 6], devices=[0, 1], chunks=2) device = model.devices[0] optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): tic = time.perf_counter() train(args, model, device, train_loader, optimizer, epoch) toc = time.perf_counter() print(f">>> TRANING Time {toc - tic:0.4f} seconds") tic = time.perf_counter() test(model, device, test_loader) toc = time.perf_counter() print(f">>> TESTING Time {toc - tic:0.4f} seconds") scheduler.step() if args.save_model: torch.save(model.state_dict(), "mnist_cnn.pt") if __name__ == "__main__": main()
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Site-Campus/sitecampus/migrations/0007_auto_20191121_1902.py
Benedito-Medeiros-Neto-UnB/TacProgWeb
1
1605940
<filename>Site-Campus/sitecampus/migrations/0007_auto_20191121_1902.py<gh_stars>1-10 # Generated by Django 2.2.7 on 2019-11-21 19:02 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('sitecampus', '0006_post_sutia'), ] operations = [ migrations.AlterField( model_name='autor', name='semestre', field=models.CharField(max_length=6), ), migrations.AlterField( model_name='post', name='semestrepub', field=models.CharField(max_length=6), ), ]
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src/simmate/database/base_data_types/thermodynamics.py
laurenmm/simmate-1
0
152181
<reponame>laurenmm/simmate-1 # -*- coding: utf-8 -*- from tqdm import tqdm from simmate.toolkit import Structure as ToolkitStructure from simmate.database.base_data_types import DatabaseTable, table_column from simmate.utilities import get_chemical_subsystems # , dask_batch_submit from pymatgen.analysis.phase_diagram import PDEntry from pymatgen.analysis.phase_diagram import PhaseDiagram class Thermodynamics(DatabaseTable): class Meta: abstract = True base_info = ["energy"] energy = table_column.FloatField(blank=True, null=True) """ The calculated total energy. Units are in eV. """ energy_per_atom = table_column.FloatField(blank=True, null=True) """ The `energy` divided by `nsites`. Units are in eV. """ # TODO: These columns below should be updated on a schedule. I'd run # a prefect flow every night that makes sure all values are up to date. energy_above_hull = table_column.FloatField(blank=True, null=True) """ The hull energy (aka "stability") of this structure compared to all other structures in this database table. Units are in eV. """ is_stable = table_column.BooleanField(blank=True, null=True) """ Whether `energy_above_hull` is 0 -- if so, this is considered stable. """ decomposes_to = table_column.JSONField(blank=True, null=True) """ If `energy_above_hull` is above 0, these are the compositions that the structure will decompose to (e.g. ["Y2C", "C", "YF3"]) """ formation_energy = table_column.FloatField(blank=True, null=True) """ The formation energy of the structure relative to all other structures in this database table. Units are in eV. """ formation_energy_per_atom = table_column.FloatField(blank=True, null=True) """ The `formation_energy` divided by `nsites`. """ # Other fields to consider # equilibrium_reaction_energy_per_atom # energy_uncertainy_per_atom # energy_per_atom_uncorrected # decompose_amount (to each decomp type) """ Properties """ # These are some extra fields to consider adding # energy_type = ... (DFT, machine-learned, LJ, etc.) # calc_types = (list) https://github.com/materialsproject/emmet/blob/main/emmet-core/emmet/core/vasp/calc_types/utils.py # psudeopotential # functional(PBE) # label(Y_sv) # pot_type(PAW) # type(GGA,GGAU,HF) # is_hubbard @classmethod def _from_toolkit( cls, structure: ToolkitStructure, energy: float = None, as_dict: bool = False, ): # TODO: should structure be optional? # Given energy, this function builds the rest of the required fields # for this class as an object (or as a dictionary). data = ( dict( energy=energy, energy_per_atom=energy / structure.num_sites, energy_above_hull=None, is_stable=None, decomposes_to=None, ) if energy else {} ) # If as_dict is false, we build this into an Object. Otherwise, just # return the dictionary return data if as_dict else cls(**data) @classmethod def update_chemical_system_stabilities(cls, chemical_system: str): # NOTE: I assume we are using a Child(Structure, Thermodynamics) # if we have a multi-element system, we need to include subsystems as # well. ex: Na --> Na, Cl, Na-Cl subsystems = get_chemical_subsystems(chemical_system) # grab all entries for this chemical system entries = ( cls.objects.filter( chemical_system__in=subsystems, ) .only("energy", "formula_full") .all() ) # convert to pymatgen PDEntries and build into PhaseDiagram object entries_pmg = [PDEntry(entry.formula_full, entry.energy) for entry in entries] phase_diagram = PhaseDiagram(entries_pmg) # now go through the entries and update stability values for entry, entry_pmg in zip(entries, entries_pmg): decomp, hull_energy = phase_diagram.get_decomp_and_e_above_hull(entry_pmg) entry.energy_above_hull = hull_energy entry.is_stable = True if hull_energy == 0 else False # OPTIMIZE: I would like this to point to another entry specifically # but this will take more work. entry.decomposes_to = ( [d.composition.formula for d in decomp] if hull_energy != 0 else [] ) entry.formation_energy = phase_diagram.get_form_energy(entry_pmg) entry.formation_energy_per_atom = phase_diagram.get_form_energy_per_atom( entry_pmg ) # Now that we updated our objects, we want to collectively update them cls.objects.bulk_update( objs=entries, fields=[ "energy_above_hull", "is_stable", "decomposes_to", "formation_energy", "formation_energy_per_atom", ], # updating extremely large systems (>3k structures) can cause this # to time-out and crash. We therefore update in batches of 500 batch_size=500, ) @classmethod def update_all_stabilities(cls): # grab all unique chemical systems chemical_systems = cls.objects.values_list( "chemical_system", flat=True ).distinct() # Now go through each and run the analysis. # OPTIMIZE: Some systems will be analyzed multiple times. For example, # C would be repeatedly updated through C, C-O, Y-C-F, etc. for chemical_system in tqdm(chemical_systems): try: cls.update_chemical_system_stabilities(chemical_system) except ValueError as exception: print(f"Failed for {chemical_system} with error: {exception}") # BUG: can't use parallel=True as an input # Because different systems may need to update a single one at the same # time, errors will be thrown due to row locking. 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cosmoboost/lib/KernelODE.py
maamari/CosmoBoost
0
87429
""" library containing differential equation solver for d=1 kernel elements """ __author__ = " <NAME>" __email__ = "<EMAIL>" import os import numpy as np from scipy.special import factorial import pdb np.seterr(divide='ignore', invalid='ignore') from scipy import special from scipy.integrate import odeint, solve_ivp from cosmoboost.lib import FileHandler as fh from cosmoboost.lib import MatrixHandler as mh from cosmoboost.lib.mytimer import timeit import logging logger = logging.getLogger(__name__) logger.setLevel(logging.WARN) # number of steps for returning the solution to the Differential Equation N = 2 # first and last def dK_deta(eta, Kstore, Bmatrix): '''The derivative of the Kernel for index m and L''' K_return = mh.shift_left(Bmatrix * Kstore) - Bmatrix * mh.shift_right(Kstore) return K_return def est_K_T_ODE(pars, save_kernel=True): '''constructs the kernel analytically using the unmarked equation on page 10 of Dai, Chluba 2014 arXiv:1403.6117v2 Parameters ---------- pars : dict dictionary of the kernel parameters save_kernel : bool, optional If True, the kernel elements will be saved to a file for later use Returns ------- K_T : 2D numpy array Each row corresponds to the (m,ell') index calculated with the getindx scheme in the file_handler . The rows correspond to different values of ell for a neighborhood of delta_ell around ell'. ''' # logger.info("rtol = {}\natol = {}".format(rtol, atol)) with timeit("Analytically determining the Doppler and aberration Kernel elements"): # ------------------------------ # set up parameters and matrices # ------------------------------ beta = pars['beta'] s = pars['s'] delta_ell = pars['delta_ell'] lmax = pars['lmax'] # initialize matrices file name from input parameters matrices_file_name = fh.get_matrices_filename(pars) # if they already exist, load them from disc if fh.file_exists(matrices_file_name): Mmatrix = fh.load_matrix(matrices_file_name, key='M') Lmatrix = fh.load_matrix(matrices_file_name, key='L') else: Mmatrix, Lmatrix = mh.get_ML_matrix(delta_ell=delta_ell, lmax=lmax) # construct the Bmatrix Blms, _ = mh.get_Blm_Clm(delta_ell, lmax, s=s) Bmatrix = Blms[Lmatrix, Mmatrix] Bmatrix[np.isnan(Bmatrix)] = 0 # construct delta_ell matrix dl = np.array([np.arange(delta_ell, -delta_ell - 1, -1)] * Bmatrix.shape[0]) # use J(v) function from scipy to analytically estimate K matrix eta = np.arctanh(beta) #pdb.set_trace() # use J(v) function from scipy to analytically estimate K matrix K_T = special.jv(dl, 2. * Bmatrix * eta) #K_T = analytical(dl, Bmatrix, eta) # ------------------------------ # save to file # ------------------------------ if save_kernel: save_KT2file(pars, K_T) return K_T def solve_K_T_ODE(pars, save_kernel=True, rtol=1.e-3, atol=1.e-6, mxstep=0): '''solves the ODE to find the temperature aberration kernel elements uses Eq. 44 in Dai, Chluba 2014 arXiv:1403.6117v2 Parameters ---------- pars : dict dictionary of the kernel parameters save_kernel : bool, optional If True, the kernel elements will be saved to a file for later use rtol, atol, mxstep: scalars passed to scipy.odeint to set precision Returns ------- K_T : 2D numpy array Each row corresponds to the (m,ell') index calculated with the getindx scheme in the file_handler . The rows correspond to different values of ell for a neighborhood of delta_ell around ell'. ''' logger.info("rtol = {}\natol = {}".format(rtol, atol)) with timeit("calculating the Doppler and aberration Kernel elements"): # ------------------------------ # set up parameters and matrices # ------------------------------ beta = pars['beta'] s = pars['s'] delta_ell = pars['delta_ell'] lmax = pars['lmax'] # lmin= pars['lmin'] # set the height and width of the aberration kernel storage matrix. # the storage matrix is set around each value of ell' for a neighborhood of delta_ell # on each side. The middle value of each row corresponds to ell'=ell or delta_ell=0 # the number of columns corresponds to different values of ell' for each m mode. height, width = ((lmax + 1) * (lmax + 2) // 2, 2 * delta_ell + 1) # initialize the K0 = dirac_delta(ell,ell') (initial condition for the ODE) K0 = np.zeros(width) K0[delta_ell] = 1 K0 = np.tensordot(np.ones(height), K0, axes=0) # initialize matrices file name from input parameters matrices_file_name = fh.get_matrices_filename(pars) # if they already exist, load them from disc if fh.file_exists(matrices_file_name): Mmatrix = fh.load_matrix(matrices_file_name, key='M') Lmatrix = fh.load_matrix(matrices_file_name, key='L') else: Mmatrix, Lmatrix = mh.get_ML_matrix(delta_ell=delta_ell, lmax=lmax) # construct the Bmatrix corresponding to the elements of K0 Blms, _ = mh.get_Blm_Clm(delta_ell, lmax, s=s) Bmatrix = Blms[Lmatrix, Mmatrix] Bmatrix[np.isnan(Bmatrix)] = 0 # (safety) pad K and B matrices to avoid leakage from the edges # necessary when using odeint solver # add two zeros to the end of each row # FIXME: is two enough for all ell? K0 = np.insert(K0, [2 * delta_ell + 1, 2 * delta_ell + 1], 0, axis=1) Bmatrix = np.insert(Bmatrix, [2 * delta_ell + 1, 2 * delta_ell + 1], 0, axis=1) # reshape all the 2D matrices to 1D arrays so that the ODE can be solved in vectorized mode K0 = K0.reshape((width + 2) * height) Bmatrix = Bmatrix.reshape((width + 2) * height) # ------------------------------ # solve ODE # ------------------------------ # initialize the eta = np.arctanh(beta) array for ODE iterations # the index (N-1) will give the final result eta = np.linspace(0, np.arctanh(beta), N) print("beta (v/c) : ", beta) print("eta (arctanh(beta)) : ", eta[-1]) # solve the ODE for a range of ell' between lmin=0 and lmax # dK_deta is the derivative of the aberration kernel with respect to eta is defined sol = solve_ivp(dK_deta, [eta[0],eta[-1]], K0, t_eval=[eta[0],eta[-1]], args=(Bmatrix,), rtol=rtol, atol=atol) sol = sol.y.T # store the results in the K_T matrix K_T = sol[N - 1].reshape(height, width + 2) # remove the zero padding from the final solution K_T = np.delete(K_T, [2 * delta_ell + 1, 2 * delta_ell + 2], axis=1) default_dell = int((lmax*0.00123*2)+4) if delta_ell>default_dell: default_dell = delta_ell-default_dell mask_pos = ((K_T[:,0] > 0.) & (K_T[:,0] < 1.e-5)) K_T[mask_pos,:default_dell] = 0. K_T[mask_pos,-default_dell:] = 0. mask_neg = ((K_T[:,0] < 0.) & (K_T[:,0] > -1.e-5)) K_T[mask_neg,:default_dell] = 0. K_T[mask_neg,-default_dell:] = 0. # ------------------------------ # save to file # ------------------------------ if save_kernel: save_KT2file(pars, K_T) return K_T def save_KT2file(pars, K_T): lmax = pars["lmax"] beta = pars["beta"] kernel_file_name = fh.get_kernel_filename(pars) dir_name = fh.dirname(lmax=lmax, beta=beta) print(f"dirname = {dir_name}") if not os.path.exists(dir_name): os.makedirs(dir_name) print(f"The following directory was created to save the kernel file:\n{dir_name}") # save the kernel to file # tag as D1 (Doppler weight =1) fh.save_kernel(kernel_file_name, K_T, 'D1') print(f"Kernel saved in:\n{kernel_file_name}")
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29911, 13, 13, 13, 1753, 4078, 29918, 29968, 29911, 29906, 1445, 29898, 862, 29879, 29892, 476, 29918, 29911, 1125, 13, 1678, 301, 3317, 353, 610, 29879, 3366, 29880, 3317, 3108, 13, 1678, 21762, 353, 610, 29879, 3366, 3571, 3108, 13, 13, 1678, 8466, 29918, 1445, 29918, 978, 353, 285, 29882, 29889, 657, 29918, 17460, 29918, 9507, 29898, 862, 29879, 29897, 13, 1678, 4516, 29918, 978, 353, 285, 29882, 29889, 25721, 29898, 29880, 3317, 29922, 29880, 3317, 29892, 21762, 29922, 3571, 29897, 13, 1678, 1596, 29898, 29888, 29908, 25721, 353, 426, 3972, 29918, 978, 27195, 13, 1678, 565, 451, 2897, 29889, 2084, 29889, 9933, 29898, 3972, 29918, 978, 1125, 13, 4706, 2897, 29889, 29885, 12535, 12935, 29898, 3972, 29918, 978, 29897, 13, 4706, 1596, 29898, 29888, 29908, 1576, 1494, 3884, 471, 2825, 304, 4078, 278, 8466, 934, 3583, 29876, 29912, 3972, 29918, 978, 27195, 13, 13, 1678, 396, 4078, 278, 8466, 304, 934, 13, 1678, 396, 4055, 408, 360, 29896, 313, 6132, 407, 1358, 7688, 353, 29896, 29897, 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sap/xssec/security_context.py
51acorsi/cloud-pysec
0
150607
<reponame>51acorsi/cloud-pysec<gh_stars>0 """ This module is deprecated. """ import warnings from sap.xssec import SecurityContextXSUAA warnings.warn("Class security_context.SecurityContext is deprecated, " "use security_context_xsuaa.SecurityContextXSUAA instead ", DeprecationWarning, stacklevel=2) SecurityContext = SecurityContextXSUAA
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geolocate/tests/test_parser.py
kayodebristol/geolocate
4
65151
<reponame>kayodebristol/geolocate """ test_logparser.py Programmed by: <NAME> email: <EMAIL> """ import io import sys import unittest import unittest.mock from collections import namedtuple import geolocate.classes.config as config import geolocate.classes.geowrapper as geoip import geolocate.classes.parser as parser import geolocate.tests.test_geowrapper as test_geowrapper import geolocate.tests.testing_tools as testing_tools WORKING_DIR = "./geolocate" TEST_STRING = """TRACEROUTE OUTPUT traceroute to www.google.com (172.16.17.32), 30 hops max, 60 byte packets 1 192.168.1.1 (1192.168.127.12) 2.923 ms 4.180 ms 4.092 ms 2 90.Red-80-58-67.staticIP.rima-tde.net (80.58.67.90) 7.713 ms 7.657 ms 7.601 ms 3 57.Red-80-58-76.staticIP.rima-tde.net (172.16.17.32) 11.990 ms 11.948 ms 13.062 ms 4 145.Red-80-58-86.staticIP.rima-tde.net (192.168.127.12) 9.630 ms 14.929 ms 14.872 ms 5 AE3-GRCMADJV1.red.telefonica-wholesale.net (172.16.58.3) 9.431 ms 9.337 ms 11.313 ms 6 192.168.127.12 (192.168.127.12) 12.499 ms 13.972 ms 15.852 ms 7 172.16.17.32 (172.16.17.32) 15.817 ms 15.798 ms 192.168.3.11 (192.168.3.11) 15.779 ms 8 192.168.3.11 (192.168.3.11) 55.590 ms 55.581 ms 55.550 ms 9 192.168.127.12 (192.168.127.12) 37.949 ms 37.937 ms 192.168.3.11 (192.168.3.11) 50.830 ms 10 172.16.17.32 (6192.168.127.12) 40.635 ms 37.871 ms 40.588 ms 11 par03s12-in-f19.1e100.net (172.16.17.32) 37.817 ms 40.528 ms 37.954 ms """ TEST_STRING_IP_ADDRESSES = ("172.16.17.32", "192.168.1.1", "172.16.58.3", "172.16.17.32", "192.168.127.12", "172.16.58.3", "192.168.127.12", "172.16.17.32", "192.168.3.11", "192.168.3.11", "192.168.127.12", "192.168.3.11", "172.16.17.32", "172.16.17.32") TEST_IP = "172.16.17.32" TEST_IP_FIELDS = { TEST_IP : {"continent_name": "North America", "country_name": "United States", "city_name": "Mountain View", "lat-long": "37.419200000000004, -122.0574"} } TEST_IP_LOCATION_STRINGS = { TEST_IP : {0: "172.16.17.32 [North America]", 1: "172.16.17.32 [North America | United States]", 2: "172.16.17.32 [North America | United States | " "Mountain View]", 3: "172.16.17.32 [North America | United States | " "Mountain View | 37.419200000000004, -122.0574]"} } TEST_IP_GEOLOCATION_STRINGS = { TEST_IP : {0: "[North America]", 1: "[North America | United States]", 2: "[North America | United States | " "Mountain View]", 3: "[North America | United States | " "Mountain View | 37.419200000000004, -122.0574]"} } locate_response = namedtuple("locate_response", "continent country" " city location") city_response = namedtuple("city", "name") country_response = namedtuple("country", "name") continent_response = namedtuple("continent", "name") location_response = namedtuple("location", "latitude longitude") MOCKED_LOCATE_RESPONSE = locate_response(continent_response("North America"), country_response("United States"), city_response("Mountain View"), location_response("37.419200000000004", "-122.0574")) MOCKED_LOCATE_RESPONSE_CONTINENT_UNKNOWN = locate_response(continent_response(None), country_response("Spain"), city_response("Madrid"), location_response("40", "-4")) MOCKED_LOCATE_RESPONSE_COUNTRY_UNKNOWN = locate_response(continent_response("Europe"), country_response(None), city_response("Madrid"), location_response("40", "-4")) MOCKED_LOCATE_RESPONSE_CITY_UNKNOWN = locate_response(continent_response("Europe"), country_response("Spain"), city_response(None), location_response("40", "-4")) MOCKED_LOCATE_RESPONSE_LATLONG_UNKNOWN = locate_response(continent_response("Europe"), country_response("Spain"), city_response("None"), location_response(None, None)) IP_NOT_FOUND_MESSAGE = "IP not found" MOCKED_RESULTS_VERBOSITY_0 = {"172.16.17.32": 'North America', "192.168.1.1": IP_NOT_FOUND_MESSAGE, "172.16.58.3": 'Europe', "172.16.17.32": 'Europe', "192.168.127.12": 'Europe', "172.16.58.3": 'Europe', "192.168.127.12": 'Europe', "172.16.17.32": 'North America', "192.168.3.11": 'North America', "192.168.3.11": 'North America', "192.168.127.12": 'North America', "192.168.3.11": 'North America', "172.16.17.32": 'North America', "172.16.17.32": 'North America'} CORRECT_RESULT_TEXT = """TRACEROUTE OUTPUT traceroute to www.google.com (172.16.17.32 [North America]), 30 hops max, 60 byte packets 1 192.168.1.1 [IP not found] (192.168.1.1 [IP not found]) 2.923 ms 4.180 ms 4.092 ms 2 90.Red-80-58-67.staticIP.rima-tde.net (172.16.58.3 [Europe]) 7.713 ms 7.657 ms 7.601 ms 3 57.Red-80-58-76.staticIP.rima-tde.net (172.16.17.32 [Europe]) 11.990 ms 11.948 ms 13.062 ms 4 145.Red-80-58-86.staticIP.rima-tde.net (192.168.127.12 [Europe]) 9.630 ms 14.929 ms 14.872 ms 5 AE3-GRCMADJV1.red.telefonica-wholesale.net (172.16.58.3 [Europe]) 9.431 ms 9.337 ms 11.313 ms 6 192.168.127.12 [Europe] (192.168.127.12 [Europe]) 12.499 ms 13.972 ms 15.852 ms 7 172.16.17.32 [North America] (172.16.31.100 [North America]) 15.817 ms 15.798 ms 192.168.3.11 [North America] (192.168.3.11 [North America]) 15.779 ms 8 192.168.3.11 [North America] (192.168.3.11 [North America]) 55.590 ms 55.581 ms 55.550 ms 9 192.168.127.12 [North America] (192.168.127.12 [North America]) 37.949 ms 37.937 ms 192.168.3.11 [North America] (192.168.3.11 [North America]) 50.830 ms 10 172.16.17.32 [North America] (172.16.17.32 [North America]) 40.635 ms 37.871 ms 40.588 ms 11 par03s12-in-f19.1e100.net (172.16.17.32 [North America]) 37.817 ms 40.528 ms 37.954 ms """ class TestParser(unittest.TestCase): @staticmethod def setUpClass(): TestInputReader._inject_to_stdin = TestInputReader._replace_stdin() @staticmethod def tearDownClass(): TestInputReader._restore_stdin() def test_GeolocateInputParser_next_stdin(self): """Check class is able to analyze input line by line and return lines with geodata strings included. """ with testing_tools.WorkingDirectoryChanged(WORKING_DIR): TestInputReader._inject_to_stdin(TEST_STRING) geoip_database = test_geowrapper._create_default_geoip_database() self._geolocate_input_parsing(0, geoip_database) def test_GeolocateInputParser_next_text(self): """Check class is able to analyze a text and return it with geodata strings included. """ with testing_tools.WorkingDirectoryChanged(WORKING_DIR): geoip_database = test_geowrapper._create_default_geoip_database() self._geolocate_input_parsing(0, geoip_database, TEST_STRING, "\n") def _geolocate_input_parsing(self, verbosity, geoip_database, text=None, join_char=""): if text is None: input_parser = parser.GeolocateInputParser(0, geoip_database) else: input_parser = parser.GeolocateInputParser(0, geoip_database, text) lines = [] for line in input_parser: lines.append(line) result_text = _rejoin_lines(lines, join_char) self.assertEqual(result_text, CORRECT_RESULT_TEXT, msg="Rebuilt text and test string doesn't match.\n" "Rebuilt text:\n {0}\n" "Test string:\n {1}".format(result_text, CORRECT_RESULT_TEXT)) @staticmethod def _mocked_locate_results(ip_address): if MOCKED_RESULTS_VERBOSITY_0[ip_address] == IP_NOT_FOUND_MESSAGE: raise geoip.IPNotFound(ip_address) else: mocked_location = {"continent_name": MOCKED_RESULTS_VERBOSITY_0[ip_address]} return mocked_location def test_GeolocateInputParser_locate(self): """Check locate() returns an string correctly formatted for every verbosity level. """ with testing_tools.WorkingDirectoryChanged(WORKING_DIR): verbosity_levels = parser.GeolocateInputParser.VERBOSITY_LEVELS ip_to_find = TEST_IP test_configuration = config.Configuration() geoip_database = geoip.load_geoip_database(test_configuration) for verbosity in verbosity_levels: input_parser = parser.GeolocateInputParser(verbosity, geoip_database) # ## TODO: Remove Mock when _geoip_database.locate is implemented. # input_parser._geoip_database.locate = unittest.mock.MagicMock( # return_value=MOCKED_LOCATE_RESPONSE) location_string = input_parser._locate(ip_to_find) self.assertEqual(location_string, TEST_IP_LOCATION_STRINGS[TEST_IP][verbosity]) def test_GeolocateInputParser_format_location_string(self): """Check format_location_string() returns an string correctly formatted for every verbosity level. """ verbosity_levels = parser.GeolocateInputParser.VERBOSITY_LEVELS for verbosity in verbosity_levels: input_parser = parser.GeolocateInputParser(verbosity, None) location_string = input_parser._format_location_string( MOCKED_LOCATE_RESPONSE) self.assertEqual(location_string, TEST_IP_GEOLOCATION_STRINGS[TEST_IP][verbosity]) def test_find_ips_in_text(self): """Check all embedded IP addresses are found.""" returned_IP_addresses = set() test_text_lines = TEST_STRING.splitlines() test_ip_addresses = set(TEST_STRING_IP_ADDRESSES) for line in test_text_lines: line_IP_addresses = parser._find_ips_in_text(line) returned_IP_addresses.update(line_IP_addresses) self.assertEqual(returned_IP_addresses, test_ip_addresses) def test_include_location_in_line(self): """Check IP addresses are properly replaced with geodata string.""" line = ("traceroute to www.google.com (172.16.17.32), 30 hops max, " "60 byte packets") ip = "172.16.17.32" location = "172.16.17.32 [North America | United States]" correct_line = ("traceroute to www.google.com (172.16.17.32 " "[North America | United States]), 30 hops max, " "60 byte packets") returned_line = parser._include_location_in_line(line, ip, location) self.assertEqual(returned_line, correct_line, msg="Rebuilt text and test string doesn't match.\n" "Rebuilt text:\n {0}\n" "Test string:\n {1}".format(returned_line, correct_line)) def test_get_lines(self): """Check text lines are correctly retrieved.""" text = "Locate this: 172.16.31.10 \n and this too: 172.16.17.32" lines = ["Locate this: 172.16.31.10 ", " and this too: 172.16.17.32"] line_generator = parser._get_lines(text) obtained_line_list = [line for line in line_generator] self.assertEqual(lines, obtained_line_list) def test_find_unknowns(self): """ Check unknowns attributes are correctly translated to informational strings. """ location_strings = parser._find_unknowns(MOCKED_LOCATE_RESPONSE_CONTINENT_UNKNOWN) self.assertEqual(location_strings["continent_name"], "Unknown continent") self.assertEqual(location_strings["country_name"], "Spain") location_strings = parser._find_unknowns(MOCKED_LOCATE_RESPONSE_COUNTRY_UNKNOWN) self.assertEqual(location_strings["country_name"], "Unknown country") self.assertEqual(location_strings["continent_name"], "Europe") location_strings = parser._find_unknowns(MOCKED_LOCATE_RESPONSE_CITY_UNKNOWN) self.assertEqual(location_strings["city_name"], "Unknown city") self.assertEqual(location_strings["lat-long"]["latitude"], "40") self.assertEqual(location_strings["lat-long"]["longitude"], "-4") location_strings = parser._find_unknowns(MOCKED_LOCATE_RESPONSE_LATLONG_UNKNOWN) self.assertEqual(location_strings["lat-long"]["latitude"], "Unknown latitude") self.assertEqual(location_strings["lat-long"]["longitude"], "Unknown longitude") class TestInputReader(unittest.TestCase): _inject_to_stdin = None _old_stdin = None @classmethod def setUpClass(cls): cls._inject_to_stdin = cls._replace_stdin() @classmethod def _replace_stdin(cls): cls._old_stdin = sys.stdin def _inject_string(string): sys.stdin = io.StringIO(string) return _inject_string @classmethod def tearDownClass(cls): cls._restore_stdin() @classmethod def _restore_stdin(cls): sys.stdin = cls._old_stdin def test_InputReader(self): """Inject text in stdin and compare what is read from there to check it is the same. """ self.__class__._inject_to_stdin(TEST_STRING) input_reader = parser.InputReader() lines = _read_stdin(input_reader) rebuilt_text = _rejoin_lines(lines) self.assertEqual(rebuilt_text, TEST_STRING, msg="Rebuilt text and test string doesn't match.\n" "Rebuilt text:\n {0}\n" "Test string:\n {1}".format(rebuilt_text, TEST_STRING)) def _read_stdin(input_reader): lines_read = (line for line in input_reader) return lines_read def _rejoin_lines(lines, join_char=""): rebuilt_text = join_char.join(lines) return rebuilt_text
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29918, 726, 13, 2 ]
Week 1/id_165/LeetCode_283_165.py
larryRishi/algorithm004-05
1
178333
#-*- encoding: utf-8 -*- # 给定一个数组 nums，编写一个函数将所有 0 移动到数组的末尾，同时保持非零元素的相对顺序。 # # 示例: # # 输入: [0,1,0,3,12] # 输出: [1,3,12,0,0] # # 说明: # # # 必须在原数组上操作，不能拷贝额外的数组。 # 尽量减少操作次数。 # # Related Topics 数组 双指针 # leetcode submit region begin(Prohibit modification and deletion) # class Solution(object): # def moveZeroes(self, nums): # """ # :type nums: List[int] # :rtype: None Do not return anything, modify nums in-place instead. # """ # # j = 0 # # for idx, d in enumerate(nums): # # if d != 0: # nums[j] = nums[idx] # if j != idx: # nums[idx] = 0 # j只有在d!=0时才移动，d=0时不动，所以d其实就是指向0 # j += 1 # class Solution(object): def moveZeroes(self, nums): """ :type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead. """ zeroCount = 0 for idx, d in enumerate(nums): if d == 0: zeroCount += 1 elif zeroCount != 0: nums[idx - zeroCount] = d nums[idx] = 0 class Solution2(object): def moveZeroes(self, nums): """ :type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead. """ zeroP = -1 for idx , data in enumerate(nums): if data == 0 and zeroP == -1: zeroP = idx elif data != 0 and zeroP > -1: nums[zeroP] = data nums[idx] = 0 zeroP += 1 if data[zeroP] != 0: zeroP = -1 # leetcode submit region end(Prohibit modification and deletion)
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nhoods/profile_builder.py
MSLADevServGIS/NhoodProfiles
0
148602
#!/usr/bin/env python # -*- coding: utf-8 -*- """ Neighborhood Profiler Script <NAME>; Feb-April 2016 This script uses an mxd map template to query neighborhood information, places that info into an HTML template, and finally kicks those HTML files out to PDF. This script relies on up-to-date data that is managed manually and is described in the NhoodProfile_Documentation.docx file. Simply double-click the script file to run. """ # TODO: better error handling from __future__ import print_function import os import sys from datetime import datetime from glob import glob from re import sub from time import sleep # import arcpy # -- moved due to issues with datetime, see below import pandas as pd from jinja2 import Environment, FileSystemLoader # Custom libs from tkit import cli sys.path.insert(0, r"\\cityfiles\DEVServices\WallyG\Scripts\conversion") import pdftools # noqa # Print part of the info message above print(__doc__.split("\n\n")[0]) print("\nExecuting...\n") status = cli.StatusLine() # ============================================================================= # PARAMETERS status.write("Setting parameters...") # Project directory nhood_dir = r"\\cityfiles\DEVServices\WallyG\projects\NhoodProfiles\nhoods" # Template names (MUST be saved in "templates" folder) profile_template = "profile_template.html" map_template = "nhood_template.mxd" wards_dir = os.path.join(nhood_dir, "data", "WardReps.csv") guides_dir = os.path.join(nhood_dir, "data", "GuidingDocs.xlsx") desc_dir = os.path.join(nhood_dir, "descriptions") profile_dir = os.path.join(nhood_dir, "profiles") template_dir = os.path.join(nhood_dir, "templates") _all_dirs = [nhood_dir, wards_dir, desc_dir, profile_dir, template_dir] # Testing vars OUTPUT_PDF = True # Default True OVERWRITE_PDF = False # Default False # =============== DON'T TOUCH =============== # arcpy botches up the datetime module import year = datetime.now().year import arcpy # noqa # =========================================== status.success() # ============================================================================= # FUNCTIONS def query_assets(layer_name, name_field): """Gets names of features that "INTERSECT" nhood_buffers. Args: layer_name (str): the name of the layer to be queried. name_field (str): field name of feature containing name attributes. Returns: A string of names seperated by an HTML break <br> (newline). """ arcpy.SelectLayerByLocation_management( layers[layer_name], "INTERSECT", layers["nhood_buffers"], "", "NEW_SELECTION") assets = set([n[0] for n in arcpy.da.SearchCursor(layers[layer_name], name_field)]) arcpy.SelectLayerByAttribute_management(layers[layer_name], "CLEAR_SELECTION") if None in assets: assets.remove(None) if "" in assets: assets.remove("") if not assets: return "None" assets = list(assets) assets.sort() return "<br>".join(assets) def query_nhood(field): """Queries attributes of the neighborhood feature. Args: field (str): the field name of attributes to be queried. Returns a string-ified list. """ return str([row[0] for row in arcpy.da.SearchCursor(layers["Nhoods"], field)][0]) def get_trail_mi(): """Queries the trail miles within a neighborhood. Manually pre-process trail data: 1) Get trails from City of Missoula Parks 2) Run intersect on the 'trails' and 'nhoods' with the output 'Line' 3) Add that output data to the database and map template 4) Re-calculate trail length in Miles """ arcpy.SelectLayerByLocation_management( layers["Trails"], "WITHIN", layers["Nhoods"], "", "NEW_SELECTION") mi = round(sum([float(row[0]) for row in arcpy.da.SearchCursor(layers["Trails"], "trail_miles")]), 1) arcpy.SelectLayerByAttribute_management(layers["Trails"], "CLEAR_SELECTION") return mi def get_guidedocs(nhood_name): """Looks for neighborhood names in the open GuideDocs spreadsheet.""" # Format for linking the plan name to the plan document on the web url = '<a href="{1}" title="{0}">{0}</a>' ndocs = [] for doc, nhoods in guide_docs.iteritems(): if nhood_name in list(nhoods): if urls[doc] is not pd.np.nan: ndocs.append(url.format(doc, urls[doc])) else: ndocs.append(doc) if not ndocs: return "None" return "<br>".join(ndocs) def get_population(): """Queries the census block population within the neighborhood.""" arcpy.SelectLayerByLocation_management( layers["Blocks"], "HAVE_THEIR_CENTER_IN", layers["Nhoods"], "", "NEW_SELECTION") pop = int(sum([float(row[0]) for row in arcpy.da.SearchCursor(layers["Blocks"], "TOTAL_POP.D001")])) arcpy.SelectLayerByAttribute_management(layers["Blocks"], "CLEAR_SELECTION") return pop def get_new_population(): """Queries permit_blocks layer (population form permit data).""" arcpy.SelectLayerByLocation_management( layers["permit_blocks"], "HAVE_THEIR_CENTER_IN", layers["Nhoods"], "", "NEW_SELECTION") new_pop = int(sum([float(row[0]) for row in arcpy.da.SearchCursor(layers["permit_blocks"], "new_pop")])) arcpy.SelectLayerByAttribute_management(layers["permit_blocks"], "CLEAR_SELECTION") return new_pop def sum_field(layer_name, field_name): """Sums a field.""" arcpy.SelectLayerByLocation_management( layers[layer_name], "HAVE_THEIR_CENTER_IN", layers["Nhoods"], "", "NEW_SELECTION") field_sum = sum([float(row[0]) for row in arcpy.da.SearchCursor(layers[layer_name], field_name)]) arcpy.SelectLayerByAttribute_management(layers[layer_name], "CLEAR_SELECTION") return field_sum def clean_name(n): """Removes characters from neighborhood names.""" return sub("&|/", "", n.replace(" ", "_")) def get_desc(nhood_name): """Gets the neighborhood extent description from the nhood's text file. Args: nhood_name (str): the name of the neighborhood. Returns the nhood's description. """ txt_file = clean_name(nhood_name) + ".txt" desc_file = os.path.join(desc_dir, txt_file) if not os.path.exists(desc_file): return "None" with open(desc_file, "r") as f: desc = f.readline() return desc def get_reps(nhood): """Queries the WardReps.csv file for the Ward(s) and Reps for each nhood. """ wards = [] reps = pd.read_csv(wards_dir).to_dict() for key, value in reps.items(): if nhood in value.values(): wards.append(key) wards.sort() return "<br>".join(wards) def get_data(nhood_name): """Runs all queries for a single neighborhood by name. Returns dict. Args: nhood_name (str): neighborhood name. Returns dictionary of data collected from queries. """ data = {} data["neighborhood_name"] = nhood_name arcpy.SelectLayerByAttribute_management( layers["Nhoods"], "NEW_SELECTION", "Name = '{}'".format(nhood_name)) arcpy.SelectLayerByAttribute_management( layers["nhood_buffers"], "NEW_SELECTION", "Name = '{}'".format(nhood_name)) data["loc_desc"] = get_desc(nhood_name) data["date_est"] = query_nhood("Year_Created") data["area"] = round(float(query_nhood("Acres")), 1) data["council_reps"] = get_reps(nhood_name) data["parks"] = query_assets("ParksAndCommons", "Name") data["park_acres"] = round(sum_field("ParksAndCommons", "Acres"), 1) data["trail_mi"] = get_trail_mi() data["pub_fac"] = query_assets("PublicFacilities", "FACILITY_NAME") data["schools"] = query_assets("Schools", "School") data["groceries"] = query_assets("SuperMarkets", "STORE_NAME") data["hist_res"] = query_assets("HistoricSites", "Name") data["current_year"] = year data["pop10"] = get_population() data["pop_current"] = get_new_population() + data["pop10"] # NEW data["house10"] = sum_field("ORION_2014", "LIVUNITS") # 2014 data["house_current"] = data["house10"] + sum_field("permit_blocks", "dwellings") data["new_dev"] = "PENDING" # TODO: data["guide_docs"] = get_guidedocs(nhood_name) return data def make_profile(data): """Converts dict of queried data to HTML and outputs an HTML profile. Args: data (dict): dictionary of data collected in queries. Returns None. """ html_out = template.render(data) outfile = os.path.join(profile_dir, clean_name(data["neighborhood_name"]) + ".html") with open(outfile, "wb") as f: f.write(html_out) return # ============================================================================= # RUN IT if __name__ == '__main__': # Check that directories exist status.write("Checking environments...") try: if not all([os.path.exists(_dir) for _dir in _all_dirs]): raise IOError("Directory does not exist: {}".format(_dir)) # Prevent any possibility of overwriting the PDFs if glob(os.path.join(profile_dir, "*.pdf")) and not OVERWRITE_PDF: raise IOError("PDF Overwrite Disabled") sleep(1) status.success() except IOError as e: status.failure() raw_input(e.message) #cli.GetError(wait=True) sys.exit(1) # Load HTML and Map templates status.write("Loading templates...") try: # Set working directory to "templating" folder os.chdir(template_dir) # Set working directory / jinja2 environment env = Environment(loader=FileSystemLoader(template_dir)) # Open template html template file template = env.get_template(profile_template) # Map (.mxd) and dataframe data mxd = arcpy.mapping.MapDocument(map_template) df = arcpy.mapping.ListDataFrames(mxd)[0] status.success() except Exception as e: status.failure() raw_input(e.message) #cli.GetError(wait=True) sys.exit(1) # Make dictionary of layers in mxd template status.write("Loading map layers...") try: layers = {layer.name: layer for layer in arcpy.mapping.ListLayers(df)} status.success() except Exception as e: status.failure() raw_input(e.message) #cli.GetError(wait=True) sys.exit(1) # List neighborhood names status.write("Loading neighborhood names...") try: nhood_names = [n[0] for n in arcpy.da.SearchCursor(layers["Nhoods"], "NAME")] status.success() except Exception as e: status.failure() raw_input(e.message) #cli.GetError(wait=True) sys.exit(1) # Load misc data status.write("Loading other datasets...") try: guide_docs = pd.read_excel(guides_dir, sheetname="Main") links = pd.read_excel(guides_dir, "PlanList") urls = {v[0]: v[2] for k, v in links.iterrows()} status.success() except Exception as e: status.failure() raw_input(e.message) #cli.GetError(wait=True) sys.exit(1) # Make profiles status.write("Beginning profile generation...") try: # List comprehension to make all profiles [make_profile(get_data(nhood)) for nhood in set(nhood_names)] status.success() except Exception as e: status.failure() print(cli.GetError(wait=True)) raw_input() sys.exit(1) # Convert all HTML profiles into PDFs if OUTPUT_PDF: status.write("Exporting to pdf...") try: # Convert all profiles to PDF # TODO: Change? [pdftools.to_pdf(os.path.join(profile_dir, f)) for f in os.listdir(profile_dir)] status.success() except Exception as e: status.failure() raw_input(e.message) #cli.GetError(wait=True) sys.exit(1) # TODO: push pdfs & html files to separate folders on FTP print("\n") status.custom("Complete.", "cyan", wait=True)
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4660, 29889, 14057, 545, 580, 13, 4706, 10650, 29918, 2080, 29898, 29872, 29889, 4906, 29897, 29871, 396, 11303, 29889, 2577, 2392, 29898, 10685, 29922, 5574, 29897, 13, 4706, 10876, 29889, 13322, 29898, 29896, 29897, 13, 13, 1678, 396, 2391, 18403, 2983, 13, 1678, 4660, 29889, 3539, 703, 23456, 18403, 2983, 856, 1159, 13, 1678, 1018, 29901, 13, 4706, 302, 6614, 29918, 7039, 353, 518, 29876, 29961, 29900, 29962, 363, 302, 297, 15232, 2272, 29889, 1388, 29889, 7974, 19890, 29898, 29277, 3366, 29940, 6614, 29879, 12436, 13, 462, 462, 462, 965, 376, 5813, 13531, 13, 4706, 4660, 29889, 8698, 580, 13, 1678, 5174, 8960, 408, 321, 29901, 13, 4706, 4660, 29889, 14057, 545, 580, 13, 4706, 10650, 29918, 2080, 29898, 29872, 29889, 4906, 29897, 29871, 396, 11303, 29889, 2577, 2392, 29898, 10685, 29922, 5574, 29897, 13, 4706, 10876, 29889, 13322, 29898, 29896, 29897, 13, 13, 1678, 396, 16012, 3984, 29883, 848, 13, 1678, 4660, 29889, 3539, 703, 23456, 916, 20035, 856, 1159, 13, 1678, 1018, 29901, 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11303, 29889, 2577, 2392, 29898, 10685, 29922, 5574, 29897, 13, 9651, 10876, 29889, 13322, 29898, 29896, 29897, 13, 13, 1678, 396, 14402, 29901, 5503, 13552, 29879, 669, 3472, 2066, 304, 5004, 16495, 373, 383, 3557, 13, 13, 1678, 1596, 14182, 29876, 1159, 13, 1678, 4660, 29889, 6341, 703, 17813, 19602, 376, 1270, 273, 613, 4480, 29922, 5574, 29897, 13, 2 ]
recommendations/kujikatsu/249/c.py
takelifetime/competitive-programming
0
28486
a, b, n = map(int, input().split()) x = min(b - 1, n) print(a * x // b - a * (x // b))
[ 1, 263, 29892, 289, 29892, 302, 353, 2910, 29898, 524, 29892, 1881, 2141, 5451, 3101, 13, 29916, 353, 1375, 29898, 29890, 448, 29871, 29896, 29892, 302, 29897, 13, 2158, 29898, 29874, 334, 921, 849, 289, 448, 263, 334, 313, 29916, 849, 289, 876, 2 ]
vcf/old/read_test.py
TuanjieNew/jubilant-fiesta
0
1600930
#!/usr/bin/env python # read_test.py FILE = open('wanrenInfo_snp.txt', 'r') DICFILE = open('/haplox/users/huanghuiqiang/project/wanren/VCF/vcf/caobo.final.snp.hg19_multianno.vcf', 'r') dic = {} for line in DICFILE: line = line.strip('\n') ln_ls = line.split('\t') if line[0] == '#': continue chr_pos = ln_ls[0]+'_'+str(ln_ls[1]) dic[chr_pos] = 'test' for line in FILE: count = 0 line = line.strip('\n') ln_ls = line.split('\t') if line[0] == '#': continue chrom_pos = ln_ls[0] if chrom_pos in dic.keys(): count+=1
[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 1303, 29918, 1688, 29889, 2272, 13, 13, 7724, 353, 1722, 877, 11440, 1267, 3401, 29918, 29879, 9302, 29889, 3945, 742, 525, 29878, 1495, 13, 29928, 2965, 7724, 353, 1722, 11219, 29882, 481, 417, 29916, 29914, 7193, 29914, 6905, 574, 15669, 26461, 574, 29914, 4836, 29914, 11440, 1267, 29914, 8257, 29943, 29914, 29894, 6854, 29914, 1113, 711, 29877, 29889, 8394, 29889, 29879, 9302, 29889, 29882, 29887, 29896, 29929, 29918, 4713, 713, 1217, 29889, 29894, 6854, 742, 525, 29878, 1495, 13, 27774, 353, 6571, 13, 1454, 1196, 297, 360, 2965, 7724, 29901, 13, 1678, 1196, 353, 1196, 29889, 17010, 28909, 29876, 1495, 13, 1678, 301, 29876, 29918, 3137, 353, 1196, 29889, 5451, 28909, 29873, 1495, 13, 1678, 565, 1196, 29961, 29900, 29962, 1275, 16321, 2396, 13, 4706, 6773, 13, 1678, 18460, 29918, 1066, 353, 301, 29876, 29918, 3137, 29961, 29900, 10062, 15972, 18717, 710, 29898, 3083, 29918, 3137, 29961, 29896, 2314, 13, 1678, 12124, 29961, 22495, 29918, 1066, 29962, 353, 525, 1688, 29915, 13, 13, 13, 1454, 1196, 297, 24080, 29901, 13, 1678, 2302, 353, 29871, 29900, 13, 1678, 1196, 353, 1196, 29889, 17010, 28909, 29876, 1495, 13, 1678, 301, 29876, 29918, 3137, 353, 1196, 29889, 5451, 28909, 29873, 1495, 13, 1678, 565, 1196, 29961, 29900, 29962, 1275, 16321, 2396, 13, 4706, 6773, 13, 1678, 25173, 29918, 1066, 353, 301, 29876, 29918, 3137, 29961, 29900, 29962, 13, 1678, 565, 25173, 29918, 1066, 297, 12124, 29889, 8149, 7295, 13, 4706, 2302, 23661, 29896, 13, 268, 13, 268, 13, 2 ]
Examples And Benchmarks/HTTP over Raw Connection/SERVERS/http_server__http_tools.py
FI-Mihej/FI-ASockIOCore
0
12958
from simple_network.tcp_app_server import * import httptools """ Module Docstring Docstrings: http://www.python.org/dev/peps/pep-0257/ """ __author__ = 'ButenkoMS <<EMAIL>>' # ====================================================================== # ===================GLOBAL SETTINGS FOR ALL TESTS====================== # SERVER_KEYWORD = b'http server inline' SERVER_ADDRESS = ('localhost', 25000) BSC__USE_READ_WITH_FIXED_BUFFER = True # "Optimized for speed". Good for Named Clients. # BSC__USE_READ_WITH_FIXED_BUFFER = False # "Optimized for memory". Good for big amount of Unknown Clients (raw, # http, etc.) if you have small server. BSC__SOCKET_READ_FIXED_BUFFER_SIZE = 1024 ** 2 BSC__USE_NODELAY_INET = True BSC__REUSE_GATE_ADDR = True BSC__REUSE_GATE_PORT = True LINE_TRACE_ALLOWED = True # # ===================GLOBAL SETTINGS FOR ALL TESTS====================== # ====================================================================== class RawClientCheckerAllRaw(CheckIsRawConnection): def __call__(self, app_server: ASockIOCore, client_info: Connection): return True def run_http_server(): io_iteration_timeout = 0.5 # ADD SERVER GATE CONNECTIONS set_of_tcp_settings = set() tcp_settings = ConnectionSettings(ConnectionDirectionRole.server, SERVER_ADDRESS, SERVER_KEYWORD) set_of_tcp_settings.add(tcp_settings) # CREATE SERVER http_server = ASockIOCore(set_of_tcp_settings) # SET SERVER SETTINGS http_server.raw_checker_for_new_incoming_connections = RawClientCheckerAllRaw() http_server.unknown_clients_are_allowed = True http_server.should_get_client_addr_info_on_connection = False http_server.use_speed_optimized_socket_read = BSC__USE_READ_WITH_FIXED_BUFFER http_server.socket_read_fixed_buffer_size.result = BSC__SOCKET_READ_FIXED_BUFFER_SIZE http_server.use_nodelay_inet = BSC__USE_NODELAY_INET http_server.reuse_gate_addr = BSC__REUSE_GATE_ADDR http_server.reuse_gate_port = BSC__REUSE_GATE_PORT # START SERVER with asock_io_core_connect(http_server, True, backlog=1000) as server: http_server.need_to_auto_check_incoming_raw_connection = True clients_per_transport_id = dict() # RUN SERVER LOOP while True: io_iteration_result = server.io_iteration(io_iteration_timeout) # CLIENT CONNECTED for another_client_id in io_iteration_result.newly_connected_unknown_clients: clients_per_transport_id[another_client_id] = HttpClientData(another_client_id, server) # CLIENT HAVE DATA TO READ for another_client_id in io_iteration_result.clients_have_data_to_read: clients_per_transport_id[another_client_id].data_received() # CLIENT CLOSED for another_client_id in io_iteration_result.clients_with_disconnected_connection: if clients_per_transport_id[another_client_id].socket_error(): del clients_per_transport_id[another_client_id] print('Server had been Shut Down.') # ============================================================================================================== # !!!!! IMPORTANT !!!!! # NEXT CODE SHOULD BE EQUIVALENT TO ASYNCIO HTTP SERVER'S CODE FROM "https://github.com/MagicStack/vmbench" PROJECT # (BENCHMARKING TOOL FROM 'UVLOOP' DEVELOPERS) FOR FAIR COMPARISON, SO IT'S SO DIRTY. # (IT'S ALMOST EQUIVALENT: IT DOES NOT HAVE FEW CRITICAL vmbench's BUGS) _RESP_CACHE = {} class HttpRequest: __slots__ = ('_protocol', '_url', '_headers', '_version') def __init__(self, protocol, url, headers, version): self._protocol = protocol self._url = url self._headers = headers self._version = version class HttpResponse: __slots__ = ('_protocol', '_request', '_headers_sent') def __init__(self, protocol, request: HttpRequest): self._protocol = protocol self._request = request self._headers_sent = False def write(self, data): self._protocol.output_list.append(b''.join([ 'HTTP/{} 200 OK\r\n'.format( self._request._version).encode('latin-1'), b'Content-Type: text/plain\r\n', 'Content-Length: {}\r\n'.format(len(data)).encode('latin-1'), b'\r\n', data ])) class HttpClientData: __slots__ = ('server', 'output_list', 'transport_id', '_current_request', '_current_parser', '_current_url', '_current_headers', '_last_piece_of_data', '_previous_piece_of_data') def __init__(self, transport_id, server: ASockIOCore): self.server = server self.transport_id = transport_id self.output_list = list() self._current_parser = httptools.HttpRequestParser(self) self._current_headers = list() self._current_request = None self._current_url = None self._last_piece_of_data = None self._previous_piece_of_data = None def data_received(self): try: for message in self.server.get_messages_from_client(self.transport_id): # print('IN {}: {}'.format(self.transport_id, bytes(message))) self._current_parser.feed_data(message) self.server.send_messages_to_client(self.transport_id, self.output_list) except Exception as err: print('EXCEPTION:', err) self.server.mark_client_connection_as_should_be_closed_immediately(self.transport_id, False) # raise err del self.output_list[:] # self.output_list.clear() def socket_error(self): self._current_request = self._current_parser = None self.server.remove_client(self.transport_id) return True # ============================================= # ==== BEGIN of HttpRequestParser methods: ==== # def on_message_begin(self): # pass def on_url(self, url): if self._current_url: self._current_url += url else: self._current_url = url # def on_status(self, data): # pass def on_header(self, name, value): self._current_headers.append((name, value)) def on_headers_complete(self): try: self._current_request = HttpRequest( self, self._current_url, self._current_headers, self._current_parser.get_http_version()) self.handle(self._current_request, HttpResponse(self, self._current_request)) except: print('ON HEADERS COMPLETE. ID: {}. Last: {}. Previous : {}.'.format( self.transport_id, self._last_piece_of_data, self._previous_piece_of_data)) raise # def on_body(self, data): # pass # def on_message_complete(self): # pass # def on_chunk_header(self): # pass # def on_chunk_complete(self): # pass # ==== END of HttpRequestParser methods==== # ========================================= def handle(self, request, response: HttpResponse): parsed_url = httptools.parse_url(self._current_url) payload_size = parsed_url.path.decode('ascii')[1:] if not payload_size: payload_size = 1024 else: payload_size = int(payload_size) resp = _RESP_CACHE.get(payload_size) if resp is None: resp = b'X' * payload_size _RESP_CACHE[payload_size] = resp response.write(resp) self._current_request = None self._current_url = None self._current_headers = list() # print('KEEP ALIVE:', self._current_parser.should_keep_alive()) if not self._current_parser.should_keep_alive(): self.server.mark_client_connection_as_ready_to_be_closed(self.transport_id, False) if __name__ == '__main__': run_http_server()
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MYONN/myonn.py
bfelder/MYONN
3
144319
#!/usr/bin/env python3 import csv import datetime import os import shelve from time import time import matplotlib.pyplot as plt import numpy as np from scipy.special import expit as activation_function # sigmoid function from scipy.special import logit as inverse_activation_function import wget # CONFIG DATA_DIR = "data" DATA_FILE = "data.db" # for statistics # DEBUG = True will use smaller dataset, single run and show pictures DEBUG = False INPUT_NODES = 28**2 OUTPUT_NODES = 10 DATA_DIR = "test_data/" TRAIN_FILE = os.path.join(DATA_DIR, "mnist_train.csv") TEST_FILE = os.path.join(DATA_DIR, "mnist_test.csv") TRAINFILE_URL = "http://www.pjreddie.com/media/files/mnist_train.csv" TESTFILE_URL = "http://www.pjreddie.com/media/files/mnist_test.csv" def reversed_enumerate(sequence): return zip( reversed(range(len(sequence))), reversed(sequence), ) class NeuralNetwork: def __init__(self, hidden_layers, input_nodes, hidden_nodes, output_nodes, learning_rate): """ set number of nodes in each input, hidden, output layer """ self.lr = learning_rate # defining weights # input to hidden layer l_ih = self.__init_weights(input_nodes, hidden_nodes) # hidden to hidden layer l_hh = self.__init_weights(hidden_nodes, hidden_nodes) # hidden to output layer l_ho = self.__init_weights(hidden_nodes, output_nodes) self.layer_weights = [l_ih] for i in range(0, hidden_layers - 1): self.layer_weights.append(l_hh) self.layer_weights.append(l_ho) # closing with output layer def __init_weights(self, this_layer, next_layer): return np.random.normal(0.0, pow(next_layer, -0.5), (next_layer, this_layer)) def __calculate_outputs(self, weights, inputs): # Step 1 - calculate signals into hidden layer weighted_inputs = np.dot(weights, inputs) # calculate the signals emerging from hidden layer outputs = activation_function(weighted_inputs) return outputs def __update_weights(self, this_errors, this_outputs, previous_outputs): x = this_errors * this_outputs * (1.0 - this_outputs) updated_weights = self.lr * np.dot(x, np.transpose(previous_outputs)) return updated_weights def __propagate(self, inputs_list): # convert inputs list to 2d array inputs = np.array(inputs_list, ndmin=2).T # initialize with inputs, as "outputs from picture" outputs_list = [inputs] for i, weights in enumerate(self.layer_weights): this_output = self.__calculate_outputs(weights, outputs_list[i]) outputs_list.append(this_output) return outputs_list def train(self, inputs_list, targets_list): outputs_list = self.__propagate(inputs_list) # transpose the inputs list to a vertical array targets = np.array(targets_list, ndmin=2).T # backpropagate new_weights_rev = [] this_errors = targets - outputs_list[-1] # starting with output_errors for i, weights in reversed_enumerate(self.layer_weights): # print(i) # for weights, outputs in reversed(list(zip(self.layer_weights, outputs_list))): w_delta = self.__update_weights(this_errors, outputs_list[i + 1], outputs_list[i]) new_weight = weights + w_delta new_weights_rev.append(new_weight) this_errors = np.dot(weights.T, this_errors) # update weights self.layer_weights = new_weights_rev[::-1] # must be reversed def query(self, inputs_list): output_list = self.__propagate(inputs_list) return output_list[-1] # return final outputs # backquery the neural network # we'll use the same termnimology to each item, # eg target are the values at the right of the network, albeit used as input # eg hidden_output is the signal to the right of the middle nodes def backquery(self, targets_list): # transpose the targets list to a vertical array outputs = np.array(targets_list, ndmin=2).T for weights in reversed(self.layer_weights): # calculate the signal into the final output layer inputs = inverse_activation_function(outputs) # calculate the signal out of the hidden layer outputs = np.dot(weights.T, inputs) # scale them back to 0.01 to .99 outputs -= np.min(outputs) outputs /= np.max(outputs) outputs *= 0.98 outputs += 0.01 return outputs # HELPER FUNCTIONS ----------------------------------- def ensure_files(): if not os.path.exists(DATA_DIR): os.makedirs(DATA_DIR) if not os.path.exists(TRAIN_FILE): print("Downloading: {}".format(TRAIN_FILE)) wget.download(TRAINFILE_URL, out=DATA_DIR) if not os.path.exists(TEST_FILE): print("Downloading: {}".format(TEST_FILE)) wget.download(TESTFILE_URL, out=DATA_DIR) def scale_matrix(matrix): """ nn works best with values between 0.01 and 1 """ return matrix / 255 * 0.99 + 0.01 def import_data(file_name): with open(file_name, "r") as f: reader = csv.reader(f) for row in reader: label = int(row.pop(0)) matrix = scale_matrix(np.asfarray(row)) # .reshape((28, 28))) yield label, matrix def gen_target_array(onodes): """ generates list of lists, like: [[0.01, 0.01, 0.01, 0.01, 0.01, 0.99, 0.01, 0.01, 0.01, 0.01], ... ] """ o_list = [] for index in range(onodes): targets = np.zeros(onodes) + 0.01 targets[index] = 0.99 o_list.append(targets) return o_list def plot_matrix(matrix): fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.set_aspect('equal') plt.imshow( matrix, interpolation='nearest', cmap=plt.cm.Greys) plt.colorbar() plt.show() def calc_accuracy(scorecard): scorecard_array = np.asarray(scorecard) accuracy = scorecard_array.sum() / scorecard_array.size return accuracy def get_sample_size(file_name): if DEBUG: return 1000 sample_size = 0 with open(file_name, "r") as f: reader = csv.reader(f) sample_size = len(list(reader)) return sample_size def best_accuracy(list): max_L = max(records, key=lambda x: x["accuracy"]) return max_L["accuracy"] def run_experiment(hidden_layers, hidden_nodes, learning_rate, epochs): # init network nn = NeuralNetwork( input_nodes=INPUT_NODES, hidden_layers=hidden_layers, hidden_nodes=hidden_nodes, output_nodes=OUTPUT_NODES, learning_rate=learning_rate ) target_ar = gen_target_array(OUTPUT_NODES) # train network training_data = import_data(TRAIN_FILE) if DEBUG: # use less training data for debug training_data = list(training_data)[1000:] t0 = time() for e in range(epochs): for record in training_data: label, matrix = record nn.train(inputs_list=matrix, targets_list=target_ar[label]) t1 = time() - t0 # test network scorecard = [] # scorecard for how well the network performs test_data = import_data(TEST_FILE) if DEBUG: # use less test data for debug test_data = list(test_data)[100:] for record in test_data: correct_label, matrix = record outputs = nn.query(matrix) label = np.argmax( outputs) # the index of the highest value corresponds to the label if (label == correct_label): # network's answer matches correct answer, add 1 to scorecard scorecard.append(1) else: # network's answer doesn't match correct answer, add 0 to scorecard scorecard.append(0) accuracy = calc_accuracy(scorecard) training_time = str(datetime.timedelta(seconds=t1)) # print some info regarding training run print("accuracy: {0:.1f}%".format(calc_accuracy(scorecard) * 100)) print("training time: {}".format(training_time)) #print("sample size: {}".format(get_sample_size(TRAIN_FILE))) print("epochs: {}".format(epochs)) print("hidden layers: {}".format(hidden_layers)) print("hidden nodes: {}".format(hidden_nodes)) print("learning rate: {}".format(learning_rate)) print("-" * 15 + "\n") if DEBUG: print("\n\nBackquerying the network") for i, t in enumerate(target_ar): image_data = nn.backquery(t) plt.imshow(image_data.reshape(28, 28), cmap='Greys', interpolation='None') plt.title("Target: " + str(i)) plt.show() else: # save current model to db if it has better accuracy d = shelve.open(DATA_FILE, writeback=True) records = d["records"] # # get best accuracy if records: # list must be non-empty current_best = best_accuracy(records) # save model only if accuracy higher than current best if accuracy > current_best: # del d["data"] # TODO: is this needed? or just writeback=True? d["model"] = nn.layer_weights # save current parameter set for later analysis records.append({ "accuracy": 0, "hidden_layers": 0, "hidden_nodes": 0, "learning_rate": 0, "training_time": training_time, "epochs": 0}) # don't forget to close db connection d.close() def init_db(): d = shelve.open(DATA_FILE, writeback=True) try: d["records"] except KeyError: d["records"] = [] # initialize records list try: d["model"] except KeyError: d["model"] = [] # initialize model list d.close() if __name__ == '__main__': ensure_files() # download training and test data if not present init_db() # make sure key value pairs are present if DEBUG: print("Running in DEBUG mode.\n\n") run_experiment(hidden_layers=0, hidden_nodes=50, learning_rate=0.2, epochs=1) else: start_time = time() print("Running combinatorial query over parameters.\n\n") for hidden_layers in range(2): for hidden_nodes in [50, 80, 150, 200, 300]: for learning_rate in [0.05, 0.1, 0.2, 0.3, 0.5]: for epochs in [2, 3, 5]: run_experiment(hidden_layers, hidden_nodes, learning_rate, epochs) time_delta = time() - start_time # print some summarizing statistics d = shelve.open(DATA_FILE, writeback=False) records = d["records"] d.close() current_best = best_accuracy(records) print("-" * 15) print("Best accuracy: {}".format()) print("Total training time: {}".format(str(datetime.timedelta(seconds=time_delta)))) print("Number of experiments: {}".format(len(records))) print("Sample size: {}".format(get_sample_size(TRAIN_FILE))) print("-" * 15) print("\n\nBest model saved to db file. Access via key \"model\" and have fun!")
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custom_components/ecowitt/sensor.py
synth3tk/homeassistant_ecowitt
15
51737
<gh_stars>10-100 """Support for Ecowitt Weather Stations.""" import logging import homeassistant.util.dt as dt_util from homeassistant.components.sensor import DOMAIN as SENSOR_DOMAIN from homeassistant.components.sensor import SensorEntity from homeassistant.const import DEVICE_CLASS_BATTERY from homeassistant.const import DEVICE_CLASS_TIMESTAMP from homeassistant.const import PERCENTAGE from homeassistant.const import STATE_UNKNOWN from homeassistant.helpers.dispatcher import async_dispatcher_connect from . import async_add_ecowitt_entities from . import EcowittEntity from .const import DOMAIN from .const import REG_ENTITIES from .const import SIGNAL_ADD_ENTITIES from .const import TYPE_SENSOR _LOGGER = logging.getLogger(__name__) async def async_setup_entry(hass, entry, async_add_entities): """Add sensors if new.""" def add_entities(discovery_info=None): async_add_ecowitt_entities( hass, entry, EcowittSensor, SENSOR_DOMAIN, async_add_entities, discovery_info, ) signal = f"{SIGNAL_ADD_ENTITIES}_{SENSOR_DOMAIN}" async_dispatcher_connect(hass, signal, add_entities) add_entities(hass.data[DOMAIN][entry.entry_id][REG_ENTITIES][TYPE_SENSOR]) class EcowittSensor(EcowittEntity, SensorEntity): def __init__(self, hass, entry, key, name, dc, uom, icon, sc): """Initialize the sensor.""" super().__init__(hass, entry, key, name) self._icon = icon self._uom = uom self._dc = dc self._sc = sc @property def native_value(self): """Return the state of the sensor.""" if self._key in self._ws.last_values: # The lightning time is reported in UTC, hooray. if self._dc == DEVICE_CLASS_TIMESTAMP: if not isinstance(self._ws.last_values[self._key], int): return STATE_UNKNOWN return dt_util.as_local( dt_util.utc_from_timestamp(self._ws.last_values[self._key]) ).isoformat() # Battery value is 0-5 if self._dc == DEVICE_CLASS_BATTERY and self._uom == PERCENTAGE: return self._ws.last_values[self._key] * 20.0 return self._ws.last_values[self._key] _LOGGER.warning( "Sensor %s not in last update, check range or battery", self._key ) return STATE_UNKNOWN @property def native_unit_of_measurement(self): """Return the unit of measurement.""" return self._uom @property def icon(self): """Return the icon to use in the fronend.""" return self._icon @property def device_class(self): """Return the device class.""" return self._dc @property def state_class(self): """Return the state_class of the device.""" return self._sc
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laboratorios/4-Analisis-algoritmos-2/B - Friend Circle Queries.py
yeisonbarreto/ayed-2019-1
0
155854
from sys import stdin def main(): print('Número de enteros a procesar') n = stdin.readline().strip() f = [] print('Secuencia de consultas') cont = 1 for i in range(int(n)): x = stdin.readline().strip().split(' ') if x in f: print('2') elif x[0] in f: cont += 1 f += (x[1]) elif x[1] in f: cont += 1 f += (x[0]) else: cont += 1 f += (x) print('2') print(cont) main()
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cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_ipv4_telnet_mgmt_cfg.py
tkamata-test/ydk-py
0
30753
<reponame>tkamata-test/ydk-py """ Cisco_IOS_XR_ipv4_telnet_mgmt_cfg This module contains a collection of YANG definitions for Cisco IOS\-XR ipv4\-telnet\-mgmt package configuration. This module contains definitions for the following management objects\: telnet\: Global Telnet configuration commands Copyright (c) 2013\-2016 by Cisco Systems, Inc. All rights reserved. """ import re import collections from enum import Enum from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict from ydk.errors import YPYError, YPYModelError class Telnet(object): """ Global Telnet configuration commands .. attribute:: vrfs VRF name for telnet service **type**\: :py:class:`Vrfs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_telnet_mgmt_cfg.Telnet.Vrfs>` """ _prefix = 'ipv4-telnet-mgmt-cfg' _revision = '2015-11-09' def __init__(self): self.vrfs = Telnet.Vrfs() self.vrfs.parent = self class Vrfs(object): """ VRF name for telnet service .. attribute:: vrf VRF name for telnet service **type**\: list of :py:class:`Vrf <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_telnet_mgmt_cfg.Telnet.Vrfs.Vrf>` """ _prefix = 'ipv4-telnet-mgmt-cfg' _revision = '2015-11-09' def __init__(self): self.parent = None self.vrf = YList() self.vrf.parent = self self.vrf.name = 'vrf' class Vrf(object): """ VRF name for telnet service .. attribute:: vrf_name <key> VRF name **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: ipv4 IPv4 configuration **type**\: :py:class:`Ipv4 <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_telnet_mgmt_cfg.Telnet.Vrfs.Vrf.Ipv4>` """ _prefix = 'ipv4-telnet-mgmt-cfg' _revision = '2015-11-09' def __init__(self): self.parent = None self.vrf_name = None self.ipv4 = Telnet.Vrfs.Vrf.Ipv4() self.ipv4.parent = self class Ipv4(object): """ IPv4 configuration .. attribute:: dscp Specify the DSCP value **type**\: int **range:** 0..63 """ _prefix = 'ipv4-telnet-mgmt-cfg' _revision = '2015-11-09' def __init__(self): self.parent = None self.dscp = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:ipv4' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return True def _has_data(self): if not self.is_config(): return False if self.dscp is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_telnet_mgmt_cfg as meta return meta._meta_table['Telnet.Vrfs.Vrf.Ipv4']['meta_info'] @property def _common_path(self): if self.vrf_name is None: raise YPYModelError('Key property vrf_name is None') return '/Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:telnet/Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:vrfs/Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:vrf[Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:vrf-name = ' + str(self.vrf_name) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return True def _has_data(self): if not self.is_config(): return False if self.vrf_name is not None: return True if self.ipv4 is not None and self.ipv4._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_telnet_mgmt_cfg as meta return meta._meta_table['Telnet.Vrfs.Vrf']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:telnet/Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:vrfs' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return True def _has_data(self): if not self.is_config(): return False if self.vrf is not None: for child_ref in self.vrf: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_telnet_mgmt_cfg as meta return meta._meta_table['Telnet.Vrfs']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-ipv4-telnet-mgmt-cfg:telnet' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return True def _has_data(self): if not self.is_config(): return False if self.vrfs is not None and self.vrfs._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_telnet_mgmt_cfg as meta return meta._meta_table['Telnet']['meta_info']
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Lib/contextlib.py
uburuntu/cpython
0
130199
<filename>Lib/contextlib.py """Utilities for with-statement contexts. See PEP 343.""" import abc import sys import _collections_abc from collections import deque from functools import wraps from types import MethodType, GenericAlias __all__ = ["asynccontextmanager", "contextmanager", "closing", "nullcontext", "AbstractContextManager", "AbstractAsyncContextManager", "AsyncExitStack", "ContextDecorator", "ExitStack", "redirect_stdout", "redirect_stderr", "suppress", "aclosing"] class AbstractContextManager(abc.ABC): """An abstract base class for context managers.""" __class_getitem__ = classmethod(GenericAlias) def __enter__(self): """Return `self` upon entering the runtime context.""" return self @abc.abstractmethod def __exit__(self, exc_type, exc_value, traceback): """Raise any exception triggered within the runtime context.""" return None @classmethod def __subclasshook__(cls, C): if cls is AbstractContextManager: return _collections_abc._check_methods(C, "__enter__", "__exit__") return NotImplemented class AbstractAsyncContextManager(abc.ABC): """An abstract base class for asynchronous context managers.""" __class_getitem__ = classmethod(GenericAlias) async def __aenter__(self): """Return `self` upon entering the runtime context.""" return self @abc.abstractmethod async def __aexit__(self, exc_type, exc_value, traceback): """Raise any exception triggered within the runtime context.""" return None @classmethod def __subclasshook__(cls, C): if cls is AbstractAsyncContextManager: return _collections_abc._check_methods(C, "__aenter__", "__aexit__") return NotImplemented class ContextDecorator(object): "A base class or mixin that enables context managers to work as decorators." def _recreate_cm(self): """Return a recreated instance of self. Allows an otherwise one-shot context manager like _GeneratorContextManager to support use as a decorator via implicit recreation. This is a private interface just for _GeneratorContextManager. See issue #11647 for details. """ return self def __call__(self, func): @wraps(func) def inner(*args, **kwds): with self._recreate_cm(): return func(*args, **kwds) return inner class AsyncContextDecorator(object): "A base class or mixin that enables async context managers to work as decorators." def _recreate_cm(self): """Return a recreated instance of self. """ return self def __call__(self, func): @wraps(func) async def inner(*args, **kwds): async with self._recreate_cm(): return await func(*args, **kwds) return inner class _GeneratorContextManagerBase: """Shared functionality for @contextmanager and @asynccontextmanager.""" def __init__(self, func, args, kwds): self.gen = func(*args, **kwds) self.func, self.args, self.kwds = func, args, kwds # Issue 19330: ensure context manager instances have good docstrings doc = getattr(func, "__doc__", None) if doc is None: doc = type(self).__doc__ self.__doc__ = doc # Unfortunately, this still doesn't provide good help output when # inspecting the created context manager instances, since pydoc # currently bypasses the instance docstring and shows the docstring # for the class instead. # See http://bugs.python.org/issue19404 for more details. def _recreate_cm(self): # _GCMB instances are one-shot context managers, so the # CM must be recreated each time a decorated function is # called return self.__class__(self.func, self.args, self.kwds) class _GeneratorContextManager( _GeneratorContextManagerBase, AbstractContextManager, ContextDecorator, ): """Helper for @contextmanager decorator.""" def __enter__(self): # do not keep args and kwds alive unnecessarily # they are only needed for recreation, which is not possible anymore del self.args, self.kwds, self.func try: return next(self.gen) except StopIteration: raise RuntimeError("generator didn't yield") from None def __exit__(self, typ, value, traceback): if typ is None: try: next(self.gen) except StopIteration: return False else: raise RuntimeError("generator didn't stop") else: if value is None: # Need to force instantiation so we can reliably # tell if we get the same exception back value = typ() try: self.gen.throw(typ, value, traceback) except StopIteration as exc: # Suppress StopIteration *unless* it's the same exception that # was passed to throw(). This prevents a StopIteration # raised inside the "with" statement from being suppressed. return exc is not value except RuntimeError as exc: # Don't re-raise the passed in exception. (issue27122) if exc is value: return False # Avoid suppressing if a StopIteration exception # was passed to throw() and later wrapped into a RuntimeError # (see PEP 479 for sync generators; async generators also # have this behavior). But do this only if the exception wrapped # by the RuntimeError is actually Stop(Async)Iteration (see # issue29692). if ( isinstance(value, StopIteration) and exc.__cause__ is value ): return False raise except BaseException as exc: # only re-raise if it's *not* the exception that was # passed to throw(), because __exit__() must not raise # an exception unless __exit__() itself failed. But throw() # has to raise the exception to signal propagation, so this # fixes the impedance mismatch between the throw() protocol # and the __exit__() protocol. if exc is not value: raise return False raise RuntimeError("generator didn't stop after throw()") class _AsyncGeneratorContextManager( _GeneratorContextManagerBase, AbstractAsyncContextManager, AsyncContextDecorator, ): """Helper for @asynccontextmanager decorator.""" def __call__(self, func): @wraps(func) async def inner(*args, **kwds): async with self.__class__(self.func, self.args, self.kwds): return await func(*args, **kwds) return inner async def __aenter__(self): # do not keep args and kwds alive unnecessarily # they are only needed for recreation, which is not possible anymore del self.args, self.kwds, self.func try: return await anext(self.gen) except StopAsyncIteration: raise RuntimeError("generator didn't yield") from None async def __aexit__(self, typ, value, traceback): if typ is None: try: await anext(self.gen) except StopAsyncIteration: return False else: raise RuntimeError("generator didn't stop") else: if value is None: # Need to force instantiation so we can reliably # tell if we get the same exception back value = typ() try: await self.gen.athrow(typ, value, traceback) except StopAsyncIteration as exc: # Suppress StopIteration *unless* it's the same exception that # was passed to throw(). This prevents a StopIteration # raised inside the "with" statement from being suppressed. return exc is not value except RuntimeError as exc: # Don't re-raise the passed in exception. (issue27122) if exc is value: return False # Avoid suppressing if a Stop(Async)Iteration exception # was passed to athrow() and later wrapped into a RuntimeError # (see PEP 479 for sync generators; async generators also # have this behavior). But do this only if the exception wrapped # by the RuntimeError is actually Stop(Async)Iteration (see # issue29692). if ( isinstance(value, (StopIteration, StopAsyncIteration)) and exc.__cause__ is value ): return False raise except BaseException as exc: # only re-raise if it's *not* the exception that was # passed to throw(), because __exit__() must not raise # an exception unless __exit__() itself failed. But throw() # has to raise the exception to signal propagation, so this # fixes the impedance mismatch between the throw() protocol # and the __exit__() protocol. if exc is not value: raise return False raise RuntimeError("generator didn't stop after athrow()") def contextmanager(func): """@contextmanager decorator. Typical usage: @contextmanager def some_generator(<arguments>): <setup> try: yield <value> finally: <cleanup> This makes this: with some_generator(<arguments>) as <variable>: <body> equivalent to this: <setup> try: <variable> = <value> <body> finally: <cleanup> """ @wraps(func) def helper(*args, **kwds): return _GeneratorContextManager(func, args, kwds) return helper def asynccontextmanager(func): """@asynccontextmanager decorator. Typical usage: @asynccontextmanager async def some_async_generator(<arguments>): <setup> try: yield <value> finally: <cleanup> This makes this: async with some_async_generator(<arguments>) as <variable>: <body> equivalent to this: <setup> try: <variable> = <value> <body> finally: <cleanup> """ @wraps(func) def helper(*args, **kwds): return _AsyncGeneratorContextManager(func, args, kwds) return helper class closing(AbstractContextManager, AbstractAsyncContextManager): """Context to automatically close something at the end of a block. Code like this: with closing(<module>.open(<arguments>)) as f: <block> is equivalent to this: f = <module>.open(<arguments>) try: <block> finally: f.close() In asynchronous case, code like this: async with closing(<module>.open(<arguments>)) as f: <block> is equivalent to this: f = <module>.open(<arguments>) try: <block> finally: await f.aclose() """ def __init__(self, thing): self.thing = thing def __enter__(self): return self.thing def __exit__(self, *exc_info): self.thing.close() async def __aenter__(self): return self.thing async def __aexit__(self, *exc_info): await self.thing.aclose() class aclosing(AbstractAsyncContextManager): """Async context manager for safely finalizing an asynchronously cleaned-up resource such as an async generator, calling its ``aclose()`` method. Code like this: async with aclosing(<module>.fetch(<arguments>)) as agen: <block> is equivalent to this: agen = <module>.fetch(<arguments>) try: <block> finally: await agen.aclose() """ def __init__(self, thing): self.thing = thing async def __aenter__(self): return self.thing async def __aexit__(self, *exc_info): await self.thing.aclose() class _RedirectStream(AbstractContextManager): _stream = None def __init__(self, new_target): self._new_target = new_target # We use a list of old targets to make this CM re-entrant self._old_targets = [] def __enter__(self): self._old_targets.append(getattr(sys, self._stream)) setattr(sys, self._stream, self._new_target) return self._new_target def __exit__(self, exctype, excinst, exctb): setattr(sys, self._stream, self._old_targets.pop()) class redirect_stdout(_RedirectStream): """Context manager for temporarily redirecting stdout to another file. # How to send help() to stderr with redirect_stdout(sys.stderr): help(dir) # How to write help() to a file with open('help.txt', 'w') as f: with redirect_stdout(f): help(pow) """ _stream = "stdout" class redirect_stderr(_RedirectStream): """Context manager for temporarily redirecting stderr to another file.""" _stream = "stderr" class suppress(AbstractContextManager): """Context manager to suppress specified exceptions After the exception is suppressed, execution proceeds with the next statement following the with statement. with suppress(FileNotFoundError): os.remove(somefile) # Execution still resumes here if the file was already removed """ def __init__(self, *exceptions): self._exceptions = exceptions def __enter__(self): pass def __exit__(self, exctype, excinst, exctb): # Unlike isinstance and issubclass, CPython exception handling # currently only looks at the concrete type hierarchy (ignoring # the instance and subclass checking hooks). While Guido considers # that a bug rather than a feature, it's a fairly hard one to fix # due to various internal implementation details. suppress provides # the simpler issubclass based semantics, rather than trying to # exactly reproduce the limitations of the CPython interpreter. # # See http://bugs.python.org/issue12029 for more details return exctype is not None and issubclass(exctype, self._exceptions) class _BaseExitStack: """A base class for ExitStack and AsyncExitStack.""" @staticmethod def _create_exit_wrapper(cm, cm_exit): return MethodType(cm_exit, cm) @staticmethod def _create_cb_wrapper(callback, /, *args, **kwds): def _exit_wrapper(exc_type, exc, tb): callback(*args, **kwds) return _exit_wrapper def __init__(self): self._exit_callbacks = deque() def pop_all(self): """Preserve the context stack by transferring it to a new instance.""" new_stack = type(self)() new_stack._exit_callbacks = self._exit_callbacks self._exit_callbacks = deque() return new_stack def push(self, exit): """Registers a callback with the standard __exit__ method signature. Can suppress exceptions the same way __exit__ method can. Also accepts any object with an __exit__ method (registering a call to the method instead of the object itself). """ # We use an unbound method rather than a bound method to follow # the standard lookup behaviour for special methods. _cb_type = type(exit) try: exit_method = _cb_type.__exit__ except AttributeError: # Not a context manager, so assume it's a callable. self._push_exit_callback(exit) else: self._push_cm_exit(exit, exit_method) return exit # Allow use as a decorator. def enter_context(self, cm): """Enters the supplied context manager. If successful, also pushes its __exit__ method as a callback and returns the result of the __enter__ method. """ # We look up the special methods on the type to match the with # statement. cls = type(cm) try: _enter = cls.__enter__ _exit = cls.__exit__ except AttributeError: raise TypeError(f"'{cls.__module__}.{cls.__qualname__}' object does " f"not support the context manager protocol") from None result = _enter(cm) self._push_cm_exit(cm, _exit) return result def callback(self, callback, /, *args, **kwds): """Registers an arbitrary callback and arguments. Cannot suppress exceptions. """ _exit_wrapper = self._create_cb_wrapper(callback, *args, **kwds) # We changed the signature, so using @wraps is not appropriate, but # setting __wrapped__ may still help with introspection. _exit_wrapper.__wrapped__ = callback self._push_exit_callback(_exit_wrapper) return callback # Allow use as a decorator def _push_cm_exit(self, cm, cm_exit): """Helper to correctly register callbacks to __exit__ methods.""" _exit_wrapper = self._create_exit_wrapper(cm, cm_exit) self._push_exit_callback(_exit_wrapper, True) def _push_exit_callback(self, callback, is_sync=True): self._exit_callbacks.append((is_sync, callback)) # Inspired by discussions on http://bugs.python.org/issue13585 class ExitStack(_BaseExitStack, AbstractContextManager): """Context manager for dynamic management of a stack of exit callbacks. For example: with ExitStack() as stack: files = [stack.enter_context(open(fname)) for fname in filenames] # All opened files will automatically be closed at the end of # the with statement, even if attempts to open files later # in the list raise an exception. """ def __enter__(self): return self def __exit__(self, *exc_details): received_exc = exc_details[0] is not None # We manipulate the exception state so it behaves as though # we were actually nesting multiple with statements frame_exc = sys.exc_info()[1] def _fix_exception_context(new_exc, old_exc): # Context may not be correct, so find the end of the chain while 1: exc_context = new_exc.__context__ if exc_context is old_exc: # Context is already set correctly (see issue 20317) return if exc_context is None or exc_context is frame_exc: break new_exc = exc_context # Change the end of the chain to point to the exception # we expect it to reference new_exc.__context__ = old_exc # Callbacks are invoked in LIFO order to match the behaviour of # nested context managers suppressed_exc = False pending_raise = False while self._exit_callbacks: is_sync, cb = self._exit_callbacks.pop() assert is_sync try: if cb(*exc_details): suppressed_exc = True pending_raise = False exc_details = (None, None, None) except: new_exc_details = sys.exc_info() # simulate the stack of exceptions by setting the context _fix_exception_context(new_exc_details[1], exc_details[1]) pending_raise = True exc_details = new_exc_details if pending_raise: try: # bare "raise exc_details[1]" replaces our carefully # set-up context fixed_ctx = exc_details[1].__context__ raise exc_details[1] except BaseException: exc_details[1].__context__ = fixed_ctx raise return received_exc and suppressed_exc def close(self): """Immediately unwind the context stack.""" self.__exit__(None, None, None) # Inspired by discussions on https://bugs.python.org/issue29302 class AsyncExitStack(_BaseExitStack, AbstractAsyncContextManager): """Async context manager for dynamic management of a stack of exit callbacks. For example: async with AsyncExitStack() as stack: connections = [await stack.enter_async_context(get_connection()) for i in range(5)] # All opened connections will automatically be released at the # end of the async with statement, even if attempts to open a # connection later in the list raise an exception. """ @staticmethod def _create_async_exit_wrapper(cm, cm_exit): return MethodType(cm_exit, cm) @staticmethod def _create_async_cb_wrapper(callback, /, *args, **kwds): async def _exit_wrapper(exc_type, exc, tb): await callback(*args, **kwds) return _exit_wrapper async def enter_async_context(self, cm): """Enters the supplied async context manager. If successful, also pushes its __aexit__ method as a callback and returns the result of the __aenter__ method. """ cls = type(cm) try: _enter = cls.__aenter__ _exit = cls.__aexit__ except AttributeError: raise TypeError(f"'{cls.__module__}.{cls.__qualname__}' object does " f"not support the asynchronous context manager protocol" ) from None result = await _enter(cm) self._push_async_cm_exit(cm, _exit) return result def push_async_exit(self, exit): """Registers a coroutine function with the standard __aexit__ method signature. Can suppress exceptions the same way __aexit__ method can. Also accepts any object with an __aexit__ method (registering a call to the method instead of the object itself). """ _cb_type = type(exit) try: exit_method = _cb_type.__aexit__ except AttributeError: # Not an async context manager, so assume it's a coroutine function self._push_exit_callback(exit, False) else: self._push_async_cm_exit(exit, exit_method) return exit # Allow use as a decorator def push_async_callback(self, callback, /, *args, **kwds): """Registers an arbitrary coroutine function and arguments. Cannot suppress exceptions. """ _exit_wrapper = self._create_async_cb_wrapper(callback, *args, **kwds) # We changed the signature, so using @wraps is not appropriate, but # setting __wrapped__ may still help with introspection. _exit_wrapper.__wrapped__ = callback self._push_exit_callback(_exit_wrapper, False) return callback # Allow use as a decorator async def aclose(self): """Immediately unwind the context stack.""" await self.__aexit__(None, None, None) def _push_async_cm_exit(self, cm, cm_exit): """Helper to correctly register coroutine function to __aexit__ method.""" _exit_wrapper = self._create_async_exit_wrapper(cm, cm_exit) self._push_exit_callback(_exit_wrapper, False) async def __aenter__(self): return self async def __aexit__(self, *exc_details): received_exc = exc_details[0] is not None # We manipulate the exception state so it behaves as though # we were actually nesting multiple with statements frame_exc = sys.exc_info()[1] def _fix_exception_context(new_exc, old_exc): # Context may not be correct, so find the end of the chain while 1: exc_context = new_exc.__context__ if exc_context is old_exc: # Context is already set correctly (see issue 20317) return if exc_context is None or exc_context is frame_exc: break new_exc = exc_context # Change the end of the chain to point to the exception # we expect it to reference new_exc.__context__ = old_exc # Callbacks are invoked in LIFO order to match the behaviour of # nested context managers suppressed_exc = False pending_raise = False while self._exit_callbacks: is_sync, cb = self._exit_callbacks.pop() try: if is_sync: cb_suppress = cb(*exc_details) else: cb_suppress = await cb(*exc_details) if cb_suppress: suppressed_exc = True pending_raise = False exc_details = (None, None, None) except: new_exc_details = sys.exc_info() # simulate the stack of exceptions by setting the context _fix_exception_context(new_exc_details[1], exc_details[1]) pending_raise = True exc_details = new_exc_details if pending_raise: try: # bare "raise exc_details[1]" replaces our carefully # set-up context fixed_ctx = exc_details[1].__context__ raise exc_details[1] except BaseException: exc_details[1].__context__ = fixed_ctx raise return received_exc and suppressed_exc class nullcontext(AbstractContextManager, AbstractAsyncContextManager): """Context manager that does no additional processing. Used as a stand-in for a normal context manager, when a particular block of code is only sometimes used with a normal context manager: cm = optional_cm if condition else nullcontext() with cm: # Perform operation, using optional_cm if condition is True """ def __init__(self, enter_result=None): self.enter_result = enter_result def __enter__(self): return self.enter_result def __exit__(self, *excinfo): pass async def __aenter__(self): return self.enter_result async def __aexit__(self, *excinfo): pass
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examples/old-examples/GLWindow/conways_game_of_life_texel_fetch.py
asnt/moderngl
96
173260
import struct import numpy import GLWindow import ModernGL # This example is not working with NPOT Textures width, height = 640, 460 pixels = numpy.round(numpy.random.rand(width, height)).astype('float32') grid = numpy.dstack(numpy.mgrid[0:height, 0:width][::-1]).astype('int32') wnd = GLWindow.create_window(width, height) ctx = ModernGL.create_context() prog = ctx.program( ctx.vertex_shader(''' #version 330 in vec2 vert; out vec2 text; void main() { text = vert; gl_Position = vec4((vert * 2.0 - 1.0) * 1, 0.0, 1.0); } '''), ctx.fragment_shader(''' #version 330 uniform sampler2D Texture; in vec2 text; out vec4 color; void main() { color = texture(Texture, text); } '''), ]) trans = ctx.program( ctx.vertex_shader(''' #version 330 uniform sampler2D Texture; uniform int Width; uniform int Height; in ivec2 text; out float vert; #define LIVING 0.0 #define DEAD 1.0 bool cell(int x, int y) { return texelFetch(Texture, ivec2((x + Width) % Width, (y + Height) % Height), 0).r < 0.5; } void main() { bool living = cell(text.x, text.y); int neighbours = 0; if (cell(text.x - 1, text.y - 1)) neighbours++; if (cell(text.x - 1, text.y + 0)) neighbours++; if (cell(text.x - 1, text.y + 1)) neighbours++; if (cell(text.x + 1, text.y - 1)) neighbours++; if (cell(text.x + 1, text.y + 0)) neighbours++; if (cell(text.x + 1, text.y + 1)) neighbours++; if (cell(text.x + 0, text.y + 1)) neighbours++; if (cell(text.x + 0, text.y - 1)) neighbours++; if (living) { vert = (neighbours == 2 || neighbours == 3) ? LIVING : DEAD; } else { vert = (neighbours == 3) ? LIVING : DEAD; } } '''), varyings=['vert'] ) trans.uniforms['Width'].value = width trans.uniforms['Height'].value = height texture = ctx.texture((width, height), 1, pixels.tobytes(), floats=True) texture.filter = ModernGL.NEAREST texture.swizzle = 'RRR1' texture.use() vbo = ctx.buffer(struct.pack('8f', 0, 0, 0, 1, 1, 0, 1, 1)) vao = ctx.simple_vertex_array(prog, vbo, ['vert']) text = ctx.buffer(grid.tobytes()) tao = ctx.simple_vertex_array(trans, text, ['text']) pbo = ctx.buffer(reserve=pixels.nbytes) while wnd.update(): ctx.viewport = wnd.viewport tao.transform(pbo) texture.write(pbo) vao.render(ModernGL.TRIANGLE_STRIP)
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Processing.py
Christoper-Harvey/ecg-file-processing
0
113745
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import base64 import glob from scipy.signal import medfilt from scipy.integrate import trapz import xml.etree.ElementTree as et from datetime import date today = date.today() np.warnings.filterwarnings('ignore') sns.set(style="darkgrid") roots = [] root_names = [] for n in glob.glob('*.xml'): roots.append(et.parse(n).getroot()) root_names.append(n) def modified_z_score(intensity): median_int = np.median(intensity) mad_int = np.median([np.abs(intensity - median_int)]) if mad_int == 0: mad_int = 1 modified_z_scores = 0.6745 * (intensity - median_int) / mad_int return modified_z_scores def df_fixer(y,n): threshold = 0 x = 0 while threshold == 0: if np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) > 150: if abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+55: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+55: x += 5 elif np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) <= 150: if abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+55: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+55: x += 5 spikes = abs(np.array(modified_z_score(np.diff(y)))) > threshold y_out = y.copy() for i in np.arange(len(spikes)): if spikes[i] != 0: y_out[i+y_out.index[0]] = None return y_out def half_df_fixer(y,n): threshold = 0 x = 0 while threshold == 0: if np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) > 150: if abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+60: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+60: x += 2 elif np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) <= 150: if abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+60: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+60: x += 2 spikes = abs(np.array(modified_z_score(np.diff(y)))) > threshold y_out = y.copy() for i in np.arange(len(spikes)): if spikes[i] != 0: y_out[i+y_out.index[0]] = None return y_out def hanging_line(point1, point2): a = (point2[1] - point1[1])/(np.cosh(point2[0] % 600) - np.cosh(point1[0] % 600)) b = point1[1] - a*np.cosh(point1[0] % 600) x = np.linspace(point1[0], point2[0], (point2[0] - point1[0])+1) y = a*np.cosh(x % 600) + b return (x,y) Tags = {'tags':[]} tags = {'tags':[]} for root in roots: if len(root.find('{http://www3.medical.philips.com}waveforms').getchildren()) == 2: if int(root.find('{http://www3.medical.philips.com}waveforms')[1].attrib['samplespersec']) == 1000: for elem in root.find('{http://www3.medical.philips.com}waveforms')[1]: tag = {} tag['Lead'] = elem.attrib['leadname'] if (root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid') and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == '\n ' or root[6][1][0][14].text == 'Failed': tag['Ponset'] = 0 tag['Pdur'] = 0 tag['Print'] = 0 tag['Poffset'] = 0 else: tag['Ponset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) tag['Pdur'] = 0 tag['Print'] = int(root[6][1][0][14].text) tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + 0 elif root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Failed' or root[6][1][0][14].text == 'Failed' or (root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid'): tag['Ponset'] = 0 tag['Pdur'] = 0 tag['Print'] = 0 tag['Poffset'] = 0 else: tag['Ponset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) tag['Pdur'] = int(elem[0].text) tag['Print'] = int(root[6][1][0][14].text) tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + int(elem[0].text) if (root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][0][29].text == 'Invalid' or elem[4].text == 'Invalid' or root[6][1][0][18].text == 'Invalid'): tag['Qonset'] = np.nan tag['Qrsdur'] = np.nan tag['Qoffset'] = np.nan tag['Tonset'] = np.nan tag['Qtint'] = np.nan tag['Toffset'] = np.nan tag['Tdur'] = np.nan else: tag['Qonset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) tag['Qrsdur'] = int(root[6][0][29].text) tag['Qoffset'] = tag['Qonset'] + tag['Qrsdur'] tag['Tonset'] = int(elem[4].text) tag['Qtint'] = int(root[6][1][0][18].text) tag['Toffset'] = tag['Qonset'] + tag['Qtint'] tag['Tdur'] = tag['Qoffset'] - tag['Qonset'] if root[7].tag == '{http://www3.medical.philips.com}interpretations' and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[7][0][1][0].text != None and (root[7][0][1][0].text).isdigit(): tag['HeartRate'] = int(root[7][0][1][0].text) if root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text != None: tag['RRint'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text) if root[6][1][0][9].text != None: tag['AtrialRate'] = int(root[6][1][0][9].text) if root[6][0][15].text != None and root[6][0][15].text != 'Indeterminate': tag['QRSFrontAxis'] = int(root[6][0][15].text) if root[6][0][31].text != None and root[6][0][31].text != 'Failed': tag['QTC'] = int(root[6][0][31].text) tag['Target'] = [] for n in range(len(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):])): tag['Target'].append(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):][n][0].text) else: tag['HeartRate'] = np.nan tag['RRint'] = np.nan tag['AtrialRate'] = np.nan tag['QRSFrontAxis'] = np.nan tag['QTC'] = np.nan tag['Target'] = [] if root[3].tag == '{http://www3.medical.philips.com}reportinfo' and root[5].tag == '{http://www3.medical.philips.com}patient': time = root[3].attrib tag['Date'] = time['date'] tag['Time'] = time['time'] tag['Sex'] = root[5][0][6].text tag['ID'] = root[5][0][0].text tag['Name'] = root[5][0].find('{http://www3.medical.philips.com}name')[0].text + ', ' + root[5][0].find('{http://www3.medical.philips.com}name')[1].text if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}dateofbirth': tag['Age'] = int(today.strftime("%Y")) - int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text[0:4]) if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}years': tag['Age'] = int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text) tag['Waveform'] = elem[6].text # tag['LongWaveform'] = root[8][0].text tags['tags'].append(tag) else: for elem in root.find('{http://www3.medical.philips.com}waveforms')[1]: Tag = {} Tag['Lead'] = elem.attrib['leadname'] if (root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid') and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == '\n ' or root[6][1][0][14].text == 'Failed': Tag['Ponset'] = 0 Tag['Pdur'] = 0 Tag['Print'] = 0 Tag['Poffset'] = 0 else: Tag['Ponset'] = float(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) Tag['Pdur'] = 0 Tag['Print'] = int(root[6][1][0][14].text) Tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + 0 elif root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == None or root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid' and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': Tag['Ponset'] = 0 Tag['Pdur'] = 0 Tag['Print'] = 0 Tag['Poffset'] = 0 else: Tag['Ponset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) Tag['Pdur'] = int(elem[0].text) Tag['Print'] = int(root[6][1][0][14].text) Tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + int(elem[0].text) if (root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][18].text == None or root[6][0][29].text == 'Invalid' or elem[4].text == 'Invalid' or root[6][1][0][18].text == 'Invalid'): Tag['Qonset'] = np.nan Tag['Qrsdur'] = np.nan Tag['Qoffset'] = np.nan Tag['Tonset'] = np.nan Tag['Qtint'] = np.nan Tag['Toffset'] = np.nan Tag['Tdur'] = np.nan else: Tag['Qonset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) Tag['Qrsdur'] = int(root[6][0][29].text) Tag['Qoffset'] = Tag['Qonset'] + Tag['Qrsdur'] Tag['Tonset'] = int(elem[4].text) Tag['Qtint'] = int(root[6][1][0][18].text) Tag['Toffset'] = Tag['Qonset'] + Tag['Qtint'] Tag['Tdur'] = Tag['Qoffset'] - Tag['Qonset'] if root[7].tag == '{http://www3.medical.philips.com}interpretations' and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[7][0][1][0].text != None and (root[7][0][1][0].text).isdigit(): Tag['HeartRate'] = int(root[7][0][1][0].text) if root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text != None: Tag['RRint'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text) if root[6][1][0][9].text != None: Tag['AtrialRate'] = int(root[6][1][0][9].text) if root[6][0][15].text != None and root[6][0][15].text != 'Indeterminate': Tag['QRSFrontAxis'] = int(root[6][0][15].text) if root[6][0][31].text != None: Tag['QTC'] = int(root[6][0][31].text) Tag['Target'] = [] for n in range(len(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):])): Tag['Target'].append(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):][n][0].text) else: Tag['HeartRate'] = np.nan Tag['RRint'] = np.nan Tag['AtrialRate'] = np.nan Tag['QRSFrontAxis'] = np.nan Tag['QTC'] = np.nan Tag['Target'] = [] if root[3].tag == '{http://www3.medical.philips.com}reportinfo' and root[5].tag == '{http://www3.medical.philips.com}patient': time = root[3].attrib Tag['Date'] = time['date'] Tag['Time'] = time['time'] Tag['Sex'] = root[5][0][6].text Tag['ID'] = root[5][0][0].text Tag['Name'] = root[5][0].find('{http://www3.medical.philips.com}name')[0].text + ', ' + root[5][0].find('{http://www3.medical.philips.com}name')[1].text if len(root[5][0].find('{http://www3.medical.philips.com}age')) > 0: if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}dateofbirth': Tag['Age'] = int(today.strftime("%Y")) - int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text[0:4]) if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}years': Tag['Age'] = int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text) Tag['Waveform'] = elem[6].text # Tag['LongWaveform'] = root[8][0].text Tags['tags'].append(Tag) half_data = pd.DataFrame(Tags['tags']) data = pd.DataFrame(tags['tags']) del roots del root del elem count1000 = int(len(data)/12) count500 = int(len(half_data)/12) count = count1000 + count500 if len(data) > 0: array = np.unique(data[data.isnull().any(axis=1)][['ID', 'Date', 'Time']]) missing_data = data.loc[data['ID'].isin(array) & data['Date'].isin(array) & data['Time'].isin(array)] data.drop(missing_data.index, axis=0,inplace=True) missing_data = missing_data.reset_index(drop=True) del tag del tags data = data.reset_index(drop=True) for n in range(count1000): data.Tonset[n*12:(n+1)*12] = np.repeat(int(data.Tonset[n*12:(n+1)*12].sum()/12), 12) data.Pdur[n*12:(n+1)*12] = np.repeat(int(data.Pdur[n*12:(n+1)*12].sum()/12), 12) x = 0 p = [] for x in range(len(data.Waveform)): t = base64.b64decode(data.Waveform[x]) p.append(np.asarray(t)) x+=1 p = np.asarray(p) a = [] for i in p: o = [] for x in i: o.append(x) a.append(o) df = pd.DataFrame(a) df.insert(0, 'Lead', data['Lead']) blank = [] for n in range(count1000): blank.append(pd.pivot_table(df[(n*12):(n+1)*12], columns=df.Lead)) test = pd.concat(blank) new = [] array = [] for n in range(13): for index, num in zip(test.iloc[:, n-1][::2], test.iloc[:, n-1][1::2]): if num > 128: new.append(index - (256 * (256 - num))) elif num < 128: new.append(index + (256 * num)) elif num == 0: new.append(index) else: new.append(index) new = [] array.append(new) array = np.asarray([array[0], array[1], array[2], array[3], array[4], array[5], array[6], array[7], array[8], array[9], array[10], array[11]]) df = pd.DataFrame(array) df = pd.pivot_table(df, columns=test.columns) df = df.fillna(0) del a del p del o del t del blank del new del array for n in range(count1000): for x in range(12): if (data.Toffset[n*12]-data.RRint[n*12]) >= data.Ponset[n*12] or (data.Ponset[n*12] + data.RRint[n*12]) - data.Toffset[n*12] == 1: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: rrint = data.RRint[n*12] if (rrint + data.Ponset[n*12]) > 1200 and (data.Toffset[n*12]-rrint) < 0: temp = df.iloc[:,x][int(n*1200):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif (rrint + data.Ponset[n*12]) > 1200 and (data.Toffset[n*12]-rrint) > 0: temp = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)-rrint):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif rrint + data.Ponset[n*12] < 1200 and (data.Toffset[n*12]-rrint) < 0: temp = df.iloc[:,x][int(n*1200):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int(rrint + data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: temp = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)-rrint):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int(rrint + data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 unfiltered_leads = df.copy() for n in range(count1000): for inx in range(12): test = df_fixer(df.iloc[:,inx][n*1200:(n+1)*1200], n) gaps = [] lstOfNs = [] gap = [] for num in test[test.isna() == True].index: lstOfNs.append(num) if len(lstOfNs) == 1: gap.append(lstOfNs[0]) if len(lstOfNs) > 1: if lstOfNs[-1] - lstOfNs[-2] < 5: gap.append(num) elif lstOfNs[-1] - lstOfNs[-2] > 5: gaps.append(gap) gap = [] gap.append(num) gaps.append(gap) if gaps != [[]]: x = [] y = [] for g in gaps: if len(g) == 1: x.append([g[-1]+1]) y.append(test[g[-1]+1]) if np.isnan(test.iloc[0]): point1 = [g[0], test[g[-1]+1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) else: point1 = [g[0]-1, test[g[0]-1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) for i in range(len(x)): test[x[i]] = y[i] if (trapz(abs(test[int(data.Qonset[n*12]):int(data.Qoffset[n*12])]))/trapz(abs(df.iloc[:,inx][int(data.Qonset[12*n]+(1200*n)):int(data.Qoffset[12*n]+(1200*n))]))) < .60: test = df.iloc[:,inx][n*1200:(n+1)*1200] test = medfilt(test, kernel_size=9) df.iloc[:,inx][n*1200:(n+1)*1200] = test del gaps del lstOfNs del gap del test VTI_leads = df[['III', 'aVF', 'aVL', 'aVR']] df = df[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] Unfiltered_VTI_leads = unfiltered_leads[['III', 'aVF', 'aVL', 'aVR']] unfiltered_leads = unfiltered_leads[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] matrix = [[.38, -.07, -.13, .05, -.01, .14, .06, .54], [-.07, .93, .06, -.02, -.05, .06, -.17, .13], [.11, -.23, -.43, -.06, -.14, -.20, -.11, .31]] x = matrix[0] y = matrix[1] z = matrix[2] n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(df)): xtemp.append((df.iloc[n].values * x).sum()) ytemp.append((df.iloc[n].values * y).sum()) ztemp.append((df.iloc[n].values * z).sum()) n+=1 df['x'] = xtemp df['y'] = ytemp df['z'] = ztemp n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(unfiltered_leads)): xtemp.append((unfiltered_leads.iloc[n].values * x).sum()) ytemp.append((unfiltered_leads.iloc[n].values * y).sum()) ztemp.append((unfiltered_leads.iloc[n].values * z).sum()) n+=1 df['Unfiltered_x'] = xtemp df['Unfiltered_y'] = ytemp df['Unfiltered_z'] = ztemp del xtemp del ytemp del ztemp df['Date'] = data['Date'] df['ID'] = data['ID'] df['Time'] = data['Time'] df['Print'] = data['Print'] df['Ponset'] = data['Ponset'] df['Pdur'] = data['Pdur'] df['Poffset'] = data['Poffset'] df['Qonset'] = data['Qonset'] df['Qrsdur'] = data['Qrsdur'] df['Qtint'] = data['Qtint'] df['Qoffset'] = data['Qoffset'] df['Tonset'] = data['Tonset'] df['Tdur'] = data['Tdur'] df['Toffset'] = data['Toffset'] df['HeartRate'] = data['HeartRate'] df['QRSFrontAxis'] = data['QRSFrontAxis'] df['Sex'] = data['Sex'] df['QTC'] = data['QTC'] df['Age'] = data['Age'] df['Name'] = data['Name'] for n in range(count1000): df['Ponset'][(n*1200):(n+1)*1200] = data['Ponset'][n*12] df['Print'][(n*1200):(n+1)*1200] = data['Print'][n*12] df['Pdur'][(n*1200):(n+1)*1200] = data['Pdur'][n*12] df['Poffset'][(n*1200):(n+1)*1200] = data['Poffset'][n*12] df['Qonset'][(n*1200):(n+1)*1200] = data['Qonset'][n*12] df['Qrsdur'][(n*1200):(n+1)*1200] = data['Qrsdur'][n*12] df['Qtint'][(n*1200):(n+1)*1200] = data['Qtint'][n*12] df['Qoffset'][(n*1200):(n+1)*1200] = data['Qoffset'][n*12] df['Tonset'][(n*1200):(n+1)*1200] = data['Tonset'][n*12] df['Tdur'][(n*1200):(n+1)*1200] = data['Tdur'][n*12] df['Toffset'][(n*1200):(n+1)*1200] = data['Toffset'][n*12] df['HeartRate'][(n*1200):(n+1)*1200] = data['HeartRate'][n*12] df['QRSFrontAxis'][(n*1200):(n+1)*1200] = data['QRSFrontAxis'][n*12] df['Sex'][(n*1200):(n+1)*1200] = data['Sex'][n*12] df['QTC'][(n*1200):(n+1)*1200] = data['QTC'][n*12] df['Age'][(n*1200):(n+1)*1200] = data['Age'][n*12] df['Date'][(n*1200):(n+1)*1200] = data['Date'][12*n] df['Time'][(n*1200):(n+1)*1200] = data['Time'][12*n] df['ID'][(n*1200):(n+1)*1200] = data['ID'][12*n] df['Name'][(n*1200):(n+1)*1200] = data['Name'][12*n] df[['III', 'aVF', 'aVL', 'aVR']] = VTI_leads unfiltered_leads[['III', 'aVF', 'aVL', 'aVR']] = Unfiltered_VTI_leads df[['Unfiltered_I', 'Unfiltered_II', 'Unfiltered_III', 'Unfiltered_V1', 'Unfiltered_V2', 'Unfiltered_V3', 'Unfiltered_V4', 'Unfiltered_V5', 'Unfiltered_V6', 'Unfiltered_aVF', 'Unfiltered_aVL', 'Unfiltered_aVR']] = unfiltered_leads[['I', 'II', 'III', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'aVF', 'aVL', 'aVR']] del unfiltered_leads del VTI_leads if len(half_data) > 0: array = np.unique(half_data[half_data.isnull().any(axis=1)][['ID', 'Date', 'Time']]) missing_half_data = half_data.loc[half_data['ID'].isin(array) & half_data['Date'].isin(array) & half_data['Time'].isin(array)] half_data.drop(missing_half_data.index, axis=0,inplace=True) missing_half_data = missing_half_data.reset_index(drop=True) del Tag del Tags half_data = half_data.reset_index(drop=True) for n in range(count500): half_data.Tonset[n*12:(n+1)*12] = np.repeat(int(half_data.Tonset[n*12:(n+1)*12].sum()/12), 12) half_data.Pdur[n*12:(n+1)*12] = np.repeat(int(half_data.Pdur[n*12:(n+1)*12].sum()/12), 12) x = 0 p = [] for x in range(len(half_data.Waveform)): t = base64.b64decode(half_data.Waveform[x]) p.append(np.asarray(t)) x+=1 p = np.asarray(p) a = [] for i in p: o = [] for x in i: o.append(x) a.append(o) half_df = pd.DataFrame(a) half_df.insert(0, 'Lead', half_data['Lead']) blank = [] for n in range(count500): blank.append(pd.pivot_table(half_df[(n*12):(n+1)*12], columns=half_df.Lead)) test = pd.concat(blank) new = [] array = [] for n in range(13): for index, num in zip(test.iloc[:, n-1][::2], test.iloc[:, n-1][1::2]): if num > 128: new.append(index - (256 * (256 - num))) elif num < 128: new.append(index + (256 * num)) elif num == 0: new.append(index) else: new.append(index) new = [] array.append(new) array = np.asarray([array[0], array[1], array[2], array[3], array[4], array[5], array[6], array[7], array[8], array[9], array[10], array[11]]) half_df = pd.DataFrame(array) half_df = pd.pivot_table(half_df, columns=test.columns) half_df = half_df.fillna(0) blank = [] for n in range(count500): blank.append(half_df[(n*1200):((n+1)*1200)-600]) test = pd.concat(blank) half_df = test half_df = half_df.reset_index(drop=True) half_df = pd.pivot_table(half_df, columns=half_df.index) array = [] for i in range(count500): for x in range(12): temp = [] new = [] for n in half_df.iloc[x,i*600:(i+1)*600]: temp.append(n) if len(temp) > 1: new.append(temp[-2]) if len(temp) < 601 and len(temp) > 1: new.append((temp[-1]+temp[-2])/2) if len(temp) == 600: new.append(temp[-1]) new.append(temp[-1]) array.append(new) I = (np.asarray(array[::12])).reshape(count500*1200) II = (np.asarray(array[1::12])).reshape(count500*1200) III = (np.asarray(array[2::12])).reshape(count500*1200) V1 = (np.asarray(array[3::12])).reshape(count500*1200) V2 = (np.asarray(array[4::12])).reshape(count500*1200) V3 = (np.asarray(array[5::12])).reshape(count500*1200) V4 = (np.asarray(array[6::12])).reshape(count500*1200) V5 = (np.asarray(array[7::12])).reshape(count500*1200) V6 = (np.asarray(array[8::12])).reshape(count500*1200) aVF = (np.asarray(array[9::12])).reshape(count500*1200) aVL = (np.asarray(array[10::12])).reshape(count500*1200) aVR = (np.asarray(array[11::12])).reshape(count500*1200) half_df = pd.pivot_table(pd.DataFrame([I, II, III, V1, V2, V3, V4, V5, V6, aVF, aVL, aVR]), columns=test.columns) half_df = half_df.fillna(0) del I del II del III del V1 del V2 del V3 del V4 del V5 del V6 del aVF del aVL del aVR del a del p del o del t del blank del new del array del temp for n in range(count500): for x in range(12): if ((half_data.Toffset[n*12]-half_data.RRint[n*12]) >= half_data.Ponset[n*12]) or ((half_data.Ponset[n*12] + half_data.RRint[n*12]) - half_data.Toffset[n*12] == 1): half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: rrint = half_data.RRint[n*12] if (rrint + half_data.Ponset[n*12]) > 1200 and (half_data.Toffset[n*12]-rrint) < 0: temp = half_df.iloc[:,x][int(n*1200):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif (rrint + half_data.Ponset[n*12]) > 1200 and (half_data.Toffset[n*12]-rrint) > 0: temp = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)-rrint):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif rrint + half_data.Ponset[n*12] < 1200 and (half_data.Toffset[n*12]-rrint) < 0: temp = half_df.iloc[:,x][int(n*1200):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int(rrint + half_data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: temp = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)-rrint):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int(rrint + half_data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 for x in range(12): half_df.iloc[:,x] = half_df.iloc[:,x]*2.5 unfiltered_half_leads = half_df.copy() for n in range(count500): for inx in range(12): test = half_df_fixer(half_df.iloc[:,inx][n*1200:(n+1)*1200], n) gaps = [] lstOfNs = [] gap = [] for num in test[test.isna() == True].index: lstOfNs.append(num) if len(lstOfNs) == 1: gap.append(lstOfNs[0]) if len(lstOfNs) > 1: if lstOfNs[-1] - lstOfNs[-2] < 5: gap.append(num) elif lstOfNs[-1] - lstOfNs[-2] > 5: gaps.append(gap) gap = [] gap.append(num) gaps.append(gap) if gaps != [[]]: x = [] y = [] for g in gaps: if len(g) == 1: x.append([g[-1]+1]) y.append(test[g[-1]+1]) if np.isnan(test.iloc[0]): point1 = [g[0], test[g[-1]+1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) else: point1 = [g[0]-1, test[g[0]-1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) for i in range(len(x)): test[x[i]] = y[i] if (trapz(abs(test[int(half_data.Qonset[n*12]):int(half_data.Qoffset[n*12])]))/trapz(abs(half_df.iloc[:,inx][int(half_data.Qonset[12*n]+(1200*n)):int(half_data.Qoffset[12*n]+(1200*n))]))) < .60: test = half_df.iloc[:,inx][n*1200:(n+1)*1200] test = medfilt(test, kernel_size=9) half_df.iloc[:,inx][n*1200:(n+1)*1200] = test del gaps del lstOfNs del gap del test half_VTI_leads = half_df[['III', 'aVF', 'aVL', 'aVR']] half_df = half_df[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] Unfiltered_half_VTI_leads = unfiltered_half_leads[['III', 'aVF', 'aVL', 'aVR']] unfiltered_half_leads = unfiltered_half_leads[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] matrix = [[.38, -.07, -.13, .05, -.01, .14, .06, .54], [-.07, .93, .06, -.02, -.05, .06, -.17, .13], [.11, -.23, -.43, -.06, -.14, -.20, -.11, .31]] x = matrix[0] y = matrix[1] z = matrix[2] n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(half_df)): xtemp.append((half_df.iloc[n].values * x).sum()) ytemp.append((half_df.iloc[n].values * y).sum()) ztemp.append((half_df.iloc[n].values * z).sum()) n+=1 half_df['x'] = xtemp half_df['y'] = ytemp half_df['z'] = ztemp x = matrix[0] y = matrix[1] z = matrix[2] n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(unfiltered_half_leads)): xtemp.append((unfiltered_half_leads.iloc[n].values * x).sum()) ytemp.append((unfiltered_half_leads.iloc[n].values * y).sum()) ztemp.append((unfiltered_half_leads.iloc[n].values * z).sum()) n+=1 half_df['Unfiltered_x'] = xtemp half_df['Unfiltered_y'] = ytemp half_df['Unfiltered_z'] = ztemp del xtemp del ytemp del ztemp half_df['Date'] = half_data['Date'] half_df['ID'] = half_data['ID'] half_df['Time'] = half_data['Time'] half_df['Ponset'] = half_data['Ponset'] half_df['Print'] = half_data['Print'] half_df['Pdur'] = half_data['Pdur'] half_df['Poffset'] = half_data['Poffset'] half_df['Qonset'] = half_data['Qonset'] half_df['Qrsdur'] = half_data['Qrsdur'] half_df['Qtint'] = half_data['Qtint'] half_df['Qoffset'] = half_data['Qoffset'] half_df['Tonset'] = half_data['Tonset'] half_df['Tdur'] = half_data['Tdur'] half_df['Toffset'] = half_data['Toffset'] half_df['HeartRate'] = half_data['HeartRate'] half_df['QRSFrontAxis'] = half_data['QRSFrontAxis'] half_df['Sex'] = half_data['Sex'] half_df['QTC'] = half_data['QTC'] half_df['Age'] = half_data['Age'] half_df['Name'] = half_data['Name'] for n in range(count500): half_df['Ponset'][(n*1200):(n+1)*1200] = half_data['Ponset'][n*12] half_df['Print'][(n*1200):(n+1)*1200] = half_data['Print'][n*12] half_df['Pdur'][(n*1200):(n+1)*1200] = half_data['Pdur'][n*12] half_df['Poffset'][(n*1200):(n+1)*1200] = half_data['Poffset'][n*12] half_df['Qonset'][(n*1200):(n+1)*1200] = half_data['Qonset'][n*12] half_df['Qrsdur'][(n*1200):(n+1)*1200] = half_data['Qrsdur'][n*12] half_df['Qtint'][(n*1200):(n+1)*1200] = half_data['Qtint'][n*12] half_df['Qoffset'][(n*1200):(n+1)*1200] = half_data['Qoffset'][n*12] half_df['Tonset'][(n*1200):(n+1)*1200] = half_data['Tonset'][n*12] half_df['Tdur'][(n*1200):(n+1)*1200] = half_data['Tdur'][n*12] half_df['Toffset'][(n*1200):(n+1)*1200] = half_data['Toffset'][n*12] half_df['HeartRate'][(n*1200):(n+1)*1200] = half_data['HeartRate'][n*12] half_df['QRSFrontAxis'][(n*1200):(n+1)*1200] = half_data['QRSFrontAxis'][n*12] half_df['Sex'][(n*1200):(n+1)*1200] = half_data['Sex'][n*12] half_df['QTC'][(n*1200):(n+1)*1200] = half_data['QTC'][n*12] half_df['Name'][(n*1200):(n+1)*1200] = half_data['Name'][12*n] half_df['Age'][(n*1200):(n+1)*1200] = half_data['Age'][12*n] half_df['ID'][(n*1200):(n+1)*1200] = half_data['ID'][12*n] half_df['Date'][(n*1200):(n+1)*1200] = half_data['Date'][12*n] half_df['Time'][(n*1200):(n+1)*1200] = half_data['Time'][12*n] half_df[['III', 'aVF', 'aVL', 'aVR']] = half_VTI_leads unfiltered_half_leads[['III', 'aVF', 'aVL', 'aVR']] = Unfiltered_half_VTI_leads half_df[['Unfiltered_I', 'Unfiltered_II', 'Unfiltered_III', 'Unfiltered_V1', 'Unfiltered_V2', 'Unfiltered_V3', 'Unfiltered_V4', 'Unfiltered_V5', 'Unfiltered_V6', 'Unfiltered_aVF', 'Unfiltered_aVL', 'Unfiltered_aVR']] = unfiltered_half_leads[['I', 'II', 'III', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'aVF', 'aVL', 'aVR']] del unfiltered_half_leads del half_VTI_leads if (len(half_data) > 0) and (len(data) > 0): df = pd.concat([df, half_df]) df = df.reset_index(drop=True) del half_data del data del half_df if (len(half_data) > 0) and (len(data) == 0): df = half_df del half_df del half_data if (len(half_data) == 0) and (len(data) > 0): df = df del data df['total_xyz'] = ((df.x)**2 + (df.y)**2 + (df.z)**2)**0.5 QRSVTI = [] for n in range(count): QRSVTI.append(trapz(df.total_xyz[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))])) QRSVTI = np.repeat(QRSVTI, 1200) df['QRSVTI'] = QRSVTI del QRSVTI QRStVTI = [] for n in range(count): QRStVTI.append(trapz(df.total_xyz[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))])) QRStVTI = np.repeat(QRStVTI, 1200) df['QRStVTI'] = QRStVTI del QRStVTI XVTI = [] for n in range(count): XVTI.append(trapz(abs(df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) XVTI = np.repeat(XVTI, 1200) df['XVTI'] = XVTI del XVTI YVTI = [] for n in range(count): YVTI.append(trapz(abs(df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) YVTI = np.repeat(YVTI, 1200) df['YVTI'] = YVTI del YVTI ZVTI = [] for n in range(count): ZVTI.append(trapz(abs(df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) ZVTI = np.repeat(ZVTI, 1200) df['ZVTI'] = ZVTI del ZVTI df['QRS3DArea'] = ((df.XVTI)**2 + (df.YVTI)**2 + (df.ZVTI)**2)**0.5 XtVTI = [] for n in range(count): XtVTI.append(trapz(abs(df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) XtVTI = np.repeat(XtVTI, 1200) df['XtVTI'] = XtVTI del XtVTI YtVTI = [] for n in range(count): YtVTI.append(trapz(abs(df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) YtVTI = np.repeat(YtVTI, 1200) df['YtVTI'] = YtVTI del YtVTI ZtVTI = [] for n in range(count): ZtVTI.append(trapz(abs(df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) ZtVTI = np.repeat(ZtVTI, 1200) df['ZtVTI'] = ZtVTI del ZtVTI df['QRSt3DArea'] = ((df.XtVTI)**2 + (df.YtVTI)**2 + (df.ZtVTI)**2)**0.5 XVTI = [] for n in range(count): XVTI.append(trapz((df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) XVTI = np.repeat(XVTI, 1200) df['XVector_VTI'] = XVTI del XVTI YVTI = [] for n in range(count): YVTI.append(trapz((df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) YVTI = np.repeat(YVTI, 1200) df['YVector_VTI'] = YVTI del YVTI ZVTI = [] for n in range(count): ZVTI.append(trapz((df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) ZVTI = np.repeat(ZVTI, 1200) df['ZVector_VTI'] = ZVTI del ZVTI df['QRS3DVector_Area'] = ((df.XVector_VTI)**2 + (df.YVector_VTI)**2 + (df.ZVector_VTI)**2)**0.5 XtVTI = [] for n in range(count): XtVTI.append(trapz((df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) XtVTI = np.repeat(XtVTI, 1200) df['XtVector_VTI'] = XtVTI del XtVTI YtVTI = [] for n in range(count): YtVTI.append(trapz((df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) YtVTI = np.repeat(YtVTI, 1200) df['YtVector_VTI'] = YtVTI del YtVTI ZtVTI = [] for n in range(count): ZtVTI.append(trapz((df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) ZtVTI = np.repeat(ZtVTI, 1200) df['ZtVector_VTI'] = ZtVTI del ZtVTI df['QRSt3DVector_Area'] = ((df.XtVector_VTI)**2 + (df.YtVector_VTI)**2 + (df.ZtVector_VTI)**2)**0.5 Tamp = [] XTamp = [] YTamp = [] ZTamp = [] TpTe = [] XTpTe = [] YTpTe = [] ZTpTe = [] QpQe = [] for x in range(count): if int(df.Tonset[1200*x]+(1200*x)) > int(df.Toffset[1200*x]+(1200*x)): XTamp.append(np.nan) XTpTe.append(np.nan) YTamp.append(np.nan) YTpTe.append(np.nan) ZTamp.append(np.nan) ZTpTe.append(np.nan) Tamp.append(np.nan) TpTe.append(np.nan) Qa = [abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]])) QpQe.append(len([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]][Qa:])) elif df.Tonset[1200*x] == df.Toffset[1200*x]: XTamp.append(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) XTpTe.append(len([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) YTamp.append(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) YTpTe.append(len([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) ZTamp.append(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) ZTpTe.append(len([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) Tamp.append(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) TpTe.append(len([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) Qa = [abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]])) QpQe.append(len([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]][Qa:])) else: XTamp.append(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) XTpTe.append(len([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) YTamp.append(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) YTpTe.append(len([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) ZTamp.append(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) ZTpTe.append(len([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) Tamp.append(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) TpTe.append(len([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) Qa = [abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]])) QpQe.append(len([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]][Qa:])) QpQe = np.repeat(QpQe, 1200) df['QpQe'] = QpQe Tamp = np.repeat(Tamp, 1200) df['Tamp'] = Tamp XTamp = np.repeat(XTamp, 1200) df['XTamp'] = XTamp YTamp = np.repeat(YTamp, 1200) df['YTamp'] = YTamp ZTamp = np.repeat(ZTamp, 1200) df['ZTamp'] = ZTamp XTpTe = np.repeat(XTpTe, 1200) df['XTpTe'] = XTpTe YTpTe = np.repeat(YTpTe, 1200) df['YTpTe'] = YTpTe ZTpTe = np.repeat(ZTpTe, 1200) df['ZTpTe'] = ZTpTe TpTe = np.repeat(TpTe, 1200) df['TpTe'] = TpTe del Tamp del XTamp del YTamp del ZTamp del XTpTe del YTpTe del ZTpTe del TpTe del QpQe temp = df[['I', 'II', 'III', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'aVR', 'aVL', 'aVF', 'x', 'y', 'z', 'total_xyz']] Qamp = [] for x in range(count): for i in range(16): if min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0 and max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) > 0: Qamp.append(max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) - min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) elif min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) > 0 and max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0: Qamp.append(max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) - min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) elif min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0 and max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0: Qamp.append(min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) else: Qamp.append(max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) del temp XQamp = Qamp[12::16] XQamp = np.repeat(XQamp, 1200) YQamp = Qamp[13::16] YQamp = np.repeat(YQamp, 1200) ZQamp = Qamp[14::16] ZQamp = np.repeat(ZQamp, 1200) Qamp = Qamp[15::16] Qamp = np.repeat(Qamp, 1200) df['XQamp'] = XQamp df['YQamp'] = YQamp df['ZQamp'] = ZQamp df['Qamp'] = Qamp del XQamp del YQamp del ZQamp del Qamp text_df = df[['ID', 'Name', 'Age', 'Sex', 'Date', 'Time','HeartRate','Pdur','Print','Qrsdur','Qtint','QTC','TpTe','QRSFrontAxis', 'QRSVTI','XVector_VTI', 'YVector_VTI','ZVector_VTI', 'QRStVTI', 'XtVTI','YtVTI', 'ZtVTI', 'Qamp','XQamp','YQamp', 'ZQamp','Tamp','XTamp', 'YTamp','ZTamp']] text_df = text_df[::1200] # text_df.to_csv('Entresto_Final_Data.csv', index=False) # signal_df.to_pickle('Entresto_Final_ML.pkl') text_df.to_csv('data.csv', index=False) for n in range(count): # pd.DataFrame(text_df.iloc[n,:]).T.to_csv('{}.csv'.format(root_names[n][:-4]), index=False) x = df.x[n*1200:(n+1)*1200] y = df.y[n*1200:(n+1)*1200] z = df.z[n*1200:(n+1)*1200] rms = df.total_xyz[n*1200:(n+1)*1200] fig, ((ax, ax1), (ax2, ax3)) = plt.subplots(2, 2, figsize=(15, 8)) ax.plot(x) ax.set_title('Lead X') ax1.plot(y) ax1.set_title('Lead Y') ax2.plot(z) ax2.set_title('Lead Z') ax3.plot(rms) ax3.set_title('XYZ RMS') fig.subplots_adjust(hspace=.3) fig.subplots_adjust(wspace=.1) fig.savefig('{}.png'.format(root_names[n][:-4]), dpi=1800, format='png') del df
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src/core/migrations/0020_auto_20181125_0510.py
metabolism-of-cities/ARCHIVED-metabolism-of-cities-platform-v3
0
176673
# Generated by Django 2.1.3 on 2018-11-25 05:10 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0019_auto_20181125_0501'), ] operations = [ migrations.AlterField( model_name='reference', name='title_original_language', field=models.CharField(blank=True, max_length=255, null=True), ), ]
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spNQueenTheorem/generator.py
lightofanima/Adventures
0
166795
# This script generates a theorem for them Z3 SAT solver. The output of this program # is designed to be the input for http://rise4fun.com/z3 # The output of z3 is the coordinates of the queens for a solution to the N Queen problem :) #Prints an assert statement def zassert(x): print("( assert ( {} ) )".format(x)) #Prints a declaration def zdeclare(x, type="Int"): print("( declare-const {} {} )".format(x,type)) #Generates a Z3 proof. # N = number of queens. # G = grid size (8 = chess board) def generate(N, G) : zdeclare("N") #Nuber of queens zdeclare("G") #Board size zassert("= N {}".format(N)) #Init N zassert("= G {}".format(G)) #Init G #Generate queen names queensX = ["P{}_x".format(n) for n in range(0, N) ] queensY = ["P{}_y".format(n) for n in range(0, N) ] #Declare queens for i in range(N): zdeclare(queensX[i]) zdeclare(queensY[i]) #For each queen Position for P in range(N): #Assert bounds zassert(">= {} 0".format(queensX[P])) zassert(">= {} 0".format(queensY[P])) zassert("< {} G".format(queensX[P])) zassert("< {} G".format(queensY[P])) for PP in range(P+1, N): #Assert Horizontal and Vertical Uniqueness zassert("not ( or (= {ax} {bx} ) (= {ay} {by} ) )" .format(ax=queensX[P], bx=queensX[PP], ay=queensY[P], by=queensY[PP])) #Assert Diagonal uniqueness # / angle zassert("not ( exists (( t Int )) ( and ( and ( and ( = (+ {ax} t) {bx} ) ( >= (+ {ax} t) 0 ) ) ( < (+ {ax} t) G ) ) ( and ( and ( = (+ {ay} t) {by} ) ( >= (+ {ay} t) 0 ) ) ( < (+ {ay} t) G ) ) ) )" .format(ax=queensX[P], bx=queensX[PP], ay=queensY[P], by=queensY[PP])) # \ angle zassert("not ( exists (( t Int )) ( and ( and ( and ( = (+ {ax} t) {bx} ) ( >= (+ {ax} t) 0 ) ) ( < (+ {ax} t) G ) ) ( and ( and ( = (- {ay} t) {by} ) ( >= (- {ay} t) 0 ) ) ( < (- {ay} t) G ) ) ) )" .format(ax=queensX[P], bx=queensX[PP], ay=queensY[P], by=queensY[PP])) print("(check-sat)") print("(get-model)") #Generate proof for 8 queens on an 8x8 grid generate(8,8)
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mypyc/test/test_irbuild.py
aghast/mypy
0
137251
"""Test cases for IR generation.""" import os.path from mypy.test.config import test_temp_dir from mypy.test.data import DataDrivenTestCase from mypy.errors import CompileError from mypyc.common import TOP_LEVEL_NAME from mypyc.ir.func_ir import format_func from mypyc.test.testutil import ( ICODE_GEN_BUILTINS, use_custom_builtins, MypycDataSuite, build_ir_for_single_file, assert_test_output, remove_comment_lines, replace_native_int ) from mypyc.options import CompilerOptions files = [ 'irbuild-basic.test', 'irbuild-lists.test', 'irbuild-dict.test', 'irbuild-statements.test', 'irbuild-nested.test', 'irbuild-classes.test', 'irbuild-optional.test', 'irbuild-tuple.test', 'irbuild-any.test', 'irbuild-generics.test', 'irbuild-try.test', 'irbuild-set.test', 'irbuild-str.test', 'irbuild-strip-asserts.test', 'irbuild-int.test', ] class TestGenOps(MypycDataSuite): files = files base_path = test_temp_dir optional_out = True def run_case(self, testcase: DataDrivenTestCase) -> None: # Kind of hacky. Not sure if we need more structure here. options = CompilerOptions(strip_asserts='StripAssert' in testcase.name) """Perform a runtime checking transformation test case.""" with use_custom_builtins(os.path.join(self.data_prefix, ICODE_GEN_BUILTINS), testcase): expected_output = remove_comment_lines(testcase.output) expected_output = replace_native_int(expected_output) try: ir = build_ir_for_single_file(testcase.input, options) except CompileError as e: actual = e.messages else: actual = [] for fn in ir: if (fn.name == TOP_LEVEL_NAME and not testcase.name.endswith('_toplevel')): continue actual.extend(format_func(fn)) assert_test_output(testcase, actual, 'Invalid source code output', expected_output)
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applications/ParticleMechanicsApplication/tests/test_search_mpm_particle.py
lcirrott/Kratos
2
76864
<reponame>lcirrott/Kratos from __future__ import print_function, absolute_import, division import KratosMultiphysics import KratosMultiphysics.ParticleMechanicsApplication as KratosParticle import KratosMultiphysics.KratosUnittest as KratosUnittest class TestSearchMPMParticle(KratosUnittest.TestCase): def _generate_particle_element(self, current_model, dimension, geometry_element, is_structured, is_fine=False): KratosMultiphysics.Logger.GetDefaultOutput().SetSeverity(KratosMultiphysics.Logger.Severity.WARNING) # Initialize model part ## Material model part definition material_point_model_part = current_model.CreateModelPart("dummy_name") material_point_model_part.ProcessInfo.SetValue(KratosMultiphysics.DOMAIN_SIZE, dimension) ## Initial material model part definition initial_mesh_model_part = current_model.CreateModelPart("Initial_dummy_name") initial_mesh_model_part.ProcessInfo.SetValue(KratosMultiphysics.DOMAIN_SIZE, dimension) ## Grid model part definition grid_model_part = current_model.CreateModelPart("Background_Grid") grid_model_part.ProcessInfo.SetValue(KratosMultiphysics.DOMAIN_SIZE, dimension) # Create Background Grid sub_background = grid_model_part.CreateSubModelPart("test") if is_structured: self._create_background_nodes_structured(sub_background, dimension, geometry_element) else: self._create_background_nodes_unstructured(sub_background, dimension, geometry_element, is_fine) self._create_background_elements(sub_background,dimension, geometry_element, is_structured) # Create element and nodes sub_mp = initial_mesh_model_part.CreateSubModelPart("test") sub_mp.GetProperties()[1].SetValue(KratosParticle.PARTICLES_PER_ELEMENT, 1) if is_structured: self._create_nodes_structured(sub_mp, dimension, geometry_element) else: self._create_nodes_unstructured(sub_mp, dimension, geometry_element, is_fine) self._create_elements(sub_mp,dimension, geometry_element) # Set active KratosMultiphysics.VariableUtils().SetFlag(KratosMultiphysics.ACTIVE, True, initial_mesh_model_part.Elements) # Generate MP Elements KratosParticle.GenerateMaterialPointElement(grid_model_part, initial_mesh_model_part, material_point_model_part, False, False) def _create_nodes_structured(self, model_part, dimension, geometry_element): if geometry_element == "Triangle": model_part.CreateNewNode(1, 0.0, 0.0, 0.0) model_part.CreateNewNode(2, 1.0, 0.0, 0.0) model_part.CreateNewNode(3, 0.0, 1.0, 0.0) if (dimension == 3): model_part.CreateNewNode(4, 0.0, 0.0, 1.0) elif geometry_element == "Quadrilateral": model_part.CreateNewNode(1, -0.5, -0.5, 0.0) model_part.CreateNewNode(2, 0.5, -0.5, 0.0) model_part.CreateNewNode(3, 0.5, 0.5, 0.0) model_part.CreateNewNode(4, -0.5, 0.5, 0.0) if (dimension == 3): model_part.CreateNewNode(5, -0.5, -0.5, 1.0) model_part.CreateNewNode(6, 0.5, -0.5, 1.0) model_part.CreateNewNode(7, 0.5, 0.5, 1.0) model_part.CreateNewNode(8, -0.5, 0.5, 1.0) def _create_background_nodes_structured(self, model_part, dimension, geometry_element): self._create_nodes_structured(model_part, dimension, geometry_element) if geometry_element == "Triangle": model_part.CreateNewNode(5, 1.0, 1.0, 0.0) if (dimension == 3): model_part.CreateNewNode(6, 1.0, 0.0, 1.0) model_part.CreateNewNode(7, 0.0, 1.0, 1.0) model_part.CreateNewNode(8, 1.0, 1.0, 1.0) elif geometry_element == "Quadrilateral": model_part.CreateNewNode(9 , 1.5, -0.5, 0.0) model_part.CreateNewNode(10, 1.5, 0.5, 0.0) if (dimension == 3): model_part.CreateNewNode(11, 1.5, -0.5, 1.0) model_part.CreateNewNode(12, 1.5, 0.5, 1.0) def _create_nodes_unstructured(self, model_part, dimension, geometry_element, is_fine): if is_fine: modulus=1.e-7 else: modulus=1 if geometry_element == "Triangle": model_part.CreateNewNode(1, 0.9*modulus, 2.9*modulus, 0.0*modulus) model_part.CreateNewNode(2, 0.0*modulus, 0.0*modulus, 0.0*modulus) model_part.CreateNewNode(3, 2.9*modulus, 2.4*modulus, 0.0*modulus) if (dimension == 3): model_part.CreateNewNode(4, 0.5*modulus, 0.5*modulus, 1.5*modulus) elif geometry_element == "Quadrilateral": model_part.CreateNewNode(1, -0.734*modulus, -0.621*modulus, 0.0*modulus) model_part.CreateNewNode(2, 0.497*modulus, -0.432*modulus, 0.0*modulus) model_part.CreateNewNode(3, 0.587*modulus, 0.402*modulus, 0.0*modulus) model_part.CreateNewNode(4, -0.809*modulus, 0.522*modulus, 0.0*modulus) if (dimension == 3): model_part.CreateNewNode(5, -0.621*modulus, -0.734*modulus, 0.93*modulus) model_part.CreateNewNode(6, 0.432*modulus, -0.497*modulus, 1.11*modulus) model_part.CreateNewNode(7, 0.402*modulus, 0.587*modulus, 0.89*modulus) model_part.CreateNewNode(8, -0.522*modulus, 0.809*modulus, 1.21*modulus) def _create_background_nodes_unstructured(self, model_part, dimension, geometry_element, is_fine): self._create_nodes_unstructured(model_part, dimension, geometry_element, is_fine) if is_fine: modulus=1.e-7 else: modulus=1 if geometry_element == "Triangle": model_part.CreateNewNode(5, 2.1*modulus, 0.1*modulus, 0.0*modulus) elif geometry_element == "Quadrilateral": model_part.CreateNewNode(9, 1.343*modulus, -0.451*modulus, 0.0*modulus) model_part.CreateNewNode(10, 1.512*modulus, 0.392*modulus, 0.0*modulus) if (dimension == 3): model_part.CreateNewNode(11, 1.742*modulus, -0.620*modulus, 0.999*modulus) model_part.CreateNewNode(12, 1.520*modulus, 0.671*modulus, 1.120*modulus) def _create_elements(self, model_part, dimension, geometry_element): if geometry_element == "Triangle": if (dimension == 2): model_part.CreateNewElement("UpdatedLagrangian2D3N", 1, [1,2,3], model_part.GetProperties()[1]) if (dimension == 3): model_part.CreateNewElement("UpdatedLagrangian3D4N", 1, [1,2,3,4], model_part.GetProperties()[1]) elif geometry_element == "Quadrilateral": if (dimension == 2): model_part.CreateNewElement("UpdatedLagrangian2D4N", 1, [1,2,3,4], model_part.GetProperties()[1]) if (dimension == 3): model_part.CreateNewElement("UpdatedLagrangian3D8N", 1, [1,2,3,4,5,6,7,8], model_part.GetProperties()[1]) def _create_background_elements(self, model_part, dimension, geometry_element, is_structured): self._create_elements(model_part, dimension, geometry_element) if geometry_element == "Triangle": if (dimension == 2): model_part.CreateNewElement("UpdatedLagrangian2D3N", 2, [2,3,5], model_part.GetProperties()[1]) if (dimension == 3): if (is_structured): model_part.CreateNewElement("UpdatedLagrangian3D4N", 2, [2,8,4,6], model_part.GetProperties()[1]) model_part.CreateNewElement("UpdatedLagrangian3D4N", 3, [4,8,3,7], model_part.GetProperties()[1]) model_part.CreateNewElement("UpdatedLagrangian3D4N", 4, [2,5,3,8], model_part.GetProperties()[1]) model_part.CreateNewElement("UpdatedLagrangian3D4N", 5, [8,3,2,4], model_part.GetProperties()[1]) else: model_part.CreateNewElement("UpdatedLagrangian3D4N", 2, [2,3,5,4], model_part.GetProperties()[1]) elif geometry_element == "Quadrilateral": if (dimension == 2): model_part.CreateNewElement("UpdatedLagrangian2D4N", 2, [2,9,10,3], model_part.GetProperties()[1]) if (dimension == 3): model_part.CreateNewElement("UpdatedLagrangian3D8N", 2, [2,9,10,3,6,11,12,7], model_part.GetProperties()[1]) def _move_and_search_element(self, current_model, new_coordinate, max_num_results = 1000, specific_tolerance = 1.e-5): # Get model part material_point_model_part = current_model.GetModelPart("dummy_name") grid_model_part = current_model.GetModelPart("Background_Grid") # Apply before search for mpm in material_point_model_part.Elements: mpm.SetValue(KratosParticle.MP_COORD, new_coordinate) # Search element KratosParticle.SearchElement(grid_model_part, material_point_model_part, max_num_results, specific_tolerance) def _check_connectivity(self, current_model, expected_connectivity_node=[]): # Get model part material_point_model_part = current_model.GetModelPart("dummy_name") grid_model_part = current_model.GetModelPart("Background_Grid") # Check the searched node as expected connectivity if not expected_connectivity_node: for mpm in material_point_model_part.Elements: self.assertEqual(mpm.GetNodes(), []) else: for mpm in material_point_model_part.Elements: for i in range (len(expected_connectivity_node)): self.assertEqual(mpm.GetNode(i).Id, grid_model_part.GetNode(expected_connectivity_node[i]).Id) self.assertEqual(mpm.GetNode(i).X, grid_model_part.GetNode(expected_connectivity_node[i]).X) self.assertEqual(mpm.GetNode(i).Y, grid_model_part.GetNode(expected_connectivity_node[i]).Y) self.assertEqual(mpm.GetNode(i).Z, grid_model_part.GetNode(expected_connectivity_node[i]).Z) def test_SearchMPMParticleTriangle2DStructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=2, geometry_element="Triangle", is_structured=True) new_coordinate = [0.5, 0.5, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3]) new_coordinate = [0.50001, 0.50001, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,3,5]) new_coordinate = [1.00001, 1.00001, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleTriangle3DStructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=3, geometry_element="Triangle", is_structured=True) new_coordinate = [0.5, 0.25, 0.20] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4]) new_coordinate = [0.90, 0.55, 0.90] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,8,4,6]) new_coordinate = [0.10, 0.90, 0.55] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [4,8,3,7]) new_coordinate = [0.90, 0.90, 0.55] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,5,3,8]) new_coordinate = [0.50, 0.50, 0.50] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [8,3,2,4]) new_coordinate = [1.0001, 1.0001, 1.0001] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleQuadrilateral2DStructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=2, geometry_element="Quadrilateral", is_structured=True) new_coordinate = [-0.11111, 0.12345, 1.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4]) new_coordinate = [0.6, 0.12345, 1.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,9,10,3]) new_coordinate = [1.00001, 1.00001, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleQuadrilateral3DStructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=3, geometry_element="Quadrilateral", is_structured=True) new_coordinate = [0.5, 0.25, 0.20] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4,5,6,7,8]) new_coordinate = [0.7, 0.35, 0.3] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,9,10,3,6,11,12,7]) new_coordinate = [0.50001, 0.50001, 0.50001] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleTriangle2DUnstructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=2, geometry_element="Triangle", is_structured=False) new_coordinate = [1.31967, 1.85246, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3]) new_coordinate = [1.72951, 0.491803, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,3,5]) new_coordinate = [3.00001, 3.00001, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleTriangle3DUnstructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=3, geometry_element="Triangle", is_structured=False) new_coordinate = [1.31967, 1.85246, 1.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4]) new_coordinate = [1.72951, 0.491803, 0.1] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,3,5,4]) new_coordinate = [3.00001, 3.00001, 1.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleQuadrilateral2DUnstructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=2, geometry_element="Quadrilateral", is_structured=False) new_coordinate = [-0.11111, 0.12345, 1.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4]) new_coordinate = [0.6, 0.12345, 1.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,9,10,3]) new_coordinate = [1.00001, 1.00001, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleQuadrilateral3DUnstructured(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=3, geometry_element="Quadrilateral", is_structured=False) new_coordinate = [0.5, 0.25, 0.20] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4,5,6,7,8]) new_coordinate = [0.7, 0.35, 0.3] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,9,10,3,6,11,12,7]) new_coordinate = [0.70001, 0.20001, 1.20001] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleTriangle2DUnstructuredFine(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=2, geometry_element="Triangle", is_structured=False, is_fine=True) new_coordinate = [1.31967e-7, 1.85246e-7, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3]) new_coordinate = [1.72951e-7, 0.491803e-7, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,3,5]) new_coordinate = [3.00001e-7, 3.00001e-7, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleTriangle3DUnstructuredFine(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=3, geometry_element="Triangle", is_structured=False, is_fine=True) new_coordinate = [1.31967e-7, 1.85246e-7, 1.0e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4]) new_coordinate = [1.72951e-7, 0.491803e-7, 0.1e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,3,5,4]) new_coordinate = [3.00001e-7, 3.00001e-7, 1.0e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleQuadrilateral2DUnstructuredFine(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=2, geometry_element="Quadrilateral", is_structured=False, is_fine=True) new_coordinate = [-0.11111e-7, 0.12345e-7, 1.0e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4]) new_coordinate = [0.6e-7, 0.12345e-7, 1.0e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,9,10,3]) new_coordinate = [1.00001e-7, 1.00001e-7, 0.0] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) def test_SearchMPMParticleQuadrilateral3DUnstructuredFine(self): current_model = KratosMultiphysics.Model() self._generate_particle_element(current_model, dimension=3, geometry_element="Quadrilateral", is_structured=False, is_fine=True) new_coordinate = [0.5e-7, 0.25e-7, 0.20e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [1,2,3,4,5,6,7,8]) new_coordinate = [0.7e-7, 0.35e-7, 0.3e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model, [2,9,10,3,6,11,12,7]) new_coordinate = [0.70001e-7, 0.20001e-7, 1.20001e-7] self._move_and_search_element(current_model, new_coordinate) self._check_connectivity(current_model) if __name__ == '__main__': KratosUnittest.main()
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Hello.py
pechimuthu369/pythonapp
0
123985
<reponame>pechimuthu369/pythonapp msg="Hello World" print(msg)
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setup.py
vhdeluca/PyStacking
0
51102
from setuptools import setup, find_packages setup(name='pystacking', version='0.1.0', description='Python Machine Learning Stacking Maker', author='<NAME>', author_email='<EMAIL>', license='MIT', packages=find_packages(), python_requires=">=3.5", tests_require=['pytest'])
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server.py
IT-Department-Projects/HCI-Project
0
137884
from imports import * from utils import * from auth import * """ REST API Call to detect image in a particular picture Argument takes in image with POST Key as 'file' Returns JSON of 'face_exists' and 'number_of_faces' """ @app.route('/image_detection', methods=['GET', 'POST']) def image_detection(): # Check if a valid image file was uploaded if request.method == 'POST': if 'file' not in request.files: return redirect(request.url) file = request.files['file'] if file.filename == '': return redirect(request.url) if file and allowed_file(file.filename): # The image file seems valid! Detect faces and return the result. return detect_faces_in_image(file) # If no valid image file was uploaded, show the file upload form: return ''' <!doctype html> <title>Have you uploaded a picture yet?</title> <h1>Upload a picture and see the number of people in the picture!</h1> <form method="POST" enctype="multipart/form-data"> <input type="file" name="file"> <input type="submit" value="Upload"> </form> ''' """ REST API Call to recognise images in a particular picture Argument takes in image with POST Key as 'file' Returns JSON of 'face_exists' and 'number_of_faces' """ @app.route('/image_recongnition', methods=['GET', 'POST']) def image_recognition(): # Check if a valid image file was uploaded if request.method == 'POST': if 'file' not in request.files: return redirect(request.url) file = request.files['file'] if file.filename == '': return redirect(request.url) if file and allowed_file(file.filename): # The image file seems valid! Detect faces and return the result. return recognise_faces_in_image(file) # If no valid image file was uploaded, show the file upload form: return ''' <!doctype html> <title>Have you uploaded a picture yet?</title> <h1>Upload a picture and see the who all are the people in the picture!</h1> <form method="POST" enctype="multipart/form-data"> <input type="file" name="file"> <input type="submit" value="Upload"> </form> ''' """ REST APT to render the static page index.html """ @app.route('/') @auth.login_required def index(): return render_template('index.html', name='Cycle Project') """ Main method to run the Flask Application """ if __name__ == '__main__': # Set server address 0.0.0.0:5000/ app.run(host="0.0.0.0", port=5000, debug=True)
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logger.py
bekaaa/xgboost_tuner
0
2880
<reponame>bekaaa/xgboost_tuner<gh_stars>0 #! /usr/bin/env python import logging #--------------------------------------- class logger : ''' A ready to use logging class. All you need to do is set an object with the parameters (log_filename, directory to save it) then whenever you want to add text, type obj.add("some text"). The function obj.close() is not important, I just added it for coverage. You can edit any of the below configuration to whatever you like. ''' def __init__(self, filename, log_dir='../data/log'): self.log = None self.handler = None LOG_PATH = log_dir assert type(filename) == str or filename != '' self.logger = logging.getLogger(); self.logger.setLevel(logging.INFO) filename = LOG_PATH + str(filename) self.handler = logging.FileHandler(filename) self.handler.setLevel(logging.INFO) formatter = logging.Formatter( fmt='%(asctime)s : %(message)s', datefmt='%d-%m %H:%M' ) self.handler.setFormatter(formatter) self.logger.addHandler(self.handler) return #------------------------------------ def add(self, message): assert type(message) == str self.logger.info(message); return #------------------------------------ def close(self): self.logger.removeHandler(self.handler) return #----------------------------------------
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app/Http/python/DNVGLRPF103/DNVGLRPF103.py
asavadorndeja/sttools
0
41610
#!/usr/bin/python; import sys import ast import json import math as m import numpy as np # from scipy.interpolate import interp1d # from scipy.optimize import fsolve # Version Controller sTitle = 'DNVGL RP F103 Cathodic protection of submarine pipelines' sVersion = 'Version 1.0.0' # Define constants pi = m.pi e = m.e precision = 2 fFormat = "{:.{}f}" separate = "--------------------" Table62 = np.array([ [25, 0.050, 0.020], [50, 0.060, 0.030], [80, 0.075, 0.040], [120, 0.100, 0.060], [200, 0.130, 0.080], ]) Table63 = np.array([ ['Al-Zn-In', 30, -1.050, 2000, -1.000, 1500], ['Al-Zn-In', 60, -1.050, 1500, -1.000, 680], ['Al-Zn-In', 80, -1.000, 720, -1.000, 320], ['Zn', 30, -1.030, 780, -0.980, 750], ['Zn', 50, -1.050, 2000, -0.750, 580], ]) TableA1 = np.array([ ['Glass fibre reincored alphat enamel', True, 70, 0.01, 0.0003], ['FBE', True, 90, 0.030, 0.0003], ['FBE', False, 90, 0.030, 0.0010], ['3-layer FBE/PE', True, 80, 0.001, 0.00003], ['3-layer FBE/PE', False, 80, 0.001, 0.00003], ['3-layer FBE/PP', True, 110, 0.001, 0.00003], ['3-layer FBE/PP', False, 80, 0.001, 0.00003], ['FBE/PP Thermally insulating coating', False, 140, 0.0003, 0.00001], ['FBE/PU Thermally insulating coating', False, 70, 0.01, 0.003], ['Polychloroprene', False, 90, 0.01, 0.001], ]) TableA2 = np.array([ ['none', '4E(1) moulded PU on top bare steel (with primer)', 70, 0.30, 0.030], ['1D Adhesive Tape or 2A(1)/2A-(2) HSS (PE/PP backing) with mastic adhesive', '4E(2) moulded PU on top 1D or 2A(1)/2A(2)', 70, 0.10, 0.010], ['2B(1) HSS (backing + adhesive in PE with LE primer)', 'none', 70, 0.03, 0.003], ['2B(1) HSS (backing + adhesive in PE with LE primer)', '4E(2) moulded PU on 0.03 0.003 top 2B(1)', 70, 0.03, 0.003], ['2C (1) HSS (backing + adhesive in PP, LE primer)', 'none', 110, 0.03, 0.003], ['2C (1) HSS (backing + adhesive in PP, LE primer)', '4E(2) moulded PU on top 2B(1)', 110, 0.03, 0.003], ['3A FBE', 'none', 90, 0.10, 0.010], ['3A FBE', '4E(2) moulded PU on top', 90, 0.03, 0.003], ['2B(2) FBE with PE HSS', 'none', 70, 0.01, 0.0003], ['2B(2) FBE with PE HSS', '4E(2) moulded PU on top FBE + PE HSS', 70, 0.01, 0.0003], ['5D(1) and 5E FBE with PE applied as flame spraying or tape, respectively', 'none', 70, 0.01, 0.0003], ['2C(2) FBE with PP HSS', 'none', 140, 0.01, 0.0003], ['5A/B/C(1) FBE, PP adhesive and PP (wrapped, flame sprayed or moulded)', 'none', 140, 0.01, 0.0003], ['NA', '5C(1) Moulded PE on top FBE with PE adhesive', 70, 0.01, 0.0003], ['NA', '5C(2) Moulded PP on top FBE with PP adhesive', 140, 0.01, 0.0003], ['8A polychloroprene', 'none', 90, 0.03, 0.001], ]) # This function final mean current demand, M, in accodance with Eq. 3 of [1] def Icf(Ac, fcf, icm, k): Icf = Ac*fcf*icm*k return Icf; def fcf(a, b, t): fcf = a + b*tf return fcf; # This function return required anode mass, M, in accodance with Eq.5 of [1] def M(Icm, tf, u, e): M = (Icm*tf*8760)/(u*e) return M; def DNVGLRPF113(D, lPL, lFJ, tAmb, tAno, tf, rhoSea, aGap, aThk, aDen, aMat, coatLP, coatFJ, nJoints, burial, u=0.8): k = 1.0 EA0 = -0.85 # determine mean current demand from Table 6-2 of [1] icm = [x for x in Table62 if x[0] > tAmb][0][burial] # print(icm) if aMat == False: aMaterial = 'Al-Zn-In' else: aMaterial = 'Zn' # determine anode properties from Table 6-3 of [1] anode = [x for x in Table63 if (x[0] == aMaterial and float(x[1]) >= float(tAno)) ][0] if burial == 1: EC0 = float(anode[2]) e = float(anode[3]) else: EC0 = float(anode[4]) e = float(anode[5]) # print(anode) # print(EC0) # print(e) # determine coating breakdown factor from Table A-1 of [1] coatingPL = TableA1[coatLP] aPL = float(coatingPL[3]) bPL = float(coatingPL[4]) # print(coatingPL) # print(aPL) # print(bPL) # determine field joint coating breakdown factor from Table A-2 of [1] coatingFJ = TableA2[coatFJ] aFJ = float(coatingFJ[3]) bFJ = float(coatingFJ[4]) # print(coatingFJ) # print(aFJ) # print(bFJ) # determine coating area Acl = pi*D*(lPL)*nJoints AclPL = pi*D*(lPL-2*lFJ)*nJoints AclFJ = pi*D*(2*lFJ)*nJoints # print(AclPL) # print(AclFJ) # print(Acl) #determine mean coating breakdown factor, Eq 2 of [1] fcmPL = aPL + 0.5*bPL*tf fcmFJ = aFJ + 0.5*bFJ*tf # print(fcmPL) # print(fcmFJ) #determine mean current demand, Eq 1 of [1] IcmPL = AclPL*fcmPL*icm*k IcmFJ = AclFJ*fcmFJ*icm*k Icm = IcmPL + IcmFJ # print(IcmPL) # print(IcmFJ) # print(Icm) #determine final coating breakdown factor, Eq 4 of [1] fcfPL = aPL + bPL*tf fcfFJ = aFJ + bFJ*tf # print(fcfPL) # print(fcfFJ) #determine final coating breakdown factor, Eq 3 of [1] IcfPL = AclPL*fcfPL*icm*k IcfFJ = AclFJ*fcfFJ*icm*k Icf = IcfPL + IcfFJ # print(IcfPL) # print(IcfFJ) # print(Icf) #determine minimun required anode mass, Eq. 5 of [1] reqM = (Icm*tf*8760)/(0.80*e) reqV = reqM/aDen # print('required anode mass',reqM) # print('required anode volume', reqV) unitV = (0.25*pi*((D + 2*aThk)**2) - 0.25*pi*(D**2) - 2*aGap*aThk) massLength = reqV/unitV # print('required anode length by mass', massLength) deltaE = EC0 - EA0 reqA = (0.315*rhoSea*Icf/deltaE)**2 unitA = pi*(D+2*(1-u)*aThk) - 2*aGap areaLength = reqA/unitA # print('required anode length by area', areaLength) input = [D, lPL, lFJ, tAmb, tAno, tf, rhoSea, aGap, aThk, aDen, aMat, coatLP, coatFJ, nJoints, burial] # output = [icm, anode, coatingPL, coatingFJ, reqM, reqV, massLength, areaLength] output = [icm, reqM, reqA, massLength, areaLength] report = [] resultRaw = [input, output, report] inputJson = { 'Outer diameter, m':D, 'Length of pipeline, m':lPL, 'Length of field joint':lFJ, 'Ambient temperature, degC':tAmb, 'Design life, year':tf, 'Seawater density, kg/cu.m':rhoSea, } outPutJson = { 'No of joints, N:':nJoints, 'Mean current demand, A/Sq.m.': fFormat.format(icm, precision ), 'Min. required anode mass, kg':fFormat.format(reqM, precision ), 'Min. required surface area, Sq.m':fFormat.format(reqA, precision ), 'Min. required length by anode mass, m':fFormat.format(massLength, precision ), 'Min. required length by anode area, m':fFormat.format(areaLength, precision ), } resultJson = {'input':inputJson, 'output':outPutJson, 'report':report} result = [resultRaw, resultJson] return result; D = 273.05E-03 lPL = 12 lFJ = 0.30 tAmb = 30 tAno = 30 tf = 30 rhoSea = 1 aGap = 25E-03 aThk = 50E-03 aDen = 2700 aMat = 0 coatLP = 0 coatFJ = 0 spaceMin = 10 spaceMax = 10 burial = 1 # 1 for non burial and 2 for burial if __name__ == "__main__": D = float(sys.argv[1]) lPL = float(sys.argv[2]) lFJ = float(sys.argv[3]) tAmb = float(sys.argv[4]) tAno = float(sys.argv[5]) tf = float(sys.argv[6]) rhoSea = float(sys.argv[7]) aGap = float(sys.argv[8]) aThk = float(sys.argv[9]) aDen = float(sys.argv[10]) aMat = int(sys.argv[11]) coatLP = int(sys.argv[12]) coatFJ = int(sys.argv[13]) spaceMin = int(sys.argv[14]) spaceMax = int(sys.argv[15]) burial = int(sys.argv[16]) resultJson = [] for nJoints in range(spaceMin, spaceMax + 1): result = DNVGLRPF113(D, lPL, lFJ, tAmb, tAno, tf, rhoSea, aGap, aThk, aDen, aMat, coatLP, coatFJ, nJoints, burial) resultJson.append(result[1]) print (json.dumps(resultJson))
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python3/aoc-01-04/main.py
brunobell/aoc-2019
0
118083
import typing def get_repeat_info(n: int) -> (bool, typing.Dict): digit, is_asc, repeat, hashmap = '0', True, 0, dict() for c in str(n): if c in hashmap: hashmap[c] = hashmap[c] + 1 else: hashmap[c] = 1 if digit <= c: if digit == c: digit, is_asc, repeat = c, is_asc, repeat + 1 else: digit, is_asc, repeat = c, is_asc, 1 else: digit, is_asc, repeat = c, False, 1 return is_asc, hashmap class Solution: def is_password_part_1(self, n: int, min_: int, max_: int) -> bool: is_asc, hashmap = get_repeat_info(n) min_repeat = min([x for x in hashmap.values() if x > 1] or [1]) max_repeat = hashmap[max(hashmap.keys())] if is_asc and max_repeat <= max_ and min_repeat >= min_: return True else: return False def is_password_part_2(self, n: int, repeat: int) -> bool: is_asc, hashmap = get_repeat_info(n) valid = len([x for x in hashmap.values() if x == repeat]) > 0 if is_asc and valid: return True else: return False if __name__ == '__main__': solution = Solution() start, end = 353096, 843212 answer_1 = [] for n in range(start, end + 1): if solution.is_password_part_1(n, 2, 6): answer_1.append(n) print(f"the answer to part-1 is {len(answer_1)}") answer_2 = [] for n in range(start, end + 1): if solution.is_password_part_2(n, 2): answer_2.append(n) print(f"the answer to part-2 is {len(answer_2)}")
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ogreyLevelAttributesPanel.py
opengd/OgreyTool
0
154152
import wx from ogreyEntity import * from FoldPanelBar import * from ogreyScriptEditor import * import ogre.renderer.OGRE as ogre class ogreyLevelAttributesPanel(wx.Panel): def __init__(self, parent, window, resourceInformation, middle): wx.Panel.__init__(self, parent, -1, style = wx.FULL_REPAINT_ON_RESIZE) self.current = None self.Bind(wx.EVT_SIZE, self.OnSize) self.window = window self.resourceInformation = resourceInformation self.parent = parent self.middle = middle self.ogreView = None self.ogreScene = None self.selected = True def select(self, selected = True): self.selected = selected def showAttributes(self, type, object, options = None, item = None): self.current = [type, object, options, item] #self.foldPanel = FoldPanelBar(self, wx.ID_ANY, wx.DefaultPosition, # self.GetSize(), style = wx.FULL_REPAINT_ON_RESIZE) if type == "EntityInstance": EntityInstanceAttributes(self, object, options, item) if type == "Entity": EntityAttributes(self, object, options, item) if not type == None: pass #Comments(self, object, options, item) #self.Show() def Show(self, type, object, options = None, item = None): self.current = [type, object, options, item] if self.selected == True: self.foldPanel = FoldPanelBar(self, wx.ID_ANY, wx.DefaultPosition, self.GetSize(), style = wx.FULL_REPAINT_ON_RESIZE) if not self.current == None: self.showAttributes(self.current[0], self.current[1], self.current[2], self.current[3]) if not self.ogreView == None: EnviromentOptions(self, (self.ogreScene, self.ogreView), self.Level ) def bindView(self, view): self.ogreView = view self.update() def bindScene(self, scene): self.ogreScene = scene self.update() def bindLevel(self, level): self.Level = level def OnSize(self, event): self.update() def clear(self): self.DestroyChildren() def update(self, event = None): self.clear() if not self.current == None: self.clear() self.Show(self.current[0], self.current[1], self.current[2], self.current[3]) #self.Show(n, self.current[1], self.current[2], self.current[3]) def showObjectAttributes(self, Attributes, name): column = (5, 105, 205) row = 22 pan = self.foldPanel.AddFoldPanel(name, False) r = 0 tulo = wx.Panel(pan, -1, style = wx.FULL_REPAINT_ON_RESIZE) for attrib in Attributes: c = 0 for rowItem in attrib: if rowItem["type"] == "text": obj = wx.TextCtrl(tulo, -1,"", style=rowItem["style"], pos = (column[c], row*r)) if not rowItem["attribs"] == None: obj.SetDefaultStyle(rowItem["attribs"]) obj.WriteText(rowItem["value"]) obj.SetEditable(rowItem["editable"]) if not rowItem["event"] == None: obj.Bind(wx.EVT_TEXT, rowItem["event"]) elif rowItem["type"] == "bitmapbutton": modelButton = wx.BitmapButton(tulo, -1,rowItem["image"], pos = (column[c], row*r)) modelButton.Bind(wx.EVT_BUTTON, rowItem["event"]) elif rowItem["type"] == "combobox": comboBox = wx.ComboBox(tulo, -1, pos=(column[c], row*r), choices=rowItem["value"], style=rowItem["style"], size = (self.GetSizeTuple()[0] -8, -1)) comboBox.Bind(wx.EVT_COMBOBOX, rowItem["event"]) elif rowItem["type"] == "button": modelButton = wx.Button(tulo, -1,rowItem["value"], pos = (column[c], row*r)) modelButton.Bind(wx.EVT_BUTTON, rowItem["event"]) elif rowItem["type"] == "checkbox": checkbox = wx.CheckBox(tulo, -1, rowItem["value"], pos = (column[c]+5, row*r)) checkbox.Bind(wx.EVT_CHECKBOX, rowItem["event"]) checkbox.SetValue(rowItem["state"]) elif rowItem["type"] == "slider": slider = wx.Slider(tulo, -1, value = rowItem["value"], minValue = rowItem["minValue"], maxValue = rowItem["maxValue"], style = rowItem["style"], pos = (column[c], row*r)) slider.Bind(wx.EVT_SLIDER, rowItem["event"]) elif rowItem["type"] == "panel": panel = wx.Panel(tulo, -1, pos = (column[c], row*r), size = rowItem["size"], style= rowItem["style"]) panel.SetBackgroundColour(rowItem["bgcolor"]) panel.Refresh(True, None) elif rowItem["type"] == "textctrl": self.text = wx.TextCtrl(tulo, -1, rowItem["value"], pos=(column[c], row*r), size = (self.GetSizeTuple()[0] -8,rowItem["height"]),style = rowItem["style"]) self.text.Bind(wx.EVT_TEXT, rowItem["event"]) elif rowItem["type"] == "comment": text = wx.StaticText(tulo, -1,label = rowItem["label"],pos = (column[c], row*r)) elif rowItem["type"] == "empty": pass c += 1 r += 1 tulo.Fit() self.foldPanel.AddFoldPanelWindow(pan, tulo) class EntityAttributes: def __init__(self, panel, object, tree, item): self.panel = panel self.object = object self.tree = tree self.item = item self.Attributes = [ [ {"type" : "text", "editable" : False, "value" : "Name", "style" : wx.TE_LEFT | wx.TE_RICH2, "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "text", "editable" : False, "value" : self.object.name, "style" : wx.TE_RIGHT, "event" : self.OnEntityName, "attribs" : None}, ], [ {"type" : "text", "editable" : False, "value" : "Name", "style" : wx.TE_LEFT | wx.TE_RICH2, "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "text", "editable" : False, "value" : self.object.filepath, "style" : wx.TE_RIGHT, "event" : self.OnEntityName, "attribs" : None}, ], ] self.panel.showObjectAttributes(self.Attributes, "Entity") def OnEntityName(self, event): self.object.name = event.GetClientObject().GetValue() self.tree.SetItemText(self.item, self.object.name + " - Entity") class EntityInstanceAttributes: def __init__(self, panel, object, tree, item): self.panel = panel self.object = object self.tree = tree self.item = item self.Attributes = [ [ {"type" : "text", "editable" : False, "value" : "Name", "style" : wx.TE_LEFT | wx.TE_RICH2, "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "text", "editable" : True, "value" : self.object.name, "style" : wx.TE_RIGHT, "event" : self.OnEntityName, "attribs" : None}, ] # World Position [{"type" : "text", "editable" : False, "value" : "World Position", "style" : wx.TE_LEFT | wx.TE_RICH2 , "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)},], [{"type" : "text", "editable" : False, "value" : "X", "style" : wx.TE_LEFT | wx.TE_RICH2, "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "text", "editable" : True, "value" : str(self.object.position[0]), "style" : wx.TE_RIGHT, "event" : self.OnEntityPositionX, "attribs" : None}, ], [{"type" : "text", "editable" : False, "value" : "Y", "style" : wx.TE_LEFT | wx.TE_RICH2, "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "text", "editable" : True, "value" : str(self.object.position[1]), "style" : wx.TE_RIGHT, "event" : self.OnEntityPositionY, "attribs" : None}, ], [{"type" : "text", "editable" : False, "value" : "Z", "style" : wx.TE_LEFT | wx.TE_RICH2, "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "text", "editable" : True, "value" : str(self.object.position[2]), "style" : wx.TE_RIGHT, "event" : self.OnEntityPositionZ, "attribs" : None}, ], ] self.panel.showObjectAttributes(self.Attributes, "Entity Instance") def OnEntityName(self, event): self.object.name = event.GetClientObject().GetValue() self.tree.SetItemText(self.item, self.object.name + " - Entity Instance") def OnEntityPositionX(self, event): self.object.position.x = float(event.GetClientObject().GetValue()) #self.update() def OnEntityPositionY(self, event): self.object.position.y = float(event.GetClientObject().GetValue()) #self.update() def OnEntityPositionZ(self, event): self.object.position.z = float(event.GetClientObject().GetValue()) #self.update() class EnviromentOptions: def __init__(self, parent, window, object): self.parent = parent self.window, self.ogreView = window self.object = object self.bgcolour = wx.Colour(0,0,0) tempcolor = self.ogreView.viewport.backgroundColour self.bgcolour = wx.Colour(int(tempcolor.r*255.0),int(tempcolor.g*255.0), int(tempcolor.b*255.0)) self.ambientcolour = wx.Colour(0,0,0) tempcolor = self.window.sceneManager.ambientLight self.ambientcolour = wx.Colour(int(tempcolor.r*255.0),int(tempcolor.g*255.0), int(tempcolor.b*255.0)) self.Attributes = [ [{"type" : "text", "editable" : False, "value" : "Background Color", "style" : wx.TE_LEFT | wx.TE_RICH2 , "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "button", "value" : "Choose Colour...", "event" : self.OnChooseColor}, {"type" : "panel", "size" : (20,20), "style" : wx.SUNKEN_BORDER, "bgcolor" : self.bgcolour},], [{"type" : "text", "editable" : False, "value" : "Ambient Light Color", "style" : wx.TE_LEFT | wx.TE_RICH2 , "return" : None, "event" : None, "attribs" : wx.TextAttr(alignment = wx.TEXT_ALIGNMENT_RIGHT)}, {"type" : "button", "value" : "Choose Colour...", "event" : self.OnAmbientChooseColor}, {"type" : "panel", "size" : (20,20), "style" : wx.SUNKEN_BORDER, "bgcolor" : self.ambientcolour},], [{"type" : "empty"},], ] self.parent.showObjectAttributes(self.Attributes, "Level Enviroment") def OnChooseColor(self, event): dlg = wx.ColourDialog(self.parent) dlg.GetColourData().SetChooseFull(True) if dlg.ShowModal() == wx.ID_OK: data = dlg.GetColourData() (r, g, b) = data.GetColour().Get() #print "Changing Background color to: ", str(float(r)) ,str(float(g)),str(float(b)) self.bgcolour.Set(r, g, b) self.ogreView.viewport.backgroundColour = ((float(r)/255), (float(g)/255), (float(b)/255)) self.object.clearcolor = ((float(r)/255), (float(g)/255), (float(b)/255)) dlg.Destroy() self.parent.update() def OnAmbientChooseColor(self, event): dlg = wx.ColourDialog(self.parent) dlg.GetColourData().SetChooseFull(True) if dlg.ShowModal() == wx.ID_OK: data = dlg.GetColourData() (r, g, b) = data.GetColour().Get() #print "Changing Background color to: ", str(float(r)) ,str(float(g)),str(float(b)) self.ambientcolour.Set(r, g, b) self.window.sceneManager.ambientLight = ((float(r)/255), (float(g)/255), (float(b)/255)) self.object.ambientlight = ((float(r)/255), (float(g)/255), (float(b)/255)) dlg.Destroy() self.parent.update()
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litex_boards/targets/digilent_cmod_a7.py
vacajk/litex-boards
0
67400
<gh_stars>0 #!/usr/bin/env python3 # # This file is part of LiteX-Boards. # # Copyright (c) 2021 <NAME> <<EMAIL>> # SPDX-License-Identifier: BSD-2-Clause import os import sys import argparse from migen import * from litex.build.io import CRG from litex.build.xilinx.vivado import vivado_build_args, vivado_build_argdict from litex_boards.platforms import digilent_cmod_a7 from litex.soc.cores.clock import * from litex.soc.integration.soc_core import * from litex.soc.integration.soc import SoCRegion from litex.soc.integration.builder import * from litex.soc.cores.led import LedChaser from litex.soc.interconnect import wishbone from litex.soc.integration.soc import colorer kB = 1024 mB = 1024*kB class _CRG(Module): def __init__(self, platform, sys_clk_freq): self.rst = Signal() self.clock_domains.cd_sys = ClockDomain() # # # # Clk/Rst. clk12 = platform.request("clk12") rst = platform.request("cpu_reset") # PLL. self.submodules.pll = pll = S7MMCM(speedgrade=-1) self.comb += pll.reset.eq(rst | self.rst) pll.register_clkin(clk12, 12e6) pll.create_clkout(self.cd_sys, sys_clk_freq) platform.add_false_path_constraints(self.cd_sys.clk, pll.clkin) # Ignore sys_clk to pll.clkin path created by SoC's rst. # AsyncSRAM ------------------------------------------------------------------------------------------ class AsyncSRAM(Module): def __init__(self, platform, size): addr_width = size//8 data_width = 8 self.bus = wishbone.Interface(data_width=data_width,adr_width=addr_width) issiram = platform.request("issiram") addr = issiram.addr data = issiram.data wen = issiram.wen cen = issiram.cen ######################## tristate_data = TSTriple(data_width) self.specials += tristate_data.get_tristate(data) ######################## chip_ena = self.bus.cyc & self.bus.stb & self.bus.sel[0] write_ena = (chip_ena & self.bus.we) ######################## # external write enable, # external chip enable, # internal tristate write enable ######################## self.comb += [ cen.eq(~chip_ena), wen.eq(~write_ena), tristate_data.oe.eq(write_ena) ] ######################## # address and data ######################## self.comb += [ addr.eq(self.bus.adr[:addr_width]), self.bus.dat_r.eq(tristate_data.i[:data_width]), tristate_data.o.eq(self.bus.dat_w[:data_width]) ] ######################## # generate ack ######################## self.sync += [ self.bus.ack.eq(self.bus.cyc & self.bus.stb & ~self.bus.ack), ] ######################## def addAsyncSram(soc, platform, name, origin, size): ram_bus = wishbone.Interface(data_width=soc.bus.data_width) ram = AsyncSRAM(platform,size) soc.bus.add_slave(name, ram.bus, SoCRegion(origin=origin, size=size, mode="rw")) soc.check_if_exists(name) soc.logger.info("ISSIRAM {} {} {}.".format( colorer(name), colorer("added", color="green"), soc.bus.regions[name])) setattr(soc.submodules, name, ram) # BaseSoC ------------------------------------------------------------------------------------------ class BaseSoC(SoCCore): def __init__(self, variant="a7-35", toolchain = "vivado", sys_clk_freq = int(100e6), with_led_chaser = True, **kwargs): platform = digilent_cmod_a7.Platform(variant=variant, toolchain=toolchain) # SoCCore ---------------------------------------------------------------------------------- SoCCore.__init__(self, platform, sys_clk_freq, ident = "LiteX SoC on Digilent CmodA7", **kwargs) # CRG -------------------------------------------------------------------------------------- self.submodules.crg = _CRG(platform, sys_clk_freq) addAsyncSram(self,platform,"main_ram",0x40000000,512*1024) # Leds ------------------------------------------------------------------------------------- if with_led_chaser: self.submodules.leds = LedChaser( pads = platform.request_all("user_led"), sys_clk_freq = sys_clk_freq) # Build -------------------------------------------------------------------------------------------- def main(): parser = argparse.ArgumentParser(description="LiteX SoC on CMOD A7") parser.add_argument("--toolchain", default="vivado", help="FPGA toolchain (vivado or symbiflow).") parser.add_argument("--build", action="store_true", help="Build bitstream.") parser.add_argument("--load", action="store_true", help="Load bitstream.") parser.add_argument("--variant", default="a7-35", help="Board variant (a7-35 or a7-100).") parser.add_argument("--sys-clk-freq", default=48e6, help="System clock frequency.") builder_args(parser) soc_core_args(parser) vivado_build_args(parser) args = parser.parse_args() soc = BaseSoC( variant = args.variant, toolchain = args.toolchain, sys_clk_freq = int(float(args.sys_clk_freq)), **soc_core_argdict(args) ) builder_argd = builder_argdict(args) builder = Builder(soc, **builder_argd) builder_kwargs = vivado_build_argdict(args) if args.toolchain == "vivado" else {} builder.build(**builder_kwargs, run=args.build) if args.load: prog = soc.platform.create_programmer() prog.load_bitstream(os.path.join(builder.gateware_dir, soc.build_name + ".bit")) if __name__ == "__main__": main()
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venv/Lib/site-packages/pymessenger/bot.py
shivamsahni/Bot-to-live
0
41116
import json import requests from requests_toolbelt import MultipartEncoder from pymessenger.graph_api import FacebookGraphApi import pymessenger.utils as utils class Bot(FacebookGraphApi): def __init__(self, *args, **kwargs): super(Bot, self).__init__(*args, **kwargs) def send_text_message(self, recipient_id, message): '''Send text messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/text-message Input: recipient_id: recipient id to send to message: message to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': { 'text': message } } return self.send_raw(payload) def send_message(self, recipient_id, message): '''Send text messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/text-message Input: recipient_id: recipient id to send to message: raw message to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': message } return self.send_raw(payload) def send_generic_message(self, recipient_id, elements): '''Send generic messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/generic-template Input: recipient_id: recipient id to send to elements: generic message elements to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': { "attachment": { "type": "template", "payload": { "template_type": "generic", "elements": elements } } } } return self.send_raw(payload) def send_button_message(self, recipient_id, text, buttons): '''Send text messages to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/button-template Input: recipient_id: recipient id to send to text: text of message to send buttons: buttons to send Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'message': { "attachment": { "type": "template", "payload": { "template_type": "button", "text": text, "buttons": buttons } } } } return self.send_raw(payload) def send_image(self, recipient_id, image_path): '''Send an image to the specified recipient. Image must be PNG or JPEG or GIF (more might be supported). https://developers.facebook.com/docs/messenger-platform/send-api-reference/image-attachment Input: recipient_id: recipient id to send to image_path: path to image to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'image', 'payload': {} } } ), 'filedata': (image_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_image_url(self, recipient_id, image_url): '''Send an image to specified recipient using URL. Image must be PNG or JPEG or GIF (more might be supported). https://developers.facebook.com/docs/messenger-platform/send-api-reference/image-attachment Input: recipient_id: recipient id to send to image_url: url of image to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'image', 'payload': { 'url': image_url } } } ) } return self.send_raw(payload) def send_action(self, recipient_id, action): '''Send typing indicators or send read receipts to the specified recipient. Image must be PNG or JPEG. https://developers.facebook.com/docs/messenger-platform/send-api-reference/sender-actions Input: recipient_id: recipient id to send to action: action type (mark_seen, typing_on, typing_off) Output: Response from API as <dict> ''' payload = { 'recipient': { 'id': recipient_id }, 'sender_action': action } return self.send_raw(payload) def _send_payload(self, payload): ''' Deprecated, use send_raw instead ''' return self.send_raw(payload) def send_raw(self, payload): request_endpoint = '{0}/me/messages'.format(self.graph_url) response = requests.post( request_endpoint, params=self.auth_args, json=payload ) result = response.json() return result def send_audio(self, recipient_id, audio_path): '''Send audio to the specified recipient. Audio must be MP3 or WAV https://developers.facebook.com/docs/messenger-platform/send-api-reference/audio-attachment Input: recipient_id: recipient id to send to audio_path: path to audio to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': {} } } ), 'filedata': (audio_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_audio_url(self, recipient_id, audio_url): '''Send audio to specified recipient using URL. Audio must be MP3 or WAV https://developers.facebook.com/docs/messenger-platform/send-api-reference/audio-attachment Input: recipient_id: recipient id to send to audio_url: url of audio to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': { 'url': audio_url } } } ) } return self.send_raw(payload) def send_video(self, recipient_id, video_path): '''Send video to the specified recipient. Video should be MP4 or MOV, but supports more (https://www.facebook.com/help/218673814818907). https://developers.facebook.com/docs/messenger-platform/send-api-reference/video-attachment Input: recipient_id: recipient id to send to video_path: path to video to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': {} } } ), 'filedata': (video_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_video_url(self, recipient_id, video_url): '''Send video to specified recipient using URL. Video should be MP4 or MOV, but supports more (https://www.facebook.com/help/218673814818907). https://developers.facebook.com/docs/messenger-platform/send-api-reference/video-attachment Input: recipient_id: recipient id to send to video_url: url of video to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'audio', 'payload': { 'url': video_url } } } ) } return self.send_raw(payload) def send_file(self, recipient_id, file_path): '''Send file to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/file-attachment Input: recipient_id: recipient id to send to file_path: path to file to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'file', 'payload': {} } } ), 'filedata': (file_path, open(image_path, 'rb')) } multipart_data = MultipartEncoder(payload) multipart_header = { 'Content-Type': multipart_data.content_type } return requests.post(self.base_url, data=multipart_data, headers=multipart_header).json() def send_file_url(self, recipient_id, file_url): '''Send file to the specified recipient. https://developers.facebook.com/docs/messenger-platform/send-api-reference/file-attachment Input: recipient_id: recipient id to send to file_url: url of file to be sent Output: Response from API as <dict> ''' payload = { 'recipient': json.dumps( { 'id': recipient_id } ), 'message': json.dumps( { 'attachment': { 'type': 'file', 'payload': { 'url': file_url } } } ) } return self.send_raw(payload)
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utils/augmentations.py
kogans1107/AR_yolact
2
170501
<reponame>kogans1107/AR_yolact import torch import cv2 import numpy as np import types #from numpy import random from math import sqrt from scipy.spatial import ConvexHull import imageio import copy # Force same sequence of random numbers to be drawn by augmentations. If two # training processes are otherwise the same, i.e. same data coming in, then they # will see the same sequence of augmentations. from numpy.random import MT19937 from numpy.random import RandomState, SeedSequence rs = RandomState(MT19937(SeedSequence(123456789))) from skimage import draw from data import cfg, MEANS, STD def combine_masks(masks): object_combo = np.zeros(masks[0].shape) for m in masks: object_combo += m object_combo[object_combo > 0] = 1 return object_combo def fill_boundary(shape, bpts): t_or_f = draw.polygon2mask(shape, bpts[:,[1,0]]) mask = np.zeros_like(t_or_f, dtype=np.uint8) mask[t_or_f] = 1 return mask def intersect(box_a, box_b): max_xy = np.minimum(box_a[:, 2:], box_b[2:]) min_xy = np.maximum(box_a[:, :2], box_b[:2]) inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf) return inter[:, 0] * inter[:, 1] def jaccard_numpy(box_a, box_b): """Compute the jaccard overlap of two sets of boxes. The jaccard overlap is simply the intersection over union of two boxes. E.g.: A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) Args: box_a: Multiple bounding boxes, Shape: [num_boxes,4] box_b: Single bounding box, Shape: [4] Return: jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]] """ inter = intersect(box_a, box_b) area_a = ((box_a[:, 2]-box_a[:, 0]) * (box_a[:, 3]-box_a[:, 1])) # [A,B] area_b = ((box_b[2]-box_b[0]) * (box_b[3]-box_b[1])) # [A,B] union = area_a + area_b - inter return inter / union # [A,B] class Compose(object): """Composes several augmentations together. Args: transforms (List[Transform]): list of transforms to compose. Example: >>> augmentations.Compose([ >>> transforms.CenterCrop(10), >>> transforms.ToTensor(), >>> ]) """ def __init__(self, transforms): self.transforms = transforms def __call__(self, img, masks=None, boxes=None, labels=None): for t in self.transforms: img, masks, boxes, labels = t(img, masks, boxes, labels) return img, masks, boxes, labels class Lambda(object): """Applies a lambda as a transform.""" def __init__(self, lambd): assert isinstance(lambd, types.LambdaType) self.lambd = lambd def __call__(self, img, masks=None, boxes=None, labels=None): return self.lambd(img, masks, boxes, labels) class ConvertFromInts(object): def __call__(self, image, masks=None, boxes=None, labels=None): return image.astype(np.float32), masks, boxes, labels class ToAbsoluteCoords(object): def __call__(self, image, masks=None, boxes=None, labels=None): height, width, channels = image.shape boxes[:, 0] *= width boxes[:, 2] *= width boxes[:, 1] *= height boxes[:, 3] *= height return image, masks, boxes, labels class ToPercentCoords(object): def __call__(self, image, masks=None, boxes=None, labels=None): height, width, channels = image.shape boxes[:, 0] /= width boxes[:, 2] /= width boxes[:, 1] /= height boxes[:, 3] /= height return image, masks, boxes, labels class Pad(object): """ Pads the image to the input width and height, filling the background with mean and putting the image in the top-left. Note: this expects im_w <= width and im_h <= height """ def __init__(self, width, height, mean=MEANS, pad_gt=True): self.mean = mean self.width = width self.height = height self.pad_gt = pad_gt def __call__(self, image, masks, boxes=None, labels=None): im_h, im_w, depth = image.shape expand_image = np.zeros( (self.height, self.width, depth), dtype=image.dtype) expand_image[:, :, :] = self.mean expand_image[:im_h, :im_w] = image if self.pad_gt: expand_masks = np.zeros( (masks.shape[0], self.height, self.width), dtype=masks.dtype) expand_masks[:,:im_h,:im_w] = masks masks = expand_masks return expand_image, masks, boxes, labels class Resize(object): """ If preserve_aspect_ratio is true, this resizes to an approximate area of max_size * max_size """ @staticmethod def calc_size_preserve_ar(img_w, img_h, max_size): """ I mathed this one out on the piece of paper. Resulting width*height = approx max_size^2 """ ratio = sqrt(img_w / img_h) w = max_size * ratio h = max_size / ratio return int(w), int(h) def __init__(self, resize_gt=True): self.resize_gt = resize_gt self.max_size = cfg.max_size self.preserve_aspect_ratio = cfg.preserve_aspect_ratio def __call__(self, image, masks, boxes, labels=None): img_h, img_w, _ = image.shape if self.preserve_aspect_ratio: width, height = Resize.calc_size_preserve_ar(img_w, img_h, self.max_size) else: width, height = self.max_size, self.max_size image = cv2.resize(image, (width, height)) if self.resize_gt: # Act like each object is a color channel masks = masks.transpose((1, 2, 0)) masks = cv2.resize(masks, (width, height)) # OpenCV resizes a (w,h,1) array to (s,s), so fix that if len(masks.shape) == 2: masks = np.expand_dims(masks, 0) else: masks = masks.transpose((2, 0, 1)) # Scale bounding boxes (which are currently absolute coordinates) boxes[:, [0, 2]] *= (width / img_w) boxes[:, [1, 3]] *= (height / img_h) # Discard boxes that are smaller than we'd like w = boxes[:, 2] - boxes[:, 0] h = boxes[:, 3] - boxes[:, 1] keep = (w > cfg.discard_box_width) * (h > cfg.discard_box_height) masks = masks[keep] boxes = boxes[keep] labels['labels'] = labels['labels'][keep] labels['num_crowds'] = (labels['labels'] < 0).sum() return image, masks, boxes, labels class RandomSaturation(object): def __init__(self, lower=0.5, upper=1.5): self.lower = lower self.upper = upper assert self.upper >= self.lower, "contrast upper must be >= lower." assert self.lower >= 0, "contrast lower must be non-negative." def __call__(self, image, masks=None, boxes=None, labels=None): if rs.randint(2): image[:, :, 1] *= rs.uniform(self.lower, self.upper) return image, masks, boxes, labels class RandomHue(object): def __init__(self, delta=18.0): assert delta >= 0.0 and delta <= 360.0 self.delta = delta def __call__(self, image, masks=None, boxes=None, labels=None): if rs.randint(2): image[:, :, 0] += rs.uniform(-self.delta, self.delta) image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0 image[:, :, 0][image[:, :, 0] < 0.0] += 360.0 return image, masks, boxes, labels class RandomLightingNoise(object): def __init__(self): self.perms = ((0, 1, 2), (0, 2, 1), (1, 0, 2), (1, 2, 0), (2, 0, 1), (2, 1, 0)) def __call__(self, image, masks=None, boxes=None, labels=None): # Don't shuffle the channels please, why would you do this # if random.randint(2): # swap = self.perms[random.randint(len(self.perms))] # shuffle = SwapChannels(swap) # shuffle channels # image = shuffle(image) return image, masks, boxes, labels class ConvertColor(object): def __init__(self, current='BGR', transform='HSV'): self.transform = transform self.current = current def __call__(self, image, masks=None, boxes=None, labels=None): # print('In ConvertColor, shape is', image.shape) # print('and data type is',image.dtype) if self.current == 'BGR' and self.transform == 'HSV': image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) elif self.current == 'HSV' and self.transform == 'BGR': image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) else: raise NotImplementedError return image, masks, boxes, labels class RandomContrast(object): def __init__(self, lower=0.5, upper=1.5): self.lower = lower self.upper = upper assert self.upper >= self.lower, "contrast upper must be >= lower." assert self.lower >= 0, "contrast lower must be non-negative." # expects float image def __call__(self, image, masks=None, boxes=None, labels=None): if rs.randint(2): alpha = rs.uniform(self.lower, self.upper) image *= alpha return image, masks, boxes, labels class RandomBrightness(object): def __init__(self, delta=32): assert delta >= 0.0 assert delta <= 255.0 self.delta = delta def __call__(self, image, masks=None, boxes=None, labels=None): if rs.randint(2): delta = rs.uniform(-self.delta, self.delta) image += delta return image, masks, boxes, labels class ToCV2Image(object): def __call__(self, tensor, masks=None, boxes=None, labels=None): return tensor.cpu().numpy().astype(np.float32).transpose((1, 2, 0)), masks, boxes, labels class ToTensor(object): def __call__(self, cvimage, masks=None, boxes=None, labels=None): return torch.from_numpy(cvimage.astype(np.float32)).permute(2, 0, 1), masks, boxes, labels class RandomSampleCrop(object): """Crop Arguments: img (Image): the image being input during training boxes (Tensor): the original bounding boxes in pt form labels (Tensor): the class labels for each bbox mode (float tuple): the min and max jaccard overlaps Return: (img, boxes, classes) img (Image): the cropped image boxes (Tensor): the adjusted bounding boxes in pt form labels (Tensor): the class labels for each bbox """ def __init__(self): self.sample_options = ( # using entire original input image None, # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9 (0.1, None), (0.3, None), (0.7, None), (0.9, None), # randomly sample a patch (None, None), ) def __call__(self, image, masks, boxes=None, labels=None): height, width, _ = image.shape while True: # randomly choose a mode mode = rs.choice(self.sample_options) if mode is None: return image, masks, boxes, labels min_iou, max_iou = mode if min_iou is None: min_iou = float('-inf') if max_iou is None: max_iou = float('inf') # max trails (50) for _ in range(50): current_image = image w = rs.uniform(0.3 * width, width) h = rs.uniform(0.3 * height, height) # aspect ratio constraint b/t .5 & 2 if h / w < 0.5 or h / w > 2: continue left = rs.uniform(width - w) top = rs.uniform(height - h) # convert to integer rect x1,y1,x2,y2 rect = np.array([int(left), int(top), int(left+w), int(top+h)]) # calculate IoU (jaccard overlap) b/t the cropped and gt boxes overlap = jaccard_numpy(boxes, rect) # This piece of code is bugged and does nothing: # https://github.com/amdegroot/ssd.pytorch/issues/68 # # However, when I fixed it with overlap.max() < min_iou, # it cut the mAP in half (after 8k iterations). So it stays. # # is min and max overlap constraint satisfied? if not try again if overlap.min() < min_iou and max_iou < overlap.max(): continue # cut the crop from the image current_image = current_image[rect[1]:rect[3], rect[0]:rect[2], :] # keep overlap with gt box IF center in sampled patch centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0 # mask in all gt boxes that above and to the left of centers m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1]) # mask in all gt boxes that under and to the right of centers m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1]) # mask in that both m1 and m2 are true mask = m1 * m2 # [0 ... 0 for num_gt and then 1 ... 1 for num_crowds] num_crowds = labels['num_crowds'] crowd_mask = np.zeros(mask.shape, dtype=np.int32) if num_crowds > 0: crowd_mask[-num_crowds:] = 1 # have any valid boxes? try again if not # Also make sure you have at least one regular gt if not mask.any() or np.sum(1-crowd_mask[mask]) == 0: continue # take only the matching gt masks current_masks = masks[mask, :, :].copy() # take only matching gt boxes current_boxes = boxes[mask, :].copy() # take only matching gt labels labels['labels'] = labels['labels'][mask] current_labels = labels # We now might have fewer crowd annotations if num_crowds > 0: labels['num_crowds'] = np.sum(crowd_mask[mask]) # should we use the box left and top corner or the crop's current_boxes[:, :2] = np.maximum(current_boxes[:, :2], rect[:2]) # adjust to crop (by substracting crop's left,top) current_boxes[:, :2] -= rect[:2] current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:], rect[2:]) # adjust to crop (by substracting crop's left,top) current_boxes[:, 2:] -= rect[:2] # crop the current masks to the same dimensions as the image current_masks = current_masks[:, rect[1]:rect[3], rect[0]:rect[2]] return current_image, current_masks, current_boxes, current_labels class Expand(object): def __init__(self, mean): self.mean = mean def __call__(self, image, masks, boxes, labels): # print('Entering Expand...') if rs.randint(2): return image, masks, boxes, labels height, width, depth = image.shape ratio = rs.uniform(1, 4) left = rs.uniform(0, width*ratio - width) top = rs.uniform(0, height*ratio - height) expand_image = np.zeros( (int(height*ratio), int(width*ratio), depth), dtype=image.dtype) expand_image[:, :, :] = self.mean expand_image[int(top):int(top + height), int(left):int(left + width)] = image image = expand_image expand_masks = np.zeros( (masks.shape[0], int(height*ratio), int(width*ratio)), dtype=masks.dtype) expand_masks[:,int(top):int(top + height), int(left):int(left + width)] = masks masks = expand_masks boxes = boxes.copy() boxes[:, :2] += (int(left), int(top)) boxes[:, 2:] += (int(left), int(top)) # print('Doen with Expand...') return image, masks, boxes, labels class NothingToSeeHere(object): # THis is my very weird idea that we should show the model the vast bleak # reaches of irrelevant image space. Does it help? WJP 14-Dec-2021 # # Oops multiboxloss is just not equipped to handle an image with no # targets. ARGH! Bolya himself said he doesn't want to make the many # required changes. def __call__(self, image, masks, boxes, labels): # print('in NTSH',labels,type(labels)) if rs.randint(20): # skip this 19 out of 20 times. return image, masks, boxes, labels h,w,d = image.shape noise = np.round(rs.uniform(low=0.0, high=256.0, size=(h, w, d))).astype(np.uint8) masks = np.ones((h,w), dtype=np.uint8) boxes = np.asarray([0,0,w,h],dtype=np.float).reshape((1,4)) labels = {'num_crowds': 0, 'labels': np.array([0.])} return noise, masks, boxes, labels class D415noise(object): # After we observed a lot of frame-to-frame variability in the output # of YOLACT imported into ACAM2, I decided to try to simulate the sensor # noise from the D415 camera we are using, to see if we could teach the # model that such noise is irrelevant. I initially went for a sophisticated # model to include all aspects of inter-pixel correlation etc., but for # now I am going to punt and see if I can get any improvement at at all # this way. WJP 1-Dec-2021 def __call__(self, image, masks, boxes, labels): if rs.randint(2): return image, masks, boxes, labels h,w,d = image.shape width = 11 # of blur kernel ksize = 66 # base_noise = rs.normal(loc=0.0, scale=3, size=(h, w, d)) ctr = ksize/2.0 w2 = width/2.0 x0 = ctr-w2; x1 = ctr+w2; y0 = ctr-1; y1 = ctr+1 # tht = rs.uniform(0,2*np.pi) # R = np.asarray([[np.cos(tht), -np.sin(tht)],[np.sin(tht), np.cos(tht)]]) # origin = np.asarray([[ctr,ctr]]) bpts = np.asarray([[x0,y0],[x1,y0],[x1,y1],[x0,y1]]) kern = fill_boundary((ksize, ksize), bpts).astype(np.float64) kern = cv2.blur(kern, (3,3)) kern = kern/np.sum(kern) xyran = np.linspace(-width/2,width/2,num=width,endpoint=True) x, y = np.meshgrid(xyran, xyran) r = np.sqrt(x*x + y*y) r[r==0] = 1 kern = np.exp(-np.power(4*r/width, 2)) ddepth = -1 # keep same dtype. Nothing to do with channels. noise = cv2.filter2D(base_noise, ddepth , kern) image += noise ; return image, masks, boxes, labels class Mover(object): # # I want to make some objects appear to have motion blur. For now, this # augmentation will move all objects in view, but for many objects, this # does not make sense; they do not actually ever move in practice. # On the other hand, through the Hololens, when the operator turns their head, # everything in the scene has some motion blur. # # WJP def __call__(self, image, masks, boxes, labels): if rs.randint(2): return image, masks, boxes, labels # # Motion is normally distributed with a std of 0.5 m/s. The FOV is about # 70 degrees wide, corresponding to 640 pixels. Camera is 0.8 m above # bench, Hololens will be about 1 meter from objects. So within 20% # or so we expect an std of 0.5 *320 * 0.9 *atan(35 deg)/30 per # sec = 2.6 pixels. # # So make the blur kernels have an std of 4. # # In other words, 0.5 m/s corresponds to about 4 pixels per frame. # h,w,d = image.shape width = np.abs(np.round(rs.normal(0,4))) + 1 # of blur kernel ksize = 33 # ctr = ksize/2.0 w2 = width/2.0 x0 = ctr-w2; x1 = ctr+w2; y0 = ctr-1; y1 = ctr+1 tht = rs.uniform(0,2*np.pi) R = np.asarray([[np.cos(tht), -np.sin(tht)],[np.sin(tht), np.cos(tht)]]) origin = np.asarray([[ctr,ctr]]) bpts = (np.asarray([[x0,y0],[x1,y0],[x1,y1],[x0,y1]])-origin) @ R \ + origin kern = fill_boundary((ksize, ksize), bpts).astype(np.float64) kern = cv2.blur(kern, (3,3)) kern = kern/np.sum(kern) ddepth = -1 # keep same dtype. Nothing to do with channels. for mask in masks: mask = mask.reshape((h,w,1)) img_blur = cv2.filter2D(image*mask, ddepth , kern); bmask = cv2.filter2D(mask.astype(np.float32), ddepth,\ kern.astype(np.float32)).reshape((h,w,1)) image = img_blur*bmask + image*(1- bmask) mask = bmask return image, masks, boxes, labels class CameraShaker(object): # # I want to make some objects appear to have motion blur. For now, this # augmentation will move all objects in view, but for many objects, this # does not make sense; they do not actually ever move in practice. # On the other hand, through the Hololens, when the operator turns their head, # everything in the scene has some motion blur. # # WJP def __call__(self, image, masks, boxes, labels): if rs.randint(2): return image, masks, boxes, labels # # Typical camera motion is ?????. NEED TO MODEL OR MEASURE TYPICAL TECHNICIAN # HEAD MOTIONS # The FOV is about # 70 degrees wide, corresponding to 640 pixels. Camera is 0.8 m above # bench, Hololens will be about 1 meter from objects. So within 20% # or so we expect an std of 0.5 *320 * 0.9 *atan(35 deg)/30 per # sec = 2.6 pixels. # # So make the blur kernels have an std of 4. # # In other words, 0.5 m/s corresponds to about 4 pixels per frame. # h,w,d = image.shape width = np.abs(np.round(rs.normal(0,4))) + 1 # of blur kernel ksize = 33 # ctr = ksize/2.0 w2 = width/2.0 x0 = ctr-w2; x1 = ctr+w2; y0 = ctr-1; y1 = ctr+1 tht = rs.uniform(0,2*np.pi) R = np.asarray([[np.cos(tht), -np.sin(tht)],[np.sin(tht), np.cos(tht)]]) origin = np.asarray([[ctr,ctr]]) bpts = (np.asarray([[x0,y0],[x1,y0],[x1,y1],[x0,y1]])-origin) @ R + origin kern = fill_boundary((ksize, ksize), bpts).astype(np.float64) kern = cv2.blur(kern, (3,3)) kern = kern/np.sum(kern) ddepth = -1 # keep same dtype. Nothing to do with channels. # print('Motion blur:', width/8, 'meters per second at',\ # int(tht*180/np.pi),'degrees') # print('image data type is',image.dtype) image = cv2.filter2D(image, ddepth , kern); for mask in masks: mask = mask.reshape((h,w,1)) mask = cv2.filter2D(mask.astype(np.float32), ddepth,\ kern.astype(np.float32)).reshape((h,w,1)) return image, masks, boxes, labels class Shrinker(object): # I want to zoom out some of our images so that objects appear smaller. # WJP def __call__(self, image, masks, boxes, labels): # if random.randint(2)<0: if rs.randint(2): return image, masks, boxes, labels ratio = rs.uniform(0.33,0.9) masksum = np.zeros(masks.shape[1:]) ioverlap = [] n_more_than_one = 0 for i, m in enumerate(masks): masksum += m nmto_new = np.sum(masksum > 1) if nmto_new > n_more_than_one: n_more_than_one = nmto_new ioverlap.append(i) # # Check all the masks that have overlap, and drop some of them until # no masks have overlap. use_mask = [True] * len(masks) if n_more_than_one > 0: for io in ioverlap: for j in range(io): if use_mask[j] and use_mask[io]: if rs.randint(2): use_mask[io] = False else: use_mask[j] = False height, width, depth = image.shape xx, yy = np.meshgrid(np.arange(width), np.arange(height)) i1dmatrix = xx*height + yy r = ratio ishrnk = np.zeros(image.shape) newmasksum = np.zeros(masks.shape[1:]) all_objs = combine_masks(masks) for imask, m in enumerate(masks): if not use_mask[imask]: masksum = masksum - m continue # skip everything for all but one of each crowd mgtz = m > 0 imgtz = np.ravel(xx[mgtz]*height + yy[mgtz]).astype(int) x0, y0 = np.mean(xx[mgtz]), np.mean(yy[mgtz]) # F collapes the coordinates of the masked pixels around # (x0, y0), by a factor r, since r < 1. # Use F to find locations of shrunken object pixels, round to nearest. F = lambda r: np.asmatrix([[ r, 0, x0*(1-r)],\ [ 0, r, y0*(1-r)],\ [ 0, 0, 1 ]]) # xmask and ymask are the coordinates of masked pixels, as rows. ymask = yy[mgtz].reshape((1,-1)) xmask = xx[mgtz].reshape((1,-1)) # v is a set augmented vectors, compatible with F, based # on masked pixels, i.e. concatenate the rows into columns, add 3rd row of 1's. # v.shape is (3,len(mgtz)) v = \ np.concatenate((xmask, ymask, np.ones(ymask.shape)), axis=0) xy_p = ((np.matmul(F(r),v))[0:2,:] + 0.5).astype(int) # I had to use np.ravel in the following to avoid what I think # is a bug in np.unique. It was giving me "per column" unique # values, when I had not asked for that. # # One thing I learned is that xy_p is a matrix, rather than an array, # which is a distinction I am not used to drawing, and it seems # to make the difference here. # iu = np.unique(np.ravel(xy_p[0,:]*height + xy_p[1,:])).astype(int) # iu are the indices to the new mask pixels. xpu = (iu // height) ypu = (iu % height) xypu = np.concatenate((xpu.reshape(1,-1), \ ypu.reshape(1,-1), \ np.ones(ypu.shape).reshape(1,-1))) Finv = np.linalg.inv(F(r)) xy_orig = np.asarray((np.matmul(Finv, xypu)[0:2,:] + 0.5).astype(int)) ffs = (xy_orig.shape)[1] x = xy_orig[0,:].reshape(ffs) y = xy_orig[1,:].reshape(ffs) xy_orig[0, x >= width] = width-1 xy_orig[0, x < 0] = 0 xy_orig[1, y >= height] = height-1 xy_orig[1, y < 0] = 0 ishrnk[ypu, xpu,:] = image[xy_orig[1,:].T, xy_orig[0,:].T,:].reshape(-1,3) # # Border pixels are those that are in the old mask (imgtz) but not in the # new mask (iu). "Border" is not really the right name. They are the pixels # left vacant when the object shrinks. # ibordset = set(imgtz) - set(iu) ibord = np.fromiter(ibordset, int, len(ibordset)) xbord = ibord // height ybord = ibord % height pts3 = np.concatenate((xbord.reshape(1,-1),\ ybord.reshape(1,-1),\ np.ones(ybord.shape).reshape(1,-1))) xy_fill = np.asarray((np.matmul(Finv, pts3)[0:2,:] + 0.5).astype(int)) fillshape = (xy_fill.shape)[1] xf = xy_fill[0,:].reshape(fillshape) yf = xy_fill[1,:].reshape(fillshape) all_good = False foldback_reduction = 10 while not all_good: wide = xf >= width xy_fill[0, wide] = (width-1) - (xf[wide]-width)//foldback_reduction neg = xf < 0 xy_fill[0, neg] = -xf[neg]//foldback_reduction high = yf >= height xy_fill[1, high] = (height-1) - (yf[high]-height)//foldback_reduction low = yf < 0 xy_fill[1, low] = -yf[low]//foldback_reduction all_good = True # just do it once for now bumpmax = 100 bump = 0 while True: i1dfill = (xy_fill[0,:]*height + xy_fill[1,:]).reshape(-1) i1dobjs = i1dmatrix[all_objs > 0].reshape(-1) i_from_objs_set = set(i1dobjs).intersection(i1dfill) if len(i_from_objs_set) == 0: break if bump > bumpmax: # too crowded!! print('Shrinker, too crowded to fill!') return image, masks, boxes, labels xy_fill[0,:] = (xy_fill[0,:] + \ np.int(np.random.uniform(0,width))) % width xy_fill[1,:] = (xy_fill[1,:] + \ np.int(np.random.uniform(0,height))) % height bump += 1 ishrnk[ybord, xbord, :] = image[xy_fill[1,:].T, \ xy_fill[0,:].T,:].reshape(-1,3) b = boxes[imask] bv = np.asarray([[b[0], b[2]], [b[1], b[3]],[1, 1]]) bvp = np.matmul(F(r), bv) xmin, ymin, xmax, ymax = bvp[0,0], bvp[1,0], bvp[0,1], bvp[1,1] boxes[imask] = np.asarray([xmin, ymin, xmax, ymax]) # Now shrink the current mask m and accumulate into newmasksum. m[:] = 0 m[ypu, xpu] = 1 newmasksum += m mss = masksum.shape allmaskcombo = np.asarray(masksum + newmasksum, dtype=np.float) allmaskcombo[allmaskcombo > 0] = 1.0 ileave_alone = (1-allmaskcombo.reshape(mss[0], mss[1], 1))*image image = (ishrnk + ileave_alone).astype(np.float32) return image, masks, boxes, labels class Twirler(object): # I want to twirl some of our objects! James saw that orientation seemed # to affect detectability. Not cool. # WJP def __call__(self, image, masks, boxes, labels): # if random.randint(2)<0: if rs.randint(2): return image, masks, boxes, labels masksum = np.zeros(masks.shape[1:]) ioverlap = [] n_more_than_one = 0 for i, m in enumerate(masks): masksum += m nmto_new = np.sum(masksum > 1) if nmto_new > n_more_than_one: n_more_than_one = nmto_new ioverlap.append(i) # # Check all the masks that have overlap, and drop some of them until # no masks have overlap. use_mask = [True] * len(masks) if n_more_than_one > 0: for io in ioverlap: for j in range(io): if use_mask[j] and use_mask[io]: if rs.randint(2): use_mask[io] = False else: use_mask[j] = False height, width, depth = image.shape xx, yy = np.meshgrid(np.arange(width), np.arange(height)) i1dmatrix = xx*height + yy itwirl = np.zeros(image.shape) newmasksum = np.zeros(masks.shape[1:]) all_objs = combine_masks(masks) for imask, m in enumerate(masks): if not use_mask[imask]: masksum = masksum - m continue # skip everything for all but one of each crowd angle = rs.uniform(-np.pi, np.pi) mgtz = m > 0 imgtz = np.ravel(xx[mgtz]*height + yy[mgtz]).astype(int) x0, y0 = np.mean(xx[mgtz]), np.mean(yy[mgtz]) # twirl center C = np.cos(angle) S = np.sin(angle) # These are the elements of the 3rd column that put the rotation # center at (x0, y0) instead of (0,0) R13 = -C*x0 - S*y0 + x0 R23 = S*x0 - C*y0 + y0 # R rotates the coordinates of the masked pixels around # (x0, y0), by angle. # Use R to find locations of twirled object pixels, round to # nearest. R = np.asmatrix([[ C, S, R13],\ [-S, C, R23],\ [ 0, 0, 1 ]]) # xmask and ymask are the coordinates of masked pixels, as rows. ymask = yy[mgtz].reshape((1,-1)) xmask = xx[mgtz].reshape((1,-1)) # v is a set augmented vectors, compatible with R, based # on masked pixels, i.e. concatenate the rows into columns, add # 3rd row of 1's. # v.shape is (3,len(mgtz)) v = \ np.concatenate((xmask, ymask, np.ones(ymask.shape)), axis=0) xy_p = ((np.matmul(R,v))[0:2,:] + 0.5).astype(int) x_ok = np.logical_and(xy_p[0,:] >= 0, xy_p[0,:] < width) y_ok = np.logical_and(xy_p[1,:] >= 0, xy_p[1,:] < height) xy_ok = np.squeeze(np.array(np.logical_and(x_ok, y_ok))) # print('xy_p shape is',xy_p.shape) # print('xy_ok shape is',xy_ok.shape) # print('squeezed xy_ok shape is',np.squeeze(xy_ok).shape) xy_p = xy_p[:, xy_ok] # I had to use np.ravel in the following to avoid what I think # is a bug in np.unique. It was giving me "per column" unique # values, when I had not asked for that. # # One thing I learned is that xy_p is a matrix, rather than an array, # which is a distinction I am not used to drawing, and it seems # to make the difference here. # iu = np.unique(np.ravel(xy_p[0,:]*height + xy_p[1,:])).astype(int) # iu are the indices to the new mask pixels. xpu = (iu // height) ypu = (iu % height) # I need to check for xy_p being in bounds, up above, rather than doing it after # the mod function which can wrap around in y, oops. # # xin_bounds = np.logical_and(xpu < width, xpu >= 0) # yin_bounds = np.logical_and(ypu < height,ypu >= 0) # in_bounds = np.logical_and(xin_bounds, yin_bounds) # xpu = xpu[in_bounds] # ypu = ypu[in_bounds] xypu = np.concatenate((xpu.reshape(1,-1), \ ypu.reshape(1,-1), \ np.ones(ypu.shape).reshape(1,-1))) Rinv = np.linalg.inv(R) xy_orig = \ np.asarray((np.matmul(Rinv, xypu)[0:2,:] + 0.5).astype(int)) ffs = (xy_orig.shape)[1] x = xy_orig[0,:].reshape(ffs) y = xy_orig[1,:].reshape(ffs) xy_orig[0, x >= width] = width-1 xy_orig[0, x < 0] = 0 xy_orig[1, y >= height] = height-1 xy_orig[1, y < 0] = 0 itwirl[ypu, xpu,:] = image[xy_orig[1,:].T,\ xy_orig[0,:].T,:].reshape(-1,3) # # Border pixels are those that are in the old mask (imgtz) but not # in the new mask (iu). "Border" is not really the right name. # They are the pixels left vacant when the object twirls. # ibordset = set(imgtz) - set(iu) ibord = np.fromiter(ibordset, int, len(ibordset)) xbord = ibord // height ybord = ibord % height pts3 = np.concatenate((xbord.reshape(1,-1),\ ybord.reshape(1,-1),\ np.ones(ybord.shape).reshape(1,-1))) xy_fill = \ np.asarray((np.matmul(Rinv, pts3)[0:2,:] + 0.5).astype(int)) fillshape = (xy_fill.shape)[1] xf = xy_fill[0,:].reshape(fillshape) yf = xy_fill[1,:].reshape(fillshape) all_good = False foldback_reduction = 10 while not all_good: wide = xf >= width xy_fill[0, wide] = \ (width-1) - (xf[wide]-width)//foldback_reduction neg = xf < 0 xy_fill[0, neg] = -xf[neg]//foldback_reduction high = yf >= height xy_fill[1, high] = \ (height-1) - (yf[high]-height)//foldback_reduction low = yf < 0 xy_fill[1, low] = -yf[low]//foldback_reduction all_good = True # just do it once for now bumpmax = 100 bump = 0 while True: i1dfill = (xy_fill[0,:]*height + xy_fill[1,:]).reshape(-1) i1dobjs = i1dmatrix[all_objs > 0].reshape(-1) i_from_objs_set = set(i1dobjs).intersection(i1dfill) if len(i_from_objs_set) == 0: break if bump > bumpmax: # too crowded!! print('Twirler, too crowded to fill!') return image, masks, boxes, labels xy_fill[0,:] = (xy_fill[0,:] + \ np.int(np.random.uniform(0,width))) % width xy_fill[1,:] = (xy_fill[1,:] + \ np.int(np.random.uniform(0,height))) % height bump += 1 itwirl[ybord, xbord, :] = image[xy_fill[1,:].T, \ xy_fill[0,:].T,:].reshape(-1,3) # The following is how I transformed boxes in Shrinker. It does not work for # Twirler, because we do not want rotated boxes, we want image-aligned # bounding boxes of rotated masks. For Shrinker, those are the same, but not here. # # b = boxes[imask] # bv = np.asarray([[b[0], b[2]], [b[1], b[3]],[1, 1]]) # bvp = np.matmul(R, bv) # xmin, ymin, xmax, ymax = bvp[0,0], bvp[1,0], bvp[0,1], bvp[1,1] # # The following is necessary in Twirler but not Shrinker # if xmin > xmax: # xmin, xmax = xmax, xmin # if ymin > ymax: # ymin, ymax = ymax, ymin # # boxes[imask] = np.asarray([xmin, ymin, xmax, ymax]) # Now twirl the current mask m and accumulate into newmasksum. m[:] = 0 m[ypu, xpu] = 1 newmasksum += m # Now that the mask is rotated, get the box. This is much more # robust than what I did above and in Shrinker. boxes[imask] = np.asarray([np.min(xpu), np.min(ypu),\ np.max(xpu), np.max(ypu)]) mss = masksum.shape allmaskcombo = np.asarray(masksum + newmasksum, dtype=np.float) allmaskcombo[allmaskcombo > 0] = 1.0 ileave_alone = (1-allmaskcombo.reshape(mss[0], mss[1], 1))*image image = (itwirl + ileave_alone).astype(np.float32) return image, masks, boxes, labels class Sheepdog(object): # I want to herd our objects into small groups. Sometimes it seems if # they are too close they become indetectable. # WJP # # Mutating these from Shrinker etc. has become clumsy. Here is a new algorithm # # Pick a pair of objects. # Compute the convex hull of 1. # Repeat: # Check each mask boundary point of the other to see if it lies in hull # take bisection step toward if no points in hull away if points in hull # until step size is too small to matter. # # When point is inside a hull, all angles formed by adjacent hull points, with # test test point as the vertex, will have the same sense, as we step around # the hull in a consistent direction. # def pt_in_hull(self, pt, hull): # both args should be torch.tensor. # only works for convex hulls dp = torch.tensor(hull - pt) Xi = dp[:,0] Yi = dp[:,1] rot_sense = torch.sign(Xi.roll(1,0)*Yi - Xi*Yi.roll(1,0)) return all(rot_sense == rot_sense[0]) def hull_pts(self, pts): CH = ConvexHull(pts) return CH.points[CH.vertices] def step_to1_from0(self, p1, p0): x1, y1 = p1; x0, y0 = p0 step = np.array([x1-x0, y1-y0]).reshape((1,2)) dist = np.sqrt(np.sum(step**2)) return step / dist, dist def pts_from_xy(self, x, y): xc = np.array(x).reshape((-1,1)) yc = np.array(y).reshape((-1,1)) pts = np.concatenate((xc,yc), 1) return pts def get_step_vector(self, pA, pB): # best step for moving object A toward object B in this context. # Just finding nearest pair of points and bringing them into contact. # # Both pA and pB are Nx2. aclose, bclose = pA[0], pB[0] mindist = 1.0e30 for a in pA: for b in pB: dist = sum((a-b)**2) if dist < mindist: mindist = dist aclose = a bclose = b return (bclose - aclose).reshape((1,2)) def __call__(self, image, masks, boxes, labels): if rs.randint(1) or masks.shape[0] < 2: return image, masks, boxes, labels # RETURN 1/3 new_image = copy.copy(image) height, width, depth = image.shape xx, yy = np.meshgrid(np.arange(width), np.arange(height)) i1dmatrix = xx*height + yy nmasks = masks.shape[0] has_overlap = [False] * nmasks i1d = [i1dmatrix[m > 0].reshape(-1) for m in masks] for i in range(nmasks-1): for j in range(i+1,nmasks): if not set(i1d[i]).isdisjoint(i1d[j]): has_overlap[i] = True has_overlap[j] = True count = 0 for hov in has_overlap: if not hov: count+=1 if count < 2: return image, masks, boxes, labels # RETURN 2/3 print('Sheepdog on the loose!') seq = np.arange(nmasks).astype(int); rs.shuffle(seq) # # I need to avoid filling vacated pixels with pieces of labeled objects. # So I need a mask that tells me all pixels belonging to any labeled # object. object_combo = combine_masks(masks) oc1d = i1dmatrix[object_combo > 0].reshape(-1) # import matplotlib.pyplot as plt # plt.imshow(object_combo) # plt.pause(0.1) # plt.show() # ilabel = labels['labels'].astype(int) for i in range(nmasks): if not has_overlap[i]: print(cfg.dataset.class_names[ilabel[seq[i]]]) first = True for imask in seq: if has_overlap[imask]: continue m = masks[imask] mgtz = m > 0 x = xx[mgtz] y = yy[mgtz] if first: first = False pts0 = self.pts_from_xy(x,y) # all object points hull0 = self.hull_pts(pts0) continue # with next imask, no else block needed pts1 = self.pts_from_xy(x,y) hull1 = self.hull_pts(pts1) step_vector = self.get_step_vector(hull1, hull0) this_move = np.round(step_vector).astype(int) hull1 += this_move hull0 = self.hull_pts(np.concatenate((hull0, hull1), 0)) pts0 = np.concatenate((pts1,pts0),0) xdest = x + this_move[0,0] ydest = y + this_move[0,1] ixgood = np.logical_and(xdest >= 0, xdest < width) iygood = np.logical_and(ydest >=0, ydest < height) good = np.logical_and(ixgood, iygood) xdest = xdest[good] ydest = ydest[good] masks[imask] = 0 masks[imask][ydest, xdest] = 1 boxes[imask] = np.asarray([np.min(xdest), np.min(ydest),\ np.max(xdest), np.max(ydest)]) new_image[ydest, xdest,:] = image[y[good], x[good], :] # Figure out which pixels are vacated by the move. Fill these with # whatever was present in the original image at the destination spot, # unless those already belong to a labeled object. # # Vacated pixels belonged to the object in the original image, but # are not present in the moved object. # xyg1d = x[good]*height + y[good] xyg1d = x*height + y xyd1d = xdest*height + ydest xyvacset = set(xyg1d) - set(xyd1d) xyvac = np.fromiter(xyvacset, int, len(xyvacset)) xvac = xyvac // height yvac = xyvac % height try_move = this_move while True: xfill = (xvac + try_move[0,0]) % width yfill = (yvac + try_move[0,1]) % height if not set(xfill*height + yfill).isdisjoint(oc1d): try_move += np.random.uniform(-50, 50, size=(1,2)).astype(np.int32) else: break new_image[yvac, xvac, :] = image[yfill, xfill, :] image = new_image return image, masks, boxes, labels # RETURN 3/3 class Shrink_XOR_Twirl(object): """ Choose between Shrinker or Twirler at random. """ def __init__(self, mean=MEANS, std=STD): self.shrink = Compose([Shrinker()]) self.twirl = Compose([Twirler()]) def __call__(self, img, masks=None, boxes=None, labels=None): # print('SHRINKER AND TWIRLER ARE TURNED OFF!!!') # return img, masks, boxes, labels if rs.randint(2): # print('Shrinking!') return self.shrink(img, masks, boxes, labels) else: # print('Twirling!') return self.twirl(img, masks, boxes, labels) class PhotoBomb(object): """ Take objects out of their lab bench context and drop them into random scenes """ def __init__(self): import glob non_iconic_path = './data/non_iconic/' self.imgfiles = glob.glob(non_iconic_path+'*.jpg') if len(self.imgfiles) > 0: self.no_files = False else: print('**************************************') print('I cannot find the PhotoBomb background files.') print('They should be in data/non_iconic/.') print('Deactivating PhotoBomb augmentation!') print('**************************************') self.no_files = True self.once_every = 8 # On average, this is invoked once every once_every images. def __call__(self, image, masks, boxes, labels): if rs.randint(self.once_every) or self.no_files: return image, masks, boxes, labels bkgfile = self.imgfiles[rs.randint(0,len(self.imgfiles))] bkg = cv2.cvtColor(imageio.imread(bkgfile), cv2.COLOR_RGB2BGR) mshape = masks[0].shape masksum = np.zeros(mshape) for m in masks: masksum += m masksum[masksum > 0] = 1.0 masksum = np.reshape(masksum, (mshape[0], mshape[1],1)) objects = image * masksum bkg = bkg * (1-masksum) non_icon_image = objects + bkg return non_icon_image, masks, boxes, labels class RandomMirror(object): def __call__(self, image, masks, boxes, labels): # print('Entering RandomMirror...') _, width, _ = image.shape if rs.randint(2): image = image[:, ::-1] masks = masks[:, :, ::-1] boxes = boxes.copy() boxes[:, 0::2] = width - boxes[:, 2::-2] # print('Done with RandomMirror...') return image, masks, boxes, labels class RandomFlip(object): def __call__(self, image, masks, boxes, labels): height , _ , _ = image.shape if rs.randint(2): image = image[::-1, :] masks = masks[:, ::-1, :] boxes = boxes.copy() boxes[:, 1::2] = height - boxes[:, 3::-2] return image, masks, boxes, labels class RandomRot90(object): def __call__(self, image, masks, boxes, labels): old_height , old_width , _ = image.shape k = rs.randint(4) image = np.rot90(image,k) masks = np.array([np.rot90(mask,k) for mask in masks]) boxes = boxes.copy() for _ in range(k): boxes = np.array([[box[1], old_width - 1 - box[2], box[3], old_width - 1 - box[0]] for box in boxes]) old_width, old_height = old_height, old_width return image, masks, boxes, labels class SwapChannels(object): """Transforms a tensorized image by swapping the channels in the order specified in the swap tuple. Args: swaps (int triple): final order of channels eg: (2, 1, 0) """ def __init__(self, swaps): self.swaps = swaps def __call__(self, image): """ Args: image (Tensor): image tensor to be transformed Return: a tensor with channels swapped according to swap """ # if torch.is_tensor(image): # image = image.data.cpu().numpy() # else: # image = np.array(image) image = image[:, :, self.swaps] return image class PhotometricDistort(object): def __init__(self): self.pd = [ RandomContrast(), ConvertColor(transform='HSV'), RandomSaturation(), RandomHue(), ConvertColor(current='HSV', transform='BGR'), RandomContrast() ] self.rand_brightness = RandomBrightness() self.rand_light_noise = RandomLightingNoise() def __call__(self, image, masks, boxes, labels): im = image.copy() im, masks, boxes, labels = self.rand_brightness(im, masks, boxes, labels) if rs.randint(2): distort = Compose(self.pd[:-1]) else: distort = Compose(self.pd[1:]) im, masks, boxes, labels = distort(im, masks, boxes, labels) return self.rand_light_noise(im, masks, boxes, labels) class PrepareMasks(object): """ Prepares the gt masks for use_gt_bboxes by cropping with the gt box and downsampling the resulting mask to mask_size, mask_size. This function doesn't do anything if cfg.use_gt_bboxes is False. """ def __init__(self, mask_size, use_gt_bboxes): self.mask_size = mask_size self.use_gt_bboxes = use_gt_bboxes def __call__(self, image, masks, boxes, labels=None): if not self.use_gt_bboxes: return image, masks, boxes, labels height, width, _ = image.shape new_masks = np.zeros((masks.shape[0], self.mask_size ** 2)) for i in range(len(masks)): x1, y1, x2, y2 = boxes[i, :] x1 *= width x2 *= width y1 *= height y2 *= height x1, y1, x2, y2 = (int(x1), int(y1), int(x2), int(y2)) # +1 So that if y1=10.6 and y2=10.9 we still have a bounding box cropped_mask = masks[i, y1:(y2+1), x1:(x2+1)] scaled_mask = cv2.resize(cropped_mask, (self.mask_size, self.mask_size)) new_masks[i, :] = scaled_mask.reshape(1, -1) # Binarize new_masks[new_masks > 0.5] = 1 new_masks[new_masks <= 0.5] = 0 return image, new_masks, boxes, labels class BackboneTransform(object): """ Transforms a BRG image made of floats in the range [0, 255] to whatever input the current backbone network needs. transform is a transform config object (see config.py). in_channel_order is probably 'BGR' but you do you, kid. """ def __init__(self, transform, mean, std, in_channel_order): self.mean = np.array(mean, dtype=np.float32) self.std = np.array(std, dtype=np.float32) self.transform = transform # Here I use "Algorithms and Coding" to convert string permutations to numbers self.channel_map = {c: idx for idx, c in enumerate(in_channel_order)} self.channel_permutation = [self.channel_map[c] for c in transform.channel_order] def __call__(self, img, masks=None, boxes=None, labels=None): img = img.astype(np.float32) if self.transform.normalize: img = (img - self.mean) / self.std elif self.transform.subtract_means: img = (img - self.mean) elif self.transform.to_float: img = img / 255 img = img[:, :, self.channel_permutation] return img.astype(np.float32), masks, boxes, labels class BaseTransform(object): """ Transorm to be used when evaluating. """ def __init__(self, mean=MEANS, std=STD): self.augment = Compose([ ConvertFromInts(), Resize(resize_gt=False), BackboneTransform(cfg.backbone.transform, mean, std, 'BGR') ]) def __call__(self, img, masks=None, boxes=None, labels=None): return self.augment(img, masks, boxes, labels) import torch.nn.functional as F class FastBaseTransform(torch.nn.Module): """ Transform that does all operations on the GPU for super speed. This doesn't suppport a lot of config settings and should only be used for production. Maintain this as necessary. """ def __init__(self): super().__init__() self.mean = torch.Tensor(MEANS).float().cuda()[None, :, None, None] self.std = torch.Tensor( STD ).float().cuda()[None, :, None, None] self.transform = cfg.backbone.transform def forward(self, img): self.mean = self.mean.to(img.device) self.std = self.std.to(img.device) # img assumed to be a pytorch BGR image with channel order [n, h, w, c] if cfg.preserve_aspect_ratio: _, h, w, _ = img.size() img_size = Resize.calc_size_preserve_ar(w, h, cfg.max_size) img_size = (img_size[1], img_size[0]) # Pytorch needs h, w else: img_size = (cfg.max_size, cfg.max_size) img = img.permute(0, 3, 1, 2).contiguous() img = F.interpolate(img, img_size, mode='bilinear', align_corners=False) if self.transform.normalize: img = (img - self.mean) / self.std elif self.transform.subtract_means: img = (img - self.mean) elif self.transform.to_float: img = img / 255 if self.transform.channel_order != 'RGB': raise NotImplementedError img = img[:, (2, 1, 0), :, :].contiguous() # Return value is in channel order [n, c, h, w] and RGB return img def do_nothing(img=None, masks=None, boxes=None, labels=None): return img, masks, boxes, labels def enable_if(condition, obj): return obj if condition else do_nothing def enable_if_else(condition, obj1, obj2): if condition: obj = obj1 else: obj = obj2 return obj class SSDAugmentation(object): """ Transform to be used when training. """ def __init__(self, mean=MEANS, std=STD): self.augment = Compose([ ConvertFromInts(), ToAbsoluteCoords(), enable_if(cfg.augment_photometric_distort, PhotometricDistort()), RandomMirror(), RandomFlip(), RandomRot90(), Resize(), Shrink_XOR_Twirl(), Mover(), CameraShaker(), D415noise(), Sheepdog(), enable_if(not cfg.preserve_aspect_ratio, \ Pad(cfg.max_size, cfg.max_size, mean)), ToPercentCoords(), PrepareMasks(cfg.mask_size, cfg.use_gt_bboxes), BackboneTransform(cfg.backbone.transform, mean, std, 'BGR')]) print('Augmentations in use:') for aug in self.augment.__dict__['transforms']: print(self.get_aug_name(aug)) def get_aug_name(self,aug): saug = str(aug) i0 = saug.rfind('.') + 1 # from the last dot.... i1 = saug.find(' ', i0) # ...to the first space return saug[i0:i1] def __call__(self, img, masks, boxes, labels): return self.augment(img, masks, boxes, labels) # enable_if_else(rs.randint(2), Twirler(), Shrinker()),
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tscli/stats/commands.py
pnemade/transtats-cli
1
169064
# Copyright 2017-2019 Red Hat, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import click from tscli.restapi import ConsumeAPIs from tscli.textoutput import TextOutputAPIs @click.command() @click.option( '--server-url', envvar='TRANSTATS_SERVER', help="Transtats Server URL") @click.option("--exist", is_flag=True, help="Determine if the package exist in Transtats or not.") @click.option("--health", is_flag=True, help="Get package health") @click.option( '--json', is_flag=True, envvar='JSON_OUTPUT', help="Print in JSON format") @click.argument('package-name') @click.pass_obj def package(app_context, server_url, package_name, exist, health, json): """Translation status of a package. e.g. transtats package anaconda """ api_obj = ConsumeAPIs(server_url or app_context.server_url) if json \ else TextOutputAPIs(server_url or app_context.server_url) if exist: response = api_obj.package_status(package_name, exist=True, health=None) elif health: response = api_obj.package_status(package_name, exist=None, health=True) else: response = api_obj.package_status(package_name, exist=None, health=None) if isinstance(response, dict): app_context.print_r(response) @click.command() @click.option( '--server-url', envvar='TRANSTATS_SERVER', help="Transtats Server URL") @click.option("--detail", is_flag=True, help="For individual packages grouped by languages.") @click.option("--locale", is_flag=False, help="For individual language specific output.") @click.option( '--json', is_flag=True, envvar='JSON_OUTPUT', help="Print in JSON format") @click.argument('release-slug') @click.pass_obj def release(app_context, server_url, detail, release_slug, locale, json): """Translation status of a release slug/branch. e.g. transtats release fedora-29 """ api_obj = ConsumeAPIs(server_url or app_context.server_url) if json \ else TextOutputAPIs(server_url or app_context.server_url) if detail: if locale: print("Ignoring locale option as detail option also given") response = api_obj.release_status(release_slug, locale=None, detail=True) elif locale: response = api_obj.release_status(release_slug, locale, detail=False) else: response = api_obj.release_status(release_slug) if isinstance(response, dict): app_context.print_r(response) @click.command() @click.option( '--server-url', envvar='TRANSTATS_SERVER', help="Transtats Server URL") @click.option( '--json', is_flag=True, envvar='JSON_OUTPUT', help="Print in JSON format") @click.argument('coverage-rule') @click.pass_obj def coverage(app_context, server_url, coverage_rule, json): """Translation coverage as per coverage rule. e.g. transtats coverage rhinstaller """ api_obj = ConsumeAPIs(server_url or app_context.server_url) if json \ else TextOutputAPIs(server_url or app_context.server_url) response = api_obj.rule_coverage(coverage_rule) if isinstance(response, dict): app_context.print_r(response)
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custom_components/here_weather/utils.py
eifinger/homeassistant-conf
60
196426
<reponame>eifinger/homeassistant-conf<gh_stars>10-100 """Utility functions for here_weather.""" from __future__ import annotations from homeassistant.const import ( CONF_UNIT_SYSTEM_METRIC, LENGTH_CENTIMETERS, LENGTH_INCHES, LENGTH_KILOMETERS, LENGTH_MILES, PRESSURE_INHG, PRESSURE_MBAR, SPEED_KILOMETERS_PER_HOUR, SPEED_MILES_PER_HOUR, TEMP_CELSIUS, TEMP_FAHRENHEIT, ) def convert_temperature_unit_of_measurement_if_needed( unit_system: str, unit_of_measurement: str ) -> str: """Convert the temperature unit of measurement to imperial if configured.""" if unit_system != CONF_UNIT_SYSTEM_METRIC: unit_of_measurement = TEMP_FAHRENHEIT return unit_of_measurement def convert_unit_of_measurement_if_needed( unit_system: str, unit_of_measurement: str | None ) -> str | None: """Convert the unit of measurement to imperial if configured.""" if unit_system != CONF_UNIT_SYSTEM_METRIC: if unit_of_measurement == TEMP_CELSIUS: unit_of_measurement = TEMP_FAHRENHEIT elif unit_of_measurement == LENGTH_CENTIMETERS: unit_of_measurement = LENGTH_INCHES elif unit_of_measurement == SPEED_KILOMETERS_PER_HOUR: unit_of_measurement = SPEED_MILES_PER_HOUR elif unit_of_measurement == PRESSURE_MBAR: unit_of_measurement = PRESSURE_INHG elif unit_of_measurement == LENGTH_KILOMETERS: unit_of_measurement = LENGTH_MILES return unit_of_measurement def get_attribute_from_here_data( here_data: list, attribute_name: str, sensor_number: int = 0 ) -> str | None: """Extract and convert data from HERE response or None if not found.""" try: state = here_data[sensor_number][attribute_name] except KeyError: return None state = convert_asterisk_to_none(state) return state def convert_asterisk_to_none(state: str) -> str | None: """Convert HERE API representation of None.""" if state == "*": return None return state
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