xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

42b27e1114addb6efa22983ea1b8536333e5b90e

3,096

py

Python

datar/forcats/misc.py

stjordanis/datar

4e2b5db026ad35918954576badef9951928c0cb1

[ "MIT" ]

110

2021-03-09T04:10:40.000Z

2022-03-13T10:28:20.000Z

datar/forcats/misc.py

sthagen/datar

1218a549e2f0547c7b5a824ca6d9adf1bf96ba46

[ "MIT" ]

54

2021-06-20T18:53:44.000Z

2022-03-29T22:13:07.000Z

datar/forcats/misc.py

sthagen/datar

1218a549e2f0547c7b5a824ca6d9adf1bf96ba46

[ "MIT" ]

11

2021-06-18T03:03:14.000Z

2022-02-25T11:48:26.000Z

"""Provides other helper functions for factors""" from typing import Any, Iterable import numpy from pandas import Categorical, DataFrame from pipda import register_verb from pipda.utils import CallingEnvs from ..core.types import ForcatsRegType, ForcatsType, is_null, is_scalar from ..core.utils import Array from ..core.contexts import Context from ..core.defaults import f from ..base import ( factor, tabulate, prop_table, nlevels, levels, NA, setdiff, is_ordered, ) from ..dplyr import arrange, desc, mutate from .utils import check_factor from .lvl_order import fct_inorder @register_verb(ForcatsRegType, context=Context.EVAL) def fct_count(_f: ForcatsType, sort: bool = False, prop=False) -> Categorical: """Count entries in a factor Args: _f: A factor sort: If True, sort the result so that the most common values float to the top prop: If True, compute the fraction of marginal table. Returns: A data frame with columns `f`, `n` and `p`, if prop is True """ f2 = check_factor(_f) n_na = sum(is_null(f2)) df = DataFrame( { "f": fct_inorder( levels(f2, __calling_env=CallingEnvs.REGULAR), __calling_env=CallingEnvs.REGULAR, ), "n": tabulate( f2, nlevels(f2, __calling_env=CallingEnvs.REGULAR), __calling_env=CallingEnvs.REGULAR, ), } ) if n_na > 0: df = df.append({"f": NA, "n": n_na}, ignore_index=True) if sort: df = arrange( df, desc(f.n, __calling_env=CallingEnvs.PIPING), __calling_env=CallingEnvs.REGULAR, ) if prop: df = mutate( df, p=prop_table(f.n, __calling_env=CallingEnvs.PIPING), __calling_env=CallingEnvs.REGULAR, ) return df @register_verb(ForcatsRegType, context=Context.EVAL) def fct_match(_f: ForcatsType, lvls: Any) -> Iterable[bool]: """Test for presence of levels in a factor Do any of `lvls` occur in `_f`? Args: _f: A factor lvls: A vector specifying levels to look for. Returns: A logical factor """ _f = check_factor(_f) if is_scalar(lvls): lvls = [lvls] bad_lvls = setdiff( lvls, levels(_f, __calling_env=CallingEnvs.REGULAR), __calling_env=CallingEnvs.REGULAR, ) if len(bad_lvls) > 0: bad_lvls = Array(bad_lvls)[~is_null(bad_lvls)] if len(bad_lvls) > 0: raise ValueError(f"Levels not present in factor: {bad_lvls}.") return numpy.isin(_f, lvls) @register_verb(ForcatsRegType) def fct_unique(_f: ForcatsType) -> Categorical: """Unique values of a factor Args: _f: A factor Returns: The factor with the unique values in `_f` """ lvls = levels(_f, __calling_env=CallingEnvs.REGULAR) is_ord = is_ordered(_f, __calling_env=CallingEnvs.REGULAR) return factor(lvls, lvls, exclude=None, ordered=is_ord)

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null

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42b603082633608e2a31d1e0d368cdcfc8b30d98

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py

Python

qucumber/utils/training_statistics.py

silky/QuCumber

f0dd8725b8dd3a0c94f10f1a3b88a769c63a567f

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

1

2019-06-27T11:26:29.000Z

qucumber/utils/training_statistics.py

silky/QuCumber

f0dd8725b8dd3a0c94f10f1a3b88a769c63a567f

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

null

qucumber/utils/training_statistics.py

silky/QuCumber

f0dd8725b8dd3a0c94f10f1a3b88a769c63a567f

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

null

# Copyright 2018 PIQuIL - All Rights Reserved # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import torch import qucumber.utils.cplx as cplx import qucumber.utils.unitaries as unitaries def fidelity(nn_state, target_psi, space, **kwargs): r"""Calculates the square of the overlap (fidelity) between the reconstructed wavefunction and the true wavefunction (both in the computational basis). :param nn_state: The neural network state (i.e. complex wavefunction or positive wavefunction). :type nn_state: WaveFunction :param target_psi: The true wavefunction of the system. :type target_psi: torch.Tensor :param space: The hilbert space of the system. :type space: torch.Tensor :param \**kwargs: Extra keyword arguments that may be passed. Will be ignored. :returns: The fidelity. :rtype: torch.Tensor """ Z = nn_state.compute_normalization(space) F = torch.tensor([0.0, 0.0], dtype=torch.double, device=nn_state.device) target_psi = target_psi.to(nn_state.device) for i in range(len(space)): psi = nn_state.psi(space[i]) / Z.sqrt() F[0] += target_psi[0, i] * psi[0] + target_psi[1, i] * psi[1] F[1] += target_psi[0, i] * psi[1] - target_psi[1, i] * psi[0] return cplx.norm_sqr(F) def rotate_psi(nn_state, basis, space, unitaries, psi=None): r"""A function that rotates the reconstructed wavefunction to a different basis. :param nn_state: The neural network state (i.e. complex wavefunction or positive wavefunction). :type nn_state: WaveFunction :param basis: The basis to rotate the wavefunction to. :type basis: str :param space: The hilbert space of the system. :type space: torch.Tensor :param unitaries: A dictionary of (2x2) unitary operators. :type unitaries: dict :param psi: A wavefunction that the user can input to override the neural network state's wavefunction. :type psi: torch.Tensor :returns: A wavefunction in a new basis. :rtype: torch.Tensor """ N = nn_state.num_visible v = torch.zeros(N, dtype=torch.double, device=nn_state.device) psi_r = torch.zeros(2, 1 << N, dtype=torch.double, device=nn_state.device) for x in range(1 << N): Upsi = torch.zeros(2, dtype=torch.double, device=nn_state.device) num_nontrivial_U = 0 nontrivial_sites = [] for jj in range(N): if basis[jj] != "Z": num_nontrivial_U += 1 nontrivial_sites.append(jj) sub_state = nn_state.generate_hilbert_space(num_nontrivial_U) for xp in range(1 << num_nontrivial_U): cnt = 0 for j in range(N): if basis[j] != "Z": v[j] = sub_state[xp][cnt] cnt += 1 else: v[j] = space[x, j] U = torch.tensor([1.0, 0.0], dtype=torch.double, device=nn_state.device) for ii in range(num_nontrivial_U): tmp = unitaries[basis[nontrivial_sites[ii]]] tmp = tmp[ :, int(space[x][nontrivial_sites[ii]]), int(v[nontrivial_sites[ii]]) ].to(nn_state.device) U = cplx.scalar_mult(U, tmp) if psi is None: Upsi += cplx.scalar_mult(U, nn_state.psi(v)) else: index = 0 for k in range(len(v)): index = (index << 1) | int(v[k].item()) Upsi += cplx.scalar_mult(U, psi[:, index]) psi_r[:, x] = Upsi return psi_r def KL(nn_state, target_psi, space, bases=None, **kwargs): r"""A function for calculating the total KL divergence. :param nn_state: The neural network state (i.e. complex wavefunction or positive wavefunction). :type nn_state: WaveFunction :param target_psi: The true wavefunction of the system. :type target_psi: torch.Tensor :param space: The hilbert space of the system. :type space: torch.Tensor :param bases: An array of unique bases. :type bases: np.array(dtype=str) :param \**kwargs: Extra keyword arguments that may be passed. Will be ignored. :returns: The KL divergence. :rtype: torch.Tensor """ psi_r = torch.zeros( 2, 1 << nn_state.num_visible, dtype=torch.double, device=nn_state.device ) KL = 0.0 unitary_dict = unitaries.create_dict() target_psi = target_psi.to(nn_state.device) Z = nn_state.compute_normalization(space) eps = 0.000001 if bases is None: num_bases = 1 for i in range(len(space)): KL += ( cplx.norm_sqr(target_psi[:, i]) * (cplx.norm_sqr(target_psi[:, i]) + eps).log() ) KL -= ( cplx.norm_sqr(target_psi[:, i]) * (cplx.norm_sqr(nn_state.psi(space[i])) + eps).log() ) KL += cplx.norm_sqr(target_psi[:, i]) * Z.log() else: num_bases = len(bases) for b in range(1, len(bases)): psi_r = rotate_psi(nn_state, bases[b], space, unitary_dict) target_psi_r = rotate_psi( nn_state, bases[b], space, unitary_dict, target_psi ) for ii in range(len(space)): if cplx.norm_sqr(target_psi_r[:, ii]) > 0.0: KL += ( cplx.norm_sqr(target_psi_r[:, ii]) * cplx.norm_sqr(target_psi_r[:, ii]).log() ) KL -= ( cplx.norm_sqr(target_psi_r[:, ii]) * cplx.norm_sqr(psi_r[:, ii]).log().item() ) KL += cplx.norm_sqr(target_psi_r[:, ii]) * Z.log() return KL / float(num_bases)

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null

0

1

0

42b7e07ad45d9d0be2cad9161c36276cb3b1762f

1,433

py

Python

14.py

niharikasingh/aoc2018

21d430d393321e6066eca22d7c6b49e5eb42d756

[ "MIT" ]

null

14.py

niharikasingh/aoc2018

21d430d393321e6066eca22d7c6b49e5eb42d756

[ "MIT" ]

null

14.py

niharikasingh/aoc2018

21d430d393321e6066eca22d7c6b49e5eb42d756

[ "MIT" ]

null

import copy def next10(i): # start condition board = [3, 7] elves = [0, 1] found = False # while (len(board) < i + 10): while (not found): to_add = board[elves[0]] + board[elves[1]] if (to_add < 10): board.append(to_add) if (board[-1*len(i):] == i): found = len(board[:-1*len(i)]) else: board.append(1) board.append(to_add%10) if (board[-1*len(i):] == i): found = len(board[:-1*len(i)]) elif (board[-1*len(i)-1:-1] == i): found = len(board[:-1*len(i)-1]) elves[0] = (elves[0] + board[elves[0]] + 1) % len(board) elves[1] = (elves[1] + board[elves[1]] + 1) % len(board) # print board # to_print = copy.deepcopy(board) # to_print[elves[0]] = "(" + str(to_print[elves[0]]) + ")" # to_print[elves[1]] = "[" + str(to_print[elves[1]]) + "]" # print(to_print) # print(board[i:i+10]) # return board[i:i+10] print(found) return found # assert next10(5) == [0,1,2,4,5,1,5,8,9,1] # assert next10(9) == [5,1,5,8,9,1,6,7,7,9] # assert next10(18) == [9,2,5,1,0,7,1,0,8,5] # assert next10(2018) == [5,9,4,1,4,2,9,8,8,2] # print(next10(760221)) assert next10([0,1,2,4,5]) == 5 assert next10([5,1,5,8,9]) == 9 assert next10([9,2,5,1,0]) == 18 assert next10([5,9,4,1,4]) == 2018 print(next10([7,6,0,2,2,1]))

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null

0

null

0

1

0

42b83fe05de3f7690454c9ae7844d6d5c0896fb0

892

py

Python

rpc inv matriz/ServerRPC.py

Aldair47x/DISTRIBUIDOS-UTP

182f143b3a5d73744f78eb4fe1428cbca22387c2

[ "MIT" ]

null

rpc inv matriz/ServerRPC.py

Aldair47x/DISTRIBUIDOS-UTP

182f143b3a5d73744f78eb4fe1428cbca22387c2

[ "MIT" ]

null

rpc inv matriz/ServerRPC.py

Aldair47x/DISTRIBUIDOS-UTP

182f143b3a5d73744f78eb4fe1428cbca22387c2

[ "MIT" ]

null

import xmlrpclib from SimpleXMLRPCServer import SimpleXMLRPCServer from SimpleXMLRPCServer import SimpleXMLRPCRequestHandler import numpy as np from io import StringIO from numpy.linalg import inv from scipy.linalg import * # Restrict to a particular path. class RequestHandler(SimpleXMLRPCRequestHandler): rpc_paths = ('/RPC2',) # Create server server = SimpleXMLRPCServer(("localhost", 9000), requestHandler=RequestHandler) server.register_introspection_functions() s = xmlrpclib.ServerProxy('http://localhost:9000') def operacion(name): matriz = [] print ("Franquito") archivo = open(name) for linea in archivo: matriz.append(linea.strip().split()) archivo.close() matrizInv=inv(matriz) return str(matrizInv) server.register_function(operacion, 'operacion') # Run the server's main loop server.serve_forever()

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null

0

1

0

42ba676a4b1855f63fba242958ff64fc7b10d468

1,524

py

Python

damq/api/management/commands/check_settings.py

zhanghui9700/clouddam

18c7c7578fb727bcab50737b51b8fb5c09070b48

[ "Apache-2.0" ]

null

damq/api/management/commands/check_settings.py

zhanghui9700/clouddam

18c7c7578fb727bcab50737b51b8fb5c09070b48

[ "Apache-2.0" ]

null

damq/api/management/commands/check_settings.py

zhanghui9700/clouddam

18c7c7578fb727bcab50737b51b8fb5c09070b48

[ "Apache-2.0" ]

null

#!/usr/bin/env python # coding=utf-8 from smtplib import SMTPException from django.conf import settings from django.core.management import BaseCommand from django.core.mail import send_mail class Command(BaseCommand): def _log(self, tag, result): label = self.style.ERROR("XXX") if result: label = self.style.MIGRATE_SUCCESS(":-)") self.stdout.write("{:<30}{:<5}".format(tag, label)) def _check_mail(self): if len(settings.ADMINS) < 1: self._log("CHECK_MAIL No Admin", False) return try: title = "%sCheck Settings" % settings.EMAIL_SUBJECT_PREFIX msg = "This message used for checking email settings." result = send_mail(title, msg, settings.EMAIL_FROM, [settings.ADMINS[0]]) except SMTPException as e: result = False raise e self._log("CHECK_MAIL", result) def _check_rpc_send(self): try: from rpc import notify msg = "{'test': 'This message used for checking email settings.'}" notify(msg, routing="transResponse") except Exception as e: raise e self._log("CHECK_RPC_SEND", True) def handle(self, *args, **kwargs): self.stdout.write(self.style.WARNING("************CHECK START************")) self._check_mail() self._check_rpc_send() self.stdout.write(self.style.WARNING("************CHECK END*************"))

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0

null

0

null

0

1

0

42ba9ea7e400e5ef293ccdc589dfbbce586a2405

4,113

py

Python

sidomo/sidomo.py

noajshu/sdpm

b70825d9017eb0c2c6b6389345cccbcbd52cf669

[ "Unlicense" ]

358

2016-02-24T01:36:55.000Z

2022-02-20T00:10:22.000Z

sidomo/sidomo.py

noajshu/sdpm

b70825d9017eb0c2c6b6389345cccbcbd52cf669

[ "Unlicense" ]

5

2016-02-24T22:50:25.000Z

2017-01-30T07:58:00.000Z

sidomo/sidomo.py

noajshu/sdpm

b70825d9017eb0c2c6b6389345cccbcbd52cf669

[ "Unlicense" ]

27

2016-02-24T13:40:22.000Z

2021-06-30T12:04:41.000Z

"""Manages the lifecycle of a docker container. Use via the with statement: with Container(some_image) as c: for line in c.run("some_command"): print line """ import docker import click, os # sets the docker host from your environment variables client = docker.Client( **docker.utils.kwargs_from_env(assert_hostname=False)) class Container: """ Represents a single docker container on the host. Volumes should be a list of mapped paths, e.g. ['/var/log/docker:/var/log/docker']. """ def __init__(self, image, memory_limit_gb=4, stderr=True, stdout=True, volumes=[], cleanup=False, environment=[]): self.image = image self.memory_limit_bytes = int(memory_limit_gb * 1e9) self.stderr = stderr self.stdout = stdout self.volumes = [x[1] for x in map(lambda vol: vol.split(':'), volumes)] self.binds = volumes self.cleanup = cleanup self.environment = environment def __enter__(self): """Power on.""" self.container_id = client.create_container( image=self.image, volumes=self.volumes, host_config=client.create_host_config( mem_limit=self.memory_limit_bytes, binds=self.binds, ), environment=self.environment, stdin_open=True )['Id'] client.start(self.container_id) return self def __exit__(self, type, value, traceback): """Power off.""" client.stop(self.container_id) if self.cleanup: client.remove_container(self.container_id) def run(self, command): """Just like 'docker run CMD'. This is a generator that yields lines of container output. """ exec_id = client.exec_create( container=self.container_id, cmd=command, stdout=self.stdout, stderr=self.stderr )['Id'] for line in client.exec_start(exec_id, stream=True): yield line @click.command() @click.argument('do', nargs=-1) @click.option('--image', '-i', help='Image name in which to run do', default=None) @click.option('--sharedir', '-s', help='Directory on host machine to mount to docker.', default=os.path.abspath(os.getcwd())) @click.option('--display', '-d', help='Display variable to set for X11 forwarding.', default=None) def dodo(do, image, sharedir, display): """ dodo (like sudo but for docker) runs argument in a docker image. do is the command to run in the image. image taken from (1) command-line, (2) "DODOIMAGE" environment variable, or (3) first built image. sharedir (e.g., to pass data to command) is mounted (default: current directory). empty string does no mounting. display is environment variable to set in docker image that allows X11 forwarding. """ # try to set image three ways if not image: if 'DODOIMAGE' in os.environ: image = os.environ['DODOIMAGE'] else: ims = client.images() if len(ims) >= 1: image = [im['RepoTags'][0] for im in client.images()][0] assert image, 'No image given or found locally.' # get image if not available locally imnames = [im['RepoTags'][0] for im in client.images()] if (not any([image in imname for imname in imnames])) and client.search(image): print('Image {} not found locally. Pulling from docker hub.'.format(image)) client.pull(image) # mount directory in docker if sharedir: volumes = ['{}:/home'.format(sharedir)] else: volumes = [] # set docker environment to display X11 locally if display: environment = ['DISPLAY={}'.format(display)] elif 'DODODISPLAY' in os.environ: environment = ['DISPLAY={}'.format(os.environ['DODODISPLAY'])] else: environment = [] with Container(image, volumes=volumes, cleanup=True, environment=environment) as c: for output_line in c.run(do): print('{}:\t {}'.format(image, output_line.decode('utf-8')))

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null

0

null

0

1

0

42bb4531b3deb62a4952ce2f40bb5fa396ce9810

4,321

py

Python

scripts/utils/prepare_data.py

Harshs27/mGLAD

f85d5a7cb2091a4528c762dc550d8c9b35d190b1

[ "MIT" ]

null

scripts/utils/prepare_data.py

Harshs27/mGLAD

f85d5a7cb2091a4528c762dc550d8c9b35d190b1

[ "MIT" ]

null

scripts/utils/prepare_data.py

Harshs27/mGLAD

f85d5a7cb2091a4528c762dc550d8c9b35d190b1

[ "MIT" ]

null

import networkx as nx import numpy as np from sklearn import covariance import torch def convertToTorch(data, req_grad=False, use_cuda=False): """Convert data from numpy to torch variable, if the req_grad flag is on then the gradient calculation is turned on. """ if not torch.is_tensor(data): dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor data = torch.from_numpy(data.astype(np.float, copy=False)).type(dtype) data.requires_grad = req_grad return data def eigVal_conditionNum(A): """Calculates the eigenvalues and the condition number of the input matrix A condition number = max(|eig|)/min(|eig|) """ eig = [v.real for v in np.linalg.eigvals(A)] condition_number = max(np.abs(eig)) / min(np.abs(eig)) return eig, condition_number def getCovariance(Xb, offset = 0.1): """Calculate the batch covariance matrix Args: Xb (3D np array): The input sample matrices (B x M x D) offset (float): The eigenvalue offset in case of bad condition number Returns: Sb (3D np array): Covariance matrices (B x D x D) """ Sb = [] for X in Xb: S = covariance.empirical_covariance(X, assume_centered=False) # calculate the eigenvalue of the covariance S eig, con = eigVal_conditionNum(S) if min(eig)<=1e-3: # adjust the eigenvalue print(f'Adjust the eval: min {min(eig)}, con {con}') S += np.eye(S.shape[-1]) * (offset-min(eig)) Sb.append(S) return np.array(Sb) def generateRandomGraph(num_nodes, sparsity, seed=None): """Generate a random erdos-renyi graph with a given sparsity. Args: num_nodes (int): The number of nodes in the DAG sparsity (float): = #edges-present/#total-edges seed (int, optional): set the numpy random seed Returns: edge_connections (2D np array (float)): Adj matrix """ if seed: np.random.seed(seed) G = nx.generators.random_graphs.gnp_random_graph( num_nodes, sparsity, seed=seed, directed=False ) edge_connections = nx.adjacency_matrix(G).todense() return edge_connections def simulateGaussianSamples( num_nodes, edge_connections, num_samples, seed=None, u=0.1, w_min=0.5, w_max=1.0, ): """Simulating num_samples from a Gaussian distribution. The precision matrix of the Gaussian is determined using the edge_connections Args: num_nodes (int): The number of nodes in the DAG edge_connections (2D np array (float)): Adj matrix num_sample (int): The number of samples seed (int, optional): set the numpy random seed u (float): Min eigenvalue offset for the precision matrix w_min (float): Precision matrix entries ~Unif[w_min, w_max] w_max (float): Precision matrix entries ~Unif[w_min, w_max] Returns: X (2D np array (float)): num_samples x num_nodes precision_mat (2D np array (float)): num_nodes x num_nodes """ # zero mean of Gaussian distribution mean_value = 0 mean_normal = np.ones(num_nodes) * mean_value # Setting the random seed if seed: np.random.seed(seed) # uniform entry matrix [w_min, w_max] U = np.matrix(np.random.random((num_nodes, num_nodes)) * (w_max - w_min) + w_min) theta = np.multiply(edge_connections, U) # making it symmetric theta = (theta + theta.T)/2 + np.eye(num_nodes) smallest_eigval = np.min(np.linalg.eigvals(theta)) # Just in case : to avoid numerical error in case an # epsilon complex component present smallest_eigval = smallest_eigval.real # making the min eigenvalue as u precision_mat = theta + np.eye(num_nodes)*(u - smallest_eigval) # print(f'Smallest eval: {np.min(np.linalg.eigvals(precision_mat))}') # getting the covariance matrix (avoid the use of pinv) cov = np.linalg.inv(precision_mat) # get the samples if seed: np.random.seed(seed) # Sampling data from multivariate normal distribution data = np.random.multivariate_normal( mean=mean_normal, cov=cov, size=num_samples ) return data, precision_mat # MxD, DxD

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null

0

null

0

1

0

42bc62f46cb6d0412a2527cc37f497de098a673f

1,475

py

Python

Exercicios/multplica_matriz.py

eduardodarocha/Introducao_Ciencia_da_Computacao_com_Python_Parte_2_Coursera

b5b9198e16b4b67894b85766eb521ae96010accf

[ "MIT" ]

1

2020-08-28T20:29:23.000Z

Exercicios/multplica_matriz.py

eduardodarocha/Introducao_Ciencia_da_Computacao_com_Python_Parte_2_Coursera

b5b9198e16b4b67894b85766eb521ae96010accf

[ "MIT" ]

null

Exercicios/multplica_matriz.py

eduardodarocha/Introducao_Ciencia_da_Computacao_com_Python_Parte_2_Coursera

b5b9198e16b4b67894b85766eb521ae96010accf

[ "MIT" ]

null

def multiplica_matrizes(m1, m2): '''Minha solução para multiplicação de matrizes''' matriz = [] cont = 0 b1 = 0 for t in range(len(m1)): # números de linhas mat1 linhanova = [] for t1 in range(len(m2[0])): #números de colunas mat2 while cont < len(m2): #a1 = m1[t][cont] * m2[cont][t1] #b1 = b1 + a1 b1 = b1 + m1[t][cont] * m2[cont][t1] # refatorado cont += 1 linhanova.append(b1) cont = b1 = 0 matriz.append(linhanova) return matriz def mat_mul (A, B): num_linhas_A, num_colunas_A = len(A), len(A[0]) num_linhas_B, num_colunas_B = len(B), len(B[0]) assert num_colunas_A == num_linhas_B C = [] for linha in range(num_linhas_A): C.append([]) for coluna in range(num_colunas_B): C[linha].append(0) for k in range(num_colunas_A): C[linha][coluna] += A[linha][k] * B[k][coluna] return C # mat1 = [[2,3,1], [-1, 0, 2]] # mat2 = [[1, -2], [0, 5],[4, 1]] # mat1 = [[5, 8, -4], [6, 9, -5],[4, 7, -2]] # mat2 = [[2], [-3], [1]] # mat1 = [[2,5,9], [3, 6, 8]] mat1 = [[1, 2, 3], [4, 5, 6]] mat2 = [[1, 2],[3, 4],[5, 6]] # mat2 = [[2,7],[4,3],[5,2]] #https://brasilescola.uol.com.br/matematica/multiplicacao-matrizes.htm # print(multiplica_matrizes(mat1, mat2)) # print(mat_mul (mat1, mat2))

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null

0

1

0

42c012688f03cf2033f2ea77e4e8d937fb973de4

996

py

Python

bifacialvf/tests/test_vf.py

shirubana/bifacialvf

7cd1c4c658bb7a68f0815b2bd1a6d5c492ca7300

[ "BSD-3-Clause" ]

16

2018-01-17T06:03:23.000Z

2021-11-08T18:54:20.000Z

bifacialvf/tests/test_vf.py

shirubana/bifacialvf

7cd1c4c658bb7a68f0815b2bd1a6d5c492ca7300

[ "BSD-3-Clause" ]

36

2018-03-16T15:17:58.000Z

2022-03-18T17:54:49.000Z

bifacialvf/tests/test_vf.py

shirubana/bifacialvf

7cd1c4c658bb7a68f0815b2bd1a6d5c492ca7300

[ "BSD-3-Clause" ]

15

2018-01-11T09:11:13.000Z

2022-03-21T11:37:42.000Z

""" Tests of the view factors module """ import pytest import numpy as np from bifacialvf.vf import getSkyConfigurationFactors from bifacialvf.tests import ( SKY_BETA160_C05_D1, SKY_BETA20_C05_D1, SKY_BETA20_C0_D1, SKY_BETA160_C0_D1, SKY_BETA160_C1_D1, SKY_BETA20_C1_D1, SKY_BETA20_C1_D0, SKY_BETA160_C1_D0, SKY_BETA160_C05_D0, SKY_BETA20_C05_D0) @pytest.mark.parametrize('beta, C, D, expected', [(160, 0.5, 1, SKY_BETA160_C05_D1), (20, 0.5, 1, SKY_BETA20_C05_D1), (20, 0, 1, SKY_BETA20_C0_D1), (160, 0, 1, SKY_BETA160_C0_D1), (160, 1, 1, SKY_BETA160_C1_D1), (20, 1, 1, SKY_BETA20_C1_D1), (20, 1, 0, SKY_BETA20_C1_D0), (160, 1, 0, SKY_BETA160_C1_D0), (160, 0.5, 0, SKY_BETA160_C05_D0), (20, 0.5, 0, SKY_BETA20_C05_D0)]) def test_getSkyConfigurationFactors(beta, C, D, expected): """ Benchmark against to the master branch on 2018-08-20 at 91e785d. """ assert np.allclose( getSkyConfigurationFactors("interior", beta, C, D), expected)

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3.835294

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996

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0

null

0

null

0

1

0

42c0646e767e46f559cbd944cee5d0ed57e7deeb

732

py

Python

test_horovod.py

lu-wang-dl/test-horovod

0b1699057fe03f84bbea46c3e63197a6c9e21c14

[ "Apache-2.0" ]

null

test_horovod.py

lu-wang-dl/test-horovod

0b1699057fe03f84bbea46c3e63197a6c9e21c14

[ "Apache-2.0" ]

null

test_horovod.py

lu-wang-dl/test-horovod

0b1699057fe03f84bbea46c3e63197a6c9e21c14

[ "Apache-2.0" ]

null

# Databricks notebook source import horovod.tensorflow.keras as hvd def run_training_horovod(): # Horovod: initialize Horovod. hvd.init() import os print(os.environ.get('PYTHONPATH')) print(os.environ.get('PYTHONHOME')) print(f"Rank is: {hvd.rank()}") print(f"Size is: {hvd.size()}") # COMMAND ---------- from sparkdl import HorovodRunner hr = HorovodRunner(np=-spark.sparkContext.defaultParallelism, driver_log_verbosity="all") hr.run(run_training_horovod) # COMMAND ---------- from sparkdl import HorovodRunner hr = HorovodRunner(np=spark.sparkContext.defaultParallelism, driver_log_verbosity="all") hr.run(run_training_horovod) # manually stopping b/c it's just hanging # COMMAND ----------

24.4

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0

null

0

null

0

1

0

42c3250899086a2d423b9d8448bed7aa2e3d35b4

1,832

py

Python

datasets.py

Liuhongzhi2018/Car_detection

f32fea9c348c691ccc30b9804a4f3fa32732bbae

[ "MIT" ]

1

2022-03-05T04:20:46.000Z

datasets.py

Liuhongzhi2018/Car_detection

f32fea9c348c691ccc30b9804a4f3fa32732bbae

[ "MIT" ]

null

datasets.py

Liuhongzhi2018/Car_detection

f32fea9c348c691ccc30b9804a4f3fa32732bbae

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Mar 12 10:11:09 2020 @author: NAT """ import torch from torch.utils.data import Dataset import json import os from PIL import Image from utils import transform class VOCDataset(Dataset): def __init__(self, DataFolder, split): """ DataFolder: folder where data files are stored split: split {"TRAIN", "TEST"} """ self.split = str(split.upper()) if self.split not in {"TRAIN", "TEST"}: print("Param split not in {TRAIN, TEST}") assert self.split in {"TRAIN", "TEST"} self.DataFolder = DataFolder #read data file from json file with open(os.path.join(DataFolder, self.split+ '_images.json'), 'r') as j: self.images = json.load(j) with open(os.path.join(DataFolder, self.split+ '_objects.json'), 'r') as j: self.objects = json.load(j) assert len(self.images) == len(self.objects) def __len__(self): return len(self.images) def __getitem__(self, i): image = Image.open(self.images[i], mode= "r") image = image.convert("RGB") #Read objects in this image objects = self.objects[i] boxes = torch.FloatTensor(objects["boxes"]) labels = torch.LongTensor(objects['labels']) difficulties = torch.ByteTensor(objects['difficulties']) #Apply transforms new_image, new_boxes, new_labels, new_difficulties = transform(image, boxes, labels, difficulties, self.split) return new_image, new_boxes, new_labels, new_difficulties

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0.543668

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null

0

null

0

1

0

42c34955df6c0e7aa377ac0cc57e813a5826e6fa

7,564

py

Python

roles/gitlab_runner/filter_plugins/from_toml.py

wikimedia/operations-gitlab-ansible

f6433674ff812ea6e07ee192ff6fd848ba252aaa

[ "MIT" ]

17

2019-03-08T15:33:46.000Z

2021-11-02T18:22:47.000Z

roles/gitlab_runner/filter_plugins/from_toml.py

wikimedia/operations-gitlab-ansible

f6433674ff812ea6e07ee192ff6fd848ba252aaa

[ "MIT" ]

8

2018-12-23T21:17:36.000Z

2019-12-10T13:52:13.000Z

roles/gitlab_runner/filter_plugins/from_toml.py

wikimedia/operations-gitlab-ansible

f6433674ff812ea6e07ee192ff6fd848ba252aaa

[ "MIT" ]

12

2019-01-26T15:00:32.000Z

2022-03-15T08:04:17.000Z

#!/usr/bin/python DOCUMENTATION = ''' --- module: to_toml, from_toml version_added: "2.8" short_description: Converts Python data to TOML and TOML to Python data. author: - "Samy Coenen (contact@samycoenen.be)" ''' import datetime import sys from collections import OrderedDict #pip3 install python-toml def to_toml(data): ''' Convert the value to TOML ''' return dumps(data) def from_toml(data): ''' Convert TOML to Python data ''' return loads(data) class FilterModule(object): ''' Ansible TOML jinja2 filters ''' def filters(self): return { # toml 'to_toml': to_toml, 'from_toml': from_toml } def loads(s, *args, **kwargs): if not isinstance(s, basestring): raise TypeError("It's not a string.") try: s = s.decode('utf-8') except AttributeError: pass s = _clear_r_n_t(s) return _loads(s) def load(file, *args, **kwargs): return loads(_read(file, *args, **kwargs)) def dumps(s, *args, **kwargs): if not isinstance(s, dict): raise TypeError("It's not a dict.") return un_utf_8(_json_transition_str(s)) def dump(file, s, *args, **kwargs): _write(file, dumps(s)) def _clear_r_n_t(v): return v.replace('\r', '').replace('\t', '').split('\n') def _clear_empty_l_r(v): return v.rstrip(' ').lstrip(' ') def _clear_empty(v): return v.replace(' ', '') def _is_empty(v): return v[0] if v else v def _get_key(v): key = _re('\[\[(.*?)\]\]', v) if key: return key, True return _re('\[(.*?)\]', v), False def _loads(s): items, nd, it, fg = ordict(), ordict(), [], False key_status = False for v in s: if not v or _is_empty(_clear_empty(v)) == '#': continue if '[' == _is_empty(_clear_empty(v)) and ']' in v: key, key_status = _get_key(v) nd = ordict() else: _it = v.split('=') _it[0] = _clear_empty(_is_empty(_it)) """ arr_arr = [ 'zbc', 'sdf', ] """ try: if '[' not in _it[0] and _it[0][-1] == ']': it.append(_it[0]) fg = False elif _it[1].replace(' ', '')[0] == '[' and ']' not in _it[1]: it.append(_it[0]) fg = True except Exception as e: pass if fg: it.append(_it[1] if len(_it) > 1 else _it[0]) elif not fg and it: _it = [it[0], ''.join(it[1:])] it = [] nd.update(_str_transition_json(_it)) ite = items try: # [1][:-1] = [] for k in key[:-1]: try: ite = ite[k] except Exception as e: ite[k] = ordict() ite = ite[k] if isinstance(ite, list): ite = ite[-1] try: ite[key[-1]] if key_status: ite[key[-1]].append(nd) except Exception as e: ite[key[-1]] = [nd] if key_status else nd finally: key_status = False except Exception as e: ite.update(nd) pass return items def _str_transition_json(v): item = ordict() if not isinstance(v, (list, tuple)): raise TypeError("It's not a list/tuple.") if (len(v) == 2): item[v[0]] = _str_transition_obj(_clear_empty_l_r(v[1])) elif (len(v) > 2): item[v[0]] = _str_transition_obj(_clear_empty_l_r('='.join(v[1:]))) return item def _str_transition_obj(v): if not isinstance(v, basestring): raise TypeError("It's not a string") if v.lower() == 'true': return True elif v.lower() == 'false': return False try: if _re('\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z', v): return str_to_datetime(v) except Exception as e: raise e try: _veal = eval(v.replace(',', ', ')) if isinstance(_veal, basestring): return escape(_veal) return _veal except SyntaxError as e: pass return v def _json_transition_str(s, _k='', index=0): _s = '' for k, v in s.items(): _k = _k.rstrip('.') + '.' if _k else '' if isinstance(v, dict): _s += '\n' + '\t' * index + '[{}]\n'.format(_k + k) _s += _json_transition_str(v, _k + k, index=index + 1) elif isinstance(v, list) and isinstance(v[0], dict): for _v in v: _s += '\n' + '\t' * index + '[[{}]]\n'.format(_k + k) _s += _json_transition_str(_v, _k + k, index=index + 1) elif not isinstance(v, dict): _s += '\t' * index + _key_equal_value(k, v) else: _s += '\n' return _s def _key_equal_value(k, v): if isinstance(v, datetime.datetime): v = datetime_to_str(v) elif isinstance(v, bool): v = str(v).lower() elif not isinstance(v, basestring): v = str(v) else: v = '"' + str(v) + '"' return k + ' = ' + _utf_8(v) + '\n' def _read(file, *args, **kwargs): if PY3: with open(file, encoding='utf-8', *args, **kwargs) as fp: v = fp.read() else: with open(file, *args, **kwargs) as fp: v = fp.read() return v def _write(file, text, model='w', *args, **kwargs): if PY3: with open(file, model, encoding='utf-8', *args, **kwargs) as fp: fp.write(text) else: with open(file, model, *args, **kwargs) as fp: fp.write(text) def _re(reg, text): reg = re.findall(re.compile(reg), text) reg = reg[0].split('.') if reg else [] return reg def escape(v): if not isinstance(v, basestring): return v return v.replace( '\\', '\\\\').replace( '\b', '\\b').replace( '\t', '\\t').replace( '\f', '\\f').replace( '\r', '\\r').replace( '\"', '\\"').replace( '\/', '\\/').replace( '\n', '\\n') def escape_u(v): if not isinstance(v, basestring): return v # v = escape(v) v = v.encode('unicode-escape').decode() if PY2: return v.replace('\\x', '\\u00') return v def unescape_u(v): if not isinstance(v, basestring): return v v = unescape(v) return v.encode().decode('unicode-escape') def _utf_8(v): if PY2: return v.decode('utf-8') return v def un_utf_8(v): if PY2: return v.encode('utf-8') return v def str_to_datetime(dtstr, strftime='%Y-%m-%dT%H:%M:%SZ'): if not isinstance(dtstr, basestring): raise TypeError("It's not a string.") return datetime.datetime.strptime(dtstr, strftime) def datetime_to_str(dttime, strftime='%Y-%m-%dT%H:%M:%SZ'): if not isinstance(dttime, datetime.datetime): raise TypeError("It's not a datetime.") return dttime.strftime(strftime) PY2 = sys.version_info[0] == 2 PY3 = sys.version_info[0] == 3 PY35 = sys.version_info[:2] == (3, 5) PY36 = sys.version_info[:2] == (3, 6) if PY3: basestring = str, integer_types = int, unicode = str unichr = chr _range = range else: integer_types = (int, long) _range = xrange def ordict(): return {} if PY36 else OrderedDict() if __name__ == '__main__': pass

22.714715

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7,564

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0.30303

0

null

0

null

0

1

0

42c37f3f064078bde91e95903b77950bc9bd114f

414

py

Python

ABC190/D.py

shimomura314/AtcoderCodes

db1d62a7715f5f1b3c40eceff8d34f0f34839f41

[ "MIT" ]

null

ABC190/D.py

shimomura314/AtcoderCodes

db1d62a7715f5f1b3c40eceff8d34f0f34839f41

[ "MIT" ]

null

ABC190/D.py

shimomura314/AtcoderCodes

db1d62a7715f5f1b3c40eceff8d34f0f34839f41

[ "MIT" ]

null

def divisor(n: int): divisors = [] for integer in range(1, int(n**0.5)+1): if not n % integer: divisors.append(integer) divisors.append(n//integer) divisors.sort() return divisors n = int(input()) divisors = divisor(2*n) answer = 0 for integer in divisors: pair = 2*n // integer a2 = pair + 1 - integer if a2 % 2 == 0: answer += 1 print(answer)

21.789474

43

0.562802

59

414

3.949153

0.389831

0.103004

0

0.044983

0.301932

414

19

44

21.789474

0.761246

0

1

0.058824

false

0

0.117647

0.058824

0

null

0

null

0

1

0

42c55d5c799cf1af35cb63cb32b363a33a23a6ae

862

py

Python

TV/models/episode.py

viswas163/Parse-bot

881df2767cc5bdf88ff5dcc451a97c2ed96fc073

[ "MIT" ]

null

TV/models/episode.py

viswas163/Parse-bot

881df2767cc5bdf88ff5dcc451a97c2ed96fc073

[ "MIT" ]

null

TV/models/episode.py

viswas163/Parse-bot

881df2767cc5bdf88ff5dcc451a97c2ed96fc073

[ "MIT" ]

null

from mongoengine import Document, IntField, StringField, FloatField, connect from pymongo import UpdateOne class Episode(Document): title = StringField(required=True) show = StringField(required=True) rating = FloatField(required=True) votes = IntField(required=True) def bulk_upsert(episodes): bulk_operations = [] for entity in episodes: try: entity.validate() filter = { 'title': entity.title, 'show': entity.show } bulk_operations.append( UpdateOne(filter, {'$set': entity.to_mongo().to_dict()}, upsert=True) ) except ValidationError: pass if bulk_operations: with connect("tvdb") as c: collection = Episode._get_collection().bulk_write(bulk_operations, ordered=False)

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862

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0.088975

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862

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0

0.333333

0

null

0

null

0

1

0

42caa75d97d78a5da176444b0c283b314888e8e5

4,161

py

Python

BasicReport.py

nikneural/Report

414b08c157ef14345372fd5b84f134eb7c911fe4

[ "MIT" ]

null

BasicReport.py

nikneural/Report

414b08c157ef14345372fd5b84f134eb7c911fe4

[ "MIT" ]

null

BasicReport.py

nikneural/Report

414b08c157ef14345372fd5b84f134eb7c911fe4

[ "MIT" ]

null

import subprocess import docx.table import pandas as pd from docx import Document from docx.enum.text import WD_PARAGRAPH_ALIGNMENT from docx.oxml import OxmlElement from docx.oxml import ns from docx.oxml.ns import qn from docx.shared import Inches, Pt from docx.table import _Cell from docx2pdf import convert class BasicReport: def __init__(self): pass @staticmethod def column_text_change(data: pd.DataFrame, table: docx.table.Table, size: int, bold: bool = False): for i in range(len(data.columns)): run = table.cell(0, i).paragraphs[0].runs[0] run.font.size = Pt(size) run.font.bold = bold @staticmethod def cell_text_change(data: pd.DataFrame, table: docx.table.Table, size: int, bold: bool = False): for i in range(data.shape[0]): for j in range(data.shape[1]): run = table.cell(i + 1, j).paragraphs[0].runs[0] run.font.size = Pt(size) run.font.bold = bold @staticmethod def delete_columns(table, columns): # sort columns descending columns.sort(reverse=True) grid = table._tbl.find("w:tblGrid", table._tbl.nsmap) for ci in columns: for cell in table.column_cells(ci): cell._tc.getparent().remove(cell._tc) # Delete column reference. col_elem = grid[ci] grid.remove(col_elem) @staticmethod def generate_pdf_windows(doc_path: str, out_path: str): """Generate pdf file for windows system""" convert(doc_path, out_path) @staticmethod def generate_pdf_Linux(doc_path, out_path): """Generate pdf file for windows system""" subprocess.call(['soffice', # '--headless', '--convert-to', 'pdf', '--outdir', out_path, doc_path]) return doc_path @staticmethod def set_row_height(row, height): trPr = row.tr.get_or_add_trPr() trHeight = OxmlElement('w:trHeight') trHeight.set(qn('w:val'), str(height)) trPr.append(trHeight) @staticmethod def set_vertical_cell_direction(cell: _Cell, direction: str): # direction: tbRl -- top to bottom, btLr -- bottom to top assert direction in ("tbRl", "btLr") tc = cell._tc tcPr = tc.get_or_add_tcPr() textDirection = OxmlElement('w:textDirection') textDirection.set(qn('w:val'), direction) # btLr tbRl tcPr.append(textDirection) @staticmethod def create_element(name): return OxmlElement(name) @staticmethod def create_attribute(element, name, value): element.set(ns.qn(name), value) def create_document(self, header): document = Document() section = document.sections[-1] section.left_martin = Inches(0.1) paragraph_format = document.styles['Normal'].paragraph_format paragraph_format.space_before = 0 paragraph_format.space_after = 0 document.add_paragraph().add_run(header).bold = True document.add_paragraph(" ") section.footer.paragraphs[0].text = header section.footer.add_paragraph() self.add_page_number(section.footer.paragraphs[1].add_run()) section.footer.paragraphs[1].alignment = WD_PARAGRAPH_ALIGNMENT.RIGHT return document def add_page_number(self, run): fldChar1 = self.create_element('w:fldChar') self.create_attribute(fldChar1, 'w:fldCharType', 'begin') instrText = self.create_element('w:instrText') self.create_attribute(instrText, 'xml:space', 'preserve') instrText.text = "PAGE" fldChar2 = self.create_element('w:fldChar') self.create_attribute(fldChar2, 'w:fldCharType', 'end') run._r.append(fldChar1) run._r.append(instrText) run._r.append(fldChar2)

32.76378

77

0.59553

485

4,161

4.954639

0.286598

0.05618

0.014981

0.022472

0.177278

0.151477

0.114856

0

0.007901

0.300409

4,161

126

78

33.02381

0.817588

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0

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0

0.010101

1

0.121212

false

0.010101

0.111111

0.010101

0.272727

0

null

0

null

0

1

0

42cd0e4c33a465776d2f55cc4beb83f4edfc71a6

4,568

py

Python

main.py

meaug/indoor_air_quality_dht22_sgp30

a746a9955903de1f7ce5e5d84493f860c1fd0b16

[ "MIT" ]

null

main.py

meaug/indoor_air_quality_dht22_sgp30

a746a9955903de1f7ce5e5d84493f860c1fd0b16

[ "MIT" ]

null

main.py

meaug/indoor_air_quality_dht22_sgp30

a746a9955903de1f7ce5e5d84493f860c1fd0b16

[ "MIT" ]

null

from network import WLAN import urequests as requests # from ubidots tutorial https://help.ubidots.com/en/articles/961994-connect-any-pycom-board-to-ubidots-using-wi-fi-over-http from machine import I2C import adafruit_sgp30 # from https://github.com/alexmrqt/micropython-sgp30 from machine import Pin from dht import DHT # from https://github.com/JurassicPork/DHT_PyCom import machine import time #Ubidots TOKEN TOKEN = "INSERT UBIDOTS TOKEN HERE" #wifi setup wlan = WLAN(mode=WLAN.STA) wlan.antenna(WLAN.INT_ANT) # Wi-Fi credentials wlan.connect("INSERT WIFI SSI", auth=(WLAN.WPA2, "INSERT WIFI PASSWORD"), timeout=5000) while not wlan.isconnected (): machine.idle() print("Connected to Wifi\n") # Initialize I2C bus i2c = I2C(0, I2C.MASTER) i2c.init(I2C.MASTER, baudrate=100000) # Create library object on our I2C port sgp30 = adafruit_sgp30.Adafruit_SGP30(i2c) print("SGP30 serial #", [hex(i) for i in sgp30.serial]) # Initialize SGP-30 internal drift compensation algorithm. sgp30.iaq_init() # Wait 15 seconds for the SGP30 to properly initialize print("Waiting 15 seconds for SGP30 initialization.") time.sleep(15) # Retrieve previously stored baselines, if any (helps the compensation algorithm). has_baseline = False try: f_co2 = open('co2eq_baseline.txt', 'r') f_tvoc = open('tvoc_baseline.txt', 'r') co2_baseline = int(f_co2.read()) tvoc_baseline = int(f_tvoc.read()) #Use them to calibrate the sensor sgp30.set_iaq_baseline(co2_baseline, tvoc_baseline) f_co2.close() f_tvoc.close() has_baseline = True except: print('No SGP30 baselines found') #Store the time at which last baseline has been saved baseline_time = time.time() #Initialize dht22 th = DHT(Pin('P23', mode=Pin.OPEN_DRAIN), 1) #1 because dht22, change to 0 if using a DHT11 print("Waiting 2 seconds for DHT22 initialization.") time.sleep(2) # Builds the json to send the post request to ubidots def build_json(variable1, value1, variable2, value2, variable3, value3, variable4, value4): try: #lat = 6.217 #lng = -75.567 data = {variable1: {"value": value1}, variable2: {"value": value2}, variable3: {"value": value3}, variable4: {"value": value4}} return data except: return None # Sends the post request to ubidots using the REST API def post_var(device, value1, value2, value3, value4): try: url = "https://industrial.api.ubidots.com/" url = url + "api/v1.6/devices/" + device headers = {"X-Auth-Token": TOKEN, "Content-Type": "application/json"} data = build_json("temperature", value1, "humidity", value2, "CO2", value3, "TVOC", value4) if data is not None: print(data) req = requests.post(url=url, headers=headers, json=data) return req.json() else: pass except: pass while True: #gets the temperature and humidity measurements from dht22 result = th.read() while not result.is_valid(): time.sleep(.5) result = th.read() print('Temp.:', result.temperature) print('RH:', result.humidity) #sends the humidity and temperature from DHT22 to SGP30 for a more accurate output sgp30.set_iaq_rel_humidity(result.humidity, result.temperature) #gets the co2 and tvoc measurements co2_eq, tvoc = sgp30.iaq_measure() print('co2eq = ' + str(co2_eq) + ' ppm \t tvoc = ' + str(tvoc) + ' ppb') #sends the data to Ubidots temperature = result.temperature humidity = result.humidity post_var("pycom", temperature, humidity, co2_eq, tvoc) #sends the data to pybytes pybytes.send_signal(1,result.temperature) pybytes.send_signal(2,result.humidity) pybytes.send_signal(3,co2_eq) pybytes.send_signal(4,tvoc) #writes baselines after 12 hours (first time) or 1 hour if (has_baseline and (time.time() - baseline_time >= 3600)) \ or ((not has_baseline) and (time.time() - baseline_time >= 43200)): print('Saving baseline') baseline_time = time.time() try: f_co2 = open('co2eq_baseline.txt', 'w') f_tvoc = open('tvoc_baseline.txt', 'w') bl_co2, bl_tvoc = sgp30.get_iaq_baseline() f_co2.write(str(bl_co2)) f_tvoc.write(str(bl_tvoc)) f_co2.close() f_tvoc.close() has_baseline = True except: print('Impossible to write SGP30 baselines!') # Measures every 5 minutes (300 seconds) time.sleep(300)

31.722222

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0.668345

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4,568

4.749206

0.353968

0.008021

0.022727

0.012032

0.102273

0.086898

0.070856

0.03008

0

0.046512

0.218695

4,568

143

154

31.944056

0.791818

0.240587

0

0.212766

0

0.14846

0

1

0.021277

false

0.031915

0.085106

0

0.138298

0.117021

0

null

0

null

0

1

0

42cd2ea8378c0d8edecc4b1ef21bb286fd030c27

5,278

py

Python

drivers/get_imu_data.py

ndkjing/usv

132e021432a0344a22914aaf68da7d7955d7331f

[ "MIT" ]

null

drivers/get_imu_data.py

ndkjing/usv

132e021432a0344a22914aaf68da7d7955d7331f

[ "MIT" ]

null

drivers/get_imu_data.py

ndkjing/usv

132e021432a0344a22914aaf68da7d7955d7331f

[ "MIT" ]

1

2021-09-04T10:27:30.000Z

# coding:UTF-8 import queue import serial import time import threading ACCData = [0.0] * 8 GYROData = [0.0] * 8 AngleData = [0.0] * 8 FrameState = 0 # 通过0x后面的值判断属于哪一种情况 Bytenum = 0 # 读取到这一段的第几位 CheckSum = 0 # 求和校验位 a = [0.0] * 3 w = [0.0] * 3 Angle = [0.0] * 3 count=0 start_time = time.time() interval=0.01 def DueData(inputdata,q): # 新增的核心程序，对读取的数据进行划分，各自读到对应的数组里 global FrameState # 在局部修改全局变量，要进行global的定义 global Bytenum global CheckSum global a global w global Angle global count global start_time for data in inputdata: # 在输入的数据进行遍历 # data = ord(data) if FrameState == 0: # 当未确定状态的时候，进入以下判断 if data == 0x55 and Bytenum == 0: # 0x55位于第一位时候，开始读取数据，增大bytenum CheckSum = data Bytenum = 1 continue elif data == 0x51 and Bytenum == 1: # 在byte不为0 且识别到 0x51 的时候，改变frame CheckSum += data FrameState = 1 Bytenum = 2 elif data == 0x52 and Bytenum == 1: # 同理 CheckSum += data FrameState = 2 Bytenum = 2 elif data == 0x53 and Bytenum == 1: CheckSum += data FrameState = 3 Bytenum = 2 elif FrameState == 1: # acc #已确定数据代表加速度 if Bytenum < 10: # 读取8个数据 ACCData[Bytenum - 2] = data # 从0开始 CheckSum += data Bytenum += 1 else: if data == (CheckSum & 0xff): # 假如校验位正确 a = get_acc(ACCData) CheckSum = 0 # 各数据归零，进行新的循环判断 Bytenum = 0 FrameState = 0 elif FrameState == 2: # gyro if Bytenum < 10: GYROData[Bytenum - 2] = data CheckSum += data Bytenum += 1 else: if data == (CheckSum & 0xff): w = get_gyro(GYROData) CheckSum = 0 Bytenum = 0 FrameState = 0 elif FrameState == 3: # angle if Bytenum < 10: AngleData[Bytenum - 2] = data CheckSum += data Bytenum += 1 else: if data == (CheckSum & 0xff): Angle = get_angle(AngleData) d = a + w + Angle # 元组类型 # print("a(g):%10.3f %10.3f %10.3f w(deg/s):%10.3f %10.3f %10.3f Angle(deg):%10.3f %10.3f %10.3f" % d) q.put(d) count+=1 if count%1000==0: print('count 1 cost time',(time.time()-start_time)/count) if count>100000000: count=0 # return d CheckSum = 0 Bytenum = 0 FrameState = 0 def get_acc(datahex): axl = datahex[0] axh = datahex[1] ayl = datahex[2] ayh = datahex[3] azl = datahex[4] azh = datahex[5] k_acc = 16.0 acc_x = (axh << 8 | axl) / 32768.0 * k_acc acc_y = (ayh << 8 | ayl) / 32768.0 * k_acc acc_z = (azh << 8 | azl) / 32768.0 * k_acc if acc_x >= k_acc: acc_x -= 2 * k_acc if acc_y >= k_acc: acc_y -= 2 * k_acc if acc_z >= k_acc: acc_z -= 2 * k_acc return acc_x, acc_y, acc_z def get_gyro(datahex): wxl = datahex[0] wxh = datahex[1] wyl = datahex[2] wyh = datahex[3] wzl = datahex[4] wzh = datahex[5] k_gyro = 2000.0 gyro_x = (wxh << 8 | wxl) / 32768.0 * k_gyro gyro_y = (wyh << 8 | wyl) / 32768.0 * k_gyro gyro_z = (wzh << 8 | wzl) / 32768.0 * k_gyro if gyro_x >= k_gyro: gyro_x -= 2 * k_gyro if gyro_y >= k_gyro: gyro_y -= 2 * k_gyro if gyro_z >= k_gyro: gyro_z -= 2 * k_gyro return gyro_x, gyro_y, gyro_z def get_angle(datahex): rxl = datahex[0] rxh = datahex[1] ryl = datahex[2] ryh = datahex[3] rzl = datahex[4] rzh = datahex[5] k_angle = 180.0 angle_x = (rxh << 8 | rxl) / 32768.0 * k_angle angle_y = (ryh << 8 | ryl) / 32768.0 * k_angle angle_z = (rzh << 8 | rzl) / 32768.0 * k_angle if angle_x >= k_angle: angle_x -= 2 * k_angle if angle_y >= k_angle: angle_y -= 2 * k_angle if angle_z >= k_angle: angle_z -= 2 * k_angle return angle_x, angle_y, angle_z class GetImuData: def __init__(self, port, baud, timeout=0.5): self.q = queue.Queue() try: self.serial_obj = serial.Serial(port, baud, timeout=timeout) except Exception as e: print(e) exit(-1) print('串口打开',self.serial_obj.is_open) def get_data(self): while True: datahex = self.serial_obj.read(33) DueData(datahex,self.q) def imu_integration(q): """ imu积分计算 :param d: 当前检测到加速度与角速度 :return: """ if __name__ == '__main__': obj = GetImuData(port='com4',baud=115200) # 打印数据 t1 = threading.Thread(target=obj.get_data) t2 = threading.Thread(target=obj.imu_integration) t1.start() t2.start() t1.join() t2.join()

26.656566

122

0.48939

669

5,278

3.718984

0.22571

0.016077

0.025322

0.019293

0.18127

0.116158

0.060289

0.043408

0

0.079708

0.403372

5,278

198

123

26.656566

0.710384

0.082228

0

0.220126

0

0.006901

0

0.005855

0

1

0.044025

false

0

0.025157

0

0.09434

0.018868

0

null

0

null

0

1

0

42cdb0ad159342fdea9a675f50b583e29f8c7d2a

3,082

py

Python

test/test_utils.py

dilettacal/nmt_seq2seq_evo

1de7647fb50445d17aa0eab8f300fdcbe6b8145e

[ "MIT" ]

null

test/test_utils.py

dilettacal/nmt_seq2seq_evo

1de7647fb50445d17aa0eab8f300fdcbe6b8145e

[ "MIT" ]

null

test/test_utils.py

dilettacal/nmt_seq2seq_evo

1de7647fb50445d17aa0eab8f300fdcbe6b8145e

[ "MIT" ]

null

import os import unittest from torchtext.data import Field, Iterator from project.utils.utils_metrics import AverageMeter from project.utils.utils_logging import Logger from project.utils.datasets import Seq2SeqDataset data_dir = os.path.join(".", "test", "test_data") class TestIOUtils(unittest.TestCase): def test_load_data(self): src_vocab = Field(pad_token="<p>", unk_token="<u>", lower=True) trg_vocab = Field(init_token="<s>", eos_token="</s>",pad_token="<p>", unk_token="<u>", lower=True ) exts = (".de", ".en") samples = Seq2SeqDataset.splits(root="", path=data_dir, exts=exts, train="samples", fields=(src_vocab, trg_vocab), validation="",test="") self.assertIsInstance(samples, tuple) samples = samples[0] self.assertIsInstance(samples, Seq2SeqDataset) self.assertIsNotNone(samples.examples) self.assertAlmostEqual(len(samples.examples), 15) self.assertEqual(list(samples.fields.keys()), ["src", "trg"]) src_vocab.build_vocab(samples) trg_vocab.build_vocab(samples) self.assertIsNotNone(src_vocab.vocab.stoi) self.assertIsNotNone(trg_vocab.vocab.stoi) def test_logger(self): path = os.path.join(data_dir, "log.log") if os.path.exists(path): os.remove(path) logger = Logger(path=data_dir) self.assertIsNotNone(logger) logger.log("test_logging", stdout=False) logger.log("test_second_logging", stdout=False) with open(path, mode="r") as f: content = f.read().strip().split("\n") self.assertEqual(content[0], "test_logging") self.assertEqual(content[1], "test_second_logging") def test_save_model(self): path = os.path.join(data_dir, "log.log") if os.path.exists(path): os.remove(path) logger = Logger(path=data_dir) self.assertIsNotNone(logger) model = dict({"model": [1,2,3,4,2]}) logger.save_model(model) files = os.listdir(data_dir) self.assertIn("model.pkl", files) os.remove(os.path.join(data_dir, "model.pkl")) def test_plot_metrics(self): path = os.path.join(data_dir, "log.log") if os.path.exists(path): os.remove(path) logger = Logger(path=data_dir) self.assertIsNotNone(logger) metric = [1,2,5,1,6,1] logger.plot(metric, "", "", "metric") files = os.listdir(data_dir) self.assertIn("metric.png", files) os.remove(os.path.join(data_dir, "metric.png")) def test_metric(self): metric = AverageMeter() for i in range(10): metric.update(i) self.assertEqual(metric.count, 10) self.assertEqual(metric.val, 9) self.assertEqual(metric.avg, 4.5) self.assertEqual(metric.sum, 45) metric.reset() self.assertEqual(metric.count, 0) self.assertEqual(metric.val, 0) self.assertEqual(metric.avg, 0) self.assertEqual(metric.sum, 0)

36.690476

110

0.621999

390

3,082

4.797436

0.276923

0.044896

0.089792

0.037413

0.270978

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0.174773

0

0.013605

0.236859

3,082

84

111

36.690476

0.781888

0

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0

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false

0

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0.169014

0

null

0

null

0

1

0

42d1f1c104a654530b6968dd6b6bff5cdf01c509

2,156

py

Python

networks/cifar_net.py

DQle38/Fair-Feature-Distillation-for-Visual-Recognition

f0f98728f36528218bf19dce9a26d6ee1ba96e58

[ "MIT" ]

5

2021-09-07T13:33:45.000Z

2022-02-12T18:56:45.000Z

networks/cifar_net.py

DQle38/Fair-Feature-Distillation-for-Visual-Recognition

f0f98728f36528218bf19dce9a26d6ee1ba96e58

[ "MIT" ]

null

networks/cifar_net.py

DQle38/Fair-Feature-Distillation-for-Visual-Recognition

f0f98728f36528218bf19dce9a26d6ee1ba96e58

[ "MIT" ]

4

2021-09-25T06:56:38.000Z

2022-03-24T18:06:08.000Z

import torch import torch.nn as nn import numpy as np class Net(nn.Module): def __init__(self, num_classes=10): super().__init__() self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1) s = compute_conv_output_size(32, 3, padding=1) # 32 self.conv2 = nn.Conv2d(32, 32, kernel_size=3, padding=1) s = compute_conv_output_size(s, 3, padding=1) # 32 s = s // 2 # 16 self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1) s = compute_conv_output_size(s, 3, padding=1) # 16 self.conv4 = nn.Conv2d(64, 64, kernel_size=3, padding=1) s = compute_conv_output_size(s, 3, padding=1) # 16 s = s // 2 # 8 self.conv5 = nn.Conv2d(64, 128, kernel_size=3, padding=1) s = compute_conv_output_size(s, 3, padding=1) # 8 self.conv6 = nn.Conv2d(128, 128, kernel_size=3, padding=1) s = compute_conv_output_size(s, 3, padding=1) # 8 s = s // 2 # 4 self.fc1 = nn.Linear(s * s * 128, 256) # 2048 self.drop1 = nn.Dropout(0.25) self.drop2 = nn.Dropout(0.5) self.MaxPool = torch.nn.MaxPool2d(2) self.last = torch.nn.Linear(256, num_classes) self.relu = torch.nn.ReLU() def forward(self, x, get_inter=False, before_fc=False): act1 = self.relu(self.conv1(x)) act2 = self.relu(self.conv2(act1)) h = self.drop1(self.MaxPool(act2)) act3 = self.relu(self.conv3(h)) act4 = self.relu(self.conv4(act3)) h = self.drop1(self.MaxPool(act4)) act5 = self.relu(self.conv5(h)) act6 = self.relu(self.conv6(act5)) h = self.drop1(self.MaxPool(act6)) h = h.view(x.shape[0], -1) act7 = self.relu(self.fc1(h)) # h = self.drop2(act7) y=self.last(act7) if get_inter: if before_fc: return act6, y else: return act7, y else: return y def compute_conv_output_size(l_in, kernel_size, stride=1, padding=0, dilation=1): return int(np.floor((l_in + 2 * padding - dilation * (kernel_size - 1) - 1) / float(stride) + 1))

35.933333

101

0.574212

333

2,156

3.582583

0.243243

0.080469

0.090528

0.123219

0.317687

0.264878

0

0.092978

0.286642

2,156

59

102

36.542373

0.682705

0.022263

0

0.204082

0

1

0.061224

false

0

0.061224

0.020408

0.22449

0

null

0

null

0

1

0

42d4aca626e7056c3cd312d444ec2606808efc07

1,207

py

Python

solutions/python3/problem654.py

tjyiiuan/LeetCode

abd10944c6a1f7a7f36bd9b6218c511cf6c0f53e

[ "MIT" ]

null

solutions/python3/problem654.py

tjyiiuan/LeetCode

abd10944c6a1f7a7f36bd9b6218c511cf6c0f53e

[ "MIT" ]

null

solutions/python3/problem654.py

tjyiiuan/LeetCode

abd10944c6a1f7a7f36bd9b6218c511cf6c0f53e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ 654. Maximum Binary Tree Given an integer array with no duplicates. A maximum tree building on this array is defined as follow: The root is the maximum number in the array. The left subtree is the maximum tree constructed from left part subarray divided by the maximum number. The right subtree is the maximum tree constructed from right part subarray divided by the maximum number. Construct the maximum tree by the given array and output the root node of this tree. """ # Definition for a binary tree node. class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def constructMaximumBinaryTree(self, nums) -> TreeNode: if not nums: return None max_val = nums[0] max_ind = 0 for ind, val in enumerate(nums): if val > max_val: max_ind = ind max_val = val l_node = self.constructMaximumBinaryTree(nums[:max_ind]) r_node = self.constructMaximumBinaryTree(nums[max_ind + 1:]) root = TreeNode(val=max_val, left=l_node, right=r_node) return root

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105

0.666114

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1,207

4.554913

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0.076142

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0.048223

0.30203

0.190355

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0.26512

1,207

38

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1

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false

0

0.315789

0

null

0

null

0

1

0

42d54535865b205f51d1935bf40792c7ce95c829

5,189

py

Python

pparser.py

deadsurgeon42/StarryPy3k

9291e5a7ca97004675a4868165ce5690c111c492

[ "WTFPL" ]

44

2015-11-18T07:45:11.000Z

2022-03-30T06:32:18.000Z

pparser.py

deadsurgeon42/StarryPy3k

9291e5a7ca97004675a4868165ce5690c111c492

[ "WTFPL" ]

110

2016-08-01T06:45:13.000Z

2021-11-30T18:45:36.000Z

pparser.py

deadsurgeon42/StarryPy3k

9291e5a7ca97004675a4868165ce5690c111c492

[ "WTFPL" ]

32

2015-01-31T09:54:38.000Z

2022-03-31T06:12:21.000Z

import asyncio import traceback from configuration_manager import ConfigurationManager from data_parser import * parse_map = { 0: ProtocolRequest, 1: ProtocolResponse, 2: ServerDisconnect, 3: ConnectSuccess, 4: ConnectFailure, 5: HandshakeChallenge, 6: ChatReceived, 7: None, 8: None, 9: PlayerWarpResult, 10: None, 11: None, 12: None, 13: ClientConnect, 14: ClientDisconnectRequest, 15: None, 16: PlayerWarp, 17: FlyShip, 18: ChatSent, 19: None, 20: ClientContextUpdate, 21: WorldStart, 22: WorldStop, 23: None, 24: None, 25: None, 26: None, 27: None, 28: None, 29: None, 30: None, 31: GiveItem, 32: None, 33: None, 34: None, 35: None, 36: None, 37: None, 38: None, 39: ModifyTileList, 40: None, 41: None, 42: None, 43: SpawnEntity, 44: None, 45: None, 46: None, 47: None, 48: None, 49: None, 50: EntityCreate, 51: None, 52: None, 53: EntityInteract, 54: EntityInteractResult, 55: None, 56: DamageRequest, 57: DamageNotification, 58: EntityMessage, 59: EntityMessageResponse, 60: DictVariant, 61: StepUpdate, 62: None, 63: None, 64: None, 65: None, 66: None, 67: None, 68: None } class PacketParser: """ Object for handling the parsing and caching of packets. """ def __init__(self, config: ConfigurationManager): self._cache = {} self.config = config self.loop = asyncio.get_event_loop() self._reaper = self.loop.create_task(self._reap()) @asyncio.coroutine def parse(self, packet): """ Given a packet preped packet from the stream, parse it down to its parts. First check if the packet is one we've seen before; if it is, pull its parsed form from the cache, and run with that. Otherwise, pass it to the appropriate parser for parsing. :param packet: Packet with header information parsed. :return: Fully parsed packet. """ try: if packet["size"] >= self.config.config["min_cache_size"]: packet["hash"] = hash(packet["original_data"]) if packet["hash"] in self._cache: self._cache[packet["hash"]].count += 1 packet["parsed"] = self._cache[packet["hash"]].packet[ "parsed"] else: packet = yield from self._parse_and_cache_packet(packet) else: packet = yield from self._parse_packet(packet) except Exception as e: print("Error during parsing.") print(traceback.print_exc()) finally: return packet @asyncio.coroutine def _reap(self): """ Prune packets from the cache that are not being used, and that are older than the "packet_reap_time". :return: None. """ while True: yield from asyncio.sleep(self.config.config["packet_reap_time"]) for h, cached_packet in self._cache.copy().items(): cached_packet.count -= 1 if cached_packet.count <= 0: del (self._cache[h]) @asyncio.coroutine def _parse_and_cache_packet(self, packet): """ Take a new packet and pass it to the parser. Once we get it back, make a copy of it to the cache. :param packet: Packet with header information parsed. :return: Fully parsed packet. """ packet = yield from self._parse_packet(packet) self._cache[packet["hash"]] = CachedPacket(packet=packet) return packet @asyncio.coroutine def _parse_packet(self, packet): """ Parse the packet by giving it to the appropriate parser. :param packet: Packet with header information parsed. :return: Fully parsed packet. """ res = parse_map[packet["type"]] if res is None: packet["parsed"] = {} else: #packet["parsed"] = yield from self.loop.run_in_executor( # self.loop.executor, res.parse, packet["data"]) # Removed due to issues with testers. Need to evaluate what's going # on. packet["parsed"] = res.parse(packet["data"]) return packet # def __del__(self): # self._reaper.cancel() class CachedPacket: """ Prototype for cached packets. Keep track of how often it is used, as well as the full packet's contents. """ def __init__(self, packet): self.count = 1 self.packet = packet def build_packet(packet_id, data, compressed=False): """ Convenience method for building a packet. :param packet_id: ID value of packet. :param data: Contents of packet. :param compressed: Whether or not to compress the packet. :return: Built packet object. """ return BasePacket.build({"id": packet_id, "data": data, "compressed": compressed})

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false

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0.015625

0

null

0

null

0

1

0

42d72c0c58e56c65e8f873c5b25c452eaaf9e7cc

3,032

py

Python

deploy/testData.py

yaoguxiao/insightface

731f9ec7503cda3a5f3433525aa57709a78b2118

[ "MIT" ]

null

deploy/testData.py

yaoguxiao/insightface

731f9ec7503cda3a5f3433525aa57709a78b2118

[ "MIT" ]

null

deploy/testData.py

yaoguxiao/insightface

731f9ec7503cda3a5f3433525aa57709a78b2118

[ "MIT" ]

null

import sys import os import mxnet as mx import argparse sys.path.append(os.path.join(os.getcwd(), "../src/common")) sys.path.append(os.path.join(os.getcwd(), "../src/eval")) import verification def argParser(): parser = argparse.ArgumentParser(description='test network') parser.add_argument('--model', default='../../insightface/models/model-res4-8-16-4-dim512/model,0', help='path of model') parser.add_argument('--data-dir', default='../../insightface/datasets/faces_ms1m_112x112/', help='path of test data') parser.add_argument('--target', default='lfw', help='name of test data') parser.add_argument('--output', default='fc1', help='output name') parser.add_argument('--batch-size', default=50, help='batch size') # parser.add_argument('add_argument') args = parser.parse_args() return args def reaTestData(): verList = {} for name in args.target.split(','): print("============", name) path = os.path.join(args.data_dir,name+".bin") print(path) if not os.path.exists(path):break verList[name] = verification.load_bin(path, [112,112]) print('ver', name) return verList def verTest(model, nbatch): results = [] verList = reaTestData() print("===============, line:", sys._getframe().f_lineno) if verList is None: print("read test data err") return print("===============, line:", sys._getframe().f_lineno) for i in verList: print("===============, line:", sys._getframe().f_lineno) acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(verList[i], model, args.batch_size, 10, None, None) print('[%s][%d]XNorm: %f' % (i, nbatch, xnorm)) # print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (i, nbatch, acc1, std1)) print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (i, nbatch, acc2, std2)) results.append(acc2) return results # class faceMode: # def __init__(self, args): # self.arts = args # modelid = args.model.split(',') # print(modelid[0], modelid[1]) # sym, argParams, auxParams = mx.model.load_checkpoint(modelid[0], int(modelid[1]))#type:mx.symbol.symbol.Symbol # sym = sym.get_internals()[args.output + '_output'] # self.model = mx.mod.Module(symbol=sym, label_names=None) # self.model.bind(('data', (1, 3, 112,112))) # self.model.set_params(argParams, auxParams) # print(type(sym)) if __name__ == "__main__": args = argParser() # faceMode(args) modelid = args.model.split(',') print(modelid[0], modelid[1]) sym, argParams, auxParams = mx.model.load_checkpoint(modelid[0], int(modelid[1])) # type:mx.symbol.symbol.Symbol sym = sym.get_internals()[args.output + '_output'] model = mx.mod.Module(symbol=sym, context=mx.gpu(0), label_names=None) # model.bind(data_shapes=('data', (args.batch_size, 3, 112, 112))) model.bind(data_shapes=[('data', (args.batch_size, 3, 112,112))]) model.set_params(argParams, auxParams) verTest(model, args.batch_size)

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125

0.632256

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3,032

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0.29484

0.041176

0.054545

0.032086

0.405882

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0.261497

0.225134

0

0.029786

0.169525

3,032

73

126

41.534247

0.712867

0.240765

0

0.058824

0

0.189934

0.054705

0

1

0.058824

false

0

0.098039

0

0.235294

0.196078

0

null

0

null

0

1

0

42dc77f7900d79cb250ea17552132e0f738917bd

4,482

py

Python

test/test_plugin_spontit.py

NiNiyas/apprise

8d96e95acd7cb89f082685ae161bd0e268203f0c

[ "MIT" ]

1

2022-01-19T01:40:04.000Z

test/test_plugin_spontit.py

NiNiyas/apprise

8d96e95acd7cb89f082685ae161bd0e268203f0c

[ "MIT" ]

null

test/test_plugin_spontit.py

NiNiyas/apprise

8d96e95acd7cb89f082685ae161bd0e268203f0c

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright (C) 2021 Chris Caron <lead2gold@gmail.com> # All rights reserved. # # This code is licensed under the MIT License. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files(the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and / or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions : # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import requests from apprise import plugins from helpers import AppriseURLTester # Disable logging for a cleaner testing output import logging logging.disable(logging.CRITICAL) # Our Testing URLs apprise_url_tests = ( ('spontit://', { # invalid url 'instance': TypeError, }), # Another bad url ('spontit://:@/', { 'instance': TypeError, }), # No user specified ('spontit://%s' % ('a' * 100), { 'instance': TypeError, }), # Invalid API Key specified ('spontit://user@%%20_', { 'instance': TypeError, }), # Provide a valid user and API Key ('spontit://%s@%s' % ('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, # Our expected url(privacy=True) startswith() response: 'privacy_url': 'spontit://{}@b...b/'.format('u' * 11), }), # Provide a valid user and API Key, but provide an invalid channel ('spontit://%s@%s/#!!' % ('u' * 11, 'b' * 100), { # An instance is still created, but the channel won't be notified 'instance': plugins.NotifySpontit, }), # Provide a valid user, API Key and a valid channel ('spontit://%s@%s/#abcd' % ('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, }), # Provide a valid user, API Key, and a subtitle ('spontit://%s@%s/?subtitle=Test' % ('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, }), # Provide a valid user, API Key, and a lengthy subtitle ('spontit://%s@%s/?subtitle=%s' % ('u' * 11, 'b' * 100, 'c' * 300), { 'instance': plugins.NotifySpontit, }), # Provide a valid user and API Key, but provide a valid channel (that is # not ours). # Spontit uses a slash (/) to delimite the user from the channel id when # specifying channel entries. For Apprise we need to encode this # so we convert the slash (/) into %2F ('spontit://{}@{}/#1245%2Fabcd'.format('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, }), # Provide multipe channels ('spontit://{}@{}/#1245%2Fabcd/defg'.format('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, }), # Provide multipe channels through the use of the to= variable ('spontit://{}@{}/?to=#1245/abcd'.format('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, }), ('spontit://%s@%s' % ('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, # force a failure 'response': False, 'requests_response_code': requests.codes.internal_server_error, }), ('spontit://%s@%s' % ('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, # throw a bizzare code forcing us to fail to look it up 'response': False, 'requests_response_code': 999, }), ('spontit://%s@%s' % ('u' * 11, 'b' * 100), { 'instance': plugins.NotifySpontit, # Throws a series of connection and transfer exceptions when this flag # is set and tests that we gracfully handle them 'test_requests_exceptions': True, }), ) def test_plugin_spontit_urls(): """ NotifySpontit() Apprise URLs """ # Run our general tests AppriseURLTester(tests=apprise_url_tests).run_all()

37.663866

79

0.629407

573

4,482

4.891798

0.366492

0.012843

0.015697

0.027471

0.281127

0.236889

0.211916

0.197289

0.171602

0

0.025865

0.232262

4,482

118

80

37.983051

0.788724

0.499554

0

0.6

0

0.258494

0.109275

0

1

0.016667

false

0

0.066667

0

0.083333

0

null

0

null

0

1

0

42dc9cb1aa466dc4d81d1303416d9c0741104c68

2,751

py

Python

img_striper.py

tacensi/image_striper

d361c5c4b7e9b8588b50d8f992b90d14fd64d4f0

[ "MIT" ]

null

img_striper.py

tacensi/image_striper

d361c5c4b7e9b8588b50d8f992b90d14fd64d4f0

[ "MIT" ]

null

img_striper.py

tacensi/image_striper

d361c5c4b7e9b8588b50d8f992b90d14fd64d4f0

[ "MIT" ]

null

#! /usr/bin/env python3 import argparse import textwrap import math from PIL import Image parser = argparse.ArgumentParser( prog='img_striper.py', formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent('''\ Image striper This is a simple program to make stripes out of images and join them together again. It was inspired by this great video: https://www.instagram.com/p/BhZU4XMgdYA/ This script follows the WTFPL, so go ahead and do whatever the fuck you want with it. '''), epilog=textwrap.dedent('''\ This is just a simple exercise. Please don't hate me for my noobiness. ''')) parser.add_argument('--i', '-input', help='File to be shifted', type=argparse.FileType('rb', 0), required=True ) parser.add_argument('--o', '-output', help='Image to be saved', type=argparse.FileType('wb', 0), required=True ) args = parser.parse_args() # open image and create new one original_doggo = Image.open(args.i) original_w, original_h = original_doggo.size inter_w = int(math.floor(original_w / 2)) inter_h = original_h * 2 inter_doggo = Image.new('RGB', [inter_w, inter_h], 'white') # calculate the number of strips no_strips = int(math.floor(original_w / 15)) for n in range(0, no_strips): # calculate xs from the cropped strip x1 = n * 15 x2 = x1 + 15 # create crop box crop_box = (x1, 0, x2, original_h) # cropped section section = original_doggo.crop(crop_box) y1 = 0 # calculate xs for the placement of the paste if n % 2: y1 = original_h y2 = y1 + original_h x3 = 15 * int(math.floor(n / 2)) x4 = x3 + 15 paste_box = (x3, y1, x4, y2) inter_doggo.paste(section, paste_box) original_w, original_h = inter_doggo.size new_h = int(math.floor(inter_h / 2)) new_w = inter_w * 2 new_doggo = Image.new('RGB', [new_w, new_h], 'white') # calculate the number of strips no_strips = int(math.floor(inter_h / 15)) for n in range(0, no_strips): # calculate xs from the cropped strip y1 = n * 15 y2 = y1 + 15 # create crop box crop_box = (0, y1, inter_w, y2) # cropped section section = inter_doggo.crop(crop_box) x1 = 0 # calculate xs for the placement of the paste if n % 2: x1 = inter_w x2 = x1 + inter_w y3 = 15 * int(math.floor(n / 2)) y4 = y3 + 15 paste_box = (x1, y3, x2, y4) new_doggo.paste(section, paste_box) new_doggo.save(args.o) # print(original_w, original_h) # parser.print_help()

25.472222

67

0.607779

400

2,751

4.045

0.34

0.038937

0.044499

0.033375

0.290482

0.226205

0.179234

0

0.035696

0.287168

2,751

107

68

25.71028

0.789393

0.14104

0

0.085714

0

0.212857

0

1

0

false

0

0.057143

0

0.057143

0

null

0

null

0

1

0

42dcb97d77131e74ecfe71c62c27b3b22cca853a

7,590

py

Python

ds/web/views.py

brainmorsel/python-dhcp-sprout

c8da1b19558e404fdfef24304e1996c696fc13b1

[ "MIT" ]

null

ds/web/views.py

brainmorsel/python-dhcp-sprout

c8da1b19558e404fdfef24304e1996c696fc13b1

[ "MIT" ]

1

2019-05-03T07:54:57.000Z

ds/web/views.py

brainmorsel/python-dhcp-sprout

c8da1b19558e404fdfef24304e1996c696fc13b1

[ "MIT" ]

null

import datetime from aiohttp import web from aiohttp_jinja2 import template import sqlalchemy as sa from sqlalchemy.dialects import postgresql as pg import psycopg2 from ds import db from . import forms @template('index.jinja2') async def index(request): return {} @template('profile_list.jinja2') async def profile_list(request): async with request.app.db.acquire() as conn: items = await (await conn.execute( sa.select([ db.profile, 'ips_used', (sa.func.broadcast(db.profile.c.network_addr) - db.profile.c.network_addr - 2).label('ips_total') ]). select_from( db.profile. join(sa.select([ db.owner.c.profile_id, sa.func.count(db.owner.c.id).label('ips_used') ]).group_by(db.owner.c.profile_id).alias('cnts')) ). order_by(db.profile.c.name) )).fetchall() return {'items': items} def _cast_str_to_inet_arr(ip_list_str): return sa.cast(map(str, forms.str_to_ip_list(ip_list_str)), pg.ARRAY(pg.INET)) @template('profile_edit.jinja2') async def profile_edit(request): tbl = db.profile item_id = request.match_info.get('id') await request.post() async with request.app.db.acquire() as conn: async with conn.begin(): item = await (await conn.execute( tbl.select().where(tbl.c.id == item_id) )).fetchone() form = forms.ProfileEditForm(await request.post(), item) if request.method == 'POST' and form.validate(): params = db.fit_params_dict(form.data, tbl.c.keys()) print(params['dns_ips']) params['dns_ips'] = _cast_str_to_inet_arr(params['dns_ips']) params['ntp_ips'] = _cast_str_to_inet_arr(params['ntp_ips']) if item_id is None: await conn.execute(tbl.insert().values(params)) else: await conn.execute( tbl.update().values(params).where(tbl.c.id == item_id) ) await conn.execute( sa.select([sa.func.pg_notify('dhcp_control', 'RELOAD_PROFILE {}'.format(item_id))]) ) return web.HTTPFound('/profile/') return {'form': form} async def profile_delete(request): tbl = db.profile item_id = request.match_info.get('id') async with request.app.db.acquire() as conn: await conn.execute(tbl.delete().where(tbl.c.id == item_id)) return web.HTTPFound('/profile/') @template('staging_list.jinja2') async def staging_list(request): async with request.app.db.acquire() as conn: items = await (await conn.execute( sa.select([ db.owner, db.profile.c.name.label('profile_name'), db.profile.c.relay_ip, ]). select_from( db.owner. join(db.profile) ). where(db.owner.c.ip_addr == None). order_by(sa.desc(db.owner.c.create_date)) )).fetchall() return {'items': items} async def staging_assign_ip(request): item_id = int(request.match_info.get('id')) async with request.app.db.acquire() as conn: async with conn.begin(): profile_id = await conn.scalar( sa.select([db.owner.c.profile_id]).where(db.owner.c.id == item_id) ) gen = sa.select([ (sa.cast('0.0.0.0', pg.INET) + sa.func.generate_series( sa.cast(db.profile.c.network_addr, pg.INET) - '0.0.0.0' + 1, sa.func.broadcast(db.profile.c.network_addr) - '0.0.0.0' - 1 )).label('ip_addr') ]).\ select_from(db.profile.join(db.owner)). \ where(db.profile.c.id == profile_id) sel = sa.select([db.owner.c.ip_addr]). \ where(db.owner.c.profile_id == profile_id). \ where(db.owner.c.ip_addr != None) ip_addr = gen.except_(sel).order_by('ip_addr').limit(1) await conn.execute( db.owner.update().values( ip_addr=ip_addr, modify_date=sa.func.now() ). where(db.owner.c.id == item_id) ) await conn.execute( sa.select([sa.func.pg_notify('dhcp_control', 'RELOAD_ITEM {}'.format(item_id))]) ) if 'edit' in request.rel_url.query: return web.HTTPFound('/assigned/{}/edit?redirect=/staging/'.format(item_id)) return web.HTTPFound('/staging/') async def staging_delete(request): tbl = db.owner item_id = request.match_info.get('id') async with request.app.db.acquire() as conn: async with conn.begin(): mac_addr = await conn.scalar( sa.select([tbl.c.mac_addr]). where(tbl.c.id == item_id) ) await conn.execute(tbl.delete().where(tbl.c.id == item_id)) await conn.execute( sa.select([sa.func.pg_notify('dhcp_control', 'REMOVE_STAGING {}'.format(mac_addr))]) ) return web.HTTPFound('/staging/') @template('assigned_list.jinja2') async def assigned_list(request): async with request.app.db.acquire() as conn: items = await (await conn.execute( sa.select([ db.owner, db.profile.c.name.label('profile_name'), db.profile.c.relay_ip, ]). select_from( db.owner. join(db.profile) ). where(db.owner.c.ip_addr != None). order_by(sa.desc(db.owner.c.lease_date)) )).fetchall() return {'items': items} @template('assigned_edit.jinja2') async def assigned_edit(request): item_id = request.match_info.get('id') await request.post() async with request.app.db.acquire() as conn: item = await (await conn.execute( sa.select([ db.owner, db.profile.c.name.label('profile_name'), db.profile.c.relay_ip, ]). select_from( db.owner. join(db.profile) ). where(db.owner.c.id == item_id) )).fetchone() form = forms.AssignedItemEditForm(await request.post(), item) if request.method == 'POST' and form.validate(): params = db.fit_params_dict(form.data, db.owner.c.keys()) await conn.execute( db.owner.update().values(params).where(db.owner.c.id == item_id) ) if 'redirect' in request.rel_url.query: return web.HTTPFound(request.rel_url.query['redirect']) return web.HTTPFound('/assigned/') return {'item': item, 'form': form} async def assigned_delete(request): tbl = db.owner item_id = request.match_info.get('id') async with request.app.db.acquire() as conn: async with conn.begin(): mac_addr = await conn.scalar( sa.select([tbl.c.mac_addr]). where(tbl.c.id == item_id) ) await conn.execute(tbl.delete().where(tbl.c.id == item_id)) await conn.execute( sa.select([sa.func.pg_notify('dhcp_control', 'REMOVE_ACTIVE {}'.format(mac_addr))]) ) return web.HTTPFound('/assigned/')

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941

7,590

4.274176

0.134963

0.046992

0.06365

0.024615

0.673794

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0.564147

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7,590

212

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35.801887

0.770476

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0.005435

0

null

0

null

0

1

0

42e0044ddc8db8684b032fa92b309e589628c115

6,123

py

Python

etsyapi/__init__.py

DempDemp/etsyapi

995250d2f76dcac7edf3b2404bfbce1df732765e

[ "BSD-3-Clause" ]

1

2021-02-19T01:45:49.000Z

etsyapi/__init__.py

DempDemp/etsyapi

995250d2f76dcac7edf3b2404bfbce1df732765e

[ "BSD-3-Clause" ]

null

etsyapi/__init__.py

DempDemp/etsyapi

995250d2f76dcac7edf3b2404bfbce1df732765e

[ "BSD-3-Clause" ]

2

2016-04-10T21:28:05.000Z

2019-09-20T19:51:37.000Z

import six import json import logging import requests from requests_oauthlib import OAuth1 if six.PY3: from urllib.parse import parse_qs from urllib.parse import urlencode else: from urlparse import parse_qs from urllib import urlencode log = logging.getLogger(__name__) class EtsyError(Exception): def __init__(self, message, response): super(EtsyError, self).__init__(message) self.response = response class Etsy(object): """ Represents the etsy API """ url_base = "https://openapi.etsy.com/v2" def __init__(self, consumer_key, consumer_secret, oauth_token=None, oauth_token_secret=None, sandbox=False): self.params = {'api_key': consumer_key} self.consumer_key = consumer_key self.consumer_secret = consumer_secret if sandbox: self.url_base = "http://sandbox.openapi.etsy.com/v2" # generic authenticated oauth hook self.simple_oauth = OAuth1(consumer_key, client_secret=consumer_secret) if oauth_token and oauth_token_secret: # full oauth hook for an authenticated user self.full_oauth = OAuth1(consumer_key, client_secret=consumer_secret, resource_owner_key=oauth_token, resource_owner_secret=oauth_token_secret) def show_listings(self, color=None, color_wiggle=5): """ Show all listings on the site. color should be a RGB ('#00FF00') or a HSV ('360;100;100') """ endpoint = '/listings/active' params = {} if color: params['color'] = color params['color_accuracy'] = color_wiggle response = self.execute(endpoint, params=params) return response def get_user_info(self, user): """ Get basic info about a user, pass in a username or a user_id """ endpoint = '/users/%s' % user auth = {} if user == '__SELF__': auth = {'oauth': self.full_oauth} response = self.execute(endpoint, **auth) return response def find_user(self, keywords): """ Search for a user given the """ endpoint = '/users' params = {'keywords': keywords} response = self.execute(endpoint, params=params) return response def get_auth_url(self, permissions=[]): """ Returns a url that a user is redirected to in order to authenticate with the etsy API. This is step one in the authentication process. oauth_token and oauth_token_secret need to be saved for step two. """ endpoint = '/oauth/request_token' params = {} if permissions: params = {'scope': " ".join(permissions)} self.oauth = self.simple_oauth response = self.execute(endpoint, oauth=self.oauth, params=params) parsed = parse_qs(response) url = parsed['login_url'][0] token = parsed['oauth_token'][0] secret = parsed['oauth_token_secret'][0] return {'oauth_token': token, 'url': url, 'oauth_token_secret': secret} def get_auth_token(self, verifier, oauth_token, oauth_token_secret): """ Step two in the authentication process. oauth_token and oauth_token_secret are the same that came from the get_auth_url function call. Returned is the permanent oauth_token and oauth_token_secret that will be used in every subsiquent api request that requires authentication. """ endpoint = '/oauth/access_token' oauth = OAuth1(self.consumer_key, client_secret=self.consumer_secret, resource_owner_key=oauth_token, resource_owner_secret=oauth_token_secret, verifier=verifier) response = requests.post(url="%s%s" % (self.url_base, endpoint), auth=oauth) parsed = parse_qs(response.text) return {'oauth_token': parsed['oauth_token'][0], 'oauth_token_secret': parsed['oauth_token_secret'][0]} def execute(self, endpoint, method='get', oauth=None, params=None, files=None, **hooks): """ Actually do the request, and raise exception if an error comes back. """ if oauth: # making an authenticated request, add the oauth hook to the request hooks['auth'] = oauth if params is None: params = {} else: if params is None: params = self.params else: params.update(self.params) querystring = urlencode(params) url = "%s%s" % (self.url_base, endpoint) if querystring: url = "%s?%s" % (url, querystring) response = getattr(requests, method)(url, files=files, **hooks) if response.status_code > 201: e = response.text code = response.status_code raise EtsyError('API returned %s response: %s' % (code, e), response) try: return json.loads(response.text) except (TypeError, ValueError): return response.text def execute_authed(self, endpoint, method='get', params=None, **hooks): return self.execute(endpoint, method, oauth=self.full_oauth, params=params, **hooks) def iterate_pages(self, f, *p, **d): ''' Iterates through pages in a response. Use this method when the response is valid json and has pagination Example: pages = e.iterate_pages('execute_authed', '/shops/GreenTurtleTshirts/receipts', params={'was_paid': True, 'was_shipped': False}) for page in pages: print page ''' f = getattr(self, f) r = f(*p, **d) yield r while r['pagination']['next_page'] is not None: if not d: d = {} if 'params' not in d: d['params'] = {} d['params']['page'] = r['pagination']['next_page'] r = f(*p, **d) yield r

36.230769

112

0.594806

721

6,123

4.882108

0.260749

0.068182

0.054545

0.020455

0.252841

0.182955

0.144318

0.130682

0.107386

0

0.006828

0.306386

6,123

168

113

36.446429

0.821992

0.194349

0

0.160377

0

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0

1

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false

0

0.084906

0.009434

0.283019

0

null

0

null

0

1

0

42e109eb76a25424069247c9b529582b0044ded2

2,996

py

Python

SatTrack/tle.py

ed-ortizm/satellite-tracking

9eb2b4a7f31b43035a425d8e2e51044f2e80712d

[ "MIT" ]

2

2021-10-05T10:04:56.000Z

2021-10-13T18:31:35.000Z

SatTrack/tle.py

ed-ortizm/satellite-tracking

9eb2b4a7f31b43035a425d8e2e51044f2e80712d

[ "MIT" ]

14

2021-09-01T12:30:59.000Z

2022-02-14T18:53:44.000Z

SatTrack/tle.py

ed-ortizm/satellite-tracking

9eb2b4a7f31b43035a425d8e2e51044f2e80712d

[ "MIT" ]

null

import datetime import os import re import sys import urllib from SatTrack.superclasses import FileDirectory ############################################################################### # CONSTANTS TLE_URL = f"https://celestrak.com/NORAD/elements/supplemental" ############################################################################### class TLE(FileDirectory): def __init__(self, satellite_brand: str, directory: str): """ Handles tle files PARAMETERS satellite_brand: Name of satellite type, e.g, oneweb directory: The location of the tle files """ self.satellite_brand = satellite_brand self.directory = directory ########################################################################### def download(self) -> str: """ Downloads the tle_file pass in the costructor from TLE_URL = f"https://celestrak.com/NORAD/elements/supplemental" OUTPUTS string with name of the tle file in the format "tle_{satellite_brand}_{time_stamp}.txt". time_stamp -> "%Y-%m-%d %H:%M:%S" example: "tle_oneweb_2021-10-09 16:18:16.txt" """ tle_query = f"{TLE_URL}/{self.satellite_brand}.txt" time_stamp = self._get_time_stamp() tle_file_name = f"tle_{self.satellite_brand}_{time_stamp}.txt" super().check_directory(directory=self.directory, exit=False) urllib.request.urlretrieve( tle_query, f"{self.directory}/{tle_file_name}" ) return tle_file_name ########################################################################### def get_satellites_from_tle(self, file_location: str) -> list: """ Retrieves the names of satellites present in tle file. The tle file must be stored locally. PARAMETERS file_location: path of the tle file RETURNS list with all the sattelites available in tle file example: [oneweb-000, ...] """ super().file_exists(file_location, exit=True) # oneweb -> ONEWEB satellite = self.satellite_brand.upper() regular_expression = f"{satellite}-[0-9]*.*\)|{satellite}.[0-9]*" pattern = re.compile(regular_expression) with open(f"{file_location}", "r") as tle: content = tle.read() satellites = pattern.findall(content) return satellites ########################################################################### def _get_time_stamp(self) -> str: """ Returns time stamp for tle file download: "2021-10-09 16:18:16" """ now = datetime.datetime.now(tz=datetime.timezone.utc) time_stamp = f"{now:%Y-%m-%d %H:%M:%S}" return time_stamp ########################################################################### ###########################################################################

32.215054

79

0.496996

302

2,996

4.754967

0.354305

0.048747

0.062674

0.016713

0.132312

0.0961

0.068245

0

0.015351

0.238985

2,996

92

80

32.565217

0.614474

0.261015

0

0.163599

0.103613

0

1

0.121212

false

0

0.181818

0

0.424242

0

null

0

null

0

1

0

42e13e620ce8965d49cd0e6e2ae37165c0735674

21,970

py

Python

vinfo/dataset.py

john-hewitt/conditional-probing

bebc90aa0c910395e2370910409076a945279fe0

[ "Apache-2.0" ]

13

2021-09-21T11:07:33.000Z

2022-03-25T08:46:46.000Z

vinfo/dataset.py

john-hewitt/conditional-probing

bebc90aa0c910395e2370910409076a945279fe0

[ "Apache-2.0" ]

2

2021-09-25T15:45:19.000Z

2021-12-10T15:57:35.000Z

vinfo/dataset.py

john-hewitt/conditional-probing

bebc90aa0c910395e2370910409076a945279fe0

[ "Apache-2.0" ]

2

2021-09-27T01:21:49.000Z

2021-09-28T06:08:19.000Z

import os import h5py import torch import torch.nn as nn from torch.utils.data import Dataset, IterableDataset, DataLoader import Levenshtein as levenshtein from tqdm import tqdm from yaml import YAMLObject from transformers import AutoTokenizer, AutoModel from allennlp.modules.elmo import batch_to_ids from utils import TRAIN_STR, DEV_STR, TEST_STR, InitYAMLObject BATCH_SIZE = 50 """ Classes for loading, caching, and yielding text datasets """ #class Dataset(Dataset, InitYAMLObject): # """ # Base class for objects that serve batches of # tensors. For decoration/explanation only # """ # yaml_tag = '!Dataset' class IterableDatasetWrapper(Dataset):#(IterableDataset): """ Wrapper class to pass to a DataLoader so it doesn't think the underlying generator should have a len() fn. But I gave up on this for various reasons so it's just a normal dataset, here in case I try again. """ def __init__(self, generator): self.generator = generator #[x for x in generator] def __iter__(self): return iter(self.generator) def __len__(self): return len(self.generator) def __getitem__(self, idx): return self.generator[idx] class ListDataset(Dataset, InitYAMLObject): """ Container class for collecting multiple annotation or representation datasets and a single target task dataset , and serving all of them """ yaml_tag = '!ListDataset' def __init__(self, args, data_loader, output_dataset, input_datasets): """ Arguments: output_datset: """ self.args = args self.input_datasets = input_datasets self.output_dataset = output_dataset self.data_loader = data_loader self.train_data = None self.dev_data = None self.test_data = None def get_train_dataloader(self, shuffle=True): """Returns a PyTorch DataLoader object with the training data """ if self.train_data is None: self.train_data = list(self.load_data(TRAIN_STR)) #generator = IterableDatasetWrapper(self.load_data(TRAIN_STR)) generator = IterableDatasetWrapper(self.train_data) return DataLoader(generator, batch_size=BATCH_SIZE, shuffle=shuffle, collate_fn=self.collate_fn) def get_dev_dataloader(self, shuffle=False): """Returns a PyTorch DataLoader object with the dev data """ if self.dev_data is None: self.dev_data = list(self.load_data(DEV_STR)) #generator = IterableDatasetWrapper(self.load_data(DEV_STR)) generator = IterableDatasetWrapper(self.dev_data) return DataLoader(generator, batch_size=BATCH_SIZE, shuffle=shuffle, collate_fn=self.collate_fn) def get_test_dataloader(self, shuffle=False): """Returns a PyTorch DataLoader object with the test data """ if self.test_data is None: self.test_data = list(self.load_data(TEST_STR)) #generator = IterableDatasetWrapper(self.load_data(TEST_STR)) generator = IterableDatasetWrapper(self.test_data) return DataLoader(generator, batch_size=BATCH_SIZE, shuffle=shuffle, collate_fn=self.collate_fn) def load_data(self, split_string): """Loads data from disk into RAM tensors for passing to a network on GPU Iterates through the training set once, passing each sentence to each input Dataset and the output Dataset """ for sentence in tqdm(self.data_loader.yield_dataset(split_string),desc='[loading]'): input_tensors = [] for dataset in self.input_datasets: input_tensors.append(dataset.tensor_of_sentence(sentence, split_string)) output_tensor = self.output_dataset.tensor_of_sentence(sentence, split_string) yield (input_tensors, output_tensor, sentence) def collate_fn(self, observation_list): """ Combines observations (input_tensors, output_tensor, sentence) tuples input_tensors is of the form ((annotation, alignment), ..., (annotation, alignment)) output_tensor is of the form (annotation, alignment), to batches of observations ((batches_input_1, batches_input_2), batches_output, sentences) """ sentences = (x[2] for x in observation_list) max_corpus_token_len = max((len(x) for x in sentences)) input_annotation_tensors = [] input_alignment_tensors = [] input_tensor_count = len(observation_list[0][0]) for input_tensor_index in range(input_tensor_count): max_annotation_token_len = max([x[0][input_tensor_index][0].shape[0] for x in observation_list]) intermediate_annotation_list = [] intermediate_alignment_list = [] for input_annotation, input_alignment in ((x[0][input_tensor_index][0], x[0][input_tensor_index][1]) for x in observation_list): if len(input_annotation.shape) == 1: # word-level ids new_annotation_tensor = torch.zeros(max_annotation_token_len, dtype=torch.long) new_annotation_tensor[:len(input_annotation)] = input_annotation elif len(input_annotation.shape) == 2: # characeter-level ids new_annotation_tensor = torch.zeros(max_annotation_token_len, input_annotation.shape[1]).long() new_annotation_tensor[:len(input_annotation),:] = input_annotation intermediate_annotation_list.append(new_annotation_tensor) new_alignment_tensor = torch.zeros(max_annotation_token_len, max_corpus_token_len) new_alignment_tensor[:input_alignment.shape[0], :input_alignment.shape[1]] = input_alignment intermediate_alignment_list.append(new_alignment_tensor) input_annotation_tensors.append(torch.stack(intermediate_annotation_list).to(self.args['device'])) input_alignment_tensors.append(torch.stack(intermediate_alignment_list).to(self.args['device'])) intermediate_annotation_list = [] intermediate_alignment_list = [] max_output_annotation_len = max([x[1][0].shape[0] for x in observation_list]) for output_annotation, output_alignment in (x[1] for x in observation_list): new_annotation_tensor = torch.zeros(max_output_annotation_len, dtype=torch.long) new_annotation_tensor[:len(output_annotation)] = output_annotation intermediate_annotation_list.append(new_annotation_tensor) output_annotation_tensor = torch.stack(intermediate_annotation_list).to(self.args['device']) sentences = [x[2] for x in observation_list] return ((input_annotation_tensors, input_alignment_tensors), output_annotation_tensor, sentences) class ELMoData(InitYAMLObject): """ Loading and serving minibatches of tokens to input to ELMo, as mediated by allennlp. """ yaml_tag = '!ELMoData' def __init__(self, args): self.args = args def tensor_of_sentence(self, sentence, split_string): """ Provides character indices for a single sentence. """ words = [x[1] for x in sentence] alignment = torch.eye(len(words)) return batch_to_ids([words])[0,:,:], alignment #for index, token in enumerate([x[1] for x in sentence]): class HuggingfaceData(InitYAMLObject): """ Loading and serving minibatches of tokens to input to a Huggingface-loaded model. """ yaml_tag = '!HuggingfaceData' def __init__(self, args, model_string, cache=None): print('Constructing HuggingfaceData of {}'.format(model_string)) self.tokenizer = AutoTokenizer.from_pretrained(model_string) #, add_prefix_space=True) self.args = args self.cache = cache self.task_name = 'hfacetokens.{}'.format(model_string) self.cache_is_setup = False def levenshtein_matrix(self, string1, string2): opcodes = levenshtein.opcodes(string1, string2) mtx = torch.zeros(len(string1), len(string2)) cumulative = 0 for opcode in opcodes: opcode_type, str1b, str1e, str2b, str2e = opcode if opcode_type in {'equal', 'replace'}: diff = str1e - str1b for i in range(diff): mtx[str1b+i,str2b+i] = 1 if opcode_type == 'delete': diff = str1e - str1b for i in range(diff): mtx[str1b+i, str2b] = 1 if opcode_type == 'insert': diff = str2e - str2b for i in range(diff): mtx[str1b, str2b+i] = 1 return mtx def token_to_character_alignment(self, tokens): ptb_sentence_length = sum((len(tok) for tok in tokens)) ptb_string_token_alignment = [] cumulative = 0 for token in tokens: new_alignment = torch.zeros(ptb_sentence_length) for i, char in enumerate(token): if char == ' ': continue new_alignment[i+cumulative] = 1 new_alignment = new_alignment / sum(new_alignment) cumulative += len(token) ptb_string_token_alignment.append(new_alignment) return torch.stack(ptb_string_token_alignment) def de_ptb_tokenize(self, tokens): tokens_with_spaces = [] new_tokens_with_spaces = [] ptb_sentence_length = sum((len(tok) for tok in tokens)) token_alignments = [] cumulative = 0 for i, _ in enumerate(tokens): token = tokens[i] next_token = tokens[i+1] if i < len(tokens)-1 else '<EOS>' # Handle LaTeX-style quotes if token.strip() in {"``", "''"}: new_token = '"' elif token.strip() == '-LRB-': new_token = '(' elif token.strip() == '-RRB-': new_token = ')' elif token.strip() == '-LSB-': new_token = '[' elif token.strip() == '-RSB-': new_token = ']' elif token.strip() == '-LCB-': new_token = '{' elif token.strip() == '-RCB-': new_token = '}' else: new_token = token use_space = (token.strip() not in {'(', '[', '{', '"', "'", '``', "''"} and next_token.strip() not in {"'ll", "'re", "'ve", "n't", "'s", "'LL", "'RE", "'VE", "N'T", "'S", '"', "'", '``', "''", ')', '}', ']', '.', ';', ':', '!', '?'} and i != len(tokens) - 1) new_token = new_token.strip() + (' ' if use_space else '') new_tokens_with_spaces.append(new_token) tokens_with_spaces.append(token) new_alignment = torch.zeros(ptb_sentence_length) for index, char in enumerate(token): new_alignment[index+cumulative] = 1 #new_alignment = new_alignment / sum(new_alignment) for new_char in new_token: token_alignments.append(new_alignment) cumulative += len(token) return new_tokens_with_spaces, torch.stack(token_alignments) def hface_ontonotes_alignment(self, sentence): tokens = [x[1] for x in sentence] tokens = [ x + (' ' if i !=len(tokens)-1 else '') for (i, x) in enumerate(tokens)] raw_tokens, ptb_to_deptb_alignment = self.de_ptb_tokenize(tokens) raw_string = ''.join(raw_tokens) ptb_token_to_ptb_string_alignment = self.token_to_character_alignment(tokens) #tokenizer = transformers.AutoTokenizer.from_pretrained('roberta-base') hface_tokens = self.tokenizer.tokenize(raw_string) hface_tokens_with_spaces = [x+ (' ' if i != len(hface_tokens)-1 else '')for (i, x) in enumerate(hface_tokens)] hface_token_to_hface_string_alignment = self.token_to_character_alignment(hface_tokens_with_spaces) hface_string = ' '.join(hface_tokens) hface_character_to_deptb_character_alignment = self.levenshtein_matrix(hface_string, raw_string) unnormalized_alignment = torch.matmul(torch.matmul(hface_token_to_hface_string_alignment.to(self.args['device']), hface_character_to_deptb_character_alignment.to(self.args['device'])), torch.matmul(ptb_token_to_ptb_string_alignment.to(self.args['device']), ptb_to_deptb_alignment.to(self.args['device']).t()).t()) return (unnormalized_alignment / torch.sum(unnormalized_alignment, dim=0)).cpu(), hface_tokens, raw_string def _setup_cache(self): """ Constructs readers for caches that exist and writers for caches that do not. """ if self.cache is None: return if self.cache_is_setup: return # Check cache readable/writeable train_cache_path, train_cache_readable, train_cache_writeable = \ self.cache.get_cache_path_and_check(TRAIN_STR, self.task_name) dev_cache_path, dev_cache_readable, dev_cache_writeable = \ self.cache.get_cache_path_and_check(DEV_STR, self.task_name) test_cache_path, test_cache_readable, test_cache_writeable = \ self.cache.get_cache_path_and_check(TEST_STR, self.task_name) # If any of the train/dev/test are neither readable nor writeable, do not use cache. if ((not train_cache_readable and not train_cache_writeable) or (not dev_cache_readable and not dev_cache_writeable) or (not test_cache_readable and not test_cache_writeable)): self.cache = None print("Not using the cache at all, since at least of one " "of {train,dev,test} cache neither readable nor writable.") return # Load readers or writers self.train_cache_writer = None self.dev_cache_writer = None self.test_cache_writer = None if train_cache_readable: f = h5py.File(train_cache_path, 'r') self.train_cache_tokens = (torch.tensor(f[str(i)+'tok'][()]) for i in range(len(f.keys()))) self.train_cache_alignments = (torch.tensor(f[str(i)+'aln'][()]) for i in range(len(f.keys()))) elif train_cache_writeable: #self.train_cache_writer = h5py.File(train_cache_path, 'w') self.train_cache_writer = self.cache.get_hdf5_cache_writer(train_cache_path) self.train_cache_tokens = None self.train_cache_alignments = None else: raise ValueError("Train cache neither readable nor writeable") if dev_cache_readable: f2 = h5py.File(dev_cache_path, 'r') self.dev_cache_tokens = (torch.tensor(f2[str(i)+'tok'][()]) for i in range(len(f2.keys()))) self.dev_cache_alignments = (torch.tensor(f2[str(i)+'aln'][()]) for i in range(len(f2.keys()))) elif dev_cache_writeable: #self.dev_cache_writer = h5py.File(dev_cache_path, 'w') self.dev_cache_writer = self.cache.get_hdf5_cache_writer(dev_cache_path) self.dev_cache_tokens = None self.dev_cache_alignments = None else: raise ValueError("Dev cache neither readable nor writeable") if test_cache_readable: f3 = h5py.File(test_cache_path, 'r') self.test_cache_tokens = (torch.tensor(f3[str(i)+'tok'][()]) for i in range(len(f3.keys()))) self.test_cache_alignments = (torch.tensor(f3[str(i)+'aln'][()]) for i in range(len(f3.keys()))) elif test_cache_writeable: #self.test_cache_writer = h5py.File(test_cache_path, 'w') self.test_cache_writer = self.cache.get_hdf5_cache_writer(test_cache_path) self.test_cache_tokens = None self.test_cache_alignments = None else: raise ValueError("Test cache neither readable nor writeable") self.cache_is_setup = True def tensor_of_sentence(self, sentence, split): self._setup_cache() if self.cache is None: labels = self._tensor_of_sentence(sentence, split) return labels # Otherwise, either read from or write to cache if split == TRAIN_STR and self.train_cache_tokens is not None: return next(self.train_cache_tokens), next(self.train_cache_alignments) if split == DEV_STR and self.dev_cache_tokens is not None: return next(self.dev_cache_tokens), next(self.dev_cache_alignments) if split == TEST_STR and self.test_cache_tokens is not None: return next(self.test_cache_tokens), next(self.test_cache_alignments) cache_writer = (self.train_cache_writer if split == TRAIN_STR else ( self.dev_cache_writer if split == DEV_STR else ( self.test_cache_writer if split == TEST_STR else None))) if cache_writer is None: raise ValueError("Unknown split: {}".format(split)) wordpiece_indices, alignments = self._tensor_of_sentence(sentence, split) tok_string_key = str(len(list(filter(lambda x: 'tok' in x, cache_writer.keys())))) + 'tok' tok_dset = cache_writer.create_dataset(tok_string_key, wordpiece_indices.shape) tok_dset[:] = wordpiece_indices aln_string_key = str(len(list(filter(lambda x: 'aln' in x, cache_writer.keys())))) + 'aln' aln_dset = cache_writer.create_dataset(aln_string_key, alignments.shape) aln_dset[:] = alignments return wordpiece_indices, alignments def _tensor_of_sentence(self, sentence, split): alignment, wordpiece_strings, raw_string = self.hface_ontonotes_alignment(sentence) # add [SEP] and [CLS] empty alignments empty = torch.zeros(1, alignment.shape[1]) alignment = torch.cat((empty, alignment, empty)) #wordpiece_indices = torch.tensor(self.tokenizer(wordpiece_strings) wordpiece_indices = torch.tensor(self.tokenizer(raw_string).input_ids) #, is_split_into_words=True)) return wordpiece_indices, alignment def _naive_tensor_of_sentence(self, sentence, split_string): """ Converts from a tuple-formatted sentence (e.g, from conll-formatted data) to a Torch tensor of integers representing subword piece ids for input to a Huggingface-formatted neural model """ # CLS token given by tokenizer wordpiece_indices = [] wordpiece_alignment_vecs = [torch.zeros(len(sentence))] # language tokens for index, token in enumerate([x[1] for x in sentence]): new_wordpieces = self.tokenizer.tokenize(token) wordpiece_alignment = torch.zeros(len(sentence)) wordpiece_alignment[index] = 1 for wordpiece in new_wordpieces: wordpiece_alignment_vecs.append(torch.clone(wordpiece_alignment)) wordpiece_indices.extend(new_wordpieces) # SEP token given by tokenizer wordpiece_indices = torch.tensor(self.tokenizer.encode(wordpiece_indices)) wordpiece_alignment_vecs.append(torch.zeros(len(sentence))) wordpiece_alignment_vecs = torch.stack(wordpiece_alignment_vecs) return wordpiece_indices, wordpiece_alignment_vecs class AnnotationData(InitYAMLObject): """ Loading and serving minibatches of data from annotations """ yaml_tag = '!AnnotationDataset' def __init__(self, args, task): self.args = args self.task = task #self.task.setup_cache() def tensor_of_sentence(self, sentence, split_string): """ Converts from a tuple-formatted sentence (e.g, from conll-formatted data) to a Torch tensor of integers representing the annotation """ alignment = torch.eye(len(sentence)) return self.task.labels_of_sentence(sentence, split_string), alignment class Loader(InitYAMLObject): """ Base class for objects that read datasets from disk and yield sentence buffers for tokenization and labeling Strictly for description """ yaml_tag = '!Loader' class OntonotesReader(Loader): """ Minutae for reading the Ontonotes dataset, as formatted as described in the readme """ yaml_tag = '!OntonotesReader' def __init__(self, args, train_path, dev_path, test_path, cache): print('Constructing OntoNotesReader') self.train_path = train_path self.dev_path = dev_path self.test_path = test_path self.cache = cache @staticmethod def sentence_lists_of_stream(ontonotes_stream): """ Yield sentences from raw ontonotes stream Arguments: ontonotes_stream: iterable of ontonotes file lines Yields: a buffer for each sentence in the stream; elements in the buffer are lists defined by TSV fields of the ontonotes stream """ buf = [] for line in ontonotes_stream: if line.startswith('#'): continue if not line.strip(): yield buf buf = [] else: buf.append([x.strip() for x in line.split('\t')]) if buf: yield buf def yield_dataset(self, split_string): """ Yield a list of attribute lines, given by ontonotes_fields, for each sentence in the training set of ontonotes """ path = (self.train_path if split_string == TRAIN_STR else (self.dev_path if split_string == DEV_STR else (self.test_path if split_string == TEST_STR else None))) if path is None: raise ValueError("Unknown split string: {}".format(split_string)) with open(path) as fin: for sentence in OntonotesReader.sentence_lists_of_stream(fin): yield sentence class SST2Reader(Loader): """ Minutae for reading the Stanford Sentiment (SST-2) dataset, as downloaded from the GLUE website. """ yaml_tag = '!SST2Reader' def __init__(self, args, train_path, dev_path, test_path, cache): print('Constructing SST2Reader') self.train_path = train_path self.dev_path = dev_path self.test_path = test_path self.cache = cache @staticmethod def sentence_lists_of_stream(sst2_stream): """ Yield sentences from raw sst2 stream Arguments: sst2_stream: iterable of sst2_stream lines Yields: a buffer for each sentence in the stream; elements in the buffer are lists defined by TSV fields of the ontonotes stream """ _ = next(sst2_stream) # Get rid of the column labels for line in sst2_stream: word_string, label_string = [x.strip() for x in line.split('\t')] word_tokens = word_string.split(' ') indices = [str(i) for i, _ in enumerate(word_tokens)] label_tokens = [label_string for _ in word_tokens] yield list(zip(indices, word_tokens, label_tokens)) def yield_dataset(self, split_string): """ Yield a list of attribute lines, given by ontonotes_fields, for each sentence in the training set of ontonotes """ path = (self.train_path if split_string == TRAIN_STR else (self.dev_path if split_string == DEV_STR else (self.test_path if split_string == TEST_STR else None))) if path is None: raise ValueError("Unknown split string: {}".format(split_string)) with open(path) as fin: for sentence in SST2Reader.sentence_lists_of_stream(fin): yield sentence

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null

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42e6a0854dc4ea36c5a33692e83aa3d38c0f49cc

2,505

py

Python

function/python/brightics/function/statistics/test/correlation_test.py

parkjh80/studio

6d8d8384272e5e1b2838b12e5557272a19408e89

[ "Apache-2.0" ]

202

2018-10-23T04:37:35.000Z

2022-01-27T05:51:10.000Z

function/python/brightics/function/statistics/test/correlation_test.py

data-weirdo/studio

48852c4f097f773ce3d408b59f79fda2e2d60470

[ "Apache-2.0" ]

444

2018-11-07T08:41:14.000Z

2022-03-16T06:48:57.000Z

function/python/brightics/function/statistics/test/correlation_test.py

data-weirdo/studio

48852c4f097f773ce3d408b59f79fda2e2d60470

[ "Apache-2.0" ]

99

2018-11-08T04:12:13.000Z

2022-03-30T05:36:27.000Z

""" Copyright 2019 Samsung SDS Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import unittest import numpy as np from brightics.function.statistics import correlation from brightics.common.datasets import load_iris import HtmlTestRunner import os class CorrelationTest(unittest.TestCase): def setUp(self): print("*** Correlation UnitTest Start ***") self.testdata = load_iris() def tearDown(self): print("*** Correlation UnitTest End ***") def test_first(self): cr = correlation(self.testdata, vars=['sepal_length', 'sepal_width'], method='pearson', display_plt=True, height=2.5, corr_prec=2) DF1 = cr['result']['corr_table'].values # print(DF1) np.testing.assert_equal(DF1[0][0], 'sepal_width') np.testing.assert_equal(DF1[0][1], 'sepal_length') np.testing.assert_almost_equal(DF1[0][2], -0.10936924995064935, 10) np.testing.assert_almost_equal(DF1[0][3], 0.1827652152713665, 10) def test_second(self): cr = correlation(self.testdata, vars=['sepal_width', 'petal_length', 'petal_width'], method='spearman', display_plt=False, height=2.5, corr_prec=2) DF2 = cr['result']['corr_table'].values # print(DF2) np.testing.assert_almost_equal(DF2[0][2], -0.3034206463815157, 10) np.testing.assert_almost_equal(DF2[0][3], 0.0001603809454660342, 10) np.testing.assert_almost_equal(DF2[1][2], -0.2775110724763029, 10) np.testing.assert_almost_equal(DF2[1][3], 0.0005856929405699988, 10) np.testing.assert_almost_equal(DF2[2][2], 0.9360033509355782, 10) np.testing.assert_almost_equal(DF2[2][3], 5.383649646072797e-69, 10) if __name__ == '__main__': filepath = os.path.dirname(os.path.abspath(__file__)) reportFoler = filepath + "/../../../../../../../reports" unittest.main(testRunner=HtmlTestRunner.HTMLTestRunner(combine_reports=True, output=reportFoler))

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0.0625

0

null

0

null

0

1

0

42e77bb6f8a615aa18b12b83385ee014877a332f

340

py

Python

fdp/__init__.py

cffbots/fairdatapoint

6142b31408b5746d1a7e9f59e61735b7ad8bfde9

[ "Apache-2.0" ]

9

2020-03-27T12:58:51.000Z

2021-01-21T16:22:46.000Z

fdp/__init__.py

MaastrichtU-IDS/fairdatapoint

f9f38903a629acbdb74a6a20014ac424cc3d3206

[ "Apache-2.0" ]

26

2016-05-26T22:22:34.000Z

2020-02-13T07:12:37.000Z

fdp/__init__.py

MaastrichtU-IDS/fairdatapoint

f9f38903a629acbdb74a6a20014ac424cc3d3206

[ "Apache-2.0" ]

4

2020-06-09T18:37:33.000Z

2020-12-16T08:05:01.000Z

# -*- coding: utf-8 -*- import logging from .__version__ import __version__ logging.getLogger(__name__).addHandler(logging.NullHandler()) __author__ = "Rajaram Kaliyaperumal, Arnold Kuzniar, Cunliang Geng, Carlos Martinez-Ortiz" __email__ = 'c.martinez@esciencecenter.nl' __status__ = 'beta' __license__ = 'Apache License, Version 2.0'

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0

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0

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false

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0.285714

0

null

0

null

0

1

0

42e8e15830841aa965ec225fd7e1715fe1c14fdd

60,795

py

Python

fluids/flow_meter.py

rddaz2013/fluids

acde6a6edc2110c152c59341574739b24a2f1bad

[ "MIT" ]

null

fluids/flow_meter.py

rddaz2013/fluids

acde6a6edc2110c152c59341574739b24a2f1bad

[ "MIT" ]

null

fluids/flow_meter.py

rddaz2013/fluids

acde6a6edc2110c152c59341574739b24a2f1bad

[ "MIT" ]

null

# -*- coding: utf-8 -*- '''Chemical Engineering Design Library (ChEDL). Utilities for process modeling. Copyright (C) 2018 Caleb Bell <Caleb.Andrew.Bell@gmail.com> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.''' from __future__ import division from math import cos, sin, tan, atan, pi, radians, exp, acos, log10 import numpy as np from fluids.friction import friction_factor from fluids.core import Froude_densimetric from scipy.optimize import newton, brenth from scipy.constants import g, inch __all__ = ['C_Reader_Harris_Gallagher', 'differential_pressure_meter_solver', 'differential_pressure_meter_dP', 'orifice_discharge', 'orifice_expansibility', 'Reader_Harris_Gallagher_discharge', 'discharge_coefficient_to_K', 'K_to_discharge_coefficient', 'dP_orifice', 'velocity_of_approach_factor', 'flow_coefficient', 'nozzle_expansibility', 'C_long_radius_nozzle', 'C_ISA_1932_nozzle', 'C_venturi_nozzle', 'orifice_expansibility_1989', 'dP_venturi_tube', 'diameter_ratio_cone_meter', 'diameter_ratio_wedge_meter', 'cone_meter_expansibility_Stewart', 'dP_cone_meter', 'C_wedge_meter_Miller', 'C_Reader_Harris_Gallagher_wet_venturi_tube', 'dP_Reader_Harris_Gallagher_wet_venturi_tube' ] CONCENTRIC_ORIFICE = 'concentric' ECCENTRIC_ORIFICE = 'eccentric' SEGMENTAL_ORIFICE = 'segmental' CONDITIONING_4_HOLE_ORIFICE = 'Rosemount 4 hole self conditioing' ORIFICE_HOLE_TYPES = [CONCENTRIC_ORIFICE, ECCENTRIC_ORIFICE, SEGMENTAL_ORIFICE, CONDITIONING_4_HOLE_ORIFICE] ORIFICE_CORNER_TAPS = 'corner' ORIFICE_FLANGE_TAPS = 'flange' ORIFICE_D_AND_D_2_TAPS = 'D and D/2' ISO_5167_ORIFICE = 'ISO 5167 orifice' LONG_RADIUS_NOZZLE = 'long radius nozzle' ISA_1932_NOZZLE = 'ISA 1932 nozzle' VENTURI_NOZZLE = 'venuri nozzle' AS_CAST_VENTURI_TUBE = 'as cast convergent venturi tube' MACHINED_CONVERGENT_VENTURI_TUBE = 'machined convergent venturi tube' ROUGH_WELDED_CONVERGENT_VENTURI_TUBE = 'rough welded convergent venturi tube' CONE_METER = 'cone meter' WEDGE_METER = 'wedge meter' __all__.extend(['ISO_5167_ORIFICE', 'LONG_RADIUS_NOZZLE', 'ISA_1932_NOZZLE', 'VENTURI_NOZZLE', 'AS_CAST_VENTURI_TUBE', 'MACHINED_CONVERGENT_VENTURI_TUBE', 'ROUGH_WELDED_CONVERGENT_VENTURI_TUBE', 'CONE_METER', 'WEDGE_METER']) def orifice_discharge(D, Do, P1, P2, rho, C, expansibility=1.0): r'''Calculates the flow rate of an orifice plate based on the geometry of the plate, measured pressures of the orifice, and the density of the fluid. .. math:: m = \left(\frac{\pi D_o^2}{4}\right) C \frac{\sqrt{2\Delta P \rho_1}} {\sqrt{1 - \beta^4}}\cdot \epsilon Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] P1 : float Static pressure of fluid upstream of orifice at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of orifice at the cross-section of the pressure tap, [Pa] rho : float Density of fluid at `P1`, [kg/m^3] C : float Coefficient of discharge of the orifice, [-] expansibility : float, optional Expansibility factor (1 for incompressible fluids, less than 1 for real fluids), [-] Returns ------- m : float Mass flow rate of fluid, [kg/s] Notes ----- This is formula 1-12 in [1]_ and also [2]_. Examples -------- >>> orifice_discharge(D=0.0739, Do=0.0222, P1=1E5, P2=9.9E4, rho=1.1646, ... C=0.5988, expansibility=0.9975) 0.01120390943807026 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates. ''' dP = P1 - P2 beta = Do/D return (pi*Do*Do/4.)*C*(2*dP*rho)**0.5/(1.0 - beta**4)**0.5*expansibility def orifice_expansibility(D, Do, P1, P2, k): r'''Calculates the expansibility factor for orifice plate calculations based on the geometry of the plate, measured pressures of the orifice, and the isentropic exponent of the fluid. .. math:: \epsilon = 1 - (0.351 + 0.256\beta^4 + 0.93\beta^8) \left[1-\left(\frac{P_2}{P_1}\right)^{1/\kappa}\right] Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] P1 : float Static pressure of fluid upstream of orifice at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of orifice at the cross-section of the pressure tap, [Pa] k : float Isentropic exponent of fluid, [-] Returns ------- expansibility : float, optional Expansibility factor (1 for incompressible fluids, less than 1 for real fluids), [-] Notes ----- This formula was determined for the range of P2/P1 >= 0.80, and for fluids of air, steam, and natural gas. However, there is no objection to using it for other fluids. Examples -------- >>> orifice_expansibility(D=0.0739, Do=0.0222, P1=1E5, P2=9.9E4, k=1.4) 0.9974739057343425 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates. ''' beta = Do/D return (1.0 - (0.351 + 0.256*beta**4 + 0.93*beta**8)*( 1.0 - (P2/P1)**(1./k))) def orifice_expansibility_1989(D, Do, P1, P2, k): r'''Calculates the expansibility factor for orifice plate calculations based on the geometry of the plate, measured pressures of the orifice, and the isentropic exponent of the fluid. .. math:: \epsilon = 1- (0.41 + 0.35\beta^4)\Delta P/\kappa/P_1 Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] P1 : float Static pressure of fluid upstream of orifice at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of orifice at the cross-section of the pressure tap, [Pa] k : float Isentropic exponent of fluid, [-] Returns ------- expansibility : float Expansibility factor (1 for incompressible fluids, less than 1 for real fluids), [-] Notes ----- This formula was determined for the range of P2/P1 >= 0.75, and for fluids of air, steam, and natural gas. However, there is no objection to using it for other fluids. This is an older formula used to calculate expansibility factors for orifice plates. In this standard, an expansibility factor formula transformation in terms of the pressure after the orifice is presented as well. This is the more standard formulation in terms of the upstream conditions. The other formula is below for reference only: .. math:: \epsilon_2 = \sqrt{1 + \frac{\Delta P}{P_2}} - (0.41 + 0.35\beta^4) \frac{\Delta P}{\kappa P_2 \sqrt{1 + \frac{\Delta P}{P_2}}} [2]_ recommends this formulation for wedge meters as well. Examples -------- >>> orifice_expansibility_1989(D=0.0739, Do=0.0222, P1=1E5, P2=9.9E4, k=1.4) 0.9970510687411718 References ---------- .. [1] American Society of Mechanical Engineers. MFC-3M-1989 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2005. .. [2] Miller, Richard W. Flow Measurement Engineering Handbook. 3rd edition. New York: McGraw-Hill Education, 1996. ''' return 1.0 - (0.41 + 0.35*(Do/D)**4)*(P1 - P2)/(k*P1) def C_Reader_Harris_Gallagher(D, Do, rho, mu, m, taps='corner'): r'''Calculates the coefficient of discharge of the orifice based on the geometry of the plate, measured pressures of the orifice, mass flow rate through the orifice, and the density and viscosity of the fluid. .. math:: C = 0.5961 + 0.0261\beta^2 - 0.216\beta^8 + 0.000521\left(\frac{ 10^6\beta}{Re_D}\right)^{0.7}\\ + (0.0188 + 0.0063A)\beta^{3.5} \left(\frac{10^6}{Re_D}\right)^{0.3} \\ +(0.043 + 0.080\exp(-10L_1) -0.123\exp(-7L_1))(1-0.11A)\frac{\beta^4} {1-\beta^4} \\ - 0.031(M_2' - 0.8M_2'^{1.1})\beta^{1.3} .. math:: M_2' = \frac{2L_2'}{1-\beta} A = \left(\frac{19000\beta}{Re_{D}}\right)^{0.8} Re_D = \frac{\rho v D}{\mu} If D < 71.12 mm (2.8 in.): .. math:: C += 0.11(0.75-\beta)\left(2.8-\frac{D}{0.0254}\right) If the orifice has corner taps: .. math:: L_1 = L_2' = 0 If the orifice has D and D/2 taps: .. math:: L_1 = 1 L_2' = 0.47 If the orifice has Flange taps: .. math:: L_1 = L_2' = \frac{0.0254}{D} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] rho : float Density of fluid at `P1`, [kg/m^3] mu : float Viscosity of fluid at `P1`, [Pa*s] m : float Mass flow rate of fluid through the orifice, [kg/s] taps : str The orientation of the taps; one of 'corner', 'flange', 'D', or 'D/2', [-] Returns ------- C : float Coefficient of discharge of the orifice, [-] Notes ----- The following limits apply to the orifice plate standard [1]_: The measured pressure difference for the orifice plate should be under 250 kPa. There are roughness limits as well; the roughness should be under 6 micrometers, although there are many more conditions to that given in [1]_. For orifice plates with D and D/2 or corner pressure taps: * Orifice bore diameter muse be larger than 12.5 mm (0.5 inches) * Pipe diameter between 50 mm and 1 m (2 to 40 inches) * Beta between 0.1 and 0.75 inclusive * Reynolds number larger than 5000 (for :math:`0.10 \le \beta \le 0.56`) or for :math:`\beta \ge 0.56, Re_D \ge 16000\beta^2` For orifice plates with flange pressure taps: * Orifice bore diameter muse be larger than 12.5 mm (0.5 inches) * Pipe diameter between 50 mm and 1 m (2 to 40 inches) * Beta between 0.1 and 0.75 inclusive * Reynolds number larger than 5000 and also larger than :math:`170000\beta^2 D`. This is also presented in Crane's TP410 (2009)publication, whereas the 1999 and 1982 editions showed only a graph for discharge coefficients. Examples -------- >>> C_Reader_Harris_Gallagher(D=0.07391, Do=0.0222, rho=1.165, mu=1.85E-5, ... m=0.12, taps='flange') 0.5990326277163659 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates. .. [3] Reader-Harris, M. J., "The Equation for the Expansibility Factor for Orifice Plates," Proceedings of FLOMEKO 1998, Lund, Sweden, 1998: 209-214. .. [4] Reader-Harris, Michael. Orifice Plates and Venturi Tubes. Springer, 2015. ''' A_pipe = pi/4.*D*D v = m/(A_pipe*rho) Re_D = rho*v*D/mu beta = Do/D if taps == 'corner': L1, L2_prime = 0.0, 0.0 elif taps == 'D' or taps == 'D/2': L1 = 1.0 L2_prime = 0.47 elif taps == 'flange': L1 = L2_prime = 0.0254/D else: raise Exception('Unsupported tap location') beta2 = beta*beta beta4 = beta2*beta2 beta8 = beta4*beta4 A = (19000.0*beta/Re_D)**0.8 M2_prime = 2*L2_prime/(1.0 - beta) delta_C_upstream = ((0.043 + 0.080*exp(-1E1*L1) - 0.123*exp(-7.0*L1)) *(1.0 - 0.11*A)*beta4/(1.0 - beta4)) # The max part is not in the ISO standard delta_C_downstream = (-0.031*(M2_prime - 0.8*M2_prime**1.1)*beta**1.3 *(1.0 + 8*max(log10(3700./Re_D), 0.0))) # C_inf is discharge coefficient with corner taps for infinite Re # Cs, slope term, provides increase in discharge coefficient for lower # Reynolds numbers. # max term is not in the ISO standard C_inf_C_s = (0.5961 + 0.0261*beta2 - 0.216*beta8 + 0.000521*(1E6*beta/Re_D)**0.7 + (0.0188 + 0.0063*A)*beta**3.5*( max((1E6/Re_D)**0.3, 22.7 - 4700.0*(Re_D/1E6)))) C = (C_inf_C_s + delta_C_upstream + delta_C_downstream) if D < 0.07112: # Limit is 2.8 inches, .1 inches smaller than the internal diameter of # a sched. 80 pipe. # Suggested to be required not becausue of any effect of small # diameters themselves, but because of edge radius differences. # max term is given in [4]_ Reader-Harris, Michael book delta_C_diameter = 0.011*(0.75 - beta)*max((2.8 - D/0.0254), 0.0) C += delta_C_diameter return C def Reader_Harris_Gallagher_discharge(D, Do, P1, P2, rho, mu, k, taps='corner'): r'''Calculates the mass flow rate of fluid through an orifice based on the geometry of the plate, measured pressures of the orifice, and the density, viscosity, and isentropic exponent of the fluid. This solves an equation iteratively to obtain the correct flow rate. Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] P1 : float Static pressure of fluid upstream of orifice at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of orifice at the cross-section of the pressure tap, [Pa] rho : float Density of fluid at `P1`, [kg/m^3] mu : float Viscosity of fluid at `P1`, [Pa*s] k : float Isentropic exponent of fluid, [-] taps : str The orientation of the taps; one of 'corner', 'flange', 'D', or 'D/2', [-] Returns ------- m : float Mass flow rate of fluid through the orifice, [kg/s] Notes ----- Examples -------- >>> Reader_Harris_Gallagher_discharge(D=0.07366, Do=0.05, P1=200000.0, ... P2=183000.0, rho=999.1, mu=0.0011, k=1.33, taps='D') 7.702338035732167 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates. ''' def to_solve(m): C = C_Reader_Harris_Gallagher(D=D, Do=Do, rho=rho, mu=mu, m=m, taps=taps) epsilon = orifice_expansibility(D=D, Do=Do, P1=P1, P2=P2, k=k) m_calc = orifice_discharge(D=D, Do=Do, P1=P1, P2=P2, rho=rho, C=C, expansibility=epsilon) return m - m_calc return newton(to_solve, 2.81) def discharge_coefficient_to_K(D, Do, C): r'''Converts a discharge coefficient to a standard loss coefficient, for use in computation of the actual pressure drop of an orifice or other device. .. math:: K = \left[\frac{\sqrt{1-\beta^4(1-C^2)}}{C\beta^2} - 1\right]^2 Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] C : float Coefficient of discharge of the orifice, [-] Returns ------- K : float Loss coefficient with respect to the velocity and density of the fluid just upstream of the orifice, [-] Notes ----- If expansibility is used in the orifice calculation, the result will not match with the specified pressure drop formula in [1]_; it can almost be matched by dividing the calculated mass flow by the expansibility factor and using that mass flow with the loss coefficient. Examples -------- >>> discharge_coefficient_to_K(D=0.07366, Do=0.05, C=0.61512) 5.2314291729754 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates. ''' beta = Do/D beta2 = beta*beta beta4 = beta2*beta2 return ((1.0 - beta4*(1.0 - C*C))**0.5/(C*beta2) - 1.0)**2 def K_to_discharge_coefficient(D, Do, K): r'''Converts a standard loss coefficient to a discharge coefficient. .. math:: C = \sqrt{\frac{1}{2 \sqrt{K} \beta^{4} + K \beta^{4}} - \frac{\beta^{4}}{2 \sqrt{K} \beta^{4} + K \beta^{4}} } Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] K : float Loss coefficient with respect to the velocity and density of the fluid just upstream of the orifice, [-] Returns ------- C : float Coefficient of discharge of the orifice, [-] Notes ----- If expansibility is used in the orifice calculation, the result will not match with the specified pressure drop formula in [1]_; it can almost be matched by dividing the calculated mass flow by the expansibility factor and using that mass flow with the loss coefficient. This expression was derived with SymPy, and checked numerically. There were three other, incorrect roots. Examples -------- >>> K_to_discharge_coefficient(D=0.07366, Do=0.05, K=5.2314291729754) 0.6151200000000001 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates. ''' beta = Do/D beta2 = beta*beta beta4 = beta2*beta2 root_K = K**0.5 common_term = 2.0*root_K*beta4 + K*beta4 return (-beta4/(common_term) + 1.0/(common_term))**0.5 def dP_orifice(D, Do, P1, P2, C): r'''Calculates the non-recoverable pressure drop of an orifice plate based on the pressure drop and the geometry of the plate and the discharge coefficient. .. math:: \Delta\bar w = \frac{\sqrt{1-\beta^4(1-C^2)}-C\beta^2} {\sqrt{1-\beta^4(1-C^2)}+C\beta^2} (P_1 - P_2) Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] P1 : float Static pressure of fluid upstream of orifice at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of orifice at the cross-section of the pressure tap, [Pa] C : float Coefficient of discharge of the orifice, [-] Returns ------- dP : float Non-recoverable pressure drop of the orifice plate, [Pa] Notes ----- This formula can be well approximated by: .. math:: \Delta\bar w = \left(1 - \beta^{1.9}\right)(P_1 - P_2) The recoverable pressure drop should be recovered by 6 pipe diameters downstream of the orifice plate. Examples -------- >>> dP_orifice(D=0.07366, Do=0.05, P1=200000.0, P2=183000.0, C=0.61512) 9069.474705745388 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates. ''' beta = Do/D beta2 = beta*beta beta4 = beta2*beta2 dP = P1 - P2 delta_w = ((1.0 - beta4*(1.0 - C*C))**0.5 - C*beta2)/( (1.0 - beta4*(1.0 - C*C))**0.5 + C*beta2)*dP return delta_w def velocity_of_approach_factor(D, Do): r'''Calculates a factor for orifice plate design called the `velocity of approach`. .. math:: \text{Velocity of approach} = \frac{1}{\sqrt{1 - \beta^4}} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] Returns ------- velocity_of_approach : float Coefficient of discharge of the orifice, [-] Notes ----- Examples -------- >>> velocity_of_approach_factor(D=0.0739, Do=0.0222) 1.0040970074165514 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. ''' return (1.0 - (Do/D)**4)**-0.5 def flow_coefficient(D, Do, C): r'''Calculates a factor for differential pressure flow meter design called the `flow coefficient`. This should not be confused with the flow coefficient often used when discussing valves. .. math:: \text{Flow coefficient} = \frac{C}{\sqrt{1 - \beta^4}} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of flow meter characteristic dimension at flow conditions, [m] C : float Coefficient of discharge of the flow meter, [-] Returns ------- flow_coefficient : float Differential pressure flow meter flow coefficient, [-] Notes ----- This measure is used not just for orifices but for other differential pressure flow meters [2]_. It is sometimes given the symbol K. It is also equal to the product of the diacharge coefficient and the velocity of approach factor [2]_. Examples -------- >>> flow_coefficient(D=0.0739, Do=0.0222, C=0.6) 0.6024582044499308 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] Miller, Richard W. Flow Measurement Engineering Handbook. 3rd edition. New York: McGraw-Hill Education, 1996. ''' return C*(1.0 - (Do/D)**4)**-0.5 def nozzle_expansibility(D, Do, P1, P2, k): r'''Calculates the expansibility factor for a nozzle or venturi nozzle, based on the geometry of the plate, measured pressures of the orifice, and the isentropic exponent of the fluid. .. math:: \epsilon = \left\{\left(\frac{\kappa \tau^{2/\kappa}}{\kappa-1}\right) \left(\frac{1 - \beta^4}{1 - \beta^4 \tau^{2/\kappa}}\right) \left[\frac{1 - \tau^{(\kappa-1)/\kappa}}{1 - \tau} \right] \right\}^{0.5} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice of the venturi or nozzle, [m] P1 : float Static pressure of fluid upstream of orifice at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of orifice at the cross-section of the pressure tap, [Pa] k : float Isentropic exponent of fluid, [-] Returns ------- expansibility : float Expansibility factor (1 for incompressible fluids, less than 1 for real fluids), [-] Notes ----- This formula was determined for the range of P2/P1 >= 0.75. Examples -------- >>> nozzle_expansibility(D=0.0739, Do=0.0222, P1=1E5, P2=9.9E4, k=1.4) 0.9945702344566746 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-3:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 3: Nozzles and Venturi Nozzles. ''' beta = Do/D beta2 = beta*beta beta4 = beta2*beta2 tau = P2/P1 term1 = k*tau**(2.0/k )/(k - 1.0) term2 = (1.0 - beta4)/(1.0 - beta4*tau**(2.0/k)) term3 = (1.0 - tau**((k - 1.0)/k))/(1.0 - tau) return (term1*term2*term3)**0.5 def C_long_radius_nozzle(D, Do, rho, mu, m): r'''Calculates the coefficient of discharge of a long radius nozzle used for measuring flow rate of fluid, based on the geometry of the nozzle, mass flow rate through the nozzle, and the density and viscosity of the fluid. .. math:: C = 0.9965 - 0.00653\beta^{0.5} \left(\frac{10^6}{Re_D}\right)^{0.5} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of long radius nozzle orifice at flow conditions, [m] rho : float Density of fluid at `P1`, [kg/m^3] mu : float Viscosity of fluid at `P1`, [Pa*s] m : float Mass flow rate of fluid through the nozzle, [kg/s] Returns ------- C : float Coefficient of discharge of the long radius nozzle orifice, [-] Notes ----- Examples -------- >>> C_long_radius_nozzle(D=0.07391, Do=0.0422, rho=1.2, mu=1.8E-5, m=0.1) 0.9805503704679863 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-3:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 3: Nozzles and Venturi Nozzles. ''' A_pipe = pi/4.*D*D v = m/(A_pipe*rho) Re_D = rho*v*D/mu beta = Do/D return 0.9965 - 0.00653*beta**0.5*(1E6/Re_D)**0.5 def C_ISA_1932_nozzle(D, Do, rho, mu, m): r'''Calculates the coefficient of discharge of an ISA 1932 style nozzle used for measuring flow rate of fluid, based on the geometry of the nozzle, mass flow rate through the nozzle, and the density and viscosity of the fluid. .. math:: C = 0.9900 - 0.2262\beta^{4.1} - (0.00175\beta^2 - 0.0033\beta^{4.15}) \left(\frac{10^6}{Re_D}\right)^{1.15} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of nozzle orifice at flow conditions, [m] rho : float Density of fluid at `P1`, [kg/m^3] mu : float Viscosity of fluid at `P1`, [Pa*s] m : float Mass flow rate of fluid through the nozzle, [kg/s] Returns ------- C : float Coefficient of discharge of the nozzle orifice, [-] Notes ----- Examples -------- >>> C_ISA_1932_nozzle(D=0.07391, Do=0.0422, rho=1.2, mu=1.8E-5, m=0.1) 0.9635849973250495 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-3:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 3: Nozzles and Venturi Nozzles. ''' A_pipe = pi/4.*D*D v = m/(A_pipe*rho) Re_D = rho*v*D/mu beta = Do/D C = (0.9900 - 0.2262*beta**4.1 - (0.00175*beta**2 - 0.0033*beta**4.15)*(1E6/Re_D)**1.15) return C def C_venturi_nozzle(D, Do): r'''Calculates the coefficient of discharge of an Venturi style nozzle used for measuring flow rate of fluid, based on the geometry of the nozzle. .. math:: C = 0.9858 - 0.196\beta^{4.5} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of nozzle orifice at flow conditions, [m] Returns ------- C : float Coefficient of discharge of the nozzle orifice, [-] Notes ----- Examples -------- >>> C_venturi_nozzle(D=0.07391, Do=0.0422) 0.9698996454169576 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-3:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 3: Nozzles and Venturi Nozzles. ''' beta = Do/D return 0.9858 - 0.198*beta**4.5 # Relative pressure loss as a function of beta reatio for venturi nozzles # Venturi nozzles should be between 65 mm and 500 mm; there are high and low # loss ratios , with the high losses corresponding to small diameters, # low high losses corresponding to large diameters # Interpolation can be performed. venturi_tube_betas = np.array( [0.299160, 0.299470, 0.312390, 0.319010, 0.326580, 0.337290, 0.342020, 0.347060, 0.359030, 0.365960, 0.372580, 0.384870, 0.385810, 0.401250, 0.405350, 0.415740, 0.424250, 0.434010, 0.447880, 0.452590, 0.471810, 0.473090, 0.493540, 0.499240, 0.516530, 0.523800, 0.537630, 0.548060, 0.556840, 0.573890, 0.582350, 0.597820, 0.601560, 0.622650, 0.626490, 0.649480, 0.650990, 0.668700, 0.675870, 0.688550, 0.693180, 0.706180, 0.713330, 0.723510, 0.749540, 0.749650]) venturi_tube_dP_high = np.array( [0.164534, 0.164504, 0.163591, 0.163508, 0.163439, 0.162652, 0.162224, 0.161866, 0.161238, 0.160786, 0.160295, 0.159280, 0.159193, 0.157776, 0.157467, 0.156517, 0.155323, 0.153835, 0.151862, 0.151154, 0.147840, 0.147613, 0.144052, 0.143050, 0.140107, 0.138981, 0.136794, 0.134737, 0.132847, 0.129303, 0.127637, 0.124758, 0.124006, 0.119269, 0.118449, 0.113605, 0.113269, 0.108995, 0.107109, 0.103688, 0.102529, 0.099567, 0.097791, 0.095055, 0.087681, 0.087648]) venturi_tube_dP_low = np.array( [0.089232, 0.089218, 0.088671, 0.088435, 0.088206, 0.087853, 0.087655, 0.087404, 0.086693, 0.086241, 0.085813, 0.085142, 0.085102, 0.084446, 0.084202, 0.083301, 0.082470, 0.081650, 0.080582, 0.080213, 0.078509, 0.078378, 0.075989, 0.075226, 0.072700, 0.071598, 0.069562, 0.068128, 0.066986, 0.064658, 0.063298, 0.060872, 0.060378, 0.057879, 0.057403, 0.054091, 0.053879, 0.051726, 0.050931, 0.049362, 0.048675, 0.046522, 0.045381, 0.043840, 0.039913, 0.039896]) #ratios_average = 0.5*(ratios_high + ratios_low) D_bound_venturi_tube = np.array([0.065, 0.5]) def dP_venturi_tube(D, Do, P1, P2): r'''Calculates the non-recoverable pressure drop of a venturi tube differential pressure meter based on the pressure drop and the geometry of the venturi meter. .. math:: \epsilon = \frac{\Delta\bar w }{\Delta P} The :math:`\epsilon` value is looked up in a table of values as a function of beta ratio and upstream pipe diameter (roughness impact). Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of venturi tube at flow conditions, [m] P1 : float Static pressure of fluid upstream of venturi tube at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of venturi tube at the cross-section of the pressure tap, [Pa] Returns ------- dP : float Non-recoverable pressure drop of the venturi tube, [Pa] Notes ----- The recoverable pressure drop should be recovered by 6 pipe diameters downstream of the venturi tube. Note there is some information on the effect of Reynolds number as well in [1]_ and [2]_, with a curve showing an increased pressure drop from 1E5-6E5 to with a decreasing multiplier from 1.75 to 1; the multiplier is 1 for higher Reynolds numbers. This is not currently included in this implementation. Examples -------- >>> dP_venturi_tube(D=0.07366, Do=0.05, P1=200000.0, P2=183000.0) 1788.5717754177406 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-4:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 4: Venturi Tubes. ''' # Effect of Re is not currently included beta = Do/D epsilon_D65 = np.interp(beta, venturi_tube_betas, venturi_tube_dP_high) epsilon_D500 = np.interp(beta, venturi_tube_betas, venturi_tube_dP_low) epsilon = np.interp(D, D_bound_venturi_tube, [epsilon_D65, epsilon_D500]) return epsilon*(P1 - P2) def diameter_ratio_cone_meter(D, Dc): r'''Calculates the diameter ratio `beta` used to characterize a cone flow meter. .. math:: \beta = \sqrt{1 - \frac{d_c^2}{D^2}} Parameters ---------- D : float Upstream internal pipe diameter, [m] Dc : float Diameter of the largest end of the cone meter, [m] Returns ------- beta : float Cone meter diameter ratio, [-] Notes ----- Examples -------- >>> diameter_ratio_cone_meter(D=0.2575, Dc=0.184) 0.6995709873957624 References ---------- .. [1] Hollingshead, Colter. "Discharge Coefficient Performance of Venturi, Standard Concentric Orifice Plate, V-Cone, and Wedge Flow Meters at Small Reynolds Numbers." May 1, 2011. https://digitalcommons.usu.edu/etd/869. ''' D_ratio = Dc/D return (1.0 - D_ratio*D_ratio)**0.5 def cone_meter_expansibility_Stewart(D, Dc, P1, P2, k): r'''Calculates the expansibility factor for a cone flow meter, based on the geometry of the cone meter, measured pressures of the orifice, and the isentropic exponent of the fluid. Developed in [1]_, also shown in [2]_. .. math:: \epsilon = 1 - (0.649 + 0.696\beta^4) \frac{\Delta P}{\kappa P_1} Parameters ---------- D : float Upstream internal pipe diameter, [m] Dc : float Diameter of the largest end of the cone meter, [m] P1 : float Static pressure of fluid upstream of cone meter at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid at the end of the center of the cone pressure tap, [Pa] k : float Isentropic exponent of fluid, [-] Returns ------- expansibility : float Expansibility factor (1 for incompressible fluids, less than 1 for real fluids), [-] Notes ----- This formula was determined for the range of P2/P1 >= 0.75; the only gas used to determine the formula is air. Examples -------- >>> cone_meter_expansibility_Stewart(D=1, Dc=0.9, P1=1E6, P2=8.5E5, k=1.2) 0.9157343 References ---------- .. [1] Stewart, D. G., M. Reader-Harris, and NEL Dr RJW Peters. "Derivation of an Expansibility Factor for the V-Cone Meter." In Flow Measurement International Conference, Peebles, Scotland, UK, 2001. .. [2] ISO 5167-5:2016 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 5: Cone meters. ''' dP = P1 - P2 beta = diameter_ratio_cone_meter(D, Dc) return 1.0 - (0.649 + 0.696*beta**4)*dP/(k*P1) def dP_cone_meter(D, Dc, P1, P2): r'''Calculates the non-recoverable pressure drop of a cone meter based on the measured pressures before and at the cone end, and the geometry of the cone meter according to [1]_. .. math:: \Delta \bar \omega = (1.09 - 0.813\beta)\Delta P Parameters ---------- D : float Upstream internal pipe diameter, [m] Dc : float Diameter of the largest end of the cone meter, [m] P1 : float Static pressure of fluid upstream of cone meter at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid at the end of the center of the cone pressure tap, [Pa] Returns ------- dP : float Non-recoverable pressure drop of the orifice plate, [Pa] Notes ----- The recoverable pressure drop should be recovered by 6 pipe diameters downstream of the cone meter. Examples -------- >>> dP_cone_meter(1, .7, 1E6, 9.5E5) 25470.093437973323 References ---------- .. [1] ISO 5167-5:2016 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 5: Cone meters. ''' dP = P1 - P2 beta = diameter_ratio_cone_meter(D, Dc) return (1.09 - 0.813*beta)*dP def diameter_ratio_wedge_meter(D, H): r'''Calculates the diameter ratio `beta` used to characterize a wedge flow meter as given in [1]_ and [2]_. .. math:: \beta = \left(\frac{1}{\pi}\left\{\arccos\left[1 - \frac{2H}{D} \right] - 2 \left[1 - \frac{2H}{D} \right]\left(\frac{H}{D} - \left[\frac{H}{D}\right]^2 \right)^{0.5}\right\}\right)^{0.5} Parameters ---------- D : float Upstream internal pipe diameter, [m] H : float Portion of the diameter of the clear segment of the pipe up to the wedge blocking flow; the height of the pipe up to the wedge, [m] Returns ------- beta : float Wedge meter diameter ratio, [-] Notes ----- Examples -------- >>> diameter_ratio_wedge_meter(D=0.2027, H=0.0608) 0.5022531424646643 References ---------- .. [1] Hollingshead, Colter. "Discharge Coefficient Performance of Venturi, Standard Concentric Orifice Plate, V-Cone, and Wedge Flow Meters at Small Reynolds Numbers." May 1, 2011. https://digitalcommons.usu.edu/etd/869. .. [2] IntraWedge WEDGE FLOW METER Type: IWM. January 2011. http://www.intra-automation.com/download.php?file=pdf/products/technical_information/en/ti_iwm_en.pdf ''' H_D = H/D t0 = 1.0 - 2.0*H_D t1 = acos(t0) t2 = 2.0*(t0) t3 = (H_D - H_D*H_D)**0.5 t4 = t1 - t2*t3 return (1./pi*t4)**0.5 def C_wedge_meter_Miller(D, H): r'''Calculates the coefficient of discharge of an wedge flow meter used for measuring flow rate of fluid, based on the geometry of the differential pressure flow meter. For half-inch lines: .. math:: C = 0.7883 + 0.107(1 - \beta^2) For 1 to 1.5 inch lines: .. math:: C = 0.6143 + 0.718(1 - \beta^2) For 1.5 to 24 inch lines: .. math:: C = 0.5433 + 0.2453(1 - \beta^2) Parameters ---------- D : float Upstream internal pipe diameter, [m] H : float Portion of the diameter of the clear segment of the pipe up to the wedge blocking flow; the height of the pipe up to the wedge, [m] Returns ------- C : float Coefficient of discharge of the wedge flow meter, [-] Notes ----- There is an ISO standard being developed to cover wedge meters as of 2018. Wedge meters can have varying angles; 60 and 90 degree wedge meters have been reported. Tap locations 1 or 2 diameters (upstream and downstream), and 2D upstream/1D downstream have been used. Some wedges are sharp; some are smooth. [2]_ gives some experimental values. Examples -------- >>> C_wedge_meter_Miller(D=0.1524, H=0.3*0.1524) 0.7267069372687651 References ---------- .. [1] Miller, Richard W. Flow Measurement Engineering Handbook. 3rd edition. New York: McGraw-Hill Education, 1996. .. [2] Seshadri, V., S. N. Singh, and S. Bhargava. "Effect of Wedge Shape and Pressure Tap Locations on the Characteristics of a Wedge Flowmeter." IJEMS Vol.01(5), October 1994. ''' beta = diameter_ratio_wedge_meter(D, H) if D <= 0.7*inch: # suggested limit 0.5 inch for this equation C = 0.7883 + 0.107*(1 - beta*beta) elif D <= 1.4*inch: # Suggested limit is under 1.5 inches C = 0.6143 + 0.718*(1 - beta*beta) else: C = 0.5433 + 0.2453*(1 - beta*beta) return C def C_Reader_Harris_Gallagher_wet_venturi_tube(mg, ml, rhog, rhol, D, Do, H=1): r'''Calculates the coefficient of discharge of the wet gas venturi tube based on the geometry of the tube, mass flow rates of liquid and vapor through the tube, the density of the liquid and gas phases, and an adjustable coefficient `H`. .. math:: C = 1 - 0.0463\exp(-0.05Fr_{gas, th}) \cdot \min\left(1, \sqrt{\frac{X}{0.016}}\right) Fr_{gas, th} = \frac{Fr_{\text{gas, densionetric }}}{\beta^{2.5}} \phi = \sqrt{1 + C_{Ch} X + X^2} C_{Ch} = \left(\frac{\rho_l}{\rho_{1,g}}\right)^n + \left(\frac{\rho_{1, g}}{\rho_{l}}\right)^n n = \max\left[0.583 - 0.18\beta^2 - 0.578\exp\left(\frac{-0.8 Fr_{\text{gas, densiometric}}}{H}\right),0.392 - 0.18\beta^2 \right] X = \left(\frac{m_l}{m_g}\right) \sqrt{\frac{\rho_{1,g}}{\rho_l}} {Fr_{\text{gas, densiometric}}} = \frac{v_{gas}}{\sqrt{gD}} \sqrt{\frac{\rho_{1,g}}{\rho_l - \rho_{1,g}}} = \frac{4m_g}{\rho_{1,g} \pi D^2 \sqrt{gD}} \sqrt{\frac{\rho_{1,g}}{\rho_l - \rho_{1,g}}} Parameters ---------- mg : float Mass flow rate of gas through the venturi tube, [kg/s] ml : float Mass flow rate of liquid through the venturi tube, [kg/s] rhog : float Density of gas at `P1`, [kg/m^3] rhol : float Density of liquid at `P1`, [kg/m^3] D : float Upstream internal pipe diameter, [m] Do : float Diameter of venturi tube at flow conditions, [m] H : float, optional A surface-tension effect coefficient used to adjust for different fluids, (1 for a hydrocarbon liquid, 1.35 for water, 0.79 for water in steam) [-] Returns ------- C : float Coefficient of discharge of the wet gas venturi tube flow meter (includes flow rate of gas ONLY), [-] Notes ----- This model has more error than single phase differential pressure meters. The model was first published in [1]_, and became ISO 11583 later. The limits of this correlation according to [2]_ are as follows: .. math:: 0.4 \le \beta \le 0.75 0 < X \le 0.3 Fr_{gas, th} > 3 \frac{\rho_g}{\rho_l} > 0.02 D \ge 50 \text{ mm} Examples -------- >>> C_Reader_Harris_Gallagher_wet_venturi_tube(mg=5.31926, ml=5.31926/2, ... rhog=50.0, rhol=800., D=.1, Do=.06, H=1) 0.9754210845876333 References ---------- .. [1] Reader-harris, Michael, and Tuv Nel. An Improved Model for Venturi-Tube Over-Reading in Wet Gas, 2009. .. [2] ISO/TR 11583:2012 Measurement of Wet Gas Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits. ''' V = 4*mg/(rhog*pi*D**2) Frg = Froude_densimetric(V, L=D, rho1=rhol, rho2=rhog, heavy=False) beta = Do/D beta2 = beta*beta Fr_gas_th = Frg*beta**-2.5 n = max(0.583 - 0.18*beta2 - 0.578*exp(-0.8*Frg/H), 0.392 - 0.18*beta2) C_Ch = (rhol/rhog)**n + (rhog/rhol)**n X = ml/mg*(rhog/rhol)**0.5 OF = (1.0 + C_Ch*X + X*X)**0.5 C = 1.0 - 0.0463*exp(-0.05*Fr_gas_th)*min(1.0, (X/0.016)**0.5) return C def dP_Reader_Harris_Gallagher_wet_venturi_tube(D, Do, P1, P2, ml, mg, rhol, rhog, H=1): r'''Calculates the non-recoverable pressure drop of a wet gas venturi nozzle based on the pressure drop and the geometry of the venturi nozzle, the mass flow rates of liquid and gas through it, the densities of the vapor and liquid phase, and an adjustable coefficient `H`. .. math:: Y = \frac{\Delta \bar \omega}{\Delta P} - 0.0896 - 0.48\beta^9 Y_{max} = 0.61\exp\left[-11\frac{\rho_{1,g}}{\rho_l} - 0.045 \frac{Fr_{gas}}{H}\right] \frac{Y}{Y_{max}} = 1 - \exp\left[-35 X^{0.75} \exp \left( \frac{-0.28Fr_{gas}}{H}\right)\right] X = \left(\frac{m_l}{m_g}\right) \sqrt{\frac{\rho_{1,g}}{\rho_l}} {Fr_{\text{gas, densiometric}}} = \frac{v_{gas}}{\sqrt{gD}} \sqrt{\frac{\rho_{1,g}}{\rho_l - \rho_{1,g}}} = \frac{4m_g}{\rho_{1,g} \pi D^2 \sqrt{gD}} \sqrt{\frac{\rho_{1,g}}{\rho_l - \rho_{1,g}}} Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of venturi tube at flow conditions, [m] P1 : float Static pressure of fluid upstream of venturi tube at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of venturi tube at the cross- section of the pressure tap, [Pa] ml : float Mass flow rate of liquid through the venturi tube, [kg/s] mg : float Mass flow rate of gas through the venturi tube, [kg/s] rhol : float Density of liquid at `P1`, [kg/m^3] rhog : float Density of gas at `P1`, [kg/m^3] H : float, optional A surface-tension effect coefficient used to adjust for different fluids, (1 for a hydrocarbon liquid, 1.35 for water, 0.79 for water in steam) [-] Returns ------- C : float Coefficient of discharge of the wet gas venturi tube flow meter (includes flow rate of gas ONLY), [-] Notes ----- The model was first published in [1]_, and became ISO 11583 later. Examples -------- >>> dP_Reader_Harris_Gallagher_wet_venturi_tube(D=.1, Do=.06, H=1, ... P1=6E6, P2=6E6-5E4, ml=5.31926/2, mg=5.31926, rhog=50.0, rhol=800.,) 16957.43843129572 References ---------- .. [1] Reader-harris, Michael, and Tuv Nel. An Improved Model for Venturi-Tube Over-Reading in Wet Gas, 2009. .. [2] ISO/TR 11583:2012 Measurement of Wet Gas Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits. ''' dP = P1 - P2 beta = Do/D X = ml/mg*(rhog/rhol)**0.5 V = 4*mg/(rhog*pi*D**2) Frg = Froude_densimetric(V, L=D, rho1=rhol, rho2=rhog, heavy=False) Y_ratio = 1.0 - exp(-35.0*X**0.75*exp(-0.28*Frg/H)) Y_max = 0.61*exp(-11.0*rhog/rhol - 0.045*Frg/H) Y = Y_max*Y_ratio rhs = -0.0896 - 0.48*beta**9 dw = dP*(Y - rhs) return dw # Venturi tube loss coefficients as a function of Re as_cast_convergent_venturi_Res = [4E5, 6E4, 1E5, 1.5E5] as_cast_convergent_venturi_Cs = [0.957, 0.966, 0.976, 0.982] machined_convergent_venturi_Res = [5E4, 1E5, 2E5, 3E5, 7.5E5, # 5E5 to 1E6 1.5E6, # 1E6 to 2E6 5E6] # 2E6 to 1E8 machined_convergent_venturi_Cs = [0.970, 0.977, 0.992, 0.998, 0.995, 1.000, 1.010] rough_welded_convergent_venturi_Res = [4E4, 6E4, 1E5] rough_welded_convergent_venturi_Cs = [0.96, 0.97, 0.98] as_cast_convergent_entrance_machined_venturi_Res = [1E4, 6E4, 1E5, 1.5E5, 3.5E5, # 2E5 to 5E5 3.2E6] # 5E5 to 3.2E6 as_cast_convergent_entrance_machined_venturi_Cs = [0.963, 0.978, 0.98, 0.987, 0.992, 0.995] CONE_METER_C = 0.82 ROUGH_WELDED_CONVERGENT_VENTURI_TUBE_C = 0.985 MACHINED_CONVERGENT_VENTURI_TUBE_C = 0.995 AS_CAST_VENTURI_TUBE_C = 0.984 def _differential_pressure_C_epsilon(D, D2, m, P1, P2, rho, mu, k, meter_type, taps=None): '''Helper function only. ''' if meter_type == ISO_5167_ORIFICE: C = C_Reader_Harris_Gallagher(D=D, Do=D2, rho=rho, mu=mu, m=m, taps=taps) epsilon = orifice_expansibility(D=D, Do=D2, P1=P1, P2=P2, k=k) elif meter_type == LONG_RADIUS_NOZZLE: epsilon = nozzle_expansibility(D=D, Do=D2, P1=P1, P2=P2, k=k) C = C_long_radius_nozzle(D=D, Do=D2, rho=rho, mu=mu, m=m) elif meter_type == ISA_1932_NOZZLE: epsilon = nozzle_expansibility(D=D, Do=D2, P1=P1, P2=P2, k=k) C = C_ISA_1932_nozzle(D=D, Do=D2, rho=rho, mu=mu, m=m) elif meter_type == VENTURI_NOZZLE: epsilon = nozzle_expansibility(D=D, Do=D2, P1=P1, P2=P2, k=k) C = C_venturi_nozzle(D=D, Do=D2) elif meter_type == AS_CAST_VENTURI_TUBE: epsilon = nozzle_expansibility(D=D, Do=D2, P1=P1, P2=P2, k=k) C = AS_CAST_VENTURI_TUBE_C elif meter_type == MACHINED_CONVERGENT_VENTURI_TUBE: epsilon = nozzle_expansibility(D=D, Do=D2, P1=P1, P2=P2, k=k) C = MACHINED_CONVERGENT_VENTURI_TUBE_C elif meter_type == ROUGH_WELDED_CONVERGENT_VENTURI_TUBE: epsilon = nozzle_expansibility(D=D, Do=D2, P1=P1, P2=P2, k=k) C = ROUGH_WELDED_CONVERGENT_VENTURI_TUBE_C elif meter_type == CONE_METER: epsilon = cone_meter_expansibility_Stewart(D=D, Dc=D2, P1=P1, P2=P2, k=k) C = CONE_METER_C elif meter_type == WEDGE_METER: epsilon = orifice_expansibility_1989(D=D, Do=D2, P1=P1, P2=P2, k=k) C = C_wedge_meter_Miller(D=D, H=D2) return epsilon, C def differential_pressure_meter_solver(D, rho, mu, k, D2=None, P1=None, P2=None, m=None, meter_type=ISO_5167_ORIFICE, taps=None): r'''Calculates either the mass flow rate, the upstream pressure, the second pressure value, or the orifice diameter for a differential pressure flow meter based on the geometry of the meter, measured pressures of the meter, and the density, viscosity, and isentropic exponent of the fluid. This solves an equation iteratively to obtain the correct flow rate. Parameters ---------- D : float Upstream internal pipe diameter, [m] rho : float Density of fluid at `P1`, [kg/m^3] mu : float Viscosity of fluid at `P1`, [Pa*s] k : float Isentropic exponent of fluid, [-] D2 : float, optional Diameter of orifice, or venturi meter orifice, or flow tube orifice, or cone meter end diameter, or wedge meter fluid flow height, [m] P1 : float, optional Static pressure of fluid upstream of differential pressure meter at the cross-section of the pressure tap, [Pa] P2 : float, optional Static pressure of fluid downstream of differential pressure meter or at the prescribed location (varies by type of meter) [Pa] m : float, optional Mass flow rate of fluid through the flow meter, [kg/s] meter_type : str, optional One of ('ISO 5167 orifice', 'long radius nozzle', 'ISA 1932 nozzle', 'venuri nozzle', 'as cast convergent venturi tube', 'machined convergent venturi tube', 'rough welded convergent venturi tube', 'cone meter', 'wedge meter'), [-] taps : str, optional The orientation of the taps; one of 'corner', 'flange', 'D', or 'D/2'; applies for orifice meters only, [-] Returns ------- ans : float One of `m`, the mass flow rate of the fluid; `P1`, the pressure upstream of the flow meter; `P2`, the second pressure tap's value; and `D2`, the diameter of the measuring device; units of respectively, [kg/s], [Pa], [Pa], or [m] Notes ----- See the appropriate functions for the documentation for the formulas and references used in each method. The solvers make some assumptions about the range of values answers may be in. Note that the solver for the upstream pressure uses the provided values of density, viscosity and isentropic exponent; whereas these values all depend on pressure (albeit to a small extent). An outer loop should be added with pressure-dependent values calculated in it for maximum accuracy. It would be possible to solve for the upstream pipe diameter, but there is no use for that functionality. Examples -------- >>> differential_pressure_meter_solver(D=0.07366, D2=0.05, P1=200000.0, ... P2=183000.0, rho=999.1, mu=0.0011, k=1.33, ... meter_type='ISO 5167 orifice', taps='D') 7.702338035732168 >>> differential_pressure_meter_solver(D=0.07366, m=7.702338, P1=200000.0, ... P2=183000.0, rho=999.1, mu=0.0011, k=1.33, ... meter_type='ISO 5167 orifice', taps='D') 0.04999999990831885 ''' if m is None: def to_solve(m): C, epsilon = _differential_pressure_C_epsilon(D, D2, m, P1, P2, rho, mu, k, meter_type, taps=taps) m_calc = orifice_discharge(D=D, Do=D2, P1=P1, P2=P2, rho=rho, C=C, expansibility=epsilon) return m - m_calc return newton(to_solve, 2.81) elif D2 is None: def to_solve(D2): C, epsilon = _differential_pressure_C_epsilon(D, D2, m, P1, P2, rho, mu, k, meter_type, taps=taps) m_calc = orifice_discharge(D=D, Do=D2, P1=P1, P2=P2, rho=rho, C=C, expansibility=epsilon) return m - m_calc return brenth(to_solve, D*(1-1E-9), D*5E-3) elif P2 is None: def to_solve(P2): C, epsilon = _differential_pressure_C_epsilon(D, D2, m, P1, P2, rho, mu, k, meter_type, taps=taps) m_calc = orifice_discharge(D=D, Do=D2, P1=P1, P2=P2, rho=rho, C=C, expansibility=epsilon) return m - m_calc return brenth(to_solve, P1*(1-1E-9), P1*0.7) elif P1 is None: def to_solve(P1): C, epsilon = _differential_pressure_C_epsilon(D, D2, m, P1, P2, rho, mu, k, meter_type, taps=taps) m_calc = orifice_discharge(D=D, Do=D2, P1=P1, P2=P2, rho=rho, C=C, expansibility=epsilon) return m - m_calc return brenth(to_solve, P2*(1+1E-9), P2*1.4) else: raise Exception('Solver is capable of solving for one of P2, D2, or m only.') def differential_pressure_meter_dP(D, D2, P1, P2, C=None, meter_type=ISO_5167_ORIFICE): r'''Calculates either the non-recoverable pressure drop of a differential pressure flow meter based on the geometry of the meter, measured pressures of the meter, and for most models the meter discharge coefficient. Parameters ---------- D : float Upstream internal pipe diameter, [m] D2 : float Diameter of orifice, or venturi meter orifice, or flow tube orifice, or cone meter end diameter, or wedge meter fluid flow height, [m] P1 : float Static pressure of fluid upstream of differential pressure meter at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of differential pressure meter or at the prescribed location (varies by type of meter) [Pa] C : float, optional Coefficient of discharge of the wedge flow meter, [-] meter_type : str, optional One of ('ISO 5167 orifice', 'long radius nozzle', 'ISA 1932 nozzle', 'as cast convergent venturi tube', 'machined convergent venturi tube', 'rough welded convergent venturi tube', 'cone meter'), [-] Returns ------- dP : float Non-recoverable pressure drop of the differential pressure flow meter, [Pa] Notes ----- See the appropriate functions for the documentation for the formulas and references used in each method. Wedge meters, and venturi nozzles do not have standard formulas available for pressure drop computation. Examples -------- >>> differential_pressure_meter_dP(D=0.07366, D2=0.05, P1=200000.0, ... P2=183000.0, meter_type='as cast convergent venturi tube') 1788.5717754177406 ''' if meter_type == ISO_5167_ORIFICE: dP = dP_orifice(D=D, Do=D2, P1=P1, P2=P2, C=C) elif meter_type == LONG_RADIUS_NOZZLE: dP = dP_orifice(D=D, Do=D2, P1=P1, P2=P2, C=C) elif meter_type == ISA_1932_NOZZLE: dP = dP_orifice(D=D, Do=D2, P1=P1, P2=P2, C=C) elif meter_type == VENTURI_NOZZLE: raise Exception(NotImplemented) elif meter_type == AS_CAST_VENTURI_TUBE: dP = dP_venturi_tube(D=D, Do=D2, P1=P1, P2=P2) elif meter_type == MACHINED_CONVERGENT_VENTURI_TUBE: dP = dP_venturi_tube(D=D, Do=D2, P1=P1, P2=P2) elif meter_type == ROUGH_WELDED_CONVERGENT_VENTURI_TUBE: dP = dP_venturi_tube(D=D, Do=D2, P1=P1, P2=P2) elif meter_type == CONE_METER: dP = dP_cone_meter(D=D, Dc=D2, P1=P1, P2=P2) elif meter_type == WEDGE_METER: raise Exception(NotImplemented) return dP

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0.607385

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60,795

4.097409

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0.016153

0.014413

0.017616

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0.61946

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0.569429

0.547036

0.533313

0

0.092302

0.278625

60,795

1,716

109

35.428322

0.733514

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0

0.188172

0

null

0

null

0

1

0

42eb0db02ed2cdde4c36688526176ef0796f32f2

1,370

py

Python

git_plan/cli/commands/delete.py

synek/git-plan

4cf5429348a71fb5ea8110272fb89d20bfa38c38

[ "MIT" ]

163

2021-03-06T12:01:06.000Z

2022-03-01T22:52:36.000Z

git_plan/cli/commands/delete.py

synek/git-plan

4cf5429348a71fb5ea8110272fb89d20bfa38c38

[ "MIT" ]

61

2021-03-06T07:00:39.000Z

2021-04-13T10:25:58.000Z

git_plan/cli/commands/delete.py

synek/git-plan

4cf5429348a71fb5ea8110272fb89d20bfa38c38

[ "MIT" ]

9

2021-03-07T17:52:57.000Z

2021-10-18T21:35:23.000Z

"""Delete command Author: Rory Byrne <rory@rory.bio> """ from typing import Any from git_plan.cli.commands.command import Command from git_plan.service.plan import PlanService from git_plan.util.decorators import requires_initialized, requires_git_repository @requires_initialized @requires_git_repository class Delete(Command): """Delete an existing commit""" subcommand = 'delete' def __init__(self, plan_service: PlanService, **kwargs): super().__init__(**kwargs) assert plan_service, "Plan service not injected" self._plan_service = plan_service def command(self, **kwargs): """Create a new commit""" commits = self._plan_service.get_commits(self._repository) if not commits: self._ui.bold('No commits found.') return chosen_commit = self._ui.choose_commit(commits, 'Which plan do you want to delete?') self._ui.bold(f'{chosen_commit.message.headline}\n') confirm_msg = 'Are you sure you want to delete this commit?' if not self._ui.confirm(confirm_msg): self._ui.bold("Stopped.") return self._plan_service.delete_commit(chosen_commit) self._ui.bold('Deleted.') def register_subparser(self, subparsers: Any): subparsers.add_parser(Delete.subcommand, help='Delete a planned commit.')

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0.687591

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1,370

5.196532

0.404624

0.097887

0.066741

0.088988

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1,370

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93

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null

0

null

0

1

0

42ef38196b7af8975b40694b6eb1954f2a48845e

1,926

py

Python

vision_module.py

seongdong2/GRADUATION

c38b13a2dd82a58bdba7673916408daa0d9b471e

[ "Unlicense" ]

2

2021-09-19T13:52:05.000Z

2021-10-04T01:09:21.000Z

vision_module.py

seongdong2/graduation

c38b13a2dd82a58bdba7673916408daa0d9b471e

[ "Unlicense" ]

1

2021-10-14T06:19:44.000Z

vision_module.py

seongdong2/graduation

c38b13a2dd82a58bdba7673916408daa0d9b471e

[ "Unlicense" ]

null

import numpy as np import cv2 CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] net = cv2.dnn.readNetFromCaffe( "MobileNetSSD_deploy.prototxt.txt", "MobileNetSSD_deploy.caffemodel") BLACK_CRITERIA = 60 def detect(frame): (h, w) = frame.shape[:2] blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 0.007843, (300, 300), 127.5) net.setInput(blob) detections = net.forward() result_all = [] result_black = [] for i in np.arange(0, detections.shape[2]): confidence = detections[0, 0, i, 2] if confidence > 0.3: idx = int(detections[0, 0, i, 1]) if CLASSES[idx] == "person": box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) startX, startY, endX, endY = box.astype("int") x, y, w, h = startX, startY, endX - startX, endY - startY result_all.append((confidence, (x, y, w, h))) cut_size = int(min(w, h) / 6) black_value = np.mean(frame[y + cut_size:y + h - cut_size, x + cut_size:x + w - cut_size]) if black_value < BLACK_CRITERIA: result_black.append((confidence, (x, y, w, h))) if result_black: result_black.sort(key=lambda x: x[0]) return True, result_black[-1][1] else: return False, None def find_template(template, full_img): h, w, _ = template.shape full_img_copy = full_img.copy() res = cv2.matchTemplate(full_img_copy, template, cv2.TM_CCOEFF) _, max_val, _, max_loc = cv2.minMaxLoc(res) top_left = max_loc x = top_left[0] y = top_left[1] return full_img[y:y + h, x:x + w], (x, y, w, h)

30.571429

106

0.555556

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3.961686

0.413793

0.01354

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0.015474

0.073501

0.038685

0

0.038771

0.290239

1,926

63

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0

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0

0.159091

0

null

0

null

0

1

0

42efd3e55b344db382180d65f36b45d066baab96

618

py

Python

riccipy/metrics/lewis_papapetrou.py

cjayross/riccipy

2cc0ca5e1aa4af91b203b3ff2bb1effd7d2f4846

[ "MIT" ]

4

2019-08-17T04:28:06.000Z

2021-01-02T15:19:18.000Z

riccipy/metrics/lewis_papapetrou.py

grdbii/riccipy

2cc0ca5e1aa4af91b203b3ff2bb1effd7d2f4846

[ "MIT" ]

3

2019-08-02T04:07:43.000Z

2020-06-18T07:49:38.000Z

riccipy/metrics/lewis_papapetrou.py

grdbii/riccipy

2cc0ca5e1aa4af91b203b3ff2bb1effd7d2f4846

[ "MIT" ]

null

""" Name: Lewis Papapetrou References: Ernst, Phys. Rev., v167, p1175, (1968) Coordinates: Cartesian """ from sympy import Function, Rational, exp, symbols, zeros coords = symbols("t x y z", real=True) variables = () functions = symbols("k r s w", cls=Function) t, x, y, z = coords k, r, s, w = functions metric = zeros(4) metric[0, 0] = -exp(2 * s(x, y)) metric[3, 3] = (exp(-s(x, y)) * r(x, y) - w(x, y) * exp(s(x, y))) * ( exp(-s(x, y)) * r(x, y) + w(x, y) * exp(s(x, y)) ) metric[0, 3] = metric[3, 0] = -w(x, y) * exp(2 * s(x, y)) metric[1, 2] = metric[2, 1] = Rational(1, 2) * exp(2 * k(x, y) - 2 * s(x, y))

30.9

77

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618

2.826446

0.330579

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0.061404

0.070175

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0.202265

618

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0.63286

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0.076923

0

null

0

null

0

1

0

42f8e8791025cfd39e8878d6744a088d9902c8a3

1,206

py

Python

test/variable_type.py

bourne7/demo-python

0c4dd12475bcada4e5826b7117bd4c4bdcedfd9f

[ "MIT" ]

null

test/variable_type.py

bourne7/demo-python

0c4dd12475bcada4e5826b7117bd4c4bdcedfd9f

[ "MIT" ]

null

test/variable_type.py

bourne7/demo-python

0c4dd12475bcada4e5826b7117bd4c4bdcedfd9f

[ "MIT" ]

null

def do_loop(): print('Being Invoked.') # * 表示参数为元组 def fun1(*args): # 相当于 def fun1(1,2,3) ==> args 就相当于（1,2,3） for a in args: print(a) # ** 表示参数为字典 def fun2(**args): # 相当于 def fun2({a:1,b:2,c:3}) ==>args 就相当于{a:1,b:2,c:3} for k, v in args: print(k, ":", v) # Python3 的六个标准数据类型 def show_type(): # 不可变对象 var_int = 123 # 注意 isinstance(1, int) 这种可以判断父类，type不行 print('Number 数字', type(var_int)) var_str = 'Hello' print('String 字符串', type(var_str)) var_tuple = ('Hi', 786, 2.23, 'john', 70.2) print('Tuple 元组', type(var_tuple)) # 可变对象 var_set = {1, 2, 3, 4, 5} print('Sets 集合', type(var_set)) var_list = [1, 2, 3, 4, 5, 6] print('List 列表', type(var_list)) var_dict = {'a': 'apple', 'b': 'banana', 'z': 1000} print('Dictionary 字典', type(var_dict)) def test_mutable(): a1 = [1, 2, 3] a2 = a1 print(id(a1), id(a2)) # 这3种都不会导致对象id变化，因为都是调用内部函数。 a2.append(4) a2 += [4] a2.extend([4]) # 会导致对象id变化，因为创建了新的对象。 # a2 = a2 + [4] print(id(a1), id(a2)) print(a1) print(a2) if __name__ == '__main__': print('Start test as main.') show_type() test_mutable()

19.451613

74

0.543118

195

1,206

3.230769

0.405128

0.066667

0.02381

0.012698

0.07619

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0.07545

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1,206

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0.142857

0.4

0

null

0

null

0

1

0

42f979541235624972aa7beb6b4040036e613c33

951

py

Python

scrapystsytem/spiders/doubanmoviespider.py

mezhou887/ScrapySystem2017

888ac42bba36b541845244596db1644e332bf291

[ "Apache-2.0" ]

null

scrapystsytem/spiders/doubanmoviespider.py

mezhou887/ScrapySystem2017

888ac42bba36b541845244596db1644e332bf291

[ "Apache-2.0" ]

null

scrapystsytem/spiders/doubanmoviespider.py

mezhou887/ScrapySystem2017

888ac42bba36b541845244596db1644e332bf291

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import logging from scrapystsytem.misc.commonspider import CommonSpider from scrapy.spiders import Rule from scrapy.linkextractors import LinkExtractor as sle logger = logging.getLogger(__name__) class DoubanMovieSpider(CommonSpider): name = "doubanmovie" allowed_domains = ["douban.com"] start_urls = [ "https://movie.douban.com/chart" ] rules = [ Rule(sle(allow=("/subject/[0-9]+/$")), callback='parse_subject', follow=True), ] content_css_rules = { 'rating_per': '.rating_per::text', 'rating_num': '.rating_num::text', 'title': 'h1 span:nth-child(1)::text', 'rating_people': '.rating_people span::text', } def parse_subject(self, response): item = self.parse_with_rules(response, self.content_css_rules, dict) logger.info('function: parse_subject, url: '+response.url+' , item: '+str(item)); return item

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0.041667

false

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0.5

0

null

0

null

0

1

0

42fe26b4d9e2cf96a145d2ebd3a33d07d37ab54e

2,476

py

Python

09/09b.py

thejoeejoee/aoc-2021

1ae7650aea42b5fbb60e891687cf7bc84c81bd66

[ "MIT" ]

1

2021-12-01T17:43:38.000Z

09/09b.py

thejoeejoee/aoc-2021

1ae7650aea42b5fbb60e891687cf7bc84c81bd66

[ "MIT" ]

null

09/09b.py

thejoeejoee/aoc-2021

1ae7650aea42b5fbb60e891687cf7bc84c81bd66

[ "MIT" ]

null

#!/bin/env python3 import operator from _operator import attrgetter, itemgetter from collections import defaultdict, Counter from functools import reduce, partial from itertools import chain from aocd import get_data EMPTY = type('EMPTY', (int,), dict(__repr__=(f := lambda s: 'EMPTY'), __str__=f))(10) def windowed(seq, n): for i in range(len(seq) - n + 1): yield seq[i: i + n] def compose(*fs): return reduce(lambda f, g: lambda x: f(g(x)), fs, lambda x: x) heights = get_data().strip().splitlines() HEIGHT = len(heights) + 2 WIDTH = len(heights[0]) + 2 def get_neighbors(data, pos): row, col = pos for p in ( (row, col + 1), (row, col - 1), (row + 1, col), (row - 1, col), ): r, c = p if 0 <= r < HEIGHT and 0 <= c < WIDTH: yield p, data[r * WIDTH + c] def find_low_points(levels): for triplet_i, triplet in filter( # turbo magic to get triples (with indexes) with center item which is NOT EMPTY compose(partial(operator.ne, EMPTY), itemgetter(1), itemgetter(1)), enumerate(windowed(levels, 3), start=1) # wtf dunno why to start at 1 ): row = triplet_i // WIDTH col = triplet_i % WIDTH left, center, right = triplet top = levels[(row - 1) * WIDTH + col] bottom = levels[(row + 1) * WIDTH + col] if all(map(partial(operator.lt, center), (left, right, top, bottom))): yield row, col def main(): data = tuple(chain( (EMPTY for _ in range(WIDTH)), *(((EMPTY,) + tuple(int(c) for c in line) + (EMPTY,)) for line in heights), (EMPTY for _ in range(WIDTH)), )) basins = Counter() for low_point in find_low_points(data): known = set() to_explore = {low_point} # not BFS, dot DFS? just JoeFS while to_explore: exploring = to_explore.pop() known.add(exploring) r, c = exploring current = data[r * WIDTH + c] for neighbor, level in get_neighbors(data, exploring): if level in known: continue if level > current and level not in (EMPTY, 9): to_explore.add(neighbor) basins[low_point] = len(known) return reduce( operator.mul, map(itemgetter(1), basins.most_common(3)) ) if __name__ == '__main__': print(main())

26.340426

91

0.560582

330

2,476

4.090909

0.363636

0.017778

0.023704

0.014815

0.056296

0

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0.317851

2,476

93

92

26.623656

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0

0.061538

0

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0

1

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false

0

0.092308

0.015385

0.2

0.015385

0

null

0

null

0

1

0

42ff0390633d326bb027aa10d5b16efa20802940

1,343

py

Python

tests/test_window.py

yogeshkumarpilli/detectron2

f4f276dc8239b2c5a1bbbf6ed234acd25c75a522

[ "Apache-2.0" ]

null

tests/test_window.py

yogeshkumarpilli/detectron2

f4f276dc8239b2c5a1bbbf6ed234acd25c75a522

[ "Apache-2.0" ]

null

tests/test_window.py

yogeshkumarpilli/detectron2

f4f276dc8239b2c5a1bbbf6ed234acd25c75a522

[ "Apache-2.0" ]

3

2021-12-17T04:28:02.000Z

2022-02-22T18:18:03.000Z

from detectron2.engine import DefaultPredictor from detectron2.data import MetadataCatalog from detectron2.config import get_cfg from detectron2.utils.visualizer import ColorMode, Visualizer from detectron2 import model_zoo import cv2 import numpy as np import requests # Load an image res = requests.get("https://thumbor.forbes.com/thumbor/fit-in/1200x0/filters%3Aformat%28jpg%29/https%3A%2F%2Fspecials-images.forbesimg.com%2Fimageserve%2F5f15af31465263000625ce08%2F0x0.jpg") image = np.asarray(bytearray(res.content), dtype="uint8") image = cv2.imdecode(image, cv2.IMREAD_COLOR) config_file = 'COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml' cfg = get_cfg() cfg.merge_from_file(model_zoo.get_config_file(config_file)) cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.75 # Threshold cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(config_file) cfg.MODEL.DEVICE = "cuda" # cpu or cuda # Create predictor predictor = DefaultPredictor(cfg) # Make prediction output = predictor(image) print(output) v = Visualizer(image[:, :, ::-1], scale=0.8, metadata=MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), instance_mode=ColorMode.IMAGE ) v = v.draw_instance_predictions(output["instances"].to("cpu")) cv2.imshow('images', v.get_image()[:, :, ::-1]) cv2.waitKey(0)

37.305556

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0.737156

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42ff644535c1107deafd0fab424dd9161db0897b

9,920

py

Python

hydra/cli.py

albertoa/hydra

8161e75829e4e76cb91ce516bbf03c258a87ce9e

[ "Apache-2.0" ]

28

2020-11-05T16:04:51.000Z

2021-02-16T22:58:10.000Z

hydra/cli.py

albertoa/hydra

8161e75829e4e76cb91ce516bbf03c258a87ce9e

[ "Apache-2.0" ]

43

2020-11-06T19:21:39.000Z

2021-02-25T19:04:42.000Z

hydra/cli.py

albertoa/hydra

8161e75829e4e76cb91ce516bbf03c258a87ce9e

[ "Apache-2.0" ]

4

2020-11-06T08:54:57.000Z

2021-01-18T03:26:00.000Z

import os import yaml import json import click import hydra.utils.constants as const from hydra.utils.git import check_repo from hydra.utils.utils import dict_to_string, inflate_options from hydra.cloud.local_platform import LocalPlatform from hydra.cloud.fast_local_platform import FastLocalPlatform from hydra.cloud.google_cloud_platform import GoogleCloudPlatform from hydra.cloud.aws_platform import AWSPlatform from hydra.version import __version__ @click.group() @click.version_option(__version__) def cli(): pass @cli.command() # Generic options @click.option('-y', '--yaml_path', default='hydra.yaml', type=str) @click.option('-p', '--project_name', default=None, type=str) @click.option('-m', '--model_path', default=None, type=str) @click.option('--cloud', default=None, type=click.Choice(['fast_local','local', 'aws', 'gcp', 'azure'], case_sensitive=False)) @click.option('--github_token', envvar='GITHUB_TOKEN') # Takes either an option or environment var # Cloud specific options @click.option('--cpu_count', default=None, type=click.IntRange(0, 96), help='Number of CPU cores required') @click.option('--memory_size', default=None, type=click.IntRange(0, 624), help='GB of RAM required') @click.option('--gpu_count', default=None, type=click.IntRange(0, 8), help="Number of accelerator GPUs") @click.option('--gpu_type', default=None, type=str, help="Accelerator GPU type") @click.option('--region', default=None, type=str, help="Region of cloud server location") # AWS specific options @click.option('--metadata_db_hostname', default=None, type=str, help="Hostname of the RDS instance storing job metadata") @click.option('--metadata_db_username_secret', default=None, type=str, help="Secret name in AWS of the username of the RDS instance storing job metadata") @click.option('--metadata_db_password_secret', default=None, type=str, help="Secret name in AWS of the password of the RDS instance storing job metadata") @click.option('--metadata_db_name', default=None, type=str, help="Database name of the RDS instance storing job metadata") # Docker Options @click.option('-t', '--image_tag', default=None, type=str, help="Docker image tag name") @click.option('-u', '--image_url', default=None, type=str, help="Url to the docker image on cloud") # Env variable of model file @click.option('-o', '--options', default=None, type=str, help='Environmental variables for the script') def run( yaml_path, project_name, model_path, cloud, github_token, cpu_count, memory_size, gpu_count, gpu_type, region, metadata_db_hostname, metadata_db_username_secret, metadata_db_password_secret, metadata_db_name, image_tag, image_url, options): # If YAML config file available to supplement the command line arguments if os.path.isfile(yaml_path): with open(yaml_path) as f: print("[Hydra Info]: Loading run info from {}...".format(yaml_path)) data = yaml.load(f, Loader=yaml.FullLoader) run_data = data.get('run', '') project_name = run_data.get('project_name') if project_name is None: raise ValueError("project_name option is required") model_path = run_data.get('model_path', const.MODEL_PATH_DEFAULT) if model_path is None else model_path cloud = run_data.get('cloud', const.CLOUD_DEFAULT).lower() if cloud is None else cloud image_tag = run_data.get('image_tag', const.IMAGE_TAG_DEFAULT) if image_tag is None else image_tag image_url = run_data.get('image_url', const.IMAGE_URL_DEFAULT) if image_url is None else image_url if image_tag == '' and image_url != '': raise Exception("image_tag is required when passing a custom image_url") if cloud == 'gcp' or cloud == 'aws': region = run_data.get('region', const.REGION_DEFAULT) if region is None else region cpu_count = run_data.get('cpu_count', const.CPU_COUNT_DEFAULT) if cpu_count is None else cpu_count memory_size = run_data.get('memory_size', const.MEMORY_SIZE_DEFAULT) if memory_size is None else memory_size gpu_count = run_data.get('gpu_count', const.GPU_COUNT_DEFAULT) if gpu_count is None else gpu_count gpu_type = run_data.get('gpu_type', const.GPU_TYPE_DEFAULT) if gpu_type is None else gpu_type if cloud == 'aws': metadata_db_hostname = run_data.get('metadata_db_hostname', const.METADATA_DB_HOSTNAME) if metadata_db_hostname is None else metadata_db_hostname metadata_db_username_secret = run_data.get('metadata_db_username_secret', const.METADATA_DB_USERNAME_SECRET) if metadata_db_username_secret is None else metadata_db_username_secret metadata_db_password_secret = run_data.get('metadata_db_password_secret', const.METADATA_DB_PASSWORD_SECRET) if metadata_db_password_secret is None else metadata_db_password_secret metadata_db_name = run_data.get('metadata_db_name', const.METADATA_DB_NAME) if metadata_db_name is None else metadata_db_name elif cloud == 'local' or cloud == 'fast_local': pass else: raise RuntimeError("Reached parts of Hydra that are either not implemented or recognized.") options_list = run_data.get('options', const.OPTIONS_DEFAULT) if options is None else options if type(options_list) is str: options_list = json.loads(options_list) # Read the options for run from CIL else: model_path = const.MODEL_PATH_DEFAULT if model_path is None else model_path cloud = const.CLOUD_DEFAULT if cloud is None else cloud region = const.REGION_DEFAULT if region is None else region cpu_count = const.CPU_COUNT_DEFAULT if cpu_count is None else cpu_count memory_size = const.MEMORY_SIZE_DEFAULT if memory_size is None else memory_size gpu_count = const.GPU_COUNT_DEFAULT if gpu_count is None else gpu_count gpu_type = const.GPU_TYPE_DEFAULT if gpu_type is None else gpu_type image_tag = const.IMAGE_TAG_DEFAULT if image_tag is None else image_tag image_url = const.IMAGE_URL_DEFAULT if image_url is None else image_url options = str(const.OPTIONS_DEFAULT) if options is None else options options_list = json.loads(options) if cloud == 'aws': metadata_db_hostname = const.METADATA_DB_HOSTNAME if metadata_db_hostname is None else metadata_db_hostname metadata_db_username_secret = const.METADATA_DB_USERNAME_SECRET if metadata_db_username_secret is None else metadata_db_username_secret metadata_db_password_secret = const.METADATA_DB_PASSWORD_SECRET if metadata_db_password_secret is None else metadata_db_password_secret metadata_db_name = const.METADATA_DB_NAME if metadata_db_name is None else metadata_db_name if isinstance(options_list, dict): options_list = [options_list] options_list_inflated = inflate_options(options_list) if cloud == 'aws': git_url, commit_sha = '', '' else: git_url, commit_sha = check_repo(github_token) hydra_core_configs = { 'HYDRA_PLATFORM': cloud, 'HYDRA_GIT_URL': git_url or '', 'HYDRA_COMMIT_SHA': commit_sha or '', 'HYDRA_OAUTH_TOKEN': github_token, 'HYDRA_MODEL_PATH': model_path } print("\n[Hydra Info]: Executing experiments with the following options: \n {}\n".format(options_list_inflated)) for i, options in enumerate(options_list_inflated): options_str = dict_to_string(options) hydra_core_configs_str = dict_to_string(hydra_core_configs) print("\n[Hydra Info]: Runnning experiment #{} with the following options: \n {}\n".format(i, options)) if cloud == 'fast_local': platform = FastLocalPlatform(model_path, f"{options_str} {hydra_core_configs_str}") platform.run() continue if cloud == 'local': platform = LocalPlatform( model_path=model_path, options=options_str, git_url=git_url, commit_sha=commit_sha, github_token=github_token, image_url=image_url, image_tag=image_tag) elif cloud == 'gcp': platform = GoogleCloudPlatform( model_path=model_path, github_token=github_token, cpu=cpu_count, memory=memory_size, gpu_count=gpu_count, gpu_type=gpu_type, region=region, git_url=git_url, commit_sha=commit_sha, image_url=image_url, image_tag=image_tag, options=options_str) elif cloud == 'aws': platform = AWSPlatform( model_path=model_path, project_name=project_name, github_token=github_token, cpu=cpu_count, memory=memory_size, gpu_count=gpu_count, region=region, git_url=git_url, commit_sha=commit_sha, hydra_version=__version__, metadata_db_hostname=metadata_db_hostname, metadata_db_username_secret=metadata_db_username_secret, metadata_db_password_secret=metadata_db_password_secret, metadata_db_name=metadata_db_name, image_url=image_url, image_tag=image_tag, options=options ) else: raise RuntimeError("Reached parts of Hydra that are not yet implemented.") platform.run() return 0

44.684685

200

0.674698

1,328

9,920

4.745482

0.13253

0.082513

0.04443

0.049508

0.544906

0.501269

0.471279

0.445255

0.43034

0.391622

0

0.001327

0.240323

9,920

221

201

44.886878

0.834926

0.025

0

0.209302

0

0.15668

0.016351

0

1

0.011628

false

0.046512

0.069767

0

0.087209

0.017442

0

null

0

null

0

1

0

6e0596f60ea2aacca4a2e542940c06bbc4f394b7

25,458

py

Python

utils/dataset_utils.py

Daipuwei/YOLO-tf2

1b2e7133c99507573f419c8a367a8dba4abeae5b

[ "MIT" ]

null

utils/dataset_utils.py

Daipuwei/YOLO-tf2

1b2e7133c99507573f419c8a367a8dba4abeae5b

[ "MIT" ]

null

utils/dataset_utils.py

Daipuwei/YOLO-tf2

1b2e7133c99507573f419c8a367a8dba4abeae5b

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Time : 2021/9/18 下午11:23 # @Author : DaiPuWei # @Email : 771830171@qq.com # @File : dataset_utils.py # @Software: PyCharm """ 这是YOLO模型数据集 """ import cv2 import numpy as np from PIL import Image from matplotlib.colors import rgb_to_hsv, hsv_to_rgb from utils.model_utils import get_classes from utils.model_utils import get_anchors def resize_keep_aspect_ratio(image_src, dst_size, value=[128, 128, 128]): ''' 这是opencv将源图像扩充边界成正方形，并完成图像尺寸变换 Args: image_src: 源图像 dst_size: 缩放尺寸 value: 填充像素值 Returns: ''' # 获取源图像和目标图像的尺寸 src_h, src_w, _ = np.shape(image_src) dst_h, dst_w = dst_size # 首先确定哪个方向进行填充 if src_h < src_w: # 在h方向进行填充 delta = src_w - src_h # 计算需要填充的像素个数，然后均分到上下两侧 top = int(delta // 2) down = delta - top left = 0 right = 0 else: # 在w方向进行填充 delta = src_h - src_w # 计算需要填充的像素个数，然后均分到左右两侧 top = 0 down = 0 left = int(delta // 2) right = delta - left borderType = cv2.BORDER_CONSTANT image_dst = cv2.copyMakeBorder(image_src, top, down, left, right, borderType, None, value) image_dst = cv2.resize(image_dst, dst_size) return image_dst def letterbox_image(image, size): ''' 这是PIL将源图像扩充边界成正方形，并完成图像尺寸变换 Args: image: 图像 size: 缩放尺寸 Returns: ''' iw, ih = image.size w, h = size scale = min(w/iw, h/ih) nw = int(iw*scale) nh = int(ih*scale) image = image.resize((nw,nh), Image.BICUBIC) new_image = Image.new('RGB', size, (128,128,128)) new_image.paste(image, ((w-nw)//2, (h-nh)//2)) return new_image def rand(a=0, b=1): return np.random.rand()*(b-a) + a class Dataset(object): def __init__(self,dataset_path,classes_path,anchors_path,batch_size,target_size, max_boxes_num=20,use_mosaic=False,random=True,model_name='yolov3'): ''' 这是目标检测数据集初始化类 Args: dataset_path: COCO格式的数据集txt地址 classes_path: 目标分类txt地址 anchors_path: 模版框txt地址 batch_size: 小批量数规模 target_size: 目标尺寸 max_boxes_num: 最大目标框个数,默认为20 use_mosaic: 是否使用mosaic数据增强，默认为False random: 是否进行随机数据增强标志量，默认为True model_name: 模型名称，默认为‘yolov3’ ''' self.dataset_path = dataset_path self.classes_path = classes_path self.anchors_path = anchors_path self.target_size = target_size self.max_boxes_num = max_boxes_num self.use_mosaic = use_mosaic self.random = random self.model_name = model_name self.annotation_lines = [] with open(self.dataset_path, 'r') as f: for line in f.readlines(): self.annotation_lines.append(line) self.annotation_lines = np.array(self.annotation_lines) self.annotation_lines = np.random.permutation(self.annotation_lines) self.size = len(self.annotation_lines) self.batch_size = batch_size self.iter_num = self.size // self.batch_size if self.size % self.batch_size != 0: self.iter_num += 1 # 初始化anchors与classes self.anchors = get_anchors(self.anchors_path) self.classes_names = get_classes(self.classes_path) self.num_anchors = len(self.anchors) self.num_classes = len(self.classes_names) # 初始化相关数据增强参数 self.jitter = 0.3 self.hue=.1 self.sat=1.5 self.val=1.5 def get_batch_data_with_mosaic(self,batch_annotation_lines): ''' 这是获取批量图像及其标签并使用mosaic数据增强的函数 Args: batch_annotation_lines: 批量yolo数据集格式标注 Returns: ''' batch_image_data = [] batch_boxes = [] size = len(batch_annotation_lines) for start in np.arange(0,len(batch_annotation_lines),4): end = int(np.min([start+4,size])) _batch_annotation_lines = batch_annotation_lines[start:end] image_data,box_data = self.get_random_data_with_mosaic(_batch_annotation_lines) batch_image_data.append(image_data) batch_boxes.append(box_data) batch_image_data = np.array(batch_image_data) batch_boxes = np.array(batch_boxes) return batch_image_data,batch_boxes def get_random_data_with_mosaic(self,batch_lines): """ 这是4张图像及其目标标签，并对图像法进行mosaic数据增强操作的函数 :param batch_lines: 4张yolo格式数据 :return: """ h, w = self.target_size min_offset_x = 0.3 min_offset_y = 0.3 scale_low = 1 - min(min_offset_x, min_offset_y) scale_high = scale_low + 0.2 image_datas = [] box_datas = [] index = 0 place_x = [0, 0, int(w * min_offset_x), int(w * min_offset_x)] place_y = [0, int(h * min_offset_y), int(h * min_offset_y), 0] # 批量图像可能不足4张，随机补充 size = len(batch_lines) if size < 4: dif = 4 - len(batch_lines) _batch_line = [line for line in batch_lines] for i in np.arange(dif): random_index = np.random.randint(0,size) _batch_line.append(batch_lines[random_index]) batch_lines = np.array(_batch_line) # 便利所有图像，加载真实标签 for line in batch_lines: # 每一行进行分割 line_content = line.split() # 打开图片 image = Image.open(line_content[0]) image = image.convert("RGB") # 图片的大小 iw, ih = image.size # 保存框的位置 box = np.array([np.array(list(map(int, box.split(',')))) for box in line_content[1:]]) # 是否翻转图片 flip = rand() < .5 if flip and len(box) > 0: image = image.transpose(Image.FLIP_LEFT_RIGHT) box[:, [0, 2]] = iw - box[:, [2, 0]] # 对输入进来的图片进行缩放 new_ar = w / h scale = rand(scale_low, scale_high) if new_ar < 1: nh = int(scale * h) nw = int(nh * new_ar) else: nw = int(scale * w) nh = int(nw / new_ar) image = image.resize((nw, nh), Image.BICUBIC) # 进行色域变换 hue = rand(-self.hue, self.hue) sat = rand(1, self.sat) if rand() < .5 else 1 / rand(1, self.sat) val = rand(1, self.val) if rand() < .5 else 1 / rand(1, self.val) x = cv2.cvtColor(np.array(image, np.float32) / 255, cv2.COLOR_RGB2HSV) x[..., 0] += hue * 360 x[..., 0][x[..., 0] > 1] -= 1 x[..., 0][x[..., 0] < 0] += 1 x[..., 1] *= sat x[..., 2] *= val x[x[:, :, 0] > 360, 0] = 360 x[:, :, 1:][x[:, :, 1:] > 1] = 1 x[x < 0] = 0 image = cv2.cvtColor(x, cv2.COLOR_HSV2RGB) # numpy array, 0 to 1 image = Image.fromarray((image * 255).astype(np.uint8)) # 将图片进行放置，分别对应四张分割图片的位置 dx = place_x[index] dy = place_y[index] new_image = Image.new('RGB', (w, h), (128, 128, 128)) new_image.paste(image, (dx, dy)) image_data = np.array(new_image) / 255 index = index + 1 box_data = [] # 对box进行重新处理 if len(box) > 0: np.random.shuffle(box) box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy box[:, 0:2][box[:, 0:2] < 0] = 0 box[:, 2][box[:, 2] > w] = w box[:, 3][box[:, 3] > h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w > 1, box_h > 1)] box_data = np.zeros((len(box), 5)) box_data[:len(box)] = box image_datas.append(image_data) box_datas.append(box_data) # 将图片分割，放在一起 cutx = np.random.randint(int(w * min_offset_x), int(w * (1 - min_offset_x))) cuty = np.random.randint(int(h * min_offset_y), int(h * (1 - min_offset_y))) new_image = np.zeros([h, w, 3]) new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :] new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :] new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :] new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :] # 归并边界框 merge_bbox = self.merge_bboxes(box_datas,cutx,cuty) #print(np.shape(merge_bbox)) bbox = np.zeros((self.max_boxes_num, 5)) if len(merge_bbox) != 0: if len(merge_bbox) > self.max_boxes_num: merge_bbox = merge_bbox[:self.max_boxes_num] bbox[:len(merge_bbox)] = merge_bbox return new_image,bbox def merge_bboxes(self,bbox_data,cutx,cuty): ''' 这是mosaic数据增强中对4张图片的边界框标签进行合并的函数 Args: bbox_data: 边界框标签数组 cutx: x坐标轴分界值 cuty: y坐标轴分界值 Returns: ''' merge_bbox = [] for i,bboxes in enumerate(bbox_data): if bboxes is not None: for box in bboxes: tmp_box = [] x1, y1, x2, y2 = box[0], box[1], box[2], box[3] if i == 0: if y1 > cuty or x1 > cutx: continue if y2 >= cuty and y1 <= cuty: y2 = cuty if y2 - y1 < 5: # 相差过小则放弃 continue if x2 >= cutx and x1 <= cutx: x2 = cutx if x2 - x1 < 5: # 相差过小则放弃 continue if i == 1: if y2 < cuty or x1 > cutx: continue if y2 >= cuty and y1 <= cuty: y1 = cuty if y2 - y1 < 5: # 相差过小则放弃 continue if x2 >= cutx and x1 <= cutx: x2 = cutx if x2 - x1 < 5: # 相差过小则放弃 continue if i == 2: if y2 < cuty or x2 < cutx: continue if y2 >= cuty and y1 <= cuty: y1 = cuty if y2 - y1 < 5: # 相差过小则放弃 continue if x2 >= cutx and x1 <= cutx: x1 = cutx if x2 - x1 < 5: # 相差过小则放弃 continue if i == 3: if y1 > cuty or x2 < cutx: continue if y2 >= cuty and y1 <= cuty: y2 = cuty if y2 - y1 < 5: # 相差过小则放弃 continue if x2 >= cutx and x1 <= cutx: x1 = cutx if x2 - x1 < 5: # 相差过小则放弃 continue tmp_box.append(x1) tmp_box.append(y1) tmp_box.append(x2) tmp_box.append(y2) tmp_box.append(box[-1]) merge_bbox.append(tmp_box) del bbox_data return np.array(merge_bbox) def get_batch_data(self,batch_annotation_lines): ''' 这是获取批量图像及其目标框标签的函数，不使用mosaic数据增强 Args: batch_annotation_lines: 批量yolo数据集格式标注 Returns: ''' batch_images = [] batch_boxes = [] for annotation_line in batch_annotation_lines: image,box_data = self.get_random_data(annotation_line) batch_images.append(image) batch_boxes.append(box_data) batch_images = np.array(batch_images) batch_boxes = np.array(batch_boxes) return batch_images,batch_boxes def get_random_data(self,line): ''' 这是获取图像及其目标标签，并对图像法进行实时数据增强操作的函数 Args: line: yolo格式数据 Returns: ''' lines =line.split() image = Image.open(lines[0]) iw, ih = image.size h, w = self.target_size box = np.array([np.array(list(map(int, box.split(',')))) for box in lines[1:]]) if not self.random: # resize image scale = min(w / iw, h / ih) nw = int(iw * scale) nh = int(ih * scale) dx = (w - nw) // 2 dy = (h - nh) // 2 image = image.resize((nw, nh), Image.BICUBIC) new_image = Image.new('RGB', (w, h), (128, 128, 128)) new_image.paste(image, (dx, dy)) image_data = np.array(new_image, np.float32) / 255 # correct boxes box_data = np.zeros((self.max_boxes_num, 5)) if len(box) > 0: np.random.shuffle(box) box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy box[:, 0:2][box[:, 0:2] < 0] = 0 box[:, 2][box[:, 2] > w] = w box[:, 3][box[:, 3] > h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w > 1, box_h > 1)] # discard invalid box if len(box) > self.max_boxes_num: box = box[:self.max_boxes_num] box_data[:len(box)] = box return image_data, box_data # resize image new_ar = w / h * rand(1 - self.jitter, 1 + self.jitter) / rand(1 - self.jitter, 1 + self.jitter) scale = rand(.25, 2) if new_ar < 1: nh = int(scale * h) nw = int(nh * new_ar) else: nw = int(scale * w) nh = int(nw / new_ar) image = image.resize((nw, nh), Image.BICUBIC) # place image dx = int(rand(0, w - nw)) dy = int(rand(0, h - nh)) new_image = Image.new('RGB', (w, h), (128, 128, 128)) new_image.paste(image, (dx, dy)) image = new_image # flip image or not flip = rand() < .5 if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) # distort image hue = rand(-self.hue, self.hue) sat = rand(1, self.sat) if rand() < .5 else 1 / rand(1, self.sat) val = rand(1, self.val) if rand() < .5 else 1 / rand(1, self.val) x = rgb_to_hsv(np.array(image) / 255.) x[..., 0] += hue x[..., 0][x[..., 0] > 1] -= 1 x[..., 0][x[..., 0] < 0] += 1 x[..., 1] *= sat x[..., 2] *= val x[x > 1] = 1 x[x < 0] = 0 image_data = hsv_to_rgb(x) # numpy array, 0 to 1 # correct boxes box_data = np.zeros((self.max_boxes_num, 5)) if len(box) > 0: np.random.shuffle(box) box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy if flip: box[:, [0, 2]] = w - box[:, [2, 0]] box[:, 0:2][box[:, 0:2] < 0] = 0 box[:, 2][box[:, 2] > w] = w box[:, 3][box[:, 3] > h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w > 1, box_h > 1)] # discard invalid box if len(box) > self.max_boxes_num: box = box[:self.max_boxes_num] box_data[:len(box)] = box return image_data, box_data # ---------------------------------------------------# # 读入xml文件，并输出y_true # ---------------------------------------------------# def preprocess_true_boxes(self,true_boxes): ''' 这是根据真实标签转换成不同yolo预测输出的函数 Args: true_boxes: 真实目标框标签 Returns: ''' assert (true_boxes[..., 4] < self.num_classes).all(), 'class id must be less than num_classes' # -----------------------------------------------------------# # 获得框的坐标和图片的大小 # -----------------------------------------------------------# true_boxes = np.array(true_boxes, dtype='float32') input_shape = np.array(self.target_size, dtype='int32') # 根据不同yolo模型初始化不同anchor掩膜、网格尺寸和输出层数 if self.model_name == 'yolov3': # yolov3 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] num_layers = 3 grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] elif self.model_name == 'yolov3-spp': # yolov3-spp anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] num_layers = 3 grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] elif self.model_name == 'yolov4': # yolov4 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] num_layers = 3 grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] elif self.model_name == 'yolov4-csp': # yolov4-csp anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] num_layers = 3 grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] elif self.model_name == 'yolov4-p5': # yolov4-p5 anchor_mask = [[8, 9, 10, 11], [4, 5, 6, 7], [0, 1, 2, 3]] num_layers = 3 grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] elif self.model_name == 'yolov4-p6': # yolov4-p6 anchor_mask = [[12, 13, 14, 15], [8, 9, 10, 11], [4, 5, 6, 7], [0, 1, 2, 3]] num_layers = 4 grid_shapes = [input_shape // {0: 64, 1: 32, 2: 16, 3: 8}[l] for l in range(num_layers)] elif self.model_name == 'yolov4-p7': # yolov4-p7 anchor_mask = [[16, 17, 18, 19], [12, 13, 14, 15], [8, 9, 10, 11], [4, 5, 6, 7], [0, 1, 2, 3]] num_layers = 5 grid_shapes = [input_shape // {0:128, 1: 64, 2: 32, 3: 16, 4: 8}[l] for l in range(num_layers)] elif self.model_name == 'poly-yolo': # poly-yolo(v3) anchor_mask = [[0,1,2,3,4,5,6,7,8]] num_layers = 1 grid_shapes = [input_shape // {0: 8}[l] for l in range(num_layers)] elif self.model_name == 'yolov3-tiny': # yolov3-tiny anchor_mask = [[3, 4, 5], [0, 1, 2]] num_layers = 2 grid_shapes = [input_shape // {0: 32, 1: 16}[l] for l in range(num_layers)] elif self.model_name == 'yolov4-tiny': # yolov4-tiny anchor_mask = [ [3, 4, 5], [0, 1, 2]] num_layers = 2 grid_shapes = [input_shape // {0: 32, 1: 16}[l] for l in range(num_layers)] print(grid_shapes) else: # 默认为yolov3 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] num_layers = 3 grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] # -----------------------------------------------------------# # 通过计算获得真实框的中心和宽高 # 中心点(m,n,2) 宽高(m,n,2) # -----------------------------------------------------------# boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2 boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2] # -----------------------------------------------------------# # 将真实框归一化到小数形式 # -----------------------------------------------------------# true_boxes[..., 0:2] = boxes_xy / input_shape[::-1] true_boxes[..., 2:4] = boxes_wh / input_shape[::-1] # m为图片数量，grid_shapes为网格的shape m = true_boxes.shape[0] #grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] # -----------------------------------------------------------# # y_true的格式为(m,13,13,3,85)(m,26,26,3,85)(m,52,52,3,85) # -----------------------------------------------------------# y_true = [np.zeros((m, grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + self.num_classes), dtype='float32') for l in range(num_layers)] # -----------------------------------------------------------# # [9,2] -> [1,9,2] # -----------------------------------------------------------# anchors = np.expand_dims(self.anchors, 0) anchor_maxes = anchors / 2. anchor_mins = -anchor_maxes # -----------------------------------------------------------# # 长宽要大于0才有效 # -----------------------------------------------------------# valid_mask = boxes_wh[..., 0] > 0 for b in range(m): # 对每一张图进行处理 wh = boxes_wh[b, valid_mask[b]] if len(wh) == 0: continue # -----------------------------------------------------------# # [n,2] -> [n,1,2] # -----------------------------------------------------------# wh = np.expand_dims(wh, -2) box_maxes = wh / 2. box_mins = -box_maxes # -----------------------------------------------------------# # 计算所有真实框和先验框的交并比 # intersect_area [n,9] # box_area [n,1] # anchor_area [1,9] # iou [n,9] # -----------------------------------------------------------# intersect_mins = np.maximum(box_mins, anchor_mins) intersect_maxes = np.minimum(box_maxes, anchor_maxes) intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.) intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] box_area = wh[..., 0] * wh[..., 1] anchor_area = anchors[..., 0] * anchors[..., 1] iou = intersect_area / (box_area + anchor_area - intersect_area) # -----------------------------------------------------------# # 维度是[n,] 感谢消尽不死鸟的提醒 # -----------------------------------------------------------# best_anchor = np.argmax(iou, axis=-1) for t, n in enumerate(best_anchor): # -----------------------------------------------------------# # 找到每个真实框所属的特征层 # -----------------------------------------------------------# for l in range(num_layers): if n in anchor_mask[l]: # -----------------------------------------------------------# # floor用于向下取整，找到真实框所属的特征层对应的x、y轴坐标 # -----------------------------------------------------------# i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype('int32') # -----------------------------------------------------------# # k指的的当前这个特征点的第k个先验框 # -----------------------------------------------------------# k = anchor_mask[l].index(n) # -----------------------------------------------------------# # c指的是当前这个真实框的种类 # -----------------------------------------------------------# c = true_boxes[b, t, 4].astype('int32') # -----------------------------------------------------------# # y_true的shape为(m,13,13,3,85)(m,26,26,3,85)(m,52,52,3,85) # 最后的85可以拆分成4+1+80，4代表的是框的中心与宽高、 # 1代表的是置信度、80代表的是种类 # -----------------------------------------------------------# y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5 + c] = 1 return y_true def generator(self): ''' 这是数据生成器定义函数 Returns: ''' while True: # 随机打乱数据集 self.annotation_lines = np.random.permutation(self.annotation_lines) for start in np.arange(0,self.size,self.batch_size): end = int(np.min([start+self.batch_size,self.size])) batch_annotation_lines = self.annotation_lines[start:end] if self.use_mosaic: batch_images,batch_boxes = self.get_batch_data_with_mosaic(batch_annotation_lines) else: batch_images, batch_boxes = self.get_batch_data(batch_annotation_lines) # 对box数组进行处理，生成符合YOLO v4模型输出的标签 batch_y_true = self.preprocess_true_boxes(batch_boxes) batch_loss = np.zeros(len(batch_images)) yield [batch_images,*batch_y_true],batch_loss

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0

null

0

null

0

1

0

6e080db2602e0c90c09249fc8d6eeaeabeabd005

750

py

Python

caesar_cipher.py

DomirScire/Basic_Ciphers

7425b306f8d0ce9ceb5ba3a59e73a52892bee5ca

[ "MIT" ]

1

2021-03-31T23:29:00.000Z

caesar_cipher.py

DomirScire/Ciphers_Py

127c82b14c9bd5595f924bc267b6bf238f654c22

[ "MIT" ]

null

caesar_cipher.py

DomirScire/Ciphers_Py

127c82b14c9bd5595f924bc267b6bf238f654c22

[ "MIT" ]

null

import string def caesar_cipher(text, shift, decrypt=False): if not text.isascii() or not text.isalpha(): raise ValueError("Text must be ASCII and contain no numbers.") lowercase = string.ascii_lowercase uppercase = string.ascii_uppercase result = "" if decrypt: shift = shift * -1 for char in text: if char.islower(): index = lowercase.index(char) result += lowercase[(index + shift) % 26] else: index = uppercase.index(char) result += uppercase[(index + shift) % 26] return result if __name__ == "__main__": print(caesar_cipher("meetMeAtOurHideOutAtTwo", 10)) print(caesar_cipher("woodWoKdYebRsnoYedKdDgy", 10, decrypt=True))

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0.1

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null

0

null

0

1

0

6e0977041deef6fa7bf74e2fadd3b0a89bcf73e3

6,953

py

Python

hume/hume/app.py

megacorpincorporated/HOME

0eb8009b028fabf64abb03acc0a081b2b8207eb0

[ "MIT" ]

1

2018-02-18T15:51:57.000Z

hume/hume/app.py

megacorpincorporated/HOME

0eb8009b028fabf64abb03acc0a081b2b8207eb0

[ "MIT" ]

null

hume/hume/app.py

megacorpincorporated/HOME

0eb8009b028fabf64abb03acc0a081b2b8207eb0

[ "MIT" ]

null

import json import logging from app.abc import StartError from app.device import DeviceApp, DeviceMessage from app.device.models import Device from app.hint import HintApp from app.hint.defs import HintMessage from util.storage import DataStore LOGGER = logging.getLogger(__name__) class Hume: def __init__(self, cli_args): self.storage = DataStore() self.device_app = DeviceApp(cli_args, self.storage) self.hint_app = HintApp(cli_args, self.storage) def start(self): """Starts the HUME.""" LOGGER.info("hume start") self.device_app.pre_start() self.hint_app.pre_start() # Register callbacks prior to starting Apps in case of any # confirmation-type messages happen on connection establishment, or in # case of queued up messages from HINT. self.device_app.register_callback(self._on_device_message) self.hint_app.register_callback(self._on_hint_message) try: self.device_app.start() self.hint_app.start() except StartError: self.stop() # may or may not raise another exception # raise runtime error to ensure stop raise RuntimeError("failed to start an app") self.device_app.post_start() self.hint_app.post_start() def stop(self): """Stops the HUME.""" LOGGER.info("hume stop") # Important to maintain same stop order as the start order! self.device_app.pre_stop() self.hint_app.pre_stop() self.device_app.stop() self.hint_app.stop() self.device_app.post_stop() self.hint_app.post_stop() """ Private """ def _on_device_message(self, device: Device, msg_type: int, msg: bytearray): """ Registered to be called by the Device app when a new message is received from a connected device. """ LOGGER.debug("HUME handling device message") if msg_type == DeviceMessage.CAPABILITY.value: decoded_msg = json.loads(msg) LOGGER.info(f"device {device.uuid[:4]} sent capability response") capabilities = decoded_msg capabilities["identifier"] = device.uuid if self.hint_app.create_device(capabilities): LOGGER.info("device created in HINT successfully") # This is done since BLE devices cannot provide UUID before # capability response is gotten and are thus saved with their # address as their primary key prior to attach success. device = self.storage.get(Device, device.uuid) device.uuid = capabilities["uuid"] device.attached = True self.storage.set(device) else: LOGGER.error("failed to create device in HINT") # Detach device to clean up after unsuccessful attach. self.device_app.detach(device) self.hint_app.attach_failure(device.uuid) elif msg_type == DeviceMessage.ACTION_STATEFUL.value: decoded_msg = msg.decode() self.hint_app.action_response(device, HintMessage.ACTION_STATEFUL, { "group_id": int(decoded_msg[0]), "state_id": int(decoded_msg[1]) }) else: LOGGER.warning(f"got message from device {device.uuid[:4]} of an " f"unknown type: {msg_type}, msg: {msg}") def _on_hint_message(self, msg_type, msg): """ Registered to be called by the Hint app when a new message is received from HINT. """ LOGGER.debug("HUME handling HINT message") if msg_type == HintMessage.DISCOVER_DEVICES.value: LOGGER.info("HINT requested device discovery") self.device_app.discover(self._discovered_devices) elif msg_type == HintMessage.ATTACH.value: identifier = msg["identifier"] LOGGER.info(f"HINT requested device {identifier[:4]} to " f"be attached") device = self.storage.get(Device, identifier) if device is not None: if not self.device_app.request_capabilities(device): LOGGER.error(f"failed to attach device {identifier[:4]}") self.hint_app.attach_failure(identifier) elif msg_type == HintMessage.DETACH.value: device_uuid = msg["device_uuid"] LOGGER.info(f"HINT requested detaching device {device_uuid[:4]}") device = self.storage.get(Device, device_uuid) if device is not None: self.device_app.detach(device) else: LOGGER.error(f"can't detach device {device_uuid[:4]}, " f"does not exist") elif msg_type == HintMessage.UNPAIR.value: LOGGER.info("HINT requested unpairing, factory resetting HUME") self.device_app.reset() self.storage.delete_all() elif msg_type == HintMessage.ACTION_STATEFUL.value: device_uuid = msg.pop("device_uuid") LOGGER.info(f"HINT requested stateful action for device " f"{device_uuid[:4]}") msg.pop("type") device = self.storage.get(Device, device_uuid) if device is not None: self.device_app.stateful_action(device, **msg) else: LOGGER.error("could not execute stateful action since device " "does not exist") elif msg_type == HintMessage.ACTION_STATES.value: device_uuid = msg["device_uuid"] LOGGER.info(f"HINT requested all stateful action states for " f"device {device_uuid[:4]}") device = self.storage.get(Device, device_uuid) if device is not None: self.device_app.action_states(device) else: LOGGER.error("could not fetch stateful action states since " "the device did not exist") else: LOGGER.warning(f"got message from hint of an unknown type: " f"{msg_type}, msg: {msg}") def _discovered_devices(self, devices: [Device]): """ Callback provided to the device app when discovering devices. """ for device in devices: # Store discovered devices to remember the transport type reported # by the individual connection types. self.storage.set(device) self.hint_app.discovered_devices(devices)

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null

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null

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1

0

6e0c62be30176a8297c1bf84eb84e82bffd0d9ee

3,281

py

Python

scripts/generate_demo_requests.py

onedata/onezone-gui-plugin-ecrin

2bf38b0994d1c0bf8148b1b8c5990bcf0aa4a62b

[ "MIT" ]

null

scripts/generate_demo_requests.py

onedata/onezone-gui-plugin-ecrin

2bf38b0994d1c0bf8148b1b8c5990bcf0aa4a62b

[ "MIT" ]

null

scripts/generate_demo_requests.py

onedata/onezone-gui-plugin-ecrin

2bf38b0994d1c0bf8148b1b8c5990bcf0aa4a62b

[ "MIT" ]

null

# # Author: Michał Borzęcki # # This script creates empty files with study and data object metadata in # specified space and Oneprovider. It uses JSON files located in directories # `studies_dir` (= studies) and `data_object_dir` (= data_objects). Positional # arguments: # 1. Oneprovider location (IP address or domain). # 2. Space name (it must be supported by passed Oneprovider). # 3. Access token (can be obtained via Onezone). # 4. Number of files metadata to upload ("100" means 100 studies and 100 data # objects) # 5. Name of a directory (in space), where files with metadata should be # uploaded. Warning: if that directory already exists, it will be removed. # Example of usage: # python3 generate_demo_requests.py 172.17.0.16 s1 MDAzMvY...ZlOGCg 1000 ecrin1 # # Example studies and data objects can be found at # https://github.com/beatmix92/ct.gov_updated # import os import sys import subprocess import json from natsort import natsorted provider = sys.argv[1] space = sys.argv[2] token = sys.argv[3] files = int(sys.argv[4]) directory = sys.argv[5] studies_dir = 'studies' data_object_dir = 'data_objects' FNULL = open(os.devnull, 'w') curl = [ 'curl', '-k', '-H', 'X-Auth-Token: ' + token, '-H', 'X-CDMI-Specification-Version: 1.1.1', '-H', 'Content-Type: application/cdmi-container', '-X', 'DELETE', 'https://' + provider + '/cdmi/' + space + '/' + directory + '/' ] remove_dir_proc = subprocess.Popen(curl, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) remove_dir_proc.wait() curl = [ 'curl', '-k', '-H', 'X-Auth-Token: ' + token, '-H', 'X-CDMI-Specification-Version: 1.1.1', '-H', 'Content-Type: application/cdmi-container', '-X', 'PUT', 'https://' + provider + '/cdmi/' + space + '/' + directory + '/' ] create_dir_proc = subprocess.Popen(curl, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) create_dir_proc.wait() processes = [] for source in [studies_dir, data_object_dir]: index = 0 for (dirpath, _, filenames) in os.walk(source): filenames = natsorted(filenames) for filename in filenames[:files]: path = dirpath + '/' + filename with open(path, 'r') as json_file: metadata = json_file.read() metadata_json = json.loads(metadata) if metadata_json['object_type'] == 'study': linked_data_objects = metadata_json['linked_data_objects'] start_id = linked_data_objects[0]['id'] for i in range(1, 20): linked_data_objects.append({ 'id': start_id + i }) else: related_studies = metadata_json['related_studies'] start_id = related_studies[0]['id'] for i in range(1, 20): related_studies.append({ 'id': start_id - i }) curl = [ 'curl', '-k', '-H', 'X-Auth-Token: ' + token, '-H', 'X-CDMI-Specification-Version: 1.1.1', '-H', 'Content-Type: application/cdmi-object', '-X', 'PUT', '-d', '{"metadata": {"onedata_json": ' + json.dumps(metadata_json) + '}}', 'https://' + provider + '/cdmi/' + space + '/' + directory + '/' + filename ] processes.append(subprocess.Popen(curl, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)) for proc in processes: proc.wait()

33.824742

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0.643401

431

3,281

4.786543

0.357309

0.042656

0.032962

0.014542

0.329132

0.245274

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0.073529

0

null

0

null

0

1

0

6e0cbccdccc4307ec0cd8efe2c3cb65f9c612951

1,925

py

Python

backend/routes/user.py

mradzikowski/flask-trackerproductivity

029103b80e21b6c64801816fe8dc27585317cb02

[ "MIT" ]

null

backend/routes/user.py

mradzikowski/flask-trackerproductivity

029103b80e21b6c64801816fe8dc27585317cb02

[ "MIT" ]

null

backend/routes/user.py

mradzikowski/flask-trackerproductivity

029103b80e21b6c64801816fe8dc27585317cb02

[ "MIT" ]

null

from flask import jsonify, request import backend.services.user as user_services from . import bp @bp.route('/user', methods=['POST', 'GET']) def create_user(): if request.method == "POST": data_json = request.json body, status = user_services.create_user(data_json) elif request.method == "GET": body, status = user_services.get_all_users() else: body, status = None, 405 return jsonify(body), status @bp.route('/user/<pk>', methods=['GET', 'DELETE']) def get_user(pk): if request.method == "GET": body, status = user_services.get_user(pk) elif request.method == "DELETE": body, status = user_services.delete_user(pk) else: body, status = None, 405 return jsonify(body), status @bp.route('/user/<pk>/tasks', methods=['GET']) def get_all_tasks_for_user(pk): if request.method == "GET": active = request.args.get('active') if active is None: body, status = user_services.get_all_tasks_for_user(pk) if active.upper() == "TRUE": active = True elif active.upper() == "FALSE": active = False else: return {"success": False, "message": "Invalid argument key."}, 400 body, status = user_services.get_all_active_tasks_for_user(pk, active) else: body, status = None, 405 return jsonify(body), status @bp.route('/user/<pk>/tasks/productivity', methods=['GET']) def get_productivity_for_user(pk): if request.method == "GET": body, status = user_services.get_all_tasks_and_calculate_productivity(pk) else: body, status = None, 405 return jsonify(body), status @bp.route('/user/get/all', methods=['GET']) def get_all_users(): if request.methdod == "GET": body, status = user_services.get_all_users() else: body, status = None, 405 return jsonify(body), status

27.112676

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0.628052

250

1,925

4.664

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0.154374

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0.531732

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0.403087

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1,925

70

82

27.5

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0.269231

0

null

0

null

0

1

0

6e0db8ed1374b74b17dc4c64dad644332a33ce07

7,205

py

Python

src/modu/editable/datatypes/date.py

philchristensen/modu

795f3bc413956b98522ac514dafe35cbab0d57a3

[ "MIT" ]

null

src/modu/editable/datatypes/date.py

philchristensen/modu

795f3bc413956b98522ac514dafe35cbab0d57a3

[ "MIT" ]

null

src/modu/editable/datatypes/date.py

philchristensen/modu

795f3bc413956b98522ac514dafe35cbab0d57a3

[ "MIT" ]

null

# modu # Copyright (c) 2006-2010 Phil Christensen # http://modu.bubblehouse.org # # # See LICENSE for details """ Datatypes for managing stringlike data. """ import time, datetime from zope.interface import implements from modu.editable import IDatatype, define from modu.util import form, tags, date from modu.persist import sql from modu import persist, assets DAY = 86400 MONTH = DAY * 31 YEAR = DAY * 365 class CurrentDateField(define.definition): """ Display a checkbox that allows updating a date field with the current date. """ def get_element(self, req, style, storable): """ @see: L{modu.editable.define.definition.get_element()} """ value = getattr(storable, self.get_column_name(), None) if(value): output = date.strftime(value, self.get('format_string', '%B %d, %Y at %I:%M%p')) else: output = '' if(style == 'search'): frm = form.FormNode(self.name) return frm elif(style == 'listing'): frm = form.FormNode(self.name) if(self.get('date_in_listing', True)): if(output == ''): output = '(none)' frm(type='label', value=output) else: frm(type='checkbox', disabled=True, checked=bool(output)) return frm elif(style == 'detail' and self.get('read_only', False)): if(output == ''): output = '(none)' frm = form.FormNode(self.name) frm(type='label', value=output) return frm checked = False if(storable.get_id() == 0 and self.get('default_checked', False)): checked = True frm = form.FormNode(self.name)( type = 'checkbox', # this is only True if default_checked is true and it's a new item checked = checked, suffix = '  ' + tags.small()[output], ) if(bool(output)): if(self.get('one_time', True)): frm(attributes=dict(disabled='disabled')) else: frm( text = '  ' + tags.small(_class='minor-help')['check to set current date'] ) return frm def update_storable(self, req, form, storable): if(form[self.name].attr('checked', False)): value = datetime.datetime.now() save_format = self.get('save_format', 'timestamp') if(save_format == 'timestamp'): setattr(storable, self.get_column_name(), date.convert_to_timestamp(value)) else: setattr(storable, self.get_column_name(), value) return True class DateField(define.definition): """ Allow editing of date data via a multiple select interface or javascript popup calendar. """ implements(IDatatype) def get_element(self, req, style, storable): """ @see: L{modu.editable.define.definition.get_element()} """ value = getattr(storable, self.get_column_name(), None) if(isinstance(value, (int, long, float))): value = datetime.datetime.utcfromtimestamp(value) if(style == 'search'): frm = form.FormNode(self.name) frm['from'] = self.get_form_element(req, '_detail', storable)( prefix='<div>from date:', suffix=tags.br() + '</div>', ) frm['to'] = self.get_form_element(req, '_detail', storable)( prefix='<div>to date:', suffix='</div>', ) return frm elif(style == 'listing' or (style == 'detail' and self.get('read_only', False))): if(value): output = date.strftime(value, self.get('format_string', '%B %d, %Y at %I:%M%p')) else: output = '' frm = form.FormNode(self.name) frm(type='label', value=output) return frm current_year = datetime.datetime.now().year if(value is not None): current_year = getattr(value, 'year', current_year) start_year = self.get('start_year', current_year - 2) end_year = self.get('end_year', current_year + 5) months, days = date.get_date_arrays() frm = form.FormNode(self.name) frm(type='fieldset', style='brief') frm['null'](type='checkbox', text="no value", weight=-1, suffix=tags.br(), attributes=dict(onChange='enableDateField(this);')) assets.activate_jquery(req) req.content.report('header', tags.script(type='text/javascript')[""" function enableDateField(checkboxField){ var formItem = $(checkboxField).parent().parent(); if($(checkboxField).attr('checked')){ formItem.children(':enabled').attr('disabled', true); } else{ formItem.children(':disabled').attr('disabled', false); } } """]) attribs = {} if(value is None): frm['null'](checked=True) #attribs['disabled'] = None if(self.get('default_now', False)): value = datetime.datetime.now() frm['null'](checked=False) frm['date']( type = self.get('style', 'datetime'), value = value, attributes = attribs, suffix = tags.script(type="text/javascript")[""" enableDateField($('#form-item-%s input')); """ % self.name], ) frm.validate = self.validate return frm def validate(self, req, frm): if(not frm[self.name]['date'].attr('value', '') and self.get('required', False)): frm.set_error(self.name, 'You must enter a value for this field.') return False return True def update_storable(self, req, form, storable): """ @see: L{modu.editable.define.definition.update_storable()} """ save_format = self.get('save_format', 'timestamp') if(self.get('read_only')): if(self.get('default_now', False) and not storable.get_id()): if(save_format == 'timestamp'): setattr(storable, self.get_column_name(), int(time.time())) else: setattr(storable, self.get_column_name(), datetime.datetime.now()) return True data = form[self.name]['date'] if(data.attr('null', 0)): setattr(storable, self.get_column_name(), None) return True date_data = req.data[form.name][self.name].get('date', None) # if it's not a dict, it must be None, or broken if(isinstance(date_data, dict)): value = date.get_dateselect_value(date_data, self.get('style', 'datetime')) else: value = None if(save_format == 'timestamp'): setattr(storable, self.get_column_name(), date.convert_to_timestamp(value)) else: setattr(storable, self.get_column_name(), value) return True def get_search_value(self, value, req, frm): form_data = frm[self.name] to_value = 0 from_value = 0 if not(value['to'].get('null')): start_year = form_data['to']['date'].start_year end_year = form_data['to']['date'].end_year date_data = value['to'].get('date', None) if(date_data): to_value = date.get_dateselect_value(date_data, self.get('style', 'datetime'), start_year, end_year) to_value = time.mktime(to_value.timetuple()) if not(value['from'].get('null')): start_year = form_data['from']['date'].start_year end_year = form_data['from']['date'].end_year date_data = value['from'].get('date', None) if(date_data): from_value = date.get_dateselect_value(date_data, self.get('style', 'datetime'), start_year, end_year) from_value = time.mktime(from_value.timetuple()) if(to_value and from_value): if(self.get('save_format', 'timestamp') == 'datetime'): return sql.RAW('UNIX_TIMESTAMP(%%s) BETWEEN %s AND %s' % (from_value, to_value)) else: return sql.RAW('%%s BETWEEN %s AND %s' % (from_value, to_value)) elif(to_value): return sql.LT(to_value) elif(from_value): return sql.GT(from_value) else: return None

28.82

106

0.658015

988

7,205

4.673077

0.194332

0.047

0.02924

0.040936

0.455491

0.390513

0.34178

0.292831

0.261209

0.214425

0

0.0042

0.173907

7,205

249

107

28.935743

0.771505

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0.038308

0

1

0.03352

false

0

0.03352

0

0.178771

0

null

0

null

0

1

0

6e11fb05adb494991b86d4b22a22f936a7c8a876

1,908

py

Python

cactusbot/commands/magic/alias.py

CactusBot/CactusBot

6d035bf74bdc8f7fb3ee1e79f8d443f5b17e7ea5

[ "MIT" ]

23

2016-02-16T05:09:11.000Z

2016-09-20T14:22:51.000Z

cactusbot/commands/magic/alias.py

Alkali-Metal/CactusBot

6d035bf74bdc8f7fb3ee1e79f8d443f5b17e7ea5

[ "MIT" ]

190

2016-09-30T05:31:59.000Z

2018-12-22T08:46:49.000Z

cactusbot/commands/magic/alias.py

Alkali-Metal/CactusBot

6d035bf74bdc8f7fb3ee1e79f8d443f5b17e7ea5

[ "MIT" ]

16

2016-10-09T16:51:48.000Z

2017-10-25T05:29:10.000Z

"""Alias command.""" from . import Command from ...packets import MessagePacket class Alias(Command): """Alias command.""" COMMAND = "alias" @Command.command(role="moderator") async def add(self, alias: "?command", command: "?command", *_: False, raw: "packet"): """Add a new command alias.""" _, _, _, _, *args = raw.split() if args: packet_args = MessagePacket.join( *args, separator=' ').json["message"] else: packet_args = None response = await self.api.add_alias(command, alias, packet_args) if response.status == 201: return "Alias !{} for !{} created.".format(alias, command) elif response.status == 200: return "Alias !{} for command !{} updated.".format(alias, command) elif response.status == 404: return "Command !{} does not exist.".format(command) @Command.command(role="moderator") async def remove(self, alias: "?command"): """Remove a command alias.""" response = await self.api.remove_alias(alias) if response.status == 200: return "Alias !{} removed.".format(alias) elif response.status == 404: return "Alias !{} doesn't exist!".format(alias) @Command.command("list", role="moderator") async def list_aliases(self): """List all aliases.""" response = await self.api.get_command() if response.status == 200: commands = (await response.json())["data"] return "Aliases: {}.".format(', '.join(sorted( "{} ({})".format( command["attributes"]["name"], command["attributes"]["commandName"]) for command in commands if command.get("type") == "aliases" ))) return "No aliases added!"

32.338983

78

0.545597

191

1,908

5.387435

0.319372

0.116618

0.073858

0.061224

0.2138

0.137998

0

0.013544

0.303459

1,908

58

79

32.896552

0.760722

0.015199

0

0.15

0

0.162934

0

1

0

false

0

0.05

0

0.275

0

null

0

null

0

1

0

6e13a8102a55ae649fda3dcfedbae946ebff32c0

2,828

py

Python

explorer/util.py

brianhouse/rlab

4d878abd2299fd340a645ebd8b92a68c2b48f41e

[ "MIT" ]

null

explorer/util.py

brianhouse/rlab

4d878abd2299fd340a645ebd8b92a68c2b48f41e

[ "MIT" ]

null

explorer/util.py

brianhouse/rlab

4d878abd2299fd340a645ebd8b92a68c2b48f41e

[ "MIT" ]

null

import numpy as np def combine(signal_x, signal_y): return np.stack((signal_x, signal_y), axis=-1) def normalize(signal, minimum=None, maximum=None): """Normalize a signal to the range 0, 1. Uses the minimum and maximum observed in the data unless explicitly passed.""" signal = np.array(signal).astype('float') if minimum is None: minimum = np.min(signal) if maximum is None: maximum = np.max(signal) signal -= minimum maximum -= minimum signal /= maximum signal = np.clip(signal, 0.0, 1.0) return signal def resample(ts, values, num_samples): """Convert a list of times and a list of values to evenly spaced samples with linear interpolation""" assert np.all(np.diff(ts) > 0) ts = normalize(ts) return np.interp(np.linspace(0.0, 1.0, num_samples), ts, values) def smooth(signal, size=10, window='blackman'): """Apply weighted moving average (aka low-pass filter) via convolution function to a signal""" signal = np.array(signal) if size < 3: return signal s = np.r_[2 * signal[0] - signal[size:1:-1], signal, 2 * signal[-1] - signal[-1:-size:-1]] w = np.ones(size,'d') y = np.convolve(w / w.sum(), s, mode='same') return y[size - 1:-size + 1] def detect_peaks(signal, lookahead=10, delta=0): """ Detect the local maximas and minimas in a signal lookahead -- samples to look ahead from a potential peak to see if a bigger one is coming delta -- minimum difference between a peak and surrounding points to be considered a peak (no hills) and makes things faster Note: careful if you have flat regions, may affect lookahead """ signal = np.array(signal) peaks = [] valleys = [] min_value, max_value = np.Inf, -np.Inf for index, value in enumerate(signal[:-lookahead]): if value > max_value: max_value = value max_pos = index if value < min_value: min_value = value min_pos = index if value < max_value - delta and max_value != np.Inf: if signal[index:index + lookahead].max() < max_value: peaks.append([max_pos, max_value]) drop_first_peak = True max_value = np.Inf min_value = np.Inf if index + lookahead >= signal.size: break continue if value > min_value + delta and min_value != -np.Inf: if signal[index:index + lookahead].min() > min_value: valleys.append([min_pos, min_value]) drop_first_valley = True min_value = -np.Inf max_value = -np.Inf if index + lookahead >= signal.size: break return peaks, valleys

40.985507

132

0.597242

391

2,828

4.232737

0.340153

0.043505

0.042296

0.03142

0.101511

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0.049547

0

0.014141

0.299859

2,828

69

133

40.985507

0.821717

0.220297

0

0.142857

0

0.008353

0

0.017857

1

0.089286

false

0

0.017857

0.214286

0

null

0

null

0

1

0

6e14c71363bc33135f20b63aec47306b9531737a

2,839

py

Python

dooly/converters/kobart_utils.py

jinmang2/DOOLY

961c7b43b06dffa98dc8a39e72e417502e89470c

[ "Apache-2.0" ]

17

2022-03-06T05:06:14.000Z

2022-03-31T00:25:06.000Z

dooly/converters/kobart_utils.py

jinmang2/DOOLY

961c7b43b06dffa98dc8a39e72e417502e89470c

[ "Apache-2.0" ]

6

2022-03-27T18:18:40.000Z

2022-03-31T17:35:34.000Z

dooly/converters/kobart_utils.py

jinmang2/DOOLY

961c7b43b06dffa98dc8a39e72e417502e89470c

[ "Apache-2.0" ]

1

2022-03-31T13:07:41.000Z

import os import sys import hashlib import importlib def is_available_boto3(): return importlib.util.find_spec("boto3") if is_available_boto3(): import boto3 from botocore import UNSIGNED from botocore.client import Config else: raise ModuleNotFoundError("Please install boto3 with: `pip install boto3`.") class AwsS3Downloader(object): def __init__( self, aws_access_key_id=None, aws_secret_access_key=None, ): self.resource = boto3.Session( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, ).resource("s3") self.client = boto3.client( "s3", aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, config=Config(signature_version=UNSIGNED), ) def __split_url(self, url: str): if url.startswith("s3://"): url = url.replace("s3://", "") bucket, key = url.split("/", maxsplit=1) return bucket, key def download(self, url: str, local_dir: str): bucket, key = self.__split_url(url) filename = os.path.basename(key) file_path = os.path.join(local_dir, filename) os.makedirs(os.path.dirname(file_path), exist_ok=True) meta_data = self.client.head_object(Bucket=bucket, Key=key) total_length = int(meta_data.get("ContentLength", 0)) downloaded = 0 def progress(chunk): nonlocal downloaded downloaded += chunk done = int(50 * downloaded / total_length) sys.stdout.write( "\r{}[{}{}]".format(file_path, "█" * done, "." * (50 - done)) ) sys.stdout.flush() try: with open(file_path, "wb") as f: self.client.download_fileobj(bucket, key, f, Callback=progress) sys.stdout.write("\n") sys.stdout.flush() except Exception as e: # E722 do not use bare 'except' print(f"Exception occured: {e}.\ndownloading file is failed. {url}") return file_path def download(url, chksum=None, cachedir=".cache"): cachedir_full = os.path.join(os.getcwd(), cachedir) os.makedirs(cachedir_full, exist_ok=True) filename = os.path.basename(url) file_path = os.path.join(cachedir_full, filename) if os.path.isfile(file_path): if hashlib.md5(open(file_path, "rb").read()).hexdigest()[:10] == chksum: print(f"using cached model. {file_path}") return file_path, True s3 = AwsS3Downloader() file_path = s3.download(url, cachedir_full) if chksum: assert ( chksum == hashlib.md5(open(file_path, "rb").read()).hexdigest()[:10] ), "corrupted file!" return file_path, False

31.898876

80

0.610426

352

2,839

4.71875

0.34375

0.062613

0.036123

0.042143

0.145695

0.124022

0.077062

0

0.015429

0.269461

2,839

88

81

32.261364

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1

0.082192

false

0

0.109589

0.013699

0.273973

0.027397

0

null

0

null

0

1

0

6e15e9506e9a75c167124e23e066dc0069217190

1,565

py

Python

tests/uv/util/test_env.py

hartikainen/uv-metrics

7b47b8ce1dff5fc41cdd540f816ea41a0cd27c21

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

9

2020-06-17T17:33:05.000Z

2022-03-30T17:32:05.000Z

tests/uv/util/test_env.py

hartikainen/uv-metrics

7b47b8ce1dff5fc41cdd540f816ea41a0cd27c21

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

28

2020-06-16T18:32:08.000Z

2020-11-12T17:51:20.000Z

tests/uv/util/test_env.py

hartikainen/uv-metrics

7b47b8ce1dff5fc41cdd540f816ea41a0cd27c21

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

4

2020-08-07T20:05:49.000Z

2021-10-21T01:43:00.000Z

#!/usr/bin/python # # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import uv.util.env as ue def test_extract_params(monkeypatch): def mem_env(prefix): return { f"{prefix}_MY_KEY": "face", f"{prefix}_ANOTHER_KEY": "sandwich", f"{prefix}THIRD_KEY": "ham" } expected = {"my_key": "face", "another_key": "sandwich", "third_key": "ham"} # with various prefixes, a custom-supplied environment will return the # correctly parsed env variables. assert expected == ue.extract_params(prefix="ENVVAR", env=mem_env("ENVVAR")) assert expected == ue.extract_params(prefix="funky", env=mem_env("funky")) k = f"{ue._ENV_VAR_PREFIX}_RANDOM_KEY" v = "better_not_be_set" # make sure we don't have some random value set if os.environ.get(k): monkeypatch.delenv(k) # the environment should be empty. assert ue.extract_params() == {} # set our expected kv pair... monkeypatch.setenv(k, v) # and get it back from the env. assert ue.extract_params() == {"random_key": v}

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0.705431

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52

79

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0.263158

0

null

0

null

0

1

0

6e1651dd40e1ae6c43644b4a77456f4eb701c53a

1,054

py

Python

models/fleet.py

gnydick/qairon

e67af1f88ac6c614ae33adc4f42ab2ec3cc5b257

[ "MIT" ]

null

models/fleet.py

gnydick/qairon

e67af1f88ac6c614ae33adc4f42ab2ec3cc5b257

[ "MIT" ]

null

models/fleet.py

gnydick/qairon

e67af1f88ac6c614ae33adc4f42ab2ec3cc5b257

[ "MIT" ]

null

from sqlalchemy import * from sqlalchemy.orm import relationship from db import db class Fleet(db.Model): __tablename__ = "fleet" id = Column(String, primary_key=True) deployment_target_id = Column(String, ForeignKey('deployment_target.id')) fleet_type_id = Column(String, ForeignKey('fleet_type.id')) name = Column(String(256)) defaults = Column(Text) native_id = Column(String) deployment_target = relationship("DeploymentTarget", back_populates="fleets") subnets = relationship("Subnet", secondary='subnets_fleets', back_populates="fleets") type = relationship("FleetType", back_populates="fleets") capacities = relationship("Capacity", back_populates="fleet") def __repr__(self): return self.id @db.event.listens_for(Fleet, 'before_update') @db.event.listens_for(Fleet, 'before_insert') def my_before_insert_listener(mapper, connection, fleet): __update_id__(fleet) def __update_id__(fleet): fleet.id = ':'.join([fleet.deployment_target_id, fleet.fleet_type_id, fleet.name])

30.114286

89

0.736243

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1,054

5.682171

0.387597

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1,054

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0.130435

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0.826087

0

null

0

null

0

1

0

6e1773f3e2177f91fdf46e022af55af83edbbcb5

1,568

py

Python

logs/followup_email.py

vreyespue/Movie_Bot

192c74be62afcfda77a0984ff4da3014226c3432

[ "Apache-2.0" ]

26

2019-02-04T04:55:09.000Z

2021-09-22T14:58:46.000Z

logs/followup_email.py

vreyespue/Movie_Bot

192c74be62afcfda77a0984ff4da3014226c3432

[ "Apache-2.0" ]

2

2019-05-07T16:33:09.000Z

2021-02-13T18:25:35.000Z

logs/followup_email.py

vreyespue/Movie_Bot

192c74be62afcfda77a0984ff4da3014226c3432

[ "Apache-2.0" ]

27

2018-12-10T12:13:50.000Z

2020-10-11T17:43:22.000Z

################################################################### ######## Follow up email ############# ################################################################### """ followup_email.py This is special use case code written to assist bot developers. It consolidates topics that are not familiar to the bot and sends it in a nicely formatted email to the developers team. """ from email.mime.text import MIMEText from email.mime.image import MIMEImage from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email import encoders import smtplib import os,string,sys sys.path.append(os.path.normpath(os.getcwd())) from config import location SERVER = " " FROM = ["xxxx@gmail.com"] TO = ["xxxx@gmail.com"] # must be a list SUBJECT = "Follow up questions email" TEXT = """Hello, Here are the various questions users asked me today which I have no idea about. Could you help me learn these topics? Regards, Kelly """ msg = MIMEMultipart() msg['From'] = ", ".join(FROM) msg['To'] = ", ".join(TO) msg['Subject'] = SUBJECT body = TEXT msg.attach(MIMEText(body, 'plain')) filename = 'followup_file.TXT' attachment = open(location + 'followup_file.TXT', "rb") part = MIMEBase('application', 'octet-stream') part.set_payload((attachment).read()) encoders.encode_base64(part) part.add_header('Content-Disposition', "attachment; filename= %s" % filename) msg.attach(part) message = msg.as_string() server = smtplib.SMTP(SERVER) server.sendmail(FROM, TO, message) server.quit()

26.133333

122

0.646684

201

1,568

5.00995

0.557214

0.044687

0.051639

0

0.001497

0.147959

1,568

60

123

26.133333

0.752246

0.169643

0

0.028571

0.284192

0

1

0

false

0

0.228571

0

0.228571

0

null

0

null

0

1

0

6e1b6e602b092d059fb5b4b96bb130aa002770f4

1,213

py

Python

wiwo/sender.py

CoreSecurity/wiwo

44bd44b8ebea7e33105a7f4dac6480493cbb9623

[ "Apache-1.1" ]

76

2015-08-01T23:24:43.000Z

2018-07-02T11:13:16.000Z

wiwo/sender.py

6e726d/wiwo

44bd44b8ebea7e33105a7f4dac6480493cbb9623

[ "Apache-1.1" ]

1

2016-01-28T22:11:17.000Z

2016-02-03T22:14:46.000Z

wiwo/sender.py

6e726d/wiwo

44bd44b8ebea7e33105a7f4dac6480493cbb9623

[ "Apache-1.1" ]

27

2015-08-11T07:24:42.000Z

2018-10-05T11:09:54.000Z

#!/usr/bin/env python # -*- coding: iso-8859-15 -*- # # Copyright 2003-2015 CORE Security Technologies # # 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. # # Authors: # Andres Blanco (6e726d) # Andres Gazzoli # import ethernet import pcapy class Sender(object): @staticmethod def send(frame_obj, iface_name): """ Method that inject/send a frame. """ frame = frame_obj.get_packet() if len(frame) < ethernet.ETHERNET_MIN_SIZE: padding = "\x00" * (ethernet.ETHERNET_MIN_SIZE - len(frame)) frame += padding pd = pcapy.open_live(iface_name, ethernet.ETHERNET_MTU, 0, 100) pd.sendpacket(frame) return frame

28.880952

74

0.678483

164

1,213

4.95122

0.664634

0.073892

0.03202

0.039409

0

0.029979

0.230008

1,213

41

75

29.585366

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0.587799

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0

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0.083333

false

0

0.166667

0

0.416667

0

null

0

null

0

1

0

6e201007363380e4d643bfc71a7961525d34bdc2

4,073

py

Python

email_scrapper/readers/gmail_reader.py

datmellow/email-scrapper

614e99a4b33f3a0d3d85d5eb9c359818991673a6

[ "MIT" ]

2

2018-01-07T23:12:28.000Z

2018-01-10T00:58:17.000Z

email_scrapper/readers/gmail_reader.py

LucasCoderT/email-scrapper

614e99a4b33f3a0d3d85d5eb9c359818991673a6

[ "MIT" ]

null

email_scrapper/readers/gmail_reader.py

LucasCoderT/email-scrapper

614e99a4b33f3a0d3d85d5eb9c359818991673a6

[ "MIT" ]

1

2019-12-09T17:01:08.000Z

import base64 import datetime import email import logging import os import typing from email.message import Message from googleapiclient import errors from email_scrapper.models import Stores from email_scrapper.readers.base_reader import BaseReader logger = logging.getLogger(__name__) class GmailReader(BaseReader): SCOPES = ['https://www.googleapis.com/auth/gmail.readonly'] def __init__(self, service, user_id: str = "me", user_email: str = None, email_mapping: dict = None, date_from: datetime.datetime = None): """ Parameters ---------- service: The Gmail API service email_mapping: dict Mapping of class:Stores: to str representing the email to search from """ super(GmailReader, self).__init__(date_from=date_from, user_email=user_email, email_mapping=email_mapping) self.service = service self.user_id = user_id @classmethod def authenticate_with_browser(cls, credentials_json: dict = None, date_from: datetime.datetime = None): """ Login to gmail through the browser. Requires a credentials.json file or a credentials_json dict passed Returns ------- GmailReader """ try: from google_auth_oauthlib.flow import InstalledAppFlow from googleapiclient.discovery import build import pickle creds = None if os.path.exists('token.pickle'): with open('token.pickle', 'rb') as token: creds = pickle.load(token) if not creds or not creds.valid: if credentials_json: flow = InstalledAppFlow.from_client_config(credentials_json, GmailReader.SCOPES) else: flow = InstalledAppFlow.from_client_secrets_file( 'credentials.json', GmailReader.SCOPES) creds = flow.run_local_server(port=0) # Save the credentials for the next run with open('token.pickle', 'wb') as token: pickle.dump(creds, token) service = build('gmail', 'v1', credentials=creds) response = service.users().getProfile(userId="me").execute() return cls(service, user_id="me", user_email=response.get("emailAddress"), date_from=date_from) except (ImportError, ModuleNotFoundError): raise BaseException("Google Auth library not found") def _get_search_date_range(self): return self.search_date_range.strftime("%Y-%m-%d") def _get_email_details(self, message) -> Message: response = self.service.users().messages().get(userId=self.user_id, id=message['id'], format="raw").execute() msg_str = base64.urlsafe_b64decode(response['raw'].encode('ASCII')) mime_msg = email.message_from_bytes(msg_str) return mime_msg def _get_search_query(self, store: Stores, subject: str = None): return f"from:{self._get_store_email(store)} after:{self._get_search_date_range()}" def read_store_emails(self, store: Stores, subject: str = None) -> typing.Generator[str, None, None]: query = self._get_search_query(store, subject) try: response = self.service.users().messages().list(userId=self.user_id, q=query).execute() if 'messages' in response: for message in response['messages']: yield self._get_email_details(message) while 'nextPageToken' in response: page_token = response['nextPageToken'] response = self.service.users().messages().list(userId=self.user_id, q=query, pageToken=page_token).execute() for message in response['messages']: yield self._get_email_details(message) except errors.HttpError as error: print('An error occurred: %s' % error)

41.141414

117

0.615762

456

4,073

5.302632

0.335526

0.01737

0.016543

0.029777

0.163772

0.150538

0.126551

0.096774

0

0.002758

0.287749

4,073

98

118

41.561224

0.830748

0.076848

0

0.089552

0

0.088211

0.019786

0

1

0.089552

false

0

0.208955

0.029851

0.38806

0.014925

0

null

0

null

0

1

0

6e2255b8f77a18ad6776515831039d97cfa15e3a

748

py

Python

Advanced_algorithm/oj_test/test04.py

mndream/MyOJ

ee92fb657475d998e6c201f222cb20bcbc2bfd64

[ "Apache-2.0" ]

1

2018-12-27T08:06:38.000Z

Advanced_algorithm/oj_test/test04.py

mndream/MyPythonOJ

ee92fb657475d998e6c201f222cb20bcbc2bfd64

[ "Apache-2.0" ]

null

Advanced_algorithm/oj_test/test04.py

mndream/MyPythonOJ

ee92fb657475d998e6c201f222cb20bcbc2bfd64

[ "Apache-2.0" ]

null

''' A+B for Input-Output Practice (IV) 描述 Your task is to Calculate the sum of some integers. 输入 Input contains multiple test cases. Each test case contains a integer N, and then N integers follow in the same line. A test case starting with 0 terminates the input and this test case is not to be processed. 输出 For each group of input integers you should output their sum in one line, and with one line of output for each line in input. 输入样例 4 1 2 3 4 5 1 2 3 4 5 0 输出样例 10 15 ''' while(True): input_list = list(map(int, input().split())) # split()默认为所有的空字符，包括空格、换行(\n)、制表符(\t)等。 # 使用split(" ") 报RE n = input_list[0] if n == 0: break sum = 0 for i in range(n): sum = sum + input_list[i + 1] print(sum)

24.933333

91

0.669786

142

748

3.507042

0.528169

0.048193

0.012048

0.016064

0.02008

0

0.037037

0.241979

748

30

92

24.933333

0.84127

0.705882

0

1

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false

0

0.111111

0

null

0

null

0

1

0

6e22c62fbf96771a37ae5b157b23776e81cda2c5

2,421

py

Python

pre-processing/obtain_audio_spectrogram.py

GeWu-Lab/OGM-GE_CVPR2022

08b3f2498dd3e89f57fe9a12b5bf0c162eba1fbf

[ "MIT" ]

4

2022-03-06T17:57:24.000Z

2022-03-24T04:26:32.000Z

pre-processing/obtain_audio_spectrogram.py

GeWu-Lab/OGM-GE_CVPR2022

08b3f2498dd3e89f57fe9a12b5bf0c162eba1fbf

[ "MIT" ]

null

pre-processing/obtain_audio_spectrogram.py

GeWu-Lab/OGM-GE_CVPR2022

08b3f2498dd3e89f57fe9a12b5bf0c162eba1fbf

[ "MIT" ]

1

2022-03-31T08:12:15.000Z

import multiprocessing import os import os.path import pickle import librosa import numpy as np from scipy import signal def audio_extract(path, audio_name, audio_path, sr=16000): save_path = path samples, samplerate = librosa.load(audio_path) resamples = np.tile(samples, 10)[:160000] resamples[resamples > 1.] = 1. resamples[resamples < -1.] = -1. frequencies, times, spectrogram = signal.spectrogram(resamples, samplerate, nperseg=512, noverlap=353) spectrogram = np.log(spectrogram + 1e-7) mean = np.mean(spectrogram) std = np.std(spectrogram) spectrogram = np.divide(spectrogram - mean, std + 1e-9) assert spectrogram.shape == (257, 1004) save_name = os.path.join(save_path, audio_name + '.pkl') print(save_name) with open(save_name, 'wb') as fid: pickle.dump(spectrogram, fid) class Consumer(multiprocessing.Process): def __init__(self, task_queue): multiprocessing.Process.__init__(self) self.task_queue = task_queue def run(self): proc_name = self.name while True: next_task = self.task_queue.get() if next_task is None: # Poison pill means shutdown print('{}: Exiting'.format(proc_name)) self.task_queue.task_done() break # print(next_task) audio_extract(next_task[0], next_task[1], next_task[2]) self.task_queue.task_done() if __name__ == '__main__': # Establish communication queues tasks = multiprocessing.JoinableQueue() # Start consumers num_consumers = multiprocessing.cpu_count() print('Creating {} consumers'.format(num_consumers)) consumers = [ Consumer(tasks) for i in range(num_consumers) ] for w in consumers: w.start() # path='data/' save_dir = '/home/xiaokang_peng/data/AVE_av/audio_spec' if not os.path.exists(save_dir): os.mkdir(save_dir) path_origin = '/home/xiaokang_peng/data/AVE_av/audio' audios = os.listdir(path_origin) for audio in audios: audio_name = audio audio_path = os.path.join(path_origin, audio) tasks.put([save_dir, audio_name[:-4], audio_path]) # Add a poison pill for each consumer for i in range(num_consumers): tasks.put(None) # Wait for all of the tasks to finish tasks.join() print("ok")

28.482353

106

0.646014

312

2,421

4.807692

0.391026

0.036

0.043333

0.034

0.098667

0.070667

0.04

0

0.020868

0.247831

2,421

84

107

28.821429

0.802856

0.072284

0

0.033333

0

0.056747

0.035299

0

0.016667

1

0.05

false

0

0.116667

0

0.183333

0.066667

0

null

0

null

0

1

0

6e237945177ee47426cc1fcc873291dbba403f32

3,317

py

Python

src/protean/core/event_handler.py

mpsiva89/protean

315fa56da3f64178bbbf0edf1995af46d5eb3da7

[ "BSD-3-Clause" ]

null

src/protean/core/event_handler.py

mpsiva89/protean

315fa56da3f64178bbbf0edf1995af46d5eb3da7

[ "BSD-3-Clause" ]

null

src/protean/core/event_handler.py

mpsiva89/protean

315fa56da3f64178bbbf0edf1995af46d5eb3da7

[ "BSD-3-Clause" ]

null

import inspect import logging from protean.container import Element, OptionsMixin from protean.core.event import BaseEvent from protean.exceptions import IncorrectUsageError from protean.utils import DomainObjects, derive_element_class, fully_qualified_name from protean.utils.mixins import HandlerMixin logger = logging.getLogger(__name__) class BaseEventHandler(Element, HandlerMixin, OptionsMixin): """Base Event Handler to be inherited by all event handlers""" element_type = DomainObjects.EVENT_HANDLER class Meta: abstract = True @classmethod def _default_options(cls): aggregate_cls = ( getattr(cls.meta_, "aggregate_cls") if hasattr(cls.meta_, "aggregate_cls") else None ) return [ ("aggregate_cls", None), ("stream_name", aggregate_cls.meta_.stream_name if aggregate_cls else None), ("source_stream", None), ] def __new__(cls, *args, **kwargs): if cls is BaseEventHandler: raise TypeError("BaseEventHandler cannot be instantiated") return super().__new__(cls) def event_handler_factory(element_cls, **opts): element_cls = derive_element_class(element_cls, BaseEventHandler, **opts) if not (element_cls.meta_.aggregate_cls or element_cls.meta_.stream_name): raise IncorrectUsageError( { "_entity": [ f"Event Handler `{element_cls.__name__}` needs to be associated with an aggregate or a stream" ] } ) # Iterate through methods marked as `@handle` and construct a handler map # # Also, if `_target_cls` is an event, associate it with the event handler's # aggregate or stream methods = inspect.getmembers(element_cls, predicate=inspect.isroutine) for method_name, method in methods: if not ( method_name.startswith("__") and method_name.endswith("__") ) and hasattr(method, "_target_cls"): # `_handlers` is a dictionary mapping the event to the handler method. if method._target_cls == "$any": # This replaces any existing `$any` handler, by design. An Event Handler # can have only one `$any` handler method. element_cls._handlers["$any"] = {method} else: element_cls._handlers[fully_qualified_name(method._target_cls)].add( method ) # Associate Event with the handler's stream if inspect.isclass(method._target_cls) and issubclass( method._target_cls, BaseEvent ): # Order of preference: # 1. Stream name defined in event # 2. Stream name defined for the event handler # 3. Stream name derived from aggregate stream_name = element_cls.meta_.stream_name or ( element_cls.meta_.aggregate_cls.meta_.stream_name if element_cls.meta_.aggregate_cls else None ) method._target_cls.meta_.stream_name = ( method._target_cls.meta_.stream_name or stream_name ) return element_cls

36.855556

114

0.619234

363

3,317

5.38292

0.319559

0.06653

0.053736

0.052201

0.136643

0.085977

0

0.001306

0.307507

3,317

89

115

37.269663

0.849369

0.177872

0

0.032787

0

0.082288

0.008856

0

1

0.04918

false

0

0.114754

0

0.262295

0

null

0

null

0

1

0

6e246664f07a32e8eef7dfd24b7f3cda19fa9734

7,508

py

Python

read_prepare_data.py

jlu-ilr-hydro/IPCC-Repots-Focus-Overview

bf631975eb6c3ea2cf2f8fe9382e3361ad700a6e

[ "Apache-2.0" ]

null

read_prepare_data.py

jlu-ilr-hydro/IPCC-Repots-Focus-Overview

bf631975eb6c3ea2cf2f8fe9382e3361ad700a6e

[ "Apache-2.0" ]

null

read_prepare_data.py

jlu-ilr-hydro/IPCC-Repots-Focus-Overview

bf631975eb6c3ea2cf2f8fe9382e3361ad700a6e

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Sep 17 10:12:26 2021 @author: Florian Jehn """ import os import pandas as pd import numpy as np def read_ipcc_counts_temp(): """reads all counts of temperatures for all reports and makes on df""" files = os.listdir(os.getcwd()+os.sep+"Results"+ os.sep + "temperatures") all_df = pd.DataFrame() for file in files: file_df = pd.read_csv("Results" + os.sep + "temperatures" + os.sep + file, sep=";", index_col=0) file_df.columns = [file[:-4]] all_df = pd.concat([all_df, file_df], axis=1) return all_df.transpose() def read_ipcc_counts_rfc(): """reads all counts of reasons of concern for all reports and makes on df""" files = os.listdir(os.getcwd()+os.sep+"Results"+ os.sep + "reasons_for_concern") all_df = pd.DataFrame() for file in files: file_df = pd.read_csv("Results" + os.sep + "reasons_for_concern" + os.sep + file, sep=";", index_col=0) file_df.columns = [file[:-4]] all_df = pd.concat([all_df, file_df], axis=1) return all_df.transpose() def read_false_positive(): """reads in all the counted false/true positive rates for the temperatres in the IPCC and calculates a true positive rate for each entry""" files = os.listdir(os.getcwd()+os.sep+"Results"+ os.sep + "false_positive_check_files") all_df = pd.DataFrame() for file in files: # only read those files that contains the counting results if "results" not in file: continue file_df = pd.read_csv("Results" + os.sep + "false_positive_check_files" + os.sep + file, sep=",", index_col=0) # calculate the true positive rate file_df["True Positive Rate [%]"] = (file_df["n true positive"]/(file_df["n true positive"]+file_df["n false positive"]))*100 # Arange the df for seaborn file_df["Temperature [°C]"] = file_df.index file_df.reset_index(inplace=True, drop=True) all_df = pd.concat([all_df, file_df]) return all_df def scale_counts(ipcc_counts): """scale the counts by overall sum""" sums = ipcc_counts.sum(axis=1) for col in ipcc_counts: ipcc_counts[col] = ipcc_counts[col]/sums*100 return ipcc_counts def read_meta(): """reads in the meta data of the reports""" meta = pd.read_csv("Reports" + os.sep + "meta_data_reports.tsv", sep="\t") meta["Year"] = meta["Year"].astype("str") return meta def group_temps(ipcc_counts): """groups the temperatures into three categories""" ipcc_counts["0.5°C - 2°C"] = ipcc_counts[" 0.5°C"] + ipcc_counts[" 1°C"] + ipcc_counts[" 1.5°C"] +ipcc_counts[" 2°C"] ipcc_counts["2.5°C - 4°C"] = ipcc_counts[" 2.5°C"] + ipcc_counts[" 3°C"] + ipcc_counts[" 3.5°C"] +ipcc_counts[" 4°C"] ipcc_counts["≥ 4.5°C"] = ipcc_counts[" 4.5°C"] + ipcc_counts[" 5°C"] + ipcc_counts[" 5.5°C"] +ipcc_counts[" 6°C"] +ipcc_counts[" 6.5°C"] + ipcc_counts[" 7°C"] + ipcc_counts[" 7.5°C"] +ipcc_counts[" 8°C"] + ipcc_counts[" 8.5°C"] + ipcc_counts[" 9°C"] + ipcc_counts[" 9.5°C"] +ipcc_counts[" 10°C"] return ipcc_counts.iloc[:,20:] def merge_counts_meta(ipcc_counts, meta): """merges the df with the counted temperatures/rfcs with the metadata""" return pd.merge(meta, ipcc_counts, right_index=True, left_on="count_names") def lookup_names(): """"Returns lookup dict for different files names to merge them""" lookup_dict = { "IPCC_AR6_WGI_Full_Report":"counts_IPCC_AR6_WGI_Full_Report_parsed", "SROCC_FullReport_FINAL":"counts_SROCC_FullReport_FINAL_parsed", "210714-IPCCJ7230-SRCCL-Complete-BOOK-HRES":"counts_210714-IPCCJ7230-SRCCL-Complete-BOOK-HRES_parsed", "SR15_Full_Report_Low_Res":"counts_SR15_Full_Report_Low_Res_parsed", "SYR_AR5_FINAL_full":"counts_SYR_AR5_FINAL_full_wcover_parsed", "ipcc_wg3_ar5_full":"counts_ipcc_wg3_ar5_full_parsed", "WGIIAR5-PartA_FINAL":"counts_WGIIAR5-PartA_FINAL_parsed", "WGIIAR5-PartB_FINAL":"counts_WGIIAR5-PartB_FINAL_parsed", "WG1AR5_all_final":"counts_WG1AR5_all_final_parsed", "SREX_Full_Report-1":"counts_SREX_Full_Report-1_parsed", "SRREN_Full_Report-1":"counts_SRREN_Full_Report-1_parsed", "ar4_syr_full_report":"counts_ar4_syr_full_report_parsed", "ar4_wg2_full_report":"counts_ar4_wg2_full_report_parsed", "ar4_wg1_full_report-1":"counts_ar4_wg1_full_report-1_parsed", "ar4_wg3_full_report-1":"counts_ar4_wg3_full_report-1_parsed", "sroc_full-1":"counts_sroc_full-1_parsed", "srccs_wholereport-1":"counts_srccs_wholereport-1_parsed", "SYR_TAR_full_report":"counts_SYR_TAR_full_report_parsed", "WGII_TAR_full_report-2":"counts_WGII_TAR_full_report-2_parsed", "WGI_TAR_full_report":"counts_WGI_TAR_full_report_parsed", "WGIII_TAR_full_report":"counts_WGIII_TAR_full_report_parsed", "srl-en-1":"counts_srl-en-1_parsed", "srtt-en-1":"counts_srtt-en-1_parsedd", "emissions_scenarios-1":"counts_emissions_scenarios-1_parsed", "av-en-1":"counts_av-en-1_parsed", "The-Regional-Impact":"counts_The-Regional-Impact_parsed", "2nd-assessment-en-1":"counts_2nd-assessment-en-1_parsed", "ipcc_sar_wg_III_full_report":"counts_ipcc_sar_wg_III_full_report_parsed", "ipcc_sar_wg_II_full_report":"counts_ipcc_sar_wg_II_full_report_parsed", "ipcc_sar_wg_I_full_report":"counts_ipcc_sar_wg_I_full_report_parsed", "climate_change_1994-2":"counts_climate_change_1994-2_parsed", # "ipcc-technical-guidelines-1994n-1":"", # could not read in, but also contains no temp mentions "ipcc_wg_I_1992_suppl_report_full_report":"counts_ipcc_wg_I_1992_suppl_report_full_report_parsed", "ipcc_wg_II_1992_suppl_report_full_report":"counts_ipcc_wg_II_1992_suppl_report_full_report_parsed", "ipcc_90_92_assessments_far_full_report":"counts_ipcc_90_92_assessments_far_full_report_parsed", "ipcc_far_wg_III_full_report":"counts_ipcc_far_wg_III_full_report_parsed", "ipcc_far_wg_II_full_report":"counts_ipcc_far_wg_II_full_report_parsed", "ipcc_far_wg_I_full_report":"counts_ipcc_far_wg_I_full_report_parsed", } return lookup_dict def create_temp_keys(): """Creates a list of strings for all temperatures the paper looked at""" temps = [] for i,temp in enumerate(np.arange(0.5,10.1,0.5)): if i % 2 != 0: temps.append(" "+str(int(temp))+"°C") else: temps.append(" "+str(temp)+"°C" ) return temps def combine_all_raw_strings(): """combines all raw strings into one big file to search through""" reports = [file for file in os.listdir(os.getcwd() + os.sep + "Raw IPCC Strings") if file[-4:] == ".csv" ] all_reports = " " for report in reports: print("Starting with " + report) report_df = pd.read_csv(os.getcwd() + os.sep + "Raw IPCC Strings" + os.sep + report, sep="\t", usecols=[0]) report_list = report_df[report_df.columns[0]].tolist() report_str = " ".join([str(item) for item in report_list]) all_reports += report_str with open(os.getcwd() + os.sep + "Raw IPCC Strings" + os.sep + "all_ipcc_strings.csv", 'w', encoding='utf-8') as f: # this file is not included in the repository, as it is too large for Github f.write(all_reports) if __name__ == "__main__": combine_all_raw_strings()

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6e2666a6e406e4ebd7fe6e6904bdb4696b8d2f47

404

py

Python

has33.py

CombatPompano81/Python-Snippets-Galore

c2fb9c6ebef0477895749db9f2aa0f87132a72d6

[ "Apache-2.0" ]

null

has33.py

CombatPompano81/Python-Snippets-Galore

c2fb9c6ebef0477895749db9f2aa0f87132a72d6

[ "Apache-2.0" ]

null

has33.py

CombatPompano81/Python-Snippets-Galore

c2fb9c6ebef0477895749db9f2aa0f87132a72d6

[ "Apache-2.0" ]

null

# main function def has33(nums): # iterates through the list and tries to find two 3s next to each other for i in range(0, len(nums) - 1): # if indice i has a 3 and the indice next to it has a 3, print true if nums[i] == 3 and nums[i + 1] == 3: return print('True') return print('False') has33([1, 3, 3]) has33([3, 1, 3]) has33([3, 3, 3]) has33([1, 3, 1, 3])

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null

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17,692

py

Python

survos2/frontend/plugins/objects.py

DiamondLightSource/SuRVoS2

42bacfb6a5cc267f38ca1337e51a443eae1a9d2b

[ "MIT" ]

4

2017-10-10T14:47:16.000Z

2022-01-14T05:57:50.000Z

survos2/frontend/plugins/objects.py

DiamondLightSource/SuRVoS2

42bacfb6a5cc267f38ca1337e51a443eae1a9d2b

[ "MIT" ]

1

2022-01-11T21:11:12.000Z

2022-01-12T08:22:34.000Z

survos2/frontend/plugins/objects.py

DiamondLightSource/SuRVoS2

42bacfb6a5cc267f38ca1337e51a443eae1a9d2b

[ "MIT" ]

2

2018-03-06T06:31:29.000Z

2019-03-04T03:33:18.000Z

from survos2.config import Config import numpy as np from numpy.lib.function_base import flip from qtpy import QtWidgets from qtpy.QtWidgets import QPushButton, QRadioButton from survos2.frontend.components.base import * from survos2.frontend.components.entity import ( SmallVolWidget, TableWidget, setup_entity_table, setup_bb_table, ) from survos2.frontend.components.icon_buttons import IconButton from survos2.frontend.control import Launcher from survos2.frontend.plugins.base import * from survos2.frontend.plugins.plugins_components import MultiSourceComboBox from survos2.frontend.utils import FileWidget from survos2.improc.utils import DatasetManager from survos2.model import DataModel from survos2.server.state import cfg from survos2.frontend.plugins.features import FeatureComboBox from survos2.frontend.plugins.annotations import LevelComboBox from survos2.entity.patches import PatchWorkflow, organize_entities, make_patches class ObjectComboBox(LazyComboBox): def __init__(self, full=False, header=(None, "None"), parent=None): self.full = full super().__init__(header=header, parent=parent) def fill(self): params = dict(workspace=True, full=self.full) result = Launcher.g.run("objects", "existing", **params) logger.debug(f"Result of objects existing: {result}") if result: # self.addCategory("Points") for fid in result: if result[fid]["kind"] == "points": self.addItem(fid, result[fid]["name"]) elif result[fid]["kind"] == "boxes": self.addItem(fid, result[fid]["name"]) @register_plugin class ObjectsPlugin(Plugin): __icon__ = "fa.picture-o" __pname__ = "objects" __views__ = ["slice_viewer"] __tab__ = "objects" def __init__(self, parent=None): super().__init__(parent=parent) self.vbox = VBox(self, spacing=10) self.objects_combo = ComboBox() self.vbox.addWidget(self.objects_combo) self.existing_objects = {} self.objects_layout = VBox(margin=0, spacing=5) self.objects_combo.currentIndexChanged.connect(self.add_objects) self.vbox.addLayout(self.objects_layout) self._populate_objects() def _populate_objects(self): self.objects_params = {} self.objects_combo.clear() self.objects_combo.addItem("Add objects") params = dict( workspace=DataModel.g.current_session + "@" + DataModel.g.current_workspace ) result = Launcher.g.run("objects", "available", **params) print(result) logger.debug(f"objects available: {result}") if result: all_categories = sorted(set(p["category"] for p in result)) for i, category in enumerate(all_categories): self.objects_combo.addItem(category) self.objects_combo.model().item( i + len(self.objects_params) + 1 ).setEnabled(False) for f in [p for p in result if p["category"] == category]: self.objects_params[f["name"]] = f["params"] self.objects_combo.addItem(f["name"]) def add_objects(self, idx): logger.debug(f"Add objects with idx {idx}") if idx == 0 or idx == -1: return # self.objects_combo.setCurrentIndex(0) print(idx) order = idx - 2 if order == 1: params = dict( order=order, workspace=DataModel.g.current_session + "@" + DataModel.g.current_workspace, fullname="survos2/entity/blank_boxes.csv", ) else: params = dict( order=order, workspace=DataModel.g.current_session + "@" + DataModel.g.current_workspace, fullname="survos2/entity/blank_entities.csv", ) result = Launcher.g.run("objects", "create", **params) if result: objectsid = result["id"] objectsname = result["name"] objectsfullname = result["fullname"] objectstype = result["kind"] self._add_objects_widget( objectsid, objectsname, objectsfullname, objectstype, True ) def _add_objects_widget( self, objectsid, objectsname, objectsfullname, objectstype, expand=False ): logger.debug( f"Add objects {objectsid} {objectsname} {objectsfullname} {objectstype}" ) widget = ObjectsCard(objectsid, objectsname, objectsfullname, objectstype) widget.showContent(expand) self.objects_layout.addWidget(widget) src = DataModel.g.dataset_uri(objectsid, group="objects") with DatasetManager(src, out=None, dtype="uint32", fillvalue=0) as DM: src_dataset = DM.sources[0] src_dataset.set_metadata("fullname", objectsfullname) self.existing_objects[objectsid] = widget return widget def clear(self): for objects in list(self.existing_objects.keys()): self.existing_objects.pop(objects).setParent(None) self.existing_objects = {} def setup(self): self._populate_objects() params = dict( workspace=DataModel.g.current_session + "@" + DataModel.g.current_workspace ) result = Launcher.g.run("objects", "existing", **params) logger.debug(f"objects result {result}") if result: # Remove objects that no longer exist in the server print(self.existing_objects.keys()) for objects in list(self.existing_objects.keys()): if objects not in result: self.existing_objects.pop(objects).setParent(None) # Populate with new entity if any for entity in sorted(result): if entity in self.existing_objects: continue enitity_params = result[entity] objectsid = enitity_params.pop("id", entity) objectsname = enitity_params.pop("name", entity) objectsfullname = enitity_params.pop("fullname", entity) objectstype = enitity_params.pop("kind", entity) print(f"type: {objectstype}") if objectstype != "unknown": widget = self._add_objects_widget( objectsid, objectsname, objectsfullname, objectstype ) widget.update_params(params) self.existing_objects[objectsid] = widget else: logger.debug( "+ Skipping loading entity: {}, {}, {}".format( objectsid, objectsname, objectstype ) ) class ObjectsCard(Card): def __init__( self, objectsid, objectsname, objectsfullname, objectstype, parent=None ): super().__init__( title=objectsname, collapsible=True, removable=True, editable=True, parent=parent, ) self.objectsid = objectsid self.objectsname = objectsname self.object_scale = 1.0 self.objectsfullname = objectsfullname self.objectstype = objectstype self.widgets = {} self.filewidget = FileWidget(extensions="*.csv", save=False) self.filewidget.path.setText(self.objectsfullname) self.add_row(self.filewidget) self.filewidget.path_updated.connect(self.load_data) self.compute_btn = PushButton("Compute") self.view_btn = PushButton("View", accent=True) self.get_btn = PushButton("Get", accent=True) self._add_param("scale", title="Scale: ", type="Float", default=1) self._add_param("offset", title="Offset: ", type="FloatOrVector", default=0) self._add_param( "crop_start", title="Crop Start: ", type="FloatOrVector", default=0 ) self._add_param( "crop_end", title="Crop End: ", type="FloatOrVector", default=9000 ) self.flipxy_checkbox = CheckBox(checked=True) self.add_row(HWidgets(None, self.flipxy_checkbox, Spacing(35))) self.add_row(HWidgets(None, self.view_btn, self.get_btn, Spacing(35))) self.view_btn.clicked.connect(self.view_objects) self.get_btn.clicked.connect(self.get_objects) cfg.object_scale = self.widgets["scale"].value() cfg.object_offset = self.widgets["offset"].value() cfg.object_crop_start = self.widgets["crop_start"].value() cfg.object_crop_end = self.widgets["crop_end"].value() cfg.object_scale = 1.0 cfg.object_offset = (0,0,0) cfg.object_crop_start = (0,0,0) cfg.object_crop_end = (1e9,1e9,1e9) if self.objectstype == "patches": self._add_annotations_source() self.entity_mask_bvol_size = LineEdit3D(default=64, parse=int) self._add_feature_source() self.make_entity_mask_btn = PushButton("Make entity mask", accent=True) self.make_entity_mask_btn.clicked.connect(self.make_entity_mask) self.make_patches_btn = PushButton("Make patches", accent=True) self.make_patches_btn.clicked.connect(self.make_patches) self.add_row(HWidgets(None, self.entity_mask_bvol_size, self.make_entity_mask_btn, Spacing(35))) self.add_row(HWidgets(None, self.make_patches_btn, Spacing(35))) self.table_control = TableWidget() self.add_row(self.table_control.w, max_height=500) cfg.entity_table = self.table_control def _add_param(self, name, title=None, type="String", default=None): if type == "Int": p = LineEdit(default=default, parse=int) elif type == "Float": p = LineEdit(default=default, parse=float) elif type == "FloatOrVector": p = LineEdit3D(default=default, parse=float) elif type == "IntOrVector": p = LineEdit3D(default=default, parse=int) else: p = None if title is None: title = name if p: self.widgets[name] = p self.add_row(HWidgets(None, title, p, Spacing(35))) def load_data(self, path): self.objectsfullname = path print(f"Setting objectsfullname: {self.objectsfullname}") def card_deleted(self): params = dict(objects_id=self.objectsid, workspace=True) result = Launcher.g.run("objects", "remove", **params) if result["done"]: self.setParent(None) self.table_control = None def _add_annotations_source(self): self.annotations_source = LevelComboBox(full=True) self.annotations_source.fill() self.annotations_source.setMaximumWidth(250) widget = HWidgets( "Annotation:", self.annotations_source, Spacing(35), stretch=1 ) self.add_row(widget) def card_title_edited(self, newtitle): logger.debug(f"Edited entity title {newtitle}") params = dict(objects_id=self.objectsid, new_name=newtitle, workspace=True) result = Launcher.g.run("objects", "rename", **params) return result["done"] def view_objects(self): logger.debug(f"Transferring objects {self.objectsid} to viewer") cfg.ppw.clientEvent.emit( { "source": "objects", "data": "view_objects", "objects_id": self.objectsid, "flipxy": self.flipxy_checkbox.value(), } ) def update_params(self, params): if "fullname" in params: self.objectsfullname = params["fullname"] def _add_feature_source(self): self.feature_source = FeatureComboBox() self.feature_source.fill() self.feature_source.setMaximumWidth(250) widget = HWidgets("Feature:", self.feature_source, Spacing(35), stretch=1) self.add_row(widget) def get_objects(self): cfg.object_scale = self.widgets["scale"].value() cfg.object_offset = self.widgets["offset"].value() cfg.object_crop_start = self.widgets["crop_start"].value() cfg.object_crop_end = self.widgets["crop_end"].value() dst = DataModel.g.dataset_uri(self.objectsid, group="objects") print(f"objectsfullname: {self.objectsfullname}") params = dict( dst=dst, fullname=self.objectsfullname, scale=cfg.object_scale, offset=cfg.object_offset, crop_start=cfg.object_crop_start, crop_end=cfg.object_crop_end, ) logger.debug(f"Getting objects with params {params}") result = Launcher.g.run("objects", "update_metadata", workspace=True, **params) if self.objectstype == "points": tabledata, self.entities_df = setup_entity_table( self.objectsfullname, scale=cfg.object_scale, offset=cfg.object_offset, crop_start=cfg.object_crop_start, crop_end=cfg.object_crop_end, flipxy=self.flipxy_checkbox.value() ) elif self.objectstype == "boxes": tabledata, self.entities_df = setup_bb_table( self.objectsfullname, scale=cfg.object_scale, offset=cfg.object_offset, crop_start=cfg.object_crop_start, crop_end=cfg.object_crop_end, flipxy=self.flipxy_checkbox.value() ) elif self.objectstype == "patches": tabledata, self.entities_df = setup_entity_table( self.objectsfullname, scale=cfg.object_scale, offset=cfg.object_offset, crop_start=cfg.object_crop_start, crop_end=cfg.object_crop_end, flipxy=self.flipxy_checkbox.value() ) cfg.tabledata = tabledata self.table_control.set_data(tabledata) print(f"Loaded tabledata {tabledata}") self.table_control.set_data(tabledata) self.collapse() self.expand() def make_entity_mask(self): src = DataModel.g.dataset_uri(self.feature_source.value(), group="features") with DatasetManager(src, out=None, dtype="float32", fillvalue=0) as DM: src_array = DM.sources[0][:] entity_arr = np.array(self.entities_df) bvol_dim = self.entity_mask_bvol_size.value() entity_arr[:, 0] -= bvol_dim[0] entity_arr[:, 1] -= bvol_dim[1] entity_arr[:, 2] -= bvol_dim[2] from survos2.entity.entities import make_entity_mask gold_mask = make_entity_mask( src_array, entity_arr, flipxy=True, bvol_dim=bvol_dim )[0] # create new raw feature params = dict(feature_type="raw", workspace=True) result = Launcher.g.run("features", "create", **params) if result: fid = result["id"] ftype = result["kind"] fname = result["name"] logger.debug(f"Created new object in workspace {fid}, {ftype}, {fname}") dst = DataModel.g.dataset_uri(fid, group="features") with DatasetManager(dst, out=dst, dtype="float32", fillvalue=0) as DM: DM.out[:] = gold_mask cfg.ppw.clientEvent.emit( {"source": "objects_plugin", "data": "refresh", "value": None} ) def make_patches(self): src = DataModel.g.dataset_uri(self.feature_source.value(), group="features") with DatasetManager(src, out=None, dtype="float32", fillvalue=0) as DM: src_array = DM.sources[0][:] objects_scale = 1.0 entity_meta = { "0": { "name": "class1", "size": np.array((15, 15, 15)) * objects_scale, "core_radius": np.array((7, 7, 7)) * objects_scale, }, } entity_arr = np.array(self.entities_df) combined_clustered_pts, classwise_entities = organize_entities( src_array, entity_arr, entity_meta, plot_all=False ) wparams = {} wparams["entities_offset"] = (0, 0, 0) wparams["entity_meta"] = entity_meta wparams["workflow_name"] = "Make_Patches" wparams["proj"] = DataModel.g.current_workspace wf = PatchWorkflow( [src_array], combined_clustered_pts, classwise_entities, src_array, wparams, combined_clustered_pts ) src = DataModel.g.dataset_uri(self.annotations_source.value().rsplit("/", 1)[-1], group="annotations") with DatasetManager(src, out=None, dtype="uint16", fillvalue=0) as DM: src_dataset = DM.sources[0] anno_level = src_dataset[:] & 15 logger.debug(f"Obtained annotation level with labels {np.unique(anno_level)}") logger.debug(f"Making patches in path {src_dataset._path}") train_v_density = make_patches(wf, entity_arr, src_dataset._path, proposal_vol=(anno_level > 0)* 1.0, padding=self.entity_mask_bvol_size.value(), num_augs=0, max_vols=-1) self.patches = train_v_density cfg.ppw.clientEvent.emit( {"source": "panel_gui", "data": "view_patches", "patches_fullname": train_v_density} )

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37.642553

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0.052083

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0.018229

0

null

0

null

0

1

0

6e28319339ecb10a654afec47c04531f1e4fc2e5

5,459

py

Python

tests/benchmark/preprocess_img/preproc.py

mpascucci/AST-image-processing

54111e874237f0c146760d514eea96131177878a

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

6

2020-11-24T15:55:35.000Z

2021-12-31T11:52:56.000Z

tests/benchmark/preprocess_img/preproc.py

mpascucci/AST-image-processing

54111e874237f0c146760d514eea96131177878a

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

1

2020-11-24T15:46:15.000Z

tests/benchmark/preprocess_img/preproc.py

mpascucci/AST-image-processing

54111e874237f0c146760d514eea96131177878a

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

3

2021-02-04T10:08:43.000Z

2022-02-21T02:00:47.000Z

from tqdm import tqdm import os import glob import pickle import numpy as np from imageio import imread, imwrite import astimp from multiprocessing import Pool, cpu_count from functools import partial class ErrorInPreproc(Exception): pass class Dataset(): """Datasets consisting of several files in a given input_folder.""" def __init__(self, base_path, glob_patterns=('*.jpg', '*.JPG', '*.png', "*.PNG")): """base_path : path to the folder where the files are stored glob_patterns : a list of patterns for selecting files (e.g. ['*.jpg'])""" assert os.path.exists( base_path), "input folder '{}' not found".format(base_path) self.base_path = base_path self.paths = [] for pattern in glob_patterns: self.paths += glob.glob(os.path.join(base_path, pattern)) self.names = [os.path.basename(path).split('.')[0] for path in self.paths] class PreprocResults(): """Access to preprocessed pickled AST images""" def __init__(self, pickles_folder): if not os.path.exists(pickles_folder): raise FileNotFoundError("{} does not exit".format(pickles_folder)) self.pf = pickles_folder self.ds = Dataset(self.pf, glob_patterns=("*.pickle",)) self.names = self.ds.names errorlog_path = os.path.join(pickles_folder, "error_log.txt") if os.path.exists(errorlog_path): with open(errorlog_path, 'r') as f: lines = f.readlines() self.errors = {line.split(',')[0]: line.split(',')[ 1] for line in lines} else: self.errors = [] def get_by_name(self, name): """Load a pickle by name. Pickles have the same name than images example: 234_SLR_ESBL.jpg <-> 234_SLR_ESBL.jpg.pickle""" if name in self.errors and self.errors[name].split(" ") != 'INFO': raise ErrorInPreproc(self.errors[name].strip()) path = os.path.join(self.pf, name+'.pickle') if not os.path.exists(path): raise FileNotFoundError("Pickle {} not found.".format(path)) with open(path, 'rb') as f: p = pickle.load(f) return p def __getitem__(self, name): return self.get_by_name(name) def get_all(self): """Load all pickles in input folder""" output = [] for path in tqdm(self.ds.paths, desc="Loading pickles"): with open(path, 'rb') as f: p = pickle.load(f) output.append(p) return output def preprocess_one_image(path): img = np.array(imread(path)) # load image ast = astimp.AST(img) crop = ast.crop circles = ast.circles pellets = ast.pellets labels = ast.labels_text # create preprocessing object # NOTE the preprocessing object is not created it no pellets where found. preproc = ast.preproc if len(circles) != 0 else None pobj = {"ast":ast, "preproc": preproc, "circles": circles, "pellets": pellets, "labels": labels, "crop": crop, "fname": os.path.basename(path), "inhibitions": ast.inhibitions} return pobj def pickle_one_preproc(idx, output_path, image_paths, error_list, skip_existing=False, mute=True): if mute: log_function = lambda x : x else: log_function = tqdm.write path = image_paths[idx] try: # create output path fname = os.path.basename(path) # file name from path ofpath = os.path.join( output_path, f"{fname}.pickle") # output file path if skip_existing: # skip if output file exists already if os.path.exists(ofpath): return None # WARNING for an unknown reason the pickle call must be inside this function pobj = preprocess_one_image(path) with open(ofpath, 'wb') as f: pickle.dump(pobj, f) if len(pobj['circles']) == 0: # if no pellet found error_list[idx] = "INFO : {}, No pellets found".format(fname) log_function("No pellet found in {}".format(fname)) except Exception as e: ex_text = ', '.join(map(lambda x: str(x), e.args)) error_list[idx] = "{}, {}".format(fname, ex_text) log_function("Failed images: {} - {}".format(len(error_list), ex_text)) return None def preprocess(img_paths, output_path, skip_existing=False, parallel=True): """preprocess images and pickle the preproc object. img_paths : a list of paths of the image files.""" if not os.path.exists(output_path): os.mkdir(output_path) errors = [""]*len(img_paths) if parallel: jobs = cpu_count() print("Running in parallel on {} processes".format(jobs)) f = partial(pickle_one_preproc, image_paths=img_paths, output_path=output_path, error_list=errors, skip_existing=skip_existing ) with Pool(jobs) as p: list(tqdm(p.imap(f,range(len(img_paths))), total=len(img_paths))) errors = [e for e in errors if e != ""] else: for idx in tqdm(range(len(img_paths)), desc="Preprocessing"): pickle_one_preproc(idx, output_path, img_paths, errors, skip_existing, mute=False) return errors

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98

0.596263

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5,459

4.499289

0.257468

0.02466

0.022763

0.017072

0.067341

0.036674

0.018337

0

0.00283

0.287965

5,459

170

99

32.111765

0.810908

0.14325

0

0.078261

0

0.073446

0

0.008696

1

0.069565

false

0.008696

0.078261

0.008696

0.234783

0.008696

0

null

0

null

0

1

0

6e28b70b57732d2994e0b212e99122e11d61d96f

1,024

py

Python

src/main.py

Evelkos/PAM-and-CLARA

26fbb8d2d4a7924ce1d0d504c4b23bac38238c69

[ "MIT" ]

null

src/main.py

Evelkos/PAM-and-CLARA

26fbb8d2d4a7924ce1d0d504c4b23bac38238c69

[ "MIT" ]

null

src/main.py

Evelkos/PAM-and-CLARA

26fbb8d2d4a7924ce1d0d504c4b23bac38238c69

[ "MIT" ]

null

from clustering_algorithms import CLARA, PAM, get_initial_points from data_loaders import load_data from timer import Timer from visualizers import plot_data # FILENAME = "datasets/artificial/sizes3.arff" FILENAME = "datasets/artificial/zelnik4.arff" # FILENAME = "datasets/artificial/xclara.arff" # FILENAME = "datasets/real-world/glass.arff" def run_clara(data, points): clara = CLARA(points, len(data["classes"]), labels=data["classes"]) clara.run() return clara.get_result_df() def run_pam(data, points): pam = PAM(points, len(data["classes"]), labels=data["classes"]) pam.run() return pam.get_result_df() if __name__ == "__main__": data = load_data(FILENAME) # plot_data(data["df"], data["classes"], data["class_column"]) points = get_initial_points(data["df"], data["coordinates_columns"]) # result = run_clara(data, points) result = run_pam(data, points) plot_data( result, data["classes"], "cluster", attributes_names=data["coordinates_columns"] )

30.117647

88

0.709961

133

1,024

5.233083

0.323308

0.094828

0.112069

0.086207

0.106322

0

0.002296

0.149414

1,024

33

89

31.030303

0.796785

0.22168

0

0.154235

0.040455

0

1

0.1

false

0

0.2

0

0.4

0

null

0

null

0

1

0

6e2a9766e0a79f77304a55be682d4bc167bde209

4,459

py

Python

src/utils.py

zimonitrome/AbstractionNet

a037b696ccac015936d60026cb1ac4ebafc68371

[ "MIT" ]

null

src/utils.py

zimonitrome/AbstractionNet

a037b696ccac015936d60026cb1ac4ebafc68371

[ "MIT" ]

null

src/utils.py

zimonitrome/AbstractionNet

a037b696ccac015936d60026cb1ac4ebafc68371

[ "MIT" ]

null

import torch from einops import rearrange import svgwrite ########################################### # Normalization / Standardization functions ########################################### def normalize_functional(tensor: torch.Tensor, mean: list, std: list): """ Standardizes tensor in the channel dimension (dim -3) using mean and std. [... C H W] -> [... C H W] """ mean = torch.tensor(mean).view(-1, 1, 1).to(tensor.device) std = torch.tensor(std).view(-1, 1, 1).to(tensor.device) return (tensor-mean)/std def unnormalize_functional(tensor: torch.Tensor, mean: list, std: list): """ Un-standardizes tensor in the channel dimension (dim -3) using mean and std. Also clips the tensor to be in the range [0, 1]. [... C H W] -> [... C H W] """ mean = torch.tensor(mean).view(-1, 1, 1).to(tensor.device) std = torch.tensor(std).view(-1, 1, 1).to(tensor.device) return ((tensor*std)+mean).clamp(0, 1) def unnormalize_to(x, x_min, x_max): """ Linear normalization of x to [x_min, x_max]. In other words maps x.min() -> x_min and x.max() -> x_max. """ return x * (x_max - x_min) + x_min ############################ # Image convertion functions ############################ def rgba_to_rgb(rgba: torch.Tensor): """ Converts tensor from 3 channels into 4. Multiplies first 3 channels with the last channel. [... 4 H W] -> [... 3 H W] """ return rgba[..., :-1, :, :] * rgba[..., -1:, :, :] def rgb_to_rgba(rgb: torch.Tensor, fill: float = 1.0): """ Converts tensor from 4 channels into 3. Alpha layer will be filled with 1 by default, but can also be specified. [... 3 H W] -> [... 4 H W] """ alpha_channel = torch.full_like(rgb[..., :1, :, :], fill_value=fill) return torch.concat([rgb, alpha_channel], dim=-3) ########################################### # Alpha compositing/decompositing functions ########################################### def alpha_composite(base, added, eps=1e-8): """ Composite two tensors, i.e., layers `added` on top of `base`, where the last channel is assumed to be an alpha channel. [... C H W], [... C H W] -> [... C H W] """ # Separate color and alpha alpha_b = base[..., -1:, :, :] alpha_a = added[..., -1:, :, :] color_b = base[..., :-1, :, :] color_a = added[..., :-1, :, :] # https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending alpha_0 = (1 - alpha_a) * alpha_b + alpha_a color_0 = ((1-alpha_a) * alpha_b*color_b + alpha_a*color_a) / (alpha_0 + eps) # Re-combine new color and alpha return torch.concat([color_0, alpha_0], dim=-3) def alpha_composite_multiple(images_tensor): """ Composite tensor of N images into a single image. Assumes last channel is an alpha channel. [... N C H W] -> [... C H W] """ image_iterator = rearrange(images_tensor, "... N C H W -> N ... C H W") # Get first image compositioned_image = image_iterator[0] # Add the rest of the images for image in image_iterator[1:]: # TODO: Possibly need to add .copy() to prevent assignment error in autograd. compositioned_image = alpha_composite(compositioned_image, image) return compositioned_image def get_visible_mask(shapes): """ Inputs a set of rendered images where C > 1 and the last channel is an alpha channel. Assuming that images were to be compositioned first to last (N=0, 1, 2...), returns a mask for each image that show what pixels of that image is visible in the final composition. [... N C H W] -> [... N H W] """ shape_iterator = rearrange(shapes, "... N C H W -> N ... C H W").flip(0) accumulated_alpha = torch.zeros_like(shape_iterator[0,..., 0, :, :]) # empty like first image, single channel shape_maks = torch.zeros_like(shape_iterator[..., 0, :, :]) # empty image for each shape layer for i, shape in enumerate(shape_iterator): # a over b alpha compositioning # alpha_0 = (1 - alpha_a) * alpha_b + alpha_a # get b # alpha_b = (alpha_0 - alpha_a) / (1 - alpha_a) shape_alpha = shape[..., -1, :, :] alpha_visible = shape_alpha - accumulated_alpha * shape_alpha shape_maks[i] = alpha_visible accumulated_alpha = (1 - shape_alpha) * accumulated_alpha + shape_alpha return rearrange(shape_maks.flip(0), "N ... H W -> ... N H W").unsqueeze(-3)

36.54918

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0.589146

637

4,459

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0.241758

0.016503

0.00943

0.267584

0.262868

0.179961

0.140668

0.121022

0

0.019009

0.22135

4,459

122

114

36.54918

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0.389549

0

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0

0.032188

0

0.008197

0

1

0.190476

false

0

0.071429

0

0.452381

0

null

0

null

0

1

0

6e2c7487821c1b466bfeb152a868353bd01ba3f7

3,742

py

Python

CellMQ.py

edjuaro/cell-migration-quantification

b6479cc8525a1ac8bdaf0abfc66dec57de0be21e

[ "MIT" ]

null

CellMQ.py

edjuaro/cell-migration-quantification

b6479cc8525a1ac8bdaf0abfc66dec57de0be21e

[ "MIT" ]

null

CellMQ.py

edjuaro/cell-migration-quantification

b6479cc8525a1ac8bdaf0abfc66dec57de0be21e

[ "MIT" ]

null

import cv2 import numpy as np from skimage import draw from skimage import io # Read image im_in = cv2.imread("analyses/MDA231_stopper_1_c3.tif", cv2.IMREAD_GRAYSCALE); # Threshold. # Set values equal to or above 220 to 0. # Set values below 220 to 255. th, im_th = cv2.threshold(im_in, 20, 255, cv2.THRESH_BINARY_INV); # Copy the thresholded image. im_floodfill = im_th.copy() # Mask used to flood filling. # Notice the size needs to be 2 pixels than the image. h, w = im_th.shape[:2] mask = np.zeros((h+2, w+2), np.uint8) # Floodfill from point (0, 0) cv2.floodFill(im_floodfill, mask, (0,0), 255); # Invert floodfilled image im_floodfill_inv = cv2.bitwise_not(im_floodfill) # Combine the two images to get the foreground. im_out = im_th | im_floodfill_inv io.imsave(fname='temp_output.png', arr=im_out) # im_out_inv = cv2.bitwise_not(im_out) # dilate the mask: k_size = 2 k_half = k_size/2 kernel = np.ones((k_size,k_size),np.uint8) coords = draw.circle(k_half, k_half, k_half, shape=im_th.shape) kernel[coords] = 1 erosion = cv2.erode(im_out,kernel,iterations = 1) dilation = cv2.dilate(cv2.bitwise_not(erosion),kernel,iterations = 1) dilation = cv2.bitwise_not(dilation) # io.imshow(dilation) io.imsave(fname='mask.png', arr=dilation) # Display images. # io.imsave(fname='mask.png', arr=im_out) # # mostly from http://nickc1.github.io/python,/matlab/2016/05/17/Standard-Deviation-(Filters)-in-Matlab-and-Python.html # import cv2 # from skimage import draw # from skimage import io # filename = 'analyses/MDA231_stopper_1_c3.tif' # plate = io.imread(filename,as_grey=True) # image = plate # #io.imshow(image) # # io.imsave(fname='temp_output.png', arr=image) # import numpy as np # # img = cv2.imread('....') # Read in the image # sobelx = cv2.Sobel(image,cv2.CV_64F,1,0) # Find x and y gradients # sobely = cv2.Sobel(image,cv2.CV_64F,0,1) # # Find magnitude and angle # I2 = np.sqrt(sobelx**2.0 + sobely**2.0) # # angle = np.arctan2(sobely, sobelx) * (180 / np.pi) # # io.imshow(I2) # # io.imsave(fname='temp_output.png', arr=I2) # from scipy.ndimage.filters import uniform_filter # import numpy as np # def window_stdev(X, window_size): # c1 = uniform_filter(X, window_size, mode='reflect') # c2 = uniform_filter(X*X, window_size, mode='reflect') # return np.sqrt(c2 - c1*c1) # # x = np.arange(16).reshape(4,4).astype('float') # kernel_size = 3 # I1 = window_stdev(I2,kernel_size)*np.sqrt(kernel_size**2/(kernel_size**2 - 1)) # # io.imshow(I1) # # io.imsave(fname='temp_output.png', arr=I1) # from scipy.signal import medfilt2d # I1 = medfilt2d(I1, kernel_size=3) # # io.imshow(I1) # # io.imsave(fname='temp_output.png', arr=I1) # import numpy as np # from skimage.morphology import reconstruction # from skimage.exposure import rescale_intensity # # image = rescale_intensity(I1, in_range=(50, 200)) # image = I1 # seed = np.copy(image) # seed[1:-1, 1:-1] = image.max() # mask = image # filled = reconstruction(seed, mask, method='erosion') # io.imsave(fname='temp_output.png', arr=filled) # # kernel = np.zeros((80,80),np.uint8) # # coords = draw.circle(40, 40, 40, shape=image.shape) # # kernel[coords] = 1 # # erosion = cv2.erode(I1,kernel,iterations = 1) # # # io.imshow(erosion) # # # # kernel = np.ones((40,40),np.uint8) # # # # erosion = cv2.erode(I1,kernel,iterations = 1) # # # # io.imshow(erosion) # # # io.imsave(fname='temp_output.png', arr=erosion) # # from skimage.morphology import reconstruction # # fill = reconstruction(I1, erosion, method='erosion') # # # io.imshow(fill) # # # io.imsave(fname='temp_output.png', arr=fill) # # dilation = cv2.dilate(fill,kernel,iterations = 1) # # # io.imshow(dilation) # # io.imsave(fname='temp_output.png', arr=dilation)

27.925373

120

0.69829

601

3,742

4.231281

0.264559

0.034605

0.056233

0.060165

0.395596

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0.127802

0.070783

0

0.046671

0.141101

3,742

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121

27.925373

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0

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0

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0

0.181818

0

0.181818

0

null

0

null

0

1

0

6e2d9335521cea1ce24ba509b262882641d75542

1,344

py

Python

test/unit/messages/bloxroute/test_txs_message.py

dolphinridercrypto/bxcommon

8f70557c1dbff785a5dd3fcdf91176066e085c3a

[ "MIT" ]

12

2019-11-06T17:39:10.000Z

2022-03-01T11:26:19.000Z

test/unit/messages/bloxroute/test_txs_message.py

dolphinridercrypto/bxcommon

8f70557c1dbff785a5dd3fcdf91176066e085c3a

[ "MIT" ]

8

2019-11-06T21:31:11.000Z

2021-06-02T00:46:50.000Z

test/unit/messages/bloxroute/test_txs_message.py

dolphinridercrypto/bxcommon

8f70557c1dbff785a5dd3fcdf91176066e085c3a

[ "MIT" ]

5

2019-11-14T18:08:11.000Z

2022-02-08T09:36:22.000Z

from bxcommon.test_utils.abstract_test_case import AbstractTestCase from bxcommon.messages.bloxroute.txs_message import TxsMessage from bxcommon.models.transaction_info import TransactionInfo from bxcommon.test_utils import helpers from bxcommon.utils.object_hash import Sha256Hash class TxsMessageTests(AbstractTestCase): def test_txs_with_short_ids_message(self): txs_info = [ TransactionInfo(Sha256Hash(helpers.generate_bytearray(32)), helpers.generate_bytearray(200), 111), TransactionInfo(Sha256Hash(helpers.generate_bytearray(32)), helpers.generate_bytearray(300), 222), TransactionInfo(Sha256Hash(helpers.generate_bytearray(32)), helpers.generate_bytearray(400), 333) ] msg = TxsMessage(txs=txs_info) msg_bytes = msg.rawbytes() self.assertTrue(msg_bytes) parsed_msg = TxsMessage(buf=msg_bytes) self.assertTrue(parsed_msg) parsed_txs_info = parsed_msg.get_txs() self.assertEqual(len(parsed_txs_info), len(txs_info)) for index in range(len(txs_info)): self.assertEqual(parsed_txs_info[index].short_id, txs_info[index].short_id) self.assertEqual(parsed_txs_info[index].contents, txs_info[index].contents) self.assertEqual(parsed_txs_info[index].hash, txs_info[index].hash)

38.4

110

0.738095

164

1,344

5.780488

0.317073

0.088608

0.151899

0.126582

0.369198

0.341772

0.237342

0

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0.171131

1,344

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false

0

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0

0.304348

0

null

0

null

0

1

0

6e2fe086028f0377c018ceee95df734b7ae1f811

986

py

Python

BLAST/make_fasta.py

cdiaza/bootcamp

2fda661a44930f70ac8ef15218cc99d099fc4019

[ "MIT" ]

1

2021-01-16T20:39:41.000Z

BLAST/make_fasta.py

cdiaza/bootcamp

2fda661a44930f70ac8ef15218cc99d099fc4019

[ "MIT" ]

null

BLAST/make_fasta.py

cdiaza/bootcamp

2fda661a44930f70ac8ef15218cc99d099fc4019

[ "MIT" ]

1

2021-01-16T20:31:17.000Z

import random def format_fasta(title, sequence): """ This formats a fasta sequence Input: title - String - Title of the sequence sequence - String - Actual sequence Output: String - Fully formatted fasta sequence """ fasta_width = 70 # Number of characters in one line n_lines = 1 + len(sequence) // fasta_width # Number of lines lines = [ sequence[i*fasta_width: (i+1)*fasta_width] for i in range(n_lines)] lines = "\n".join(lines) formatted = f"> {title}\n{lines}\n\n" return formatted bases = "actg" # Bases for our randon protein # Writing random sequences in a file with open("random_sequences.fa", "w") as f: for length in range(1, 25): # Sequences of different lengths for run in range(10): # Trying several times title = f"length_{length} run_{run}" sequence = "".join(random.choices(bases, k=length)) f.write(format_fasta(title, sequence))

29.878788

81

0.631846

134

986

4.567164

0.447761

0.065359

0.052288

0.078431

0

0.01238

0.262677

986

32

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0.829436

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false

0

0.066667

0

0.2

0

null

0

null

0

1

0

6e330bec332cbcb5e47190df3547281fe5168a28

903

py

Python

tests/test_echo_server_contextvar.py

rednafi/think-async

3642afc0d8661b10affd953ce3b239f3e6b3009b

[ "MIT" ]

87

2021-04-14T09:51:30.000Z

2022-03-24T10:38:41.000Z

tests/test_echo_server_contextvar.py

rednafi/think-async

3642afc0d8661b10affd953ce3b239f3e6b3009b

[ "MIT" ]

3

2021-06-27T18:06:11.000Z

2022-03-24T19:56:38.000Z

tests/test_echo_server_contextvar.py

rednafi/think-async

3642afc0d8661b10affd953ce3b239f3e6b3009b

[ "MIT" ]

4

2021-05-12T01:36:14.000Z

2022-01-28T04:06:12.000Z

from unittest.mock import Mock, patch import pytest import patterns.echo_server_contextvar as main @patch.object(main, "client_addr_var", Mock()) def test_render_goodbye(capsys): # Call 'render_goodbye' goodbye_string = main.render_goodbye() print(goodbye_string) # Assert. out, err = capsys.readouterr() assert err == "" assert "Good bye, client @" in out @pytest.mark.asyncio @patch("patterns.echo_server_contextvar.asyncio.start_server", autospec=True) @patch("patterns.echo_server_contextvar.asyncio.sleep", autospec=True) async def test_server(mock_asyncio_sleep, mock_asyncio_start_server): stop_after = 5 # Call 'server()'. await main.server(stop_after=stop_after) # Assert. assert mock_asyncio_sleep.call_count == stop_after args = main.handle_request, "127.0.0.1", 8081 mock_asyncio_start_server.assert_called_once_with(*args)

25.8

77

0.743079

123

903

5.170732

0.430894

0.069182

0.084906

0.132075

0.125786

0

0.014342

0.150609

903

34

78

26.558824

0.814863

0.059801

0

0.164692

0.114929

0

0.210526

1

0.052632

false

0

0.157895

0

0.210526

0.052632

0

null

0

null

0

1

0

6e3355f7d36e6d39cee7c23d5acd90666f7629a8

693

py

Python

test.py

riquedev/SSLProxies24Feed

93ab23a6794ae7f40002eb464a9c443afe44db86

[ "MIT" ]

null

test.py

riquedev/SSLProxies24Feed

93ab23a6794ae7f40002eb464a9c443afe44db86

[ "MIT" ]

1

2017-09-15T13:27:09.000Z

2017-09-15T14:43:28.000Z

test.py

riquedev/SSLProxies24Feed

93ab23a6794ae7f40002eb464a9c443afe44db86

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Autor: rique_dev (rique_dev@hotmail.com) from SSLProxies24.Feed import Feed from SSLProxies24.Check import CheckProxy import time import gc # Recupera a listagem prx = Feed().PROXY_LIST # Inicia classe chk = CheckProxy() # Começa validação chk.validatelist(prx) # Ativa garbage gc.enable() time.sleep(30) # Contagem print('Sucesso: '+str(chk.getsucesscount())) print('Falhas: '+str(chk.getfailcount())) print('Total de Proxys: '+str(chk.getproxycount())) print('Restam: '+str(chk.getproxycount()-(chk.getsucesscount()+chk.getfailcount()))) # Lista de Proxys print(chk.getproxylist()) del prx del chk print('Classes eliminadas.') exit(0)

19.25

84

0.730159

93

693

5.408602

0.602151

0.047714

0.075547

0

0.012987

0.111111

693

36

85

19.25

0.803571

0.249639

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0.119141

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false

0

0.222222

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0.222222

0.333333

0

null

0

null

0

1

0

6e33da3d320ddccf5c2863568bc4b5fb0505e125

577

py

Python

euler.py

user3719431/tna_lab1

183c34d927c39f502fea7d6a81f2945104d7b75b

[ "MIT" ]

null

euler.py

user3719431/tna_lab1

183c34d927c39f502fea7d6a81f2945104d7b75b

[ "MIT" ]

null

euler.py

user3719431/tna_lab1

183c34d927c39f502fea7d6a81f2945104d7b75b

[ "MIT" ]

null

import math as m def yakobi(a, n, k): if a < 0: k *= pow(-1, (n - 1) // 2) yakobi(-a, n, k) if a % 2 == 0: k *= (-1) ** ((pow(n, 2) - 1) / 8) yakobi(a / 2, n, k) if a == 1: return k if a < n: k *= pow(-1, ((n - 1)(a - 1)) / 4) yakobi(n % a, a, k) def euler_test(p, x): if pow(x, (p - 1) / 2) % p == yakobi(x, p, k = 1): return bool(True) elif pow(x, (p - 1) / 2) % p - p == yakobi(x, p, k = 1): return bool(True) else: return bool(False)

24.041667

60

0.363951

99

577

2.111111

0.262626

0.038278

0.076555

0.07177

0.492823

0.425837

0.239234

0

0.067278

0.433276

577

24

61

24.041667

0.571865

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0.1

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0.1

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0

0.05

0

0.35

0

null

0

null

0

1

0

6e34180a8de5ed1a630ffd86a9a830130bbd1076

3,787

py

Python

src/b2d/hud_b2d.py

VgTajdd/neuroevolver

248c96b25ad936e15cfffc7a4223926db83ad540

[ "MIT" ]

null

src/b2d/hud_b2d.py

VgTajdd/neuroevolver

248c96b25ad936e15cfffc7a4223926db83ad540

[ "MIT" ]

null

src/b2d/hud_b2d.py

VgTajdd/neuroevolver

248c96b25ad936e15cfffc7a4223926db83ad540

[ "MIT" ]

null

## ========================================================================= ## ## Copyright (c) 2019 Agustin Durand Diaz. ## ## This code is licensed under the MIT license. ## ## hud_b2d.py ## ## ========================================================================= ## from core.hud_base import HudBase from enums import ScreenType, SimulationType from core.utils import getPathWithoutExtension, existsFile, getImageSize import settings class HudB2D(HudBase): def __init__(self, width, height): HudBase.__init__(self, width, height) def init(self): self.showFPS() self.addLabel((80, 30), (150, 30), 'Box2D') self.addButton((725, 40), (100, 50), 'Back', self.gotoMetamap) def gotoMetamap(self): self.m_manager.gotoScreen(ScreenType.META_MAP) class HudB2DNEATDIP(HudB2D): def __init__(self, width, height, params): self.params = params HudB2D.__init__(self, width, height) def init(self): self.showFPS() self.addLabel((75, 15), (150, 30), 'NEAT DIP') if 'isTraining' in self.params and self.params['isTraining']: self.addLabel((75, 45), (150, 30), str(self.params['currentStep']) + "/" + str(settings.NEAT_DIP_EVOLVING_STEPS)) else: imgPath = self.params['genomePath'] imgPath = getPathWithoutExtension(imgPath) + '.png' if existsFile(imgPath): size = getImageSize(imgPath) w, h = size if size[0] > 450: w = 450 if size[1] > 450: h = 450 self.addImage(((w/2) + 30, (h/2) + 30), (w, h), imgPath) self.addButton((770, 15), (60, 30), 'Back', self.gotoMetamap, alpha = 200) self.addButton((670, 15), (60, 30), 'Reset', self.resetDIP, alpha = 200) def resetDIP(self): self.m_manager.gotoScreen(ScreenType.SIMULATION, {'simulationType': SimulationType.NEAT_B2D_DIP}) class HudB2DNEATTIP(HudB2D): def __init__(self, width, height, params): self.params = params HudB2D.__init__(self, width, height) def init(self): self.showFPS() self.addLabel((75, 15), (150, 30), 'NEAT TIP') if 'isTraining' in self.params and self.params['isTraining']: self.addLabel((75, 45), (150, 30), str(self.params['currentStep']) + "/" + str(settings.NEAT_TIP_EVOLVING_STEPS)) else: imgPath = 'net_neat_tip.png' if existsFile(imgPath): size = getImageSize(imgPath) self.addImage(((size[0]/2) + 30, (size[1]/2) + 30), size, imgPath) self.addButton((770, 15), (60, 30), 'Back', self.gotoMetamap, alpha = 200) class HudB2DNEATWalker(HudB2D): def __init__(self, width, height, params): self.params = params HudB2D.__init__(self, width, height) def init(self): self.showFPS() self.addLabel((75, 15), (150, 30), 'NEAT Walker') if 'isTraining' in self.params and self.params['isTraining']: self.addLabel((75, 45), (150, 30), str(self.params['currentStep']) + "/" + str(settings.NEAT_WALKER_EVOLVING_STEPS)) else: imgPath = 'net_neat_walker.png' if existsFile(imgPath): size = getImageSize(imgPath) self.addImage(((size[0]/2) + 30, (size[1]/2) + 30), size, imgPath) self.addButton((770, 15), (60, 30), 'Back', self.gotoMetamap, alpha = 200)

44.034884

108

0.520201

393

3,787

4.880407

0.24173

0.067779

0.045881

0.079249

0.658498

0.647028

0.577164

0.553702

0

0.066743

0.311592

3,787

86

109

44.034884

0.668968

0.098495

0

0.528571

0

0.064087

0

1

0.142857

false

0

0.057143

0

0.257143

0

null

0

null

0

1

0

6e358277ee18f33ce73fddfacb850dc985cb0977

1,958

py

Python

grblc/search/gcn/parser/combine.py

youngsm/adsgrb

a89b56b371888deb67788a9f5a91300b281784a6

[ "MIT" ]

null

grblc/search/gcn/parser/combine.py

youngsm/adsgrb

a89b56b371888deb67788a9f5a91300b281784a6

[ "MIT" ]

null

grblc/search/gcn/parser/combine.py

youngsm/adsgrb

a89b56b371888deb67788a9f5a91300b281784a6

[ "MIT" ]

null

def get_final_txt(grb, tables, sentences, output_path): """ Combine the data from [grb]_final_sentences.txt and [grb]_final_tables.txt. If a piece of data in tables and another piece in sentecnes are originially from the same GCN. Put them in the same GCN in [grb]_final.txt. """ # Avoid modifying the data for the later use. tables = tables.copy() sentences = sentences.copy() # Open up the file. file = open(f"{output_path}{grb}/{grb}_final.txt", 'w') # Loop through the sentences and for each sentence, check if there is any table # that are originially from the same GCN. for sentence in sentences: # The number of the GCN. num = sentence['number'] # The final string that we dumps into the text file. result = "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=\n\n" result += f"GCN Number: {sentence['number']}\n\n" result += f"SENTENCE DATA:\n\n{sentence['sentences']}\n\n" # The variable to help check how many tables are from the same GCN. table_with_the_same_number = 0 # Loop through the tables to see if there are any tables in the same GCN. for idx, table in enumerate(tables): # If we find any tables in the same GCN. if table['number'] == num: if table_with_the_same_number == 0: result += "TABLE DATA:\n\n" table_with_the_same_number += 1 result += '\n'.join(table['table']) + '\n\n' tables.pop(idx) file.write(result) # Write the remaining tables to the text file. for table in tables: result = "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=\n\n" result += f"GCN Number: {table['number']}\n" result += "TABLE DATA:\n\n" + '\n'.join(table['table']) + '\n\n' file.write(result)

36.943396

88

0.550051

262

1,958

4.026718

0.278626

0.018957

0.056872

0.03981

0.267299

0.214218

0.045498

0

0.002135

0.282431

1,958

52

89

37.653846

0.748754

0.353422

0

0.173913

0

0.283049

0.188159

0.043478

0

1

0.043478

false

0

0.043478

0

null

0

null

0

1

0

6e35f3a7bd64997a4e302cd1d8e7454d8298b774

972

py

Python

hardware/headband.py

davidji/roundbot

2ca34a83c9feb3331f1b818106f06b3182c4970e

[ "Apache-2.0" ]

null

hardware/headband.py

davidji/roundbot

2ca34a83c9feb3331f1b818106f06b3182c4970e

[ "Apache-2.0" ]

null

hardware/headband.py

davidji/roundbot

2ca34a83c9feb3331f1b818106f06b3182c4970e

[ "Apache-2.0" ]

null

from solid import * from solid.utils import * import util from util import * from math import pi def headband(r1=64.0, r2=85.0, t=3.0, w=12.0): combe = right(r1-t/2)(linear_extrude(1)(square([1,1], center=True) + left(0.5)(circle(d=1)))) combe_spacing = 3.0 # mm combe_count = pi*r1/combe_spacing combes = union()(*[ rotate([0,0, i*180.0/combe_count])(combe) for i in range(-int(combe_count/2), int(combe_count/2))]) def arcshell(r, ends): start, end = ends return (arc(rad=r+t/6, start_degrees = start, end_degrees=end) - arc(rad=r-t/6, start_degrees = start, end_degrees=end)) return (linear_extrude(w)( offset(r=t/3)( arcshell(r1, (-90, 90)) + forward(r2 - r1)(arcshell(r2, (-130, -90))) + back(r2 - r1)(arcshell(r2, (90, 130))))) + combes) def export_scad(): util.save('headband', headband()) if __name__ == '__main__': export_scad()

31.354839

123

0.588477

152

972

3.618421

0.421053

0.072727

0.047273

0.050909

0.141818

0

0.07537

0.235597

972

30

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0

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0

1

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false

0

0.208333

0

0.416667

0

null

0

null

0

1

0

6e364089d40bdc8f90fe2c5aa5081ef11b937f59

3,482

py

Python

climlab/dynamics/meridional_advection_diffusion.py

nfeldl/climlab

2cabb49e2c3f54c1795f24338ef5ee44e49fc7e7

[ "BSD-3-Clause", "MIT" ]

160

2015-02-25T15:56:37.000Z

2022-03-14T23:51:23.000Z

climlab/dynamics/meridional_advection_diffusion.py

nfeldl/climlab

2cabb49e2c3f54c1795f24338ef5ee44e49fc7e7

[ "BSD-3-Clause", "MIT" ]

137

2015-12-18T17:39:31.000Z

2022-02-04T20:50:53.000Z

climlab/dynamics/meridional_advection_diffusion.py

nfeldl/climlab

2cabb49e2c3f54c1795f24338ef5ee44e49fc7e7

[ "BSD-3-Clause", "MIT" ]

54

2015-04-28T05:57:39.000Z

2022-02-17T08:15:11.000Z

r"""General solver of the 1D meridional advection-diffusion equation on the sphere: .. math:: \frac{\partial}{\partial t} \psi(\phi,t) &= -\frac{1}{a \cos\phi} \frac{\partial}{\partial \phi} \left[ \cos\phi ~ F(\phi,t) \right] \\ F &= U(\phi) \psi(\phi) -\frac{K(\phi)}{a} ~ \frac{\partial \psi}{\partial \phi} for a state variable :math:`\psi(\phi,t)`, arbitrary diffusivity :math:`K(\phi)` in units of :math:`x^2 ~ t^{-1}`, and advecting velocity :math:`U(\phi)`. :math:`\phi` is latitude and :math:`a` is the Earth's radius (in meters). :math:`K` and :math:`U` can be scalars, or optionally vector *specified at grid cell boundaries* (so their lengths must be exactly 1 greater than the length of :math:`\phi`). :math:`K` and :math:`U` can be modified by the user at any time (e.g., after each timestep, if they depend on other state variables). A fully implicit timestep is used for computational efficiency. Thus the computed tendency :math:`\frac{\partial \psi}{\partial t}` will depend on the timestep. In addition to the tendency over the implicit timestep, the solver also calculates several diagnostics from the updated state: - ``diffusive_flux`` given by :math:`-\frac{K(\phi)}{a} ~ \frac{\partial \psi}{\partial \phi}` in units of :math:`[\psi]~[x]`/s - ``advective_flux`` given by :math:`U(\phi) \psi(\phi)` (same units) - ``total_flux``, the sum of advective, diffusive and prescribed fluxes - ``flux_convergence`` (or instantanous scalar tendency) given by the right hand side of the first equation above, in units of :math:`[\psi]`/s Non-uniform grid spacing is supported. The state variable :math:`\psi` may be multi-dimensional, but the diffusion will operate along the latitude dimension only. """ from __future__ import division import numpy as np from .advection_diffusion import AdvectionDiffusion, Diffusion from climlab import constants as const class MeridionalAdvectionDiffusion(AdvectionDiffusion): """A parent class for meridional advection-diffusion processes. """ def __init__(self, K=0., U=0., use_banded_solver=False, prescribed_flux=0., **kwargs): super(MeridionalAdvectionDiffusion, self).__init__(K=K, U=U, diffusion_axis='lat', use_banded_solver=use_banded_solver, **kwargs) # Conversion of delta from degrees (grid units) to physical length units phi_stag = np.deg2rad(self.lat_bounds) phi = np.deg2rad(self.lat) self._Xcenter[...,:] = phi*const.a self._Xbounds[...,:] = phi_stag*const.a self._weight_bounds[...,:] = np.cos(phi_stag) self._weight_center[...,:] = np.cos(phi) # Now properly compute the weighted advection-diffusion matrix self.prescribed_flux = prescribed_flux self.K = K self.U = U class MeridionalDiffusion(MeridionalAdvectionDiffusion): """A parent class for meridional diffusion-only processes, with advection set to zero. Otherwise identical to the parent class. """ def __init__(self, K=0., use_banded_solver=False, prescribed_flux=0., **kwargs): # Just initialize the AdvectionDiffusion class with U=0 super(MeridionalDiffusion, self).__init__( U=0., K=K, prescribed_flux=prescribed_flux, use_banded_solver=use_banded_solver, **kwargs)

42.463415

143

0.661401

473

3,482

4.754757

0.365751

0.024011

0.040018

0.028012

0.169409

0.114718

0.066696

0

0.005117

0.214245

3,482

81

144

42.987654

0.816886

0.605112

0

0.352941

0

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0

1

0.058824

false

0

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0

0.235294

0

null

0

null

0

1

0

6e397c403213c314186ad9c8dc4d66123671cfea

620

py

Python

Day14/main.py

dloibl/AOC2021

80672a7ee8ebc1a7970c155e4e15e0ed2351e085

[ "MIT" ]

null

Day14/main.py

dloibl/AOC2021

80672a7ee8ebc1a7970c155e4e15e0ed2351e085

[ "MIT" ]

null

Day14/main.py

dloibl/AOC2021

80672a7ee8ebc1a7970c155e4e15e0ed2351e085

[ "MIT" ]

null

data = open("input.txt", "r").readlines() polymer = data[0] pair_insertion = {} for line in data[2:]: [token, replacement] = line.strip().split(" -> ") pair_insertion[token] = replacement result = [i for i in polymer.strip()] for step in range(0, 10): next = [] for i, si in enumerate(result): if i < len(result)-1: next.append(si) next.append(pair_insertion[result[i]+result[i+1]]) else: next.append(si) result = next count = [result.count(a) for a in set(pair_insertion.values())] print("The answer of part 1 is", max(count) - min(count))

23.846154

63

0.596774

90

620

4.066667

0.477778

0.142077

0.065574

0

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0.240323

620

25

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0

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0

1

0

false

0

0.055556

0

null

0

null

0

1

0

6e399f9876b8a0c8affd85f404dc546dcab1961f

1,199

py

Python

raster/migrations/0006_auto_20141016_0522.py

bpneumann/django-raster

74daf9d396f2332a2cd83723b7330e6b10d73b1c

[ "BSD-3-Clause" ]

null

raster/migrations/0006_auto_20141016_0522.py

bpneumann/django-raster

74daf9d396f2332a2cd83723b7330e6b10d73b1c

[ "BSD-3-Clause" ]

null

raster/migrations/0006_auto_20141016_0522.py

bpneumann/django-raster

74daf9d396f2332a2cd83723b7330e6b10d73b1c

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('raster', '0005_auto_20141014_0955'), ] operations = [ migrations.AddField( model_name='rastertile', name='tilex', field=models.IntegerField(null=True, db_index=True), preserve_default=True, ), migrations.AddField( model_name='rastertile', name='tiley', field=models.IntegerField(null=True, db_index=True), preserve_default=True, ), migrations.AddField( model_name='rastertile', name='tilez', field=models.IntegerField(db_index=True, null=True, choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9), (10, 10), (11, 11), (12, 12), (13, 13), (14, 14), (15, 15), (16, 16), (17, 17), (18, 18)]), preserve_default=True, ), migrations.AlterField( model_name='rastertile', name='level', field=models.IntegerField(null=True, db_index=True), ), ]

31.552632

236

0.539616

131

1,199

4.793893

0.419847

0.057325

0.121019

0.146497

0.457006

0.39172

0.324841

0

0.084827

0.301918

1,199

37

237

32.405405

0.665472

0.017515

0

0.548387

0

0.07568

0.019558

0

1

0

false

0

0.064516

0

0.16129

0

null

0

null

0

1

0

6e3b1af1bee45ddc7a412b33a2fead806c9ec302

1,765

py

Python

djangorecipebook/templating.py

tkhyn/djangorecipebook

2cbb3d46631630e2c7a3c511b504de2088aac115

[ "MIT" ]

null

djangorecipebook/templating.py

tkhyn/djangorecipebook

2cbb3d46631630e2c7a3c511b504de2088aac115

[ "MIT" ]

null

djangorecipebook/templating.py

tkhyn/djangorecipebook

2cbb3d46631630e2c7a3c511b504de2088aac115

[ "MIT" ]

null

""" Carry out template-based replacements in project files """ import os import sys from string import Template def replace_name(path, mapping): """ Handles replacement strings in the file or directory name """ # look for replacement strings in filename f_split = list(os.path.split(path)) name = f_split[1] if '${' in name: new_name = Template(name).substitute(mapping) new_path = os.path.join(f_split[0], new_name) os.rename(path, new_path) else: new_path = path return new_path def replace_ctnt(f, mapping): """ Handles replacement strings in the file content """ if not os.path.isfile(f): return try: # look for replacement strings in file t_file = open(f, 'r+') t = Template(t_file.read()) t_file.seek(0) t_file.write(t.substitute(mapping)) t_file.truncate() except Exception as e: sys.stderr.write(""" ERROR: while running template engine on file %s """ % f) raise e finally: t_file.close() def process(path, mapping): """ Performs all templating operations on the given path """ replace_ctnt(replace_name(path, mapping), mapping) def process_tree(directory, mapping): """ Performs all templating operations on the directory and its children """ directory = replace_name(directory, mapping) for dirpath, dirnames, filenames in os.walk(directory): for f in filenames: process(os.path.join(dirpath, f), mapping) for d in dirnames: dirnames.remove(d) dirnames.append(replace_name(os.path.join(dirpath, d), mapping))

25.214286

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0.607932

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1,765

4.691964

0.370536

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0.294051

1,765

69

78

25.57971

0.841091

0.204533

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0.043444

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false

0

0.076923

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0.230769

0

null

0

null

0

1

0

6e3c23f713b7a54ba361ed5b6913012fed253e5e

1,747

py

Python

toHash.py

ElTarget/-

fcf774386514a7f070be25d643be7bbf1a92af1e

[ "MIT" ]

1

2022-02-22T02:39:52.000Z

toHash.py

ElTarget/-

fcf774386514a7f070be25d643be7bbf1a92af1e

[ "MIT" ]

1

2022-03-08T04:46:17.000Z

toHash.py

ElTarget/get_malware_bazaar

fcf774386514a7f070be25d643be7bbf1a92af1e

[ "MIT" ]

null

import hashlib import os # 生成字符串的MD5值 def str2md5(content=None): if not content: return '' md5gen = hashlib.md5() md5gen.update(content.encode()) return md5gen.hexdigest() # 生成字符串的SHA256值 def str2sha256(content=None): if not content: return '' sha256gen = hashlib.sha256() sha256gen.update(content.encode()) return sha256gen.hexdigest() # 生成文件的MD5值 def file2md5(filename): hash_value = '' if os.path.exists(filename): try: md5obj = hashlib.md5() with open(filename, 'rb') as f: md5obj.update(f.read()) hash_value = md5obj.hexdigest() except Exception as e: print(e) return hash_value def file2sha256(filename): hash_value = '' if os.path.exists(filename): try: sha256obj = hashlib.sha256() with open(filename, "rb") as f: sha256obj.update(f.read()) hash_value = sha256obj.hexdigest() except Exception as e: print(e) return hash_value def file2sha1(filename): hash_value = '' if os.path.exists(filename): try: sha1obj = hashlib.sha1() with open(filename, 'rb') as f: sha1obj.update(f.read()) hash_value = sha1obj.hexdigest() except Exception as e: print(e) return hash_value def file2sha3(filename): hash_value = '' if os.path.exists(filename): try: sha3obj = hashlib.sha3_384() with open(filename, 'rb') as f: sha3obj.update(f.read()) hash_value = sha3obj.hexdigest() except Exception as e: print(e) return hash_value

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0.567258

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1,747

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0

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0.068966

0

null

0

null

0

1

0

6e3ec2b42c30f989802844d030b6a4725567d1ae

442

py

Python

config.py

benperove/oneliner.sh

0c6eb25f2dd32cdd5cc275ef5849b5e12c76e9db

[ "Apache-2.0" ]

4

2019-02-15T01:35:17.000Z

2020-07-08T17:47:33.000Z

config.py

benperove/oneliner.sh

0c6eb25f2dd32cdd5cc275ef5849b5e12c76e9db

[ "Apache-2.0" ]

1

2019-05-24T21:00:37.000Z

config.py

benperove/oneliner.sh

0c6eb25f2dd32cdd5cc275ef5849b5e12c76e9db

[ "Apache-2.0" ]

1

2020-04-10T08:03:16.000Z

import os #github login SITE = 'https://api.github.com' CALLBACK = 'https://oneliner.sh/oauth2' AUTHORIZE_URL = 'https://github.com/login/oauth/authorize' TOKEN_URL = 'https://github.com/login/oauth/access_token' SCOPE = 'user' #redis config REDIS_HOST = os.environ['REDIS_HOST'] #REDIS_HOST = 'localhost' REDIS_PORT = 6379 REDIS_DB = 0 DATA_DIR = 'oneliners' DEBUG = True #app SUBMISSION_PATH = 'incoming'

26

61

0.68552

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0.176471

442

16

62

27.625

0.788462

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0.418605

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1

0

false

0

0.083333

0

0.083333

0

null

0

null

0

1

0

6e4153ef83e21bf087ec6ed89dceeb002c6fc185

319

py

Python

examples/pybullet/examples/signedDistanceField.py

frk2/bullet3

225d823e4dc3f952c6c39920c3f87390383e0602

[ "Zlib" ]

27

2018-05-21T14:28:10.000Z

2021-12-31T03:12:35.000Z

examples/pybullet/examples/signedDistanceField.py

frk2/bullet3

225d823e4dc3f952c6c39920c3f87390383e0602

[ "Zlib" ]

1

2018-11-19T19:07:47.000Z

examples/pybullet/examples/signedDistanceField.py

frk2/bullet3

225d823e4dc3f952c6c39920c3f87390383e0602

[ "Zlib" ]

13

2019-11-08T12:48:44.000Z

2022-01-04T04:13:33.000Z

import pybullet as p import pybullet import time p.connect(p.GUI) p.loadURDF("toys/concave_box.urdf") p.setGravity(0,0,-10) for i in range (10): p.loadURDF("sphere_1cm.urdf",[i*0.02,0,0.5]) p.loadURDF("duck_vhacd.urdf") timeStep = 1./240. p.setTimeStep(timeStep) while (1): p.stepSimulation() time.sleep(timeStep)

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0.0625

0.097179

319

15

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0.732639

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0.065625

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1

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false

0

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0.214286

0

null

0

null

0

1

0

6e415d21c97c8bf5b7c0199061ba4f235f80c0f3

2,472

py

Python

Old/TitleTable.py

StephanM87/Sofie-Herrmann-Praktikum

3fa7e715061e35aade8eb93756c30ebf10971059

[ "MIT" ]

null

Old/TitleTable.py

StephanM87/Sofie-Herrmann-Praktikum

3fa7e715061e35aade8eb93756c30ebf10971059

[ "MIT" ]

2

2021-10-04T08:22:40.000Z

2021-10-05T13:30:02.000Z

Old/TitleTable.py

StephanM87/Sofie-Herrmann-Praktikum

3fa7e715061e35aade8eb93756c30ebf10971059

[ "MIT" ]

null

from pylatex import Document, Tabular, Section, NoEscape, Command, MultiRow from Old.BioCatHubDatenmodell import DataModel first_name = "some firstname" last_name = "some lastname" e_mail = "some@adress.com" institution = "some institution" vessel_type = "some vessel" volume = int(42) vol_unit = "mol/l" add_attributes = [{"Sektor": "Kruzifix"}, {"Bereich": "Eisheiligen"}] temp = int(42) temp_unit = "°C" ph_value = int(7) buffer = "some buffer" class PdfLibrary (Document): def __init__(self, data_model): self.biocathub_model = data_model def create_pdf(self): geometry_options = { "margin": "2cm", "includeheadfoot": True } doc = Document(page_numbers=True, geometry_options=geometry_options) doc.preamble.append(Command("title", self.biocathub_model["title"])) doc.append(NoEscape(r"\maketitle")) with doc.create(Section("User:")): with doc.create(Tabular("|c|c|")) as table: table.add_hline() table.add_row(["First Name", first_name]) table.add_hline() table.add_row(["Last Name", last_name]) table.add_hline() table.add_row(["E-Mail", e_mail]) table.add_hline() table.add_row(["Institution", institution]) table.add_hline() with doc.create(Section("Vessel:")): with doc.create(Tabular("|c|c|")) as table2: for i in DataModel["vessel"]: key = list(i.keys()) table2.add_row([key, i[key]]) table2.add_hline() with doc.create(Section("Condition:")): with doc.create(Tabular("|c|c|")) as table3: table3.add_hline() table3.add_row(["Temperature", temp]) table3.add_hline() table3.add_row(["Unit", temp_unit]) table3.add_hline() table3.add_row(["pH", ph_value]) table3.add_hline() table3.add_row(["Buffer", buffer]) table3.add_hline() for i in add_attributes: key = list(i.keys())[0] table3.add_row([key, i[key]]) table3.add_hline() doc.generate_pdf("Gesamt_Test", compiler="pdflatex", clean_tex=False) doc = PdfLibrary(DataModel) doc.create_pdf()

34.333333

76

0.552589

277

2,472

4.743682

0.33935

0.073059

0.059361

0.054795

0.275495

0.255708

0.097412

0

0.012995

0.315129

2,472

71

77

34.816901

0.762552

0

0.180328

0

0.114887

0

1

0.032787

false

0

0.032787

0

0.081967

0

null

0

null

0

1

0

6e41787cb64edb79c7312a9c056163a1f57400e3

535

py

Python

Lab2/la2_4.py

ThomCruz/ImageAnalysisLab

6a524696ecf4aab96336931d22ead8e8c9ec9e30

[ "MIT" ]

null

Lab2/la2_4.py

ThomCruz/ImageAnalysisLab

6a524696ecf4aab96336931d22ead8e8c9ec9e30

[ "MIT" ]

null

Lab2/la2_4.py

ThomCruz/ImageAnalysisLab

6a524696ecf4aab96336931d22ead8e8c9ec9e30

[ "MIT" ]

null

import cv2 import numpy as np import matplotlib.pyplot as plt pic = cv2.imread('image2.png',0) #pic = imageio.imread('img/parrot.jpg') gray = lambda rgb : np.dot(rgb[... , :3] , [0.299 , 0.587, 0.114]) gray = gray(pic) ''' log transform -> s = c*log(1+r) So, we calculate constant c to estimate s -> c = (L-1)/log(1+|I_max|) ''' max_ = np.max(gray) def log_transform(): return (255/np.log(1+max_)) * np.log(1+gray) plt.figure(figsize = (5,5)) plt.imshow(log_transform(), cmap = plt.get_cmap(name = 'gray')) plt.axis('off');

20.576923

67

0.637383

96

535

3.489583

0.541667

0.047761

0.035821

0

0.059603

0.153271

535

25

68

21.4

0.679912

0.071028

0

0.044156

0

1

0.083333

false

0

0.25

0.083333

0.416667

0

null

0

null

0

1

0

6e45ae2f0c35533b4360de6c8858cfc005287327

4,100

py

Python

metafilter/model/__init__.py

exhuma/metafilter

dfbc01877a3020f7fe58b9fda3e14ed073684f25

[ "BSD-3-Clause" ]

null

metafilter/model/__init__.py

exhuma/metafilter

dfbc01877a3020f7fe58b9fda3e14ed073684f25

[ "BSD-3-Clause" ]

null

metafilter/model/__init__.py

exhuma/metafilter

dfbc01877a3020f7fe58b9fda3e14ed073684f25

[ "BSD-3-Clause" ]

null

from ConfigParser import SafeConfigParser from cStringIO import StringIO import sqlalchemy from sqlalchemy import create_engine from sqlalchemy import MetaData from sqlalchemy.orm import sessionmaker from os.path import sep from hashlib import md5 from datetime import datetime, timedelta import re import logging import functools NON_LTREE = re.compile(r'[^a-zA-Z0-9/]') LOG = logging.getLogger(__name__) CONFIG = None metadata = MetaData() Session = sessionmaker() def loadconfig(filename): defaults=StringIO("""\ [cli_logging] error_log= """) config = SafeConfigParser() config.readfp(defaults) config.read(filename) dsn = config.get('database', 'dsn', None) if not dsn: raise ValueError('No DSN found in the config file! This is required!') set_dsn(dsn) return config class memoized(object): """Decorator that caches a function's return value each time it is called. If called later with the same arguments, the cached value is returned, and not re-evaluated. """ def __init__(self, func): self.func = func self.cache = {} def __call__(self, *args): obsoletion = datetime.now() - timedelta(seconds=60*5) if args in self.cache and self.cache[args][1] < obsoletion: # value too old. Remove it from the cache LOG.debug("Removing obsolete value for args %r from cache." % (args,)) del(self.cache[args]) try: output = self.cache[args][0] LOG.debug("Cache hit for args %r." % (args,)) return output except KeyError: LOG.debug("Initialising cache for args %r." % (args,)) value = self.func(*args) if isinstance(value, sqlalchemy.orm.query.Query): result = value.all() self.cache[args] = (result, datetime.now()) return result else: self.cache[args] = (value, datetime.now()) return value except TypeError: # uncachable -- for instance, passing a list as an argument. # Better to not cache than to blow up entirely. LOG.warning("Uncachable function call for args %r" % (args,)) return self.func(*args) def __repr__(self): """Return the function's docstring.""" return self.func.__doc__ def __get__(self, obj, objtype): """Support instance methods.""" return functools.partial(self.__call__, obj) def uri_depth(uri): "determines the depth of a uri" if not uri: return 0 if uri.endswith(sep): uri = uri[0:-1] return len(uri.split(sep)) def file_md5(path): """ Return the MD5 hash of the file """ hash = md5() fptr = open(path, "rb") chunk = fptr.read(1024) while chunk: hash.update(chunk) chunk = fptr.read(1024) fptr.close() return hash.hexdigest() def uri_to_ltree(uri): if not uri or uri == "/": return "ROOT" if uri.endswith(sep): uri = uri[0:-1] if uri.startswith(sep): ltree = "ROOT%s%s" % (sep, uri[1:]) else: ltree = uri # the ltree module uses "." as path separator. Replace dots by # underscores and path separators by dots ltree = NON_LTREE.sub("_", ltree) ltree = ltree.replace(sep, ".") return ltree def set_dsn(dsn): engine = create_engine(dsn) metadata.bind = engine Session.bind = engine from metafilter.model.nodes import Node from metafilter.model.queries import Query from metafilter.model.tags import Tag # # Parse the config file # from os.path import join, exists, expanduser from os import getcwd paths = [ join(getcwd(), 'config.ini'), join(expanduser("~"), '.metafilter', 'config.ini'), join('/', 'etc', 'metafilter', 'config.ini'), ] for path in paths: if not exists(path): continue LOG.debug('Reading config from %s' % path) CONFIG = loadconfig(path) if not CONFIG: LOG.error('Unable to open config file (search order: %s)' % (', '.join(paths)))

26.973684

83

0.621463

528

4,100

4.748106

0.354167

0.02513

0.025927

0.01436

0.033506

0.019146

0

0.008286

0.264146

4,100

151

84

27.152318

0.822672

0.135122

0

0.073395

0

0.115745

0

1

0.082569

false

0

0.155963

0

0.357798

0

null

0

null

0

1

0

6e46d398600e4b5a657c138522f24f0eef1938e9

3,067

py

Python

manager/base.py

monocleface/viewer

8ab47a9e846bd2716fe0208c34f33565513fc3f6

[ "Apache-2.0" ]

6

2020-02-28T21:18:16.000Z

2020-03-13T16:45:57.000Z

manager/base.py

monocleface/viewer

8ab47a9e846bd2716fe0208c34f33565513fc3f6

[ "Apache-2.0" ]

6

2020-02-28T12:42:52.000Z

2020-03-16T03:49:09.000Z

manager/base.py

monocleface/viewer

8ab47a9e846bd2716fe0208c34f33565513fc3f6

[ "Apache-2.0" ]

6

2020-03-05T13:04:25.000Z

2020-03-13T16:46:03.000Z

from pathlib import Path from typing import Union import yaml class Config(object): """Basic Config Class""" def __init__(self, cfg_yaml_path:str, root:str=".", data_path:str="./data"): r""" Configuration of Settings Args: root: root path of project, default="." data_path: data path that contains data directories cfg_yaml_path: argument file path(`str`) It will create directory automatically by `cfg_yaml_path`, ``` checkpoints └── data_type └── eval_type ├── exp_arg1 │ ├── exp1_summary │ ├── model_type + attr_type1 <-weights │ ├── model_type + attr_type2 │ └── model_type + attr_type3 ├── exp_arg2 └── exp_arg3 ``` `cfg_yaml_path` file shuould like below. ```yaml # confiugre.yaml type: data_type: mnist eval_type: roar model_type: resnet18 attr_type: ["vanillagrad", "gradcam"] ... ``` """ self.prj_path = Path(root) self.data_path = Path(data_path) with open(cfg_yaml_path, mode="r") as f: conf = yaml.load(f, Loader=yaml.FullLoader) # vars(self).update(conf) self.__dict__.update(conf) self.check_type_args() def check_type_args(self): r""" Check arguments and create experiment path """ type_args = self.conf["type_args"] check_types = ["data_type", "eval_type", "model_type", "attr_type"] for c_type in check_types: if not (c_type in type_args): raise KeyError(f"Configure file dosen't have {c_type}, check your argument file") self.exp_path = self.prj_path / "checkpoints" / type_args["data_type"] / type_args["eval_type"] self.check_dir_exist(self.exp_path) def check_dir_exist(self, path:Union[str, Path], file:bool=False): r""" Check directory file is exists, if not exists will create one Args: path: `str` or `pathlib.Path` type file: if True, will create a file, not a directory path """ if not isinstance(path, Path): path = Path(path) if file: if not path.exists(): path.touch() print(f"Given path doesn't exists, created {path}") else: if not path.exists(): path.mkdir(parents=True) print(f"Given path doesn't exists, created {path}") @property def conf(self): return self.__dict__ class Checkpoints(object): """Model Checkpoint Manager""" def __init__(self, cfg): r""" Save details about model weights and summaries """ def save_model(self): r""" Save model weights """ def save_summary(self): r""" Save training stats """

29.209524

103

0.538637

369

3,067

4.355014

0.330623

0.034848

0.034225

0.017424

0.092097

0.047293

0

0.004557

0.356048

3,067

104

29.490385

0.793418

0.345615

0

0.093023

0

0.141081

0

1

0.162791

false

0

0.069767

0.023256

0.302326

0.046512

0

null

0

null

0

1

0

6e486d2de9698c2208f5c29100b107e8de344209

307

py

Python

007 - Intro List Comprehension.py/016 - Maior.py

rodrigoviannini/meus_Primeiros_Codigos

828dec1c4ce06889efd491145e631c30a45e858f

[ "MIT" ]

2

2021-07-22T23:26:54.000Z

2021-07-22T23:27:27.000Z

007 - Intro List Comprehension.py/016 - Maior.py

rodrigoviannini/meus_Primeiros_Codigos

828dec1c4ce06889efd491145e631c30a45e858f

[ "MIT" ]

null

007 - Intro List Comprehension.py/016 - Maior.py

rodrigoviannini/meus_Primeiros_Codigos

828dec1c4ce06889efd491145e631c30a45e858f

[ "MIT" ]

null

""" List Comprehension Aninhada OBJ: Encontrar o maior ou os maiores números de uma lista e imprimir outra lista """ listaGenerica = [1, 2, 3, 4, 1, 2, 3, 4, 10, 10, 10, 5, 3, -4] listaMaior = [x for x in listaGenerica if not False in [True if x >= y else False for y in listaGenerica]] print(listaMaior)

30.7

106

0.693811

55

307

3.872727

0.636364

0.028169

0.037559

0

0.069106

0.198697

307

10

107

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0.796748

0.351792

0

1

0

false

0

0.333333

0

null

0

null

0

1

0

6e487df26dabde97ea3f1c6bd9a631bd068d4b7f

357

py

Python

thehardway/practice3.py

sunquan9301/pythonLearn

f10760a4e32c3ac267e39d835c08f45800d081b6

[ "Apache-2.0" ]

null

thehardway/practice3.py

sunquan9301/pythonLearn

f10760a4e32c3ac267e39d835c08f45800d081b6

[ "Apache-2.0" ]

null

thehardway/practice3.py

sunquan9301/pythonLearn

f10760a4e32c3ac267e39d835c08f45800d081b6

[ "Apache-2.0" ]

null

def main(): # age = input("How old are you?") # print("I am %s year old" % age) file = open("demo1") lines = file.readlines() print("lines",lines) for i in range(len(lines)): print(lines[i]) file.close() c,d = addOne(1,2) print(c,d) def addOne(a,b): return a+1, b+1 if __name__ == '__main__': main()

17

37

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56

357

3.267857

0.589286

0.10929

0

0.019531

0.282913

357

20

38

17.85

0.695313

0.176471

0

0.061856

0

1

0.153846

false

0

0.076923

0.230769

0

null

0

null

0

1

0

6e4b454f9d9a661e964992d4f53efcc35fd88de8

651

py

Python

ipt/td1/3.3-nbracines.py

lucas8/MPSI

edefa2155071910d95633acf87b9f3a9d34f67d3

[ "MIT" ]

null

ipt/td1/3.3-nbracines.py

lucas8/MPSI

edefa2155071910d95633acf87b9f3a9d34f67d3

[ "MIT" ]

null

ipt/td1/3.3-nbracines.py

lucas8/MPSI

edefa2155071910d95633acf87b9f3a9d34f67d3

[ "MIT" ]

null

#!/usr/bin/python3 def nbracines(a, b, c): if a == 0: print("Le coefficient dominant est nul, ce n'est pas un trinome !") return d = b*b - 4*a*c k = 2 if abs(d) < 1e-10: k = 1 d = 0 elif d < 0: k = 0 print("Le polynome " + str(a) + "X^2 + " + str(b) + "X + " + str(c) + " admet " + str(k) + " racines distinctes (det = " + str(d) + ")") a = float(input("Entrez le coefficient dominant du trinome : ")) b = float(input("Entrez le coefficient d'ordre 1 du trinome : ")) c = float(input("Entrez la constante du trinome : ")) nbracines(a, b, c) nbracines(0, 3, 1) nbracines(1, 0.2, 0.01)

28.304348

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0.537634

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651

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0

0.111111

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null

0

null

0

1

0

6e52fb33dd28eee7b106bc48ba5c34f08261ca0b

2,309

py

Python

src/pynorare/__main__.py

concepticon/pynorare

3cf5ea2d1597c5acc84963f781ff49d96b4d7e02

[ "MIT" ]

null

src/pynorare/__main__.py

concepticon/pynorare

3cf5ea2d1597c5acc84963f781ff49d96b4d7e02

[ "MIT" ]

5

2020-07-20T11:05:07.000Z

2022-03-11T15:51:52.000Z

src/pynorare/__main__.py

concepticon/pynorare

3cf5ea2d1597c5acc84963f781ff49d96b4d7e02

[ "MIT" ]

null

""" Main command line interface to the pynorare package. """ import sys import pathlib import contextlib from cldfcatalog import Config, Catalog from clldutils.clilib import register_subcommands, get_parser_and_subparsers, ParserError, PathType from clldutils.loglib import Logging from pyconcepticon import Concepticon from pynorare import NoRaRe import pynorare.commands def main(args=None, catch_all=False, parsed_args=None): try: # pragma: no cover repos = Config.from_file().get_clone('concepticon') except KeyError: # pragma: no cover repos = pathlib.Path('.') parser, subparsers = get_parser_and_subparsers('norare') parser.add_argument( '--repos', help="clone of concepticon/concepticon-data", default=repos, type=PathType(type='dir')) parser.add_argument( '--repos-version', help="version of repository data. Requires a git clone!", default=None) parser.add_argument( '--norarepo', default=pathlib.Path('.'), type=PathType(type='dir')) register_subcommands(subparsers, pynorare.commands) args = parsed_args or parser.parse_args(args=args) if not hasattr(args, "main"): # pragma: no cover parser.print_help() return 1 with contextlib.ExitStack() as stack: stack.enter_context(Logging(args.log, level=args.log_level)) if args.repos_version: # pragma: no cover # If a specific version of the data is to be used, we make # use of a Catalog as context manager: stack.enter_context(Catalog(args.repos, tag=args.repos_version)) args.repos = Concepticon(args.repos) args.api = NoRaRe(args.norarepo, concepticon=args.repos) args.log.info('norare at {0}'.format(args.repos.repos)) try: return args.main(args) or 0 except KeyboardInterrupt: # pragma: no cover return 0 except ParserError as e: # pragma: no cover print(e) return main([args._command, '-h']) except Exception as e: # pragma: no cover if catch_all: # pragma: no cover print(e) return 1 raise if __name__ == '__main__': # pragma: no cover sys.exit(main() or 0)

32.985714

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0.644435

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2,309

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33.463768

0.844898

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0

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0.055556

0

null

0

null

0

1

0

6e53df58b8e50b1065505ed5b573aa01243270d1

12,263

py

Python

yolov3_deepsort.py

h-enes-simsek/deep_sort_pytorch

0a9ede55e53355c19455197cc8daa60336c652bb

[ "MIT" ]

1

2021-02-28T15:22:43.000Z

yolov3_deepsort.py

h-enes-simsek/deep_sort_pytorch

0a9ede55e53355c19455197cc8daa60336c652bb

[ "MIT" ]

null

yolov3_deepsort.py

h-enes-simsek/deep_sort_pytorch

0a9ede55e53355c19455197cc8daa60336c652bb

[ "MIT" ]

null

import os import cv2 import time import argparse import torch import warnings import numpy as np from detector import build_detector from deep_sort import build_tracker from utils.draw import draw_boxes from utils.parser import get_config from utils.log import get_logger from utils.io import write_results from numpy import loadtxt #gt.txt yi almak için class VideoTracker(object): def __init__(self, cfg, args, video_path): self.cfg = cfg self.args = args self.video_path = video_path self.logger = get_logger("root") use_cuda = args.use_cuda and torch.cuda.is_available() if not use_cuda: warnings.warn("Running in cpu mode which maybe very slow!", UserWarning) if args.display: cv2.namedWindow("test", cv2.WINDOW_NORMAL) cv2.resizeWindow("test", args.display_width, args.display_height) if args.cam != -1: print("Using webcam " + str(args.cam)) self.vdo = cv2.VideoCapture(args.cam) else: self.vdo = cv2.VideoCapture() self.detector = build_detector(cfg, use_cuda=use_cuda) self.deepsort = build_tracker(cfg, use_cuda=use_cuda) self.class_names = self.detector.class_names def __enter__(self): if self.args.cam != -1: ret, frame = self.vdo.read() assert ret, "Error: Camera error" self.im_width = frame.shape[0] self.im_height = frame.shape[1] else: assert os.path.isfile(self.video_path), "Path error" self.vdo.open(self.video_path) self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH)) self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT)) assert self.vdo.isOpened() if self.args.save_path: os.makedirs(self.args.save_path, exist_ok=True) # path of saved video and results self.save_video_path = os.path.join(self.args.save_path, "results.avi") self.save_results_path = os.path.join(self.args.save_path, "results.txt") # create video writer fourcc = cv2.VideoWriter_fourcc(*'MJPG') self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 20, (self.im_width, self.im_height)) # logging self.logger.info("Save results to {}".format(self.args.save_path)) #eğer gt'den veriler okunacaksa if self.args.gt: gtFolder = self.video_path + "/../gt/gt.txt" gt = loadtxt(gtFolder, delimiter=",") def sortwithFrame(elem): return elem[0] # sort list with key gt_sorted = sorted(gt,key=sortwithFrame) #----------------------------- # object_type=1 olmayanları sil, def filterType(param): if (param[7]==1): return True else: return False gt_filtered = list(filter(filterType, gt_sorted)) #------------------------------- #not_ignored=0 olanları sil def filterIgnore(param): if (param[6]==1): return True else: return False gt_filtered2 = list(filter(filterIgnore, gt_filtered)) self.gt = np.array(gt_filtered2) return self def __exit__(self, exc_type, exc_value, exc_traceback): if exc_type: print(exc_type, exc_value, exc_traceback) #deep_sort içindeki fonksiyon doğru çalışmadığı için düzenleyip buraya fonksiyon olarak yazdım. #input: frame görüntüsü, xywh formatında bbox matrisi (shape=#ofDetections,4) #output: xywh formatında matrisin xyxy formatında matris karşılığı def my_xywh_to_xyxy(self,ori_img, bbox_xywh): x,y,w,h = bbox_xywh[:,0],bbox_xywh[:,1],bbox_xywh[:,2],bbox_xywh[:,3] x = x.reshape((x.size,1)) y = y.reshape((y.size,1)) w = w.reshape((w.size,1)) h = h.reshape((h.size,1)) #ekranın boyutu alınıyor height, width = ori_img.shape[:2] x1 = np.maximum(np.int_(x-w/2),0) x2 = np.minimum(np.int_(x+w/2),width-1) y1 = np.maximum(np.int_(y-h/2),0) y2 = np.minimum(np.int_(y+h/2),height-1) arr = np.concatenate((x1,y1,x2,y2),axis=1) return arr def my_tlwh_to_xywh(self,ori_img, bbox_tlwh): x,y,w,h = bbox_tlwh[:,0],bbox_tlwh[:,1],bbox_tlwh[:,2],bbox_tlwh[:,3] x = x.reshape((x.size,1)) y = y.reshape((y.size,1)) w = w.reshape((w.size,1)) h = h.reshape((h.size,1)) #ekranın boyutu alınıyor height, width = ori_img.shape[:2] x1 = np.minimum(np.int_(x+w/2),width-1) y1 = np.minimum(np.int_(y+h/2),height-1) arr = np.concatenate((x1,y1,w,h),axis=1) return arr #topleft(xy)wh >> xyxy dönüştürücü #gt içinde veriler tlxy şeklinde verilmiş. yolo verilerini xywh olarak üretiyor. (xy orta nokta) def my_tlwh_to_xyxy(self,ori_img, bbox_tlwh): x,y,w,h = bbox_tlwh[:,0],bbox_tlwh[:,1],bbox_tlwh[:,2],bbox_tlwh[:,3] x = x.reshape((x.size,1)) y = y.reshape((y.size,1)) w = w.reshape((w.size,1)) h = h.reshape((h.size,1)) #ekranın boyutu alınıyor height, width = ori_img.shape[:2] x1 = np.maximum(np.int_(x),0) x2 = np.minimum(np.int_(x+w),width-1) y1 = np.maximum(np.int_(y),0) y2 = np.minimum(np.int_(y+h),height-1) arr = np.concatenate((x1,y1,x2,y2),axis=1) return arr def run(self): results = [] idx_frame = 0 while self.vdo.grab(): idx_frame += 1 if idx_frame % self.args.frame_interval: continue start = time.time() _, ori_im = self.vdo.retrieve() im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB) #print(im.shape) #video_boyu,video_eni,3 # do detection bbox_xywh, cls_conf, cls_ids = self.detector(im) #bbox_xywh, confidence, labels #gt'leri gt'den okuyarak yolo yerine veren kısım if (self.args.gt): #py çalıştırılırken --gt yazıldıysa if(idx_frame == 1 or idx_frame == 2 or idx_frame == 3): #üç frame boyunca gt verileri yolo yerine veriliyor gt_curr_frame = self.gt[self.gt[:,0]==idx_frame].astype('float64') #filtreli gt verilerinden içinde bulunuğunuz kısım çıkarılıyor gt_curr_frame = gt_curr_frame[:,2:6] #tlwh tipinde veriler alınıyor #print(gt_curr_frame) #print(self.my_tlwh_to_xywh(im, gt_curr_frame)) bbox_xywh = self.my_tlwh_to_xywh(im, gt_curr_frame) #yolo yerine gt bboxları cls_conf = np.ones((bbox_xywh.shape[0],), dtype=int) #yolo conf skorları yerine (tüm skorlar 1) cls_ids = np.zeros(bbox_xywh.shape[0]) #bütün bboxlar yolo için 0 id'li yani person. ori_im = draw_boxes(ori_im, self.my_tlwh_to_xyxy(im,gt_curr_frame)) #gt'deki bboxları çizdir print("yolo yerine gt kullanıldı, frame: ",idx_frame) #test amaçlı bilerek yanlış vererek başlangıçtaki verilerin tracker üzerindeki etkisini incelemek için """ bbox_xywh = np.array([[100,200,400.1,600.1],[500,600.1,600.1,800.1]]) #test amaçlı bilerek yanlış vermek için cls_conf = np.ones((bbox_xywh.shape[0],), dtype=int) #test amaçlı bilerek yanlış vermek için cls_ids = np.zeros(bbox_xywh.shape[0]) #test amaçlı bilerek yanlış vermek için ori_im = draw_boxes(ori_im, bbox_xywh) """ """ labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"] """ # select person class 0-people 22-zebra 20-elephant #mask = (cls_ids == 20) + (cls_ids == 22) mask = cls_ids == 0 bbox_xywh = bbox_xywh[mask] # bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector bbox_xywh[:, 3:] *= 1.2 cls_conf = cls_conf[mask] # do tracking outputs = self.deepsort.update(bbox_xywh, cls_conf, im) #im.shape = video_boyu,video_eni,3 #print(bbox_xywh) # number_of_detection, 4 #print(cls_conf) # number_of_detection, # draw boxes for visualization if len(outputs) > 0: bbox_tlwh = [] bbox_xyxy = outputs[:, :4] identities = outputs[:, -1] #detection'ları ekrana çizen kendi yazdığım kod #ori_im = draw_boxes(ori_im, self.my_xywh_to_xyxy(im,bbox_xywh)) #doğru eşleşmeleri ekrana çizen orjinal kod ori_im = draw_boxes(ori_im, bbox_xyxy, identities) for bb_xyxy in bbox_xyxy: bbox_tlwh.append(self.deepsort._xyxy_to_tlwh(bb_xyxy)) results.append((idx_frame - 1, bbox_tlwh, identities)) end = time.time() if self.args.display: cv2.imshow("test", ori_im) cv2.waitKey(1) if self.args.save_path: self.writer.write(ori_im) # save results write_results(self.save_results_path, results, 'mot') # logging self.logger.info("time: {:.03f}s, fps: {:.03f}, detection numbers: {}, tracking numbers: {}" \ .format(end - start, 1 / (end - start), bbox_xywh.shape[0], len(outputs))) def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("VIDEO_PATH", type=str) parser.add_argument("--config_detection", type=str, default="./configs/yolov3.yaml") parser.add_argument("--config_deepsort", type=str, default="./configs/deep_sort.yaml") # parser.add_argument("--ignore_display", dest="display", action="store_false", default=True) parser.add_argument("--display", action="store_true") parser.add_argument("--gt", action="store_true") #gt'den alınan verileri kullanmak istiyorsak parser.add_argument("--frame_interval", type=int, default=1) parser.add_argument("--display_width", type=int, default=800) parser.add_argument("--display_height", type=int, default=600) parser.add_argument("--save_path", type=str, default="./output/") parser.add_argument("--cpu", dest="use_cuda", action="store_false", default=True) parser.add_argument("--camera", action="store", dest="cam", type=int, default="-1") return parser.parse_args() if __name__ == "__main__": args = parse_args() cfg = get_config() cfg.merge_from_file(args.config_detection) cfg.merge_from_file(args.config_deepsort) with VideoTracker(cfg, args, video_path=args.VIDEO_PATH) as vdo_trk: vdo_trk.run()

42.432526

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0.572698

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12,263

4.30568

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12,263

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0.204545

0.017045

0

null

0

null

0

1

0

280906641aae735ca1d3dbc649fdb86d59c81472

1,172

py

Python

aerosandbox/numpy/array.py

askprash/AeroSandbox

9e82966a25ced9ce96ca29bae45a4420278f0f1d

[ "MIT" ]

null

aerosandbox/numpy/array.py

askprash/AeroSandbox

9e82966a25ced9ce96ca29bae45a4420278f0f1d

[ "MIT" ]

null

aerosandbox/numpy/array.py

askprash/AeroSandbox

9e82966a25ced9ce96ca29bae45a4420278f0f1d

[ "MIT" ]

1

2021-09-11T03:28:45.000Z

import numpy as onp import casadi as cas def array(object, dtype=None): try: a = onp.array(object, dtype=dtype) if a.dtype == "O": raise Exception return a except (AttributeError, Exception): # If this occurs, it needs to be a CasADi type. # First, determine the dimension def make_row(row): try: return cas.horzcat(*row) except (TypeError, Exception): # If not iterable or if it's a CasADi MX type return row return cas.vertcat( *[ make_row(row) for row in object ] ) def length(array) -> int: """ Returns the length of an 1D-array-like object. Args: array: Returns: """ try: return len(array) except TypeError: # array has no function len() -> either float, int, or CasADi type try: if len(array.shape) >= 1: return array.shape[0] else: raise AttributeError except AttributeError: # array has no attribute shape -> either float or int return 1

25.478261

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0.529863

139

1,172

4.453237

0.446043

0.035541

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0.391638

1,172

45

90

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0

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false

0

0.064516

0

0.387097

0

null

0

null

0

1

0

280b7ce2e2cb3f65d56ba5e4705455b1cbb3bb0e

3,283

py

Python

capspayment/api_payin.py

agorapay/python-sdk

c5b7fd6894f95e6862446248b26c16253c8fd4f4

[ "MIT" ]

null

capspayment/api_payin.py

agorapay/python-sdk

c5b7fd6894f95e6862446248b26c16253c8fd4f4

[ "MIT" ]

null

capspayment/api_payin.py

agorapay/python-sdk

c5b7fd6894f95e6862446248b26c16253c8fd4f4

[ "MIT" ]

null

""" Payin API """ from dataclasses import dataclass from typing import Union from api_payin_model import ( PayinAdjustPaymentRequest, PayinCancelRequest, PayinCancelResponse, PayinCaptureRequest, PayinCaptureResponse, PayinMandateRequest, PayinMandateResponse, PayinOrderDetailsRequest, PayinOrderDetailsResponse, PayinPaymentDetailsRequest, PayinPaymentDetailsResponse, PayinPaymentIframeRequest, PayinPaymentIframeResponse, PayinPaymentMethodsRequest, PayinPaymentMethodsResponse, PayinPaymentRequest, PayinPaymentResponse, PayinRefundRequest, PayinRefundResponse, PayinTicketRequest, PayinTicketResponse, ) from base import BaseRequest from model import Response @dataclass class ApiPayin(BaseRequest): """Payin API requests""" def payment( self, payload: PayinPaymentRequest ) -> Union[PayinPaymentResponse, Response]: """Submit a payment""" return self.request("POST", "/payin/payment", payload) def payment_details( self, payload: PayinPaymentDetailsRequest ) -> Union[PayinPaymentDetailsResponse, Response]: """Submit additionnal payment details""" return self.request("POST", "/payin/paymentDetails", payload) def payment_methods( self, payload: PayinPaymentMethodsRequest ) -> Union[PayinPaymentMethodsResponse, Response]: """Submit an order/get payment methods""" return self.request("POST", "/payin/paymentMethods", payload) def capture( self, payload: PayinCaptureRequest ) -> Union[PayinCaptureResponse, Response]: """Capture a transaction/order""" return self.request("POST", "/payin/capture", payload) def cancel( self, payload: PayinCancelRequest ) -> Union[PayinCancelResponse, Response]: """Cancel a transaction/order""" return self.request("POST", "/payin/cancel", payload) def order_details( self, payload: PayinOrderDetailsRequest ) -> Union[PayinOrderDetailsResponse, Response]: """Get all the order details""" return self.request("GET", "/payin/orderDetails", payload) def adjust_payment(self, payload: PayinAdjustPaymentRequest) -> Response: """Adjust the amount of the payment/change the breakdown of the payment""" return self.request("POST", "/payin/adjustPayment", payload) def payment_iframe( self, payload: PayinPaymentIframeRequest ) -> Union[PayinPaymentIframeResponse, Response]: """Submit an order/get an authent code""" return self.request("POST", "/payin/paymentIframe", payload) def refund( self, payload: PayinRefundRequest ) -> Union[PayinRefundResponse, Response]: """Refund a transaction/order""" return self.request("POST", "/payin/refund", payload) def mandate( self, payload: PayinMandateRequest ) -> Union[PayinMandateResponse, Response]: """Get signed mandate file""" return self.request("GET", "/payin/mandate", payload) def ticket( self, payload: PayinTicketRequest ) -> Union[PayinTicketResponse, Response]: """Get card payment ticket""" return self.request("GET", "/payin/ticket", payload)

32.186275

82

0.687786

276

3,283

8.155797

0.271739

0.053754

0.083074

0.074634

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3,283

101

83

32.504951

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0

null

0

null

0

1

0

280c4e3ff6e2c8be5af4beb5882bf9b9cd5ee1c7

3,626

py

Python

script/gen_canonical_combining_class.py

CyberZHG/UChar

e59ee5e3ad166288380407df6d5e6c0fe20681cf

[ "MIT" ]

1

2020-07-15T16:16:20.000Z

script/gen_canonical_combining_class.py

CyberZHG/UChar

e59ee5e3ad166288380407df6d5e6c0fe20681cf

[ "MIT" ]

null

script/gen_canonical_combining_class.py

CyberZHG/UChar

e59ee5e3ad166288380407df6d5e6c0fe20681cf

[ "MIT" ]

1

2020-06-01T01:15:29.000Z

#!/usr/bin/env python """ Copyright 2020 Zhao HG Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ with open('UnicodeData.txt', 'r') as reader: last, indices, canonicals, classes = '', [], [], {} for line in reader: parts = line.strip().split(';') if parts[3] != last: last = parts[3] indices.append(parts[0]) canonicals.append(parts[3]) classes[parts[3]] = parts[0] with open('include/unicode_data.h', 'a') as writer: writer.write('/** The total number of indices used to store the canonical combing class. */\n') writer.write('const int32_t CANONICAL_COMBINING_NUM = {};\n'.format(len(indices))) writer.write('/** The indices of the first character that have a different type. */\n') writer.write('extern const int32_t CANONICAL_COMBINING_INDEX[];\n') writer.write('/** The canonical combining class data. */\n') writer.write('extern const int32_t CANONICAL_COMBINING_CLASS[];\n\n') with open('src/canonical_combining_class.cpp', 'w') as writer: with open('copyright.txt', 'r') as reader: writer.write(reader.read()) writer.write('#include "unicode_data.h"\n\n') writer.write('namespace unicode {\n\n') writer.write('\nconst int32_t CANONICAL_COMBINING_INDEX[] = {') for i, index in enumerate(indices): if i == 0: writer.write('\n ') elif i % 8 == 0: writer.write(',\n ') else: writer.write(', ') writer.write('0x' + index) writer.write('\n};\n') writer.write('\nconst int32_t CANONICAL_COMBINING_CLASS[] = {') for i, canonical in enumerate(canonicals): if i == 0: writer.write('\n ') elif i % 8 == 0: writer.write(',\n ') else: writer.write(', ') writer.write(canonical) writer.write('\n};\n\n') writer.write('} // namespace unicode\n') with open('tests/test_canonical_combining_class_gen.cpp', 'w') as writer: with open('copyright.txt', 'r') as reader: writer.write(reader.read()) writer.write('#include "test.h"\n') writer.write('#include "unicode_char.h"\n\n') writer.write('namespace test {\n\n') writer.write('class CanonicalCombiningClassGenTest : public UnitTest {};\n\n') writer.write('__TEST_U(CanonicalCombiningClassGenTest, test_classes) {\n') for canonical, code in classes.items(): writer.write(' __ASSERT_EQ({}, unicode::getCanonicalCombiningClass({}));\n'.format( canonical, '0x' + code )) writer.write('}\n\n') writer.write('} // namespace test\n')

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0

null

0

null

0

1

0

280cef3837d316af797287a2c5c707f3a00a10c1

3,676

py

Python

server.py

Timothylock/twillio-buzzer-connector

9ac7e4763a5eee7d04daa054841e17332c0bac13

[ "Apache-2.0" ]

null

server.py

Timothylock/twillio-buzzer-connector

9ac7e4763a5eee7d04daa054841e17332c0bac13

[ "Apache-2.0" ]

null

server.py

Timothylock/twillio-buzzer-connector

9ac7e4763a5eee7d04daa054841e17332c0bac13

[ "Apache-2.0" ]

null

from flask import Flask, request from twilio.twiml.voice_response import VoiceResponse, Gather import datetime import os import json import http.client app = Flask(__name__) allowUntil = datetime.datetime.now() # Fetch env vars whitelisted_numbers = os.environ['WHITELISTED_NUMBERS'].split(",") # Numbers allowed to dial into the system forward_number = os.environ['FORWARD_NUMBER'] # Number that will be forwarded to if not whitelisted forward_number_from = os.environ['FORWARD_NUMBER_FROM'] # Number that will be forwarded to if not whitelisted buzzcode = os.environ['BUZZCODE'] # Digits to dial to let them in minutes = int(os.environ['MINUTES']) # Number of minutes to unlock the system slack_path = os.environ['SLACK_PATH'] # Slack path for slack message say_message = os.environ['SAY_MESSAGE'] # The message to be said to the dialer # Buzzer ########################################################################## @app.route("/buzzer/webhook", methods=['GET', 'POST']) def voice(): """Respond to incoming phone calls""" resp = VoiceResponse() incoming_number = request.values['From'] # If an unknown number, filter out robo callers and forward to cell if incoming_number not in whitelisted_numbers: gather = Gather(num_digits=1, action='/buzzer/forward') gather.say('Press 1 to continue') resp.append(gather) return str(resp) # Tell the user a nice message that they are not permitted to enter if not allowed_to_buzz(): resp.say("The system cannot let you in. Did you dial the right buzzcode?") send_message("A visitor was just rejected as the buzzer system was not unlocked") return str(resp) # Otherwise, unlock the door resp.say(say_message, language='zh-CN') resp.play(digits=buzzcode) send_message("A visitor was just let in") return str(resp) @app.route("/buzzer/forward", methods=['GET', 'POST']) def forward(): resp = VoiceResponse() incoming_number = request.values['From'] send_message("About to forward a call from " + str(incoming_number)) resp.say("Please note your call may be recorded for the benefit of both parties") resp.dial(forward_number, caller_id=forward_number_from) return str(resp) @app.route("/buzzer/state", methods=['POST']) def change_state(): """Tells the buzzer to unlock the door for the next 30 minutes""" global allowUntil c = request.json if "active" not in c: return "missing \"active\" field", 400 if c["active"] == "true": allowUntil = datetime.datetime.now() + datetime.timedelta(minutes=minutes) if c["active"] == "false": allowUntil = datetime.datetime.now() return "OK", 200 @app.route("/buzzer/state", methods=['GET']) def status(): """Fetches whether the system will buzz people in""" return json.dumps({"is_active": str(allowed_to_buzz()).lower()}), 200 def allowed_to_buzz(): """Fetches whether the system is allowed to buzz somebody in""" global allowUntil return allowUntil > datetime.datetime.now() def send_message(message): try: conn = http.client.HTTPSConnection("hooks.slack.com") payload = "{\"text\": \"" + message + "\"}" headers = { 'content-type': "application/json", } conn.request("POST", slack_path, payload, headers) conn.getresponse() except: print("error sending message") if __name__ == "__main__": app.run(host='0.0.0.0', port=8080)

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null

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null

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0

2810be0978f433319136f58db93ce028bbbb9a9c

8,151

py

Python

cosmos/ingestion/ingest/process/hierarchy_extractor/bert_hierarchy_extractor/train/bert_extractor_trainer.py

ilmcconnell/Cosmos

84245034727c30e20ffddee9e02c7e96f3aa115e

[ "Apache-2.0" ]

30

2019-03-14T08:24:34.000Z

2022-03-09T06:05:44.000Z

cosmos/ingestion/ingest/process/hierarchy_extractor/bert_hierarchy_extractor/train/bert_extractor_trainer.py

ilmcconnell/Cosmos

84245034727c30e20ffddee9e02c7e96f3aa115e

[ "Apache-2.0" ]

78

2019-02-07T22:14:48.000Z

2022-03-09T05:59:18.000Z

cosmos/ingestion/ingest/process/hierarchy_extractor/bert_hierarchy_extractor/train/bert_extractor_trainer.py

ilmcconnell/Cosmos

84245034727c30e20ffddee9e02c7e96f3aa115e

[ "Apache-2.0" ]

11

2019-03-02T01:20:06.000Z

2022-03-25T07:25:46.000Z

from bert_hierarchy_extractor.datasets.train_dataset import TrainHierarchyExtractionDataset from bert_hierarchy_extractor.datasets.utils import cudafy from bert_hierarchy_extractor.logging.utils import log_metrics import numpy as np from torch.utils.data import DataLoader from transformers import AdamW, get_linear_schedule_with_warmup import torch import time from tqdm import tqdm from comet_ml import Experiment def placeholder_num_correct(x, y, print_result=False): result = torch.argmax(x, dim=1) result = result.view(-1) y2 = y.view(-1) mask = (y2 != -1) y2 = y2[mask] result = result[mask] if print_result: print('*************') y1mask = (y[0] != -1) print(y[0][y1mask]) print('-------------') rez = torch.argmax(x[0], dim=0) print(rez[y1mask]) print('**************') total_correct = (result == y2).sum().detach().cpu().numpy() total = result.shape[0] return total_correct, total class BertExtractorTrainer: def __init__( self, experiment: Experiment, model, data_path: str, base_model: str, bsz: int, num_workers: int, lr: float, weight_decay: float, warmup_updates: int, max_updates: int, accumulation_steps: int, validate_interval: int, save_metric: str, save_min: bool, device: str, seed=1, num_correct=placeholder_num_correct, ): """ :param model: Initialized model :param dataset_path: Path to dataset :param base_model: Path to base model :param bsz: Batch size :param num_workers: Num workers available :param lr: Learning rate :param weight_decay: weight decay :param warmup_updates: number of samples to warmup learning rate :param max_updates: max number of samples :param accumulation_steps: Number of batches to accumulate loss over before running an update :param validate_interval: num updates before validating :param save_metric: metric to use to save best model :param save_min: Whether we're looking to minimize or maximize the save metric :param seed: Random seed for iteration """ torch.manual_seed(seed) self.experiment = experiment self.device = device print(device) self.model = model.to(device) self.max_accumulation = accumulation_steps print("Loading training dataset") self.train_dataset = TrainHierarchyExtractionDataset(data_path) num_classes = len(self.train_dataset.label_map)-1 class_counts = np.zeros(num_classes) for i in range(len(self.train_dataset)): _, l = self.train_dataset[i] for cl in l: class_counts[cl] += 1 effective_num = 1.0 - np.power(0.9999, class_counts) weights = (1.0 - 0.9999) / np.array(effective_num) weights = weights / np.sum(weights * num_classes) self.weights = torch.FloatTensor(weights).to(device) print(self.weights) #print("Loading validation dataset") #self.val_dataset = TrainHierarchyExtractionDataset(data_path, base_model, "val") self.train_dataloader = DataLoader( self.train_dataset, batch_size=bsz, num_workers=num_workers, pin_memory=True, shuffle=True, collate_fn=TrainHierarchyExtractionDataset.collate, ) self.val_dataloader = DataLoader( self.train_dataset, batch_size=bsz, num_workers=num_workers, pin_memory=True, shuffle=True, collate_fn=TrainHierarchyExtractionDataset.collate, ) self.bsz = bsz self.optimizer = AdamW(model.parameters(), lr=lr, weight_decay=0.01) self.scheduler = get_linear_schedule_with_warmup( self.optimizer, num_warmup_steps=warmup_updates, num_training_steps=max_updates, ) self.max_updates = max_updates self.validate_interval = validate_interval self.num_correct = num_correct self.save_metric = save_metric self.current_best_metric = float('inf') def validate(self, validate_cap=None, best_save_metric=None): self.model.eval() val_cap = validate_cap if validate_cap is not None else len(self.val_dataloader) with tqdm(total=val_cap) as pbar: total_loss = 0 total_correct = 0 total_instances = 0 for ind, batch in enumerate(self.val_dataloader): if ind > val_cap: break xs, labels = cudafy(batch) loss, logits = self.model(xs, labels=labels, weights=self.weights) nc, t = self.num_correct(logits, labels, print_result=True if ind < 5 else False) total_correct += nc total_instances += t total_loss += loss.detach().cpu().numpy() pbar.update(1) loss_per_sample = total_loss / val_cap / self.bsz accuracy = total_correct / total_instances metrics = {} metrics["val_loss"] = loss_per_sample metrics["val_accuracy"] = accuracy metrics["val_per_sample_loss"] = total_loss if best_save_metric is not None: if metrics[best_save_metric] <= self.current_best_metric: self.model.save_pretrained('best') self.current_best_metric = metrics[best_save_metric] return metrics def train(self): """ """ start_time = time.time() # Verify forward pass using validation loop metrics = self.validate(validate_cap=5) self.model.train() with tqdm(total=self.max_updates, desc='Number of updates') as pbar: total_updates = 1 val_updates = 1 while total_updates < self.max_updates: accumulation_steps = 0 accumulation_loss = None for batch in self.train_dataloader: xs, labels = cudafy(batch) loss, _ = self.model(xs, labels=labels, weights=self.weights) if accumulation_loss is None: accumulation_steps += 1 accumulation_loss = loss elif accumulation_steps > self.max_accumulation: self.optimizer.zero_grad() accumulation_loss.backward() torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) self.optimizer.step() self.scheduler.step() pbar.update(1) total_updates += 1 accumulation_steps = 0 accumulation_loss = loss l = loss.detach().cpu().numpy() metrics = {} metrics["train_update_loss"] = l metrics["train_per_sample_loss"] = l / self.bsz # TODO: Accuracy, f1, etc metrics log_metrics(self.experiment, metrics, total_updates) if total_updates % self.validate_interval == 0: metrics = self.validate(validate_cap=100, best_save_metric=self.save_metric) val_updates += 1 log_metrics(self.experiment, metrics, val_updates) else: accumulation_steps += 1 accumulation_loss += loss metrics = self.validate(validate_cap=1000) print(f"Final validation metrics: {metrics}") torch.save(self.model.state_dict(), 'last.pt') val_updates += 1 log_metrics(self.experiment, metrics, val_updates) end_time = time.time() total_time = end_time - start_time print(f"Total train time: {total_time}")

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0.024028

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0

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0

null

0

null

0

1

0

28137bb29b2acdc147558b677e97f5e615bea160

2,900

py

Python

adduser.py

Vignesh424/Face-Recognition-Attendance-Python

5d9c33b64bd41918edc55290a320f73bc4afa4e5

[ "Apache-2.0" ]

null

adduser.py

Vignesh424/Face-Recognition-Attendance-Python

5d9c33b64bd41918edc55290a320f73bc4afa4e5

[ "Apache-2.0" ]

null

adduser.py

Vignesh424/Face-Recognition-Attendance-Python

5d9c33b64bd41918edc55290a320f73bc4afa4e5

[ "Apache-2.0" ]

null

import cv2 import os import sqlite3 import dlib import re,time from playsound import playsound import pyttsx3 cam = cv2.VideoCapture(0) cam.set(3, 640) # set video width cam.set(4, 480) # set video height face_detector = cv2.CascadeClassifier('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/HaarCascade/haarcascade_frontalface_default.xml') detector = dlib.get_frontal_face_detector() # init function to get an engine instance for the speech synthesis engine1 = pyttsx3.init() engine2 = pyttsx3.init() # For each person, enter one numeric face id detector = dlib.get_frontal_face_detector() regex = '^\w+([\.-]?\w+)*@\w+([\.-]?\w+)*(\.\w{2,3})+$' Id =int(input("Enter ID:")) fullname = input("Enter FullName : ") email=input("Enter Email:") match = re.match(regex,email) if match == None: print('Invalid Email') raise ValueError('Invalid Email') rollno = int(input("Enter Roll Number : ")) print("\n [INFO] Initializing face capture. Look the camera and wait ...") # say method on the engine that passing input text to be spoken playsound('sound.mp3') engine1.say('User Added Successfully') # run and wait method, it processes the voice commands. engine2.runAndWait() connects = sqlite3.connect("C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/sqlite3/Studentdb.db")# connecting to the database c = connects.cursor() c.execute('CREATE TABLE IF NOT EXISTS Student (ID INT NOT NULL UNIQUE PRIMARY KEY, FULLNAME TEXT NOT NULL, EMAIL NOT NULL, ROLLNO INT UNIQUE NOT NULL , STATUS TEXT DATE TIMESTAMP)') c.execute("INSERT INTO Student(ID, FULLNAME, EMAIL,ROLLNO) VALUES(?,?,?,?)",(Id,fullname,email,rollno)) print('Record entered successfully') connects.commit()# commiting into the database c.close() connects.close()# closing the connection # Initialize individual sampling face count count = 0 while(True): ret, img = cam.read() img = cv2.flip(img,1) # flip video image vertically gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_detector.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in faces: cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2) count += 1 # Save the captured image into the datasets folder cv2.imwrite("dataset/User." + str(Id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w]) cv2.imshow('image', img) k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting video if k == 27: break elif count >= 30: # Take 30 face sample and stop video playsound('sound.mp3') engine2.say('DataSets Captured Successfully') # run and wait method, it processes the voice commands. engine2.runAndWait() break # Doing a bit of cleanup print("\n [INFO] Exiting Program and cleanup stuff") cam.release() cv2.destroyAllWindows()

43.283582

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0.024979

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null

0

null

0

1

0

2814df1e327e7a389483fc7f28c047ef76e86e37

8,753

py

Python

conet/datasets/duke_oct_flat_sp.py

steermomo/conet

21d60fcb4ab9a01a00aa4d9cd0bdee79ea35cc4b

[ "MIT" ]

null

conet/datasets/duke_oct_flat_sp.py

steermomo/conet

21d60fcb4ab9a01a00aa4d9cd0bdee79ea35cc4b

[ "MIT" ]

null

conet/datasets/duke_oct_flat_sp.py

steermomo/conet

21d60fcb4ab9a01a00aa4d9cd0bdee79ea35cc4b

[ "MIT" ]

1

2020-05-18T10:05:24.000Z

import multiprocessing as mp # mp.set_start_method('spawn') import math import os import pickle import random from glob import glob from os import path import albumentations as alb import cv2 import numpy as np import skimage import torch import imageio from albumentations.pytorch import ToTensorV2 from skimage.color import gray2rgb from torch.utils.data import Dataset from conet.config import get_cfg # https://github.com/albumentations-team/albumentations/pull/511 # Fix grid distortion bug. #511 # GridDistortion bug修复..... train_size_aug = alb.Compose([ # alb.RandomSizedCrop(min_max_height=(300, 500)), alb.PadIfNeeded(min_height=100, min_width=600, border_mode=cv2.BORDER_REFLECT101), alb.Rotate(limit=6), alb.RandomScale(scale_limit=0.05,), alb.ElasticTransform(), # alb.GridDistortion(p=1, num_steps=20, distort_limit=0.5), # alb.GridDistortion(num_steps=10, p=1), # alb.OneOf([ # alb.OpticalDistortion(), # ]), # alb.MaskDropout(image_fill_value=0, mask_fill_value=-1,p=0.3), alb.HorizontalFlip(), # alb.VerticalFlip(), # alb.RandomBrightness(limit=0.01), alb.PadIfNeeded(min_height=224, min_width=512, border_mode=cv2.BORDER_REFLECT101), alb.RandomCrop(224, 512), # alb.Normalize(), # alb.pytorch.ToTensor(), # ToTensorV2() ]) train_content_aug = alb.Compose([ # alb.MedianBlur(3), # alb.GaussianBlur(3), alb.RGBShift(r_shift_limit=5, g_shift_limit=5, b_shift_limit=5), alb.RandomBrightnessContrast(brightness_limit=0.05), alb.Normalize(), # ToTensorV2() ]) val_aug = alb.Compose([ # alb.PadIfNeeded(512, border_mode=cv2.BORDER_REFLECT101), # alb.Normalize(), # alb.Resize(512, 512), alb.PadIfNeeded(min_height=224, min_width=512, border_mode=cv2.BORDER_REFLECT101), alb.CenterCrop(224, 512), # ToTensorV2(), ]) val_c_aug = alb.Compose([ alb.Normalize(), # ToTensorV2() ]) # train_aug_f = alb.Compose([ # # alb.RandomSizedCrop(min_max_height=(300, 500)), # alb.RandomScale(), # # alb.HorizontalFlip(), # alb.VerticalFlip(), # alb.RandomBrightness(limit=0.01), # alb.Rotate(limit=30), # # 224 548 # alb.PadIfNeeded(min_height=224, min_width=548, border_mode=cv2.BORDER_REFLECT101), # alb.RandomCrop(224, 512), # alb.Normalize(), # # alb.pytorch.ToTensor(), # ToTensorV2() # ]) # val_aug_f = alb.Compose([ # alb.PadIfNeeded(min_height=224, min_width=512, border_mode=cv2.BORDER_REFLECT101), # alb.Normalize(), # # alb.Resize(512, 512), # alb.CenterCrop(224, 512), # ToTensorV2(), # ]) class DukeOctFlatSPDataset(Dataset): def __init__(self, split='train', n_seg=0): cfg = get_cfg() self.cfg = cfg self.data_dir = path.join(cfg.dme_flatten_sp, str(n_seg)) print(f'Load data from {self.data_dir}') # with open(path.join(cfg.data_dir, 'split.dp'), 'rb') as infile: # self.d_split = pickle.load(infile) self.split = split data_files = glob(path.join(self.data_dir, '*.jpg')) # img_bname = ['_'.join(path.basename(x).split('_')[:-1]) for x in img_files] data_bnames = [path.basename(x).split('.')[0] for x in data_files] # self.data_bnames = data_bnames subject_ids = [int(x.split('_')[1]) for x in data_bnames] if split == 'train': self.bnames = [data_bnames[i] for i in range(len(data_files)) if subject_ids[i] < 6] else: self.bnames = [data_bnames[i] for i in range(len(data_files)) if subject_ids[i] >= 6] if split == 'train': self.b_aug = train_size_aug self.c_aug = train_content_aug elif split == 'val': self.b_aug = val_aug self.c_aug = val_c_aug else: raise NotImplementedError self.cache = [] for idx in range(len(self)): bname = self.bnames[idx] img_fp = path.join(self.data_dir, f'{bname}.jpg') label_fp = path.join(self.data_dir, f'{bname}_label.npy') softlabel_fp = path.join(self.data_dir, f'{bname}_softlabel.npy') img = imageio.imread(img_fp) label = np.load(label_fp) softlabel = np.load(softlabel_fp) self.cache.append((img_fp, img, label, softlabel)) def __len__(self): # return len(self.d_basefp) return len(self.bnames) def __getitem__(self, idx): # carr = np.load(path.join(self.data_dir, self.d_basefp[idx])) # carr = np.load(self.bnames[idx]) # if idx in self.cache.keys(): # img_fp, img, label, soft_label = self.cache[idx] # else: # bname = self.bnames[idx] # img_fp = path.join(self.data_dir, f'{bname}.jpg') # label_fp = path.join(self.data_dir, f'{bname}_label.npy') # softlabel_fp = path.join(self.data_dir, f'{bname}_softlabel.npy') # img = imageio.imread(img_fp) # label = np.load(label_fp) # softlabel = np.load(softlabel_fp) # self.cache[idx] = (img_fp, img, label, softlabel) img_fp, img, label, softlabel = self.cache[idx] img_fp, img, label, softlabel = img_fp, img.copy(), label.copy(), softlabel.copy() # img = gray2rgb(img) # if self.split == 'train': # auged = train_aug_f(image=img, mask=label) # else: # auged = val_aug_f(image=img, mask=label) # auged['fname'] = img_fp # auged['softlabel'] = torch.tensor(0.) # return auged # img = np.transpose(img, (1, 2, 0)) softlabel = np.transpose(softlabel, (1, 2, 0)) img = np.expand_dims(img, axis=-1) img_a = np.concatenate([img, softlabel], axis=-1) # img = gray2rgb(img) # grid_distortion 可能不支持负数 label[label == -1] = 255 auged = self.b_aug(image=img_a, mask=label) img = auged['image'] label = auged['mask'] label[label == 255] = -1 softlabel = img[:, :, 1:] image = img[:, :, 0] # print(image.shape, image.max(), image.min()) image = np.clip(image, 0, 255).astype('uint8') # image = skimage.img_as_ubyte(image) image = gray2rgb(image) image = self.c_aug(image=image)['image'] # normi # image = alb.Normalize()(image)['image'] image = np.transpose(image, (2, 0, 1)) softlabel = np.transpose(softlabel, (2, 0, 1)) loss_mask = (label !=-1).astype("float") image = torch.from_numpy(image) softlabel = torch.from_numpy(softlabel).float() label = torch.from_numpy(label) loss_mask = torch.from_numpy(loss_mask) # img = auged['image'] # print(img.shape) return { 'image': image, 'softlabel': softlabel, 'mask': label, 'fname': img_fp, 'loss_mask': loss_mask } if __name__ == "__main__": from skimage import segmentation, color, filters, exposure import skimage import os from os import path import imageio from matplotlib import pyplot as plt from torch.utils.data import DataLoader import random np.random.seed(42) random.seed(42) save_dir = '/data1/hangli/oct/debug' os.makedirs(save_dir, exist_ok=True) cmap = plt.cm.get_cmap('jet') n_seg = 1200 training_dataset = DukeOctFlatSPDataset(split='train', n_seg=n_seg) # val_dataset = DukeOctFlatSPDataset(split='val', n_seg=n_seg) data_loader = DataLoader(training_dataset, batch_size=16, shuffle=False, num_workers=8, pin_memory=False) # val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False, num_workers=2, pin_memory=True) for t in range(40): for bidx, batch in enumerate(data_loader): data = batch['image'] target = batch['mask'] for b_i in range(len(data)): img = data[b_i] img = img.permute(1, 2, 0).cpu().numpy() img = (img - img.min()) / (img.max() - img.min()) img = skimage.img_as_ubyte(img) mask = target[b_i] # mask_color = cmap(mask) mask_color = color.label2rgb(mask.cpu().numpy()) mask_color = skimage.img_as_ubyte(mask_color) print(img.shape, mask_color.shape) save_img = np.hstack((img, mask_color)) p = path.join(save_dir, f'{t}_{bidx}_{b_i}.jpg') print(f'=> {p}') imageio.imwrite(p, save_img)

30.498258

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0.595224

1,134

8,753

4.409171

0.207231

0.012

0.022

0.0256

0.3316

0.2814

0.2614

0.2546

0

0.033967

0.266766

8,753

286

110

30.604895

0.745092

0.31395

0

0.161765

0

0.03956

0.007439

0

1

0.022059

false

0

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0.022059

0

null

0

null

0

1

0

281720b5fdc07905c3eb03b6c213540b162d5693

1,109

py

Python

tests/config/test_project.py

gaborbernat/toxn

1ecb1121b3e3dc30b892b0254cb5566048b5d2e7

[ "MIT" ]

4

2018-04-15T15:12:32.000Z

2019-06-03T12:41:06.000Z

tests/config/test_project.py

gaborbernat/tox3

1ecb1121b3e3dc30b892b0254cb5566048b5d2e7

[ "MIT" ]

3

2018-03-15T11:06:30.000Z

2018-04-15T15:17:29.000Z

tests/config/test_project.py

gaborbernat/tox3

1ecb1121b3e3dc30b892b0254cb5566048b5d2e7

[ "MIT" ]

1

2019-09-25T19:53:09.000Z

from io import StringIO from pathlib import Path import pytest from toxn.config import from_toml @pytest.mark.asyncio async def test_load_from_io(): content = StringIO(""" [build-system] requires = ['setuptools >= 38.2.4'] build-backend = 'setuptools:build_meta' [tool.toxn] default_tasks = ['py36'] """) build, project, filename = await from_toml(content) assert build.backend == 'setuptools:build_meta' assert build.requires == ['setuptools >= 38.2.4'] assert project == {'default_tasks': ['py36']} assert filename is None @pytest.mark.asyncio async def test_load_from_path(tmpdir): filename: Path = Path(tmpdir) / 'test.toml' with open(filename, 'wt') as f: f.write(""" [build-system] requires = ['setuptools >= 38.2.4'] build-backend = 'setuptools:build_meta' [tool.toxn] default_tasks = ['py36'] """) build, project, config_path = await from_toml(filename) assert build.backend == 'setuptools:build_meta' assert build.requires == ['setuptools >= 38.2.4'] assert project == {'default_tasks': ['py36']} assert filename == config_path

25.790698

59

0.6844

144

1,109

5.138889

0.298611

0.097297

0.108108

0.113514

0.675676

0.575676

0

0.02603

0.16862

1,109

42

60

26.404762

0.776573

0

0.588235

0

0.35257

0.079351

0

0.235294

1

0

false

0

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0

0.117647

0

null

0

null

0

1

0

2820ef5bc2fdcf7913515a4a45ac8b19c189a6ce

1,340

py

Python

longest path in matrix.py

buhuhaha/python

4ff72ac711f0948ae5bcb0886d68e8df77fe515b

[ "MIT" ]

null

longest path in matrix.py

buhuhaha/python

4ff72ac711f0948ae5bcb0886d68e8df77fe515b

[ "MIT" ]

null

longest path in matrix.py

buhuhaha/python

4ff72ac711f0948ae5bcb0886d68e8df77fe515b

[ "MIT" ]

null

row = [-1, -1, -1, 0, 0, 1, 1, 1] col = [-1, 0, 1, -1, 1, -1, 0, 1] def isValid(x, y, mat): return 0 <= x < len(mat) and 0 <= y < len(mat[0]) def findMaxLength(mat, x, y, previous): if not isValid(x, y, mat) or chr(ord(previous) + 1) != mat[x][y]: return 0 max_len = 0 for k in range(len(row)): length = findMaxLength(mat, x + row[k], y + col[k], mat[x][y]) max_len = max(max_len, 1 + length) return max_len def findMaximumLength(mat, ch): if not mat or not len(mat): return 0 (M, N) = (len(mat), len(mat[0])) max_len = 0 for x in range(M): for y in range(N): if mat[x][y] == ch: for k in range(len(row)): length = findMaxLength(mat, x + row[k], y + col[k], ch) max_len = max(max_len, 1 + length) return max_len if __name__ == '__main__': mat = [ ['D', 'E', 'H', 'X', 'B'], ['A', 'O', 'G', 'P', 'E'], ['D', 'D', 'C', 'F', 'D'], ['E', 'B', 'E', 'A', 'S'], ['C', 'D', 'Y', 'E', 'N'] ] ch = 'C' print("The length of the longest path with consecutive characters starting from " "character", ch, "is", findMaximumLength(mat, ch))

20.30303

85

0.435075

200

1,340

2.835

0.27

0.084656

0.021164

0.014109

0.326279

0.292769

0

0.032143

0.373134

1,340

66

86

20.30303

0.642857

0

0.285714

0

0.08806

0

1

0.085714

false

0

0.028571

0.228571

0.028571

0

null

0

null

0

1

0

28226ec9ea67dad00950fa1852a66dbf14540c2c

4,653

py

Python

AnimalProfile/session/batchAnimals.py

AtMostafa/AnimalProfile

866f55659b80291f840ecacd090afada5f4de674

[ "MIT" ]

null

AnimalProfile/session/batchAnimals.py

AtMostafa/AnimalProfile

866f55659b80291f840ecacd090afada5f4de674

[ "MIT" ]

null

AnimalProfile/session/batchAnimals.py

AtMostafa/AnimalProfile

866f55659b80291f840ecacd090afada5f4de674

[ "MIT" ]

null

__all__ = ('get_session_list', 'get_animal_list', 'get_event', 'get_tag_pattern', 'get_pattern_animalList', 'get_current_animals') import datetime import logging from .. import Root from .. import File from .. import Profile from ..Profile import EventProfile from .singleAnimal import * def get_session_list(root: Root, animalList: list = None, profile: Profile = None): """ This function returns list of sessions with certain 'profile' for all the animals in animalList. if animalList=Nonr, it will search all the animals. """ if profile is None: profile = Profile(root=root) if animalList is None or animalList == '' or animalList == []: animalList = root.get_all_animals() profileOut = Profile(root=root) for animal in animalList: tagFile = File(root, animal) sessionProfile = tagFile.get_profile_session_list(profile) profileOut += sessionProfile return profileOut def get_animal_list(root: Root, profile: Profile = None): """ this function returns list of animals with at least one session matching the "profile" """ if profile is None: profile = Profile(root=root) allProfiles = get_session_list(root, animalList=None, profile=profile) sessionList = allProfiles.Sessions animalList = [] for session in sessionList: animalList.append(session[:len(profile._prefix) + 3]) animalList = list(set(animalList)) return sorted(animalList) def get_event(root: Root, profile1: Profile, profile2: Profile, badAnimals: list = None): """ This function finds the animals that match both profile1 and profile2 IN SUCCESSION I.E., when the conditions changed """ if badAnimals is None: badAnimals = [] animalList1 = get_animal_list(root, profile1) animalList2 = get_animal_list(root, profile2) animalList0 = set(animalList1).intersection(set(animalList2)) animalList0 = [animal for animal in animalList0 if animal not in badAnimals] # remove bad animals from animalList0 animalList0.sort() eventProfile = EventProfile(profile1, profile2) for animal in animalList0: sessionProfile1 = get_session_list(root, animalList=[animal], profile=profile1) sessionProfile2 = get_session_list(root, animalList=[animal], profile=profile2) sessionTotal = get_session_list(root, animalList=[animal], profile=root.get_profile()) try: index = sessionTotal.Sessions.index(sessionProfile1.Sessions[-1]) if sessionProfile2.Sessions[0] == sessionTotal.Sessions[index + 1]: # Two profiles succeed, meaning the Event happended. eventProfile.append(sessionProfile1.Sessions, sessionProfile2.Sessions) except Exception: pass return eventProfile def get_tag_pattern(root: Root, animalList: list = None, tagPattern: str = '*'): """ applies 'get_pattern_session_list' to a list of animals """ if animalList is None or animalList == []: animalList = root.get_all_animals() profileDict = root.get_profile() for animal in animalList: tagFile = File(root, animal) profileDict += tagFile.get_pattern_session_list(tagPattern=tagPattern) return profileDict def get_pattern_animalList(root: Root, tagPattern: str): """ this function returns list of animals with at least one session matching the 'tagPattern' """ allProfile = get_tag_pattern(root, animalList=None, tagPattern=tagPattern) sessionList = allProfile.Sessions animalList = [] for session in sessionList: animalList.append(session[:len(root.prefix) + 3]) animalList = list(set(animalList)) return sorted(animalList) def get_current_animals(root: Root, days_passed: int = 4): """ this function returns the list of animals with a new session within the last few ('days_passed') days """ now = datetime.datetime.now() all_animals = root.get_all_animals() if all_animals == []: logging.warning('No animal found!') return [] animalList = [] for animal in all_animals: animalTag = File(root, animal) sessionList = animalTag.get_all_sessions() if not sessionList: continue lastSessionDate = animalTag.get_session_date(sessionList[-1]) if (now - lastSessionDate).days <= days_passed: animalList.append(animal) return animalList

33.47482

119

0.663873

514

4,653

5.877432

0.223735

0.032771

0.027805

0.029791

0.309169

0.282688

0.266799

0.200265

0.127772

0

0.009445

0.249087

4,653

138

120

33.717391

0.85518

0.148077

0

0.225806

0

0.029275

0.005699

0

1

0.064516

false

0.032258

0.075269

0

0.215054

0

null

0

null

0

1

0

282403dbaa1f17f6e0d6f80a9faabdc5990009bd

10,747

py

Python

IsaacAgent.py

dholmdahl/connect4-1

cdcd92ee30f45e89a9f01ebc87a8b6d797cc4a81

[ "MIT" ]

null

IsaacAgent.py

dholmdahl/connect4-1

cdcd92ee30f45e89a9f01ebc87a8b6d797cc4a81

[ "MIT" ]

null

IsaacAgent.py

dholmdahl/connect4-1

cdcd92ee30f45e89a9f01ebc87a8b6d797cc4a81

[ "MIT" ]

null

from random import choice from copy import deepcopy from game_data import GameData from agents import Agent import numpy as np import random import pickle import pandas as pd class IsaacAgent(Agent): def __init__(self, max_time=2, max_depth=300): self.max_time = max_time self.max_depth = max_depth # self.heuristic = [ # [0], [0], [0], [0], [0], [0], [0], # [0], [0], [0], [0], [0], [0], [0], # [0], [0], [0], [0], [0], [0], [0], # [0], [0], [0], [0], [0], [0], [0], # ... # [0], [0], [-1], [-1], [-1], [0], [0], # odd player # [0], [1, -1], [0], [0], [0], [1, -1], [0] # even player # ] self.heuristic = [ [0], [0], [0], [0], [0], [0], [0], [0], [0], [1, -1], [2, -2], [1, -1], [0], [0], [0], [0], [1, -2], [2, -2], [1, -2], [0], [0], [0], [0], [3, -2], [3, -2], [3, -2], [0], [0], [0], [0], [2, -3], [2, -3], [2, -3], [0], [0], [0], [1, -1], [3, -3], [4, -4], [3, -3], [1, -1], [0] ] self.game_data = None self.model = pickle.load(open("./c4model.sav", 'rb')) def get_name(self) -> str: return "IsaacAgent" def get_move(self, game_data) -> int: self.game_data = game_data rows_reversed_connect4_board = [] for row in list(game_data.game_board): rows_reversed_connect4_board.append(row[::-1]) connect4_board = list(np.concatenate(rows_reversed_connect4_board).flat)[::-1] for sn, sv in enumerate(connect4_board): if sv == 0: connect4_board[sn] = ' ' elif sv == 1: connect4_board[sn] = 'R' else: connect4_board[sn] = 'B' # self.print_board(connect4_board) turn = self.player(connect4_board) actions = self.actions(connect4_board) best_action = random.choice(actions) if turn == 'R': # max player local_best_min_v = -float('inf') for action in actions: self.current_depth = 0 min_v = self.min_value(self.result(connect4_board, action)) # print(f"Action: {action + 1}, Min Value: {min_v}") if min_v > local_best_min_v: local_best_min_v = min_v best_action = action else: # min player local_best_max_v = float('inf') for action in actions: self.current_depth = 0 max_v = self.max_value(self.result(connect4_board, action)) # print(f"Action: {action + 1}, Max Value: {max_v}") if max_v < local_best_max_v: local_best_max_v = max_v best_action = action return best_action def print_board(self, board): for l in range(0, 42, 7): row = ''.join([board[l + i] + '|' for i in range(7)]) print(row[:13]) print('-+-+-+-+-+-+-') def player(self, board): return 'B' if board.count('R') > board.count('B') else 'R' def is_tie(self, board): return len([sq for sq in board if sq == ' ']) == 0 def utility(self, board): return 0 if self.is_tie(board) else -1000 if self.player(board) == "R" else 1000 def terminal(self, board): # use modulo 7 to detect new row row = 0 for sq in range(42): if sq % 7 == 0: row += 1 distance_to_new_row = 7 * row - (sq + 1) distance_to_column_end = [i for i in range(6) if (sq + 1) + i * 7 > 35][0] if board[sq] == ' ': continue # 4 horizontally if distance_to_new_row >= 3 and board[sq] == board[sq + 1] and board[sq] == board[sq + 2] and board[sq] == board[sq + 3]: return True # 4 vertically elif distance_to_column_end > 2 and board[sq] == board[sq + 7] and board[sq] == board[sq + 14] and board[sq] == board[sq + 21]: return True # 4 diagonally elif distance_to_new_row >= 3 and distance_to_column_end >= 2 and sq + 24 < len(board) and board[sq] == board[sq + 8] and board[sq] == board[sq + 16] and board[sq] == board[sq + 24]: return True elif distance_to_new_row >= 3 and distance_to_column_end <= 2 and 0 <= sq - 18 < len(board) and board[sq] == board[sq - 6] and board[sq] == board[sq - 12] and board[sq] == board[sq - 18]: return True return self.is_tie(board) def actions(self, board): return [sn for sn in range(7) if board[sn] == ' '] def result(self, board, action): result = board[:] for r in range(6): current_sq = board[action + 35 - r * 7] if current_sq == ' ': result[action + 35 - r * 7] = self.player(board) break return result def count_two_in_row(self, board, player): two_in_row = 0 row = 0 for sq in range(42): if sq % 7 == 0: row += 1 distance_to_new_row = 7 * row - (sq + 1) distance_to_column_end = [i for i in range(6) if (sq + 1) + i * 7 > 35][0] if board[sq] != player or board[sq].isdigit() or board[sq] == ' ': continue # 4 horizontally if distance_to_new_row >= 3 and board[sq] == board[sq + 1]: two_in_row += 1 # 4 vertically elif distance_to_column_end > 2 and board[sq] == board[sq + 7]: two_in_row += 1 # 4 diagonally elif distance_to_new_row >= 3 and distance_to_column_end >= 2 and sq + 8 < len(board) and board[sq] == board[sq + 8]: two_in_row += 1 elif distance_to_new_row >= 3 and distance_to_column_end <= 2 and 0 <= sq - 6 < len(board) and board[sq] == board[sq - 6]: two_in_row += 1 return two_in_row def count_three_in_row(self, board, player): three_in_row = 0 row = 0 for sq in range(42): if sq % 7 == 0: row += 1 distance_to_new_row = 7 * row - (sq + 1) distance_to_column_end = [i for i in range(6) if (sq + 1) + i * 7 > 35][0] if board[sq] != player or board[sq].isdigit() or board[sq] == ' ': continue # 4 horizontally if distance_to_new_row >= 3 and board[sq] == board[sq + 1] and board[sq] == board[sq + 2]: three_in_row += 1 # 4 vertically elif distance_to_column_end > 2 and board[sq] == board[sq + 7] and board[sq] == board[sq + 14]: three_in_row += 1 # 4 diagonally elif distance_to_new_row >= 3 and distance_to_column_end >= 2 and sq + 16 < len(board) and board[sq] == board[sq + 8] and board[sq] == board[sq + 16]: three_in_row += 1 elif distance_to_new_row >= 3 and distance_to_column_end <= 2 and 0 <= sq - 12 < len(board) and board[sq] == board[sq - 6] and board[sq] == board[sq - 12]: three_in_row += 1 return three_in_row def evaluate(self, board): """ Heuristic: - Squares value: [0, 0, -1, -1, -1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, -2, -2, -2, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, -3, -3, -3, 0, 0, 0, 0, 1, 1, 1, 0, 0] - Include win squares of each player and where they are located. Heuristic based off Odd-Even strategy: https://www.youtube.com/watch?v=YqqcNjQMX18 """ total_score = 0 for vn, values in enumerate(self.heuristic): for value in values: if value < 0 and board[vn] == 'B': total_score += value elif value > 0 and board[vn] == 'R': total_score += value # three_in_row_modifier = 10 # total_score += self.count_three_in_row(board, 'R') * three_in_row_modifier # total_score -= self.count_three_in_row(board, 'B') * three_in_row_modifier # total_score += self.count_two_in_row(board, 'R') * three_in_row_modifier # total_score -= self.count_two_in_row(board, 'B') * three_in_row_modifier # divisor = 5 # for i in range(7): # action_result = self.result(board, i) # if self.terminal(action_result): # total_score += self.utility(action_result) / divisor # print(total_score) # multiplier = 2 # r_win_states = 0 # b_win_states = 0 # for i in range(7): # action_result = self.result(board, i) # if self.terminal(action_result): # if self.utility(action_result) == 1000: # r_win_states += 1 # else: # b_win_states += 1 # total_score += r_win_states * multiplier # total_score -= b_win_states * multiplier # if r_win_states >= 2: # total_score += 400 # elif b_win_states >= 2: # total_score -= 400 # print(f"Red Win States: {r_win_states}, Blue Win States: {b_win_states}") # multiplier = 30 # conv_data = [] # for sq in board: # if sq.isdigit() or sq == ' ': # conv_data.append(0) # elif sq == 'R': # conv_data.append(1) # else: # conv_data.append(-1) # c4_board = pd.Series(conv_data, index=[f"pos_{sn + 1}" for sn, sv in enumerate(board)]) # total_score += self.model.predict([c4_board])[0][0] return total_score def min_value(self, board): if self.terminal(board): return self.utility(board) if self.current_depth > self.max_depth: return self.evaluate(board) self.current_depth += 1 v = float('inf') for action in self.actions(board): max_v = self.max_value(self.result(board, action)) v = min(v, max_v) return v def max_value(self, board): if self.terminal(board): return self.utility(board) if self.current_depth > self.max_depth: return self.evaluate(board) self.current_depth += 1 v = -float('inf') for action in self.actions(board): min_v = self.min_value(self.result(board, action)) v = max(v, min_v) return v

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2826bae5797a9d9d95a636c0a99581f2619ca237

5,872

py

Python

algorand-oracle-smart-contracts/src/algorand_oracle.py

damees/algorand-oracle

f7f078f9d153341d1ba546ff66e8afbf2685f114

[ "MIT" ]

null

algorand-oracle-smart-contracts/src/algorand_oracle.py

damees/algorand-oracle

f7f078f9d153341d1ba546ff66e8afbf2685f114

[ "MIT" ]

null

algorand-oracle-smart-contracts/src/algorand_oracle.py

damees/algorand-oracle

f7f078f9d153341d1ba546ff66e8afbf2685f114

[ "MIT" ]

null

from pyteal import * ADMIN_KEY = Bytes("admin") WHITELISTED_KEY = Bytes("whitelisted") REQUESTS_BALANCE_KEY = Bytes("requests_balance") MAX_BUY_AMOUNT = Int(1000000000) MIN_BUY_AMOUNT = Int(10000000) REQUESTS_SELLER = Addr("N5ICVTFKS7RJJHGWWM5QXG2L3BV3GEF6N37D2ZF73O4PCBZCXP4HV3K7CY") MARKET_EXCHANGE_NOTE = Bytes("algo-oracle-app-4") def approval_program(): on_creation = Seq( [ Assert(Txn.application_args.length() == Int(0)), App.localPut(Int(0), ADMIN_KEY, Int(1)), Return(Int(1)) ] ) is_contract_admin = App.localGet(Int(0), ADMIN_KEY) # set/remove an admin for this contract admin_status = Btoi(Txn.application_args[2]) set_admin = Seq( [ Assert( And( is_contract_admin, Txn.application_args.length() == Int(3), Txn.accounts.length() == Int(1), ) ), App.localPut(Int(1), ADMIN_KEY, admin_status), Return(Int(1)), ] ) register = Seq( [ App.localPut(Int(0), WHITELISTED_KEY, Int(0)), Return(Int(1)) ] ) # Depending on what you do, you should always consider implementing a whitelisting to # control who access your app. This will allow you to process offchain validation before # allowing an account to call you app. # You may also consider case by case whitelisting to allow access to specific business methods. whitelist = Seq( [ Assert( And( is_contract_admin, Txn.application_args.length() == Int(2), Txn.accounts.length() == Int(1) ) ), App.localPut(Int(1), WHITELISTED_KEY, Int(1)), Return(Int(1)) ] ) # This should be added to the checklist of business methods. is_whitelisted = App.localGet(Int(0), WHITELISTED_KEY) # An admin can increase the request balance of a user. requests_amount = Btoi(Txn.application_args[1]) allocate_requests = Seq( [ Assert( And( is_contract_admin, # Sent by admin Txn.application_args.length() == Int(3), # receiver and amount are provided Txn.accounts.length() == Int(1), App.localGet(Int(1), WHITELISTED_KEY), # receiver is whitelisted ) ), App.localPut( Int(1), REQUESTS_BALANCE_KEY, App.localGet(Int(1), REQUESTS_BALANCE_KEY) + requests_amount ), Return(Int(1)) ] ) # a client can buy requests buy_requests = Seq( [ Assert( And( is_whitelisted, Global.group_size() == Int(2), # buying requests must be done using an atomic transfer Gtxn[0].type_enum() == TxnType.Payment, # the first transaction must be a payment... Gtxn[0].receiver() == REQUESTS_SELLER, # ...to our address Gtxn[0].amount() >= MIN_BUY_AMOUNT, # we don't sell for less than 10... Gtxn[0].amount() <= MAX_BUY_AMOUNT, # ...or more than 1000 ALGO Txn.group_index() == Int(1), # call to the contract is the second transaction Txn.application_args.length() == Int(2), Txn.accounts.length() == Int(1) # the address which will use the requests must be provided ) ), App.localPut( Int(1), REQUESTS_BALANCE_KEY, App.localGet(Int(1), REQUESTS_BALANCE_KEY) + (Gtxn[0].amount() / Int(100000)), ), Return(Int(1)) ] ) market_exchange_rate_request = Seq( [ Assert( And( is_whitelisted, Txn.note() == MARKET_EXCHANGE_NOTE, Txn.application_args.length() == Int(4), Txn.accounts.length() == Int(0), App.localGet(Int(0), REQUESTS_BALANCE_KEY) >= Int(1) ) ), App.localPut( Int(0), REQUESTS_BALANCE_KEY, App.localGet(Int(0), REQUESTS_BALANCE_KEY) - Int(1), ), Return(Int(1)) ] ) # Implement other oracle methods... program = Cond( [Txn.application_id() == Int(0), on_creation], [Txn.on_completion() == OnComplete.DeleteApplication, Return(is_contract_admin)], [Txn.on_completion() == OnComplete.UpdateApplication, Return(is_contract_admin)], [Txn.on_completion() == OnComplete.CloseOut, Return(Int(1))], [Txn.on_completion() == OnComplete.OptIn, register], [Txn.application_args[0] == Bytes("set_admin"), set_admin], [Txn.application_args[0] == Bytes("whitelist"), whitelist], [Txn.application_args[0] == Bytes("allocate_requests"), allocate_requests], [Txn.application_args[0] == Bytes("buy_requests"), buy_requests], [Txn.application_args[0] == Bytes("get_market_exchange_rate"), market_exchange_rate_request] ) return program def clear_state_program(): program = Seq( [ Return(Int(1)) ] ) return program if __name__ == "__main__": with open("algorand_oracle_approval.teal", "w") as f: compiled = compileTeal(approval_program(), mode=Mode.Application, version=5) f.write(compiled) with open("algorand_oracle_clear_state.teal", "w") as f: compiled = compileTeal(clear_state_program(), mode=Mode.Application, version=5) f.write(compiled)

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null

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null

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0

28271eebbca12a80c721021d335930842259d168

20,198

py

Python

custom_components/shelly/__init__.py

astrandb/ShellyForHASS

f404d3007a26945f310a801c6c7d196d7fa1fe23

[ "MIT" ]

null

custom_components/shelly/__init__.py

astrandb/ShellyForHASS

f404d3007a26945f310a801c6c7d196d7fa1fe23

[ "MIT" ]

null

custom_components/shelly/__init__.py

astrandb/ShellyForHASS

f404d3007a26945f310a801c6c7d196d7fa1fe23

[ "MIT" ]

null

""" Support for Shelly smart home devices. For more details about this component, please refer to the documentation at https://home-assistant.io/components/shelly/ """ # pylint: disable=broad-except, bare-except, invalid-name, import-error from datetime import timedelta import logging import time import asyncio import voluptuous as vol from homeassistant.const import ( CONF_DEVICES, CONF_DISCOVERY, CONF_ID, CONF_NAME, CONF_PASSWORD, CONF_SCAN_INTERVAL, CONF_USERNAME, EVENT_HOMEASSISTANT_STOP) from homeassistant import config_entries from homeassistant.helpers import discovery from homeassistant.helpers.dispatcher import async_dispatcher_send from homeassistant.helpers.entity import Entity from homeassistant.helpers.script import Script from homeassistant.util import slugify from .const import * from .configuration_schema import CONFIG_SCHEMA REQUIREMENTS = ['pyShelly==0.1.16'] _LOGGER = logging.getLogger(__name__) __version__ = "0.1.6.b6" VERSION = __version__ BLOCKS = {} DEVICES = {} BLOCK_SENSORS = [] DEVICE_SENSORS = [] #def _get_block_key(block): # key = block.id # if not key in BLOCKS: # BLOCKS[key] = block # return key def get_block_from_hass(hass, discovery_info): """Get block from HASS""" if SHELLY_BLOCK_ID in discovery_info: key = discovery_info[SHELLY_BLOCK_ID] return hass.data[SHELLY_BLOCKS][key] def _dev_key(dev): key = dev.id + "-" + dev.device_type if dev.device_sub_type is not None: key += "-" + dev.device_sub_type return key #def _get_device_key(dev): # key = _dev_key(dev) # if not key in DEVICES: # DEVICES[key] = dev # return key def get_device_from_hass(hass, discovery_info): """Get device from HASS""" device_key = discovery_info[SHELLY_DEVICE_ID] return hass.data[SHELLY_DEVICES][device_key] async def async_setup(hass, config): """Set up this integration using yaml.""" if DOMAIN not in config: return True hass.data[DOMAIN] = config hass.async_create_task( hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data={} ) ) return True async def async_setup_entry(hass, config_entry): """Setup Shelly component""" _LOGGER.info("Starting shelly, %s", __version__) config = hass.data[DOMAIN] conf = config.get(DOMAIN, {}) #todo! hass.data[SHELLY_CONFIG] = conf hass.data[SHELLY_DEVICES] = DEVICES hass.data[SHELLY_BLOCKS] = BLOCKS if conf.get(CONF_WIFI_SENSOR) is not None: _LOGGER.warning("wifi_sensor is deprecated, use rssi in sensors instead.") if conf.get(CONF_WIFI_SENSOR) and SENSOR_RSSI not in conf[CONF_SENSORS]: conf[CONF_SENSORS].append(SENSOR_RSSI) if conf.get(CONF_UPTIME_SENSOR) is not None: _LOGGER.warning("uptime_sensor is deprecated, use uptime in sensors instead.") if conf.get(CONF_UPTIME_SENSOR) and SENSOR_UPTIME not in conf[CONF_SENSORS]: conf[CONF_SENSORS].append(SENSOR_UPTIME) hass.data["SHELLY_INSTANCE"] = ShellyInstance(hass, config_entry, conf) #def update_status_information(): # pys.update_status_information() #for _, block in pys.blocks.items(): # block.update_status_information() #async def update_domain_callback(_now): # """Update the Shelly status information""" # await hass.async_add_executor_job(update_status_information) #if conf.get(CONF_ADDITIONAL_INFO): # hass.helpers.event.async_track_time_interval( # update_domain_callback, update_interval) return True class ShellyInstance(): """Config instance of Shelly""" def __init__(self, hass, config_entry, conf): self.hass = hass self.config_entry = config_entry self.platforms = {} self.pys = None self.conf = conf self.discover = conf.get(CONF_DISCOVERY) hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, self._stop) hass.loop.create_task( self.start_up() ) async def start_up(self): conf = self.conf if conf.get(CONF_LOCAL_PY_SHELLY): _LOGGER.info("Loading local pyShelly") #pylint: disable=no-name-in-module from .pyShelly import pyShelly else: from pyShelly import pyShelly additional_info = conf.get(CONF_ADDITIONAL_INFO) update_interval = conf.get(CONF_SCAN_INTERVAL) self.pys = pys = pyShelly() _LOGGER.info("pyShelly, %s", pys.version()) pys.cb_block_added.append(self._block_added) pys.cb_device_added.append(self._device_added) pys.cb_device_removed.append(self._device_removed) pys.username = conf.get(CONF_USERNAME) pys.password = conf.get(CONF_PASSWORD) pys.cloud_auth_key = conf.get(CONF_CLOUD_AUTH_KEY) pys.cloud_server = conf.get(CONF_CLOUD_SEREVR) pys.tmpl_name = conf.get(CONF_TMPL_NAME, pys.tmpl_name) if additional_info: pys.update_status_interval = update_interval pys.only_device_id = conf.get(CONF_ONLY_DEVICE_ID) pys.igmp_fix_enabled = conf.get(CONF_IGMPFIX) pys.mdns_enabled = conf.get(CONF_MDNS) pys.host_ip = conf.get(CONF_HOST_IP, '') pys.start() pys.discover() discover_by_ip = conf.get(CONF_DISCOVER_BY_IP) for ip_addr in discover_by_ip: pys.add_device_by_ip(ip_addr, 'IP-addr') if conf.get(CONF_VERSION): attr = {'version': VERSION, 'pyShellyVersion': pys.version()} self._add_device("sensor", attr) fake_block = { 'id' : "694908", 'fake_block': True, 'info_values': {'temperature':5}, 'cb_updated' : [], } attr = {'sensor_type':'temperature', 'itm': fake_block} self._add_device("sensor", fake_block) async def _stop(self, _): """Stop Shelly.""" _LOGGER.info("Shutting down Shelly") self.pys.close() def _get_specific_config_root(self, key, *ids): item = self._get_specific_config(key, None, *ids) if item is None: item = self.conf.get(key) return item def _find_device_config(self, device_id): device_conf_list = self.conf.get(CONF_DEVICES) for item in device_conf_list: if item[CONF_ID].upper() == device_id: return item return None def _get_device_config(self, device_id, id_2=None): """Get config for device.""" item = self._find_device_config(device_id) if item is None and id_2 is not None: item = self._find_device_config(id_2) if item is None: return {} return item def _get_specific_config(self, key, default, *ids): for device_id in ids: item = self._find_device_config(device_id) if item is not None and key in item: return item[key] return default def _get_sensor_config(self, *ids): sensors = self._get_specific_config(CONF_SENSORS, None, *ids) if sensors is None: sensors = self.conf.get(CONF_SENSORS) if SENSOR_ALL in sensors: return [*SENSOR_TYPES.keys()] if sensors is None: return {} return sensors def _add_device(self, platform, dev): self.hass.add_job(self._async_add_device(platform, dev)) async def _async_add_device(self, platform, dev): if platform not in self.platforms: self.platforms[platform] = asyncio.Event() await self.hass.config_entries.async_forward_entry_setup( self.config_entry, platform) self.platforms[platform].set() await self.platforms[platform].wait() async_dispatcher_send(self.hass, "shelly_new_" + platform \ , dev, self) def _block_updated(self, block): hass_data = block.hass_data if hass_data['discover']: if hass_data['allow_upgrade_switch']: has_update = block.info_values.get('has_firmware_update', False) update_switch = getattr(block, 'firmware_switch', None) if has_update: if update_switch is None: attr = {'firmware': True, 'block':block} self._add_device("switch", attr) elif update_switch is not None: update_switch.remove() #block_key = _get_block_key(block) for key, _value in block.info_values.items(): ukey = block.id + '-' + key if not ukey in BLOCK_SENSORS: BLOCK_SENSORS.append(ukey) for sensor in hass_data['sensor_cfg']: if SENSOR_TYPES[sensor].get('attr') == key: attr = {'sensor_type':key, 'itm': block} self._add_device("sensor", attr) def _block_added(self, block): self.hass.add_job(self._async_block_added(block)) async def _async_block_added(self, block): block.cb_updated.append(self._block_updated) discover_block = self.discover \ or self._get_device_config(block.id) != {} block.hass_data = { 'allow_upgrade_switch' : self._get_specific_config_root(CONF_UPGRADE_SWITCH, block.id), 'sensor_cfg' : self._get_sensor_config(block.id), 'discover': discover_block } #Config block if block.unavailable_after_sec is None: block.unavailable_after_sec \ = self._get_specific_config_root(CONF_UNAVALABLE_AFTER_SEC, block.id) #if conf.get(CONF_ADDITIONAL_INFO): #block.update_status_information() # cfg_sensors = conf.get(CONF_SENSORS) # for sensor in cfg_sensors: # sensor_type = SENSOR_TYPES[sensor] # if 'attr' in sensor_type: # attr = {'sensor_type':sensor_type['attr'], # SHELLY_BLOCK_ID : block_key} # discovery.load_platform(hass, 'sensor', DOMAIN, attr, # config) def _device_added(self, dev, _code): self.hass.add_job(self._async_device_added(dev, _code)) async def _async_device_added(self, dev, _code): device_config = self._get_device_config(dev.id, dev.block.id) if not self.discover and device_config == {}: return if dev.device_type == "ROLLER": self._add_device("cover", dev) if dev.device_type == "RELAY": if device_config.get(CONF_LIGHT_SWITCH): self._add_device("light", dev) else: self._add_device("switch", dev) elif dev.device_type == 'POWERMETER': sensor_cfg = self._get_sensor_config(dev.id, dev.block.id) if SENSOR_POWER in sensor_cfg: self._add_device("sensor", dev) elif dev.device_type == 'SWITCH': sensor_cfg = self._get_sensor_config(dev.id, dev.block.id) if SENSOR_SWITCH in sensor_cfg: self._add_device("sensor", dev) elif dev.device_type in ["SENSOR"]: #, "INFOSENSOR"]: self._add_device("sensor", dev) elif dev.device_type in ["LIGHT", "DIMMER"]: self._add_device("light", dev) def _device_removed(self, dev, _code): dev.shelly_device.remove() try: pass #key = _dev_key(dev) #del DEVICES[key] except KeyError: pass class ShellyBlock(Entity): """Base class for Shelly entities""" def __init__(self, block, instance, prefix=""): conf = instance.conf id_prefix = conf.get(CONF_OBJECT_ID_PREFIX) self._unique_id = slugify(id_prefix + "_" + block.type + "_" + block.id + prefix) self.entity_id = "." + self._unique_id entity_id = instance._get_specific_config(CONF_ENTITY_ID , None, block.id) if entity_id is not None: self.entity_id = "." + slugify(id_prefix + "_" + entity_id + prefix) self._unique_id += "_" + slugify(entity_id) #self._name = None #block.type_name() #if conf.get(CONF_SHOW_ID_IN_NAME): # self._name += " [" + block.id + "]" self.fake_block = isinstance(block, dict) #:'fake_block' in block self._show_id_in_name = conf.get(CONF_SHOW_ID_IN_NAME) self._block = block self.hass = instance.hass self.instance = instance self._block.cb_updated.append(self._updated) block.shelly_device = self self._name = instance._get_specific_config(CONF_NAME, None, block.id) self._name_ext = None self._is_removed = False self.hass.add_job(self.setup_device(block)) async def setup_device(self, block): dev_reg = await self.hass.helpers.device_registry.async_get_registry() dev_reg.async_get_or_create( config_entry_id=self.entity_id, identifiers={(DOMAIN, block.id)}, manufacturer="Shelly", name=block.friendly_name(), model=block.type_name(), sw_version="0.0.1", ) @property def name(self): """Return the display name of this device.""" if self.fake_block: name = 'Fake' if self._name is None: name = self._block.friendly_name() else: name = self._name if self._name_ext: name += ' - ' + self._name_ext if self._show_id_in_name: name += " [" + self._block.id + "]" return name def _updated(self, _block): """Receive events when the switch state changed (by mobile, switch etc)""" if self.entity_id is not None and not self._is_removed: self.schedule_update_ha_state(True) @property def device_state_attributes(self): """Show state attributes in HASS""" if self.fake_block: return {} attrs = {'ip_address': self._block.ip_addr, 'shelly_type': self._block.type_name(), 'shelly_id': self._block.id, 'discovery': self._block.discovery_src } room = self._block.room_name() if room: attrs['room'] = room if self._block.info_values is not None: for key, value in self._block.info_values.items(): attrs[key] = value return attrs @property def device_info(self): return { 'identifiers': { (DOMAIN, self._block.id) } # 'name': self.name, # 'manufacturer': "Shelly", # 'model': self._block.type, # 'sw_version': '0.0.1', # #'via_device': (hue.DOMAIN, self.api.bridgeid), } def remove(self): self._is_removed = True self.hass.add_job(self.async_remove) class ShellyDevice(Entity): """Base class for Shelly entities""" def __init__(self, dev, instance): conf = instance.conf id_prefix = conf.get(CONF_OBJECT_ID_PREFIX) self._unique_id = id_prefix + "_" + dev.type + "_" + dev.id self.entity_id = "." + slugify(self._unique_id) entity_id = instance._get_specific_config(CONF_ENTITY_ID, None, dev.id, dev.block.id) if entity_id is not None: self.entity_id = "." + slugify(id_prefix + "_" + entity_id) self._unique_id += "_" + slugify(entity_id) self._show_id_in_name = conf.get(CONF_SHOW_ID_IN_NAME) #self._name = dev.type_name() #if conf.get(CONF_SHOW_ID_IN_NAME): # self._name += " [" + dev.id + "]" # 'Test' #light.name self._dev = dev self.hass = instance.hass self.instance = instance self._dev.cb_updated.append(self._updated) dev.shelly_device = self self._name = instance._get_specific_config(CONF_NAME, None, dev.id, dev.block.id) self._sensor_conf = instance._get_sensor_config(dev.id, dev.block.id) self._is_removed = False def _updated(self, _block): """Receive events when the switch state changed (by mobile, switch etc)""" if self.entity_id is not None and not self._is_removed: self.schedule_update_ha_state(True) if self._dev.info_values is not None: for key, _value in self._dev.info_values.items(): ukey = self._dev.id + '-' + key if not ukey in DEVICE_SENSORS: DEVICE_SENSORS.append(ukey) for sensor in self._sensor_conf: if SENSOR_TYPES[sensor].get('attr') == key: attr = {'sensor_type':key, 'itm':self._dev} conf = self.hass.data[SHELLY_CONFIG] #discovery.load_platform(self.hass, 'sensor', # DOMAIN, attr, conf) @property def name(self): """Return the display name of this device.""" if self._name is None: name = self._dev.friendly_name() else: name = self._name if self._show_id_in_name: name += " [" + self._dev.id + "]" return name @property def device_state_attributes(self): """Show state attributes in HASS""" attrs = {'ip_address': self._dev.ip_addr, 'shelly_type': self._dev.type_name(), 'shelly_id': self._dev.id, 'discovery': self._dev.discovery_src } room = self._dev.room_name() if room: attrs['room'] = room if self._dev.block.info_values is not None: for key, value in self._dev.block.info_values.items(): attrs[key] = value if self._dev.info_values is not None: for key, value in self._dev.info_values.items(): attrs[key] = value if self._dev.sensor_values is not None: for key, value in self._dev.sensor_values.items(): attrs[key] = value return attrs @property def device_info(self): return { 'identifiers': { # Serial numbers are unique identifiers within a specific domain (DOMAIN, self._dev.block.id) }, # 'name': self._dev.block.friendly_name(), # 'manufacturer': "Shelly", # 'model': self._dev.block.type_name(), # 'sw_version': '0.0.1', #'via_device': (hue.DOMAIN, self.api.bridgeid), } @property def unique_id(self): """Return the ID of this device.""" return self._unique_id @property def available(self): """Return true if switch is available.""" return self._dev.available() def remove(self): self._is_removed = True self.hass.add_job(self.async_remove) @property def should_poll(self): """No polling needed.""" return False

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