text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
This module provides a function to check the SNR of the white and gray matter
"""
# -----------------------------------------------------------------------------
def checkSNR(subjects_dir, subject, nb_erode=3, ref_image="norm.mgz", aparc_image="aparc+aseg.mgz"):
"""
A function to check the SNR of the whi... | {"hexsha": "1660796b9050554de1d724342236a62593473237", "size": 4444, "ext": "py", "lang": "Python", "max_stars_repo_path": "qatoolspython/checkSNR.py", "max_stars_repo_name": "AhmedFaisal95/qatools-python", "max_stars_repo_head_hexsha": "580530b24f7f29cc1c7ab3f9211998493f49be7c", "max_stars_repo_licenses": ["MIT"], "ma... |
from sage.all import EllipticCurve
def is_embedding_degree(E: EllipticCurve, k):
return (E.base_field().order() ** k - 1) % E.order() == 0
| {"hexsha": "27055f3a60cf0a74448b782252282910cca89bc3", "size": 144, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "adamivora/pairing_friendly_curves_generation", "max_stars_repo_head_hexsha": "7cad587e26f420fad5f9becb99bf1ec85d6b884d", "max_stars_repo_licenses": ["MIT"], "max_... |
'''
BSD 3-Clause License
Copyright (c) 2017, Jack Miles Hunt
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of cond... | {"hexsha": "6bd7918268f1d9ec40debaf0379bfc6a00974822", "size": 6300, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo.py", "max_stars_repo_name": "JackHunt/GP-LVM", "max_stars_repo_head_hexsha": "2f2aade7207db2f54d5ab13fd304c93eaeb8fc2a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11, "m... |
export testcase1a, testcase1a_point, testcase1b, testcase1c
export testcase1abis, testcase1ater
export testcase2
export testcase4a, testcase4b, testcase4c
# Test case 1.a
function testcase1a_point()
x = PointE([Matrix{Float64}(I, 3, 3)], Float64[])
end
function testcase1a(; symmetric=false)
if symmetric
... | {"hexsha": "0ae57c8580ed6d6f6fccb539cb806fc969affb71", "size": 7273, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/test_cases.jl", "max_stars_repo_name": "GillesBareilles/SDCO.jl", "max_stars_repo_head_hexsha": "f1514689b77d4410224d472c82776e9c26fa1cc4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
//////////////////////////////////////////////////////////////////////////////
// Boost.Assign v2 //
// //
// Copyright (C) 2003-2004 Thorsten Ottosen //
// ... | {"hexsha": "7966ec087a2e3201bc6413d9f773d41d4c3d80d0", "size": 2570, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/assign/v2/support/check/equal_container/deduce.hpp", "max_stars_repo_name": "rogard/assign_v2", "max_stars_repo_head_hexsha": "8735f57177dbee57514b4e80c498dd4b89f845e5", "max_stars_repo_licens... |
import sys
import numpy as np
from . import nn_translator
from . import predict
from . import visualizer
inputfile = sys.argv[1]
nn_input, input_mat, empirical \
= nn_translator.nn_translator(inputfile, train=True)
nn_input = np.array(nn_input).reshape((1, predict._N_DIMS_IN))
p = predict.Predictor(net='hyak_l... | {"hexsha": "9a824aec1dfd9bef6251e83c304f19eff26ad54d", "size": 597, "ext": "py", "lang": "Python", "max_stars_repo_path": "sspinn/__main__.py", "max_stars_repo_name": "awild82/SSPINN", "max_stars_repo_head_hexsha": "8b2680b6556f73ee75847b1d5e842f66e2af2f59", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "ma... |
import sys
sys.path.insert(0,'./../../..')
from limix.core.mean.mean_base import MeanBase as lin_mean
from limix.core.covar import SQExpCov
from limix.core.covar import FixedCov
from limix.core.covar import SumCov
from limix.core.gp import GP
import pdb
import scipy as sp
import scipy.linalg as LA
import time as TIME... | {"hexsha": "94f92fb7c4431f5e10e038ab4e4cadbbb6c5006d", "size": 1817, "ext": "py", "lang": "Python", "max_stars_repo_path": "svca_limix/demos/demo_gp_regression.py", "max_stars_repo_name": "DenisSch/svca", "max_stars_repo_head_hexsha": "bd029c120ca8310f43311253e4d7ce19bc08350c", "max_stars_repo_licenses": ["Apache-2.0"]... |
//==============================================================================
// Copyright 2003 - 2011 LASMEA UMR 6602 CNRS/Univ. Clermont II
// Copyright 2009 - 2011 LRI UMR 8623 CNRS/Univ Paris Sud XI
//
// Distributed under the Boost Software License, Version 1.0.
// ... | {"hexsha": "ca9ba1402006db1e572f499a074509bfa2969ba9", "size": 1954, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/core/trigonometric/include/nt2/trigonometric/constants/radindeg.hpp", "max_stars_repo_name": "psiha/nt2", "max_stars_repo_head_hexsha": "5e829807f6b57b339ca1be918a6b60a2507c54d0", "max_stars... |
#include <json/json.h>
#include <thread>
#include <mutex>
#include <list>
#include "settings.h"
#include <boost/uuid/uuid.hpp>
#include <boost/uuid/uuid_io.hpp>
#include <boost/lexical_cast.hpp>
OverlaySettingsWithDirtyFlag globalSettings;
//OverlaySettingsServer settingServer;
//OverlaySettingsManager settingManager... | {"hexsha": "d5bd9d7e139f49d8c08f29042d28ef0265b0f5b9", "size": 8566, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/settings/settings.cpp", "max_stars_repo_name": "ZCube/OverlayProc", "max_stars_repo_head_hexsha": "b57bab06644be6c7fe3468c726591f5a3a0f1a6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import numpy as np
# Ask user which folder we want to merge with
print("Merge 'processed_data' with: ")
print(" 1. 'train_data'")
print(" 2. 'test_data'")
while True:
val = input("Enter '1' or '2': ")
if val == "1":
path = "train_data\\"
break
elif val == "2":
path = "te... | {"hexsha": "26fe0c62b51cc703bcc90ddffe3caed391bf416e", "size": 1271, "ext": "py", "lang": "Python", "max_stars_repo_path": "windows_code/merge_data.py", "max_stars_repo_name": "daniel-luper/self-driving-picar", "max_stars_repo_head_hexsha": "d8d7bb3ee450db8995b85273d3015555a9ed3b1b", "max_stars_repo_licenses": ["MIT"],... |
cd(@__DIR__) # changes the directory to the current directory, the default I guess is the HOME
using Pkg; Pkg.activate("."); Pkg.instantiate()
#=
Pkg is Julia's built-in package manager, and handles operations such as installing, updating and removing packages.
Just like cargo it creates a toml-file that describes the ... | {"hexsha": "b4385f99c88147dbd9a51825e2f4b4614a150479", "size": 9455, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "seir/seir.jl", "max_stars_repo_name": "carlos-hernani/julia101", "max_stars_repo_head_hexsha": "3c5c877cf585ddab5674aa839880029db31387b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import numpy as np
from tqdm import tqdm
from joblib import Parallel, delayed
class ParticleFilter():
def __init__(self, dimension, n_particles, exploration_factor, keep_best, RandomSampler, Likelihood, Diffuser, n_jobs=-1, joblib_backend="loky"):
# Particles will simply be stored as numpy arrays (of size "dimensio... | {"hexsha": "5b3ef2368038b4af512fa64fa7fab588bdd4e80d", "size": 4419, "ext": "py", "lang": "Python", "max_stars_repo_path": "particle_filter.py", "max_stars_repo_name": "cohnt/Deformable-Object-Manifold-Learning", "max_stars_repo_head_hexsha": "81b6d757df78fbd4427db7ab87051ed1514180ee", "max_stars_repo_licenses": ["MIT"... |
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... | {"hexsha": "f22162f5212c320959db19cb6ae001a4e8cb5fd5", "size": 6277, "ext": "py", "lang": "Python", "max_stars_repo_path": "tuframework/evaluation/model_selection/summarize_results_with_plans.py", "max_stars_repo_name": "Magnety/tuFramework", "max_stars_repo_head_hexsha": "b31cb34d476ef306b52da955021f93c91c14ddf4", "ma... |
!NORMAL, REAL GREEN'S FUNCTION
subroutine vca_get_gimp_real_full(Greal)
complex(8),dimension(Nlat,Nlat,Nspin,Nspin,Norb,Norb,Lreal),intent(inout) :: Greal
Greal = impGreal
end subroutine vca_get_gimp_real_full
subroutine vca_get_gimp_real_ij(Greal,ilat,jlat)
integer ... | {"hexsha": "c1dc6dcc13cdac5815e19c37a1abc7fcbc11ee07", "size": 514, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "VCA_IO/get_gimp_realaxis.f90", "max_stars_repo_name": "QcmPlab/VCA", "max_stars_repo_head_hexsha": "01f5001db0ab0016043fb990a56b381858f7f9e0", "max_stars_repo_licenses": ["FSFAP"], "max_stars_cou... |
#to add support for Python 3.x
from __future__ import division
from __future__ import print_function
import os, sys
import matplotlib
import gtk
# gtk.set_interactive(False)
matplotlib.use('TkAgg') # use WXAgg for smoother graphs; GTKAgg is faster
import matplotlib.pyplot as plt
import numpy as np
import xml.etr... | {"hexsha": "cfc7e960f63be95144e9e96d3f83842ef5bac16c", "size": 9800, "ext": "py", "lang": "Python", "max_stars_repo_path": "figures/results/BckUpPlotStats.py", "max_stars_repo_name": "RobertHue/LatexBeamerRosenheim", "max_stars_repo_head_hexsha": "dbe568806053c8b4ca3a5470a011822f6220d511", "max_stars_repo_licenses": ["... |
!========================================================================
!
! T o m o f a s t - x
! -----------------------
!
! Authors: Vitaliy Ogarko, Jeremie Giraud, Roland Martin.
!
! (c) 2021 The University of Western Australia.
!
! The full ... | {"hexsha": "5097f695f5c9d3da32cbaf7e267a581ebf186a9e", "size": 5539, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/utils/vector.f90", "max_stars_repo_name": "RichardScottOZ/Tomofast-x", "max_stars_repo_head_hexsha": "af2d0b8ad59cdc18d9c348bec274ca4371ae94c0", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env /usr/bin/python
from pyrosetta import *
import re,sys
import os, shutil
import random
import numpy as np
import pickle
import math
os.environ["OPENBLAS_NUM_THREADS"] = "1"
phi=[]
psi=[]
phi_prob=[]
psi_prob=[]
#exit()
pcut = float(sys.argv[1])
k=int(sys.argv[2])
if(os.path.isfile("phipsi.npz")):
... | {"hexsha": "e60f034cb2003ac578e6e662cd96eda478657657", "size": 21845, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/TrRosetta/TR/fold_from_tor_split.py", "max_stars_repo_name": "ruiyangsong/mCNN", "max_stars_repo_head_hexsha": "889f182245f919fb9c7a8d97965b11576b01a96c", "max_stars_repo_licenses": ["MIT"], ... |
from sklearn.model_selection import StratifiedKFold, KFold
from reval.relative_validation import RelativeValidation
from collections import namedtuple
from scipy import stats
import numpy as np
import math
class FindBestClustCV(RelativeValidation):
"""Child class of :class:`reval.relative_validation.RelativeValid... | {"hexsha": "785ad29203b2cc3fe2438a1d361280ac03e24fed", "size": 5206, "ext": "py", "lang": "Python", "max_stars_repo_path": "reval/best_nclust_cv.py", "max_stars_repo_name": "landiisotta/relative_validation_clustering", "max_stars_repo_head_hexsha": "8842abd1674d899eee9997ea4f0cbe2429df0732", "max_stars_repo_licenses": ... |
from .context import skip_if_no_cuda_device
import numpy as np
import os
from km3net.util import *
#this test verifies that we are testing
#the current repository package rather than the installed package
def test_get_kernel_path():
path = "/".join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-1])
... | {"hexsha": "4d28f2b23bdfe5fee000a3943c69f16a434459f0", "size": 3218, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_util.py", "max_stars_repo_name": "remenska/KM3Net", "max_stars_repo_head_hexsha": "4c175662465b9a880fc1864f62219ce9702311f1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
// Copyright (C) 2021 Christian Brommer, Control of Networked Systems, University of Klagenfurt, Austria.
//
// All rights reserved.
//
// This software is licensed under the terms of the BSD-2-Clause-License with
// no commercial use allowed, the full terms of which are made available
// in the LICENSE file. No licens... | {"hexsha": "2ebe568f7ebaff89bba3c6c338c4f66ce7b31546", "size": 1212, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "source/tests/mars-test/mars_type_erasure.cpp", "max_stars_repo_name": "eallak/mars_lib", "max_stars_repo_head_hexsha": "9657fb669c48be39471e7504c3648319126c020b", "max_stars_repo_licenses": ["BSD-2-... |
import os
import sys
from glob import glob
import numpy as np
import h5py
import freqent.freqentn as fen
import multiprocessing
import argparse
def calc_epr_spectral(file):
'''
function to pass to multiprocessing pool to calculate epr in parallel
'''
print('Reading {f}'.format(f=file.split(os.path.sep)... | {"hexsha": "92f11bd80af8b148edd61a56297d37faef18124c", "size": 2463, "ext": "py", "lang": "Python", "max_stars_repo_path": "freqent/tests/brussfield/calculations/calculate_epr.py", "max_stars_repo_name": "lab-of-living-matter/freqent", "max_stars_repo_head_hexsha": "210d8f25a59894d903c42d52e5475900303f9631", "max_stars... |
import face_recognition
from scipy import misc
import numpy as np
from skimage import transform
import os.path
for i in range(1200):
image_numpy = misc.imread('/media/rob/Ma Book1/mugshots/aligned/alignedFace'+str(i)+'.jpg')
image_numpy = np.flip(image_numpy, axis=1)
image_numpy = misc.imsave('/media/rob/M... | {"hexsha": "01d6b3ac13622fe1f8a78bc4a04ae265422f6916", "size": 462, "ext": "py", "lang": "Python", "max_stars_repo_path": "datavis/faceFlipper.py", "max_stars_repo_name": "carykh/celebrityFaces", "max_stars_repo_head_hexsha": "7513ae9562a51e89e0ae5ec33db309e2cb16192b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python
# Copyright 2014-2018 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | {"hexsha": "a7d3910d21a71c536578588f608ef48af95ac53c", "size": 2989, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/prop/gtensor/dhf.py", "max_stars_repo_name": "y-yao/pyscf_arrow", "max_stars_repo_head_hexsha": "079088a5d92af1570167004f411207deb104a1bb", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
#!/usr/bin/env python
import numpy as np
import os
import torch
from torch import nn
import warnings
import models
from scipy.signal import resample
import math
import pandas as pd
import shutil
from get_12ECG_features import get_12ECG_features
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
if torch.cuda.is_available():
... | {"hexsha": "c13ebc85125eb130063052fdaa1265dc4a5647ab", "size": 7427, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_12ECG_classifier.py", "max_stars_repo_name": "ZhaoZhibin/Physionet2020model", "max_stars_repo_head_hexsha": "ea7379bd1e4c145c84fd254faa0d5d1330cd2f6e", "max_stars_repo_licenses": ["BSD-2-Claus... |
//
// The MIT License(MIT)
//
// Copyright(c) 2014 Demonsaw LLC
//
// 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, c... | {"hexsha": "ec245c7b234d40842ca6ed275b92666e1745b42d", "size": 19455, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ds2/qt_demonsaw/pane/client/client_communication_pane.cpp", "max_stars_repo_name": "demonsaw/Code", "max_stars_repo_head_hexsha": "b036d455e9e034d7fd178e63d5e992242d62989a", "max_stars_repo_license... |
import theano
import theano.tensor as T
import theano.tensor.nlinalg as nlinalg
import theano.gof as gof
import numpy as np
import numerical.numpyext.linalg as ntl
class CholeskyInvJitterOp(theano.Op):
__props__ = ('lower', 'destructive')
def __init__(self, lower=True, maxiter=10):
self.lower = lower... | {"hexsha": "498b3a070ca9b24d836c22aec91d2aab63ba84de", "size": 2169, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/numerical/theanoext/operations/cholesky.py", "max_stars_repo_name": "dmytrov/gaussianprocess", "max_stars_repo_head_hexsha": "7044bd2d66f44e10656fee17e94fdee0c24c70bb", "max_stars_repo_licens... |
import shapely.geometry
import numpy as np
import fiona.crs
import pyproj
from shapely.geometry.point import Point
UTM_ZONE30 = pyproj.Proj(
proj='utm',
zone=30,
datum='WGS84',
units='m',
errcheck=True)
schema = {'geometry': 'LineString', 'properties': {'PhysID': 'int'}}
crs = fiona.crs.from_string... | {"hexsha": "1a41d2112e579b449fc47301d00b17852994dafa", "size": 1535, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/discrete_turbines/qmesh/shapefile_generation.py", "max_stars_repo_name": "jrper/thetis", "max_stars_repo_head_hexsha": "3c08a2e6947552119232fefd7380fa61b2a9b84b", "max_stars_repo_licenses... |
module Endpoints
using ..Pages
export Endpoint, endpoints, method, servefile, servefolder
export GET, HEAD, POST, PUT, DELETE, CONNECT, OPTIONS, TRACE, PATCH
struct Method{M} end
struct Endpoint
handlers::Dict{Symbol,HTTP.RequestHandlerFunction}
route::String
function Endpoint(handle,route,method::Meth... | {"hexsha": "9d43397a9564deb15745e93c383958b0f6350b42", "size": 1774, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Endpoints.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Pages.jl-7c165e09-dada-5b64-9fdc-39b801c58527", "max_stars_repo_head_hexsha": "b626454e82da21659e9ca822e94dbdf220652917", "... |
# -*- coding: utf-8 -*-
__all__ = ["USE_AESARA", "aesara", "sparse", "change_flags", "ifelse"]
USE_AESARA = False
try:
import aesara
except ImportError:
aesara = None
else:
try:
import pymc3.theanof # noqa
except ImportError:
USE_AESARA = True
if aesara is None or not USE_AESARA:
... | {"hexsha": "665f49940ae0710f5643411d2b8753087e00a6bb", "size": 1050, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/aesara_theano_fallback/compat.py", "max_stars_repo_name": "dfm/aesara-theano-fallback", "max_stars_repo_head_hexsha": "9b7ba725ed9c25fa0ec457b183d4dfa3ad6874ab", "max_stars_repo_licenses": ["M... |
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from ccgnet import experiment as exp
from ccgnet import layers
import tensorflow as tf
import numpy as np
import time
from sklearn.metrics import balanced_accuracy_score
from ccgnet.Dataset import Dataset, DataLoader
d... | {"hexsha": "f75ddd83bbbdb79643ecaca64767c649ff74c687", "size": 9283, "ext": "py", "lang": "Python", "max_stars_repo_path": "BayesOpt/BayesOpt-GraphCNN.py", "max_stars_repo_name": "Saoge123/ccgnet", "max_stars_repo_head_hexsha": "9359c642bd1faa4c15cae829615385761ebd8d92", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
C Copyright(C) 1999-2020 National Technology & Engineering Solutions
C of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with
C NTESS, the U.S. Government retains certain rights in this software.
C
C See packages/seacas/LICENSE for details
SUBROUTINE VERSION(QAINFO)
include 'params.blk'
... | {"hexsha": "727c9050c20d1afbf392b603379f7e057a9f0c29", "size": 591, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/blot/bl_version.f", "max_stars_repo_name": "jschueller/seacas", "max_stars_repo_head_hexsha": "14c34ae08b757cba43a3a03ec0f129c8a168a9d3", "max_stars_repo_licenses": ["P... |
from skimage import measure
import numpy as np
np.random.seed(123)
try:
from MulticoreTSNE import MulticoreTSNE as TSNE
except:
from sklearn.manifold import TSNE
from tqdm import tqdm
from phathom.preprocess.filtering import gaussian_blur
try:
from mayavi import mlab
except:
mlab = None
import matplotli... | {"hexsha": "e887491dd8cb3e8fec2bfd8cef8828cb6e7a2fc2", "size": 8891, "ext": "py", "lang": "Python", "max_stars_repo_path": "phathom/phenotype/mesh.py", "max_stars_repo_name": "chunglabmit/phathom", "max_stars_repo_head_hexsha": "304db7a95e898e9b03d6b2640172752d21a7e3ed", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
program problem_4b
!! In your batch file, how many passports are valid?
use aoc_utilities
use iso_fortran_env
implicit none
integer,parameter :: chunk_size = 256
integer :: iunit, istat, n_lines, record_num, i, j, n_valid, ival, n, c
character(len=:),allocatable :: line, key, val
logical :: status_ok
type(string),... | {"hexsha": "b169d14676a6a1500854ec5a93ef5a6e8166d2fb", "size": 4684, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/day4/problem_4b.f90", "max_stars_repo_name": "jacobwilliams/AoC-2020", "max_stars_repo_head_hexsha": "2adf673a0ac62710fc5461576feb95bf5fae4cf2", "max_stars_repo_licenses": ["BSD-2-Clause"], ... |
import numpy.random as rnd
from scipy import stats
import numpy as np
def AWGN_IS(x, snr, seed=None):
rng = rnd.default_rng(0)
noise_sigma = 10 ** (-snr / 20)
n, n_trials = x.shape
mu, sigma = 0, noise_sigma
mu_biased, sigma_biased = 0.5, noise_sigma
noise = np.zeros(x.shape, dtype=float)
... | {"hexsha": "513cf8aa3149a3cff19230c5a9a932a61fb36f02", "size": 949, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyldpc/channel.py", "max_stars_repo_name": "LingruiZhu/pyldpc-master", "max_stars_repo_head_hexsha": "b85dc1121a821e48c5e18168dd68ca5a21cf5a22", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
"""
Class for serving and recording post-processed live data
The functions which are the tasks to be performed must be defined outside the
class. I don't recall why. This should be looked into.
The generalplan here is this::
-------------------------------- reader hands packets to 16 unpackers
... | {"hexsha": "507537a5ef25b8aaa30e86b68045915254ab0155", "size": 21380, "ext": "py", "lang": "Python", "max_stars_repo_path": "BackEnds/data_server-daemon.py", "max_stars_repo_name": "SDRAST/MonitorControl", "max_stars_repo_head_hexsha": "3aaa0b93be3e6d5c2ad8f8e3423cf51fed6dcd8e", "max_stars_repo_licenses": ["Apache-2.0"... |
import gpflow
import tensorflow as tf
tf.config.run_functions_eagerly(True)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
import os
import operator
plt.style.use("ggplot")
warnings.filterwarnings('ignore')
np.random.seed(0)
def pred_x(model, patient_idx, X, Y,
cl... | {"hexsha": "7fc2a6315c9140012ed3de402683890c0075e393", "size": 4002, "ext": "py", "lang": "Python", "max_stars_repo_path": "hgpmoe/plot.py", "max_stars_repo_name": "bee-hive/HGP-MOE", "max_stars_repo_head_hexsha": "b1f753b3e82f8cedcbb3d29381ae0eb6a4755bc1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
from numpy.testing import *
import numpy as np
rlevel = 1
class TestRegression(TestCase):
def test_polyfit_build(self,level=rlevel):
"""Ticket #628"""
ref = [-1.06123820e-06, 5.70886914e-04, -1.13822012e-01,
9.95368241e+00, -3.14526520e+02]
x = [90, 91, 92, 93, 94, 95, 96, ... | {"hexsha": "189b2e48131a8b33c2d231c9030808384c3eff24", "size": 1408, "ext": "py", "lang": "Python", "max_stars_repo_path": "GlyphProofer/dist/GlyphProofer.app/Contents/Resources/lib/python2.6/numpy/lib/tests/test_regression.py", "max_stars_repo_name": "miguelsousa/robothon", "max_stars_repo_head_hexsha": "f2ac88884e04a... |
#include <boost/spirit/home/support/utree/utree_traits_fwd.hpp>
| {"hexsha": "826eb38d64fe9441912ebaf540d8b87697a1fd27", "size": 64, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_spirit_home_support_utree_utree_traits_fwd.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo... |
import torch
import torch.nn as nn
import torch.nn.utils.prune as prune
import numpy as np
import custom_modules.custom_modules as modules
def compute_group_lasso_mask(inputTensor: torch.Tensor, clusterSize: int, threshold: float) -> torch.Tensor:
mask = torch.zeros_like(inputTensor, dtype=torch.float)
input... | {"hexsha": "1a2516513a2f299090c0e07fa4a3c53696b23d6c", "size": 14243, "ext": "py", "lang": "Python", "max_stars_repo_path": "develop/pruning/pruning.py", "max_stars_repo_name": "mustard-seed/SparseNN_training", "max_stars_repo_head_hexsha": "267a1fb5bed650e66ad5cf3d98069891bb307aec", "max_stars_repo_licenses": ["Apache... |
#!/usr/bin/env python
# coding: utf-8
# # MLFlow Pre-packaged Model Server AB Test Deployment
# In this example we will build two models with MLFlow and we will deploy them as an A/B test deployment. The reason this is powerful is because it allows you to deploy a new model next to the old one, distributing a percent... | {"hexsha": "4b38bf16f1a86b506d9a09c6ade7c394440d1a47", "size": 12019, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/jupyter_execute/examples/models/mlflow_server_ab_test_ambassador/mlflow_server_ab_test_ambassador.py", "max_stars_repo_name": "edshee/seldon-core", "max_stars_repo_head_hexsha": "78c10fbca16a... |
// Warning! This file is autogenerated.
#include <boost/text/collation_table.hpp>
#include <boost/text/collate.hpp>
#include <boost/text/data/all.hpp>
#ifndef LIMIT_TESTING_FOR_CI
#include <boost/text/save_load_table.hpp>
#include <boost/filesystem.hpp>
#endif
#include <gtest/gtest.h>
using namespace boost::text;
... | {"hexsha": "e16b40e6b9fe0ae2292d2021dee8f5cc72b0cc33", "size": 161782, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/tailoring_rule_test_my_standard_001.cpp", "max_stars_repo_name": "eightysquirrels/text", "max_stars_repo_head_hexsha": "d935545648777786dc196a75346cde8906da846a", "max_stars_repo_licenses": [... |
import copy
import vnmrjpy as vj
import numpy as np
import matplotlib.pyplot as plt
class Lmafit():
"""Low-rank matrix fitting algorithm
Fills missing matrix elements by low rank approximation
ref.: paper
"""
def __init__(self,init_data,\
known_data='NOT GIVEN',\
t... | {"hexsha": "8fb63a9fda5d3cd0d08613403f4f13a427ec181d", "size": 6409, "ext": "py", "lang": "Python", "max_stars_repo_path": "vnmrjpy/aloha/lmafit.py", "max_stars_repo_name": "hlatkydavid/vnmrjpy", "max_stars_repo_head_hexsha": "48707a1000dc87e646e37c8bd686e695bd31a61e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Low Level Functions
import types
import random
import math
import time
euler = 2.718281828
##############################################
# 1. Matrix Initializations ---
##############################################
def size(matrix):
return len(matrix),len(matrix[0])
def zeros(m,n):
# Create zero mat... | {"hexsha": "7219c48835a870c36554a78c0ec53856e9ab47fd", "size": 38005, "ext": "py", "lang": "Python", "max_stars_repo_path": "Utils/matrix.py", "max_stars_repo_name": "MKLab-ITI/DanceAnno", "max_stars_repo_head_hexsha": "fad9985bf0843c3b95895df946c3caeee4e42210", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
from __future__ import division, print_function, absolute_import
import pytest
import numpy as np
from scipy.spatial.transform import Rotation
from scipy.optimize import linear_sum_assignment
from scipy.spatial.distance import cdist
from scipy.constants import golden as phi
from scipy.spatial import cKDTree
TOL = 1... | {"hexsha": "5407bcc643f76cac64d7489d046c3a3ecaace146", "size": 5060, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/spatial/transform/tests/test_rotation_groups.py", "max_stars_repo_name": "TNonet/scipy", "max_stars_repo_head_hexsha": "84d0b611f08187a2259a86a4ad5ed7295632c570", "max_stars_repo_licenses": ... |
/-
Copyright (c) 2020 Markus Himmel. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Markus Himmel
-/
import category_theory.category
import pseudoelements
import tactic.combinators
import tactic.chase_tactic
open category_theory
open category_theory.abelian
open cate... | {"author": "TwoFX", "repo": "lean-homological-algebra", "sha": "e3a8e4ecaf49bec6c7b38b34c0b8f9749e941aa8", "save_path": "github-repos/lean/TwoFX-lean-homological-algebra", "path": "github-repos/lean/TwoFX-lean-homological-algebra/lean-homological-algebra-e3a8e4ecaf49bec6c7b38b34c0b8f9749e941aa8/src/tactic/commutativity... |
# Copyright (c) 2020 Matthew Earl
#
# 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, distr... | {"hexsha": "35cc72f9540d05918002837cba0c2a1fd2bd4d53", "size": 9447, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyquake/blendmdl.py", "max_stars_repo_name": "proteanblank/pyquake", "max_stars_repo_head_hexsha": "26818b92bf648d897975993a3c40a78d7a5c1a9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# coding: utf8
# coding: utf8
import sys
import os
from sys import argv
sys.path.insert(0, os.getcwd()) # adds current directory to python path
import numpy as np
import matplotlib.pylab as plt
####################
# Recovery of Data
####################
folder_name = ""
pathIn = "crocoddyl_eval/test_4/log_eval/"... | {"hexsha": "4a3cf0ebbf9dcb5b6fa817c7123663bfad625166", "size": 1948, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/quadruped_reactive_walking/crocoddyl_eval/test_4/analyse_simu.py", "max_stars_repo_name": "nim65s/quadruped-reactive-walking", "max_stars_repo_head_hexsha": "1e0f4069fd11af85abf10bfc8f9d662... |
# targets for phase in / phase out of policies
pinft = 0.4*pinf_o # infection prob at meeting
#socialmaxyyt = 2 # max no. people met outside firm young_young
#socialmaxoyt = 1 # max no. people met outside firm old_young
#socialmaxoot = 0.5 # max no. people met outside firm old_old
#phomeofficet = 1
#pshopt = [[0.85... | {"hexsha": "a4ff8ec76637bfc0292f33425fc69db1686ec9e5", "size": 719, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "policy_only_xi.jl", "max_stars_repo_name": "jasperhepp/ace_covid19", "max_stars_repo_head_hexsha": "d2ba0c066ccfdb2523c03f3fc334e0ef4c102adc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
module fftpack_precision
! Explicit typing only
implicit none
! Everything is private unless stated otherwise
private
public :: wp, ip
public :: pimach, epmach
!-----------------------------------------------
! Dictionary: precision constants
!-------------------------------------... | {"hexsha": "00b8720ad320c94ba8e10946260ece6162f14fca", "size": 1647, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/fftpack_precision.f90", "max_stars_repo_name": "jbdv-no/modern_fftpack", "max_stars_repo_head_hexsha": "6909d44988925dcae1ee478c06be31e5605d3974", "max_stars_repo_licenses": ["MIT"], "max_st... |
import datetime
import glob
import imageio
import json
import numpy as np
import os
import psutil
import subprocess
import sys
import time
import models
import tensorflow as tf
import keras.backend as K
from keras.utils import generic_utils
from keras.optimizers import Adam, SGD
# Utils
sys.path.append("../utils")
... | {"hexsha": "77b18e35a369fa155e8e4ce837ccff7c0eafbf5d", "size": 16066, "ext": "py", "lang": "Python", "max_stars_repo_path": "pix2pix/src/model/train_only_generator.py", "max_stars_repo_name": "voletiv/DeepLearningImplementations", "max_stars_repo_head_hexsha": "22ec85cdc7daa308ff2bec81962ca77e5959a70b", "max_stars_repo... |
#!/usr/bin/env python
import rospy, roslib, sys, cv2, time
import numpy as np
from std_msgs.msg import Int32
from std_msgs.msg import Float64
from sensor_msgs.msg import JointState
from sensor_msgs.msg import Image
from visual_servoing.srv import *
from std_srvs.srv import Empty as EmptySrv
from gazebo_ros_link_attach... | {"hexsha": "1671c579e698b00f2a7edc5ae5c76f149f5d3a3a", "size": 6632, "ext": "py", "lang": "Python", "max_stars_repo_path": "visual_servoing/scripts/3_PickPlace_VS.py", "max_stars_repo_name": "nsabhari/Visual_Servoing", "max_stars_repo_head_hexsha": "36905434a0ad425fd44f0b5997f09e5f5a76af45", "max_stars_repo_licenses": ... |
from Good_Boids_module.Update_Boids import Boids
import numpy as np
from nose.tools import assert_almost_equal, assert_greater
from nose.tools import assert_less, assert_equal
from numpy.testing import assert_array_equal
import os
import yaml
from Good_Boids_module.tests.record_fixtures import configuration_file
fixt... | {"hexsha": "790d7f2469b623df18560268f0e89fc2f0e10bab", "size": 2558, "ext": "py", "lang": "Python", "max_stars_repo_path": "Good_Boids_module/tests/test_the_Good_Boids.py", "max_stars_repo_name": "anest1s/Refactoring_the_Bad_Boids", "max_stars_repo_head_hexsha": "d569de4372d96917ef6aa7f1ca8acdaa09c26e0f", "max_stars_re... |
Require Import Coq.Strings.String.
Require Import Coq.PArith.BinPos.
Require Import ExtLib.Core.RelDec.
Require Import ExtLib.Data.String.
Require Import ExtLib.Data.Nat.
Require Import ExtLib.Data.HList.
Require Import MirrorCore.Lemma.
Require Import MirrorCore.TypesI.
Require Import MirrorCore.Lambda.Expr.
Require ... | {"author": "jesper-bengtson", "repo": "MirrorCharge", "sha": "cb0fe1da80be70ba4b744d4178a4e6e3afa38e62", "save_path": "github-repos/coq/jesper-bengtson-MirrorCharge", "path": "github-repos/coq/jesper-bengtson-MirrorCharge/MirrorCharge-cb0fe1da80be70ba4b744d4178a4e6e3afa38e62/MirrorCharge!/src/MirrorCharge/Java/SymEx.v"... |
import numpy as np
import pandas as pd
from scipy.integrate import odeint
from scipy import interpolate
#import pressure_estimation
def func(x, *params):
y = np.zeros_like(x)
for i in range(0, len(params), 3):
ctr = params[i]
amp = params[i+1]
wid = params[i+2]
y = y + amp * np... | {"hexsha": "0fe1a2e54ca2a4c239aac9f64012135ccf7d94d5", "size": 2132, "ext": "py", "lang": "Python", "max_stars_repo_path": "5_simulation/ode_solver_pat2.py", "max_stars_repo_name": "xi2pi/elastance-function", "max_stars_repo_head_hexsha": "ac3422b55a1958fe0ce579a2b49a977545159ccd", "max_stars_repo_licenses": ["Apache-2... |
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
from torch import tensor, float32
import json
from collections import defaultdict
# представление очищенного датасета в pytorch
class DatasetModel(Dataset):
def __init__(self, df, vectorizer):
self.df = df
self._vectorize... | {"hexsha": "a61a876fde60c3480062cf3265e8bdb93edfb1cd", "size": 9722, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/preprocessor.py", "max_stars_repo_name": "doksketch/happy-dating", "max_stars_repo_head_hexsha": "680c63f38fe039b6567f5fce94c3d0fa3b968019", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import os
import os.path as osp
import sys
import argparse
import json
import numpy as np
import pandas as pd
import time
import subprocess
RESULTS_DIR = './results'
if not osp.exists(RESULTS_DIR):
os.makedirs(RESULTS_DIR)
def get_args():
parser = argparse.ArgumentParser(description='gkm Protein Experiments')... | {"hexsha": "077b34ab97657959ce3db209465d11f86b14d896", "size": 1527, "ext": "py", "lang": "Python", "max_stars_repo_path": "results/other_scripts/gkm_prot_tests.py", "max_stars_repo_name": "dblakely/FastSK", "max_stars_repo_head_hexsha": "bd0d4cef89c3d7d661f4c6abc094423ab6d1c7e1", "max_stars_repo_licenses": ["Apache-2.... |
#!/usr/bin/env python
import sys
import math
import numpy as np
ph2Kcal = 1.364
Kcal2kT = 1.688
class Microstate:
def __init__(self, state, E, count):
self.state = state
self.E = E
self.count = count
class Conformer:
def __init__(self):
self.iconf = 0
self.ires = 0
... | {"hexsha": "87c9679bf26844c79f2c522c4555931069adb57e", "size": 12887, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/ms_analysis.py", "max_stars_repo_name": "umeshkhaniya/Stable-MCCE", "max_stars_repo_head_hexsha": "b037a417e722f46030fdd5e24e5bb44513440559", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# ======================================================================
# Copyright CERFACS (October 2018)
# Contributor: Adrien Suau (adrien.suau@cerfacs.fr)
#
# This software is governed by the CeCILL-B license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify an... | {"hexsha": "16e924ae2eeebb12e8f96413b7d33463b3315d62", "size": 3031, "ext": "py", "lang": "Python", "max_stars_repo_path": "qtoolkit/maths/matrix/sud/group_commutator.py", "max_stars_repo_name": "nelimee/qtoolkit", "max_stars_repo_head_hexsha": "1e99bd7d3a143a327c3bb92595ea88ec12dbdb89", "max_stars_repo_licenses": ["CE... |
from __future__ import print_function, absolute_import, division
from future.builtins import *
from future import standard_library
standard_library.install_aliases()
# Copyright 2017 Autodesk Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with... | {"hexsha": "82ad69f93697bb2e3ebb91de69b7a31670866059", "size": 6277, "ext": "py", "lang": "Python", "max_stars_repo_path": "moldesign/_tests/test_pdbfixer_xface.py", "max_stars_repo_name": "Autodesk/molecular-design-toolkit", "max_stars_repo_head_hexsha": "5f45a47fea21d3603899a6366cb163024f0e2ec4", "max_stars_repo_lice... |
import numpy as np
from numpy.linalg import slogdet, solve
from numpy import log, pi
import pandas as pd
from scipy.special import expit
from .constants import mass_pion
from .kinematics import momentum_transfer_cm, cos0_cm_from_lab, omega_cm_from_lab
from .constants import omega_lab_cusp, dsg_label, DesignLabels
from ... | {"hexsha": "5dce49741ed41519529ab27946095fd786abe07f", "size": 33825, "ext": "py", "lang": "Python", "max_stars_repo_path": "compton/convergence.py", "max_stars_repo_name": "buqeye/compton-scattering", "max_stars_repo_head_hexsha": "867703fc21e75155af50d543b61f794dc5bfe5a7", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from __future__ import absolute_import
import os.path
import numpy as np
from PIL import Image
import Levenshtein
from ocrd_utils import (
getLogger, concat_padded,
coordinates_for_segment,
polygon_from_bbox,
points_from_polygon,
MIMETYPE_PAGE
)
from ocrd_modelfactory import page_from_file
from o... | {"hexsha": "f046f51a967b847f065ceacb1c731c56fd5ebeb3", "size": 11740, "ext": "py", "lang": "Python", "max_stars_repo_path": "ocrd_cis/ocropy/recognize.py", "max_stars_repo_name": "stweil/ocrd_cis", "max_stars_repo_head_hexsha": "e8c20e67ca78682059e20445c9a10849b9bdd7ba", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
Require Import Crypto.Arithmetic.PrimeFieldTheorems.
Require Import Crypto.Specific.montgomery32_2e127m1_4limbs.Synthesis.
(* TODO : change this to field once field isomorphism happens *)
Definition opp :
{ opp : feBW_small -> feBW_small
| forall a, phiM_small (opp a) = F.opp (phiM_small a) }.
Proof.
Set Ltac Pr... | {"author": "anonymous-code-submission-01", "repo": "sp2019-54-code", "sha": "8867f5bed0821415ec99f593b1d61f715ed4f789", "save_path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code", "path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code/sp2019-54-code-8867f5bed0821415ec99f593b1d61f715ed4f7... |
#!/usr/bin/env python
import os
from time import time
from typing import Generator, Tuple
import numpy as np
import click
import json
from .lib import *
from cloudvolume import CloudVolume
from cloudvolume.lib import Vec, yellow
from chunkflow.lib.aws.sqs_queue import SQSQueue
from chunkflow.lib.bounding_boxes impo... | {"hexsha": "cf32bd34a11df55a41df8b1367563d74cc081f97", "size": 67881, "ext": "py", "lang": "Python", "max_stars_repo_path": "chunkflow/flow/flow.py", "max_stars_repo_name": "julesberman/chunkflow", "max_stars_repo_head_hexsha": "c6af0d036bc2f308c64c591d49c94c414c569241", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
# coding: utf-8
import numpy as np
import talib as ta
from settings import evaluete
MAKER_COST = evaluete["maker_cost"]
TAKER_COST = evaluete["taker_cost"]
IMPACT = evaluete["impact"]
SLIDE = evaluete["slide"]
LEVER = evaluete["lever"]
MAX_POSITION = evaluete["max_position"]
STOP_EARN = evaluete["stop_earn"]
STOP_LOSS... | {"hexsha": "b2099af20730ae29f4350ce5f34a843518b3270e", "size": 17023, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluete.py", "max_stars_repo_name": "gLhookniano/AlgTradeTest", "max_stars_repo_head_hexsha": "ab9bb92afe3c4ce3516fcaec0e401c2dad405080", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
from aip import AipNlp
import pandas as pd
import numpy as np
import time
# 此处输入baiduAIid
APP_ID = ''
API_KEY = ''
SECRET_KEY = ''
client = AipNlp(APP_ID, API_KEY, SECRET_KEY)
def isPostive(text):
try:
if client.sentimentClassify(text)['items'][0]['positive_prob']>0.5:
return... | {"hexsha": "6dc93cde2750b81f4abb9adf099b8b5448179552", "size": 1110, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis.py", "max_stars_repo_name": "huihui7987/weibo-topic-spider", "max_stars_repo_head_hexsha": "a7e93f1a8fac4146be36b8a594b7977fbac019f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma has_one_imp_equal:
assumes "\<one> \<in> I"
shows "I = R"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. I = R
[PROOF STEP]
by (metis assms lideal subset multiplicative.right_unit subsetI subset_antisym) | {"llama_tokens": 94, "file": "Grothendieck_Schemes_Comm_Ring", "length": 1} |
!
!
! AMG4PSBLAS version 1.0
! Algebraic Multigrid Package
! based on PSBLAS (Parallel Sparse BLAS version 3.7)
!
! (C) Copyright 2021
!
! Salvatore Filippone
! Pasqua D'Ambra
! Fabio Durastante
!
! Redistribution and us... | {"hexsha": "2c07fc4ac0fc943361561dfceb9c387f06296bcd", "size": 19255, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "amgprec/amg_s_base_aggregator_mod.f90", "max_stars_repo_name": "sfilippone/amg4psblas", "max_stars_repo_head_hexsha": "45fabb5214b27d6c67cdf8f6a82277374a059e34", "max_stars_repo_licenses": ["BS... |
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
from solarescape_env import SolarescapeEnv
import pygame
from p... | {"hexsha": "f7fb00f9c1bd6ed9ba70af36b34cda97083dc27d", "size": 1889, "ext": "py", "lang": "Python", "max_stars_repo_path": "learningAgentKeras.py", "max_stars_repo_name": "kaiobarb/solarescape", "max_stars_repo_head_hexsha": "18f2c432a48e4b2fe9dc116ec7b9190ee5637401", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/env python
"""Tests and validates classes from :py:mod:`plastid.genomics.genome_array`,
these being |GenomeArray|, |SparseGenomeArray| and |BAMGenomeArray|,
using test data found in plastid.test.data.
This module additionally contains utilites to generate other test datasets.
To do, please see the document... | {"hexsha": "04be15ba5578baa6bcaa9a43b79d648a5dd3c6dd", "size": 76671, "ext": "py", "lang": "Python", "max_stars_repo_path": "plastid/test/unit/genomics/test_genome_array.py", "max_stars_repo_name": "joshuagryphon/plastid", "max_stars_repo_head_hexsha": "e63a818e33766b01d84b3ac9bc9f55e6a1ece42f", "max_stars_repo_license... |
import os
import sys
import numpy as np
from datetime import datetime
from functools import wraps
from time import time
def stop_watch(func):
@wraps(func)
def wrapper(*args, **kargs):
start = time()
log = "[START] {}: {}() | PID: {} ({})".format(sys.argv[0], func.__qualname__, os.getpid(), da... | {"hexsha": "f3a5c70c632d9aa7c024166164e712b6f72881dd", "size": 2711, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/common.py", "max_stars_repo_name": "j20232/kaggle_earthquake", "max_stars_repo_head_hexsha": "47fac5f2e8d2ad4fab82426a0b6af18b71e4b57b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
!----------------------------------------------------------------------------- best with 100 columns
!> finite element discretizations
module modBasicFEM
public
contains
!> find Laplacian operator
!> NOTE: A must be pre-initialized to contain the temporary CSR matrix with duplications
subroutine findLapl... | {"hexsha": "539016d7a005dd5ffb883c0807e79105aa75e8e0", "size": 4428, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/libfosolvers/FEM/basicFEM.f90", "max_stars_repo_name": "mianzhi/fosolvers", "max_stars_repo_head_hexsha": "be4877a9cccd7bf6b97d4e01c58e10634684415d", "max_stars_repo_licenses": ["BSD-2-Claus... |
import numpy as np
import torch
import torch.nn
from .nnutils import Network, one_hot, extract
class QNet(Network):
def __init__(self, n_features, n_actions, n_hidden_layers=1, n_units_per_layer=32):
super().__init__()
self.n_actions = n_actions
self.layers = []
if n_hidden_layers... | {"hexsha": "7e883e083b9bdf2eaf1bc78f3c870a14abb0d75b", "size": 867, "ext": "py", "lang": "Python", "max_stars_repo_path": "markov_abstr/gridworld/models/qnet.py", "max_stars_repo_name": "camall3n/regenernet", "max_stars_repo_head_hexsha": "c1b23624bf8ded7c1dadb858de90f58838586413", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 4 10:08:24 2020
@author: hannes
"""
#General imports
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import numpy as np
import skimage as skimage
"""
NOTE:
In order to generate the image sequence we can run following ... | {"hexsha": "35e328eb78014fabbd3a0e9796442715aa1ba084", "size": 2206, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Utilities/BariumCloudPreprocessing.py", "max_stars_repo_name": "hsiehScalAR/AdaptiveSamplingIntermittentComms", "max_stars_repo_head_hexsha": "5aec4677fbe3f3bf19213ae6abe4d5ea8b4d052c", "max_s... |
[STATEMENT]
lemma lemma_2_8_i1:
"a \<in> supremum A \<Longrightarrow> a r\<rightarrow> b \<in> infimum ((\<lambda> x . x r\<rightarrow> b)`A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a \<in> supremum A \<Longrightarrow> a r\<rightarrow> b \<in> infimum ((\<lambda>x. x r\<rightarrow> b) ` A)
[PROOF STEP]
by ... | {"llama_tokens": 147, "file": "PseudoHoops_PseudoHoops", "length": 1} |
from .provider_test import ProviderTest
from gunpowder import (
RandomLocation,
BatchProvider,
Roi,
Coordinate,
ArrayKeys,
ArrayKey,
ArraySpec,
Array,
Roi,
Coordinate,
Batch,
BatchRequest,
BatchProvider,
RandomLocation,
MergeProvider,
build,
)
import numpy... | {"hexsha": "46c4b2a97ceb2077366e92ba1bb426b9bbad0887", "size": 4599, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/cases/random_location.py", "max_stars_repo_name": "trivoldus28/gunpowder", "max_stars_repo_head_hexsha": "97e9e64709fb616e2c47567b22d5f11a9234fe48", "max_stars_repo_licenses": ["MIT"], "max_... |
"""Provides a data proxy for deferring access to data from a mongoDB query."""
from bson.objectid import ObjectId
import numpy as np
import pymongo
# Inspired by https://github.com/SciTools/iris/blob/master/lib/iris/fileformats/netcdf.py#L418.
class MongoDBDataProxy:
"""A proxy to the data of a single TileDB ar... | {"hexsha": "fede0828c39328da7781c617b8e1ca9510c99dec", "size": 2112, "ext": "py", "lang": "Python", "max_stars_repo_path": "metadatabase/data_proxy.py", "max_stars_repo_name": "informatics-lab/metadatabase", "max_stars_repo_head_hexsha": "380cfd683cc28d57bfc20b1965ed884541e63a6c", "max_stars_repo_licenses": ["BSD-3-Cla... |
import argparse
import copy
import json
import os
import random
import torch
import sys
import numpy as np
import multiprocessing as mp
from audio_conditioned_unet.dataset import iterate_dataset, load_dataset, NonSequentialDatasetWrapper
from audio_conditioned_unet.network import ConditionalUNet
from audio_conditio... | {"hexsha": "a755e8f0d0d91c58b422a434e05b50846b312721", "size": 9864, "ext": "py", "lang": "Python", "max_stars_repo_path": "audio_conditioned_unet/train_model.py", "max_stars_repo_name": "CPJKU/audio_conditioned_unet", "max_stars_repo_head_hexsha": "68f20f5280079e99be260f9fe9933c0064eb2d7f", "max_stars_repo_licenses": ... |
import os
import os.path as osp
import pandas as pd
import numpy as np
from PIL import Image
import multiprocessing
import argparse
################################################################################
# Evaluate the performance by computing mIoU.
# It assumes that every CAM or CRF dict file is already infe... | {"hexsha": "381e8ed2e6ad8d070064cbd4f888eb6cea607b8f", "size": 6870, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation.py", "max_stars_repo_name": "KAIST-vilab/OC-CSE", "max_stars_repo_head_hexsha": "35703390e13621a865aef4d9b75202c8c9e5822b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "m... |
import aiofiles
import asyncio
import simpleaudio as sa
import numpy as np
import struct
from datetime import datetime
import av
from av.audio.fifo import AudioFifo
'''
卡顿原因 读取 io操作会导致进程卡住，所以必须异步化
测试AudioFifo
'''
async def read_header_wav_async(f):
#RIFF
await f.read(12)
#FORMAT
id_chunk = a... | {"hexsha": "88083bb429a32d9ba2132278a7cd23be4bfc6f73", "size": 7497, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo_play_parse_wav_fifo_async.py", "max_stars_repo_name": "xuqinghan/flv-extract-audio-and-video", "max_stars_repo_head_hexsha": "e4c0c42119e6ea4478817c04e21ffe341bfc4189", "max_stars_repo_licens... |
# TODO: Need to finish
# Need to test
function fit_fs_imcmc_pt!(cfs::ConstantsFS,
dfs::DataFS;
nmcmc::Int, nburn::Int,
# Args for PT:
tempers::Vector{Float64},
inits=nothing,
... | {"hexsha": "404e7715dd8e3171af5b918681e8f4a0e17f439b", "size": 9297, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Model/FeatureSelect/fit_feature_select_imcmc_pt.jl", "max_stars_repo_name": "luiarthur/CytofRepFAM.jl", "max_stars_repo_head_hexsha": "1f997d1620d74861c5bde5559ebdd1e6c449b9e7", "max_stars_repo... |
[STATEMENT]
lemma ideal_generated_subset2:
assumes ac: "ideal_generated {a} \<subseteq> ideal_generated {c}"
and bc: "ideal_generated {b} \<subseteq> ideal_generated {c}"
shows "ideal_generated {a,b} \<subseteq> ideal_generated {c}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ideal_generated {a, b} \<subset... | {"llama_tokens": 3276, "file": "Echelon_Form_Rings2", "length": 43} |
function class_info=gen_class_info_nyud()
class_info=[];
class_info.class_names={
'wall'
'floor'
'cabinet'
'bed'
'chair'
'sofa'
'table'
'door'
'window'
'bookshelf'
'picture'
'counter'
'blinds'
'desk'
'shelves'
'curtain'
'dresser'
'pillow'
'm... | {"author": "guosheng", "repo": "refinenet", "sha": "0d62007bd60ba983d48acaee6ee29988c7171a91", "save_path": "github-repos/MATLAB/guosheng-refinenet", "path": "github-repos/MATLAB/guosheng-refinenet/refinenet-0d62007bd60ba983d48acaee6ee29988c7171a91/main/gen_class_info_nyud.m"} |
#!/usr/bin/env python3
import numpy as np
import pickle
#this file is a bit messy as both value models and deep cfr models
#both use these methods
stateSize = 3883
#3883 is the state size
#186 is the action size
inputShape = (stateSize + 2 * 186,)
#number of possible actions, which is used for our enumeration
numA... | {"hexsha": "1a9edc0feb6d63a6f79340ea487fd9500ef14fd0", "size": 11177, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/modelInput.py", "max_stars_repo_name": "samhippie/shallow-red", "max_stars_repo_head_hexsha": "5690cdf380c6e138e25d88e85093738951438298", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from __future__ import absolute_import, division, print_function
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=int, default=0)
args = parser.parse_args()
gpu_id = args.gpu_id # set GPU id to use
import os; os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
import numpy as np
... | {"hexsha": "e0d0fdcc89fe1b9335d19a415e0b625b6a6d9c5d", "size": 9107, "ext": "py", "lang": "Python", "max_stars_repo_path": "exp_shapes/train_shapes_gt_layout.py", "max_stars_repo_name": "YuJiang01/n2nnmn", "max_stars_repo_head_hexsha": "f0d751313ca756fe40ece1a7bbb0205ab899adf8", "max_stars_repo_licenses": ["BSD-2-Claus... |
from edgetpu.detection.engine import DetectionEngine
import numpy as np
from PIL import Image
class face_detection():
MODEL = 'models/ssd_mobilenet_v2_face_quant_postprocess_edgetpu.tflite'
def __init__(self, threshold=0.5, num_results=10):
self.engine = DetectionEngine(face_detection.MODEL)
self.objs = None
s... | {"hexsha": "38be4936bb1d9fb1db75548ed9b1d63e90623475", "size": 1028, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/ctrlengine/ai/face_detection.py", "max_stars_repo_name": "0xJeremy/ctrl.engine", "max_stars_repo_head_hexsha": "19abba70df149a05edc5722cc95ceacc538448e6", "max_stars_repo_licenses": ["MI... |
import pytest
import xarray as xr
import numpy as np
import dask.array as da
from xrspatial.utils import has_cuda
from xrspatial.utils import doesnt_have_cuda
from xrspatial.multispectral import arvi
from xrspatial.multispectral import ebbi
from xrspatial.multispectral import evi
from xrspatial.multispectral import ... | {"hexsha": "52e4c6e8e4b998273214d9bfb55a78d2848db1cc", "size": 23503, "ext": "py", "lang": "Python", "max_stars_repo_path": "xrspatial/tests/test_multispectral.py", "max_stars_repo_name": "brendancol/xarray-spatial", "max_stars_repo_head_hexsha": "36d53b75086b760cab5100a12fcbda946dd85a25", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 25 18:04:22 2019
@author: wt4452
"""
from time import time
import numpy as np
import numpy.linalg as la
import meshio as mo
reload(mo)
in_abq = False
try:
from abaqus import *
from abaqusConstants import (
NODAL,
INTEGRATION_POINT,
CEN... | {"hexsha": "9f99c9e56b53f8eef0d398ba2819588adf30c7c2", "size": 30308, "ext": "py", "lang": "Python", "max_stars_repo_path": "plugins/abaqus/abq_meshio/abq_meshio_converter.py", "max_stars_repo_name": "siegfriedgalkinkit/meshio", "max_stars_repo_head_hexsha": "8c2ccf62d1841258df92fe6badd424fe845f9ff9", "max_stars_repo_l... |
import pytest
from ffai.core.model import D3, D6, D8, BBDie
from ffai.core.table import BBDieResult
import numpy as np
@pytest.mark.parametrize("die", [D3, D6, D8, BBDie])
def test_d_die(die):
results = []
n = 6
if die == D3:
n = 3
elif die == D8:
n = 8
elif die == BBDieResult:
... | {"hexsha": "51af5d3ad267bfd4a865544c37f350b0c8f39c19", "size": 3333, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/game/test_dice.py", "max_stars_repo_name": "gsverhoeven/ffai", "max_stars_repo_head_hexsha": "673ff00e1aac905381cdfb1228ccfcfccda97d1f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
/*
Copyright (c) 2013, Illumina Inc.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following ... | {"hexsha": "5fcf895b181fbb0c6531bd19ed3efb645e85246c", "size": 23427, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "es3/connection.cpp", "max_stars_repo_name": "Cyberax/extremes3", "max_stars_repo_head_hexsha": "dc95b65a84778defc8fcc6d55554de2670cef6fc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
# *-* coding: utf-8 *-*
"""Read MPT DAS-1 data files.
TODO:
"""
import re
import pandas as pd
import numpy as np
from reda.tdip.decay_curve import DecayCurveObj
# from reda.importers.utils.decorators import enable_result_transforms
def get_frequencies(filename, header_row):
"""Read the used frequencies in he... | {"hexsha": "ebe42df0f92d8a76e12e9bba56f1aca6d03c392c", "size": 17057, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/reda/importers/mpt_das1.py", "max_stars_repo_name": "j-hase/reda", "max_stars_repo_head_hexsha": "b6419c39842cfbdd9380a27a5c6e9a04ccaeb294", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python
######################
## written by Wojciech Dudek
######################
__author__ = "Wojciech Dudek"
from nav_msgs.msg import Odometry
from tf import transformations
import tf
import rospy
import sys
import signal
from sensor_msgs.msg import Imu
from rapp_ros_naoqi_wrappings.srv import GetRob... | {"hexsha": "9bd355fe52abfc1da48af0219701738858737282", "size": 21111, "ext": "py", "lang": "Python", "max_stars_repo_path": "rapp_ros_naoqi_wrappings/nodes/rapp_navigation/acore_estimator_server.py", "max_stars_repo_name": "rapp-project/rapp-robot-nao", "max_stars_repo_head_hexsha": "588061c630b1a8f69791eb603ff52db1e3e... |
import math
def RP(x,y,z):
s = 1;
while(x>0):
if(x%2==1):
s=(s*y)%z;
x=x//2;
y=(y*y)%z;
return int(s);
def egcd(a, b):
u1=1;
v1=0;
u2=0;
v2=1;
while(b>0):
r=a%b
q=a//b ... | {"hexsha": "b31e5c03ee58998b825a666af1837105a2efffd4", "size": 2851, "ext": "py", "lang": "Python", "max_stars_repo_path": "Modules/15IT152_15IT119_M2/15IT152_15It119_M2_attacks/silver-pohlig-hellman.py", "max_stars_repo_name": "HimadriP/RSA_attacks", "max_stars_repo_head_hexsha": "db58478a33f0dffe492b9a626bf0a4a055682... |
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
from sklearn.svm import LinearSVC
from neuraxle.base import ExecutionContext
from neuraxle.data_container import DataContainer
from neuraxle.hyperparams.distributions impo... | {"hexsha": "a29d1e69dd9e073eec78e4d6662e3a53246348df", "size": 11526, "ext": "py", "lang": "Python", "max_stars_repo_path": "testing/metaopt/test_automl.py", "max_stars_repo_name": "vincent-antaki/Neuraxle", "max_stars_repo_head_hexsha": "cef1284a261010c655f8ef02b4fca5b8bb45850c", "max_stars_repo_licenses": ["Apache-2.... |
"""Data utility functions."""
import os
import numpy as np
import scipy.io
import torch
import torch.utils.data as data
import h5py
class ImdbData(data.Dataset):
def __init__(self, X, y, w):
self.X = X
self.y = y
self.w = w
def __getitem__(self, index):
img = self.X[index]
... | {"hexsha": "f6416ecd1ab0a3fe4e43792d677eec16a0e74dbd", "size": 3350, "ext": "py", "lang": "Python", "max_stars_repo_path": "networks/data_utils.py", "max_stars_repo_name": "Nikolay1998/relaynet_pytorch", "max_stars_repo_head_hexsha": "b4eecc28020b2a7f7a8cf9618558f968788b14c2", "max_stars_repo_licenses": ["MIT"], "max_s... |
r"""Diffusion of an acoustic wave in 1-d (5 minutes)
Propagation of acoustic wave
particles have properties according
to the following distribuion
.. math::
\rho = \rho_0 + \Delta\rho sin(kx)
p = p_0 + c_0^2\Delta\rho sin(kx)
u = c_0\rho_0^{-1}\Delta\rho sin(kx)
with :math:`\Delta\rho = 1e-6` ... | {"hexsha": "517b4b735ff29ae4d1227aadc7c588ccd834345e", "size": 5467, "ext": "py", "lang": "Python", "max_stars_repo_path": "pysph/examples/gas_dynamics/acoustic_wave.py", "max_stars_repo_name": "nauaneed/pysph", "max_stars_repo_head_hexsha": "9cb9a859934939307c65a25cbf73e4ecc83fea4a", "max_stars_repo_licenses": ["BSD-3... |
#!/usr/bin/python
"""
Test to compare PPF loop calculations against Maryam's MATLAB code
"""
import numpy as np
from riglib.bmi import ppfdecoder, state_space_models as ssm
from scipy.io import loadmat, savemat
from riglib.bmi.sim_neurons import PointProcessEnsemble
import matplotlib.pyplot as plt
from riglib.bmi impo... | {"hexsha": "1e6813b230f1f2efcc13864bd6d235d1dcdb54b2", "size": 3321, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/ppf/test_point_proc_decoding.py", "max_stars_repo_name": "DerekYJC/bmi_python", "max_stars_repo_head_hexsha": "7b9cf3f294a33688db24b0863c1035e9cc6999ea", "max_stars_repo_licenses": ["Apache-... |
# # -*- coding：utf-8 -*-
# &Author AnFany
# 将mnist数据集或者Fashion-MNIST数据集转换为图片
# 因为两个数据集的格式是完全一致的，因此程序可以共用
import struct
from PIL import Image
import numpy as np
import os
Path = r'C:\Users\GWT9\Desktop' # 存储下面4个文件的路径
os.chdir(Path) # 设置为当前的工作路径
# 训练图片文件
train_images = 'train-images-idx3-ubyte' ... | {"hexsha": "352c378b9caa509d78cf7ea46be5d16a7799ca40", "size": 3341, "ext": "py", "lang": "Python", "max_stars_repo_path": "CNN/mnist_to_fig.py", "max_stars_repo_name": "Jojoxiao/Machine-Learning-for-Beginner-by-Python3", "max_stars_repo_head_hexsha": "71b91c9cba5803bd78d4d31be6dabb1d3989e968", "max_stars_repo_licenses... |
from rdkit import Chem
from rdkit.Chem import rdchem, Descriptors
import numpy
periodicTable = rdchem.GetPeriodicTable()
def getChinp(mol,NumPath=2):
"""
#################################################################
Calculation of molecular connectivity chi index for path order n
################... | {"hexsha": "c391b043939fe06f6e69d9ac5a2d53eacdac6ccb", "size": 12681, "ext": "py", "lang": "Python", "max_stars_repo_path": "Desc1D2D/connectivity.py", "max_stars_repo_name": "ABorrel/molecular-descriptors", "max_stars_repo_head_hexsha": "cdc08c7242e929ecf4dcb362331c7226127c3589", "max_stars_repo_licenses": ["MIT"], "m... |
cd(@__DIR__); include("setups/grid23x22.jl")
gr(dpi = 200)
##
frame = sgwt_frame(W; nf = 6)
x = 242
for j = 1:6
plt = heatmap(reshape(frame[:, x, j], (Nx, Ny))', c = :viridis, ratio = 1,
frame = :none, xlim = [1, Nx], size = (500, 400))
savefig(plt, "../figs/Grid$(Nx)x$(Ny)_SGWT_frame_j$(j-1)_x$(x)... | {"hexsha": "8a573bd89bd84febd3404fd89c3f58734dc4052c", "size": 331, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/dissertations/htli/scripts/Figure10.5.jl", "max_stars_repo_name": "BoundaryValueProblems/MTSG.jl", "max_stars_repo_head_hexsha": "8cf8e2b3035876b5ceda45109b0847a60b581a7c", "max_stars_repo_lice... |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 20 14:38:00 2021
@author: 14488
"""
import pyrealsense2 as rs
import numpy as np
import cv2
class IntelRealSense():
def __init__(self,
RGB_resolution = (320,240),
Depth_resolution = (640,480)):
self.pi... | {"hexsha": "1d5b7e7c8496fc4ae5eeb5c27176d091c70325f8", "size": 2091, "ext": "py", "lang": "Python", "max_stars_repo_path": "Real UR5e/IntelRealSense.py", "max_stars_repo_name": "wq13552463699/UCD_UR5E", "max_stars_repo_head_hexsha": "513acb7e235ab940fd03c3038208678e285690f3", "max_stars_repo_licenses": ["MIT"], "max_st... |
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