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""" Some parts of the code are adapted from the LSST stack club:
https://nbviewer.jupyter.org/github/LSSTScienceCollaborations/StackClub/blob/rendered/Validation/image_quality_demo.nbconvert.ipynb
"""
import numpy as np
from astropy import units as u
from lsst.afw.geom.ellipses import Quadrupole, SeparableDistortionTr... | {"hexsha": "37bbdf5a1219a05b80317a59dbb15b4f644c8a12", "size": 3326, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/huntsman/drp/metrics/calexp.py", "max_stars_repo_name": "AstroHuntsman/huntsman-drp", "max_stars_repo_head_hexsha": "00f045ccccc1f7545da491457a2b17b9aabea89a", "max_stars_repo_licenses": ["MIT... |
import numpy, scipy.io
import os
import pandas as pd
import datetime
#os.chdir('C:\\Users\\name\\folders') #get the correct working directory
remove_data_up_to_year = 2006
num_years = 10
num_contracts = 20
#READ DATA
data = pd.read_excel('S&S.xlsx', sheet_name='Futures2')
data = data.fillna('-') # with 0s rather tha... | {"hexsha": "9e32088758a9bff10016f2a4b5885a657d2b3222", "size": 2970, "ext": "py", "lang": "Python", "max_stars_repo_path": "two_factor_estimation/GenerateMatlabData.py", "max_stars_repo_name": "SteffenBakker/MatureFieldDevelopment", "max_stars_repo_head_hexsha": "d733091c686d1f333f29f8cfe1b743f456a0dcd9", "max_stars_re... |
# coding: utf-8
import sys
sys.path.append('../..')
import numpy as np
from common.util import preprocess, create_co_matrix, cos_similarity, ppmi
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
C = create_co_matrix(corpus, vocab_size)
W = ppmi(C... | {"hexsha": "d4da11a882e160f376405b3ed7359527ed81cc85", "size": 440, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch02/lecture/ppmi.py", "max_stars_repo_name": "intlabSeJun/deep-learning2", "max_stars_repo_head_hexsha": "bd02497adb627e1e07b4ed71044f675d2208ddb6", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#ifndef AWARE_ANDROID_FACTORY_HPP
#define AWARE_ANDROID_FACTORY_HPP
///////////////////////////////////////////////////////////////////////////////
//
// http://github.com/breese/aware
//
// Copyright (C) 2013 Bjorn Reese <breese@users.sourceforge.net>
//
// Distributed under the Boost Software License, Version 1.0.
/... | {"hexsha": "0568bed3b62e65288fd525fd4a2a2047152fb8c1", "size": 1896, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/aware/android/factory.hpp", "max_stars_repo_name": "xpgdk/aware", "max_stars_repo_head_hexsha": "985e4120d93c1e88b26d6a7420de01c146584902", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars... |
##
# Sparse BroadcastStyle
##
for Typ in (:Diagonal, :SymTridiagonal, :Tridiagonal, :Bidiagonal)
@eval begin
BroadcastStyle(::StructuredMatrixStyle{<:$Typ}, ::BandedStyle) =
BandedStyle()
BroadcastStyle(::BandedStyle, ::StructuredMatrixStyle{<:$Typ}) =
BandedStyle()
en... | {"hexsha": "ce4f8a6be2a308f687a338d022c69a4f7fb507b7", "size": 2966, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/interfaceimpl.jl", "max_stars_repo_name": "xzackli/BandedMatrices.jl", "max_stars_repo_head_hexsha": "803edd6f3bcb150ba5e9e226adf22904f5fc4690", "max_stars_repo_licenses": ["BSD-3-Clause-Open-M... |
$$
\newcommand{\dt}{\Delta t}
\newcommand{\udt}[1]{u^{({#1})}(T)}
\newcommand{\Edt}[1]{E^{({#1})}}
$$
This is the first in a series of posts on testing scientific software. For this to make sense, you'll need to have skimmed [the motivation and background](http://ianhawke.github.io/blog/close-enough.html).
We're star... | {"hexsha": "e696aa7ba8f4a3c37618bb6fae41c2cea87c6770", "size": 74773, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "content/notebooks/01-Close-Enough-Errorbars.ipynb", "max_stars_repo_name": "IanHawke/blog", "max_stars_repo_head_hexsha": "aa47807bf5a96cc97ecfbe48e41b8f795b88cba9", "max_stars_repo_... |
def parcat_analysis(TaXon_table_xlsx, path_to_outdirs, template, height, width, meta_data_to_test, plotly_colors, available_taxonomic_levels_list, taxonomic_level, theme, font_size, color_discrete_sequence):
import PySimpleGUI as sg
import pandas as pd
import numpy as np
import plotly.graph_objects as ... | {"hexsha": "53c35f6f60a3176620b39bddc78e90a97772ba74", "size": 6824, "ext": "py", "lang": "Python", "max_stars_repo_path": "taxontabletools/parcat_analysis.py", "max_stars_repo_name": "TillMacher/TaxonTableTools", "max_stars_repo_head_hexsha": "9b5c6356acd9890465d39f16b671eb346ceec25a", "max_stars_repo_licenses": ["MIT... |
abstract type LocalFieldParameter end
abstract type EisensteinLocalField <: LocalFieldParameter end
abstract type UnramifiedLocalField <: LocalFieldParameter end
abstract type GenericLocalField <: LocalFieldParameter end
mutable struct LocalField{S, T} <: Field
defining_polynomial::Generic.Poly{S}
S::Symbol
prec... | {"hexsha": "0ebea63cb5b8dee593e170a242d6057257758e5c", "size": 2350, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LocalField/Types.jl", "max_stars_repo_name": "mohammed198246/Hecke.jl", "max_stars_repo_head_hexsha": "6f77b6ff0779e62840265704c3ede44e7c20da70", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
"""
PLot distirbution
"""
import pylab as P
import numpy as np
import random as rdm
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.inset_locator import inset_axes
from scipy.stats import norm
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, F... | {"hexsha": "3497622f4d79c52b5d5160e02981aa26a5f217ec", "size": 4073, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/distri_distance.py", "max_stars_repo_name": "laurencee9/Symposium_optimization", "max_stars_repo_head_hexsha": "8d70111c3269f749fc4379023d7103be3091f2a0", "max_stars_repo_licenses": ["MIT"], ... |
"""This module provides out-of-the box plots to analyse models whee titles, axes labels, additional information is automatically
added to the resuling figures from the information stored in the repository.
"""
import logging
import math
import numpy as np
import pandas as pd
import pailab.analysis.plot_helper as plot_... | {"hexsha": "9c1518f164f5bf12ab1f1c639959ac3c805ba637", "size": 30613, "ext": "py", "lang": "Python", "max_stars_repo_path": "pailab/analysis/plot.py", "max_stars_repo_name": "pailabteam/pailab", "max_stars_repo_head_hexsha": "3995b25f105827ae631e6120f380748d7d284c9f", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
#include <appbase/application.hpp>
#include <eosio/http_plugin/http_plugin.hpp>
#include <eosio/net_plugin/net_plugin.hpp>
#include <eosio/producer_plugin/producer_plugin.hpp>
#include <potato/version/version.hpp>
#include <boost/exception/diagnostic_information.hpp>
#include <fc/filesystem.hpp>
#include <fc/log/logg... | {"hexsha": "fc8cc7178e09025087622a9655c6c5551570a68e", "size": 4813, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "programs/nodeos/main.cpp", "max_stars_repo_name": "rise-worlds/GeneralServer", "max_stars_repo_head_hexsha": "1901e9bf1c8654752153859ea5ab0244053264d5", "max_stars_repo_licenses": ["MIT"], "max_star... |
/*=============================================================================
Copyright (c) 2017 Daniel James
Use, modification and distribution is subject to the Boost Software
License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt)
=============================... | {"hexsha": "ec822d9827b6559671121ae7f5bcd77cbf90466a", "size": 5243, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tools/quickbook/src/boostbook_chunker.cpp", "max_stars_repo_name": "cpp-pm/boost", "max_stars_repo_head_hexsha": "38c6c8c07f2fcc42d573b10807fef27ec14930f8", "max_stars_repo_licenses": ["BSL-1.0"], "... |
/-
Copyright (c) 2021 Yaël Dillies. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Yaël Dillies
-/
import data.finset.locally_finite
/-!
# Intervals as multisets
> THIS FILE IS SYNCHRONIZED WITH MATHLIB4.
> Any changes to this file require a corresponding PR to mathl... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/data/multiset/locally_finite.lean"} |
# Copyright - Transporation, Bots, and Disability Lab - Carnegie Mellon University
# Released under MIT License
import numpy as np
""" Representation of a Ray
"""
class Ray():
origin: np.array # (3,) Origin of the Ray
direction: np.array # (3,) Unit Vector describing the direction of ray from or... | {"hexsha": "eef433b97e1cb87c8044c789ea7a9497e3548155", "size": 1766, "ext": "py", "lang": "Python", "max_stars_repo_path": "alloy/spatial/primitives/ray.py", "max_stars_repo_name": "CMU-TBD/alloy", "max_stars_repo_head_hexsha": "cf66738e044613fb274bd1b159864a7600e15cb5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import unittest
from typing import Tuple
import numpy as np
from frc.delay import Delay # pylint: disable=import-error
class TestDelay(unittest.TestCase):
def test_delay(self) -> None:
x: Delay[str] = Delay(5, 0.2)
self.assertIsNone(x.get(0)) # nothing there yet
x.put("foo", 0)
self... | {"hexsha": "2e30e63b57490804e61a4d03b1b65655efb00d92", "size": 950, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulator/tests/test_delay.py", "max_stars_repo_name": "truher/FRC2022", "max_stars_repo_head_hexsha": "fb8a39d0212e8460669feb282f8b9977f17fdd93", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
using Base.Test, GaussianProcesses
using GaussianProcesses: distance, KernelData, StationaryARDData, SEArd
srand(1)
d, n = 5, 10
logℓ, logσ = rand(d), rand()
X = rand(d,n)
kern = SEArd(logℓ, logσ)
data = KernelData(kern, X)
@test_approx_eq distance(kern, X, data) distance(kern, X)
| {"hexsha": "f96d365402a8461c38b0636f1af373e7c0aac1ec", "size": 285, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_utils.jl", "max_stars_repo_name": "JuliaPackageMirrors/GaussianProcesses.jl", "max_stars_repo_head_hexsha": "76a0070b01e996d56888f56ba9d144940cbb7578", "max_stars_repo_licenses": ["MIT"], ... |
#!/usr/bin/env python
"""
Copyright 2018 Jesus Villalba (Johns Hopkins University)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
from __future__ import absolute_import
from __future__ import print_function
import sys
import os
import argparse
import time
import logging
import numpy as np
from six.mo... | {"hexsha": "03a1c603b5520d6cf4af7eea013cfbc32a0c9ed5", "size": 2055, "ext": "py", "lang": "Python", "max_stars_repo_path": "hyperion/bin/compute-energy-vad.py", "max_stars_repo_name": "jsalt2019-diadet/hyperion", "max_stars_repo_head_hexsha": "14a11436d62f3c15cd9b1f70bcce3eafbea2f753", "max_stars_repo_licenses": ["Apac... |
# Parsed file utility
"""
A cache of file content, parsed with an arbitrary parser function.
This is a modified and generalized version of `Base.CachedTOMLDict`.
Getting the value of the cache with `f[]` will automatically update the parsed
value whenever the file changes.
"""
mutable struct CachedParsedFile{T}
p... | {"hexsha": "c4844307980649b291d32177f4bf4ad23d001f6e", "size": 6286, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/file_data_projects.jl", "max_stars_repo_name": "JuliaComputing/DataSets.jl", "max_stars_repo_head_hexsha": "195d0b65ebdb56a69917a8c70452cd05a6083783", "max_stars_repo_licenses": ["MIT"], "max_s... |
##########################################################
############### Dataset management class #################
##########################################################
import cv2
import os
import numpy as np
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
from keras.uti... | {"hexsha": "2462550d85441cec5d7fdb268cdc144e19f624ec", "size": 3084, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/data_manager1.py", "max_stars_repo_name": "nurwalyanto/SteganalysisCNN", "max_stars_repo_head_hexsha": "5c0b954332b3ff04648fad50a5bf8a379d03394b", "max_stars_repo_licenses": ["MIT"], "max_st... |
function default_in_partition(sites::Tuple{Int}, p::Integer, nparts::Integer)
return p == mod1(sites[1], nparts)
end
# i ≥ j
_f(i, j) = (i - 1) * i ÷ 2 + j
function default_in_partition(sites::NTuple{2,Int}, p, nparts)
i, j = sites
if i ≤ j
return p == mod1(_f(j, i), nparts)
end
return p == mod1(_f(i, j)... | {"hexsha": "bc07a59c52c5dc05648b6721c0a6d00f4f803ecc", "size": 675, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/default_in_partition.jl", "max_stars_repo_name": "mtfishman/ITensorParallel", "max_stars_repo_head_hexsha": "4614a948b25c65a55d3cad84464e52935faf6c81", "max_stars_repo_licenses": ["MIT"], "max_s... |
""" io_utils.py
Utilities for reading and writing logs.
"""
import os
import statistics
import re
import csv
import numpy as np
import pandas as pd
import scipy as sc
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import networkx as nx
import tensorboardX
import cv2
import ... | {"hexsha": "131d1dfa9c240b467560347a0b9d4141502c9a6e", "size": 24642, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/io_utils.py", "max_stars_repo_name": "kangyongxin/gnn-model-explainer", "max_stars_repo_head_hexsha": "c601ea4222cf60e04f954e563239d0c6717086eb", "max_stars_repo_licenses": ["Apache-2.0"], ... |
# Copyright 2020 The Private Cardinality Estimation Framework Authors
#
# 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 b... | {"hexsha": "7bc5910d59a16532faa3712e9d9d0c9738d5a48d", "size": 11299, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluations/evaluator.py", "max_stars_repo_name": "hpnhxxwn/cardinality_estimation_evaluation_framework", "max_stars_repo_head_hexsha": "13578a4d4d4e15934b5d6d48c5367bb044778010", "max_stars_repo... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Multi-page composite figure of all basins
Created on Tue Jul 13 09:15:09 2021
@author: lizz
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.patches import Rectangle
import gSPEI as gSPEI
## Confirm... | {"hexsha": "54a03412cd7eb2297869ab297f004e1efe61e657", "size": 5670, "ext": "py", "lang": "Python", "max_stars_repo_path": "supplement_multiplot-ens_mean.py", "max_stars_repo_name": "ehultee/glacial-SPEI", "max_stars_repo_head_hexsha": "6f81db8a398fde626fc21b0c5e03ba97f131c0a0", "max_stars_repo_licenses": ["MIT"], "max... |
# 1950083 自动化 刘智宇
import numpy as np
import ToolFunction
from WFLWdataset import WFLW_Dataset
from Consts import *
from KeyPointNet import KeyPointNet
if __name__ == "__main__":
# ---------------------- 网络模型验证 ----------------------
print(separate_bar*2, "网络模型验证：", separate_bar*2)
print(use_model_name)
... | {"hexsha": "50d5559837665d7255ad4d918a72f471630549d9", "size": 2103, "ext": "py", "lang": "Python", "max_stars_repo_path": "KeyPointsDetect/EvalKeyPointNet.py", "max_stars_repo_name": "leizhenyu-lzy/DrowsyDrivingDetect", "max_stars_repo_head_hexsha": "9416769c4ceb5c5775cb98e7ccb11faa0fd760dd", "max_stars_repo_licenses"... |
"""
Module to predict superconductivity in a material
"""
import sdmetrics
from sdmetrics.single_table import MLEfficacy
import pandas as pd
import numpy as np
class Validator():
"""
Docstring
"""
def __init__(self, predicted_data, verbose, aggregate = True):
"""
Docstring
"""... | {"hexsha": "11219db21d48f7e1bc531952add931bfd22c460b", "size": 1731, "ext": "py", "lang": "Python", "max_stars_repo_path": "SuperconGAN/validate.py", "max_stars_repo_name": "RajeevAtla/SuperconGAN", "max_stars_repo_head_hexsha": "3078fd43308b31243c9f8b364b147725c58c397e", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\documentclass[11pt]{article}
\input{preamble}
% Add your bibtex library here
\addbibresource{tutorial.bib}
\newcommand{\includeimage}[2][]{%
%HEVEA\imgsrc{#2.hevea.png}%
%BEGIN LATEX
\includegraphics[#1]{#2}
%END LATEX
}
% BEAST book specific commands
\newcommand{\BEASTVersion}{2.4.x}
\newcommand{\TracerVersion}{1.... | {"hexsha": "882d0511aa000960c6ef6c4644f48d7fdfd771cd", "size": 36974, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "main.tex", "max_stars_repo_name": "Taming-the-BEAST/starBeast-Tutorial", "max_stars_repo_head_hexsha": "128840d025a64b8d5e562098c6f91e270dc63c0c", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_sta... |
# Copyright 2014-2020 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
#
# Unless required by appl... | {"hexsha": "cc07a608a86cbea078c862f15b913ccd0e506cdf", "size": 22824, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/agf2/aux.py", "max_stars_repo_name": "QuESt-Calculator/pyscf", "max_stars_repo_head_hexsha": "0ed03633b699505c7278f1eb501342667d0aa910", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
-- a hodge-podge of tests
module Test where
import Test.Class
import Test.EquivalenceExtensionṖroperty
import Test.EquivalenceṖroperty
import Test.EquivalentCandidates
import Test.EquivalentCandidates-2
import Test.Factsurj3
import Test.Functor -- FIXME doesn't work with open import Everything
import Test.ProblemWi... | {"hexsha": "898dd38655e5164deed65da1e72a45b9983769e5", "size": 1177, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "archive/agda-3/src/Test.agda", "max_stars_repo_name": "m0davis/oscar", "max_stars_repo_head_hexsha": "52e1cdbdee54d9a8eaee04ee518a0d7f61d25afb", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_c... |
import multinet as mn
import networkx as nx
g1 = nx.Graph()
nodes = ['A', 'B', 'C', 'D', 'E']
edges = [('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'A')]
g1.add_nodes_from(nodes)
g1.add_edges_from(edges)
g2 = nx.Graph()
nodes = ['A', 'B', 'C', 'D', 'E']
edges = [('A', 'C'), ('B', 'D'), ('C', 'E'), ('D', 'A')... | {"hexsha": "9be181782c64632233d9f2ad3aabd06b4e96c527", "size": 1373, "ext": "py", "lang": "Python", "max_stars_repo_path": "multinet/tests/test_bipartite.py", "max_stars_repo_name": "wuhaochen/multinet", "max_stars_repo_head_hexsha": "b43a1ad8b82a3ad6557f2ee0a30f1ab142bc47bd", "max_stars_repo_licenses": ["MIT"], "max_s... |
import importlib
import pandas as pd
import xarray as xr
import numpy as np
from numpy import nan
import sys
import warnings
import xesmf as xe
from glob import glob
from CASutils import readdata_utils as read
from CASutils import calendar_utils as cal
from CASutils import filter_utils as filt
importlib.reload(read)
... | {"hexsha": "91f692f5b810166a0b0d950271e4a33ce7b7b6ab", "size": 2140, "ext": "py", "lang": "Python", "max_stars_repo_path": "DATA_SORT/deseasonalized_tvar/CMIP6/outputcmip6_trefhtminvar.py", "max_stars_repo_name": "islasimpson/snowpaper_2022", "max_stars_repo_head_hexsha": "d6ee677f696d7fd6e7cadef8168ce4fd8b184cac", "ma... |
module DualDecompositionSolver
using ModelGraphs
using JuMP
using MathOptInterface
const MOI = MathOptInterface
using SparseArrays
using LinearAlgebra
using Distributed
export DDModel, dual_decomposition_solve, DDOptimizer
include("utils.jl")
include("solution.jl")
include("dual_decomp_model.jl")
include("multip... | {"hexsha": "1b43655f978259eaebd9ffe969fb7ccb3d37237f", "size": 368, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DualDecomposition/DualDecompositionSolver.jl", "max_stars_repo_name": "jalving/ModelGraphSolvers.jl", "max_stars_repo_head_hexsha": "36af5c13815db634a0345ea1f0e226f496e65a20", "max_stars_repo_li... |
ENV["JULIA_CXX_RTTI"]=1
using Cxx
push!(LOAD_PATH,"../src/")
using Documenter, ROS
makedocs(
modules = [ROS],
authors = "George Stavrinos",
sitename = "ROS.jl",
format = Documenter.HTML(prettyurls = false, footer = nothing),
pages = [
"Home" => "index.md",
"Features" => "features.md... | {"hexsha": "0ef1867f798e6377478163f57484df60c83d58f7", "size": 759, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "gstavrinos/ROS.jl", "max_stars_repo_head_hexsha": "05c79ff213af1bb6a40e10228b2cffb5c0e006c3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3... |
"""Loss functions for Soft Actor-Critic."""
from acme import types
import haiku as hk
import jax
import jax.numpy as jnp
def alpha_loss_fn(
log_alpha: jnp.ndarray, entropy: jnp.ndarray, target_entropy: float
) -> jnp.ndarray:
"Compute the temperature loss for EC-SAC."
return log_alpha * (entropy - target_... | {"hexsha": "9739cb9d95b45c202ab52240409abe2b9da4c91e", "size": 1923, "ext": "py", "lang": "Python", "max_stars_repo_path": "magi/agents/sac/losses.py", "max_stars_repo_name": "akbir/magi", "max_stars_repo_head_hexsha": "cff26ddb87165bb6e19796dc77521e3191afcffc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY ... | {"hexsha": "c30d53f992d654913fc2d27d2935e6d3cd7d7103", "size": 10725, "ext": "py", "lang": "Python", "max_stars_repo_path": "imperative/python/test/unit/quantization/test_module.py", "max_stars_repo_name": "Olalaye/MegEngine", "max_stars_repo_head_hexsha": "695d24f24517536e6544b07936d189dbc031bbce", "max_stars_repo_lic... |
Author - Adwait P Naik
Created on 13th February
#packages to import
from __future__ import print_function, division
import random
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
print("A-star grid based implementation using matplotlib")
print("matplotlib - https://matplotl... | {"hexsha": "ef26b96b51d0b10aa9ada6723d934f2e82afc880", "size": 2913, "ext": "py", "lang": "Python", "max_stars_repo_path": "Motion planning/A star/Astar_matplotlib.py", "max_stars_repo_name": "addy1997/python-RRT", "max_stars_repo_head_hexsha": "93983e17f2e6e93ff79c8f04a86ce28718ba2779", "max_stars_repo_licenses": ["MI... |
Require Import Crypto.Arithmetic.PrimeFieldTheorems.
Require Import Crypto.Specific.solinas32_2e174m3_7limbs.Synthesis.
(* TODO : change this to field once field isomorphism happens *)
Definition freeze :
{ freeze : feBW_tight -> feBW_limbwidths
| forall a, phiBW_limbwidths (freeze a) = phiBW_tight a }.
Proof.
S... | {"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... |
#This script will find the yield point, Young's modulus, and
#Poisson's ratio.
#License information:
#
#MIT License
#
#Copyright (c) 2019 Will Pisani
#
# 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 Sof... | {"hexsha": "ad350891bb07994fbad17d524bb7315d51ae0b6e", "size": 6637, "ext": "r", "lang": "R", "max_stars_repo_path": "R/YMPlot.r", "max_stars_repo_name": "wapisani/PhD", "max_stars_repo_head_hexsha": "9f6ba9bde659e6032cc7a5fa10532e480163e779", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo... |
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* ----... | {"hexsha": "c4bf0480c5daf8352a511859f75895ede1983284", "size": 6616, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/testDoglegOptimizer.cpp", "max_stars_repo_name": "ashariati/gtsam-3.2.1", "max_stars_repo_head_hexsha": "f880365c259eb7532b9c1d20979ecad2eb04779c", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
[STATEMENT]
lemma distinct_cnt: "distinct xs \<Longrightarrow> cnt x xs \<le> 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. distinct xs \<Longrightarrow> cnt x xs \<le> 1
[PROOF STEP]
apply (induction xs)
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. distinct [] \<Longrightarrow> cnt x [] \<le> 1
2. \<And>... | {"llama_tokens": 728, "file": "Timed_Automata_DBM_Basics", "length": 6} |
# coding: utf-8
# ## Import dependencies
import os
import subprocess
import numpy as np
from glob import glob
from keras.preprocessing.image import *
from model import CNN_model
#print('Connect your smartphone to this system, mount your Internal Storage and note the absolute path of WhatsApp folder')
#print('For ex... | {"hexsha": "e8e200a0bdb617d8dd0e9ab48c920bef2f36bf04", "size": 2839, "ext": "py", "lang": "Python", "max_stars_repo_path": "Quora-Screen-Shots-Notes/extract.py", "max_stars_repo_name": "send2manoo/Quora-CNN", "max_stars_repo_head_hexsha": "286b0d40c10a02d2d9724ae08bf16d1f1979d21e", "max_stars_repo_licenses": ["Apache-2... |
#ifndef _SRC_DIFF_HPP_
#define _SRC_DIFF_HPP_
#include <boost/filesystem.hpp>
#include <string>
#include <map>
#include "Index.hpp"
namespace Sit {
namespace Diff {
/**
* File Status
*/
enum FileStatus { Added, Modified, Deleted, Same, Untracked };
/**
* An item of a `Diff` object
*/
struct DiffItem {
std::str... | {"hexsha": "708ce65b4d28922ef2aa108caac941f9d9fa929f", "size": 1036, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/Diff.hpp", "max_stars_repo_name": "abcdabcd987/s", "max_stars_repo_head_hexsha": "bcd982bcf78554d243c1f6be38fd295ffb85c78b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 40.0, "max_sta... |
# Copyright 2018-2019 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the 'license' fil... | {"hexsha": "21c7ff26da15f2f603faf75e93dc331e6028856f", "size": 3054, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sagemaker_containers/_runner.py", "max_stars_repo_name": "samuel-massinon/sagemaker-containers", "max_stars_repo_head_hexsha": "356ab704ca99e6f35904093fbc2b8558eb54fe57", "max_stars_repo_licen... |
# Copyright 2020 The SQLFlow Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | {"hexsha": "e6f2620bf4452f55a0a3c2fa5af32dfd042e3a39", "size": 6634, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/sqlflow_submitter/optimize/runner.py", "max_stars_repo_name": "Kelang-Tian/sqlflow", "max_stars_repo_head_hexsha": "36eab7802cff31d862c1983d23407647339fd18a", "max_stars_repo_licenses": ["A... |
import os
import numpy as np
import pandas as pd
import yaml
from tqdm import tqdm
from joblib import Parallel, delayed
project_dir = os.path.normpath(os.path.dirname(os.path.abspath(__file__)) + os.sep + os.pardir)
from lucrl.utils.coordinator import Coordinator
crd = Coordinator(project_dir)
from lucrl.utils.logge... | {"hexsha": "0335350ec7bb2dc74442ebecefc74294c97ac356", "size": 5167, "ext": "py", "lang": "Python", "max_stars_repo_path": "lucrl/scripts/ml_to_df.py", "max_stars_repo_name": "tyomj/linucrl", "max_stars_repo_head_hexsha": "8438330bdc20bacf5f985835c1d2e70aec6b00b8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import numpy as np
from .base import ClassifierModule
from .bert import BERTClassifier
from ..model.base import ClsDecoder
from ..model.bert import BERTConfig, get_decay_power
from ..model.stockbert import StockBERTEncoder
from ..third import tf
from .. import com
class StockBERTClassifier(BERTClassifier, Classifier... | {"hexsha": "03fddf2da87ac7c492fb444fd3131ec53a8856ce", "size": 6960, "ext": "py", "lang": "Python", "max_stars_repo_path": "uf/apps/stockbert.py", "max_stars_repo_name": "dumpmemory/unif", "max_stars_repo_head_hexsha": "a301d7207791664fb107edda607c55f2d50dd17d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# SPDX-License-Identifier: BSD-3-Clause
# Copyright (c) 2022 Osyris contributors (https://github.com/osyris-project/osyris)
from enum import Enum
import numpy as np
from . import utils
from ..core import Array
class ReaderKind(Enum):
AMR = 0
SINK = 1
PART = 2
class Reader():
def __init__(self, kind... | {"hexsha": "9ba87554dceb9413b5e1bad0d896f2792061683f", "size": 3033, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/osyris/io/reader.py", "max_stars_repo_name": "osyris-project/osyris", "max_stars_repo_head_hexsha": "bff42d864a7d5d248f7023216e32fe97bc06dca6", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# Distribution of home values for all US counties
import pandas as pd
import numpy as np
import requests
# Adding column descriptions
mort_df = pd.read_csv('Census_Home_Value.csv') # Copied from [https://api.census.gov/data/2018/acs/acs5/variables.html] and slightly formatted in excel
mort_df['Vals'] = mort_df['Vals'... | {"hexsha": "436ebf57a5c7e1254a85c2e328358eac746e1774", "size": 987, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/Census API Data/Census_API_Home_Value_mort_nomort_17.py", "max_stars_repo_name": "hassenmorad/CA-Migration", "max_stars_repo_head_hexsha": "762434b3a013f2488c382dbdc3d2dc7b7f91c572", "max_s... |
import matplotlib.pyplot as plt
import numpy as np
def split_data(original, position = 1):
trial_1_linear = original.split("\n")
trial_1_linear = [float(line.split(" ")[position]) for line in trial_1_linear]
return trial_1_linear
def model_1_data():
trial_1_linear = '''1.0 0.20545053482055664
1.0 0.82... | {"hexsha": "62c501df74eca40945a4cfdfa49dbd6a74c5d0c6", "size": 5605, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project/6669_code/figures.py", "max_stars_repo_name": "ranliu98/ISYE-6669-Deterministic-Optimization", "max_stars_repo_head_hexsha": "0d6eefaa34f3e8e909f4109504fc3e2212d8725f", "max_stars_repo_lic... |
#include <petsc.h>
#include <petscmath.h>
#include "compressibleFlow.h"
#include "mesh.h"
#include "petscdmplex.h"
#include "petscts.h"
//MMS from Verification of a Compressible CFD Code using the Method of Manufactured Solutions, Christopher J. Roy,† Thomas M. Smith,‡ and Curtis C. Ober§
// Define
#define Pi PETSC_P... | {"hexsha": "70551590619ff2c38b05f12f08d7f3c08f7e073d", "size": 44048, "ext": "c", "lang": "C", "max_stars_repo_path": "euler2DMMS.c", "max_stars_repo_name": "mmcgurn/MattFlowCases", "max_stars_repo_head_hexsha": "1ef7ca77b447a07fdd14d1e2e902abe3e281b651", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": ... |
"""
Example call on NT infrastructure:
export STORAGE_ROOT=<your desired storage root>
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
python -m padertorch.contrib.examples.source_separation.or_pit.train with database_jsons=${paths to your JSONs}
"""
import copy
import lazy_dataset
import torch
from paderbox.io imp... | {"hexsha": "932a01a8b85baf3f560f5a5526a10389fd72ddbd", "size": 11553, "ext": "py", "lang": "Python", "max_stars_repo_path": "padertorch/contrib/examples/source_separation/or_pit/train.py", "max_stars_repo_name": "sibange/padertorch", "max_stars_repo_head_hexsha": "494692d877f04c66847c2943795b23aea488217d", "max_stars_r... |
#=
Copyright (c) 2015, Intel Corporation
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 disclaime... | {"hexsha": "84ba3f11b0c3374a08acda8fa3dfc21013eca13d", "size": 3456, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/stdlib/layers/dropout.jl", "max_stars_repo_name": "IntelLabs/Latte.jl", "max_stars_repo_head_hexsha": "8782f7bf33453f24d0eff4adf76e098968e77892", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy import stats
import statsmodels.api as sm
from CER_data import CER_data_list, liquid_storable_prod
import math
data_set = liquid_storable_prod
x_data = data_set.x_data
y_data = data_set.y_data
log_x = []
log_y = []
for... | {"hexsha": "ab87e8551b96eba515385492bd5d853877de3739", "size": 885, "ext": "py", "lang": "Python", "max_stars_repo_path": "lvreuse/data/CER_data/CER_data_fit.py", "max_stars_repo_name": "mvernacc/lvreuse", "max_stars_repo_head_hexsha": "e2ac6aca334b49b0d4f5f881861cb42ce86dd130", "max_stars_repo_licenses": ["MIT"], "max... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
===========================
sbpy Production Rate Module
===========================
:author: Giannina Guzman (gguzman2@villanova.edu)
created on June 26, 2019
"""
import tempfile
import numpy as np
import astropy
import astropy.constants as con
impo... | {"hexsha": "1cdc49f2145fbeb7dd4b3a74970da2f4b648975c", "size": 34332, "ext": "py", "lang": "Python", "max_stars_repo_path": "sbpy/activity/gas/productionrate.py", "max_stars_repo_name": "dirac-institute/sbpy", "max_stars_repo_head_hexsha": "9eb0523610f497ba2d068a071aae05ebfd67ed9d", "max_stars_repo_licenses": ["BSD-3-C... |
# coding: utf8
import sys
from os.path import dirname, exists, join
import numpy as np
import pinocchio
from pinocchio.robot_wrapper import RobotWrapper
def getModelPath(subpath, printmsg=False):
base = '../../../share/example-robot-data/robots'
for p in sys.path:
path = join(p, base.strip('/'))
... | {"hexsha": "ef8535a2f53f9e59f27f3fa9b12a043d7b2e3318", "size": 7128, "ext": "py", "lang": "Python", "max_stars_repo_path": "robots_loader.py", "max_stars_repo_name": "thomascbrs/mpc-tsid", "max_stars_repo_head_hexsha": "a20da07fd285a628c6dd32afd76075e3963bf005", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
# --------------
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
# Load the data
df = pd.read_csv(path)
# replace the $ symbol
columns = ['INCOME','HOME_VAL','BLUEBOOK','... | {"hexsha": "657613b41182eef8a14384a53475232e1b089b10", "size": 4223, "ext": "py", "lang": "Python", "max_stars_repo_path": "car_Insurance_claim/code.py", "max_stars_repo_name": "ManojSingh0302/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "11818ca3a58d2cbb8818eddcac7dff9f1216f2a6", "max_stars_repo_licenses": ["M... |
import pandas as pd
import os.path as ospath
import numpy as np
from os import makedirs
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import IsolationForest
from sklearn.decomposition import PCA, KernelPCA
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
im... | {"hexsha": "19d28b718696c969a248321b4856d1d1816ae93f", "size": 3047, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "MeMoFa-Machine-Learning/aml_project2", "max_stars_repo_head_hexsha": "7a09baba6923d038bce8282b84fb6b1d2b86cba2", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
r"""
Index of decoders
The ``codes.decoders`` object may be used to access the decoders that Sage can build.
**Generic decoders**
- :class:`linear_code.LinearCodeSyndromeDecoder <sage.coding.linear_code.LinearCodeSyndromeDecoder>`
- :class:`linear_code.LinearCodeNearestNeighborDecoder <sage.coding.linear_code.Linear... | {"hexsha": "0dfbd0186b42ea9ec9c149c5cddd9d45d8583ae9", "size": 1735, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/coding/decoders_catalog.py", "max_stars_repo_name": "fredstro/sage", "max_stars_repo_head_hexsha": "c936d2cda81ec7ec3552a3bdb29c994b40d1bb24", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
import unittest
import math
import numpy
import wireless as rf
class TurboCodecTests(unittest.TestCase):
def test_001_test_vectors_encode(self):
# input and output bits were prepared using CML, using Cdma2000 scenario
# number 5.
messageStr = file('test-message-1530.dat').read()
... | {"hexsha": "2e87caf79f0a84505e0369404e06badb924c278d", "size": 2367, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/codes/turbo/TurboCodecTests.py", "max_stars_repo_name": "yonch/wireless", "max_stars_repo_head_hexsha": "5e5a081fcf3cd49d901f25db6c4c1fabbfc921d5", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import pandas as pd
import statsmodels.api as sm
import gc
import operator
import networkx as nx
class causalPartition:
df = None # the whole dataset
probabilities = None # the Monte Carlo probabilities, a dict, each element represents a dimension of the intervention vector
# each eleme... | {"hexsha": "d107b5d0c92e33c3a108065928d70ce7e1a34d08", "size": 33633, "ext": "py", "lang": "Python", "max_stars_repo_path": "causalPartition.py", "max_stars_repo_name": "facebookresearch/CausalMotifs", "max_stars_repo_head_hexsha": "cee4fc71e2a72edbff4d01a69368634e255d19a7", "max_stars_repo_licenses": ["MIT"], "max_sta... |
!============================================================================!
module bspline
integer :: ns, kord
real, allocatable :: knot(:)
real, allocatable :: bs(:,:)
real, external :: bsder
real :: rhoe, ue, ve, we, te, pe, phie
real :: alpha, M... | {"hexsha": "3952040e5d36681b62e1cd6d1252a492feeecdbd", "size": 56708, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "util/npost.f90", "max_stars_repo_name": "sscollis/lns3d", "max_stars_repo_head_hexsha": "f87b06abbb188cce9248092075f71dcce4570493", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
"""
This piece of code is used for generating triplets on the fly while training
the embednet
"""
import pdb
import torch
import numpy as np
from tqdm import tqdm
import Levenshtein as lev
from torch.utils.data import DataLoader
from sklearn.metrics import pairwise_distances
class Triplets():
def __init__(
... | {"hexsha": "73b6005d01f3ade14a2804a66ba9c7221d68c7fa", "size": 16386, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/triplets.py", "max_stars_repo_name": "Sid2697/Word-recognition-EmbedNet-CAB", "max_stars_repo_head_hexsha": "cfc8b2d6841e2cbfd69dde27ea613c814bd4ac85", "max_stars_repo_licenses": ["MIT"], "ma... |
theory Test_Suite_ATC_RBT
imports Test_Suite_ATC "HOL-Library.RBT_Set" "HOL-Library.RBT_Mapping" (*"HOL-Data_Structures.AVL_Set"*)
begin
(* from RBT_Set : *)
(*
Users should be aware that by including this file all code equations
outside of List.thy using 'a list as an implementation of sets cannot be
used for c... | {"author": "RobertSachtleben", "repo": "Refined-Adaptive-State-Counting", "sha": "3691de6f16cec5ec74282465495c12e6a40133aa", "save_path": "github-repos/isabelle/RobertSachtleben-Refined-Adaptive-State-Counting", "path": "github-repos/isabelle/RobertSachtleben-Refined-Adaptive-State-Counting/Refined-Adaptive-State-Count... |
#include <boost/python.hpp>
#include "TelloPro.h"
#include "takeoff.h"
#include "land.h"
#include "up.h"
#include "flip.h"
TelloPro* get_instance(boost::python::str _inst, int _value)
{
std::string instance = boost::python::extract<std::string>(_inst);
if(instance == "takeoff")
return new Takeoff... | {"hexsha": "ee023ab5a26d791ddf89791a7a1f5a48b2973df9", "size": 860, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/python_interface.cpp", "max_stars_repo_name": "hgu-sit22005/telloproject-joypark88", "max_stars_repo_head_hexsha": "f35745dd085b2e18aaf922298a141703ddc92716", "max_stars_repo_licenses": ["MIT"], ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
LIVE DEMO
This script loads a pre-trained model (for best results use pre-trained weights for classification block)
and classifies American Sign Language finger spelling frame-by-frame in real-time
"""
import string
import cv2
import time
from processing import square... | {"hexsha": "b044475c3b8a25898a8527a87ed6dc1d9dadbb1d", "size": 6670, "ext": "py", "lang": "Python", "max_stars_repo_path": "live_demo.py", "max_stars_repo_name": "GerryZhang7/ASL-Translator-", "max_stars_repo_head_hexsha": "3963311d8dd1f010ee5a19b3760b451bc287ab1e", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import copy
import random
import enum
from collections import defaultdict
from typing import List
import pyglet
import math
import numpy as np
from Box2D import *
from math import sin, cos
from numpy import ndarray
from ped_env.utils.colors import ColorRed, exit_type_to_color, ColorYellow
from ped_env.functions imp... | {"hexsha": "d7fa0bdc43d83a89adbbc89dd961ba7a96b69d92", "size": 17586, "ext": "py", "lang": "Python", "max_stars_repo_path": "ped_env/objects.py", "max_stars_repo_name": "unkper/PedestrainSimulationModule", "max_stars_repo_head_hexsha": "039ed0903a0861130566d8d1d862594064b8e0db", "max_stars_repo_licenses": ["MIT"], "max... |
# The MIT License (MIT)
# Copyright (c) 2022 by the xcube development team and contributors
#
# 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 limit... | {"hexsha": "399efe454670a160d91f1e3fc63b108051b13c67", "size": 24646, "ext": "py", "lang": "Python", "max_stars_repo_path": "xcube/webapi/controllers/ogc/wmts.py", "max_stars_repo_name": "bcdev/xcube", "max_stars_repo_head_hexsha": "9d275ef3baef8fbcea5c1fbbfb84c3d0164aecd3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from __future__ import print_function, division, absolute_import
import time
import matplotlib
matplotlib.use('Agg') # fix execution of tests involving matplotlib on travis
import numpy as np
import six.moves as sm
import cv2
import imgaug as ia
from imgaug import augmenters as iaa
import imgaug.augmenters.size as ... | {"hexsha": "4333c65a99afb522393f58dc9dc3e2e8b9f9c9a2", "size": 62714, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/augmenters/test_size.py", "max_stars_repo_name": "dynamicguy/imgaug", "max_stars_repo_head_hexsha": "f58c06323eb04416c76de1f18952ca5875caf883", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Sebastian Raschka 2014-2020
# mlxtend Machine Learning Library Extensions
#
# A function for plotting enrichment plots.
# Author: Sebastian Raschka <sebastianraschka.com>
#
# License: BSD 3 clause
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from itertools import cycle
def enrichment_plo... | {"hexsha": "41241a8015d058a5b9e129ffc9decec90b131b45", "size": 3702, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlxtend/mlxtend/plotting/enrichment_plot.py", "max_stars_repo_name": "WhiteWolf21/fp-growth", "max_stars_repo_head_hexsha": "01e1d853b09f244f14e66d7d0c87f139a0f67c81", "max_stars_repo_licenses": [... |
import numpy as np
import tensorflow as tf
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
from datetime import datetime
housing = fetch_california_housing()
m, n = housing.data.shape
scaler = StandardScaler()
scaled_housing_data = scaler.fit_transform(housing.da... | {"hexsha": "6d92872fa879858e4c33b028e81506ddad32932e", "size": 3159, "ext": "py", "lang": "Python", "max_stars_repo_path": "drafts/basic_tf/linear.py", "max_stars_repo_name": "quanhua92/deeplearning_tutorials", "max_stars_repo_head_hexsha": "32fec492ca21c248dd1fb234db0a95a532df3469", "max_stars_repo_licenses": ["MIT"],... |
"""Demo Linear and Ridge Regression.
MPyC demo accompanying the paper 'Efficient Secure Ridge Regression from
Randomized Gaussian Elimination' by Frank Blom, Niek J. Bouman, Berry
Schoenmakers, and Niels de Vreede, presented at TPMPC 2019 by Frank Blom.
See https://eprint.iacr.org/2019/773 (or https://ia.cr/2019/773).... | {"hexsha": "0ecaec7b2fcf0ff37300ae56a57630fee9836e6c", "size": 15909, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/ridgeregression.py", "max_stars_repo_name": "cyckun/mpyc", "max_stars_repo_head_hexsha": "ed8546ab20d77b9d612528e82cb1501b85c2b673", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
# input 256x256x3
dis_channel = 64
dis_kern... | {"hexsha": "38c8ea4e52340b4405b8d80516c263d5147234f1", "size": 1285, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/cycle_gan/dis_model.py", "max_stars_repo_name": "syfrankie/IE498-Deep-Learning", "max_stars_repo_head_hexsha": "d19e28ca19047c73411dcbf32673dcc03f462c10", "max_stars_repo_licenses": ["MIT"... |
import json, random, time, os, base64
import numpy as np
from pprint import pprint
from collections import Counter, defaultdict
import matplotlib.pyplot as plt
np.set_printoptions(precision=4)
from sentence_transformers import SentenceTransformer
import torch
dataset = json.load(open("WebQA_train_val.json", "r"))
mo... | {"hexsha": "1d2efd41abbefc094400ae6b2c46c38f58a26781", "size": 1425, "ext": "py", "lang": "Python", "max_stars_repo_path": "unimodal/language/sentence_embedding_extractor.py", "max_stars_repo_name": "shubham-gupta-iitr/mmmlX", "max_stars_repo_head_hexsha": "3485e6191e0e45bf1c8168e4e928a36ab9264d22", "max_stars_repo_lic... |
# -*- coding: utf-8 -*-
"""
Integration d'une DataFrame Pandas dans un serveur Postgres avec psycopg2
@author: Victor MARTY-JOURJON
license = "MIT"
Input:
schemas='public'
table='temp' :nom de la table dans la base de données postgres (schéma public) (attention!! : si la table existe déjà, ell... | {"hexsha": "a8f7de4f42fc3e0a22f6f06977b250a8cfc60deb", "size": 3849, "ext": "py", "lang": "Python", "max_stars_repo_path": "insert_postgres.py", "max_stars_repo_name": "VMarty/DataFrameToPostgresPostgis", "max_stars_repo_head_hexsha": "8017635a87655a8adf3233d08fdb695239b7e2db", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma cbox_Pair_eq: "cbox (a, c) (b, d) = cbox a b \<times> cbox c d"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. cbox (a, c) (b, d) = cbox a b \<times> cbox c d
[PROOF STEP]
by (force simp: cbox_def Basis_prod_def) | {"llama_tokens": 112, "file": null, "length": 1} |
#include <boost/container_hash/hash.hpp>
#include <vector>
#include <algorithm>
#include <iterator>
#include <cassert>
//[ get_hashes
template <class Container>
std::vector<std::size_t> get_hashes(Container const& x)
{
std::vector<std::size_t> hashes;
std::transform(x.begin(), x.end(), std::back_inserter(hashe... | {"hexsha": "b11609b4463dc90973e7f88efb974e8fbc766f73", "size": 672, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "thirdparty/boost_1_71_0/libs/container_hash/doc/samples/tutorial.cpp", "max_stars_repo_name": "anonymouscode1/djxperf", "max_stars_repo_head_hexsha": "b6073a761753aa7a6247f2618977ca3a2633e78a", "max_... |
"""
Deep Worm project
Victor Kulikov 2018 Skoltech
"""
import os
import urllib2
import zipfile
from os import listdir
from os.path import basename, join, exists
from shutil import copyfile
import numpy as np
from skimage.draw import circle
from skimage.feature import corner_harris, corner_peaks
from skimage.io impor... | {"hexsha": "85c0973d8c2701e0cfb3c604386e691d68411f55", "size": 3829, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess/1_preprocess.py", "max_stars_repo_name": "kulikovv/DeepWorm", "max_stars_repo_head_hexsha": "8c6b857e2f11cacb975e2436515ca78014c6b93a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import numpy as np
import matplotlib.pyplot as plt
from module import Module
class RNN(Module):
r""" Simple recurrent neural network (RNN) class for an input sequence.
This RNN initializes weight and gradients. And contains the forward
and backward pass. The network is optimized using Adagrad.... | {"hexsha": "433f1a4795759503ca352567ca1cf2fb09b5bf70", "size": 5656, "ext": "py", "lang": "Python", "max_stars_repo_path": "additional/src/RNN.py", "max_stars_repo_name": "Mees-Molenaar/protein_location", "max_stars_repo_head_hexsha": "ce4f3e83e7dcaa2628a354823dc6b96c4175e7a0", "max_stars_repo_licenses": ["MIT"], "max_... |
import PIL.Image as Image
import scipy.misc
import sys
sys.path.append('./python')
from dehaze import load_model, transform, cuda # pylint: disable=E0401
def run_test():
net = load_model()
input_image = './download/canyon1.jpg'
output_filename = './download/canyon1_dh.jpg'
#===== Load input image =====
img ... | {"hexsha": "22951383910c3fabe25a68ea83d9381770a8a5bf", "size": 685, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test-dehaze.py", "max_stars_repo_name": "opteroncx/MoePhoto", "max_stars_repo_head_hexsha": "8e60803c02cbba0d1445cdb7570df1f836c9dff2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
"""
Utility module to help define pages (dashboards) as bundles of route, view and model.
"""
module Pages
using Genie.Router
import Genie.Router: Route
using Genie.Renderers
using Genie.Renderers.Html
using Stipple
export Page
export pages
@reactive struct EmptyModel <: ReactiveModel
end
mutable struct Page{M<:Re... | {"hexsha": "81a7c28b21bab18bb19a5c9536b74fe3819c47d2", "size": 1468, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Pages.jl", "max_stars_repo_name": "essenciary/Stipple.jl", "max_stars_repo_head_hexsha": "a7ae7d83f9f3cd67a7fd1a7e46414962a32525e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
# Copyright 2019 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "d8e663102935c9e0064fcc8835336da5381199ba", "size": 9153, "ext": "py", "lang": "Python", "max_stars_repo_path": "jax/dtypes.py", "max_stars_repo_name": "apaszke/jax", "max_stars_repo_head_hexsha": "a3997ba98c67f96ac1b44fa3e6f2420ea4ccacdd", "max_stars_repo_licenses": ["ECL-2.0", "Apache-2.0"], "max_stars_cou... |
///////////////////////////////////////////////////////////////////////////////
//
// Copyright (C) 2008-2012 Artyom Beilis (Tonkikh) <artyomtnk@yahoo.com>
//
... | {"hexsha": "2cb7dc2e643473a1d479a41335e47b984fa6b1cb", "size": 926, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cppcms_error.cpp", "max_stars_repo_name": "gatehouse/cppcms", "max_stars_repo_head_hexsha": "61da055ffeb349b4eda14bc9ac393af9ce842364", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 388.... |
# -*- coding: utf-8 -*-
from osgeo import gdal
import numpy
import random
import Utils
def checkMacroDemand(thisYearsMacroDemand):
tot = 0
for luNr in thisYearsMacroDemand.keys():
tot += thisYearsMacroDemand[luNr]
return tot
def applyMacroDemand(macroDemand, year, luNrsDyn, luNrsStat, ... | {"hexsha": "17ddcd76251cdf204bfff2535a782a597deefb37", "size": 5107, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/MacroDemand.py", "max_stars_repo_name": "johanlahti/urban-lu-model", "max_stars_repo_head_hexsha": "a8f7419b6f13bc4c273f1c0c6262a5daf4f87deb", "max_stars_repo_licenses": ["MIT"], "max_stars... |
##############################################################################
#
# Copyright (c) 2003-2018 by The University of Queensland
# http://www.uq.edu.au
#
# Primary Business: Queensland, Australia
# Licensed under the Apache License, version 2.0
# http://www.apache.org/licenses/LICENSE-2.0
#
# Development unt... | {"hexsha": "6b7bef4f64607684295d1d0d801ba137aae790f1", "size": 19230, "ext": "py", "lang": "Python", "max_stars_repo_path": "speckley/test/python/run_readWriteOnSpeckley.py", "max_stars_repo_name": "markendr/esys-escript.github.io", "max_stars_repo_head_hexsha": "0023eab09cd71f830ab098cb3a468e6139191e8d", "max_stars_re... |
import sys
from pprint import pprint
from silk import Silk, ValidationError
def adder(self, other):
return other + self.x
s = Silk()
s.__add__ = adder
s.bla = adder
s.x = 80
print(s.x.data)
print(s.bla(5))
print(s+5)
s2 = Silk(schema=s.schema)
s2.x = 10
print(s2+5)
s3 = Silk(schema=s2.schema)
s3.x = 10
print(s3... | {"hexsha": "f74c9e33fd93a4e7a72ae4a9d0aab764ad6e9dee", "size": 4189, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/silk/test-complex.py", "max_stars_repo_name": "sjdv1982/silk", "max_stars_repo_head_hexsha": "232e759cabfc7a87550d1e50ed9c4de4e0e57bf4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/-
Copyright (c) 2022 Antoine Labelle, Rémi Bottinelli. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Antoine Labelle, Rémi Bottinelli
! This file was ported from Lean 3 source module combinatorics.quiver.cast
! leanprover-community/mathlib commit 448144f7ae193a8990c... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/Combinatorics/Quive... |
import matplotlib
import logging
from sklearn import metrics
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pdb
import logging
import os
def generate_matrix(df, goal):
X = df.drop([goal], axis=1)
y = df[goal].astype(float)
return X, y
def ape(y_test, y_pred):
return np.a... | {"hexsha": "07c6e66e6dfb8e6e31ebce6ed39e740f8b7e9711", "size": 3778, "ext": "py", "lang": "Python", "max_stars_repo_path": "immo/scikit/helper.py", "max_stars_repo_name": "bhzunami/Immo", "max_stars_repo_head_hexsha": "9b2cda72a7c5cfc7fb95596f629aebef9eaa2e98", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, ... |
module DefinedMacros
! -- modules
use KindModule, only: I4B
use ConstantsModule, only: OSUNDEF, OSLINUX, OSMAC, OSWIN
implicit none
private
public :: get_os
contains
function get_os() result(ios)
integer(I4B) :: ios
!
!... | {"hexsha": "338b4a4db0773bfccebc0b70364857a2d67c1a87", "size": 873, "ext": "fpp", "lang": "FORTRAN", "max_stars_repo_path": "source/MODFLOW6/src/Utilities/defmacro.fpp", "max_stars_repo_name": "usgs/neversink_workflow", "max_stars_repo_head_hexsha": "acd61435b8553e38d4a903c8cd7a3afc612446f9", "max_stars_repo_licenses":... |
import subprocess
import numpy as np
import vplot
# Run vplanet
try:
subprocess.check_output(['vplanet', 'vpl.in', '-q'])
except subprocess.CalledProcessError:
raise AssertionError("This bug is still present.")
# Check
output = vplot.GetOutput()
assert not np.all([output.bodies[2].EnvelopeMass ==
... | {"hexsha": "ebe9fae41a976d7cf01a01a76bc7fc7e6de8c217", "size": 410, "ext": "py", "lang": "Python", "max_stars_repo_path": "bugs/twins/bug.py", "max_stars_repo_name": "decaelus/vplanet", "max_stars_repo_head_hexsha": "f59bd59027f725cc12a2115e8d5df58784c53477", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from tensorflow.keras.preprocessing.image import img_to_array
from PIL import Image
import numpy as np
import streamlit as st
from huggingface_hub import from_pretrained_keras
@st.cache(persist=True,allow_output_mutation=True,show_spinner=False,suppress_st_warning=True)
def instantiate_model():
model = f... | {"hexsha": "a4259dce6b2f87b8fad6fcaa1b02309024970d0e", "size": 1466, "ext": "py", "lang": "Python", "max_stars_repo_path": "app_funcs.py", "max_stars_repo_name": "prateekralhan/Streamlit-based-Low-Light-Image-Enhancer", "max_stars_repo_head_hexsha": "372a59f49e68b4a704073444d5ecedc55bd3985e", "max_stars_repo_licenses":... |
import matplotlib.pyplot as plt
import numpy as np
"""
This code computes and plots the efficacy of contact tracing
as a function of app uptake among Android and iOS users.
The model for tracing efficacy is outline in the Corona
paper draft, and the probabilities of detecting a contact
are taken from Smittestopp data.... | {"hexsha": "5616aa48d7c8362b548c7e4132a83e9b6517088c", "size": 2229, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandemic-control/uptake2efficacy2D.py", "max_stars_repo_name": "sundnes/smittestopp_data_model", "max_stars_repo_head_hexsha": "ccea25f91bbabd8c724fc8e20c3b48b9727e5cb5", "max_stars_repo_licenses"... |
"""
Deproject 2-d circular annular spectra to 3-d object properties.
This module implements the "onion-skin" approach popular in X-ray
analysis of galaxy clusters and groups to estimate the three-dimensional
temperature, metallicity, and density distributions of an optically-thin
plasma from the observed (projected) t... | {"hexsha": "abed2b7adb0208cc172356f416e439a4df902095", "size": 72959, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/deproject/deproject.py", "max_stars_repo_name": "DougBurke/deproject", "max_stars_repo_head_hexsha": "e03e56f0ded9b4c6ce080e41c47c0ce71b9b2be7", "max_stars_repo_licenses": ["BSD-2-Clause"], "... |
# author Dominik Capkovic
# contact: domcapkovic@gmail.com; https://www.linkedin.com/in/dominik-čapkovič-b0ab8575/
# GitHub: https://github.com/kilimetr
# Description: Calculation liquid flow at FLOODING point
import numpy as np
def calc_liq_flooding(pars,yvec):
uVFl = pars[0]
g = pars[1]
epsilon = pars... | {"hexsha": "e3cdc98fad01caee6afb5dfaa340c2af6e9a4581", "size": 1084, "ext": "py", "lang": "Python", "max_stars_repo_path": "calc_liq_Fl.py", "max_stars_repo_name": "kilimetr/zkouska", "max_stars_repo_head_hexsha": "859a3b7918d76a70cf794e06c08390a2ae807880", "max_stars_repo_licenses": ["BSD-3-Clause-Attribution"], "max_... |
from scipy import stats,optimize
print(stats.entropy([2],[3]))
optimize.brentq | {"hexsha": "2887d47618931933b18838ce84cdf04a1060ba38", "size": 78, "ext": "py", "lang": "Python", "max_stars_repo_path": "Paper_Related/test.py", "max_stars_repo_name": "bvsk35/Hopping_Bot", "max_stars_repo_head_hexsha": "5a8c7d4fdb4ae0a5ddf96002deb3c9ba1116c216", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/python3
"""
Class for predictor object that determines whether or not to turn off
electricity.
Script contains loop for mkaing predictions and scraping data into an SQL database
"""
import argparse
from copy import deepcopy
from datetime import datetime, timedelta
import os
import pickle
import signal
import... | {"hexsha": "08653043e591760839bc437963366ce4022671d6", "size": 16578, "ext": "py", "lang": "Python", "max_stars_repo_path": "peakPredictor.py", "max_stars_repo_name": "Clickedbigfoot/comedHourly", "max_stars_repo_head_hexsha": "30e87ad866eac0bad3eff9f10d8105072f249285", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
module stppblhmod
!$$$ module documentation block
! . . . .
! module: stppblhmod module for stppblh
! prgmmr:
!
! abstract: module for stppblh
!
! program history log:
! 2009-02-24 zhu
! 2016-05-18 guo - replaced ob_type with polymorphic obsNode t... | {"hexsha": "08e178b46523b4c5448bf6c142a48fb0ad4ba0fe", "size": 4285, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "GEOSaana_GridComp/GSI_GridComp/stppblh.f90", "max_stars_repo_name": "GEOS-ESM/GEOSana_GridComp", "max_stars_repo_head_hexsha": "cf33607613754313a2383bb7e7b3d29c856b9daf", "max_stars_repo_license... |
[STATEMENT]
lemma estep_Cn:
assumes "c = (((Cn n f gs, xs, ls) # fs), rv)"
shows "estep_Cn (encode_config c) = encode_config (step c)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. estep_Cn (encode_config c) = encode_config (step c)
[PROOF STEP]
using encode_frame
[PROOF STATE]
proof (prove)
using this:
encode_... | {"llama_tokens": 245, "file": "Inductive_Inference_Universal", "length": 2} |
from __future__ import division
import numpy as np
def line(p1, p2):
A = (p1[1] - p2[1])
B = (p2[0] - p1[0])
C = (p1[0]*p2[1] - p2[0]*p1[1])
return A, B, -C
def intersection(L1, L2):
D = L1[0] * L2[1] - L1[1] * L2[0]
Dx = L1[2] * L2[1] - L1[1] * L2[2]
Dy = L1[0] * L2[2] - L1[2] * L2[0]... | {"hexsha": "7cfa1698c63ad0587d1a6cc2196617fd5fb7db7d", "size": 1247, "ext": "py", "lang": "Python", "max_stars_repo_path": "image-processing/skimage/intersection.py", "max_stars_repo_name": "ikhovryak/PyTorchHackathon", "max_stars_repo_head_hexsha": "7a75edeccaee15ff142f9561c1c98fe49ca81e8c", "max_stars_repo_licenses":... |
from skimage.measure import compare_ssim as ssim
import matplotlib.pyplot as plt
import cv2
import urllib.request
import urllib.parse
import urllib.error
import tkinter as tk
from tkinter import font as tkfont
from tkinter import *
import os
from tkinter import filedialog
import sqlite3
import numpy as np
import shuti... | {"hexsha": "6677462ec91ddcb2f0c157e75d9903feff286d57", "size": 21686, "ext": "py", "lang": "Python", "max_stars_repo_path": "IRIS.py", "max_stars_repo_name": "monchiestevez/Iris", "max_stars_repo_head_hexsha": "2d187bb540df4c67018c585644565952b6a30668", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_sta... |
\subsection{Linear finite element cannot be recovered by ${\rm DNN}_1$ for $d\ge2$}
In view of Theorem~\ref{thm:1dLFEMDNN} and the fact that ${\rm{DNN}_J}
\subseteq {\rm{DNN}_{J+1}} $, it is natural to ask that how many
layers are needed at least to recover all linear finite element
functions in $\mathbb{R}^d$ for $d\... | {"hexsha": "14f1b8239bdea41d6e9fa960f5969f318c7f915c", "size": 6797, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "6DL/DNN1FEM-2D.tex", "max_stars_repo_name": "liuzhengqi1996/math452", "max_stars_repo_head_hexsha": "635b6ce53cb792e316abf4f47396f2e4f0686815", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import os
import pandas as pd
import numpy as np
import vaex as vx
import h5py
import time
HDF5_AES_FILTER = 444
t1 = time.time()
# create data 4 cols x10mio rows
a = np.random.uniform(-1, 1, 10000000)
b = np.random.uniform(-1, 1, 10000000)
c = np.random.uniform(-1, 1, 10000000)
d = np.random.uniform(-1, 1, 100000... | {"hexsha": "17e22691e26abc9a24fe9a01ba44e446f05961b4", "size": 1049, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/vaex_example.py", "max_stars_repo_name": "mkst/hdf5-aes", "max_stars_repo_head_hexsha": "c745f2c1c58cec3ed8fb9aeb7ef528cd01540e9b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
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