text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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(* Property from Productive Use of Failure in Inductive Proof,
Andrew Ireland and Alan Bundy, JAR 1996.
This Isabelle theory is produced using the TIP tool offered at the following website:
https://github.com/tip-org/tools
This file was originally provided as part of TIP benchmark at the following web... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/UR/TIP_with_Proof/Prod/Prod/TIP_prop_20.thy"} |
subroutine choose_model
use bio_MOD
implicit none
integer :: i
namelist /Model/ Stn, Model_ID, nutrient_uptake, grazing_formulation
character(len=100) :: format_string
! open the namelist file and read station name.
open(namlst,file='Model.nml',status='old',action='read')
read(namlst,nml=Model)
close(... | {"hexsha": "7cb523d2dc5221dc0bd8e0d81dfc1a31c160f4e5", "size": 10713, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "DRAM/NPZDFix_sRun/HOT_g2/KTW/Compile/choose_model.f90", "max_stars_repo_name": "BingzhangChen/NPZDFeCONT", "max_stars_repo_head_hexsha": "0083d46dadc4fb8fed8728816755678da8294e16", "max_stars_r... |
from numpy.polynomial.polynomial import Polynomial
from functools import reduce
class CustomPolynomial(Polynomial):
def __init__(self, coefficients):
super(CustomPolynomial, self).__init__(coefficients)
"""
input: coefficients are a list form of [a_0, a_1, ..., a_n]
"""
# s... | {"hexsha": "910370d94e866c846db335ad257e2d14fdaac34b", "size": 1253, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/share/polynomial.py", "max_stars_repo_name": "bingrao/mapreduce_sss", "max_stars_repo_head_hexsha": "be469b86f3fc133e1586149f0b850a76cb85f216", "max_stars_repo_licenses": ["BSD-2-Clause"], "ma... |
/*
* This file is part of SRS project.
*
* SRS is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* SRS is distribut... | {"hexsha": "6440e9390befe096d1ed1afce3d577dd5df1a324", "size": 11144, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "SRS/srs.cpp", "max_stars_repo_name": "1flei/lccs-lsh", "max_stars_repo_head_hexsha": "c903e557587de366e6ad67e8681b04061989bd6f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8.0, "max_sta... |
from imutils import paths
import numpy as np
import imutils
import cv2
from random import randint
new_image = cv2.imread('./dataset/%d.png' %randint(0, 1000))
# loop over the image paths
for i in range(1):
cv2.imwrite("org.jpg", new_image)
# Load the image and convert it to grayscale
image = new_image
... | {"hexsha": "a16f21417cf743b9021c6e49918e21d53ba57b57", "size": 3921, "ext": "py", "lang": "Python", "max_stars_repo_path": "contributed/Course-Project_winter96-097/captcha_break/show/contour.py", "max_stars_repo_name": "moh3n9595/class.vision", "max_stars_repo_head_hexsha": "cbcc65fd1f226273d26e44576ca7c3950faea75c", "... |
#!/bin/bash
# -*- mode: julia -*-
#=
JULIA="${JULIA:-julia --color=yes --startup-file=no}"
export JULIA_PROJECT="$(dirname ${BASH_SOURCE[0]})"
set -ex
${JULIA} -e 'using Pkg; Pkg.instantiate()'
export JULIA_LOAD_PATH="@"
exec ${JULIA} "${BASH_SOURCE[0]}" "$@"
=#
import BangBang
import JSON
import Literate
import Lit... | {"hexsha": "7ba538e5c6a36199ae4386c851c280493817dcad", "size": 5252, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "maartenvd/Transducers.jl", "max_stars_repo_head_hexsha": "1e5e530241370bda913988417c33d396630182dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18... |
# -------------------------------------------------------------------------------------------------
# scientific
import numpy as np
# -------------------------------------------------------------------------------------------------
# system
from math import sqrt
from PyQuantum.Common.html import *
from PyQuantum.Bipart... | {"hexsha": "bf8c099ebe6efa4dbb0e34e60ebc2d42776a4114", "size": 4870, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/PyQuantum/Bipartite/Hamiltonian.py", "max_stars_repo_name": "alexfmsu/pyquantum", "max_stars_repo_head_hexsha": "78b09987cbfecf549e67b919bb5cb2046b21ad44", "max_stars_repo_licenses": ["M... |
# PyZX - Python library for quantum circuit rewriting
# and optimization using the ZX-calculus
# Copyright (C) 2018 - Aleks Kissinger and John van de Wetering
# 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 ... | {"hexsha": "49ef72ad4acb6bd0159c2f6fe17e52b9ad41a8af", "size": 21566, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyzx/routing/architecture.py", "max_stars_repo_name": "VladMoldoveanu/pyzx", "max_stars_repo_head_hexsha": "8a7d7dabf8492f570fc3f0851bcdb7377144dbc4", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import h5py
import logging
import numpy as np
from pybind_isce3.core import LUT2d, DateTime, Orbit, Attitude, EulerAngles
from pybind_isce3.product import RadarGridParameters
from pybind_isce3.geometry import DEMInterpolator
from pybind_nisar.h5 import set_string
from pybind_nisar.types import complex32
from pybind_nis... | {"hexsha": "5a846aafbf6b2f6a8adbcc53ab400aceb5cd427b", "size": 19071, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/packages/pybind_nisar/products/writers/SLC.py", "max_stars_repo_name": "isce-framework/isce3", "max_stars_repo_head_hexsha": "59cdd2c659a4879367db5537604b0ca93d26b372", "max_stars_repo_lic... |
'''
Test Sciris printing functions.
'''
import numpy as np
import sciris as sc
def test_colorize():
sc.heading('Test text colorization')
sc.colorize(showhelp=True)
print('Simple example:')
sc.colorize('green', 'hi')
print('More complicated example:')
sc.colorize(['yellow', 'bgblack'])
pri... | {"hexsha": "f88127afd2cbfc6499e07dd46845200b513c1a6f", "size": 3527, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_printing.py", "max_stars_repo_name": "optimamodel/sciris", "max_stars_repo_head_hexsha": "fc0148fd9352e443a1c9b1a790275bc2904b30b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <boost/fusion/container/vector/vector20.hpp>
| {"hexsha": "f6501b4c598f6eb8c7ce212555f1601b78ab02b8", "size": 54, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_fusion_container_vector_vector20.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses"... |
import numpy as np
import matplotlib.pyplot as plt
# plt.switch_backend('agg')
def plot_sequence_heatmap(data, filename=None, title=None, x_label="Positions", y_label="Nucleotides", y_ticks=None):
""" Plots heatmap of sequence matrix (rows corresponds nucleotides, columns correspond to genomic location). """
... | {"hexsha": "24c816312c8e24bc26efd768cb84122db947f7e3", "size": 1250, "ext": "py", "lang": "Python", "max_stars_repo_path": "virusnn/plotting.py", "max_stars_repo_name": "Lamicc/Corona_mutants", "max_stars_repo_head_hexsha": "89ab80da65f23e42906e0ab57da6ca50f001cdfb", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
########################################################################################################################
# Module: tests/test_resampling.py
# Description: Tests for resampling schemes.
#
# Web: https://github.com/SamDuffield/bayesian-traffic
##############################################################... | {"hexsha": "9c039df8a7f4895fef06d6e909523c2b94d63064", "size": 2864, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_resampling.py", "max_stars_repo_name": "SamDuffield/bayesian-map-matching", "max_stars_repo_head_hexsha": "f1557f584d4411f1ee77665dae3644111b3a3a42", "max_stars_repo_licenses": ["MIT"],... |
(*:maxLineLen=78:*)
theory Sessions
imports Base
begin
chapter \<open>Isabelle sessions and build management \label{ch:session}\<close>
text \<open>
An Isabelle \<^emph>\<open>session\<close> consists of a collection of related theories that may
be associated with formal documents (\chref{ch:present}). There is ... | {"author": "m-fleury", "repo": "isabelle-emacs", "sha": "756c662195e138a1941d22d4dd7ff759cbf6b6b9", "save_path": "github-repos/isabelle/m-fleury-isabelle-emacs", "path": "github-repos/isabelle/m-fleury-isabelle-emacs/isabelle-emacs-756c662195e138a1941d22d4dd7ff759cbf6b6b9/src/Doc/System/Sessions.thy"} |
# -*- coding: utf-8 -*
import numpy as np
import preprocess
import logging
import math
import random
import io
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger("paddle")
logger.setLevel(logging.INFO)
class NumpyRandomInt(object):
def __init__(self, a, b, buf_siz... | {"hexsha": "92d51706abce63463943189ee1d620248cd07126", "size": 3553, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/quant/quant_embedding/reader.py", "max_stars_repo_name": "ZichaoGuo/PaddleSlim", "max_stars_repo_head_hexsha": "2550fb4ec86aee6155c1c8a2c9ab174e239918a3", "max_stars_repo_licenses": ["Apache-... |
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import pyeparse as pp
fname = '../pyeparse/tests/data/test_raw.edf'
raw = pp.read_raw(fname)
# visualize initial calibration
raw.plot_calibration(title='5-Point Calibration')
# creat... | {"hexsha": "a8d4d348ca415ef24037df093d1f3905ba41e84d", "size": 1328, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/plot_from_raw_to_epochs.py", "max_stars_repo_name": "Eric89GXL/pyeparse", "max_stars_repo_head_hexsha": "c1907c39276aacb0fad80034d69b537b07f82786", "max_stars_repo_licenses": ["BSD-3-Clau... |
''' Applying Adversarial Auto-encoder for Estimating Human Walking Gait Index
BSD 2-Clause "Simplified" License
Author: Trong-Nguyen Nguyen'''
import argparse, sys, os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec a... | {"hexsha": "b94d2f3eea60966f2ea03874c58826c6adf331e8", "size": 17059, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "nguyetn89/cylindrical-histogram-AAE", "max_stars_repo_head_hexsha": "fa88ef321402d8e7ad6bf6af4a6e86685c2d63f9", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
@testset "parser" begin
b = Bobby.fen_to_bitboard(
"rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1")
@test b.free == ~b.taken
@test_throws ArgumentError Bobby.fen_to_bitboard(
"rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBN")
end
| {"hexsha": "696aa905adeee290c14c26d2e0c218b21b5adc25", "size": 265, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_parser.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Bobby.jl-bd34264e-e812-11e8-1ee8-bfb20fea2fb4", "max_stars_repo_head_hexsha": "9814a1d987fcf64e61b1626395e6e221a09b6004",... |
function s = spm_existfile(filename)
% Check if a file exists on disk - a compiled routine
% FORMAT S = SPM_EXISTFILE(FILENAME)
% FILENAME - filename (can also be a relative or full pathname to a file)
% S - logical scalar, true if the file exists and false otherwise
%____________________________________________... | {"author": "leoliuf", "repo": "MRiLab", "sha": "5cdcf1f7b67759700685d3a26ffeb70e55325567", "save_path": "github-repos/MATLAB/leoliuf-MRiLab", "path": "github-repos/MATLAB/leoliuf-MRiLab/MRiLab-5cdcf1f7b67759700685d3a26ffeb70e55325567/External/MatrixUser2.2/External/spm8/spm_existfile.m"} |
import json
import logging
import os
import re
import string
from collections import Counter
from scipy.cluster.hierarchy import ward, dendrogram
import nltk
from nltk.stem.snowball import SnowballStemmer
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifo... | {"hexsha": "1728d739f826af0d758ffed486c8473e3ce159cb", "size": 6158, "ext": "py", "lang": "Python", "max_stars_repo_path": "aarhus/sklearn_kmeans_clustering.py", "max_stars_repo_name": "mikedelong/aarhus", "max_stars_repo_head_hexsha": "0c0e94fadd65be8428fe3bd2c92928e1b23fc2a1", "max_stars_repo_licenses": ["Apache-2.0"... |
// Copyright 2008 by BBN Technologies Corp.
// All Rights Reserved.
#include "Generic/common/leak_detection.h" // This must be the first #include
#include "Generic/common/SessionLogger.h"
#include "Generic/common/NullSessionLogger.h"
#include "Generic/common/ConsoleSessionLogger.h"
#include "Generic/common/Ou... | {"hexsha": "9316c51f2dbb10065bf9db6a5a933af086781d53", "size": 29921, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Generic/common/SessionLogger.cpp", "max_stars_repo_name": "BBN-E/serif", "max_stars_repo_head_hexsha": "1e2662d82fb1c377ec3c79355a5a9b0644606cb4", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/python2.7
# _*_ coding: utf-8 _*_
"""
@Author: MarkLiu
"""
import matplotlib.pyplot as plt
import numpy as np
def plotSigmoidTest():
"""
绘制Sigmoid函数
:return:
"""
figure = plt.figure(figsize=(10, 10), facecolor="white")
figure.clear()
pltaxes = plt.subplot(111)
num = np.li... | {"hexsha": "8a03b0a905311b08bfa08a95df8936db16c11e85", "size": 634, "ext": "py", "lang": "Python", "max_stars_repo_path": "LogisticRegression/plotSigmoid.py", "max_stars_repo_name": "SunnyLQ/Machine_Learning_In_Action_Algorithms", "max_stars_repo_head_hexsha": "8753e0798dd47a210f77306558185a54888e532d", "max_stars_repo... |
import unittest
from pyalink.alink import *
import numpy as np
import pandas as pd
class TestVectorBiFunctionStreamOp(unittest.TestCase):
def test_vectorbifunctionstreamop(self):
df = pd.DataFrame([
["1 2 3", "2 3 4"]
])
data = StreamOperator.fromDataframe(df, schemaStr="vec1 st... | {"hexsha": "7871115abccb4d410281a979c9859336fff20818", "size": 553, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/src/main/python/pyalink/alink/tests/examples/from_docs/test_vectorbifunctionstreamop.py", "max_stars_repo_name": "wenwei8268/Alink", "max_stars_repo_head_hexsha": "c00702538c95a32403985ebd34... |
% !TEX root = Main.tex
\chapter{Change Log}
Changes in Bold were required by the Graduate School
\section{Changes in v1.15}
\begin{itemize}
\end{itemize}
\section{Changes in v1.14}
\begin{itemize}
\item{\bfseries Ensured double spacing in chapter titles.}
\item{\bfseries Removed extra space above chapter titles.}
\i... | {"hexsha": "f9f5a602be8a4ec1481e192d1b662f566415ab02", "size": 8976, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Documentation/AppB.tex", "max_stars_repo_name": "rpspringuel/maine-thesis", "max_stars_repo_head_hexsha": "c0a4c2b600f25e9c33578fe07de1b271bef4a4b0", "max_stars_repo_licenses": ["LPPL-1.3c"], "max_s... |
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
\copyright Copyright (c) 2022 Visual Computing group of Ulm University,
Germany. See the LICENSE file at the top-level directory of
this distribution.
''''''''''''''''''''''''''''''''''''''''''''''''''''... | {"hexsha": "a33ef6817df7facc4b4fbc0f1896881e1ba700bd", "size": 7371, "ext": "py", "lang": "Python", "max_stars_repo_path": "code_dl/data_ops/geom_ops_numpy.py", "max_stars_repo_name": "schellmi42/RADU", "max_stars_repo_head_hexsha": "60b86743bb1e6896d1ae7729a19bb263256fd28f", "max_stars_repo_licenses": ["MIT"], "max_st... |
(** * Wand: The Magic Wand Operator *)
Set Implicit Arguments.
From SLF Require Import LibSepReference.
From SLF Require Repr.
Close Scope trm_scope.
Implicit Types h : heap.
Implicit Types P : Prop.
Implicit Types H : hprop.
Implicit Types Q : val->hprop.
(* #########################################################... | {"author": "blainehansen", "repo": "coq-playground", "sha": "93619e132a7fabf9d3b796465e253469815a0266", "save_path": "github-repos/coq/blainehansen-coq-playground", "path": "github-repos/coq/blainehansen-coq-playground/coq-playground-93619e132a7fabf9d3b796465e253469815a0266/SLF/Wand.v"} |
"""
Adapted from https://github.com/john-hewitt/structural-probes
"""
import numpy as np
from collections import namedtuple, defaultdict
from argparse import ArgumentParser
import os
import torch
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import AutoTokenizer
import math
a... | {"hexsha": "c88899ed04bc994c64cb7f07141f0a9f3092df2b", "size": 23464, "ext": "py", "lang": "Python", "max_stars_repo_path": "unsupervised_parsing/mutual_info_eval.py", "max_stars_repo_name": "tatsu-lab/mlm_inductive_bias", "max_stars_repo_head_hexsha": "2d99e2477293036949ba356c88513729244dc1f9", "max_stars_repo_license... |
subroutine GetTime2(net,n,t_start,t_stop)
c
c read time interval to estimate a RATE change
c
c net is input for the time format
c n is an index of the rate change
c t_start and _stop are output: time span, in days since 1960, to analyze data
c
character*3 net
character*80 string1,string2
do... | {"hexsha": "6247f705ce756cccfa1d9f50f34e1872a3aca20e", "size": 6704, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/GetTime.f", "max_stars_repo_name": "langbein-usgs/est_noise", "max_stars_repo_head_hexsha": "c29e13c3f900310f20cbc808ba30f8b1daade7b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
\chapter{Acknowledgements}
This work concludes a long and at times difficult journey.
I express my deep gratitude to my supervisors, Nicolas Couffin, for his guidance and knowledge, and Stefano Calzavara, for his patience and valuable comments. My thanks extend to Quarkslab for a challenging internship in an office fu... | {"hexsha": "918f380896b898455a3614a52861b9a966c7ceee", "size": 730, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "content/ack.tex", "max_stars_repo_name": "integeruser/yet-another-latex-thesis-template", "max_stars_repo_head_hexsha": "76ada9c9196a47269f22d1043e5d51c61e9ac4b5", "max_stars_repo_licenses": ["MIT"],... |
using Graphs
using AtomicLevels
type LevelVertex
config::Config
term::Term
calc_props::Dict
exp_eng
wfn
end
import Graphs.attributes
function attributes(l::LevelVertex, g::AbstractGraph)
Dict{UTF8String,Any}("label" => "$(l.config) $(l.term)")
end
LevelVertex(config::Config, term::Term,
... | {"hexsha": "02202ae560d03ee7bfb6980055afcac393271697", "size": 3579, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/level_graph.jl", "max_stars_repo_name": "mansour2014/AST", "max_stars_repo_head_hexsha": "fcd0026958d0006dba3bde2173e416d683a16201", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
[STATEMENT]
lemma (in merkle_interface) merkle_interfaceD: "merge_on UNIV h bo m"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. merge_on UNIV h bo m
[PROOF STEP]
using merkle_interface_aux[of h bo m, symmetric]
[PROOF STATE]
proof (prove)
using this:
merge_on UNIV h bo m = merkle_interface h bo m
goal (1 subgoal):... | {"llama_tokens": 158, "file": "ADS_Functor_Merkle_Interface", "length": 2} |
import numpy as np
import pytest
from py3nj import clebsch_gordan
rng = np.random.RandomState(0)
# reference values from
# https://en.wikipedia.org/wiki/Table_of_Clebsch%E2%80%93Gordan_coefficients
CG = (
# j1 = 1/2, j2 = 1/2
((1, 1, 2, 1, 1, 2), 1),
((1, 1, 2, -1, -1, -2), 1),
((1, 1, 2, 1, -1, 0)... | {"hexsha": "9dc7cf5a2afd529caeed7a7d877a4694ad635b26", "size": 956, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_cg.py", "max_stars_repo_name": "fujiisoup/py3nj", "max_stars_repo_head_hexsha": "15f179ecc21033022b05e27c681a1512e2b0e604", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import numpy as np
def half_hour(t):
"""
Computes the half-hour of timestamp t as integer between 0 and 47.
"""
return 2 * t.hour + t.minute // 30
def standardize(x, mean, std):
"""
Standardizes the input x by subtracting the mean and dividing by the standard deviation.
"""
return (x... | {"hexsha": "e1a0e349281b453f5fd0a8f42259a2eddabe90b5", "size": 2612, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "kitteltom/probabilistic-energy-forecasting", "max_stars_repo_head_hexsha": "6ebd4130e42d1b0808e5e9499acf3fb401b47315", "max_stars_repo_licenses": ["MIT"], "max_s... |
# This file is based on https://github.com/onnx/onnx-tensorflow
import json
import warnings
import sys
import onnx
from onnx import TensorProto
from onnx import numpy_helper
from onnx import shape_inference
from onnx import ModelProto
from onnx import GraphProto
from onnx import helper
# from onnx_tf.common import da... | {"hexsha": "9e9acbd4c53b1b7137eb3ace4e3beb6939c0df5d", "size": 4837, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/onnx2ln/backend.py", "max_stars_repo_name": "zhaozhixu/LightNet", "max_stars_repo_head_hexsha": "d21f056b6fbc1a241364e7c6797dbdc57e61866b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import tactic -- hide
open function nat -- hide
/-
## The `assumption` tactic
The first tactic that we'll learn is the `assumption` tactic. This can be used
when your goal is exactly one of your hypotheses. In the following example,
there are three hypotheses, namely the fact that $a$ is $3$ (hypothesis `ha`), the
fa... | {"author": "mmasdeu", "repo": "fundamental", "sha": "ef60218d34c089beda66b39a85a4604b3604651f", "save_path": "github-repos/lean/mmasdeu-fundamental", "path": "github-repos/lean/mmasdeu-fundamental/fundamental-ef60218d34c089beda66b39a85a4604b3604651f/src/tutorial_world/00_assumption.lean"} |
"""
@test_reference filename expr [by] [kw...]
Tests that the expression `expr` with reference `filename` using
equality test strategy `by`.
The pipeline of `test_reference` is:
1. preprocess `expr`
2. read and preprocess `filename`
3. compare the results using `by`
4. if test fails in an interactive session (e.... | {"hexsha": "211f510e5e0cd2b91f1354d4bbabf1ef469cbb8f", "size": 4827, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/test_reference.jl", "max_stars_repo_name": "timholy/ReferenceTests.jl", "max_stars_repo_head_hexsha": "cb4125e14656a12ea9660c22e545c5a620f85502", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\section{Packages: a few favourites}
\begin{frame}{\href{https://ctan.org/pkg/cleveref?lang=en}%
{\text{\color{white} \tb usepackage\{cleveref\}}}}
cleveref formats cross-references automatically
See \cref{fig:lion}.
\begin{figure}
\centering
\includegraphics[width=0.35\textwidth]{lio... | {"hexsha": "9a44e7eaac1ca8513d6171841a4b9dcebee81ab5", "size": 8611, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "slides/sections/packages.tex", "max_stars_repo_name": "jwalton3141/sage_latex", "max_stars_repo_head_hexsha": "6aed684ad18d4d7b7ae19bb60f99f03b5d95006d", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python3
import sys
import math
import numpy as np
class CountTwitterPartitions(object):
def __init__(self, filename, identifier, bits_per_vertex):
self.meta_info = {
'twitter_rv': {
'count': 40103281,
'seperator' : '\t'
},
'... | {"hexsha": "0b1d122c7674bf50bedb73637b920579e9b5c50b", "size": 4606, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tools/scripts/twitter_hilbertiled_partitions.py", "max_stars_repo_name": "rekords-uw/mosaic", "max_stars_repo_head_hexsha": "6484cc7f5e7918a363d578a89283e94771987d8a", "max_stars_repo_licenses... |
import os
from os.path import join
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from vae_model import BimodalVAE, CancerSamplesDataset
from mmd_vae_model import Bimodal_MMD_VAE
if __name__ == "__main__":
if not os.path.exists("embeddings"):
os.mkdir("embeddings")
# Embeddi... | {"hexsha": "679cf6ca35da4fc1ae2d3918507107a610aaa4b2", "size": 1372, "ext": "py", "lang": "Python", "max_stars_repo_path": "embed_cancer_samples.py", "max_stars_repo_name": "gvisona/COSMIC_Embedding", "max_stars_repo_head_hexsha": "ee9d5f50f7ada832a83abf40b9f9ba30fc4a5220", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import cv2
import math
import sys
if len(sys.argv) < 2:
print("Usage: compress.py [video]")
print("Note: video must have a resolution lower than or equal to 384x216 px.")
exit()
totalBytes = 0
video = cv2.VideoCapture(sys.argv[1])
frameCount = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
w... | {"hexsha": "075b9065e560c1ac4dcdeb3a533d04b795bc888e", "size": 2014, "ext": "py", "lang": "Python", "max_stars_repo_path": "compression/compress.py", "max_stars_repo_name": "oxixes/bad-apple-cg50", "max_stars_repo_head_hexsha": "9ef92d25fb723b122f8d7c65fc213e616c850fd2", "max_stars_repo_licenses": ["Unlicense"], "max_s... |
import numpy as np
from holoviews.element import BoxWhisker
from .test_plot import TestMPLPlot, mpl_renderer
class TestMPLBoxWhiskerPlot(TestMPLPlot):
def test_boxwhisker_simple(self):
values = np.random.rand(100)
boxwhisker = BoxWhisker(values)
plot = mpl_renderer.get_plot(boxwhisker)
... | {"hexsha": "1da9dd2410eaaa544fffde88820eec2c834b4624", "size": 1137, "ext": "py", "lang": "Python", "max_stars_repo_path": "holoviews/tests/plotting/matplotlib/test_boxwhisker.py", "max_stars_repo_name": "TheoMathurin/holoviews", "max_stars_repo_head_hexsha": "0defcef994d6dd6d2054f75a0e332d02d121f8b0", "max_stars_repo_... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
##################################################################################################################
# ### Miscellaneous Functions
# ### Module responsible for storing extra data processing functions, accuracy measures and others.
################################... | {"hexsha": "d211602357bb9f80881a08ae535b7821ded4b9e4", "size": 4099, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/misc.py", "max_stars_repo_name": "pedroananias/abd", "max_stars_repo_head_hexsha": "e5781a05704b3061e382030e8ec65c6030ccd98d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
[STATEMENT]
lemma list_assn_simps[simp]:
"hn_ctxt (list_assn P) [] l' = (\<up>(l'=[]))"
"hn_ctxt (list_assn P) l [] = (\<up>(l=[]))"
"hn_ctxt (list_assn P) [] [] = emp"
"hn_ctxt (list_assn P) (a#as) (c#cs) = hn_ctxt P a c * hn_ctxt (list_assn P) as cs"
"hn_ctxt (list_assn P) (a#as) [] = false"
"hn_ctxt (lis... | {"llama_tokens": 1486, "file": "Refine_Imperative_HOL_Sepref_HOL_Bindings", "length": 9} |
# Quick and dirty XOR 2-3-1 example network
# for an example on /r/MLQuestions
# Vanilla gradient descent with no momentum
# Adam Smith 1.26.2017
import numpy as np
# X are the inputs, Y are the outputs (the XOR truth table)
X = np.array([[[1],[1]], [[0],[1]], [[1],[0]], [[0],[0]]])
Y = np.array([0, 1, 1, 0])
#Learni... | {"hexsha": "148c7d3ee17575267f7d94e9f8dfaddb953ed24b", "size": 2094, "ext": "py", "lang": "Python", "max_stars_repo_path": "xor.py", "max_stars_repo_name": "tamlovertt/DenseInL29", "max_stars_repo_head_hexsha": "d6546e3c2f4fa107bd0d34a352257972237b7cfe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import inspect
import os
import sys
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
import numpy as np
from keras.models import Model
from keras.layers import Dense, CuDNNLSTM, Bidirectional, Input, Dropout, c... | {"hexsha": "c48d825ac04cae098a999350bb316110c8826816", "size": 3640, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/a_cnn_bilstm.py", "max_stars_repo_name": "etfrenchvcu/DeepPrime2Sec", "max_stars_repo_head_hexsha": "a61146c7c7d8a0f8821717b9f7aed8703cfdb2a1", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
"""
Implements the Multimedia Self-Supervised Grid-based (proposal-free) CNN framework
"""
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.image_list import to_image_list
from ..backbone import build_backbone
from ..language_backbone import build_language_backbone
from ..mmss_h... | {"hexsha": "67064f763ed642f7cc55bdfc2773efbecfc6ed85", "size": 4946, "ext": "py", "lang": "Python", "max_stars_repo_path": "maskrcnn_benchmark/modeling/detector/mmss_gcnn.py", "max_stars_repo_name": "mmaaz60/ovr-cnn", "max_stars_repo_head_hexsha": "28d125ae51f0216575e12b5d5ded878874131b22", "max_stars_repo_licenses": [... |
import numpy as np
from sklearn.linear_model import LinearRegression
import os
import pandas as pd
import statsmodels.api as sm
# Define working folder
out_folder = 'results'
if not os.path.exists(out_folder):
os.makedirs(out_folder)
os.chdir(out_folder)
# Define countries and states to loop over
file_list = ['A... | {"hexsha": "42e05c5c91881b919c47aa26f040fd15c15b96f7", "size": 4148, "ext": "py", "lang": "Python", "max_stars_repo_path": "req_016_facebook_average_surface_temperature/regression_and_change.py", "max_stars_repo_name": "resource-watch/blog-analysis", "max_stars_repo_head_hexsha": "b1047f0d5bb9eb7ad037f9cb7a41f1bc8d6f3d... |
[STATEMENT]
lemma 7: "{} \<turnstile> P IFF
(Q\<^sub>1 AND Neg (PfP \<guillemotleft>P IMP Q\<^sub>1\<guillemotright>) XOR
Q\<^sub>2 AND Neg (PfP \<guillemotleft>P AND Neg Q\<^sub>1 IMP Q\<^sub>2\<guillemotright>))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {} \<turnstile> local.P IFF (Q\<^sub>1 AND N... | {"llama_tokens": 662, "file": "Surprise_Paradox_Surprise_Paradox", "length": 3} |
import requests
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
from datetime import date
import time
def add():
try:
df = pd.read_csv('data pool/raw_android.csv', index_col=0)
except:
df = pd.DataFrame()
to_add = dict()
month = input('Month: ')
if month in df.columns:
command = inp... | {"hexsha": "7757c9f7a51d3ef3389c73cf08a0797884498ffd", "size": 3665, "ext": "py", "lang": "Python", "max_stars_repo_path": "android/sensortower_android.py", "max_stars_repo_name": "zachkongoyu/Sensor-Tower", "max_stars_repo_head_hexsha": "4d4ebebde206b11316d8814a89659e1e79a8550a", "max_stars_repo_licenses": ["MIT"], "m... |
"""Unit tests for custom dot operation"""
import numpy as np
import torch
from absl.testing import absltest
from dlrm.cuda_ext import dotBasedInteract
DECIMAL_MATRIX = 0
DECIMAL_LINEAR = 0
MAX_INT_VALUE = 1024 # clip integers larger than `MAX_INT_VALUE` (used in debugging only).
SEED = 12345
SCALE = 1 # Scale the r... | {"hexsha": "913d36081051eaba589aec1df7e853e6643cd453", "size": 11175, "ext": "py", "lang": "Python", "max_stars_repo_path": "NVIDIA/benchmarks/dlrm/implementations/pytorch/tests/dot_based_interact.py", "max_stars_repo_name": "goswamig/training_results_v0.7", "max_stars_repo_head_hexsha": "4278ce8a0f3d4db6b5e6054277724c... |
from scipy import misc
import numpy as np
from training import train_net
import inception
from inference import infere
def train(location='./train/'):
"""
The training procedure is triggered here. OPTIONAL to run; everything that is required for testing the model
must be saved to file (e.g., pickle) so t... | {"hexsha": "af06b74404d4a848fe3c856a7f8cf369a621be84", "size": 2411, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "Remeus/simage", "max_stars_repo_head_hexsha": "5a81f43288a594d499a894a2311497efc48b151d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_rep... |
SUBROUTINE reset_params
USE precon2d, ONLY: ictrl_prec2d
USE vmec_main, ONLY: iequi, ivac, ftolv, fsqr, fsqz
USE vsvd, ONLY: pfac, phifac
USE timer_sub, ONLY: timer
IMPLICIT NONE
! 2d preconditioner
ictrl_prec2d = 0
iequi = 0
ivac = -1
fsqr = 1
f... | {"hexsha": "dd51f6529b3757a74a540afee004858ff54974e6", "size": 434, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/VMEC2000/Sources/Initialization_Cleanup/reset_params.f", "max_stars_repo_name": "jonathanschilling/VMEC_8_49", "max_stars_repo_head_hexsha": "9f1954d83b2db13f4f4b58676badda4425caeeee", "max_sta... |
"""
pyrad.io.read_data_mxpol
========================
Functions for reading radar mxpol data files
.. autosummary::
:toctree: generated/
classes - MXPOL:
pyrad_MXPOL
classes - MCH:
pyrad_MCH
utilities - read:
row_stack
findTimes
int2float_radar
readMXPOLRa... | {"hexsha": "c8db5e6a5f7700a4bf84b2a63a7247c6274d7867", "size": 37565, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyrad_proc/pyrad/io/read_data_mxpol.py", "max_stars_repo_name": "jfigui/pyrad", "max_stars_repo_head_hexsha": "7811d593bb09a7f8a621c0e8ae3f32c2b85a0254", "max_stars_repo_licenses": ["BSD-3-Cl... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "b8c383e5ae321b29a52959ea2173826dd02ea466", "size": 68655, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/DBImportConfig/common_config.py", "max_stars_repo_name": "Middlecon/DBImport", "max_stars_repo_head_hexsha": "8f19776a5f6a6e5d6eebe5c312432b8058a2e579", "max_stars_repo_licenses": ["Apache-2.... |
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivat... | {"hexsha": "8d5c46dd9880f301b24271e448741200c6e12a58", "size": 3719, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/neural_networks/test_two_layer_qnn.py", "max_stars_repo_name": "FrankFeenix/qiskit-machine-learning", "max_stars_repo_head_hexsha": "ec800bc8a58b32f3052ff399d54f19a74417fc7d", "max_stars_repo... |
#!/usr/bin/env python -O
import os
import sys
import bisect
import os.path
import json
import numpy as np
from datetime import date, datetime, timedelta
from netCDF4 import Dataset
def main(elFile, ncFile):
# read all of json in one go
fp = open(elFile, "r+")
lines = fp.readlines()
slines = "".join... | {"hexsha": "8ef1e55f0531b033c484645c01f8d07a0ae9067e", "size": 2644, "ext": "py", "lang": "Python", "max_stars_repo_path": "scratch/EnvironmentLogger2NetCDF.py", "max_stars_repo_name": "terraref/extractors-hyperspectral", "max_stars_repo_head_hexsha": "782e3c62088257bb9c8f0f4f1d8943fa02881dc0", "max_stars_repo_licenses... |
function export_protocol(iProtocol, iSubject, OutputFile)
% EXPORT_PROTOCOL: Export a protocol into a zip file.
%
% USAGE: export_protocol(iProtocol, iSubject, OutputFile)
% export_protocol(iProtocol, iSubject) : Ask for the output filename
% export_protocol(iProtocol) : Expor... | {"author": "brainstorm-tools", "repo": "brainstorm3", "sha": "a892cfaabde1eaa2f9a3ac015c05b73f3739433a", "save_path": "github-repos/MATLAB/brainstorm-tools-brainstorm3", "path": "github-repos/MATLAB/brainstorm-tools-brainstorm3/brainstorm3-a892cfaabde1eaa2f9a3ac015c05b73f3739433a/toolbox/io/export_protocol.m"} |
# Utility Functions
# Authors: Adam Li
# Edited by: Adam Li
'''
Used by the user to define channels that are hard coded for analysis.
'''
# Imports necessary for this function
import numpy as np
import re
from itertools import combinations
def splitpatient(patient):
stringtest = patient.find('seiz')
if stri... | {"hexsha": "6a9f1fc42031b58ef28519e5270318bad5e2fb93", "size": 41983, "ext": "py", "lang": "Python", "max_stars_repo_path": "datainterface/utils/utils.py", "max_stars_repo_name": "adam2392/eegdatastorage", "max_stars_repo_head_hexsha": "fc6fbaaa4d31df83b45e6d420d023fada62cfa8e", "max_stars_repo_licenses": ["Apache-2.0"... |
## @type x : character
f <- function(x) x
for (i in 1:10) {
<warning descr="x expected to be of type character, found type numeric">f(i)</warning>
} | {"hexsha": "a7c89d57d82eafb85f44443c503a21b1e5c18700", "size": 151, "ext": "r", "lang": "R", "max_stars_repo_path": "testData/typing/slice.r", "max_stars_repo_name": "ktisha/TheRPlugin", "max_stars_repo_head_hexsha": "be2593c26dee8f6287d5ec0a82aebbca9c74f657", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, ... |
#
# Copyright (c) 2021 The Markovflow Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | {"hexsha": "825bf0d6f8d8c149a140e7a62e68f038289e4560", "size": 3078, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/tools/generate_random_objects.py", "max_stars_repo_name": "prakharverma/markovflow", "max_stars_repo_head_hexsha": "9b7fafc199dae2f7f3207c2945fd43f674386dc1", "max_stars_repo_licenses": ["Ap... |
import os,sys
import imageio
import numpy as np
import argparse
import math
from models import ResNet18
import torchvision.transforms as transforms
import torch
import cv2 as cv
import glob as glob
from numpy import clip
# GPUID = 0
# os.environ["CUDA_VISIBLE_DEVICES"] = str(GPUID)
# print ("PACKAGES LOADED")
""" htt... | {"hexsha": "2f31bdaaf6c0891051653f61430c35b57914daaa", "size": 4881, "ext": "py", "lang": "Python", "max_stars_repo_path": "MNIST_score.py", "max_stars_repo_name": "YilongJu/Implicit-Bias-towards-the-Kernel-RegimeCauses-Mode-Collapse-in-GANs", "max_stars_repo_head_hexsha": "983fcfde19c17b4d61223df8d7433c286db6b3db", "m... |
# ## Animation
z = get_maximal_state(storage)
z = [[z[1] for t = 1:100]..., z..., [z[end] for t = 1:100]...]
T = length(z)
anim = MeshCat.Animation(convert(Int, floor(1.0 / 0.01)))
build_robot(mech, vis=vis)
for t = 1:T
MeshCat.atframe(anim, t) do
set_robot(vis, mech, z[t])
end
end
MeshCat.setanimation!... | {"hexsha": "7296c3c4ce1357fc9c6894c762ed132b2a61b194", "size": 568, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/animations/generate/atlas_drop.jl", "max_stars_repo_name": "mcx/Dojo.jl", "max_stars_repo_head_hexsha": "0d54f76c3458f2eb979af0feb368c9c7b270100e", "max_stars_repo_licenses": ["MIT"], "max_... |
import logging
import items
import networkx as nx
import time
import socket
from typing import List
from collections import defaultdict
from nodes import BaseNode, ProcessSpec, SingleItemNode, NodeState
import basecases
from metrics import Metrics
log = logging.getLogger()
class ClusterBlueprint(object):
"""
... | {"hexsha": "12b837d99266e409a75ca4c8384dfd483e26fd82", "size": 10748, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cluster.py", "max_stars_repo_name": "raokrutarth/manufacturing-os", "max_stars_repo_head_hexsha": "3186d5a2f1add5bd87b728056990613f5594148f", "max_stars_repo_licenses": ["CC0-1.0"], "max_star... |
import pandas as pd
import codecs
import matplotlib.pyplot as plt
import numpy as np
import odf
import math
import pdfkit
from uncertainties import ufloat, ufloat_fromstr
from sklearn.linear_model import LinearRegression
from lmfit.models import LorentzianModel
from IPython.display import display, Latex
pd.set_option('... | {"hexsha": "cd6493cc4cb724e4bc61dc5a516f9a91cc3ee4c0", "size": 618, "ext": "py", "lang": "Python", "max_stars_repo_path": "Advanced Physics Lab I/Ultrasonic Waves/test.py", "max_stars_repo_name": "n-khatiwada/Advanced-Physics-Lab", "max_stars_repo_head_hexsha": "61afdcecac37a5ed8ab94bd5b2ca971843dae32f", "max_stars_rep... |
##########################################################################
# NSAp - Copyright (C) CEA, 2016
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########... | {"hexsha": "2da7e42c3f95a51412946144246f6c479020f15e", "size": 7500, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyconnectomist/tests/tests_preproc/test_qspace.py", "max_stars_repo_name": "neurospin/pyconnectomist", "max_stars_repo_head_hexsha": "81881fd88d94b3c2bd401602783261a64e818a05", "max_stars_repo_lic... |
from pandas.core.indexes.datetimes import date_range
import frappe
from frappe.utils import nowdate, add_to_date, cstr, cint, getdate
import itertools
import pandas as pd
import numpy as np
import time
from frappe import _
import json
import multiprocessing
import os
from multiprocessing.pool import ThreadP... | {"hexsha": "8cfe826b6dab1b259077936ead4daf8018b4e2cd", "size": 43534, "ext": "py", "lang": "Python", "max_stars_repo_path": "one_fm/one_fm/page/roster/roster.py", "max_stars_repo_name": "ks093/One-FM", "max_stars_repo_head_hexsha": "d89b6739f12a8ad527355f7ba31184bb635e1dad", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import logging
import collections
from typing import Dict, List, Optional, Union, Tuple, Any, Callable
import numpy
from dataclasses import dataclass
from cephlib.units import b2ssize, b2ssize_10
from . import html
from .cluster_classes import CephInfo, OSDStatus, DiskType, CephVersion, BlueStoreInfo, FileStoreInfo,... | {"hexsha": "332b3c845805db3807f80017f3763562b5308c4b", "size": 22386, "ext": "py", "lang": "Python", "max_stars_repo_path": "ceph_monitoring/visualize_osds.py", "max_stars_repo_name": "izadorozhna/ceph-monitoring", "max_stars_repo_head_hexsha": "71c8c75987ed175b335074db2741d008796078c8", "max_stars_repo_licenses": ["Ap... |
import binascii
import numpy as np
from PIL import Image
from encryption import AESCipher
import sys
class ImageSteganography:
def image_to_matrix(self, path):
image = Image.open(path)
matrix = np.array(image)
return matrix
def matrix_to_image(self, matrix, output_image):
ima... | {"hexsha": "966e4be1ddb24d126e4b1311c420b28cbe9678ed", "size": 4701, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/Steganography.py", "max_stars_repo_name": "MacBox7/Image-Steganography", "max_stars_repo_head_hexsha": "e277afc415960638ce4ecfb25caf0c7c88f0659b", "max_stars_repo_licenses": ["MIT"], "max_... |
using ArgParse
include("functions_closest.jl")
function parse_commandline()
s = ArgParseSettings()
@add_arg_table! s begin
"--target"
help = "The alignment to search for closest matches, in fasta format"
required = true
"--query"
help = "The sequence(s) to ... | {"hexsha": "e3e04efbe33f72dd99e30b6a41e014411b23b1c2", "size": 1375, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/closest.jl", "max_stars_repo_name": "cov-ert/julialign", "max_stars_repo_head_hexsha": "6bddd1825b6c5d0205876252fffe252411dadc5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
\chapter{General Markov Chains}
In this chapter we will study Markov chains on a general state space, i.e., state spaces that are not necessarily finite or countable.
\section{Markov Chain Monte Carlo}
This Section is under \work.
The methods described so far are generally unsuitable for complex multivariate or mult... | {"hexsha": "1d04d6d452130b782288d80b2cadd871cadbcb48", "size": 16340, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "matlab/csebook/MCMC.tex", "max_stars_repo_name": "raazesh-sainudiin/computational-statistical-experiments", "max_stars_repo_head_hexsha": "edb33db9a05b32645e8337c03729c0b8d02fa728", "max_stars_repo... |
from itertools import chain
from itertools import combinations
import numpy
def powerset(iterable, min_size):
s = list(iterable)
return chain.from_iterable(combinations(s, r)
for r in range(min_size, len(s) + 1))
def reduced_combinations(candidate, numbers):
max_pos = num... | {"hexsha": "95d98a790179198a6c9fd43e09193b7d12c12846", "size": 1957, "ext": "py", "lang": "Python", "max_stars_repo_path": "201705/peque/main.py", "max_stars_repo_name": "rocasan/vigotech-reto", "max_stars_repo_head_hexsha": "6b45dacb80139121ae6204cc614db8e4441fc444", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
"""Module including useful functions relative to rigid motion.
Functions:
augment_matrix_coord: returns augmented vector
get_rotation_mat_single_axis: computes rotation matrix around specificied axis (x,y or z)
get_rigid_motion_mat_from_euler: computes 4X4 rigid transformation matrix, from the specified se... | {"hexsha": "6c9e318ecd4995c00046b2f500dd833a917bdbe5", "size": 3734, "ext": "py", "lang": "Python", "max_stars_repo_path": "3D_2D_Registration/modules/RigidMotionModule.py", "max_stars_repo_name": "lekooooook/Opt-GO-3D-2D-Registration", "max_stars_repo_head_hexsha": "b12f22d99ed1d8a44663c8baf5d18f0bc563388c", "max_star... |
#!/usr/bin/env python -W ignore
import numpy as np
# матриця Леслі
L = np.array([
[ 0, 6, 15],
[0.5, 0, 0],
[ 0, 0.5, 0]
])
# початковий розподіл популяції
x0 = np.array([1, 1, 1])
# розподіл через t = 5 кроків обчислюємо як L^t * x0
t = 5
xt = np.dot(np.linalg.matrix_power(L, t), x0)
print(f'x({t}) = {xt... | {"hexsha": "2e192d4510eba10bb5f53622e949de438673580e", "size": 986, "ext": "py", "lang": "Python", "max_stars_repo_path": "labs/examples/1/py/2.py", "max_stars_repo_name": "Sky-Nik/ecology-and-economics", "max_stars_repo_head_hexsha": "25dfc7bccc9b8c430eb31729805f75daebda24d0", "max_stars_repo_licenses": ["MIT"], "max_... |
using Test;
using LinearAlgebra;
using SparseArrays;
using ParamLevelSet;
using Random;
n = 10;
B = getSpMatBuilder(Int64,Float64,n, n, 30);
for k=1:10
setNext!(B,k,k,1.0);
end
II = getSparseMatrix(B);
@test norm(II - SparseMatrixCSC(1.0I,n,n)) < 1e-14
B.V.*=100.0;
reset!(B);
for i=1:10
JJ = randperm(n)[1:5];
fo... | {"hexsha": "523f7aa725b9f9b487a307b3c33b7359056efbcb", "size": 483, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testSpMatBuilder.jl", "max_stars_repo_name": "JuliaInv/ParamLevelSet.jl", "max_stars_repo_head_hexsha": "d6bd68665080aeb0718e97cc873b2a5e234bd4f8", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys
IN_NOSETESTS = False
if sys.argv and sys.argv[0].endswith('nosetests'): # pragma: no cover
IN_NOSETESTS = True
import warnings
import re
# Make sure that DeprecationWarning within this package always gets printed
warnings.filterwarnings('always', category=DeprecationWarning,
m... | {"hexsha": "a964584dc77e1c4f5fa736f874dfa89b4f57f46e", "size": 2780, "ext": "py", "lang": "Python", "max_stars_repo_path": "root_numpy/__init__.py", "max_stars_repo_name": "douglasdavis/root_numpy", "max_stars_repo_head_hexsha": "d337ed0bae3d59f096bcd4ea427bf3244a9a34be", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
''' All DUT alignment functions in space and time are listed here plus additional alignment check functions'''
from __future__ import division
import logging
import sys
import os
from collections import Iterable
import math
import tables as tb
import numpy as np
import scipy
from matplotlib.backends.backend_pdf impor... | {"hexsha": "b0d92d6ce6ddf1824814e9c83d5a5866b69734bc", "size": 129777, "ext": "py", "lang": "Python", "max_stars_repo_path": "beam_telescope_analysis/dut_alignment.py", "max_stars_repo_name": "SiLab-Bonn/beam-telescope-analysis", "max_stars_repo_head_hexsha": "69a6a79a93ddb5e32064ec61f7c5bc323165bb99", "max_stars_repo_... |
import os
import warnings
import numpy as np
import pandas as pd
from tensorflow.keras import backend as K
from tensorflow.keras import Model
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.keras.engine import data_adapter
from tensorflow.python.eager import monitoring
from tensorflow.pytho... | {"hexsha": "5752f6143e8573e9d6d1fd5501fc53e57a33956b", "size": 4810, "ext": "py", "lang": "Python", "max_stars_repo_path": "top_down_attention/keras_custom/engine/training.py", "max_stars_repo_name": "don-tpanic/CostsBenefitsAttention", "max_stars_repo_head_hexsha": "7221b873283c210cafd54fa895ba10b418d8ca67", "max_star... |
# Linear Discriminant Analysis
#### Type to represent a linear discriminant functional
abstract type Discriminant{T} end
struct LinearDiscriminant{T<:Real} <: Discriminant{T}
w::Vector{T}
b::T
end
length(f::LinearDiscriminant) = length(f.w)
evaluate(f::LinearDiscriminant, x::AbstractVector) = dot(f.w, x) +... | {"hexsha": "46601146e7066e9e13ac6a3d7911f9a3e6ee1e6d", "size": 10150, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lda.jl", "max_stars_repo_name": "yingqiuz/MultivariateStats.jl", "max_stars_repo_head_hexsha": "b84cbc94c04057297acc42168a58b599a984f87d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: © 2021 Massachusetts Institute of Technology.
# SPDX-FileCopyrightText: © 2021 Lee McCuller <mcculler@mit.edu>
# NOTICE: authors should document their contributions in concisely in NOTICE
# with details inline ... | {"hexsha": "817576b09521c527afccf13d690703f05029eeb9", "size": 12575, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/wavestate/iirrational/representations/zpktf.py", "max_stars_repo_name": "wavestate/wavestate-iirrational", "max_stars_repo_head_hexsha": "01d6dba8b2131fa2a099a74f17e6540f30cee606", "max_stars... |
import numpy as np
import tensorflow as tf
from model import GCN_LPA
def print_statistics(features, labels, adj):
n_nodes = features[2][0]
n_edges = (len(adj[0]) - labels.shape[0]) // 2
n_features = features[2][1]
n_labels = labels.shape[1]
labeled_node_rate = 20 * n_labels / n_nodes
n_intra_... | {"hexsha": "170f6e329cc1064dc67bdb2445128f0989efac20", "size": 2374, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train.py", "max_stars_repo_name": "hwwang55/GCN-LPA", "max_stars_repo_head_hexsha": "21df23afee0912380ac682b0f80ac244140c33e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 70, "max_... |
import sys
import pandas as pd
import numpy as np
import re
import nltk
import pickle
from sqlalchemy import create_engine
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.multioutput imp... | {"hexsha": "fd4800c3dfd941068bbd9800305444bee7504a3c", "size": 4994, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/train_classifier.py", "max_stars_repo_name": "minhtuan2000/disaster-response-pipeline-project", "max_stars_repo_head_hexsha": "bef0f33e2c5a397e6525d53dc636553edd73d002", "max_stars_repo_lic... |
from __future__ import print_function
from __future__ import with_statement
import os
import math
import numpy
from tqdm import tqdm
from PIL import Image
from configparser import ConfigParser, Error
from tigre.utilities.geometry import Geometry
def BrukerDataLoader(filepath, **kwargs):
# BrukerDataLoader(filep... | {"hexsha": "c492ffcff48aa1df9b8570de2400eaa139a6b110", "size": 9586, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/tigre/utilities/io/BrukerDataLoader.py", "max_stars_repo_name": "tsadakane/TIGRE", "max_stars_repo_head_hexsha": "a853cd2d4a6bc9509c01414b85ca75b4448fd700", "max_stars_repo_licenses": ["BSD... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 07:06:17 2020
@author: A. Goulart
"""
import numpy as np
import matplotlib.pyplot as plt
from M1_FileReader_v2 import FileReader_1
class Method_1:
def __init__(self, foldername, filename):
self.it_lim = 1000
self.erro_max... | {"hexsha": "04821209fb94dfd2c60caad3c7a6b2b754509223", "size": 49623, "ext": "py", "lang": "Python", "max_stars_repo_path": "M1/M1_Method_v2.py", "max_stars_repo_name": "alexandrecg/SID", "max_stars_repo_head_hexsha": "2da81a2e18d4faa329e5c722b4b544212cf3530a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# coding: utf-8
"""
description:
Scikit-learn compatible implementation of the Gibberish detector
based on https://github.com/rrenaud/Gibberish-Detector
original author: rrenaud@github
author: Suraj Iyer
"""
__all__ = ['GibberishDetectorClassifier']
from sklearn.base import BaseEstimator,... | {"hexsha": "6cfe419d26b1fb196bc3e4d6d89f9b5bb4cc1a08", "size": 3920, "ext": "py", "lang": "Python", "max_stars_repo_path": "python_data_utils/nlp/gibberish_detector.py", "max_stars_repo_name": "surajiyer/python-data-utils", "max_stars_repo_head_hexsha": "d6e9bf81204a01545a3edb165c5724eb24f37c18", "max_stars_repo_licens... |
#include <boost/test/unit_test.hpp>
#include <boost/algorithm/string/predicate.hpp>
#include <enumivo/testing/tester.hpp>
#include <enumivo/chain/abi_serializer.hpp>
#include <enumivo/chain/wasm_enumivo_constraints.hpp>
#include <enumivo/chain/resource_limits.hpp>
#include <enumivo/chain/exceptions.hpp>
#include <enum... | {"hexsha": "b77afc716b6289919f6bb2435b85dfe690dc17be", "size": 4117, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/get_table_tests.cpp", "max_stars_repo_name": "dvlaicu/enumivo", "max_stars_repo_head_hexsha": "f347de9dde0571b55e3ef2b624377e49a30c6f4e", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
sleep <- function(time=1)
{
message("Sleeping...")
flush.console()
Sys.sleep(time)
message("Awake!")
}
sleep()
| {"hexsha": "12ba992a0db59d172d35b848b78196d4fffff1d5", "size": 124, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Sleep/R/sleep.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_sta... |
From Heapster Require Import
Permissions
PermissionsSpred2.
From Coq Require Import
Classes.RelationClasses.
Section PermSet.
Context {config : Type}.
Record Perms2 :=
{
in_Perms2 : forall {spred}, @perm { x | spred x } -> Prop;
(* Perms_upwards_closed1 : forall (spred1 spred2 : co... | {"author": "GaloisInc", "repo": "heapster-formalization", "sha": "1c5ed40a556a2b45f8f137ba475eb4bca76dcc70", "save_path": "github-repos/coq/GaloisInc-heapster-formalization", "path": "github-repos/coq/GaloisInc-heapster-formalization/heapster-formalization-1c5ed40a556a2b45f8f137ba475eb4bca76dcc70/src/PermS.v"} |
import unittest
import os
from shutil import rmtree
import numpy as np
import nifty
import nifty.graph.rag as nrag
class TestAccumulateStacked(unittest.TestCase):
shape = (10, 256, 256)
# shape = (3, 128, 128)
@staticmethod
def make_labels(shape):
labels = np.zeros(shape, dtype='uint32')
... | {"hexsha": "6439e4c49ee906cd7c0f4e67a756f1e3ec572bd3", "size": 16326, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/test/graph/rag/test_accumulate_stacked.py", "max_stars_repo_name": "konopczynski/nifty", "max_stars_repo_head_hexsha": "dc02ac60febaabfaf9b2ee5a854bb61436ebdc97", "max_stars_repo_licen... |
function visualize(states::AbstractArray, states2::AbstractArray; statenames = string.(1:length(states)), statenames2 = string.(1:length(states2)), aspect = false, resolution = (2880, 1080), statistics = false, title = "Field = ", title2 = "Field = ", units1 = ["" for i in eachindex(states)], units2 = ["" for i in eac... | {"hexsha": "ba6d7b198d1317453eb347bfbc9719f2a32e84e5", "size": 9656, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/compare.jl", "max_stars_repo_name": "sandreza/Mesoscale", "max_stars_repo_head_hexsha": "cd3f00d9697ca3c97a51a5bf617edeb3a69e4178", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
C
C************************** FXVDMC ************************************
C
C calculate derivative of moisture content with respect to pressure
C head using extended van Genuchten characteristic equation.
C for the extended van Genuchten equation this gives the value of the
C overall storage coefficient, or gener... | {"hexsha": "b89a09c590bd399c224d457cb303153fa93ef445", "size": 943, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "examples/weil_exemple_withDA/my_cathy_prj/src/fxvdmc.f", "max_stars_repo_name": "BenjMy/pycathy_wrapper", "max_stars_repo_head_hexsha": "8639713acbb5bc5c5cd293b94bdafe017d03f199", "max_stars_repo_l... |
# Copyright 2021 The FastEstimator 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 appl... | {"hexsha": "e17312c24a9ce0cafe32206111db432e05327f67", "size": 13197, "ext": "py", "lang": "Python", "max_stars_repo_path": "apphub/neural_architecture_search/naswot/naswot_torch.py", "max_stars_repo_name": "DwijayDS/fastestimator", "max_stars_repo_head_hexsha": "9b288cb2bd870f971ec4cee09d0b3205e1316a94", "max_stars_re... |
from scipy.integrate import solve_dde
import matplotlib.pyplot as plt
import numpy as np
def fun(t,y,Z):
if y[0] >= 0.0:
return [-1.0]
else:
return [-10.0]
y0 = 1.0
jumps = [1.]
tf = 2.0
tspan = [0.0, tf]
delays = []
rtol = 1e-5
atol = 1e-10
sol45 = solve_dde(fun, tspan, delays, [y0], [y0]... | {"hexsha": "21290f477d9d982272d6914bd42036cb4ca98093", "size": 2005, "ext": "py", "lang": "Python", "max_stars_repo_path": "DDEs_models_test/discODEex2_RK45.py", "max_stars_repo_name": "jrmejansen/scipy", "max_stars_repo_head_hexsha": "77f4f5172f8e718de96b89bf3f015a8729a7613c", "max_stars_repo_licenses": ["BSD-3-Clause... |
# Copyright 2017 IBM Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | {"hexsha": "6939ffeb3aca0c2287b26e1f61dbeb21fbaf96f2", "size": 12415, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymqttbench/bench.py", "max_stars_repo_name": "mtreinish/pymqttbench", "max_stars_repo_head_hexsha": "7b3bbd1a4de747492c3803667b2c48f9f432fb2d", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['text.usetex'] = True
import numpy as np
# vector field
def f(X, Y):
U = np.maximum(0.3, X + 1)
V = 0.5 * Y
return 0.07 * U, 0.07 * V
# action of the group
def act(X, Y, a, tx=0, ty=0):
newX = np.cos(a) * X - np.sin(a) * Y + tx
... | {"hexsha": "a302dd57d2d415c94b653eaa5c8a7c8c0a96f976", "size": 1508, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/image/plots/rotate_field.py", "max_stars_repo_name": "mariogeiger/se3cnn", "max_stars_repo_head_hexsha": "afd027c72e87f2c390e0a2e7c6cfc8deea34b0cf", "max_stars_repo_licenses": ["MIT"], "m... |
import json
from mpl_toolkits.mplot3d import Axes3D
import matplotlib
import pandas as pd
import numpy as np
import scipy.io as sio
import _pickle as cPickle
import time, os, math
import collections
from tqdm import tqdm
import matplotlib.pyplot as plt
import pickle
import math
#加载数据, 筛选重要的，三个相机都可以用
src_dir = '/media/c... | {"hexsha": "b64aca59dc7a48bbd5b8e8195ab968ebd0f26b89", "size": 11564, "ext": "py", "lang": "Python", "max_stars_repo_path": "watch_all_output_image_NYU_suoluan.py", "max_stars_repo_name": "Chenzhoujia/Mobile_hand_tf-gnn-samples-master", "max_stars_repo_head_hexsha": "56236752bb06bcc60b4befb024cdd100aa9ce9ac", "max_star... |
# author : Group 27
# date : 2021-11-25
"""Performs some statistical or machine learning analysis and summarizes the results as a figure(s) and a table(s)
Usage: preprocess_n_model.py --file_path=<file_path> --out_file=<out_file>
Options:
--file_path=<file_path> Path to train processed data file for which to perfo... | {"hexsha": "d032c09722116e24b79016ac0200d7d3d21dbe39", "size": 5132, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/preprocess_n_model.py", "max_stars_repo_name": "rezam747/DSCI_522_Spotify_Track_Popularity_Predictor", "max_stars_repo_head_hexsha": "56abceb68a57f72de855e65bdc18bdd02d229311", "max_stars_repo... |
Euler Problem 51
================
By replacing the 1st digit of the 2-digit number \*3, it turns out that six of the nine possible values: 13, 23, 43, 53, 73, and 83, are all prime.
By replacing the 3rd and 4th digits of 56\*\*3 with the same digit, this 5-digit number is the first example having seven primes among t... | {"hexsha": "84b8522e4872c6835633386fe82d58673a2c3697", "size": 3907, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Euler 051 - Prime digit replacements.ipynb", "max_stars_repo_name": "Radcliffe/project-euler", "max_stars_repo_head_hexsha": "5eb0c56e2bd523f3dc5329adb2fbbaf657e7fa38", "max_stars_rep... |
#!/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": "9ad0058f1ab7325e2fc26d3a117473e0d2eb5cbd", "size": 2232, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/mcscf/chkfile.py", "max_stars_repo_name": "crisely09/pyscf", "max_stars_repo_head_hexsha": "cb92f7974bd9c87c0ef5b2b52abf5d3219b3d6b6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
# -*- coding: utf-8 -*-
#
# plot_sequence_I_networks_perlin_size.py
#
# Copyright 2017 Sebastian Spreizer
# The MIT License
import numpy as np
import matplotlib as mpl
import pylab as pl
from lib.circular_colormap import gen_circular_cmap
from lib.panel_label import panel_label
from lib.ploscb_formatting import set_f... | {"hexsha": "77de7b2ffe12d48ce7b26e49ae36913a9069316a", "size": 4980, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/plot/sequence_I_networks_perlin_size.py", "max_stars_repo_name": "babsey/spatio-temporal-activity-sequence", "max_stars_repo_head_hexsha": "bb82c7d4d3b2f85e9d2635a5479d0478868d33bb", "max_... |
import copy
import time
import torch
import argparse
import numpy as np
from torch.utils.data import DataLoader
from deepsnap.batch import Batch
from deepsnap.dataset import GraphDataset
from torch_geometric.datasets import TUDataset
def arg_parse():
parser = argparse.ArgumentParser(description='Pagerank argument... | {"hexsha": "54f335aaa08f79ee2a345113fcae32ab1d9765eb", "size": 8202, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmark/transform/ego_net.py", "max_stars_repo_name": "ruth-ann/deepsnap", "max_stars_repo_head_hexsha": "35eeb5abdb304c53b2e0a68cbbeeaa55dca286a0", "max_stars_repo_licenses": ["MIT"], "max_star... |
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