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/**
* @copyright Copyright 2018 The J-PET Framework 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 find a copy of the License in the LICENCE file.
*
* Unless required by applicable la... | {"hexsha": "e6fa8f62642c8aa877b67caf1692073b7558e2f9", "size": 4227, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/MC/JPetMCHit/JPetMCHitTest.cpp", "max_stars_repo_name": "BlurredChoise/j-pet-framework", "max_stars_repo_head_hexsha": "f6728e027fae2b6ac0bdf274141254689894aa08", "max_stars_repo_licenses": ["... |
import numpy as np
import logging
logger = logging.getLogger('Solve it like a human')
class SolveItLikeAHuman:
"""
The idea behind this algorithm is to emulate how would a human being solve a sudoku
"""
def __is_number_valid_in_grid(self, number, grid, row_position, column_position):
grid_ro... | {"hexsha": "14ebbd3e7615f2a51b9b5ebe86adf05e1010b2e8", "size": 3061, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/base_solver/solve_it_like_a_human.py", "max_stars_repo_name": "Mai13/sudoku", "max_stars_repo_head_hexsha": "1e9c1da612a78476ef60dad0f9943c8760e631ed", "max_stars_repo_licenses": ["CC0-1.0"], ... |
using Test
using FlightMechanicsSimulator
using FlightMechanicsUtils
# Stevens, B. L., Lewis, F. L., & Johnson, E. N. (2015). Aircraft control
# and simulation: dynamics, controls design, and autonomous systems. John Wiley
# & Sons. (page 193 table 3.6-2)
trim_test_data = [
# TAS thtl AOA DE thtl_... | {"hexsha": "1169eee0c3ee874d8318c7eec929466f971aa300", "size": 8616, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/f16/trimmer.jl", "max_stars_repo_name": "jonniedie/FlightMechanicsSimulator.jl", "max_stars_repo_head_hexsha": "2097237a5ffb814fcaf81830c5dd419c322a5389", "max_stars_repo_licenses": ["MIT"], "... |
""" Module for image processing core methods
.. include common links, assuming primary doc root is up one directory
.. include:: ../include/links.rst
"""
from IPython import embed
import numpy as np
from scipy import signal, ndimage
from scipy.optimize import curve_fit
from pypeit import msgs
from pypeit import util... | {"hexsha": "386436341792000b5b9e87d515d858711f91f334", "size": 49017, "ext": "py", "lang": "Python", "max_stars_repo_path": "pypeit/core/procimg.py", "max_stars_repo_name": "brackham/PypeIt", "max_stars_repo_head_hexsha": "8769f06ae8e8f18d3a55d12b01dd3dde50b98040", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import numpy as np
def measure_correlation(snapshots, correlation_threshold):
correlated_inputs = get_list_of_correlated_inputs(
snapshots, correlation_threshold)
if len(correlated_inputs) > 0:
print(("Caution!\nCorrelation between input data can affect the "
+ "reliability of th... | {"hexsha": "414877ad1c4d3dfc00a21e9e7bb74a4deb3ff7c4", "size": 1439, "ext": "py", "lang": "Python", "max_stars_repo_path": "NucleationModel/correlation_measure.py", "max_stars_repo_name": "MFrassek/CommittorEAE", "max_stars_repo_head_hexsha": "88a467e4500bc9ab69834209f4eaec9f2d0d7a61", "max_stars_repo_licenses": ["MIT"... |
%Protein processing II process test case
%
% Author: Jared Jacobs, jmjacobs@stanford.edu
% Author: Jonathan Karr, jkarr@stanford.edu
% Affilitation: Covert Lab, Department of Bioengineering, Stanford University
% Last updated: 8/9/2010
classdef ProteinProcessingII_Test < edu.stanford.covert.cell.sim.ProcessTestCase
... | {"author": "CovertLab", "repo": "WholeCell", "sha": "6cdee6b355aa0f5ff2953b1ab356eea049108e07", "save_path": "github-repos/MATLAB/CovertLab-WholeCell", "path": "github-repos/MATLAB/CovertLab-WholeCell/WholeCell-6cdee6b355aa0f5ff2953b1ab356eea049108e07/src_test/+edu/+stanford/+covert/+cell/+sim/+process/ProteinProcessin... |
[STATEMENT]
lemma linorder_rank_set_sorted_wrt:
assumes "linorder_on B R" "set xs \<subseteq> B" "sorted_wrt R xs" "x \<in> set xs" "distinct xs"
shows "linorder_rank R (set xs) x = index xs x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. linorder_rank R (set xs) x = index xs x
[PROOF STEP]
proof -
[PROOF ST... | {"llama_tokens": 3662, "file": "Comparison_Sort_Lower_Bound_Linorder_Relations", "length": 38} |
/* integration/qk.c
*
* Copyright (C) 1996, 1997, 1998, 1999, 2000 Brian Gough
*
* This program 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 2 of the License, or (at
* your option) a... | {"hexsha": "4b62a17365495ca9a189a3863dd0ad1fefc79d74", "size": 3172, "ext": "c", "lang": "C", "max_stars_repo_path": "code/em/treba/gsl-1.0/integration/qk.c", "max_stars_repo_name": "ICML14MoMCompare/spectral-learn", "max_stars_repo_head_hexsha": "91e70bc88726ee680ec6e8cbc609977db3fdcff9", "max_stars_repo_licenses": ["... |
"""
2021 Simon Bing, ETHZ, MPI IS
"""
import numpy as np
from absl import flags
class BaseProcessor(object):
def __init__(self):
self.name = None
def transform(self, x):
raise NotImplementedError | {"hexsha": "630b8bdc1ca9842989623aa8a26806eb73524765", "size": 221, "ext": "py", "lang": "Python", "max_stars_repo_path": "healthgen/data_access/preprocessing/base_processor.py", "max_stars_repo_name": "simonbing/HealthGen", "max_stars_repo_head_hexsha": "d5886a5a41dc36c6a70dece3dba3c60a90bf1fdd", "max_stars_repo_licen... |
/**
* Copyright (c) 2017 Melown Technologies SE
*
* 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 f... | {"hexsha": "fdf9805304abc1419a308eb51dc34ea5ecd20eb5", "size": 33211, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "mapproxy/src/generatevrtwo/generatevrtwo.cpp", "max_stars_repo_name": "melowntech/vts-mapproxy", "max_stars_repo_head_hexsha": "241ba43c1f7dcc226ec0f2089d47e11c699c2587", "max_stars_repo_licenses":... |
// kv_dictionary_test_harness.cpp
/**
* Copyright (C) 2014 MongoDB Inc.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License, version 3,
* as published by the Free Software Foundation.
*
* This program is distribut... | {"hexsha": "b94383cda60b97134dce5190f9f7e23e0971907e", "size": 15925, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mongo/db/storage/kv/dictionary/kv_dictionary_test_harness.cpp", "max_stars_repo_name": "leifwalsh/mongo", "max_stars_repo_head_hexsha": "4cf51324255f76a110246f6d1646dc8cda570141", "max_stars_re... |
"""Functions for specific to timelapse datasets."""
import numpy as np
from skimage.util import img_as_ubyte
from skimage.exposure import rescale_intensity
from .tissue import epithelium_watershed, largest_object_mask, segment_hemijunctions
from ..utils import validate_mask
def segment_epithelium_timelapse(
ims_... | {"hexsha": "67ead659c27864df64a853a12b2ad9272b98f00d", "size": 3701, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/functions/segment/timelapse.py", "max_stars_repo_name": "a9w/Fat2_polarizes_WAVE", "max_stars_repo_head_hexsha": "be39ba21245a9b532a70954a38139976a2355a7d", "max_stars_repo_licenses": ["MIT"]... |
"""
Copyright (c) 2020 Intel Corporation
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 writin... | {"hexsha": "960a45c3b295eee79eae1ec7ab5bfcc60af20dfa", "size": 6959, "ext": "py", "lang": "Python", "max_stars_repo_path": "beta/tests/tensorflow/sparsity/magnitude/test_algorithm.py", "max_stars_repo_name": "xiao1228/nncf", "max_stars_repo_head_hexsha": "307262119ee3f50eec2fa4022b2ef96693fd8448", "max_stars_repo_licen... |
from numpy import array, full, sqrt, sin, abs
from benchmarks.benchmark import Benchmark
class Schwefel(Benchmark):
"""dim: n"""
def __init__(self, lower=-500, upper=500, dimension=2):
super(Schwefel, self).__init__(lower, upper, dimension)
def get_optimum(self):
return array([full(self.d... | {"hexsha": "155a2877445ffda9d227bd667a746820a27bc86b", "size": 537, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/schwefel.py", "max_stars_repo_name": "buctlab/NIO", "max_stars_repo_head_hexsha": "094e688dd1cd3def7f31cd16ff927d4324651422", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
using Decomp
using Base.Test
a = zeros(3,3)
for i=1:100
a[1,1] = rand(-2.:10e-8:2.)
a[2,2] = rand(-2.:10e-8:2.)
a[3,3] = rand(-2.:10e-8:2.)
a[1,2] = rand(-2.:10e-8:2.)
a[1,3] = rand(-2.:10e-8:2.)
a[2,3] = rand(-2.:10e-8:2.)
a[2,1] = a[1,2]
a[3,1] = a[1,3]
a[3,2] = a[2,3]
eigv,eigvec1,eigvec2,eigvec... | {"hexsha": "d957f38cb89cf46918a69e09b190e5d7770adf68", "size": 507, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "favba/Decomp.jl", "max_stars_repo_head_hexsha": "edc0df4cb2964334a5c02fcb2af60bc027b09c67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
#!/usr/bin/python
'''
Program:
This is a program for doing photometry on observation data table.
Usage:
photometry.py [option file]
The input table should follow the form in TAT_env.obs_data_titles
Editor:
Jacob975
20181029
#################################
update log
20181029 version alpha 1:
... | {"hexsha": "e7d18330ac1afb829c3e44bcf6d0a45e6b0b802a", "size": 11181, "ext": "py", "lang": "Python", "max_stars_repo_path": "photometry.py", "max_stars_repo_name": "jacob975/TATIRP", "max_stars_repo_head_hexsha": "2d81fa280e039aa931c6f8456632a23ef123282a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import numpy as np
import codecs
import os
def init(root_training, root_emb):
global emb_dir, train_dir
emb_dir = root_emb
train_dir = root_training
def get_embeddings(what='expression'):
uri_file = '%s/%s.emb.u' % (emb_dir, what)
vector_file = '%s/%s.emb.v' % (emb_dir, what)
header_file = '... | {"hexsha": "a49fa3cb1476c524a2f4948b255d465496237649", "size": 2168, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/doremus_data.py", "max_stars_repo_name": "DOREMUS-ANR/recommender", "max_stars_repo_head_hexsha": "027e0dcb3639f03204c67777e2e10aac8505a70a", "max_stars_repo_licenses": ["MIT"], "max_star... |
module rsdft_allgather_module
implicit none
private
public :: d_rsdft_allgatherv_div
integer :: nblock_default=4
integer :: n_opt, n_opt_h
contains
subroutine d_rsdft_allgatherv_div( n, a, ir, id, comm, nblk_in )
implicit none
integer,intent(in) :: n
real(8),intent(inout) :: a(n)
intege... | {"hexsha": "4a202b833421672dbeda9f95e9ef12f8170f0c31", "size": 4620, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/rsdft_allgather_module.f90", "max_stars_repo_name": "j-iwata/RSDFT_DEVELOP", "max_stars_repo_head_hexsha": "14e79a4d78a19e5e5c6fd7b3d2f2f0986f2ff6df", "max_stars_repo_licenses": ["Apache-2.0... |
# ADG with two real variables and Covariance inequality
**author:Alessio Benavoli**
<a href="http://www.alessiobenavoli.com"> alessiobenavoli.com </a>
We will learn how to build a PyRational **ADG (Almost Desirable Gambles)** belief model on the outcome of an experiment whose space of possibility is $\mathbb{R}^2$.
... | {"hexsha": "95f13e53350607ac6333ea540a3c5138a6ea6bdb", "size": 15607, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/Two_Real_Variables_and_Covariance_Inequality.ipynb", "max_stars_repo_name": "PyRational/PyRational", "max_stars_repo_head_hexsha": "dad982bbd7a0b1f9a27f87e0c1d069a922384450... |
module SE
using DataFrames
using Random
using XLSX
using StructArrays
using StatsBase
using CSV
using Main.JOH
using JuMP
using JSON
""" create a variety of SSIT methods. Accept a parameter to multiply each time
limit by. """
function make_SSIT_methods(m=60; n_threads=6)
[
JOH.Matheur.SSIT.make_SSIT_metho... | {"hexsha": "f500b1f0d074bc805bed95247b1346a5a8578237", "size": 6304, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "experiments/SSIT.jl", "max_stars_repo_name": "Dibillilia/JuliaOptHeuristics", "max_stars_repo_head_hexsha": "e7acc803d8037969b98534cccba87b99028a0f5b", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys
import itertools
sys.path.append('/home/shunan/Code/CNN_Doc2Vec/imdb')
sys.path.append('/home/shunan/Code/CNN_Doc2Vec/Amazon_Doc2Vec')
import imdb_experiments
import amazon_experiments
import os
import cPickle
import subprocess
import numpy as np
from training import train
from training import tools
from sci... | {"hexsha": "cc33fd32bb8b4230f2d820a057aa9dfc30297dbd", "size": 9972, "ext": "py", "lang": "Python", "max_stars_repo_path": "skip_thought_grid_search.py", "max_stars_repo_name": "zashuna/skip-thoughts", "max_stars_repo_head_hexsha": "dec2c97f47d2ad139f5ae8602faca40c81ac096b", "max_stars_repo_licenses": ["Apache-2.0"], "... |
[STATEMENT]
lemma new\<^sub>E\<^sub>l\<^sub>e\<^sub>m\<^sub>e\<^sub>n\<^sub>t_get\<^sub>S\<^sub>h\<^sub>a\<^sub>d\<^sub>o\<^sub>w\<^sub>R\<^sub>o\<^sub>o\<^sub>t [simp]:
assumes "new\<^sub>E\<^sub>l\<^sub>e\<^sub>m\<^sub>e\<^sub>n\<^sub>t h = (new_element_ptr, h')"
shows "get\<^sub>S\<^sub>h\<^sub>a\<^sub>d\<^sub>o... | {"llama_tokens": 389, "file": "Shadow_DOM_classes_ShadowRootClass", "length": 2} |
import numpy as np
def scale_convert(self,list_to_convert):
"""Takes a list of values and scales using NumPy
log10() and rounds two decimal places.
Arguments:
list_to_convert {list} -- List of values int or float
Returns:
list -- List of float values two decimal pl... | {"hexsha": "033bf7829f7b8326672ab91dd96765723e7d96a2", "size": 1229, "ext": "py", "lang": "Python", "max_stars_repo_path": "HelperFunctions.py", "max_stars_repo_name": "brianRingler/EDA-Tools-", "max_stars_repo_head_hexsha": "1870e786f1cd009f03a51243177e5b22a98bb921", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import datetime
import datetime
from osgeo import gdal, gdalnumeric, ogr, osr
from datetime import timedelta
import numpy as np
from PIL import ImageDraw
def convert_time(time_since_1900):
d = datetime.datetime(1900, 1, 1)
return (str(d+timedelta(hours=time_since_1900)))
def convert_time_reverse(... | {"hexsha": "8298fda8a840d8a0dc91798780555a634a79ee52", "size": 12382, "ext": "py", "lang": "Python", "max_stars_repo_path": "auxiliary_classes.py", "max_stars_repo_name": "JiriVales/agroclimatic-factors", "max_stars_repo_head_hexsha": "36e2924a0a19f16f6be151d457df9c465bb39a44", "max_stars_repo_licenses": ["BSD-3-Clause... |
/*
* BSD 2-Clause License
*
* Copyright (c) 2021, Christoph Neuhauser
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright ... | {"hexsha": "ec5ebbca02ad9130f2affe292b2a8813aa946ea6", "size": 20928, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Graphics/Vulkan/Shader/ShaderManager.cpp", "max_stars_repo_name": "chrismile/sgl", "max_stars_repo_head_hexsha": "03748cadbd1661285081c47775213091b665cb86", "max_stars_repo_licenses": ["MIT", "... |
from torch.utils.tensorboard import SummaryWriter
from PIL import Image
import numpy as np
"""
TensorBoard主要用来对训练过程中的参数等数据做可视化，比如你可以看到训练过程中loss、梯度等数据的变化。
1、使用之前先安装TensorBoard包：
conda install TensorBoard
2、编写代码，展示需要可视化的数据：
3、使用命令启动TensorBoard页面;
tensorboard --logdir=Pytorch/2-Tensor... | {"hexsha": "f46286efb17ec3a2b0373c7d119958b96a04711a", "size": 801, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pytorch/2-TensorBoard/2_add_img_tensorboard.py", "max_stars_repo_name": "pengchenyu111/PaperCodeReplication", "max_stars_repo_head_hexsha": "7b8681654e25b7d707f4b4d7ebcfb85ffc0fd52a", "max_stars_re... |
import matplotlib
matplotlib.use('TkAgg')
from numpy import arange, sin, pi
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
# implement the default mpl key bindings
from matplotlib.backend_bases import key_press_handler
from matplotlib.figure import Figure
from tkinter ... | {"hexsha": "ef646fe33eb9c250b8e4cf8290858ed2555b0733", "size": 19843, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Caroline/client_side/GUI.py", "max_stars_repo_name": "smalbadger/TradeBot", "max_stars_repo_head_hexsha": "a6d4b443a6584af3e91b2d9bf0162db2b4c362e5", "max_stars_repo_licenses": ["Apache-2.0"]... |
module tcai2 use adj_mod use tcai1 implicit none integer private nx contains subroutine tcai2_init aa nx_in integer nx_in real dimension pointer aa nx nx_in call tcai1_init aa end subroutine function tcai2_lop adj add x r result stat integer stat logical intent in adj add real dimension x r call adjnull adj add x r cal... | {"hexsha": "634b37141065faff3332bcdb267ce17e5c84ef00", "size": 569, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "pa1-skeleton/pa1-data/5/sepwww.stanford.edu_sep_prof_geelib_tcai2.f90", "max_stars_repo_name": "yzhong94/cs276-spring-2019", "max_stars_repo_head_hexsha": "a4780a9f88b8c535146040fe11bb513c91c5693... |
from functools import cache
from typing import Optional, Union
import numpy as np
import torch
from mtutils.mtutils import BatchedLinear, BatchedSequential, broadcast_xwb
from torch.nn import Module, MSELoss, Tanh
from torch.nn.parameter import Parameter
from torch.nn.utils.clip_grad import clip_grad_norm_
from torch.... | {"hexsha": "2080d2991ac45128808d5e002c8f6b894ed3c4fc", "size": 5997, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mtmlp/mtmlp.py", "max_stars_repo_name": "michaelvolpp/mtbnn_pyro", "max_stars_repo_head_hexsha": "225360d62cdebeb800833ae8fba1364e4a8601c5", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# SVR(Support Vector Regression)
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
# The StandardScaler class expects the input in a cert... | {"hexsha": "f4b3af1ca491a65c8ee0b8747ec1db97caa5abce", "size": 5061, "ext": "py", "lang": "Python", "max_stars_repo_path": "Projects/Python/Machine_Learning_Project/Regression/svr.py", "max_stars_repo_name": "kshivam654/hacktoberfest2020", "max_stars_repo_head_hexsha": "41632803eff3f6cf5a1684fe5ab98f17e59cb765", "max_s... |
import numpy as np
def sigmoid(x):
return 1/(1+np.exp(-x))
def relu(x):
return np.maximum(0, x)
def relu_deriv(x):
return np.where(x < 0, 0, 1)
x = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])
y = np.array([[0],
[1],
[1],
... | {"hexsha": "b66042bfbfe66cd23153d76f7f9349e93da8a72d", "size": 745, "ext": "py", "lang": "Python", "max_stars_repo_path": "2nn3.py", "max_stars_repo_name": "kimtg/neural-network-test", "max_stars_repo_head_hexsha": "4072347d785e4e9b6624c45f8341181c18c9f5d3", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": ... |
##############################################################################
##
## Gensys solver adapted from phactsolver.m
##
##############################################################################
function gensys(Γ0, Γ1, c, Ψ, Π; clean = true, continuous = true, check_existence = true, check_uniqueness = tr... | {"hexsha": "570b102b509527ffdc8f010519b59550aa0f6dab", "size": 2107, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/aiyagari/gensys.jl", "max_stars_repo_name": "sglyon/HJBFiniteDifference.jl", "max_stars_repo_head_hexsha": "f678d57731bcadee0e770493418b86a55fac9197", "max_stars_repo_licenses": ["MIT"], "max_s... |
\documentclass[10pt, a4paper, twoside]{basestyle}
\usepackage[backend=biber,firstinits=true,maxnames=100,style=alphabetic,maxalphanames=4,doi=true,isbn=false,url=false,eprint=true]{biblatex}
\bibliography{bibliography}
\usepackage{tikz}
\usetikzlibrary{cd}
\usepackage[Mathematics]{semtex}
\usepackage{chngcntr}
\count... | {"hexsha": "e62c1cc761233ddb8d89eb0e3a2d7da49f14fe3b", "size": 31024, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "documentation/Celledoni.tex", "max_stars_repo_name": "erplsf/Principia", "max_stars_repo_head_hexsha": "1f2a1fc53f8a73c1bc67f12213169e6969c8488f", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE DeriveDataTypeable, DeriveGeneric #-}
-- |
-- Module : Statistics.Distribution.Poisson
-- Copyright : (c) 2009, 2011 Bryan O'Sullivan
-- License : BSD3
--
-- Maintainer : bos@serpentine.com
-- Stability : experimental
-- Portability : portable
--
-- The Poisson di... | {"hexsha": "e07a7b45982bfe5588af271e71068d6fd7feae18", "size": 3223, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "Statistics/Distribution/Poisson.hs", "max_stars_repo_name": "vmchale/statistics", "max_stars_repo_head_hexsha": "7f19ba0569ff34891c3ec18293a23ffb7eac8edf", "max_stars_repo_licenses": ["BSD-2-Clau... |
import unittest
import pandas as pd
import numpy as np
from src.models.QuantumSLIM.Aggregators.AggregatorFirst import AggregatorFirst
from src.models.QuantumSLIM.Aggregators.AggregatorUnion import AggregatorUnion
class MyTestCase(unittest.TestCase):
def setUp(self) -> None:
data1 = [[0, 1, 0, -20, 1],... | {"hexsha": "df42c1b7f4f77719453b94307a32a326395dc184", "size": 1745, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/models/QuantumSLIM/aggregators_test.py", "max_stars_repo_name": "tangtang95/qslim-recommender", "max_stars_repo_head_hexsha": "31b3825a171f3010890491f1b9675072a37252aa", "max_stars_repo_licen... |
# -*- coding: utf-8 -*-
import itertools
from copy import deepcopy
import networkx as nx
from networkx import MultiGraph
from bg.edge import BGEdge, BGEdge_JSON_SCHEMA_JSON_KEY
from bg.genome import BGGenome, BGGenome_JSON_SCHEMA_JSON_KEY
from bg.kbreak import KBreak
from bg.multicolor import Multicolor
from bg.utils... | {"hexsha": "846bcd8a094780bc3ab9d22a4268afca9f21f2a2", "size": 78102, "ext": "py", "lang": "Python", "max_stars_repo_path": "bg/breakpoint_graph.py", "max_stars_repo_name": "sergey-aganezov-jr/bg", "max_stars_repo_head_hexsha": "1ec758193441e49e7b34e0da09571480f4c24455", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from utils import zscore_normalize
import boss_utils
np.random.seed(0)
def encode_dna(s):
if s=='A':
return 0
if s=='C':
return 1
if s=='G':
return 2
if s=='T':
return 3
def encode_data(S):
# S is an N-list of L-stri... | {"hexsha": "195d146eb13e02046c17a38961e43e357948bc83", "size": 3133, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/Old/boss_8mer.py", "max_stars_repo_name": "always-newbie161/pyprobml", "max_stars_repo_head_hexsha": "eb70c84f9618d68235ef9ba7da147c009b2e4a80", "max_stars_repo_licenses": ["MIT"], "max_st... |
from scipy import stats
def test_scaling_exponent_estimation(desired_alpha, result, size=0.01):
"""
Test whether the desired alpha lies within some specified confidence
interval of the estimated scaling exponent.
"""
critical_value = stats.norm.ppf(size / 2) # this is negative!
alpha_hat, al... | {"hexsha": "f0819af56302cfb55289c9415960e23c451fcea9", "size": 528, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyreto/tests/utilities.py", "max_stars_repo_name": "davidrpugh/Pyreto", "max_stars_repo_head_hexsha": "7a9ca92ea1204c89f0dbffb08d3f08b344a8d7dd", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright (c) Facebook, Inc. and its affiliates.
import os, sys, shutil
import os.path as osp
import cv2
from collections import OrderedDict
import mocap_utils.general_utils as gnu
import numpy as np
import json
import subprocess as sp
def setup_render_out(out_dir):
if out_dir is not None:
gnu.build_di... | {"hexsha": "96a032bb87aae09809eff0dc3ea5659fbb19a5ff", "size": 11781, "ext": "py", "lang": "Python", "max_stars_repo_path": "mocap_utils/demo_utils.py", "max_stars_repo_name": "Zhora1337/umnik", "max_stars_repo_head_hexsha": "12a0b18d542c46625bd627d7b6a14958eb16c503", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import numpy as np
import time
#import rtlsdr
import kid_readout.equipment.rtlkid
import kid_readout.equipment.agilent_33220
import kid_readout.equipment.lockin_controller
lockin = kid_readout.equipment.lockin_controller.lockinController()
print lockin.get_idn()
fg = kid_readout.equipment.agilent_33220.FunctionGene... | {"hexsha": "09cb7f5b07469b83b275c4ed98d1c0f5dbf1591d", "size": 3140, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/data_taking_scripts/old_scripts/rtl_time_constant_with_pin_atten.py", "max_stars_repo_name": "danielflanigan/kid_readout", "max_stars_repo_head_hexsha": "07202090d468669200cab78297122880c1c03... |
from typing import List, Dict, Iterable
import hypothesis
import numpy as np
from gl0learn import fit
from hypothesis.strategies import composite
def is_mosek_installed() -> bool:
try:
import mosek
except ModuleNotFoundError:
return False
else:
return True
def is_scipy_installed... | {"hexsha": "47f6b97a1513efa263edb6ecf87fcc80f755abfd", "size": 3845, "ext": "py", "lang": "Python", "max_stars_repo_path": "pypkg/tests/helper/utils.py", "max_stars_repo_name": "TNonet/gL0Learn", "max_stars_repo_head_hexsha": "cfa94ffd83b294faf94c8c7820f195d6b93c620b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
using MLStyle
using DataFrames
include("MQuery.ConstantNames.jl")
include("MQuery.DynamicInfer.jl")
include("MQuery.Interfaces.jl")
include("MQuery.MacroProcessor.jl")
include("MQuery.Impl.jl")
using Base.Enums
@enum TypeChecking Dynamic Static
df = DataFrame(
Symbol("Type checking") => [
Dynamic... | {"hexsha": "78e03aa8f9836a8520ba05422a2f1706d246a43b", "size": 793, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "MQuery/MQuery.jl", "max_stars_repo_name": "thautwarm/MLStyle-Playground", "max_stars_repo_head_hexsha": "89095cab786e40476bc6c8f5e7b028ea02b8c4ff", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#ifndef OPENGM_PYTHON_INTERFACE
#define OPENGM_PYTHON_INTERFACE 1
#endif
#include <stdexcept>
#include <stddef.h>
#include <string>
#include <boost/python.hpp>
#include <opengm/graphicalmodel/graphicalmodel.hxx>
#include <opengm/inference/inference.hxx>
#include <opengm/inference/lazyflipper.hxx>
#include "../export_t... | {"hexsha": "ea488bd1f5f2a8384dabb055670da41cf5153aef", "size": 1682, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "src/interfaces/python/opengm/inference/pyLazyflipper.cxx", "max_stars_repo_name": "amueller/opengm", "max_stars_repo_head_hexsha": "bf2d0c611ade9bbf1d2ae537fee0df4cb6553777", "max_stars_repo_license... |
!
! LBLRTM_Fhdr_netCDF_IO
!
! Module containing routine to read and write LBLRTM Fhdr objects as
! groups to a netCDF format file.
!
!
! CREATION HISTORY:
! Written by: Paul van Delst, 19-Feb-2014
! paul.vandelst@noaa.gov
!
MODULE LBLRTM_Fhdr_netCDF_IO
! -----------------
! Environ... | {"hexsha": "5d510dccc3bc2ee988b9919d135478408d96ea67", "size": 24785, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/TauProd/LBL/lblrtm/io/netcdf/fhdr/LBLRTM_Fhdr_netCDF_IO.f90", "max_stars_repo_name": "hsbadr/crtm", "max_stars_repo_head_hexsha": "bfeb9955637f361fc69fa0b7af0e8d92d40718b1", "max_stars_repo... |
# Import Dependencies
import numpy as np
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
from flask import Flask, jsonify
import datetime as dt
#################################################
# Database Setup
#########... | {"hexsha": "39d30314d3ff0ba746f6696aabb862f06a7a3119", "size": 4174, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "tylerlvaughn/SQLAlchemy-Challenge", "max_stars_repo_head_hexsha": "0d09acb421f48fc1167602ee62e18867c5f06089", "max_stars_repo_licenses": ["ADSL"], "max_stars_count... |
[STATEMENT]
lemma remove_term_keys:
shows "keys (mapping_of p) - {m} = keys (mapping_of (remove_term m p))" (is "?A = ?B")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. keys (mapping_of p) - {m} = keys (mapping_of (remove_term m p))
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. keys (mapping... | {"llama_tokens": 1378, "file": "Polynomials_More_MPoly_Type", "length": 18} |
setwd("/home/yuanhao/github_repositories/DISC/reproducibility")
utilities_path = "./source/utilities.r"
source(utilities_path)
#### STEP 1
#Here, we use BONE_MARROW dataset. The detail information of this dataset can be seen at https://raw.githack.com/iyhaoo/DISC/master/reproducibility/data_preparation_and_imputation/d... | {"hexsha": "a9016efee5c23b1c4c5fc86842fff26ea055f908", "size": 14211, "ext": "r", "lang": "R", "max_stars_repo_path": "reproducibility/Down-stream Analysis Improvement/raw_scripts/pseudotemporal_analysis_source.r", "max_stars_repo_name": "iyhaoo/DISC", "max_stars_repo_head_hexsha": "42bcb570bc76ac28bba1681e905efc5189c1... |
"""
# @Time : 2021/7/3 8:04 上午
# @Author : hezhiqiang01
# @Email : hezhiqiang01@baidu.com
# @File : naiveAC.py
"""
import argparse
import torch
import gym
import numpy as np
import collections
import torch.nn as nn
from torch.distributions import Categorical
import torch.nn.functional as F
Experience = coll... | {"hexsha": "10e2c2bdc9c606da02f65559c3db306ce8004cf7", "size": 5210, "ext": "py", "lang": "Python", "max_stars_repo_path": "chap05 Actor Critic/naiveAC.py", "max_stars_repo_name": "18279406017/awesome-reinforcement-learning", "max_stars_repo_head_hexsha": "88644e65f6c18ad74a84eb87e7ce433fa65530bf", "max_stars_repo_lice... |
import preprocessing
import unittest
import numpy as np
class PreprocessingTest(unittest.TestCase):
def setUp(self):
self.raw_small_image = np.random.uniform(0, 255, (16,17,3)).astype(int)
self.char2ind = {'a': 0,
'b': 1,
'c': 2}
self.ind2c... | {"hexsha": "ecc7fa963fc641e0f40e5795b51bc2de95d327cc", "size": 937, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_preprocessing.py", "max_stars_repo_name": "denkuzin/captcha_solver", "max_stars_repo_head_hexsha": "cea3a3673df2d9c9529811d0ed4ee0a2244166d3", "max_stars_repo_licenses": ["Unlicense"], "... |
#!/usr/bin/env python
"""
Author:
Yixin Li
Email: liyixin@mit.edu
"""
import numpy as np
from of.utils import *
from of.gpu.KernelThinWrapper import KernelThinWrapper
from .gpu import dirname_of_cuda_files
cuda_filename = os.path.join(dirname_of_cuda_files,'rgb_to_lab.cu')
FilesDirs.raise_if_file_does_not_exist(cuda_f... | {"hexsha": "549e223c9731a5fe38116a869eebafd1b0fe1732", "size": 1282, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/superpixels/rgb_to_lab.py", "max_stars_repo_name": "BGU-CS-VIL/fastScsp", "max_stars_repo_head_hexsha": "32e36d2ee2a6636303bd4cbf8b7cc91190af2202", "max_stars_repo_licenses": ["MIT"], "max_... |
import time
import math
import numpy as np
from pykeops.numpy import LazyTensor, ComplexLazyTensor
M, N, D = 1000, 1000, 3
dtype = "float32"
do_warmup = False
x = np.random.rand(M, 1, D).astype(dtype) + 1j * np.random.rand(M, 1, D).astype(dtype)
y = np.random.rand(1, N, D).astype(dtype) + 1j * np.random.rand(1, N,... | {"hexsha": "4220802187b671e101890e5f1116f7f24d4873c1", "size": 1426, "ext": "py", "lang": "Python", "max_stars_repo_path": "pykeops/sandbox/test_complex_numpy.py", "max_stars_repo_name": "mdiazmel/keops", "max_stars_repo_head_hexsha": "52a3d2ee80a720639f52898305f85399b7b45a63", "max_stars_repo_licenses": ["MIT"], "max_... |
from flask import Flask, Response
from flask_socketio import SocketIO, send, emit
from queue import Queue
import base64
import cv2
import numpy as np
from PIL import Image
import io
d = dirname(dirname(abspath(__file__)))
app = Flask(__name__)
app.queue = Queue()
socketio = SocketIO(app)
@socketio.on('connect', names... | {"hexsha": "b0c8862e6df6caae2f0853b66dcfd9234c8fded0", "size": 833, "ext": "py", "lang": "Python", "max_stars_repo_path": "server.py", "max_stars_repo_name": "DevconX/Tello-Python", "max_stars_repo_head_hexsha": "0e7ef8375e6904a536ff274ec7c868388424327e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_... |
/*=============================================================================
Copyright (c) 1999-2003 Jaakko Jarvi
Copyright (c) 2001-2011 Joel de Guzman
Copyright (c) 2006 Dan Marsden
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at htt... | {"hexsha": "9c03b4268adcb094447a74edb42e9de5a2b47552", "size": 3707, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/fusion/test/sequence/map_construction.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "ma... |
"""
ImageSpace: image matrix, inc dimensions, voxel size, vox2world matrix and
inverse, of an image. Used for resampling operations between different
spaces and also for saving images into said space (eg, save PV estimates
into the space of an image)
"""
import copy
from textwrap import dedent
import nibabel
impor... | {"hexsha": "35d83ecfedaae803957903dd9a4536d98a6ec81e", "size": 11652, "ext": "py", "lang": "Python", "max_stars_repo_path": "regtricks/image_space.py", "max_stars_repo_name": "tomfrankkirk/regtools", "max_stars_repo_head_hexsha": "844fb1f108aa7dd02e8b9d36a2975022bce2c98d", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
! MODULE: params_obs
!
! This module contains all of the necessary parameters related to the
! observations, and observation operators.
!
! Author: Prof. Stephen G. Penny
! University of Maryland, College Park
! Department of Atmospheric and Oceanic Science
!
! 2016.4.7
MODULE params_obs
USE common, ... | {"hexsha": "28e12617717fe0c7b2d9937292dc78106f7a60b8", "size": 7727, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/obs/params_obs.f90", "max_stars_repo_name": "GEOS-ESM/Ocean-LETKF", "max_stars_repo_head_hexsha": "a7c4bbf86cdbff078212914dcc059d0b1450accf", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
#
# colormaps.jl --
#
# Implements management of colors and colormaps for using with the PGPlot
# library.
#
module Colormaps
export
RGBVec,
palette
using Colors
using PGPlot.Bindings
import PGPlot.Bindings: pgqcr, pgscr
const DATA_DIR = normpath(joinpath(@__DIR__, "..", "data"))
"""
`RGBVec{T}(r,g,b)` re... | {"hexsha": "84f64f9e021b12536a359ebf55585daef9ddb9aa", "size": 12804, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/colormaps.jl", "max_stars_repo_name": "emmt/PGPlot.jl", "max_stars_repo_head_hexsha": "884ddc3624c071e081a804e0d55ae371204c6549", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
import os
import h5py
import numpy as np
from sklearn.model_selection import train_test_split
from utilsTrain import generator, ensureDir
from modelLib import makeModel
from keras.models import load_model
from keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger, ReduceLROnPlateau
import tensorflow as tf
... | {"hexsha": "80a3fedad662ac9111726e2d07390abf54b45044", "size": 2408, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/train.py", "max_stars_repo_name": "suhailnajeeb/tcga-cancer-predict", "max_stars_repo_head_hexsha": "60e7061311f36f722abeae0ad26ed34c44095843", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import re
import itertools as it
import numpy as np
import pandas as pd
from string import punctuation
import unicodedata
from sklearn.feature_extraction.text import CountVectorizer
import nltk
from nltk.tokenize import TweetTokenizer
# import tweepy
import matplotlib.pyplot as plt
from matplotlib.ticker import Func... | {"hexsha": "8f57ee4ba3e4a1b7a8441d6ae2adf2985d30c6d5", "size": 12227, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/timeseries_utils.py", "max_stars_repo_name": "mehdimansouri/pictet", "max_stars_repo_head_hexsha": "449378dd3df2e54968e31de13635b509b1f4572b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import inspect
import tubular.testing.helpers as h
import tubular
import pandas as pd
import numpy as np
from unittest import mock
from _pytest.mark.structures import ParameterSet
def test_arguments():
"""Test arguments for arguments of tubular.testing.helpers.index_preserved_params."""
expected_arguments = ... | {"hexsha": "fadd0f638192b360a9791e9fb5688c6a03ccce21", "size": 4322, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/testing/helpers/test_index_preserved_params.py", "max_stars_repo_name": "munichpavel/tubular", "max_stars_repo_head_hexsha": "53e277dea2cc869702f2ed49f2b495bf79b92355", "max_stars_repo_licen... |
/// @file TwitterSpark.cpp
/// @brief TwitterSpark class implementation.
#include "TwitterSpark.h"
#include <algorithm>
#include <boost/tokenizer.hpp>
/* LOG4CPLUS Headers */
#include <log4cplus/logger.h>
#include <log4cplus/fileappender.h>
#include <log4cplus/layout.h>
#include <log4cplus/ndc.h>
#include <log4cplus/... | {"hexsha": "a515a408d2eea65955c6a6ea921f24ec54826d3a", "size": 11076, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "SPARKS/TwitterSpark/TwitterSpark.cpp", "max_stars_repo_name": "adele-robots/fiona", "max_stars_repo_head_hexsha": "1ef1fb18e620e18b2187e79e4cca31d66d3f1fd2", "max_stars_repo_licenses": ["MIT"], "ma... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from attention import MHSATransformerPos
def xy2uv(xyz, eps = 0.001):
x, y, z = torch.unbind(xyz, dim=2)
x = x+eps
y = y+eps
z = z+eps
u = torch.atan2(x, -y)
... | {"hexsha": "cf1da77af59c11b885b755c6467620f94b6d6f39", "size": 3279, "ext": "py", "lang": "Python", "max_stars_repo_path": "gaf.py", "max_stars_repo_name": "crs4/Deep3DLayout", "max_stars_repo_head_hexsha": "881a66cfeac52b043f0aaffef40e3a2aea5c22ec", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 6, "m... |
# just a example
# use it in each script
import numpy as np
import keras.backend as K
from keras import Model
from keras.layers import Dense, Input
def get_model(num_class):
input = Input([5,])()
print(base_model.summary())
x = base_model.get_layer("bn").output
# x = base_model.get_layer("block5_pool"... | {"hexsha": "20579af9102f3c3203e49d460cf3aabe0b83304f", "size": 1080, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict_with_uncertainty.py", "max_stars_repo_name": "OsciiArt/Cookpad", "max_stars_repo_head_hexsha": "b2245f84db0650d6282c97c98600de825c6ed6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
Torn numbers in cpmpy.
From
http://www.comp.nus.edu.sg/~henz/projects/puzzles/digits/torn.html?19
---
The Torn Number from 'Amusements in Mathematics', Dudeney, number 113
I had the other day in my possession a label bearing the number 3025
in large figures. This got accidentally torn in half, so that 30 was
... | {"hexsha": "a2284201b3d7516ec84eccba8fafb21193935aed", "size": 1642, "ext": "py", "lang": "Python", "max_stars_repo_path": "cpmpy/torn_numbers.py", "max_stars_repo_name": "hakank/hakank", "max_stars_repo_head_hexsha": "313e5c0552569863047f6ce9ae48ea0f6ec0c32b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 279... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 4 16:27:43 2020
@author: bernardo
"""
import matplotlib.pyplot as plt
import numpy as np
import csv
import sys
from datetime import datetime, timezone
ts = []
p = []
tmp = []
iaq = []
iaqAcq = []
gRes = []
hum = []
cO2 = []
voc = []
staticIaq = [... | {"hexsha": "5647988ac60264a56cfff6929b48f3e2f13dd1b4", "size": 1378, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_bme680_iaq_csv.py", "max_stars_repo_name": "bernardocarvalho/esp32-bme680", "max_stars_repo_head_hexsha": "83d143aab831ab6444f157c9f170433f384371c0", "max_stars_repo_licenses": ["MIT"], "max_... |
Jo Hatcher is a licensed marriage and family Counselors and Therapists Therapist (license: MFC #33486).
It is easy to get swept away in the busyness of life and drift from that which is truly meaningful and important to us. When stress and unplanned events happen, we sometimes lose our balance. In my work with wome... | {"hexsha": "6083cc2881ee563cf1453daa3e1b49df169b8d04", "size": 603, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Jo_Hatcher%2C_LMFT.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\clearpage
\section{Kidney Droplet}
\subsection{All Cells, labeled by \emph{Cell Ontology Class}}
\subsubsection{Table of cell counts in All Cells, per \emph{Cell Ontology Class}}\begin{table}[h]
\centering
\label{my-label}
\begin{tabular}{@{}ll@{}}
\toprule
\emph{Cell Ontology Class}& Number of cells \\ \midrule
kid... | {"hexsha": "a04d7ee652a407d8e419729a64775814b2968810", "size": 10316, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "31_tissue_supplement_tex/Kidney_droplet_auto_generated.tex", "max_stars_repo_name": "mjoppich/tabula-muris", "max_stars_repo_head_hexsha": "c1a7b7854b7b9a191141c6f2c4d89179ec41603b", "max_stars_rep... |
# import the necessary packages
# coding:utf-8
import json
import os
import cv2 as cv
import keras.backend as K
import numpy as np
from keras.applications.inception_resnet_v2 import preprocess_input
from tqdm import tqdm
from config import train_data, test_a_image_folder, img_height, img_width
from model import build... | {"hexsha": "2bd848af5710a7e3865286131861067dc3c874a1", "size": 1572, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval.py", "max_stars_repo_name": "foamliu/Crop-Disease-Detection", "max_stars_repo_head_hexsha": "fca9baefca48ad2fbdbc62075580fa27e5ed3531", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from bpdb import set_trace
from scipy.constants import c
from sympy import Matrix, symbols
from sympy.utilities.lambdify import lambdify
class Sensors:
def __init__(self, env):
self.env = env
self.define_measurement_models()
def define_measurement_models(self):
sel... | {"hexsha": "70472caae5474c58c5ec0250390eb31b09901712", "size": 2907, "ext": "py", "lang": "Python", "max_stars_repo_path": "pntddf/sensors.py", "max_stars_repo_name": "COHRINT/pnt-ddf", "max_stars_repo_head_hexsha": "5ea00957e74452ab41a34b00a60b9e4001144280", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
[STATEMENT]
lemma f_make_mono_less:
"\<forall>n. f n < oLimit f \<Longrightarrow> f (make_mono f n) < f (make_mono f (Suc n))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>n. f n < oLimit f \<Longrightarrow> f (make_mono f n) < f (make_mono f (Suc n))
[PROOF STEP]
apply (drule_tac x="make_mono f n" in spe... | {"llama_tokens": 348, "file": "Ordinal_OrdinalInduct", "length": 4} |
import numpy as np
import matplotlib.pyplot as plt
plt.imshow(np.zeros((100, 100)))
plt.show() | {"hexsha": "6264e8c1e70682f46f6dfb3471f2ac8af48a8990", "size": 95, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/plt.py", "max_stars_repo_name": "f-sky/DeepV2D", "max_stars_repo_head_hexsha": "5c1c6f58ee359d045a7efd5161445ea87d83bdbe", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "m... |
"""Tests for SIR model in this repo
* Compares conserved quantities
* Compares model against Penn CHIME w/wo social policies
* Checks logistic policies in extreme limit
"""
from typing import Tuple
from datetime import date
from pytest import fixture
from numpy import zeros
from pandas import DataFrame, Series
from p... | {"hexsha": "0f6923a9170d6828a2358f14f60bddc04ec10c6e", "size": 5871, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/sir_test.py", "max_stars_repo_name": "ckoerber/covid-19-analysis", "max_stars_repo_head_hexsha": "d6b3324cdac37b20aebc18932ffd9077e9d1b969", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
from pathlib import Path
import csv
import tensorflow as tf
import sqlite3
import numpy as np
DATA_PATH = Path(__file__).resolve().parents[3] / "parsed_data"
DB_PATH = Path(__file__).resolve().parents[3] / "webserver" / "app.db"
RATING_TRAIN_FILENAME = "ratings_train.csv"
RATING_TEST_FILENAME = "ratings_test... | {"hexsha": "e0248eff98feace23f859ba58717df7adc41fb0f", "size": 3252, "ext": "py", "lang": "Python", "max_stars_repo_path": "recommenders/recommenders/datasets/dataset.py", "max_stars_repo_name": "hojinYang/tfrs-movierec-serving", "max_stars_repo_head_hexsha": "bef4f19a8be99cde510d761082de7602151a7d99", "max_stars_repo_... |
import argparse
import os
from plyfile import PlyData, PlyElement
import numpy as np
from sklearn.decomposition import PCA
parser = argparse.ArgumentParser()
parser.add_argument("--rootdir", type=str, required=True)
parser.add_argument("--destdir", type=str, required=True)
parser.add_argument("--test", action="store_t... | {"hexsha": "852051c3217a4dfda6f958bbc4424d607e1d203b", "size": 2818, "ext": "py", "lang": "Python", "max_stars_repo_path": "legacy_examples/npm3d/prepare_data.py", "max_stars_repo_name": "StructuralNeurobiologyLab/LightConvPoint", "max_stars_repo_head_hexsha": "3f353f45e9e910fa390a74520dfd478e3e88f104", "max_stars_repo... |
abstract type AbstractGrid{T, N} <: AbstractArray{T, N} end
"""
struct Grid{T, N, S <: AbstractCoordinateSystem, AT} <: AbstractGrid{T, N}
Collection of `N` axes that define the dimensions of the grid needed to calculate
[`ElectricPotential`](@ref), [`ElectricField`](@ref) or [`WeightingPotential`](@ref).
... | {"hexsha": "9b9012ea0f415ab77eb29d7f9c00ea9678647822", "size": 9849, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Grids/Grids.jl", "max_stars_repo_name": "hervasa2/SolidStateDetectors.jl", "max_stars_repo_head_hexsha": "c640fc84c617fb5dc360aba43550c86e959e47a1", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Copyright 2020 The TensorFlow 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 applica... | {"hexsha": "81e9b299ebaa4bb3e254ecc2c19253154d85278d", "size": 3139, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/python/ipu/tests/iteration_counter_test.py", "max_stars_repo_name": "chenzhengda/tensorflow", "max_stars_repo_head_hexsha": "8debb698097670458b5f21d728bc6f734a7b5a53", "max_stars_repo_l... |
// All content Copyright (C) 2018 Genomics plc
#ifndef WECALL_REDUCE_HPP
#define WECALL_REDUCE_HPP
#include <iomanip>
#include <boost/program_options.hpp>
#include <boost/asio/io_service.hpp>
#include <boost/bind.hpp>
#include <boost/thread/thread.hpp>
#include <boost/algorithm/string.hpp>
#include <boost/filesystem/... | {"hexsha": "09b6c682f706b8ca945a037786e4adc6ec97f8b3", "size": 651, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cpp/src/weCallReduce.hpp", "max_stars_repo_name": "dylex/wecall", "max_stars_repo_head_hexsha": "35d24cefa4fba549e737cd99329ae1b17dd0156b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8.0,... |
# Anthony Krivonos
# Nov 9th, 2018
# src/models/price.py
# Imports
import sys
import json
# Pandas
import pandas as pd
# NumPy
import numpy as np
# SciPy
import scipy.optimize as optimize
# Enums
from enums import *
# Math
from math import exp
# PriceModel
from models.price import *
# QuoteModel
from models.qu... | {"hexsha": "7da6f80d5902c184da615060b8a1a409555eee32", "size": 15611, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/portfolio.py", "max_stars_repo_name": "derricksmith/Quantico", "max_stars_repo_head_hexsha": "e8ff3da3813e0373b67ec489c8b78d70b5565034", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python
try:
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
except Exception as e:
print(e)
print("Not possible to set gpu allow growt... | {"hexsha": "d96362a477fbb0703ce5e057bfd123d215ef4345", "size": 5391, "ext": "py", "lang": "Python", "max_stars_repo_path": "tunings/run_tuning.py", "max_stars_repo_name": "natmourajr/CERN-ATLAS-Qualify", "max_stars_repo_head_hexsha": "9b40106df97df5f75ba09a7acbbc763a9fdbb8b9", "max_stars_repo_licenses": ["MIT"], "max_s... |
{-# OPTIONS --safe #-}
module Cubical.Algebra.CommAlgebra.FreeCommAlgebra.Properties where
open import Cubical.Foundations.Prelude
open import Cubical.Foundations.Equiv
open import Cubical.Foundations.Isomorphism
open import Cubical.Foundations.HLevels
open import Cubical.Foundations.Structure
open import Cubical.Fou... | {"hexsha": "e02ddc0e7f46d17ba799a046defa42d5c6f48558", "size": 17635, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/Algebra/CommAlgebra/FreeCommAlgebra/Properties.agda", "max_stars_repo_name": "howsiyu/cubical", "max_stars_repo_head_hexsha": "1b9c97a2140fe96fe636f4c66beedfd7b8096e8f", "max_stars_repo_l... |
source("utils/rtools.r");
list.packages = c("stats", "utils", "Rcpp", "stringr", "jsonlite")
install_missing(list.packages)
sourceCpp('utils/parseParams.cpp')
params <- list(
wantedCol="x_OfSpectra",
pthreshold=0.05
);
params$twoStats <- list( # stats comparing 2 test groups
"wilcoxon"=function(x,y) tryCatch(wil... | {"hexsha": "db322fc66b957b37b5ce6302bd207c1e51b38595", "size": 5076, "ext": "r", "lang": "R", "max_stars_repo_path": "statTests.r", "max_stars_repo_name": "UnsignedByte/MassSpec-Data-Visualizer", "max_stars_repo_head_hexsha": "c75d242768d99aa61d87e2bc01462389c83a7028", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import re
import json
from typing import Dict
import numpy as np
import sklearn
from gensim.utils import tokenize
from gensim.models import KeyedVectors
from sklearn.cluster import AgglomerativeClustering
from models.models_tools import filter_data
class BaselineWord2Vec:
def __init__(self, filepath: str, path... | {"hexsha": "6bf2ae6ebbca0ba689f56a3c9a810893e9f2fae1", "size": 5648, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/baseline/baseline_Word2Vec.py", "max_stars_repo_name": "datactivist/dataoutai", "max_stars_repo_head_hexsha": "a1db230a31e78d17cd1c79daa8c13a508d493f9f", "max_stars_repo_licenses": ["MIT"],... |
SUBROUTINE PS_USTB ( datain, nparm, plev, outdat, iret )
C************************************************************************
C* PS_USTB *
C* *
C* This subroutine finds the most unstable level of a sounding from *
C* surface up to PLEV. The most unstable level is defined as the level *
C* which ha... | {"hexsha": "75d8add6e8e96b0b67b41a40d39b9da23550ba7a", "size": 2433, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/prmcnvlib/ps/psustb.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import logging
from functools import lru_cache
from typing import Optional, Tuple, Any
import numpy as np
from opensfm import features as ft
from opensfm.dataset import DataSetBase
logger = logging.getLogger(__name__)
class FeatureLoader(object):
def clear_cache(self):
self.load_mask.cache_clear()
... | {"hexsha": "4993359dc58e0ac1040addc00bc6515325e654c3", "size": 3983, "ext": "py", "lang": "Python", "max_stars_repo_path": "opensfm/feature_loading.py", "max_stars_repo_name": "whuaegeanse/OpenSfM", "max_stars_repo_head_hexsha": "1c261fbd1330f9a4483597ceb5cb1098fcdbd97f", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
#!/bin/env python
import numpy as np
# controls printing array corners
# np.set_printoptions(threshold='nan')
zero = np.zeros(10)
one = np.ones(20)
print zero
print one
# read file into a numpy array
data = np.loadtxt('../data/strlist10k.txt', dtype='string')
print data
| {"hexsha": "dc825129c647abc8738e151719b952e42eb507e6", "size": 277, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/arrayops.py", "max_stars_repo_name": "ketancmaheshwari/hello-goog", "max_stars_repo_head_hexsha": "fc479ea0017edfc96f3b109eff336fb9954e1f3e", "max_stars_repo_licenses": ["Apache-2.0"], "... |
"""
===============================================
Creating a timeline with lines, dates, and text
===============================================
How to create a simple timeline using Matplotlib release dates.
Timelines can be created with a collection of dates and text. In this example,
we show how to create a sim... | {"hexsha": "087e7320f6b84f1fbc1c695596b5e045a3c012bf", "size": 4243, "ext": "py", "lang": "Python", "max_stars_repo_path": "matplotlib-3.4.3/matplotlib-3.4.3/examples/lines_bars_and_markers/timeline.py", "max_stars_repo_name": "JohnLauFoo/clc_packages_Yu", "max_stars_repo_head_hexsha": "259f01d9b5c02154ce258734d519ae89... |
"""
This is the implementation of the User MAD ranking metric.
It proceeds from a user-wise computation, and average the values over the users.
"""
__version__ = '0.3.1'
__author__ = 'Vito Walter Anelli, Claudio Pomo'
__email__ = 'vitowalter.anelli@poliba.it, claudio.pomo@poliba.it'
import math
import typing as t
im... | {"hexsha": "584827fa8272a47f0228a439163ded9c05dc74fb", "size": 5384, "ext": "py", "lang": "Python", "max_stars_repo_path": "elliot/evaluation/metrics/fairness/MAD/UserMADranking.py", "max_stars_repo_name": "gategill/elliot", "max_stars_repo_head_hexsha": "113763ba6d595976e14ead2e3d460d9705cd882e", "max_stars_repo_licen... |
# stdlib imports
from datetime import timedelta, datetime
import tempfile
import os.path
import io
import urllib
import ftplib
import logging
import shutil
# third party imports
import pytz
import numpy as np
import requests
from openquake.hazardlib.geo.geodetic import geodetic_distance
from obspy.core.utcdatetime imp... | {"hexsha": "dcb5feb0b34ccdbb9f9fba66c0528c86bae80f76", "size": 10978, "ext": "py", "lang": "Python", "max_stars_repo_path": "gmprocess/io/geonet/geonet_fetcher.py", "max_stars_repo_name": "smithj382/groundmotion-processing", "max_stars_repo_head_hexsha": "b6c8284dc945deb868e90c6e674b1743a424b4f9", "max_stars_repo_licen... |
#!/usr/bin/env python
# coding:utf-8
import torch.nn as nn
from models.structure_model.graphcnn import HierarchyGCN
from models.structure_model.tree import Tree
import json
import os
import numpy as np
from helper.utils import get_hierarchy_relations
from models.structure_model.weighted_tree_lstm import WeightedHierar... | {"hexsha": "4c510a231d9ce1d45c010b8bbb4997e37efea665", "size": 3403, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/structure_model/structure_encoder.py", "max_stars_repo_name": "TownShaw/HiAGM", "max_stars_repo_head_hexsha": "26c20362467ab703cde4eb0352809de622deddf0", "max_stars_repo_licenses": ["MIT"],... |
Agricultural and Environmental Education (AEE) is a major offered by the College of Agricultural and Environmental Sciences as of 2010. This major prepares students to enter a teacher credential program in either science or agricultural and environmental education. Students in AEE take classes on a variety of subjects ... | {"hexsha": "2f10bac2328a702547755103e203a5f6ed22f5c1", "size": 1030, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Agricultural_and_Environmental_Education.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses... |
(* *********************************************************************)
(* *)
(* The CertiKOS Certified Kit Operating System *)
(* *)
(* The... | {"author": "npe9", "repo": "certikos", "sha": "dd2631a096523a29a2e8a3101d8a224b754ea56a", "save_path": "github-repos/coq/npe9-certikos", "path": "github-repos/coq/npe9-certikos/certikos-dd2631a096523a29a2e8a3101d8a224b754ea56a/mcertikos/trap/TTrapArgCode2.v"} |
#Outlier Detection
# WARNING : DATA SET USED FOR OUTLIER DETECTION MUST BE ENTIERLY FILLING IN (NO MISSING VALUES)
# HERE WE USED THE MEAN METHOD TO FILLING MISSING VALUES, REPLACE "MEAN" BY "MEDIAN" or "KNN" TO USE ANOTHER METHOD
######################################## PERCENTILE ###################################... | {"hexsha": "170ef4386e73ff75c74096f73d6409e16f4832b8", "size": 6873, "ext": "r", "lang": "R", "max_stars_repo_path": "outlier_detection.r", "max_stars_repo_name": "AugusteLef/MRI", "max_stars_repo_head_hexsha": "58e48e9e7026dd3a9e043a5d5f912202bf5a2b3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
#!/usr/bin/env python
import os
import os.path as osp
import numpy as np
import skimage.io
import instance_occlsegm_lib
def main():
root_dir = osp.expanduser('~/.ros/instance_occlsegm')
for save_dir in sorted(os.listdir(root_dir)):
save_dir = osp.join(root_dir, save_dir)
print('-' * 79)
... | {"hexsha": "397d0ab92b9d1c2cbe467312a91e7b0a5a8141fd", "size": 1196, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/instance_occlsegm/ros/instance_occlsegm/scripts/view_collected_data.py", "max_stars_repo_name": "pazeshun/jsk_apc", "max_stars_repo_head_hexsha": "0ff42000ad5992f8a31e719a5360a39cf4fa1fde", ... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
from hypothesis import assume, given, settings
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import n... | {"hexsha": "98e9d61b5bd02820fbc4aa4bac41f460cc484745", "size": 4483, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch-frontend/caffe2/python/operator_test/im2col_col2im_test.py", "max_stars_repo_name": "AndreasKaratzas/stonne", "max_stars_repo_head_hexsha": "2915fcc46cc94196303d81abbd1d79a56d6dd4a9", "max... |
from nanograd.tensor import Tensor
from nanograd.device import Device
import nanograd.nn.module as nnn
import nanograd.optim.optimizer as optim
import torch
import torch.nn.functional as F
import torch.optim
import numpy as np
import unittest
x_init = np.random.randn(1, 3).astype(np.float32)
W_init = np.random.ran... | {"hexsha": "f0fcee3735978d2c8428e5f2e7da396ccb74652f", "size": 2904, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_step.py", "max_stars_repo_name": "PABannier/nanograd", "max_stars_repo_head_hexsha": "5acd355c638885cbfc0fd0f1c4903964e7fb7de9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
const GR_SUPPORTED_TYPES = Union{
MIME"image/svg", MIME"image/svg+xml", MIME"image/png", MIME"image/jpeg",
MIME"image/tiff", MIME"image/bmp", MIME"application/pdf",
MIME"application/postscript", MIME"application/x-tex"
}
backend_showable(::GRBackend, ::GR_SUPPORTED_TYPES, scene::SceneLike) = true
functio... | {"hexsha": "c24a3ae16b260d08e5a64e1687327f587ea0e403", "size": 2393, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/saving.jl", "max_stars_repo_name": "JuliaPlots/GRMakie.jl", "max_stars_repo_head_hexsha": "a027c79236de47bda98cf7d6592bc83f340c9f08", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "... |
[STATEMENT]
lemma inv_is_iD [elim]:
fixes ip rt
assumes "ip\<in>kD(rt)"
and "the (flag rt ip) = inv"
shows "ip\<in>iD(rt)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ip \<in> iD rt
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
ip \<in> kD rt
the (flag rt ip) = Aodv_Basic.inv
g... | {"llama_tokens": 271, "file": "AODV_variants_e_all_abcd_E_Aodv_Data", "length": 3} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Routines to import atmospheric data from text files.
Created on Thu Nov 17 09:57:08 2016
@author: maxwell
"""
__all__ = ['readprof']
import numpy as np
def readprof(fname):
return readprof_full(fname)
def readprof_full(fname):
"""
Read ASCII table o... | {"hexsha": "9549c5aae2e64f7905a132b477aaebd58107b91b", "size": 1328, "ext": "py", "lang": "Python", "max_stars_repo_path": "atmosphere/readprof.py", "max_stars_repo_name": "msmithsm/rce", "max_stars_repo_head_hexsha": "91e6fd2ee93b64a471aa7e0ca62bb4649c1b3b19", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import numpy as np
from menpo.transform.piecewiseaffine.base import barycentric_vectors
from menpo.image import BooleanImage, MaskedImage
def _pixels_to_check_python(start, end, _):
pixel_locations = []
tri_indices = []
for i, ((s_x, s_y), (e_x, e_y)) in enumerate(zip(start, end)):
for x in range... | {"hexsha": "beefb9549763ebeceaa3f6e7066d824d5ca8191e", "size": 5314, "ext": "py", "lang": "Python", "max_stars_repo_path": "menpo3d/rasterize/cpu.py", "max_stars_repo_name": "nontas/menpo3d", "max_stars_repo_head_hexsha": "f29324b12a147f5b716ae5c3048d2c6b7a298752", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import sightlines as los
import numpy as np
def test_halfway():
short_z_r_list = [(0,0,1), (1,0,10)]
seg_dict = los.compute_len_in_each_cell(short_z_r_list)
assert(len(seg_dict)==2)
assert(seg_dict[1]==0.5)
assert(seg_dict[10]==0.5)
def test_equal_10():
nice_z_r_list = [(0,1,10), (1,1,11), (2,... | {"hexsha": "3ca1a93d6763ce4e88eac78040b4017075531d31", "size": 2677, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_segment_lister.py", "max_stars_repo_name": "astrobenji/sightlines", "max_stars_repo_head_hexsha": "e81464a672dae7105d2507d1753d415a51f5a512", "max_stars_repo_licenses": ["MIT"], "max_st... |
# python 2/3 compatibility
from __future__ import division, print_function
# global imports
import numpy
import pandas
import json
class InfoMatrices(object):
"""
Class holding information on the compartments in the model.
Attributes
----------
Reaction_Reaction : pandas.DataFrame
... | {"hexsha": "2a164f979eeda7291f33c90b6a0ebd9d14ff3237", "size": 6826, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulator/static/python/rbatools/infoMatrices.py", "max_stars_repo_name": "tlubitz/rba", "max_stars_repo_head_hexsha": "073b591ff6047ee8df00288ecfe45094e2b7d195", "max_stars_repo_licenses": ["MIT"... |
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