text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
import scipy
from scipy.spatial import ConvexHull
import matplotlib.pyplot as plt
def basic_cube():
"""
Cube based on ordering in program
"""
return np.array([
[-7.156285 , -3.80337925, -1.95817204],
[-7.156285 , -3.80337925, -1.70817204],
[-7.156285 , -3.... | {"hexsha": "e96d4aee45de7723967f618b9e373afe3dd49741", "size": 40845, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymove/molecules/marching_cubes_lookup.py", "max_stars_repo_name": "manny405/PyMoVE", "max_stars_repo_head_hexsha": "82045fa27b3bd31f2159d3ad72dc0a373c5e7b23", "max_stars_repo_licenses": ["BSD-3-... |
#!/usr/bin/python2
# This script computes every artist's Snoop Dogg Number their shortest path to Snoop Dogg by
# performing a breadth-first traversal and computing the results in a single pass of the vertices.
# This method can only be applied to the unweighted graph.
#
# This script runs in O(|E|) as far as I know. ... | {"hexsha": "d6d4963b67d8067d78bbbe32d026ae2a20bc60f7", "size": 2137, "ext": "py", "lang": "Python", "max_stars_repo_path": "compute_snoopdogg_number_bfs.py", "max_stars_repo_name": "basimr/SnoopDoggNumber", "max_stars_repo_head_hexsha": "9574ba6c611ecfac100e8dcccc87c3ec5e33ed76", "max_stars_repo_licenses": ["MIT"], "ma... |
import logging
import warnings
import keras
import keras.backend as K
import numpy as np
def load_model(path):
return keras.models.load_model(
path,
custom_objects={
'OffsetAndScale': OffsetAndScale,
'_sigmoid2': _sigmoid2
}
)
def simple_model(data_x,
... | {"hexsha": "ef16163d3331262ed99ec56ba6164c21d86a199c", "size": 4012, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras_utils.py", "max_stars_repo_name": "SuperSourav/transliterationLangugageDetect", "max_stars_repo_head_hexsha": "df2a812ed9488daeb33b262679c4716a26e26cfb", "max_stars_repo_licenses": ["MIT"], ... |
from sklearn.base import RegressorMixin
from ...predictors.predictor import DL85Predictor
import numpy as np
from math import floor, ceil
class DL85Regressor(DL85Predictor, RegressorMixin):
"""An optimal binary decision tree regressor.
Parameters
----------
max_depth : int, default=1
Maximum d... | {"hexsha": "fd3b24209d117c4f77fe8b9662ceb76414dca280", "size": 6104, "ext": "py", "lang": "Python", "max_stars_repo_path": "dl85/supervised/regressors/regressor.py", "max_stars_repo_name": "valentinlemaire/pydl8.5", "max_stars_repo_head_hexsha": "a846f3c36bacbbe01ff87c31413342069b0cf61b", "max_stars_repo_licenses": ["M... |
import numpy as np
from collections import OrderedDict
from rlcard.envs import Env
from rlcard.games.blackjack import Game
DEFAULT_GAME_CONFIG = {
'game_num_players': 1,
}
class BlackjackEnv(Env):
''' Blackjack Environment
'''
def __init__(self, config):
''' Initialize the Blackj... | {"hexsha": "e0797a542775e26c25f5c7447276198bd122ffad", "size": 3269, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlcard/envs/blackjack.py", "max_stars_repo_name": "syntaxp/rlcard", "max_stars_repo_head_hexsha": "3dbccfd9a046f0ccb0996bc2bb83969fb553d024", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
'''
Defines a scalar field over a grid
.. codeauthor:: David Zwicker <david.zwicker@ds.mpg.de>
'''
from typing import (List, TypeVar, Iterator, Union, Optional, # @UnusedImport
TYPE_CHECKING)
from pathlib import Path
import numpy as np
from .base import DataFieldBase
from ..grids import UnitGri... | {"hexsha": "9fb50e626652d8614f026e5c5f095182282b0847", "size": 7840, "ext": "py", "lang": "Python", "max_stars_repo_path": "pde/fields/scalar.py", "max_stars_repo_name": "xuanxu/py-pde", "max_stars_repo_head_hexsha": "de33d938aea8680eff872ae1b64569895662a248", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import json
import numpy as np
import tables
import os
import pandas as pd
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QPainter, QPen
from PyQt5.QtWidgets import QApplication, QMessageBox
from tierpsy.gui.SWTrackerViewer_ui import Ui_SWTrackerViewer
from tierpsy.gui.TrackerViewerAux import TrackerViewerAuxGUI... | {"hexsha": "dbbbcdfcc308ac5467521248c6cc9046a7149e43", "size": 10137, "ext": "py", "lang": "Python", "max_stars_repo_path": "tierpsy/gui/SWTrackerViewer.py", "max_stars_repo_name": "mgh17/tierpsy-tracker", "max_stars_repo_head_hexsha": "a18c06aa80a5fb22fd51563d82c639b520742777", "max_stars_repo_licenses": ["MIT"], "max... |
'''
Created on Mar 30, 2015
@author: Ming Jiang and Jean-Luc Starck
CLASS FUNCTION class starlet2d()
Allow to perform a starlet transform, manipulate it (visualisation, thresholding, statistics, etc),
and to reconstruct.
If pysap is installed, then the pysparse module shoul... | {"hexsha": "e2058dee5789f16c56bcd6cf6026ed0e0c6d19a4", "size": 35135, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycs/sparsity/sparse2d/starlet.py", "max_stars_repo_name": "sfarrens/cosmostat", "max_stars_repo_head_hexsha": "a475315cda06dca346095a1e83cb6ad23979acae", "max_stars_repo_licenses": ["MIT"], "max... |
# encoding: utf-8
# ******************************************************
# Author : zzw922cn
# Last modified: 2017-12-09 11:00
# Email : zzw922cn@gmail.com
# Filename : ed.py
# Description : Calculating edit distance for Automatic Speech Recognition
# ************************************************... | {"hexsha": "a2101ef9f8dc3ebd51a8d78d58fedb1f4dea34fb", "size": 3010, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/ed.py", "max_stars_repo_name": "HanSeokhyeon/Automatic_Speech_Recognition", "max_stars_repo_head_hexsha": "73b92e7b2b12e8b43294caa8eec5727d0ffc7a47", "max_stars_repo_licenses": ["MIT"], "max... |
"""
MPO
A finite size matrix product operator type.
Keeps track of the orthogonality center.
"""
mutable struct MPO <: AbstractMPS
data::Vector{ITensor}
llim::Int
rlim::Int
end
function MPO(A::Vector{<:ITensor}; ortho_lims::UnitRange=1:length(A))
return MPO(A, first(ortho_lims) - 1, last(ortho_lims) + 1... | {"hexsha": "e28acde1c7feb3846ab636cbdc0d313b8c6210b6", "size": 27936, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mps/mpo.jl", "max_stars_repo_name": "LinjianMa/ITensors.jl", "max_stars_repo_head_hexsha": "579bd97f45e1723367ba569f094dd1569817b8d7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
using Wakame
using Documenter
DocMeta.setdocmeta!(Wakame, :DocTestSetup, :(using Wakame); recursive=true)
makedocs(;
modules=[Wakame],
authors="Bernard Brenyah",
repo="https://github.com/PyDataBlog/Wakame.jl/blob/{commit}{path}#{line}",
sitename="Wakame.jl",
format=Documenter.HTML(;
pretty... | {"hexsha": "cbc7a54a3cf549b7f2d37c87f9418723586a7af3", "size": 585, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "PyDataBlog/Wakame.jl", "max_stars_repo_head_hexsha": "7492c73884a447a1a62101db01839b36e7c02c32", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# Copyright 2022 The DDSP Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | {"hexsha": "ecaee9cd77beab7cf1ec429babfe997dfb5fbbaf", "size": 11158, "ext": "py", "lang": "Python", "max_stars_repo_path": "ddsp/spectral_ops_test.py", "max_stars_repo_name": "vvolhejn/ddsp", "max_stars_repo_head_hexsha": "f99c192473c84bbf5d083e8630bf105520ad6ad0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
\section{Developed Method}
\label{sec:HomographyDevelopedMethod}
Our work aimed to devise a systematic approach to select the ``best'' homography according to the proposed score function. The assumption was that there was no prior knowledge about the quality of individual markers.
Here is the description of the ... | {"hexsha": "a995ec5bd85680e5de0e3b659e7d0bab88b57a0d", "size": 14322, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/chapters/homography/sections/methodology.tex", "max_stars_repo_name": "mondrasovic/phd_thesis", "max_stars_repo_head_hexsha": "68a3a6d1687ea43dc6cdfafcd5e6d9ce35f424e8", "max_stars_repo_license... |
# -*- coding: utf-8 -*-
# Copyright 2017 Kakao, Recommendation Team
#
# 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 a... | {"hexsha": "a368e1a317718b189d568011dff84f65a7e2d23a", "size": 11336, "ext": "py", "lang": "Python", "max_stars_repo_path": "classifier.py", "max_stars_repo_name": "junwoopark92/kakao_shopping_classification", "max_stars_repo_head_hexsha": "3d2669a8c946a1d810da7c0fd896ad42e0361fbe", "max_stars_repo_licenses": ["Apache-... |
import numpy as np
from overrides import overrides
import torch
from datasets.dataset_base import DatasetBase
from services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService
from services.process.evaluation_process_service import EvaluationProcessService
from services.log_service import LogServ... | {"hexsha": "7cf418f1b9e9eead55798b30aecec42a75f6393c", "size": 1030, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/evaluation_dataset.py", "max_stars_repo_name": "ktodorov/historical-ocr", "max_stars_repo_head_hexsha": "d4d7bf0addf5ff98b7182c00ff716e79c97e050e", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import matplotlib.pyplot as plt
from numpy import pi
class Vector(object):
def __init__(self,x,y,z):
self.x = np.array(x)
self.y = np.array(y)
self.z = np.array(z)
def duplicate(self):
return Vector(self.x,self.y,self.z)
def size(self):
return... | {"hexsha": "43cfe4dc7712e7c72ed7805c92a56851746f4cc7", "size": 3115, "ext": "py", "lang": "Python", "max_stars_repo_path": "Vector.py", "max_stars_repo_name": "Yiones/raytests", "max_stars_repo_head_hexsha": "d88cc28f4775e00edaf206fb08944aae0f9a6bc7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars... |
[STATEMENT]
lemma powser_split_head:
fixes f :: "nat \<Rightarrow> 'a::{real_normed_div_algebra,banach}"
assumes "summable (\<lambda>n. f n * z ^ n)"
shows "suminf (\<lambda>n. f n * z ^ n) = f 0 + suminf (\<lambda>n. f (Suc n) * z ^ n) * z"
and "suminf (\<lambda>n. f (Suc n) * z ^ n) * z = suminf (\<lambda>n... | {"llama_tokens": 2191, "file": null, "length": 18} |
module NewtonsMethod
using ForwardDiff
function newtonroot(f, fp; x0, tol=1e-7, maxiter=1000)
abserror = Inf
iter = 1
x = x0
while abserror > tol && iter <= maxiter
x_new = x - f(x)/fp(x)
iter = iter +1
abserror = abs(x_new - x)
x = x_new
end
return x
end
funct... | {"hexsha": "714223a8ed0f07c0cc6004a95cae21a65847afcf", "size": 493, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NewtonsMethod.jl", "max_stars_repo_name": "ykkan/NewtonsMethod.jl", "max_stars_repo_head_hexsha": "7cde23f25d50a3a5469492d481aad00c701a4849", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#Python3
##--------------------------------Main file------------------------------------
##
## Copyright (C) 2020 by Belinda Brown Ramírez (belindabrownr04@gmail.com)
## Image recognition system for diagnosis of a network switch
##
##-----------------------------------------------------------------------------
###... | {"hexsha": "6fd075585f173e86afb5d595e0854b87b3152327", "size": 11369, "ext": "py", "lang": "Python", "max_stars_repo_path": "Images_Recognition/main.py", "max_stars_repo_name": "brown9804/EIE-Project_Recognition-System_ARUBA-Switch", "max_stars_repo_head_hexsha": "45382bed52b977280a4bd4562a14b6447cd3a39e", "max_stars_r... |
[STATEMENT]
lemma card_nonzero:"\<lbrakk>finite A; card A \<noteq> 0\<rbrakk> \<Longrightarrow> A \<noteq> {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>finite A; card A \<noteq> 0\<rbrakk> \<Longrightarrow> A \<noteq> {}
[PROOF STEP]
by (rule contrapos_pp, simp+) | {"llama_tokens": 115, "file": "Group-Ring-Module_Algebra1", "length": 1} |
from __future__ import print_function
import os
import json
import logging
import numpy as np
from tqdm import tqdm, trange
from datetime import datetime
from collections import defaultdict
import _pickle as cPickle
import torch as t
import torch
from torch.autograd import Variable
##########################
# Torc... | {"hexsha": "3e464cd8f700a8e00f9088cf1018e990cf65e772", "size": 4479, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "tuyunbin/TTA_AVS", "max_stars_repo_head_hexsha": "71d7c3a8220550169e731268144ebae397f04163", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
using Base64
using Sockets
function main()
str_size = 131072
tries = 8192
str = repeat("a", str_size)
str2 = base64encode(str)
str3 = String(base64decode(str2))
notify("Julia\t$(getpid())")
t = time()
s_encoded = 0
for i = 1:tries
s_encoded += length(base64encode(str))
... | {"hexsha": "b3c9a614ddba57c483441870877f99068266657e", "size": 1273, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "base64/test.jl", "max_stars_repo_name": "clemenswasser/benchmarks", "max_stars_repo_head_hexsha": "d1d22c42c107ffb3ad0a7489ef1dd439c237559c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Wang Yu, the University of Yamanashi, Japan
# Oct 2, 2020
import numpy as np
import os,sys
DIR=os.path.dirname(os.path.dirname(__file__))
sys.path.append(DIR)
from gmm import gmm
from collections import namedtuple
import copy
class NaiveDiscreteHMM:
'''
A naive HMM with discrete observation probability.
'''... | {"hexsha": "ac12b925d495cb712542dd106e01c5f88d83beb9", "size": 12224, "ext": "py", "lang": "Python", "max_stars_repo_path": "hmm/hmm.py", "max_stars_repo_name": "wangyu09/asr_memo", "max_stars_repo_head_hexsha": "e51b0232a4d3f79126d151edc61cdd02c8c68680", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1,... |
module Pseudospectra
#=
Eigenvalue and Pseudospectrum Analysis for Julia
The Pseudospectra.jl package is a translation of EigTool, but no endorsement
or promotion by the authors of EigTool is implied.
This package is released under a BSD license, as described in the LICENSE file.
Julia code and supplements
Copyright... | {"hexsha": "6dedf189caaceec0f3467b2840ba3c8cead94a27", "size": 26684, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Pseudospectra.jl", "max_stars_repo_name": "csimal/Pseudospectra.jl", "max_stars_repo_head_hexsha": "1cbbdd17fb84c1bdf604d5d122778df2664ca8ca", "max_stars_repo_licenses": ["BSD-3-Clause", "MIT"... |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns; sns.set()
import pandas as pd
from sklearn.cluster import KMeans
store_data = pd.read_csv('D:\\Datasets\\NIPS_1987-2015.csv')
x = store_data.... | {"hexsha": "8ca6ddd0305faf8f19a139800bff269475d67e80", "size": 1086, "ext": "py", "lang": "Python", "max_stars_repo_path": "2.1.2_clustering_between_to_first_article_and_the_last_article_(all_rows)[1].py", "max_stars_repo_name": "dianagut1987/BigData-Unsupervised-for-articles", "max_stars_repo_head_hexsha": "f3413f02b3... |
SUBROUTINE MA_CGDT ( iret )
C************************************************************************
C* MA_CGDT *
C* *
C* This subroutine sets the report date/time using system and bulleti... | {"hexsha": "1090fd69c0d9e97f0d487a6b0b0a3c0f84b79b8a", "size": 3051, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/bridge/ma/macgdt.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
import argparse
import torch
import numpy as np
import time as tm
from torch.autograd import Variable
# Compute error
def compute_error(A, sA):
normA = torch.sqrt(torch.sum(torch.sum(A * A, dim=1),dim=1))
error = A - torch.bmm(sA, sA)
error = torch.sqrt((error * error).sum(dim=1).sum(dim=1)) / normA
re... | {"hexsha": "1c10ca63b4f71d160e89276b9e430af5e3b81430", "size": 4120, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/matrix_sqrt.py", "max_stars_repo_name": "milesgray/CALAE", "max_stars_repo_head_hexsha": "a2ab2f7d9ee17cc6c24ff6ac370b0373537079ac", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# Import general libraries
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import pprint
import os
# Import dash
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output, State
... | {"hexsha": "ba4ea0b4f774332ab70abafae0eb4a0c3e719339", "size": 3815, "ext": "py", "lang": "Python", "max_stars_repo_path": "news.py", "max_stars_repo_name": "bensjx/covid-dashboard", "max_stars_repo_head_hexsha": "c4204d984719137c3cbdd224b50ced385b4f5f49", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import matplotlib.pyplot as plt
import numpy as np
import wave
import scipy.io.wavfile as wav
from audiolazy.lazy_lpc import levinson_durbin
from pip._vendor.distlib.compat import raw_input
from scipy import signal
import scipy as sk
from audiolazy import *
import audiolazy.lazy_lpc
from audiolazy import lpc
from sklea... | {"hexsha": "7785425bf259fb3f975125b22c835333752f6312", "size": 9356, "ext": "py", "lang": "Python", "max_stars_repo_path": "spam_code/lpc_gmm.py", "max_stars_repo_name": "chondroseto/Virtual_Assistant", "max_stars_repo_head_hexsha": "b52945d255176b711795d61da54d72000cf3561b", "max_stars_repo_licenses": ["MIT"], "max_st... |
contactList{1,1} = 'title 1'; contactList{1,2} = 'author name 1'; contactList{1,3} = 'spam@email.com';
contactList{2,1} = 'title 2'; contactList{2,2} = 'author name 2'; contactList{2,3} = 'spam@email.com';
subjectLine = 'title of the emails';
bodyLine = ['Dear %s,\n',...
'\n',...
'We will be organizing XXX\n',...
'S... | {"author": "jianxiongxiao", "repo": "ProfXkit", "sha": "7376c50abf5ead846247774a36be026e6f24953c", "save_path": "github-repos/MATLAB/jianxiongxiao-ProfXkit", "path": "github-repos/MATLAB/jianxiongxiao-ProfXkit/ProfXkit-7376c50abf5ead846247774a36be026e6f24953c/batchEmail.m"} |
[STATEMENT]
lemma project_extend_Join: "project h UNIV ((extend h F)\<squnion>G) = F\<squnion>(project h UNIV G)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. project h UNIV (extend h F \<squnion> G) = F \<squnion> project h UNIV G
[PROOF STEP]
apply (rule program_equalityI)
[PROOF STATE]
proof (prove)
goal (3 sub... | {"llama_tokens": 621, "file": null, "length": 5} |
macro do_while(condition, block)
quote
let
$block
while $condition
$block
end
end
end |> esc
end
function _reg(s, quoted_attr, attr_name)
@eval begin
#$get_attr_name(x) = getattr( x, $quoted_attr)
#$set_attr_name(x, va... | {"hexsha": "929a935f082f013aa84a03181a344ae9115b00fe", "size": 1106, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/macros.jl", "max_stars_repo_name": "PPKFS/julia_if_old", "max_stars_repo_head_hexsha": "b46abb43aa89daf038e2f822c184c0f2c75d1b6f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
#pragma once
#include <memory>
#include <boost/asio.hpp>
#include "eventReceiver.hpp"
#include "serviceParamTypes.h"
namespace mln::net {
struct NetCommonObjects
{
public:
NetCommonObjects(ServiceParams& svcParams)
: _ioc(svcParams.ioc_)
, _strand(svcParams.ioc_)
, _keepAliveTimeMs(svcParams.keepAliveT... | {"hexsha": "ecf87dbb2ea6af169f8fcd557cc599d8b8f89d3c", "size": 2214, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/net/netCommonObjects.hpp", "max_stars_repo_name": "lazychase/mlnsdk", "max_stars_repo_head_hexsha": "599303c37b83c03827a3050c42aeb3af649ee968", "max_stars_repo_licenses": ["MIT"], "max_stars... |
using Documenter, Query
makedocs(
modules = [Query],
sitename = "Query.jl",
pages = [
"Introduction" => "index.md",
"Getting Started" => "gettingstarted.md",
"Standalone Query Commands" => "standalonequerycommands.md",
"LINQ Style Query Commands" => "linqquerycommands.md",
"Data Sources" => "sources.md"... | {"hexsha": "cfed2a1e6129c56000b14b86654f6fec7b9fd4ef", "size": 498, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "petershintech/Query.jl", "max_stars_repo_head_hexsha": "7ab5f58ec82d51c42cf6dba7e916eee7a78a2c51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from automan.api import Problem, Automator, Simulation
from automan.api import CondaClusterManager
from matplotlib import pyplot as plt
import numpy as np
class Squares(Problem):
def get_name(self):
return 'squares'
def get_commands(self):
commands = [(str(i), 'python square.py %d' % i, None)... | {"hexsha": "f08c5ee1ff401ba18c615465837f85e02b692de3", "size": 1904, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/edm_conda_cluster/automate_conda.py", "max_stars_repo_name": "pypr/automan", "max_stars_repo_head_hexsha": "80619f0cb58ad1e996dc7c9ea66effecf8df5acc", "max_stars_repo_licenses": ["BSD-3-C... |
library(ggplot2)
library(dplyr)
library(reshape2)
#load data
data <- read.csv("Downloads/MachineLearning-master/Example Data/PCA_Example_1.csv", stringsAsFactors=F)
#change the first column format as Date
data$Date = as.Date(data$Date)
#transform stock data into Date Stock1 Stock2 .... Stock24
data <- reshape(data, id... | {"hexsha": "b31e0e37f7e716cec489fb841f9fb682381996a1", "size": 1712, "ext": "r", "lang": "R", "max_stars_repo_path": "R/pca.r", "max_stars_repo_name": "Hennrik/machine_learning_examples", "max_stars_repo_head_hexsha": "8263fb95aa18ae56e4dc9690d389fe8ac25c2b3a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
# -*- coding: utf-8 -*-
"""
Contains the definition of the SuddenDecay class.
"""
from __future__ import unicode_literals
from __future__ import print_function
import logging
import numpy as np
from . import SampleBasedDecay
logger = logging.getLogger('decay.sudden')
class SuddenDecay(SampleBasedDecay):
"""
... | {"hexsha": "fff977053334258989b6ac0d6b95c314d3c3b6f4", "size": 2032, "ext": "py", "lang": "Python", "max_stars_repo_path": "decay/decays/sample/sudden.py", "max_stars_repo_name": "pyl1b/decay", "max_stars_repo_head_hexsha": "7200516455fc03351ad658af66b5cc39b2b2d50a", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
@testset "map" begin
m = sprand(5, 5, 0.25)
n = GBMatrix(m)
@test map(UnaryOps.LOG, n)[1,1] == map(log, m)[1,1]
o = map!(>, GBMatrix{Bool}(5, 5), 0.1, n)
@test o[1,4] == (0.1 > m[1,4])
@test map(second, n, 1.5)[1,1] == 1.5
@test (n .* 10)[1,1] == n[1,1] * 10
# Julia will map over the en... | {"hexsha": "4dd13310e8b11ce993532cc15cb21f4b70ab0b8a", "size": 444, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/operations/map.jl", "max_stars_repo_name": "JuliaSparse/SuiteSparseGraphBLAS.jl", "max_stars_repo_head_hexsha": "73466763044fb8a8c80c92180b294c482440c2b0", "max_stars_repo_licenses": ["MIT"], "... |
(*
Title: Catoids
Author: Georg Struth
Maintainer: Georg Struth <g.struth at sheffield.ac.uk>
*)
section \<open>Catoids\<close>
theory Catoid
imports Main
begin
subsection \<open>Multimagmas\<close>
text \<open>Multimagmas are sets equipped with multioperations. Multioperations are isomorphic to ternary relatio... | {"author": "gstruth", "repo": "catoids", "sha": "1b7c623d742bcacfecf1a60518106c31716bf2dd", "save_path": "github-repos/isabelle/gstruth-catoids", "path": "github-repos/isabelle/gstruth-catoids/catoids-1b7c623d742bcacfecf1a60518106c31716bf2dd/Catoid.thy"} |
[STATEMENT]
lemma mask_inj_hlp1: "inj_on (mask :: nat \<Rightarrow> 16 word) {0..16}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. inj_on mask {0..16}
[PROOF STEP]
proof(intro inj_onI, goal_cases)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>x y. \<lbrakk>x \<in> {0..16}; y \<in> {0..16}; mask x = mask ... | {"llama_tokens": 1681, "file": "LOFT_LinuxRouter_OpenFlow_Translation", "length": 16} |
/* test_uniform_int_distribution.cpp
*
* Copyright Steven Watanabe 2011
* Distributed under the Boost Software License, Version 1.0. (See
* accompanying file LICENSE_1_0.txt or copy at
* http://www.boost.org/LICENSE_1_0.txt)
*
* $Id$
*
*/
#include <boost/random/uniform_int_distribution.hpp>
#inclu... | {"hexsha": "227a0b31050a5a8db32daa1984dacef8fd052fd6", "size": 1232, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/random/test/test_uniform_int_distribution.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e1... |
import os
import numpy as np
from tqdm import tqdm
import laspy
import argparse
from tqdm import tqdm
def get_predictions(pred_file, las_file):
result = np.loadtxt(pred_file)
labels = result[:, 3]
points = result[:, 0:3]
las = laspy.create(file_version = "1.2", point_format = 3)
las.x = poi... | {"hexsha": "7255ec5834cfd5b7897ebe5db8dd44d2b85b2512", "size": 1501, "ext": "py", "lang": "Python", "max_stars_repo_path": "predictions.py", "max_stars_repo_name": "BenCurran98/FugroDGCNN", "max_stars_repo_head_hexsha": "7033cc4992f975e836289cae59d4990d9edb8b6b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
from multiprocessing import Pool #parallel processing
import multiprocessing as mp
import structure
from structure.global_constants import *
from structure.cell import Tissue, BasicSpringForceNoGrowth
import structure.initialisation as init
import sys
import os
import numpy as np
import libs.pd_lib_neutral as lib
impo... | {"hexsha": "370f1ffde2f500968a4e3d043bee9a5dfebacc18", "size": 2726, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_files/pd_original/cluster_stats_vary_MU.py", "max_stars_repo_name": "jessiesrr/VTdyn", "max_stars_repo_head_hexsha": "6f71ef94525d95221f5bd5e5290f4df10648cd18", "max_stars_repo_licenses": ["MI... |
"""
Interact with the grizli AWS database
"""
import os
import numpy as np
FLAGS = {'init_lambda': 1,
'start_beams': 2,
'done_beams': 3,
'no_run_fit': 4,
'start_redshift_fit': 5,
'fit_complete': 6}
COLUMNS = ['root', 'id', 'status', 'ra', 'dec', 'ninput', 'redshift', 'as_e... | {"hexsha": "aceef6e53447813622c94473a304064efa0e32fe", "size": 105785, "ext": "py", "lang": "Python", "max_stars_repo_path": "grizli/aws/db.py", "max_stars_repo_name": "jkmatharu/grizli", "max_stars_repo_head_hexsha": "7e2eb918667ac9f845d0847452a72138fc22fbcd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import logging
from typing import Any, Callable, Collection, Dict, Optional, Sequence, Tuple, Union
import numpy
import skimage.transform
import skimage.transform
import torch
from hylfm.utils.for_log import DuplicateLogFilter
from .affine_utils import get_lf_roi_in_raw_lf, get_ls_roi
from .base import Transform
from... | {"hexsha": "07531758331cb9e879c4c02239b59a1c6732c1d1", "size": 10346, "ext": "py", "lang": "Python", "max_stars_repo_path": "hylfm/transforms/image.py", "max_stars_repo_name": "kreshuklab/hylfm-net", "max_stars_repo_head_hexsha": "9f1013640e40e998674b65176023367b1e978782", "max_stars_repo_licenses": ["MIT"], "max_stars... |
module EMLstoic
using DanaTypes
using DotPlusInheritance
using Reexport
@reexport using ...reactors.EMLtank_basic
import EMLtypes.length
include("stoic/stoic_vap.jl")
include("stoic/stoic_liq.jl")
include("stoic/stoic_extent_vap.jl")
include("stoic/stoic_extent_liq.jl")
include("stoic/stoic_conv_vap.jl")
inc... | {"hexsha": "bf2371eba0499bf5350be90fe8b35d59463d8dd9", "size": 355, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "JuliaEMSOModels/reactors/stoic.jl", "max_stars_repo_name": "DANA-Laboratory/EMSOModelLibrary.jl", "max_stars_repo_head_hexsha": "e28904cc1bdf8f67c6839ad35b4658dd399c0e47", "max_stars_repo_licenses":... |
from __future__ import annotations
import os
from typing import Optional, Union
import tensorflow as tf
from numpy import random
from GNN import GNN_metrics as mt, GNN_utils as utils
from GNN.GNN import GNNnodeBased, GNNedgeBased, GNNgraphBased
from GNN.LGNN import LGNN
from GNN.MLP import MLP, get_inout_... | {"hexsha": "5a386c93e1d9ca1e44f62a1f5ac5e27d4d5e72d8", "size": 9273, "ext": "py", "lang": "Python", "max_stars_repo_path": "starter.py", "max_stars_repo_name": "vishalbelsare/GNN_tf_2.x", "max_stars_repo_head_hexsha": "4b6429ed58f2c0922257600a9287d5cc5a10395b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
#!/usr/bin/env python
import rospy
import tf
from auv_msgs.msg import NavSts
from uw_vs.msg import PilotRequest
from geometry_msgs.msg import Pose, TwistStamped
import numpy as np
global TOPIC_NAV # get the vehicle simulated pose
global TOPIC_POSE # publishes simulated pose on UWSim
global TOPIC_CMD # get the controll... | {"hexsha": "93a56dbdb95fad0f7aed04864c4999658668f3ce", "size": 2083, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dyn_interface.py", "max_stars_repo_name": "LaboratoireCosmerTOULON/uwvs_osl", "max_stars_repo_head_hexsha": "c3d790c451d13bebc1265b5d6011655ef660232e", "max_stars_repo_licenses": ["BSD-2-Claus... |
import pandas as pd
import numpy as np
import click
import h5py
import os
import logging
from array import array
from copy import deepcopy
from tqdm import tqdm
from astropy.io import fits
from fact.credentials import create_factdb_engine
from zfits import FactFits
from scipy.optimize import curve_fit
from joblib imp... | {"hexsha": "857b18b35686656b46bc34d4625e565c64b6ccff", "size": 23133, "ext": "py", "lang": "Python", "max_stars_repo_path": "drs4Calibration/timelapse/drs4CalibrationTool_time.py", "max_stars_repo_name": "fact-project/DrsTemperatureCalibration", "max_stars_repo_head_hexsha": "3702ee390c16cf2c5930d4a0f24c1354d036d645", ... |
#!/usr/bin/env python3
# coding: utf-8
# ------------ #
# Lum Analysis #
# ------------ #
### Modules
# std library
import os
from os.path import join
from collections import OrderedDict
# dependencies
import scipy.signal as signal
import numpy as np
from datetime import datetime, timedelta
# custom
from pupil_code... | {"hexsha": "c8628051d0dda665df699d728dd2eb6dbe349693", "size": 9897, "ext": "py", "lang": "Python", "max_stars_repo_path": "pupil_code/lum_analysis.py", "max_stars_repo_name": "pignoniG/cognitive_analysis_tool", "max_stars_repo_head_hexsha": "90568fc83493a10b567c1f957a57b3ef3a1cf69f", "max_stars_repo_licenses": ["MIT"]... |
""" Draw wiring diagrams (aka string diagrams) using Graphviz.
"""
module GraphvizWiringDiagrams
export to_graphviz
import ...Doctrines: HomExpr
using ...WiringDiagrams, ...WiringDiagrams.WiringDiagramSerialization
import ..Graphviz
import ..Graphviz: to_graphviz
# Constants and data types
##########################
... | {"hexsha": "e11ece1f585272b967a97a3479ae51cb8ca14df3", "size": 11852, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/graphics/GraphvizWiringDiagrams.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Catlab.jl-134e5e36-593f-5add-ad60-77f754baafbe", "max_stars_repo_head_hexsha": "b8e5e1eab26b53ec7e53c503c1dd5... |
import torch
import cv2
import lib.dataset_handler
import lib.generate_gt_anchor
import lib.tag_anchor
import Net
import numpy as np
import os
import time
import random
import copy
def val(net, criterion, batch_num, using_cuda, logger, img_list):
random_list = random.sample(img_list, batch_num)
total_loss = 0... | {"hexsha": "5bcd8a8f9ad964048335dfcb35bf59fc2f9fa225", "size": 3697, "ext": "py", "lang": "Python", "max_stars_repo_path": "detector/ctpn/evaluate.py", "max_stars_repo_name": "qiu9yu/Lets_OCR", "max_stars_repo_head_hexsha": "62d68b044250d02a9d5ac8c4fbd08cec83faa0d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import sys
import pyqtgraph as pg
import datetime
import time
import numpy as np
import logging
from PyQt5.QtWidgets import QDialog
from .gui.chartWindowGui import *
from .pg_time_axis import DateAxisItem
"""
Charts are plotted using pyqtgrpah library.
Data are read directly from the image list
model (imageListModel... | {"hexsha": "7c6be6cf967db9ec28aef0f9a1ff9df2b29f12d7", "size": 15964, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrodom/chartWindow.py", "max_stars_repo_name": "fenriques/AstroDom", "max_stars_repo_head_hexsha": "84b54d3299cf591c39b214248339a201ae8ae6ca", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import git
import numpy as np
import os
import argparse
import sys
import json
import torch
from utils.spec_reader import SpecReader
from policy_gradients import models
from policy_gradients.agent import Trainer
from cox.store import Store, schema_from_dict
# Tee object allows for logging to both stdout and to file... | {"hexsha": "6a167e0df80b4dcc0003c7c9ed215c4ad80e5683", "size": 9699, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/run.py", "max_stars_repo_name": "tristan-ka/ppo-wmg", "max_stars_repo_head_hexsha": "e26ab78ab77cc6f42cb24e03f71a3315489478f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
(* Title: OInvariants.thy
License: BSD 2-Clause. See LICENSE.
Author: Timothy Bourke
*)
section "Open reachability and invariance"
theory OInvariants
imports Invariants
begin
subsection "Open reachability"
text \<open>
By convention, the states of an open automaton are pairs. The first com... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/AWN/OInvariants.thy"} |
#!/usr/bin/env python
import math
import average_vector
from scipy import spatial # Cosine similarity calculation
def getDistanceAverageEpsilonNeighborhoodAndNegative( source_word, eps_plus, eps_minus, model, np ):
"""
Get distance (angle by cosine similarity)
between 1. epsilon-neighborhood of word ... | {"hexsha": "01e82042c5ee93417c5e64979c5ae290c413a8a8", "size": 7467, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lib/epsilon_neighborhood.py", "max_stars_repo_name": "componavt/wcorpus.py", "max_stars_repo_head_hexsha": "4433c8de62ffb8e3c9cec6eb8a23dd64e5349700", "max_stars_repo_licenses": ["Unlicense"],... |
import random
import cv2
import numpy as np
import math
def ellipse_bbox(h, k, a, b, theta):
ux = a * math.cos(theta)
uy = a * math.sin(theta)
vx = b * math.cos(theta + math.pi / 2)
vy = b * math.sin(theta + math.pi / 2)
box_halfwidth = np.ceil(math.sqrt(ux ** 2 + vx ** 2))
box_halfheight = np... | {"hexsha": "4e42172ee1b68c5e0b9d4f450dabf11fa2c6f4d1", "size": 3131, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/snow.py", "max_stars_repo_name": "LeikvollE/pytorch-superpoint", "max_stars_repo_head_hexsha": "52144a760e0cc46259e57397a5a55f0585fe6d0b", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/7/5 10:42 AM
# @Author : Ject.Y
# @Site :
# @File : create_h5_cls.py
# @Software: PyCharm
# @Copyright: BSD2.0
# @Function : Genarate HDF5 data for Face classification
# How to run : run
import h5py, os
import numpy as np
import random
import cv2
... | {"hexsha": "31c5fd6fb2b0eeb00d9efaf7fa3d3a8cf33c7abf", "size": 4148, "ext": "py", "lang": "Python", "max_stars_repo_path": "train/Fstage/create_h5_cls.py", "max_stars_repo_name": "yungs2017/CNNFaceDetection", "max_stars_repo_head_hexsha": "38aa7d052beb8993bf3b5282d03d68ab8cca4439", "max_stars_repo_licenses": ["BSD-2-Cl... |
function compute_derivatives(obj::ComplexOrdinaryDifferentialEquation, arg0::jdouble, arg1::Vector{JComplex})
return jcall(obj, "computeDerivatives", Vector{JComplex}, (jdouble, Vector{JComplex}), arg0, arg1)
end
function get_dimension(obj::ComplexOrdinaryDifferentialEquation)
return jcall(obj, "getDimension",... | {"hexsha": "56a644edd3b97cff15305b767c97193058a71ca4", "size": 543, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "gen/HipparchusWrapper/OdeWrapper/complex_ordinary_differential_equation.jl", "max_stars_repo_name": "JuliaAstrodynamics/Orekit.jl", "max_stars_repo_head_hexsha": "e2dd3d8b2085dcbb1d2c75471dab42d6ddf... |
# Splitting the data
# December 22nd 2020
'''This script splits the data.
Usage: split_data.py --clean_train_path=<clean_train_path>
Options:
--clean_train_path=<clean_train_path> : Relative file path for the cleaned train csv
'''
import numpy as np
import pandas as pd
from docopt import docopt
from skle... | {"hexsha": "204ce36f09338e5045f7f6c94221e81a558c78e0", "size": 1118, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/split_data.py", "max_stars_repo_name": "nphaterp/house_price_prediction", "max_stars_repo_head_hexsha": "376c9a052e5de552e431210ead548d0a5b238377", "max_stars_repo_licenses": ["MIT"], "max_sta... |
From iris.proofmode Require Import proofmode.
From iris.program_logic Require Import weakestpre adequacy lifting.
From stdpp Require Import base.
From cap_machine Require Export logrel.
From cap_machine.ftlr Require Import ftlr_base.
From cap_machine.rules Require Import rules_Jmp.
Section fundamental.
Context {Σ:gF... | {"author": "logsem", "repo": "cerise", "sha": "a578f42e55e6beafdcdde27b533db6eaaef32920", "save_path": "github-repos/coq/logsem-cerise", "path": "github-repos/coq/logsem-cerise/cerise-a578f42e55e6beafdcdde27b533db6eaaef32920/theories/ftlr/Jmp.v"} |
import gym
import numpy as np
import matplotlib.pyplot as plt
def value_iteration():
V_states = np.zeros(n_states) # init values as zero
theta = 1e-8
gamma = 0.8
# TODO: implement the value iteration algorithm and return the policy
# Hint: env.P[state][action] gives you tuples (p, n_state, r, is_... | {"hexsha": "3ee52a30e4f470b92f22d246f47b05171ac778ac", "size": 4143, "ext": "py", "lang": "Python", "max_stars_repo_path": "ex06-nstep/ex06-nstep.py", "max_stars_repo_name": "vijaykumarprabhu/rl-course", "max_stars_repo_head_hexsha": "cc9db0236bd1908e0fa54eae1b2fcfd609ec0ae4", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import numpy as np
import sys
i = sys.argv[1]
uf = sys.argv[2]
size = sys.argv[3]
page = sys.argv[4]
latency_path = "./log/latency_" + str(i) + "_" + str(uf) + "k_" + str(size) + "_" + str(page) + "_0.log"
prepare_path = ... | {"hexsha": "92853417f679d78bfa38c1ee138b4f26f189512c", "size": 999, "ext": "py", "lang": "Python", "max_stars_repo_path": "cmake-build-debug/result.py", "max_stars_repo_name": "bombework/FrequentSnapshot", "max_stars_repo_head_hexsha": "cd1266a2c7dbc44b0ab38637d1704b54175da895", "max_stars_repo_licenses": ["Apache-2.0"... |
import pandas as pd
import numpy as np
import json
import csv
import matplotlib.pyplot as plt
# import seaborn as sns
from tqdm import tqdm
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.stem import WordNetLemmatizer
import nltk
# nltk.... | {"hexsha": "e6acef970b439f71bc0061f34b020b67a66415d6", "size": 2323, "ext": "py", "lang": "Python", "max_stars_repo_path": "p3.py", "max_stars_repo_name": "swarooprm/DQI", "max_stars_repo_head_hexsha": "8de54cc60e489af49d063fb6a14235b9abcc2839", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_star... |
program t
external a,b,c
print *,'ok'
end program t
| {"hexsha": "314a3d957ed0bde0b4ae6e7ca2e8cbf96fd6e448", "size": 56, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/t0172r/t.f90", "max_stars_repo_name": "maddenp/ppp", "max_stars_repo_head_hexsha": "81956c0fc66ff742531817ac9028c4df940cc13e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2... |
from random import choice
import numpy as np
from PIL import Image
from scipy.ndimage import gaussian_gradient_magnitude
from wordcloud import WordCloud, ImageColorGenerator
COLORMAP = 'ocean'
COLORS = (
'#0F468C',
'#1665CC',
'#072040'
)
BACKGROUND_COLOR = '#ffffff'
HEIGHT = 768
WIDTH = 1536
PREFER_HORIZONTAL = 1
#... | {"hexsha": "701a0d013a4bf65357540cbbd771ece4139df880", "size": 1482, "ext": "py", "lang": "Python", "max_stars_repo_path": "wordcloud_generator/generate.py", "max_stars_repo_name": "liviakuhn/wordcloud-generator", "max_stars_repo_head_hexsha": "b0f28f57361fa7f801b9179afb5c6b2a1cd2d37c", "max_stars_repo_licenses": ["MIT... |
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE UniqueIdMapTest
#include <boost/test/unit_test.hpp>
#include <iostream>
#include "utils/UniqueIdMap.hpp"
using namespace pcw;
////////////////////////////////////////////////////////////////////////////////
BOOST_AUTO_TEST_CASE(UniqueIds) {
UniqueIdMap<std::stri... | {"hexsha": "feeff9953bc669f1fa398da02e2a0527301fbf57", "size": 1024, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "rest/src/utils/tests/TestUniqueIdMap.cpp", "max_stars_repo_name": "cisocrgroup/pocoweb", "max_stars_repo_head_hexsha": "93546d026321744602f6ee90fd82503da56da3b7", "max_stars_repo_licenses": ["Apache... |
#!/usr/bin/env python
# Black-Scholes PDE solving using DGM paper
# __author__ = "Abdollah Rida"
# __email__ = "abdollah.rida@berkeley.edu"
# Import needed packages
import numpy as np
import scipy.stats as spstats
from __params__ import *
# Black-Scholes European call price
# Analytical known solution
def lambd_H(... | {"hexsha": "f8f94efcbb50d5b5463569475d72e381672969f8", "size": 1378, "ext": "py", "lang": "Python", "max_stars_repo_path": "f_BS/f_BS/f_Call.py", "max_stars_repo_name": "AbdollahRida/MathFi", "max_stars_repo_head_hexsha": "bf392e76793940c477c73016f44c5192e902c6b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from PIL import Image
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Generate pixel portraits from an image.')
parser.add_argument('file', type=str, help='Input file.')
parser.add_argument('-out', type=str, default='', help='Output file.')
parser.add_argument('-compression', type=int,... | {"hexsha": "4d39902eeaf64a8217231f06d910b55e8b2f62ef", "size": 730, "ext": "py", "lang": "Python", "max_stars_repo_path": "run.py", "max_stars_repo_name": "ErikBoesen/portrify", "max_stars_repo_head_hexsha": "ab14ab6112b915364b50dd3b35c223cb79f50376", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars... |
# Copyright 2019 The TensorFlow Probability Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | {"hexsha": "f8f026f826335371632ca881609701f49c924fc5", "size": 18235, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_probability/python/util/deferred_tensor.py", "max_stars_repo_name": "chrism0dwk/probability", "max_stars_repo_head_hexsha": "ab260f15cae94c6802c2f2769fb448ad213b79cd", "max_stars_repo_... |
#! /usr/bin/env python
# standard library imports
import argparse
import textwrap
import sys # NOQA importing sys so I can mock sys.argv in tests
from pandashells.lib import module_checker_lib, arg_lib
module_checker_lib.check_for_modules(['pandas'])
from pandashells.lib import io_lib
import pandas as pd
import n... | {"hexsha": "3d7fcc62ce3475a6c94465402a6be948ccd05ed0", "size": 5729, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandashells/bin/p_rand.py", "max_stars_repo_name": "timgates42/pandashells", "max_stars_repo_head_hexsha": "4b565435a25ac713eeeacf28c3e5b52fe94530d8", "max_stars_repo_licenses": ["BSD-2-Clause-Fre... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% [velo2, tmax]= SINCHRONIZE(qini, qfinal, velocity) Finds a mean speed and the required
% time to perform a movement between the joint coordinates qini and qfinal.
% If the speed of each joint is different, the maxi... | {"author": "4rtur1t0", "repo": "ARTE", "sha": "6e836f3156bb36af63b70bd93375c8ff4ee643c4", "save_path": "github-repos/MATLAB/4rtur1t0-ARTE", "path": "github-repos/MATLAB/4rtur1t0-ARTE/ARTE-6e836f3156bb36af63b70bd93375c8ff4ee643c4/RAPID/functions/synchronize.m"} |
"""
Standalone script to load all bad odometers in an astropy table.
This can also be used for any other google sheet by changing the id and
the tab.
"""
import requests
from astropy.table import Table
URL_BASE = ('https://docs.google.com/spreadsheets/d/'
'{}/gviz/tq?tqx=out:csv&sheet={}')
SHEET_ID = '1gv... | {"hexsha": "8ca958cea2b5e6a969777fc6386e405ab9ffa357", "size": 586, "ext": "py", "lang": "Python", "max_stars_repo_path": "spirou/sandbox/bad_odo_list/load_bad_odo.py", "max_stars_repo_name": "njcuk9999/apero-utils", "max_stars_repo_head_hexsha": "f77de4c9123874e5bb6ed6bd03a7de3b27057402", "max_stars_repo_licenses": ["... |
"""
VariantMap plot
Author: CY THAM
Version: 1.0.0
"""
import math
import numpy as np
import pandas as pd
import plotly.graph_objects as go
def VariantMap(
dataframe,
entries_per_batch=2500,
batch_no=1,
annotation=None,
filter_sample=None,
filter_file=None,
... | {"hexsha": "0aad4105f98e73380d24b53fa95cdd8e98a6b87e", "size": 11917, "ext": "py", "lang": "Python", "max_stars_repo_path": "dash_bio/component_factory/_variant.py", "max_stars_repo_name": "cytham/dash-bio", "max_stars_repo_head_hexsha": "331c8b3c80b5243c3cfa5261583a3595072edf15", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy
import networkx as nx
def map_step(p1, p2):
u = numpy.unique(p1)
splt = [p2[p1 == _u] for _u in u]
counter = [numpy.unique(_s, return_counts=True)
for _s in splt]
counter = [_x[0][numpy.argsort(_x[1])].tolist()
for _x in counter]
idx = numpy.max(u) + 1
... | {"hexsha": "9def99f7517aaa6bb25278b19cac23e5814151a4", "size": 18477, "ext": "py", "lang": "Python", "max_stars_repo_path": "white_matter/wm_recipe/p_types/ptype_tree_model.py", "max_stars_repo_name": "alex4200/Long-range-micro-connectome", "max_stars_repo_head_hexsha": "833aad78bc71e49a5059b276e65d3fef21686f9d", "max_... |
// Copyright Carl Philipp Reh 2009 - 2016.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <fcppt/make_ref.hpp>
#include <fcppt/noncopyable.hpp>
#include <fcppt/reference_compariso... | {"hexsha": "d8bc53e30291eed631749e32d5e04d5397b876e1", "size": 2153, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/optional/map.cpp", "max_stars_repo_name": "vinzenz/fcppt", "max_stars_repo_head_hexsha": "3f8cc5babdee178a9bbd06ca3ce7ad405d19aa6a", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": n... |
# -*- coding: utf-8 -*-
"""RegressionTorchModel Base class for model with no cell specific parameters"""
import matplotlib.pyplot as plt
# +
import numpy as np
import pandas as pd
from cell2location.models.base.torch_model import TorchModel
class RegressionTorchModel(TorchModel):
r"""Base class for regression ... | {"hexsha": "863b6d52d93164d18b9b68bea616aaad12fb1a5b", "size": 8540, "ext": "py", "lang": "Python", "max_stars_repo_path": "cell2location/models/base/regression_torch_model.py", "max_stars_repo_name": "nadavyayon/cell2location", "max_stars_repo_head_hexsha": "54141fb85d4b0d64825dfdb6d1bf147b025c856b", "max_stars_repo_l... |
"""Read, Write, and Convert between different word vector serialization formats."""
__version__ = "4.0.0"
from typing import Dict, Tuple
from enum import Enum
import numpy as np
#: A mapping of word to integer index. This index is used pull the this words
#: vector from the matrix of word vectors.
Vocab = Dict[str,... | {"hexsha": "e1cc2a2ad53d37b074b6a0c162feb68c71caaa9f", "size": 4012, "ext": "py", "lang": "Python", "max_stars_repo_path": "word_vectors/__init__.py", "max_stars_repo_name": "blester125/word-vectors", "max_stars_repo_head_hexsha": "4f6d8b2b6d8b87fad453a37000c6d0d236a6cb96", "max_stars_repo_licenses": ["MIT"], "max_star... |
import cv2
import numpy as np
import pytesseract
from PIL import Image
# Path of working folder on Disk
src_path = "Gamer"
def get_string(img_path):
# Read image with opencv
img = cv2.imread(img_path)
# Convert to gray
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply dilation a... | {"hexsha": "e2380b79d726a6eb3da7035140e365d620688224", "size": 1187, "ext": "py", "lang": "Python", "max_stars_repo_path": "untitled0.py", "max_stars_repo_name": "Vrittik/PyPiTess", "max_stars_repo_head_hexsha": "498e3a97f5148f6c85344a68d058e51465fa49ef", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nu... |
program ut_dyn_ipert
use m_dyn, only: dyn_init
use m_dyn, only: dyn_vect
use m_dyn, only: dyn_get
use m_dyn, only: dyn_put
use m_dyn, only: dyn_clean
use m_set_eta, only: set_eta
use m_mapz_pert, only: mapz_pert_set
use m_mapz_pert, only: mapz_pert_interp
implicit none
type(dyn_vect) :: xpi ! input vector
type(dyn_ve... | {"hexsha": "c96afa967972268067fe0df0018409678560e783", "size": 1610, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Shared/GMAO_hermes/ut_dyn_ipert.f90", "max_stars_repo_name": "joeylamcy/gchp", "max_stars_repo_head_hexsha": "0e1676300fc91000ecb43539cabf1f342d718fb3", "max_stars_repo_licenses": ["NCSA", "Apac... |
module mod_settings
use iso_fortran_env, only: wp=>real64
implicit none
private
public :: t_settings
type t_settings
integer :: length = 0
integer :: width = 0
end type t_settings
end module mod_settings
| {"hexsha": "6d8d463d303617074af81e5a0171ba88d19da3da", "size": 275, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/settings/mod_settings.f90", "max_stars_repo_name": "cbcoutinho/learn_dg", "max_stars_repo_head_hexsha": "b22bf91d1a0daedb6b48590c7361c3a9c3c7f371", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
[STATEMENT]
lemma gen_in_free_hull: "x \<in> G \<Longrightarrow> x \<in> \<langle>\<BB>\<^sub>F G\<rangle>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> G \<Longrightarrow> x \<in> \<langle>\<BB>\<^sub>F G\<rangle>
[PROOF STEP]
using free_hull.free_gen_in[folded basis_gen_hull_free]
[PROOF STATE]
proof (pr... | {"llama_tokens": 212, "file": "Combinatorics_Words_Submonoids", "length": 2} |
SUBROUTINE INVP2 (*)
C
C INVP2 INITIALIZES THEN CALLS EITHER SDCOMP OR DECOMP DEPENDING ON
C THE OPTION SELECTED ON THE EIGR CARD
C
INTEGER FILEA ,FILEL ,FILEU ,SCR1 ,
1 SCR2 ,SCR3 ,SCR4 ,SCR5 ,
2 SR1FIL ... | {"hexsha": "3e042b8778516f409dab2dd4b092fae77d3df84c", "size": 2611, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/invp2.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": 1... |
# Autogenerated wrapper script for Exodus_jll for x86_64-linux-gnu
export libexodus
using Zlib_jll
using NetCDF_jll
using HDF5_jll
JLLWrappers.@generate_wrapper_header("Exodus")
JLLWrappers.@declare_library_product(libexodus, "libexodus.so.2")
function __init__()
JLLWrappers.@generate_init_header(Zlib_jll, NetCDF_... | {"hexsha": "54e65572a2a12d5bcd438ea1f8958e85a3758115", "size": 521, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/x86_64-linux-gnu.jl", "max_stars_repo_name": "JuliaBinaryWrappers/Exodus_jll.jl", "max_stars_repo_head_hexsha": "738e0ef23ee095ae1d0818453088caf9a2151f3d", "max_stars_repo_licenses": ["... |
import os
import cv2
import time
import numpy as np
import pyautogui
import matplotlib.pyplot as plt
from input_feeder import InputFeeder
from mouse_controller import MouseController
from face_detection import Model_fd
from gaze_estimation import Model_ge
from facial_landmarks_detection import Model_fld
from head_pose_... | {"hexsha": "df67b484bc799e1c36c9aac365dd46cca21d5596", "size": 7200, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "ojsindher/Computer-Pointer-Controller-OPENVINO", "max_stars_repo_head_hexsha": "7e6c286d0eb90005cc5fc881439f09776c31755e", "max_stars_repo_licenses": ["MIT"], "ma... |
// Copyright (C) 2012-2016 Internet Systems Consortium, Inc. ("ISC")
//
// This Source Code Form is subject to the terms of the Mozilla Public
// License, v. 2.0. If a copy of the MPL was not distributed with this
// file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <config.h>
#include <cstddef>
#incl... | {"hexsha": "607a395712cd205ed69d8b133100b3c8a6a71468", "size": 36166, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/bin/perfdhcp/tests/command_options_unittest.cc", "max_stars_repo_name": "nchaigne/kea", "max_stars_repo_head_hexsha": "2badfd4d9b4f2420b0e9683db5da16a3ab90dd81", "max_stars_repo_licenses": ["Apa... |
from keras.layers import Dense, Dropout, Conv2D, Flatten
from keras.models import Sequential
from snake import NUM_CHANNELS, NUM_ACTIONS
from collections import deque
import random
import numpy as np
import keras
class DQNAgent:
def __init__(self, field_size, gamma, batch_size, min_replay_memory_size, replay_memo... | {"hexsha": "07fa5552af3800bd3c915959f8293a217bbbd67c", "size": 3259, "ext": "py", "lang": "Python", "max_stars_repo_path": "dqn_agent.py", "max_stars_repo_name": "choyi0521/snake-reinforcement-learning", "max_stars_repo_head_hexsha": "4881dfc163378f615654d85262901480858e5e65", "max_stars_repo_licenses": ["MIT"], "max_s... |
(*
Copyright (C) 2017 M.A.L. Marques
This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
*)
(* type: work_gga_x *)
theta0 := 1.0008:
theta1 := 0.1926:
theta2 := 1.8962:
f0... | {"hexsha": "5c6446a1796123e920f015c157fb5624e6789362", "size": 406, "ext": "mpl", "lang": "Maple", "max_stars_repo_path": "libxc-4.2.3/maple/gga_x_bayesian.mpl", "max_stars_repo_name": "rdietric/lsms", "max_stars_repo_head_hexsha": "8d0d5f01186abf9a1cc54db3f97f9934b422cf92", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
[STATEMENT]
lemma fv_assignment_rhs_subset_fv_st'[simp]: "fv\<^sub>s\<^sub>e\<^sub>t (assignment_rhs\<^sub>s\<^sub>t S) \<subseteq> fv\<^sub>s\<^sub>t S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fv\<^sub>s\<^sub>e\<^sub>t (assignment_rhs\<^sub>s\<^sub>t S) \<subseteq> fv\<^sub>s\<^sub>t S
[PROOF STEP]
by (indu... | {"llama_tokens": 172, "file": "Stateful_Protocol_Composition_and_Typing_Strands_and_Constraints", "length": 1} |
#ifndef CANARD_NET_OFP_DETAIL_ANY_TYPE_HPP
#define CANARD_NET_OFP_DETAIL_ANY_TYPE_HPP
#include <canard/net/ofp/detail/config.hpp>
#include <cstddef>
#include <cstdint>
#include <memory>
#include <type_traits>
#include <utility>
#include <boost/mpl/contains.hpp>
#include <boost/mpl/deref.hpp>
#include <boost/mpl/integ... | {"hexsha": "9f1381f82eced5938e86577444c07c94a7374b3a", "size": 7850, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/canard/net/ofp/detail/any_type.hpp", "max_stars_repo_name": "amedama41/bulb", "max_stars_repo_head_hexsha": "2e9fd8a8c35cfc2be2ecf5f747f83cf36ffbbdbb", "max_stars_repo_licenses": ["BSL-1.0"]... |
from __future__ import print_function
from sklearn.feature_extraction.text import CountVectorizer
import argparse
import logging
from time import time
import numpy as np
import codecs
from gensim import corpora, matutils
from gensim.models import TfidfModel, LsiModel
import os
import ntpath
from pathlib import Path
fr... | {"hexsha": "3f33201df68c6aa4eb1eefea2e388fb52a64ff40", "size": 18061, "ext": "py", "lang": "Python", "max_stars_repo_path": "word2igf.py", "max_stars_repo_name": "iarroyof/discrimative_attributes", "max_stars_repo_head_hexsha": "1f18eddd5f114f45704d96955199ba686098d2e6", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
Require ClassicalEpsilon.
Require Import Reals Psatz.
From stdpp Require Import tactics.
From mathcomp Require Import ssrfun ssreflect eqtype ssrbool seq fintype choice bigop.
From discprob.basic Require Import base sval order monad bigop_ext nify.
From discprob.prob Require Import prob countable finite stochastic_orde... | {"author": "jtassarotti", "repo": "coq-proba", "sha": "11d69b2286940ff532421252a7d9b1384c2f674a", "save_path": "github-repos/coq/jtassarotti-coq-proba", "path": "github-repos/coq/jtassarotti-coq-proba/coq-proba-11d69b2286940ff532421252a7d9b1384c2f674a/theories/monad/idxval/irrel_equiv.v"} |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 3 21:15:20 2020
@author: lukemcculloch
"""
import os
import weakref
try:
from memory_profiler import profile
MEM_PROFILE = True
except:
print 'please install memory_profiler'
MEM_PROFILE = False
#
import numpy as np
import matplotli... | {"hexsha": "2f25693e4278f71dafa76fac5488a105b1780180", "size": 52973, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Solvers.py", "max_stars_repo_name": "LukeMcCulloch/PyCFD", "max_stars_repo_head_hexsha": "6720e6575e25f8c274ef591d6c215de90a740935", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
from zoopt import Objective
from zoopt import Parameter
from zoopt import Dimension
from zoopt import Solution
import numpy as np
def ackley(solution):
"""
Ackley function for continuous optimization
"""
x = solution.get_x()
bias = 0.2
ave_seq = sum([(i - bias) * (i - bias) for i in x]) / len(... | {"hexsha": "ea7ff81f8cb60d89c03666e17d4fb78bb8f61527", "size": 1566, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_objective.py", "max_stars_repo_name": "HowardHu97/ZOOpt", "max_stars_repo_head_hexsha": "01568e8e6b0e65ac310d362af2da5245ac375e53", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
def read_file(f):
best_result = (-1, -1, [])
ponctuations = []
with open(f, 'r') as f:
for line in f.readlines():
line = line.split(',')
ponct = float(line[0])
ponctuations.... | {"hexsha": "1ed9b2cd2eebd5dcca1071e753c9330b946c3fc0", "size": 3296, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotter/plot.py", "max_stars_repo_name": "vitornl/pokemongo-raid", "max_stars_repo_head_hexsha": "67a35de0c67c04a0dca78a8767db4f9da8769e51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import re
import sys
sys.path.append('..')
import numpy as np
import scipy.special
import matplotlib.pyplot as plt
import matplotlib.colors
import palettable
import pandas as pd
import glob
import os.path
from lib import *
from lib.analytical import *
from lib.fitting import *
def growthlaw(T, d, t0, gamma):
retu... | {"hexsha": "fcecc28f19db26347d00dc4b19b4eeb85b28f91f", "size": 1744, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/model_growth_and_fluctuations/run_sampling.py", "max_stars_repo_name": "andim/paper-tcellimprint", "max_stars_repo_head_hexsha": "e89605e51014fa3f347f96bab3d3d84c2b013a2f", "max_stars_repo_li... |
import pytest
import numpy as np
import toppra
import toppra.constraint as constraint
@pytest.fixture(params=[(0, 0)])
def vel_accel_robustaccel(request):
"Velocity + Acceleration + Robust Acceleration constraint"
dtype_a, dtype_ra = request.param
vlims = np.array([[-1, 1], [-1, 2], [-1, 4]], dtype=float... | {"hexsha": "0d969f5f1fe9e35fc1bc366b139381680ea5a617", "size": 815, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/tests/solverwrapper/conftest.py", "max_stars_repo_name": "stevegolton/toppra", "max_stars_repo_head_hexsha": "846e2a7f5b87e0e1884b244b07d5fd661edcd9bd", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# indexhandlers.py - Waqas Bhatti (wbhatti@astro.princeton.edu) - Apr 2018
'''
These are Tornado handlers for the AJAX actions.
'''
####################
## SYSTEM IMPORTS ##
####################
import logging
import json
from datetime import datetime
import numpy as n... | {"hexsha": "e289431808d23770b07d69540d3a8581c73ba23c", "size": 19567, "ext": "py", "lang": "Python", "max_stars_repo_path": "vizinspect/frontend/actionhandlers.py", "max_stars_repo_name": "johnnygreco/viz-inspect", "max_stars_repo_head_hexsha": "3fc24e00062e28ccbc5fea70c20ed76d380a4e16", "max_stars_repo_licenses": ["MI... |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 7 13:26:06 2017
@author: nblago
"""
from __future__ import print_function
import datetime
from astropy.io import votable
import numpy as np
import os
import logging
import warnings
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.tab... | {"hexsha": "3c268e4f8083266094f8b9d84282315b5a5ef55c", "size": 11999, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/photometry/QueryCatalogue.py", "max_stars_repo_name": "nblago/utils", "max_stars_repo_head_hexsha": "862a34eb9820474d1071e5ac2eec58d66d297649", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# --------------------------------------------------------------------------
# ACE1.jl: Julia implementation of the Atomic Cluster Expansion
# Copyright (c) 2019 Christoph Ortner <christophortner0@gmail.com>
# Licensed under ASL - see ASL.md for terms and conditions.
# -------------------------------------------------... | {"hexsha": "ea20acb890681784e6783b208b8b3ec5585cbdd2", "size": 1720, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/prototypes.jl", "max_stars_repo_name": "casv2/ACE1.jl", "max_stars_repo_head_hexsha": "40d7cdfc8141193aeea4ee666b8cedb746928489", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_count": null,... |
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