text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import ase
import numpy as np
from .helpers import *
from toolz.curried import curry, pipe
@curry
def get_scaled_positions(coords, cell, pbc, wrap=True):
"""Get positions relative to unit cell.
If wrap is True, atoms outside the unit cell will be wrapped into
the cell in those directions with periodic bou... | {"hexsha": "8350cc9d8dcee5bb501d42432559bda3d3ded7d8", "size": 3489, "ext": "py", "lang": "Python", "max_stars_repo_path": "poremks/grid_generator.py", "max_stars_repo_name": "auag92/atomMKS", "max_stars_repo_head_hexsha": "60f957379b4acdeb64b7ff5c4e82c49e445a4c28", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
program i
integer, dimension(2, 2) :: array
array(1, 1) = 1 + 1
array(1, 2) = 1 * 2
array(2, 1) = 1 ** 2
array(2, 2) = array(1, 1) + array(1, 2) * array(2, 1)
end program i
| {"hexsha": "631aaf248d6ce1ac1fbd601109fc3f74057ccd99", "size": 186, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/expr/arrays/expr_array2d.f90", "max_stars_repo_name": "clementval/fc", "max_stars_repo_head_hexsha": "a5b444963c1b46e4eb34d938d992836d718010f7", "max_stars_repo_licenses": ["BSD-2-Clause"], ... |
import numpy as np
def getSamples( f, n, xmin=0, dx=1.0, fddx=0.1 ):
ddx=dx*fddx
xs = np.arange( xmin-dx, xmin+dx*(n+2)+1e-8, dx )
xs[0 ]=xs[ 1]+ddx
xs[-1]=xs[-2]-ddx
#print xs
ys = f(xs)
dy0 = (ys[0 ] - ys[1 ])/ddx
dy1 = (ys[-2] - ys[-1])/ddx
ys[ 0] = ys[2 ] - dy0*2*dx
ys[-1]... | {"hexsha": "44811a6e1ce666c556afa65338e50b45d31d15ad", "size": 1355, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/pyGaussAtom/splines.py", "max_stars_repo_name": "ProkopHapala/SimpleSimulationEngine", "max_stars_repo_head_hexsha": "240f9b7e85b3a6eda7a27dc15fe3f7b8c08774c5", "max_stars_repo_licenses": [... |
-makelib ies/xil_defaultlib -sv \
"C:/Xilinx/Vivado/2016.2/data/ip/xpm/xpm_cdc/hdl/xpm_cdc.sv" \
-endlib
-makelib ies/xpm \
"C:/Xilinx/Vivado/2016.2/data/ip/xpm/xpm_VCOMP.vhd" \
-endlib
-makelib ies/xil_defaultlib \
"../../../../Pipeline.srcs/sources_1/ip/clk_div/clk_div_clk_wiz.v" \
"../../../../Pipeline.srcs/... | {"hexsha": "5458b40cb53a6a106f299a063ca162e36eb13d00", "size": 410, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Pipeline/Pipeline.ip_user_files/sim_scripts/clk_div/ies/run.f", "max_stars_repo_name": "HandsomeBrotherShuaiLi/pipelineCPU", "max_stars_repo_head_hexsha": "06789d4e396895f1be33bfe96abd756eede74da5"... |
[STATEMENT]
lemma seq_msg_ok:
"ptoy i \<TTurnstile>\<^sub>A onll \<Gamma>\<^sub>T\<^sub>O\<^sub>Y (\<lambda>((\<xi>, _), a, _).
anycast (\<lambda>m. case m of Pkt num' sid' \<Rightarrow> num' = no \<xi> \<and> sid' = i | _ \<Rightarrow> True) a)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ptoy ... | {"llama_tokens": 257, "file": "AWN_Toy", "length": 1} |
import platform
import pyrealsense2 as rs # for realsense api
import numpy as np
import sys
import time
from enum import IntEnum
from pyaidoop.camera.camera_dev_abc import CameraDev
class RealSensePreset(IntEnum):
Custom = 0
Default = 1
Hand = 2
HighAccuracy = 3
HighDensity = 4
MediumDensity... | {"hexsha": "5425d3e147341eee55c374ceadf1f1239701694e", "size": 7388, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyaidoop/camera/camera_dev_realsense.py", "max_stars_repo_name": "aidoop/pyaidoop", "max_stars_repo_head_hexsha": "fa42d0010b95b6641227033700f7dc31844cbe3b", "max_stars_repo_licenses": ["MIT"], "m... |
c=======================================================================
c
c SIMSKEW
c
c This function implements the simulator for the allocation with
c skewness.
c
c-----------------------------------------------------------------------
subroutine simskew ( indic, sime... | {"hexsha": "ca99927bdf38afc2673d912b8c91e4eea41a0136", "size": 5372, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/math/optim_portfolio/simskew.f", "max_stars_repo_name": "alpgodev/numx", "max_stars_repo_head_hexsha": "d2d3a0713020e1b83b65d3a661c3801ea256aff9", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
% Master file
% Documentclass
\def\style{0}
\if\style0 % My own publication template
\documentclass[9pt, twocolumn, lineno]{templates/pi/pi-article}
\fi\if\style1 % Elsevier publications
\documentclass[12pt, preprint]{elsarticle}
\fi\if\style2 % RSC publications
\documentclass[9pt, twoside, twocolumn]{... | {"hexsha": "139635d9466944b6bc241b1e894114c81751fd47", "size": 1704, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "manuscript.tex", "max_stars_repo_name": "pauliacomi/paper-2021-d4-sorption", "max_stars_repo_head_hexsha": "52abbc1e84f1314afb58239954a9ee205422e99a", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
! { dg-do compile }
! { dg-options "-std=f2003" }
!
! PR fortran/33197
!
! Check for Fortran 2008's ATAN(Y,X) - which is equivalent
! to Fortran 77's ATAN2(Y,X).
!
real(4) :: r4
real(8) :: r8
complex(4) :: c4
complex(8) :: c8
r4 = atan2(r4,r4)
r8 = atan2(r8,r8)
r4 = atan(r4,r4) ! { dg-error "Too many arguments ... | {"hexsha": "407e83a701d227a3b667024e2645e9fa0b09a6a9", "size": 827, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/atan2_2.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_licenses... |
#!/usr/bin/env python3
import sys
import re
import json
import math
import argparse
import logging
from itertools import count
import random
from datetime import datetime, timedelta, MINYEAR, MAXYEAR
import pathlib
import pytz
from yattag import Doc
import gpxpy
from aerofiles.igc.reader import Reader
from pykml import... | {"hexsha": "601528a2c77e99085c9957de9567947670f70e64", "size": 70928, "ext": "py", "lang": "Python", "max_stars_repo_path": "genczml.py", "max_stars_repo_name": "mhaberler/gpx2czml", "max_stars_repo_head_hexsha": "9cb657652c941ac7d01d2405bef7e794c4531df0", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count":... |
import multiprocessing as mp
import numpy as np
import subprocess
import concurrent.futures
import os
from integrations import long_integration, short_integration, check_resonance_make_plots, kozai_integration, check_kozai_make_plots
# make function needed for multiprocessing
def do_integration(intN):
""" when r... | {"hexsha": "683da9cede694fe6e1714759cb9607dc7736a2b8", "size": 3873, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/migrate.py", "max_stars_repo_name": "sbalaji718/KBR", "max_stars_repo_head_hexsha": "143c886f7740f4cc44c54e8657098d1381f2d66d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
!++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++!
! Futility Development Group !
! All rights reserved. !
! ... | {"hexsha": "6c9f8a0bedae9f156fc5f711a4ea58f333881326", "size": 7818, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/Interpolators.f90", "max_stars_repo_name": "picmc/Futility", "max_stars_repo_head_hexsha": "158950c2c3aceffedf547ed4ea777e023035ca6e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
import sys
sys.path.append("../")
import numpy as np
import pandas as pd
import networkx as nx
import pickle
# katz centrlaity: computes the relative influence of a node by measuring the number of
# immediate neighbors (first degree nodes) and also all other nodes that
# connect to the node under consideration thro... | {"hexsha": "166259672ebd63fedae21c5371818ad599a4cfc9", "size": 3106, "ext": "py", "lang": "Python", "max_stars_repo_path": "static_network.py", "max_stars_repo_name": "ellymeng/Disease-Transmission-Analysis-Toolkit", "max_stars_repo_head_hexsha": "0785db949acba28bb7bcc675e0340006faf4a481", "max_stars_repo_licenses": ["... |
"""
Tests for callbacks.
"""
import os
import unittest
from shutil import rmtree
import numpy as np
import pytest
from gpso import GPSOptimiser, ParameterSpace
from gpso.callbacks import (
GPFlowCheckpoints,
PostIterationPlotting,
PostUpdateLogging,
PreFinaliseSave,
)
from gpso.optimisation import Cal... | {"hexsha": "7fc39cf3cb2b095fbd28de828fe17160c6b8df70", "size": 5290, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_callbacks.py", "max_stars_repo_name": "jajcayn/pygpso", "max_stars_repo_head_hexsha": "3e769067904a0ed19e77327876386689ae3f8cab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
from numpy import sqrt
from numpy.random import shuffle
from ArmMovementPredictionStudien.PREPROCESSING.smooth import smooth_data_utils
import matplotlib.pyplot as plt
from ArmMovementPredictionStudien.PREPROCESSING.utils.velocity import calculate_velocity_vector_for_dataset_filename
def generate_smooth... | {"hexsha": "f43152f3d5ce0bde8af404580a5434a9d771a9a6", "size": 3809, "ext": "py", "lang": "Python", "max_stars_repo_path": "PREPROCESSING/smooth/check_different_window_sizes.py", "max_stars_repo_name": "MobMonRob/ArmMovementPredictionStudien", "max_stars_repo_head_hexsha": "7086f7b044d54b023c7d40e9413c35178a1ad084", "m... |
import numpy as np
import logging
import config
from utils import setup_logger
import loggers as lg
class Node():
def __init__(self, state):
self.state = state
self.playerTurn = state.playerTurn
self.id = state.id
self.edges = []
def isLeaf(self):
if len(self.edges) > 0:
return False
else:
retur... | {"hexsha": "602db14313700e328ee0cdd99c58aa127dfc9452", "size": 3177, "ext": "py", "lang": "Python", "max_stars_repo_path": "DeepReinforcementLearning/MCTS.py", "max_stars_repo_name": "Christoper-Harvey/1st-Capstone", "max_stars_repo_head_hexsha": "93630a4d5f4a2d939c8b5f74f11b5b33052e3f72", "max_stars_repo_licenses": ["... |
import cv2 as cv
import numpy as np
import time
def surf_image_match(left_image, right_image):
gray_left_image = cv.cvtColor(left_image, cv.COLOR_BGR2GRAY)
gray_right_image = cv.cvtColor(right_image, cv.COLOR_BGR2GRAY)
gpu_gray_left_image = cv.cuda_GpuMat(gray_left_image)
gpu_gray_right_image = cv.cud... | {"hexsha": "692a22ce6a6e3b4aca0464defe9e20e53fb9ebbe", "size": 8806, "ext": "py", "lang": "Python", "max_stars_repo_path": "SURF/surf.py", "max_stars_repo_name": "Oumourin/Moving-object-tracking-recognition-and-ranging-in-multi-channel-video", "max_stars_repo_head_hexsha": "201a6b260581f2e38ed4b4c348d6f4506eee549b", "m... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as md
import datetime as dt
from matplotlib.ticker import FormatStrFormatter
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
from matplotlib.axes import Axes
from cartopy.m... | {"hexsha": "2f240641f2506cf81d2dd19258ce6d9d9a2b24fb", "size": 18475, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyxlma/plot/xlma.py", "max_stars_repo_name": "Vsalinas91/xlma-python", "max_stars_repo_head_hexsha": "bb95c11954e5b815ad3ee2b63707589a829e4566", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# -*- coding: utf-8 -*-
# @Time : 2020/3/20 9:28
# @Author : kanmendashu2020
# @File : news_spider_init.py
# @Software: PyCharm
# @Description:
# 全部代码的视频：
# 新闻爬虫系列：
# 1、https://www.bilibili.com/video/BV15E411P7ey?p=1
#
# 2、https://mp.weixin.qq.com/s/DZb0lw391xkV2tCovCLaPQ
#
# 3、https://mp.weixin.qq.com/s/7tInLyxpuj5iII... | {"hexsha": "45c2a774e86ca4cd1157eba3c21869d731b2b7be", "size": 1820, "ext": "py", "lang": "Python", "max_stars_repo_path": "B\u7ad9/Python\u722c\u866b\u6848\u4f8b\u5b9e\u6218\uff082020 \u00b7 \u5468\u66f4\uff09/n1-l-\u722c\u53d6\u65b0\u95fb\u7f51\u7ad9\u5e76\u4fdd\u5b58\u5230Excel\u4e2d/news_spider_init.py", "max_stars... |
import numpy as np
from .optimization_problem import OptimizationProblem
class Rosenbrock(OptimizationProblem):
"""Rosenbrock function
.. math::
f(x_1,\\ldots,x_n) = \\sum_{j=1}^{n-1} \
\\left( 100(x_j^2-x_{j+1})^2 + (1-x_j)^2 \\right)
subject to
.. math::
-2.048 \\leq x_i ... | {"hexsha": "e11353ab36d210023204064c7dc84af303f08a9a", "size": 1434, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySOT/optimization_problems/rosenbrock.py", "max_stars_repo_name": "WY-Wang/pySOT", "max_stars_repo_head_hexsha": "a2de59a0ab00da0465b964e070199fe4773918cd", "max_stars_repo_licenses": ["BSD-3-Cla... |
import numpy as np
import hashlib
import logging
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
mpi_rank = comm.Get_rank()
mpi_size = comm.Get_size()
barrier = comm.barrier
except ImportError:
mpi_rank = 0
mpi_size = 1
barrier = lambda: None
MPI_fail_params_nonuniform = True # ... | {"hexsha": "600a503456bb83eead5898e4abece00f10a2fba0", "size": 3958, "ext": "py", "lang": "Python", "max_stars_repo_path": "bmtk/builder/builder_utils.py", "max_stars_repo_name": "mjhyman/bmtk", "max_stars_repo_head_hexsha": "42dcce944fe8ff8cab02b19d2d983f73a8cbc0d1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
Debats du Senat (hansard)
1ere Session, 36 e Legislature,
Volume 137, Numero 142
Le mardi 1 er juin 1999
L'honorable Gildas L. Molgat, President
Remise d'un doctorat honorifique de l'Universite de Montreal
Les effets negatifs des accords de libre-echange
Avis de motion d'affirmation et de resolution appuyant leu... | {"hexsha": "52b106e4d1f8a4dc33436018507e6edcfd2ebad3", "size": 26011, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "data/Hansard/Training/hansard.36.1.senate.debates.1999-06-01.142.f", "max_stars_repo_name": "j1ai/Canadian_Hansards_Neural_Machine_Translation", "max_stars_repo_head_hexsha": "554666a89090fc1b1d1... |
"""
Copyright (c) 2021 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": "e06f09bb38d33a24b7faa87bb91cf625378b6b43", "size": 17798, "ext": "py", "lang": "Python", "max_stars_repo_path": "nncf/torch/quantization/metrics.py", "max_stars_repo_name": "evgeniya-egupova/nncf", "max_stars_repo_head_hexsha": "39a3c5b2e5cc7d33723154d2e622d4d7882a99a4", "max_stars_repo_licenses": ["Apache-... |
import math
import multiprocessing
import os
import random
import re
import sys
import time
import traceback
from itertools import chain
from multiprocessing import Process, Manager
from shutil import copyfile
import numpy as np
from goprime import Constants
from goprime.board import Position
from goprime.elo import ... | {"hexsha": "99ab7b8b14f2201508a9290cdf1c75d4a68eb2f8", "size": 26125, "ext": "py", "lang": "Python", "max_stars_repo_path": "goprime/game.py", "max_stars_repo_name": "BenisonSam/goprime", "max_stars_repo_head_hexsha": "3613f643ee765b4ad48ebdc27bd9f1121b1c5298", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
theory ComputeHOL
imports Complex_Main "Compute_Oracle/Compute_Oracle"
begin
lemma Trueprop_eq_eq: "Trueprop X == (X == True)" by (simp add: atomize_eq)
lemma meta_eq_trivial: "x == y \<Longrightarrow> x == y" by simp
lemma meta_eq_imp_eq: "x == y \<Longrightarrow> x = y" by auto
lemma eq_trivial: "x = y \<Longrightar... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Matrix_LP/ComputeHOL.thy"} |
import cv2
import numpy as np
import time
import requests
from utils import helpers
import matplotlib.pylab as plt
class config(object):
def __init__(self):
self.width = 352 # 图片宽
self.height = 288 #图片高
self.color = (255,240,0) # 掩模颜色
self.url = "http://127.0.0.1:8500" #请求url 端口8500
self.label... | {"hexsha": "2896d4f89e49bf725032ad32a180789715fd1575", "size": 2960, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo.py", "max_stars_repo_name": "YifengChen94/Track-limit-recognition", "max_stars_repo_head_hexsha": "0be8cc1f3ef34703d3252f8e569d49332d339adf", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#!/usr/bin/env python3
import numpy as np
import json
import os
import sys
import scipy.io as sio
import wfdb
"""
Written by: Xingyao Wang, Chengyu Liu
School of Instrument Science and Engineering
Southeast University, China
chengyu@seu.edu.cn
"""
R = np.array([[1, -1, -.5], ... | {"hexsha": "37f968aa3679a4db135f400fde59bb7cf033c51d", "size": 6663, "ext": "py", "lang": "Python", "max_stars_repo_path": "score_2021.py", "max_stars_repo_name": "syxiaobai/AF-NEU", "max_stars_repo_head_hexsha": "0cf19f50ce3af53c75bf28564ca74d78fdaf7309", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
Users/WilliamLewis has 500 stickers. Leave a message here if you want one.
Also, are there good places to leave any? Places to give them out?
20100103 17:49:00 nbsp Id like two. I also have a few more Wiki Button Wiki Buttons. Users/JasonAller
20100103 19:07:07 nbsp Maybe a few could be placed next to where copie... | {"hexsha": "e9b149cd30b11c5f7af47e7760fe92483537840c", "size": 1287, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Requests.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
'''
Author: Dr. Mohamed A. Bouhlel <mbouhlel@umich>
Dr. John T. Hwang <hwangjt@umich.edu>
This package is distributed under New BSD license.
'''
from __future__ import print_function, division
import numpy as np
from scipy import linalg
from smt.utils import compute_rms_error
from smt.problems import Sphere,... | {"hexsha": "b2894d475ace6037062550f2fd6d9af359901958", "size": 19674, "ext": "py", "lang": "Python", "max_stars_repo_path": "smt/examples/run_examples.py", "max_stars_repo_name": "meliani09/smt", "max_stars_repo_head_hexsha": "af729143be09b012257bf81dcd3e2c8c40f65c96", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
import os
import json
import html
import re
from collections import OrderedDict
from collections import Counter
from string import punctuation
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader
from hparams import hps_data
def read_data(dataset, max_count=hps_... | {"hexsha": "030d07899a476dec51a1e28d9dcac1d777f78627", "size": 6667, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/preprocess.py", "max_stars_repo_name": "eugli/news-engagement-prediction", "max_stars_repo_head_hexsha": "192eb166397e1576577a97babfeebae353344123", "max_stars_repo_licenses": ["MIT"], "max_st... |
import pandas as pd
import numpy as np
import os
claim_path = '.\\After_Process\\Claim\\'
personal_path = '.\\After_Process\\personal_process\\'
output_path = '.\\After_Process\\monthly_process\\'
multiply = 1000
'''
Create File
'''
create_path = ''
for i in output_path.split('\\'):
if i == '.':
create_path += ... | {"hexsha": "44985b3773ae203ce155dd530c80d186db8b8dd8", "size": 2197, "ext": "py", "lang": "Python", "max_stars_repo_path": "monthly_process.py", "max_stars_repo_name": "jui-sheng/Insurance_Reclassifier", "max_stars_repo_head_hexsha": "06c61e3da1af7ec2ac569bfe3d43574ee4611a1a", "max_stars_repo_licenses": ["MIT"], "max_s... |
include("rigidbody.jl")
using Statistics
mass1 = 1.0 # kg
force1 = 4.0 # Newton
Δt = 0.1
body = RigidBody(mass1, force1)
ts = 0:Δt:200
# calculate the approximation using the integration
approx = Float64[]
for t in ts
integrate!(body, Δt)
push!(approx, body.position[1])
end
# analytic calculation using the... | {"hexsha": "c4c1eefe0ca1e5fba4d732786cd479e8c15df357", "size": 1122, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "rigidbodytest.jl", "max_stars_repo_name": "FourMInfo/JuliaforBeginners", "max_stars_repo_head_hexsha": "4908cd82c777cc9934056bbada36bccd67158906", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
SUBROUTINE RKQC(Y,DYDX,N,X,HTRY,EPS,YSCAL,HDID,HNEXT,DERIVS)
PARAMETER (NMAX=10,FCOR=.0666666667,
* ONE=1.,SAFETY=0.9,ERRCON=6.E-4)
EXTERNAL DERIVS
DIMENSION Y(N),DYDX(N),YSCAL(N),YTEMP(NMAX),YSAV(NMAX),DYSAV(NMAX)
PGROW=-0.20
PSHRNK=-0.25
XSAV=X
DO 11 I=1,N
... | {"hexsha": "d1a5058ac374160d5fc8402f43f67f7bfcbf133a", "size": 1152, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "Math3/NumRec/source/rkqc.for", "max_stars_repo_name": "domijin/MM3", "max_stars_repo_head_hexsha": "cf696d0cf26ea8e8e24c86287cf8856cab7eaf77", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import chess
import chess.pgn
import random
def choosePositions(positions, moves, nExcludeStarting=5, nPositions=10):
"""
Returns positions that will be used in our model
Inputs:
positions: List of all chessboard positions of a game in... | {"hexsha": "3fdb5dc638729bee8dae7c5119c01540692913d2", "size": 4429, "ext": "py", "lang": "Python", "max_stars_repo_path": "pgn2bitboard/main.py", "max_stars_repo_name": "anirudhganwal06/pgn2bitboard", "max_stars_repo_head_hexsha": "9a1218bb664ee33d884f52ce9f840d6a7e20dd93", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#load the datasets library to access the iris data set
library(datasets)
#load and view the iris dataset
data(iris)
head(iris)
#load ggplot 2 for plotting
#install.packages("ggplot2")
library(ggplot2)
#install.packages("GGally")
library(GGally)
ggplot(iris, aes(Petal.Length, Petal.Width, color=Species))... | {"hexsha": "1c70b8f1253f13621cdc41040f23e1a7ee9aa20c", "size": 4359, "ext": "r", "lang": "R", "max_stars_repo_path": "kmeans.r", "max_stars_repo_name": "LizMGagne/Modeling-with-R", "max_stars_repo_head_hexsha": "475da952e359ea5671a0468838134d8e1fcd7ddb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from kutu.logistic_regression import LogisticRegressionNumpy
from sklearn.metrics import r2_score
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metr... | {"hexsha": "9166b0be332fb22056b969fbd8956fe3e76b8c64", "size": 1639, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/logistic_regression.py", "max_stars_repo_name": "bozcani/kutu", "max_stars_repo_head_hexsha": "2bfc8a18ad65baf7ad103e4413211147c75c914c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# us... | {"hexsha": "4658a3f5cc480b146fa9901d56844425944138c4", "size": 6543, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lab 2/utils/display_utils.py", "max_stars_repo_name": "heber-augusto/aws-deepcomposer-samples", "max_stars_repo_head_hexsha": "f0be9809f8a7e663c3cb0e2a18510d4d931cfd7a", "max_stars_repo_licenses":... |
[STATEMENT]
lemma Or\<^sub>n_dnf:
"finite \<Phi> \<Longrightarrow> dnf (Or\<^sub>n \<Phi>) = Finite_Set.fold (\<lambda>\<phi>. (\<union>) (dnf \<phi>)) {} \<Phi>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite \<Phi> \<Longrightarrow> dnf (Or\<^sub>n \<Phi>) = Finite_Set.fold (\<lambda>\<phi>. (\<union>) (d... | {"llama_tokens": 1418, "file": "LTL_Disjunctive_Normal_Form", "length": 11} |
#!/usr/bin/env python3
import argparse
from pathlib import Path
import numpy as np
import statsmodels.api as sm
from scipy import stats
from matplotlib import pyplot as plt
parser = argparse.ArgumentParser(
description="Train generalized linear model to get coefficient for each variable."
)
parser.add_argument(
... | {"hexsha": "561e33ce0c5fe3519ceaa13941cef714b7924fe7", "size": 11548, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/few_image_train.py", "max_stars_repo_name": "tommyfuu/flower_map_new", "max_stars_repo_head_hexsha": "6488b118c2d41c41829f83087761342c81d0ef8c", "max_stars_repo_licenses": ["MIT"], "max_s... |
import cv2
import numpy as np
yield_Cascade = cv2.CascadeClassifier('haarCascade.xml')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('Cedez passage.avi', fourcc, 10.0, (640,480))
cap = cv2.VideoCapture(0)
threshold = 150
while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, ... | {"hexsha": "8690123fb5b095180381ecd4a210e7f947606a11", "size": 896, "ext": "py", "lang": "Python", "max_stars_repo_path": "yieldSign.py", "max_stars_repo_name": "IemProg/Road_Panel_Recognition", "max_stars_repo_head_hexsha": "5acd5e1b82ce2d161243a3e92e2449ebe2bde479", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
from sklearn.datasets import load_boston, load_diabetes
from sklearn.linear_model import LinearRegression, Lasso, ElasticNet, TheilSenRegressor, RANSACRegressor, Ridge
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.tree import DecisionTreeRegressor
from s... | {"hexsha": "eef024b94d75fcd78c3f8885e18f517ba4236e29", "size": 5005, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/regressionPipelineCrossValidation.py", "max_stars_repo_name": "Tiziana-I/project-covid-mask-classifier", "max_stars_repo_head_hexsha": "e1619172656f8de92e8faae5dcb7437686f7ca5e", "max_stars_... |
program t
implicit none
! io-control-spec write stmt iostat (with error)
integer::ierr=0
open (95, status='new', file='tmpfile', access='direct', recl=3)
write (95, iostat=ierr) 'hello'
if (ierr .ne. 0) then
print *,'test successful'
endif
close (95,status='delete')
endprogram t
| {"hexsha": "a234b4be21e7a8ffa91c706d08843ca43ee1809f", "size": 302, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/t0292r/t.f90", "max_stars_repo_name": "maddenp/ppp", "max_stars_repo_head_hexsha": "81956c0fc66ff742531817ac9028c4df940cc13e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
"""
Bounded(block, size) <: WrapperBlock
A [`WrapperBlock`](#) for annotating spatial data blocks with
size information for their spatial bounds. As an example,
`Image{2}()` doesn't carry any size information since it
supports variable-size images, but sometimes it can be
useful to have the exact size as informati... | {"hexsha": "a170961b0e3d7e3c595ed573f33c634d8eba8582", "size": 1434, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Vision/blocks/bounded.jl", "max_stars_repo_name": "manikyabard/FastAI.jl", "max_stars_repo_head_hexsha": "01fcfa93a3c8ae0748c3394692651df66f878f43", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import tensorflow as tf
import numpy as np
from tensorflow import keras
import matplotlib.pyplot as plt
fashion_mnist = keras.datasets.fashion_mnist
#--------------> LOAD THE DATA
(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()
#since the range of values are from 0 to 25... | {"hexsha": "e6402da131e8e959f4136eda9046e2f216add910", "size": 2572, "ext": "py", "lang": "Python", "max_stars_repo_path": "Deep Learning/Keras/Learning/Fashion/fashion.py", "max_stars_repo_name": "deepaksing/DeepLearning", "max_stars_repo_head_hexsha": "295ac190d92fc6822f16ac781e955ea594ff4dc4", "max_stars_repo_licens... |
!> @file classTimeIterator.f90
!! @brief time Iterator
!! @detail F03 format
!! @date 2017.2.18
!! @date Last Update
!! @author
MODULE classTimeIterator
IMPLICIT NONE
PRIVATE
TYPE, PUBLIC :: timeIterator
DOUBLE PRECISION, PRIVATE :: tend
DOUBLE PRECISION, PRIVATE :: now
DOUBLE PR... | {"hexsha": "f3bab367d5c02751e4bbc97ba035526281dbfb45", "size": 1898, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "classTimeIterator.f90", "max_stars_repo_name": "computational-sediment-hyd/fakeVOF", "max_stars_repo_head_hexsha": "339c42b4dbc5f7d09d8d098b9bc033616daf34c0", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma reachable_While: "reachable (WHILE b DO c) \<subseteq>
{WHILE b DO c, IF b THEN c ;; WHILE b DO c ELSE SKIP, SKIP} \<union>
(\<lambda>c'. c' ;; WHILE b DO c) ` reachable c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. reachable (WHILE b DO c) \<subseteq> {WHILE b DO c, IF b THEN c;; WHILE b D... | {"llama_tokens": 358, "file": null, "length": 2} |
import numpy as np
import pytest
from nvtabular import Dataset, Workflow, WorkflowNode, dispatch
from nvtabular.graph.schema import Schema
from nvtabular.graph.selector import ColumnSelector
from nvtabular.ops import (
Categorify,
DifferenceLag,
FillMissing,
LambdaOp,
Operator,
Rename,
Targ... | {"hexsha": "b92a34003cb208a14f23b13b05c39b8212a74e2a", "size": 11282, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/workflow/test_workflow_node.py", "max_stars_repo_name": "SimonCW/NVTabular", "max_stars_repo_head_hexsha": "229d6da5cfcf26dece7867ff43a1414b711b07be", "max_stars_repo_licenses": ["Apac... |
from functools import lru_cache
import json
import numpy as np
import pims
import cv2 as cv
import scipy.ndimage
import skimage.draw as skdraw
import skimage.feature as skfeature
import skimage.filters as skfilters
import skimage.measure as skmeasure
import skimage.morphology as skmorph
import skimage.segmentation as s... | {"hexsha": "a3ad4233181aae9df5265c25160d7e05bc69149f", "size": 15581, "ext": "py", "lang": "Python", "max_stars_repo_path": "ant_tracker/tracker/segmenter.py", "max_stars_repo_name": "fd-sturniolo/AntTracker", "max_stars_repo_head_hexsha": "0677ec1757c33aaddd013eb0a65481c3aca25881", "max_stars_repo_licenses": ["MIT"], ... |
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ion()
import cPickle as pkl
import sys
import pdb
import h5py
class VideoPatchDataHandler(object):
def __init__(self,sequence_length=20,batch_size=80,down_sample_rate_=1,dataset_name='train'):
stats = pkl.loa... | {"hexsha": "e5d8147a3d311c6ea8360665dc88fdc3e24317a8", "size": 6853, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/data_handler.py", "max_stars_repo_name": "mjfarooq/Sensor_Array_Prediction", "max_stars_repo_head_hexsha": "42f63e4293ca68e886af813f480d5852c74b58d3", "max_stars_repo_licenses": ["MIT"], "max... |
import json
import pdb
import re
import argparse
from operator import itemgetter
from multiprocessing import Pool
from functools import partial
from copy import deepcopy
import os
import numpy as np
from skmultilearn.problem_transform import LabelPowerset, \
BinaryRelevance#,... | {"hexsha": "d136d008739f0f5bd33974aa1e85085f9dc2e5e2", "size": 10685, "ext": "py", "lang": "Python", "max_stars_repo_path": "plastering/inferencers/scrabble/naive_baseline.py", "max_stars_repo_name": "MingzheWu418/plastering", "max_stars_repo_head_hexsha": "322531e934c3acf2ecc8f520b37a6d255b9959c2", "max_stars_repo_lic... |
#!/usr/bin/env python
import numpy as np
class stats(object):
""" Based on SpectralInfoClass.m
"""
def __init__(self,S=None,w=None,dw=None):
self.M0 = None # [(response units)^2*(rad/s)^0] Zeroth spectral moment
self.M1 = None # [(response units)^2*(rad/s)^... | {"hexsha": "e837ec3327ffd9b19fdaca0de684053d050fdaf9", "size": 2690, "ext": "py", "lang": "Python", "max_stars_repo_path": "WDRT/MLER_toolbox/mler/spectrum.py", "max_stars_repo_name": "zmorrell-sand/WDRT", "max_stars_repo_head_hexsha": "5a28ef268a1ea7431bdba5bc2ac02d03f39ab608", "max_stars_repo_licenses": ["Apache-2.0"... |
import matplotlib.pyplot as plt
import seaborn as sns
from numpy import histogram, interp, round, log
from numpy import max as npmax
sns.axes_style("white")
def InvarianceTestKolSmirn(epsi, y1, y2, band_int, cdf_1, cdf_2, up_band, low_band, pos=None, name='Invariance Test',
bound=(0, 0)):
... | {"hexsha": "3230a1f7578eda71bba759368ae576e7310aa614", "size": 4235, "ext": "py", "lang": "Python", "max_stars_repo_path": "functions_legacy/InvarianceTestKolSmirn.py", "max_stars_repo_name": "dpopadic/arpmRes", "max_stars_repo_head_hexsha": "ddcc4de713b46e3e9dcb77cc08c502ce4df54f76", "max_stars_repo_licenses": ["MIT"]... |
#
# Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons, Josef Kircher
# Licensed under the MIT license. See LICENSE file in the project root for details.
#
###############
# Prepare FMU #
###############
cd(dirname(@__FILE__))
pathToFMU = joinpath(pwd(), "../model/IO.fmu")
myFMU = fmiLoad(pathToFMU)
##############... | {"hexsha": "a899e79a5baf08aca793111cd4571a50e2900ad4", "size": 1928, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/getterSetterTest_fmu.jl", "max_stars_repo_name": "AnHeuermann/FMI.jl", "max_stars_repo_head_hexsha": "f7814f830d38119e4181162be1f5b5fa63c6d4a0", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy
from typing import List, Tuple
def ngrams(sequence, n: int):
"""
:return: The ngrams of the message in order
"""
assert isinstance(n, int)
mlen = len(sequence)
ngramlist = ( sequence[start:end] for start, end in
zip( range(mlen - n + 1),
range(n, ml... | {"hexsha": "22abe5d66969d4ad5177da4e9356e42d5f0fc6f3", "size": 5528, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/nemere/utils/baseAlgorithms.py", "max_stars_repo_name": "jaredchandler/nemesys", "max_stars_repo_head_hexsha": "9f8d87ac545748766e58495cd7c2f447ae2bd225", "max_stars_repo_licenses": ["MIT"], "... |
# -*- coding: utf-8 -*-
"""
Created on Sat May 22 00:39:09 2021
@author: whong
"""
import pathlib
import tensorflow as tf
import numpy as np
import tqdm
np.set_printoptions(threshold=5)
def AISdata_train():
print ("1")
data_root=r"/content/AISda... | {"hexsha": "1875b96930688a32c21e9b7664b95a29c8f3976e", "size": 2194, "ext": "py", "lang": "Python", "max_stars_repo_path": "inputdata.py", "max_stars_repo_name": "whongfeiHK/AIS-curve-prediction-Deep-Learning", "max_stars_repo_head_hexsha": "b1f321c6fa6fbd2c854e7d9d16953ca49519bc1a", "max_stars_repo_licenses": ["Apache... |
using NamedDims
using NamedDims: names
using SparseArrays
using Test
@testset "get the parent array that was wrapped" begin
orig = [1 2; 3 4]
@test parent(NamedDimsArray(orig, (:x, :y))) === orig
end
@testset "get the named array that was wrapped" begin
@test names(NamedDimsArray([10 20; 30 40], (:x, :... | {"hexsha": "498772b2c6c9803254b77936962eff1fd37716db", "size": 4305, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/wrapper_array.jl", "max_stars_repo_name": "mcabbott/NamedDims.jl", "max_stars_repo_head_hexsha": "0e74fec43ef969b99118deabff84758644097167", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma nth_ucast:
"(ucast (w::'a::len word)::'b::len word) !! n =
(w !! n \<and> n < min LENGTH('a) LENGTH('b))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. UCAST('a \<rightarrow> 'b) w !! n = (w !! n \<and> n < min LENGTH('a) LENGTH('b))
[PROOF STEP]
by (auto simp add: bit_simps not_le dest: bit_... | {"llama_tokens": 158, "file": "Word_Lib_Word_Lib_Sumo", "length": 1} |
import flwr as fl
import os
import argparse
from datetime import datetime
from typing import Tuple, Optional
import torch
from torchvision.transforms import transforms
import numpy as np
from leafdp.utils import model_utils
from leafdp.femnist.cnn import FemnistModel
from leafdp.vanilla.train import test_model
from l... | {"hexsha": "171e5784249836fa0b12163816817a7e36fa9b24", "size": 9722, "ext": "py", "lang": "Python", "max_stars_repo_path": "leafdp/flower/server.py", "max_stars_repo_name": "matturche/fl_dpsgd_strategies", "max_stars_repo_head_hexsha": "81d29edc86be75b9d8cae0db66277b47b81f4db0", "max_stars_repo_licenses": ["Apache-2.0"... |
"""
This example shows how to use the facade class StratifiedThermalStorage to add a storage to a model
that optimizes operation with oemof.solph.
"""
import os
import pandas as pd
import numpy as np
from oemof.solph import Source, Sink, Bus, Flow, Model, EnergySystem # noqa
from oemof.thermal import facades
from oe... | {"hexsha": "739a740c32e72ab40931f327ed4406d2940b8d62", "size": 2637, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/stratified_thermal_storage/02_operation_facade.py", "max_stars_repo_name": "oemof/oemof-thermal", "max_stars_repo_head_hexsha": "1c1fb8f6ea6255d854c7a535c8d199a4ba0abc5e", "max_stars_repo... |
import numpy as np
import tensorflow as tf
import random as rn
np.random.seed(123)
rn.seed(123)
#single thread
session_conf = tf.ConfigProto(
intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1)
from keras import backend as K
tf.set_random_seed(123)
sess = tf.Session(graph=tf.get_default_graph(), config=ses... | {"hexsha": "9b107236891b9649f4a6708a913fbc25df5c4ff0", "size": 5554, "ext": "py", "lang": "Python", "max_stars_repo_path": "NN.py", "max_stars_repo_name": "akash13singh/thesis", "max_stars_repo_head_hexsha": "c9c1aae3545e0412a042f5838e81622c82e8668a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 215, "max_sta... |
// Copyright (c) 2001-2010 Hartmut Kaiser
//
// 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)
#if !defined(BOOST_SPIRIT_WHAT_MAY_04_2007_0116PM)
#define BOOST_SPIRIT_WHAT_MAY_04_2007_0116PM
#if defined(_MSC_VE... | {"hexsha": "9c0efe324b4f653c0f5541f355cf885fbbe104fe", "size": 961, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external/boost_1_44_0/boost/spirit/home/karma/what.hpp", "max_stars_repo_name": "RaptDept/slimtune", "max_stars_repo_head_hexsha": "a9a248a342a51d95b7c833bce5bb91bf3db987f3", "max_stars_repo_licenses... |
import os
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import pdb
import random
from add_pieces_mosaic import *
from parameters import *
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
d... | {"hexsha": "32770ce3f523a814dc575733ebfe43dceb54dd77", "size": 4396, "ext": "py", "lang": "Python", "max_stars_repo_path": "build_mosaic.py", "max_stars_repo_name": "bleotiu/Mosaics", "max_stars_repo_head_hexsha": "3ef0fcd9ea37de03c15bc8eac4dd02e80f0cb4f4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
module splay_stuff
! integer, parameter :: bs=4
integer :: bs
logical dowrite
type abc
! integer high,low
integer,allocatable :: bytes(:)
end type abc
interface operator(<=)
module procedure less_equal
end interface
interface operator(==)
module procedure equal
end interface
... | {"hexsha": "e310d03ff35962a78c35cf14281b0ef89b2a73a7", "size": 15107, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "darwin19/sep_allocate.f90", "max_stars_repo_name": "timkphd/examples", "max_stars_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_stars_repo_licenses": ["Unlicense"], "max_s... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Base class for Mask objects. Contains many common utilities used for accessing masks. The mask itself is
represented under the hood as a three dimensional numpy :obj:`ndarray` object. The dimensions are
``[NUM_FREQ, NUM_HOPS, NUM_CHAN]``. Safe accessors for these arra... | {"hexsha": "dd832b3e79306a46388e4fff217d35f408802f61", "size": 10915, "ext": "py", "lang": "Python", "max_stars_repo_path": "nussl/separation/masks/mask_base.py", "max_stars_repo_name": "gomoto/nussl", "max_stars_repo_head_hexsha": "5ff8899d2da3c7704465d5e9ab7969657ed83263", "max_stars_repo_licenses": ["MIT"], "max_sta... |
using PlotUtils: zscale
using PyCall
using SiriusB
fits = pyimport("astropy.io.fits")
pro = pyimport("proplot")
# rcParams
pro.rc["image.origin"] = "lower"
pro.rc["image.cmap"] = "inferno"
pro.rc["grid"] = false
data_cube = fits.getdata(datadir("epoch_2020nov21", "processed", "2020nov21_sirius-b_cube_calib.fits"))
f... | {"hexsha": "dda4a0a6247bf4573308e735fa61e643aac6ebf1", "size": 562, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "paper/figures/spike.jl", "max_stars_repo_name": "mileslucas/sirius-b", "max_stars_repo_head_hexsha": "eb0bc6fa31836506bf755662677e00842db92358", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_... |
"""
This script is working demo of our project
"""
import numpy as np
import cv2
from timer import Timer
from framesandoflow import frames_downsample, images_crop, frames2flows
from videocapture import video_start, frame_show, video_show, video_capture
from datagenerator import VideoClasses
from model_i3d ... | {"hexsha": "51302395a37a2578563ff00f0046f60c464e659f", "size": 3504, "ext": "py", "lang": "Python", "max_stars_repo_path": "livedemo.py", "max_stars_repo_name": "SocialHelpers/Inception3d_for_Indian_Sign_language", "max_stars_repo_head_hexsha": "c48cd95fd1b87b25ba5aba6da12f9bf7462311bf", "max_stars_repo_licenses": ["MI... |
import NetworkLayer
import Utils
from NetworkConfig import NetworkConfig
from NetworkPerformanceTuner import NetworkPerformanceTuner
from NeuralNetworkMLP import NeuralNetwork
import numpy as np
nodes_per_layer = [3,2]
weights = 0
def weight_provider(num):
return np.arange(num)
config = NetworkConfig(nodes_per_... | {"hexsha": "8a086c96fd517496e046d9a953daa6700fc103f5", "size": 1258, "ext": "py", "lang": "Python", "max_stars_repo_path": "smoketest.py", "max_stars_repo_name": "WayEq/NeuralNetworkMLP", "max_stars_repo_head_hexsha": "17f6637c4f12b219a1249c18431b1f81eb14e6ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import matplotlib.pyplot as plt
import numpy as np
from StringUtil import StringUtil as String
class GraphUtil:
"""Contains utility methods used for plotting graphs"""
def __init__(self):
self.labels = [ "Time (Milliseconds)", "Time" ,"P1", "P2", "P3", "P4", "P5", "P6", "P7", "P8", "P9"]
#sa... | {"hexsha": "744025af44412b6d3c08c5faf2c9bf6b4cbfdd6a", "size": 3339, "ext": "py", "lang": "Python", "max_stars_repo_path": "GraphUtil.py", "max_stars_repo_name": "AcerNoobchio/CSCI5957MachineLearningPhase1", "max_stars_repo_head_hexsha": "a90d28b2013f4f94870efd2e160f57e6aac88069", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python3
import argparse, os
import pandas as pd
import numpy as np
from tuba_seq.shared import logPrint
from pathlib import Path
tuba_seq_dir = os.path.dirname(__file__)
############################## Input #########################################
parser = argparse.ArgumentParser( description="Cons... | {"hexsha": "5a6e774f56a903e6764f0f1531d44dcba2e49a13", "size": 3799, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/combine_mutation_tallies.py", "max_stars_repo_name": "petrov-lab/tuba-seq", "max_stars_repo_head_hexsha": "d257988659f79c2fecfec72b1d7fe260c245b7dc", "max_stars_repo_licenses": ["MIT"], "max_s... |
# very slow (even on TPU :( )
import os
from IPython.display import clear_output
!pip install distrax optax dm-haiku
clear_output()
try:
import brax
except ImportError:
!pip install git+https://github.com/google/brax.git@main
clear_output()
import brax
if 'COLAB_TPU_ADDR' in os.environ:
from jax.tools imp... | {"hexsha": "196a64fcadcfd78181ed98b2ac1f3ff3391c0b57", "size": 10363, "ext": "py", "lang": "Python", "max_stars_repo_path": "ppo/ppo_brax.py", "max_stars_repo_name": "gebob19/rl_with_jax", "max_stars_repo_head_hexsha": "a30df06de3035c460e5339611974664a2130ca6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5,... |
# -*- coding:utf-8 -*-
#
# Import OBJ files
#
# External dependencies
import os
import numpy as np
import MeshToolkit as mtk
# Import a mesh from a OBJ / SMF file
def ReadObj( filename ) :
# Initialisation
vertices = []
faces = []
normals = []
colors = []
texcoords = []
material = ""
# Read each line in the ... | {"hexsha": "536094bbd94a3e2ca26e0561a2f968cc1c84141e", "size": 1285, "ext": "py", "lang": "Python", "max_stars_repo_path": "MeshToolkit/File/Obj.py", "max_stars_repo_name": "microy/MeshToolkit", "max_stars_repo_head_hexsha": "df239e73fcd78c726e14c6b92eef7318da5e4297", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
from brancher.standard_variables import DirichletVariable, GeometricVariable, Chi2Variable, \
GumbelVariable, HalfCauchyVariable, HalfNormalVariable, NegativeBinomialVariable, PoissonVariable, StudentTVariable, UniformVariable, BernoulliVariable
## Distributions and samples ##
a = DirichletVari... | {"hexsha": "9971a8bb2ab871a7aec394d81495abc76c7f7a88", "size": 1287, "ext": "py", "lang": "Python", "max_stars_repo_path": "development_playgrounds/distributions_test.py", "max_stars_repo_name": "ai-di/Brancher", "max_stars_repo_head_hexsha": "01d51137b0e6fc81512994c21cc3a19287353767", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
from sklearn.model_selection import train_test_split
import funcy
from tabulate import tabulate
import coloredlogs, logging
from glob import glob
import itertools, os, json, urllib.request
from tqdm import tqdm
from os.path import join as opj
import cv2
coloredlogs.install()
logging.basicConfig(form... | {"hexsha": "1a641e7251a21a4ba61b2d79208e5a0ee17eaef0", "size": 7293, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "jichengYUAN/mmdetectionCust", "max_stars_repo_head_hexsha": "a45bfffc101ed4c901b6c9a07ed67629a3413091", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
##
# This software was developed and / or modified by Raytheon Company,
# pursuant to Contract DG133W-05-CQ-1067 with the US Government.
#
# U.S. EXPORT CONTROLLED TECHNICAL DATA
# This software product contains export-restricted data whose
# export/transfer/disclosure is restricted by U.S. law. Dissemination
# to non... | {"hexsha": "6f1f9754bb7f6d41b30e4a4c10cead5e654ca04e", "size": 2743, "ext": "py", "lang": "Python", "max_stars_repo_path": "edexOsgi/com.raytheon.edex.plugin.gfe/utility/cave_static/user/GFETEST/gfe/userPython/smartTools/ExUtil1.py", "max_stars_repo_name": "srcarter3/awips2", "max_stars_repo_head_hexsha": "37f31f5e8851... |
SUBROUTINE ssmied
c
c PROCESS SSMIGR
c ------- ------
c
c Document date:
c
c Split-Step Migration
c
c Reference: Stoffa et al., Split-step Fourier Migration,
c Geophysics,55,p.410-421,1990.
c
c This process ... | {"hexsha": "0ac0e7b940a7a24deccdfc334e6c5febc94f3e0b", "size": 18842, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ssmied.f", "max_stars_repo_name": "apthorpe/sioseis", "max_stars_repo_head_hexsha": "28965a8b4a5b3ffaf169588dd1a385a1f9c9ccce", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3, ... |
from itertools import product
from astropy import units as u, constants as const
import numpy as np
from ..nuclear import (nuclear_binding_energy, nuclear_reaction_energy)
import pytest
def test_nuclear_binding_energy():
assert nuclear_binding_energy('p') == 0
assert nuclear_binding_energy('n') == 0
asse... | {"hexsha": "b862aca605c65c44b34d1b1c8f8fd983d7089eeb", "size": 2885, "ext": "py", "lang": "Python", "max_stars_repo_path": "plasmapy/atomic/tests/test_nuclear.py", "max_stars_repo_name": "ludoro/PlasmaPy", "max_stars_repo_head_hexsha": "69712cb40b8b588400301edfd6925d41d2f13eac", "max_stars_repo_licenses": ["BSD-2-Claus... |
\chapter{Data analytics}
In this chapter I propose three hypotheses, each to be supported by analysis in the coming sections. The hypotheses are:
\begin{enumerate}
\item Skedge's differences from and additions to CDCS are \textbf{usable and have real need}.
\item Skedge’s \emph{navigations-per-add} demonstrate ... | {"hexsha": "81317462983fc82f33631c1934a4124689aa3b70", "size": 838, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "analytics.tex", "max_stars_repo_name": "dingbat/skedge-thesis", "max_stars_repo_head_hexsha": "b5c89fe83f59adc0dd389e9712930ce26aa92824", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
#include "cache.hpp"
#include "tools.hpp"
#include <yaml-cpp/yaml.h>
#include <boost/filesystem/fstream.hpp>
#include <boost/filesystem/operations.hpp>
#include <sstream>
namespace charge
{
namespace
{
void write_info(
std::string const & hostname,
boost::filesystem::path const & script_abspath,
boo... | {"hexsha": "e812a42501c79fdfd27b5b106a8f54a398234e4d", "size": 2152, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "charge_engine/src/cache.cpp", "max_stars_repo_name": "philtherobot/quickc", "max_stars_repo_head_hexsha": "9ade79ce22856e968543f3d443d9b13e9d30afcb", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import cv2
import numpy as np
def getLocation( min_point: tuple, max_point: tuple ) -> tuple:
"""Gets the bottom center location from the bounding box of an occupant."""
# Unpack the tuples into min/max values
xmin, ymin = min_point
xmax, ymax = max_point
# Take midpoint of x-coordinate and ymax fo... | {"hexsha": "03ec3f78a35d8ec505e001209f8ad07dc93ed702", "size": 12103, "ext": "py", "lang": "Python", "max_stars_repo_path": "testing/transform/transform_test.py", "max_stars_repo_name": "cac765/ee486-capstone-team10", "max_stars_repo_head_hexsha": "fdc986e12c31df91f59ded40ba5822e629a03dcd", "max_stars_repo_licenses": [... |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__... | {"hexsha": "86ada66e46deab1838a537b97e60241d50e4dad5", "size": 4353, "ext": "py", "lang": "Python", "max_stars_repo_path": "pose_estimation/demo.py", "max_stars_repo_name": "padeler/human-pose-estimation.pytorch", "max_stars_repo_head_hexsha": "9093d1a0083ab593ae91d1d36348e501f4181caa", "max_stars_repo_licenses": ["MIT... |
import numpy as np
import os
import json
from keras.layers import Input, Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten, RNN, Reshape, Permute, Dot, LSTM, Softmax
from keras.layers import LeakyReLU, UpSampling2D, Conv2DTranspose, Multiply, Activation,TimeDistributed
from keras.layers.normalization import BatchN... | {"hexsha": "b2f5fc2ec4887433057e7477e8135cb930edfa58", "size": 30197, "ext": "py", "lang": "Python", "max_stars_repo_path": "apnet/model.py", "max_stars_repo_name": "pzinemanas/APNet", "max_stars_repo_head_hexsha": "5b44ced037c94d087befc578a5e922f963074e2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "ma... |
import numpy as np
def strategy(history, memory):
n = history.shape[1]
if n == 0:
return 1, np.array([0] * 16)
elif n >= 2:
olderMove = 2 * history[0, -2] + history[1, -2]
recentMove = 2 * history[0, -1] + history[1, -1]
memory[4 * olderMove + recentMove] += 1
# measure... | {"hexsha": "105d3ffefd4f78ebca6b5a4a5f5c5610cf68758b", "size": 912, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/st3v3nmw/Absolution3679.py", "max_stars_repo_name": "usedToBeTomas/PrisonersDilemmaTournament", "max_stars_repo_head_hexsha": "b5ce72e4e0b943dd2fa8cca35a191bd3b4c4d5aa", "max_stars_repo_licens... |
[STATEMENT]
lemma fMax_ffilter_less: "x |\<in>| P \<Longrightarrow> x < n \<Longrightarrow> fMax (ffilter (\<lambda>i. i < n) P) < n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>x |\<in>| P; x < n\<rbrakk> \<Longrightarrow> fMax (ffilter (\<lambda>i. i < n) P) < n
[PROOF STEP]
by (metis fMax_in ffilter_e... | {"llama_tokens": 153, "file": "Formula_Derivatives_FSet_More", "length": 1} |
create table categories (
id integer not null,
catname varchar(40) not null,
primary key(id)
);
create table quotes (
id integer not null,
cid integer not null,
author varchar(100),
quoname varchar(250) not null,
primary key(id)
);
| {"hexsha": "432a7af7cbe2f3b1c50edeb057fedb2125e30baa", "size": 259, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Module 3/Chapter09/apt.jl", "max_stars_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_stars_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_stars_repo_lic... |
using Documenter, LiveDisplay
makedocs(;
modules=[LiveDisplay],
format=Documenter.HTML(),
pages=[
"Home" => "index.md",
],
repo="https://github.com/tkf/LiveDisplay.jl/blob/{commit}{path}#L{line}",
sitename="LiveDisplay.jl",
authors="Takafumi Arakaki",
)
deploydocs(;
repo="githu... | {"hexsha": "50959ea055dd7816f48d816eb54212fc34d615c5", "size": 349, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "tkf/LiveDisplay.jl", "max_stars_repo_head_hexsha": "73811dc298baad24ecf072747b8e5ef39f321455", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
[STATEMENT]
lemma execn_dynCall_Normal_elim:
assumes exec: "\<Gamma>\<turnstile>\<langle>dynCall init p return c,Normal s\<rangle> =n\<Rightarrow> t"
assumes "\<Gamma>\<turnstile>\<langle>call init (p s) return c,Normal s\<rangle> =n\<Rightarrow> t \<Longrightarrow> P"
shows "P"
[PROOF STATE]
proof (prove)
goal... | {"llama_tokens": 403, "file": "Simpl_Semantic", "length": 5} |
from pathlib import Path
import sys
from typing import Tuple
import numpy as np # type: ignore
def calc_epsilon(gamma_bin: str) -> Tuple[str, int]:
"""Using XOR operation to invert gamma and return binary and decimal form."""
gamma_dec = int(gamma_bin, base=2)
# apply XOR operator
inverse_gamma = ga... | {"hexsha": "94f15b4d9f8e7e20a212d2cc115977280ac1b5b8", "size": 2542, "ext": "py", "lang": "Python", "max_stars_repo_path": "03-december/run_diagnostics.py", "max_stars_repo_name": "acatovic/AoC2021", "max_stars_repo_head_hexsha": "aae151c73ab9c21c69e39d95126b9ffdd98e462e", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
###############################################################################
# Copyright (c), Forschungszentrum Jülich GmbH, IAS-1/PGI-1, Germany. #
# All rights reserved. #
# This file is part of the Masci-tools package. ... | {"hexsha": "e58ec3a6df4962ab6852ce0d70cb4b2177f83e47", "size": 14472, "ext": "py", "lang": "Python", "max_stars_repo_path": "masci_tools/vis/kkr_plot_dos.py", "max_stars_repo_name": "soumyajyotih/masci-tools", "max_stars_repo_head_hexsha": "e4d9ea2fbf6e16378d0cbfb8828a11bdb09c2139", "max_stars_repo_licenses": ["MIT"], ... |
module AllStdLib where
-- Ensure that the entire standard library is compiled.
import README
open import Data.Unit.Polymorphic using (⊤)
open import Data.String
open import IO using (putStrLn; run)
open import IO.Primitive using (IO; _>>=_)
import DivMod
import HelloWorld
import HelloWorldPrim
import ShowNat
import... | {"hexsha": "92f5e8d2ef197db79b19b6224155c8a5b42fc84d", "size": 608, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Compiler/with-stdlib/AllStdLib.agda", "max_stars_repo_name": "shlevy/agda", "max_stars_repo_head_hexsha": "ed8ac6f4062ea8a20fa0f62d5db82d4e68278338", "max_stars_repo_licenses": ["BSD-3-Clause"... |
from functools import partial
import os
from pathlib import Path
import warnings
import numpy as np
import xarray as xr
from . import arakawa_points as akp
from .domcfg import open_domain_cfg
from .tools import _dir_or_files_to_files
def nemo_preprocess(ds, domcfg):
"""
Preprocess function for the nemo file... | {"hexsha": "983579b6827a1deed0e9958e46040e34d0953cd3", "size": 4094, "ext": "py", "lang": "Python", "max_stars_repo_path": "xnemogcm/nemo.py", "max_stars_repo_name": "rcaneill/xnemogcm", "max_stars_repo_head_hexsha": "3b1c93d2d09c442a4440f47cab956e2ff89d25d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "... |
from __future__ import division
from qm.columbus.columbus import Columbus
from misc import data, amu_to_au, call_name
import os, shutil, re
import numpy as np
class MRCI(Columbus):
""" Class for MRCI method of Columbus program
:param object molecule: Molecule object
:param string basis_set: Basis ... | {"hexsha": "d742f05b6861bbe8edb22e59bd261c76c0486f50", "size": 19774, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/qm/columbus/mrci.py", "max_stars_repo_name": "hkimaf/unixmd", "max_stars_repo_head_hexsha": "616634c720d0589fd600e3268afab9da957e18bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# coding=utf-8
from __future__ import print_function
import numpy as np
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
from PIL import ImageDraw
import cv2
import math
import sys, os
import common
def load_engine(trt_runtime, plan_path):
with open(engine_path, 'rb') as f:
engi... | {"hexsha": "9af525c7fd5f9b688f0b7ede6e6332084edbe720", "size": 5150, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorrt_optimize/inference_trt.py", "max_stars_repo_name": "saicoco/SA-Text", "max_stars_repo_head_hexsha": "353a73d84246a54962f3b7ffb7c7ca2e35f235e8", "max_stars_repo_licenses": ["MIT"], "max_st... |
\section{Introduction}
\label{sec:intro}
We consider a food delivery application with three types of users:
\begin{itemize}[noitemsep]
\item \textbf{Customers}: can place orders and check their status
\item \textbf{Administrators}: can insert new products and change their availability
\item \textbf{Delive... | {"hexsha": "e46dd933dc99776ea1379c5239db92f8d1811897", "size": 2443, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/sections_report/introduction.tex", "max_stars_repo_name": "fuljo/pub-sub-delivered", "max_stars_repo_head_hexsha": "7dfe57bdf7aaaddd7aa069f3581517d7298c8b6d", "max_stars_repo_licenses": ["MIT... |
import os
import sys
import time
import torch
import torch.nn as nn
import random
import numpy as np
import torchvision.transforms as transforms
FILE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_ROOT = os.path.join(FILE_DIR, '../../../data')
sys.path.append(os.path.join(FILE_DIR, '../'))
sys.path.append(os.pa... | {"hexsha": "fae76c8d74e47c824e067609790c8f64a424bb8f", "size": 4020, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/cifar/defense/base.py", "max_stars_repo_name": "DingfanChen/RelaxLoss", "max_stars_repo_head_hexsha": "d21c82dee7016cd0cb6688a408104eeb0d832790", "max_stars_repo_licenses": ["MIT"], "max_st... |
/-
Copyright © 2018 François G. Dorais. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
-/
import .basic .cons fin.extra
variables {α : Type*} [dlo : decidable_linear_order α]
include dlo
open tup
definition tup.max : Π {n : ℕ}, α ^ (n+1) → α
| 0 xs := xs.head
| (n+1) xs := ... | {"author": "fgdorais", "repo": "tup", "sha": "ac4a2f8ca2ccc8aea091498439a0a47d43ac4700", "save_path": "github-repos/lean/fgdorais-tup", "path": "github-repos/lean/fgdorais-tup/tup-ac4a2f8ca2ccc8aea091498439a0a47d43ac4700/src/tup/minmax.lean"} |
# coding=utf-8
import time
import h5py
import numpy as np
from util import utils
'''
将线上系统生成的所有视频帧特征转为h5格式，在本系统中只需要对视频库进行处理，不需要对查询视频特征进行转换，
只需要在result_generator.py脚本中直接产生结果。
'''
def bow2h5f(dir_name, file_feature_output):
bows = utils.get_all_files_suffix(dir_name, '.bow')
img_names = utils.get_all_files_s... | {"hexsha": "6b7d07b3d4bbd27f8b3a2ee6fb0d5655c66e4560", "size": 2189, "ext": "py", "lang": "Python", "max_stars_repo_path": "result_generator/result_feature_db/format_bow2h5f.py", "max_stars_repo_name": "shijack/feature_extract", "max_stars_repo_head_hexsha": "2c45750ea42a30a1f0b5cbe305edc4c8ab0461d7", "max_stars_repo_l... |
# Copyright 2018 Xanadu Quantum Technologies Inc.
# 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 agre... | {"hexsha": "6653d97e27767deeca60bfd165fd983fca24fa54", "size": 28712, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_simulator_device.py", "max_stars_repo_name": "kalufinnle/pennylane-cirq", "max_stars_repo_head_hexsha": "c239239a661cd01adb671ad7b7254a2fa3684c6b", "max_stars_repo_licenses": ["Apache-... |
# Copyright 2020 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": "92e637b649624848e47501d02d9472e6cc8c2b97", "size": 4071, "ext": "py", "lang": "Python", "max_stars_repo_path": "spinoffs/inference_gym/inference_gym/tools/stan/logistic_regression.py", "max_stars_repo_name": "mederrata/probability", "max_stars_repo_head_hexsha": "bc6c411b0fbd83141f303f91a27343fe3c43a797", "... |
import copy
import os
import sys
from collections import defaultdict
import numpy as np
import pandas as pd
from abc import ABC
from sklearn.metrics import auc
from odin.classes import DatasetLocalization
from odin.classes.analyzer_interface import AnalyzerInterface
from odin.utils import get_root_logger
from odin.u... | {"hexsha": "9a7380069793d183e138b82b12470464c1129e84", "size": 36255, "ext": "py", "lang": "Python", "max_stars_repo_path": "odin/classes/analyzer_localization.py", "max_stars_repo_name": "rnt-pmi/odin", "max_stars_repo_head_hexsha": "8cfddf04f964393ef30217aa5f4aa61229d7e811", "max_stars_repo_licenses": ["Apache-2.0"],... |
#!/usr/bin/env python
# %!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%#
# %!%!% ------------------------------ FPTE_Setup_VASP---- ------------------------------- %!%!%#
# %!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%!%... | {"hexsha": "2925f34e1be5fd8fc9b36be18223599ba5b267e3", "size": 24203, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/FPTE/FPTE_Setup_VASP.py", "max_stars_repo_name": "MahdiDavari/Elastic_Constants_Finite_pressures", "max_stars_repo_head_hexsha": "bba28acc7024710a253dd270f11a2e5adb252b3d", "max_stars_r... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.