text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
/*
* Copyright (C) 2021 FISCO BCOS.
* SPDX-License-Identifier: Apache-2.0
* 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
*
* Unl... | {"hexsha": "6bc8722fc3f6ce13670cf49595b3820fa8cd5931", "size": 3218, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "bcos-executor/src/vm/VMFactory.cpp", "max_stars_repo_name": "contropist/FISCO-BCOS", "max_stars_repo_head_hexsha": "1605c371448b410674559bb1c9e98bab722f036b", "max_stars_repo_licenses": ["Apache-2.0... |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 29 14:08:21 2018
@author: tih
"""
def run(input_nc, Inflow_Text_Files):
'''
This functions add inflow to the runoff dataset before the channel routing.
The inflow must be a text file with a certain format. The first line of this format are the latitude and lo... | {"hexsha": "0af20e8c3d82510abf1d06ff99f00a6c2fc81aaf", "size": 2195, "ext": "py", "lang": "Python", "max_stars_repo_path": "Models/SurfWAT/Part0_Add_Inlets.py", "max_stars_repo_name": "ali1100/wa", "max_stars_repo_head_hexsha": "700e5014533c45f38a245c3abdeacc537cb307bc", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
try:
from .pkg import rolex
from .parse_datetime import any2datetime
except: # pragma: no cover
from convert2.pkg.rolex import rolex
from convert2.parse_datetime import any2datetime
class Anything2Date(object):
"""Parse anything to... | {"hexsha": "dc0ec26e3f08d9c8ca2c1b0207e0ebc25e4301cf", "size": 1543, "ext": "py", "lang": "Python", "max_stars_repo_path": "convert2/parse_date.py", "max_stars_repo_name": "MacHu-GWU/convert2-project", "max_stars_repo_head_hexsha": "aad1837676b3702aa22f53f6462beb8c5c17d4e1", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# coding: utf-8
"""
Author: Arnaud Ferré
Mail: arnaud.ferre.pro@gmail.com
Description: Training module for C-Norm method
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 cop... | {"hexsha": "804df5e345a19fbd0e940f19119eaec2491a4aeb", "size": 13136, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "ArnaudFerre/C-Norm_PostLab", "max_stars_repo_head_hexsha": "edb9462a32b3e47b38bfd62836427d3a90f510ab", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# Implementation of the Douglas-Peucker algorithm for line thinning
# http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
simplify_rec <- function(points, tol = 0.01) {
n <- nrow(points)
if (n <= 2) return() # c(1, n))
dist <- with(points, point_line_dist(x, y, x[1], y[1], x[n], y[n]))
if (max(dist,... | {"hexsha": "edbf20b3138c8f8fc93ddb53695948b70d8deb6c", "size": 1431, "ext": "r", "lang": "R", "max_stars_repo_path": "data/r/4d3b204f419e13c711e427f1e3aae40b_thin.r", "max_stars_repo_name": "maxim5/code-inspector", "max_stars_repo_head_hexsha": "14812dfbc7bac1d76c4d9e5be2cdf83fc1c391a1", "max_stars_repo_licenses": ["Ap... |
SUBROUTINE ADDEND( SPDBSP, LASCEN, SAMPLE, ENDRAD,
& CVECT, PERPVE, PERPVN,
& ATMAX, ATNO, ATXYZ, ATVDW, CUTSIZE)
IMPLICIT NONE
C ********************************************************************
C * ... | {"hexsha": "4e8d9c86cbbc3598e0ea05e9a27769b32a3bea9b", "size": 4961, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/addend.f", "max_stars_repo_name": "MDAnalysis/hole2", "max_stars_repo_head_hexsha": "ed28b2ac0dfc714933ddf2befe0a06ed87ede98e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 15... |
#!python
import os
from os.path import join
import sys
import click
import json
import csv
from tqdm import tqdm
import hashlib
from glob import glob
import cv2 as cv
import imutils
import faiss
import pickle
import numpy as np
import pandas as pd
import sqlite3
import time
import csv
# TODO change to relative
sys.pat... | {"hexsha": "4848c7fbf4c2a028685944d476191cc3865325ca", "size": 7146, "ext": "py", "lang": "Python", "max_stars_repo_path": "vframe/vframe/tools/faiss/train_ah.py", "max_stars_repo_name": "kant/vframe", "max_stars_repo_head_hexsha": "28e49ca62d9036a78a25b26eb0fb7e3cf8c79031", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
from dedalus import public as de
from pySDC.core.Problem import ptype
from pySDC.core.Errors import ParameterError
from pySDC.implementations.datatype_classes.mesh import mesh, imex_mesh
class dynamogp_2d_dedalus(ptype):
"""
"""
def __init__(self, problem_params, dtype_u=mesh, dtype_... | {"hexsha": "099ba8af28bddede6fdfec5fee7cdaee9ca9c4bb", "size": 6224, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySDC/playgrounds/deprecated/Dedalus/DynamoGP_2D_Dedalus_NEW.py", "max_stars_repo_name": "brownbaerchen/pySDC", "max_stars_repo_head_hexsha": "31293859d731646aa09cef4345669eac65501550", "max_stars... |
theory nat_pow_le_factorial
imports Main
"$HIPSTER_HOME/IsaHipster"
begin
datatype Nat = Z | S "Nat"
fun plus :: "Nat => Nat => Nat" where
"plus (Z) y = y"
| "plus (S n) y = S (plus n y)"
fun mult :: "Nat => Nat => Nat" where
"mult (Z) y = Z"
| "mult (S n) y = plus y (mult n y)"
fun pow :: "Nat => Nat => Na... | {"author": "moajohansson", "repo": "IsaHipster", "sha": "91f6ea3f1166a9de547722ece6445fe843ad89b4", "save_path": "github-repos/isabelle/moajohansson-IsaHipster", "path": "github-repos/isabelle/moajohansson-IsaHipster/IsaHipster-91f6ea3f1166a9de547722ece6445fe843ad89b4/benchmark/tip2015/nat_pow_le_factorial.thy"} |
"""
.. currentmodule: arim.geometry
Utilities for geometric operations: translation, rotation, change of basis, etc.
Points
======
A :class:`Points` object contains the Cartesian coordinates of one or more
points. The points can be stored as a ndarray.
However ray tracing and many parts of arim expects as input an 1... | {"hexsha": "f4e662c29f5a389c03705061d5ae23080eb8a010", "size": 41425, "ext": "py", "lang": "Python", "max_stars_repo_path": "arim/geometry.py", "max_stars_repo_name": "will-jj/arim", "max_stars_repo_head_hexsha": "fc15efe171a41355090123fcea10406ee75efe31", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max... |
import argparse
import os
import torch
import numpy as np
import torch
import random
def set_deterministic(seed):
# seed by default is None
if seed is not None:
print(f"Deterministic with seed = {seed}")
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
... | {"hexsha": "35ff1d403d5b16d2b9d9aa10621be2c87764b34e", "size": 3598, "ext": "py", "lang": "Python", "max_stars_repo_path": "configs/__init__.py", "max_stars_repo_name": "rjean/SimSiam", "max_stars_repo_head_hexsha": "d754dc0b5adf408c57cc23f4eb207276d559b3c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
(*
* Unique.v
* Wolf Honore
*
* Defines an interface for a generator of unique values.
*)
Require Import Le.
Require Import List.
Require Import Max.
Module Type UNIQUE.
Parameter t : Set.
Parameter init : list t.
Parameter new : list t -> list t * t.
Parameter eq : forall (u1 u2 : t), {u1 = u2} + {u1 ... | {"author": "whonore", "repo": "CerTiger", "sha": "b99908ce11a2e8095d7f33353cb69f8b0d6315b8", "save_path": "github-repos/coq/whonore-CerTiger", "path": "github-repos/coq/whonore-CerTiger/CerTiger-b99908ce11a2e8095d7f33353cb69f8b0d6315b8/util/Unique.v"} |
#!/usr/bin/env python3
import numpy as np
import cv2
import sys
import rospkg
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import rospy
def lane_callback(msg):
#print(msg)
br = CvBridge()
current_frame = br.imgmsg_to_cv2(msg)
cv2.imshow("test", current_frame)
def main():
print... | {"hexsha": "36cddcb49a5c6fda84343ff9686a3107342e6f50", "size": 565, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/binocular_p3/src/basic_python3.py", "max_stars_repo_name": "PatWatson/Queens-Autonomous-Taxi-2021-Capstone", "max_stars_repo_head_hexsha": "2706927e9c98c3ddddf7f5f03301d178a827e147", "max_stars... |
"""
Copyright (c) 2019 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 writing,... | {"hexsha": "60d4b5d9cb66efb7bc8cff3526fa055767263c00", "size": 26829, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/accuracy_checker/accuracy_checker/adapters/detection.py", "max_stars_repo_name": "allnes/open_model_zoo", "max_stars_repo_head_hexsha": "693ba31b3b7671f5fb8ecf8f9b8d670cfec21bc3", "max_star... |
// Copyright Gavin Band 2008 - 2012.
// 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)
#ifndef COMPONENTS_RELATEDNESS_COMPONENT_PCA_COMPUTER_HPP
#define COMPONENTS_RELATEDNESS_COMPONENT_PCA_C... | {"hexsha": "610e3270b49511fb1c1921350829cbac56e293ce", "size": 3564, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "components/RelatednessComponent/include/components/RelatednessComponent/PCAComputer.hpp", "max_stars_repo_name": "CreRecombinase/qctool", "max_stars_repo_head_hexsha": "6dad3a15c461177bf6940ba7b9913... |
from lltk.imports import *
def newpolyfit(X,Y):
newdf=pd.DataFrame({'X':X, 'Y':Y})
results = smf.ols('Y ~ X + I(X**2)', data=newdf).fit()
return results.rsquared, results.f_pvalue
def regressions(df,resultdf=None):
word2rp={}
RRs=[]
Ps=[]
for word in df.index:
Y=list(df.loc[word])
... | {"hexsha": "9da686ffba36749208eee6345e1a9c029a79e890", "size": 7999, "ext": "py", "lang": "Python", "max_stars_repo_path": "lltk/tools/stats.py", "max_stars_repo_name": "literarylab/lltk", "max_stars_repo_head_hexsha": "0e516d7fa0978c1a3bd2cb7636f0089772e515ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5,... |
import unittest
class PackagesTestCase(unittest.TestCase):
def test_pygame_installation(self):
try:
import pygame
pygame.init()
pygame.display.set_mode((500, 400), 0, 32)
pygame.quit()
except ModuleNotFoundError:
self.fail("PyGame is not ... | {"hexsha": "41766b2c008b581ca2b4f9c6701ceba7102bd822", "size": 569, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/environment_test.py", "max_stars_repo_name": "jfajkowski/PyCheckers", "max_stars_repo_head_hexsha": "2747ec645fe78bbaa9306987cfad0117b366dcc1", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import os
import timeit
import numpy as np
import argparse
from psyneulink import *
from double_dqn import DoubleDQNAgent
from gym_forager.envs.forager_env import ForagerEnv
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int,
default=int.from_bytes(os.urandom(4), byteorder="big"),
... | {"hexsha": "0eb89fb2ed7109fa04aeb17f2e138fc497fa7bc6", "size": 21477, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/Debug/predator_prey_opt/predator_prey_dmt.py", "max_stars_repo_name": "MetaCell/PsyNeuLink", "max_stars_repo_head_hexsha": "aeddf3e8ea62504a5d928b100b59aa18e593156c", "max_stars_repo_lice... |
# -*- coding: utf-8 -*-
""" Module to implement acoustic shadowing along with spherical spreading
of sound
Created on Mon Jun 17 16:08:50 2019
@author: tbeleyur
"""
import time
import sys
sys.path.append('..//bridson//')
sys.path.append('..//')
import numpy as np
import pandas as pd
import scipy.spatial as spatial
... | {"hexsha": "bde9076396af94c726fc4471c10686e14a136a25", "size": 15041, "ext": "py", "lang": "Python", "max_stars_repo_path": "acoustics/detailed_sound_propagation.py", "max_stars_repo_name": "thejasvibr/the_cocktail_party_nightmare", "max_stars_repo_head_hexsha": "385bd4c6d1b8bfc7079b3ba68a0fc3ce79cec3f6", "max_stars_re... |
c*********************************************************
c
c Test program for subroutine ABLE77V2
c
c*********************************************************
c
INCLUDE 'VICMAIN_FOR'
SUBROUTINE MAIN44
c
INTEGER IND, ARR(50), UNIT, NUM(3)
DATA NUM /39,6,22/
CHARACTER*132 MSG
c
... | {"hexsha": "ebc7015d3884beb580ad726ad0b93dcc035fba12", "size": 4068, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p2/sub/able77v2/test/table77v2.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Claus... |
# hack until we figure out how to detect automatically
if Sys.iswindows()
const libniscope = "C:/Program Files/IVI Foundation/IVI/Bin/niScope_64"
const libnidmm = "C:/Program Files/IVI Foundation/IVI/Bin/nidmm_64"
const libnifgen = "C:/Program Files/IVI Foundation/IVI/Bin/niFgen_64"
const libnihsdio = "C:/Progra... | {"hexsha": "9986d2e8160a14448022667c8573731b0f086fd8", "size": 1012, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lib_locations.jl", "max_stars_repo_name": "iuliancioarca/GenericInstruments.jl", "max_stars_repo_head_hexsha": "93d411d026b8fad36c46a8e99206b2d788176f8f", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/python
import csv
import time
import json
import talib
import requests
import numpy as np
import pandas as pd
from datetime import datetime, date, timedelta, timezone
from backtesting import Backtest
from backtesting import Strategy
from backtesting.lib import crossover
from sklearn.pipeline import Pipeline
... | {"hexsha": "7baec853bf26e394a40c3621490078738595c920", "size": 5972, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "yuyasugano/ml-classifier-Ta-Lib-ohlc", "max_stars_repo_head_hexsha": "02f4b24bc2b80f962dca49e6469f45ee0cb6d37d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from common.utils import AverageMeter, split_states, generate_eom, gen_loader
from common.model import DeLaN, init_weights
from common.obj import Visualizer
... | {"hexsha": "f2f309ef7680b1c9f9e8474942484fa9ec27d670", "size": 3905, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "xth430/DeepLagrangian", "max_stars_repo_head_hexsha": "acec4cc4429b6d08e9f7b8caadea9a2750837598", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import os
import sys
# Add /Modules to sys path so that program has access to the files in the Modules folder
sys.path.append(os.getcwd() + '/Modules')
import time
import mysql.connector
import numpy as np
import pandas as pd
import json
from datetime import datetime
import os.path
import csv
from DepartureDelay imp... | {"hexsha": "ba06601ca826bba0b50ebaed269dab8f7fc16406", "size": 24799, "ext": "py", "lang": "Python", "max_stars_repo_path": "CronJob/cron_job.py", "max_stars_repo_name": "md3997/FYP", "max_stars_repo_head_hexsha": "428a54714255dcd4cfd222cd4e3f8b28285c8b6f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
module TestCount
using Test
using LazyGroupBy
@testset begin
@test count.(_ -> true, grouped(isodd, [0, 7, 3, 1, 5, 9, 4, 3, 0, 5])) ==
Dict(false => 3, true => 7)
@test count.(grouped(identity, [true, false, true, true, true])) ==
Dict(false => 0, true => 4)
@test_throws(
Exce... | {"hexsha": "88db30ff12ddae142829ec1edc3d96183f606512", "size": 426, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_count.jl", "max_stars_repo_name": "JuliaFolds/LazyGroupBy.jl", "max_stars_repo_head_hexsha": "293c338d6a1efc4d17453bf85eb3addf7fa90194", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[GOAL]
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
f : E → F
s : Set E
x : E
f' : E →L[ℝ] F
f_diff : DifferentiableOn ℝ f s
s_conv : Convex ℝ s
s_open : IsOpen s
f_cont : ∀ (y : E), y ∈ closure s → ContinuousWithinAt f s y
h : Te... | {"mathlib_filename": "Mathlib.Analysis.Calculus.FDeriv.Extend", "llama_tokens": 51779} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# this file is from https://github.com/ucbdrive/dla/blob/master/dla.py.
import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as wei... | {"hexsha": "2a33c66bf3d5b97bf882eaf0b80de012151a62b4", "size": 17423, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/CenterNet2/centernet/modeling/backbone/dlafpn.py", "max_stars_repo_name": "collector-m/CenterNet2", "max_stars_repo_head_hexsha": "68c0a468254b013e1d08309cd7a506756120ca62", "max_stars_r... |
import argparse
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
# https://zhuanlan.zhihu.com/p/359524837
class AddModelFunction(Function):
@staticmethod
def forward(ctx, a, b, n):
c = torch.empty(n).to(device="cuda:0")
if args.compiler == 'jit':
... | {"hexsha": "e25ba968602b69de0dd0db4c2b6660b1c9086220", "size": 2459, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch/train.py", "max_stars_repo_name": "yuanzhedong/NN-CUDA-Example", "max_stars_repo_head_hexsha": "15f37a07591d29459abc290173e95aa9918615be", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
##~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~##
## ##
## This file forms part of the Badlands surface processes modelling application. ##
## ... | {"hexsha": "16ebd6945c0d1a323ed381040dffe53e61f28f9a", "size": 3373, "ext": "py", "lang": "Python", "max_stars_repo_path": "badlands/badlands/forcing/pelagicGrowth.py", "max_stars_repo_name": "intelligentEarth/surrogateBayeslands", "max_stars_repo_head_hexsha": "24462cafed05ac0c377865d8fe039cafa0aa59d4", "max_stars_rep... |
[STATEMENT]
lemma bc_mt_corresp_IAdd: "
bc_mt_corresp [IAdd] (replST 2 (PrimT Integer))
(PrimT Integer # PrimT Integer # ST, LT) cG rT mxr (Suc 0)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bc_mt_corresp [IAdd] (replST 2 (PrimT Integer)) (PrimT Integer # PrimT Integer # ST, LT) cG rT mxr (Suc 0)
[PR... | {"llama_tokens": 1014, "file": null, "length": 7} |
#include "c8-sq-lite/SelectResult.hpp"
#include <boost/format.hpp>
#include <gmock/gmock.h>
#include <stdexcept>
namespace C8::SqLite {
using namespace testing;
class SelectResultTestSuite : public Test {
public:
void SetUp() override {
sut.addColumn("id", 0);
sut.addColumn("name", 1);
sut... | {"hexsha": "5dbd9f19ffb46e082f92586c7647de9f81c4189a", "size": 1539, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/c8-sq-lite/SelectResultTestSuite.cpp", "max_stars_repo_name": "chaos0x8/cpp-c8", "max_stars_repo_head_hexsha": "f64004e59339a5e3e066128aa75e6a090449b644", "max_stars_repo_licenses": ["MIT"], "m... |
#! /usr/bin/env python3
###########################################################
# The example shows how to get mapping data #
# The peak ratio at 1315 cm^-1 and 1380 cm^-1 are plotted #
# Details see Small 14, 1804006 (2018). #
#######################################################... | {"hexsha": "8a70aebddaee853d32090cf748aea94d45f0b9c8", "size": 2391, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/ex5_mapping.py", "max_stars_repo_name": "lovaulonze/py-wdf-reader", "max_stars_repo_head_hexsha": "f9632eaf35fe51634bdce71dc0a6937e1454707f", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys
import numpy as np
import pandas as pd
import requests
from epw import epw
from datetime import datetime
import calendar
def download_epw(lat, lon, year, location, attributes, interval, utc, your_name, api_key, reason_for_use,
your_affiliation, your_email, mailing_list, leap_year):
cu... | {"hexsha": "0ec8999d3574ec862b49c1d6739f4af7dea1db4f", "size": 11887, "ext": "py", "lang": "Python", "max_stars_repo_path": "assets.py", "max_stars_repo_name": "kastnerp/NREL-PSB3-2-EPW", "max_stars_repo_head_hexsha": "94a4407073f09e2718a9efe8ed24c9a78e1990f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
//
// Copyright (c) 2008, 2009 Boris Schaeling <boris@highscore.de>
//
// 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)
//
#define BOOST_AUTO_TEST_MAIN
#include <boost/test/auto_unit_test.hpp>
#include <boost/filesys... | {"hexsha": "445ff1ebf9eb6f1a200d3e7e266455357e9b2fa4", "size": 4092, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/test_sync.cpp", "max_stars_repo_name": "Librevault/dir_monitor", "max_stars_repo_head_hexsha": "1b7220520ac3a6a543d4c63f0a5ff4a9ff113835", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_co... |
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distribu... | {"hexsha": "fc423231af76b78ce392a12bcc2b8068369293a8", "size": 2364, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/nisqai/encode/_plus_minus_encoding_test.py", "max_stars_repo_name": "obliviateandsurrender/nisqai-dev", "max_stars_repo_head_hexsha": "cf6c38d34acb8fe776913c69212845c547cb47a6", "max_stars_rep... |
import streamlit as st
import numpy as np
import pandas as pd
from PIL import Image
import cv2
#inittalise haarcascade CascadeClassifier and read in model xml file for face detction
face_cascade = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
#takes in image from streamlit fileuploader as P... | {"hexsha": "eb24a971bb41122073eb8536313f22aaa44457f6", "size": 1611, "ext": "py", "lang": "Python", "max_stars_repo_path": "harcasscade_stremlit_demo.py", "max_stars_repo_name": "poipiii/stream-lit-demo", "max_stars_repo_head_hexsha": "a8624ebf1c8e4d84911f569e8692d9d11f35cac1", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 14 15:08:56 2019
@author: riselAir
"""
#unit testing for peakdetect_RI.py
import unittest
import math
import numpy as np
from scipy.stats import norm
from peakfunctions import second_largest, fit_gaussian_to_shifted_data, xvalues_for_continuous_cu... | {"hexsha": "9e8ccc3cf7eb2d01c6b58f43a35a09ff8824853b", "size": 2704, "ext": "py", "lang": "Python", "max_stars_repo_path": "peakdetect_testing.py", "max_stars_repo_name": "riselin/Intensity_measurement", "max_stars_repo_head_hexsha": "7edb458fe49ae3bcf30b4d025b8fb2131b99ab65", "max_stars_repo_licenses": ["MIT"], "max_s... |
import random
import unittest
import numpy as np
from skratch.datasets import load_boston
from skratch.datasets import load_diabetes
from skratch.bonsai import DecisionBonsaiRegressor
def r2_score(y_true, y_pred):
"""
Very similar to the Sklearn implementation but without weights and single
output.
... | {"hexsha": "e686594078f249954c62c5fb1807177019439de4", "size": 3942, "ext": "py", "lang": "Python", "max_stars_repo_path": "skratch/test/test_bonsai_regressor.py", "max_stars_repo_name": "DelgadoPanadero/Scratch-Python-Forest", "max_stars_repo_head_hexsha": "1dbeeab8a0cc5c9078dbe700ccee42c8afdcc597", "max_stars_repo_li... |
#!/usr/bin/env python
# Copyright (c) 2011-2019, wradlib developers.
# Distributed under the MIT License. See LICENSE.txt for more info.
"""
Xarray powered Data I/O
^^^^^^^^^^^^^^^^^^^^^^^
Reads data from netcdf-based CfRadial1, CfRadial2 and hdf5-based ODIM_H5 and
other hdf5-flavours (GAMIC).
Writes data to CfRadi... | {"hexsha": "fb8bdc8406fb18a18c38eb11660da1c5404d6808", "size": 65078, "ext": "py", "lang": "Python", "max_stars_repo_path": "wradlib/io/xarray.py", "max_stars_repo_name": "nschaffner/wradlib", "max_stars_repo_head_hexsha": "eaa74701464e4ca2e01c96be7d47667983b6cfd6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python
import numpy as np
import sys
def avg_data(d_f):
data = np.loadtxt(d_f)
vs = []
print len(data)
for i in range(len(data)):
vs.append(data[i][1])
avg = np.average(vs)
return avg
avg = avg_data(sys.argv[1])
print avg
| {"hexsha": "3fdc6986d0b17c9c10f68b3115761e69a49b5362", "size": 249, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/lipid/misc/get_avgv.py", "max_stars_repo_name": "kmckiern/scripts", "max_stars_repo_head_hexsha": "acc8326ca653d804ee06752af9e7f5b011fc6e0e", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 11 20:02:37 2017
@author: Anastasios Tzavellas
"""
import numpy as np
import matplotlib.pyplot as plt
import interpolation
def f(x):
return np.exp(-x)
def df(x):
return -np.exp(-x)
def d2f(x):
return f(x)
def err2(x):
return np.exp(-x) * (x-0.25) ... | {"hexsha": "727fd25f1c910c848caeff6621dcd4cb4b24d2b3", "size": 883, "ext": "py", "lang": "Python", "max_stars_repo_path": "Assignment3/ex5.py", "max_stars_repo_name": "tzavellas/ComputationalPhysics", "max_stars_repo_head_hexsha": "7dd95d1a55faa4d467b9a96c7ee1933e88652370", "max_stars_repo_licenses": ["MIT"], "max_star... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, TensorDataset
from sklearn.model_selection import train_test_split
import numpy as np
import logging
from pdb import set_trace as st
import os.path as osp
import os
import copy
import wandb
from fedml... | {"hexsha": "b500a9913816ffd3452097de4d0061d7dfa91b40", "size": 13275, "ext": "py", "lang": "Python", "max_stars_repo_path": "privacy_fedml/MI_attack/NN_attack.py", "max_stars_repo_name": "ziqi-zhang/FedML", "max_stars_repo_head_hexsha": "83cfc1245609c50f21d13be5f180f67576c57735", "max_stars_repo_licenses": ["Apache-2.0... |
#!/usr/bin/env python
"""Tests for `datawrangler` package (decorate module)."""
import os
import datawrangler as dw
import pandas as pd
import numpy as np
# noinspection PyTypeChecker
def test_list_generalizer():
@dw.decorate.list_generalizer
def f(x):
return x ** 2
assert f(3) == 9
assert ... | {"hexsha": "b9f934a8376d0258765f1733a983cf48c3d49593", "size": 5147, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/wrangler/test_decorate.py", "max_stars_repo_name": "ContextLab/data-wrangler", "max_stars_repo_head_hexsha": "80cebdb1acaf0c1a2af34f56161e80d81e962ab1", "max_stars_repo_licenses": ["MIT"], "... |
import importlib
import logging
import os
import pkg_resources
import sys
import time
import numpy as np
import pandas as pd
import tables
import yaml
def setup_logging(config, log_dir, debug, log_to_file):
# Log configuration to a text file in the log dir
time_str = time.strftime('%Y%m%d_%H%M%S')
config... | {"hexsha": "999881516f5de581f406dd1f5006208493a9f300", "size": 7236, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctlearn/utils.py", "max_stars_repo_name": "bryankim96/deep-learning-gamma", "max_stars_repo_head_hexsha": "cac4f2d90b6536eedf95c3a07ea73d4c7a69c5b7", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
export BackwardPlanner, BackwardGreedyPlanner, BackwardAStarPlanner
"Heuristic-guided best-first backward search."
@kwdef mutable struct BackwardPlanner <: Planner
heuristic::Heuristic = GoalCountHeuristic(:backward)
g_mult::Float64 = 1.0 # Path cost multiplier
h_mult::Float64 = 1.0 # Heuristic multiplier
... | {"hexsha": "e969f7f2b0e55789190b680dba96a75ffd5bb98b", "size": 4224, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/planners/backward.jl", "max_stars_repo_name": "owenps/SymbolicPlanners.jl", "max_stars_repo_head_hexsha": "824dedf9a73b9ee4f4881af0c16fe8963f033e4f", "max_stars_repo_licenses": ["Apache-2.0"], ... |
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this... | {"hexsha": "7481637669d33bf88f72b953f4d3040cc8e71d33", "size": 2490, "ext": "py", "lang": "Python", "max_stars_repo_path": "figure4/plot.py", "max_stars_repo_name": "ChriPiv/stinespring-algo-paper", "max_stars_repo_head_hexsha": "d61cf46c302c511286280e5de2b22d01284a4379", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
import os
import numpy as np
import pandas as pd
import time
import ray
import tensorflow.keras as keras
from cerebro.backend.ray.backend import RayBackend
from cerebro.storage import LocalStore
from cerebro.keras import RayEstimator
from cerebro.tune import RandomSearch, GridSearch, hp_choice
import rand... | {"hexsha": "8e4b9f0832584870319dd94e7291d6e64132b1b3", "size": 3060, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mnist/model_selection.py", "max_stars_repo_name": "Abhishek2304/Cerebro-System-Ray", "max_stars_repo_head_hexsha": "1e2f2ae291cd449573f87bb83fb2bda12e606b3a", "max_stars_repo_licenses": [... |
# help manual of "Data input" functions
?read.table
?read.csv
# read csv to frame
my_data <- read.csv('evals.csv')
# open data table in window
View(mydata)
# structure of frame
str(mydata)
# return vector of value's names
names(mydata)
# create new var in frame
mydata$nvar <- 0
# amount of rows in frame
nrow(myd... | {"hexsha": "4b4810f169088ee0881b6eeff57c8dfa36e5f5bd", "size": 1243, "ext": "r", "lang": "R", "max_stars_repo_path": "dataframes.r", "max_stars_repo_name": "Sokel/R-shchu", "max_stars_repo_head_hexsha": "0b5b48b018bf85caca89fefb1896468f6039568c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_... |
from os import listdir
from os.path import isfile, join,isdir
import sys
from pathlib import Path
import numpy as np
from PIL import Image
import cv2
import os
class DataPrepare():
def __init__(self):
self.basedir=r'./DeepLabv3FineTuning/ADEChallengeData2016/'
self.basedir_annt=r'..\freespacetrai... | {"hexsha": "0040dbad23a817ac42fffbcd07ba2d8239533a59", "size": 2558, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataprepare.py", "max_stars_repo_name": "angrysword/DeepLabv3FineTuning", "max_stars_repo_head_hexsha": "d97dd31620c0e65dd7cf4fd24581ded5af17465c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import argparse
import os
import pickle
import torch
import numpy as np
import matplotlib.pyplot as plt
from util.bioinformatics_algorithms.edit_distance import cross_distance_matrix_threads
from util.data_handling.string_generator import string_to_list
class ClosestStringGenomicDatasetGenerator:
def __init__(s... | {"hexsha": "758d1773af9eeecf6f1f8a511c0ea0d4d9dce95f", "size": 3276, "ext": "py", "lang": "Python", "max_stars_repo_path": "closest_string/task/dataset_generator_genomic.py", "max_stars_repo_name": "pchlenski/NeuroSEED", "max_stars_repo_head_hexsha": "6318431dcce22df70948251b103374f4a6e96fff", "max_stars_repo_licenses"... |
# B.C. Goodwin
# Oscillatory behavior in enzymatic control processes
# https://doi.org/10.1016/0065-2571(65)90067-1
using Logging
using StructuralIdentifiability
logger = Logging.SimpleLogger(stdout, Logging.Info)
global_logger(logger)
ode = @ODEmodel(
x1'(t) = -b * x1(t) + 1 / (c + x4(t)),
x2'(t) = alpha *... | {"hexsha": "a6716bc0d752d99f492bd01c3ffb68581a3c5177", "size": 518, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/Goodwin.jl", "max_stars_repo_name": "iliailmer/StructuralIdentifiability.jl", "max_stars_repo_head_hexsha": "29f1104e357f2f2942ff5477871856d80518a6ed", "max_stars_repo_licenses": ["MIT"], "... |
# standard library
import sys
import re
from typing import Any, Dict, List, Literal, Tuple, Union
import os
import json
import subprocess
import time
# third-party libraries
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
# local
from validate_utils import write_report... | {"hexsha": "91447de326a6694923d1d117820be2b0251adee7", "size": 20509, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/validate_GWASSS_entries.py", "max_stars_repo_name": "Titorat/SSrehab", "max_stars_repo_head_hexsha": "6691ee1ed442073bfa00a51f0d9ab74b9252d302", "max_stars_repo_licenses": ["MIT"], "max_stars... |
@testset "678.valid-parenthesis-string.jl" begin
@test check_valid_string("(())((())()()(*)(*()(())())())()()((()())((()))(*") == false
@test check_valid_string(")(") == false
@test check_valid_string("(*))") == true
@test check_valid_string("(*)") == true
@test check_valid_string("()") == true
end | {"hexsha": "c70c5c4347c7c1ab92203ea069f822acca167b91", "size": 319, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems/678.valid-parenthesis-string.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MI... |
import torch
import torch.nn as nn
import torch.nn.functional as func
from torch.autograd import Variable
from torchvision.utils import save_image
import os
import numpy as np
class Discriminator(nn.Module):
def __init__(self, config):
super(Discriminator, self).__init__()
self.label... | {"hexsha": "1344a8dc65b804a057029c39b4542cad017b2104", "size": 2206, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/cGan.py", "max_stars_repo_name": "Ls-Dai/Pytorch_FL_GAN", "max_stars_repo_head_hexsha": "63b38600751c6245e07b022f64e63d783e531b5e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
function Y = cosh(X)
% Symbolic hyperbolic cosine.
% Convert inputs to SymExpression
% X = SymExpression(X);
% construct the operation string
sstr = ['Cosh[' X.s ']'];
% create a new object with the evaluated string
Y = SymExpression(sstr);
end
| {"author": "ayonga", "repo": "frost-dev", "sha": "e5dc0624d834520872bfa588dd3eda5643da71de", "save_path": "github-repos/MATLAB/ayonga-frost-dev", "path": "github-repos/MATLAB/ayonga-frost-dev/frost-dev-e5dc0624d834520872bfa588dd3eda5643da71de/matlab/symbolic/@SymExpression/cosh.m"} |
import requests
import re
import time
import datetime
import numpy as np
"""
Prerequisite: Create access tokens
You need private access token to have full access to Github search
API.
Generate your access token in [here](https://github.com/settings/tokens)
you don't need to tick on any access permission because you ar... | {"hexsha": "076ff7f288ae3e101b0b411debeee5fea728fdf5", "size": 2265, "ext": "py", "lang": "Python", "max_stars_repo_path": "find_other_frameworks_usage.py", "max_stars_repo_name": "koreyou/chainer-usage-analysis", "max_stars_repo_head_hexsha": "f394b7e38f0608110a4e70e04c0fc91449a4351b", "max_stars_repo_licenses": ["CC0... |
import dask.array as da
import numpy as np
import abmvisum.engines.choice_engine as choice_engine
import abmvisum.tools.utilities as utilities
daskfactory = choice_engine.DaskFactory(10000)
def calc_dest_zone_utility_matrix(origin_zones, betas, utility_matrices,
target_zones_for_ru... | {"hexsha": "6c2621be24393402b9549cb29d26760b67bf5e8b", "size": 9099, "ext": "py", "lang": "Python", "max_stars_repo_path": "abmvisum/engines/location_choice_engine.py", "max_stars_repo_name": "SchweizerischeBundesbahnen/abm-in-visum", "max_stars_repo_head_hexsha": "b67451c331482a70fab20c7340d27e78b9e50461", "max_stars_... |
function load_xyz_data(file_path :: String; atom_names = nothing, radii = [3.5], weights = [1.0], masses = [1.0], boundary_type = ["p", "p", "p"], units = "lj")
# frames = read_frames(file_path)
# n = length(frames)
# r = Vector{Configuration}(undef, n)
# if atom_names == nothing
# atom_names =... | {"hexsha": "2ca3dc241b0445739ed72fb0a69b0ec4017a3fc5", "size": 1873, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/IO/io_xyz.jl", "max_stars_repo_name": "stefanbringuier/InteratomicPotentials.jl", "max_stars_repo_head_hexsha": "96d6808d4ccd974817d0220b72b29ed4dd173e4f", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
from core import GraphicalObject, DrawContext
import typing as tp
class Point(GraphicalObject):
def __init__(self, name: str, x: float, y: float, z: float = ...):
if z is ...:
self.is_3d = False
super().__init__(name, np.array([[x, y, 1.]]))
else:
... | {"hexsha": "1159ab058cfe5d1d1d64dded00d9d60d2528e160", "size": 1361, "ext": "py", "lang": "Python", "max_stars_repo_path": "shapes/point.py", "max_stars_repo_name": "HENRDS/INE5408", "max_stars_repo_head_hexsha": "3216fe813200eff6480bd8db3d63299fb5255cda", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_... |
! =================================================================================================================================
! MODULE : hydrolc
!
! CONTACT : orchidee-help _at_ listes.ipsl.fr
!
! LICENCE : IPSL (2006)
! This software is governed by the CeCILL licence see ORCHIDEE/O... | {"hexsha": "a1540f8dd85d4f423868ae2674c50199d197d261", "size": 213320, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/examples_large/ORCHIDEE_hydrolc.f90", "max_stars_repo_name": "mbdevpl/open-fortran-parser-xml", "max_stars_repo_head_hexsha": "127f4ec2ba7cd06eb010794560eb8e4b9494fdfe", "max_stars_repo_l... |
module VermontTerrestrialPassageTool
using ArchGDAL
using DelimitedFiles
using GeoData
using Omniscape
using Shapefile
using Statistics
include("utils.jl")
include("main.jl")
include("consts.jl")
export compute_all_culverts
end
| {"hexsha": "d6d0d2943d5f50faa30aba936871f95e915277a2", "size": 232, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/VermontTerrestrialPassageTool.jl", "max_stars_repo_name": "csp-inc/VermontTerrestrialPassageTool.jl", "max_stars_repo_head_hexsha": "9c5273012bcdb77241ec6caddf2bc02c99c4e817", "max_stars_repo_li... |
/****************************************************************************
* hipipe library
* Copyright (c) 2017, Cognexa Solutions s.r.o.
* Copyright (c) 2018, Iterait a.s.
* Author(s) Filip Matzner
*
* This file is distributed under the MIT License.
* See the accompanying file LICENSE.txt for the comp... | {"hexsha": "3a624e818493180be0319f99347f08d5b0f56f15", "size": 1603, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/tensorflow/run_graph.cpp", "max_stars_repo_name": "iterait/hipipe", "max_stars_repo_head_hexsha": "c2a6cc13857dce93e5ae3f76a86e8f029ca3f921", "max_stars_repo_licenses": ["BSL-1.0", "MIT"], "max... |
subroutine difu(icreep ,timest ,lundia ,nst ,icx , &
& icy ,j ,nmmaxj ,nmmax ,kmax , &
& lstsci ,lstsc ,lsal ,ltem ,lsecfl , &
& lsec ,lsed ,lsts ,norow ,irocol , &
& kcs ... | {"hexsha": "1d7ca2ec6776a1f20d8beeff045e7dabc98a884c", "size": 49066, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/flow2d3d/packages/kernel/src/compute/difu.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02... |
from vpython import *
from cubes.configs import *
from cubes.cube import *
from cubes.util import *
from time import time
import numpy as np
def smooth_step(x):
"""
Smooth interpolation function
"""
return 0.5 - np.cos(x * pi) / 2
class Cube3D(Cube):
"""
Initialize the cube structure... | {"hexsha": "86e7168fb5792e92bab81e59daa74a183d8ba426", "size": 7397, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cubes/cube_3d.py", "max_stars_repo_name": "pabloqb2000/py-rubik_solver", "max_stars_repo_head_hexsha": "c1cf83fa62b6ff0dce9859fe59296c85a57f8d3f", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
[STATEMENT]
lemma ZfunD: "Zfun f F \<Longrightarrow> 0 < r \<Longrightarrow> eventually (\<lambda>x. norm (f x) < r) F"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Zfun f F; 0 < r\<rbrakk> \<Longrightarrow> \<forall>\<^sub>F x in F. norm (f x) < r
[PROOF STEP]
by (simp add: Zfun_def) | {"llama_tokens": 123, "file": null, "length": 1} |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import tempfile
import argparse
import os
from tabulate import tabulate
import pickle
header = '''data_nef_MELD_{}
save_nef_nmr_meta_data
_nef_nmr_meta_data.sf_category nef_nmr_meta_data
_nef_nmr_meta_data.sf_frameco... | {"hexsha": "30d7c13ff330b83433c9f4a89eae54e0599628e9", "size": 13294, "ext": "py", "lang": "Python", "max_stars_repo_path": "MELD_NEF_Classes.py", "max_stars_repo_name": "alberto99/MELDNMRParser", "max_stars_repo_head_hexsha": "cabffff5dcb6fbc9ff01446367659d42ce236644", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma cxt_inv:
"\<lbrakk> cxt_to_stmt E c = c' ; \<And> p q. c' \<noteq> Seq p q \<rbrakk> \<Longrightarrow> E = [] \<and> c' = c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>cxt_to_stmt E c = c'; \<And>p q. c' \<noteq> p ;; q\<rbrakk> \<Longrightarrow> E = [] \<and> c' = c
[PROOF STEP]
by ... | {"llama_tokens": 181, "file": "Dependent_SIFUM_Type_Systems_Language", "length": 1} |
import bw2data as bd
import numpy as np
from scipy.stats import norm
import stats_arrays as sa
# Local files
from .constants import air_molecular_weight, atmosphere_total_mass, substances_data
def get_uncertain_flows(time_horizon=100, verbose=False):
method = ('IPCC 2013', 'climate change', 'GWP {}a'.format(t... | {"hexsha": "a48992b3004e3cd44cb75679d302b2e6fd07b1c5", "size": 11226, "ext": "py", "lang": "Python", "max_stars_repo_path": "gwp_uncertainties/gwp_computations.py", "max_stars_repo_name": "aleksandra-kim/bw_gwp_uncertainties", "max_stars_repo_head_hexsha": "de428c60b2ff54def15f976657ebf55823ccb86d", "max_stars_repo_lic... |
\section{PWGrid version 1}
\subsection{The implementation: {\tt PWGrid\_01.jl}}
The type definition
\begin{juliacode}
type PWGrid
Ns::Array{Int64}
LatVecs::Array{Float64,2}
RecVecs::Array{Float64,2}
Npoints::Int
Ω::Float64
r::Array{Float64,2}
G::Array{Float64,2}
G2::Array{Float64}
end
\end{juliacode}... | {"hexsha": "890c23178c50e7e7a1b8d37e67fe95dcb3aee533", "size": 3007, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "PW/annotated/PWGrid_01.tex", "max_stars_repo_name": "f-fathurrahman/ffr-ElectronicStructure.jl", "max_stars_repo_head_hexsha": "35dca9831bfc6a3e49bb0f3a5872558ffce4b211", "max_stars_repo_licenses": ... |
*DECK SCOEF
SUBROUTINE SCOEF (YH, YP, NCOMP, NROWB, NFC, NIC, B, BETA, COEF,
+ INHOMO, RE, AE, BY, CVEC, WORK, IWORK, IFLAG, NFCC)
C***BEGIN PROLOGUE SCOEF
C***SUBSIDIARY
C***PURPOSE Subsidiary to BVSUP
C***LIBRARY SLATEC
C***TYPE SINGLE PRECISION (SCOEF-S, DCOEF-D)
C***AUTHOR Watts, H. A., (SNLA... | {"hexsha": "c53d812c3bbb6eef1123f0c47045a8adc9102279", "size": 5511, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "slatec/src/scoef.f", "max_stars_repo_name": "andremirt/v_cond", "max_stars_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count... |
{-# OPTIONS --cubical --no-import-sorts --safe #-}
module Cubical.HITs.Truncation.FromNegOne where
open import Cubical.HITs.Truncation.FromNegOne.Base public
open import Cubical.HITs.Truncation.FromNegOne.Properties public
| {"hexsha": "fdbb77ecd12a49b7635e67c6a865c42578a19d8d", "size": 224, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/HITs/Truncation/FromNegOne.agda", "max_stars_repo_name": "bijan2005/univalent-foundations", "max_stars_repo_head_hexsha": "737f922d925da0cd9a875cb0c97786179f1f4f61", "max_stars_repo_license... |
################################################################################
# interHapLabels function
#
# Author: Heidi Lischer
# Date: 10.2008
################################################################################
interHapLabels <- function(xmlText){
tagData2 <- as.character(xmlText)
tag... | {"hexsha": "f913e9408a79d6ae0bafaea3655ab04eae4534c9", "size": 735, "ext": "r", "lang": "R", "max_stars_repo_path": "code/tools/arlequin/arlecore_linux/Rfunctions/interHapLabels.r", "max_stars_repo_name": "kibet-gilbert/co1_metaanalysis", "max_stars_repo_head_hexsha": "1089cc03bc4dbabab543a8dadf49130d8e399665", "max_st... |
[STATEMENT]
lemma Spy_not_see_NB:
"[| A \<notin> bad; B \<notin> bad |]
==> Nprg \<in> Always
{s. Says B A (Crypt (pubK A) \<lbrace>Nonce NA, Nonce NB\<rbrace>) \<in> set s &
(\<forall>C. Says A C (Crypt (pubK C) (Nonce NB)) \<notin> set s)
--> Nonce NB \<notin>... | {"llama_tokens": 11201, "file": null, "length": 14} |
# -*- coding: utf-8 -*-
#==========================================
# Title: util.py
# Author: Binxin Ru and Ahsan Alvi
# Date: 20 August 2019
# Link: https://arxiv.org/abs/1906.08878
#==========================================
from typing import Tuple
import numpy as np
def add_hallucinations_to_x_and_y(bo, o... | {"hexsha": "6e1ce39236517ddd8add2e4ed3432963bd4fc2e7", "size": 2977, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/bayesopt/util.py", "max_stars_repo_name": "ChangYong-Oh/CoCaBO_code", "max_stars_repo_head_hexsha": "d7eab8c8419de8df68355f1e410b8c4b4e697e44", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Copyright - Transporation, Bots, and Disability Lab - Carnegie Mellon University
# Released under MIT License
"""
Convert Functions for roslibpy
"""
import numpy as np
import base64
import sys
import logging
logging.basicConfig()
def _get_encoding_information(encoding):
if encoding == 'bgra8':
retur... | {"hexsha": "2341f34b64ccd6a300739731b72130ee5aecee57", "size": 2381, "ext": "py", "lang": "Python", "max_stars_repo_path": "alloy/ros/roslibpy.py", "max_stars_repo_name": "CMU-TBD/alloy", "max_stars_repo_head_hexsha": "cf66738e044613fb274bd1b159864a7600e15cb5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# coding: utf-8
import logging
import sys
import time
import numpy as np
import pytest
import ray.cluster_utils
from ray._private.test_utils import (
client_test_enabled,
SignalActor,
)
if client_test_enabled():
from ray.util.client import ray
else:
import ray
logger = logging.getLogger(__name__)
... | {"hexsha": "0bda228682f4c17f72575843529b6586900a48d8", "size": 7318, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ray/tests/test_advanced_5.py", "max_stars_repo_name": "jianoaix/ray", "max_stars_repo_head_hexsha": "1701b923bc83905f8961c06a6a173e3eba46a936", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
#include <boost/log/sinks/basic_sink_frontend.hpp>
| {"hexsha": "53c787c860d9565abaa498078196dfb7f23b03ee", "size": 51, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_log_sinks_basic_sink_frontend.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": [... |
import numpy as np
import tensorflow as tf
from sklearn.ensemble import IsolationForest
class EarlyStoppingBeforeOverfit(tf.keras.callbacks.Callback):
"""
"""
def __init__(self, patience=15, min_acc=0.95):
super(EarlyStoppingBeforeOverfit, self).__init__()
self.min_acc = min_acc
s... | {"hexsha": "134b1b6c6674e64f977ddd5c3921921ec22451ed", "size": 3410, "ext": "py", "lang": "Python", "max_stars_repo_path": "exp_table2/aosr_utility.py", "max_stars_repo_name": "Anjin-Liu/Openset_Learning_AOSR", "max_stars_repo_head_hexsha": "8b9435dfd33abf5969122beddf07bf9314d0969a", "max_stars_repo_licenses": ["MIT"],... |
from scipy.signal import butter, lfilter, resample, firwin, decimate
from sklearn.decomposition import FastICA, PCA
from sklearn import preprocessing
import numpy as np
import pandas as np
import matplotlib.pyplot as plt
import scipy
import pandas as pd
class SpectrogramImage:
"""
Plot spectrogram for each ch... | {"hexsha": "767f756df6c9b01fb861185b1d3f165d996d8fe9", "size": 12247, "ext": "py", "lang": "Python", "max_stars_repo_path": "transforms.py", "max_stars_repo_name": "A-Jacobson/iEEG_Seizure_Prediction", "max_stars_repo_head_hexsha": "bdee7f4aab72674e01af7ec254b5d6ec7f65e620", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import sigpy as sp
import math
from subtle_data_crimes.functions import new_poisson
import matplotlib.pyplot as plt
# ===================== 2D Variable-density Sampling (based on Miki Lustig's Sparse MRI toolbox) ============================
def create_samp_mask(R, imSize, calib=[24, 24],... | {"hexsha": "58151f4d1b634b3b26644f2126dbb4f0f46ed993", "size": 5712, "ext": "py", "lang": "Python", "max_stars_repo_path": "subtle_data_crimes/crime_2_jpeg/Fig7/DL/utils/sampling_funcs.py", "max_stars_repo_name": "mikgroup/subtle_data_crimes", "max_stars_repo_head_hexsha": "210025d9cb8f92583f5f983be15af06b57cfea36", "m... |
import gc
import os
import numpy as np
import tensorflow as tf
from tensorflow.contrib.image import translate as tf_translate
from scipy.ndimage import gaussian_filter
from utils.distance_measures import DistL2
from models.minibatch_wrapper import MinibatchWrapper
from utils import saliency, dataset_imagenet
from util... | {"hexsha": "7ebd9fe264e36fbf306a04603ddc626fe83b1347", "size": 14391, "ext": "py", "lang": "Python", "max_stars_repo_path": "starting_point_finder.py", "max_stars_repo_name": "ttbrunner/blackbox_starting_points", "max_stars_repo_head_hexsha": "7d489cc712a3ee53bfd630ce2830bf643feecb0c", "max_stars_repo_licenses": ["MIT"... |
function tapas_validate_data(data)
%% Check that data complies with the inferface.
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
if ~isstruct(data)
error('tapas:validate:data', 'data is not a structure')
end
if ~isfield(data, 'y')
error('tapas:validate:data', 'data lacks field y');
end
if ~isfield(data,... | {"author": "translationalneuromodeling", "repo": "tapas", "sha": "604c56843c15411f5bd80190f81d845ac57d8592", "save_path": "github-repos/MATLAB/translationalneuromodeling-tapas", "path": "github-repos/MATLAB/translationalneuromodeling-tapas/tapas-604c56843c15411f5bd80190f81d845ac57d8592/tools/ti/tapas_validate_data.m"} |
abstract Initializer
type NormalInit <: Initializer
mu :: AbstractFloat
sd :: AbstractFloat
function NormalInit(mu=0.0, sd=1.0)
new(mu, sd)
end
end
type UniformInit <: Initializer
min :: AbstractFloat
max :: AbstractFloat
function UniformInit(min=0.0, max=1.0)
new(min, max)... | {"hexsha": "d2a9863fb3ade191aaf299ba6ce4bfc0586c258c", "size": 650, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/initializer.jl", "max_stars_repo_name": "peakbook/BlockMatchingSOMs.jl", "max_stars_repo_head_hexsha": "e70d20a4e6dce296f6b489a3344090e97db1085d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import matplotlib.pyplot as plt
from numpy import *
import Model
def read_data_from_file(filename):
file = open(filename, "r")
file.readline()
data = file.readlines()
x_true, y_true = [], []
for line in data:
temp = line.split(",")
x_true.append(int(temp[0]))
... | {"hexsha": "eb165e96358ce4fa0a45b5fc31956ef3d361a997", "size": 1358, "ext": "py", "lang": "Python", "max_stars_repo_path": "HW1/Experiment1.py", "max_stars_repo_name": "efkandurakli/Pattern-Recognition-Homewoks", "max_stars_repo_head_hexsha": "0d24a2c5d38fb70b02313ea6c1b8342bdcfa4495", "max_stars_repo_licenses": ["MIT"... |
import os
import sys
import numpy as np
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
DATA_PATH = os.path.join(ROOT_DIR, 'data/')
out_fname = ['rgbnss_trn', 'rgbnss_tst', 'depth_trn', 'depth_tst']
xtrn_list = [line.rstrip() for line in open(os.path.join(DATA_PATH, 'xtrn_list.txt'))]
xtst_l... | {"hexsha": "d30ca922ec617070f128fb08b1254f810e36c951", "size": 931, "ext": "py", "lang": "Python", "max_stars_repo_path": "stack_all_data.py", "max_stars_repo_name": "yustisiardhitasari/sdbcnn", "max_stars_repo_head_hexsha": "5b591009c1576c6879bf883c6eec23b0b743d2cd", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
\section{Conclusion}
\label{sec:conc}
Blockchains have the potential to disrupt incumbent applications on the data sharing economy. In addition to this, the
Transportation industry is going through a radical shift due to the new types of data and applications that have become
available. For instance the proliferation ... | {"hexsha": "b2e8d0fc92d13be2a554aa2c87c5fd0f18aa7a54", "size": 1535, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "conclusion.tex", "max_stars_repo_name": "arunesh/latwp", "max_stars_repo_head_hexsha": "a84fff7e01cfea691a81024f13cd730bea6c8c05", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# 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 a... | {"hexsha": "82f331067cbf449dccaf1ca643ded164e42d9dbf", "size": 3379, "ext": "py", "lang": "Python", "max_stars_repo_path": "mars/learn/metrics/pairwise/tests/test_pariwise_distances.py", "max_stars_repo_name": "tomzhang/mars-1", "max_stars_repo_head_hexsha": "6f1d85e37eb1b383251314cb0ba13e06288af03d", "max_stars_repo_l... |
[STATEMENT]
lemma set_attribute_get_attribute:
"h \<turnstile> set_attribute ptr k v \<rightarrow>\<^sub>h h' \<Longrightarrow> h' \<turnstile> get_attribute ptr k \<rightarrow>\<^sub>r v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. h \<turnstile> set_attribute ptr k v \<rightarrow>\<^sub>h h' \<Longrightarrow>... | {"llama_tokens": 221, "file": "Core_DOM_common_Core_DOM_Functions", "length": 1} |
# -*- coding: utf-8 -*-
import numpy as np
import statistics
from skimage import filters, morphology, measure
import matplotlib.pyplot as plt
def kymo_to_coords(kymo, thres=15, pixel_length = 0.1833333):
"""
This function takes a kymograph and extract the membrane position from the image.
Parameters
... | {"hexsha": "9ddef7453170ff5287628ed61ad810197cbd1558", "size": 2784, "ext": "py", "lang": "Python", "max_stars_repo_path": "kymo_to_coords.py", "max_stars_repo_name": "ernestiu/Cell-spreading-analysis", "max_stars_repo_head_hexsha": "5a8807be9e25b6cc8cc4850f03810eacc48fe8e0", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
import unittest
import pytest
import numpy as np
import pickle
from s2and.data import ANDData
from s2and.model import Clusterer
from s2and.featurizer import FeaturizationInfo, many_pairs_featurize
from s2and.consts import LARGE_DISTANCE
from sklearn.linear_model import LogisticRegression
class TestClusterer(unittest... | {"hexsha": "66579e70df0c64235d8dccafc00bc12f5eb65e1a", "size": 3368, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_cluster_incremental_incompatible.py", "max_stars_repo_name": "atypon/S2AND", "max_stars_repo_head_hexsha": "d1af114edf5941d0ec00f56b11508582e1091913", "max_stars_repo_licenses": ["Apach... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import openravepy
import numpy
import random
from copy import deepcopy
if not __openravepy_build_doc__:
from openravepy import *
from numpy import *
class Node:
def __init__(self, _input, id_in, parentid_in):
self.config = _input
self.id = id_i... | {"hexsha": "c5bd2174ba11d708ebe4816cf5f4f785a2a3c928", "size": 7075, "ext": "py", "lang": "Python", "max_stars_repo_path": "RRT/rrt_fun.py", "max_stars_repo_name": "Simon0323/A-star-RRT-Motion-Planning", "max_stars_repo_head_hexsha": "3319a4da07b9a81fdc6ab64aeef119abb5aedd93", "max_stars_repo_licenses": ["MIT"], "max_s... |
// Copyright (c) 2012, Yahoo! Inc. All rights reserved.
// Copyrights licensed under the New BSD License. See the accompanying LICENSE file for terms.
#include <tap/trivial.h>
#include <gearbox/core/util.h>
#include <gearbox/core/TempFile.h>
using namespace Gearbox;
#include "log4cxx/propertyconfigurator.h"
#includ... | {"hexsha": "e35b8394c24a7ecb6c0eef6d57a97414bdb5de8e", "size": 1468, "ext": "cc", "lang": "C++", "max_stars_repo_path": "gearbox/t/core/ffdigest.t.cc", "max_stars_repo_name": "coryb/gearbox", "max_stars_repo_head_hexsha": "88027f2f101c2d1fab16093928963052b9d3294d", "max_stars_repo_licenses": ["Artistic-1.0-Perl", "BSD-... |
import numpy as np
from flask import Flask, jsonify, request
import pickle
#model
my_model = pickle.load(open('iris_model2.pkl', 'rb'))
app = Flask(__name__)
@app.route('/api', methods=['POST'])
def make_predict():
#get data
data = request.get_json(force=True)
#transform/parse
predict_request = [data... | {"hexsha": "99e3fd6a4ec6768a5b8f3e33e66481c19064c5db", "size": 1009, "ext": "py", "lang": "Python", "max_stars_repo_path": "module3-adding-data-science-to-a-web-application/iris_model_app_two.py", "max_stars_repo_name": "nrvanwyck/DS-Unit-3-Sprint-3-Productization-and-Cloud", "max_stars_repo_head_hexsha": "186a2420ddaf... |
module day24
include("inputs.jl")
ex = inputs.ex_d24()
input = inputs.input_d24()
# https://www.redblobgames.com/grids/hexagons/#coordinates-cube
struct Tile
# cube coordinates for hex grid (see link)
x::Int
y::Int
z::Int
end
function parse_input(input)
rgx = r"(ne|se|sw|nw|e|w)"
return map(... | {"hexsha": "81685e76ae7e8b529b74434be4aa9fb2ea921c59", "size": 3104, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/day_24.jl", "max_stars_repo_name": "danjburns/AdventOfCode2020", "max_stars_repo_head_hexsha": "2c7cb0d1cbc41960da26691d55a572b9806bdb32", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
\documentclass[]{book}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\usepackage{fixltx2e} % provides \textsubscript
\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\else % if luatex or xelatex
\ifxetex
\usepackage{mathsp... | {"hexsha": "19169839b95ff0711fb4b83344b1e701534451b4", "size": 6675, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "bookdown-start.tex", "max_stars_repo_name": "watzek/BLTdocs", "max_stars_repo_head_hexsha": "e21c8f809de6bf20d235ee7e4f4c6617df52bb53", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": nul... |
PROGRAM SECEME
C
C CALCULATES AVERAGE VALUE OF 1/SQRT(ES) FOR SECONDARY
C EMISSION FROM COPPER, SEAH, SURFACE SCIENCE 17,132-160,(1969)
C
DELE = .05
VAL1 = 0.
VAL2 = 0.
DO N = 1,1000
ES = .05*N
FSR = 1 + (N.AND.1)
Y = ES/(ES +.35)/(ES + 4.5)**1.6
VAL1 = VAL1 + DELE*FSR*Y
VAL2 = VAL2 + DELE*FSR*Y/SQRT(ES)
END... | {"hexsha": "e7164c9fb75d9e805c3badd47e11457c6356ab76", "size": 388, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "WAVES_VMS_Fortran/PJK_Fortran/waves_dir/seceme_1.for", "max_stars_repo_name": "lynnbwilsoniii/Wind_Decom_Code", "max_stars_repo_head_hexsha": "ef596644fe0ed3df5ff3b462602e7550a04323e2", "max_star... |
# Copyright 2017 Battelle Energy Alliance, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... | {"hexsha": "dcf7dbc44b8c4a0b18e3ce5cd3d8b8f3c360df53", "size": 15558, "ext": "py", "lang": "Python", "max_stars_repo_path": "framework/Models/PostProcessors/Metric.py", "max_stars_repo_name": "FlanFlanagan/raven", "max_stars_repo_head_hexsha": "bd7fca18af94376a28e2144ba1da72c01c8d343c", "max_stars_repo_licenses": ["Apa... |
function x = Adj_USFFT(n,y,omega,D,L,center)
% USFFT_simple -- 1d adjoint unequispaced Fourier transform
% Usage:
% x = USFFT(n,y,omega,D,L,center)
% Inputs:
% n length of output x
% y vector of length m,
% omega vector of sampled frequencies (length m)
% center 0 for unbiased FT, 1 otherwise
% Outp... | {"author": "thomaskuestner", "repo": "CS_MoCo_LAB", "sha": "a26e8e483624b2e4ee669e7a069ba9c74d2d2e4b", "save_path": "github-repos/MATLAB/thomaskuestner-CS_MoCo_LAB", "path": "github-repos/MATLAB/thomaskuestner-CS_MoCo_LAB/CS_MoCo_LAB-a26e8e483624b2e4ee669e7a069ba9c74d2d2e4b/reconstruction/matlab/CS_LAB_matlab/utils/uti... |
From Test Require Import tactic.
Section FOFProblem.
Variable Universe : Set.
Variable UniverseElement : Universe.
Variable wd_ : Universe -> Universe -> Prop.
Variable col_ : Universe -> Universe -> Universe -> Prop.
Variable col_swap1_1 : (forall A B C : Universe, (col_ A B C -> col_ B A C)).
Variable col_swap2_... | {"author": "janicicpredrag", "repo": "Larus", "sha": "a095ca588fbb0e4a64a26d92946485bbf85e1e08", "save_path": "github-repos/coq/janicicpredrag-Larus", "path": "github-repos/coq/janicicpredrag-Larus/Larus-a095ca588fbb0e4a64a26d92946485bbf85e1e08/benchmarks/coq-problems/col-trans/col_trans_0888.v"} |
import h5py
import numpy as np
import os
from numpy.lib.shape_base import expand_dims
import open3d as o3d
import torch
from torch.utils.data import Dataset
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import minkowski
def farthest_subsample_points(pointcloud1, pointcloud2, num_subsampl... | {"hexsha": "65fddea90094215400f8ea85d65c4f143087e9a8", "size": 6861, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dataset.py", "max_stars_repo_name": "paul007pl/GMCNet", "max_stars_repo_head_hexsha": "ebe3005874ec6040ac6d2b2f4aee7aeb9b21e7ee", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
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