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/////////1/////////2/////////3/////////4/////////5/////////6/////////7/////////8
// serializer_map.cpp:
// (C) Copyright 2002 Robert Ramey - http://www.rrsd.com .
// Use, modification and distribution is subject to the Boost Software
// License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http:/... | {"hexsha": "4bd56407b8e0eeab7ac60ff5a1e79072590149b1", "size": 2435, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/serialization/src/basic_serializer_map.cpp", "max_stars_repo_name": "coxlab/boost_patched_for_objcplusplus", "max_stars_repo_head_hexsha": "5316cd54bbd03994ae785185efcde62b57fd5e93", "max_stars... |
#!/usr/bin/python
import chainer
import numpy as np
from PIL import Image
from chainer import Variable
from net import StarGenerator
from model_utils import crop, transpose, save_image, resize, preprocess_img
def make_gen_fun(gen, att_num=20, image_size=128):
def make_image(pic, att, dst, name):
xp = gen... | {"hexsha": "2f7fbde1677973ae3bf10487b344aa871e90939a", "size": 1781, "ext": "py", "lang": "Python", "max_stars_repo_path": "ML/FaceGen/StarMain.py", "max_stars_repo_name": "AlongWY/HIT-WorkShop", "max_stars_repo_head_hexsha": "7169666a3e04c087e4760b119bf375f798466e43", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numba
import numpy
from numba import jit, stencil
from numpy.typing import ArrayLike
def fast_edge_filter(array: ArrayLike, axis: int = 0, gpu: bool = True):
# Cast to float:
array = array.astype(dtype=numpy.float32, copy=False)
negative = ['0'] * array.ndim
negative[axis] = '-1'
positive... | {"hexsha": "385f6533eb4dc490e3ba0c77ba03ff4c3bf053f7", "size": 901, "ext": "py", "lang": "Python", "max_stars_repo_path": "aydin/util/edge_filter/fast_edge_filter.py", "max_stars_repo_name": "AhmetCanSolak/aydin", "max_stars_repo_head_hexsha": "e8bc81ee88c96e0f34986df30a63c96468a45f70", "max_stars_repo_licenses": ["BSD... |
// Copyright 2021 Anthony Paul Astolfi
//
#include <batteries/async/watch.hpp>
//
#include <batteries/async/watch.hpp>
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include <batteries/async/task.hpp>
#include <boost/asio/io_context.hpp>
namespace {
using namespace batt::int_types;
class MockStringHandler
{
... | {"hexsha": "29da12bb6e31baadaac6a02b9af39447e593b3ef", "size": 27113, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/batteries/async/watch.test.cpp", "max_stars_repo_name": "tonyastolfi/batteries", "max_stars_repo_head_hexsha": "67349930e54785f44eab84f1e56da6c78c66a5f9", "max_stars_repo_licenses": ["Apache-2.... |
# Copyright 2018/2019 The RLgraph authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "943273a5e55a029b652b51104e1c080e61496f57", "size": 13391, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlgraph/tests/performance/test_python_memory_performance.py", "max_stars_repo_name": "RLGraph/RLGraph", "max_stars_repo_head_hexsha": "428fc136a9a075f29a397495b4226a491a287be2", "max_stars_repo_l... |
from exojax.spec import xsection
from exojax.spec.hitran import SijT, doppler_sigma, gamma_hitran, gamma_natural
from exojax.spec.exomol import gamma_exomol
from exojax.spec import moldb
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import time
# Setting wavenumber bins and loading HITEMP dat... | {"hexsha": "259577e367b444985772051f409e0da0f820df4a", "size": 558, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/xs/mdbhit_extract.py", "max_stars_repo_name": "dcmvdbekerom/exojax", "max_stars_repo_head_hexsha": "9b9305f8e383c73bdb97c1cfb0e276ddafcd75de", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
!-------------------------------------------------------------------
! class-hpc-smoke-ring: A simple sample field solver.
!
! by Akira Kageyama, Kobe University, Japan.
! email: sgks@mac.com
!
! Copyright 2018 Akira Kageyama
!
! This software is released under the MIT License.
!
!-----------------------... | {"hexsha": "09ed14dec7df1a913a65ea8cd96788bfe13b77c9", "size": 5212, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "slice_grapher/gnuplot_data_generator.f90", "max_stars_repo_name": "TZWizard/smrs", "max_stars_repo_head_hexsha": "f99746accbcf97a1d3b075dbc02b08abafde96ea", "max_stars_repo_licenses": ["MIT"], "... |
[STATEMENT]
lemma yield_Seq [simp, code]:
"yield (Lazy_Sequence f) = f ()"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. yield (Lazy_Sequence f) = f ()
[PROOF STEP]
by (cases "f ()") (simp_all add: yield_def split_def) | {"llama_tokens": 94, "file": null, "length": 1} |
from typing import Tuple, Iterator, List
from catboost import Pool
from numpy import ndarray
from pandas import Series, DataFrame
from sklearn.model_selection import GroupKFold
def yield_folds(group_ids: Series, n_folds: int) -> Iterator[Tuple[ndarray, ndarray]]:
n_samples = len(group_ids)
group_k_fold = Gro... | {"hexsha": "4fac4cb818bd12b2c65c308354c11be2b59a6dbc", "size": 768, "ext": "py", "lang": "Python", "max_stars_repo_path": "homework_2/train_utils.py", "max_stars_repo_name": "SpirinEgor/HSE.recommendation_systems", "max_stars_repo_head_hexsha": "d6e3c19a1bfb08156451cbc3c622f6610288c17e", "max_stars_repo_licenses": ["Ap... |
function test_bug3417
% WALLTIME 00:20:00
% MEM 3gb
% DEPENDENCY
% ... works fine on his computer, which has a 2012 version of FieldTrip and Matlab 2013a.
% But when we try to run either a later version of Fieldtrip (e.g. 2015) or a later version
% of Matlab (e.g. 2017b), we get the following bug:
%
% Error using fin... | {"author": "fieldtrip", "repo": "fieldtrip", "sha": "c2039be598a02d86b39aae76bfa7aaa720f9801c", "save_path": "github-repos/MATLAB/fieldtrip-fieldtrip", "path": "github-repos/MATLAB/fieldtrip-fieldtrip/fieldtrip-c2039be598a02d86b39aae76bfa7aaa720f9801c/test/test_bug3417.m"} |
import numpy as np
import pandas as pd
import sys
sys.path.insert(0, 'C:/Users/Serhan/Documents/SLACwork/VADER-Analytics/mlpowerflow')
import forward_mlpf
import inverse_mlpf
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
def removeValues(data, percentage, inplace... | {"hexsha": "4cb18e278affe6eb2110e1cd0fd0bd44be92402d", "size": 12359, "ext": "py", "lang": "Python", "max_stars_repo_path": "virtual-scada/vs.py", "max_stars_repo_name": "slaclab/VADER-Analytics", "max_stars_repo_head_hexsha": "9d2dd5b11b4f632eb511278c52aa8236f9e252f5", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"],... |
"""
=====================================================
Reprojecting to a Map Projection with a Custom Origin
=====================================================
In this example, we show how to reproject a map to a map projection with a
custom origin. Here, we choose the target map projection to be the
`azimuthal ... | {"hexsha": "366cd64ee317164aab8f1840077d428e856dccd0", "size": 3595, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/map_transformations/projection_custom_origin.py", "max_stars_repo_name": "akaszynski/sunpy", "max_stars_repo_head_hexsha": "d300d5015b2b37f8c75bdaf05c6df7865760eadc", "max_stars_repo_lice... |
from random import *
from matplotlib import pyplot as plt
import numpy as np
numOfClusters = 8
iterations = 50
def column(matrix, i):
return [row[i] for row in matrix]
def generateClusterPositions(numOfPlots):
randomData = []
for i in range(0, numOfPlots):
randomData.append([randint(0,1000)/1000,... | {"hexsha": "737545f35744cffe8977cdb89830ef8691b5b639", "size": 2300, "ext": "py", "lang": "Python", "max_stars_repo_path": "program.py", "max_stars_repo_name": "Rhysj125/K-Means-Clustering", "max_stars_repo_head_hexsha": "33ea91daccfb96663209b5f13f786a659da45c76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
subroutine stringsh(q,h5,docc)
! input: momenta q, helicity of 5 is h5,
! flag docc to turn on c.c. - appropriate for t~ calculation
! computes currents for given q and helicity combination
! stores as common block as will be re-used.
! add more currents as needed
implicit none
include 'constan... | {"hexsha": "a7477bbb6de54ad204cd2d752993b7a8b091dfc6", "size": 4386, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/SingletopH/stringsh.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0"... |
/* Copyright 2022 Zuru Tech HK Limited.
*
* 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 ... | {"hexsha": "5d02716f42bf7785329190c18f9e3185c429682d", "size": 6758, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "solvers/src/solvers/SparseSystem.cpp", "max_stars_repo_name": "zurutech/stand", "max_stars_repo_head_hexsha": "a341f691d991072a61d07aac6fa7e634e2d112d3", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import cv2
import numpy as np
import glob
import random
# Load Yolo
net = cv2.dnn.readNet("yolov3_training_last.weights", "yolov3_testing.cfg")
# Name custom object
classes = ["tank"]
# Images path
images_path = glob.glob(r"E:\python\images\*.jpg")
layer_names = net.getLayerNames()
output_layer... | {"hexsha": "be2ba5f3a36728eac925ef282ca9e990e2f36a0b", "size": 2282, "ext": "py", "lang": "Python", "max_stars_repo_path": "yolo_object_detection.py", "max_stars_repo_name": "yazan2241/Tank-Detection", "max_stars_repo_head_hexsha": "451de901ce041e19fe8486b9ca3002601519a686", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import rirgenerator as RG
import matplotlib.pyplot as plt
c = 340 # Sound velocity (m/s)
fs = 16000 # Sample frequency (samples/s)
r = [2,1.5,2] # Receiver position [x y z] (m)
s = [2,3.5,2] # Source position [x y z] (m)
L = [5,4,6] # Room dimensions [x y z] (m)
beta = 0.4 # Reverbe... | {"hexsha": "f7d82533722d2f6318f323aec06674550627cd0d", "size": 457, "ext": "py", "lang": "Python", "max_stars_repo_path": "example_1.py", "max_stars_repo_name": "yyj2013/RIR-Generator", "max_stars_repo_head_hexsha": "cbb219582585db1c17c72a913fc97c0ba9087cca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""
This SumTree code is modified version of Morvan Zhou:
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/5.2_Prioritized_Replay_DQN/RL_brain.py
"""
from typing import Tuple
from typing import TypeVar, Generic
import numpy as np
E = TypeVar('E') # type of the experience
c... | {"hexsha": "91984e992a91fe8ca8d8254ca653f7865d9c91d5", "size": 3913, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/deepword/sum_tree.py", "max_stars_repo_name": "kartik2112/deepword", "max_stars_repo_head_hexsha": "d6be4bcbb61e5c15db57d534a7deab9d6455b085", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import os
import shutil
from argparse import ArgumentParser
from typing import Text
from datetime import datetime
import tensorflow as tf
import numpy as np
from kerastuner.tuners import BayesianOptimization, RandomSearch
from tabnet.models import TabNetClassifier
from local.original_dataset import (
input_fn,
... | {"hexsha": "c54a7c06029f74ac2f7cd5d3da18d1e90aaa8346", "size": 4157, "ext": "py", "lang": "Python", "max_stars_repo_path": "local/tuner_orig_dataset.py", "max_stars_repo_name": "ostamand/tensorflow-tabnet", "max_stars_repo_head_hexsha": "cc676d75a5879df61d3b154ea783fbc364caf2a2", "max_stars_repo_licenses": ["MIT"], "ma... |
"""
Sequential Probability Ratio Tests
"""
import numpy as np
def sprt(likelihood_ratio, alpha, beta, x, random_order = True):
"""
Performs sequential probability ratio test with desired likelihood ratio.
Parameters
----------
likelihood_ratio : function
likelihood ratio function with one parameter, x,... | {"hexsha": "b621d399cc5cc762731f6633048623e33e80e068", "size": 1615, "ext": "py", "lang": "Python", "max_stars_repo_path": "permute/sprt.py", "max_stars_repo_name": "Claybarn/permute", "max_stars_repo_head_hexsha": "292d1925f27f91f1971baac4b27d10731716be2d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count... |
Dear Princess Celestia: Letter One.
Today I learned how to sing Applejack's Drinking Song.
Did you know that Applejack likes the number 99?
As long as Applejack had more than 1…
I sang Applejack" jugs of cider on the wall, "Applejack" jugs of cider,".
Applejack got one less. (Jug of cider)
When Applejack had more t... | {"hexsha": "ee5c34ed9aaaef04406636f1bdba4d4c6e90be02", "size": 1253, "ext": "fpp", "lang": "FORTRAN", "max_stars_repo_path": "examples/applejacks_drinking_song.fpp", "max_stars_repo_name": "evant/sparkle", "max_stars_repo_head_hexsha": "a171e8830f38136aff149628f03d53130da10768", "max_stars_repo_licenses": ["Apache-2.0"... |
# -*- coding: utf-8 -*-
"""
# Kristine m. Larson
removed station input
"""
import sys
import numpy as np
import math
import gps as g
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("x", help="X coordinate (m) ", type=float)
parser.add_argument("y", help="Y coordinate (m) ", type=float)
parser.a... | {"hexsha": "fb39e72066a99ab4cd88375ae8394cacc9d96f74", "size": 670, "ext": "py", "lang": "Python", "max_stars_repo_path": "easy_llh.py", "max_stars_repo_name": "wlcable/gnssIR_python", "max_stars_repo_head_hexsha": "4e6e66cfb06cb114af98608e50f77c10834f2696", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21, "m... |
------------------------------------------------------------------------
-- Parser monad
------------------------------------------------------------------------
open import Relation.Binary
open import Relation.Binary.OrderMorphism
open import Relation.Binary.PropositionalEquality hiding (poset)
import Relation.Binary... | {"hexsha": "ebe16f2e27a97e320bdb2786a165aa3e8dc9f76d", "size": 6600, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "benchmark/monad/MonadPostulates.agda", "max_stars_repo_name": "KDr2/agda", "max_stars_repo_head_hexsha": "98c9382a59f707c2c97d75919e389fc2a783ac75", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
subroutine runCY_00i1i2(k,l,i1,i2,Xtwiddle,Gtwiddle,Shat4,N0)
implicit none
C--- Expression for Eq. 5.58c
C--- Calculates C00i1i2, requires C00li1,C00li2
C--- Small terms of order Xtwiddle(0,k)*Ciii,Xtwiddle(0,0)*Ciiii
C--- Denominator Gtwiddle(k,l)
include 'pvCnames.f'
include 'pvCv.f'
... | {"hexsha": "8d9e83ebc27a35c25b75e4aa67350bf3ca3d6dbd", "size": 1056, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/TensorReduction/recur/smallY/runCY_00i1i2.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licens... |
import torch
import hydra
import numpy as np
from lib.smpl.body_models import SMPL
class SMPLServer(torch.nn.Module):
def __init__(self, gender='neutral', betas=None, v_template=None):
super().__init__()
self.smpl = SMPL(model_path=hydra.utils.to_absolute_path('lib/smpl/smpl_model'),
... | {"hexsha": "02af1dc210a5fdfe7c1631c9b0dfd5d0038e0780", "size": 3429, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/model/smpl.py", "max_stars_repo_name": "xuchen-ethz/snarf", "max_stars_repo_head_hexsha": "69b50ce796087c13d0094dd3b6d435ccaa678698", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 150... |
import pandas as pd
import sys
from confluent_kafka import avro
from confluent_kafka.avro import AvroProducer
import numpy as np
from model import PaintingModel, ImgLoader
def batched(batch_size, iterable):
batch = []
for item in iterable:
batch.append(item)
if len(batch) == batch_size:
... | {"hexsha": "b705306fe07839c52d9b00f3f60991b18362c87c", "size": 4537, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml-model/painting-vectorizer.py", "max_stars_repo_name": "schocco/img-similarity-search", "max_stars_repo_head_hexsha": "7ebad18d0fe7f783b666181d7c88193a087841ca", "max_stars_repo_licenses": ["MIT... |
################################################################################
##
## This library is free software; you can redistribute it and/or
## modify it under the terms of the GNU Lesser General Public
## License as published by the Free Software Foundation; either
## version 2.1 of the License, or (at your op... | {"hexsha": "9921cd8e8d3c97ee1e3ca188ce1085cc46109d93", "size": 30520, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/ADFRsuite/CCSBpckgs/ADFR/__init__.py", "max_stars_repo_name": "AngelRuizMoreno/Jupyter_Dock_devel", "max_stars_repo_head_hexsha": "6d23bc174d5294d1e9909a0a1f9da0713042339e", "max_stars_repo_l... |
import numpy as np
from warnings import warn
from .Numeric import tovoigt, tovoigt3
__all__ = ['unique2d','in2d','intersect2d','in2d_unsorted','shuffle_along_axis',
'shuffle_along_axis_bothway','shuffle_along_axis_robust','itemfreq',
'SecondTensor2Vector','Voigt','UnVoigt','remove_duplicates_2D','totuple',
'prime_num... | {"hexsha": "74d2f5c0385378292d405a45e0b58094ba43f003", "size": 24608, "ext": "py", "lang": "Python", "max_stars_repo_path": "Florence/Tensor/Tensors.py", "max_stars_repo_name": "jdlaubrie/florence", "max_stars_repo_head_hexsha": "830dca4a34be00d6e53cbec3007c10d438b27f57", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Julia wrapper for header: /usr/include/scip/pub_prop.h
# Automatically generated using Clang.jl wrap_c
function SCIPpropComp(elem1, elem2)
ccall((:SCIPpropComp, libscip), Cint, (Ptr{Cvoid}, Ptr{Cvoid}), elem1, elem2)
end
function SCIPpropCompPresol(elem1, elem2)
ccall((:SCIPpropCompPresol, libscip), Cint, ... | {"hexsha": "716ed10ed6a80098142c864ea7b9f800b23c2827", "size": 4739, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrapper/pub_prop.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SCIP.jl-82193955-e24f-5292-bf16-6f2c5261a85f", "max_stars_repo_head_hexsha": "65fa90d17c9f8f5e59bcce9d01fa37a61762b6... |
import pickle
import os
import argparse
import matplotlib.pyplot as plt
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument("-a", "--a", dest = "a", default = True, help="All (print)")
args = parser.parse_args()
print( "Print all? {}".format(args.a))
if args.a == "False":
var = input("Numb... | {"hexsha": "21220820b55253e37e6ceffa4049e0a09b25eec4", "size": 2951, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/evals/pickle_handler.py", "max_stars_repo_name": "Alexjsenn/FedScale", "max_stars_repo_head_hexsha": "2048cabfba94d029b562a62d765a27225b7c1016", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/env julia
export pe34
function pe34()
intlength(n::Integer)=length(digits(n))
fdsum(n::Integer)=sum(factorial.(digits(n)))
max_digit=2
while intlength(fdsum(10^max_digit-1))>max_digit
max_digit +=1
end
filter(n->fdsum(n)==n,10:(10^max_digit-1))|>sum
end
if !haskey(ENV,"PROJ... | {"hexsha": "1776c39a5c4e7cdd55743ec6022fc2778bffc285", "size": 369, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "34. Digit factorials/34.jl", "max_stars_repo_name": "miRoox/ProjectEuler", "max_stars_repo_head_hexsha": "020e8efddbf0dde33f2b2561e7b636fa199be75c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
# from mpl_toolkits.mplot3d import Axes3D
# from matplotlib import collections as mc
# import matplotlib.animation as animation
class DoublePendulum:
def __init__(self, g, l1, l2, m1, m2, omega1, omega2, phi1, phi2, lambda1, lam... | {"hexsha": "a2207df83c056943070010451bef15e71442a128", "size": 1243, "ext": "py", "lang": "Python", "max_stars_repo_path": "cgi-bin/dp.py", "max_stars_repo_name": "schifzt/Kalman_Pendulum", "max_stars_repo_head_hexsha": "b9d8f66c304a35f61ce581c4401e2f4b06f0444f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
"""
Copyright (c) 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
distributed under the License ... | {"hexsha": "a3e7f47fdb1cb7a8057b5568aab92bae10eab41c", "size": 31355, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/quantization/test_unified_scales.py", "max_stars_repo_name": "LeonidBeynenson/nncf_pytorch", "max_stars_repo_head_hexsha": "f8ded2752aded28d3559308c354235e5011ccbe0", "max_stars_repo_licens... |
import pandas as pd
import numpy as np
import pytest
from rdkit import Chem
from rdkit.Chem import AllChem
from nfp.preprocessing import SmilesPreprocessor, MolPreprocessor
@pytest.fixture()
def get_2d_smiles():
train = ['CC', 'CCC', 'C(C)C', 'C']
test = ['CO', 'CCO']
return train, test
# data = ... | {"hexsha": "5c99cbc07f06f6d5f27ad74dde5f3d2b9751e988", "size": 2406, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/preprocessing/test_preprocessor.py", "max_stars_repo_name": "MolecularMaterials/MPNN-Mo2C", "max_stars_repo_head_hexsha": "c0ea4cb793901b7ae86fdfc91e108f3912d7a750", "max_stars_repo_licenses... |
'''use the opencv implenmented image stitcher'''
import numpy as np
import cv2
import glob
import imutils
WINDOW_NAME = "Test Stitching On Mac"
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_AUTOSIZE)
# initialtime = time.time()
cv2.startWindowThread()
imgs_path = glob.glob('img/*')
images = []
num = len(imgs_path)
ino =... | {"hexsha": "898f98c5734832d2c5e25ff3e93b8b304967594d", "size": 1057, "ext": "py", "lang": "Python", "max_stars_repo_path": "file/BenBen/peixuan/stitcher_many_images.py", "max_stars_repo_name": "lingwsh/2020-Computer-Version-image-stitching-with-RANSAC", "max_stars_repo_head_hexsha": "b3a067d7a3653026e288d2682b2d1d3fc59... |
!> This module contains some definitions that are used in every module.
module common
integer, parameter :: sp = selected_real_kind(9,49) !< Short precision
integer, parameter :: lp = selected_real_kind(15,99) !< Long precision
integer, parameter :: wp = lp !< Working precision
end module
| {"hexsha": "9e6508a896ee0cce138e2b638b4ad2a50fc505e6", "size": 299, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/common.f90", "max_stars_repo_name": "Chr1sWilliams/waveqlab_co", "max_stars_repo_head_hexsha": "405a0efe3d444796d6df27b637ec80600ca5e278", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
// Warning! This file is autogenerated.
#include <boost/text/collation_table.hpp>
#include <boost/text/collate.hpp>
#include <boost/text/data/all.hpp>
#ifndef LIMIT_TESTING_FOR_CI
#include <boost/text/save_load_table.hpp>
#include <boost/filesystem.hpp>
#endif
#include <gtest/gtest.h>
using namespace boost::text;
... | {"hexsha": "ba747e7eb24bd31ce281b095f725f499c3ba4c5e", "size": 333629, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/tailoring_rule_test_zh_zhuyin_005.cpp", "max_stars_repo_name": "jan-moeller/text", "max_stars_repo_head_hexsha": "c61e51c82dfb0ae6e74200c01ce040fa6db730c4", "max_stars_repo_licenses": ["BSL-1... |
import warnings
from copy import deepcopy
from functools import partial
import numpy as np
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises, assert_true, assert_equal
from numpy.testing import assert_allclose
from genz.static.expyfun import ExperimentController, wait_secs, visual
from genz.... | {"hexsha": "f19ad4b969faee6c6d5ab4cecbb2d7357799f285", "size": 21555, "ext": "py", "lang": "Python", "max_stars_repo_path": "genz/static/expyfun/tests/test_experiment_controller.py", "max_stars_repo_name": "larsoner/genz-1", "max_stars_repo_head_hexsha": "dc7a73b4597f976c0274d696c2610c79b7a1f7c1", "max_stars_repo_licen... |
import os.path as osp
import numpy as np
from six.moves import zip_longest
from jenks_natural_breaks import classify
def fp_approx_equal(v1, v2):
return abs(v1 - v2) < 10e-6
def assert_all_approx_equal(result, expected):
assert all(fp_approx_equal(r, e) for r, e in zip_longest(result, expecte... | {"hexsha": "00c7dedfea4a2288f061ca8a868ddc38ce9b73fe", "size": 1268, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_jenks_natural_breaks.py", "max_stars_repo_name": "cwalv/jenks_natural_breaks", "max_stars_repo_head_hexsha": "439a6288b790f14235c8c6e1e13f1786613f0362", "max_stars_repo_licenses": ["MIT... |
/*
* Copyright 2009-2017 The VOTCA Development Team
* (http://www.votca.org)
*
* 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
*
* h... | {"hexsha": "d961cefac46073e2f73d88014b07b74a1f161ba6", "size": 4701, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/libxtp/qmpackages/xtpdft.cc", "max_stars_repo_name": "choudarykvsp/xtp", "max_stars_repo_head_hexsha": "9a249fd34615abcf790d5f0ecd3ddf1ed0ac0e7a", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
[STATEMENT]
lemma nmod2: "n mod 2 = 0 \<or> n mod 2 = 1"
for n :: int
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. n mod 2 = 0 \<or> n mod 2 = 1
[PROOF STEP]
by arith | {"llama_tokens": 88, "file": "Word_Lib_More_Divides", "length": 1} |
import pandas as pd
import argparse
import scipy.stats as ss
p = argparse.ArgumentParser()
p.add_argument(
"--tidy_spectra",
required=True,
help="""tidy dataframe containing BXD mutation spectra""",
)
p.add_argument(
"-sig_profiler_activities",
default="data/sigprofiler_data/COSMIC_SBS96_activities... | {"hexsha": "5e472ec534a589d5fe133dc32c75a844500468af", "size": 2196, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_scripts/count_samples_with_sbs18.py", "max_stars_repo_name": "harrispopgen/bxd_mutator_manuscript", "max_stars_repo_head_hexsha": "89d86bdbec2fc1e660453e786ca360bdd045caa5", "max_stars_repo_lic... |
module fftw3
use, intrinsic :: iso_c_binding
implicit none
include 'fftw3.f03'
end module | {"hexsha": "3d5cc59937adfed743ebc326623553387497f9d6", "size": 101, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/fft/fftw3.f90", "max_stars_repo_name": "Nkzono99/mpifft-solver", "max_stars_repo_head_hexsha": "e1ccf0595718145caf8e57bf1d0f6bf5a17dfd9b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
from pykalman import KalmanFilter
import scipy.linalg
class KLF(object):
def __init__(self):
self._trans_mat = np.eye(6)
self._trans_conv = scipy.linalg.block_diag(np.eye(3)*0.05, np.eye(3)*0.2)
self._trans_conv[2,2] = 0.0872665
self._trans_conv[5,5] = 0.349066
... | {"hexsha": "ec85661c015c540fc33cf14f6107834bfb658c54", "size": 1989, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/kalman_filter.py", "max_stars_repo_name": "CMU-ARM/HRI18-Bystander-Intervention-Study", "max_stars_repo_head_hexsha": "9113897d25bfab3410e7a3cd5b0a6488528f1848", "max_stars_repo_licenses":... |
import os
import numpy as np
import pandas as pd
import random
from scarv import scarv_assess
import sys
ancestry = sys.argv[1]
window_size = 575
chr_list = ["chr" + str(i) for i in range(1, 23)]
chr_list.extend(["chrXnonPAR", "chrXPAR"])
chr_lengths_raw = [248956422, 242193529, 198295559, 190214555, 181538259, 170... | {"hexsha": "b6eb699e483c4b79fc01fa1a9db31bb2f0e3b9f2", "size": 3806, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/scarv_pipeline/compute_SCARV_6.py", "max_stars_repo_name": "jtenwolde/SCARV", "max_stars_repo_head_hexsha": "ef31f5e610cf40cedfccd93bf189111c6586979b", "max_stars_repo_licenses": ["MIT"], ... |
# Copyright 2021, The TensorFlow Federated 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": "60ca9ff63d2ce8a060ba0508d1763a116586fcb9", "size": 8242, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_federated/python/analytics/hierarchical_histogram/hierarchical_histogram.py", "max_stars_repo_name": "Tensorflow-Devs/federated", "max_stars_repo_head_hexsha": "5df96d42d72fa43a050df646... |
## use "source( 'extremesBoxPlot.r' )" from R prompt or "R -f extremesBoxPlot.r" from command line.
tab1 <- read.csv(file="ch14ExtremesR.csv",head=TRUE,sep=",")
## or X11(type="Xlib")
jpeg('median_precip.jpg')
par(fig=c(0.01, 0.95, 0.2, 0.95) )
par(mgp = c(4.2, .8, 0))
boxplot(pr~Scenario*Season, data=tab1, notch=FA... | {"hexsha": "5bf9ec62664e9d22d05e09920c300204ced268e4", "size": 668, "ext": "r", "lang": "R", "max_stars_repo_path": "ddc_scripts/ar5_wg2_scatter/extremesBoxPlot.r", "max_stars_repo_name": "cp4cds/cmip6_range_check_2", "max_stars_repo_head_hexsha": "6fca2632029a2adb9736bfc1382b39f82d8a27e1", "max_stars_repo_licenses": [... |
function mv_plotScans(view,groupFlag);
% mv_plotScans(view,[groupFlag]);
%
% Shell/dialog for calling multi voxel UI for multiple scans.
%
% The idea here is to concatenate similar scans together and view the
% concatenated time course (mainly event-related or non-AB block designs,
% but can also be used if you have ma... | {"author": "vistalab", "repo": "vistasoft", "sha": "7f0102c696c091c858233340cc7e1ab02f064d4c", "save_path": "github-repos/MATLAB/vistalab-vistasoft", "path": "github-repos/MATLAB/vistalab-vistasoft/vistasoft-7f0102c696c091c858233340cc7e1ab02f064d4c/mrBOLD/EventRelated/MultiVoxelUI/mv_plotScans.m"} |
import xml.etree.ElementTree as ET
import numpy as np
from random import seed
from random import gauss,randint
class Bus_stop():
def __init__(self,id,lat,lon ):
'''
:param id: bus stop unique id
:param lat: bus stop latitude in real-world
:param lon: bus stop longitude in real-w... | {"hexsha": "2a92390b467c8d99a2d1fc4306a579f0635daec4", "size": 4453, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/transit_control_research_caac/sim/Busstop.py", "max_stars_repo_name": "TransitGym/TransitGym.github.io", "max_stars_repo_head_hexsha": "65edf4250708a86a96030516ee48a4b5f78b1c4b", "max_sta... |
# Lint as: python3
# Copyright 2019 Google 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | {"hexsha": "1328c721d906ed5a2c51301f23c44a8db7ccd7ee", "size": 38293, "ext": "py", "lang": "Python", "max_stars_repo_path": "integrations/tensorflow/bindings/python/pyiree/tf/support/module_utils.py", "max_stars_repo_name": "xinan-jiang/iree", "max_stars_repo_head_hexsha": "334aa4a5ac033399255fbd836d4fbe807c741230", "m... |
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Manuel Guenther <Manuel.Guenther@idiap.ch>
# Tue May 1 18:12:43 CEST 2012
#
# Copyright (C) 2011-2013 Idiap Research Institute, Martigny, Switzerland
"""Tests bob interior point Linear Programming solvers
"""
import os, sys
from bob.math import histogram_inters... | {"hexsha": "0a133157ca9f225a8af7fab235448cafd8288426", "size": 3028, "ext": "py", "lang": "Python", "max_stars_repo_path": "bob/math/test_histogram.py", "max_stars_repo_name": "bioidiap/bob.math", "max_stars_repo_head_hexsha": "e0efcd51a609755e55b723e97dca2b5eac6b7976", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import numpy as np
import os
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch import autograd
from torch.autograd import Variable
from torch.nn.utils import clip_grad_norm
from .utils import plot_img, plot_scalar, save_images, to_device
def Critic(netD, real, f... | {"hexsha": "421a68ea9d4851bd9976d759c848829172dce453", "size": 6776, "ext": "py", "lang": "Python", "max_stars_repo_path": "Gans/cramerGan.py", "max_stars_repo_name": "ypxie/pytorch_gans", "max_stars_repo_head_hexsha": "aff334d3bef84f5d8f1702f99402295e7cd66ddf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7,... |
#!/usr/bin/env python3
# Tobit Flatscher - github.com/2b-t (2022)
# @file main.py
# @brief Command line interface for stereo matching
import argparse
import matplotlib.pyplot as plt
import numpy as np
from matching_algorithm.matching_algorithm import MatchingAlgorithm
from matching_algorithm.semi_global_matching imp... | {"hexsha": "26caeb78785965ea217e03aaf14bf96546207d2d", "size": 6350, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/main.py", "max_stars_repo_name": "2b-t/stereo-matching", "max_stars_repo_head_hexsha": "6c2e6944a2859763f4110f3e90e99f9267e97e78", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
(* Title: HOL/Auth/Message.thy
Author: Lawrence C Paulson, Cambridge University Computer Laboratory
Copyright 1996 University of Cambridge
Datatypes of agents and messages;
Inductive relations "parts", "analz" and "synth"
*)
section\<open>Theory of Agents and Messages for Security Protocols\<clos... | {"author": "dtraytel", "repo": "HOLRLT", "sha": "e9029da59bb3af0c835604a65308498f9696a364", "save_path": "github-repos/isabelle/dtraytel-HOLRLT", "path": "github-repos/isabelle/dtraytel-HOLRLT/HOLRLT-e9029da59bb3af0c835604a65308498f9696a364/HOLRLT/Auth/Message.thy"} |
import pandas as pd
from scripts.python.routines.manifest import get_manifest
import numpy as np
import os
from scripts.python.pheno.datasets.filter import filter_pheno, get_passed_fields
from scipy.stats import spearmanr
import matplotlib.pyplot as plt
from scripts.python.pheno.datasets.features import get_column_name... | {"hexsha": "774232201559288c369c2bc9cb8a73fe37283a96", "size": 3653, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/python/dataset_specific/GSEUNN/tasks/007_heterogeneity_dead_alive.py", "max_stars_repo_name": "AaronBlare/dnam", "max_stars_repo_head_hexsha": "4d97c879cb24447eee0852eaf48fc5b3ef8e159b", "... |
[STATEMENT]
lemma unrest_true [unrest]: "x \<sharp> true"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<sharp> true
[PROOF STEP]
by (pred_auto) | {"llama_tokens": 65, "file": "UTP_utp_utp_pred", "length": 1} |
import matplotlib
from matplotlib.ticker import ScalarFormatter
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import statsmodels.api as sm
import numpy as np
from sortedcontainers import SortedList
class TaskCPUTimeCDF(object):
def __init__(self, workload_name, df, image_folder_location):
... | {"hexsha": "6cf165b33426b6f25a985ef93efe30db359bbf22", "size": 2047, "ext": "py", "lang": "Python", "max_stars_repo_path": "statistic_scripts/task_cpu_time_cdf.py", "max_stars_repo_name": "lfdversluis/wta-tools", "max_stars_repo_head_hexsha": "e9d505df03fff9bb57208dfb82212977ef5e7ca2", "max_stars_repo_licenses": ["Apac... |
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_0879.v"} |
! Copyright 2016-2021 Lawrence Livermore National Security, LLC and other
! IREP Project Developers. See the top-level LICENSE file for details.
!
! SPDX-License-Identifier: MIT
module mainmod
use, intrinsic :: iso_c_binding
implicit none
integer(kind=c_int), parameter :: LUA_NOREF = -2
integer(k... | {"hexsha": "90e12fe35888bf148a5f15043f8065bb3815871e", "size": 9553, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "examples/fortran/f_main.f", "max_stars_repo_name": "RikkiButler20/irep", "max_stars_repo_head_hexsha": "eb65a123810fc68c8e1748958d5d6101130d8255", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python
#
# timeseriesplot.py - a matplotlib wrapper library to plot time-series data.
#
# - Only accepts CSV data when used from commandline.
# - See '--help' for Useage from command line.
#
# LICENSE
# This script is free to use and/or redistribute under MIT license.
#
# TODO:
# * moving averag... | {"hexsha": "9f20b3d6f2bd15e95436e8c5c76ce66eebd61b6a", "size": 20073, "ext": "py", "lang": "Python", "max_stars_repo_path": "timeseriesplot.py", "max_stars_repo_name": "ryo1kato/timeseriesplot", "max_stars_repo_head_hexsha": "18b014dec93bc5975b1bcdee1b881dd658e499cc", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma subcocycle_max:
assumes "subcocycle u" "subcocycle v"
shows "subcocycle (\<lambda>n x. max (u n x) (v n x))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subcocycle (\<lambda>n x. max (u n x) (v n x))
[PROOF STEP]
unfolding subcocycle_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<f... | {"llama_tokens": 2997, "file": "Ergodic_Theory_Kingman", "length": 26} |
//
// A base class for classes which print different types of products
// It serves to simplify the module code a bit
//
#ifndef Print_inc_ProductPrinter_hh
#define Print_inc_ProductPrinter_hh
#include "art/Framework/Principal/Event.h"
#include "art/Framework/Principal/Run.h"
#include "art/Framework/Principal/SubRun... | {"hexsha": "c12cb480a79bb7f6dbd7d5940ecd898b96b4ca73", "size": 2646, "ext": "hh", "lang": "C++", "max_stars_repo_path": "Print/inc/ProductPrinter.hh", "max_stars_repo_name": "NamithaChitrazee/Offline-1", "max_stars_repo_head_hexsha": "bcb6c74954f95dd48433d79a69d3ef3bdb99022e", "max_stars_repo_licenses": ["Apache-2.0"],... |
/-
Copyright (c) 2018 Kenny Lau. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kenny Lau, Yury Kudryashov
-/
import algebra.char_p.basic
import data.equiv.ring
import algebra.group_with_zero.power
import algebra.iterate_hom
/-!
# The perfect closure of a field
-/
un... | {"author": "JLimperg", "repo": "aesop3", "sha": "a4a116f650cc7403428e72bd2e2c4cda300fe03f", "save_path": "github-repos/lean/JLimperg-aesop3", "path": "github-repos/lean/JLimperg-aesop3/aesop3-a4a116f650cc7403428e72bd2e2c4cda300fe03f/src/field_theory/perfect_closure.lean"} |
# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
import numpy as np
from pyiron_base import state, InteractiveBase
from pyiron_atomistics.atomistics.structure.periodic_t... | {"hexsha": "340747fba19de274f4188395ce49e04c0a3b5db9", "size": 20824, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyiron_atomistics/atomistics/job/interactive.py", "max_stars_repo_name": "dgehringer/pyiron_atomistics", "max_stars_repo_head_hexsha": "f8f2d573a483e802c8e5840998a0769378b95e31", "max_stars_repo_... |
[STATEMENT]
lemma (in comm_group) finprod_comp:
assumes "inj_on g A" "(f \<circ> g) ` A \<subseteq> carrier G"
shows "finprod G f (g ` A) = finprod G (f \<circ> g) A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finprod G f (g ` A) = finprod G (f \<circ> g) A
[PROOF STEP]
using finprod_reindex[OF _ assms(1), o... | {"llama_tokens": 499, "file": "Finitely_Generated_Abelian_Groups_Finite_Product_Extend", "length": 4} |
# converters from selected RSEXPREC to Hash
# They are used to translate SEXPREC attributes into Hash
using Dates
function Base.convert(::Type{Hash}, pl::RPairList)
res = Hash()
for i in eachindex(pl.items)
@inbounds setindex!(res, pl.items[i], pl.tags[i])
end
res
end
########################... | {"hexsha": "8b7115b174296cfb0df54fd13e34e3150368cdfc", "size": 7851, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/convert.jl", "max_stars_repo_name": "munckymagik/RData.jl", "max_stars_repo_head_hexsha": "1f4f7e2d41a5a52760d6f399c5893d45fe39629e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from sqlalchemy import create_engine
import pandas as pd
import numpy as np
from importlib import reload
import collections
from pandas import json_normalize
import json
import argparse
import sys
from sqlalchemy import create_engine
import sqlite3
from importlib import reload
import os
def search_bypfam(dataset_path)... | {"hexsha": "f40b94cf1ce1fa9f3c724e8feda873768916ef9c", "size": 3841, "ext": "py", "lang": "Python", "max_stars_repo_path": "patho_chembl/chembldb_pfam_mech.py", "max_stars_repo_name": "florenciacastello/target_chembl", "max_stars_repo_head_hexsha": "f3927a70bafa360791218f7b50238eea8175b9e6", "max_stars_repo_licenses": ... |
# -*- coding: utf-8 -*-
"""
Created on Tue May 10 15:18:07 2016
@author: hb1g13
Remapping into temperature space in Parallel.
Start by running:
ipcluster start --profile=thalassa -n 6
this starts 6 processors. - Be polite do not use more than half
the machines CPUs!
This is a little more complex (even though sim... | {"hexsha": "0e95bf0e1c661631a8dd14ee044c10a359fae864", "size": 7491, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/parallel_test.py", "max_stars_repo_name": "hlburns/PhD", "max_stars_repo_head_hexsha": "7aff6584b0a5cd1318954b7f17a36f14f00d851b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
'''For attacking GraphCodeBERT models'''
import sys
import os
sys.path.append('../../../')
sys.path.append('../../../python_parser')
import csv
import copy
import pickle
import logging
import argparse
import warnings
import torch
import numpy as np
import json
import time
from model import Model
from utils import set... | {"hexsha": "ced252b1cc06de997b365d1ba4daac02d77f7e95", "size": 9918, "ext": "py", "lang": "Python", "max_stars_repo_path": "GraphCodeBERT/clonedetection/code/attack.py", "max_stars_repo_name": "soarsmu/attack-pretrain-models-of-code", "max_stars_repo_head_hexsha": "38fa2a6316859efde1507ef6186b4879e7b13748", "max_stars_... |
[STATEMENT]
lemma prj_chine:
shows "\<tau>\<mu>.p\<^sub>0 \<star> chine \<cong> \<chi>.chine \<star> \<rho>\<sigma>.p\<^sub>0"
and "\<tau>\<mu>.p\<^sub>1 \<star> chine \<cong> \<omega>.chine \<star> \<rho>\<sigma>.p\<^sub>1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. t\<^sub>0u\<^sub>1.p\<^sub>0 \<star> ... | {"llama_tokens": 541786, "file": "Bicategory_BicategoryOfSpans", "length": 826} |
#!/usr/bin/env python3
# See: https://github.com/pr3d4t0r/COVIDvu/blob/master/LICENSE
# vim: set fileencoding=utf-8:
from covidvu.visualize import plotTimeSeries
from covidvu.visualize import plotTimeSeriesInteractive
from covidvu.visualize import plotPrediction
from covidvu.visualize import plotDataAndPredictionsWith... | {"hexsha": "7b6b0bedbaa64e8e01b154737e792e6085faca49", "size": 5972, "ext": "py", "lang": "Python", "max_stars_repo_path": "work/test/covidvu/test_visualize.py", "max_stars_repo_name": "dlwhitehurst/COVIDvu", "max_stars_repo_head_hexsha": "eef75eb9571f8ed2dc6f5af9dbd52d4cd2ff0074", "max_stars_repo_licenses": ["BSD-3-Cl... |
# This script defines and tests a function for generating a sequence of normally
# distributed random numbers using the Box-Muller method.
#
# Ref. 1: Numerical Recipes in C 2nd ed.
# Ref. 2: Statistics for Engineers and Scientists 2nd ed. Navidi
#
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats im... | {"hexsha": "9a9714213b126feca904b9a33310047e27c154d3", "size": 1782, "ext": "py", "lang": "Python", "max_stars_repo_path": "random/box-muller.py", "max_stars_repo_name": "nalyd88/modeling-and-simulation", "max_stars_repo_head_hexsha": "96e5fc3994ded8782229425e43ed9237966c9567", "max_stars_repo_licenses": ["MIT"], "max_... |
\section{Conclusions}
\label{sec-conc}
Writing archetypes can be a daunting task because reasonably accurate models
require knowledge of physics, economics, and computer science to solve a single nuclear engineering
problem. Unlike other spheres of nuclear engineering, decoupling these domains from
one another is o... | {"hexsha": "109b34988c095eb640022cf521fbe0bbea0fdb87", "size": 5835, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "conclusion.tex", "max_stars_repo_name": "mbmcgarry/archetype-paper", "max_stars_repo_head_hexsha": "f7f39bb7abe4a838bb88d15fb601ffd55598652f", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_coun... |
from typing import List, Tuple, Dict
import logging
import onnx
import numpy as np
from onnx import TensorProto, TensorAnnotation, StringStringEntryProto
import onnxruntime as ort
from onnxruntime_tools.quantization.quantize import _attribute_to_kwarg
logger = logging.getLogger("Furiosa-Quantizer")
logging.basicCo... | {"hexsha": "931d16e8d7cdc8d21ea4f5e2e83ff06f765a2f0e", "size": 9264, "ext": "py", "lang": "Python", "max_stars_repo_path": "closed/FuriosaAI/code/quantization/furiosa_sdk_quantizer/frontend/onnx/quantizer/utils.py", "max_stars_repo_name": "ctuning/inference_results_v1.1", "max_stars_repo_head_hexsha": "d9176eca28fcf6d7... |
# -*- coding: utf-8 -*-
# ProDy: A Python Package for Protein Dynamics Analysis
#
# Copyright (C) 2010-2012 Ahmet Bakan
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of th... | {"hexsha": "9a7e91bc4a62b12f0e60e67aef5fe3f5a84f6c3a", "size": 6191, "ext": "py", "lang": "Python", "max_stars_repo_path": "prody/atomic/__init__.py", "max_stars_repo_name": "gokceneraslan/ProDy", "max_stars_repo_head_hexsha": "74d40c372c53bd68f5e9f9c47b991b2e2b1b9f27", "max_stars_repo_licenses": ["NCSA"], "max_stars_c... |
# Copyright (c) 2015, Ecole Polytechnique Federale de Lausanne, Blue Brain Project
# All rights reserved.
#
# This file is part of NeuroM <https://github.com/BlueBrain/NeuroM>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are ... | {"hexsha": "0ca456ec12dab5dc6b8c022966fb10cfaa95243f", "size": 5046, "ext": "py", "lang": "Python", "max_stars_repo_path": "neurom/tests/test_viewer.py", "max_stars_repo_name": "NeuroDataDesign/NeuroM", "max_stars_repo_head_hexsha": "61a7b5de0c3bf3c07d6eb3270c28d21be6ea7865", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
# -*- coding: utf-8 -*-
"""Train and evaluate MiniVGGNet on Cifar10 dataset.
1. Load the CIFAR-10 dataset from disk.
2. Instantiate the MiniVGGNet architecture.
3. Train MiniVGGNet using the training data.
4. Evaluate network performance with the testing data.
Example:
$ python minivggnet_cifar10.py --output outp... | {"hexsha": "dc8889588e2146b11b1a523d40aba970fed4c70d", "size": 3163, "ext": "py", "lang": "Python", "max_stars_repo_path": "starter_bundle/ch15-minivggnet/minivggnet_cifar10.py", "max_stars_repo_name": "romanroson/pis_code", "max_stars_repo_head_hexsha": "1221c39c23bec62ba419f9a324f88b0d8e5e4b5b", "max_stars_repo_licen... |
/*
Copyright (C) 2012-2014 Brian S O'Neill
Copyright (C) 2014 Vishal Parakh
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 req... | {"hexsha": "c4dfd5020362d1bd27dc38686212ce505c82b42b", "size": 1180, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libTupl/src/tupl/pvt/Latch.hpp", "max_stars_repo_name": "cojen/TuplNative", "max_stars_repo_head_hexsha": "4a8fa2b90825fdfe8f563e910fa188f1ed851404", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
library(methods)
library(atSNP)
library(chunked)
options(stringsAsFactors = FALSE)
snpfile = {{i.snpfile | R}}
tffile = {{i.tffile | R}}
outfile = {{o.outfile | R}}
outdir = {{o.outdir | R}}
tfmotifs = {{args.tfmotifs | R}}
fdr = {{args.fdr | :'BH' if a is True else a | R}}
pval = {{args.pval | R}}
nthr... | {"hexsha": "81b41ac12fce032d9f3e0e5ab905a2e6a540c6b8", "size": 3865, "ext": "r", "lang": "R", "max_stars_repo_path": "bioprocs/scripts/tfbs/pAtSnp.r", "max_stars_repo_name": "pwwang/biopipen", "max_stars_repo_head_hexsha": "d53b78aa192fd56a5da457463b099b2aa833b284", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
library(optCluster)
d = read.table ({{i.infile | quote}}, sep="\t", header={{args.cnames | R}}, row.names={{args.rnames | lambda x: 'NULL' if not x else int(x)}}, check.names=F)
if ({{args.transpose | R}}) {
d = t(d)
}
names = rownames(d)
{% if args.methods == 'all' %}
#methods = c("agnes", "clara", "diana", "hierar... | {"hexsha": "b1c0d6a6949fcc684a7331dd82fee95ee03e4785", "size": 1847, "ext": "r", "lang": "R", "max_stars_repo_path": "bioprocs/scripts/cluster/pCluster.r", "max_stars_repo_name": "LeaveYeah/bioprocs", "max_stars_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python3
### Eval option 1: onnxruntime
import numpy as np
import onnxruntime as rt
sess = rt.InferenceSession("test.onnx")
input_name_x = sess.get_inputs()[0].name
input_name_shape = sess.get_inputs()[0].shape
input_x = np.ones(input_name_shape , dtype="float32")
pred_onx = sess.run(None, {input_name... | {"hexsha": "f0d78dc34a8ec43aec4c4e3e70c612b48e629813", "size": 544, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/example_06.py", "max_stars_repo_name": "owlbarn/owl_onnx", "max_stars_repo_head_hexsha": "956f45dd80cc28d85f9b4c061e21c878f969eefd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6... |
[STATEMENT]
lemma WhileInvPost:
assumes P_I: "P \<subseteq> I"
assumes termi_body:
"\<forall>\<sigma>. \<Gamma>,\<Theta>\<turnstile>\<^sub>t\<^bsub>/UNIV\<^esub> ({\<sigma>} \<inter> I \<inter> b) c ({t. (t, \<sigma>) \<in> V} \<inter> P),A"
assumes deriv_body:
"\<Gamma>,\<Theta>\<turnstile>\<^bsub>/F\<^esub>... | {"llama_tokens": 2090, "file": "Simpl_HoareTotal", "length": 15} |
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 12 18:07:05 2018
@author: Denis
"""
import time
import numpy as np
def borders_m(data_cube, infos):
print('%s : Creating borders masks - start' % time.asctime())
masks = 0*data_cube
nan_masks = np.isnan(masks)
ag = infos.height
bg = 1
... | {"hexsha": "bcadeba86b051f41b0fd02533eb1dba159169157", "size": 2262, "ext": "py", "lang": "Python", "max_stars_repo_path": "irisdct/masks/borders.py", "max_stars_repo_name": "DenisUllmann/IRIS", "max_stars_repo_head_hexsha": "c7963843594ee4cca44134d21816c1d11d8d6203", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import time
import pathlib
import cv2
import torch
import torchvision
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
class SuperResolver:
def __init__(self, model,
name="test",
extension="png",
directory="/content/results",
... | {"hexsha": "c63db97e9d2fa830cef769b8f655a54a15e51750", "size": 13766, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/super_resolve.py", "max_stars_repo_name": "milesgray/ImageFunctions", "max_stars_repo_head_hexsha": "35e4423b94149b0ba291eafb0cd98260a70d5f31", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
function basic_lev_multi(dataframe; sequence_id="sequence_id",sequence_event="sequence_event",sequence_order="sequence_order")
# The lev_master Array is the key object of this funtion. consider it the spine, or memory
# the Elements are: The Sequence, Lev matrix, Lev Value, Trace
lev_master = Array[]
... | {"hexsha": "3a47d743c1f27964e251fe07a3ef1849b5081f98", "size": 4011, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "wip/lev_multipe_threads.jl", "max_stars_repo_name": "ruthless-nz/SequenceReduction.jl", "max_stars_repo_head_hexsha": "8d72595f35b7a5349d0cb5c3ab15058e110db710", "max_stars_repo_licenses": ["MIT"],... |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from pandas.compat import range, lrange
import numpy as np
from pandas import DataFrame, Series, Index, MultiIndex
from pandas.util.testing import (assert_series_equal,
assert_frame_equal,
... | {"hexsha": "fe3f3c554a9b5fb9cf3dfa6f220c0e278a92f071", "size": 8607, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/tests/frame/test_mutate_columns.py", "max_stars_repo_name": "tacaswell/pandas", "max_stars_repo_head_hexsha": "81c57e20da278494dfebc2f1043f5ff361a234f3", "max_stars_repo_licenses": ["PSF-2.... |
"""
Tests specific to the lines module.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import itertools
import matplotlib.lines as mlines
import pytest
from timeit import repeat
import numpy as np
from cycler import cycler
import matplotlib
import matp... | {"hexsha": "83939cfce0a79eb74920ec2b1950e63eb084ac18", "size": 5747, "ext": "py", "lang": "Python", "max_stars_repo_path": "autocalibration/lib/python2.7/site-packages/matplotlib/tests/test_lines.py", "max_stars_repo_name": "prcalopa/reactable-autocalibration", "max_stars_repo_head_hexsha": "eb67a5b5ee0e50f1effa773f6f3... |
\documentclass[t]{beamer}
\title{Software Toolkit for ML}
\date{\today}
\author{Patrick O'Hara}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{graphicx}
\usepackage{hyperref}
% custom colours
\definecolor{mygreen}{RGB}{0, 100, 0}
\definecolor{myred}{RGB}{150, 0, 0}
\definecolor{myblue}{RGB}{0, ... | {"hexsha": "09d943e965f45a56214bf51ea63f73e081766b0a", "size": 7976, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "presentation/presentation.tex", "max_stars_repo_name": "warwick-machine-learning-group/pywmlg", "max_stars_repo_head_hexsha": "03fb908d1622cef556406f88e9d29f43f91c2e9e", "max_stars_repo_licenses": [... |
"""
A module providing some utility functions regarding Bezier path manipulation.
"""
from functools import lru_cache
import math
import warnings
import numpy as np
import matplotlib.cbook as cbook
# same algorithm as 3.8's math.comb
@np.vectorize
@lru_cache(maxsize=128)
def _comb(n, k):
if k > n:
retur... | {"hexsha": "3fcd31d7dea38694a645aff38550d0fc983422a2", "size": 19482, "ext": "py", "lang": "Python", "max_stars_repo_path": "book_env/Lib/site-packages/matplotlib/bezier.py", "max_stars_repo_name": "als0052/Hands-On-Data-Analysis-with-Pandas", "max_stars_repo_head_hexsha": "ea4e94f93a1e00cbccb493f22dde5c8007ed5e76", "m... |
# coding: utf-8
# ## We aim to find the keywords in file.pdf and create a distribution chart of the same.
# ### Using File: file.pdf
# ### Stopwords file: stopwords.txt
# In[349]:
# Importing
import numpy as np
import pandas as pd
import PyPDF2 # For extracting text
import matplotlib.pyplot as plt
from sklearn.fea... | {"hexsha": "958a27183ad31d2a3e644c036c114292e4bbaa68", "size": 4444, "ext": "py", "lang": "Python", "max_stars_repo_path": "solution.py", "max_stars_repo_name": "akapoorx00/assignment-data-science", "max_stars_repo_head_hexsha": "771d55564d7d7a273df402417561a02be9bcc860", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
from __future__ import division
import cv2
import numpy as np
import threading
import data_provider
from data_provider import DataProvider
class ListImageDataProvider(DataProvider):
"""
Pass in a list of image file path in a plain text file.
"""
def __init__(self, fname, inp_height=None, inp_width=N... | {"hexsha": "eba7de519c160de26b68be5e6488dabf1a44c58c", "size": 2095, "ext": "py", "lang": "Python", "max_stars_repo_path": "tfplus/data/list_image_data_provider.py", "max_stars_repo_name": "renmengye/tfplus", "max_stars_repo_head_hexsha": "9b1f71d33f24af510c701c7d6f6bb816720ed701", "max_stars_repo_licenses": ["MIT"], "... |
def orientation_evaluation(gt_pose, pred_rotmat, batch_size, curr_batch_size, step):
import torch
import numpy as np
from scipy.spatial.transform import Rotation as R
# Orientation evaluation
# Taking as input gt_pose in axis-angle representation and pred_rotmat in rotation matrix representati... | {"hexsha": "c14d5cdb2904f98458075c8d2343f103b435eb76", "size": 2347, "ext": "py", "lang": "Python", "max_stars_repo_path": "orientation_evaluation.py", "max_stars_repo_name": "arturbecker/SPIN", "max_stars_repo_head_hexsha": "66ee585df1574887e1e7f7668e3918b3c9c6f1d8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
#coding:utf-8
import tensorflow as tf
import numpy as np
def dynamic_bi_rnn(inputs, seqlen, n_hidden, keep_prob, cell_name='', reuse=False):
batch_size = tf.shape(inputs)[0]
with tf.variable_scope(cell_name + 'fw', initializer=tf.contrib.layers.xavier_initializer(), dtype = tf.float32, reuse=reuse):
f... | {"hexsha": "07590e2734044daeb2c9915ee9a1708f23dc21f3", "size": 3008, "ext": "py", "lang": "Python", "max_stars_repo_path": "components.py", "max_stars_repo_name": "misonuma/strsum", "max_stars_repo_head_hexsha": "fae85afc06969904e54fb76b594769e34c308736", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 27... |
ccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
c written by the UFO converter
ccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
SUBROUTINE MP_COUP2()
IMPLICIT NONE
REAL*16 MP__PI
PARAMETER (MP__PI=3.1415926535897932384626433832795... | {"hexsha": "17c3ff0a3badd96bf2fcc98885cafcc1537f3f79", "size": 2874, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "tests/input_files/IOTestsComparison/IOTestsComparison/long_ML_SMQCD_optimized/dux_mumvmxg/%..%..%Source%MODEL%mp_couplings2.f", "max_stars_repo_name": "valassi/mg5amc_test", "max_stars_repo_head_h... |
# from two_layer_net import TwoLayerNet
import numpy as np
from two_layer_net import TwoLayerNet
X = np.random.rand(10, 2)
nn = TwoLayerNet(2, 4, 3)
print(nn.predict(X))
print(nn.params)
| {"hexsha": "b4452d57b7245bff22e1b308815fffeae4ffed87", "size": 193, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml_nlp/zero_deep_learning_nlp/ch01/train.py", "max_stars_repo_name": "o3c9/playgrounds", "max_stars_repo_head_hexsha": "1363dca0ece0283b5cb1f5c4e26b7a12cf5badab", "max_stars_repo_licenses": ["MIT"]... |
[STATEMENT]
lemma intvs_decr_h:
"{l::int..<h - 1} = {l..<h} - {h-1}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {l..<h - 1} = {l..<h} - {h - 1}
[PROOF STEP]
by auto | {"llama_tokens": 98, "file": "IMP2_lib_IMP2_Aux_Lemmas", "length": 1} |
Require Import Ring.
Require Import Field.
Require Import Setoid.
Require Import Classes.RelationClasses.
Require Import Classes.Morphisms.
Require Import Ensembles.
Require Import Vector.
Require Import Logic.FunctionalExtensionality.
Require Import Logic.PropExtensionality.
Fixpoint zipwith {A B C : Type} {n : nat}
... | {"author": "quinn-dougherty", "repo": "ladr", "sha": "a3137394831791ad29c5bbfe6241d1fb233d67cb", "save_path": "github-repos/coq/quinn-dougherty-ladr", "path": "github-repos/coq/quinn-dougherty-ladr/ladr-a3137394831791ad29c5bbfe6241d1fb233d67cb/src/old/StartingOver.v"} |
import os
from functools import reduce
from typing import Any, List
from ....utils.format_output import format_output, dict_format
import numpy as np
import onnxruntime as rt
from ...abc import BatchAlgorithm
from ....auto_config import get_provider
from ....data import load
from ....utils import DictExtraction
from tr... | {"hexsha": "aaf341bb882fc08888a2bf4c41284bf2bddf75b1", "size": 4134, "ext": "py", "lang": "Python", "max_stars_repo_path": "oknlp/algorithm/cws/bert_cws/BertCWS.py", "max_stars_repo_name": "PLNUHT/ink", "max_stars_repo_head_hexsha": "3542bdfbd7281118c62e0b1ab965dc7ddf279a6d", "max_stars_repo_licenses": ["MIT"], "max_st... |
# -*- coding: utf-8 -*-
import numpy as np
from math import sin, cos, pi, sqrt, atan2
## Function converts from WGS84 to ECEF
# @param lon Longitude in degrees (np.array, 1xN)
# @param lat Longitude in degrees (np.array, 1xN)
# @param h Height in meters (optional, default = 0)
# @return Matrix (3xN) containing... | {"hexsha": "449b1257047124ca0768cea164f7cc3f21497de0", "size": 4948, "ext": "py", "lang": "Python", "max_stars_repo_path": "clustering/COStransforms.py", "max_stars_repo_name": "sansastra/uncertainty_edl_graph", "max_stars_repo_head_hexsha": "21c20bef323b520f6031ca54c2eb1fd020841f9d", "max_stars_repo_licenses": ["Apach... |
"""Training/evaluation hooks which make visualization or custom logging easier
"""
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, Optional
import cv2
import numpy as np
from torch import Tensor
from ..envs import EnvExt, EnvTransition
from ..prelude import Action, Array, St... | {"hexsha": "bf7641b0262f4e1e7d1d9591645ca901dda0aa7d", "size": 5075, "ext": "py", "lang": "Python", "max_stars_repo_path": "rainy/lib/hooks.py", "max_stars_repo_name": "kngwyu/Rainy", "max_stars_repo_head_hexsha": "535f8f49d9efe0324a8480d0165ab1b35d83dc19", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
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