text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
# Copyright 2018-2020 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the 'license' fil... | {"hexsha": "3f7036d4a856a91f6eeb370adf8ca5cb1924ae5d", "size": 4954, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/unit/test_server.py", "max_stars_repo_name": "wiltonwu/sagemaker-containers", "max_stars_repo_head_hexsha": "8ece6809f1af6c32a95dc5330ac29c48d34ed12d", "max_stars_repo_licenses": ["Apache-2.0... |
\chapter{Theoretical Background}
\section{Basics in Modelling Light in Computer Graphics}
\subsection{Radiometry}
One purpose of Computer Graphics is to simulate the interaction of light with a surface and how a real-world observer, such as a human eye, will perceive this. These visual sensations of an eye are modelled... | {"hexsha": "7e53cde6557f38a17d8d510a5da1b837bc3c9049", "size": 45172, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "document/Source/Chapters/theoretical_background.tex", "max_stars_repo_name": "simplay/Bachelor-Thesis", "max_stars_repo_head_hexsha": "ef450c5420b768b2a1fd84c9ad768f34db12fc88", "max_stars_repo_lic... |
classdef NonSeqEvtsState < matlab.mixin.SetGet
%NonSeqEvtsState Summary of this class goes here
% Detailed explanation goes here
properties
nonSeqEvts LaunchVehicleNonSeqEvents
event LaunchVehicleEvent
end
methods
function obj = NonSeqEvtsState(event, nonSeqEvts)
... | {"author": "Arrowstar", "repo": "ksptot", "sha": "2b414440d3b167ba2294f56dafce0f465c07f982", "save_path": "github-repos/MATLAB/Arrowstar-ksptot", "path": "github-repos/MATLAB/Arrowstar-ksptot/ksptot-2b414440d3b167ba2294f56dafce0f465c07f982/helper_methods/ksptot_lvd/classes/StateLog/@NonSeqEvtsState/NonSeqEvtsState.m"} |
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
BLOCK_SIZE = 4
### read image ###
img = Image.open('test_B.bmp') # color image
img = np.array(img).astype(np.float32) / 256
print(img.shape, img.dtype)
row,col=img.shape[:2]
data=[]
for i in range(0,row,BLOCK_SIZE):
for j in range(0... | {"hexsha": "176d6cec5d325f9d5113c23368e2ce50e192fe32", "size": 1455, "ext": "py", "lang": "Python", "max_stars_repo_path": "digitmedia/2 compress/imgtest.py", "max_stars_repo_name": "fffasttime/cs_misc", "max_stars_repo_head_hexsha": "abff0dcaa840d07e2d948c50d9a9e53996c744fb", "max_stars_repo_licenses": ["MIT"], "max_s... |
import os
import argparse
import warnings
import numpy as np
import torch
from torch import nn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from tqdm import tqdm
class Generator(nn.Module):
"""Generator structure from InfoGAN... | {"hexsha": "dc26338c761280465e0c0f119159be4ec053448f", "size": 8676, "ext": "py", "lang": "Python", "max_stars_repo_path": "main_infogan.py", "max_stars_repo_name": "elingaard/infogan-mnist", "max_stars_repo_head_hexsha": "fc50097b7b416400d00c48286c2de9c3a5190eef", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
from keras.layers import Input, Embedding, SpatialDropout1D, Bidirectional, LSTM, Flatten, Concatenate, Dense
from keras.initializers import glorot_normal, orthogonal
from keras.models import Model
from sklearn.model_selection import StratifiedKFold
from sklearn import utils
from common.nn.element... | {"hexsha": "c7d7f9a5c164b8c1385129880be6f55e81a22851", "size": 7653, "ext": "py", "lang": "Python", "max_stars_repo_path": "quora/sequence_models.py", "max_stars_repo_name": "CC0210/jads_kaggle", "max_stars_repo_head_hexsha": "6897a43426b7a54325e5301ced9e714d79541c4a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import unittest
import torch
import torch.nn as nn
import torch.optim
import numpy as np
import FrEIA.modules as Fm
import FrEIA.framework as Ff
def F_conv(cin, cout):
'''Simple convolutional subnetwork'''
net = nn.Sequential(nn.Conv2d(cin, 32, 3, padding=1),
nn.ReLU(),
... | {"hexsha": "2cb1542a8a5f2f78712107cd107b1ce838a94454", "size": 5356, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_conditioning.py", "max_stars_repo_name": "RussellALA/FrEIA", "max_stars_repo_head_hexsha": "f7a9fd469741fcab7912425047bf9a3965876512", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
! ******************************************************************************************************************************** !
! cpl_comp_rokocn.f90
! rokgem interface rokd compositional integrator
! ****************************************************************************************************************... | {"hexsha": "e90b334b9e70aea9b5a23d9996ac2274067602bf", "size": 2872, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "genie-rokgem/src/fortran/cpl_comp_rokocn.f90", "max_stars_repo_name": "JUNPENGZ/cgenie.muffin", "max_stars_repo_head_hexsha": "43bc8dc025428a5141866d762129b2cfaf1345ed", "max_stars_repo_licenses... |
# This is barely modified from Kivy tutorials:
# https://kivy.org/doc/stable/tutorials/pong.html
# ...to integrate serial input from the MSP430FR5994
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.properties import (
NumericProperty, ReferenceListProperty, ObjectProperty
)
from kivy.vec... | {"hexsha": "a69cd2dec9ff226f5a3cf2663f00a9c4e81ec583", "size": 5301, "ext": "py", "lang": "Python", "max_stars_repo_path": "lab4/pong/main.py", "max_stars_repo_name": "EvansTDingwiza/ce346-code", "max_stars_repo_head_hexsha": "b938463d4a6d25e0a017aedbf4f50977adc88f40", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import json
import os
import random
from argparse import ArgumentParser
import cv2
import keras.backend as K
import numpy as np
from keras import Input, Model, metrics
from keras.callbacks import Callback, TensorBoard
from keras.layers import Conv2D, Flatten, Dense, Lambda, Reshape, Conv2DTranspose
from sonicrl.envir... | {"hexsha": "24d73cf05ee1988a9e74d8066bf02d61c91a6307", "size": 7210, "ext": "py", "lang": "Python", "max_stars_repo_path": "sonicrl/worldmodel/autoencoder.py", "max_stars_repo_name": "bharris47/sonic-rl", "max_stars_repo_head_hexsha": "5a819e92299f7eeaa9853b4991c9829603752bf6", "max_stars_repo_licenses": ["MIT"], "max_... |
from typing import List, Dict, Tuple, Callable
import numpy as np
import pytest
def static_test(f: Callable, l_tests: List[Dict[str, Tuple]],
key_in: str = 'Input', key_out: str = 'Output'):
"""Validates the 'f' function on the list of tests 'l_tests'
Parameters
--------------------
f... | {"hexsha": "cac0f8b5054dd09652366846e06eda59042eaeae", "size": 1167, "ext": "py", "lang": "Python", "max_stars_repo_path": "covidxpert/utils/test_utils.py", "max_stars_repo_name": "LucaCappelletti94/covidxpert", "max_stars_repo_head_hexsha": "8adda25f3d6fb648607c0f8af7d3ff54b42c59fb", "max_stars_repo_licenses": ["MIT"]... |
Require Import rt.util.all.
Require Import rt.model.arrival.basic.task.
From mathcomp Require Import ssreflect ssrbool ssrnat eqtype seq.
Module ConcreteTask.
Import SporadicTaskset.
Section Defs.
(* Definition of a concrete task. *)
Record concrete_task :=
{
task_id: nat; (* for uniquen... | {"author": "cd-public", "repo": "rt-proofs", "sha": "ebef0b65460fe009c51f638fe2b459f16a6d1dd5", "save_path": "github-repos/coq/cd-public-rt-proofs", "path": "github-repos/coq/cd-public-rt-proofs/rt-proofs-ebef0b65460fe009c51f638fe2b459f16a6d1dd5/implementation/uni/susp/dynamic/task.v"} |
from sklearn.datasets import fetch_lfw_people
import numpy as np
import pdb
MALENESS_THRESHOLD = 0 # threshold at which the person is classified as a male
MIN_FACES = 5
TRAIN_CUT = 0.75
print("Fetching people with at least " + str(MIN_FACES) + " pictures.")
lfw_people = fetch_lfw_people(color=True, min_faces_per_per... | {"hexsha": "7d2228ed17ca5af8e5f4bcfba05b8bb1d578ccb7", "size": 1379, "ext": "py", "lang": "Python", "max_stars_repo_path": "ML/Pytorch/data/lfw/parse_lfw_maleness.py", "max_stars_repo_name": "DistributedML/Biscotti", "max_stars_repo_head_hexsha": "dfba71b3924e1bafd2ab2545881fb741193f224e", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 19 22:52:36 2020
@author: Woody
"""
from model import Model
from agent import Agent
from algorithm import PolicyGradient
import gym
import numpy as np
import os
from parl.utils import logger
def run_episode(env, agent):
obs_list, action_list, reward_list = [], [],... | {"hexsha": "0c972a37f8cbb8bfc355a5b5d778962b75250753", "size": 3481, "ext": "py", "lang": "Python", "max_stars_repo_path": "results/pg/train_pg.py", "max_stars_repo_name": "star2dust/parl-tutorials", "max_stars_repo_head_hexsha": "a7bae8b9a8968b7cad77a04f104f1c846eb4ddf2", "max_stars_repo_licenses": ["MIT"], "max_stars... |
include("censored.jl")
include("cross_validate.jl")
include("precision_at_k.jl")
include("simple_glrms.jl")
#include("fit_rdataset.jl") | {"hexsha": "f321a716cd48caeec7660660067c526171aab22f", "size": 135, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/runexamples.jl", "max_stars_repo_name": "heyi19931225/Lowrankmodels", "max_stars_repo_head_hexsha": "b87cadec54dd0e431c0b901ae405dcc490bdc79a", "max_stars_repo_licenses": ["MIT"], "max_star... |
! the initilization module, used to initialize the phase field data
! Created by Z. Guo on Jan 17, 2014
! Last modified ib Jan 22, 2014
module init_phi_module
use multifab_module
use ml_layout_module
use define_bc_module
use multifab_physbc_module
use multifab_fill_ghost_module
!use ml_restriction_module
... | {"hexsha": "4e8f957d82796804e4438530cdedc42d3077d9c3", "size": 33877, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Work/FLOW_PHASEFIELD/init_phi.f90", "max_stars_repo_name": "Marsfish1981/HPC-Phase_Field", "max_stars_repo_head_hexsha": "80a6b367cb6395541150ea68ab9dd3802f37850b", "max_stars_repo_licenses": [... |
# stdlib
from functools import lru_cache
from typing import Dict
# third party
from autodp import dp_bank
from autodp import fdp_bank
from autodp.autodp_core import Mechanism
import numpy as np
@lru_cache(maxsize=None)
def _individual_RDP_gaussian(
sigma: float, value: float, L: float, alpha: float
) -> float:
... | {"hexsha": "e87011da3841abc315e22906bde74485e41a1fa8", "size": 3894, "ext": "py", "lang": "Python", "max_stars_repo_path": "packages/syft/src/syft/core/adp/idp_gaussian_mechanism.py", "max_stars_repo_name": "callezenwaka/PySyft", "max_stars_repo_head_hexsha": "2545c302441cfe727ec095c4f9aa136bff02be32", "max_stars_repo_... |
# Main waveform class location
# Copyright (C) 2020 Michael L. Katz, Alvin J.K. Chua, Niels Warburton, Scott A. Hughes
#
# 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 the ... | {"hexsha": "be7b77f6e62a95a80db9fac8e2ae6aab92a0b4ad", "size": 28545, "ext": "py", "lang": "Python", "max_stars_repo_path": "FastEMRIWaveforms/few/waveform.py", "max_stars_repo_name": "basuparth/ICERM_Workshop", "max_stars_repo_head_hexsha": "ebabce680fc87e90ff1de30246dcda9beb384bb4", "max_stars_repo_licenses": ["MIT"]... |
import io
import math
import pdb
import timeit
from quantities import mV, ms, s, V
import sciunit
from neo import AnalogSignal
import neuronunit.capabilities as cap
import numpy as np
from .base import *
import quantities as qt
from quantities import mV, ms, s, V
import matplotlib as mpl
# try:
# import asciiplotli... | {"hexsha": "92e8f3ce05be7c7693ee18f892651c5172b269ff", "size": 18687, "ext": "py", "lang": "Python", "max_stars_repo_path": "jithub/models/backends/neuron_hh.py", "max_stars_repo_name": "russelljjarvis/numba_reduced_neuronal_models", "max_stars_repo_head_hexsha": "bc500aefab267a1a1eaf2a1d8dac83da676d7ee6", "max_stars_r... |
'''Collection of terms that form loss functionals
Author: Hwan Goh, Oden Institute, Austin, Texas 2020
'''
import numpy as np
import tensorflow as tf
import pdb #Equivalent of keyboard in MATLAB, just add "pdb.set_trace()"
###############################################################################
# ... | {"hexsha": "f31c2caa4c3fb72373b0df5131eb8cb21f222a02", "size": 6600, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/src/utils_training/functionals.py", "max_stars_repo_name": "hwangoh/uq-vae", "max_stars_repo_head_hexsha": "382548e6f6dd7f9d72feff0e0752beec871db348", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import torch
import numpy as np
import librosa as la
from utils import listDir, tick, tock
FRAMES_PER_SAMPLE: int = 336 # number of frames per sample
HOP_LENGTH: int = 42 # number of frames to hop, to get to next sample
# number of samples to extract from a performance
SAMPLES_PER_PERFORMANCE: int = 120
#... | {"hexsha": "6f6515c0ca8544426648f1f4dcae3b9af008fedc", "size": 3610, "ext": "py", "lang": "Python", "max_stars_repo_path": "comparing-rnn-params/model/dataset.py", "max_stars_repo_name": "pasinducw/scs-4224-fyp", "max_stars_repo_head_hexsha": "753dd2cc6db84bcb9823a24ce5f495d94f55b162", "max_stars_repo_licenses": ["MIT"... |
#pragma once
#include <memory>
#include <iterator>
#include <cstddef>
#include <gsl/gsl>
namespace dr {
/// \brief round `s` up to the nearest multiple of n
template<typename T>
T round_up(T s, unsigned int n) { return ((s + n - 1) / n) * n; }
template<typename T, typename Allocator = std::allocator<T>>
struct gap_... | {"hexsha": "30128633a412da2b11f1833ec52d0b0e2c142bc9", "size": 17324, "ext": "h", "lang": "C", "max_stars_repo_path": "include/gap_buffer.h", "max_stars_repo_name": "lie-yan/gapbuffer", "max_stars_repo_head_hexsha": "b6b3d621430029d989ebed8a672eee769c214ea5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, ... |
"""
inputfile
Abstract type for all kinds of input files
"""
abstract type inputfile end
"""
Inputconstants = new(lx, ly, maxruntime, dumping, gravity, γ, δ, kbt)
Struct containing input parameters.
Contains `.lx` lattice points in x-direction, `.ly` lattice points in y-direction.
Other fields are `.maxrun... | {"hexsha": "3404f78641c7fb9a981a3eff91f25d5f79cd2762", "size": 4538, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/readinput.jl", "max_stars_repo_name": "Zitzeronion/JuSwalbe", "max_stars_repo_head_hexsha": "eb0aca0eabe327d4f9ca5756b4fc5b0e4fb2876b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
import os
import json
import numpy as np
try:
import cv2
except:
pass
from copy import deepcopy
from tqdm import tqdm
from transformers import BertTokenizer, LayoutLMTokenizer
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from public.data_provider.doc2dial import load_tocr, load_doc
from... | {"hexsha": "e62ab32977f188ca54fb1a9d8f4b42e8aa026cc1", "size": 13450, "ext": "py", "lang": "Python", "max_stars_repo_path": "public/data_provider/ocr.py", "max_stars_repo_name": "Coldog2333/DGDS", "max_stars_repo_head_hexsha": "7c9b6904ab1d86fe2b430f01a3b583609bc095e2", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
clf reset;
echo on
% This script demonstrates the use of the RSOM.
clc;
% load the example dissimilarity data
load exampleDissimilarity.mat;
% display the eigenvalue spectrum
[V, D] = eig(Dissim);
eVals = diag(D);
figure; bar(eVals);
pause % Strike any key to continues...
clc;
% init RSOM
sMap = rsom_lininit(D... | {"author": "ilarinieminen", "repo": "SOM-Toolbox", "sha": "f2597abc1ae33c2060e0443d49e854011ff21831", "save_path": "github-repos/MATLAB/ilarinieminen-SOM-Toolbox", "path": "github-repos/MATLAB/ilarinieminen-SOM-Toolbox/SOM-Toolbox-f2597abc1ae33c2060e0443d49e854011ff21831/contrib/rsom/rsom_demo.m"} |
import numpy as np
from sklearn import datasets
# 设置数据集
n_samples = 1500
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = np.random.rand(n... | {"hexsha": "4a9d2b400b46b60ed4abaeb38252abe352f4c7f0", "size": 605, "ext": "py", "lang": "Python", "max_stars_repo_path": "Dataset/toy_dataset/cluster_toy_dataset.py", "max_stars_repo_name": "pengchenyu111/PaperCodeReplication", "max_stars_repo_head_hexsha": "7b8681654e25b7d707f4b4d7ebcfb85ffc0fd52a", "max_stars_repo_l... |
import sys
sys.path.insert(0, 'data')
import pandas as pd
import numpy as np
from matplotlib import pyplot
import collections
from sklearn.model_selection import train_test_split, cross_val_score, RepeatedStratifiedKFold
from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, f1_score, auc, accura... | {"hexsha": "2e371fe7480f7424a8e659d1c39521a5472d37ee", "size": 19594, "ext": "py", "lang": "Python", "max_stars_repo_path": "advanced-ml.py", "max_stars_repo_name": "skouras-io/advanced-ml", "max_stars_repo_head_hexsha": "68cfaa3edb9592b8183f607f9ff61628fdb0c2cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
theory SimplyTypedLambdaCalculus
imports Main
begin
type_synonym var = string
no_notation Set.member ("(_/ \<in> _)" [51, 51] 50)
datatype type = TUnit
| TApp type type ("_ \<rightarrow> _")
datatype expr =
Unit
| Var var
| Abs var type expr
| App expr expr
(* it is important to choose a list here, s... | {"author": "ThreeFx", "repo": "toy-examples", "sha": "b687b0c48742d01a1839f71068f51fcc026aad34", "save_path": "github-repos/isabelle/ThreeFx-toy-examples", "path": "github-repos/isabelle/ThreeFx-toy-examples/toy-examples-b687b0c48742d01a1839f71068f51fcc026aad34/SimplyTypedLambdaCalculus.thy"} |
"""
MultiComplexMat implements a wrapper for any objects (reals, np.arrays
and sparse matrices) which can have multiple imaginary like units.
E.g. rules i*i = -1, j*j = -1 but i*j = j*i will not simplify further.
The MultiComplexMat overloads all common
numerical operations: +,-,*,@ etc. such that these rules are... | {"hexsha": "5ebc854b1e159abedaf0bda958a78d32355fd444", "size": 15380, "ext": "py", "lang": "Python", "max_stars_repo_path": "multicomplexmat.py", "max_stars_repo_name": "Ehtycs/modeling-spork", "max_stars_repo_head_hexsha": "9aeb8ce5f69f7456feb872744c07341c2966de33", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import tensorflow as tf
from Transformer import MHA
from Transformer.TransformerEncoder import TransformerEncoder
from Transformer.TransformerDecoder import TransformerDecoder
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from Transformer.TransformerCore import Get_Custom... | {"hexsha": "d7fd0567a8d6c8dec65c9507876f3cc93d6e2236", "size": 6103, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests.py", "max_stars_repo_name": "antoniorv6/Transformer-Keras", "max_stars_repo_head_hexsha": "9566f4211f92922a668977e72dbb72b722d4de5e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# -*- coding: utf-8 -*-
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, roc_auc_score, confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import itertools
import re
def data_load_home_credit(path):
"""
To load dataset from the di... | {"hexsha": "73e021f7572832485684ffd424767ae891476b53", "size": 4229, "ext": "py", "lang": "Python", "max_stars_repo_path": "home_credit/common.py", "max_stars_repo_name": "ismaeelnawaz/msds19029_thesis", "max_stars_repo_head_hexsha": "83eac1aa22105404d0551bfa49be5b3e960a6aa2", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
Copyright 2019 Samsung SDS
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 ... | {"hexsha": "e5846c27474b5bc85798194321dd09efcdbd7f52", "size": 4053, "ext": "py", "lang": "Python", "max_stars_repo_path": "function/python/brightics/function/regression/test/random_forest_regression_test.py", "max_stars_repo_name": "parkjh80/studio", "max_stars_repo_head_hexsha": "6d8d8384272e5e1b2838b12e5557272a19408... |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 9 16:07:58 2019
@author: Yaqiong Su
"""
import numpy as np
import pandas as pd
import linecache as lc
f1 = open('POSCAR','rb')
f2 = open('head','wb')
f3 = open('direct','wb')
f4 = open('lattice','wb')
i = 0
while True:
line = f1.readline()
i... | {"hexsha": "42cebeaede80a4cf61d0ca0345c7374f090ccc8b", "size": 1600, "ext": "py", "lang": "Python", "max_stars_repo_path": "coordinate.py", "max_stars_repo_name": "YaqiongSu/transformation-between-direct-and-cartesian-coordinate-VASP", "max_stars_repo_head_hexsha": "30109019681af8ffa7f6d2ddaef909cc4c7c854b", "max_stars... |
! Last change: HO 27 May 2000 11:47 pm
MODULE mpeg
IMPLICIT NONE
PUBLIC
TYPE:: mpeg_parameters
INTEGER :: mtype
INTEGER :: layer
INTEGER :: ibit_rate
INTEGER :: isample_rate
INTEGER :: ipsychoacoustic
INTEGER :: iemphasis
INTEGER :: ipadding
INTEGER :: icrc
INTEGER :: mode
INTEGER :: iextension
INTE... | {"hexsha": "ce8c2609fbff2aa6bee56dd702cbe13da70ea371", "size": 1447, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "mpeg.f90", "max_stars_repo_name": "cure-honey/uzura2", "max_stars_repo_head_hexsha": "2ab63fa966daa7e5331fcd3f33be4417bd6222ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
"""
routines to compute derivatives of spherical functions
"""
import numpy as np
def d_r_dx(x, y):
"""
derivative of r with respect to x
:param x:
:param y:
:return:
"""
return x / np.sqrt(x**2 + y**2)
def d_r_dy(x, y):
"""
:param x:
:param y:
:return:
"""
retur... | {"hexsha": "9d4f232e2f067c59df83436b583a50c8d541175b", "size": 951, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrofunc/LensingProfiles/calc_util.py", "max_stars_repo_name": "LBJ-Wade/astrofunc_lensing_profile", "max_stars_repo_head_hexsha": "d2223705bc44d07575a5e93291375ab8e69ebfa8", "max_stars_repo_licen... |
\section{Privacy Preserving Voting}
\todo{
consider the liquid democracy requirement that individual voters' votes remain private, while delegates' votes are public. This can be achieved by blinding the votes but still allowing a final tally.
} | {"hexsha": "e8c4582913cf11594d15d0546d074579e7657b28", "size": 244, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "papers/working-document/privacy.tex", "max_stars_repo_name": "MitchellTesla/decentralized-software-updates", "max_stars_repo_head_hexsha": "89f5873f82c0ff438e2cd3fff83cc030a46e29da", "max_stars_repo_... |
A popular snack to munch while watching Movies.
| {"hexsha": "90dbd3b81388c99694558415ab008926279ad162", "size": 50, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Popcorn.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#include <boost/mpl/aux_/preprocessed/no_ttp/quote.hpp>
| {"hexsha": "165ad44d5f598b55102ff9a60f3a25e222a372f3", "size": 56, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_mpl_aux__preprocessed_no_ttp_quote.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_license... |
## GeomUtils.jl --- constructs for geometry
export distance, anglespan, dihedral
export centroid, RMSD
"""
distance(a, b) -> Real
Calculate Euclidean distance from `a` to `b`.
"""
distance(a, b) = norm(a-b)
"""
anglespan(a, b, c) ∈ [0, π]
Calculate smaller angle between two vectors ``AB`` and ``BC`` meetin... | {"hexsha": "b9239d18d4c5d5b8d73e21c6af3bb55e7adc0613", "size": 2295, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GeomUtils.jl", "max_stars_repo_name": "bldamalla/ProtStructs.jl", "max_stars_repo_head_hexsha": "98f73e61d123b8f319c597368f00b4b83a416d62", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python
import cv2
import numpy as np
import rospy
import math
from cv_bridge import CvBridge, CvBridgeError
from sensor_msgs.msg import CompressedImage, Image
def crop_line_image(image_raw):
OK = 20
CK = 20
hough_thresh = 220
hough_min_line = 100
hough_max_gap = 100
col = 100
... | {"hexsha": "690ab22d21b21af36e00cf2f29db39b1878daf86", "size": 2851, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/crop_row_follow.py", "max_stars_repo_name": "sonyccd/crop_row_follow", "max_stars_repo_head_hexsha": "26ee58804d0b569608da4b825c3b14c227cfd245", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
"Split a subspace into two"
split(ss::UInt64) = hash((ss, 0)), hash((ss, 1)) | {"hexsha": "b8150b2996449861ec57bacc2f51d1cb1d5bfc6d", "size": 76, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "OmegaCore/src/space/subspace.jl", "max_stars_repo_name": "zenna/expect", "max_stars_repo_head_hexsha": "48bd661df410777eeb8940876a5cc8817eed2ac5", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
\chapter{Abstract}
Here goes the abstract, a summary of your thesis work. You may add some keyword at the end that clearly identify the research field of the thesis. The abstract should not contain any reference to related works.
\paragraph{Keywords} keyword1; keyword2; keyword3 | {"hexsha": "b6922dacf4724bc1a551a989ac732888642af9ec", "size": 282, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapters/Abstract.tex", "max_stars_repo_name": "ste7en/master-thesis-template-polimi", "max_stars_repo_head_hexsha": "b56d8dd68cf80061d92e9cf783248b31ecbae0d1", "max_stars_repo_licenses": ["MIT"], "m... |
\section*{Publication Data}
\textcopyright Copyright The Rexx Language Association, 2011-\splice{java TexYear}\\
All original material in this publication is published under the Creative Commons - Share Alike 3.0 License as stated at \url{http://creativecommons.org/licenses/by-nc-sa/3.0/us/legalcode}.\\[0.5cm]
The re... | {"hexsha": "166bce9afbbc3866201228c2d0bf6079ce923928", "size": 775, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "documentation/boilerplate/bookmeta.tex", "max_stars_repo_name": "RexxLA/NetRexx", "max_stars_repo_head_hexsha": "ec27b6e3f908fbc50cb6dc54696daea68ae59103", "max_stars_repo_licenses": ["ICU"], "max_st... |
import numpy as np
from hw2 import Transform, Translation, Rotation, Base
from hw3 import Quaternion, link, symLink
import matplotlib.pyplot as plt
from simulation import Simulation
import sympy
from pprint import pprint
from gradient import gradientVariable, forwardVariable
# exit()
print("Q1")
t01 = Transform(rot=R... | {"hexsha": "16441702e9b3fe3112f4bf9f5ae6866d423023ba", "size": 2482, "ext": "py", "lang": "Python", "max_stars_repo_path": "midterm.py", "max_stars_repo_name": "linnil1/2020Robotics", "max_stars_repo_head_hexsha": "fdb18abe020f7123b5112dac9b49deb51850f054", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# Copyright 2018-2019 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or... | {"hexsha": "1d05117bc5e3068029200857ca3898be613dc137", "size": 1873, "ext": "py", "lang": "Python", "max_stars_repo_path": "pennylane/about.py", "max_stars_repo_name": "InduManimaran/pennylane", "max_stars_repo_head_hexsha": "375d25acc7bd2e6d5243b5273958b26513c33189", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
<!-- dom:TITLE: Demo - 3D Poisson's equation -->
# Demo - 3D Poisson's equation
<!-- dom:AUTHOR: Mikael Mortensen Email:mikaem@math.uio.no at Department of Mathematics, University of Oslo. -->
<!-- Author: -->
**Mikael Mortensen** (email: `mikaem@math.uio.no`), Department of Mathematics, University of Oslo.
Date: **... | {"hexsha": "d49b96fa656fc77c983bbd0174c000bb6fdb73c0", "size": 38890, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "content/poisson3d.ipynb", "max_stars_repo_name": "mikaem/shenfun-demos", "max_stars_repo_head_hexsha": "c2ad13d62866e0812068673fdb6a7ef68ecfb7f2", "max_stars_repo_licenses": ["BSD-2-... |
$\newcommand{\xv}{\mathbf{x}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\vv}{\mathbf{v}}
\newcommand{\yv}{\mathbf{y}}
\newcommand{\zv}{\mathbf{z}}
\newcommand{\av}{\mathbf{a}}
\newcommand{\Chi}{\mathcal{X}}
\newcommand{\R}{\rm I\!R}
\newcommand{\sign}{\text{sign}}
\newcommand{\Tm}{\mathbf{T}}
\newcommand{\Xm}{... | {"hexsha": "6f81f00dced9be42e450c3bc18375b2726e81e57", "size": 356271, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "reading_assignments/questions/7_Note-NonlinearLogReg.ipynb", "max_stars_repo_name": "biqar/Fall-2020-ITCS-8156-MachineLearning", "max_stars_repo_head_hexsha": "ce14609327e5fa13f7af7... |
*DECK D9ATN1
DOUBLE PRECISION FUNCTION D9ATN1 (X)
C***BEGIN PROLOGUE D9ATN1
C***SUBSIDIARY
C***PURPOSE Evaluate DATAN(X) from first order relative accuracy so
C that DATAN(X) = X + X**3*D9ATN1(X).
C***LIBRARY SLATEC (FNLIB)
C***CATEGORY C4A
C***TYPE DOUBLE PRECISION (R9ATN1-S, D9ATN1-D)
C***K... | {"hexsha": "8f64e14b7a7a9280df8f0dd49b419822efc203ba", "size": 5371, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "slatec/src/d9atn1.f", "max_stars_repo_name": "andremirt/v_cond", "max_stars_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_stars_repo_licenses": ["Unlicense"], "max_stars_coun... |
from math import log, sqrt, floor
from typing import List
import numpy as np
from scipy import integrate
from scipy.signal import argrelextrema
from cell_models import protocols, trace
from cell_models.current_models import ExperimentalArtefactsThesis
from cell_models.protocols import VoltageClampProtocol
class CellM... | {"hexsha": "2946b50c5b6bea7c4c3629d161fe3b46c991fd9e", "size": 24351, "ext": "py", "lang": "Python", "max_stars_repo_path": "cell_models/cell_model.py", "max_stars_repo_name": "Christini-Lab/cell-models", "max_stars_repo_head_hexsha": "a2a4b2b8678e3846b77b48c93eb1df080281490e", "max_stars_repo_licenses": ["MIT"], "max_... |
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import os
from datetime import datetime
from IPython.core.display import ProgressBar
import numpy as np
import pandas as pd
from gql import gql, Client, AIOHTTPTransport
import asyncio
TOP = 100
TOKEN = "bearer " + os.getenv(... | {"hexsha": "9a884625895381c9903a18d692d86f46caa92fdb", "size": 5502, "ext": "py", "lang": "Python", "max_stars_repo_path": "contributions.py", "max_stars_repo_name": "oleksis/github-cuba", "max_stars_repo_head_hexsha": "bbb2aa4b52cdcaa74bff0532a627dc715f65ead5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy import units as u
from astropy.coordinates import Angle
from regions import PixCoord
from gammapy.datasets import SpectrumDatasetOnOff
from gammapy.maps import RegionGeom, RegionNDMap, WcsNDMap
from gammapy.ut... | {"hexsha": "425772fbbd1c9b1b3fd0dbf9f290487b5c9d2560", "size": 12881, "ext": "py", "lang": "Python", "max_stars_repo_path": "gammapy/makers/background/reflected.py", "max_stars_repo_name": "kabartay/gammapy", "max_stars_repo_head_hexsha": "015206d2418b1d254f1c9d3ea819ab0c5ece99e9", "max_stars_repo_licenses": ["BSD-3-Cl... |
import cv2
import numpy as np
import time
import autopy
import mediapipe as mp
import math
import wx
import threading
thread = None
# noinspection PyAttributeOutsideInit,PyShadowingNames
class TraceThread(threading.Thread):
def __init__(self, *args, **keywords):
threading.Thread.__init__(self, *args, **ke... | {"hexsha": "a643643c8e2ae4cea1d63084d87f425fb8fc3a45", "size": 11116, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/MouseCTRL.py", "max_stars_repo_name": "MouseCTRL/MouseCTRL", "max_stars_repo_head_hexsha": "920c6b99c6729b41a34af77d47936c8893bf46f3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import os
import sys
import numpy as np
import cv2
def parse_csv_annotations(filepath, num_classes=60):
"""
Extract annotations from the csv file from one epoch produced in main.py
:param filepath: path of the csv file
:param num_classes: number of classes used
:return: return a list[dict] of rec... | {"hexsha": "573fb427365bf7981c7d389afc31dec9b2e7f5d0", "size": 8903, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/visualize.py", "max_stars_repo_name": "AlexandreDh/ACAR-Net", "max_stars_repo_head_hexsha": "db28009388512e31cb6ff8e86725dc9b026886b6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
! { dg-do compile }
! PR fortran/41940
integer, allocatable :: a
TYPE :: x
integer, allocatable :: a
END TYPE
TYPE (x) :: y
allocate(a(4)) ! { dg-error "Shape specification for allocatable scalar" }
allocate(y%a(4)) ! { dg-error "Shape specification for allocatable scalar" }
end
| {"hexsha": "0fa9ce1fce9c2d8ea2e4b2bbf956c80ace473e15", "size": 291, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/allocate_scalar_with_shape.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_... |
// embeddedprolog.cpp : Defines the entry point for the console application.
//
#include "stdafx.h"
#include <stdexcept>
#include <utility>
#include <boost/any.hpp>
#include <vector>
#include <boost/regex.hpp>
#include <boost/variant.hpp>
#include <boost/variant/recursive_variant.hpp>
#include <boost/intrusiv... | {"hexsha": "460488a3572d776eb49dc07376a823d20a335f91", "size": 64754, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "embeddedprolog.cpp", "max_stars_repo_name": "differentprogramming/logic_in_cpp", "max_stars_repo_head_hexsha": "f0ccf8d9a5108ff358c5cec189357205534b23ce", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python
from __future__ import division
from sklearn.cross_validation import StratifiedKFold
from sklearn.datasets import load_svmlight_file
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import log_loss
import argparse
import logging
import numpy as np
import os
import time
import x... | {"hexsha": "72e863a2f3b5ad6ff90852fde12c27baeca5e926", "size": 2522, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train_predict_esb_xg_grid_colsub.py", "max_stars_repo_name": "drivendata/countable-care-3rd-place", "max_stars_repo_head_hexsha": "d1bba2f09ba0196cc3f35d2a41ea93bfbc4086a2", "max_stars_repo_li... |
!! SRN 04502674
!!
!! WRONG RESULT FROM POLYMORPHIC POINTER ASSIGNMENT
!!
!! This example shows that the polymorphic pointer assignment
!! is being done incorrectly, giving either wrong results or
!! a segfault. This occurs for version 19.1 and is a regression
!! from 18 and 19.0.
!!
!! $ ifort --version
!! ifort (IFOR... | {"hexsha": "d40dc463aba4c3f3616d7c87e10c60253067f4c4", "size": 2009, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "intel-bugs/intel-20200125.f90", "max_stars_repo_name": "dhnza/fortran-compiler-tests", "max_stars_repo_head_hexsha": "f60d1c2a6e67153fa92f5653e0a3df771d5ed5bb", "max_stars_repo_licenses": ["MIT"... |
"""
The algorithms based on centered aligmnent proposed in
C. Cortes, M. Mohri, and A. Rostamizadeh, "Algorithms for Learning Kernels Based on Centered Alignment," J. Mach. Learn. Res., vol. 13, pp. 795-828, Mar. 2012.
Given :math:`p` kernel matrices :math:`\mathbf{K}_1, \mathbf{K}_2, ..., \mathbf{K}_p`, centered ker... | {"hexsha": "88465c2608963df3004cc629e9554548399a2562", "size": 7739, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project_Health/src/mklaren/mkl/alignf.py", "max_stars_repo_name": "Anonymous633671/STABILIZER", "max_stars_repo_head_hexsha": "5a1ab8099a2d75ace7e053afc78055f1f4d359c0", "max_stars_repo_licenses":... |
# Copyright 2014, Jerome Fung, Rebecca W. Perry, Thomas G. Dimiduk
#
# flyvbjerg_petersen_std_err 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 the
# License, or (at your option) any l... | {"hexsha": "3001ecd252c6cc31aa3cc86326266420171eace6", "size": 5967, "ext": "py", "lang": "Python", "max_stars_repo_path": "US/diffusivity/2_fblockavg/flyvbjerg_petersen_std_err.py", "max_stars_repo_name": "vtlim/permeability", "max_stars_repo_head_hexsha": "56c9379b82ab586e76224937b72397011fd6acc0", "max_stars_repo_li... |
#!/usr/bin/env python
from funlib.show.neuroglancer import add_layer
import argparse
import daisy
import glob
import neuroglancer
import os
import webbrowser
import numpy as np
import zarr
parser = argparse.ArgumentParser()
parser.add_argument(
'--file',
'-f',
type=str,
action='append',
help="The ... | {"hexsha": "f782ab579af857f8f3720eea3f0a98c731407db6", "size": 6061, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/visualize.py", "max_stars_repo_name": "rhoadesScholar/daisy", "max_stars_repo_head_hexsha": "78cdd2ed0d67647a6602fb53cc952214450f3753", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#-------------------------------------------------------------------------------
# Author: Lukasz Janyst <lukasz@jany.st>
# Date: 14.06.2017
#-------------------------------------------------------------------------------
import random
import cv2
import argparse
import sys,os
import os.path as ops
sys.path.append(o... | {"hexsha": "16163245957ed80e038d66fae7a617d41e12981d", "size": 3629, "ext": "py", "lang": "Python", "max_stars_repo_path": "split_train_test_data_ubuntu.py", "max_stars_repo_name": "ivo-gilles/breast-cancer-classification", "max_stars_repo_head_hexsha": "eb79275ac281d0340ede5d21e3d4a1116d612ddd", "max_stars_repo_licens... |
from preprocess.removeOutliers import remove_outliers
from preprocess.scale import scale
from preprocess.pca import pca
import numpy as np
def pre_process(arr, test, y=[]):
test = test.to_numpy()
arr = arr.to_numpy()
arr, y = remove_outliers(arr, y)
#arr, test = pca(arr, test)
arr, test = scale(... | {"hexsha": "ddfd4866b689364311d5e0ce7c2036a2fed6a34a", "size": 357, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess/index.py", "max_stars_repo_name": "ahmedgaafer/pattern-project", "max_stars_repo_head_hexsha": "b20cd9b0c3c62ef761fa6d2969ea3d7133e40632", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#include <iostream>
#include <Eigen/Dense>
void gramSchmidtOrthogonalization(Eigen::MatrixXd &matrix,Eigen::MatrixXd &orthonormalMatrix)
{
/*
In this method you make every column perpendicular to it's previous columns,
here if a and b are representation vector of two columns, c=b-((b.a)/|a|).a
^
... | {"hexsha": "4827f06fe844f4826a12fe584c7c179c64f364b5", "size": 2541, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gram_schmidt_orthogonalization.cpp", "max_stars_repo_name": "behnamasadi/Mastering_Eigen", "max_stars_repo_head_hexsha": "99edbc819c89a4805b777eef69044a1658d96206", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
# © 2017-2019, ETH Zurich, Institut für Theoretische Physik
# Author: Dominik Gresch <greschd@gmx.ch>
"""
Defines inline calculations to automatically get an initial window guess.
"""
import numpy as np
from aiida import orm
from aiida.engine import calcfunction
from .._calcfunctions import ... | {"hexsha": "2772c7ea847bd52038ef11a6b0883c9ca33939b3", "size": 3023, "ext": "py", "lang": "Python", "max_stars_repo_path": "aiida_tbextraction/energy_windows/auto_guess.py", "max_stars_repo_name": "DanielMarchand/aiida-tbextraction", "max_stars_repo_head_hexsha": "6f9513c0c16ef0874aeb5801481287f233277620", "max_stars_r... |
using MPI
using ClimateMachine
using Logging
using ClimateMachine.Mesh.Topologies
using ClimateMachine.Mesh.Grids
using ClimateMachine.DGmethods
using ClimateMachine.DGmethods.NumericalFluxes
using ClimateMachine.MPIStateArrays
using ClimateMachine.LowStorageRungeKuttaMethod
using LinearAlgebra
using ClimateMachine.Gen... | {"hexsha": "33cc2a70407efac916ef6715b6ed1e4a5e0328c3", "size": 693, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tutorials/topo.jl", "max_stars_repo_name": "ChrisRackauckas/ClimateMachine.jl", "max_stars_repo_head_hexsha": "195bdaa323086c67a7aa4d1b5d99612f077ff3fe", "max_stars_repo_licenses": ["Apache-2.0"], "... |
module cube_to_vtk_c_binding
! Usage:
! Reference:
! http://fortranwiki.org/fortran/show/c_interface_module
use iso_c_binding
use c_f_string_c_binding, only : c_f_string
use asflowf_cube_to_vtk, only : cube_to_vtk
implicit none
contains
subroutine c_cube_to_vtk(cube_fi... | {"hexsha": "6caf56ae30f4f93ded874bc4003e32074762a35f", "size": 811, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "fortran/atomsciflowf/c_binding/cube_to_vtk_c_binding.f90", "max_stars_repo_name": "DeqiTang/pymatflow", "max_stars_repo_head_hexsha": "bd8776feb40ecef0e6704ee898d9f42ded3b0186", "max_stars_repo_l... |
import unittest
import numpy as np
import tensorflow as tf
import tf_encrypted as tfe
from tf_encrypted.layers.activation import Relu
class TestRelu(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
def test_forward(self):
input_shape = [2, 2, 2, 50]
input_relu = np.rand... | {"hexsha": "fbb435cae931769aa12d19aab95ee1c12290fc4b", "size": 1238, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_relu.py", "max_stars_repo_name": "robert-wagner/tf-encrypted", "max_stars_repo_head_hexsha": "d2cb60d68f263dfd5da46dbac55b8858d701bc47", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
#include "Gui.h"
#include "mycq.hpp"
#include <Windows.h>
#include "MyJson.h"
#include "Update.h"
//#include <regex>
#include <boost/regex.hpp>
#include <nana/gui.hpp>
#include <nana/gui/widgets/button.hpp>
#include <nana/gui/widgets/checkbox.hpp>
#include <nana/gui/widgets/combox.hpp>
#include <nana/gui/widgets/gr... | {"hexsha": "0e33101ac5411560a650236cdd6cf35b688694df", "size": 65648, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gui.cpp", "max_stars_repo_name": "zhaoguoqingit/GroupMonitor", "max_stars_repo_head_hexsha": "26d250048dd8c1a20fd1849e616ada6ea9996464", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8... |
[STATEMENT]
lemma degree_pderiv_le: "degree (pderiv f) \<le> degree f - 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. degree (pderiv f) \<le> degree f - 1
[PROOF STEP]
proof (rule ccontr)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<not> degree (pderiv f) \<le> degree f - 1 \<Longrightarrow> False
[PROOF ... | {"llama_tokens": 1541, "file": "Polynomial_Interpolation_Missing_Polynomial", "length": 19} |
struct QuickMDP{ID,S,A,D<:NamedTuple} <: MDP{S,A}
data::D
end
"""
QuickMDP(gen::Function, [id]; kwargs...)
Construct a generative MDP model with the function `gen` and keyword arguments.
`gen` should take three arguments: a state, an action, and a random number generator. It should return a `NamedTuple` with... | {"hexsha": "e6633ee66c7d2644e2a9a80262cebebd2da9832e", "size": 8114, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/quick.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/QuickPOMDPs.jl-8af83fb2-a731-493c-9049-9e19dbce6165", "max_stars_repo_head_hexsha": "3e1ce5ef3d1c8da474dc0b2b685e66ea7403058f",... |
@doc doc"""
GraphManifoldType
This type represents the type of data on the graph that the [`GraphManifold`](@ref)
represents.
"""
abstract type GraphManifoldType end
@doc doc"""
EdgeManifoldManifold <: GraphManifoldType
A type for a [`GraphManifold`](@ref) where the data is given on the edges.
"""
struct Edg... | {"hexsha": "1f49dbed5a9845472a01692ecb389f98afd27a19", "size": 7286, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/manifolds/GraphManifold.jl", "max_stars_repo_name": "manuelweisser/Manifolds.jl", "max_stars_repo_head_hexsha": "07f889a290ece01569c6c53bb0c96a5608923a0c", "max_stars_repo_licenses": ["MIT"], "... |
# -*- coding: utf-8 -*-
from __future__ import division, print_function
__all__ = ["IntegratedLimbDarkOp"]
import theano
from theano import gof
import theano.tensor as tt
from .base_op import StarryBaseOp
class IntegratedLimbDarkOp(StarryBaseOp):
params_type = gof.ParamsType(
tol=theano.scalar.float6... | {"hexsha": "cc4890424359e922e3ed9c226308dddcc50678ba", "size": 2974, "ext": "py", "lang": "Python", "max_stars_repo_path": "exoplanet/theano_ops/starry/integrated_limbdark.py", "max_stars_repo_name": "exowanderer/exoplanet", "max_stars_repo_head_hexsha": "dfd4859525ca574f1936de7b683951c35c292586", "max_stars_repo_licen... |
import matplotlib
matplotlib.use('Agg')
import numpy as np
import scipy.stats
import matplotlib.pylab as plt
import os
import sys
from .context import vfe
from .context import config
import pdb
np.random.seed(42)
# We first define several utility functions
def kink_true(x):
fx = np.zeros(x.shape)
for t in ran... | {"hexsha": "5890d5e238d799e32b4fc3db64cede2b2b021fb0", "size": 4276, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/gpssm_vfe_examples.py", "max_stars_repo_name": "MattAshman/geepee", "max_stars_repo_head_hexsha": "ae71998579cb80e160f7ea5eb5adfa1c937fb90a", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/python3
# coding:utf-8
from flask import render_template,json,jsonify,request
from app import app
import base64
import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim
import pickle
from PIL import Image,ImageFont, ImageDraw
__global_times = 0
__chinese_word_count = 3755 # 常见汉字... | {"hexsha": "060efe2544b9bdd047ccb62863ce6544fb3de599", "size": 6881, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/views.py", "max_stars_repo_name": "Stevenchooo/tensorflow-master", "max_stars_repo_head_hexsha": "1b7ee0fdcfd166cbbfabbbcf801cda56d8faca4e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
function gen_player(model, number)
if model == "uniform"
# generate n uniformly spread over a given range
elseif model == "random"
# generate randomly according to given distribution
end
end
| {"hexsha": "61b8f59ef6008626bff1fe2101136f41ac007d00", "size": 231, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Generation/PlayerGen.jl", "max_stars_repo_name": "JuliaTagBot/RatPack.jl", "max_stars_repo_head_hexsha": "44d5735c1bbfaa97b12d3f418b0e1c4b967da1df", "max_stars_repo_licenses": ["MIT"], "max_star... |
# encoding=utf-8
"""
Created on 17:25 2018/11/13
@author: Jindong Wang
"""
import numpy as np
import scipy.io
import bob.learn
import bob.learn.linear
import bob.math
from sklearn.neighbors import KNeighborsClassifier
class GFK:
def __init__(self, Xs, Ys, Xt, Yt, dim=20):
'''
Init func
... | {"hexsha": "22e7a5de13cd31a59f8e25ae67ee2d7468179b3f", "size": 6710, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/traditional/GFK/GFK.py", "max_stars_repo_name": "HyoKong/TransferLearning", "max_stars_repo_head_hexsha": "ae8c7104ca40705bc2437bdbc34483c46509ff57", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import os
import pickle
import re
import time
import warnings
import cc3d
import ants
import numpy as np
import scipy
import scipy.io as spio
from matplotlib import pyplot as plt
from scipy import ndimage
from skimage.measure import regionprops
from sklearn.de... | {"hexsha": "bd715ed80229ab9ba1fe0cd87433acc274b8c3a5", "size": 40819, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/preprocLeadCT.py", "max_stars_repo_name": "dpedrosac/cDBS", "max_stars_repo_head_hexsha": "75ddb6a37a6f3b25f428005afc4e882bf31b09bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""Main script to execute simulation (and inference)."""
import argparse
from datetime import datetime
from pathlib import Path
import numpy as np
import pandas as pd
from forecast.protocol.simulation_steps import sorting_and_sequencing
from forecast.util.simulation import Simulation
def parse_args():
"""Parse ... | {"hexsha": "aa35ab80bd05cbd4b26a9a8e21a508667fbd8ece", "size": 3185, "ext": "py", "lang": "Python", "max_stars_repo_path": "forecast/run_simulation.py", "max_stars_repo_name": "Pierre-Aurelien/forecast", "max_stars_repo_head_hexsha": "d19c62bc7313c62836699bba2246afd0e79f1b53", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
cifar-10 dataset, with support for random labels
"""
import numpy as np
import torch
import torchvision.datasets as datasets
class CIFAR10RandomLabels(datasets.CIFAR10):
"""CIFAR10 dataset, with support for randomly corrupt labels.
Params
------
corrupt_prob: float
Default 0.0. The probability of a l... | {"hexsha": "35164f6889ebcf8f52932a006c748ed82be1edd5", "size": 3493, "ext": "py", "lang": "Python", "max_stars_repo_path": "cifar10_data.py", "max_stars_repo_name": "huweiATgithub/fitting-random", "max_stars_repo_head_hexsha": "e5527f3c75cc19807c8cbd25790d99d48aed7e2c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#include "tool/container/mapbox_vector_tile.hpp"
#include <protozero/pbf_writer.hpp>
#include <protozero/varint.hpp>
#include <boost/assert.hpp>
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-parameter"
#pragma GCC diagnostic ignored "-Wsign-conversion"
#include <boost/geometry.hpp>
#include <b... | {"hexsha": "65758dac181e5aa02fd349ea4d5ee880dc12fa95", "size": 13508, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tool/container/mapbox_vector_tile.cpp", "max_stars_repo_name": "mapbox/nepomuk", "max_stars_repo_head_hexsha": "8771482edb9b16bb0f5a152c15681c57eb3bb6b6", "max_stars_repo_licenses": ["MIT"], "m... |
export TensorMesh3D, getTensorMesh3D
export getCellCenteredGrid, getNodalGrid, getFaceGrids, getEdgeGrids
export getCellCenteredAxes, getNodalAxes,getBoundaryNodes
export getVolume, getVolumeInv, getFaceArea, getFaceAreaInv, getLength, getLengthInv
"""
mutable struct jInv.Mesh.TensorMesh3D <: AbstractTensorMesh
F... | {"hexsha": "23566e23ce4e0c870bacbcef1fae6d1e0ab90137", "size": 11649, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Mesh/tensor.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/jInv.jl-3dacf901-f8cd-5544-86ed-7a705f85c244", "max_stars_repo_head_hexsha": "2e7305f231a29bd8e1e803b82cc2bc8e9b7a205a",... |
# -*-coding:utf-8-*-
import sys
sys.path.append('.')
import tensorflow as tf
import tensorflow.contrib.slim as slim
import time
import numpy as np
import cv2
from lib.dataset.dataietr import DataIter
from lib.core.model.net.pruned_ssd import DSFD
from mac_config import config as cfg
from lib.helper.l... | {"hexsha": "7c6053ad852b1a1a7fe78b14192fd02d64f51b55", "size": 20740, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/core/base_trainer/mac_net_work.py", "max_stars_repo_name": "airacid/pruned-face-detector", "max_stars_repo_head_hexsha": "ef587e274ccf87633af653694890eb6712d6b3eb", "max_stars_repo_licenses":... |
from DSPbox import MFCC
import scipy.io.wavfile as wav
import numpy as np
import librosa
rate, signal = wav.read('./Observation.wav')
obser = MFCC(signal, rate)
result = []
for i in range(5):
rate, signal = wav.read('./{:d}.wav'.format(i+1))
compare = MFCC(signal, rate)
d = np.zeros((len(obser)+1, len(comp... | {"hexsha": "7812175b4f8bfa2665340fb47d9aabe1ed398ae7", "size": 855, "ext": "py", "lang": "Python", "max_stars_repo_path": "HW16/HW16.py", "max_stars_repo_name": "JasonLiTW/assignment-speech-recognition", "max_stars_repo_head_hexsha": "794907a417d054477812c1f50695312601eae929", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""Custom Transform """
from typing import Optional, Union, Tuple
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
from torchvision.transforms import functional as F
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img,... | {"hexsha": "4e8f097e5ac4c7cb0044cc4b37048a6a342e97d0", "size": 3807, "ext": "py", "lang": "Python", "max_stars_repo_path": "yolo/data/transforms/transforms.py", "max_stars_repo_name": "DavianYang/yolo.ai", "max_stars_repo_head_hexsha": "0856d4f1e84428667046ee27270ff1bf742e658a", "max_stars_repo_licenses": ["MIT"], "max... |
\documentclass[full]{subfiles}
\ifthenelse{\value{singlefile} = 1}
{\title{SUBJECT -- Lecture 1}
\date{31st July 2013}}
{}
\begin{document}\ifthenelse{\value{singlefile} = 1}{\maketitle}{\chapter{Lecture 1}}
\section*{Subject}
\end{document}
| {"hexsha": "1c5ef1998c64401500f553f4645a45e40662e7ec", "size": 245, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "LectureNotes/Lecture1.tex", "max_stars_repo_name": "harrisony/uni-latex-template", "max_stars_repo_head_hexsha": "b7051ee69743c5923d426b6e5a8132d409f17844", "max_stars_repo_licenses": ["Unlicense"], ... |
from numpy import linspace, sin
from chaco.api import ArrayPlotData, Plot
from chaco.tools.api import PanTool, ZoomTool, DragZoom
from enable.component_editor import ComponentEditor
from traits.api import HasTraits, Instance, List
from traitsui.api import Item, View, CheckListEditor
class ToolChooserExample(HasTrait... | {"hexsha": "16c79b50472f65f244dde738a7fc74ac85bc99b1", "size": 1779, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/tutorials/scipy2008/tool_chooser.py", "max_stars_repo_name": "martinRenou/chaco", "max_stars_repo_head_hexsha": "1888da3ecee89f9b2d11900cda9333b32fc5e89a", "max_stars_repo_licenses": ["BS... |
#!/usr/bin/python
# Author: Srikanth Malla
# Date: 28 Aug, 2020
# project lidar points in top view image
import matplotlib.pyplot as plt
import numpy as np
import glob
from joblib import Parallel, delayed
import multiprocessing
def lidar_top_view(lidar_file):
lidar_points = np.load(lidar_file)
fig = plt.figure(f... | {"hexsha": "546ccae43bd90b6d6af028f3bf2c69adfdab00f7", "size": 2186, "ext": "py", "lang": "Python", "max_stars_repo_path": "top_view_lidar.py", "max_stars_repo_name": "srikanthmalla/waymo_data_utils", "max_stars_repo_head_hexsha": "26588bb2cfc04dd1034d122ae8b14d3c2b3920c6", "max_stars_repo_licenses": ["MIT"], "max_star... |
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import BernoulliNB, MultinomialNB, GaussianNB
from sklearn.tree import DecisionTreeClassifier
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cross_validation import KFold
from sklearn.ensemble ... | {"hexsha": "dc8aa13eea7f7ac5abd2535aec196da176f8a5e3", "size": 1704, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/features/build_features.py", "max_stars_repo_name": "satishkt/lending_club_classifier", "max_stars_repo_head_hexsha": "0b09d7dc7ff2dd998afe5ead5486e8156bc559f7", "max_stars_repo_licenses": ["F... |
from pathlib import Path
import cv2
import numpy as np
from tqdm import tqdm
pascal_classes_id = {
1: 'aeroplane',
2: 'bicycle',
4: 'boat',
5: 'bottle',
6: 'bus',
7: 'car',
9: 'chair',
11: 'diningtable',
14: 'motorbike',
18: 'sofa',
19: 'train',
20: 'tvmonitor'
}
def ... | {"hexsha": "42bd08c69de8b389d98c47941d07e61d16860faa", "size": 3565, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/pascal3d/preprocess_VOC_hariharan_masks.py", "max_stars_repo_name": "aimagelab/MCMR", "max_stars_repo_head_hexsha": "eb3556bffebc734c19e7f3e39dcf018ba28c63b3", "max_stars_repo_licenses": ... |
r"""
Emulator
--------------
Precomputed synthetic spectral models are awesome but imperfect and rigid. Here we clone the most prominent spectral lines and continuum appearance of synthetic spectral models to turn them into tunable, flexible, semi-empirical models. We can ultimately learn the properties of the pre-c... | {"hexsha": "df4bdfcd40a3ed719aad6f61ec6bf165c82eeea5", "size": 17470, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/blase/emulator.py", "max_stars_repo_name": "gully/blase", "max_stars_repo_head_hexsha": "1f7f4b2cf77bd4a5d0e4591b4fd3581d7c0bfe25", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "... |
from csv_parser import Parser
import random
a = Parser('transport_data.csv')
a.open()
dots = a.get_data()
zero = []
first = []
second = []
q = []
for dot in dots:
if dot.label == '0':
zero.append(dot)
if dot.label == '1':
first.append(dot)
if dot.label == '2':
second.append(dot)
... | {"hexsha": "5418f3b83eb7b149b25090adfd2c34b6e11ad277", "size": 3493, "ext": "py", "lang": "Python", "max_stars_repo_path": "transport_data_analis/neural_network.py", "max_stars_repo_name": "levkovalenko/pm_task_2018", "max_stars_repo_head_hexsha": "8bffa5327dc59b1640d922c4faa7381eb4255ead", "max_stars_repo_licenses": [... |
# Illustrate einstein summation
# https://rockt.github.io/2018/04/30/einsum
# https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html
import superimport
import numpy as np
np.random.seed(42)
a = np.arange(3)
b = np.arange(3)
A = np.arange(6).reshape(2,3)
B = np.arange(15).reshape(3,5)
S = np.arange(9)... | {"hexsha": "0a88c90e645debd1e52822284b20b674cca52104", "size": 5388, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/einsum_demo.py", "max_stars_repo_name": "vipavlovic/pyprobml", "max_stars_repo_head_hexsha": "59a2edc682d0163955db5e2f27491ad772b60141", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Forecasting with quadratic model
The quadratic (non-linear) regression model explores a linear relationship
between the forecast variabl... | {"hexsha": "db6568b9fbce199ac9c6f919d451f4cc2259265b", "size": 6019, "ext": "py", "lang": "Python", "max_stars_repo_path": "kats/models/quadratic_model.py", "max_stars_repo_name": "utkucanaytac/Kats", "max_stars_repo_head_hexsha": "9781615750a2f3b49f16cccf335b5c29fdfd181a", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import matplotlib.pyplot as plt
import stan_utils as stan
from mpl_utils import (mpl_style, common_limits)
plt.style.use(mpl_style)
def generate_data(N, M, D, scales=None, seed=None):
"""
Generate some toy data to play with. Here we assume all :math:`N` stars have
been observed by al... | {"hexsha": "a873e6e776032792b2aee21f0ea9cce795362399", "size": 3657, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "andycasey/uber-chemi-cal", "max_stars_repo_head_hexsha": "8fc3cfbf7b54145779b1635496426c5473cedb56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""
Functions and methods for creating ancillary quality control variables
and filters (masks) which can be used with various corrections
routines in ACT.
"""
import numpy as np
import xarray as xr
import dask
from act.qc import qctests, comparison_tests
@xr.register_dataset_accessor('qcfilter')
class QCFilter(qct... | {"hexsha": "6b3a80fc6fa0d9a2ddfa98b2a6a0d2109d799776", "size": 38882, "ext": "py", "lang": "Python", "max_stars_repo_path": "act/qc/qcfilter.py", "max_stars_repo_name": "zssherman/ACT", "max_stars_repo_head_hexsha": "db87008aa6649d3d21b79ae97ea0f11d7f1f1935", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
##python practice file
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
import astropy.units as u
from astropy.time import Time
from astropy.coordinates import SkyCoord, EarthLocation, AltAz
impo... | {"hexsha": "48e859e9885fad690fe3e4a45089af694cef3624", "size": 2622, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/samedec.py", "max_stars_repo_name": "luciencd/starhopper", "max_stars_repo_head_hexsha": "564eae99bc770b143855312d583bbeaaad676ff1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
# -*- coding: utf-8 -*-
import numpy as np
from pyam.logger import logger
from pyam.utils import isstr, cast_years_to_int
# %%
def fill_series(x, year):
"""Returns the value of a timeseries (indexed over years) for a year
by linear interpolation.
Parameters
----------
x: pandas.Series
a... | {"hexsha": "c4c932f56e3e16447c8c262809ff6b5b214dde01", "size": 4360, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyam/timeseries.py", "max_stars_repo_name": "khaeru/pyam", "max_stars_repo_head_hexsha": "c91551d753c739316fca7dae043d4605211e05cc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": n... |
[STATEMENT]
lemma finter_transfer [transfer_rule]:
assumes "bi_unique A"
shows "(rel_fset A ===> rel_fset A ===> rel_fset A) finter finter"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (rel_fset A ===> rel_fset A ===> rel_fset A) (|\<inter>|) (|\<inter>|)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
us... | {"llama_tokens": 506, "file": null, "length": 4} |
struct LayerNotFoundException <: Exception
var::String
end
function Base.showerror(io::IO, e::LayerNotFoundException)
println(io, typeof(e), ": ", e.var)
end | {"hexsha": "6ad20ba4b48e4b5b0936b69511564c6ba81872e1", "size": 166, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/exceptions.jl", "max_stars_repo_name": "quatrix/HTTP.jl", "max_stars_repo_head_hexsha": "657dbf9a5d1d87e6e085158a5a053553fcaed032", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
"""Tests ring polymer contraction.
Copyright (C) 2013, Joshua More and Michele Ceriotti
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 the License, or
(at your option) any later... | {"hexsha": "ad9556fbea41f9e72ba5fd5bdd1baefb08fe4297", "size": 3843, "ext": "py", "lang": "Python", "max_stars_repo_path": "lammps-master/tools/i-pi/ipi/tests/test_contraction.py", "max_stars_repo_name": "rajkubp020/helloword", "max_stars_repo_head_hexsha": "4bd22691de24b30a0f5b73821c35a7ac0666b034", "max_stars_repo_li... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.