text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma dg_prod_Dom_app_component_app[dg_cs_simps]:
assumes "f \<in>\<^sub>\<circ> (\<Prod>\<^sub>D\<^sub>Gi\<in>\<^sub>\<circ>I. \<AA> i)\<lparr>Arr\<rparr>" and "i \<in>\<^sub>\<circ> I"
shows "(\<Prod>\<^sub>D\<^sub>Gi\<in>\<^sub>\<circ>I. \<AA> i)\<lparr>Dom\<rparr>\<lparr>f\<rparr>\<lparr>i\<rparr> =... | {"llama_tokens": 582, "file": "CZH_Foundations_czh_digraphs_CZH_DG_PDigraph", "length": 3} |
"""
Copyright 2016 Erik Jan de Vries
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": "10bebcb1c839e96abd603589ceef57734b9ee042", "size": 5513, "ext": "py", "lang": "Python", "max_stars_repo_path": "catch.py", "max_stars_repo_name": "erikjandevries/QLearnCatch", "max_stars_repo_head_hexsha": "ddc37f86ec6a57d2b0c1da88207d9880e1de43b7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import cv2
import os
import argparse
import random
from imutils import paths
import numpy as np
from tqdm import trange
provinces = ["皖", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", \
"苏", "浙", "京", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", \
"桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新"]
al... | {"hexsha": "8a217b37f8560d58943eaf81d2e5c528292d0d05", "size": 2788, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/deep_learning/data_process.py", "max_stars_repo_name": "ThreeSRR/License-Plate-Recogonition", "max_stars_repo_head_hexsha": "f2161c8fa0e161b1e03cf2fb1d5a7f4cba4d5449", "max_stars_repo_license... |
"""Trains a model, saving checkpoints and tensorboard summaries along
the way."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
from datetime import datetime
import json
import math
import os
import shutil
import sys
from timeit import d... | {"hexsha": "54c0f0f1c487b3c648a94d9a41ff07e6dad3c93f", "size": 5813, "ext": "py", "lang": "Python", "max_stars_repo_path": "compute_corr.py", "max_stars_repo_name": "MadryLab/backdoor_data_poisoning", "max_stars_repo_head_hexsha": "c15096fdd79836e3e94a5cab54d50fe97a9c140f", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright 2019 Intel Corporation.
import functools
import inspect
import logging
import math
import os
from collections import defaultdict
from contextlib import contextmanager
import six
import numpy as np
import scipy.stats
import plaidml
import plaidml.edsl as edsl
import plaidml.exec
import plaidml.op as plaid... | {"hexsha": "82caf4f368525cba7398204353f156e10f1f07d3", "size": 55054, "ext": "py", "lang": "Python", "max_stars_repo_path": "plaidml/bridge/keras/__init__.py", "max_stars_repo_name": "hfp/plaidml", "max_stars_repo_head_hexsha": "c86852a910e68181781b3045f5a306d2f41a775f", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
CU SUBROUTINE shoot(n2,v,f) is named "funcv" for use with "newt"
SUBROUTINE funcv(n2,v,f)
INTEGER n2,nvar,kmax,kount,KMAXX,NMAX
REAL f(n2),v(n2),x1,x2,dxsav,xp,yp,EPS
PARAMETER (NMAX=50,KMAXX=200,EPS=1.e-6)
COMMON /caller/ x1,x2,nvar
COMMON /path/ kmax,kount,dxsav,xp(KMAXX),... | {"hexsha": "ab9ac40bc797d3dcdd35de16813c6b57276f6b52", "size": 652, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "NR-Functions/Numerical Recipes- Example & Functions/Functions/shoot.for", "max_stars_repo_name": "DingdingLuan/nrfunctions_fortran", "max_stars_repo_head_hexsha": "37e376dab8d6b99e63f6f1398d0c33d... |
!-------------------------------------------------------------------------------
!> module ATMOSPHERE / Physics Radiative Transfer
!!
!! @par Description
!! 2-stream, k-distribution broadband radiative transfer scheme mstrnX
!! Reference : Nakajima and Tanaka(1986) : J.Quant.Spectrosc.Radiat.Transfe... | {"hexsha": "97222371ea240195e39bb9b7ffd7ff8f9d4cde05", "size": 160345, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "physicskernel_radiation/src/mod_rd_mstrnx.f90", "max_stars_repo_name": "hisashiyashiro/nicam_dckernel_2016", "max_stars_repo_head_hexsha": "a614da10bad7cd1c2eb9a778bd04bad2dc195696", "max_star... |
/*
This file is part of solidity.
solidity 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 version.
solidity is distributed in the hope that i... | {"hexsha": "c0fdd9e3082d609283db448ca1bbd4f5c1158371", "size": 15325, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libsolidity/analysis/ContractLevelChecker.cpp", "max_stars_repo_name": "step21/solidity", "max_stars_repo_head_hexsha": "2a0d701f709673162e8417d2f388b8171a34e892", "max_stars_repo_licenses": ["MIT"... |
import sys
import pandas as pd
import numpy as np
import itertools
from sklearn.preprocessing import RobustScaler
from sklearn.linear_model import SGDClassifier
from evaluate_model import evaluate_model
dataset = sys.argv[1]
pipeline_components = [RobustScaler, SGDClassifier]
pipeline_parameters = {}
loss_values = [... | {"hexsha": "b4e55cba53a37eed1e275e931e460e40ed06a2d6", "size": 1597, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_code/grid_search/SGDClassifier.py", "max_stars_repo_name": "lacava/sklearn-benchmarks", "max_stars_repo_head_hexsha": "bec1d5468f40b1fea08b605a11d5f7795fe5bb1b", "max_stars_repo_licenses": [... |
\section{Design Stakeholders and Concerns}
\subsection{Design Stakeholders}
\begin{itemize}
\item Light Water Reactor Sustainability (LWRS) program
\item Nuclear Energy Advanced Modeling and Simulation Program (NEAMS)
\item Nuclear-Renewable Hybrid Energy Systems (NHES)
\item Open-source community
\end{itemiz... | {"hexsha": "f5e5983d478e0d7f2d6349927c9e0f33ab0e4fc6", "size": 627, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/sqa/sdd/ravenDesignStakeHoldersAndConcerns.tex", "max_stars_repo_name": "rinelson456/raven", "max_stars_repo_head_hexsha": "1114246136a2f72969e75b5e99a11b35500d4eef", "max_stars_repo_licenses": [... |
\section{Lineage-based Reuse}
\label{sec:reuse}
The lineage of an intermediate carries all information to identify and recompute this intermediate. LIMA leverages this characteristic in a lineage-based reuse cache for eliminating fine-grained redundancy (see Section~\ref{sec:redundancy}). Figure~\ref{fig:reuse} gives ... | {"hexsha": "d0d71ea93be4864ad12c6c2f9b006e1176a7219d", "size": 18239, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sigmod2021-LIMA-p32/paper/Reuse.tex", "max_stars_repo_name": "damslab/reproducibility", "max_stars_repo_head_hexsha": "f7804b2513859f7e6f14fa7842d81003d0758bf8", "max_stars_repo_licenses": ["Apache... |
from __future__ import print_function, absolute_import, division, unicode_literals
import numpy as np
import pytest
import pdb
from astropy import units as u
from linetools.analysis.absline import aodm, log_clm, linear_clm, photo_cross,\
sum_logN, get_tau0, Wr_from_N_b, Wr_from_N_b_transition, Wr_from_N, Wr_from_... | {"hexsha": "3391c94bc0b7f1c5a1a89b6a3e55fdb109934f28", "size": 5166, "ext": "py", "lang": "Python", "max_stars_repo_path": "linetools/analysis/tests/test_absline.py", "max_stars_repo_name": "jchowk/linetools", "max_stars_repo_head_hexsha": "5a0eafa96ab854c52c070ce756033c0499414dde", "max_stars_repo_licenses": ["BSD-3-C... |
```python
%matplotlib inline
```
Adversarial Example Generation
==============================
**Author:** `Nathan Inkawhich <https://github.com/inkawhich>`__
If you are reading this, hopefully you can appreciate how effective some
machine learning models are. Research is constantly pushing ML models to
be faster, ... | {"hexsha": "e67d3baadeb9cb7de80c79957906048882e61516", "size": 19697, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "docs/_downloads/8c22803172fb62b19326a346e208ba61/fgsm_tutorial.ipynb", "max_stars_repo_name": "leejh1230/PyTorch-tutorials-kr", "max_stars_repo_head_hexsha": "ebbf44b863ff96c597631e2... |
from qiskit.circuit.gate import Gate
from qiskit_cold_atom import QiskitColdAtomError, add_gate
import numpy as np
class LoadGate(Gate):
"""The load gate."""
def __init__(self,num_atoms:int) -> None:
"""Create a new gate.
Args:
params: A list of parameters.
"""
su... | {"hexsha": "10e7101fec237c26ad8cf2846d9034dda172bb11", "size": 1900, "ext": "py", "lang": "Python", "max_stars_repo_path": "qiskit_synqs/synqs_single_qudit/sqs_gate_library.py", "max_stars_repo_name": "synqs/qiskit-synqs", "max_stars_repo_head_hexsha": "435c3944900963c61aa479f2a13739083f6495a7", "max_stars_repo_license... |
Require Import Coqlib.
Require Import Asm.
Require Import Integers.
Require Import PeekTactics.
Require Import PeepsLib.
Require Import PregTactics.
Require Import StepIn.
Require Import AsmBits.
Require Import Values.
Require Import ValEq.
Require Import Integers.
Require Import PeepsTactics.
Definition peep_add_neg_... | {"author": "uwplse", "repo": "peek", "sha": "4943735ed39fd5ddadf2c28fc2ada31504228561", "save_path": "github-repos/coq/uwplse-peek", "path": "github-repos/coq/uwplse-peek/peek-4943735ed39fd5ddadf2c28fc2ada31504228561/compcert/peeps/Peep_AddNeg1ToDec.v"} |
\hypertarget{classglite_1_1wms_1_1jdl_1_1AdConverter}{
\section{glite::wms::jdl::Ad\-Converter Class Reference}
\label{classglite_1_1wms_1_1jdl_1_1AdConverter}\index{glite::wms::jdl::AdConverter@{glite::wms::jdl::AdConverter}}
}
utilities for converting classad expression into requestad known classes and to create Job\... | {"hexsha": "70572844a3bbde113ada8e0c7c40e5661388315d", "size": 33494, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "users-guide/WMS/autogen/jdl/classglite_1_1wms_1_1jdl_1_1AdConverter.tex", "max_stars_repo_name": "italiangrid/wms", "max_stars_repo_head_hexsha": "5b2adda72ba13cf2a85ec488894c2024e155a4b5", "max_st... |
#Day 1: Data Prepocessing
#Step 1: Importing the libraries
import numpy as np
import pandas as pd
#Step 2: Importing dataset
dataset = pd.read_csv('../datasets/Data.csv')
print(dataset.head())
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values
print("Step 2: Importing dataset")
print("X")
print(X)
prin... | {"hexsha": "b119fec552a63cc507ff7ffecc198a95b40e138f", "size": 1916, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/Day 1_Data_Preprocessing.py", "max_stars_repo_name": "fengdu1127/tmp", "max_stars_repo_head_hexsha": "ecbd3828832ce3c66cd748a6764ed7c1ae19bade", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# !usr/bin/env python
# coding:utf-8
"""
决策树实践
author: prucehuang
email: 1756983926@qq.com
date: 2019/01/08
"""
import os
from matplotlib.colors import ListedColormap
from sklearn.datasets import load_iris, make_moons
from sklearn.tree import DecisionTreeClassifier, export_graphviz, DecisionTreeRegressor
import ... | {"hexsha": "a8b1c4f7896def7a9e6ee286738a5b10b1d0c192", "size": 7603, "ext": "py", "lang": "Python", "max_stars_repo_path": "Hands-On Machine Learning with Scikit-Learn and TensorFlow/code/06_decision_trees.py", "max_stars_repo_name": "prucehuang/quickly-start-python", "max_stars_repo_head_hexsha": "8af5f339b6d324a769d5... |
import math
from scipy.stats import binom
def ncr(n,r):
f = math.factorial
return f(n) / f(r) / f(n-r)
# setting the values
# of n and p
beta = 10
p = 1.0/(4.3*30*24)#-math.exp(-1/(4.3))
print p
# obtaining the mean and variance
mean, var = binom.stats(beta, p)
# list of pmf values
# printing mean an... | {"hexsha": "e70dbf1b69d7c81c218f72c981c5f2af02610854", "size": 1008, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/exp/sstable_availability.py", "max_stars_repo_name": "ruihong123/NovaLSM", "max_stars_repo_head_hexsha": "8a661197ce5b993f2baeef608f34192d1ef0adf5", "max_stars_repo_licenses": ["BSD-3-Clau... |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule x264_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("x264")
JLLWrappers.@generate_main_file("x264", UUID("1270edf5-f2f9-52d2-97e9-ab00b5d0237a"))
end # module x264_jll
| {"hexsha": "b5170b9b072e287703c188b0bd10a37fcda37c22", "size": 294, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/x264_jll.jl", "max_stars_repo_name": "JuliaBinaryWrappers/x264_jll.jl", "max_stars_repo_head_hexsha": "a7ba0419794606a373fa1ac2ef3e7368e75fc493", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/visualization/experiment_visualization.ipynb (unless otherwise specified).
__all__ = ['plot_multiple_histories', 'plot_metric_relationship', 'visualize_experiments']
# Cell
import numpy as np
#import matplotlib.pyplot as plt
import os
import pickle
import pandas as pd
f... | {"hexsha": "13ce6fb69fd51bbf9ec86ed8faa0be9224ebfdd5", "size": 8278, "ext": "py", "lang": "Python", "max_stars_repo_path": "hpsearch/visualization/experiment_visualization.py", "max_stars_repo_name": "Jaume-JCI/hpsearch", "max_stars_repo_head_hexsha": "168d81f49e1a4bd4dbab838baaa8ff183a422030", "max_stars_repo_licenses... |
# kuramoto_7-27-19.jl
# Starting off the day strong with some code from my other project,
# grok-the-dot.
function f(x)
return 2x+1
end
function g(x)
return x^2
end
function h(x)
return 4x^2 + 4x + 11 # (2x + 1)^2 = 4x^2 + 4x + 11
end
G = collect(1:1_000_000) # You can use underscores to
... | {"hexsha": "a01b8c85672186b8c3a83235a943afe87b301051", "size": 629, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "diary/kuramoto_7-27-19.jl", "max_stars_repo_name": "AndrewQuinn2020/Kuramoto", "max_stars_repo_head_hexsha": "db0d9873b71cb5904f25c8806aaccc62b8dedaaa", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Read in a shapefile of a fault and plot river profile morphology along it.
# FJC 26/11/18
# set backend to run on server
#import matplotlib
#matplotlib.use('Agg')
# general modules
import numpy as np
import numpy.ma as ma
import pandas as pd
import math
import matplotlib.pyplot as plt
import os
import time
from mat... | {"hexsha": "29d2ec869f8d4b08b5723b815e04bd926316b9b5", "size": 91387, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotting/swath_profile.py", "max_stars_repo_name": "fclubb/fault-swath", "max_stars_repo_head_hexsha": "687b1af26a969b1ed4e94cc597b23762fd1bb3da", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import time
import math
import numpy as np
import scipy
import theano
import theano.tensor as T
class Distance:
def __init__(self, norm='l2', verbose=False):
"""Construct an object, with the primary method transform, there can
create a sparse distance matrix.
Parameters
---------... | {"hexsha": "d51aa914bc371823d66f7f448e05023e3ee29b64", "size": 3048, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/distance.py", "max_stars_repo_name": "AndreasMadsen/bachelor-code", "max_stars_repo_head_hexsha": "115fd2b955de07f34cdec998ba2a7f103ae253e3", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import ast
import numpy as np
import scipy as sp
import cv2
from pywt import wavedec2, dwt2
from ..third_party.funque_atoms import pyr_features
from ..third_party.vmaf_atoms import vmaf_features
from ..core.feature_extractor import FeatureExtractor, VmafexecFeatureExtractorMixin
from ..tools.reader import YuvReader... | {"hexsha": "2a955054d53e5e30863c5710029bc6eb88d5ea2b", "size": 12368, "ext": "py", "lang": "Python", "max_stars_repo_path": "funque/core/custom_feature_extractors.py", "max_stars_repo_name": "abhinaukumar/funque", "max_stars_repo_head_hexsha": "47f030af78e4e64b9d0e5c7632193373eb94afcd", "max_stars_repo_licenses": ["BSD... |
import numpy as np
from collections import OrderedDict
from gym.spaces import Space, Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict
_BaseGymSpaces = (Box, Discrete, MultiDiscrete, MultiBinary)
__all__ = ["_BaseGymSpaces", "batch_space"]
def batch_space(space, n=1):
"""Create a (batched) space, containin... | {"hexsha": "ac4727de5a08fa3d77e14177bf38b3d94fa5fe4e", "size": 2849, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/gymcore/vector/utils/spaces.py", "max_stars_repo_name": "maxgold/icml22", "max_stars_repo_head_hexsha": "49f026dd2314091639b52f5b8364a29e8000b738", "max_stars_repo_licenses": ["MIT"], "max_st... |
######## Webcam hand-digit Detection Using Tensorflow mnist Model #########
#
# Author: jihwan Lee
# Date: 02/10/20
# Revised by: LUMI
# Date: Nov-21,'21
# Description:
# This program uses a TensorFlow Lite model to perform object detection on a live webcam feed
#
# This code is based off the TensorFlow Lite image cl... | {"hexsha": "ff19af8ad183087f8735cca43c6dab5f157318fa", "size": 7735, "ext": "py", "lang": "Python", "max_stars_repo_path": "TFLite_mnist_webcam.py", "max_stars_repo_name": "poohaboy/raspberrypi-mnist", "max_stars_repo_head_hexsha": "ad30766e8ccbefc49e3907b9eb2af6aefd542797", "max_stars_repo_licenses": ["MIT"], "max_sta... |
/-
Copyright (c) 2014 Parikshit Khanna. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Parikshit Khanna, Jeremy Avigad, Leonardo de Moura, Floris van Doorn, Mario Carneiro
-/
import data.option.defs
import logic.basic
import tactic.cache
/-!
## Definitions on lists
Th... | {"author": "JLimperg", "repo": "aesop3", "sha": "a4a116f650cc7403428e72bd2e2c4cda300fe03f", "save_path": "github-repos/lean/JLimperg-aesop3", "path": "github-repos/lean/JLimperg-aesop3/aesop3-a4a116f650cc7403428e72bd2e2c4cda300fe03f/src/data/list/defs.lean"} |
import os
import numpy as np
from pprint import pprint
from collections import defaultdict
from ..utils import *
from .io_base import DataDecoder
from pygama import lh5
from .ch_group import *
class FlashCamEventDecoder(DataDecoder):
"""
decode FlashCam digitizer event data.
"""
def __init__(self, *... | {"hexsha": "d4f74ed7f8b2f65f44a20c6c3ebddc0d642ad617", "size": 14907, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygama/io/fcdaq.py", "max_stars_repo_name": "Kermaidy/pygama", "max_stars_repo_head_hexsha": "d75f3d8d79f78c29f50171e24010a0644684f591", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import os
import tensorflow as tf
import gym
# Must import gym_powerworld for the environments to get registered.
# noinspection PyUnresolvedReferences
import gym_powerworld
from gym_powerworld.envs.voltage_control_env import OutOfScenariosError
import numpy as np
import time
import shutil
from copy import deepcopy
imp... | {"hexsha": "717d9562029c2d3aee3a68ea0930b3be3ba84389", "size": 18469, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/train.py", "max_stars_repo_name": "blthayer/drl-powerworld", "max_stars_repo_head_hexsha": "b265dc2e43f2e723f5d6e0aad7ba4946880efa53", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# ***************************************************************
# Copyright (c) 2020 Jittor. Authors: Dun Liang <randonlang@gmail.com>. All Rights Reserved.
# This file is subject to the terms and conditions defined in
# file 'LICENSE.txt', which is part of this source code package.
# ********************************... | {"hexsha": "b05885fde5c432bd581cdf493c77f61a8acf188c", "size": 3754, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/jittor/test/test_scope.py", "max_stars_repo_name": "xmyqsh/jittor", "max_stars_repo_head_hexsha": "1260e19235e301a67cba57aebbc187a5c1386e1a", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
"""Class to run max-product linear programming for linear-programming MAP inference."""
import numpy as np
from .MatrixBeliefPropagator import sparse_dot
from .MaxProductBeliefPropagator import MaxProductBeliefPropagator
class MaxProductLinearProgramming(MaxProductBeliefPropagator):
"""
Class to run max-prod... | {"hexsha": "3c72315d25c7e0cee9d0c1830abee09a2a502b0e", "size": 1694, "ext": "py", "lang": "Python", "max_stars_repo_path": "mrftools/MaxProductLinearProgramming.py", "max_stars_repo_name": "berty38/mrftools", "max_stars_repo_head_hexsha": "9cd1984a2ff178bd1ee87b678ace8b803111654e", "max_stars_repo_licenses": ["MIT"], "... |
/-
Copyright (c) 2022 Jun Yoshida. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
-/
import Algdata.Init.Sigma
import Algdata.Data.List.Ascending
universe u v
/-!
List of key-value pairs with values dependent on keys
The elements are stored as the dependent pair `Sigma β... | {"author": "Junology", "repo": "algdata", "sha": "ef0e552747c3f1004705755a3afc7ccedec92bf6", "save_path": "github-repos/lean/Junology-algdata", "path": "github-repos/lean/Junology-algdata/algdata-ef0e552747c3f1004705755a3afc7ccedec92bf6/Algdata/Data/KVChain/Basic.lean"} |
\documentclass[a4paper,12pt]{article}
% Font
\usepackage[T1]{fontenc}
\usepackage{gentium}
% Math packages
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsthm}
\usepackage{bm}
% Define symbol shortcuts
\newcommand{\cc}{\mathcal{C}}
\newcommand{\dd}{\mathcal{D}}
\newcommand{\hh}{\mathca... | {"hexsha": "bf071a686e70bdfa322ee39e0874712c3df7a5d2", "size": 13748, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "lecture_notes/chap08_trees.tex", "max_stars_repo_name": "chagaz/ma2823_2016", "max_stars_repo_head_hexsha": "9a963f800b4eb35ccee4f8fe347f11ea221cad58", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma size_filter_unsat_elem:
assumes "x \<in># M" and "\<not> P x"
shows "size {#x \<in># M. P x#} < size M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. size (filter_mset P M) < size M
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. size (filter_mset P M) < size M
[PROOF ST... | {"llama_tokens": 579, "file": "Lambda_Free_RPOs_Lambda_Free_Util", "length": 8} |
\chapter{Experiment and Results}
\section{Evaluation Metrics}
We evaluate the performance of the model on individual typos through various measures of accuracy. In particular, we
compute the \textsc{Top-1 Accuracy}, comparing the intended word and the best candidate predicted by the
model. Then we compute the \texts... | {"hexsha": "bc54a2f3f4f2bf31d54aa42b88dc3560c6ffac0c", "size": 22814, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/sections/05-results.tex", "max_stars_repo_name": "GiorgiaAuroraAdorni/hmm-misspelling", "max_stars_repo_head_hexsha": "90f16726d368764edbd94a53c36b3882112ce8c0", "max_stars_repo_licenses": [... |
(* Title: HOL/Library/Lattice_Constructions.thy
Author: Lukas Bulwahn
Copyright 2010 TU Muenchen
*)
theory Lattice_Constructions
imports Main
begin
subsection \<open>Values extended by a bottom element\<close>
datatype 'a bot = Value 'a | Bot
instantiation bot :: (preorder) preorder
begin
defin... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Library/Lattice_Constructions.thy"} |
[STATEMENT]
lemma ereal_infty_mult[simp]:
"(\<infinity>::ereal) * a = (if a = 0 then 0 else if 0 < a then \<infinity> else - \<infinity>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<infinity> * a = (if a = 0 then 0 else if 0 < a then \<infinity> else - \<infinity>)
[PROOF STEP]
by (cases a) auto | {"llama_tokens": 134, "file": null, "length": 1} |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Tests for the rectangle module.
"""
from astropy.coordinates import Angle, SkyCoord
import astropy.units as u
import numpy as np
import pytest
from .test_aperture_common import BaseTestAperture
from ..rectangle import (RectangularAperture, Rectangul... | {"hexsha": "ef7aa79dcf263c8e9a0dac631fc2cec2191340a9", "size": 5336, "ext": "py", "lang": "Python", "max_stars_repo_path": "photutils/aperture/tests/test_rectangle.py", "max_stars_repo_name": "rosteen/photutils", "max_stars_repo_head_hexsha": "5821bddc2d3fa2709b8de79c18efe99cff1ecb71", "max_stars_repo_licenses": ["BSD-... |
The following are restaurants one can get Fish n Chips
Black Bear Diner
Carls Jr.
De Veres Irish Pub
G St. Wunderbar
The Dumpling House
Fishs Wild
The Graduate
Outside of Davis
http://www.oceanfishandchips.com/ Ocean Fish And Chips
wiki:Sacramento:Streets of London
I like a nice generous plate of fi... | {"hexsha": "a35db5d9dd48e50097d491ca0a8443ad2351c5e9", "size": 777, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Fish_n_Chips.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import random
import numpy
import time
import evaluation as ev
import networkx as nx
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
# Constants
init_pop_size = 300
number_of_nodes = ev.GRAPH_SIZE
# squared because that many elements in adj matrix
ind_size = number_of... | {"hexsha": "10abb1cc4d454f77cc24d380bc3fded8242ec70d", "size": 8217, "ext": "py", "lang": "Python", "max_stars_repo_path": "EvoFramwork.py", "max_stars_repo_name": "taomsakal/SeymourSearch", "max_stars_repo_head_hexsha": "eb098eab2d73eb36eeedbe7d200ffe10c3a9506f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# 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 agreed to in writing, ... | {"hexsha": "0fa86c469cc3338296f0e135026ae69d6dd21a41", "size": 12980, "ext": "py", "lang": "Python", "max_stars_repo_path": "opencv/facemask_2.py", "max_stars_repo_name": "vanduc103/coral_examples", "max_stars_repo_head_hexsha": "a514d003a3948cb0888d2dabc0bdd93939f8ddd0", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
import torch.nn.functional as F
import torch.nn as nn
import torch
import torch.optim as optim
import numpy as np
import math
from torch.nn import init
from torch.distributions.normal import Normal
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class CnnActorCrit... | {"hexsha": "f79f351528771e6c169d5e865e3155402866bf55", "size": 9129, "ext": "py", "lang": "Python", "max_stars_repo_path": "models_sep.py", "max_stars_repo_name": "akhandait/curiosity-driven-world-models", "max_stars_repo_head_hexsha": "544326f1ed4274c2e96addebd414f05dead721a9", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
import argparse, os
import numpy as np
import pickle
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser("Plot errors")
parser.add_argument('--config', dest='config', type=str, default='config.txt',
help='config file containing error directories'
... | {"hexsha": "2e3637ac19370de2fcac50a1b4a92529a435823e", "size": 3422, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_errors.py", "max_stars_repo_name": "schinmayee/object-tracking", "max_stars_repo_head_hexsha": "d0c53eb1b059da8456deb1ee0f0f23e96f9099ae", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed ... | {"hexsha": "e740e840c38381bafd7a1a7fcde5f963832ac1fb", "size": 12990, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3rdparty/libprocess/include/process/address.hpp", "max_stars_repo_name": "zagrev/mesos", "max_stars_repo_head_hexsha": "eefec152dffc4977183089b46fbfe37dbd19e9d7", "max_stars_repo_licenses": ["Apach... |
# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
import pytest # noqa: F401
import numpy as np # noqa: F401
import awkward as ak # noqa: F401
def test():
layout = ak._v2.contents.ListOffsetArray(
ak._v2.index.Index64(np.array([0, 1], dtype=np.int64)),
ak.... | {"hexsha": "f6ffc420e1ef7c0270aedc3d3aab867cc470a8cc", "size": 753, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/v2/test_1071-mask-identity-false-should-not-return-option-type.py", "max_stars_repo_name": "douglasdavis/awkward-1.0", "max_stars_repo_head_hexsha": "f00775803a5568efb0a8e2dae3b1a4f23228fa40"... |
import numpy as np
# Use "new-style" classes for easier inheritance
__metaclass__ = type
class transformation:
def __init__(self):
self.matrix = np.zeros((4,4), dtype = np.float64)
self.invmatrix = np.zeros((4,4), dtype = np.float64)
def getMatrix(self):
return self.matrix
def getInvMatrix(self):
... | {"hexsha": "8c81669c3c4b815873dc3f5275152532d1cf8497", "size": 4733, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/transformation.py", "max_stars_repo_name": "tinyendian/relativity", "max_stars_repo_head_hexsha": "e6f3812bcbb491beea4d4deff134ee2c874a822e", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
def log_gradient_(x, y_true, y_pred):
print(type(y_pred))
print(type(y_true))
if (isinstance(y_true, (list,np.ndarray)) and isinstance(y_pred, (list,np.ndarray))):
error = y_pred - y_true
nabela = [0] * len(x[0])
print(len(x[0][:]))
print(x[0])
for i in range(len(x[0])):
# print(x[:... | {"hexsha": "c5eaa14496a699fee674322f3dc615589c3dfa04", "size": 829, "ext": "py", "lang": "Python", "max_stars_repo_path": "day02/log_gradient.py", "max_stars_repo_name": "elbourki1/Machine-Learning-bootcamp-42", "max_stars_repo_head_hexsha": "cf6a987ede555d8d208aed5b915cafe8078dd848", "max_stars_repo_licenses": ["Apach... |
#!/usr/bin/env python3
# Programa simple para aprender a usar Qt
from __future__ import with_statement
import sys
import matplotlib
matplotlib.use('Qt4Agg')
from PyQt4 import QtGui, QtCore
from propagator import Ui_MainWindow
from polarization_routines import plot_ellipse, getAnglesFromEllipse, getAnglesFromJones
fr... | {"hexsha": "a27fa7241a36b62290d34c04462cc9ab7a79d40c", "size": 7638, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python_version/propagator_app.py", "max_stars_repo_name": "RomaniukVadim/ellipsometric-calculator", "max_stars_repo_head_hexsha": "629ae5c962dacd4416a180e0904b8ddcf642371a", "max_stars_repo_licens... |
!-----------------------------------------------------------------------
!
! Copyright (c) 2016 Tom L. Underwood
!
! Permission is hereby granted, free of charge, to any person obtaining
! a copy of this software and associated documentation files (the
! "Software"), to deal in the Software without restriction, inclu... | {"hexsha": "72b240d90e50cc95147b810136bc81308184ee6f", "size": 7189, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "lattices_in_bcc_hcp.f95", "max_stars_repo_name": "tomlunderwood/monteswitch", "max_stars_repo_head_hexsha": "1dd95dd46c0a1301fb4e520d2024e9b70bc71958", "max_stars_repo_licenses": ["MIT"], "max_s... |
import torch
import numpy.random as np_random
from vap_turn_taking.backchannel import Backchannel
from vap_turn_taking.hold_shifts import HoldShift
from vap_turn_taking.utils import (
time_to_frames,
find_island_idx_len,
get_dialog_states,
get_last_speaker,
)
class TurnTakingEvents:
def __init__(... | {"hexsha": "ba88746b7a07174746d50b45118233f164187372", "size": 10373, "ext": "py", "lang": "Python", "max_stars_repo_path": "vap_turn_taking/events.py", "max_stars_repo_name": "ErikEkstedt/vad_turn_taking", "max_stars_repo_head_hexsha": "c24e0ddfe9c739328872310e56f4b8c17f82c92c", "max_stars_repo_licenses": ["MIT"], "ma... |
#%%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import phd.viz
colors, palette = phd.viz.phd_style()
# Load the data.
data = pd.read_csv('../../data/ch9_mscl_si/MLG910_electrophysiology.csv')
data.columns = ['time', 'pa', 'mmHg']
# Instantiate the fig... | {"hexsha": "de817e952791e2294c4f8b145150d3b8f2bb2406", "size": 1658, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/chapter_09/code/ch9_figS1.py", "max_stars_repo_name": "gchure/phd", "max_stars_repo_head_hexsha": "cf5941e467ee57c6c93c78dda151335cb320f831", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
{-# OPTIONS --without-K --safe #-}
module Categories.Functor.Representable where
-- A Presheaf (into Setoids) is representation if it is naturally isomorphic to a Hom functor
-- over a particular object A of the base category.
open import Level
open import Categories.Category using (Category)
open import Categories.... | {"hexsha": "56f3630c38ec838d02f31100b9ead771c8ecacd4", "size": 740, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Categories/Functor/Representable.agda", "max_stars_repo_name": "MirceaS/agda-categories", "max_stars_repo_head_hexsha": "58e5ec015781be5413bdf968f7ec4fdae0ab4b21", "max_stars_repo_licenses": ["... |
import numpy as np
def QRDecomposition(A):
n = np.shape(A)[0] #pegando o tamanho das linhas de A
m = np.shape(A)[1] #pegando o tamanho das colunas de A
Q = np.zeros((n,m)) #declarando a matriz Q
R = np.zeros((m,m)) #declarando a matriz R
for j in range(0, m):
A_column = A[:, j] #pegando as colunas da matriz ... | {"hexsha": "e6b56d914513e4b3037f8049b84067c644cd368e", "size": 2431, "ext": "py", "lang": "Python", "max_stars_repo_path": "QRDecomposition.py", "max_stars_repo_name": "igortakeo/Calculo-Numerico", "max_stars_repo_head_hexsha": "96ed1892c3d2ba80039f2f2ce7de4ca834aa6283", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import tensorflow as tf
from models.ops import *
from models.Attention import self_attention_layer, multihead_attention, dot_product_attention
import tensorflow_probability as tfp
lr = 5e-5
beta1 = 0.5
beta2 = 0.999
dtype = tf.float32
jitter = 1e-3 if dtype == tf.float64 else 1e-1
class ResidualN... | {"hexsha": "41cad2097187e8ab70b503a1a26a5f74765c7f6e", "size": 8408, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/toy1d.py", "max_stars_repo_name": "dlqudwns/Anonymous-Repository", "max_stars_repo_head_hexsha": "40e6d68781427268ab21c662f04855cc0e75833d", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
from autograd import numpy as np
from autograd import grad, jacobian
import numpy.matlib as nm
from svgd import SVGD
import sys
#from mpltools import style
#from mpltools import layout
from multiprocessing import Process, Manager
#style.use('ggplot')
import matplotlib.pyplot as plt
#-(1.0/(2*observation_variance))... | {"hexsha": "53b176cec7979b19e43d7891d23fd1524f8012e2", "size": 3589, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/llg.py", "max_stars_repo_name": "gcgibson/ssvgd", "max_stars_repo_head_hexsha": "8f47dca7588a3ccbc13069860f342efcd5bbf644", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
[STATEMENT]
lemma infsetsum_Un_Int:
assumes "f abs_summable_on (A \<union> B)"
shows "infsetsum f (A \<union> B) = infsetsum f A + infsetsum f B - infsetsum f (A \<inter> B)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. infsetsum f (A \<union> B) = infsetsum f A + infsetsum f B - infsetsum f (A \<inter> B)
[... | {"llama_tokens": 1298, "file": null, "length": 13} |
# python regression_randombag_sample.py MSA_NAME LEN_SEEDS NUM_SAMPLE SAMPLE_FRAC
# python regression_randombag_sample.py Atlanta 3 NUM_SAMPLE SAMPLE_FRAC
import setproctitle
setproctitle.setproctitle("covid-19-vac@chenlin")
import sys
import os
import constants
import functions
import numpy as np
impo... | {"hexsha": "2692935ba3f41ccde3b427a3e01f7866ff4883f5", "size": 25208, "ext": "py", "lang": "Python", "max_stars_repo_path": "regression_randombag_sample.py", "max_stars_repo_name": "LinChen-65/utility-equity-covid-vac", "max_stars_repo_head_hexsha": "9194ee0e019b3160254401b84d369900a527da7e", "max_stars_repo_licenses":... |
mutable struct FriendSets
var::Vector{Symbol}
sets::Vector{String}
counts::Vector{Int64}
items::Vector{Vector{Int64}}
FriendSets(var, sets, counts, items) = new(var, sets, counts, items)
FriendSets() = new(Symbol[], String[], Int64[], Vector{Vector{Int64}}(undef, 0))
end
| {"hexsha": "57a9ad64d60f4c902a7a701e4061cb79bf103462", "size": 296, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/structs/constraints/friend_sets_struct.jl", "max_stars_repo_name": "giadasp/ATA.jl", "max_stars_repo_head_hexsha": "8ec4227c418784521e8d14623626e072a348fc79", "max_stars_repo_licenses": ["MIT"],... |
import numpy as np
import logging
from scipy import interpolate
def areas(ip):
p = ip.tri.points[ip.tri.vertices]
q = p[:, :-1, :] - p[:, -1, None, :]
areas = abs(q[:, 0, 0] * q[:, 1, 1] - q[:, 0, 1] * q[:, 1, 0]) / 2
return areas
def scale(points, xy_mean, xy_scale):
points = np.asarray(points,... | {"hexsha": "d00f235ff6a023f5f2787129fee1f83586aaf433", "size": 6558, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycqed/analysis/tools/plot_interpolation.py", "max_stars_repo_name": "nuttamas/PycQED_py3", "max_stars_repo_head_hexsha": "1ee35c7428d36ed42ba4afb5d4bda98140b2283e", "max_stars_repo_licenses": ["M... |
!-------------------------------------------------------------------------------------------------------------
!
!> \file CompExcessGibbsEnergyQKTO.f90
!> \brief Compute the partial molar excess Gibbs energy of mixing of solution phase constituents in a QKTO
!! solution phase.
!>... | {"hexsha": "916cacd1207dfc11f66b91cfbaddb7bc14f27016", "size": 10930, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/gem/CompExcessGibbsEnergyQKTO.f90", "max_stars_repo_name": "elementx54/thermochimica", "max_stars_repo_head_hexsha": "ea7bb8f64b03d8583d326f737e9699083c304847", "max_stars_repo_licenses": [... |
module trim_test
use iso_varying_string, only: char, trim
use veggies, only: &
input_t, &
result_t, &
string_input_t, &
test_item_t, &
assert_equals, &
describe, &
fail, &
it, &
ASCII_STRING_GENERATOR
... | {"hexsha": "4177fca07a2e2a37107a63db0f6135376b6b0e59", "size": 1195, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/unit_test/trim_test.f90", "max_stars_repo_name": "everythingfunctional/iso_varying_string", "max_stars_repo_head_hexsha": "f330d7a246d81aacfbf92f8085a4eca9d2506820", "max_stars_repo_license... |
[STATEMENT]
lemma powr_eventually_exp_ln':
assumes "x > 0"
shows "eventually (\<lambda>x. (x::real) powr p = exp (p * ln x)) (nhds x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>\<^sub>F x in nhds x. x powr p = exp (p * ln x)
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \... | {"llama_tokens": 693, "file": "Landau_Symbols_Landau_Library", "length": 9} |
import ipywidgets as ipyw
import traitlets as t
import numpy as np
import bqplot as bq
import traittypes as tt
M = 1e10
MAXIMUM_COST_CURVE_SEGMENTS = 50
MINIMUM_COST_CURVE_SEGMENTS = 1
class Generator(t.HasTraits):
'''Generator Model'''
name = t.CUnicode(default_value='GenCo0', help='Name of Generator (str... | {"hexsha": "253bfbe9b6231682ebd80e5f027e250033be4cce", "size": 14506, "ext": "py", "lang": "Python", "max_stars_repo_path": "psst/case/generator.py", "max_stars_repo_name": "ayadabd000/psst", "max_stars_repo_head_hexsha": "8c116fb7afd183881ecc605e017dffc87cdc49e6", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""
Display one shapes layer ontop of one image layer using the add_shapes and
add_image APIs. When the window is closed it will print the coordinates of
your shapes.
"""
import numpy as np
from skimage import data
import napari
# create the list of polygons
triangle = np.array([[11, 13], [111, 113], [22, 246]])
per... | {"hexsha": "acf08972861f5ccf70588d2ad76a104456027684", "size": 1139, "ext": "py", "lang": "Python", "max_stars_repo_path": "test-examples/add_shapes2.py", "max_stars_repo_name": "tlambert03/image-demos", "max_stars_repo_head_hexsha": "a2974bcc7f040fd4d14e659c4cbfeabcf726c707", "max_stars_repo_licenses": ["BSD-3-Clause"... |
from __future__ import print_function, division
from itertools import product
import numpy as np
from scipy.stats import multivariate_normal
from ahoy import positions, fields
from ahoy.mesh import uniform_mesh_factory
import test
def get_nearest_cell_ids_manual(f, ps):
rs = ps.r_w.T
ccs = f.mesh.cellCenters.... | {"hexsha": "f586d34293d91e249bb8371798823d21a8eadd68", "size": 4632, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_field.py", "max_stars_repo_name": "eddiejessup/ships", "max_stars_repo_head_hexsha": "acb30dfed5524fa2d20e988d08cef0e66ffcdc60", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
import random
import cv2
import numpy as np
from augraphy.base.augmentation import Augmentation
class Folding(Augmentation):
"""Emulates folding effect from perspective transformation
:param fold count: Number of applied foldings
:type fold_count: int, optional
:param fold_noise: Level of noise add... | {"hexsha": "9b6414d4ffcc898bedd012df43cfdcc3ec09504c", "size": 9656, "ext": "py", "lang": "Python", "max_stars_repo_path": "augraphy/augmentations/folding.py", "max_stars_repo_name": "kwcckw/augraphy", "max_stars_repo_head_hexsha": "b0e8cc4192eaf827e36fb67ae1b7b9c762f9578b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Copyright (c) 2021, NVIDIA CORPORATION. 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 applic... | {"hexsha": "ebe601b5187403d93fe8ec144486e00864424a62", "size": 1767, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/test_tensor_pyt.py", "max_stars_repo_name": "triton-inference-server/model_navigator", "max_stars_repo_head_hexsha": "ec2915f4f5a6b9ed7e1b59290899e2b56b98bcc7", "max_stars_repo_licenses... |
/**
* @example types/test/quadtree_test.cc
*/
#include <boost/test/unit_test.hpp>
#include <usml/types/quadtree.h>
#include <usml/types/test/quadtree_test_support.h>
#include <iostream>
#include <list>
#include <cstdlib>
BOOST_AUTO_TEST_SUITE(quadtree_test)
using namespace boost::unit_test ;
using namespace usml::t... | {"hexsha": "8a6a12e3bd181ce0410d289fc5857573848e3894", "size": 1953, "ext": "cc", "lang": "C++", "max_stars_repo_path": "types/test/quadtree_test.cc", "max_stars_repo_name": "fraclipe/UnderSeaModelingLibrary", "max_stars_repo_head_hexsha": "52ef9dd03c7cbe548749e4527190afe7668ff4e7", "max_stars_repo_licenses": ["BSD-2-C... |
"""The ``surfaces`` module provides functions for generating **surfaces**.
Surfaces are 2D matrices which act as an elevation map.
"""
from scipy.ndimage.filters import gaussian_filter
import numpy as np
DEFAULT_DIMS = (500, 500)
def make_noise_surface(dims=DEFAULT_DIMS, blur=10, seed=None):
"""Makes a surface b... | {"hexsha": "8593b2fec984cbfc3681a58fb27e8d463aa7a8cb", "size": 2089, "ext": "py", "lang": "Python", "max_stars_repo_path": "penkit/surfaces.py", "max_stars_repo_name": "djma/penkit", "max_stars_repo_head_hexsha": "ab0bde49d6045bd09598cf87d3dcb5766e9387bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_... |
#!/usr/bin/python3
#-*- coding: utf-8 -*-
import gi, sys, os, signal, stat, warnings, re, time, pathlib
import numpy as np
import traceback, faulthandler ## Debugging library crashes
faulthandler.enable()
# https://docs.python.org/3/library/sys.html#sys.settrace
gi.require_version('Gtk', '3.0')
from gi.repository imp... | {"hexsha": "7dd920d4ce97b7d3b30f9ec52ac1298458844dc1", "size": 81190, "ext": "py", "lang": "Python", "max_stars_repo_path": "nihilnovi.py", "max_stars_repo_name": "FilipDominec/nihilnovi", "max_stars_repo_head_hexsha": "e1af97f500cb1ad5bf08605815803c58b9698cfd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
export ElectronVolt, Torr
const ElectronVolt = NonSIUnit{typeof(Joule),:eV}()
convert(::Type{SIQuantity},::typeof(ElectronVolt)) = 1.60217656535e-19Joule
const Torr = NonSIUnit{typeof(Pascal),:torr}()
convert(::Type{SIQuantity},::typeof(Torr)) = 133.322368Pascal
| {"hexsha": "ee419730338310ee6f30b81a479cae026615b1ca", "size": 265, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/nonsiunits.jl", "max_stars_repo_name": "mbauman/SIUnits.jl", "max_stars_repo_head_hexsha": "a04d6f94f4f246dce32f6edcd08060fea59673ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import os
import numpy as np
import scipy.io
import requests
# ultility functions
def load_annotation(file_path):
"""
Load temporal annotation file (.mat)
Input: file_path: *.mat file path
Return: a numpy array, temporal interval value pairs (can have more than 1 pair)
ex: array([[x,y]])
"""
... | {"hexsha": "e899057c3acf0ea5e13741e1cea251287c54f579", "size": 1910, "ext": "py", "lang": "Python", "max_stars_repo_path": "catalog/utils/utils.py", "max_stars_repo_name": "srichakradhar/DTechtive", "max_stars_repo_head_hexsha": "79cae0b7b702b304bd1e44ed24e084387e760234", "max_stars_repo_licenses": ["CC0-1.0"], "max_st... |
#!/usr/bin/env python3
import os
import sys
import cv2 as cv
import numpy as np
import open3d as o3d
sys.path.append("..")
from utils.cache import get_cache, memoize
class ICLDataset:
def __init__(self, data_source, get_color=False):
# Cache
self.use_cache = True
self.cache = get_cache(d... | {"hexsha": "dc7b9dd3f3067ac037e1cc0853baf9e267b82216", "size": 3652, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/python/datasets/icl.py", "max_stars_repo_name": "nachovizzo/vdbfusion", "max_stars_repo_head_hexsha": "05ebd56b1d9226deee2c5ddc01e72b97ef0b1595", "max_stars_repo_licenses": ["MIT"], "max_... |
# Vendor
import numpy as np
from numpy import tensordot, roll, transpose, stack
# Project
from gates.Gate import Gate
class Add(Gate):
def __call__(self, M: np.array, A: np.array = None, B: np.array = None) -> (np.array, np.array):
rows = [roll(B[:, ::-1], shift=shift + 1, axis=1)
for sh... | {"hexsha": "dcc4053ed1e7cf7977dfa0d87dee5359a6ced6f0", "size": 470, "ext": "py", "lang": "Python", "max_stars_repo_path": "gates/Add.py", "max_stars_repo_name": "DrugoLebowski/nram-executor", "max_stars_repo_head_hexsha": "3abb49b3f28cc1457f246b158167f664eaf37a8e", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from __future__ import print_function
from vice.yields.sneia import single
from vice.yields.sneia import fractional
from vice._globals import _RECOGNIZED_ELEMENTS_
import warnings
try:
ModuleNotFoundError
except NameError:
ModuleNotFoundError = ImportError
try:
import numpy as np
_N_ = np.linspace(.001, ... | {"hexsha": "e701f21bceeeebb8774aa77e3fd61efee2effe5a", "size": 2423, "ext": "py", "lang": "Python", "max_stars_repo_path": "vice/tests/test_ia_yields.py", "max_stars_repo_name": "NessMayker/VICE", "max_stars_repo_head_hexsha": "c8862baebf0795e227196800ebce6702e0f75ba0", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#include <boost/preprocessor/punctuation.hpp>
| {"hexsha": "e688862a006b3becdf5a155ef5b1f0d600919c77", "size": 46, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_preprocessor_punctuation.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-... |
mkdir(resultsdir)
mkdir(resultsdir*"/tags")
mkdir(resultsdir*"/images")
fid = open(resultsdir*"/readme.txt", "w")
println(fid, currdirtime)
println(fid, datafolder)
println(fid, camidxs)
println(fid, ARGS)
close(fid)
fid = open(resultsparentdir*"/racecar.log", "a")
println(fid, "$(currdirtime), $datafolder, $(camid... | {"hexsha": "276e39229f524dd1a3c9f9d031b18c965e30cd88", "size": 346, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/wheeled/racecar/createResultsDirs.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Caesar.jl-62eebf14-49bc-5f46-9df9-f7b7ef379406", "max_stars_repo_head_hexsha": "8438ee9adf649f3d66493f1e... |
#!/usr/bin/env python
#
# Code to query the VETO mask of objects/randoms
# It takes the NOISES extension as an input
# It writers a VETO extension.
# Usage, see python query_veto.py -h
#
from __future__ import print_function
__author__ = "Yu Feng and Martin White"
__version__ = "1.0"
__email__ = "yfeng1@berkeley.ed... | {"hexsha": "6150aa4c86be0709bceaea10c0cd77d46a394ca4", "size": 2447, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/imglss-query-tycho-veto.py", "max_stars_repo_name": "desihub/imaginglss", "max_stars_repo_head_hexsha": "09258d20015869fead9bad6020da2bc0d161f670", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Machine/LamSlotMulti.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Machine/LamSlotMulti
"""
from os import linesep
from sys import getsizeof... | {"hexsha": "0ebd11f63c440b9e6dc893180fabec8df47a4e05", "size": 14881, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyleecan/Classes/LamSlotMulti.py", "max_stars_repo_name": "carbon-drive/pyleecan", "max_stars_repo_head_hexsha": "e89d4fe97f23f6182c19127d2c6a2133614e169d", "max_stars_repo_licenses": ["Apache-2.... |
def f : Fin 2 → Nat
| 0 => 5
| 1 => 45
example : f 0 = 5 := rfl
example : f 1 = 45 := rfl
def g : Fin 11 → Nat
| 0 => 5
| 1 => 10
| 2 => 15
| 3 => 2
| 4 => 48
| 5 => 0
| 6 => 87
| 7 => 64
| 8 => 32
| 9 => 64
| 10 => 21
def h : Fin 15 → Nat
| 0 => 5
| 1 => 45
| _ => 50
| {"author": "leanprover", "repo": "lean4", "sha": "742d053a97bdd109a41a921facd1cd6a55e89bc7", "save_path": "github-repos/lean/leanprover-lean4", "path": "github-repos/lean/leanprover-lean4/lean4-742d053a97bdd109a41a921facd1cd6a55e89bc7/tests/lean/run/finLit.lean"} |
#
# Copyright (c) 2016, Nikolay Polyarnyi
# All rights reserved.
#
import numpy as np
import pyopencl as cl
import pyopencl.array
from triangulum.utils import support
from triangulum.utils.cl import create_context
class CentralLineExtractionProcessor:
""" This is OpenCL implementation for central line extractio... | {"hexsha": "cf14eea9f2616441cb789639082eadc1cf836e7e", "size": 3713, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/triangulum/algos/central_line_extraction/central_line_extraction.py", "max_stars_repo_name": "PolarNick239/Triangulum3D", "max_stars_repo_head_hexsha": "85c6a44f5c8f620bdc58164bd50ff89e1897f59... |
# -*- coding: utf-8 -*-
#
# hl_api.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of the License, or
# (a... | {"hexsha": "77208384f62cdcde2dcb2ed1329384932b2c1ec4", "size": 71159, "ext": "py", "lang": "Python", "max_stars_repo_path": "NEST-14.0-FPGA/topology/pynest/hl_api.py", "max_stars_repo_name": "OpenHEC/SNN-simulator-on-PYNQcluster", "max_stars_repo_head_hexsha": "14f86a76edf4e8763b58f84960876e95d4efc43a", "max_stars_repo... |
import logging
import sys
sys.path.append('./modules/')
import time
from docopt import docopt
from scipy.spatial.distance import cosine as cosine_distance
from utils_ import Space
def main():
"""
Compute cosine distance for targets in two matrices.
"""
# Get the arguments
args = docopt("""Compu... | {"hexsha": "c3d1b7e704f84a45e289eda067ce250ad74d48c3", "size": 3265, "ext": "py", "lang": "Python", "max_stars_repo_path": "measures/cd.py", "max_stars_repo_name": "ichristod/language-drift", "max_stars_repo_head_hexsha": "ebd720afe83dd3e72bc1d389371a1f84393e86ee", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
"""
Validate post-ETL FERC Form 1 data and the associated derived outputs.
These tests depend on a FERC Form 1 specific PudlTabl output object, which is
a parameterized fixture that has session scope.
"""
import logging
import numpy as np
import pytest
from pudl import validate as pv
logger = logging.getLogger(__na... | {"hexsha": "4a57b261336131a357ffb91e96aec8b7b067a954", "size": 3857, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/validate/fbp_ferc1_test.py", "max_stars_repo_name": "cschloer/pudl", "max_stars_repo_head_hexsha": "32705ecc77443eb0d8c1d460df428f6f5f5b5037", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import tensorflow as tf
import numpy as np
import cv2
import time
import argparse
import math
import random
from ffpyplayer.player import MediaPlayer
import posenet
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=int, default=101)
parser.add_argument('--cam_id', type=int, default=0)
parser.add_... | {"hexsha": "6f0563ec301c91323ec0f15b9bb00e32c75ead64", "size": 5592, "ext": "py", "lang": "Python", "max_stars_repo_path": "webcam_demo_try.py", "max_stars_repo_name": "ShreshthSaxena/posenet-python", "max_stars_repo_head_hexsha": "a974b2db32222f2b88718f31e05f00d4e87863bf", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
{-# OPTIONS --without-K #-}
open import HoTT
open import cohomology.Exactness
open import cohomology.Theory
module cohomology.Sn {i} (OT : OrdinaryTheory i) where
open OrdinaryTheory OT
C-Sphere-≠ : (n : ℤ) (m : ℕ) → (n ≠ ℕ-to-ℤ m)
→ C n (⊙Lift (⊙Sphere m)) == Lift-Unit-group
C-Sphere-≠ n O neq = C-dimension n ne... | {"hexsha": "76fcca1a137dc97cf374e063f3af451cbb254c66", "size": 1190, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "theorems/cohomology/Sn.agda", "max_stars_repo_name": "cmknapp/HoTT-Agda", "max_stars_repo_head_hexsha": "bc849346a17b33e2679a5b3f2b8efbe7835dc4b6", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
macro x:ident noWs "(" ys:term,* ")" : term => `($x $ys*)
#check id(1)
macro "foo" &"only" : tactic => `(trivial)
example : True := by foo only
| {"author": "Kha", "repo": "lean4-nightly", "sha": "b4c92de57090e6c47b29d3575df53d86fce52752", "save_path": "github-repos/lean/Kha-lean4-nightly", "path": "github-repos/lean/Kha-lean4-nightly/lean4-nightly-b4c92de57090e6c47b29d3575df53d86fce52752/tests/lean/run/macroParams.lean"} |
import numpy as np
import json
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
if isinstance(obj, np.floating):
return float(obj)
if isinstance(obj, np.ndarray):
return obj.tolist()... | {"hexsha": "18d6a8ccf05c88d99fd37f30bcaa7837e9bb2e67", "size": 456, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/JsonUtil.py", "max_stars_repo_name": "liuyuns/keras-yolo3", "max_stars_repo_head_hexsha": "512a18f39115a203bb65bea627df69eb8133b5bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
"""
MNIST based datasets are not useful benchamrks if the task identity
is known at both train and test times.
"""
from typing import List
import numpy as np
import torchvision.transforms as transforms
from numpy.random import default_rng
from datasets.modmnist import ModMNIST
from datasets.data import MultiTaskDataH... | {"hexsha": "f1afc094ce4e7cee34550c84874939cd631326be", "size": 7291, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/mnist.py", "max_stars_repo_name": "Laknath1996/modelzoo_continual", "max_stars_repo_head_hexsha": "23f657e9aac428d2ac6233b86bd717cd8960fe83", "max_stars_repo_licenses": ["MIT"], "max_star... |
from eval_model import cap
from flask import Flask, render_template, request, jsonify,Response
import cv2
import numpy as np
import jsonpickle
app = Flask(__name__)
@app.route('/uploads', methods=['POST'])
def test():
r = request
#filename = secure_filename(file.filename)
#file.save(os.path... | {"hexsha": "aecebddeafd6143364867a71c42dec3363f702ab", "size": 1088, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "huyquoctrinh/cap_Flask", "max_stars_repo_head_hexsha": "126044494819e86431bc4dee355176931eae246c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
////////////////////////////////////////////////////////////////////////////////
/// @brief test suite for string utility functions
///
/// @file
///
/// DISCLAIMER
///
/// Copyright 2012 triagens GmbH, Cologne, Germany
///
/// Licensed under the Apache License, Version 2.0 (the "License");
/// you may not use this fil... | {"hexsha": "33f58c36ba74b90a50d815e7328db7671f454e4d", "size": 9808, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "UnitTests/Basics/string-utf8-normalize-test.cpp", "max_stars_repo_name": "asaaki/ArangoDB", "max_stars_repo_head_hexsha": "a1d4f6f33c09ffd6f67744dbe748e83dc0fe6b82", "max_stars_repo_licenses": ["Apa... |
import numpy as np
from nicegui.ui import Ui
from nicegui.events import KeyEventArguments
import logging
from ..actors import Steerer
from ..world import Point
class KeyboardControl:
steerer: Steerer # will be set by rosys.ui.configure
ui: Ui # will be set by rosys.ui.configure
def __init__(self, *, de... | {"hexsha": "dbe3f64f0fb5c68e20b3a007b09719dd625b5ee1", "size": 2007, "ext": "py", "lang": "Python", "max_stars_repo_path": "rosys/ui/keyboard_control.py", "max_stars_repo_name": "zauberzeug/rosys", "max_stars_repo_head_hexsha": "10271c88ffd5dcc4fb8eec93d46fe4144a9e40d8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma PK8: "Kh_3 \<phi> \<Longrightarrow> \<forall>A. \<phi>(\<^bold>\<midarrow>(\<phi>(\<phi> A))) \<^bold>\<approx> \<phi>\<^sup>d(\<phi>(\<^bold>\<midarrow>(\<phi> A)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Kh_3 \<phi> \<Longrightarrow> \<forall>A w. \<phi> (\<phi>\<^sup>c (\<phi> A)) w = \... | {"llama_tokens": 2134, "file": "Topological_Semantics_topo_operators_derivative", "length": 18} |
# Author(s): Sehoon Ha <sehoon.ha@gmail.com>
# : Seungmoon Song <ssm0445@gmail.com>
import numpy as np
class MusculoTendonUnit(object):
"""
"""
# f_lce
W = .56
C = np.log(.05)
# f_vce
N = 1.5
K = 5
# f_pe
E_REF_PE = W
# f_be
E_REF_BE = .5 * W
E_REF_BE2 = ... | {"hexsha": "f130cbc5a3f36c8a0b84ab6a0885ee291f654347", "size": 3223, "ext": "py", "lang": "Python", "max_stars_repo_path": "flappy/envs/fwmav/pydart2/muscle/tendon_unit.py", "max_stars_repo_name": "ArbalestV/flappy", "max_stars_repo_head_hexsha": "9e715cacc842fab6b9dfd4d6f0f7fb65b769204f", "max_stars_repo_licenses": ["... |
"""
Name: inherent
Coder: HaoLing ZHANG (BGI-Research)[V1]
Current Version: 1
Function(s):
(1) Some inherent concepts.
"""
import numpy
# mapping of integer and char
A = 65 # ord('A')
B = 66 # ord('B')
C = 67 # ord('C')
D = 68 # ord('D')
E = 69 # ord('E')
F = 70 # ord('F')
G = 71 # ord('G'... | {"hexsha": "493620e27df70ce08782664a4bfb4206b7ae99b5", "size": 1667, "ext": "py", "lang": "Python", "max_stars_repo_path": "methods/inherent.py", "max_stars_repo_name": "HaolingZHANG/GenomeCompact", "max_stars_repo_head_hexsha": "b118161dc470e3c07f9b3a58d059dbd8c7540981", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#include<iostream>
# define BOOST_TEST_MAIN
#include <boost/test/included/unit_test.hpp>
#include <my_ip_country_detector.hpp>
std::string setting_ipv4_cvs_path = "./res/ip2country/IP2LOCATION-LITE-DB1.CSV";
std::string setting_ipv6_cvs_path = "./res/ip2country/IP2LOCATION-LITE-DB1.IPV6.CSV";
BOOST_AUTO_TEST_CASE(te... | {"hexsha": "6252d8e285879344a0ad45058fdf359a5509a2dd", "size": 1141, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test_my_ip_country_detector.cpp", "max_stars_repo_name": "kyorohiro/torrent_chaser", "max_stars_repo_head_hexsha": "b212d51c96de893b3dd4f0f9ad41f66dced377f5", "max_stars_repo_licenses": ["BSD-So... |
import random
import unittest
import datetime
from dagger.codec import decode_header, encode_header, EventType, pack_message, unpack_payload
class TestProto(unittest.TestCase):
def test_decode_and_encode_header(self):
max = 2 ** 32 - 1
for i in range(10000):
a = random.randint(0, max)... | {"hexsha": "f48b8824f342d785e16634c6e99530c9600eadd5", "size": 2011, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_proto.py", "max_stars_repo_name": "ko-han/dagger", "max_stars_repo_head_hexsha": "839de31f7b23f6bcf14c33d822e23d4ce5323815", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import pickle
import re
import pandas as pd
import numpy as np
import math
from statsmodels.tsa.stattools import adfuller, kpss
from arch.unitroot import PhillipsPerron
"""
Refs:
- https://www.machinelearningplus.com/time-series/time-series-analysis-python/
- https://machinelearningmastery.com/time-series-data-statio... | {"hexsha": "29386e60ff9f8feb639377842ba31af1c5ede782", "size": 5537, "ext": "py", "lang": "Python", "max_stars_repo_path": "matilda/quantitative_analysis/time_series_analysis/forecasting.py", "max_stars_repo_name": "AlainDaccache/Quantropy", "max_stars_repo_head_hexsha": "6cfa06ed2b764471382ebf94d40af867f10433bb", "max... |
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
import os
from tempfile import mkdtemp
from shutil import rmtree
import numpy as np
import nibabel as nb
from nipype.testing import (assert_equal, assert_raises, skipif)
from nipype.interfaces.base impor... | {"hexsha": "297b80a47d8fc05832a1d490c4085440babd61cc", "size": 15189, "ext": "py", "lang": "Python", "max_stars_repo_path": "nipype/interfaces/fsl/tests/test_maths.py", "max_stars_repo_name": "sebastientourbier/nipype_lts5", "max_stars_repo_head_hexsha": "3b9718d154443574cc6a5d0bbd76ccf7964e6a45", "max_stars_repo_licen... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.