text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
export Storage, update_storage
# Create a structure that will hold evaluation of the basis functions,
# as well as their derivative and second derivative
"""
$(TYPEDEF)
An immutable structure to hold the evaluation of basis functions
## Fields
$(TYPEDFIELDS)
"""
struct Storage
m::Int64
Nψ::Int64
Nx::... | {"hexsha": "d2826300438daf6e5ed60017cf2dbaed56439b2c", "size": 4406, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/hermitemap/storage.jl", "max_stars_repo_name": "mleprovost/TransportBasedInference.jl", "max_stars_repo_head_hexsha": "bdcedf72e9ea23c24678fe6af7a00202c5f9d5d7", "max_stars_repo_licenses": ["MI... |
import os
from timeit import default_timer as timer
import fire
import h5py
import numpy as np
import torch
from torch.utils.data import Subset
from cnn_gp import save_K
from plotting.createStartPlot import loadDataset
from utils import load_kern, constructSymmetricMatrix, deleteValues, loadNormalizedModel
def comp... | {"hexsha": "d37091818eecf361657d2e309b333113441e9c93", "size": 5848, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotting/computeKernel.py", "max_stars_repo_name": "meinma/master-thesis", "max_stars_repo_head_hexsha": "0b1c3fa124eef97c4759064576bf6d25e8c60efd", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
import pandas
import numpy as np
import click
from bitstring import BitArray
from base58 import b58encode_int, b58decode_int
class Clusterer:
def __init__(self):
pass
def cluster(self, n, state_processor, pca = False, model_type = 'kmeans', z_score_exclude = 0.0, seed = None, quiet = False):
from sklearn.... | {"hexsha": "02c4b250b261c6bb504888c067ab3db8c6959e7e", "size": 2828, "ext": "py", "lang": "Python", "max_stars_repo_path": "wethepeopletoolkit/clusterer.py", "max_stars_repo_name": "alexpeattie/wethepeopletoolkit", "max_stars_repo_head_hexsha": "665881ef536355552b936f7a8341bdcc2711efeb", "max_stars_repo_licenses": ["MI... |
"""Set up the environment for doctests
This file is automatically evaluated by py.test. It ensures that we can write
doctests without distracting import statements in the doctest.
"""
import inspect
from collections import OrderedDict
import numpy
import pytest
import krotov
@pytest.fixture(autouse=T... | {"hexsha": "6dec8efc1d332cd75b4bb703fc649da5a2cad937", "size": 545, "ext": "py", "lang": "Python", "max_stars_repo_path": "Mixed/Linear/src/conftest.py", "max_stars_repo_name": "mcditoos/krotov", "max_stars_repo_head_hexsha": "6a70cc791fa21186997ad2ca5a72f6d30574e7a0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
from panda3d.core import PNMImage, TextNode
from direct.gui.DirectGui import DirectFrame, DirectButton, DirectLabel, DirectEntry, DGG, DirectOptionMenu
from direct.showbase.ShowBase import ShowBase
from direct.showbase.DirectObject import DirectObject
import numpy as np
from typing import Tuple, Union, List, Any, Dic... | {"hexsha": "7c00fb4896d425bfd2af1cfc7b23e7a31ccad597", "size": 19678, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/simulator/views/gui/simulator_config.py", "max_stars_repo_name": "ed741/PathBench", "max_stars_repo_head_hexsha": "50fe138eb1f824f49fe1a862705e435a1c3ec3ae", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma CondLowCompositionality:
assumes "nonInterference \<Gamma> c1" and "nonInterference \<Gamma> c2" and "\<Gamma> \<turnstile> b : Low"
shows "nonInterference \<Gamma> (if (b) c1 else c2)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. nonInterference \<Gamma> (if (b) c1 else c2)
[PROOF STEP]
pro... | {"llama_tokens": 5524, "file": "VolpanoSmith_secTypes", "length": 40} |
# (c) Nelen & Schuurmans. GPL licensed, see LICENSE.rst.
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import absolute_import
try:
from osgeo import ogr
except ImportError:
ogr = None
import numpy as np
from threedigrid.admin import con... | {"hexsha": "0f7e9113e7e993990bd7574227129eab0cfb6423", "size": 3398, "ext": "py", "lang": "Python", "max_stars_repo_path": "threedigrid/admin/breaches/prepare.py", "max_stars_repo_name": "nens/threedigrid", "max_stars_repo_head_hexsha": "7bff5f9efa5edb8335a8ed601b9a096f85e19711", "max_stars_repo_licenses": ["BSD-3-Clau... |
% Options for packages loaded elsewhere
\PassOptionsToPackage{unicode}{hyperref}
\PassOptionsToPackage{hyphens}{url}
%
\documentclass[
12pt,
]{book}
\usepackage{amsmath,amssymb}
\usepackage{lmodern}
\usepackage{iftex}
\ifPDFTeX
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{textcomp} % provide... | {"hexsha": "ecd9b4852978972ed0146563386369668e44f907", "size": 457700, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/wam_tuto.tex", "max_stars_repo_name": "wamwiki/wam_tuto", "max_stars_repo_head_hexsha": "c8e275d0c91421a09602293e31168a5534705979", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count"... |
# maintained by rajivak@utexas.edu
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
def format_numsel(numsel):
ss = ''
for i,j in enumerate(numsel):
ss = ss + " %d:%d " %(i,j)
return ss
def get_train_testindi... | {"hexsha": "4a77a0ffdc93c5e262c47320e055e138b37f2dbd", "size": 1019, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/mmd/MMD-critic/Helper.py", "max_stars_repo_name": "sthagen/christophM-interpretable-ml-book", "max_stars_repo_head_hexsha": "d8b82b8e6ab82c78d95de784a601e71025621ab2", "max_stars_repo_lice... |
[STATEMENT]
lemma benv_in_eval:
assumes "\<forall>\<beta>'\<in>benv_in_ve ve. Q \<beta>'"
and "Q \<beta>"
shows "\<forall>\<beta>\<in>benv_in_d (\<A> v \<beta> ve). Q \<beta>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>\<beta>\<in>benv_in_d (\<A> v \<beta> ve). Q \<beta>
[PROOF STEP]
proof(cas... | {"llama_tokens": 4615, "file": "Shivers-CFA_ExCFSV", "length": 41} |
###_______________________________ SymPy ___________________________________###
# SymPy es una biblioteca de Python para matemática simbólica. Apunta a convertirse
# en un sistema de algebra computacional (CAS) con todas sus prestaciones manteniendo
# el código tan simple como sea posible para manterlo comprensible y... | {"hexsha": "9cf8d0e957967da7b5eceea824704c0222e6b602", "size": 7000, "ext": "py", "lang": "Python", "max_stars_repo_path": "MyScripts/040-SymPy.py", "max_stars_repo_name": "diegoomataix/Curso_AeroPython", "max_stars_repo_head_hexsha": "c2cf71a938062bc70dbbf7c2f21e09653fa2cedd", "max_stars_repo_licenses": ["CC-BY-4.0"],... |
[STATEMENT]
lemma PO_m3_inv1_keys_init [iff]:
"init m3 \<subseteq> m3_inv1_keys"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. init m3 \<subseteq> m3_inv1_keys
[PROOF STEP]
by (auto simp add: PO_hoare_def m3_defs intro!: m3_inv1_keysI) | {"llama_tokens": 112, "file": "Security_Protocol_Refinement_Auth_simple_m3_enc", "length": 1} |
theory Proof_1_6
imports HandDryer VCTheoryLemmas Extra
begin
theorem proof_1_6:
"VC6 inv1 s0 hands_value"
apply(simp only: VC6_def inv1_def R1_def dryer_def)
apply(rule impI; rule conjI)
proof -
print_state
assume VC: "((toEnvP s0 \<and>
(\<forall>s1 s2.
substate s1 s2 \<and>
subs... | {"author": "ivchernenko", "repo": "post_vcgenerator", "sha": "fadfff131086870a027d6bd1c78b8d5a3baf183b", "save_path": "github-repos/isabelle/ivchernenko-post_vcgenerator", "path": "github-repos/isabelle/ivchernenko-post_vcgenerator/post_vcgenerator-fadfff131086870a027d6bd1c78b8d5a3baf183b/case-studies/HandDryer/Proof_1... |
# Copyright 2020 Makani Technologies LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "31b2f46e7786004b90af6206c189ee907be116a8", "size": 29994, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/control/replay/estimator_helper.py", "max_stars_repo_name": "leozz37/makani", "max_stars_repo_head_hexsha": "c94d5c2b600b98002f932e80a313a06b9285cc1b", "max_stars_repo_licenses": ["Apach... |
[STATEMENT]
lemma sinvar_mono_I_proofrule_simple:
"\<lbrakk> (\<forall> G nP. sinvar G nP = (\<forall> (e1, e2) \<in> edges G. P e1 e2 nP) ) \<rbrakk> \<Longrightarrow> sinvar_mono"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>G nP. sinvar G nP = (\<forall>(e1, e2)\<in>edges G. P e1 e2 nP) \<Longrighta... | {"llama_tokens": 559, "file": "Network_Security_Policy_Verification_TopoS_withOffendingFlows", "length": 4} |
from contextlib import ExitStack as does_not_raise # noqa: N813
import numpy as np
import pandas as pd
import pytest
from sid.msm import _flatten_index
from sid.msm import _harmonize_input
from sid.msm import _is_diagonal
from sid.msm import get_diag_weighting_matrix
from sid.msm import get_flat_moments
from sid.msm ... | {"hexsha": "15dea6b997cdf3f126269b3a6e883217dcfdae17", "size": 3776, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_msm.py", "max_stars_repo_name": "covid-19-impact-lab/sid", "max_stars_repo_head_hexsha": "d867f55d4d005b01c672bd2edd0e1dc974cb182b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
This module is a part of system for the automatic enrichment
of a WordNet-like taxonomy.
Copyright 2020 Ivan Bondarenko, Tatiana Batura
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... | {"hexsha": "e4c1efdfa3ced5b4314eede90dfcbb9ae2201e9b", "size": 8607, "ext": "py", "lang": "Python", "max_stars_repo_path": "prepare_contexts_for_submission.py", "max_stars_repo_name": "CT2020Hypernym/Hypernym", "max_stars_repo_head_hexsha": "50ab2c38f93d596dd78cdfe84cb6c8adae21b6ca", "max_stars_repo_licenses": ["Apache... |
import argparse
import os
import subprocess
import SimpleITK as sitk
import numpy as np
from nipype.interfaces.ants import N4BiasFieldCorrection
from natsort import natsorted
def ReadImage(file_path):
''' This code returns the numpy nd array for a MR image at path'''
return sitk.GetArrayFromImage(sitk.ReadI... | {"hexsha": "2c332ecb8add7b7f1f4549b682051e9b4dfd96f0", "size": 4429, "ext": "py", "lang": "Python", "max_stars_repo_path": "createBrainParcellation.py", "max_stars_repo_name": "pykao/BraTS2018-tumor-segmentation", "max_stars_repo_head_hexsha": "6c81ab670f7bd035312f7ccd729776c5c05c47a3", "max_stars_repo_licenses": ["MIT... |
import random
import matplotlib.pyplot as plt
import numpy as np
import torch
import copy
import utils.pytorch_util as ptu
def eval_np(module, *args, **kwargs):
"""
Eval this module with a numpy interface
Same as a call to __call__ except all Variable input/outputs are
replaced with numpy equivalents.... | {"hexsha": "7d6befd6cc380fb082bc726abc53978029f4893a", "size": 5153, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/core.py", "max_stars_repo_name": "amarildolikmeta/oac-explore", "max_stars_repo_head_hexsha": "e3d63992a4ff33c8df593941f498457e94f81eb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import numpy as np
from torch.utils import data
from .parsers.atis import readATISFile
class PropheseeNCars(data.Dataset):
"""Prophesee N-Cars dataset from:
Amos Sironi, Manuele Brambilla, Nicolas Bourdis, Xavier Lagorce, Ryad Benosman
“HATS: Histograms of Averaged Time Surfaces for Robust Event... | {"hexsha": "b34633ed569a725692defe073ddc391a99d60d1e", "size": 1597, "ext": "py", "lang": "Python", "max_stars_repo_path": "ebdataset/vision/prophesee_ncars.py", "max_stars_repo_name": "tihbe/python-ebdataset", "max_stars_repo_head_hexsha": "4d16822a3a6b45882124a8d7f7e124bd39a75868", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma continuous_on_const[continuous_intros,simp]: "continuous_on s (\<lambda>x. c)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. continuous_on s (\<lambda>x. c)
[PROOF STEP]
unfolding continuous_on_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>x\<in>s. ((\<lambda>x. c) \<longlongright... | {"llama_tokens": 150, "file": null, "length": 2} |
# Adapted from : VGG 16 model : https://github.com/machrisaa/tensorflow-vgg
import time
import os
import inspect
import numpy as np
from termcolor import colored
import tensorflow as tf
from fcn.losses import sigmoid_cross_entropy_balanced
from fcn.utils.io import IO
class PickNet():
def __init__(self, cfgs, r... | {"hexsha": "1026b7909d61272bcf49c9b31b89dbd696c52cbc", "size": 15318, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/PickNet/fcn/models/picknet.py", "max_stars_repo_name": "MrXiaoXiao/ESPRH", "max_stars_repo_head_hexsha": "c4bbebba001523fbd86f9de4b09cb931665b7a71", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This set of functions is used for plotting the results from
CT Crash data analysis
@author: Anna Konstorum (konstorum.anna@gmail.com)
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
def myround(x, base=5... | {"hexsha": "433c6973882a25d71d4c0d9e420bb0f036eba472", "size": 5831, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/plot_results.py", "max_stars_repo_name": "akonstodata/CT_crash_analysis", "max_stars_repo_head_hexsha": "66a8ecce5279f4dfc9f1cc3766a00573229812ca", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python
# coding: utf-8
# # import required library
# In[1]:
# Import numpy, pandas for data manipulation
import numpy as np
import pandas as pd
# Import matplotlib, seaborn for visualization
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
# I... | {"hexsha": "f2551e934ec02c72327ff261b91868d8569a6187", "size": 9361, "ext": "py", "lang": "Python", "max_stars_repo_path": "Time Series Analysis/Weather Forecasting using SRIMAX Model/weather prediction.py", "max_stars_repo_name": "shreejitverma/Data-Scientist", "max_stars_repo_head_hexsha": "03c06936e957f93182bb18362b... |
@testset "Robots: biped" begin
q0 = [0.0; 0.0; 0.5 * π * ones(7)]
v0 = zeros(9)
@test norm(lagrangian(biped, q0, v0)) < 1.0e-8
# visualize
vis = RoboDojo.Visualizer();
@test visualize!(vis, biped, [q0], Δt=0.1);
end | {"hexsha": "def11798a6d8bf8bf45dba96832e4c872e2f727f", "size": 241, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/robots/biped.jl", "max_stars_repo_name": "mcx/RoboDojo.jl", "max_stars_repo_head_hexsha": "b31fa17ee84285f45b76de78d9e660a83f5ddc9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Twenty Seconds Resume/CV
% LaTeX Template
% Version 1.0 (14/7/16)
%
% This template has been downloaded from:
% http://www.LaTeXTemplates.com
%
% Original author:
% Carmine Spagnuolo (cspagnuolo@unisa.it) with major modifications by
% Vel (vel@LaTeXTemplates.com)
%
% License... | {"hexsha": "282a49fcbf7d94a35b76159512de83de5af722d9", "size": 6382, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "template.tex", "max_stars_repo_name": "latexstudio/Data-Engineer-Resume-LaTeX", "max_stars_repo_head_hexsha": "0ca7e94b54375276550a10da8041eea2d26786ed", "max_stars_repo_licenses": ["Apache-2.0"], "... |
library(RMySQL)
lin_sem_distance <- function (data) {
options(warn = -1)
con <- dbConnect(MySQL(),
user="user", password="password",
dbname="snomed_20160731", host="127.0.0.1")
codes <- sort(na.omit(unique(as.vector(data))))
n_codes <- length(codes)
weight <- matri... | {"hexsha": "26745d90e8e9072c2b3753f1a941045f0a1cb4fe", "size": 1515, "ext": "r", "lang": "R", "max_stars_repo_path": "sem_distance.r", "max_stars_repo_name": "danka74/SnomedAgreement", "max_stars_repo_head_hexsha": "4eb3d0764846c8bd25f178915f671603978058d7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2018 denglixi edenglixi@xgpd2>
#
# Distributed under terms of the MIT license.
"""
"""
import numpy
import os
from xml_process import parse_rec
def create_dishes(canteen):
"""create_dishes"""
# each dish may have more than 1 ima... | {"hexsha": "bf6c2224e63c9953cc6804e19ea902a1893da37b", "size": 2537, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/datasets/clean_val.py", "max_stars_repo_name": "denglixi/faster-rcnn.pytorch", "max_stars_repo_head_hexsha": "12158fa2ec998ba3733a4696b7a4e08a35c157e3", "max_stars_repo_licenses": ["MIT"], "ma... |
from pathlib import Path
from typing import Union, Dict, List
import numpy as np
from .eeg import EEG
from .transforms import HighPass, RemoveBeginning, RemoveLineNoise, Standardize
def ingest_session(
data_path: Path, output_dir: Path
) -> Dict[str, Union[int, List[int]]]:
eeg = EEG.from_hdf5(data_path... | {"hexsha": "a294aa3fce6d2cc0bb3d7713b0da7200fc507b43", "size": 2262, "ext": "py", "lang": "Python", "max_stars_repo_path": "eegdrive/ingestion/ingest.py", "max_stars_repo_name": "lucagrementieri/eegdrive", "max_stars_repo_head_hexsha": "65b122246e2a75c0c7c80db3e544f6a6741ceb53", "max_stars_repo_licenses": ["Apache-2.0"... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 14 18:16:26 2020
@author: arslan
"""
from pyit2fls import IT2FS_Gaussian_UncertMean, join, IT2FS_plot, \
max_s_norm, probabilistic_sum_s_norm, bounded_sum_s_norm, \
drastic_s_norm, nilpotent_maximum_s_norm, einstein_sum_s_norm
from numpy im... | {"hexsha": "7656caa9f56b779574285fec981fef6ed383ae94", "size": 1472, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/ex_13.py", "max_stars_repo_name": "Haghrah/PyIT2FLS", "max_stars_repo_head_hexsha": "ca2763032a4f441c3c4456570c18faa68cfee3e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 33, ... |
import math, torch
import numpy as np
from numpy.random import normal as normrnd
from scipy.stats import multivariate_normal, norm
from scipy.linalg import sqrtm, expm
from pdb import set_trace as bp
from include.DNN import DNN
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from include.dataSt... | {"hexsha": "c28ebb519de9bc9d9ac37cfb68bb3d8715cb4b51", "size": 31290, "ext": "py", "lang": "Python", "max_stars_repo_path": "include/localize.py", "max_stars_repo_name": "sahibdhanjal/DeepLocNet", "max_stars_repo_head_hexsha": "a3a5973a0cb549d0a16f17b96a9c78c200cf0c7e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
class Solution:
def minMoves2(self, nums: List[int]) -> int:
nums.sort()
length = len(nums)
median = nums[length//2]
left, right = 0, length - 1
# for i in nums:
while left <= right:
mid = left + (right - left) // 2
if num... | {"hexsha": "71b7ef56ab5f6800c93ce1452c4e63bd4f259deb", "size": 804, "ext": "py", "lang": "Python", "max_stars_repo_path": "leetcode/462.py", "max_stars_repo_name": "strawsyz/straw", "max_stars_repo_head_hexsha": "db313c78c2e3c0355cd10c70ac25a15bb5632d41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_s... |
//=============================================================================
//
// Copyright (c) Kitware, Inc.
// All rights reserved.
// See LICENSE.txt for details.
//
// This software is distributed WITHOUT ANY WARRANTY; without even
// the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
// ... | {"hexsha": "3e9944e5dec445a88596757d8add3d863ba2c729", "size": 4145, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "remus/worker/detail/testing/UnitTestMessageRouterWorkerTermination.cxx", "max_stars_repo_name": "robertmaynard/Remus", "max_stars_repo_head_hexsha": "090a14c9a4b0e628a86590dcfa7e46ba728e9c04", "max_... |
[STATEMENT]
theorem min_of_list3_correct: "(min_of_list3,min_of_list) \<in> (array_assn nat_assn)\<^sup>k \<rightarrow>\<^sub>a nat_assn"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (min_of_list3, min_of_list) \<in> (array_assn nat_assn)\<^sup>k \<rightarrow>\<^sub>a nat_assn
[PROOF STEP]
using min_of_list3.refin... | {"llama_tokens": 292, "file": "Refine_Imperative_HOL_Userguides_Sepref_Guide_Quickstart", "length": 2} |
Name: Jesse Unger.
Office: FC
Personality:
Activities:
going to college... pretty much a full time job
work in Wickson Hall for Dr. Hildegarde Heymann
founder and prez of SBA AT UCD http://www.sbaatucd.com
working for qualcomm over the summer
going abroad
20051225 19:23:33 nbsp hey i called your number and ... | {"hexsha": "a8dc5785c46c690cb8937dbaf976315e67a3828f", "size": 587, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/JesseUnger.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
import scipy.io as io
import numpy as np
import torch
from .. import LIB_DATA_PATH
from .spatial import SpatialModel
from .spatial_OLD.spatial_model import SpatialModel as SpatialModelOriginal
from .spatial_OLD.spatial_hist import SpatialHist
from ..util.general import aeq
class Library(object):
"""
... | {"hexsha": "18aa18bb038b0bd03fa0b0fa9ef2aac028fa706b", "size": 7306, "ext": "py", "lang": "Python", "max_stars_repo_path": "pybpl/library/library.py", "max_stars_repo_name": "lucast4/pyBPL", "max_stars_repo_head_hexsha": "fc5d9a87266df4a7b0014d09feec713a7052bc39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
/-
Copyright (c) 2018 Simon Hudon. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Simon Hudon
-/
import tactic.hint
namespace tactic
open expr
open tactic.interactive ( casesm constructor_matching )
/--
find all assumptions of the shape `¬ (p ∧ q)` or `¬ (p ∨ q)` ... | {"author": "JLimperg", "repo": "aesop3", "sha": "a4a116f650cc7403428e72bd2e2c4cda300fe03f", "save_path": "github-repos/lean/JLimperg-aesop3", "path": "github-repos/lean/JLimperg-aesop3/aesop3-a4a116f650cc7403428e72bd2e2c4cda300fe03f/src/tactic/tauto.lean"} |
program set_threads
! Load the OpenMP functions library
use omp_lib
! Set variables
implicit none
integer :: tnum
! Create a parallel block of four threads (including master thread)
!$OMP PARALLEL PRIVATE(tnum) NUM_THREADS(4)
tnum = OMP_GET_THREAD_NUM()
print *, "I am thre... | {"hexsha": "561d9b4744e8dad78f94450968f59cdf016ae88b", "size": 608, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "files/arc_openmp/fortran/setthreads.f90", "max_stars_repo_name": "ARCCA/Introduction-to-Parallel-Programming-using-OpenMP", "max_stars_repo_head_hexsha": "830c240a041c32928b6c1fb0f76693268114cd2e... |
-- Andreas, 2012-01-30, bug reported by Nisse
-- {-# OPTIONS -v tc.term.absurd:50 -v tc.signature:30 -v tc.conv.atom:30 -v tc.conv.elim:50 #-}
module Issue557 where
data ⊥ : Set where
postulate
A : Set
a : (⊥ → ⊥) → A
F : A → Set
f : (a : A) → F a
module M (I : Set → Set) where
x : A
x = a (λ ())
y : A... | {"hexsha": "4e0ca7e6bd1ca737b7b4b7252e4f18163452cd0f", "size": 500, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Succeed/Issue557.agda", "max_stars_repo_name": "cruhland/agda", "max_stars_repo_head_hexsha": "7f58030124fa99dfbf8db376659416f3ad8384de", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
! This file is part of HolmMHD
! Copyright (C) 2019 Daniel Verscharen (d.verscharen@ucl.ac.uk)
!All rights reserved.
!
!Redistribution and use in source and binary forms, with or without
!modification, are permitted provided that the following conditions are met:
!
!1. Redistributions of source code must retain the abo... | {"hexsha": "03dcbee201d118583b2d5db0ce8c7a114f108cdf", "size": 2222, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/parameters.f90", "max_stars_repo_name": "danielver02/HolmMHD", "max_stars_repo_head_hexsha": "e32a42e77377bbc6632cb0e1aa975c45a83d1a66", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"], ... |
// Copyright 2005 Alexander Nasonov.
// Distributed under the Boost Software License, Version 1.0. (See
// accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#ifndef FILE_boost_type_traits_integral_promotion_hpp_INCLUDED
#define FILE_boost_type_traits_integral_promotion_hpp_INCLUDED
... | {"hexsha": "92d2a6745017f91d14b4bee89396e1faa4d05053", "size": 7804, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "openbmc/build/tmp/deploy/sdk/witherspoon-2019-08-08/sysroots/armv6-openbmc-linux-gnueabi/usr/src/debug/boost/1.69.0-r0/boost_1_69_0/boost/type_traits/integral_promotion.hpp", "max_stars_repo_name": ... |
!> @brief This function returns the overall index for any step within 2 nested do loops
!>
!> @param[in] i1 the index of the first do loop
!>
!> @param[in] i2 the index of the second do loop
!>
!> @param[in] n2 the end of the second do loop
!>
!> @warning This function assumes that all indexes go from 1 to n (inclusive... | {"hexsha": "b9698895e529b59eac695d871a80736ca3fee923", "size": 866, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "mod_safe/func_overall_index/func_overall_index_2loops.f90", "max_stars_repo_name": "Guymer/fortranlib", "max_stars_repo_head_hexsha": "30e27b010cf4bc5acf0f3a63d50f11789640e0e3", "max_stars_repo_l... |
import LinearAlgebra.dot
import GeometryBasics.Point
import Random.GLOBAL_RNG
export random_vector_matrix, perlin_noise
"""
random_vector_matrix([rng,] rows, cols)
Produce a matrix with the given number of rows and columns, in which every
entry is a random unit vector. If provided, the given random number genera... | {"hexsha": "8b294a533be8d6da707ec7c15a761d02e223284f", "size": 1507, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/noise.jl", "max_stars_repo_name": "fonsp/PenPlots.jl", "max_stars_repo_head_hexsha": "f330f8014049fad0c915759181798f823dd42757", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, "max_... |
function codepart(cnt,prevc)
codepart=cnt>1 ? string(cnt) : ""
return codepart * prevc
end
function encode(s)
# prevc=s
prevc = length(s)>0 ? s[1] : s
coded = ""
cnt=0
for c in s
if c == prevc
cnt = cnt + 1
else
# codepart=cnt>1 ? string(cnt) : ""
... | {"hexsha": "6e94bfd99e75414006ca21bb0b23e6b3d633cc59", "size": 1002, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "run-length-encoding/run-length-encoding.jl", "max_stars_repo_name": "stepinski/julia-in", "max_stars_repo_head_hexsha": "08c82e2de236ead3b1eb356d60a04c61e275d4fa", "max_stars_repo_licenses": ["MIT"... |
#if COMPILATION_INSTRUCTIONS
(echo "#include\""$0"\"" > $0x.cpp) && mpic++ -O3 -std=c++14 -Wall -Wextra -Wfatal-errors -D_TEST_MPI3_SHARED_COMMUNICATOR $0x.cpp -o $0x.x && time mpirun -n 3 $0x.x $@ && rm -f $0x.x $0x.cpp; exit
#endif
#ifndef MPI3_SHARED_COMMUNICATOR_HPP
#define MPI3_SHARED_COMMUNICATOR_HPP
#include ".... | {"hexsha": "287c377a7a2b398404977b5db7e54b744febd198", "size": 5728, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external_codes/mpi_wrapper/mpi3/shared_communicator.hpp", "max_stars_repo_name": "djstaros/qmcpack", "max_stars_repo_head_hexsha": "280f67e638bae280448b47fa618f05b848c530d2", "max_stars_repo_license... |
"""
Keep different states of pipeline using reposition and position.
If we add preprocessing and postprocessing to pipeline steps, we can play with state and capture specific
inputs and outputs as separate elements of the state. In this example the final state elements are:
- 0: initial dataframe with two columns (sub... | {"hexsha": "ca3c6b83c17a96c033200c8b1b619bb03bc322e3", "size": 3753, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/example_reposition.py", "max_stars_repo_name": "ftrojan/e2epipeline", "max_stars_repo_head_hexsha": "e337539010aa3128d021ebcb48a473c712b271b1", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
\documentclass[12pt,a4paper]{article}
\usepackage[]{graphicx}
\usepackage[]{color}
\usepackage{chngcntr}
\usepackage{pdfpages}
\usepackage{pdflscape}
\usepackage{subfig}
\usepackage[backend=biber]{biblatex}
\addbibresource{biblio_supmat.bib}
\usepackage[
colorlinks,
citecolor=blue,
urlcolor=cyan,
bookmarks=true,
hypert... | {"hexsha": "689a250619235aea976e3b3ab1435d6d52282a50", "size": 36426, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ms/appendix.tex", "max_stars_repo_name": "palmaraz/SaniVult", "max_stars_repo_head_hexsha": "4ad91b093c5b553c13a70a3c3946e9580f03ac33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
import torch
import torchvision
#import skimage.io as io
import numpy as np
import torchvision.transforms as t
import torch.nn as nn
import os
import matplotlib.pyplot as plt
import torchvision.models as model
#from sklearn.metrics import accuracy_score
torch.cuda.set_device(0)
#device=torch.device(#"cuda" i... | {"hexsha": "5312b6629308f46336fbdf54c9a5d501cd3126ac", "size": 12178, "ext": "py", "lang": "Python", "max_stars_repo_path": "DeepFeatureExtraction/EXTRACTOR_GoogLeNet.py", "max_stars_repo_name": "SohamChattopadhyayEE/DeepGA", "max_stars_repo_head_hexsha": "034e0b254244b00bc1bc6daaaeec610d5c73dd55", "max_stars_repo_lice... |
using HDF5
using JLD2
using Pkg
using SparseVertex
include("./triqs_conv_functions.jl")
file = ARGS[1]
outdir = ARGS[2]
# Gimp
gImp = triqs_read_gf(file, "G_imp")
# chi
chiupdo_mesh = h5read(file, "chi_updn_ph_imp/mesh")
mesh = triqs_build_freqGrid(chiupdo_mesh);
chiupdo_raw = h5read(file, "chi_updn_ph_imp/data")
c... | {"hexsha": "46e0e81fd0188c133ed4092850f302d3cb533fca", "size": 1584, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/triqs_conv.jl", "max_stars_repo_name": "Atomtomate/LadderDGA.jl", "max_stars_repo_head_hexsha": "8cd39fe2ae2aa1130bff706171266d3cf2d4c8e7", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# Copyright (c) 2003-2015 by Mike Jarvis
#
# TreeCorr is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of condi... | {"hexsha": "b22b2b71e2a6528482a735a0f37e83b4736e4ad2", "size": 28203, "ext": "py", "lang": "Python", "max_stars_repo_path": "treecorr/celestial.py", "max_stars_repo_name": "kstoreyf/TreeCorr", "max_stars_repo_head_hexsha": "f81b4b762c8672b9047ed045c300730cc2983eb0", "max_stars_repo_licenses": ["Python-2.0", "OLDAP-2.7"... |
@testset "Main" begin
@testset "Simulate with trace" begin
n = 3
ε = .1
reward = Distribution[Bernoulli(.5 + ((i == j) ? ε : 0.)) for i in 1:n, j in 1:n]
instance = UncorrelatedPerfectBipartiteMatching(reward, PerfectBipartiteMatchingMunkresSolver())
Random.seed!(1)
n_rounds = 2
s, t = si... | {"hexsha": "08a2ff674b08f44929172efe982c2104c9e0e442", "size": 867, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/main.jl", "max_stars_repo_name": "dourouc05/-CombinatorialBandits.jl", "max_stars_repo_head_hexsha": "05470f136ac8832e6f8afc2ee07705ac3f769271", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
//
// Boost.Process
// ~~~~~~~~~~~~~
//
// Copyright (c) 2006, 2007 Julio M. Merino Vidal
// Copyright (c) 2008 Ilya Sokolov, Boris Schaeling
// Copyright (c) 2009 Boris Schaeling
// Copyright (c) 2010 Felipe Tanus, Boris Schaeling
//
// Distributed under the Boost Software License, Version 1.0. (See accompany... | {"hexsha": "98e3c89ad9d9692502f2bdfd5d75a85166fbc8a6", "size": 625, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libraries/fc/vendor/boost_1.51/include/boost/process.hpp", "max_stars_repo_name": "techsharesteam/techshares", "max_stars_repo_head_hexsha": "47c58630a578204147057b7504e571e19546444f", "max_stars_rep... |
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2016-2018 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to d... | {"hexsha": "4db2863999400d13815344a9cbed90531de6e1af", "size": 21174, "ext": "py", "lang": "Python", "max_stars_repo_path": "silx/gui/data/DataViewer.py", "max_stars_repo_name": "vallsv/silx", "max_stars_repo_head_hexsha": "834bfe9272af99096faa360e1ad96291bf46a2ac", "max_stars_repo_licenses": ["CC0-1.0", "MIT"], "max_s... |
import os
from tesserocr import PSM, PyTessBaseAPI
import cv2
import numpy as np
from PIL import Image
from typing import List, Optional
from constants import SIDES_DIR
from cv_helpers import contour_bounding_box_for_contour, extract_color, four_point_transform,\
get_center_for_contour, get_classifier_directories... | {"hexsha": "e001462526598e8938908ba58cb103101ba491c2", "size": 8516, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sides/serial_number_cv.py", "max_stars_repo_name": "FuegoFro/KeepTalkingBot", "max_stars_repo_head_hexsha": "c4a66750e253aae667f561a9863f163da0eeb68b", "max_stars_repo_licenses": ["MIT"], "max... |
-- Estudante: Lucas Emanuel Resck Domingues
-- Exercise 1
section
parameters {A : Type} {R : A → A → Prop}
parameter (irreflR : irreflexive R)
parameter (transR : transitive R)
local infix < := R
def R' (a b : A) : Prop := R a b ∨ a = b
local infix ≤ := R'
theorem reflR' (a : A) : a ≤ a... | {"author": "lucasresck", "repo": "Discrete-Mathematics", "sha": "0a08081c5f393e5765259d3f1253c3a6dd043dac", "save_path": "github-repos/lean/lucasresck-Discrete-Mathematics", "path": "github-repos/lean/lucasresck-Discrete-Mathematics/Discrete-Mathematics-0a08081c5f393e5765259d3f1253c3a6dd043dac/Lists of exercises/List 7... |
*DECK CARG
FUNCTION CARG (Z)
C***BEGIN PROLOGUE CARG
C***PURPOSE Compute the argument of a complex number.
C***LIBRARY SLATEC (FNLIB)
C***CATEGORY A4A
C***TYPE COMPLEX (CARG-C)
C***KEYWORDS ARGUMENT OF A COMPLEX NUMBER, ELEMENTARY FUNCTIONS, FNLIB
C***AUTHOR Fullerton, W., (LANL)
C***DESCRIPTION
C
C C... | {"hexsha": "f6e44aabc2242534dfcad53e33d0d6ef5618e49f", "size": 950, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "slatec/src/carg.f", "max_stars_repo_name": "andremirt/v_cond", "max_stars_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count":... |
import os
import requests
from string import Template
import json
import boto3
import uuid
import shutil
import sys
from PIL import Image
from skimage import feature
from skimage.filters import gaussian
from fil_finder import FilFinder2D
from astropy import units as u
import numpy as np
import networkx as nx
# https:/... | {"hexsha": "0fcb5344529f25b2eb5b1baf9130692de297d4e9", "size": 5102, "ext": "py", "lang": "Python", "max_stars_repo_path": "ingest-lamdba/src/lambda.py", "max_stars_repo_name": "Ricool06/TheGlowGetters", "max_stars_repo_head_hexsha": "e0f22073ec51671f0eb6f8ab49472f7981978039", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
def cls_type_to_id(cls_type):
type_to_id = {'Car': 1, 'Pedestrian': 2, 'Cyclist': 3, 'Van': 4}
if cls_type not in type_to_id.keys():
return -1
return type_to_id[cls_type]
class Object3d(object):
def __init__(self, line, gt=False): # if read from ground truth label, the txt f... | {"hexsha": "5eb0dd8f0b9476ec0a596f5ec7af8ea258374b82", "size": 2398, "ext": "py", "lang": "Python", "max_stars_repo_path": "pcdet/utils/object3d_jrdb.py", "max_stars_repo_name": "brudermueller/OpenPCDet", "max_stars_repo_head_hexsha": "dc6e16abb03363e5b307225e4c02297c231d56da", "max_stars_repo_licenses": ["Apache-2.0"]... |
import numpy as np
import math
from ..miniworld import MiniWorldEnv, Room
from ..entity import Box, ImageFrame
from gym import spaces
class Hallway(MiniWorldEnv):
"""
Environment in which the goal is to go to a red box
at the end of a hallway
"""
def __init__(self, length=10, stochastic=False, den... | {"hexsha": "66c9a4391f84429e26357fcf0b7b5a1921a4a4b2", "size": 3221, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_miniworld/envs/hallway.py", "max_stars_repo_name": "PrieureDeSion/gym-miniworld", "max_stars_repo_head_hexsha": "896509fe59c18650ed8483e4df3394f098f07c3c", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
class Dataset(object):
def __init__(self, X, Y, T, n_candidate, n_safety, n_test, seed=None, include_T=False, include_intercept=True, standardize=False):
n_train = n_candidate + n_saf... | {"hexsha": "7778588d8cae9e8d700aada4a53c61caf0b1d776", "size": 11707, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/datasets/dataset.py", "max_stars_repo_name": "sgiguere/RobinHood-NeurIPS-2019-", "max_stars_repo_head_hexsha": "4bc3283b1cba13b1addf07f3fccf667f4c8f4a08", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/python3
import matplotlib.pyplot as plt
import numpy as np
import plawt
# Simple data to display in various forms
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
plt.close('all')
f, axarr = plt.subplots(2, 2)
f.suptitle('Matplotlib: Grid of subplots')
axarr[0, 0].plot(x, y)
axarr[0, 0].set_title('Ax... | {"hexsha": "337c43ab08a86e08bf7dcca140778c381449db75", "size": 1228, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/subplotgridtest.py", "max_stars_repo_name": "mef51/plawt", "max_stars_repo_head_hexsha": "7d5dbcd64d97499eaf7896d2f6e50826d54d2e6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
\section{Development}
When trying to follow a traditional software engineering approach to in
Haskell, one soon runs into several dead ends: due to the different paradigm
and style, trying to apply some methods feels forced or unnatural.
Traditionally, in Haskell the approach when formalizing a piece of code just
invo... | {"hexsha": "fd8ad20b5db79530fd413c7e80afaf406137fd0c", "size": 66787, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "thesis/4-development.tex", "max_stars_repo_name": "DiegoVicen/bachelor-thesis", "max_stars_repo_head_hexsha": "feb1657ef4082402434d5e6519ec57eac85ac7a6", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python3
# coding: utf-8
import argparse
SD_FACTOR1 = 2.5
SD_FACTOR2 = 4
def main(args):
import os
import numpy as np
import allel
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def read_vcfs(vcf_list, fields):
callset = {f: np.array([],dtype='float... | {"hexsha": "d9e235b01dce536bc14fa34e7c4794e09dfa533d", "size": 4696, "ext": "py", "lang": "Python", "max_stars_repo_path": "workflow/scripts/estimate_parental_filtering_params.py", "max_stars_repo_name": "ibebio/vc-gatk4-snakemake", "max_stars_repo_head_hexsha": "154074c72c847fb251cf5f8fd878b42053c07c92", "max_stars_re... |
import torch
from torch.optim import Optimizer
from typing import Callable, Union, List
import numpy as np
import matplotlib.pyplot as plt
__all__ = ['get_lr', 'change_lr', 'plot_schedule', 'save_optimizer', 'load_optimizer']
def get_lr(optim: Optimizer) -> List[float]:
return [param_group['lr'] for param_group... | {"hexsha": "04009bf2a126efedb10850acff4f499e18c2d40f", "size": 1794, "ext": "py", "lang": "Python", "max_stars_repo_path": "nntoolbox/optim/utils.py", "max_stars_repo_name": "nhatsmrt/nn-toolbox", "max_stars_repo_head_hexsha": "689b9924d3c88a433f8f350b89c13a878ac7d7c3", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# Para visualizar gráficos en la terminal interactiva
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'ipympl')
"""
# Actividad en clases: Series de Fourier
Objetivos:
- Componer señales periodicas en base a sinusoides
- Visualizar señales con matplotlib
- Escuchar las señales con IPython... | {"hexsha": "a93385068d32710faa1ef462b518bf1eaac50ed5", "size": 1890, "ext": "py", "lang": "Python", "max_stars_repo_path": "class-activities/unit1/sinewaves.py", "max_stars_repo_name": "phuijse/UACH-INFO183", "max_stars_repo_head_hexsha": "0e1b6bef0bd80cda2753bd11e62016268f2de638", "max_stars_repo_licenses": ["MIT"], "... |
"""Basic definitions for the transforms module."""
import numpy as np
import torch
from torch import nn
import nflows.utils.typechecks as check
class InverseNotAvailable(Exception):
"""Exception to be thrown when a transform does not have an inverse."""
pass
class InputOutsideDomain(Exception):
"""Ex... | {"hexsha": "860b3494d05c106795e3d0b83d66052581c998ca", "size": 8253, "ext": "py", "lang": "Python", "max_stars_repo_path": "nflows/transforms/base.py", "max_stars_repo_name": "mshakerinava/nflows", "max_stars_repo_head_hexsha": "d86cb1478ff36ffd3e005e980d92a3b0bbffbf02", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma infer_v_pair2I:
fixes v\<^sub>1::v and v\<^sub>2::v
assumes "\<Theta>; \<B>; \<Gamma> \<turnstile> v\<^sub>1 \<Rightarrow> \<tau>\<^sub>1" and "\<Theta>; \<B>; \<Gamma> \<turnstile> v\<^sub>2 \<Rightarrow> \<tau>\<^sub>2"
shows "\<exists>\<tau>. \<Theta>; \<B>; \<Gamma> \<turnstile> V_pair v\<... | {"llama_tokens": 6191, "file": "MiniSail_TypingL", "length": 25} |
[STATEMENT]
lemma fbd_ifbd_inv2_iff: "((bd\<^sub>\<F> \<circ> bd\<^sup>-\<^sub>\<F>) \<phi> = \<phi>) = (Sup_pres \<phi>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((bd\<^sub>\<F> \<circ> bd\<^sup>-\<^sub>\<F>) \<phi> = \<phi>) = Sup_pres \<phi>
[PROOF STEP]
using fbd_ifbd_inv2 fbd_ifbd_inv2_inv
[PROOF STATE]
... | {"llama_tokens": 306, "file": "Transformer_Semantics_Kleisli_Transformers", "length": 2} |
from vg.compat import v1 as vg
def find_rigid_transform(a, b, compute_scale=False, fail_in_degenerate_cases=True):
"""
Args:
a: a Nx3 array of vertex locations
b: a Nx3 array of vertex locations
a and b are in correspondence -- we find a transformation such that the first
point... | {"hexsha": "81bf573d53978fb85b030717097383f3ec4aafb9", "size": 3082, "ext": "py", "lang": "Python", "max_stars_repo_path": "entente/rigid_transform.py", "max_stars_repo_name": "metabolize/entente", "max_stars_repo_head_hexsha": "c1b16bb7c7fb83b31db4e8ddaf65f1504374fe7a", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import unittest
import numpy as np
try:
import bokeh
from openmdao.visualization.meta_model_viewer.meta_model_visualization import MetaModelVisualization
except ImportError:
bokeh = None
import openmdao.api as om
@unittest.skipUnless(bokeh, "Bokeh is required")
class UnstructuredMetaModelCompTests(unitt... | {"hexsha": "55b1f90f88a32f8fd39727176d800bcf076dccc7", "size": 6696, "ext": "py", "lang": "Python", "max_stars_repo_path": "openmdao/visualization/meta_model_viewer/tests/test_unstruct.py", "max_stars_repo_name": "bollwyvl/OpenMDAO", "max_stars_repo_head_hexsha": "4d7a31b2bb39674e2be0d6a13cbe22de3f5353af", "max_stars_r... |
# PART 5 – ASSESSMENT OF LOCAL METAL LOSS
# Determine Asessment Applicability
#Determine the assessment applicability
# @doc DesignCodeCriteria
# @doc MaterialToughness
# @doc CyclicService
# @doc Part5ComponentType
print("Begin -- Assessment Applicability and Component Type Checks\n")
creep_range = CreepRangeTemperat... | {"hexsha": "297229c5f0a513de7307302825811ccabd213fd8", "size": 10184, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/part5_local_metal_loss_assessment.jl", "max_stars_repo_name": "JuliaTagBot/FitnessForService.jl", "max_stars_repo_head_hexsha": "530fa8c4764967275220561bcf525291c88d7cd8", "max_stars_repo_lice... |
#!/usr/bin/env python3
#
# Author: Yipeng Sun
# License: BSD 2-clause
# Last Change: Thu Jul 29, 2021 at 02:36 AM +0200
import numpy as np
from .io import read_branches
# Find total number of events (unique events) out of total number of candidates.
def extract_uid(ntp, tree, run_branch='runNumber', event_branch='e... | {"hexsha": "25c33303e20e088f5f750d531d12b81bc3065476", "size": 2000, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyTuplingUtils/utils.py", "max_stars_repo_name": "umd-lhcb/pyTuplingUtils", "max_stars_repo_head_hexsha": "dd2efe154f1418a70295eabd8919e16ace2785cc", "max_stars_repo_licenses": ["BSD-2-Clause"], "... |
import os
import random
import cv2
import torch
import numpy as np
from torch.utils.data import Dataset
from PIL import Image, ImageFile
def read_image(img_path):
"""Keep reading image until succeed.
This can avoid IOError incurred by heavy IO process."""
got_img = False
if not os.path.... | {"hexsha": "8e64a17c9a76a4b508c43bbea9a22899e51c662a", "size": 1529, "ext": "py", "lang": "Python", "max_stars_repo_path": "addition_module/DMUE/pretrain/utils/dataset.py", "max_stars_repo_name": "weihaoxie/FaceX-Zoo", "max_stars_repo_head_hexsha": "db0b087e4f4d28152e172d6c8d3767a8870733b4", "max_stars_repo_licenses": ... |
import group_theory.subgroup data.equiv.basic data.fintype algebra.big_operators
open equiv
variables {α : Type*}
def is_transposition (f : perm α) : Prop :=
∃ x y, f x = y ∧ f y = x ∧ ∀ a, a ≠ x → a ≠ y → f a = a
lemma is_transposition_inv {f : perm α} : is_transposition f →
is_transposition (f⁻¹) :=
λ ⟨x, y, h⟩... | {"author": "ChrisHughes24", "repo": "leanstuff", "sha": "9efa85f72efaccd1d540385952a6acc18fce8687", "save_path": "github-repos/lean/ChrisHughes24-leanstuff", "path": "github-repos/lean/ChrisHughes24-leanstuff/leanstuff-9efa85f72efaccd1d540385952a6acc18fce8687/transpostions.lean"} |
[STATEMENT]
lemma continuous_on_swap_args:
assumes "continuous_on (A\<times>B) (\<lambda>(x,y). d x y)"
shows "continuous_on (B\<times>A) (\<lambda>(x,y). d y x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. continuous_on (B \<times> A) (\<lambda>(x, y). d y x)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state... | {"llama_tokens": 590, "file": null, "length": 7} |
from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer
from transformers import AutoTokenizer, MBartTokenizer
from src.envs import build_env
import torch.nn.functional as F
import datasets
import random
import pandas as pd
from datasets import Dataset
import tor... | {"hexsha": "94139dc9d92526188c959b6d9d100c21e828b6ff", "size": 4371, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer.py", "max_stars_repo_name": "softsys4ai/differentiable-proving", "max_stars_repo_head_hexsha": "ed9b0c1a2803a3d2f75b60b78ec864c6e57fb8c4", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
"""customer-conversion-prediction.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1fwoTRHqz3_T-_RekFYT9qNkeqasMEBQ7
**Predict Customer Conversion (Churn) with Machine Learning**
Importing necessary libraries
"""
import n... | {"hexsha": "ef8cf21938d283f4685b5e2b87cec041a3a25187", "size": 3178, "ext": "py", "lang": "Python", "max_stars_repo_path": "customer-conversion-prediction/customer_conversion_prediction.py", "max_stars_repo_name": "rajgmishra/machine-learning-datasets", "max_stars_repo_head_hexsha": "26df1446dc6140d4ab19503c3108e192733... |
module LogSynth
using Counters, Markdown, Random
export SkipListDistribution, AliasTableDistribution
md"""
A `SkipListDistribution` provides an implementation of a multinomial
distribution that has ``O(log(n))`` sample time, but which allows the
underlying probability for any element to be adjusted in ``O(log(n))`` ... | {"hexsha": "031ebe69d13aabceed57db1dffd722daae009d71", "size": 7024, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LogSynth.jl", "max_stars_repo_name": "tdunning/LogSynth.jl", "max_stars_repo_head_hexsha": "f585182b5aa16230fecabdbd50ad77776bfe24a2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
SUBROUTINE HC_FVLD ( advise, nfcst, flat, flon, fstype, f34kt,
+ f50kt, f64kt, iret)
C************************************************************************
C* HC_FVLD *
C* *
C* This subroutine finds the forecasted latitudes and longitudes, and *
C* the 34, 50 and the 64 knot or 100 kt ... | {"hexsha": "f71abcec4e8d10c92d1ce9a221c804e910017b28", "size": 8278, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/bridge/hc/hcfvld.f", "max_stars_repo_name": "sgdecker/gempak", "max_stars_repo_head_hexsha": "92f9a3a8ee667ec49a9082f44380e27f61ca716b", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
# import json
# import os
# import cv2
# import numpy as np
# from tqdm import tqdm
# from pycocotools import mask as maskUtils
# parent_path = '/data/zequn/datasets/coco/val2017'
# json_file = '/data/zequn/datasets/coco/annotations/instances_val2017.json'
# with open(json_file) as anno_:
# annotations = json.load... | {"hexsha": "397b6234bb915fc75f7c2d9c6af48b824a9eaed4", "size": 2280, "ext": "py", "lang": "Python", "max_stars_repo_path": "mmdetection/vis_coco.py", "max_stars_repo_name": "InukKang/Pedestron", "max_stars_repo_head_hexsha": "b592292389f313907c18b38cc9066c3b6f8ad5a4", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "... |
# coding=utf-8
import pandas as pd
import numpy as np
from config.neighborhoods import Neighborhoods
dataFrame = pd.read_csv('data/uber_map.csv')
def rgb(minimum, maximum, value):
minimum, maximum = float(minimum), float(maximum)
r = 255
ratio = (value-minimum)/(maximum - minimum)
bg = 20 + int(max(0,... | {"hexsha": "b4e811ffe9f4f29f5fc937c5f59f7964a28500ec", "size": 929, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/calculate_avgs.py", "max_stars_repo_name": "vandrefonseca/Copia-Uber-Natal", "max_stars_repo_head_hexsha": "09c453d24bc2d817f18508b217875a8351cdc967", "max_stars_repo_licenses": ["Apache-2.0"],... |
struct DeepEnsemble
models::Array
DeepEnsemble(generator, N::Int) = new([generator() for _=1:N])
end
Flux.@functor DeepEnsemble
Flux.trainable(m::DeepEnsemble) = (Flux.trainable(model) for model in m.models)
# Get the mean and variance estimate from each network individually
function individual_forw... | {"hexsha": "a1bf42c1548edcdb00169566e7c698887aab939d", "size": 2381, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/extras/deep_ensembles.jl", "max_stars_repo_name": "ancorso/Shard.jl", "max_stars_repo_head_hexsha": "77b3b891494ca7f12113d86854641404ac961ad6", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
abstract type Grid{d} end
#
# # backward backward compatibility
nodes(::Type{<:Union{ListOfPoints,ListOfPoints{d}}}, grid::Grid{d}) where d = nodes(grid)
nodes(::Type{<:Matrix}, grid::Grid) = copy(from_LOP(nodes(grid)))
node(::Type{<:Union{Point,Point{d}}}, grid::Grid{d}, i::Int) where d = node(grid,i)
node(::Type{<:... | {"hexsha": "88c5d179a68e2c8fdb2eeaccc5571864f8860174", "size": 5969, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/numeric/grids.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Dolo.jl-9d24351c-2990-5e1b-a277-04c4b809c898", "max_stars_repo_head_hexsha": "f507e1b812d8980d87ccac33bfd56677c8d70ec0"... |
'''
@lanhuage: python
@Descripttion:
@version: beta
@Author: xiaoshuyui
@Date: 2020-05-07 08:55:29
@LastEditors: xiaoshuyui
@LastEditTime: 2020-05-07 10:58:40
'''
'''
this file define a class to save the result of the mask of parse
the mask will be save as a gray image using different color to represent different
obje... | {"hexsha": "71d489e54ff6cb3389fc121604a53c731f2d8bd2", "size": 2223, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/saveMaskImage.py", "max_stars_repo_name": "guchengxi1994/LabelImgTool", "max_stars_repo_head_hexsha": "e7595f6758a3756379c8a1534e778bb57b71730f", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma borel_measurable_ereal_prod[measurable (raw)]:
fixes f :: "'c \<Rightarrow> 'a \<Rightarrow> ereal"
assumes "\<And>i. i \<in> S \<Longrightarrow> f i \<in> borel_measurable M"
shows "(\<lambda>x. \<Prod>i\<in>S. f i x) \<in> borel_measurable M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (... | {"llama_tokens": 277, "file": null, "length": 2} |
#!/usr/bin/env python
"""Extract subcatchment and routing information from SWMM input file to GIS.
Reads subcatchment geometries and routing from a SWMM input (.inp) file and
saves them as shapefiles into the same folder as the SWMM input file.
Copyright (C) 2018 Tero Niemi, Aalto University School of Engineering
TO... | {"hexsha": "dd2585f0fe32a6a8fa0a7fbcba7dc2ea9f07c843", "size": 13702, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/inp2gis.py", "max_stars_repo_name": "AaltoUAW/GisToSWMM5", "max_stars_repo_head_hexsha": "b3435006e084a14f6f0f325c0962a4c2a5213559", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
% ClientServerProtocol.tex: Sedna Client/Server Protocol
% Copyright (C) 2010 ISP RAS
% The Institute for System Programming of the Russian Academy of Sciences
\documentclass[a4paper,12pt]{article}
\usepackage{alltt} % Like verbatim but supports commands inside
\usepackage{theorem}
\newtheorem{note}{Note} ... | {"hexsha": "d49460c442762d4e470bebea89b85586f69a81c6", "size": 30119, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/ClientServerProtocol/ClientServerProtocol.tex", "max_stars_repo_name": "TonnyRed/sedna", "max_stars_repo_head_hexsha": "06ff5a13a16f2d820d3cf0ce579df23f03a59eda", "max_stars_repo_licenses": ["E... |
using CircuitscapeUI
using Circuitscape
using Distributed
w = run_ui()
oldpwd = pwd()
cd(CircuitscapeUI.TESTPATH)
Circuitscape.runtests()
cd(oldpwd)
| {"hexsha": "788d222ecedf3635d0d8645a01993a40dd000222", "size": 149, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "build/snoop.jl", "max_stars_repo_name": "JuliaTagBot/CircuitscapeUI.jl", "max_stars_repo_head_hexsha": "87f392d993d95ce787d84460c96ebe22c9499a3a", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright (C) 2019-2022, François-Guillaume Fernandez.
# This program is licensed under the Apache License version 2.
# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.
from typing import Callable, Dict, List
import numpy as np
import torch
import torch.nn as nn
from... | {"hexsha": "629c1098b4b921842afbea6f318d4e012a4b1f53", "size": 5715, "ext": "py", "lang": "Python", "max_stars_repo_path": "holocron/nn/modules/downsample.py", "max_stars_repo_name": "frgfm/torch-zoo", "max_stars_repo_head_hexsha": "c97beacf3d49eaa34398abf47f378ea6b48a70f3", "max_stars_repo_licenses": ["Apache-2.0"], "... |
[STATEMENT]
lemma "\<FF> \<F> \<Longrightarrow> ci"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<FF> \<F> \<Longrightarrow> \<forall>P. contains (\<bullet>P) (\<^bold>\<not> ((\<^bold>\<and>) P\<^sup>c (\<^bold>\<not> P)))
[PROOF STEP]
nitpick
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<FF> \<F> \<Longri... | {"llama_tokens": 181, "file": "Topological_Semantics_ex_LFIs", "length": 2} |
# ---
# jupyter:
# jupytext:
# formats: ipynb,.pct.py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.3.3
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
... | {"hexsha": "e8338c1efebee55c77bff5ef6061c9f7e79772e8", "size": 5502, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/source/notebooks/tailor/kernel_design.pct.py", "max_stars_repo_name": "christabella/GPflow", "max_stars_repo_head_hexsha": "30824d289f8ee3f58d4249238c8b7267e6a0b2fc", "max_stars_repo_licenses"... |
#import libraries
import math
#import matplotlib
import numpy
#import sympy
import subprocess as sub
#import sys
import time
import keyboard
#define variables
res=""
exe=""
cmd=""
timsl=0.25
lcmd=""
#define redirections
cmred={"quit":"m0","basic":"m1","math":"m2","numpy":"m3","emulationstation":"m0","(":"m0",")":"m... | {"hexsha": "b2f009598f616ccf2fb8ce54b79c6785ba40e9b6", "size": 11980, "ext": "py", "lang": "Python", "max_stars_repo_path": "Raspberry/Interface/main.old.py", "max_stars_repo_name": "miguiss27/Calculadora", "max_stars_repo_head_hexsha": "f4f488abd156b22082f425200291d9f27c6dff54", "max_stars_repo_licenses": ["MIT"], "ma... |
import argparse
import os
import numpy as np
from sklearn.model_selection import train_test_split
def read_sentence_data(gold_sent_fh):
gold_scores = [float(line.strip()) for line in open(gold_sent_fh, 'r')]
return gold_scores
def read_data(fname):
data = [line.strip() for line in open(fname, 'r')]
... | {"hexsha": "9d6793434f7aecd6c6904f1a84a8ed1f979d611b", "size": 2072, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/split_dev_set.py", "max_stars_repo_name": "deep-spin/explainable-qe-shared-task", "max_stars_repo_head_hexsha": "da517a9a76f6dc0c68113e2d6be830f5b57726a7", "max_stars_repo_licenses": ["MIT... |
import os
import tornado.web
import tornado.ioloop
import codecs
import math
import numpy as np
import os
import sys
import json
import torch
from torch.utils.data import DataLoader
from config import Config
from dataset.classification_dataset import ClassificationDataset
from dataset.collator import ClassificationC... | {"hexsha": "8716884d212ffb9de36800f52f3795d49682deb1", "size": 4177, "ext": "py", "lang": "Python", "max_stars_repo_path": "api_tornado.py", "max_stars_repo_name": "TechnologyInstitute/NeuralNLP-NeuralClassifier", "max_stars_repo_head_hexsha": "cd2c46c8d99dd8fa537206b5f4444a777c7b5d89", "max_stars_repo_licenses": ["Apa... |
[STATEMENT]
lemma Spy_see_shrK_D [dest!]:
"\<lbrakk>Key (shrK A) \<in> parts (knows Spy evs); evs \<in> otway\<rbrakk> \<Longrightarrow> A \<in> bad"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Key (shrK A) \<in> parts (knows Spy evs); evs \<in> otway\<rbrakk> \<Longrightarrow> A \<in> bad
[PROOF S... | {"llama_tokens": 159, "file": null, "length": 1} |
#!/usr/bin/env python
import argparse
import os
import os.path
import shutil
import cv2
import duckietown_utils
import numpy as np
import numpy.random
import rosbag
# Example usage: ./sample_images.py path_to_bag /scbb/camera_rectifier/image/compressed 500
if __name__ == '__main__':
parser = argparse.ArgumentPa... | {"hexsha": "d2d3f3b53ab6cf5e9146b5de3f99880774534f5e", "size": 1357, "ext": "py", "lang": "Python", "max_stars_repo_path": "catkin_ws/src/10-lane-control/anti_instagram/sandbox/annotation/sample_images.py", "max_stars_repo_name": "johnson880319/Software", "max_stars_repo_head_hexsha": "045894227f359e0a3a3ec5b7a53f8d1eb... |
import unittest
import numpy as np
import pandas as pd
import numpy.testing as np_testing
import pandas.testing as pd_testing
import os
import import_ipynb
from tensorflow import random
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from keras.preprocessing import image
class Test(uni... | {"hexsha": "679b3440d01906f8cfa6bb4714b2a981aa8293fb", "size": 1411, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter08/Activity8.01/Activity8.01_Unit_test.py", "max_stars_repo_name": "PacktWorkshops/Applied-Deep-Learning-with-Keras", "max_stars_repo_head_hexsha": "d1372a6109e2ee9434ae47df59440577566badaa... |
[STATEMENT]
lemma strong_supplementation: "\<not> P x y \<Longrightarrow> (\<exists> z. P z x \<and> \<not> O z y)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> P x y \<Longrightarrow> \<exists>z. P z x \<and> \<not> O z y
[PROOF STEP]
nitpick [expect = genuine]
[PROOF STATE]
proof (prove)
goal (1 subgoal):... | {"llama_tokens": 167, "file": "Mereology_GMM", "length": 2} |
\documentclass[letterpaper,10pt]{article}
\usepackage[margin=2cm]{geometry}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage[colorlinks]{hyperref}
\newcommand{\panhline}{\begin{center}\rule{\textwidth}{1pt}\end{center}}
\title{\textbf{LectureTitle}}
\author{Authors}
... | {"hexsha": "c25cec0ac92a09f03bcc3ae0d16df9704900e3be", "size": 565, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Notes/R3_MleMapVectorMatrixDifferentiation/document.tex", "max_stars_repo_name": "MengwenHe-CMU/17S_10701_MachineLearning", "max_stars_repo_head_hexsha": "613a3087a57a206b83d79855cec359e04cb440f7", "... |
import sys
import numpy as np
print("Make space between values.")
n = map(float, input("Reflactive Index>> ").split())
r = map(float, input("Curvature>> ").split())
d = map(float, input("Thickness>> ").split())
n = list(n)
n.insert(0, 1.0)
r = list(r)
d = list(d)
print(n)
all_matrix = []
def calculate_mat... | {"hexsha": "d5197d496b4ead9c85488cfee5b89548fc1d4274", "size": 1454, "ext": "py", "lang": "Python", "max_stars_repo_path": "matrix.py", "max_stars_repo_name": "ilikemap2/RayMatrix", "max_stars_repo_head_hexsha": "d894ed1b0bda85aad2c3b155cbcf2a277c30e8be", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
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