text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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(******************************************************************************
Mixed distributive laws distributive laws in bicategories
Monads in the bicategory of comonads are the same as mixed distributive laws
******************************************************************************)
Require Import UniM... | {"author": "UniMath", "repo": "UniMath", "sha": "7de5cc98a7f6718af63a429ea88d80411eca2977", "save_path": "github-repos/coq/UniMath-UniMath", "path": "github-repos/coq/UniMath-UniMath/UniMath-7de5cc98a7f6718af63a429ea88d80411eca2977/UniMath/Bicategories/Monads/MixedDistributiveLaws.v"} |
\documentclass[12pt]{article}
\usepackage[margin=1.1in]{geometry}
\input{../../syllabi/preamble}
\newcommand{\coursedept}{Math}
\newcommand{\coursenumber}{342W}
\newcommand{\coursenumbercrosslisted}{/ 650.03~}
\newcommand{\semester}{Spring}
\newcommand{\numcredits}{6}
\newcommand{\lectimeandloc}{Mon and Wed 5-6:50PM ... | {"hexsha": "0b5102bc4a5bb0cb7a8d9a8bf17304412a87063f", "size": 11666, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "syllabus/syllabus.tex", "max_stars_repo_name": "jakemanthebakeman/QC_MATH_342W_Spring_2021", "max_stars_repo_head_hexsha": "494c3dd2df6fb7647d0de5cd271eb85527ef0192", "max_stars_repo_licenses": ["M... |
% Copyright (c) 2019, Betsalel (Saul) Williamson, Jordan Henderson (the Authors)
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
% * Redistributions of source code must retain the above copyri... | {"hexsha": "ad84b046f1fa088ed888c9cb0f989effce2b22f4", "size": 2912, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "latex-template/examples/listings.tex", "max_stars_repo_name": "betsalel-williamson/source-2-pdf", "max_stars_repo_head_hexsha": "e2087df98814a83f1b0810dd7e809b9c2dc47b98", "max_stars_repo_licenses":... |
## SCENARIO PARTITION
function GAPM(Ind_old, duals; α_val = 0.)
dualRange = maximum(duals)-minimum(duals)
MaxDiff = α_val*dualRange # Maximum difference between scenario duals
NP = maximum(Ind_old)
Scenarios = [duals Ind_old collect(1:N)]
Ind,NsubParts = ones(N), 1
for p in 1:NP
... | {"hexsha": "0cc7e23b6cf050de97bf335bfcadd66b1922b616", "size": 935, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/functions/GAPM.jl", "max_stars_repo_name": "gonzalez-alvaro/MultidimensionalGAPM", "max_stars_repo_head_hexsha": "605ca8f13feb189b5932ac72a77504e65bbd02c7", "max_stars_repo_licenses": ["MIT"], "... |
module MesoscaleML
"""
func(x)
Returns double the number `x` plus `1`.
"""
func(x) = 2x + 1
export func
end # module
| {"hexsha": "2c947af19558a17c75bcb2133cb4f8d3ec09320e", "size": 124, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MesoscaleML.jl", "max_stars_repo_name": "upiterbarg/MesoscaleML.jl", "max_stars_repo_head_hexsha": "5bb7f6d3ca431c63023efc5a9951f1cc16ad5f35", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy
import scipy.special
import csv
import datetime
import os
from pathlib import Path
# TODO Softmax Function for Output with cross entropy cost
'''
Neural network that can have n amount of layers and nodes per layer.
It can also have tanh, sigmoid or relu as an activation function for the hidden layers
and... | {"hexsha": "84aed9b4f414938067cf5f010300d6e454c39dff", "size": 7033, "ext": "py", "lang": "Python", "max_stars_repo_path": "library/neural_network.py", "max_stars_repo_name": "sunilson/Neural-Network-Backpropagation", "max_stars_repo_head_hexsha": "4aabbe22cb11ded7506d722c447f442862bd9494", "max_stars_repo_licenses": [... |
section \<open> Circus Trace Merge \<close>
theory utp_circus_traces
imports "UTP1-Stateful-Failures.utp_sf_rdes"
begin
subsection \<open> Function Definition \<close>
fun tr_par ::
"'\<theta> set \<Rightarrow> '\<theta> list \<Rightarrow> '\<theta> list \<Rightarrow> '\<theta> list set" where
"tr_par cs [] [] =... | {"author": "isabelle-utp", "repo": "utp-main", "sha": "27bdf3aee6d4fc00c8fe4d53283d0101857e0d41", "save_path": "github-repos/isabelle/isabelle-utp-utp-main", "path": "github-repos/isabelle/isabelle-utp-utp-main/utp-main-27bdf3aee6d4fc00c8fe4d53283d0101857e0d41/theories/circus/utp_circus_traces.thy"} |
from __future__ import print_function
from __future__ import division
import os
import gdal
import shutil
import numpy as np
def WriteRaster(InputArray, file_name, dimension):
# create the 3-band raster file
dst_ds = gdal.GetDriverByName('GTiff').Create(file_name, dimension, dimension, 13, gdal.GDT_Int16)
... | {"hexsha": "d42f6551dacbfd4e65c17fce193317b65a5548cc", "size": 3573, "ext": "py", "lang": "Python", "max_stars_repo_path": "labelling/get_all_images_from_large_images.py", "max_stars_repo_name": "annusgit/forestcoverUnet", "max_stars_repo_head_hexsha": "8ba4eafc6e5d637d3b08fa20d029e25173f96074", "max_stars_repo_license... |
#!/usr/bin/env python
# dp2mr.py
import numpy as num
from dp2e import dp2e
from e2mr import e2mr
def dp2mr(p,t,dp,Tconvert=None):
"""w = dp2mr(p,t,dp,Tconvert)
compute water vapor mixing ratio (g/kg) given total
pressure p (mb), air temperature t (K), and dew point
temperature (K).
if input, Tconvert is used as ... | {"hexsha": "3f8af490e8abbde1ed80e6b1260b78ac1b9533b0", "size": 2038, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyuwphysret/common/pyfiles/atmos/dp2mr.py", "max_stars_repo_name": "graziano-giuliani/pythoncode", "max_stars_repo_head_hexsha": "4e505af5be3e32519cf4e62b85c101a63c885f77", "max_stars_repo_license... |
from time import time
import numpy as np
from pulp import LpMaximize, LpProblem, LpStatus, lpSum, LpVariable, LpMinimize
import copy
from verification.utils import massage_proj, LBFs_UBFs_onReLU, LBFs_UBFs_onSigmoid, solver_log_filename, lower_bound_from_logs, my_sigmoid, discretise_sigmoid_interval, get_solver
from m... | {"hexsha": "10be0758f25fe8ca578c1c52b38a4ea012472d99", "size": 10745, "ext": "py", "lang": "Python", "max_stars_repo_path": "verification/global_v.py", "max_stars_repo_name": "eliasbenussi/nn-cert-individual-fairness", "max_stars_repo_head_hexsha": "d5298902190caccb91c2762e0c9d96f98b1fbb84", "max_stars_repo_licenses": ... |
#!/usr/bin/env python3
# Copyright (c) 2021 CINN Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | {"hexsha": "93109edc6283e27182393dff33ac4078f331b10a", "size": 3222, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/tests/ops/test_slice_op.py", "max_stars_repo_name": "Avin0323/CINN", "max_stars_repo_head_hexsha": "093217619c821e73cec15511fa54cb0026ed0476", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
\begin{abstract}
\pagenumbering{roman}
\setcounter{page}{1}
\paragraph{}
Unstructured data like doc, pdf, accdb is lengthy to search and filter for desired information. We need to go through every file manually for finding information. It is very time consuming and frustrating. It doesn’t need to be done this way if w... | {"hexsha": "c1f9a7648749a2a4c39e5accee3d505b29b6b2a5", "size": 1352, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/assignments/final-report/abstract.tex", "max_stars_repo_name": "arks-api/arks-api", "max_stars_repo_head_hexsha": "692093762bfd855a5ad72f2b23cced34b6827baf", "max_stars_repo_licenses": ["Apache-... |
import argparse
import os
import sys
from collections import defaultdict
import numpy as np
from mir_eval.multipitch import evaluate as evaluate_frames
from mir_eval.transcription import precision_recall_f1_overlap as evaluate_notes
from mir_eval.transcription_velocity import precision_recall_f1_overlap as evaluate_no... | {"hexsha": "13f1049fef6a261e82a9e48d6ea777ac3e308784", "size": 7349, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluate.py", "max_stars_repo_name": "brianc118/onsets-and-frames", "max_stars_repo_head_hexsha": "40ae19f7c44454a7ebb7414761e718fcffa7d400", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from typing import List
import luigi
import sklearn
import gokart
import numpy as np
class CalculateWordEmbedding(gokart.TaskOnKart):
task_namespace = 'redshells.word_item_similarity'
word_task = gokart.TaskInstanceParameter()
word2item_task = gokart.TaskInstanceParameter()
item2embedding_task = gok... | {"hexsha": "46311fd2ab5452c0e1e89c28612aed86c74f4dc3", "size": 2136, "ext": "py", "lang": "Python", "max_stars_repo_path": "redshells/app/word_item_similarity/calculate_word_embedding.py", "max_stars_repo_name": "mski-iksm/redshells", "max_stars_repo_head_hexsha": "1e956fed9b000ea3f6ba1c96e25d5dd953025155", "max_stars_... |
from tkinter import *
from tkinter import filedialog
from PIL import ImageTk , Image # We need pillow to visualize image in tkinter in an easy way
import cv2
from ttkbootstrap import Style
from tkinter import ttk
import numpy as np
# 1- Color Tracking
def lower_upper(color_no):
# define range of blue c... | {"hexsha": "cab82ebaf7a2aa27e27a12fb78e4cae83333fc67", "size": 17867, "ext": "py", "lang": "Python", "max_stars_repo_path": "Computer-Vision/Lec-04/ImageControlPanel.py", "max_stars_repo_name": "ashme2/ElectroPi", "max_stars_repo_head_hexsha": "9c28c65613a744af8a3bd58e557536644728df30", "max_stars_repo_licenses": ["MIT... |
function [ candidates, scores ] = sample_bing_windows( im, num_samples)
%SAMPLE_BING_WINDOWS Will generate equaly distributed windows in space,
%following Bing sizes
% Bing uses 29 specific sizes, this method spread this sizes homogenously
% inside the image
scores = [];
im_wh = [size(im, 2), size(im, 1)];
origina... | {"author": "hosang", "repo": "detection-proposals", "sha": "858368afffde5ff4028020fcb1dd4381705ccbfb", "save_path": "github-repos/MATLAB/hosang-detection-proposals", "path": "github-repos/MATLAB/hosang-detection-proposals/detection-proposals-858368afffde5ff4028020fcb1dd4381705ccbfb/baselines/sample_bing_windows.m"} |
#!/usr/bin/env python
#_*_coding:utf-8_*_
import sys, os, re
import math
import numpy as np
pPath = re.sub(r'codes$', '', os.path.split(os.path.realpath(__file__))[0])
sys.path.append(pPath)
from codes import readFasta
def Sim(a, b):
blosum62 = [
[ 4, -1, -2, -2, 0, -1, -1, 0, -2, -1, -1, -1, -1, -2, -1, 1, 0,... | {"hexsha": "bbfccc41e43df612685bc25ab362133c35b2a5a1", "size": 4591, "ext": "py", "lang": "Python", "max_stars_repo_path": "profab/utils/feature_extraction_module/iFeature/codes/KNNpeptide.py", "max_stars_repo_name": "Sametle06/PROFAB", "max_stars_repo_head_hexsha": "571b691df2c5e98df0bfc4d6335f3ecd245314fd", "max_star... |
source("eaglesoft-caplan-functions.r")
## get triples
res <- get.caplan.data(limit="30")
## fill in missing column names
## res <- fill.missing.caplan.columns(res)
## ## dates in res are in form "YYYY-MM-DD^^http://www.w3.org/2001/XMLSchema#date"
## ## so I need to lop off the "^^http://www.w3.org/2001/XMLSchema#dat... | {"hexsha": "e9dcac9b848eb30059b4146e38e33d0c3998347a", "size": 962, "ext": "r", "lang": "R", "max_stars_repo_path": "src/tools/eaglesoft-query-all.r", "max_stars_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_stars_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_stars_repo_licen... |
###################################
# Routing module
###################################
"""
Route selector
Finds a route by using a choosen mode (fastest, shortest or based on Google Distances API) and returns intersections indeces for the route
**Arguments**
* `start_node` : unique start node id selected for an ag... | {"hexsha": "a7f396afcf77ac7836bc1f143b67722e43056cd1", "size": 6979, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/routing_module.jl", "max_stars_repo_name": "pszufe/OpenStreetMapXSim.jl", "max_stars_repo_head_hexsha": "65fcfe6d366a4cab6cfb6f1fd3a4b0c051145f66", "max_stars_repo_licenses": ["MIT"], "max_star... |
ccc By Trifon Trifonov trifon@hku.hk
ccc You can modify if as you want, but please
ccc do not distribute without permision.
ccc This is not a final version!!!
ccc The final version will be available in the Python RVMod lib
ccc Trifonov et al. (in prep).
implicit none
real*8 PI, twopi
... | {"hexsha": "66fc0500c701c814876a6a4a261e1c71d8ebacb1", "size": 40440, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "source/latest_f/kepfit_amoeba.f", "max_stars_repo_name": "Simske/exostriker", "max_stars_repo_head_hexsha": "587b0af4c9cadb46637a4ac61a5392a596e966b1", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python
# Copyright (c) 2015 Andrew J. Hesford. All rights reserved.
# Restrictions are listed in the LICENSE file distributed with this package.
import numpy as np, os, sys, pyfftw, getopt
from fwht import fwht
from collections import defaultdict, OrderedDict
from functools import partial, reduce
im... | {"hexsha": "166be7c15f66a7558f923a566a7a6173757a164c", "size": 19358, "ext": "py", "lang": "Python", "max_stars_repo_path": "shell/fhfft.py", "max_stars_repo_name": "ahesford/habis-tools", "max_stars_repo_head_hexsha": "82f82b99fa18452697404100edcf83bd03d35abc", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
# Regular Python Libraries
import cv2, os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # get rid of any TF warning messages
import numpy as np
from PIL import Image
# Python GUI
import PySimpleGUI as sg
# Model Libraries
import tensorflow as tf
# Multi Image Classifier Library
from Multi_Classification.Multi_Image_Class... | {"hexsha": "d963fd96da1e77a66dfd4d8e357703d68bcb7138", "size": 7012, "ext": "py", "lang": "Python", "max_stars_repo_path": "Application.py", "max_stars_repo_name": "KKanda900/UW-Pollution-Detector", "max_stars_repo_head_hexsha": "5caffb41a7f38bf003bd7e8c37b884fd30b645f7", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import matplotlib.pyplot as plt
import numpy.random as rnd
from matplotlib.patches import Ellipse
NUM = 250
ells = [Ellipse(xy=rnd.rand(2)*10, width=rnd.rand(), height=rnd.rand(), angle=rnd.rand()*360)
for i in range(NUM)]
fig = plt.figure(0)
ax = fig.add_subplot(111, aspect='equal')
for e in ells:
ax.ad... | {"hexsha": "d77e06fa86733c3b6164292916851025cf9ee6e3", "size": 472, "ext": "py", "lang": "Python", "max_stars_repo_path": "matplotlib_examples/examples_src/pylab_examples/ellipse_demo.py", "max_stars_repo_name": "xzlmark/webspider", "max_stars_repo_head_hexsha": "133c620c65aa45abea1718b0dada09618c2115bf", "max_stars_re... |
# usage:
# num_files h5_filenames updates
import numpy as np
import h5py
import sys
import os
from tqdm import tqdm
import pandas as pd
from keyname import keyname as kn
from fileshash import fileshash as fsh
import re
from collections import Counter, defaultdict
from joblib import delayed, Parallel
import json
num_f... | {"hexsha": "9227ceffb9735d22693de8acb11db9f33cb70199", "size": 2597, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/script/FractionResevoirPrep.py", "max_stars_repo_name": "schregardusc/dishtiny", "max_stars_repo_head_hexsha": "b0b1841a457a955fa4c22f36a050d91f12484f9e", "max_stars_repo_licenses": ["MIT"], "... |
// Copyright (C) 2001 Jeremy Siek, Douglas Gregor, Brian Osman
//
// 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 BOOST_GRAPH_ISOMORPHISM_HPP
#define BOOST_GRAPH_ISOMORPHISM_HPP
#include <utility>
#inclu... | {"hexsha": "29f6ef2c58e72b859218ba0b71030a52fe507581", "size": 17641, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/lib/boost/graph/isomorphism.hpp", "max_stars_repo_name": "EricBoittier/vina-carb-docker", "max_stars_repo_head_hexsha": "e8730d1ef90395e3d7ed3ad00264702313b0766a", "max_stars_repo_licenses": ["... |
/-
Copyright (c) 2020 Scott Morrison. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Scott Morrison
! This file was ported from Lean 3 source module category_theory.limits.colimit_limit
! leanprover-community/mathlib commit 59382264386afdbaf1727e617f5fdda511992eb9
! P... | {"author": "leanprover-community", "repo": "mathlib4", "sha": "b9a0a30342ca06e9817e22dbe46e75fc7f435500", "save_path": "github-repos/lean/leanprover-community-mathlib4", "path": "github-repos/lean/leanprover-community-mathlib4/mathlib4-b9a0a30342ca06e9817e22dbe46e75fc7f435500/Mathlib/CategoryTheory/Limits/ColimitLimit.... |
[STATEMENT]
lemma [simp]: "\<forall>fs_opt. (fields P ty fs_opt) = (fields_f P ty = fs_opt)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>fs_opt. fields P ty fs_opt = (fields_f P ty = fs_opt)
[PROOF STEP]
apply(rule)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>fs_opt. fields P ty fs_opt = (fie... | {"llama_tokens": 3001, "file": "LightweightJava_Lightweight_Java_Equivalence", "length": 24} |
"""
losses.py
=======================
Some additional loss functions that can be called using the pipeline, some of which still to be implemented.
"""
import torch
import numpy as np
from typing import Iterable, List, Set, Tuple
# from typing import Any, Callable, TypeVar, Union
from torch import Tensor, einsum
impor... | {"hexsha": "70d680384069f2dd5baa693a41dd0ce5f2da3a47", "size": 13147, "ext": "py", "lang": "Python", "max_stars_repo_path": "pathflowai/losses.py", "max_stars_repo_name": "sumanthratna/PathFlowAI", "max_stars_repo_head_hexsha": "70324e78da7ad9452789478b9be7cc76515ea3ab", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import pandas as pd
import dask.dataframe as dd
import numpy as np
from itertools import combinations, permutations
from multiprocessing import Pool, cpu_count
def pairwise(df, operation, columns = None):
"""Form interactions between all pairs of numeric columns
Arguments:
df -- the DataFrame to run o... | {"hexsha": "5d514d7612a4aa5c9872c67a2a520164b786d19e", "size": 3739, "ext": "py", "lang": "Python", "max_stars_repo_path": "featurama/interactions/numeric.py", "max_stars_repo_name": "atomichighfive/featurama", "max_stars_repo_head_hexsha": "725481957fc56bd709bfa70e4892f8dbe6e48b0b", "max_stars_repo_licenses": ["MIT"],... |
import shutil
import unittest
import numpy as np
import discretize
from SimPEG import (
utils,
maps,
regularization,
data_misfit,
optimization,
inverse_problem,
directives,
inversion,
)
from SimPEG.potential_fields import gravity
np.random.seed(43)
class GravInvLinProblemTest(unittes... | {"hexsha": "5a53194faed066ab0110e6870f165d3fe8b8377c", "size": 4606, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pf/test_grav_inversion_linear.py", "max_stars_repo_name": "ElliotCheung/simpeg", "max_stars_repo_head_hexsha": "ce5bde154179ca63798a62a12787a7ec3535472c", "max_stars_repo_licenses": ["MIT"],... |
# Train a new network on a data set with train.py
# Basic usage: python train.py data_directory
# Prints out training loss, validation loss, and validation accuracy as the network trains
# Options:
# Set directory to save checkpoints: python train.py data_dir --save_dir save_directory
# Choose architecture: python tra... | {"hexsha": "0bc2ad2f07d2dc1257d3122b9c0e980251392bc5", "size": 8140, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "AlanACruz/aipnd-project", "max_stars_repo_head_hexsha": "e0d5dcb49865cced1a9e88f03adaf71f6d0bf1a6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#!/usr/bin/env python
# coding: utf-8
#%% global packages
import mesa.batchrunner as mb
import numpy as np
import networkx as nx
#import uuid
#import pandas as pd
from IPython.core.display import display
import matplotlib as mpl
#import matplotlib.figure as figure
mpl.rc('text', usetex = True)
mpl.rc('font', size = ... | {"hexsha": "97205eb9dbea4093de18794154e0423a55429b77", "size": 9845, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model/Janosik_grid2d_batch.py", "max_stars_repo_name": "jmiszczak/matthew_reduction_game", "max_stars_repo_head_hexsha": "377c699f4ee908f8f7b84eafaf6a749149c59b81", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma "\<forall>(x::'a::linordered_field) y. x \<noteq> y \<and> 5 * x \<le> y \<longrightarrow> 500 * x \<le> 100 * y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>x y. x \<noteq> y \<and> (5::'a) * x \<le> y \<longrightarrow> (500::'a) * x \<le> (100::'a) * y
[PROOF STEP]
by ferrack | {"llama_tokens": 146, "file": null, "length": 1} |
import numpy as np
# The Geometry and Polygon classes are adapted from
# https://github.com/Oktosha/DeepSDF-explained/blob/master/deepSDF-explained.ipynb
class Geometry(object):
EPS = 1e-12
@staticmethod
def distance_from_point_to_segment(a, b, p):
res = min(np.linalg.norm(a - p), np.linalg.norm(... | {"hexsha": "5abeb5b3cb91dbebbae327440a5eb92944aaa45b", "size": 1782, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/shape.py", "max_stars_repo_name": "mintpancake/2d-sdf-net", "max_stars_repo_head_hexsha": "6170f53f7eb5fc9fe84d1a164c615f9348115958", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4,... |
import flask
from flask import Flask, redirect, url_for, request, render_template
import tensorflow as tf
import os
from PIL import Image
import numpy as np
import base64
import io
import tensorflow_hub as hub
MODELS_PATH = './models/'
BASE_MODEL = 'SRWNNbase.h5'
srwnnModelPaht = MODELS_PATH + BASE_MODEL
denoise1Mo... | {"hexsha": "50195361cb08c4bf8fd1eeaaeefd4ee6c8fab97f", "size": 4881, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "Exusai/srwnn-generator-server", "max_stars_repo_head_hexsha": "2223a0ad8a7caf6b2283073402dda632a9bea547", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
using Gridap
using Gridap.Io
using GridapGmsh
model = GmshDiscreteModel("elasticFlag_coarse.msh")
writevtk(model,"elasticFlag_coarse")
fn = "elasticFlag_coarse.json"
to_json_file(model,fn)
| {"hexsha": "a3491586cc4dd1413d228bae88ead5aa44760fb9", "size": 192, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "models/elasticFlag_coarse.jl", "max_stars_repo_name": "gridapapps/GridapFSI.jl", "max_stars_repo_head_hexsha": "7924fccb46b7bbcd04715564559698aabf452190", "max_stars_repo_licenses": ["MIT"], "max_st... |
# -------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ----------------------------------------------------------------------... | {"hexsha": "654c9e5518d21bab19782db9bc1650ed69c69877", "size": 1415, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyStationB/libraries/StaticCharacterization/tests/test_integral.py", "max_stars_repo_name": "BrunoKM/station-b-libraries", "max_stars_repo_head_hexsha": "ea3591837e4a33f0bef789d905467754c27913b3",... |
#/opt/local/bin/python3
import sys, math, re, time, os
import numpy as np
import numpy.random as rand
import random
import hashlib
from copy import deepcopy
#helpful resources: https://www.youtube.com/c/learnmeabitcoin/videos
#Txn for transaction
#BlkChn for blockchain
#################################################... | {"hexsha": "bb58913ba987d29cd64a9c2ea2744314253dc281", "size": 11982, "ext": "py", "lang": "Python", "max_stars_repo_path": "bitCoinFuncs.py", "max_stars_repo_name": "Rabbitybunny/zMisc_bitCoinSim", "max_stars_repo_head_hexsha": "9ce7e9fded23778afe38a5fd00a2c13b602554a3", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python2.7
# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding: utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 fileencoding=utf-8
#
# Cadishi --- CAlculation of DIStance HIstograms
#
# Copyright (c) Klaus Reuter, Juergen Koefinger
# See the file AUTHORS.rst for the full list o... | {"hexsha": "b089b4188098edb376c63129ef708d27f8c793c4", "size": 1759, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/scripts/plot_hdf5_data.py", "max_stars_repo_name": "bio-phys/cadishi", "max_stars_repo_head_hexsha": "b44351fcb77737c6a6da5249a0c24ee8e34f72d2", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import cv2
import numpy as np
import matplotlib.pyplot as plt
cmap = plt.cm.viridis
def rgb2gray(rgb):
return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])
def convert_2d_to_3d(u, v, z, K):#将2d图像转到3d空间中
v0 = K[1][2]
u0 = K[0][2]
fy = K[1][1]
fx = K[0][0]
x = (u-u0)*z/fx
y = (v-v0)*z/fy
r... | {"hexsha": "c06c4e452f17a4642b2cc747e9fbbbbdccd4edfd", "size": 5344, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloaders/pose_estimator.py", "max_stars_repo_name": "Hansry/Semi-supervised-depth-estimation", "max_stars_repo_head_hexsha": "1e8e77d9074ce8e5e2471705d843627a0111b8e0", "max_stars_repo_licenses... |
import numpy as np
import numpy.ma as ma
def find_peak(field, comp=0, max_radius=None, min_radius=None):
"""Find the peak magnitude of a component in the field.
Args:
field ``GraspField``: The field to work on.
comp int: The field component to look at.
max_radius float: Ignore portion... | {"hexsha": "a628abc7a266cab97f4ec4fb372b994afedb76a7", "size": 3740, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/graspfile/analysis/grid.py", "max_stars_repo_name": "Spect4tor/python-graspfile", "max_stars_repo_head_hexsha": "b1d623c018edb5f27714b083e967d924527b7509", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma poly_compose_mult:
assumes "is_poly_tuple m fs"
assumes "length fs = n"
assumes "f \<in> carrier (Q\<^sub>p[\<X>\<^bsub>n\<^esub>])"
assumes "g \<in> carrier (Q\<^sub>p[\<X>\<^bsub>n\<^esub>])"
shows "Qp_poly_comp m fs (f \<otimes>\<^bsub>Q\<^sub>p[\<X>\<^bsub>n\<^esub>]\<^esub> g) = (Qp_pol... | {"llama_tokens": 1144, "file": "Padic_Field_Padic_Field_Powers", "length": 3} |
from utils.templates.func import dict_to_par
def template_class(name,message,default,className,estimator):
string = """
class {0}(object):
def __init__(self):
print("This is {1} Model")
self.default ={3}
def getClassName(self):
return "{2}"
def getLibraryName(self):
retu... | {"hexsha": "ad26a94064b81da518447dfd6c84e6f260096fa2", "size": 3785, "ext": "py", "lang": "Python", "max_stars_repo_path": "fatigue/DriverFatigueness/utils/templates/template_Gen.py", "max_stars_repo_name": "jkapila/paper-codebase", "max_stars_repo_head_hexsha": "35198a924b66299cab0bf405d4f5ab54ca504be9", "max_stars_re... |
import ast
import json
import numpy as np
from methods.utils import isSquared, progressiveSustitution, regresiveSustitution
def doolittle(A, b):
A = ast.literal_eval(A)
b = ast.literal_eval(b)
n = len(A[0])
A = np.array(A).astype(float)
b = np.array(b).astype(float)
U = np.zeros((n... | {"hexsha": "b7255811b806d48ca5f5524b02809a39edc70ca8", "size": 1989, "ext": "py", "lang": "Python", "max_stars_repo_path": "methods/doolittle.py", "max_stars_repo_name": "eechava6/NumericalAnalysis", "max_stars_repo_head_hexsha": "1b44349fe4c5e24413c3d5faeca7d227272814ec", "max_stars_repo_licenses": ["MIT"], "max_stars... |
subroutine edgeele(edge,mrng,neface,ne,bcel,n_bcel)
! Find the element index which containing part of edge and return as bcel
! edge is from 1 to 4 fro 2-D case
integer edge ! edge index what to find
integer mrng(neface,ne) ! boundary information
integer ne ! number of element
integer neface ! edge per element
integer... | {"hexsha": "dc2ba949d70680613e8a27028a8ca2db6e3df505", "size": 602, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "edgeele.f90", "max_stars_repo_name": "biofluids/IFEM-archive", "max_stars_repo_head_hexsha": "ed14a3ff980251ba98e519a64fb0549d4c991744", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
module TypeDB_tutorial
# Write your package code here.
end
| {"hexsha": "23c8e6f8c75ccef10a0b329f48b76dd0b15fd2e7", "size": 61, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TypeDB_tutorial.jl", "max_stars_repo_name": "FrankUrbach/TypeDB_tutorial", "max_stars_repo_head_hexsha": "51937c34a8d410861313368ed0d6a4569ef3c369", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
import numpy
import torch
class Metric:
def __init__(self, num_scales):
self.num_scales = num_scales
self.sum_metric = [0.0 for i in range(num_scales * 2)]
self.num_update = 0
self.multiply_factor = 10000
def update(self, loss_branch):
for i in... | {"hexsha": "a9b00606aafc9bd679b07d5ccd5e101b2c4707d9", "size": 1030, "ext": "py", "lang": "Python", "max_stars_repo_path": "face_detection/metric_farm/metric_default.py", "max_stars_repo_name": "CNN-NISER/lffd-pytorch", "max_stars_repo_head_hexsha": "7d6476ece79cf75c6265c89346ddac48929ce8f6", "max_stars_repo_licenses":... |
from __future__ import division
import cv2
import numpy as np
import rtmp
import analyzer
import drawer
import processor
##
# Drangonfly - Main video analyzer
# Takes a video and analyze it for features
# Edmund
##
capture = rtmp.captureVideo("rtmp://192.168.1.139:1935/live/edmund live=1 buffer=10")
oldFrame ... | {"hexsha": "5c613fba88c18bc8e796e7fc1234fa01ba4ecfe5", "size": 5020, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "edfungus/Text-Detection-from-RTMP-Stream", "max_stars_repo_head_hexsha": "f84a9185cbf2d49a24f372687b0960fe85a0e195", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import pandas as pd
from remodnav.clf import deg_per_pixel, EyegazeClassifier
from neurogaze.analyze import _get_screen_x_y
from neurogaze.gaze import SAMPLING_RATE
def longest_stretch(df, col='left_gaze_point_on_display_area_x'):
a = df[col].values
m = np.concatenate(([True], np.isnan(a),... | {"hexsha": "8342b56704decee50ff659a5450e8a5dfcbd1ed0", "size": 1249, "ext": "py", "lang": "Python", "max_stars_repo_path": "neurogaze/analyze/gaze_classification.py", "max_stars_repo_name": "chrizzzlybear/neurogaze_research", "max_stars_repo_head_hexsha": "6d22a1e54b9f941333a935db4795f014fa1efe26", "max_stars_repo_lice... |
import numpy as np
import pytest
from kiez.neighbors import HNSW, NNG, Annoy, SklearnNN
rng = np.random.RandomState(2)
@pytest.mark.parametrize("algo_cls", [HNSW, SklearnNN, NNG, Annoy])
def test_str_rep(algo_cls, n_samples=20, n_features=5):
source = rng.rand(n_samples, n_features)
algo = algo_cls()
ass... | {"hexsha": "d181a3df22c5539a6556b39c75d244e2cad56453", "size": 900, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/neighbors/test_base.py", "max_stars_repo_name": "cthoyt/kiez", "max_stars_repo_head_hexsha": "25f9f103ed51d4084e10f7ac532bb24183fe3894", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import argparse
import datetime
import glob
import os
import pickle
import numpy as np
import time
import sys
from loguru import logger
import torch
from torch import autograd
from utils.load_synth_data import process_loaded_sequences
from train_functions.train_sahp import make_model, train_eval_sahp
DEFAULT_BATCH_S... | {"hexsha": "5ca9e381bb38093fdfbd289d76938189c2a7056d", "size": 10193, "ext": "py", "lang": "Python", "max_stars_repo_path": "sahp/sahp_training/main_func.py", "max_stars_repo_name": "yangalan123/anhp-andtt", "max_stars_repo_head_hexsha": "b907f3808ed2ce1616edb1bc2229993a6742cee9", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python
# coding=utf-8
"""
Fine-tuning a 🤗 Transformers model on summarization.
"""
import argparse
import logging
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_metric
from torch.utils.data import DataLoader
from t... | {"hexsha": "1e64159fba23e7b1ba376892c80c2d6931654b5b", "size": 27050, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_summarization_no_trainer.py", "max_stars_repo_name": "mcao516/soft-q-learning-for-text-summarization", "max_stars_repo_head_hexsha": "b06050741de444490b2534fce24f31b7f5258e52", "max_stars_rep... |
# sys
import os
import sys
import numpy as np
import random
import pickle
# torch
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets, transforms
# visualization
import time
class Feeder(torch.utils.data.D... | {"hexsha": "3bfaf5f25f0b8555871ea914d8a48d1ec0a2530d", "size": 2699, "ext": "py", "lang": "Python", "max_stars_repo_path": "newapproach/feeder.py", "max_stars_repo_name": "chigur/pose", "max_stars_repo_head_hexsha": "3e8ecebbc24ea59a1cb217b15a9b2a1a1de09085", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""
"""
import unittest
import os.path
import numpy as np
import carribean
from carribean.points_grid import PointsGridGraph, four_points_connectivity, eight_points_connectivity
from carribean.carribean import get_best_island
class PointsGridGraphTest(unittest.TestCase):
"""
Simple test case for the Points... | {"hexsha": "4d7867aa73b1a8cdef1a21cda31b18c782f2d093", "size": 2245, "ext": "py", "lang": "Python", "max_stars_repo_path": "carribean_test.py", "max_stars_repo_name": "areshytko/interview-challenge-2", "max_stars_repo_head_hexsha": "495b999961401d1a50f5c8216f40f3c77342e21c", "max_stars_repo_licenses": ["Apache-2.0"], "... |
[STATEMENT]
lemma properties_loop:
assumes "mu \<le> i"
shows "seq (i + j * lambda) = seq i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. seq (i + j * lambda) = seq i
[PROOF STEP]
using P assms
[PROOF STATE]
proof (prove)
using this:
local.properties lambda mu
mu \<le> i
goal (1 subgoal):
1. seq (i + j * lam... | {"llama_tokens": 154, "file": "TortoiseHare_Basis", "length": 2} |
\documentclass[letterpaper]{article}
\usepackage{fullpage}
\usepackage{nopageno}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{tikz}
\usepackage[utf8]{luainputenc}
\usepackage{aeguill}
\usepackage{setspace}
\tikzstyle{edge} = [fill,opacity=.5,fill opacity=.5,line cap=round, line join=round, line width=50pt]
\... | {"hexsha": "b54fd5a1c425984f653b6458ba32ef63687f77b0", "size": 1629, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "graph/graph-notes-2015-03-09.tex", "max_stars_repo_name": "ylixir/school", "max_stars_repo_head_hexsha": "66d433f2090b6396c8dd2a53a733c25dbe7bc90f", "max_stars_repo_licenses": ["Unlicense"], "max_st... |
[STATEMENT]
lemma ta_union_der_disj_states:
assumes "\<Q> \<A> |\<inter>| \<Q> \<B> = {||}" and "q |\<in>| ta_der (ta_union \<A> \<B>) t"
shows "q |\<in>| ta_der \<A> t \<or> q |\<in>| ta_der \<B> t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. q |\<in>| ta_der \<A> t \<or> q |\<in>| ta_der \<B> t
[PROOF STEP]... | {"llama_tokens": 4560, "file": "Regular_Tree_Relations_Tree_Automata_Tree_Automata", "length": 20} |
import h5py
import numpy as np
import logging
from . import parse_as
from . import color
logger = logging.getLogger(__name__)
def report_default(key, value):
logger.info("Using default '{}': {}".format(key, value))
def apply_defaults(scene):
# Note: Only set defaults for options the user would expect to ha... | {"hexsha": "7568f6f4b5aab24b6c08935794030141eb66fff7", "size": 4785, "ext": "py", "lang": "Python", "max_stars_repo_path": "gwpv/scene_configuration/defaults.py", "max_stars_repo_name": "damibabayemi/gwpv", "max_stars_repo_head_hexsha": "e6705787fc2e25b72eaef2508357b1f0b9258581", "max_stars_repo_licenses": ["MIT"], "ma... |
from kapteyn import maputils
import numpy
from service import *
fignum = 37
fig = plt.figure(figsize=figsize)
frame = fig.add_axes((0.1,0.15,0.8,0.75))
title = 'WCS polyconic (PGSBOX fig.1)'
rot = 30.0 *numpy.pi/180.0
header = {'NAXIS' : 2, 'NAXIS1': 512, 'NAXIS2': 512,
'CTYPE1' : 'RA---PCO',
'PC1... | {"hexsha": "7b622f849f9f912e5b64c20fc75fe2dd11fdacda", "size": 1960, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/source/EXAMPLES/allskyf37.py", "max_stars_repo_name": "kapteyn-astro/kapteyn", "max_stars_repo_head_hexsha": "f12332cfd567c7c0da40628dcfc7b297971ee636", "max_stars_repo_licenses": ["BSD-3-Clau... |
include("attributes.jl")
function SMD1_leader(xu, xl)
r = floor(Int,length(xu)/2)
p = length(xu) - r
q = length(xl) - r
xu1 = xu[1:p]
xu2 = xu[p+1:p+r]
xl1 = xl[1:q]
xl2 = xl[q+1:q+r]
functionValue = sum((xu1).^2) + sum((xl1).^2) + sum((xu2).^2) + sum((xu2 - tan.(xl2)).^2)
##... | {"hexsha": "57b43845d08a2d577f2a0ec05bfd05617135f9bf", "size": 15164, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Bilevel/SMD/SMD.jl", "max_stars_repo_name": "jmejia8/HardTestProblems.jl", "max_stars_repo_head_hexsha": "cde9e6c654f046fc8b9f01a434f7b213a0fab182", "max_stars_repo_licenses": ["MIT"], "max_st... |
\documentclass[a4paper,11pt,leqno,fleqn]{artikel3}
\usepackage[dvips]{color}
%\definecolor{backgray}{gray}{0.925}
%\definecolor{verylightgray}{gray}{0.95}
\usepackage{fullpage, fancyvrb, amssymb, listings, url}
%\usepackage[breqn, inline]{emaxima}
%\usepackage[cmbase]{flexisym}
%% \usepackage{breqn}
%% \setkeys{breqn... | {"hexsha": "cc8f99e18627d0b27160af3ac328fbfca93c1151", "size": 28424, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "maxima/src/maxima/share/contrib/gf/gf_manual.tex", "max_stars_repo_name": "nilqed/spadlib", "max_stars_repo_head_hexsha": "d317f6abdeff4fedc24231a9a39c51c3121f3475", "max_stars_repo_licenses": ["MI... |
"""
ModelGrid.py
Author: Jordan Mirocha
Affiliation: University of Colorado at Boulder
Created on: Thu Dec 5 15:49:16 MST 2013
Description: For working with big model grids. Setting them up, running them,
and analyzing them.
"""
from __future__ import print_function
import os
import gc
import re
import copy
impor... | {"hexsha": "dbffb8b531b30f229661ca366ff0485594da535d", "size": 39476, "ext": "py", "lang": "Python", "max_stars_repo_path": "ares/inference/ModelGrid.py", "max_stars_repo_name": "mirochaj/ares", "max_stars_repo_head_hexsha": "b3335ad30435ee0d7f17d0110aa164a35f252d78", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
Created on Apr 10, 2014
@author: sstober
"""
import logging
import os
log = logging.getLogger(__name__)
import numpy as np
from pylearn2.utils.timing import log_timing
from deepthought3.experiments.ismir2014.util import load_config
from deepthought3.util.yaml_util import load_yaml_file, save_yaml_file
from de... | {"hexsha": "ad60934d6a1f55fd685585f3cf1018233ab25e49", "size": 1817, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepthought3/.experiments/ismir2014/train_convnet.py", "max_stars_repo_name": "chanhakim/deepthought", "max_stars_repo_head_hexsha": "9f5dd5c7a21da51b65d6049e7a19e29fc3a072f9", "max_stars_repo_lic... |
# coding: utf-8
"""Module for utility functions."""
from __future__ import (print_function, division, absolute_import,
unicode_literals)
import math
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
def fov_to_cell_size(fov, im_size):
... | {"hexsha": "f94747e0f320e6b1da222b15aadb4b91bc735c59", "size": 6766, "ext": "py", "lang": "Python", "max_stars_repo_path": "simple_w_imager/utils.py", "max_stars_repo_name": "OxfordSKA/simple_w_imager", "max_stars_repo_head_hexsha": "465fdb813f5b2662fdd20a410fc3ce739fec1d34", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
# <editor-fold desc="definition and handling of parameters"
# XXX defines struct for handling parameter data
"""
Type including data and additional information on parameters. Fields relate to what is provided in [Parameter list](@ref) and include:
* `name::Symbol`: name of the parameter
* `dim::Tuple`: potential dime... | {"hexsha": "15b72397db973b8a0465c99f42bb72a2f2ce33cb", "size": 20426, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/objects.jl", "max_stars_repo_name": "wookay/AnyMOD.jl", "max_stars_repo_head_hexsha": "14fdae26d6c8dd88001b2b5e4aadb468a3856b42", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# coding: utf-8
import pandas as pd
import numpy as np
import scipy
import scipy.sparse
import sklearn
import sklearn.svm
import sklearn.datasets
import sklearn.cross_validation
import warnings
warnings.filterwarnings('ignore')
X, y = sklearn.datasets.load_svmlight_file('data/news20.binary')
instance_ids = np.ar... | {"hexsha": "30faface74d25b023f5f24cde319c8de26252aa8", "size": 3680, "ext": "py", "lang": "Python", "max_stars_repo_path": "incremental_tsvm_news.py", "max_stars_repo_name": "CalculatedContent/tsvm", "max_stars_repo_head_hexsha": "0b59212c5dd682105dbb4be3be8a64832845cb01", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Test cases for functions in general_utils.py
-- kandasamy@cs.cmu.edu
"""
# pylint: disable=import-error
# pylint: disable=no-member
# pylint: disable=invalid-name
# pylint: disable=relative-import
import numpy as np
from utils import general_utils
from utils.base_test_class import BaseTestClass, execute_tests... | {"hexsha": "d3b8f76669994d04c782bea160e705706bebec73", "size": 5152, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/unittest_general_utils.py", "max_stars_repo_name": "lengjia/NAS_NPU", "max_stars_repo_head_hexsha": "600c05ed27c9b1ce63a5c1c4f1fc862d510cfcf0", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import cv2
import os
import numpy as np
import cPickle
CVCONTOUR_APPROX_LEVEL = 2
CVCLOSE_ITR = 1
def main():
mask = cv2.imread('/Users/asafvaladarsky/Documents/pic3.png', cv2.CV_LOAD_IMAGE_GRAYSCALE)
findConnectedComponents(mask)
def findConnectedComponents(mask,
poly1Hull0 = 1,
... | {"hexsha": "7f29fde3522f0e32eff87802469375619ffbd250", "size": 2844, "ext": "py", "lang": "Python", "max_stars_repo_path": "engine/ocr/Test2.py", "max_stars_repo_name": "hasadna/OpenPress", "max_stars_repo_head_hexsha": "7aa99ed92c6aef975f59c0295681f02211fc7ab5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import sys
sys.path.append('deepv2d')
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
import os
import time
import argparse
import glob
import tqdm
import vis
from core import config
from data_stream.scannet_twoview import ScanNet
from deepv2d import DeepV2D
import eval_utils
... | {"hexsha": "7d67fa4fa2f02bb061b6e28f40ec84eea4731cb9", "size": 4205, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/eval_scannet_twoview.py", "max_stars_repo_name": "TWJianNuo/Deepv2d", "max_stars_repo_head_hexsha": "e8d9658d974fac9734ebb0a9b9ad56dbfad5c8ee", "max_stars_repo_licenses": ["BSD-3-Clause... |
# -*- coding: utf-8 -*-
# The class DB allows the user to create a conection with the database
import calendar
import csv
import datetime as dt
import math
import os
import pickle
import re
import warnings
import dotenv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import psycopg2
import pym... | {"hexsha": "d261edd36c22f24da71ba06872fb1d9e82f66714", "size": 411, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/get_datasets.py", "max_stars_repo_name": "ilce-catbug/xbox-games-sales-analysis", "max_stars_repo_head_hexsha": "661a7194c73049e8a07ea3f96eafd9a09c236841", "max_stars_repo_licenses": ["MIT... |
#include <btcb/node/common.hpp>
#include <btcb/node/wallet.hpp>
#include <btcb/secure/blockstore.hpp>
#include <boost/polymorphic_cast.hpp>
btcb::summation_visitor::summation_visitor (btcb::transaction const & transaction_a, btcb::block_store & store_a) :
transaction (transaction_a),
store (store_a)
{
}
void btcb::s... | {"hexsha": "bb96606afda4d54b1d38411ebdcfdf95c51eda3b", "size": 7326, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "btcb/secure/blockstore.cpp", "max_stars_repo_name": "melnaquib/btcb", "max_stars_repo_head_hexsha": "f55c9867113d403118c3028d5ba11a0debcd7609", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
// Copyright Abel Sinkovics (abel@sinkovics.hu) 2015.
// 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)
#include <boost/metaparse/sequence_apply.hpp>
#include <boost/metaparse/is_error.hpp>
#inc... | {"hexsha": "179ba25c2230c46ec8c434cf2981776b06edc377", "size": 4330, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/metaparse/test/sequence_apply.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e188fe871f47c2... |
struct fixedIncome :> Income end | {"hexsha": "e62a9591267dbe6de0b8fe6cfe2ae9b1ab42a9a9", "size": 33, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Accounting/CrAccount/Capital/Income/fixedIncome/fixedIncome.jl", "max_stars_repo_name": "adamwillisXanax/accountingJulia", "max_stars_repo_head_hexsha": "59aea20f42c73ae2e740d3cd279828d5ad52e1fc"... |
from numpy import prod
from torch import einsum
from torch.nn import Conv1d, Conv2d, Conv3d
from torch.nn.grad import _grad_input_padding
from torch.nn.functional import conv1d, conv2d, conv3d
from torch.nn.functional import conv_transpose1d, conv_transpose2d, conv_transpose3d
from backpack.core.derivatives.basederiva... | {"hexsha": "0a261b880de5ba8cdf8aedee728c1d1da9c62bfe", "size": 6144, "ext": "py", "lang": "Python", "max_stars_repo_path": "backpack/core/derivatives/convnd.py", "max_stars_repo_name": "maryamhgf/backpack", "max_stars_repo_head_hexsha": "63d2717656df2e0f18b3b6ee50320e82ce7358b6", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import matplotlib.pyplot as plt
from skyfield.api import Loader, EarthSatellite, Topos
# We really just want the filedialog from tkinter
import tkinter as tk
from tkinter import filedialog
from datetime import datetime
from os import path
def _calculateGroundTrack(earth, satellite, timeset):
... | {"hexsha": "783a7630ce87a030398ce9cab297deb95f9560bf", "size": 1991, "ext": "py", "lang": "Python", "max_stars_repo_path": "TLEPlot.py", "max_stars_repo_name": "Uniliterally/TLEPlot", "max_stars_repo_head_hexsha": "4928eb7db2d5b7c9e37decb2f8f367c16dd02c01", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": n... |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import tensorflow as tf
import numpy as np
import copy
from abc import ABC, abstractmethod
from rl.core.function_approximators.normalizers.normalizer import Normalizer, NormalizerStd, NormalizerMax
from rl.core.utils.tf_util... | {"hexsha": "52612a3d8eb311eb7831cdac9543f1b065cc2453", "size": 4201, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl/core/function_approximators/normalizers/keras_normalizer.py", "max_stars_repo_name": "gtrll/librl", "max_stars_repo_head_hexsha": "39709c3e485e232865b3e08b7211cd9d871c666a", "max_stars_repo_lic... |
from typing import List, Dict, Any, Optional, Union
from ..core.schemas import ANNOTATION_SCHEMA, SEGMENTATION_SCHEMA
from abc import ABC, abstractmethod
import numpy as np
class Scene(ABC):
def __init__(self):
self.frames = Optional[Dict]
self.cameras = {}
self.lidars = {}
self.r... | {"hexsha": "0071510c39abf3ec0a3fbd0e9bedc19f781b89b6", "size": 1101, "ext": "py", "lang": "Python", "max_stars_repo_path": "frames/scene.py", "max_stars_repo_name": "jacobbieker/3dml", "max_stars_repo_head_hexsha": "f4b0e49343a18b4935c1502112e7bef0ff448986", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
\documentclass{article}
%%%%%% Include Packages %%%%%%
\usepackage{sectsty}
\usepackage{amsmath,amsfonts,amsthm,amssymb}
\usepackage{fancyhdr}
\usepackage{lastpage}
\usepackage{setspace}
\usepackage{graphicx}
%%%%%% Formatting Modifications %%%%%%
\usepackage[margin=2.5cm]{geometry} %% Set margins
\sectionfont{\sect... | {"hexsha": "01d7ed558e1a4951a8e3992c95eb466673883d99", "size": 2829, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Spring 2013/STAT W1211 - Statistics/Homework/Homework 5/Homework 5.tex", "max_stars_repo_name": "bendrucker/columbia", "max_stars_repo_head_hexsha": "0661e729fa0c7cb792fc31a2da77f2b44874d8a1", "max_... |
lemma one_add_le_self (x : mynat) : x ≤ 1 + x :=
begin
-- rw le_iff_exists_add,
use 1,
rw add_comm,
end
| {"author": "chanha-park", "repo": "naturalNumberGame", "sha": "4e0d7100ce4575e1add92feefa38b1250431b879", "save_path": "github-repos/lean/chanha-park-naturalNumberGame", "path": "github-repos/lean/chanha-park-naturalNumberGame/naturalNumberGame-4e0d7100ce4575e1add92feefa38b1250431b879/Inequality/1.lean"} |
#include "kmers/dirichlet-sampler.hpp"
#include "kmers/fasta-parser.hpp"
#include <Eigen/Dense>
#include <Eigen/Sparse>
#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <locale>
#include <stdexcept>
#include <vector>
int64_t num_kmers(int64_t K) {
int64_t y = 1;
for (int k = 0;... | {"hexsha": "2f312effe76bd1c99058700362795f92344dee7c", "size": 6152, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "kmers/src/builder.cpp", "max_stars_repo_name": "bob-carpenter/case-studies", "max_stars_repo_head_hexsha": "d9ac886989b08629f5fcedf6c9e06f3f1f1faff8", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
example (a b c : ℕ) : a + b + c = a + c + b :=
begin
rw [add_assoc, add_comm b, ←add_assoc]
end
example (a b c : ℕ) : a + b + c = a + c + b :=
begin
rw [add_assoc, add_assoc, add_comm b]
end
example (a b c : ℕ) : a + b + c = a + c + b :=
begin
rw [add_assoc, add_assoc, add_comm _ b]
end
| {"author": "Ailrun", "repo": "Theorem_Proving_in_Lean", "sha": "2eb1b5caf93c6a5a555c79e9097cf2ba5a66cf68", "save_path": "github-repos/lean/Ailrun-Theorem_Proving_in_Lean", "path": "github-repos/lean/Ailrun-Theorem_Proving_in_Lean/Theorem_Proving_in_Lean-2eb1b5caf93c6a5a555c79e9097cf2ba5a66cf68/src/ch5/ex0605.lean"} |
using Compat
using Dates
using Infinity
using Infinity.Utils
using Random
using Test
using TimeZones: ZonedDateTime
@testset "Infinity" begin
include("utils.jl")
include("infinite.jl")
include("infextendedreal.jl")
include("infextendedtime.jl")
end
| {"hexsha": "0b638bbc67fac393e1357d0c4b000abf603f2309", "size": 266, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "cjdoris/Infinity.jl", "max_stars_repo_head_hexsha": "c73a7142ded9bd4bdff35b86fd3891216a47714e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, ... |
(* Title: HOL/HOLCF/One.thy
Author: Oscar Slotosch
*)
section {* The unit domain *}
theory One
imports Lift
begin
type_synonym
one = "unit lift"
translations
(type) "one" <= (type) "unit lift"
definition ONE :: "one"
where "ONE == Def ()"
text {* Exhaustion and Elimination for type @{typ one} ... | {"author": "Josh-Tilles", "repo": "isabelle", "sha": "990accf749b8a6e037d25012258ecae20d59ca62", "save_path": "github-repos/isabelle/Josh-Tilles-isabelle", "path": "github-repos/isabelle/Josh-Tilles-isabelle/isabelle-990accf749b8a6e037d25012258ecae20d59ca62/src/HOL/HOLCF/One.thy"} |
from __future__ import print_function
from numpy import *
'''
NAME
host
PURPOSE
to get properties of host galaxies, given redshift, host galaxy type and i band magnitude of QSO
INPUT:
z (redshift), mQi (i band magnitude of QSO), type ("e"=early type, "l"= late type)
OUTPUT:
magnitude of ... | {"hexsha": "536ce49e149b6a1bf9344b4d07a84103c2ef0a58", "size": 1143, "ext": "py", "lang": "Python", "max_stars_repo_path": "om10/host.py", "max_stars_repo_name": "drphilmarshall/OM10", "max_stars_repo_head_hexsha": "009c16f0ef4e1c5f8f78c78df3c7711b7be24938", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "ma... |
import spotipy
import os
import spotipy.util as util
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import altair as alt
from sklearn.preprocessing import MinMaxScaler
import... | {"hexsha": "06114e9fc2eb03f89c24be8bbe8409a39ff44832", "size": 13293, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "sejaldua/spotify-ops", "max_stars_repo_head_hexsha": "a5676668734d409e1706a3bebe072c58658d469e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 42, "max_s... |
#!/usr/bin/python
# -*- coding: utf8 -*-
"""
analyse results from BluePyOpt checkpoints
author: András Ecker last update: 11.2017
"""
import os
import sys
import pickle
import numpy as np
import sim_evaluator
import matplotlib.pyplot as plt
SWBasePath = os.path.sep.join(os.path.abspath('__file__').split(os.path.sep)[:... | {"hexsha": "fe7b5c7d9f40be9f7bbba1cccdf53118b2219578", "size": 4382, "ext": "py", "lang": "Python", "max_stars_repo_path": "optimization/analyse_checkpoints.py", "max_stars_repo_name": "andrisecker/KOKI_sharp_waves", "max_stars_repo_head_hexsha": "e6375a2574559172a7036c2def177064a7b6def8", "max_stars_repo_licenses": ["... |
import numpy as np
import pytest
from numpy.testing import assert_array_equal
from six import StringIO
from landlab import (
CLOSED_BOUNDARY,
HexModelGrid,
NetworkModelGrid,
RadialModelGrid,
RasterModelGrid,
VoronoiDelaunayGrid,
)
from landlab.grid.hex import from_dict as hex_from_dict
from lan... | {"hexsha": "0ba7084200eccd8573752809f95462c6da930aa2", "size": 7332, "ext": "py", "lang": "Python", "max_stars_repo_path": "landlab/grid/tests/test_constructors.py", "max_stars_repo_name": "cctrunz/landlab", "max_stars_repo_head_hexsha": "4e4ef12f4bae82bc5194f1dcc9af8ff1a7c20939", "max_stars_repo_licenses": ["MIT"], "m... |
from warnings import warn
import os
from multiprocessing import Pool
import numpy as np
from tqdm import tqdm
from keras.models import Model
from scipy.stats import gaussian_kde
from coverage.tools.surprise_adequacy.sa_utils import *
from coverage.tools.common_utils import ScoreUtils
from coverage.tools.... | {"hexsha": "f7d23664dc2153df5f8262e4d3aadbecbee16388", "size": 19682, "ext": "py", "lang": "Python", "max_stars_repo_path": "coverage/tools/surprise_adequacy/sa.py", "max_stars_repo_name": "anonymousprojs/ISSTA2022-study", "max_stars_repo_head_hexsha": "94cef7fc4c098c03bb08ff8865d0c1d9a5de86b2", "max_stars_repo_license... |
import copy
import gym
import numpy as np
import rlf.algos.utils as autils
import rlf.rl.utils as rutils
import torch
import torch.nn.functional as F
from rlf.algos.il.base_il import BaseILAlgo
from rlf.args import str2bool
from rlf.storage.base_storage import BaseStorage
from tqdm import tqdm
class BehavioralClonin... | {"hexsha": "f81b5de94a7d0bce5cee7b4c30afb75c153428bd", "size": 7142, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl-toolkit/rlf/algos/il/bc.py", "max_stars_repo_name": "clvrai/goal_prox_il", "max_stars_repo_head_hexsha": "7c809b2ee575a69a14997068db06f3c1f3c8bd08", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import pandas as pd
import numpy as np
import re
from datetime import datetime as dt
from datetime import date, timedelta
# This script cleans the fetched tweets from the previous task "fetching_tweets"
LOCAL_DIR = '/tmp/'
def main():
# Read the csv produced by the "fetching_tweets" task
tweets = pd.read_cs... | {"hexsha": "a8f5c44ce603d3c0a99d7fc7972842ba270b6bcd", "size": 1707, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_pipelines/cleaning_tweet.py", "max_stars_repo_name": "rodrigoarenas456/airflow-course", "max_stars_repo_head_hexsha": "8ffda59b8ac4cfa18b4cd614bc0f75ee18324b28", "max_stars_repo_licenses": ["... |
import torch
import h5py
import numpy as np
#from cspnet import CSPNet_p3p4p5, ConvBlock
def load_conv_weights(conv, f, layer_name):
w = np.asarray(f[layer_name][layer_name + '_1/kernel:0'], dtype='float32')
b = np.asarray(f[layer_name][layer_name + '_1/bias:0'], dtype='float32')
conv.weight = torch.nn.Par... | {"hexsha": "44fbdc6017d5bae69394f72f34be34ada35c9a61", "size": 4170, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/keras_weights_loader.py", "max_stars_repo_name": "wang-xinyu/csp.pytorch", "max_stars_repo_head_hexsha": "8d5187358c1eb0fbf7ba5f184b9afed6c2518ad3", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import pandas as pd
import os
import librosa
from utilities import compute_time_consumed
import time
import soundfile
import tqdm
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
base_path = os.path.join(os.path.expanduser('~'), 'DCase/data/TUT-urban-acoustic-scenes-2018-developm... | {"hexsha": "026aa8e80e4f3f8910a8231abcfc1969f685b62e", "size": 4246, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/data_augmentation.py", "max_stars_repo_name": "rongxuanhong/task1", "max_stars_repo_head_hexsha": "3e3f8eaa936a73df60a93a410e369f803d302903", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "2aadb54c111d2e210d227f1fd406003b8af9a9a2", "size": 2174, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/cv/EDSR/export.py", "max_stars_repo_name": "leelige/mindspore", "max_stars_repo_head_hexsha": "5199e05ba3888963473f2b07da3f7bca5b9ef6dc", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import CompactBasisFunctions: Basis, nodes, nnodes
@testset "$(rpad("Basis Tests",80))" begin
struct BasisTest{T} <: Basis{T} end
test_basis = BasisTest{Float64}()
@test_throws ErrorException basis(test_basis)
@test_throws ErrorException nodes(test_basis)
@test_throws ErrorException nbasis(tes... | {"hexsha": "d39594407878d7fc44c15a276e19c81d6dfcfa9a", "size": 542, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/basis_tests.jl", "max_stars_repo_name": "JuliaGNI/CompactBasisFunctions.jl", "max_stars_repo_head_hexsha": "5a76714aca25c399d0856643aff3683d8e0f103a", "max_stars_repo_licenses": ["MIT"], "max_s... |
from __future__ import absolute_import, division, print_function
import numpy as np
import explorers
from .. import tools
from ..tools import chrono
def astd(xs):
m = np.average(xs)
plus = [x-m for x in xs if x >= m]
minus = [x-m for x in xs if x <= m]
sigma_plus = np.std([-1*x for x in plus] + p... | {"hexsha": "29d57357ad2ce12a14d5411d27d26183a7a64799", "size": 2592, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/runs/result.py", "max_stars_repo_name": "humm/experiments", "max_stars_repo_head_hexsha": "44770110e51349cc5e2a225322f57ede4f9fdfa7", "max_stars_repo_licenses": ["OML"], "max_stars_cou... |
#https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb
from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np
# MNIST dataset parameters.
num_classes = 10 # 0 to 9 digits
num_features = 784 # 28*28... | {"hexsha": "f6c83197c8d0bf69f07f4f4aea25dc57cbb6f6a4", "size": 4116, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_neural_networks/save_and_restore_tensorflow_models.py", "max_stars_repo_name": "parphane/udacity-self_driving_cars", "max_stars_repo_head_hexsha": "069762a5320a109ebe4f7c23997631a2998a0076", ... |
#include <stan/math/rev.hpp>
#include <test/unit/math/test_ad.hpp>
#include <gtest/gtest.h>
#include <boost/math/differentiation/finite_difference.hpp>
TEST(mathMixScalFun, neg_binomial_lpmf_derivatives) {
auto f = [](const int y) {
return [=](const auto& alpha, const auto& beta) {
return stan::math::neg_b... | {"hexsha": "a17998ded933d92b9267b26283d099ae5a55589a", "size": 858, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/unit/math/mix/prob/neg_binomial_test.cpp", "max_stars_repo_name": "LaudateCorpus1/math", "max_stars_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_stars_repo_licenses": ["BS... |
* PROGRAM RIBBON
*
* Program to set up input for RENDER (RASTER3D package)
* to draw ribbon diagram. The RIBBON routine itself is simply
* extracted from CCP FRODO. The original invoked a bspline feature
* of the ps300; I have replaced this with a spline equation gotten
* from Larry Andrews. Conversion from... | {"hexsha": "154e637ec48020051b1af88eb101073d45df2c0f", "size": 18299, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ribbon1.f", "max_stars_repo_name": "wkpark/raster3d", "max_stars_repo_head_hexsha": "1fc0f8e363c6c5a63a9fe64e7adeb4f2538bacb3", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": nul... |
[STATEMENT]
lemma D_mndet1 : "D(mndet {} P) = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. D (mndet {} P) = {}
[PROOF STEP]
unfolding mndet_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. D (if {} = {} then STOP else Abs_process (\<Union>x\<in>{}. F (x \<rightarrow> P x), \<Union>x\<in>{}. D (x \<rightar... | {"llama_tokens": 168, "file": "HOL-CSP_Mndet", "length": 2} |
import os
import numpy as np
import vipy.video
import vipy.videosearch
import vipy.object
from vipy.util import tempjpg, tempdir, Failed, isurl, rmdir
from vipy.geometry import BoundingBox
import pdb
from vipy.data.kinetics import Kinetics400, Kinetics600, Kinetics700
from vipy.data.activitynet import ActivityNet
from ... | {"hexsha": "06905d82b034ea65d424ad564ffe13609073d408", "size": 1451, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_dataset.py", "max_stars_repo_name": "williford/vipy", "max_stars_repo_head_hexsha": "d7ce90cfa3c11363ca9e9fcb1fcea9371aa1b74d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13,... |
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