text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma poly_altdef':
assumes gr: "k \<ge> degree p"
shows "poly p (z::complex) = (\<Sum>i\<le>k. coeff p i * z ^ i)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. poly p z = (\<Sum>i\<le>k. coeff p i * z ^ i)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. poly p z = (\<Sum>i\<... | {"llama_tokens": 2467, "file": "Gauss_Sums_Finite_Fourier_Series", "length": 26} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on %(date)s
@author: %(username)s
"""
#%%
import numpy as np
from astropy.io import fits
from laspec.normalization import normalize_spectrum_iter
from scipy import signal
from twodspec import extract
from twodspec.aperture import Aperture
from ... | {"hexsha": "76e1e829b4cbd2257cd98f24096ffaa69022405a", "size": 7167, "ext": "py", "lang": "Python", "max_stars_repo_path": "twodspec/deprecated/newtrace.py", "max_stars_repo_name": "hypergravity/songcn", "max_stars_repo_head_hexsha": "e2b071c932720d02e5f085884c83c46baba7802d", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 5 17:04:32 2018
@author: matteo
"""
import numpy as np
import itertools
from gym import spaces
"""Feature functions"""
class RBF(object):
def __init__(self, kernel_centers, std):
self.kernel_centers = kernel_centers
self.std ... | {"hexsha": "cf88fdf845ef4e980afe60473b291d5e67bb3381", "size": 1123, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/interaction/features.py", "max_stars_repo_name": "T3p/baselines", "max_stars_repo_head_hexsha": "5623c9160d1e86ebca3e673f142fe6b14a1db06c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# ------------------------------------------------------------------
# Licensed under the MIT License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
PoissonProcess(λ)
A Poisson process with intensity `λ`.
"""
struct PoissonProcess{L<:Union{Real,Function}}... | {"hexsha": "7d78a0bfe6f97806dc4499fd57f9eade1cc98e7e", "size": 2078, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/processes/poisson.jl", "max_stars_repo_name": "PeetoomHeida/PointPatterns.jl", "max_stars_repo_head_hexsha": "4225b21a4724032cd83100c638733a63325e8fa6", "max_stars_repo_licenses": ["MIT"], "max... |
def topsiscode(data, matrix, sign):
import numpy as np
import pandas as pd
dataset = pd.read_csv('data')
x = dataset.shape[0]
y = dataset.shape[1]
summ = sum(matrix)
s = []
for j in range(y):
s.append(0)
for i in range(x):
s[j] = s[j]+... | {"hexsha": "94877f55b222f02440809725a8584e6c3a9fcca9", "size": 1608, "ext": "py", "lang": "Python", "max_stars_repo_path": "Topsissss/as.py", "max_stars_repo_name": "deepak199922/Topsisss", "max_stars_repo_head_hexsha": "63436342d4327a9e018dff54f5b1f3d86d27ec96", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import pandas as pd
import numpy as np
from keras.models import load_model
from sklearn.metrics import roc_curve, roc_auc_score, auc, precision_recall_curve, average_precision_score
import os
import pickle
from scipy.special import softmax
from prg import prg
class MetricsGenerator(object):
def __init__(self, data... | {"hexsha": "3e549ad1164dfec9640eaffc06582c10fc91d254", "size": 4839, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation-approach/lib/metric.py", "max_stars_repo_name": "Bruno81930/EXIMB-SMDP", "max_stars_repo_head_hexsha": "9483b18d61dddab5fffe53b1419102e70dbe5499", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma keys_to_list_MP_oalist [code]: "keys_to_list (MP_oalist xs) = OAlist_sorted_domain_ntm cmp_term xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. keys_to_list (MP_oalist xs) = OAlist_sorted_domain_ntm cmp_term xs
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. keys_to_list (... | {"llama_tokens": 3190, "file": "Polynomials_MPoly_Type_Class_OAlist", "length": 34} |
[STATEMENT]
lemma elementary_union_interval:
fixes a b :: "'a::euclidean_space"
assumes "p division_of \<Union>p"
obtains q where "q division_of (cbox a b \<union> \<Union>p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>q. q division_of cbox a b \<union> \<Union> p \<Longrightarrow> thesis) \<Longrig... | {"llama_tokens": 15423, "file": null, "length": 118} |
import sys, skvideo.io, json, base64
import numpy as np
from PIL import Image
from io import BytesIO, StringIO
import tensorflow as tf
import scipy.misc
import argparse
import os
import scipy
file = sys.argv[-1]
image_shape = (192, 256)
if file == 'demo.py':
print ("Error loading video")
quit
# Define encode... | {"hexsha": "7bd806bfc742263338a529cee581d0f94387d6d8", "size": 2336, "ext": "py", "lang": "Python", "max_stars_repo_path": "submit.py", "max_stars_repo_name": "xfqbuaa/Lyft_Challenge_Carla", "max_stars_repo_head_hexsha": "df32613cc203be3aa4b568da34994edf86b2ea15", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""Utilities and definitions shared by reward-related code."""
from typing import Callable
import numpy as np
from stable_baselines3.common import vec_env
RewardFn = Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray], np.ndarray]
def build_norm_reward_fn(
*,
reward_fn: RewardFn,
vec_normalize: v... | {"hexsha": "e37b88b4f5b9748276b2605cc4347051133df49a", "size": 1710, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/imitation/rewards/common.py", "max_stars_repo_name": "dreamfyrian/imitation", "max_stars_repo_head_hexsha": "682bc4b919baf57bdd959ac646caba21d92cdf71", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
from mpl_toolkits.mplot3d import Axes3D
from skimage.morphology import skeletonize_3d
from pathlib import Path
from typing import NamedTuple, Tuple
from ptg.pixel_shape import PixelSphere as ps
# utilities
serialize = False
latex = False
if ... | {"hexsha": "6aef8da51a9354efff4e1d6e16d15b16b8341588", "size": 5069, "ext": "py", "lang": "Python", "max_stars_repo_path": "geo/examples/pixlet.py", "max_stars_repo_name": "sandialabs/sibl", "max_stars_repo_head_hexsha": "010cbc3fbbd14cdfa3742ec4c95100f05146dce8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 17 22:38:43 2021
@author: stvyh
this file is used to complete program with Qt Designer output
C:\Users\stvyh\OneDrive\Desktop\ImageProcessing\ImageProcessing
pyuic5 -x m1.ui -o elilik.py
C:/Temp/Data/test/shapes.png
ERROR: ImportError: QtWebEngineWidgets must be i... | {"hexsha": "da5f3f403ab5c445b17fc7f872cef1949833519b", "size": 15541, "ext": "py", "lang": "Python", "max_stars_repo_path": "MonkeyBusiness/m3Utils.py", "max_stars_repo_name": "svyhlidka/Python-Projects", "max_stars_repo_head_hexsha": "7c297bf7248a1e61699a69d62eb83f15cf90d352", "max_stars_repo_licenses": ["MIT"], "max_... |
using GeometryTypes
using ComputerVision
using Test
digital_camera = DigitalCamera()
camera_model = get_model(digital_camera)
pictures = get_pictures(digital_camera)
@test typeof(camera_model) <: AbstractCameraModel
@test isnothing(pictures)
intrinsics = get_intrinsics(camera_model)
@test typeof(intrinsics) <: Abstr... | {"hexsha": "f5b7642919aed414fd856c022adf28656da115ed", "size": 441, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/camera.jl", "max_stars_repo_name": "JuliaTagBot/ComputerVision.jl", "max_stars_repo_head_hexsha": "a83bd0d271217090798407de3aa8d40cc7adbdf1", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "f17effa82beb565fd4a0efa09f42b8ba711b78a2", "size": 9610, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/python/ops/signal/window_ops.py", "max_stars_repo_name": "EricRemmerswaal/tensorflow", "max_stars_repo_head_hexsha": "141ff27877579c81a213fa113bd1b474c1749aca", "max_stars_repo_licenses... |
#!/usr/bin/env python3
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx.operators
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq... | {"hexsha": "e81539dae452dd15890673931b9b6f6ea44d266f", "size": 58155, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch_translate/rnn.py", "max_stars_repo_name": "ROCmSoftwarePlatform/translate", "max_stars_repo_head_hexsha": "32a6380d914ebe1a6c38c4992aac9600ed3d9810", "max_stars_repo_licenses": ["BSD-3-Cl... |
import os
import numpy as np
import torch.utils.data as data
import torchvision
from numpy.random import randint
import transforms as t
from .video_record import VideoRecord
class VideoDataset(data.Dataset):
""" A generic video dataset.
Args:
root_path: Full path to the dataset videos directory.
... | {"hexsha": "29de5538b6c852dde408be645d82c45fb2de9165", "size": 6762, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/video_dataset.py", "max_stars_repo_name": "patykov/OnlineAction", "max_stars_repo_head_hexsha": "efab480382e9a2a14bfe7fceaf6ade35d22efc21", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | {"hexsha": "13c1883af6184f2971d43c6bbd88496912c5ec67", "size": 4327, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/mkldnn/test_softmax_mkldnn_op.py", "max_stars_repo_name": "zmxdream/Paddle", "max_stars_repo_head_hexsha": "04f042a5d507ad98f7f2cfc3cbc44b06d7a7f45c", "max_star... |
# coding=utf-8
# Copyright 2021 The Uncertainty Baselines Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | {"hexsha": "8c909fe5153b02745603c3ae61576fdf32f06ed8", "size": 8698, "ext": "py", "lang": "Python", "max_stars_repo_path": "uncertainty_baselines/models/bit_resnet.py", "max_stars_repo_name": "y0ast/uncertainty-baselines", "max_stars_repo_head_hexsha": "8d32c77ba0803ed715c1406378adf10ebd61ab74", "max_stars_repo_license... |
""" Convert dataset to HDF5
This script preprocesses a dataset and saves it (images and labels) to
an HDF5 file for improved I/O. """
import logging
import os
import sys
from argparse import ArgumentParser
from tqdm import tqdm, trange
import h5py as h5
import numpy as np
import torch
import torchvision.datas... | {"hexsha": "8885d59cd367d60c64c70e238e158515edb6faaa", "size": 6567, "ext": "py", "lang": "Python", "max_stars_repo_path": "make_hdf5.py", "max_stars_repo_name": "PeterouZh/BigGAN-PyTorch-1", "max_stars_repo_head_hexsha": "722fe2e3b721f350c8e9b991d2839b0291f5cea7", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
C$Procedure ZZDAFNFR ( Private --- DAF write New File Record )
SUBROUTINE ZZDAFNFR ( LUN,
. IDWORD,
. ND,
. NI,
. IFNAME,
. FWARD,
. BWARD,
. ... | {"hexsha": "62ef361e9ad7f426c2e38420772edf4737a6c7ad", "size": 10703, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "source/nasa_f/zzdafnfr.f", "max_stars_repo_name": "agforero/FTFramework", "max_stars_repo_head_hexsha": "6caf0bc7bae8dc54a62da62df37e852625f0427d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python3
import unittest
import warnings
import matplotlib.pyplot as plt
import numpy as np
import numpy.testing as nptest
import pandas as pd
import pandas.testing as pdtest
import scipy.sparse
from scipy.spatial.distance import pdist, squareform
from datafold.pcfold.distance import compute_distance_ma... | {"hexsha": "30c65f55a43eed8a8acd9091bde39e8d2ad436a2", "size": 21013, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/ModelSelection/datafold-master/datafold/pcfold/tests/test_kernels.py", "max_stars_repo_name": "sazio/SSVEP_IEEE_SMC_2021", "max_stars_repo_head_hexsha": "8cae603d04dd8d623f01cf1e8979cf8ac9e0... |
import unittest
import numpy as np
import vnmrjpy as vj
from scipy.ndimage.filters import convolve
from scipy.signal import fftconvolve
import matplotlib.pyplot as plt
class Test_fftconvolve(unittest.TestCase):
def test_fftconvolve(self):
hankel = np.random.rand(50,20)
kernel = np.fliplr(np.eye(h... | {"hexsha": "74ac0701fd1ec83e45037c7acc16947c07cba66f", "size": 922, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_mathutils.py", "max_stars_repo_name": "hlatkydavid/vnmrjpy", "max_stars_repo_head_hexsha": "48707a1000dc87e646e37c8bd686e695bd31a61e", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
module ApproxBayes
using Distributions
using ProgressMeter
using StatsBase
using RecipesBase
using Printf
using Distances
using DelimitedFiles
using Random
using Statistics
using Base.Threads
import Base.show
export
# types
ABCtype,
Prior,
Particle,
abctype,
ParticleRejection,
ABCRejection,
ABCSMC,
... | {"hexsha": "3ca75fc5ac99ddc7b9bb44a0aef94efb8a3d25fc", "size": 633, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ApproxBayes.jl", "max_stars_repo_name": "wilwxk/ApproxBayes.jl", "max_stars_repo_head_hexsha": "8ca364c66a9f94ccd09910a4f73232909d91c399", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#References
#https://stackoverflow.com/
#questions/47330812/find-the-longest-diagonal-of-an-element-in-a-matrix-python
#https://codereview.stackexchange.com/questions/146935/find-diagonal-positions-for-bishop-movement
import numpy as np
import pandas as pd
from itertools import chain
import matplotlib.pyplot as plt
# ... | {"hexsha": "c8f97e2fff6ad9f33aaeed9e87cf9d427a4c49ca", "size": 4742, "ext": "py", "lang": "Python", "max_stars_repo_path": "dtw_diag.py", "max_stars_repo_name": "kibol/data-analytics", "max_stars_repo_head_hexsha": "89228849e9897c753dfd95507a9148422735f4fb", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count... |
"""
Image Cleaning Algorithms (identification of noisy pixels)
"""
__all__ = ['tailcuts_clean', 'dilate']
import numpy as np
from scipy.sparse.csgraph import connected_components
def tailcuts_clean(geom, image, picture_thresh=7, boundary_thresh=5,
keep_isolated_pixels=False,
mi... | {"hexsha": "0b3187ba9809390a735c2df1867c3c3383e49a06", "size": 4762, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctapipe/image/cleaning.py", "max_stars_repo_name": "mpecimotika/ctapipe", "max_stars_repo_head_hexsha": "ffd7930921f7139b761fbf1208da16dd302e97a6", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
(*
* Copyright 2014, General Dynamics C4 Systems
*
* SPDX-License-Identifier: GPL-2.0-only
*)
theory EmptyFail_H
imports Refine
begin
crunch_ignore (empty_fail)
(add: handleE' getCTE getObject updateObject
CSpaceDecls_H.resolveAddressBits
doMachineOp suspend restart schedule)
context begin inte... | {"author": "NICTA", "repo": "l4v", "sha": "3c3514fe99082f7b6a6fb8445b8dfc592ff7f02b", "save_path": "github-repos/isabelle/NICTA-l4v", "path": "github-repos/isabelle/NICTA-l4v/l4v-3c3514fe99082f7b6a6fb8445b8dfc592ff7f02b/proof/refine/X64/EmptyFail_H.thy"} |
Fake Larry Vanderhoef, aka Larry Vanderhumpf, is an internet spoof version of Larry Vanderhoef, complete with blog. He was elected to the Office of the Channeler by the Minions of Mrak, and is serving his term of the eternal now.
In His Own Words
You are my connections. Email Mailto(greyseal AT att DOT net) with... | {"hexsha": "e218f171905d2951a787e29d58e1e08863a557e9", "size": 562, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Fake_Larry_Vanderhoef_-_Dirt_at_UCD.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["M... |
__precompile__()
module ReadGlobal
using DelimitedFiles
export readglobal, getdimsize, readpadded, readpadded!, readfield, readfield!, checkinput, getnfilter, doinchunks, read_info, readcsv, testinput
function findglobal()
filename="global"
if !isfile(filename)
for i = 1:10
filename = "..... | {"hexsha": "3c64623b57f0184686a4243a0589acbfd9e195d2", "size": 6644, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ReadGlobal.jl", "max_stars_repo_name": "favba/ReadGlobal.jl", "max_stars_repo_head_hexsha": "a9b69382f969471ba393eac83f09a283c8173e4b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
SUBROUTINE ML5_0_MP_BORN_AMPS_AND_WFS(P)
C
C Generated by MadGraph5_aMC@NLO v. %(version)s, %(date)s
C By the MadGraph5_aMC@NLO Development Team
C Visit launchpad.net/madgraph5 and amcatnlo.web.cern.ch
C
C Computes all the AMP and WFS in quadruple precision for the
C phase space poi... | {"hexsha": "3415f0cef1c5d40b513c9d395bd4820979e81d91", "size": 13499, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "tests/input_files/IOTestsComparison/short_ML_SMQCD_default/gg_ttx/mp_born_amps_and_wfs.f", "max_stars_repo_name": "valassi/mg5amc_test", "max_stars_repo_head_hexsha": "2e04f23353051f64e1604b23105... |
program p
1 variable = a .eqv. b .eqv. c == 0
variable = a .eqv. b .and. .not. c == 0
variable = 10._8 > 1 .and. 1.E+1_8 > 0
a(:) = 0
name_check = line(i)(1:pos-1)
pressure = 949. + real(reshape( (/ (counter, counter = 1, numLats * numLons * numFrTimes) /), &
... | {"hexsha": "a56af779a8a93b7e43c326652ae40fd3db2d8609", "size": 382, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "src/test/resources/psi/Expressions.f95", "max_stars_repo_name": "LChernigovskaya/fortran-plugin", "max_stars_repo_head_hexsha": "6750933e79c5bf420937160aecedc39b142ca5f6", "max_stars_repo_license... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: peter
csvtools.py: Tools for handling metadata CSV files
**CSV types**
Four types of CSV files are supported, differentiated by the presence of 'run'
and 'probe columns.
Experiment constants -> No runs column, no probe column. Data that applies to
... | {"hexsha": "c907e4f8591c1fa7a653f2d6fec0d218ada958ff", "size": 14501, "ext": "py", "lang": "Python", "max_stars_repo_path": "uclahedp/tools/csv.py", "max_stars_repo_name": "uclahedp/uclahedp", "max_stars_repo_head_hexsha": "122ecb180cc58c3aadb47de016427a46094cc8fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from .__parser__ import Parser as _P
from lxml import etree
import numpy as np
import pandas as pd
import os.path as op
class Parser(_P):
def __init__(self, path, app):
super().__init__(path, app)
if not op.isfile(path):return
if not path.endswith(".xml"):return
rootxml = etree.pars... | {"hexsha": "cc51b2f80fdecb3212aa968a99b76eea672db309", "size": 4699, "ext": "py", "lang": "Python", "max_stars_repo_path": "plt.mk3/plugins/data/parsers/parser_visionxml.py", "max_stars_repo_name": "mb-89/plt.mk3", "max_stars_repo_head_hexsha": "45e06f608e6051c92b8e4718e4ef480fd9ca800b", "max_stars_repo_licenses": ["MI... |
import numpy as np
import cv2
class BackGroundSubtractor:
# When constructing background subtractor, we
# take in two arguments:
# 1) alpha: The background learning factor, its value should
# be between 0 and 1. The higher the value, the more quickly
# your program learns the changes in the background. Therefore... | {"hexsha": "481c17fe4fcc1c94360f059d1c56cafaba624c47", "size": 2271, "ext": "py", "lang": "Python", "max_stars_repo_path": "hard-gists/7c3d01ad13b0e7e5985a/snippet.py", "max_stars_repo_name": "jjhenkel/dockerizeme", "max_stars_repo_head_hexsha": "eaa4fe5366f6b9adf74399eab01c712cacaeb279", "max_stars_repo_licenses": ["A... |
"""
Metrics for computing evalutation results
Modified from vanilla PANet code by Wang et al.
"""
import numpy as np
class Metric(object):
"""
Compute evaluation result
Args:
max_label:
max label index in the data (0 denoting background)
n_scans:
number of test sca... | {"hexsha": "6d4f0241efc6e11f79f7781d137f395296683710", "size": 11243, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/metric.py", "max_stars_repo_name": "tanghaotommy/Self-supervised-Fewshot-Medical-Image-Segmentation", "max_stars_repo_head_hexsha": "9ff8cd2421ee2f7c038d8eec15b0296b365e0c46", "max_stars_rep... |
import argparse
import json
import os
import sys
import numpy as np
import torch
from PIL import Image
from network import Network
from train import initializeDevice
device = None
cat_to_name = []
def initializeCatgories(category_names_file):
print(f"Loading category names from [{category_names_file}].")
w... | {"hexsha": "80d67cacc540d957e34fb0ce746f851cc91d642f", "size": 4183, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "elfnews/image_classifier", "max_stars_repo_head_hexsha": "b5d0c46250c7793bbf0729895541078fd2626252", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
(* seplog (c) AIST 2005-2013. R. Affeldt, N. Marti, et al. GNU GPLv3. *)
(* seplog (c) AIST 2014-2018. R. Affeldt et al. GNU GPLv3. *)
Require Import Epsilon.
From mathcomp Require Import ssreflect ssrfun ssrbool eqtype ssrnat seq.
Require Import Init_ext ssrZ ZArith_ext seq_ext machine_int uniq_tac multi_int.
Import M... | {"author": "affeldt-aist", "repo": "seplog", "sha": "b08516d34f5dedd0aafbe77d8ef270fa838e8f85", "save_path": "github-repos/coq/affeldt-aist-seplog", "path": "github-repos/coq/affeldt-aist-seplog/seplog-b08516d34f5dedd0aafbe77d8ef270fa838e8f85/begcd/multi_is_even_u_and_simu.v"} |
import os
import matplotlib.pyplot as plt
import csv
import numpy as np
def map_visible(value):
if value == 'True':
return 1
elif value == 'False':
return 0
else:
return -1
def map_unsure3dBox(valuevisible, valueBox):
if valuevisible == 'True' and valueBox>=0:
return 1... | {"hexsha": "84845aba1d0af21a6eb5176c765a838d98cc9964", "size": 6359, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/DatasetStatisticsTools/lib_stats/util.py", "max_stars_repo_name": "ladt/SeeingThroughFog", "max_stars_repo_head_hexsha": "c714a4c3e8f8e604494b1db6e9eef529b0326405", "max_stars_repo_licenses"... |
# This file implements a MultiVolumeVisual class that can be used to show
# multiple volumes simultaneously. It is derived from the original VolumeVisual
# class in vispy.visuals.volume, which is releaed under a BSD license included
# here:
#
# ===========================================================================... | {"hexsha": "f38fff8af772102b9fdd89dc435573268164ea80", "size": 9740, "ext": "py", "lang": "Python", "max_stars_repo_path": "phathom/multivol/multi_volume_visual.py", "max_stars_repo_name": "chunglabmit/phathom", "max_stars_repo_head_hexsha": "304db7a95e898e9b03d6b2640172752d21a7e3ed", "max_stars_repo_licenses": ["MIT"]... |
From hydras Require Import T1 E0.
From Coq Require Import Lia.
Open Scope E0_scope.
Lemma ge_omega_iff (alpha : E0):
E0omega o<= alpha <-> (forall i:nat, i + alpha = alpha).
Proof.
destruct alpha as [a Ha]; unfold E0le; cbn.
destruct a; cbn; split; intros H.
- rewrite Le_iff in H; destruct H as (H, (Hle, Hnf)... | {"author": "coq-community", "repo": "hydra-battles", "sha": "2d211e0b5030d5f77aaaf6366b01fc64ed998c61", "save_path": "github-repos/coq/coq-community-hydra-battles", "path": "github-repos/coq/coq-community-hydra-battles/hydra-battles-2d211e0b5030d5f77aaaf6366b01fc64ed998c61/theories/ordinals/solutions_exercises/ge_omega... |
\chapter{Ordinary Least Squares for prediction}
| {"hexsha": "eeb716b4014ada1a8df616b32a612be83ffe0fb1", "size": 50, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/statistics/ols/00-00-Chapter_name.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo_licen... |
"""Contains the highway with ramps network class."""
from flow.networks.base import Network
from flow.core.params import InitialConfig, TrafficLightParams
from collections import defaultdict
from numpy import pi, sin, cos
ADDITIONAL_NET_PARAMS = {
# lengths of highway, on-ramps and off-ramps respectively
"hi... | {"hexsha": "d2c004a914fa9fff3699096784328a95bc5ee01e", "size": 9460, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl/net/highway_ramps.py", "max_stars_repo_name": "KarlRong/Safe-RL-for-Driving", "max_stars_repo_head_hexsha": "67484911ca8ad9f1476e96043c379c01cd5ced8c", "max_stars_repo_licenses": ["Apache-2.0"]... |
import pandas as pd
import numpy as np
import asteval
import sys
from cytoolz.curried import map, curry
from cytoolz.functoolz import thread_last
from cytoolz.dicttoolz import assoc_in
from dask import delayed
import re
from survey_stats import log
from survey_stats.etl import download as dl
from survey_stats import pd... | {"hexsha": "02bbae551f8b847322f27b31eff58c0af6cc4eae", "size": 8492, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/survey_stats/etl/socrata.py", "max_stars_repo_name": "semanticbits/survey_stats", "max_stars_repo_head_hexsha": "10d74ea6f25f98e55aefc89cb612e1e668ab236e", "max_stars_repo_licenses": ["BSD-2-C... |
import argparse
import time
import numpy as np
import os
import json
import pickle
import random
import sklearn.metrics as metrics
import torch
from torch.utils import data
from data_generator import Dataset
from mlp_model import mlp_model
torch.manual_seed(123)
torch.cuda.manual_seed(123)
np.random.seed(123)
random.s... | {"hexsha": "db7f8a3d6f4e1a54183be1fdc00cd2970164a4e1", "size": 9448, "ext": "py", "lang": "Python", "max_stars_repo_path": "graph2vec_optimization/train_graph2vec.py", "max_stars_repo_name": "Guannan1900/GraphDTI_preprocess", "max_stars_repo_head_hexsha": "60bb448666e6922b2e3b80a3409b090014af86ce", "max_stars_repo_lice... |
/*!
@file
Defines `boost::hana::convert` and `boost::hana::to`.
@copyright Louis Dionne 2014
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
#ifndef BOOST_HANA_CORE_CONVERT_HPP
#define BOOST_HANA_CORE_CONVERT_HPP
#include <... | {"hexsha": "bed46bad1c08fdd726a60d76b4095bce3a30a30a", "size": 3334, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/hana/core/convert.hpp", "max_stars_repo_name": "rbock/hana", "max_stars_repo_head_hexsha": "2b76377f91a5ebe037dea444e4eaabba6498d3a8", "max_stars_repo_licenses": ["BSL-1.0"], "max_star... |
/*!
* @file
* Contains unit tests for the intrinsics for Boost.Fusion sequences.
*/
#include <react/extension/fusion.hpp>
#include <react/computation/implementing.hpp>
#include <react/concept/assert.hpp>
#include <react/concepts.hpp>
#include <react/detail/dont_care.hpp>
#include <react/intrinsic/augment.hpp>
#inc... | {"hexsha": "4d6bd3c703f3b13141d82cbc0284cca34922c7f7", "size": 1770, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/extension/fusion.cpp", "max_stars_repo_name": "ldionne/react", "max_stars_repo_head_hexsha": "57b51d179661a9c21bc1f987d124722ac36399ac", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count... |
from CHECLabPy.core.reducer import WaveformReducer, column
import numpy as np
from numba import njit, prange, float64, float32, int64
@njit([
(float64[:, :], int64, int64),
(float32[:, :], int64, int64),
])
def obtain_pulse_timing(waveforms, window_start, window_end):
n_pixels, n_samples = waveforms.shape... | {"hexsha": "ec6019c98ffb1aa7a9f0e98f49afe1e3f16b877f", "size": 7946, "ext": "py", "lang": "Python", "max_stars_repo_path": "CHECLabPy/waveform_reducers/timing.py", "max_stars_repo_name": "ConteFrancesco/CHECLabPy", "max_stars_repo_head_hexsha": "b2d0a12cae062603b618132957a555c404a4a4c9", "max_stars_repo_licenses": ["BS... |
from typing import List, Optional, Type
import gym
import numpy as np
import torch as T
from gym import Env
from pearll.agents.base_agents import BaseAgent
from pearll.buffers import BaseBuffer, RolloutBuffer
from pearll.callbacks.base_callback import BaseCallback
from pearll.common.type_aliases import Log
from pearl... | {"hexsha": "81fc9c82438f0ecfeab8a905446205c3c542631c", "size": 7403, "ext": "py", "lang": "Python", "max_stars_repo_path": "pearll/agents/a2c.py", "max_stars_repo_name": "LondonNode/Anvil", "max_stars_repo_head_hexsha": "bc50fd7b16af36051157814e2548a98e787b03de", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
"""
=============
Unicode minus
=============
You can use the proper typesetting `Unicode minus`__ or the ASCII hyphen for
minus, which some people prefer. :rc:`axes.unicode_minus` controls the default
behavior.
__ https://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
The default is to use the Unicode ... | {"hexsha": "4acfc07a58fb750cc98160a4418a9bb645cc4696", "size": 674, "ext": "py", "lang": "Python", "max_stars_repo_path": "extraPackages/matplotlib-3.0.3/examples/text_labels_and_annotations/unicode_minus.py", "max_stars_repo_name": "dolboBobo/python3_ios", "max_stars_repo_head_hexsha": "877f8c2c5890f26292ddd14909bea62... |
import cv2
import numpy as np
import os
import mysql.connector
import attendence
from datetime import datetime
def main():
n, d, r = "", "", ""
cnt = 0
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_defau... | {"hexsha": "830c238d72101472e30d03cd9e5f4b848a113662", "size": 3756, "ext": "py", "lang": "Python", "max_stars_repo_path": "facerecog.py", "max_stars_repo_name": "nitishmishra617/Python-Face_Recognition_Attendance_System", "max_stars_repo_head_hexsha": "596e68a2100d212961096a17c2c3c17fc229647d", "max_stars_repo_license... |
#!/usr/bin/env python3
import argparse
import copy
from collections import defaultdict
from pathlib import Path
import os
import sys
import time
import numpy as np
import pandas as pd
from sklearn.metrics import f1_score, precision_recall_fscore_support, log_loss, average_precision_score
import torch
import torch.opt... | {"hexsha": "00ec1447dec7415200abb8866ebabc979cd65ddb", "size": 11629, "ext": "py", "lang": "Python", "max_stars_repo_path": "workdir/train.py", "max_stars_repo_name": "lisosia/kaggle-rsna-str", "max_stars_repo_head_hexsha": "262d3530158952587ec69e7a97f874e8cd34588b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import pickle
import shutil
from pathlib import Path, PurePath
from typing import Dict, List, Optional, Tuple, Union
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from skimage.color import label2rgb
import inc.python_image_utilities.image_util as iutil
import ut... | {"hexsha": "7c2475010ee4dff69443fe5f145043e6c64aa63b", "size": 13687, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "wwarriner/unsupervised_onh_histo", "max_stars_repo_head_hexsha": "48ec23781af203a14ff590f3074a2d3559957560", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include <fstream>
#include <sstream>
#include <iostream>
#include <vector>
#include <bits/stdc++.h>
#include <boost/format.hpp>
#include <boost/iostreams/filtering_stream.hpp>
#include <boost/iostreams/filter/zlib.hpp>
#include <boost/iostreams/copy.hpp>
int main(int argc, char *argv[]){
if(argc!=3){
st... | {"hexsha": "8ff40ca28c39411f7333a51054019f5a4d9f772f", "size": 3274, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gaden/test_env/decompress/src/toASCII.cpp", "max_stars_repo_name": "chinaheyu/pydog_ws", "max_stars_repo_head_hexsha": "c5ff65647845e6c96901fb925a2357507dfeecf0", "max_stars_repo_licenses": ["MI... |
Lemma LP12P13 : forall P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 ,
rk(P1 :: P2 :: P3 :: nil) = 3 -> rk(P4 :: P5 :: P6 :: nil) = 3 -> rk(P1 :: P2 :: P3 :: P4 :: P5 :: P6 :: nil) = 4 ->
rk(P7 :: P8 :: P9 :: nil) = 3 -> rk(P1 :: P2 :: P3 :: P7 :: P8 :: P9 :: nil) = 4 -> rk(P4 :: P5 :: P6 :: P7 :: P8 :: P9 :: nil) =... | {"author": "pascalschreck", "repo": "MatroidIncidenceProver", "sha": "e492d375a2264e6c908c9c47fe719c39e3f847f8", "save_path": "github-repos/coq/pascalschreck-MatroidIncidenceProver", "path": "github-repos/coq/pascalschreck-MatroidIncidenceProver/MatroidIncidenceProver-e492d375a2264e6c908c9c47fe719c39e3f847f8/matroidbas... |
import numpy as np
import cv2
#red detection
vid = cv2.VideoCapture(0) #Webcam=0
while True:
_, frame = vid.read() #_ is used for those returned things whch are of no use
# HSV = HUE, SATURATION, VALUE
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_red = np.array([0,70,50]) #provides a range
u... | {"hexsha": "f7c40848ea0625c4ebf432aa880678459e3d8bbb", "size": 870, "ext": "py", "lang": "Python", "max_stars_repo_path": "PycharmProjects/OpenCV/Dhairya_OpenCV/6_VideoFilters.py", "max_stars_repo_name": "dhairyashah1/Eklavya20-CatchPracticeBot", "max_stars_repo_head_hexsha": "60434bf5e280d7495eab75b21566bd1eb3bbd14e",... |
Primes <- read.csv("Primes.csv", header=F)
avg <- (Primes[3]+Primes[4]+Primes[5]+Primes[6]+Primes[7]+Primes[8]+Primes[9]+Primes[10]+Primes[11]+Primes[12])/10
Data.name <- Primes[[1]]
Data.val <- Primes[[2]]
avg <- avg[[1]]
pdf(file = "Primes.pdf", width = 6.25, height = 4.7, family = "Times", pointsize = 18)
par(mai=... | {"hexsha": "7a132463b23f3762c7f47b1575f75c287ed5ddfd", "size": 5132, "ext": "r", "lang": "R", "max_stars_repo_path": "code/scripts/out3/MakeGraphs.r", "max_stars_repo_name": "joseproenca/ip-constraints", "max_stars_repo_head_hexsha": "0d478b44d696e2107e4c75e465b23ef4e677a6ba", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/python2.7
# -*- coding:utf-8 -*-
# Author: NetworkRanger
# Date: 2018/11/5 下午9:40
# 4.2 线性支持向量机的使用
# 1. 导入必要的编程库，包括导入scikit learn的datasets库来访问iris数据集
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets
"""
安装scikit learn可使用: $ pip install -U scikit-lear... | {"hexsha": "106ac67b7b6f92b5c42fc45d6b084cb35651992a", "size": 4526, "ext": "py", "lang": "Python", "max_stars_repo_path": "chapter04/demo_4.2.py", "max_stars_repo_name": "OsbornHu/tensorflow-ml", "max_stars_repo_head_hexsha": "56c3051e7085a919a603481709b63e4a6614192a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
import torch
import logging
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
# from https://palikar.github.io/posts/pytorch_datasplit/
class DataSplit:
def __init__(self, dataset, test_train_split=0.8, val_train_split=0.1, shuffle=False):
... | {"hexsha": "ae4512544a723002c0aba90dcf75b8c6c5bbefba", "size": 2898, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/datasplit/datasplit.py", "max_stars_repo_name": "mcomunita/gfx_classifier", "max_stars_repo_head_hexsha": "94e5f09084837886990e05821da9ff4a55ed180a", "max_stars_repo_licenses": ["BSD-3-Clause"... |
[STATEMENT]
lemma bnds_cos: "\<forall>(x::real) lx ux. (l, u) =
bnds_cos prec lx ux \<and> x \<in> {lx .. ux} \<longrightarrow> l \<le> cos x \<and> cos x \<le> u"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>x lx ux. (l, u) = bnds_cos prec lx ux \<and> x \<in> {real_of_float lx..real_of_float ux} \<lon... | {"llama_tokens": 107162, "file": null, "length": 275} |
""""
Copyright (c) 2018. All rights reserved.
Created by Resnick Xing on 2018/5/10
"""
import numpy as np
import cv2
def get_test_patches(img, config,rl=False):
"""
将待分割图预处理后，分割成patch
:param img: 待分割图
:param config: 配置文件
:return:
"""
test_img = []
test_img.append(img)
test_img=np... | {"hexsha": "97775d5d344420484a9b23a26ede019e637b0f5e", "size": 9253, "ext": "py", "lang": "Python", "max_stars_repo_path": "configs/utils/img_utils.py", "max_stars_repo_name": "kant/DL_Segmention_Template", "max_stars_repo_head_hexsha": "cd791d79fefb1f9a7ee4fbd691c09f1f23180a9a", "max_stars_repo_licenses": ["MIT"], "ma... |
from .Custom_Legend import *
from .set_Axes_Color import *
from scipy.stats import spearmanr
import matplotlib.pyplot as plt
import numpy as np
def _zero_to_nan(values):
"""Replace every 0 with 'nan' and return a copy."""
return [float('nan') if x==0 else x for x in values]
def Pair_Plot(parameter_array, labe... | {"hexsha": "c0ed165fad24f3e476f4644503ce2146ef9ebe3c", "size": 10299, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_visualization/Pair_Plot.py", "max_stars_repo_name": "CashabackLab/DataVisualization", "max_stars_repo_head_hexsha": "91b2a2d6020ae2fb5b8277f5c7bca69d620be1cb", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma spectral_radius_max: assumes "eigen_value A v"
shows "norm v \<le> spectral_radius A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. cmod v \<le> HMA_Connect.spectral_radius A
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. cmod v \<le> HMA_Connect.spectral_radius A
[PROOF ... | {"llama_tokens": 578, "file": "Perron_Frobenius_Perron_Frobenius_Aux", "length": 8} |
// Copyright Gavin Band 2008 - 2012.
// 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 QCTOOL_CALLCOMPARER_COMPONENT_ACCEPT_ALL_CALL_MERGER_HPP
#define QCTOOL_CALLCOMPARER_COMPONENT_A... | {"hexsha": "7c3f38d2e0381e9b3a06cd63d02838e98bbca8c5", "size": 1271, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "components/SNPSummaryComponent/include/components/SNPSummaryComponent/AcceptAllCallMerger.hpp", "max_stars_repo_name": "CreRecombinase/qctool", "max_stars_repo_head_hexsha": "6dad3a15c461177bf6940ba... |
[STATEMENT]
lemma pfx_not_empty: "valid_prefix pfx \<Longrightarrow> prefix_to_wordset pfx \<noteq> {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. valid_prefix pfx \<Longrightarrow> prefix_to_wordset pfx \<noteq> {}
[PROOF STEP]
unfolding valid_prefix_def prefix_to_wordset_def
[PROOF STATE]
proof (prove)
goal (1... | {"llama_tokens": 202, "file": "IP_Addresses_Prefix_Match", "length": 2} |
import numpy as np
from pyriemann.utils.test import (
is_square, is_sym, is_skew_sym, is_real, is_hermitian,
is_pos_def, is_pos_semi_def,
is_sym_pos_def, is_sym_pos_semi_def
)
n_channels = 3
def test_is_square():
assert is_square(np.eye(n_channels))
assert not is_square(np.ones((n_channels, n_c... | {"hexsha": "b4b1e8c293dc9eb06ee00baa9aaff16e3a6f3c5d", "size": 2469, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_utils_test.py", "max_stars_repo_name": "c-cameron/pyRiemann", "max_stars_repo_head_hexsha": "11298a81d34b173dfca19f653329bd0c8bf5a534", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
/* Boost.MultiIndex example of use of rearrange facilities.
*
* Copyright 2003-2008 Joaquin M Lopez Munoz.
* 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)
*
* See http://www.boost.org/libs/multi_index ... | {"hexsha": "593ac8d5a0c35f8c5ff78963f0c66361e74768ba", "size": 6649, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/multi_index/example/rearrange.cpp", "max_stars_repo_name": "zyiacas/boost-doc-zh", "max_stars_repo_head_hexsha": "689e5a3a0a4dbead1a960f7b039e3decda54aa2c", "max_stars_repo_licenses": ["BSL-1.0... |
import meep as mp
import math
import cmath
import numpy as np
import pickle
def run_nanodisk_simulation(name, empty, sx, sy, theta, resolution, heights_vector, materials_vector, geometries_vector,
characteristic_vector, abs_layer, air_layer, lmin, lmax, coating_layer=None,
magnetic_field=mp.Vector3(0,... | {"hexsha": "819f56b90f9f6adcb3730e0d1c0423f3544aa43a", "size": 7702, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/multicore.py", "max_stars_repo_name": "oscarmunoz20/owlmPy", "max_stars_repo_head_hexsha": "c5a41bef431f86da213566dda4382afd13bd6883", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
"""A very simple animation."""
import numpy as np
from pysketcher import Angle, Circle, Figure, Line, Point
from pysketcher.backend.matplotlib import MatplotlibBackend
from pysketcher.composition import Composition
def main():
circle = Circle(Point(0, 0), 1)
line = Line(Point(0, 0), Point(0, 1))
def fun... | {"hexsha": "c7b7efa78931291a6d36cfb3ed7bfdba2548fc9c", "size": 677, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/animation.py", "max_stars_repo_name": "rddaz2013/pysketcher", "max_stars_repo_head_hexsha": "9d4079baf0aa04f8fa80dc6edcf03bf1c14f70a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
using Documenter, Hashpipe
makedocs(sitename="Hashpipe Documentation",
modules = [Hashpipe],
)
deploydocs(
repo = "github.com/max-Hawkins/Hashpipe.jl.git",
devbranch = "main"
) | {"hexsha": "894bb4bef3d01b7770fe84943aa2da0a2e68ee92", "size": 195, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "max-Hawkins/Hashpipe.jl", "max_stars_repo_head_hexsha": "cec9ec56830f094df05f6c98badaa8ea84612b87", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
module NamedIndexing
export NamedAxisArray
export axisnames
struct NamedAxisArray{T, N, A<:AbstractArray{T, N}, Names} <: AbstractArray{T, N}
data::A
end
function NamedAxisArray(data::A, names::NTuple{N, Symbol}) where {T, N, A<:AbstractArray{T, N}}
NamedAxisArray{T, N, A, names}(data)
end
Base.size(AA::Nam... | {"hexsha": "0c0687067b95d9551d60eeb8886431fa5dbd979a", "size": 1055, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NamedIndexing.jl", "max_stars_repo_name": "iamed2/NamedIndexing.jl", "max_stars_repo_head_hexsha": "043097ba4ad29591c7407b60a8a024c2bf6005a8", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from math import fabs as fabs
from math import floor as floor
from math import sqrt as sqrt
from scipy.special import erfc as erfc
from scipy.special import gammaincc as gammaincc
class FrequencyTest:
@staticmethod
def monobit_test(binary_data:str, verbose=False):
"""
The focus of the test is ... | {"hexsha": "c7cbcb99e05b0172a53e491c20ba5a4d7a568115", "size": 5258, "ext": "py", "lang": "Python", "max_stars_repo_path": "nist_randomness_testsuite/FrequencyTest.py", "max_stars_repo_name": "Goluck-Konuko/cellular_automata_prng", "max_stars_repo_head_hexsha": "da8bb374ff4c9f0b508c767e2787754f6de1e56a", "max_stars_rep... |
__doc__ = \
"""
================================================================
Application Input/Output utilities (:mod:`mango.application.io`)
================================================================
.. currentmodule:: mango.application.io
Application specific input/output utilities.
Classes
=======
.. a... | {"hexsha": "8d926162db1a577139cf18cf227dfe7d73b98a5d", "size": 11037, "ext": "py", "lang": "Python", "max_stars_repo_path": "misc/python/mango/application/io.py", "max_stars_repo_name": "pymango/pymango", "max_stars_repo_head_hexsha": "b55f831f0194b214e746b2dfb4d9c6671a1abc38", "max_stars_repo_licenses": ["BSD-2-Clause... |
# Copyright (c) 2020 Jeff Irion and contributors
#
# This file originated from the `graphslam` package:
#
# https://github.com/JeffLIrion/python-graphslam
r"""A ``Graph`` class that stores the edges and vertices required for Graph SLAM.
"""
from collections import defaultdict
from functools import reduce
import w... | {"hexsha": "83c181dc792c98a9b27a0b70b23dfd7072aa3b9d", "size": 8832, "ext": "py", "lang": "Python", "max_stars_repo_path": "SLAM/GraphBasedSLAM/graphslam/graph.py", "max_stars_repo_name": "pruidzeko/PythonRobotics", "max_stars_repo_head_hexsha": "5ff9b70d737121c2947d844ecfb1fa07abdd210c", "max_stars_repo_licenses": ["M... |
struct Identity <: LocalOperator
end
kernelvals(biop::Identity, x) = nothing
integrand(op::Identity, kernel, x, g, f) = dot(f[1], g[1])
scalartype(op::Identity) = Union{}
struct NCross <: LocalOperator
end
kernelvals(op::NCross, mp) = nothing
integrand(op::NCross, kernel, x, g, f) = dot(g[1], normal(x) × f[1])
scala... | {"hexsha": "ad362fb559554dce29615b25077e7a4ff5a048b7", "size": 4075, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/identityop.jl", "max_stars_repo_name": "paresula/BEAST.jl", "max_stars_repo_head_hexsha": "44be41a27c6ab2cb6b9ecf43a6a90a4ff8f3488a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import torch
import torch.nn
import torch.nn.functional as nn
import torch.autograd as autograd
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from torch.autograd import Variable
from tensorflow.examples.tutorials.mnist import in... | {"hexsha": "3f26844890a761f6fce3402749f35a3d21a617b1", "size": 4849, "ext": "py", "lang": "Python", "max_stars_repo_path": "GAN/dual_gan/dualgan_pytorch.py", "max_stars_repo_name": "vtabbott/generative-models", "max_stars_repo_head_hexsha": "c97ae1b396a32ef8908e27468aef78b758202d1c", "max_stars_repo_licenses": ["Unlice... |
# http://hdfeos.org/zoo/index_openLAADS_Examples.php
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
FILE_NAME = 'MOD08_D3.A2010001.006.2015041224130.hdf'
DATAFIELD_NAME = 'Cloud_F... | {"hexsha": "86e3c91c800e2a5f8102c1044c5b8380fa7487fa", "size": 2064, "ext": "py", "lang": "Python", "max_stars_repo_path": "gis/hdf_tests/laads_example.py", "max_stars_repo_name": "natelowry/data_visualization", "max_stars_repo_head_hexsha": "8d01b6ae5337ff5c7a4eda59e657a53d19af5f32", "max_stars_repo_licenses": ["MIT"]... |
#-*- coding:utf-8 -*-
#'''
# Created on 19-5-15 上午11:48
#
# @Author: Greg Gao(laygin)
#'''
import numpy as np
import cv2
import xml.etree.ElementTree as ET
from skimage.draw import polygon as drawpoly
from functools import reduce
from utils import compute_distance_of_2pts, compute_angle_of_2pts
from keras.applications.... | {"hexsha": "4498dda7402455cc705174cf92198283483fab81", "size": 7583, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/data_utils.py", "max_stars_repo_name": "opconty/keras_std_plus_plus", "max_stars_repo_head_hexsha": "36939047573c4c768e6008e9c475a62c5b23e5f6", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
SUBROUTINE SCHDC(A,LDA,P,WORK,JPVT,JOB,INFO)
INTEGER LDA,P,JPVT(*),JOB,INFO
REAL A(LDA,*),WORK(*)
INTEGER PU,PL,PLP1,J,JP,JT,K,KB,KM1,KP1,L,MAXL
REAL TEMP
REAL MAXDIA
LOGICAL SWAPK,NEGK
PL=1
PU=0
INFO=P
IF(JOB.EQ.0)GOTO160
DO70K=1,P
SWAPK=JPV... | {"hexsha": "3c0a86517e00614d5e44a83587a083cf804a41e3", "size": 74183, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "math/blas/sgeco.f", "max_stars_repo_name": "fluffynukeit/mdsplus", "max_stars_repo_head_hexsha": "a204d2e9d26554bb035945595210f2a57d187250", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
import numpy as np
import tensorflow as tf
import os
import sys
def fatal_error(message):
tf.logging.log(tf.logging.ERROR, message)
sys.exit(1)
def init_logging(verbosity):
tf.logging.set_verbosity(verbosity)
tf.logging.log(tf.logging.INFO, "Using Python version %s" % sys.version)
tf.logging.log... | {"hexsha": "772dcf0ab49998b10a783704e2d173b2e5b5e196", "size": 4880, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/util.py", "max_stars_repo_name": "kleag/LISA", "max_stars_repo_head_hexsha": "7fa9812577a517d51d0c5006400d292292aefad6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "ma... |
#!/usr/bin/env python
# import sys
# import os
#
# # Using https://stackoverflow.com/questions/51520/how-to-get-an-absolute-file-path-in-python
# utils_path = os.path.abspath("utils")
#
# # Using https://askubuntu.com/questions/470982/how-to-add-a-python-module-to-syspath/471168
# sys.path.insert(0, utils_path)
impo... | {"hexsha": "1d09d04ed22ed23ff0982d6a11a6d380345bb303", "size": 23722, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_extraction.py", "max_stars_repo_name": "varun-invent/feature-extractor", "max_stars_repo_head_hexsha": "82a4f2e0a0523e0e6bcd5d7cd0d434e6d76951f0", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import datetime as dt
import pandas as pd
import bs4 as bs
import pickle
import requests
import os
import pandas_datareader.data as web
import yfinance as yf
style.use('ggplot')
# Extract Current top 50 Stocks which are part of Nifty50
de... | {"hexsha": "008cfd303e6e6c99dd1c3b5b62bb56cbe33cc4ee", "size": 2958, "ext": "py", "lang": "Python", "max_stars_repo_path": "heatmap.py", "max_stars_repo_name": "vbyravarasu/Real-Time-Stock-Market-Prediction-using-Ensemble-DL-and-Rainbow-DQN", "max_stars_repo_head_hexsha": "4ed7fc2a3995c33cf7b009810c73d01223ea85e4", "ma... |
using Test
using ExportWebAssembly
myfun(x) = sum((x, x, 1.0))
write_bitcode("myfun.bc", myfun, Tuple{Float64})
f() = @extern(:myfun, Int32, Tuple{Float64, Int64}, 1.1, 3)
@code_llvm f() | {"hexsha": "a2de112dd7cfb46158d0503394545f50d07d5411", "size": 199, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "tshort/ExportWebAssembly.jl", "max_stars_repo_head_hexsha": "da1b1c221510baa020b89dbfd2c3c9d321aa8869", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
def make_ngrams(tokens: list, n: int) -> list:
"""Creates n-grams for the given token sequence.
Args:
tokens (list): a list of tokens as strings
n (int): the length of n-grams to create
Returns:
list: list of tuples of strings, each tuple being one of the individual n-grams... | {"hexsha": "b1219ad260df87d873413c14951392f7761d81c3", "size": 5115, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/ngram.py", "max_stars_repo_name": "JohnnyWang1998/ukraineRussiaTweetsSentimentAnalysis", "max_stars_repo_head_hexsha": "80132e1b453b4439c1610fc6b073dcce76307fb5", "max_stars_repo_licens... |
import math
import numpy as np
class Interval:
def __init__(self, x):
self.x = x.copy()
def __repr__(self):
"""
Representation of the interval as [a, b]
:return: rounded ends of the interval
"""
return "[" + str(round(self.x[0], 3)) + ", " + str(round(self.x[1... | {"hexsha": "0e54560849d4c32b4b8524a9858cd9bcba8d46f4", "size": 11226, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/intervalpy/interval.py", "max_stars_repo_name": "artemmam/intervalpy", "max_stars_repo_head_hexsha": "bf550dc26457f3786fc52c3056b9c716588d03d7", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "d168af6b10aa64ce3c4d3bcc3cab32ecdbdf21fa", "size": 15197, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/mxnet/numpy_extension/_op.py", "max_stars_repo_name": "blazej-wieliczko/incubator-mxnet", "max_stars_repo_head_hexsha": "66a65924f03e6e62ca0619afb02e2a674fcccbfd", "max_stars_repo_licenses... |
import numpy as np
import ipdb
import torch
import torch.nn as nn
import numpy as np
import random
from model import Policy, StateGen
from baselines.common.segment_tree import SumSegmentTree, MinSegmentTree
from utils_bw import select_mj, evaluate_mj, zero_mean_unit_std
class ReplayBuffer:
"""
Replay buffer...... | {"hexsha": "ce17066850db0fa6384234b6c03273700d206de8", "size": 19160, "ext": "py", "lang": "Python", "max_stars_repo_path": "bw_module_mujoco.py", "max_stars_repo_name": "soumye/bwmodel", "max_stars_repo_head_hexsha": "d6e321f23145c653598c979c884ee1bc70aaefe2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
# # Comparison with MathOptInterface on a Probability Simplex
# In this example, we project a random point onto a probability simplex with the Frank-Wolfe algorithm using
# either the specialized LMO defined in the package or a generic LP formulation using `MathOptInterface.jl` (MOI) and
# `GLPK` as underlying LP solv... | {"hexsha": "4b40e832bcf88edbc45b8df06ac98a83296c829c", "size": 3797, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/docs_1_mathopt_lmo.jl", "max_stars_repo_name": "gdalle/FrankWolfe.jl-2", "max_stars_repo_head_hexsha": "c3b3903c4808e24aa9e0f655aa2f8de0f2c1571c", "max_stars_repo_licenses": ["MIT"], "max_... |
! @@name: target_update.2f
! @@type: F-free
! @@compilable: yes
! @@linkable: no
! @@expect: success
subroutine vec_mult(p, v1, v2, N)
interface
logical function maybe_init_again (v1, N)
real :: v1(N)
integer :: N
end function
end interface
real :: p(N), v1(N), v2(N)
integer :: ... | {"hexsha": "38d44f39bc6c4aa2a426803a2ea3e02d5b5186e0", "size": 939, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "test/openmp_examples/sources/Example_target_update.2f.f", "max_stars_repo_name": "kammerdienerb/flang", "max_stars_repo_head_hexsha": "8cc4a02b94713750f09fe6b756d33daced0b4a74", "max_stars_repo_lic... |
from collections import namedtuple
from numpy import std, zeros, cumsum, mean, r_, copy
from numpy import sum as npsum, min as npmin, max as npmax
def PnlStats(pnl_contr):
# This function computes some Profit and loss (P&L) statistics
# INPUT
# pnl_contr :[matrix] (n_ x t_end) portfolio P&L contribu... | {"hexsha": "df1fc4de2d2c320ed0f170bb754fdbeefb0ad586", "size": 1441, "ext": "py", "lang": "Python", "max_stars_repo_path": "functions_legacy/PnlStats.py", "max_stars_repo_name": "dpopadic/arpmRes", "max_stars_repo_head_hexsha": "ddcc4de713b46e3e9dcb77cc08c502ce4df54f76", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from __future__ import division
from __future__ import print_function
import copy
import csv
import json
import numpy as np
import scipy.linalg
import scipy.io as sio
import os
import os.path as osp
import cPickle as pickle
import cPickle as pkl
import torch
from torch.autograd import Variable
from . import transformat... | {"hexsha": "cb08eb56217c26219e9f65d00ef8b74ae9a24d8f", "size": 3258, "ext": "py", "lang": "Python", "max_stars_repo_path": "acsm/utils/cub_parse.py", "max_stars_repo_name": "eldar/acsm", "max_stars_repo_head_hexsha": "04069e8bb4c12185473dc10c3355e5367fa98968", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
from new_keras_rnn import _rnn_and_lstm
from transformer import _transformer
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical
import tensorflow as tf
from data_process import load_text,load_label
from data_process import histogram_bui... | {"hexsha": "e355f28b19f8c948f8748a46a98ca13e557e2832", "size": 2474, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/DNN_Based_Classification.py", "max_stars_repo_name": "Adonis-galaxy/AI_Final_Project", "max_stars_repo_head_hexsha": "98b9a815a2053e85fd07ac47cb7fbac47d7b83a0", "max_stars_repo_licenses": ["MI... |
SUBROUTINE CALHDMF
! *** CALDMF CALCULATES THE HORIZONTAL VISCOSITY AND
! *** DIFFUSIVE MOMENTUM FLUXES. THE VISCOSITY, AH IS CALCULATED USING
! *** SMAGORINSKY'S SUBGRID SCALE FORMULATION PLUS ... | {"hexsha": "22a2ee3dcd4db95f219f9ad831175e258b1c5550", "size": 11596, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "EFDC/calhdmf.f90", "max_stars_repo_name": "dsi-llc/EFDCPlus", "max_stars_repo_head_hexsha": "27ece1cd0bb9e02a46d1ad20f343bc5d109acfb3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
import operator
import numpy as np
import pandas as pd
from typing import Any
from typing import List
from typing import Dict
from typing import Tuple
from typing import Union
from typing import Optional
from typing import Iterable
from pyrobot.stock_frame import StockFrame
class Indicators():
"""
Represen... | {"hexsha": "43aa569d33cca72d3da7aa4de577a4d447ddc451", "size": 36546, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyrobot/indicators.py", "max_stars_repo_name": "Saby-Bishops/Stock-Prediction-using-different-models", "max_stars_repo_head_hexsha": "123f1e26c417300829a8ccb1007995dfa574db65", "max_stars_repo_li... |
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2011, Willow Garage, Inc.
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
... | {"hexsha": "4400aa7dc5284b7113d34d6c8e2ccce1fa7d87ce", "size": 5442, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "kfusion/src/color_volume.cpp", "max_stars_repo_name": "libin1987832/kinfu_remake", "max_stars_repo_head_hexsha": "7e44feba65daf67bb59447d5128f4a669b841ece", "max_stars_repo_licenses": ["BSD-3-Clause... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import os
from sklearn.metrics import f1_score
import graphviz
from sklearn import tree
import seaborn as sns
import tensorflow as tf
from sklearn.model_selection import train_test_... | {"hexsha": "91f68a51f6aee14d391c6c27b222d8f40dd4d351", "size": 3693, "ext": "py", "lang": "Python", "max_stars_repo_path": "ion/preproc.py", "max_stars_repo_name": "nizamphoenix/kaggle", "max_stars_repo_head_hexsha": "a9c993d0441a6d9260d605a630f95d938e6329db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
values_per = values_per + 1
indices(values_per) = insert_row
values(values_per) = insert_value
| {"hexsha": "22579a81b42fd4faf9f4d4f4f459e47c8e3ad465", "size": 101, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/Fortran/solver_includes/AppendToVector.f90", "max_stars_repo_name": "Kokookster/NTPoly", "max_stars_repo_head_hexsha": "717b2e344e800ea6c2de7061b96dd51ffd089f36", "max_stars_repo_licenses"... |
import numpy as np
import pyomo.environ as pyo
from pyomo.core.expr import numeric_expr
from pyomo.opt import SolverFactory
from sklearn.preprocessing import StandardScaler
from penlm.base_estimators import BaseClassifier, BaseRegressor
from abc import ABC, abstractmethod
from typing import Dict, Tuple
class BaseSmo... | {"hexsha": "81ffbc49c6aad9a7bb64df9f303d37e77565e1bb", "size": 6983, "ext": "py", "lang": "Python", "max_stars_repo_path": "penlm/smooth_linear_model.py", "max_stars_repo_name": "bellibot/penlm", "max_stars_repo_head_hexsha": "cd3e82c319ed221cfdbd6c566ee63be26b3aa6fb", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from . import ZClient, UClient
from .. import TRIGGER_SETUP_SEQ, START_DAQ, STOP_DAQ
import numpy as np
import curio
class ZClientCurioBase(ZClient):
def __init__(self, *args, max_events=None, **kwargs):
super().__init__(*args, **kwargs)
self.cmd_lock = curio.Lock()
async def __cntrl_recv(sel... | {"hexsha": "74b490741027b4cc691ab112ca69a107cb7a6ed6", "size": 4085, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygerm/client/curio_zmq.py", "max_stars_repo_name": "NSLS-II/GeRM", "max_stars_repo_head_hexsha": "b9251d171ca768b6a1a8d6af774afe5c670b6661", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import numpy as np
def intersection(line1, line2):
"""Finds the intersection of two lines given in Hesse normal form.
Returns closest integer pixel locations.
See https://stackoverflow.com/a/383527/5087436
"""
rho1, theta1 = line1[0]
rho2, theta2 = line2[0]
A = np.array([
[np.cos(t... | {"hexsha": "a5d97a6992957fb11767d85eed4af4c723e3e2d5", "size": 992, "ext": "py", "lang": "Python", "max_stars_repo_path": "intersection.py", "max_stars_repo_name": "sahil2kuppal/sudoku", "max_stars_repo_head_hexsha": "7b81459d229efc494a67ea3b4077ed42dd6d81c1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
# Copyright 2020 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 a... | {"hexsha": "96ada060899917c90d9bd8b1baed8223bde5fb3e", "size": 15758, "ext": "py", "lang": "Python", "max_stars_repo_path": "mindspore/nn/probability/toolbox/uncertainty_evaluation.py", "max_stars_repo_name": "dongkcs/mindspore", "max_stars_repo_head_hexsha": "cd7df6dbf463ff3128e9181e9d0c779cecb81320", "max_stars_repo_... |
import JuliaCLI
@async JuliaCLI.serve() # TODO: don't
let path = joinpath(DEPOT_PATH[1], "jlcli", "socket")
for i in 1:100
isfile(path) && break
sleep(0.1)
end
end
include("cli_test.jl")
| {"hexsha": "5de84195f00b4cd28f81e7d95804562a9c90dcfc", "size": 214, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "tkf/JuliaCLI.jl", "max_stars_repo_head_hexsha": "34f519837af9e2706a44b7945c5e09f93b41f1de", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_... |
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