text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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from forse.tools.nn_tools import *
from forse.tools.img_tools import *
from forse.tools.mix_tools import *
from keras.models import Sequential, Model, load_model
from keras.layers import UpSampling2D, Conv2D, Activation, BatchNormalization
from keras.layers import Reshape, Dense, Input
from keras.layers import LeakyReL... | {"hexsha": "fd07f0162854861fa2803bb8d173efcca8b7848a", "size": 5530, "ext": "py", "lang": "Python", "max_stars_repo_path": "forse/networks/dcgan.py", "max_stars_repo_name": "ai4cmb/ForSE", "max_stars_repo_head_hexsha": "8ceab3b2e47f077b9d5dbaee879a5385c3a76073", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#!/bin/bash
"""
Create timeseries averages for the NOAA water vapour data.
"""
from datetime import datetime
from pathlib import Path
import numpy
import h5py
import pandas
from wagl.geobox import GriddedGeoBox
from wagl.hdf5.compression import H5CompressionFilter
from wagl.hdf5 import read_h5_table, write_h5_image
... | {"hexsha": "141bd2c1ab3e5eae94262dfdddbbdf92d17d05e7", "size": 4420, "ext": "py", "lang": "Python", "max_stars_repo_path": "average_water_vapour.py", "max_stars_repo_name": "ASVincent/swfo", "max_stars_repo_head_hexsha": "17ef3c32047a5069c4db04fa04368a9268f19d93", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
from covariant_compositional_networks_tf2.CCN_Model import CCN_Model
import tensorflow as tf
from functools import reduce
from operator import mul
from ordered_set import OrderedSet
import numpy as np
from sklearn.metrics import accuracy_score
from graphColoring import randomNPGraph, checkIfGraphConnected
channels_in... | {"hexsha": "587fce2a9fa2bd4c8aa54c3be218882bc8cf144c", "size": 4050, "ext": "py", "lang": "Python", "max_stars_repo_path": "covariant_compositional_networks_tf2/tests/testModel_2.py", "max_stars_repo_name": "PiotrKaszuba/Covariant_Compositional_Networks_Tf2", "max_stars_repo_head_hexsha": "dcd287a5f063bf2c3a37120e3247a... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler
# set to True to enable training
# if set to False, training is skipped,
# and the weights are loaded from the session storage.
# this way, you can train the net... | {"hexsha": "1ca682958c7d5bca43cf5c045fdaaaa899a0ff90", "size": 6129, "ext": "py", "lang": "Python", "max_stars_repo_path": "shakespeare.py", "max_stars_repo_name": "christofferaakre/shakespeare", "max_stars_repo_head_hexsha": "c2563d19232465edbda2edaeebb7b93f491512d2", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
/*
* 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 ... | {"hexsha": "bff260c5b8de395b17e6b3d2c4ba12a33d6b7877", "size": 3710, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "modules/platforms/cpp/core-test/src/cache_store_test.cpp", "max_stars_repo_name": "geertjanw/ignite", "max_stars_repo_head_hexsha": "521149998a76d78a72628cf49d1ffad162ed5a01", "max_stars_repo_licens... |
[STATEMENT]
lemma split_list_first_unique:
assumes "u\<^sub>1 @ [a] @ u\<^sub>2 = v\<^sub>1 @ [a] @ v\<^sub>2" "a \<notin> set u\<^sub>1" "a \<notin> set v\<^sub>1"
shows "u\<^sub>1 = v\<^sub>1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. u\<^sub>1 = v\<^sub>1
[PROOF STEP]
proof -
[PROOF STATE]
proof (sta... | {"llama_tokens": 1116, "file": "Partial_Order_Reduction_Extensions_List_Extensions", "length": 8} |
/*
* Copyright (c) 2020-2022 The reone project contributors
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
... | {"hexsha": "0bda89a102dc37ba06bf28be2e2988b0b2093121", "size": 28553, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/script/expressiontree.cpp", "max_stars_repo_name": "seedhartha/revan", "max_stars_repo_head_hexsha": "b9a98007ca2f510b42894ecd09fb623571b433dc", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma in_atlas_order_le: "c \<in> c_manifold.atlas charts l" if "l \<le> k" "c \<in> atlas"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. c \<in> c_manifold.atlas charts l
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. c \<in> c_manifold.atlas charts l
[PROOF STEP]
interpret l: c... | {"llama_tokens": 494, "file": "Smooth_Manifolds_Differentiable_Manifold", "length": 8} |
#*- coding:UTF-8 -*-
"""
## ==========================================================================
##
## author : Liang He, heliang@mail.tsinghua.edu.cn
## Xianhong Chen, chenxianhong@mail.tsinghua.edu.cn
## descrption : sre10 demo
## comparison of LDA and LPLDA
## ... | {"hexsha": "4b611c88f3daa29e7fb63feb960dc18a11700f53", "size": 9026, "ext": "py", "lang": "Python", "max_stars_repo_path": "sre10_demo.py", "max_stars_repo_name": "sanphiee/LPLDA", "max_stars_repo_head_hexsha": "95941de0a84010dc8c8bdd12e39a276331c0d286", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 13,... |
import numpy as np
def int_type(img):
if img.max() > 255:
dtype = np.uint16
else:
dtype = np.uint8
return dtype
def normalize(img, maxval=255, pmin=0, pmax=100):
img = img.astype(np.float32)
mn, mx = [np.percentile(img, p) for p in [pmin, pmax]]
img = np.clip((img - mn) / (mx... | {"hexsha": "64f5ef014f453035f157c0c817ad5a7cd3367d9e", "size": 358, "ext": "py", "lang": "Python", "max_stars_repo_path": "care_batch/utils.py", "max_stars_repo_name": "amedyukhina/care_batch", "max_stars_repo_head_hexsha": "7670eb7bbd9339dcc580cf8686c79900253392eb", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import pandas as pd
import numpy as np
#import os
from Clean_function import clean_note
from collections import OrderedDict
from progressbar import Percentage, ProgressBar,Bar,ETA
from sklearn.model_selection import train_test_split
import tensorflow as tf
import pickle
maxlen=2500
min_word_frequency=5
from tensorfl... | {"hexsha": "0d3c4949c6abfb53eecd2750caee270923ab9564", "size": 8863, "ext": "py", "lang": "Python", "max_stars_repo_path": "update_and_write.py", "max_stars_repo_name": "PiSchool/icd9-labelling", "max_stars_repo_head_hexsha": "1007643c7b96b9ba72d73678a75ffc68e5a2d882", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
SUBROUTINE TAVISPAK3X(EA,FCN,A,B,C,LUERR,IERR)
IMPLICIT REAL*8 (A-H,O-Z)
C
C THIS ROUTINE SOLVES FOR THE A,B,C COEFFICIENTS OF THE FUNCTION
C
C FCN = A*COS(EA) + B*SIN(EA) - C
C
C WHICH IS USED TO APPROXIMATE THE RAM AND SHADOW FUNCTIONS. THREE
C PAIRS OF ECCENTRIC ANOMALY AND FUNCTION VALUES ARE INP... | {"hexsha": "7a7291d853467b0101291399b1e873c1fc3ff0da", "size": 3298, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "gsc-13083/tavispak3x.for", "max_stars_repo_name": "SteveDoyle2/nasa-cosmic", "max_stars_repo_head_hexsha": "c8015a9851a04f0483b978d92c2cbaee31c81fe3", "max_stars_repo_licenses": ["BSD-Source-Cod... |
import inspect
import random
import re
import statistics
from bisect import bisect_left
from functools import lru_cache
from typing import Dict, List, Set, Tuple
import numpy as np
import pandas as pd
class _StringSpans:
__slots__ = ("string", "spans")
def __init__(self, string, spans):
self.string ... | {"hexsha": "630c99857f6d521043b8c6fb2af9896f87dff57a", "size": 18769, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lemon/_lemon_utils.py", "max_stars_repo_name": "NilsBarlaug/lemon", "max_stars_repo_head_hexsha": "ee82f20253c50eb5a958fc5507b0df8ca51fa317", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
Distance functions to define how "far" apart two vectors are.
"""
import numpy as np
from squidward.utils import exactly_1d
np.seterr(over="raise")
# ---------------------------------------------------------------------------------------------------------------------
# Radial Basis Function
# -------------------... | {"hexsha": "27d06e12d208fe9ab0fb15bb0434c5ac914173ea", "size": 3620, "ext": "py", "lang": "Python", "max_stars_repo_path": "squidward/kernels/distance.py", "max_stars_repo_name": "looyclark/Gaussian_Processes", "max_stars_repo_head_hexsha": "f02aa64bfbca8b3086e403a81178e6ae4702b48a", "max_stars_repo_licenses": ["MIT"],... |
import os
import numpy as np
import pandas as pd
import matplotlib.mlab as ml
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
from mpl_toolkits.axes_grid1 import make_axes_locatable
import networkx
import matplotlib.pyplot as plt
import shapely.geometry.linestring as shapely
from shapely.geomet... | {"hexsha": "533918d205e73429774ab8b93fcbd2405fa0cea2", "size": 18317, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/routemap_cluster_the_shapes.py", "max_stars_repo_name": "jweckstr/westmetro_scripts", "max_stars_repo_head_hexsha": "a16385b00ac8d80f0068f348226ed89e2d0425a9", "max_stars_repo_licenses": ... |
using LinearAlgebra, ForwardDiff, NLPModels
export Bisseccao, Newton_rc_bissec
function Bisseccao(g, a, b, max_bissec; λ = 0)
ϵ = 1e-4
status= :resolvido
iter = 0
while abs(g(λ)) > ϵ
if g(a)*g(b)==0 && g(b)==0
λ=b
else
λ=a
end
if g(a) * g(b) <... | {"hexsha": "87deea35c473f1911df10821588679bc0386d3f2", "size": 3648, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Newton_rc_bissec.jl", "max_stars_repo_name": "RogerioOMDS/JSOSolverTemplate.jl", "max_stars_repo_head_hexsha": "fd57f4de0d9253003f15566fc502cca37abd890e", "max_stars_repo_licenses": ["MIT"], "m... |
import os
import imageio
import numpy as np
from elf.io import open_file
from elf.util import normalize_index
from ..data import ConcatDataset, ImageCollectionDataset, SegmentationDataset
from .util import get_trainer, get_normalizer
from .prediction import predict_with_halo
try:
import napari
except ImportError... | {"hexsha": "31d1db6b1e2088ef15d7b0131c9ebd75e0651fc0", "size": 8516, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_em/util/validation.py", "max_stars_repo_name": "JonasHell/torch-em", "max_stars_repo_head_hexsha": "2e008e0cd2f0ea6681581374fce4f9f47b986d55", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import sys
import matplotlib.pyplot as plt
from pathlib import Path
from loguru import logger
import numpy as np
sys.path.append("./")
from fcutils.plot.figure import clean_axes
from myterial import blue_grey
from analysis.visuals import plot_probe_electrodes
"""
Running this script will save a figure with the... | {"hexsha": "655a18219a03f0f212253cdbabbdbb6d9e02cd00", "size": 3314, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/ephys/probe_n_units_per_channel.py", "max_stars_repo_name": "FedeClaudi/LocomotionControl", "max_stars_repo_head_hexsha": "1281f7894825096ad212407351463a2105c5152a", "max_stars_repo_licen... |
One of the many Hotels in Davis. Amenties include Wifi Hot Spots Wireless Internet and cable TV.
University B&B is closed June 29, 2006.
| {"hexsha": "d7980641e841efea31047973a2718ff3a537478d", "size": 141, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/University_Bed_and_Breakfast.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "... |
import torch
import torch.nn as nn
import numpy as np
from torch.nn import functional as F
import math
from utils.tools import make_positions
def Embedding(num_embeddings, embedding_dim, padding_idx=None):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean... | {"hexsha": "5e6bc55e268372029e7f57bc85f8e7bb87ff1abd", "size": 25543, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/blocks.py", "max_stars_repo_name": "ishine/DiffSinger", "max_stars_repo_head_hexsha": "d5dbe05ee1c7da0878393c73129089a67d0fe935", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
""" Utility for storing common lib and data structures """
import math
from collections import namedtuple
from itertools import product
import numpy as np
import simplejson as json
__author__ = 'Ari Saha (arisaha@icloud.com)'
__date__ = 'Wednesday, March 14th 2018, 2:3... | {"hexsha": "1e767451bedee75bd0182d975de385a08042269a", "size": 13021, "ext": "py", "lang": "Python", "max_stars_repo_path": "rainman2/lib/environment/cellular/dev/utils.py", "max_stars_repo_name": "att-innovate/rainman2", "max_stars_repo_head_hexsha": "edd07c03a9d33a2e44b3a333fc28dc73c8cbe56e", "max_stars_repo_licenses... |
__precompile__()
module JFVM
# global mumps_solver
# using PyPlot
try
import MUMPS
global mumps_solver = MUMPS
catch
@info "MUMPS solver (optional) is not available."
end
using SparseArrays, FFTW
# using PyCall
# I prefer not to use the following command for the issues that it has on windows machines
# pygui_s... | {"hexsha": "d255c54a2d03af51f7b92e325b1db1f1c7ce17b4", "size": 2027, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/JFVM.jl", "max_stars_repo_name": "simulkade/JFVM", "max_stars_repo_head_hexsha": "3e6bf931a430c4e4ccb7ca7824a947c6b09f8fe2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 38, "max_star... |
[STATEMENT]
lemma lset_P_V [simp]: "lset P \<subseteq> V"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lset P \<subseteq> V
[PROOF STEP]
by (simp add: valid_path_in_V) | {"llama_tokens": 77, "file": "Parity_Game_ParityGame", "length": 1} |
module Searching
export bfs_parents, bfs_tree, dfs_parents, dfs_tree
using ...CSetDataStructures, ..BasicGraphs
"""
tree(parents)
Convert a parents array into a directed graph.
"""
function tree(parents::AbstractVector{Int})
n = T(length(parents))
t = Graph(n)
for (v, u) in enumerate(parents)
... | {"hexsha": "461d1d6b29523dc479300e0d6afe246ee8d0aa5e", "size": 3486, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/graphs/Searching.jl", "max_stars_repo_name": "slwu89/Catlab.jl", "max_stars_repo_head_hexsha": "d197b0c12c65fe72198baf9c990e6a4e1f3aebe0", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
@testset "reshape" begin
test_rrule(reshape, rand(4, 5), (2, 10) ⊢ nothing)
test_rrule(reshape, rand(4, 5), 2 ⊢ nothing, 10 ⊢ nothing)
end
@testset "hcat" begin
A = randn(3, 2)
B = randn(3)
C = randn(3, 3)
test_rrule(hcat, A, B, C; check_inferred=false)
end
@testset "reduce hcat" begin
A =... | {"hexsha": "26d000bd3016fc9a1fcedb8c69d8ffb175c99993", "size": 846, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/rulesets/Base/array.jl", "max_stars_repo_name": "DhairyaLGandhi/ChainRules.jl", "max_stars_repo_head_hexsha": "76ef95c326e773c6c7140fb56eb2fd16a2af468b", "max_stars_repo_licenses": ["MIT"], "ma... |
module sqlite
use iso_c_binding, only: c_int
implicit none
private c_int
include "constants.f90"
interface
function sqlite3_bind_text(stmt, index, text, bytes, destructor) bind(c)
use iso_c_binding, only: c_int, c_ptr
type(c_ptr), value :: stmt, text, destructor
integer(c_int), v... | {"hexsha": "ea301a28f5e87e3a50b4662c3ba4f8c148fd9c0b", "size": 2442, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "sources/sqlite.f90", "max_stars_repo_name": "dram/fortran-sqlite", "max_stars_repo_head_hexsha": "9912de17549db247cbcf9768a462e52ed7907af0", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_sta... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 04 10:35:33 2018
@author: ldh
"""
# utils.py
import datetime as dt
import numpy as np
import pandas as pd
def array_decorator(func):
return np.frompyfunc(func,1,1)
@array_decorator
def time_matlab2py(date_time_ordinal):
'''
Convert matlab format of time or ... | {"hexsha": "a83807024b1c264ffd6da45d52cca5350f23f68a", "size": 2344, "ext": "py", "lang": "Python", "max_stars_repo_path": "matlab_convert/utils.py", "max_stars_repo_name": "orxg/helper", "max_stars_repo_head_hexsha": "6cbad158213028e64407c8ef0fd4e66a9aff9917", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
\openepigraph{Science knows no country, because knowledge belongs to humanity, and is the torch which illuminates the world.}{---Louis Pasteur}
\openepigraph{We live in a society exquisitely dependent on science and technology, in which hardly anyone knows anything about science and technology.}{---Carl Sagan}
Many... | {"hexsha": "443af29b9435f0d488b7107f3c6e7be2ed323c58", "size": 50713, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "LatexVersion/Chapter1_PsychologicalScience.tex", "max_stars_repo_name": "danBurrell/research_methods_with_R", "max_stars_repo_head_hexsha": "74745c4bd69d185f1a36ef38638be8cc55966b06", "max_stars_re... |
from scipy import integrate, interpolate
from matplotlib import pyplot as plt
import numpy as np
import utils
# Options
nb_nodes = 30
nb_phases = 4
nb_frame_inter = 500
nb_dim = 3
output_files = "Eocar"
# read states
nb_points = (nb_phases * nb_nodes) + 1
i = 0
t = np.ndarray(nb_points) # initialization of the tim... | {"hexsha": "5b2eb8d6bd0931d296c51bc1909735456fd7ee0a", "size": 3116, "ext": "py", "lang": "Python", "max_stars_repo_path": "analyses/show_eocar.py", "max_stars_repo_name": "ValKanAll/ViolinOptimalControl", "max_stars_repo_head_hexsha": "556311aecc3e13b1fd2dd6927d22510b127c38c4", "max_stars_repo_licenses": ["MIT"], "max... |
from os import PathLike
from pathlib import Path
import cv2
import numpy as np
import torch
from PIL import Image
from numpy import linalg
from torch import nn
from torchvision import transforms, models
from kts.cpd_auto import cpd_auto
class FeatureExtractor(object):
def __init__(self):
self.preprocess... | {"hexsha": "2be1e9271b87d120178eac1bf5af58ac09403450", "size": 2891, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/helpers/video_helper.py", "max_stars_repo_name": "wqliu657/DSNet", "max_stars_repo_head_hexsha": "1804176e2e8b57846beb063667448982273fca89", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
displayer.py is responsible for saving the rendered animation into file.
"""
import subprocess as sp
import os
import time
import numpy as np
import pygame as pg
import configs as cfg
FFMPEG_BIN = "ffmpeg" # on Windows
def savevideo(animation):
""" Saves the simulation as a video """
command = [FFMPE... | {"hexsha": "cd0f9b9cbb768ac81cae72525b6e78b39f1b6f9b", "size": 2923, "ext": "py", "lang": "Python", "max_stars_repo_path": "displayer.py", "max_stars_repo_name": "naummo/swarm_maze_opencl_solver", "max_stars_repo_head_hexsha": "1047e1293e90f484ccc4ff77cfe61196fb7cbbc6", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
using ParameterisedModule
using Test
Functor = Function
# Write your own tests here.
# :(@sig struct S{A}
# x :: Int
# y :: A
# struct K end
# end) |> (x -> macroexpand(ParameterisedModule, x)) |> println
@testset "I'm here?" begin
@sig struct S{A}
x :: Int
y :: A
struct K end
end
... | {"hexsha": "51eee4efdaa26bb0178a58567f7b1bc465f18897", "size": 2678, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaTagBot/ParameterisedModule.jl", "max_stars_repo_head_hexsha": "5e8eb12915093479382db225760958204c8e7e8e", "max_stars_repo_licenses": ["MIT"], "max_st... |
{-# OPTIONS --prop #-}
{-# TERMINATING #-}
makeloop : {P : Prop} → P → P
makeloop p = makeloop p
postulate
A : Set
B C : A → Prop
record AB : Set where
no-eta-equality -- the problem goes away if this is left out
constructor _,_
field
a : A
b : B a
open AB public
-- -- Same problem if replacing t... | {"hexsha": "a9c1a3b5aec68878487262818032e08d62bbb3f9", "size": 914, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Fail/Issue4118.agda", "max_stars_repo_name": "shlevy/agda", "max_stars_repo_head_hexsha": "ed8ac6f4062ea8a20fa0f62d5db82d4e68278338", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
# from typing import ?
import torch
import torch.nn as nn
from numpy import exp, sqrt
from numpy.random import normal
class VAE(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
# linear (size of input, 2d), size of input= max possible size i... | {"hexsha": "33d97c280b59e3be525682e98fc1ab82f95f868e", "size": 3976, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/prototypes/vae.py", "max_stars_repo_name": "avishvj/3d-reactions", "max_stars_repo_head_hexsha": "8326fbecf2e8a0d0445508ae809dc61e3116d161", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
Tensor Products of Crystals
Main entry points:
- :class:`~sage.combinat.crystals.tensor_product.TensorProductOfCrystals`
- :class:`~sage.combinat.crystals.tensor_product.CrystalOfTableaux`
AUTHORS:
- Anne Schilling, Nicolas Thiery (2007): Initial version
- Ben Salisbury, Travis Scrimshaw (2013): Refactored tens... | {"hexsha": "7cacfeb335bf5999c026802106511ca8e50523f8", "size": 43529, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/combinat/crystals/tensor_product.py", "max_stars_repo_name": "haiyashah/sage", "max_stars_repo_head_hexsha": "55a711e3d6251f2ff4f3bcccc4c6a8b7a2a8d1b2", "max_stars_repo_licenses": ["BSL-... |
(* Useful properties of our Simple.v specification *)
Require Import Simple.
(* Dominates is transitive *)
Theorem dom_trans {D : Domain} :
forall {s1 s2 s3},
Dominates s1 s2 -> Dominates s2 s3 -> Dominates s1 s3.
(* Break apart our Dominates arguments *)
intros. destruct H. destruct H0. ref... | {"author": "Warbo", "repo": "powerplay", "sha": "8792220032f8a277b775d52e46225ab58ddb6928", "save_path": "github-repos/coq/Warbo-powerplay", "path": "github-repos/coq/Warbo-powerplay/powerplay-8792220032f8a277b775d52e46225ab58ddb6928/SimpleTests.v"} |
using AxisArrays
using AxisKeys
using CSV
using Combinatorics
using DataFrames
using Dates
using Distances
using Documenter
using HypothesisTests
using LinearAlgebra
using Random
using Statistics
using StatsBase
using TableOperations
using Tables
using Test
using Impute
using Impute:
Impute,
Imputor,
Chain... | {"hexsha": "536ae24551569c03b2c1f3b90d31859a2a875cd2", "size": 1447, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "pitmonticone/Impute.jl", "max_stars_repo_head_hexsha": "bd2e1f2c62a7b9d29cf25cb0bd2d5290cc569d07", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 23 08:16:00 2015
@author: marc
"""
import numpy as np
import matplotlib.pyplot as plt
from dgp import DGP
from dgp import BCM
from dgp import GP
from dgp import rBCM
from dgp import gPoE
from dgp.utils import tictoc
N = 1000 # no of training inputs
d = 1 # no of inp... | {"hexsha": "4f0211ca853f99215663d99cae741c433df6a1d7", "size": 2715, "ext": "py", "lang": "Python", "max_stars_repo_path": "dgp/dgp/tests/test_rBCM.py", "max_stars_repo_name": "nick-terry/Splitting-GP", "max_stars_repo_head_hexsha": "efd886f6442f096833460cf8cd28ff3e18da732a", "max_stars_repo_licenses": ["MIT"], "max_st... |
# -*- coding: utf-8 -*-
"""
All K-Nearest Neighbors
"""
# Author: Dayvid Victor <victor.dvro@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.validation import check_X_y
from ..base import InstanceReductionMixin
from protopy.selection.enn import ENN
class AllKNN(InstanceReductionMixin):... | {"hexsha": "83aaf58677957706e3e3dc8d0986053d0edf79bd", "size": 2560, "ext": "py", "lang": "Python", "max_stars_repo_path": "protopy/selection/allknn.py", "max_stars_repo_name": "mjasher/scikit-protopy", "max_stars_repo_head_hexsha": "f4deddc42c5883b527d7bb1bfc6d0ece7d01979d", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
Require Import Lia.
Require Export smpl.Smpl.
Require Import Undecidability.Shared.Libs.PSL.FiniteTypes.BasicDefinitions.
From Complexity.Libs Require Export PSLCompat.
From Complexity.Libs.CookPrelim Require Import MorePrelim.
(** * Representation of finite types by natural numbers *)
(** This is needed as working ... | {"author": "uds-psl", "repo": "coq-library-complexity", "sha": "5a996877f16fd6fe16dc5f0c3b933486957869df", "save_path": "github-repos/coq/uds-psl-coq-library-complexity", "path": "github-repos/coq/uds-psl-coq-library-complexity/coq-library-complexity-5a996877f16fd6fe16dc5f0c3b933486957869df/theories/Libs/CookPrelim/Fla... |
import random
import torch
import numpy as np
import scipy.io as sio
from lwrl.memories import Memory
class SequentialMemory(Memory):
def __init__(self, max_length, history_length=1):
super().__init__()
self.max_length = max_length
self.obs_buffer = None
self.history_length = hi... | {"hexsha": "f09ac62ffa89e53c27117b1f84717a31465af47e", "size": 3562, "ext": "py", "lang": "Python", "max_stars_repo_path": "lwrl/memories/sequential.py", "max_stars_repo_name": "sealday/lwrl", "max_stars_repo_head_hexsha": "52bcd67751e605c38db4afa609c58938c7034e8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import simplejson as json, os
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from kettle.utils import get_beers
import numpy as np
class BeerMLData(list):
def __init__(self):
self.proj = None
self.arr = None
self.beer_map... | {"hexsha": "93833a4661a59da31899b3702fe70c0ffb4ac536", "size": 5376, "ext": "py", "lang": "Python", "max_stars_repo_path": "kettle/scripts/formatData.py", "max_stars_repo_name": "hacktobacillus/fermenter", "max_stars_repo_head_hexsha": "198e739aa71b13290c542773658928a33709b8f2", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
lemma invar_butlast: "invar (bq @ [t]) \<Longrightarrow> invar bq"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. invar (bq @ [t]) \<Longrightarrow> invar bq
[PROOF STEP]
unfolding invar_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. queue_invar (bq @ [t]) \<and> rank_invar (bq @ [t]) \<Longrighta... | {"llama_tokens": 819, "file": "Binomial-Heaps_BinomialHeap", "length": 5} |
from math import log, isnan
import numpy as np
from bokeh.models import *
from bokeh.plotting import figure
from itertools import cycle
from hail.expr import aggregators
from hail.expr.expressions import *
from hail.expr.expressions import Expression
from hail.typecheck import *
from hail import Table
import hail
pa... | {"hexsha": "c7fc88075892798bc6bbb5c4e02a976ba6eace76", "size": 14577, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/hail/plot/plots.py", "max_stars_repo_name": "maccum/hail", "max_stars_repo_head_hexsha": "e9e8a40bb4f0c2337e5088c26186a4da4948bed2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
function spm_progress_bar(action,varargin)
% Display a 'Progress Bar' in the 'Interactive' window
% FORMAT spm_progress_bar('Init',height,xlabel,ylabel,flgs)
% Initialise the bar in the 'Interactive' window.
% If flgs contains a 't', then use tex interpreter for labels.
%
% FORMAT spm_progress_bar('Set',value)
% Set th... | {"author": "fieldtrip", "repo": "fieldtrip", "sha": "c2039be598a02d86b39aae76bfa7aaa720f9801c", "save_path": "github-repos/MATLAB/fieldtrip-fieldtrip", "path": "github-repos/MATLAB/fieldtrip-fieldtrip/fieldtrip-c2039be598a02d86b39aae76bfa7aaa720f9801c/external/spm12/spm_progress_bar.m"} |
from typing import Union, Any
import numpy as np
import quaternion
from framegraph.utils import transform_vecs, transform_vec
from framegraph.pose_abc import AbstractPose
class Pose(AbstractPose):
def __init__(self,
rotation: Union[np.ndarray, np.quaternion] = None,
translation:... | {"hexsha": "70dbfd00949138388d1fd8ddc90c1d8b97bffa26", "size": 5159, "ext": "py", "lang": "Python", "max_stars_repo_path": "framegraph/pose.py", "max_stars_repo_name": "vi-robotics/framegraph", "max_stars_repo_head_hexsha": "554d3058059ef3c31f940fb38c93d67381d9df2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma cf_comma_proj_left_ObjMap_vrange:
assumes "\<GG> : \<AA> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>" and "\<HH> : \<BB> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>"
shows "\<R>\<^sub>\<circ> (\<GG> \<^sub>C\<^sub>F\<Sqinter> \<HH>\<lparr>ObjMap\<rparr>) \<subseteq>\<^sub... | {"llama_tokens": 2362, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_Comma", "length": 11} |
import numpy as np
from configs.DataPath import TRAIN_PATH, ROOT_PATH, DET_PATH, TRAIN_JSON_PATH
from utils.rand import random_sys
import cv2
import json
import random
class DataLoader(object):
def __init__(self, data_settings, read_all_boxes=False):
self.dataset_trained = []
se... | {"hexsha": "1cc39b563237297c0916e6886975b5e5bc43f314", "size": 9192, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/DataLoader.py", "max_stars_repo_name": "bit-bcilab/SiamDCA", "max_stars_repo_head_hexsha": "78a520f2bf6b89f8dee8b05ca7a9399813f77e92", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# -*- coding: utf-8 -*-
import sys
from PySide2 import QtWidgets
from PySide2.QtTest import QTest
from numpy import pi
from Tests.GUI import gui_option # Set unit as [m]
from pyleecan.Classes.LamSlotMag import LamSlotMag
from pyleecan.Classes.SlotM18 import SlotM18
from pyleecan.GUI.Dialog.DMachineSetup.SMSlot.PMSlo... | {"hexsha": "8687b976809ba757ffbdd486ac876e9b5d6d9250", "size": 2406, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tests/GUI/DMachineSetup/PMSlot/test_PMSlot18.py", "max_stars_repo_name": "tobsen2code/pyleecan", "max_stars_repo_head_hexsha": "5b1ded9e389e0c79ed7b7c878b6e939f2d9962e9", "max_stars_repo_licenses"... |
import numpy as np
class Pilha:
def __init__(self, capacidade):
self.__capacidade = capacidade
self.__topo = -1
self.__valores = np.chararray(self.__capacidade, unicode=True)
def __pilha_cheia(self):
if self.__topo == self.__capacidade - 1:
return True
else:
return False
def ... | {"hexsha": "2fd0be5d98bb51020c1d8d1205fe2ab06a3142ff", "size": 1331, "ext": "py", "lang": "Python", "max_stars_repo_path": "05_pilha_validador_expressoes.py", "max_stars_repo_name": "AlissonRaphael/algorithm_and_data_structures", "max_stars_repo_head_hexsha": "d970299c40ce779e6826d36ca28ebfb1ec6f8a88", "max_stars_repo_... |
from collections import OrderedDict
from typing import List, Dict
import cma
import numpy as np
import torch
from torch import nn
def rnn_adjust_parameters(state_dict: Dict[str, torch.Tensor]) -> OrderedDict:
state_dict = {
k.replace("model.", ""): v for k, v in state_dict.items() if "vae" not in k
}... | {"hexsha": "789fb27819504aeedf9713d08fb8cc2749ae602a", "size": 2783, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pl_modules/controller_utils.py", "max_stars_repo_name": "mikcnt/dlai-project", "max_stars_repo_head_hexsha": "56fa0d1e682d07cd89cb011400b0a4ef92ec9265", "max_stars_repo_licenses": ["MIT"], "ma... |
# inspired by https://github.com/drawbridge/keras-mmoe
import tensorflow as tf
import os
import numpy as np
import pandas as pd
from utils_mod import tf_itr, MAP_at_10
from keras import backend as K
from keras.optimizers import Adam
from keras.layers import Input, Dense, Concatenate
from keras.initializers import Vari... | {"hexsha": "04f0d803b9c583e2e6916cce81c68e72607eee6e", "size": 2810, "ext": "py", "lang": "Python", "max_stars_repo_path": "MMOE/train_mmoe.py", "max_stars_repo_name": "innovator-zero/CS410_AI_Project2", "max_stars_repo_head_hexsha": "2d33eb43274dcf6875f48b656ab7c7504ad2f7fa", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
Copyright (c) 2021 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writin... | {"hexsha": "77fb0f3514fd36d10e75f5c49a87fd7c5f8b0deb", "size": 12310, "ext": "py", "lang": "Python", "max_stars_repo_path": "nncf/tensorflow/quantization/initializers/collectors.py", "max_stars_repo_name": "sarthakpati/nncf", "max_stars_repo_head_hexsha": "29ad62c664c1dd53b3c8c50fc001a1b36bd1e8ac", "max_stars_repo_lice... |
using SuccessiveConvexProgrammings
using LinearAlgebra
function my_func_array(x, u, t, k)
A = [1 2;
3 4;
5 6]
B = Matrix(I, 3, 3)
return A*x + B*u
end
x = [1, 2]
u = [3, 4, 5]
t = [6, 7]
k = [8, 9]
jacob = get_jacobian(my_func_array, x, u, t, k)
| {"hexsha": "62fd8d9370558bcea4583722ceeb68a99f7051a7", "size": 279, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/linearisers.jl", "max_stars_repo_name": "JinraeKim/SuccessiveConvexProgrammings.jl", "max_stars_repo_head_hexsha": "a367a5dac91a459d889a745611227cb14b0152e7", "max_stars_repo_licenses": ["MIT"]... |
from random import shuffle, choice, random, sample, randint
from typing import List, Type, Set, Dict, Tuple
from datetime import datetime
import networkx as nx
import numpy as np
import math
from LAMARCK_ML.architectures import DataFlow
from LAMARCK_ML.architectures.IOMapping_pb2 import IOMappingProto
from LAMARCK_ML... | {"hexsha": "32576376237bd3f90b3248a4fcb2c48f5d736380", "size": 42624, "ext": "py", "lang": "Python", "max_stars_repo_path": "LAMARCK_ML/architectures/neuralNetwork.py", "max_stars_repo_name": "JonasDHomburg/LAMARCK", "max_stars_repo_head_hexsha": "0e372c908ff59effc6fd68e6477d04c4d89e6c26", "max_stars_repo_licenses": ["... |
import unittest
import numpy as np
from matplotlib.pylab import plt
from mpl_toolkits.mplot3d import Axes3D as _3d
def mean_squared_error(y, t):
"""均方误差(mean squared error)"""
return .5 * np.sum((y - t) ** 2)
def cross_entropy_error(y, t):
"""交叉熵误差(cross entropy error)"""
if y.ndim == 1:
t =... | {"hexsha": "1a92e3cdc07a64d45c742c95aba845bae6f89c63", "size": 5066, "ext": "py", "lang": "Python", "max_stars_repo_path": "third_party/deep_leaning_from_scratch/c4/test_neural_network_learning.py", "max_stars_repo_name": "KentWangYQ/py3-poc", "max_stars_repo_head_hexsha": "52b993716192acaf13094dc77f3f6347ee580996", "m... |
#!/usr/bin/env python
from pbpl.common.units import *
import numpy as np
E0 = 3.5*MeV
gamma0 = (me*c_light**2 + E0)/(me*c_light**2)
p0 = gamma0 * me * c_light
quad_f = 250*mm
quad_length = 10*mm
quad_gradient = p0 / (quad_f * quad_length * eplus)
print('gradient = ', quad_gradient / (tesla/meter))
Ld = quad_f * (np.... | {"hexsha": "0e8a37ea8d76fbdf0cf56f05a08e0e5ea8bb17b1", "size": 411, "ext": "py", "lang": "Python", "max_stars_repo_path": "share/double-quad/calc.py", "max_stars_repo_name": "ucla-pbpl/pbpl-gpt", "max_stars_repo_head_hexsha": "783d8ce3e72debed6ab20b1828d99102bfbb9360", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""Utilities for performing affine transformations on image data.
"""
import numpy as np
from .utils import array_to_img, img_to_array
try:
import scipy
# scipy.ndimage cannot be accessed until explicitly imported
from scipy import ndimage
except ImportError:
scipy = None
try:
from PIL import Ima... | {"hexsha": "01b8f52a21cfa4d0fe5a998342ef2ec27e7abed4", "size": 15048, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras_preprocessing/image/affine_transformations.py", "max_stars_repo_name": "smedegaard/keras-preprocessing", "max_stars_repo_head_hexsha": "1b36f97450b14ed8a88018891473944be1587b47", "max_stars... |
import os
import random
import numpy as np
from PIL import Image
import torch.utils.data as data_utils
class ImagePairDataset(data_utils.dataset.Dataset):
"""
"""
def __init__(self, data_dir=u'image_data/',
view_size=48, train=True, transform=None):
self.data_dir = data_dir.replace... | {"hexsha": "3fb24722f9f057b2aeeed81131f3f653818ed079", "size": 6819, "ext": "py", "lang": "Python", "max_stars_repo_path": "pair_datasets.py", "max_stars_repo_name": "minz95/lmvcnn_pytorch", "max_stars_repo_head_hexsha": "ff60996c8fd45ffe370b1ba31533276d6eb6c440", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""This functions are based on my own technical analysis library:
https://github.com/bukosabino/ta
You should check it if you need documentation of this functions.
"""
import pandas as pd
import numpy as np
"""
Volatility Indicators
"""
def bollinger_hband(close, n=20, ndev=2):
mavg = ... | {"hexsha": "15046f842f58b09826585fce6e2d9718bc450df2", "size": 3076, "ext": "py", "lang": "Python", "max_stars_repo_path": "ta.py", "max_stars_repo_name": "Abxhor/Coldairarrow", "max_stars_repo_head_hexsha": "3735beec8a6fa7ad9356375081229c68f0e83f3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 40, "max_star... |
/*
Copyright Rene Rivera 2011-2012
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)
*/
#include <string>
#include <iostream>
#include <set>
#define BOOST_PREDEF_INTERNAL_GENERATE_TESTS
namespace
{
struc... | {"hexsha": "d7835e16989e854bff1079ab251c73a9f5f7b2e0", "size": 2245, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/predef/test/info_as_cpp.cpp", "max_stars_repo_name": "Abce/boost", "max_stars_repo_head_hexsha": "2d7491a27211aa5defab113f8e2d657c3d85ca93", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_c... |
# measure version 1.8.7
from measure import npfarray,sqrt,ln,exp,arctan,lst,tbl,sig,val,mv,dsto_mv,dsys_mv,dtot_mv,plt,pltext,expreg,pi,curve_fit
# Aufgabe 1
R_A1 = npfarray([1,10,1])*1e3
R_A1_dsys = 0.05 * R_A1
C_A1 = npfarray([470,4.7,47])*1e-9
C_A1_dsys = 0.10 * C_A1
g_thalb = npfarray([312,32.6,32.6])*1e-6
g_thalb... | {"hexsha": "53ef4f6f300f56f970b130bf04a1470f97f04c00", "size": 7402, "ext": "py", "lang": "Python", "max_stars_repo_path": "PAP22-241.py", "max_stars_repo_name": "stephanlachnit/PAP", "max_stars_repo_head_hexsha": "13dad27dadac706edaa6ec61a522d5ba0b9b100d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
using DrWatson
@quickactivate "PECUZAL_Julia"
using DynamicalSystems
using DelayEmbeddings
using DelimitedFiles
using BenchmarkTools
include("../../src/pecuzal_method.jl")
include("../../src/data_analysis_functions.jl")
## We analyze the computational complexity of the proposed PECUZAL method in
# comparison to TDE,... | {"hexsha": "c6d8c2ce34d1ab70ad922854e006f519da3590cf", "size": 2795, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/performance/comm_performance.jl", "max_stars_repo_name": "hkraemer/PECUZAL_Julia", "max_stars_repo_head_hexsha": "f68ae7bc3b3dc6116cbdf9682798345e80c22c0a", "max_stars_repo_licenses": ["MIT... |
"""
Copyright 2020 Samsung SDS
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law ... | {"hexsha": "ecd4098ed92dc831e6e6c517adc422fdc82d0751", "size": 6408, "ext": "py", "lang": "Python", "max_stars_repo_path": "function/python/brightics/function/textanalytics/gsdmm.py", "max_stars_repo_name": "jhpark428/studio", "max_stars_repo_head_hexsha": "539457b3026dda827c1b17b4cb851946e34e3b85", "max_stars_repo_lic... |
import csv
import json
import requests
import pandas as pd
import time
import numpy as np
import rawgpy
rawg = rawgpy.RAWG("student project for university")
date_from = "2019-01-01"
date_to = "2019-01-02"
results = rawg.get_request("https://api.rawg.io/api/games?dates=" + date_from + "," + date_to + "&platforms=4&sto... | {"hexsha": "c9b76bfde44f0997ebe039fdac65ef0d1f254c36", "size": 8256, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_collection.py", "max_stars_repo_name": "GillinedUp/video-games-data-integration", "max_stars_repo_head_hexsha": "8ed6fd5af3d67dd9ba9154de2ec9e196d121b7ee", "max_stars_repo_licenses": ["Apache... |
from __future__ import print_function
import runai.mp
runai.mp.init(splits=2, method=runai.mp.Method.Cout)
#runai.mp.init(splits=2, method=runai.mp.Method.Cin)
import keras
from keras.applications.resnet50 import ResNet50,preprocess_input
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
from k... | {"hexsha": "80a779c0fc6b3a77378c5745f715f692e807d36d", "size": 20217, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mp/keras/cifar10.py", "max_stars_repo_name": "bamps53/runai", "max_stars_repo_head_hexsha": "0c868160f64e1e063c6eb6f660d42917322d40c5", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Internal Imports
from os import error
from alpha import Alpha
from learnt_model import LearntModel
from model import Model
from operations import OPS
from util import load_alpha
# External Imports
from copy import deepcopy
from datetime import datetime
from lucent.modelzoo import inceptionv1, util
from lucent.misc.c... | {"hexsha": "c608ff60db6f9cd3431f78613fd57f3959601208", "size": 10751, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_visualization.py", "max_stars_repo_name": "sjoshi804/neural-architecture-search-project", "max_stars_repo_head_hexsha": "b28c23383dc5d8f9da8023a70786313dc5696cf1", "max_stars_repo_license... |
import os,random
os.environ["KERAS_BACKEND"] = "tensorflow"
os.environ["THEANO_FLAGS"] = "device=gpu%d"%(1)
os.environ["MKL_THREADING_LAYER"] = "GNU"
import numpy as np
from keras.utils import np_utils
import keras.models as models
from keras.layers.core import Reshape,Dense,Dropout,Activation,Flatten
from keras.layer... | {"hexsha": "d903fa37d9c767d4ad5d8eb67af9bf31d44f452c", "size": 2623, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn/network.py", "max_stars_repo_name": "mlcomm/deepsensing", "max_stars_repo_head_hexsha": "ede1b0431b0eb5554b63c35c094eecdf544c4795", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#!/usr/bin/env python
###############################################################################
# Copyright Kitware Inc. and Contributors
# Distributed under the Apache License, 2.0 (apache.org/licenses/LICENSE-2.0)
# See accompanying Copyright.txt and LICENSE files for details
##################################... | {"hexsha": "6a55fe366c3680b389bbb0134bda8bd73ec5aeae", "size": 4240, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/generate_dsm.py", "max_stars_repo_name": "willdunklin/Danesfield", "max_stars_repo_head_hexsha": "686cfd331250c00a93b3778c6faaa646fec65de5", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
/*! \file
\brief A timetable_value vocabulary.
Copyright (C) 2019-2022 kaoru https://www.tetengo.org/
*/
#include <algorithm>
#include <any>
#include <cassert>
#include <istream>
#include <iterator>
#include <limits>
#include <memory>
#include <optional>
#include <stdexcept>
#include <string>
... | {"hexsha": "c4f146906c3a1f45ea0459a7da752a4a3c79f8ba", "size": 20788, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sample/transfer_trains/src/timetable.cpp", "max_stars_repo_name": "tetengo/tetengo", "max_stars_repo_head_hexsha": "66e0d03635583c25be4320171f3cc1e7f40a56e6", "max_stars_repo_licenses": ["MIT"], "m... |
import os
from os import listdir
from os import makedirs
from os.path import join, isdir
import json
import random
import shutil
import argparse
import numpy as np
import pandas as pd
import librosa
import sox
def filter_single_labeled(ann, inter_nodes):
# get single-labeled filenames and classes
class_to_fil... | {"hexsha": "c938cf9cdfde788518d07e07c92fd11ed679f563", "size": 7820, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/preprocess_foreground_sounds.py", "max_stars_repo_name": "wangyu/rethink-audio-fsl", "max_stars_repo_head_hexsha": "6e9626efc0fddadfe2f032e18d1794066a08c8b1", "max_stars_repo_licenses": ["MIT... |
import torch
import numpy as np
from torch.nn import functional as F
import matplotlib.pylab as plt
def logsumexp(inputs, dim=None, keepdim=True):
# From: https://github.com/YosefLab/scVI/issues/13
return (inputs - F.log_softmax(inputs, dim=dim)).sum(dim, keepdim=keepdim)
class VAE(torch.nn.Module):
d... | {"hexsha": "b1c275c5ee5b3f6e8164826b38d4e3dd98bdbed7", "size": 2441, "ext": "py", "lang": "Python", "max_stars_repo_path": "vae.py", "max_stars_repo_name": "phuijse/tutorial", "max_stars_repo_head_hexsha": "0b6c6ada8509e4ceff52a2c05d962b6a82f461d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_r... |
import numpy as np
import torch
from model import Actor, Critic
class Memory():
def __init__(self,batch_size):
# init state, action, reward, state_, done
self.state = []
self.action = []
self.reward = []
self.val = []
self.prob = []
self.done = []
se... | {"hexsha": "0fb1a241e57cfb4b0dc6ecf6badc0d7aa3b82676", "size": 4476, "ext": "py", "lang": "Python", "max_stars_repo_path": "ppo.py", "max_stars_repo_name": "dkolosa/carla_pilot", "max_stars_repo_head_hexsha": "05ab3cb50788ad9a9af7c0db06d99307c7454aa6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_star... |
from qtpy.QtGui import QGuiApplication
from qtpy.QtWidgets import QMenu
from qtpy import QtGui
import numpy as np
import logging
from __code._utilities.list_widget import ListWidget
from __code._utilities.status_message import StatusMessageStatus, show_status_message
from __code.extract_evenly_spaced_files.manual_mode... | {"hexsha": "a8786f2aa206d399bb3b19cebd2d90c99fc438c0", "size": 4660, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/__code/extract_evenly_spaced_files/event_handler.py", "max_stars_repo_name": "mabrahamdevops/python_notebooks", "max_stars_repo_head_hexsha": "6d5e7383b60cc7fd476f6e85ab93e239c9c32330", ... |
# Copyright 2019 The Forte 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 applicable ... | {"hexsha": "d1506fa0db5e872b7dc14e6af47dc1ca5e844a86", "size": 8401, "ext": "py", "lang": "Python", "max_stars_repo_path": "forte/processors/text_generation_processor.py", "max_stars_repo_name": "swapnull7/forte", "max_stars_repo_head_hexsha": "737a72afd440d40c3826c3a7c5e4e44235c0f701", "max_stars_repo_licenses": ["Apa... |
// Copyright (C) 2016-2018 T. Zachary Laine
//
// 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)
//[ lazy_vector
// Defining this allows the assignment below of an expression to a double
// without writing a... | {"hexsha": "70c89d440de7bec636b50da60ed0012a82312efd", "size": 4022, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/yap/example/lazy_vector.cpp", "max_stars_repo_name": "Talustus/boost_src", "max_stars_repo_head_hexsha": "ffe074de008f6e8c46ae1f431399cf932164287f", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
[STATEMENT]
lemma WS_silent_move:
assumes "S,kind \<turnstile> (ms\<^sub>1,s\<^sub>1) -a\<rightarrow>\<^sub>\<tau> (ms\<^sub>1',s\<^sub>1')" and "((ms\<^sub>1,s\<^sub>1),(ms\<^sub>2,s\<^sub>2)) \<in> WS S"
shows "((ms\<^sub>1',s\<^sub>1'),(ms\<^sub>2,s\<^sub>2)) \<in> WS S"
[PROOF STATE]
proof (prove)
goal (1 subgo... | {"llama_tokens": 121726, "file": "HRB-Slicing_StaticInter_WeakSimulation", "length": 439} |
"""
Fourier Reconstruction of RR-Lyrae Templates
--------------------------------------------
This figure demonstrates Fourier decomposition using RR-Lyrae templates
"""
# Author: Jake VanderPlas
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine L... | {"hexsha": "d25d5d1b639ddf47bee65ea99d2b1306982647f2", "size": 2324, "ext": "py", "lang": "Python", "max_stars_repo_path": "book_figures/chapter10/fig_rrlyrae_reconstruct.py", "max_stars_repo_name": "larsmans/astroML", "max_stars_repo_head_hexsha": "01ee67ea6e1c5a8dedc2498ec7397653d65b2c8d", "max_stars_repo_licenses": ... |
import os
import sys
import numpy as np
import pandas as pd
from pprint import pprint
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from utils import model_methods
from utils.data_utils import *
from utils.arguments import Arguments
from utils.mappings import Mappings, Labe... | {"hexsha": "6f573ac0cce8e0f857d4eb5e6f8f55851bbfb03d", "size": 14133, "ext": "py", "lang": "Python", "max_stars_repo_path": "finetuning.py", "max_stars_repo_name": "jfcann/va-transformer", "max_stars_repo_head_hexsha": "bbf04612770c95d38915f41045cf9f9acb5dad21", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# -*-coding: utf-8-*-
from lightgbm import LGBMClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from bayes_opt import BayesianOptimization
import numpy as np
def parm_format(parms, intdeal, middledeal, maxdeal):
'''
整理模型参数的格式，intdeal是int类参数的列表，middl... | {"hexsha": "74122080abbd045eeb2d4286a6c790d0499ce1c5", "size": 3111, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/optimize.py", "max_stars_repo_name": "kaiwang0112006/project_demo", "max_stars_repo_head_hexsha": "4067d245be5139aec236adf2179b5880df507cf0", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma ground_head: "ground s \<Longrightarrow> is_Sym (head s)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ground s \<Longrightarrow> is_Sym (head s)
[PROOF STEP]
by (cases s rule: tm_exhaust_apps) (auto simp: is_Var_def) | {"llama_tokens": 92, "file": "Lambda_Free_RPOs_Lambda_Free_Term", "length": 1} |
__pytorch_version__ = "1.3.0"
import logging
from datetime import timedelta
import numpy as np
import torch
import torch.distributed as dist
from ftlib.commlib.basic_commlib import BasicCommLib
from ftlib.commlib.commlib_status import CommLibStatus
class PyTorch(BasicCommLib):
def __init__(
self, grad_... | {"hexsha": "07d1ba8e4c11b4c4b521bad0b92fd3d9c2f47e69", "size": 2861, "ext": "py", "lang": "Python", "max_stars_repo_path": "ftlib/commlib/pytorch/impl.py", "max_stars_repo_name": "terrytangyuan/ftlib", "max_stars_repo_head_hexsha": "7d2862dafe9d338d733300047b03c514d1893201", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import numpy as np
import napari
from .utils import *
# Shift, Control, Alt, Meta, Up, Down, Left, Right, PageUp, PageDown, Insert,
# Delete, Home, End, Escape, Backspace, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10,
# F11, F12, Space, Enter, Tab
KEYS = {"focus_next": "]",
"focus_previous": "[",
"hide_o... | {"hexsha": "f89b74609a13d5d07a33d234f32d494938fafd39", "size": 5206, "ext": "py", "lang": "Python", "max_stars_repo_path": "impy/viewer/keybinds.py", "max_stars_repo_name": "hanjinliu/impy", "max_stars_repo_head_hexsha": "d35b21be7739c3073ae87486673af68b1cdb2853", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
"""Module with logic for a 1-D signal dataset."""
from typing import Callable, NamedTuple, Union
import matplotlib.pyplot as plt
import numpy as np
import torch
from .fourier_feature_models import FourierFeatureMLP
class SignalData(NamedTuple("FunctionData", [("x", torch.FloatTensor),
... | {"hexsha": "01bd169e170e801d3a808449d900f8ffc6ce6845", "size": 5406, "ext": "py", "lang": "Python", "max_stars_repo_path": "fourier_feature_nets/signal_dataset.py", "max_stars_repo_name": "matajoh/fourier_feature_nets", "max_stars_repo_head_hexsha": "784140f01464e34a0dd4b813c50d20c4c15a8a59", "max_stars_repo_licenses":... |
from __future__ import print_function
import argparse, os, copy
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
import prismnet.model as arch
from prismnet.utils import log_print, metrics, datautils
def train(args, model, device, train_loader, criterion, optimizer):
model.train()
... | {"hexsha": "67a78795ffee76d555d412ce07330f5f261efeee", "size": 7493, "ext": "py", "lang": "Python", "max_stars_repo_path": "prismnet/engine/train_loop.py", "max_stars_repo_name": "kuixu/PrismNet", "max_stars_repo_head_hexsha": "aef6f0bdfef765c2fd431762e27a35625d0bd2d8", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from copy import deepcopy
from pathlib import Path
import itertools
import time
import numpy as np
import tempfile
import os
from python.solver import *
from python.config import TORCHSCRIPT_MODEL_PATH
from python.deploy_model import *
SVCOMP_PATH = ""
SATCOMP18_PATH = ""
BENCHMARKS = [SVCOMP_PATH, SATCOMP18_PATH]
... | {"hexsha": "896d4a6cbd45a19e784166a1ac167e705396e3d8", "size": 9606, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/solver_eval.py", "max_stars_repo_name": "negotiatorvivian/neuro-cadical", "max_stars_repo_head_hexsha": "ea7d052a5e03b33d3fb8e4a47bcecf7b9f99551d", "max_stars_repo_licenses": ["Apache-2.0"]... |
import pandas as pd
import numpy as np
DATA_PATH = "./data/EVconsumption/"
d1 = pd.read_csv(DATA_PATH + "data_1_selected.csv")
d1.head()
ids = np.unique(d1['trip_id'])
N = len(ids)
N_train = int(N * 0.7)
N_val = int(N * 0.8)
ids_train = ids[:N_train]
ids_val = ids[N_train:N_val]
ids_test = ids[N_val:]
data_trai... | {"hexsha": "c8ece8236f88b31e77bdf2a070b4f5c75359cf71", "size": 1498, "ext": "py", "lang": "Python", "max_stars_repo_path": "prep_scripts/2_data_split.py", "max_stars_repo_name": "linas-p/EVDPEP", "max_stars_repo_head_hexsha": "2062e20ef784a76eebaf71ebbe4f9006cde5bbd5", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars... |
# -*- coding: utf-8 -*-
import datetime
import pandas as pd
import numpy as np
from rqdatac.services.calendar import get_previous_trading_date
from rqdatac.validators import (
ensure_string_in,
ensure_order_book_id,
ensure_order_book_ids,
ensure_date_range,
ensure_list_of_string
)
from rqdatac.uti... | {"hexsha": "d18d9128f5bddf6e126d45b4dc9c87806f88a75e", "size": 6938, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/lib/python3.7/site-packages/rqdatac/services/ksh_auction_info.py", "max_stars_repo_name": "CatTiger/vnpy", "max_stars_repo_head_hexsha": "7901a0fb80a5b44d6fc752bd4b2b64ec62c8f84b", "max_stars... |
import numpy as np
import pandas as pd
import gc
from sklearn import metrics
from tqdm import tqdm
import torch
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
from sklearn.model_selection import train_test_split
from datasets import WakeWordDataset, get_loaders
from model import SimpleRNN, Simp... | {"hexsha": "c7ce6e7f94f69bdde5afd6d8207387688afb048f", "size": 4584, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "streamride/wakeworddetection", "max_stars_repo_head_hexsha": "da162a93c90d0c139293f4479da6ed44897f492e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
from pathlib import Path
import cv2
import numpy as np
from medhack.dataset import CovidImageDataset
import albumentations as A
import pytorch_lightning as pl
from pytorch_lightning.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS
from torch.utils.data import DataLoader
import torch.distributed as dist
fro... | {"hexsha": "9fc70199894edf1cd92023cdc4fd7e2a95e0e319", "size": 5071, "ext": "py", "lang": "Python", "max_stars_repo_path": "medhack/data_loading.py", "max_stars_repo_name": "mibaumgartner/hackathon_health", "max_stars_repo_head_hexsha": "e3ab4971ecb4efd0e43c583104b8485c548320d5", "max_stars_repo_licenses": ["Apache-2.0... |
def run_pyexocross():
import numpy as np
import argparse
import os
from .pyexocross import PyExocross
from .util import create_grid_res, convert_to_wavenumber
parser = argparse.ArgumentParser()
parser.add_argument("--linelist",type=str,dest="linelist",required=True)
parser.add_argument... | {"hexsha": "d566975a1b2e77dcad44a85a5e6cfa12a093ccb2", "size": 5287, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyexocross/run.py", "max_stars_repo_name": "ucl-exoplanets/pyexocross", "max_stars_repo_head_hexsha": "703341cd0fddafcbb04e935c89ddc9d02dda9f59", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
import matplotlib.pyplot as plt
import glob
import numpy as np
def parse_RMSF_file(rmsf_file: str):
if glob.glob(rmsf_file):
return np.genfromtxt(rmsf_file,skip_header=1,usecols=1,dtype=float)
else:
print("File not found: ",rmsf_file)
return None
def rmsf(temp_rmsf_array,ax=None... | {"hexsha": "ff0b335680c085268fe696fa707f2b3655f323c0", "size": 1916, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydrachem/Subplots/rmsf.py", "max_stars_repo_name": "markahix/pydrachem", "max_stars_repo_head_hexsha": "56a55260bbcbb3759629a36625920f4094a49202", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# pylint: disable=C0103,W0102,R0914
import numpy as np
from omnizart.feature.cfp import extract_cfp
from omnizart.utils import get_logger
logger = get_logger("HCFP Feature")
def fetch_harmonic(data, cenf, ith_har, start_freq=27.5, num_per_octave=48, is_reverse=False):
ith_har += 1
if ith_har != 0 and is_r... | {"hexsha": "cfd5d318799a7c14d55d91a92d2b0b50e2dab8c5", "size": 1972, "ext": "py", "lang": "Python", "max_stars_repo_path": "omnizart/feature/hcfp.py", "max_stars_repo_name": "nicolasanjoran/omnizart", "max_stars_repo_head_hexsha": "b0e74af39b2e3a312ef32dbf0837626b2e043cb6", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import time
import torch
import torch.nn as nn
# from flair.parser.modules.dropout import SharedDropout
from torch.nn.modules.rnn import apply_permutation
from torch.nn.utils.rnn import PackedSequence
from torch.nn.utils.rnn import (pack_padded_sequence, pad_packed_sequence,
pad_sequence)
i... | {"hexsha": "183b7cf6737730be32eb70c4b37b94fc5d059621", "size": 23168, "ext": "py", "lang": "Python", "max_stars_repo_path": "flair/models/biaffine_dp.py", "max_stars_repo_name": "db-bionlp/CLNER", "max_stars_repo_head_hexsha": "77910311acf0411252b9fea8c3e6efb7175eb21f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# Copyright 2020 NXP
# SPDX-License-Identifier: MIT
import numpy as np
from PIL import Image
import argparse
import os
def imload(filename: str, im_width: int, im_height: int, datatype: str):
"""Converts an image to a numpy array and resizes.
Args:
filename (str): Image filename.
... | {"hexsha": "e4c654a10561d3d96cc4d39bf2f4902033512a7c", "size": 2476, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/pyarmnn/scripts/image_to_npy.py", "max_stars_repo_name": "PetervdPerk-NXP/pyarmnn-release", "max_stars_repo_head_hexsha": "2008c270f7c7c84a930842c845138628c8b95713", "max_stars_repo_license... |
from pprint import pprint
import numpy as np
from skimage.data import camera
from skimage.exposure import rescale_intensity
from skimage.metrics import peak_signal_noise_ratio as psnr
from skimage.metrics import structural_similarity as ssim
from aydin.io.datasets import add_noise
from aydin.it.transforms.padding imp... | {"hexsha": "13d2842b2c14045cbc80ad866454c9831edab4fc", "size": 1481, "ext": "py", "lang": "Python", "max_stars_repo_path": "aydin/restoration/denoise/test/test_n2s_fgr.py", "max_stars_repo_name": "royerloic/aydin", "max_stars_repo_head_hexsha": "f9c61a24030891d008c318b250da5faec69fcd7d", "max_stars_repo_licenses": ["BS... |
from __future__ import print_function
from __future__ import division
from sklearn.utils import check_random_state
from sklearn import preprocessing as prep
from utils.data import load_data, show_data_splits, shape_data
from utils.evaluation import evaluate
from utils.profiles import select_model, show_design, train,... | {"hexsha": "844aff8b757e567eab04101d17c08cb3e245797f", "size": 8032, "ext": "py", "lang": "Python", "max_stars_repo_path": "profiles_weak.py", "max_stars_repo_name": "andreuvall/HybridPlaylistContinuation", "max_stars_repo_head_hexsha": "6e31e50050c61a2c3ae55183e18b665fd54c7250", "max_stars_repo_licenses": ["BSD-2-Clau... |
import numpy as np
class Kinematics:
def __init__(self):
self._l = 0.14
self._w = 0.075
self._hip = 0.04
self._leg = 0.1
self._foot = 0.1
self.y_dist = 0.11
self.x_dist = self._l
self.height = 0.15
# frame vectors
self._hip_front_righ... | {"hexsha": "2022875ea045399b33707d0016f65a84a383b92f", "size": 6936, "ext": "py", "lang": "Python", "max_stars_repo_path": "rex_gym/model/kinematics.py", "max_stars_repo_name": "elvinaqa/rex-gym", "max_stars_repo_head_hexsha": "a57b5df5f356e228e47d4e0b778617a7f74834c4", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
\chapter{Constructing an formula}
\label{chapter:constructingaformula}
The class \formulaClass represents SMT formulas, which are
defined according to the following abstract grammar
\[
\begin{array}{rccccccccccccc}
p &\quad ::=\quad & a & | & b & | & x & | & (p + p) & | & (p \cdot p) & | & (p^e) \\
v &\quad ::=\qu... | {"hexsha": "a386713f08ee5608914bf385bda64818ef350a2e", "size": 8958, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "manual/constructingformulas.tex", "max_stars_repo_name": "minemebarsha/smtrat", "max_stars_repo_head_hexsha": "eaada50cdf9bbfe4dd4f6a54776387484c37b0f2", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
"""
Created on Fri May 4 21:34:01 2018
@author: 大茄茄
"""
#对原始数据进行四阶巴特沃斯滤波
from scipy.signal import butter, lfilter
import pandas as pd
import os
from sklearn.externals.joblib import Parallel, delayed
SampFreq = 256
ChannelNum = 22
def butter_bandpass_filter(data, lowcu... | {"hexsha": "a520116991dd80361b958218c340e7a654118bc7", "size": 2069, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_process/band_filter.py", "max_stars_repo_name": "yolle103/eeg-lstm", "max_stars_repo_head_hexsha": "24a236a3ffa4af02b81a5a772f9a1f3130817ad4", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
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