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# This code is written by Sunita Nayak at BigVision LLC. It is based on the OpenCV project. It is subject to the license terms in the LICENSE file found in this distribution and at http://opencv.org/license.html
# Usage example: python3 augmented_reality_with_aruco.py --image=test.jpg
# python3 aug... | {"hexsha": "31da7a673b40ef9c5ff689595fa802b3eff8af2d", "size": 5489, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/playground/arucoAR.py", "max_stars_repo_name": "manavjain99/oscar_buggy", "max_stars_repo_head_hexsha": "b5dab0848f8667c9515bcfb078730cd0c4060000", "max_stars_repo_licenses": ["MIT"], "max_st... |
/**
\file
\author Datta Ramadasan
//==============================================================================
// Copyright 2015 INSTITUT PASCAL UMR 6602 CNRS/Univ. Clermont II
//
// Distributed under the Boost Software License, Version 1.0.
// See accompanying file LICENSE.txt or ... | {"hexsha": "d5eaf23a5088be447e9619f8c9c025bef6655ada", "size": 4152, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/libv/lma/lm/trait/use_estimator.hpp", "max_stars_repo_name": "bezout/LMA", "max_stars_repo_head_hexsha": "9555e41eed5f44690c5f6e3ea2d22d520ff1a9d2", "max_stars_repo_licenses": ["BSL-1.0"], "max_... |
import sys
sys.path.append("./models")
import numpy as np
import torch
from datasets.VNRiceDataset import VNRiceDataset
from models.TransformerEncoder import TransformerEncoder
from models.multi_scale_resnet import MSResNet
from models.TempCNN import TempCNN
from models.rnn import RNN
from datasets.ConcatDataset impo... | {"hexsha": "698bba5b7804837ae48b6714c357825e331c53a6", "size": 10216, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train.py", "max_stars_repo_name": "Pratyush1991/crop-type-mapping", "max_stars_repo_head_hexsha": "d9d99ec92c3a090ec5576f9e46c89dfcc6f50cf3", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
% test for ti quicunx
name = 'turbulence';
name = 'lena';
n = 256;
M = load_image(name);
M = rescale( crop(M,n) );
Jmax = log2(n)-1;
Jmin = Jmax-5;
% boundary conditions
options.bound = 'per';
options.bound = 'sym';
% vanishing moments
vm = 6;
options.primal_vm = vm;
options.dual_vm = vm;
% transform
disp('Computing... | {"author": "gpeyre", "repo": "matlab-toolboxes", "sha": "0cd622c988cda6f63f64d35cd7bd096fa578e5c6", "save_path": "github-repos/MATLAB/gpeyre-matlab-toolboxes", "path": "github-repos/MATLAB/gpeyre-matlab-toolboxes/matlab-toolboxes-0cd622c988cda6f63f64d35cd7bd096fa578e5c6/toolbox_wavelets/tests/test_quincunx_ti.m"} |
import numpy as np
from PIL import Image
import glob
import torch
from torch.utils.data.dataset import Dataset
import torchvision.transforms as transforms
class FairFaceDataset(Dataset):
def __init__(self, folder_path, dimensions):
"""
Args:
folder_path (string): path to image folder
... | {"hexsha": "10eac2687199ba2a90d991bacd3cd91e3f864cf0", "size": 1240, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/fairface_dataset.py", "max_stars_repo_name": "Asap7772/DeepCriminalize", "max_stars_repo_head_hexsha": "c171c6ce6e87e126e6e2b0ed1d9709ee7d0ce667", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma higher_pderiv_0 [simp]: "(pderiv ^^ n) 0 = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (pderiv ^^ n) 0 = 0
[PROOF STEP]
by (induction n) simp_all | {"llama_tokens": 85, "file": "E_Transcendental_E_Transcendental", "length": 1} |
struct FluidParams
eta2d :: Float64 # length scale introduced by difference in viscosity of of surface fluid and external fluids
end
abstract type Object end
| {"hexsha": "f9c764dba0802372ff882b309938cb62cda95c9f", "size": 163, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "physics/fluid.jl", "max_stars_repo_name": "sarthakbagaria/curved-membrane-fluid-dynamics", "max_stars_repo_head_hexsha": "6b6223c89011a179595a39656a4f866816dffc8c", "max_stars_repo_licenses": ["MIT"... |
import argparse, time, os, pickle
import numpy as np
import dgl
import torch
import torch.optim as optim
from models import LANDER
from dataset import LanderDataset
from utils import evaluation, decode, build_next_level, stop_iterating
###########
# ArgParser
parser = argparse.ArgumentParser()
# Dataset
parser.add_... | {"hexsha": "149d58336f468e4f5ce292bab15d1f7cb28f5a23", "size": 4086, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/pytorch/hilander/test.py", "max_stars_repo_name": "ketyi/dgl", "max_stars_repo_head_hexsha": "a1b859c29b63a673c148d13231a49504740e0e01", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
from typing import Tuple
import tensorflow as tf
import tensorflow.keras as keras
import numpy as np
# Build the actual model to run
class ActorCritic(keras.Model):
def __init__ (self, num_actions, num_hidden_units):
"""
Builds an actor critic network
Args:
num_actions: Num... | {"hexsha": "5cc1f0cffff7b19b7e088d33b8db56f980b41908", "size": 1718, "ext": "py", "lang": "Python", "max_stars_repo_path": "ActorCriticNetwork/ActorCritic.py", "max_stars_repo_name": "esslushy/actor-critic-ml-practice", "max_stars_repo_head_hexsha": "1a49b8a3ea5cc92f26a290fa7b032c126e00a3f0", "max_stars_repo_licenses":... |
line5
help! world
order
line 4
help! world
order
line 3
help! world
order
line 2
help! world
order
line 1
help! world
order
| {"hexsha": "578a44b6b21c0200af43fa88a0d6d22c4984bef6", "size": 124, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/ed/test/g1.r", "max_stars_repo_name": "lambdaxymox/DragonFlyBSD", "max_stars_repo_head_hexsha": "6379cf2998a4a073c65b12d99e62988a375b4598", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
open methanol.xyz
bond 1.55
%hybridize 0 SP2
model GAFF
thermostat ANDERSEN
energy
minimize 0.0001 0.00001 40000 1000 min.xyz
energy
heat 300 50 400 0.01 0.01 0.0001 0.00001 0.3 heat.xyz
temperature
energy
prod 0.0001 0.0001 300 .3 500000 1000 prod.xyz
| {"hexsha": "efca66b77e0904f3871f6202d7c29bbf3f6072b1", "size": 254, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/methanol/script.r", "max_stars_repo_name": "ThatPerson/MolecularDynamics", "max_stars_repo_head_hexsha": "967a9db20528f1c4806b46445df17464525f4a2e", "max_stars_repo_licenses": ["MIT"], "max_star... |
import time
import edgeiq
import cv2
import numpy as np
"""
detect objects edges based on thermal detection
"""
def main():
fps = edgeiq.FPS()
try:
with edgeiq.WebcamVideoStream(cam=1) as video_stream, \
edgeiq.Streamer() as streamer:
# Allow Webcam to warm up
... | {"hexsha": "680fe6a1ae7bd6b37aaf783f6742fa8dc7b80b25", "size": 1404, "ext": "py", "lang": "Python", "max_stars_repo_path": "thermal-edges/app.py", "max_stars_repo_name": "alwaysai/thermal-imaging", "max_stars_repo_head_hexsha": "70a2d0b337c29b762a4dbce19309ab0dfae77a38", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import subprocess
import sys
def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])
#source activate py36-udify-direct
#python biasCDA/biasCDA/e2e-scripts/step1-stanza-conllu.py
##subprocess.call(["source activate", "py36-udify-direct"])
##install('stanza')
import stanza
#... | {"hexsha": "72b7c240e8787f14ab68869af19a84f1b52ed3a5", "size": 7076, "ext": "py", "lang": "Python", "max_stars_repo_path": "e2e-scripts/step1-stanza-conllu.py", "max_stars_repo_name": "talktovishal/biasCDA", "max_stars_repo_head_hexsha": "270e4bdda72b12018f9a803b3e5c9e4476990011", "max_stars_repo_licenses": ["MIT"], "m... |
# Copyright 2019 Pascal Audet
#
# This file is part of RfPy.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, mod... | {"hexsha": "44fedf54f7fa7cd9a00d0778620363c6afebb669", "size": 6760, "ext": "py", "lang": "Python", "max_stars_repo_path": "obstools/orient/options.py", "max_stars_repo_name": "wbythewood/OBStools", "max_stars_repo_head_hexsha": "1ce1e39aeb4200bb3120735bef27b77c3d52c956", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
! @Copyright 2007 Kristjan Haule
module RealBubble
! ###############################
! # Computing real axis Bubble #
! ###############################
IMPLICIT NONE
REAL*8, allocatable :: Ome(:)
REAL*8, allocatable :: chi0r0(:,:,:)
INTEGER :: norb, nOme
CONTAINS
SUBROUTINE RealBubble__Init__()
... | {"hexsha": "1747b03d5fff52f729fe7729b468684b472c2272", "size": 10881, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/magnetSusc/dmft_bubble/dmft_real_bubble.f90", "max_stars_repo_name": "dmft-wien2k/dmft-wien2k-v2", "max_stars_repo_head_hexsha": "83481be27e8a9ff14b9635d6cc1cd9d96f053487", "max_stars_repo_... |
import os
import random
import numpy as np
import torch
from einops import repeat
def expand_to_batch(tensor, desired_size):
tile = desired_size // tensor.shape[0]
return repeat(tensor, 'b ... -> (b tile) ...', tile=tile)
def init_random_seed(seed, gpu=False):
random.seed(seed)
np.random.seed(seed)... | {"hexsha": "ff294275fdb68b6eba02e56a43280ed7fefa1da6", "size": 526, "ext": "py", "lang": "Python", "max_stars_repo_path": "self_attention_cv/common.py", "max_stars_repo_name": "MooseMouse/self-attention-cv", "max_stars_repo_head_hexsha": "867e7b1f08bf838c29770e43b7746b2a945d75e3", "max_stars_repo_licenses": ["MIT"], "m... |
module libnpcf
use iso_c_binding
private
public :: npcf
include "ganpcf_cdef.f90"
type npcf
private
type(c_ptr) :: ptr
contains
#ifdef __GNUC__
procedure :: delete => delete_npcf_polymorph
#else
final :: delete_npcf
#endif
proce... | {"hexsha": "9620e454741d61c0a4bb71b6799ff8904a807663", "size": 4131, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "source/ganpcf_mod.f90", "max_stars_repo_name": "dpearson1983/ganpcf", "max_stars_repo_head_hexsha": "d75fddfb094045a81916ffed10fec19d96b6d52e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
/*
* ping_pong_fiber_test.cpp
*
* Created on: Mar 25, 2017
* Author: zmij
*/
#ifndef WITH_BOOST_FIBERS
#define WITH_BOOST_FIBERS
#endif
#include <gtest/gtest.h>
#include <test/ping_pong.hpp>
#include <wire/core/connector.hpp>
#include <wire/core/connection.hpp>
#include "sparring/sparring_test.hpp"
#inclu... | {"hexsha": "371f9a9f07ebf1d224a6a73a464b17c7a5b118f9", "size": 6868, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/connector/ping_pong_fiber_test.cpp", "max_stars_repo_name": "zmij/wire", "max_stars_repo_head_hexsha": "9981eb9ea182fc49ef7243eed26b9d37be70a395", "max_stars_repo_licenses": ["Artistic-2.0"], "... |
import tensorflow as tf
import numpy as np
import os
from PIL import Image
filename = 'model.pb'
labels_filename = 'labels.txt'
graph_def = tf.GraphDef()
labels = []
# Import the TF graph
with tf.gfile.FastGFile(filename, 'rb') as f:
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name=''... | {"hexsha": "e0e42e26904a9aacb1ec0b0994ba0b2d7c2222fd", "size": 1527, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lab-02/source/inference.py", "max_stars_repo_name": "alexandergg/Cognitive-Services-AI-Labs", "max_stars_repo_head_hexsha": "e1e0aef5e9a4ad43144f2ba8003d29d587f6bf4c", "max_stars_repo_licenses": [... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
import numpy as np
import typing
def algae(relative_humidity: typing.List[float], temperature: typing.List[float], material_name, porosity, roughness,
total_pore_area):
"""
UNIVPM Algae Model
Currently a dummy function!
:param relativ... | {"hexsha": "593a19f940fc0de9ce0bf0c512b23cfc156370cd", "size": 9472, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_process/algae_script/algae_model.py", "max_stars_repo_name": "ribuild/delphin_6_automation", "max_stars_repo_head_hexsha": "12024381fc1042b46314c55d88b6349229ea33b7", "max_stars_repo_licenses... |
// (C) Copyright 2008 CodeRage, LLC (turkanis at coderage dot com)
// (C) Copyright 2004-2007 Jonathan Turkanis
// 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/iostreams for d... | {"hexsha": "ad4ae05d7cddbf471e20e839d8f3119185f77ae0", "size": 4902, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ios/Pods/boost-for-react-native/boost/iostreams/device/array.hpp", "max_stars_repo_name": "rudylee/expo", "max_stars_repo_head_hexsha": "b3e65a7a5b205f14a3eb6cd6fa8d13c8d663b1cc", "max_stars_repo_li... |
subroutine foo(d)
real(kind=8) :: d
print *, d
end subroutine foo
program test
call foo(0.01d0)
end program
| {"hexsha": "92bc813c9a7f9847c965be852777a8d34862479e", "size": 115, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/mlir_out_tests/const_arg.f90", "max_stars_repo_name": "clementval/fc", "max_stars_repo_head_hexsha": "a5b444963c1b46e4eb34d938d992836d718010f7", "max_stars_repo_licenses": ["BSD-2-Clause"], ... |
# https://www.kaggle.com/maniyar2jaimin/interactive-plotly-guide-to-pca-lda-t-sne
# PCA (Principal Component Analysis),
# LDA ( Linear Discriminant Analysis) and
# TSNE ( T-Distributed Stochastic Neighbour Embedding)
import numpy as np
import pandas as pd
df = pd.read_csv('https://covid.ourworldindata.org/data/owi... | {"hexsha": "ccb28f561b826bfeedc96a195b2de1cee7593e71", "size": 6523, "ext": "py", "lang": "Python", "max_stars_repo_path": "30DayChartChallenge/20210415-multivariate.py", "max_stars_repo_name": "vivekparasharr/Challenges-and-Competitions", "max_stars_repo_head_hexsha": "c99d67838a0bb14762d5f4be4993dbcce6fe0c5a", "max_s... |
import numpy as np
import torch
from torch import nn
from torch import optim
import matplotlib.pyplot as plt
from torchvision import datasets, transforms, models
import torch.nn.functional as F
from collections import OrderedDict
import json
import argparse
import os
from image_processing import transform_image
from mo... | {"hexsha": "a5ad751eec191766b5151790886e0d93113e6f97", "size": 5073, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "ryahollands99/Udacity-AI-Programming-Final-Project", "max_stars_repo_head_hexsha": "936dcddf72385c15e37a360a713f2c59e7b183de", "max_stars_repo_licenses": ["MIT"]... |
"""This module tests the PauliGate class."""
from __future__ import annotations
import numpy as np
import pytest
from hypothesis import given
from hypothesis.strategies import floats
from hypothesis.strategies import integers
from bqskit.ir.gates import IdentityGate
from bqskit.ir.gates import PauliGate
from bqskit.i... | {"hexsha": "3c98b4d92c6fde1af6165b51067ec1f98324512a", "size": 3221, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/ir/gates/parameterized/test_pauli.py", "max_stars_repo_name": "jkalloor3/bqskit", "max_stars_repo_head_hexsha": "ad34a6eae3c0e62d2bd960cd4cd841ba8e845811", "max_stars_repo_licenses": ["BSD-3... |
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.autograd import Variable
from torch.optim import lr_scheduler
import numpy as np
import h5py
from .EDSR_models.rcan import RCAN
from .RegreClass import *
from .weights_init import *
def get_norm_layer(norm_type='instance'):
i... | {"hexsha": "269859d73eeb9450502983887a3dd5d4988d4aa2", "size": 8630, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/networks.py", "max_stars_repo_name": "YilinLiu97/MR_Fingerprinting", "max_stars_repo_head_hexsha": "dbcfc85352c58f7a9027f2f4e02674ff85e59681", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#pragma once
#include <vector>
#include <Eigen/Dense>
#include "Utils/IO/IOUtilities.hpp"
class TVRParameter {
public:
double swing_time;
Eigen::Vector2d des_loc;
Eigen::Vector3d stance_foot_loc;
bool b_positive_sidestep;
double yaw_angle;
};
class TVROutput {
public:
double time_modif... | {"hexsha": "c0d79be7ce5701a46ea814ad48f8ab15656793c3", "size": 2485, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "PnC/PlannerSet/LIPMPlanner/TVRPlanner.hpp", "max_stars_repo_name": "shbang91/PnC", "max_stars_repo_head_hexsha": "880cbbcf96a48a93a0ab646634781e4f112a71f6", "max_stars_repo_licenses": ["MIT"], "max_... |
import os
import random
from glob import glob
import numpy as np
import cv2
import matplotlib.pyplot as plt
from Udacity_self_driving_car_challenge_4.image_processing.calibration import camera_cal, found_chessboard, read_camera_cal_file
from Udacity_self_driving_car_challenge_4.image_processing.edge_detection import ... | {"hexsha": "bb60ef9946f9dd26727e16e9ffd39d96849119d1", "size": 7805, "ext": "py", "lang": "Python", "max_stars_repo_path": "Advanced_Lane_Lines.py", "max_stars_repo_name": "KUASWoodyLIN/Udacity_self_driving_car_challenge_4", "max_stars_repo_head_hexsha": "36ca5ed50f74c49645b43ffcab1b27055540d8e5", "max_stars_repo_licen... |
#
# File: gofALAAM.py
# Author: Alex Stivala
# Created: May 2020
#
"""ALAAM goodness-of-fit by simulating from estimated parameters, and
comparing observed statistics to statistics of simulated outcome vectors,
including statistics not included in the estimated model.
The ALAAM is described in:
G. Darag... | {"hexsha": "88bb2587525150e12f720590556461a8265b25f5", "size": 3692, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/gofALAAM.py", "max_stars_repo_name": "stivalaa/ALAAMEE", "max_stars_repo_head_hexsha": "30147b4227488dca07ab48812aa98ab68df79f4f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11,... |
#!/usr/bin/env python
""" Abstract class representing a HIAS AI OpenVINO Model.
Represents a HIAS AI OpenVINO Model. HIAS AI OpenVINO
Models are used by AI Agents to process incoming data.
MIT License
Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial
Para la Leucemia Peter Moss
Permission is... | {"hexsha": "59a0f0dc32674ae84999a6265fdc33a88caac7af", "size": 7908, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/model_openvino.py", "max_stars_repo_name": "AdamMiltonBarker/hias-all-oneapi-classifier", "max_stars_repo_head_hexsha": "7afdbcde0941b287df2e153d64e14d06f2341aa2", "max_stars_repo_licenses... |
@testset "test BasisMatrices.lookup" begin
table1 = [1.0, 4.0]
table2 = [1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]
x = [0.5, 1.0, 1.5, 4.0, 5.5]
x2 = [0.5, 2.0]
@test BasisMatrices.lookup(table1, x, 0) == [0, 1, 1, 2, 2]
@test BasisMatrices.lookup(table1, x, 1) == [1, 1, 1, 2,... | {"hexsha": "a2a86edf848f9aaa2f70db12a1b724a50404652c", "size": 6345, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/util.jl", "max_stars_repo_name": "magerton/BasisMatrices.jl", "max_stars_repo_head_hexsha": "093925e67c2452a1f2da872571aff84d93cfac2d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
Load LFindLoad.
From lfind Require Import LFind.
Unset Printing Notations.
Set Printing Implicit.
From QuickChick Require Import QuickChick.
Inductive natural : Type := Succ : natural -> natural | Zero : natural.
Derive Show for natural. Derive Arbitrary for natural. Instance Dec_Eq_natural : Dec_Eq natural. Proof. ... | {"author": "ana-brendel", "repo": "coq-benchmarks", "sha": "78b9ca2993b0fb579d814ed17e63c6859e79681c", "save_path": "github-repos/coq/ana-brendel-coq-benchmarks", "path": "github-repos/coq/ana-brendel-coq-benchmarks/coq-benchmarks-78b9ca2993b0fb579d814ed17e63c6859e79681c/modifications/quickchick_fails/test110_goal10/lf... |
import numpy as np
import scipy.sparse as ss
import logging
import time
import warnings
from .feature_selection import get_significant_genes
from .feature_selection import calculate_minmax
warnings.simplefilter("ignore")
logging.basicConfig(format='%(process)d - %(levelname)s : %(asctime)s - %(message)s', level=logg... | {"hexsha": "1551ac1a6a79309991708839cd5a99e957a025cb", "size": 6263, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycdr/pycdr.py", "max_stars_repo_name": "wlchin/pycdr", "max_stars_repo_head_hexsha": "96e64a05f1b84fd01fbb003d3256e297d6492df4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
#!/usr/bin/env python3
import asyncio
import logging
import tempfile
import argparse
import sys
import numpy as np
from aiocron import crontab
from pyppeteer import launch
from PIL import Image
# Import the waveshare folder (containing the waveshare display drivers) without refactoring it to a module
# find the laste... | {"hexsha": "010d0528448d03acfc88213403071719f148841b", "size": 5052, "ext": "py", "lang": "Python", "max_stars_repo_path": "show_image.py", "max_stars_repo_name": "lhoggatt17/rpi-magicmirror-eink", "max_stars_repo_head_hexsha": "2a47a257c9a4ca0d5af63c7f27406b1c8a73c4c9", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/*
* The MIT License (MIT)
*
* Copyright (c) 2018 Sylko Olzscher
*
*/
#include "test-async-005.h"
#include <iostream>
#include <boost/test/unit_test.hpp>
#include <cyng/async/mux.h>
#include <cyng/io/io_chrono.hpp>
#include <iomanip>
#include <atomic>
#include <fstream>
// unit_test --run_test=ASYNC/async_005... | {"hexsha": "87023745adcca2d4a1bd6d3de185027722fff1d9", "size": 1391, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/unit-test/src/test-async-005.cpp", "max_stars_repo_name": "solosTec/cyng", "max_stars_repo_head_hexsha": "3862a6b7a2b536d1f00fef20700e64170772dcff", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from typing import Callable, List, Union
import numpy as np
from numpy import argsort, ceil, exp, mod, zeros
from numpy.random import geometric, rand, randint, randn
from ..search_space import SearchSpace
from ..solution import Solution
from ..utils import dynamic_penalty, handle_box_constraint
__author__ = "Hao Wan... | {"hexsha": "5b47355b857ec0c865c5569289af8bdee20cc68c", "size": 13254, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayes_optim/acquisition_optim/mies.py", "max_stars_repo_name": "zdanial/Bayesian-Optimization", "max_stars_repo_head_hexsha": "a4779e992da15d21fa3fc425293cfb1f2621f81f", "max_stars_repo_licenses"... |
import face_recognition
from torch.utils.data import Dataset
from facenet_pytorch.models.mtcnn import MTCNN
from PIL import Image
import cv2
from typing import List
from collections import OrderedDict
from abc import ABC, abstractmethod
import os
import numpy as np
from retinaface.pre_trained_models import get_model
im... | {"hexsha": "8ab7f1ff677b4be796a741940d7c087a44b0e0ed", "size": 3652, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing/face_detector.py", "max_stars_repo_name": "windysavage/dfdc_deepfake_challenge", "max_stars_repo_head_hexsha": "d10b54cf933282366157a031954b046d87d57009", "max_stars_repo_licenses": ... |
// Copyright Louis Dionne 2013-2016
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
#include <boost/hana/assert.hpp>
#include <boost/hana/core/tag_of.hpp>
#include <boost/hana/ext/std/integral_constant.hpp>
#include <boost/... | {"hexsha": "5d9624412d03adac260aca847b96a908bcd41d23", "size": 6087, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.62.0/libs/hana/test/ext/std/integral_constant.cpp", "max_stars_repo_name": "sita1999/arangodb", "max_stars_repo_head_hexsha": "6a4f462fa209010cd064f99e63d85ce1d432c500", "max_stars_... |
# Copyright (c) 2020 DeNA Co., Ltd.
# Licensed under The MIT License [see LICENSE for details]
# agent classes
import random
import numpy as np
from .util import softmax, get_action_code, get_random_action
class RandomAgent:
def reset(self, env, show=False):
pass
def action(self, env, player, sho... | {"hexsha": "0728cb8c10fcf93b5a73c48167f6c549ecc06573", "size": 3505, "ext": "py", "lang": "Python", "max_stars_repo_path": "HandyRL/handyrl/agent.py", "max_stars_repo_name": "Fkaneko/kaggle_lux_ai", "max_stars_repo_head_hexsha": "cc2e7ad88b8817cb96e081061e363e357831e132", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -*- coding: utf-8 -*-
"""
run file for neural walker
@author: hongyuan
"""
import pickle
import time
import numpy
import theano
from theano import sandbox
import theano.tensor as tensor
import os
import scipy.io
from collections import defaultdict
from theano.tensor.shared_randomstreams import RandomStreams
import ... | {"hexsha": "21428b40f271f5639df03ea68f63cd0cfb89a526", "size": 1327, "ext": "py", "lang": "Python", "max_stars_repo_path": "check_results.py", "max_stars_repo_name": "jam-world/hongyuan_code", "max_stars_repo_head_hexsha": "8bfb6872fdb4cbe6742d6df4ec5942fb2d9fc87f", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma Diff_triv_mset: "M \<inter># N = {#} \<Longrightarrow> M - N = M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. M \<inter># N = {#} \<Longrightarrow> M - N = M
[PROOF STEP]
by (metis diff_intersect_left_idem diff_zero) | {"llama_tokens": 103, "file": "Nested_Multisets_Ordinals_Multiset_More", "length": 1} |
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
from src.utility.config import Config, Option
from pipeline import SentimentAnalyzer
evaluate_exp_name = "exp-p1-2.1"
evaluate_fe_option = "bert"
evaluate_clf_option = "bert"
config = Config(evaluate_exp_name)
option = Option(evaluate_fe... | {"hexsha": "ffc99a7d04f36d824d883e7fcde16de44ef3f7ff", "size": 811, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluate.py", "max_stars_repo_name": "William9923/IF4072-SentimentClassification", "max_stars_repo_head_hexsha": "5e22a6da418056955243c310bab0382e4683b781", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
"""
Created on Sun May 24 11:05:18 2020
@author: Nicolai
"""
import sys
sys.path.append("../../testbed/pde0A/")
import CiPde0A as pde0A
sys.path.append("../../testbed/pde0B/")
import CiPde0B as pde0B
sys.path.append("../../testbed/pde1/")
import CiPde1 as pde1
sys.path.append("../../testbed/pd... | {"hexsha": "7eef58c3d9077143ab5b1ba05979b954596eecab", "size": 8884, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/experiments/experiment_0/experiment_0_time.py", "max_stars_repo_name": "nicolai-schwartze/Masterthesis", "max_stars_repo_head_hexsha": "7857af20c6b233901ab3cedc325bd64704111e16", "max_stars_r... |
// Andrew Naplavkov
#ifndef BARK_DB_SLIPPY_LAYERS_HPP
#define BARK_DB_SLIPPY_LAYERS_HPP
#include <bark/db/provider.hpp>
#include <bark/db/slippy/detail/arcgis.hpp>
#include <bark/db/slippy/detail/bing.hpp>
#include <bark/db/slippy/detail/cartodb.hpp>
#include <bark/db/slippy/detail/double_gis.hpp>
#include <bark/db/s... | {"hexsha": "8b4190d4c4e7d0d86e53eda262eacaa101d8fe72", "size": 1772, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "db/slippy/detail/layers.hpp", "max_stars_repo_name": "storm-ptr/bark", "max_stars_repo_head_hexsha": "e4cd481183aba72ec6cf996eff3ac144c88b79b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import io
import zipfile
import matplotlib.pyplot as plt
import networkx as nx
import urllib.request as urllib
from zipfile import ZipFile
import pandas as pd
def football():
print('Loading football network...')
url = "http://websensors.net.br/projects/biased-deep-walk/football.zip"
sock = urllib.urlopen(url) ... | {"hexsha": "b34cefa67819d17d2b57a099814143603008c223", "size": 7284, "ext": "py", "lang": "Python", "max_stars_repo_path": "bdw/load_networks.py", "max_stars_repo_name": "rmarcacini/bdw", "max_stars_repo_head_hexsha": "55adc880b5dd4621ed48c94a6e084a90be571bd6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
"""
Generate samples with GPT-2 and filter out those that are likely to be
memorized samples from the training set.
"""
import logging
logging.basicConfig(level='ERROR')
import argparse
import numpy as np
from pprint import pprint
import sys
import torch
import zlib
from transformers import AutoTokenizer, AutoModelFo... | {"hexsha": "584a44ebd4d557844aa021d72c4149f95e153099", "size": 7416, "ext": "py", "lang": "Python", "max_stars_repo_path": "extraction.py", "max_stars_repo_name": "puffy310/LM_Memorization", "max_stars_repo_head_hexsha": "fc607352cd4b5db3bdcd0d764d20d475e9029ed4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from __future__ import print_function
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.rcParams["font.family"] = "serif"
plt.rcParams["mathtext.fontset"] = "cm"
#plt.rcParams["mathtext.fontset"] = "dejavuserif"
from orphics import maps,io,cosmology,mpi,stats
from pixell import enmap,curvedsky... | {"hexsha": "c6569f74de73b149458adf0e9f10186bd933a906", "size": 3754, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/investigate_sims.py", "max_stars_repo_name": "ACTCollaboration/tilec", "max_stars_repo_head_hexsha": "11ed8d027ad6ffac09b3e291a047f33e97673f14", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# Estimators.
function estimator1(R::Float64, beta::Float64, int::Integrator,
s::State{P,D,A,N}) where {P,D,A,N}
function estim()
result = 2.0 / R # 1 / nm
dxdr = 0.5 .* normalize(com_disp(CN1, CN2, CJ, s))
for a in 1:A
for d in 1:D
for j in... | {"hexsha": "51e6cf1bbe742ba931e8400727ae7a94e62f19f3", "size": 1536, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/estimator.jl", "max_stars_repo_name": "0/WaterMeanMeanForce.jl", "max_stars_repo_head_hexsha": "a10068ecce6456bc57215cce8e60cd6976cae608", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
### Analyze object sizes ###
import json
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from collections import defaultdict
from collections import Counter
data = json.load(open('/BS/rshetty-wrk/archive00/data/cocoDataStuff/datasetBoxAnn.json','r'))
catid2attr = {}
select_attr_list = set(['pe... | {"hexsha": "1fa0d3224a6b4e6d297257dfd8e02599a8e86302", "size": 35131, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/miscCode.py", "max_stars_repo_name": "baudm/adversarial-object-removal", "max_stars_repo_head_hexsha": "2ad7caa0d0ffdb3fbe5fa59c66edd4e77e3557ed", "max_stars_repo_licenses": ["MIT"], "max_s... |
import random, math
from collections import deque, namedtuple
import itertools
import numpy as np
import gym
from gym import error, spaces, utils
from gym.utils import seeding
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
def generate_random_point_on_circle(radius=0.1):
"""
# usin... | {"hexsha": "ffdb2267c7bd788700a3a3a183ecd1446fb5613c", "size": 7850, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym-added-environments/gym-synthetic2Dplane/gym_synthetic2Dplane/envs/synthetic2Dplane_env_backup.py", "max_stars_repo_name": "azarafrooz/corgail", "max_stars_repo_head_hexsha": "a1e6084054084fab9... |
import sys
from glob import glob
from os import path
from pathlib import Path
from unittest import TestCase
from PIL import Image
from numpy import array
sys.path.append("..")
from src.solver.captcha import get_captcha_text
class TestCaptcha(TestCase):
def test_captcha(self):
cwd = Path(__file__).paren... | {"hexsha": "12ff7ae6b3f558f8d02c8e8982f308cfdaf280c0", "size": 782, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_tesseract.py", "max_stars_repo_name": "mt-hack/nptu-auto-checkin", "max_stars_repo_head_hexsha": "65597630629eb3bb7b7efac7419cd7229f7467d7", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import functools as fun
import itertools as it
import collections as coll
import re
import numpy as np
from scipy import ndimage as nd
from skimage import io
from scipy.stats.mstats import mquantiles as quantiles
from skimage import morphology as skmorph, filter as imfilter
import skimage.filter.rank as rank
... | {"hexsha": "afa96043fa776acd0fcf81791d70530109ae10ad", "size": 18721, "ext": "py", "lang": "Python", "max_stars_repo_path": "husc/preprocess.py", "max_stars_repo_name": "gitter-badger/husc", "max_stars_repo_head_hexsha": "6e7ae2879caef304de7bfb77f19f99d5308ca256", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import numpy as np
#import pandas as pd
import matplotlib
# import seaborn as sns
import matplotlib.pyplot as plt
names = "LR SVM CNN LSTM BERT".split(" ")
colors = ['tab:gray', 'tab:green','tab:orange', 'tab:red', 'tab:blue', 'orange' ]
patterns = ('--', '\\', '////', '\\\\', '\\\\', '\\\\', '.', '*')... | {"hexsha": "cbd7a2b9373878ddd24d74d27416d1d31cd8b675", "size": 8564, "ext": "py", "lang": "Python", "max_stars_repo_path": "appendix/auc_accuracy/xl_plot.py", "max_stars_repo_name": "rit-git/tagging", "max_stars_repo_head_hexsha": "b075ce1553492be7088026b67f525a529bf03770", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import torch
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Args:
mean (tuple): means for each channel.
std (tuple): standard deviations for each channel.
"""
def __ini... | {"hexsha": "e05c33be05d74c291614f0a51d80fa37aa90439a", "size": 1728, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloaders/custom_transforms_test.py", "max_stars_repo_name": "SteveSZF/Traffic-Lane-Detection", "max_stars_repo_head_hexsha": "8217808178cdf2d655d02632eb71c543d39f5258", "max_stars_repo_licenses... |
"""This is how I made the digits_of_pi.txt file because I wanted to go
a little further than the book did."""
# Install using pipinstall mpmath
from mpmath import mp
import os
THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))
filename = os.path.join(THIS_FOLDER, "pi_digits.txt")
# Set the number of digits of p... | {"hexsha": "1077c3d2fff72a0b2604d5719751971d9b2f46ec", "size": 839, "ext": "py", "lang": "Python", "max_stars_repo_path": "Jupyter/PythonCrashCourse2ndEdition/ch10_files/generate_pi.py", "max_stars_repo_name": "awakun/LearningPython", "max_stars_repo_head_hexsha": "578f9290c8065df37ade49abe4b0ab4e6b35a1bd", "max_stars_... |
import code
import sys
import argparse
import os
import time
import json
import shutil
import cv2
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.optim.lr_scheduler as lrsched
from Network ... | {"hexsha": "c7900cabe8d091ef40eb948f083291759e014ebc", "size": 5560, "ext": "py", "lang": "Python", "max_stars_repo_path": "L2/TrainingController.py", "max_stars_repo_name": "derangedhk417/ML-Lessons", "max_stars_repo_head_hexsha": "3433e3fa6324791b74771fcfd8a6c5361ba69c53", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../shared")
from analytic_tools import fractal_latent_heat_alex
from wednesdaySPEED import simulation
# %%
tau = 9
pi_2_vals = [0.0, 0.1, 0.2, 0.3, 0.5]
plt.figure(figsize=(10,5))
for i, val in enumerate(pi_2_va... | {"hexsha": "7324471ad5f7eb701c44cc42ce5e268c8586c20c", "size": 2844, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/james/OTHER/entropy_test.py", "max_stars_repo_name": "charelF/ComplexSystems", "max_stars_repo_head_hexsha": "3efc9b577ec777fcecbd5248bbbaf77b7d90fc65", "max_stars_repo_licenses": ["MIT"], "m... |
# coding: utf-8
# In[1]:
import pandas as pd
import gensim
import os
import collections
import smart_open
import random
import numpy as np
# In[2]:
df = pd.read_csv(open('library.corr','rU'), encoding='utf8',header=None, engine='c',delimiter=',', error_bad_lines=False, low_memory=False, index_col=None)
# In[3]:... | {"hexsha": "caf947a8cefa3cf1ba838be28b84034afbc96160", "size": 848, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc2vec.py", "max_stars_repo_name": "Skeftical/Research-Paper-Recommender", "max_stars_repo_head_hexsha": "14f676f54627bf7f834f594787b17fc52d3530d4", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma in_outs_rpv [iff]: "out \<in> outs'_rpv rpv \<longleftrightarrow> (\<exists>input. out \<in> outs'_gpv (rpv input))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (out \<in> outs'_rpv rpv) = (\<exists>input. out \<in> outs'_gpv (rpv input))
[PROOF STEP]
by(simp add: outs'_rpv_def) | {"llama_tokens": 135, "file": "CryptHOL_Generative_Probabilistic_Value", "length": 1} |
import pickle
import numpy as np
import cf_recommender as cf
import similarity_functions as sf
import movie_reviews_compiler as mrc
path = '../data/'
def run_test_top_k(cosine=True,k=10):
'''compute the predictions for masked values in the testing set (user review vectors) using the training set (critic review matri... | {"hexsha": "2be8cfe203783df00627c55aa54172eb2f784462", "size": 2656, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/test_data_top_k.py", "max_stars_repo_name": "alaw1290/CS591B1", "max_stars_repo_head_hexsha": "57e9a3425e84405f1bfff76cc14e14e4501acc1e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
//==================================================================================================
/*!
@file
@Copyright 2016 Numscale SAS
@copyright 2016 J.T.Lapreste
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_... | {"hexsha": "7c1f797fe477f756aaf4c666d7fa3ef8b385a2f2", "size": 2556, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/simd/arch/x86/sse4_1/simd/function/max.hpp", "max_stars_repo_name": "yaeldarmon/boost.simd", "max_stars_repo_head_hexsha": "561316cc54bdc6353ca78f3b6d7e9120acd11144", "max_stars_repo_l... |
MODULE Euler_CharacteristicDecompositionModule_NonRelativistic_TABLE
USE KindModule, ONLY: &
DP, &
Zero, &
Half, &
One
USE GeometryFieldsModule, ONLY: &
nGF, &
iGF_Gm_dd_11, &
iGF_Gm_dd_22, &
iGF_Gm_dd_33
USE UnitsModule, ONLY: &
Gram, &
Centimeter
USE FluidFieldsModule,... | {"hexsha": "bf0d1dcf04821f73041f51971be2166463e1537b", "size": 18599, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Modules/Euler/Euler_CharacteristicDecompositionModule_NonRelativistic_TABLE.f90", "max_stars_repo_name": "srichers/thornado", "max_stars_repo_head_hexsha": "bc6666cbf9ae8b39b1ba5feffac80303c2b1... |
"""Minimum jerk trajectory."""
import numpy as np
from .base import PointToPointMovement
from .data._minimum_jerk import generate_minimum_jerk
class MinimumJerkTrajectory(PointToPointMovement):
"""Precomputed point to point movement with minimum jerk.
Parameters
----------
n_dims : int
State ... | {"hexsha": "183072fccb2a7a806b23652b48413b0e0c4a3189", "size": 1771, "ext": "py", "lang": "Python", "max_stars_repo_path": "movement_primitives/minimum_jerk_trajectory.py", "max_stars_repo_name": "maotto/movement_primitives", "max_stars_repo_head_hexsha": "b79c78a5a0667cc24a26b7b6cc64a5762d8f4dd4", "max_stars_repo_lice... |
/-
Copyright (c) 2018 Jeremy Avigad. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Jeremy Avigad, Mario Carneiro, Simon Hudon, Alex Keizer
-/
import Qpf.MathlibPort.Fin2
import Qpf.Util.HEq
-- import Mathlib
universe u v w
abbrev DVec {n : Nat} (αs : Fin2 n → Type ... | {"author": "alexkeizer", "repo": "qpf4", "sha": "980f97425b9d5a5e3897073df33794192b3b3124", "save_path": "github-repos/lean/alexkeizer-qpf4", "path": "github-repos/lean/alexkeizer-qpf4/qpf4-980f97425b9d5a5e3897073df33794192b3b3124/Qpf/Util/Vec.lean"} |
import sys
import numpy as np
from matplotlib import pyplot as plt
sys.path.append("../../")
from spook import SpookPosL1
from spook.utils import normalizedATA
np.random.seed(1996)
Na = 17
Nb = 9
Ns = 10000
Ng = 11
A = np.random.rand(Ns, Na) * 50
Xtrue = np.zeros((Na, Nb))
bb, aa = np.meshgrid(np.arange(Nb), np.ara... | {"hexsha": "da972869f01f1df2a8777bd38ba34e3e3ba8845d", "size": 1177, "ext": "py", "lang": "Python", "max_stars_repo_path": "spook/unittest/testPreNorm.py", "max_stars_repo_name": "congzlwag/spook", "max_stars_repo_head_hexsha": "0c728086b811ce829c6226e0a9a10a350772ec15", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
from setuptools import setup
from setuptools import Extension
import numpy
short_desc = "Package for evaluating the NRSur7dq2 surrogate model"
long_desc = \
"""
NRSur7dq2 is a surrogate model for gravitational waves from numerical
relativity simulations of binary black hole mergers.
It is descri... | {"hexsha": "4d9ae7e413b14d0620e5106d7cecae562a35a11c", "size": 1536, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "jblackma/NRSur7dq2", "max_stars_repo_head_hexsha": "fd2383f5a49f180598af25dd5ea2994dccfab2f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_sta... |
import numpy as np
import matrix
import tkinter as tk
from PIL import Image, ImageTk
class MyApp(tk.Tk):
def __init__(self):
super().__init__()
self.geometry("600x400+10+10")
self.bind('<Button-1>', self.callback)
game = matrix.create_field(12,8)
matrix.rand_array(game,15)
... | {"hexsha": "18cbc25fd89bd90868b841105c2e18893c7d77b1", "size": 925, "ext": "py", "lang": "Python", "max_stars_repo_path": "form.py", "max_stars_repo_name": "rvgorod122/pointer", "max_stars_repo_head_hexsha": "99bb95e6b75bc5de3a35f4b8a2d300dd6294292c", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "... |
using Ejemplo
using Test
@testset "Ejemplo.jl" begin
@test f_xy(1,5) == 6# Write your tests here.
end
| {"hexsha": "dd6b051524fbeb0f9d1adf2c28fbd53e3745cbe3", "size": 107, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "EdwardAngelino/Ejemplo.jl", "max_stars_repo_head_hexsha": "e59cd0c957eefbbd5259190fd30e6cb34c4660c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#pragma once
#include "AxisString.hpp"
#include "SymbolTable.hpp"
#include <boost/spirit/include/qi.hpp>
#include <list>
#include "foundation/definitions/AxisInputLanguage.hpp"
#include "services/language/primitives/OrExpressionParser.hpp"
#include "services/language/primitives/GeneralExpressionParser.hpp"
namespace a... | {"hexsha": "8f252e2fe7afede74c087403a1349df08483e0ec", "size": 4359, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Axis.Core/application/parsing/preprocessing/ExistenceExpressionParser.hpp", "max_stars_repo_name": "renato-yuzup/axis-fem", "max_stars_repo_head_hexsha": "2e8d325eb9c8e99285f513b4c1218ef53eb0ab22", ... |
import os
import os.path as osp
from argparse import ArgumentParser
import mmcv
import numpy as np
from xtcocotools.coco import COCO
from mmpose.apis import (inference_pose_lifter_model,
inference_top_down_pose_model, vis_3d_pose_result)
from mmpose.apis.inference import init_pose_model
from ... | {"hexsha": "730622c3f850fe3e700ed8a94f3e5c67de944aed", "size": 9999, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/body3d_two_stage_img_demo.py", "max_stars_repo_name": "jlgzb/mmpose", "max_stars_repo_head_hexsha": "0ecf06e3580f141f6ab44645768a0d6d8ba48383", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
\section{\module{hmac} ---
Keyed-Hashing for Message Authentication}
\declaremodule{standard}{hmac}
\modulesynopsis{Keyed-Hashing for Message Authentication (HMAC)
implementation for Python.}
\moduleauthor{Gerhard H{\"a}ring}{ghaering@users.sourceforge.net}
\sectionauthor{Gerhard H{\"a}ring}{g... | {"hexsha": "1d49417853ceb34813d8057fc02ef05b2278eac8", "size": 1879, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/lib/libhmac.tex", "max_stars_repo_name": "deadsnakes/python2.4", "max_stars_repo_head_hexsha": "f493d5415b662e99a73d017bcafe2148c5bc8fb5", "max_stars_repo_licenses": ["PSF-2.0"], "max_stars_coun... |
#!/usr/bin/env python3
# bagus@ep.its.ac.id,
# changelog:
# 2019-04-16: init code from avec
# 2019-07-02: modify to extract 10039 iemocap data
import numpy as np
import os
import time
import ntpath
import pickle
feature_type = 'egemaps'
exe_opensmile = '~/opensmile-2.3.0/bin/linux_x64_standalone_static/SMILExtract' ... | {"hexsha": "7a11d41cac91c0bb07d08d9d442bce352c67fc54", "size": 2096, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/extract_feature/save_egemaps_10039-pc.py", "max_stars_repo_name": "bagustris/dimensional-ser", "max_stars_repo_head_hexsha": "ce9bfae1d962b3581dd7022e4f145429615e2771", "max_stars_repo_licens... |
from __future__ import division
import itertools
import logging
import numpy as np
import time
import cv2 as cv
import sys
import tqdm
from scipy import stats
def exhaustive_search_block_matching(reference_img, search_img, block_size=16, max_search_range=16, norm='l1',
verbose... | {"hexsha": "d49ff39603ca028cce74d8e8eed29aed89f2925d", "size": 12021, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/optical_flow.py", "max_stars_repo_name": "mcv-m6-video/mcv-m6-2018-team5", "max_stars_repo_head_hexsha": "bb7ee72a06ce021bab751c8e8773ec128170b524", "max_stars_repo_licenses": ["MIT"], "max... |
using Cairo
using Rsvg
abstract type AbstractGraphics <: AbstractResource end
mutable struct Texture{T} <: AbstractGraphics
ptr::Ptr{T}
width::Int
height::Int
center_x::Int
center_y::Int
end
function Texture(render_ptr::Ptr{SDL.Renderer}, sdl_surface::Ptr{SDL.Surface}; halign = 0.5, valign = 0.5)... | {"hexsha": "a85cdfcdfbb8f1e739c0567989734607b85d1f59", "size": 2740, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/texture.jl", "max_stars_repo_name": "freemin7/Gloria.jl", "max_stars_repo_head_hexsha": "35ab01e48c19e414320b2bb2800a9cbd57e86075", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 42, "m... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the Kramers-Kronig Calculator software package.
#
# Copyright (c) 2013 Benjamin Watts, Daniel J. Lauk
#
# The software is licensed under the terms of the zlib/libpng license.
# For details see LICENSE.txt
import kkcalc as kk
import numpy as np
impor... | {"hexsha": "66493732f5f10b7673a567dc6ee63eb7118b7ba5", "size": 1414, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/example.py", "max_stars_repo_name": "dschick/kkcalc", "max_stars_repo_head_hexsha": "9218d557fb3217ff1339dcc81230380e2cc0059b", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": null... |
#!/usr/bin/env python
'''Testing for tracking.py
@author: Zach Hafen
@contact: zachary.h.hafen@gmail.com
@status: Development
'''
import copy
import mock
import numpy as np
import numpy.testing as npt
import pytest
import unittest
import unyt
import galaxy_dive.read_data.ahf as read_ahf
import galaxy_dive.analyze_da... | {"hexsha": "10f57350fb7b26cf453486ea9ad46dcf88ba8301", "size": 54829, "ext": "py", "lang": "Python", "max_stars_repo_path": "galaxy_dive/tests/test_galaxy_linker/test_linker.py", "max_stars_repo_name": "zhafen/galaxy-dive", "max_stars_repo_head_hexsha": "e1127da25d10f699b3ada01b1b4635255f4f3917", "max_stars_repo_licens... |
/*****************************************************************************/
/* Copyright (c) 2017, Aleksandrs Ecins */
/* All rights reserved. */
/* */
... | {"hexsha": "02c7ebf358a27fbf5a7813e90a74a15f55c13473", "size": 9022, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "utilities/graph/min_cut.hpp", "max_stars_repo_name": "Cznielsen/symseg", "max_stars_repo_head_hexsha": "b1c1e1e2f21f6a3d8b65e4f68d3516bc0bbbf06e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import numpy as np
from layers.base import Layer
class Output(Layer):
def __init__(self, input_layers, output_shape, loss_function=None, learning_rate=0.1):
super().__init__(input_layers, output_shape)
self.loss_function = loss_function
self.cur_y_true = None
self.learning_rate = l... | {"hexsha": "051900c2ab26a6db2fae2e94a6048f0db0789230", "size": 1099, "ext": "py", "lang": "Python", "max_stars_repo_path": "layers/output.py", "max_stars_repo_name": "WallFacer5/ArtifIdiot", "max_stars_repo_head_hexsha": "698aac564901f64138b1e6287ab1996792a8f2fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Information Retrieval in High Dimensional Data
## Lab #6, 23.11.2017
## Principal Component Analysis
### Task 1
In this task we will once again work with the MNIST training set as provided on Moodle. Chose three digit classes, e.g. 1, 2 and 3 and load N=1000 images from each of the clsses to the workspace. Store t... | {"hexsha": "0cf86713f443311e6980a06107baf12f561c033f", "size": 37081, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "information-retrieval-exercises/lab06.ipynb", "max_stars_repo_name": "achmart/inforet", "max_stars_repo_head_hexsha": "3596ff971207728a42b335e71608b0b96e241228", "max_stars_repo_lice... |
##################################################################
# Match two patterns in one two-pattern image
# Works for color images only
#
# Copyright (c) 2017 Alexey Yastrebov
# MIT License, see LICENSE file.
##################################################################
#coding=cp1251
from keras.... | {"hexsha": "e52e633efc095d22e93630767e43fde1b80f5149", "size": 1509, "ext": "py", "lang": "Python", "max_stars_repo_path": "classifier/match_patterns.py", "max_stars_repo_name": "a-yastrebov/keratools", "max_stars_repo_head_hexsha": "6a20564174e11e0a8430edc052b60f3acca2b732", "max_stars_repo_licenses": ["MIT"], "max_st... |
from mod_copeland_yateesh import sample_complexity
args = {}
# args['heuristic'] = 'random'
args['heuristic'] = 'greedy'
# args['heuristic'] = 'mod_dcb'
args['n_voters'] = 4639
args['alpha'] = 0.05
args['seed'] = 42
args['ques_limit'] = 5
args['gamma'] = 0.5
args['probs'] = [0.05, 0.1, 0.2, 0.4]
q_limits = [1, 2, 3, ... | {"hexsha": "40a0ac9ef1f0942324ba2dd8c8677bdc56cbdcb2", "size": 3525, "ext": "py", "lang": "Python", "max_stars_repo_path": "cswor/runner_us_data.py", "max_stars_repo_name": "satvikmashkaria/CS748_project", "max_stars_repo_head_hexsha": "57185b6a467e638d6db96cf1c7d3dbe8d6bf5032", "max_stars_repo_licenses": ["BSD-3-Claus... |
from contextlib import contextmanager
from typing import Tuple
import numpy as np
import scipy.sparse as sp
@contextmanager
def random_state_context(seed: int):
state = np.random.get_state()
try:
np.random.seed(seed)
yield
finally:
np.random.set_state(state)
def with_cliques(
... | {"hexsha": "d92c3e021641a226137786a5342b693d51c519a6", "size": 3103, "ext": "py", "lang": "Python", "max_stars_repo_path": "gud/anomolies.py", "max_stars_repo_name": "jackd/gud", "max_stars_repo_head_hexsha": "7bd4befca53fa5a6b9d14a60e11d0898ade78267", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
# google imports
# standard library imports
import sys
import copy
import pickle
import os
from collections import Counter
from io import BytesIO
from zipfile import ZipFile
import copy
import pickle
from math import ceil
import importlib
import urllib.request
# math imports
from matplotlib import pyplot as plt
impor... | {"hexsha": "a41e0939fa40849de09879bb18a2ace11e3c95ba", "size": 37272, "ext": "py", "lang": "Python", "max_stars_repo_path": "jowilder_utils.py", "max_stars_repo_name": "fielddaylab/OGDUtils", "max_stars_repo_head_hexsha": "7f8252cafb0783f706db5adebf167d2edf069d00", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""Read pkl files created by process_*_catalog.py and *_ML.py and make plots of chromatic biases
as functions of redshift, both before and after photometric corrections are estimated. Run
`python plot_bias.py --help` for a list of command line options.
"""
import cPickle
from argparse import ArgumentParser
import nu... | {"hexsha": "d490ea85277b4207f2cd60d40b9482e8b2f3cd2c", "size": 24733, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/analytic/catalog/plot_bias_panel.py", "max_stars_repo_name": "DarkEnergyScienceCollaboration/chroma", "max_stars_repo_head_hexsha": "64fc123a065334b307654f29b3bea52885b46ec8", "max_stars_repo... |
program test_ewald2
! This test compares lattice energy 'eew' calculated from the Ewald summation
! against the energy calculated using the Madelung constant for various lattice
! constants L. The agreement is essentially to machine precision.
! Similar to test_ewald, but here we the diagonal Na atom is moved by 3/8
... | {"hexsha": "7e5f456ade68d8c38a7899bf7ed161c24e4a7ac1", "size": 5302, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/tests/test_ewald2.f90", "max_stars_repo_name": "certik/hfsolver", "max_stars_repo_head_hexsha": "b4c50c1979fb7e468b1852b144ba756f5a51788d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
#!/usr/bin/python
# coding=utf-8
# Copyright 2016-2019 Angelo Ziletti
#
# 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... | {"hexsha": "edc73565f43a66ea14e152a2fcf0bf5fc3fe1881", "size": 2569, "ext": "py", "lang": "Python", "max_stars_repo_path": "ai4materials/descriptors/diffraction1d.py", "max_stars_repo_name": "hpleva/ai4materials", "max_stars_repo_head_hexsha": "5b5548f4fbfd4751cd1f9d57cedaa1e1d7ca04b2", "max_stars_repo_licenses": ["Apa... |
import numpy as np
import pandas as pd
import xarray as xr
def istat_deaths_to_pandas(path):
istat = pd.read_csv(path, encoding="8859", na_values="n.d.", dtype={"GE": str})
# make a date index from GE
def ge2month_day(x):
return f"{x[:2]}-{x[2:]}"
month_day = istat["GE"].map(ge2month_day).va... | {"hexsha": "08fa4f3d8c2484a9c0197f48167adb6018f2835d", "size": 2089, "ext": "py", "lang": "Python", "max_stars_repo_path": "xpop/data/italy.py", "max_stars_repo_name": "alexamici/xpop", "max_stars_repo_head_hexsha": "940f935dfd125d5d51ab7b71a281196c55b29da4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
import Pkg; Pkg.activate(joinpath(@__DIR__, "../../../../../"))
using Distributions
using PyPlot
"""
Paper: Bayesian inference for finite mixtures of univariate and multivariate
skew-normal and skew-t distributions, Biostatistics 2010.
skew (delta): a real number in (-1, 1)
"""
function rand_skewnormal(loc, sca... | {"hexsha": "d57867463fd6192147031c96695cf7aca8c38ee7", "size": 740, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "runs/sim-study/configs/test-sim-6-7-19/notebook/scripts/sketch-data-gen.jl", "max_stars_repo_name": "luiarthur/CytofRepFAM.jl", "max_stars_repo_head_hexsha": "1f997d1620d74861c5bde5559ebdd1e6c449b9e... |
######################
##### ROSENBROOK #####
######################
function rosenbrook1()
model = Model()
@variable(model, x)
@variable(model, y)
@NLobjective(model, Min, (2.0 - x)^2 + 100 * (y - x^2)^2)
return model
end
function test_rosenbrook1(solver)
@testset "test_rosenbrook1" begin
... | {"hexsha": "ce39a147d36cf8a2f4bbd531da89deeb74babd38", "size": 10840, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems.jl", "max_stars_repo_name": "ohinder/OnePhase.jl", "max_stars_repo_head_hexsha": "386f4c09ec43b3cb1d41d711bbb7b0bab097015c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21... |
import numpy as np
from bokeh.plotting import figure, show
from bio_rtd import pdf, uo
# Define inlet profiles.
t = np.linspace(0, 10, 201) # time
c_in = np.ones([1, t.size]) # concentration (constant)
f = np.ones_like(t) * 3.5 # flow rate
# Define unit operation.
ft_uo = uo.fc_uo.FlowThrough(
t=t, uo_id="ft_e... | {"hexsha": "622f1010f51f3369eaaef4ba1644437882692cf5", "size": 762, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/documentation/tutorial_1d.py", "max_stars_repo_name": "open-biotech/bio-rtd", "max_stars_repo_head_hexsha": "c3e2cf4d7d646bda719e5fc6f694a1cae0e412c0", "max_stars_repo_licenses": ["MIT"], ... |
struct Quadrotor2DArm{I, T} <: Model{I, T}
n::Int
m::Int
d::Int
# body
lb # length
mb # mass
Jb # inertia
# link 1
l1
lc1
m1
J1
# link
l2
lc2
m2
J2
g # gravity
end
function kinematics(model::Quadrotor2DArm, q)
@SVector [q[1] + mod... | {"hexsha": "19fc81e0f4ced912f09dc3e833b4ed8847fcdcd8", "size": 1370, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "models/quadrotor2D_arm.jl", "max_stars_repo_name": "jmichaux/motion_planning", "max_stars_repo_head_hexsha": "9a36f394261ff11ca8325d8a5e9d8a79f18b2744", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma lift_resumption_bind: "lift_resumption (r \<bind> f) = lift_resumption r \<bind> lift_resumption \<circ> f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lift_resumption (r \<bind> f) = lift_resumption r \<bind> lift_resumption \<circ> f
[PROOF STEP]
by(coinduction arbitrary: r rule: gpv.coinduct_... | {"llama_tokens": 164, "file": "CryptHOL_Generative_Probabilistic_Value", "length": 1} |
from flask import Flask, render_template, request
import json
import plotly
import time
#import pandas as pd
import numpy as np
from plotly import graph_objects as go
import os
app = Flask(__name__)
app.debug = False
datapath = os.path.join('..',"data")
statusfile = os.path.join(datapath,"statusfile.txt")
def modifyl... | {"hexsha": "68f085e7a13a697ab6e1d62131b07a4e3dc7fefd", "size": 3675, "ext": "py", "lang": "Python", "max_stars_repo_path": "flaskapp/testapp.py", "max_stars_repo_name": "dr3y/biomassSensorPy", "max_stars_repo_head_hexsha": "3b86af0b0811a39973b5da592ad19bbd35444c70", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
Stacked area plot for 1D arrays inspired by Douglas Y'barbo's stackoverflow
answer:
https://stackoverflow.com/q/2225995/
(https://stackoverflow.com/users/66549/doug)
"""
import numpy as np
from matplotlib import _api
__all__ = ['stackplot']
def stackplot(axes, x, *args,
labels=()... | {"hexsha": "58c9b4fde5c0ffc23970031cef4426cd61c9ddda", "size": 4268, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/Lib/site-packages/matplotlib/stackplot.py", "max_stars_repo_name": "arnoyu-hub/COMP0016miemie", "max_stars_repo_head_hexsha": "59af664dcf190eab4f93cefb8471908717415fea", "max_stars_repo_licen... |
import tkinter as tk
import tkinter.ttk as ttk
import numpy as np
from ..functions import dp, pdd
from ..resources.language import Text
from .measurement_import import GetType
class RadCalc:
def __init__(self, filepath, parent):
self.filepath = filepath
self.parent = parent
self.filenam... | {"hexsha": "b8c8e117af7c0256e03aeebdb1fb30fbb88bbd3d", "size": 1898, "ext": "py", "lang": "Python", "max_stars_repo_path": "topasgraphsim/src/classes/radcalc_import.py", "max_stars_repo_name": "sebasj13/topasgraphsim", "max_stars_repo_head_hexsha": "6027c7c098b319159c32108dd4ec63f4b44e8676", "max_stars_repo_licenses": ... |
#!/usr/bin/env python
import os, re
import networkx as NX
import matplotlib.pyplot as PLT
import numpy, scipy
from numpy import random, array, triu, linalg
from scipy.sparse.linalg import eigs
from draggableNode import DraggableNode
from graph import Graph
class GraphCoOccurrence(Graph):
def __init__(self, par... | {"hexsha": "e689575723f58294acf79e89836ee1e93c0aa597", "size": 9564, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/graphCoOccurrence.py", "max_stars_repo_name": "fdesjardins/nara-stepbrowser", "max_stars_repo_head_hexsha": "27703717e0e5a69fe16d3df3ba285d22b56595cb", "max_stars_repo_licenses": ["MIT"], "max... |
\documentclass{article}
\usepackage{epic}
\usepackage{eepic}
\title{Efficient Planning In Simple Cases:\\
Lessons From The Blocks World}
\author{Bart Massey}
\date{June 12, 1995}
\newcommand{\sbw}{{\em primitive-blocks-world}}
\newcommand{\astar}{{$\mbox{A}^{\!\mbox{\tt *}}$}}
\newcommand{\idastar}{{$\mbox{IDA}^{\!... | {"hexsha": "d9dcea60f173f22ba0c268ec310b98824e6da06f", "size": 15375, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/fastplan/fastplan.tex", "max_stars_repo_name": "BartMassey/blocks", "max_stars_repo_head_hexsha": "6dbb39186595b6e2b80c9a5dcd616056f6cb3117", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright 2020 Magic Leap, Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing... | {"hexsha": "48cf0552e77f2ed1d55c24d9b9e1f689eeed1bfc", "size": 10265, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference.py", "max_stars_repo_name": "fuy34/indoorMVS", "max_stars_repo_head_hexsha": "440ba357de50d47e5868e6009bc608f8bcb9e9f6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1,... |
# -*- coding: utf-8 -*-
"""
We'd like to know a bit more about the dose we inflict on the patient.
This script is used to calculate said dose based on the x-ray spectra that we
will be able to set (see Source-Specifications).
"""
from __future__ import division # fix integer division
from optparse import OptionParse... | {"hexsha": "03fd3059180732229a1fb98f061254002823eb50", "size": 10870, "ext": "py", "lang": "Python", "max_stars_repo_path": "DoseCalculation.py", "max_stars_repo_name": "habi/GlobalDiagnostiX", "max_stars_repo_head_hexsha": "5171ccee3c8a8ccc7f0b82d52d7fdac327e8d7c7", "max_stars_repo_licenses": ["Unlicense"], "max_stars... |
import mlflow
import os.path
import plotly.express as px
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from src.visualization.plot import track_plot, plot
from src.substitute_dynamic_symbols import run, lambdify
from IPython.display import display
from os import stat
from sklearn.metrics import... | {"hexsha": "0ec4a0e67cf9d16b198fd7a0bd705bab1db4eec1", "size": 12006, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/result.py", "max_stars_repo_name": "martinlarsalbert/wPCC", "max_stars_repo_head_hexsha": "16e0d4cc850d503247916c9f5bd9f0ddb07f8930", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
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