text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
struct DisksApi <: SwaggerApi
client::Swagger.Client
end
"""
Creates or updates a disk.
Param: subscriptionId::String (required)
Param: resourceGroupName::String (required)
... | {"hexsha": "d9304bef539dc952efcc89682b2b3d6b6f2b9fde", "size": 13792, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Compute/DiskResourceProviderClient/api_DisksApi.jl", "max_stars_repo_name": "JuliaComputing/Azure.jl", "max_stars_repo_head_hexsha": "0e2b55e7602352d86bdf3579e547a74a9b5f44f8", "max_stars_repo... |
import mmap
import re
import numpy as np
import six
def parse_quasiparticle_data(qp_file):
import yaml
with open(qp_file, "r") as handle:
quasiparticle_data = yaml.load(handle)
data_dict = {}
for i, data in enumerate(quasiparticle_data):
data_dict['q_point_{}'.format(i)] = data
... | {"hexsha": "dba88fcdc3c1ef17a39f0ca56abd49604dc4267d", "size": 23280, "ext": "py", "lang": "Python", "max_stars_repo_path": "aiida_lammps/common/raw_parsers.py", "max_stars_repo_name": "chenggroup/aiida-lammps", "max_stars_repo_head_hexsha": "f91e3c8bc2d8d522b3476f31edbc7e08fbec01de", "max_stars_repo_licenses": ["MIT"]... |
using StringDistances, Test
# Compare
@test compare("", "abc", Hamming()) ≈ 0.0 atol = 1e-4
@test compare("acc", "abc", Hamming()) ≈ 2/3 atol = 1e-4
@test compare("saturday", "sunday", Hamming()) ≈ 1/8 atol = 1e-4
@test compare("", "abc", QGram(1)) ≈ 0.0 atol = 1e-4
@test compare("abc", "cba", QGram(1)) ≈ 1.0 atol =... | {"hexsha": "31519cee293e58493e9c904ad3e89d8de711d101", "size": 8413, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/modifiers.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/StringDistances.jl-88034a9c-02f8-509d-84a9-84ec65e18404", "max_stars_repo_head_hexsha": "e6bd25e583c00f799367ffb5cb8a0e8c1... |
#!/usr/bin/env python
######################################################################
## File: create_public_lumi_plots.py
######################################################################
import sys
import csv
import os
import commands
import time
import datetime
import calendar
import copy
import math
i... | {"hexsha": "c75ec952e12db2affaf765af11d7880196c5622a", "size": 19375, "ext": "py", "lang": "Python", "max_stars_repo_path": "RecoLuminosity/LumiDB/plotdata/create_public_peakpu_plots.py", "max_stars_repo_name": "pasmuss/cmssw", "max_stars_repo_head_hexsha": "566f40c323beef46134485a45ea53349f59ae534", "max_stars_repo_li... |
[STATEMENT]
lemma dep_tc_imp_in_dom:
fixes PROB :: "(('a, 'b) fmap \<times> ('a, 'b) fmap) set" and v1 v2
assumes "\<not>(v1 = v2)" "(dep_tc PROB v1 v2)"
shows "(v1 \<in> prob_dom PROB)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. v1 \<in> prob_dom PROB
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal... | {"llama_tokens": 8232, "file": "Factored_Transition_System_Bounding_Dependency", "length": 69} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Ivo Marvan"
__email__ = "ivo@marvan.cz"
__description__ = """
Insightface face detector as img processor.
@credit https://github.com/deepinsight/insightface
"""
import sys
import os
import numpy as np
import insightface
# root of project repository
T... | {"hexsha": "8399d51474f765360b0920e6d2e670153490d7e6", "size": 2494, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/img/processor/faces/face_detector/insightface_face_detector.py", "max_stars_repo_name": "ivomarvan/quick_faces", "max_stars_repo_head_hexsha": "ba75ba1f0a217b1e845d7c09f8d18bb7a6913584", "max_... |
[STATEMENT]
lemma mono2mono2:
assumes f: "monotone (rel_prod ordb ordc) leq (\<lambda>(x, y). f x y)"
and t: "monotone orda ordb (\<lambda>x. t x)"
and t': "monotone orda ordc (\<lambda>x. t' x)"
shows "monotone orda leq (\<lambda>x. f (t x) (t' x))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. monotone or... | {"llama_tokens": 970, "file": null, "length": 10} |
# -*- coding: utf-8 -*-
"""
Created on Wed May 13 02:16:09 2020
@author: Will Kew
"""
# import modules
import pandas as pd
import numpy as np
import os
import csv
from io import BytesIO
from pathlib import Path
from s3path import S3Path
# import corems modules
from corems.transient.input.brukerSolarix import ReadBru... | {"hexsha": "ae460348af8e747f071fad9201f7fa41d77a1f69", "size": 11507, "ext": "py", "lang": "Python", "max_stars_repo_path": "corems/mass_spectrum/calc/Calibration.py", "max_stars_repo_name": "deweycw/CoreMS", "max_stars_repo_head_hexsha": "c88bf7aedaca26e2268c53b96c4a19e6b3ed6fbf", "max_stars_repo_licenses": ["BSD-2-Cl... |
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "Optimization.hpp"
#include <boost/core/ignore_unused.hpp>
namespace armnn
{
namespace optimizations
{
template <typename Comparable>
class SquashEqualSiblingsImpl
{
public:
/// Run for every connectio... | {"hexsha": "12637ba9b6d9985c0b598ae73c1886407b355fb2", "size": 2616, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/armnn/optimizations/SquashEqualSiblings.hpp", "max_stars_repo_name": "korabelnikov/armnn", "max_stars_repo_head_hexsha": "8c3259fa007d43fcc5ea56fe6928526dbe79f3c0", "max_stars_repo_licenses": ["... |
import unittest
import numpy as np
import tensorflow as tf
import torch
from fastestimator.op.tensorop.gradient import Watch
from fastestimator.test.unittest_util import is_equal
class TestWatch(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.tf_data = tf.Variable([1., 2., 4.])
cls... | {"hexsha": "8e570cb375c1c81f65d388e0d655ec037af82e3c", "size": 1171, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/PR_test/integration_test/op/tensorop/gradient/test_watch.py", "max_stars_repo_name": "hanskrupakar/fastestimator", "max_stars_repo_head_hexsha": "1c3fe89ad8b012991b524a6c48f328b2a80dc9f6", "m... |
import numpy as np
import utils
def binarize_predictions_2d(predictions, threshold=0.5):
"""Convert prediction probabilities to binary values.
This function is intended for audio tagging predictions. The
predictions should be passed in a 2D array in which the first
dimension is the sample axis and t... | {"hexsha": "7c7d93dc2f6fd355eb6aae397162d615a5d9e303", "size": 5703, "ext": "py", "lang": "Python", "max_stars_repo_path": "gccaps/inference.py", "max_stars_repo_name": "turab95/gccaps", "max_stars_repo_head_hexsha": "6bfc36aadc3ca7aeba40f1b8dbf58051e40cb1e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 15, ... |
id : forall {k}{X : Set k} -> X -> X
id x = x
_o_ : forall {i j k}
{A : Set i}{B : A -> Set j}{C : (a : A) -> B a -> Set k} ->
(f : {a : A}(b : B a) -> C a b) ->
(g : (a : A) -> B a) ->
(a : A) -> C a (g a)
f o g = \ a -> f (g a)
data List (X : Set) : Set where
[] : List X
_,_ : X → List X → List X
data... | {"hexsha": "bb0da65225f51970fb3dabd76a89b0fcef70c69a", "size": 1804, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Fail/Issue2993.agda", "max_stars_repo_name": "shlevy/agda", "max_stars_repo_head_hexsha": "ed8ac6f4062ea8a20fa0f62d5db82d4e68278338", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
! This file was automatically generated by SWIG (http://www.swig.org).
! Version 4.0.0
!
! Do not make changes to this file unless you know what you are doing--modify
! the SWIG interface file instead.
! ---------------------------------------------------------------
! Programmer(s): Auto-generated by swig.
! --------... | {"hexsha": "639e8a86a08cda258fd4d1eb6dd67322232ac98f", "size": 40279, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/nvector/serial/fmod/fnvector_serial_mod.f90", "max_stars_repo_name": "bassenj/sundials", "max_stars_repo_head_hexsha": "73c280cd55ca2b42019c8a9aa54af10e41e27b9d", "max_stars_repo_licenses":... |
import numpy as np
import torch
from torch import nn
from torchvision.transforms import transforms
np.random.seed(0)
class GaussianBlur(object):
"""blur a single image on CPU"""
def __init__(self, kernel_size):
radias = kernel_size // 2
kernel_size = radias * 2 + 1
self.blur_h = nn.Co... | {"hexsha": "57ffca425b5b75d4ffc518b7214245a3c53df804", "size": 1819, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_aug/gaussian_blur.py", "max_stars_repo_name": "LouGalata/SimCLR", "max_stars_repo_head_hexsha": "c5ae0309183bbb4c6aac701f60488d54e8753fe5", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
'''
Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related docu... | {"hexsha": "f1e026ad86cf3d8f17754f21fabd8cbf7f0a4a23", "size": 3755, "ext": "py", "lang": "Python", "max_stars_repo_path": "sdk/packages/pyalice/tests/pymessage_test.py", "max_stars_repo_name": "ddr95070/RMIsaac", "max_stars_repo_head_hexsha": "ee3918f685f0a88563248ddea11d089581077973", "max_stars_repo_licenses": ["FSF... |
subroutine m70get (fcb, error)
c
c Routine to get (allocate) the model 70
c
c arguments:
c
c fcb function communications block
c
c error -2 => device already allocated
c -1 => m70 not acquired
c 0 => success
c 1 => timeout
c ... | {"hexsha": "c9d0b1c296012f2c8fe66121e080d6a4b3a0eb5b", "size": 1053, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "iraf.v2161/unix/gdev/m70vms/m70get.f", "max_stars_repo_name": "ysBach/irafdocgen", "max_stars_repo_head_hexsha": "b11fcd75cc44b01ae69c9c399e650ec100167a54", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
from PIL import Image
datalist=np.load('./pgdx.npy')
print(type(datalist))
print(type(datalist[0,:,:,:]))
for i in range(500):
array=datalist[i,:,:,:]
img=Image.fromarray(np.uint8(array))
img.save('./imgs/'+str(i)+'.jpg') | {"hexsha": "80441c712c66c156eb6aa54622ceb6fb71aa87f9", "size": 253, "ext": "py", "lang": "Python", "max_stars_repo_path": "AT/npy2img.py", "max_stars_repo_name": "chciw/Adversary-about-RGBD-SOD", "max_stars_repo_head_hexsha": "19edc78b3abf45313f63212750aaedefb3b82de0", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import tensorrt as trt
import torch
from torch import nn
import torch2trt
from torch2trt.inference.inference import TorchInferenceContext
from torch2trt.utils import get_torch_forward_name
from torch.utils import dlpack
try:
import tvm
from tvm.relay import expr, analysis
from tvm impor... | {"hexsha": "b32b49a79a7c204a9ac22ed62182529414d18bd3", "size": 15786, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch2trt/module.py", "max_stars_repo_name": "traveller59/torch2trt", "max_stars_repo_head_hexsha": "ad64a31217e1356a76fd491ee354eaf3b1d0b5ba", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "134e67089831fc43cc2133d53cf8affa4897365f", "size": 35064, "ext": "py", "lang": "Python", "max_stars_repo_path": "python3/lib/python3.6/site-packages/tensorflow/_api/v1/v1.py", "max_stars_repo_name": "TruongThuyLiem/keras2tensorflow", "max_stars_repo_head_hexsha": "726f2370160701081cb43fbd8b56154c10d7ad63", ... |
from scipy import integrate
import numpy as np
import matplotlib.pyplot as plt
# Define constants
i0 = 100.0
R = 0.5
t0 = 0.01
integrand = lambda t: R*(((i0)*np.exp(-t/t0)*(np.sin((2*t)/t0)))**2)
E, error = integrate.quad(integrand,0,np.inf)
print "E = " + str(E) + " with an error of " + str(error) | {"hexsha": "59eb1df5331ff5b355503ece62e4712f71006335", "size": 302, "ext": "py", "lang": "Python", "max_stars_repo_path": "ExamPrep/Shit Comp/ShitComp/(Past paper) 1314/Q3_integral.py", "max_stars_repo_name": "FHomewood/ScientificComputing", "max_stars_repo_head_hexsha": "bc3477b4607b25a700f2d89ca4f01cb3ea0998c4", "max... |
[STATEMENT]
lemma (in aGroup) ag_l_inv1:"x \<in> carrier A \<Longrightarrow> (-\<^sub>a x) \<plusminus> x = \<zero>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> carrier A \<Longrightarrow> -\<^sub>a x \<plusminus> x = \<zero>
[PROOF STEP]
by (simp add:l_m) | {"llama_tokens": 113, "file": "Group-Ring-Module_Algebra4", "length": 1} |
import json
import os
import pickle as pkl
import typing
from copy import copy
from time import time
import numpy as np
import umap
from gensim.models import KeyedVectors, Word2Vec
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial import distance
from sklea... | {"hexsha": "9194986891d6735a8ac258ba5c007e0cd791ccdb", "size": 1502, "ext": "py", "lang": "Python", "max_stars_repo_path": "K-Cap_2021/2A_KB_embeddings/get_reduced.py", "max_stars_repo_name": "cultural-ai/ConConCor", "max_stars_repo_head_hexsha": "f5c30dfb7d38392f492f9c6e44c8d242f2820ce4", "max_stars_repo_licenses": ["... |
"""
Script for showing use of Profile.average_into_grid_boxes(). This routines
takes all data in a Profile obejct and averages it into lat/lon grid boxes.
This script can be used for comparing observed and modelled climatologies.
It should be run AFTER the nearest profiles have been extracted from the model
data, suc... | {"hexsha": "a419aef7cf369868c1ec6d929793c8f868308209", "size": 2417, "ext": "py", "lang": "Python", "max_stars_repo_path": "example_scripts/profile_validation/analysis_average_into_grid_boxes.py", "max_stars_repo_name": "British-Oceanographic-Data-Centre/NEMO-ENTRUST", "max_stars_repo_head_hexsha": "41ed278e56428404ab8... |
@doc raw"""
TranslationGroup{T<:Tuple,𝔽} <: GroupManifold{Euclidean{T,𝔽},AdditionOperation}
Translation group $\mathrm{T}(n)$ represented by translation arrays.
# Constructor
TranslationGroup(n₁,...,nᵢ; field = 𝔽)
Generate the translation group on
$𝔽^{n₁,…,nᵢ}$ = `Euclidean(n₁,...,nᵢ; field = 𝔽)`, which... | {"hexsha": "6bf7f03e0dae8ad7a128e0c06a2dd3163b85f567", "size": 994, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/groups/translation_group.jl", "max_stars_repo_name": "Nikdwal/Manifolds.jl", "max_stars_repo_head_hexsha": "10d264d146159015fdebab94b7394b9f74728381", "max_stars_repo_licenses": ["MIT"], "max_st... |
import unittest
import pandas as pd
import numpy as np
from numpy.testing import assert_array_almost_equal
from epyestim.smoothen import renormalise_series, smoothen_series
class MyTestCase(unittest.TestCase):
def test_renormalise_series(self):
ser = pd.Series([-1,2,3], pd.date_range('2020-03-01', perio... | {"hexsha": "b8a223dc56c9ed65e0631f01fc7f52eb88040eba", "size": 870, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_smoothen.py", "max_stars_repo_name": "lo-hfk/epyestim", "max_stars_repo_head_hexsha": "ca2ca928b744f324dade248c24a40872b69a5222", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
from keras.utils import to_categorical
import copy
from common.utils import eligibility_traces, default_config, make_env, RunningMeanStd, str2bool, discount_rewards
from common.ppo_independant import PPOPolicyNetwork, ValueNetwork
render = False
normalize_inputs = True
config = default_config()
LAM... | {"hexsha": "a65bf9f82d272be18bc03edc6c073ad285e16e10", "size": 5724, "ext": "py", "lang": "Python", "max_stars_repo_path": "FEN-FD.py", "max_stars_repo_name": "matthieu637/distributed-fair-rl", "max_stars_repo_head_hexsha": "6ccc5f2dd657d5123bf9d34330b5ae44304fefcb", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
Basic REINFORCE algorithm.
Simple policy gradient method for discounted reward MDPs.
http://kvfrans.com/simple-algoritms-for-solving-cartpole/
"""
from __future__ import division, print_function, absolute_import
import theano
import theano.tensor as T
from .generic import get_n_step_value_reference, get_values_f... | {"hexsha": "e740546379bee96ebb88ccb8b1526e52af140308", "size": 3211, "ext": "py", "lang": "Python", "max_stars_repo_path": "agentnet/learning/reinforce.py", "max_stars_repo_name": "mraihan19/AgentNet", "max_stars_repo_head_hexsha": "240b7ac351d5c7a156a6c9c25432d5a7afffe3cc", "max_stars_repo_licenses": ["MIT"], "max_sta... |
%% The MTEX Documentation
%
% Documenting a project like MTEX is a challenging and ongoing task for the
% entire community. Therefore we are extremely happy about any spelling
% fixes, examples, theoretical explainations, special use cases, etc. As a
% bonus everybody who contributed to MTEX will automatically appear a... | {"author": "mtex-toolbox", "repo": "mtex", "sha": "f0ce46a720935e9ae8106ef919340534bca1adcb", "save_path": "github-repos/MATLAB/mtex-toolbox-mtex", "path": "github-repos/MATLAB/mtex-toolbox-mtex/mtex-f0ce46a720935e9ae8106ef919340534bca1adcb/doc/GeneralConcepts/Contribute2Doc.m"} |
PATH <- '00_data/babies-stoerche.csv'
JAHR <- c(1965, 1971, 1974, 1977, 1978, 1979, 1980)
BABIES <- c(1061, 788, 631, 583, 577, 580, 654)
STOERCHE <- c(1910, 1295, 1071, 904, 1019, 974, 910)
TAB <- cbind(JAHR, BABIES, STOERCHE)
DF <- data.frame(TAB)
#DF <- data.frame(JAHR, BABIES, STOERCHE)
str(DF)
summary(DF)
writ... | {"hexsha": "6ca7ff48e1d2639064c45d1419244566691bd208", "size": 428, "ext": "r", "lang": "R", "max_stars_repo_path": "workshop-1-3.r", "max_stars_repo_name": "StrubT/CASBIDataAnalytics", "max_stars_repo_head_hexsha": "8ea466f8a0ef1b5af7d9e19a7929e3ad95c03ee4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #내 맥북에서 발생되는 에러를 없애기 위한 코드
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
import pandas as pd
import tensorflow as tf
import numpy
seed = 0
numpy.random.seed(seed)
tf.set_random_seed(seed)
df... | {"hexsha": "b44904315dc37a09935e8422c85d6c4f04c9124b", "size": 991, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_code_myself_modu/13_Boston.py", "max_stars_repo_name": "94JuHo/study_for_deeplearning", "max_stars_repo_head_hexsha": "ababf482b6a24d94b5f860ea9a68e34fe324d182", "max_stars_repo_licenses": ["M... |
module TestExamples3DPart2
using Test
using Trixi
include("test_trixi.jl")
# Start with a clean environment: remove Trixi output directory if it exists
outdir = "out"
isdir(outdir) && rm(outdir, recursive=true)
@testset "3D-Part2" begin
# Run basic tests
@testset "Examples 3D" begin
# MHD
include("test_example... | {"hexsha": "9c747fcf8d1bbaa8e9844095fa8ff4351f9f7da8", "size": 1434, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_examples_3d_part2.jl", "max_stars_repo_name": "atiebing/Trixi.jl", "max_stars_repo_head_hexsha": "63ba0f9799a7cf6712af3f8e9731d54eeb7a3c3e", "max_stars_repo_licenses": ["MIT"], "max_stars... |
using Makie
Base.@ccallable function julia_main(ARGS::Vector{String})::Cint
scene = Scene()
scatter(scene, rand(50), rand(50), markersize = 0.01)
a = axis(scene, range(0, stop = 1, length = 4), range(0, stop = 1, length = 4), textsize = 0.1, axisnames = ("", "", ""))
tf = to_value(a, :tickfont2d)
a... | {"hexsha": "ab67c8077667237781a6d24a79f92565f23c2e33", "size": 428, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/tmp/makietest.jl", "max_stars_repo_name": "MaximeRivest/Makie.jl", "max_stars_repo_head_hexsha": "331f183c024b031a1ec425a4ccb3c25583f130b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from functools import reduce
from astropy.io import fits
from scipy.optimize import curve_fit
from scipy.special import wofz
from lmfit.models import GaussianModel, VoigtModel, LinearModel, ConstantModel
from sklearn.metrics import... | {"hexsha": "72fddab25cd83e96ec1937fbe4261b8946e7575d", "size": 5624, "ext": "py", "lang": "Python", "max_stars_repo_path": "stat_model_v3.py", "max_stars_repo_name": "pablonavarrob/analysis-of-stellar-spectra", "max_stars_repo_head_hexsha": "8efb9142b6b1b463c377837426d2f09f8b314878", "max_stars_repo_licenses": ["MIT"],... |
\documentclass[12pt]{beamer}
\usepackage[T1]{fontenc}
\usepackage{lmodern}
\usepackage{fancyvrb}
\usepackage{transparent}
\usepackage{stmaryrd}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\usepackage{fixltx2e} % provides \textsubscript
% use upquote if available, for straight quotes in verbatim environ... | {"hexsha": "99789dc8cdf516686dc0ddce822115ffeaae0f8b", "size": 16338, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "bachelor-presentation/bachelor-presentation.tex", "max_stars_repo_name": "folkertdev/reversible-debugger", "max_stars_repo_head_hexsha": "34a474bc2217e0abfb678e8916438de7f2fe88f6", "max_stars_repo_... |
\input{docs/preamble}
\title{The Speed of Light}
\author{Max Bigras and David Frawley}
\begin{document}
\maketitle
\section{The speed of light}
\subsection{Light speed in air}
Using a pulse modulated diode laser and fast photodiode detector we measured the time lag between the laser pulse and detection. We adjusted ... | {"hexsha": "15cf8db9d4d49da994feb97b07ec6a0b16fada1d", "size": 2786, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "senior_project/report/speed_of_light/report/lab_report.tex", "max_stars_repo_name": "mbigras/physics_projects", "max_stars_repo_head_hexsha": "7dd29707b3ac8adea7ed8b63786245e34097345e", "max_stars_r... |
import os
import argparse
import matplotlib.pyplot as plt
import numpy as np
def main(args):
path=args.out_path
# losses in train
lossD = np.load(os.path.join(path, "lossD.npy"))
lossH = np.load(os.path.join(path, "lossH.npy"))
lossL = np.load(os.path.join(path, "lossL.npy"))
auc_all = np.lo... | {"hexsha": "9c6048437e6090bec8d36652dfaf4e70edb5c20f", "size": 3048, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot.py", "max_stars_repo_name": "tanyinghui/Minimal-Hand-pytorch", "max_stars_repo_head_hexsha": "3e991af9be0475ebc761fec3f13d00f81146631a", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#include <boost/spirit/home/x3/support/traits/container_traits.hpp>
| {"hexsha": "cbdaa5e3c0ade11000f25c482afedfa40936dcbb", "size": 68, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_spirit_home_x3_support_traits_container_traits.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_... |
from keras.models import model_from_json
import numpy as np
class FModel(object):
TARGET_LIST = ["1","2","3","4","5","6"]
def __init__(self, model_json_file,model_weights_file):
with open(model_json_file,"r") as json_file:
loaded_model_json = json_file.read()
self.loaded_model ... | {"hexsha": "4a7ae6456f1928a0fa8c121a053deb8b6b5b2366", "size": 611, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera_recognition/FModel.py", "max_stars_repo_name": "kwonbosung02/2019_ICT_COC", "max_stars_repo_head_hexsha": "9b330e1aff8cff5fbaf12c1f1bbae29ab57fd81d", "max_stars_repo_licenses": ["MIT"], "max... |
#include <ros/ros.h>
#include <sensor_msgs/JointState.h>
#include <boost/asio.hpp>
#include <boost/asio/serial_port.hpp>
#include <thread>
#include <string>
#include <vector>
#include <functional>
#include <mutex>
#include <realtime_tools/realtime_publisher.h>
using namespace std;
using namespace boost;
const stri... | {"hexsha": "60e45f3a20344054b636133a9480d49fec4eeda9", "size": 4892, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dyros_jet_ext_encoder/src/dyros_jet_ext_encoder.cpp", "max_stars_repo_name": "DaegyuLim/dyros_jet", "max_stars_repo_head_hexsha": "969233737fb49b42d7fd9e5ec49694953baf4e7d", "max_stars_repo_licenses... |
[STATEMENT]
lemma max_ex_gr: "\<exists>x \<in> X. k < x \<Longrightarrow> finite X \<Longrightarrow> X \<noteq> {} \<Longrightarrow> k < Max X"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<exists>x\<in>X. k < x; finite X; X \<noteq> {}\<rbrakk> \<Longrightarrow> k < Max X
[PROOF STEP]
by (simp add: Max_... | {"llama_tokens": 129, "file": "CakeML_Codegen_Utils_Compiler_Utils", "length": 1} |
import numpy as np
from tqdm import tqdm
from sklearn.neighbors import KDTree
from scipy.spatial import Voronoi
from config import *
from multiprocessing import Pool
import os
import traceback
import time
from matplotlib import pyplot as plt
from matplotlib import collections as mc
from sklearn.cluster import DBSCAN
f... | {"hexsha": "54a5f6e73e70a94f43bb4ace87beee930d3ee5da", "size": 5864, "ext": "py", "lang": "Python", "max_stars_repo_path": "move_points.py", "max_stars_repo_name": "Phoenix-Chen/butterflies", "max_stars_repo_head_hexsha": "06abf393c69f7a7c54d4e8a8775de81de7908285", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import time
import sys
import networkx as nx
sys.path.insert(0, '../')
from dijkstra import Dijkstra
class TestAlgorithm:
def __init__(self, amountOfTest):
self.amountOfTest = amountOfTest
self.resultProcess = np.zeros([amountOfTest, 9]) #Matriz que armazena: Origem | Destino |... | {"hexsha": "7e49bb51a596f1d2d638b86411f41cf71f35dd61", "size": 3980, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/tester.py", "max_stars_repo_name": "Jhon3/BestRoute_Graphs", "max_stars_repo_head_hexsha": "2957580d7f43e294f011df0a8e52ebe534c9d48b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
PhD student, Computer Science.
http://amitsahoo.blogspot.com
| {"hexsha": "dc104a2d3ec19063555863766c9f230ebaef3e75", "size": 63, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/AmitSahoo.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import numpy as np
import pyqtgraph as pg
from PyQt5.QtCore import pyqtSignal
from PyQt5.QtGui import QKeySequence
from PyQt5.QtWidgets import QShortcut
from pyqtgraph import PlotWidget
pg.setConfigOption("background", "w") # white background
pg.setConfigOption("foreground", "k") # black peaks
class TICWidget(Plot... | {"hexsha": "0e8e46f2db92e108f88e268c875a0e70cb9b07a7", "size": 12314, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/view/TICWidget.py", "max_stars_repo_name": "jpfeuffer/pyopenms-extra", "max_stars_repo_head_hexsha": "b10c64356ccef147234f0adb08c40ddb043f2376", "max_stars_repo_licenses": ["Zlib", "Apache-2.... |
(************************************************************************)
(* v * The Coq Proof Assistant / The Coq Development Team *)
(* <O___,, * INRIA - CNRS - LIX - LRI - PPS - Copyright 1999-2011 *)
(* \VV/ **************************************************************)
(* // * Th... | {"author": "JasonGross", "repo": "category-coq-experience-tests", "sha": "f9949ede618788fd051fe8327f997ee683388e49", "save_path": "github-repos/coq/JasonGross-category-coq-experience-tests", "path": "github-repos/coq/JasonGross-category-coq-experience-tests/category-coq-experience-tests-f9949ede618788fd051fe8327f997ee6... |
__author__ = 'vid'
import os
import numpy as np
import lmfit as lm
import matplotlib.pyplot as plt
from matplotlib.patches import *
import natsort
import tkinter.filedialog as tk
def beri(poti):
lagtime = []
corr = []
with open(poti, encoding='windows-1250') as file:
for i in range(33):
... | {"hexsha": "c277604fddad3f7fb209bb96c107febdc323192e", "size": 4721, "ext": "py", "lang": "Python", "max_stars_repo_path": "fitDLS.py", "max_stars_repo_name": "Ossada/DLS-UVVis", "max_stars_repo_head_hexsha": "810e7c394a6815ef61a0c22626915e9bea81ef94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_star... |
import glob
import os
import re
import sys
import dask
import deepblink as pink
import numpy as np
import pandas as pd
import skimage.measure
import skimage.util
DTYPES = {
"radius": np.float16,
"threshold": np.float16,
"x": np.float16,
"y": np.float16,
"q": np.float64,
}
def print_results(resul... | {"hexsha": "be03ce1fef4d936fb2afec4de11d0ac4a2bc5347", "size": 2782, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/util.py", "max_stars_repo_name": "resace3/deepBlink", "max_stars_repo_head_hexsha": "1085f9fddca9dbf511f9cbb727b72f030bb7e303", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
from __future__ import print_function
import os
import numpy as np
# from six.moves.urllib.request import urlretrieve
# from six.moves import cPickle as pickle
import cPickle as pickle
from not_mnist.img_pickle import maybe_pickle, save_obj
image_size = 28 # Pixel width and height.
def make_arrays(nb_rows, img_s... | {"hexsha": "866ee50698a4c2831d476f540e3b2caf0acbbbbb", "size": 4065, "ext": "py", "lang": "Python", "max_stars_repo_path": "GDLnotes/src/not_mnist/merge_prune.py", "max_stars_repo_name": "dachmx/tfnotes", "max_stars_repo_head_hexsha": "da86835766a3764856de3308c75cb7258cf10811", "max_stars_repo_licenses": ["Apache-2.0"]... |
"""
Create a table plot of the brightness ratio as a function of the
contrast and the separation.
"""
# -----------------------------------------------------------------------------
# IMPORTS
# -----------------------------------------------------------------------------
from itertools import product
from pathlib imp... | {"hexsha": "ef3d224ab386ecbd834eecf1b86cf9ba4e55ca80", "size": 7602, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/main/5.3_photometry-artificial-planets/02_make_plot.py", "max_stars_repo_name": "timothygebhard/hsr4hci", "max_stars_repo_head_hexsha": "0b38c26fac2fee9e564a9ab981fca715d5577e1e", "max... |
from __future__ import division
import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import re
if len(sys.argv) == 1:
print("enter log file path")
sys.exit(1)
filepath = sys.argv[1]
accuracy = list()
ce = list()
rmse = list()
with open(filepath) as f:
for line in f:... | {"hexsha": "d88be405d8cf399ac573e5d39ee9d922390a3f25", "size": 1610, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/scripts/plotModelMetrics.py", "max_stars_repo_name": "smihir/gdml", "max_stars_repo_head_hexsha": "932cab10b7091bbcfa29000e57b2e032ca85e37d", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
SEED = 42
tf.set_random_seed(SEED)
class CNN():
def __init__(self, num_features, num_historical_days, is_train=True):
self.X = tf.placeholder(tf.float32, shape=[None, num_historical_days, num_features])
X =... | {"hexsha": "57e47780d2c0f2440b29180410d10bacd79a7eed", "size": 3820, "ext": "py", "lang": "Python", "max_stars_repo_path": "StockMarketGAN/cnn.py", "max_stars_repo_name": "sevmardi/ml-projects", "max_stars_repo_head_hexsha": "0eb218c77cda61285cfcf599599ff28a8a8deba7", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from argparse import ArgumentParser
from pathlib import Path
from tqdm import tqdm, trange
from tempfile import TemporaryDirectory
import shelve
from random import random, randrange, randint, shuffle, choice, sample
from pytorch_pretrained_bert.tokenization import BertTokenizer
import numpy as np
import json
class D... | {"hexsha": "8a59e0d6a7bc6b1ffdfda0b2593f19f0ac32c1bc", "size": 13813, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/lm_finetuning/pregenerate_training_data.py", "max_stars_repo_name": "Barqawiz/pytorch-pretrained-BERT", "max_stars_repo_head_hexsha": "c4fe56dcc0ca30d777650ba95167ec72582fdfea", "max_sta... |
[STATEMENT]
lemma "map opt_MatchAny_match_expr (normalize_src_ports
(MatchAnd (Match ((Src_Ports (L4Ports UDP [(21,21), (22,22)])) :: 32 common_primitive))
(Match (Prot (Proto UDP)))))
=
[MatchAnd (Match (Src_Ports (L4Ports UDP [(21, 22)]))) (Match (Prot (Proto UDP)))]"
[PROOF STATE]... | {"llama_tokens": 264, "file": "Iptables_Semantics_Primitive_Matchers_Ports_Normalize", "length": 1} |
import numpy as np
from scipy.special import loggamma, gammaln, gamma
from matplotlib import pyplot as plt
from scipy.optimize import minimize
from scipy.optimize import root
from mpl_toolkits import mplot3d
np.seterr(divide = 'raise')
logmoments = np.load("logmoments_Harmonic_4.npy")
moments = np.load("momen... | {"hexsha": "94fe4ad65857231f8b1c7d56992d11e69559300d", "size": 4117, "ext": "py", "lang": "Python", "max_stars_repo_path": "HarmonicOscillator/fit_gammas_H4.py", "max_stars_repo_name": "BenedictIrwin/ExactLearning", "max_stars_repo_head_hexsha": "5f1185eec560d36b33d2a3cc090bc0737b1c926e", "max_stars_repo_licenses": ["M... |
from typing import List
import re
import numpy as np
import pandas as pd
import networkx as nx
from scipy.spatial import distance
from latent_semantic_analysis import *
import constants
# ideas is in streamlit app, create one function to load an object of this class
# and add a node to the graph inside t... | {"hexsha": "b9953f155c61c63d7fe7856fc474113ded9fad80", "size": 10249, "ext": "py", "lang": "Python", "max_stars_repo_path": "recommender/core/job_recommender.py", "max_stars_repo_name": "huynhnhathao/job_recommender", "max_stars_repo_head_hexsha": "67ec56abfa9ad09f804778e51a7a1ee62142a7da", "max_stars_repo_licenses": [... |
import numpy as np
import vrep
import ctypes
import math
import nengo
vrep_mode = vrep.simx_opmode_oneshot
def b( num ):
""" forces magnitude to be 1 or less """
if abs( num ) > 1.0:
return math.copysign( 1.0, num )
else:
return num
def convert_angles( ang ):
""" Converts Euler angles from x-y-z to z... | {"hexsha": "9d4fb4b1f9c183afc7f0bb0e3a478c1452cfcf8b", "size": 19838, "ext": "py", "lang": "Python", "max_stars_repo_path": "vr_omnirob/robotics_simulator/robots.py", "max_stars_repo_name": "caxenie/neuromorphic-sensorimotor-adaptation", "max_stars_repo_head_hexsha": "e2f75bab83c0c08009d0ac5080e0bd63b77f61a8", "max_sta... |
#!/usr/bin/env python
"""
Prisma SDWAN Bulk Device Upgrades
tkamath@paloaltonetworks.com
Version: 1.0.1 b1
"""
# standard modules
import getpass
import json
import logging
import datetime
import os
import sys
import csv
import time
import numpy as np
import pandas as pd
#standard modules
import argparse
import logging... | {"hexsha": "03564e1a4ed67d49ef6ba0295b215afbee07d6d8", "size": 14424, "ext": "py", "lang": "Python", "max_stars_repo_path": "bulkupgrade.py", "max_stars_repo_name": "ktanushree/bulkupgrade", "max_stars_repo_head_hexsha": "8dfaa07b58a345c0ebfef9cc05b981272ce9e997", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma ceiling_divide_upper:
fixes q :: "'a::floor_ceiling"
shows "q > 0 \<Longrightarrow> p \<le> of_int (ceiling (p / q)) * q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (0::'a) < q \<Longrightarrow> p \<le> of_int \<lceil>p / q\<rceil> * q
[PROOF STEP]
by (meson divide_le_eq le_of_int_ceiling) | {"llama_tokens": 138, "file": null, "length": 1} |
function backtrack_constrained(ϕ, α::Real, αmax::Real, αImax::Real,
Lcoefsα::Tuple{<:Real,<:Real,<:Real}, c1::Real = 0.5,
ρ::Real=oftype(α, 0.5),
αminfrac::Real = sqrt(eps(one(α)));
show_linesearc... | {"hexsha": "0bf0a9ef23c062345e2f8950ca08a4dd77abe47d", "size": 2634, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/multivariate/solvers/constrained/ipnewton/iplinesearch.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Optim.jl-429524aa-4258-5aef-a3af-852621145aeb", "max_stars_repo_head_hexsha": ... |
"""
# RandomGeometricGraphs.jl
A small package for the generation of [random geometric graphs](https://en.wikipedia.org/wiki/Random_geometric_graph) in Julia. The package is heavily inspired by the generator used in NetworkX.
It's about 10-15x faster than `LightGraphs.euclidean_graph` because it uses KDTrees from `Ne... | {"hexsha": "db9b972afb4eb6d6ae60180a731723c8bb1d8131", "size": 2046, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RandomGeometricGraphs.jl", "max_stars_repo_name": "bovine3dom/RandomGeometricGraphs.jl", "max_stars_repo_head_hexsha": "83835fee20c6e5ebc63771ebd083045502680374", "max_stars_repo_licenses": ["B... |
module test_bukdu_changeset
using Test
using Bukdu # Assoc Changeset
using Bukdu.HTML5.Form # change
struct User
name
age::Int
salary::Float64
end
changeset = Changeset(User)
@test changeset.changes == NamedTuple()
params = Assoc("user_name" => "Alex", "user_age" => "20")
changeset = change(User, params... | {"hexsha": "bff651bd207e158c2b697a07e846dd1f4d471fad", "size": 2020, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/bukdu/changeset.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Bukdu.jl-3e78a19a-cc83-51d8-955b-515f39fd7955", "max_stars_repo_head_hexsha": "3c2f188e2050873f612c1e356ef5fcfd1de7aacb", "ma... |
# Base VAE class definition
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from utils import get_mean, kl_divergence
from vis import embed_umap, tensors_to_df
class VAE(nn.Module):
def __init__(self, prior_dist, likelihood_dist, post_dist, enc, dec, params):
... | {"hexsha": "7885fc04b3b3e1168120369b26434e3ec1bb4cc7", "size": 2987, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/vae.py", "max_stars_repo_name": "kodaim1115/test", "max_stars_repo_head_hexsha": "c5c8579dd088883a31f220170c39393084ff4c03", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "m... |
import numpy as np
import copy
class Graph():
""" The Graph to model the skeletons extracted by the openpose
Args:
strategy (string): must be one of the follow candidates
- uniform: Uniform Labeling
- distance: Distance Partitioning
- spatial: Spatial Configuration
For... | {"hexsha": "d4a311614df81e50f664276d76ace7eb0fe43993", "size": 27256, "ext": "py", "lang": "Python", "max_stars_repo_path": "net/utils/graph.py", "max_stars_repo_name": "julycrow/IST-GCN", "max_stars_repo_head_hexsha": "99e054377af4e054d61f2f043e8cacdeb2de73b8", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
/**
* @file lleventdispatcher_test.cpp
* @author Nat Goodspeed
* @date 2011-01-20
* @brief Test for lleventdispatcher.
*
* $LicenseInfo:firstyear=2011&license=viewerlgpl$
* Copyright (c) 2011, Linden Research, Inc.
* $/LicenseInfo$
*/
// Precompiled header
#include "linden_common.h"
// associated header... | {"hexsha": "9da1ecfd67a71fc87cf889f118c986e6b7a79ffe", "size": 55479, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "indra/llcommon/tests/lleventdispatcher_test.cpp", "max_stars_repo_name": "SaladDais/LSO2-VM-Performance", "max_stars_repo_head_hexsha": "d7ec9ad9daa9a2c9e48c5f06cd768606e3e50638", "max_stars_repo_l... |
# Alfonso del Carre
import numpy as np
import sharpy.utils.algebra as algebra
class Element(object):
"""
This class stores all the required data for the definition of
a linear or quadratic beam element.
"""
ordering = [0, 2, 1]
max_nodes_elem = 3
def __init__(self,
ielem... | {"hexsha": "33995aae4c2c646924325f36bdb329c0d3b48455", "size": 5602, "ext": "py", "lang": "Python", "max_stars_repo_path": "sharpy/structure/models/beamstructures.py", "max_stars_repo_name": "ostodieck/sharpy", "max_stars_repo_head_hexsha": "b85aa1c001a0ec851af4eb259cce7c01dfa68b9e", "max_stars_repo_licenses": ["BSD-3-... |
# ----------------------------------------------------------------------------
# This software is in the public domain, furnished "as is", without technical
# support, and with no warranty, express or implied, as to its usefulness for
# any purpose.
#
# RevertTopo.py
#
# Restores the GFE Topo from the most recent backu... | {"hexsha": "a2e7c6088115bc2cf032f83e36e60a3bac268632", "size": 2640, "ext": "py", "lang": "Python", "max_stars_repo_path": "cave/com.raytheon.viz.gfe/localization/gfe/userPython/procedures/RevertTopo.py", "max_stars_repo_name": "srcarter3/awips2", "max_stars_repo_head_hexsha": "37f31f5e88516b9fd576eaa49d43bfb762e1d174"... |
MODULE domwri
USE dom_oce
USE phycst, ONLY: rsmall
USE wet_dry, ONLY: ll_wd
USE in_out_manager
USE iom
USE lbclnk
USE lib_mpp
IMPLICIT NONE
PRIVATE
PUBLIC :: dom_wri
PUBLIC :: dom_stiff
CONTAINS
SUBROUTINE dom_wri
INTEGER :: inum
CHARACTER(LEN = 21) :: clnam
INTEGER :: ji, jj, jk
... | {"hexsha": "473ca15b30761db46c28dde8ddbbe14855c0cc59", "size": 7693, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "domwri.f90", "max_stars_repo_name": "deardenchris/psycloned_nemo_CDe", "max_stars_repo_head_hexsha": "d0040fb20daa5775575b8220cb5f186857973fdb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
include("experiment_helpers.jl")
# Create Roadway
roadway_opts = MergingRoadwayOptions(lane_width=0.26)
roadway = build_roadway(roadway_opts)
# Create players
T = Float64
p = 2
model = DoubleIntegratorGame(p=p)
n = model.n
m = model.m
# Define the horizon of the problem
N = 20 # N time steps
dt = 0.1 # each step las... | {"hexsha": "9d3612eb601c9752d18f36e591183e31db296185", "size": 4236, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "autonomous_robots_figures/figure_5a_ne_isolated.jl", "max_stars_repo_name": "simon-lc/AlgamesPlots.jl", "max_stars_repo_head_hexsha": "18851ea53168bbd1ab5c1c7f1116f8194d2c3091", "max_stars_repo_lic... |
"""
Copyright 2017 The Johns Hopkins University Applied Physics Laboratory LLC
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
Unles... | {"hexsha": "ed37e896491718af3078557c5acc4b64e6bf4d8e", "size": 10376, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/data_ml_functions/mlFunctions.py", "max_stars_repo_name": "kyoon3/hadoopPractice", "max_stars_repo_head_hexsha": "96fc3157e7db0caec918abfee29ace5547aa4f39", "max_stars_repo_licenses": ["ECL-... |
# A simple python script to plot the GW
# signals over time, for a chosen mode
import numpy as np;
import matplotlib.pyplot as plt;
# output data for setup
M = 1.0
mu = 0.5
r = 30
a = 0.99
symmetry = 2
N = 3000 # when to normalise to
alpha = M * mu
r_plus = M + np.sqrt(M*M - a*a)
omega_Re = mu * (1.0 - 0.5 * alpha**2... | {"hexsha": "8cd63dfdd029dcdd576ecdb2b86dac5828a3c28a", "size": 1270, "ext": "py", "lang": "Python", "max_stars_repo_path": "FixedBGExamples/SISuperradiance/c4ism2/plotEJ.py", "max_stars_repo_name": "KAClough/GRChombo-1", "max_stars_repo_head_hexsha": "03f2f305e44cb8e8cca35e8f1b4a5bd3e266e259", "max_stars_repo_licenses"... |
import numpy as np
import scipy.sparse
import pytest
import pyamgx
class TestMatrix:
@classmethod
def setup_class(self):
pyamgx.initialize()
self.cfg = pyamgx.Config().create("")
self.rsrc = pyamgx.Resources().create_simple(self.cfg)
@classmethod
def teardown_class(self):
... | {"hexsha": "71921971e69a0c6caee26ca60f99ab42bfca1b2b", "size": 2710, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_matrix.py", "max_stars_repo_name": "fizmat/pyamgx", "max_stars_repo_head_hexsha": "ef7758d40ea177bc3f14e2bf87348a6f30825521", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
Davis now has one parklet, with perhaps more parklets to come. The parklet is located on E Street in front of the (nowclosed) Beach Hut Deli, Sugar Daddies, and Thai Canteen. However, you dont have to purchase food from these restaurants to sit there; you can bring your own food if you like or just hang out without e... | {"hexsha": "2efac6201d9c6f532594fd1c26bca21acc0cf394", "size": 807, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Parklets.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
from abc import ABC, abstractmethod
from typing import Optional, Tuple, Union
import numpy as np
class BaseDataGenerator(ABC):
def __init__(
self,
n_samples: int,
n_features: int,
n_informative: int,
n_redundant: int,
noise: float,
... | {"hexsha": "f68407c1e9d8db65d779c264f6672b498f2616cf", "size": 4315, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sparsely/data_generator.py", "max_stars_repo_name": "jivanhoe/sparsely", "max_stars_repo_head_hexsha": "c180716227462ccc8c4c7f04f5232aa33dcb1eaf", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
from compas.plugins import plugin
from compas.geometry import Point
from compas.geometry import Vector
from compas.geometry import Plane
from compas.geometry import Polyline
from compas.geometry import Box
from compas.geometry import bounding_box
import time
import sys
folder = "C:/IBOIS57/_Code... | {"hexsha": "10d3a34e2fe37dc4448328d72db3432e3cb50c26", "size": 5645, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/compas_wood/CGAL/connectionDetection.py", "max_stars_repo_name": "brgcode/compas_wood", "max_stars_repo_head_hexsha": "5b43d1a77053523e6a5132dbfcbd99b808cf5a52", "max_stars_repo_licenses": ["M... |
"""Compute embeddings and predictions from a saved holparam checkpoint."""
from __future__ import absolute_import
from __future__ import division
# Import Type Annotations
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from typing import List
from typing import Optional
fro... | {"hexsha": "cbc08f1558fe4f86e58c69de2f497c4ed4bf1f64", "size": 9608, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepmath/deephol/holparam_predictor.py", "max_stars_repo_name": "LaudateCorpus1/deepmath", "max_stars_repo_head_hexsha": "b5b721f54de1d5d6a02d78f5da5995237f9995f9", "max_stars_repo_licenses": ["Ap... |
"""
Copyright (c) 2021, Electric Power Research Institute
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this li... | {"hexsha": "9f2871f9507346bf0f4858f5ce4d11cba0c4fa16", "size": 12344, "ext": "py", "lang": "Python", "max_stars_repo_path": "dervet/MicrogridDER/LoadControllable.py", "max_stars_repo_name": "epri-dev/dervet", "max_stars_repo_head_hexsha": "2d74d8b3f00fd0ebcf562900c0cb2bff0e995b42", "max_stars_repo_licenses": ["Apache-2... |
% \name{Caleb}{Kisby}
% \title{Curriculum Vitae} % optional, remove / comment the line if not wanted
% \address{312 West Kenwood Drive}{Bloomington, IN 47404}% optional, remove / comment the line if not wanted; the "postcode city" and "country" arguments can be omitted or provided empty
%... | {"hexsha": "62931f76b48fed0f6bf2a43ea704c5a6dcf5c407", "size": 4704, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "files/cv.tex", "max_stars_repo_name": "ais-climber/ais-climber.github.io", "max_stars_repo_head_hexsha": "ef347015860e9b3ba77dacea1f3c54771fa73e5b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# wahpenayo at gmail dot com
# 2018-04-16
#-----------------------------------------------------------------
if (file.exists('e:/porta/projects/taiga')) {
setwd('e:/porta/projects/taiga')
} else {
setwd('c:/porta/projects/taiga')
}
#source('src/scripts/r/functions.r')
#----------------------------------------------... | {"hexsha": "d16dc87a3f92685e9c4d87e79f085cb682378887", "size": 4274, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/r/quantreg.r", "max_stars_repo_name": "wahpenayo/taiga", "max_stars_repo_head_hexsha": "9d142149240f3f5db5dd8fbfbc0ce0552b81d490", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
#include <boost/filesystem.hpp>
#include<bits/stdc++.h>
#include <iostream>
#include <fstream>
#include <string>
#include <gtest/gtest.h>
#include "fcs-genome/BackgroundExecutor.h"
#include "fcs-genome/common.h"
#include "fcs-genome/config.h"
#include "fcs-genome/Worker.h"
#include "fcs-genome/workers/BlazeWorker.h"... | {"hexsha": "13088fc175540dac7aa3bf5bf676cf4c7d76facc", "size": 2277, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/TestExecutor.cpp", "max_stars_repo_name": "FCS-holding/falcon-genome", "max_stars_repo_head_hexsha": "bbba762ec54139392be843e9edff21766d5d7f5b", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
/*--------------------------------------------------------------------
* $Id$
*
* This file is part of libRadtran.
* Copyright (c) 1997-2012 by Arve Kylling, Bernhard Mayer,
* Claudia Emde, Robert Buras
*
* ######### Contact info: http://www.libradtran.org #########
*
* This program... | {"hexsha": "c128ed21b5192897ba52c0c7c4c9738fd2781e5e", "size": 598888, "ext": "c", "lang": "C", "max_stars_repo_path": "ubuntu20/projects/libRadtran-2.0.4/src/ancillary.c", "max_stars_repo_name": "AmberCrafter/docker-compose_libRadtran", "max_stars_repo_head_hexsha": "0182f991db6a13e0cacb3bf9f43809e6850593e4", "max_sta... |
import tensorflow as tf
import numpy as np
from tensorflow.python.framework import ops
from tflearn.initializations import truncated_normal
from tflearn.activations import relu
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1,dtype=tf.float32)
return tf.Variable(initial, dtype=tf.fl... | {"hexsha": "eabc61fc9a5f6c72d1e54dd003adc4ebc9998818", "size": 1441, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "kilotwo/NeoDTI", "max_stars_repo_head_hexsha": "a338b9b937d2458338f63d82295f9c3c91807737", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 59, ... |
import os
import sys
import math
import random
import numpy as np
from datetime import datetime
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import data_utils
def _stoke_decoding(stoke):
lift_pen_padding = 2.0
lines = []
points = []
x_prev = 0
y_prev = 0
was_dr... | {"hexsha": "ebc1844ea998fd0a8ff14da62166fd6130e38e6b", "size": 6970, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_conversions/quick_draw_utils.py", "max_stars_repo_name": "mateoKutnjak/PointCNN", "max_stars_repo_head_hexsha": "bfbc2cad7e250b7a3635a0ed56d7d0c1b8df25ea", "max_stars_repo_licenses": ["MIT"],... |
import streamlit as st
import pandas as pd
import numpy as np
from mortgage import Loan
from datetime import date
# Setup Sidebar
st.sidebar.markdown("# Assumptions")
st.sidebar.markdown("## Mortgage")
mortgage_remaining = st.sidebar.number_input(
'Mortgage Remaining', value=178850, min_value=0)
term_remaining ... | {"hexsha": "1101fd8749dd6b2207e4f2a788eb5f4453fa76e4", "size": 2782, "ext": "py", "lang": "Python", "max_stars_repo_path": "mortgages.py", "max_stars_repo_name": "mmmoli/streamlit-play", "max_stars_repo_head_hexsha": "9a906c3a262e3b6fce09640e7f91ed9001c3d761", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
//#include "math.hpp"
#include "array.hpp"
//#include "globals.hpp"
//#include "lcao_wavefunction.hpp"
//#include "scf.hpp"
//#include "timer.hpp"
//#include <boost/timer/timer.hpp>
#include <omp.h>
#include <cstdlib>
#include <iostream>
//
//
//
inline void print_array(const array<double, 1> &C, const std::string name... | {"hexsha": "2ee610c9336f74649375f7938448750c798a43d9", "size": 9376, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/main.cpp", "max_stars_repo_name": "violador/catalyst", "max_stars_repo_head_hexsha": "40d5c1dd04269a0764a9804711354a474bc43c15", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null... |
'''
Created on Feb 21, 2022
@author: vivi
'''
import os
import time
import random
import argparse
import re
import scipy
from multiprocessing import Process, Queue
import ioutils
from viz.common import load_embeddings
def get_distance(a, b, dist):
if "cos" in dist:
return scipy.spatial.distanc... | {"hexsha": "2fb5d984c8c1dfaa6ac1537b49af331dd335edc5", "size": 6265, "ext": "py", "lang": "Python", "max_stars_repo_path": "semantic_displacement.py", "max_stars_repo_name": "vivinastase/histwords", "max_stars_repo_head_hexsha": "bb3117434e76679fb38f649e2dbf11b15f5ef03b", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
from __future__ import print_function
from ast import literal_eval
import numpy as np
from pymol import cmd
from pymol.querying import get_color_indices
import os
from sys import platform
#tk GUI progress bar
import tkinter as tk
from tkinter import ttk
if platform == "linux" or platform == "linux2":
# linux
... | {"hexsha": "1f27adddcab2680bedb0c9d2dc50ae100a51a18a", "size": 11444, "ext": "py", "lang": "Python", "max_stars_repo_path": "pcn/pcn_miner/pcn_pymol_scripts.py", "max_stars_repo_name": "hguzzi/ProteinContactNetworks", "max_stars_repo_head_hexsha": "86e54e658730d3ef5e62a6b365ed5539d7c660e8", "max_stars_repo_licenses": [... |
//
// Copyright 2005-2007 Adobe Systems Incorporated
//
// Distributed under the Boost Software License, Version 1.0
// See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt
//
#ifndef BOOST_GIL_CONCEPTS_PIXEL_HPP
#define BOOST_GIL_CONCEPTS_PIXEL_HPP
#include <boost/gil/concepts/basi... | {"hexsha": "b471667c9f9d03372f8c09df25f59d9829ce5c1a", "size": 8343, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/gil/concepts/pixel.hpp", "max_stars_repo_name": "sdebionne/gil-reformated", "max_stars_repo_head_hexsha": "7065d600d7f84d9ef2ed4df9862c596ff7e8a8c2", "max_stars_repo_licenses": ["BSL-1... |
import numpy as np
from numpy.linalg import inv, norm, matrix_rank
import itertools
p = lambda x, y: np.array([1.0, x, y, x*y, x**2, y**2])
dpx = lambda x, y: np.array([0, 1, 0, y, 2*x, 0])
dpx2 = lambda x, y: np.array([0, 0, 0, 0, 2, 0])
dpy = lambda x, y: np.array([0, 0, 1, x, 0, 2*y])
dpy2 = lambda x, y: np.arra... | {"hexsha": "387b301b34450443c007e0fe6dca9b4225a800dc", "size": 876, "ext": "py", "lang": "Python", "max_stars_repo_path": "OLD/PIM.py", "max_stars_repo_name": "IgorBaratta/pyMLPG", "max_stars_repo_head_hexsha": "e005e4abe56a405a01c113efc63bb022339d728c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
# Algoritmo genético n°8
# aumentar a taxa de mutacao quando a diversidade for baixa
# diminuir a taxa de mutacao quando a diversidade for alta
# diversidade: dostancia de hamming
from random import random, seed
import numpy as np
from matplotlib import pyplot as plt
from math import sqrt
# from calc_pop import calc_p... | {"hexsha": "7db3bbfeaefc004ea016cfda43265931739c189a", "size": 4897, "ext": "py", "lang": "Python", "max_stars_repo_path": "homework_08/main.py", "max_stars_repo_name": "ufpa-organization-repositories/evolutionary-computing", "max_stars_repo_head_hexsha": "e16786f9619e2b357b94ab91ff3a7b352e6a0d92", "max_stars_repo_lice... |
model_name= 'hp_non_static_biobert_lstm'
import sys
sys.path.append('../')
import os
import tensorflow
import numpy as np
import random
if not os.path.isdir('hp_results/'):
os.mkdir('hp_results')
global seed_value
seed_value = 123123
#seed_value = None
environment_name = sys.executable.split('/')[-3]
print(... | {"hexsha": "86f170934625acfdd48ba19734999565f19d6f13", "size": 8592, "ext": "py", "lang": "Python", "max_stars_repo_path": "pipelines/hp_biobert_lstm_ft.py", "max_stars_repo_name": "BioSystemsUM/biotmpy", "max_stars_repo_head_hexsha": "f981d58cf7f53a2aa09708e13d6561533c164e1f", "max_stars_repo_licenses": ["MIT"], "max_... |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "183d26317694b71b86caa4c945fcec89fa21ed5c", "size": 15054, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/cv/PAMTRI/infer/sdk/eval_mt.py", "max_stars_repo_name": "mindspore-ai/models", "max_stars_repo_head_hexsha": "9127b128e2961fd698977e918861dadfad00a44c", "max_stars_repo_licenses": ["Apac... |
# widgets/plot/pol.py --- Polarimeter plot classes
#
# Copyright (C) 2018 Stefano Sartor - stefano.sartor@inaf.it
from widgets.plot import MplCanvas
from web.wamp.base import WampBase
from config import Config
import asyncio
from threading import Thread
import numpy as np
import time
import astropy.time as at
import da... | {"hexsha": "fef93af1c8a4364c4c3c66e078d2ded4ef077eef", "size": 2785, "ext": "py", "lang": "Python", "max_stars_repo_path": "widgets/plot/base.py", "max_stars_repo_name": "lopezcaraballoch/striptease", "max_stars_repo_head_hexsha": "86bde677d46af8c7fc14becdf32e456685074d66", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright 2021 by Haozhe Wu, Tsinghua University, Department of Computer Science and Technology.
# All rights reserved.
# This file is part of the pytorch-nicp,
# and is released under the "MIT License Agreement". Please see the LICENSE
# file that should have been included as part of this package.
import torch
impo... | {"hexsha": "4d0a54d324a76a73f4f3a8a392a5611ea0dc212b", "size": 2475, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo_nicp.py", "max_stars_repo_name": "wuhaozhe/pytorch-nicp", "max_stars_repo_head_hexsha": "099f189b749154e97d0f6fa5a1e16e8fb885cce0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 46, ... |
(*
Author: Norbert Schirmer
Maintainer: Norbert Schirmer, norbert.schirmer at web de
License: LGPL
*)
(* Title: Hoare.thy
Author: Norbert Schirmer, TU Muenchen
Copyright (C) 2004-2008 Norbert Schirmer
Some rights reserved, TU Muenchen
This library is free software; you can redist... | {"author": "LVPGroup", "repo": "TimSort", "sha": "16437b6b6e2df9f6d32b2a32be7d0d650d83f980", "save_path": "github-repos/isabelle/LVPGroup-TimSort", "path": "github-repos/isabelle/LVPGroup-TimSort/TimSort-16437b6b6e2df9f6d32b2a32be7d0d650d83f980/Simpl/Hoare.thy"} |
import numpy as np
import pytest
import crowsetta.formats
from .asserts import assert_rounded_correct_num_decimals
def test_from_file(a_textgrid_path):
textgrid = crowsetta.formats.seq.TextGrid.from_file(annot_path=a_textgrid_path)
assert isinstance(textgrid, crowsetta.formats.seq.TextGrid)
def test_from_... | {"hexsha": "8744a1370af9a93054f63d03e08151acf8dc6aa3", "size": 3381, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_formats/test_seq/test_textgrid.py", "max_stars_repo_name": "NickleDave/conbirt", "max_stars_repo_head_hexsha": "71db6c6fd68dfef1bdbdcfacd8b2a16b21b86089", "max_stars_repo_licenses": ["B... |
include("../../fancy_toys.jl")
version = v"1.2.0"
name = "CUTENSOR_CUDA$(cuda_version.major)$(cuda_version.minor)"
sources_linux_x64 = [
ArchiveSource("https://developer.nvidia.com/compute/cutensor/secure/1.2.0/local_installers/libcutensor-linux-x86_64-1.2.0.tar.gz",
"0b33694d391bca537cad0f349b... | {"hexsha": "2975f41fd683d72b484c25f4b366ee911065b490", "size": 1877, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "C/CUTENSOR/common.jl", "max_stars_repo_name": "mortenpi/Yggdrasil", "max_stars_repo_head_hexsha": "dfde83cadb8f2b614c77e0a8a3bef38592fbbf56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import time, copy
import os, os.path
import sys
import numpy
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from scipy import optimize
from echem_plate_ui import *
from echem_plate_math import *
p='C:/Users/Gregoire/Documents/CaltechWork/echemdrop/2012-9_FeCoNiTi/2012-9FeCoNiTi_500C_CAill_plate1'
os.chdir('C:/U... | {"hexsha": "c377f772b9805f1d622104c24e32ae678f241416", "size": 3166, "ext": "py", "lang": "Python", "max_stars_repo_path": "echem_plate_CAill_test.py", "max_stars_repo_name": "johnmgregoire/JCAPdatavis", "max_stars_repo_head_hexsha": "6d77a510e00acf31de9665828d27ea33aba6ab78", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import uuid
import satoyama
from satoyama.models import *
from nodes import NodeSeeder
from random import random
from datetime import datetime
from multiprocessing import Process
from numpy.random import shuffle
import time
def notest(func):
setattr(func, 'notest', True)
return func
class SiteSeeder():
@staticm... | {"hexsha": "52958c6f514f58be39a612cc42a81158c0284ec8", "size": 1917, "ext": "py", "lang": "Python", "max_stars_repo_path": "seeds/sites.py", "max_stars_repo_name": "DgFutureLab/satoyama-api", "max_stars_repo_head_hexsha": "cc8d80ad4c437d67aa53ba619aa4904350be09f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import sys
import gym
import numpy as np
from gym import spaces, utils
from gym.utils import seeding
class NKnobEnv(gym.Env):
metadata = {'render.modes': ['human']}
def __init__(self, n=7):
self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(n,), dtype=np.float32)
self.observation_spac... | {"hexsha": "d7f422e829d1c41d4bb67ce805528ecd3a6df55c", "size": 2209, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_quickcheck/envs/n_knob_env.py", "max_stars_repo_name": "SwamyDev/gym-quickcheck", "max_stars_repo_head_hexsha": "d271f509c11998c9f210c5d8131906f712553123", "max_stars_repo_licenses": ["MIT"], ... |
///////////////////////////////////////////////////////////////////////////////
// comm.hpp
//
// unicomm - Unified Communication protocol C++ library.
//
// Communication service. Represents transport layer of unicomm.
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1... | {"hexsha": "5f2ef07c7f9be645de46c0060e1dafb66f2346f4", "size": 24288, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/unicomm/comm.hpp", "max_stars_repo_name": "Chrizzly/libunicomm", "max_stars_repo_head_hexsha": "3aefc02445a5b1e047cc40daaddb7cf9b5082404", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars... |
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