text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
#pragma once
#include <Eigen/Dense>
using namespace Eigen;
class lrsgd
{
public:
lrsgd();
~lrsgd() {};
VectorXf sigmoid(VectorXf& a);
void lr_objective(float& cost, VectorXf& grad, VectorXf& theta);
void fit(void);
void generate_data(MatrixXf& X, VectorXi& y);
int num_iter; //max number of iterations
V... | {"hexsha": "ebcbe36e769fac34b1da05d76f0c1f9ee2553b53", "size": 729, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "machine_learning/logreg/logreg.hpp", "max_stars_repo_name": "vishalbelsare/cpp", "max_stars_repo_head_hexsha": "772178d911e8f90c23e9d3c1d8d32482bc397fc5", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma lcmof_leastUpper:
fixes G (structure)
assumes carr[simp]: "a \<in> carrier G" "b \<in> carrier G"
shows "(x \<in> carrier G \<and> x lcmof a b) = least (division_rel G) x (Upper (division_rel G) {a, b})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (x \<in> carrier G \<and> x lcmof a b) = ... | {"llama_tokens": 182, "file": null, "length": 1} |
import numpy as np
import cv2
from id.trafficmon.objecttracking import ObjectTrackingAbstract
__author__ = 'Luqman'
class OpticalFlowHS(ObjectTrackingAbstract):
"""
class OpticalFlowHS
implementation of classical Horn-Schunck optical flow (Horn, 1981)
"""
def __init__(self):
ObjectTrac... | {"hexsha": "84a37a1420a7b629009b1bee6c16de1fb4ab5af9", "size": 2436, "ext": "py", "lang": "Python", "max_stars_repo_path": "id/trafficmon/objecttracking/OpticalFlow.py", "max_stars_repo_name": "umanium/trafficmon", "max_stars_repo_head_hexsha": "86c138bda3c8a3e38fff273e5d61610acee123b5", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/python3
import matplotlib.pyplot as plt
import numpy as np
import csv
# CPU UTILIZATION
# Make the data
num = 0
# Amount of x ticks
x_ticks=np.arange(0, 900, 10)
plt.xticks(x_ticks)
for files in range(6):
x = []
y = []
num += 1
with open('APM' + str(num) + '_metrics.csv', 'r') as file:
lines = csv.r... | {"hexsha": "d6b5474536e9465d594c0a08338620bdb5d7acd9", "size": 3629, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python Tutorials/Graphing/Lab4.py", "max_stars_repo_name": "GarrettMaury7921/OpenAI_Tutorials", "max_stars_repo_head_hexsha": "6db988c249565264c94efda65739b2e87007b87e", "max_stars_repo_licenses":... |
c###findf.for
SUBROUTINE FINDF(K)
C--------------------------------
C
C THIS ROUTINE DOES AREA COVERAGE FOR A SPECIFIED FREQUENCY
C (FIND ALL MODES FOR AN OPERATING FREQUENCY)
C INSERTS PENETRATION ANGLES INTO THE ANGLE TABLE AND COMPUTES ALL
C RAY PATH PARAMETERS FOR EACH ANGLE AT THE FREQ... | {"hexsha": "e0b886614176047f481b578117d6c74960befa3f", "size": 8494, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "src/voacapw/findf.for", "max_stars_repo_name": "jawatson/voacapl", "max_stars_repo_head_hexsha": "6861fbad041ff7a56b8875c735c9242c81a94320", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_co... |
# coding=utf-8
# Copyright 2018 Google LLC & Hwalsuk Lee.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | {"hexsha": "d51f36b29a6e3f8c7947a64ead69957fd88e98fc", "size": 9724, "ext": "py", "lang": "Python", "max_stars_repo_path": "DiffAugment-biggan-imagenet/compare_gan/gans/ssgan.py", "max_stars_repo_name": "Rian-T/data-efficient-gans", "max_stars_repo_head_hexsha": "1d7e58be909524c86c863b5ab7c9d591afc00d1d", "max_stars_re... |
-- Math 52: Week 5
import .utils
open classical
-- The following lemmas may be useful for the next proof.
-- mul_lt_mul_of_pos_left (a b c : ℝ) : a < b → 0 < c → c * a < c * b
-- mul_lt_mul_of_pos_right (a b c : ℝ) : a < b → 0 < c → a * c < b * c
-- Lakins 2.1.2: For all real numbers a and b, if 0 < a < b, then a² <... | {"author": "UVM-M52", "repo": "week-5-maddiehutchinson", "sha": "7fd99c56b0a9a313ed1b462e9a8e50c0d66857aa", "save_path": "github-repos/lean/UVM-M52-week-5-maddiehutchinson", "path": "github-repos/lean/UVM-M52-week-5-maddiehutchinson/week-5-maddiehutchinson-7fd99c56b0a9a313ed1b462e9a8e50c0d66857aa/src/week05.lean"} |
import sys
sys.path.append('..')
import numpy as np
import apis
from apis import apis_system
from apis import apis_basic
from .hvdc import calculate_dc_line_power
def init_powerflow_solution():
'''
Initial power flow solution with flat start.
Args: None
Rets:
(1) S, array, no... | {"hexsha": "0824f72b7bbd3081fe2180d3f0d66718bd08bbde", "size": 6815, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/powerflow/branch.py", "max_stars_repo_name": "yanzhaochang/PSATools-Python", "max_stars_repo_head_hexsha": "7524d7eeed26db9fba93c0ea03a7c8c0bfee7410", "max_stars_repo_licenses": ["Apache-2.0"]... |
[STATEMENT]
lemma sturm_meta_spec: "(\<And>x::real. P x) \<Longrightarrow> P x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>x. P x) \<Longrightarrow> P x
[PROOF STEP]
by simp | {"llama_tokens": 79, "file": "Sturm_Sequences_Sturm_Method", "length": 1} |
#--------------------------
# Tensorflow Keras imports
#--------------------------
import os
import warnings
import logging
from distutils.util import strtobool
from packaging import version
import re
os.environ['NUMEXPR_MAX_THREADS'] = '8' # suppress warning from NumExpr on machines with many CPUs
# TensorFlow
SUPP... | {"hexsha": "f52ec79dde08035d4ad963f47fe0d7fddf97aab4", "size": 6415, "ext": "py", "lang": "Python", "max_stars_repo_path": "ktrain/imports.py", "max_stars_repo_name": "rubensmau/ktrain", "max_stars_repo_head_hexsha": "de32b87b9d3996f35882e7ca18273356c72d342c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
simulationMode = False
if not simulationMode:
import TensorflowProcessingModule as TPM
from imutils.video.pivideostream import PiVideoStream
import MathModule as MM
import math, time, copy
import cv2
import numpy as np
from multiprocessing import Process, RawValue, RawArray
#program sluzacy do analizy obraz... | {"hexsha": "2702ded874a778118a2f2e4c08e01dbcf139ffa0", "size": 11360, "ext": "py", "lang": "Python", "max_stars_repo_path": "PythonCode/ImageProcessingModule.py", "max_stars_repo_name": "janw23/Ballance", "max_stars_repo_head_hexsha": "f085d2d03c31a8e3be74d4c82300f571cc3cad65", "max_stars_repo_licenses": ["MIT"], "max_... |
import pandas as pd
import numpy as np
from dataclasses import dataclass, InitVar, field
from enum import Enum, Flag, auto, unique
from functools import reduce
print("\nWarning: pre-loading selected modules, see your config", end='')
| {"hexsha": "9948661f9f157cc1a137345ce1e15902114b75be", "size": 236, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ipython/profile_default/startup/00-import_selected_modules.py", "max_stars_repo_name": "dpopchev/linux", "max_stars_repo_head_hexsha": "b5348b9543276b06366497681924441ef4f43005", "max_stars_rep... |
# used from predict.py
import pandas as pd
from collections import OrderedDict
import numpy as np
import random
from sato.extract.helpers import utils
from sherlock.features.bag_of_characters import extract_bag_of_characters_features
from sherlock.features.bag_of_words import extract_bag_of_words_features
from she... | {"hexsha": "35569439a3077dcea88b8a06490fd25423a989a6", "size": 2385, "ext": "py", "lang": "Python", "max_stars_repo_path": "sato/extract/feature_extraction/sherlock_features.py", "max_stars_repo_name": "VIDA-NYU/sato", "max_stars_repo_head_hexsha": "8fb51787b36114df13f54c1acd11df12a66ad3e4", "max_stars_repo_licenses": ... |
#include <Server.h>
#include <Session.h>
#include <Database.h>
#include <iterator>
#include <boost/asio.hpp>
#include <signal.h>
namespace NHttpProxy {
TServerError::TServerError(const std::string& message)
: std::runtime_error(message)
{}
class TServer::TImpl {
public:
TImpl()
: IOContext_(1)
... | {"hexsha": "8c1c61df83b8f41ff8a8beb6db653a0f52aab3d1", "size": 2700, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "individual/lib/Server.cpp", "max_stars_repo_name": "vasalf/NetworksLab2020HSE", "max_stars_repo_head_hexsha": "b1971981921ae6288e741a578182bfd5279d792f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma [code_unfold]:
fixes literal :: Literal and clause :: Clause
shows "literal el clause = List.member clause literal"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. literal el clause = List.member clause literal
[PROOF STEP]
by (auto simp add: member_def) | {"llama_tokens": 91, "file": "SATSolverVerification_SatSolverCode", "length": 1} |
'''
This is the homework for SJTU IE308 Image Processing by Prof. Yi Xu
Copy right by Sizhe Wei, Dec 2019; ID: 517021910796
HW No.2 BM3D Denoising Implement & False Color Transfer
If you have any question, feel free to contact me at sizhewei@sjtu.edu.cn
'''
import cv2
import numpy
import math
import numpy.matli... | {"hexsha": "dd36d33f761f75bbae4f5de8335a6da57c2611b6", "size": 19180, "ext": "py", "lang": "Python", "max_stars_repo_path": "Codework_and_Images/bm3d/bm3d.py", "max_stars_repo_name": "SizheWei/bm3d", "max_stars_repo_head_hexsha": "e6f3a084cafb2bc87b3860dd80eb0aa42398f51b", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import cv2
import numpy as np
from matplotlib import pyplot as plt
def find_matching_points(img1, img2, method='sift', match_method='bf', plot=False):
'''
Find matching points in img1 and img2.
'''
if method == 'orb':
# Initiate ORB detector
detector = cv2.ORB_create()
nor... | {"hexsha": "5f5d4317913e35398061494faa90d952ac0308fe", "size": 2319, "ext": "py", "lang": "Python", "max_stars_repo_path": "registration.py", "max_stars_repo_name": "duembgen/NIRdehazing", "max_stars_repo_head_hexsha": "b985762a5a80f20874751abd7f4b3683e4f2e8b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14... |
import Flux
struct PointwiseFeedForward
FirstFilter::Flux.Conv
SecondFilter::Flux.Conv
end
PointwiseFeedForward(dims::Integer) = PointwiseFeedForward(
Flux.Conv((1, 1), dims => 4 * dims, Flux.relu) |> Flux.gpu,
Flux.Conv((1, 1), 4 * dims => dims) |> Flux.gpu
)
Flux.@treelike PointwiseFeedForward
func... | {"hexsha": "a74580f6ceab5c8e56395231d25a67bbc61126d5", "size": 583, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FeedForward.jl", "max_stars_repo_name": "brainsqueeze/SequenceModels.jl", "max_stars_repo_head_hexsha": "990d8217150807fcc8989eac562af3d63153f43c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Bookcrossing dataset transformation methods
"""
import os
import shutil
import urllib
import zipfile
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
def download_bookcrossing(url='http://www2.informatik.uni-freiburg.de/~cziegler/BX/BX-CSV-Dump.zip',
... | {"hexsha": "9b054ddf19c9b5e044e4e0e560e6b5c6c8626906", "size": 1622, "ext": "py", "lang": "Python", "max_stars_repo_path": "rec_tool/transformations/bookcrossing.py", "max_stars_repo_name": "Ilyushin/rec-tool", "max_stars_repo_head_hexsha": "f9e3338ee19f93c5dee483feb5301958591abf80", "max_stars_repo_licenses": ["MIT"],... |
# Copyright (c) 2020, NVIDIA Corporation. All rights reserved.
#
# This work is made available
# under the Nvidia Source Code License (1-way Commercial).
# To view a copy of this license, visit
# https://nvlabs.github.io/Dancing2Music/License.txt
import torch
import torch.nn as nn
import torch.nn.parallel
import torch... | {"hexsha": "f1d84875073abb4bac6836d0ff13df50c664e68a", "size": 14704, "ext": "py", "lang": "Python", "max_stars_repo_path": "networks.py", "max_stars_repo_name": "Neoutlier/Dancing2Music", "max_stars_repo_head_hexsha": "b1b51ed5319415238869a9bc78a34d4d90662dc0", "max_stars_repo_licenses": ["BSD-Source-Code"], "max_star... |
// This file is part of the dune-xt project:
// https://zivgitlab.uni-muenster.de/ag-ohlberger/dune-community/dune-xt
// Copyright 2009-2021 dune-xt developers and contributors. All rights reserved.
// License: Dual licensed as BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
// or GPL-2.0+ (h... | {"hexsha": "2fff57ef690d0b7a223974fec44fed1c0d5dcb74", "size": 15094, "ext": "hh", "lang": "C++", "max_stars_repo_path": "dune/xt/la/container/matrix-market.hh", "max_stars_repo_name": "dune-community/dune-xt", "max_stars_repo_head_hexsha": "da921524c6fff8d60c715cb4849a0bdd5f020d2b", "max_stars_repo_licenses": ["BSD-2-... |
import numpy as np
import os, sys
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch import autograd
from torch.autograd import Variable
from torch.nn import Parameter
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.n... | {"hexsha": "8892c27e328788c7cde3f51068e89f534f6cf790", "size": 5801, "ext": "py", "lang": "Python", "max_stars_repo_path": "gan/neuralDist/trainNeuralDist.py", "max_stars_repo_name": "pedroryzewski/TCC", "max_stars_repo_head_hexsha": "ba5167928d5a14dc0c6e144d0927c050090950a6", "max_stars_repo_licenses": ["MIT"], "max_s... |
export snr, smooth, smooth!, abs_max, abs_max!, standardize, standardize!
export mad, std_threshold
"""
snr(A)
Signal to noise ratio of cross-correlations in matrix `A`.
Follows method of Clarke et. al, 2011. Measures SNR at each point.
"""
function snr(A::AbstractArray)
Nrows,Ncols = size(A)
A_mean = me... | {"hexsha": "691666aa8eeb7fc041ae0be3065515a9ba6e3eae", "size": 3025, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tools.jl", "max_stars_repo_name": "Denolle-Lab/SeisNoise.jl", "max_stars_repo_head_hexsha": "0eba10ca056ddbc73da50a137b4d2c13cea956ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 46... |
from astropy import units as u
from astropy.coordinates import SkyCoord, AltAz, Angle
def azToDirection(az): #azimut to direction
az = float(az)
if (az >=360): az -= 360
if (az <0): az += 360
if (az >= 0 and az < 22.5) or (az >= 337.5 and az < 360): lettre='North'
elif az >= 22.5 and az < ... | {"hexsha": "d393986883dcc6098350bc5698987cda4f06bdf5", "size": 2491, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/functions.py", "max_stars_repo_name": "serge-golovanow/SpectroStars", "max_stars_repo_head_hexsha": "19e1ca2fbecd8dd814ceea18a54c54603b9efbf4", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
'''
wrapper for change detector that is called by 3D solver based on tomostream
'''
from roi_utils.roi import roi_search, load_seg_nn, roi_search_subtraction, rescale_vol_for_NN
from roi_utils.patches import Patches
from roi_utils.voxel_processing import modified_autocontrast
from roi_utils.ADet4RoI import roi_searc... | {"hexsha": "b80bb9d7442ba7c2982b6dabd1a39508ee1e8d40", "size": 3405, "ext": "py", "lang": "Python", "max_stars_repo_path": "scratchpad/tomostream_roi/tomostream/change_detector.py", "max_stars_repo_name": "arshadzahangirchowdhury/TomoEncoders", "max_stars_repo_head_hexsha": "9c2b15fd515d864079f198546821faee5d78df17", "... |
import re
import collections.abc
import datetime
import hashlib
import logging
import os
import time
import random
from copy import deepcopy
from concurrent.futures import (
ProcessPoolExecutor,
as_completed,
ThreadPoolExecutor,
)
import github3
import networkx as nx
import requests
from xonsh.lib.collec... | {"hexsha": "6f672b510f766ec63db2fdf2e1688577b203bee1", "size": 7570, "ext": "py", "lang": "Python", "max_stars_repo_path": "conda_forge_tick/make_graph.py", "max_stars_repo_name": "sdvillal/cf-scripts", "max_stars_repo_head_hexsha": "026a90a0a0e4e5ef09c3f0cb8e1a8659977250b2", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
\documentclass{article}
\usepackage{jobapp}
%% Contact info
\SetName{PHILLIP FRY}
\SetProfessionalTitle{A Real Person}
\SetAddress{1600 Pennsylvania Avenue, N.W. \\ Washington, DC 20500}
\SetPhone{(555) 555-5555}
\SetEmail{p.fry@dev.null}
\begin{document}
\section*{Skills \& Expertise}
\ResumeLayout
{\te... | {"hexsha": "523660cac8c5352fee0406478c84693f559b014d", "size": 1619, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "example.tex", "max_stars_repo_name": "cahna/JobApp", "max_stars_repo_head_hexsha": "74d369b016e050c25d151155e3018e99726271d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
export l_bfgs_rcst
export Steighaug
export BhaskaraTop
export LimitedMemory
"""
l_bfgs_rcst(nlp; options...) - Ainda não chegamos nesse!
Este método é chamado L-BFGS com região de confiança por Steihaug-Toint.
Tenta-se resolver B_k d = - ∇f(xₖ) usando Gradientes Conjugados.
Se em algum momento a direção ficar maior... | {"hexsha": "f9fe58f57bdbefaaedbf7914347524dd7f37c9ce", "size": 8224, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/l_bfgs_rcst.jl", "max_stars_repo_name": "FKrukoski/Projeto2Solvers.jl", "max_stars_repo_head_hexsha": "8d48093e9bc1fca9470f568c2415df7bdbaa1672", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import category_theory.category category_theory.epi_mono tactic-- this transitively imports
open category_theory
universes v u
variables (C : Type u) [category.{v} C]
/-Prove Lemma 1.2.11 by proving either (i) or (i') and either (ii) or (i'),
then arguing by duality. Conclude that the monomorphisms in any category... | {"author": "agusakov", "repo": "riehl-solutions", "sha": "3cba620de377649e9269c53e17a65cfccd7db651", "save_path": "github-repos/lean/agusakov-riehl-solutions", "path": "github-repos/lean/agusakov-riehl-solutions/riehl-solutions-3cba620de377649e9269c53e17a65cfccd7db651/src/category_theory_stuff/solutions/chapter_1/secti... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import pickle
import random
from abc import ABC, abstractmethod
from dataclasses import dataclass,... | {"hexsha": "22e5d087398964c1be75899619889bfe28bdc2af", "size": 22886, "ext": "py", "lang": "Python", "max_stars_repo_path": "parlai/crowdsourcing/tasks/model_chat/model_chat_blueprint.py", "max_stars_repo_name": "Uzornd/ParlAI", "max_stars_repo_head_hexsha": "5c07877f493db75847029ed9906aabc48c4d9f54", "max_stars_repo_l... |
from __future__ import print_function
# This lets us use the python3-style print() function even in python2. It should have no effect if you're already running python3.
import os
import dwl
import numpy as np
# Configure the printing
np.set_printoptions(suppress=True)
# Construct an instance of the FloatingBaseSyste... | {"hexsha": "48893f398e94989c34633abff9d9adef53fd4b0a", "size": 5445, "ext": "py", "lang": "Python", "max_stars_repo_path": "sample/python/FloatingBaseSystem.py", "max_stars_repo_name": "EricWang1hitsz/dwl", "max_stars_repo_head_hexsha": "b45e6babfcace928af2601f3d453e3f27e2e76c8", "max_stars_repo_licenses": ["BSD-3-Clau... |
/-
Copyright (c) 2018 Johannes Hölzl. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johannes Hölzl, Jens Wagemaker, Aaron Anderson
-/
import ring_theory.coprime.basic
import ring_theory.principal_ideal_domain
/-!
# Divisibility over ℕ and ℤ
This file collects result... | {"author": "jjaassoonn", "repo": "projective_space", "sha": "11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce", "save_path": "github-repos/lean/jjaassoonn-projective_space", "path": "github-repos/lean/jjaassoonn-projective_space/projective_space-11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce/src/ring_theory/int/basic.lean"} |
import numpy as np
import halotools.empirical_models as htem
import halotools.sim_manager as htsm
import galtab
def test_bolshoi_zheng_placeholder_weights(use_jax=False):
redshift = 0
threshold = -21
halocat = htsm.CachedHaloCatalog(simname="bolshoi", redshift=redshift,
... | {"hexsha": "704084b5da172b351eb33282feb40f74c05d4a06", "size": 4726, "ext": "py", "lang": "Python", "max_stars_repo_path": "galtab/tests/test_galtab.py", "max_stars_repo_name": "AlanPearl/hodtab", "max_stars_repo_head_hexsha": "d10e1ba2c314a068665d2cfc1bfc4c4f9a16c10f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import re
from collections import defaultdict
import numpy as np
from utensor_cgen.frontend import FrontendSelector
from utensor_cgen.frontend.base import Parser
from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph
from utensor_cgen.ir.converter import (AttrValueConverter,
... | {"hexsha": "d7229a35c23cf079e8934b813e719dd62e54a998", "size": 15662, "ext": "py", "lang": "Python", "max_stars_repo_path": "utensor_cgen/frontend/tflite.py", "max_stars_repo_name": "uTensor/utensor_cgen", "max_stars_repo_head_hexsha": "eccd6859028d0b6a350dced25ea72ff02faaf9ad", "max_stars_repo_licenses": ["Apache-2.0"... |
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from torch.optim import Adam
import torch.nn.functional as functional
from torch.utils.data import DataLoader
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from config import Config
from dataset import Vocabulary, DualNovelD... | {"hexsha": "9d757ddcea374b48451a0be014e63fb5edcaa26b", "size": 11129, "ext": "py", "lang": "Python", "max_stars_repo_path": "autoencoder.py", "max_stars_repo_name": "RunTimeError2/TextStyleTransfer_PTO", "max_stars_repo_head_hexsha": "870ee837696d8eddffed6a3fd162ea1ad403ce44", "max_stars_repo_licenses": ["Apache-2.0"],... |
import time
t0t=time.time()
from os.path import join
import os
import numpy as n
import glob
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as p
import astropy.io.fits as fits
from scipy.interpolate import interp1d
from scipy.stats import norm as gaussD
import GalaxySpectrumFIREFLY as ... | {"hexsha": "70ac254fe73b4cc60cf93afb21c4beec904fb417", "size": 8527, "ext": "py", "lang": "Python", "max_stars_repo_path": "spm/bin_simulated_spectra/combine_model_spectra_GAMA_SNR.py", "max_stars_repo_name": "AndresSixtos/pyeBOSS", "max_stars_repo_head_hexsha": "4750908c8bc409633bef8f790133e3a1f3f0c9e4", "max_stars_re... |
#ifndef _PS_
#define _PS_
#include <stdio.h>
#include <stddef.h>
#include <stdlib.h>
#include <ctype.h>
#include <math.h>
#include <unistd.h>
#include "../Parameter_files/COSMOLOGY.H"
#include "../Parameter_files/INIT_PARAMS.H"
#include <gsl/gsl_interp.h>
#include <gsl/gsl_spline.h>
#include "cosmo_progs.c"
#include "... | {"hexsha": "38bb9b2f8a04e9bc4060d300282b7b180a9060e2", "size": 55391, "ext": "c", "lang": "C", "max_stars_repo_path": "public_21CMvFAST_MC/Cosmo_c_files/ps.c", "max_stars_repo_name": "JulianBMunoz/21cmvFAST", "max_stars_repo_head_hexsha": "14c053c301a7f10081071e815281f9c3879efa6b", "max_stars_repo_licenses": ["MIT"], "... |
from lcc.utils.data_analysis import to_PAA, normalize
import numpy as np
class SAX(object):
"""
This class manages symbolic representation of data series via
Symbolic Aggregate approXimation method. It translates
series of data to a words, which can then be compared with other
such words in sym... | {"hexsha": "6a23fa50d5b12cccbb528df83af52255473f4508", "size": 8047, "ext": "py", "lang": "Python", "max_stars_repo_path": "lcc/stars_processing/utilities/sax.py", "max_stars_repo_name": "mavrix93/LightCurvesClassifier", "max_stars_repo_head_hexsha": "a0a51f033cb8adf45296913f0de0aa2568e0530c", "max_stars_repo_licenses"... |
(*
Copyright 2014 Cornell University
Copyright 2015 Cornell University
Copyright 2016 Cornell University
Copyright 2017 Cornell University
This file is part of VPrl (the Verified Nuprl project).
VPrl is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public Li... | {"author": "vrahli", "repo": "NuprlInCoq", "sha": "0c3d7723836d3f615ea47f56e58b2ea6173e7d98", "save_path": "github-repos/coq/vrahli-NuprlInCoq", "path": "github-repos/coq/vrahli-NuprlInCoq/NuprlInCoq-0c3d7723836d3f615ea47f56e58b2ea6173e7d98/bar_induction/bar_induction_cterm2.v"} |
import numpy as np
from utils import peak_skewness, peak_kurtosis
class Gene:
"""
Gene object, save a gene's information
"""
def __init__(self, gene_id, celltype, label, chr, start, end, step=10, signal=None, exp=None, cur_signal=None):
"""
:param gene_id: unique gene identifier
... | {"hexsha": "1d30f43350287fd67eed217088296d5b807eeafe", "size": 5784, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gene.py", "max_stars_repo_name": "bxia888/CEFCIG", "max_stars_repo_head_hexsha": "5431d3bba91847c99f9facc67d1996b08705e8e9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_star... |
#!/usr/bin/env
"""
ctd.py
Seabird CNV only
Built using Anaconda packaged Python:
Original code reference:
--------------
purpose: Some classes and functions to work with CTD data.
author: Filipe P. A. Fernandes
e-mail: ocefpaf@gmail
web: http://ocefpaf.tiddlyspot.com/
created: 22-Jun-2012
... | {"hexsha": "0c2070ae3d9b7cb6c93c7f0cec5db8ccc5b6701d", "size": 9958, "ext": "py", "lang": "Python", "max_stars_repo_path": "OnCruiseRoutines/CTD_Vis/ctd.py", "max_stars_repo_name": "shaunwbell/AtSeaPrograms", "max_stars_repo_head_hexsha": "839ee4dc1cf7a85bce1de82b04379c6d1670c414", "max_stars_repo_licenses": ["MIT"], "... |
function jed = ymdf_to_jed_islamic_b ( y, m, d, f )
%*****************************************************************************80
%
%% YMDF_TO_JED_ISLAMIC_B converts an Islamic B YMDF date to a JED.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 13 March 2013
%
% ... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/calpak/ymdf_to_jed_islamic_b.m"} |
// Copyright (c) 2012 - 2017 Object Computing, Inc.
// All rights reserved.
// See the file license.txt for licensing information.
#define BOOST_TEST_NO_MAIN LiquibookTest
#include <boost/test/unit_test.hpp>
#include "ut_utils.h"
#include "changed_checker.h"
#include <book/order_book.h>
#include <simple/simple_order.... | {"hexsha": "d1f1322653dc32ea8fa3cc05b7f7dc5c3a1685c6", "size": 3041, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/unit/ut_order_book_shared_ptr.cpp", "max_stars_repo_name": "coinderex/liquibook", "max_stars_repo_head_hexsha": "110695cf0a234888555323c94d8b65edb45fe8cf", "max_stars_repo_licenses": ["BSD-3-Cl... |
#include <boost/phoenix/statement/while.hpp>
| {"hexsha": "7b177457ab074ecc679ae6fcd23015b111d5b3af", "size": 45, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_phoenix_statement_while.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1... |
#ifndef GRAPHCLASS
#define GRAPHCLASS
#include "graph_node.hpp"
#include <Eigen/Dense>
#include <memory>
#include <stddef.h>
#include <vector>
class UndirectedGraph {
public:
// constructors
UndirectedGraph() = delete;
UndirectedGraph(const size_t num_nodes, const double lower_weight,
const d... | {"hexsha": "9a67827514109914706659c064585d9c2a471304", "size": 1941, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/graph_utils/graph_class.hpp", "max_stars_repo_name": "jweber94/dijkstras_shortest_path", "max_stars_repo_head_hexsha": "ec175081895b5bca924c7c98d403fa61db59b992", "max_stars_repo_licenses": ... |
import numpy as np
def deprojectVis(data, incl=0., PA=0., offset=[0., 0.], wsc=1.):
# - read in, parse data
u, v, real, imag = data
# - convert keywords into relevant units
inclr = np.radians(incl)
PAr = np.radians(PA)
offr = 1e3*offset*np.pi/(180.*3600.)
# - change to an appropriate coo... | {"hexsha": "32b44303f7e2350a5f1e97020161e314d05160d8", "size": 689, "ext": "py", "lang": "Python", "max_stars_repo_path": "deprojectVis.py", "max_stars_repo_name": "seanandrews/discrete-SB", "max_stars_repo_head_hexsha": "28571ebcef2c01cc2403eff6d3183beb7bf3810e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# import numpy as np
import pandas as pd
class PandasUtil():
def __init__(self, datetime_format=None):
self.datetime_format = datetime_format
def fix_string(self, series):
return series.astype(str)
def fix_bool(self, series):
return series.astype(bool)
def fix_float(self, se... | {"hexsha": "0eba977abb2f03a7d418f095454d843b1753ccba", "size": 785, "ext": "py", "lang": "Python", "max_stars_repo_path": "bqsqoop/utils/pandas_util.py", "max_stars_repo_name": "therako/bqsqoop", "max_stars_repo_head_hexsha": "43bb120a34811bfc62dc49095c39c68be9a49aba", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
------------------------------------------------------------------------
-- The Agda standard library
--
-- Conversion of < to ≤, along with a number of properties
------------------------------------------------------------------------
-- Possible TODO: Prove that a conversion ≤ → < → ≤ returns a
-- relation equivale... | {"hexsha": "85434fdcf388404262c64bc5c5e2f7b2251b5305", "size": 3212, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "agda-stdlib-0.9/src/Relation/Binary/StrictToNonStrict.agda", "max_stars_repo_name": "qwe2/try-agda", "max_stars_repo_head_hexsha": "9d4c43b1609d3f085636376fdca73093481ab882", "max_stars_repo_licen... |
#=
6 digit numbers
From
Gunnar Blom, Lars Holst, Dennis Sandell:
"Problems and Snapshots from the World of Probability"
Page 19f, Problem 2.5 Problems concerning random numbers
Given the 6 digits numbers:
a) Problem 1
find the probability that at least one of the digits 0..9
appears ... | {"hexsha": "a17849009f77187cb2d8952aa17d3ede7a5d6e9a", "size": 3139, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/turing/6_digit_numbers.jl", "max_stars_repo_name": "Wikunia/hakank", "max_stars_repo_head_hexsha": "030bc928d2efe8dcbc5118bda3f8ae9575d0fd13", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Parameters
beta = .96
y = [1.0, 2.0]
b0 = 0.0
P = [0.8 0.2;
0.4 0.6]
cp = ConsumptionProblem(beta, y, b0, P)
Q = beta*P
N_simul = 150
c_bar, b1, b2 = consumption_complete(cp)
debt_complete = [b1, b2]
println("P = ", P)
println("Q= ", Q, "\n")
println("Govt expenditures in peace and war =", y... | {"hexsha": "e099082e7b3f45a8f32a3c8f25039eb5bbc5359b", "size": 1449, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "smoothing/war_peace_example.jl", "max_stars_repo_name": "parkjt0506/QuantEconPractice", "max_stars_repo_head_hexsha": "2d83848dab7ed8d40efc9bbcf1e73aed7e5e532f", "max_stars_repo_licenses": ["BSD-3-... |
import numpy as np
from pyscf import gto
from pyscf.dft import rks
from pyscf.pbc import gto as pbcgto
from pyscf.pbc.dft import rks as pbcrks
def test_ke_cutoff(pseudo=None):
# The periodic calculation
eke_cut = []
eno_cut = []
max_ke = []
Ls = [5, 10, 15, 20, 25, 30, 40, 50]
for L in Ls:
... | {"hexsha": "dca0e7da041ca1b7148c65a6194a921723d6a5c6", "size": 2167, "ext": "py", "lang": "Python", "max_stars_repo_path": "pbc/tests/test_ke_cutoff.py", "max_stars_repo_name": "gmwang18/pyscf", "max_stars_repo_head_hexsha": "fcd6877751661c8a9743c1c872a4a2b65f6dd7ac", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_s... |
# -*- coding: utf-8 -*-
""" gutils/numpy_/test/test_numpy_ """
import unittest
import numpy as np
from scipy import linalg
from gutils.numpy_.numpy_ import colnorms_squared_new, normcols, LabelMatrixManager, \
scale_using_general_min_max_values, split_numpy_array
class MatrixMixin:
def setUp(self):
... | {"hexsha": "55a699d0076a64d7c62c627952478dc8df5329c1", "size": 7536, "ext": "py", "lang": "Python", "max_stars_repo_path": "gutils/numpy_/test/test_numpy_.py", "max_stars_repo_name": "giussepi/gutils", "max_stars_repo_head_hexsha": "f43a207a4a10884096a5d92d81183c2213169c14", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 30 20:35:22 2019
@author: icbab
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.font_manager as fm
font_location = 'C:/HANDotum.ttf'
# ex - 'C:/asiahead4.ttf'
font_name = fm.FontProperties(fname = font... | {"hexsha": "d91d1d6f6990117bc6b0adebe63a66031e2c19e9", "size": 1222, "ext": "py", "lang": "Python", "max_stars_repo_path": "pics/2019-07-27_PCA/score_dist.py", "max_stars_repo_name": "reporeferences/angeloyeo.github.io", "max_stars_repo_head_hexsha": "c249e860665b918d1144f9cc5093dbe24b384224", "max_stars_repo_licenses"... |
[STATEMENT]
lemma forward_UV_lists_arg_min_ex:
"\<lbrakk>finite xs; ys \<noteq> {};
ys = {x. set x = xs \<and> distinct x \<and> take 1 x = [r] \<and> forward x \<and> (\<forall>xs \<in> Y. sublist xs x)}\<rbrakk>
\<Longrightarrow> \<exists>y \<in> ys. \<forall>z \<in> ys. (f :: 'a list \<Rightarrow> real) y ... | {"llama_tokens": 583, "file": "Query_Optimization_IKKBZ_Optimality", "length": 2} |
import os
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, classification_report
from plot_cm import plot_cm
def pat_meta_info(pro_data_dir):
"""
Get patient and scan metadata for chest CT
@params:
data_sitk - requir... | {"hexsha": "776fbf74cafd94ce127050ccea0e383062295e21", "size": 17319, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/HN_patient_meta.py", "max_stars_repo_name": "xmuyzz/IVContrast", "max_stars_repo_head_hexsha": "f3100e54f1808e1a796acd97ef5d23d0a2fd4f6c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 26 11:08:30 2018
@author: wangyf
"""
'''
Objective Oriented Version of Lattice Building functon
'''
import numpy as np
import math
import json
import networkx as nx
from networkx.algorithms import isomorphism as iso
from itertools import combinations
import matplotlib.p... | {"hexsha": "7d8fb93fd591dafe012b1df1a3ee641ec49dd48c", "size": 52301, "ext": "py", "lang": "Python", "max_stars_repo_path": "lasso-assisted-CE/lattice_functions.py", "max_stars_repo_name": "VlachosGroup/Pdn-Cluster-Structure-Optimization", "max_stars_repo_head_hexsha": "470fe57bd0dcfdfc131c9b7138b6c6480dfafd4b", "max_s... |
from os.path import dirname, join
import sys
import platform
from ctypes import *
from .xi_wintypes import *
from .xidefs import *
try:
import numpy as np
except ImportError:
pass
#import platform; platform.architecture - not reliable on Mac OSX
if platform.machine().startswith('arm') or plat... | {"hexsha": "5c4275fd801db0afc31cd779218accf15d3c0d51", "size": 195478, "ext": "py", "lang": "Python", "max_stars_repo_path": "Catkin_PKG_Car/src/ximea_cam/src/ximea/xiapi.py", "max_stars_repo_name": "jessecha/OPCAS", "max_stars_repo_head_hexsha": "2b51543b4ad1ee37dba2e45a0c7d0b872309d418", "max_stars_repo_licenses": ["... |
#include <boost/filesystem/operations.hpp>
#include <palette_loader.hxx>
#include <global_state.hxx>
auto asset_loader<palette>::load_asset(const ::std::string& p_name) const
-> palette
{
// Retrieve data path
const auto t_dataPath = global_state<path_manager>().data_path();
// Build path to requested palette f... | {"hexsha": "061c3c8aa401125ecf7a43577df9c2cdb755e1f5", "size": 670, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "src/palette_loader.cxx", "max_stars_repo_name": "nshcat/gl_app", "max_stars_repo_head_hexsha": "49b97f07f81dc252695a379c46213ad9c1d94e1a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, ... |
#!/usr/bin/env python
from PyQt4 import QtGui
from PyQt4 import QtCore
from PyQt4 import Qt
import PyQt4.Qwt5 as Qwt
import numpy as np
from datetime import datetime as date
import sys
from Relay_QCheckBox import *
class MainWindow(QtGui.QWidget):
def __init__(self):
QtGui.QMainWindow.__init__(self)
... | {"hexsha": "31bf7d8cf09a918fefed7540bfdcb7504feae807", "size": 16782, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/python/rr_main_gui.py", "max_stars_repo_name": "vt-gs/relay_daemon", "max_stars_repo_head_hexsha": "9d77cd3222a3fe3e588f7c2196a4a06e8a73a471", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#redirect wiki:Sacramento:Sapor
| {"hexsha": "eb4c22f3d7b12dcbfda15640ba03c062af7e8152", "size": 32, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Sapor.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import argparse
import os
import pickle
import re
import glob
import numpy as np
import PIL.Image
from PIL import Image
from cv2 import VideoWriter, VideoWriter_fourcc, imread
import dnnlib
import dnnlib.tflib as tflib
def generate_images(arrs, network_pkl, truncation_psi=1.0,noise_mode='const', outdir='out', save=T... | {"hexsha": "2704adc790c3a791b91340ffdda9f4be19e8f0e7", "size": 1590, "ext": "py", "lang": "Python", "max_stars_repo_path": "StyleganTools/custom_generate_tf.py", "max_stars_repo_name": "LeoLinRui/SSTP", "max_stars_repo_head_hexsha": "2cad02fd14d1fd76f24a58fe609421796dee59f9", "max_stars_repo_licenses": ["CC0-1.0"], "ma... |
import os
import numpy as np
import pytest
import torch
from skimage.metrics import peak_signal_noise_ratio as ski_psnr
import ignite.distributed as idist
from ignite.exceptions import NotComputableError
from ignite.metrics import PSNR
from ignite.utils import manual_seed
def test_zero_div():
psnr = PSNR()
... | {"hexsha": "6374390fdb5f24d0fb5c0c809c058f8c8038b96d", "size": 9774, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/ignite/metrics/test_psnr.py", "max_stars_repo_name": "DecSnowFlake/ignite", "max_stars_repo_head_hexsha": "b48354236a9993b75bcab12c8657fbf2f2318c70", "max_stars_repo_licenses": ["BSD-3-Claus... |
using Knet
Pkg.test("Knet")
load_only = true
for (p,f,o1,o2,o3) =
(
(:LinReg, "linreg.jl", "--gcheck 2", "--fast", "--fast"),
(:Housing, "housing.jl", "--gcheck 2 --atype Array{Float64}", "--fast", "--fast"),
(:MNIST, "mnist.jl", "--gcheck 2", "--fast", "--fast"),
(:LeNet, "lenet.jl", "--gchec... | {"hexsha": "9f0ddf07249e68b74cf376e375efffdd51646b2c", "size": 576, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/runexamples.jl", "max_stars_repo_name": "enzotarta/Knet.jl", "max_stars_repo_head_hexsha": "4330f3fec812ccbbfeabbcf6aa63450ca743f9c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
\documentclass[12pt,oneside,a4]{article}
\usepackage{float}
\usepackage[utf8]{inputenc}
\usepackage[a4paper,width=160mm,top=25mm,bottom=25mm]{geometry}
\usepackage[lining,tabular]{fbb} % so math uses tabular lining figures
\usepackage{graphicx}
\usepackage{enumitem}
\usepackage{listings}
\usepackage[svgnames]{xcolor}
\... | {"hexsha": "9e9710859c1fc0ddc4d4ef00fe85e5d9583d4526", "size": 31109, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/marble_user_guide/Marble_User_Guide.tex", "max_stars_repo_name": "BerkeleyLab/Marble", "max_stars_repo_head_hexsha": "df6742000361e6a103fdec6a5667684df13a7a9e", "max_stars_repo_licenses": ["Un... |
import numpy as np
import csv
class Grid(object):
def __init__(self,n):
self.n = n
self.x = np.zeros(self.n,dtype=np.float64)
self.conc = np.zeros(self.n*4,dtype=np.float64)
self.concA = np.zeros(self.n,dtype=np.float64)
self.concB = np.zeros(self.n,dtype=np.float64)
... | {"hexsha": "6739c2896cff75fcf02831712353222f1fab5a00", "size": 4016, "ext": "py", "lang": "Python", "max_stars_repo_path": "Simulation Python/grid.py", "max_stars_repo_name": "nmerovingian/dissociativeCE-Simulation-MachineLearning", "max_stars_repo_head_hexsha": "cfbc8b8e6c9e3f2efc994fcf1d207c6266eedf2e", "max_stars_re... |
from typing import Callable, Iterable, Sized
from itertools import product
import numpy as np
def convert_tuple_to_array(elements: Iterable, **kw) -> np.ndarray:
if "dtype" in kw:
dtype = kw["dtype"]
else:
dtype = np.result_type(*elements)
return np.array(elements, dtype=dtype)
def car... | {"hexsha": "080d6517f0615fa74a48ca6f5647538f23f77769", "size": 1035, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mckit_meshes/utils/cartesian_product.py", "max_stars_repo_name": "MC-kit/mckit-meshes", "max_stars_repo_head_hexsha": "8aa06ff95ffd1dabac95f399d45047325c265f78", "max_stars_repo_licenses": ["M... |
function y = tdis_prb(x,n)
% PURPOSE: calculates t-probabilities for elements in x-vector
%---------------------------------------------------
% USAGE: y = tdis_prb(x,n)
% where: x = vector containing computed t-values
% n = degrees of freedom parameter
%---------------------------------------------------
% RET... | {"author": "ambropo", "repo": "VAR-Toolbox", "sha": "9fe5d763da307cdded2827851325766b3a7c60e1", "save_path": "github-repos/MATLAB/ambropo-VAR-Toolbox", "path": "github-repos/MATLAB/ambropo-VAR-Toolbox/VAR-Toolbox-9fe5d763da307cdded2827851325766b3a7c60e1/OldVersions/v2dot0/Auxiliary/tdis_prb.m"} |
[STATEMENT]
theorem f0_asymptotic_space_complexity:
"f0_space_usage \<in> O[at_top \<times>\<^sub>F at_right 0 \<times>\<^sub>F at_right 0](\<lambda>(n, \<epsilon>, \<delta>). ln (1 / of_rat \<epsilon>) *
(ln (real n) + 1 / (of_rat \<delta>)\<^sup>2 * (ln (ln (real n)) + ln (1 / of_rat \<delta>))))"
(is "_ \<in>... | {"llama_tokens": 35859, "file": "Frequency_Moments_Frequency_Moment_0", "length": 144} |
%HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
\section{Understanding the "right-hand side"\label{RHS}}
%HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
As can be seen from the above example, you need to know the exact appearance
of the "right-hand side"... | {"hexsha": "b9068821446117fee4b5c832d5ee28648e39fed3", "size": 1993, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Mouse/source/manual/RHS.tex", "max_stars_repo_name": "celer/mouse", "max_stars_repo_head_hexsha": "021a81f0c02fc079a944569ba382f2c9d7b9b9eb", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
# ======================================================
# Copyright (C) 2020 repa1030
# This program and the accompanying materials
# are made available under the terms of the MIT license.
# ======================================================
import numpy as np
import os
import math
class KNearestNeighbor:
de... | {"hexsha": "4ec68ab564c60ba91bd907d45f2d91704ba72400", "size": 2699, "ext": "py", "lang": "Python", "max_stars_repo_path": "classificator1/k_nearest_neighbors.py", "max_stars_repo_name": "repa1030/simple_object_classification", "max_stars_repo_head_hexsha": "14c75f262c512ae6404174a96d818dfda10ed060", "max_stars_repo_li... |
import pytest
import numpy as np
from FindTheTail.ftt import Ftt
@pytest.fixture
def ftt_with_parameters():
ftt = Ftt(np.arange(10), 'test_data', 100)
return ftt
@pytest.fixture
def ftt_with_data():
ftt = Ftt(np.arange(10), 'test_data')
return ftt
@pytest.fixture
def ftt_data_with_dublicates():
... | {"hexsha": "dc32cfcc078bcc5139ac9aa7ba665f061442d794", "size": 1128, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_ftt.py", "max_stars_repo_name": "fstroth/findthetail", "max_stars_repo_head_hexsha": "f4525a1393ab362886395bfb3a789446c1ac5143", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
#ifndef INCLUDED_STDDEFX
#include "stddefx.h"
#define INCLUDED_STDDEFX
#endif
#ifndef INCLUDED_CALC_OBJECTLINKRUNTIME
#include "calc_objectlinkruntime.h"
#define INCLUDED_CALC_OBJECTLINKRUNTIME
#endif
// Library headers.
#ifndef INCLUDED_BOOST_FORMAT
#include <boost/format.hpp>
#define INCLUDED_BOOST_FORMAT
#endif
#if... | {"hexsha": "de7bfad6850806d4a538a7b98a4fae5a2cfe7b52", "size": 3230, "ext": "cc", "lang": "C++", "max_stars_repo_path": "pcraster/pcraster-4.2.0/pcraster-4.2.0/source/pcraster_model_engine/calc_objectlinkruntime.cc", "max_stars_repo_name": "quanpands/wflow", "max_stars_repo_head_hexsha": "b454a55e4a63556eaac3fbabd97f8a... |
from flask import Flask, jsonify
from flask_restplus import Resource, Api
from google_api import google
from pubmed_api import pubmed
from bioarchive_api import bioarchive
from medrxiv_api import medrxiv
import math
import json
from rake_nltk import Rake
import requests
import datetime
import re
from statistics import ... | {"hexsha": "bdd5a24c05763c349b9ca76fed777d0ad1059765", "size": 5506, "ext": "py", "lang": "Python", "max_stars_repo_path": "sci_search_api.py", "max_stars_repo_name": "forensx/hatch2020", "max_stars_repo_head_hexsha": "982e6821ec8f0c1862c208a12bdabbf7fad15101", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# coding=utf-8
import numpy as np
import scipy.sparse as sp
from pymg.problem_base import ProblemBase
class Helmholtz1D_Periodic(ProblemBase):
"""Implementation of the 1D Helmholtz problem.
Here we define the 1D Poisson problem :math:`-\Delta u - \sigma u = 0` with
Dirichlet-Zero boundary conditions. Th... | {"hexsha": "6a9b4e563cce0254d6859a5041bc1db5e637da84", "size": 2511, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/helmholtz1d_periodic.py", "max_stars_repo_name": "amit17133129/pyMG-2016", "max_stars_repo_head_hexsha": "b82a60811bb0a8b91d8793c47177a240221f9176", "max_stars_repo_licenses": ["BSD-2-Clau... |
import numpy as np
import matplotlib.pyplot as plt
from ML_functions import gen_events
from density_funcs import rho_baryon
from astropy.io import fits
hdu = fits.open('galaxy1.fits')
data = hdu[1].data
def IMF(m):
#use a Kroupa IMF
#calculated in a seperate jupyter notebook
if (m<0.08):
alph... | {"hexsha": "182a885424d5da616fe7c65756c0593a6a942ba3", "size": 2420, "ext": "py", "lang": "Python", "max_stars_repo_path": "genbaryons.py", "max_stars_repo_name": "HarrisonWinch96/DarkDisk_Microlensing", "max_stars_repo_head_hexsha": "e25d59051771318239116a8d2036aca8ce70236d", "max_stars_repo_licenses": ["BSD-3-Clause"... |
#include <procedural_graph/reader/graph_reader_grammar.h>
#include <procedural_graph/reader/named_argument.h>
#include <procedural_graph/reader/node_definition_node.h>
#include <procedural_graph/reader/node_link_node.h>
#include <boost/spirit/include/qi.hpp>
#include <gtest/gtest.h>
using namespace pagoda;
using nam... | {"hexsha": "67e0bf57baff940bee5f4eb83a28a639c2351f9b", "size": 9787, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/unit_tests/procedural_graph/graph_reader_grammar.cpp", "max_stars_repo_name": "diegoarjz/selector", "max_stars_repo_head_hexsha": "976abd0d9e721639e6314e2599ef7e6f3dafdc4f", "max_stars_repo_li... |
###############################################################
# _ _ _ _ _
# | |__ (_) ___ _ __ __ _ _ __| |_(_) ___| | ___
# | '_ \| |/ _ \| '_ \ / _` | '__| __| |/ __| |/ _ \
# | |_) | | (_) | |_) | (_| | | | |_| | (__| | __/
# |_.__/|_|\___/| .__/ \__,_|_| \__|_|\___|_|\___... | {"hexsha": "ab3999a4fe3f9be668941a919d000ce4634e049d", "size": 2557, "ext": "py", "lang": "Python", "max_stars_repo_path": "doeTests/3DUnstructuredCylinder/createMesh.py", "max_stars_repo_name": "edsaac/bioparticle", "max_stars_repo_head_hexsha": "67e191329ef191fc539b290069524b42fbaf7e21", "max_stars_repo_licenses": ["... |
# https://github.com/JuliaDiffEq/DifferentialEquations.jl/issues/525
using OrdinaryDiffEq, StaticArrays, Test
mutable struct SimType{T} <: DEDataVector{T}
x::Array{T,1}
f1::T
end
function f(u,p,t) # new out-of-place definition
SimType([-0.5*u[1] + u.f1,
-0.5*u[2]],u.f1)
end
function f!... | {"hexsha": "3debbad17a3fcb2a544804ac71b1bcf1b7ab8e9d", "size": 4162, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/downstream/data_array_regression_tests.jl", "max_stars_repo_name": "frankschae/DiffEqBase.jl", "max_stars_repo_head_hexsha": "406640750d8bf6e38009a40d377fa6d95717fe04", "max_stars_repo_license... |
#!/usr/bin/env /usr/bin/python3
import numpy as np
################################################################################
#===============================================================================
# geometry.py
#===============================================================================
###########... | {"hexsha": "e97a81fc1db11353e8ac5b43c5bff45b44cdaf0f", "size": 1230, "ext": "py", "lang": "Python", "max_stars_repo_path": "flat_source/geometry.py", "max_stars_repo_name": "HopyanLab/ConPT2D", "max_stars_repo_head_hexsha": "4e5cb2de8959c88b79613431fb9fb71675db17ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
from simple_convnet.helpers import (
filter2D, batch_filter3D, padarray, atleast, safe_exp, safe_log, choice, imshow
)
from matplotlib import pyplot as plt
from time import time
from skimage.transform import downscale_local_mean
class Layer(object):
def __init__(self, input_shape, rand_... | {"hexsha": "a178e7757858331e2d0db6e767e6d1fd3f157c95", "size": 34064, "ext": "py", "lang": "Python", "max_stars_repo_path": "simple_convnet/convnet.py", "max_stars_repo_name": "bbabenko/simple_convnet", "max_stars_repo_head_hexsha": "015dd33be80365f704049790a547a2b3e0251f2a", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 18 06:01:03 2021
@author: hakimbmkg
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import pandas as pd
import librosa
import librosa.display
import matplotlib.pyplot as plt
plt.rcParams['agg.path.chunksize'] = 1000000
from tqdm import tqdm... | {"hexsha": "8011e3b1d463d765905ca870bdd66ed4e534fb3b", "size": 18234, "ext": "py", "lang": "Python", "max_stars_repo_path": "LinduAI/preprocessing/transform.py", "max_stars_repo_name": "hakimbmkg/linduAI", "max_stars_repo_head_hexsha": "7d413ba03d54498dfe4871136748d95527e6d575", "max_stars_repo_licenses": ["MIT"], "max... |
"""
Benchmarks for code in pandas/_libs, excluding pandas/_libs/tslibs,
which has its own directory
"""
import numpy as np
from pandas._libs.lib import (
is_list_like,
is_scalar,
)
from pandas import (
NA,
NaT,
)
# TODO: share with something in pd._testing?
scalars = [
0,
1.0,
1 + 2j,
... | {"hexsha": "f5c2397945cea52fd66be72ca57beaa9f654cc9d", "size": 746, "ext": "py", "lang": "Python", "max_stars_repo_path": "asv_bench/benchmarks/libs.py", "max_stars_repo_name": "oricou/pandas", "max_stars_repo_head_hexsha": "9405e58d9268041f5416711c051cf5429a19bf49", "max_stars_repo_licenses": ["PSF-2.0", "Apache-2.0",... |
import os
import pickle
import numpy as np
from numbers import Number
from typing import Union, Optional, Dict
from .common import get_datetime, mkdir
from .metric import Metric
class Logger:
"""
Log training statistics and visualize them via Tensorboard.
Parameters
----------
root : str
... | {"hexsha": "e0b0099f96ea27c1a5f2188c749a84f9d1f17dd8", "size": 4006, "ext": "py", "lang": "Python", "max_stars_repo_path": "alkaid/utils/logger.py", "max_stars_repo_name": "Renovamen/alkaid", "max_stars_repo_head_hexsha": "78bb19c3d18856234dec9444235b749c6006655f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
SUBROUTINE setvars ! set variables we have found are undefined
C--------------------------------
common /cantenna/ numants,iats(20),anttype(20),antname(20),
+ xfqs(20),xfqe(20),designfreq(20),antfile(20),
+ beammain(20),offazim(20),cond(20),diel(20),
... | {"hexsha": "7de8e50775ec691e905b297fbe28d4dd6257af85", "size": 1892, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "src/voacapw/setvars.for", "max_stars_repo_name": "jawatson/voacapl", "max_stars_repo_head_hexsha": "6861fbad041ff7a56b8875c735c9242c81a94320", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_... |
"""
Purpose: Sample size
Date created: 2020-11-19
Ref:
https://www.qualtrics.com/experience-management/research/determine-sample-size/
https://github.com/shawnohare/samplesize/blob/master/samplesize.py
Contributor(s):
Mark M.
"""
try:
from secrets import SystemRandom
except ModuleNotFoundError:
... | {"hexsha": "e1115fe56b498d3201b93529f7eeb195c50a5133", "size": 4840, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebook-samples/sample_size.py", "max_stars_repo_name": "MarkMoretto/python-examples-main", "max_stars_repo_head_hexsha": "37b8c41d2f175029f4536ca970f037ff19b4e951", "max_stars_repo_licenses": ["... |
import numpy as np
from ..constants import log
def load_assimp(file_obj, file_type=None):
'''
Use the assimp library to load a mesh, from a file object and type,
or filename (if file_obj is a string)
Assimp supports a huge number of mesh formats.
Performance notes: in tests on binary STL pyassim... | {"hexsha": "ad57009bb8fb670e41ca2595c2bfd2ec3102bf9b", "size": 2042, "ext": "py", "lang": "Python", "max_stars_repo_path": "basis/trimesh/io/assimp.py", "max_stars_repo_name": "takuya-ki/wrs", "max_stars_repo_head_hexsha": "f6e1009b94332504042fbde9b39323410394ecde", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2020 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
from pymor.core.config import config
if config.HAVE_TORCH:
from numbers import Number
i... | {"hexsha": "f41c80b8a97130bbfcfcbd769a79cc83c383f9f8", "size": 24315, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pymor/reductors/neural_network.py", "max_stars_repo_name": "TreeerT/pymor", "max_stars_repo_head_hexsha": "e8b18d2d4c4b5998f0bd84f6728e365e0693b753", "max_stars_repo_licenses": ["Unlicense"],... |
module SD_ale_boundary_operator
#include <messenger.h>
use mod_kinds, only: rk,ik
use mod_constants, only: ZERO,ONE,TWO,HALF
use type_operator, only: operator_t
use type_chidg_worker, only: chidg_worker_t
use type_properties, only: prope... | {"hexsha": "b29cd5909cd835efd4abf865c73fe40aeeb66294", "size": 4211, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/equations/scalar/scalar_diffusion_ale/SD_ale_boundary_operator.f90", "max_stars_repo_name": "wanglican/ChiDG", "max_stars_repo_head_hexsha": "d3177b87cc2f611e66e26bb51616f9385168f338", "max_... |
#!/usr/bin/env python
# Author: Tony Zheng
import rospy
import time
import roslib
import sys
import cv2
import scipy.linalg
import numpy as np
from geometry_msgs.msg import Twist
from std_msgs.msg import String, Int32, Float32, Float32MultiArray, Bool, Float64
from sensor_msgs.msg import Image, CompressedImage
from m... | {"hexsha": "9415ed165b5f0ec75a45d328542366bf9e875a4d", "size": 23839, "ext": "py", "lang": "Python", "max_stars_repo_path": "workspace/src/labs/src/lab7/image_processing_lane_keeping.py", "max_stars_repo_name": "treyfortmuller/barc", "max_stars_repo_head_hexsha": "29c1e6e2f5ad530b4020c552eea8e82411c2a0af", "max_stars_r... |
import numpy as np
def bridge(var, steps, state=None):
"""1D Brownian bridge in the time interval [0,1]
# Arguments
var: variance of the Brownian bridge
steps: number of time steps to simulate
state: state of random number generator
# Result
trace of the bridge
"""
i... | {"hexsha": "ce283dc38e469488f503a939fd9f39ab37d1d148", "size": 620, "ext": "py", "lang": "Python", "max_stars_repo_path": "jellium/bridge.py", "max_stars_repo_name": "Christian-Hirsch/jellium", "max_stars_repo_head_hexsha": "f3887af722a1793207ecfeeb2fd2d8f81bbfc5c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
This module provides the EvaluateModel class.
"""
import logging
import os
import warnings
import numpy as np
import torch
import torch.nn as nn
from selene_sdk.sequences import Genome
from selene_sdk.utils import (
PerformanceMetrics,
initialize_logger,
load_model_from_state_dict,
)
from sklearn.metri... | {"hexsha": "8a691c6aef7419221efe5ffbeaeab2da92ab23b6", "size": 7243, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/evaluation/evaluate_model.py", "max_stars_repo_name": "AIRI-Institute/DeepCT", "max_stars_repo_head_hexsha": "8e23fda101bd4a2bce2c98c5a73d97072a3892de", "max_stars_repo_licenses": ["Apache-2.0... |
import pandas as pd
import numpy as np
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
import sklearn.metrics as metrics
import matplotlib.pyplot as plt
import ipdb
blabels = pd.read_csv('bad_label_losses.csv')
glabels = pd.read_csv('good_label_losses.csv')
blabels['labe... | {"hexsha": "46c2f9ebc86355e490e1e7f71a31dc200dc6c76b", "size": 1422, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainLR.py", "max_stars_repo_name": "bhaprayan/keras-yolo3", "max_stars_repo_head_hexsha": "9ad5ff761f3baf9042a17327c0149591ce1307c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
c
c ----------------------------------------------------------------------
c
subroutine fsicxx(fic,sc,gc,fmass,fwidth , fsic)
c
c this subroutine computes an off-shell antifermion wavefunction from a
c flowing-in external antifermion and a vector boson.
c ... | {"hexsha": "549de7c5ba112c72c43162dfaa7ee4b7d6a45e13", "size": 1910, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "HELAS/fsicxx.f", "max_stars_repo_name": "valassi/mg5amc_test", "max_stars_repo_head_hexsha": "2e04f23353051f64e1604b23105fe3faabd32869", "max_stars_repo_licenses": ["NCSA"], "max_stars_count": 5, ... |
""" Class average finetuning functions. Before using any of these finetuning
functions, ensure that the model is set up with nb_classes=2.
"""
from __future__ import print_function
import sys
import uuid
import numpy as np
from os.path import dirname
from time import sleep
from keras.optimizers import Adam
from g... | {"hexsha": "13bc1b6b4db0459eef58b85f4a7175fca07ba0e4", "size": 13300, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepmoji/class_avg_finetuning.py", "max_stars_repo_name": "Bodzio-2/DeepMoji", "max_stars_repo_head_hexsha": "4081db22f9bcb0305ee930bf75c1e4cc76aa1c2f", "max_stars_repo_licenses": ["MIT"], "max_s... |
'''
xECG Project Repository (https://github.com/jtrpinto/xECG)
File: train_model_uoftdb.py
- Uses data from prepare_data.py and the Model class from models.py to train a model
for biometric identification on the UofTDB database. The training routine can be found
at trainers.py.
"Explaining ECG Biometric... | {"hexsha": "8a159e5be644219e8fa919a7e35e40985893172c", "size": 4060, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_model_uoftdb.py", "max_stars_repo_name": "jtrpinto/xECG", "max_stars_repo_head_hexsha": "e062cd94defb49ae946c1874267a36713733baba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
import numpy as np
class MeshIOInterface:
""" Mesh reader/writer interface for meshio """
def read(self, mesh_file):
assert mesh_file[-4:] == '.ply', "Only PLY format for input mesh"
# Read mesh
import meshio
mesh = meshio.read(mesh_file)
# Check that it is triangulat... | {"hexsha": "50f2bc0eea6962f97ad19d6da4aa1eaba88d0ba3", "size": 6533, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tools/mesh_interfaces.py", "max_stars_repo_name": "rolanddenis/MeshDD", "max_stars_repo_head_hexsha": "83e2f0faae3f4e90075a7f3135c8c66fd8721b7f", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# # -*- coding: utf-8 -*-
# """
# Created on Wed Feb 3 12:49:07 2021
# @author: user
# """
import mph
from polygen import random_poly
from polygen import poly_add
from polygen import poly_draw
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
import jpype
import pandas as pd
import numpy as np
import matplotlib... | {"hexsha": "49be98cc777aa17fad93c30f5590b5c7df3b86f0", "size": 673, "ext": "py", "lang": "Python", "max_stars_repo_path": "comsol/comsol_connect.py", "max_stars_repo_name": "ITMO-NSS-team/cell-trap-design", "max_stars_repo_head_hexsha": "161c4c98c0c068516277ab096d407013427cf340", "max_stars_repo_licenses": ["BSD-3-Clau... |
Subroutine ptoh
Parameter (maxstr=150001)
Double Precision gxp, gyp, gzp, ftp, pxp, pyp, pzp, pep, pmp
Double Precision gxp0, gyp0, gzp0, ft0fom, drlocl
Double Precision enenew, pxnew, pynew, pznew, beta2, gam
Double Precision ftavg0, gxavg0, gyavg0, gzavg0, bex, bey, bez
Double Precision pxsgs, pysgs, pzsg... | {"hexsha": "ec13827f7ef6733cccea14bfaa89fd8c68732818", "size": 10915, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/ptoh.f90", "max_stars_repo_name": "xiaohaijin/AMPT", "max_stars_repo_head_hexsha": "90c7a1ab4dc04a092e64af759d53e22f6fea5b02", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
[STATEMENT]
lemma typing_swp:
assumes "\<Gamma>(a \<mapsto> \<sigma>) \<turnstile> M : \<tau>" "b \<notin> fvs M"
shows "\<Gamma>(b \<mapsto> \<sigma>) \<turnstile> [a \<leftrightarrow> b] \<cdot> M : \<tau>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Gamma>(b \<mapsto> \<sigma>) \<turnstile> [a \<leftrigh... | {"llama_tokens": 1736, "file": "Name_Carrying_Type_Inference_SimplyTyped", "length": 16} |
import numpy as np
import aoc_helper
FLOOR = np.array(list(map(list, aoc_helper.day(25).splitlines())))
EMPTY, EAST, SOUTH = ".>v"
def step():
moving_east = (FLOOR == EAST) & np.roll(FLOOR == EMPTY, -1, 1)
FLOOR[moving_east] = EMPTY
FLOOR[np.roll(moving_east, 1, 1)] = EAST
moving_south = (FLOOR == ... | {"hexsha": "a9409507828f50d5dd86e749c4b051fc5db56d7c", "size": 575, "ext": "py", "lang": "Python", "max_stars_repo_path": "2021/day_25.py", "max_stars_repo_name": "salt-die/Advent-of-Code", "max_stars_repo_head_hexsha": "b0344dafda4576b41023134b8289c08af877a77a", "max_stars_repo_licenses": ["Unlicense"], "max_stars_cou... |
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