text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma rquot_D: "x \<preceq>\<^sub>R y \<Longrightarrow> z = rquot y x \<Longrightarrow> D x z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>x \<preceq>\<^sub>R y; z = rquot y x\<rbrakk> \<Longrightarrow> D x z
[PROOF STEP]
using gR_rel_def rquot_prop
[PROOF STATE]
proof (prove)
using this:
(?x... | {"llama_tokens": 256, "file": "PSemigroupsConvolution_Partial_Semigroups", "length": 2} |
\documentclass[../main.tex]{subfiles}
\begin{document}
\subsubsection{Comparing different radii}
The first objective we set was determining the optimal beacon range.
For this we tested all datasets, ceteris paribus, with the following levels of beacon radius: 0.25 till 3 with increments of 0.25 and also a radius of 0.1... | {"hexsha": "4ee20fc5ec4558210183e259a04a95d3c44f0b09", "size": 3469, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/subfiles/results.tex", "max_stars_repo_name": "JDevlieghere/MAS", "max_stars_repo_head_hexsha": "b00d2e5fa487b74a31f4092086d94ed01e348822", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import scipy.signal
import scipy.fftpack as fftpack
import numpy as np
def sin(f,fs,time):
x = np.linspace(0, 2*np.pi*f*time, fs*time)
return np.sin(x)
def downsample(signal,fs1=0,fs2=0,alpha=0,mod = 'just_down'):
if alpha == 0:
alpha = int(fs1/fs2)
if mod == 'just_down':
return signal... | {"hexsha": "c525c02c602fdd4e1753775dcc277abfb909af62", "size": 2969, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/dsp.py", "max_stars_repo_name": "HiYKY/candock", "max_stars_repo_head_hexsha": "fdbfced6f91f1d9a264bd6cdf9f957c03ec5d5d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
[STATEMENT]
lemma converged_cd_diverge_cs: assumes \<open>is_path \<pi>\<close> and \<open>is_path \<pi>'\<close> and \<open>cs\<^bsup>\<pi>\<^esup> j = cs\<^bsup>\<pi>'\<^esup> j'\<close> and \<open>j<l\<close> and \<open>\<not> (\<exists>l'. cs\<^bsup>\<pi>\<^esup> l = cs\<^bsup>\<pi>'\<^esup> l')\<close> and \<open... | {"llama_tokens": 9129, "file": "IFC_Tracking_IFC", "length": 59} |
"""
Asif Khan
"""
import numpy as np
import cv2
from mtcnn.mtcnn import MTCNN
detector = MTCNN()
def one_face(frame, bbs, pointss):
# process only one face (center ?)
offsets = [(bbs[:,0]+bbs[:,2])/2-frame.shape[1]/2,
(bbs[:,1]+bbs[:,3])/2-frame.shape[0]/2]
offset_dist = np.sum... | {"hexsha": "e63b10d62f081cd601194af7988dbf3483501c3a", "size": 7403, "ext": "py", "lang": "Python", "max_stars_repo_path": "pose_detection_mtcnn.py", "max_stars_repo_name": "fisakhan/Face_Pose", "max_stars_repo_head_hexsha": "b98e7338d1b64357e5842b507fb9e845dc48d126", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
<a href="https://colab.research.google.com/github/deep-learning-indaba/indaba-pracs-2019/blob/master/4a_recurrent_nets.ipynb" target="_parent"></a>
# Practical 4a: Recurrent Neural Networks (RNNs)
© Deep Learning Indaba. Apache License 2.0.
## Introduction
Feedforward models (eg deep MLPs and ConvNets) map fixed-s... | {"hexsha": "bb7d0594ad046de47fa01f44f35c24be45f3557b", "size": 131300, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "4a_recurrent_nets.ipynb", "max_stars_repo_name": "amrrs/indaba-pracs-2019", "max_stars_repo_head_hexsha": "33f6121a8aec5856936254524c385ea847635e5a", "max_stars_repo_licenses": ["Ap... |
#include <boost/program_options.hpp>
#include <boost/format.hpp>
#include <iostream>
#include "../include/OutputFormatter.h"
#include "../include/Genre.h"
namespace RomViewer {
/* Init Functions */
OutputFormatter::OutputFormatter() { }
OutputFormatter::~OutputFormatter() { }
/* Primary Functions */
void OutputForm... | {"hexsha": "991d9c674aa2ba63ca0a2ad654941a5ab3363078", "size": 1057, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/OutputFormatter.cpp", "max_stars_repo_name": "vyth/lsrom", "max_stars_repo_head_hexsha": "b926a1a2774f3c25d4e7d491d6de9296763557d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import numpy as np
from torch.utils.data import DataLoader
from typing import Tuple
class TorchDataset:
"""`TorchDataset` class.
Represents a dataset class for PyTorch.
"""
DATA_ROOT = "./data"
@classmethod
def numpy(
cls,
one_hot_encode: bool = True,
transformers: s... | {"hexsha": "9d3a54205ca0770ae4f59b65f102cd9889b9d352", "size": 4040, "ext": "py", "lang": "Python", "max_stars_repo_path": "privacy_evaluator/datasets/torch/torch.py", "max_stars_repo_name": "chen-yuxuan/privacy-evaluator", "max_stars_repo_head_hexsha": "ed4852408108c3e6a01216af4183261945fd7e67", "max_stars_repo_licens... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# BCDI: tools for pre(post)-processing Bragg coherent X-ray diffraction imaging data
# (c) 07/2017-06/2019 : CNRS UMR 7344 IM2NP
# (c) 07/2019-present : DESY PHOTON SCIENCE
# authors:
# Jerome Carnis, carnis_jerome@yahoo.fr
import numpy as np
from matpl... | {"hexsha": "a27c5a5c9602b07cb78a0344ea522ae7eb85c0e2", "size": 4165, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/postprocessing/bcdi_strain_mean_var_rms.py", "max_stars_repo_name": "sjleake/bcdi", "max_stars_repo_head_hexsha": "bf071ad085a11622158e1e651857a8a172c51cf1", "max_stars_repo_licenses": ["C... |
"""Python library for backtesting and analyzing trading strategies at scale.
While there are many great backtesting packages for Python, vectorbt was designed specifically for data mining:
it excels at processing performance and offers interactive tools to explore complex phenomena in trading.
With it you can traver... | {"hexsha": "01d79e96f5179598fd5b16fe9b900a963bb1457a", "size": 18721, "ext": "py", "lang": "Python", "max_stars_repo_path": ".venv/lib/python3.8/site-packages/vectorbt/__init__.py", "max_stars_repo_name": "eo1989/VectorBTanalysis", "max_stars_repo_head_hexsha": "bea3deaf2ee3fc114b308146f2af3e4f35f70197", "max_stars_rep... |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "8350c65ed54ba75222e685754d26b950fc50dbea", "size": 2941, "ext": "py", "lang": "Python", "max_stars_repo_path": "multiple_user_representations/models/task_test.py", "max_stars_repo_name": "xxdreck/google-research", "max_stars_repo_head_hexsha": "dac724bc2b9362d65c26747a8754504fe4c615f8", "max_stars_repo_lice... |
import logging
import networkx as nx
import numpy as np
from joblib import Parallel, delayed
from tqdm import tqdm
from pygkernels.data.utils import np2nx
class GraphGenerator:
@classmethod
def params_from_adj_matrix(cls, A, partition, name=None):
return cls.params_from_graph(np2nx(A, partition), na... | {"hexsha": "6185783b6bc1942c051bac6124dc2406bf6b4cd4", "size": 1686, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygkernels/data/graph_generator.py", "max_stars_repo_name": "vlivashkin/pygraphs", "max_stars_repo_head_hexsha": "ec0ec0d064c0fc7f5a3620e94152bc3fe2f9feaf", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
from qtpy.QtWidgets import QFileDialog
from qtpy import QtGui
import numpy as np
from collections import OrderedDict
import glob
from NeuNorm.normalization import Normalization
from __code.file_handler import make_or_reset_folder
from __code.panoramic_stitching_for_tof.image_handler import HORIZONTAL_MARGIN... | {"hexsha": "c0fd8056178ae75d9d3a3225502f90ae80e89fcd", "size": 5916, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/__code/panoramic_stitching_for_tof/export.py", "max_stars_repo_name": "mabrahamdevops/python_notebooks", "max_stars_repo_head_hexsha": "6d5e7383b60cc7fd476f6e85ab93e239c9c32330", "max_st... |
'''
An implementation of a subset of the following symbolic nomenclature:
http://www.ncbi.nlm.nih.gov/books/NBK310273/table/symbolnomenclature.T.monosaccharide_symb/?report=objectonly
'''
import logging
from collections import Counter
from functools import partial
import numpy as np
from matplotlib.path import Pa... | {"hexsha": "80d543dc857b5c1b0a32dd4b33f51ef947a82599", "size": 19547, "ext": "py", "lang": "Python", "max_stars_repo_path": "glypy/plot/cfg_symbols.py", "max_stars_repo_name": "dcambie/glypy", "max_stars_repo_head_hexsha": "ecbf849b9686dc617a2e65ea171bcc33881a8db7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
ah1_file = path*"/SampleFiles/AH/ah1.f"
ah1_fstr = path*"/SampleFiles/AH/ah1.*"
ahc_file = path*"/SampleFiles/AH/lhz.ah"
ah_resp = path*"/SampleFiles/AH/BRV.TSG.DS.lE21.resp"
ah2_file = path*"/SampleFiles/AH/ah2.f"
ah2_fstr = path*"/SampleFiles/AH/ah2.*"
printstyled(" AH (Ad Hoc)\n", color=:light_green)
print... | {"hexsha": "cab826290ca78b8bb7e2769053cfdbe7418fd56d", "size": 3523, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/DataFormats/test_ah.jl", "max_stars_repo_name": "UnofficialJuliaMirror/SeisIO.jl-b372bb87-02dd-52bb-bcf6-c30dd83fd342", "max_stars_repo_head_hexsha": "ae4ddd969c4c42281f36e218d5d3039af6c3146a"... |
###############################################################################
# 重要: 请务必把任务(jobs)中需要保存的文件存放在 results 文件夹内
# Important : Please make sure your files are saved to the 'results' folder
# in your jobs
# 本代码来源于 Notebook cell 里面的模型，大家进行离线任务时尽量只训练模型，不要进行模型评估等操作
################################################... | {"hexsha": "08639803373506e53e1796d3ce90c8750f0f5bdd", "size": 9860, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_main.py", "max_stars_repo_name": "Stella925/Programming-Projects", "max_stars_repo_head_hexsha": "5d6357390e7494a10b675f02bd47cee75424c95f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import GPy
import numpy as np
from emukit.bayesian_optimization.acquisitions import ExpectedImprovement
from emukit.bayesian_optimization.loops import UnknownConstraintBayesianOptimizationLoop
from emukit.core import ContinuousParameter, ParameterSpace
from emukit.core.loop import FixedIterationsStoppingCondition, Use... | {"hexsha": "3dad65944e11c75f72b4ae98d3c66fc2a3261171", "size": 1379, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/emukit/bayesian_optimization/test_constrained_loop.py", "max_stars_repo_name": "lfabris-mhpc/emukit", "max_stars_repo_head_hexsha": "ccb07f6bed0e9ae41dbeefdb3ad2ab247d3991e2", "max_stars_rep... |
# Created by msinghal at 09/04/22
import os
import numpy as np
import networkx as nx
import pandas as pd
from sklearn import preprocessing
from stellargraph import StellarGraph
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from stellargraph.datasets import DatasetLoader
class MovieLens(
DatasetLoader,
name="Mov... | {"hexsha": "b284b830f7c9254aeb9f24e0e643bf684b625b7a", "size": 8003, "ext": "py", "lang": "Python", "max_stars_repo_path": "GraphicoRango/steller_graph_implementation/datasets.py", "max_stars_repo_name": "madansinghal/graphranko", "max_stars_repo_head_hexsha": "c975aa42104216237f4651c5b300a09ac8db02e8", "max_stars_repo... |
import tensorflow as tf
import os
import time
import json
import pandas as pd
import numpy as np
"""
Script to train a sequential NN.
NN trains on training data, all results output to disk.
Use this script over `train_and_predict.py`
"""
#GPU configuration - dont use too much memory
os.environ["TF_GPU_ALLOCATOR"]="c... | {"hexsha": "4930cefa30c222d5a64e2e77b25b4e583e03ceb2", "size": 4744, "ext": "py", "lang": "Python", "max_stars_repo_path": "legacy/legacy_scripts/.ipynb_checkpoints/feature_importance-checkpoint.py", "max_stars_repo_name": "tomkimpson/ML4L", "max_stars_repo_head_hexsha": "ffa8360cb80df25bd6af4fa5cc39b42bd6f405cd", "max... |
import os.path as osp
import json, pickle
import sys
from math import sqrt
from itertools import product
from numpy import random
import jittor as jt
import numpy as np
max_image_size = 550
augment_idx = 0
dump_file = 'weights/bboxes_aug.pkl'
box_file = 'weights/bboxes.pkl'
def augment_boxes(bboxes):
bboxes_rel =... | {"hexsha": "71f236b773b43118ab1403c46fa3d02b5edb3b80", "size": 3985, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/augment_bbox.py", "max_stars_repo_name": "li-xl/Yolact.jittor", "max_stars_repo_head_hexsha": "10d93335b7e8c7a10cb9e62dc64b4ba9f9409ea5", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Lint as: python3
# Copyright 2020 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 ... | {"hexsha": "f08c80ad602fc8619425ed56ca3be8d2b150ef8b", "size": 2837, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/lite/tools/optimize/sparsity/format_converter_wrapper_pybind11_test.py", "max_stars_repo_name": "neochristou/tensorflow", "max_stars_repo_head_hexsha": "50b55bfc5c9132c3bd82505181380bff... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
from models.model import create_model, load_model
from utils.image import get_affine_transform
from utils.debugger import Deb... | {"hexsha": "4975e5cd1c91381bef6854f2f96249966e2ee258", "size": 8266, "ext": "py", "lang": "Python", "max_stars_repo_path": "chapter_08/lib/detectors/base_detector.py", "max_stars_repo_name": "XiangLiK/cv_course", "max_stars_repo_head_hexsha": "da7c2318fd4128bbdab96db26ddbb2524f37d0a0", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python3
# coding: utf-8
import logging
import os
import sys
import warnings
from argparse import ArgumentParser
from typing import List, Tuple, Dict
import numpy as np
import pandas
from pandas import DataFrame
try:
import matchbox
except ImportError:
sys.path.append(os.path.join(os.path.dirna... | {"hexsha": "9b922d598759cfc0bbcf6337cac53741db954a2c", "size": 4102, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/ds-summary.py", "max_stars_repo_name": "ayllon/MatchBox", "max_stars_repo_head_hexsha": "367b69c51f1ef4b574ce2a534d3e5441b2b2933b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from pathlib import Path
from typing import Dict, List, Set, Tuple
import networkx as nx
import numpy as np
import pandas as pd
def sepset_dict_to_ndarray(
sepsets: Dict[Tuple[int, int], Set[int]], variable_count: int, max_level: int
) -> np.ndarray:
separation_sets = np.full((variable_count, variable_count,... | {"hexsha": "60dcbd3134ca87dadb257051e16aff424956d7af", "size": 2515, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/fixtures/file_readers.py", "max_stars_repo_name": "hpi-epic/gpucsl", "max_stars_repo_head_hexsha": "f461c47ce17105f7cf25aa65d39cb671021f07e4", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
__precompile__()
module Utils
# export @define, @reexport
#
# include("utils/macro_utils.jl")
export istriustrict, istrilstrict
export simd_scale!, simd_copy!, simd_copy_scale!,
simd_copy_xy_first!, simd_copy_yx_first!,
simd_copy_yx_first_last!,
simd_xpy!, simd_ax... | {"hexsha": "11766ffc7a03ba27343cad20fb584aedbfead27e", "size": 404, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Utils.jl", "max_stars_repo_name": "ChrisRackauckas/GeomDAE.jl", "max_stars_repo_head_hexsha": "dbaf5d909dc1686d211312f4143ad7355ffbc325", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 12 14:51:16 2019
@author: Devendra Mishra
"""
#%%
"""
santa is matrix produced from the previous code synthetic
outputs as out and input as inp
took learning rate as lr=0.01
7 layers with tanh activation function and 1 layer of sigmoid activation function is used to tra... | {"hexsha": "e71af5997549b56dbd8a2c5ca023a494ca43afa6", "size": 4663, "ext": "py", "lang": "Python", "max_stars_repo_path": "Ann.py", "max_stars_repo_name": "Devendra1225mishra/Optimisations", "max_stars_repo_head_hexsha": "dc59819693728807740cc7b510a5b643b3717fc7", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import glob
filnames = glob.glob('data_transport/u_*')
filnames.sort()
dates = [filname.split('.')[-2].split('_')[-1] for filname in filnames]
gammas = [filname.split('.')[-2].split('_')[-2] for filname in filnames]
opt_types = [filname.s... | {"hexsha": "6989ad6fdc7f385acd78d9b3e5ef8638216531a9", "size": 6004, "ext": "py", "lang": "Python", "max_stars_repo_path": "make_check_list.py", "max_stars_repo_name": "clinfo/2021_Patients_Transport", "max_stars_repo_head_hexsha": "4f14cd0b1350eca98dfbe9d4ae530fda34759811", "max_stars_repo_licenses": ["Apache-2.0"], "... |
"""
Baseline
Bi-GRU encoder -> MLP -> MST/ILP
"""
import tensorflow as tf
import numpy as np
import os, json, random, time, re, math
from Sentence_Encoder import Sentence_Encoder
from utils import load_data, build_vocab
from os import path as fp
from ilp import load_scip_output, mk_zimpl_input, dump_scores_to_dat_file... | {"hexsha": "94cbe4e57b6a09632f9a33d1d0bcb25acf65bd89", "size": 21805, "ext": "py", "lang": "Python", "max_stars_repo_path": "baseline/main.py", "max_stars_repo_name": "amillert/DialogueDiscourseParsing", "max_stars_repo_head_hexsha": "9baf9f957f054b0ed0e0f2668eabb26c74e38759", "max_stars_repo_licenses": ["BSD-2-Clause"... |
"""
@author: LXA
Date: 2021年 2 月 20 日
"""
import os
import sys
import tensorflow as tf
import numpy as np
import matplotlib
import platform
import shutil
import DNN_data
import time
import DNN_base
import DNN_tools
import plotData
import saveData
# 记录字典中的一些设置
def dictionary_out2file(R_dic, log_fi... | {"hexsha": "9bf0b0b7d0faba7351c5ae21c625ba879193e85f", "size": 21872, "ext": "py", "lang": "Python", "max_stars_repo_path": "MissData_DNN.py", "max_stars_repo_name": "Blue-Giant/DNN2missdata_EQ", "max_stars_repo_head_hexsha": "be049af314ff0f85fcf7dd4f454be7ac59a93374", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
Ray teaches electric bass and is one of many area music teachers in Davis.
| {"hexsha": "e0fe8c998fec95d2c6b422e4892c456df781f07c", "size": 78, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Ray_Yukich.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
"""
Cantera Simulator Adapter module
Used to run mechanism analysis with Cantera as an ideal gas in a batch reactor at constant V-U (adiabatic)
"""
import cantera as ct
import numpy as np
from typing import List, Optional, Type
from rmgpy.tools.canteramodel import generate_cantera_conditions
from rmgpy.tools.data imp... | {"hexsha": "d2bcf9b87bfc65b3aecd5774cf4d14995518fb88", "size": 26604, "ext": "py", "lang": "Python", "max_stars_repo_path": "t3/simulate/cantera_constantUV.py", "max_stars_repo_name": "ReactionMechanismGenerator/T3", "max_stars_repo_head_hexsha": "13e50482282c1ae4b82a9057eeaba5f3b8de2e8c", "max_stars_repo_licenses": ["... |
# library imports
library(tidyverse)
library(scales)
library(limma)
library(edgeR)
library(psych)
# get the default plot width and height
width <- options()$repr.plot.width
height <- options()$repr.plot.height
# load the IRS-normalized data and check the table
data_import <- read_tsv("labeled_grouped_protein_summary... | {"hexsha": "4aa8590521f34b4bedc9a33b1899000b2a4c3e20", "size": 14805, "ext": "r", "lang": "R", "max_stars_repo_path": "Nat-Comm-2019_TMT_QE_averages.r", "max_stars_repo_name": "pwilmart/BCP-ALL_QE-TMT_Nat-Comm-2019", "max_stars_repo_head_hexsha": "646f1f4cf667df4f71aa31b3c994845e18c9a05d", "max_stars_repo_licenses": ["... |
# *****************************************************************************
# © Copyright IBM Corp. 2018-2020. All Rights Reserved.
#
# This program and the accompanying materials
# are made available under the terms of the Apache V2.0
# which accompanies this distribution, and is available at
# http://www.apache.... | {"hexsha": "942249580b5a13fbb06468e74ca6e977de3b3718", "size": 79597, "ext": "py", "lang": "Python", "max_stars_repo_path": "iotfunctions/anomaly.py", "max_stars_repo_name": "TheTheseus/functions", "max_stars_repo_head_hexsha": "526bb01598c80c5db8995a979a613a7aa6a1e126", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
\documentclass[11pt,letterpaper,roman]{moderncv}
\usepackage{luapackageloader}
\directlua{resume = require("resume")}
% Modern CV type
\moderncvstyle{classic}
\moderncvcolor{grey}
% Packages
\usepackage{verbatim}
\usepackage[margin=0.5in]{geometry}
\usepackage{import}
% Custom comand definitions
\definecolor{cvblue}{... | {"hexsha": "14c506c7c442fea7bebea49643e3083c264cdb31", "size": 1379, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "EricLondresResume.tex", "max_stars_repo_name": "slondr/Resume", "max_stars_repo_head_hexsha": "4791a8e78ed32c8013e4f72fadd77e09639136b0", "max_stars_repo_licenses": ["0BSD"], "max_stars_count": 1, "... |
# -*- coding: utf-8 -*-
"""
Colour Blindness Plotting
=========================
Defines the colour blindness plotting objects:
- :func:`plot_cvd_simulation_Machado2009`
"""
from __future__ import division
from colour.blindness import cvd_matrix_Machado2009
from colour.plotting import CONSTANTS_COLOUR_STYLE, plot_... | {"hexsha": "43610d30deeec1ca2da7f47c005de25460cee557", "size": 2754, "ext": "py", "lang": "Python", "max_stars_repo_path": "colour/plotting/blindness.py", "max_stars_repo_name": "njwardhan/colour", "max_stars_repo_head_hexsha": "fedf769764b46cd0b4484cde7e4f59a09b37515c", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
import argparse
import os
import random
import sys
import time
import struct
from collections import Counter
from collections import deque
from operator import itemgetter
from tempfile import NamedTemporaryFile as NTF
import SharedArray as sa
import numpy as np
from numba import jit
from text_embedding.documents import... | {"hexsha": "1a585e927d9b23d55ccfb3d92f3eaecfabdbc3f1", "size": 33728, "ext": "py", "lang": "Python", "max_stars_repo_path": "solvers.py", "max_stars_repo_name": "NLPrinceton/text_embedding", "max_stars_repo_head_hexsha": "ee269727863982669ffb95c984c4220c1fba2834", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import torch
import random
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import atexit
from os import path
import gym
from torch.utils.tensorboard import SummaryWriter
from models import StaticReconstructor, DiscriminatorConv
from utils import WarpFrame, NoopResetEnv, MaxAndSkipEnv
BATCH_SI... | {"hexsha": "ba7188e28f2107d0841394459a6bf3a0a70742a2", "size": 3083, "ext": "py", "lang": "Python", "max_stars_repo_path": "endecode.py", "max_stars_repo_name": "klemenkotar/optimus", "max_stars_repo_head_hexsha": "c454d48934a422a5033da2c5e8f4073dcbf60500", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#pragma once
#include "kissfft.hh"
#include <boost/math/constants/constants.hpp>
#include <boost/optional.hpp>
#include <cstddef>
namespace vv
{
template <class T, class U>
auto lerp(T x0, T x1, U ratio)
{
return x0 + (x1 - x0) * ratio;
}
template <class T>
auto invlerp(T x0, T x1, T x)
{
return (x - x0) ... | {"hexsha": "c3db6cc05ad1805b7094c9412c731bfde37ff443", "size": 6847, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "vv/src/processor.hpp", "max_stars_repo_name": "planaria/vv", "max_stars_repo_head_hexsha": "08aebfbe37338fe1735fd3431f1178237941dde1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import datetime as dt
import os
import io
import requests
from dateutil.relativedelta import relativedelta
from Modules.Utils import Listador, FindOutlier, FindOutlierMAD, Cycles
from Modules.Graphs import GraphSerieOutliers, GraphSerieOutliersMAD
def SST... | {"hexsha": "1e0e356cf22f78253be57bc1b26be4f731e1d446", "size": 6600, "ext": "py", "lang": "Python", "max_stars_repo_path": "Modules/ENSO.py", "max_stars_repo_name": "cmcuervol/HydroBalbo", "max_stars_repo_head_hexsha": "0c70536305d12f6fb9fb8fe7ce7cdb08d88472af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#include <cassert>
#include <cmath>
#include <functional>
#include <numeric>
#include <sstream>
#include <boost/multiprecision/gmp.hpp>
#include <QtDebug>
using s64 = int64_t;
using namespace std;
using boost::multiprecision::mpq_rational;
QDebug operator<<(QDebug d, const mpq_rational& r) {
d.nospace();
... | {"hexsha": "d0082201219a366ebef5645dd13d7f9f37241af0", "size": 2919, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "project-euler/205/main.cpp", "max_stars_repo_name": "hydroo/coding-and-math-exercises", "max_stars_repo_head_hexsha": "c0c9b8ae48e043b0809e4c592444f3e4bc3222d8", "max_stars_repo_licenses": ["CC0-1.0... |
(* Author: Simon Wimmer *)
theory TA_Graphs
imports
More_List Stream_More
"HOL-Library.Rewrite"
begin
chapter \<open>Graphs\<close>
section \<open>Basic Definitions and Theorems\<close>
locale Graph_Defs =
fixes E :: "'a \<Rightarrow> 'a \<Rightarrow> bool"
begin
inductive steps where
Single: "steps [... | {"author": "wimmers", "repo": "archive-of-graph-formalizations", "sha": "cf49dd3379174cca7f3f1de16214e1c66238841e", "save_path": "github-repos/isabelle/wimmers-archive-of-graph-formalizations", "path": "github-repos/isabelle/wimmers-archive-of-graph-formalizations/archive-of-graph-formalizations-cf49dd3379174cca7f3f1de... |
# ! /usr/bin/env python
import argparse
import os
import numpy as np
import json
from voc import parse_voc_annotation
from yolo import create_yolov3_model, create_yolov3_tiny_model, dummy_loss
from generator import BatchGenerator
from utils.utils import normalize, evaluate, makedirs
from keras.callbacks impo... | {"hexsha": "7ad3cefe6c8bfe8a5319dfe5522e111ef59b2782", "size": 13713, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "LachlanMares/keras-yolo3-generic", "max_stars_repo_head_hexsha": "abc0ad0488e727b3d997a924b57356ace2caf444", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import os
import sys
import glob
import time
import copy
import logging
import argparse
import random
import numpy as np
import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import utils
from controller import NAO
from nasbench import api
def build... | {"hexsha": "e83003b219003c665e787cb06dad344461680f20", "size": 13410, "ext": "py", "lang": "Python", "max_stars_repo_path": "nasbench/train_seminas.py", "max_stars_repo_name": "PeterouZh/SemiNAS", "max_stars_repo_head_hexsha": "39731663271b994571160d43d796b2bb93386b3b", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
!COMPILER-GENERATED INTERFACE MODULE: Fri Mar 6 15:34:29 2020
! This source file is for reference only and may not completely
! represent the generated interface used by the compiler.
MODULE DIAGOSC_EMBM__genmod
INTERFACE
SUBROUTINE DIAGOSC_EMBM(ISTEP,IOUT,EXT,FX0... | {"hexsha": "14d659bf0dd4c68daf0a774c8ed172d535db112e", "size": 708, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "genie-embm/src/fortran/diagosc_embm__genmod.f90", "max_stars_repo_name": "crem33/EcoGENIE_LA", "max_stars_repo_head_hexsha": "89032945316dc2827478100ed6bf60143e31f34d", "max_stars_repo_licenses":... |
#!/usr/bin/env python
""" Generate events and perform jet finding.
Can be invoked via ``python -m jet_hadron.event_gen.jet_analyis -c ...``.
.. codeauthor:: Raymond Ehlers <raymond.ehlers@cern.ch>, Yale University
"""
import abc
import enlighten
import logging
import numpy as np
import os
import pyjet
from scipy.sp... | {"hexsha": "b5dc1fb47e42de9e3d59c43c14ba2a63b4a33a68", "size": 27353, "ext": "py", "lang": "Python", "max_stars_repo_path": "jet_hadron/event_gen/jet_analysis.py", "max_stars_repo_name": "raymondEhlers/alice-jet-hadron", "max_stars_repo_head_hexsha": "8526567935c0339cebb9ef224b09a551a0b96932", "max_stars_repo_licenses"... |
import numpy as np
import scipy.stats as sp
import matplotlib.pyplot as plt
import h5py
def manifold(gridSize, binary, epoch):
f = h5py.File('params/ff_epoch_' + str(epoch) + '.hdf5','r')
wsig = np.matrix(f["wsig"])
bsig = np.matrix(f["bsig"]).T
if binary:
shape = (28,28)
activation ... | {"hexsha": "fc54241a36be6b027a4628567af9f78211205098", "size": 1427, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot.py", "max_stars_repo_name": "TobiasMR/AEVB", "max_stars_repo_head_hexsha": "78b780f69d5d5136b49bcaff7da96eabb34de31a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
"""
collection of useful miscellaneous functions
"""
def get_dim_exp(exp):
"""
outputs hard-coded data dimensions (lat-lon-lev-time)
for a given simulation
"""
if exp == "QSC5.TRACMIP.NH01.L.pos.Q0.300.lon0.150.lond.45.lat0.0.latd.30":
from ds21grl import dim_aqua_short as dim
else... | {"hexsha": "134f15917aa2fbb9ad54862e24e8149153f10b8a", "size": 8804, "ext": "py", "lang": "Python", "max_stars_repo_path": "ds21grl/misc.py", "max_stars_repo_name": "edunnsigouin/ds21grl", "max_stars_repo_head_hexsha": "b6544cbc97529943da86e48a437ce68dc00e0f82", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
#pragma once
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <boost/shared_ptr.hpp>
#include "macros.h"
namespace cloudproc {
class CloudGrabberImpl;
struct RGBD {
typedef boost::shared_ptr<RGBD> Ptr;
std::vector<unsigned char> rgb;
std::vector<unsigned short> depth;
RGBD() : rgb(480*640*3... | {"hexsha": "fe31f59a1987a06f40e43e688d707c07011b2366", "size": 795, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/cloudproc/cloudgrabber.hpp", "max_stars_repo_name": "HARPLab/trajopt", "max_stars_repo_head_hexsha": "40e2260d8f1e4d0a6a7a8997927bd65e5f36c3a4", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
"""
SE-ResNet for CUB-200-2011, implemented in Gluon.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['seresnet10_cub', 'seresnet12_cub', 'seresnet14_cub', 'seresnetbc14b_cub', 'seresnet16_cub',
'seresnet18_cub', 'seresnet26_cub', 'seresnetbc26b_cu... | {"hexsha": "e0575014302c61a6366d08182911d69a0fffacd2", "size": 15666, "ext": "py", "lang": "Python", "max_stars_repo_path": "gluon/gluoncv2/models/seresnet_cub.py", "max_stars_repo_name": "naviocean/imgclsmob", "max_stars_repo_head_hexsha": "f2993d3ce73a2f7ddba05da3891defb08547d504", "max_stars_repo_licenses": ["MIT"],... |
from housing_df.utils import build_housing_df_registry_for_all_regions, save_all_dfs_in_registry
from housing_df.specific import RegionDF, MetroDF
from housing_df.workflow.report import Report
import pandas as pd
import numpy as np
class MetroAreasRanked(Report):
def __init__(self, housing_type):
self.reg... | {"hexsha": "ad38bb400fd00a13706d31a58af5e73222883758", "size": 1278, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/housing_df/workflow/metro_areas_ranked.py", "max_stars_repo_name": "ojarrett/housing-data-utils", "max_stars_repo_head_hexsha": "f041e789ff8de7f08550a1d39641dfd1b683f324", "max_stars_repo_lice... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Author: Jie Yang, Wei wu, Xiaoy LI
# Last update: 2019.03.12
# First create: 2017.06.15
# Concate:
#
import os
import sys
root_path = "/".join(os.path.realpath(__file__).split("/")[:-3])
if root_path not in sys.path:
sys.path.insert(0, root_path)
impo... | {"hexsha": "4072c072a9617f750953160b1b3c4e2eead567f7", "size": 19712, "ext": "py", "lang": "Python", "max_stars_repo_path": "glyce/bin/run_lattice_lstm.py", "max_stars_repo_name": "TimSYQQX/glyce", "max_stars_repo_head_hexsha": "1542ed30ce104c25aa5c69ffcc9cc5ef2fcda975", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
# Copyright 2020 MIT Probabilistic Computing Project.
# See LICENSE.txt
from math import log
import pytest
from numpy import linspace
from sppl.distributions import bernoulli
from sppl.distributions import beta
from sppl.distributions import randint
from sppl.compilers.ast_to_spe import IfElse
from sppl.compilers.a... | {"hexsha": "0706de0a01ab04120ddced102df2d39cb131bbc6", "size": 3919, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_ast_switch.py", "max_stars_repo_name": "SEICS/sppl", "max_stars_repo_head_hexsha": "902c8c0ec0144fbabc8c0f33b15850af238e8d38", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
# Morphological operation functions for HDI data preparation
# Developer: Joshua M. Hess, BSc
# Developed at the Vaccine & Immunotherapy Center, Mass. General Hospital
# Import external modules
import numpy as np
import skimage.filters
import skimage.morphology
import skimage.color
import scipy.sparse
# Define funct... | {"hexsha": "fa7f4c00fbefe7b385405e62c94afe2faece0159", "size": 7067, "ext": "py", "lang": "Python", "max_stars_repo_path": "HDIprep/morphology.py", "max_stars_repo_name": "JoshuaHess12/hdi-prep", "max_stars_repo_head_hexsha": "224994b17b229abb30c29e9e70579ad8fdfeff8a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import torch
import numpy as np
# From https://github.com/soumith/dcgan.torch/issues/14
def np_slerp(val, low, high):
omega = np.arccos(np.clip(np.dot(low/np.linalg.norm(low), high/np.linalg.norm(high)), -1, 1))
so = np.sin(omega)
if so == 0:
return (1.0-val) * low + val * high # L'Hopital's rule/... | {"hexsha": "a03f484ad7f67a0aa74ca93641d9b84dcdef954b", "size": 717, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/slerp.py", "max_stars_repo_name": "li012589/NeuralWavelet", "max_stars_repo_head_hexsha": "6e593ded5cb4ae80579cbf56eb9c346d808669cb", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
import numpy as np
import pandas as pd
import logging
from math import pi
from datetime import datetime
BASE_FEATURES = ['ip', 'app', 'device', 'os', 'channel']
def feature_creation(data_directory):
""" Reads in the raw data stored in the data_directory
(either sample, train, or test) and builds the requ... | {"hexsha": "adbf425532707ae32389878b15dd9cfacbbd18c0", "size": 1631, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/features/build_features.py", "max_stars_repo_name": "KennedyMurphy/AdTracking", "max_stars_repo_head_hexsha": "dfe69b89ada8fc3a3b5aae59de018e49267d7201", "max_stars_repo_licenses": ["FTL"], "m... |
(**
* Copyright (C) 2022 BedRock Systems, Inc.
* All rights reserved.
*
* SPDX-License-Identifier: LGPL-2.1 WITH BedRock Exception for use over network, see repository root for details.
*)
Require Import iris.algebra.agree.
Require Import iris.proofmode.proofmode.
Require Import bedrock.lang.bi.spec.frac_splittab... | {"author": "bedrocksystems", "repo": "BRiCk", "sha": "23d7e64cc53706de608dbff0be75d1c4b8c3a7ec", "save_path": "github-repos/coq/bedrocksystems-BRiCk", "path": "github-repos/coq/bedrocksystems-BRiCk/BRiCk-23d7e64cc53706de608dbff0be75d1c4b8c3a7ec/theories/lang/cpp/logic/lib/auth_frac.v"} |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 8 17:03:43 2020
@author: Shoba Banik
"""
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
train = pd.read_csv('C:/Users/S... | {"hexsha": "9b29c9c5f7f714207377b0a2d2134c7018b066d4", "size": 3529, "ext": "py", "lang": "Python", "max_stars_repo_path": "Loan_SVM/Loan_svm.py", "max_stars_repo_name": "ShobaBanik/SVM_shoba", "max_stars_repo_head_hexsha": "3a428156fb085504ff0c5560b37f438f276d59d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""High-level API for cubic splines"""
import numpy
import numpy as np
from ..cartesian import mlinspace
class CubicSpline:
"""Class representing a cubic spline interpolator on a regular cartesian grid.."""
__grid__ = None
__values__ = None
__coeffs__ = None
def __init__(self, a, b, orders, v... | {"hexsha": "008f6000bd66928040f8ac4be05c0b00bc84069a", "size": 7070, "ext": "py", "lang": "Python", "max_stars_repo_path": "interpolation/splines/splines.py", "max_stars_repo_name": "vishalbelsare/interpolation.py", "max_stars_repo_head_hexsha": "116d144700a1c78b5ad86eee097d064610be8325", "max_stars_repo_licenses": ["B... |
import h5py
from mpi4py import MPI
import numpy as np
import time
comm = MPI.COMM_WORLD
rank = comm.rank # The process ID (integer 0-3 for 4-process run)
size = comm.size
def MPI_open(comm,handle,key,nparts):
## figure out how many particles each MPI task is supposed to read
num_per_task = int(nparts//comm.... | {"hexsha": "726abcfebf99abaa7911c38fd5778dd93db8a6f2", "size": 1869, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/abg_python/parallel/parallel_read.py", "max_stars_repo_name": "agurvich/abg_python", "max_stars_repo_head_hexsha": "f76425481781e6e8e28caf9e8290c0b5b920ab91", "max_stars_repo_licenses": ["MIT"... |
/-
Copyright (c) 2022 Yaël Dillies. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Yaël Dillies
! This file was ported from Lean 3 source module combinatorics.double_counting
! leanprover-community/mathlib commit 327c3c0d9232d80e250dc8f65e7835b82b266ea5
! Please do no... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/Combinatorics/Doubl... |
// Copyright Oliver Kowalke 2013.
// 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_FIBERS_H
#define BOOST_FIBERS_H
#include <boost/fiber/algo/algorithm.hpp>
#include... | {"hexsha": "b1919dbbf0252cbdd4c865caa29f12affd601da9", "size": 1557, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/boost/fiber/all.hpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e188fe871f47c2ef9cdef36d8a66bc8",... |
# coding: utf-8
# In[ ]:
# Copyright (c) 2017 Andrew Glassner
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, mod... | {"hexsha": "bc365c6474f60802f021ca3f3e24a21bd7c3bb78", "size": 5646, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/lectures/lecture18/notes/DLBasics_Utilities.py", "max_stars_repo_name": "sytseng/2019-CS109B", "max_stars_repo_head_hexsha": "3ecde086bcc77dbac2e930fa3bdc93f94a1f2b6d", "max_stars_repo_licens... |
# -*- coding: utf-8 -*-
from flask import Flask, render_template, request
import torch
from torch import nn
from torch.utils.data import Dataset
import gluonnlp as nlp
import numpy as np
import kss
from googletrans import Translator
from itertools import combinations
from krwordrank.word import summarize_with_keywor... | {"hexsha": "ce4499dbb538ddffbdcf9b6542bc6d1814445646", "size": 6225, "ext": "py", "lang": "Python", "max_stars_repo_path": "web/server.py", "max_stars_repo_name": "hanna56/Teddy", "max_stars_repo_head_hexsha": "c89a2a81ca4cb4c6cc2e41e70b49028d81d5e391", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
%-----------------------------------------------------------------------------------------------
\section*{Conflict of Interest}
The authors declare that there is no conflict of interest in this work.
%-----------------------------------------------------------------------------------------------
| {"hexsha": "14a9eafb333534d8a7a940b431e4b301a7bdf1f9", "size": 305, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "02-01-conflicts.tex", "max_stars_repo_name": "cnaak/man-CarnotPrinciple", "max_stars_repo_head_hexsha": "b258fa7508d8049d2c1a958ffab58ff9b17c78ff", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_star... |
"""
Sampling from a poisson distribution
"""
abstract type AbstractPoissonDistribution <: AbstractSampleDistribution end
struct PoissonSampleDistribution{T} <: AbstractPoissonDistribution
lambda::T
function PoissonSampleDistribution(lambda::T) where {T <: AbstractFloat}
return new{T}(lambda)
end
end
"""
... | {"hexsha": "69b7e843109beea3ddd73b50e96ca4c27f3029d8", "size": 3781, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "glmSolverjl/src/simulate.jl", "max_stars_repo_name": "dataPulverizer/glmSolver", "max_stars_repo_head_hexsha": "d82623caac1e14d29ee09fbafa7efbbd5d6ee0bf", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
"""CNN.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1K2l75LJfglEOZG4M09pzFUqdUfemlWQ3
"""
!unzip -qq /content/drive/MyDrive/Data/joined.zip
import numpy as np
import pandas as pd
import keras
from keras.layers import... | {"hexsha": "b2a4be031d7427c9cb32a69382aa070b6c210a0b", "size": 3316, "ext": "py", "lang": "Python", "max_stars_repo_path": "Models/cnn.py", "max_stars_repo_name": "GunH-colab/CovidOps", "max_stars_repo_head_hexsha": "6a64a85d60f158fbe15a7f116a972ce419366996", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
# -*- coding: utf-8 -*-
"""
MWT collision graph manipulation general utilities
"""
from __future__ import (
absolute_import, division, print_function, unicode_literals)
import six
from six.moves import (zip, filter, map, reduce, input, range)
# standard library
import itertools
import collections
import types
... | {"hexsha": "409c62698872e36a9a170c160825a90538bcf75b", "size": 5340, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/waldo/collider/simplifications/util.py", "max_stars_repo_name": "amarallab/waldo", "max_stars_repo_head_hexsha": "e38d23d9474a0bcb7a94e685545edb0115b12af4", "max_stars_repo_licenses": ["MIT"]... |
import os
import json
import datetime
import numpy as np
import glob
from easydict import EasyDict
def edict2dict(edict_obj):
dict_obj = {}
for key, vals in edict_obj.items():
if isinstance(vals, EasyDict):
dict_obj[key] = edict2dict(vals)
else:
dict_obj[key] = vals
return dict_obj
def ge... | {"hexsha": "7d4e562eaa2179c162bbdff292406f9d143f15dc", "size": 2121, "ext": "py", "lang": "Python", "max_stars_repo_path": "framework/run_utils.py", "max_stars_repo_name": "DeLightCMU/ElaborativeRehearsal", "max_stars_repo_head_hexsha": "0cf2a234b4716ff7a16fdbf4b18c173cc42fbcc4", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma invariant_start:
"\<lbrakk>wf_state r; wf_state s\<rbrakk> \<Longrightarrow> invariant r s ([([], r, s)], [], {(post r, post s)})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>wf_state r; wf_state s\<rbrakk> \<Longrightarrow> invariant r s ([([], r, s)], [], {(post r, post s)})
[PROOF ... | {"llama_tokens": 150, "file": "MSO_Regex_Equivalence_Pi_Equivalence_Checking", "length": 1} |
import numpy as np
from matplotlib.axes import Axes
from matplotlib.patches import Ellipse
from .c_ellipsoid2 import AsymmetricEllipsoidalShell, EllipsoidalShellWithSizeDistribution
from .c_gauss_ellipsoid import I0Rgfromrho, rhofromI0Rg, F2GaussianEllipsoid
from ..core import FitFunction
class F2AsymmetricCoreShell... | {"hexsha": "3d4c0046eb6ca878084f7ac829d8bcf88d830601", "size": 7843, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/saxsfittool/fitfunction/ellipsoid/ellipsoid.py", "max_stars_repo_name": "awacha/saxsfittool", "max_stars_repo_head_hexsha": "20e9a8aecf680cfc5f3b84264b932c9a50e47085", "max_stars_repo_licenses... |
! requires pulchra.dat
module nmr
implicit none
! variables for adding backbone atoms
integer,parameter:: num_stat = 2363
integer,parameter:: num_stat_pro = 1432 ! stats for proline
integer, dimension(num_stat,3),save:: nco_stat_bin
integer, dimension(num_stat_pro,3),save:: nco_stat_pro_bin
... | {"hexsha": "397065ba9863641975816eaa7b2da93a9590859e", "size": 79213, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "itasser/nmr.f90", "max_stars_repo_name": "fanufree/assign-it", "max_stars_repo_head_hexsha": "7eae0828aea4964f1d459a14a0e13025fefc4c9a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
from numpy.polynomial.legendre import legval
from scipy import linalg
from ..fixes import einsum
from ..utils import logger, warn, verbose
from ..io.pick import pick_types, pick_channels, pick_info
from ..surface impor... | {"hexsha": "9c6fdafa2353d4f82397df632436ffe9bfb9d6f1", "size": 6725, "ext": "py", "lang": "Python", "max_stars_repo_path": "mne/channels/interpolation.py", "max_stars_repo_name": "jasmainak/mne-python", "max_stars_repo_head_hexsha": "039cb1bf52770019bd48ac028795af0861792fa2", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2020 Ryan L. Collins <rlcollins@g.harvard.edu>
# and the Talkowski Laboratory
# Distributed under terms of the MIT license.
"""
Identify, cluster, and refine all significant segments per HPO from rCNV sliding window analysis
"""
from os import path
imp... | {"hexsha": "c2fb42258db4999471fc799258135708138ae41f", "size": 74900, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/sliding_windows/refine_significant_regions.py", "max_stars_repo_name": "talkowski-lab/rCNV2", "max_stars_repo_head_hexsha": "fcc1142d8c13b58d18a37fe129e9bb4d7bd6641d", "max_stars_repo_li... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "bc447b0f1c55fbb96d9fe98c7fbffa0431203d71", "size": 42525, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/mxnet/gluon/loss.py", "max_stars_repo_name": "franciscocalderon2/incubator-mxnet", "max_stars_repo_head_hexsha": "3260862c1ea928e99af5517b8e8ce16e670205a9", "max_stars_repo_licenses": ["Ap... |
# -*- coding: utf-8 -*-
from base import Parameter, Channel, Estimator, Report, Constant
import numpy as np
def _cos_partial(x):
return (x/2.0 + 1/4.0 * np.sin(2.0*x))
def _sin_partial(x):
return (x/2.0 - 1/4.0 * np.sin(2.0*x))
class ThetaFFT(Parameter):
"""An angle of decesion from the |0> pole."""
... | {"hexsha": "0c3b7a4322ea156977b4721db332d0015dd23d99", "size": 3766, "ext": "py", "lang": "Python", "max_stars_repo_path": "drift_qec/oneangleheuristic.py", "max_stars_repo_name": "janmtl/drift_qec", "max_stars_repo_head_hexsha": "3b1c703d151f9dc2833b761f85586cd09666557b", "max_stars_repo_licenses": ["0BSD"], "max_star... |
import numpy as np
def process_state_static(state, dist_norm):
location = state.physics.location
rotation = state.physics.rotation
velocity = state.physics.velocity
ang_vel = state.physics.angular_velocity
boost = state.boost_amount
jumped = 1 if state.jumped else -1
double_j = 1 if state.d... | {"hexsha": "c92a0abb863034e70da15cf8ba04528ae270b06e", "size": 2790, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ml_dir/experience_replay_buffer.py", "max_stars_repo_name": "danielbairamian/RL-RL", "max_stars_repo_head_hexsha": "4fc4ac14bd10088e83e7a15c3319370f74d0a756", "max_stars_repo_licenses": ["MIT"... |
import cv2 as cv
import numpy as np
def read():
return cv.imread("images/bolt.jpg")
| {"hexsha": "1386ea9f5c3c4b3cf6776f105e777a65e6ae61f6", "size": 89, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/test_camera.py", "max_stars_repo_name": "karagenit/material-sorter", "max_stars_repo_head_hexsha": "9bd195ab1fcfb72cb41e3ad3d35cf7b8f55bafe3", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import rospy
import bisect
import numpy as np
import time
import logging
import math
from time_msg_container import *
from plan_scoring import *
# Messages
from geometry_msgs.msg import Twist, PoseStamped, Point, Quaternion
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Path, Odometry
from move_base_m... | {"hexsha": "c1200d0e294ac9def4718b3efe4e077eb288e617", "size": 7040, "ext": "py", "lang": "Python", "max_stars_repo_path": "planner_comparison/python/planner_comparison/plan_scoring.py", "max_stars_repo_name": "IhabMohamed/deep_motion_planning", "max_stars_repo_head_hexsha": "6512f651bafbb56710ddbae501a5b4c22d56ac66", ... |
function solve()
n, a, b = [parse(Int, x) for x in split(readline())]
n - a + b
end
println(solve())
| {"hexsha": "1361fb38b1b7de81cff696041844c8a6f57b4079", "size": 110, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "abc171-180/abc180/a.jl", "max_stars_repo_name": "aishikawa/atcoder-julia", "max_stars_repo_head_hexsha": "93339ea6dd954b0739b3895a5625f94433e33baf", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
### A Pluto.jl notebook ###
# v0.15.1
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
loc... | {"hexsha": "f0db2d1922c610c433d56609e77a7044b998b223", "size": 6592, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/fluxExamples.jl", "max_stars_repo_name": "aniketjivani/variational-autoencoders", "max_stars_repo_head_hexsha": "bba54d571b2c674e73dee3c018fdce5701d06940", "max_stars_repo_licenses": ["MI... |
C Copyright restrictions apply - see stsdas$copyright.stsdas
C
SUBROUTINE GEOPOS(LATI,LONGI,MLAT,MLONG,ISTAT)
*
* Module number:
*
* Module name: GEOPOS
*
* Keyphrase:
* ----------
* Calculate geomagnetic position from geographic position
*
* Description:
* ------------
* This routine calcu... | {"hexsha": "a41d0e0269ffba7cb3122989ceb3c9ce27964052", "size": 2827, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "stsdas/pkg/hst_calib/stpoa/poa_fos/poa_calfos/geopos.f", "max_stars_repo_name": "iraf-community/stsdas", "max_stars_repo_head_hexsha": "043c173fd5497c18c2b1bfe8bcff65180bca3996", "max_stars_repo_l... |
"""
Script to compare different approaches to selecting Q-values according to integer action indices.
Compares two approaches:
1. Create a new one-hot encoding, apply the element-wise product, and reduce
2. Use torch.gather()
"""
import time
from numpy import dtype
import torch
import torch.nn as nn
class Stopwatch:... | {"hexsha": "ca74e23b38f761beb152521981049286b092c722", "size": 2652, "ext": "py", "lang": "Python", "max_stars_repo_path": "junk/torch_benchmarks.py", "max_stars_repo_name": "oliehoek-research/interactive_agents", "max_stars_repo_head_hexsha": "fddf99fed8e6aaf213c658897c2e232fe5323053", "max_stars_repo_licenses": ["MIT... |
# coding=utf-8
import tensorflow as tf
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import cv2
import forward
import backward
import file
Z1_PREDICT_PATH = '.\\predict\\Z1\\Z1-'
def restore_model(time):
with tf.Graph().as_default() as tg:
x = tf.placeholder(tf.float32, [None,... | {"hexsha": "4b1e5739504bcd79aa9a55bb7e392b7013e74792", "size": 1669, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "DolorHunter/model_build_B", "max_stars_repo_head_hexsha": "7b189ebe4a95c328f13bde458fb245d3c5a37830", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
# Databricks notebook source
# MAGIC %md ** Credit Card Fraud Detection ** : Supervised Machine Learning model is built to classify whether the transaction is fraud or not.<br>
# MAGIC ** Dataset Source ** : The dataset used in this experiment has been downloaded from kaggle. The input dataset contains 3075 rows and 1... | {"hexsha": "4cb7e9284faf812375ba1a942f8ee4c8cdf017d0", "size": 5163, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/Users/shamir.alavi@cra-arc.gc.ca/Training.py", "max_stars_repo_name": "dg1223/ML-pipeline", "max_stars_repo_head_hexsha": "b421fd8dddb695689ffe6dcf58c7640625066074", "max_stars_repo_lice... |
epsilon=1e-9
from math import *
from alpha_zero.player.player_inherit_from import Player
import random
class Node:
""" A node in the game tree. Note wins is always from the viewpoint of playerJustMoved.
Crashes if state not specified.
"""
def __init__(self, move=None, parent=None, state=None):
... | {"hexsha": "6b89e609d7ab0ab2c38eaa38cf10da7994713dfd", "size": 7721, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/alpha_zero/player/mcts.py", "max_stars_repo_name": "theputernerd/alpha-zero-theputernerd", "max_stars_repo_head_hexsha": "7d0defbdbd4824a6104f8a889ca4c24d330c4ed2", "max_stars_repo_licenses": ... |
PIPaginationLinks <- function(first = NULL, previous = NULL, last = NULL) {
if (is.null(first) == FALSE) {
if (is.character(first) == FALSE) {
return (print(paste0("Error: first must be a string.")))
}
}
if (is.null(previous) == FALSE) {
if (is.character(previous) == FALSE) {
return (print(paste0("Error:... | {"hexsha": "cc5e2c1f3036053ca2bd3bce856cb1a70cb937fd", "size": 678, "ext": "r", "lang": "R", "max_stars_repo_path": "R/PIPaginationLinks.r", "max_stars_repo_name": "frbl/PI-Web-API-Client-R", "max_stars_repo_head_hexsha": "1e12907493053fe10c8b9feb229584b741d4ae2e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import logging
import re
import time
from typing import List, Optional, Tuple, Union
import numpy as np
import pandas as pd
try:
from tqdm import tqdm
except ImportError:
def tqdm(*args, **kwargs):
if args:
return args[0]
return kwargs.get("iterable", None)
from collections impo... | {"hexsha": "094a20343361b66630c9236717f89eac1e38f46c", "size": 11846, "ext": "py", "lang": "Python", "max_stars_repo_path": "blm_header/header_maker.py", "max_stars_repo_name": "loiccoyle/blm_header", "max_stars_repo_head_hexsha": "f3430ad5013bcd6f1918ca1ec1ef9b676335c885", "max_stars_repo_licenses": ["MIT"], "max_star... |
\documentclass[12pt, titlepage]{article}
\usepackage{booktabs}
\usepackage{tabularx}
\usepackage{hyperref}
\usepackage{verbatim}
\usepackage{fancyhdr}
\usepackage{graphicx}
\pagestyle{fancy}
\hypersetup{
colorlinks,
citecolor=black,
filecolor=blue,
linkcolor=red,
urlcolor=blue
}
\usepackage[round]... | {"hexsha": "802178fcf06440084ff029cf42ffa5c4ee3680f3", "size": 22268, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/TestPlan/TestPlan.tex", "max_stars_repo_name": "curiouskunal/Ultimate_TicTacToe", "max_stars_repo_head_hexsha": "e77381d526e4f2e8ef325238d3e73419d7ed35ea", "max_stars_repo_licenses": ["MIT"], "... |
# Copyright 2017 Martin Haesemeyer. All rights reserved.
#
# Licensed under the MIT license
"""
Script to create movie frames of network activations upon temperature stimulation and behavior generation
"""
import sys
import numpy as np
import h5py
import matplotlib as mpl
import matplotlib.pyplot as pl
import tkint... | {"hexsha": "e0b08a249bd160f91a206348dca94120a01c3a74", "size": 2737, "ext": "py", "lang": "Python", "max_stars_repo_path": "activationMovie.py", "max_stars_repo_name": "haesemeyer/GradientPrediction", "max_stars_repo_head_hexsha": "679b48768ad74dccd58f8c2f434ad60036fc5cb7", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma lift_add : "insertion (f::nat\<Rightarrow>real) (liftPoly 0 z (a + b)) = insertion f (liftPoly 0 z a + liftPoly 0 z b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. insertion f (liftPoly 0 z (a + b)) = insertion f (liftPoly 0 z a + liftPoly 0 z b)
[PROOF STEP]
using liftPoly_add[of 0 z a b]
[PRO... | {"llama_tokens": 250, "file": "Virtual_Substitution_Debruijn", "length": 2} |
import os
import sys
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import cv2
from skimage import io
from skimage.transform import resize
sys.path.append('../')
from model.models import CRNet
from config.cfg import cfg
def prepare_data(model):
"""
prepare training and test set
... | {"hexsha": "8dc75af8a824cfa892c3a79ad1dfc88508012d4c", "size": 3579, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/fer.py", "max_stars_repo_name": "lucasxlu/CRNet", "max_stars_repo_head_hexsha": "17d27e39a77181921cc2bd5a5a8866a25282b4de", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_sta... |
import sys
import mwapi
import toolforge
import pandas as pd
import pymysql
import numpy as np
import argparse
from urllib.parse import unquote
import utils.db_access as db_acc
import constants
pymysql.converters.encoders[np.int64] = pymysql.converters.escape_int
pymysql.converters.conversions = pymysql.converters.enc... | {"hexsha": "32f01865cdd781d826ce9369782f7459daf4321b", "size": 18622, "ext": "py", "lang": "Python", "max_stars_repo_path": "fetch_content.py", "max_stars_repo_name": "wikimedia/abstract-wikipedia-data-science", "max_stars_repo_head_hexsha": "e71cee92f3c8273749b747a155f802e62a425d0a", "max_stars_repo_licenses": ["MIT"]... |
// -*- C++ -*-
//
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
//
// Jiao Lin
// California Institute of Technology
// (C) 2007 All Rights Reserved
//
// {LicenseText}
//
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... | {"hexsha": "1452919b77b36469c5c96f2832b06e4eb80ef854", "size": 894, "ext": "cc", "lang": "C++", "max_stars_repo_path": "packages/mccomponents/mccomponentsbpmodule/wrap_IsotropicKernel.cc", "max_stars_repo_name": "mcvine/mcvine", "max_stars_repo_head_hexsha": "42232534b0c6af729628009bed165cd7d833789d", "max_stars_repo_l... |
#=
ported from:
COTD Entry submitted by John W. Ratcliff [jratcliff@verant.com]
THIS IS A CODE SNIPPET WHICH WILL EFFICIEINTLY TRIANGULATE ANY
POLYGON/CONTOUR (without holes) AS A STATIC CLASS. THIS SNIPPET
IS COMPRISED OF 3 FILES, TRIANGULATE.H, THE HEADER FILE FOR THE
TRIANGULATE BASE CLASS, TRIANGULATE.CPP, THE IM... | {"hexsha": "866076c452d6d957651485835d58e9f147729d5c", "size": 5132, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/polygons.jl", "max_stars_repo_name": "JuliaPackageMirrors/GeometryTypes.jl", "max_stars_repo_head_hexsha": "705e5a646dd2177bbb4b9f8c26b52bc832b38d65", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Getting predictions from a deployed resnet-18 model
import json
import requests
import numpy as np
from pathlib import Path
import typer
import decord
from mlserve.common.logger import logger
from mlserve.common.misc import stopwatch
app = typer.Typer(name="Deployment tests", add_completion=False)
def load_video(... | {"hexsha": "338b6f5d24816138874af197a38e067ab1a885a0", "size": 1551, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlserve/local_serve/client.py", "max_stars_repo_name": "svats2k/mlserve", "max_stars_repo_head_hexsha": "3f93e3db872612fc6222cb585b425304d9429e45", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma erf_minus [simp]: "erf (-z) = - erf z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. erf (- z) = - erf z
[PROOF STEP]
unfolding erf_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<Sum>n. of_real (erf_coeffs n) * (- z) ^ n) = - (\<Sum>n. of_real (erf_coeffs n) * z ^ n)
[PROOF STEP]
by (sub... | {"llama_tokens": 190, "file": "Error_Function_Error_Function", "length": 2} |
# -*- coding: utf-8 -*-
# /usr/bin/python3.9
# import cv2
# import elasticdeform
import numpy as np
from skimage.exposure import rescale_intensity
class Background:
"""Background definition"""
def __init__(
self,
img_size,
perlin_noise_level,
poisson_noise_level,
perl... | {"hexsha": "c4f7af17b31798233eee70ed35ebc5e11e1248b2", "size": 3470, "ext": "py", "lang": "Python", "max_stars_repo_path": "spheroid_simulator/background.py", "max_stars_repo_name": "ebouilhol/neuron_simulator", "max_stars_repo_head_hexsha": "bcaca4710d3f9b423ecd5253f9a67bd468b1af87", "max_stars_repo_licenses": ["MIT"]... |
[STATEMENT]
lemma approx_HComplex:
"\<lbrakk>a \<approx> b; c \<approx> d\<rbrakk> \<Longrightarrow> HComplex a c \<approx> HComplex b d"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>a \<approx> b; c \<approx> d\<rbrakk> \<Longrightarrow> HComplex a c \<approx> HComplex b d
[PROOF STEP]
unfolding approx... | {"llama_tokens": 229, "file": null, "length": 2} |
[STATEMENT]
lemma headconst_zero:
fixes p::"'a::zero poly"
shows "isnpolyh p n0 \<Longrightarrow> headconst p = 0 \<longleftrightarrow> p = 0\<^sub>p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. isnpolyh p n0 \<Longrightarrow> (headconst p = (0::'a)) = (p = 0\<^sub>p)
[PROOF STEP]
by (induct p arbitrary: n0 ... | {"llama_tokens": 140, "file": "Taylor_Models_Polynomial_Expression", "length": 1} |
#include <cstdlib>
#include <string>
#include <boost/filesystem.hpp>
#include <ros/ros.h>
#include <rosbag/recorder.h>
namespace fs = boost::filesystem;
using namespace std::string_literals;
int main(int argc, char** argv) {
ros::init(argc, argv, "record_snapshot");
rosbag::RecorderOptions opts;
const char* h... | {"hexsha": "351631a9055322c15cda5f4a4dd5a885ad9309b6", "size": 1519, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/kitcar_rosbag/src/record_snapshot/record_snapshot.cpp", "max_stars_repo_name": "KITcar-Team/kitcar-rosbag", "max_stars_repo_head_hexsha": "5fcbdcab16765862802e4c4cb69ed71deceea4f3", "max_stars_r... |
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