text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import os
from glob import glob
import h5py
import numpy as np
import torch
from skimage import draw
from scipy.ndimage import gaussian_filter
import elf
import nifty
from threading import Thread
from affogato.segmentation import compute_mws_segmentation
from utils.affinities import get_naive_affinities, get_edge_fe... | {"hexsha": "91c025503c2ffb0c8fc768dee03e9547856123fb", "size": 20150, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/leptin_data.py", "max_stars_repo_name": "edosedgar/RLForSeg", "max_stars_repo_head_hexsha": "fc748d8e7d2f2a1e7ac0dddb3f268ec3025d40ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 6 01:36:12 2019
Last updated on Aug 7 2019
@author: Shengjie Liu
@Email: liushengjie0756@gmail.com
"""
import numpy as np
from scipy import stats
import rscls
import matplotlib.pyplot as plt
import time
import networks as nw
from keras.utils import to_cat... | {"hexsha": "4727dba02ba74e1f7034d937c324e3d58f287445", "size": 5776, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo_keras.py", "max_stars_repo_name": "chemaMR/Remote-Sensing-Image-Classification", "max_stars_repo_head_hexsha": "ddc5d7e17c1bb8ecf6f0c8982115327be3f0dbbe", "max_stars_repo_licenses": ["MIT"], ... |
import abc
import numpy as np
import pandas as pd
from reinvent_chemistry.conversions import Conversions
from reinvent_scoring.scoring.diversity_filters.curriculum_learning import DiversityFilterParameters, \
DiversityFilterMemory
from reinvent_scoring.scoring.diversity_filters.curriculum_learning.loggable_data_d... | {"hexsha": "91942532b0eaeb87ed80dcfeb1849e5a69722131", "size": 2614, "ext": "py", "lang": "Python", "max_stars_repo_path": "reinvent_scoring/scoring/diversity_filters/curriculum_learning/base_diversity_filter.py", "max_stars_repo_name": "MolecularAI/reinvent-scoring", "max_stars_repo_head_hexsha": "f7e052ceeffd29e17e16... |
"""
Alignment plans for the HXRSnD
"""
import logging
import numpy as np
from lmfit.models import LorentzianModel
from bluesky import Msg
from bluesky.plans import scan, list_scan
from bluesky.utils import short_uid as _short_uid
from bluesky.plan_stubs import abs_set, checkpoint, trigger_and_read
from bluesky.preproc... | {"hexsha": "6e0dcc98500d3c8e6c90ba452c5716baeef4eb5d", "size": 8997, "ext": "py", "lang": "Python", "max_stars_repo_path": "hxrsnd/plans/alignment.py", "max_stars_repo_name": "klauer/hxrsnd", "max_stars_repo_head_hexsha": "aa78f3cdbdd59cd4b3aa7fc72d066158364b5ee2", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], "max... |
/*! \file
\brief An entry.
Copyright (C) 2019-2022 kaoru https://www.tetengo.org/
*/
#include <any>
#include <memory>
#include <string>
#include <string_view>
#include <utility>
#include <boost/preprocessor.hpp>
#include <boost/scope_exit.hpp>
#include <boost/test/unit_test.hpp>
#include <t... | {"hexsha": "140c1d131c326efdb63f87c21554fd5c05b734f3", "size": 8679, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "library/lattice/test/src/test_tetengo.lattice.entry.cpp", "max_stars_repo_name": "tetengo/tetengo", "max_stars_repo_head_hexsha": "66e0d03635583c25be4320171f3cc1e7f40a56e6", "max_stars_repo_licenses... |
function [tissueModel,coreRxnBool,coreMetBool,coreCtrsBool] = fastcore(model, coreRxnInd, epsilon, printLevel)
% Use the FASTCORE algorithm ('Vlassis et al, 2014') to extract a context
% specific model. FASTCORE algorithm defines one set of core
% reactions that is guaranteed to be active in the extracted model and fin... | {"author": "opencobra", "repo": "cobratoolbox", "sha": "e60274d127f65d518535fd0814d20c53dc530f73", "save_path": "github-repos/MATLAB/opencobra-cobratoolbox", "path": "github-repos/MATLAB/opencobra-cobratoolbox/cobratoolbox-e60274d127f65d518535fd0814d20c53dc530f73/src/dataIntegration/transcriptomics/FASTCORE/fastcore.m"... |
__author__ = "Nestor Bermudez"
__license__ = "MIT"
__version__ = "1.0.0"
__email__ = "nab6@illinois.edu"
__status__ = "Development"
import numpy as np
import pandas as pd
import tensorflow as tf
flags = tf.flags
FLAGS = flags.FLAGS
flags.DEFINE_string("data_dir", "./data", "Root of input data")
flags.DEFINE_string... | {"hexsha": "b7566981b51ba1623eed2bdae98b9b61971358d5", "size": 4444, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/preprocess_homologs.py", "max_stars_repo_name": "nbermudezs/HoGEm", "max_stars_repo_head_hexsha": "a8f04e88b4ca9c1667502d408c2177a4466be344", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# pylint: disable=missing-docstring, invalid-name, import-error
import numpy as np
import pandas as pd
from mltils.preprocessing.encoders import InfrequentValueEncoder
from mltils.utils.test_utils import _test_immutability
def test_infrequent_value_encoder_1():
ive = InfrequentValueEncoder()
assert ive is no... | {"hexsha": "ef79d564fd77d03b91ef64900254490bf9c0882b", "size": 2476, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/mltils/test_infrequent_value_encoder.py", "max_stars_repo_name": "rladeira/mltils", "max_stars_repo_head_hexsha": "ed9c9f1e4f2eb0bb4c4457df82d5c28058223bfd", "max_stars_repo_licenses": ["MIT... |
/*!
@file
Defines `boost::hana::extend`.
@copyright Louis Dionne 2015
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
#ifndef BOOST_HANA_EXTEND_HPP
#define BOOST_HANA_EXTEND_HPP
#include <boost/hana/fwd/extend.hpp>
#includ... | {"hexsha": "61251ed0b71b4f9d5ebab114622a6bdb86935982", "size": 1463, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/hana/extend.hpp", "max_stars_repo_name": "qicosmos/hana", "max_stars_repo_head_hexsha": "b0f8cf2bf19d491b7b739dcb7b8d7497b0e5829f", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_c... |
import numpy as np
ndarry = np.array([[35, 20, 66], [23, 67, 89], [13, 244, 67]], np.int32)
print(ndarry.shape, ndarry.size)
print(ndarry.dtype)
print(ndarry[1:2, 1:2])
sdarry = np.array([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]
])
print(sdarry[1, 2])
print(sdarry[:, 2])
print(sdarry[2, :])
print(s... | {"hexsha": "943830220a32cf7705f90c40ccceba6968eaa0bf", "size": 404, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpy/nparray.py", "max_stars_repo_name": "cloudgc/data-statistics", "max_stars_repo_head_hexsha": "b5c0f60fd6caccf9a2735602be864b4d537d8fb5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
\section{Honours and Awards}
\begin{tabular}{rll}
2022 & Certificate of Distinction (top 25\%) & Canadian Computing Competition Senior \\
2021 & Certificate of Distinction (top 25\%) & Canadian Computing Competition Junior \\
2021 & & Google Code Jam Round 1 Qualifier \\
2021 & Certificate of Excellence (top 10\... | {"hexsha": "654460a94bb27a351fc4a3fa70b39987b59a58b2", "size": 541, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/awards.tex", "max_stars_repo_name": "isobarbaric/my-resume", "max_stars_repo_head_hexsha": "b7c616e4114f1ade5aa65517b69df75c8b77c687", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
program main
use mpi
DOUBLE PRECISION f(10,2)
integer code,np,world,ierr
do i=1,10
f(i,1)=1
f(i,2)=2
enddo
call mpi_init(ierr)
call mpi_comm_dup(mpi_comm_world,world,ierr)
call mpi_comm_rank(world,code,ierr)
call mpi_comm_size(world,np,ierr)
do i=1,3
call update()
if(code.eq.0)then
print*,'global f',f
endif
endd... | {"hexsha": "579e997dee40faee56f9a34e4f49cd5943535fc0", "size": 1300, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Model5/yinheqing1.f90", "max_stars_repo_name": "CaptainYin/ParLTRANS", "max_stars_repo_head_hexsha": "760734f058589ab464897dcf01f3f5528803d43b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
using StatsPlots
using CSV
using DataFrames
jt = CSV.read(joinpath(@__DIR__, "julia_num_apertures.csv")) |> DataFrame
pt = CSV.read(joinpath(@__DIR__, "python_num_apertures.csv")) |> DataFrame
jt_ell = CSV.read(joinpath(@__DIR__, "julia_num_apertures-ellipse.csv")) |> DataFrame
pt_ell = CSV.read(joinpath(@__DIR__, "p... | {"hexsha": "8f94fb435c3dcc821d3e5ac27ee69d6a7fd5ad02", "size": 1484, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "bench/num_apertures/plotting.jl", "max_stars_repo_name": "mileslucas/Photometry.jl", "max_stars_repo_head_hexsha": "fa5e225266db1eb3df92ff77082a857e41515cb0", "max_stars_repo_licenses": ["MIT"], "m... |
'''
Urban-PLUMBER processing code
Associated with the manuscript: Harmonized, gap-filled dataset from 20 urban flux tower sites
Maps developed based on:
Hrisko, J. (2020). Geographic Visualizations in Python with Cartopy. Maker Portal.
https://makersportal.com/blog/2020/4/24/geographic-visualizations-in-python-with-... | {"hexsha": "6bbe27d0c3c1326baaf35ce648fe06a7d7a80050", "size": 14948, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_sitemaps.py", "max_stars_repo_name": "matlipson/urban-plumber_pipeline", "max_stars_repo_head_hexsha": "d4ab7ef3942d502b422041a364be5eddd56e301b", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import array
import typing
import numpy as np
import pandas as pd
import six
import logging
from flask import json
log = logging.getLogger(__name__)
class Metadata:
STATUS = "status"
QUERY = "query"
MESSAGE = "message"
DATA_TYPE = "type"
EXECUTION_TIME = "time"
class Error:
CODE = "code"
... | {"hexsha": "8ec448afb4491c648b57a2503c4b9627c315b521", "size": 4345, "ext": "py", "lang": "Python", "max_stars_repo_path": "libraries/unified-model/unified_model/server/response_format.py", "max_stars_repo_name": "felixridinger/machine-learning-lab", "max_stars_repo_head_hexsha": "410e2f5fecb7ea91dcec12a5b9cb9161331191... |
"""
The two primary classes this code uses are bfio.BioReader and bfio.BioWriter:
bfio.BioReader will read any image that the Bioformats tool can read.
bfio.BioWriter will only save images as an ome tiled tiff.
Example usage is provided in the comments to each class.
Required packages:
javabridge (also requires jdk >... | {"hexsha": "584b0e28e80173a0a453a482277f34bf87adc8cf", "size": 77720, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/polus-bfio-util/bfio/bfio.py", "max_stars_repo_name": "blowekamp/polus-plugins", "max_stars_repo_head_hexsha": "87f9c36647b4cf95cf107cfede3a5a1d749415a5", "max_stars_repo_licenses": ["MIT"]... |
import os
import numpy as np
import cv2
i=0
for root, dirs, file in os.walk("Published_database_FV-USM_Dec2013/2nd_session/extractedvein/"):
for dir in dirs:
for file in os.walk(root+dir):
for each in file[2]:
print(root+dir+each)
x = cv2.imread(root+dir+"/"+e... | {"hexsha": "e3445a808b807df2dd3b76c82cd35cdd18cb20ab", "size": 501, "ext": "py", "lang": "Python", "max_stars_repo_path": "deliverable/data/regroup_db.py", "max_stars_repo_name": "samdubuis/ma-semester-project", "max_stars_repo_head_hexsha": "058e7be9782727cb4428772f8384322686e82000", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python3
import asyncio
import json
import keras.preprocessing
import numpy as np
import re
import spacy
import sys
import tensorflow as tf
from pathlib import Path
from sklearn.feature_extraction.text import TfidfVectorizer
from spacy import displacy
from spacy.matcher import Matcher
from textblob import... | {"hexsha": "59a19f905e09c0155f9063180efbef3584b87c9d", "size": 5472, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml/processtext.py", "max_stars_repo_name": "QEDK/clarity", "max_stars_repo_head_hexsha": "cca58cce33e273b77190de50a5bdde3f5a199c4c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 4... |
import ipywidgets as widgets
from ipywidgets import Button, HBox, VBox, Layout, Label
import lmfit
import numpy as np
import functools
def layers2t(layers):
t = []
for key in layers.keys():
t.append(layers[key].value)
return t
def build_gui_layer_v2(name, guess, nk):
cb = widgets.Checkbox(
... | {"hexsha": "31f70c74726da71ff2c17d34d80837dcbc6e61c1", "size": 7798, "ext": "py", "lang": "Python", "max_stars_repo_path": "tes_optical_stack/jupyter_gui.py", "max_stars_repo_name": "saewoonam/tes_optical_stack", "max_stars_repo_head_hexsha": "975361064702b9e294c4815ee84bb51c7d6ccc4c", "max_stars_repo_licenses": ["MIT"... |
"""Tests for GenomeCorrelation."""
import unittest
from microbepy.common import constants as cn
from microbepy.common import helpers
from microbepy.common import util
from microbepy.correlation import genome_correlation as gc
from microbepy.correlation.genome_correlation import GenomeCorrelation
import numpy as np
i... | {"hexsha": "71c2d49bdd2f018a40447a8b0f166fa489d66245", "size": 5832, "ext": "py", "lang": "Python", "max_stars_repo_path": "microbepy/tests/correlation/test_genome_correlation.py", "max_stars_repo_name": "ScienceStacks/MicrobEPy", "max_stars_repo_head_hexsha": "704435e66c58677bab24f27820458870092924e2", "max_stars_repo... |
import os
import re
import time
from tqdm import tqdm
import argparse
import numpy as np
import tensorflow as tf
import imageio
from model import PWCDCNet
from flow_utils import vis_flow_pyramid
def factor_crop(image, factor = 64):
assert image.ndim == 3
h, w, _ = image.shape
image = image[:factor*(h//fac... | {"hexsha": "344719358b2a9dde62bde2741e940e4e33466fe1", "size": 3087, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "daigo0927/pwcnet", "max_stars_repo_head_hexsha": "d0b749967940b8fac2ccf82502c3fe7923c3afab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 49, "max_stars... |
from __future__ import division, print_function
import argparse
import math
import os
import sys
from timeit import Timer
from collections import defaultdict
import pkg_resources
from PIL import Image
import cv2
from tqdm import tqdm
import numpy as np
import pandas as pd
import torchvision.transforms.functional as to... | {"hexsha": "c7a9894fdbbdc4d750a682753b2124c35f644c58", "size": 9789, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmark/benchmark.py", "max_stars_repo_name": "daisukelab/albumentations", "max_stars_repo_head_hexsha": "f5e9f4d46e7abaddfc137c7f697b4a5a98af5fb7", "max_stars_repo_licenses": ["MIT"], "max_star... |
from numpy import percentile
from a2e.datasets.bearing import load_data
from a2e.experiment import Experiment
from a2e.processing.stats import mad
from a2e.utility import z_score as compute_z_score, compute_classification_metrics, compute_roc
config = {
# See https://medias.schaeffler.us/en/product/rotary/rolling-... | {"hexsha": "5097d59f0d49e49390a4fd6d465f37b3ecd1274c", "size": 5894, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/benchmark/frequency_analysis.py", "max_stars_repo_name": "maechler/a2e", "max_stars_repo_head_hexsha": "c28f546ca5fc3fdb9c740ea5f0f85d2aca044a00", "max_stars_repo_licenses": ["MIT"], "... |
[STATEMENT]
theorem T_monotonic:
"MonotProblem TE_wtFsym wtPsym TE_arOf TE_resOf parOf tPB"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. MonotProblem TE_wtFsym wtPsym TE_arOf TE_resOf parOf tPB
[PROOF STEP]
.. | {"llama_tokens": 99, "file": "Sort_Encodings_T", "length": 1} |
#!venv/bin/python3
'''
Author: Massimo Clementi
Date: 2021-04-04
Display the 4D IRIS dataset and train a simple MLP on it
'''
# %% Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import urllib.request
# %% Download, sho... | {"hexsha": "dd78168bdbaa1dea0a8e4c2a5f72da2942077cad", "size": 3260, "ext": "py", "lang": "Python", "max_stars_repo_path": "python_scripts/1_IRIS_dataset_and_MLP.py", "max_stars_repo_name": "alezuech/tf-neural-networks", "max_stars_repo_head_hexsha": "f601043de7e9a8789923189e2b1eafe62f00abdd", "max_stars_repo_licenses"... |
#!/usr/bin/env python
import numpy as np
import cv2
import itertools
class SegmentationFunctions():
def __init__(self, mask_values=[12]):
self.mask_values=mask_values
def overlay_mask(self, image, mask, mask_color=[255,0,0], alpha=0.5):
orig_image_size=image.shape
# reshape image for ... | {"hexsha": "6ea19e6e7b71f4bf3c6b1e4bc3695aa9c32ab9de", "size": 1968, "ext": "py", "lang": "Python", "max_stars_repo_path": "rr_mxnet/scripts/mxnet_segmentation_custom_functions.py", "max_stars_repo_name": "DavidFernandezChaves/ViMantic-Client", "max_stars_repo_head_hexsha": "cb28b459724b24a8acb1ed09387f7921960344bd", "... |
# -*- coding: utf-8 -*-
r"""Data handling
"""
import copy
import numbers
import warnings
import numpy as np
from collections import OrderedDict
from multiprocessing import cpu_count, Pool # @UnresolvedImport
from .analysis import Analysis
from .plot import PlotData
def _call_bin_parallel(arg, **kwarg):
r"""Wra... | {"hexsha": "15a4c125114f53f51bcb566e43835dfb128f246c", "size": 21927, "ext": "py", "lang": "Python", "max_stars_repo_path": "neutronpy/data/data.py", "max_stars_repo_name": "neutronpy/neutronpy", "max_stars_repo_head_hexsha": "44ca74a0bef25c03397a77aafb359bb257de1fe6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Python 3.7.6
# -*- coding: utf-8 -*-
# Author: Ines Pisetta
import os
import torch
from torch import nn
import numpy as np
#from fast_ctc_decode import beam_search, viterbi_search
#import tensorflow as tf
char_list = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
# len_char_list = 62
... | {"hexsha": "b3fee49dd621d29c4a5b22288217f692671eb77d", "size": 10969, "ext": "py", "lang": "Python", "max_stars_repo_path": "network.py", "max_stars_repo_name": "inlpi/ocr_crnn", "max_stars_repo_head_hexsha": "ddae200d00ecdbfc5d21d4ea7acc02026a4aadf0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
C @(#)putchr_8.f 20.1 1/4/99
subroutine putchr_8 (n, b, r8)
character b*(*), format*4
double precision r8
C * PUTCHR_8 MOVES CHARACTER DATA INTO REAL * 8 STORAGE
C * CURRENTLY USES READ/WRITE TO SIMULATE ENCODE
C * NOTE THAT VARIABLES ARE SWITCHED FROM ENCODE ORDER
C
C ... | {"hexsha": "1d987a87c513b801f6cfdfe5d3fd9a83f14068cc", "size": 601, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/putchr_8.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, ... |
[STATEMENT]
lemma bound_main_lemma_charles:
fixes PROB :: "'a problem"
assumes "finite PROB"
shows "problem_plan_bound_charles PROB \<le> 2 ^ (card (prob_dom PROB)) - 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. problem_plan_bound_charles PROB \<le> 2 ^ card (prob_dom PROB) - 1
[PROOF STEP]
proof -
[PROOF... | {"llama_tokens": 1663, "file": "Factored_Transition_System_Bounding_TopologicalProps", "length": 17} |
/*
MIT License
Copyright (c) 2020 7Mersenne
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, modify, merge, publish, dist... | {"hexsha": "13caa1d4d73e7210a0cc24ea4142ebef5e616028", "size": 3676, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "AIDataGen.cpp", "max_stars_repo_name": "sikkey/AIDataGenerator", "max_stars_repo_head_hexsha": "dc10ac7424258ed0d282442ec63148154075dfd3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
# coding: utf-8
# # Nengo Example: Many neurons
#
# This demo shows how to construct and manipulate a population of neurons.
#
# These are 100 leaky integrate-and-fire (LIF) neurons. The neuron tuning properties have been randomly selected.
#
# The input is a sine wave to show the effects of increasing or decreasi... | {"hexsha": "ef9f2b43decb055c0b3e248f679000581bd72235", "size": 2717, "ext": "py", "lang": "Python", "max_stars_repo_path": "mul_neuron.py", "max_stars_repo_name": "harshkothari410/snn-image-segmentation", "max_stars_repo_head_hexsha": "18fb28e8b2fee3d7583f6e62fd512ba90863c0ee", "max_stars_repo_licenses": ["MIT"], "max_... |
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: binbinzhang@mobvoi.com (Binbin Zhang)
import argparse
import logging
import random
import sys
import codecs
import math
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import Dataset, DataLoader
from PIL impo... | {"hexsha": "d9e7a9a3b0e2b53751c54d2257acb867c3ac3cdf", "size": 13514, "ext": "py", "lang": "Python", "max_stars_repo_path": "wenet/dataset/dataset.py", "max_stars_repo_name": "glynpu/wenet", "max_stars_repo_head_hexsha": "a3c9bbba96a9e57d85c0aa6a2b5bff96fd0e48f7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
#coding:utf-8
'''
Created on 2018年9月6日
@author: xyj
'''
from __future__ import division
import pandas as pd
import numpy as np
import xgboost as xgb
import LoadData
from sklearn.model_selection import KFold
def train():
# 将已经生成的DataFrame数据读取出来
df = LoadData.readDataSet()
df_label = df['label']
# df_features = ... | {"hexsha": "fb67417889a1ea7c930067b38b1b879b194516b1", "size": 2281, "ext": "py", "lang": "Python", "max_stars_repo_path": "sklearn/Xgboost.py", "max_stars_repo_name": "xyj77/Machine-Learning-Record", "max_stars_repo_head_hexsha": "5b25f630db7fb95a4b7c6c8993b6e97efeaa8de7", "max_stars_repo_licenses": ["MIT"], "max_star... |
from PIL import Image
import numpy as np
from torch.tensor import Tensor
def list_to_tensor(image_list):
for i, img in enumerate(image_list):
image_list[i] = Image.fromarray(img).resize([220,155])
X_arr = np.stack(image_list,axis=0)
X_arr = X_arr / 255.0
return Tensor(X_arr).view(len(image_lis... | {"hexsha": "ac100469857776742162425dc0a941d2a36f809f", "size": 346, "ext": "py", "lang": "Python", "max_stars_repo_path": "Preprocessing.py", "max_stars_repo_name": "khizar-anjum/signature_extraction", "max_stars_repo_head_hexsha": "5740408cb0a30895e59c33b624da43943d824ccb", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma convert_eval: "peval P a = ppeval (convert P) a v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. peval P a = ppeval (convert P) a v
[PROOF STEP]
(* implicit for all v *)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. peval P a = ppeval (convert P) a v
[PROOF STEP]
by (induction P, auto) | {"llama_tokens": 134, "file": "DPRM_Theorem_Diophantine_Parametric_Polynomials", "length": 2} |
# AUTOGENERATED! DO NOT EDIT! File to edit: 02_figures.ipynb (unless otherwise specified).
__all__ = ['new_x_y', 'add_trace', 'get_figure']
# Cell
import plotly.graph_objects as go
import numpy as np
from scipy.interpolate import interp1d
import pandas as pd
from pathlib2 import Path
import datetime
# Cell
def new_x... | {"hexsha": "85322ec2b6286d997f951fb531a969d162d74cc9", "size": 1612, "ext": "py", "lang": "Python", "max_stars_repo_path": "ds18b20/figures.py", "max_stars_repo_name": "eandreas/ds18b20", "max_stars_repo_head_hexsha": "eac5da4d26504a3b8f9472955ec7fe38b104e732", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import serial
import serial.tools.list_ports
import numpy as np
import os,sys
import time
import matplotlib.pyplot as plt
pwd = os.path.abspath(os.path.abspath(__file__))
father_path = os.path.abspath(os.path.dirname(pwd) + os.path.sep + "..")
sys.path.append(father_path)
data_path = os.path.abspath(
os.path.dirn... | {"hexsha": "0a92a8b41615070c53bb550c38483fd2ab6ada66", "size": 7793, "ext": "py", "lang": "Python", "max_stars_repo_path": "Sensors/Infrared_Sensor.py", "max_stars_repo_name": "Forence1999/SmartWalker-master", "max_stars_repo_head_hexsha": "ec153f5d50ddd43bd5be88209b66ca7178aef7cb", "max_stars_repo_licenses": ["MIT"], ... |
import os
import numpy as np
from tqdm import tqdm
import json
from collections import OrderedDict
class SimpleKPLoader(object):
def __init__(self, root, image_size, image_set='test', data_set='tusimple', norm=False):
self.image_set = image_set
self.data_set = data_set
self.root = root
... | {"hexsha": "cddbb704fdb31a4bea171bedb03c78a4f7ad3056", "size": 4803, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/curve_fitting_tools/loader.py", "max_stars_repo_name": "voldemortX/DeeplabV3_PyTorch1.3_Codebase", "max_stars_repo_head_hexsha": "d22d23e74800fafb58eeb61d6649008745c1a287", "max_stars_repo_l... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 2 10:22:25 2020
@author: shanjuyeh
"""
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold, train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.metric... | {"hexsha": "19a86b3c5f6deb73f28d6b7b795659f560152475", "size": 5721, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml-prediction/GPL570_XGB_age.py", "max_stars_repo_name": "Bin-Chen-Lab/covid19_sex", "max_stars_repo_head_hexsha": "b593ed877b4868278c4546280d05572d0d6addb9", "max_stars_repo_licenses": ["MIT"], "... |
#ifndef vef2vef_ogrpoly_hpp_included_
#define vef2vef_ogrpoly_hpp_included_
#include <string>
#include <vector>
#include <boost/optional.hpp>
#include <boost/filesystem/path.hpp>
#include "math/geometry.hpp"
#include "geo/srsdef.hpp"
using Polygon = math::Polygon;
using Polygons = math::MultiPolygon;
Polygons loa... | {"hexsha": "870e4d6aa552bef6792f5aed4c779e97619dfc0f", "size": 773, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "vef/tools/ogrpoly.hpp", "max_stars_repo_name": "Melown/libvef", "max_stars_repo_head_hexsha": "cb7df31d09a58d5ed4b894a4dc3bb66c6b8be825", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count... |
% Copyright 2011-2015 David Hadka. All Rights Reserved.
%
% This file is part of the MOEA Framework User Manual.
%
% Permission is granted to copy, distribute and/or modify this document under
% the terms of the GNU Free Documentation License, Version 1.3 or any later
% version published by the Free Software Fou... | {"hexsha": "b43b11acd2087fcaef0872338e7a60760a291026", "size": 16006, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "manual/installation.tex", "max_stars_repo_name": "BrunoGrisci/EngineeringDesignusingMultiObjectiveEvolutionaryAlgorithms", "max_stars_repo_head_hexsha": "6b15dfe67521249ef1747f52a1ef709401eee377", ... |
theory flash18Bra imports flash18Rev
begin
lemma onInv18:
assumes a1:"iInv1 \<le> N" and a2:"iInv2 \<le> N" and a3:"iInv1~=iInv2 " and
b1:"r \<in> rules N" and b2:"invf=inv18 iInv1 iInv2 "
shows "invHoldForRule' s invf r (invariants N)"
proof -
have c1:"ex1P N (% iRule1 . r=NI_Local_Ge... | {"author": "lyj238Gmail", "repo": "IsabelleCourse", "sha": "cd49d944d3504328ad8210fbd987abebdf192ed8", "save_path": "github-repos/isabelle/lyj238Gmail-IsabelleCourse", "path": "github-repos/isabelle/lyj238Gmail-IsabelleCourse/IsabelleCourse-cd49d944d3504328ad8210fbd987abebdf192ed8/flash/flash18Bra.thy"} |
"""Tests for normalization related utility functions."""
import numpy as np
from neurodsp.utils.norm import *
###################################################################################################
###################################################################################################
def te... | {"hexsha": "8aaf3bc2160f0edcf5e62eb7d770ab9119912b85", "size": 1191, "ext": "py", "lang": "Python", "max_stars_repo_path": "neurodsp/tests/test_utils_norm.py", "max_stars_repo_name": "josepfont65/neurodsp", "max_stars_repo_head_hexsha": "a7c5b72665eed6368e29bf4f15443a28a2e18732", "max_stars_repo_licenses": ["Apache-2.0... |
from scipy.io import loadmat
from MvKernelLapSRC import MvKernelLapSRC
from construct_features import *
from load_data import load_4mC_data
from measurement_tools import performance
def normalization(data, dim=2, V=1):
data_v = []
if dim == 2:
for i in range(np.shape(data)[1]):
... | {"hexsha": "ba8edf9c0bc572c9458e4bb513ab1fd752e90f02", "size": 7301, "ext": "py", "lang": "Python", "max_stars_repo_path": "MvLapKSRC_HSIC-master/main_crossSpecies.py", "max_stars_repo_name": "guofei-tju/MvLapKSRC_HSIC", "max_stars_repo_head_hexsha": "dc9e6d54b834cee5ef9a088f71eb506307672902", "max_stars_repo_licenses"... |
"""
Module containing the factor and belief classes.
"""
# License: BSD 3 clause
import numpy
class DiscreteFactor(object):
"""
A factor containing only discrete variables. Factors are immutable and basically a container for a probability table
and its metadata.
"""
def __init__(self, variables,... | {"hexsha": "e226003a682ba08c8af0af216f73ac5cbafe2c2d", "size": 8041, "ext": "py", "lang": "Python", "max_stars_repo_path": "Statistical_methods/LoopyBeliefPropagation/pyugm/factor.py", "max_stars_repo_name": "Ali-Sahili/Background-Subtraction-Unsupervised-Learning", "max_stars_repo_head_hexsha": "445b2cf8736a4a28cff2b0... |
"""
`smooth(y, span=5)`
Smooth a vector using a moving average filter. Endpoints are handled by
collapsing the length of the filter as showed below.
```
yy[1] = y[1]
yy[2] = (y[1] + y[2] + y[3]) / 3
yy[3] = (y[1] + y[2] + y[3] + y[4] + y[5]) / 5
yy[4] = (y[2] + y[3] + y[4] + y[4] + y[6]) / 5
```
**Arguments**
- `y`: ... | {"hexsha": "c387d0e602a8868a0956a5b0f176298bf1ffef3f", "size": 737, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MiscFunctions/smooth.jl", "max_stars_repo_name": "vkumpost/stoosc", "max_stars_repo_head_hexsha": "2a1fd4dc3adf9e6066877aa4134530f9d79a16cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""Test the poisson model."""
import numpy as onp
import pandas as pd
from numpyro import distributions as dist
from scipy.special import expit, logit
from shabadoo import Bernoulli
def logit(p):
"""Quick numpy logit function."""
return -onp.log(1 / p - 1)
def test_single_coef_is_about_right_boolean():
... | {"hexsha": "f529a27e2f5e77db03252df0124aec4a6c4bca51", "size": 4232, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_bernoulli.py", "max_stars_repo_name": "nolanbconaway/shabadoo", "max_stars_repo_head_hexsha": "a4d34993e2921b0ac853854bfcba5d90ce025b8c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
Uses CRTM to compute surface transmittance.
"""
import crtmmodis_ as crtm
from numpy import array, zeros
def getSfcTrans(sample):
"""
Uses CRTM to compute surface transmittance.
tau_21, tau_31 = getSfcTrans(sample)
"""
N = len(sample.tsh)
u = sample.u.T
v = sample.v... | {"hexsha": "35e71056e6ea79a54b84adc097604acfdf33715a", "size": 1390, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Components/qfed/qfed/sfctrans.py", "max_stars_repo_name": "GEOS-ESM/AeroApps", "max_stars_repo_head_hexsha": "874dad6f34420c014d98eccbe81a061bdc0110cf", "max_stars_repo_licenses": ["NASA-1.3",... |
The Resource Manager/Monitor and Control Interface is intended to access both low level and abstracted information from the monitor and control system (if available), much like the Resource Manager/Operating System Interface (section \ref{sec:RMOS}).
The resource manager is in a somewhat unique position of providing a ... | {"hexsha": "cc3432ee8b13e93537653261ac98c55d5fab820e", "size": 3206, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "RMMC.tex", "max_stars_repo_name": "regrant/powerapi_spec-1", "max_stars_repo_head_hexsha": "e3b74b0c62fa7e6104b8b18c4334e71afb745802", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
!
! Copyright (C) by Argonne National Laboratory
! See COPYRIGHT in top-level directory
!
subroutine MPI_Graph_map_f08(comm, nnodes, index, edges, newrank, ierror)
use, intrinsic :: iso_c_binding, only : c_int
use :: mpi_f08, only : MPI_Comm
use :: mpi_c_interface, only : c_Comm
use :: mpi_c_interf... | {"hexsha": "dc79ed99ccee049d910d06b166f1cb12b5b54761", "size": 1257, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "torch-test/mpich-3.4.3/src/binding/fortran/use_mpi_f08/wrappers_f/graph_map_f08ts.f90", "max_stars_repo_name": "alchemy315/NoPFS", "max_stars_repo_head_hexsha": "f3901e963e2301e8a6f1c7aac0511d0c... |
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.preprocessing import StandardScaler
def get_multiple_choice_value_cou... | {"hexsha": "120f983ddc4ba8f659c81606e2b6a0f2d6b90f08", "size": 14752, "ext": "py", "lang": "Python", "max_stars_repo_path": "stackoverflow_survey_analysis/src/data_utils.py", "max_stars_repo_name": "raminzohouri/udacity-data-scientist", "max_stars_repo_head_hexsha": "974665b13f7248bd5834f9b798669ba970662231", "max_star... |
# utility functions for loading data
import sys
sys.path.insert(0,'/Users/neelguha/Dropbox/NeelResearch/fairness/code/tensorflow_constrained_optimization/')
import math
import random
import numpy as np
import pandas as pd
import warnings
from six.moves import xrange
import tensorflow as tf
import tensorflow_constraine... | {"hexsha": "8071e8f44551a5232919f742707dab413842ef16", "size": 10018, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/data_utils.py", "max_stars_repo_name": "neelguha/tensorflow_constrained_optimization", "max_stars_repo_head_hexsha": "46b34d1c2d6ec05ea1e46db3bcc481a81e041637", "max_stars_repo_licens... |
#
# Copyright (c) 2021 The Markovflow Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | {"hexsha": "0d9538a24207079fad3919d09b4cd35802097d3a", "size": 4817, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/test_constant_kernel.py", "max_stars_repo_name": "prakharverma/markovflow", "max_stars_repo_head_hexsha": "9b7fafc199dae2f7f3207c2945fd43f674386dc1", "max_stars_repo_licenses": ["Apache... |
"""Script containing the base scenario kernel class."""
import logging
import random
import numpy as np
# length of vehicles in the network, in meters
VEHICLE_LENGTH = 5
class KernelScenario(object):
"""Base scenario kernel.
This kernel subclass is responsible for generating any simulation-specific
com... | {"hexsha": "0a3d75a29bee4ba86c58700fcab4c0acf44b4f47", "size": 20361, "ext": "py", "lang": "Python", "max_stars_repo_path": "flow/core/kernel/scenario/base.py", "max_stars_repo_name": "mawright/flow", "max_stars_repo_head_hexsha": "6e3f3da04b289a3f9e754c84915b60f0689dc78d", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma P25_invariant:
shows "invariant (composition) P25"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. invariant composition P25
[PROOF STEP]
proof (auto simp only:invariant_def)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>a b. reachable composition (a, b) \<Longrightarrow> P25 (a, b)
[PROOF... | {"llama_tokens": 8203, "file": "Abortable_Linearizable_Modules_Idempotence", "length": 87} |
#define BOOST_TEST_MODULE "test_lennard_jones_wall_potential"
#ifdef BOOST_TEST_DYN_LINK
#include <boost/test/unit_test.hpp>
#else
#include <boost/test/included/unit_test.hpp>
#endif
#include <mjolnir/forcefield/external/LennardJonesWallPotential.hpp>
BOOST_AUTO_TEST_CASE(LennardJonesWallPotential_double)
{
usin... | {"hexsha": "e71ba35a11a2ec634a19f2a20ddb5cc88b606f6d", "size": 1519, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/core/test_lennard_jones_wall_potential.cpp", "max_stars_repo_name": "yutakasi634/Mjolnir", "max_stars_repo_head_hexsha": "ab7a29a47f994111e8b889311c44487463f02116", "max_stars_repo_licenses": [... |
#!/usr/bin/env python
'''
A generator for some arbitrary test data roughly matching the description
of the expected data from SAIL.
Notes:
* Additional covariate: Baseline intensity varies by care home size.
* Data is daily number of cases from February to July.
* 1000 Care homes
* 330 homes had cases, 670 no cases
*... | {"hexsha": "3f962ed8030e6148cb1c3be294a350702f406581", "size": 3238, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_sample_data.py", "max_stars_repo_name": "sa2c/care-home-fit", "max_stars_repo_head_hexsha": "58a2639c74b53e24f062d0dfc3e21df6d53b3077", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""Implements the Kaplan-Meier estimator for non-parametric survival function
estimation.
References
----------
E. L. Kaplan and P. Meier. "Nonparametric estimation from incomplete
observations". Journal of the American Statistical Association, Volume 53,
Issue 282 (1958), 457--481. doi:10.2307/2281868
"""
im... | {"hexsha": "617cdb8bd6fc707743e2c59f52b4646e5ec360d2", "size": 5387, "ext": "py", "lang": "Python", "max_stars_repo_path": "stattools/survival/kaplan_meier.py", "max_stars_repo_name": "artemmavrin/SLTools", "max_stars_repo_head_hexsha": "04525b5d6777be3ccdc6ad44e4cbfe24a8875933", "max_stars_repo_licenses": ["MIT"], "ma... |
# -*- coding: utf-8 -*-
"""
Input: Food listing in csv format (e.g. output of Phenix platform)
Output: Previous listing + food category added in dataframe format
"""
__author__ = 'Julie Seguela'
__license__ = 'MIT License'
__version__ = '0.1'
__maintainer__ = 'Julie Seguela'
__status__ = 'Development'
import os
impor... | {"hexsha": "8a57d9be7bf623213b97dbca76102d8eb544bdd0", "size": 6393, "ext": "py", "lang": "Python", "max_stars_repo_path": "import_demo.py", "max_stars_repo_name": "dataforgoodfr/batch5_happy_meal", "max_stars_repo_head_hexsha": "952e14d117ef57b06606b6ab6f6fb7d0c947f220", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
module CSV2Parquet
using DataConvenience
Base.@ccallable function julia_main()::Cint
try
real_main()
catch
Base.invokelatest(Base.display_error, Base.catch_stack())
return 1
end
return 0
end
function real_main()
for file in ARGS
if !isfile(file)
error("... | {"hexsha": "b96987d1d43957a3dc3b3b3654a197b7d416f02b", "size": 643, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "CSV2Parquet.jl", "max_stars_repo_name": "xiaodaigh/csv-to-parquet", "max_stars_repo_head_hexsha": "e271989d9a9e6ee8c1beb83268070d4953a09b43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
#
# implementation greatly inspired from: https://github.com/geodynamics/seismic_cpml/blob/master
# # Credits
# Author: Pawan Bharadwaj
# (bharadwaj.pawan@gmail.com)
#
# * original code in FORTRAN90: March 2013
# * modified: 11 Sept 2013
# * major update: 25 July 2014
# * code optimization with help from J... | {"hexsha": "e1a7ae62c8eab41ca138f11f82a5561c0f4f0252", "size": 21476, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/fdtd/core.jl", "max_stars_repo_name": "pawbz/SeismicInversion.jl", "max_stars_repo_head_hexsha": "d17c91c6e589f0444fb4f28a77bb5194bc242079", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
\begin{notsw}
\section{Reflection}
\textit{This section is added for IN4334 - Machine Learning for Software Engineering and should not be considered as a part of this paper.} \\
\noindent
During the project we faced the following challenges:
\begin{itemize}
\item Because we are not using a ready-made dataset, we ... | {"hexsha": "26f65e72980541a0f304796f00adb27279024cce", "size": 2720, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/6-reflection.tex", "max_stars_repo_name": "petroolg/dltpy", "max_stars_repo_head_hexsha": "8f8d522945f2362efc29e4d5c1aa3d64f30f4f6a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12... |
# -*- coding: utf-8 -*-
# *****************************************************************************
# NICOS, the Networked Instrument Control System of the MLZ
# Copyright (c) 2009-2021 by the NICOS contributors (see AUTHORS)
#
# This program is free software; you can redistribute it and/or modify it under
# the t... | {"hexsha": "290b78750bc7ae6fef6f80cdf33276e204d32a4f", "size": 9818, "ext": "py", "lang": "Python", "max_stars_repo_path": "nicos_mlz/kws1/devices/kwsfileformat.py", "max_stars_repo_name": "ebadkamil/nicos", "max_stars_repo_head_hexsha": "0355a970d627aae170c93292f08f95759c97f3b5", "max_stars_repo_licenses": ["CC-BY-3.0... |
module procedures
implicit none
contains
!----------------------------
! Function to solve:
!----------------------------
real(kind=8) function g (x)
real(kind=8), intent (in) :: x
g = 8.0d0 - 4.5d0*(x-sin(x))
end function g
!----------------------------
! Bisection routine:
!----------------------------
sub... | {"hexsha": "1484af096bfa7a51e9867a0fd57afddccebeeb94", "size": 1947, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "ME_Numerical_Methods/HW3/ex2/mod.f95", "max_stars_repo_name": "ElenaKusevska/Fortran_exercises", "max_stars_repo_head_hexsha": "69bab3c2ac6a17612e28e71e8a7bd322f4260153", "max_stars_repo_license... |
# imports
import serial
import numpy as np
import matplotlib.pyplot as plt
import time
import glob
from StringIO import StringIO
################################################################################
# NOTE: TO DO
# - check the grab
# - turn into numpy
# - add a matplotlib output
############################... | {"hexsha": "961edcd70bb4951cd93979d650b964fcfd6d55a9", "size": 8802, "ext": "py", "lang": "Python", "max_stars_repo_path": "ReadingGauges/ComputerSide/GrabberGaugesOutput/Grabber.py", "max_stars_repo_name": "jerabaul29/PaddleAndUltrasonicGauges", "max_stars_repo_head_hexsha": "5c6ba80ddfd44190eb21d5c61979ac802a54cb99",... |
import serial, sys, time, os, gzip, math
import Sigma_koki
import numpy as np
class Stage(object):
def __init__(self):
#self.Range_Speed = 1
self.Min_Speed = 50
self.Max_Speed = 20000
self.w_x = 1000 / 2. #/1mm SGSP 33-200
self.w_z = 1000 / 6. #/1mm SGSP 26-200
... | {"hexsha": "38797fda58238e9960cff61d411c69c1cb151e91", "size": 7067, "ext": "py", "lang": "Python", "max_stars_repo_path": "measurement/Stage_control.py", "max_stars_repo_name": "akira-okumura/isee_sipm", "max_stars_repo_head_hexsha": "dff98c82ed8ef950c450c83ad8951743e3799e94", "max_stars_repo_licenses": ["MIT"], "max_... |
function [center, rotMat] = imPrincipalAxes(img, varargin)
% Computes principal axes of a 2D/3D binary image.
%
% [CENTER, ROTMAT] = imPrincipalAxes(IMG)
%
% (Note: currently only implemented for binary images)
%
% Example
% % Compute principal axes of a discretized 3D ellipsoid
% % (requires the MatGeom ... | {"author": "mattools", "repo": "matImage", "sha": "94d892c7beac0db32daadf2646ce37f58e894caf", "save_path": "github-repos/MATLAB/mattools-matImage", "path": "github-repos/MATLAB/mattools-matImage/matImage-94d892c7beac0db32daadf2646ce37f58e894caf/matImage/imMeasures/imPrincipalAxes.m"} |
#! /usr/bin/env python
"""Tests for the ``instrument_properties`` module.
Authors
-------
- Bryan Hilbert
Use
---
These tests can be run via the command line (omit the ``-s`` to
suppress verbose output to stdout):
::
pytest -s test_instrument_properties.py
"""
import os
import pytest
imp... | {"hexsha": "dc074ead26b07a2fd374c90e1932dd881070c55b", "size": 3340, "ext": "py", "lang": "Python", "max_stars_repo_path": "jwql/tests/test_instrument_properties.py", "max_stars_repo_name": "penaguerrero/jwql", "max_stars_repo_head_hexsha": "0e6eb58e7a631c1d6356ce6c1b192c7dd52962bf", "max_stars_repo_licenses": ["BSD-3-... |
#!/usr/bin/env python3
import argparse
import whois
import time
import numpy as np
import csv
import socket
#import tld
#run domains through a whois query
#exclude results from domains where whois reports None, meaning domain not found
def whoIsQuery(domain):
try:
data = whois.query(domain)
if dat... | {"hexsha": "a238b8711fc900d9769c900ac4479ae8de12178c", "size": 3040, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/shank_hooper_whois.py", "max_stars_repo_name": "vaishnavi-sridhar/CU_HIN", "max_stars_repo_head_hexsha": "686a0cf883fbface41acdcdeba44eb8805bb74e1", "max_stars_repo_licenses": ["MIT"], "max_st... |
import datetime
import cmor
import dateutil
import logging
import netCDF4
import numpy
import os
import unittest
from nose.tools import eq_
from ece2cmor3 import ifs2cmor, ece2cmorlib
logging.basicConfig(level=logging.DEBUG)
calendar_ = "proleptic_gregorian"
def write_postproc_timestamps(filename, startdate, refd... | {"hexsha": "0fb3fc6992a7a2fa23e4f9e9be37b8f14952cf9b", "size": 6094, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/ifs2cmor_test.py", "max_stars_repo_name": "etiennesky/ece2cmor3", "max_stars_repo_head_hexsha": "af51e99dd496b5202623569a23fe52c5506761e0", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
C LAST UPDATE 16/03/89
C+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
C
SUBROUTINE MULFIL
IMPLICIT NONE
C
C PURPOSE: Multiply an image by a single file/raster.
C
INCLUDE 'COMMON.FOR'
C
C Calls 8: WFRAME , GETHDR , OUTFIL , GETVAL
C IMDISP , RFRAME , OPNNEW , ... | {"hexsha": "17b659dc5d3d8d58530fe2259db042c5dee25d7e", "size": 4360, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "bsl/mulfil.f", "max_stars_repo_name": "scattering-central/CCP13", "max_stars_repo_head_hexsha": "e78440d34d0ac80d2294b131ca17dddcf7505b01", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
import tempfile
import tensorflow_model_optimization as tfmot
import tensorflow as tf
import numpy as np
import gzip
class ModelOptimizers:
@staticmethod
def callbacks(log_dir):
return []
@staticmethod
def modify(model):
return model
@staticmethod
def save(model):
retu... | {"hexsha": "94aae6d4d9596655ef1e4fe9480a52b8add1999f", "size": 4023, "ext": "py", "lang": "Python", "max_stars_repo_path": "libmot.py", "max_stars_repo_name": "4g/tf-model-optimization", "max_stars_repo_head_hexsha": "cb719bf105a25393f78bdd7342e61a1f4be75d9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
[STATEMENT]
lemma addr_in_ipv4set_from_cidr_code[code_unfold]:
fixes addr :: ipv4addr
shows "addr \<in> (ipset_from_cidr pre len) \<longleftrightarrow>
(pre AND ((mask len) << (32 - len))) \<le> addr \<and> addr \<le> pre OR (mask (32 - len))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (add... | {"llama_tokens": 200, "file": "IP_Addresses_IPv4", "length": 1} |
import sys, os
file_path = os.path.abspath(__file__)
project_path = os.path.dirname(os.path.dirname(file_path))
sys.path.append(project_path)
fcgf_path = os.path.join(project_path, 'ext', 'FCGF')
sys.path.append(fcgf_path)
from dataset.threedmatch_test import Dataset3DMatchTest
from perception3d.adaptor import FCGFC... | {"hexsha": "ffae7b66a740f450d28853b13d719213f90e9ed6", "size": 1265, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/perception3d/test.py", "max_stars_repo_name": "theNded/SGP", "max_stars_repo_head_hexsha": "63d33cc8bffde53676d9c4800f4b11804b53b360", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "22af5af6298f108dd8b9aa88ec9849473c26005e", "size": 4097, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/parallelwave_gan/baker/batch_fn.py", "max_stars_repo_name": "lym0302/Parakeet", "max_stars_repo_head_hexsha": "97b7000aa2be182d3ff4681f435f8c1463e97083", "max_stars_repo_licenses": ["Apac... |
# -*- coding: utf-8 -*-
r"""
The On-Line Encyclopedia of Integer Sequences (OEIS)
You can query the OEIS (Online Database of Integer Sequences) through Sage in
order to:
- identify a sequence from its first terms.
- obtain more terms, formulae, references, etc. for a given sequence.
AUTHORS:
- Thierry Mont... | {"hexsha": "be0138b6b76fc1faad1b51f07b0c0cc897c4aa04", "size": 73495, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/databases/oeis.py", "max_stars_repo_name": "LaisRast/sage", "max_stars_repo_head_hexsha": "5fb2a6ea44400e469caee82748cf863ca0c5f724", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_c... |
import warnings
from pathlib import Path
import numpy as np
from tqdm.auto import tqdm
from amset.constants import int_to_spin, numeric_types, spin_to_int
from amset.wavefunction.common import (
get_gpoint_indices,
get_gpoints,
get_min_gpoints,
get_overlap,
is_ncl,
sample_random_kpoints,
)
__... | {"hexsha": "ab642fe87f329a225c9f12192285bd8faeae040f", "size": 6617, "ext": "py", "lang": "Python", "max_stars_repo_path": "amset/wavefunction/vasp.py", "max_stars_repo_name": "hackingmaterials/amset", "max_stars_repo_head_hexsha": "a99a9a9c33fc8d2e2937f3e51c7221e9620a33fc", "max_stars_repo_licenses": ["BSD-3-Clause-LB... |
using PyPlot
# 2000 samples with 0.1s per model eval
Nprocs = [1, 5, 10, 20, 50, 80, 100, 120, 150, 180 ]
t = [13287.663 2697.591854 1375.82991 709.99628 313.435787 218.478622 181.348302 161.611588 141.589949 127.132717]'
# 2000 samples with 1s per model eval
Nprocs2 = [5, 10, 20, 50, 80, 100, 120, 150, 180 ]
t2 = [2... | {"hexsha": "dc5bde337fcf84eef6e1da8efc7559baee1829e1", "size": 1628, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "slurm_benchmarks/modelEvals/results.jl", "max_stars_repo_name": "AnderGray/TransitionalMCMC.jl", "max_stars_repo_head_hexsha": "547862f38a6ccb7880f780f497ab3f04dda34fb7", "max_stars_repo_licenses":... |
[STATEMENT]
lemma index_zero_implies_one_group:
assumes "ps \<subseteq> \<V>"
and "card ps = 2"
and "\<B> index ps = 0"
shows "size {#b \<in># mset_set \<G> . ps \<subseteq> b#} = 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. size (filter_mset ((\<subseteq>) ps) (mset_set \<G>)) = 1
[PROOF STEP]
pro... | {"llama_tokens": 2242, "file": "Design_Theory_Group_Divisible_Designs", "length": 21} |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 25 14:34:04 2016
@author: devd
"""
from __future__ import division
import logging
import math
from choose import nCr
import numpy as np
from scipy.misc import comb
import createRandomString as c
import meshers
import time
import random
import functools
import json
import ... | {"hexsha": "bb8acb8fa29a80c2fb2c8c9d044170333243fc96", "size": 1823, "ext": "py", "lang": "Python", "max_stars_repo_path": "theory/test.py", "max_stars_repo_name": "johnterickson/Mesh", "max_stars_repo_head_hexsha": "829f6183a6fb06eaef5ff16532677124d827bda5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
\documentclass [11pt,a4paper]{moderncv}
\usepackage{multicol}
\usepackage[scale=0.75]{geometry}
\moderncvtheme[blue]{classic}
% colors include blue, orange, green, red, purple, grey, and black
% styles casual, classic, oldstyle, banking
%\nopagenumbers{} %used to remove line numbers
%--------------------------------... | {"hexsha": "512fd37d98ad989ac32c31d617d807ff73c94329", "size": 6645, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "resume.tex", "max_stars_repo_name": "westrope/Resume", "max_stars_repo_head_hexsha": "7aeb23aa093529070f54723ecb081236326458af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
import numpy as np
import copy
def hreflect1D(x):
"""
Calculate Householder reflection: Q = I - 2*uu'.
Parameters:
X: numpy array.
Returns:
Qx: reflected vector.
Q: Reflector (matrix).
"""
# Construct v:
v = copy.deepcopy(x)
v[0] += np.linalg.norm(x)
# Co... | {"hexsha": "021d34ddaf95214215c4e24758e26ffd7307ced3", "size": 1387, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithms/helpers.py", "max_stars_repo_name": "thsis/NIS18", "max_stars_repo_head_hexsha": "1f2a7be1ab209fa7c0a25cb8eace744336b07c1f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import pytest
import torch
import torch.nn.functional as F
import numpy as np
from cplxmodule import cplx
def cplx_allclose(input, other):
return torch.allclose(input.real, other.real) and \
torch.allclose(input.imag, other.imag)
def cplx_allclose_numpy(input, other):
other = np.asarray(other)... | {"hexsha": "18ca75799db755bb30a616975e215460d58c7e67", "size": 22730, "ext": "py", "lang": "Python", "max_stars_repo_path": "cplxmodule/tests/test_cplx.py", "max_stars_repo_name": "veya2ztn/mltool", "max_stars_repo_head_hexsha": "4ed151152845ebe3de128e1f53c478581c1492e4", "max_stars_repo_licenses": ["IJG"], "max_stars_... |
"""
See networks.csv https://github.com/GeoNet/delta/blob/master/network/networks.csv
for what the codes mean. The relevant codes for QuakeCoRE are (I am guessing here)
NZ (included per Brendon's advice
SM National strong motion network
SC Canterbury regional strong motion network
SB is included in http... | {"hexsha": "07bfea4dd3c7da31dd35f48cfdf1b8ab0a9d1cb9", "size": 3737, "ext": "py", "lang": "Python", "max_stars_repo_path": "geoNet/extract_geoNet_stations.py", "max_stars_repo_name": "ucgmsim/Pre-processing", "max_stars_repo_head_hexsha": "c4b9ae20a9e5e4f96f930bde29aa15176d9c8b64", "max_stars_repo_licenses": ["MIT"], "... |
import torch
import numpy as np
import wandb
import torch.nn as nn
from models.gnn import EdgeGnn, NodeGnn, QGnn, GlobalEdgeGnn
from utils.distances import CosineDistance
from utils.sigmoid_normal import SigmNorm
class Agent(torch.nn.Module):
def __init__(self, cfg, StateClass, distance, device, with_temp=True):
... | {"hexsha": "b3f7972370ba725293d38b51b1ffc457b68c8de3", "size": 7875, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/agent_model.py", "max_stars_repo_name": "edosedgar/RLForSeg", "max_stars_repo_head_hexsha": "fc748d8e7d2f2a1e7ac0dddb3f268ec3025d40ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
Jump to Timeline #Navigation Navigation
## If you are adding something, go ahead and remove the notice below!
The Terminal Hotel opens
## ################## Leave this navigation here:
| {"hexsha": "20590904bb5e371dcbcc2e3499c9633c1a2a5e46", "size": 203, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/1925.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#cd /Users/iikura/Desktop/TerrainParam
# terrain.py kiban50_450F.tif
import sys
import numpy as np
import cv2
import terrain_util as ut
#reload(ut)
param=sys.argv
if (len(param) <2) or (len(param) > 4):
print " * Usage : terrain.py dem.tif => for slope and aspect"
... | {"hexsha": "dbf783051250dc518a1c8f0291f7dc89a33e03cf", "size": 1172, "ext": "py", "lang": "Python", "max_stars_repo_path": "terrain.py", "max_stars_repo_name": "y-iikura/TerrainParam", "max_stars_repo_head_hexsha": "58a1461e00f6209a56f5659c0f61950f3fde4b0b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
From mathcomp Require Import all_ssreflect.
Implicit Type P Q R : Prop.
(** *** Exercise 0:
- Define not. In type theory negation is defined in terms
of [False].
*)
Definition not P :=
.
(** *** Exercise 1:
- Prove the negation of the excluded middle.
*)
Lemma ex0 P : not (P /\ not P).
Proof.
Qed.
(... | {"author": "math-comp", "repo": "tutorial_material", "sha": "3e5fcef3a25d2a43115fb645645b437640624ad3", "save_path": "github-repos/coq/math-comp-tutorial_material", "path": "github-repos/coq/math-comp-tutorial_material/tutorial_material-3e5fcef3a25d2a43115fb645645b437640624ad3/SummerSchoolSophia/exercise4_todo.v"} |
submodule (h5mpi:hdf5_read) hdf5_reader
use hdf5, only: h5dread_f, h5sclose_f
implicit none (type, external)
contains
module procedure h5exist
type(hdf5_file) :: h
call h%open(filename, action='r', mpi=mpi)
h5exist = h%exist(dname)
call h%close()
end procedure h5exist
module procedure h5read_1d
include "reade... | {"hexsha": "55d176926b69daeb76e5dfb72a56aea62d9bce4f", "size": 879, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/reader.f90", "max_stars_repo_name": "geospace-code/h5fortran-mpi", "max_stars_repo_head_hexsha": "f18d5037f1fffcea954fedf2aedcf0f77605a937", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
[STATEMENT]
lemma energy_graph_subsets_ge0 [simp]:
"energy_graph_subsets U W G \<ge> 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 \<le> energy_graph_subsets U W G
[PROOF STEP]
by (auto simp: energy_graph_subsets_def) | {"llama_tokens": 97, "file": "Szemeredi_Regularity_Szemeredi", "length": 1} |
##
## i n t e g r a t i o n . j l Integration Routines
##
function trapz(x::Vector{Tx}, y::Vector{Ty}) where {Tx<:Number, Ty<:Number}
# Trapezoidal integration rule
local n = length(x)
if (length(y) != n)
error("Vectors 'x', 'y' must be of same length")
end
r = zero(zero(Tx) + zero(Ty))
... | {"hexsha": "c1b7d7a566394f2ded2bdff4cbe3efa1c979481e", "size": 1537, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/integrate.jl", "max_stars_repo_name": "danielbalzer/NumericalMath.jl", "max_stars_repo_head_hexsha": "8623632136da2b77a4d162eaaf7aca73109c5062", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
def demo_linearized_gpr():
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# matplotlib.use("Agg")
rng = np.random.RandomState(0)
# Generate sample data
noise = 1.0
input_noise = 0.2
n_train = 1_000
n_test = 1_000
n_inducing = 100
batch_size = Non... | {"hexsha": "7b4ba4fdb3e9c5c3846aa839e35a0b108d57bd1d", "size": 3617, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/uncertain.py", "max_stars_repo_name": "IPL-UV/gp_error_propagation", "max_stars_repo_head_hexsha": "5d37b4bc48b3fc9521d819970ba24b8bb8ea5b16", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python
import random
import sys
import numpy as np
from bst_vector import BSTVector
from bst_matrix import BSTMatrix
if '--seed' not in sys.argv:
seed = random.randint(0, 10000)
random.seed(seed)
else:
seed = sys.argv[1 + sys.argv.index('--seed')]
print "Using seed {}".format(seed)
seed = i... | {"hexsha": "0fc712774080968cbf706512b18b4676fc08c1e6", "size": 983, "ext": "py", "lang": "Python", "max_stars_repo_path": "bst_test.py", "max_stars_repo_name": "nuchi/bst-matrix-vector", "max_stars_repo_head_hexsha": "4abf5c8adf8352978d80fbbc596db20d2456449b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, ... |
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import sklearn
import json
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegr... | {"hexsha": "3a7cd276b38e946b5049fd2c5ab85f370c11bff8", "size": 2644, "ext": "py", "lang": "Python", "max_stars_repo_path": "public/Python_Scripts/Main_Script.py", "max_stars_repo_name": "CodeDude19/Dell_Hackathon-Won-", "max_stars_repo_head_hexsha": "ab85ca4573296b343a8d655f59d3791a0c0711fa", "max_stars_repo_licenses":... |
[STATEMENT]
lemma closed_UN [continuous_intros, intro]:
"finite A \<Longrightarrow> \<forall>x\<in>A. closed (B x) \<Longrightarrow> closed (\<Union>x\<in>A. B x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>finite A; \<forall>x\<in>A. closed (B x)\<rbrakk> \<Longrightarrow> closed (\<Union> (B ` A))
[... | {"llama_tokens": 260, "file": null, "length": 2} |
function bessel_i0_values_test ( )
%*****************************************************************************80
%
%% BESSEL_I0_VALUES_TEST demonstrates the use of BESSEL_I0_VALUES.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 02 February 2009
%
% Author:
%
% ... | {"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/test_values/bessel_i0_values_test.m"} |
import numpy as np
import sys
import os
sys.path.append(os.path.abspath('wethebestOLS'))
import ols
import matplotlib.pyplot as plt
nsim = 1000
nObs = 1000
nParams = 3
const = np.ones((nObs,1))
XX = np.random.random((nObs, nParams))
XX[:,0] = 1
beta_list = []
sigma_list = []
mu_list = []
proportion_list = []
for s ... | {"hexsha": "238ab6fd501ff8dc498879a2e15dc27601f9ea04", "size": 915, "ext": "py", "lang": "Python", "max_stars_repo_path": "Projects/project_2_packages/wethebestOLS/sbc_excercise.py", "max_stars_repo_name": "FrancoCalle/modularizationandtesting", "max_stars_repo_head_hexsha": "d44b39b19fc1d7009a8b9be7624cecff2a3156dc", ... |
const CONFIG = MOIT.Config(Int)
const OPTIMIZER = JaCoP.Optimizer()
const BRIDGED_OPTIMIZER = MOI.Bridges.full_bridge_optimizer(OPTIMIZER, Int32)
COIT.runtests(
# BRIDGED_OPTIMIZER,
OPTIMIZER,
CONFIG,
)
| {"hexsha": "48eeb4d90016f73ee6d0612df563d9d12786f161", "size": 217, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/MOI.jl", "max_stars_repo_name": "JuliaConstraints/JaCoP.jl", "max_stars_repo_head_hexsha": "cf774819da9822ac982a6ec119f8e9f8a9a73c4a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import datetime as dt
import pandas as pd
import numpy as np
from pandas.plotting import table
import matplotlib.pyplot as plt
def ann_return(DF):
"function to calculate the Annualized return from monthly prices of a fund/sript"
df = DF.copy()
df["mon_ret"] = df["NAV"].pct_change()
df["cum_return"] = (... | {"hexsha": "1c30b0b0b2dd41db25f62e5e8c870bf536a3300b", "size": 2172, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project_2.py", "max_stars_repo_name": "jainrachit1008/Titans-of-Wall-Street-Program-Projects", "max_stars_repo_head_hexsha": "2d71499a0942ed506330c412eae3b0822c837aa7", "max_stars_repo_licenses": ... |
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