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# Copyright (c) 2020-2021 - for information on the respective copyright owner
# see the NOTICE file and/or the repository
# https://github.com/boschresearch/pylife
#
# 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 co... | {"hexsha": "870da316ff987b91e6a2efc529b4719f486a0c0b", "size": 20382, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pylife/vmap/vmap_import.py", "max_stars_repo_name": "alexander-maier/pylife", "max_stars_repo_head_hexsha": "a9dceb3f364af16bf0a2d3015e34fa47192bfcf6", "max_stars_repo_licenses": ["Apache-2.0... |
# -*- coding: utf-8 -*-
""" RandOm Convolutional KErnel Transform (ROCKET)
"""
__author__ = "Matthew Middlehurst"
__all__ = ["ROCKETClassifier"]
import numpy as np
from sklearn.linear_model import RidgeClassifierCV
from sklearn.pipeline import make_pipeline
from sklearn.utils import check_random_state
from sklearn.ut... | {"hexsha": "a53926510f81443183811393ae71863ac3f53d50", "size": 5540, "ext": "py", "lang": "Python", "max_stars_repo_path": "sktime/classification/shapelet_based/_rocket_classifier.py", "max_stars_repo_name": "abdulelahsm/sktime", "max_stars_repo_head_hexsha": "ce056a56246ec26f2bfdaef2d5a58a949ed3bd52", "max_stars_repo_... |
import cv2
import torch
from scipy import io as mat_io
from skimage import io
from torch.utils.data import Dataset
from torchvision import transforms
from .auto_augment import AutoAugment, ImageNetAutoAugment
import numpy as np
from base import BaseDataLoader
class CarsDataset(Dataset):
"""
Cars Dataset
... | {"hexsha": "07e67ccd5517e49ca4d722be1ddab56a640ac9d2", "size": 2814, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_processing/data_loaders.py", "max_stars_repo_name": "zah-tane/stanford-cars-model", "max_stars_repo_head_hexsha": "b299f9d9b7c55c6e925484ee31355f062066fdf4", "max_stars_repo_licenses": ["MIT"... |
from os import times
import matplotlib.pyplot as plt
import numpy as np
import json
import cv2
from matplotlib.colors import hsv_to_rgb as hsv2rgb
from glob import glob
import matplotlib.ticker as plticker
from tqdm import tqdm
import os
def _flow2rgb(flow):
# cart to polar
ang = (np.arctan2(flow[0], flow[1]) ... | {"hexsha": "819988c6e93a83e9d1b187ca7fa9335765e3ac20", "size": 9408, "ext": "py", "lang": "Python", "max_stars_repo_path": "Json/SequenceVisualizer.py", "max_stars_repo_name": "asd2511/mriSequence", "max_stars_repo_head_hexsha": "de6002fc892acdec5bfe1988bcc85b69b6d16f67", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import os, sys
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
try:
from data_handle.mid_object import *
except:
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from data_handle.mid_object import *
'''
Th... | {"hexsha": "970e1f7fef998ab31085dc663a1674dc001390c6", "size": 8182, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/util/utils_data.py", "max_stars_repo_name": "georghess/EBM_ONGOING", "max_stars_repo_head_hexsha": "c5cabe71d26972a7346c13248549b6b4b4fca9f0", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#include "integration.hpp"
#include <iostream>
#include <Eigen/Sparse>
#include <Eigen/IterativeLinearSolvers>
using namespace std;
void explicitEulerStep(PhysicalSystem *system, double dt) {
forwardEulerStep(system, dt);
}
void forwardEulerStep(PhysicalSystem *system, double dt) {
int n = system->getDOFs();
... | {"hexsha": "23fe0e1eb896f5c26b0196064b425ad8a722b47f", "size": 663, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "C++/1_MassSpring_Explicit/integration.cpp", "max_stars_repo_name": "mmmovania/ConstrainedDynamicsExperiments", "max_stars_repo_head_hexsha": "9ebcd7256037fb50785bb2a1ddb7473b034a0c5b", "max_stars_rep... |
import torch
import gym
import random
import numpy as np
torch.backends.cudnn.deterministic=True
class Environment:
def __init__(self, render=False, seed=None):
self.render = render
self.env_seed = seed
def set_seed(self):
if self.env_seed is not None:
self.env.seed(self... | {"hexsha": "79b83a40b798807a926b898565300343c23fecfa", "size": 4794, "ext": "py", "lang": "Python", "max_stars_repo_path": "VR_REINFORCE/environments.py", "max_stars_repo_name": "gargiani/VRPG", "max_stars_repo_head_hexsha": "429fe58b089df2f4cdedab01b05564230e2317ac", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright (c) 2019 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": "86e22444cb8cb92b20d30e9d59d5bb7eee4aff06", "size": 31519, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/contrib/layers/nn.py", "max_stars_repo_name": "itminner/Paddle", "max_stars_repo_head_hexsha": "f41da8a4ad3caf2a11d3cc2ca89bbcd243438c7f", "max_stars_repo_licenses": ["Apache-... |
"""Nexar Dataset Classes
"""
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import torch.utils.data as data
from utils.utils import load_image
NEXAR_CLASSES = ( # always index 0
'car',)
# for making bounding boxes pretty
COLORS = ((255, 0, 0, 128), (0, 255, 0, 128), (0, 0, 255, ... | {"hexsha": "8cc778255bc48ef508244207a10ca17fae51d265", "size": 5485, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/nexar2.py", "max_stars_repo_name": "ternaus/nexar2_ssd", "max_stars_repo_head_hexsha": "338dbdef74ebc44098613b1fd1f5124d6e877a33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
import numpy
import vtreat.stats_utils
def test_linear_cor():
y_true = [1, 1, 0, 1, 0, 1, 1, 0, 1, 0]
y_pred = [0.8, 1, 0.2, 0.5, 0.5, 0.8, 1, 0.2, 0.5, 0.5]
cor, sig = vtreat.stats_utils.our_corr_score(y_true=y_true, y_pred=y_pred)
# R:
# y_true = c(1, 1, 0, 1, 0, 1, 1, 0, 1, 0)
# y_pred = c... | {"hexsha": "fb6a51d070f0db394c9a31c0d4f62601edcf5018", "size": 5515, "ext": "py", "lang": "Python", "max_stars_repo_path": "pkg/tests/test_stats.py", "max_stars_repo_name": "WinVector/pyvtreat", "max_stars_repo_head_hexsha": "eb71be1b43e9310cdf77f89c2ae2d26be3a59d4b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
"""
Geometry
====================
Change or get info on the current geometrical configuration, e.g.
number of cells in the three crystal translation directions.
"""
import spirit.spiritlib as spiritlib
import ctypes
### Load Library
_spirit = spiritlib.load_spirit_library()
### Imports
from spirit.scalar import sca... | {"hexsha": "2cdc274d47988889a2c786e800fbdb6c01e4b774", "size": 9577, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/python/spirit/geometry.py", "max_stars_repo_name": "bck2302000/spirit", "max_stars_repo_head_hexsha": "14ed7782bd23f4828bf23ab8136ae31a21037bb3", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""
This is a stand alone python program that plots the phase plot for a damped
oscillator. It starts with the limiting case of
all three being approximately critical
damping. Then you can adjust gamma higher to show the overdamped case or adjust
gamma lower to show the underdampmed case. You can also adjust the ini... | {"hexsha": "c032db1aeb5fd666694a5dc36319e1ae8946bea3", "size": 2681, "ext": "py", "lang": "Python", "max_stars_repo_path": "phase-oscillator.py", "max_stars_repo_name": "drjenncash/MechanicsInteractives", "max_stars_repo_head_hexsha": "6d2756de6d24beaa6c93396a4a975c055b103121", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
# 概要
光に関するモジュール
# 使い方
# references
these data have been downloaded from following site.
[Munsell Color Science Laboratory](https://www.rit.edu/cos/colorscience/rc_useful_data.php)
"""
import os
import numpy as np
from scipy import linalg
import matplotlib.pyplot a... | {"hexsha": "2d2ae9dbb422ced53835957c81af93c420fe9960", "size": 3331, "ext": "py", "lang": "Python", "max_stars_repo_path": "ty_lib/light.py", "max_stars_repo_name": "toru-ver4/sample_code", "max_stars_repo_head_hexsha": "9165b4cb07a3cb1b3b5a7f6b3a329be081bddabe", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
SUBROUTINE DT_NUC2CM
C***********************************************************************
C Lorentz-transformation of all wounded nucleons from Lab. to nucl.- *
C nucl. cms. (This subroutine replaces NUCMOM.) *
C This version dated 15.01.95 is written by S. Roesler ... | {"hexsha": "f4aa789f61f2b1c64668196d5256a882d919e102", "size": 2551, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/dpmjet/DT_NUC2CM.f", "max_stars_repo_name": "pzhristov/DPMJET", "max_stars_repo_head_hexsha": "946e001290ca5ece608d7e5d1bfc7311cda7ebaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
"""
---
pandoctools:
profile: Default
out: "*.ipynb"
input: True
eval: False
echo: True
...
"""
# %%
# from importlib import reload
from typing import Tuple, Optional as Opt, Any, Iterable, Type, List
import warnings
from dataclasses import dataclass
from itertools import chain
import math
# noinspection PyPep8Nam... | {"hexsha": "42d7075873f6fc7df6bab3daca6ac60ec6ca56f4", "size": 65291, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "kiwi0fruit/jats-semi-supervised-pytorch", "max_stars_repo_head_hexsha": "67e9bb85f09f8ef02e17e495784d1d9a71c3adec", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
from scipy.linalg import block_diag
import openmdao.api as om
from openmdao.utils.units import unit_conversion
from ...transcriptions.grid_data import GridData
from ...utils.lagrange import lagrange_matrices
from ...options import options as dymos_options
class TimeseriesOutputCompBase(om.Explici... | {"hexsha": "b473f2963c38911c89c21b540f1afb85ff8ecc25", "size": 8956, "ext": "py", "lang": "Python", "max_stars_repo_path": "dymos/transcriptions/common/timeseries_output_comp.py", "max_stars_repo_name": "kaushikponnapalli/dymos", "max_stars_repo_head_hexsha": "3fba91d0fc2c0e8460717b1bec80774676287739", "max_stars_repo_... |
From aneris.aneris_lang Require Import ast.
From aneris.aneris_lang.lib Require Import set_code.
From aneris_examples.transaction_commit Require Import two_phase_code.
Definition runner : expr :=
let: "TM" := MakeAddress #"tm" #80 in
let: "RM1" := MakeAddress #"rm.01" #80 in
let: "RM2" := MakeAddress #"rm.02" #... | {"author": "fresheed", "repo": "trillium-experiments", "sha": "a9c38a9e9566fb8057ae97ecb8d1a0c09c799aef", "save_path": "github-repos/coq/fresheed-trillium-experiments", "path": "github-repos/coq/fresheed-trillium-experiments/trillium-experiments-a9c38a9e9566fb8057ae97ecb8d1a0c09c799aef/theories/transaction_commit/two_p... |
import matplotlib.pyplot as plt
import numpy as np
from pysdd import hyperparameter
if __name__ == "__main__":
for encoding in ['standard_enc1_full', 'standard_enc1_noisy', 'standard_enc2_full', 'standard_enc2_noisy']:
print(encoding)
f = 'cnf/' + encoding + '_pysdd.cnf'
plt.figure()
... | {"hexsha": "0cb5271680bba74333d16afe5eeb774180e6da7f", "size": 761, "ext": "py", "lang": "Python", "max_stars_repo_path": "hyperparam-cli.py", "max_stars_repo_name": "sventhijssen/PySDD", "max_stars_repo_head_hexsha": "561804d59f5b362410c3a53bca637ae97e967edc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import pandas as pd
import numpy as np
def label_to_pos_map(all_codes):
label_to_pos = dict([(code,pos) for code, pos in zip(sorted(all_codes),range(len(all_codes)))])
pos_to_label = dict([(pos,code) for code, pos in zip(sorted(all_codes),range(len(all_codes)))])
return label_to_pos, pos_to_la... | {"hexsha": "4118ca437d0f414d59c41402546621c7a13fb1f3", "size": 3093, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset_creation/src/utils/train_test_split_experiment.py", "max_stars_repo_name": "neuron1682/cross-lingual-phenotype-prediction", "max_stars_repo_head_hexsha": "3cf139283a39487969bff646dee1eb03f... |
import numpy as np
arr= np.arange(0,12)
print arr
print arr[0:5] #this is know as slicing
print arr[2:6]
arr[0:5]=19,20,21,22,23
print arr
| {"hexsha": "156a7c8d0b712fdd82fec2b53e7a65c5b5fbbb0c", "size": 145, "ext": "py", "lang": "Python", "max_stars_repo_path": "Section3/L3,4 Array Index/array_index1.2.py", "max_stars_repo_name": "Mohit-Sharma1/Takenmind_Internship_assignments", "max_stars_repo_head_hexsha": "7099ae3a70fca009f6298482e90e988124868148", "max... |
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
# from pandas_profiling import ProfileReport
def isCategorical(col):
unis = np.unique(col)
if len(unis)<0.2*len(col):
return True
return False
# def getProfile(data):
# report = ProfileReport(data)
# rep... | {"hexsha": "e0d7421fe93238410694cf263490be15cf88c140", "size": 2343, "ext": "py", "lang": "Python", "max_stars_repo_path": "pages/utils.py", "max_stars_repo_name": "rypaik/Streamlit_Ref", "max_stars_repo_head_hexsha": "5ce11cecbe8307238463c126b88b3beed66c99fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 59,... |
#include <iostream>
#include <boost/log/trivial.hpp>
int
main (int argc, char **argv)
{
BOOST_LOG_TRIVIAL(trace)
<< "A trace severity message";
BOOST_LOG_TRIVIAL(debug)
<< "A debug severity message";
BOOST_LOG_TRIVIAL(info)
<< "An informational severity message";
BOOST_LOG_TRIVIAL(warning)
<< "A warnin... | {"hexsha": "b4bd2b492df270dd06f1230b73b40ea6158cc7f3", "size": 478, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "codes/cpp/cplusplus-codesnippet/src/boost/example_boost_log.cpp", "max_stars_repo_name": "zhoujiagen/learning-system-programming", "max_stars_repo_head_hexsha": "2a18e9f8558433708837ba4bd0fae5d7c11bf... |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modif... | {"hexsha": "5e8076eccee9e4f3361cc089cd9c05ec0400d6eb", "size": 5732, "ext": "py", "lang": "Python", "max_stars_repo_path": "qiskit/aqua/circuits/fourier_transform_circuits.py", "max_stars_repo_name": "gillenhaalb/qiskit-aqua", "max_stars_repo_head_hexsha": "e2be4401dfdd69def7f842944b761bd3972d7ea2", "max_stars_repo_lic... |
using DataFrames
function parse_numbers(s)
pieces = split(s, ' ', keepempty=false)
map(pieces) do piece
parse(Float64, piece)
end
end
"""
load_H(path)
Load a homography matrix from path `path`
"""
function load_H(path)
lines = readlines(path)
H = Array{Float64}(undef, (3,3))
for (... | {"hexsha": "fbb639459191e4a7a6334be1d1252eb8c4059a58", "size": 4850, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/birl.jl", "max_stars_repo_name": "Kunz-David/LAP_Julia", "max_stars_repo_head_hexsha": "4978fce94e1ab47224337cac563c5ad0a49e7c8e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
from pyod.models.iforest import IForest
from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print
import numpy as np
import pickle
X_train = np.loadtxt('X_train.txt', dtype=float)
y_train = np.loadtxt('y_train.txt', dtype=float)
X_test = np.loadtxt('X_test.txt', dtype=float)
y_test = np.loa... | {"hexsha": "e3a5c0582215c813df6c7f40adfc4b261ed0a427", "size": 674, "ext": "py", "lang": "Python", "max_stars_repo_path": "labs/ai-edge/safety-monitor/IsolatedForrest.py", "max_stars_repo_name": "kartben/iot-curriculum", "max_stars_repo_head_hexsha": "11b930b11123575cc4f68ce106f945c6c0c46dd5", "max_stars_repo_licenses"... |
using NBodySimulator, Dates
const T = 370 # °K
const T0 = 275 # °K
const kb = 8.3144598e-3 # kJ/(K*mol)
const ϵOO = 0.1554253*4.184 # kJ
const σOO = 0.3165492 # nm
const ρ = 997/1.6747# Da/nm^3
const mO = 15.999 # Da
const mH = 1.00794 # Da
const mH2O = mO+2*mH
const N = 216#floor(Int, ρ * L^3 / m)
const L = (mH2O*N/... | {"hexsha": "7b5ba6184f19606813bb5b62965c114b52efe676", "size": 1363, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/water_for_visualization.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/NBodySimulator.jl-0e6f8da7-a7fc-5c8b-a220-74e902c310f9", "max_stars_repo_head_hexsha": "bc24532487c7b0b2... |
theory CoBinaryTree
imports "$HIPSTER_HOME/IsaHipster"
begin
setup Misc_Data.set_noisy
setup Tactic_Data.set_sledgehammer_coinduct
(* setup Tactic_Data.set_no_proof *)
setup Misc_Data.set_time (* Print out timing info *)
setup Misc_Data.set_hammer_timeout_medium (* set sledgehammer timeout to 20 s*)
codatatype 'a Tr... | {"author": "moajohansson", "repo": "IsaHipster", "sha": "91f6ea3f1166a9de547722ece6445fe843ad89b4", "save_path": "github-repos/isabelle/moajohansson-IsaHipster", "path": "github-repos/isabelle/moajohansson-IsaHipster/IsaHipster-91f6ea3f1166a9de547722ece6445fe843ad89b4/Examples/CoBinaryTree.thy"} |
#!/usr/bin/env python3
import os
import sys
import csv
import struct
from sympy import *
from sympy.parsing.sympy_parser import parse_expr
MATH_CHANNELS = [ ("Math_Roll", -180, 180, "rad", 10, "(180/pi) * atan2(AccelX, sqrt(AccelY**2 + AccelZ**2))"),
("Math_Roll2", -180, 180, "rad", 10, "(180/pi) *... | {"hexsha": "ed82141c17120b54c4791d13bcddd997844bd694", "size": 3093, "ext": "py", "lang": "Python", "max_stars_repo_path": "rcp-python-parser/rcp-parse.py", "max_stars_repo_name": "eacmen/hpde", "max_stars_repo_head_hexsha": "7f95038f3f29a5ee9ad4ab78ddc7a9d8553f9b48", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma C_mult_propagate:
"C x * y = C x * C y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. C x * y = C x * C y
[PROOF STEP]
by (smt (z3) C_n_mult_closed order.eq_iff inf.left_commute inf.sup_monoid.add_commute mult_left_sub_dist_inf_right n_L_T_meet_mult_propagate) | {"llama_tokens": 135, "file": "Correctness_Algebras_N_Algebras", "length": 1} |
[STATEMENT]
lemma max_ipv4_addr_max[simp]: "\<forall>a. a \<le> max_ipv4_addr"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>a. a \<le> max_ipv4_addr
[PROOF STEP]
by(simp add: max_ipv4_addr_max_word) | {"llama_tokens": 101, "file": "IP_Addresses_IPv4", "length": 1} |
isdefined(:FourierOptics) || include("../src/FourierOptics.jl")
module TestFourierOptics
using Base.Test
using FourierOptics
using FourierOptics.CoordinateTransforms
using FourierOptics.Regions
Base.isapprox(x::NTuple{2,Real}, y::NTuple{2,Real}; kwds...) =
(isapprox(x[1], y[1]; kwds...) && isapprox(x[2], y[2]; ... | {"hexsha": "9e2a67021c7aed1c4668b5ac41bcfe423d004137", "size": 1990, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "emmt/FourierOptics.jl", "max_stars_repo_head_hexsha": "8d55510b85c6e31f84522db2a68df7a9db183c1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3... |
#!/usr/bin/env python
#
# Copyright (C) 2017 - Massachusetts Institute of Technology (MIT)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option... | {"hexsha": "3ec22234523dec385c72cec8a6d18e184d0c93d0", "size": 7847, "ext": "py", "lang": "Python", "max_stars_repo_path": "SEAS_Main/Physics/astrophysics.py", "max_stars_repo_name": "zhuchangzhan/SEAS", "max_stars_repo_head_hexsha": "d844ceecc54a475a5384925f45a2078eef3416ee", "max_stars_repo_licenses": ["MIT"], "max_s... |
SUBROUTINE loop36_F77(N, x, e)
INTEGER i, N
REAL*8 x(N), e(N)
DO i=1,N
x(i) = exp(e(i));
END DO
RETURN
END
SUBROUTINE loop36_F77Overhead(N, x, e)
INTEGER i, N
REAL*8 x(N), e(N)
RETURN
END
| {"hexsha": "225e734c0907d10fe9cd32a1f76f6bde293221bf", "size": 274, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "depspawn-blitz-0.10/benchmarks/loop36f.f", "max_stars_repo_name": "fraguela/depspawn", "max_stars_repo_head_hexsha": "b5760f4c0d38a1b245ee5274e2ccc5c5fe2d3d45", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma \<psi>_nonneg [intro]: "\<psi> x \<ge> 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 \<le> \<psi> x
[PROOF STEP]
unfolding \<psi>_def sum_upto_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 \<le> sum mangoldt {i. 0 < i \<and> real i \<le> x}
[PROOF STEP]
by (intro sum_nonneg mangoldt... | {"llama_tokens": 157, "file": "Prime_Number_Theorem_Prime_Counting_Functions", "length": 2} |
import sys
import torch
import numpy as np
device = torch.device('cuda')
epsilon = 0.031
# epsilon -= np.finfo(np.float32(1.0)).eps
x = np.load('data_attack/cifar10_X.npy')
x = torch.tensor(x, device=device)
xadv = torch.load(sys.argv[1], map_location=device)['adv_complete']
xadv = xadv.permute(0, 2, 3, 1).contiguous... | {"hexsha": "9565d05e9829b70bf857d31ec5a08c6df72be15c", "size": 471, "ext": "py", "lang": "Python", "max_stars_repo_path": "convert.py", "max_stars_repo_name": "admk/TRADES", "max_stars_repo_head_hexsha": "e84865c5b9f5e19af89d44c61e0171a05caa1bee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
import moviepy.editor
from pydub import AudioSegment
import librosa as lr
import numpy as np
# GLOBAL VARIABLES
sound_vid = ['Muppets-03-04-03','Muppets-02-01-01','Muppets-02-04-04']
sound_path = ['Muppets-03-04-03-sound/','Muppets-02-01-01-sound/','Muppets-02-04-04-sound/']
sound_target = ['Muppets-03-04-03-sound.csv... | {"hexsha": "30f3fb661cb84b6f6b1d648bb35dc9af76814543", "size": 3583, "ext": "py", "lang": "Python", "max_stars_repo_path": "sm2-classical/create_sound.py", "max_stars_repo_name": "Bassileios/similarityModeling", "max_stars_repo_head_hexsha": "544f266cbca2fe92253a7526fc3d0232b1667692", "max_stars_repo_licenses": ["BSD-2... |
import numpy as np
import MakeAndPlotDim as ploter
import AutoDimUtil as util
N_DIMS = 10 # DIM size
DNA_SIZE = N_DIMS # DNA (real number)
DNA_BOUND = [1, N_DIMS + 1] # solution upper and lower bounds
N_GENERATIONS = 8000
POP_SIZE = 100 # population size
HALF_POP_SIZE = int(POP_SIZE/2)
N_KI... | {"hexsha": "c330aae6e5a0c0ca5345eea42f8004429e905919", "size": 6180, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorial-contents/DimAutoLayout/EVOForDim.py", "max_stars_repo_name": "lidegao899/Evolutionary-Algorithm", "max_stars_repo_head_hexsha": "2b36038ecfe6d7bc848eb8ee72d66f9b0f5ff265", "max_stars_repo... |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import numpy as np
import os
import sys
from observations.util import maybe_download_and_extract
def strike(path):
"""Strike Duration Data
a cross-section from 1968 to... | {"hexsha": "214b0eadf298d034e8b0b6db4290755c716437be", "size": 1466, "ext": "py", "lang": "Python", "max_stars_repo_path": "observations/r/strike.py", "max_stars_repo_name": "hajime9652/observations", "max_stars_repo_head_hexsha": "2c8b1ac31025938cb17762e540f2f592e302d5de", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
#ifndef BOOST_SMART_PTR_DETAIL_SP_FORWARD_HPP_INCLUDED
#define BOOST_SMART_PTR_DETAIL_SP_FORWARD_HPP_INCLUDED
// MS compatible compilers support #pragma once
#if defined(_MSC_VER)
# pragma once
#endif
// detail/sp_forward.hpp
//
// Copyright 2008,2012 Peter Dimov
//
// Distributed under the Boost Software License... | {"hexsha": "5de47dd20029a05f6c02853fbedf0c87171cd989", "size": 767, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/smart_ptr/detail/sp_forward.hpp", "max_stars_repo_name": "ballisticwhisper/boost", "max_stars_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_stars_repo_licenses": ["BSL-1.0... |
# Goal:
# Read the Grease/no Grease data files and plot the graph
import sys
import os
from decimal import Decimal
from spikesafe_python.SpikeSafeError import SpikeSafeError
from matplotlib import pyplot as plt
import numpy as np
slow_sampling_string = "SLOWLOG"
medium_sampling_string = "MEDIUMLOG"
fast_sampling_... | {"hexsha": "6bd4b15f65465b52a7311db424f9e5fb6979f9bd", "size": 6541, "ext": "py", "lang": "Python", "max_stars_repo_path": "application_specific_examples/making_transient_dual_interface_measurement/produce_conductive_thermal_resistance.py", "max_stars_repo_name": "sjdemartini/SpikeSafePythonSamples", "max_stars_repo_he... |
# Assignment 1
**CS283 Computer Vision, Harvard University, Fall 2019**
**Due Wednesday, Sep. 18, at 5:00pm**
Name: *(<font color=red>fill name here</font>)*
---
The intended outcomes of this assignment are for you to be familiar with Python as a tool for manipulating images and to deepen your understanding of mode... | {"hexsha": "ba78fa7fdbde915fcfc9ce48239e33a39009677e", "size": 23229, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "pset1 (1).ipynb", "max_stars_repo_name": "nilavghosh/ProtoBuf_Net", "max_stars_repo_head_hexsha": "ee23634dbbb89cc2b91f8054d7b683e4405b964e", "max_stars_repo_licenses": ["Apache-2.0"... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
//===========================... | {"hexsha": "b174292759eb33fc063252d35c472cf6e237d0cc", "size": 1829, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/detail/constant/powlowlim.hpp", "max_stars_repo_name": "xmar/pythran", "max_stars_repo_head_hexsha": "dbf2e8b70ed1e4d4ac6b5f26ead4add940a72592", "max_stars_repo_licenses": ["B... |
"""
Copyright (c) 2018, University of Oxford, Rama Cont and ETH Zurich, Marvin S. Mueller
lobpy - model - lob_linear
Defines classes for linear SPDE models:
LOBLinearTwoFactor: Linear 2-factor models
LOBLinearTwoFactorS: Linear 2-factor models with rescaling to fit the order book profile
OrderVolumeMeanRev: Re... | {"hexsha": "53ea375f283a09c1aa08bfc4356cf1a8b5c2b95a", "size": 18969, "ext": "py", "lang": "Python", "max_stars_repo_path": "lobpy/models/loblinear.py", "max_stars_repo_name": "bohblue2/lobpy", "max_stars_repo_head_hexsha": "6bb1c0c70d44ab7d93761e22dd3d42148b469a37", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
(* Title: HOL/Hoare/Arith2.thy
Author: Norbert Galm
Copyright 1995 TUM
More arithmetic. Much of this duplicates ex/Primes.
*)
theory Arith2
imports Main
begin
definition cd :: "[nat, nat, nat] \<Rightarrow> bool"
where "cd x m n \<longleftrightarrow> x dvd m \<and> x dvd n"
definition gcd :: ... | {"author": "landonf", "repo": "isabelle-legacy", "sha": "e40f3ca7e9a42bb91e57fd15f969388e6e83f692", "save_path": "github-repos/isabelle/landonf-isabelle-legacy", "path": "github-repos/isabelle/landonf-isabelle-legacy/isabelle-legacy-e40f3ca7e9a42bb91e57fd15f969388e6e83f692/src/HOL/Hoare/Arith2.thy"} |
import numpy as np
from collections import deque
from PIL import (
Image,
ImageSequence
)
import tensorflow as tf
def get_val_train_indices(length, fold, ratio=0.8):
if ratio <= 0 or ratio > 1:
raise ValueError("Train/total data ratio must be in range (0.0, 1.0]")
np.random.seed(0)
indi... | {"hexsha": "5ab334bb2542e1b0ad120e468e007d68518df942", "size": 2669, "ext": "py", "lang": "Python", "max_stars_repo_path": "tftrt/examples/nvidia_examples/unet_medical_tf2/utils.py", "max_stars_repo_name": "tanayvarshney/tensorrt", "max_stars_repo_head_hexsha": "4c3b61837e2e696caa6c09d35577be93beacaca1", "max_stars_rep... |
\chapter{Bla}
There is something called \acrlong{MIDI}, which is abbreviated \acrshort{MIDI}. This process is similar to that used for the \acrfull{MIDI}.
\newpage
| {"hexsha": "d207671587909db89cf604dd17460dbfcb9433b4", "size": 166, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "thesis/appa.tex", "max_stars_repo_name": "peter-parque/tiny-trainable-instruments", "max_stars_repo_head_hexsha": "6849aa78b337e7a2092b759aa927758fcc5b9d5b", "max_stars_repo_licenses": ["MIT"], "max_... |
from collections import namedtuple
import numpy as np
from keras.utils import to_categorical
from constants import NO_ENTITY_TOKEN, MAX_LEN, PAD, MAX_LEN_CHAR
from .data_processor import numericalize
from .vocab import TextVocab, LabelVocab, PosVocab, CharacterVocab
def load_dataset():
# load examples
train... | {"hexsha": "2395abb5b0d9e786431738a6542ccdcd93c1361e", "size": 3604, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/api.py", "max_stars_repo_name": "lbasek/named-entity-recognition", "max_stars_repo_head_hexsha": "d21e41442b67161285efe02a6cb032ce63b8ecf2", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import os
import sys
import numpy as np
import math
class level2():
def __init__(self):
pass
def chunks(self, l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
def checkLeft(self,v,row,col,arr):
return not... | {"hexsha": "ed353fd319b3430e8d022fcca1e5de51261e2819", "size": 2140, "ext": "py", "lang": "Python", "max_stars_repo_path": "Catalyst-Coding/level3.py", "max_stars_repo_name": "userforbidden/Python-Samples", "max_stars_repo_head_hexsha": "95c4cd12d59ff2f974744c335597ca3e3dcb9f2d", "max_stars_repo_licenses": ["MIT"], "ma... |
Citizens Who Care provides social support services to senior citizens older adults and their family caregivers through In Home, Convalescent Hospital and Time Off for Caregivers programs. These services are provided to residents of Yolo County and neighboring communities by trained, caring Volunteer Opportunities vol... | {"hexsha": "88a4d35986a4b140a8d81ea5a07d8af3197320dd", "size": 755, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Citizens_Who_Care.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma bal_substitute: "\<lbrakk>bal (Node (ls@(a,b)#rs) t); height t = height c; bal c\<rbrakk> \<Longrightarrow> bal (Node (ls@(c,b)#rs) t)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>bal (Node (ls @ (a, b) # rs) t); height t = height c; bal c\<rbrakk> \<Longrightarrow> bal (Node (ls @ (c, ... | {"llama_tokens": 323, "file": "BTree_BPlusTree", "length": 2} |
import numpy as np
from tensorflow.keras import utils
import math
import random
from tensorflow.keras.preprocessing.sequence import pad_sequences
class SequenceBuckets(utils.Sequence):
"""
Sequence bucket padding
Args:
sequences - (list) A list of sequences of tokens
choose_length - (functi... | {"hexsha": "25ea78e34ebe3bf65aba614cdba9635c83c5be9e", "size": 2176, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/buckets.py", "max_stars_repo_name": "xiaojoey/eNS", "max_stars_repo_head_hexsha": "e3b1b51c7027f5b6845cbffe5a035dc30c9bb5b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
# flake8: noqa
import tempfile
import nltk
import numpy as np
from ..preprocessing.nlp import tokenize_words
from ..ngram import AdditiveNGram, MLENGram
from ..utils.testing import random_paragraph
class MLEGold:
def __init__(
self, N, K=1, unk=True, filter_stopwords=True, filter_punctuation=True
):... | {"hexsha": "0fd4252d39b73ef147d5405084543b8526eb1b4b", "size": 8272, "ext": "py", "lang": "Python", "max_stars_repo_path": "book-code/numpy-ml/numpy_ml/tests/test_ngram.py", "max_stars_repo_name": "yangninghua/code_library", "max_stars_repo_head_hexsha": "b769abecb4e0cbdbbb5762949c91847a0f0b3c5a", "max_stars_repo_licen... |
# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python
# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python
# http://lazyprogrammer.me
# Create a Markov model for site data.
import numpy as np
transitions = {}
row_sums = {}
# collect counts
for lin... | {"hexsha": "617863f5db4cec20942828051d972be0c523f370", "size": 890, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/hmm_class/sites.py", "max_stars_repo_name": "JouniVatanen/NLP-and-Deep-Learning", "max_stars_repo_head_hexsha": "2fddcc2c39787713d33d17e80565de4ed073ca60", "max_stars_repo_licenses": ["MIT"], "... |
import matplotlib.pyplot as plt
import netomaton as ntm
import numpy as np
if __name__ == '__main__':
# NKS page 443 - Rule 122R
network = ntm.topology.cellular_automaton(n=100)
# carefully chosen initial conditions
previous_state = [1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, ... | {"hexsha": "de989aea79a8e72f3dd815c5a0b4ea442b7509b1", "size": 1961, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/reversible_ca/rule122R_reverse_demo.py", "max_stars_repo_name": "lantunes/netomaton", "max_stars_repo_head_hexsha": "fef60a787d031c9c7b1eb4ff990f7c12145579ef", "max_stars_repo_licenses": ["A... |
[STATEMENT]
theorem ltl_to_generalized_rabin\<^sub>C_af\<^sub>\<UU>_correct:
assumes "range w \<subseteq> set \<Sigma>"
shows "w \<Turnstile> \<phi> \<longleftrightarrow> accept\<^sub>G\<^sub>R_LTS (ltl_to_generalized_rabin\<^sub>C_af\<^sub>\<UU> \<Sigma> \<phi>) w"
(is "?lhs \<longleftrightarrow> ?rhs")
[PROOF ... | {"llama_tokens": 3756, "file": "LTL_to_DRA_Impl_LTL_Rabin_Impl", "length": 17} |
#!/usr/bin/env python -u
# -*- coding: utf-8 -*-
import os
import wave
import re
import argparse
import textgrid
import linecache
import numpy as np
import random
import soundfile as sf
import re
import itertools
from itertools import product
from subprocess import call
import pandas as pd
import re
... | {"hexsha": "e35fb9f3b1fc82dead645e4638fce942c75f5618", "size": 3012, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval/calcer_nospk.py", "max_stars_repo_name": "DanBerrebbi/AISHELL-4", "max_stars_repo_head_hexsha": "75ea24e547b671eae05aac365e32b3cd09bb3b3c", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
SUBROUTINE ZERRTR( PATH, NUNIT )
*
* -- LAPACK test routine (version 3.1) --
* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..
* November 2006
*
* .. Scalar Arguments ..
CHARACTER*3 PATH
INTEGER NUNIT
* ..
*
* Purpose
* =======
*
* ZERRTR tests the ... | {"hexsha": "40ac95d3de120a2dd386d049773eedbed55b18eb", "size": 16340, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "TESTING/LIN/zerrtr.f", "max_stars_repo_name": "nashmit/lapack-release", "max_stars_repo_head_hexsha": "fd9237f4f4cd45f10a8be62b35e63a702c93328f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
# Copyright 2017 Rice University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | {"hexsha": "d8aea68ca03d71dd8be66d2ee175ac800e00772f", "size": 5156, "ext": "py", "lang": "Python", "max_stars_repo_path": "program_helper/sequence/field_reader.py", "max_stars_repo_name": "jajajaqlt/nsg", "max_stars_repo_head_hexsha": "1873f2b5e10441110c3c69940ceb4650f9684ac0", "max_stars_repo_licenses": ["Apache-2.0"... |
from __init__ import OUTPUT
print("run _chemprop.sh in chemprop conda environment")
print("run _grover.sh in grover conda environment")
import pandas as pd
import numpy as np
import os
ROOT = os.path.dirname(os.path.abspath(__file__))
print("DEEP LEARNING PREDICTIONS")
df = pd.read_csv(os.path.join(OUTPUT, "data_1... | {"hexsha": "3d141c52b39c865c843e0366a9bf70979669f660", "size": 1455, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/13_deepactivity.py", "max_stars_repo_name": "ersilia-os/osm-series4-candidates-2", "max_stars_repo_head_hexsha": "a0b7f55d79c65182dcc4c102791d2ababbfb176e", "max_stars_repo_licenses": ["MI... |
import numpy as np
from matplotlib import pyplot as plt
np.set_printoptions(precision=6)
def plotData(X, y):
"""Plots the data points X and y into a new figure
plotData(x,y) plots the data points with + for the positive examples
and o for the negative examples. X is assumed to be a Mx2 matrix.
:para... | {"hexsha": "d580390d2962ce2658e0050dc0c97afebbe7437a", "size": 3880, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine-learning-python/machine-learning-ex6/ex6.py", "max_stars_repo_name": "StevenPZChan/ml_dl_coursera_Andrew_Ng", "max_stars_repo_head_hexsha": "c14f3490007392c16547e429c3028e9c80221013", "max... |
from datetime import datetime
import warnings
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.tsa.stattools import adfuller as ADF
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
i... | {"hexsha": "b8d1d6c23332c2e10ddd57cb30d17e2d730dca1f", "size": 9831, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/data_analysis/data_analysis.py", "max_stars_repo_name": "fakecoinbase/ChenYuHoslashvnpy", "max_stars_repo_head_hexsha": "928c83b5cf970f31554fe72da4fb89f4afc4221b", "max_stars_repo_license... |
import numpy as np
from scipy.ndimage import convolve1d as convolve1d_serial
from scipy.signal import lfilter as lfilter_serial
from nitime.algorithms import multi_taper_psd as multi_taper_psd_serial
from ecogdata.filt.time.blocked_filter import bfilter as bfilter_serial
from ecogdata.filt.time.blocked_filter import o... | {"hexsha": "5e045c0f78d04c5c813ded360cf4b4dd04627819", "size": 2822, "ext": "py", "lang": "Python", "max_stars_repo_path": "ecogdata/parallel/split_methods.py", "max_stars_repo_name": "miketrumpis/ecogdata", "max_stars_repo_head_hexsha": "ff65820198e69608634c12686a86b97ac3a77558", "max_stars_repo_licenses": ["BSD-3-Cla... |
import os
import numpy as np
import pytest
from jina import Document, DocumentArray
from ...custom_image_torch_encoder import CustomImageTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture
def encoder(tmpdir):
model_state_dict_path = os.path.join(cur_dir, '../model/model_state_dic... | {"hexsha": "5b1aad84e69245bfc280e0a89f5461761c5e78cb", "size": 1491, "ext": "py", "lang": "Python", "max_stars_repo_path": "jinahub/encoders/image/CustomImageTorchEncoder/tests/unit/test_custom_image_torch_encoder.py", "max_stars_repo_name": "makram93/executors", "max_stars_repo_head_hexsha": "9e88b56650ee154e811b8ecfa... |
import numpy as np
import math
def zadoff_chu(u,N,q=0):
cf = N%2
n = np.linspace(0,N-1,N);
return np.exp(-1j*math.pi*u*np.multiply(n,n+cf+2*q)/N)
def barker_13(up_sample_factor):
b13 = np.array([1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1])
return b13.repeat(up_sample_factor, axis=0)
def weier... | {"hexsha": "68a8d5f180b12a4b0cbb9e1f13e7193d68c7de94", "size": 822, "ext": "py", "lang": "Python", "max_stars_repo_path": "sequences.py", "max_stars_repo_name": "efreneau/Ambiguity-Function", "max_stars_repo_head_hexsha": "63828f85ad5d07290d267c42efe6edfd27d45f4f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 4 08:55:48 2018
@author: C Winkler
"""
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 25 16:24:34 2017
@author: C Winkler
"""
import pandas as pd
import numpy as np
from cycler import cycler
import matplotlib.pyplot as plt
import glob, os
from scipy.signal import argr... | {"hexsha": "446cd6f306295df426b9be58fbe272ff1109a1a9", "size": 3560, "ext": "py", "lang": "Python", "max_stars_repo_path": "4_curve_fitting/curve_fitting.py", "max_stars_repo_name": "xi2pi/elastance-function", "max_stars_repo_head_hexsha": "ac3422b55a1958fe0ce579a2b49a977545159ccd", "max_stars_repo_licenses": ["Apache-... |
function test01 ( input_file_name )
%*****************************************************************************80
%
%% TEST01 tests STLA_CHECK.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 24 September 2005
%
% Author:
%
% John Burkardt
%
fprintf ( 1, '\n' ... | {"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/stla_io/stla_io_test01.m"} |
from ReID_CNN.Model_Wrapper import ResNet_Loader
from track import Track
import argparse
import numpy as np
import cv2
import os
from scipy.misc import imsave
from progressbar import ProgressBar
import sys
import pathlib
import pickle
def import_pkl(pkl_file):
with open(pkl_file, 'rb') as f:
return pickl... | {"hexsha": "73d038c28370d64a4c6c97a516064f3b706dbd39", "size": 3947, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/AIC2018_iamai/ReID/SCT.py", "max_stars_repo_name": "gordonjun2/CenterTrack", "max_stars_repo_head_hexsha": "358f94c36ef03b8ae7d15d8a48fbf70fff937e79", "max_stars_repo_licenses": ["MIT"], "max_... |
open import Prelude renaming (_<_ to _N<_)
module Implicits.Resolution.GenericFinite.Algorithm where
open import Induction.WellFounded
open import Induction.Nat
open import Data.Vec
open import Data.List
open import Data.Fin.Substitution
open import Data.Nat.Base using (_<′_)
open import Data.Maybe
open import Data.N... | {"hexsha": "2377d1eb8d548ba65cd0f1e56248495f0bdf286a", "size": 3995, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Implicits/Resolution/GenericFinite/Algorithm.agda", "max_stars_repo_name": "metaborg/ts.agda", "max_stars_repo_head_hexsha": "7fe638b87de26df47b6437f5ab0a8b955384958d", "max_stars_repo_license... |
[STATEMENT]
lemma lookup_monom_mult_plus:
"lookup (monom_mult c t p) (t \<oplus> v) = (c::'b::semiring_0) * lookup p v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lookup (monom_mult c t p) (t \<oplus> v) = c * lookup p v
[PROOF STEP]
by (simp add: lookup_monom_mult term_simps) | {"llama_tokens": 124, "file": "Polynomials_MPoly_Type_Class", "length": 1} |
[STATEMENT]
lemma LANDAU_PROD'_fold:
"BIGTHETA_CONST e \<Theta>(\<lambda>_. d) = BIGTHETA_CONST (e*d) \<Theta>(eval_primfuns [])"
"LANDAU_PROD' c (\<lambda>_. 1) = LANDAU_PROD' c (eval_primfuns [])"
"eval_primfun f = eval_primfuns [f]"
"eval_primfuns fs x * eval_primfuns gs x = eval_primfuns (fs @ gs) x"
[PROOF... | {"llama_tokens": 709, "file": "Landau_Symbols_Landau_Real_Products", "length": 3} |
SUBROUTINE FFREAD (*,CARD)
C
C THIS ROUTINE READS INPUT CARDS IN FREE FIELD OR FIXED FIELD
C FORMATS.
C
C IF READFILE COMMAND IS ENCOUNTERED, IT SWITCHE THE INPUT FILE TO
C THE ONE SPECIFIED BY READFILE UNTIL EOF IS REACHED. THEN IT
C SWITCHES BACK TO THE NORMAL CARD READER. NESTED READ... | {"hexsha": "e8a9f33b01fa3bdb83bb51c0b41fa39dad80cef1", "size": 28403, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/ffread.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count":... |
Require Export Term.
Require Export Formula.
Require Export SignedSubformula.
Require Export Sequent.
Require Import Base.Lists.
Require Import Base.Permutation.
Require Import Tactics.FormulaEquality.
Require Import Tactics.TermEquality.
Require Import Tactics.WellFormedFormulaNoProofs.
Require Import Tactics.TermHas... | {"author": "FLAFOL", "repo": "flafol-coq", "sha": "1803453d86ba8be103b513f2300a377aaf84326b", "save_path": "github-repos/coq/FLAFOL-flafol-coq", "path": "github-repos/coq/FLAFOL-flafol-coq/flafol-coq-1803453d86ba8be103b513f2300a377aaf84326b/Logic/Consistency.v"} |
import scipy.spatial
import numpy as np
def as_unc_core(S,r,theta):
# Compute Auger variance on signal S core distance r, zenith theta
#r should be in m
#theta in deg.
a = 0.865
b = 0.593
c = 0.023
beta = -2.2
theta = theta * np.pi / 180.
avgtheta = 35. * np.pi / 180.
ab1 = a**2 * (1+b*(1/np.cos(th... | {"hexsha": "239cd3778d9c82d16ad3c25ad45f3a1a1fc695fa", "size": 4664, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/comparative/calc_mahal_boot_w_cuts.py", "max_stars_repo_name": "seanpquinn/augerta", "max_stars_repo_head_hexsha": "43862fd6b5360c9b7c5a7b3502fb7738ea2e8d75", "max_stars_repo_licenses": [... |
section \<open>PDDL and STRIPS Semantics\<close>
theory PDDL_STRIPS_Semantics
imports
"Propositional_Proof_Systems.Formulas"
"Propositional_Proof_Systems.Sema"
"Propositional_Proof_Systems.Consistency"
"Automatic_Refinement.Misc"
"Automatic_Refinement.Refine_Util"
begin
no_notation insert ("_ \<triangleright>... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/AI_Planning_L... |
\documentclass{cascadilla-xelatex-biblatex}
\ifthenelse{\boolean{usegb4e}}{}{%
\usepackage{xcolor}
\usepackage{mdframed}
\surroundwithmdframed[backgroundcolor=gray!10!white]{verbatim}
}
\title{Using \texttt{cascadilla-xelatex-biblatex.cls}\thanks{András Bárány,
Bielefeld University,
\url{andras.ba... | {"hexsha": "20fb8ed7bb507467ac907f7ef9b6c632e1583ed6", "size": 4910, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "example.tex", "max_stars_repo_name": "andrasbarany/cascadilla-xelatex-biblatex", "max_stars_repo_head_hexsha": "b222ef349eb05b4412308b43f0e807e2b6548fb2", "max_stars_repo_licenses": ["Unlicense"], "... |
Mike Ivanov and Philip Neustrom founded Davis Wiki in 2004.
Mike has a very informative personal page: Users/MikeIvanov.
| {"hexsha": "8a1190ea50dd8702f48834706cda6f7125a374bf", "size": 122, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Mike_Ivanov.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
"""
Parsers provided by aiida_sirius.
Register parsers via the "aiida.parsers" entry point in setup.json.
"""
from __future__ import absolute_import
from aiida.engine import ExitCode
from aiida.parsers.parser import Parser
from aiida.plugins import CalculationFactory, DataFactory
import numpy... | {"hexsha": "c8573a3fe2f680b54f2edcb768e18898fc2ffba4", "size": 3307, "ext": "py", "lang": "Python", "max_stars_repo_path": "aiida_sirius/parsers/nlcg_cpp.py", "max_stars_repo_name": "simonpintarelli/aiida-sirius", "max_stars_repo_head_hexsha": "5dc968cc4a98a5d0b018f54c4c7023b2a2682795", "max_stars_repo_licenses": ["MIT... |
import numpy as np
import time
import cv2
import requests
import json
from typing import Tuple
import os
class TensorflowNetwork:
def __init__(self):
self.model_uri = 'http://localhost:8501/v1/models/model:predict'
self.init_tensorflow_serve()
@staticmethod
def shape_image(file_route):
... | {"hexsha": "922d3163825e84bb65016bb065e5471dc89e97e0", "size": 1421, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/service/tensorflow_network.py", "max_stars_repo_name": "A-Ortiz-L/hyperspectral-imaging-cnn-final-degree-work", "max_stars_repo_head_hexsha": "e8571a063d24d2ef45039914abe4d59939dfcad8", "max_s... |
import numpy
import svm_funcs
def dataset3_params(x_train, y_train, x_valid, y_valid):
"""
Returns your choice of reg_C and sigma for Part 3 of the exercise
where you select the optimal (reg_C, sigma) learning parameters to use for SVM
with RBF kernel.
Parameters
----------
x_train : arr... | {"hexsha": "8c9c0766c332fb9c65a92453dd1b66ff52ceb71b", "size": 2536, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ex6_support_vector_machines/svm_funcs/dataset3_params.py", "max_stars_repo_name": "ashu-vyas-github/AndrewNg_MachineLearning_Coursera", "max_stars_repo_head_hexsha": "1be5124b07df61f7295dd1... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% University Assignment Title Page
% LaTeX Template
% Version 1.0 (27/12/12)
%
% This template has been downloaded from:
% http://www.LaTeXTemplates.com
%
% Original author:
% WikiBooks (http://en.wikibooks.org/wiki/LaTeX/Title_Creation)
%
% License:
% CC BY-NC-SA 3.0 (http://... | {"hexsha": "86c88aa3ab6668346fd13e4d6e6d9378bd689525", "size": 51038, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "arxiv/prediction_market.tex", "max_stars_repo_name": "kandluis/CS186-Final-Project", "max_stars_repo_head_hexsha": "eec4e6527b857e962262af582f6ed5d5e0f9e08b", "max_stars_repo_licenses": ["Apache-2.... |
"""
#################################
# Find Qmax for the Next State
#################################
"""
#########################################################
# import libraries
import numpy as np
from statefromloc import getstateloc
from copy import deepcopy
########################################... | {"hexsha": "965ae66c15285a2980f08dd17afaf84713e057e8", "size": 3158, "ext": "py", "lang": "Python", "max_stars_repo_path": "findq.py", "max_stars_repo_name": "purbe/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task", "max_stars_repo_head_hexsha": "7434d21a17c0e9c80f0aba5a95e17e5dd3b3480e", "max_s... |
import numpy as np
import multiprocessing
import os
import sqlite3 as sql
import pandas as pd
from itertools import repeat
from contextlib import closing
from tqdm import tqdm
import interaction3.abstract as abstract
from interaction3.mfield.simulations import TransmitBeamplot
from interaction3.mfield.simulations imp... | {"hexsha": "f55cc792ff9e6d4f8f5f3424b51ebd493937bfd6", "size": 6421, "ext": "py", "lang": "Python", "max_stars_repo_path": "interaction3/mfield/scripts/simulate_transmit_beamplot_with_full_data.py", "max_stars_repo_name": "bdshieh/interaction3", "max_stars_repo_head_hexsha": "b44c390045cf3b594125e90d2f2f4f617bc2433b", ... |
"""
n_man(sem_fit::SemFit)
n_man(model::AbstractSemSingle)
Return the number of manifest variables.
"""
function n_man end
n_man(sem_fit::SemFit) = n_man(sem_fit.model)
n_man(model::AbstractSemSingle) = n_man(model.observed) | {"hexsha": "8262a78c0269bc9d6ac7dab003dfdd7e0297fb3e", "size": 235, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/frontend/fit/fitmeasures/n_man.jl", "max_stars_repo_name": "sem-jl/SEM.jl", "max_stars_repo_head_hexsha": "456c30170e34ff07c97667a8fc11e5fb6a80d6a2", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
@authors: Chase Coleman, Balint Szoke, Tom Sargent
"""
import numpy as np
import scipy as sp
import scipy.linalg as la
import quantecon as qe
import matplotlib.pyplot as plt
from scipy.stats import norm, lognorm
class AMF_LSS_VAR:
"""
This class transforms an additive (multipilcative)
functional in... | {"hexsha": "86c32bd1f3525f7f7a9aca9220f911361e38e1d1", "size": 14590, "ext": "py", "lang": "Python", "max_stars_repo_path": "additive_functionals/amflss.py", "max_stars_repo_name": "chenwang/QuantEcon.lectures.code", "max_stars_repo_head_hexsha": "8832a74acd219a71cb0a99dc63c5e976598ac999", "max_stars_repo_licenses": ["... |
import os
import numpy as np
import copy
from tensorboardX import SummaryWriter
import sys
sys.path.append('..')
from ding.config import compile_config
from ding.worker import BaseLearner, BattleSampleSerialCollector, BattleInteractionSerialEvaluator, NaiveReplayBuffer
from ding.envs import SyncSubprocessEnvManager, B... | {"hexsha": "43d5413899a477ef85797abba64eb5f442434215", "size": 3803, "ext": "py", "lang": "Python", "max_stars_repo_path": "my_submission/entry/gobigger_vsbot_baseline_simple_eval.py", "max_stars_repo_name": "abcdcamey/Gobigger-Explore", "max_stars_repo_head_hexsha": "75864164f3e45176a652154147740c34905d1958", "max_sta... |
import torch
from functools import partial
import numpy as np
def collate_fn(batch):
data = {}
imgs = []
depths = []
# sketch_1_1s = []
sketch_1_4s = []
tsdf_1_1s = []
tsdf_1_4s = []
targets = []
# nonempties = []
occ_1_1s = []
mapping_1_1s = []
mapping_1_4s = []
CP_m... | {"hexsha": "0850c1598316dda42880ee94346f5680b53c9e05", "size": 3524, "ext": "py", "lang": "Python", "max_stars_repo_path": "xmuda/data/semantic_kitti/kitti_collate.py", "max_stars_repo_name": "anhquancao/xmuda-extend", "max_stars_repo_head_hexsha": "4b670ec2f6766e3a624e81dbe5d97b209c1c4f76", "max_stars_repo_licenses": ... |
from io import StringIO
from pathlib import Path
import numpy as np
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.pdfpage import PDFPage
from sklearn.ensemble import RandomForestClassifier
from sklea... | {"hexsha": "c5784f2a7c1091c404025de43357b2babc3a1fdb", "size": 4375, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/utils.py", "max_stars_repo_name": "ojwalch/sleep_classifiers", "max_stars_repo_head_hexsha": "5fbed3d9d076d891a75e5de6678b14b11c2ee724", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
$(TYPEDEF)
Defines a total Liouvillian to feed to the solver using the `DiffEqOperator` interface. It contains both closed-system and open-system Liouvillians.
# Fields
$(FIELDS)
"""
struct DiffEqLiouvillian{diagonalization,adiabatic_frame}
"Hamiltonian"
H::AbstractHamiltonian
"Open system in eigenba... | {"hexsha": "50f06b9b948d83d24edabddbc64a5827b26de716", "size": 4941, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/opensys/diffeq_liouvillian.jl", "max_stars_repo_name": "neversakura/QTBase.jl", "max_stars_repo_head_hexsha": "937a3236f1b9578bc223b21817dec2e7a8512ee2", "max_stars_repo_licenses": ["MIT"], "ma... |
import torch
import random
import networkx as nx
from rdkit.Chem import AllChem
from .loader import graph_data_obj_to_nx_simple, nx_to_graph_data_obj_simple
from .loader import MoleculeDataset
def check_same_molecules(s1, s2):
mol1 = AllChem.MolFromSmiles(s1)
mol2 = AllChem.MolFromSmiles(s2)
return All... | {"hexsha": "8a9b5c75881b05137aabeaf5fe9ff5d80deabc58", "size": 18022, "ext": "py", "lang": "Python", "max_stars_repo_path": "contextPred/chem/util.py", "max_stars_repo_name": "thomasly/slgnn", "max_stars_repo_head_hexsha": "caa1e7814498da41ad025b4e62c569fe511848ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from myskimage import gaussian
import numpy as np
import imp
try:
imp.find_module('pyfftw')
pyfftw_installed = True
import pyfftw
except ImportError:
pyfftw_installed = False
class Spectrogram:
fftw_inps = {}
fftw_rfft = {}
han_wins = {}
def __init__(self, use_pyfftw=True):
if... | {"hexsha": "d53a534648e57b05532836c04d8409a7c88fc9c0", "size": 4177, "ext": "py", "lang": "Python", "max_stars_repo_path": "bat_eval/spectrogram.py", "max_stars_repo_name": "bgotthold-usgs/batdetect", "max_stars_repo_head_hexsha": "0d4a70f1cda9f6104f6f785f0d953f802fddf0f1", "max_stars_repo_licenses": ["BSD-Source-Code"... |
import time
import struct
import socket
import logging
from contextlib import closing
import cPickle
import numpy as np
# TODO: verify that the log levels are correct here.
logger = logging.getLogger(__name__)
def get_udp_packets(ri, npkts, streamid, stream_reg='streamid', addr=('192.168.1.1', 12345)):
ri.r.wri... | {"hexsha": "de2a04d303bad8579f82ed8c2ff1d54a594c9cea", "size": 5178, "ext": "py", "lang": "Python", "max_stars_repo_path": "kid_readout/roach/udp_catcher.py", "max_stars_repo_name": "danielflanigan/kid_readout", "max_stars_repo_head_hexsha": "07202090d468669200cab78297122880c1c03e87", "max_stars_repo_licenses": ["BSD-2... |
from RQ2 import t2v_w2v_lstm_k_cross, w2v_lstm_k_cross, t2v_lstm_k_cross
from RQ2.utils import file_opt
import jsonlines
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
from numpy import *
from collections import Counter
import numpy as np
import pandas as pd
import config
def run():
... | {"hexsha": "c1452f59b3e570336750dab284a0d893ab0c66ce", "size": 10762, "ext": "py", "lang": "Python", "max_stars_repo_path": "RQ2/cmp_t2v_w2v.py", "max_stars_repo_name": "SuShu19/TiTIC", "max_stars_repo_head_hexsha": "7dd83a1527ee0e57e354eb7843c75ad2e53d69fc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
Require Import Kami.All Kami.Compiler.Compiler.
Require Import Kami.Notations.
Require Import Kami.Compiler.CompilerSimple.
Section SemSimple.
Local Notation UpdRegT := RegsT.
Local Notation UpdRegsT := (list UpdRegT).
Local Notation RegMapType := (RegsT * UpdRegsT)%type.
Inductive Sem_RmeSimple: (RmeSimpl... | {"author": "sifive", "repo": "Kami", "sha": "ffb77238f27b603dbd42d2622ba911740bf5eadf", "save_path": "github-repos/coq/sifive-Kami", "path": "github-repos/coq/sifive-Kami/Kami-ffb77238f27b603dbd42d2622ba911740bf5eadf/Compiler/CompilerSimpleSem.v"} |
# import the necessary packages
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.ker... | {"hexsha": "94bf4b8d9acfb5581d5605b75b087444cdba14f6", "size": 4258, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainmaskdetector.py", "max_stars_repo_name": "maryamnasir65834/face-mask-detector", "max_stars_repo_head_hexsha": "a31314558620e28c5573cbf87dc128efa8addd6c", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
import matplotlib.pyplot as plt
def get_random(freqs):
freq_vec = sum([[k]*num for k, num in freqs.items()], [])
N = len(freq_vec)
while True:
idx = np.random.randint(0, N)
yield freq_vec[idx]
def write_random_sequence(fn, freqs, n):
with open(fn, "w") as f:
... | {"hexsha": "2e83df72b77f7ad6b64c23e0293df4ad33170a9a", "size": 3119, "ext": "py", "lang": "Python", "max_stars_repo_path": "garageofcode/compression/arithmetic.py", "max_stars_repo_name": "tpi12jwe/garageofcode", "max_stars_repo_head_hexsha": "3cfaf01f6d77130bb354887e6ed9921c791db849", "max_stars_repo_licenses": ["MIT"... |
CHECKSUM = 'VtP-A2'
import argparse
import mlflow
import torch
import time
from torch import optim
from torch.nn import functional as F
from torch.optim import lr_scheduler
from torch.utils.data import Dataset
from torchvision import transforms
from torchsummary import summary
from sklearn.manifold import TSNE
from sk... | {"hexsha": "cd376a86d118a4d0f388d8d4462471dcfbd34866", "size": 14619, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/VtPVAE.py", "max_stars_repo_name": "Micky774/NewRepo", "max_stars_repo_head_hexsha": "d3c6f9882fd7f03ff8c33ca07b9584f587e2451e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "... |
from enum import Enum
import itertools
import operator
import numpy as np
def input_():
return int(input("Please supply a number: "))
def output_(input_value):
print(input_value)
def jump_if_true(condition, target):
if condition != 0:
return target
else:
return None
def jump_if_... | {"hexsha": "c78fd7f7f8e226c8e68ff126fe418a3c5ccd1dd1", "size": 3673, "ext": "py", "lang": "Python", "max_stars_repo_path": "day5/python/main.py", "max_stars_repo_name": "freidrichen/advent-of-code-2019", "max_stars_repo_head_hexsha": "08aca50e86700504d35c934a308a640a95de586e", "max_stars_repo_licenses": ["MIT"], "max_s... |
import json
#import dbmanager
from pprint import pprint
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime
from collections import defaultdict, OrderedDict
"""
Plot/statistics ideas:
- multi-histogram of stress as a function of weekday
- -||- of alkoholi as a funct... | {"hexsha": "989a16aec1ad8f6ed8fd2a24b41072f946180937", "size": 19081, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis.py", "max_stars_repo_name": "fyysikkokilta/hyvinvointibot", "max_stars_repo_head_hexsha": "8fa94dfe051937a1d108b0295a3c5edffdd08dc9", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
Address(Stanford Place) is a residential Culdesacs culdesac in Central Davis.
Intersecting Streets
Sycamore Lane and across the intersection Stanford Drive
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import pickle
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import math
from scipy import signal
objectRep = open("C:\\Users\\asus\\OneDrive\\BSC_brain_math\\year_c\\Yearly\\BCI\\bci4als\\recordings\\adi\\9\\trials.pickle", "rb")
file = pickle.load(objectRep)
all_data = np.zeros... | {"hexsha": "6d13ec8e122a1440249d9bd2f67a0977b2a59f9f", "size": 2770, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/bci2021.py", "max_stars_repo_name": "Chgabri2/bci4als", "max_stars_repo_head_hexsha": "cfa8bfb6190389e473100cc37281c304b6a3bc4a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
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