text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
theorem \<theta>_asymptotics: "\<theta> \<sim>[at_top] (\<lambda>x. x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<theta> \<sim>[at_top] (\<lambda>x. x)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<theta> \<sim>[at_top] (\<lambda>x. x)
[PROOF STEP]
from \<MM>_minus_ln_li... | {"llama_tokens": 4441, "file": "Prime_Number_Theorem_Prime_Number_Theorem", "length": 48} |
import numpy
'''a=list(map(int,input().split()))
n=a[0]
m=a[1]
print( numpy.eye(n, m))'''
import numpy
print(str(numpy.eye(*map(int,input().split()))).replace('1',' 1').replace('0',' 0'))
#helps make identity matrices
| {"hexsha": "6e163b891a054dc976b32d4e88858f948a101dd7", "size": 220, "ext": "py", "lang": "Python", "max_stars_repo_path": "Numpy/eyeandidentity.py", "max_stars_repo_name": "TheG0dfath3r/Python", "max_stars_repo_head_hexsha": "73f40e9828b953c3e614a21a8980eaa81b5c066e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import cv2
import turicreate as tc
from tqdm import tqdm
import numpy as np
from tools.utils.draw import *
from tools.utils.segment import *
def predict_on_video(video_path, model_path, confidence_threshold=0.75, iou_threshold=0.25, target_label=None, num_objs=-1, draw_masks=False, draw_frame_num=True):
model = tc.l... | {"hexsha": "d9c39ec336e5f4dbde09b4738d4add5c7c8e4d67", "size": 3795, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/utils/parse.py", "max_stars_repo_name": "vitae-gravitas/model-tester", "max_stars_repo_head_hexsha": "c6de6f7e26043047fd30c9ed66f4dfb75a68a29b", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Precompute Fraunhofer fixed representations (logSTFT, logMel)
"""
import os
from pathlib import Path
#
from omegaconf import OmegaConf
import numpy as np
#
from d2021umaps.utils import IncrementalHDF5
from d2021umaps.logging import ColorLogger, make_timestamp
from d2... | {"hexsha": "e8bc0e0cfb812d7c32521e012f61a1e17efddd71", "size": 4763, "ext": "py", "lang": "Python", "max_stars_repo_path": "00c_precompute_fraunhofer_fixed.py", "max_stars_repo_name": "andres-fr/dcase2021_umaps", "max_stars_repo_head_hexsha": "0418b256d484a66958763061170bb2346cb6030a", "max_stars_repo_licenses": ["MIT"... |
\subsection{Content Analyzer}
\label{sec:content-analyzer}
\index{Content Analyzer}
For this project the data describing the products, offered by an online shop have been semi-structured.
It was a text file where each line described a product.
An example is given in listing~\ref{lst:product-data}.
\begin{lstlisting}[... | {"hexsha": "6164b915564edd4963cac9c9b6c599aa85dba70f", "size": 19833, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "thesis/inc/implementation/contentanalyzer/contentanalyzer.tex", "max_stars_repo_name": "dustywind/bachelor-thesis", "max_stars_repo_head_hexsha": "be06aaeb1b4d73f727a19029a3416a9b8043194d", "max_st... |
-- An Agda example file
module test where
open import Coinduction
open import Data.Bool
open import {- pointless comment between import and module name -} Data.Char
open import Data.Nat
open import Data.Nat.Properties
open import Data.String
open import Data.List hiding ([_])
open import Data.Vec hiding ([_])
open im... | {"hexsha": "d930a77b6abbd22305d4945b4169cee9ab1a80f0", "size": 2558, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "vendor/bundle/ruby/2.0.0/gems/pygments.rb-0.6.1/vendor/pygments-main/tests/examplefiles/test.agda", "max_stars_repo_name": "agent010101/agent010101.github.io", "max_stars_repo_head_hexsha": "b8bf8... |
/****************************************************************************
*
* fkie_potree_rviz_plugin
* Copyright © 2018 Fraunhofer FKIE
* Author: Timo Röhling
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obt... | {"hexsha": "955dbed0a04680a7d3ecd4df79107c1e4a83b0c2", "size": 8716, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "fkie_potree_rviz_plugin/src/cloud_loader.cpp", "max_stars_repo_name": "fkie/potree_rviz_plugin", "max_stars_repo_head_hexsha": "883c305dd924b8c8ae35c192c087f2bb25899f8d", "max_stars_repo_licenses": ... |
// Copyright (c) 2007-2013 Hartmut Kaiser
// Copyright (c) 2011 Bryce Lelbach
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#if !defined(HPX_UTIL_FULLEMPTYSTORE_JUN_16_2008_0128APM)
#define HPX_UTIL_F... | {"hexsha": "02eb15eb5953f8c048f2011af7dd6337c2cf8797", "size": 17787, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "hpx/lcos/detail/full_empty_entry.hpp", "max_stars_repo_name": "andreasbuhr/hpx", "max_stars_repo_head_hexsha": "4366a90aacbd3e95428a94ab24a1646a67459cc2", "max_stars_repo_licenses": ["BSL-1.0"], "m... |
import cv2
import numpy as np
import time
cv2.namedWindow('Mywindow')
cameraCapture = cv2.VideoCapture(0)
#cameraCapture.set(3,64)
#cameraCapture.set(4,64)
success, image = cameraCapture.read(0)
while success and cv2.waitKey(1) == -1:
image = cv2.resize(image, (64, 64),interpolation=cv2.INTER_AREA)
cv2.imshow('Myw... | {"hexsha": "d221e0837c81dcc2e1726953735f56afdab09122", "size": 559, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera.py", "max_stars_repo_name": "Thisislegit/RTCS_hand-_counting_project", "max_stars_repo_head_hexsha": "1c6bbc48cab9dd579809e0919ec00e2be3721dac", "max_stars_repo_licenses": ["MIT"], "max_star... |
from __future__ import absolute_import
from tensorflow.keras import activations, constraints, initializers, regularizers
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer, Dropout, LeakyReLU, Dense, Concatenate,Reshape
import tensorflow as tf
import numpy as np
import pdb
#Custom Lay... | {"hexsha": "b7cf183fbaa8cd02b46ffb7ee70f2157d3200937", "size": 964, "ext": "py", "lang": "Python", "max_stars_repo_path": "ncaabGNNs/src/extract_team_GAT.py", "max_stars_repo_name": "joewilaj/sportsGNNs", "max_stars_repo_head_hexsha": "78beb1a7908afa0ff2c6b2d425f4e81fd7dee3c4", "max_stars_repo_licenses": ["MIT"], "max_... |
struct Start <: EdgeModifier
s
end
@testset "Unmeta Node" begin
@test unmeta(Node(:a)) == Node(:a)
@test unmeta(Node(:a) * Start(4)) == Node(:a)
end
@testset "Unmeta Edge" begin
@test unmeta(Edge(Node(:a), Node(:b))) == Edge(Node(:a), Node(:b))
@test unmeta(Edge(Node(:a), Node(:b)) * Start(4)) == ... | {"hexsha": "2df7f8c2b1ab787c01d53904adcc55ec8ed2c840", "size": 350, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/keep.jl", "max_stars_repo_name": "aaronpeikert/Semi.jl", "max_stars_repo_head_hexsha": "7fbb585ccd8068c3fc48077693d9fff71a024561", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max... |
"""
Collection of tests for unified device functions
"""
# global
import math
import pytest
import numpy as np
from numbers import Number
# local
import ivy
import ivy.functional.backends.numpy
import ivy_tests.test_ivy.helpers as helpers
# Tests #
# ------#
# Device Queries #
# dev
@pytest.mark.parametrize("x", ... | {"hexsha": "6b200402418eb2e91b33f481f753b719c2bfd9fd", "size": 14729, "ext": "py", "lang": "Python", "max_stars_repo_path": "ivy_tests/test_ivy/test_functional/test_core/test_device.py", "max_stars_repo_name": "mattbarrett98/ivy", "max_stars_repo_head_hexsha": "a706e59b907c0f78edb819959cc2035ebf48946f", "max_stars_repo... |
from __future__ import absolute_import
import numpy as np
from sklearn.metrics import pairwise_distances
from graphs import Graph
__all__ = ['incremental_neighbor_graph']
def incremental_neighbor_graph(X, precomputed=False, k=None, epsilon=None,
weighting='none'):
'''See neighbor_g... | {"hexsha": "a3349b2b40d0e93044d2c461f3bf11ac984eb63e", "size": 1809, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphs/construction/incremental.py", "max_stars_repo_name": "vishalbelsare/graphs", "max_stars_repo_head_hexsha": "4fbeb025dfe33340335f34300f58dd3809228822", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 25 08:30:01 2021
@author: Ngoc Anh
"""
from .MovieLens import MovieLens
from surprise import KNNBasic
from surprise import NormalPredictor
from .Evaluator import Evaluator
import random
import numpy as np
def LoadMovieLensData():
ml = MovieLens()
print("Loading... | {"hexsha": "61e26f1c06755c02e5b5f9ea9a809852751c6048", "size": 1410, "ext": "py", "lang": "Python", "max_stars_repo_path": "my_code/Process.py", "max_stars_repo_name": "anhtpn/app_flask", "max_stars_repo_head_hexsha": "ba1509a9bffdec8c4e6c5c98d211d75e3d87541f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import matplotlib.pyplot as plt
import numpy as np
import os
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.gridspec as gridspec
sample_num = 5
def connect_2D_line(inputs_use, sample_num):
# sample_joint = np.reshape( np.asarray(s), (16,2))
sample_joint = np.reshape( np.asarray(inputs_use[sample_num]... | {"hexsha": "022fa02e2cbb4106ed3651e5702b31c1cf874551", "size": 6177, "ext": "py", "lang": "Python", "max_stars_repo_path": "joint_visualization.py", "max_stars_repo_name": "damonchang23/3d_pose_baseline_pytorch", "max_stars_repo_head_hexsha": "5fedd6b2026be43155829a87d5c7ba6d5db64af6", "max_stars_repo_licenses": ["MIT"... |
import argparse
import os
import torch
import json
import numpy as np
import src.utils.interface_train_tool as train_tool
import src.utils.interface_audio_io as audio_io
import matplotlib.pyplot as plt
import src.trainers.trainer as trainer
import src.trainers.tester as tester
import src.utils.interface_tensorboard as ... | {"hexsha": "d7b583004a825117f2f701077401c48cd90fff02", "size": 2853, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/extract_latent_space.py", "max_stars_repo_name": "waverDeep/WaveBYOL", "max_stars_repo_head_hexsha": "ab062c26598e0fa6ab8426498f9920048988b5c1", "max_stars_repo_licenses": ["MIT"], "max_... |
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from src.embed import L2Embedding as Embedding
from src.module import Encoder, Decoder, Postnet, CBHG, Linear
#from src.util import get_audio_feat_mask
class Tacotron2(nn.Module):
"""Tacotron2 text-to-speech model (w/o stop predi... | {"hexsha": "f479e58ffc4c6a7806f98758c5b8cb79f85a048c", "size": 3683, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tts.py", "max_stars_repo_name": "ttaoREtw/semi-tts", "max_stars_repo_head_hexsha": "46750fc68d1547e82bda9341f5029595ded984c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 40, "max_s... |
////////////////////////////////////////////////////////////////////////
//
// This file is part of gmic-8bf, a filter plug-in module that
// interfaces with G'MIC-Qt.
//
// Copyright (c) 2020, 2021 Nicholas Hayes
//
// This file is licensed under the MIT License.
// See LICENSE.txt for complete licensing and attributi... | {"hexsha": "345cbe6d2abc8c53194f77660b3a16bf3c1c49d4", "size": 12331, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/win/ClipboardUtilWin.cpp", "max_stars_repo_name": "ganego/gmic-8bf", "max_stars_repo_head_hexsha": "ee49ae507da60d648df582772163e059faa9f4f1", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
```python
from IPython.display import Image
Image('../../../python_for_probability_statistics_and_machine_learning.jpg')
```
# Support Vector Machines
Support Vector Machines (SVM) originated from the statistical learning theory
developed by Vapnik-Chervonenkis. As such, it represents a deep applica... | {"hexsha": "8bf326becb141dc929f1ec4ead3eac5c14561ade", "size": 236626, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "chapters/machine_learning/notebooks/svm.ipynb", "max_stars_repo_name": "nsydn/Python-for-Probability-Statistics-and-Machine-Learning", "max_stars_repo_head_hexsha": "d3e0f8ea475525a... |
{-
True
0
False
False
4
42
42
True
False
[33, 42, 42, 42, 42]
[42, 42, 33, 42, 42]
[42, 42, 42, 42, 33]
[33, 42, 42, 42]
[42, 42, 33, 42]
[42, 42, 42, 33]
False
True
-}
import Data.Vector
main : IO ()
main = do
let e = the (Vector Int) empty
printLn (null e)
printLn (length e)
printLn (elem 42 e)
let a = re... | {"hexsha": "2a9896936aea44753a6b0e1cd06d05375a37a551", "size": 837, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Tests/Vector.idr", "max_stars_repo_name": "timjs/iris-clean", "max_stars_repo_head_hexsha": "b2ed1f982beec936cb6fe32e8fa6b97a1da4a4f6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 146, "... |
C Copyright(C) 1999-2020 National Technology & Engineering Solutions
C of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with
C NTESS, the U.S. Government retains certain rights in this software.
C
C See packages/seacas/LICENSE for details
SUBROUTINE LINE3 (COORD, NUMNP, DIST, T, NDIM... | {"hexsha": "0b57d85ddca2d18e8aa274d5420295cabb30d141", "size": 3495, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/numbers/nu_line3.f", "max_stars_repo_name": "jschueller/seacas", "max_stars_repo_head_hexsha": "14c34ae08b757cba43a3a03ec0f129c8a168a9d3", "max_stars_repo_licenses": [... |
/**
* Copyright (C) 2012 ciere consulting, ciere.com
* Copyright (C) 2011, 2012 Object Modeling Designs
*
* Distributed under the Boost Software License, Version 1.0. (See accompanying
* file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
*
*
*/
#ifndef CIERE_JSON_IO_IMPL_HP... | {"hexsha": "0b310382052277597cc06ec1ea1d648ef775a45d", "size": 6120, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/spirit/example/qi/json/json/detail/io_impl.hpp", "max_stars_repo_name": "Abce/boost", "max_stars_repo_head_hexsha": "2d7491a27211aa5defab113f8e2d657c3d85ca93", "max_stars_repo_licenses": ["BSL-... |
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np
# 定义模型
if False: # 一种定义模型的写法
model1 = Sequential([
Dense(32, units=784),
Activation('relu'),
Dense(10),
Activation('softmax'),
])
if False: # 一种定义模型的写法
model2 = Sequential()
... | {"hexsha": "5ec818a97cacf311726f4c886fb22bc7452e2854", "size": 942, "ext": "py", "lang": "Python", "max_stars_repo_path": "kkeras/sequential_first_try.py", "max_stars_repo_name": "daigouwei/TensorFlow", "max_stars_repo_head_hexsha": "3716b1cdf79f9203adfc2bc77eb3a367a153cc22", "max_stars_repo_licenses": ["Apache-2.0"], ... |
# -*- coding: utf-8 -*-
"""
Copyright 2020 Andrea López Incera.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
Please acknowledge the authors when re-using this code and maintain this notice intact.
Code written by Andrea López I... | {"hexsha": "2f08f9a7e5f29f666a7083e8369d18f210e5dca2", "size": 6353, "ext": "py", "lang": "Python", "max_stars_repo_path": "learning.py", "max_stars_repo_name": "qic-ibk/CollectiveStinging", "max_stars_repo_head_hexsha": "da59a19e9e4cab6dbfd91bfb63d982bfd4ee0f70", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
#include <stdlib.h>
#include <math.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_permutation.h>
#include <gsl/gsl_randist.h>
#include <gsl/gsl_rng.h>
#include "design.h"
/*This function computes the p-distance between 2 points in D dimensions:
the exponent p is tunable*/
double distance(double *x,double *y,int D... | {"hexsha": "f99fec28cf49bce2c454c7f2beae670453f83130", "size": 5389, "ext": "c", "lang": "C", "max_stars_repo_path": "lenstools/extern/design.c", "max_stars_repo_name": "asabyr/LensTools", "max_stars_repo_head_hexsha": "e155d6d39361e550906cec00dbbc57686a4bca5c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.... |
'''This script will perform a classification task on the data generated in sim_data.py.
Each positive sample has approximately half it's variants a specific sequence.
It is a simple task so should quickly achieve perfect accuracy unless you start with bad weights.
Note: the loss will be much higher than the cross entro... | {"hexsha": "4064535a4ff1ca8023bbd401ae9c122e3644c0ef", "size": 5256, "ext": "py", "lang": "Python", "max_stars_repo_path": "figures/controls/samples/sim_run.py", "max_stars_repo_name": "OmnesRes/ATGC", "max_stars_repo_head_hexsha": "c4fc4d6a0ac99bf083232686dcd0b634ff597f8a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import torch
import numpy as np
import torch.nn as nn
import warnings
warnings.filterwarnings('ignore')
if torch.cuda.is_available():
device = 'cuda'
else :
device = 'cpu'
def entropy_threshold(teacher,confidence_loader,n_class):
dct = {}
sample_entropy = {}
soft = nn.Softmax()
for i in rang... | {"hexsha": "b9368fdceb17df772463c7191e5b3358766f1f29", "size": 1401, "ext": "py", "lang": "Python", "max_stars_repo_path": "entropy.py", "max_stars_repo_name": "shauryat97/SampleSelectionBasedKnowledgeDistillation", "max_stars_repo_head_hexsha": "13cb7e201378230cada0cc7476d1b517da75129e", "max_stars_repo_licenses": ["M... |
/*
Copyright (c) 2016, 2020, Arvid Norberg
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and... | {"hexsha": "0b5c5351084b83272316284995bc5fee7a99732f", "size": 3585, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/libtorrent/aux_/store_buffer.hpp", "max_stars_repo_name": "bitwiseworks/libtorrent-os2", "max_stars_repo_head_hexsha": "6bb656e0938ee517b87ecdc3f9309890691a0d11", "max_stars_repo_licenses": ... |
function convective_adjust!(x)
# remove negative gradients from temperature profile
for i in length(x)-3:-1:2
if x[i] > x[i+1]
if x[i-1] > x[i]; x[i] = x[i+1]
else; x[i] = (x[i-1]+x[i+1])/2
end
end
end
end
| {"hexsha": "26ddac46de15d47d64fd46c5f7fbe62e23049f75", "size": 270, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/data/convective_adjust.jl", "max_stars_repo_name": "adelinehillier/LearnConvection", "max_stars_repo_head_hexsha": "2a5b0cebe1a31777578293d59bff60b0808343b0", "max_stars_repo_licenses": ["MIT"],... |
function p = vonMises_prob( x, m, k, use_log )
%VONMIS_PROB Calculates the probability of x coming from a Von Mises
%distribution with mean mu and concentration parameter k.
% p = vonMises_prob( x, m, k, use_log )
if nargin < 4, use_log = 0; end
[d N] = size(x);
m = m(:);
M = m*ones(1,N);
denom = (2*pi)*besseli(0... | {"author": "bayesnet", "repo": "bnt", "sha": "bebba5f437b4e1e29169f0f3669df59fb5392e62", "save_path": "github-repos/MATLAB/bayesnet-bnt", "path": "github-repos/MATLAB/bayesnet-bnt/bnt-bebba5f437b4e1e29169f0f3669df59fb5392e62/KPMstats/vonMises_prob.m"} |
function extract_params!(pm)
if haskey(pm, "user_defined_params")
user_defined_params = pm["user_defined_params"]
pm = delete!(pm, "user_defined_params")
# else
# user_defined_param = NaN
end
return pm, user_defined_param
end
| {"hexsha": "4bf1f6fb194bef239890b34535036939fb658242", "size": 266, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/input/tools.jl", "max_stars_repo_name": "lvzhibai/PandaModels.jl", "max_stars_repo_head_hexsha": "4f69c5d4bac95904039413478d0dbc8e734b01cd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'maxim'
import ast
import collections
import numbers
import os
import random
import string
import sys
from six import iteritems
from six.moves import urllib
import numpy as np
def smart_str(val):
if type(val) in [float, np.float32, np.float64] and val:... | {"hexsha": "bb7cdc8c38bf6aa850a48f04de72443577c3a532", "size": 3243, "ext": "py", "lang": "Python", "max_stars_repo_path": "hyperengine/base/util.py", "max_stars_repo_name": "KOLANICH/hyper-engine", "max_stars_repo_head_hexsha": "60ba73438fdbef9320a849ee65f36da977f68eca", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
[STATEMENT]
lemma subgraph_no_last_branch_chain:
assumes "subgraph C T"
and "finite (verts T)"
and "verts C \<subseteq> verts T - {x. \<exists>y\<in>last_branching_points. x \<rightarrow>\<^sup>*\<^bsub>T\<^esub> y}"
shows "wf_digraph.is_chain C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. wf_di... | {"llama_tokens": 503, "file": "Query_Optimization_Directed_Tree_Additions", "length": 2} |
! nicked and adapted from IFEFFIT, the Interactive XAFS Analysis Library
SUBROUTINE PGVPORT (XLEFT, XRIGHT, YBOT, YTOP)
REAL XLEFT, XRIGHT, YBOT, YTOP
END
SUBROUTINE PGWNAD (X1, X2, Y1, Y2)
REAL X1, X2, Y1, Y2
END
SUBROUTINE PGCONS (A, IDIM, JDIM, I1, I2, J1, J... | {"hexsha": "476174d2b9e29cded3aee245bd8cfb913f6f39f9", "size": 4642, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/amuse/community/galactics/gas_src/src/pgstub.f", "max_stars_repo_name": "rknop/amuse", "max_stars_repo_head_hexsha": "85d5bdcc29cfc87dc69d91c264101fafd6658aec", "max_stars_repo_licenses": ["Ap... |
# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseT... | {"hexsha": "b9f74fdf8520385a79653a557631fa4a9ac1b9fc", "size": 33011, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorials/motr/motr_det.py", "max_stars_repo_name": "hyperfraise/ByteTrack", "max_stars_repo_head_hexsha": "d742a3321c14a7412f024f2218142c7441c1b699", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import sys, os
sys.path.append(os.pardir) # parent directory
#import tensorflow as tf
import numpy as np
#import matplotlib.pyplot as plt
#import matplotlib.animation as animation
#import matplotlib.gridspec as gridspec
fr... | {"hexsha": "c289d5e3350925e05d7996f6907e5262870a0e73", "size": 4494, "ext": "py", "lang": "Python", "max_stars_repo_path": "PaintCode_classification/gen_data.py", "max_stars_repo_name": "sjk0709/PaintCode_classification", "max_stars_repo_head_hexsha": "0663f68592b7685dc1c1008f6433ae1d60f21dc4", "max_stars_repo_licenses... |
"""The orbit solution class."""
import numpy as np
import scipy.optimize as sp_optimize
import opihiexarata.library as library
import opihiexarata.library.error as error
import opihiexarata.library.hint as hint
import opihiexarata.orbit as orbit
class OrbitSolution(hint.ExarataSolution):
"""This is the class ... | {"hexsha": "26df490e6a1fa040c74f9251d2d9fe5adbbd4ece", "size": 15598, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/opihiexarata/orbit/solution.py", "max_stars_repo_name": "psmd-iberutaru/OpihiExarata", "max_stars_repo_head_hexsha": "f0b595d7712ec68c972a7261e6bacc66410ba8b3", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma lran_empty[simp]:
"lran a l l = []"
"lran a l h = [] \<longleftrightarrow> h\<le>l"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lran a l l = [] &&& (lran a l h = []) = (h \<le> l)
[PROOF STEP]
by (subst lran.simps; auto)+ | {"llama_tokens": 116, "file": "IMP2_lib_IMP2_Aux_Lemmas", "length": 1} |
"""
abstract type AbstractMetricParams{T} end
Abstract type used to dispatch different geodesic problems.
"""
abstract type AbstractMetricParams{T} end
# contains the full metric components (this type needed for DiffGeoSymbolics)
abstract type AbstractMetric{T} <: AbstractMatrix{T} end
metric_params(m::Abstract... | {"hexsha": "83db162a9298d212973cc037331f9f8ed77cd345", "size": 2719, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/metric-params.jl", "max_stars_repo_name": "astro-group-bristol/GeodesicBase.jl", "max_stars_repo_head_hexsha": "0cb60aeba81a2fe21e363824ae3fe86dbb52a15d", "max_stars_repo_licenses": ["MIT"], "m... |
"""
Kindly install these libraries before executing this code:
1. numpy
2. matplotlib
3. scipy
"""
import numpy as np
import matplotlib.pyplot as plt
import cmath
import math
from numpy import random
from scipy.special import beta
from scipy import stats
import time
# if using a Jupyter noteb... | {"hexsha": "f956182779aad5caf5a3c003695ada5b370d4272", "size": 2536, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lab5/Submission Files/180123053_VishishtPriyadarshi_q2.py", "max_stars_repo_name": "vishishtpriyadarshi/Monte-Carlo-Simulation", "max_stars_repo_head_hexsha": "0e162bdecf774e06ec209914ff16bc31b0f8... |
"""
rendering.py
--------------
Functions to convert trimesh objects to pyglet/opengl objects.
"""
import numpy as np
try:
import pyglet
pyglet.options['shadow_window'] = False
from pyglet import gl
# bring in mode enum
GL_LINES, GL_POINTS, GL_TRIANGLES = (
gl.GL_LINES,
gl.GL_POIN... | {"hexsha": "5a5aba2baaf9f7bbc4d2dbdf3245cf7e1c3daf21", "size": 10778, "ext": "py", "lang": "Python", "max_stars_repo_path": "trimesh/rendering.py", "max_stars_repo_name": "LinJiarui/trimesh", "max_stars_repo_head_hexsha": "5f925bbab447e733d6f1ebf0956b202d18271ee1", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#Imports
import os, sys
import base64
import glob
import time, sched
import datetime
from datetime import timezone
from datetime import timedelta
from collections import OrderedDict
import numpy as np
import pandas as pd
import socket
import psycopg2
import subprocess
import pytz
import json
import matplotlib as mpl
... | {"hexsha": "fcd5770a3afb534ad08b737f2011c576f9e7470a", "size": 50147, "ext": "py", "lang": "Python", "max_stars_repo_path": "dni/report.py", "max_stars_repo_name": "ClairePpt/desilo", "max_stars_repo_head_hexsha": "a3f64012d4aa899ed43cebfca06460172d20487d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
[STATEMENT]
lemma Disj_commute: "H \<turnstile> B OR A \<Longrightarrow> H \<turnstile> A OR B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. H \<turnstile> B OR A \<Longrightarrow> H \<turnstile> A OR B
[PROOF STEP]
using DisjConj [of B A B] Ident [of B]
[PROOF STATE]
proof (prove)
using this:
B OR A IMP (B IMP B)... | {"llama_tokens": 209, "file": "Goedel_HFSet_Semanticless_SyntaxN", "length": 2} |
write_to_table <- function(conn, name, value, indices=c(), new=F) {
if (RSQLite::dbExistsTable(conn, name) && new) RSQLite::dbRemoveTable(conn, name)
if (!RSQLite::dbExistsTable(conn, name)) {
RSQLite::dbCreateTable(conn, name, value)
for (i in indices) {
RSQLite::dbExecute(conn, sprintf('DROP INDEX ... | {"hexsha": "ad4cf5b636b13b73fe0ba4e73c92fa75f592348a", "size": 3088, "ext": "r", "lang": "R", "max_stars_repo_path": "R/lib_db.r", "max_stars_repo_name": "vanatteveldt/shinyBZtopics", "max_stars_repo_head_hexsha": "524cad31395d20c9d33ad92660a38522d70342a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import socket
import argparse
import numpy as np
import logging
BUFF_SIZE = 1024
class Lakeshore240_Simulator:
def __init__(self, port, num_channels=8, sn="LSSIM"):
self.log = logging.getLogger()
self.port = port
self.sn = sn
self.modname = "SIM_MODULE"
self.num_channel... | {"hexsha": "f6fdd31a041cebd798d1c562b76dc9771df69594", "size": 9451, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulators/lakeshore240/ls240_simulator.py", "max_stars_repo_name": "gdevenyi/socs", "max_stars_repo_head_hexsha": "2f94cbee0246d23a200afdf1dec8208f2c561c71", "max_stars_repo_licenses": ["BSD-2-Cl... |
import warnings
import numpy as np
import empca
from apogee.tools.path import change_dr
from apogee.tools import bitmask as bm
from sklearn.decomposition import PCA
from delfiSpec import util, specproc, specsim
from fpca import FPCA
# Ignore warnings
warnings.filterwarnings("ignore")
# Read APOGEE DR14 catalogue
c... | {"hexsha": "18d061e76abd1af7309cf629a3a4cee482535d8d", "size": 5127, "ext": "py", "lang": "Python", "max_stars_repo_path": "apogee_fpca.py", "max_stars_repo_name": "aaryapatil/specdims", "max_stars_repo_head_hexsha": "acfc644aa06b13c8b34cde984e207b42e948af41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "... |
"""
Segmentation Viewer
This class allows you to view examples from the Fusion Gallery segmentation dataset.
Additionally you can generate an html view for all the files.
"""
import argparse
from pathlib import Path
import numpy as np
import igl
import meshplot as mp
import math
class SegmentationView... | {"hexsha": "1865e0f373d7052422cec106adc7278856efb65f", "size": 3868, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/segmentation_viewer/segmentation_viewer.py", "max_stars_repo_name": "AutodeskAILab/Fusion360GalleryDataset", "max_stars_repo_head_hexsha": "b6424f4c06535c426b59839a9355d49bd1d8a364", "max_st... |
#include <iostream>
#include <cmath>
#include <Eigen/Dense>
#include "traji.hpp"
using namespace std;
using namespace std::placeholders;
namespace traji
{
TFloat PathPosition::to_t(const Trajectory &traj) const
{
return traj._timestamps[segment] + (traj._timestamps[segment+1] - traj._timestamps[segmen... | {"hexsha": "ab792c4674c6397f13b45f6363d730c661a00bb9", "size": 11440, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Trajectory.cpp", "max_stars_repo_name": "cmpute/traji", "max_stars_repo_head_hexsha": "192141dfdea26012a17cb0b5ddb99c0d085de0dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "ma... |
"""
ReducedSpaceEvaluator{T} <: AbstractNLPEvaluator
Evaluator working in the reduced space corresponding to the
control variable `u`. Once a new point `u` is passed to the evaluator,
the user needs to call the method `update!` to find the corresponding
state `x(u)` satisfying the equilibrium equation `g(x(u), u)... | {"hexsha": "570427146d398506578901a0558fca64734a2113", "size": 9328, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Evaluators/reduced_evaluator.jl", "max_stars_repo_name": "lcw/ExaPF.jl", "max_stars_repo_head_hexsha": "9435f8a24ac44d08047169378bdd745269af3ef1", "max_stars_repo_licenses": ["MIT"], "max_stars... |
/*
[auto_generated]
boost/numeric/odeint/external/thrust/thrust_algebra_dispatcher.hpp
[begin_description]
algebra_dispatcher specialization for thrust
[end_description]
Copyright 2013 Karsten Ahnert
Copyright 2013 Mario Mulansky
Distributed under the Boost Software License, Version 1.0.
(See accom... | {"hexsha": "5deba2cb570a3919fc432705522db32fa10d9801", "size": 1339, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/numeric/odeint/external/thrust/thrust_algebra_dispatcher.hpp", "max_stars_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_stars... |
#
# author: Jungtaek Kim (jtkim@postech.ac.kr)
# last updated: March 22, 2021
#
"""It defines Gaussian process regression."""
import time
import numpy as np
import scipy.stats
from bayeso import covariance
from bayeso import constants
from bayeso.gp import gp_kernel
from bayeso.utils import utils_gp
from bayeso.utils... | {"hexsha": "b6db27deaeae8f1971599a29771cdd4314445fff", "size": 8729, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayeso/gp/gp.py", "max_stars_repo_name": "jungtaekkim/bayeso", "max_stars_repo_head_hexsha": "d11c9ff8037cf7fd3f9b41362eaab120f1224c71", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 76, ... |
using LinearAlgebra
using Test
using CompScienceMeshes
using SauterSchwabQuadrature
using StaticArrays
pI = point(1,5,3)
pII = point(2,5,3)
pIII = point(7,1,0)
pIV = point(5,1,-3)
Sourcechart = simplex(pI,pIII,pII)
Testchart = simplex(pI,pIV,pII)
Accuracy = 12
ce = CommonEdge(SauterSchwabQuadrature._legendre(Accura... | {"hexsha": "86fc04fa7978f9f6fde04ad7b39484174ea10d12", "size": 3131, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_ce_p_verification.jl", "max_stars_repo_name": "UnofficialJuliaMirror/SauterSchwabQuadrature.jl-535c7bfe-2023-5c1d-b712-654ef9d93a38", "max_stars_repo_head_hexsha": "419fb564912814b5e033df... |
# coding: UTF8
from sklearn.pipeline import FeatureUnion
from sklearn import preprocessing
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
import sklearn.linear_model
import img_to_pickle as i_p
import features as f
import classify
import pickle
import numpy as np
import pandas as pd
impo... | {"hexsha": "7da1535713aa8d8d73902b0f4f3ea56bbb02855a", "size": 2854, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/repredict.py", "max_stars_repo_name": "haisland0909/Denoising-Dirty-Documents", "max_stars_repo_head_hexsha": "dcf4be659d045633f7b369db5fa9ad89793669f0", "max_stars_repo_licenses": ["Apache... |
#include <config.h>
#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <boost/asio.hpp>
#include <libtorrent/session.hpp>
#include "command_server.hpp"
using namespace boost::asio::ip;
namespace lt = libtorrent;
const auto UNIX_SOCKET_PATH = "/tmp/arr-torrent-cmd-srv.sock";
void print_usage(cons... | {"hexsha": "cb92c235b703e3e0446cf9ed88470f7b7af47574", "size": 937, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "daemon/main.cpp", "max_stars_repo_name": "IT-Syndikat/arr-torrent", "max_stars_repo_head_hexsha": "d14e7959294d2bb8a3b7332a269be70838e8109a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
subroutine apaga_lista(lista)
integer esco,nl,nc,j,i,ii,m,iostat,ierr,tam(256,10),r,t,aux(256),a,b
character (512) arq(256,10),arqaux(256,10),text
character (50) tit(1,10)
character (50) lista , listaux
write(*,"(A41)")'Digite o nome da lista que deseja apagar.'
8 read(*,"(A)",iostat=ierr)listaux
i... | {"hexsha": "654855179774f34228648fe22b360b08416c7089", "size": 980, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "codes/Projeto designe/designe final/apaga_lista.f90", "max_stars_repo_name": "danielsanfr/fortran-study", "max_stars_repo_head_hexsha": "101ff0aa552f40542b5bc3e90ee0265f9a74eb48", "max_stars_repo... |
using Dates
using TimeZones
using CBinding
packagedir = joinpath(dirname(pathof(DWDataReader)), "..")
includedir = joinpath(packagedir, "src", "include")
# CBinding.jl: Set up compiler context
c`-std=c99 -Wall -I$(includedir) -lDWDataReaderLib64 -L$(packagedir)`
const c"int64_t" = Int64
# CBinding.jl: Create Julia ... | {"hexsha": "5a98e1404acea6ff395b945af4c0e8120cd02e55", "size": 4617, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/file.jl", "max_stars_repo_name": "fleimgruber/DWDataReader.jl", "max_stars_repo_head_hexsha": "4a8a280ed9a34a6af63a295d976e578aeaca1e97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# coding=utf-8
import numpy as np
import torch
from PIL import Image
def _is_numpy(input):
"""
Check if input is a numpy object.
Args:
input (:obj:): input.
Returns:
(bool): True if input is a numpy object.
"""
return isinstance(input, np.ndarray)
def _is_pil_image(input):... | {"hexsha": "8d906e0edf9e2f0cd2f90955b8cd8b179df83686", "size": 1816, "ext": "py", "lang": "Python", "max_stars_repo_path": "cheblienet/datas/functionals.py", "max_stars_repo_name": "haguettaz/ChebLieNet", "max_stars_repo_head_hexsha": "8545122c85513a4b4e8cc34c9f01bacca9140110", "max_stars_repo_licenses": ["MIT"], "max_... |
####
# install.packages("devtools")
# devtools::install_github("iobis/obistools")
# devtools::install_github("EMODnet/skosxml")
# devtools::install_github("EMODnet/EMODnetBiocheck")
####
library(obistools)
library(EMODnetBiocheck)
check_shark_dwca <- function(data_dir_path, dwca_file_name) {
dwca_zip_path = paste... | {"hexsha": "89fa5b20eb7b2ac31501f189a754f950aa965524", "size": 2431, "ext": "r", "lang": "R", "max_stars_repo_path": "R_scripts/iobistools_validation.r", "max_stars_repo_name": "sharkdata/darwincore", "max_stars_repo_head_hexsha": "28937763353ce75b8897c5d8ab1fadb188b302b2", "max_stars_repo_licenses": ["MIT"], "max_star... |
# coding: utf-8
# ## This notebook will help you train a vanilla Point-Cloud AE with the basic architecture we used in our paper.
# (it assumes latent_3d_points is in the PYTHONPATH and the structural losses have been compiled)
import os
import sys
sys.path.insert(0, "/home/gy46/")
import tqdm
import numpy as np
... | {"hexsha": "78f9656e50e0b59e54cd6898e3b28d1b95b817c1", "size": 4721, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/MN_clf.py", "max_stars_repo_name": "stevenygd/latent_3d_points", "max_stars_repo_head_hexsha": "cf8c0888f4489690fa5b692cbd44638f8db2d0ba", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import sympy as sp
def calc_taylor_series(equation, xInit, a, n:int):
"""
Method to estimate a function using taylor series
Parameters:
equation: The equation f(x)
xInit: Initial value of x
a: Another value of x
n: number of derivatives
"""
#Variables and settings
x = sp.Sy... | {"hexsha": "4f563e106e1c1af8c9e6273a37d5948bd35301c7", "size": 1512, "ext": "py", "lang": "Python", "max_stars_repo_path": "mid_exam/taylor_series_calculator.py", "max_stars_repo_name": "GiantSweetroll/Computational-Mathematics", "max_stars_repo_head_hexsha": "f94457d1943a7d17379296cac284da88aefa862c", "max_stars_repo_... |
!========================================================================
!
! S P E C F E M 2 D Version 7 . 0
! --------------------------------
!
! Main historical authors: Dimitri Komatitsch and Jeroen Tromp
! Princeton University, USA
! an... | {"hexsha": "f6dda70703ba1c02a5a85ac72c9aba83464af763", "size": 12304, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "specfem2d/src/specfem2D/compute_forces_acoustic_backward.f90", "max_stars_repo_name": "PanIGGCAS/SeisElastic2D_1.1", "max_stars_repo_head_hexsha": "2872dc514b638237771f4071195f7b8f90e0ce3d", "m... |
"""Utility functions"""
from math import log, pow
import numpy as np
from ..exceptions import WaveletException
def getExponent(value):
"""Returns the exponent for the data Ex: 8 -> 3 [2 ^ 3]"""
return int(log(value) / log(2.))
def scalb(f, scaleFactor):
"""Return the scale for the factor"""
retur... | {"hexsha": "fa345d02ad160b20613de5d8c9bc313a8e9e4cd8", "size": 4948, "ext": "py", "lang": "Python", "max_stars_repo_path": "wavelet/util/utility.py", "max_stars_repo_name": "AP-Atul/wavelets-ext", "max_stars_repo_head_hexsha": "00ced22462c369584ebd32f9b5f357f092de0142", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
Copyright (C) 2016, 2017, 2018, 2019, 2020, 2021
Caleb Bell <Caleb.Andrew.Bell@gmail.com>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation fi... | {"hexsha": "43859292910a325435a452907759586a392f2abf", "size": 422590, "ext": "py", "lang": "Python", "max_stars_repo_path": "thermo/eos_mix.py", "max_stars_repo_name": "RoryKurek/thermo", "max_stars_repo_head_hexsha": "985279467faa028234ab422a19b69385e5100149", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 38... |
import gym
from .GridInterface import *
import numpy
import os
class GridTargetSearchAEnv(gym.Env, GridInterface):
def __init__(self, render = False, view_camera_distance = 1.5, view_camera_angle = -80.0):
gym.Env.__init__(self)
GridInterface.__init__(self, render, view_camera_distance, view... | {"hexsha": "d244988980f1e8553c80a1c7f6847eaf2fed747e", "size": 3579, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym-aeris/gym_aeris/envs/grid_target_search_a_env.py", "max_stars_repo_name": "michalnand/gym-aeris", "max_stars_repo_head_hexsha": "e3b924ff767073bf3e42339b2763a736851664c5", "max_stars_repo_lice... |
import copy
import numpy as np
from nn_lib import *
def debug_net():
'''Debug network by calculating gradient and numerical gradient'''
frst_layer = 60
hidden = [40, 20]
out_layer = 10
# First without regularization
network = NetworkObject(inpt = frst_layer, hidden = hidden,
outpt... | {"hexsha": "88b181dd77683e4c39f5a3268259e938dad56a2a", "size": 2448, "ext": "py", "lang": "Python", "max_stars_repo_path": "debug_network.py", "max_stars_repo_name": "AndresYague/neural_network_numbers", "max_stars_repo_head_hexsha": "049c6ece317127b96ea50be589c79e14c7f04e32", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma rel_mset_size: "rel_mset R M N \<Longrightarrow> size M = size N"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. rel_mset R M N \<Longrightarrow> size M = size N
[PROOF STEP]
unfolding multiset.rel_compp_Grp Grp_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((\<lambda>a b. b = image_mset fs... | {"llama_tokens": 240, "file": null, "length": 2} |
import os
import shutil
import numpy as np
import pandas as pd
import time
from scipy.special import softmax
import sys
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROn... | {"hexsha": "02402d7a8505a94dbe6f7dba7a3fe4069f6aa8bb", "size": 6459, "ext": "py", "lang": "Python", "max_stars_repo_path": "enkd_scnn_feat_student-lstm/main_kd.py", "max_stars_repo_name": "mvp18/KD-SVD", "max_stars_repo_head_hexsha": "35b208a2455256b721189dbb0580e22654479761", "max_stars_repo_licenses": ["MIT"], "max_s... |
from abc import abstractmethod, ABC
from collections import deque
import numpy as np
import time
import cv2
def timit(func):
def wrapper(*args, **kwargs):
tick = time.perf_counter()
res = func(*args, **kwargs)
tock = time.perf_counter()
print(f"[runtime] --- {func.__name__}: {(toc... | {"hexsha": "f13f6f0ce9f379ce5f0e1645b4249746ec5c31be", "size": 4742, "ext": "py", "lang": "Python", "max_stars_repo_path": "odom.py", "max_stars_repo_name": "loaywael/VisualOdom", "max_stars_repo_head_hexsha": "c090a78d7166ce12e0b526df0219015949c3de79", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null... |
import shutil
import sys
import subprocess
import glob
from tqdm import tqdm
import numpy as np
import os
import argparse
from PIL import Image
import torch
from torch import nn
import torch.nn.functional as F
import torchvision.models as models
import transforms as TF
import utils
import torchvision
C, H, W = 3, 112... | {"hexsha": "fc58defc84fbda476a6c5db952d810b3b18017e4", "size": 3516, "ext": "py", "lang": "Python", "max_stars_repo_path": "feat_script/extract_visual_feat/extract_3D_feat.py", "max_stars_repo_name": "GeWu-Lab/MUSIC-AVQA_CVPR2022", "max_stars_repo_head_hexsha": "f704130f37a342b5ff861780282c75cc875221b2", "max_stars_rep... |
[STATEMENT]
lemma proj_same_not_active:
assumes "n \<le> n'"
and "enat (n'-1) < llength t"
and "\<pi>\<^bsub>c\<^esub>(ltake n' t) = \<pi>\<^bsub>c\<^esub>(ltake n t)"
shows "\<nexists>k. k\<ge>n \<and> k<n' \<and> \<parallel>c\<parallel>\<^bsub>lnth t k\<^esub>"
[PROOF STATE]
proof (prove)
goal (1 subgoal)... | {"llama_tokens": 10274, "file": "DynamicArchitectures_Configuration_Traces", "length": 69} |
# Aiyagari (1994) in Continuous Time
#### By [SeHyoun Ahn](http://www.princeton.edu/~sehyouna/) and [Benjamin Moll](http://www.princeton.edu/~moll/)
The material in this notebook is based on [Achdou et al. (2015) "Heterogeneous Agent Models in Continuous Time"](http://www.princeton.edu/~moll/HACT.pdf) and follows clo... | {"hexsha": "8cf60ff625fbf1aa0b4fc7f89866c4977734787f", "size": 94602, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "aiyagari_continuous_time.ipynb", "max_stars_repo_name": "a-parida12/QuantEcon.notebooks", "max_stars_repo_head_hexsha": "b8794ae7d869a0cbc585b56e2c71cefcd2d9cdc6", "max_stars_repo_li... |
import jax
import jax.numpy as np
@jax.jit
def interp_dim(x_new, x, y):
return jax.vmap(np.interp, in_axes=(0, 0, 0))(x_new, x, y)
def searchsorted(bin_locations, inputs, eps=1e-6):
# add noise to prevent zeros
# bin_locations = bin_locations[..., -1] + eps
bin_locations = bin_locations + eps
#... | {"hexsha": "5f9ac71d77b422d0c231819f83fadc6fd9ded747", "size": 561, "ext": "py", "lang": "Python", "max_stars_repo_path": "rbig_jax/utils.py", "max_stars_repo_name": "jejjohnson/rbig_jax", "max_stars_repo_head_hexsha": "112e064d5b62631aa03b7563c9eb9f115ab23eb0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#**
function cost_eens(eqp,ks)
cp_tbl=cap_prob_table(eqp)#make capacity probability table
eens_all=[]#Create eens array
eens=0.0
for i=1:length(cp_tbl[:,1])#loop through rows of cpt
ratio_curt=cp_tbl[i,1]/eqp.mva#find PU curtailment ratio
diff=wind_module.wind_profs[eqp.wnd].pu.-ratio_c... | {"hexsha": "0af769a885101963c50058e1c61956cc4ae7599b", "size": 4053, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/economics/eens/functions.jl", "max_stars_repo_name": "sdwhardy/cordoba.jl", "max_stars_repo_head_hexsha": "49de8a6a5862c6ee9a70f241a498e0a48ef41eed", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""
pyart.aux_io.d3r_gcpex_nc
=========================
Routines for reading GCPEX D3R files.
.. autosummary::
:toctree: generated/
read_d3r_gcpex_nc
_ncvar_to_dict
"""
import datetime
import numpy as np
import netCDF4
from ..config import FileMetadata
from ..io.common import make_time_unit_str, _tes... | {"hexsha": "a3bc28e5a8c0d72f58acf48731b481f1956f676f", "size": 7742, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyart/aux_io/d3r_gcpex_nc.py", "max_stars_repo_name": "josephhardinee/pyart", "max_stars_repo_head_hexsha": "909cd4a36bb4cae34349294d2013bc7ad71d0969", "max_stars_repo_licenses": ["OLDAP-2.6", "Py... |
[STATEMENT]
lemma chainI:
assumes "Y 0 = false" "\<And> i. Y (Suc i) \<sqsubseteq> Y i"
shows "chain Y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. chain Y
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
Y 0 = false
Y (Suc ?i) \<sqsubseteq> Y ?i
goal (1 subgoal):
1. chain Y
[PROOF STEP]
by ... | {"llama_tokens": 153, "file": "UTP_utp_utp_recursion", "length": 2} |
"""Generation and plot of an events file : the neurospin/localizer events.
==========================================================================
The protocol described is the simplified version of the so-called
"archi standard" localizer event sequence.
See Pinel et al., BMC neuroscience 2007 for reference.
""... | {"hexsha": "94184fce103fa31f63812c9da65e31133127f12f", "size": 2950, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/04_glm_first_level_models/plot_events_file.py", "max_stars_repo_name": "ariekahn/nilearn", "max_stars_repo_head_hexsha": "baa77b18ecee7c4507579214af59d715cc9292f9", "max_stars_repo_licens... |
import numpy as np
import scipy.sparse as sp
import torch
import torch.utils.data
import typing as _typing
import torch_geometric
from . import target_dependant_sampler
class _LayerDependentImportanceSampler(
target_dependant_sampler.BasicLayerWiseTargetDependantSampler
):
"""
Obsolete implementation, unu... | {"hexsha": "bc66bdf83323bc3f67dd616bce77dfeebb6ef677", "size": 19191, "ext": "py", "lang": "Python", "max_stars_repo_path": "autogl/module/train/sampling/sampler/layer_dependent_importance_sampler.py", "max_stars_repo_name": "dedsec-9/AutoGL", "max_stars_repo_head_hexsha": "487f2b2f798b9b1363ad5dc100fb410b12222e06", "m... |
import tensorflow as tf
import numpy as np
from pylab import mpl
import matplotlib.pyplot as plt
import math
plt.rcParams['axes.unicode_minus'] = False
mpl.rcParams['font.sans-serif'] = ['SimHei']
# 同时我们要在中文前面加上u
# 随机种子
tf.set_random_seed(1)
np.random.seed(1)
# 设置超参数
BATCH_SIZE = 64 # 批量大小
LR_G = 0.... | {"hexsha": "9ef885d620cc9cb2ea9d1399165ba056b64131ed", "size": 4034, "ext": "py", "lang": "Python", "max_stars_repo_path": "gan_DAY_2.py", "max_stars_repo_name": "SoulProficiency/MyRepository", "max_stars_repo_head_hexsha": "3738e558190bacb596a89a305408ad6621342930", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import tensorflow as tf
import numpy as np
#from data_utils import get_batch
import data_utils
import pdb
import json
from mod_core_rnn_cell_impl import LSTMCell #modified to allow initializing bias in lstm
#from tensorflow.contrib.rnn import LSTMCell
tf.logging.set_verbosity(tf.logging.ERROR)
import mmd
from... | {"hexsha": "e012127130a83978fbced65a3ab2d82372ce86d2", "size": 23651, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "gebob19/RGAN", "max_stars_repo_head_hexsha": "cb8c4c36ff7af0395611f10d5b17c8719fff0b00", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
"""
Basics
~~~~~~
Tensor trains are a versatile tensor decomposition. They consist of a list of
order-3 tensors known as `cores`. A tensor train encoding an order `d` dense
tensor, has `d` cores. The second dimension of these cores coincides with the
dimensions of the dense tensor. The first and third dimensions of th... | {"hexsha": "c568f6287003ca87c826ac420aca1c7affe694ee", "size": 11248, "ext": "py", "lang": "Python", "max_stars_repo_path": "ttml/_tensor_train_doc.py", "max_stars_repo_name": "RikVoorhaar/ttml", "max_stars_repo_head_hexsha": "3786cfc02976f7d6cd5f045f213e28793f4ece61", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# Reference from http://bluewhale.cc/2017-09-22/use-python-opencv-for-image-template-matching-match-template.html
import cv2
import numpy as np
def similarity(img,tar):
# img=cv2.imread(image,0)
# tar=cv2.imread(target,0)
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
tar=cv2.cvtColor(tar,cv2.COLOR_BGR2GRAY... | {"hexsha": "e98258596f9453490514e698b83b93a26beb3653", "size": 1550, "ext": "py", "lang": "Python", "max_stars_repo_path": "matcher.py", "max_stars_repo_name": "Jerry-Terrasse/LlfSystem", "max_stars_repo_head_hexsha": "069d9e6935cfae19f1d2c17dfe3dcf1a75515f53", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, ... |
#pragma once
#include <Core/RaCore.hpp>
#include <Eigen/Core>
#include <Eigen/Geometry>
namespace Ra {
namespace Core {
namespace Geometry {
/// An oriented bounding box.
class Obb
{
public:
using Transform = Eigen::Transform<Scalar, 3, Eigen::Affine>;
using Aabb = Eigen::AlignedBox<Scalar, 3>;
//... | {"hexsha": "fd44f0b1dc52762bd8b2cf14650f3b07e7977089", "size": 2034, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/Core/Geometry/Obb.hpp", "max_stars_repo_name": "Yasoo31/Radium-Engine", "max_stars_repo_head_hexsha": "e22754d0abe192207fd946509cbd63c4f9e52dd4", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import json
import numpy as np
import os
import random
import re
import sklearn.linear_model
import sklearn.preprocessing
import time
EMB_DIR = '/tmp/basilica-embeddings/'
files = [f for f in os.listdir(EMB_DIR)]
random.shuffle(files)
train_size = int(len(files)*0.8)
x_train = np.zeros((train_size, 2048))
x_test = np... | {"hexsha": "b128c62dcc41477cd869efb0daf8071ff53646ff", "size": 1406, "ext": "py", "lang": "Python", "max_stars_repo_path": "module2-consuming-data-from-an-api/training_a_classifier.py", "max_stars_repo_name": "nrvanwyck/DS-Unit-3-Sprint-3-Productization-and-Cloud", "max_stars_repo_head_hexsha": "186a2420ddaf0ca1b229982... |
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.autograd import Variable
from src.models.lang_model.w2v_averager_model import W2vAveragerModel
from src.models.lang_model.embedding_model import EmbeddingModel
import src.models.time_series.ts_models as ts
... | {"hexsha": "44f54c4d3ea788c108f41fab41d22180e0d12c39", "size": 22519, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/content_aware/strip_temporal_cf.py", "max_stars_repo_name": "oughtinc/psj", "max_stars_repo_head_hexsha": "e7c5e987039ce7978234e137167991a61371604b", "max_stars_repo_licenses": ["MIT"]... |
#-*-coding: utf-8 -*-
#Imagelerde Aritmatik Islemler - Resim Birlestirme
import numpy as np
import cv2
img1=cv2.imread('resimler/cameraman.tif')
img2=cv2.imread('resimler/text.tif')
#birlestirilecek resimler aynı boyutta olmalı.
#g(X)=(1-a)xF0(X)+axF1(X) denklemi kullanılarak eklenecek
#resimlerin agırlık d... | {"hexsha": "1bc34249d85da80faa6d6efd0d8aef3ee70e04c3", "size": 561, "ext": "py", "lang": "Python", "max_stars_repo_path": "Goruntu Isleme/Beginning/ornk12.py", "max_stars_repo_name": "NevzatBOL/Paket-Kurulumlar-", "max_stars_repo_head_hexsha": "f5ce3b8205b11d072b9dadd305c11c278f184388", "max_stars_repo_licenses": ["MIT... |
\chapter*{Introduction}
\addcontentsline{toc}{section}{Introduction}
\chaptermark{Introduction}
\pagenumbering{arabic}
% ! TODO: Delete this line
\lipsum[1-3]
Random citation \cite{DUMMY:1} embeddeed in text.
\lipsum[4-6] | {"hexsha": "e4434d7cc7bca22bb18871660f8b9bdd3b9d3777", "size": 222, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/03.Introduction.tex", "max_stars_repo_name": "MECHEDDAL-Hani/Thesis-template", "max_stars_repo_head_hexsha": "dbb83c1686b0be1cc56419a07f81effcad5d6ec6", "max_stars_repo_licenses": ["MIT"], "... |
import time
import numpy as np
import collections
import so3g
class Timer():
def __init__(self, block_name=None):
self.t0 = time.time()
def __enter__(self):
return self
def report(self):
return time.time() - self.t0
def __exit__(self, exc_type, exc_value, exc_traceback):
... | {"hexsha": "742c6eabf7f689ee568ae3f1e6866b585334f6f9", "size": 3336, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/test_utils.py", "max_stars_repo_name": "tskisner/so3g", "max_stars_repo_head_hexsha": "75c1d8dea84f862bdd2c9fa2c2f9d1c5b8da5eec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "m... |
"""Data Augmentaion for scaling n.i.O images from powertrain"""
import argparse
from operator import index
import os, cv2, random
import numpy as np
import pandas as pd
import scipy
import tensorflow as tf
import glob
import PIL
from keras_preprocessing.image import ImageDataGenerator, img_to_array, load_img
# ======... | {"hexsha": "635c1be42f077c875f3954a421bff6eda46db309", "size": 2777, "ext": "py", "lang": "Python", "max_stars_repo_path": "augmentation.py", "max_stars_repo_name": "molu1019/CycleGAN-Tensorflow-2", "max_stars_repo_head_hexsha": "69e51007718b76595313b24ed1fb7c3ee5ea346c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
/**
* Copyright (c) 2020 libnuls developers (see AUTHORS)
*
* This file is part of libnuls.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
*... | {"hexsha": "ff2e48e5313c8d4f9b9f6d9f01aa249d5352de72", "size": 3989, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/nuls/system/chain/point.hpp", "max_stars_repo_name": "ccccbjcn/nuls-v2-cplusplus-sdk", "max_stars_repo_head_hexsha": "3d5a76452fe0673eba490b26e5a95fea3d5788df", "max_stars_repo_licenses": ["... |
from __future__ import print_function
import numpy as np
import theano
import theano.tensor as T
import lasagne
try:
input_text = open("shakespeare_input.txt", "r").read()
input_text = input_text.decode("utf-8-sig").encode("utf-8")
except Exception as e:
raise IOError("Couldn't read input file"... | {"hexsha": "6743f057dc07be234f855ee223216b5ecd6df3cf", "size": 5587, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/Experiments/Shakespeare/shakespeare.py", "max_stars_repo_name": "matthijsvk/convNets", "max_stars_repo_head_hexsha": "7e65db7857a4e6abfbcab264953eb7741319de6c", "max_stars_repo_licenses": ["A... |
import unittest
import matplotlib.pyplot as plt
import numpy as np
import GooseMPL as gplt
class Test_ticks(unittest.TestCase):
"""
Functions generating ticks.
"""
def test_log_ticks(self):
ticks, labels = gplt.log_ticks((0, 3))
self.assertEqual(list(ticks), [1, 10, 100, 1000])
... | {"hexsha": "fd3b5f45c31c24a372f40b55c96521b29a67beec", "size": 10751, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/main.py", "max_stars_repo_name": "tdegeus/pyplot_ext", "max_stars_repo_head_hexsha": "d084fb6f5a824d9c9c3d1bf9a3c9ef9e579b4d7f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
[STATEMENT]
lemma in_Gr[simp]:
shows "(x,y) \<in> BNF_Def.Gr A f \<longleftrightarrow> x \<in> A \<and> f x = y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((x, y) \<in> BNF_Def.Gr A f) = (x \<in> A \<and> f x = y)
[PROOF STEP]
unfolding BNF_Def.Gr_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((x, y) ... | {"llama_tokens": 199, "file": "Graph_Saturation_MissingRelation", "length": 2} |
"""
Test `sinethesizer.envelopes.user_defined` module.
Author: Nikolay Lysenko
"""
from typing import Any, Dict, List
import numpy as np
import pytest
from sinethesizer.envelopes.user_defined import create_user_defined_envelope
from sinethesizer.synth.core import Event
@pytest.mark.parametrize(
"duration, ve... | {"hexsha": "e3d2ef7a39e79996234d8eb0ee0538503cdc4745", "size": 4149, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/envelopes/test_user_defined.py", "max_stars_repo_name": "Nikolay-Lysenko/sinethesizer", "max_stars_repo_head_hexsha": "fe6855186a00e701113ea5bb4fac104bf8497035", "max_stars_repo_licenses": [... |
subroutine trace_bend_2d(xzt,yzt,xst,yst,ips, tout)
real xrmin(1000),yrmin(1000)
real t_segm(1000)
integer indexx(100)
real dxtmp(1000),dytmp(1000),xtmp(1000),ytmp(1000)
common/ray_param/ds_ini,ds_segm_min,bend_min0,bend_max0
common/ray/ nodes,xray(1000),yray(1000)
common/ray_part/ npart,xpart(1000),ypart(... | {"hexsha": "22e95078a3fb481d57bd35fb9ab467122ddcfdf1", "size": 4453, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "PROGRAMS/subr/trace_2d_iso/trace_bend_2d.f90", "max_stars_repo_name": "ilyasnsk/colima_lotos_2019", "max_stars_repo_head_hexsha": "d3ff4f32034e49a32560f170e980b6847b6ea9c7", "max_stars_repo_lice... |
module HFMod
if length(findin("C:\\Users\\Clinton\\Dropbox\\Projects\\SummerProject17",LOAD_PATH)) == 0
push!(LOAD_PATH,"C:\\Users\\Clinton\\Dropbox\\Projects\\SummerProject17")
end
#=TODO:
1) IV=#
#2) Run specifications
using DataFrames, Distributions, StatsBase, GZip, JLD, Gadfly, CTMod
#importall CT
#Set this... | {"hexsha": "0ca49fc85973ab3032d65eb15c726efc61cc84d6", "size": 28318, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Old/SummerProject17/SAVE- HF Data Functions-preNLRedux.jl", "max_stars_repo_name": "clintonTE/CCA", "max_stars_repo_head_hexsha": "a555cc1fa4b6d5f1464de44e2e322d32336d1e3a", "max_stars_repo_licens... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#==============================================================================
# DOCS
#==============================================================================
"""Move data
"""
#==============================================================================
# IMP... | {"hexsha": "bdd0b5a2c7eccc4bf7ae664e464e4eb1b7daf026", "size": 1772, "ext": "py", "lang": "Python", "max_stars_repo_path": "carpyncho1/skdjango/management/commands/skshell.py", "max_stars_repo_name": "carpyncho/yeolde_carpyncho", "max_stars_repo_head_hexsha": "fba72ebf9d4a3e4e4ea18160310058c6812a0457", "max_stars_repo_... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Computes the spectrogram of a test signal using cupy and cuFFT.
Author: Jan Schlüter
"""
import sys
import os
import timeit
import numpy as np
import cupy as cp
INPUT_ON_GPU = True
OUTPUT_ON_GPU = True
from testfile import make_test_signal
def spectrogram(signal... | {"hexsha": "bc93ed322f15833ada38ade26d0df82b04900ca0", "size": 1908, "ext": "py", "lang": "Python", "max_stars_repo_path": "bench_cupy.py", "max_stars_repo_name": "zhouxzh/Jetson_nano_stft_benchmark", "max_stars_repo_head_hexsha": "ffa97984f95b9862ac2a10b8459bb7ef241c6c72", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""Defines a segmentation module for evaluation only."""
import os
import imageio
import numpy as np
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
class SegmentationModule:
def __init__(self, load_checkpoint_path: str, debug_dir: str):
... | {"hexsha": "11bc22236dfdf51889e750ffc409cc22110b387b", "size": 2021, "ext": "py", "lang": "Python", "max_stars_repo_path": "system/seg_module.py", "max_stars_repo_name": "jyf588/pytorch-rl-bullet", "max_stars_repo_head_hexsha": "3ac1835d01e658b2078126895ffa0eb11304abb4", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
""" Estimate the correlations in terms of robustness between existing natural and synthetic corruption benchmarks.
The computed correlation scores and their associated p-values are saved in pickles at ../Results/benchmark_correlations.
Require: performances on ImageNet-C and ImageNet-P of the models defined in ../mode... | {"hexsha": "6ebbb44064172bc61a2477aa649a0fd2268dfb16", "size": 2801, "ext": "py", "lang": "Python", "max_stars_repo_path": "bench_correlations/get_existing_bench_correlations.py", "max_stars_repo_name": "bds-ailab/common_corruption_benchmark", "max_stars_repo_head_hexsha": "b6888f1591a2eb03d186628e25550ebd132e0024", "m... |
from functools import partial
import gc
from multiprocessing import Pool
import sys
import numpy as np
from tqdm import tqdm
from components.spectrum.alignment import pafft
if __name__ == '__main__':
MZS = sys.argv[1]
REFERENCE = sys.argv[2]
SPECTRA = sys.argv[3]
POOL_SIZE = int(sys.argv[4])
DES... | {"hexsha": "aa75cc2538fba88d3926a9a3f035953e045761ee", "size": 785, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/alignment.py", "max_stars_repo_name": "gmrukwa/msi-preprocessing-pipeline", "max_stars_repo_head_hexsha": "bc6d26daba42575babcdf5287999f1f844cf2e8e", "max_stars_repo_licenses": ["Apache-2.0"], ... |
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