text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import sys
import gif
from os import getcwd, path
def hilbert(i):
index = i & 3
points = np.array([[0,0],[0,1],[1,1],[1,0]])
v = points[index]
for iOrder in range(1,order):
i = i >> 2
index = i & 3
... | {"hexsha": "5c25ea7ce1b2996cd6e231e9c1f9f34dd3c8cf08", "size": 1945, "ext": "py", "lang": "Python", "max_stars_repo_path": "HilbertCurve.py", "max_stars_repo_name": "damuopel/HilbertCurve", "max_stars_repo_head_hexsha": "b0a93275a57090d21144a35b600f8a2d93391389", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
% !TEX root = ../../main.tex
% !TEX encoding = UTF-8 Unicode
\chapter{The Large Hadron Collider}
\label{ch:lhc}
The Large Hadron Collider (LHC)~\cite{LHC,LHC_design_v1,LHC_design_v2,LHC_design_v3} is a circular particle accelerator designed to probe physics at the \TeV\,scale. By colliding protons or heavy-ions with h... | {"hexsha": "8423fe2e2c58b9e1760ade666e61201a2755c7bd", "size": 23292, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/preface/LHC.tex", "max_stars_repo_name": "rynecarbone/Thesis_lvJ", "max_stars_repo_head_hexsha": "46ba8b142e945d5f3103a364630f661da3405479", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_c... |
[STATEMENT]
lemma SubstAtomicP_unique: "{SubstAtomicP v tm x y, SubstAtomicP v tm x y'} \<turnstile> y' EQ y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {SubstAtomicP v tm x y, SubstAtomicP v tm x y'} \<turnstile> y' EQ y
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. {SubstAtomicP v tm x... | {"llama_tokens": 18208, "file": "Incompleteness_Functions", "length": 23} |
C Copyright(C) 1988-2017 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 Redistribution and use in source and binary forms, with or without
C modification, ar... | {"hexsha": "05b6c4a7e43f46a46bccb9e4d0218b4dc086403b", "size": 3489, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/numbers/nu_lissel.f", "max_stars_repo_name": "mathstuf/seacas", "max_stars_repo_head_hexsha": "49b3466e3bba12ec6597e364ce0f0f149f9ca909", "max_stars_repo_licenses": ["... |
using TypeClasses
using Test
using DataTypesBasic
using Suppressor
splitln(str) = split(strip(str), "\n")
# Combine
# =======
a = Callable(x -> "hello $x")
b = Callable(x -> "!")
(a ⊕ b)(:Albert)
# FunctorApplicativeMonad
# =======================
g = Callable(x -> x*2)
f = Callable(x -> x*x)
# just function com... | {"hexsha": "5fbd5bab3981f97b03ffe30471698e8e7ee7a39c", "size": 714, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/TypeInstances/Callable.jl", "max_stars_repo_name": "JuliaFunctional/TypeClasses.jl", "max_stars_repo_head_hexsha": "815ab57bc0b15a57132191d4989b3bbe7abcabbe", "max_stars_repo_licenses": ["MIT"]... |
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
import random
class Atom:
"""A simple atom in a 2-D crystal grain, with its coordinates."""
def __init__(self, grain, coords):
self.grain = grain
self.coords = coords
... | {"hexsha": "716abcf8a0899063ece5c16e0f636da5a989ec90", "size": 14619, "ext": "py", "lang": "Python", "max_stars_repo_path": "crystal_para_mexer.py", "max_stars_repo_name": "adrianopls/SonicSim", "max_stars_repo_head_hexsha": "77c77c49781cbd947c70869aabed859323e0e07b", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import detectron2
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2 import model_zoo
from detectron2.modeling import build_model
from detectron2.checkpoint import ... | {"hexsha": "86aff4e493606150d824b4bb55ad2775467ec8e8", "size": 7372, "ext": "py", "lang": "Python", "max_stars_repo_path": "Detectron_roadside.py", "max_stars_repo_name": "jinsuwang/16824-Fall2021-Project", "max_stars_repo_head_hexsha": "a60d33b1413db350be1d47660abec051071e5296", "max_stars_repo_licenses": ["MIT"], "ma... |
(* Title: Jive Data and Store Model
Author: Norbert Schirmer <schirmer at informatik.tu-muenchen.de>, 2003
Maintainer: Nicole Rauch <rauch at informatik.uni-kl.de>
License: LGPL
*)
section \<open>Store Properties\<close>
theory StoreProperties
imports Store
begin
text \<open>This theory ... | {"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/JiveDataStore... |
"""
Copyright 2019 Manuel Olguín
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 writing, ... | {"hexsha": "541482ea0a061d022f20323e8b42b5e8b2562563", "size": 4212, "ext": "py", "lang": "Python", "max_stars_repo_path": "aggregate.py", "max_stars_repo_name": "molguin92/EdgeDroidResults", "max_stars_repo_head_hexsha": "083906c3b1e95d81327494e2d6465c3a39547050", "max_stars_repo_licenses": ["Apache-2.0", "CC-BY-4.0"]... |
# Creating the Two Zone Example Data
#
# Transform the TM1 TAZ-based model 25 zone inputs to a two-zone (MAZ and TAZ) set of inputs for software development.
#
# The 25 zones are downtown San Francisco and they are converted to 25 MAZs.
# MAZs 1,2,3,4 are small and adjacent and assigned TAZ 2 and TAP 10002.
# MAZs 13,1... | {"hexsha": "bc49a2a0ef1eb65b17c7517449f611fba6556cf6", "size": 9084, "ext": "py", "lang": "Python", "max_stars_repo_path": "activitysim/examples/example_multiple_zone/scripts/three_zone_example_data.py", "max_stars_repo_name": "mxndrwgrdnr/activitysim", "max_stars_repo_head_hexsha": "722d6e36b2210d5d24dfa2ac4a3504c1e8f... |
import math
import numpy as np
import sys
print("Inflow(Cs),")
f=np.load('outfile.npz')
x=sys.argv[1]
for item in f[x+'PredictPlot']:
if not math.isnan(item):
print(item[0],",")
| {"hexsha": "d138c8a18bbea7ae7b8abbc8c06d422217a9af05", "size": 190, "ext": "py", "lang": "Python", "max_stars_repo_path": "LSTM/graphs/read_outfile.py", "max_stars_repo_name": "Anurag14/Inflow-prediction-Bhakra", "max_stars_repo_head_hexsha": "d440ec552032084991878877ba5154ea2c452264", "max_stars_repo_licenses": ["MIT"... |
SUBROUTINE XLAENV( ISPEC, NVALUE )
*
* -- LAPACK auxiliary routine (version 3.1) --
* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..
* November 2006
*
* .. Scalar Arguments ..
INTEGER ISPEC, NVALUE
* ..
*
* Purpose
* =======
*
* XLAENV sets certain machine- and... | {"hexsha": "648c0c85b154787d8de22682e3e47d95afb6ca6b", "size": 2622, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "external_src/oomph_flapack/xlaenv.f", "max_stars_repo_name": "pkeuchel/oomph-lib", "max_stars_repo_head_hexsha": "37c1c61425d6b9ea1c2ddceef63a68a228af6fa4", "max_stars_repo_licenses": ["RSA-MD"], ... |
function e = calcError(pf,rec,varargin)
% RP and mean square error
%
% *calcError(pf,rec)* calculates reconstruction error between meassured
% intensities and the recalcuated ODF or between two meassured pole
% figures. It can be specified whether the RP
% error or the mean square error is calculated. The scaling coe... | {"author": "mtex-toolbox", "repo": "mtex", "sha": "f0ce46a720935e9ae8106ef919340534bca1adcb", "save_path": "github-repos/MATLAB/mtex-toolbox-mtex", "path": "github-repos/MATLAB/mtex-toolbox-mtex/mtex-f0ce46a720935e9ae8106ef919340534bca1adcb/PoleFigureAnalysis/@PoleFigure/calcError.m"} |
#!/usr/local/bin/python3
"""
Requirements:
- sklearn
- numpy
Python:
- 3.7
Hierarchical clustering (HC) is a method of cluster analysis which seeks to build
a hierarchy of clusters. The code contains Agglomerative approach for
hierarchical clustering.
Agglomerative: This is a "bottom-up" approach. Each observatio... | {"hexsha": "330c1ffc40ce9baef22aab480c6634c9b7bcc2f2", "size": 7627, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine_learning/hierarchial_clustering.py", "max_stars_repo_name": "jindal2309/Python", "max_stars_repo_head_hexsha": "894293574d3439b13ec848d223afec34cd9a075f", "max_stars_repo_licenses": ["MIT"... |
# -*- coding: utf-8 -*-
"""
Logistic regression (yes, pretty basic)
Created on Sun Jun 7 21:05:59 2020
@author: Gurpinder
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import fmin_tnc
X = []
y = []
#Reading the data
with open("ex2data1.txt") as f:
lines = f.readl... | {"hexsha": "14dcec5b009c2fd575f37b11298afa19aa602a2f", "size": 2100, "ext": "py", "lang": "Python", "max_stars_repo_path": "Logistic_Regression/Logistic_Regression.py", "max_stars_repo_name": "Gurpinder98/Fun_scripts", "max_stars_repo_head_hexsha": "32e3c512196a0db7326cbb06475ab30e8e083738", "max_stars_repo_licenses": ... |
import os
from glob import glob
from tqdm import tqdm
import numpy as np
import cv2
import skimage.measure
def i3d_prediction(model, features_folder, output_folder):
feature_paths = glob(os.path.join(features_folder, '*'))
feature_paths.sort()
os.makedirs(output_folder, exist_ok=True)
for feature_pa... | {"hexsha": "6927ae4e2be45fa8b4a2ca80ec54ef05b5f6b642", "size": 3901, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_model/libs/i3d_funcs.py", "max_stars_repo_name": "wpfhtl/Drone-Detection-1", "max_stars_repo_head_hexsha": "d3aeb2f731739475b1b63e74337876ea14de08ac", "max_stars_repo_licenses": ["MIT"], "max... |
! Generated by cart. DO NOT EDIT
program main
implicit none
if (.not.run()) stop 1
contains
function run() result(passed)
use acceleration_test, only: &
acceleration_acceleration => &
test_acceleration
use amount_rate_test, only: &
amount_... | {"hexsha": "d7316fba3fff1afcfec95cea6db20b1efcbc0d8e", "size": 6605, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/main.f90", "max_stars_repo_name": "everythingfunctional/quaff", "max_stars_repo_head_hexsha": "72a4a4611e18b799196a5f962b4c653ace7a3671", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
Module to gather various high-level algorithms based on the kernel methods,
such as kernel-based predictive models for classification and regression.
"""
from abc import abstractmethod
from copy import deepcopy
import numpy as np
from sklearn.base import (BaseEstimator, ClassifierMixin, RegressorMixin,
... | {"hexsha": "20cd02eb193d6753b226abf37bbf5f0b24c81116", "size": 22358, "ext": "py", "lang": "Python", "max_stars_repo_path": "kernelmethods/algorithms.py", "max_stars_repo_name": "vishalbelsare/kernelmethods", "max_stars_repo_head_hexsha": "f49eae7057e6223fe1bae52ca4f308af807fe347", "max_stars_repo_licenses": ["Apache-2... |
"""Helios Force-Directed Layout using octree
References
----------
[1] Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph Drawing
by Force-Directed Placement. Software: Practice and Experience, 21(11).
[2] Y. Hu, “Efficient, High-Quality Force-Directed Graph Drawing,” The
Mathematica Journal, p. 35... | {"hexsha": "5dcd43607dc6cfb7bf8d5387192594bf7b0a3d3b", "size": 3734, "ext": "py", "lang": "Python", "max_stars_repo_path": "helios/layouts/force_directed.py", "max_stars_repo_name": "fury-gl/helios", "max_stars_repo_head_hexsha": "14e39e0350b4b9666775ba0c4840d2e9887678c2", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np, pandas as pd
from scipy import stats
"""
Notes on Analysis:
- we have mean, se, & worst on radius, texture, perimeter, area, smoothness, compactness,
concavity, concave points, symmetry, fractal dimensions
1st preprocessing: normalize each columns using z-score
"""
print("Import data")
df = pd... | {"hexsha": "0da2013ae98f721642c9b166bc24b9b3e83bef63", "size": 750, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing.py", "max_stars_repo_name": "jayliu99/bcbn", "max_stars_repo_head_hexsha": "6ec0977299c66c224877ec953b64d34f9b74c6f6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import cv2
import time
import numpy as np
import pose_module as pm
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
class Video:
def __init__(self, video_path: str):
"""
:param video_path: path of video to analyse
Object video has poseDetector inner or nested class... | {"hexsha": "9141e205a6eb77b5cdd6e57a3483746e5abeca4f", "size": 5588, "ext": "py", "lang": "Python", "max_stars_repo_path": "video_pose.py", "max_stars_repo_name": "lucamagnasco/human_pose_test", "max_stars_repo_head_hexsha": "c2041fa35f9150c262e91b4b1fc342f53518d4e9", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
module Mod_TempeDriver
use typre
use MPI
use Mod_BroadCastBuffer
use Mod_Listen
use Mod_caseVariables
use Mod_PhysicalProblemDriver
use Mod_PhysicalProblem
use Mod_DistributedContainer
use Mod_DC_ip
use Mod_DC_rp
use Mod_InChannel
use Mod_MasterVariables
use Mod_Temp... | {"hexsha": "015108b48aff4c029ffa0b442d5b3f3ed7de235b", "size": 10185, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Sources/master/Drivers/PhysicalProblems/Mod_TempeDriver.f90", "max_stars_repo_name": "ciaid-colombia/InsFEM", "max_stars_repo_head_hexsha": "be7eb35baa75c31e3b175e95286549ccd84f8d40", "max_star... |
function decompose(_dataset::AbstractVTKStructuredData, target::String = "Faces", decompose_cell_data = false)
dataset = VTKStructuredData(_dataset)
_dim = dim(dataset)
if decompose_cell_data
if target == "Faces"
if _dim == 2
return decompose_to_faces_2d_with_cell_data(d... | {"hexsha": "018705362da80b5a1ddfdc14ef79e0143c7a7f09", "size": 33531, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/decompose_struct.jl", "max_stars_repo_name": "mohamed82008/VTKDataTypes.jl", "max_stars_repo_head_hexsha": "f1434139863b5d2c6b46f0c0d133dca56993c1b3", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma InvariantsAfterAddClause:
fixes state::State and clause :: Clause and Vbl :: "Variable set"
assumes
"InvariantConsistent (getM state)"
"InvariantUniq (getM state)"
"InvariantWatchListsContainOnlyClausesFromF (getWatchList state) (getF state)" and
"InvariantWatchListsUniq (getWatchList state)" ... | {"llama_tokens": 385976, "file": "SATSolverVerification_Initialization", "length": 456} |
# Eric Xu, 2019-09
###### PACKAGES ######
import pandas as pd
import logging
import time
import dateutil.parser
import flask
import numpy as np
from functools import wraps
from flask import Flask, request, Response, render_template
import numpy as np
import math
from flask_limiter import Limiter
from flask_limiter.uti... | {"hexsha": "c29a0e4d6cefb8ee8924a10b5f7a8e119b8bf22c", "size": 2454, "ext": "py", "lang": "Python", "max_stars_repo_path": "subscription_service.py", "max_stars_repo_name": "eric-xu-ownerIQ/klaviyo-weather-app", "max_stars_repo_head_hexsha": "aa01332f18b1fe6ca3a6df2a6d7f2ae304f001d4", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import unittest
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorlayerx
import tensorlayerx as tlx
from tests.utils import CustomTestCase
import numpy as np
class Layer_BinaryLinear_Test(CustomTestCase):
@classmethod
def setUpClass(self):
... | {"hexsha": "d3f12fbccf184a06268e049f718b76d43d2b2605", "size": 4810, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/layers/test_layers_linear.py", "max_stars_repo_name": "tensorlayer/TensorLayerX", "max_stars_repo_head_hexsha": "4e3e6f13687309dda7787f0b86e35a62bb3adbad", "max_stars_repo_licenses": ["Apach... |
"""test_production.py
This contains validation scripts comparing circuit-based Z2 simulation to
Erik's numerics code. To run the pre-arranged test suite just call
python3 -m pytest test_production.py
Alternatively, you can arrange a custom test suite using the __name__==__main__
logic at the end of this module.... | {"hexsha": "15bb3edf95603300677fcbc92a92c46e2a34f062", "size": 8018, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_production.py", "max_stars_repo_name": "Fermilab-Quantum-Science/Z2Sim-public", "max_stars_repo_head_hexsha": "dfbefffd933aa2e39a0cb9f668b424596dfa7d35", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma run_snth:
assumes "run r p"
shows "enabled (r !! k) (target (stake k r) p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. enabled (r !! k) (target (stake k r) p)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
run r p
goal (1 subgoal):
1. enabled (r !! k) (target (st... | {"llama_tokens": 176, "file": "Transition_Systems_and_Automata_Transition_Systems_Transition_System_Extra", "length": 2} |
/* vim: set tabstop=4 expandtab shiftwidth=4 softtabstop=4: */
/**
* \file boost/numeric/ublasx/operation/abs.hpp
*
* \brief Apply the \c std::abs function to a vector or matrix expression.
*
* \author Marco Guazzone (marco.guazzone@gmail.com)
*
* <hr/>
*
* Copyright (c) 2010, Marco Guazzone
*
* Distribute... | {"hexsha": "7912ddd63fe2d2e4dae911667903bb748fb3ef2f", "size": 3930, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/numeric/ublasx/operation/abs.hpp", "max_stars_repo_name": "sguazt/boost-ublasx", "max_stars_repo_head_hexsha": "21c9b393d33a6ec2a8071ba8d48680073d766409", "max_stars_repo_licenses": ["BSL-1.0"... |
(AxisVector{T, A, SVector{1, T}} where {T})(
a::Real,
::LocalGeometry,
) where {A} = AxisVector(A.instance, SVector(a))
# standard conversions
ContravariantVector(u::ContravariantVector, ::LocalGeometry) = u
CovariantVector(u::CovariantVector, ::LocalGeometry) = u
LocalVector(u::LocalVector, ::LocalGeometry) =... | {"hexsha": "c82a0e4324c7756f0820b9171b5a0821a0a93a7c", "size": 14062, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Geometry/conversions.jl", "max_stars_repo_name": "CliMA/ClimaCore.jl", "max_stars_repo_head_hexsha": "e28309249a4c0dea0e8bb897b4dc9ebc376fa94e", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
#!/usr/bin/env python3
import argparse
import sys
import pandas as p
import numpy as np
from numpy.random import RandomState
from scipy.optimize import minimize_scalar
from numpy.random import RandomState
from scipy.stats import chi2
from collections import defaultdict
from scipy.special import gammaln
#class to perfo... | {"hexsha": "80be5c44a2e75a685eb4a59c312586c10ea809dd", "size": 10549, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/Variant_eval.py", "max_stars_repo_name": "MatthewWolff/DESMAN", "max_stars_repo_head_hexsha": "3f683e75830c4862b5f2c7577ef31b4cc86bdd61", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD... |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
import os
import numpy as np
import boto3
import time
import sagemaker
import sagemaker.session
from sagemaker.workflow.parameters import ParameterInteger, ParameterString
from sagemaker.sklearn.processing import SKLe... | {"hexsha": "e1904458e9960fc48085767b14f0b32283301954", "size": 7516, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cloud/pipelines/semantic_segmentation/pipeline.py", "max_stars_repo_name": "aws-samples/amazon-sagemaker-edge-defect-detection-computer-vision", "max_stars_repo_head_hexsha": "61d762ef43a59a5e... |
[STATEMENT]
lemma (in Module) lin_span_sub_carrier:"\<lbrakk>ideal R A;
H \<subseteq> carrier M\<rbrakk> \<Longrightarrow> linear_span R M A H \<subseteq> carrier M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>ideal R A; H \<subseteq> carrier M\<rbrakk> \<Longrightarrow> linear_span R M A H \<sub... | {"llama_tokens": 2285, "file": "Group-Ring-Module_Algebra7", "length": 15} |
SUBROUTINE CLUKM(X,NX,N,NATT,NCLUST,IASSGN,LIST,NUM,SS,MAXIT,
* IWORK,RW,NW)
C***********************************************************************
C* *
C* FORTRAN CODE WRITTEN FOR INCLUSION IN IBM RESEARCH REPORT RC20525, *
C* 'FORTRAN... | {"hexsha": "aeb4d4a620165729e5f0a11bf489b7836a2a6c1b", "size": 5383, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "contrib/lmoments/Src/clukm.for", "max_stars_repo_name": "xylar/cdat", "max_stars_repo_head_hexsha": "8a5080cb18febfde365efc96147e25f51494a2bf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
"""
permutation-flowshop repository
Module that implements constructive heuristics for the
flowshop scheduling problem.
"""
import random
import numpy as np
def NEH(solution, tie_breaking=False, order_jobs="SD"):
"""Create initial solution with NEH heuristic.
Apply the Nawaz, Enscore and Hans heuristic (198... | {"hexsha": "c9f6723e641d91fe154c6bc846eb771b832fc7a0", "size": 2399, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/constructive_heuristic.py", "max_stars_repo_name": "sukanyakudva/permutation-flowshop", "max_stars_repo_head_hexsha": "11ac52f309cf443da9bff5a11b9e83602fdae84a", "max_stars_repo_licenses": ["M... |
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confus... | {"hexsha": "d5bd87f189968255cc85d71e1498df14242a236c", "size": 2991, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/mlservice/classes/protomodel.py", "max_stars_repo_name": "hamed225/Channel-Sensor-Error-Detection", "max_stars_repo_head_hexsha": "12aba311c4334fec2fbbe0a69e0c764d6db5988c", "max_stars_repo_l... |
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import batman
import numpy as np
from .cache import planet_props
from .limbdarkening import quad
__all__ = ['kic_to_params', 'transit_model']
def kic_to_params(kic):
"""
For a KIC number ``kic``, re... | {"hexsha": "b4fa88d3d6199fb932c51c6519b6cfc658b4fc4b", "size": 3801, "ext": "py", "lang": "Python", "max_stars_repo_path": "salter/params.py", "max_stars_repo_name": "bmorris3/salter", "max_stars_repo_head_hexsha": "f7471aa074fb9fdd024ba2f4dc7ac51ae9180093", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import cv2
import numpy as np
from pathlib import Path
import json
from settings_folder import settings_folder
circles_detection_file = f'{settings_folder}circles_detection_data.txt'
def nothing(_):
pass
def get_circles(image):
cv2.namedWindow('Circle detection')
erosion = 0
minDist = 10
param1 ... | {"hexsha": "ec1c6fe5ae087186cb087fe8845da7ebe5f6dee9", "size": 4686, "ext": "py", "lang": "Python", "max_stars_repo_path": "circles_calibration.py", "max_stars_repo_name": "Dikzamen/dbd_bloodweb_python", "max_stars_repo_head_hexsha": "51c13a917d49740f15fdb38189308851a1369e63", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma sub_point_rep_number_le: "x \<in> \<U> \<Longrightarrow> \<A> rep x \<le> \<B> rep x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> \<U> \<Longrightarrow> \<A> rep x \<le> \<B> rep x
[PROOF STEP]
by (simp add: point_replication_number_def blocks_subset multiset_filter_mono size_mset_mono) | {"llama_tokens": 129, "file": "Design_Theory_Sub_Designs", "length": 1} |
import Dates
import HTTP
import JSON
function compare_http_date_header(header_value::String, timestamp_request_completed::Dates.DateTime) :: Nothing
header_value_timestamp::Dates.DateTime = Dates.DateTime(split(header_value, " UTC")[1], "e, d u Y H:M:S")
@test header_value_timestamp <= timestamp_request_compl... | {"hexsha": "326490d3c9e6c8aab8e77afaca9ca74a61b1f1b2", "size": 1685, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/utils/request.jl", "max_stars_repo_name": "DanceJL/Flamenco.jl", "max_stars_repo_head_hexsha": "c0d16201f3265d2b265b6114e5af07555a467720", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
Simple Linear Regression
========================
See `LinearRegression <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html>`_.
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
def plot_linear_regression():
a = ... | {"hexsha": "b799b8b48b1368a36b67073ade9931539af7a66f", "size": 868, "ext": "py", "lang": "Python", "max_stars_repo_path": "_doc/examples/sklearn_ensae_course/plot_linear_regression.py", "max_stars_repo_name": "Jerome-maker/ensae_teaching_cs", "max_stars_repo_head_hexsha": "43ea044361ee60c00c85aea354a7b25c21c0fd07", "ma... |
"""
calc_grm()
---
Merge imputed data and calculate GRM.
e.g., two.bed + norge.bed, or, dutch.bed, german.bed, norge.bed
"""
function calc_grm(source)
title("Data imputed within country")
cd(work_dir)
fra = joinpath(work_dir, "data/genotypes/step-8.plk")
ped = joinpath(work_dir, "data/pedigree"... | {"hexsha": "ded30e2cbbbed119a3069986fc3191542ee788a2", "size": 14019, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/grm/calc-grm.jl", "max_stars_repo_name": "xijiang/ReDiverse.jl", "max_stars_repo_head_hexsha": "02212b981934b43997b942a78ea8e6f7ac1b9fa4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
!
! Copyright © 2011 The Numerical Algorithms Group Ltd. 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 n... | {"hexsha": "498593b19933250f0d2660337d73deb4d35f6e2b", "size": 19427, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "control/plasma_f90.f90", "max_stars_repo_name": "zhuangsc/Plasma-ompss1", "max_stars_repo_head_hexsha": "bcc99c164a256bc7df7c936b9c43afd38c12aea2", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
[STATEMENT]
lemma O'_O:
assumes "Orig.validFrom s tr"
and "Orig.reach s"
shows "Prime.O (translateTrace tr) = translateO (Orig.O tr)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. O (translateTrace tr) = translateO (Orig.O tr)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
Orig.validFrom s tr
Orig... | {"llama_tokens": 2115, "file": "BD_Security_Compositional_Transporting_Security", "length": 13} |
#! /usr/bin/env python
import os
import logging
import re
import pathlib
from datetime import date, datetime
from collections import namedtuple
import time as timer
import scipy.io as spio
import numpy as np
import pandas as pd
import decimal
import warnings
import datajoint as dj
from pybpodgui_api.models.project ... | {"hexsha": "1d119e13fd297645eceb215fa081e4b3546bcaf1", "size": 71298, "ext": "py", "lang": "Python", "max_stars_repo_path": "pipeline/ingest/behavior.py", "max_stars_repo_name": "Yambottle/map-ephys", "max_stars_repo_head_hexsha": "bea24e29070a53ad97561a66d30a12fc4d8e5409", "max_stars_repo_licenses": ["MIT"], "max_star... |
import cv2 as cv
import numpy as np
img = cv.imread("img.png",0)
img = cv.GaussianBlur(img,(9,9),10)
th2 , ret2 = cv.threshold(img,0,255,cv.THRESH_BINARY + cv.THRESH_OTSU)
kernel = np.ones((5,5))
ret2 = cv.morphologyEx(ret2, cv.MORPH_OPEN, kernel)
ret2 = cv.dilate(ret2 , kernel,50)
cv.imshow("the",ret2)
... | {"hexsha": "903434185fa9189a5a59b7911f75378f87f9af50", "size": 887, "ext": "py", "lang": "Python", "max_stars_repo_path": "lab 10/main.py", "max_stars_repo_name": "AbdulMoeed-140212/DIP-Labs", "max_stars_repo_head_hexsha": "0b083e381ef9f9e2a780dc64ef6a4f04bcde6946", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
subroutine nut_nrain
!! ~ ~ ~ PURPOSE ~ ~ ~
!! this subroutine adds nitrate from rainfall to the soil profile
!! ~ ~ ~ INCOMING VARIABLES ~ ~ ~
!! name |units |definition
!! ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
!! ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~... | {"hexsha": "f92774bf788325a2ded70fa878b8bebbf76c9680", "size": 3183, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/data/program_analysis/multifile_multimod/mfmm_02/nut_nrain.f90", "max_stars_repo_name": "mikiec84/delphi", "max_stars_repo_head_hexsha": "2e517f21e76e334c7dfb14325d25879ddf26d10d", "max_st... |
import numpy as np
from scipy.interpolate import UnivariateSpline
from scipy.optimize import fmin_slsqp
from quantecon import MarkovChain
from scipy.optimize import root
class RecursiveAllocation:
'''
Compute the planner's allocation by solving Bellman
equation.
'''
def __init__(self, model, μgr... | {"hexsha": "c02e16421c500e8eaa6892b02ccc4d94bab1d376", "size": 9244, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/_static/lecture_specific/opt_tax_recur/recursive_allocation.py", "max_stars_repo_name": "gliptak/lecture-source-py", "max_stars_repo_head_hexsha": "6d8fc91acc6668f31e5c163991db7dedb133b8b7"... |
""" helper utility to work with global map / map layout"""
import os
import numpy
import matplotlib.pyplot as plt
import networkx as nx
from scipy.misc import imread
from utils import root
def plot_map():
""" Older utility, new stuff should use GlobalMap.plot() """
filename = os.path.join(root, 'flash', 'f... | {"hexsha": "45a22f488444d8fe422ea119d597e45b55700377", "size": 1899, "ext": "py", "lang": "Python", "max_stars_repo_path": "planning/astar/global_map.py", "max_stars_repo_name": "opikalo/pyfire", "max_stars_repo_head_hexsha": "1a56a39532e1a1d6ca938f46ca5f8eb09fb43957", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# make sure the rest of the ABXpy package is accessible
import os
import sys
package_path = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
if not(package_path in sys.path):
sys.path.append(package_path)
# remove this dependency to ABXpy and create separate repository for this ?
... | {"hexsha": "142aa6cad0919db08b6a602125658adad8ccd5fd", "size": 3912, "ext": "py", "lang": "Python", "max_stars_repo_path": "ABXpy/database/database.py", "max_stars_repo_name": "elinlarsen/ABXpy", "max_stars_repo_head_hexsha": "7b254b99d6ce3f386f45d3da9c92cd45720dd9dd", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Imports
import numpy as np
import sys
sys.path.append('../stationsim/')
from ensemble_kalman_filter import EnsembleKalmanFilter
from ensemble_kalman_filter import EnsembleKalmanFilterType
# from ensemble_kalman_filter import ActiveAgentNormaliser
from stationsim_gcs_model import Model
# Functions
def make_observa... | {"hexsha": "21d7edcbdc438919d2906718e458c5c95423714b", "size": 2663, "ext": "py", "lang": "Python", "max_stars_repo_path": "Projects/ABM_DA/tests/utils.py", "max_stars_repo_name": "Urban-Analytics/dust", "max_stars_repo_head_hexsha": "952ca8991ac1f7ff65387a1b21a3fd36cf98314a", "max_stars_repo_licenses": ["MIT"], "max_s... |
include("functions/GenerateCareerPaths.jl")
using Compose, GraphPlot
FirstLevel = Vector{AbstractLevel}()
g = DiGraph(4)
# 1 => world
add_edge!(g, 1, 2)
#add_edge!(g, 1, 4)
# 2 => 1B-BDL
add_edge!(g, 2, 3)
add_edge!(g, 2, 4)
#add_edge!(g, 2, 5)
#add_edge!(g, 2, 6)
push!(FirstLevel, AcademicLevel("1B-BDL", next = ["... | {"hexsha": "ebf53f3343815853a6214cffc0a4595a59cb572b", "size": 2062, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TempTestsSmall.jl", "max_stars_repo_name": "Omazaria/CareerPathBeDef", "max_stars_repo_head_hexsha": "da5b1cc1a70382444a348da82725d4cfe670d3c9", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 50)
y1 = 2*x + 1
y2 = x**2
# 生成窗口来显示图像
plt.figure()
plt.plot(x, y1)
# 生成新的窗口来显示图像
plt.figure(num=3, figsize=(4,4))
l1, = plt.plot(x, y2, label="y=2*x+1")
l2, = plt.plot(x, y1, color='r', linewidth=1.0, linestyle='--', label='y=x**2')
# 设置x轴显... | {"hexsha": "b1a6dbc33adf0e2236224ba393bba3d762d87e7f", "size": 1658, "ext": "py", "lang": "Python", "max_stars_repo_path": "matplotlib/sample_matplotlib.py", "max_stars_repo_name": "zjhdota/practice", "max_stars_repo_head_hexsha": "de28003e7adf6140dfc06a1ffa3a808e514dbbc0", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy
import sklearn.ensemble
try:
from typing import OrderedDict
except ImportError:
from typing import MutableMapping as OrderedDict
class RandomForestClassifier:
def __init__(self, **kwargs):
self.clf = sklearn.ensemble.RandomForestClassifier(**kwargs)
def __call__(self, raw: numpy... | {"hexsha": "e45c6b38d8590a09398eef706a41bfb0a79d57a3", "size": 781, "ext": "py", "lang": "Python", "max_stars_repo_path": "bioimageio/sklearn/models.py", "max_stars_repo_name": "k-dominik/python-bioimage-io", "max_stars_repo_head_hexsha": "aecaa3412c31672ce159335db083ee9fb4fca519", "max_stars_repo_licenses": ["MIT"], "... |
from numpy import arange
from bokeh.plotting import figure, show
x = arange(1, 4.5, 0.25)
y = 1 / x
plot = figure(height=200)
plot.circle(x, y, fill_color="blue", size=5)
plot.line(x, y, color="darkgrey")
plot.xaxis.axis_label = "Resistance"
plot.xaxis.ticker = [1, 2, 3, 4]
plot.yaxis.axis_label = "Current at 1 V"
... | {"hexsha": "4af6a64f61e29cb76d67da33772cf096304c831f", "size": 464, "ext": "py", "lang": "Python", "max_stars_repo_path": "sphinx/source/docs/user_guide/examples/styling_math_text_latex_tick_labels.py", "max_stars_repo_name": "Suicoleiro/bokeh", "max_stars_repo_head_hexsha": "a212acdf091a7a4df639fa9d443be6ade0018039", ... |
import argparse
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from nltk.tokenize import word_tokenize
#this function uses the train data to create a dictionary, which contains w... | {"hexsha": "2f65920b601e3fd84da6dd05687ed61affa3a498", "size": 3663, "ext": "py", "lang": "Python", "max_stars_repo_path": "binary_data_creation.py", "max_stars_repo_name": "simonmarty/data-mining-project", "max_stars_repo_head_hexsha": "4801be11c29bea7f05e7b499babf67cc37584098", "max_stars_repo_licenses": ["MIT"], "ma... |
#-*- coding:utf-8 -*-
import torch
from torchvision import transforms
import cv2
from PIL import Image, ImageOps
import numpy as np
class MultiViewDataInjector():
def __init__(self, transform_list):
self.transform_list = transform_list
def __call__(self, sample):
output = [transform(sample).uns... | {"hexsha": "7635389fc0335626aa9aec3710d32f29934c0fca", "size": 2222, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/byol_transform.py", "max_stars_repo_name": "SuhongMoon/BYOL-PyTorch", "max_stars_repo_head_hexsha": "fa8eea6c4cc65436aa458a1a48c79fd0d9d46d51", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
clc;
close all
%% which one to show?
idx = 24;
coordIndices = [1,2,3];
imgFig1 = figure(1);
set(imgFig1, 'Position', [100 100 1400 900]) % [1 1 width height]
subplot(2,2,1);
imagesc(imgMat(:,:,:,idx)); axis off image;
subplot(2,2,2);
imagesc(instanceMaskMat(:,:,:,idx)); axis off image;
subplot(2,2,3);
A = (predInstan... | {"author": "aimerykong", "repo": "Recurrent-Pixel-Embedding-for-Instance-Grouping", "sha": "748ade6b969c7861c2a9009cd0f0ffb27004677c", "save_path": "github-repos/MATLAB/aimerykong-Recurrent-Pixel-Embedding-for-Instance-Grouping", "path": "github-repos/MATLAB/aimerykong-Recurrent-Pixel-Embedding-for-Instance-Grouping/Re... |
#Hasan Avcı 170401035
from sympy import Symbol,pprint
file = open("veriler.txt", "r")
data = file.readlines()
for i in range(len(data)):
data[i] = int(data[i])
def detectPolynominal(m1, m2, m3, m4, m5, m6):
if m1 > m6 and m1 > m5 and m1 > m4 and m1 > m3 and m1 > m2:
print(str(m1) + " en uygun 1. poli... | {"hexsha": "ad60904106c4e2de6211f8ed6a7e5e05bc8113f0", "size": 5286, "ext": "py", "lang": "Python", "max_stars_repo_path": "final/170401035.py", "max_stars_repo_name": "yigitcanustek/blm2010", "max_stars_repo_head_hexsha": "2e86dab3fc225a7679b6c660fb01902423476a94", "max_stars_repo_licenses": ["Unlicense"], "max_stars_... |
from sklearn.datasets import load_boston
from tqdm import tqdm
from sklearn.utils import shuffle, resample
import numpy as np
from xhp_flow.nn.node import Placeholder,Linear,Sigmoid,ReLu,Leakrelu,Elu,Tanh,LSTM
from xhp_flow.optimize.optimize import toplogical_sort,run_steps,forward,save_model,load_model,Auto_update_lr,... | {"hexsha": "fe98652eabeaf0e0efb57589c4365ddd56e2dfda", "size": 4791, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/LSTM.py", "max_stars_repo_name": "xhpxiaohaipeng/xhp_flow_frame", "max_stars_repo_head_hexsha": "903b67bee0fa56373ce8751ca604b601ab8be8fd", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""
NE method: naively combine AttrPure and DeepWalk (AttrComb)
by Chengbin Hou 2018
"""
import numpy as np
from . import node2vec
from .utils import dim_reduction
class ATTRCOMB(object):
def __init__(self, graph, dim, comb_method='concat', comb_with='deepWalk', number_walks=10, walk_length=80, window=10, work... | {"hexsha": "954306fac28279f0a092e57cf7e28328af9758e0", "size": 4399, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/libnrl/attrcomb.py", "max_stars_repo_name": "houchengbin/OpenANE", "max_stars_repo_head_hexsha": "d608cb3ece77f45f417d85aad257767ac209f206", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import rospy
import ros_numpy
import numpy as np
import copy
import json
import os
import sys
import torch
import time
from std_msgs.msg import Header
import sensor_msgs.point_cloud2 as pc2
from sensor_msgs.msg import PointCloud2, PointField
from jsk_recognition_msgs.msg import BoundingBox, BoundingBoxArray
from pyq... | {"hexsha": "b514bf323bb801b9fdd15d38ea3182afcb24dee4", "size": 9443, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/single_infernece_ros.py", "max_stars_repo_name": "xyupeng/CenterPoint", "max_stars_repo_head_hexsha": "091f4245da1c1b6b9b2f057b4256e415d983ed1b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/python
# -*- coding utf-8 -*-
#
# Hyperbel - Klasse von agla
#
#
# This file is part of agla
#
#
# Copyright (c) 2019 Holger Böttcher hbomat@posteo.de
#
#
# Licensed under the Apache License, V... | {"hexsha": "2081d19f6317ce815665e436e2b633c2af554383", "size": 20113, "ext": "py", "lang": "Python", "max_stars_repo_path": "agla/lib/objekte/hyperbel.py", "max_stars_repo_name": "HBOMAT/AglaUndZufall", "max_stars_repo_head_hexsha": "3976fecf024a5e4e771d37a6b8056ca4f7eb0da1", "max_stars_repo_licenses": ["Apache-2.0"], ... |
module EdgeTest
using Test
using ForneyLab: Interface, Edge, Variable, Interface, FactorNode, FactorGraph, currentGraph, addNode!, disconnect!, generateId
# Integration helper
mutable struct MockNode <: FactorNode
id::Symbol
interfaces::Vector{Interface}
i::Dict{Symbol,Interface}
function MockNode(; ... | {"hexsha": "9fd5f3a9d505132990a83bdd9ddb59057aaf429f", "size": 1602, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_edge.jl", "max_stars_repo_name": "chmathys/ForneyLab.jl", "max_stars_repo_head_hexsha": "30933f28f9fe3aaaf343547a76a309314ea982cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import sys
import colorsys
import os
import numpy as np
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from scipy import stats
from itertools import combinations
from scipy.stats import ks_2samp
from matplotlib.lines import Line2D
from lokki.lib import PipelineComponents
# Description: Retu... | {"hexsha": "9921659ddb4ea529c9b9916e68c370462a4b6446", "size": 14081, "ext": "py", "lang": "Python", "max_stars_repo_path": "lokki/visualize/enrichment.py", "max_stars_repo_name": "bzhanglab/Lokki", "max_stars_repo_head_hexsha": "035087d58f2b194b3a1644b932683ded0fb71af4", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
#!/usr/bin/env python
"""
find minimum and maximum, stats in an HDF5 variable
"""
from pathlib import Path
import h5py
import numpy as np
import warnings
from argparse import ArgumentParser
def main():
p = ArgumentParser()
p.add_argument('fn', help='HDF5 filename')
p.add_argument('var', help='HDF5 variabl... | {"hexsha": "9b095dac5d5472a2704f78e6dde5ff2ea84bc7ca", "size": 1010, "ext": "py", "lang": "Python", "max_stars_repo_path": "hdf5stats.py", "max_stars_repo_name": "scienceopen/cvhst", "max_stars_repo_head_hexsha": "0613fdcc11cd086cdd375aae05677b33bfbbcfd0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6... |
# Copyright 2019 Xanadu Quantum Technologies Inc.
# 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 agre... | {"hexsha": "58de699eb6c0bd1e8132577ccd69364c14fc42a6", "size": 13911, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/gbs/test_sample.py", "max_stars_repo_name": "rajeshkumarkarra/strawberryfields", "max_stars_repo_head_hexsha": "138d8459fe773a9d645569d7af3ecd1f86e65f5a", "max_stars_repo_licenses": ["Apach... |
"""
Example configurations for the models that worked well
"""
import enum
from typing import Dict
import numpy as np
from reconstruction.model.bunny import FixedBunny
from reconstruction.model.dragon import Dragon
from reconstruction.model.model_mesh import MeshModelLoader
from reconstruction.model.model_pts import ... | {"hexsha": "2225f3eaf9ecba08c4f1ebffb438975afcae45f1", "size": 1688, "ext": "py", "lang": "Python", "max_stars_repo_path": "example.py", "max_stars_repo_name": "mickare/Robust-Reconstruction-of-Watertight-3D-Models", "max_stars_repo_head_hexsha": "c3afd98a8732c0447c153d38bfcefb5c4441bc7b", "max_stars_repo_licenses": ["... |
import time
import numpy as np
from sklearn.metrics import (accuracy_score, confusion_matrix, f1_score,
precision_score, recall_score)
def classify_folds(clf, X, Y, folds):
"""Performs the full-learning procedure (training, testing and metrics).
Args:
clf (Classifier): A... | {"hexsha": "2d1ea5ae99e537b774b3c7e98a3abd77c2dee476", "size": 5618, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/wrapper.py", "max_stars_repo_name": "malvesbertoni/ensemble_opf", "max_stars_repo_head_hexsha": "d36289e19db1ac9c55f3b404f90ba1894fc0e62a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
class ActionFreeReplayBuffer():
def __init__(self, observation, observation_img, observation_img_raw, done):
self.n = len(observation) - 1
self.observation = observation
self.observation_img = observation_img
self.observation_img_raw = observation_img_raw
... | {"hexsha": "f407ebb101f5156929de766ead4064941838af88", "size": 1720, "ext": "py", "lang": "Python", "max_stars_repo_path": "RLV/torch_rlv/utils/action_free_buffer.py", "max_stars_repo_name": "simonr98/Reinforcement-Learning-with-Videos", "max_stars_repo_head_hexsha": "e40cec6b8d817276375e940696b290fc4e1e8bc7", "max_sta... |
# This is a Python port of Joy's affinity maturation flexibility code
# available at: https://github.com/jlouveau/Toy_Model_for_John
import csv
import sys
import os
import importlib
import numpy as np # numerical tools
from copy import deepcopy # deepcopy copies a dat... | {"hexsha": "4fbc7977bcaba625bd6283115eab6f2e5cb1fb23", "size": 34329, "ext": "py", "lang": "Python", "max_stars_repo_path": "Mixture.py", "max_stars_repo_name": "vanouk/Affinity-Maturation-PNAS", "max_stars_repo_head_hexsha": "43839ef01c7164914eb8ef7b338d2a7b1ba769d2", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from __future__ import print_function
from __future__ import division
import click
import os
import json
import numpy as np
import matplotlib.pyplot as plt
def visualize_test_evaluation(input_file, output_folder, experiment_title = ''):
assert os.path.isfile(input_file)
with open(input_file) as infile:
... | {"hexsha": "2568acffc8448e974a76a6cfbfb678510a94b1da", "size": 1924, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/tools/visulization.py", "max_stars_repo_name": "jingkunchen/MS-CMR_miccai_2019", "max_stars_repo_head_hexsha": "ce4b67e017c0891533efadbdce4947b1c4821d6c", "max_stars_repo_licenses": ["MIT"],... |
import os
from flask import Flask, request, render_template
from numpy.lib.polynomial import poly
from werkzeug.utils import secure_filename
import base64
import cv2
import numpy as np
from model import Model
import glob
import argparse
import tifffile as tiff
app = Flask(__name__)
app.config['UPLOAD_IMAGES'] = 'uploa... | {"hexsha": "50d4a2eed79040643823b1bdb578c9cc858d4bae", "size": 8988, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo_server/app.py", "max_stars_repo_name": "abcxs/polyrnn", "max_stars_repo_head_hexsha": "92eee689fe62585529deb1c44fbf1c889f414fa2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
# TODO add ToroidalGeometry, LinearGeometry and other structs that let you pick parameters (like Toroidal parameters to define a plasma) | {"hexsha": "1267474fe0097736fb0adc2675c8779f4873a3f0", "size": 136, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/geometry.jl", "max_stars_repo_name": "SebastianM-C/Plasma.jl", "max_stars_repo_head_hexsha": "e01f53c4ffa08f81971db9a04ee43f5f6d4abdf0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21... |
# This is an approximate model of an acceleration controlled differential drive.
# The angular velocities and the orientation are updated using their closed form
# expression, the position along x and y axes are updated with the midway constant
# angular velocities. The error in this heuristic increases with time and
#... | {"hexsha": "d017d4ed0f5be083549f5f5f9c3ca863416ac353", "size": 2691, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "model/AccelerationControlledDifferentialDrive.jl", "max_stars_repo_name": "grgomrton/robot-model", "max_stars_repo_head_hexsha": "e3c4fb37919a6cadc091a434d692e8f77bd809c6", "max_stars_repo_licenses... |
# -*- mode: python; coding: utf-8 -*
# Copyright (c) 2022 Radio Astronomy Software Group
# Licensed under the 3-clause BSD License
"""
Estimate scalings on different axes from profiling data.
NB:
This script cannot tell the difference between lines that are
hit once and axes with length 1.
It is only usefu... | {"hexsha": "e0f93fda7f2eff9a526e6b2a49475b5cbc36535b", "size": 1907, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarking/analyze_runtimes.py", "max_stars_repo_name": "HERA-Team/pyuvsim", "max_stars_repo_head_hexsha": "d5ae72b06e4bad07433c3fc8073bebb43a3be399", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
C****************************************************************
C
C File: out_cmde.f
C Purpose: Routine to solve COMMON-MODE-OUTAGE
C
C Author: Walt Powell Date: 7 Mar 1995
C Modified:
C Called by: apsoln
C
C*********************************************************... | {"hexsha": "d3cc425db15abeeefd5a8e9c40a39c8fa7bbba87", "size": 6974, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/out_cmde.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14,... |
# -*- coding: utf-8 -*-
"""
Project: neurohacking
File: helpers.py
Author: wffirilat
"""
import numpy as np
from numpy.fft import fft, rfft
import plugin_interface as plugintypes
from open_bci_v3 import OpenBCISample
class PluginHelpers(plugintypes.IPluginExtended):
def __init__(self):
self.packetnum = ... | {"hexsha": "8620a278157169d2532fc920a61686f7c5d4aa59", "size": 1641, "ext": "py", "lang": "Python", "max_stars_repo_path": "plugins/helpers.py", "max_stars_repo_name": "wffirilat/Neurohacking", "max_stars_repo_head_hexsha": "4407566ddb1b4a19a27f585a0050ab04b2bc5efe", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
/*
High Performance Astrophysical Reconstruction and Processing (HARP)
(c) 2014-2015, The Regents of the University of California,
through Lawrence Berkeley National Laboratory. See top
level LICENSE file for details.
*/
// Test that we can instantiate our plugin.
#include <iostream>
#include <cstdio>
#incl... | {"hexsha": "eb3314bd78101b0ad193eb2ae1dc9fe4f6b46fdb", "size": 1320, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "plugin_example/harp_mpi_example.cpp", "max_stars_repo_name": "tskisner/HARP", "max_stars_repo_head_hexsha": "e21435511c3dc95ce1318c852002a95ca59634b1", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL... |
function PascalDistribution(arg0::jint, arg1::jdouble)
return PascalDistribution((jint, jdouble), arg0, arg1)
end
function cumulative_probability(obj::PascalDistribution, arg0::jint)
return jcall(obj, "cumulativeProbability", jdouble, (jint,), arg0)
end
function get_number_of_successes(obj::PascalDistribution... | {"hexsha": "9c5959395cc59c93e500896a9bd13774605cff2a", "size": 1356, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "gen/HipparchusWrapper/DistributionWrapper/DiscreteWrapper/pascal_distribution.jl", "max_stars_repo_name": "JuliaAstrodynamics/Orekit.jl", "max_stars_repo_head_hexsha": "e2dd3d8b2085dcbb1d2c75471dab... |
#include "Media/MediaSinkPad.h"
#include "Media/IMediaFilter.h"
#include <boost/foreach.hpp>
//SharedMediaFormat MediaSinkPad::accept(SharedMediaPad sourcePad, SharedMediaFormat format)
//{}
//
//SharedMediaFormat MediaSinkPad::onAccept(SharedMediaPad sourcePad, SharedMediaFormat format)
//{
// SharedMediaFormats sour... | {"hexsha": "8377d37d54fc1dc4852429a733d433f9b7fa6ead", "size": 3824, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sdk/Media/MediaSinkPad.cpp", "max_stars_repo_name": "InfiniteInteractive/LimitlessSDK", "max_stars_repo_head_hexsha": "cb71dde14d8c59cbf8a1ece765989c5787fffefa", "max_stars_repo_licenses": ["MIT"], ... |
#include <boost/asio/bind_executor.hpp>
#include <boost/asio/signal_set.hpp>
#include <cstdlib>
#include <memory>
#include <thread>
#include <vector>
#include "http_listener.h"
int main(int argc, char *argv[]) {
auto const address = boost::asio::ip::make_address("0.0.0.0");
auto const port = 8080;
auto con... | {"hexsha": "05d7ab8c0c815c4336b4d661c8270e28083d37b1", "size": 1445, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "jeprof_in_use/src/main.cpp", "max_stars_repo_name": "phantom9999/mithril", "max_stars_repo_head_hexsha": "d7c5ff22a6396a77f7f92a1ee20706573e065c13", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# -*- coding:utf-8 -*-
# @author leone
# @desc 摄像头实时人脸识别
# @version 2018-12-13
import cv2
import dlib
import numpy as np
import pandas as pd
# 人脸识别模型，提取 128D 的特征矢量
face_recognition_model = dlib.face_recognition_model_v1("../data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
# 计算两个向量间的欧式距离
def return_euclid... | {"hexsha": "c2f1e595271b4f3ed787f0a5d7a1f6a0ff3932a9", "size": 4350, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/recognition_from_camera.py", "max_stars_repo_name": "janlle/dlib-face-recognition", "max_stars_repo_head_hexsha": "394b3665612dd98d5a9104545b1efa55b146e52b", "max_stars_repo_licenses": ["Apach... |
import math
import os
import numpy as np
from file_handling import binary_classes
from margrie_libs.signal_processing.metadata_handling import store_meta_data
def detrend_single_channel(trace):
from scipy.signal import savgol_filter
return trace - savgol_filter(trace, 1001, 3)
def detrend_al... | {"hexsha": "aded0748d07b5aba731492c8e6d26c668b0cd2e0", "size": 5541, "ext": "py", "lang": "Python", "max_stars_repo_path": "margrie_libs/margrie_libs/signal_processing/detrending.py", "max_stars_repo_name": "Sepidak/spikeGUI", "max_stars_repo_head_hexsha": "25ae60160308c0a34e7180f3e39a1c4dc6aad708", "max_stars_repo_lic... |
import paddle
import numpy as np
import argparse
import os
import os.path as osp
import sys
import time
import json
from mmcv import Config
from dataset import build_data_loader
from models import build_model
from utils import ResultFormat, AverageMeter
# import warnings
# warnings.filterwarnings('ig... | {"hexsha": "5641993c5962d89683172de67c502e2af1b0a939", "size": 2158, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "RoseSakurai/PSENet_paddle", "max_stars_repo_head_hexsha": "6b45f95059724080932b116a98d5af14ea0e1640", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
""" Module implementing GAN which will be trained using the Progressive growing
technique -> https://arxiv.org/abs/1710.10196
"""
import datetime
import os
import time
import timeit
import copy
import numpy as np
import torch as th
class Generator(th.nn.Module):
""" Generator of the GAN network """
def _... | {"hexsha": "d24f2040dd603f46e6aeed183a0af5f8206d07cd", "size": 21510, "ext": "py", "lang": "Python", "max_stars_repo_path": "sourcecode/MSG_GAN/GAN.py", "max_stars_repo_name": "manojtld/BMSG-GAN", "max_stars_repo_head_hexsha": "b3d85cd4fa4dd648e05c7881457c39a6d5379cb5", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import time, copy
import os, os.path
import sys
import numpy
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from scipy import optimize
from echem_plate_ui import *
from echem_plate_math import *
homepath='C:/Users/Gregoire/Documents/CaltechWork/echemdrop/20130301_CuZnSnFe_Plate3_3654'
mainapp=QApplication... | {"hexsha": "3e39a7e6b490aff3c1e626932a3db19c6b1852c3", "size": 1459, "ext": "py", "lang": "Python", "max_stars_repo_path": "echem_testphoto.py", "max_stars_repo_name": "johnmgregoire/JCAPdatavis", "max_stars_repo_head_hexsha": "6d77a510e00acf31de9665828d27ea33aba6ab78", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
from fvcore.common.file_io import PathManager
import xml.etree.ElementTree as ET
from typing import Dict
from tqdm import tqdm
import numpy as np
import os
def bias_pascal_voc(
dirname: str,
noise_ratio: float,
bias_rule: Dict[str, str]
):
"""
Add Noise to Pascal VOC detection annotations.
Ar... | {"hexsha": "2804b3fe9e9e71c922b4b225ad28edad7e927613", "size": 2103, "ext": "py", "lang": "Python", "max_stars_repo_path": "autoqa/add_noise.py", "max_stars_repo_name": "Umbrasi/qa-automation", "max_stars_repo_head_hexsha": "5ac1d97bc77289399132cf8fdda45bde3adb158b", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
#!/usr/bin/python3
"""
Counterfactual explanation of a user query.
Based on application on the knapsack problem of Korikov, A., & Beck, J. C. mming, CP2021. Counterfactual Explanations via Inverse Constraint Programming.
Usecase:
1) Some optimal solution x* is provided to the user by a constraint optimization solver... | {"hexsha": "e5838f112a52745f7cdff9e96e9a589b1ade8c22", "size": 9119, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/advanced/counterfactual_explain.py", "max_stars_repo_name": "vishalbelsare/cpmpy", "max_stars_repo_head_hexsha": "42b1795d268c4e634d49d6d6aa2bb243aea67b0c", "max_stars_repo_licenses": ["A... |
section \<open>Commonly used Lemmas\<close>
theory Common
imports
Main
"HOL-Library.Extended_Nat"
"HOL-Eisbach.Eisbach"
begin
declare [[coercion_enabled = false]]
subsection \<open>Miscellaneous\<close>
lemma split_sym_rel:
fixes G :: "'a rel"
assumes "sym G" "irrefl G"
obtains E where "E\<inter>E\<in... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Prim_Dijkstra_Simple/Common.thy"} |
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 8 22:40:32 2016
@author: au194693
"""
import mne
import numpy as np
from my_settings import *
subject = 1
raw = mne.io.Raw(data_folder + "sub_%s-raw.fif" % subject, preload=True)
raw.filter(8, 12)
picks = mne.pick_types(raw.info, "grad")
raw.apply_hilbert(picks)
ev... | {"hexsha": "38e3c539a469ff8cac4b778c90f941c6822e4046", "size": 2248, "ext": "py", "lang": "Python", "max_stars_repo_path": "hilbert_transform.py", "max_stars_repo_name": "MadsJensen/biomeg_class", "max_stars_repo_head_hexsha": "470ce736d8103e6d90ca203a0b2e0a96b756a780", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
// ----------------- BEGIN LICENSE BLOCK ---------------------------------
//
// Copyright (C) 2018-2019 Intel Corporation
//
// SPDX-License-Identifier: MIT
//
// ----------------- END LICENSE BLOCK -----------------------------------
#pragma once
#include <boost/program_options/options_description.hpp>
#include "ad... | {"hexsha": "55517e654c47a4beb4e64ba9a1009abf692c42af", "size": 732, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "tools/map_maker/common/include/ad/map/maker/common/InternalToRoad5ConfigDescription.hpp", "max_stars_repo_name": "seowwj/map", "max_stars_repo_head_hexsha": "2afacd50e1b732395c64b1884ccfaeeca0040ee7"... |
#!/usr/bin/env python
#Copyright (c) 2014,
#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 t... | {"hexsha": "5be417a67762f027128cc84ffec9bb277860a136", "size": 26448, "ext": "py", "lang": "Python", "max_stars_repo_path": "hydrogeol_utils/grid_utils.py", "max_stars_repo_name": "GeoscienceAustralia/hydrogeol_utils", "max_stars_repo_head_hexsha": "586c8289f5a9931c25c04c141dbdf3cd3b2ae3dc", "max_stars_repo_licenses": ... |
# -*- coding: utf-8 -*-
# !/usr/bin/python
################################### PART0 DESCRIPTION #################################
# Filename: class_compute_meta_data_of_network.py
# Description:
#
# Author: Shuai Yuan
# E-mail: ysh329@sina.com
# Create: 2015-12-06 21:49:46
# Last:
__author__ = 'yuens'
#############... | {"hexsha": "0dc01f78e3eb29a92d8ae9e9240ce763142543e5", "size": 31897, "ext": "py", "lang": "Python", "max_stars_repo_path": "mypackage/class_compute_edge_property.py", "max_stars_repo_name": "ysh329/link-prediction", "max_stars_repo_head_hexsha": "1ffbd1ecdf3b80a6ddebb02cf20a0487e36adb3b", "max_stars_repo_licenses": ["... |
import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.parameterize(
{'axis': 0, 'start': 2, 'out_shape': (3, 2, 4)},... | {"hexsha": "60cdeb368af47d793e9962fd8d9ba25b81b4dde2", "size": 3523, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/chainer_tests/functions_tests/array_tests/test_rollaxis.py", "max_stars_repo_name": "disktnk/chainer", "max_stars_repo_head_hexsha": "133798db470f6fd95973b882b9ccbd0c9726ac13", "max_stars_re... |
[STATEMENT]
lemma cast\<^sub>n\<^sub>o\<^sub>d\<^sub>e\<^sub>_\<^sub>p\<^sub>t\<^sub>r\<^sub>2\<^sub>o\<^sub>b\<^sub>j\<^sub>e\<^sub>c\<^sub>t\<^sub>_\<^sub>p\<^sub>t\<^sub>r_inject [simp]:
"cast\<^sub>n\<^sub>o\<^sub>d\<^sub>e\<^sub>_\<^sub>p\<^sub>t\<^sub>r\<^sub>2\<^sub>o\<^sub>b\<^sub>j\<^sub>e\<^sub>c\<^sub>t\<... | {"llama_tokens": 397, "file": "Core_SC_DOM_common_pointers_NodePointer", "length": 1} |
"""
Tests the InputMixl class to ensure it is constructed correctly and that
input validation works as expected.
"""
import unittest
import numpy as np
from src.models.base_model_inputs import InputMixl
class InputMixlTests(unittest.TestCase):
"""
Unit test class for storing the various tests of the InputMix... | {"hexsha": "f048882adf60c618048b61209a5aa5a65ce90e82", "size": 1439, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/test_input_mixl.py", "max_stars_repo_name": "timothyb0912/check-yourself", "max_stars_repo_head_hexsha": "bbca2afcd63c068401913de2dd98f64569373c97", "max_stars_repo_licenses": ["MIT"], ... |
"""
```
transform_data(m::AbstractModel, levels::DataFrame; verbose::Symbol = :low)
```
Transform data loaded in levels and order columns appropriately for the DSGE model. Returns
DataFrame of transformed data.
The DataFrame `levels` is output from `load_data_levels`. The series in levels are
transformed as specified... | {"hexsha": "df5ff598ed781a487d6650c5d20c6d1a390850a4", "size": 3493, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/data/transform_data.jl", "max_stars_repo_name": "bgoodri/DSGE.jl", "max_stars_repo_head_hexsha": "6f9077ab8e9912bbade15b40c9c78c298c26e8f0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
"""Connect four game.
"""
import numpy as np
class Connect_four_game:
def __init__(self):
self.n_in_a_row = 4
self.board_dim = 6
self.reset()
self.bad_move = 0
#States:
# 0: start
# 1: player 1
# 2: check for win?
# 3: player 2
# 4: ... | {"hexsha": "8688771ada387d4775ccd5dcbd80cd3ccc2f6058", "size": 5435, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/connect_four/game.py", "max_stars_repo_name": "kasperskov01/pysnaike", "max_stars_repo_head_hexsha": "401a4601cbbd151470fa9ac0575288f89fa93240", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/bin/python
import numpy as np
class LabelGroup():
def __init__(self, baseNames):
self.base = baseNames
self.cropped = self.crop(self.base)
# lowercase greek letters for niceness;
self.clusterSymbolMap = [chr(945 + x) for x in range(55)]
print(self.cropped)
@static... | {"hexsha": "ebfcfc74e664e488aab9327c8da13db4a321a77a", "size": 2364, "ext": "py", "lang": "Python", "max_stars_repo_path": "straintables/Viewer/MatrixLabelGroup.py", "max_stars_repo_name": "Gab0/linkageMapper", "max_stars_repo_head_hexsha": "549b292e5b6ab22e03373483cd27236aa2f635eb", "max_stars_repo_licenses": ["MIT"],... |
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