text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import unittest
import numpy as np
from pydrake.autodiffutils import (InitializeAutoDiff, AutoDiffXd,
ExtractGradient)
from .normalization_derivatives import calc_normalization_derivatives
class TestNormalizationDerivatives(unittest.TestCase):
def test_normalization_derivative... | {"hexsha": "6f44b9010e5ed69f76152799ed9bf74fae2ef77f", "size": 635, "ext": "py", "lang": "Python", "max_stars_repo_path": "qsim/test_normalization_derivatives.py", "max_stars_repo_name": "pangtao22/quasistatic_simulator", "max_stars_repo_head_hexsha": "7c6f99cc7237dd922f6eb0b54c580303e86b5223", "max_stars_repo_licenses... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "d99bee58b99bbe350ee8d7e516301f4750753537", "size": 9932, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/relay/test_pass_gradient.py", "max_stars_repo_name": "ttyang1018/tvm", "max_stars_repo_head_hexsha": "ade26cacd0767cf14dc053ac4d7778859f83a32c", "max_stars_repo_licenses": ["Apache-2.... |
import arviz as az
import numpy as np
import os
import pystan
import matplotlib.pyplot as plt
from lib.stan_utils import compile_model, get_pickle_filename, get_model_code
from lib.drug_classes import DRUG_CLASSES
from prepare_data import get_formatted_data, add_rank_column, aggregate_treatment_arms, get_variability_ef... | {"hexsha": "a14eedae83a38337c5f84825e067d50ff4ad9f4f", "size": 3532, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/subgroup_analysis.py", "max_stars_repo_name": "volkale/advr", "max_stars_repo_head_hexsha": "f817ce31c50a5bb976eb29bffe9832e2aeb6f7c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# This solution was build using python 2.7
# Author: Tomas F. Venegas Bernal tf.venegas10@uniandes.edu.co
import random
import numpy as np
# This is a simple class that builds the Board that will be shown to the user.
class Board:
def __init__(self, height, width):
self.matrix = np.chararray((height, wid... | {"hexsha": "6534e0a1ec1c980ecdeb1ef9cd4ce1220f48ae03", "size": 7932, "ext": "py", "lang": "Python", "max_stars_repo_path": "Minesweeper.py", "max_stars_repo_name": "tf-venegas10/Minesweeper", "max_stars_repo_head_hexsha": "dfb41284f5bb2469d101c4b05abe5d6897bf69f6", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
using GeometricAlgebra
using DataStructures
using MacroTools: postwalk, prewalk, @capture
import BenchmarkTools
import Grassmann
include("printing.jl")
include("utils.jl")
function run_benchmark(name, expr::Expr, level)
indent_level = level * 2
result = @eval @localbenchmark $expr
replprint(string(minimum... | {"hexsha": "faa76f1b0c10718dfcf6849b923cedcd18137440", "size": 2149, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmarks/benchmarks.jl", "max_stars_repo_name": "serenity4/ConformalGeometry.jl", "max_stars_repo_head_hexsha": "70ec88954d0d0acfb9d480e17a45593305333c04", "max_stars_repo_licenses": ["MIT"], "ma... |
{-# OPTIONS --no-universe-polymorphism #-}
open import Data.Product hiding (map)
open import Relation.Binary.Core
open import Function
open import Data.List
open import Data.Unit using (⊤)
open import Data.Empty
open import Equivalence
module BagEquality where
infixr 5 _⊕_
data _⊕_ (A B : Set) : Set where
... | {"hexsha": "33b53bfd9c67ca6d5dd50499a1915729c52c1e81", "size": 12091, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "BagEquality.agda", "max_stars_repo_name": "NAMEhzj/Divide-and-Conquer-in-Agda", "max_stars_repo_head_hexsha": "99bd3a5e772563153d78f61c1bbca48d7809ff48", "max_stars_repo_licenses": ["MIT"], "max_... |
"""Lekhnitskii solutions to homogeneous anisotropic plates with loaded and unloaded holes
Notes
-----
This module uses the following acronyms
* CLPT: Classical Laminated Plate Theory
References
----------
.. [1] Esp, B. (2007). *Stress distribution and strength prediction of composite
laminates with multiple holes... | {"hexsha": "86d1f038438f52414722894dd3c43ec5092b044d", "size": 36019, "ext": "py", "lang": "Python", "max_stars_repo_path": "bjsfm/lekhnitskii.py", "max_stars_repo_name": "BenjaminETaylor/bjsfm", "max_stars_repo_head_hexsha": "a952183f5acca8139a1dd8ab2191c8dd3dc14710", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from mpl_toolkits.axes_grid1 import make_axes_locatable
import os
# dictionary useful in indexing
# of loaded numpy arrays as each
# column in each of these numpy
# array represents a class
c={1:0,2:1,4:2,5:3,6:4,8:5,13:6}
# crea... | {"hexsha": "0ea65948174ebd6bbe21e49c3c8d668c0492ac0e", "size": 16784, "ext": "py", "lang": "Python", "max_stars_repo_path": "periodic variable classification/code/experiments/cnn/blanking_experiments/average_blanking_exp_subplots.py", "max_stars_repo_name": "MeetGandhi/MeetGandhi-Post-hoc-Explainability-of-Deep-Learnin... |
{-# OPTIONS --without-K --safe #-}
module Categories.Category.Monoidal.Construction.Minus2 where
-- Any -2-Category is Monoidal. Of course, One is Monoidal, but
-- we don't need to shrink to do this, it can be done directly.
-- The assumptions in the construction of a -2-Category are all
-- needed to make things wor... | {"hexsha": "5c8d655828961c270eeeb76b4d5291097b569bf0", "size": 1228, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Categories/Category/Monoidal/Construction/Minus2.agda", "max_stars_repo_name": "Trebor-Huang/agda-categories", "max_stars_repo_head_hexsha": "d9e4f578b126313058d105c61707d8c8ae987fa8", "max_st... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from flask import Flask, render_template, request, redirect,Response
from flask_socketio import SocketIO, emit,send
import flask_socketio
from threading import Lock
import torch
from torch import nn
import os
import itertools
import glob
import math
import sys
import numpy ... | {"hexsha": "a0d762488c013ddba2b2f63483df46797db059ac", "size": 2631, "ext": "py", "lang": "Python", "max_stars_repo_path": "etc/individual modules/Voice_Activity_Detection/performance_test/whole process/app.py", "max_stars_repo_name": "yuzhouhe2000/video-conference-enhancer", "max_stars_repo_head_hexsha": "46aa130c0b7f... |
# This file is part of the bapsflib package, a Python toolkit for the
# BaPSF group at UCLA.
#
# http://plasma.physics.ucla.edu/
#
# Copyright 2017-2019 Erik T. Everson and contributors
#
# License: Standard 3-clause BSD; see "LICENSES/LICENSE.txt" for full
# license terms and contributor agreement.
#
"""
Module for ... | {"hexsha": "1733eceb33f31b8807ab32c01cbe6c93b604d9dc", "size": 7733, "ext": "py", "lang": "Python", "max_stars_repo_path": "bapsflib/_hdf/maps/controls/nixyz.py", "max_stars_repo_name": "BaPSF/bapsflib", "max_stars_repo_head_hexsha": "999c88f813d3a7c5c244a77873850c5c5a4042b8", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import matplotlib.pyplot as plt
import numpy as np
model, explain, faith = [],[],[]
model.extend([0,0.07])
explain.extend([0,0.073])
for i in range(10):
model.append(0.073)
explain.append(0.073+(i+1)*0.001)
faith.append(np.corrcoef(model, explain)[0, 1])
print("model ", model)
print("explain", explain)
pr... | {"hexsha": "ceeff486cb2060eadbae6cd72c4acb03becfabbe", "size": 643, "ext": "py", "lang": "Python", "max_stars_repo_path": "faithfulness test.py", "max_stars_repo_name": "marnixm/lime_experiments", "max_stars_repo_head_hexsha": "0b6b2acddff3f55c022c7a5eb28a150ab9e4c846", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
##########################################################
#
#
#This IFMR comes from Raithel et al. 2017
#https://arxiv.org/pdf/1712.00021.pdf
#
#
#########################################################
import numpy as np
class IFMR(object):
"""
The IFMR base class. The IFMR is a combination of the
WD... | {"hexsha": "90265cd9d440643ae2ee89f166aed949469744e0", "size": 8989, "ext": "py", "lang": "Python", "max_stars_repo_path": "popstar/ifmr.py", "max_stars_repo_name": "samrose30/PyPopStar", "max_stars_repo_head_hexsha": "de32db0662c61dbb1141d3acedb7cc2be06bb1dd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
#!/usr/bin/env python3
# MHD linear modes convergence plots
import os,sys
import numpy as np
import matplotlib.pyplot as plt
import pyHARM
from pyHARM.parameters import parse_parthenon_dat
RES = [int(x) for x in sys.argv[1].split(",")]
BASE = "../../"
LONG = sys.argv[2]
SHORT = sys.argv[3]
NVAR = 8
VARS = ['rho', '... | {"hexsha": "3b59dea05acf5d2c0585c062db2add8d11001533", "size": 3223, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/convergence/plot_convergence_modes.py", "max_stars_repo_name": "vedantdhruv96/kharma", "max_stars_repo_head_hexsha": "1159aa53d060087e1723166ceb922bd634c14a97", "max_stars_repo_licenses": ["... |
//////////////////////////////////////////////////////////////////
//
// FreeLing - Open Source Language Analyzers
//
// Copyright (C) 2004 TALP Research Center
// Universitat Politecnica de Catalunya
//
// This library is free software; you can redistribute it and/or
// modify it ... | {"hexsha": "46a5b7f6dadf1cd85d33ec557a173d6c8da8ff45", "size": 16334, "ext": "cc", "lang": "C++", "max_stars_repo_path": "FreeLingModules/deprecated/squoia_server_analyzer.cc", "max_stars_repo_name": "ariosquoia/squoia", "max_stars_repo_head_hexsha": "3f3c3c253bdb2d891889e0427790e6c972870f08", "max_stars_repo_licenses"... |
struct AggressivenessBeliefMDP <: MDP{AggressivenessBelief, MLAction}
up::AggressivenessUpdater
end
function generate_sr(p::AggressivenessBeliefMDP, b_old::AggressivenessBelief, a::MLAction, rng::AbstractRNG)
up = p.up
pomdp = get(up.problem)
s = rand(rng, b_old)
sp = generate_s(pomdp, s, a, rng)
... | {"hexsha": "26cf50b76e4d4581bd6ea4125b487afabca2da73", "size": 3539, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/aggressiveness_belief_mdp.jl", "max_stars_repo_name": "zsunberg/Multilane.jl", "max_stars_repo_head_hexsha": "2f19dd2a60a0786e6bbcf6a150a173d35be068a3", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
import os,sys
import numpy as np
import json
sys.path.append('/opt/render')
from tools import mysql
from tools import bash
from colorama import *
init(autoreset=True)
from tools.decorators import *
####################################################################... | {"hexsha": "b89331c197cea079a1417c1dbbb8058482bcf74d", "size": 2567, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "bashgu/NetVidSeg", "max_stars_repo_head_hexsha": "93d5549713497f77806f1589032270ff42c61327", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_... |
(* seplog (c) AIST 2005-2013. R. Affeldt, N. Marti, et al. GNU GPLv3. *)
(* seplog (c) AIST 2014-2018. R. Affeldt et al. GNU GPLv3. *)
Require Import Div2 Even.
From mathcomp Require Import ssreflect ssrfun ssrbool eqtype ssrnat seq choice.
From mathcomp Require Import tuple.
Require Import ssrZ ZArith_ext String_ext s... | {"author": "affeldt-aist", "repo": "seplog", "sha": "b08516d34f5dedd0aafbe77d8ef270fa838e8f85", "save_path": "github-repos/coq/affeldt-aist-seplog", "path": "github-repos/coq/affeldt-aist-seplog/seplog-b08516d34f5dedd0aafbe77d8ef270fa838e8f85/seplogC/POLAR_parse_client_hello_triple4.v"} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 10 15:02:51 2020
@author: parsotak
"""
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset
from mpl_toolkits.basemap import Basemap
#Define input file
infile = '/wx/storage/halpea17/wx272/20170909_erai.nc'
#Read in the... | {"hexsha": "be8dcdd5e6052a8c94cb931f9827b3e7c2fdc5e3", "size": 1874, "ext": "py", "lang": "Python", "max_stars_repo_path": "example_grib_2_10_20.py", "max_stars_repo_name": "Kyl67899/python-labs", "max_stars_repo_head_hexsha": "aafc6fc94837ee43c9ef2e1b103d86f80dfc9814", "max_stars_repo_licenses": ["FSFAP"], "max_stars_... |
# Copyright 2019 D-Wave Systems 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 agreed to in wri... | {"hexsha": "b305cde76e4f814cd51622fd34eaf9ee146ba063", "size": 4739, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_search.py", "max_stars_repo_name": "zeta1999/dwave-tabu", "max_stars_repo_head_hexsha": "2a6393c4e7444c0c60d9718840160c83eadb7c31", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
[STATEMENT]
lemma SMP_subterms_subset: "subterms\<^sub>s\<^sub>e\<^sub>t M \<subseteq> SMP M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subterms\<^sub>s\<^sub>e\<^sub>t M \<subseteq> SMP M
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>x. x \<sqsubseteq>\<^sub>s\<^sub>e\<^sub>t M \<L... | {"llama_tokens": 851, "file": "Stateful_Protocol_Composition_and_Typing_Typed_Model", "length": 10} |
import numpy as np
from sensors.msg import RawMeasurement
from sensors.msg import ProcessedMeasurement
def C_to_F(cel):
return cel * 9/5 + 32
def F_to_C(far):
return (far - 32) * 5/9
def process_measurement(measurement):
proc_measurement = ProcessedMeasurement()
proc_measurement.day = measurement.d... | {"hexsha": "df696beadf4d1bf544cf98ce2028276aad85f84c", "size": 987, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sensors/scripts/utils/analysis.py", "max_stars_repo_name": "IvanovicM/ros", "max_stars_repo_head_hexsha": "ef9f4b0661459f3911f5a937af74d18ac7170173", "max_stars_repo_licenses": ["MIT"], "max_st... |
section \<open>Cones\<close>
text \<open>We define the notions like cone, polyhedral cone, etc. and prove some basic facts about them.\<close>
theory Cone
imports
Basis_Extension
Missing_VS_Connect
Integral_Bounded_Vectors
begin
context gram_schmidt
begin
definition "nonneg_lincomb c Vs b = (lincomb c... | {"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/Linear_Inequalities/Cone.thy"} |
import math
import numpy as np
"""
This function calculates the roots of the quadratic inequality for the Rh reuse factor.
Parameters:
lx - list of input sizes of the lstms. The size of this list is equal to the number of layers.
lh - list of input sizes of the hidden layers. The size of this ... | {"hexsha": "8fbadd797079a59bb7c16b5b056dc3c3435e7089", "size": 1390, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/reuse_factors_examples.py", "max_stars_repo_name": "walkieq/LSTM-HLS", "max_stars_repo_head_hexsha": "f90bc769153e667eb8a30c7c4147bd53620f02bb", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
The base image interface.
"""
import numpy as np
from scipy import ndimage
# Local imports
from .image import Image
from ..transforms.affines import to_matrix_vector
from ..reference.coordinate_system... | {"hexsha": "96e358ba5b0d984090a93647829bb204053bc846", "size": 15070, "ext": "py", "lang": "Python", "max_stars_repo_path": "nipy/core/image/affine_image.py", "max_stars_repo_name": "neurospin/nipy", "max_stars_repo_head_hexsha": "cc54600a0dca1e003ad393bc05c46f91eef30a68", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
# Copyright (c) 2018, Curious AI Ltd. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View... | {"hexsha": "f11dbac353b5cda43a1097feb2726b11f781ea2d", "size": 7325, "ext": "py", "lang": "Python", "max_stars_repo_path": "MT-CNV/mean_teacher/data.py", "max_stars_repo_name": "Wangzheaos/DARD-Net", "max_stars_repo_head_hexsha": "4b0dc7e87c82c7f6f5892c257fd397d7217fd7f1", "max_stars_repo_licenses": ["Unlicense"], "max... |
[STATEMENT]
lemma i0_less[simp]: "(0::enat) < n \<longleftrightarrow> n \<noteq> 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (0 < n) = (n \<noteq> 0)
[PROOF STEP]
by (rule zero_less_iff_neq_zero) | {"llama_tokens": 95, "file": null, "length": 1} |
[STATEMENT]
lemma mreq_end2: "applied_rule_rev C x b = applied_rule_rev C x c \<Longrightarrow>
applied_rule_rev C x (a#b) = applied_rule_rev C x (a#c)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. applied_rule_rev C x b = applied_rule_rev C x c \<Longrightarrow> applied_rule_rev C x (a # b) = applied_rule_r... | {"llama_tokens": 382, "file": "UPF_Normalisation", "length": 3} |
# 1 "./libxc_master.F90"
# 1 "<built-in>"
# 1 "<command-line>"
# 1 "./libxc_master.F90"
!! Copyright (C) 2003-2006 M. Marques, A. Castro, A. Rubio, G. Bertsch
!!
!! This program is free software; you can redistribute it and/or modify
!! it under the terms of the GNU Lesser General Public License as published by
!! the ... | {"hexsha": "30cefb7279f6fe574ec2a20f301d91056dd4148b", "size": 19638, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "libxc-2.2.0/src/libxc.f90", "max_stars_repo_name": "rdietric/lsms", "max_stars_repo_head_hexsha": "8d0d5f01186abf9a1cc54db3f97f9934b422cf92", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
# © 2021 Nokia
#
# Licensed under the BSD 3 Clause license
# SPDX-License-Identifier: BSD-3-Clause
import subprocess
import numpy as np
def gpuCount():
query = [
'nvidia-smi',
'--list-gpus'
]
res = subprocess.run(query, stdout=subprocess.PIPE).stdout.decode('ascii')[0:-1]
# Count ... | {"hexsha": "d1278720a9b2cf7634d0e16c7e1abcc4240843e4", "size": 1695, "ext": "py", "lang": "Python", "max_stars_repo_path": "common/utils/gpu.py", "max_stars_repo_name": "nokia/integratedimputation", "max_stars_repo_head_hexsha": "ca72bda54cb66e99d79ff0b174cf8f99ccb554ba", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
"""
varN(B::LogBinner[, lvl])
Calculates the variance/N of a given level in the Binning Analysis.
"""
function varN(B::LogBinner, lvl::Integer = _reliable_level(B))
n = B.count[lvl]
var(B, lvl) / n
end
"""
var(B::LogBinner[, lvl])
Calculates the variance of a given level in the Binning Analysis.
"""... | {"hexsha": "05b20af320f553d5a0d6594928488da6d487df5a", "size": 5231, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/log/statistics.jl", "max_stars_repo_name": "UnofficialJuliaMirror/BinningAnalysis.jl-b7192094-8e58-5052-a244-180a858778ee", "max_stars_repo_head_hexsha": "9eb6cf6ae6e623d76a778cc72311fb058cc751... |
module Sessions
using DataFrames, NaturalSort, DataStructures, HDF5
export PROCESSED_DATA_DIR, REGIONS, REGION_LABELS, Session, ClusterMetadata, sessionnames, readsession, patient, siteregion
const RAW_DATA_DIR = joinpath(dirname(@__FILE__), "..", "raw")
const PROCESSED_DATA_DIR = joinpath(dirname(@__FILE__), "..", "... | {"hexsha": "88f8523031e449051baab996f4969161f720cab0", "size": 3540, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Sessions.jl", "max_stars_repo_name": "simonster/Persistent-Single-Neuron-Activity-during-Working-Memory-in-the-Human-Medial-Temporal-Lobe", "max_stars_repo_head_hexsha": "4b46fef287b3d849695ec9... |
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from horsetailmatching import UncertainParameter, HorsetailMatching
from horsetailmatching import UniformParameter, IntervalParameter
from horsetailmatching.demoproblems import TP2
def main():
u1 = IntervalParameter(lower_bound=-1, upper_bound=1... | {"hexsha": "dca083cb9d6ec48804deec1ec7a114f49c649012", "size": 1378, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mixed_uncertainties.py", "max_stars_repo_name": "lwcook/horsetail-matching", "max_stars_repo_head_hexsha": "f3d5f8d01249debbca978f412ce4eae017458119", "max_stars_repo_licenses": ["MIT"], ... |
import pydevd
pydevd.settrace('localhost', port=51234, stdoutToServer=True, stderrToServer=True)
import os
import sys
import plyvel
import numpy
import matplotlib.pyplot as plt
import numpy as np
import leveldb
# First compile the Datum, protobuf so that we can load using protobuf
# This will create datum_pb2.py
os.s... | {"hexsha": "c6c6928b89f50b1d25ae9956a3d6b9dd91b20a72", "size": 2343, "ext": "py", "lang": "Python", "max_stars_repo_path": "distanceMetricLearning/leveldbToNP.py", "max_stars_repo_name": "KareemYousrii/2015-DL-TIWafer", "max_stars_repo_head_hexsha": "5aa7d2ecfcfd3da95811a0a49c855ba1bcb3f034", "max_stars_repo_licenses":... |
"""
File name: extracted_features_gridsearch.py
Author: Esra Zihni
Date created: 29.04.2019
"""
import numpy as np
import sys
import os
import yaml
import pickle
import pandas as pd
import pandas.core.indexes
sys.modules['pandas.indexes'] = pandas.core.indexes
import json
import time
import keras
import tensorflow ... | {"hexsha": "994c12e38f2a4f62bbd27c7c76592e6520b353ab", "size": 7656, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/extracted_features_gridsearch.py", "max_stars_repo_name": "prediction2020/multimodal-classification", "max_stars_repo_head_hexsha": "0805d5b48b640e89ab942c5e44be22e2315a8079", "max_stars_repo... |
"""
Author: michealowen
Last edited: 2019.9.20,Friday
DBSCAN聚类,使用sklearn生成数据集
"""
#encoding=UTF-8
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import DBSCAN
from queue import Queue
from time import time
from scipy.spatial import KDTree
def genData():
'''
... | {"hexsha": "6d7a021de2377ab8a53d3346c043e79afacab500", "size": 6468, "ext": "py", "lang": "Python", "max_stars_repo_path": "cluster/density/DBSCAN.py", "max_stars_repo_name": "michealowen/MachingLearning", "max_stars_repo_head_hexsha": "9dcc908f2d3e468390e5abb7f051b449b0ecb455", "max_stars_repo_licenses": ["Apache-2.0"... |
/*
* Copyright (C) 2019 LEIDOS.
*
* 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 w... | {"hexsha": "e0f6fa957c1c3b25cbd1bdd4f4cb9c4709a89a7e", "size": 32173, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "carma_wm/src/CARMAWorldModel.cpp", "max_stars_repo_name": "harderthan/carma-platform", "max_stars_repo_head_hexsha": "29921896a761a866db9cfee473f02a481d8bb9c9", "max_stars_repo_licenses": ["Apache-... |
\documentclass[letterpaper]{article}
\usepackage{common/ohpc-doc}
\setcounter{secnumdepth}{5}
\setcounter{tocdepth}{5}
% Include git variables
\input{vc.tex}
% Define Base OS and other local macros
\newcommand{\baseOS}{CentOS8.4}
\newcommand{\OSRepo}{CentOS\_8.4}
\newcommand{\OSTree}{CentOS\_8}
\newcommand{\OSTag}{el... | {"hexsha": "21cfca89cd5cb661964125cc18b1876a7feb5325", "size": 11277, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/recipes/install/rocky8/x86_64/xcat_stateful/slurm/steps.tex", "max_stars_repo_name": "viniciusferrao/ohpc", "max_stars_repo_head_hexsha": "edae86737aeeeee1a9d0c1e6a1ac7139d5fce971", "max_stars... |
import os
os.chdir('seqFISH_AllenVISp/')
import numpy as np
import pandas as pd
import pickle
import matplotlib
matplotlib.use('qt5agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
import matplotlib.pyplot as plt
#from matplotlib import cm
import scipy.stats as st
with open ('dat... | {"hexsha": "76d7f42aadff293229047298824135aa97f40b4b", "size": 5040, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmark/seqFISH_AllenVISp/Performance_evaluation.py", "max_stars_repo_name": "tabdelaal/SpaGE", "max_stars_repo_head_hexsha": "7533cbf2275c3049561e8a17b9f7866e0e324743", "max_stars_repo_licenses... |
[STATEMENT]
lemma wf\<^sub>s\<^sub>s\<^sub>t_prefix[dest]: "wf'\<^sub>s\<^sub>s\<^sub>t V (S@S') \<Longrightarrow> wf'\<^sub>s\<^sub>s\<^sub>t V S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. wf'\<^sub>s\<^sub>s\<^sub>t V (S @ S') \<Longrightarrow> wf'\<^sub>s\<^sub>s\<^sub>t V S
[PROOF STEP]
by (induct S rule: w... | {"llama_tokens": 169, "file": "Stateful_Protocol_Composition_and_Typing_Stateful_Strands", "length": 1} |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import time
EXP_ID = '2021-09-10 16:48:03.161207'
# Load odometry data
odom_addr = f'train/odometry/odometry_log_{EXP_ID}.csv'
odom_data_df = pd.read_csv(odom_addr, delimiter=',')
# Setup odometry as a image
map_res = 10
min_... | {"hexsha": "9ca691fe1ba68201ccb13bdc8d57f7ee2b07a3c5", "size": 2879, "ext": "py", "lang": "Python", "max_stars_repo_path": "kairos_minerl/src/kairos_minerl/viz_odometry.py", "max_stars_repo_name": "viniciusguigo/kairos_minerl_basalt", "max_stars_repo_head_hexsha": "8f76e1d293dbcf62653ed3f7f326bd090a0af6f0", "max_stars_... |
[STATEMENT]
lemma vlrestriction_VLambda: "(\<lambda>a\<in>\<^sub>\<circ>A. f a) \<restriction>\<^sup>l\<^sub>\<circ> B = (\<lambda>a\<in>\<^sub>\<circ>A \<inter>\<^sub>\<circ> B. f a)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. VLambda A f \<restriction>\<^sup>l\<^sub>\<circ> B = VLambda (A \<inter>\<^sub>\<circ... | {"llama_tokens": 136, "file": "CZH_Foundations_czh_sets_CZH_Sets_BRelations", "length": 1} |
import tensorflow as tf
from tensorflow.python.ops import variable_scope as vs
import numpy as np
import pickle
import os,json
from samples import read_clips
from laplace_temporal_net import create_r3d
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
gpus=[0]
video_json_path = 'three_samples.json'
data_root = '/home/pr606/P... | {"hexsha": "6a17d806ac31c4144395144b5f8e1b1ef7c3944d", "size": 6815, "ext": "py", "lang": "Python", "max_stars_repo_path": "samples/fetch_gpa_feat.py", "max_stars_repo_name": "shenqiang-Yuan/GPA", "max_stars_repo_head_hexsha": "ad8bb4540ef4126c817c5fe007dad93a5a7ddc2a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
/**
* Copyright 2015 Christian Dreher (dreher@charlydelta.org)
*
* 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 ... | {"hexsha": "648ac5077e3428d4520c74a82540ffdc37a39c43", "size": 11872, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Main.cpp", "max_stars_repo_name": "crgdreher/nddlgen-cli", "max_stars_repo_head_hexsha": "3efbde7ee95bd51f76ba3a067725bc0f5f3d010c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
#! /usr/bin/python
# -*- coding: utf-8 -*-
u"""
Fast Nearest Neighbor Search on python using kd-tree
author Atsushi Sakai
usage: see test codes as below
license: MIT
"""
import numpy as np
import scipy.spatial
class NNS:
def __init__(self, data):
# store kd-tree
self.tree = scipy.spatial.cKDTr... | {"hexsha": "fa979d99d943c91e162afdc3a7ed34e0d14ca7ae", "size": 2121, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyfastnns.py", "max_stars_repo_name": "AtsushiSakai/pyfastnns", "max_stars_repo_head_hexsha": "d3baabba8d2639c6b065bcfcf756adc93b4e8326", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
import numpy as np
from numba import jit
from ..constants import Constants as c
from .universal_propagate import propagateUniversal
__all__ = [
"addLightTime",
"addStellarAberration"
]
MU = c.MU
C = c.C
@jit(["Tuple((f8[:,:], f8[:]))(f8[:,:], f8[:], f8[:,:], f8, f8, i8, f8)"], nopython=True, cache=True)
def... | {"hexsha": "0636601bfcc2b9dabe271b7798b5b2c06b755633", "size": 4256, "ext": "py", "lang": "Python", "max_stars_repo_path": "thor/orbits/aberrations.py", "max_stars_repo_name": "KatKiker/thor", "max_stars_repo_head_hexsha": "ffc8ab3fbaa8af046f531e8111907a891998d14b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
using Knet
# sample usage:
# m128 = MLP([3644,128,1])
# mlprun(data; model=m128, epochs=20)
# define a model type with weights and optimization params so we can
# use the Adam optimizer which works faster than SGD:
type MLP
weights
oparams
function MLP(sizes; optimizer=Adam, winit=0.1, atype=Array{Float3... | {"hexsha": "bfc2c854f5151e76927d0327db8a52a4e98b2298", "size": 2455, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "mlp.jl", "max_stars_repo_name": "JuliaTagBot/melseg", "max_stars_repo_head_hexsha": "8f63c00516624be8625244fc64dcbfb26d5d5851", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_... |
import argparse
import numpy as np
def quantize(data,pred,error_bound):
radius=32768
diff = data - pred
quant_index = (int) (abs(diff)/ error_bound) + 1
#print(quant_index)
if (quant_index < radius * 2) :
quant_index =quant_index>> 1
half_index = quant_index
quant_index... | {"hexsha": "fc0880df6f1e2ecce1b350f11b3fbf397fb28950", "size": 2263, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantize.py", "max_stars_repo_name": "Meso272/PyTorch-VAE", "max_stars_repo_head_hexsha": "08c44bdcb30ba8795a7c0da5597af80c8c42e9f0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
[STATEMENT]
lemma constant_function_eq':
assumes "a \<in> carrier R"
assumes "b \<notin> carrier R"
shows "\<cc>\<^bsub>a\<^esub> b = undefined"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. constant_function (carrier R) a b = undefined
[PROOF STEP]
by (simp add: constant_function_closed assms(1) assms(2) fun... | {"llama_tokens": 127, "file": "Padic_Ints_Function_Ring", "length": 1} |
# wczytanie zestawu danych
import numpy as np
dataset = 'australian'
dataset = np.genfromtxt("%s.csv" % (dataset), delimiter=",")
X = dataset[:, :-1]
y = dataset[:, -1].astype(int)
# zdefiniowanie klasyfikatorów
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn... | {"hexsha": "3a04c28800d08731619b6a9e8d1d97f298fcc5b9", "size": 3150, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/kod4.py", "max_stars_repo_name": "metsi/metsi.github.io", "max_stars_repo_head_hexsha": "4a195236d73ea90b46be1662c45ab473c893af29", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
/*
* This file is part of the Sequoia MSO Solver.
*
* Copyright 2012 Alexander Langer, Theoretical Computer Science,
* RWTH Aachen University
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
*... | {"hexsha": "a1ae32591427d51ac3cfc805d328c18ce0e8eea9", "size": 2020, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/leaf_game_factory.hpp", "max_stars_repo_name": "sequoia-mso/sequoia-core", "max_stars_repo_head_hexsha": "d2a6a461ffbe38dc8abb005b2c8f1f3bc1dc2bbe", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
#!/usr/bin/env python
#
# This software is distributed under BSD 3-clause license (see LICENSE file).
#
# Authors: Sergey Lisitsyn
from numpy import *
from numpy.random import randn
# generate some overlapping training vectors
num_vectors=100
vec_distance=1
traindat=concatenate((randn(2,num_vectors)-vec_distance,
ra... | {"hexsha": "e0c311e74457a139b8e119abaae4c8964d777752", "size": 2821, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/undocumented/python/modelselection_random_search_liblinear.py", "max_stars_repo_name": "shiyi001/shogun", "max_stars_repo_head_hexsha": "287f02d11d5914ded2d410ab9c6f38712e11ca2b", "max_st... |
using Fn
using MacroTools
using Test
@testset "Fn.jl" begin
@testset "no _ creates 0-argument function" begin
@test @capture(Fn.fn(:(exp(5 * √π))), function () exp(5 * √π) end)
end
@testset "_ creates single argument function" begin
@test @capture(Fn.fn(:(5 + _)), function (x_) 5 + x_ end... | {"hexsha": "75fb2bd64bb6242217876115bc38623db6bff6ac", "size": 1692, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "cqql/Fn.jl", "max_stars_repo_head_hexsha": "b6784f1b5b8a9592e56decaea3d68d8a0f942944", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['mathtext.fontset'] = 'cm'
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
cm = plt.get_cmap('Set1')
deficits_nice = ['Gait speed', 'Dom grip str', 'Ndom grip str', 'ADL score','IADL score', '5 Chai... | {"hexsha": "e3ac5175a6226bbcc5b1b766fa48af6c07c5a9ca", "size": 6013, "ext": "py", "lang": "Python", "max_stars_repo_path": "Plotting_code/plot_ELSA_missing.py", "max_stars_repo_name": "Spencerfar/djin-aging", "max_stars_repo_head_hexsha": "f6513226e879e6061996d819b4de0e2873860fbc", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
@np.vectorize
def vectorized_get(dictionary, key):
"""
Helper vectorized function to get keys
from a dictionary.
"""
return dictionary.get(key, -1)
def is_iter(something):
"""
Helper vectorized function to test
if something is an iterable other
than a str.
... | {"hexsha": "1171bd9deec381480e4c6fafcbc2ec974f7a239d", "size": 1332, "ext": "py", "lang": "Python", "max_stars_repo_path": "mapsbr/helpers/utils.py", "max_stars_repo_name": "phelipetls/mapsbr", "max_stars_repo_head_hexsha": "36e2637c612d333a327199fd0687dbba09e964ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python
# vim: fdm=indent
'''
author: Fabio Zanini
date: 05/08/13
content: Correct the allele frequencies comparing read types and write to file.
'''
# Modules
import subprocess as sp
import argparse
from operator import itemgetter
import numpy as np
from hivwholeseq.sequencing.samples impor... | {"hexsha": "2f37e663a317d2d653f74ae96ded391cbd9bca32", "size": 4179, "ext": "py", "lang": "Python", "max_stars_repo_path": "hivwholeseq/sequencing/filter_allele_frequencies.py", "max_stars_repo_name": "neherlab/hivwholeseq", "max_stars_repo_head_hexsha": "978ce4060362e4973f92b122ed5340a5314d7844", "max_stars_repo_licen... |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct AccountSasParameters <: SwaggerModel
signedServices::Any # spec type: Union{ Nothing, String } # spec name: signedServices
signedResourceTypes::Any # spec ... | {"hexsha": "91bb4d90d497e89b11748be1396cb19fad10ea00", "size": 4798, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Storage/StorageManagementClient/model_AccountSasParameters.jl", "max_stars_repo_name": "JuliaComputing/Azure.jl", "max_stars_repo_head_hexsha": "0e2b55e7602352d86bdf3579e547a74a9b5f44f8", "max_... |
from collections import Counter
import numpy as np
import sklearn
from imblearn.base import BaseSampler
class GlobalCS(BaseSampler):
"""
Global CS is an algorithm that equalizes number of samples in each class. It duplicates all samples equally
for each class to achieve majority class size
"""
d... | {"hexsha": "8991e0b22c105cedfbdcffdda36902103e6b31aa", "size": 2296, "ext": "py", "lang": "Python", "max_stars_repo_path": "multi_imbalance/resampling/global_cs.py", "max_stars_repo_name": "NaIwo/multi-imbalance", "max_stars_repo_head_hexsha": "237c5842b27a58edfdfb88073faa0021eb243348", "max_stars_repo_licenses": ["MIT... |
import numpy
import theano
from theano import scalar, gof
from theano.tests.unittest_tools import SkipTest, assert_allclose
from theano.tensor.tests import test_elemwise
from .config import mode_with_gpu, test_ctx_name
from .test_basic_ops import rand_gpuarray
from ..elemwise import (GpuElemwise, GpuDimShuffle,
... | {"hexsha": "1c9a2b1fb969c88a2e7c12fae35a4f466207db68", "size": 9912, "ext": "py", "lang": "Python", "max_stars_repo_path": "theano/sandbox/gpuarray/tests/test_elemwise.py", "max_stars_repo_name": "oplatek/Theano", "max_stars_repo_head_hexsha": "09605e7cae876e15c5502c4edaba6a9644c50c11", "max_stars_repo_licenses": ["BSD... |
import csv
import nltk
import pandas as pd
import numpy as np
import spacy
from nltk.tokenize import word_tokenize
import string
def load_book(book_path, lower=False):
'''
Reads in a novel from a .txt file, and returns it in (optionally
lowercased) string form
Parameters
----------
book_path ... | {"hexsha": "4330fe6a853c314c59d81009c80518c0e93ffaf9", "size": 9222, "ext": "py", "lang": "Python", "max_stars_repo_path": "bookworm/build_network.py", "max_stars_repo_name": "harrisonpim/bookworm", "max_stars_repo_head_hexsha": "d5fffe9630079236a64708f767186aa0748de4cf", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
module mod_output
use mod_error
use mod_graph
implicit none
save
private
! public procedures
public :: write_input_graph, &
write_graph_paths
! private variables
integer, parameter :: fout_numb = 700
character(*), parameter :: fout_name = "graph.out"
contains
!!! Public !!!!!!... | {"hexsha": "167dc95c954a68bdc0753cbbe70e39db7fb1e867", "size": 2363, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/mod_output.f90", "max_stars_repo_name": "marberti/pathgen", "max_stars_repo_head_hexsha": "9ba8df5c1577492d49ea8423516f477bb8dfbfb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#!/usr/bin/env python
# coding: utf-8
# # Problem 3 - Purchasing Paint
# In[98]:
from tqdm import tqdm
import numpy as np
import os
# Sometimes an assertion fails. Just shuffle the seed again when that happens. #ostrich
rng = np.random.default_rng(42838858382)
# In[99]:
class Case:
def __init__(self, name,... | {"hexsha": "96b463f5749d3ae6cd36250207a5aa5bc57d2908", "size": 5355, "ext": "py", "lang": "Python", "max_stars_repo_path": "paint/data/generator.py", "max_stars_repo_name": "acio-olympiad/ACIO2022Contest2", "max_stars_repo_head_hexsha": "53c7ced66ae916b118cfab8c72e4531dd08defd2", "max_stars_repo_licenses": ["MIT"], "ma... |
import math
from dataclasses import dataclass
from typing import Dict
import numpy as np
import jax.numpy as jnp
from jax import random, ops
import nltk
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from data_collator import DataCollatorForTextInfilling, SentenceTokenize, DataCollatorForSen... | {"hexsha": "50c154d549f4bde8d2749167f7a9d2e0871006ce", "size": 981, "ext": "py", "lang": "Python", "max_stars_repo_path": "sentence_permutation.py", "max_stars_repo_name": "patrickvonplaten/rotobart", "max_stars_repo_head_hexsha": "eb1482677889b6de3f1708621ad8d2da263afadb", "max_stars_repo_licenses": ["MIT"], "max_star... |
#! /opt/anaconda3/envs/align/bin/python
# -*- coding: utf-8 -*-
# Copyright 2021 The align-experiment Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... | {"hexsha": "c134f0ac889bbcca03c8fa629b1fd2015e4e019e", "size": 5847, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/processing/process_utils.py", "max_stars_repo_name": "kaarinaaho/learning_alignment", "max_stars_repo_head_hexsha": "605f730834b7ef2d57b01eb519a10bf60c93f7bc", "max_stars_repo_licenses": ["MI... |
\section{Analysis of soft matter scattering}
The use of neutron and X-ray scattering experiments for the study of soft matter is well developed, with early research into the structure of phospholipid monolayers by reflectometry methods being conducted in the late 1970s by Albrecht \emph{et al.}\autocite{albrecht_polymo... | {"hexsha": "32f447900e6636e9c079048b45e641ad91e49165", "size": 4458, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "reports/chapters/introduction/scattering.tex", "max_stars_repo_name": "arm61/thesis", "max_stars_repo_head_hexsha": "4c76e837b1041472a5522427de0069a5a28d40c9", "max_stars_repo_licenses": ["CC-BY-4.0... |
{-# OPTIONS --sized-types #-}
module SList.Order {A : Set}(_≤_ : A → A → Set) where
open import List.Sorted _≤_
open import Size
open import SList
data _*≤_ : {ι : Size} → A → SList A {ι} → Set where
genx : {ι : Size}{b : A}
→ (_*≤_) {↑ ι} b snil
gecx : {ι : Size}{b x : A}{xs : SList A {ι}}
... | {"hexsha": "f1aab2fd78ddcd60d189b7079ad13e2aa3dac2f9", "size": 678, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "agda/SList/Order.agda", "max_stars_repo_name": "bgbianchi/sorting", "max_stars_repo_head_hexsha": "b8d428bccbdd1b13613e8f6ead6c81a8f9298399", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
% $Id$
%
% ObitSingle dish and OTF/GBT Tables class definitions
%
%\def\section #1.#2.{\medskip\leftline{\bf #1. #2.}\smallskip}
%\def\bfitem #1#2{{{\bf (#1)}}{\it #2}}
\def\bfi #1#2{{\bf #1:}{ #2}\par}
\def\extname #1#2{ {\bf 1} Extension Name {\it #2}\smallskip}
%\def\tabname #1#2{\bfitem 1{#1 Table Name #2}}
\def\... | {"hexsha": "735e5888d8ce5b1d0cb2f5da1a6d12e534b7530d", "size": 65928, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ObitSystem/ObitSD/doc/ObitSD.tex", "max_stars_repo_name": "sarrvesh/Obit", "max_stars_repo_head_hexsha": "e4ce6029e9beb2a8c0316ee81ea710b66b2b7986", "max_stars_repo_licenses": ["Linux-OpenIB"], "ma... |
import mujoco as mj
import numpy as np
from mujoco.glfw import glfw
from mujoco_base import MuJoCoBase
# Define states for the finite state machine
FSM_HOLD = 0
FSM_SWING1 = 1
FSM_SWING2 = 2
FSM_STOP = 3
class LegSwing(MuJoCoBase):
def __init__(self, xml_path):
super().__init__(xml_path)
self.si... | {"hexsha": "cb9bd05abed9219f6269694466b643224693d646", "size": 4828, "ext": "py", "lang": "Python", "max_stars_repo_path": "example_leg_swing.py", "max_stars_repo_name": "BolunDai0216/PyMuJoCoBase", "max_stars_repo_head_hexsha": "3d9250feacd6129e44d99342663616aaf06c5d43", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import pygame
from src.pgassets import pgObject
class pgSlider(pgObject):
def __init__(self, pos, size):
pgObject.__init__(self, pos, size)
self.slider_rect = pygame.Rect(pos, (size[0] * 0.8, 4))
self.slider_rect.center = self.rect.center
self.slider_button = se... | {"hexsha": "d8f21b8da3c790ee3e72d31ddcbc78ba5837a507", "size": 1135, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pgassets/common/pgSlider.py", "max_stars_repo_name": "Blackdevil132/machineLearning", "max_stars_repo_head_hexsha": "de048bb1473994052f8ed1afb11a15b7833b506d", "max_stars_repo_licenses": ["MIT... |
from functools import partial
import jax
import jax.numpy as jnp
from e3nn_jax import IrrepsData, index_add
from e3nn_jax.util import prod
from jax import lax
@partial(jax.jit, static_argnums=(1, 2, 3, 4))
def lowpass_filter(input, scale, strides, transposed=False, steps=(1, 1, 1)):
r"""Lowpass filter for 3D fie... | {"hexsha": "77c3311ba025cb0ae67c1a2970b4c6c9f8480f95", "size": 6488, "ext": "py", "lang": "Python", "max_stars_repo_path": "e3nn_jax/experimental/voxel_pooling.py", "max_stars_repo_name": "yilunliao/e3nn-jax", "max_stars_repo_head_hexsha": "dfe472eb3dcc58abb07ae91eedc39f6fa6926bc8", "max_stars_repo_licenses": ["Apache-... |
import packages.mdp.gridworld as gw
import numpy as np
def test_mapcreation_booleanWalls():
walls = np.array([[0, 1, 0], [0, 0, 0], [0, 0, 1]])
walls = walls.astype('bool')
world = gw.Gridworld(walls)
desired = np.array([[0, np.nan, 1], [2, 3, 4], [5, 6, np.nan]])
assert np.allclose(world.map, des... | {"hexsha": "fbde81ddcbafc76549391e85dad56c1db85f9514", "size": 763, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_gridworld.py", "max_stars_repo_name": "bastianalt/correlation_priors_for_rl", "max_stars_repo_head_hexsha": "9a98f345ac10e9767d854cd7a9681057a50a9737", "max_stars_repo_licenses": ["MIT"],... |
import torch
import numpy as np
import pytest
import deepspeed
from deepspeed.ops.adagrad import DeepSpeedCPUAdagrad
from deepspeed.ops.op_builder import CPUAdagradBuilder
if not deepspeed.ops.__compatible_ops__[CPUAdagradBuilder.NAME]:
pytest.skip("cpu-adagrad is not compatible")
def check_equal(fi... | {"hexsha": "b8a025fe02a8145c1f4295728e8b1d4453c89275", "size": 4861, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/test_cpu_adagrad.py", "max_stars_repo_name": "Seong-yeop/DeepSpeed", "max_stars_repo_head_hexsha": "76f2b5e51d8cf68d1966dceaf1a562a6f02d73fb", "max_stars_repo_licenses": ["MIT"], "max_s... |
# libraries
import argparse
import logging
from google.cloud import storage
import joblib
import numpy as np
import pandas as pd
import pickle
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
def run(argv=None):
# downlo... | {"hexsha": "3fb3372f93717899061ede3b655307d8c458bf35", "size": 1953, "ext": "py", "lang": "Python", "max_stars_repo_path": "pitch-predictor/answers/components/trainRF/train_rf.py", "max_stars_repo_name": "data-describe/awesome-data-science-models", "max_stars_repo_head_hexsha": "aa9b3aa8137a30b47fa044c7b4db46568c0d6316... |
import multiprocessing as mp
import random
import numpy as np
import nltk
from nltk.corpus import stopwords
import csv
import gzip
import pandas as pd
from nltk.corpus import wordnet as wn
from nltk.util import ngrams
from flashtext import KeywordProcessor
from seqeval.metrics import accuracy_score, classification_repo... | {"hexsha": "1871ac557871c8a015232888b9113bbab6a3ad0f", "size": 1059, "ext": "py", "lang": "Python", "max_stars_repo_path": "leaqi/src/oracles.py", "max_stars_repo_name": "xkianteb/leaqi", "max_stars_repo_head_hexsha": "924435590e74421ed16488429056f26747c99421", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12,... |
import pandas as pd
import numpy as np
import math
from tensorflow.keras.models import load_model
from agents.agent import Agent
from agents.meter import Meter
from agents.prediction_market_adapter import NUM_PREDICTIONS, ACCOUNT_0
class LstmMultiAgent(Agent):
"""Agent that uses multivariate LSTM with private a... | {"hexsha": "d3c5696945946171234c8e3fba9de0d67a5dfbca", "size": 2759, "ext": "py", "lang": "Python", "max_stars_repo_path": "agent/agents/lstm_multi_agent.py", "max_stars_repo_name": "rampopat/charje", "max_stars_repo_head_hexsha": "3af178bd72800e339c45637356440780c3b0563a", "max_stars_repo_licenses": ["MIT"], "max_star... |
The Peoples Vanguard of Davis (http://davisvanguard.org/ link) is a political blogs blog that was launched in July 2006. It is dedicated to exposing what it calls the dark underbelly of the Peoples Republic of Davis by writing about things that arent completely reported in the Davis Enterprise mainstream press. Its ... | {"hexsha": "cf6db6bbea706d55673a7747af120a5f47d45a64", "size": 31439, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/The_People%27s_Vanguard_of_Davis.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MI... |
function t = isascii(c)
%ISASCII True for decimal digits.
%
% For a string C, ISASCII(C) is 1 for any ASCII character code between 0
% and 127, inclusive, and 0 otherwise.
%
% See also: ISALNUM, ISALPHA, ISDIGIT, ISLOWER, ISPRTCHR, ISUPPER,
% ISXDIGIT.
% Author: Peter J. Acklam
% Time-stamp: 2002-03-... | {"author": "CovertLab", "repo": "WholeCell", "sha": "6cdee6b355aa0f5ff2953b1ab356eea049108e07", "save_path": "github-repos/MATLAB/CovertLab-WholeCell", "path": "github-repos/MATLAB/CovertLab-WholeCell/WholeCell-6cdee6b355aa0f5ff2953b1ab356eea049108e07/lib/util/matutil/isascii.m"} |
! ###################################################################
! Copyright (c) 2013-2019, Marc De Graef Research Group/Carnegie Mellon University
! All rights reserved.
!
! Redistribution and use in source and binary forms, with or without modification, are
! permitted provided that the following conditions are... | {"hexsha": "5f2c5477b39a13b6b4491e3b048eac4687c85d86", "size": 4630, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/Utilities/EMmkxtal.f90", "max_stars_repo_name": "ZachClayburn/EMsoft", "max_stars_repo_head_hexsha": "5852e630fd0ce6c9538d7c0b7b1653dda28d0f1f", "max_stars_repo_licenses": ["Unlicense"], ... |
/* vim:set ts=3 sw=3 sts=3 et: */
/**
* Copyright © 2008-2013 Last.fm Limited
*
* This file is part of libmoost.
*
* Permission is hereby granted, free of charge, to any person
* obtaining a copy of this software and associated documentation
* files (the "Software"), to deal in the Software without restriction,
... | {"hexsha": "ad3bd424b05bb0771c3ccb8d77e43202e90971af", "size": 3966, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/moost/io/detail/helper_win32.hpp", "max_stars_repo_name": "lastfm/libmoost", "max_stars_repo_head_hexsha": "895db7cc5468626f520971648741488c373c5cff", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import pickle
import jieba
import operator
import statistics
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
from datetime import datetime
from collections import Counter
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from... | {"hexsha": "3e529828b07347ebe33d57409b34a62103117a24", "size": 8757, "ext": "py", "lang": "Python", "max_stars_repo_path": "final_demo/modules.py", "max_stars_repo_name": "A2Zntu/HW0_Political_News_Analysis", "max_stars_repo_head_hexsha": "898c593ee2ed514a0052edd672a44ec3c68bb9fd", "max_stars_repo_licenses": ["MIT"], "... |
import os
import sys
from pathlib import Path
_package_path = Path(__file__).parent.absolute()
_package_search_path = _package_path.parent
sys.path.append(str(_package_search_path))
import json
import numpy as np
import h5py
from data.vsum_tool import generate_summary, evaluate_summary
# example usage: python compu... | {"hexsha": "56529ea8c314e61a4bacc7fb46b55c04c44a2764", "size": 2543, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/compute_fscores.py", "max_stars_repo_name": "jongwookyi/AC-SUM-GAN", "max_stars_repo_head_hexsha": "2408d231f316be1265cda2abf231a21a2a1f5399", "max_stars_repo_licenses": ["FSFAP"], "max... |
[STATEMENT]
lemma const_res_subseq_prop_1:
assumes "s \<in> closed_seqs Zp"
shows "(\<forall>m.(const_res_subseq k s) m k = (const_res k s) )"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>m. const_res_subseq k s m k = const_res k s
[PROOF STEP]
using const_res_subseq_prop_0[of s] const_res_def[of k s... | {"llama_tokens": 349, "file": "Padic_Ints_Zp_Compact", "length": 2} |
import numpy as np
class Method:
def __call__(self, x):
raise NotImplementedError()
def disable(self, index, x):
raise NotImplementedError
def __repr__(self):
raise NotImplementedError
class Max(Method):
def __call__(self, x):
return x.argmax()
def disable(self... | {"hexsha": "8e6112ccc4d5760092e699a7f8dd640290964132", "size": 1298, "ext": "py", "lang": "Python", "max_stars_repo_path": "griffig/infer/selection.py", "max_stars_repo_name": "pantor/griffig", "max_stars_repo_head_hexsha": "0b10ef5d69902b14a4d648a809a51933a8f5fe8a", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
import pickle
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from keras.layers import Dropout
from sklearn.preprocessing import MinMax... | {"hexsha": "7ff1c1bbe7f9c7e18674f2e9a439193de6c243a8", "size": 6602, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/hyperparameter_search.py", "max_stars_repo_name": "jbae11/depletion_rom", "max_stars_repo_head_hexsha": "06d68465e81be13de5b6d6c8c8030a8ae9315b1b", "max_stars_repo_licenses": ["BSD-3-Clause... |
import struct
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
import pyaudio
plt.rcParams['toolbar'] = 'None'
class LiveViewer(object):
def __init__(self, approx_fps, border_color):
self.pause_time = 1 / approx_fps
self.fig = plt.figure()
... | {"hexsha": "1f49796f67a87277d01e1a137aa78e3ff9112d4c", "size": 4047, "ext": "py", "lang": "Python", "max_stars_repo_path": "synethesia/network/io/live_viewer.py", "max_stars_repo_name": "RunOrVeith/SyNEThesia", "max_stars_repo_head_hexsha": "0ef5de759b4bf74cb318fc5e6e9be64520b8faf5", "max_stars_repo_licenses": ["MIT"],... |
import os
import cv2
from pathlib import Path
import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms, datasets
import torchvision.transforms.functional as tf
from albumentations import (Compose, RandomCrop, Resize, Horizont... | {"hexsha": "d2e4278a34917a669221a32f1061736a62744c1f", "size": 3639, "ext": "py", "lang": "Python", "max_stars_repo_path": "RGB_Segmentation/data/Carvana_Dataset/ValDS.py", "max_stars_repo_name": "JonnyD1117/RGB-D-Plant-Segmentation", "max_stars_repo_head_hexsha": "b98eb0f32c27205abc9801eca4b2ad3f61ad80d8", "max_stars_... |
#!/usr/bin/env python
# _*_ coding:utf-8 _*_
from __future__ import division
import os
import numpy as np
import argparse
from glob import glob
from pose_evaluation_utils import *
import sys
parser = argparse.ArgumentParser()
parser.add_argument("--gtruth_dir", type=str,
help='Path to the directory with ground-trut... | {"hexsha": "184ce84f652a9e02c5db0e134297d16f17b8c077", "size": 1413, "ext": "py", "lang": "Python", "max_stars_repo_path": "kitti_eval/eval_pose_all.py", "max_stars_repo_name": "xuyufan936831611/vo_imu", "max_stars_repo_head_hexsha": "8a5753384b4a5c08dc83edf718d76a2ac308a298", "max_stars_repo_licenses": ["MIT"], "max_s... |
// Copyright Carl Philipp Reh 2009 - 2016.
// 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 FCPPT_CONTAINER_GRID_POS_ITERATOR_DECL_HPP_INCLUDED
#define FCPPT_CONTAINER_GRID_POS_ITERA... | {"hexsha": "3dcc752f29617ed49f0b814406a3235893816c9b", "size": 2171, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/fcppt/container/grid/pos_iterator_decl.hpp", "max_stars_repo_name": "vinzenz/fcppt", "max_stars_repo_head_hexsha": "3f8cc5babdee178a9bbd06ca3ce7ad405d19aa6a", "max_stars_repo_licenses": ["BS... |
"""
This module evaluates the results from multiple runs and generates plots.
"""
import csv
import os
from itertools import product
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import cycler
from data import read_compas
class Evaluation(object):
... | {"hexsha": "b2958b0a35cb2f64879e5aeba35a7be9e5a42cc9", "size": 19394, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/src/evaluation.py", "max_stars_repo_name": "nikikilbertus/blind-justice", "max_stars_repo_head_hexsha": "2344609e55a2af20396ec042627ffed368e01e56", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# License: BSD-3 (https://tldrlegal.com/license/bsd-3-clause-license-(revised))
# Copyright (c) 2016-2021, Cabral, Juan; Luczywo, Nadia
# Copyright (c) 2022, QuatroPe
# All rights reserved.
# =============================================================================
# D... | {"hexsha": "d3410a8d49cb4643fe3aff9c3851c453a0fb4f06", "size": 2962, "ext": "py", "lang": "Python", "max_stars_repo_path": "skcriteria/preprocessing/increment.py", "max_stars_repo_name": "leliel12/scikitcriteria", "max_stars_repo_head_hexsha": "f13a75b5a39cd2d3db30a37b69e61a2814a5cea4", "max_stars_repo_licenses": ["BSD... |
# imports
import matplotlib
from matplotlib import pyplot as plt
import numpy as np
from datetime import datetime
import urllib.request # the module we'll need
import shutil
# 1. DOWNLOAD DATA from the web
url = 'http://cdn.knmi.nl/knmi/map/page/seismologie/all_induced.csv' # the url ... | {"hexsha": "47e79c42c249a91117d8f070255794830d60cabf", "size": 3568, "ext": "py", "lang": "Python", "max_stars_repo_path": "2_visualisation/scatter_plot.py", "max_stars_repo_name": "ddempsey/python_for_geoscientists", "max_stars_repo_head_hexsha": "ac3e3e9951b530ecd5f0ed3128083edd4f55b2c0", "max_stars_repo_licenses": [... |
import unittest
import numpy as np
from coremltools.models import datatypes, MLModel
from coremltools.models.neural_network import NeuralNetworkBuilder
class BasicNumericCorrectnessTest(unittest.TestCase):
def test_undefined_shape_single_output(self):
W = np.ones((3,3))
input_features ... | {"hexsha": "56968be924239642c19de139f36c77d9f7112c97", "size": 1097, "ext": "py", "lang": "Python", "max_stars_repo_path": "coremltools/test/test_nn_builder.py", "max_stars_repo_name": "Vijayrajsinh/Core-ML", "max_stars_repo_head_hexsha": "b103f513cfd42cdf5b60f6261448d1ce667f590b", "max_stars_repo_licenses": ["BSD-3-Cl... |
import os
import csv
import cv2
import numpy as np # for np.array() np.append()
from datetime import datetime # print timestamps
# for loss history visualization image
import matplotlib; matplotlib.use('agg')
import matplotlib.pyplot as plt
from scipy import ndimage # to convert to RBG due to np.imread reading in BG... | {"hexsha": "848d408ebe7d89625baf89d47b03ff7ca1de3dd1", "size": 7168, "ext": "py", "lang": "Python", "max_stars_repo_path": "commonFunctions_v14.py", "max_stars_repo_name": "remichartier/014_selfDrivingCarND_BehavioralCloningProject", "max_stars_repo_head_hexsha": "1dcaa7c5a937929d4481e5efbf7ccc856c04c4ff", "max_stars_r... |
from phylo._core.phylogenytree import *
import networkx as nx
import pytest as pt
def dummy_graph():
G = nx.DiGraph()
G.add_node('0', label = 'sabbia')
G.add_node('1', label = 'pollo,boh')
G.add_node('2', label = 'sBin')
G.add_edge('0', '1')
G.add_edge('0', '2')
return G
def dummy_tree():
... | {"hexsha": "6457e6de343a068d85fc9162f0b439bb24b9ed25", "size": 2838, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/core/test_phylogenytree.py", "max_stars_repo_name": "plastic-phy/plastic", "max_stars_repo_head_hexsha": "101dcfd2c4e32be432d46d89ab151f48e34d6458", "max_stars_repo_licenses": ["MIT"], "max_... |
from copy import copy
import numpy as np
class IndicatorBase(object):
def __init__(self, marketevent):
self.instrument = None
self.preload_bar_list = []
self.instrument = marketevent.instrument
self.iteral_buffer = marketevent.feed.iteral_buffer
self.bar_list = copy(marke... | {"hexsha": "d4ae48090d025328df9db7655269ca599daed187", "size": 859, "ext": "py", "lang": "Python", "max_stars_repo_path": "OnePy/indicators/indicatorbase.py", "max_stars_repo_name": "sibuzu/OnePy", "max_stars_repo_head_hexsha": "464fca1c68a10f90ad128da3bfb03f05d2fc24bc", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#####################################################
## Read bag from file ##
#####################################################
import pyrealsense2 as rs
import numpy as np
import cv2
import argparse
import os
import shutil
from mmcv import ProgressBar
def parse_args():
# Create... | {"hexsha": "28524cd2aade959f02baa7c193badd93a4b9ad15", "size": 7544, "ext": "py", "lang": "Python", "max_stars_repo_path": "gradslam/utils/convert_bagfile.py", "max_stars_repo_name": "chuong98/gradslam", "max_stars_repo_head_hexsha": "4f744c54605980aa6e5a5ef3e3b6a04db0fe6597", "max_stars_repo_licenses": ["MIT"], "max_s... |
using SimpleCTF
using Test
@testset "SimpleCTF.jl" begin
# Write your tests here.
@test isapprox(SimpleCTF.wavelength_from_voltage(200), 2.5079, atol=1e-4)
@test isapprox(SimpleCTF.wavelength_from_voltage(300), 1.9687, atol=1e-4)
end
| {"hexsha": "6dcb7e7c7b4240ddffc22e0786cd0c21fd2c37a2", "size": 248, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "alberttxu/SimpleCTF.jl", "max_stars_repo_head_hexsha": "e5052ef99274ffd09952c240fd86598f07a5ce54", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
[STATEMENT]
lemma fls_X_conv_shift_1: "fls_X = fls_shift (-1) 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fls_X = fls_shift (- 1) 1
[PROOF STEP]
by (intro fls_eqI) simp | {"llama_tokens": 91, "file": null, "length": 1} |
#encoding=utf-8
import argparse
from distutils.dir_util import copy_tree
import os
def is_valid_file(parser, arg):
if not os.path.exists(arg):
parser.error("The file %s does not exist!" % arg)
else:
return arg
parser = argparse.ArgumentParser(description="Easy trainable text generation RNN mo... | {"hexsha": "15cee439caedbe704ac7a68f38db94b80987d70d", "size": 13740, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "artificialis-ai/rnn-training", "max_stars_repo_head_hexsha": "0e38be1fce7e7c76e8653306fff1dced8565f48e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
"""
Point collocation method, or regression based polynomial chaos expansion builds
open the idea of fitting a polynomial chaos expansion to a set of generated
samples and evaluations. The experiment can be done as follows:
- Select a :ref:`distributions`::
>>> distribution = chaospy.Iid(chaospy.Normal(0, 1), 2)
... | {"hexsha": "94fa1a73f1b145ed7131d08922c5dd14dafa7c5f", "size": 4917, "ext": "py", "lang": "Python", "max_stars_repo_path": "chaospy/regression.py", "max_stars_repo_name": "lblonk/chaospy", "max_stars_repo_head_hexsha": "1759a4307c6134b74ce63ff44973195f1e185f94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
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