text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import networkx as nx
class Graph:
def __init__(self, _vertices=[], _AdjMatrix = None):
assert isinstance(_vertices, list)
self.vertices = _vertices
self.get_vertex_id = {v: i for i, v in enumerate(_vertices)}
self.V = len(_vertices)
self.E = 0
self.AdjMatrix = [[[]... | {"hexsha": "5034ca3c808b5e3303baa14ab21ba6eaffaf3bb4", "size": 2073, "ext": "py", "lang": "Python", "max_stars_repo_path": "app_backend/graph.py", "max_stars_repo_name": "Unicorn-Dev/ProGraph", "max_stars_repo_head_hexsha": "4ec7a2c09b243562d5eb5f7cfeace0887fd162af", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import torch
import numpy as np
import cv2
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
model=torch.hub.load("ultralytics/yolov5",'custom',path="best3.pt",force_reload=True)
cap =cv2.VideoCapture("yourvideo.mp4")
while cap.isOpened():
ret, frame = cap.read()
# Make detections
results = model(frame... | {"hexsha": "3f534211363ae5575daacfb586b6019e20b86ed3", "size": 1145, "ext": "py", "lang": "Python", "max_stars_repo_path": "VideoDetector.py", "max_stars_repo_name": "emportent/HandFace", "max_stars_repo_head_hexsha": "755dfabb7da2aea59b40152b2c9626fd5cf9ade6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
[STATEMENT]
lemma "foldr/cons": "foldr\<cdot>k\<cdot>z\<cdot>(x:xs) = k\<cdot>x\<cdot>(foldr\<cdot>k\<cdot>z\<cdot>xs)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. foldr\<cdot>k\<cdot>z\<cdot>(x : xs) = k\<cdot>x\<cdot>(foldr\<cdot>k\<cdot>z\<cdot>xs)
[PROOF STEP]
by simp | {"llama_tokens": 130, "file": "HOLCF-Prelude_examples_GHC_Rewrite_Rules", "length": 1} |
# Copyright 2019 Huawei Technologies Co., Ltd
#
# 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... | {"hexsha": "4667dc1cfcee3f493c6b5aee5dd4548463809c25", "size": 3431, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/common/test_run/apply_momentum_run.py", "max_stars_repo_name": "KnowingNothing/akg-test", "max_stars_repo_head_hexsha": "114d8626b824b9a31af50a482afc07ab7121862b", "max_stars_repo_licenses":... |
# -*- coding: utf-8 -*-
"""
Created on Sun May 22 10:30:01 2016
SC process signups functions
@author: tkc
"""
#%%
import pandas as pd
import numpy as np
from datetime import datetime, date
import re, glob, math
from openpyxl import load_workbook # writing to Excel
from PIL import Image, ImageDraw, ImageFont
import tkin... | {"hexsha": "e94cbfd507644d88cde1207a5b9df902d5022e48", "size": 135800, "ext": "py", "lang": "Python", "max_stars_repo_path": "pkg/SC_signup_functions.py", "max_stars_repo_name": "tkcroat/SC", "max_stars_repo_head_hexsha": "4c2c7663298cbd454ff7aba535b689b44b48a7d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#define BOOST_TEST_MODULE g_test
#include <boost/test/unit_test.hpp>
#include <boost/test/floating_point_comparison.hpp>
#include <cassert>
#include <iostream>
#include <fstream>
#include <vector>
#include "trule.h"
#include "tdict.h"
#include "grammar.h"
#include "bottom_up_parser.h"
#include "hg.h"
#include "ff.h"
#... | {"hexsha": "11df1e3f96984d9271bb6eb345916af43d771f33", "size": 1807, "ext": "cc", "lang": "C++", "max_stars_repo_path": "decoder/grammar_test.cc", "max_stars_repo_name": "pks/cdec-dtrain-legacy", "max_stars_repo_head_hexsha": "8d900ca0af90dff71d68a7b596571df3e64c2101", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import random
import seqlib as sl
import numpy as np
# n is the length of an individual sequence
# N is the number of sequences
def simple_seqgen(n, N):
seq_list = []
for i in range(1, N + 1):
#id='>s'+str(i)
seq=sl.random_dna(n,0.20,0.30,0.30,0.20)
seq_list.append(seq)
return(seq_list)
def make_pfm(seqs, n... | {"hexsha": "8e85f4ac03c81ff6b6171e850d116b0759841aa1", "size": 2629, "ext": "py", "lang": "Python", "max_stars_repo_path": "modelib.py", "max_stars_repo_name": "icacedo/splicing-practice", "max_stars_repo_head_hexsha": "8acc2c0aa5e50632791b9a8ef4a4ad7c732d34ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import numpy as np
from networks.errors import *
from networks.optimizers import *
from networks.activations import ReLu
from networks.std.network import Model
from networks.std.layer import Layer
from unsupervised.KMC import KMC
from unsupervised.linear_model import LinearModel
import os
import sys
import ... | {"hexsha": "88db401334b4ab7305a5740cab248572198ca2ff", "size": 4949, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests.py", "max_stars_repo_name": "Chappie733/MLPack", "max_stars_repo_head_hexsha": "223b142ff22dc35b9122183435afdc473a2c0b47", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
from challenge.agoda_cancellation_estimator import AgodaCancellationEstimator
from IMLearn.utils import split_train_test
from data_cleaner import DataCleaner, TARGET_NAME
from IMLearn import BaseEstimator
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
def load_data(filename: str, dat... | {"hexsha": "428cc675193c5ff9f4f4647fb8aad036a423bebd", "size": 3671, "ext": "py", "lang": "Python", "max_stars_repo_path": "challenge/agoda_cancellation_prediction.py", "max_stars_repo_name": "noamblum/IML.HUJI", "max_stars_repo_head_hexsha": "72e848d6d2549be3b5401eac93023c601f880cc4", "max_stars_repo_licenses": ["MIT"... |
import os, sys
import argparse
import random
import numpy as np
import pandas as pd
import torch
from torch import optim
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
import torch.nn as nn
import torch.multiprocessing as mp
from tqdm import tqdm
import pickle
from copy import deepcopy
from tra... | {"hexsha": "7385f2f9158012cf4ec19be193a8c53a106fd620", "size": 5431, "ext": "py", "lang": "Python", "max_stars_repo_path": "build_influence_matrix.py", "max_stars_repo_name": "yjbang/math6380", "max_stars_repo_head_hexsha": "045bf9dd877b4b387580459fd747a4e428cbe8ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import SelectKBest
from ... | {"hexsha": "86d634eb771c3d8f8dc0141426464842541a89a0", "size": 6412, "ext": "py", "lang": "Python", "max_stars_repo_path": "tercera_iteracion.py", "max_stars_repo_name": "EdgarOPG/Final-Proyect-Data-Mining", "max_stars_repo_head_hexsha": "438cf5ebd9acfa0b82813c900328335c476e274c", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python
import os, sys
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import numpy as np
import tensorflow as tf
import cv2
import pickle
from test_utils import *
bilateral_filters = load_func_from_lib()
path2file = os.path.dirname(os.path.realpath(__file__))
#------------------------------------------------... | {"hexsha": "a3baafe507336f8e7554a2192ce8957daf9baff8", "size": 3946, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_segment.py", "max_stars_repo_name": "jasonbunk/tensorflow-bilateral-permutohedral", "max_stars_repo_head_hexsha": "7d59e0c18491263528b087c6e9efa21c88e15f75", "max_stars_repo_licenses": [... |
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{amssymb}
\usepackage{enumitem}
\newcommand{\problem}[2]{\subsection*{#1 - Problem #2}}
| {"hexsha": "d666441b980d6aaae9cb537258f78f2394847b6e", "size": 193, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sample_formats/sample_preamble.tex", "max_stars_repo_name": "jonathanbcarlson/hw-parse", "max_stars_repo_head_hexsha": "1f022bee07046e4a7b670d8cd4782894ade82d0c", "max_stars_repo_licenses": ["MIT"], ... |
import numpy
import data_algebra.test_util
from data_algebra.data_ops import *
import data_algebra
import data_algebra.util
import data_algebra.test_util
import data_algebra.SQLite
import pytest
def test_free_fn():
# show unknown fns are not allowed, unless registered
d = data_algebra.default_data_model.pd... | {"hexsha": "1d49414566d3899cbbdc69226b1e2c29a57bb5f6", "size": 1440, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_free_fn.py", "max_stars_repo_name": "WinVector/data_algebra", "max_stars_repo_head_hexsha": "3d6002ddf8231d310e03537a0435df0554b62234", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import unittest
import numpy as np
from src.expression.abstract_expression import ExpressionType
from src.expression.constant_value_expression import ConstantValueExpression
from src.expression.tuple_value_expression import TupleValueExpression
from src.expression.arithmetic_expression import ArithmeticExpression
from... | {"hexsha": "0e6aa76a5db0f88b7315628046304f0ca3eed44f", "size": 3485, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/expression/test_arithmetic.py", "max_stars_repo_name": "SND96/Eva", "max_stars_repo_head_hexsha": "d58084bf6431dff99416d3cae5a564a747496a75", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from astropy.nddata.utils import NoOverlapError
from astropy.coordinates import Angle
from ..maps import Map, WcsGeom
from .counts import fill_map_counts
from... | {"hexsha": "10e3228fa8663ff3b5460976ac70e6bf4a7f8ef4", "size": 9015, "ext": "py", "lang": "Python", "max_stars_repo_path": "gammapy/cube/make.py", "max_stars_repo_name": "qpiel/gammapy", "max_stars_repo_head_hexsha": "cfb976909e63f4d5d578e1495245c0baad69482b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
import glob
import os
import re
import cv2
import matplotlib.pyplot as plt
import numpy as np
from utils import load_data, pixel_evaluation, f1_score
TestDirectory = '../test_results/foreground/highway/'
GTDirectory = '../databases/highway/'
PlotsDirectory = 'Week1/plots/task4/'
if not os.path.exists(PlotsDirectory... | {"hexsha": "8d0bf25f17192825cbff09111e14a5ede40a7786", "size": 2120, "ext": "py", "lang": "Python", "max_stars_repo_path": "Week1/task4.py", "max_stars_repo_name": "Ivancaminal72/mcv-m6-2018-team3", "max_stars_repo_head_hexsha": "dcdbc97d6d9534f1c0479e98113f35bca0084d86", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""Transforms for preprocessing images during data loading"""
import PIL
import torch
import copy
import numpy as np
def img_pad(img, mode='warp', size=224):
"""
Pads a given image.
Crops and/or pads a image given the boundries of the box needed
img: the image to be coropped and/or padded
bbox: th... | {"hexsha": "e1f938758c27328ca0c2bbf6a234259e9ac63470", "size": 6739, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/transform/transforms.py", "max_stars_repo_name": "DongxuGuo1997/TransNet", "max_stars_repo_head_hexsha": "a720c0b1ac18db19796409b51e1cab96b744a4f0", "max_stars_repo_licenses": ["MIT"], "max_st... |
program test_tensor_rot
! Test that my own implementation of CIJ_rotate3() is correct
! It is, but it's much slower than Mainprice's
use anisotropy_ajn
implicit none
integer,parameter :: rs=8
real,parameter :: pi = 3.141592653589793238462643_rs
real(rs) :: T(3,3,3,3), Tr(3,3,3,3), CIJ(6,... | {"hexsha": "4d96f5b9658bf8aa3ccdf7b64d41f5f05c076248", "size": 3220, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "anisotropy_ajn/test_tensor_rot.f90", "max_stars_repo_name": "JackWalpole/seismo-fortran-fork", "max_stars_repo_head_hexsha": "18ba57302ba2dbd39028e6ff55deb448d02bd919", "max_stars_repo_licenses"... |
! PR 25048
! { dg-do compile }
! Originally contributed by Joost VandeVondele
INTEGER, POINTER :: I
CALL S1((I)) ! { dg-error "Actual argument for .i. must be a pointer" }
CONTAINS
SUBROUTINE S1(I)
INTEGER, POINTER ::I
END SUBROUTINE S1
END
| {"hexsha": "bc2acd8e71de748aae2fd6cc4a1c862e968ef222", "size": 246, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/parens_2.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_license... |
# ******************************************************************************
# Copyright 2017-2020 Intel Corporation
#
# 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.apa... | {"hexsha": "811c16bf890d4956897b3bb539dc9d96d33e7b48", "size": 9480, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/test/ngraph/test_basic.py", "max_stars_repo_name": "pqLee/ngraph", "max_stars_repo_head_hexsha": "ddfa95b26a052215baf9bf5aa1ca5d1f92aa00f7", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
#semeion.py
from numpy import *
from matplotlib.pyplot import *
import pandas as pd
from sklearn.svm import SVC
df = pd.read_csv('semeion.data', sep=' ', header=None).as_matrix()
X = df[:, 0:256]
y = df[:, 256:266].argmax(1)
index = arange(len(y))
random.shuffle(index)
N = 1000
train_index = index[:N]
test_index ... | {"hexsha": "d2bd033659f13b397f6710e1b0162639959378f3", "size": 432, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python-lesson/semeion.py", "max_stars_repo_name": "minhouse/python-dev", "max_stars_repo_head_hexsha": "9fc19d28d85547ae8a58fb61b6dff36917a6f832", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from sympy import *
import sys
sys.path.insert(1, '..')
from tait_bryan_R_utils import *
from rodrigues_R_utils import *
from quaternion_R_utils import *
a_1, b_1, c_1, d_1 = symbols('a_1 b_1 c_1 d_1')
px_1, py_1, pz_1 = symbols('px_1 py_1 pz_1')
om_1, fi_1, ka_1 = symbols('om_1 fi_1 ka_1')
#sx_1, sy_1, sz_1 = symbols... | {"hexsha": "6732413e786ad15d212c7f2444c51b7ef86fc49f", "size": 3350, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/python-scripts/feature-to-feature-metrics/plane_to_plane_tait_bryan_wc_jacobian.py", "max_stars_repo_name": "karolmajek/observation_equations", "max_stars_repo_head_hexsha": "ae4c84f4488c3f9... |
from unittest import TestCase
from pydmd import SpDMD, DMD
import scipy.io
import numpy as np
data = np.load("tests/test_datasets/heat_90.npy")
gammas = [1.0e-1, 0.5, 2, 5, 10, 20, 40, 50, 100]
class TestSpDmd(TestCase):
def test_number_nonzero_amplitudes_rho1(self):
zeros = np.load("tests/test_datasets/... | {"hexsha": "c848057cd64ba2acdc520079885e7207b1eec635", "size": 9689, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_spdmd.py", "max_stars_repo_name": "kathryn-garside/PyDMD-fork", "max_stars_repo_head_hexsha": "0158c4144019f0899ce34ec44286b0f700c56b38", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import time
import torch
import numpy as np
import helper
from torch.functional import F
from hvplot import hvPlot
from torch import nn
from torch import optim
from intro import view_classify
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
# now we'll make a bigger structure for computer ... | {"hexsha": "5892522dc6438f648e8f7dc8830ee5d2de14bf9a", "size": 1878, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyTorch/Udacity/network_mnist.py", "max_stars_repo_name": "ashirwadsangwan/Python", "max_stars_repo_head_hexsha": "b4e570bb31783178d241b9f2a7145343d830b698", "max_stars_repo_licenses": ["MIT"], "m... |
theory Verification
imports Language
begin
record store =
s_f\<^sub>A :: nat
s_f\<^sub>B :: nat
s_i\<^sub>A :: nat
s_i\<^sub>B :: nat
s_fmax :: nat
s_A :: "nat list"
type_synonym program = "(store \<times> store) llist set"
datatype var = f\<^sub>A | f\<^sub>B | i\<^sub>A | i\<^sub>B | fmax
primrec de... | {"author": "Alasdair", "repo": "Thesis", "sha": "8face4b62adfd73803b387e95c24f06e09736e30", "save_path": "github-repos/isabelle/Alasdair-Thesis", "path": "github-repos/isabelle/Alasdair-Thesis/Thesis-8face4b62adfd73803b387e95c24f06e09736e30/Verification.thy"} |
// Copyright (c) 2010 Satoshi Nakamoto
// Copyright (c) 2009-2014 The Dacrs developers
// Distributed under the MIT/X11 software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include "base58.h"
#include "rpcserver.h"
#include "init.h"
#include "net.h"
#include "n... | {"hexsha": "fe8ba0eea1e4deb1fe8e16b42c44af0dcb4fb5ca", "size": 38753, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/rpc/rpcwallet.cpp", "max_stars_repo_name": "sharkfund001/sharkfund", "max_stars_repo_head_hexsha": "4a53c7c2e8b8398bd3ff910dba67256d545b4014", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Displaying a custom label for each individual point
===================================================
mpldatacursor's *point_labels* functionality can be emulated with an event
handler that sets the annotation text with a label selected from the target
index.
"""
import matplotlib.pyplot as plt
import mplcursor... | {"hexsha": "6aa233fee811c4af2d0a83f06363ae6aa86c1830", "size": 578, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/labeled_points.py", "max_stars_repo_name": "jelumpp/mplcursors", "max_stars_repo_head_hexsha": "b322b3d97553f95e074827ca4d3b4f7425a6774b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
{-# OPTIONS --safe --without-K #-}
module CF.Types where
open import Data.Unit using (⊤; tt)
open import Data.Empty using (⊥)
open import Data.Product
open import Data.List as L
open import Data.String
open import Relation.Binary
open import Relation.Binary.PropositionalEquality
open import Relation.Nullary.Decidable
... | {"hexsha": "e4ca293b6a22a603d9e51d69198c11558aa83727", "size": 463, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/CF/Types.agda", "max_stars_repo_name": "ajrouvoet/jvm.agda", "max_stars_repo_head_hexsha": "c84bc6b834295ac140ff30bfc8e55228efbf6d2a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
"""
Tests scikit-onehotencoder converter.
"""
import unittest
import numpy
from sklearn.preprocessing import OneHotEncoder
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType, Int64TensorType, StringTensorType
from test_utils import dump_data_and_model
class TestSklearnOneHotEn... | {"hexsha": "6fab8c80c17a0546542ae85e5f2423ca1bd38af1", "size": 3167, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_SklearnOneHotEncoderConverter.py", "max_stars_repo_name": "wenbingl/sklearn-onnx", "max_stars_repo_head_hexsha": "b18cf687f3ffa5fe7f6d23e2f06f2095da622e26", "max_stars_repo_licenses": [... |
subroutine switch (nmat, nmatb, islord, idof, iswitch, nsizea)
!***********************************************************************
! Copyright, 1993, 2004, The Regents of the University of California.
! This program was prepared by the Regents of the University of
! California at Los Alamos National La... | {"hexsha": "fc86e64d2f38869c588aabefb7171d13049e1c16", "size": 7429, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/switch.f", "max_stars_repo_name": "satkarra/FEHM", "max_stars_repo_head_hexsha": "5d8d8811bf283fcca0a8a2a1479f442d95371968", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 24,... |
------------------------------------------------------------------------
-- Closure properties for h-levels
------------------------------------------------------------------------
{-# OPTIONS --without-K --safe #-}
-- Partly based on Voevodsky's work on so-called univalent
-- foundations.
open import Equality
modu... | {"hexsha": "bdc3ce21717fa026124c87240070d5afdeebdf69", "size": 31206, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/H-level/Closure.agda", "max_stars_repo_name": "nad/equality", "max_stars_repo_head_hexsha": "402b20615cfe9ca944662380d7b2d69b0f175200", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
\section{Ideal Bose gas}
\begin{align}
N &= gV \int \frac{d^3k}{\left( 2\pi \right)^3}
\frac{1}{\frac{1}{Z} e^{\beta E_{k}} - 1}
+ N_0\\
&=
\frac{gV}{\lambda_T^3} \underbrace{g_{3/2}(z)}_{\zeta(3/2)\approx 2.6\ldots}
+ N_0
\end{align}
That's it for now.
Let's do phase transitions.
The interesti... | {"hexsha": "7d8a8f1b06c22a488676c7a7854201458f6fb88c", "size": 7796, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "phys612/lecture30.tex", "max_stars_repo_name": "ehua7365/umdphysnotes", "max_stars_repo_head_hexsha": "00e4e2b6aba3d03baaec5caa36903e5135b014de", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
extern "C" {
#include "photospline/splinetable.h"
#include "photospline/bspline.h"
}
#include <I3Test.h>
#include <boost/filesystem.hpp>
#include <sys/time.h>
#include <limits>
namespace fs = boost::filesystem;
struct TableSet {
fs::path abs, prob;
};
static void
splinetable_destructor(struct splinetable *table... | {"hexsha": "c3202304deec4f71bdac6ae5c3909765d56984b1", "size": 23922, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "photospline/private/test/test_bounds.cxx", "max_stars_repo_name": "hschwane/offline_production", "max_stars_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_stars_repo_licenses":... |
# ===========================================================================
# rsviscv.py -------------------------------------------------------------
# ===========================================================================
# import ------------------------------------------------------------------
# ------... | {"hexsha": "245abea45ac5abec787fd3890aa68cb7fa0fe21b", "size": 16529, "ext": "py", "lang": "Python", "max_stars_repo_path": "rsvis/tools/canvas/rsviscv.py", "max_stars_repo_name": "Tom-Hirschberger/DataVisualization", "max_stars_repo_head_hexsha": "1aec6a85e2af7ba62ba47e6ee93dc9a7d99c6221", "max_stars_repo_licenses": [... |
C***********************************************************************ABSH0001
C*****MDRIV IS THE MAIN DRIVING FOR SOLVING THE MOMENT EQUATIONS OF THE*ABSH0002
C*****GRAD-SHAFRANOV EQUATION USING A VARIATIONAL METHOD. *ABSH0003
C***********************************************************************ABSH0... | {"hexsha": "d90e3310ce61b6a85183540fcbdab2fc28b8dc08", "size": 3564, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/vmoms.f", "max_stars_repo_name": "jonathanschilling/VMOMS", "max_stars_repo_head_hexsha": "346c0fe49fe61c3badad549d7cb5497776d0052d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
mutable struct WallTimer
starttime_ns::typeof(Base.time_ns())
paused_elapsed_ns::typeof(Base.time_ns())
WallTimer() = new(0,0)
end
function start!(timer::WallTimer)
timer.starttime_ns = (Base.time_ns)()
return nothing
end
started(timer::WallTimer) = (timer.starttime_ns ≠ 0)
""" Return nanoseconds since ti... | {"hexsha": "6892fddceaaa85030b0ed453a10949f56cbeadb6", "size": 777, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/timer/WallTimer.jl", "max_stars_repo_name": "albinahlback/GameZero.jl", "max_stars_repo_head_hexsha": "85a5ac34538755400d31ddc563ae12078801c507", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\subsection{AIC, AICc, Bayes factor, BIC}
| {"hexsha": "25c4030d8d447188f825d1ea4442afcefb614bb9", "size": 44, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/statistics/choosing/03-01-AID.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo_licenses"... |
"""Model for image attribution to news sources including quantization matrices.
In this improved model, we include the file mime type, the image compression
quality level, AND (for jpeg's only) the quantization matrices in the
features. Note that the quantization matrices allow us to differentiate
different jpeg enc... | {"hexsha": "5d8fb2365ba63fb3daf4f6e78f77097dc6411558", "size": 15913, "ext": "py", "lang": "Python", "max_stars_repo_path": "image_compression_attribution/common/code/models/quant_matrices.py", "max_stars_repo_name": "Kitware/image_attribution", "max_stars_repo_head_hexsha": "a1a2e0f44af97c9a2ad98bd2257ba0db011b6bfe", ... |
import pika
import cv2
import json
import time
import numpy
def open_amqp_conn():
print('OPENING: AMQP connection')
credentials = pika.PlainCredentials('guest', 'guest')
parameters = pika.ConnectionParameters('MaragiRabbit', 5672, '/', credentials)
connection = pika.BlockingConnection(paramete... | {"hexsha": "8354e2dbed4817e43fcae9784322de38e98cd237", "size": 966, "ext": "py", "lang": "Python", "max_stars_repo_path": "amqp_pub.py", "max_stars_repo_name": "daveshap/maragi_sensor_video", "max_stars_repo_head_hexsha": "9142638212ad406c07902f41687ffc5deed01ed4", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import warnings
import numpy as np
import xarray as xr
from .utils import _is_180, _wrapAngle, equally_spaced
def _mask(
self,
lon_or_obj,
lat=None,
lon_name="lon",
lat_name="lat",
method=None,
xarray=None,
wrap_lon=None,
):
"""
create a grid as mask of a set of regions for g... | {"hexsha": "48ef31d6e565c0f8ef59f57e965377fae02071cd", "size": 10460, "ext": "py", "lang": "Python", "max_stars_repo_path": "regionmask/core/mask.py", "max_stars_repo_name": "COVID-Weather/regionmask", "max_stars_repo_head_hexsha": "54f2bebe5f99bd73da1341eec7d6b1d569bf5436", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
data_dir = "smiles-data/"
src_data_name_list = ["train.src",
"test.src",
"val.src"
]
tgt_data_name_list = ["train.tgt",
"test.tgt",
"val.tgt"
]
train_proportion = 0.6
t... | {"hexsha": "09e32bc8da3d9fe3eca51ab3f75ece75878f4ccd", "size": 1837, "ext": "py", "lang": "Python", "max_stars_repo_path": "randomselection.py", "max_stars_repo_name": "TingtingWang021/molecular-fairseq", "max_stars_repo_head_hexsha": "f62e4ea5a746b975a2a1852b7cf5170963d9d1cb", "max_stars_repo_licenses": ["MIT"], "max_... |
"""MAVLink log parsing utilities."""
import argparse
from pymavlink.dialects.v10 import ceaufmg as mavlink
from pymavlink import mavutil
import numpy as np
def main():
"""Parse a MAVLink log."""
parser = argparse.ArgumentParser(description=main.__doc__)
parser.add_argument("--condition", default=None,
... | {"hexsha": "62bfe85bbec69aee93797af981fbec0a80142b2f", "size": 1342, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyfdas/mavlogparse.py", "max_stars_repo_name": "dimasad/pyfdas3", "max_stars_repo_head_hexsha": "d495a7dbfa3f8e96ac9c216e7317c164aa907da1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
/**************************************************************
*
* 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 y... | {"hexsha": "f6ea49f08122d095bbf86cf50d5c0b4b229086d5", "size": 7813, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "main/slideshow/source/engine/activitiesqueue.cxx", "max_stars_repo_name": "jimjag/openoffice", "max_stars_repo_head_hexsha": "74746a22d8cc22b031b00fcd106f4496bf936c77", "max_stars_repo_licenses": ["... |
c$Header: /data/petsun4/data1/src_solaris/imglin/RCS/t4inv.f,v 1.1 2007/05/01 01:18:57 avi Exp $
c$Log: t4inv.f,v $
c Revision 1.1 2007/05/01 01:18:57 avi
c Initial revision
c
subroutine t4inv(t,tinv)
c extracted from param12opr.f
real*4 t(4,4),tinv(4,4)
real*4 sr(3,3),d(3),g(3,3),q(3,3)
... | {"hexsha": "a13ecfb02e500d418327da46a3c21fbf511e9791", "size": 631, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "4dfp/imglin/t4inv.f", "max_stars_repo_name": "llevitis/PUP", "max_stars_repo_head_hexsha": "850cf50dc29672db9d0d6781f7fe467f77f94204", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
import numpy as np
import os.path
import shutil
import torch
import unittest
from source.environment.atari.evaluation import EpisodeStats
from source.utilities.logging.list_logger import ListLogger
from unittest.mock import MagicMock, patch
class LoggerTest(unittest.TestCase):
def setUp(self) -> None:
se... | {"hexsha": "d270f85ef5431df4950294ede26855e11b91efd8", "size": 7071, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/utilities/test_list_logger.py", "max_stars_repo_name": "Aethiles/ppo-pytorch", "max_stars_repo_head_hexsha": "b3fb6bdb466056cf84115ca7b0af21d2b48185ae", "max_stars_repo_licenses": ["MIT"], "m... |
/*
* This file is part of the CitizenFX project - http://citizen.re/
*
* See LICENSE and MENTIONS in the root of the source tree for information
* regarding licensing.
*/
#include "StdInc.h"
#if defined(LAUNCHER_PERSONALITY_MAIN) || defined(COMPILING_GLUE)
#include <CfxLocale.h>
#include <tinyxml2.h>
... | {"hexsha": "4fee4ac5cd8711080886cbd9dfafecde0e515f12", "size": 18912, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "code/client/launcher/Updater.cpp", "max_stars_repo_name": "MateqB/fivem", "max_stars_repo_head_hexsha": "f4befbfee415e8f7aea486b61a9cdbc30408cf09", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
/******************************** Inclusions. ********************************/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_randist.h>
/******************************** Definitions. *******************************/
#define MAX(x, y) ((x)>(y) ? (... | {"hexsha": "fae780686c6e0229c4149362f2d12213b1ca7048", "size": 370, "ext": "h", "lang": "C", "max_stars_repo_path": "hypocrisy.h", "max_stars_repo_name": "mgastner/impact-of-hypocrisy", "max_stars_repo_head_hexsha": "d5eb2828b31f139ff79e9aaac072dd15c707772e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
using DataFrames
"""
estimate_volume(
model,
p_fun,
df::SubDataFrame,
bounds,
args...;
n_sim = 10_000,
parm_names,
kwargs...
)
Estimate volume of region with an eillipsoid and hit or miss bias adjustment.
# Arguments
- `model`: a mode... | {"hexsha": "f2d08e8e6b4b50de2011b63fdf19d4c152ad694e", "size": 1413, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/volume_df.jl", "max_stars_repo_name": "itsdfish/ParameterSpacePartitions", "max_stars_repo_head_hexsha": "fac247a26549fde108419ed2dd52d165ea97f1e8", "max_stars_repo_licenses": ["MIT"], "max_sta... |
\subsection{Testing data}
Victorian statewide daily testing data by date of test are obtained from \href{https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data.csv}{Our World in Data} and applied identically to all health service clusters to provide a broad profile of the variation in testing cap... | {"hexsha": "85d3e6462c1808fc7cfeca456872306221d483d7", "size": 980, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/papers/covid_19/projects/victoria/case_detection_data.tex", "max_stars_repo_name": "monash-emu/AuTuMN", "max_stars_repo_head_hexsha": "fa3b81ef54cf561e0e7364a48f4ff96585dc3310", "max_stars_repo_... |
"""Module providing basic functions for familiarisation phase."""
import scipy.io
import matplotlib.ticker
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from matplotlib import colors
def load_mat_img(img, img_info, cmap_info={}):
"""
Load a .mat image into python.
... | {"hexsha": "d4107c6ef85eeca261deb697b708fcea4e2caa09", "size": 4309, "ext": "py", "lang": "Python", "max_stars_repo_path": "cued_sf2_lab/familiarisation.py", "max_stars_repo_name": "VarunBabbar/Image_Compressor", "max_stars_repo_head_hexsha": "254d8d411f7cd16f3ce242275532c9fca537269c", "max_stars_repo_licenses": ["MIT"... |
import cv2
import numpy as np
from utils import line
from utils import threshold
from utils import camera
from utils import fit_lane
# Define a class to receive the characteristics of each line detection
class Processor():
def __init__(self, Mtx, Dist, line):
# self.M = M
# self.Minv = Minv
... | {"hexsha": "e265161c718cc86cf2d2c6105198ebc3e0f14170", "size": 2690, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/Processor.py", "max_stars_repo_name": "rpanday/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "23ec93728d552ddec04f5611e823ac80f41e08bd", "max_stars_repo_licenses": ["MIT"], "max_... |
# -*- coding: utf-8 -*-
# Copyright (c) Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
from __future__ import division
import math
import numpy as np
from .base_camera import BaseCamera
from ...util import keys, transforms
from ...visuals.transf... | {"hexsha": "86bd248d3cd92756b1d8631b5c4fac50cd133c1c", "size": 11627, "ext": "py", "lang": "Python", "max_stars_repo_path": "vispy/scene/cameras/perspective.py", "max_stars_repo_name": "hmaarrfk/vispy", "max_stars_repo_head_hexsha": "7f3f6f60c8462bb8a3a8fa03344a2e6990b86eb2", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
# -*- coding: utf-8 -*-
"""Miscellaneous utility functions"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import statsmodels.api as sm
def wind_regression(wdf, elevation=75, max_se=1):
ncols = wdf.shape[1]
colnames = wdf.columns
los = wdf.index.get_level_value... | {"hexsha": "1ef5030b1c1ef81d354b3ec5118b06628cf2d33c", "size": 3383, "ext": "py", "lang": "Python", "max_stars_repo_path": "wxprofilers/utils.py", "max_stars_repo_name": "ASRCsoft/skykit", "max_stars_repo_head_hexsha": "c4deb3bfec09497c5a7755bfa92c53f9b4c8feb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
import numpy as np
import cv2
import pyopengv
import networkx as nx
import logging
import sys
import math
from collections import defaultdict
from itertools import combinations
from opensfm import context
from opensfm import types
from opensfm.unionfind import UnionFind
logger = logging.getLogger(__name__)
# pairw... | {"hexsha": "18c269199fdaef6e2f7558a3dc47a211375d59c3", "size": 13246, "ext": "py", "lang": "Python", "max_stars_repo_path": "opensfm/matching.py", "max_stars_repo_name": "CogChameleon/MarkerSfM", "max_stars_repo_head_hexsha": "dbceb273a39870c934b2fbab6c5b5551a887e12f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
\section{Limit Definition}
\begin{definition}
Let $f : D \subseteq \R \to \R$.
Let $c \in R$ be a limit point (ie $c \in D$ or $c$ is on the boundary of $D$).
$f$ has a limit $L$ as $x$ approaches $c$ if for any given positive real number $\epsilon$, there is a positive real number $\delta$ such that for all $x ... | {"hexsha": "1fa0d9577de8cc515ab36c3b6271642365fa6fc1", "size": 1998, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "calc/limits_continuity/limit_definition.tex", "max_stars_repo_name": "aneziac/Math-Summaries", "max_stars_repo_head_hexsha": "20a0efd79057a1f54e093b5021fbc616aab78c3f", "max_stars_repo_licenses": ["... |
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
PeriodIndex,
Series,
TimedeltaIndex,
Timestamp,
date_range,
)
import pandas._testing as tm
from pandas.tests.frame.common import _check_mixed_... | {"hexsha": "58016be82c405f3e435093e5ffdd70a46d27b02d", "size": 18443, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/tests/frame/methods/test_fillna.py", "max_stars_repo_name": "oricou/pandas", "max_stars_repo_head_hexsha": "9405e58d9268041f5416711c051cf5429a19bf49", "max_stars_repo_licenses": ["PSF-2.0"... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.collections
from typing import List, Dict, Tuple
import celeri
EPS = np.finfo(float).eps
def test_plot():
plt.figure()
plt.plot(np.random.rand(3), "-r")
plt.show()
def plot_matrix_abs_log(matrix):
plt.figure(f... | {"hexsha": "fd9d9e925abe6bee81efb6a83afcdc505524f2be", "size": 22514, "ext": "py", "lang": "Python", "max_stars_repo_path": "celeri/celeri_vis.py", "max_stars_repo_name": "brendanjmeade/celeri", "max_stars_repo_head_hexsha": "4322ebe420e9635b3ec01f216afaf146545d8be2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import mnist
from numba import jit
#Data Prep---
x_train, y_train, x_test, y_test = mnist.load()
x_train = x_train.reshape(-1,28,28)
mnistx = x_train/255
@jit
def ForwardConv(inputarray, stride, weights, flatten = True):
filternumber = weight... | {"hexsha": "8e0a4a9cb7627de0872e6b9bbf2bc696c3689534", "size": 1992, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnnvis.py", "max_stars_repo_name": "mburaksayici/FullNumPyCNN-NN-LogReg", "max_stars_repo_head_hexsha": "77a7931e0a3ecc602de9de5c05f8fcf0e4c2bab5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import rinobot_plugin as bot
import numpy.testing as npt
import unittest
import shutil
import os
import sys
from mock import patch
_dir = os.path.join(os.path.dirname(__file__), 'text-fixtures')
class Test(unittest.TestCase):
@classmethod
def setUpClass(cls):
os.mkdir(_dir)
@classmethod
def... | {"hexsha": "a3bcefbacdc3f9adb4688033c2ccc743e9a776c0", "size": 2683, "ext": "py", "lang": "Python", "max_stars_repo_path": "rinobot_plugin/test/test.py", "max_stars_repo_name": "rinocloud/rinobot-plugin", "max_stars_repo_head_hexsha": "0196f2a5a01a85a2f4859755b262bf093cd4eb45", "max_stars_repo_licenses": ["MIT"], "max_... |
### A Pluto.jl notebook ###
# v0.12.4
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
loc... | {"hexsha": "5552be9eb621afdcd786f3779030a5a0eed9e5e0", "size": 2658, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "pluto/dump_labs.jl", "max_stars_repo_name": "usnistgov/NeXLDatabase.jl", "max_stars_repo_head_hexsha": "7c1cd0bbf1407a7216b3760bcf361490a3dbc8b0", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
## @package auxiliary_functions some additional useful functions
#
# A collection of sever additional function useful during the running of the code.
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
from pyquil.api import get_qc
import torch
import sys
def AllBinaryStrin... | {"hexsha": "af6ff82e4b70cf6a7516c07a39fe86bf340e6583", "size": 10652, "ext": "py", "lang": "Python", "max_stars_repo_path": "auxiliary_functions.py", "max_stars_repo_name": "mrnp95/IsingBornMachine", "max_stars_repo_head_hexsha": "23cf1917a8aa977bb25f0113d8df51f0643d72f1", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
.. _tut_creating_data_structures:
Creating MNE-Python data structures from scratch
================================================
This tutorial shows how to create MNE-Python's core data structures using an
existing :class:`NumPy array <numpy.ndarray>` of (real or synthetic) data.
We begin by importing the nec... | {"hexsha": "2952eef340d31f93c92c15b8a56b446b361cb16e", "size": 8174, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorials/simulation/10_array_objs.py", "max_stars_repo_name": "rylaw/mne-python", "max_stars_repo_head_hexsha": "aa526c8ed7049046734ca28493d99e841672b0eb", "max_stars_repo_licenses": ["BSD-3-Clau... |
module RulesNormalMixtureOutTest
using Test
using ReactiveMP
using Random
using Distributions
import ReactiveMP: @test_rules
@testset "rules:NormalMixture:out" begin
@testset "Variational : (m_μ::PointMass{ <: Real }..., m_p::PointMass{ <: Real }...)" begin
@test_rules [ with_float_conversions... | {"hexsha": "31f14742990c702f3c7d48f50db5d106f42ad2b6", "size": 4214, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/rules/normal_mixture/test_out.jl", "max_stars_repo_name": "HoangMHNguyen/ReactiveMP.jl", "max_stars_repo_head_hexsha": "f3e848ab171e0786e3d8eb6a0843dbf6dacc7415", "max_stars_repo_licenses": ["... |
import pandas as pd
import numpy as np
import datetime
import pytrends
import os
from pytrends.request_1 import TrendReq
pytrend = TrendReq()
country = pd.read_csv(r"C:\Users\Dell\Desktop\livinglabcountries.csv")
country_list = list(country['living lab countries'])
city = pd.DataFrame()
city =... | {"hexsha": "700ceaca76c98a1bf842f968b9f2948582ed65b8", "size": 1815, "ext": "py", "lang": "Python", "max_stars_repo_path": "internshipvenv.py", "max_stars_repo_name": "infernus616/Internship_Projects", "max_stars_repo_head_hexsha": "cb8ddcc55e7fa8558d0e6c09a3c29bddcefca640", "max_stars_repo_licenses": ["Apache-2.0"], "... |
Dlist = (2,5,7,12)
@testset "PeriodicCMPS: environments and gauging with bond dimension $D" for D in Dlist
for T in (Float64, ComplexF64)
Q = FourierSeries([exp(-4*(j>>1))*randn(T, (D,D)) for j=1:5])
R = FourierSeries([exp(-4*(j>>1))*randn(T, (D,D))/D for j=1:3])
Ψ = InfiniteCMPS(Q, R)
... | {"hexsha": "2d0c81633331d69a24aa04370d342f75ce199301", "size": 7475, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/periodiccmps.jl", "max_stars_repo_name": "Jutho/CMPSKit.jl", "max_stars_repo_head_hexsha": "462a4f030243061a179d6c9807db61372524cba1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17... |
!--------------------------------------------------------------------------------
! Copyright (c) 2016 Peter Grünberg Institut, Forschungszentrum Jülich, Germany
! This file is part of FLEUR and available as free software under the conditions
! of the MIT license as expressed in the LICENSE file in more detail.
!------... | {"hexsha": "1c22afb0e284260c9857a8f77e9dfae42fc806e6", "size": 3330, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "vgen/od_vvac.f90", "max_stars_repo_name": "MRedies/FLEUR", "max_stars_repo_head_hexsha": "84234831c55459a7539e78600e764ff4ca2ec4b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
isdefined(Base, :__precompile__) && __precompile__(false)
module SMC
using Dates, Distributed, Distributions
using FileIO, HDF5, JLD2, LinearAlgebra, Random
using ModelConstructors
using Roots: fzero, ConvergenceFailed
using StatsBase: sample, Weights
import Base.<, Base.isempty, Base.min, Ba... | {"hexsha": "03863f958fd99040d378c3da487633b90105efc9", "size": 834, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SMC.jl", "max_stars_repo_name": "FRBNY-DSGE/SequentialMonteCarlo.jl", "max_stars_repo_head_hexsha": "de6c3180572bfe397917c69059fc242ba8bfb7ca", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
#include <iostream>
#include <fstream>
#include <Eigen/Eigen>
//include the bie header files
#include "material.hh"
#include "precomputed_kernel.hh"
#include "bimat_interface.hh"
#include "infinite_boundary.hh"
//include the fem header files
#include "mesh_Generated.hpp"
#include "bcdof.hpp"
#include "cal_ke.hpp"
#incl... | {"hexsha": "f13d1c9c65e9b04bc8d3675e22b07a5d95f812a6", "size": 7884, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tests/test_simulation/main_v1.cc", "max_stars_repo_name": "XiaoMaResearch/hybrid_FEM_SBI", "max_stars_repo_head_hexsha": "32fcf1e21a7f78907e01585d892777c11ff1c21e", "max_stars_repo_licenses": ["MIT"]... |
[STATEMENT]
lemma single_valued_monom_rel: \<open>single_valued monom_rel\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. single_valued monom_rel
[PROOF STEP]
by (rule list_rel_sv)
(auto intro!: frefI simp: string_rel_def
rel2p_def single_valued_def p2rel_def) | {"llama_tokens": 116, "file": "PAC_Checker_PAC_Checker_Relation", "length": 1} |
from config import MNIST_config as config
from model_architecture import CNN
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
# PREPROCESSING
# Read dataset
training_data = pd.read_csv(config.DATASET + 'tr... | {"hexsha": "1e02a03c2bad116d6d7ed6d78bda6573ffa4135b", "size": 3087, "ext": "py", "lang": "Python", "max_stars_repo_path": "MNIST/main.py", "max_stars_repo_name": "kevinesg/kaggle_competitions", "max_stars_repo_head_hexsha": "d7af1e31a6c0090002b350e0fbab61d9216de49d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#' Adds a new category to the given weight matrix and map field.
#'
#' This function returns a new weight matrix which is identical to the
#' given weight matrix except that it contains one more category which
#' is initialized to all 1's. It also returns a new map field matrix
#' which is identical to the given map f... | {"hexsha": "8208f674994b452d915a03537ce7e16dbb132b90", "size": 1685, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ARTMAP_Add_New_Category.r", "max_stars_repo_name": "gbaquer/fuzzyARTMAP", "max_stars_repo_head_hexsha": "dc5378a742673f5279d054e7cc3bd92d601235cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""Gauss-Legendre quadrature rule."""
try:
from functools import lru_cache
except ImportError: # pragma: no cover
from functools32 import lru_cache
import numpy
import chaospy
from .hypercube import hypercube_quadrature
def legendre(order, lower=-1., upper=1., physicist=False):
"""
Gauss-Legendre q... | {"hexsha": "33a9c2941b34a16cfc261d8c8d9280c7c6bbafa5", "size": 5424, "ext": "py", "lang": "Python", "max_stars_repo_path": "chaospy/quadrature/legendre.py", "max_stars_repo_name": "utsekaj42/chaospy", "max_stars_repo_head_hexsha": "0fb23cbb58eb987c3ca912e2a20b83ebab0514d0", "max_stars_repo_licenses": ["MIT"], "max_star... |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 parts of this notebook are from ([this Jupyter notebook](https://nbviewer.jupyter.org/github/heinerigel/coursera/blob/master/Notebooks4Coursera/W2/W2_P1.ipynb)) by Heiner Igel ([@heinerigel](https://github.com/he... | {"hexsha": "f958f15486ae0319123ea883c06bc5692cf59030", "size": 205981, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "02_finite_difference_intro/2_fd_optimum_gridpoint_dist.ipynb", "max_stars_repo_name": "daniel-koehn/Differential-equations-earth-system", "max_stars_repo_head_hexsha": "3916cbc968da... |
using ModelingToolkit
using Test
MT = ModelingToolkit
@variables t x
struct MyNLS <: MT.AbstractSystem
name::Any
systems::Any
end
@test_logs (:warn,) tmp=independent_variables(MyNLS("sys", []))
tmp = independent_variables(MyNLS("sys", []))
@test tmp == []
struct MyTDS <: MT.AbstractSystem
iv::Any
name... | {"hexsha": "85379bdad6eab83959c6c1d3a1a9da24c9aa864d", "size": 732, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/abstractsystem.jl", "max_stars_repo_name": "ArnoStrouwen/ModelingToolkit.jl", "max_stars_repo_head_hexsha": "57564bf8bbf966c062ba45a60a17c2ccde921c99", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python3
# Used to access filesystem
import numpy
import os
import shutil
# Used for song file metadata (getting title, artist, album)
from tinytag import TinyTag
# Used for args
import click
import json
# Used to access spotify
import spotipy
import spotipy.util as util
def get_album_queries_from_direct... | {"hexsha": "09de4bdd4feb0b8f1ba5344bab115bb710b3a0b4", "size": 3094, "ext": "py", "lang": "Python", "max_stars_repo_path": "cd_to_spotify.py", "max_stars_repo_name": "Crejaud/cd_to_spotify", "max_stars_repo_head_hexsha": "ddfd2d66df238a2dd181da4fe06a5b08d929910a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
Ann Marie Sanchez unsuccessfully ran for ASUCD ASUCD Senate Senate on the LEAD slate in the Winter 2005 ASUCD Election. She is one of the 20072008 Student Assistants to the Chancellor.
| {"hexsha": "9c92a4f35f18834e643383d78317a2771240aa2a", "size": 185, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Ann_Marie_Sanchez.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
(*<*)
theory TAO_10_PossibleWorlds
imports TAO_9_PLM
begin
(*>*)
section\<open>Possible Worlds\<close>
text\<open>\label{TAO_PossibleWorlds}\<close>
locale PossibleWorlds = PLM
begin
subsection\<open>Definitions\<close>
text\<open>\label{TAO_PossibleWorlds_Definitions}\<close>
definition Situation where
"Situ... | {"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/PLM/TAO_10_PossibleWorlds.thy"} |
\subsection{Computer Science}
Offical course plan found here:
\href{http://www.uq.edu.au/study/plan_display.html?acad_plan=COSCIX2030}{\nolinkurl{http://www.uq.edu.au/study/plan_display.html?acad_plan=COSCIX2030}}
For Science students there is also this helpful guide
\href{http://planner.science.uq.edu.au/content/b... | {"hexsha": "1bd9c037d7dadf953f40ff69c27d3ad4ca29b95b", "size": 1398, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/courses/csci.tex", "max_stars_repo_name": "UQComputingSociety/subject-guide", "max_stars_repo_head_hexsha": "a026bfdb8f7b2085509995bd0f99ece30fa51442", "max_stars_repo_licenses": ["MIT"], "max_s... |
/*
MIT License
Copyright (c) 2019 Xiaohong Chen
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, ... | {"hexsha": "a1553b5fe969e45f06872725e7ca794d18e057a0", "size": 5270, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/conjugate_gradient.hpp", "max_stars_repo_name": "xiaohongchen1991/krylov-solvers", "max_stars_repo_head_hexsha": "148d7bb4107a80c9e1771d77a0d589afb74d5744", "max_stars_repo_licenses": ["MIT"... |
"""CLI submodule for predicting on images."""
from typing import List
import logging
import os
import numpy as np
import pandas as pd
from ..inference import get_intensities
from ..inference import predict
from ..io import EXTENSIONS
from ..io import basename
from ..io import grab_files
from ..io import load_image
f... | {"hexsha": "fd79210f6602bf1c79d433f61ac59dda6a8bfa2c", "size": 6961, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepblink/cli/_predict.py", "max_stars_repo_name": "BioinfoTongLI/deepBlink", "max_stars_repo_head_hexsha": "aa819b71f380507f9fcfa0664ab0f5a8eca4b209", "max_stars_repo_licenses": ["MIT"], "max_sta... |
//****************************************************************************
// (c) 2008, 2009 by the openOR Team
//****************************************************************************
// The contents of this file are available under the GPL v2.0 license
// or under the openOR commercial license. see
// /Do... | {"hexsha": "97d9a7e6f0713a812f27ff2980a77436f2ac401b", "size": 8878, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/core/Plugin/Config.cpp", "max_stars_repo_name": "avinfinity/UnmanagedCodeSnippets", "max_stars_repo_head_hexsha": "2bd848db88d7b271209ad30017c8f62307319be3", "max_stars_repo_licenses": ["MIT"], ... |
#include <boost/core/lightweight_test.hpp>
#include <msqlite/open.hpp>
#include <msqlite/exec.hpp>
#include <msqlite/prepare.hpp>
using namespace std;
using namespace msqlite;
auto create() {
return open()
| exec("create table person(name TEXT);"
"insert into person values('abc');"
... | {"hexsha": "676b48a4e08bc46f648519ff02406a8c96fded58", "size": 2436, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/prepare.cpp", "max_stars_repo_name": "ricardocosme/msqlite", "max_stars_repo_head_hexsha": "95af5b04831c7af449f87346301b6e26bf749e9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19.... |
""" Utility functions relevant to Lindblad forms and projections """
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the ... | {"hexsha": "0c33f213ad560439cdf766bdcc851d283172591b", "size": 8043, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygsti/tools/lindbladtools.py", "max_stars_repo_name": "drewrisinger/pyGSTi", "max_stars_repo_head_hexsha": "dd4ad669931c7f75e026456470cf33ac5b682d0d", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import pytest
from hypothesis import strategies as st, given, settings
from finntk.omor.extract import (
extract_lemmas,
extract_lemmas_combs,
extract_lemmas_recurs,
extract_lemmas_span,
)
from finntk.wordnet.reader import fiwn
from scipy.spatial.distance import cosine
import heapq
import itertools
d... | {"hexsha": "fc4b735a05efb2e5dffa2fd7392239d131aeb830", "size": 3977, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests.py", "max_stars_repo_name": "frankier/finntk", "max_stars_repo_head_hexsha": "d2fb4e2e4c5c73eadbe5064b46d776c4e3a2ba27", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 4, "max... |
# TODO:
# - test bidirectional rnns
# - test new interface
# - test keepstate
include("header.jl")
using Knet: rnntest
if gpu() >= 0; @testset "rnn" begin
function rmulti(r,xs,hs...)
h = Any[hs...]
y = Any[]
for x in xs
push!(y, r(x; hidden=h))
end
y = reshape(... | {"hexsha": "71a3f8749af0719aca28cf76c71a2260f5f91db4", "size": 7622, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/rnn.jl", "max_stars_repo_name": "petershintech/Knet.jl", "max_stars_repo_head_hexsha": "9ed953d568f2ce94265bcc9663a671ac8364d8b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 15 09:48:31 2013
@author: Thomas Schatz
"""
"""
Sort rows of a several two dimenional numeric dataset (possibly with just one column) according to numeric key in two-dimensional key dataset
with just one column (the first dimension of all datasets involved must match).... | {"hexsha": "988ab84a3c29085e317a60731c6fe1fc9de24589", "size": 13236, "ext": "py", "lang": "Python", "max_stars_repo_path": "ABXpy/h5tools/h5_handler.py", "max_stars_repo_name": "elinlarsen/ABXpy", "max_stars_repo_head_hexsha": "7b254b99d6ce3f386f45d3da9c92cd45720dd9dd", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
Core OpenBCI object for handling connections and samples from the board.
EXAMPLE USE:
def handle_sample(sample):
print(sample.channel_data)
board = OpenBCIBoard()
board.print_register_settings()
board.start_streaming(handle_sample)
NOTE: If daisy modules is enabled, the callback will occur every two samples, ... | {"hexsha": "d2b4c2669d316dfa7109ef82fc1323f0f501864f", "size": 19529, "ext": "py", "lang": "Python", "max_stars_repo_path": "open_bci_v3.py", "max_stars_repo_name": "wffirilat/Neurohacking", "max_stars_repo_head_hexsha": "4407566ddb1b4a19a27f585a0050ab04b2bc5efe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""
Generate the foursquare POI feature of Chicago.
Use the POI data at
../data/all_POIs_chicago
Created on Tue Jan 26 11:09:49 2016
@author: kok
"""
from Crime import Tract
from shapely.geometry import Point
import pickle
import numpy as np
import os.path
here = os.path.dirnam... | {"hexsha": "8968361f5032283b5b0311d2bbd01cd8ca66fbb0", "size": 6166, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/foursquarePOI.py", "max_stars_repo_name": "thekingofkings/chicago-crime", "max_stars_repo_head_hexsha": "30550697402aa3a5a074096a0032b0c1e1264313", "max_stars_repo_licenses": ["MIT"], "max_... |
import sys
import math
import time
import os
import shutil
import torch
import torch.distributions as dist
from torch.autograd import Variable, Function, grad
import numpy as np
import torch.nn as nn
def lexpand(A, *dimensions):
"""Expand tensor, adding new dimensions on left."""
return A.expand(tuple(dimensi... | {"hexsha": "2975614ab796cb1a15fd471f3ffd77649e9c5e00", "size": 9601, "ext": "py", "lang": "Python", "max_stars_repo_path": "pvae/utils.py", "max_stars_repo_name": "thanosvlo/Causal-Future-Prediction-in-a-Minkowski-Space-Time", "max_stars_repo_head_hexsha": "0e0539a122484ce9869aca9acd436a24c2597908", "max_stars_repo_lic... |
import numpy as np
import pandas as pd
import matplotlib
df= pd.read_csv("MLCourse/PastHires.csv")
print(df.head())
#print(df.head(10))
print(df.shape)
print(df.size)
len(df)
print(df.columns)
print(df['Hired'])
print(df['Hired'][5])
degree = df['Level of Education'].value_counts()
print(degree)
... | {"hexsha": "4027f3db5d688e362eae7e7082e37a522f7ab9a5", "size": 407, "ext": "py", "lang": "Python", "max_stars_repo_path": "basic-pandas.py", "max_stars_repo_name": "kaiiyer/deep-learning", "max_stars_repo_head_hexsha": "c100d8c0834956184e1c9c871f3ffd090c344813", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
using Pkg; Pkg.activate(@__DIR__)
using StructArrays
import JuKeBOX as Jk
using GLMakie
using Makie
using JuKeBOX
using ForwardDiff
using ComradeBase
x = range(-15.0, 15.0, length=128)
y = range(-15.0, 15.0, length=128)
ix,iy = Tuple(ind)
g, acc = bam(2, spin, 1.0, 6.0, 5.0, 0.9, π/2, π/2)
o = Observer(1.0, inc)
s... | {"hexsha": "9a8c28ccb58de9d0f181a2cbd4c6edefb6c441d6", "size": 2545, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "widget/widget.jl", "max_stars_repo_name": "ptiede/JuKeBOX.jl", "max_stars_repo_head_hexsha": "664db03f8c7e72f9b505d00ed33aae22b2f8ed64", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
__author__ = 'breddels'
import unittest
import vaex as vx
import vaex.utils
import vaex.image
import numpy as np
default_size = 2
default_shape = (default_size, default_size)
class TestImage(unittest.TestCase):
def test_blend(self):
black = vaex.image.background(default_shape, "black")
white = vae... | {"hexsha": "dc03f95d21114f83426b3fa009bdc96dfbd8356d", "size": 1519, "ext": "py", "lang": "Python", "max_stars_repo_path": "packages/vaex-core/vaex/test/misc.py", "max_stars_repo_name": "cyrusradfar/vaex", "max_stars_repo_head_hexsha": "6a37bd4509c9a0823b4f01075049f3331fabea77", "max_stars_repo_licenses": ["MIT"], "max... |
(***************************************************************************
* Safety for STLC with Datatypes - Infrastructure *
* Extented from "Type Safety for STLC" by *
* Arthur Charguéraud, July 2007, Coq v8.1 *
************... | {"author": "spl", "repo": "formal_binders", "sha": "392d6e5c52d54d9b0bddc22f9ccbd2a0d765acbb", "save_path": "github-repos/coq/spl-formal_binders", "path": "github-repos/coq/spl-formal_binders/formal_binders-392d6e5c52d54d9b0bddc22f9ccbd2a0d765acbb/STLC_Data_Infrastructure.v"} |
import numpy as np
import copy
from tkinter import *
from MapGen import maze_generate
from DFS import *
from BFS import *
from A_star import *
DARK_SQUARE_COLOR = "black"
LIGHT_SQUARE_COLOR = "white"
PATH_COLOR = "green"
class Application(Frame):
def __init__(self, master=None, map=np.zeros((10,10))):
... | {"hexsha": "9fb8c9520d4c75ff34f109193791274e86ab71cb", "size": 5984, "ext": "py", "lang": "Python", "max_stars_repo_path": "Assignments/Assignment1/MazeRunner_submission/MazeRunner_submission/code/app.py", "max_stars_repo_name": "billgoo/Rutgers-CS520-Intro-to-AI", "max_stars_repo_head_hexsha": "e3c67af8a1d0efdec763b44... |
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from ops.utils import get_logger, AverageMeter, accuracy
from archs.i3d_model import I3D
from tools.metric import ConfusionMatrix
import random
from archs.fusion_i3d import fusion... | {"hexsha": "6f0d586b5ec039630cc1bcded21c9816e36ac96a", "size": 7442, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_i3d.py", "max_stars_repo_name": "SCUT-AILab/BPAI-Net", "max_stars_repo_head_hexsha": "d71c92366222c9e226e15f8263fc2d72361735c3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
\subsection{Analog simulations} \label{sec:analogSimulations}
All simulations of the analog circuitry were done using the AimSpice SPICE backend~\cite{AIMSpice} along with the AIMPlot~\cite{aimplot} frontend.
The resulting figures~\ref{fig:analog7502}~through~\ref{fig:analogLeakingM2} display the same voltage signals ... | {"hexsha": "0a77ccd007913300803a795c8440dcb211c3d5be", "size": 5882, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/text/simulations.tex", "max_stars_repo_name": "torsteinnh/TFE4152-project", "max_stars_repo_head_hexsha": "ae6903882593caf3dc5db7b4cd9c56b4131fe7b0", "max_stars_repo_licenses": ["MIT"], "max_... |
import scipy as sp
#from scipy.stats import wishart, chi2
from scipy import linalg
#discrete cosine transform
#from scipy.fftpack import dct
import numpy as np
import math
import random
import matplotlib.pyplot as plt
#from spec import *
import sys
import os
#this_dir = os.getcwd()
#sys.path.insert(0, this... | {"hexsha": "2da5c4b686e1928d6215c899b8a1a4c77fe54bac", "size": 6274, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/matrix_util.py", "max_stars_repo_name": "ThayaFluss/cnl", "max_stars_repo_head_hexsha": "485345e730a4cbf5cff6dbdeeb5e1fb7c4283733", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 5 09:58:24 2019
@author: juangabriel
"""
# XGBoost
# Las instrucciones de instalación se pueden consultar en http://xgboost.readthedocs.io/en/latest/build.html
# Cómo importar las librerías
import numpy as np
import matplotlib.pyplot as plt
impor... | {"hexsha": "fad51374d4359729d1b84548a289db91f5a2a4d6", "size": 1644, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/Part 10 - Model Selection & Boosting/Section 49 - XGBoost/xgboost.py", "max_stars_repo_name": "Aguilerimon/machinelearning-az", "max_stars_repo_head_hexsha": "0baa8ae0cfd4322d86cb3fe4508b... |
[STATEMENT]
lemma fNext_not_member_subset: "S |\<subseteq>| S' \<Longrightarrow> fNext S' |\<notin>| S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. S |\<subseteq>| S' \<Longrightarrow> fNext S' |\<notin>| S
[PROOF STEP]
by transfer (rule Next_not_member_subset) | {"llama_tokens": 104, "file": "Higher_Order_Terms_Fresh_Monad", "length": 1} |
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