text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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#!/usr/bin/env python
from pda.dataset import init_aggregate_and_appliance_dataset_figure
import slicedpy.feature_detectors as fd
from slicedpy.plot import plot_steady_states
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
SLIDING_MEANS_STEADY_STATES = False
RD_STEADY_STATES = Tru... | {"hexsha": "d36a30f37d37c84128503a93eb258496549ca6b4", "size": 4213, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/plot_decays.py", "max_stars_repo_name": "JackKelly/slicedpy", "max_stars_repo_head_hexsha": "c2fa7eb4c7b7374f8192a43d8e617b63c9e25e62", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import
from numba import *
@autojit
def zip1(L1, L2):
"""
>>> zip1(range(2), range(5, 8))
[(0, 5), (1, 6)]
"""
return list(zip(L1, L2))
@autojit
def zip2(L1, L2, L3):
"""
>>> zip2(range(2), range(5, 8), range... | {"hexsha": "5839da7c461edc4566039a792272752b67edef07", "size": 811, "ext": "py", "lang": "Python", "max_stars_repo_path": "oldnumba/tests/builtins/test_builtin_zip.py", "max_stars_repo_name": "meawoppl/numba", "max_stars_repo_head_hexsha": "bb8df0aee99133c6d52465ae9f9df2a7996339f3", "max_stars_repo_licenses": ["BSD-2-C... |
"""
The `mode` type parameter must be either `:inflate` or `:deflate`.
"""
mutable struct Source{mode,T<:BufferedInputStream}
input::T
zstream::ZStream
state::State
reset_on_end::Bool
end
# inflate source constructors
# ---------------------------
function InflateSource(input::T, raw::Bool, gzip::Boo... | {"hexsha": "a3790aab49845c3310aa47a345a31828c130aa5d", "size": 8833, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/source.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Libz.jl-2ec943e9-cfe8-584d-b93d-64dcb6d567b7", "max_stars_repo_head_hexsha": "5b6e825f67bb72b528fa85a78fdd6ba0c1d5c724", "max_stars_rep... |
import random
import csv
import numpy as np
import torch
import torch.utils.data as torchdata
from torchvision import transforms
import torchaudio
import librosa
from PIL import Image
from . import video_transforms as vtransforms
class BaseDataset(torchdata.Dataset):
def __init__(self, list_sample, opt, max_samp... | {"hexsha": "534b367b18668c80e5e893e58d214da608546231", "size": 7721, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/base.py", "max_stars_repo_name": "TaoStarlit/Sound-of-Pixels", "max_stars_repo_head_hexsha": "06cd37a75836e22208f2e59bcc263b89938e065e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
program kind
REAL( KIND = 4 ) :: four
REAL( KIND = 8 ) :: eight
REAL( KIND = 16 ) :: sixteen
INTEGER :: i4, i8, i16
i4 = SIZEOF( four )
i8 = SIZEOF( eight )
i16 = SIZEOF( sixteen )
IF( i4 == 4 .AND. i8 == 8 .AND. i16 == 16 ) THEN
call EXIT( 0 )
ELSE
call EXIT( 1 )
END IF
... | {"hexsha": "6475061db9e4c7778694528a3b2d6ae4e08fb1f7", "size": 332, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/simple/kind.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma inv_end:
assumes "invariant ({}, B)"
shows "B = saturate"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. B = saturate
[PROOF STEP]
proof (intro set_eqI iffI, goal_cases lr rl)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. \<And>x. x \<in> B \<Longrightarrow> x \<in> saturate
2. \<And>x. x... | {"llama_tokens": 800, "file": "Regular_Tree_Relations_Horn_Setup_Horn_Inference", "length": 13} |
/*
* The MIT License
*
* Copyright (c) 2012-2018 The University of Utah
*
* 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
* right... | {"hexsha": "dab6cce6b96889b739622ca1c6437445535bc207", "size": 3174, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/CCA/Components/Wasatch/ConvectiveInterpolationMethods.cc", "max_stars_repo_name": "damu1000/Uintah", "max_stars_repo_head_hexsha": "0c768664c1fe0a80eff2bbbd9b837e27f281f0a5", "max_stars_repo_lice... |
#define PY_ARRAY_UNIQUE_SYMBOL pyhip_ARRAY_API
#include <hip.hpp>
#include <utility>
#include <numeric>
#include <algorithm>
#include "tools.hpp"
#include "wrap_helpers.hpp"
#include <boost/python/stl_iterator.hpp>
using namespace pyhip;
using boost::shared_ptr;
namespace
{
py::handle<>
HipError,... | {"hexsha": "3a6cb09d29dd191f4f9d53fdab4e18f42a7218a7", "size": 44195, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/wrapper/wrap_hipdriv.cpp", "max_stars_repo_name": "ahmed-f-alrefaie/pyhip", "max_stars_repo_head_hexsha": "713280b65ca5a375cdf4d303330e4ec10df606e7", "max_stars_repo_licenses": ["Apache-2.0"], ... |
\section{Overview}
\label{s:overview}
We start with an overview of how \sys works by describing
how it can prove the equivalence of two functions: a
recursive function |sumTo| that adds up the numbers
from |1| to |n|, and a \emph{tail-recursive} variant
|sumToTR| that uses a helper |loop| with an accumulator
|acc| to ... | {"hexsha": "b81c3670fe170e5bf0c0539848b64dc5d7485a28", "size": 1097, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "papers/syn-refinements/overview.tex", "max_stars_repo_name": "qizhou92/icfp", "max_stars_repo_head_hexsha": "2f84c30e8f564f4bec2933a8b736ae58fd91821e", "max_stars_repo_licenses": ["MIT"], "max_stars... |
[STATEMENT]
lemma exactly_result:
assumes "exactly x s = Inr (y, r)"
shows "\<exists> w. s = x @ w @ r \<and> y = x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>w. s = x @ w @ r \<and> y = x
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<exists>w. s = x @ w @ r \<and> y = x
... | {"llama_tokens": 3794, "file": "Certification_Monads_Parser_Monad", "length": 38} |
import streamlit as st
import pandas as pd
import numpy as np
import requests
import time
import matplotlib.pyplot as plt
import seaborn as sns
from io import BytesIO
def get_last_8_days_hourly_bitcoin_data():
"""Call Coincap API and request last 8 days of hourly Bitcoin USD data,
return DataFrame with 'date'... | {"hexsha": "3df93b1da1d4e8b6a7b2b42b5c1c65de365b285f", "size": 1772, "ext": "py", "lang": "Python", "max_stars_repo_path": "deploy/deploy_helpers.py", "max_stars_repo_name": "theadammurphy/bitcoin_price_predictor", "max_stars_repo_head_hexsha": "7d4cb3eb85f3800ac7d4f651881d5672457ae538", "max_stars_repo_licenses": ["MI... |
#include <array>
#include <cstdint>
#include <ostream>
#include <string>
#include <boost/format.hpp>
#include <zcpm/core/processor.hpp>
#include <zcpm/core/registers.hpp>
#include "writer.hpp"
namespace
{
const std::array<const char*, 8> ByteRegMask{ "B", "C", "D", "E", "H", "L", "(HL)", "A" };
const std::... | {"hexsha": "4f6e95cf25b8fc5ad8004040a9e39ab9a5342c3d", "size": 26486, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "debugger/writer.cpp", "max_stars_repo_name": "VictorRandomCode/zcpm", "max_stars_repo_head_hexsha": "c121396bceda11a605e995bd10f46b82e7df6ced", "max_stars_repo_licenses": ["Xnet", "Linux-OpenIB", "... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import os
import time
import dgl.function as fn
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
from ogb.nodepr... | {"hexsha": "14ef27af8ac89902d656f56f7264a5f23c7cc272", "size": 11570, "ext": "py", "lang": "Python", "max_stars_repo_path": "ogbn-arxiv/src/main.py", "max_stars_repo_name": "skepsun/adaptive_graph_diffusion_convolution_networks", "max_stars_repo_head_hexsha": "a1b5e0d9c600a42a36d3c15cafdf36b7ccfb47c4", "max_stars_repo_... |
/**
@file sysid_actuator_gazebo.cpp
ROS node for collecting data for sysid to determine
an appropriate scaling for thrust and body angular
acceleration commands over ActuatorControl messages.
*/
#include <ros/ros.h>
#include <ros/console.h>
#include <mavros_msgs/ParamSet.h>
#include <mavros_msgs/Command... | {"hexsha": "d163aadc3a35c7951aff811d5a087b36d6d7985d", "size": 6922, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "scripts/sysid/sysid_actuator_gazebo.cpp", "max_stars_repo_name": "jlorenze/asl_fixedwing", "max_stars_repo_head_hexsha": "9cac7c8d31f5d1c9f7d059d4614d6b60f1a3fbef", "max_stars_repo_licenses": ["MIT"... |
# encoding: utf-8
"""
This module contains chord evaluation functionality.
It provides the evaluation measures used for the MIREX ACE task, and
tries to follow [1]_ and [2]_ as closely as possible.
Notes
-----
This implementation tries to follow the references and their implementation
(e.g., https://github.com/jpauwe... | {"hexsha": "90fadfd6250c35127c876ead5da9eeb19509a614", "size": 32062, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/lib/python3.6/site-packages/madmom/evaluation/chords.py", "max_stars_repo_name": "metu-sparg/higrid", "max_stars_repo_head_hexsha": "ebee0f35ea1712a01f3fdbaae132127ce4833baf", "max_stars_rep... |
# Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "7d83f743d58959574f0f773535ba00b4370bbeb3", "size": 5703, "ext": "py", "lang": "Python", "max_stars_repo_path": "uisrnn/tests/uisrnn_test.py", "max_stars_repo_name": "RoyalStorm/Speaker-Diarization", "max_stars_repo_head_hexsha": "1080449decb535d1eebe8064c2fcf9877da9655c", "max_stars_repo_licenses": ["Apache... |
import numpy as np
from dbspy.core import base
from dbspy.core.analyze import _analyze as analyze
from dbspy.core.utils.indexing import search_nearest, index_nearest
from dbspy.core.utils.neighborhood import neighborhood
from dbspy.core.utils.variance import add_var, sum_var, divide_var
# define
class Conf(analyze.... | {"hexsha": "ac8e0c3b05967be56bf735adce1195df104054b2", "size": 2929, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dbspy/core/analyze/sw/_sw.py", "max_stars_repo_name": "ZhengKeli/PositronSpector", "max_stars_repo_head_hexsha": "be0281fe50fe634183b6f239f03b7140c1dc0b7f", "max_stars_repo_licenses": ["MIT"],... |
# This code is used in the paper
# "Model-based exploration of the frontier of behaviours for deep learning system testing"
# by V. Riccio and P. Tonella
# https://doi.org/10.1145/3368089.3409730
import numpy as np
from random import randint
from typing import List, Tuple
from shapely.geometry import Point
import mat... | {"hexsha": "ace17a4933cd4d41ec0226d29a12a3bbaac691fd", "size": 5937, "ext": "py", "lang": "Python", "max_stars_repo_path": "roadsearch/utils/catmull.py", "max_stars_repo_name": "ERATOMMSD/roadsearch", "max_stars_repo_head_hexsha": "e5b32b70835a51d56d10547720d90e34ade08564", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
"""
http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
parametrization by error is still in progress
"""
from time import time
import numpy as np
from scipy import linalg
import matplotlib.pyplot as pl
def unsparse(v, idx, length):
"""Transform a vector-index pair to a den... | {"hexsha": "7e1334a01d35eede71c0c9d30a26d3f7f5ef981e", "size": 7117, "ext": "py", "lang": "Python", "max_stars_repo_path": "hard-gists/996771/snippet.py", "max_stars_repo_name": "jjhenkel/dockerizeme", "max_stars_repo_head_hexsha": "eaa4fe5366f6b9adf74399eab01c712cacaeb279", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import json
import os
import time
import numpy as np
from .base import BaseExperiment, OUTPUT_DIRECTORY
import solvers
if not os.path.exists(OUTPUT_DIRECTORY + '/Q'):
os.makedirs(OUTPUT_DIRECTORY + '/Q')
if not os.path.exists(OUTPUT_DIRECTORY + '/Q/pkl'):
os.makedirs(OUTPUT_DIRECTORY + '/Q/pkl')
if not os.pa... | {"hexsha": "6aef0e71798a119026ef87dd71bbd5189cbc6010", "size": 8193, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignment4/experiments/q_learner.py", "max_stars_repo_name": "jonhilgart22/CS-7641-assignments", "max_stars_repo_head_hexsha": "a69eca1f7a6f82f80674d98188d11910b0673c13", "max_stars_repo_licenses... |
//
// Copyright 2020 Mateusz Loskot <mateusz at loskot dot net>
//
// 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
//
#include <boost/gil/color_convert.hpp>
#include <boost/gil/gray.hpp>
#include <boost/gil/rgb.hpp>... | {"hexsha": "a2ea7fd9ef3413855b07c7c002c9cdbc68ba7cc4", "size": 3093, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "venv/boost_1_73_0/libs/gil/test/core/color/default_color_converter_impl.cpp", "max_stars_repo_name": "uosorio/heroku_face", "max_stars_repo_head_hexsha": "7d6465e71dba17a15d8edaef520adb2fcd09d91e", ... |
From mathcomp
Require Import ssreflect ssrbool ssrnat eqtype seq ssrfun.
From fcsl
Require Import prelude pred pcm unionmap heap.
From HTT
Require Import stmod stsep stlog stlogR.
Set Implicit Arguments.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
Definition llist (T : Type) := ptr.
Section LList.
Vari... | {"author": "ilyasergey", "repo": "pnp", "sha": "dc32861434e072ed825ba1952cbb7acc4a3a4ce0", "save_path": "github-repos/coq/ilyasergey-pnp", "path": "github-repos/coq/ilyasergey-pnp/pnp-dc32861434e072ed825ba1952cbb7acc4a3a4ce0/solutions/HTT_solutions.v"} |
from os import listdir
from os.path import isfile, join
import string
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import nltk
import re
from nltk.tokenize import RegexpTokenizer
#download latest stopwords
nl... | {"hexsha": "a0638da06fc2dacfa1f5d7d7f42bc107ede8dec5", "size": 10032, "ext": "py", "lang": "Python", "max_stars_repo_path": "MachineLearning/NaiveBayes_NewsGroup/NaiveBayes_NewsGroup.py", "max_stars_repo_name": "sindura93/SchoolProjects", "max_stars_repo_head_hexsha": "13cdca18c7d1711072373b50e25ad84ff124cfa5", "max_st... |
import numpy as np
from collections import defaultdict
results = '/Users/tdmeeste/workspace/inferbeddings/logs/synth/synth_paper_closedform_aggregated.txt'
models_lst = ['DistMult', 'ComplEx']
clauses_lst = ['symm', 'impl', 'impl_inv']
confs_lst = ['0.0']
versions_lst = ['v0', 'v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7... | {"hexsha": "22231c63bc7c5c92ebd676f35bb159798c86c481", "size": 6208, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/synth/create_table_closed_form.py", "max_stars_repo_name": "issca/inferbeddings", "max_stars_repo_head_hexsha": "80492a7aebcdcac21e758514c8af403d77e8594a", "max_stars_repo_licenses": ["MIT... |
\section{Introduction}
Intro into the topic. Brief overview of paper structure. | {"hexsha": "a931b37e15c1616a5284cb86deb6f8a65881b453", "size": 82, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/sections/intro.tex", "max_stars_repo_name": "0ortmann/tex-template", "max_stars_repo_head_hexsha": "808f156ee36c0a5a71661941c990db5d6b169e41", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
println("£")
println("\302\243"); # works if your terminal is utf-8
| {"hexsha": "50965815a3a8abcbc81db9028fad67c2bb1556c3", "size": 68, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/terminal-control-display-an-extended-character.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_sta... |
#include <boost/fusion/container.hpp>
| {"hexsha": "cdadc164910bade89d3c7c659a03e71f48dc331e", "size": 38, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_fusion_container.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0"], "... |
# coding:utf-8
import os
import logging
import json
from collections import Counter, OrderedDict
from itertools import product
import copy
import numpy as np
import json
from cotk.metric import MetricChain, BleuCorpusMetric
import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from utils import... | {"hexsha": "212f963959ee523ae759732ed0b0b45a45627a03", "size": 7493, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval/eval_yelp.py", "max_stars_repo_name": "thu-coai/NAST", "max_stars_repo_head_hexsha": "ef765d412f6e9a2ebdcc7d62c99ec2e883d0e17a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max... |
/*!
* Copyright (c) 2021 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for
* license information.
*/
#include "FreeForm2Result.h"
#include <boost/lexical_cast.hpp>
#include <iomanip>
#include <sstream>
#include "FreeForm2Assert.h"
#include "Free... | {"hexsha": "59cffa6c795a366c4169a9b0cefca1372ba2e3ad", "size": 6445, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/DynamicRank.FreeForm.Library/libs/External/FreeForm2Result.cpp", "max_stars_repo_name": "ltxtech/lightgbm-transform", "max_stars_repo_head_hexsha": "ca3bdaae4e594c1bf74503c5ec151f2b794f855c", "m... |
!
! CalculiX - A 3-dimensional finite element program
! Copyright (C) 1998-2021 Guido Dhondt
!
! This program is free software; you can redistribute it and/or
! modify it under the terms of the GNU General Public License as
! published by the Free Software Foundation(version 2);
!
! ... | {"hexsha": "9621e925c4ed8f218699c5656a9846aadfbcadc0", "size": 33480, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ccx_prool/CalculiX/ccx_2.19/src/e_c3d_v1rhs.f", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars_repo_li... |
SUBROUTINE clawpack5_setaux_manifold(mbc,mx,my, &
xlower,ylower,dx,dy,maux,aux, &
xnormals,ynormals,edgelengths,area)
IMPLICIT NONE
INTEGER mx,my,mbc,maux
DOUBLE PRECISION xlower,ylower,dx,dy
DOUBLE PRECISION aux(maux,1-mbc:mx+mbc,1-mbc:my+mbc)
DOUBLE PRECISION area(-mbc:mx+mbc+1,-mbc... | {"hexsha": "5a221a084c5ffd88bef7d5f53847692d07a467d1", "size": 1043, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "applications/clawpack/acoustics/2d/radial/user_5.0/setaux.f90", "max_stars_repo_name": "ECLAIRWaveS/ForestClaw", "max_stars_repo_head_hexsha": "0a18a563b8c91c55fb51b56034fe5d3928db37dd", "max_st... |
import unittest
import numpy as np
from tests import genImage
class TestHyImage(unittest.TestCase):
def test_image(self):
# create test image
image = genImage(dimx = 1464, dimy=401, nbands=10)
self.assertEqual(image.xdim(), 1464)
self.assertEqual(image.ydim(), 401)
self.as... | {"hexsha": "60db65fed59c77b8e73caacad69d240ae7ec016a", "size": 1369, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_hyimage.py", "max_stars_repo_name": "npucino/hylite", "max_stars_repo_head_hexsha": "dff1314a2a0c281fd2fc1a5ee03bdba3e0c49f28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18... |
// Copyright (c) 2017 Computer Vision Center (CVC) at the Universitat Autonoma
// de Barcelona (UAB).
//
// This work is licensed under the terms of the MIT license.
// For a copy, see <https://opensource.org/licenses/MIT>.
#include "carla/streaming/detail/tcp/Client.h"
#include "carla/Debug.h"
#include "carla/Loggin... | {"hexsha": "454acfecde6d484b3b71fbdd15288f19e91b19e9", "size": 5736, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "LibCarla/source/carla/streaming/detail/tcp/Client.cpp", "max_stars_repo_name": "edufford/carla", "max_stars_repo_head_hexsha": "427a77e895b6da40581ea73a6cec25420eb6b498", "max_stars_repo_licenses": ... |
\subsection{Torsion tensor}
| {"hexsha": "bc3927d87407ea4583b44b09ef0eec450073edde", "size": 31, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/geometry/manifoldsRiemann/02-02-torsion.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo... |
\documentclass[12pt,a4paper]{article}
\usepackage[a4paper,text={16.5cm,25.2cm},centering]{geometry}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{bm}
\usepackage{graphicx}
\usepackage{microtype}
\usepackage{hyperref}
\setlength{\parindent}{0pt}
\setlength{\parskip}{1.2ex}
\hypersetup
{ pdfaut... | {"hexsha": "441e624f801cf49770e20b75508d063f4d696776", "size": 12174, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "output/Lecture0.tex", "max_stars_repo_name": "MarcoFasondini/M3M6AppliedComplexAnalysis", "max_stars_repo_head_hexsha": "a29356b8d42e5ce2ca764c440386d6524b8dcd31", "max_stars_repo_licenses": ["MIT"... |
subroutine minmax2(m,n,i,j,k,l)
i = min(m,n)
j = min0(m,n)
k = max(m,n)
l = max0(m,n)
print *, i, j, k, l
i = min(i, j, k, l, m, n)
print *, i
end
| {"hexsha": "003165f643b9d891980c1aafef40120d56c50001", "size": 208, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/PIPS/validation/Semantics/minmax2.f", "max_stars_repo_name": "DVSR1966/par4all", "max_stars_repo_head_hexsha": "86b33ca9da736e832b568c5637a2381f360f1996", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python
# Python 2.7.14
import argparse
import os
import pandas
import numpy
import matplotlib.pyplot
import matplotlib.dates
import datetime
fig_dir = 'fig'
table_dir = 'table'
class Pointing:
def __init__(self, data_path):
self.file_base, _ = os.path.splitext(os.path.basename(data_path))... | {"hexsha": "ba8c78a029782866d6ada689b3edb1e95c5ce1af", "size": 14018, "ext": "py", "lang": "Python", "max_stars_repo_path": "qlp_plot.py", "max_stars_repo_name": "mmatsuo0/qlp", "max_stars_repo_head_hexsha": "b280dc2d97ebc731e2bb14ec25a1afc736cd5a29", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
import logging, urllib2, time, json, os, math
import pymongo, bson
import numpy as np
import StringIO, gzip
import csv
import pandas as pd
from astropy.io import fits
from astropy import wcs, coordinates as coord, units as u
from astropy.cosmology import Planck13 as cosmo
from scipy.optimize import brentq, curve_fit, l... | {"hexsha": "25088cc76d03bd607e6d63a5b1b6f8bdfe8710b3", "size": 126005, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/bending_analysis.py", "max_stars_repo_name": "willettk/rgz-analysis", "max_stars_repo_head_hexsha": "11c34b1b2d0eb8b9c1c71757e6e2f771c169e993", "max_stars_repo_licenses": ["MIT"], "max_st... |
from abc import ABC, abstractmethod
from fastquant import get_stock_data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
from scipy.ndimage.filters import gaussian_filter
class Model(ABC):
# params should be a dict of your parameters that you want to pass to the model
# name... | {"hexsha": "babf032a8e004b5102a6e592d515822c327d0f2d", "size": 5341, "ext": "py", "lang": "Python", "max_stars_repo_path": "frac_change_forecasting/regressors/model.py", "max_stars_repo_name": "rlavelle/stock-forecasting", "max_stars_repo_head_hexsha": "732df75e9802e9c2ce24ae305565df96a649d760", "max_stars_repo_license... |
#!/path/to/your/python3/interpreter
import sys
from os import stat
from PIL import Image
from numpy import (uint8,diag,asarray,array,zeros)
from numpy.linalg import svd
from time import perf_counter
def image_compressor(image_path,output,rank):
with Image.open(image_path) as image:
print('Compressing the g... | {"hexsha": "ca9dcd93702afe58cb741672f87ad71e0ba3f29f", "size": 1955, "ext": "py", "lang": "Python", "max_stars_repo_path": "svd_compression.py", "max_stars_repo_name": "grafdim/SVD-Image-Compressor", "max_stars_repo_head_hexsha": "2310a869a45a48b350003275e1f71b38abbe0c9d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
'''
John Farmer
1. a. Done.
b. The second array is the frequency bins. The FFT algorithm I used arranged the bins in a different order, so I used a different freq. array in my plots.
I checked the normalization by verifying Parseval's theorem. I found that a normali... | {"hexsha": "de5c3ea0f0656416ecae3f21645233a6c4222739", "size": 4902, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw6/hw6_farmer.py", "max_stars_repo_name": "farmerjm/PHYS38600", "max_stars_repo_head_hexsha": "1fe861360307efd09b3eed38d5502a3f97cc9686", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# -*- coding: utf-8 -*-
# CCP in Tomographic Imaging (CCPi) Core Imaging Library (CIL).
# Copyright 2017 UKRI-STFC
# Copyright 2017 University of Manchester
# 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 ... | {"hexsha": "310132a80544b8ae83c66dbd63f2e778ad228038", "size": 23960, "ext": "py", "lang": "Python", "max_stars_repo_path": "Wrappers/Python/ccpi/framework/BlockDataContainer.py", "max_stars_repo_name": "rijobro/CCPi-Framework", "max_stars_repo_head_hexsha": "ff08216d4e6fef84659b43155c5c52484b1dc543", "max_stars_repo_l... |
import cv2
import numpy as np
import operator
import keras
import solve_sudoku
import pytesseract
def preprocess_img(image, dilate_single_digit):
# convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Blur
blur_image = cv2.GaussianBlur(gray, (3, 3), 0)
show_image(blur_image, "blur... | {"hexsha": "a6deb99a73f37866159ebf963aa9b551d5cac079", "size": 12459, "ext": "py", "lang": "Python", "max_stars_repo_path": "read_sudoku.py", "max_stars_repo_name": "carlostling/computervision-sudoku-solver", "max_stars_repo_head_hexsha": "0bd3c9aba05a49113ad82b2e93e53a55ed08dc34", "max_stars_repo_licenses": ["MIT"], "... |
using MinAtar
using Test
@testset "MinAtar.jl" begin
# Write your own tests here.
env = MinAtarEnv("space_invaders")
end
| {"hexsha": "1d81bf18aedacbc78a6dd5b183b40e16f7ed21d4", "size": 130, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "mkschleg/MinAtar.jl", "max_stars_repo_head_hexsha": "97bdf79992e74fb44a6acab6515388f6d6fa9214", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
import os
import cv2
import numpy as np
from utils.error_utils import SUCCESS
from utils.recognition_definitions import *
UPOL = 1
CASIA_1 = 2
MMU = 3
UBIRIS = 4
UPOL_STR = "UPOL"
CASIA_1_STR = "CASIA 1"
MMU_STR = "MMU"
UBIRIS_STR = "UBIRIS"
UPOL_PATH = "./databases/upol/"
CASIA_1_PATH = "./databases/casia1/"
MMU_... | {"hexsha": "5860d49593d6279bf4a739a80fcf00094f9cb7e1", "size": 3611, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/testing_utils.py", "max_stars_repo_name": "ryuzakyl/yapir", "max_stars_repo_head_hexsha": "e0a3b6f5799fbc84295004d849106f707739106f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
from cachpy import cachpy
base_path = 'pickles/lda/'
@cachpy(base_path + 'sb_matrix.pickle')
def calculate_sb_matrix(mean_vectors, overall_mean, classes_matrices):
# noinspection PyPep8Naming
S_b = 0
for idx, m_v in enumerate(mean_vectors):
diff = m_v - overall_mean
ou... | {"hexsha": "9b5f8d36b92598eb7ac4898cacea8be767f74e12", "size": 2076, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/lda.py", "max_stars_repo_name": "amrufathy/face-ification", "max_stars_repo_head_hexsha": "409b851df1777dd987450ff5bf767d594f9dc8a8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
__doc__ = "a module housing posterior functions used in our inference of anisotropies"
__author__ = "reed.essick@ligo.org"
#-------------------------------------------------
import healpy as hp
import numpy as np
### non-standard libraries
from gpr_isotropy import likelihood
from gpr_isotropy.utils import DEFAULT_NU... | {"hexsha": "29e2d4fd54b55a8ba8a0ee6529fc271a00bb6351", "size": 2787, "ext": "py", "lang": "Python", "max_stars_repo_path": "gpr_isotropy/posterior.py", "max_stars_repo_name": "reedessick/gpr-isotropy", "max_stars_repo_head_hexsha": "95fdb58328e3b3ff7d3a974f408afa2e0169c57a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma is_sup_binary: "is_sup x y (\<Squnion>{x, y})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. is_sup x y (\<Squnion>{x, y})
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. is_sup x y (\<Squnion>{x, y})
[PROOF STEP]
have "is_Sup {x, y} (\<Squnion>{x, y})"
[PROOF STATE]
proof (... | {"llama_tokens": 459, "file": null, "length": 7} |
import numpy as np
from tomoxtal.utils import cctbx_tools
from tomoxtal.utils import phases as phases_utils
from tomoxtal.pipeline import MergeCrystals
class TestMergeCrystals:
def setup_class(self):
"""
Prepare a few simulated datasets.
"""
args = {'pdb_path':'/sdf/home/a/apec... | {"hexsha": "a94cd6fc8da9a0814c572fccc7e5b166858e9d33", "size": 2709, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_merge_crystals.py", "max_stars_repo_name": "apeck12/tomoxtal", "max_stars_repo_head_hexsha": "d2b3407708da2a35ecf061fb62ba397d837b980c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#
# Copyright 2020 Joshua Maglione
#
# Distributed under MIT License
#
from rationalPoints import _guess_polynomial
from globalVars import _DEFAULT_VERBOSE as _verbose
# A useful function for multiple lines
_cat_with_space = lambda x, y: x + "\n" + y
# Get the name of the atlas, which is the last folder of the... | {"hexsha": "9f40c5127149d394a01d3ac0257bec78cf8ab683", "size": 24365, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/atlasReport.py", "max_stars_repo_name": "joshmaglione/SingularZeta", "max_stars_repo_head_hexsha": "5ff9167cec8233c575fa421e4c99b95f06eb90d0", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
using RomanNumerals
using Test
using Random
const MT = MersenneTwister
using RomanNumerals: InvalidRomanNumeral
@testset "Construction" begin
@test RomanNumeral("I") == RomanNumeral(1)
@test RomanNumeral("V") == RomanNumeral(5)
@test RomanNumeral("X") == RomanNumeral(10)
@test RomanNumeral("L") == Ro... | {"hexsha": "5c0e578acca3b1e3c3e4e9bc0ac6345013511a91", "size": 3702, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "harryscholes/RomanNumerals.jl", "max_stars_repo_head_hexsha": "ee9d38cdae7d12a9afa48c6a7595e23d776de4c6", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
using StructJuMP, JuMP
using StructJuMPSolverInterface
include("select_solver.jl")
#############
# A sample model
#############
scen = 1
m = StructuredModel(num_scenarios=scen)
@variable(m, x[1:4])
# @variable(m, -100<=x[1:4]<=100)
@NLobjective(m, Min, 1*x[1] + 3*x[3] + 4*x[4] )
for i in 1:scen
bl = StructuredM... | {"hexsha": "3befbb5502a4b9bc0eb393242e9d3ea541c87960", "size": 1400, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/pips/parmodel_eqieq_lp.jl", "max_stars_repo_name": "matbesancon/StructJuMP.jl", "max_stars_repo_head_hexsha": "0bcbdd33cbf2d881067ede924f79ea6d1d0b1a2d", "max_stars_repo_licenses": ["MIT"]... |
"""
examples to use nsdcode
"""
import os
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
from nsdcode.nsd_mapdata import NSDmapdata
from nsdcode.nsd_datalocation import nsd_datalocation
from nsdcode.nsd_output import nsd_write_fs
from nsdcode.utils import makeimagestack
# Map T1 anatomical t... | {"hexsha": "8e925afb8dfcd5b2e8ab759b0a3027adc01a5e28", "size": 13422, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/examples_nsdmapdata.py", "max_stars_repo_name": "kjamison/nsdcode", "max_stars_repo_head_hexsha": "3f34e17bf4ee2492910bffebf104a2cb7a8fa823", "max_stars_repo_licenses": ["BSD-2-Clause"],... |
# Copyright (c) 2017 The Khronos Group 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 ... | {"hexsha": "6dba4ecf9df8b917131dad5bcd0356a37b65271b", "size": 52508, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorial_exercises/vgg16-nnef-openvx/tf2nnef.py", "max_stars_repo_name": "relrotciv/openvx_tutorial", "max_stars_repo_head_hexsha": "0e08191776e5c8012f72f63137dc370534e673ba", "max_stars_repo_lic... |
[STATEMENT]
lemma generalized_sfwSomeD: "generalized_sfw fw p = Some (r,d) \<Longrightarrow> (r,d) \<in> set fw \<and> simple_matches r p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. generalized_sfw fw p = Some (r, d) \<Longrightarrow> (r, d) \<in> set fw \<and> simple_matches r p
[PROOF STEP]
unfolding generaliz... | {"llama_tokens": 225, "file": "Simple_Firewall_Generic_SimpleFw", "length": 2} |
using EzXML
function getxml(; from="", until="")
baseuri = "http://export.arxiv.org/oai2?verb=ListRecords"
uri = "$baseuri&metadataPrefix=arXiv"
if !isempty(from)
@assert !isempty(until)
uri = "$uri&from=$from&until=$until"
end
while true
xml = readxml(download(uri))
... | {"hexsha": "7e61c70cee25fd2bb0d9f22f4d2c057e28d7d317", "size": 765, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/download.jl", "max_stars_repo_name": "hshindo/ArXivTools.jl", "max_stars_repo_head_hexsha": "c5c7d4e0b44291a07691e760e4d8a2c39b9dd127", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#include "modelvisu.h"
#ifndef GLM_ENABLE_EXPERIMENTAL
#define GLM_ENABLE_EXPERIMENTAL
#endif
#include <glm/gtx/euler_angles.hpp>
#include "constants.h"
#include "recenter.h"
#include <iostream>
// eigen stuf
#include <Eigen/Dense>
#include <Eigen/Eigenvalues>
#include "barycenter.h"
ModelVisu::ModelVisu( QWidge... | {"hexsha": "7f57434df4b79bd1563a937c10a22dea64142916", "size": 5889, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gui/modelvisu.cpp", "max_stars_repo_name": "fossabot/datura", "max_stars_repo_head_hexsha": "d8a09c4d5ae13a6984a5a8e89c69ecb8a6023037", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
import os
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from collections import OrderedDict
from tensorflow.python.keras.models import load_model
from pkg_resources import resource_filename
from transomaly.prepare_input import PrepareI... | {"hexsha": "aed2e03907d2feb0e60a11d7939e84c63632febd", "size": 11229, "ext": "py", "lang": "Python", "max_stars_repo_path": "transomaly/predict_several_timesteps_at_each_timestep.py", "max_stars_repo_name": "daniel-muthukrishna/transomaly", "max_stars_repo_head_hexsha": "5ecccd958a11b9c13100190116a8e6ff5fff1fae", "max_... |
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from PyQt5.QtWidgets import *
import numpy as np
import cv2
import math
class Paint_CV:
def __init__(self):
pass
def Filter(self, image, flag, Ksize=None, depth=None, colspa... | {"hexsha": "7fd3d050b37453d71e81d1e5aeda0bb3904a7d20", "size": 13257, "ext": "py", "lang": "Python", "max_stars_repo_path": "ToothPaint_CV.py", "max_stars_repo_name": "JunHong-1998/OpenCV-ToothPaint2-Digital-Image-Editor", "max_stars_repo_head_hexsha": "30b0c902f41aca43e98c09b7af479016760075bc", "max_stars_repo_license... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.1.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # S_Ou... | {"hexsha": "a3a4e87c197e0d9b78db94b4a37b78716bde23ff", "size": 5390, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/sources/S_OutDetectFPdependence.py", "max_stars_repo_name": "dpopadic/arpmRes", "max_stars_repo_head_hexsha": "ddcc4de713b46e3e9dcb77cc08c502ce4df54f76", "max_stars_repo_licenses": ["MIT"]... |
from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import jax.scipy.stats as jstats
from jax import grad
def main():
# various beta distribution shapes
x_vals = np.linspace(0.0, 1.0, 100)
plt.plot(x_vals, stats.beta.pdf(x_vals, a=0.5, b=0.5), label=f'a={0.5}, b={0.5}')
plt.plo... | {"hexsha": "d1907c6be743f6fc87bda4e9ec5e12a4bfb20130", "size": 2135, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/case_studies/mountain-car-continuous-figs/beta-dist.py", "max_stars_repo_name": "MatthewGerber/rl", "max_stars_repo_head_hexsha": "c323524be2a541b43b420a3da58e4675521b594f", "max_stars_repo_l... |
import numpy as np # Import numpy library
# Integers:
i = 10 # integer
print(type(i)) # Print out the data type of 1
print(" ")
a_i = np.zeros(i,dtype=int) #declare an array of ints. Otherwise will be floats
print(type(a_i)) #will return ndarray?
print(type(a_i[0])) #wi... | {"hexsha": "339f149c47a5fff07e205e4262e8d9197047a71a", "size": 748, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_types.py", "max_stars_repo_name": "spausanc/astr-119-hw-1", "max_stars_repo_head_hexsha": "f2e17dbea70f0eebdd3555718285cafce2ac3cf4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import os
import sys
import sympy
from sympy.galgebra.GA import MV, ZERO, ONE, HALF
from sympy import collect, symbols
def F(x, n, nbar):
"""
Conformal Mapping Function
"""
Fx = HALF*((x*x)*n + 2*x - nbar)
return(Fx)
if __name__ == '__main__':
| {"hexsha": "41b3bb5c672cebf6b950e023ad510f815fffcc24", "size": 287, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/src/modules/galgebra/GA/headerGAtest.py", "max_stars_repo_name": "eriknw/sympy", "max_stars_repo_head_hexsha": "b7544e2bb74c011f6098a7e886fd77f41776c2c4", "max_stars_repo_licenses": ["BSD-3-Cla... |
[STATEMENT]
lemma ipurge_fail_aux_t_intro_2:
"\<lbrakk>ipurge_fail_aux_t_inv_2 I D U xs X Y; ipurge_fail_aux_t_form Y\<rbrakk> \<Longrightarrow>
snd (ipurge_fail_aux_t_out Y) = ipurge_ref_aux I D U xs X"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>ipurge_fail_aux_t_inv_2 I D U xs X Y; ipurge_fail_au... | {"llama_tokens": 412, "file": "Noninterference_Sequential_Composition_Propaedeutics", "length": 2} |
#! /usr/bin/python
import random,argparse,sys,subprocess,os
parser = argparse.ArgumentParser()
import numpy as np
random.seed(0)
import time
input_file_ls = ["data/maze/grid10.txt","data/maze/grid20.txt","data/maze/grid30.txt","data/maze/grid40.txt","data/maze/grid50.txt","data/maze/grid60.txt","data/maze/grid70.txt","... | {"hexsha": "c0148cb20e418fe0b6cfb9aec0bd8cc004c61b75", "size": 4513, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignment2/MazeVerifyOutput.py", "max_stars_repo_name": "cybershiptrooper/CS747-assignments", "max_stars_repo_head_hexsha": "5b4b2bce8321b8fc48e578615034bb16df3ca88e", "max_stars_repo_licenses": ... |
import numpy as np
from sklearn.linear_model import LogisticRegression
from .model import User
from .twitter import BASILICA
def predict_user(user1_name, user2_name, tweet_text):
"""
Determine and return which user is more likeley to say a given TWEEN
ex__ run: predict('austen, "e;on", 'lambda school')
... | {"hexsha": "b15d8042308cc0eb1451b7297fa207225d9ffb99", "size": 1176, "ext": "py", "lang": "Python", "max_stars_repo_path": "twitoff/predict.py", "max_stars_repo_name": "JonRivera/TwitOff", "max_stars_repo_head_hexsha": "69bb121139e8a76ffba62d51cb0ef4c215c45167", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 25 16:24:34 2017
@author: C Winkler
"""
import pandas as pd
import numpy as np
from cycler import cycler
import matplotlib.pyplot as plt
import glob, os
from scipy.signal import argrelextrema
from statsmodels.nonparametric.smoothers_lowess import lowess
plt.rc('axes', p... | {"hexsha": "db20bd7f9b0720e498e066ae96d26fa442f7937f", "size": 2368, "ext": "py", "lang": "Python", "max_stars_repo_path": "2_espvr/espvr.py", "max_stars_repo_name": "xi2pi/elastance-function", "max_stars_repo_head_hexsha": "ac3422b55a1958fe0ce579a2b49a977545159ccd", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 12 21:06:37 2018
@author: user
"""
# %% libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#%% İnformation
data = pd.read_csv("oasis_cross-sectional.csv")
data.info()
data.head()
data.describe()
#%% WE need to fi... | {"hexsha": "07d35ef163f96b0bce7bec3f9fb81d0644571434", "size": 5057, "ext": "py", "lang": "Python", "max_stars_repo_path": "alzheimers_machine_learning.py", "max_stars_repo_name": "tolgakurtulus/Machine-Learning-For-Alzehimers-Gender", "max_stars_repo_head_hexsha": "c29079849c3e97e614a7d6b0c1f0d5912cc6b30e", "max_stars... |
import theano
import theano.tensor as T
import lasagne
from lasagne import init
from lasagne import nonlinearities
from .common import get_common_nonlinearity
__all__ = [
'RestrictedDenseLayer',
'rdense'
]
class RestrictedDenseLayer(lasagne.layers.DenseLayer):
def __init__(self, incoming, num_units, W=init.G... | {"hexsha": "5040c033c65cb7c5b4ff95ad38666b04f9542816", "size": 1943, "ext": "py", "lang": "Python", "max_stars_repo_path": "craynn/layers/rdense.py", "max_stars_repo_name": "maxim-borisyak/craynn", "max_stars_repo_head_hexsha": "fceabd33f5969033fb3605f894778c42c42f3e08", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'PHM.ui'
#
# Created by: PyQt5 UI code generator 5.9.2
#
# WARNING! All changes made in this file will be lost!
import os
import h5py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from PyQt5 import QtCore, QtWidgets... | {"hexsha": "43e5211ba7ea9f86a1499a9e735c86e9b742a4a4", "size": 27633, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/TF-gui/PHM.py", "max_stars_repo_name": "jeetsagar/turbojet", "max_stars_repo_head_hexsha": "9b17edde0a7e01d0fa320261fbc2734ce53577d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
/**
* @file cpp_ptr.cpp
* @author Maximilian Harr <maximilian.harr@daimler.com>
* @date 21.11.2016
*
* @brief Investigation of smart pointers.
* Smart pointers mimic "normal" pointers (by means of operator overloading),
* but furthermore provide additional memory management features (deletion... | {"hexsha": "258bcdef124a38c0b3badd3dacfdcb17e1735d9a", "size": 6286, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cpp/cpp_stdlib_boost/src/cpp_ptr.cpp", "max_stars_repo_name": "maximilianharr/code_snippets", "max_stars_repo_head_hexsha": "8b271e6fa9174e24200e88be59e417abd5f2f59a", "max_stars_repo_licenses": ["B... |
from src.utils.fft.fft import fft
from src.utils.fft.ifft import ifft
from tools import mirror, halve
import numpy as np
import pandas as pd
# Calculates the spectral derivative from x
def compute_spectral_derivative(x, dt, mirroring=True):
"""
x (DataFrame): State measurements
dt (Float): Sampling period
... | {"hexsha": "152e220279229708ff50df95a3ab02c8b2c06b02", "size": 632, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/differentiation/spectral_derivative.py", "max_stars_repo_name": "BystrickyK/SINDy", "max_stars_repo_head_hexsha": "f5b887d230079ffd60eacfe0221b47d1c288342e", "max_stars_repo_licenses": ["... |
import os
import sys
import time
import math
import threading
from ctypes import *
from typing import List
import cv2
import numpy as np
import xir
import vart
from utils import preprocess_one_image_fn
from resnet_thread import ResNetThread
global THREAD_NUM
THREAD_NUM = 1
def get_child_subgraph_dpu(graph: "Graph"... | {"hexsha": "7e68195b768b639822012e0e845cbbe0e73a7128", "size": 3579, "ext": "py", "lang": "Python", "max_stars_repo_path": "AIoT/Vitis-AI/VART/example/resnet50_py/main.py", "max_stars_repo_name": "kaka-lin/ML-Notes", "max_stars_repo_head_hexsha": "047b88d59346b2ec719b1b3e2fcd605e1ccfaf91", "max_stars_repo_licenses": ["... |
# Copyright 2020 The TensorFlow Probability Authors.
#
# 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 o... | {"hexsha": "640aeb7eada9945fd631e8ad6e015939b0ec3eb1", "size": 5146, "ext": "py", "lang": "Python", "max_stars_repo_path": "spinoffs/oryx/oryx/experimental/nn/normalization.py", "max_stars_repo_name": "jakee417/probability-1", "max_stars_repo_head_hexsha": "ae7117f37ac441bc7a888167ea23e5e620c5bcde", "max_stars_repo_lic... |
/* LOOT
A load order optimisation tool for
Morrowind, Oblivion, Skyrim, Skyrim Special Edition, Skyrim VR,
Fallout 3, Fallout: New Vegas, Fallout 4 and Fallout 4 VR.
Copyright (C) 2014 WrinklyNinja
This file is part of LOOT.
LOOT is free software: you can redistribute
it and/or modify i... | {"hexsha": "b05d61ab13fd7b83b02c5adb2aca1071efffa183", "size": 7332, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/shared/src/gui/state/loot_state.cpp", "max_stars_repo_name": "haifengkao/HugeLoot", "max_stars_repo_head_hexsha": "ef30c828d4fffc55a1fdedebaea8cb843f4e32cb", "max_stars_repo_licenses": ["BS... |
import numpy as np
import pdb
def sum_product_p(uscores, bscores, umargs, bmargs):
"""Apply the sum-product algorithm on a chain
:param uscores: array T*K, (unary) scores on individual nodes
:param bscores: array (T-1)*K*K, (binary) scores on the edges
:return: log-marginals on nodes, log-marginals on ... | {"hexsha": "c2de769f95247bfc8965bbfc3f4537849880d78c", "size": 4139, "ext": "py", "lang": "Python", "max_stars_repo_path": "struntho/inference/sum_product_chain.py", "max_stars_repo_name": "alexnowakvila/maxminloss", "max_stars_repo_head_hexsha": "15c45da5b8c4c214ba2aa596931aff998e3f1c92", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
__author__ = "Konstantin Klementiev"
__date__ = "1 Oct 2015"
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage
def plot_NOM_2D(fname):
xL, yL, zL = np.loadtxt(fname+'.dat', unpack=True)
nX = (yL == yL[0]).sum()
nY = (xL == xL[0]).sum()
x = xL[:nX]
... | {"hexsha": "c4fb74084e7d2299ee994e7017e3deeae468633a", "size": 6163, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/raycing/read_NOM_maps.py", "max_stars_repo_name": "adinatan/xrt", "max_stars_repo_head_hexsha": "75b884c0cba7e1aac15b30f2d0d803597328a208", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
module MechGluecode
using Requires
export value, ODE_DEFAULT_NORM, UNITLESS_ABS2, Unitfu, norm
function __init__()
@require MechanicalUnits = "e6be9192-89dc-11e9-36e6-5dbcb28f419e" begin
@require Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" begin
@info "Plots => using MechGluePlots"
... | {"hexsha": "43819f0c49777cca7d12347fce949c1c747c74db", "size": 1363, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MechGluecode.jl", "max_stars_repo_name": "hustf/MechGluecode.jl", "max_stars_repo_head_hexsha": "e631d09f13428c70d46d2a3b903e1c6936b30c43", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
range = getattr(__builtins__, 'xrange', range)
# end of py2 compatability boilerplate
import os
import pytest
import nump... | {"hexsha": "24abe958f075c7152dee35a73299f3c67f437846", "size": 2692, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_stomp.py", "max_stars_repo_name": "KSaiRahul21/matrixprofile", "max_stars_repo_head_hexsha": "d8250e30d90ed0453bb7c35bb34ab0c04ae7b334", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
"""analyze.py: Runs the FF-trEFM Analysis for a set of given files."""
__author__ = "Rajiv Giridharagopal"
__copyright__ = "Copyright 2019, Ginger Lab"
__maintainer__ = "Rajiv Giridharagopal"
__email__ = "rgiri@uw.edu"
__status__ = "Development"
import os
import sys
import time
import multiprocessing
import logging
i... | {"hexsha": "9991cbe97dd49127289d4e8e36658ae42dceda6e", "size": 6327, "ext": "py", "lang": "Python", "max_stars_repo_path": "ffta/_legacy_functions/analyze.py", "max_stars_repo_name": "GingerLabUW/FFTA", "max_stars_repo_head_hexsha": "576591d6ba23731c26f7dfa90591e94795f1b288", "max_stars_repo_licenses": ["MIT"], "max_st... |
module POMDPPolicies
using LinearAlgebra
using Random
using StatsBase # for Weights
using SparseArrays # for sparse vectors in alpha_vector.jl
using Parameters
using Distributions # For logpdf extenstion in playback policy
using Printf
using POMDPs
import POMDPs: action, value, solve, updater
using BeliefUpdaters
us... | {"hexsha": "e798f8994467f4165fa14a9b565f5b6d9dca52a1", "size": 1332, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/POMDPPolicies.jl", "max_stars_repo_name": "Wu-Chenyang/POMDPPolicies.jl", "max_stars_repo_head_hexsha": "51a83b8193adac8a92afb83aa0e1abe987593f55", "max_stars_repo_licenses": ["MIT"], "max_star... |
% !TEX root = ../main.tex
% = = = = = = = = = = = = = = = = = = = %
% Introduction %
% = = = = = = = = = = = = = = = = = = = %
\let\clearpage\relax
\chapter{Introduction}
\section{Figures}
\subsection{Single Figure}
\begin{figure}[!htp]
\centering
\includegraphics[scale=0.5]{examp... | {"hexsha": "a342d63d3270e749a6e9972bf6467835847b77d5", "size": 2098, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/2-intro.tex", "max_stars_repo_name": "Mars-tin/SJTUThesis", "max_stars_repo_head_hexsha": "189d447e0f43a9774727cc70655ce8821cd7215e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
# -*- coding: utf-8 -*-
#
#
# Created by: PyQt5 UI code generator 5.12.3
#
#FIB needle rotation calculator v0.1
#Written by Tao Ma, taoma@umich.edu
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
import matplotlib
matplotlib.use('Qt5Agg')
#%from matp... | {"hexsha": "d943988fc9125968cd565d276578ef839e607a47", "size": 15375, "ext": "py", "lang": "Python", "max_stars_repo_path": "Main/FIB_geom.py", "max_stars_repo_name": "matao1984/FIB-geom-calculator", "max_stars_repo_head_hexsha": "993de21a745d5398968bb5b7bebb3df884b60b9e", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#tbeg = time()
using LinearAlgebra
BLAS.set_num_threads(4)
using Flux, Statistics # Flux.Data.MNIST
using Flux: onehotbatch, onecold, crossentropy, throttle
using Base.Iterators: repeated, partition
using BSON, HDF5, JLD, Random
using MLDataUtils
using NPZ
include("genDataScripts.jl")
n_sites = 100
periods = [2,3,4,5,6... | {"hexsha": "1ef2c866f4ff654db23b273e313e2582e2ba4c84", "size": 6757, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mainJob.jl", "max_stars_repo_name": "lazarusA/noisySignals", "max_stars_repo_head_hexsha": "ebbf36c2b92f0ca5351eabc00746c89d111fb70d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# https://github.com/marcharper/python-ternary
import ternary
import random
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
def random_points(num_points=25, scale=40):
points = []
for i in range(num_points):
x = random.randint(1, scale)
y = random.randint(0, scal... | {"hexsha": "eccac528adc5fe5dcfd2511a27cb896436579eb2", "size": 2975, "ext": "py", "lang": "Python", "max_stars_repo_path": "TernaryPlots.py", "max_stars_repo_name": "Wright4TheJob/CobModelGPR", "max_stars_repo_head_hexsha": "714c8d85d91817bd1abb560359afe4abda116996", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
np.random.seed(0)
def convert_str_columns_to_float(df):
df['expected_outcome_st_treatment'] = df['expected_outcome_st_treatment'].str[1:-1]
df['expected_outcome_st_treatment'] = df['expected_outcome_st_treatment'].astype(np.float64)
df[... | {"hexsha": "abbf84b283c5fc28ebcd22dbfd65cfc29c62e928", "size": 1740, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/result_processing/helpers.py", "max_stars_repo_name": "dveni/causal-text-embeddings", "max_stars_repo_head_hexsha": "82104f3fb6fd540cf98cb4ca0fd5b5d1fb5f757a", "max_stars_repo_licenses": ["MIT... |
effective_particles(pf) = effective_particles(expweights(pf))
effective_particles(we::AbstractVector) = 1/sum(abs2, we)
function shouldresample(pf::AbstractParticleFilter)
resample_threshold(pf) == 1 && (return true)
th = num_particles(pf)*resample_threshold(pf)
ne = effective_particles(pf)
retur... | {"hexsha": "e1264644ab015119e5277e163463062414db02c4", "size": 2145, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/resample.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/LowLevelParticleFilters.jl-d9d29d28-c116-5dba-9239-57a5fe23875b", "max_stars_repo_head_hexsha": "b4077d2f27520e32a755a1ecebc... |
[STATEMENT]
lemma D_imp_CR: assumes "\<forall>P. (peak ars P \<longrightarrow> (\<exists> \<sigma>' \<tau>'. DD ars r (fst P,snd P,\<sigma>',\<tau>')))" shows "CR (unlabel ars)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. CR (unlabel ars)
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>... | {"llama_tokens": 3357, "file": "Decreasing-Diagrams_Decreasing_Diagrams", "length": 27} |
"""
Implement wrapper that uses pseudo relevance feedback to expand the initial query with additional terms
"""
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from preprocessing import Corpus
from retrieval_algorithms import RetrievalAlgorithm
from .identity import i... | {"hexsha": "f35dd8f5c17d69dbdf552eb9ba0e800405db9a07", "size": 3264, "ext": "py", "lang": "Python", "max_stars_repo_path": "paper_retrieval/retrieval_algorithms/prf_wrapper.py", "max_stars_repo_name": "JNKielmann/Master-Thesis", "max_stars_repo_head_hexsha": "47475d15a2d63e11320405cc60b0e49ccda2c468", "max_stars_repo_l... |
import numpy as np
import pkg_resources
DTYPE = np.float64
ICA_THRESHOLD_CONST = 0.005
PATH_TO_SESSION = "MNINonLinear/Results"
SESSION_IDS = [('1', 'LR'), ('1', 'RL'), ('2', 'LR'), ('2', 'RL')]
PATH_TO_SESSIONS = "MNINonlinear/Results"
SESSION_NAME_TEMPLATE = "rfMRI_REST%s_%s/rfMRI_REST%s_%s_Atlas_MSMAll_hp2000_cle... | {"hexsha": "c4d51a9c65f7ca2a694cecca60cc8e811e2e12c4", "size": 877, "ext": "py", "lang": "Python", "max_stars_repo_path": "neuralocalize/utils/constants.py", "max_stars_repo_name": "kessido/Neuroscience-seminar", "max_stars_repo_head_hexsha": "09c137638e49a12f9389fb37ed1a810be3cbb7be", "max_stars_repo_licenses": ["MIT"... |
Require Import floyd.proofauto.
Require Import sha.sha.
Require Import sha.SHA256.
Require Import sha.spec_sha.
Require Import sha.sha_lemmas.
Require Import sha.bdo_lemmas.
Local Open Scope logic.
Definition block_data_order_loop2 :=
nth 1 (loops (fn_body f_sha256_block_data_order)) Sskip.
Fixpoint Xarray' (b: li... | {"author": "rbowden91", "repo": "cs260r-fp", "sha": "a1593bdcd91b5aa2e4977e67cbf0c34bc8fa561e", "save_path": "github-repos/coq/rbowden91-cs260r-fp", "path": "github-repos/coq/rbowden91-cs260r-fp/cs260r-fp-a1593bdcd91b5aa2e4977e67cbf0c34bc8fa561e/seplog/VST/sha/verif_sha_bdo7.v"} |
#
# Mosaic.py -- Mosaic plugin for Ginga reference viewer
#
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
import math
import time
import numpy
import os.path
import threading
from ginga import AstroImage
from ginga.util import mosaic
from ginga.util impor... | {"hexsha": "c7e5ff3ff620c9b6c96d824b7ec5e69b64a045ad", "size": 20700, "ext": "py", "lang": "Python", "max_stars_repo_path": "ginga/misc/plugins/Mosaic.py", "max_stars_repo_name": "Cadair/ginga", "max_stars_repo_head_hexsha": "5afdd8824f27c7ae7d8d82b5013b0ff0068bd8b8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import numpy as np
import math
from scipy.special import hyp2f1
################################################################
################ Define some helper functions ##################
################################################################
def lennard_jones(r , sigma , epsilon , LJ_form = 'standard... | {"hexsha": "3de3e2c254e2ad1600ecbddf4fd6e6a5aadef3a7", "size": 8876, "ext": "py", "lang": "Python", "max_stars_repo_path": "nanoscopy/afm/AFMForceRecovery.py", "max_stars_repo_name": "darianSmalley/NanoscoPy", "max_stars_repo_head_hexsha": "dfb6784f5ad3f439765bfb0fb67d9cde5aec87d5", "max_stars_repo_licenses": ["MIT"], ... |
module TestBasisPursuit
using Test
using LinearAlgebra
using SparseArrays
using CompressedSensing: bp, bp_candes, bp_ard, bpd, bpd_candes, bpd_ard, sparse_data, perturb
n, m = 32, 48
k = 3
A, x, b = sparse_data(n = n, m = m, k = k, rescaled = true)
δ = 1e-2
y = perturb(b, δ/2)
@testset "Basis Pursuit" begin
# equ... | {"hexsha": "338a52f95749129cb2cd41d15df58dfbc01420dc", "size": 1184, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/basispursuit.jl", "max_stars_repo_name": "SebastianAment/CompressedSensing.jl", "max_stars_repo_head_hexsha": "ea296503966c4c50ba943bed1f9bb8168683bb2b", "max_stars_repo_licenses": ["MIT"], "m... |
from matplotlib import pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import os
from pandas.tseries.offsets import MonthEnd
from qcmr.parse.utils import get_fiscal_months
from .. import utils
from ..style import default_style, palette
__all__ = [
"monthly_actuals_this_quarter",
"his... | {"hexsha": "f404e843d15a68e4a1e5f05b51417b60e4649362", "size": 23581, "ext": "py", "lang": "Python", "max_stars_repo_path": "cash_viz/general_fund/core.py", "max_stars_repo_name": "PhiladelphiaController/cash_viz", "max_stars_repo_head_hexsha": "2124d1d7859fc20e2a7f79754697fd5a41b2ee47", "max_stars_repo_licenses": ["MI... |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime
import pickle
def plot_pies(df, drop_columns=None): # kwargs for subplots call?
if drop_columns:
columns = df.columns.drop(drop_columns)
else:
columns = df.columns
rows = 4
plt.subplots(len(c... | {"hexsha": "f5d56ea1fb54654df903e47210f9ed4019734275", "size": 678, "ext": "py", "lang": "Python", "max_stars_repo_path": "dtcj/plot_pies.py", "max_stars_repo_name": "np1919/DTCJ", "max_stars_repo_head_hexsha": "766d67dc73a0bc5ea59972c52cb9c7db81c43daf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
# Copyright (c) 2021, NVIDIA 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.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "9379ff501ac1942dd32834ae720c90e37097b877", "size": 1974, "ext": "py", "lang": "Python", "max_stars_repo_path": "qa/L0_e2e/test_model.py", "max_stars_repo_name": "divyegala/rapids-triton", "max_stars_repo_head_hexsha": "8ff2a8dbad029e9379d9e7808d868924c4b60590", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib
matplotlib.rcParams.update({'font.size': 20})
type_colors = { 'transient_ON': 'green', 'transient_OFF': 'magenta', 'transient_ON_OFF': 'cyan' }
map_df = pd.read_csv('../build/ll2_inputs_from_LGN.csv', sep=' ')
gids = np.array(li... | {"hexsha": "73680e1338e774056e6be5eb1e15bffc35d045cd", "size": 2161, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis_codes_v2/plot_LGN_vis_space_positions.py", "max_stars_repo_name": "zqwei/LIF_Vis_model", "max_stars_repo_head_hexsha": "16f651ac827ba5f0feb40a0e619e600f1251d009", "max_stars_repo_licenses... |
#include <kazen/camera.h>
#include <kazen/rfilter.h>
#include <kazen/warp.h>
#include <Eigen/Geometry>
NAMESPACE_BEGIN(kazen)
/**
* \brief Perspective camera with depth of field
*
* This class implements a simple perspective camera model. It uses an
* infinitesimally small aperture, creating an infinite depth of ... | {"hexsha": "c8c7caacb46e2b6141c47d31c4b9a000cff15209", "size": 9587, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/kazen/camera.cpp", "max_stars_repo_name": "ZhongLingXiao/nano-kazen", "max_stars_repo_head_hexsha": "0f4311b6cfe1d964af4e49263e8cc9b089d53e2e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
# coding: utf-8
from __future__ import print_function
import scipy.io
import tensorflow as tf
from numpy import *
import os
from pylab import *
import numpy as np
import matplotlib
import PIL
from PIL import ImageFile
from PIL import Image
ImageFile.LOAD_TRUNCATED_IMAGES = True
import matplotlib.pyplot as plt
import s... | {"hexsha": "114d4696439f4bfc13faca156556517970676bca", "size": 19940, "ext": "py", "lang": "Python", "max_stars_repo_path": "alexnet/alexnet_50_species.py", "max_stars_repo_name": "peace195/latefusion", "max_stars_repo_head_hexsha": "bc2b6a06613a9d979bb95538b62334471c1b008c", "max_stars_repo_licenses": ["MIT"], "max_st... |
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