text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
def min_max(numbers):
return min(numbers), max(numbers)
class Person:
def __init__(self,name):
self.name = name
pass
class Student(Person):
def __init__(self, name, id):
super().__init__(name)
self.id = id
pass
import numpy as np
import pandas as pd
def nonpositives(x):
... | {"hexsha": "941bdddcb07fb6d3ea88ad678f3b28577f6cfd29", "size": 3029, "ext": "py", "lang": "Python", "max_stars_repo_path": "module1/src/test.py", "max_stars_repo_name": "gbrsouza/NLP", "max_stars_repo_head_hexsha": "a94f9ab21351b4592725fb4f11c0ed594622fc81", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
"""
"""
import numpy as np
__all__ = ["bazin09", "karpenka12", "firth17",
"bazin09_listarg", "karpenka12_listarg", "firth17_listarg",
"_defined_models"]
_defined_models = ["bazin09", "karpenka12", "firth17"]
def bazin09(x, a, t_0, t_rise, t_fall):
return a * np.exp(-(x - t_0) / t_fall) ... | {"hexsha": "ec076698b4e161ecafbeef71caf5a40ce65c0977", "size": 1428, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycocosn/models.py", "max_stars_repo_name": "RobFirth/pycoco", "max_stars_repo_head_hexsha": "1c9be662b6dd67f8be5423568bb3ecbff2979492", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
/* Copyright (c) 2010-2014, Delft University of Technology
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are
* permitted provided that the following conditions are met:
* - Redistributions of source code must retain the above copyright ... | {"hexsha": "5942811209d10c7d48666c735b694204f45d2192", "size": 5745, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tudat/Astrodynamics/Ephemerides/UnitTests/unitTestKeplerEphemeris.cpp", "max_stars_repo_name": "JPelamatti/ThesisTUDAT", "max_stars_repo_head_hexsha": "b94ce35fb7c8fa44ae83238e296a979dfa3adfe8", "ma... |
'''
2048 GAME PROJECT: AI Bot.
Date created:
03/2022
Date edited:
04/2022
Author:
Filip J. Cierkosz
'''
import random
import numpy as np
import pygame
from pygame.locals import *
from time import sleep, time
from graphics import GRID_COLOR, CELL_COLORS, GRID_FONT_COLOR, FONT_BOARD, FONT_SIZES, WINDOW_FO... | {"hexsha": "1005e468dd6ead31f860a1a3571830f7a05689d6", "size": 15092, "ext": "py", "lang": "Python", "max_stars_repo_path": "AI-Implementation/bot.py", "max_stars_repo_name": "chizo4/2048-Project", "max_stars_repo_head_hexsha": "34d062b4b4a5eb9d9ec55d3d8b6e876dcb2967da", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#
# This file is part of KwarqsDashboard.
#
# KwarqsDashboard 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 3.
#
# KwarqsDashboard is distributed in the hope that it will be useful,
# but... | {"hexsha": "c9cf9bcb0b35a18cc1a5624e7ae8aed236fcdbab", "size": 14404, "ext": "py", "lang": "Python", "max_stars_repo_path": "driver_station/src/common/image_capture.py", "max_stars_repo_name": "frc1418/2014", "max_stars_repo_head_hexsha": "2fc75b13efd5a986522a16e20ff0ccee66938724", "max_stars_repo_licenses": ["BSD-3-Cl... |
#!/usr/bin/python
# Copyright (C) 2010, 2011 by Eric Brochu
#
# 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, mo... | {"hexsha": "c3e3e527d81ec6b4d050c740a815804ef64a3e81", "size": 18612, "ext": "py", "lang": "Python", "max_stars_repo_path": "ego/gaussianprocess/__init__.py", "max_stars_repo_name": "misterwindupbird/IBO", "max_stars_repo_head_hexsha": "17e502e4fab4b1c20abbe26c6ce8a258c32bf49a", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
import time
import numpy
import quantarhei as qr
from quantarhei import LabSetup
from quantarhei.utils.vectors import X #, Y, Z
###############################################################################
#
#
# PARAMETERS
#
#
##############################################################... | {"hexsha": "ba5d902553cb3248878f7dc1fa3f626d9823c703", "size": 5698, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantarhei/wizard/examples/ex_035_2D_Vibronic.py", "max_stars_repo_name": "slamavl/quantarhei", "max_stars_repo_head_hexsha": "d822bc2db86152c418e330a9152e7866869776f7", "max_stars_repo_licenses":... |
import torch
import torch.nn as nn
import numpy as np
from transforms import *
class PlanarFlow(nn.Module):
def __init__(self, dim=20, K=16):
super().__init__()
self.transforms = nn.ModuleList([PlanarTransform(dim) for k in range(K)])
def forward(self, z, logdet=False):
zK = z
... | {"hexsha": "245d67b4f2e79a8f5b738dadc8cea71c373b2cc4", "size": 848, "ext": "py", "lang": "Python", "max_stars_repo_path": "flows.py", "max_stars_repo_name": "kaiqi-ken/planar-flow-pytorch", "max_stars_repo_head_hexsha": "3761883bbb53a40f903b5840c18e772bfd3e3d99", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
function voxels = carveall( voxels, cameras )
%CARVEALL carve away voxels using all cameras
%
% VOXELS = CARVEALL(VOXELS, CAMERAS) simple calls CARVE for each of the
% cameras specified
% Copyright 2005-2009 The MathWorks, Inc.
% $Revision: 1.0 $ $Date: 2006/06/30 00:00:00 $
for ii=1:numel(cameras);
v... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/26160-carving-a-dinosaur/SpaceCarving/+space... |
import jieba
import pandas as pd
# import gensim
import pickle as pkl
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../data/train_first.csv')
train_line = train['Discuss'].values
dict = {}
for idx, line in enumerate(train_line):
words = list(jieba.cut(line.strip().replace('\n','')))
... | {"hexsha": "bde0bf2d94eaa93fdeafed109d58774be8fa56ee", "size": 758, "ext": "py", "lang": "Python", "max_stars_repo_path": "Analyse/process3.py", "max_stars_repo_name": "TwT520Ly/YUN-2020", "max_stars_repo_head_hexsha": "1abdfbe399dc841e64ae546b22eed6c17d58a6e0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
\section{Algorithms}
In this section we will describe the randomized algorithms in detail, provide the
corresponding computational complexity analysis, and state the main theoretical results
that guarantee the accuracy of the approximation.
We split this section in three main parts: Stage 1 \ref{sec:stage1} and Stage... | {"hexsha": "57f30c917031213f9c88e42a01fde935b14fe6ec", "size": 21485, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/sections/algorithms.tex", "max_stars_repo_name": "alexnowakvila/ProbAlgosProj", "max_stars_repo_head_hexsha": "819251313cb2b9ca4748892ee62f40c602453ddc", "max_stars_repo_licenses": ["MIT"], ... |
import gsum as gm
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from sklearn.gaussian_process.kernels import RBF, WhiteKernel
from stats_utils import *
from matter import *
import seaborn as sns
import time
from os import path
mpl.r... | {"hexsha": "7c5ad9dd05212ef993942cf8561ad267a4e0e674", "size": 8861, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/populate_fig_database.py", "max_stars_repo_name": "buqeye/nuclear-matter-convergence", "max_stars_repo_head_hexsha": "6500e686c3b0579a1ac7c7570d84ffe8e09ad085", "max_stars_repo_licenses":... |
[STATEMENT]
lemma subd_0 [simp]: "subd p 0 = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subd p 0 = 0
[PROOF STEP]
by (induction p) auto | {"llama_tokens": 71, "file": null, "length": 1} |
\section{Notes}
\todo{\begin{enumerate}
\item Merge this delta section - use for snippets
\item Consider moving Code optimization out of intro
\item Optimizable range is only a small part of this part
\item Write a clear limitations section
\end{enumerate}
}
\todo{Say how the baseline does not include halts, but h... | {"hexsha": "605e079d4a32d865f9b28725abf788ce6dfcfb1a", "size": 3471, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Paper/shadow.tex", "max_stars_repo_name": "csrhau/Iconoclast", "max_stars_repo_head_hexsha": "a77cf2604ea7687415e12ccae6ee79f57f7c4a57", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
# 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, s... | {"hexsha": "ee5e4c058e693ab28ff269e9b5b71abd09b6cfaa", "size": 3309, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/game_example.py", "max_stars_repo_name": "moorugi98/hanabi-learning-environment", "max_stars_repo_head_hexsha": "3c294fe98b6764a5a700b15f1c407dc8c171b593", "max_stars_repo_licenses": ["Ap... |
from glob import glob
import os
import cv2
import numpy as np
from tqdm import tqdm
from collections import defaultdict
colors = [[128, 64, 128],
[244, 35, 232],
[70, 70, 70],
[102, 102, 156],
[190, 153, 153],
[153, 153, 153],
[250, 170, 30],
[220, ... | {"hexsha": "8385e64081764db09d886fe8e2fde540879bec7b", "size": 2224, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/translate_labels.py", "max_stars_repo_name": "drkostas/SemiSeg-Contrastive", "max_stars_repo_head_hexsha": "af6b133400368911ef77f401b7673894fe6aa05c", "max_stars_repo_licenses": ["Apache-2.0... |
/*
* ******************************************************************************
* Copyright 2014-2016 Spectra Logic Corporation. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use
* this file except in compliance with the License. A copy of the License i... | {"hexsha": "1e99ff5c13cc5230f9482f8d24f5296605cd9536", "size": 20888, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/bulk_get.cpp", "max_stars_repo_name": "RachelTucker/ds3_c_sdk", "max_stars_repo_head_hexsha": "b0a32aef4ccc4eb87ed6ce4f08b3b7c01e047234", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
function arcsinh_values_test ( )
%*****************************************************************************80
%
%% ARCSINH_VALUES_TEST demonstrates the use of ARCSINH_VALUES.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 23 June 2007
%
% Author:
%
% John Burk... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/test_values/arcsinh_values_test.m"} |
#include "redist.h"
#include "../Log.h"
#include "../Config.h"
#include <map>
#include <boost/foreach.hpp>
#define foreach BOOST_FOREACH
void Redistribution(GameState& state) {
static bool redist = Config::Value<bool>("redist");
static bool use_future = Config::Value<bool>("redist.future");
if ( !redist ... | {"hexsha": "c36bf802f0fc5259e3db73998cbfd9eb4a209e49", "size": 4517, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/strategy/redist.cc", "max_stars_repo_name": "sigh/Planet-Wars", "max_stars_repo_head_hexsha": "7855ea11143ac97c32cd13bcac42d212c8ed3c30", "max_stars_repo_licenses": ["Apache-1.1"], "max_stars_cou... |
MODULE m_kkintgr
!------------------------------------------------------------------------------
!
! MODULE: m_kkintgr
!
!> @author
!> Henning Janßen
!
! DESCRIPTION:
!> Performs the Kramer-Kronig-Transformation to obtain the Green's function
!> in the complex plane from the imaginary p... | {"hexsha": "b3877bdfcb8e0fe738d1adb39bbba91068a99e71", "size": 7883, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "greensf/kkintgr.f90", "max_stars_repo_name": "MRedies/FLEUR", "max_stars_repo_head_hexsha": "84234831c55459a7539e78600e764ff4ca2ec4b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
from nvidia.dali.pipeline import Pipeline
from nvidia.dali import fn
import nvidia.dali.ops as ops
import nvidia.dali.types as types
import scipy.io.wavfile
import numpy as np
import math
import json
import librosa
import tempfile
import os
from test_audio_decoder_utils import generate_waveforms, rosa_resample
tmp_dir... | {"hexsha": "b52f86cd6353a191a0df54197744c1883a06a6fd", "size": 7168, "ext": "py", "lang": "Python", "max_stars_repo_path": "dali/test/python/test_operator_nemo_asr_reader.py", "max_stars_repo_name": "a-sansanwal/DALI", "max_stars_repo_head_hexsha": "83aeb96792d053f60dd4252b8efa0fc8fdd9012a", "max_stars_repo_licenses": ... |
from pathlib import Path
import pandas as pd
import numpy as np
def next_monday(date):
return pd.date_range(start=date, end=date + pd.offsets.Day(6), freq='W-MON')[0]
def get_relevant_dates(dates):
wds = pd.Series(d.day_name() for d in dates)
next_mondays = pd.Series(next_monday(d) for d in dates)
rel... | {"hexsha": "7f08f62e7b776457185193eda0555588a3260b32", "size": 2136, "ext": "py", "lang": "Python", "max_stars_repo_path": "app_forecasts_de/code/data_preparation.py", "max_stars_repo_name": "QEDHamburg/covid19-forecast-hub-de", "max_stars_repo_head_hexsha": "0fa2daa6ebe4fcb3b91a9c5a852d86d1a36e6d9a", "max_stars_repo_l... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from tempfile import mkdtemp
from shutil import rmtree
from nipype.testing import (assert_equal, example_data, skipif, assert_true)
from nipype.algorithms.confounds import FramewiseDisplacement, ComputeDVARS
import numpy as np
nonitime = True
try:
import ni... | {"hexsha": "c41ed485a19cef6a4c8c42610d69d438a016dcb2", "size": 1375, "ext": "py", "lang": "Python", "max_stars_repo_path": "nipype/algorithms/tests/test_confounds.py", "max_stars_repo_name": "Conxz/nipype", "max_stars_repo_head_hexsha": "1281723ae56eacd103597ff4081a205583706e62", "max_stars_repo_licenses": ["Apache-2.0... |
import os
import shutil
import unittest
import numpy as np
import bilby_pipe
from bilby_pipe.input import Input
from bilby_pipe.utils import BilbyPipeError, parse_args
class TestParser(unittest.TestCase):
def test_parser_defaults(self):
example_prior_file = "tests/example_prior.prior"
known_arg... | {"hexsha": "98b4b32cf19a7000ec44b68a40f24c0a93ff009c", "size": 8035, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/create_injection_test.py", "max_stars_repo_name": "Samanwaya1301/tidal-heating-bilby-pipe", "max_stars_repo_head_hexsha": "b495d4f3ffe3ef61a46ce5b87c826e10b087e2e1", "max_stars_repo_licenses... |
import sys
import os
import numpy as np
from PIL import Image
from external_model import load_external_model, pred_by_external_model
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
APS = 100;
TileFolder = sys.argv[1] + '/';
CNNModel = sys.argv[2];
#CNNModel = '/home/shahira/quip_classification/NNFr... | {"hexsha": "027b1e467945350f04d7ed170c3a2e2af73f7b39", "size": 4642, "ext": "py", "lang": "Python", "max_stars_repo_path": "u24_lymphocyte/prediction/lymphocyte/pred_by_external_model.py", "max_stars_repo_name": "ShahiraAbousamra/til_classification", "max_stars_repo_head_hexsha": "cede5453cb46b9c168a1f50f76ded43f8ca3fc... |
# 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 i... | {"hexsha": "b259c2567dcfbc7013f1d5eec7ca924adf753c7e", "size": 2628, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cugraph/tests/test_egonet.py", "max_stars_repo_name": "mike-wendt/cugraph", "max_stars_repo_head_hexsha": "1f0f14eba2e6253423b1a58ca38989261308df6c", "max_stars_repo_licenses": ["Apache-2.0... |
import matplotlib.pyplot as plt
import numpy as np
from ROOT import TFile,TAxis,TH1,gROOT
import os
import numpy as np
import pickle
# from dataclasses import dataclass
# @dataclass
class PyHist:
""" Basic wrapper for ROOT histogram
Should contain no ROOT functionality, just a container for the information
... | {"hexsha": "c3ebdf3364e7f00596ee2e40a38828e565bbb24f", "size": 3813, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/heptools/histplot/PyHist_Class.py", "max_stars_repo_name": "ethansimpson285/HEPTools", "max_stars_repo_head_hexsha": "7ccb54d6264e693667d97966ab0cd6a4f815d120", "max_stars_repo_licenses": ["MI... |
# Copyright 2020, Battelle Energy Alliance, LLC
# ALL RIGHTS RESERVED
import numpy as np
def run(self,Input):
t_shutdown = 10 # days
repl_cost = 4.48 # M$
risk_free_rate = 0.03
hard_savings = 0.
self.sm_npv_a = Input['sm_p_failure'] * t_shutdown + repl_cost + hard_savings
self.sm_npv_b = self.sm_npv_a / (... | {"hexsha": "3ddb29dd25251f4f91fbceee35d7b8e971e888e3", "size": 399, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/MilestoneTestsSeptFY19/use_case_II/MC/seismic_modification.py", "max_stars_repo_name": "dgarrett622/LOGOS", "max_stars_repo_head_hexsha": "7234b8b5e80bc79526b4cbced7efd5ae482f7c44", "max_star... |
[STATEMENT]
lemma map_values_cong:
assumes "\<And>x y. Mapping.lookup t x = Some y \<Longrightarrow> f x y = f' x y"
shows "Mapping.map_values f t = Mapping.map_values f' t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Mapping.map_values f t = Mapping.map_values f' t
[PROOF STEP]
proof -
[PROOF STATE]
proof (s... | {"llama_tokens": 689, "file": "Eval_FO_Ailamazyan", "length": 8} |
import pandas as pd
import numpy as np
import yfinance as yf
def book_to_market():
"""
Calculates the book to market ratio (shareholders equity/ market cap) for every company based on the latest
stock price and annual financial statement.
:return: DataFrame with ratio for each company.
"... | {"hexsha": "d53ef58aa587d920dcd7a992b24a728612323f42", "size": 24014, "ext": "py", "lang": "Python", "max_stars_repo_path": "strategies.py", "max_stars_repo_name": "TimBstn/Fundamental-Trading-Strategies", "max_stars_repo_head_hexsha": "b620c129a8826e3cf99ecf19eb2a32d63058e352", "max_stars_repo_licenses": ["MIT"], "max... |
"""
Use Gaussian distributions to randomly generate two sets. Then use bhatta_dist() on the sets. Compare the results to the
theoretical Bhattacharyya distance for the distributions.
The Bhattacharyya distance between two Gaussian distributions is given on this page:
https://en.wikipedia.org/wiki/Bhattacharyya... | {"hexsha": "2fdfebe18856dd94f431455f647129433f854060", "size": 3153, "ext": "py", "lang": "Python", "max_stars_repo_path": "verification/bhatta_test.py", "max_stars_repo_name": "EricPWilliamson/bhattacharyya-distance", "max_stars_repo_head_hexsha": "d67498d58bed342151c9d820a520254a503abdc8", "max_stars_repo_licenses": ... |
import torch.utils.data as data
from torchvision import datasets, models, transforms
IN_SIZE = 224
import pickle
from PIL import Image
import matplotlib.pyplot as plt
import os
import os.path
import sys
import numpy as np
import torch
project_root = os.getcwd()
data_root = "%s/data"%project_root
def get_image_attribu... | {"hexsha": "ed0a48e2686c7720cd6b7206fc3a55a7eeb6762a", "size": 3470, "ext": "py", "lang": "Python", "max_stars_repo_path": "res/loader/multi_attribute_loader_file_list_ilab.py", "max_stars_repo_name": "GregoryEHunter/generalization_to_OOD_category_viewpoint_combinations", "max_stars_repo_head_hexsha": "52aacbb3420639ca... |
##
# @file Placer.py
# @author Yibo Lin (DREAMPlace), Rachel Selina Rajarathnam (DREAMPlaceFPGA)
# @date Sep 2020
# @brief Main file to run the entire placement flow.
#
import matplotlib
matplotlib.use('Agg')
import os
import sys
import time
import numpy as np
import logging
# for consistency between python2... | {"hexsha": "cc17d54c09603aa8a280b2c8ce1048f9611a9171", "size": 8787, "ext": "py", "lang": "Python", "max_stars_repo_path": "dreamplacefpga/Placer.py", "max_stars_repo_name": "rachelselinar/DREAMPlaceFPGA", "max_stars_repo_head_hexsha": "b8dd961718144a7c2471dd670379c3d1923171f9", "max_stars_repo_licenses": ["BSD-3-Claus... |
import numpy as np
import pandas as pd
import importlib
from qlib.data.ops import ElemOperator, PairOperator
from qlib.config import C
from qlib.data.cache import H
from qlib.data.data import Cal
from qlib.contrib.ops.high_freq import get_calendar_day
class DayLast(ElemOperator):
"""DayLast Operator
Paramete... | {"hexsha": "175f4f66be772e35d95ab0e9d4eaec76b66c732c", "size": 4524, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/highfreq/highfreq_ops.py", "max_stars_repo_name": "wan9c9/qlib", "max_stars_repo_head_hexsha": "cc95099d7696ca850205b8ca220a99fba35a637a", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
""" Tonti Diagrams
This file includes a framework for constructing and evaluating Tonti diagrams.
Tonti diagrams are stored as ACSets, and have an imperative interface for
describing physical variables and the relationships between them. This tooling
also lets a Tonti diagram be converted to a vectorfield, allowing fo... | {"hexsha": "7c049720c9bf73dd36f55886302992897b0ee06d", "size": 17536, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TontiDiagrams.jl", "max_stars_repo_name": "mgatlin3-gtri/Decapods.jl", "max_stars_repo_head_hexsha": "793c5992f2219e5d066a437ae9a719a8ed809a23", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""
This file generates the html for the Flood Risk Map.
"""
# -------
# IMPORTS
# -------
import geopandas as gpd # for loading/manipulating vector data
from shapely.geometry import Polygon
import rasterio # for loading/manipulating raster data
import folium # for creating the interactive map
import numpy as np
... | {"hexsha": "719429cc1f5c154a111f72aab29c09b625e8ceaf", "size": 15957, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/create_map.py", "max_stars_repo_name": "NatalieThurlby/useful_flood_data", "max_stars_repo_head_hexsha": "66b041ef5176239fd370209afd0bbf910806ffdc", "max_stars_repo_licenses": ["MIT"], "m... |
import ccxt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from utils.constants import OHLCV_COLS
class BinanceAPICallException(Exception):
pass
class IndicatorNotFoundException(Exception):
pass
exchange = ccxt.binance()
def get_price_by_coin_pair(pair: str = "BTC/USDT") -> floa... | {"hexsha": "b29ad5c9b25680ff1103dfeca85191e4bead9c3d", "size": 3024, "ext": "py", "lang": "Python", "max_stars_repo_path": "technical_analysis/moving_averages.py", "max_stars_repo_name": "elmesaoudee/signarly", "max_stars_repo_head_hexsha": "b642ea3a6d6064e6e9705115880cc93072c4ccb3", "max_stars_repo_licenses": ["MIT"],... |
import numpy as np
import nimfa
V = np.random.rand(40, 100)
pmfcc = nimfa.Pmfcc(V, seed="random_vcol", rank=10, max_iter=30,
theta=np.random.rand(V.shape[1], V.shape[1]))
pmfcc_fit = pmfcc()
| {"hexsha": "93703c5327a3dc23e98491211b68f0e72f75163a", "size": 213, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/code/snippet_pmfcc.py", "max_stars_repo_name": "askerdb/nimfa", "max_stars_repo_head_hexsha": "3e3353e60d53fd409b53c46fde23f4f6fef64aaf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
# 2018-9-10
# 函数
import cv2
import numpy as np
def resize(img, scale_factor):
"""
缩小图像尺寸
"""
return cv2.resize(img, (int(img.shape[1] * (1 / scale_factor)), int(img.shape[0] * (1 / scale_factor))), interpolation=cv2.INTER_AREA)
def pyramid(image, scale=1.5, min_size=(200, 80)):
"""
图像金字塔
... | {"hexsha": "e1cd2602900333131ccf54f8a63393b163d018d0", "size": 2406, "ext": "py", "lang": "Python", "max_stars_repo_path": "Image/OpenCV/car_detect/func.py", "max_stars_repo_name": "YangXiaoo/NoteBook", "max_stars_repo_head_hexsha": "37056acad7a05b876832f72ac34d3d1a41e0dd22", "max_stars_repo_licenses": ["CNRI-Python", ... |
## ---- load data ---- ##
# data
df<-read.table("rdata.csv", sep="\t", encoding="UTF-8", header=TRUE)
# module names
modules<-c("ALP1", "ALP2", "ALP3", "ALP4", "ALP5", "SWP", "MafI1", "MafI2", "MafI3", "GTI", "PS", "DBS", "TI1", "TI2", "TI3", "TI4", "AWS", "SWT")
# weeks per sem
wps <-c( 17, 13, 17, 13, ... | {"hexsha": "a02b00a666ff10887e84e3f9ffbfe163b5747a59", "size": 40139, "ext": "r", "lang": "R", "max_stars_repo_path": "survey/start.r", "max_stars_repo_name": "xconnect/fub.msc.empirical-evaluation", "max_stars_repo_head_hexsha": "7a995bfc8df23e43ed03b334d6c8bcb186129d78", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import numpy as np
import os
import pandas as pd
from load_paths import load_box_paths
from datetime import date, timedelta, datetime
datapath, projectpath, wdir,exe_dir, git_dir = load_box_paths()
def load_sim_data(exp_name, region_suffix ='_All', input_wdir=None, fname='trajectoriesDat.csv',
inpu... | {"hexsha": "c528aadc832c18f25d5d556c376e3bc7fae80ddc", "size": 22051, "ext": "py", "lang": "Python", "max_stars_repo_path": "processing_helpers.py", "max_stars_repo_name": "reeserich/covid-chicago", "max_stars_repo_head_hexsha": "88bdc556aebdd7e443e4756e7421160d230f5a01", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
import os
import yaml
import glob
import numpy as np
from hexrd import imageseries
from PySide2.QtGui import QCursor
from PySide2.QtCore import QObject, Qt, QPersistentModelIndex, QThreadPool, Signal
from PySide2.QtWidgets import QTableWidgetItem, QFileDialog, QMenu, QMessageBox
from hexrd.ui.async_worker import Asy... | {"hexsha": "6499e3654ac695d909bce9dd12cc93e07f8450b3", "size": 24151, "ext": "py", "lang": "Python", "max_stars_repo_path": "hexrd/ui/load_panel.py", "max_stars_repo_name": "psavery/hexrdgui", "max_stars_repo_head_hexsha": "2a02dff4284bdddc48aa0bb17473cc651e161759", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
#include <iostream>
#include <fstream>
#include <vector>
#include <string>
#include <boost/archive/text_oarchive.hpp>
#include <boost/archive/text_iarchive.hpp>
#include <boost/archive/binary_oarchive.hpp>
#include <boost/archive/binary_iarchive.hpp>
#include "featurizer.h"
int main() {
Featurizer* feat = new Fea... | {"hexsha": "65f491f81bf4e8d314028e088292ea1029eb27e8", "size": 734, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/serializetest.cpp", "max_stars_repo_name": "shurain/codesprint2014r1", "max_stars_repo_head_hexsha": "980b0191e9e90adc54778bdc5dbfbb41538e96f1", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
// Copyright John Maddock 2008.
// Use, modification and distribution are subject to 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 <pch.hpp>
#ifndef BOOST_MATH_TR1_SOURCE
# define BOOST_MATH_TR1_SOURCE
#... | {"hexsha": "4f4efaa2ba872131fad2d0838858dc348ce8c3b4", "size": 654, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/math/src/tr1/cyl_bessel_if.cpp", "max_stars_repo_name": "zyiacas/boost-doc-zh", "max_stars_repo_head_hexsha": "689e5a3a0a4dbead1a960f7b039e3decda54aa2c", "max_stars_repo_licenses": ["BSL-1.0"], ... |
import numpy
import pylab
import tables
import math
import matplotlib.transforms as mtransforms
pylab.rc('text', usetex=True)
# open HDF5 file
lcFh = tables.openFile("s34-rte-slab_sol.h5")
mu = lcFh.root.mu.read()
muM = mu*0.0
for i in range(mu.shape[0]):
muM[i] = -mu[mu.shape[0]-i-1]
muExtended = numpy.zeros( ... | {"hexsha": "eae2e0407983cb003278ce5e5f73d389b78f7ad4", "size": 2125, "ext": "py", "lang": "Python", "max_stars_repo_path": "sims/s34/s34-mkplots.py", "max_stars_repo_name": "ammarhakim/ammar-simjournal", "max_stars_repo_head_hexsha": "85b64ddc9556f01a4fab37977864a7d878eac637", "max_stars_repo_licenses": ["MIT", "Unlice... |
C * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
C * *
C * copyright (c) 1999 by UCAR *
C * *
C * UNIVERSITY CORPORA... | {"hexsha": "0287df6a19803891fd9c3aa621e19c139a2d84d1", "size": 7519, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/omuse/community/qgmodel/src/fishpack4.1/test/tpoistg.f", "max_stars_repo_name": "ipelupessy/omuse", "max_stars_repo_head_hexsha": "83850925beb4b8ba6050c7fa8a1ef2371baf6fbb", "max_stars_repo_li... |
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors
def make_plots():
'''
A dummy function.
'''
pass
def plot_detected_planet_contrasts(planet_table,wv_index,detected,flux_ratios,instrument,telescope,
show=True,save=False,ymin=1e-9,ymax=1e-4,xmin=0.,xmax=1.,... | {"hexsha": "0635b561796402814ff51cd4e08da51d9c081716", "size": 10358, "ext": "py", "lang": "Python", "max_stars_repo_path": "psisim/plots.py", "max_stars_repo_name": "abgibbs/psisim", "max_stars_repo_head_hexsha": "9b0a6ac4f134cabcd2b10a03e20b2fcb58c8afe7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
Require Export SfLib.
Require Export HelperFunctions.
Inductive bplustree (b: nat) (X:Type) : Type :=
| bptLeaf : list (nat * X) -> bplustree b X
| bptNode : list (nat * (bplustree b X)) -> bplustree b X
.
Notation "[[ b , X | x , .. , y ]]" := (bptLeaf b X (cons x .. (cons y []) ..)) (at level 100, format
... | {"author": "nicolaidahl", "repo": "BPlusTrees", "sha": "f017e4d3a334f72e1fd1cfb777e5bdd78cd9ca49", "save_path": "github-repos/coq/nicolaidahl-BPlusTrees", "path": "github-repos/coq/nicolaidahl-BPlusTrees/BPlusTrees-f017e4d3a334f72e1fd1cfb777e5bdd78cd9ca49/code/InductiveDataTypes.v"} |
%!TEX root = ../../report.tex
\subsection{Undiscovered City} % (fold)
\label{sub:undiscovered_city}
In \cite{Greuter2003} Stefan Greuter et al. presented a system that generates in real-time pseudo infinite virtual cities which can be interactively explored from a first person perspective. In their approach ``all geo... | {"hexsha": "b56a8502a95cbfdbf3b84e7c80b56b20b256e726", "size": 3204, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/Works/2-Undiscovered-City.tex", "max_stars_repo_name": "arturalkaim/v2ProceduralGeneration", "max_stars_repo_head_hexsha": "496ecd2bf9885b6fa634cb958b696dad7a2166b7", "max_stars_repo_licens... |
import numpy as np
import cv2
"""使用均值漂移检测目标移动的例子
效果很不好
这种方式存在一个问题，就是窗口的大小不与跟踪帧中的目标大小一起变化
"""
cap = cv2.VideoCapture(0)
# 获得第一帧图像
ret, frame = cap.read()
# 标志 ROI的区域
r, h, c, w = 10, 200, 10, 200
track_window = (c, r, w, h)
# 提取roi区域
roi = frame[r:r + h, c:c + w]
# 将图片转为HSV格式
hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR... | {"hexsha": "4d334a9424fec2560832aa79f7776ad83b3b83af", "size": 1507, "ext": "py", "lang": "Python", "max_stars_repo_path": "my_cv/08/08_04.py", "max_stars_repo_name": "strawsyz/straw", "max_stars_repo_head_hexsha": "db313c78c2e3c0355cd10c70ac25a15bb5632d41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
import csv
import collections
import operator
from csv import DictReader
from datetime import datetime
import argparse
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from itertools import islice
import random
import numpy as np
def setup_seed(seed):
np.random.seed(seed)
random.seed(se... | {"hexsha": "4d10ef934d657a93e99d031973283906ecbe8345", "size": 11391, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctr/encode/data_.py", "max_stars_repo_name": "JiaXingBinggan/FAB_expr", "max_stars_repo_head_hexsha": "354d274b28f4a9933695b82494d829c87531a772", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import numpy as np
from sklearn.dummy import DummyClassifier
################################################################################
def array2c (array, fmt = None):
"Converts an array in a C string. fmt can be a %format, a callable or None"
if fmt is None:
fmt_ = lambda x: "%.20f" % x
elif isins... | {"hexsha": "fd3e311f7300e9bcd79ba02717b49004a2eeb8af", "size": 2703, "ext": "py", "lang": "Python", "max_stars_repo_path": "scikinC/_tools.py", "max_stars_repo_name": "landerlini/scikinC", "max_stars_repo_head_hexsha": "c408e2b63a32eecefc514193a4483b9d95b8d0fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import cv2
import numpy as np
from open3d import PointCloud, Vector3dVector, draw_geometries
import open3d as o3d
import time
import random
from interaction import opt
import sys
brush_temp = None
points = np.zeros((1, 3))
color = np.zeros((1, 3))
def pc_cube(pt1, pt2):
x = np.linspace(pt1[0], pt2[0])
y = n... | {"hexsha": "10080abdeb8e06a358a6409d50ab452d2b648c06", "size": 6446, "ext": "py", "lang": "Python", "max_stars_repo_path": "gen3d.py", "max_stars_repo_name": "HarryXD2018/3DPainter", "max_stars_repo_head_hexsha": "d58e705c203eddd6e46007e6640543f7a4bf44d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_... |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.1.7
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %%
import os
import... | {"hexsha": "8b6d0ac15ac6918a33e708c221bec294fcc694a1", "size": 1870, "ext": "py", "lang": "Python", "max_stars_repo_path": "kernels/jpeg_io_test/jpeg_io_test.py", "max_stars_repo_name": "ar90n/siim-acr-pneumothorax-segmentation", "max_stars_repo_head_hexsha": "e9b88f0a023798431dfd76704047388ba81a86e3", "max_stars_repo_... |
program persist
implicit none
! Explicit types for blas calls
integer, parameter :: i32 = 4
integer, parameter :: i64 = 8
integer, parameter :: f32 = kind(1.e0)
integer, parameter :: f64 = kind(1.d0)
real(f64) :: ddot
! Iteration variables
integer(i32) i, j
! Physi... | {"hexsha": "5a643cd6514751b65f971effd4c4dc0b956c2481", "size": 2515, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "persist/persist.f95", "max_stars_repo_name": "jasondark/dissertation", "max_stars_repo_head_hexsha": "3e1117ef0d14aa8d659f80df3edde1c266815856", "max_stars_repo_licenses": ["Unlicense"], "max_st... |
[STATEMENT]
lemma Macaulay_list_Nil [simp]: "Macaulay_list [] = ([]::('t \<Rightarrow>\<^sub>0 'b::field) list)" (is "?l = _")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Macaulay_list [] = []
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. Macaulay_list [] = []
[PROOF STEP]
have "length ?l... | {"llama_tokens": 912, "file": "Groebner_Bases_Macaulay_Matrix", "length": 11} |
REBOL [
Title: "Builds a set of Red/System Float Tests to run on an ARM host"
File: %build-arm-float-tests.r
Author: "Peter W A Wood"
Version: 0.1.0
License: "BSD-3 - https://github.com/dockimbel/Red/blob/master/BSD-3-License.txt"
]
;; This script must be run from the Red/system/tests dir
;; supress script... | {"hexsha": "d34fa8845227e262936e5700882f30fa199340b2", "size": 1655, "ext": "r", "lang": "R", "max_stars_repo_path": "system/tests/build-arm-float-tests.r", "max_stars_repo_name": "7hi4g0/red", "max_stars_repo_head_hexsha": "a9f561552696d9922198bb41a19b2f9fc0052adc", "max_stars_repo_licenses": ["BSL-1.0", "BSD-3-Clause... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 19 09:55:36 2017
@author: cheers
"""
import scipy.io as sio
import matplotlib.pyplot as plt
import numpy as np
image_size = 32
num_labels = 10
def display_data():
print 'loading Matlab data...'
train = sio.loadmat('train_32x32.mat')
d... | {"hexsha": "989114d5e7ea12a03c96522b5a640b3dd6d2221f", "size": 1669, "ext": "py", "lang": "Python", "max_stars_repo_path": "svhn_data.py", "max_stars_repo_name": "Yisongsong/TFlearn-SVHN", "max_stars_repo_head_hexsha": "fd655853a0d8a495cebb35b035f2007581120332", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
#%%
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRest... | {"hexsha": "7c66ce7f899f2c972f26f73fa13fd390b3f90e01", "size": 7184, "ext": "py", "lang": "Python", "max_stars_repo_path": "WR/WR_LASSO.py", "max_stars_repo_name": "mutazag/ilab1", "max_stars_repo_head_hexsha": "c37ae969d0fa13029ee08e7c0e102990e98e65b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
# Script to replicate figure 4 and 5
using StatsBase, Statistics, LinearAlgebra, StatsPlots, XLSX, PrettyTables, GLM
# -------------- Figure 4 --------------------------------------------------------------------------------------------------------------------------------
t_fig_4 = DataFrame(XLSX.readtable("clean/t_f... | {"hexsha": "8909334194c4a82f286e03941cc2c5f5717c2210", "size": 3960, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/figure_4_5_6.jl", "max_stars_repo_name": "forsthuber92/antras_chor_2018.jl", "max_stars_repo_head_hexsha": "7c520db82566fa75a6b2eb41ae0cab3ca26f522f", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
from research import wrappers
from collections import defaultdict
from torch.utils.tensorboard import SummaryWriter
from research.define_config import env_fn
import gym
from gym.vector.async_vector_env import AsyncVectorEnv
import torch as th
from research.nets import net_map
from jax.tree_util impor... | {"hexsha": "7b8e48cc38ef616416d6f26dfb023f9a162cb65a", "size": 6987, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/rl/_base.py", "max_stars_repo_name": "matwilso/boxLCD", "max_stars_repo_head_hexsha": "7505e27f47e6694026303aa6cf12477959fc9fba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
# -*- coding: utf-8 -*-
"""
_____________________________________________________________________________
This file contain code for converting pretrain Pytorch model into TensorRT engine
_____________________________________________________________________________
"""
from icecream import ic
import sys
import os
from... | {"hexsha": "b750d7a0aa7cb01b60126a174c02a9280320c80b", "size": 3259, "ext": "py", "lang": "Python", "max_stars_repo_path": "converters/torch2trt.py", "max_stars_repo_name": "k9ele7en/torch2tensorRT-dynamic-CRAFT-pytorch", "max_stars_repo_head_hexsha": "40191e9ac0d6f3a8d2763ab11d02d391f4880944", "max_stars_repo_licenses... |
import os
from tqdm import tqdm
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import cv2
import torch
from torchvision import transforms
from models.model_with_tcn_big import Model
from utils.hwdb2_0_chars import char_set
from utils.get_dgrl_data import get_pred_data
from utils.pred_utils import get_a... | {"hexsha": "fe644e89a72d4bf2d85db3d724b66fedcc34674e", "size": 5641, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval_hwdb_with_center_line.py", "max_stars_repo_name": "BruceHan98/OCHTPS", "max_stars_repo_head_hexsha": "5bee02bcbff36029cd47b4802178216f980a4298", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
from scipy import interpolate
import os
import shutil
import cv2
import argparse
import _init_paths
from datasets.json_dataset import JsonDataset
from six.moves import cPickle as pickle
import pdb
np.seterr(divide='ignore',invalid='ignore')
# windows: origin and multi-window 1,2,3
windows = [[-1024... | {"hexsha": "56e9761352fb5a83eaa38450dafcda33200d2220", "size": 5439, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/case_study.py", "max_stars_repo_name": "xixiobba/MVP-Net", "max_stars_repo_head_hexsha": "07bf00390080670b5d9a643b99f633419322a1ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18... |
import numpy as np
import tensorflow as tf
#import cv2
import matplotlib.pyplot as plt
from PIL import Image
import csv
import math
import os
from keras.layers import Dense, Flatten, Lambda, Activation, MaxPooling2D, ELU, Dropout
from keras.layers.convolutional import Conv2D
from keras.models import Sequential, model_f... | {"hexsha": "08dd50406111d9a6ed28cdff4de6c4d01b250235", "size": 6420, "ext": "py", "lang": "Python", "max_stars_repo_path": "experimental/model.py", "max_stars_repo_name": "tho15/tfplusplus", "max_stars_repo_head_hexsha": "e151986f7d449ee5ccb440fbb947fbc64fd62f49", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import AnalysisFunctions as af
import pandas as pd
#defaultdict to use nested dictionaries
from collections import defaultdict
import matplotlib.pyplot as plt
import statsmodels.api as sm
import numpy as np
import dill
"""
-------------------------------------------------------------------------------------------... | {"hexsha": "30a2c28ddb8c1fce4d3f3a6e587c15218c9e8515", "size": 16940, "ext": "py", "lang": "Python", "max_stars_repo_path": "AnalysisScriptNEW.py", "max_stars_repo_name": "Ciccuz/ensemble-flood-forecasting", "max_stars_repo_head_hexsha": "a3e1eb5cb3625bd4228513c73a7188c9cfafc3ef", "max_stars_repo_licenses": ["BSD-3-Cla... |
#!/usr/bin/env python
import unittest
import numpy as np
from plico.utils.decorator import override
from plico.rpc.dummy_remote_procedure_call import DummyRpcHandler
from plico.rpc.dummy_sockets import DummySockets
from plico_dm.client.deformable_mirror_client import DeformableMirrorClient
from plico_dm.utils.timeout i... | {"hexsha": "8ebf6336f72b5460565e8a9817db5a0b49af588f", "size": 5661, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/client/deformable_mirror_client_test.py", "max_stars_repo_name": "lbusoni/palpao", "max_stars_repo_head_hexsha": "95ffeb3733437ab9d96ea47c4a266f73142acca6", "max_stars_repo_licenses": ["MIT"]... |
'''
Pan_X tracker
====================
Made by: Jan-Jaap van de Velde
Keys
----
ESC - exit
'''
import numpy as np
import cv2
import datetime
lk_params = dict( winSize = (10, 10),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, ... | {"hexsha": "b68ad1d4104abc88397659ce1b8876d1962a8dec", "size": 6889, "ext": "py", "lang": "Python", "max_stars_repo_path": "process_video.py", "max_stars_repo_name": "Jan-Jaap/gps_video_sync", "max_stars_repo_head_hexsha": "dcdbce948a1e37a15fbc497b8fcc8dae831a8b5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
const Vec2 = Vec{2, Float32}
const Vec3 = Vec{3, Float32}
const Vec4 = Vec{4, Float32}
const iVec2 = Vec{2, Cint}
const iVec3 = Vec{3, Cint}
const iVec4 = Vec{4, Cint}
const uVec2 = Vec{2, Cuint}
const uVec3 = Vec{3, Cuint}
const uVec4 = Vec{4, Cuint}
function test_textures()
N = 100
t1 = Texture(RGBA{N0f8}... | {"hexsha": "ce5262246f9399857f7c6d4376ba81f1973142da", "size": 1372, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/texture.jl", "max_stars_repo_name": "Tuebel/GLAbstraction.jl", "max_stars_repo_head_hexsha": "5dd969f1d1313d4ff3fc423de90feaa532d5f608", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
(* Author: Alexander Bentkamp, Universität des Saarlandes
*)
section \<open>Matrix Rank\<close>
theory DL_Rank
imports VS_Connect DL_Missing_List
Determinant
Missing_VectorSpace
begin
lemma (in vectorspace) full_dim_span:
assumes "S \<subseteq> carrier V"
and "finite S"
and "vectorspace.dim K (span_vs S) = card S"
... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Evaluation/Jordan_Normal_Form/DL_Rank.thy"} |
# -*- coding: utf-8 -*-
"""
Helper functions to visualize the data in plotly
"""
import plotly.graph_objs as go
import numpy as np
"""Visualizaiton functions do the scatter plots in plotly since it seems to be more efficient."""
def get_plotly_scatter_plot(
data_in: np.ndarray,
lat_mat: np.ndarray,
skip... | {"hexsha": "2467ce2f52a85b3063ff76cb21746cd18dd90592", "size": 2951, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyrho/vis/plotly.py", "max_stars_repo_name": "mattmcdermott/pyrho", "max_stars_repo_head_hexsha": "7ab3bd893a8b310b8be61f33a1105b090a46cd32", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], ... |
\section{Moves in Detail}
\section{Multiclass Moves}
\section{Multiclass Dabbler}
\section{Multiclass Initiate}
\section{Multiclass Master}
For the purposes of these multiclass moves the cleric's commune and cast a spell count as one move. Likewise for the wizard's spellbook, prepare Spells, and cast a Spell.
Whe... | {"hexsha": "0f15b67febb612633ff99459fec42fa5fc0b8132", "size": 6990, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/Class_Moves_Discussion.tex", "max_stars_repo_name": "Hegz/DW-Latex", "max_stars_repo_head_hexsha": "49a230f82fdeab7faa7c736ef81ef13266ac399d", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_star... |
import numpy as np
from Utils.Data.DatasetUtils import is_test_or_val_set, get_train_set_id_from_test_or_val_set, \
get_test_or_val_set_id_from_train
from Utils.Data.Dictionary.TweetTextFeaturesDictArray import TweetTokenLengthFeatureDictArray, \
TweetTokenLengthUniqueFeatureDictArray
from Utils.Data.Features.... | {"hexsha": "7e52ebc151d6aff155b7245a742b18311eaaabe7", "size": 5593, "ext": "py", "lang": "Python", "max_stars_repo_path": "Utils/Data/Features/Generated/CreatorFeature/CreatorFrequencyUniqueTokens.py", "max_stars_repo_name": "MaurizioFD/recsys-challenge-2020-twitter", "max_stars_repo_head_hexsha": "95dc024fb4f8777aa62... |
[STATEMENT]
lemma InvariantWatchesElNotifyWatchesLoop:
fixes literal :: Literal and Wl :: "nat list" and newWl :: "nat list" and state :: State
assumes
"InvariantWatchesEl (getF state) (getWatch1 state) (getWatch2 state)" and
"\<forall> (c::nat). c \<in> set Wl \<longrightarrow> 0 \<le> c \<and> c < length (getF... | {"llama_tokens": 60965, "file": "SATSolverVerification_AssertLiteral", "length": 232} |
import numpy as np
import scipy as sp
import openmdao.api as om
import random
from . import VariableType
def hyperplane_coefficients(points):
A = np.c_[points[:, :-1], np.ones(points.shape[0])]
B = points[:, -1]
coeff, _, _, _ = sp.linalg.lstsq(A, B)
return coeff
def is_assertion_error(err, *args):... | {"hexsha": "e7ad3830bdb61702b4b8538d6abce23f65e4562f", "size": 1558, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/openmdao_omt/testing.py", "max_stars_repo_name": "ovidner/openmdao-utils", "max_stars_repo_head_hexsha": "dfc4041cac48bf7d1d4537c23a6d18c29339f6f3", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""Methods for computing, reading, and writing occlusion maps."""
import numpy
import netCDF4
from gewittergefahr.gg_utils import file_system_utils
from gewittergefahr.gg_utils import error_checking
from ml4tc.machine_learning import neural_net
from ml4tc.machine_learning import gradcam
NUM_EXAMPLES_PER_BATCH = 32
E... | {"hexsha": "333ed325682e2682481bc098cdaccb1e3fee3957", "size": 15608, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml4tc/machine_learning/occlusion.py", "max_stars_repo_name": "NOAA-GSL/ml4tc", "max_stars_repo_head_hexsha": "e9f8faa51e5bfb86b2a78648d7b1d0e61d09b6c0", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 29 16:30:36 2020
@author: aparravi
"""
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
import matplotlib.lines as lines
import pandas as pd
import numpy as np
import scipy.stats as st
from mat... | {"hexsha": "47104339ad9a180f917bb6bdcd546e859db0c97b", "size": 24781, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/resources/python/plotting/plot_errors.py", "max_stars_repo_name": "AlbertoParravicini/approximate-spmv-topk", "max_stars_repo_head_hexsha": "f98dd1846d81a5c21faa9af48bb5b531543424c7", "max_st... |
"Update the header bounding box and count based on point data"
function update!(h::LasHeader, pvec::Vector{T}) where T <: LasPoint
x_min, y_min, z_min = Inf, Inf, Inf
x_max, y_max, z_max = -Inf, -Inf, -Inf
for p in pvec
x, y, z = xcoord(p, h), ycoord(p, h), zcoord(p, h)
if x < x_min
... | {"hexsha": "098dd6dee516e084703810f38109d567ca5ee747", "size": 800, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/util.jl", "max_stars_repo_name": "vernimmen/LasIO.jl", "max_stars_repo_head_hexsha": "bf4ae3f87c77fa2628da36c47e9bbdc049d2687e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max_s... |
import numpy as np
from sigmoid import sigmoid
def predict(Theta1, Theta2, X):
""" outputs the predicted label of X given the
trained weights of a neural network (Theta1, Theta2)
"""
if X.ndim == 1:
X = X.reshape(1, -1)
# Useful values
m = len(X)
# ====================== YOUR CO... | {"hexsha": "2bdf2fc934208963eb168970500b5c921c3b8f4c", "size": 1320, "ext": "py", "lang": "Python", "max_stars_repo_path": "ex3/predict.py", "max_stars_repo_name": "junwon1994/Coursera-ML", "max_stars_repo_head_hexsha": "91e96c3c14c058cd6d745a4fada1baf40d91458f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3... |
from sage.matrix.constructor import matrix
from sage.matrix.matrix import is_Matrix
from sage.rings.arith import legendre_symbol
from sage.rings.integer_ring import ZZ
def is_triangular_number(n):
"""
Determines if the integer n is a triangular number.
(I.e. determine if n = a*(a+1)/2 for some natural num... | {"hexsha": "5d145b7688666198fa9ccc2f0dcf4576ea2b870f", "size": 5301, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/quadratic_forms/extras.py", "max_stars_repo_name": "bopopescu/classic_diff_geom", "max_stars_repo_head_hexsha": "2b1d88becbc8cb30962e0995cc78e429e0f5589f", "max_stars_repo_licenses": ["BS... |
\documentclass[8pt]{beamer}
\usepackage[utf8]{inputenc}
\usetheme{default}
\fontfamily{ppl}
\usetheme{Antibes}
\usecolortheme{spruce}
\usefonttheme{serif}
\title{APC Project: SimpleQuadTree}
\author{Thomas Bellotti}
\date{24 - 05 - 2019}
%\usepackage{mathpazo} % add possibly `sc` and `osf` options
\begin{documen... | {"hexsha": "b9a3b6ed69fd98bfd928a77ef16a48216f75733e", "size": 22159, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "presentation/slides.tex", "max_stars_repo_name": "tbellotti/SimpleQuadTree", "max_stars_repo_head_hexsha": "227b0ed368ca71849f9cdc0a58788d5b0dcfd2a0", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""Scarf Algotihm util functions."""
import numpy as np
def check_single_preflist(s, pair_list):
assert(np.all([len(p) == 2 and p[0] == s for p in pair_list]))
assert(pair_list[-1] == (s, -1))
def check_couple_preflist(c, pair_list):
assert(np.all([len(p) == 3 and p[0] == c for p in pair_list]))
assert(pai... | {"hexsha": "061d1f7a3ad6bd582a627a3b8030780a704907e4", "size": 3980, "ext": "py", "lang": "Python", "max_stars_repo_path": "scarf/utils.py", "max_stars_repo_name": "dwtang/scarf", "max_stars_repo_head_hexsha": "62b58c62b2bca0552aec95500fad2c6d9555a0d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
/*
BSD 3-Clause License
Copyright (c) 2017, Alibaba Cloud
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditi... | {"hexsha": "17c716e8cc87f22fde9446aa5518712c4f2df168", "size": 3745, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tablestore/util/random.cpp", "max_stars_repo_name": "TimeExceed/aliyun-tablestore-cpp-sdk", "max_stars_repo_head_hexsha": "f8d2fdf500badf70073dff4e21a5d2d7aa7d3853", "max_stars_repo_licenses": [... |
\section{Performance Notes} | {"hexsha": "cc7f0ab35eacafd27440606861e7035bb0b1a31a", "size": 27, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "day-4/trio_score/trio_score/builds/assets/performance-notes.tex", "max_stars_repo_name": "DaviRaubach/intensive", "max_stars_repo_head_hexsha": "f2abce0f888f1231d0f8da6f24d41c921ea6aca1", "max_stars_r... |
||| ported from https://github.com/pepijnkokke/FirstOrderUnificationInAgda
module Unification
import Data.Fin
%default total
%access public export
||| An identifier
data Name = MkName String
Eq Name where
(MkName x) == (MkName y) = x == y
||| A term in an untyped lambda-calculus with variables indexed by `v`.
... | {"hexsha": "45e1bd5f7bc9b301080e069c7045b0e343afbfa9", "size": 6629, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Unification.idr", "max_stars_repo_name": "sammthomson/IdrisUnification", "max_stars_repo_head_hexsha": "c3f2a8b63d4d57f0668f18caf505baf632e9b363", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
/*****************************************************************************
* Licensed to Qualys, Inc. (QUALYS) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* QUALYS licenses this file to You under ... | {"hexsha": "778c620c8edf54dcd141f5f2119a7acdacc522e1", "size": 2418, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "predicate/validate_graph.cpp", "max_stars_repo_name": "crustymonkey/ironbee", "max_stars_repo_head_hexsha": "8350b383244e33b18c7a7b6ba989f67ffcbd945a", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 22 13:13:26 2017
@author: wd
"""
import io
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import math
import tensorflow as tf
import numpy as np
class CNN_SL:
def __init__(self, sess, input_size, output_size, name='main'):
... | {"hexsha": "852fdec407bba33173502a37cf55fa3f6eac9ae4", "size": 5014, "ext": "py", "lang": "Python", "max_stars_repo_path": "PG_supervised_CNN_action_class.py", "max_stars_repo_name": "blackpigg/RL_landmark_finder_scale_action", "max_stars_repo_head_hexsha": "2005741bc0d54d4361331342d522859231ad1955", "max_stars_repo_li... |
# -*- coding: utf-8 -*-
"""
Deep Learning with Python by Francois Chollet
4. Fundamentals of machine learning
4.4 Overfitting and underfitting
"""
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.regularizers import l2
import ... | {"hexsha": "0f465a43dafca4730f6b979607782409bb607bea", "size": 5294, "ext": "py", "lang": "Python", "max_stars_repo_path": "4_fundamentals_of_machine_learning/overfitting_and_underfitting.py", "max_stars_repo_name": "agaitanis/deep_learning_with_python", "max_stars_repo_head_hexsha": "590e4171c4e4e83136a8633665586e07f0... |
from ast import Mod
import numpy as np
import scipy
from multiprocessing import Pool
from enum import Enum
class Direction(Enum):
N = 0
NE = 1
E = 2
SE = 3
S = 4
SW = 5
W = 6
NW = 7
transform = {
'N': (-1,0),
'NE': (-1,1),
'E': (0,1),
'SE': (1,1),
'S': (1,0),
'... | {"hexsha": "f5ffc2ccc7aed4db119be4e39e6c925fdd5f7d69", "size": 1789, "ext": "py", "lang": "Python", "max_stars_repo_path": "boundary_refinement.py", "max_stars_repo_name": "Yusoi/mmdetection", "max_stars_repo_head_hexsha": "cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
#include <huaweicloud/eip/v2/EipClient.h>
#include <huaweicloud/core/utils/MultipartFormData.h>
#include <unordered_set>
#include <boost/algorithm/string/replace.hpp>
template <typename T>
std::string toString(const T value)
{
std::ostringstream out;
out << std::setprecision(std::numeric_limits<T>::digits10)... | {"hexsha": "99f10ca0c0be0b2bf0d1e4c6a90870074167307b", "size": 51440, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "eip/src/v2/EipClient.cpp", "max_stars_repo_name": "huaweicloud/huaweicloud-sdk-cpp-v3", "max_stars_repo_head_hexsha": "d3b5e07b0ee8367d1c7f6dad17be0212166d959c", "max_stars_repo_licenses": ["Apache... |
import os, sys
from csv import DictReader
import numpy as np
from cupcake.io.BioReaders import GMAPSAMReader
REF_LENGTH = 29903
def process_sam_to_wig(sam_filename, output_wig, cov_threshold=200, meta_info=None):
cov = np.zeros(REF_LENGTH)
reader = GMAPSAMReader(sam_filename, True)
f_sam = open(sam_filen... | {"hexsha": "6a46def310fa45132982b11000a1623fe140ef33", "size": 1970, "ext": "py", "lang": "Python", "max_stars_repo_path": "cosa/utils/process_sam_to_wig.py", "max_stars_repo_name": "Zuhayr-PacBio/CoSA", "max_stars_repo_head_hexsha": "a8bfccd301a367aac2858b48f13ca708833f6c8f", "max_stars_repo_licenses": ["BSD-3-Clause-... |
#!/usr/bin/env python3
import numpy as np
import h5py as h5
import argparse
import os
import subprocess
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument('--npts', type=int, default=201,
help='Number of gridpoints per dimension.')
parser.add_argument('--target'... | {"hexsha": "17e7efd7f4fc01716cdf2e6e2bb61659678e5793", "size": 1345, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/scripts/eikonal2d_periodic.py", "max_stars_repo_name": "mcpca/fsm", "max_stars_repo_head_hexsha": "df4081fa0e595284ddbb1f30f20c5fb2063aa41f", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Testcase from example given in Mocking.jl's README
@testset "readme" begin
# Note: Function only works in UNIX environments.
function randdev(n::Integer)
@mock open("/dev/urandom") do fp
reverse(read(fp, n))
end
end
n = 10
if Sys.isunix()
result = randdev(n) #... | {"hexsha": "c6ad21d2610bf5868e5c7cd5026b3ab135b9beeb", "size": 1065, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/readme.jl", "max_stars_repo_name": "invenia/Patchwork.jl", "max_stars_repo_head_hexsha": "1eb1e7fb3783c0a78900d6880bcac368905a464f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 41, ... |
import pyrtools as pyr
import numpy
class FakeSFPyr(pyr.SFpyr):
def __init__(self, pyr, pind):
self.pyr = list()
self.pyrSize = pind
# decompose pyr vector into each bands
start = 0
for shape in pind:
ind = numpy.prod(shape)
self.pyr.append(pyr[star... | {"hexsha": "3167f427e2164b14ca66b2d26c2992f16cad2225", "size": 377, "ext": "py", "lang": "Python", "max_stars_repo_path": "texturesynth/fakesfpyr.py", "max_stars_repo_name": "tochikuji/pyTextureSynth", "max_stars_repo_head_hexsha": "6e1746fa1cc931ea083e3f04004a42a4894c762e", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import gym
from gym import wrappers
n_states = 40
max_episodes = 10000
initial_lr = 1.0 #Initial Learning rate
min_lr = 0.003
discount_factor = 1.0
max_iterations = 10000
eps = 0.02
env_name = 'MountainCar-v0'
env = gym.make(env_name)
env.seed(0)
np.random.seed(0)
q_table = np.zeros((n_states, n_sta... | {"hexsha": "cbe170ff2c5dd74b0db866aaf5a0e6be34700e3b", "size": 2733, "ext": "py", "lang": "Python", "max_stars_repo_path": "MountainCar-v0/mountainCar-v0.py", "max_stars_repo_name": "spirosbax/HittingTheGym", "max_stars_repo_head_hexsha": "9bac19cb159d8e62d9518ee3703e6f18d32e5cb3", "max_stars_repo_licenses": ["MIT"], "... |
%\documentclass[ebook,12pt,openany]{memoir} %ebook
\documentclass[ebook,12pt,openany,onesided]{memoir} %physical book
\usepackage[utf8x]{inputenc}
\usepackage[english]{babel}
\usepackage{url}
\usepackage{graphicx}
\usepackage{imakeidx} % for how to use the index see https://www.sharelatex.com/learn/Indices
\usepackage... | {"hexsha": "89180b261c73205cbbb89ccf1a38953fd5bf544c", "size": 1113, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "book6x9.tex", "max_stars_repo_name": "LafeLabs/trashmagic", "max_stars_repo_head_hexsha": "2eb29db9e29c6f90ac7cc7a0b477b0e00fec5eab", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null,... |
import TimeZones: TimeZone, localzone
import Compat: is_linux
# Ensure that the current system's local time zone is supported. If this test fails make
# sure to report it as an issue.
@test isa(localzone(), TimeZone)
if is_linux()
# Bad TZ environmental variables
withenv("TZ" => "") do
@test_throws E... | {"hexsha": "2178c48ee033c733781997acd9334558dadcf863", "size": 1647, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/local.jl", "max_stars_repo_name": "JuliaPackageMirrors/TimeZones.jl", "max_stars_repo_head_hexsha": "d997abb7398ca8514007d1cbc77a031f721c727b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright 2016-2019 David Robillard <d@drobilla.net>
# Copyright 2013 Kaspar Emanuel <kaspar.emanuel@gmail.com>
#
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in ... | {"hexsha": "3db89182ac4d77a0c5252df3aa6111b3e90c9d90", "size": 16224, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib-src/lv2/lilv/bindings/test/python/test_api.py", "max_stars_repo_name": "ECSE437-Audacity/audacity", "max_stars_repo_head_hexsha": "eff572509488f2df25f244f71cf45564b62027c0", "max_stars_repo_l... |
\documentclass[Physics.tex]{subfiles}
\begin{document}
\chapter{Lasers and Semiconductors}
\section{Lasers}
The word `\sldef{laser}' is an acronym meaning light amplification by stimulated emission of radiation. Light emitted from a laser is monochromatic, coherent, unidirectional and focused.
\subsection{Principl... | {"hexsha": "3f9e054c661218c9b5c5a2a2633ca0774f160846", "size": 10456, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "TeX/Physics/ch19_materials.tex", "max_stars_repo_name": "oliverli/A-Level-Notes", "max_stars_repo_head_hexsha": "5afdc9a71c37736aacf3ae1db9d0384cdb6a0348", "max_stars_repo_licenses": ["CC0-1.0"], "... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: load-cpm.py
# Author: Yuxin Wu
import argparse
import numpy as np
import cv2
import tensorflow as tf
from tensorpack import *
from tensorpack.utils import viz
from tensorpack.utils.argtools import memoized
"""
15 channels:
0-1 head, neck
2-4 right shoulder, righ... | {"hexsha": "0a5dd42e9266d4c405aea8ed1df2ec534d452c8b", "size": 3989, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/CaffeModels/load-cpm.py", "max_stars_repo_name": "Neovairis/tensorpack", "max_stars_repo_head_hexsha": "ca0969089847c37a893a8e99317214c5899278db", "max_stars_repo_licenses": ["Apache-2.0"... |
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