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Repository: kwikteam/npy-matlab
Branch: master
Commit: 7e61a884f7f5
Files: 54
Total size: 2.4 MB

Directory structure:
gitextract_utzabjxi/

├── .gitignore
├── .travis.yml
├── LICENSE
├── README.md
├── examples/
│   └── exampleMemmap.m
├── npy-matlab/
│   ├── constructNPYheader.m
│   ├── datToNPY.m
│   ├── readNPY.m
│   ├── readNPYheader.m
│   └── writeNPY.m
└── tests/
    ├── data/
    │   ├── chelsea_float32.npy
    │   ├── chelsea_float64.npy
    │   ├── chelsea_int16.npy
    │   ├── chelsea_int32.npy
    │   ├── chelsea_int64.npy
    │   ├── chelsea_int8.npy
    │   ├── chelsea_uint16.npy
    │   ├── chelsea_uint32.npy
    │   ├── chelsea_uint64.npy
    │   ├── chelsea_uint8.npy
    │   ├── matlab_chelsea_float32.npy
    │   ├── matlab_chelsea_float64.npy
    │   ├── matlab_chelsea_int16.npy
    │   ├── matlab_chelsea_int32.npy
    │   ├── matlab_chelsea_int64.npy
    │   ├── matlab_chelsea_int8.npy
    │   ├── matlab_chelsea_uint16.npy
    │   ├── matlab_chelsea_uint32.npy
    │   ├── matlab_chelsea_uint64.npy
    │   ├── matlab_chelsea_uint8.npy
    │   ├── matlab_sine_float32.npy
    │   ├── matlab_sine_float64.npy
    │   ├── matlab_sine_int16.npy
    │   ├── matlab_sine_int32.npy
    │   ├── matlab_sine_int64.npy
    │   ├── matlab_sine_int8.npy
    │   ├── matlab_sine_uint16.npy
    │   ├── matlab_sine_uint32.npy
    │   ├── matlab_sine_uint64.npy
    │   ├── matlab_sine_uint8.npy
    │   ├── sine_float32.npy
    │   ├── sine_float64.npy
    │   ├── sine_int16.npy
    │   ├── sine_int32.npy
    │   ├── sine_int64.npy
    │   ├── sine_int8.npy
    │   ├── sine_uint16.npy
    │   ├── sine_uint32.npy
    │   ├── sine_uint64.npy
    │   ├── sine_uint8.npy
    │   └── test.npy
    ├── npy.ipynb
    ├── test_npy_roundtrip.py
    └── test_readNPY.m

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
*.asv
.ipynb_checkpoints/
__pycache__


================================================
FILE: .travis.yml
================================================
# Using several other .travis.yml files as inspiration. See for example:
# https://github.com/MOxUnit/MOxUnit
# https://github.com/scottclowe/matlab-continuous-integration/
# https://github.com/fieldtrip/fieldtrip/blob/master/.travis.yml

language: python

cache:
    - apt

before_install:
    # to prevent IPv6 being used for APT
    - sudo bash -c "echo 'Acquire::ForceIPv4 \"true\";' > /etc/apt/apt.conf.d/99force-ipv4"
    - travis_retry sudo apt-get -y -qq update
    - travis_retry sudo apt-get install -y -qq software-properties-common python-software-properties
    - travis_retry sudo apt-add-repository -y ppa:octave/stable
    - travis_retry sudo apt-get -y -qq update
    # get Octave 4.0
    - travis_retry sudo apt-get -y -qq install octave liboctave-dev

    # Get conda
    - wget -q http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
    - chmod +x miniconda.sh
    - ./miniconda.sh -b -p /home/travis/miniconda
    - export PATH=/home/travis/miniconda/bin:$PATH
    - conda update --yes --quiet conda

install:
    - conda create -n testenv --yes python=3.6
    - source activate testenv
    - conda install --yes --quiet numpy
    - conda install --yes -c conda-forge scikit-image
    - pip install pytest==3.3.2 pytest-sugar

script:
    - echo "Octave version:"
    - octave --no-gui --eval "version()"
    - pytest


================================================
FILE: LICENSE
================================================
BSD 2-Clause License

Copyright (c) 2015, npy-matlab developers
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


================================================
FILE: README.md
================================================
[![Travis](https://api.travis-ci.org/kwikteam/npy-matlab.svg?branch=master "Travis")](https://travis-ci.org/kwikteam/npy-matlab)
# npy-matlab

Code to read and write NumPy's NPY format (`.npy` files) in MATLAB.

This is experimental code and still work in progress. For example, this code:
- Only reads a subset of all possible NPY files, specifically N-D arrays of
  certain data types.
- Only writes little endian, fortran (column-major) ordering
- Only writes with NPY version number 1.0.
- Always outputs a shape according to matlab's convention, e.g. (10, 1)
  rather than (10,).

Feel free to open an issue and/or send a pull request for improving the
state of this project!

For the complete specification of the NPY format, see the [NumPy documentation](https://www.numpy.org/devdocs/reference/generated/numpy.lib.format.html).

## Installation
After downloading npy-matlab as a zip file or via git, just add the
npy-matlab directory to your search path:

```matlab
>> addpath('my-idiosyncratic-path/npy-matlab/npy-matlab')  
>> savepath
```

## Usage example
```matlab
>> a = rand(5,4,3);
>> writeNPY(a, 'a.npy');
>> b = readNPY('a.npy');
>> sum(a(:)==b(:))
ans =

    60
```

## Tests
Roundtrip testing is performed using Travis CI and GNU Octave, see
the `.travis.yml` file and `tests/test_npy_roundtrip.py`.

You can also use two "manual testing scripts":

- See `tests/npy.ipynb` for Python tests.
- See `tests/test_readNPY.m` for MATLAB reading/writing tests.

## Memory mapping npy files
See `examples/exampleMemmap.m` for an example of how to memory map a `.npy` file in MATLAB, which is not trivial when the file uses C-ordering (i.e., row-major order) rather than Fortran-ordering (i.e., column-major ordering). MATLAB's memory mapping only supports Fortran-ordering, but Python's default is C-ordering so `.npy` files created with Python defaults are not straightforward to read in MATLAB.


================================================
FILE: examples/exampleMemmap.m
================================================


% Example implementation of memory mapping an NPY file using readNPYheader

filename = 'data/chelsea_float64.npy';

[arrayShape, dataType, fortranOrder, littleEndian, totalHeaderLength, npyVersion] = readNPYheader(filename);

figure;

if fortranOrder
    f = memmapfile(filename, 'Format', {dataType, arrayShape, 'd'}, 'Offset', totalHeaderLength);
    image(f.Data.d)
    
else
    % Note! In this case, the dimensions of the array will be transposed,
    % e.g. an AxBxCxD array becomes DxCxBxA. 
    f = memmapfile(filename, 'Format', {dataType, arrayShape(end:-1:1), 'd'}, 'Offset', totalHeaderLength);
    
    tmp = f.Data.d;
    img = permute(tmp, length(arrayShape):-1:1); % note here you have to reverse the dimensions. 
    image(img./255)
end

================================================
FILE: npy-matlab/constructNPYheader.m
================================================



function header = constructNPYheader(dataType, shape, varargin)

	if ~isempty(varargin)
		fortranOrder = varargin{1}; % must be true/false
		littleEndian = varargin{2}; % must be true/false
	else
		fortranOrder = true;
		littleEndian = true;
	end

    dtypesMatlab = {'uint8','uint16','uint32','uint64','int8','int16','int32','int64','single','double', 'logical'};
    dtypesNPY = {'u1', 'u2', 'u4', 'u8', 'i1', 'i2', 'i4', 'i8', 'f4', 'f8', 'b1'};

    magicString = uint8([147 78 85 77 80 89]); %x93NUMPY
    
    majorVersion = uint8(1);
    minorVersion = uint8(0);

    % build the dict specifying data type, array order, endianness, and
    % shape
    dictString = '{''descr'': ''';
    
    if littleEndian
        dictString = [dictString '<'];
    else
        dictString = [dictString '>'];
    end
    
    dictString = [dictString dtypesNPY{strcmp(dtypesMatlab,dataType)} ''', '];
    
    dictString = [dictString '''fortran_order'': '];
    
    if fortranOrder
        dictString = [dictString 'True, '];
    else
        dictString = [dictString 'False, '];
    end
    
    dictString = [dictString '''shape'': ('];
    
%     if length(shape)==1 && shape==1
%         
%     else
%         for s = 1:length(shape)
%             if s==length(shape) && shape(s)==1
%                 
%             else
%                 dictString = [dictString num2str(shape(s))];
%                 if length(shape)>1 && s+1==length(shape) && shape(s+1)==1
%                     dictString = [dictString ','];
%                 elseif length(shape)>1 && s<length(shape)
%                     dictString = [dictString ', '];
%                 end            
%             end
%         end
%         if length(shape)==1
%             dictString = [dictString ','];
%         end
%     end

    for s = 1:length(shape)
        dictString = [dictString num2str(shape(s))];
        if s<length(shape)
            dictString = [dictString ', '];
        end
    end
    
    dictString = [dictString '), '];
    
    dictString = [dictString '}'];
    
    totalHeaderLength = length(dictString)+10; % 10 is length of magicString, version, and headerLength
    
    headerLengthPadded = ceil(double(totalHeaderLength+1)/16)*16; % the whole thing should be a multiple of 16
                                                                  % I add 1 to the length in order to allow for the newline character

	% format specification is that headerlen is little endian. I believe it comes out so using this command...
    headerLength = typecast(int16(headerLengthPadded-10), 'uint8');
	
    zeroPad = zeros(1,headerLengthPadded-totalHeaderLength, 'uint8')+uint8(32); % +32 so they are spaces
    zeroPad(end) = uint8(10); % newline character
    
    header = uint8([magicString majorVersion minorVersion headerLength dictString zeroPad]);

end

================================================
FILE: npy-matlab/datToNPY.m
================================================


function datToNPY(inFilename, outFilename, dataType, shape, varargin)
% function datToNPY(inFilename, outFilename, shape, dataType, [fortranOrder, littleEndian])
%
% make a NPY file from a flat binary file, given that you know the shape,
% dataType, ordering, and endianness of the flat binary file. 
% 
% The point here is you don't want to read in all the data from the
% existing binary file - instead you can just create the appropriate header
% and then concatenate it with the data. 
%
% ** completely untested

if ~isempty(varargin)
    fortranOrder = varargin{1}; % must be true/false
    littleEndian = varargin{2}; % must be true/false
else
    fortranOrder = true;
    littleEndian = true;
end

header = constructNPYheader(dataType, shape, fortranOrder, littleEndian);

% ** TODO: need to put the header into a temp file instead, in case the
% outFilename is the same as the inFilename (and then delete the temp file
% later)
fid = fopen(tempFilename, 'w');
fwrite(fid, header, 'uint8');
fclose(fid)

str = computer;
switch str
    case {'PCWIN', 'PCWIN64'}
        [~,~] = system(sprintf('copy /b %s+%s %s', tempFilename, inFilename, outFilename));
    case {'GLNXA64', 'MACI64'}
        [~,~] = system(sprintf('cat %s %s > %s', tempFilename, inFilename, outFilename));
        
    otherwise
        fprintf(1, 'I don''t know how to concatenate files for your OS, but you can finish making the NPY youself by concatenating %s with %s.\n', tempFilename, inFilename);
end
    


================================================
FILE: npy-matlab/readNPY.m
================================================


function data = readNPY(filename)
% Function to read NPY files into matlab.
% *** Only reads a subset of all possible NPY files, specifically N-D arrays of certain data types.
% See https://github.com/kwikteam/npy-matlab/blob/master/tests/npy.ipynb for
% more.
%

[shape, dataType, fortranOrder, littleEndian, totalHeaderLength, ~] = readNPYheader(filename);

if littleEndian
    fid = fopen(filename, 'r', 'l');
else
    fid = fopen(filename, 'r', 'b');
end

try

    [~] = fread(fid, totalHeaderLength, 'uint8');

    % read the data
    data = fread(fid, prod(shape), [dataType '=>' dataType]);

    if length(shape)>1 && ~fortranOrder
        data = reshape(data, shape(end:-1:1));
        data = permute(data, [length(shape):-1:1]);
    elseif length(shape)>1
        data = reshape(data, shape);
    end

    fclose(fid);

catch me
    fclose(fid);
    rethrow(me);
end


================================================
FILE: npy-matlab/readNPYheader.m
================================================


function [arrayShape, dataType, fortranOrder, littleEndian, totalHeaderLength, npyVersion] = readNPYheader(filename)
% function [arrayShape, dataType, fortranOrder, littleEndian, ...
%       totalHeaderLength, npyVersion] = readNPYheader(filename)
%
% parse the header of a .npy file and return all the info contained
% therein.
%
% Based on spec at http://docs.scipy.org/doc/numpy-dev/neps/npy-format.html

fid = fopen(filename);

% verify that the file exists
if (fid == -1)
    if ~isempty(dir(filename))
        error('Permission denied: %s', filename);
    else
        error('File not found: %s', filename);
    end
end

try
    
    dtypesMatlab = {'uint8','uint16','uint32','uint64','int8','int16','int32','int64','single','double', 'logical'};
    dtypesNPY = {'u1', 'u2', 'u4', 'u8', 'i1', 'i2', 'i4', 'i8', 'f4', 'f8', 'b1'};
    
    
    magicString = fread(fid, [1 6], 'uint8=>uint8');
    
    if ~all(magicString == [147,78,85,77,80,89])
        error('readNPY:NotNUMPYFile', 'Error: This file does not appear to be NUMPY format based on the header.');
    end
    
    majorVersion = fread(fid, [1 1], 'uint8=>uint8');
    minorVersion = fread(fid, [1 1], 'uint8=>uint8');
    
    npyVersion = [majorVersion minorVersion];
    
    headerLength = fread(fid, [1 1], 'uint16=>uint16');
    
    totalHeaderLength = 10+headerLength;
    
    arrayFormat = fread(fid, [1 headerLength], 'char=>char');
    
    % to interpret the array format info, we make some fairly strict
    % assumptions about its format...
    
    r = regexp(arrayFormat, '''descr''\s*:\s*''(.*?)''', 'tokens');
    if isempty(r)
        error('Couldn''t parse array format: "%s"', arrayFormat);
    end
    dtNPY = r{1}{1};    
    
    littleEndian = ~strcmp(dtNPY(1), '>');
    
    dataType = dtypesMatlab{strcmp(dtNPY(2:3), dtypesNPY)};
        
    r = regexp(arrayFormat, '''fortran_order''\s*:\s*(\w+)', 'tokens');
    fortranOrder = strcmp(r{1}{1}, 'True');
    
    r = regexp(arrayFormat, '''shape''\s*:\s*\((.*?)\)', 'tokens');
    shapeStr = r{1}{1}; 
    arrayShape = str2num(shapeStr(shapeStr~='L'));

    
    fclose(fid);
    
catch me
    fclose(fid);
    rethrow(me);
end


================================================
FILE: npy-matlab/writeNPY.m
================================================


function writeNPY(var, filename)
% function writeNPY(var, filename)
%
% Only writes little endian, fortran (column-major) ordering; only writes
% with NPY version number 1.0.
%
% Always outputs a shape according to matlab's convention, e.g. (10, 1)
% rather than (10,).


shape = size(var);
dataType = class(var);

header = constructNPYheader(dataType, shape);

fid = fopen(filename, 'w');
fwrite(fid, header, 'uint8');
fwrite(fid, var, dataType);
fclose(fid);


end



================================================
FILE: tests/npy.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NPY format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some links:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [NPY Spec](http://docs.scipy.org/doc/numpy-dev/neps/npy-format.html)\n",
    "* [C++ library](https://github.com/rogersce/cnpy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scope\n",
    "\n",
    "The initial MATLAB implementation could have the following limitations:\n",
    "\n",
    "* **Supported data types**: `bool`, `[u]int[8|16|32|64]`, `float32`, `float64` (no strings for now?). **No support for record arrays**.\n",
    "* **C order only**: an error is raised if FORTRAN order is used.\n",
    "* **Arbitrary number of dimensions**: if a generic implementation supporting ndarrays is not possible, then it is okay to only support 1, 2, and 3 dimensions, and raise an error if there are more dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a few sample datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset 1: 1D sine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 10000\n",
    "t = np.linspace(-10., 10., n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sine = (1 + np.sin(t)) * 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000L,), dtype('float64'))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sine.shape, sine.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xab5e3c8>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2MGxYem7KPN6QkC6vMI8eIaSrjYJRYI8eydoRN2F1wKkQd0jXTbl0qZsnyBtp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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab35860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, sine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset 2: 2D image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import skimage.data\n",
    "chelsea = skimage.data.chelsea()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300L, 451L, 3L)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chelsea.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chelsea.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[143, 120, 104],\n",
       "        [143, 120, 104],\n",
       "        [141, 118, 102]],\n",
       "\n",
       "       [[146, 123, 107],\n",
       "        [145, 122, 106],\n",
       "        [143, 120, 104]],\n",
       "\n",
       "       [[148, 126, 112],\n",
       "        [147, 125, 111],\n",
       "        [146, 122, 109]]], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chelsea[:3,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb0cb1d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CVBM5pgKexYC5dFjZRSopBZScKGxjnjCaqO3iBlUhLEWZrJlEsfyIlaXYUzzt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/Ijy3wmLn5N0xaPBl0ES1VRMLWIRmXpJGNKFJ+wBtpa0VRgSLLlNoZcJmJc8FMQZacGrSLaBOZw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hE4Ll5794xbObC7744iXjOPFwe0/TWsK7hc3qiqvLK1zT4jpLSQuFSy42a148u+YnMfK4PfDV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R71kHI6YX0KAzleKLkiacX5wuPJE/wHhrDw9WVhPb/xo2y4Hz7z8HBUtz5nGY8zNasPSNoS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\nnr89Yd1AmAdVG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Download .txt
gitextract_utzabjxi/

├── .gitignore
├── .travis.yml
├── LICENSE
├── README.md
├── examples/
│   └── exampleMemmap.m
├── npy-matlab/
│   ├── constructNPYheader.m
│   ├── datToNPY.m
│   ├── readNPY.m
│   ├── readNPYheader.m
│   └── writeNPY.m
└── tests/
    ├── data/
    │   ├── chelsea_float32.npy
    │   ├── chelsea_float64.npy
    │   ├── chelsea_int16.npy
    │   ├── chelsea_int32.npy
    │   ├── chelsea_int64.npy
    │   ├── chelsea_int8.npy
    │   ├── chelsea_uint16.npy
    │   ├── chelsea_uint32.npy
    │   ├── chelsea_uint64.npy
    │   ├── chelsea_uint8.npy
    │   ├── matlab_chelsea_float32.npy
    │   ├── matlab_chelsea_float64.npy
    │   ├── matlab_chelsea_int16.npy
    │   ├── matlab_chelsea_int32.npy
    │   ├── matlab_chelsea_int64.npy
    │   ├── matlab_chelsea_int8.npy
    │   ├── matlab_chelsea_uint16.npy
    │   ├── matlab_chelsea_uint32.npy
    │   ├── matlab_chelsea_uint64.npy
    │   ├── matlab_chelsea_uint8.npy
    │   ├── matlab_sine_float32.npy
    │   ├── matlab_sine_float64.npy
    │   ├── matlab_sine_int16.npy
    │   ├── matlab_sine_int32.npy
    │   ├── matlab_sine_int64.npy
    │   ├── matlab_sine_int8.npy
    │   ├── matlab_sine_uint16.npy
    │   ├── matlab_sine_uint32.npy
    │   ├── matlab_sine_uint64.npy
    │   ├── matlab_sine_uint8.npy
    │   ├── sine_float32.npy
    │   ├── sine_float64.npy
    │   ├── sine_int16.npy
    │   ├── sine_int32.npy
    │   ├── sine_int64.npy
    │   ├── sine_int8.npy
    │   ├── sine_uint16.npy
    │   ├── sine_uint32.npy
    │   ├── sine_uint64.npy
    │   ├── sine_uint8.npy
    │   └── test.npy
    ├── npy.ipynb
    ├── test_npy_roundtrip.py
    └── test_readNPY.m
Download .txt
SYMBOL INDEX (2 symbols across 1 files)

FILE: tests/test_npy_roundtrip.py
  function _save_testing_data (line 23) | def _save_testing_data():
  function test_roundtrip (line 62) | def test_roundtrip():
Condensed preview — 54 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,510K chars).
[
  {
    "path": ".gitignore",
    "chars": 38,
    "preview": "*.asv\n.ipynb_checkpoints/\n__pycache__\n"
  },
  {
    "path": ".travis.yml",
    "chars": 1371,
    "preview": "# Using several other .travis.yml files as inspiration. See for example:\n# https://github.com/MOxUnit/MOxUnit\n# https://"
  },
  {
    "path": "LICENSE",
    "chars": 1331,
    "preview": "BSD 2-Clause License\n\nCopyright (c) 2015, npy-matlab developers\nAll rights reserved.\n\nRedistribution and use in source a"
  },
  {
    "path": "README.md",
    "chars": 1909,
    "preview": "[![Travis](https://api.travis-ci.org/kwikteam/npy-matlab.svg?branch=master \"Travis\")](https://travis-ci.org/kwikteam/npy"
  },
  {
    "path": "examples/exampleMemmap.m",
    "chars": 755,
    "preview": "\n\n% Example implementation of memory mapping an NPY file using readNPYheader\n\nfilename = 'data/chelsea_float64.npy';\n\n[a"
  },
  {
    "path": "npy-matlab/constructNPYheader.m",
    "chars": 2844,
    "preview": "\n\n\nfunction header = constructNPYheader(dataType, shape, varargin)\n\n\tif ~isempty(varargin)\n\t\tfortranOrder = varargin{1};"
  },
  {
    "path": "npy-matlab/datToNPY.m",
    "chars": 1490,
    "preview": "\n\nfunction datToNPY(inFilename, outFilename, dataType, shape, varargin)\n% function datToNPY(inFilename, outFilename, sha"
  },
  {
    "path": "npy-matlab/readNPY.m",
    "chars": 878,
    "preview": "\n\nfunction data = readNPY(filename)\n% Function to read NPY files into matlab.\n% *** Only reads a subset of all possible "
  },
  {
    "path": "npy-matlab/readNPYheader.m",
    "chars": 2181,
    "preview": "\n\nfunction [arrayShape, dataType, fortranOrder, littleEndian, totalHeaderLength, npyVersion] = readNPYheader(filename)\n%"
  },
  {
    "path": "npy-matlab/writeNPY.m",
    "chars": 470,
    "preview": "\n\nfunction writeNPY(var, filename)\n% function writeNPY(var, filename)\n%\n% Only writes little endian, fortran (column-maj"
  },
  {
    "path": "tests/npy.ipynb",
    "chars": 2458493,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# NPY format\"\n   ]\n  },\n  {\n   \"cel"
  },
  {
    "path": "tests/test_npy_roundtrip.py",
    "chars": 2925,
    "preview": "\"\"\"Roundtrip testing of npy-matlab.\n\nWrite data in NPY format to be read and rewritten using npy-matlab. Then\nre-read th"
  },
  {
    "path": "tests/test_readNPY.m",
    "chars": 1303,
    "preview": "\n\n%% Test the readNPY function with given data\ndtypes = {'uint8','uint16','uint32','uint64','int8','int16','int32','int6"
  }
]

// ... and 41 more files (download for full content)

About this extraction

This page contains the full source code of the kwikteam/npy-matlab GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 54 files (2.4 MB), approximately 620.1k tokens, and a symbol index with 2 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

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