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S)�NumpyArrayWrapperap An object to be persisted instead of numpy arrays.
This object is used to hack into the pickle machinery and read numpy
array data from our custom persistence format.
More precisely, this object is used for:
* carrying the information of the persisted array: subclass, shape, order,
dtype. Those ndarray metadata are used to correctly reconstruct the array
with low level numpy functions.
* determining if memmap is allowed on the array.
* reading the array bytes from a file.
* reading the array using memorymap from a file.
* writing the array bytes to a file.
Attributes
----------
subclass: numpy.ndarray subclass
Determine the subclass of the wrapped array.
shape: numpy.ndarray shape
Determine the shape of the wrapped array.
order: {'C', 'F'}
Determine the order of wrapped array data. 'C' is for C order, 'F' is
for fortran order.
dtype: numpy.ndarray dtype
Determine the data type of the wrapped array.
allow_mmap: bool
Determine if memory mapping is allowed on the wrapped array.
Default: False.
Fc C s1 | | _ | | _ | | _ | | _ | | _ d S)z4Constructor. Store the useful information for later.N)�subclass�shape�order�dtype�
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S)z�Write array bytes to pickler file handle.
This function is an adaptation of the numpy write_array function
available in version 1.10.1 in numpy/lib/format.py.
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This function is an adaptation of the numpy read_array function
available in version 1.10.1 in numpy/lib/format.py.
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� | S)z!Read an array using numpy memmap.zw+zr+r r r �mode�offset) r' �tell� mmap_moder( �memmap�filenamer r r �seek�nbytes)r r8 r= Zmarrayr r r � read_mmap� s zNumpyArrayWrapper.read_mmapc C s� | j d k r* | j r* | j | � } n | j | � } t | d � r� | j | j j | j j f k r� | j j j
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Read the array corresponding to this wrapper.
Use the unpickler to get all information to correctly read the array.
Parameters
----------
unpickler: NumpyUnpickler
Returns
-------
array: numpy.ndarray
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__module__� __qualname__�__doc__r r, r; rD rI r r r r r " s 6 r c @ sL e Z d Z d Z e j j � Z d d d � Z d d � Z d d � Z d S) � NumpyPicklera� A pickler to persist big data efficiently.
The main features of this object are:
* persistence of numpy arrays in a single file.
* optional compression with a special care on avoiding memory copies.
Attributes
----------
fp: file
File object handle used for serializing the input object.
protocol: int
Pickle protocol used. Default is pickle.DEFAULT_PROTOCOL under
python 3, pickle.HIGHEST_PROTOCOL otherwise.
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zNumpyPickler.__init__c C sg | j j r | j j r d n d } | j o9 | j j } t t | � | j | | j d | �} | S)z<Create and returns a numpy array wrapper from a numpy array.r. r! r ) r � f_contiguous� c_contiguousr r r$ r �typer )r r* r r �wrapperr r r �_create_array_wrapper� s z"NumpyPickler._create_array_wrapperc C s� | j d k r� t | � | j j | j j | j j f k r� t | � | j j k rf | j j | � } | j | � } t j | | � | j d k r� | j
j d d � | j | | � d St j | | � S)a� Subclass the Pickler `save` method.
This is a total abuse of the Pickler class in order to use the numpy
persistence function `save` instead of the default pickle
implementation. The numpy array is replaced by a custom wrapper in the
pickle persistence stack and the serialized array is written right
after in the file. Warning: the file produced does not follow the
pickle format. As such it can not be read with `pickle.load`.
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j <d S)�NumpyUnpicklera� A subclass of the Unpickler to unpickle our numpy pickles.
Attributes
----------
mmap_mode: str
The memorymap mode to use for reading numpy arrays.
file_handle: file_like
File object to unpickle from.
filename: str
Name of the file to unpickle from. It should correspond to file_handle.
This parameter is required when using mmap_mode.
np: module
Reference to numpy module if numpy is installed else None.
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| j | � � d S)aO Called to set the state of a newly created object.
We capture it to replace our place-holder objects, NDArrayWrapper or
NumpyArrayWrapper, by the array we are interested in. We
replace them directly in the stack of pickler.
NDArrayWrapper is used for backward compatibility with joblib <= 0.9.
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d � r�d } n d } | t k r�| d k r�d } t r)| d k r)t d j | t j d t j d � � � | d k rTt j d j | � t d d �| d k r�t | d | | f �� } t | d | �j | � Wd QRXnT | r�t | d � � } t | d | �j | � Wd QRXn t | d | �j | � | r�d S| g S) a� Persist an arbitrary Python object into one file.
Parameters
-----------
value: any Python object
The object to store to disk.
filename: str or pathlib.Path
The path of the file in which it is to be stored. The compression
method corresponding to one of the supported filename extensions ('.z',
'.gz', '.bz2', '.xz' or '.lzma') will be used automatically.
compress: int from 0 to 9 or bool or 2-tuple, optional
Optional compression level for the data. 0 or False is no compression.
Higher value means more compression, but also slower read and
write times. Using a value of 3 is often a good compromise.
See the notes for more details.
If compress is True, the compression level used is 3.
If compress is a 2-tuple, the first element must correspond to a string
between supported compressors (e.g 'zlib', 'gzip', 'bz2', 'lzma'
'xz'), the second element must be an integer from 0 to 9, corresponding
to the compression level.
protocol: positive int
Pickle protocol, see pickle.dump documentation for more details.
cache_size: positive int, optional
This option is deprecated in 0.10 and has no effect.
Returns
-------
filenames: list of strings
The list of file names in which the data is stored. If
compress is false, each array is stored in a different file.
See Also
--------
joblib.load : corresponding loader
Notes
-----
Memmapping on load cannot be used for compressed files. Thus
using compression can significantly slow down loading. In
addition, compressed files take extra extra memory during
dump and load.
Nr) �zlibT� r zlCompress argument tuple should contain exactly 2 elements: (compress method, compress level), you passed {0}F�
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r� } z) t r� t d � } | | _ | � � WYd d } ~ Xn X| S)z Internal unpickling function.r? NzjThe file '%s' has been generated with a joblib version less than 0.10. Please regenerate this pickle file.rn ri zyYou may be trying to read with python 3 a joblib pickle generated with python 2. This feature is not supported by joblib.)
r_ r2 rc rz r{ r| �UnicodeDecodeErrorr rs � __cause__)�fobjrA r? r8 r\ �exc�new_excr r r � _unpickle� s r� c C s� t d k r' t | t � r' t | � } t | d � rp t | d � rp t | d | � � } t | � } Wd QRXn` t | d � �N } t | | | � �2 } t | t � r� t | � St | | | � } Wd QRXWd QRX| S)a Reconstruct a Python object from a file persisted with joblib.dump.
Parameters
-----------
filename: str or pathlib.Path
The path of the file from which to load the object
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
If not None, the arrays are memory-mapped from the disk. This
mode has no effect for compressed files. Note that in this
case the reconstructed object might not longer match exactly
the originally pickled object.
Returns
-------
result: any Python object
The object stored in the file.
See Also
--------
joblib.dump : function to save an object
Notes
-----
This function can load numpy array files saved separately during the
dump. If the mmap_mode argument is given, it is passed to np.load and
arrays are loaded as memmaps. As a consequence, the reconstructed
object might not match the original pickled object. Note that if the
file was saved with compression, the arrays cannot be memmaped.
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