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"""
Define a simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able create a solution in his preferred programming language to
read most ``.npy`` files that he has been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmep`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total length of
``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment
purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Notes
-----
The ``.npy`` format, including reasons for creating it and a comparison of
alternatives, is described fully in the "npy-format" NEP.
"""
import cPickle
import numpy
from numpy.lib.utils import safe_eval
MAGIC_PREFIX = '\x93NUMPY'
MAGIC_LEN = len(MAGIC_PREFIX) + 2
def magic(major, minor):
""" Return the magic string for the given file format version.
Parameters
----------
major : int in [0, 255]
minor : int in [0, 255]
Returns
-------
magic : str
Raises
------
ValueError if the version cannot be formatted.
"""
if major < 0 or major > 255:
raise ValueError("major version must be 0 <= major < 256")
if minor < 0 or minor > 255:
raise ValueError("minor version must be 0 <= minor < 256")
return '%s%s%s' % (MAGIC_PREFIX, chr(major), chr(minor))
def read_magic(fp):
""" Read the magic string to get the version of the file format.
Parameters
----------
fp : filelike object
Returns
-------
major : int
minor : int
"""
magic_str = fp.read(MAGIC_LEN)
if len(magic_str) != MAGIC_LEN:
msg = "could not read %d characters for the magic string; got %r"
raise ValueError(msg % (MAGIC_LEN, magic_str))
if magic_str[:-2] != MAGIC_PREFIX:
msg = "the magic string is not correct; expected %r, got %r"
raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2]))
major, minor = map(ord, magic_str[-2:])
return major, minor
def dtype_to_descr(dtype):
"""
Get a serializable descriptor from the dtype.
The .descr attribute of a dtype object cannot be round-tripped through
the dtype() constructor. Simple types, like dtype('float32'), have
a descr which looks like a record array with one field with '' as
a name. The dtype() constructor interprets this as a request to give
a default name. Instead, we construct descriptor that can be passed to
dtype().
Parameters
----------
dtype : dtype
The dtype of the array that will be written to disk.
Returns
-------
descr : object
An object that can be passed to `numpy.dtype()` in order to
replicate the input dtype.
"""
if dtype.names is not None:
# This is a record array. The .descr is fine.
# XXX: parts of the record array with an empty name, like padding bytes,
# still get fiddled with. This needs to be fixed in the C implementation
# of dtype().
return dtype.descr
else:
return dtype.str
def header_data_from_array_1_0(array):
""" Get the dictionary of header metadata from a numpy.ndarray.
Parameters
----------
array : numpy.ndarray
Returns
-------
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
"""
d = {}
d['shape'] = array.shape
if array.flags.c_contiguous:
d['fortran_order'] = False
elif array.flags.f_contiguous:
d['fortran_order'] = True
else:
# Totally non-contiguous data. We will have to make it C-contiguous
# before writing. Note that we need to test for C_CONTIGUOUS first
# because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS.
d['fortran_order'] = False
d['descr'] = dtype_to_descr(array.dtype)
return d
def write_array_header_1_0(fp, d):
""" Write the header for an array using the 1.0 format.
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
"""
import struct
header = ["{"]
for key, value in sorted(d.items()):
# Need to use repr here, since we eval these when reading
header.append("'%s': %s, " % (key, repr(value)))
header.append("}")
header = "".join(header)
# Pad the header with spaces and a final newline such that the magic
# string, the header-length short and the header are aligned on a 16-byte
# boundary. Hopefully, some system, possibly memory-mapping, can take
# advantage of our premature optimization.
current_header_len = MAGIC_LEN + 2 + len(header) + 1 # 1 for the newline
topad = 16 - (current_header_len % 16)
header = '%s%s\n' % (header, ' '*topad)
if len(header) >= (256*256):
raise ValueError("header does not fit inside %s bytes" % (256*256))
header_len_str = struct.pack('<H', len(header))
fp.write(header_len_str)
fp.write(header)
def read_array_header_1_0(fp):
"""
Read an array header from a filelike object using the 1.0 file format
version.
This will leave the file object located just after the header.
Parameters
----------
fp : filelike object
A file object or something with a `.read()` method like a file.
Returns
-------
shape : tuple of int
The shape of the array.
fortran_order : bool
The array data will be written out directly if it is either C-contiguous
or Fortran-contiguous. Otherwise, it will be made contiguous before
writing it out.
dtype : dtype
The dtype of the file's data.
Raises
------
ValueError :
If the data is invalid.
"""
# Read an unsigned, little-endian short int which has the length of the
# header.
import struct
hlength_str = fp.read(2)
if len(hlength_str) != 2:
msg = "EOF at %s before reading array header length"
raise ValueError(msg % fp.tell())
header_length = struct.unpack('<H', hlength_str)[0]
header = fp.read(header_length)
if len(header) != header_length:
raise ValueError("EOF at %s before reading array header" % fp.tell())
# The header is a pretty-printed string representation of a literal Python
# dictionary with trailing newlines padded to a 16-byte boundary. The keys
# are strings.
# "shape" : tuple of int
# "fortran_order" : bool
# "descr" : dtype.descr
try:
d = safe_eval(header)
except SyntaxError, e:
msg = "Cannot parse header: %r\nException: %r"
raise ValueError(msg % (header, e))
if not isinstance(d, dict):
msg = "Header is not a dictionary: %r"
raise ValueError(msg % d)
keys = d.keys()
keys.sort()
if keys != ['descr', 'fortran_order', 'shape']:
msg = "Header does not contain the correct keys: %r"
raise ValueError(msg % (keys,))
# Sanity-check the values.
if (not isinstance(d['shape'], tuple) or
not numpy.all([isinstance(x, (int,long)) for x in d['shape']])):
msg = "shape is not valid: %r"
raise ValueError(msg % (d['shape'],))
if not isinstance(d['fortran_order'], bool):
msg = "fortran_order is not a valid bool: %r"
raise ValueError(msg % (d['fortran_order'],))
try:
dtype = numpy.dtype(d['descr'])
except TypeError, e:
msg = "descr is not a valid dtype descriptor: %r"
raise ValueError(msg % (d['descr'],))
return d['shape'], d['fortran_order'], dtype
def write_array(fp, array, version=(1,0)):
"""
Write an array to an NPY file, including a header.
If the array is neither C-contiguous or Fortran-contiguous AND if the
filelike object is not a real file object, then this function will have
to copy data in memory.
Parameters
----------
fp : filelike object
An open, writable file object or similar object with a `.write()`
method.
array : numpy.ndarray
The array to write to disk.
version : (int, int), optional
The version number of the format.
Raises
------
ValueError
If the array cannot be persisted.
Various other errors
If the array contains Python objects as part of its dtype, the
process of pickling them may raise arbitrary errors if the objects
are not picklable.
"""
if version != (1, 0):
msg = "we only support format version (1,0), not %s"
raise ValueError(msg % (version,))
fp.write(magic(*version))
write_array_header_1_0(fp, header_data_from_array_1_0(array))
if array.dtype.hasobject:
# We contain Python objects so we cannot write out the data directly.
# Instead, we will pickle it out with version 2 of the pickle protocol.
cPickle.dump(array, fp, protocol=2)
elif array.flags.f_contiguous and not array.flags.c_contiguous:
# Use a suboptimal, possibly memory-intensive, but correct way to
# handle Fortran-contiguous arrays.
fp.write(array.data)
else:
if isinstance(fp, file):
array.tofile(fp)
else:
# XXX: We could probably chunk this using something like
# arrayterator.
fp.write(array.tostring('C'))
def read_array(fp):
"""
Read an array from an NPY file.
Parameters
----------
fp : filelike object
If this is not a real file object, then this may take extra memory and
time.
Returns
-------
array : numpy.ndarray
The array from the data on disk.
Raises
------
ValueError
If the data is invalid.
"""
version = read_magic(fp)
if version != (1, 0):
msg = "only support version (1,0) of file format, not %r"
raise ValueError(msg % (version,))
shape, fortran_order, dtype = read_array_header_1_0(fp)
if len(shape) == 0:
count = 1
else:
count = numpy.multiply.reduce(shape)
# Now read the actual data.
if dtype.hasobject:
# The array contained Python objects. We need to unpickle the data.
array = cPickle.load(fp)
else:
if isinstance(fp, file):
# We can use the fast fromfile() function.
array = numpy.fromfile(fp, dtype=dtype, count=count)
else:
# This is not a real file. We have to read it the memory-intensive
# way.
# XXX: we can probably chunk this to avoid the memory hit.
data = fp.read(count * dtype.itemsize)
array = numpy.fromstring(data, dtype=dtype, count=count)
if fortran_order:
array.shape = shape[::-1]
array = array.transpose()
else:
array.shape = shape
return array
def open_memmap(filename, mode='r+', dtype=None, shape=None,
fortran_order=False, version=(1,0)):
"""
Open a .npy file as a memory-mapped array.
This may be used to read an existing file or create a new one.
Parameters
----------
filename : str
The name of the file on disk. This may not be a file-like object.
mode : str, optional
The mode to open the file with. In addition to the standard file modes,
'c' is also accepted to mean "copy on write". See `numpy.memmap` for
the available mode strings.
dtype : dtype, optional
The data type of the array if we are creating a new file in "write"
mode.
shape : tuple of int, optional
The shape of the array if we are creating a new file in "write"
mode.
fortran_order : bool, optional
Whether the array should be Fortran-contiguous (True) or
C-contiguous (False) if we are creating a new file in "write" mode.
version : tuple of int (major, minor)
If the mode is a "write" mode, then this is the version of the file
format used to create the file.
Returns
-------
marray : numpy.memmap
The memory-mapped array.
Raises
------
ValueError
If the data or the mode is invalid.
IOError
If the file is not found or cannot be opened correctly.
See Also
--------
numpy.memmap
"""
if not isinstance(filename, basestring):
raise ValueError("Filename must be a string. Memmap cannot use" \
" existing file handles.")
if 'w' in mode:
# We are creating the file, not reading it.
# Check if we ought to create the file.
if version != (1, 0):
msg = "only support version (1,0) of file format, not %r"
raise ValueError(msg % (version,))
# Ensure that the given dtype is an authentic dtype object rather than
# just something that can be interpreted as a dtype object.
dtype = numpy.dtype(dtype)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
d = dict(
descr=dtype_to_descr(dtype),
fortran_order=fortran_order,
shape=shape,
)
# If we got here, then it should be safe to create the file.
fp = open(filename, mode+'b')
try:
fp.write(magic(*version))
write_array_header_1_0(fp, d)
offset = fp.tell()
finally:
fp.close()
else:
# Read the header of the file first.
fp = open(filename, 'rb')
try:
version = read_magic(fp)
if version != (1, 0):
msg = "only support version (1,0) of file format, not %r"
raise ValueError(msg % (version,))
shape, fortran_order, dtype = read_array_header_1_0(fp)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
offset = fp.tell()
finally:
fp.close()
if fortran_order:
order = 'F'
else:
order = 'C'
# We need to change a write-only mode to a read-write mode since we've
# already written data to the file.
if mode == 'w+':
mode = 'r+'
marray = numpy.memmap(filename, dtype=dtype, shape=shape, order=order,
mode=mode, offset=offset)
return marray
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