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""" Define a simple format for saving numpy arrays to disk with the full
information about them.
WARNING: THE FORMAT IS CURRENTLY UNSTABLE. DO NOT STORE CRITICAL DATA WITH IT.
While this code is in an SVN branch, the format may change without
notice, without backwards compatibility, and without changing the
format's version number. When the code moves into the trunk the format
will be stabilized, the version number will increment as changes occur,
and backwards compatibility with older versions will be maintained.
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 the 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, this dictionary is formatted using
pprint.pformat() so the keys are in alphabetic order.
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.
"""
import cPickle
import pprint
import struct
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:
raise ValueError("could not read %d characters for the magic string; got %r" % (MAGIC_LEN, magic_str))
if magic_str[:-2] != MAGIC_PREFIX:
raise ValueError("the magic string is not correct; expected %r, got %r" % (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().
"""
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.
"""
header = pprint.pformat(d)
# 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
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
Raises
------
ValueError if the data is invalid.
"""
# Read an unsigned, little-endian short int which has the length of the
# header.
hlength_str = fp.read(2)
if len(hlength_str) != 2:
raise ValueError("EOF at %s before reading array header length" % 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:
raise ValueError("Cannot parse header: %r\nException: %r" % (header, e))
if not isinstance(d, dict):
raise ValueError("Header is not a dictionary: %r" % d)
keys = d.keys()
keys.sort()
if keys != ['descr', 'fortran_order', 'shape']:
raise ValueError("Header does not contain the correct keys: %r" % (keys,))
# Sanity-check the values.
if (not isinstance(d['shape'], tuple) or
not numpy.all([isinstance(x, int) for x in d['shape']])):
raise ValueError("shape is not valid: %r" % (d['shape'],))
if not isinstance(d['fortran_order'], bool):
raise ValueError("fortran_order is not a valid bool: %r" % (d['fortran_order'],))
try:
dtype = numpy.dtype(d['descr'])
except TypeError, e:
raise ValueError("descr is not a valid dtype descriptor: %r" % (d['descr'],))
return d['shape'], d['fortran_order'], dtype
def write_array(fp, array, version=(1,0)):
""" Write an array to a 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
array : numpy.ndarray
version : (int, int), optional
The version number of the format.
Raises
------
ValueError if the array cannot be persisted.
Various other errors from pickling if the array contains Python objects as
part of its dtype.
"""
if version != (1, 0):
raise ValueError("we only support format version (1,0), not %s" % (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 a 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
Raises
------
ValueError if the data is invalid.
"""
version = read_magic(fp)
if version != (1, 0):
raise ValueError("only support version (1,0) of file format, not %r" % (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.
Parameters
----------
filename : str
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".
dtype : dtype, optional
shape : tuple of int, optional
fortran_order : bool, optional
If the mode is a "write" mode, then the file will be created using this
dtype, shape, and contiguity.
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
Raises
------
ValueError if the data or the mode is invalid.
IOError if the file is not found or cannot be opened correctly.
"""
if 'w' in mode:
# We are creating the file, not reading it.
# Check if we ought to create the file.
if version != (1, 0):
raise ValueError("only support version (1,0) of file format, not %r" % (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:
raise ValueError("the dtype includes Python objects; the array cannot be memory-mapped")
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):
raise ValueError("only support version (1,0) of file format, not %r" % (version,))
shape, fortran_order, dtype = read_array_header_1_0(fp)
if dtype.hasobject:
raise ValueError("the dtype includes Python objects; the array cannot be memory-mapped")
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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