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io.imageformats.spm2analyze

Module: io.imageformats.spm2analyze

Inheritance diagram for nipy.io.imageformats.spm2analyze:

Header reading functions for SPM2 version of analyze format

Classes

Spm2AnalyzeHeader

class nipy.io.imageformats.spm2analyze.Spm2AnalyzeHeader(binaryblock=None, endianness=None, check=True)

Bases: nipy.io.imageformats.spm99analyze.Spm99AnalyzeHeader

SPM2 header; adds possibility of reading, but not writing DC offset for data

Methods

as_byteswapped
check_fix
copy Generic (shallow and deep) copying operations.
diagnose_binaryblock
for_file_pair
from_fileobj
from_mapping
get_base_affine
get_best_affine
get_data_dtype
get_data_offset
get_data_shape
get_datatype
get_origin_affine
get_slope_inter
get_zooms
items
keys
set_data_dtype
set_data_shape
set_origin_from_affine
set_slope_inter
set_zooms
values
write_to
__init__(binaryblock=None, endianness=None, check=True)

Initialize header from binary data block

Parameters :

binaryblock : {None, string} optional

binary block to set into header. By default, None, in which case we insert the default empty header block

endianness : {None, ‘<’,’>’, other endian code} string, optional

endianness of the binaryblock. If None, guess endianness from the data.

check : bool, optional

Whether to check content of header in initialization. Default is True.

Examples

>>> hdr1 = AnalyzeHeader() # an empty header
>>> hdr1.endianness == native_code
True
>>> hdr1.get_data_shape()
(0,)
>>> hdr1.set_data_shape((1,2,3)) # now with some content
>>> hdr1.get_data_shape()
(1, 2, 3)

We can set the binary block directly via this initialization. Here we get it from the header we have just made

>>> binblock2 = hdr1.binaryblock
>>> hdr2 = AnalyzeHeader(binblock2)
>>> hdr2.get_data_shape()
(1, 2, 3)

Empty headers are native endian by default

>>> hdr2.endianness == native_code
True

You can pass valid opposite endian headers with the endianness parameter. Even empty headers can have endianness

>>> hdr3 = AnalyzeHeader(endianness=swapped_code)
>>> hdr3.endianness == swapped_code
True

If you do not pass an endianness, and you pass some data, we will try to guess from the passed data.

>>> binblock3 = hdr3.binaryblock
>>> hdr4 = AnalyzeHeader(binblock3)
>>> hdr4.endianness == swapped_code
True
as_byteswapped(endianness=None)

return new byteswapped header object with given endianness

Guaranteed to make a copy even if endianness is the same as the current endianness.

Parameters :

endianness : None or string, optional

endian code to which to swap. None means swap from current endianness, and is the default

Returns :

hdr : header object

hdr object with given endianness

Examples

>>> hdr = AnalyzeHeader()
>>> hdr.endianness == native_code
True
>>> bs_hdr = hdr.as_byteswapped()
>>> bs_hdr.endianness == swapped_code
True
>>> bs_hdr = hdr.as_byteswapped(swapped_code)
>>> bs_hdr.endianness == swapped_code
True
>>> bs_hdr is hdr
False
>>> bs_hdr == hdr
True

If you write to the resulting byteswapped data, it does not change the original.

>>> bs_hdr['dim'][1] = 2
>>> bs_hdr == hdr
False

If you swap to the same endianness, it returns a copy

>>> nbs_hdr = hdr.as_byteswapped(native_code)
>>> nbs_hdr.endianness == native_code
True
>>> nbs_hdr is hdr
False
binaryblock

binary block of data as string

Returns :

binaryblock : string

string giving binary data block

Examples

>>> # Make default empty header
>>> hdr = AnalyzeHeader()
>>> len(hdr.binaryblock)
348
check_fix(logger=<logging.Logger object at 0x950fcac>, error_level=40)

Check header data with checks

copy()

Return copy of header

>>> hdr = AnalyzeHeader()
>>> hdr['dim'][0]
0
>>> hdr['dim'][0] = 2
>>> hdr2 = hdr.copy()
>>> hdr2 is hdr
False
>>> hdr['dim'][0] = 3
>>> hdr2['dim'][0]
2
classmethod diagnose_binaryblock(klass, binaryblock, endianness=None)

Run checks over header binary data, return string

endianness

endian code of binary data

The endianness code gives the current byte order interpretation of the binary data.

Notes

Endianness gives endian interpretation of binary data. It is read only because the only common use case is to set the endianness on initialization, or occasionally byteswapping the data - but this is done via the as_byteswapped method

Examples

>>> hdr = AnalyzeHeader()
>>> code = hdr.endianness
>>> code == native_code
True
for_file_pair(is_pair=True)

Adapt header to separate or same image and header file

This is a rare and exotic case for Analyze files, common for Nifti1. For Analyze, we only need to check that, if the file is single, then the data offset is large enough to leave room for the header.

Parameters :

is_pair : bool, optional

True if adapting header to file pair state, False for single

Returns :

hdr : header

copied and possibly modified header

Examples

The header starts off as being for two files

>>> hdr = AnalyzeHeader()
>>> hdr.get_data_offset()
0

This is the same as the default behavior for this method

>>> pair_hdr = hdr.for_file_pair()
>>> pair_hdr.get_data_offset()
0

But we can switch it to be for one

>>> unpair_hdr = hdr.for_file_pair(False)
>>> unpair_hdr.get_data_offset()
352

The original header is not affected (a copy is returned)

>>> hdr.get_data_offset()
0
classmethod from_fileobj(klass, fileobj, endianness=None, check=True)

Return read header with given or guessed endiancode

Parameters :

fileobj : file-like object

Needs to implement read method

endianness : None or endian code, optional

Code specifying endianness of read data

Returns :

hdr : AnalyzeHeader object

AnalyzeHeader object initialized from data in fileobj

Examples

>>> import StringIO
>>> hdr = AnalyzeHeader()
>>> fileobj = StringIO.StringIO(hdr.binaryblock)
>>> fileobj.seek(0)
>>> hdr2 = AnalyzeHeader.from_fileobj(fileobj)
>>> hdr2.binaryblock == hdr.binaryblock
True

You can write to the resulting object data

>>> hdr2['dim'][1] = 1
classmethod from_mapping(klass, field_mapping=None, endianness=None, check=True)

Initialize header from mapping

get_base_affine()

Get affine from basic (shared) header fields

Note that we get the translations from the center of the image.

Examples

>>> hdr = AnalyzeHeader()
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.set_zooms((3, 2, 1))
>>> hdr.default_x_flip
True
>>> hdr.get_base_affine() # from center of image
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -3.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr.set_data_shape((3, 5))
>>> hdr.get_base_affine()
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -0.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.get_base_affine() # from center of image
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -3.],
       [ 0.,  0.,  0.,  1.]])
get_best_affine()

Get affine from header, using SPM origin field if sensible

The default translations are got from the origin field, if set, or from the center of the image otherwise.

Examples

>>> hdr = Spm99AnalyzeHeader()
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.set_zooms((3, 2, 1))
>>> hdr.default_x_flip
True
>>> hdr.get_origin_affine() # from center of image
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -3.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr['origin'][:3] = [3,4,5]
>>> hdr.get_origin_affine() # using origin
array([[-3.,  0.,  0.,  6.],
       [ 0.,  2.,  0., -6.],
       [ 0.,  0.,  1., -4.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr['origin'] = 0 # unset origin
>>> hdr.set_data_shape((3, 5))
>>> hdr.get_origin_affine()
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -0.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.get_origin_affine() # from center of image
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -3.],
       [ 0.,  0.,  0.,  1.]])
get_data_dtype()

Get numpy dtype for data

For examples see set_data_dtype

get_data_offset()

Return offset into data file to read data

Examples

>>> hdr = AnalyzeHeader()
>>> hdr.get_data_offset()
0
>>> hdr['vox_offset'] = 12
>>> hdr.get_data_offset()
12
get_data_shape()

Get shape of data

Examples

>>> hdr = AnalyzeHeader()
>>> hdr.get_data_shape()
(0,)
>>> hdr.set_data_shape((1,2,3))
>>> hdr.get_data_shape()
(1, 2, 3)

Expanding number of dimensions gets default zooms

>>> hdr.get_zooms()
(1.0, 1.0, 1.0)
get_datatype(code_repr='label')

Return representation of datatype code

This method returns the datatype code, or a string label for the code. Usually you are more interested in the data dtype. To do that more useful thing, use get_data_dtype

Parameters :

code_repr : string

string giving output form of datatype code representation. Default is ‘label’; use ‘code’ for integer representation.

Returns :

datatype_code : string or integer

string label for datatype code or code

Examples

>>> hdr = AnalyzeHeader()
>>> hdr['datatype'] = 4 # int16
>>> hdr.get_datatype()
'int16'
get_origin_affine()

Get affine from header, using SPM origin field if sensible

The default translations are got from the origin field, if set, or from the center of the image otherwise.

Examples

>>> hdr = Spm99AnalyzeHeader()
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.set_zooms((3, 2, 1))
>>> hdr.default_x_flip
True
>>> hdr.get_origin_affine() # from center of image
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -3.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr['origin'][:3] = [3,4,5]
>>> hdr.get_origin_affine() # using origin
array([[-3.,  0.,  0.,  6.],
       [ 0.,  2.,  0., -6.],
       [ 0.,  0.,  1., -4.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr['origin'] = 0 # unset origin
>>> hdr.set_data_shape((3, 5))
>>> hdr.get_origin_affine()
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -0.],
       [ 0.,  0.,  0.,  1.]])
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.get_origin_affine() # from center of image
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -3.],
       [ 0.,  0.,  0.,  1.]])
get_slope_inter()

Get data scaling (slope) and offset (intercept) from header data

Uses the algorithm from SPM2 spm_vol_ana.m by John Ashburner

Parameters :

self : header

Mapping with fields: * scl_slope - slope * scl_inter - possible intercept (SPM2 use - shared by nifti) * glmax - the (recorded) maximum value in the data (unscaled) * glmin - recorded minimum unscaled value * cal_max - the calibrated (scaled) maximum value in the dataset * cal_min - ditto minimum value

Returns :

scl_slope : None or float

scaling (slope). None if there is no valid scaling from these fields

scl_inter : None or float

offset (intercept). Also None if there is no valid scaling, offset

Examples

>>> fields = {'scl_slope':1,'scl_inter':0,'glmax':0,'glmin':0,'cal_max':0, 'cal_min':0}
>>> hdr = Spm2AnalyzeHeader()
>>> for key, value in fields.items():
...     hdr[key] = value
>>> hdr.get_slope_inter()
(1.0, 0.0)
>>> hdr['scl_inter'] = 0.5
>>> hdr.get_slope_inter()
(1.0, 0.5)
>>> hdr['scl_inter'] = np.nan
>>> hdr.get_slope_inter()
(1.0, 0.0)

If ‘scl_slope’ is 0, nan or inf, cannot use ‘scl_slope’. Without valid information in the gl / cal fields, we cannot get scaling, and return None

>>> hdr['scl_slope'] = 0
>>> hdr.get_slope_inter()
(None, None)
>>> hdr['scl_slope'] = np.nan
>>> hdr.get_slope_inter()
(None, None)

Valid information in the gl AND cal fields are needed

>>> hdr['cal_max'] = 0.8
>>> hdr['cal_min'] = 0.2
>>> hdr.get_slope_inter()
(None, None)
>>> hdr['glmax'] = 110
>>> hdr['glmin'] = 10
>>> np.allclose(hdr.get_slope_inter(), [0.6/100, 0.2-0.6/100*10])
True
get_zooms()

Get zooms from header

Returns :

z : tuple

tuple of header zoom values

Examples

>>> hdr = AnalyzeHeader()
>>> hdr.get_zooms()
()
>>> hdr.set_data_shape((1,2))
>>> hdr.get_zooms()
(1.0, 1.0)
>>> hdr.set_zooms((3, 4))
>>> hdr.get_zooms()
(3.0, 4.0)
items()

Return items from header data

keys()

Return keys from header data

set_data_dtype(datatype)

Set numpy dtype for data from code or dtype or type

Examples

>>> hdr = AnalyzeHeader()
>>> hdr.set_data_dtype(np.uint8)
>>> hdr.get_data_dtype()
dtype('uint8')
>>> hdr.set_data_dtype(np.dtype(np.uint8))
>>> hdr.get_data_dtype()
dtype('uint8')
>>> hdr.set_data_dtype('implausible')
Traceback (most recent call last):
   ...
HeaderDataError: data dtype "implausible" not recognized
>>> hdr.set_data_dtype('none')
Traceback (most recent call last):
   ...
HeaderDataError: data dtype "none" known but not supported
>>> hdr.set_data_dtype(np.void)
Traceback (most recent call last):
   ...
HeaderDataError: data dtype "<type 'numpy.void'>" known but not supported
set_data_shape(shape)

Set shape of data

set_origin_from_affine(affine)

Set SPM origin to header from affine matrix.

The origin field was read but not written by SPM99 and 2. It was used for storing a central voxel coordinate, that could be used in aligning the image to some standard position - a proxy for a full translation vector that was usually stored in a separate matlab .mat file.

Nifti uses the space occupied by the SPM origin field for important other information (the transform codes), so writing the origin will make the header a confusing Nifti file. If you work with both Analyze and Nifti, you should probably avoid doing this.

Parameters :

affine : array-like, shape (4,4)

Affine matrix to set

Returns :

None :

Examples

>>> hdr = Spm99AnalyzeHeader()
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.set_zooms((3,2,1))
>>> hdr.get_origin_affine()
array([[-3.,  0.,  0.,  3.],
       [ 0.,  2.,  0., -4.],
       [ 0.,  0.,  1., -3.],
       [ 0.,  0.,  0.,  1.]])
>>> affine = np.diag([3,2,1,1])
>>> affine[:3,3] = [-6, -6, -4]
>>> hdr.set_origin_from_affine(affine)
>>> np.all(hdr['origin'][:3] == [3,4,5])
True
>>> hdr.get_origin_affine()
array([[-3.,  0.,  0.,  6.],
       [ 0.,  2.,  0., -6.],
       [ 0.,  0.,  1., -4.],
       [ 0.,  0.,  0.,  1.]])
set_slope_inter(slope, inter)
set_zooms(zooms)

Set zooms into header fields

See docstring for get_zooms for examples

structarr

header data, with data fields

Examples

>>> hdr1 = AnalyzeHeader() # an empty header
>>> sz = hdr1.structarr['sizeof_hdr']
>>> hdr1.structarr = None
Traceback (most recent call last):
   ...
AttributeError: can't set attribute
values()

Return values from header data

write_to(fileobj)

Write header to fileobj

Write starts at fileobj current file position.

Parameters :

fileobj : file-like object

Should implement write method

Returns :

None :

Examples

>>> hdr = AnalyzeHeader()
>>> import StringIO
>>> str_io = StringIO.StringIO()
>>> hdr.write_to(str_io)
>>> hdr.binaryblock == str_io.getvalue()
True

Spm2AnalyzeImage

class nipy.io.imageformats.spm2analyze.Spm2AnalyzeImage(data, affine, header=None, extra=None)

Bases: nipy.io.imageformats.spm99analyze.Spm99AnalyzeImage

__init__(data, affine, header=None, extra=None)
static filespec_to_files(filespec)
classmethod from_filename(klass, filename)
classmethod from_files(klass, files)
classmethod from_filespec(klass, img, filespec)
classmethod from_image(klass, img)

Create new instance of own class from img

This is a class method

Parameters :

img : spatialimage instance

In fact, an object with the API of spatialimage - specifically get_data, get_affine, get_header and extra.

Returns :

cimg : spatialimage instance

Image, of our own class

get_affine()
get_data()

Lazy load of data

get_data_dtype()
get_header()

Return header

Update header to match data, affine etc in object

get_shape()
get_unscaled_data()

Return image data without image scaling applied

Summary: please use the get_data method instead of this method unless you are sure what you are doing, and that you will only be using image formats for which this method exists and returns sensible results.

Use this method with care; the modified Analyze-type formats such as SPM formats, and nifti1, specify that the image data array, as they are expecting to return it, is given by the raw data on disk, multiplied by a scalefactor and maybe with the addition of a constant. This method returns the data on the disk, without these format-specific scalings applied. Please use this method only if you absolutely need the unscaled data, and the magnitude of the data, as given by the scalefactor, is not relevant to your application. The Analyze-type formats have a single scalefactor +/- offset per image on disk. If you do not care about the absolute values, and will be removing the mean from the data, then the unscaled values will have preserved intensity ratios compared to the mean-centered scaled data. However, this is not necessarily true of other formats with more complicated scaling - such as MINC.

Note that - unlike the scaled get_data method, we do not cache the array, to minimize the memory taken by the object.

classmethod instance_to_filename(klass, img, filename)

Save img in our own format, to name implied by filename

This is a class method

Parameters :

img : spatialimage instance

In fact, an object with the API of spatialimage - specifically get_data, get_affine, get_header and extra.

filename : str

Filename, implying name to which to save image.

classmethod load(klass, filename)
classmethod save(klass, img, filename)
set_data_dtype(dtype)
to_filename(filename)

Write image to files implied by filename string

Returns :None :
to_files(files=None)
to_filespec(filename)