Inheritance diagram for nipy.io.imageformats.analyze:
Header and image for the basic Mayo Analyze format
The basic principle of the header object is that it manages and contains header information. Each header type may have different attributes that can be set. Some headers can contain only subsets of possible passed values - for example the basic Analyze header can only encode the zooms in an affine transform - not shears, rotations, translations.
The attributes and methods of the object guarantee that the set values will be consistent and valid with the header standard, in some sense. The object API therefore gives “safe” access to the header. You can reach all the named fields in the header directly with the header_data attribute. If you futz with these, the object makes no guarantee that the data in the header are consistent.
Headers do not have filenames, they refer only the block of data in the header. The containing object manages the filenames, and therefore must know how to predict image filenames from header filenames, whether these are different, and so on.
You can access and set fields of a particular header type using standard __getitem__ / __setitem__ syntax:
hdr[‘field’] = 10
Headers also implement general mappingness:
hdr.keys() hdr.items() hdr.values()
Basic attributes of the header object are:
.endianness (read only)
.binaryblock (read only)
.structarr (read only)
Class attributes are:
.default_x_flip
with methods:
.get/set_data_shape
.get/set_data_dtype
.get/set_zooms
.get_base_affine()
.get_best_affine()
.check_fix()
.as_byteswapped(endianness)
.write_to(fileobj)
.__str__
.__eq__
.__ne__
and class methods:
.diagnose_binaryblock(string)
.from_fileobj(fileobj)
More sophisticated headers can add more methods and attributes.
We have a file, and we would like feedback as to whether there are any problems with this header, and whether they are fixable:
hdr = AnalyzeHeader.from_fileobj(fileobj, check=False)
AnalyzeHeader.diagnose_binaryblock(hdr.binaryblock)
This will run all known checks, with no fixes, outputing to stdout
In creating a header object, we might want to check the header data. If it passes the error threshold, it goes through:
hdr = AnalyzeHeader.from_fileobj(good_fileobj)
whereas:
hdr = AnalyzeHeader.from_fileobj(bad_fileobj)
would raise some error, with output to logging (see below).
We set the error level (the level of problem that the check=True versions will accept as OK) from global defaults:
nifti.imageglobals.error_level = 30
The same for logging:
nifti.logger = logger
Bases: object
Class for basic analyze header
Implements zoom-only setting of affine transform, and no image scaling
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_slope_inter | |
get_zooms | |
items | |
keys | |
set_data_dtype | |
set_data_shape | |
set_slope_inter | |
set_zooms | |
values | |
write_to |
Initialize header from binary data block
Parameters : | binaryblock : {None, string} optional
endianness : {None, ‘<’,’>’, other endian code} string, optional
check : bool, optional
|
---|
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
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
|
---|---|
Returns : | hdr : header object
|
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
binary block of data as string
Returns : | binaryblock : string
|
---|
Examples
>>> # Make default empty header
>>> hdr = AnalyzeHeader()
>>> len(hdr.binaryblock)
348
Check header data with checks
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
Run checks over header binary data, return string
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
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
|
---|---|
Returns : | hdr : 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
Return read header with given or guessed endiancode
Parameters : | fileobj : file-like object
endianness : None or endian code, optional
|
---|---|
Returns : | hdr : AnalyzeHeader object
|
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
Initialize header from mapping
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 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 numpy dtype for data
For examples see set_data_dtype
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 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)
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
|
---|---|
Returns : | datatype_code : string or integer
|
Examples
>>> hdr = AnalyzeHeader()
>>> hdr['datatype'] = 4 # int16
>>> hdr.get_datatype()
'int16'
Get scalefactor and intercept
These are not implemented for basic Analyze
Get zooms from header
Returns : | z : tuple
|
---|
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)
Return items from header data
Return keys from header data
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 shape of data
Set raises error for Analyze header
Set zooms into header fields
See docstring for get_zooms for examples
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
Return values from header data
Write header to fileobj
Write starts at fileobj current file position.
Parameters : | fileobj : file-like object
|
---|---|
Returns : | None : |
Examples
>>> hdr = AnalyzeHeader()
>>> import StringIO
>>> str_io = StringIO.StringIO()
>>> hdr.write_to(str_io)
>>> hdr.binaryblock == str_io.getvalue()
True
Bases: nipy.io.imageformats.spatialimages.SpatialImage
Create new instance of own class from img
This is a class method
Parameters : | img : spatialimage instance
|
---|---|
Returns : | cimg : spatialimage instance
|
Lazy load of data
Return header
Update header to match data, affine etc in object
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.
Save img in our own format, to name implied by filename
This is a class method
Parameters : | img : spatialimage instance
filename : str
|
---|
Write image to files implied by filename string
Returns : | None : |
---|
Write image to files passed, or self._files