mvpa2.misc.plot.lightbox.SpatialImage

class mvpa2.misc.plot.lightbox.SpatialImage(dataobj, affine, header=None, extra=None, file_map=None)

Initialize image

The image is a combination of (array, affine matrix, header), with optional metadata in extra, and filename / file-like objects contained in the file_map mapping.

Parameters:

dataobj : object

Object containg image data. It should be some object that retuns an array from np.asanyarray. It should have a shape attribute or property

affine : None or (4,4) array-like

homogenous affine giving relationship between voxel coordinates and world coordinates. Affine can also be None. In this case, obj.affine also returns None, and the affine as written to disk will depend on the file format.

header : None or mapping or header instance, optional

metadata for this image format

extra : None or mapping, optional

metadata to associate with image that cannot be stored in the metadata of this image type

file_map : mapping, optional

mapping giving file information for this image format

Methods

affine

dataobj

files_types = (('image', None),)

classmethod filespec_to_file_map(klass, filespec)

Make file_map for this class from filename filespec

Class method

Parameters:

filespec : str

Filename that might be for this image file type.

Returns:

file_map : dict

file_map dict with (key, value) pairs of (file_type, FileHolder instance), where file_type is a string giving the type of the contained file.

Raises:

ImageFileError :

if filespec is not recognizable as being a filename for this image type.

classmethod filespec_to_files(klass, filespec)

classmethod from_file_map(klass, file_map)

classmethod from_filename(klass, filename)

classmethod from_files(klass, file_map)

classmethod from_filespec(klass, filespec)

classmethod from_image(klass, img)

Class method to create new instance of own class from img

Parameters:

img : spatialimage instance

In fact, an object with the API of spatialimage - specifically dataobj, affine, header and extra.

Returns:

cimg : spatialimage instance

Image, of our own class

get_affine()

Get affine from image

Please use the affine property instead of get_affine; we will deprecate this method in future versions of nibabel.

get_data(caching='fill')

Return image data from image with any necessary scalng applied

The image dataobj property can be an array proxy or an array. An array proxy is an object that knows how to load the image data from disk. An image with an array proxy dataobj is a proxy image; an image with an array in dataobj is an array image.

The default behavior for get_data() on a proxy image is to read the data from the proxy, and store in an internal cache. Future calls to get_data will return the cached array. This is the behavior selected with caching == “fill”`.

Once the data has been cached and returned from an array proxy, if you modify the returned array, you will also modify the cached array (because they are the same array). Regardless of the caching flag, this is always true of an array image.

Parameters:

caching : {‘fill’, ‘unchanged’}, optional

See the Notes section for a detailed explanation. This argument specifies whether the image object should fill in an internal cached reference to the returned image data array. “fill” specifies that the image should fill an internal cached reference if currently empty. Future calls to get_data will return this cached reference. You might prefer “fill” to save the image object from having to reload the array data from disk on each call to get_data. “unchanged” means that the image should not fill in the internal cached reference if the cache is currently empty. You might prefer “unchanged” to “fill” if you want to make sure that the call to get_data does not create an extra (cached) reference to the returned array. In this case it is easier for Python to free the memory from the returned array.

Returns:

data : array

array of image data

See also

uncache

empty the array data cache

Notes

All images have a property dataobj that represents the image array data. Images that have been loaded from files usually do not load the array data from file immediately, in order to reduce image load time and memory use. For these images, dataobj is an array proxy; an object that knows how to load the image array data from file.

By default (caching == “fill”), when you call get_data on a proxy image, we load the array data from disk, store (cache) an internal reference to this array data, and return the array. The next time you call get_data, you will get the cached reference to the array, so we don’t have to load the array data from disk again.

Array images have a dataobj property that already refers to an array in memory, so there is no benefit to caching, and the caching keywords have no effect.

For proxy images, you may not want to fill the cache after reading the data from disk because the cache will hold onto the array memory until the image object is deleted, or you use the image uncache method. If you don’t want to fill the cache, then always use get_data(caching='unchanged'); in this case get_data will not fill the cache (store the reference to the array) if the cache is empty (no reference to the array). If the cache is full, “unchanged” leaves the cache full and returns the cached array reference.

The cache can effect the behavior of the image, because if the cache is full, or you have an array image, then modifying the returned array will modify the result of future calls to get_data(). For example you might do this:

>>> import os
>>> import nibabel as nib
>>> from nibabel.testing import data_path
>>> img_fname = os.path.join(data_path, 'example4d.nii.gz')

>>> img = nib.load(img_fname) # This is a proxy image
>>> nib.is_proxy(img.dataobj)
True

The array is not yet cached by a call to “get_data”, so: >>> img.in_memory False

After we call get_data using the default caching='fill', the cache contains a reference to the returned array ``data`:

>>> data = img.get_data()
>>> img.in_memory
True

We modify an element in the returned data array:

>>> data[0, 0, 0, 0]
0
>>> data[0, 0, 0, 0] = 99
>>> data[0, 0, 0, 0]
99

The next time we call ‘get_data’, the method returns the cached reference to the (modified) array:

>>> data_again = img.get_data()
>>> data_again is data
True
>>> data_again[0, 0, 0, 0]
99

If you had initially used caching == ‘unchanged’ then the returned data array would have been loaded from file, but not cached, and:

>>> img = nib.load(img_fname)  # a proxy image again
>>> data = img.get_data(caching='unchanged')
>>> img.in_memory
False
>>> data[0, 0, 0] = 99
>>> data_again = img.get_data(caching='unchanged')
>>> data_again is data
False
>>> data_again[0, 0, 0, 0]
0

get_data_dtype()

get_filename()

Fetch the image filename

Parameters:

None :

Returns:

fname : None or str

Returns None if there is no filename, or a filename string. If an image may have several filenames assoctiated with it (e.g Analyze .img, .hdr pair) then we return the more characteristic filename (the .img filename in the case of Analyze’)

get_header()

Get header from image

Please use the header property instead of get_header; we will deprecate this method in future versions of nibabel.

get_shape()

Return shape for image

This function deprecated; please use the shape property instead

header

header_class

alias of Header

in_memory

True when array data is in memory

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 dataobj, affine, header and extra.

filename : str

Filename, implying name to which to save image.

classmethod load(klass, filename)

classmethod make_file_map(klass, mapping=None)

Class method to make files holder for this image type

Parameters:

mapping : None or mapping, optional

mapping with keys corresponding to image file types (such as ‘image’, ‘header’ etc, depending on image class) and values that are filenames or file-like. Default is None

Returns:

file_map : dict

dict with string keys given by first entry in tuples in sequence klass.files_types, and values of type FileHolder, where FileHolder objects have default values, other than those given by mapping

set_data_dtype(dtype)

set_filename(filename)

Sets the files in the object from a given filename

The different image formats may check whether the filename has an extension characteristic of the format, and raise an error if not.

Parameters:

filename : str

If the image format only has one file associated with it, this will be the only filename set into the image .file_map attribute. Otherwise, the image instance will try and guess the other filenames from this given filename.

shape

to_file_map(file_map=None)

to_filename(filename)

Write image to files implied by filename string

Parameters:

filename : str

filename to which to save image. We will parse filename with filespec_to_file_map to work out names for image, header etc.

Returns:

None :

to_files(file_map=None)

to_filespec(filename)

uncache()

Delete any cached read of data from proxied data

Remember there are two types of images:

• array images where the data img.dataobj is an array
• proxy images where the data img.dataobj is a proxy object

If you call img.get_data() on a proxy image, the result of reading from the proxy gets cached inside the image object, and this cache is what gets returned from the next call to img.get_data(). If you modify the returned data, as in:

data = img.get_data()
data[:] = 42

then the next call to img.get_data() returns the modified array, whether the image is an array image or a proxy image:

assert np.all(img.get_data() == 42)

When you uncache an array image, this has no effect on the return of img.get_data(), but when you uncache a proxy image, the result of img.get_data() returns to its original value.

update_header()

Harmonize header with image data and affine

>>> data = np.zeros((2,3,4))
>>> affine = np.diag([1.0,2.0,3.0,1.0])
>>> img = SpatialImage(data, affine)
>>> img.shape == (2, 3, 4)
True
>>> img.update_header()
>>> img.header.get_data_shape() == (2, 3, 4)
True
>>> img.header.get_zooms()
(1.0, 2.0, 3.0)