File-based images that have data arrays
The class:DataObjImage class defines an image that extends the FileBasedImage by adding an array-like object, named dataobj. This can either be an actual numpy array, or an object that:
DataobjImage(dataobj[, header, extra, file_map]) | Template class for images that have dataobj data stores |
Bases: nibabel.filebasedimages.FileBasedImage
Template class for images that have dataobj data stores
Initialize dataobj image
The datobj image is a combination of (dataobj, header), with optional metadata in extra, and filename / file-like objects contained in the file_map mapping.
Parameters: | dataobj : object
header : None or mapping or header instance, optional
extra : None or mapping, optional
file_map : mapping, optional
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Initialize dataobj image
The datobj image is a combination of (dataobj, header), with optional metadata in extra, and filename / file-like objects contained in the file_map mapping.
Parameters: | dataobj : object
header : None or mapping or header instance, optional
extra : None or mapping, optional
file_map : mapping, optional
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Return image data from image with any necessary scaling applied
Warning
We recommend you use the get_fdata method instead of the get_data method, because it is easier to predict the return data type. We will deprecate the get_data method around April 2018, and remove it around April 2020.
If you don’t care about the predictability of the return data type, and you want the minimum possible data size in memory, you can replicate the array that would be returned by img.get_data() by using np.asanyarray(img.dataobj).
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
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Returns: | data : array
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See also
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 affect 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
Return floating point image data with necessary scaling 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_fdata() on a proxy image is to read the data from the proxy, and store in an internal cache. Future calls to get_fdata 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
dtype : numpy dtype specifier
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Returns: | fdata : array
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See also
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_fdata 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_fdata, 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_fdata(caching='unchanged'); in this case get_fdata 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_fdata(). 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_fdata”, so:
>>> img.in_memory
False
After we call get_fdata using the default caching == ‘fill’, the cache contains a reference to the returned array data:
>>> data = img.get_fdata()
>>> img.in_memory
True
We modify an element in the returned data array:
>>> data[0, 0, 0, 0]
0.0
>>> data[0, 0, 0, 0] = 99
>>> data[0, 0, 0, 0]
99.0
The next time we call ‘get_fdata’, the method returns the cached reference to the (modified) array:
>>> data_again = img.get_fdata()
>>> data_again is data
True
>>> data_again[0, 0, 0, 0]
99.0
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_fdata(caching='unchanged')
>>> img.in_memory
False
>>> data[0, 0, 0] = 99
>>> data_again = img.get_fdata(caching='unchanged')
>>> data_again is data
False
>>> data_again[0, 0, 0, 0]
0.0
Return shape for image
get_shape method is deprecated. Please use the img.shape property instead.
True when any array data is in memory cache
There are separate caches for get_data reads and get_fdata reads. This property is True if either of those caches are set.
Delete any cached read of data from proxied data
Remember there are two types of images:
If you call img.get_fdata() 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_fdata(). If you modify the returned data, as in:
data = img.get_fdata()
data[:] = 42
then the next call to img.get_fdata() returns the modified array, whether the image is an array image or a proxy image:
assert np.all(img.get_fdata() == 42)
When you uncache an array image, this has no effect on the return of img.get_fdata(), but when you uncache a proxy image, the result of img.get_fdata() returns to its original value.