Change scan and pattern size

Patterns in an EBSD object s are stored in the s.data attribute as either numpy.ndarray or dask.array.Array. HyperSpy’s user guide explains how to access, i.e. index, the data. This section details example uses of scan and pattern indexing specific to EBSD objects.

Crop scan or pattern

A new EBSD object s2 can be created from a region of interest in another EBSD object s by using HyperSpy’s navigation indexing method inav. The new scan keeps the metadata and original_metadata of s. Say we, after plotting and inspecting the data, want to create a new, smaller data set of the patterns within a rectangle defined by the upper left pattern with index (53, 82) and the bottom right pattern with index (74, 105):

>>> s
<EBSD, title: , dimensions: (200, 149|60, 60)>
>>> s2 = s.inav[53:74, 82:105]
>>> s2
<EBSD, title: , dimensions: (21, 23|60, 60)>

Patterns can also be cropped with the signal indexing method isig. Say we wanted to remove the ten outermost pixels in our (60 x 60) pixel patterns:

>>> s3 = s.isig[10:50, 10:50]
>>> s3
<EBSD, title: , dimensions: (200, 149|40, 40)>

The same pattern in scan s before cropping (left) and in scan s3 after cropping (right).

Binning

A new signal with patterns binned e.g. by 2 can be obtained using the rebin() method provided by HyperSpy, explained further in their user guide, by passing in either the scale or new_shape parameter:

>>> print(s, s.data.dtype)
<EBSD, title: , dimensions: (200, 149|60, 60)> uint8
>>> s3 = s.rebin(scale=(1, 1, 2, 2))
>>> print(s3, s.data.dtype)
<EBSD, title: , dimensions: (200, 149|30, 30)> uint64

Note that rebin() casts the data to uint64. This means that in this example, each pixel in the binned scan s3 takes up eight times the memory size of pixels in the original scan s. If you want, you can rescale the intensities to e.g. the uint8 data type range.