### From a file¶

kikuchipy can read and write experimental EBSD patterns and EBSD master patterns from/to multiple formats (see supported formats. To load patterns from file use the load() function. Let’s import the necessary libraries and read the Nickel EBSD test data set directly from file (not via kikuchipy.data.nickel_ebsd_small()):

[1]:

# exchange inline for qt5 for interactive plotting from the pyqt package
%matplotlib inline

import os
import hyperspy.api as hs
import kikuchipy as kp
import numpy as np
import matplotlib.pyplot as plt

nordif_ebsd = "nordif/Pattern.dat"
s

[1]:

<EBSD, title: Pattern, dimensions: (3, 3|60, 60)>


Or, load the spherical projection of the northern hemisphere of an EBSD master pattern for a 20 keV beam energy from a modified version of EMsoft’s master pattern file, returned from their EMEBSDmaster.f90 program:

[2]:

emsoft_master_pattern = (
"emsoft_ebsd_master_pattern/ni_mc_mp_20kv_uint8_gzip_opts9.h5"
)
s_mp

[2]:

<EBSDMasterPattern, title: ni_mc_mp_20kv_uint8_gzip_opts9, dimensions: (|401, 401)>


Both the spherical and Lambert projections of this master pattern data is available via kikuchipy.data.nickel_ebsd_master_pattern_small().

All file readers support accessing the data without loading it into memory (with the Dask library), which can be useful when processing large data sets to avoid memory errors:

[3]:

s_lazy = kp.load(datadir + nordif_ebsd, lazy=True)
s_lazy

[3]:

<LazyEBSD, title: Pattern, dimensions: (3, 3|60, 60)>


Parts or all of the data can be read into memory by calling compute():

[4]:

s_lazy_copy = s_lazy.inav[:2, :].deepcopy()
s_lazy_copy.compute()
s_lazy_copy

[########################################] | 100% Completed |  0.1s

[4]:

<EBSD, title: Pattern, dimensions: (2, 3|60, 60)>

[5]:

s_lazy.compute()
s_lazy

[########################################] | 100% Completed |  0.1s

[5]:

<EBSD, title: Pattern, dimensions: (3, 3|60, 60)>


Note

When lazily loaded EBSD patterns are processed, they are processed chunk by chunk, which in many cases leads to longer processing times, so processing lazy data sets should be done with some care. See the relevant HyperSpy user guide for information on how to do this.

Visualization of data is done by navigating navigation space, showing the signal in each navigation point:

[6]:

s.plot()


Upon loading, kikuchipy tries to read all scan information from the file and stores everything it can read in the original_metadata attribute:

[7]:

# s.original_metadata  # Long output


Also, some information may be stored in a standard location in the metadata attribute where it can be used by EBSD class methods:

[8]:

s.metadata

[8]:

• Acquisition_instrument
• SEM
• Detector
• EBSD
• azimuth_angle = 0.0
• binning = 1
• detector = NORDIF UF1100
• elevation_angle = 0.0
• exposure_time = 0.0035
• frame_number = -1
• frame_rate = 202
• gain = 0.0
• grid_type = square
• manufacturer = NORDIF
• sample_tilt = 70.0
• scan_time = 148
• static_background = array([[84, 87, 90, ..., 27, 29, 30], [87, 90, 93, ..., 27, 28, 30], ... 80, 82, ..., 28, 26, 26], [76, 78, 80, ..., 26, 26, 25]], dtype=uint8)
• version = 3.1.2
• xpc = -1.0
• ypc = -1.0
• zpc = -1.0
• beam_energy = 20.0
• magnification = 200
• microscope = Hitachi SU-6600
• working_distance = 24.7
• General
• original_filename = ../kikuchipy/data/nordif/Pattern.dat
• title = Pattern
• Sample
• Phases
• 1
• atom_coordinates
• 1
• atom =
• coordinates = array([0., 0., 0.])
• debye_waller_factor = 0.0
• site_occupation = 0.0
• formula =
• info =
• lattice_constants = array([0., 0., 0., 0., 0., 0.])
• laue_group =
• material_name = Ni
• point_group =
• setting = 0
• source =
• space_group = 0
• symmetry = 0
• Signal
• binned = False
• signal_type = EBSD

The number of patterns in horizontal and vertical direction, pattern size in pixels, scan step size and detector pixel size is stored in the axes_manager attribute:

[9]:

s.axes_manager

[9]:


< Axes manager, axes: (3, 3|60, 60) >

Navigation axis name size index offset scale units
x 3 0 0.0 1.5 um
y 3 0 0.0 1.5 um
Signal axis name size offset scale units
dx 60 0.0 1.0 um
dy 60 0.0 1.0 um

This information can be modified directly, and information in metadata and axes_manager can also be modified by the EBSD class methods set_experimental_parameters(), set_phase_parameters(), set_scan_calibration() and set_detector_calibration(). For example, to set or change the accelerating voltage, horizontal pattern centre coordinate and static background pattern (stored as a numpy.ndarray):

[10]:

s.set_experimental_parameters(
beam_energy=15,
xpc=0.5073,
)
)


In addition to the HyperSpy provided metadata, original_metadata and axes_manager properties, kikuchipy tries to read a CrystalMap object with indexing results into a xmap property and an EBSDDetector object into a detector property:

[11]:

s.xmap  # This is empty for this signal

[12]:

s.detector

[12]:

EBSDDetector (60, 60), px_size 1.0 um, binning 1, tilt 0, pc (0.5, 0.5, 0.5)


### From a NumPy array¶

An EBSD or EBSDMasterPattern signal can also be created directly from a numpy.ndarray. To create a data set of (60 x 60) pixel patterns in a (10 x 20) grid, i.e. 10 and 20 patterns in the horizontal and vertical scan directions respectively, of random intensities:

[13]:

s_np = kp.signals.EBSD(np.random.random((20, 10, 60, 60)))
s_np

[13]:

<EBSD, title: , dimensions: (10, 20|60, 60)>


When processing large data sets, it is useful to load data lazily with the Dask library. This can be done upon reading patterns from a file by setting lazy=True when using the load() function, or directly from a dask.array.Array:

[14]:

s_da = kp.signals.LazyEBSD(
da.random.random((20, 10, 60, 60), chunks=(2, 10, 60, 60))
)
print(s_da)
s_da.data

<LazyEBSD, title: , dimensions: (10, 20|60, 60)>

[14]:

 Array Chunk 5.76 MB 576.00 kB (20, 10, 60, 60) (2, 10, 60, 60) 10 Tasks 10 Chunks float64 numpy.ndarray

### From a HyperSpy signal¶

HyperSpy provides the method set_signal_type() to change between BaseSignal subclasses, of which EBSD, EBSDMasterPattern and VirtualBSEImage are three. To get one of these objects from a HyperSpy Signal2D object:

[15]:

s_hs = hs.signals.Signal2D(np.random.random((20, 10, 60, 60)))
s_hs

[15]:

<Signal2D, title: , dimensions: (10, 20|60, 60)>

[16]:

s_hs.set_signal_type("EBSD")
s_hs

[16]:

<EBSD, title: , dimensions: (10, 20|60, 60)>

[17]:

s_hs.set_signal_type("VirtualBSEImage")
s_hs

[17]:

<VirtualBSEImage, title: , dimensions: (10, 20|60, 60)>

[18]:

s_hs.set_signal_type("EBSDMasterPattern")
s_hs

[18]:

<EBSDMasterPattern, title: , dimensions: (10, 20|60, 60)>


## Save patterns¶

To save experimental EBSD patterns to file use the save() method. For example, to save an EBSD signal in an HDF5 file, with file name patterns.h5, in our default h5ebsd format:

[20]:

tempdir = datadir + "temp/"; os.mkdir(tempdir)
s.save(tempdir + "patterns")

---------------------------------------------------------------------------
FileExistsError                           Traceback (most recent call last)
<ipython-input-20-9e22fa4f983a> in <module>
----> 1 tempdir = datadir + "temp/"; os.mkdir(tempdir)
2 s.save(tempdir + "patterns")

FileExistsError: [Errno 17] File exists: '../kikuchipy/data/temp/'


Warning

If we want to overwrite an existing file:

s.save("patterns.h5", overwrite=True)


If we want to save patterns in NORDIF’s binary .dat format instead:

[21]:

s.save(tempdir + "patterns.dat")


To save an EBSDMasterPattern to an HDF5 file, we use the save method inherited from HyperSpy to write to their HDF5 specification:

[ ]:

s_hs.save(tempdir + "master_pattern.hspy")
s_hs


These master patterns can then be read into an EBSDMasterPattern signal again via HyperSpy’s load():

[ ]:

s_mp2 = hs.load(
tempdir + "master_pattern.hspy", signal_type="EBSDMasterPattern"
)
s_mp2


Note

To save results from statistical decomposition (machine learning) of patterns to file see the section Saving and loading results in HyperSpy’s user guide. Note that the file extension .hspy must be used upon saving, s.save('patterns.hspy'), as the default extension in kikuchipy, .h5, yields a kikuchipy h5ebsd file where the decomposition results aren’t saved. The saved patterns can then be reloaded using HyperSpy’s load() function and passing the signal_type="EBSD" parameter as explained above.

## Supported EBSD formats¶

Currently, kikuchipy has readers and writers for the following formats:

Format

Write

Bruker Nano h5ebsd

Yes

No

EDAX TSL h5ebsd

Yes

No

kikuchipy h5ebsd

Yes

Yes

NORDIF binary

Yes

Yes

EMsoft simulated EBSD HDF5

Yes

No

EMsoft EBSD master pattern HDF5

Yes

No

Bruker Nano h5ebsd

Yes

No

Note

If you want to process your patterns with kikuchipy, but use an unsupported EBSD vendor software, or if you want to write your processed patterns to a vendor format that does not support writing, please request this feature in our issue tracker.

### h5ebsd¶

The h5ebsd format [JGU+14] is based on the HDF5 open standard (Hierarchical Data Format version 5). HDF5 files can be read and edited using e.g. the HDF Group’s reader HDFView or the Python package used by kikuchipy, h5py. Upon loading an HDF5 file with extension .h5, .hdf5, or .h5ebsd, the correct reader is determined from the file. Supported h5ebsd formats are listed in the table above.

If an h5ebsd file contains multiple scans, as many scans as desirable can be read from the file. For example, if the file contains two scans with names My awes0m4 Xcan #! with a long title and Scan 2:

[ ]:

kikuchipy_ebsd = "kikuchipy/patterns.h5"
scan_group_names=["My awes0m4 Xcan #! with a long title", "Scan 2"]
)
print(s_awsm)
print(s2)


Here, the h5ebsd file_reader() is called. If only Scan 2 is to be read, scan_group_names="Scan 2" can be passed:

[ ]:

s2 = kp.load(filename=datadir + kikuchipy_ebsd, scan_group_names="Scan 2")
s2


The scan_group_names parameter is unnecessary if only the first scan in the file is to be read, since reading only the first scan in the file is the default behaviour.

So far, only saving patterns to kikuchipy’s own h5ebsd format is supported. It is possible to write a new scan with a scan name Scan x, where x is an integer, to an existing, but closed, h5ebsd file in the kikuchipy format, e.g. one containing only Scan 1, by passing:

[ ]:

new_file = "patterns_new.h5"
s2.save(tempdir + new_file, scan_number=1)

filename=tempdir + new_file, scan_group_names=["Scan 1", "Scan 2"]
)
print(s2_new)
print(s_awsm_new)


Here, the h5ebsd file_writer() is called.

Note

The EBSD.xmap and EBSD.detector properties are so far not written to this file format.

### NORDIF binary¶

Patterns acquired using NORDIF’s acquisition software are stored in a binary file usually named Pattern.dat. Scan information is stored in a separate text file usually named Setting.txt, and both files usually reside in the same directory. If this is the case, the patterns can be loaded by passing the file name as the only parameter. If this is not the case, the setting file can be passed upon loading:

[ ]:

s_nordif = kp.load(
)
s_nordif


Here, the NORDIF file_reader() is called. If the scan information, i.e. scan and pattern size, in the setting file is incorrect or the setting file is not available, patterns can be loaded by passing:

[ ]:

s_nordif = kp.load(
filename=datadir + nordif_ebsd, scan_size=(1, 9), pattern_size=(60, 60)
)
s_nordif


If a static background pattern named Background acquisition.bmp is stored in the same directory as the pattern file, this is stored in metadata upon loading.

Patterns can also be saved to a NORDIF binary file, upon which the NORDIF file_writer() is called. Note, however, that so far no new setting file, background pattern, or calibration patterns are created upon saving.

### EMsoft simulated EBSD HDF5¶

Dynamically simulated EBSD patterns returned by EMsoft’s EMEBSD.f90 program as HDF5 files can be read as an EBSD signal:

[ ]:

emsoft_ebsd = "emsoft_ebsd/simulated_ebsd.h5"  # Dummy data set
s_sim


Here, the EMsoft simulated EBSD file_reader() is called, which takes the optional argument scan_size. Passing scan_size=(2, 5) will reshape the pattern data shape from (10, 10, 10) to (2, 5, 10, 10):

[ ]:

s_sim2 = kp.load(filename=datadir + emsoft_ebsd, scan_size=(2, 5))
print(s_sim2)
print(s_sim2.data.shape)


Simulated EBSD patterns can be written to the kikuchipy h5ebsd format, the NORDIF binary format, or to HDF5 files using HyperSpy’s HDF5 specification as explained above.

### EMsoft EBSD master pattern HDF5¶

Master patterns returned by EMsoft’s EMEBSDmaster.f90 program as HDF5 files can be read as an EBSDMasterPattern signal:

[ ]:

s_mp = kp.load(filename=datadir + emsoft_master_pattern)

print(s_mp)
print(s_mp.projection)
print(s_mp.hemisphere)
print(s_mp.phase)


Here, the EMsoft EBSD master pattern file_reader() is called, which takes the optional arguments projection, hemisphere and energy. The spherical projection is read by default. Passing projection="lambert" will read the square Lambert projection instead. The northern hemisphere is read by default. Passing hemisphere="south" or hemisphere="both" will read the southern hemisphere projection or both, respectively. Master patterns for all beam energies are read by default. Passing energy=(10, 20) or energy=15 will read the master pattern(s) with beam energies from 10 to 20 keV, or just 15 keV, respectively:

[ ]:

s_mp = kp.load(
projection="lambert",
hemisphere="both",
energy=20
)

print(s_mp)
print(s_mp.projection)
print(s_mp.hemisphere)


Master patterns can be written to HDF5 files using HyperSpy’s HDF5 specification as explained above.

See [JPDeGraef19] for a hands-on tutorial explaining how to simulate these patterns with EMsoft, and [CDeGraef13] for details of the underlying theory.

## From kikuchipy into other software¶

Patterns saved in the h5ebsd format can be read by the dictionary indexing and related routines in EMsoft using the EMEBSD reader. Those routines in EMsoft also have a NORDIF reader.

Patterns saved in the h5ebsd format can of course be read in Python like any other HDF5 data set:

[ ]:

import h5py
with h5py.File(datadir + kikuchipy_ebsd, mode="r") as f:
dset = f['Scan 2/EBSD/Data/patterns']
print(dset)
patterns = dset[()]
print(patterns.shape)
plt.figure()
plt.imshow(patterns[0], cmap="gray")


## Load and save virtual BSE images¶

One or more virtual backscatter electron (BSE) images in a VirtualBSEImage signal can be read and written to file using one of HyperSpy’s many readers and writers. If they are only to be used internally in HyperSpy, they can be written to and read back from HyperSpy’s HDF5 specification as explained above for EBSD master patterns.

If we want to write the images to image files, HyperSpy also provides a series of image readers/writers, as explained in their IO user guide. If we wanted to write them as a stack of TIFF images:

[ ]:

# Get virtual image from generator
vbse_gen = kp.generators.VirtualBSEGenerator(s)
print(vbse_gen)

print(vbse_gen.grid_shape)
vbse = vbse_gen.get_images_from_grid()
print(vbse)

[ ]:

vbse.rescale_intensity()
vbse

[ ]:

vbse_fname = "vbse.tif"
vbse.save(tempdir + vbse_fname)  # Easily read into e.g. ImageJ


We can also write them to e.g. png or bmp files with Matplotlib:

[ ]:

nav_size = vbse.axes_manager.navigation_size
_ = [
plt.imsave(tempdir + f"vbse{i}.png", vbse.inav[i].data)
for i in range(nav_size)
]


Read the TIFF stack back into a VirtualBSEImage signal:

[ ]:

vbse2 = hs.load(tempdir + vbse_fname, signal_type="VirtualBSEImage")
vbse2