In [24]:

# -*- coding: utf-8 -*-
#  Copyright 2024 United Kingdom Research and Innovation
#  Copyright 2024 The University of Manchester
#  Copyright 2024 Technical University of Denmark
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
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#   Authored by:    Hannah Robarts (STFC-UKRI)

Load and visualise data with ZeissDataReader¶

This how-to shows how to use the ZeissDataReader to load data from Zeiss .txrm files and quickly visualise the data and geometry

Get the example dataset dataexample.WALNUT using download_data().

In [25]:

from cil.utilities import dataexample
dataexample.WALNUT.download_data(data_dir='.', prompt=False)

Title: HDTomo TXRM micro-CT datasets
Keywords: X-ray, CT, computed tomography, projection, reconstruction
Publication date: 2021-05-27
DOI: 10.5281/zenodo.4822516
Total size: 6.4 GB

Link: https://zenodo.org/records/4822516/files/walnut.zip   size: 6.4 GB

Checksum is correct. (92e455d5dfa3ac8eeb89a37bedd4cf23)
All files have been downloaded.

Out[25]:

True

Now load a file from the dataset using the ZEISSDataReader()

We specify the file to load using file_name = '.walnut/valnut/valnut_2014-03-21_643_28/tomo-A/valnut_tomo-A.txrm"

In [26]:

from cil.io import ZEISSDataReader
file_name = "./walnut/valnut/valnut_2014-03-21_643_28/tomo-A/valnut_tomo-A.txrm"
data_reader = ZEISSDataReader(file_name=file_name)
data = data_reader.read()

View the geometry with show_geometry to display information about the source and detector setup

In [27]:

from cil.utilities.display import show_geometry
show_geometry(data.geometry)

Out[27]:

<cil.utilities.display.show_geometry at 0x7f1850cf77d0>

View the data with show2D

In [28]:

from cil.utilities.display import show2D
show2D(data)

Out[28]:

<cil.utilities.display.show2D at 0x7f185169d700>

Use the roi argument to load a subset of the data. roishould be passed as a dictionary e.g. {'axis_labels_1': (start, end, step),'axis_labels_2': (start, end, step)} with axis labels that describe the data dimension labels

To load a cropped subset of the data, change the start and end values. 'axis_label': -1 is a shortcut to load all elements along the axis.

In [29]:

roi = {'horizontal':(200, 800, 1), 'vertical':-1}
data_reader = ZEISSDataReader(file_name=file_name, roi=roi)
data = data_reader.read()
show2D(data)

Out[29]:

<cil.utilities.display.show2D at 0x7f1850c85ee0>

To load a binned subset of the data, change the step value. Here we use different binning for the horizontal and vertical dimensions which results in a different aspect ratio

In [30]:

roi = {'horizontal':(None, None, 4), 'vertical':(None, None, 2)}
data_reader = ZEISSDataReader(file_name=file_name, roi=roi)
data = data_reader.read()
show2D(data)

Out[30]:

<cil.utilities.display.show2D at 0x7f18458d7fb0>

Sometimes we might want to just read the geometry from a Zeiss file, for example if we want to load the raw TIFF files separately but get the correct geometry from the .txrm file. We can do this by instantiating the data reader and using the get_geometry() method

In [31]:

data_reader = ZEISSDataReader(file_name=file_name)
ag = data_reader.get_geometry()

In [32]:

show_geometry(ag)

Out[32]:

<cil.utilities.display.show_geometry at 0x7f1850d0aba0>