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# -*- 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# 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().
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from cil.utilities import dataexample
dataexample.WALNUT.download_data(data_dir='../data', 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.
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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"
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from cil.io import ZEISSDataReader
file_name = "../data/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
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from cil.utilities.display import show_geometry
show_geometry(data.geometry)
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<cil.utilities.display.show_geometry at 0x7f936e520f80>
View the data with show2D
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from cil.utilities.display import show2D
show2D(data)
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<cil.utilities.display.show2D at 0x7f936e521250>
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.
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roi = {'horizontal':(200, 800, 1), 'vertical':-1}
data_reader = ZEISSDataReader(file_name=file_name, roi=roi)
data = data_reader.read()
show2D(data)
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<cil.utilities.display.show2D at 0x7f936e522180>
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
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roi = {'horizontal':(None, None, 4), 'vertical':(None, None, 2)}
data_reader = ZEISSDataReader(file_name=file_name, roi=roi)
data = data_reader.read()
show2D(data)
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<cil.utilities.display.show2D at 0x7f936e28e450>
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
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data_reader = ZEISSDataReader(file_name=file_name)
ag = data_reader.get_geometry()
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show_geometry(ag)
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<cil.utilities.display.show_geometry at 0x7f936d758f80>
Uncomment the cell below to delete the dataset and its folder
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# import shutil
# shutil.rmtree('../data/walnut')