8000 Add TransmissionAbsorptionConvertor example by hrobarts · Pull Request #180 · TomographicImaging/CIL-Demos · GitHub
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Add TransmissionAbsorptionConvertor example #180

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Add TransmissionAbsorptionConvertor example #180

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138 changes: 138 additions & 0 deletions how-to/3_Prepare_data/TransmissionAbsorptionConvertor.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transmission-absorption convertor\n",
"##### This example shows how to use the `TransmissionAbsorptionConvertor` processor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the example dataset `dataexample.WALNUT` using `download_data()` then `get()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cil.utilities import dataexample\n",
"dataexample.WALNUT.download_data(data_dir='.', prompt=False)\n",
"data = dataexample.WALNUT.get('.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a `TransmissionAbsorptionConvertor` procesor to convert from x-ray transmission data to x-ray absorption data using the Beer-Lambert law "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from cil.processors import TransmissionAbsorptionConverter\n",
"processor = TransmissionAbsorptionConverter()\n",
"processor.set_input(data)\n",
"data_abs = processor.get_output()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display the data before and after applying the `TransmissionAbsorptionConverter()` processor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cil.utilities.display import show2D\n",
"show2D([data, data_abs], ['Before TransmissionAbsorptionConverter', 'After TransmissionAbsorptionConverter'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the data contains zeros or negative numbers, you may want to consider using `Normaliser` first (see our examples on the How-To's page). Otherwise you can specify a minium value to use in the `TransmissionAbsorptionConverter` to avoid 0 in the log calculation"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"min_intensity = 0.0001 # specify a minimum value to avoid 0 in log"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also specify the value corresponding to the maximum transmission (white level), which will correspond to the transmission equalling zero, this might be useful if there are outliers in the data which you want to ignore"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"white_level = 1.2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run `TransmissionAbsorptionConverter` again and plot the data with the new arguments"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"processor = TransmissionAbsorptionConverter(min_intensity=min_intensity, white_level=white_level)\n",
"processor.set_input(data)\n",
"data_abs = processor.get_output()\n",
"show2D([data, data_abs], ['Transmission', 'Absorption'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "cil_tests",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
0