mapp-tricks 0.1.12

Reusable code developed during my PhD in the Medical Applications of Particle Physics group at the University of Bern.

mapp-tricks package

Reusable code developed during my PhD in the Medical Applications of Particle Physics (MAPP) group at the University of Bern. It has several modules, some of which are explained below. The other modules I figured are too specific and probably not useful for others, but feel free to explore them.

Use at your own risk!

• Lars Eggimann

Usage of HPGeCalibration

Importing:

from mapp_tricks.spectrometer_calibrations import HPGeCalibration

Basic usage for directly getting the activity at the end of the beam:

calibration = HPGeCalibration(level=1, with_aluminum_foil=True)

A_EoB = calibration.get_activity_for_peak_at_end_of_beam(
    peak_area=...,
    peak_energy=...,
    life_time=...,
    real_time=...,
    cooling_time=...,
    branching_ratio=...,
    half_life=...,
)

print(f"Activity at end of beam: {A_EoB:.3f} Bq")

Or just the efficiency:

efficiency = calibration.evaluate_efficiency_at_energy(
    energy=...
)

print(f"Efficiency: {efficiency:.3f}")

You can also ask for activity of peak at the start of spectra measurement:

A_SoM = calibration.get_activity_for_peak_at_start_of_measurement(
    peak_area=...,
    peak_energy=...,
    life_time=...,
    real_time=...,
    branching_ratio=...,
    half_life=...,
)

print(f"Activity at start of measurement: {A_SoM:.3f} Bq")

You can also show the fit to visually verify the calibration:

calibration.plot_fit()

Usage of X-ray Spectrometer Efficiency Calibration

The math and general implementation was developed by Samuel Dominique Juillerat (TODO: add link to rhodium paper once published or his masters thesis). I wrapped it in a class to make the functionality easily accessible in a package.

Basic usage is similar to the HPGeCalibration module.

from mapp_tricks.spectrometer_calibrations import XRayCalibration

calib = XRayCalibration(level=10, source_radius=0)
eff = calib.evaluate_efficiency_at(energy_in_keV=40) # energy_in_keV can also be a ufloat

Note that for a source_radius > 0 the efficiency calculation accounts for the geometry of a disk source with given radius. This computation takes very long (several seconds) since it performs integration over the source surface and detector surface. For point like sources (source_radius=0) the computation is almost instant and is default value.

To get the plot of the efficiency calibration fit:

fig = calib.get_plot()

Which will return the matplotlib figure object (Samuel implemented this module with matplotlib).

Note that this plot will be for a point like source and does not account for extended source geometries even if the calibration was initialized with a non-zero source radius.

Usage Orbitos Utils

ORBITOS is a custom software developed to control and acquire data from various beam shaping and monitoring devices.

ElectrometerDataAnalyzer

The orbitos_utils module provides a convenient tool to plot and analyze data from the electrometer in its most simple use case. For more advanced analysis one can still use the util to easily extract the raw data and perform custom analysis.

from mapp_tricks.orbitos_utils import ElectrometerDataAnalyzer

eda = ElectrometerDataAnalyzer(path_to_csv="data/electrometer_data.csv")
electrometer_data = eda.analyze_beam_data()

The electrometer_data is a python object that contains the following attributes:

class BeamData:
    start_of_beam: datetime
    end_of_beam: datetime
    t_irradiation: float
    integrated_charge: ufloat
    plot: go.Figure

i.e. to get the integrated charge (which is a ufloat):

integrated_charge = electrometer_data.integrated_charge
print(f"Integrated charge: {integrated_charge:.3f} C")

To get only the raw data:

eda = ElectrometerDataAnalyzer(path_to_csv="data/electrometer_data.csv")

raw_data = eda.df

The raw data is a pandas dataframe which looks like this

      timestamp       current                   datetime
0  1.756286e+09  1.500000e-12 2025-08-27 11:05:49.924095
1  1.756286e+09 -1.000000e-12 2025-08-27 11:05:50.227460
2  1.756286e+09 -5.000000e-13 2025-08-27 11:05:50.530236
3  1.756286e+09 -8.000000e-13 2025-08-27 11:05:50.834038
4  1.756286e+09 -5.000000e-13 2025-08-27 11:05:51.140312
...

Integrated Correction Factor

It also calculates the integrated correction factor accounting for the decay of a isotope produced during irradiation. It accounts for irregular beam-shape and accurately integrates the needed correction factor to effectively determine i.e. the cross section of a reaction. It is implemented according to the following equation: $$ f(t) = \frac{\int_0^t P(t'),dt'}{e^{-\lambda t}\int_0^t e^{\lambda t'} P(t'),dt'} $$ , where $P(t)$ is the production rate (which is proportional to the beam current for all times $t'$) and $\lambda$ is the decay constant of the isotope. For constant $P(t) = P$ this yields: $$ f(t) = \frac{\lambda t}{1 - e^{-\lambda t}} $$ , which is the well known correction factor we use for i.e. the cross section calculation.

To get the integrated correction factor one can use the following example for a Tc101 peak:

eda = ElectrometerDataAnalyzer(path_to_csv="data/messy_beam_electrometer_data.csv")

electrometer_data = eda.analyze_beam_data()
integrated_correction_factor = eda.get_integrated_correction_factor(
    half_life=14.12 * 60
)

print(f"Integrated correction factor: {integrated_correction_factor:.3f}")

Usage of Film Analyzer

The film_analyzer module provides tools to analyze scanned images of gafchromic films. It can read the image, extract the RGB channels, and convert the pixel values to dose using a calibration curve defined in a bundled JSON file.

Basic Usage

from mapp_tricks.film_analyzer.film_analyzer import  FilmAnalyzer

fa = FilmAnalyzer(
    folder='./data/film_reader_test_data/',
    dpi=1200,
    calibration_key='EBT3_new_METAS_ImageJwRGB',
    plot_downsample=0.5
)

In order to analyze films we need information about the center positiona and ROI shape and size. This is done via a config file that can be generated as a template and then has to be edited manually, using the film analyzer is a iterative process. To generate a template config file and save it:

config = fa.generate_default_config(
    default_shape='circular',
    default_max_dose=10.0,
)
fa.save_config(config, fa.folder / 'config.json')

Now run the analysis:

fa.load_config(fa.folder / 'config.json')
fa.process_all(config)

This will create a subfolder results/ in the folder where the images are located. In this folder you will find dose maps and profiles for each film analyzed and also a summary file summary.csv that contains the mean dose and standard deviation and other information in the ROI for each film.

Now use the generated html plots to optimize the config file and re-run the analysis until satisfied. Make sure to not overwrite the config file with the generated one each time you run the analysis -> remove the saving and generating part after the first time.