Using the GammaSpectrum class

The GammaSpectrum class from the pygammaspec.spectrum module allows the storage and manipulation of gamma spectroscopy data. The data can be loaded directly from experimental datafiles using dedicated classmethods. At the moment the following file format are supported:

• ASCII pulse height histogram exported by PRA (from_PRA_histogram)

As an example, the following snippet of code can be used to create an instance of the GammaSpectrum class starting from a PRA pulse height histogram file:

from pygammaspec.spectrum import GammaSpectrum

background = GammaSpectrum.from_PRA_histogram("../utils/background.txt", 25851)

print(background)

<pygammaspec.spectrum.GammaSpectrum object at 0x7fed005e3a90>

The obtained class now contains as properties the total acquisition time, the total recorded activity, the list of recorded channels and the corresponding number of counts expressed as counts per second per channel. An example of how to access the properties if shown in the following exampls:

print(f"Acquisition time: {background.acquisition_time} s")
print(f"Total activity of the sample: {background.total_activity:.2f} cps")

print("\nChannels:")
print(background.channels)

print("\nCounts:")
print(background.counts)

Acquisition time: 25851 s
Total activity of the sample: 37.48 cps

Channels:
[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.2, 1.21, 1.22, 1.23, 1.24, 1.25, 1.26, 1.27, 1.28, 1.29, 1.3, 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37, 1.38, 1.39, 1.4, 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 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Counts:
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The data contained in a GammaSpectrum object can easily be represented using the plot_spectrum function from the pygammaspec.visualization module. The following syntax can be used:

from pygammaspec.visualization import plot_spectrum

plot_spectrum(background, ylog=True)

The noisy raw data can be smoothed using the average_smoothing method. The method applies a moving average to the data returning a smoothed out GammaSpectrum object. The size of the averaging window can be set by the user using the width argument. The user specified width will set the extension (\(w\)) the windows from the center point. In ohter terms, the average window will have a size of \(2\cdot w + 1\) centered around the cannel currently subject to the smoothing. As a consequence the first and last \(w\) channel of the spectrum will be left empty. An example of smoothing, togheter with the resulting spectrum, is presented in the following code box:

from pygammaspec.visualization import plot_spectrum

smoothed_background = background.average_smoothing(5)

plot_spectrum(smoothed_background, ylog=True)

The library also supports arithmetic operations between instances of the GammaSpectrum class. In particular given two different GammaSpectrum object, sum and subtractions can be performed using the + and - operators. If one set of data has more channels than the other, the shorter one will be completed, with empty channels, to match the longer one.

Warning

The operations between spectra are allowed only when the spectra share the same channel spacing (same sequence of channel indices). If the condition for running the operations are not met, a RuntimeError exception is raised.

Note

If the spectra involved in the operation have different energy calibrations, a RuntimeWarning will be raised and the calibration will be dropped in the result. (more details on calibration are discussed here)

To show how the operation works let us firstly load a new spectrum:

sample = GammaSpectrum.from_PRA_histogram("../utils/weak_radium.txt", 25851)

plot_spectrum(sample, ylog=True)

The newly loded spectrum represents the spectrum of a very weak Ra-226 sample. Let us use the subtraction operator (-) to compute the difference of the sample spectrum and the background.

difference = sample - background

print(f"Channels in the background: {len(background.channels)}")
print(f"Channels in the sample: {len(sample.channels)}")
print(f"Channels in the difference: {len(difference.channels)}")

plot_spectrum(difference, ylog=True)

Channels in the background: 6128
Channels in the sample: 6017
Channels in the difference: 6128

Please notice how to visualize a sample together with its background and automatically computing the difference of the two, the plot_spectrum function can be used providing the background argument. The following syntax can be used:

plot_spectrum(sample, background, ylog=True)