A small library to read output files MPA3 / MPA4 system lst file.
lstpy is full-python code but thanks to numba and multi-threading, the readout is fast enough.
pip install lstpy
lstpy requires numba and numpy. Anaconda distribution might be the easiest way to install them.
conda install numba
conda install numpy
xarray is an optional dependency, but the use of xarray greatly simplifies the further analysis.
import lstpy
header, (values, time, ch, events) = lstpy.load(filename)or with xarray, you can get a single object
>>> import lstpy
>>> lstpy.load_xr(filename)
<xarray.DataArray (ch: 2, events: 28991)>
array([[8191, 2679, 1431, ..., 2102, 1491, 1635],
[ 973, 973, 973, ..., 4379, 4380, 4381]], dtype=uint16)
Coordinates:
* ch (ch) uint64 2 3
* events (events) uint64 1 2 3 4 5 6 ... 30141 30142 30143 30144
time (events) uint32 15 163 196 223 311 ... 409913 409940
Attributes:
ctm: 400
...Load a list file and construct a histogram.
>>> import lstpy
>>> import numpy as np
>>> da = lstpy.load_histogram('test_public/data/Neon_KLL_002.lst', n_bins=2)
>>> da
<xarray.DataArray (ADC0: 1024, ADC1: 1024)>
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
Coordinates:
* ADC0 (ADC0) float64 4.0 12.0 20.0 28.0 ... 8.172e+03 8.18e+03 8.188e+03
* ADC1 (ADC1) float64 4.0 12.0 20.0 28.0 ... 8.172e+03 8.18e+03 8.188e+03
>>> np.log(da).plot()
This is equivalent to
>>> data = lstpy.load_xr('test_public/data/Neon_KLL_002.lst')
>>> da = lstpy.utils.histogram(data, n_bins=2)- Sometimes lst file is corrupted and cannot read
Try disabling multiprocessing, by setting chunk=None,
lstpy.load_xr('test_public/data/Neon_KLL_002.lst', chunk=None)Not all the features of MPA system are not yet supported. Contributions are welcome. If you have any questions or request, raise an issue at Github issue page.