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Tool to generate a HDF table of sampling interval coefficients #690
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import numpy as np | ||
from astropy.io import fits | ||
from numba import jit, prange | ||
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from ctapipe.core import Component | ||
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__all__ = ['SamplingIntervalCalculate'] | ||
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N_PIXELS = 1855 | ||
N_CAPACITORS_CHANNEL = 1024 | ||
N_SAMPLES = 40 | ||
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class SamplingIntervalCalculate(Component): | ||
""" | ||
The SamplingIntervalCalculate class to create a sampling interval coefficient table for LST readout system using chip DRS4. | ||
""" | ||
def __init__(self): | ||
self.peak_count = np.zeros([N_PIXELS, N_CAPACITORS_CHANNEL]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. thanks, I set the proper data type (uint16 is enough). |
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self.fc_count = np.zeros([N_PIXELS, N_CAPACITORS_CHANNEL]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. As above |
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self.peak_count_stuck ={} | ||
self.fc_count_stuck = {} | ||
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self.sampling_interval_coefficient = {} | ||
self.charge_array_after_corr ={} | ||
self.charge_reso_array_after_corr ={} | ||
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self.charge_reso_final = np.zeros(N_PIXELS) | ||
self.used_run = np.zeros(N_PIXELS) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Run is also integer |
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self.sampling_interval_coefficient_final = np.zeros([N_PIXELS, N_CAPACITORS_CHANNEL]) | ||
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def increment_peak_count(self, event, tel_id, gain, r0_r1_calibrator): | ||
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waveform = event.r1.tel[tel_id].waveform | ||
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first_capacitors = r0_r1_calibrator.first_cap | ||
r1_sample_start = r0_r1_calibrator.r1_sample_start.tel[tel_id] | ||
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# Check pulse | ||
pulse_event_flag = np.sum(np.max(waveform[gain], axis=1) > 100) > 1800 | ||
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if pulse_event_flag: | ||
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# find pulse peak position | ||
pulse_peak = np.argmax(waveform[gain], axis=1) | ||
fc = first_capacitors[tel_id][gain] | ||
pulse_peak_abs_pos = (fc + r1_sample_start + pulse_peak) % N_CAPACITORS_CHANNEL | ||
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# increment peak count array | ||
self.peak_count[np.arange(N_PIXELS), pulse_peak_abs_pos] += 1 | ||
self.fc_count[np.arange(N_PIXELS), fc % N_CAPACITORS_CHANNEL] += 1 | ||
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def stuck_single_sampling_interval(self, file_list, gain): | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. What does There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. sorry, it is a typo... it should be 'stack' |
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for single_file in file_list: | ||
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run_id, gain_in_file, peak_count, fc_count = load_single_fits_sampling_interval(single_file) | ||
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if gain_in_file == gain: | ||
if not run_id in self.peak_count_stuck.keys(): | ||
self.peak_count_stuck[run_id] = np.zeros([N_PIXELS, N_CAPACITORS_CHANNEL]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. dtype? |
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self.fc_count_stuck[run_id] = np.zeros([N_PIXELS, N_CAPACITORS_CHANNEL]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. dtype? |
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self.peak_count_stuck[run_id] += peak_count | ||
self.fc_count_stuck[run_id] += fc_count | ||
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def convert_to_samp_interval_coefficient(self, gain): | ||
# convert peak counts to sampling interval coefficient | ||
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for run_id in self.peak_count_stuck.keys(): | ||
self.sampling_interval_coefficient[run_id] = np.zeros([N_PIXELS, N_CAPACITORS_CHANNEL + N_SAMPLES]) | ||
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for pixel in range(N_PIXELS): | ||
self.sampling_interval_coefficient[run_id][pixel, :N_CAPACITORS_CHANNEL] = \ | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Please use implicit line continuations inside parentheses instead of these line breaks with Also, it looks like this loop is not necessary but could be avoided by using the There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. thanks for the comment. done. |
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self.peak_count_stuck[run_id][pixel] / np.sum(self.peak_count_stuck[run_id][pixel]) * N_CAPACITORS_CHANNEL | ||
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self.sampling_interval_coefficient[run_id][pixel, N_CAPACITORS_CHANNEL:] = \ | ||
self.sampling_interval_coefficient[run_id][pixel, :N_SAMPLES] | ||
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def set_charge_array(self, gain): | ||
self.charge_array_before_corr = np.zeros([N_PIXELS, 60000]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Why 60000? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I modified related codes. If There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. You can actually get the number of events by doing A more flexible solution would be to use a python list and append to it, converting to a 2d numpy array at the end. |
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self.charge_reso_array_before_corr = np.zeros(N_PIXELS) | ||
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for run_id in self.peak_count_stuck.keys(): | ||
self.charge_array_after_corr[run_id] = np.zeros([N_PIXELS, 60000]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. same as above |
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self.charge_reso_array_after_corr[run_id] = np.zeros(N_PIXELS) | ||
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def calc_charge(self, count, event, tel_id, gain, r0_r1_calibrator): | ||
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waveform = event.r1.tel[tel_id].waveform | ||
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first_capacitors = r0_r1_calibrator.first_cap | ||
r1_sample_start = r0_r1_calibrator.r1_sample_start.tel[tel_id] | ||
r1_sample_end = r0_r1_calibrator.r1_sample_end.tel[tel_id] | ||
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# Check pulse | ||
pulse_event_flag = np.sum(np.max(waveform[gain], axis=1) > 100) > 1800 | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This needs more context and probably the unnamed constants here should be transformed into either global constants or configurable traitlets. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. added contexts, and set global constants for them |
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if pulse_event_flag: | ||
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# find pulse peak position | ||
pulse_peak = np.argmax(waveform[gain], axis=1) | ||
fc = first_capacitors[tel_id][gain] | ||
pulse_peak_abs_pos = (fc + r1_sample_start + pulse_peak) % N_CAPACITORS_CHANNEL | ||
integ_start = pulse_peak - 2 | ||
integ_last = integ_start + 5 | ||
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integ_abs_start = (pulse_peak_abs_pos -2 ) % N_CAPACITORS_CHANNEL | ||
integ_abs_last = integ_abs_start + 5 | ||
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for pixel in range(N_PIXELS): | ||
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if integ_start[pixel] < 0 or integ_last[pixel] > (r1_sample_end - r1_sample_start): | ||
continue | ||
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self.charge_array_before_corr[pixel][count] = np.sum(waveform[gain, pixel,integ_start[pixel]:integ_last[pixel]]) | ||
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for run_id in self.peak_count_stuck.keys(): | ||
samp_interval_coefficient = self.sampling_interval_coefficient[run_id] | ||
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self.charge_array_after_corr[run_id][pixel][count] = \ | ||
np.sum(waveform[gain,pixel,integ_start[pixel]:integ_last[pixel]] \ | ||
* samp_interval_coefficient[pixel, integ_abs_start[pixel]:integ_abs_last[pixel]]) | ||
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def calc_charge_reso(self, gain): | ||
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# Before correction | ||
for pixel in range(N_PIXELS): | ||
charge_array_before_corr_final = self.charge_array_before_corr[pixel] | ||
charge_array_before_corr_final = charge_array_before_corr_final[charge_array_before_corr_final!=0] | ||
self.charge_reso_array_before_corr[pixel] = np.std(charge_array_before_corr_final)/np.mean(charge_array_before_corr_final) | ||
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for run_id in self.peak_count_stuck.keys(): | ||
charge_array_after_corr_final = self.charge_array_after_corr[run_id][pixel] | ||
charge_array_after_corr_final = charge_array_after_corr_final[charge_array_after_corr_final!=0] | ||
self.charge_reso_array_after_corr[run_id][pixel] = np.std(charge_array_after_corr_final)/np.mean(charge_array_after_corr_final) | ||
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def verify(self): | ||
n_keys=len(self.charge_reso_array_after_corr.keys()) | ||
run_id_array = np.zeros(n_keys) | ||
charge_reso_array_after_corr_all = np.zeros([n_keys, 1855]) | ||
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for i, ikey in enumerate(self.charge_reso_array_after_corr.keys()): | ||
run_id_array[i] = ikey | ||
charge_reso_array_after_corr_all[i] = self.charge_reso_array_after_corr[ikey] | ||
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for ipix in range(N_PIXELS): | ||
min_charge_reso_arg = np.argmin(charge_reso_array_after_corr_all.T[ipix]) | ||
self.charge_reso_final[ipix] = charge_reso_array_after_corr_all[min_charge_reso_arg, ipix] | ||
self.used_run[ipix] = int(run_id_array[min_charge_reso_arg]) | ||
self.sampling_interval_coefficient_final[ipix] = self.sampling_interval_coefficient[self.used_run[ipix]][ipix, :N_CAPACITORS_CHANNEL] | ||
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def load_single_fits_sampling_interval(input_file): | ||
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hdulist = fits.open(input_file) | ||
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run_id = hdulist[0].header['run_id'] | ||
gain = hdulist[0].header['gain'] | ||
peak_count = hdulist[1].data | ||
fc_count = hdulist[2].data | ||
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return run_id, gain, peak_count, fc_count | ||
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you can import these from
ctapipe_io_lst.constants.
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done!