# don't forget to set environment variable NECTARCAMDATA
import argparse
import os
import pickle
import sys
import matplotlib.pyplot as plt
import numpy as np
from nectarchain.trr_test_suite.tools_components import PedestalTool
from nectarchain.trr_test_suite.utils import adc_to_pe, pe2photons
[docs]
def get_args():
"""Parses command-line arguments for the linearity test script.
Returns:
argparse.ArgumentParser: The parsed command-line arguments.
"""
parser = argparse.ArgumentParser(
description="Pedestal substraction test B-TEL-1370.\n"
+ "According to the nectarchain component interface, you have to set\
a NECTARCAMDATA environment variable in the folder where you have\
the data from your runs or where you want them to be downloaded.\n"
+ "You have to give a list of runs (run numbers with spaces inbetwee\
n) and an output directory to save the final plot.\n"
+ "If the data is not in NECTARCAMDATA, the files will be\
downloaded through DIRAC.\n For the purposes of testing this script,\
default data is from the runs used for this test in the\
TRR document.\n"
+ "You can optionally specify the number of events to be processed\
(default 1200).\n"
)
parser.add_argument(
"-r",
"--runlist",
type=int,
nargs="+",
help="List of runs (numbers separated by space)",
required=False,
default=[3647],
)
parser.add_argument(
"-e",
"--evts",
type=int,
help="Number of events to process from each run. Default is 1200. 4000 or more\
gives best results but takes some time",
required=False,
default=10,
)
parser.add_argument(
"-o",
"--output",
type=str,
help="Output directory. If none, plot will be saved in current directory",
required=False,
default="./",
)
parser.add_argument(
"--temp_output", help="Temporary output directory for GUI", default=None
)
return parser
[docs]
def main():
"""The main function that runs the pedestal subtraction test. It parses command-line
arguments, processes the specified runs, and generates two plots:
1. A 2D heatmap of the pedestal RMS for all events and pixels.
2. A line plot of the mean pedestal RMS for each pixel, with the CTA requirement\
range highlighted.
The function also saves the generated plots to the specified output directory,\
and optionally saves the first plot to a temporary output file.
"""
parser = get_args()
args = parser.parse_args()
runlist = args.runlist
nevents = args.evts
# output_dir = args.output
# temp_output_file = args.temp_output
output_dir = os.path.abspath(args.output)
temp_output = os.path.abspath(args.temp_output) if args.temp_output else None
print(f"Output directory: {output_dir}") # Debug print
print(f"Temporary output dir: {temp_output}") # Debug print
sys.argv = sys.argv[:1]
output = []
for run in runlist:
print("PROCESSING RUN {}".format(run))
tool = PedestalTool(
progress_bar=True,
run_number=run,
max_events=nevents,
events_per_slice=999,
log_level=20,
peak_height=10,
window_width=16,
overwrite=True,
)
tool.initialize()
print("OUTPUT_PATH", tool.output_path)
tool.setup()
tool.start()
output.append(tool.finish())
rms_ped = pe2photons(np.array(output[0]) / adc_to_pe) # in photons
plt.figure()
plt.title("Pedestal rms for all events and pixels")
plt.pcolormesh(rms_ped.T, clim=(0.8, 1.5))
plt.colorbar()
plt.savefig(os.path.join(output_dir, "pedestal_rms_2d_graph.png"))
mean_rms_per_pix = np.mean(rms_ped, axis=0)
mean_value = np.mean(mean_rms_per_pix)
fig, ax = plt.subplots()
ax.set_title("Mean pedestal rms for each pixel")
ax.set_xlabel("Pixel")
ax.set_ylabel("p.e.")
ax.plot(mean_rms_per_pix, marker="x", linestyle="")
ax.axhline(1.2, color="black", alpha=0.8)
ax.axhline(mean_value, color="red", linestyle="--", alpha=0.5)
# Fill the region between 0.8 * mean_value and 1.2 * mean_value
ax.fill_between(
range(len(mean_rms_per_pix)),
0.8 * mean_value,
1.2 * mean_value,
color="red",
alpha=0.3,
)
right_x_position = (
len(mean_rms_per_pix) * 0.95
) # Slightly left of the right edge of the plot
ax.arrow(
right_x_position,
1.2 * mean_value,
0,
-0.4 * mean_value,
color="grey",
width=5,
head_width=50,
head_length=0.05,
length_includes_head=True,
zorder=3,
)
ax.arrow(
right_x_position,
0.8 * mean_value,
0,
0.4 * mean_value,
color="grey",
width=5,
head_width=50,
head_length=0.05,
length_includes_head=True,
zorder=3,
)
ax.text(
right_x_position + 100,
mean_value,
"±20%",
color="grey",
fontsize=12,
verticalalignment="center",
)
ax.text(10, 1.2, "CTA requirement", color="black", fontsize=12)
ax.arrow(
300,
1.2,
0,
-0.1,
color="black",
width=5,
head_width=50,
head_length=0.05,
length_includes_head=True,
zorder=5,
)
ax.arrow(
1500,
1.2,
0,
-0.1,
color="black",
width=5,
head_width=50,
head_length=0.05,
length_includes_head=True,
zorder=5,
)
plt.savefig(os.path.join(output_dir, "mean_pedestal_rms.png"))
if temp_output:
with open(os.path.join(args.temp_output, "plot1.pkl"), "wb") as f:
pickle.dump(fig, f)
plt.close("all")
if __name__ == "__main__":
main()
