"""

Create a theta-square plot .

"""

import sys

import matplotlib.pyplot as plt

import numpy as np

from astropy import units as u

from astropy.coordinates.angle_utilities import angular_separation

from ctapipe.calib import CameraCalibrator

from ctapipe.image import hillas_parameters

from ctapipe.image import tailcuts_clean

from ctapipe.io import event_source

from ctapipe.reco import HillasReconstructor

from ctapipe.utils import datasets

if len(sys.argv) >= 2:

filename = sys.argv[1]

else:

# importing data from avaiable datasets in ctapipe

filename = datasets.get_dataset_path("gamma_test_large.simtel.gz")

# reading the Monte Carlo file for LST

source = event_source(filename, allowed_tels={1, 2, 3, 4})

reco = HillasReconstructor()

calib = CameraCalibrator(r1_product="HESSIOR1Calibrator")

off_angles = []

for event in source:

# calibrating the event

calib.calibrate(event)

hillas_params = {}

subarray = event.inst.subarray

# pointing direction of the telescopes

point_azimuth = {}

point_altitude = {}

for tel_id in event.dl0.tels_with_data:

# telescope pointing direction

point_azimuth[tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad

point_altitude[tel_id] = event.mc.tel[tel_id].altitude_raw * u.rad

# print(point_azimuth,point_altitude)

# Camera Geometry required for hillas parametrization

camgeom = subarray.tel[tel_id].camera

# note the [0] is for channel 0 which is high-gain channel

image = event.dl1.tel[tel_id].image[0]

# Cleaning of the image

cleaned_image = image

# create a clean mask of pixels above the threshold

cleanmask = tailcuts_clean(

camgeom, image, picture_thresh=10, boundary_thresh=5

)

# set all rejected pixels to zero

cleaned_image[~cleanmask] = 0

# Calulate hillas parameters

# It fails for empty pixels

try:

hillas_params[tel_id] = hillas_parameters(camgeom, cleaned_image)

except:

pass

if len(hillas_params) < 2:

continue

reco_result = reco.predict(hillas_params, event.inst, point_altitude, point_azimuth)

# get angular offset between reconstructed shower direction and MC

# generated shower direction

off_angle = angular_separation(

event.mc.az,

event.mc.alt,

reco_result.az,

reco_result.alt

)

# Appending all estimated off angles

off_angles.append(off_angle.to(u.deg).value)

# calculate theta square for angles which are not nan

off_angles = np.array(off_angles)

thetasquare = off_angles[np.isfinite(off_angles)]**2

# To plot thetasquare The number of events in th data files for LSTCam is not

# significantly high to give a nice thetasquare plot for gammas One can use

# deedicated MC file for LST get nice plot

plt.figure(figsize=(10, 8))

plt.hist(thetasquare, bins=np.linspace(0, 1, 50))

plt.title(r'$\theta^2$ plot')

plt.xlabel(r'$\theta^2$ (deg)')

plt.ylabel('# of events')

plt.show()