"""
Module to analyse psf images (e.g. results from ray tracing simulations).
Main functionalities are: computing centroids, psf containers etc.
"""
import logging
from math import fabs, pi, sqrt
from pathlib import Path
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
from simtools.utils.general import collect_kwargs, set_default_kwargs
__all__ = ["PSFImage"]
[docs]
class PSFImage:
"""
Image composed of list of photon positions (2D).
Load photon list from sim_telarray file and compute centroids, psf containers, effective area,
as well as plot the image as a 2D histogram.
Internal units: photon positions in cm internally.
Parameters
----------
focal_length: float, optional
Focal length of the system in cm. If not given, PSF can only be computed in cm.
total_scattered_area: float, optional
Scatter area of all photons in cm^2. If not given, effective area cannot be computed.
"""
__PSF_RADIUS = "Radius [cm]"
__PSF_CUMULATIVE = "Cumulative PSF"
def __init__(self, focal_length=None, total_scattered_area=None):
"""Initialize PSFImage class."""
self._logger = logging.getLogger(__name__)
self._total_photons = None
self._number_of_detected_photons = None
self._effective_area = None
self.photon_pos_x = []
self.photon_pos_y = []
self.photon_r = []
self.centroid_x = None
self.centroid_y = None
self._total_area = total_scattered_area
self._stored_psf = {}
if focal_length is not None:
self._cm_to_deg = 180.0 / pi / focal_length
self._has_focal_length = True
else:
self._has_focal_length = False
[docs]
def read_photon_list_from_simtel_file(self, photons_file):
"""
Read photon list file generated by sim_telarray and store the photon positions (2D).
Parameters
----------
photons_file: str
Name of sim_telarray file with photon list.
Raises
------
RuntimeError
If photon positions X and Y are not compatible or are empty.
"""
self._logger.info(f"Reading sim_telarray file {photons_file}")
self._total_photons = 0
if Path(photons_file).suffix == ".gz":
import gzip # pylint: disable=import-outside-toplevel
file_open_function = gzip.open
else:
file_open_function = open
with file_open_function(photons_file, "rb") as f:
for line in f:
self._process_simtel_line(line)
if not self._is_photon_positions_ok():
msg = "Problems reading Simtel file - invalid data"
self._logger.error(msg)
raise RuntimeError(msg)
self.centroid_x = np.mean(self.photon_pos_x)
self.centroid_y = np.mean(self.photon_pos_y)
self._number_of_detected_photons = len(self.photon_pos_x)
self._effective_area = (
self._number_of_detected_photons * self._total_area / self._total_photons
)
self.photon_r = np.sort(
np.sqrt(
(self.photon_pos_x - self.centroid_x) ** 2
+ (self.photon_pos_y - self.centroid_y) ** 2
)
)
def _is_photon_positions_ok(self):
"""
Verify if the photon positions are ok.
Returns
-------
bool
True if photon positions are ok, False if they are not.
"""
cond1 = len(self.photon_pos_x) != 0
cond2 = len(self.photon_pos_y) != 0
cond3 = len(self.photon_pos_x) == len(self.photon_pos_y)
return cond1 and cond2 and cond3
def _process_simtel_line(self, line):
"""
Supporting function to read_photon_list_from_simtel_file.
Parameters
----------
line: str
A line from the photon list file generated by sim_telarray.
"""
words = line.split()
if b"falling on an area of" in line:
self._total_photons += int(words[4])
total_area_in_file = float(words[14])
if self._total_area is None:
self._total_area = total_area_in_file
elif total_area_in_file != self._total_area:
self._logger.warning(
"Conflicting value of the total area found"
f" {self._total_area} != {total_area_in_file}"
" - Keeping the original value"
)
else:
# Do nothing - Keep the original value of _total_area
pass
elif b"#" in line or len(words) == 0:
# Skipping comments
pass
else:
# Storing photon position from cols 2 and 3
self.photon_pos_x.append(float(words[2]))
self.photon_pos_y.append(float(words[3]))
[docs]
def get_effective_area(self):
"""
Return effective area pre calculated.
Returns
-------
float
Pre-calculated effective area. None if it could not be calculated (e.g because the total
scattering area was not set).
"""
if "_effective_area" in self.__dict__ and self._effective_area is not None:
return self._effective_area
self._logger.error("Effective Area could not be calculated")
return None
[docs]
def set_effective_area(self, value):
"""
Set effective area.
Parameters
----------
value: float
Effective area
"""
self._effective_area = value
[docs]
def get_psf(self, fraction=0.8, unit="cm"):
"""
Return PSF.
Parameters
----------
fraction: float
Fraction of photons within the containing radius.
unit: str
'cm' or 'deg'. 'deg' will not work if focal length was not set.
Returns
-------
float:
Containing diameter for a certain intensity fraction (PSF).
"""
if unit == "deg" and not self._has_focal_length:
self._logger.error("PSF cannot be computed in deg because focal length is not set")
return None
if fraction not in self._stored_psf:
self._compute_psf(fraction)
unit_factor = 1 if unit == "cm" else self._cm_to_deg
return self._stored_psf[fraction] * unit_factor
[docs]
def set_psf(self, value, fraction=0.8, unit="cm"):
"""
Set PSF calculated from other methods.
Parameters
----------
value: float
PSF value to be set
fraction: float
Fraction of photons within the containing radius.
unit: str
'cm' or 'deg'. 'deg' will not work if focal length was not set.
"""
if unit == "deg" and not self._has_focal_length:
self._logger.error("PSF cannot be set in deg because focal length is not set")
return
unit_factor = 1 if unit == "cm" else 1.0 / self._cm_to_deg
self._stored_psf[fraction] = value * unit_factor
def _compute_psf(self, fraction):
"""
Compute and store PSF.
Parameters
----------
fraction: float
Fraction of photons within the containing radius
"""
self._stored_psf[fraction] = self._find_psf(fraction)
def _find_psf(self, fraction):
"""
Try to find PSF by a smart algorithm first.
If it fails, _find_radius_by_scanning is called and do it by brute force.
Parameters
----------
fraction: float
Fraction of photons within the containing radius.
Returns
-------
float:
Diameter of the circular container with a certain fraction of the photons.
"""
self._logger.debug(f"Finding PSF for fraction = {fraction}")
x_pos_sq = [i**2 for i in self.photon_pos_x]
y_pos_sq = [i**2 for i in self.photon_pos_y]
x_pos_sig = sqrt(np.mean(x_pos_sq) - self.centroid_x**2)
y_pos_sig = sqrt(np.mean(y_pos_sq) - self.centroid_y**2)
radius_sig = sqrt(x_pos_sig**2 + y_pos_sig**2)
target_number = fraction * self._number_of_detected_photons
current_radius = 1.5 * radius_sig
start_number = self._sum_photons_in_radius(current_radius)
scale = 0.5 * sqrt(current_radius * current_radius / start_number)
delta_number = start_number - target_number
n_iter = 0
max_iter = 1000
tolerance = self._number_of_detected_photons / 1000.0
found_radius = False
while not found_radius and n_iter < max_iter:
n_iter += 1
dr = -delta_number * scale / sqrt(target_number)
while current_radius + dr < 0:
dr *= 0.5
current_radius += dr
current_number = self._sum_photons_in_radius(current_radius)
delta_number = current_number - target_number
found_radius = fabs(delta_number) < tolerance
if found_radius:
return 2 * current_radius
self._logger.warning("Could not find PSF efficiently - trying by scanning")
return 2 * self._find_radius_by_scanning(target_number, radius_sig)
def _find_radius_by_scanning(self, target_number, radius_sig):
"""
Find radius by scanning, aka brute force.
Parameters
----------
target_number: float
Number of photons inside the diameter to be found.
radius_sig: float
Sigma of the radius to be used as scale.
Returns
-------
float:
Radius of the circle with target_number photons inside.
"""
self._logger.debug("Finding PSF by scanning")
def scan(dr, rad_min, rad_max):
"""
Scan the image from rad_min to rad_max until it finds target_number photons inside.
Scanning is done in steps of dr.
Returns
-------
(float, float, float):
Average radius, min radius, max radius of the interval where target_number photons
are inside.
Raises
------
RuntimeError
if radius is not found (found_radius is False)
"""
r0, r1 = rad_min, rad_min + dr
s0, s1 = 0, 0
found_radius = False
while not found_radius:
s0, s1 = self._sum_photons_in_radius(r0), self._sum_photons_in_radius(r1)
if s0 < target_number <= s1:
found_radius = True
break
if r1 > rad_max:
break
r0 += dr
r1 += dr
if found_radius:
return (r0 + r1) / 2, r0, r1
self._logger.error("Could not find PSF by scanning")
raise RuntimeError
# Run scan few times with smaller dr to optimize search.
# Step 0
radius, rad_min, rad_max = scan(0.1 * radius_sig, 0, 4 * radius_sig)
# Step 1
radius, rad_min, rad_max = scan(0.005 * radius_sig, rad_min, rad_max)
return radius
def _sum_photons_in_radius(self, radius):
"""Return the number of photons inside a certain radius."""
return np.searchsorted(self.photon_r, radius)
[docs]
def get_image_data(self, centralized=True):
"""
Provide image data (2D photon positions in cm) as lists.
Parameters
----------
centralized: bool
Centroid of the image is set to (0, 0) if True.
Returns
-------
(x, y), the photons positions in cm.
"""
if centralized:
x_pos_data = np.array(self.photon_pos_x) - self.centroid_x
y_pos_data = np.array(self.photon_pos_y) - self.centroid_y
else:
x_pos_data = np.array(self.photon_pos_x)
y_pos_data = np.array(self.photon_pos_y)
d_type = {"names": ("X", "Y"), "formats": ("f8", "f8")}
result = np.recarray((len(x_pos_data),), dtype=d_type)
result.X = x_pos_data
result.Y = y_pos_data
return result
[docs]
def plot_image(self, centralized=True, **kwargs):
"""
Plot 2D image as histogram (in cm).
Parameters
----------
centralized: bool
Centroid of the image is set to (0, 0) if True.
**kwargs:
image_* for the histogram plot and psf_* for the psf circle.
"""
data = self.get_image_data(centralized)
kwargs = set_default_kwargs(
kwargs,
image_bins=80,
image_cmap=plt.cm.gist_heat_r,
psf_color="k",
psf_fill=False,
psf_lw=2,
psf_ls="--",
)
kwargs_for_image = collect_kwargs("image", kwargs)
kwargs_for_psf = collect_kwargs("psf", kwargs)
ax = plt.gca()
# Image histogram
ax.hist2d(data["X"], data["Y"], **kwargs_for_image)
ax.set_aspect("equal", "datalim")
# PSF circle
center = (0, 0) if centralized else (self.centroid_x, self.centroid_y)
circle = plt.Circle(center, self.get_psf(0.8) / 2, **kwargs_for_psf)
ax.add_artist(circle)
ax.axhline(0, color="k", linestyle="--", zorder=3, linewidth=0.5)
ax.axvline(0, color="k", linestyle="--", zorder=3, linewidth=0.5)
[docs]
def get_cumulative_data(self, radius=None):
"""
Provide cumulative data (intensity vs radius).
Parameters
----------
radius: array
Array with radius calculate the cumulative PSF in distance units.
Returns
-------
(radius, intensity)
"""
if radius is not None:
radius_all = radius.to(u.cm).value if isinstance(radius, u.Quantity) else radius
else:
radius_all = list(np.linspace(0, 1.6 * self.get_psf(0.8), 30))
intensity = []
for rad in radius_all:
intensity.append(self._sum_photons_in_radius(rad) / self._number_of_detected_photons)
d_type = {
"names": (self.__PSF_RADIUS, self.__PSF_CUMULATIVE),
"formats": ("f8", "f8"),
}
result = np.recarray((len(radius_all),), dtype=d_type)
result[self.__PSF_RADIUS] = radius_all
result[self.__PSF_CUMULATIVE] = intensity
return result
[docs]
def plot_cumulative(self, **kwargs):
"""Plot cumulative data (intensity vs radius).
Parameters
----------
**kwargs:
image_* for the histogram plot and psf_* for the psf circle.
"""
data = self.get_cumulative_data()
plt.plot(data[self.__PSF_RADIUS], data[self.__PSF_CUMULATIVE], **kwargs)
