esbo_ds/ESBO-ETC
esbo_ds/ESBO-ETC
11
0
Fork 1
Code Issues 1 Pull Requests Releases 1 Wiki Activity

Parse contained energy

Browse Source
This commit is contained in:
Lukas Klass 2020-04-29 17:37:07 +02:00
parent 20234d284e
commit 37ba622407
1 changed files with 11 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
esbo_etc/classes/psf/Zemax.py
View File

@ -6,6 +6,8 @@ from scipy.interpolate import RegularGridInterpolator
from scipy.integrate import nquad
from scipy.optimize import bisect
from .IPSF import IPSF
from typing import Union
from ...lib.helpers import error
class Zemax(IPSF):
@ -53,7 +55,7 @@ class Zemax(IPSF):
self.__center_point = [int(x) for x in
re.findall("[0-9]+", list(filter(re.compile("Center point is: ").match, head))[0])]
def calcReducedObservationAngle(self, contained_energy: float) -> u.Quantity:
def calcReducedObservationAngle(self, contained_energy: Union[str, int, float, u.Quantity]) -> u.Quantity:
"""
Calculate the reduced observation angle in lambda / d_ap for the given contained energy.
@ -67,6 +69,13 @@ class Zemax(IPSF):
reduced_observation_angle: Quantity
The reduced observation angle in lambda / d_ap
"""
if type(contained_energy) == str:
try:
contained_energy = float(contained_energy) / 100.0 * u.dimensionless_unscaled
except ValueError:
error("Could not convert encircled energy to float.")
elif type(contained_energy) in [int, float]:
contained_energy = contained_energy * u.dimensionless_unscaled
# Create an linear interpolation function for the PSF and the corresponding grid coordinates
x_range = np.arange(-(self.__center_point[0] - 1), self.__psf.shape[0] - self.__center_point[0] + 1)
y_range = np.arange(-(self.__center_point[1] - 1), self.__psf.shape[1] - self.__center_point[1] + 1)
@ -80,7 +89,7 @@ class Zemax(IPSF):
# Find the radius of the circle containing the given percentage of energy. Therefore, the interpolation
# function is numerically integrated within the radius. The Integration radius is optimized using bisection.
try:
r = bisect(lambda r_c: contained_energy -
r = bisect(lambda r_c: contained_energy.value -
nquad(lambda x, y: interp(np.array([y, x])),
[lambda y: [-1 * np.sqrt(r_c ** 2 - y ** 2), np.sqrt(r_c ** 2 - y ** 2)],
[-r_c, r_c]], opts={"epsrel": 1e-1})[0] / total, 0, r_max, xtol=0.1)