esbo_ds/ESBO-ETC
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Warn on overexposure

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This commit is contained in:
Lukas Klass 2020-05-15 11:18:03 +02:00
parent c1f0f981da
commit 4ec4f40423
1 changed files with 15 additions and 5 deletions
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20
esbo_etc/classes/sensor/Imager.py
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@ -9,7 +9,7 @@ from ..psf.Zemax import Zemax
from ..SpectralQty import SpectralQty
from .PixelMask import PixelMask
import astropy.constants as const
from logging import info
from logging import info, warning
class Imager(ASensor):
@ -76,8 +76,7 @@ class Imager(ASensor):
self.__center_offset = center_offset
self.__shape = shape
self.__contained_energy = contained_energy
if contained_pixels:
self.__contained_pixels = contained_pixels
self.__contained_pixels = contained_pixels
self.__common_conf = common_conf
# Calculate central wavelength
self.__central_wl = self.__common_conf.wl_min() + (
@ -145,8 +144,19 @@ class Imager(ASensor):
def __calcSNR(self, signal_current: u.Quantity, background_current: u.Quantity, read_noise: u.Quantity,
dark_current: u.Quantity, exp_time: u.Quantity) -> u.dimensionless_unscaled:
# Calculate the SNR using the CCD-equation
snr = signal_current.sum() * exp_time / np.sqrt(
exp_time * (signal_current.sum() + background_current.sum() + dark_current.sum()) + read_noise.sum() ** 2)
signal = signal_current * exp_time
background = background_current * exp_time
dark = dark_current * exp_time
total = signal + background + dark
overexposed = total > self.__well_capacity
if np.any(overexposed):
warning(str(np.count_nonzero(overexposed)) + " pixels are overexposed.")
info("Collected electrons from target: %1.2e electrons" % signal.sum().value)
info("Collected electrons from background: %1.2e electrons" % background.sum().value)
info("Electrons from dark current: %1.2e electrons" % dark.sum().value)
info("Read noise: %1.2e electrons" % (read_noise ** 2).sum().value)
info("Total collected electrons: %1.2e electrons" % total.sum().value)
snr = signal.sum() / np.sqrt(total.sum() + (read_noise ** 2).sum())
# Return the value of the SNR, ignoring the physical units (electrons^0.5)
return snr.value * u.dimensionless_unscaled