87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
from abc import ABC, abstractmethod
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import astropy.units as u
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from ..sensor.PixelMask import PixelMask
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from typing import Union
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import numpy as np
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class IPSF(ABC):
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"""
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Interface for modelling a PSF
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"""
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@abstractmethod
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def calcReducedObservationAngle(self, contained_energy: Union[str, int, float, u.Quantity],
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jitter_sigma: u.Quantity = None, obstruction: float = 0.0) -> u.Quantity:
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"""
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Calculate the reduced observation angle in lambda / d_ap for the given contained energy.
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Parameters
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----------
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contained_energy : Union[str, int, float, u.Quantity]
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The percentage of energy to be contained within a circle with the diameter reduced observation angle.
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jitter_sigma : Quantity
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Sigma of the telescope's jitter in arcsec
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obstruction : float
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The central obstruction as ratio A_ob / A_ap
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Returns
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-------
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reduced_observation_angle: Quantity
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The reduced observation angle in lambda / d_ap
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"""
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pass
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@abstractmethod
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def mapToPixelMask(self, mask: PixelMask, jitter_sigma: u.Quantity = None, obstruction: float = 0.0) -> PixelMask:
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"""
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Map the integrated PSF values to a sensor grid.
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Parameters
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----------
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obstruction
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mask : PixelMask
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The pixel mask to map the values to. The values will only be mapped onto entries with the value 1.
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jitter_sigma : Quantity
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Sigma of the telescope's jitter in arcsec
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obstruction : float
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The central obstruction as ratio A_ob / A_ap
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Returns
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-------
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mask : PixelMask
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The pixel mask with the integrated PSF values mapped onto each pixel.
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"""
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pass
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@staticmethod
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def rebin(arr: np.ndarray, factor: float):
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"""
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Rebin a 2D-array by summing or repeating the elements.
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Parameters
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----------
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arr : ndarray
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Input array.
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factor : float
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Rebinning factor
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Returns
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-------
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rebinned_array : ndarray
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If the factor is smaller than 1, the data is summed,
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if the factor is bigger than 1, array elements are repeated
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See Also
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--------
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resize : Return a new array with the specified factor.
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"""
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m, n = arr.shape
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m_new, n_new = int(m * factor), int(n * factor)
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if factor < 1:
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res = arr.reshape((m_new, int(1 / factor), n_new, int(1 / factor))).sum(3).sum(1)
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elif factor > 1:
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res = np.repeat(np.repeat(arr, int(factor), axis=0), int(factor), axis=1)
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else:
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res = arr
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if isinstance(arr, PixelMask):
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res.pixel_size = res.pixel_size / factor
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return res
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