deferredCharge.py

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# This file is part of ip_isr.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (https://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
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# the Free Software Foundation, either version 3 of the License, or
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#
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# GNU General Public License for more details.
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__all__ = ('DeferredChargeConfig', 'DeferredChargeTask', 'SerialTrap', 'DeferredChargeCalib')
 
import numpy as np
from astropy.table import Table
 
from lsst.afw.cameraGeom import ReadoutCorner
from lsst.pex.config import Config, Field
from lsst.pipe.base import Task
from .isrFunctions import gainContext
from .calibType import IsrCalib
 
import scipy.interpolate as interp
 
 
class SerialTrap():
    """Represents a serial register trap.
 
    Parameters
    ----------
    size : `float`
        Size of the charge trap, in electrons.
    emission_time : `float`
        Trap emission time constant, in inverse transfers.
    pixel : `int`
        Serial pixel location of the trap, including the prescan.
    trap_type : `str`
        Type of trap capture to use.  Should be one of ``linear``,
        ``logistic``, or ``spline``.
    coeffs : `list` [`float`]
        Coefficients for the capture process.  Linear traps need one
        coefficient, logistic traps need two, and spline based traps
        need to have an even number of coefficients that can be split
        into their spline locations and values.
 
    Raises
    ------
    ValueError
        Raised if the specified parameters are out of expected range.
    """
 
    def __init__(self, size, emission_time, pixel, trap_type, coeffs):
        if size < 0.0:
            raise ValueError('Trap size must be greater than or equal to 0.')
        self.size = size
 
        if emission_time <= 0.0:
            raise ValueError('Emission time must be greater than 0.')
        if np.isnan(emission_time):
            raise ValueError('Emission time must be real-valued, not NaN')
        self.emission_time = emission_time
 
        if int(pixel) != pixel:
            raise ValueError('Fraction value for pixel not allowed.')
        self.pixel = int(pixel)
 
        self.trap_type = trap_type
        self.coeffs = coeffs
 
        if self.trap_type not in ('linear', 'logistic', 'spline'):
            raise ValueError('Unknown trap type: %s', self.trap_type)
 
        if self.trap_type == 'spline':
            centers, values = np.split(np.array(self.coeffs, dtype=np.float), 2)
            # Ensure all NaN values are stripped out
            values = values[~np.isnan(centers)]
            centers = centers[~np.isnan(centers)]
            centers = centers[~np.isnan(values)]
            values = values[~np.isnan(values)]
            self.interp = interp.interp1d(centers, values)
 
        self._trap_array = None
        self._trapped_charge = None
 
    def __eq__(self, other):
        # A trap is equal to another trap if all of the initialization
        # parameters are equal.  All other properties are only filled
        # during use, and are not persisted into the calibration.
        if self.size != other.size:
            return False
        if self.emission_time != other.emission_time:
            return False
        if self.pixel != other.pixel:
            return False
        if self.trap_type != other.trap_type:
            return False
        if self.coeffs != other.coeffs:
            return False
        return True
 
    @property
    def trap_array(self):
        return self._trap_array
 
    @property
    def trapped_charge(self):
        return self._trapped_charge
 
    def initialize(self, ny, nx, prescan_width):
        """Initialize trapping arrays for simulated readout.
 
        Parameters
        ----------
        ny : `int`
            Number of rows to simulate.
        nx : `int`
            Number of columns to simulate.
        prescan_width : `int`
            Additional transfers due to prescan.
 
        Raises
        ------
        ValueError
            Raised if the trap falls outside of the image.
        """
        if self.pixel > nx+prescan_width:
            raise ValueError('Trap location {0} must be less than {1}'.format(self.pixel,
                                                                              nx+prescan_width))
 
        self._trap_array = np.zeros((ny, nx+prescan_width))
        self._trap_array[:, self.pixel] = self.size
        self._trapped_charge = np.zeros((ny, nx+prescan_width))
 
    def release_charge(self):
        """Release charge through exponential decay.
 
        Returns
        -------
        released_charge : `float`
            Charge released.
        """
        released_charge = self._trapped_charge*(1-np.exp(-1./self.emission_time))
        self._trapped_charge -= released_charge
 
        return released_charge
 
    def trap_charge(self, free_charge):
        """Perform charge capture using a logistic function.
 
        Parameters
        ----------
        free_charge : `float`
            Charge available to be trapped.
 
        Returns
        -------
        captured_charge : `float`
            Amount of charge actually trapped.
        """
        captured_charge = (np.clip(self.capture(free_charge), self.trapped_charge, self._trap_array)
                           - self.trapped_charge)
        self._trapped_charge += captured_charge
 
        return captured_charge
 
    def capture(self, pixel_signals):
        """Trap capture function.
 
        Parameters
        ----------
        pixel_signals : `list` [`float`]
            Input pixel values.
 
        Returns
        -------
        captured_charge : `list` [`float`]
            Amount of charge captured from each pixel.
 
        Raises
        ------
        RuntimeError
            Raised if the trap type is invalid.
        """
        if self.trap_type == 'linear':
            scaling = self.coeffs[0]
            return np.minimum(self.size, pixel_signals*scaling)
        elif self.trap_type == 'logistic':
            f0, k = (self.coeffs[0], self.coeffs[1])
            return self.size/(1.+np.exp(-k*(pixel_signals-f0)))
        elif self.trap_type == 'spline':
            return self.interp(pixel_signals)
        else:
            raise RuntimeError(f"Invalid trap capture type: {self.trap_type}.")
 
 
class DeferredChargeCalib(IsrCalib):
    r"""Calibration containing deferred charge/CTI parameters.
 
    Parameters
    ----------
    **kwargs :
        Additional parameters to pass to parent constructor.
 
    Notes
    -----
    The charge transfer inefficiency attributes stored are:
 
    driftScale : `dict` [`str`, `float`]
        A dictionary, keyed by amplifier name, of the local electronic
        offset drift scale parameter, A_L in Snyder+2021.
    decayTime : `dict` [`str`, `float`]
        A dictionary, keyed by amplifier name, of the local electronic
        offset decay time, \tau_L in Snyder+2021.
    globalCti : `dict` [`str`, `float`]
        A dictionary, keyed by amplifier name, of the mean global CTI
        paramter, b in Snyder+2021.
    serialTraps : `dict` [`str`, `lsst.ip.isr.SerialTrap`]
        A dictionary, keyed by amplifier name, containing a single
        serial trap for each amplifier.
    """
    _OBSTYPE = 'CTI'
    _SCHEMA = 'Deferred Charge'
    _VERSION = 1.0
 
    def __init__(self, **kwargs):
        self.driftScale = {}
        self.decayTime = {}
        self.globalCti = {}
        self.serialTraps = {}
 
        super().__init__(**kwargs)
        self.requiredAttributesrequiredAttributesrequiredAttributes.update(['driftScale', 'decayTime', 'globalCti', 'serialTraps'])
 
    def fromDetector(self, detector):
        """Read metadata parameters from a detector.
 
        Parameters
        ----------
        detector : `lsst.afw.cameraGeom.detector`
            Input detector with parameters to use.
 
        Returns
        -------
        calib : `lsst.ip.isr.Linearizer`
            The calibration constructed from the detector.
        """
 
        pass
 
    @classmethod
    def fromDict(cls, dictionary):
        """Construct a calibration from a dictionary of properties.
 
        Parameters
        ----------
        dictionary : `dict`
            Dictionary of properties.
 
        Returns
        -------
        calib : `lsst.ip.isr.CalibType`
            Constructed calibration.
 
        Raises
        ------
        RuntimeError
            Raised if the supplied dictionary is for a different
            calibration.
        """
        calib = cls()
 
        if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']:
            raise RuntimeError(f"Incorrect CTI supplied. Expected {calib._OBSTYPE}, "
                               f"found {dictionary['metadata']['OBSTYPE']}")
 
        calib.setMetadata(dictionary['metadata'])
 
        calib.driftScale = dictionary['driftScale']
        calib.decayTime = dictionary['decayTime']
        calib.globalCti = dictionary['globalCti']
 
        for ampName in dictionary['serialTraps']:
            ampTraps = dictionary['serialTraps'][ampName]
            calib.serialTraps[ampName] = SerialTrap(ampTraps['size'], ampTraps['emissionTime'],
                                                    ampTraps['pixel'], ampTraps['trap_type'],
                                                    ampTraps['coeffs'])
        calib.updateMetadata()
        return calib
 
    def toDict(self):
        """Return a dictionary containing the calibration properties.
        The dictionary should be able to be round-tripped through
        ``fromDict``.
 
        Returns
        -------
        dictionary : `dict`
            Dictionary of properties.
        """
        self.updateMetadata()
        outDict = {}
        outDict['metadata'] = self.getMetadata()
 
        outDict['driftScale'] = self.driftScale
        outDict['decayTime'] = self.decayTime
        outDict['globalCti'] = self.globalCti
 
        outDict['serialTraps'] = {}
        for ampName in self.serialTraps:
            ampTrap = {'size': self.serialTraps[ampName].size,
                       'emissionTime': self.serialTraps[ampName].emission_time,
                       'pixel': self.serialTraps[ampName].pixel,
                       'trap_type': self.serialTraps[ampName].trap_type,
                       'coeffs': self.serialTraps[ampName].coeffs}
            outDict['serialTraps'][ampName] = ampTrap
 
        return outDict
 
    @classmethod
    def fromTable(cls, tableList):
        """Construct calibration from a list of tables.
 
        This method uses the ``fromDict`` method to create the
        calibration, after constructing an appropriate dictionary from
        the input tables.
 
        Parameters
        ----------
        tableList : `list` [`lsst.afw.table.Table`]
            List of tables to use to construct the crosstalk
            calibration.  Two tables are expected in this list, the
            first containing the per-amplifier CTI parameters, and the
            second containing the parameters for serial traps.
 
        Returns
        -------
        calib : `lsst.ip.isr.DeferredChargeCalib`
            The calibration defined in the tables.
 
        Raises
        ------
        ValueError
            Raised if the trap type or trap coefficients are not
            defined properly.
        """
        ampTable = tableList[0]
 
        inDict = {}
        inDict['metadata'] = ampTable.meta
 
        amps = ampTable['AMPLIFIER']
        driftScale = ampTable['DRIFT_SCALE']
        decayTime = ampTable['DECAY_TIME']
        globalCti = ampTable['GLOBAL_CTI']
 
        inDict['driftScale'] = {amp: value for amp, value in zip(amps, driftScale)}
        inDict['decayTime'] = {amp: value for amp, value in zip(amps, decayTime)}
        inDict['globalCti'] = {amp: value for amp, value in zip(amps, globalCti)}
 
        inDict['serialTraps'] = {}
        trapTable = tableList[1]
 
        amps = trapTable['AMPLIFIER']
        sizes = trapTable['SIZE']
        emissionTimes = trapTable['EMISSION_TIME']
        pixels = trapTable['PIXEL']
        trap_type = trapTable['TYPE']
        coeffs = trapTable['COEFFS']
 
        for index, amp in enumerate(amps):
            ampTrap = {}
            ampTrap['size'] = sizes[index]
            ampTrap['emissionTime'] = emissionTimes[index]
            ampTrap['pixel'] = pixels[index]
            ampTrap['trap_type'] = trap_type[index]
 
            # Unpad any trailing NaN values: find the continuous array
            # of NaNs at the end of the coefficients, and remove them.
            inCoeffs = coeffs[index]
            breakIndex = 1
            nanValues = np.where(np.isnan(inCoeffs))[0]
            if nanValues is not None:
                coeffLength = len(inCoeffs)
                while breakIndex < coeffLength:
                    if coeffLength - breakIndex in nanValues:
                        breakIndex += 1
                    else:
                        break
            breakIndex -= 1  # Remove the fixed offset.
            if breakIndex != 0:
                outCoeffs = inCoeffs[0: coeffLength - breakIndex]
            else:
                outCoeffs = inCoeffs
            ampTrap['coeffs'] = outCoeffs.tolist()
 
            if ampTrap['trap_type'] == 'linear':
                if len(ampTrap['coeffs']) < 1:
                    raise ValueError("CTI Amplifier %s coefficients for trap has illegal length %d.",
                                     amp, len(ampTrap['coeffs']))
            elif ampTrap['trap_type'] == 'logistic':
                if len(ampTrap['coeffs']) < 2:
                    raise ValueError("CTI Amplifier %s coefficients for trap has illegal length %d.",
                                     amp, len(ampTrap['coeffs']))
            elif ampTrap['trap_type'] == 'spline':
                if len(ampTrap['coeffs']) % 2 != 0:
                    raise ValueError("CTI Amplifier %s coefficients for trap has illegal length %d.",
                                     amp, len(ampTrap['coeffs']))
            else:
                raise ValueError('Unknown trap type: %s', ampTrap['trap_type'])
 
            inDict['serialTraps'][amp] = ampTrap
 
        return cls.fromDictfromDict(inDict)
 
    def toTable(self):
        """Construct a list of tables containing the information in this
        calibration.
 
        The list of tables should create an identical calibration
        after being passed to this class's fromTable method.
 
        Returns
        -------
        tableList : `list` [`lsst.afw.table.Table`]
            List of tables containing the crosstalk calibration
            information.  Two tables are generated for this list, the
            first containing the per-amplifier CTI parameters, and the
            second containing the parameters for serial traps.
        """
        tableList = []
        self.updateMetadata()
 
        ampList = []
        driftScale = []
        decayTime = []
        globalCti = []
 
        for amp in self.driftScale.keys():
            ampList.append(amp)
            driftScale.append(self.driftScale[amp])
            decayTime.append(self.decayTime[amp])
            globalCti.append(self.globalCti[amp])
 
        ampTable = Table({'AMPLIFIER': ampList,
                          'DRIFT_SCALE': driftScale,
                          'DECAY_TIME': decayTime,
                          'GLOBAL_CTI': globalCti,
                          })
 
        ampTable.meta = self.getMetadata().toDict()
        tableList.append(ampTable)
 
        ampList = []
        sizeList = []
        timeList = []
        pixelList = []
        typeList = []
        coeffList = []
 
        # Get maximum coeff length
        maxCoeffLength = 0
        for trap in self.serialTraps.values():
            maxCoeffLength = np.maximum(maxCoeffLength, len(trap.coeffs))
 
        # Pack and pad the end of the coefficients with NaN values.
        for amp, trap in self.serialTraps.items():
            ampList.append(amp)
            sizeList.append(trap.size)
            timeList.append(trap.emission_time)
            pixelList.append(trap.pixel)
            typeList.append(trap.trap_type)
 
            coeffs = trap.coeffs
            if len(coeffs) != maxCoeffLength:
                coeffs = np.pad(coeffs, (0, maxCoeffLength - len(coeffs)),
                                constant_values=np.nan).tolist()
            coeffList.append(coeffs)
 
        trapTable = Table({'AMPLIFIER': ampList,
                           'SIZE': sizeList,
                           'EMISSION_TIME': timeList,
                           'PIXEL': pixelList,
                           'TYPE': typeList,
                           'COEFFS': coeffList})
 
        tableList.append(trapTable)
 
        return tableList
 
 
class DeferredChargeConfig(Config):
    """Settings for deferred charge correction.
    """
    nPixelOffsetCorrection = Field(
        dtype=int,
        doc="Number of prior pixels to use for local offset correction.",
        default=15,
    )
    nPixelTrapCorrection = Field(
        dtype=int,
        doc="Number of prior pixels to use for trap correction.",
        default=6,
    )
    useGains = Field(
        dtype=bool,
        doc="If true, scale by the gain.",
        default=False,
    )
    zeroUnusedPixels = Field(
        dtype=bool,
        doc="If true, set serial prescan and parallel overscan to zero before correction.",
        default=False,
    )
 
 
class DeferredChargeTask(Task):
    """Task to correct an exposure for charge transfer inefficiency.
 
    This uses the methods described by Snyder et al. 2021, Journal of
    Astronimcal Telescopes, Instruments, and Systems, 7,
    048002. doi:10.1117/1.JATIS.7.4.048002 (Snyder+21).
    """
    ConfigClass = DeferredChargeConfig
    _DefaultName = 'isrDeferredCharge'
 
    def run(self, exposure, ctiCalib, gains=None):
        """Correct deferred charge/CTI issues.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to correct the deferred charge on.
        ctiCalib : `lsst.ip.isr.DeferredChargeCalib`
            Calibration object containing the charge transfer
            inefficiency model.
        gains : `dict` [`str`, `float`]
            A dictionary, keyed by amplifier name, of the gains to
            use.  If gains is None, the nominal gains in the amplifier
            object are used.
 
        Returns
        -------
        exposure : `lsst.afw.image.Exposure`
            The corrected exposure.
        """
        image = exposure.getMaskedImage().image
        detector = exposure.getDetector()
 
        # If gains were supplied, they should be used.  If useGains is
        # true, but no external gains were supplied, use the nominal
        # gains listed in the detector.  Finally, if useGains is
        # false, fake a dictionary of unit gains for ``gainContext``.
        if self.config.useGains:
            if gains is None:
                gains = {amp.getName(): amp.getGain() for amp in detector.getAmplifiers()}
 
        with gainContext(exposure, image, self.config.useGains, gains):
            for amp in detector.getAmplifiers():
                ampName = amp.getName()
 
                ampImage = image[amp.getRawBBox()]
                if self.config.zeroUnusedPixels:
                    # We don't apply overscan subtraction, so zero these
                    # out for now.
                    ampImage[amp.getRawParallelOverscanBBox()].array[:, :] = 0.0
                    ampImage[amp.getRawSerialPrescanBBox()].array[:, :] = 0.0
 
                # The algorithm expects that the readout corner is in
                # the lower left corner.  Flip it to be so:
 
                ampData = self.flipData(ampImage.array, amp)
 
                if ctiCalib.driftScale[ampName] > 0.0:
                    correctedAmpData = self.local_offset_inverse(ampData,
                                                                 ctiCalib.driftScale[ampName],
                                                                 ctiCalib.decayTime[ampName],
                                                                 self.config.nPixelOffsetCorrection)
                else:
                    correctedAmpData = ampData.copy()
 
                correctedAmpData = self.local_trap_inverse(correctedAmpData,
                                                           ctiCalib.serialTraps[ampName],
                                                           ctiCalib.globalCti[ampName],
                                                           self.config.nPixelTrapCorrection)
 
                # Undo flips here.  The method is symmetric.
                correctedAmpData = self.flipData(correctedAmpData, amp)
                image[amp.getRawBBox()].array[:, :] = correctedAmpData[:, :]
 
        return exposure
 
    @staticmethod
    def flipData(ampData, amp):
        """Flip data array such that readout corner is at lower-left.
 
        Parameters
        ----------
        ampData : `numpy.ndarray`, (nx, ny)
            Image data to flip.
        amp : `lsst.afw.cameraGeom.Amplifier`
            Amplifier to get readout corner information.
 
        Returns
        -------
        ampData : `numpy.ndarray`, (nx, ny)
            Flipped image data.
        """
        X_FLIP = {ReadoutCorner.LL: False,
                  ReadoutCorner.LR: True,
                  ReadoutCorner.UL: False,
                  ReadoutCorner.UR: True}
        Y_FLIP = {ReadoutCorner.LL: False,
                  ReadoutCorner.LR: False,
                  ReadoutCorner.UL: True,
                  ReadoutCorner.UR: True}
 
        if X_FLIP[amp.getReadoutCorner()]:
            ampData = np.fliplr(ampData)
        if Y_FLIP[amp.getReadoutCorner()]:
            ampData = np.flipud(ampData)
 
        return ampData
 
    @staticmethod
    def local_offset_inverse(inputArr, drift_scale, decay_time, num_previous_pixels=15):
        r"""Remove CTI effects from local offsets.
 
        This implements equation 10 of Snyder+21.  For an image with
        CTI, s'(m, n), the correction factor is equal to the maximum
        value of the set of:
 
        .. code-block::
 
            {A_L s'(m, n - j) exp(-j t / \tau_L)}_j=0^jmax
 
        Parameters
        ----------
        inputArr : `numpy.ndarray`, (nx, ny)
            Input image data to correct.
        drift_scale : `float`
            Drift scale (Snyder+21 A_L value) to use in correction.
        decay_time : `float`
            Decay time (Snyder+21 \tau_L) of the correction.
        num_previous_pixels : `int`, optional
            Number of previous pixels to use for correction.  As the
            CTI has an exponential decay, this essentially truncates
            the correction where that decay scales the input charge to
            near zero.
 
        Returns
        -------
        outputArr : `numpy.ndarray`, (nx, ny)
            Corrected image data.
        """
        r = np.exp(-1/decay_time)
        Ny, Nx = inputArr.shape
 
        # j = 0 term:
        offset = np.zeros((num_previous_pixels, Ny, Nx))
        offset[0, :, :] = drift_scale*np.maximum(0, inputArr)
 
        # j = 1..jmax terms:
        for n in range(1, num_previous_pixels):
            offset[n, :, n:] = drift_scale*np.maximum(0, inputArr[:, :-n])*(r**n)
 
        Linv = np.amax(offset, axis=0)
        outputArr = inputArr - Linv
 
        return outputArr
 
    @staticmethod
    def local_trap_inverse(inputArr, trap, global_cti=0.0, num_previous_pixels=6):
        r"""Apply localized trapping inverse operator to pixel signals.
 
        This implements equation 13 of Snyder+21.  For an image with
        CTI, s'(m, n), the correction factor is equal to the maximum
        value of the set of:
 
        .. code-block::
 
            {A_L s'(m, n - j) exp(-j t / \tau_L)}_j=0^jmax
 
        Parameters
        ----------
        inputArr : `numpy.ndarray`, (nx, ny)
            Input image data to correct.
        trap : `lsst.ip.isr.SerialTrap`
            Serial trap describing the capture and release of charge.
        global_cti: `float`
            Mean charge transfer inefficiency, b from Snyder+21.
        num_previous_pixels : `int`, optional
            Number of previous pixels to use for correction.
 
        Returns
        -------
        outputArr : `numpy.ndarray`, (nx, ny)
            Corrected image data.
 
        """
        Ny, Nx = inputArr.shape
        a = 1 - global_cti
        r = np.exp(-1/trap.emission_time)
 
        # Estimate trap occupancies during readout
        trap_occupancy = np.zeros((num_previous_pixels, Ny, Nx))
        for n in range(num_previous_pixels):
            trap_occupancy[n, :, n+1:] = trap.capture(np.maximum(0, inputArr))[:, :-(n+1)]*(r**n)
        trap_occupancy = np.amax(trap_occupancy, axis=0)
 
        # Estimate captured charge
        C = trap.capture(np.maximum(0, inputArr)) - trap_occupancy*r
        C[C < 0] = 0.
 
        # Estimate released charge
        R = np.zeros(inputArr.shape)
        R[:, 1:] = trap_occupancy[:, 1:]*(1-r)
        T = R - C
 
        outputArr = inputArr - a*T
 
        return outputArr