isrMockLSST.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
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
__all__ = ["IsrMockLSSTConfig", "IsrMockLSST", "RawMockLSST",
           "CalibratedRawMockLSST", "ReferenceMockLSST",
           "BiasMockLSST", "DarkMockLSST", "FlatMockLSST", "FringeMockLSST",
           "BfKernelMockLSST", "DefectMockLSST", "CrosstalkCoeffMockLSST",
           "TransmissionMockLSST"]
 
import numpy as np
import galsim
 
import lsst.geom as geom
import lsst.pex.config as pexConfig
import lsst.afw.detection as afwDetection
import lsst.afw.math as afwMath
from lsst.afw.cameraGeom import ReadoutCorner
from lsst.daf.base import PropertyList
from .crosstalk import CrosstalkCalib
from .isrMock import IsrMockConfig, IsrMock
from .defects import Defects
from .assembleCcdTask import AssembleCcdTask
from .linearize import Linearizer
from .brighterFatterKernel import BrighterFatterKernel
from .deferredCharge import (
    SegmentSimulator,
    FloatingOutputAmplifier,
    DeferredChargeCalib
)
 
 
class IsrMockLSSTConfig(IsrMockConfig):
    """Configuration parameters for isrMockLSST.
    """
    # Detector parameters and "Exposure" parameters,
    # mostly inherited from IsrMockConfig.
    isLsstLike = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="If True, products have one raw image per amplifier, otherwise, one raw image per detector.",
    )
    calibMode = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Set to true to produce mock calibration products, e.g. combined bias, dark, flat, etc.",
    )
    doAdd2DBias = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add 2D bias residual frame to data.",
    )
    doAddBrightDefects = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add bright defects (bad column) to data.",
    )
    brightDefectLevel = pexConfig.Field(
        dtype=float,
        default=30000.0,
        doc="Bright defect level (electron).",
    )
    doAddBadParallelOverscanColumn = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add a bad column to the parallel overscan.",
    )
    badParallelOverscanColumnLevel = pexConfig.Field(
        dtype=float,
        default=300000.,
        doc="Bright parallel overscan column level (electron). Should be above saturation.",
    )
    doAddBadParallelOverscanColumnNeighbors = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add low-level bad columns next to parallel overscan bad column.",
    )
    badParallelOverscanColumnNeighborsLevel = pexConfig.Field(
        dtype=float,
        default=50.0,
        doc="Bright parallel overscan column neighbors level (electron).",
    )
    doAddBrighterFatter = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Add brighter fatter and/or diffusion effects to image.",
    )
    doAddDeferredCharge = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Add serial CTI at the amp level?",
    )
    bfStrength = pexConfig.Field(
        dtype=float,
        default=2.0,
        doc="The brighter fatter effect scaling parameter (cannot be zero)."
            "Nominally = 1, but = 2 is more realistic."
    )
    nRecalc = pexConfig.Field(
        dtype=int,
        default=10000,
        doc="Number of electrons to accumulate before recalculating pixel shapes.",
    )
    doAddClockInjectedOffset = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add clock-injected offset to data (on-chip bias level).",
    )
    clockInjectedOffsetLevel = pexConfig.Field(
        dtype=float,
        default=8500.0,
        doc="Clock-injected offset (on-chip bias level), in electron.",
    )
    noise2DBias = pexConfig.Field(
        dtype=float,
        default=2.0,
        doc="Noise (in electron) to generate a 2D bias residual frame.",
    )
    doAddDarkNoiseOnly = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Add only dark current noise, for testing consistency.",
    )
    doAddParallelOverscanRamp = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add overscan ramp to parallel overscan and data regions.",
    )
    doAddSerialOverscanRamp = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add overscan ramp to serial overscan and data regions.",
    )
    doAddHighSignalNonlinearity = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add high signal non-linearity to overscan and data regions?",
    )
    doAddLowSignalNonlinearity = pexConfig.Field(
        dtype=bool,
        default=False,
        doc="Add low signal non-linearity to overscan and data regions? (Not supported yet.",
    )
    highSignalNonlinearityThreshold = pexConfig.Field(
        dtype=float,
        default=40_000.,
        doc="Threshold (in adu) for the non-linearity to be considered ``high signal``.",
    )
    doApplyGain = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Add gain to data.",
    )
    doRoundAdu = pexConfig.Field(
        dtype=bool,
        default=True,
        doc="Round adu values to nearest integer.",
    )
    gainDict = pexConfig.DictField(
        keytype=str,
        itemtype=float,
        doc="Dictionary of amp name to gain; any amps not listed will use "
            "config.gain as the value. Units are electron/adu.",
        default={
            "C:0,0": 1.65,
            "C:0,1": 1.60,
            "C:0,2": 1.55,
            "C:0,3": 1.70,
            "C:1,0": 1.75,
            "C:1,1": 1.80,
            "C:1,2": 1.85,
            "C:1,3": 1.70,
        },
    )
    assembleCcd = pexConfig.ConfigurableField(
        target=AssembleCcdTask,
        doc="CCD assembly task; used for defect box conversions.",
    )
 
    def validate(self):
        super().validate()
 
        if self.doAddLowSignalNonlinearity:
            raise NotImplementedError("Low signal non-linearity is not implemented.")
 
        if self.doAddDeferredCharge and self.isTrimmed:
            raise NotImplementedError("Must be untrimmed for mock serial CTI to"
                                      "realistically add charge into overscan regions.")
 
    def validate(self): …
    def setDefaults(self):
        super().setDefaults()
 
        self.gain = 1.7  # Default value.
        self.skyLevel = 1700.0  # electron
        self.sourceFlux = [50_000.0]  # electron
        self.sourceX = [35.0]  # pixel
        self.sourceY = [37.0]  # pixel
        self.overscanScale = 170.0  # electron
        self.biasLevel = 20_000.0  # adu
        self.doAddCrosstalk = True
 
 
    def setDefaults(self): …
class IsrMockLSSTConfig(IsrMockConfig): …
class IsrMockLSST(IsrMock):
    """Class to generate consistent mock images for ISR testing.
    """
    ConfigClass = IsrMockLSSTConfig
    _DefaultName = "isrMockLSST"
 
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
 
        # Get kernel derived from imSim generated flats with BFE. The kernel
        # was used for Ops Rehearsal 3 for LSSTCam-type sensors
        # See https://rubinobs.atlassian.net/browse/DM-43059 for more details.
        self.bfKernel = np.array([[4.83499829e-01, 8.10171823e-01, 5.31096720e-01,
                                   3.54369868e-02, -8.44782871e-01, -1.64614462e+00,
                                  -3.83933101e+00, -5.60243416e+00, -6.51691578e+00,
                                  -5.60243416e+00, -3.83933101e+00, -1.64614462e+00,
                                  -8.44782871e-01, 3.54369868e-02, 5.31096720e-01,
                                   8.10171823e-01, 4.83499829e-01],
                                  [1.12382749e+00, 2.22609074e+00, 1.27877807e+00,
                                   4.55434098e-01, -1.76842385e+00, -1.90046460e+00,
                                  -8.10874526e+00, -1.20534899e+01, -1.48627948e+01,
                                  -1.20534899e+01, -8.10874526e+00, -1.90046460e+00,
                                  -1.76842385e+00, 4.55434098e-01, 1.27877807e+00,
                                   2.22609074e+00, 1.12382749e+00],
                                  [1.78571940e+00, 4.38918110e+00, 3.95098587e+00,
                                   3.70961649e-01, -3.48151981e+00, -9.61567736e+00,
                                  -1.78621172e+01, -2.32278872e+01, -2.31833727e+01,
                                  -2.32278872e+01, -1.78621172e+01, -9.61567736e+00,
                                  -3.48151981e+00, 3.70961649e-01, 3.95098587e+00,
                                   4.38918110e+00, 1.78571940e+00],
                                  [1.62986900e+00, 3.67851228e+00, 5.68645252e+00,
                                   2.15342566e-01, -8.89937202e+00, -1.44739813e+01,
                                  -2.98952660e+01, -4.37420817e+01, -4.83160958e+01,
                                  -4.37420817e+01, -2.98952660e+01, -1.44739813e+01,
                                  -8.89937202e+00, 2.15342566e-01, 5.68645252e+00,
                                   3.67851228e+00, 1.62986900e+00],
                                  [1.05524430e+00, 1.71917897e+00, 1.73105590e+00,
                                  -2.10088420e+00, -1.15118208e+01, -2.55007598e+01,
                                  -4.73056159e+01, -6.97257685e+01, -8.09264433e+01,
                                  -6.97257685e+01, -4.73056159e+01, -2.55007598e+01,
                                  -1.15118208e+01, -2.10088420e+00, 1.73105590e+00,
                                   1.71917897e+00, 1.05524430e+00],
                                  [8.71929228e-01, 5.41025574e-01, 9.47560771e-01,
                                  -5.75314708e-01, -7.46104027e+00, -4.42314481e+01,
                                  -9.54126971e+01, -1.61603201e+02, -2.07520692e+02,
                                  -1.61603201e+02, -9.54126971e+01, -4.42314481e+01,
                                  -7.46104027e+00, -5.75314708e-01, 9.47560771e-01,
                                   5.41025574e-01, 8.71929228e-01],
                                  [1.89144704e+00, 3.57543979e+00, -6.91419168e-02,
                                  -3.37950835e+00, -1.46695089e+01, -7.22850746e+01,
                                  -1.65563055e+02, -3.10820425e+02, -4.70026655e+02,
                                  -3.10820425e+02, -1.65563055e+02, -7.22850746e+01,
                                  -1.46695089e+01, -3.37950835e+00, -6.91419168e-02,
                                   3.57543979e+00, 1.89144704e+00],
                                  [3.11841913e+00, 7.84024994e+00, 1.88495248e+00,
                                  -7.69011009e+00, -2.71782400e+01, -1.04343326e+02,
                                  -2.47561370e+02, -5.32959841e+02, -1.16529012e+03,
                                  -5.32959841e+02, -2.47561370e+02, -1.04343326e+02,
                                  -2.71782400e+01, -7.69011009e+00, 1.88495248e+00,
                                   7.84024994e+00, 3.11841913e+00],
                                  [2.74197956e+00, 4.73107997e+00, -9.48352966e-01,
                                  -9.44822832e+00, -3.06477671e+01, -1.26788739e+02,
                                  -3.22828411e+02, -8.47943472e+02, -3.87702420e+03,
                                  -8.47943472e+02, -3.22828411e+02, -1.26788739e+02,
                                  -3.06477671e+01, -9.44822832e+00, -9.48352966e-01,
                                   4.73107997e+00, 2.74197956e+00],
                                  [3.11841913e+00, 7.84024994e+00, 1.88495248e+00,
                                  -7.69011009e+00, -2.71782400e+01, -1.04343326e+02,
                                  -2.47561370e+02, -5.32959841e+02, -1.16529012e+03,
                                  -5.32959841e+02, -2.47561370e+02, -1.04343326e+02,
                                  -2.71782400e+01, -7.69011009e+00, 1.88495248e+00,
                                  7.84024994e+00, 3.11841913e+00],
                                  [1.89144704e+00, 3.57543979e+00, -6.91419168e-02,
                                  -3.37950835e+00, -1.46695089e+01, -7.22850746e+01,
                                  -1.65563055e+02, -3.10820425e+02, -4.70026655e+02,
                                  -3.10820425e+02, -1.65563055e+02, -7.22850746e+01,
                                  -1.46695089e+01, -3.37950835e+00, -6.91419168e-02,
                                   3.57543979e+00, 1.89144704e+00],
                                  [8.71929228e-01, 5.41025574e-01, 9.47560771e-01,
                                  -5.75314708e-01, -7.46104027e+00, -4.42314481e+01,
                                  -9.54126971e+01, -1.61603201e+02, -2.07520692e+02,
                                  -1.61603201e+02, -9.54126971e+01, -4.42314481e+01,
                                  -7.46104027e+00, -5.75314708e-01, 9.47560771e-01,
                                   5.41025574e-01, 8.71929228e-01],
                                  [1.05524430e+00, 1.71917897e+00, 1.73105590e+00,
                                  -2.10088420e+00, -1.15118208e+01, -2.55007598e+01,
                                  -4.73056159e+01, -6.97257685e+01, -8.09264433e+01,
                                  -6.97257685e+01, -4.73056159e+01, -2.55007598e+01,
                                  -1.15118208e+01, -2.10088420e+00, 1.73105590e+00,
                                   1.71917897e+00, 1.05524430e+00],
                                  [1.62986900e+00, 3.67851228e+00, 5.68645252e+00,
                                   2.15342566e-01, -8.89937202e+00, -1.44739813e+01,
                                  -2.98952660e+01, -4.37420817e+01, -4.83160958e+01,
                                  -4.37420817e+01, -2.98952660e+01, -1.44739813e+01,
                                  -8.89937202e+00, 2.15342566e-01, 5.68645252e+00,
                                   3.67851228e+00, 1.62986900e+00],
                                  [1.78571940e+00, 4.38918110e+00, 3.95098587e+00,
                                   3.70961649e-01, -3.48151981e+00, -9.61567736e+00,
                                  -1.78621172e+01, -2.32278872e+01, -2.31833727e+01,
                                  -2.32278872e+01, -1.78621172e+01, -9.61567736e+00,
                                  -3.48151981e+00, 3.70961649e-01, 3.95098587e+00,
                                   4.38918110e+00, 1.78571940e+00],
                                  [1.12382749e+00, 2.22609074e+00, 1.27877807e+00,
                                   4.55434098e-01, -1.76842385e+00, -1.90046460e+00,
                                  -8.10874526e+00, -1.20534899e+01, -1.48627948e+01,
                                  -1.20534899e+01, -8.10874526e+00, -1.90046460e+00,
                                  -1.76842385e+00, 4.55434098e-01, 1.27877807e+00,
                                   2.22609074e+00, 1.12382749e+00],
                                  [4.83499829e-01, 8.10171823e-01, 5.31096720e-01,
                                   3.54369868e-02, -8.44782871e-01, -1.64614462+00,
                                  -3.83933101e+00, -5.60243416e+00, -6.51691578e+00,
                                  -5.60243416e+00, -3.83933101e+00, -1.64614462e+00,
                                  -8.44782871e-01, 3.54369868e-02, 5.31096720e-01,
                                   8.10171823e-01, 4.83499829e-01]]) * 1e-10
 
        # Spline trap coefficients and the ctiCalibDict are all taken from a
        # cti calibration measured from LSSTCam sensor R03_S12 during Run 5
        # EO testing. These are the coefficients for the spline trap model
        # used in the deferred charge calibration. The collection can be
        # found in /repo/ir2: u/abrought/13144/cti (processed 3/4/2024).
        self.splineTrapCoeffs = np.array([0.0, 28.1, 47.4, 56.4, 66.6, 78.6, 92.4, 109.4,
                                          129.0, 151.9, 179.4, 211.9, 250.5, 296.2, 350.0,
                                          413.5, 488.0, 576.0, 680.4, 753.0, 888.2, 1040.5,
                                          1254.1, 1478.9, 1747.0, 2055.7, 2416.9, 2855.2,
                                          3361.9, 3969.4, 4665.9, 5405.3, 6380.0, 7516.7,
                                          8875.9, 10488.6, 12681.9, 14974.2, 17257.6, 20366.5,
                                          24026.7, 28372.1, 33451.7, 39550.4, 46624.8, 55042.9,
                                          64862.7, 76503.1, 90265.6, 106384.2, 0.0, 0.0, 0.0,
                                          0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1,
                                          0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0,
                                          0.1, 0.2, 0.6, 0.1, 0.0, 0.1, 0.0, 0.6, 0.3, 0.5, 0.8,
                                          0.8, 1.5, 2.0, 1.8, 2.4, 2.6, 3.7, 5.0, 6.4, 8.4, 10.9,
                                          14.5, 21.1, 28.9])
 
        self.ctiCalibDict = {'driftScale': {'C:0,0': 0.000127,
                                            'C:0,1': 0.000137,
                                            'C:0,2': 0.000138,
                                            'C:0,3': 0.000147,
                                            'C:1,0': 0.000147,
                                            'C:1,1': 0.000122,
                                            'C:1,2': 0.000123,
                                            'C:1,3': 0.000116},
                             'decayTime': {'C:0,0': 2.30,
                                           'C:0,1': 2.21,
                                           'C:0,2': 2.28,
                                           'C:0,3': 2.34,
                                           'C:1,0': 2.30,
                                           'C:1,1': 2.40,
                                           'C:1,2': 2.51,
                                           'C:1,3': 2.21},
                             'globalCti': {'C:0,0': 5.25e-07,
                                           'C:0,1': 5.38e-07,
                                           'C:0,2': 5.80e-07,
                                           'C:0,3': 5.91e-07,
                                           'C:1,0': 6.24e-07,
                                           'C:1,1': 5.72e-07,
                                           'C:1,2': 5.60e-07,
                                           'C:1,3': 4.40e-07},
                             'signals': {'C:0,0': np.linspace(1.0e2, 1.0e5, 10),
                                         'C:0,1': np.linspace(1.0e2, 1.0e5, 10),
                                         'C:0,2': np.linspace(1.0e2, 1.0e5, 10),
                                         'C:0,3': np.linspace(1.0e2, 1.0e5, 10),
                                         'C:1,0': np.linspace(1.0e2, 1.0e5, 10),
                                         'C:1,1': np.linspace(1.0e2, 1.0e5, 10),
                                         'C:1,2': np.linspace(1.0e2, 1.0e5, 10),
                                         'C:1,3': np.linspace(1.0e2, 1.0e5, 10)},
                             'serialEper': {'C:0,0': np.full(10, 5.25e-07),
                                            'C:0,1': np.full(10, 5.38e-07),
                                            'C:0,2': np.full(10, 5.80e-07),
                                            'C:0,3': np.full(10, 5.91e-07),
                                            'C:1,0': np.full(10, 6.24e-07),
                                            'C:1,1': np.full(10, 5.72e-07),
                                            'C:1,2': np.full(10, 5.60e-07),
                                            'C:1,3': np.full(10, 4.40e-07)},
                             'parallelEper': {'C:0,0': np.full(10, 5.25e-07),
                                              'C:0,1': np.full(10, 5.38e-07),
                                              'C:0,2': np.full(10, 5.80e-07),
                                              'C:0,3': np.full(10, 5.91e-07),
                                              'C:1,0': np.full(10, 6.24e-07),
                                              'C:1,1': np.full(10, 5.72e-07),
                                              'C:1,2': np.full(10, 5.60e-07),
                                              'C:1,3': np.full(10, 4.40e-07)},
                             'serialCtiTurnoff': {'C:0,0': 1.0e5,
                                                  'C:0,1': 1.0e5,
                                                  'C:0,2': 1.0e5,
                                                  'C:0,3': 1.0e5,
                                                  'C:1,0': 1.0e5,
                                                  'C:1,1': 1.0e5,
                                                  'C:1,2': 1.0e5,
                                                  'C:1,3': 1.0e5},
                             'parallelCtiTurnoff': {'C:0,0': 1.0e5,
                                                    'C:0,1': 1.0e5,
                                                    'C:0,2': 1.0e5,
                                                    'C:0,3': 1.0e5,
                                                    'C:1,0': 1.0e5,
                                                    'C:1,1': 1.0e5,
                                                    'C:1,2': 1.0e5,
                                                    'C:1,3': 1.0e5},
                             'serialCtiTurnoffSamplingErr': {'C:0,0': 1.0e3,
                                                             'C:0,1': 1.0e3,
                                                             'C:0,2': 1.0e3,
                                                             'C:0,3': 1.0e3,
                                                             'C:1,0': 1.0e3,
                                                             'C:1,1': 1.0e3,
                                                             'C:1,2': 1.0e3,
                                                             'C:1,3': 1.0e3},
                             'parallelCtiTurnoffSamplingErr': {'C:0,0': 1.0e3,
                                                               'C:0,1': 1.0e3,
                                                               'C:0,2': 1.0e3,
                                                               'C:0,3': 1.0e3,
                                                               'C:1,0': 1.0e3,
                                                               'C:1,1': 1.0e3,
                                                               'C:1,2': 1.0e3,
                                                               'C:1,3': 1.0e3},
                             'serialTraps': {'C:0,0': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs},
                                             'C:0,1': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs},
                                             'C:0,2': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs},
                                             'C:0,3': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs},
                                             'C:1,0': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs},
                                             'C:1,1': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs},
                                             'C:1,2': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs},
                                             'C:1,3': {'size': 20000.0,
                                                       'emissionTime': 0.4,
                                                       'pixel': 1,
                                                       'trap_type': 'spline',
                                                       'coeffs': self.splineTrapCoeffs}}}
 
        self.deferredChargeCalib = self.makeDeferredChargeCalib()
 
        # Cross-talk coeffs are defined in the parent class.
 
        self.makeSubtask("assembleCcd")
 
    def __init__(self, **kwargs): …
    def run(self):
        """Generate a mock ISR product following LSSTCam ISR, and return it.
 
        Returns
        -------
        image : `lsst.afw.image.Exposure`
            Simulated ISR image with signals added.
        dataProduct :
            Simulated ISR data products.
        None :
            Returned if no valid configuration was found.
 
        Raises
        ------
        RuntimeError
            Raised if both doGenerateImage and doGenerateData are specified.
        """
        if self.config.doGenerateImage and self.config.doGenerateData:
            raise RuntimeError("Only one of doGenerateImage and doGenerateData may be specified.")
        elif self.config.doGenerateImage:
            return self.makeImage()
        elif self.config.doGenerateData:
            return self.makeData()
        else:
            return None
 
    def run(self): …
    def makeImage(self):
        """Generate a simulated ISR LSST image.
 
        Returns
        -------
        exposure : `lsst.afw.image.Exposure` or `dict`
            Simulated ISR image data.
 
        Notes
        -----
        This method constructs a "raw" data image.
        """
        exposure = self.getExposure()
 
        # Set up random number generators for consistency of components,
        # no matter the group that are configured.
        rngSky = np.random.RandomState(seed=self.config.rngSeed + 1)
        rngDark = np.random.RandomState(seed=self.config.rngSeed + 2)
        rng2DBias = np.random.RandomState(seed=self.config.rngSeed + 3)
        rngOverscan = np.random.RandomState(seed=self.config.rngSeed + 4)
        rngReadNoise = np.random.RandomState(seed=self.config.rngSeed + 5)
        rngBrighterFatter = galsim.BaseDeviate(self.config.rngSeed + 6)
 
        # Create the linearizer if we will need it.
        if self.config.doAddHighSignalNonlinearity:
            linearizer = LinearizerMockLSST().run()
 
        # We introduce effects as they happen from a source to the signal,
        # so the effects go from electron to adu.
        # The ISR steps will then correct these effects in the reverse order.
        for idx, amp in enumerate(exposure.getDetector()):
            # Get image bbox and data
            bbox = None
            if self.config.isTrimmed:
                bbox = amp.getBBox()
                bboxFull = bbox
            else:
                bbox = amp.getRawDataBBox()
                bboxFull = amp.getRawBBox()
 
            # This is the image data (excluding pre/overscans).
            ampImageData = exposure.image[bbox]
            # This is the full data (including pre/overscans if untrimmed).
            ampFullData = exposure.image[bboxFull]
 
            # Astrophysical signals are all in electron (e-).
            # These are only applied to the imaging portion of the
            # amplifier (ampImageData)
 
            if self.config.doAddSky:
                # The sky effects are in electron.
                self.amplifierAddNoise(
                    ampImageData,
                    self.config.skyLevel,
                    np.sqrt(self.config.skyLevel),
                    rng=rngSky,
                )
 
            if self.config.doAddSource:
                for sourceAmp, sourceFlux, sourceX, sourceY in zip(self.config.sourceAmp,
                                                                   self.config.sourceFlux,
                                                                   self.config.sourceX,
                                                                   self.config.sourceY):
                    if idx == sourceAmp:
                        # The source flux is in electron.
                        self.amplifierAddSource(ampImageData, sourceFlux, sourceX, sourceY)
 
            if self.config.doAddFringe:
                # Fringes are added in electron.
                self.amplifierAddFringe(amp,
                                        ampImageData,
                                        np.array(self.config.fringeScale),
                                        x0=np.array(self.config.fringeX0),
                                        y0=np.array(self.config.fringeY0))
 
            if self.config.doAddFlat:
                if self.config.calibMode:
                    # In case we are making a combined flat,
                    # add a non-zero signal so the mock flat can be multiplied
                    self.amplifierAddNoise(ampImageData, 1.0, 0.0)
                # Multiply each amplifier by a Gaussian centered on u0 and v0
                u0 = exposure.getDetector().getBBox().getDimensions().getX()/2.
                v0 = exposure.getDetector().getBBox().getDimensions().getY()/2.
                self.amplifierMultiplyFlat(amp, ampImageData, self.config.flatDrop, u0=u0, v0=v0)
 
        # On-chip electronic effects.
 
        # 1. Add bright defect(s).
        if self.config.doAddBrightDefects:
            defectList = self.makeDefectList(isTrimmed=self.config.isTrimmed)
 
            for defect in defectList:
                exposure.image[defect.getBBox()] = self.config.brightDefectLevel
 
        for idx, amp in enumerate(exposure.getDetector()):
            # Get image bbox and data
            bbox = None
            if self.config.isTrimmed:
                bbox = amp.getBBox()
                bboxFull = bbox
            else:
                bbox = amp.getRawDataBBox()
                bboxFull = amp.getRawBBox()
 
            # This is the image data (excluding pre/overscans).
            ampImageData = exposure.image[bbox]
            # This is the full data (including pre/overscans if untrimmed).
            ampFullData = exposure.image[bboxFull]
 
            # 2. Add dark current (electron) to imaging portion of the amp.
            if self.config.doAddDark or self.config.doAddDarkNoiseOnly:
                if self.config.doAddDarkNoiseOnly:
                    darkLevel = 0.0
                else:
                    darkLevel = self.config.darkRate * self.config.darkTime
                if self.config.calibMode:
                    darkNoise = 0.0
                else:
                    darkNoise = np.sqrt(self.config.darkRate * self.config.darkTime)
 
                self.amplifierAddNoise(ampImageData, darkLevel, darkNoise, rng=rngDark)
 
            # 3. Add BF effect (electron) to imaging portion of the amp.
            if self.config.doAddBrighterFatter is True:
                self.amplifierAddBrighterFatter(
                    ampImageData,
                    rngBrighterFatter,
                    self.config.bfStrength,
                    self.config.nRecalc,
                )
 
            # 4. Add serial CTI (electron) to amplifier (imaging + overscan).
            if self.config.doAddDeferredCharge:
                # Get the free charge area for the amplifier.
                self.amplifierAddDeferredCharge(exposure, amp)
 
            # 5. Add 2D bias residual (electron) to imaging portion of the amp.
            if self.config.doAdd2DBias:
                # For now we use an unstructured noise field to add some
                # consistent 2D bias residual that can be subtracted. In
                # the future this can be made into a warm corner (for example).
                self.amplifierAddNoise(
                    ampImageData,
                    0.0,
                    self.config.noise2DBias,
                    rng=rng2DBias,
                )
 
            # 6. Add clock-injected offset (electron) to amplifer
            #    (imaging + overscan).
            # This is just an offset that will be crosstalked and modified by
            # the gain, and does not have a noise associated with it.
            if self.config.doAddClockInjectedOffset:
                self.amplifierAddNoise(
                    ampFullData,
                    self.config.clockInjectedOffsetLevel,
                    0.0,
                )
 
            # 7./8. Add serial and parallel overscan slopes (electron)
            #       (imaging + overscan)
            if (self.config.doAddParallelOverscanRamp or self.config.doAddSerialOverscanRamp) and \
               not self.config.isTrimmed:
 
                if self.config.doAddParallelOverscanRamp:
                    # Apply gradient along the X axis.
                    self.amplifierAddXGradient(ampFullData, -1.0 * self.config.overscanScale,
                                               1.0 * self.config.overscanScale)
 
                if self.config.doAddSerialOverscanRamp:
                    # Apply the gradient along the Y axis.
                    self.amplifierAddYGradient(ampFullData, -1.0 * self.config.overscanScale,
                                               1.0 * self.config.overscanScale)
 
            # 9. Add non-linearity (electron) to amplifier
            #    (imaging + overscan).
            if self.config.doAddHighSignalNonlinearity:
                # The linearizer coefficients come from makeLinearizer().
                if linearizer.linearityType[amp.getName()] != "Spline":
                    raise RuntimeError("IsrMockLSST only supports spline non-linearity.")
 
                coeffs = linearizer.linearityCoeffs[amp.getName()]
                centers, values = np.split(coeffs, 2)
 
                # This is an application of high signal non-linearity, so we
                # set the lower values to 0.0 (this cut is arbitrary).
                values[centers < self.config.highSignalNonlinearityThreshold] = 0.0
 
                # The linearizer is units of adu, so convert to electron
                values *= self.config.gainDict[amp.getName()]
 
                # Note that the linearity spline is in "overscan subtracted"
                # units so needs to be applied without the clock-injected
                # offset.
                self.amplifierAddNonlinearity(
                    ampFullData,
                    centers,
                    values,
                    self.config.clockInjectedOffsetLevel if self.config.doAddClockInjectedOffset else 0.0,
                )
 
            # 10. Add read noise (electron) to the amplifier
            #     (imaging + overscan).
            #     Unsure if this should be before or after crosstalk.
            #     Probably some of both; hopefully doesn't matter.
            if not self.config.calibMode:
                # Add read noise to the imaging region.
                self.amplifierAddNoise(
                    ampImageData,
                    0.0,
                    self.config.readNoise,
                    rng=rngReadNoise,
                )
 
                # If not trimmed, add to the overscan regions.
                if not self.config.isTrimmed:
                    parallelOverscanBBox = amp.getRawParallelOverscanBBox()
                    parallelOverscanData = exposure.image[parallelOverscanBBox]
 
                    serialOverscanBBox = self.getFullSerialOverscanBBox(amp)
                    serialOverscanData = exposure.image[serialOverscanBBox]
 
                    # Add read noise of mean 0
                    # to the parallel and serial overscan regions.
                    self.amplifierAddNoise(
                        parallelOverscanData,
                        0.0,
                        self.config.readNoise,
                        rng=rngOverscan,
                    )
                    self.amplifierAddNoise(
                        serialOverscanData,
                        0.0,
                        self.config.readNoise,
                        rng=rngOverscan,
                    )
 
        # 7b. Add bad column to the parallel overscan region.
        if self.config.doAddBadParallelOverscanColumn and not self.config.isTrimmed:
            # We want to place this right above the defect, to simulate
            # bleeding into the parallel overscan region.
            amp = exposure.getDetector()[2]
            parBBox = amp.getRawParallelOverscanBBox()
            bboxBad = geom.Box2I(
                corner=geom.Point2I(50, parBBox.getMinY()),
                dimensions=geom.Extent2I(1, parBBox.getHeight()),
            )
            exposure[bboxBad].image.array[:, :] += self.config.badParallelOverscanColumnLevel
 
            if self.config.doAddBadParallelOverscanColumnNeighbors:
                for neighbor in [49, 51]:
                    bboxBad = geom.Box2I(
                        corner=geom.Point2I(neighbor, parBBox.getMinY()),
                        dimensions=geom.Extent2I(1, parBBox.getHeight()),
                    )
                    exposure[bboxBad].image.array[:, :] += self.config.badParallelOverscanColumnNeighborsLevel
 
        # 11. Add crosstalk (electron) to all the amplifiers
        #     (imaging + overscan).
        if self.config.doAddCrosstalk:
            ctCalib = CrosstalkCalib()
            # We use the regular subtractCrosstalk code but with a negative
            # sign on the crosstalk coefficients so it adds instead of
            # subtracts. We only apply the signal plane (ignoreVariance,
            # subtrahendMasking) with a very large pixel to mask to ensure
            # no crosstalk mask bits are set.
            ctCalib.subtractCrosstalk(
                exposure,
                crosstalkCoeffs=-1*self.crosstalkCoeffs,
                doSubtrahendMasking=True,
                minPixelToMask=1e100,
                ignoreVariance=True,
                fullAmplifier=True,
            )
 
        for amp in exposure.getDetector():
            # Get image bbox and data (again).
            bbox = None
            if self.config.isTrimmed:
                bbox = amp.getBBox()
                bboxFull = bbox
            else:
                bbox = amp.getRawDataBBox()
                bboxFull = amp.getRawBBox()
 
            # This is the image data (excluding pre/overscans).
            ampImageData = exposure.image[bbox]
            # This is the full data (including pre/overscans if untrimmed).
            ampFullData = exposure.image[bboxFull]
 
            # 12. Gain un-normalize (from electron to floating point adu)
            if self.config.doApplyGain:
                gain = self.config.gainDict.get(amp.getName(), self.config.gain)
                self.applyGain(ampFullData, gain)
 
            # 13. Add overall bias level (adu) to the amplifier
            #    (imaging + overscan)
            if self.config.doAddBias:
                self.addBiasLevel(ampFullData, self.config.biasLevel)
 
            # 14. Round/Truncate to integers (adu)
            if self.config.doRoundAdu:
                self.roundADU(ampFullData)
 
        # Add units metadata to calibrations.
        if self.config.calibMode:
            if self.config.doApplyGain:
                exposure.metadata["LSST ISR UNITS"] = "adu"
            else:
                exposure.metadata["LSST ISR UNITS"] = "electron"
 
            # Add a variance plane appropriate for a calibration frame.
            # We take the absolute value for biases which have no signal.
            exposure.variance.array[:, :] = np.abs(np.median(exposure.image.array)/10.)
 
        if self.config.doGenerateAmpDict:
            expDict = dict()
            for amp in exposure.getDetector():
                expDict[amp.getName()] = exposure
            return expDict
        else:
            return exposure
 
    def makeImage(self): …
    def addBiasLevel(self, ampData, biasLevel):
        """Add bias level to an amplifier's image data.
 
        Parameters
        ----------
        ampData : `lsst.afw.image.ImageF`
            Amplifier image to operate on.
        biasLevel : `float`
            Bias level to be added to the image.
        """
        ampArr = ampData.array
        ampArr[:] = ampArr[:] + biasLevel
 
    def addBiasLevel(self, ampData, biasLevel): …
    def makeDefectList(self, isTrimmed=True):
        """Generate a simple defect list.
 
        Parameters
        ----------
        isTrimmed : `bool`, optional
            Return defects in trimmed coordinates?
 
        Returns
        -------
        defectList : `lsst.meas.algorithms.Defects`
            Simulated defect list
        """
        defectBoxesUntrimmed = [
            geom.Box2I(
                geom.Point2I(50, 118),
                geom.Extent2I(1, 51),
            ),
        ]
 
        if not isTrimmed:
            return Defects(defectBoxesUntrimmed)
 
        # If trimmed, we need to convert.
        tempExp = self.getExposure(isTrimmed=False)
        tempExp.image.array[:, :] = 0.0
        for bbox in defectBoxesUntrimmed:
            tempExp.image[bbox] = 1.0
 
        assembledExp = self.assembleCcd.assembleCcd(tempExp)
 
        # Use thresholding code to find defect footprints/boxes.
        threshold = afwDetection.createThreshold(1.0, "value", polarity=True)
        footprintSet = afwDetection.FootprintSet(assembledExp.image, threshold)
 
        return Defects.fromFootprintList(footprintSet.getFootprints())
 
    def makeDefectList(self, isTrimmed=True): …
    def makeBfKernel(self):
        """Generate a simple simulated brighter-fatter kernel.
        Returns
        -------
        kernel : `lsst.ip.isr.BrighterFatterKernel`
            Simulated brighter-fatter kernel.
        """
        bfkArray = super().makeBfKernel()
        bfKernelObject = BrighterFatterKernel()
        bfKernelObject.level = 'AMP'
        bfKernelObject.gain = self.config.gainDict
 
        for amp in self.getExposure().getDetector():
            # Kernel must be in (y,x) orientation
            bfKernelObject.ampKernels[amp.getName()] = bfkArray.T
            bfKernelObject.valid[amp.getName()] = True
 
        return bfKernelObject
 
    def makeBfKernel(self): …
    def makeDeferredChargeCalib(self):
        """Generate a CTI calibration.
 
        Returns
        -------
        cti : `lsst.ip.isr.deferredCharge.DeferredChargeCalib`
            Simulated deferred charge calibration.
        """
 
        metadataDict = {'metadata': PropertyList()}
        metadataDict['metadata'].add(name="OBSTYPE", value="CTI")
        metadataDict['metadata'].add(name="CALIBCLS",
                                     value="lsst.ip.isr.deferredCharge.DeferredChargeCalib")
        self.ctiCalibDict = {**metadataDict, **self.ctiCalibDict}
        deferredChargeCalib = DeferredChargeCalib()
        self.cti = deferredChargeCalib.fromDict(self.ctiCalibDict)
 
        return self.cti
 
    def makeDeferredChargeCalib(self): …
    def amplifierAddBrighterFatter(self, ampImageData, rng, bfStrength, nRecalc):
        """Add brighter fatter effect and/or diffusion to the image.
          Parameters
          ----------
          ampImageData : `lsst.afw.image.ImageF`
              Amplifier image to operate on.
          rng : `galsim.BaseDeviate`
              Random number generator.
          bfStrength : `float`
              Scaling parameter of the brighter fatter effect (nominally = 1)
          nRecalc: 'int'
              The number of electrons to accumulate before recalculating the
              distortion of the pixel shapes.
        """
 
        incidentImage = galsim.Image(ampImageData.array, scale=1)
        measuredImage = galsim.ImageF(
            ampImageData.array.shape[1],
            ampImageData.array.shape[0],
            scale=1,
        )
        photons = galsim.PhotonArray.makeFromImage(incidentImage)
 
        sensorModel = galsim.SiliconSensor(
            strength=bfStrength,
            rng=rng,
            diffusion_factor=0.0,
            nrecalc=nRecalc,
        )
 
        totalFluxAdded = sensorModel.accumulate(photons, measuredImage)
        ampImageData.array = measuredImage.array
 
        return totalFluxAdded
 
    def amplifierAddBrighterFatter(self, ampImageData, rng, bfStrength, nRecalc): …
    def amplifierAddDeferredCharge(self, exposure, amp):
        """Add serial CTI to the amplifier data.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.ExposureF`
            The exposure object containing the amplifier
            to apply deferred charge to.
        amp : `lsst.afw.image.Amplifier`
            The amplifier object (contains geometry info).
        """
        # Get the amplifier's geometry parameters.
        # When adding deferred charge, we have already assured that
        # isTrimmed is False. Therefore we want to make sure that we
        # get the RawDataBBox.
        readoutCorner = amp.getReadoutCorner()
        prescanWidth = amp.getRawHorizontalPrescanBBox().getWidth()
        serialOverscanWidth = amp.getRawHorizontalOverscanBBox().getWidth()
        parallelOverscanWidth = amp.getRawVerticalOverscanBBox().getHeight()
        bboxFreeCharge = amp.getRawDataBBox()
        bboxFreeCharge = bboxFreeCharge.expandedTo(amp.getRawHorizontalOverscanBBox())
        bboxFreeCharge = bboxFreeCharge.expandedTo(amp.getRawHorizontalPrescanBBox())
        bboxFreeCharge = bboxFreeCharge.expandedTo(amp.getRawVerticalOverscanBBox())
 
        ampFreeChargeData = exposure.image[bboxFreeCharge]
        ampImageData = exposure.image[amp.getRawDataBBox()]
 
        # Get the deferred charge parameters for this amplifier.
        cti = self.deferredChargeCalib.globalCti[amp.getName()]
        traps = self.deferredChargeCalib.serialTraps[amp.getName()]
        driftScale = self.deferredChargeCalib.driftScale[amp.getName()]
        decayTime = self.deferredChargeCalib.decayTime[amp.getName()]
 
        # Create a fake amplifier object that contains some deferred charge
        # paramters.
        floatingOutputAmplifier = FloatingOutputAmplifier(
            gain=1.0,  # Everything is already in electrons.
            scale=driftScale,
            decay_time=decayTime,
            noise=0.0,
            offset=0.0,
        )
 
        def flipImage(arr, readoutCorner):
            # Flip an array so that the readout corner is in
            # the lower left.
            if readoutCorner == ReadoutCorner.LR:
                return np.fliplr(arr)
            elif readoutCorner == ReadoutCorner.UR:
                return np.fliplr(np.flipud(arr))
            elif readoutCorner == ReadoutCorner.UL:
                return np.flipud(arr)
            else:
                pass
 
            return arr
 
        # The algorithm expects that the readout corner is in
        # the lower left corner.  Flip it to be so:
        imageData = ampImageData.array
        imageData = flipImage(imageData, readoutCorner)
 
        # Simulate the amplifier.
        ampSim = SegmentSimulator(
            imarr=imageData,
            prescan_width=prescanWidth,
            output_amplifier=floatingOutputAmplifier,
            cti=cti,
            traps=traps,
        )
 
        # Simulate deferred charge!
        # Note that the readout() method uses the image region data and the
        # overscan dimensions provided as input parameters. It then creates
        # overscan nd adds it to the image data to create the raw image.
        result = ampSim.readout(
            serial_overscan_width=serialOverscanWidth,
            parallel_overscan_width=parallelOverscanWidth,
        )
 
        # Flip the image back to the original orientation.
        result = flipImage(result, readoutCorner)
 
        # Set the image with the deferred charge added.
        ampFreeChargeData.array[:, :] = result
 
    def amplifierAddDeferredCharge(self, exposure, amp): …
    def makeLinearizer(self):
        # docstring inherited.
 
        # The linearizer has units of adu.
        nNodes = 10
        # Set this to just above the mock saturation (adu)
        maxADU = 101_000
        nonLinSplineNodes = np.linspace(0, maxADU, nNodes)
        # These values come from cp_pipe/tests/test_linearity.py and
        # are based on a test fit to LSSTCam data, run 7193D, detector 22,
        # amp C00.
        nonLinSplineValues = np.array(
            [0.0, -8.87, 1.46, 1.69, -6.92, -68.23, -78.01, -11.56, 80.26, 185.01]
        )
 
        if self.config.doAddHighSignalNonlinearity and not self.config.doAddLowSignalNonlinearity:
            nonLinSplineValues[nonLinSplineNodes < self.config.highSignalNonlinearityThreshold] = 0.0
        elif self.config.doAddLowSignalNonlinearity:
            raise NotImplementedError("Low signal non-linearity is not implemented.")
 
        exp = self.getExposure()
        detector = exp.getDetector()
 
        linearizer = Linearizer(detector=detector)
        linearizer.updateMetadataFromExposures([exp])
 
        # We need to set override by hand because we are constructing a
        # linearizer manually and not from a serialized object.
        linearizer.override = True
        linearizer.hasLinearity = True
        linearizer.validate()
        linearizer.updateMetadata(camera=self.getCamera(), detector=detector, filterName='NONE')
        linearizer.updateMetadata(setDate=True, setCalibId=True)
 
        for amp in detector:
            ampName = amp.getName()
            linearizer.linearityType[ampName] = "Spline"
            linearizer.linearityCoeffs[ampName] = np.concatenate([nonLinSplineNodes, nonLinSplineValues])
            # We need to specify the raw bbox here.
            linearizer.linearityBBox[ampName] = amp.getRawBBox()
 
        return linearizer
 
    def makeLinearizer(self): …
    def amplifierAddNonlinearity(self, ampData, centers, values, offset):
        """Add non-linearity to amplifier data.
 
        Parameters
        ----------
        ampData : `lsst.afw.image.ImageF`
            Amplifier image to operate on.
        centers : `np.ndarray`
            Spline nodes.
        values : `np.ndarray`
            Spline values.
        offset : `float`
            Offset zero-point between linearizer (internal vs external).
        """
        # I'm not sure what to do about negative values...
 
        # Note that we are using the afw AKIMA_SPLINE to offset the
        # data but using the equivalent but faster scipy Akima1DInterpolator to
        # correct the data.
        spl = afwMath.makeInterpolate(
            centers,
            values,
            afwMath.stringToInterpStyle("AKIMA_SPLINE"),
        )
 
        delta = np.asarray(spl.interpolate(ampData.array.ravel() - offset))
 
        ampData.array[:, :] += delta.reshape(ampData.array.shape)
 
    def amplifierAddNonlinearity(self, ampData, centers, values, offset): …
    def amplifierMultiplyFlat(self, amp, ampData, fracDrop, u0=100.0, v0=100.0):
        """Multiply an amplifier's image data by a flat-like pattern.
 
        Parameters
        ----------
        amp : `lsst.afw.ampInfo.AmpInfoRecord`
            Amplifier to operate on. Needed for amp<->exp coordinate
            transforms.
        ampData : `lsst.afw.image.ImageF`
            Amplifier image to operate on.
        fracDrop : `float`
            Fractional drop from center to edge of detector along x-axis.
        u0 : `float`
            Peak location in detector coordinates.
        v0 : `float`
            Peak location in detector coordinates.
        """
        if fracDrop >= 1.0:
            raise RuntimeError("Flat fractional drop cannot be greater than 1.0")
 
        sigma = u0 / np.sqrt(2.0 * fracDrop)
 
        for x in range(0, ampData.getDimensions().getX()):
            for y in range(0, ampData.getDimensions().getY()):
                (u, v) = self.localCoordToExpCoord(amp, x, y)
                f = np.exp(-0.5 * ((u - u0)**2 + (v - v0)**2) / sigma**2)
                ampData.array[y][x] = (ampData.array[y][x] * f)
 
    def amplifierMultiplyFlat(self, amp, ampData, fracDrop, u0=100.0, v0=100.0): …
    def applyGain(self, ampData, gain):
        """Apply gain to the amplifier's data.
        This method divides the data by the gain
        because the mocks need to convert the data in electron to adu,
        so it does the inverse operation to applyGains in isrFunctions.
 
        Parameters
        ----------
        ampData : `lsst.afw.image.ImageF`
            Amplifier image to operate on.
        gain : `float`
            Gain value in electron/adu.
        """
        ampArr = ampData.array
        ampArr[:] = ampArr[:] / gain
 
    def applyGain(self, ampData, gain): …
    def roundADU(self, ampData):
        """Round adu to nearest integer.
 
        Parameters
        ----------
        ampData : `lsst.afw.image.ImageF`
            Amplifier image to operate on.
        """
        ampArr = ampData.array
        ampArr[:] = np.around(ampArr)
 
    def roundADU(self, ampData): …
    def amplifierAddXGradient(self, ampData, start, end):
        """Add a x-axis linear gradient to an amplifier's image data.
 
         This method operates in the amplifier coordinate frame.
 
        Parameters
        ----------
        ampData : `lsst.afw.image.ImageF`
            Amplifier image to operate on.
        start : `float`
            Start value of the gradient (at x=0).
        end : `float`
            End value of the gradient (at x=xmax).
        """
        nPixX = ampData.getDimensions().getX()
        ampArr = ampData.array
        ampArr[:] = ampArr[:] + (np.interp(range(nPixX), (0, nPixX - 1), (start, end)).reshape(1, nPixX)
                                 + np.zeros(ampData.getDimensions()).transpose())
 
    def amplifierAddXGradient(self, ampData, start, end): …
    def getFullSerialOverscanBBox(self, amp):
        """Get the full serial overscan bounding box from an amplifier.
 
        This includes the serial/parallel overscan region.
 
        Parameters
        ----------
        amp : `lsst.afw.ampInfo.AmpInfoRecord`
            Amplifier to operate on.
 
        Returns
        -------
        bbox : `lsst.geom.Box2I`
        """
        # This only works for untrimmed data.
        bbox = amp.getRawDataBBox()
 
        parallelOverscanBBox = amp.getRawParallelOverscanBBox()
        grownImageBBox = bbox.expandedTo(parallelOverscanBBox)
 
        serialOverscanBBox = amp.getRawSerialOverscanBBox()
        # Extend the serial overscan bbox to include corners
        serialOverscanBBox = geom.Box2I(
            geom.Point2I(serialOverscanBBox.getMinX(),
                         grownImageBBox.getMinY()),
            geom.Extent2I(serialOverscanBBox.getWidth(),
                          grownImageBBox.getHeight()))
 
        return serialOverscanBBox
 
 
    def getFullSerialOverscanBBox(self, amp): …
class IsrMockLSST(IsrMock): …
class RawMockLSST(IsrMockLSST):
    """Generate a raw exposure suitable for ISR.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.isTrimmed = False
        self.config.doGenerateImage = True
        self.config.doGenerateAmpDict = False
 
        # Add astro effects
        self.config.doAddSky = True
        self.config.doAddSource = True
 
        # Add optical effects
        self.config.doAddFringe = True
 
        # Add instrument effects
        self.config.doAddParallelOverscanRamp = True
        self.config.doAddSerialOverscanRamp = True
        self.config.doAddCrosstalk = True
        self.config.doAddBias = True
        self.config.doAddDark = True
 
        self.config.doAddFlat = True
 
 
    def __init__(self, **kwargs): …
class RawMockLSST(IsrMockLSST): …
class TrimmedRawMockLSST(RawMockLSST):
    """Generate a trimmed raw exposure.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.isTrimmed = True
        self.config.doAddParallelOverscanRamp = False
        self.config.doAddSerialOverscanRamp = False
 
 
    def __init__(self, **kwargs): …
class TrimmedRawMockLSST(RawMockLSST): …
class CalibratedRawMockLSST(RawMockLSST):
    """Generate a trimmed raw exposure.
 
    This represents a "truth" image that can be compared to a
    post-ISR cleaned image.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.isTrimmed = True
        self.config.doGenerateImage = True
 
        self.config.doAddSky = True
        self.config.doAddSource = True
 
        self.config.doAddFringe = True
 
        self.config.doAddParallelOverscanRamp = False
        self.config.doAddSerialOverscanRamp = False
        self.config.doAddCrosstalk = False
        self.config.doAddBias = False
        self.config.doAdd2DBias = False
        self.config.doAddDark = False
        self.config.doApplyGain = False
        self.config.doAddFlat = False
        self.config.doAddClockInjectedOffset = False
 
        self.config.biasLevel = 0.0
        # Assume combined calibrations are made with 16 inputs.
        self.config.readNoise *= 0.25
 
        self.config.doRoundAdu = False
 
 
    def __init__(self, **kwargs): …
class CalibratedRawMockLSST(RawMockLSST): …
class ReferenceMockLSST(IsrMockLSST):
    """Parent class for those that make reference calibrations.
    """
    def __init__(self, **kwargs):
        # If we want the calibration in adu units, we need to apply
        # the gain. Default is electron units, so do not apply the gain.
        doApplyGain = kwargs.pop("adu", False)
 
        super().__init__(**kwargs)
        self.config.isTrimmed = True
        self.config.doGenerateImage = True
 
        self.config.calibMode = True
 
        self.config.doAddSky = False
        self.config.doAddSource = False
 
        self.config.doAddFringe = False
 
        self.config.doAddParallelOverscanRamp = False
        self.config.doAddSerialOverscanRamp = False
        self.config.doAddCrosstalk = False
        self.config.doAddBias = False
        self.config.doAdd2DBias = False
        self.config.doAddDark = False
        self.config.doApplyGain = doApplyGain
        self.config.doAddFlat = False
        self.config.doAddClockInjectedOffset = False
 
        # Reference calibrations are not integerized.
        self.config.doRoundAdu = False
 
 
# Classes to generate calibration products mocks.
    def __init__(self, **kwargs): …
class ReferenceMockLSST(IsrMockLSST): …
class DarkMockLSST(ReferenceMockLSST):
    """Simulated reference dark calibration.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doAddDark = True
        self.config.darkTime = 1.0
 
 
    def __init__(self, **kwargs): …
class DarkMockLSST(ReferenceMockLSST): …
class BiasMockLSST(ReferenceMockLSST):
    """Simulated combined bias calibration.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        # This is a "2D bias residual" frame which has only
        # the 2D bias in it.
        self.config.doAdd2DBias = True
 
 
    def __init__(self, **kwargs): …
class BiasMockLSST(ReferenceMockLSST): …
class FlatMockLSST(ReferenceMockLSST):
    """Simulated reference flat calibration.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doAddFlat = True
 
 
    def __init__(self, **kwargs): …
class FlatMockLSST(ReferenceMockLSST): …
class FringeMockLSST(ReferenceMockLSST):
    """Simulated reference fringe calibration.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doAddFringe = True
 
 
    def __init__(self, **kwargs): …
class FringeMockLSST(ReferenceMockLSST): …
class BfKernelMockLSST(IsrMockLSST):
    """Simulated brighter-fatter kernel.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doGenerateImage = False
        self.config.doGenerateData = True
 
        self.config.doBrighterFatter = True
        self.config.doDefects = False
        self.config.doCrosstalkCoeffs = False
        self.config.doTransmissionCurve = False
        self.config.doLinearizer = False
 
 
    def __init__(self, **kwargs): …
class BfKernelMockLSST(IsrMockLSST): …
class DeferredChargeMockLSST(IsrMockLSST):
    """Simulated deferred charge calibration.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doGenerateImage = False
        self.config.doGenerateData = True
        self.config.doDeferredCharge = True
        self.config.doDefects = False
        self.config.doCrosstalkCoeffs = False
        self.config.doTransmissionCurve = False
 
 
    def __init__(self, **kwargs): …
class DeferredChargeMockLSST(IsrMockLSST): …
class DefectMockLSST(IsrMockLSST):
    """Simulated defect list.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doGenerateImage = False
        self.config.doGenerateData = True
 
        self.config.doBrighterFatter = False
        self.config.doDefects = True
        self.config.doCrosstalkCoeffs = False
        self.config.doTransmissionCurve = False
        self.config.doLinearizer = False
 
 
    def __init__(self, **kwargs): …
class DefectMockLSST(IsrMockLSST): …
class CrosstalkCoeffMockLSST(IsrMockLSST):
    """Simulated crosstalk coefficient matrix.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doGenerateImage = False
        self.config.doGenerateData = True
 
        self.config.doBrighterFatter = False
        self.config.doDefects = False
        self.config.doCrosstalkCoeffs = True
        self.config.doTransmissionCurve = False
        self.config.doLinearizer = False
 
 
    def __init__(self, **kwargs): …
class CrosstalkCoeffMockLSST(IsrMockLSST): …
class LinearizerMockLSST(IsrMockLSST):
    """Simulated linearizer.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doGenerateImage = False
        self.config.doGenerateData = True
 
        self.config.doBrighterFatter = False
        self.config.doDefects = False
        self.config.doCrosstalkCoeffs = False
        self.config.doTransmissionCurve = False
        self.config.doLinearizer = True
 
 
    def __init__(self, **kwargs): …
class LinearizerMockLSST(IsrMockLSST): …
class TransmissionMockLSST(IsrMockLSST):
    """Simulated transmission curve.
    """
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config.doGenerateImage = False
        self.config.doGenerateData = True
 
        self.config.doBrighterFatter = False
        self.config.doDefects = False
        self.config.doCrosstalkCoeffs = False
        self.config.doTransmissionCurve = True
        self.config.doLinearizer = False
    def __init__(self, **kwargs): …
class TransmissionMockLSST(IsrMockLSST): …