lsst.cp.pipe
20.0.0-20-g0a07b97+589c069167
|
Classes | |
class | NonexistentDatasetTaskDataIdContainer |
class | PairedVisitListTaskRunner |
class | SingleVisitListTaskRunner |
Functions | |
def | calculateWeightedReducedChi2 (measured, model, weightsMeasured, nData, nParsModel) |
def | makeMockFlats (expTime, gain=1.0, readNoiseElectrons=5, fluxElectrons=1000, randomSeedFlat1=1984, randomSeedFlat2=666, powerLawBfParams=[]) |
def | countMaskedPixels (maskedIm, maskPlane) |
def | parseCmdlineNumberString (inputString) |
def | fitLeastSq (initialParams, dataX, dataY, function, weightsY=None) |
def | fitBootstrap (initialParams, dataX, dataY, function, weightsY=None, confidenceSigma=1.) |
def | funcPolynomial (pars, x) |
def | funcAstier (pars, x) |
def | checkExpLengthEqual (exp1, exp2, v1=None, v2=None, raiseWithMessage=False) |
def | validateIsrConfig (isrTask, mandatory=None, forbidden=None, desirable=None, undesirable=None, checkTrim=True, logName=None) |
def lsst.cp.pipe.utils.calculateWeightedReducedChi2 | ( | measured, | |
model, | |||
weightsMeasured, | |||
nData, | |||
nParsModel | |||
) |
Calculate weighted reduced chi2. Parameters ---------- measured : `list` List with measured data. model : `list` List with modeled data. weightsMeasured : `list` List with weights for the measured data. nData : `int` Number of data points. nParsModel : `int` Number of parameters in the model. Returns ------- redWeightedChi2 : `float` Reduced weighted chi2.
def lsst.cp.pipe.utils.checkExpLengthEqual | ( | exp1, | |
exp2, | |||
v1 = None , |
|||
v2 = None , |
|||
raiseWithMessage = False |
|||
) |
Check the exposure lengths of two exposures are equal. Parameters: ----------- exp1 : `lsst.afw.image.exposure.ExposureF` First exposure to check exp2 : `lsst.afw.image.exposure.ExposureF` Second exposure to check v1 : `int` or `str`, optional First visit of the visit pair v2 : `int` or `str`, optional Second visit of the visit pair raiseWithMessage : `bool` If True, instead of returning a bool, raise a RuntimeError if exposure times are not equal, with a message about which visits mismatch if the information is available. Raises: ------- RuntimeError Raised if the exposure lengths of the two exposures are not equal
def lsst.cp.pipe.utils.countMaskedPixels | ( | maskedIm, | |
maskPlane | |||
) |
def lsst.cp.pipe.utils.fitBootstrap | ( | initialParams, | |
dataX, | |||
dataY, | |||
function, | |||
weightsY = None , |
|||
confidenceSigma = 1. |
|||
) |
Do a fit using least squares and bootstrap to estimate parameter errors. The bootstrap error bars are calculated by fitting 100 random data sets. Parameters ---------- initialParams : `list` of `float` initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length determines the degree of the polynomial. dataX : `numpy.array` of `float` Data in the abscissa axis. dataY : `numpy.array` of `float` Data in the ordinate axis. function : callable object (function) Function to fit the data with. weightsY : `numpy.array` of `float`, optional. Weights of the data in the ordinate axis. confidenceSigma : `float`, optional. Number of sigmas that determine confidence interval for the bootstrap errors. Return ------ pFitBootstrap : `list` of `float` List with fitted parameters. pErrBootstrap : `list` of `float` List with errors for fitted parameters. reducedChiSqBootstrap : `float` Reduced chi squared, unweighted if weightsY is not provided.
def lsst.cp.pipe.utils.fitLeastSq | ( | initialParams, | |
dataX, | |||
dataY, | |||
function, | |||
weightsY = None |
|||
) |
Do a fit and estimate the parameter errors using using scipy.optimize.leastq. optimize.leastsq returns the fractional covariance matrix. To estimate the standard deviation of the fit parameters, multiply the entries of this matrix by the unweighted reduced chi squared and take the square root of the diagonal elements. Parameters ---------- initialParams : `list` of `float` initial values for fit parameters. For ptcFitType=POLYNOMIAL, its length determines the degree of the polynomial. dataX : `numpy.array` of `float` Data in the abscissa axis. dataY : `numpy.array` of `float` Data in the ordinate axis. function : callable object (function) Function to fit the data with. weightsY : `numpy.array` of `float` Weights of the data in the ordinate axis. Return ------ pFitSingleLeastSquares : `list` of `float` List with fitted parameters. pErrSingleLeastSquares : `list` of `float` List with errors for fitted parameters. reducedChiSqSingleLeastSquares : `float` Reduced chi squared, unweighted if weightsY is not provided.
def lsst.cp.pipe.utils.funcAstier | ( | pars, | |
x | |||
) |
Single brighter-fatter parameter model for PTC; Equation 16 of Astier+19. Parameters ---------- params : `list` Parameters of the model: a00 (brightter-fatter), gain (e/ADU), and noise (e^2). x : `numpy.array` Signal mu (ADU). Returns ------- C_00 (variance) in ADU^2.
def lsst.cp.pipe.utils.funcPolynomial | ( | pars, | |
x | |||
) |
def lsst.cp.pipe.utils.makeMockFlats | ( | expTime, | |
gain = 1.0 , |
|||
readNoiseElectrons = 5 , |
|||
fluxElectrons = 1000 , |
|||
randomSeedFlat1 = 1984 , |
|||
randomSeedFlat2 = 666 , |
|||
powerLawBfParams = [] |
|||
) |
Create a pair or mock flats with isrMock. Parameters ---------- expTime : `float` Exposure time of the flats. gain : `float`, optional Gain, in e/ADU. readNoiseElectrons : `float`, optional Read noise rms, in electrons. fluxElectrons : `float`, optional Flux of flats, in electrons per second. randomSeedFlat1 : `int`, optional Random seed for the normal distrubutions for the mean signal and noise (flat1). randomSeedFlat2 : `int`, optional Random seed for the normal distrubutions for the mean signal and noise (flat2). powerLawBfParams : `list`, optional Parameters for `galsim.cdmodel.PowerLawCD` to simulate the brightter-fatter effect. Returns ------- flatExp1 : `lsst.afw.image.exposure.exposure.ExposureF` First exposure of flat field pair. flatExp2 : `lsst.afw.image.exposure.exposure.ExposureF` Second exposure of flat field pair. Notes ----- The parameters of `galsim.cdmodel.PowerLawCD` are `n, r0, t0, rx, tx, r, t, alpha`. For more information about their meaning, see the Galsim documentation https://galsim-developers.github.io/GalSim/_build/html/_modules/galsim/cdmodel.html and Gruen+15 (1501.02802). Example: galsim.cdmodel.PowerLawCD(8, 1.1e-7, 1.1e-7, 1.0e-8, 1.0e-8, 1.0e-9, 1.0e-9, 2.0)
def lsst.cp.pipe.utils.parseCmdlineNumberString | ( | inputString | ) |
def lsst.cp.pipe.utils.validateIsrConfig | ( | isrTask, | |
mandatory = None , |
|||
forbidden = None , |
|||
desirable = None , |
|||
undesirable = None , |
|||
checkTrim = True , |
|||
logName = None |
|||
) |
Check that appropriate ISR settings have been selected for the task. Note that this checks that the task itself is configured correctly rather than checking a config. Parameters ---------- isrTask : `lsst.ip.isr.IsrTask` The task whose config is to be validated mandatory : `iterable` of `str` isr steps that must be set to True. Raises if False or missing forbidden : `iterable` of `str` isr steps that must be set to False. Raises if True, warns if missing desirable : `iterable` of `str` isr steps that should probably be set to True. Warns is False, info if missing undesirable : `iterable` of `str` isr steps that should probably be set to False. Warns is True, info if missing checkTrim : `bool` Check to ensure the isrTask's assembly subtask is trimming the images. This is a separate config as it is very ugly to do this within the normal configuration lists as it is an option of a sub task. Raises ------ RuntimeError Raised if ``mandatory`` config parameters are False, or if ``forbidden`` parameters are True. TypeError Raised if parameter ``isrTask`` is an invalid type. Notes ----- Logs warnings using an isrValidation logger for desirable/undesirable options that are of the wrong polarity or if keys are missing.