PlotPhotonTransferCurveTask

class lsst.cp.pipe.PlotPhotonTransferCurveTask(datasetFilename, linearizerFileName=None, outDir='.', detNum=999, signalElectronsRelativeA=75000, plotNormalizedCovariancesNumberOfBins=10)

Bases: object

A class to plot the dataset from MeasurePhotonTransferCurveTask.

Parameters:

datasetFileName : str

datasetPtc (lsst.ip.isr.PhotonTransferCurveDataset) file name (fits).

linearizerFileName : str, optional

linearizer (isr.linearize.Linearizer) file name (fits).

outDir : str, optional

Path to the output directory where the final PDF will be placed.

detNum : int, optional

Detector number.

signalElectronsRelativeA : float, optional

Signal value for relative systematic bias between different methods of estimating a_ij (Fig. 15 of Astier+19).

plotNormalizedCovariancesNumberOfBins : float, optional

Number of bins in plotNormalizedCovariancesNumber function (Fig. 8, 10., of Astier+19).

Methods Summary

ab_vs_dist(aDict, bDict, pdfPages[, bRange])	Fig.
binData(x, y, binIndex[, wy])	Bin data (usually for display purposes).
covAstierMakeAllPlots(dataset, pdfPages[, log])	Make plots for MeasurePhotonTransferCurve task when doCovariancesAstier=True.
findGroups(x, maxDiff)	Group data into bins, with at most maxDiff distance between bins.
indexForBins(x, nBins)	Builds an index with regular binning.
plotAcoeffsSum(aDict, bDict, pdfPages)	Fig.
plotCovariances(mu, covs, covsModel, …)	Plot covariances and models: Cov00, Cov10, Cov01.
plotNormalizedCovariances(i, j, inputMu, …)	Plot C_ij/mu vs mu.
plotRelativeBiasACoeffs(aDict, aDictNoB, …)	Fig.
plot_a_b(aDict, bDict, pdfPages[, bRange])	Fig.
run(filenameFull, datasetPtc[, linearizer, log])	Make the plots for the PTC task

Methods Documentation

static ab_vs_dist(aDict, bDict, pdfPages, bRange=4)

Fig. 13 of Astier+19.

Values of a and b arrays fits, averaged over amplifiers, as a function of distance.

Parameters:

aDict : dict [numpy.array]

Dictionary keyed by amp names containing the fitted ‘a’ coefficients from the model in Eq. 20 of Astier+19 (if ptcFitType is FULLCOVARIANCE).

bDict : dict [numpy.array]

Dictionary keyed by amp names containing the fitted ‘b’ coefficients from the model in Eq. 20 of Astier+19 (if ptcFitType is FULLCOVARIANCE).

pdfPages : matplotlib.backends.backend_pdf.PdfPages

PDF file where the plots will be saved.

bRange : int

Maximum lag for b arrays.

static binData(x, y, binIndex, wy=None)

Bin data (usually for display purposes).

Parameters:

x : numpy.array

Data to bin.

y : numpy.array

Data to bin.

binIdex : list

Bin number of each datum.

wy : numpy.array

Inverse rms of each datum to use when averaging (the actual weight is wy**2).

Returns:

xbin : numpy.array

Binned data in x.

ybin : numpy.array

Binned data in y.

wybin : numpy.array

Binned weights in y, computed from wy’s in each bin.

sybin : numpy.array

Uncertainty on the bin average, considering actual scatter, and ignoring weights.

covAstierMakeAllPlots(dataset, pdfPages, log=None)

Make plots for MeasurePhotonTransferCurve task when doCovariancesAstier=True.

This function call other functions that mostly reproduce the plots in Astier+19. Most of the code is ported from Pierre Astier’s repository https://github.com/PierreAstier/bfptc

Parameters:

dataset : lsst.ip.isr.PhotonTransferCurveDataset

The dataset containing the necessary information to produce the plots.

pdfPages : matplotlib.backends.backend_pdf.PdfPages

PDF file where the plots will be saved.

log : logging.Logger, optional

Logger to handle messages

static findGroups(x, maxDiff)

Group data into bins, with at most maxDiff distance between bins.

Parameters:

x : list

Data to bin.

maxDiff : int

Maximum distance between bins.

Returns:

index : list

Bin indices.

static indexForBins(x, nBins)

Builds an index with regular binning. The result can be fed into binData.

Parameters:

x : numpy.array

Data to bin.

nBins : int

Number of bin.

Returns:

np.digitize(x, bins): numpy.array

Bin indices.

static plotAcoeffsSum(aDict, bDict, pdfPages)

Fig. 14. of Astier+19

Cumulative sum of a_ij as a function of maximum separation. This plot displays the average over channels.

Parameters:

aDict : dict [numpy.array]

Dictionary keyed by amp names containing the fitted ‘a’ coefficients from the model in Eq. 20 of Astier+19 (if ptcFitType is FULLCOVARIANCE).

bDict : dict [numpy.array]

Dictionary keyed by amp names containing the fitted ‘b’ coefficients from the model in Eq. 20 of Astier+19 (if ptcFitType is FULLCOVARIANCE).

pdfPages : matplotlib.backends.backend_pdf.PdfPages

PDF file where the plots will be saved.

static plotCovariances(mu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, covsWeightsNoB, gainDict, noiseDict, aDict, bDict, pdfPages)

Plot covariances and models: Cov00, Cov10, Cov01.

Figs. 6 and 7 of Astier+19

Parameters:

mu : dict [str, list]

Dictionary keyed by amp name with mean signal values.

covs : dict [str, list]

Dictionary keyed by amp names containing a list of measued covariances per mean flux.

covsModel : dict [str, list]

Dictionary keyed by amp names containinging covariances model (Eq. 20 of Astier+19) per mean flux.

covsWeights : dict [str, list]

Dictionary keyed by amp names containinging sqrt. of covariances weights.

covsNoB : dict [str, list]

Dictionary keyed by amp names containing a list of measued covariances per mean flux (‘b’=0 in Astier+19).

covsModelNoB : dict [str, list]

Dictionary keyed by amp names containing covariances model (with ‘b’=0 in Eq. 20 of Astier+19) per mean flux.

covsWeightsNoB : dict [str, list]

Dictionary keyed by amp names containing sqrt. of covariances weights (‘b’ = 0 in Eq. 20 of Astier+19).

gainDict : dict [str, float]

Dictionary keyed by amp names containing the gains in e-/ADU.

noiseDict : dict [str, float]

Dictionary keyed by amp names containing the rms redout noise in e-.

aDict : dict [str, numpy.array]

Dictionary keyed by amp names containing ‘a’ coefficients (Eq. 20 of Astier+19).

bDict : dict [str, numpy.array]

Dictionary keyed by amp names containing ‘b’ coefficients (Eq. 20 of Astier+19).

pdfPages : matplotlib.backends.backend_pdf.PdfPages

PDF file where the plots will be saved.

plotNormalizedCovariances(i, j, inputMu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, covsWeightsNoB, pdfPages, offset=0.004, numberOfBins=10, plotData=True, topPlot=False, log=None)

Plot C_ij/mu vs mu.

Figs. 8, 10, and 11 of Astier+19

Parameters:

i : int

Covariane lag

j : int

Covariance lag

inputMu : dict [str, list]

Dictionary keyed by amp name with mean signal values.

covs : dict [str, list]

Dictionary keyed by amp names containing a list of measued covariances per mean flux.

covsModel : dict [str, list]

Dictionary keyed by amp names containinging covariances model (Eq. 20 of Astier+19) per mean flux.

covsWeights : dict [str, list]

Dictionary keyed by amp names containinging sqrt. of covariances weights.

covsNoB : dict [str, list]

Dictionary keyed by amp names containing a list of measued covariances per mean flux (‘b’=0 in Astier+19).

covsModelNoB : dict [str, list]

Dictionary keyed by amp names containing covariances model (with ‘b’=0 in Eq. 20 of Astier+19) per mean flux.

covsWeightsNoB : dict [str, list]

Dictionary keyed by amp names containing sqrt. of covariances weights (‘b’ = 0 in Eq. 20 of Astier+19).

expIdMask : dict [str, list]

Dictionary keyed by amp names containing the masked exposure pairs.

pdfPages : matplotlib.backends.backend_pdf.PdfPages

PDF file where the plots will be saved.

offset : float, optional

Constant offset factor to plot covariances in same panel (so they don’t overlap).

numberOfBins : int, optional

Number of bins for top and bottom plot.

plotData : bool, optional

Plot the data points?

topPlot : bool, optional

Plot the top plot with the covariances, and the bottom plot with the model residuals?

log : logging.Logger, optional

Logger to handle messages.

static plotRelativeBiasACoeffs(aDict, aDictNoB, fullCovsModel, fullCovsModelNoB, signalElectrons, gainDict, pdfPages, maxr=None)

Fig. 15 in Astier+19.

Illustrates systematic bias from estimating ‘a’ coefficients from the slope of correlations as opposed to the full model in Astier+19.

Parameters:

aDict : dict

Dictionary of ‘a’ matrices (Eq. 20, Astier+19), with amp names as keys.

aDictNoB : dict

Dictionary of ‘a’ matrices (‘b’= 0 in Eq. 20, Astier+19), with amp names as keys.

fullCovsModel : dict [str, list]

Dictionary keyed by amp names containing covariances model per mean flux.

fullCovsModelNoB : dict [str, list]

Dictionary keyed by amp names containing covariances model (with ‘b’=0 in Eq. 20 of Astier+19) per mean flux.

signalElectrons : float

Signal at which to evaluate the a_ij coefficients.

pdfPages : matplotlib.backends.backend_pdf.PdfPages

PDF file where the plots will be saved.

gainDict : dict [str, float]

Dicgionary keyed by amp names with the gains in e-/ADU.

maxr : int, optional

Maximum lag.

static plot_a_b(aDict, bDict, pdfPages, bRange=3)

Fig. 12 of Astier+19

Color display of a and b arrays fits, averaged over channels.

Parameters:

aDict : dict [numpy.array]

Dictionary keyed by amp names containing the fitted ‘a’ coefficients from the model in Eq. 20 of Astier+19 (if ptcFitType is FULLCOVARIANCE).

bDict : dict [numpy.array]

Dictionary keyed by amp names containing the fitted ‘b’ coefficients from the model in Eq. 20 of Astier+19 (if ptcFitType is FULLCOVARIANCE).

pdfPages : matplotlib.backends.backend_pdf.PdfPages

PDF file where the plots will be saved.

bRange : int

Maximum lag for b arrays.

run(filenameFull, datasetPtc, linearizer=None, log=None)

Make the plots for the PTC task