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).
- datasetFileName
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
Run the Photon Transfer Curve (PTC) plotting measurement 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
isFULLCOVARIANCE
).- 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
isFULLCOVARIANCE
).- pdfPages
matplotlib.backends.backend_pdf.PdfPages
PDF file where the plots will be saved.
- bRange
int
Maximum lag for b arrays.
- aDict
- 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).
- x
- 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.
- xbin
- 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.ptcDataset.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
- dataset
- static findGroups(x, maxDiff)¶
Group data into bins, with at most maxDiff distance between bins.
- 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.
- x
- Returns:
- np.digitize(x, bins):
numpy.array
Bin indices.
- np.digitize(x, bins):
- 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
isFULLCOVARIANCE
).- 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
isFULLCOVARIANCE
).- pdfPages
matplotlib.backends.backend_pdf.PdfPages
PDF file where the plots will be saved.
- aDict
- 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.
- mu
- 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.
- i
- 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.
- aDict
- 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
isFULLCOVARIANCE
).- 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
isFULLCOVARIANCE
).- pdfPages
matplotlib.backends.backend_pdf.PdfPages
PDF file where the plots will be saved.
- bRange
int
Maximum lag for b arrays.
- aDict
- run(filenameFull, datasetPtc, linearizer=None, log=None)¶
Make the plots for the PTC task
- runDataRef()¶
Run the Photon Transfer Curve (PTC) plotting measurement task.