LSST Applications  21.0.0-131-g8cabc107+528f53ee53,22.0.0+00495a2688,22.0.0+0ef2527977,22.0.0+11a2aa21cd,22.0.0+269b7e55e3,22.0.0+2c6b6677a3,22.0.0+64c1bc5aa5,22.0.0+7b3a3f865e,22.0.0+e1b6d2281c,22.0.0+ff3c34362c,22.0.1-1-g1b65d06+c95cbdf3df,22.0.1-1-g7058be7+1cf78af69b,22.0.1-1-g7dab645+2a65e40b06,22.0.1-1-g8760c09+64c1bc5aa5,22.0.1-1-g949febb+64c1bc5aa5,22.0.1-1-ga324b9c+269b7e55e3,22.0.1-1-gf9d8b05+ff3c34362c,22.0.1-10-g781e53d+9b51d1cd24,22.0.1-10-gba590ab+b9624b875d,22.0.1-13-g76f9b8d+2c6b6677a3,22.0.1-14-g22236948+57af756299,22.0.1-18-g3db9cf4b+9b7092c56c,22.0.1-18-gb17765a+2264247a6b,22.0.1-2-g8ef0a89+2c6b6677a3,22.0.1-2-gcb770ba+c99495d3c6,22.0.1-24-g2e899d296+4206820b0d,22.0.1-3-g7aa11f2+2c6b6677a3,22.0.1-3-g8c1d971+f253ffa91f,22.0.1-3-g997b569+ff3b2f8649,22.0.1-4-g1930a60+6871d0c7f6,22.0.1-4-g5b7b756+6b209d634c,22.0.1-6-ga02864e+6871d0c7f6,22.0.1-7-g3402376+a1a2182ac4,22.0.1-7-g65f59fa+54b92689ce,master-gcc5351303a+e1b6d2281c,w.2021.32
LSST Data Management Base Package
Public Member Functions | Static Public Member Functions | Public Attributes | List of all members
lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask Class Reference

Public Member Functions

def __init__ (self, datasetFilename, linearizerFileName=None, outDir='.', detNum=999, signalElectronsRelativeA=75000, plotNormalizedCovariancesNumberOfBins=10)
 
def runDataRef (self)
 
def run (self, filenameFull, datasetPtc, linearizer=None, log=None)
 
def covAstierMakeAllPlots (self, dataset, pdfPages, log=None)
 
def plotNormalizedCovariances (self, i, j, inputMu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, covsWeightsNoB, pdfPages, offset=0.004, numberOfBins=10, plotData=True, topPlot=False, log=None)
 

Static Public Member Functions

def plotCovariances (mu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, covsWeightsNoB, gainDict, noiseDict, aDict, bDict, pdfPages)
 
def plot_a_b (aDict, bDict, pdfPages, bRange=3)
 
def ab_vs_dist (aDict, bDict, pdfPages, bRange=4)
 
def plotAcoeffsSum (aDict, bDict, pdfPages)
 
def plotRelativeBiasACoeffs (aDict, aDictNoB, fullCovsModel, fullCovsModelNoB, signalElectrons, gainDict, pdfPages, maxr=None)
 
def findGroups (x, maxDiff)
 
def indexForBins (x, nBins)
 
def binData (x, y, binIndex, wy=None)
 

Public Attributes

 datasetFilename
 
 linearizerFileName
 
 detNum
 
 signalElectronsRelativeA
 
 plotNormalizedCovariancesNumberOfBins
 
 outDir
 

Detailed Description

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).

Notes
-----
The plotting code in this file is almost identical to the code in
`plotPtcGen2.py`. If further changes are implemented in this file,
`plotPtcGen2.py` needs to be updated accordingly, and vice versa.
The file `plotPtcGen2.py` helps with maintaining backwards
compatibility with gen2 as we transition to gen3; the code
duplication is meant to only last for few month from now
(Jan, 2021). At that point only this file, `plotPtc.py`, will
remain.

Definition at line 42 of file plotPtc.py.

Constructor & Destructor Documentation

◆ __init__()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.__init__ (   self,
  datasetFilename,
  linearizerFileName = None,
  outDir = '.',
  detNum = 999,
  signalElectronsRelativeA = 75000,
  plotNormalizedCovariancesNumberOfBins = 10 
)

Definition at line 83 of file plotPtc.py.

85  plotNormalizedCovariancesNumberOfBins=10):
86  self.datasetFilename = datasetFilename
87  self.linearizerFileName = linearizerFileName
88  self.detNum = detNum
89  self.signalElectronsRelativeA = signalElectronsRelativeA
90  self.plotNormalizedCovariancesNumberOfBins = plotNormalizedCovariancesNumberOfBins
91  self.outDir = outDir
92 

Member Function Documentation

◆ ab_vs_dist()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.ab_vs_dist (   aDict,
  bDict,
  pdfPages,
  bRange = 4 
)
static 
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.

Definition at line 602 of file plotPtc.py.

602  def ab_vs_dist(aDict, bDict, pdfPages, bRange=4):
603  """Fig. 13 of Astier+19.
604 
605  Values of a and b arrays fits, averaged over amplifiers, as a function of distance.
606 
607  Parameters
608  ----------
609  aDict : `dict`, [`numpy.array`]
610  Dictionary keyed by amp names containing the fitted 'a' coefficients from the model
611  in Eq. 20 of Astier+19 (if `ptcFitType` is `FULLCOVARIANCE`).
612 
613  bDict : `dict`, [`numpy.array`]
614  Dictionary keyed by amp names containing the fitted 'b' coefficients from the model
615  in Eq. 20 of Astier+19 (if `ptcFitType` is `FULLCOVARIANCE`).
616 
617  pdfPages: `matplotlib.backends.backend_pdf.PdfPages`
618  PDF file where the plots will be saved.
619 
620  bRange : `int`
621  Maximum lag for b arrays.
622  """
623  assert (len(aDict) == len(bDict))
624  a = []
625  for amp in aDict:
626  if np.isnan(aDict[amp]).all():
627  continue
628  a.append(aDict[amp])
629  a = np.array(a)
630  y = a.mean(axis=0)
631  sy = a.std(axis=0)/np.sqrt(len(aDict))
632  i, j = np.indices(y.shape)
633  upper = (i >= j).ravel()
634  r = np.sqrt(i**2 + j**2).ravel()
635  y = y.ravel()
636  sy = sy.ravel()
637  fig = plt.figure(figsize=(6, 9))
638  ax = fig.add_subplot(211)
639  ax.set_xlim([0.5, r.max()+1])
640  ax.errorbar(r[upper], y[upper], yerr=sy[upper], marker='o', linestyle='none', color='b',
641  label='$i>=j$')
642  ax.errorbar(r[~upper], y[~upper], yerr=sy[~upper], marker='o', linestyle='none', color='r',
643  label='$i<j$')
644  ax.legend(loc='upper center', fontsize='x-large')
645  ax.set_xlabel(r'$\sqrt{i^2+j^2}$', fontsize='x-large')
646  ax.set_ylabel(r'$a_{ij}$', fontsize='x-large')
647  ax.set_yscale('log')
648  ax.tick_params(axis='both', labelsize='x-large')
649 
650  #
651  axb = fig.add_subplot(212)
652  b = []
653  for amp in bDict:
654  if np.isnan(bDict[amp]).all():
655  continue
656  b.append(bDict[amp])
657  b = np.array(b)
658  yb = b.mean(axis=0)
659  syb = b.std(axis=0)/np.sqrt(len(bDict))
660  ib, jb = np.indices(yb.shape)
661  upper = (ib > jb).ravel()
662  rb = np.sqrt(i**2 + j**2).ravel()
663  yb = yb.ravel()
664  syb = syb.ravel()
665  xmin = -0.2
666  xmax = bRange
667  axb.set_xlim([xmin, xmax+0.2])
668  cutu = (r > xmin) & (r < xmax) & (upper)
669  cutl = (r > xmin) & (r < xmax) & (~upper)
670  axb.errorbar(rb[cutu], yb[cutu], yerr=syb[cutu], marker='o', linestyle='none', color='b',
671  label='$i>=j$')
672  axb.errorbar(rb[cutl], yb[cutl], yerr=syb[cutl], marker='o', linestyle='none', color='r',
673  label='$i<j$')
674  plt.legend(loc='upper center', fontsize='x-large')
675  axb.set_xlabel(r'$\sqrt{i^2+j^2}$', fontsize='x-large')
676  axb.set_ylabel(r'$b_{ij}$', fontsize='x-large')
677  axb.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
678  axb.tick_params(axis='both', labelsize='x-large')
679  plt.tight_layout()
680  pdfPages.savefig(fig)
681 
682  return
683 
lsst::geom::all
bool all(CoordinateExpr< N > const &expr) noexcept
Return true if all elements are true.
Definition: CoordinateExpr.h:81

◆ binData()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.binData (   x,
  y,
  binIndex,
  wy = None 
)
static 
Bin data (usually for display purposes).

Patrameters
-----------
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.

Definition at line 1109 of file plotPtc.py.

1109  def binData(x, y, binIndex, wy=None):
1110  """Bin data (usually for display purposes).
1111 
1112  Patrameters
1113  -----------
1114  x: `numpy.array`
1115  Data to bin.
1116 
1117  y: `numpy.array`
1118  Data to bin.
1119 
1120  binIdex: `list`
1121  Bin number of each datum.
1122 
1123  wy: `numpy.array`
1124  Inverse rms of each datum to use when averaging (the actual weight is wy**2).
1125 
1126  Returns:
1127  -------
1128 
1129  xbin: `numpy.array`
1130  Binned data in x.
1131 
1132  ybin: `numpy.array`
1133  Binned data in y.
1134 
1135  wybin: `numpy.array`
1136  Binned weights in y, computed from wy's in each bin.
1137 
1138  sybin: `numpy.array`
1139  Uncertainty on the bin average, considering actual scatter, and ignoring weights.
1140  """
1141 
1142  if wy is None:
1143  wy = np.ones_like(x)
1144  binIndexSet = set(binIndex)
1145  w2 = wy*wy
1146  xw2 = x*(w2)
1147  xbin = np.array([xw2[binIndex == i].sum()/w2[binIndex == i].sum() for i in binIndexSet])
1148 
1149  yw2 = y*w2
1150  ybin = np.array([yw2[binIndex == i].sum()/w2[binIndex == i].sum() for i in binIndexSet])
1151 
1152  wybin = np.sqrt(np.array([w2[binIndex == i].sum() for i in binIndexSet]))
1153  sybin = np.array([y[binIndex == i].std()/np.sqrt(np.array([binIndex == i]).sum())
1154  for i in binIndexSet])
1155 
1156  return xbin, ybin, wybin, sybin
set
daf::base::PropertySet * set
Definition: fits.cc:912
std
STL namespace.

◆ covAstierMakeAllPlots()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.covAstierMakeAllPlots (   self,
  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 : `lsst.log.Log`, optional
    Logger to handle messages

Definition at line 132 of file plotPtc.py.

133  log=None):
134  """Make plots for MeasurePhotonTransferCurve task when doCovariancesAstier=True.
135 
136  This function call other functions that mostly reproduce the plots in Astier+19.
137  Most of the code is ported from Pierre Astier's repository https://github.com/PierreAstier/bfptc
138 
139  Parameters
140  ----------
141  dataset : `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset`
142  The dataset containing the necessary information to produce the plots.
143 
144  pdfPages: `matplotlib.backends.backend_pdf.PdfPages`
145  PDF file where the plots will be saved.
146 
147  log : `lsst.log.Log`, optional
148  Logger to handle messages
149  """
150  mu = dataset.finalMeans
151  # dictionaries with ampNames as keys
152  fullCovs = dataset.covariances
153  fullCovsModel = dataset.covariancesModel
154  fullCovWeights = dataset.covariancesSqrtWeights
155  aDict = dataset.aMatrix
156  bDict = dataset.bMatrix
157  fullCovsNoB = dataset.covariances
158  fullCovsModelNoB = dataset.covariancesModelNoB
159  fullCovWeightsNoB = dataset.covariancesSqrtWeights
160  aDictNoB = dataset.aMatrixNoB
161  gainDict = dataset.gain
162  noiseDict = dataset.noise
163 
164  self.plotCovariances(mu, fullCovs, fullCovsModel, fullCovWeights, fullCovsNoB, fullCovsModelNoB,
165  fullCovWeightsNoB, gainDict, noiseDict, aDict, bDict, pdfPages)
166  self.plotNormalizedCovariances(0, 0, mu, fullCovs, fullCovsModel, fullCovWeights, fullCovsNoB,
167  fullCovsModelNoB, fullCovWeightsNoB, pdfPages,
168  offset=0.01, topPlot=True,
169  numberOfBins=self.plotNormalizedCovariancesNumberOfBins,
170  log=log)
171  self.plotNormalizedCovariances(0, 1, mu, fullCovs, fullCovsModel, fullCovWeights, fullCovsNoB,
172  fullCovsModelNoB, fullCovWeightsNoB, pdfPages,
173  numberOfBins=self.plotNormalizedCovariancesNumberOfBins,
174  log=log)
175  self.plotNormalizedCovariances(1, 0, mu, fullCovs, fullCovsModel, fullCovWeights, fullCovsNoB,
176  fullCovsModelNoB, fullCovWeightsNoB, pdfPages,
177  numberOfBins=self.plotNormalizedCovariancesNumberOfBins,
178  log=log)
179  self.plot_a_b(aDict, bDict, pdfPages)
180  self.ab_vs_dist(aDict, bDict, pdfPages, bRange=4)
181  self.plotAcoeffsSum(aDict, bDict, pdfPages)
182  self.plotRelativeBiasACoeffs(aDict, aDictNoB, fullCovsModel, fullCovsModelNoB,
183  self.signalElectronsRelativeA, gainDict, pdfPages, maxr=4)
184 
185  return
186 

◆ findGroups()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.findGroups (   x,
  maxDiff 
)
static 
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.

Definition at line 1051 of file plotPtc.py.

1051  def findGroups(x, maxDiff):
1052  """Group data into bins, with at most maxDiff distance between bins.
1053 
1054  Parameters
1055  ----------
1056  x: `list`
1057  Data to bin.
1058 
1059  maxDiff: `int`
1060  Maximum distance between bins.
1061 
1062  Returns
1063  -------
1064  index: `list`
1065  Bin indices.
1066  """
1067  ix = np.argsort(x)
1068  xsort = np.sort(x)
1069  index = np.zeros_like(x, dtype=np.int32)
1070  xc = xsort[0]
1071  group = 0
1072  ng = 1
1073 
1074  for i in range(1, len(ix)):
1075  xval = xsort[i]
1076  if (xval - xc < maxDiff):
1077  xc = (ng*xc + xval)/(ng+1)
1078  ng += 1
1079  index[ix[i]] = group
1080  else:
1081  group += 1
1082  ng = 1
1083  index[ix[i]] = group
1084  xc = xval
1085 
1086  return index
1087 

◆ indexForBins()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.indexForBins (   x,
  nBins 
)
static 
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.

Definition at line 1089 of file plotPtc.py.

1089  def indexForBins(x, nBins):
1090  """Builds an index with regular binning. The result can be fed into binData.
1091 
1092  Parameters
1093  ----------
1094  x: `numpy.array`
1095  Data to bin.
1096  nBins: `int`
1097  Number of bin.
1098 
1099  Returns
1100  -------
1101  np.digitize(x, bins): `numpy.array`
1102  Bin indices.
1103  """
1104 
1105  bins = np.linspace(x.min(), x.max() + abs(x.max() * 1e-7), nBins + 1)
1106  return np.digitize(x, bins)
1107 
lsst::sphgeom::abs
Angle abs(Angle const &a)
Definition: Angle.h:106

◆ plot_a_b()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.plot_a_b (   aDict,
  bDict,
  pdfPages,
  bRange = 3 
)
static 
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.

Definition at line 549 of file plotPtc.py.

549  def plot_a_b(aDict, bDict, pdfPages, bRange=3):
550  """Fig. 12 of Astier+19
551 
552  Color display of a and b arrays fits, averaged over channels.
553 
554  Parameters
555  ----------
556  aDict : `dict`, [`numpy.array`]
557  Dictionary keyed by amp names containing the fitted 'a' coefficients from the model
558  in Eq. 20 of Astier+19 (if `ptcFitType` is `FULLCOVARIANCE`).
559 
560  bDict : `dict`, [`numpy.array`]
561  Dictionary keyed by amp names containing the fitted 'b' coefficients from the model
562  in Eq. 20 of Astier+19 (if `ptcFitType` is `FULLCOVARIANCE`).
563 
564  pdfPages: `matplotlib.backends.backend_pdf.PdfPages`
565  PDF file where the plots will be saved.
566 
567  bRange : `int`
568  Maximum lag for b arrays.
569  """
570  a, b = [], []
571  for amp in aDict:
572  if np.isnan(aDict[amp]).all():
573  continue
574  a.append(aDict[amp])
575  b.append(bDict[amp])
576  a = np.array(a).mean(axis=0)
577  b = np.array(b).mean(axis=0)
578  fig = plt.figure(figsize=(7, 11))
579  ax0 = fig.add_subplot(2, 1, 1)
580  im0 = ax0.imshow(np.abs(a.transpose()), origin='lower', norm=mpl.colors.LogNorm())
581  ax0.tick_params(axis='both', labelsize='x-large')
582  ax0.set_title(r'$|a|$', fontsize='x-large')
583  ax0.xaxis.set_ticks_position('bottom')
584  cb0 = plt.colorbar(im0)
585  cb0.ax.tick_params(labelsize='x-large')
586 
587  ax1 = fig.add_subplot(2, 1, 2)
588  ax1.tick_params(axis='both', labelsize='x-large')
589  ax1.yaxis.set_major_locator(MaxNLocator(integer=True))
590  ax1.xaxis.set_major_locator(MaxNLocator(integer=True))
591  im1 = ax1.imshow(1e6*b[:bRange, :bRange].transpose(), origin='lower')
592  cb1 = plt.colorbar(im1)
593  cb1.ax.tick_params(labelsize='x-large')
594  ax1.set_title(r'$b \times 10^6$', fontsize='x-large')
595  ax1.xaxis.set_ticks_position('bottom')
596  plt.tight_layout()
597  pdfPages.savefig(fig)
598 
599  return
600 

◆ plotAcoeffsSum()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.plotAcoeffsSum (   aDict,
  bDict,
  pdfPages 
)
static 
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.

Definition at line 685 of file plotPtc.py.

685  def plotAcoeffsSum(aDict, bDict, pdfPages):
686  """Fig. 14. of Astier+19
687 
688  Cumulative sum of a_ij as a function of maximum separation. This plot displays the average over
689  channels.
690 
691  Parameters
692  ----------
693  aDict : `dict`, [`numpy.array`]
694  Dictionary keyed by amp names containing the fitted 'a' coefficients from the model
695  in Eq. 20 of Astier+19 (if `ptcFitType` is `FULLCOVARIANCE`).
696 
697  bDict : `dict`, [`numpy.array`]
698  Dictionary keyed by amp names containing the fitted 'b' coefficients from the model
699  in Eq. 20 of Astier+19 (if `ptcFitType` is `FULLCOVARIANCE`).
700 
701  pdfPages: `matplotlib.backends.backend_pdf.PdfPages`
702  PDF file where the plots will be saved.
703  """
704  assert (len(aDict) == len(bDict))
705  a, b = [], []
706  for amp in aDict:
707  if np.isnan(aDict[amp]).all() or np.isnan(bDict[amp]).all():
708  continue
709  a.append(aDict[amp])
710  b.append(bDict[amp])
711  a = np.array(a).mean(axis=0)
712  b = np.array(b).mean(axis=0)
713  fig = plt.figure(figsize=(7, 6))
714  w = 4*np.ones_like(a)
715  w[0, 1:] = 2
716  w[1:, 0] = 2
717  w[0, 0] = 1
718  wa = w*a
719  indices = range(1, a.shape[0]+1)
720  sums = [wa[0:n, 0:n].sum() for n in indices]
721  ax = plt.subplot(111)
722  ax.plot(indices, sums/sums[0], 'o', color='b')
723  ax.set_yscale('log')
724  ax.set_xlim(indices[0]-0.5, indices[-1]+0.5)
725  ax.set_ylim(None, 1.2)
726  ax.set_ylabel(r'$[\sum_{|i|<n\ &\ |j|<n} a_{ij}] / |a_{00}|$', fontsize='x-large')
727  ax.set_xlabel('n', fontsize='x-large')
728  ax.tick_params(axis='both', labelsize='x-large')
729  plt.tight_layout()
730  pdfPages.savefig(fig)
731 
732  return
733 

◆ plotCovariances()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.plotCovariances (   mu,
  covs,
  covsModel,
  covsWeights,
  covsNoB,
  covsModelNoB,
  covsWeightsNoB,
  gainDict,
  noiseDict,
  aDict,
  bDict,
  pdfPages 
)
static 
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.

Definition at line 188 of file plotPtc.py.

189  gainDict, noiseDict, aDict, bDict, pdfPages):
190  """Plot covariances and models: Cov00, Cov10, Cov01.
191 
192  Figs. 6 and 7 of Astier+19
193 
194  Parameters
195  ----------
196  mu : `dict`, [`str`, `list`]
197  Dictionary keyed by amp name with mean signal values.
198 
199  covs : `dict`, [`str`, `list`]
200  Dictionary keyed by amp names containing a list of measued covariances per mean flux.
201 
202  covsModel : `dict`, [`str`, `list`]
203  Dictionary keyed by amp names containinging covariances model (Eq. 20 of Astier+19) per mean flux.
204 
205  covsWeights : `dict`, [`str`, `list`]
206  Dictionary keyed by amp names containinging sqrt. of covariances weights.
207 
208  covsNoB : `dict`, [`str`, `list`]
209  Dictionary keyed by amp names containing a list of measued covariances per mean flux ('b'=0 in
210  Astier+19).
211 
212  covsModelNoB : `dict`, [`str`, `list`]
213  Dictionary keyed by amp names containing covariances model (with 'b'=0 in Eq. 20 of Astier+19)
214  per mean flux.
215 
216  covsWeightsNoB : `dict`, [`str`, `list`]
217  Dictionary keyed by amp names containing sqrt. of covariances weights ('b' = 0 in Eq. 20 of
218  Astier+19).
219 
220  gainDict : `dict`, [`str`, `float`]
221  Dictionary keyed by amp names containing the gains in e-/ADU.
222 
223  noiseDict : `dict`, [`str`, `float`]
224  Dictionary keyed by amp names containing the rms redout noise in e-.
225 
226  aDict : `dict`, [`str`, `numpy.array`]
227  Dictionary keyed by amp names containing 'a' coefficients (Eq. 20 of Astier+19).
228 
229  bDict : `dict`, [`str`, `numpy.array`]
230  Dictionary keyed by amp names containing 'b' coefficients (Eq. 20 of Astier+19).
231 
232  pdfPages: `matplotlib.backends.backend_pdf.PdfPages`
233  PDF file where the plots will be saved.
234  """
235 
236  legendFontSize = 6.5
237  labelFontSize = 7
238  titleFontSize = 9
239  supTitleFontSize = 18
240  markerSize = 25
241 
242  nAmps = len(covs)
243  if nAmps == 2:
244  nRows, nCols = 2, 1
245  nRows = np.sqrt(nAmps)
246  mantissa, _ = np.modf(nRows)
247  if mantissa > 0:
248  nRows = int(nRows) + 1
249  nCols = nRows
250  else:
251  nRows = int(nRows)
252  nCols = nRows
253 
254  f, ax = plt.subplots(nrows=nRows, ncols=nCols, sharex='col', sharey='row', figsize=(13, 10))
255  f2, ax2 = plt.subplots(nrows=nRows, ncols=nCols, sharex='col', sharey='row', figsize=(13, 10))
256  fResCov00, axResCov00 = plt.subplots(nrows=nRows, ncols=nCols, sharex='col', sharey='row',
257  figsize=(13, 10))
258  fCov01, axCov01 = plt.subplots(nrows=nRows, ncols=nCols, sharex='col', sharey='row', figsize=(13, 10))
259  fCov10, axCov10 = plt.subplots(nrows=nRows, ncols=nCols, sharex='col', sharey='row', figsize=(13, 10))
260 
261  assert(len(covsModel) == nAmps)
262  assert(len(covsWeights) == nAmps)
263 
264  assert(len(covsNoB) == nAmps)
265  assert(len(covsModelNoB) == nAmps)
266  assert(len(covsWeightsNoB) == nAmps)
267 
268  for i, (amp, a, a2, aResVar, a3, a4) in enumerate(zip(covs, ax.flatten(),
269  ax2.flatten(), axResCov00.flatten(),
270  axCov01.flatten(), axCov10.flatten())):
271 
272  muAmp, cov, model, weight = mu[amp], covs[amp], covsModel[amp], covsWeights[amp]
273  if not np.isnan(np.array(cov)).all(): # If all the entries are np.nan, this is a bad amp.
274  aCoeffs, bCoeffs = np.array(aDict[amp]), np.array(bDict[amp])
275  gain, noise = gainDict[amp], noiseDict[amp]
276  (meanVecFinal, varVecFinal, varVecModelFinal,
277  varWeightsFinal, _) = getFitDataFromCovariances(0, 0, muAmp, cov, model, weight,
278  returnMasked=True)
279 
280  # Get weighted reduced chi2
281  chi2FullModelVar = calculateWeightedReducedChi2(varVecFinal, varVecModelFinal,
282  varWeightsFinal, len(meanVecFinal), 4)
283 
284  (meanVecFinalCov01, varVecFinalCov01, varVecModelFinalCov01,
285  _, _) = getFitDataFromCovariances(0, 0, muAmp, cov, model, weight, returnMasked=True)
286 
287  (meanVecFinalCov10, varVecFinalCov10, varVecModelFinalCov10,
288  _, _) = getFitDataFromCovariances(1, 0, muAmp, cov, model, weight, returnMasked=True)
289 
290  # cuadratic fit for residuals below
291  par2 = np.polyfit(meanVecFinal, varVecFinal, 2, w=varWeightsFinal)
292  varModelFinalQuadratic = np.polyval(par2, meanVecFinal)
293  chi2QuadModelVar = calculateWeightedReducedChi2(varVecFinal, varModelFinalQuadratic,
294  varWeightsFinal, len(meanVecFinal), 3)
295 
296  # fit with no 'b' coefficient (c = a*b in Eq. 20 of Astier+19)
297  covNoB, modelNoB, weightNoB = covsNoB[amp], covsModelNoB[amp], covsWeightsNoB[amp]
298  (meanVecFinalNoB, varVecFinalNoB, varVecModelFinalNoB,
299  varWeightsFinalNoB, _) = getFitDataFromCovariances(0, 0, muAmp, covNoB, modelNoB,
300  weightNoB, returnMasked=True)
301 
302  chi2FullModelNoBVar = calculateWeightedReducedChi2(varVecFinalNoB, varVecModelFinalNoB,
303  varWeightsFinalNoB, len(meanVecFinalNoB),
304  3)
305  stringLegend = (f"Gain: {gain:.4} e/ADU \n"
306  f"Noise: {noise:.4} e \n"
307  + r"$a_{00}$: %.3e 1/e"%aCoeffs[0, 0] + "\n"
308  + r"$b_{00}$: %.3e 1/e"%bCoeffs[0, 0]
309  + f"\nLast in fit: {meanVecFinal[-1]:.7} ADU ")
310  minMeanVecFinal = np.nanmin(meanVecFinal)
311  maxMeanVecFinal = np.nanmax(meanVecFinal)
312  deltaXlim = maxMeanVecFinal - minMeanVecFinal
313 
314  a.set_xlabel(r'Mean signal ($\mu$, ADU)', fontsize=labelFontSize)
315  a.set_ylabel(r'Variance (ADU$^2$)', fontsize=labelFontSize)
316  a.tick_params(labelsize=11)
317  a.set_xscale('linear')
318  a.set_yscale('linear')
319  a.scatter(meanVecFinal, varVecFinal, c='blue', marker='o', s=markerSize)
320  a.plot(meanVecFinal, varVecModelFinal, color='red', lineStyle='-')
321  a.text(0.03, 0.7, stringLegend, transform=a.transAxes, fontsize=legendFontSize)
322  a.set_title(amp, fontsize=titleFontSize)
323  a.set_xlim([minMeanVecFinal - 0.2*deltaXlim, maxMeanVecFinal + 0.2*deltaXlim])
324 
325  # Same as above, but in log-scale
326  a2.set_xlabel(r'Mean Signal ($\mu$, ADU)', fontsize=labelFontSize)
327  a2.set_ylabel(r'Variance (ADU$^2$)', fontsize=labelFontSize)
328  a2.tick_params(labelsize=11)
329  a2.set_xscale('log')
330  a2.set_yscale('log')
331  a2.plot(meanVecFinal, varVecModelFinal, color='red', lineStyle='-')
332  a2.scatter(meanVecFinal, varVecFinal, c='blue', marker='o', s=markerSize)
333  a2.text(0.03, 0.7, stringLegend, transform=a2.transAxes, fontsize=legendFontSize)
334  a2.set_title(amp, fontsize=titleFontSize)
335  a2.set_xlim([minMeanVecFinal, maxMeanVecFinal])
336 
337  # Residuals var - model
338  aResVar.set_xlabel(r'Mean signal ($\mu$, ADU)', fontsize=labelFontSize)
339  aResVar.set_ylabel(r'Residuals (ADU$^2$)', fontsize=labelFontSize)
340  aResVar.tick_params(labelsize=11)
341  aResVar.set_xscale('linear')
342  aResVar.set_yscale('linear')
343  aResVar.plot(meanVecFinal, varVecFinal - varVecModelFinal, color='blue', lineStyle='-',
344  label=r'Full fit ($\chi_{\rm{red}}^2$: %g)'%chi2FullModelVar)
345  aResVar.plot(meanVecFinal, varVecFinal - varModelFinalQuadratic, color='red', lineStyle='-',
346  label=r'Quadratic fit ($\chi_{\rm{red}}^2$: %g)'%chi2QuadModelVar)
347  aResVar.plot(meanVecFinalNoB, varVecFinalNoB - varVecModelFinalNoB, color='green',
348  lineStyle='-',
349  label=r'Full fit (b=0) ($\chi_{\rm{red}}^2$: %g)'%chi2FullModelNoBVar)
350  aResVar.axhline(color='black')
351  aResVar.set_title(amp, fontsize=titleFontSize)
352  aResVar.set_xlim([minMeanVecFinal - 0.2*deltaXlim, maxMeanVecFinal + 0.2*deltaXlim])
353  aResVar.legend(fontsize=7)
354 
355  a3.set_xlabel(r'Mean signal ($\mu$, ADU)', fontsize=labelFontSize)
356  a3.set_ylabel(r'Cov01 (ADU$^2$)', fontsize=labelFontSize)
357  a3.tick_params(labelsize=11)
358  a3.set_xscale('linear')
359  a3.set_yscale('linear')
360  a3.scatter(meanVecFinalCov01, varVecFinalCov01, c='blue', marker='o', s=markerSize)
361  a3.plot(meanVecFinalCov01, varVecModelFinalCov01, color='red', lineStyle='-')
362  a3.set_title(amp, fontsize=titleFontSize)
363  a3.set_xlim([minMeanVecFinal - 0.2*deltaXlim, maxMeanVecFinal + 0.2*deltaXlim])
364 
365  a4.set_xlabel(r'Mean signal ($\mu$, ADU)', fontsize=labelFontSize)
366  a4.set_ylabel(r'Cov10 (ADU$^2$)', fontsize=labelFontSize)
367  a4.tick_params(labelsize=11)
368  a4.set_xscale('linear')
369  a4.set_yscale('linear')
370  a4.scatter(meanVecFinalCov10, varVecFinalCov10, c='blue', marker='o', s=markerSize)
371  a4.plot(meanVecFinalCov10, varVecModelFinalCov10, color='red', lineStyle='-')
372  a4.set_title(amp, fontsize=titleFontSize)
373  a4.set_xlim([minMeanVecFinal - 0.2*deltaXlim, maxMeanVecFinal + 0.2*deltaXlim])
374 
375  else:
376  a.set_title(f"{amp} (BAD)", fontsize=titleFontSize)
377  a2.set_title(f"{amp} (BAD)", fontsize=titleFontSize)
378  a3.set_title(f"{amp} (BAD)", fontsize=titleFontSize)
379  a4.set_title(f"{amp} (BAD)", fontsize=titleFontSize)
380 
381  f.suptitle("PTC from covariances as in Astier+19 \n Fit: Eq. 20, Astier+19",
382  fontsize=supTitleFontSize)
383  pdfPages.savefig(f)
384  f2.suptitle("PTC from covariances as in Astier+19 (log-log) \n Fit: Eq. 20, Astier+19",
385  fontsize=supTitleFontSize)
386  pdfPages.savefig(f2)
387  fResCov00.suptitle("Residuals (data-model) for Cov00 (Var)", fontsize=supTitleFontSize)
388  pdfPages.savefig(fResCov00)
389  fCov01.suptitle("Cov01 as in Astier+19 (nearest parallel neighbor covariance) \n"
390  " Fit: Eq. 20, Astier+19", fontsize=supTitleFontSize)
391  pdfPages.savefig(fCov01)
392  fCov10.suptitle("Cov10 as in Astier+19 (nearest serial neighbor covariance) \n"
393  "Fit: Eq. 20, Astier+19", fontsize=supTitleFontSize)
394  pdfPages.savefig(fCov10)
395 
396  return
397 
lsst.cp.pipe.ptc.astierCovPtcUtils.getFitDataFromCovariances
def getFitDataFromCovariances(i, j, mu, fullCov, fullCovModel, fullCovSqrtWeights, gain=1.0, divideByMu=False, returnMasked=False)
Definition: astierCovPtcUtils.py:303
lsst.cp.pipe.utils.calculateWeightedReducedChi2
def calculateWeightedReducedChi2(measured, model, weightsMeasured, nData, nParsModel)
Definition: utils.py:69

◆ plotNormalizedCovariances()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.plotNormalizedCovariances (   self,
  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 : `lsst.log.Log`, optional
    Logger to handle messages.

Definition at line 398 of file plotPtc.py.

400  numberOfBins=10, plotData=True, topPlot=False, log=None):
401  """Plot C_ij/mu vs mu.
402 
403  Figs. 8, 10, and 11 of Astier+19
404 
405  Parameters
406  ----------
407  i : `int`
408  Covariane lag
409 
410  j : `int`
411  Covariance lag
412 
413  inputMu : `dict`, [`str`, `list`]
414  Dictionary keyed by amp name with mean signal values.
415 
416  covs : `dict`, [`str`, `list`]
417  Dictionary keyed by amp names containing a list of measued covariances per mean flux.
418 
419  covsModel : `dict`, [`str`, `list`]
420  Dictionary keyed by amp names containinging covariances model (Eq. 20 of Astier+19) per mean flux.
421 
422  covsWeights : `dict`, [`str`, `list`]
423  Dictionary keyed by amp names containinging sqrt. of covariances weights.
424 
425  covsNoB : `dict`, [`str`, `list`]
426  Dictionary keyed by amp names containing a list of measued covariances per mean flux ('b'=0 in
427  Astier+19).
428 
429  covsModelNoB : `dict`, [`str`, `list`]
430  Dictionary keyed by amp names containing covariances model (with 'b'=0 in Eq. 20 of Astier+19)
431  per mean flux.
432 
433  covsWeightsNoB : `dict`, [`str`, `list`]
434  Dictionary keyed by amp names containing sqrt. of covariances weights ('b' = 0 in Eq. 20 of
435  Astier+19).
436 
437  expIdMask : `dict`, [`str`, `list`]
438  Dictionary keyed by amp names containing the masked exposure pairs.
439 
440  pdfPages: `matplotlib.backends.backend_pdf.PdfPages`
441  PDF file where the plots will be saved.
442 
443  offset : `float`, optional
444  Constant offset factor to plot covariances in same panel (so they don't overlap).
445 
446  numberOfBins : `int`, optional
447  Number of bins for top and bottom plot.
448 
449  plotData : `bool`, optional
450  Plot the data points?
451 
452  topPlot : `bool`, optional
453  Plot the top plot with the covariances, and the bottom plot with the model residuals?
454 
455  log : `lsst.log.Log`, optional
456  Logger to handle messages.
457  """
458  if not topPlot:
459  fig = plt.figure(figsize=(8, 10))
460  gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
461  gs.update(hspace=0)
462  ax0 = plt.subplot(gs[0])
463  plt.setp(ax0.get_xticklabels(), visible=False)
464  else:
465  fig = plt.figure(figsize=(8, 8))
466  ax0 = plt.subplot(111)
467  ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
468  ax0.tick_params(axis='both', labelsize='x-large')
469  mue, rese, wce = [], [], []
470  mueNoB, reseNoB, wceNoB = [], [], []
471  for counter, amp in enumerate(covs):
472  muAmp, fullCov, fullCovModel, fullCovWeight = (inputMu[amp], covs[amp], covsModel[amp],
473  covsWeights[amp])
474  if len(fullCov) == 0:
475  continue
476  mu, cov, model, weightCov, _ = getFitDataFromCovariances(i, j, muAmp, fullCov, fullCovModel,
477  fullCovWeight, divideByMu=True,
478  returnMasked=True)
479 
480  mue += list(mu)
481  rese += list(cov - model)
482  wce += list(weightCov)
483 
484  fullCovNoB, fullCovModelNoB, fullCovWeightNoB = (covsNoB[amp], covsModelNoB[amp],
485  covsWeightsNoB[amp])
486  if len(fullCovNoB) == 0:
487  continue
488  (muNoB, covNoB, modelNoB,
489  weightCovNoB, _) = getFitDataFromCovariances(i, j, muAmp, fullCovNoB, fullCovModelNoB,
490  fullCovWeightNoB, divideByMu=True,
491  returnMasked=True)
492 
493  mueNoB += list(muNoB)
494  reseNoB += list(covNoB - modelNoB)
495  wceNoB += list(weightCovNoB)
496 
497  # the corresponding fit
498  fit_curve, = plt.plot(mu, model + counter*offset, '-', linewidth=4.0)
499  # bin plot. len(mu) = no binning
500  gind = self.indexForBins(mu, numberOfBins)
501 
502  xb, yb, wyb, sigyb = self.binData(mu, cov, gind, weightCov)
503  plt.errorbar(xb, yb+counter*offset, yerr=sigyb, marker='o', linestyle='none', markersize=6.5,
504  color=fit_curve.get_color(), label=f"{amp} (N: {len(mu)})")
505  # plot the data
506  if plotData:
507  points, = plt.plot(mu, cov + counter*offset, '.', color=fit_curve.get_color())
508  plt.legend(loc='upper right', fontsize=8)
509  # end loop on amps
510  mue = np.array(mue)
511  rese = np.array(rese)
512  wce = np.array(wce)
513  mueNoB = np.array(mueNoB)
514  reseNoB = np.array(reseNoB)
515  wceNoB = np.array(wceNoB)
516 
517  plt.xlabel(r"$\mu (el)$", fontsize='x-large')
518  plt.ylabel(r"$Cov{%d%d}/\mu + Cst (el)$"%(i, j), fontsize='x-large')
519  if (not topPlot):
520  gind = self.indexForBins(mue, numberOfBins)
521  xb, yb, wyb, sigyb = self.binData(mue, rese, gind, wce)
522 
523  ax1 = plt.subplot(gs[1], sharex=ax0)
524  ax1.errorbar(xb, yb, yerr=sigyb, marker='o', linestyle='none', label='Full fit')
525  gindNoB = self.indexForBins(mueNoB, numberOfBins)
526  xb2, yb2, wyb2, sigyb2 = self.binData(mueNoB, reseNoB, gindNoB, wceNoB)
527 
528  ax1.errorbar(xb2, yb2, yerr=sigyb2, marker='o', linestyle='none', label='b = 0')
529  ax1.tick_params(axis='both', labelsize='x-large')
530  plt.legend(loc='upper left', fontsize='large')
531  # horizontal line at zero
532  plt.plot(xb, [0]*len(xb), '--', color='k')
533  plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
534  plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
535  plt.xlabel(r'$\mu (el)$', fontsize='x-large')
536  plt.ylabel(r'$Cov{%d%d}/\mu$ -model (el)'%(i, j), fontsize='x-large')
537  plt.tight_layout()
538  plt.suptitle(f"Nbins: {numberOfBins}")
539  # overlapping y labels:
540  fig.canvas.draw()
541  labels0 = [item.get_text() for item in ax0.get_yticklabels()]
542  labels0[0] = u''
543  ax0.set_yticklabels(labels0)
544  pdfPages.savefig(fig)
545 
546  return
547 
list
daf::base::PropertyList * list
Definition: fits.cc:913

◆ plotRelativeBiasACoeffs()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.plotRelativeBiasACoeffs (   aDict,
  aDictNoB,
  fullCovsModel,
  fullCovsModelNoB,
  signalElectrons,
  gainDict,
  pdfPages,
  maxr = None 
)
static 
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.

Definition at line 735 of file plotPtc.py.

736  gainDict, pdfPages, maxr=None):
737  """Fig. 15 in Astier+19.
738 
739  Illustrates systematic bias from estimating 'a'
740  coefficients from the slope of correlations as opposed to the
741  full model in Astier+19.
742 
743  Parameters
744  ----------
745  aDict: `dict`
746  Dictionary of 'a' matrices (Eq. 20, Astier+19), with amp names as keys.
747 
748  aDictNoB: `dict`
749  Dictionary of 'a' matrices ('b'= 0 in Eq. 20, Astier+19), with amp names as keys.
750 
751  fullCovsModel : `dict`, [`str`, `list`]
752  Dictionary keyed by amp names containing covariances model per mean flux.
753 
754  fullCovsModelNoB : `dict`, [`str`, `list`]
755  Dictionary keyed by amp names containing covariances model (with 'b'=0 in Eq. 20 of
756  Astier+19) per mean flux.
757 
758  signalElectrons : `float`
759  Signal at which to evaluate the a_ij coefficients.
760 
761  pdfPages: `matplotlib.backends.backend_pdf.PdfPages`
762  PDF file where the plots will be saved.
763 
764  gainDict : `dict`, [`str`, `float`]
765  Dicgionary keyed by amp names with the gains in e-/ADU.
766 
767  maxr : `int`, optional
768  Maximum lag.
769  """
770 
771  fig = plt.figure(figsize=(7, 11))
772  title = [f"'a' relative bias at {signalElectrons} e", "'a' relative bias (b=0)"]
773  data = [(aDict, fullCovsModel), (aDictNoB, fullCovsModelNoB)]
774 
775  for k, pair in enumerate(data):
776  diffs = []
777  amean = []
778  for amp in pair[0]:
779  covModel = np.array(pair[1][amp])
780  if np.isnan(covModel).all():
781  continue
782  # Compute the "a" coefficients of the Antilogus+14 (1402.0725) model as in
783  # Guyonnet+15 (1501.01577, eq. 16, the slope of cov/var at a given flux mu in
784  # electrons). Eq. 16 of 1501.01577 is an approximation to the more complete
785  # model in Astier+19 (1905.08677).
786  var = covModel[0, 0, 0] # ADU^2
787  # For a result in electrons^-1, we have to use mu in electrons
788  aOld = covModel[0, :, :]/(var*signalElectrons)
789  a = pair[0][amp]
790  amean.append(a)
791  diffs.append((aOld-a))
792  amean = np.array(amean).mean(axis=0)
793  diff = np.array(diffs).mean(axis=0)
794  diff = diff/amean
795  diff = diff[:]
796  # The difference should be close to zero
797  diff[0, 0] = 0
798  if maxr is None:
799  maxr = diff.shape[0]
800  diff = diff[:maxr, :maxr]
801  ax0 = fig.add_subplot(2, 1, k+1)
802  im0 = ax0.imshow(diff.transpose(), origin='lower')
803  ax0.yaxis.set_major_locator(MaxNLocator(integer=True))
804  ax0.xaxis.set_major_locator(MaxNLocator(integer=True))
805  ax0.tick_params(axis='both', labelsize='x-large')
806  plt.colorbar(im0)
807  ax0.set_title(title[k])
808 
809  plt.tight_layout()
810  pdfPages.savefig(fig)
811 
812  return
813 

◆ run()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.run (   self,
  filenameFull,
  datasetPtc,
  linearizer = None,
  log = None 
) 
Make the plots for the PTC task

Definition at line 116 of file plotPtc.py.

116  def run(self, filenameFull, datasetPtc, linearizer=None, log=None):
117  """Make the plots for the PTC task"""
118  ptcFitType = datasetPtc.ptcFitType
119  with PdfPages(filenameFull) as pdfPages:
120  if ptcFitType in ["FULLCOVARIANCE", ]:
121  self.covAstierMakeAllPlots(datasetPtc, pdfPages, log=log)
122  elif ptcFitType in ["EXPAPPROXIMATION", "POLYNOMIAL"]:
123  self._plotStandardPtc(datasetPtc, ptcFitType, pdfPages)
124  else:
125  raise RuntimeError(f"The input dataset had an invalid dataset.ptcFitType: {ptcFitType}. \n"
126  "Options: 'FULLCOVARIANCE', EXPAPPROXIMATION, or 'POLYNOMIAL'.")
127  if linearizer:
128  self._plotLinearizer(datasetPtc, linearizer, pdfPages)
129 
130  return
131 
lsst.pipe.tasks.assembleCoadd.run
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, altMaskList=None, mask=None, supplementaryData=None)
Definition: assembleCoadd.py:764

◆ runDataRef()

def lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.runDataRef (   self) 
Run the Photon Transfer Curve (PTC) plotting measurement task.

Definition at line 93 of file plotPtc.py.

93  def runDataRef(self):
94  """Run the Photon Transfer Curve (PTC) plotting measurement task.
95  """
96 
97  datasetFile = self.datasetFilename
98  datasetPtc = PhotonTransferCurveDataset.readFits(datasetFile)
99 
100  dirname = self.outDir
101  if not os.path.exists(dirname):
102  os.makedirs(dirname)
103 
104  detNum = self.detNum
105  filename = f"PTC_det{detNum}.pdf"
106  filenameFull = os.path.join(dirname, filename)
107 
108  if self.linearizerFileName:
109  linearizer = isr.linearize.Linearizer.readFits(self.linearizerFileName)
110  else:
111  linearizer = None
112  self.run(filenameFull, datasetPtc, linearizer=linearizer, log=lsstLog)
113 
114  return
115 

Member Data Documentation

◆ datasetFilename

lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.datasetFilename

Definition at line 86 of file plotPtc.py.

◆ detNum

lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.detNum

Definition at line 88 of file plotPtc.py.

◆ linearizerFileName

lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.linearizerFileName

Definition at line 87 of file plotPtc.py.

◆ outDir

lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.outDir

Definition at line 91 of file plotPtc.py.

◆ plotNormalizedCovariancesNumberOfBins

lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.plotNormalizedCovariancesNumberOfBins

Definition at line 90 of file plotPtc.py.

◆ signalElectronsRelativeA

lsst.cp.pipe.ptc.plotPtc.PlotPhotonTransferCurveTask.signalElectronsRelativeA

Definition at line 89 of file plotPtc.py.

The documentation for this class was generated from the following file:
Generated on Wed Aug 11 2021 08:25:47 for LSST Applications by doxygen 1.9.1