23Define dataset class for MeasurePhotonTransferCurve task
26from astropy.table
import Table
30__all__ = [
'PhotonTransferCurveDataset']
34 """A simple class to hold the output data from the PTC task.
35 The dataset is made up of a dictionary
for each item, keyed by the
36 amplifiers
' names, which much be supplied at construction time.
37 New items cannot be added to the class to save accidentally saving to the
38 wrong property,
and the
class can be frozen if desired.
39 inputExpIdPairs records the exposures used to produce the data.
40 When fitPtc()
or fitCovariancesAstier()
is run, a mask
is built up, which
41 is by definition always the same length
as inputExpIdPairs, rawExpTimes,
42 rawMeans
and rawVars,
and is a list of bools, which are incrementally set
43 to
False as points are discarded
from the fits.
44 PTC fit parameters
for polynomials are stored
in a list
in ascending order
45 of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc
46 with the length of the list corresponding to the order of the polynomial
52 List
with the names of the amplifiers of the detector at hand.
55 Type of model fitted to the PTC:
"POLYNOMIAL",
"EXPAPPROXIMATION",
59 Maximum lag of covariances (size of square covariance matrices).
61 kwargs : `dict`, optional
62 Other keyword arguments to
pass to the parent init.
66 The stored attributes are:
68 List
with bad amplifiers names.
69 inputExpIdPairs : `dict`, [`str`, `list`]
70 Dictionary keyed by amp names containing the input exposures IDs.
71 expIdMask : `dict`, [`str`, `list`]
72 Dictionary keyed by amp names containing the mask produced after
73 outlier rejection. The mask produced by the
"FULLCOVARIANCE"
74 option may differ
from the one produced
in the other two PTC
76 rawExpTimes : `dict`, [`str`, `list`]
77 Dictionary keyed by amp names containing the unmasked exposure times.
78 rawMeans : `dict`, [`str`, `list`]
79 Dictionary keyed by amp namescontaining the unmasked average of the
80 means of the exposures
in each flat pair.
81 rawVars : `dict`, [`str`, `list`]
82 Dictionary keyed by amp names containing the variance of the
83 difference image of the exposures
in each flat pair.
84 gain : `dict`, [`str`, `list`]
85 Dictionary keyed by amp names containing the fitted gains.
86 gainErr : `dict`, [`str`, `list`]
87 Dictionary keyed by amp names containing the errors on the
89 noise : `dict`, [`str`, `list`]
90 Dictionary keyed by amp names containing the fitted noise.
91 noiseErr : `dict`, [`str`, `list`]
92 Dictionary keyed by amp names containing the errors on the fitted
94 ptcFitPars : `dict`, [`str`, `list`]
95 Dictionary keyed by amp names containing the fitted parameters of the
96 PTC model
for ptcFitTye
in [
"POLYNOMIAL",
"EXPAPPROXIMATION"].
97 ptcFitParsError : `dict`, [`str`, `list`]
98 Dictionary keyed by amp names containing the errors on the fitted
99 parameters of the PTC model
for ptcFitTye
in
100 [
"POLYNOMIAL",
"EXPAPPROXIMATION"].
101 ptcFitChiSq : `dict`, [`str`, `list`]
102 Dictionary keyed by amp names containing the reduced chi squared
103 of the fit
for ptcFitTye
in [
"POLYNOMIAL",
"EXPAPPROXIMATION"].
105 Flux value (
in ADU) where the variance of the PTC curve starts
106 decreasing consistently.
107 covariances : `dict`, [`str`, `list`]
108 Dictionary keyed by amp names containing a list of measured
109 covariances per mean flux.
110 covariancesModel : `dict`, [`str`, `list`]
111 Dictionary keyed by amp names containinging covariances model
112 (Eq. 20 of Astier+19) per mean flux.
113 covariancesSqrtWeights : `dict`, [`str`, `list`]
114 Dictionary keyed by amp names containinging sqrt. of covariances
116 aMatrix : `dict`, [`str`, `list`]
117 Dictionary keyed by amp names containing the
"a" parameters
from
118 the model
in Eq. 20 of Astier+19.
119 bMatrix : `dict`, [`str`, `list`]
120 Dictionary keyed by amp names containing the
"b" parameters
from
121 the model
in Eq. 20 of Astier+19.
122 covariancesModelNoB : `dict`, [`str`, `list`]
123 Dictionary keyed by amp names containing covariances model
124 (
with 'b'=0
in Eq. 20 of Astier+19)
126 aMatrixNoB : `dict`, [`str`, `list`]
127 Dictionary keyed by amp names containing the
"a" parameters
from the
128 model
in Eq. 20 of Astier+19
130 finalVars : `dict`, [`str`, `list`]
131 Dictionary keyed by amp names containing the masked variance of the
132 difference image of each flat
133 pair. If needed, each array will be right-padded
with
134 np.nan to match the length of rawExpTimes.
135 finalModelVars : `dict`, [`str`, `list`]
136 Dictionary keyed by amp names containing the masked modeled
137 variance of the difference image of each flat pair. If needed, each
138 array will be right-padded
with np.nan to match the length of
140 finalMeans : `dict`, [`str`, `list`]
141 Dictionary keyed by amp names containing the masked average of the
142 means of the exposures
in each flat pair. If needed, each array
143 will be right-padded
with np.nan to match the length of
145 photoCharge : `dict`, [`str`, `list`]
146 Dictionary keyed by amp names containing the integrated photocharge
147 for linearity calibration.
151 `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
152 Output dataset
from MeasurePhotonTransferCurveTask.
156 _SCHEMA =
'Gen3 Photon Transfer Curve'
159 def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs):
170 self.
rawMeans = {ampName: []
for ampName
in ampNames}
171 self.
rawVars = {ampName: []
for ampName
in ampNames}
174 self.
gain = {ampName: np.nan
for ampName
in ampNames}
175 self.
gainErr = {ampName: np.nan
for ampName
in ampNames}
176 self.
noise = {ampName: np.nan
for ampName
in ampNames}
177 self.
noiseErr = {ampName: np.nan
for ampName
in ampNames}
187 self.
aMatrix = {ampName: np.nan
for ampName
in ampNames}
188 self.
bMatrix = {ampName: np.nan
for ampName
in ampNames}
198 'rawMeans',
'rawVars',
'gain',
'gainErr',
'noise',
'noiseErr',
199 'ptcFitPars',
'ptcFitParsError',
'ptcFitChiSq',
'ptcTurnoff',
200 'aMatrixNoB',
'covariances',
'covariancesModel',
201 'covariancesSqrtWeights',
'covariancesModelNoB',
202 'aMatrix',
'bMatrix',
'finalVars',
'finalModelVars',
'finalMeans',
205 def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan],
206 rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan],
207 gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan],
208 ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, ptcTurnoff=np.nan, covArray=[],
209 covArrayModel=[], covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[],
210 aMatrixNoB=[], finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]):
211 """Function to initialize an amp of a PhotonTransferCurveDataset.
215 The parameters are all documented in `init`.
218 if len(covArray) == 0:
219 covArray = [nanMatrix]
220 if len(covArrayModel) == 0:
221 covArrayModel = [nanMatrix]
222 if len(covSqrtWeights) == 0:
223 covSqrtWeights = [nanMatrix]
224 if len(covArrayModelNoB) == 0:
225 covArrayModelNoB = [nanMatrix]
226 if len(aMatrix) == 0:
228 if len(bMatrix) == 0:
230 if len(aMatrixNoB) == 0:
231 aMatrixNoB = nanMatrix
239 self.
gain[ampName] = gain
240 self.
gainErr[ampName] = gainErr
241 self.
noise[ampName] = noise
251 self.
aMatrix[ampName] = aMatrix
252 self.
bMatrix[ampName] = bMatrix
262 """Update calibration metadata.
263 This calls the base class's method after ensuring the required
264 calibration keywords will be saved.
267 setDate : `bool`, optional
268 Update the CALIBDATE fields in the metadata to the current
269 time. Defaults to
False.
271 Other keyword parameters to set
in the metadata.
279 """Construct a calibration from a dictionary of properties.
280 Must be implemented by the specific calibration subclasses.
284 Dictionary of properties.
287 calib : `lsst.ip.isr.CalibType`
288 Constructed calibration.
292 Raised if the supplied dictionary
is for a different
296 if calib._OBSTYPE != dictionary[
'metadata'][
'OBSTYPE']:
297 raise RuntimeError(f
"Incorrect Photon Transfer Curve dataset supplied. "
298 f
"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}")
299 calib.setMetadata(dictionary[
'metadata'])
300 calib.ptcFitType = dictionary[
'ptcFitType']
301 calib.covMatrixSide = dictionary[
'covMatrixSide']
302 calib.badAmps = np.array(dictionary[
'badAmps'],
'str').tolist()
304 covMatrixSide = calib.covMatrixSide
306 covDimensionsProduct = len(np.array(list(dictionary[
'covariances'].values())[0]).ravel())
307 nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide))
309 for ampName
in dictionary[
'ampNames']:
310 calib.ampNames.append(ampName)
311 calib.inputExpIdPairs[ampName] = np.array(dictionary[
'inputExpIdPairs'][ampName]).tolist()
312 calib.expIdMask[ampName] = np.array(dictionary[
'expIdMask'][ampName]).tolist()
313 calib.rawExpTimes[ampName] = np.array(dictionary[
'rawExpTimes'][ampName]).tolist()
314 calib.rawMeans[ampName] = np.array(dictionary[
'rawMeans'][ampName]).tolist()
315 calib.rawVars[ampName] = np.array(dictionary[
'rawVars'][ampName]).tolist()
316 calib.gain[ampName] = np.array(dictionary[
'gain'][ampName]).tolist()
317 calib.gainErr[ampName] = np.array(dictionary[
'gainErr'][ampName]).tolist()
318 calib.noise[ampName] = np.array(dictionary[
'noise'][ampName]).tolist()
319 calib.noiseErr[ampName] = np.array(dictionary[
'noiseErr'][ampName]).tolist()
320 calib.ptcFitPars[ampName] = np.array(dictionary[
'ptcFitPars'][ampName]).tolist()
321 calib.ptcFitParsError[ampName] = np.array(dictionary[
'ptcFitParsError'][ampName]).tolist()
322 calib.ptcFitChiSq[ampName] = np.array(dictionary[
'ptcFitChiSq'][ampName]).tolist()
323 calib.ptcTurnoff[ampName] = np.array(dictionary[
'ptcTurnoff'][ampName]).tolist()
324 calib.covariances[ampName] = np.array(dictionary[
'covariances'][ampName]).reshape(
325 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
326 calib.covariancesModel[ampName] = np.array(
327 dictionary[
'covariancesModel'][ampName]).reshape(
328 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
329 calib.covariancesSqrtWeights[ampName] = np.array(
330 dictionary[
'covariancesSqrtWeights'][ampName]).reshape(
331 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
332 calib.aMatrix[ampName] = np.array(dictionary[
'aMatrix'][ampName]).reshape(
333 (covMatrixSide, covMatrixSide)).tolist()
334 calib.bMatrix[ampName] = np.array(dictionary[
'bMatrix'][ampName]).reshape(
335 (covMatrixSide, covMatrixSide)).tolist()
336 calib.covariancesModelNoB[ampName] = np.array(
337 dictionary[
'covariancesModelNoB'][ampName]).reshape(
338 (nSignalPoints, covMatrixSide, covMatrixSide)).tolist()
339 calib.aMatrixNoB[ampName] = np.array(
340 dictionary[
'aMatrixNoB'][ampName]).reshape((covMatrixSide, covMatrixSide)).tolist()
341 calib.finalVars[ampName] = np.array(dictionary[
'finalVars'][ampName]).tolist()
342 calib.finalModelVars[ampName] = np.array(dictionary[
'finalModelVars'][ampName]).tolist()
343 calib.finalMeans[ampName] = np.array(dictionary[
'finalMeans'][ampName]).tolist()
344 calib.photoCharge[ampName] = np.array(dictionary[
'photoCharge'][ampName]).tolist()
345 calib.updateMetadata()
349 """Return a dictionary containing the calibration properties.
350 The dictionary should be able to be round-tripped through
355 Dictionary of properties.
361 outDict['metadata'] = metadata
366 outDict[
'badAmps'] = self.
badAmps
371 outDict[
'rawVars'] = self.
rawVars
372 outDict[
'gain'] = self.
gain
373 outDict[
'gainErr'] = self.
gainErr
374 outDict[
'noise'] = self.
noise
383 outDict[
'aMatrix'] = self.
aMatrix
384 outDict[
'bMatrix'] = self.
bMatrix
396 """Construct calibration from a list of tables.
397 This method uses the `fromDict` method to create the
398 calibration, after constructing an appropriate dictionary from
402 tableList : `list` [`lsst.afw.table.Table`]
403 List of tables to use to construct the datasetPtc.
406 calib : `lsst.cp.pipe.`
407 The calibration defined
in the tables.
409 ptcTable = tableList[0]
411 metadata = ptcTable.meta
413 inDict['metadata'] = metadata
414 inDict[
'ampNames'] = []
415 inDict[
'ptcFitType'] = []
416 inDict[
'covMatrixSide'] = []
417 inDict[
'inputExpIdPairs'] = dict()
418 inDict[
'expIdMask'] = dict()
419 inDict[
'rawExpTimes'] = dict()
420 inDict[
'rawMeans'] = dict()
421 inDict[
'rawVars'] = dict()
422 inDict[
'gain'] = dict()
423 inDict[
'gainErr'] = dict()
424 inDict[
'noise'] = dict()
425 inDict[
'noiseErr'] = dict()
426 inDict[
'ptcFitPars'] = dict()
427 inDict[
'ptcFitParsError'] = dict()
428 inDict[
'ptcFitChiSq'] = dict()
429 inDict[
'ptcTurnoff'] = dict()
430 inDict[
'covariances'] = dict()
431 inDict[
'covariancesModel'] = dict()
432 inDict[
'covariancesSqrtWeights'] = dict()
433 inDict[
'aMatrix'] = dict()
434 inDict[
'bMatrix'] = dict()
435 inDict[
'covariancesModelNoB'] = dict()
436 inDict[
'aMatrixNoB'] = dict()
437 inDict[
'finalVars'] = dict()
438 inDict[
'finalModelVars'] = dict()
439 inDict[
'finalMeans'] = dict()
440 inDict[
'badAmps'] = []
441 inDict[
'photoCharge'] = dict()
443 for record
in ptcTable:
444 ampName = record[
'AMPLIFIER_NAME']
446 inDict[
'ptcFitType'] = record[
'PTC_FIT_TYPE']
447 inDict[
'covMatrixSide'] = record[
'COV_MATRIX_SIDE']
448 inDict[
'ampNames'].append(ampName)
449 inDict[
'inputExpIdPairs'][ampName] = record[
'INPUT_EXP_ID_PAIRS']
450 inDict[
'expIdMask'][ampName] = record[
'EXP_ID_MASK']
451 inDict[
'rawExpTimes'][ampName] = record[
'RAW_EXP_TIMES']
452 inDict[
'rawMeans'][ampName] = record[
'RAW_MEANS']
453 inDict[
'rawVars'][ampName] = record[
'RAW_VARS']
454 inDict[
'gain'][ampName] = record[
'GAIN']
455 inDict[
'gainErr'][ampName] = record[
'GAIN_ERR']
456 inDict[
'noise'][ampName] = record[
'NOISE']
457 inDict[
'noiseErr'][ampName] = record[
'NOISE_ERR']
458 inDict[
'ptcFitPars'][ampName] = record[
'PTC_FIT_PARS']
459 inDict[
'ptcFitParsError'][ampName] = record[
'PTC_FIT_PARS_ERROR']
460 inDict[
'ptcFitChiSq'][ampName] = record[
'PTC_FIT_CHI_SQ']
461 inDict[
'ptcTurnoff'][ampName] = record[
'PTC_TURNOFF']
462 inDict[
'covariances'][ampName] = record[
'COVARIANCES']
463 inDict[
'covariancesModel'][ampName] = record[
'COVARIANCES_MODEL']
464 inDict[
'covariancesSqrtWeights'][ampName] = record[
'COVARIANCES_SQRT_WEIGHTS']
465 inDict[
'aMatrix'][ampName] = record[
'A_MATRIX']
466 inDict[
'bMatrix'][ampName] = record[
'B_MATRIX']
467 inDict[
'covariancesModelNoB'][ampName] = record[
'COVARIANCES_MODEL_NO_B']
468 inDict[
'aMatrixNoB'][ampName] = record[
'A_MATRIX_NO_B']
469 inDict[
'finalVars'][ampName] = record[
'FINAL_VARS']
470 inDict[
'finalModelVars'][ampName] = record[
'FINAL_MODEL_VARS']
471 inDict[
'finalMeans'][ampName] = record[
'FINAL_MEANS']
472 inDict[
'badAmps'] = record[
'BAD_AMPS']
473 inDict[
'photoCharge'][ampName] = record[
'PHOTO_CHARGE']
477 """Construct a list of tables containing the information in this
480 The list of tables should create an identical calibration
481 after being passed to this class's fromTable method.
484 tableList : `list` [`astropy.table.Table`]
485 List of tables containing the linearity calibration
491 for i, ampName
in enumerate(self.
ampNames):
492 nPoints.append(len(list(self.
covariances.values())[i]))
493 nSignalPoints = max(nPoints)
495 for i, ampName
in enumerate(self.
ampNames):
496 nPadPoints[ampName] = nSignalPoints - len(list(self.
covariances.values())[i])
499 catalog = Table([{
'AMPLIFIER_NAME': ampName,
503 if len(self.
expIdMask[ampName])
else np.nan,
505 if len(self.
expIdMask[ampName])
else np.nan,
506 'RAW_EXP_TIMES': np.array(self.
rawExpTimes[ampName]).tolist()
508 'RAW_MEANS': np.array(self.
rawMeans[ampName]).tolist()
509 if len(self.
rawMeans[ampName])
else np.nan,
510 'RAW_VARS': np.array(self.
rawVars[ampName]).tolist()
511 if len(self.
rawVars[ampName])
else np.nan,
512 'GAIN': self.
gain[ampName],
513 'GAIN_ERR': self.
gainErr[ampName],
514 'NOISE': self.
noise[ampName],
515 'NOISE_ERR': self.
noiseErr[ampName],
516 'PTC_FIT_PARS': np.array(self.
ptcFitPars[ampName]).tolist(),
517 'PTC_FIT_PARS_ERROR': np.array(self.
ptcFitParsError[ampName]).tolist(),
520 'COVARIANCES': np.pad(np.array(self.
covariances[ampName]),
521 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
522 'constant', constant_values=np.nan).reshape(
523 nSignalPoints*covMatrixSide**2).tolist(),
525 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
526 'constant', constant_values=np.nan).reshape(
527 nSignalPoints*covMatrixSide**2).tolist(),
529 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
530 'constant', constant_values=0.0).reshape(
531 nSignalPoints*covMatrixSide**2).tolist(),
532 'A_MATRIX': np.array(self.
aMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
533 'B_MATRIX': np.array(self.
bMatrix[ampName]).reshape(covMatrixSide**2).tolist(),
534 'COVARIANCES_MODEL_NO_B':
536 ((0, nPadPoints[ampName]), (0, 0), (0, 0)),
537 'constant', constant_values=np.nan).reshape(
538 nSignalPoints*covMatrixSide**2).tolist(),
539 'A_MATRIX_NO_B': np.array(self.
aMatrixNoB[ampName]).reshape(
540 covMatrixSide**2).tolist(),
541 'FINAL_VARS': np.pad(np.array(self.
finalVars[ampName]), (0, nPadPoints[ampName]),
542 'constant', constant_values=np.nan).tolist(),
543 'FINAL_MODEL_VARS': np.pad(np.array(self.
finalModelVars[ampName]),
544 (0, nPadPoints[ampName]),
545 'constant', constant_values=np.nan).tolist(),
546 'FINAL_MEANS': np.pad(np.array(self.
finalMeans[ampName]),
547 (0, nPadPoints[ampName]),
548 'constant', constant_values=np.nan).tolist(),
549 'BAD_AMPS': np.array(self.
badAmps).tolist()
if len(self.
badAmps)
else np.nan,
550 'PHOTO_CHARGE': np.array(self.
photoCharge[ampName]).tolist(),
553 outMeta = {k: v
for k, v
in inMeta.items()
if v
is not None}
554 outMeta.update({k:
"" for k, v
in inMeta.items()
if v
is None})
555 catalog.meta = outMeta
556 tableList.append(catalog)
561 """Get the exposures used, i.e. not discarded, for a given amp.
562 If no mask has been created yet, all exposures are returned.
573 return [(exp1, exp2)
for ((exp1, exp2), m)
in zip(pairs, mask)
if bool(m)
is True]
def requiredAttributes(self, value)
def updateMetadata(self, camera=None, detector=None, filterName=None, setCalibId=False, setCalibInfo=False, setDate=False, **kwargs)
def requiredAttributes(self)
def __init__(self, ampNames=[], ptcFitType=None, covMatrixSide=1, **kwargs)
def getExpIdsUsed(self, ampName)
def fromDict(cls, dictionary)
def fromTable(cls, tableList)
def updateMetadata(self, setDate=False, **kwargs)
def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np.nan], rawExpTime=[np.nan], rawMean=[np.nan], rawVar=[np.nan], photoCharge=[np.nan], gain=np.nan, gainErr=np.nan, noise=np.nan, noiseErr=np.nan, ptcFitPars=[np.nan], ptcFitParsError=[np.nan], ptcFitChiSq=np.nan, ptcTurnoff=np.nan, covArray=[], covArrayModel=[], covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[], aMatrixNoB=[], finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan])