def testSaveSettingWithoutFilePath(self):
filePath = self._saveSettingFile()
paramReader = ParamReader(filePath=filePath)
paramReader.saveSetting()
self.assertEqual(paramReader.getFilePath(), filePath)
# Check the values are saved actually
self.assertEqual(paramReader.getSetting("znmax"), 22)
keysInContent = paramReader.getContent().keys()
self.assertTrue("dofIdx" in keysInContent)
self.assertTrue("zn3Idx" in keysInContent)
self.assertEqual(paramReader.getSetting("zn3Idx"), [1] * 19)
class MapSensorNameAndId(object):
def __init__(self, sensorNameToIdFileName="sensorNameToId.yaml"):
"""Construct a MapSensorNameAndId object.
Parameters
----------
sensorNameToIdFileName : str, optional
Configuration file name to map sensor name and Id. (the default is
"sensorNameToId.yaml".)
"""
sensorNameToIdFilePath = os.path.join(getConfigDir(),
sensorNameToIdFileName)
self._sensorNameToIdFile = ParamReader(filePath=sensorNameToIdFilePath)
def mapSensorNameToId(self, sensorName):
"""Map the sensor name to sensor Id.
Parameters
----------
sensorName : list[str] or str
List or string of abbreviated sensor names.
Returns
-------
list[int]
List of sensor Id.
"""
sensorNameList = self._changeToListIfNeed(sensorName)
sensorIdList = []
for sensor in sensorNameList:
sensorId = self._sensorNameToIdFile.getSetting(sensor)
sensorIdList.append(sensorId)
return sensorIdList
def _changeToListIfNeed(self, inputArg):
"""Change the input argument to list type if needed.
Parameters
----------
inputArg : obj
Input argument.
Returns
-------
list
Input argument as the list type.
"""
if (not isinstance(inputArg, list)):
inputArg = [inputArg]
return inputArg
def mapSensorIdToName(self, sensorId):
"""Map the sensor Id to sensor name.
If no sensor name is found for a specific Id, there will be no returned
value.
Parameters
----------
sensorId : list[int] or int
List or integer of sensor Id.
Returns
-------
list
List of abbreviated sensor names.
int
Number of sensors.
"""
sensorIdList = self._changeToListIfNeed(sensorId)
sensorNameList = []
content = self._sensorNameToIdFile.getContent()
for sensor in sensorIdList:
try:
sensorName = self._getKeyFromValueInDict(content, sensor)
sensorNameList.append(sensorName)
except ValueError:
pass
return sensorNameList, len(sensorNameList)
def _getKeyFromValueInDict(self, aDict, value):
"""Get the key from value in a dictionary object.
Parameters
----------
aDict : dict
Dictionary object.
value : str or int
Value in the dictionary.
Returns
-------
str
Dictionary key.
"""
return list(aDict.keys())[list(aDict.values()).index(value)]
class PhosimCmpt(object):
def __init__(self, tele):
"""Initialization of PhoSim component class.
WEP: wavefront estimation pipeline.
Parameters
----------
tele : TeleFacade
Telescope instance.
"""
# Configuration directory
self.configDir = getConfigDir()
# Telescope setting file
settingFilePath = os.path.join(self.configDir,
"phosimCmptSetting.yaml")
self._phosimCmptSettingFile = ParamReader(filePath=settingFilePath)
# OPD metrology
self.metr = OpdMetrology()
# TeleFacade instance
self.tele = tele
# Output directory of data
self.outputDir = ""
# Output directory of image
self.outputImgDir = ""
# Seed number
self.seedNum = 0
# M1M3 force error
self.m1m3ForceError = 0.05
def setM1M3ForceError(self, m1m3ForceError):
"""Set the M1M3 force error.
Parameters
----------
m1m3ForceError : float
Ratio of actuator force error between 0 and 1.
"""
self.m1m3ForceError = m1m3ForceError
def getM1M3ForceError(self):
"""Get the M1M3 force error.
Returns
-------
float
Ratio of actuator force error.
"""
return self.m1m3ForceError
def getSettingFile(self):
"""Get the setting file.
Returns
-------
lsst.ts.wep.ParamReader
Setting file.
"""
return self._phosimCmptSettingFile
def getTele(self):
"""Get the telescope object.
Returns
-------
TeleFacade
Telescope object.
"""
return self.tele
def getNumOfZk(self):
"""Get the number of Zk (annular Zernike polynomial).
Returns
-------
int
Number of Zk.
"""
return int(self._phosimCmptSettingFile.getSetting("numOfZk"))
def getIntraFocalDirName(self):
"""Get the intra-focal directory name.
Returns
-------
str
Intra-focal directory name.
"""
return self._phosimCmptSettingFile.getSetting("intraDirName")
def getExtraFocalDirName(self):
"""Get the extra-focal directory name.
Returns
-------
str
Extra-focal directory name.
"""
return self._phosimCmptSettingFile.getSetting("extraDirName")
def getOpdMetr(self):
"""Get the OPD metrology object.
OPD: optical path difference.
Returns
-------
OpdMetrology
OPD metrology object.
"""
return self.metr
def setOutputDir(self, outputDir):
"""Set the output directory.
The output directory will be constructed if there is no existed one.
Parameters
----------
outputDir : str
Output directory.
"""
self._makeDir(outputDir)
self.outputDir = outputDir
def _makeDir(self, newDir, exist_ok=True):
"""Make the new directory.
Super-mkdir; create a leaf directory and all intermediate ones. Works
like mkdir, except that any intermediate path segment (not just the
rightmost) will be created if it does not exist.
Parameters
----------
newDir : str
New directory.
exist_ok : bool, optional
If the target directory already exists, raise an OSError if
exist_ok is False. Otherwise no exception is raised. (the default
is True.)
"""
os.makedirs(newDir, exist_ok=exist_ok)
def getOutputDir(self):
"""Get the output directory.
Returns
-------
str
Output directory.
"""
return self.outputDir
def setOutputImgDir(self, outputImgDir):
"""Set the output image directory.
The output image directory will be constructed if there is no existed
one.
Parameters
----------
outputImgDir : str
Output image directory
"""
self._makeDir(outputImgDir)
self.outputImgDir = outputImgDir
def getOutputImgDir(self):
"""Get the output image directory.
Returns
-------
str
Output image directory
"""
return self.outputImgDir
def setSeedNum(self, seedNum):
"""Set the seed number for the M1M3 mirror surface purturbation.
Parameters
----------
seedNum : int
Seed number.
"""
self.seedNum = int(seedNum)
def getSeedNum(self):
"""Get the seed number for the M1M3 random surface purturbation.
Returns
-------
int or None
Seed number. None means there is no random purturbation.
"""
return self.seedNum
def setSurveyParam(self,
obsId=None,
filterType=None,
boresight=None,
zAngleInDeg=None,
rotAngInDeg=None):
"""Set the survey parameters.
Parameters
----------
obsId : int, optional
Observation Id. (the default is None.)
filterType : enum 'FilterType' in lsst.ts.wep.Utility, optional
Active filter type. (the default is None.)
boresight : tuple, optional
Telescope boresight in (ra, decl). (the default is None.)
zAngleInDeg : float, optional
Zenith angle in degree. (the default is None.)
rotAngInDeg : float, optional
Camera rotation angle in degree between -90 and 90 degrees. (the
default is None.)
"""
self.tele.setSurveyParam(obsId=obsId,
filterType=filterType,
boresight=boresight,
zAngleInDeg=zAngleInDeg,
rotAngInDeg=rotAngInDeg)
def addOpdFieldXYbyDeg(self, fieldXInDegree, fieldYInDegree):
"""Add the OPD new field X, Y in degree.
OPD: optical path difference.
Parameters
----------
fieldXInDegree : float, list, or numpy.ndarray
New field X in degree.
fieldYInDegree : float, list, or numpy.ndarray
New field Y in degree.
"""
self.metr.addFieldXYbyDeg(fieldXInDegree, fieldYInDegree)
def accDofInUm(self, dofInUm):
"""Accumulate the aggregated degree of freedom (DOF) in um.
idx 0-4: M2 dz, dx, dy, rx, ry
idx 5-9: Cam dz, dx, dy, rx, ry
idx 10-29: M1M3 20 bending modes
idx 30-49: M2 20 bending modes
Parameters
----------
dofInUm : list or numpy.ndarray
DOF in um.
"""
self.tele.accDofInUm(dofInUm)
def setDofInUm(self, dofInUm):
"""Set the accumulated degree of freedom (DOF) in um.
idx 0-4: M2 dz, dx, dy, rx, ry
idx 5-9: Cam dz, dx, dy, rx, ry
idx 10-29: M1M3 20 bending modes
idx 30-49: M2 20 bending modes
Parameters
----------
dofInUm : list or numpy.ndarray
DOF in um.
"""
self.tele.setDofInUm(dofInUm)
def getDofInUm(self):
"""Get the accumulated degree of freedom (DOF) in um.
idx 0-4: M2 dz, dx, dy, rx, ry
idx 5-9: Cam dz, dx, dy, rx, ry
idx 10-29: M1M3 20 bending modes
idx 30-49: M2 20 bending modes
Returns
-------
numpy.ndarray
DOF in um.
"""
return self.tele.getDofInUm()
def saveDofInUmFileForNextIter(self,
dofInUm,
dofInUmFileName="dofPertInNextIter.mat"):
"""Save the DOF in um data to file for the next iteration.
DOF: degree of freedom.
Parameters
----------
dofInUm : list or numpy.ndarray
DOF in um.
dofInUmFileName : str, optional
File name to save the DOF in um. (the default is
"dofPertInNextIter.mat".)
"""
filePath = os.path.join(self.outputDir, dofInUmFileName)
header = "The followings are the DOF in um:"
np.savetxt(filePath, np.transpose(dofInUm), header=header)
def runPhoSim(self, argString):
"""Run the PhoSim program.
Parameters
----------
argString : str
Arguments for PhoSim.
"""
self.tele.runPhoSim(argString)
def getComCamOpdArgsAndFilesForPhoSim(
self,
cmdFileName="opd.cmd",
instFileName="opd.inst",
logFileName="opdPhoSim.log",
cmdSettingFileName="opdDefault.cmd",
instSettingFileName="opdDefault.inst"):
"""Get the OPD calculation arguments and files of ComCam for the PhoSim
calculation.
OPD: optical path difference.
ComCam: commissioning camera.
Parameters
----------
cmdFileName : str, optional
Physical command file name. (the default is "opd.cmd".)
instFileName : str, optional
OPD instance file name. (the default is "opd.inst".)
logFileName : str, optional
Log file name. (the default is "opdPhoSim.log".)
cmdSettingFileName : str, optional
Physical command setting file name. (the default is
"opdDefault.cmd".)
instSettingFileName : str, optional
Instance setting file name. (the default is "opdDefault.inst".)
Returns
-------
str
Arguments to run the PhoSim.
"""
# Set the default ComCam OPD field positions
self.metr.setDefaultComcamGQ()
argString = self._getOpdArgsAndFilesForPhoSim(cmdFileName,
instFileName,
logFileName,
cmdSettingFileName,
instSettingFileName)
return argString
def _getOpdArgsAndFilesForPhoSim(self, cmdFileName, instFileName,
logFileName, cmdSettingFileName,
instSettingFileName):
"""Get the OPD calculation arguments and files for the PhoSim
calculation.
OPD: optical path difference.
Parameters
----------
cmdFileName : str
Physical command file name.
instFileName : str
OPD instance file name.
logFileName : str
Log file name.
cmdSettingFileName : str
Physical command setting file name.
instSettingFileName : str
Instance setting file name.
Returns
-------
str
Arguments to run the PhoSim.
"""
# Write the command file
cmdFilePath = self._writePertAndCmdFiles(cmdSettingFileName,
cmdFileName)
# Write the instance file
instSettingFile = self._getInstSettingFilePath(instSettingFileName)
instFilePath = self.tele.writeOpdInstFile(
self.outputDir,
self.metr,
instSettingFile=instSettingFile,
instFileName=instFileName)
# Get the argument to run the PhoSim
argString = self._getPhoSimArgs(logFileName, instFilePath, cmdFilePath)
return argString
def _writePertAndCmdFiles(self, cmdSettingFileName, cmdFileName):
"""Write the physical perturbation and command files.
Parameters
----------
cmdSettingFileName : str
Physical command setting file name.
cmdFileName : str
Physical command file name.
Returns
-------
str
Command file path.
"""
# Write the perturbation file
pertCmdFileName = "pert.cmd"
pertCmdFilePath = os.path.join(self.outputDir, pertCmdFileName)
if (not os.path.exists(pertCmdFilePath)):
self.tele.writePertBaseOnConfigFile(
self.outputDir,
seedNum=self.seedNum,
m1m3ForceError=self.m1m3ForceError,
saveResMapFig=True,
pertCmdFileName=pertCmdFileName)
# Write the physical command file
cmdSettingFile = os.path.join(self.configDir, "cmdFile",
cmdSettingFileName)
cmdFilePath = os.path.join(self.outputDir, cmdFileName)
if (not os.path.exists(cmdFilePath)):
self.tele.writeCmdFile(self.outputDir,
cmdSettingFile=cmdSettingFile,
pertFilePath=pertCmdFilePath,
cmdFileName=cmdFileName)
return cmdFilePath
def _getInstSettingFilePath(self, instSettingFileName):
"""Get the instance setting file path.
Parameters
----------
instSettingFileName : str
Instance setting file name.
Returns
-------
str
Instance setting file path.
"""
instSettingFile = os.path.join(self.configDir, "instFile",
instSettingFileName)
return instSettingFile
def _getPhoSimArgs(self, logFileName, instFilePath, cmdFilePath):
"""Get the arguments needed to run the PhoSim.
Parameters
----------
logFileName : str
Log file name.
instFilePath: str
Instance file path.
cmdFilePath : str
Physical command file path.
Returns
-------
str
Arguments to run the PhoSim.
"""
# PhoSim parameters
numPro = int(self._phosimCmptSettingFile.getSetting("numPro"))
e2ADC = int(self._phosimCmptSettingFile.getSetting("e2ADC"))
logFilePath = os.path.join(self.outputImgDir, logFileName)
argString = self.tele.getPhoSimArgs(instFilePath,
extraCommandFile=cmdFilePath,
numPro=numPro,
outputDir=self.outputImgDir,
e2ADC=e2ADC,
logFilePath=logFilePath)
return argString
def getComCamStarArgsAndFilesForPhoSim(
self,
extraObsId,
intraObsId,
skySim,
simSeed=1000,
cmdSettingFileName="starDefault.cmd",
instSettingFileName="starSingleExp.inst"):
"""Get the star calculation arguments and files of ComCam for the
PhoSim calculation.
Parameters
----------
extraObsId : int
Extra-focal observation Id.
intraObsId : int
Intra-focal observation Id.
skySim : SkySim
Sky simulator
simSeed : int, optional
Random number seed. (the default is 1000.)
cmdSettingFileName : str, optional
Physical command setting file name. (the default is
"starDefault.cmd".)
instSettingFileName : str, optional
Instance setting file name. (the default is "starSingleExp.inst".)
Returns
-------
list[str]
List of arguments to run the PhoSim.
"""
# Set the intra- and extra-focal related information
obsIdList = {"-1": extraObsId, "1": intraObsId}
instFileNameList = {"-1": "starExtra.inst", "1": "starIntra.inst"}
logFileNameList = {
"-1": "starExtraPhoSim.log",
"1": "starIntraPhoSim.log"
}
extraFocalDirName = self.getExtraFocalDirName()
intraFocalDirName = self.getIntraFocalDirName()
outImgDirNameList = {"-1": extraFocalDirName, "1": intraFocalDirName}
# Write the instance and command files of defocal conditions
cmdFileName = "star.cmd"
onFocalDofInUm = self.getDofInUm()
onFocalOutputImgDir = self.outputImgDir
argStringList = []
for ii in (-1, 1):
# Set the observation ID
self.setSurveyParam(obsId=obsIdList[str(ii)])
# Camera piston (Change the unit from mm to um)
pistonInUm = np.zeros(len(onFocalDofInUm))
pistonInUm[5] = ii * self.tele.getDefocalDistInMm() * 1e3
# Set the new DOF that considers the piston motion
self.setDofInUm(onFocalDofInUm + pistonInUm)
# Update the output image directory
outputImgDir = os.path.join(onFocalOutputImgDir,
outImgDirNameList[str(ii)])
self.setOutputImgDir(outputImgDir)
# Get the argument to run the phosim
argString = self.getStarArgsAndFilesForPhoSim(
skySim,
cmdFileName=cmdFileName,
instFileName=instFileNameList[str(ii)],
logFileName=logFileNameList[str(ii)],
simSeed=simSeed,
cmdSettingFileName=cmdSettingFileName,
instSettingFileName=instSettingFileName)
argStringList.append(argString)
# Put the internal state back to the focal plane condition
self.setDofInUm(onFocalDofInUm)
self.setOutputImgDir(onFocalOutputImgDir)
return argStringList
def getStarArgsAndFilesForPhoSim(self,
skySim,
cmdFileName="star.cmd",
instFileName="star.inst",
logFileName="starPhoSim.log",
simSeed=1000,
cmdSettingFileName="starDefault.cmd",
instSettingFileName="starSingleExp.inst"):
"""Get the star calculation arguments and files for the PhoSim
calculation.
Parameters
----------
skySim : SkySim
Sky simulator
cmdFileName : str, optional
Physical command file name. (the default is "star.cmd".)
instFileName : str, optional
Star instance file name. (the default is "star.inst".)
logFileName : str, optional
Log file name. (the default is "starPhoSim.log".)
simSeed : int, optional
Random number seed. (the default is 1000)
cmdSettingFileName : str, optional
Physical command setting file name. (the default is
"starDefault.cmd".)
instSettingFileName : str, optional
Instance setting file name. (the default is "starSingleExp.inst".)
Returns
-------
str
Arguments to run the PhoSim.
"""
# Write the command file
cmdFilePath = self._writePertAndCmdFiles(cmdSettingFileName,
cmdFileName)
# Write the instance file
instSettingFile = self._getInstSettingFilePath(instSettingFileName)
instFilePath = self.tele.writeStarInstFile(
self.outputDir,
skySim,
simSeed=simSeed,
sedName="sed_flat.txt",
instSettingFile=instSettingFile,
instFileName=instFileName)
# Get the argument to run the PhoSim
argString = self._getPhoSimArgs(logFileName, instFilePath, cmdFilePath)
return argString
def analyzeComCamOpdData(self,
zkFileName="opd.zer",
rotOpdInDeg=0.0,
pssnFileName="PSSN.txt"):
"""Analyze the ComCam OPD data.
Rotate OPD to simulate the output by rotated camera. When anaylzing the
PSSN, the unrotated OPD is used.
ComCam: Commissioning camera.
OPD: Optical path difference.
PSSN: Normalized point source sensitivity.
Parameters
----------
zkFileName : str, optional
OPD in zk file name. (the default is "opd.zer".)
rotOpdInDeg : float, optional
Rotate OPD in degree in the counter-clockwise direction. (the
default is 0.0.)
pssnFileName : str, optional
PSSN file name. (the default is "PSSN.txt".)
"""
self._writeOpdZkFile(zkFileName, rotOpdInDeg)
self._writeOpdPssnFile(pssnFileName)
def _writeOpdZkFile(self, zkFileName, rotOpdInDeg):
"""Write the OPD in zk file.
OPD: optical path difference.
Parameters
----------
zkFileName : str
OPD in zk file name.
rotOpdInDeg : float
Rotate OPD in degree in the counter-clockwise direction.
"""
filePath = os.path.join(self.outputImgDir, zkFileName)
opdData = self._mapOpdToZk(rotOpdInDeg)
header = "The followings are OPD in rotation angle of %.2f degree in um from z4 to z22:" % (
rotOpdInDeg)
np.savetxt(filePath, opdData, header=header)
def _mapOpdToZk(self, rotOpdInDeg):
"""Map the OPD to the basis of annular Zernike polynomial (Zk).
OPD: optical path difference.
Parameters
----------
rotOpdInDeg : float
Rotate OPD in degree in the counter-clockwise direction.
Returns
-------
numpy.ndarray
Zk data from OPD. This is a 2D array. The row is the OPD index and
the column is z4 to z22 in um. The order of OPD index is based on
the file name.
"""
# Get the sorted OPD file list
opdFileList = self._getOpdFileInDir(self.outputImgDir)
# Map the OPD to the Zk basis and do the collection
numOfZk = self.getNumOfZk()
opdData = np.zeros((len(opdFileList), numOfZk))
for idx, opdFile in enumerate(opdFileList):
opd = fits.getdata(opdFile)
# Rotate OPD if needed
if (rotOpdInDeg != 0):
opdRot = ndimage.rotate(opd, rotOpdInDeg, reshape=False)
opdRot[opd == 0] = 0
else:
opdRot = opd
# z1 to z22 (22 terms)
zk = self.metr.getZkFromOpd(opdMap=opdRot)[0]
# Only need to collect z4 to z22
initIdx = 3
opdData[idx, :] = zk[initIdx:initIdx + numOfZk]
return opdData
def _getOpdFileInDir(self, opdDir):
"""Get the sorted OPD files in the directory.
OPD: Optical path difference.
Parameters
----------
opdDir : str
OPD file directory.
Returns
-------
list
List of sorted OPD files.
"""
# Get the files
opdFileList = []
fileList = self._getFileInDir(opdDir)
for file in fileList:
fileName = os.path.basename(file)
m = re.match(r"\Aopd_\d+_(\d+).fits.gz", fileName)
if (m is not None):
opdFileList.append(file)
# Do the sorting of file name
sortedOpdFileList = sortOpdFileList(opdFileList)
return sortedOpdFileList
def _getFileInDir(self, fileDir):
"""Get the files in the directory.
Parameters
----------
fileDir : str
File directory.
Returns
-------
list
List of files.
"""
fileList = []
for name in os.listdir(fileDir):
filePath = os.path.join(fileDir, name)
if os.path.isfile(filePath):
fileList.append(filePath)
return fileList
def _writeOpdPssnFile(self, pssnFileName):
"""Write the OPD PSSN in file.
OPD: Optical path difference.
PSSN: Normalized point source sensitivity.
Parameters
----------
pssnFileName : str
PSSN file name.
"""
filePath = os.path.join(self.outputImgDir, pssnFileName)
# Calculate the PSSN
pssnList, gqEffPssn = self._calcComCamOpdPssn()
# Calculate the FWHM
effFwhmList, gqEffFwhm = self._calcComCamOpdEffFwhm(pssnList)
# Append the list to write the data into file
pssnList.append(gqEffPssn)
effFwhmList.append(gqEffFwhm)
# Stack the data
data = np.vstack((pssnList, effFwhmList))
# Write to file
header = "The followings are PSSN and FWHM (in arcsec) data. The final number is the GQ value."
np.savetxt(filePath, data, header=header)
def _calcComCamOpdPssn(self):
"""Calculate the ComCam PSSN of OPD.
ComCam: Commissioning camera.
OPD: Optical path difference.
PSSN: Normalized point source sensitivity.
GQ: Gaussian quadrature.
Returns
-------
list
PSSN list.
float
GQ effective PSSN.
"""
opdFileList = self._getOpdFileInDir(self.outputImgDir)
wavelengthInUm = self.tele.getRefWaveLength() * 1e-3
pssnList = []
for opdFile in opdFileList:
pssn = self.metr.calcPSSN(wavelengthInUm, opdFitsFile=opdFile)
pssnList.append(pssn)
# Calculate the GQ effectice PSSN
self._setComCamWgtRatio()
gqEffPssn = self.metr.calcGQvalue(pssnList)
return pssnList, gqEffPssn
def _setComCamWgtRatio(self):
"""Set the ComCam weighting ratio.
ComCam: Commissioning camera.
"""
comcamWtRatio = np.ones(9)
self.metr.setWeightingRatio(comcamWtRatio)
def _calcComCamOpdEffFwhm(self, pssnList):
"""Calculate the ComCam effective FWHM of OPD.
ComCam: Commissioning camera.
FWHM: Full width and half maximum.
PSSN: Normalized point source sensitivity.
GQ: Gaussian quadrature.
Parameters
----------
pssnList : list
List of PSSN.
Returns
-------
list
Effective FWHM list.
float
GQ effective FWHM of ComCam.
"""
# Calculate the list of effective FWHM
effFwhmList = []
for pssn in pssnList:
effFwhm = self.metr.calcFWHMeff(pssn)
effFwhmList.append(effFwhm)
# Calculate the GQ effectice FWHM
self._setComCamWgtRatio()
gqEffFwhm = self.metr.calcGQvalue(effFwhmList)
return effFwhmList, gqEffFwhm
def mapOpdDataToListOfWfErr(self, opdZkFileName, refSensorNameList):
"""Map the OPD data to the list of wavefront error.
OPD: Optical path difference.
Parameters
----------
opdZkFileName : str
OPD zk file name.
refSensorNameList : list
Reference sensor name list.
Returns
-------
list [lsst.ts.wep.ctrlIntf.SensorWavefrontError]
List of SensorWavefrontError object.
"""
opdZk = self._getZkFromFile(opdZkFileName)
mapSensorNameAndId = MapSensorNameAndId()
sensorIdList = mapSensorNameAndId.mapSensorNameToId(refSensorNameList)
listOfWfErr = []
for sensorId, zk in zip(sensorIdList, opdZk):
sensorWavefrontData = SensorWavefrontError(
numOfZk=self.getNumOfZk())
sensorWavefrontData.setSensorId(sensorId)
sensorWavefrontData.setAnnularZernikePoly(zk)
listOfWfErr.append(sensorWavefrontData)
return listOfWfErr
def _getZkFromFile(self, zkFileName):
"""Get the zk (z4-z22) from file.
Parameters
----------
zkFileName : str
Zk file name.
Returns
-------
numpy.ndarray
zk matrix. The colunm is z4-z22. The raw is each data point.
"""
filePath = os.path.join(self.outputImgDir, zkFileName)
zk = np.loadtxt(filePath)
return zk
def getOpdPssnFromFile(self, pssnFileName):
"""Get the OPD PSSN from file.
OPD: Optical path difference.
PSSN: Normalized point source sensitivity.
Parameters
----------
pssnFileName : str
PSSN file name.
Returns
-------
numpy.ndarray
PSSN.
"""
data = self._getDataOfPssnFile(pssnFileName)
pssn = data[0, :-1]
return pssn
def _getDataOfPssnFile(self, pssnFileName):
"""Get the data of the PSSN file.
PSSN: Normalized point source sensitivity.
Parameters
----------
pssnFileName : str
PSSN file name.
Returns
-------
numpy.ndarray
Data of the PSSN file.
"""
filePath = os.path.join(self.outputImgDir, pssnFileName)
data = np.loadtxt(filePath)
return data
def getOpdGqEffFwhmFromFile(self, pssnFileName):
"""Get the OPD GQ effective FWHM from file.
OPD: Optical path difference.
GQ: Gaussian quadrature.
FWHM: Full width at half maximum.
PSSN: Normalized point source sensitivity.
Parameters
----------
pssnFileName : str
PSSN file name.
Returns
-------
float
OPD GQ effective FWHM.
"""
data = self._getDataOfPssnFile(pssnFileName)
gqEffFwhm = data[1, -1]
return gqEffFwhm
def getListOfFwhmSensorData(self, pssnFileName, refSensorNameList):
"""Get the list of FWHM sensor data based on the OPD PSSN file.
FWHM: Full width at half maximum.
OPD: Optical path difference.
PSSN: Normalized point source sensitivity.
Parameters
----------
pssnFileName : str
PSSN file name.
refSensorNameList : list
Reference sensor name list.
Returns
-------
list [lsst.ts.ofc.ctrlIntf.FWHMSensorData]
List of FWHMSensorData which contains the sensor Id and FWHM data.
"""
# Get the FWHM data from the PSSN file
# The first row is the PSSN and the second one is the FWHM
# The final element in each row is the GQ value
data = self._getDataOfPssnFile(pssnFileName)
fwhmData = data[1, :-1]
mapSensorNameAndId = MapSensorNameAndId()
sensorIdList = mapSensorNameAndId.mapSensorNameToId(refSensorNameList)
listOfFWHMSensorData = []
for sensorId, fwhm in zip(sensorIdList, fwhmData):
fwhmSensorData = FWHMSensorData(sensorId, np.array([fwhm]))
listOfFWHMSensorData.append(fwhmSensorData)
return listOfFWHMSensorData
def repackageComCamAmpImgFromPhoSim(self):
"""Repackage the ComCam amplifier images from PhoSim to the single 16
extension MEFs for processing.
ComCam: commissioning camera.
MEF: multi-extension frames.
"""
self._repackageComCamImages(isEimg=False)
def _repackageComCamImages(self, isEimg=False):
"""Repackage the ComCam images from PhoSim for processing.
Parameters
----------
isEimg : bool, optional
Is eimage or not. (the default is False.)
"""
# Make a temporary directory
tmpDirPath = os.path.join(self.outputImgDir, "tmp")
self._makeDir(tmpDirPath)
intraFocalDirName = self.getIntraFocalDirName()
extraFocalDirName = self.getExtraFocalDirName()
for imgType in (intraFocalDirName, extraFocalDirName):
# Repackage the images to the temporary directory
command = "phosim_repackager.py"
phosimImgDir = os.path.join(self.outputImgDir, imgType)
argstring = "%s --out_dir=%s" % (phosimImgDir, tmpDirPath)
if (isEimg):
argstring += " --eimage"
runProgram(command, argstring=argstring)
# Remove the image data in the original directory
argString = "-rf %s/*.fits*" % phosimImgDir
runProgram("rm", argstring=argString)
# Put the repackaged data into the image directory
argstring = "%s/*.fits %s" % (tmpDirPath, phosimImgDir)
runProgram("mv", argstring=argstring)
# Remove the temporary directory
shutil.rmtree(tmpDirPath)
def repackageComCamEimgFromPhoSim(self):
"""Repackage the ComCam eimages from PhoSim for processing.
ComCam: commissioning camera.
"""
self._repackageComCamImages(isEimg=True)
def reorderAndSaveWfErrFile(self,
listOfWfErr,
refSensorNameList,
zkFileName="wfs.zer"):
"""Reorder the wavefront error in the wavefront error list according to
the reference sensor name list and save to a file.
The unexisted wavefront error will be a numpy zero array. The unit is
um.
Parameters
----------
listOfWfErr : list [lsst.ts.wep.ctrlIntf.SensorWavefrontData]
List of SensorWavefrontData object.
refSensorNameList : list
Reference sensor name list.
zkFileName : str, optional
Wavefront error file name. (the default is "wfs.zer".)
"""
# Get the sensor name that in the wavefront error map
wfErrMap = self._transListOfWfErrToMap(listOfWfErr)
nameListInWfErrMap = list(wfErrMap.keys())
# Reorder the wavefront error map based on the reference sensor name
# list.
reorderedWfErrMap = dict()
for sensorName in refSensorNameList:
if sensorName in nameListInWfErrMap:
wfErr = wfErrMap[sensorName]
else:
numOfZk = self.getNumOfZk()
wfErr = np.zeros(numOfZk)
reorderedWfErrMap[sensorName] = wfErr
# Save the file
filePath = os.path.join(self.outputImgDir, zkFileName)
wfsData = self._getWfErrValuesAndStackToMatrix(reorderedWfErrMap)
header = "The followings are ZK in um from z4 to z22:"
np.savetxt(filePath, wfsData, header=header)
def _transListOfWfErrToMap(self, listOfWfErr):
"""Transform the list of wavefront error to map.
Parameters
----------
listOfWfErr : list [lsst.ts.wep.ctrlIntf.SensorWavefrontData]
List of SensorWavefrontData object.
Returns
-------
dict
Calculated wavefront error. The dictionary key [str] is the
abbreviated sensor name (e.g. R22_S11). The dictionary item
[numpy.ndarray] is the averaged wavefront error (z4-z22) in um.
"""
mapSensorNameAndId = MapSensorNameAndId()
wfErrMap = dict()
for sensorWavefrontData in listOfWfErr:
sensorId = sensorWavefrontData.getSensorId()
sensorNameList = mapSensorNameAndId.mapSensorIdToName(sensorId)[0]
sensorName = sensorNameList[0]
avgErrInUm = sensorWavefrontData.getAnnularZernikePoly()
wfErrMap[sensorName] = avgErrInUm
return wfErrMap
def _getWfErrValuesAndStackToMatrix(self, wfErrMap):
"""Get the wavefront errors and stack them to be a matrix.
Parameters
----------
wfErrMap : dict
Calculated wavefront error. The dictionary key [str] is the
abbreviated sensor name (e.g. R22_S11). The dictionary item
[numpy.ndarray] is the averaged wavefront error (z4-z22) in um.
Returns
-------
numpy.ndarray
Wavefront errors as a matrix. The column is z4-z22 in um. The row
is the individual sensor. The order is the same as the input of
wfErrMap.
"""
numOfZk = self.getNumOfZk()
valueMatrix = np.empty((0, numOfZk))
for wfErr in wfErrMap.values():
valueMatrix = np.vstack((valueMatrix, wfErr))
return valueMatrix
class Instrument(object):
def __init__(self, instDir):
"""Instrument class for wavefront estimation.
Parameters
----------
instDir : str
Instrument configuration directory.
"""
self.instDir = instDir
self.instName = ""
self.dimOfDonutImg = 0
self.announcedDefocalDisInMm = 0.0
self.instParamFile = ParamReader()
self.maskParamFile = ParamReader()
self.xSensor = np.array([])
self.ySensor = np.array([])
self.xoSensor = np.array([])
self.yoSensor = np.array([])
def config(
self,
camType,
dimOfDonutImgOnSensor,
announcedDefocalDisInMm=1.5,
instParamFileName="instParam.yaml",
maskMigrateFileName="maskMigrate.yaml",
):
"""Do the configuration of Instrument.
Parameters
----------
camType : enum 'CamType'
Camera type.
dimOfDonutImgOnSensor : int
Dimension of donut image on sensor in pixel.
announcedDefocalDisInMm : float
Announced defocal distance in mm. It is noted that the defocal
distance offset used in calculation might be different from this
value. (the default is 1.5.)
instParamFileName : str, optional
Instrument parameter file name. (the default is "instParam.yaml".)
maskMigrateFileName : str, optional
Mask migration (off-axis correction) file name. (the default is
"maskMigrate.yaml".)
"""
self.instName = self._getInstName(camType)
self.dimOfDonutImg = int(dimOfDonutImgOnSensor)
self.announcedDefocalDisInMm = announcedDefocalDisInMm
# Path of instrument param file
instFileDir = self.getInstFileDir()
instParamFilePath = os.path.join(instFileDir, instParamFileName)
self.instParamFile.setFilePath(instParamFilePath)
# Path of mask off-axis correction file
# There is no such file for auxiliary telescope
maskParamFilePath = os.path.join(instFileDir, maskMigrateFileName)
if os.path.exists(maskParamFilePath):
self.maskParamFile.setFilePath(maskParamFilePath)
self._setSensorCoor()
self._setSensorCoorAnnular()
def _getInstName(self, camType):
"""Get the instrument name.
Parameters
----------
camType : enum 'CamType'
Camera type.
Returns
-------
str
Instrument name.
Raises
------
ValueError
Camera type is not supported.
"""
if camType == CamType.LsstCam:
return "lsst"
elif camType == CamType.LsstFamCam:
return "lsstfam"
elif camType == CamType.ComCam:
return "comcam"
elif camType == CamType.AuxTel:
return "auxTel"
else:
raise ValueError("Camera type (%s) is not supported." % camType)
def getInstFileDir(self):
"""Get the instrument parameter file directory.
Returns
-------
str
Instrument parameter file directory.
"""
return os.path.join(self.instDir, self.instName)
def _setSensorCoor(self):
"""Set the sensor coordinate."""
# 0.5 is the half of single pixel
ySensorGrid, xSensorGrid = np.mgrid[-(self.dimOfDonutImg / 2 - 0.5):(
self.dimOfDonutImg / 2 +
0.5), -(self.dimOfDonutImg / 2 - 0.5):(self.dimOfDonutImg / 2 +
0.5), ]
sensorFactor = self.getSensorFactor()
denominator = self.dimOfDonutImg / 2 / sensorFactor
self.xSensor = xSensorGrid / denominator
self.ySensor = ySensorGrid / denominator
def _setSensorCoorAnnular(self):
"""Set the sensor coordinate with the annular aperature."""
self.xoSensor = self.xSensor.copy()
self.yoSensor = self.ySensor.copy()
# Get the position index that is out of annular aperature range
obscuration = self.getObscuration()
r2Sensor = self.xSensor**2 + self.ySensor**2
idx = (r2Sensor > 1) | (r2Sensor < obscuration**2)
# Define the value to be NaN if it is not in pupul
self.xoSensor[idx] = np.nan
self.yoSensor[idx] = np.nan
def setAnnDefocalDisInMm(self, annDefocalDisInMm):
"""Set the announced defocal distance in mm.
Parameters
----------
annDefocalDisInMm : float
Announced defocal distance in mm.
"""
self.announcedDefocalDisInMm = annDefocalDisInMm
def getAnnDefocalDisInMm(self):
"""Get the announced defocal distance in mm.
Returns
-------
float
Announced defocal distance in mm.
"""
return self.announcedDefocalDisInMm
def getInstFilePath(self):
"""Get the instrument parameter file path.
Returns
-------
str
Instrument parameter file path.
"""
return self.instParamFile.getFilePath()
def getMaskOffAxisCorr(self):
"""Get the mask off-axis correction.
Returns
-------
numpy.ndarray
Mask off-axis correction.
"""
return self.maskParamFile.getMatContent()
def getDimOfDonutOnSensor(self):
"""Get the dimension of donut image size on sensor in pixel.
Returns
-------
int
Dimension of donut's size on sensor in pixel.
"""
return self.dimOfDonutImg
def getObscuration(self):
"""Get the obscuration.
Returns
-------
float
Obscuration.
"""
return self.instParamFile.getSetting("obscuration")
def getFocalLength(self):
"""Get the focal length of telescope in meter.
Returns
-------
float
Focal length of telescope in meter.
"""
return self.instParamFile.getSetting("focalLength")
def getApertureDiameter(self):
"""Get the aperture diameter in meter.
Returns
-------
float
Aperture diameter in meter.
"""
return self.instParamFile.getSetting("apertureDiameter")
def getDefocalDisOffset(self):
"""Get the defocal distance offset in meter.
Returns
-------
float
Defocal distance offset in meter.
"""
# Use this way to differentiate the announced defocal distance
# and the used defocal distance in the fitting of parameters by ZEMAX
# For example, in the ComCam's instParam.yaml file, they are a little
# different
offset = self.instParamFile.getSetting("offset")
defocalDisInMm = "%.1fmm" % self.announcedDefocalDisInMm
return offset[defocalDisInMm]
def getCamPixelSize(self):
"""Get the camera pixel size in meter.
Returns
-------
float
Camera pixel size in meter.
"""
return self.instParamFile.getSetting("pixelSize")
def getMarginalFocalLength(self):
"""Get the marginal focal length in meter.
Marginal_focal_length = sqrt(f^2 - (D/2)^2)
Returns
-------
float
Marginal focal length in meter.
"""
focalLength = self.getFocalLength()
apertureDiameter = self.getApertureDiameter()
marginalFL = np.sqrt(focalLength**2 - (apertureDiameter / 2)**2)
return marginalFL
def getSensorFactor(self):
"""Get the sensor factor.
Returns
-------
float
Sensor factor.
"""
offset = self.getDefocalDisOffset()
apertureDiameter = self.getApertureDiameter()
focalLength = self.getFocalLength()
pixelSize = self.getCamPixelSize()
sensorFactor = self.dimOfDonutImg / (offset * apertureDiameter /
focalLength / pixelSize)
return sensorFactor
def getSensorCoor(self):
"""Get the sensor coordinate.
Returns
-------
numpy.ndarray
X coordinate.
numpy.ndarray
Y coordinate.
"""
return self.xSensor, self.ySensor
def getSensorCoorAnnular(self):
"""Get the sensor coordinate with the annular aperature.
Returns
-------
numpy.ndarray
X coordinate.
numpy.ndarray
Y coordinate.
"""
return self.xoSensor, self.yoSensor
def calcSizeOfDonutExpected(self):
"""Calculate the size of expected donut (diameter).
Returns
-------
float
Size of expected donut (diameter) in pixel.
"""
offset = self.getDefocalDisOffset()
fNumber = self.getFocalLength() / self.getApertureDiameter()
pixelSize = self.getCamPixelSize()
return offset / fNumber / pixelSize
class WEPCalculation(object):
"""Base class for converting the wavefront images into wavefront errors.
There will be different implementations of this for different
types of CCDs (normal, full array mode, comcam, cmos, shwfs).
"""
def __init__(self, astWcsSol, camType, isrDir,
settingFileName="default.yaml"):
"""Construct an WEP calculation object.
Parameters
----------
astWcsSol : AstWcsSol
AST world coordinate system (WCS) solution.
camType : enum 'CamType'
Camera type.
isrDir : str
Instrument signature remocal (ISR) directory. This directory will
have the input and output that the data butler needs.
settingFileName : str, optional
Setting file name. (the default is "default.yaml".)
"""
super().__init__()
# This attribute is just a stakeholder here since there is no detail of
# AST WCS solution yet
self.astWcsSol = astWcsSol
# ISR directory that the data butler uses
self.isrDir = isrDir
# Number of processors for WEP to use
# This is just a stakeholder at this moment
self.numOfProc = 1
# Boresight infomation
self.raInDeg = 0.0
self.decInDeg = 0.0
# Sky rotation angle
self.rotSkyPos = 0.0
# Sky information file for the temporary use
self.skyFile = ""
# Default setting file
settingFilePath = os.path.join(getConfigDir(), settingFileName)
self.settingFile = ParamReader(filePath=settingFilePath)
# Configure the WEP controller
self.wepCntlr = self._configWepController(camType, settingFileName)
def _configWepController(self, camType, settingFileName):
"""Configure the WEP controller.
WEP: wavefront estimation pipeline.
Parameters
----------
camType : enum 'CamType'
Camera type.
settingFileName : str
Setting file name.
Returns
-------
WepController
Configured WEP controller.
"""
dataCollector = CamDataCollector(self.isrDir)
isrWrapper = CamIsrWrapper(self.isrDir)
bscDbType = self._getBscDbType()
sourSelc = self._configSourceSelector(camType, bscDbType,
settingFileName)
sourProc = SourceProcessor(settingFileName=settingFileName)
wfsEsti = self._configWfEstimator(camType)
wepCntlr = WepController(dataCollector, isrWrapper, sourSelc,
sourProc, wfsEsti)
return wepCntlr
def _getBscDbType(self):
"""Get the bright star catalog (BSC) database type.
Returns
-------
enum 'BscDbType'
BSC database type.
Raises
------
ValueError
The bscDb is not supported.
"""
bscDb = self.settingFile.getSetting("bscDb")
if (bscDb == "localDb"):
return BscDbType.LocalDb
elif (bscDb == "file"):
return BscDbType.LocalDbForStarFile
else:
raise ValueError("The bscDb (%s) is not supported." % bscDb)
def _configSourceSelector(self, camType, bscDbType, settingFileName):
"""Configue the source selector.
Parameters
----------
camType : enum 'CamType'
Camera type.
bscDbType : enum 'BscDbType'
Bright star catalog (BSC) database type.
settingFileName : str
Setting file name.
Returns
-------
SourceSelector
Configured source selector.
Raises
------
ValueError
WEPCalculation does not support this bscDbType yet.
"""
sourSelc = SourceSelector(camType, bscDbType,
settingFileName=settingFileName)
sourSelc.setFilter(FilterType.REF)
if (bscDbType == BscDbType.LocalDbForStarFile):
dbAdress = os.path.join(getModulePath(), "tests", "testData",
"bsc.db3")
sourSelc.connect(dbAdress)
else:
raise ValueError("WEPCalculation does not support %s yet." % bscDbType)
return sourSelc
def _configWfEstimator(self, camType):
"""Configure the wavefront estimator.
Returns
-------
WfEstimator
Configured wavefront estimator.
"""
configDir = getConfigDir()
instDir = os.path.join(configDir, "cwfs", "instData")
algoDir = os.path.join(configDir, "cwfs", "algo")
wfsEsti = WfEstimator(instDir, algoDir)
solver = self.settingFile.getSetting("poissonSolver")
opticalModel = self.settingFile.getSetting("opticalModel")
defocalDisInMm = self.settingFile.getSetting("dofocalDistInMm")
donutImgSizeInPixel = self.settingFile.getSetting("donutImgSizeInPixel")
wfsEsti.config(solver=solver, camType=camType,
opticalModel=opticalModel,
defocalDisInMm=defocalDisInMm,
sizeInPix=donutImgSizeInPixel)
return wfsEsti
def getWepCntlr(self):
"""Get the configured WEP controller.
Returns
-------
WepController
Configured WEP controller.
"""
return self.wepCntlr
def disconnect(self):
"""Disconnect the database."""
sourSelc = self.wepCntlr.getSourSelc()
sourSelc.disconnect()
def getIsrDir(self):
"""Get the instrument signature removal (ISR) directory.
This directory will have the input and output that the data butler
needs.
Returns
-------
str
ISR directory.
"""
return self.isrDir
def setSkyFile(self, skyFile):
"""Set the sky information file.
This is a temporary function to set the star file generated by
ts_tcs_wep_phosim module to do the query. This function will be removed
after we begin to integrate the bright star catalog database.
Parameters
----------
skyFile : str
Sky information file.
"""
self.skyFile = skyFile
def getSkyFile(self):
"""Get the sky information file.
Returns
-------
str
Sky information file.
"""
return self.skyFile
def setWcsData(self, wcsData):
"""Set the WCS data.
Parameters
----------
wcsData : WcsData
WCS data used in the WCS solution.
"""
self.astWcsSol.setWcsData(wcsData)
def setFilter(self, filterType):
"""Set the current filter.
Parameters
----------
filterType : enum 'FilterType'
The new filter configuration to use for WEP data processing.
"""
sourSelc = self.wepCntlr.getSourSelc()
sourSelc.setFilter(filterType)
def getFilter(self):
"""Get the current filter.
Returns
-------
enum 'FilterType'
The current filter configuration to use for WEP data processing.
"""
sourSelc = self.wepCntlr.getSourSelc()
return sourSelc.getFilter()
def setBoresight(self, raInDeg, decInDeg):
"""Set the boresight (ra, dec) in degree from the pointing component.
The cooridinate system of pointing component is the international
cannabinoid research society (ICRS).
Parameters
----------
raInDeg : float
Right ascension in degree. The value should be in (0, 360).
decInDeg : float
Declination in degree. The value should be in (-90, 90).
"""
self.raInDeg = raInDeg
self.decInDeg = decInDeg
def getBoresight(self):
"""Get the boresight (ra, dec) defined in the international
cannabinoid research society (ICRS).
Returns
-------
raInDeg : float
Right ascension in degree. The value should be in (0, 360).
decInDeg : float
Declination in degree. The value should be in (-90, 90).
"""
return self.raInDeg, self.decInDeg
def setRotAng(self, rotAngInDeg):
"""Set the camera rotation angle in degree from the camera rotator
control system.
Parameters
----------
rotAngInDeg : float
The camera rotation angle in degree (-90 to 90).
"""
# In the WCS solution provided by SIMS team, the input angle is sky
# rotation angle. We do not know its relationship with the camera
# rotation angle yet.
self.rotSkyPos = rotAngInDeg
def getRotAng(self):
"""Get the camera rotation angle in degree defined in the camera
rotator control system.
Returns
-------
float
The camera rotation angle in degree.
"""
# In the WCS solution provided by SIMS team, the input angle is sky
# rotation angle. We do not know its relationship with the camera
# rotation angle yet.
return self.rotSkyPos
def setNumOfProc(self, numOfProc):
"""Set the number of processor
Parameters
----------
numOfProc : int
Number of processor.
Raises
------
ValueError
Number of processor should be >=1.
"""
# Discuss with Chris that we put this into the configuration file or
# not
if (numOfProc < 1):
raise ValueError("Number of processor should be >=1.")
self.numOfProc = numOfProc
def calculateWavefrontErrors(self, rawExpData, extraRawExpData=None):
"""Calculate the wavefront errors.
Parameters
----------
rawExpData : RawExpData
Raw exposure data for the corner wavefront sensor. If the input of
extraRawExpData is not None, this input will be the intra-focal raw
exposure data.
extraRawExpData : RawExpData, optional
This is the extra-focal raw exposure data if not None. (the default
is None.)
Returns
-------
list[SensorWavefrontData]
List of SensorWavefrontData object.
Raises
------
ValueError
Corner WFS is not supported yet.
ValueError
Only single visit is allowed at this time.
"""
if (extraRawExpData is None):
raise ValueError("Corner WFS is not supported yet.")
if (len(rawExpData.getVisit()) != 1):
raise ValueError("Only single visit is allowed at this time.")
# Ingest the exposure data and do the ISR
self._ingestImg(rawExpData)
if (extraRawExpData is not None):
self._ingestImg(extraRawExpData)
self._doIsr(isrConfigfileName="isr_config.py")
# Set the butler inputs path to get the post-ISR CCD
rerunName = self._getIsrRerunName()
postIsrCcdDir = os.path.join(self.isrDir, "rerun", rerunName)
self.wepCntlr.setPostIsrCcdInputs(postIsrCcdDir)
# Get the target stars map neighboring stars
neighborStarMap = self._getTargetStar()
# Calculate the wavefront error
intraObsIdList = rawExpData.getVisit()
intraObsId = intraObsIdList[0]
if (extraRawExpData is None):
obsIdList = [intraObsId]
else:
extraObsIdList = extraRawExpData.getVisit()
extraObsId = extraObsIdList[0]
obsIdList = [intraObsId, extraObsId]
donutMap = self._calcWfErr(neighborStarMap, obsIdList)
listOfWfErr = self._populateListOfSensorWavefrontData(donutMap)
return listOfWfErr
def _ingestImg(self, rawExpData):
"""Ingest the images.
Parameters
----------
rawExpData : RawExpData
Raw exposure data.
"""
dataCollector = self.wepCntlr.getDataCollector()
rawExpDirList = rawExpData.getRawExpDir()
for rawExpDir in rawExpDirList:
rawImgFiles = os.path.join(rawExpDir, "*.fits")
dataCollector.ingestImages(rawImgFiles)
def _doIsr(self, isrConfigfileName):
"""Do the instrument signature removal (ISR).
Parameters
----------
isrConfigfileName : str
ISR configuration file name.
"""
isrWrapper = self.wepCntlr.getIsrWrapper()
isrWrapper.config(doFlat=True, fileName=isrConfigfileName)
rerunName = self._getIsrRerunName()
isrWrapper.doISR(self.isrDir, rerunName=rerunName)
def _getIsrRerunName(self):
"""Get the instrument signature removal (ISR) rerun name.
Returns
-------
str
ISR rerun name.
"""
return self.settingFile.getSetting("rerunName")
def _getTargetStar(self):
"""Get the target stars
Returns
-------
dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
Raises
------
ValueError
BSC database is not supported.
"""
sourSelc = self.wepCntlr.getSourSelc()
sourSelc.setObsMetaData(self.raInDeg, self.decInDeg, self.rotSkyPos)
camDimOffset = self.settingFile.getSetting("camDimOffset")
bscDbType = self._getBscDbType()
if (bscDbType == BscDbType.LocalDb):
return sourSelc.getTargetStar(offset=camDimOffset)[0]
elif (bscDbType == BscDbType.LocalDbForStarFile):
return sourSelc.getTargetStarByFile(self.skyFile,
offset=camDimOffset)[0]
else:
raise ValueError("BSC database (%s) is not supported." % bscDbType)
def _calcWfErr(self, neighborStarMap, obsIdList):
"""Calculate the wavefront error.
Only consider one intra-focal and one extra-focal images at this
moment
Parameters
----------
neighborStarMap : dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
obsIdList : list[int]
Observation Id list in [intraObsId, extraObsId]. If the input is
[intraObsId], this means the corner WFS.
Returns
-------
dict
Donut image map with the calculated wavefront error. The dictionary
key is the sensor name. The dictionary item is the donut image
(type: DonutImage).
"""
sensorNameList = list(neighborStarMap)
wfsImgMap = self.wepCntlr.getPostIsrImgMapByPistonDefocal(
sensorNameList, obsIdList)
doDeblending = self.settingFile.getSetting("doDeblending")
donutMap = self.wepCntlr.getDonutMap(
neighborStarMap, wfsImgMap, self.getFilter(),
doDeblending=doDeblending)
donutMap = self.wepCntlr.calcWfErr(donutMap)
return donutMap
def _populateListOfSensorWavefrontData(self, donutMap):
"""Populate the list of sensor wavefront data.
Parameters
----------
donutMap : dict
Donut image map with the calculated wavefront error. The dictionary
key is the sensor name. The dictionary item is the donut image
(type: DonutImage).
Returns
-------
list[SensorWavefrontData]
List of SensorWavefrontData object.
"""
mapSensorNameAndId = MapSensorNameAndId()
listOfWfErr = []
for sensor, donutList in donutMap.items():
sensorWavefrontData = SensorWavefrontData()
# Set the sensor Id
abbrevSensor = abbrevDectectorName(sensor)
sensorIdList = mapSensorNameAndId.mapSensorNameToId(abbrevSensor)
sensorId = sensorIdList[0]
sensorWavefrontData.setSensorId(sensorId)
sensorWavefrontData.setListOfDonut(donutList)
# Set the average zk in um
avgErrInNm = self.wepCntlr.calcAvgWfErrOnSglCcd(donutList)
avgErrInUm = avgErrInNm * 1e-3
sensorWavefrontData.setAnnularZernikePoly(avgErrInUm)
listOfWfErr.append(sensorWavefrontData)
return listOfWfErr
def ingestCalibs(self, calibsDir):
"""Ingest the calibration products.
Parameters
----------
calibsDir : str
Calibration products directory.
"""
self._genCamMapperIfNeed()
dataCollector = self.wepCntlr.getDataCollector()
calibFiles = os.path.join(calibsDir, "*")
dataCollector.ingestCalibs(calibFiles)
def _genCamMapperIfNeed(self):
"""Generate the camera mapper file if it is needed.
The mapper file is used by the data butler.
Raises
------
ValueError
Mapper is not supported yet.
"""
mapperFile = os.path.join(self.isrDir, "_mapper")
if (not os.path.exists(mapperFile)):
dataCollector = self.wepCntlr.getDataCollector()
camMapper = self.settingFile.getSetting("camMapper")
if (camMapper == "phosim"):
dataCollector.genPhoSimMapper()
else:
raise ValueError("Mapper (%s) is not supported yet." % camMapper)
class SourceSelector(object):
def __init__(self, camType, bscDbType, settingFileName="default.yaml"):
"""Initialize the source selector class.
Parameters
----------
camType : enum 'CamType'
Camera type.
bscDbType : enum 'BscDbType'
Bright star catalog (BSC) database type.
settingFileName : str, optional
Setting file name (the default is "default.yaml".)
"""
self.camera = CamFactory.createCam(camType)
self.db = DatabaseFactory.createDb(bscDbType)
self.filter = Filter()
self.maxDistance = 0.0
self.maxNeighboringStar = 0
settingFilePath = os.path.join(getConfigDir(), settingFileName)
self.settingFile = ParamReader(filePath=settingFilePath)
# Configurate the criteria of neighboring stars
starRadiusInPixel = self.settingFile.getSetting("starRadiusInPixel")
spacingCoefficient = self.settingFile.getSetting("spacingCoef")
maxNeighboringStar = self.settingFile.getSetting("maxNumOfNbrStar")
self.configNbrCriteria(starRadiusInPixel, spacingCoefficient,
maxNeighboringStar=maxNeighboringStar)
def configNbrCriteria(self, starRadiusInPixel, spacingCoefficient,
maxNeighboringStar=0):
"""Set the neighboring star criteria to decide the scientific target.
Parameters
----------
starRadiusInPixel : float, optional
Diameter of star. For the defocus = 1.5 mm, the star's radius is
63 pixel.
spacingCoefficient : float, optional
Maximum distance in units of radius one donut must be considered
as a neighbor.
maxNeighboringStar : int, optional
Maximum number of neighboring stars. (the default is 0.)
"""
self.maxDistance = starRadiusInPixel * spacingCoefficient
self.maxNeighboringStar = int(maxNeighboringStar)
def connect(self, *kwargs):
"""Connect the database.
Parameters
----------
*kwargs : str or *list
Information to connect to the database.
"""
self.db.connect(*kwargs)
def disconnect(self):
"""Disconnect the database."""
self.db.disconnect()
def setFilter(self, filterType):
"""Set the filter type.
Parameters
----------
filterType : FilterType
Filter type.
"""
self.filter.setFilter(filterType)
def getFilter(self):
"""Get the filter type.
Returns
-------
FilterType
Filter type.
"""
return self.filter.getFilter()
def setObsMetaData(self, ra, dec, rotSkyPos):
"""Set the observation meta data.
Parameters
----------
ra : float
Pointing ra in degree.
dec : float
Pointing decl in degree.
rotSkyPos : float
The orientation of the telescope in degrees.
"""
mjd = self.settingFile.getSetting("cameraMJD")
self.camera.setObsMetaData(ra, dec, rotSkyPos, mjd=mjd)
def getTargetStar(self, offset=0):
"""Get the target stars by querying the database.
Parameters
----------
offset : float, optional
Offset to the dimension of camera. If the detector dimension is 10
(assume 1-D), the star's position between -offset and 10+offset
will be seem to be on the detector. (the default is 0.)
Returns
-------
dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
dict
Information of stars with the name of sensor as a dictionary.
dict
(ra, dec) of four corners of each sensor with the name
of sensor as a list. The dictionary key is the sensor name.
"""
wavefrontSensors = self.camera.getWavefrontSensor()
lowMagnitude, highMagnitude = self.filter.getMagBoundary()
# Map the reference filter to the G filter
filterType = self.getFilter()
mappedFilterType = mapFilterRefToG(filterType)
# Query the star database
starMap = dict()
neighborStarMap = dict()
for detector, wavefrontSensor in wavefrontSensors.items():
# Get stars in this wavefront sensor for this observation field
stars = self.db.query(mappedFilterType, wavefrontSensor[0],
wavefrontSensor[1], wavefrontSensor[2],
wavefrontSensor[3])
# Set the detector information for the stars
stars.setDetector(detector)
# Populate pixel information for stars
populatedStar = self.camera.populatePixelFromRADecl(stars)
# Get the stars that are on the detector
starsOnDet = self.camera.getStarsOnDetector(populatedStar, offset)
starMap[detector] = starsOnDet
# Check the candidate of bright stars based on the magnitude
indexCandidate = starsOnDet.checkCandidateStars(
mappedFilterType, lowMagnitude, highMagnitude)
# Determine the neighboring stars based on the distance and
# allowed number of neighboring stars
neighborStar = starsOnDet.getNeighboringStar(
indexCandidate, self.maxDistance, mappedFilterType,
self.maxNeighboringStar)
neighborStarMap[detector] = neighborStar
# Remove the data that has no bright star
self._rmDataWithoutBrightStar(neighborStarMap, starMap,
wavefrontSensors)
return neighborStarMap, starMap, wavefrontSensors
def _rmDataWithoutBrightStar(self, neighborStarMap, starMap,
wavefrontSensors):
"""Remove the data that has no bright stars on the detector.
The data in inputs will be changed directly.
Parameters
----------
neighborStarMap : dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
starMap : dict
Information of stars with the name of sensor as a dictionary.
wavefrontSensors : dict
(ra, dec) of four corners of each sensor with the name
of sensor as a list. The dictionary key is the sensor name.
"""
# Collect the sensor list without the bright star
noStarSensorList = []
for detector, stars in neighborStarMap.items():
if (len(stars.getId()) == 0):
noStarSensorList.append(detector)
# Remove the data in map
for detector in noStarSensorList:
neighborStarMap.pop(detector)
starMap.pop(detector)
wavefrontSensors.pop(detector)
def getTargetStarByFile(self, skyFilePath, offset=0):
"""Get the target stars by querying the star file.
This function is only for the test. This shall be removed in the final.
Parameters
----------
skyFilePath : str
Sky data file path.
offset : float, optional
Offset to the dimension of camera. If the detector dimension is 10
(assume 1-D), the star's position between -offset and 10+offset
will be seem to be on the detector. (the default is 0.)
Returns
-------
dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
dict
Information of stars with the name of sensor as a dictionary.
dict
(ra, dec) of four corners of each sensor with the name
of sensor as a list. The dictionary key is the sensor name.
Raises
------
TypeError
The database type is incorrect.
"""
if (not isinstance(self.db, LocalDatabaseForStarFile)):
raise TypeError("The database type is incorrect.")
# Map the reference filter to the G filter
filterType = self.getFilter()
mappedFilterType = mapFilterRefToG(filterType)
# Write the sky data into the temporary table
self.db.createTable(mappedFilterType)
self.db.insertDataByFile(skyFilePath, mappedFilterType, skiprows=1)
neighborStarMap, starMap, wavefrontSensors = self.getTargetStar(offset=offset)
# Delete the table
self.db.deleteTable(mappedFilterType)
return neighborStarMap, starMap, wavefrontSensors
class M1M3Sim(MirrorSim):
def __init__(self):
"""Initiate the M1M3 simulator class."""
# M2 setting file
configDir = os.path.join(getConfigDir(), "M1M3")
settingFilePath = os.path.join(configDir, "m1m3Setting.yaml")
self._m1m3SettingFile = ParamReader(filePath=settingFilePath)
# Inner and outer radius of M1 mirror in m
radiusM1Inner = self._m1m3SettingFile.getSetting("radiusM1Inner")
radiusM1Outer = self._m1m3SettingFile.getSetting("radiusM1Outer")
# Inner and outer radius of M3 mirror in m
radiusM3Inner = self._m1m3SettingFile.getSetting("radiusM3Inner")
radiusM3Outer = self._m1m3SettingFile.getSetting("radiusM3Outer")
super(M1M3Sim,
self).__init__((radiusM1Inner, radiusM3Inner),
(radiusM1Outer, radiusM3Outer), configDir)
# Mirror surface bending mode grid file
self._gridFile = ParamReader()
# FEA model file
self._feaFile = ParamReader()
# FEA model data in zenith angle
self._feaZenFile = ParamReader()
# FEA model data in horizontal angle
self._feaHorFile = ParamReader()
# Actuator forces along zenith direction
self._forceZenFile = ParamReader()
# Actuator forces along horizon direction
self._forceHorFile = ParamReader()
# Influence matrix of actuator forces
self._forceInflFile = ParamReader()
self._config("M1M3_1um_156_force.yaml", "M1M3_LUT.yaml",
"M1M3_1um_156_grid.yaml", "M1M3_thermal_FEA.yaml",
"M1M3_dxdydz_zenith.yaml", "M1M3_dxdydz_horizon.yaml",
"M1M3_force_zenith.yaml", "M1M3_force_horizon.yaml",
"M1M3_influence_256.yaml")
def _config(self, actForceFileName, lutFileName, gridFileName, feaFileName,
feaZenFileName, feaHorFileName, forceZenFileName,
forceHorFileName, forceInflFileName):
"""Do the configuration.
LUT: Look-up table.
FEA: Finite element analysis.
Parameters
----------
actForceFileName : str
Actuator force file name.
lutFileName : str
LUT file name.
gridFileName : str
File name of bending mode data.
feaFileName : str
FEA model data file name.
feaZenFileName : str
FEA model data file name in zenith angle.
feaHorFileName : str
FEA model data file name in horizontal angle.
forceZenFileName : str
File name of actuator forces along zenith direction.
forceHorFileName : str
File name of actuator forces along horizon direction.
forceInflFileName : str
Influence matrix of actuator forces.
"""
numTerms = self._m1m3SettingFile.getSetting("numTerms")
super(M1M3Sim, self).config(numTerms=numTerms,
actForceFileName=actForceFileName,
lutFileName=lutFileName)
mirrorDataDir = self.getMirrorDataDir()
gridFilePath = os.path.join(mirrorDataDir, gridFileName)
self._gridFile.setFilePath(gridFilePath)
feaFilePath = os.path.join(mirrorDataDir, feaFileName)
self._feaFile.setFilePath(feaFilePath)
feaZenFilePath = os.path.join(mirrorDataDir, feaZenFileName)
self._feaZenFile.setFilePath(feaZenFilePath)
feaHorFilePath = os.path.join(mirrorDataDir, feaHorFileName)
self._feaHorFile.setFilePath(feaHorFilePath)
forceZenFilePath = os.path.join(mirrorDataDir, forceZenFileName)
self._forceZenFile.setFilePath(forceZenFilePath)
forceHorFilePath = os.path.join(mirrorDataDir, forceHorFileName)
self._forceHorFile.setFilePath(forceHorFilePath)
forceInflFilePath = os.path.join(mirrorDataDir, forceInflFileName)
self._forceInflFile.setFilePath(forceInflFilePath)
def getPrintthz(self, zAngleInRadian, preCompElevInRadian=0):
"""Get the mirror print in m along z direction in specific zenith
angle.
FEA: Finite element analysis.
Parameters
----------
zAngleInRadian : float
Zenith angle in radian.
preCompElevInRadian : float, optional
Pre-compensation elevation angle in radian. (the default is 0.)
Returns
-------
numpy.ndarray
Corrected projection in m along z direction.
"""
# Data needed to determine gravitational print through
data = self._feaZenFile.getMatContent()
zdx = data[:, 0]
zdy = data[:, 1]
zdz = data[:, 2]
data = self._feaHorFile.getMatContent()
hdx = data[:, 0]
hdy = data[:, 1]
hdz = data[:, 2]
# Do the M1M3 gravitational correction.
# Map the changes of dx, dy, and dz on a plane for certain zenith angle
printthxInM = zdx * np.cos(zAngleInRadian) + \
hdx * np.sin(zAngleInRadian)
printthyInM = zdy * np.cos(zAngleInRadian) + \
hdy * np.sin(zAngleInRadian)
printthzInM = zdz * np.cos(zAngleInRadian) + \
hdz * np.sin(zAngleInRadian)
# Get the bending mode information
idx1, idx3, bx, by, bz = self._getMirCoor()
# Calcualte the mirror ideal shape
zRef = self._calcIdealShape(bx * 1000, by * 1000, idx1, idx3) / 1000
# Calcualte the mirror ideal shape with the displacement
zpRef = self._calcIdealShape(
(bx + printthxInM) * 1000,
(by + printthyInM) * 1000, idx1, idx3) / 1000
# Convert printthz into surface sag to get the estimated wavefront
# error.
# Do the zenith angle correction by the linear approximation with the
# ideal shape.
printthzInM = printthzInM - (zpRef - zRef)
# Normalize the coordinate
Ri = self.getInnerRinM()[0]
R = self.getOuterRinM()[0]
normX = bx / R
normY = by / R
obs = Ri / R
# Fit the annular Zernike polynomials z0-z2 (piton, x-tilt, y-tilt)
zc = ZernikeAnnularFit(printthzInM, normX, normY, 3, obs)
# Do the estimated wavefront error correction for the mirror projection
printthzInM -= ZernikeAnnularEval(zc, normX, normY, obs)
return printthzInM
def _getMirCoor(self):
"""Get the mirror coordinate and node.
Returns
-------
numpy.ndarray[int]
M1 node.
numpy.ndarray[int]
M3 node.
numpy.ndarray
x coordinate.
numpy.ndarray
y coordinate.
numpy.ndarray
z coordinate.
"""
# Get the bending mode information
data = self._gridFile.getMatContent()
nodeID = data[:, 0].astype("int")
nodeM1 = (nodeID == 1)
nodeM3 = (nodeID == 3)
bx = data[:, 1]
by = data[:, 2]
bz = data[:, 3:]
return nodeM1, nodeM3, bx, by, bz
def _calcIdealShape(self,
xInMm,
yInMm,
idxM1,
idxM3,
dr1=0,
dr3=0,
dk1=0,
dk3=0):
"""Calculate the ideal shape of mirror along z direction.
This is described by a series of cylindrically-symmetric aspheric
surfaces.
Parameters
----------
xInMm : numpy.ndarray
Coordinate x in 1D array in mm.
yInMm : numpy.ndarray
Coordinate y in 1D array in mm.
idxM1 : numpy.ndarray [int]
M1 node.
idxM3 : numpy.ndarray [int]
M3 node.
dr1 : float, optional
Displacement of r in mirror 1. (the default is 0.)
dr3 : float, optional
Displacement of r in mirror 3. (the default is 0.)
dk1 : float, optional
Displacement of kappa (k) in mirror 1. (the default is 0.)
dk3 : float, optional
Displacement of kappa (k) in mirror 3. (the default is 0.)
Returns
-------
numpy.ndarray
Ideal mirror surface along z direction.
Raises
------
ValueError
X is unequal to y.
"""
# M1 optical design
r1 = -1.9835e4
k1 = -1.215
alpha1 = np.zeros((8, 1))
alpha1[2] = 1.38e-24
# M3 optical design
r3 = -8344.5
k3 = 0.155
alpha3 = np.zeros((8, 1))
alpha3[2] = -4.5e-22
alpha3[3] = -8.15e-30
# Get the dimension of input xInMm, yInMm
nr = xInMm.shape
mr = yInMm.shape
if (nr != mr):
raise ValueError("X[%d] is unequal to y[%d]." % (nr, mr))
# Calculation the curvature (c) and conic constant (kappa)
# Mirror 1 (M1)
c1 = 1 / (r1 + dr1)
k1 = k1 + dk1
# Mirror 3 (M3)
c3 = 1 / (r3 + dr3)
k3 = k3 + dk3
# Construct the curvature, kappa, and alpha matrixes for the ideal
# shape calculation
cMat = np.zeros(nr)
cMat[idxM1] = c1
cMat[idxM3] = c3
kMat = np.zeros(nr)
kMat[idxM1] = k1
kMat[idxM3] = k3
alphaMat = np.tile(np.zeros(nr), (8, 1))
for ii in range(8):
alphaMat[ii, idxM1] = alpha1[ii]
alphaMat[ii, idxM3] = alpha3[ii]
# Calculate the radius
r2 = xInMm**2 + yInMm**2
# Calculate the ideal surface
# The optical elements of telescopes can often be described by a
# series of cylindrically-symmetric aspheric surfaces:
# z(r) = c * r^2/[ 1 + sqrt( 1-(1+k) * c^2 * r^2 ) ] +
# sum(ai * r^(2*i)) + sum(Aj * Zj)
# where i = 1-8, j = 1-N
z0 = cMat * r2 / (1 + np.sqrt(1 - (1 + kMat) * cMat**2 * r2))
for ii in range(8):
z0 += alphaMat[ii, :] * r2**(ii + 1)
# M3 vertex offset from M1 vertex, values from Zemax model
# M3voffset = (233.8 - 233.8 - 900 - 3910.701 - 1345.500 + 1725.701
# + 3530.500 + 900 + 233.800)
M3voffset = 233.8
# Add the M3 offset (sum(Aj * Zj), j = 1 - N)
z0[idxM3] = z0[idxM3] + M3voffset
# In Zemax, z axis points from M1M3 to M2. the reversed direction
# (z0>0) is needed. That means the direction of M2 to M1M3.
return -z0
def getTempCorr(self, m1m3TBulk, m1m3TxGrad, m1m3TyGrad, m1m3TzGrad,
m1m3TrGrad):
"""Get the mirror print correction along z direction for certain
temperature gradient.
FEA: Finite element analysis.
Parameters
----------
m1m3TBulk : float
Bulk temperature in degree C (+/-2sigma spans +/-0.8C).
m1m3TxGrad : float
Temperature gradient along x direction in degree C (+/-2sigma
spans 0.4C).
m1m3TyGrad : float
Temperature gradient along y direction in degree C (+/-2sigma
spans 0.4C).
m1m3TzGrad : float
Temperature gradient along z direction in degree C (+/-2sigma
spans 0.1C).
m1m3TrGrad : float
Temperature gradient along r direction in degree C (+/-2sigma
spans 0.1C).
Returns
-------
numpy.ndarray
Corrected projection in um along z direction.
"""
# Data needed to determine thermal deformation
data = self._feaFile.getMatContent()
# These are the normalized coordinates
# In the original XLS file (thermal FEA model), max(x)=164.6060 in,
# while 4.18m = 164.5669 in for real mirror. These two numbers do
# not match.
# The data here has normalized the dimension (x, y) already.
# n.b. these may not have been normalized correctly, b/c max(tx)=1.0
tx = data[:, 0]
ty = data[:, 1]
# Below are in M1M3 coordinate system, and in micron
# Do the fitting in the normalized coordinate
bx, by = self._getMirCoor()[2:4]
R = self.getOuterRinM()[0]
normX = bx / R
normY = by / R
# Fit the bulk
tbdz = self._fitData(tx, ty, data[:, 2], normX, normY)
# Fit the x-grad
txdz = self._fitData(tx, ty, data[:, 3], normX, normY)
# Fit the y-grad
tydz = self._fitData(tx, ty, data[:, 4], normX, normY)
# Fit the z-grad
tzdz = self._fitData(tx, ty, data[:, 5], normX, normY)
# Fit the r-gradß
trdz = self._fitData(tx, ty, data[:, 6], normX, normY)
# Get the temprature correction
tempCorrInUm = m1m3TBulk*tbdz + m1m3TxGrad*txdz + m1m3TyGrad*tydz + \
m1m3TzGrad*tzdz + m1m3TrGrad*trdz
return tempCorrInUm
def _fitData(self, dataX, dataY, data, x, y):
"""Fit the data by radial basis function.
Parameters
----------
dataX : numpy.ndarray
Data x.
dataY : numpy.ndarray
Data y.
data : numpy.ndarray
Data to fit.
x : numpy.ndarray
x coordinate.
y : numpy.ndarray
y coordinate.
Returns
-------
numpy.ndarray
Fitted data.
"""
# Construct the fitting model
rbfi = Rbf(dataX, dataY, data)
# Return the fitted data
return rbfi(x, y)
def getMirrorResInMmInZemax(self, writeZcInMnToFilePath=None):
"""Get the residue of surface (mirror print along z-axis) in mm under
the Zemax coordinate.
This value is after the fitting with spherical Zernike polynomials
(zk).
Parameters
----------
writeZcInMnToFilePath : str, optional
File path to write the fitted zk in mm. (the default is None.)
Returns
------
numpy.ndarray
Fitted residue in mm after removing the fitted zk terms in Zemax
coordinate.
numpy.ndarray
X position in mm in Zemax coordinate.
numpy.ndarray
Y position in mm in Zemax coordinate.
numpy.ndarray
Fitted zk in mm in Zemax coordinate.
"""
# Get the bending mode information
bx, by, bz = self._getMirCoor()[2:5]
# Transform the M1M3 coordinate to Zemax coordinate
bxInZemax, byInZemax, surfInZemax = opt2ZemaxCoorTrans(
bx, by, self.getSurfAlongZ())
# Get the mirror residue and zk in um
RinM = self.getOuterRinM()[0]
resInUmInZemax, zcInUmInZemax = self._getMirrorResInNormalizedCoor(
surfInZemax, bxInZemax / RinM, byInZemax / RinM)
# Change the unit to mm
resInMmInZemax = resInUmInZemax * 1e-3
bxInMmInZemax = bxInZemax * 1e3
byInMmInZemax = byInZemax * 1e3
zcInMmInZemax = zcInUmInZemax * 1e-3
# Save the file of fitted Zk
if (writeZcInMnToFilePath is not None):
np.savetxt(writeZcInMnToFilePath, zcInMmInZemax)
return resInMmInZemax, bxInMmInZemax, byInMmInZemax, zcInMmInZemax
def writeMirZkAndGridResInZemax(self,
resFile=[],
surfaceGridN=200,
writeZcInMnToFilePath=None):
"""Write the grid residue in mm of mirror surface after the fitting
with Zk under the Zemax coordinate.
Parameters
----------
resFile : list, optional
File path to save the grid surface residue map. (the default
is [].)
surfaceGridN : {number}, optional
Surface grid number. (the default is 200.)
writeZcInMnToFilePath : str, optional
File path to write the fitted zk in mm. (the default is None.)
Returns
-------
str
Grid residue map related data of M1.
str
Grid residue map related data of M3.
"""
# Get the residure map
resInMmInZemax, bxInMmInZemax, byInMmInZemax = \
self.getMirrorResInMmInZemax(
writeZcInMnToFilePath=writeZcInMnToFilePath)[0:3]
# Get the mirror node
idx1, idx3 = self._getMirCoor()[0:2]
# Grid sample map for M1 and M3
for ii, idx in zip((0, 1), (idx1, idx3)):
# Change the unit from m to mm
innerRinMm = self.getInnerRinM()[ii] * 1e3
outerRinMm = self.getOuterRinM()[ii] * 1e3
# Get the residue map used in Zemax
# Content header: (NUM_X_PIXELS, NUM_Y_PIXELS, delta x, delta y)
# Content: (z, dx, dy, dxdy)
content = self._gridSampInMnInZemax(resInMmInZemax[idx],
bxInMmInZemax[idx],
byInMmInZemax[idx],
innerRinMm,
outerRinMm,
surfaceGridN,
surfaceGridN,
resFile=resFile[ii])
if (ii == 0):
contentM1 = content
elif (ii == 1):
contentM3 = content
return contentM1, contentM3
def showMirResMap(self, resFile, writeToResMapFilePath=[]):
"""Show the mirror residue map.
Parameters
----------
resFile : list
File path of the grid surface residue map.
writeToResMapFilePath : list, optional
File path to save the residue map. (the default is [].)
"""
# Get the residure map
resInMmInZemax, bxInMmInZemax, byInMmInZemax = \
self.getMirrorResInMmInZemax()[0:3]
# Get the mirror node
idx1, idx3 = self._getMirCoor()[0:2]
# Show the mirror maps
RinMtuple = self.getOuterRinM()
for ii, RinM, idx in zip((0, 1), RinMtuple, (idx1, idx3)):
outerRinMm = RinM * 1e3
plotResMap(resInMmInZemax[idx],
bxInMmInZemax[idx],
byInMmInZemax[idx],
outerRinMm,
resFile=resFile[ii],
writeToResMapFilePath=writeToResMapFilePath[ii])
def genMirSurfRandErr(self,
zAngleInRadian,
m1m3ForceError=0.05,
seedNum=0):
"""Generate the mirror surface random error.
LUT: Loop-up table.
Parameters
----------
zAngleInRadian : float
Zenith angle in radian.
m1m3ForceError : float, optional
Ratio of actuator force error. (the default is 0.05.)
seedNum : int, optional
Random seed number. (the default is 0.)
Returns
-------
numpy.ndarray
Generated mirror surface random error in m.
"""
# Get the actuator forces in N of M1M3 based on the look-up table (LUT)
zangleInDeg = np.rad2deg(zAngleInRadian)
LUTforce = self.getLUTforce(zangleInDeg)
# Assume the m1m3ForceError=0.05
# Add 5% force error to the original actuator forces
# This means from -5% to +5% of original actuator's force.
np.random.seed(int(seedNum))
nActuator = len(LUTforce)
myu = (1 + 2 *
(np.random.rand(nActuator) - 0.5) * m1m3ForceError) * LUTforce
# Balance forces along z-axis
# This statement is intentionally to make the force balance.
nzActuator = int(self._m1m3SettingFile.getSetting("numActuatorInZ"))
myu[nzActuator-1] = np.sum(LUTforce[:nzActuator]) - \
np.sum(myu[:nzActuator-1])
# Balance forces along y-axis
# This statement is intentionally to make the force balance.
myu[nActuator-1] = np.sum(LUTforce[nzActuator:]) - \
np.sum(myu[nzActuator:-1])
# Get the net force along the z-axis
zf = self._forceZenFile.getMatContent()
hf = self._forceHorFile.getMatContent()
u0 = zf * np.cos(zAngleInRadian) + hf * np.sin(zAngleInRadian)
# Calculate the random surface
G = self._forceInflFile.getMatContent()
randSurfInM = G.dot(myu - u0)
return randSurfInM
class Algorithm(object):
def __init__(self, algoDir):
"""Initialize the Algorithm class.
Algorithm used to solve the transport of intensity equation to get
normal/ annular Zernike polynomials.
Parameters
----------
algoDir : str
Algorithm configuration directory.
"""
self.algoDir = algoDir
self.algoParamFile = ParamReader()
self._inst = Instrument("")
# Show the calculation message based on this value
# 0 means no message will be showed
self.debugLevel = 0
# Image has the problem or not from the over-compensation
self.caustic = False
# Record the Zk coefficients in each outer-loop iteration
# The actual total outer-loop iteration time is Num_of_outer_itr + 1
self.converge = np.array([])
# Current number of outer-loop iteration
self.currentItr = 0
# Record the coefficients of normal/ annular Zernike polynomials after
# z4 in unit of nm
self.zer4UpNm = np.array([])
# Converged wavefront.
self.wcomp = np.array([])
# Calculated wavefront in previous outer-loop iteration.
self.West = np.array([])
# Converged Zk coefficients
self.zcomp = np.array([])
# Calculated Zk coefficients in previous outer-loop iteration
self.zc = np.array([])
# Padded mask for use at the offset planes
self.pMask = None
# Non-padded mask corresponding to aperture
self.cMask = None
# Change the dimension of mask for fft to use
self.pMaskPad = None
self.cMaskPad = None
def reset(self):
"""Reset the calculation for the new input images with the same
algorithm settings."""
self.caustic = False
self.converge = np.zeros(self.converge.shape)
self.currentItr = 0
self.zer4UpNm = np.zeros(self.zer4UpNm.shape)
self.wcomp = np.zeros(self.wcomp.shape)
self.West = np.zeros(self.West.shape)
self.zcomp = np.zeros(self.zcomp.shape)
self.zc = np.zeros(self.zc.shape)
self.pMask = None
self.cMask = None
self.pMaskPad = None
self.cMaskPad = None
def config(self, algoName, inst, debugLevel=0):
"""Configure the algorithm to solve TIE.
Parameters
----------
algoName : str
Algorithm configuration file to solve the Poisson's equation in the
transport of intensity equation (TIE). It can be "fft" or "exp"
here.
inst : Instrument
Instrument to use.
debugLevel : int, optional
Show the information under the running. If the value is higher, the
information shows more. It can be 0, 1, 2, or 3. (the default is
0.)
"""
algoParamFilePath = os.path.join(self.algoDir, "%s.yaml" % algoName)
self.algoParamFile.setFilePath(algoParamFilePath)
self._inst = inst
self.debugLevel = debugLevel
self.caustic = False
numTerms = self.getNumOfZernikes()
outerItr = self.getNumOfOuterItr()
self.converge = np.zeros((numTerms, outerItr + 1))
self.currentItr = 0
self.zer4UpNm = np.zeros(numTerms - 3)
# Wavefront related parameters
dimOfDonut = self._inst.getDimOfDonutOnSensor()
self.wcomp = np.zeros((dimOfDonut, dimOfDonut))
self.West = self.wcomp.copy()
# Used in model basis ("zer").
self.zcomp = np.zeros(numTerms)
self.zc = self.zcomp.copy()
# Mask related variables
self.pMask = None
self.cMask = None
self.pMaskPad = None
self.cMaskPad = None
def setDebugLevel(self, debugLevel):
"""Set the debug level.
If the value is higher, the information shows more. It can be 0, 1, 2,
or 3.
Parameters
----------
debugLevel : int
Show the information under the running.
"""
self.debugLevel = int(debugLevel)
def getDebugLevel(self):
"""Get the debug level.
If the value is higher, the information shows more. It can be 0, 1, 2,
or 3.
Returns
-------
int
Debug level.
"""
return self.debugLevel
def getZer4UpInNm(self):
"""Get the coefficients of Zernike polynomials of z4-zn in nm.
Returns
-------
numpy.ndarray
Zernike polynomials of z4-zn in nm.
"""
return self.zer4UpNm
def getPoissonSolverName(self):
"""Get the method name to solve the Poisson equation.
Returns
-------
str
Method name to solve the Poisson equation.
"""
return self.algoParamFile.getSetting("poissonSolver")
def getNumOfZernikes(self):
"""Get the maximum number of Zernike polynomials supported.
Returns
-------
int
Maximum number of Zernike polynomials supported.
"""
return int(self.algoParamFile.getSetting("numOfZernikes"))
def getZernikeTerms(self):
"""Get the Zernike terms in using.
Returns
-------
numpy.ndarray
Zernkie terms in using.
"""
numTerms = self.getNumOfZernikes()
zTerms = np.arange(numTerms) + 1
return zTerms
def getObsOfZernikes(self):
"""Get the obscuration of annular Zernike polynomials.
Returns
-------
float
Obscuration of annular Zernike polynomials
"""
zobsR = self.algoParamFile.getSetting("obsOfZernikes")
if (zobsR == 1):
zobsR = self._inst.getObscuration()
return float(zobsR)
def getNumOfOuterItr(self):
"""Get the number of outer loop iteration.
Returns
-------
int
Number of outer loop iteration.
"""
return int(self.algoParamFile.getSetting("numOfOuterItr"))
def getNumOfInnerItr(self):
"""Get the number of inner loop iteration.
This is for the fast Fourier transform (FFT) solver only.
Returns
-------
int
Number of inner loop iteration.
"""
return int(self.algoParamFile.getSetting("numOfInnerItr"))
def getFeedbackGain(self):
"""Get the gain value used in the outer loop iteration.
Returns
-------
float
Gain value used in the outer loop iteration.
"""
return self.algoParamFile.getSetting("feedbackGain")
def getOffAxisPolyOrder(self):
"""Get the number of polynomial order supported in off-axis correction.
Returns
-------
int
Number of polynomial order supported in off-axis correction.
"""
return int(self.algoParamFile.getSetting("offAxisPolyOrder"))
def getCompensatorMode(self):
"""Get the method name to compensate the wavefront by wavefront error.
Returns
-------
str
Method name to compensate the wavefront by wavefront error.
"""
return self.algoParamFile.getSetting("compensatorMode")
def getCompSequence(self):
"""Get the compensated sequence of Zernike order for each iteration.
Returns
-------
numpy.ndarray[int]
Compensated sequence of Zernike order for each iteration.
"""
compSequenceFromFile = self.algoParamFile.getSetting("compSequence")
compSequence = np.array(compSequenceFromFile, dtype=int)
# If outerItr is large, and compSequence is too small,
# the rest in compSequence will be filled.
# This is used in the "zer" method.
outerItr = self.getNumOfOuterItr()
compSequence = self._extend1dArray(compSequence, outerItr)
compSequence = compSequence.astype(int)
return compSequence
def _extend1dArray(self, origArray, targetLength):
"""Extend the 1D original array to the taget length.
The extended value will be the final element of original array. Nothing
will be done if the input array is not 1D or its length is less than
the target.
Parameters
----------
origArray : numpy.ndarray
Original array with 1 dimension.
targetLength : int
Target length of new extended array.
Returns
-------
numpy.ndarray
Extended 1D array.
"""
if (len(origArray) < targetLength) and (origArray.ndim == 1):
leftOver = np.ones(targetLength - len(origArray))
extendArray = np.append(origArray, origArray[-1] * leftOver)
else:
extendArray = origArray
return extendArray
def getBoundaryThickness(self):
"""Get the boundary thickness that the computation mask extends beyond
the pupil mask.
It is noted that in Fast Fourier transform (FFT) algorithm, it is also
the width of Neuman boundary where the derivative of the wavefront is
set to zero
Returns
-------
int
Boundary thickness.
"""
return int(self.algoParamFile.getSetting("boundaryThickness"))
def getFftDimension(self):
"""Get the FFT pad dimension in pixel.
This is for the fast Fourier transform (FFT) solver only.
Returns
-------
int
FFT pad dimention.
"""
fftDim = int(self.algoParamFile.getSetting("fftDimension"))
# Make sure the dimension is the order of multiple of 2
if (fftDim == 999):
dimToFit = self._inst.getDimOfDonutOnSensor()
else:
dimToFit = fftDim
padDim = int(2**np.ceil(np.log2(dimToFit)))
return padDim
def getSignalClipSequence(self):
"""Get the signal clip sequence.
The number of values should be the number of compensation plus 1.
This is for the fast Fourier transform (FFT) solver only.
Returns
-------
numpy.ndarray
Signal clip sequence.
"""
sumclipSequenceFromFile = self.algoParamFile.getSetting("signalClipSequence")
sumclipSequence = np.array(sumclipSequenceFromFile)
# If outerItr is large, and sumclipSequence is too small, the rest in
# sumclipSequence will be filled.
# This is used in the "zer" method.
targetLength = self.getNumOfOuterItr() + 1
sumclipSequence = self._extend1dArray(sumclipSequence, targetLength)
return sumclipSequence
def getMaskScalingFactor(self):
"""Get the mask scaling factor for fast beam.
Returns
-------
float
Mask scaling factor for fast beam.
"""
# m = R'*f/(l*R), R': radius of the no-aberration image
focalLength = self._inst.getFocalLength()
marginalFL = self._inst.getMarginalFocalLength()
maskScalingFactor = focalLength / marginalFL
return maskScalingFactor
def itr0(self, I1, I2, model):
"""Calculate the wavefront and coefficients of normal/ annular Zernike
polynomials in the first iteration time.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
"""
# Reset the iteration time of outer loop and decide to reset the
# defocal images or not
self._reset(I1, I2)
# Solve the transport of intensity equation (TIE)
self._singleItr(I1, I2, model)
def runIt(self, I1, I2, model, tol=1e-3):
"""Calculate the wavefront error by solving the transport of intensity
equation (TIE).
The inner (for fft algorithm) and outer loops are used. The inner loop
is to solve the Poisson's equation. The outer loop is to compensate the
intra- and extra-focal images to mitigate the calculation of wavefront
(e.g. S = -1/(delta Z) * (I1 - I2)/ (I1 + I2)).
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
tol : float, optional
Tolerance of difference of coefficients of Zk polynomials compared
with the previours iteration. (the default is 1e-3.)
"""
# To have the iteration time initiated from global variable is to
# distinguish the manually and automatically iteration processes.
itr = self.currentItr
while (itr <= self.getNumOfOuterItr()):
stopItr = self._singleItr(I1, I2, model, tol)
# Stop the iteration of outer loop if converged
if (stopItr):
break
itr += 1
def nextItr(self, I1, I2, model, nItr=1):
"""Run the outer loop iteration with the specific time defined in nItr.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
nItr : int, optional
Outer loop iteration time. (the default is 1.)
"""
# Do the iteration
ii = 0
while (ii < nItr):
self._singleItr(I1, I2, model)
ii += 1
def _singleItr(self, I1, I2, model, tol=1e-3):
"""Run the outer-loop with single iteration to solve the transport of
intensity equation (TIE).
This is to compensate the approximation of wavefront:
S = -1/(delta Z) * (I1 - I2)/ (I1 + I2)).
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
tol : float, optional
Tolerance of difference of coefficients of Zk polynomials compared
with the previours iteration. (the default is 1e-3.)
Returns
-------
bool
Status of iteration.
"""
# Use the zonal mode ("zer")
compMode = self.getCompensatorMode()
# Define the gain of feedbackGain
feedbackGain = self.getFeedbackGain()
# Set the pre-condition
if (self.currentItr == 0):
# Check this is the first time of running iteration or not
if (I1.getImgInit() is None or I2.getImgInit() is None):
# Check the image dimension
if (I1.getImg().shape != I2.getImg().shape):
print("Error: The intra and extra image stamps need to be of same size.")
sys.exit()
# Calculate the pupil mask (binary matrix) and related
# parameters
boundaryT = self.getBoundaryThickness()
I1.makeMask(self._inst, model, boundaryT, 1)
I2.makeMask(self._inst, model, boundaryT, 1)
self._makeMasterMask(I1, I2, self.getPoissonSolverName())
# Load the offAxis correction coefficients
if (model == "offAxis"):
offAxisPolyOrder = self.getOffAxisPolyOrder()
I1.setOffAxisCorr(self._inst, offAxisPolyOrder)
I2.setOffAxisCorr(self._inst, offAxisPolyOrder)
# Cocenter the images to the center referenced to fieldX and
# fieldY. Need to check the availability of this.
I1.imageCoCenter(self._inst, debugLevel=self.debugLevel)
I2.imageCoCenter(self._inst, debugLevel=self.debugLevel)
# Update the self-initial image
I1.updateImgInit()
I2.updateImgInit()
# Initialize the variables used in the iteration.
self.zcomp = np.zeros(self.getNumOfZernikes())
self.zc = self.zcomp.copy()
dimOfDonut = self._inst.getDimOfDonutOnSensor()
self.wcomp = np.zeros((dimOfDonut, dimOfDonut))
self.West = self.wcomp.copy()
self.caustic = False
# Rename this index (currentItr) for the simplification
jj = self.currentItr
# Solve the transport of intensity equation (TIE)
if (not self.caustic):
# Reset the images before the compensation
I1.updateImage(I1.getImgInit().copy())
I2.updateImage(I2.getImgInit().copy())
if (compMode == "zer"):
# Zk coefficient from the previous iteration
ztmp = self.zc
# Do the feedback of Zk from the lower terms first based on the
# sequence defined in compSequence
if (jj != 0):
compSequence = self.getCompSequence()
ztmp[int(compSequence[jj - 1]):] = 0
# Add partial feedback of residual estimated wavefront in Zk
self.zcomp = self.zcomp + ztmp*feedbackGain
# Remove the image distortion if the optical model is not
# "paraxial"
# Only the optical model of "onAxis" or "offAxis" is considered
# here
I1.compensate(self._inst, self, self.zcomp, model)
I2.compensate(self._inst, self, self.zcomp, model)
# Check the image condition. If there is the problem, done with
# this _singleItr().
if (I1.isCaustic() is True) or (I2.isCaustic() is True):
self.converge[:, jj] = self.converge[:, jj - 1]
self.caustic = True
return
# Correct the defocal images if I1 and I2 are belong to different
# sources, which is determined by the (fieldX, field Y)
I1, I2 = self._applyI1I2pMask(I1, I2)
# Solve the Poisson's equation
self.zc, self.West = self._solvePoissonEq(I1, I2, jj)
# Record/ calculate the Zk coefficient and wavefront
if (compMode == "zer"):
self.converge[:, jj] = self.zcomp + self.zc
xoSensor, yoSensor = self._inst.getSensorCoorAnnular()
self.wcomp = self.West + ZernikeAnnularEval(
np.concatenate(([0, 0, 0], self.zcomp[3:])), xoSensor,
yoSensor, self.getObsOfZernikes())
else:
# Once we run into caustic, stop here, results may be close to real
# aberration.
# Continuation may lead to disatrous results.
self.converge[:, jj] = self.converge[:, jj - 1]
# Record the coefficients of normal/ annular Zernike polynomials after
# z4 in unit of nm
self.zer4UpNm = self.converge[3:, jj]*1e9
# Status of iteration
stopItr = False
# Calculate the difference
if (jj > 0):
diffZk = np.sum(np.abs(self.converge[:, jj]-self.converge[:, jj-1]))*1e9
# Check the Status of iteration
if (diffZk < tol):
stopItr = True
# Update the current iteration time
self.currentItr += 1
# Show the Zk coefficients in interger in each iteration
if (self.debugLevel >= 2):
tmp = self.zer4UpNm
print("itr = %d, z4-z%d" % (jj, self.getNumOfZernikes()))
print(np.rint(tmp))
return stopItr
def _solvePoissonEq(self, I1, I2, iOutItr=0):
"""Solve the Poisson's equation by Fourier transform (differential) or
serial expansion (integration).
There is no convergence for fft actually. Need to add the difference
comparison and X-alpha method. Need to discuss further for this.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
iOutItr : int, optional
ith number of outer loop iteration which is important in "fft"
algorithm. (the default is 0.)
Returns
-------
numpy.ndarray
Coefficients of normal/ annular Zernike polynomials.
numpy.ndarray
Estimated wavefront.
"""
# Calculate the aperature pixel size
apertureDiameter = self._inst.getApertureDiameter()
sensorFactor = self._inst.getSensorFactor()
dimOfDonut = self._inst.getDimOfDonutOnSensor()
aperturePixelSize = apertureDiameter*sensorFactor/dimOfDonut
# Calculate the differential Omega
dOmega = aperturePixelSize**2
# Solve the Poisson's equation based on the type of algorithm
numTerms = self.getNumOfZernikes()
zobsR = self.getObsOfZernikes()
PoissonSolver = self.getPoissonSolverName()
if (PoissonSolver == "fft"):
# Use the differential method by fft to solve the Poisson's
# equation
# Parameter to determine the threshold of calculating I0.
sumclipSequence = self.getSignalClipSequence()
cliplevel = sumclipSequence[iOutItr]
# Generate the v, u-coordinates on pupil plane
padDim = self.getFftDimension()
v, u = np.mgrid[
-0.5/aperturePixelSize: 0.5/aperturePixelSize: 1./padDim/aperturePixelSize,
-0.5/aperturePixelSize: 0.5/aperturePixelSize: 1./padDim/aperturePixelSize]
# Show the threshold and pupil coordinate information
if (self.debugLevel >= 3):
print("iOuter=%d, cliplevel=%4.2f" % (iOutItr, cliplevel))
print(v.shape)
# Calculate the const of fft:
# FT{Delta W} = -4*pi^2*(u^2+v^2) * FT{W}
u2v2 = -4 * (np.pi**2) * (u*u + v*v)
# Set origin to Inf to result in 0 at origin after filtering
ctrIdx = int(np.floor(padDim/2.0))
u2v2[ctrIdx, ctrIdx] = np.inf
# Calculate the wavefront signal
Sini = self._createSignal(I1, I2, cliplevel)
# Find the just-outside and just-inside indices of a ring in pixels
# This is for the use in setting dWdn = 0
boundaryT = self.getBoundaryThickness()
struct = generate_binary_structure(2, 1)
struct = iterate_structure(struct, boundaryT)
ApringOut = np.logical_xor(binary_dilation(self.pMask, structure=struct),
self.pMask).astype(int)
ApringIn = np.logical_xor(binary_erosion(self.pMask, structure=struct),
self.pMask).astype(int)
bordery, borderx = np.nonzero(ApringOut)
# Put the signal in boundary (since there's no existing Sestimate,
# S just equals self.S as the initial condition of SCF
S = Sini.copy()
for jj in range(self.getNumOfInnerItr()):
# Calculate FT{S}
SFFT = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(S)))
# Calculate W by W=IFT{ FT{S}/(-4*pi^2*(u^2+v^2)) }
W = np.fft.fftshift(np.fft.irfft2(np.fft.fftshift(SFFT/u2v2), s=S.shape))
# Estimate the wavefront (includes zeroing offset & masking to
# the aperture size)
# Take the estimated wavefront
West = extractArray(W, dimOfDonut)
# Calculate the offset
offset = West[self.pMask == 1].mean()
West = West - offset
West[self.pMask == 0] = 0
# Set dWestimate/dn = 0 around boundary
WestdWdn0 = West.copy()
# Do a 3x3 average around each border pixel, including only
# those pixels inside the aperture
for ii in range(len(borderx)):
reg = West[borderx[ii] - boundaryT:
borderx[ii] + boundaryT + 1,
bordery[ii] - boundaryT:
bordery[ii] + boundaryT + 1]
intersectIdx = ApringIn[borderx[ii] - boundaryT:
borderx[ii] + boundaryT + 1,
bordery[ii] - boundaryT:
bordery[ii] + boundaryT + 1]
WestdWdn0[borderx[ii], bordery[ii]] = \
reg[np.nonzero(intersectIdx)].mean()
# Take Laplacian to find sensor signal estimate (Delta W = S)
del2W = laplace(WestdWdn0)/dOmega
# Extend the dimension of signal to the order of 2 for "fft" to
# use
Sest = padArray(del2W, padDim)
# Put signal back inside boundary, leaving the rest of
# Sestimate
Sest[self.pMaskPad == 1] = Sini[self.pMaskPad == 1]
# Need to recheck this condition
S = Sest
# Define the estimated wavefront
# self.West = West.copy()
# Calculate the coefficient of normal/ annular Zernike polynomials
if (self.getCompensatorMode() == "zer"):
xSensor, ySensor = self._inst.getSensorCoor()
zc = ZernikeMaskedFit(West, xSensor, ySensor, numTerms,
self.pMask, zobsR)
else:
zc = np.zeros(numTerms)
elif (PoissonSolver == "exp"):
# Use the integration method by serial expansion to solve the
# Poisson's equation
# Calculate I0 and dI
I0, dI = self._getdIandI(I1, I2)
# Get the x, y coordinate in mask. The element outside mask is 0.
xSensor, ySensor = self._inst.getSensorCoor()
xSensor = xSensor * self.cMask
ySensor = ySensor * self.cMask
# Create the F matrix and Zernike-related matrixes
F = np.zeros(numTerms)
dZidx = np.zeros((numTerms, dimOfDonut, dimOfDonut))
dZidy = dZidx.copy()
zcCol = np.zeros(numTerms)
for ii in range(int(numTerms)):
# Calculate the matrix for each Zk related component
# Set the specific Zk cofficient to be 1 for the calculation
zcCol[ii] = 1
F[ii] = np.sum(dI*ZernikeAnnularEval(zcCol, xSensor, ySensor, zobsR))*dOmega
dZidx[ii, :, :] = ZernikeAnnularGrad(zcCol, xSensor, ySensor, zobsR, "dx")
dZidy[ii, :, :] = ZernikeAnnularGrad(zcCol, xSensor, ySensor, zobsR, "dy")
# Set the specific Zk cofficient back to 0 to avoid interfering
# other Zk's calculation
zcCol[ii] = 0
# Calculate Mij matrix, need to check the stability of integration
# and symmetry later
Mij = np.zeros([numTerms, numTerms])
for ii in range(numTerms):
for jj in range(numTerms):
Mij[ii, jj] = np.sum(I0*(dZidx[ii, :, :].squeeze()*dZidx[jj, :, :].squeeze() +
dZidy[ii, :, :].squeeze()*dZidy[jj, :, :].squeeze()))
Mij = dOmega/(apertureDiameter/2.)**2 * Mij
# Calculate dz
focalLength = self._inst.getFocalLength()
offset = self._inst.getDefocalDisOffset()
dz = 2*focalLength*(focalLength-offset)/offset
# Define zc
zc = np.zeros(numTerms)
# Consider specific Zk terms only
idx = (self.getZernikeTerms() - 1).tolist()
# Solve the equation: M*W = F => W = M^(-1)*F
zc_tmp = np.linalg.lstsq(Mij[:, idx][idx], F[idx], rcond=None)[0]/dz
zc[idx] = zc_tmp
# Estimate the wavefront surface based on z4 - z22
# z0 - z3 are set to be 0 instead
West = ZernikeAnnularEval(np.concatenate(([0, 0, 0], zc[3:])),
xSensor, ySensor, zobsR)
return zc, West
def _createSignal(self, I1, I2, cliplevel):
"""Calculate the wavefront singal for "fft" to use in solving the
Poisson's equation.
Need to discuss the method to define threshold and discuss to use
np.median() instead.
Need to discuss why the calculation of I0 is different from "exp".
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
cliplevel : float
Parameter to determine the threshold of calculating I0.
Returns
-------
numpy.ndarray
Approximated wavefront signal.
"""
# Check the condition of images
I1image, I2image = self._checkImageDim(I1, I2)
# Wavefront signal S=-(1/I0)*(dI/dz) is approximated to be
# -(1/delta z)*(I1-I2)/(I1+I2)
num = I1image - I2image
den = I1image + I2image
# Define the effective minimum central signal element by the threshold
# ( I0=(I1+I2)/2 )
# Calculate the threshold
pixelList = den * self.cMask
pixelList = pixelList[pixelList != 0]
low = pixelList.min()
high = pixelList.max()
medianThreshold = (high-low)/2. + low
# Define the effective minimum central signal element
den[den < medianThreshold*cliplevel] = 1.5*medianThreshold
# Calculate delta z = f(f-l)/l, f: focal length, l: defocus distance of
# the image planes
focalLength = self._inst.getFocalLength()
offset = self._inst.getDefocalDisOffset()
deltaZ = focalLength*(focalLength-offset)/offset
# Calculate the wavefront signal. Enforce the element outside the mask
# to be 0.
den[den == 0] = np.inf
# Calculate the wavefront signal
S = num/den/deltaZ
# Extend the dimension of signal to the order of 2 for "fft" to use
padDim = self.getFftDimension()
Sout = padArray(S, padDim)*self.cMaskPad
return Sout
def _getdIandI(self, I1, I2):
"""Calculate the central image and differential image to be used in the
serial expansion method.
It is noted that the images are assumed to be co-center already. And
the intra-/ extra-focal image can overlap with one another after the
rotation of 180 degree.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
Returns
-------
numpy.ndarray
Image data of I0.
numpy.ndarray
Differential image (dI) of I0.
"""
# Check the condition of images
I1image, I2image = self._checkImageDim(I1, I2)
# Calculate the central image and differential iamge
I0 = (I1image+I2image)/2
dI = I2image-I1image
return I0, dI
def _checkImageDim(self, I1, I2):
"""Check the dimension of images.
It is noted that the I2 image is rotated by 180 degree.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
Returns
-------
numpy.ndarray
I1 defocal image.
numpy.ndarray
I2 defocal image. It is noted that the I2 image is rotated by 180
degree.
Raises
------
Exception
Check the dimension of images is n by n or not.
Exception
Check two defocal images have the same size or not.
"""
# Check the condition of images
m1, n1 = I1.getImg().shape
m2, n2 = I2.getImg().shape
if (m1 != n1 or m2 != n2):
raise Exception("Image is not square.")
if (m1 != m2 or n1 != n2):
raise Exception("Images do not have the same size.")
# Define I1
I1image = I1.getImg()
# Rotate the image by 180 degree through rotating two times of 90
# degree
I2image = np.rot90(I2.getImg(), k=2)
return I1image, I2image
def _makeMasterMask(self, I1, I2, poissonSolver=None):
"""Calculate the common mask of defocal images.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
poissonSolver : str, optional
Algorithm to solve the Poisson's equation. If the "fft" is used,
the mask dimension will be extended to the order of 2 for the "fft"
to use. (the default is None.)
"""
# Get the overlap region of mask for intra- and extra-focal images.
# This is to avoid the anormalous signal due to difference in
# vignetting.
self.pMask = I1.getPaddedMask() * I2.getPaddedMask()
self.cMask = I1.getNonPaddedMask() * I2.getNonPaddedMask()
# Change the dimension of image for fft to use
if (poissonSolver == "fft"):
padDim = self.getFftDimension()
self.pMaskPad = padArray(self.pMask, padDim)
self.cMaskPad = padArray(self.cMask, padDim)
def _applyI1I2pMask(self, I1, I2):
"""Correct the defocal images if I1 and I2 are belong to different
sources.
(There is a problem for this actually. If I1 and I2 come from different
sources, what should the correction of TIE be? At this moment, the
fieldX and fieldY of I1 and I2 should be different. And the sources are
different also.)
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
Returns
-------
numpy.ndarray
Corrected I1 image.
numpy.ndarray
Corrected I2 image.
"""
# Get the overlap region of images and do the normalization.
if (I1.fieldX != I2.fieldX or I1.fieldY != I2.fieldY):
# Get the overlap region of image
I1.updateImage(I1.getImg()*self.pMask)
# Rotate the image by 180 degree through rotating two times of 90
# degree
I2.updateImage(I2.getImg()*np.rot90(self.pMask, 2))
# Do the normalization of image.
I1.updateImage(I1.getImg()/np.sum(I1.getImg()))
I2.updateImage(I2.getImg()/np.sum(I2.getImg()))
# Return the correct images. It is noted that there is no need of
# vignetting correction.
# This is after masking already in _singleItr() or itr0().
return I1, I2
def _reset(self, I1, I2):
"""Reset the iteration time of outer loop and defocal images.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
"""
# Reset the current iteration time to 0
self.currentItr = 0
# Show the reset information
if (self.debugLevel >= 3):
print("Resetting images: I1 and I2")
# Determine to reset the images or not based on the existence of
# the attribute: Image.image0. Only after the first run of
# inner loop, this attribute will exist.
try:
# Reset the images to the first beginning
I1.updateImage(I1.getImgInit().copy())
I2.updateImage(I2.getImgInit().copy())
# Show the information of resetting image
if (self.debugLevel >= 3):
print("Resetting images in inside.")
except AttributeError:
# Show the information of no image0
if (self.debugLevel >= 3):
print("Image0 = None. This is the first time to run the code.")
pass
def outZer4Up(self, unit="nm", filename=None, showPlot=False):
"""Put the coefficients of normal/ annular Zernike polynomials on
terminal or file ande show the image if it is needed.
Parameters
----------
unit : str, optional
Unit of the coefficients of normal/ annular Zernike polynomials. It
can be m, nm, or um. (the default is "nm".)
filename : str, optional
Name of output file. (the default is None.)
showPlot : bool, optional
Decide to show the plot or not. (the default is False.)
"""
# List of Zn,m
Znm = ["Z0,0", "Z1,1", "Z1,-1", "Z2,0", "Z2,-2", "Z2,2", "Z3,-1",
"Z3,1", "Z3,-3", "Z3,3", "Z4,0", "Z4,2", "Z4,-2", "Z4,4",
"Z4,-4", "Z5,1", "Z5,-1", "Z5,3", "Z5,-3", "Z5,5", "Z5,-5",
"Z6,0"]
# Decide the format of z based on the input unit (m, nm, or um)
if (unit == "m"):
z = self.zer4UpNm*1e-9
elif (unit == "nm"):
z = self.zer4UpNm
elif (unit == "um"):
z = self.zer4UpNm*1e-3
else:
print("Unknown unit: %s" % unit)
print("Unit options are: m, nm, um")
return
# Write the coefficients into a file if needed.
if (filename is not None):
f = open(filename, "w")
else:
f = sys.stdout
for ii in range(4, len(z)+4):
f.write("Z%d (%s)\t %8.3f\n" % (ii, Znm[ii-1], z[ii-4]))
# Close the file
if (filename is not None):
f.close()
# Show the plot
if (showPlot):
plt.figure()
x = range(4, len(z) + 4)
plt.plot(x, z, marker="o", color="r", markersize=10)
plt.xlabel("Zernike Index")
plt.ylabel("Zernike coefficient (%s)" % unit)
plt.grid()
plt.show()
class Algorithm(object):
def __init__(self, algoDir):
"""Initialize the Algorithm class.
Algorithm used to solve the transport of intensity equation to get
normal/ annular Zernike polynomials.
Parameters
----------
algoDir : str
Algorithm configuration directory.
"""
self.algoDir = algoDir
self.algoParamFile = ParamReader()
self._inst = Instrument("")
# Show the calculation message based on this value
# 0 means no message will be showed
self.debugLevel = 0
# Image has the problem or not from the over-compensation
self.caustic = False
# Record the Zk coefficients in each outer-loop iteration
# The actual total outer-loop iteration time is Num_of_outer_itr + 1
self.converge = np.array([])
# Current number of outer-loop iteration
self.currentItr = 0
# Record the coefficients of normal/ annular Zernike polynomials after
# z4 in unit of nm
self.zer4UpNm = np.array([])
# Converged wavefront.
self.wcomp = np.array([])
# Calculated wavefront in previous outer-loop iteration.
self.West = np.array([])
# Converged Zk coefficients
self.zcomp = np.array([])
# Calculated Zk coefficients in previous outer-loop iteration
self.zc = np.array([])
# Padded mask for use at the offset planes
self.pMask = None
# Non-padded mask corresponding to aperture
self.cMask = None
# Change the dimension of mask for fft to use
self.pMaskPad = None
self.cMaskPad = None
def reset(self):
"""Reset the calculation for the new input images with the same
algorithm settings."""
self.caustic = False
self.converge = np.zeros(self.converge.shape)
self.currentItr = 0
self.zer4UpNm = np.zeros(self.zer4UpNm.shape)
self.wcomp = np.zeros(self.wcomp.shape)
self.West = np.zeros(self.West.shape)
self.zcomp = np.zeros(self.zcomp.shape)
self.zc = np.zeros(self.zc.shape)
self.pMask = None
self.cMask = None
self.pMaskPad = None
self.cMaskPad = None
def config(self, algoName, inst, debugLevel=0):
"""Configure the algorithm to solve TIE.
Parameters
----------
algoName : str
Algorithm configuration file to solve the Poisson's equation in the
transport of intensity equation (TIE). It can be "fft" or "exp"
here.
inst : Instrument
Instrument to use.
debugLevel : int, optional
Show the information under the running. If the value is higher, the
information shows more. It can be 0, 1, 2, or 3. (the default is
0.)
"""
algoParamFilePath = os.path.join(self.algoDir, "%s.yaml" % algoName)
self.algoParamFile.setFilePath(algoParamFilePath)
self._inst = inst
self.debugLevel = debugLevel
self.caustic = False
numTerms = self.getNumOfZernikes()
outerItr = self.getNumOfOuterItr()
self.converge = np.zeros((numTerms, outerItr + 1))
self.currentItr = 0
self.zer4UpNm = np.zeros(numTerms - 3)
# Wavefront related parameters
dimOfDonut = self._inst.getDimOfDonutOnSensor()
self.wcomp = np.zeros((dimOfDonut, dimOfDonut))
self.West = self.wcomp.copy()
# Used in model basis ("zer").
self.zcomp = np.zeros(numTerms)
self.zc = self.zcomp.copy()
# Mask related variables
self.pMask = None
self.cMask = None
self.pMaskPad = None
self.cMaskPad = None
def setDebugLevel(self, debugLevel):
"""Set the debug level.
If the value is higher, the information shows more. It can be 0, 1, 2,
or 3.
Parameters
----------
debugLevel : int
Show the information under the running.
"""
self.debugLevel = int(debugLevel)
def getDebugLevel(self):
"""Get the debug level.
If the value is higher, the information shows more. It can be 0, 1, 2,
or 3.
Returns
-------
int
Debug level.
"""
return self.debugLevel
def getZer4UpInNm(self):
"""Get the coefficients of Zernike polynomials of z4-zn in nm.
Returns
-------
numpy.ndarray
Zernike polynomials of z4-zn in nm.
"""
return self.zer4UpNm
def getPoissonSolverName(self):
"""Get the method name to solve the Poisson equation.
Returns
-------
str
Method name to solve the Poisson equation.
"""
return self.algoParamFile.getSetting("poissonSolver")
def getNumOfZernikes(self):
"""Get the maximum number of Zernike polynomials supported.
Returns
-------
int
Maximum number of Zernike polynomials supported.
"""
return int(self.algoParamFile.getSetting("numOfZernikes"))
def getZernikeTerms(self):
"""Get the Zernike terms in using.
Returns
-------
list[int]
Zernike terms in using.
"""
numTerms = self.getNumOfZernikes()
return list(range(numTerms))
def getObsOfZernikes(self):
"""Get the obscuration of annular Zernike polynomials.
Returns
-------
float
Obscuration of annular Zernike polynomials
"""
zobsR = self.algoParamFile.getSetting("obsOfZernikes")
if zobsR == 1:
zobsR = self._inst.getObscuration()
return float(zobsR)
def getNumOfOuterItr(self):
"""Get the number of outer loop iteration.
Returns
-------
int
Number of outer loop iteration.
"""
return int(self.algoParamFile.getSetting("numOfOuterItr"))
def getNumOfInnerItr(self):
"""Get the number of inner loop iteration.
This is for the fast Fourier transform (FFT) solver only.
Returns
-------
int
Number of inner loop iteration.
"""
return int(self.algoParamFile.getSetting("numOfInnerItr"))
def getFeedbackGain(self):
"""Get the gain value used in the outer loop iteration.
Returns
-------
float
Gain value used in the outer loop iteration.
"""
return self.algoParamFile.getSetting("feedbackGain")
def getOffAxisPolyOrder(self):
"""Get the number of polynomial order supported in off-axis correction.
Returns
-------
int
Number of polynomial order supported in off-axis correction.
"""
return int(self.algoParamFile.getSetting("offAxisPolyOrder"))
def getCompensatorMode(self):
"""Get the method name to compensate the wavefront by wavefront error.
Returns
-------
str
Method name to compensate the wavefront by wavefront error.
"""
return self.algoParamFile.getSetting("compensatorMode")
def getCompSequence(self):
"""Get the compensated sequence of Zernike order for each iteration.
Returns
-------
numpy.ndarray[int]
Compensated sequence of Zernike order for each iteration.
"""
compSequenceFromFile = self.algoParamFile.getSetting("compSequence")
compSequence = np.array(compSequenceFromFile, dtype=int)
# If outerItr is large, and compSequence is too small,
# the rest in compSequence will be filled.
# This is used in the "zer" method.
outerItr = self.getNumOfOuterItr()
compSequence = self._extend1dArray(compSequence, outerItr)
compSequence = compSequence.astype(int)
return compSequence
def _extend1dArray(self, origArray, targetLength):
"""Extend the 1D original array to the taget length.
The extended value will be the final element of original array. Nothing
will be done if the input array is not 1D or its length is less than
the target.
Parameters
----------
origArray : numpy.ndarray
Original array with 1 dimension.
targetLength : int
Target length of new extended array.
Returns
-------
numpy.ndarray
Extended 1D array.
"""
if (len(origArray) < targetLength) and (origArray.ndim == 1):
leftOver = np.ones(targetLength - len(origArray))
extendArray = np.append(origArray, origArray[-1] * leftOver)
else:
extendArray = origArray
return extendArray
def getBoundaryThickness(self):
"""Get the boundary thickness that the computation mask extends beyond
the pupil mask.
It is noted that in Fast Fourier transform (FFT) algorithm, it is also
the width of Neuman boundary where the derivative of the wavefront is
set to zero
Returns
-------
int
Boundary thickness.
"""
return int(self.algoParamFile.getSetting("boundaryThickness"))
def getFftDimension(self):
"""Get the FFT pad dimension in pixel.
This is for the fast Fourier transform (FFT) solver only.
Returns
-------
int
FFT pad dimention.
"""
fftDim = int(self.algoParamFile.getSetting("fftDimension"))
# Make sure the dimension is the order of multiple of 2
if fftDim == 999:
dimToFit = self._inst.getDimOfDonutOnSensor()
else:
dimToFit = fftDim
padDim = int(2 ** np.ceil(np.log2(dimToFit)))
return padDim
def getSignalClipSequence(self):
"""Get the signal clip sequence.
The number of values should be the number of compensation plus 1.
This is for the fast Fourier transform (FFT) solver only.
Returns
-------
numpy.ndarray
Signal clip sequence.
"""
sumclipSequenceFromFile = self.algoParamFile.getSetting("signalClipSequence")
sumclipSequence = np.array(sumclipSequenceFromFile)
# If outerItr is large, and sumclipSequence is too small, the rest in
# sumclipSequence will be filled.
# This is used in the "zer" method.
targetLength = self.getNumOfOuterItr() + 1
sumclipSequence = self._extend1dArray(sumclipSequence, targetLength)
return sumclipSequence
def getMaskScalingFactor(self):
"""Get the mask scaling factor for fast beam.
Returns
-------
float
Mask scaling factor for fast beam.
"""
# m = R'*f/(l*R), R': radius of the no-aberration image
focalLength = self._inst.getFocalLength()
marginalFL = self._inst.getMarginalFocalLength()
maskScalingFactor = focalLength / marginalFL
return maskScalingFactor
def getWavefrontMapEsti(self):
"""Get the estimated wavefront map.
Returns
-------
numpy.ndarray
Estimated wavefront map.
"""
return self._getWavefrontMapWithMaskApplied(self.wcomp)
def getWavefrontMapResidual(self):
"""Get the residual wavefront map.
Returns
-------
numpy.ndarray
Residual wavefront map.
"""
return self._getWavefrontMapWithMaskApplied(self.West)
def _getWavefrontMapWithMaskApplied(self, wfMap):
"""Get the wavefront map with mask applied.
Parameters
----------
wfMap : numpy.ndarray
Wavefront map.
Returns
-------
numpy.ndarray
Wavefront map with mask applied.
"""
self._checkNotItr0()
wfMapWithMask = wfMap.copy()
wfMapWithMask[self.pMask == 0] = np.nan
return wfMapWithMask
def _checkNotItr0(self):
"""Check not in the iteration 0.
TIE: Transport of intensity equation.
Raises
------
RuntimeError
Need to solve the TIE first.
"""
if self.currentItr == 0:
raise RuntimeError("Need to solve the TIE first.")
def itr0(self, I1, I2, model):
"""Calculate the wavefront and coefficients of normal/ annular Zernike
polynomials in the first iteration time.
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
"""
# Reset the iteration time of outer loop and decide to reset the
# defocal images or not
self._reset(I1, I2)
# Solve the transport of intensity equation (TIE)
self._singleItr(I1, I2, model)
def runIt(self, I1, I2, model, tol=1e-3):
"""Calculate the wavefront error by solving the transport of intensity
equation (TIE).
The inner (for fft algorithm) and outer loops are used. The inner loop
is to solve the Poisson's equation. The outer loop is to compensate the
intra- and extra-focal images to mitigate the calculation of wavefront
(e.g. S = -1/(delta Z) * (I1 - I2)/ (I1 + I2)).
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
tol : float, optional
Tolerance of difference of coefficients of Zk polynomials compared
with the previours iteration. (the default is 1e-3.)
"""
# To have the iteration time initiated from global variable is to
# distinguish the manually and automatically iteration processes.
itr = self.currentItr
while itr <= self.getNumOfOuterItr():
stopItr = self._singleItr(I1, I2, model, tol)
# Stop the iteration of outer loop if converged
if stopItr:
break
itr += 1
def nextItr(self, I1, I2, model, nItr=1):
"""Run the outer loop iteration with the specific time defined in nItr.
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
nItr : int, optional
Outer loop iteration time. (the default is 1.)
"""
# Do the iteration
ii = 0
while ii < nItr:
self._singleItr(I1, I2, model)
ii += 1
def _singleItr(self, I1, I2, model, tol=1e-3):
"""Run the outer-loop with single iteration to solve the transport of
intensity equation (TIE).
This is to compensate the approximation of wavefront:
S = -1/(delta Z) * (I1 - I2)/ (I1 + I2)).
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
tol : float, optional
Tolerance of difference of coefficients of Zk polynomials compared
with the previours iteration. (the default is 1e-3.)
Returns
-------
bool
Status of iteration.
"""
# Use the zonal mode ("zer")
compMode = self.getCompensatorMode()
# Define the gain of feedbackGain
feedbackGain = self.getFeedbackGain()
# Set the pre-condition
if self.currentItr == 0:
# Check this is the first time of running iteration or not
if I1.getImgInit() is None or I2.getImgInit() is None:
# Check the image dimension
if I1.getImg().shape != I2.getImg().shape:
print(
"Error: The intra and extra image stamps need to be of same size."
)
sys.exit()
# Calculate the pupil mask (binary matrix) and related
# parameters
boundaryT = self.getBoundaryThickness()
I1.makeMask(self._inst, model, boundaryT, 1)
I2.makeMask(self._inst, model, boundaryT, 1)
self._makeMasterMask(I1, I2, self.getPoissonSolverName())
# Load the offAxis correction coefficients
if model == "offAxis":
offAxisPolyOrder = self.getOffAxisPolyOrder()
I1.setOffAxisCorr(self._inst, offAxisPolyOrder)
I2.setOffAxisCorr(self._inst, offAxisPolyOrder)
# Cocenter the images to the center referenced to fieldX and
# fieldY. Need to check the availability of this.
I1.imageCoCenter(self._inst, debugLevel=self.debugLevel)
I2.imageCoCenter(self._inst, debugLevel=self.debugLevel)
# Update the self-initial image
I1.updateImgInit()
I2.updateImgInit()
# Initialize the variables used in the iteration.
self.zcomp = np.zeros(self.getNumOfZernikes())
self.zc = self.zcomp.copy()
dimOfDonut = self._inst.getDimOfDonutOnSensor()
self.wcomp = np.zeros((dimOfDonut, dimOfDonut))
self.West = self.wcomp.copy()
self.caustic = False
# Rename this index (currentItr) for the simplification
jj = self.currentItr
# Solve the transport of intensity equation (TIE)
if not self.caustic:
# Reset the images before the compensation
I1.updateImage(I1.getImgInit().copy())
I2.updateImage(I2.getImgInit().copy())
if compMode == "zer":
# Zk coefficient from the previous iteration
ztmp = self.zc.copy()
# Do the feedback of Zk from the lower terms first based on the
# sequence defined in compSequence
if jj != 0:
compSequence = self.getCompSequence()
ztmp[int(compSequence[jj - 1]) :] = 0
# Add partial feedback of residual estimated wavefront in Zk
self.zcomp = self.zcomp + ztmp * feedbackGain
# Remove the image distortion by forwarding the image to pupil
I1.compensate(self._inst, self, self.zcomp, model)
I2.compensate(self._inst, self, self.zcomp, model)
# Check the image condition. If there is the problem, done with
# this _singleItr().
if (I1.isCaustic() is True) or (I2.isCaustic() is True):
self.converge[:, jj] = self.converge[:, jj - 1]
self.caustic = True
return
# Correct the defocal images if I1 and I2 are belong to different
# sources, which is determined by the (fieldX, field Y)
I1, I2 = self._applyI1I2pMask(I1, I2)
# Solve the Poisson's equation
self.zc, self.West = self._solvePoissonEq(I1, I2, jj)
# Record/ calculate the Zk coefficient and wavefront
if compMode == "zer":
self.converge[:, jj] = self.zcomp + self.zc
xoSensor, yoSensor = self._inst.getSensorCoorAnnular()
self.wcomp = self.West + ZernikeAnnularEval(
np.concatenate(([0, 0, 0], self.zcomp[3:])),
xoSensor,
yoSensor,
self.getObsOfZernikes(),
)
else:
# Once we run into caustic, stop here, results may be close to real
# aberration.
# Continuation may lead to disatrous results.
self.converge[:, jj] = self.converge[:, jj - 1]
# Record the coefficients of normal/ annular Zernike polynomials after
# z4 in unit of nm
self.zer4UpNm = self.converge[3:, jj] * 1e9
# Status of iteration
stopItr = False
# Calculate the difference
if jj > 0:
diffZk = (
np.sum(np.abs(self.converge[:, jj] - self.converge[:, jj - 1])) * 1e9
)
# Check the Status of iteration
if diffZk < tol:
stopItr = True
# Update the current iteration time
self.currentItr += 1
# Show the Zk coefficients in interger in each iteration
if self.debugLevel >= 2:
print("itr = %d, z4-z%d" % (jj, self.getNumOfZernikes()))
print(np.rint(self.zer4UpNm))
return stopItr
def _solvePoissonEq(self, I1, I2, iOutItr=0):
"""Solve the Poisson's equation by Fourier transform (differential) or
serial expansion (integration).
There is no convergence for fft actually. Need to add the difference
comparison and X-alpha method. Need to discuss further for this.
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
iOutItr : int, optional
ith number of outer loop iteration which is important in "fft"
algorithm. (the default is 0.)
Returns
-------
numpy.ndarray
Coefficients of normal/ annular Zernike polynomials.
numpy.ndarray
Estimated wavefront.
"""
# Calculate the aperature pixel size
apertureDiameter = self._inst.getApertureDiameter()
sensorFactor = self._inst.getSensorFactor()
dimOfDonut = self._inst.getDimOfDonutOnSensor()
aperturePixelSize = apertureDiameter * sensorFactor / dimOfDonut
# Calculate the differential Omega
dOmega = aperturePixelSize ** 2
# Solve the Poisson's equation based on the type of algorithm
numTerms = self.getNumOfZernikes()
zobsR = self.getObsOfZernikes()
PoissonSolver = self.getPoissonSolverName()
if PoissonSolver == "fft":
# Use the differential method by fft to solve the Poisson's
# equation
# Parameter to determine the threshold of calculating I0.
sumclipSequence = self.getSignalClipSequence()
cliplevel = sumclipSequence[iOutItr]
# Generate the v, u-coordinates on pupil plane
padDim = self.getFftDimension()
v, u = np.mgrid[
-0.5
/ aperturePixelSize : 0.5
/ aperturePixelSize : 1.0
/ padDim
/ aperturePixelSize,
-0.5
/ aperturePixelSize : 0.5
/ aperturePixelSize : 1.0
/ padDim
/ aperturePixelSize,
]
# Show the threshold and pupil coordinate information
if self.debugLevel >= 3:
print("iOuter=%d, cliplevel=%4.2f" % (iOutItr, cliplevel))
print(v.shape)
# Calculate the const of fft:
# FT{Delta W} = -4*pi^2*(u^2+v^2) * FT{W}
u2v2 = -4 * (np.pi ** 2) * (u * u + v * v)
# Set origin to Inf to result in 0 at origin after filtering
ctrIdx = int(np.floor(padDim / 2.0))
u2v2[ctrIdx, ctrIdx] = np.inf
# Calculate the wavefront signal
Sini = self._createSignal(I1, I2, cliplevel)
# Find the just-outside and just-inside indices of a ring in pixels
# This is for the use in setting dWdn = 0
boundaryT = self.getBoundaryThickness()
struct = generate_binary_structure(2, 1)
struct = iterate_structure(struct, boundaryT)
ApringOut = np.logical_xor(
binary_dilation(self.pMask, structure=struct), self.pMask
).astype(int)
ApringIn = np.logical_xor(
binary_erosion(self.pMask, structure=struct), self.pMask
).astype(int)
bordery, borderx = np.nonzero(ApringOut)
# Put the signal in boundary (since there's no existing Sestimate,
# S just equals self.S as the initial condition of SCF
S = Sini.copy()
for jj in range(self.getNumOfInnerItr()):
# Calculate FT{S}
SFFT = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(S)))
# Calculate W by W=IFT{ FT{S}/(-4*pi^2*(u^2+v^2)) }
W = np.fft.fftshift(
np.fft.irfft2(np.fft.fftshift(SFFT / u2v2), s=S.shape)
)
# Estimate the wavefront (includes zeroing offset & masking to
# the aperture size)
# Take the estimated wavefront
West = extractArray(W, dimOfDonut)
# Calculate the offset
offset = West[self.pMask == 1].mean()
West = West - offset
West[self.pMask == 0] = 0
# Set dWestimate/dn = 0 around boundary
WestdWdn0 = West.copy()
# Do a 3x3 average around each border pixel, including only
# those pixels inside the aperture
for ii in range(len(borderx)):
reg = West[
borderx[ii] - boundaryT : borderx[ii] + boundaryT + 1,
bordery[ii] - boundaryT : bordery[ii] + boundaryT + 1,
]
intersectIdx = ApringIn[
borderx[ii] - boundaryT : borderx[ii] + boundaryT + 1,
bordery[ii] - boundaryT : bordery[ii] + boundaryT + 1,
]
WestdWdn0[borderx[ii], bordery[ii]] = reg[
np.nonzero(intersectIdx)
].mean()
# Take Laplacian to find sensor signal estimate (Delta W = S)
del2W = laplace(WestdWdn0) / dOmega
# Extend the dimension of signal to the order of 2 for "fft" to
# use
Sest = padArray(del2W, padDim)
# Put signal back inside boundary, leaving the rest of
# Sestimate
Sest[self.pMaskPad == 1] = Sini[self.pMaskPad == 1]
# Need to recheck this condition
S = Sest
# Calculate the coefficient of normal/ annular Zernike polynomials
if self.getCompensatorMode() == "zer":
xSensor, ySensor = self._inst.getSensorCoor()
zc = ZernikeMaskedFit(
West, xSensor, ySensor, numTerms, self.pMask, zobsR
)
else:
zc = np.zeros(numTerms)
elif PoissonSolver == "exp":
# Use the integration method by serial expansion to solve the
# Poisson's equation
# Calculate I0 and dI
I0, dI = self._getdIandI(I1, I2)
# Get the x, y coordinate in mask. The element outside mask is 0.
xSensor, ySensor = self._inst.getSensorCoor()
xSensor = xSensor * self.cMask
ySensor = ySensor * self.cMask
# Create the F matrix and Zernike-related matrixes
F = np.zeros(numTerms)
dZidx = np.zeros((numTerms, dimOfDonut, dimOfDonut))
dZidy = dZidx.copy()
zcCol = np.zeros(numTerms)
for ii in range(int(numTerms)):
# Calculate the matrix for each Zk related component
# Set the specific Zk cofficient to be 1 for the calculation
zcCol[ii] = 1
F[ii] = (
np.sum(dI * ZernikeAnnularEval(zcCol, xSensor, ySensor, zobsR))
* dOmega
)
dZidx[ii, :, :] = ZernikeAnnularGrad(
zcCol, xSensor, ySensor, zobsR, "dx"
)
dZidy[ii, :, :] = ZernikeAnnularGrad(
zcCol, xSensor, ySensor, zobsR, "dy"
)
# Set the specific Zk cofficient back to 0 to avoid interfering
# other Zk's calculation
zcCol[ii] = 0
# Calculate Mij matrix, need to check the stability of integration
# and symmetry later
Mij = np.zeros([numTerms, numTerms])
for ii in range(numTerms):
for jj in range(numTerms):
Mij[ii, jj] = np.sum(
I0
* (
dZidx[ii, :, :].squeeze() * dZidx[jj, :, :].squeeze()
+ dZidy[ii, :, :].squeeze() * dZidy[jj, :, :].squeeze()
)
)
Mij = dOmega / (apertureDiameter / 2.0) ** 2 * Mij
# Calculate dz
focalLength = self._inst.getFocalLength()
offset = self._inst.getDefocalDisOffset()
dz = 2 * focalLength * (focalLength - offset) / offset
# Define zc
zc = np.zeros(numTerms)
# Consider specific Zk terms only
idx = self.getZernikeTerms()
# Solve the equation: M*W = F => W = M^(-1)*F
zc_tmp = np.linalg.lstsq(Mij[:, idx][idx], F[idx], rcond=None)[0] / dz
zc[idx] = zc_tmp
# Estimate the wavefront surface based on z4 - z22
# z0 - z3 are set to be 0 instead
West = ZernikeAnnularEval(
np.concatenate(([0, 0, 0], zc[3:])), xSensor, ySensor, zobsR
)
return zc, West
def _createSignal(self, I1, I2, cliplevel):
"""Calculate the wavefront singal for "fft" to use in solving the
Poisson's equation.
Need to discuss the method to define threshold and discuss to use
np.median() instead.
Need to discuss why the calculation of I0 is different from "exp".
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
cliplevel : float
Parameter to determine the threshold of calculating I0.
Returns
-------
numpy.ndarray
Approximated wavefront signal.
"""
# Check the condition of images
I1image, I2image = self._checkImageDim(I1, I2)
# Wavefront signal S=-(1/I0)*(dI/dz) is approximated to be
# -(1/delta z)*(I1-I2)/(I1+I2)
num = I1image - I2image
den = I1image + I2image
# Define the effective minimum central signal element by the threshold
# ( I0=(I1+I2)/2 )
# Calculate the threshold
pixelList = den * self.cMask
pixelList = pixelList[pixelList != 0]
low = pixelList.min()
high = pixelList.max()
medianThreshold = (high - low) / 2.0 + low
# Define the effective minimum central signal element
den[den < medianThreshold * cliplevel] = 1.5 * medianThreshold
# Calculate delta z = f(f-l)/l, f: focal length, l: defocus distance of
# the image planes
focalLength = self._inst.getFocalLength()
offset = self._inst.getDefocalDisOffset()
deltaZ = focalLength * (focalLength - offset) / offset
# Calculate the wavefront signal. Enforce the element outside the mask
# to be 0.
den[den == 0] = np.inf
# Calculate the wavefront signal
S = num / den / deltaZ
# Extend the dimension of signal to the order of 2 for "fft" to use
padDim = self.getFftDimension()
Sout = padArray(S, padDim) * self.cMaskPad
return Sout
def _getdIandI(self, I1, I2):
"""Calculate the central image and differential image to be used in the
serial expansion method.
It is noted that the images are assumed to be co-center already. And
the intra-/ extra-focal image can overlap with one another after the
rotation of 180 degree.
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
Returns
-------
numpy.ndarray
Image data of I0.
numpy.ndarray
Differential image (dI) of I0.
"""
# Check the condition of images
I1image, I2image = self._checkImageDim(I1, I2)
# Calculate the central image and differential iamge
I0 = (I1image + I2image) / 2
dI = I2image - I1image
return I0, dI
def _checkImageDim(self, I1, I2):
"""Check the dimension of images.
It is noted that the I2 image is rotated by 180 degree.
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
Returns
-------
numpy.ndarray
I1 defocal image.
numpy.ndarray
I2 defocal image. It is noted that the I2 image is rotated by 180
degree.
Raises
------
Exception
Check the dimension of images is n by n or not.
Exception
Check two defocal images have the same size or not.
"""
# Check the condition of images
m1, n1 = I1.getImg().shape
m2, n2 = I2.getImg().shape
if m1 != n1 or m2 != n2:
raise Exception("Image is not square.")
if m1 != m2 or n1 != n2:
raise Exception("Images do not have the same size.")
# Define I1
I1image = I1.getImg()
# Rotate the image by 180 degree through rotating two times of 90
# degree
I2image = np.rot90(I2.getImg(), k=2)
return I1image, I2image
def _makeMasterMask(self, I1, I2, poissonSolver=None):
"""Calculate the common mask of defocal images.
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
poissonSolver : str, optional
Algorithm to solve the Poisson's equation. If the "fft" is used,
the mask dimension will be extended to the order of 2 for the "fft"
to use. (the default is None.)
"""
# Get the overlap region of mask for intra- and extra-focal images.
# This is to avoid the anormalous signal due to difference in
# vignetting.
self.pMask = I1.getPaddedMask() * I2.getPaddedMask()
self.cMask = I1.getNonPaddedMask() * I2.getNonPaddedMask()
# Change the dimension of image for fft to use
if poissonSolver == "fft":
padDim = self.getFftDimension()
self.pMaskPad = padArray(self.pMask, padDim)
self.cMaskPad = padArray(self.cMask, padDim)
def _applyI1I2pMask(self, I1, I2):
"""Correct the defocal images if I1 and I2 are belong to different
sources.
(There is a problem for this actually. If I1 and I2 come from different
sources, what should the correction of TIE be? At this moment, the
fieldX and fieldY of I1 and I2 should be different. And the sources are
different also.)
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
Returns
-------
numpy.ndarray
Corrected I1 image.
numpy.ndarray
Corrected I2 image.
"""
# Get the overlap region of images and do the normalization.
if I1.getFieldXY() != I2.getFieldXY():
# Get the overlap region of image
I1.updateImage(I1.getImg() * self.pMask)
# Rotate the pMask by 180 degree through rotating two times of 90
# degree because I2 has been rotated by 180 degree already.
I2.updateImage(I2.getImg() * np.rot90(self.pMask, 2))
# Do the normalization of image.
I1.updateImage(I1.getImg() / np.sum(I1.getImg()))
I2.updateImage(I2.getImg() / np.sum(I2.getImg()))
# Return the correct images. It is noted that there is no need of
# vignetting correction.
# This is after masking already in _singleItr() or itr0().
return I1, I2
def _reset(self, I1, I2):
"""Reset the iteration time of outer loop and defocal images.
Parameters
----------
I1 : CompensableImage
Intra- or extra-focal image.
I2 : CompensableImage
Intra- or extra-focal image.
"""
# Reset the current iteration time to 0
self.currentItr = 0
# Show the reset information
if self.debugLevel >= 3:
print("Resetting images: I1 and I2")
# Determine to reset the images or not based on the existence of
# the attribute: Image.image0. Only after the first run of
# inner loop, this attribute will exist.
try:
# Reset the images to the first beginning
I1.updateImage(I1.getImgInit().copy())
I2.updateImage(I2.getImgInit().copy())
# Show the information of resetting image
if self.debugLevel >= 3:
print("Resetting images in inside.")
except AttributeError:
# Show the information of no image0
if self.debugLevel >= 3:
print("Image0 = None. This is the first time to run the code.")
pass
def outZer4Up(self, unit="nm", filename=None, showPlot=False):
"""Put the coefficients of normal/ annular Zernike polynomials on
terminal or file ande show the image if it is needed.
Parameters
----------
unit : str, optional
Unit of the coefficients of normal/ annular Zernike polynomials. It
can be m, nm, or um. (the default is "nm".)
filename : str, optional
Name of output file. (the default is None.)
showPlot : bool, optional
Decide to show the plot or not. (the default is False.)
"""
# List of Zn,m
Znm = [
"Z0,0",
"Z1,1",
"Z1,-1",
"Z2,0",
"Z2,-2",
"Z2,2",
"Z3,-1",
"Z3,1",
"Z3,-3",
"Z3,3",
"Z4,0",
"Z4,2",
"Z4,-2",
"Z4,4",
"Z4,-4",
"Z5,1",
"Z5,-1",
"Z5,3",
"Z5,-3",
"Z5,5",
"Z5,-5",
"Z6,0",
]
# Decide the format of z based on the input unit (m, nm, or um)
if unit == "m":
z = self.zer4UpNm * 1e-9
elif unit == "nm":
z = self.zer4UpNm
elif unit == "um":
z = self.zer4UpNm * 1e-3
else:
print("Unknown unit: %s" % unit)
print("Unit options are: m, nm, um")
return
# Write the coefficients into a file if needed.
if filename is not None:
f = open(filename, "w")
else:
f = sys.stdout
for ii in range(4, len(z) + 4):
f.write("Z%d (%s)\t %8.3f\n" % (ii, Znm[ii - 1], z[ii - 4]))
# Close the file
if filename is not None:
f.close()
# Show the plot
if showPlot:
zkIdx = range(4, len(z) + 4)
plotZernike(zkIdx, z, unit)
class SourceProcessor(object):
def __init__(self,
settingFileName="default.yaml",
focalPlaneFileName="focalplanelayout.txt"):
"""Initialize the SourceProcessor class.
Parameters
----------
settingFileName : str, optional
Setting file name. (the default is "default.yaml".)
focalPlaneFileName : str, optional
Focal plane file name used in the PhoSim instrument directory. (the
default is "focalplanelayout.txt".)
"""
self.sensorName = ""
configDir = getConfigDir()
settingFilePath = os.path.join(configDir, settingFileName)
self.settingFile = ParamReader(filePath=settingFilePath)
self.sensorFocaPlaneInDeg = dict()
self.sensorFocaPlaneInUm = dict()
self.sensorDimList = dict()
self.sensorEulerRot = dict()
self._readFocalPlane(configDir, focalPlaneFileName)
# Deblending donut algorithm to use
deblendDonutType = self._getDeblendDonutTypeInSetting()
self.deblend = DeblendDonutFactory.createDeblendDonut(deblendDonutType)
def _readFocalPlane(self, folderPath, focalPlaneFileName):
"""Read the focal plane data used in PhoSim to get the ccd dimension
and fieldXY in chip center.
Parameters
----------
folderPath : str
Directory of focal plane file.
focalPlaneFileName : str
Focal plane file name used in the PhoSim instrument directory.
"""
# Read the focal plane data by the delegation
ccdData = readPhoSimSettingData(folderPath, focalPlaneFileName,
"fieldCenter")
# Collect the focal plane data
sensorFocaPlaneInDeg = dict()
sensorFocaPlaneInUm = dict()
sensorDimList = dict()
for sensorName, data in ccdData.items():
# Consider the x-translation in corner wavefront sensors
self._shiftCenterWfs(sensorName, data)
# Change the unit from um to degree
xInUm = float(data[0])
yInUm = float(data[1])
pixelSizeInUm = float(data[2])
sizeXinPixel = int(data[3])
sizeYinPixel = int(data[4])
# 1 degree = 3600 arcsec
pixelToArcsec = self.settingFile.getSetting("pixelToArcsec")
fieldX = xInUm / pixelSizeInUm * pixelToArcsec / 3600
fieldY = yInUm / pixelSizeInUm * pixelToArcsec / 3600
# Get the data
sensorFocaPlaneInDeg[sensorName] = (fieldX, fieldY)
sensorFocaPlaneInUm[sensorName] = (xInUm, yInUm)
sensorDimList[sensorName] = (sizeXinPixel, sizeYinPixel)
# Assign the values
self.sensorDimList = sensorDimList
self.sensorFocaPlaneInDeg = sensorFocaPlaneInDeg
self.sensorFocaPlaneInUm = sensorFocaPlaneInUm
self.sensorEulerRot = readPhoSimSettingData(folderPath,
focalPlaneFileName,
"eulerRot")
def _shiftCenterWfs(self, sensorName, focalPlaneData):
"""Shift the fieldXY of center of wavefront sensors.
The input data is the center of combined chips (C0+C1). The input data
will be updated directly.
Parameters
----------
sensorName : str
Abbreviated sensor name.
focalPlaneData : list
Data of focal plane: [x position (microns), y position (microns),
pixel size (microns), number of x pixels, number of y pixels].
"""
# The layout is shown in the following:
# R04_S20 R44_S00
# -------- ----------- /\ +y
# | C0 | | | | |
# |------| | C1 | C0 | |
# | C1 | | | | |
# -------- ----------- -----> +x
# R00_S22 R40_S02
# ----------- --------
# | | | | C1 |
# | C0 | C1 | |------|
# | | | | C0 |
# ----------- --------
xInUm = float(focalPlaneData[0])
yInUm = float(focalPlaneData[1])
pixelSizeInUm = float(focalPlaneData[2])
sizeXinPixel = float(focalPlaneData[3])
# Consider the x-translation in corner wavefront sensors
tempX = None
tempY = None
if sensorName in ("R44_S00_C0", "R00_S22_C1"):
# Shift center to +x direction
tempX = xInUm + sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R44_S00_C1", "R00_S22_C0"):
# Shift center to -x direction
tempX = xInUm - sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R04_S20_C1", "R40_S02_C0"):
# Shift center to -y direction
tempY = yInUm - sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R04_S20_C0", "R40_S02_C1"):
# Shift center to +y direction
tempY = yInUm + sizeXinPixel / 2 * pixelSizeInUm
# Replace the value by the shifted one
if tempX is not None:
focalPlaneData[0] = str(tempX)
elif tempY is not None:
focalPlaneData[1] = str(tempY)
def _getDeblendDonutTypeInSetting(self):
"""Get the deblend donut type in the setting.
Returns
-------
enum 'DeblendDonutType'
Deblend donut type algorithm.
"""
deblendDonutType = self.settingFile.getSetting("deblendDonutAlgo")
return getDeblendDonutType(deblendDonutType)
def config(self, sensorName=None):
"""Do the configuration.
Parameters
----------
sensorName : str, optional
Abbreviated sensor name. (the default is None.)
"""
if sensorName is not None:
self.sensorName = sensorName
def getEulerZinDeg(self, sensorName):
"""Get the Euler Z angle of sensor in degree.
Parameters
----------
sensorName : str
Abbreviated sensor name.
Returns
-------
float
Euler Z angle in degree.
"""
return float(self.sensorEulerRot[sensorName][0])
def camXYtoFieldXY(self, pixelX, pixelY):
"""Get the field X, Y from the pixel x, y position on CCD.
Parameters
----------
pixelX : float
Pixel x on camera coordinate.
pixelY : float
Pixel y on camera coordinate.
Returns
-------
float
Field x in degree.
float
Field y in degree.
"""
# The wavefront sensors will do the counter-clockwise rotation as the
# following based on the euler angle:
# R04_S20 R44_S00
# O------- -----O----O /\ +y
# | C0 | | | | |
# O------| | C1 | C0 | |
# | C1 | | | | |
# -------- ----------- O----> +x
# R00_S22 R40_S02
# ----------- --------
# | | | | C1 |
# | C0 | C1 | |------O
# | | | | C0 |
# O----O----- -------O
# Get the field X, Y of sensor's center
fieldXc, fieldYc = self.sensorFocaPlaneInDeg[self.sensorName]
# Get the center pixel position
pixelXc, pixelYc = self.sensorDimList[self.sensorName]
pixelXc = pixelXc / 2
pixelYc = pixelYc / 2
# Calculate the delta x and y in degree
# 1 degree = 3600 arcsec
pixelToArcsec = self.settingFile.getSetting("pixelToArcsec")
deltaX = (pixelX - pixelXc) * pixelToArcsec / 3600.0
deltaY = (pixelY - pixelYc) * pixelToArcsec / 3600.0
# Calculate the transformed coordinate in degree.
fieldX, fieldY = self._rotCam2FocalPlane(self.sensorName, fieldXc,
fieldYc, deltaX, deltaY)
return fieldX, fieldY
def _rotCam2FocalPlane(self,
sensorName,
centerX,
centerY,
deltaX,
deltaY,
clockWise=False):
"""Do the rotation from camera coordinate to focal plane coordinate or
vice versa.
Parameters
----------
sensorName : str
Abbreviated sensor name.
centerX : float
CCD center x.
centerY : float
CCD center y.
deltaX : float
Delta x from the CCD's center.
deltaY : float
Delta y from the CCD's center.
clockWise : bool, optional
Rotation direction (True: clockwise, False: counter-clockwise).
(the default is False.)
Returns
-------
float
Transformed x position.
float
Transformed y position.
"""
# Get the euler angle in z direction (only consider the z rotatioin at
# this moment)
eulerZ = round(self.getEulerZinDeg(sensorName))
eulerZinRad = np.deg2rad(eulerZ)
# Counter-clockwise or clockwise rotation
if clockWise:
eulerZinRad = -eulerZinRad
# Calculate the new x, y by the rotation. This is important for
# wavefront sensor.
newX = centerX + np.cos(eulerZinRad) * deltaX - np.sin(
eulerZinRad) * deltaY
newY = centerY + np.sin(eulerZinRad) * deltaX + np.cos(
eulerZinRad) * deltaY
return newX, newY
def dmXY2CamXY(self, pixelDmX, pixelDmY):
"""Transform the pixel x, y from DM library to camera to use.
Parameters
----------
pixelDmX : float
Pixel x defined in DM coordinate.
pixelDmY : float
Pixel y defined in DM coordinate.
Returns
-------
float
Pixel x defined in camera coordinate based on LCA-13381.
float
Pixel y defined in camera coordinate based on LCA-13381.
"""
# Camera coordinate is defined in LCA-13381. Define camera coordinate
# (x, y) and DM coordinate (x', y'), then the relation is:
# Camera team +y = DM team +x'
# Camera team +x = DM team -y'
# O---->y
# |
# | ----------------------
# \/ | | (x', y') = (200, 500) =>
# x | | (x, y) = (-500, 200) -> (3500, 200)
# |4000 |
# y' | |
# /\ | 4072 |
# | |----------------------
# |
# O-----> x'
# Get the CCD dimension
dimX, dimY = self.sensorDimList[self.sensorName]
# Calculate the transformed coordinate
pixelCamX = dimX - pixelDmY
pixelCamY = pixelDmX
return pixelCamX, pixelCamY
def camXY2DmXY(self, pixelCamX, pixelCamY):
"""Transform the pixel x, y from camera coordinate to DM coordinate.
Parameters
----------
pixelCamX : float
Pixel x defined in Camera coordinate based on LCA-13381.
pixelCamY : float
Pixel y defined in Camera coordinate based on LCA-13381.
Returns
-------
float
Pixel x defined in DM coordinate.
float
Pixel y defined in DM coordinate.
"""
# Check the comment in dmXY2CamXY() for the details of coordinate
# systems of DM and camera teams.
# Get the CCD dimension
dimX, dimY = self.sensorDimList[self.sensorName]
# Calculate the transformed coordinate
pixelDmX = pixelCamY
pixelDmY = dimX - pixelCamX
return pixelDmX, pixelDmY
def isVignette(self, fieldX, fieldY):
"""The donut is vignetted or not by calculating the donut's distance to
center.
Parameters
----------
fieldX : float
Field x in degree.
fieldY : float
Field y in degree.
Returns
-------
bool
True if the donut is vignette.
"""
# Calculate the distance to center in degree to judge the donut is
# vignetted or not.
fldr = np.sqrt(fieldX**2 + fieldY**2)
distVignette = self.settingFile.getSetting("distVignette")
if fldr >= distVignette:
return True
else:
return False
def simulateImg(self,
imageFolderPath,
defocalDis,
nbrStar,
filterType,
noiseRatio=0.01):
"""Simulate the defocal CCD images with the neighboring star map.
This function is only for the test use.
Parameters
----------
imageFolderPath : str
Path to image directory.
defocalDis : float
Defocal distance in mm.
nbrStar : NbrStar
Neighboring star on single detector.
filterType : FilterType
Filter type.
noiseRatio : float, optional
The noise ratio. (the default is 0.01.)
Returns
-------
numpy.ndarray
Simulated intra-focal images.
numpy.ndarray
Simulated extra-focal images.
Raises
-------
ValueError
No available donut images.
ValueError
The numbers of intra- and extra-focal images are different.
"""
# Generate the intra- and extra-focal ccd images
d1, d2 = self.sensorDimList[self.sensorName]
ccdImgIntra = np.random.random([d2, d1]) * noiseRatio
ccdImgExtra = ccdImgIntra.copy()
# Get all files in the image directory in a sorted order
fileList = sorted(os.listdir(imageFolderPath))
# Redefine the format of defocal distance
defocalDis = "%.2f" % defocalDis
# Get the available donut files
intraFileList = []
extraFileList = []
for afile in fileList:
# Get the file name
fileName, fileExtension = os.path.splitext(afile)
# Split the file name for the analysis
fileNameStr = fileName.split("_")
# Find the file name with the correct defocal distance
if len(fileNameStr) == 3 and fileNameStr[1] == defocalDis:
# Collect the file name based on the defocal type
if fileNameStr[-1] == "intra":
intraFileList.append(afile)
elif fileNameStr[-1] == "extra":
extraFileList.append(afile)
# Get the number of available files
numFile = len(intraFileList)
if numFile == 0:
raise ValueError("No available donut images.")
# Check the numbers of intra- and extra-focal images should be the same
if numFile != len(extraFileList):
raise ValueError(
"The numbers of intra- and extra-focal images are different.")
# Get the magnitude of stars
mappedFilterType = mapFilterRefToG(filterType)
starMag = nbrStar.getMag(mappedFilterType)
# Based on the nbrStar to reconstruct the image
for brightStar, neighboringStar in nbrStar.getId().items():
# Generate a random number
randNum = np.random.randint(0, high=numFile)
# Choose a random donut image from the file
donutImageIntra = self._getDonutImgFromFile(
imageFolderPath, intraFileList[randNum])
donutImageExtra = self._getDonutImgFromFile(
imageFolderPath, extraFileList[randNum])
# Get the bright star magnitude
magBS = starMag[brightStar]
# Combine the bright star and neighboring stars. Put the bright
# star in the first one.
allStars = neighboringStar[:]
allStars.insert(0, brightStar)
# Add the donut image
for star in allStars:
# Get the brigtstar pixel x, y
starX, starY = nbrStar.getRaDeclInPixel()[star]
magStar = starMag[star]
# Transform the coordiante from DM team to camera team
starX, starY = self.dmXY2CamXY(starX, starY)
# Ratio of magnitude between donuts (If the magnitudes of stars
# differs by 5, the brightness differs by 100.)
# (Magnitude difference shoulbe be >= 1.)
magDiff = magStar - magBS
magRatio = 1 / 100**(magDiff / 5.0)
# Add the donut image
self._addDonutImage(magRatio * donutImageIntra, starX, starY,
ccdImgIntra)
self._addDonutImage(magRatio * donutImageExtra, starX, starY,
ccdImgExtra)
return ccdImgIntra, ccdImgExtra
def _getDonutImgFromFile(self, imageFolderPath, fileName):
"""Read the donut image from the file.
Parameters
----------
imageFolderPath : str
Path to image directory.
fileName : str
File name.
Returns
-------
numpy.ndarray
Donut image.
"""
imageFilePath = os.path.join(imageFolderPath, fileName)
return np.loadtxt(imageFilePath)
def _addDonutImage(self, donutImage, starX, starY, ccdImg):
"""Add the donut image to simulated CCD image frame.
Parameters
----------
donutImage : numpy.ndarray
Donut image.
starX : float
Star position in pixel x.
starY : float
Star position in pixel y.
ccdImg : numpy.ndarray
CCD image.
"""
# Get the dimension of donut image
d1, d2 = donutImage.shape
# Get the interger of position to use as the index
y = int(starY)
x = int(starX)
# Add the donut image on the CCD image
ccdImg[y - int(d1 / 2):y - int(d1 / 2) + d1,
x - int(d2 / 2):x - int(d2 / 2) + d2, ] += donutImage
def getSingleTargetImage(self, ccdImg, nbrStar, index, filterType):
"""Get the image of single scientific target and related neighboring
stars.
Parameters
----------
ccdImg : numpy.ndarray
CCD image.
nbrStar : NbrStar
Neighboring star on single detector.
index : int
Index of science target star in neighboring star.
filterType : FilterType
Filter type.
Returns
-------
numpy.ndarray
Ccd image of target stars.
numpy.ndarray
Star x-positions. The arange is [neighboring stars, bright star].
numpy.ndarray
Star y-positions. The arange is [neighboring stars, bright star].
numpy.ndarray
Star magnitude ratio compared with the bright star. The arange is
[neighboring stars, bright star].
float
Offset x from the origin of target star image to the origin of CCD
image.
float
Offset y from the origin of target star image to the origin of CCD
image.
Raises
------
ValueError
Index is higher than the length of star map.
"""
# Get the target star position
nbrStarId = nbrStar.getId()
if index >= len(nbrStarId):
raise ValueError("Index is higher than the length of star map.")
# Get the star SimobjID
brightStar = list(nbrStarId)[index]
neighboringStar = nbrStarId[brightStar]
# Get all star SimobjID list
allStar = neighboringStar[:]
allStar.append(brightStar)
# Get the pixel positions
raDeclInPixel = nbrStar.getRaDeclInPixel()
allStarPosX = []
allStarPosY = []
for star in allStar:
# Get the star pixel position
starX, starY = raDeclInPixel[star]
# Transform the coordiante from DM team to camera team
starX, starY = self.dmXY2CamXY(starX, starY)
allStarPosX.append(starX)
allStarPosY.append(starY)
# Check the ccd image dimenstion
ccdD1, ccdD2 = ccdImg.shape
# Define the range of image
# Get min/ max of x, y
minX = int(min(allStarPosX))
maxX = int(max(allStarPosX))
minY = int(min(allStarPosY))
maxY = int(max(allStarPosY))
# Get the central point
cenX = int(np.mean([minX, maxX]))
cenY = int(np.mean([minY, maxY]))
# Get the image dimension
starRadiusInPixel = self.settingFile.getSetting("starRadiusInPixel")
d1 = (maxY - minY) + 4 * starRadiusInPixel
d2 = (maxX - minX) + 4 * starRadiusInPixel
# Make d1 and d2 to be symmetric and even
d = max(d1, d2)
if d % 2 == 1:
# Use d-1 instead of d+1 to avoid the boundary touch
d = d - 1
# If central x or y plus d/2 will over the boundary, shift the
# central x, y values
cenY = self._shiftCenter(cenY, ccdD1, d / 2)
cenY = self._shiftCenter(cenY, 0, d / 2)
cenX = self._shiftCenter(cenX, ccdD2, d / 2)
cenX = self._shiftCenter(cenX, 0, d / 2)
# Get the bright star and neighboring stas image
offsetX = cenX - d / 2
offsetY = cenY - d / 2
singleSciNeiImg = ccdImg[int(offsetY):int(cenY + d / 2),
int(offsetX):int(cenX + d / 2)]
# Get the stars position in the new coordinate system
# The final one is the bright star
allStarPosX = np.array(allStarPosX) - offsetX
allStarPosY = np.array(allStarPosY) - offsetY
# Get the star magnitude
mappedFilterType = mapFilterRefToG(filterType)
magList = nbrStar.getMag(mappedFilterType)
# Get the list of magnitude
magRatio = np.array([])
for star in allStar:
neiMag = magList[star]
magRatio = np.append(magRatio, neiMag)
# Calculate the magnitude ratio
magRatio = 1 / 100**((magRatio - magRatio[-1]) / 5.0)
return singleSciNeiImg, allStarPosX, allStarPosY, magRatio, offsetX, offsetY
def _shiftCenter(self, center, boundary, distance):
"""Shift the center if its distance to boundary is less than required.
Parameters
----------
center : float
Center point.
boundary : float
Boundary point.
distance : float
Required distance.
Returns
-------
float
Shifted center.
"""
# Distance between the center and boundary
delta = boundary - center
# Shift the center if needed
if abs(delta) < distance:
return boundary - np.sign(delta) * distance
else:
return center
def doDeblending(self, blendedImg, allStarPosX, allStarPosY, magRatio):
"""Do the deblending.
It is noted that the algorithm now is only for one bright star and one
neighboring star.
Parameters
----------
blendedImg : numpy.ndarray
Blended image.
allStarPosX : list or numpy.ndarray
Star's position x in pixel. The arange is [neighboring star,
bright star].
allStarPosY : list or numpy.ndarray
Star's position y in pixel. The arange is [neighboring star,
bright star].
magRatio : list or numpy.ndarray
Star magnitude ratio compared with the bright star. The arange is
[neighboring stars, bright star].
Returns
-------
numpy.ndarray
Deblended image.
float
Pixel x of bright star.
float
Pixel y of bright star.
Raises
------
ValueError
Only one neighboring star allowed.
"""
# Check there is only one bright star and one neighboring star.
# This is the limit of deblending algorithm now.
if len(magRatio) != 2:
raise ValueError("Only one neighboring star allowed.")
# Do the deblending
iniGuessXY = [(allStarPosX[0], allStarPosY[0])]
imgDeblend, realcx, realcy = self.deblend.deblendDonut(
blendedImg, iniGuessXY)
return imgDeblend, realcx, realcy
class TestParamReader(unittest.TestCase):
"""Test the ParamReaderYaml class."""
def setUp(self):
testDir = os.path.join(getModulePath(), "tests")
self.configDir = os.path.join(testDir, "testData")
self.fileName = "testConfigFile.yaml"
filePath = os.path.join(self.configDir, self.fileName)
self.paramReader = ParamReader(filePath=filePath)
self.testTempDir = os.path.join(testDir, "tmp")
self._makeDir(self.testTempDir)
def _makeDir(self, directory):
if (not os.path.exists(directory)):
os.makedirs(directory)
def tearDown(self):
shutil.rmtree(self.testTempDir)
def testGetSetting(self):
znmax = self.paramReader.getSetting("znmax")
self.assertEqual(znmax, 22)
def testGetFilePath(self):
ansFilePath = os.path.join(self.configDir, self.fileName)
self.assertEqual(self.paramReader.getFilePath(), ansFilePath)
def testSetFilePath(self):
fileName = "test.yaml"
filePath = os.path.join(self.configDir, fileName)
self.paramReader.setFilePath(filePath)
self.assertEqual(self.paramReader.getFilePath(), filePath)
def testGetContent(self):
content = self.paramReader.getContent()
self.assertTrue(isinstance(content, dict))
def testGetContentWithDefaultSetting(self):
paramReader = ParamReader()
content = paramReader.getContent()
self.assertTrue(isinstance(content, dict))
def testWriteMatToFile(self):
self._writeMatToFile()
numOfFile = self._getNumOfFileInFolder(self.testTempDir)
self.assertEqual(numOfFile, 1)
def _writeMatToFile(self):
mat = np.random.rand(3, 4, 5)
filePath = os.path.join(self.testTempDir, "temp.yaml")
ParamReader.writeMatToFile(mat, filePath)
return mat, filePath
def _getNumOfFileInFolder(self, folder):
return len([name for name in os.listdir(folder)
if os.path.isfile(os.path.join(folder, name))])
def testWriteMatToFileWithWrongFileFormat(self):
wrongFilePath = os.path.join(self.testTempDir, "temp.txt")
self.assertRaises(ValueError, ParamReader.writeMatToFile,
np.ones(4), wrongFilePath)
def testGetMatContent(self):
mat, filePath = self._writeMatToFile()
self.paramReader.setFilePath(filePath)
matInYamlFile = self.paramReader.getMatContent()
delta = np.sum(np.abs(matInYamlFile - mat))
self.assertLess(delta, 1e-10)
def testGetMatContentWithDefaultSetting(self):
paramReader = ParamReader()
matInYamlFile = paramReader.getMatContent()
self.assertTrue(isinstance(matInYamlFile, np.ndarray))
self.assertEqual(len(matInYamlFile), 0)
class Filter(object):
def __init__(self):
"""Initialize the filter class."""
# Configuration file of the limit of star's magnitude
pathMagLimitStar = os.path.join(getConfigDir(), "bsc",
"magLimitStar.yaml")
self._fileMagLimitStar = ParamReader(filePath=pathMagLimitStar)
# Filter type in use
self.filter = FilterType.U
def getFilter(self):
"""Get the filter type.
Returns
-------
FilterType
Filter type.
"""
return self.filter
def setFilter(self, filterType):
"""Set the filter type.
Parameters
----------
filterType : FilterType
Filter type.
"""
self.filter = filterType
def getMagBoundary(self):
"""Get the boundary of magnitude under the current filter type.
Returns
-------
float
Lower boundary of magnitude.
float
Higher boundary of magnitude.
Raises
------
ValueError
No filter type matches.
"""
mappedFilterType = mapFilterRefToG(self.filter)
if mappedFilterType == FilterType.U:
filterName = "filterU"
elif mappedFilterType == FilterType.G:
filterName = "filterG"
elif mappedFilterType == FilterType.R:
filterName = "filterR"
elif mappedFilterType == FilterType.I:
filterName = "filterI"
elif mappedFilterType == FilterType.Z:
filterName = "filterZ"
elif mappedFilterType == FilterType.Y:
filterName = "filterY"
else:
raise ValueError("No filter type matches.")
rangeMag = self._fileMagLimitStar.getSetting(filterName)
return rangeMag["low"], rangeMag["high"]
class Instrument(object):
def __init__(self, instDir):
"""Instrument class for wavefront estimation.
Parameters
----------
instDir : str
Instrument configuration directory.
"""
self.instDir = instDir
self.instName = ""
self.dimOfDonut = 0
self.announcedDefocalDisInMm = 0.0
self.instParamFile = ParamReader()
self.maskParamFile = ParamReader()
self.xSensor = np.array([])
self.ySensor = np.array([])
self.xoSensor = np.array([])
self.yoSensor = np.array([])
def config(self, camType, dimOfDonutOnSensor,
announcedDefocalDisInMm=1.5,
instParamFileName="instParam.yaml",
maskMigrateFileName="maskMigrate.yaml"):
"""Do the configuration of Instrument.
Parameters
----------
camType : enum 'CamType'
Camera type.
dimOfDonutOnSensor : int
Dimension of image on sensor in pixel.
announcedDefocalDisInMm : float
Announced defocal distance in mm. It is noted that the defocal
distance offset used in calculation might be different from this
value. (the default is 1.5.)
instParamFileName : str, optional
Instrument parameter file name. (the default is "instParam.yaml".)
maskMigrateFileName : str, optional
Mask migration (off-axis correction) file name. (the default is
"maskMigrate.yaml".)
"""
self.instName = self._getInstName(camType)
self.dimOfDonut = int(dimOfDonutOnSensor)
self.announcedDefocalDisInMm = announcedDefocalDisInMm
# Path of instrument param file
instFileDir = self.getInstFileDir()
instParamFilePath = os.path.join(instFileDir, instParamFileName)
self.instParamFile.setFilePath(instParamFilePath)
# Path of mask off-axis correction file
maskParamFilePath = os.path.join(instFileDir, maskMigrateFileName)
self.maskParamFile.setFilePath(maskParamFilePath)
self._setSensorCoor()
self._setSensorCoorAnnular()
def _getInstName(self, camType):
"""Get the instrument name.
Parameters
----------
camType : enum 'CamType'
Camera type.
Returns
-------
str
Instrument name.
Raises
------
ValueError
Camera type is not supported.
"""
if (camType == CamType.LsstCam):
return "lsst"
elif (camType == CamType.ComCam):
return "comcam"
else:
raise ValueError("Camera type (%s) is not supported." % camType)
def getInstFileDir(self):
"""Get the instrument parameter file directory.
Returns
-------
str
Instrument parameter file directory.
"""
return os.path.join(self.instDir, self.instName)
def _setSensorCoor(self):
"""Set the sensor coordinate."""
ySensorGrid, xSensorGrid = np.mgrid[
-(self.dimOfDonut/2-0.5):(self.dimOfDonut/2 + 0.5),
-(self.dimOfDonut/2-0.5):(self.dimOfDonut/2 + 0.5)]
sensorFactor = self.getSensorFactor()
denominator = self.dimOfDonut / 2 / sensorFactor
self.xSensor = xSensorGrid / denominator
self.ySensor = ySensorGrid / denominator
def _setSensorCoorAnnular(self):
"""Set the sensor coordinate with the annular aperature."""
self.xoSensor = self.xSensor.copy()
self.yoSensor = self.ySensor.copy()
# Get the position index that is out of annular aperature range
obscuration = self.getObscuration()
r2Sensor = self.xSensor**2 + self.ySensor**2
idx = (r2Sensor > 1) | (r2Sensor < obscuration**2)
# Define the value to be NaN if it is not in pupul
self.xoSensor[idx] = np.nan
self.yoSensor[idx] = np.nan
def setAnnDefocalDisInMm(self, annDefocalDisInMm):
"""Set the announced defocal distance in mm.
Parameters
----------
annDefocalDisInMm : float
Announced defocal distance in mm.
"""
self.announcedDefocalDisInMm = annDefocalDisInMm
def getAnnDefocalDisInMm(self):
"""Get the announced defocal distance in mm.
Returns
-------
float
Announced defocal distance in mm.
"""
return self.announcedDefocalDisInMm
def getInstFilePath(self):
"""Get the instrument parameter file path.
Returns
-------
str
Instrument parameter file path.
"""
return self.instParamFile.getFilePath()
def getMaskOffAxisCorr(self):
"""Get the mask off-axis correction.
Returns
-------
numpy.ndarray
Mask off-axis correction.
"""
return self.maskParamFile.getMatContent()
def getDimOfDonutOnSensor(self):
"""Get the dimension of donut's size on sensor in pixel.
Returns
-------
int
Dimension of donut's size on sensor in pixel.
"""
return self.dimOfDonut
def getObscuration(self):
"""Get the obscuration.
Returns
-------
float
Obscuration.
"""
return self.instParamFile.getSetting("obscuration")
def getFocalLength(self):
"""Get the focal length of telescope in meter.
Returns
-------
float
Focal length of telescope in meter.
"""
return self.instParamFile.getSetting("focalLength")
def getApertureDiameter(self):
"""Get the aperture diameter in meter.
Returns
-------
float
Aperture diameter in meter.
"""
return self.instParamFile.getSetting("apertureDiameter")
def getDefocalDisOffset(self):
"""Get the defocal distance offset in meter.
Returns
-------
float
Defocal distance offset in meter.
"""
offset = self.instParamFile.getSetting("offset")
defocalDisInMm = "%.1fmm" % self.announcedDefocalDisInMm
return offset[defocalDisInMm]
def getCamPixelSize(self):
"""Get the camera pixel size in meter.
Returns
-------
float
Camera pixel size in meter.
"""
return self.instParamFile.getSetting("pixelSize")
def getMarginalFocalLength(self):
"""Get the marginal focal length in meter.
Marginal_focal_length = sqrt(f^2 - (D/2)^2)
Returns
-------
float
Marginal focal length in meter.
"""
focalLength = self.getFocalLength()
apertureDiameter = self.getApertureDiameter()
marginalFL = np.sqrt(focalLength**2 - (apertureDiameter/2)**2)
return marginalFL
def getSensorFactor(self):
"""Get the sensor factor.
Returns
-------
float
Sensor factor.
"""
offset = self.getDefocalDisOffset()
apertureDiameter = self.getApertureDiameter()
focalLength = self.getFocalLength()
pixelSize = self.getCamPixelSize()
sensorFactor = self.dimOfDonut / (
offset * apertureDiameter / focalLength / pixelSize)
return sensorFactor
def getSensorCoor(self):
"""Get the sensor coordinate.
Returns
-------
numpy.ndarray
X coordinate.
numpy.ndarray
Y coordinate.
"""
return self.xSensor, self.ySensor
def getSensorCoorAnnular(self):
"""Get the sensor coordinate with the annular aperature.
Returns
-------
numpy.ndarray
X coordinate.
numpy.ndarray
Y coordinate.
"""
return self.xoSensor, self.yoSensor
class CamSim(object):
def __init__(self, camTBinDegC=6.5650, camRotInRad=0):
"""Initialization of camera simulator class.
This class is used to correct the camera distortion.
Parameters
----------
camTBinDegC : float, optional
Camera body temperature in degree C. (the default is 6.5650.)
camRotInRad : float, optional
Camera rotation angle in radian. (the default is 0.)
"""
# Configuration directory
self.configDir = os.path.join(getConfigDir(), "camera")
# Camera setting file
settingFilePath = os.path.join(self.configDir, "camSetting.yaml")
self._camSettingFile = ParamReader(filePath=settingFilePath)
# Camera body temperature in degree C
self.camTBinDegC = self.setBodyTempInDegC(camTBinDegC)
# Camera rotation angle in radian
self.camRotInRad = self.setRotAngInRad(camRotInRad)
def setRotAngInRad(self, rotAngInRad):
"""Set the camera rotation angle in radian.
The angle should be in (-pi/2, pi/2).
Parameters
----------
rotAngInRad : float
Rotation angle in radian.
"""
if self._valueInRange(rotAngInRad, -np.pi / 2, np.pi / 2):
self.camRotInRad = rotAngInRad
def setRotAngInDeg(self, rotAngInDeg):
"""Set the camera rotation angle in degree.
The angle should be in (-90, 90).
Parameters
----------
rotAngInDeg : float
Rotation angle in degree.
"""
if self._valueInRange(rotAngInDeg, -90, 90):
self.camRotInRad = np.deg2rad(rotAngInDeg)
def setBodyTempInDegC(self, tempInDegC):
"""Set the camera body temperature in degree C.
Parameters
----------
tempInDegC : float
Temperature in degree C.
"""
minBodyTempInDegC = self._camSettingFile.getSetting("minBodyTemp")
maxBodyTempInDegC = self._camSettingFile.getSetting("maxBodyTemp")
if self._valueInRange(tempInDegC, minBodyTempInDegC,
maxBodyTempInDegC):
self.camTBinDegC = tempInDegC
def _valueInRange(self, value, lowerBound, upperBound):
"""Check the value is in the range or not.
Parameters
----------
value : float
Set value.
lowerBound : float
Lower bound.
upperBound : float
Upper bound.
Returns
-------
bool
Ture if the value is in the range.
Raises
------
ValueError
The setting value should be in (lowerBound, upperBound).
"""
valueInRange = False
if (lowerBound <= value <= upperBound):
valueInRange = True
else:
raise ValueError("The setting value should be in (%.3f, %.3f)." %
(lowerBound, upperBound))
return valueInRange
def getCamDistortionInMm(self, zAngleInRad, camDistType):
"""Get the camera distortion correction in mm.
Parameters
----------
zAngleInRad : float
Zenith angle in radian.
camDistType : enum 'CamDistType'
Camera distortion type.
Returns
-------
numpy.ndarray
Camera distortion in mm.
"""
# Get the distortion data
distType = camDistType.name
dataFilePath = os.path.join(self.configDir, (distType + ".yaml"))
paramReader = ParamReader(filePath=dataFilePath)
data = paramReader.getMatContent()
# Calculate the gravity and temperature distortions
distortion = self._calcGravityDist(data, zAngleInRad) + \
self._calcTempDist(data)
# Reorder the index of Zernike corrections to match the PhoSim use
zIdx = self._camSettingFile.getSetting("zIdxMapping")
# The index of python begins from 0.
distortion = distortion[[x - 1 for x in zIdx]]
return distortion
def _calcGravityDist(self, camDistData, zAngleInRad):
"""Calculate the distortion from gravity.
Parameters
----------
camDistData : numpy.ndarray
Camera distortion data.
zAngleInRad : float
Zenith angle in radian.
Returns
-------
numpy.ndarray
Distortion from gravity.
"""
# Pre-compensated elevation angle in radian.
pre_elev = 0
# Pre-compensated camera rotation angle in radian.
pre_camR = 0
distortion = self._gravityDistFunc(camDistData, zAngleInRad,
self.camRotInRad) - \
self._gravityDistFunc(camDistData, pre_elev, pre_camR)
return distortion
def _gravityDistFunc(self, camDistData, zenithAngle, camRotAngle):
"""Gravity distortion function.
Parameters
----------
camDistData : numpy.ndarray
Camera distortion data.
zenithAngle : float
Zenith angle.
camRotAngle : float
Camera rotation angle.
Returns
-------
numpy.ndarray
Gravity distortion function.
"""
distFun = camDistData[0, 3:]*np.cos(zenithAngle) + \
(camDistData[1, 3:]*np.cos(camRotAngle) +
camDistData[2, 3:]*np.sin(camRotAngle)) * \
np.sin(zenithAngle)
return distFun
def _calcTempDist(self, camDistData):
"""Calculate the distortion from temperature.
Parameters
----------
camDistData : numpy.ndarray
Camera distortion data.
Returns
-------
numpy.ndarray
Distortion from temperature.
"""
# List of data:
# [ze. angle, camRot angle, temp (C), dx (mm), dy (mm), dz (mm),
# Rx (rad), Ry (rad), Rz (rad)]
startTempRowIdx = 3
endTempRowIdx = 10
# Do the temperature correction by the simple temperature
# interpolation/ extrapolation
# If the temperature is too low, use the lowest listed temperature
# to do the correction.
if (self.camTBinDegC <= camDistData[startTempRowIdx, 2]):
distortion = camDistData[startTempRowIdx, 3:]
# If the temperature is too high, use the highest listed temperature
# to do the correction.
elif (self.camTBinDegC >= camDistData[endTempRowIdx, 2]):
distortion = camDistData[endTempRowIdx, 3:]
# Get the correction value by the linear fitting
else:
# Find the temperature boundary indexes
p2 = (camDistData[startTempRowIdx:, 2] >
self.camTBinDegC).argmax() + startTempRowIdx
p1 = p2 - 1
# Calculate the linear weighting
w1 = (camDistData[p2, 2] - self.camTBinDegC) / \
(camDistData[p2, 2] - camDistData[p1, 2])
w2 = 1 - w1
distortion = w1 * camDistData[p1, 3:] + w2 * camDistData[p2, 3:]
# Minus the reference temperature correction. There is the problem
# here.
# If the pre_temp_cam is not on the data list, this statement will
# fail/ get nothing.
# Pre-compensated camera temperature in degree C.
pre_temp_cam = 0
distortion -= camDistData[(camDistData[startTempRowIdx:,
2] == pre_temp_cam).argmax() +
startTempRowIdx, 3:]
return distortion
class SourceProcessor(object):
def __init__(self, settingFileName="default.yaml",
focalPlaneFileName="focalplanelayout.txt"):
"""Initialize the SourceProcessor class.
Parameters
----------
settingFileName : str, optional
Setting file name. (the default is "default.yaml".)
focalPlaneFileName : str, optional
Focal plane file name used in the PhoSim instrument directory. (the
default is "focalplanelayout.txt".)
"""
self.sensorName = ""
self.blendedImageDecorator = BlendedImageDecorator()
configDir = getConfigDir()
settingFilePath = os.path.join(configDir, settingFileName)
self.settingFile = ParamReader(filePath=settingFilePath)
self.sensorFocaPlaneInDeg = dict()
self.sensorFocaPlaneInUm = dict()
self.sensorDimList = dict()
self.sensorEulerRot = dict()
self._readFocalPlane(configDir, focalPlaneFileName)
def _readFocalPlane(self, folderPath, focalPlaneFileName):
"""Read the focal plane data used in PhoSim to get the ccd dimension
and fieldXY in chip center.
Parameters
----------
folderPath : str
Directory of focal plane file.
focalPlaneFileName : str
Focal plane file name used in the PhoSim instrument directory.
"""
# Read the focal plane data by the delegation
ccdData = readPhoSimSettingData(folderPath, focalPlaneFileName,
"fieldCenter")
# Collect the focal plane data
sensorFocaPlaneInDeg = dict()
sensorFocaPlaneInUm = dict()
sensorDimList = dict()
for sensorName, data in ccdData.items():
# Consider the x-translation in corner wavefront sensors
self._shiftCenterWfs(sensorName, data)
# Change the unit from um to degree
xInUm = float(data[0])
yInUm = float(data[1])
pixelSizeInUm = float(data[2])
sizeXinPixel = int(data[3])
sizeYinPixel = int(data[4])
# 1 degree = 3600 arcsec
pixelToArcsec = self.settingFile.getSetting("pixelToArcsec")
fieldX = xInUm / pixelSizeInUm * pixelToArcsec / 3600
fieldY = yInUm / pixelSizeInUm * pixelToArcsec / 3600
# Get the data
sensorFocaPlaneInDeg[sensorName] = (fieldX, fieldY)
sensorFocaPlaneInUm[sensorName] = (xInUm, yInUm)
sensorDimList[sensorName] = (sizeXinPixel, sizeYinPixel)
# Assign the values
self.sensorDimList = sensorDimList
self.sensorFocaPlaneInDeg = sensorFocaPlaneInDeg
self.sensorFocaPlaneInUm = sensorFocaPlaneInUm
self.sensorEulerRot = readPhoSimSettingData(
folderPath, focalPlaneFileName, "eulerRot")
def _shiftCenterWfs(self, sensorName, focalPlaneData):
"""Shift the fieldXY of center of wavefront sensors.
The input data is the center of combined chips (C0+C1). The input data
will be updated directly.
Parameters
----------
sensorName : str
Abbreviated sensor name.
focalPlaneData : list
Data of focal plane: [x position (microns), y position (microns),
pixel size (microns), number of x pixels, number of y pixels].
"""
# The layout is shown in the following:
# R04_S20 R44_S00
# -------- ----------- /\ +y
# | C0 | | | | |
# |------| | C1 | C0 | |
# | C1 | | | | |
# -------- ----------- -----> +x
# R00_S22 R40_S02
# ----------- --------
# | | | | C1 |
# | C0 | C1 | |------|
# | | | | C0 |
# ----------- --------
xInUm = float(focalPlaneData[0])
yInUm = float(focalPlaneData[1])
pixelSizeInUm = float(focalPlaneData[2])
sizeXinPixel = float(focalPlaneData[3])
# Consider the x-translation in corner wavefront sensors
tempX = None
tempY = None
if sensorName in ("R44_S00_C0", "R00_S22_C1"):
# Shift center to +x direction
tempX = xInUm + sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R44_S00_C1", "R00_S22_C0"):
# Shift center to -x direction
tempX = xInUm - sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R04_S20_C1", "R40_S02_C0"):
# Shift center to -y direction
tempY = yInUm - sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R04_S20_C0", "R40_S02_C1"):
# Shift center to +y direction
tempY = yInUm + sizeXinPixel / 2 * pixelSizeInUm
# Replace the value by the shifted one
if (tempX is not None):
focalPlaneData[0] = str(tempX)
elif (tempY is not None):
focalPlaneData[1] = str(tempY)
def config(self, sensorName=None):
"""Do the configuration.
Parameters
----------
sensorName : str, optional
Abbreviated sensor name. (the default is None.)
"""
if (sensorName is not None):
self.sensorName = sensorName
def getEulerZinDeg(self, sensorName):
"""Get the Euler Z angle of sensor in degree.
Parameters
----------
sensorName : str
Abbreviated sensor name.
Returns
-------
float
Euler Z angle in degree.
"""
return float(self.sensorEulerRot[sensorName][0])
def camXYtoFieldXY(self, pixelX, pixelY):
"""Get the field X, Y from the pixel x, y position on CCD.
Parameters
----------
pixelX : float
Pixel x on camera coordinate.
pixelY : float
Pixel y on camera coordinate.
Returns
-------
float
Field x in degree.
float
Field y in degree.
"""
# The wavefront sensors will do the counter-clockwise rotation as the
# following based on the euler angle:
# R04_S20 R44_S00
# O------- -----O----O /\ +y
# | C0 | | | | |
# O------| | C1 | C0 | |
# | C1 | | | | |
# -------- ----------- O----> +x
# R00_S22 R40_S02
# ----------- --------
# | | | | C1 |
# | C0 | C1 | |------O
# | | | | C0 |
# O----O----- -------O
# Get the field X, Y of sensor's center
fieldXc, fieldYc = self.sensorFocaPlaneInDeg[self.sensorName]
# Get the center pixel position
pixelXc, pixelYc = self.sensorDimList[self.sensorName]
pixelXc = pixelXc / 2
pixelYc = pixelYc / 2
# Calculate the delta x and y in degree
# 1 degree = 3600 arcsec
pixelToArcsec = self.settingFile.getSetting("pixelToArcsec")
deltaX = (pixelX - pixelXc) * pixelToArcsec / 3600.0
deltaY = (pixelY - pixelYc) * pixelToArcsec / 3600.0
# Calculate the transformed coordinate in degree.
fieldX, fieldY = self._rotCam2FocalPlane(
self.sensorName, fieldXc, fieldYc, deltaX, deltaY)
return fieldX, fieldY
def _rotCam2FocalPlane(self, sensorName, centerX, centerY, deltaX, deltaY,
clockWise=False):
"""Do the rotation from camera coordinate to focal plane coordinate or
vice versa.
Parameters
----------
sensorName : str
Abbreviated sensor name.
centerX : float
CCD center x.
centerY : float
CCD center y.
deltaX : float
Delta x from the CCD's center.
deltaY : float
Delta y from the CCD's center.
clockWise : bool, optional
Rotation direction (True: clockwise, False: counter-clockwise).
(the default is False.)
Returns
-------
float
Transformed x position.
float
Transformed y position.
"""
# Get the euler angle in z direction (only consider the z rotatioin at
# this moment)
eulerZ = round(self.getEulerZinDeg(sensorName))
eulerZinRad = np.deg2rad(eulerZ)
# Counter-clockwise or clockwise rotation
if (clockWise):
eulerZinRad = -eulerZinRad
# Calculate the new x, y by the rotation. This is important for
# wavefront sensor.
newX = centerX + np.cos(eulerZinRad) * deltaX - \
np.sin(eulerZinRad) * deltaY
newY = centerY + np.sin(eulerZinRad) * deltaX + \
np.cos(eulerZinRad)*deltaY
return newX, newY
def dmXY2CamXY(self, pixelDmX, pixelDmY):
"""Transform the pixel x, y from DM library to camera to use.
Parameters
----------
pixelDmX : float
Pixel x defined in DM coordinate.
pixelDmY : float
Pixel y defined in DM coordinate.
Returns
-------
float
Pixel x defined in camera coordinate based on LCA-13381.
float
Pixel y defined in camera coordinate based on LCA-13381.
"""
# Camera coordinate is defined in LCA-13381. Define camera coordinate
# (x, y) and DM coordinate (x', y'), then the relation is:
# Camera team +y = DM team +x'
# Camera team +x = DM team -y'
# O---->y
# |
# | ----------------------
# \/ | | (x', y') = (200, 500) =>
# x | | (x, y) = (-500, 200) -> (3500, 200)
# |4000 |
# y' | |
# /\ | 4072 |
# | |----------------------
# |
# O-----> x'
# Get the CCD dimension
dimX, dimY = self.sensorDimList[self.sensorName]
# Calculate the transformed coordinate
pixelCamX = dimX - pixelDmY
pixelCamY = pixelDmX
return pixelCamX, pixelCamY
def camXY2DmXY(self, pixelCamX, pixelCamY):
"""Transform the pixel x, y from camera coordinate to DM coordinate.
Parameters
----------
pixelCamX : float
Pixel x defined in Camera coordinate based on LCA-13381.
pixelCamY : float
Pixel y defined in Camera coordinate based on LCA-13381.
Returns
-------
float
Pixel x defined in DM coordinate.
float
Pixel y defined in DM coordinate.
"""
# Check the comment in dmXY2CamXY() for the details of coordinate
# systems of DM and camera teams.
# Get the CCD dimension
dimX, dimY = self.sensorDimList[self.sensorName]
# Calculate the transformed coordinate
pixelDmX = pixelCamY
pixelDmY = dimX - pixelCamX
return pixelDmX, pixelDmY
def isVignette(self, fieldX, fieldY):
"""The donut is vignetted or not by calculating the donut's distance to
center.
Parameters
----------
fieldX : float
Field x in degree.
fieldY : float
Field y in degree.
Returns
-------
bool
True if the donut is vignette.
"""
# Calculate the distance to center in degree to judge the donut is
# vignetted or not.
fldr = np.sqrt(fieldX**2 + fieldY**2)
distVignette = self.settingFile.getSetting("distVignette")
if (fldr >= distVignette):
return True
else:
return False
def simulateImg(self, imageFolderPath, defocalDis, nbrStar, filterType,
noiseRatio=0.01):
"""Simulate the defocal CCD images with the neighboring star map.
This function is only for the test use.
Parameters
----------
imageFolderPath : str
Path to image directory.
defocalDis : float
Defocal distance in mm.
nbrStar : NbrStar
Neighboring star on single detector.
filterType : FilterType
Filter type.
noiseRatio : float, optional
The noise ratio. (the default is 0.01.)
Returns
-------
numpy.ndarray
Simulated intra-focal images.
numpy.ndarray
Simulated extra-focal images.
Raises
-------
ValueError
No available donut images.
ValueError
The numbers of intra- and extra-focal images are different.
"""
# Generate the intra- and extra-focal ccd images
d1, d2 = self.sensorDimList[self.sensorName]
ccdImgIntra = np.random.random([d2, d1])*noiseRatio
ccdImgExtra = ccdImgIntra.copy()
# Get all files in the image directory in a sorted order
fileList = sorted(os.listdir(imageFolderPath))
# Redefine the format of defocal distance
defocalDis = "%.2f" % defocalDis
# Get the available donut files
intraFileList = []
extraFileList = []
for afile in fileList:
# Get the file name
fileName, fileExtension = os.path.splitext(afile)
# Split the file name for the analysis
fileNameStr = fileName.split("_")
# Find the file name with the correct defocal distance
if (len(fileNameStr) == 3 and fileNameStr[1] == defocalDis):
# Collect the file name based on the defocal type
if (fileNameStr[-1] == "intra"):
intraFileList.append(afile)
elif (fileNameStr[-1] == "extra"):
extraFileList.append(afile)
# Get the number of available files
numFile = len(intraFileList)
if (numFile == 0):
raise ValueError("No available donut images.")
# Check the numbers of intra- and extra-focal images should be the same
if (numFile != len(extraFileList)):
raise ValueError(
"The numbers of intra- and extra-focal images are different.")
# Get the magnitude of stars
mappedFilterType = mapFilterRefToG(filterType)
starMag = nbrStar.getMag(mappedFilterType)
# Based on the nbrStar to reconstruct the image
for brightStar, neighboringStar in nbrStar.getId().items():
# Generate a random number
randNum = np.random.randint(0, high=numFile)
# Choose a random donut image from the file
donutImageIntra = self._getDonutImgFromFile(
imageFolderPath, intraFileList[randNum])
donutImageExtra = self._getDonutImgFromFile(
imageFolderPath, extraFileList[randNum])
# Get the bright star magnitude
magBS = starMag[brightStar]
# Combine the bright star and neighboring stars. Put the bright
# star in the first one.
allStars = neighboringStar[:]
allStars.insert(0, brightStar)
# Add the donut image
for star in allStars:
# Get the brigtstar pixel x, y
starX, starY = nbrStar.getRaDeclInPixel()[star]
magStar = starMag[star]
# Transform the coordiante from DM team to camera team
starX, starY = self.dmXY2CamXY(starX, starY)
# Ratio of magnitude between donuts (If the magnitudes of stars
# differs by 5, the brightness differs by 100.)
# (Magnitude difference shoulbe be >= 1.)
magDiff = magStar - magBS
magRatio = 1 / 100 ** (magDiff / 5.0)
# Add the donut image
self._addDonutImage(magRatio * donutImageIntra, starX, starY,
ccdImgIntra)
self._addDonutImage(magRatio * donutImageExtra, starX, starY,
ccdImgExtra)
return ccdImgIntra, ccdImgExtra
def _getDonutImgFromFile(self, imageFolderPath, fileName):
"""Read the donut image from the file.
Parameters
----------
imageFolderPath : str
Path to image directory.
fileName : str
File name.
Returns
-------
numpy.ndarray
Donut image.
"""
# Get the donut image from the file by the delegation
self.blendedImageDecorator.setImg(
imageFile=os.path.join(imageFolderPath, fileName))
return self.blendedImageDecorator.getImg().copy()
def _addDonutImage(self, donutImage, starX, starY, ccdImg):
"""Add the donut image to simulated CCD image frame.
Parameters
----------
donutImage : numpy.ndarray
Donut image.
starX : float
Star position in pixel x.
starY : float
Star position in pixel y.
ccdImg : numpy.ndarray
CCD image.
"""
# Get the dimension of donut image
d1, d2 = donutImage.shape
# Get the interger of position to use as the index
y = int(starY)
x = int(starX)
# Add the donut image on the CCD image
ccdImg[y-int(d1/2):y-int(d1/2)+d1, x-int(d2/2):x-int(d2/2)+d2] += \
donutImage
def getSingleTargetImage(self, ccdImg, nbrStar, index, filterType):
"""Get the image of single scientific target and related neighboring
stars.
Parameters
----------
ccdImg : numpy.ndarray
CCD image.
nbrStar : NbrStar
Neighboring star on single detector.
index : int
Index of science target star in neighboring star.
filterType : FilterType
Filter type.
Returns
-------
numpy.ndarray
Ccd image of target stars.
numpy.ndarray
Star x-positions. The arange is [neighboring stars, bright star].
numpy.ndarray
Star y-positions. The arange is [neighboring stars, bright star].
numpy.ndarray
Star magnitude ratio compared with the bright star. The arange is
[neighboring stars, bright star].
float
Offset x from the origin of target star image to the origin of CCD
image.
float
Offset y from the origin of target star image to the origin of CCD
image.
Raises
------
ValueError
Index is higher than the length of star map.
"""
# Get the target star position
nbrStarId = nbrStar.getId()
if (index >= len(nbrStarId)):
raise ValueError("Index is higher than the length of star map.")
# Get the star SimobjID
brightStar = list(nbrStarId)[index]
neighboringStar = nbrStarId[brightStar]
# Get all star SimobjID list
allStar = neighboringStar[:]
allStar.append(brightStar)
# Get the pixel positions
raDeclInPixel = nbrStar.getRaDeclInPixel()
allStarPosX = []
allStarPosY = []
for star in allStar:
# Get the star pixel position
starX, starY = raDeclInPixel[star]
# Transform the coordiante from DM team to camera team
starX, starY = self.dmXY2CamXY(starX, starY)
allStarPosX.append(starX)
allStarPosY.append(starY)
# Check the ccd image dimenstion
ccdD1, ccdD2 = ccdImg.shape
# Define the range of image
# Get min/ max of x, y
minX = int(min(allStarPosX))
maxX = int(max(allStarPosX))
minY = int(min(allStarPosY))
maxY = int(max(allStarPosY))
# Get the central point
cenX = int(np.mean([minX, maxX]))
cenY = int(np.mean([minY, maxY]))
# Get the image dimension
starRadiusInPixel = self.settingFile.getSetting("starRadiusInPixel")
d1 = (maxY - minY) + 4 * starRadiusInPixel
d2 = (maxX - minX) + 4 * starRadiusInPixel
# Make d1 and d2 to be symmetric and even
d = max(d1, d2)
if (d%2 == 1):
# Use d-1 instead of d+1 to avoid the boundary touch
d = d-1
# If central x or y plus d/2 will over the boundary, shift the
# central x, y values
cenY = self._shiftCenter(cenY, ccdD1, d / 2)
cenY = self._shiftCenter(cenY, 0, d / 2)
cenX = self._shiftCenter(cenX, ccdD2, d / 2)
cenX = self._shiftCenter(cenX, 0, d / 2)
# Get the bright star and neighboring stas image
offsetX = cenX - d / 2
offsetY = cenY - d / 2
singleSciNeiImg = \
ccdImg[int(offsetY):int(cenY + d / 2), int(offsetX):int(cenX + d / 2)]
# Get the stars position in the new coordinate system
# The final one is the bright star
allStarPosX = np.array(allStarPosX) - offsetX
allStarPosY = np.array(allStarPosY) - offsetY
# Get the star magnitude
mappedFilterType = mapFilterRefToG(filterType)
magList = nbrStar.getMag(mappedFilterType)
# Get the list of magnitude
magRatio = np.array([])
for star in allStar:
neiMag = magList[star]
magRatio = np.append(magRatio, neiMag)
# Calculate the magnitude ratio
magRatio = 1 / 100 ** ((magRatio - magRatio[-1]) / 5.0)
return singleSciNeiImg, allStarPosX, allStarPosY, magRatio, offsetX, offsetY
def _shiftCenter(self, center, boundary, distance):
"""Shift the center if its distance to boundary is less than required.
Parameters
----------
center : float
Center point.
boundary : float
Boundary point.
distance : float
Required distance.
Returns
-------
float
Shifted center.
"""
# Distance between the center and boundary
delta = boundary - center
# Shift the center if needed
if (abs(delta) < distance):
return boundary - np.sign(delta)*distance
else:
return center
def doDeblending(self, blendedImg, allStarPosX, allStarPosY, magRatio):
"""Do the deblending.
It is noted that the algorithm now is only for one bright star and one
neighboring star.
Parameters
----------
blendedImg : numpy.ndarray
Blended image.
allStarPosX : list or numpy.ndarray
Star's position x in pixel. The arange is [neighboring star,
bright star].
allStarPosY : list or numpy.ndarray
Star's position y in pixel. The arange is [neighboring star,
bright star].
magRatio : list or numpy.ndarray
Star magnitude ratio compared with the bright star. The arange is
[neighboring stars, bright star].
Returns
-------
numpy.ndarray
Deblended image.
float
Pixel x of bright star.
float
Pixel y of bright star.
Raises
------
ValueError
Only one neighboring star allowed.
"""
# Check there is only one bright star and one neighboring star.
# This is the limit of deblending algorithm now.
if (len(magRatio) != 2):
raise ValueError("Only one neighboring star allowed.")
# Set the image for the deblending
self.blendedImageDecorator.setImg(image=blendedImg)
# Do the deblending
imgDeblend, realcx, realcy = \
self.blendedImageDecorator.deblendDonut((allStarPosX[0],
allStarPosY[0]))
return imgDeblend, realcx, realcy
class TestParamReader(unittest.TestCase):
"""Test the ParamReaderYaml class."""
def setUp(self):
testDir = os.path.join(getModulePath(), "tests")
self.configDir = os.path.join(testDir, "testData")
self.fileName = "testConfigFile.yaml"
filePath = os.path.join(self.configDir, self.fileName)
self.paramReader = ParamReader(filePath=filePath)
self.testTempDir = tempfile.TemporaryDirectory(dir=testDir)
def tearDown(self):
self.testTempDir.cleanup()
def testGetSetting(self):
znmax = self.paramReader.getSetting("znmax")
self.assertEqual(znmax, 22)
def testGetSettingWithWrongParam(self):
self.assertRaises(ValueError, self.paramReader.getSetting, "wrongParam")
def testGetFilePath(self):
ansFilePath = os.path.join(self.configDir, self.fileName)
self.assertEqual(self.paramReader.getFilePath(), ansFilePath)
def testSetFilePath(self):
fileName = "test.yaml"
filePath = os.path.join(self.configDir, fileName)
with self.assertWarns(UserWarning):
self.paramReader.setFilePath(filePath)
self.assertEqual(self.paramReader.getFilePath(), filePath)
def testGetContent(self):
content = self.paramReader.getContent()
self.assertTrue(isinstance(content, dict))
def testGetContentWithDefaultSetting(self):
paramReader = ParamReader()
content = paramReader.getContent()
self.assertTrue(isinstance(content, dict))
def testWriteMatToFile(self):
self._writeMatToFile()
numOfFile = self._getNumOfFileInFolder(self.testTempDir.name)
self.assertEqual(numOfFile, 1)
def _writeMatToFile(self):
mat = np.random.rand(3, 4, 5)
filePath = os.path.join(self.testTempDir.name, "temp.yaml")
ParamReader.writeMatToFile(mat, filePath)
return mat, filePath
def _getNumOfFileInFolder(self, folder):
return len(
[
name
for name in os.listdir(folder)
if os.path.isfile(os.path.join(folder, name))
]
)
def testWriteMatToFileWithWrongFileFormat(self):
wrongFilePath = os.path.join(self.testTempDir.name, "temp.txt")
self.assertRaises(
ValueError, ParamReader.writeMatToFile, np.ones(4), wrongFilePath
)
def testGetMatContent(self):
mat, filePath = self._writeMatToFile()
self.paramReader.setFilePath(filePath)
matInYamlFile = self.paramReader.getMatContent()
delta = np.sum(np.abs(matInYamlFile - mat))
self.assertLess(delta, 1e-10)
def testGetMatContentWithDefaultSetting(self):
paramReader = ParamReader()
matInYamlFile = paramReader.getMatContent()
self.assertTrue(isinstance(matInYamlFile, np.ndarray))
self.assertEqual(len(matInYamlFile), 0)
def testUpdateSettingSeries(self):
znmaxValue = 20
zn3IdxValue = [1, 2, 3]
settingSeries = {"znmax": znmaxValue, "zn3Idx": zn3IdxValue}
self.paramReader.updateSettingSeries(settingSeries)
self.assertEqual(self.paramReader.getSetting("znmax"), znmaxValue)
self.assertEqual(self.paramReader.getSetting("zn3Idx"), zn3IdxValue)
def testUpdateSetting(self):
value = 10
param = "znmax"
self.paramReader.updateSetting(param, value)
self.assertEqual(self.paramReader.getSetting(param), value)
def testUpdateSettingWithWrongParam(self):
self.assertRaises(ValueError, self.paramReader.updateSetting, "wrongParam", -1)
def testSaveSettingWithFilePath(self):
filePath = self._saveSettingFile()
self.assertTrue(os.path.exists(filePath))
self.assertEqual(self.paramReader.getFilePath(), filePath)
def _saveSettingFile(self):
filePath = os.path.join(self.testTempDir.name, "newConfigFile.yaml")
self.paramReader.saveSetting(filePath=filePath)
return filePath
def testSaveSettingWithoutFilePath(self):
filePath = self._saveSettingFile()
paramReader = ParamReader(filePath=filePath)
paramReader.saveSetting()
self.assertEqual(paramReader.getFilePath(), filePath)
# Check the values are saved actually
self.assertEqual(paramReader.getSetting("znmax"), 22)
keysInContent = paramReader.getContent().keys()
self.assertTrue("dofIdx" in keysInContent)
self.assertTrue("zn3Idx" in keysInContent)
self.assertEqual(paramReader.getSetting("zn3Idx"), [1] * 19)
def testGetAbsPathNotExist(self):
self.assertRaises(
ValueError, self.paramReader.getAbsPath, "testFile.txt", getModulePath()
)
def testGetAbsPath(self):
filePath = "README.md"
self.assertFalse(os.path.isabs(filePath))
filePathAbs = ParamReader.getAbsPath(filePath, getModulePath())
self.assertTrue(os.path.isabs(filePathAbs))
def testNonexistentFile(self):
with self.assertWarns(UserWarning):
paramReader = ParamReader(filePath="thisFileDoesntExists")
self.assertEqual(len(paramReader.getContent().keys()), 0)
class WEPCalculation(object):
"""Base class for converting the wavefront images into wavefront errors.
There will be different implementations of this for different
types of CCDs (normal, full array mode, comcam, cmos, shwfs).
"""
def __init__(self, astWcsSol, camType, isrDir, settingFileName="default.yaml"):
"""Construct an WEP calculation object.
Parameters
----------
astWcsSol : AstWcsSol
AST world coordinate system (WCS) solution.
camType : enum 'CamType'
Camera type.
isrDir : str
Instrument signature remocal (ISR) directory. This directory will
have the input and output that the data butler needs.
settingFileName : str, optional
Setting file name. (the default is "default.yaml".)
"""
super().__init__()
# This attribute is just a stakeholder here since there is no detail of
# AST WCS solution yet
self.astWcsSol = astWcsSol
# ISR directory that the data butler uses
self.isrDir = isrDir
# Boresight infomation
self.raInDeg = 0.0
self.decInDeg = 0.0
# Sky rotation angle
self.rotSkyPos = 0.0
# Sky information file for the temporary use
self.skyFile = ""
# Default setting file
settingFilePath = os.path.join(getConfigDir(), settingFileName)
self.settingFile = ParamReader(filePath=settingFilePath)
# Configure the WEP controller
self.wepCntlr = self._configWepController(camType, settingFileName)
def _configWepController(self, camType, settingFileName):
"""Configure the WEP controller.
WEP: wavefront estimation pipeline.
Parameters
----------
camType : enum 'CamType'
Camera type.
settingFileName : str
Setting file name.
Returns
-------
WepController
Configured WEP controller.
"""
dataCollector = CamDataCollector(self.isrDir)
isrWrapper = CamIsrWrapper(self.isrDir)
bscDbType = self._getBscDbType()
sourSelc = self._configSourceSelector(camType, bscDbType, settingFileName)
sourProc = SourceProcessor(settingFileName=settingFileName)
wfsEsti = self._configWfEstimator(camType)
wepCntlr = WepController(dataCollector, isrWrapper, sourSelc, sourProc, wfsEsti)
return wepCntlr
def _getBscDbType(self):
"""Get the bright star catalog (BSC) database type.
Returns
-------
enum 'BscDbType'
BSC database type.
"""
bscDb = self.settingFile.getSetting("bscDbType")
return getBscDbType(bscDb)
def _configSourceSelector(self, camType, bscDbType, settingFileName):
"""Configue the source selector.
Parameters
----------
camType : enum 'CamType'
Camera type.
bscDbType : enum 'BscDbType'
Bright star catalog (BSC) database type.
settingFileName : str
Setting file name.
Returns
-------
SourceSelector
Configured source selector.
"""
sourSelc = SourceSelector(camType, bscDbType, settingFileName=settingFileName)
sourSelc.setFilter(FilterType.REF)
return sourSelc
def _configWfEstimator(self, camType):
"""Configure the wavefront estimator.
Returns
-------
WfEstimator
Configured wavefront estimator.
"""
configDir = getConfigDir()
instDir = os.path.join(configDir, "cwfs", "instData")
algoDir = os.path.join(configDir, "cwfs", "algo")
wfsEsti = WfEstimator(instDir, algoDir)
solver = self.settingFile.getSetting("poissonSolver")
opticalModel = self.settingFile.getSetting("opticalModel")
defocalDistInMm = self.settingFile.getSetting("defocalDistInMm")
donutImgSizeInPixel = self.settingFile.getSetting("donutImgSizeInPixel")
centroidFind = self.settingFile.getSetting("centroidFindAlgo")
centroidFindType = getCentroidFindType(centroidFind)
wfsEsti.config(
solver=solver,
camType=camType,
opticalModel=opticalModel,
defocalDisInMm=defocalDistInMm,
sizeInPix=donutImgSizeInPixel,
centroidFindType=centroidFindType,
)
return wfsEsti
def getSettingFile(self):
"""Get the setting file.
Returns
-------
ParamReader
Setting file.
"""
return self.settingFile
def getWepCntlr(self):
"""Get the configured WEP controller.
Returns
-------
WepController
Configured WEP controller.
"""
return self.wepCntlr
def getIsrDir(self):
"""Get the instrument signature removal (ISR) directory.
This directory will have the input and output that the data butler
needs.
Returns
-------
str
ISR directory.
"""
return self.isrDir
def setSkyFile(self, skyFile):
"""Set the sky information file.
This is a temporary function to set the star file generated by
ts_tcs_wep_phosim module to do the query. This function will be removed
after we begin to integrate the bright star catalog database.
Parameters
----------
skyFile : str
Sky information file.
"""
self.skyFile = skyFile
def getSkyFile(self):
"""Get the sky information file.
Returns
-------
str
Sky information file.
"""
return self.skyFile
def setWcsData(self, wcsData):
"""Set the WCS data.
Parameters
----------
wcsData : WcsData
WCS data used in the WCS solution.
"""
self.astWcsSol.setWcsData(wcsData)
def setFilter(self, filterType):
"""Set the current filter.
Parameters
----------
filterType : enum 'FilterType'
The new filter configuration to use for WEP data processing.
"""
sourSelc = self.wepCntlr.getSourSelc()
sourSelc.setFilter(filterType)
def getFilter(self):
"""Get the current filter.
Returns
-------
enum 'FilterType'
The current filter configuration to use for WEP data processing.
"""
sourSelc = self.wepCntlr.getSourSelc()
return sourSelc.getFilter()
def setBoresight(self, raInDeg, decInDeg):
"""Set the boresight (ra, dec) in degree from the pointing component.
The cooridinate system of pointing component is the international
cannabinoid research society (ICRS).
Parameters
----------
raInDeg : float
Right ascension in degree. The value should be in (0, 360).
decInDeg : float
Declination in degree. The value should be in (-90, 90).
"""
self.raInDeg = raInDeg
self.decInDeg = decInDeg
def getBoresight(self):
"""Get the boresight (ra, dec) defined in the international
cannabinoid research society (ICRS).
Returns
-------
raInDeg : float
Right ascension in degree. The value should be in (0, 360).
decInDeg : float
Declination in degree. The value should be in (-90, 90).
"""
return self.raInDeg, self.decInDeg
def setRotAng(self, rotAngInDeg):
"""Set the camera rotation angle in degree from the camera rotator
control system.
Parameters
----------
rotAngInDeg : float
The camera rotation angle in degree (-90 to 90).
"""
# In the WCS solution provided by SIMS team, the input angle is sky
# rotation angle. We do not know its relationship with the camera
# rotation angle yet.
self.rotSkyPos = rotAngInDeg
def getRotAng(self):
"""Get the camera rotation angle in degree defined in the camera
rotator control system.
Returns
-------
float
The camera rotation angle in degree.
"""
# In the WCS solution provided by SIMS team, the input angle is sky
# rotation angle. We do not know its relationship with the camera
# rotation angle yet.
return self.rotSkyPos
def calculateWavefrontErrors(self, rawExpData, extraRawExpData=None):
"""Calculate the wavefront errors.
Parameters
----------
rawExpData : RawExpData
Raw exposure data for the corner wavefront sensor. If the input of
extraRawExpData is not None, this input will be the intra-focal raw
exposure data.
extraRawExpData : RawExpData, optional
This is the extra-focal raw exposure data if not None. (the default
is None.)
Returns
-------
list[SensorWavefrontData]
List of SensorWavefrontData object.
Raises
------
ValueError
Corner WFS is not supported yet.
ValueError
Only single visit is allowed at this time.
"""
if extraRawExpData is None:
raise ValueError("Corner WFS is not supported yet.")
if len(rawExpData.getVisit()) != 1:
raise ValueError("Only single visit is allowed at this time.")
# When evaluating the eimage, the calibration products are not needed.
# Therefore, need to make sure the camera mapper file exists.
self._genCamMapperIfNeed()
# Ingest the exposure data and do the ISR
self._ingestImg(rawExpData)
if extraRawExpData is not None:
self._ingestImg(extraRawExpData)
# Only the amplifier image needs to do the ISR
imgType = self._getImageType()
if imgType == ImageType.Amp:
self._doIsr(isrConfigfileName="isr_config.py")
# Set the butler inputs path to get the images
butlerRootPath = self._getButlerRootPath()
self.wepCntlr.setPostIsrCcdInputs(butlerRootPath)
# Get the target stars map neighboring stars
neighborStarMap = self._getTargetStar()
# Calculate the wavefront error
intraObsIdList = rawExpData.getVisit()
intraObsId = intraObsIdList[0]
if extraRawExpData is None:
obsIdList = [intraObsId]
else:
extraObsIdList = extraRawExpData.getVisit()
extraObsId = extraObsIdList[0]
obsIdList = [intraObsId, extraObsId]
donutMap = self._calcWfErr(neighborStarMap, obsIdList)
listOfWfErr = self._populateListOfSensorWavefrontData(donutMap)
return listOfWfErr
def _genCamMapperIfNeed(self):
"""Generate the camera mapper file if it is needed.
The mapper file is used by the data butler.
Raises
------
ValueError
Mapper is not supported yet.
"""
mapperFile = os.path.join(self.isrDir, "_mapper")
if not os.path.exists(mapperFile):
dataCollector = self.wepCntlr.getDataCollector()
camMapper = self.settingFile.getSetting("camMapper")
if camMapper == "phosim":
dataCollector.genPhoSimMapper()
else:
raise ValueError("Mapper (%s) is not supported yet." % camMapper)
def _ingestImg(self, rawExpData):
"""Ingest the images.
Parameters
----------
rawExpData : RawExpData
Raw exposure data.
"""
dataCollector = self.wepCntlr.getDataCollector()
rawExpDirList = rawExpData.getRawExpDir()
for rawExpDir in rawExpDirList:
imgType = self._getImageType()
if imgType == ImageType.Amp:
rawImgFiles = os.path.join(rawExpDir, "*_a_*.fits*")
dataCollector.ingestImages(rawImgFiles)
elif imgType == ImageType.Eimg:
rawImgFiles = os.path.join(rawExpDir, "*_e_*.fits*")
dataCollector.ingestEimages(rawImgFiles)
def _getImageType(self):
"""Get the image type defined in the configuration file.
Returns
-------
enum 'ImageType'
ImageType enum.
"""
imgType = self.settingFile.getSetting("imageType")
return getImageType(imgType)
def _doIsr(self, isrConfigfileName):
"""Do the instrument signature removal (ISR).
Parameters
----------
isrConfigfileName : str
ISR configuration file name.
"""
isrWrapper = self.wepCntlr.getIsrWrapper()
isrWrapper.config(doFlat=True, fileName=isrConfigfileName)
rerunName = self._getIsrRerunName()
isrWrapper.doISR(self.isrDir, rerunName=rerunName)
def _getIsrRerunName(self):
"""Get the instrument signature removal (ISR) rerun name.
Returns
-------
str
ISR rerun name.
"""
return self.settingFile.getSetting("rerunName")
def _getButlerRootPath(self):
"""Get the butler root path based on the image type.
Returns
-------
str
Butler root path.
"""
imgType = self._getImageType()
if imgType == ImageType.Amp:
rerunName = self._getIsrRerunName()
return os.path.join(self.isrDir, "rerun", rerunName)
elif imgType == ImageType.Eimg:
return self.isrDir
def _getTargetStar(self):
"""Get the target stars
Returns
-------
dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
Raises
------
ValueError
WEPCalculation does not support this BSC yet.
"""
# Connect the database
sourSelc = self.wepCntlr.getSourSelc()
bscDbType = self._getBscDbType()
if bscDbType in (BscDbType.LocalDb, BscDbType.LocalDbForStarFile):
dbRelativePath = self.settingFile.getSetting("defaultBscPath")
dbAdress = os.path.join(getModulePath(), dbRelativePath)
sourSelc.connect(dbAdress)
else:
raise ValueError("WEPCalculation does not support %s yet." % bscDbType)
# Do the query
sourSelc.setObsMetaData(self.raInDeg, self.decInDeg, self.rotSkyPos)
camDimOffset = self.settingFile.getSetting("camDimOffset")
if bscDbType == BscDbType.LocalDb:
neighborStarMap = sourSelc.getTargetStar(offset=camDimOffset)[0]
elif bscDbType == BscDbType.LocalDbForStarFile:
skyFile = self._assignSkyFile()
neighborStarMap = sourSelc.getTargetStarByFile(
skyFile, offset=camDimOffset
)[0]
# Disconnect the database
sourSelc.disconnect()
return neighborStarMap
def _assignSkyFile(self):
"""Assign the sky file.
If the skyFile attribute value is "", the default one from the
configuration file will be used.
Returns
-------
str
Path of sky file.
"""
isSkyFileExist = os.path.exists(self.skyFile)
if isSkyFileExist:
skyFile = self.skyFile
else:
warnings.warn(
"No sky file assigned. Use the default one.", category=UserWarning
)
skyFileRelativePath = self.settingFile.getSetting("defaultSkyFilePath")
skyFile = os.path.join(getModulePath(), skyFileRelativePath)
return skyFile
def _calcWfErr(self, neighborStarMap, obsIdList):
"""Calculate the wavefront error.
Only consider one intra-focal and one extra-focal images at this
moment
Parameters
----------
neighborStarMap : dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
obsIdList : list[int]
Observation Id list in [intraObsId, extraObsId]. If the input is
[intraObsId], this means the corner WFS.
Returns
-------
dict
Donut image map with the calculated wavefront error. The dictionary
key is the sensor name. The dictionary item is the donut image
(type: DonutImage).
"""
sensorNameList = list(neighborStarMap)
imgType = self._getImageType()
if imgType == ImageType.Amp:
wfsImgMap = self.wepCntlr.getPostIsrImgMapByPistonDefocal(
sensorNameList, obsIdList
)
elif imgType == ImageType.Eimg:
wfsImgMap = self.wepCntlr.getEimgMapByPistonDefocal(
sensorNameList, obsIdList
)
doDeblending = self.settingFile.getSetting("doDeblending")
donutMap = self.wepCntlr.getDonutMap(
neighborStarMap, wfsImgMap, self.getFilter(), doDeblending=doDeblending
)
donutMap = self.wepCntlr.calcWfErr(donutMap)
return donutMap
def _populateListOfSensorWavefrontData(self, donutMap):
"""Populate the list of sensor wavefront data.
Parameters
----------
donutMap : dict
Donut image map with the calculated wavefront error. The dictionary
key is the sensor name. The dictionary item is the donut image
(type: DonutImage).
Returns
-------
list[SensorWavefrontData]
List of SensorWavefrontData object.
"""
mapSensorNameAndId = MapSensorNameAndId()
listOfWfErr = []
for sensor, donutList in donutMap.items():
sensorWavefrontData = SensorWavefrontData()
# Set the sensor Id
abbrevSensor = abbrevDectectorName(sensor)
sensorIdList = mapSensorNameAndId.mapSensorNameToId(abbrevSensor)
sensorId = sensorIdList[0]
sensorWavefrontData.setSensorId(sensorId)
sensorWavefrontData.setListOfDonut(donutList)
# Set the average zk in um
avgErrInNm = self.wepCntlr.calcAvgWfErrOnSglCcd(donutList)
avgErrInUm = avgErrInNm * 1e-3
sensorWavefrontData.setAnnularZernikePoly(avgErrInUm)
listOfWfErr.append(sensorWavefrontData)
return listOfWfErr
def ingestCalibs(self, calibsDir):
"""Ingest the calibration products.
Parameters
----------
calibsDir : str
Calibration products directory.
"""
self._genCamMapperIfNeed()
dataCollector = self.wepCntlr.getDataCollector()
calibFiles = os.path.join(calibsDir, "*")
dataCollector.ingestCalibs(calibFiles)
class SourceSelector(object):
def __init__(self, camType, bscDbType, settingFileName="default.yaml"):
"""Initialize the source selector class.
Parameters
----------
camType : enum 'CamType'
Camera type.
bscDbType : enum 'BscDbType'
Bright star catalog (BSC) database type.
settingFileName : str, optional
Setting file name (the default is "default.yaml".)
"""
self.camera = CamFactory.createCam(camType)
self.db = DatabaseFactory.createDb(bscDbType)
self.filter = Filter()
self.maxDistance = 0.0
self.maxNeighboringStar = 0
settingFilePath = os.path.join(getConfigDir(), settingFileName)
self.settingFile = ParamReader(filePath=settingFilePath)
# Configurate the criteria of neighboring stars
starRadiusInPixel = self.settingFile.getSetting("starRadiusInPixel")
spacingCoefficient = self.settingFile.getSetting("spacingCoef")
maxNeighboringStar = self.settingFile.getSetting("maxNumOfNbrStar")
self.configNbrCriteria(starRadiusInPixel,
spacingCoefficient,
maxNeighboringStar=maxNeighboringStar)
def configNbrCriteria(self,
starRadiusInPixel,
spacingCoefficient,
maxNeighboringStar=0):
"""Set the neighboring star criteria to decide the scientific target.
Parameters
----------
starRadiusInPixel : float, optional
Diameter of star. For the defocus = 1.5 mm, the star's radius is
63 pixel.
spacingCoefficient : float, optional
Maximum distance in units of radius one donut must be considered
as a neighbor.
maxNeighboringStar : int, optional
Maximum number of neighboring stars. (the default is 0.)
"""
self.maxDistance = starRadiusInPixel * spacingCoefficient
self.maxNeighboringStar = int(maxNeighboringStar)
def connect(self, *args):
"""Connect the database.
Parameters
----------
*args : str or *list
Information to connect to the database.
"""
self.db.connect(*args)
def disconnect(self):
"""Disconnect the database."""
self.db.disconnect()
def setFilter(self, filterType):
"""Set the filter type.
Parameters
----------
filterType : FilterType
Filter type.
"""
self.filter.setFilter(filterType)
def getFilter(self):
"""Get the filter type.
Returns
-------
FilterType
Filter type.
"""
return self.filter.getFilter()
def setObsMetaData(self, ra, dec, rotSkyPos):
"""Set the observation meta data.
Parameters
----------
ra : float
Pointing ra in degree.
dec : float
Pointing decl in degree.
rotSkyPos : float
The orientation of the telescope in degrees.
"""
self.camera.setObsMetaData(ra, dec, rotSkyPos)
def getTargetStar(self, offset=0):
"""Get the target stars by querying the database.
Parameters
----------
offset : float, optional
Offset to the dimension of camera. If the detector dimension is 10
(assume 1-D), the star's position between -offset and 10+offset
will be seem to be on the detector. (the default is 0.)
Returns
-------
dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
dict
Information of stars with the name of sensor as a dictionary.
dict
(ra, dec) of four corners of each sensor with the name
of sensor as a list. The dictionary key is the sensor name.
"""
wavefrontSensors = self.camera.getWavefrontSensor()
lowMagnitude, highMagnitude = self.filter.getMagBoundary()
# Map the reference filter to the G filter
filterType = self.getFilter()
mappedFilterType = mapFilterRefToG(filterType)
# Query the star database
starMap = dict()
neighborStarMap = dict()
for detector, wavefrontSensor in wavefrontSensors.items():
# Get stars in this wavefront sensor for this observation field
stars = self.db.query(
mappedFilterType,
wavefrontSensor[0],
wavefrontSensor[1],
wavefrontSensor[2],
wavefrontSensor[3],
)
# Set the detector information for the stars
stars.setDetector(detector)
# Populate pixel information for stars
populatedStar = self.camera.populatePixelFromRADecl(stars)
# Get the stars that are on the detector
starsOnDet = self.camera.getStarsOnDetector(populatedStar, offset)
starMap[detector] = starsOnDet
# Check the candidate of bright stars based on the magnitude
indexCandidate = starsOnDet.checkCandidateStars(
mappedFilterType, lowMagnitude, highMagnitude)
# Determine the neighboring stars based on the distance and
# allowed number of neighboring stars
neighborStar = starsOnDet.getNeighboringStar(
indexCandidate,
self.maxDistance,
mappedFilterType,
self.maxNeighboringStar,
)
neighborStarMap[detector] = neighborStar
# Remove the data that has no bright star
self._rmDataWithoutBrightStar(neighborStarMap, starMap,
wavefrontSensors)
return neighborStarMap, starMap, wavefrontSensors
def _rmDataWithoutBrightStar(self, neighborStarMap, starMap,
wavefrontSensors):
"""Remove the data that has no bright stars on the detector.
The data in inputs will be changed directly.
Parameters
----------
neighborStarMap : dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
starMap : dict
Information of stars with the name of sensor as a dictionary.
wavefrontSensors : dict
(ra, dec) of four corners of each sensor with the name
of sensor as a list. The dictionary key is the sensor name.
"""
# Collect the sensor list without the bright star
noStarSensorList = []
for detector, stars in neighborStarMap.items():
if len(stars.getId()) == 0:
noStarSensorList.append(detector)
# Remove the data in map
for detector in noStarSensorList:
neighborStarMap.pop(detector)
starMap.pop(detector)
wavefrontSensors.pop(detector)
def getTargetStarByFile(self, skyFilePath, offset=0):
"""Get the target stars by querying the star file.
This function is only for the test. This shall be removed in the final.
Parameters
----------
skyFilePath : str
Sky data file path.
offset : float, optional
Offset to the dimension of camera. If the detector dimension is 10
(assume 1-D), the star's position between -offset and 10+offset
will be seem to be on the detector. (the default is 0.)
Returns
-------
dict
Information of neighboring stars and candidate stars with the name
of sensor as a dictionary.
dict
Information of stars with the name of sensor as a dictionary.
dict
(ra, dec) of four corners of each sensor with the name
of sensor as a list. The dictionary key is the sensor name.
Raises
------
TypeError
The database type is incorrect.
"""
if not isinstance(self.db, LocalDatabaseForStarFile):
raise TypeError("The database type is incorrect.")
# Map the reference filter to the G filter
filterType = self.getFilter()
mappedFilterType = mapFilterRefToG(filterType)
# Write the sky data into the temporary table
self.db.createTable(mappedFilterType)
self.db.insertDataByFile(skyFilePath, mappedFilterType, skiprows=1)
neighborStarMap, starMap, wavefrontSensors = self.getTargetStar(
offset=offset)
# Delete the table
self.db.deleteTable(mappedFilterType)
return neighborStarMap, starMap, wavefrontSensors
class MapSensorNameAndId(object):
def __init__(self, sensorNameToIdFileName="sensorNameToId.yaml"):
"""Construct a MapSensorNameAndId object.
Parameters
----------
sensorNameToIdFileName : str, optional
Configuration file name to map sensor name and Id. (the default is
"sensorNameToId.yaml".)
"""
sensorNameToIdFilePath = os.path.join(getConfigDir(),
sensorNameToIdFileName)
self._sensorNameToIdFile = ParamReader(filePath=sensorNameToIdFilePath)
def mapSensorNameToId(self, sensorName):
"""Map the sensor name to sensor Id.
Parameters
----------
sensorName : list[str] or str
List or string of abbreviated sensor names.
Returns
-------
list[int]
List of sensor Id.
"""
sensorNameList = self._changeToListIfNeed(sensorName)
sensorIdList = []
for sensor in sensorNameList:
sensorId = self._sensorNameToIdFile.getSetting(sensor)
sensorIdList.append(sensorId)
return sensorIdList
def _changeToListIfNeed(self, inputArg):
"""Change the input argument to list type if needed.
Parameters
----------
inputArg : obj
Input argument.
Returns
-------
list
Input argument as the list type.
"""
if not isinstance(inputArg, list):
inputArg = [inputArg]
return inputArg
def mapSensorIdToName(self, sensorId):
"""Map the sensor Id to sensor name.
If no sensor name is found for a specific Id, there will be no returned
value.
Parameters
----------
sensorId : list[int] or int
List or integer of sensor Id.
Returns
-------
list
List of abbreviated sensor names.
int
Number of sensors.
"""
sensorIdList = self._changeToListIfNeed(sensorId)
sensorNameList = []
content = self._sensorNameToIdFile.getContent()
for sensor in sensorIdList:
try:
sensorName = self._getKeyFromValueInDict(content, sensor)
sensorNameList.append(sensorName)
except ValueError:
pass
return sensorNameList, len(sensorNameList)
def _getKeyFromValueInDict(self, aDict, value):
"""Get the key from value in a dictionary object.
Parameters
----------
aDict : dict
Dictionary object.
value : str or int
Value in the dictionary.
Returns
-------
str
Dictionary key.
"""
return list(aDict.keys())[list(aDict.values()).index(value)]
class TeleFacade(object):
def __init__(self):
"""Initialization of telescope facade class.
This class uses the facade pattern that the high level class telescope
helps to write the perturbations of camera, M1M3, and M2 into the
PhoSim by the interface to PhoSim.
"""
# Telescope setting file
settingFilePath = os.path.join(getConfigDir(), "teleSetting.yaml")
self._teleSettingFile = ParamReader(filePath=settingFilePath)
# Camera subsystem
self.cam = None
# M1M3 subsystem
self.m1m3 = None
# M2 subsystem
self.m2 = None
# PhoSim communication
self.phoSimCommu = PhosimCommu()
# Telescope aggregated DOF in um
# This value will be put back to PhoSim to do the perturbation
numOfDof = self.getNumOfDof()
self.dofInUm = np.zeros(numOfDof)
# Telescopt survery parameters
defocalDist = self.getDefaultDefocalDist()
self.surveyParam = {"instName": "lsst",
"defocalDistInMm": defocalDist,
"obsId": 9999,
"filterType": FilterType.REF,
"boresight": (0, 0),
"zAngleInDeg": 0,
"rotAngInDeg": 0}
# Sensors on the telescope
self.sensorOn = {"sciSensorOn": True,
"wfSensorOn": True,
"guidSensorOn": False}
def getDofInUm(self):
"""Get the accumulated degree of freedom (DOF) in um.
idx 0-4: M2 dz, dx, dy, rx, ry
idx 5-9: Cam dz, dx, dy, rx, ry
idx 10-29: M1M3 20 bending modes
idx 30-49: M2 20 bending modes
Returns
-------
numpy.ndarray
DOF in um.
"""
return self.dofInUm
def getNumOfDof(self):
"""Get the number of DOF in the setting file.
DOF: Degree of freedom.
Returns
-------
int
Number of DOF.
"""
return int(self._teleSettingFile.getSetting("numOfDof"))
def getDefaultDefocalDist(self):
"""Get the default defocal distance in mm.
Returns
-------
float
Default defocal distance in mm.
"""
return self._teleSettingFile.getSetting("defocalDist")
def getSurfGridN(self):
"""Get the number of grid of surface
Returns
-------
int
Number of grid of surface.
"""
return int(self._teleSettingFile.getSetting("surfaceGridN"))
def getCamMjd(self):
"""Get the camera MJD.
Returns
-------
float
Camera MJD.
"""
return self._teleSettingFile.getSetting("cameraMJD")
def getRefWaveLength(self):
"""Get the reference wavelength in nm.
Returns
-------
int
Reference wavelength in nm.
"""
return self._teleSettingFile.getSetting("wavelengthInNm")
def getDefocalDistInMm(self):
"""Get the defocal distance in mm.
Returns
-------
float
defocal distance in mm.
"""
return self.surveyParam["defocalDistInMm"]
def setSurveyParam(self, obsId=None, filterType=None, boresight=None,
zAngleInDeg=None, rotAngInDeg=None):
"""Set the survey parameters.
Parameters
----------
obsId : int, optional
Observation Id. (the default is None.)
filterType : enum 'FilterType' in lsst.ts.wep.Utility, optional
Active filter type. (the default is None.)
boresight : tuple, optional
Telescope boresight in (ra, decl). (the default is None.)
zAngleInDeg : float, optional
Zenith angle in degree. (the default is None.)
rotAngInDeg : float, optional
Camera rotation angle in degree between -90 and 90 degrees. (the
default is None.)
"""
self._setSurveyParamItem("obsId", obsId, int)
self._setSurveyParamItem("filterType", filterType, FilterType)
self._setSurveyParamItem("boresight", boresight, tuple)
self._setSurveyParamItem("zAngleInDeg", zAngleInDeg, (int, float))
self._setSurveyParamItem("rotAngInDeg", rotAngInDeg, (int, float))
def _setSurveyParamItem(self, dictKeyName, varValue, varType):
"""Set the item in the dictionary of survey parameters.
Parameters
----------
dictKeyName : str
Name of dictionary key.
varValue : int, tuple, float, or enum 'FilterType'
Variable value.
varType: int, tuple, float, or enum 'FilterType'
Variable type.
"""
if (isinstance(varValue, varType)):
self.surveyParam[dictKeyName] = varValue
def setSensorOn(self, sciSensorOn=True, wfSensorOn=True,
guidSensorOn=False):
"""Set the sensor on.
Parameters
----------
sciSensorOn : bool, optional
Scientific sensors are on. (the default is True.)
wfSensorOn : bool, optional
Wavefront sensors are on. (the default is True.)
guidSensorOn : bool, optional
Guider sensors are on. (the default is False.)
"""
self.sensorOn["sciSensorOn"] = sciSensorOn
self.sensorOn["wfSensorOn"] = wfSensorOn
self.sensorOn["guidSensorOn"] = guidSensorOn
def runPhoSim(self, argString):
"""
Run the PhoSim program.
Arguments:
argString {[str]} -- Arguments for PhoSim.
"""
self.phoSimCommu.runPhoSim(argstring=argString)
def getPhoSimArgs(self, instFilePath, extraCommandFile=None, numPro=1,
numThread=1, outputDir=None, sensorName=None,
e2ADC=1, logFilePath=None):
"""Get the arguments needed to run the PhoSim.
Parameters
----------
instanceFile : str
Instance catalog file.
extraCommandFile : str, optional
Command file to modify the default physics. (the default is None.)
numProc : int, optional
Number of processors. (the default is 1.)
numThread : int, optional
Number of threads. (the default is 1.)
outputDir : str, optional
Output image directory. (the default is None.)
sensorName : str, optional
Sensor chip specification (e.g., all, R22_S11, "R22_S11|R22_S12").
(the default is None.)
e2ADC : int, optional
Whether to generate amplifier images (1 = true, 0 = false). (the
default is 1.)
logFilePath : str, optional
Log file path of PhoSim calculation. (the default is None.)
Returns
-------
str
Arguments to run the PhoSim.
"""
instName = self.surveyParam["instName"]
argString = self.phoSimCommu.getPhoSimArgs(
instFilePath, extraCommandFile=extraCommandFile, numProc=numPro,
numThread=numThread, outputDir=outputDir, instrument=instName,
sensorName=sensorName, e2ADC=e2ADC, logFilePath=logFilePath)
return argString
def setInstName(self, camType, defocalDist=None):
"""Set the instrument name.
Parameters
----------
camType : enum 'CamType' in lsst.ts.wep.Utility
Camera type.
defocalDist : float, optional
Defocal distance in mm. If None, the default value will be used.
(the default is None.)
Raises
------
ValueError
Defocal distance can not be <= 0.
"""
if (defocalDist is None):
defocalDist = self.getDefaultDefocalDist()
if (defocalDist <= 0):
raise ValueError("Defocal distance can not be <= 0.")
self.surveyParam["defocalDistInMm"] = defocalDist
self._setInstNameBasedOnCamType(camType)
def _setInstNameBasedOnCamType(self, camType):
"""Set the instrument name based on the camera type.
Parameters
----------
camType : enum 'CamType' in lsst.ts.wep.Utility
Camera type.
Raises
------
ValueError
This camera type is not supported yet.
"""
if camType in (CamType.LsstCam, CamType.LsstFamCam):
instName = "lsst"
elif (camType == CamType.ComCam):
instName = "lsst"
warnings.warn("Use 'lsst' instead of 'comcam' in PhoSim.",
category=UserWarning)
else:
raise ValueError("This camera type (%s) is not supported yet."
% camType)
self.surveyParam["instName"] = instName
def setDofInUm(self, dofInUm):
"""Set the accumulated degree of freedom (DOF) in um.
idx 0-4: M2 dz, dx, dy, rx, ry
idx 5-9: Cam dz, dx, dy, rx, ry
idx 10-29: M1M3 20 bending modes
idx 30-49: M2 20 bending modes
Parameters
----------
dofInUm : list or numpy.ndarray
DOF in um.
"""
self._checkNumOfDof(dofInUm)
self.dofInUm = np.array(dofInUm, dtype=float)
def _checkNumOfDof(self, dof):
"""Check the number of DOF:
DOF: Degree of freedom.
Parameters
----------
dof : list or numpy.ndarray
DOF.
Raises
------
ValueError
The size of DOF should be 50.
"""
numOfDof = self.getNumOfDof()
if (len(dof) != numOfDof):
raise ValueError("The size of DOF should be %d." % numOfDof)
def accDofInUm(self, dofInUm):
"""Accumulate the aggregated degree of freedom (DOF) in um.
idx 0-4: M2 dz, dx, dy, rx, ry
idx 5-9: Cam dz, dx, dy, rx, ry
idx 10-29: M1M3 20 bending modes
idx 30-49: M2 20 bending modes
Parameters
----------
dofInUm : list or numpy.ndarray
DOF in um.
"""
self._checkNumOfDof(dofInUm)
self.dofInUm += np.array(dofInUm, dtype=float)
def addSubSys(self, addCam=False, addM1M3=False, addM2=False):
"""Add the sub systems to do the perturbation.
Parameters
----------
addCam : bool, optional
Add the camera. (the default is False.)
addM1M3 : bool, optional
Add the M1M3 mirror. (the default is False.)
addM2 : bool, optional
Add the M2 mirror. (the default is False.)
"""
if (addCam):
self.cam = CamSim()
if (addM1M3):
self.m1m3 = M1M3Sim()
if (addM2):
self.m2 = M2Sim()
def setPhoSimDir(self, phosimDir):
"""Set the directory of PhoSim.
Parameters
----------
phosimDir : str
Directory of PhoSim.
"""
self.phoSimCommu.setPhoSimDir(phosimDir)
def writeAccDofFile(self, outputFileDir, dofFileName="pert.mat"):
"""Write the accumulated degree of freedom (DOF) in um to file.
Parameters
----------
outputFileDir : str
Output file directory.
dofFileName : str, optional
DOF file name. (the default is "pert.mat".)
Returns
-------
str
DOF file path.
"""
dofFilePath = os.path.join(outputFileDir, dofFileName)
np.savetxt(dofFilePath, self.dofInUm)
return dofFilePath
def writeCmdFile(self, cmdFileDir, cmdSettingFile=None, pertFilePath=None,
cmdFileName="taskPert.cmd"):
"""Write the physical command file.
OPD: Optical path difference.
Parameters
----------
cmdFileDir : str
Directory to the OPD command file.
cmdSettingFile : str, optional
Physical command setting file path. (the default is None.)
pertFilePath : str, optional
Subsystem perturbation command file path. (the default is None.)
cmdFileName : str, optional
Command file name. (the default is "taskPert.cmd".)
Returns
-------
str
Command file path.
"""
# Command file path
cmdFilePath = os.path.join(cmdFileDir, cmdFileName)
self.phoSimCommu.writeToFile(cmdFilePath, content="", mode="w")
# Write the physical setting
if (cmdSettingFile is not None):
self.phoSimCommu.writeToFile(cmdFilePath,
sourceFile=cmdSettingFile)
# Add the subsystem perturbation
if (pertFilePath is not None):
self.phoSimCommu.writeToFile(cmdFilePath, sourceFile=pertFilePath)
return cmdFilePath
def writeStarInstFile(self, instFileDir, skySim, simSeed=1000,
sedName="sed_flat.txt", instSettingFile=None,
instFileName="star.inst"):
"""Write the star instance file.
Parameters
----------
instFileDir : str
Directory to instance file.
skySim : SkySim
SkySim object.
simSeed : int, optional
Random number seed. (the default is 1000.)
sedName : str, optional
The name of the SED file with a file path that is relative to the
data directory in PhoSim. (the default is "sed_flat.txt".)
instSettingFile : str optional
Instance setting file. (the default is None.)
instFileName : str, optional
Star instance file name. (the default is "star.inst".)
Returns
-------
str
Instance file path.
"""
# Instance file path
instFilePath = os.path.join(instFileDir, instFileName)
# Get the filter ID in PhoSim
aFilterId = self._getFilterIdInPhoSim()
# Write the default instance setting
obsId = self.surveyParam["obsId"]
boresight = self.surveyParam["boresight"]
ra = boresight[0]
dec = boresight[1]
rot = self.surveyParam["rotAngInDeg"]
mjd = self.getCamMjd()
self.phoSimCommu.getStarInstance(
obsId, aFilterId, ra=ra, dec=dec, rot=rot, mjd=mjd,
simSeed=simSeed, filePath=instFilePath)
if (instFilePath is not None):
self.phoSimCommu.writeToFile(instFilePath,
sourceFile=instSettingFile)
# Write the telescope accumulated degree of freedom (DOF)
content = self.phoSimCommu.doDofPert(self.dofInUm)
# Set the camera configuration
sciSensorOn = self.sensorOn["sciSensorOn"]
wfSensorOn = self.sensorOn["wfSensorOn"]
guidSensorOn = self.sensorOn["guidSensorOn"]
content += self.phoSimCommu.doCameraConfig(
sciSensorOn=sciSensorOn, wfSensorOn=wfSensorOn,
guidSensorOn=guidSensorOn)
# Use the SED file of single wavelendth if the reference filter is
# used.
filterType = self.surveyParam["filterType"]
if (filterType == FilterType.REF):
self._writeSedFileIfPhoSimDirSet()
sedName = "sed_%s.txt" % int(self.getRefWaveLength())
# Write the star source
starId = skySim.getStarId()
ra, decl = skySim.getRaDecInDeg()
mag = skySim.getStarMag()
for idx in range(len(starId)):
content += self.phoSimCommu.generateStar(
starId[idx], ra[idx], decl[idx], mag[idx], sedName)
self.phoSimCommu.writeToFile(instFilePath, content=content)
return instFilePath
def _getFilterIdInPhoSim(self):
"""Get the active filter Id used in PhoSim.
Returns
-------
int
Active filter ID in PhoSim.
"""
filterType = self.surveyParam["filterType"]
mappedFilterType = mapFilterRefToG(filterType)
return self.phoSimCommu.getFilterId(mappedFilterType)
def _writeSedFileIfPhoSimDirSet(self):
"""Write the SED file if the PhoSim directory is set.
SED: Spectral energy distribution.
"""
if os.path.isdir(self.phoSimCommu.getPhoSimDir()):
self.phoSimCommu.writeSedFile(self.getRefWaveLength())
else:
warnings.warn("No inspection of SED file for no PhoSim path.",
category=UserWarning)
def writeOpdInstFile(self, instFileDir, opdMetr, instSettingFile=None,
instFileName="opd.inst"):
"""Write the optical path difference (OPD) instance file.
Parameters
----------
instFileDir : str
Directory to instance file.
opdMetr : OpdMetrology
OpdMetrology object.
instSettingFile : str, optional
Instance setting file. (the default is None.)
instFileName : str, optional
OPD instance file name. (the default is "opd.inst".)
Returns
-------
str
Instance file path.
"""
# Instance file path
instFilePath = os.path.join(instFileDir, instFileName)
# Get the observation ID
obsId = self.surveyParam["obsId"]
# Get the filter ID in PhoSim
aFilterId = self._getFilterIdInPhoSim()
# Add the sky information
boresight = self.surveyParam["boresight"]
ra = boresight[0]
dec = boresight[1]
rot = self.surveyParam["rotAngInDeg"]
# Write the default instance setting
self.phoSimCommu.getOpdInstance(obsId, aFilterId, ra=ra, dec=dec,
rot=rot, filePath=instFilePath)
if (instSettingFile is not None):
self.phoSimCommu.writeToFile(instFilePath,
sourceFile=instSettingFile)
# Write the telescope accumulated degree of freedom (DOF)
content = self.phoSimCommu.doDofPert(self.dofInUm)
# Write the OPD source
fieldX, fieldY = opdMetr.getFieldXY()
for idx in range(len(fieldX)):
content += self.phoSimCommu.generateOpd(
idx, fieldX[idx], fieldY[idx], self.getRefWaveLength())
self.phoSimCommu.writeToFile(instFilePath, content=content)
# Write the OPD SED file if necessary
self._writeSedFileIfPhoSimDirSet()
return instFilePath
def writePertBaseOnConfigFile(self, pertCmdFileDir, seedNum=None,
m1m3ForceError=0.05, saveResMapFig=False,
pertCmdFileName="pert.cmd"):
"""Write the perturbation command file based on the telescope
configuration file.
Parameters
----------
pertCmdFileDir : str
Directory to the pertubation command file.
seedNum : int, optional
Random seed number. If the value is not None, the M1M3 mirror
will generate a random surface error. (the default is None.)
m1m3ForceError : float, optional
Ratio of actuator force error. (the default is 0.05.)
saveResMapFig : bool, optional
Save the mirror surface residue map or not. (the default is False.)
pertCmdFileName : str, optional
Perturbation command file name. (the default is "pert.cmd".)
Returns
-------
str
Perturbation command file path.
"""
# Get the perturbation of subsystems
content = ""
if (self.m1m3 is not None):
m1ResFileName = "M1res.txt"
m3ResFileName = "M3res.txt"
m1m3ZcFileName = "M1M3zlist.txt"
m1ResFilePath = os.path.join(pertCmdFileDir, m1ResFileName)
m3ResFilePath = os.path.join(pertCmdFileDir, m3ResFileName)
m1m3ZcFilePath = os.path.join(pertCmdFileDir, m1m3ZcFileName)
content = self._addPertM1M3(m1ResFilePath, m3ResFilePath,
m1m3ZcFilePath, content,
m1m3ForceError, seedNum=seedNum)
if (self.m2 is not None):
m2ResFileName = "M2res.txt"
m2ZcFileName = "M2zlist.txt"
m2ResFilePath = os.path.join(pertCmdFileDir, m2ResFileName)
m2ZcFilePath = os.path.join(pertCmdFileDir, m2ZcFileName)
content = self._addPertM2(m2ResFilePath, m2ZcFilePath, content)
if (self.cam is not None):
content = self._addPertCam(content)
# Write the perturbation command to file
pertCmdFilePath = os.path.join(pertCmdFileDir, pertCmdFileName)
self.phoSimCommu.writeToFile(pertCmdFilePath, content=content,
mode="w")
# Save the mirror residue map if necessary
if (saveResMapFig):
if (self.m1m3 is not None):
self._saveM1M3ResMapFig(m1ResFilePath, m3ResFilePath)
if (self.m2 is not None):
self._saveM2ResMapFig(m2ResFilePath)
return pertCmdFilePath
def _addPertM1M3(self, m1ResFilePath, m3ResFilePath, m1m3ZcFilePath,
content, m1m3ForceError, seedNum=None):
"""Add the perturbation of M1M3.
Parameters
----------
m1ResFilePath : str
M1 residue file path.
m3ResFilePath : str
M3 residue file path.
m1m3ZcFilePath : str
M1M3 fitted zk file path.
content : str
Perturbation without M1M3.
m1m3ForceError : float
Ratio of actuator force error.
seedNum : int, optional
Random seed number. If the value is not None, the M1M3 mirror
will generate a random surface error. (the default is None.)
Returns
-------
str
Perturbation with M1M3.
"""
# Do the gravity correction
zAngleInRad = self._getZenAngleInRad()
printthzInM = self.m1m3.getPrintthz(zAngleInRad)
# Add the surface error if necessary
randSurfInM = None
if (seedNum is not None):
randSurfInM = self.m1m3.genMirSurfRandErr(
zAngleInRad, m1m3ForceError=m1m3ForceError,
seedNum=seedNum)
# Do the temperature correction
m1m3TBulk = self._teleSettingFile.getSetting("m1m3TBulk")
m1m3TxGrad = self._teleSettingFile.getSetting("m1m3TxGrad")
m1m3TyGrad = self._teleSettingFile.getSetting("m1m3TyGrad")
m1m3TzGrad = self._teleSettingFile.getSetting("m1m3TzGrad")
m1m3TrGrad = self._teleSettingFile.getSetting("m1m3TrGrad")
tempCorrInUm = self.m1m3.getTempCorr(m1m3TBulk, m1m3TxGrad,
m1m3TyGrad, m1m3TzGrad,
m1m3TrGrad)
# Set the mirror surface in mm
if (randSurfInM is not None):
mirrorSurfInUm = (printthzInM + randSurfInM) * 1e6 + \
tempCorrInUm
else:
mirrorSurfInUm = printthzInM * 1e6 + tempCorrInUm
self.m1m3.setSurfAlongZ(mirrorSurfInUm)
resFile = [m1ResFilePath, m3ResFilePath]
surfaceGridN = self.getSurfGridN()
self.m1m3.writeMirZkAndGridResInZemax(
resFile=resFile, surfaceGridN=surfaceGridN,
writeZcInMnToFilePath=m1m3ZcFilePath)
# Get the Zk in mm
zkInMm = np.loadtxt(m1m3ZcFilePath)
# Do the surface perturbation
surfList = [SurfaceType.M1, SurfaceType.M3]
surfIdList = []
contentWithPert = content
for ii in range(len(surfList)):
surf = surfList[ii]
surfId = self.phoSimCommu.getSurfaceId(surf)
contentWithPert += self.phoSimCommu.doSurfPert(surfId, zkInMm)
# Collect the surface ID
surfIdList.append(surfId)
# Do the surface residue map perturbation
contentWithPert += self.phoSimCommu.doSurfMapPert(surfId,
resFile[ii], 1)
# Do the surface linkage
contentWithPert += self.phoSimCommu.doSurfLink(surfIdList[1],
surfIdList[0])
return contentWithPert
def _getZenAngleInRad(self):
"""Get the zenith angle in radian.
Returns
-------
float
Zenith angle in radian.
"""
zAngleInDeg = self.surveyParam["zAngleInDeg"]
zAngleInRad = np.deg2rad(zAngleInDeg)
return zAngleInRad
def _addPertM2(self, m2ResFilePath, m2ZcFilePath, content):
"""Add the perturbation of M2.
Parameters
----------
m2ResFilePath : str
M2 residue file path.
m2ZcFilePath : str
M2 fitted zk file path.
content : str
Perturbation without M2.
Returns
-------
str
Perturbation with M2.
"""
# Do the gravity correction
zAngleInRad = self._getZenAngleInRad()
printthzInUm = self.m2.getPrintthz(zAngleInRad)
# Do the temperature correction
m2TzGrad = self._teleSettingFile.getSetting("m2TzGrad")
m2TrGrad = self._teleSettingFile.getSetting("m2TrGrad")
tempCorrInUm = self.m2.getTempCorr(m2TzGrad, m2TrGrad)
# Set the mirror surface in mm
mirrorSurfInUm = printthzInUm + tempCorrInUm
self.m2.setSurfAlongZ(mirrorSurfInUm)
surfaceGridN = self.getSurfGridN()
self.m2.writeMirZkAndGridResInZemax(
resFile=m2ResFilePath, surfaceGridN=surfaceGridN,
writeZcInMnToFilePath=m2ZcFilePath)
# Get the Zk in mm
zkInMm = np.loadtxt(m2ZcFilePath)
# Do the surface perturbation
surfId = self.phoSimCommu.getSurfaceId(SurfaceType.M2)
contentWithPert = content
contentWithPert += self.phoSimCommu.doSurfPert(surfId, zkInMm)
# Do the surface residue map perturbation
contentWithPert += self.phoSimCommu.doSurfMapPert(
surfId, m2ResFilePath, 1)
return contentWithPert
def _addPertCam(self, content):
"""Add the perturbation of camera.
Parameters
----------
content : str
Perturbation without camera.
Returns
-------
str
Perturbation with camera.
"""
# Set the camera rotation angle
rotAngInDeg = self.surveyParam["rotAngInDeg"]
self.cam.setRotAngInDeg(rotAngInDeg)
# Set the temperature information
tempInDegC = self._teleSettingFile.getSetting("camTB")
self.cam.setBodyTempInDegC(tempInDegC)
# Add the perturbation of camera
zAngleInRad = self._getZenAngleInRad()
contentWithPert = content
for distType in CamDistType:
# Get the surface ID
surfaceType = self._getPhoSimCamSurf(distType.name)
surfId = self.phoSimCommu.getSurfaceId(surfaceType)
# Do the perturbation
zkInMm = self.cam.getCamDistortionInMm(zAngleInRad, distType)
contentWithPert += self.phoSimCommu.doSurfPert(surfId, zkInMm)
return contentWithPert
def _saveM1M3ResMapFig(self, m1ResFilePath, m3ResFilePath):
"""Save the figure of M1M3 residue map.
Parameters
----------
m1ResFilePath : str
M1 residue file path.
m3ResFilePath : str
M3 residue file path.
"""
resFile = [m1ResFilePath, m3ResFilePath]
writeToResMapFilePath1 = self._getImgPathFromDataFilePath(
m1ResFilePath)
writeToResMapFilePath3 = self._getImgPathFromDataFilePath(
m3ResFilePath)
writeToResMapFilePath = [writeToResMapFilePath1,
writeToResMapFilePath3]
self.m1m3.showMirResMap(
resFile=resFile,
writeToResMapFilePath=writeToResMapFilePath)
def _getImgPathFromDataFilePath(self, dataFilePath, imgType=".png"):
"""Get the image path from the data file path.
Parameters
----------
dataFilePath : str
Data file path.
imgType : str, optional
Image type (the default is ".png".)
Returns
-------
str
Image path.
"""
imgPath = os.path.splitext(dataFilePath)[0] + imgType
return imgPath
def _saveM2ResMapFig(self, m2ResFilePath):
"""Save the figure of M2 residue map.
Parameters
----------
m2ResFilePath : str
M2 residue file path.
"""
writeToResMapFilePath = self._getImgPathFromDataFilePath(
m2ResFilePath)
self.m2.showMirResMap(
resFile=m2ResFilePath,
writeToResMapFilePath=writeToResMapFilePath)
def _getPhoSimCamSurf(self, camSurfName):
"""Get the camera surface used in PhoSim.
Parameters
----------
camSurfName : str
Camera surface name (e.g. L1S1zer).
Returns
-------
enum 'SurfaceType'
Camera surface type.
Raises
------
ValueError
Can not get the camera surface name.
"""
# Get the camera surface name
m = re.match(r"(\AL\d)S(\d)zer", camSurfName)
if (m is None):
raise ValueError("Cannot get the camera surface name: %s."
% camSurfName)
# Get the surface name used in PhoSim
camPhoFaceDict = {"1": "F", "2": "B"}
surfName = m.groups()[0] + camPhoFaceDict[m.groups()[1]]
return mapSurfNameToEnum(surfName)
class M2Sim(MirrorSim):
def __init__(self):
"""Initiate the M2 simulator class."""
# M2 setting file
configDir = os.path.join(getConfigDir(), "M2")
settingFilePath = os.path.join(configDir, "m2Setting.yaml")
self._m2SettingFile = ParamReader(filePath=settingFilePath)
# Inner and outer radius of M2 mirror in m
radiusInner = self._m2SettingFile.getSetting("radiusInner")
radiusOuter = self._m2SettingFile.getSetting("radiusOuter")
super(M2Sim, self).__init__(radiusInner, radiusOuter, configDir)
# Mirror surface bending mode grid file
self._gridFile = ParamReader()
# Mirror FEA model with gradient temperature data
self._feaFile = ParamReader()
self._config("M2_1um_force.yaml", "", "M2_1um_grid.yaml",
"M2_GT_FEA.yaml")
def _config(self, actForceFileName, lutFileName, gridFileName,
feaFileName):
"""Do the configuration.
LUT: Look-up table.
FEA: Finite element analysis.
Parameters
----------
actForceFileName : str
Actuator force file name.
lutFileName : str
LUT file name.
gridFileName : str
File name of bending mode data.
feaFileName : str
FEA model data file name.
"""
numTerms = self._m2SettingFile.getSetting("numTerms")
super(M2Sim, self).config(numTerms=numTerms,
actForceFileName=actForceFileName,
lutFileName=lutFileName)
mirrorDataDir = self.getMirrorDataDir()
gridFilePath = os.path.join(mirrorDataDir, gridFileName)
self._gridFile.setFilePath(gridFilePath)
feaFilePath = os.path.join(mirrorDataDir, feaFileName)
self._feaFile.setFilePath(feaFilePath)
def getPrintthz(self, zAngleInRadian, preCompElevInRadian=0):
"""Get the mirror print in um along z direction in specific zenith
angle.
FEA: Finite element analysis.
Parameters
----------
zAngleInRadian : float
Zenith angle in radian.
preCompElevInRadian : float, optional
Pre-compensation elevation angle in radian. (the default is 0.)
Returns
------
numpy.ndarray
Corrected projection in um along z direction.
"""
# Read the FEA file
data = self._feaFile.getMatContent()
# Zenith direction in um
zdz = data[:, 2]
# Horizon direction in um
hdz = data[:, 3]
# Do the M2 gravitational correction.
# Map the changes of dz on a plane for certain zenith angle
printthzInUm = zdz * np.cos(zAngleInRadian) + \
hdz * np.sin(zAngleInRadian)
# Do the pre-compensation elevation angle correction
printthzInUm -= zdz * np.cos(preCompElevInRadian) + \
hdz * np.sin(preCompElevInRadian)
return printthzInUm
def getTempCorr(self, m2TzGrad, m2TrGrad):
"""Get the mirror print correction along z direction for certain
temperature gradient.
FEA: Finite element analysis.
Parameters
----------
m2TzGrad : float
Temperature gradient along z direction in degree C (+/-2sigma
spans 1C).
m2TrGrad : float
Temperature gradient along r direction in degree C (+/-2sigma
spans 1C).
Returns
-------
numpy.ndarray
Corrected projection in um along z direction.
"""
# Read the FEA file
data = self._feaFile.getMatContent()
# Z-gradient in um
tzdz = data[:, 4]
# r-gradient in um
trdz = data[:, 5]
# Get the temprature correction
tempCorrInUm = m2TzGrad * tzdz + m2TrGrad * trdz
return tempCorrInUm
def getMirrorResInMmInZemax(self, writeZcInMnToFilePath=None):
"""Get the residue of surface (mirror print along z-axis) in mm under
the Zemax coordinate.
This value is after the fitting with spherical Zernike polynomials
(zk).
Parameters
----------
writeZcInMnToFilePath : str, optional
File path to write the fitted zk in mm. (the default is None.)
Returns
------
numpy.ndarray
Fitted residue in mm after removing the fitted zk terms in Zemax
coordinate.
numpy.ndarray
X position in mm in Zemax coordinate.
numpy.ndarray
Y position in mm in Zemax coordinate.
numpy.ndarray
Fitted zk in mm in Zemax coordinate.
"""
# Get the bending mode information
data = self._gridFile.getMatContent()
# Get the x, y coordinate
bx = data[:, 0]
by = data[:, 1]
# Transform the M2 coordinate to Zemax coordinate
bxInZemax, byInZemax, surfInZemax = opt2ZemaxCoorTrans(
bx, by, self.getSurfAlongZ())
# Get the mirror residue and zk in um
RinM = self.getOuterRinM()
resInUmInZemax, zcInUmInZemax = self._getMirrorResInNormalizedCoor(
surfInZemax, bxInZemax / RinM, byInZemax / RinM)
# Change the unit to mm
resInMmInZemax = resInUmInZemax * 1e-3
bxInMmInZemax = bxInZemax * 1e3
byInMmInZemax = byInZemax * 1e3
zcInMmInZemax = zcInUmInZemax * 1e-3
# Save the file of fitted Zk
if (writeZcInMnToFilePath is not None):
np.savetxt(writeZcInMnToFilePath, zcInMmInZemax)
return resInMmInZemax, bxInMmInZemax, byInMmInZemax, zcInMmInZemax
def writeMirZkAndGridResInZemax(self,
resFile="",
surfaceGridN=200,
writeZcInMnToFilePath=None):
"""Write the grid residue in mm of mirror surface after the fitting
with Zk under the Zemax coordinate.
Parameters
----------
resFile : str, optional
File path to save the grid surface residue map. (the default
is "".)
surfaceGridN : int, optional
Surface grid number. (the default is 200.)
writeZcInMnToFilePath : str, optional
File path to write the fitted zk in mm. (the default is None.)
Returns
-------
str
Grid residue map related data.
"""
# Get the residure map
resInMmInZemax, bxInMmInZemax, byInMmInZemax = \
self.getMirrorResInMmInZemax(
writeZcInMnToFilePath=writeZcInMnToFilePath)[0:3]
# Change the unit from m to mm
innerRinMm = self.getInnerRinM() * 1e3
outerRinMm = self.getOuterRinM() * 1e3
# Get the residue map used in Zemax
# Content header: (NUM_X_PIXELS, NUM_Y_PIXELS, delta x, delta y)
# Content: (z, dx, dy, dxdy)
content = self._gridSampInMnInZemax(resInMmInZemax,
bxInMmInZemax,
byInMmInZemax,
innerRinMm,
outerRinMm,
surfaceGridN,
surfaceGridN,
resFile=resFile)
return content
def showMirResMap(self, resFile, writeToResMapFilePath=None):
"""Show the mirror residue map.
Parameters
----------
resFile : str
File path of the grid surface residue map.
writeToResMapFilePath : str, optional
File path to save the residue map. (the default is None.)
"""
# Get the residure map
resInMmInZemax, bxInMmInZemax, byInMmInZemax = \
self.getMirrorResInMmInZemax()[0:3]
# Change the unit
outerRinMm = self.getOuterRinM() * 1e3
plotResMap(resInMmInZemax,
bxInMmInZemax,
byInMmInZemax,
outerRinMm,
resFile=resFile,
writeToResMapFilePath=writeToResMapFilePath)