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 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
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.
"""
lowMagnitude = 0
highMagnitude = 0
mappedFilterType = mapFilterRefToG(self.filter)
if (mappedFilterType == FilterType.U):
lowMagnitude = self.U_LOW_MAG
highMagnitude = self.U_HIGH_MAG
elif (mappedFilterType == FilterType.G):
lowMagnitude = self.G_LOW_MAG
highMagnitude = self.G_HIGH_MAG
elif (mappedFilterType == FilterType.R):
lowMagnitude = self.R_LOW_MAG
highMagnitude = self.R_HIGH_MAG
elif (mappedFilterType == FilterType.I):
lowMagnitude = self.I_LOW_MAG
highMagnitude = self.I_HIGH_MAG
elif (mappedFilterType == FilterType.Z):
lowMagnitude = self.Z_LOW_MAG
highMagnitude = self.Z_HIGH_MAG
elif (mappedFilterType == FilterType.Y):
lowMagnitude = self.Y_LOW_MAG
highMagnitude = self.Y_HIGH_MAG
else:
raise ValueError("No filter type matches.")
return lowMagnitude, highMagnitude
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
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"]
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 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 positions in x. The arange is [neighboring stars, bright star].
numpy.ndarray
Star positions in y. 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
d1 = (maxY - minY) + 4 * self.STAR_RADIUS_IN_PIXEL
d2 = (maxX - minX) + 4 * self.STAR_RADIUS_IN_PIXEL
# 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
# Compare the distances from the central point to four boundaries of
# ccd image
cenYup = ccdD1 - cenY
cenXright = ccdD2 - cenX
# 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 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 testMapFilterRefToGForFilterU(self):
mappedFilterType = mapFilterRefToG(FilterType.U)
self.assertEqual(mappedFilterType, FilterType.U)
def testMapFilterRefToG(self):
mappedFilterType = mapFilterRefToG(FilterType.REF)
self.assertEqual(mappedFilterType, FilterType.G)
def testmapFilterRefToGForFilterU(self):
mappedFilterType = mapFilterRefToG(FilterType.U)
self.assertEqual(mappedFilterType, FilterType.U)
def testmapFilterRefToG(self):
mappedFilterType = mapFilterRefToG(FilterType.REF)
self.assertEqual(mappedFilterType, FilterType.G)
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 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 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
