def testGetImageData(self): eimg = self._getEimage() image = ButlerWrapper.getImageData(eimg) self.assertTrue(isinstance(image, np.ndarray)) self.assertEqual(image.shape, (4000, 4072))
def setUpClass(cls): dataDirPath = os.path.join(getModulePath(), "tests") cls.dataDir = tempfile.TemporaryDirectory(dir=dataDirPath) cls._ingestImages() cls.butlerWrapper = ButlerWrapper(cls.dataDir.name)
def testGetImageData(self): exposure = self._getRawExp() image = ButlerWrapper.getImageData(exposure) self.assertTrue(isinstance(image, np.ndarray)) self.assertEqual(image.shape, (4020, 4176))
def plotHist(exposure, name="", numOfBin=1000, log=False, saveFilePath=None): """ Plot the histogram. Arguments: exposure {[exposure]} -- Data butler of exposure image. Keyword Arguments: name {string} -- Image title name (default: {""}). numOfBin {int} -- Number of bins (default: {1000}). log {bool} -- The histogram axis will be set to a log scale if log=True (default: {False}). saveFilePath {[str]} -- Save image to file path. (default: {None}) """ # Get the image data img = ButlerWrapper.getImageData(exposure) # Plot the histogram plt.figure() plt.hist(img.flatten(), bins=int(numOfBin), log=log) plt.title(name) if (saveFilePath is not None): plt.savefig(saveFilePath, bbox_inches="tight") plt.close() else: plt.show()
def plotHist(exposure, name="", numOfBin=1000, log=False, saveFilePath=None): """Plot the histogram. Parameters ---------- exposure : Exposure Data butler of exposure image. name : str, optional Image title name. (the default is "".) numOfBin : int, optional Number of bins. (the default is 1000.) log : bool, optional The histogram axis will be set to a log scale if log=True. (the default is False.) saveFilePath : str, optional Save image to file path. (the default is None.) """ # Get the image data img = ButlerWrapper.getImageData(exposure) # Plot the histogram plt.figure() plt.hist(img.flatten(), bins=int(numOfBin), log=log) plt.title(name) if saveFilePath is None: plt.show() else: plt.savefig(saveFilePath, bbox_inches="tight") plt.close()
def plotHist(exposure, name="", numOfBin=1000, log=False, saveFilePath=None): """Plot the histogram. Parameters ---------- exposure : Exposure Data butler of exposure image. name : str, optional Image title name. (the default is "".) numOfBin : int, optional Number of bins. (the default is 1000.) log : bool, optional The histogram axis will be set to a log scale if log=True. (the default is False.) saveFilePath : str, optional Save image to file path. (the default is None.) """ # Get the image data img = ButlerWrapper.getImageData(exposure) # Plot the histogram plt.figure() plt.hist(img.flatten(), bins=int(numOfBin), log=log) plt.title(name) if (saveFilePath is not None): plt.savefig(saveFilePath, bbox_inches="tight") plt.close() else: plt.show()
class TestButlerWrapper(unittest.TestCase): """Test the butler wrapper class.""" def setUp(self): self.inputs = os.path.join(getModulePath(), "tests", "testData", "repackagedPhoSimData") self.butlerWrapper = ButlerWrapper(self.inputs) def testGetRawExp(self): exposure = self._getRawExp() self.assertEqual(exposure.getDimensions()[0], 4176) self.assertEqual(exposure.getDimensions()[1], 4020) def _getRawExp(self): visit, raft, sensor = self._getDefaultSurveyMetaData() exposure = self.butlerWrapper.getRawExp(visit, raft, sensor) return exposure def _getDefaultSurveyMetaData(self): visit = 20 raft = "R00" sensor = "S22" return visit, raft, sensor def testSetInputsAndOutputs(self): self.butlerWrapper.setInputsAndOutputs(inputs=self.inputs) exposure = self._getRawExp() self.assertEqual(exposure.getDimensions()[0], 4176) def testGetImageData(self): exposure = self._getRawExp() image = ButlerWrapper.getImageData(exposure) self.assertTrue(isinstance(image, np.ndarray)) self.assertEqual(image.shape, (4020, 4176))
def setPostIsrCcdInputs(self, inputs): """Set inputs of post instrument signature removal (ISR) CCD images. Parameters ---------- inputs : RepositoryArgs, dict, or str Can be a single item or a list. Provides arguments to load an existing repository (or repositories). String is assumed to be a URI and is used as the cfgRoot (URI to the location of the cfg file). (Local file system URI does not have to start with 'file://' and in this way can be a relative path). The 'RepositoryArgs' class can be used to provide more parameters with which to initialize a repository (such as 'mapper', 'mapperArgs', 'tags', etc. See the 'RepositoryArgs' documentation for more details). A dict may be used as shorthand for a 'RepositoryArgs' class instance. The dict keys must match parameters to the 'RepositoryArgs.__init__' function. """ self.butlerWrapper = ButlerWrapper(inputs)
def poltExposureImage(exposure, name="", scale="log", cmap="gray", vmin=None, vmax=None, saveFilePath=None): """Plot the exposure image. Parameters ---------- exposure : Exposure Data butler of exposure image. name : str, optional Image title name. (the default is "".) scale : str, optional Scale of image map (log or linear). (the default is "log".) cmap : str, optional Color map definition. (the default is "gray".) vmin : float, optional Mininum value to show. This normalizes the luminance data. (the default is None.) vmax : float, optional Maximum value to show. This normalizes the luminance data. (the default is None.) saveFilePath : str, optional Save image to file path. (the default is None.) """ # Get the image data img = ButlerWrapper.getImageData(exposure) # Change the scale if needed if scale not in ("linear", "log"): print("No %s scale to choose. Only 'linear' and 'log' scales are allowed." % scale) return # Decide the norm in imshow for the ploting if (scale == "linear"): plotNorm = None elif (scale == "log"): if (img.min()) < 0: plotNorm = SymLogNorm(linthresh=0.03) else: plotNorm = LogNorm() # Plot the image plt.figure() plt.imshow(img, cmap=cmap, origin="lower", norm=plotNorm, vmin=vmin, vmax=vmax) plt.colorbar() plt.title(name) if (saveFilePath is not None): plt.savefig(saveFilePath, bbox_inches="tight") plt.close() else: plt.show()
def poltExposureImage(exposure, name="", scale="log", cmap="gray", vmin=None, vmax=None, saveFilePath=None): """ Plot the exposure image. Arguments: exposure {[exposure]} -- Data butler of exposure image. Keyword Arguments: name {[str]} -- Image title name. (default: {""}) scale {[str]} -- Scale of image map (log or linear). (default: {"log"}) cmap {[str]} -- Color map definition. (default: {"gray"}) vmin {[float]} -- Mininum value to show. This normalizes the luminance data. (default: {None}) vmax {[float]} -- Maximum value to show. This normalizes the luminance data. (default: {None}) saveFilePath {[str]} -- Save image to file path. (default: {None}) """ # Get the image data img = ButlerWrapper.getImageData(exposure) # Change the scale if needed if scale not in ("linear", "log"): print("No %s scale to choose. Only 'linear' and 'log' scales are allowed." % scale) return # Decide the norm in imshow for the ploting if (scale == "linear"): plotNorm = None elif (scale == "log"): if (img.min()) < 0: plotNorm = SymLogNorm(linthresh=0.03) else: plotNorm = LogNorm() # Plot the image plt.figure() plt.imshow(img, cmap=cmap, origin="lower", norm=plotNorm, vmin=vmin, vmax=vmax) plt.colorbar() plt.title(name) if (saveFilePath is not None): plt.savefig(saveFilePath, bbox_inches="tight") plt.close() else: plt.show()
def setPostIsrCcdInputs(self, inputs): """Set inputs of post instrument signature removal (ISR) CCD images. Parameters ---------- inputs : RepositoryArgs, dict, or str Can be a single item or a list. Provides arguments to load an existing repository (or repositories). String is assumed to be a URI and is used as the cfgRoot (URI to the location of the cfg file). (Local file system URI does not have to start with 'file://' and in this way can be a relative path). The 'RepositoryArgs' class can be used to provide more parameters with which to initialize a repository (such as 'mapper', 'mapperArgs', 'tags', etc. See the 'RepositoryArgs' documentation for more details). A dict may be used as shorthand for a 'RepositoryArgs' class instance. The dict keys must match parameters to the 'RepositoryArgs.__init__' function. """ self.butlerWrapper = ButlerWrapper(inputs)
class WepController(object): CORNER_WFS_LIST = [ "R00_SW0", "R00_SW1", "R04_SW0", "R04_SW1", "R40_SW0", "R40_SW1", "R44_SW0", "R44_SW1", ] def __init__(self, dataCollector, isrWrapper, sourSelc, sourProc, wfEsti): """Initialize the wavefront estimation pipeline (WEP) controller class. Parameters ---------- dataCollector : CamDataCollector Camera data collector. isrWrapper : CamIsrWrapper Instrument signature removal (ISR) wrapper. sourSelc : SourceSelector Source selector. sourProc : SourceProcessor Source processor. wfEsti : WfEstimator Wavefront estimator. """ # Data collector to use DM ingest command line task self.dataCollector = dataCollector # ISR wrapper to use DM ISR command line task self.isrWrapper = isrWrapper # Source selector to query the bright star catalog self.sourSelc = sourSelc # Source processor to get the donut image or do the deblending self.sourProc = sourProc # Wavefront estimator to get zk self.wfEsti = wfEsti # Butler wrapper to use DM data butler self.butlerWrapper = None def getDataCollector(self): """Get the attribute of data collector. Returns ------- CamDataCollector Data collector. """ return self.dataCollector def getIsrWrapper(self): """Get the attribute of ISR wraper. ISR: Instrument signature removal. Returns ------- CamIsrWrapper ISR wraper. """ return self.isrWrapper def getSourSelc(self): """Get the attribute of source selector. Returns ------- SourceSelector Source selector. """ return self.sourSelc def getSourProc(self): """Get the attribute of source processor. Returns ------- SourceProcessor Source processor. """ return self.sourProc def getWfEsti(self): """Get the attribute of wavefront estimator. Returns ------- WfEstimator Wavefront estimator. """ return self.wfEsti def getButlerWrapper(self): """Get the attribute of butler wrapper. Returns ------- ButlerWrapper Butler wrapper. """ return self.butlerWrapper def setPostIsrCcdInputs(self, inputs): """Set inputs of post instrument signature removal (ISR) CCD images. Parameters ---------- inputs : RepositoryArgs, dict, or str Can be a single item or a list. Provides arguments to load an existing repository (or repositories). String is assumed to be a URI and is used as the cfgRoot (URI to the location of the cfg file). (Local file system URI does not have to start with 'file://' and in this way can be a relative path). The 'RepositoryArgs' class can be used to provide more parameters with which to initialize a repository (such as 'mapper', 'mapperArgs', 'tags', etc. See the 'RepositoryArgs' documentation for more details). A dict may be used as shorthand for a 'RepositoryArgs' class instance. The dict keys must match parameters to the 'RepositoryArgs.__init__' function. """ self.butlerWrapper = ButlerWrapper(inputs) def getPostIsrImgMapByPistonDefocal(self, sensorNameList, obsIdList): """Get the post ISR image map that the defocal images are by the pistion motion. Parameters ---------- sensorNameList : list List of sensor name. obsIdList : list Observation Id list in [intraObsId, extraObsId]. Returns ------- dict Post-ISR image map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). """ return self._getImgMapByPistonDefocal(sensorNameList, obsIdList, ImageType.Amp) def _getImgMapByPistonDefocal(self, sensorNameList, obsIdList, imageType): """Get the image map that the defocal images are by the pistion motion. Parameters ---------- sensorNameList : list List of sensor name. obsIdList : list Observation Id list in [intraObsId, extraObsId]. imageType : ImageType Image type. Returns ------- dict Image map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). Raises ------ ValueError The image type is not supported. """ # Get the waveront image map wfsImgMap = dict() for sensorName in sensorNameList: # Get the sensor name information raft, sensor = self._getSensorInfo(sensorName) # The intra/ extra defocal images are decided by obsId imgList = [] for visit in obsIdList: # Get the exposure image in ndarray if imageType == ImageType.Amp: exp = self.butlerWrapper.getPostIsrCcd(int(visit), raft, sensor) elif imageType == ImageType.Eimg: exp = self.butlerWrapper.getEimage(int(visit), raft, sensor) else: raise ValueError("The %s is not supported." % imageType) img = self.butlerWrapper.getImageData(exp) # Transform the image in DM coordinate to camera coordinate. camImg = self._transImgDmCoorToCamCoor(img) # Collect the image imgList.append(camImg) wfsImgMap[sensorName] = DefocalImage( intraImg=imgList[0], extraImg=imgList[1] ) return wfsImgMap def _getSensorInfo(self, sensorName): """Get the sensor information. Parameters ---------- sensorName : str Sensor name (e.g. "R22_S11" or "R44_SW0"). Note: SW0 and SW1 are used as the wavefront sensor in lsst.obs.lsst.LsstCamMapper. Returns ------- str Raft. str Sensor. Raises ------ ValueError Can not match the raft and sensor from sensorName. """ m = re.match(r"R(\d\d)_S([W\d]\d)", sensorName) if m is not None: raftId, sensorId = m.groups() raft = "R" + raftId sensor = "S" + sensorId return raft, sensor else: raise ValueError(f"Can not match the raft and sensor from {sensorName}") def _transImgDmCoorToCamCoor(self, dmImg): """Transfrom the image in DM coordinate to camera coordinate. The geometry transformation is defined in LSE-349. Parameters ---------- dmImg : numpy.ndarray Image in DM coordinate. Returns ------- numpy.ndarray Image is camera coordinate. """ # For the PhoSim mapper, it is the transpose (x, y) to (y, x). camImg = dmImg.T return camImg def getEimgMapByPistonDefocal(self, sensorNameList, obsIdList): """Get the eimage map that the defocal images are by the pistion motion. Parameters ---------- sensorNameList : list List of sensor name. obsIdList : list Observation Id list in [intraObsId, extraObsId]. Returns ------- dict Eimage map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). """ return self._getImgMapByPistonDefocal(sensorNameList, obsIdList, ImageType.Eimg) def getPostIsrImgMapOnCornerWfs(self, sensorNameList, obsId): """Get the post ISR image map of corner wavefront sensors. Parameters ---------- sensorNameList : list List of sensor name. obsId : int Observation Id. Returns ------- dict Post-ISR image map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). """ pass def getDonutMap(self, neighborStarMap, wfsImgMap, filterType, doDeblending=False): """Get the donut map on each wavefront sensor (WFS). Parameters ---------- neighborStarMap : dict Information of neighboring stars and candidate stars with the name of sensor as a dictionary. wfsImgMap : dict Post-ISR image map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). filterType : FilterType Filter type. doDeblending : bool, optional Do the deblending or not. If False, only consider the single donut based on the bright star catalog.(the default is False.) Returns ------- dict Donut image map. The dictionary key is the sensor name. The dictionary item is the list of donut image (type: list[DonutImage]). """ donutMap = dict() for sensorName, nbrStar in neighborStarMap.items(): # Configure the source processor self.sourProc.config(sensorName=sensorName) # Get the defocal images: [intra, extra] defocalImgList = [ wfsImgMap[sensorName].getIntraImg(), wfsImgMap[sensorName].getExtraImg(), ] # Get the bright star id list on specific sensor brightStarIdList = list(nbrStar.getId()) for starIdIdx in range(len(brightStarIdList)): # Get the single star map for jj in range(len(defocalImgList)): ccdImg = defocalImgList[jj] # Get the segment of image if ccdImg is not None: ( singleSciNeiImg, allStarPosX, allStarPosY, magRatio, offsetX, offsetY, ) = self.sourProc.getSingleTargetImage( ccdImg, nbrStar, starIdIdx, filterType ) # Only consider the single donut if no deblending if (not doDeblending) and (len(magRatio) != 1): continue # Get the single donut/ deblended image if (len(magRatio) == 1) or (not doDeblending): imgDeblend = singleSciNeiImg if len(magRatio) == 1: realcx, realcy = searchDonutPos(imgDeblend) else: realcx = allStarPosX[-1] realcy = allStarPosY[-1] # Do the deblending or not elif len(magRatio) == 2 and doDeblending: imgDeblend, realcx, realcy = self.sourProc.doDeblending( singleSciNeiImg, allStarPosX, allStarPosY, magRatio ) # Update the magnitude ratio magRatio = [1] else: continue # Extract the image if len(magRatio) == 1: sizeInPix = self.wfEsti.getSizeInPix() x0 = np.floor(realcx - sizeInPix / 2).astype("int") y0 = np.floor(realcy - sizeInPix / 2).astype("int") imgDeblend = imgDeblend[ y0 : y0 + sizeInPix, x0 : x0 + sizeInPix ] # Rotate the image if the sensor is the corner # wavefront sensor if sensorName in self.CORNER_WFS_LIST: # Get the Euler angle eulerZangle = round( self.sourProc.getEulerZinDeg(sensorName) ) # Change the sign if the angle < 0 while eulerZangle < 0: eulerZangle += 360 # Do the rotation of matrix numOfRot90 = eulerZangle // 90 imgDeblend = np.flipud( np.rot90(np.flipud(imgDeblend), numOfRot90) ) # Put the deblended image into the donut map if sensorName not in donutMap.keys(): donutMap[sensorName] = [] # Check the donut exists in the list or not starId = brightStarIdList[starIdIdx] donutIndex = self._searchDonutListId( donutMap[sensorName], starId ) # Create the donut object and put into the list if it # is needed if donutIndex < 0: # Calculate the field X, Y pixelX = realcx + offsetX pixelY = realcy + offsetY fieldX, fieldY = self.sourProc.camXYtoFieldXY( pixelX, pixelY ) # Instantiate the DonutImage class donutImg = DonutImage( starId, pixelX, pixelY, fieldX, fieldY ) donutMap[sensorName].append(donutImg) # Search for the donut index again donutIndex = self._searchDonutListId( donutMap[sensorName], starId ) # Get the donut image list donutList = donutMap[sensorName] # Take the absolute value for images, which might # contain the negative value after the ISR correction. # This happens for the amplifier images. imgDeblend = np.abs(imgDeblend) # Set the intra focal image if jj == 0: donutList[donutIndex].setImg(intraImg=imgDeblend) # Set the extra focal image elif jj == 1: donutList[donutIndex].setImg(extraImg=imgDeblend) return donutMap def _searchDonutListId(self, donutList, starId): """Search the bright star ID in the donut list. Parameters ---------- donutList : list List of DonutImage object. starId : int Star Id. Returns ------- int Index of donut image object with the specific starId. """ index = -1 for ii in range(len(donutList)): if donutList[ii].getStarId() == int(starId): index = ii break return index def calcWfErr(self, donutMap): """Calculate the wavefront error in annular Zernike polynomials (z4-z22). Parameters ---------- donutMap : dict Donut image map. The dictionary key is the sensor name. The dictionary item is the donut image (type: DonutImage). Returns ------- dict Donut image map with calculated wavefront error. """ for sensorName, donutList in donutMap.items(): for ii in range(len(donutList)): # Get the intra- and extra-focal donut images # Check the sensor is the corner WFS or not. # Only consider "intra" WFS. # For LsstSimMapper, Intra: C0 -> A; Extra: C1 -> B # For LsstCamMapper, Intra: C0 -> SW1; Extra: C1 ->SW0 # Look for the intra-focal image if sensorName.endswith("SW1"): intraDonut = donutList[ii] # Get the extra-focal sensor name extraFocalSensorName = sensorName.replace("SW1", "SW0") # Get the donut list of extra-focal sensor extraDonutList = donutMap[extraFocalSensorName] if ii < len(extraDonutList): extraDonut = extraDonutList[ii] else: continue # Pass the extra-focal image elif sensorName.endswith("SW0"): continue # Scientific sensor else: intraDonut = extraDonut = donutList[ii] # Calculate the wavefront error # Get the field X, Y position intraFieldXY = intraDonut.getFieldPos() extraFieldXY = extraDonut.getFieldPos() # Get the defocal images intraImg = intraDonut.getIntraImg() extraImg = extraDonut.getExtraImg() # Calculate the wavefront error zer4UpNm = self._calcSglWfErr( intraImg, extraImg, intraFieldXY, extraFieldXY ) # Put the value to the donut image intraDonut.setWfErr(zer4UpNm) extraDonut.setWfErr(zer4UpNm) # Intentionally to expose this return value to show the input, # donutMap, has been modified. return donutMap def _calcSglWfErr(self, intraImg, extraImg, intraFieldXY, extraFieldXY): """Calculate the wavefront error in annular Zernike polynomials (z4-z22) for single donut. Parameters ---------- intraImg : numpy.ndarray Intra-focal donut image. extraImg : numpy.ndarray Extra-focal donut image. intraFieldXY : tuple Field x, y in degree of intra-focal donut image. extraFieldXY : tuple Field x, y in degree of extra-focal donut image. Returns ------- numpy.ndarray Coefficients of Zernike polynomials (z4 - z22) in nm. """ # Set the images self.wfEsti.setImg(intraFieldXY, DefocalType.Intra, image=intraImg) self.wfEsti.setImg(extraFieldXY, DefocalType.Extra, image=extraImg) # Reset the wavefront estimator self.wfEsti.reset() # Calculate the wavefront error zer4UpNm = self.wfEsti.calWfsErr() return zer4UpNm def calcAvgWfErrOnSglCcd(self, donutList): """Calculate the average of wavefront error on single CCD. CCD: Charge-coupled device. Parameters ---------- donutList : list List of donut object (type: DonutImage). Returns ------- numpy.ndarray Average of wavefront error in nm. """ # Calculate the weighting of donut image wgtRatio = self._calcWeiRatio(donutList) # Calculate the mean wavefront error avgErr = 0 for ii in range(len(donutList)): donut = donutList[ii] zer4UpNmArr = donut.getWfErr() # Assign the zer4UpNm if len(zer4UpNmArr) == 0: zer4UpNm = 0 else: zer4UpNm = zer4UpNmArr avgErr = avgErr + wgtRatio[ii] * zer4UpNm return avgErr def _calcWeiRatio(self, donutList): """Calculate the weighting ratio of donut in the list. Parameters ---------- donutList : list List of donut object (type: DonutImage). Returns ------- numpy.ndarray Array of weighting ratio of donuts to do the average of wavefront error. """ # Weighting of donut image. Use the simple average at this moment. # Need to consider the S/N and other factors in the future. # Check the available zk and give the ratio wgtRatio = [] for donut in donutList: if len(donut.getWfErr()) == 0: wgtRatio.append(0) else: wgtRatio.append(1) # Do the normalization wgtRatioArr = np.array(wgtRatio) normalizedwgtRatioArr = wgtRatioArr / np.sum(wgtRatioArr) return normalizedwgtRatioArr def genMasterDonut(self, donutMap, zcCol=np.zeros(22)): """Generate the master donut map. Parameters ---------- donutMap : dict Donut image map. The dictionary key is the sensor name. The dictionary item is the donut image (type: DonutImage). zcCol : numpy.ndarray, optional Coefficients of wavefront (z1-z22) in nm. (the default is np.zeros(22).) Returns ------- dict Master donut image map. The dictionary key is the sensor name. The dictionary item is the master donut image (type: DonutImage). """ masterDonutMap = dict() for sensorName, donutList in donutMap.items(): # Get the master donut on single CCD masterDonut = self._genMasterImgOnSglCcd(donutList, zcCol=zcCol) # Put the master donut to donut map masterDonutMap[sensorName] = [masterDonut] return masterDonutMap def _genMasterImgOnSglCcd(self, donutList, zcCol): """Generate the master donut image on single CCD. CCD: Charge-coupled device. Parameters ---------- sensorName : str Canonical sensor name (e.g. "R:2,2 S:1,1"). donutList : list List of donut object (type: DonutImage). zcCol : numpy.ndarray Coefficients of wavefront (z1-z22) in nm. Returns ------- DonutImage Master donut. """ intraProjImgList = [] extraProjImgList = [] for donut in donutList: # Get the field x, y fieldXY = donut.getFieldPos() # Set the image intraImg = donut.getIntraImg() if intraImg is not None: # Get the projected image projImg = self._getProjImg(fieldXY, DefocalType.Intra, intraImg, zcCol) # Collect the projected donut intraProjImgList.append(projImg) extraImg = donut.getExtraImg() if extraImg is not None: # Get the projected image projImg = self._getProjImg(fieldXY, DefocalType.Extra, extraImg, zcCol) # Collect the projected donut extraProjImgList.append(projImg) # Generate the master donut stackIntraImg = self._stackImg(intraProjImgList) stackExtraImg = self._stackImg(extraProjImgList) # Put the master donut to donut map pixelX, pixelY = searchDonutPos(stackIntraImg) masterDonut = DonutImage( 0, pixelX, pixelY, 0, 0, intraImg=stackIntraImg, extraImg=stackExtraImg ) return masterDonut def _getProjImg(self, fieldXY, defocalType, defocalImg, zcCol): """Get the projected image on the pupil. Parameters ---------- fieldXY : tuple Position of donut on the focal plane in the degree for intra- and extra-focal images (field X, field Y). defocalType : enum 'DefocalType' Defocal type of image. defocalImg : numpy.ndarray Donut defocal image. zcCol : numpy.ndarray Coefficients of wavefront (z1-z22). Returns ------- numpy.ndarray Projected image. """ # Set the image self.wfEsti.setImg(fieldXY, defocalType, image=defocalImg) # Get the image in the type of CompensationImageDecorator if defocalType == DefocalType.Intra: img = self.wfEsti.getIntraImg() elif defocalType == DefocalType.Extra: img = self.wfEsti.getExtraImg() # Get the distortion correction (offaxis) algo = self.wfEsti.getAlgo() offAxisCorrOrder = algo.getOffAxisPolyOrder() inst = self.wfEsti.getInst() img.setOffAxisCorr(inst, offAxisCorrOrder) # Do the image cocenter img.imageCoCenter(inst) # Do the compensation/ projection img.compensate(inst, algo, zcCol, self.wfEsti.getOptModel()) # Return the projected image return img.getImg() def _stackImg(self, imgList): """Stack the images. Parameters ---------- imgList : list List of image. Returns ------- numpy.ndarray Stacked image. """ if len(imgList) == 0: stackImg = None else: # Get the minimun image dimension dimXlist = [] dimYlist = [] for img in imgList: dy, dx = img.shape dimXlist.append(dx) dimYlist.append(dy) dimX = np.min(dimXlist) dimY = np.min(dimYlist) deltaX = dimX // 2 deltaY = dimY // 2 # Stack the image by summation directly stackImg = np.zeros([dimY, dimX]) for img in imgList: dy, dx = img.shape cy = int(dy / 2) cx = int(dx / 2) stackImg += img[cy - deltaY : cy + deltaY, cx - deltaX : cx + deltaX] return stackImg
class WepController(object): CORNER_WFS_LIST = ["R:0,0 S:2,2,A", "R:0,0 S:2,2,B", "R:0,4 S:2,0,A", "R:0,4 S:2,0,B", "R:4,0 S:0,2,A", "R:4,0 S:0,2,B", "R:4,4 S:0,0,A", "R:4,4 S:0,0,B"] def __init__(self, dataCollector, isrWrapper, sourSelc, sourProc, wfEsti): """Initialize the wavefront estimation pipeline (WEP) controller class. Parameters ---------- dataCollector : CamDataCollector Camera data collector. isrWrapper : CamIsrWrapper Instrument signature removal (ISR) wrapper. sourSelc : SourceSelector Source selector. sourProc : SourceProcessor Source processor. wfEsti : WfEstimator Wavefront estimator. """ self.dataCollector = dataCollector self.isrWrapper = isrWrapper self.sourSelc = sourSelc self.sourProc = sourProc self.wfEsti = wfEsti self.butlerWrapper = None def getDataCollector(self): """Get the attribute of data collector. Returns ------- CamDataCollector Data collector. """ return self.dataCollector def getIsrWrapper(self): """Get the attribute of ISR wraper. ISR: Instrument signature removal. Returns ------- CamIsrWrapper ISR wraper. """ return self.isrWrapper def getSourSelc(self): """Get the attribute of source selector. Returns ------- SourceSelector Source selector. """ return self.sourSelc def getSourProc(self): """Get the attribute of source processor. Returns ------- SourceProcessor Source processor. """ return self.sourProc def getWfEsti(self): """Get the attribute of wavefront estimator. Returns ------- WfEstimator Wavefront estimator. """ return self.wfEsti def getButlerWrapper(self): """Get the attribute of butler wrapper. Returns ------- ButlerWrapper Butler wrapper. """ return self.butlerWrapper def setPostIsrCcdInputs(self, inputs): """Set inputs of post instrument signature removal (ISR) CCD images. Parameters ---------- inputs : RepositoryArgs, dict, or str Can be a single item or a list. Provides arguments to load an existing repository (or repositories). String is assumed to be a URI and is used as the cfgRoot (URI to the location of the cfg file). (Local file system URI does not have to start with 'file://' and in this way can be a relative path). The 'RepositoryArgs' class can be used to provide more parameters with which to initialize a repository (such as 'mapper', 'mapperArgs', 'tags', etc. See the 'RepositoryArgs' documentation for more details). A dict may be used as shorthand for a 'RepositoryArgs' class instance. The dict keys must match parameters to the 'RepositoryArgs.__init__' function. """ self.butlerWrapper = ButlerWrapper(inputs) def getPostIsrImgMapByPistonDefocal(self, sensorNameList, obsIdList): """Get the post ISR image map that the defocal images are by the pistion motion. Parameters ---------- sensorNameList : list List of sensor name. obsIdList : list Observation Id list in [intraObsId, extraObsId]. Returns ------- dict Post-ISR image map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). """ # Get the waveront image map wfsImgMap = dict() for sensorName in sensorNameList: # Get the sensor name information raft, sensor = self._getSensorInfo(sensorName)[0:2] # The intra/ extra defocal images are decided by obsId imgList = [] for visit in obsIdList: # Get the exposure image in ndarray exp = self.butlerWrapper.getPostIsrCcd(int(visit), raft, sensor) img = self.butlerWrapper.getImageData(exp) # Transform the image in DM coordinate to camera coordinate. camImg = self._transImgDmCoorToCamCoor(img) # Collect the image imgList.append(camImg) wfsImgMap[sensorName] = DefocalImage(intraImg=imgList[0], extraImg=imgList[1]) return wfsImgMap def _transImgDmCoorToCamCoor(self, dmImg): """Transfrom the image in DM coordinate to camera coordinate. Parameters ---------- dmImg : numpy.ndarray Image in DM coordinate. Returns ------- numpy.ndarray Image is camera coordinate. """ # The relationship between DM and camera coordinate should be 90 degree # difference. # For the PhoSim mapper, it is the transpose (x, y) to (y, x). This # should be fixed. camImg = dmImg.T return camImg def getPostIsrImgMapOnCornerWfs(self, sensorNameList, obsId): """Get the post ISR image map of corner wavefront sensors. Parameters ---------- sensorNameList : list List of sensor name. obsId : int Observation Id. Returns ------- dict Post-ISR image map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). """ pass def _getSensorInfo(self, sensorName): """Get the sensor information. Parameters ---------- sensorName : str Sensor name (e.g. "R:2,2 S:1,1" or "R:0,0 S:2,2,A") Returns ------- str Raft. str Sensor. str Channel. """ raft = sensor = channel = None # Use the regular expression to analyze the input name m = re.match(r"R:(\d,\d) S:(\d,\d)(?:,([A,B]))?$", sensorName) if (m is not None): raft, sensor, channel = m.groups()[0:3] # This is for the phosim mapper use. # For example, raft is "R22" and sensor is "S11". raftAbbName = "R" + raft[0] + raft[-1] sensorAbbName = "S" + sensor[0] + sensor[-1] return raftAbbName, sensorAbbName, channel def getDonutMap(self, neighborStarMap, wfsImgMap, filterType, doDeblending=False): """Get the donut map on each wavefront sensor (WFS). Parameters ---------- neighborStarMap : dict Information of neighboring stars and candidate stars with the name of sensor as a dictionary. wfsImgMap : dict Post-ISR image map. The dictionary key is the sensor name. The dictionary item is the defocal image on the camera coordinate. (type: DefocalImage). filterType : FilterType Filter type. doDeblending : bool, optional Do the deblending or not. If False, only consider the single donut based on the bright star catalog.(the default is False.) Returns ------- dict Donut image map. The dictionary key is the sensor name. The dictionary item is the list of donut image (type: list[DonutImage]). """ donutMap = dict() for sensorName, nbrStar in neighborStarMap.items(): # Get the abbraviated sensor name abbrevName = abbrevDectectorName(sensorName) # Configure the source processor self.sourProc.config(sensorName=abbrevName) # Get the defocal images: [intra, extra] defocalImgList = [wfsImgMap[sensorName].getIntraImg(), wfsImgMap[sensorName].getExtraImg()] # Get the bright star id list on specific sensor brightStarIdList = list(nbrStar.getId()) for starIdIdx in range(len(brightStarIdList)): # Get the single star map for jj in range(len(defocalImgList)): ccdImg = defocalImgList[jj] # Get the segment of image if (ccdImg is not None): singleSciNeiImg, allStarPosX, allStarPosY, magRatio, \ offsetX, offsetY = \ self.sourProc.getSingleTargetImage( ccdImg, nbrStar, starIdIdx, filterType) # Only consider the single donut if no deblending if (not doDeblending) and (len(magRatio) != 1): continue # Get the single donut/ deblended image if (len(magRatio) == 1) or (not doDeblending): imgDeblend = singleSciNeiImg if (len(magRatio) == 1): realcx, realcy = searchDonutPos(imgDeblend) else: realcx = allStarPosX[-1] realcy = allStarPosY[-1] # Do the deblending or not elif (len(magRatio) == 2 and doDeblending): imgDeblend, realcx, realcy = \ self.sourProc.doDeblending( singleSciNeiImg, allStarPosX, allStarPosY, magRatio) # Update the magnitude ratio magRatio = [1] else: continue # Extract the image if (len(magRatio) == 1): sizeInPix = self.wfEsti.getSizeInPix() x0 = np.floor(realcx - sizeInPix / 2).astype("int") y0 = np.floor(realcy - sizeInPix / 2).astype("int") imgDeblend = imgDeblend[y0:y0 + sizeInPix, x0:x0 + sizeInPix] # Rotate the image if the sensor is the corner # wavefront sensor if sensorName in self.CORNER_WFS_LIST: # Get the Euler angle eulerZangle = round(self.sourProc.getEulerZinDeg( abbrevName)) # Change the sign if the angle < 0 while (eulerZangle < 0): eulerZangle += 360 # Do the rotation of matrix numOfRot90 = eulerZangle // 90 imgDeblend = np.flipud( np.rot90(np.flipud(imgDeblend), numOfRot90)) # Put the deblended image into the donut map if sensorName not in donutMap.keys(): donutMap[sensorName] = [] # Check the donut exists in the list or not starId = brightStarIdList[starIdIdx] donutIndex = self._searchDonutListId( donutMap[sensorName], starId) # Create the donut object and put into the list if it # is needed if (donutIndex < 0): # Calculate the field X, Y pixelX = realcx + offsetX pixelY = realcy + offsetY fieldX, fieldY = self.sourProc.camXYtoFieldXY( pixelX, pixelY) # Instantiate the DonutImage class donutImg = DonutImage(starId, pixelX, pixelY, fieldX, fieldY) donutMap[sensorName].append(donutImg) # Search for the donut index again donutIndex = self._searchDonutListId( donutMap[sensorName], starId) # Get the donut image list donutList = donutMap[sensorName] # Take the absolute value for images, which might # contain the negative value after the ISR correction. # This happens for the amplifier images. imgDeblend = np.abs(imgDeblend) # Set the intra focal image if (jj == 0): donutList[donutIndex].setImg(intraImg=imgDeblend) # Set the extra focal image elif (jj == 1): donutList[donutIndex].setImg(extraImg=imgDeblend) return donutMap def _searchDonutListId(self, donutList, starId): """Search the bright star ID in the donut list. Parameters ---------- donutList : list List of DonutImage object. starId : int Star Id. Returns ------- int Index of donut image object with the specific starId. """ index = -1 for ii in range(len(donutList)): if (donutList[ii].getStarId() == int(starId)): index = ii break return index def calcWfErr(self, donutMap): """Calculate the wavefront error in annular Zernike polynomials (z4-z22). Parameters ---------- donutMap : dict Donut image map. The dictionary key is the sensor name. The dictionary item is the donut image (type: DonutImage). Returns ------- dict Donut image map with calculated wavefront error. """ for sensorName, donutList in donutMap.items(): for ii in range(len(donutList)): # Get the intra- and extra-focal donut images # Check the sensor is the corner WFS or not. Only consider "A" # Intra: C0 -> A; Extra: C1 -> B # Look for the intra-focal image if sensorName.endswith("A"): intraDonut = donutList[ii] # Get the extra-focal sensor name extraFocalSensorName = sensorName.replace("A", "B") # Get the donut list of extra-focal sensor extraDonutList = donutMap[extraFocalSensorName] if (ii < len(extraDonutList)): extraDonut = extraDonutList[ii] else: continue # Pass the extra-focal image elif sensorName.endswith("B"): continue # Scientific sensor else: intraDonut = extraDonut = donutList[ii] # Calculate the wavefront error # Get the field X, Y position intraFieldXY = intraDonut.getFieldPos() extraFieldXY = extraDonut.getFieldPos() # Get the defocal images intraImg = intraDonut.getIntraImg() extraImg = extraDonut.getExtraImg() # Calculate the wavefront error zer4UpNm = self._calcSglWfErr(intraImg, extraImg, intraFieldXY, extraFieldXY) # Put the value to the donut image intraDonut.setWfErr(zer4UpNm) extraDonut.setWfErr(zer4UpNm) # Intentionally to expose this return value to show the input, # donutMap, has been modified. return donutMap def _calcSglWfErr(self, intraImg, extraImg, intraFieldXY, extraFieldXY): """Calculate the wavefront error in annular Zernike polynomials (z4-z22) for single donut. Parameters ---------- intraImg : numpy.ndarray Intra-focal donut image. extraImg : numpy.ndarray Extra-focal donut image. intraFieldXY : tuple Field x, y in degree of intra-focal donut image. extraFieldXY : tuple Field x, y in degree of extra-focal donut image. Returns ------- numpy.ndarray Coefficients of Zernike polynomials (z4 - z22) in nm. """ # Set the images self.wfEsti.setImg(intraFieldXY, DefocalType.Intra, image=intraImg) self.wfEsti.setImg(extraFieldXY, DefocalType.Extra, image=extraImg) # Reset the wavefront estimator self.wfEsti.reset() # Calculate the wavefront error zer4UpNm = self.wfEsti.calWfsErr() return zer4UpNm def calcAvgWfErrOnSglCcd(self, donutList): """Calculate the average of wavefront error on single CCD. CCD: Charge-coupled device. Parameters ---------- donutList : list List of donut object (type: DonutImage). Returns ------- numpy.ndarray Average of wavefront error in nm. """ # Calculate the weighting of donut image wgtRatio = self._calcWeiRatio(donutList) # Calculate the mean wavefront error avgErr = 0 for ii in range(len(donutList)): donut = donutList[ii] zer4UpNmArr = donut.getWfErr() # Assign the zer4UpNm if (len(zer4UpNmArr) == 0): zer4UpNm = 0 else: zer4UpNm = zer4UpNmArr avgErr = avgErr + wgtRatio[ii] * zer4UpNm return avgErr def _calcWeiRatio(self, donutList): """Calculate the weighting ratio of donut in the list. Parameters ---------- donutList : list List of donut object (type: DonutImage). Returns ------- numpy.ndarray Array of weighting ratio of donuts to do the average of wavefront error. """ # Weighting of donut image. Use the simple average at this moment. # Need to consider the S/N and other factors in the future. # Check the available zk and give the ratio wgtRatio = [] for donut in donutList: if (len(donut.getWfErr()) == 0): wgtRatio.append(0) else: wgtRatio.append(1) # Do the normalization wgtRatioArr = np.array(wgtRatio) normalizedwgtRatioArr = wgtRatioArr / np.sum(wgtRatioArr) return normalizedwgtRatioArr def genMasterDonut(self, donutMap, zcCol=np.zeros(22)): """Generate the master donut map. Parameters ---------- donutMap : dict Donut image map. The dictionary key is the sensor name. The dictionary item is the donut image (type: DonutImage). zcCol : numpy.ndarray, optional Coefficients of wavefront (z1-z22) in nm. (the default is np.zeros(22).) Returns ------- dict Master donut image map. The dictionary key is the sensor name. The dictionary item is the master donut image (type: DonutImage). """ masterDonutMap = dict() for sensorName, donutList in donutMap.items(): # Get the master donut on single CCD masterDonut = self._genMasterImgOnSglCcd(donutList, zcCol=zcCol) # Put the master donut to donut map masterDonutMap[sensorName] = [masterDonut] return masterDonutMap def _genMasterImgOnSglCcd(self, donutList, zcCol): """Generate the master donut image on single CCD. CCD: Charge-coupled device. Parameters ---------- sensorName : str Canonical sensor name (e.g. "R:2,2 S:1,1"). donutList : list List of donut object (type: DonutImage). zcCol : numpy.ndarray Coefficients of wavefront (z1-z22) in nm. Returns ------- DonutImage Master donut. """ intraProjImgList = [] extraProjImgList = [] for donut in donutList: # Get the field x, y fieldXY = donut.getFieldPos() # Set the image intraImg = donut.getIntraImg() if (intraImg is not None): # Get the projected image projImg = self._getProjImg(fieldXY, DefocalType.Intra, intraImg, zcCol) # Collect the projected donut intraProjImgList.append(projImg) extraImg = donut.getExtraImg() if (extraImg is not None): # Get the projected image projImg = self._getProjImg(fieldXY, DefocalType.Extra, extraImg, zcCol) # Collect the projected donut extraProjImgList.append(projImg) # Generate the master donut stackIntraImg = self._stackImg(intraProjImgList) stackExtraImg = self._stackImg(extraProjImgList) # Put the master donut to donut map pixelX, pixelY = searchDonutPos(stackIntraImg) masterDonut = DonutImage(0, pixelX, pixelY, 0, 0, intraImg=stackIntraImg, extraImg=stackExtraImg) return masterDonut def _getProjImg(self, fieldXY, defocalType, defocalImg, zcCol): """Get the projected image on the pupil. Parameters ---------- fieldXY : tuple Position of donut on the focal plane in the degree for intra- and extra-focal images (field X, field Y). defocalType : enum 'DefocalType' Defocal type of image. defocalImg : numpy.ndarray Donut defocal image. zcCol : numpy.ndarray Coefficients of wavefront (z1-z22). Returns ------- numpy.ndarray Projected image. """ # Set the image self.wfEsti.setImg(fieldXY, defocalType, image=defocalImg) # Get the image in the type of CompensationImageDecorator if (defocalType == DefocalType.Intra): img = self.wfEsti.getIntraImg() elif (defocalType == DefocalType.Extra): img = self.wfEsti.getExtraImg() # Get the distortion correction (offaxis) algo = self.wfEsti.getAlgo() offAxisCorrOrder = algo.getOffAxisPolyOrder() inst = self.wfEsti.getInst() img.setOffAxisCorr(inst, offAxisCorrOrder) # Do the image cocenter img.imageCoCenter(inst) # Do the compensation/ projection img.compensate(inst, algo, zcCol, self.wfEsti.getOptModel()) # Return the projected image return img.getImg() def _stackImg(self, imgList): """Stack the images. Parameters ---------- imgList : list List of image. Returns ------- numpy.ndarray Stacked image. """ if (len(imgList) == 0): stackImg = None else: # Get the minimun image dimension dimXlist = [] dimYlist = [] for img in imgList: dy, dx = img.shape dimXlist.append(dx) dimYlist.append(dy) dimX = np.min(dimXlist) dimY = np.min(dimYlist) deltaX = dimX//2 deltaY = dimY//2 # Stack the image by summation directly stackImg = np.zeros([dimY, dimX]) for img in imgList: dy, dx = img.shape cy = int(dy / 2) cx = int(dx / 2) stackImg += img[cy - deltaY:cy + deltaY, cx - deltaX:cx + deltaX] return stackImg
def setUp(self): self.inputs = os.path.join(getModulePath(), "tests", "testData", "repackagedPhoSimData") self.butlerWrapper = ButlerWrapper(self.inputs)
def poltExposureImage(exposure, name="", scale="log", cmap="gray", vmin=None, vmax=None, saveFilePath=None): """Plot the exposure image. Parameters ---------- exposure : Exposure Data butler of exposure image. name : str, optional Image title name. (the default is "".) scale : str, optional Scale of image map (log or linear). (the default is "log".) cmap : str, optional Color map definition. (the default is "gray".) vmin : float, optional Mininum value to show. This normalizes the luminance data. (the default is None.) vmax : float, optional Maximum value to show. This normalizes the luminance data. (the default is None.) saveFilePath : str, optional Save image to file path. (the default is None.) """ # Get the image data img = ButlerWrapper.getImageData(exposure) # Change the scale if needed if scale not in ("linear", "log"): print( "No %s scale to choose. Only 'linear' and 'log' scales are allowed." % scale) return # Decide the norm in imshow for the ploting if scale == "linear": plotNorm = None elif scale == "log": if (img.min()) < 0: plotNorm = SymLogNorm(linthresh=0.03) else: plotNorm = LogNorm() # Plot the image plt.figure() plt.imshow(img, cmap=cmap, origin="lower", norm=plotNorm, vmin=vmin, vmax=vmax) plt.colorbar() plt.title(name) if saveFilePath is None: plt.show() else: plt.savefig(saveFilePath, bbox_inches="tight") plt.close()