def onPreProcessing(self):
self.fitImageTableWidget.currentFileInfo.currentFolderLocation = self.rawFolderLocation + '/reduced'
reducedPath = Path(
self.fitImageTableWidget.currentFileInfo.currentFolderLocation)
Path.mkdir(reducedPath, mode=0o777, exist_ok=True)
darkFitFileList = self.darkFileList
flatFitFileList = self.flatFileList
grouped = self.combFileList.groupby(["OBJECT", "EXPTIME"])
ngroup = grouped.ngroups
i = 0
for name, group in grouped:
# Todo 체크창을 띄워서 OBJECT에 있는것중에 합칠것만 선택할수 있게 하자
i = i + 1
if name[0] not in ["cali", "flat", "comp_10", "comp_15"]:
for fName in group['FILE-NAME']:
saveName = 'r_' + fName
savePath = reducedPath / saveName
print(self.rawFolderLocation + '/combine/' + fName)
objFileName = self.rawFolderLocation + '/combine/' + fName
objHdr, objData = openFitData(objFileName)
objEXPTIME = objHdr['EXPTIME']
darkFileName = self.rawFolderLocation + '/dark/' + f"cali_{objEXPTIME:.1f}.fit"
darkHdr, darkData = openFitData(darkFileName)
flatFileName = self.rawFolderLocation + '/flat/' + "masterFlat.fit"
flatHdr, flatData = openFitData(flatFileName)
reducedData = (objData - darkData) / flatData
reducedHdr = objHdr
reducedHdr['HISTORY'] = f"Dark Reduced by :{darkFileName}"
reducedHdr['HISTORY'] = f"Flat Reduced by :{flatFileName}"
reducedCCD = CCDData(data=reducedData,
header=reducedHdr,
unit="adu")
reducedCCD.write(savePath, overwrite=True)
self.step = i / ngroup * 100
self.onProgressChange()
#Todo comp image는 dark 안빼줘도 되는건가? flat도 안빼줘도 되는건가?
#일단 comp image는 combine folder에 넣자
files = list(reducedPath.glob("*.fit"))
fileInfo = fileOpener(files)
fileInfo = np.hstack((fileInfo, np.zeros((fileInfo.shape[0], 1), str)))
reducedFileList = pd.DataFrame(np.array(fileInfo),
columns=[
'FILE-NAME', 'DATE-OBS', 'EXPTIME',
'IMAGETYPE', 'OBJECT', 'REMARKS'
])
self.fitImageTableWidget.currentFileInfo.fitFileList = reducedFileList
self.reducedFileList = reducedFileList
self.fitImageTableWidget.tableEdit()
self.isFinished = True
self.preprocessingBtn.setEnabled(False)
self.doneBtn.setEnabled(True)
def onBiasSubstraction(self):
self.fitImageTableWidget.currentFileInfo.currentFolderLocation = self.rawFolderLocation + '/dark'
darkPath = Path(
self.fitImageTableWidget.currentFileInfo.currentFolderLocation)
Path.mkdir(darkPath, mode=0o777, exist_ok=True)
darkFitFileList = self.fitImageTableWidget.currentFileInfo.fitFileList[
self.fitImageTableWidget.currentFileInfo.fitFileList['IMAGETYPE']
== 'Dark Frame']
biasFitFileList = self.fitImageTableWidget.currentFileInfo.fitFileList[
self.fitImageTableWidget.currentFileInfo.fitFileList['IMAGETYPE']
== 'Bias Frame']
#bias data 불러오기
biasFileName = self.rawFolderLocation + '/combine/' + biasFitFileList[
'FILE-NAME'].iloc[0]
biasHdr, biasData = openFitData(biasFileName)
#dark data 불러오기 및 빼기
nlist = len(darkFitFileList)
i = 0
for darkFile in darkFitFileList['FILE-NAME']:
savePath = darkPath / darkFile
darkFileName = self.rawFolderLocation + '/combine/' + darkFile
darkHdr, darkData = openFitData(darkFileName)
data = darkData - biasData
hdr = darkHdr
hdr['HISTORY'] = 'BiasSub'
subbedCCD = CCDData(data=data, header=hdr, unit="adu")
subbedCCD.write(savePath, overwrite=True)
self.step = i / nlist * 100
self.onProgressChange()
files = list(darkPath.glob("*.fit"))
fileInfo = fileOpener(files)
print(fileInfo)
fileInfo = np.hstack((fileInfo, np.zeros((fileInfo.shape[0], 1), str)))
darkFileList = pd.DataFrame(np.array(fileInfo),
columns=[
'FILE-NAME', 'DATE-OBS', 'EXPTIME',
'IMAGETYPE', 'OBJECT', 'REMARKS'
])
self.fitImageTableWidget.currentFileInfo.fitFileList = darkFileList
self.darkFileList = darkFileList
self.fitImageTableWidget.tableEdit()
self.biasSubstractionBtn.setEnabled(False)
self.darkSubstractionBtn.setEnabled(True)
def onCalibnationFileOpen(self):
filePath = QFileDialog.getOpenFileName(
self, 'Open calibration file',
'./Spectroscopy_Example/20181023/combine/')[0]
hdr, data = openFitData(filePath)
self.selfImageCanvas.figure.clear()
self.selfImageAx = self.selfImageCanvas.figure.add_subplot(111)
zimshow(self.selfImageAx, data)
self.selfImageCanvas.draw()
self.imageWidth = int(data.shape[1])
self.selfData = data
def onDarkSubstraction(self):
self.fitImageTableWidget.currentFileInfo.currentFolderLocation = self.rawFolderLocation + '/flat'
flatPath = Path(
self.fitImageTableWidget.currentFileInfo.currentFolderLocation)
Path.mkdir(flatPath, mode=0o777, exist_ok=True)
darkFitFileList = self.darkFileList
flatFitFileList = self.combFileList[self.combFileList['IMAGETYPE'] ==
'Flat Field']
flatFileName = self.rawFolderLocation + '/combine/' + flatFitFileList[
'FILE-NAME'].iloc[0]
flatHdr, flatData = openFitData(flatFileName)
darkFileName = self.rawFolderLocation + '/dark/' + darkFitFileList[
'FILE-NAME'][darkFitFileList['EXPTIME'] ==
flatFitFileList['EXPTIME'].iloc[0]].iloc[0]
darkHdr, darkData = openFitData(darkFileName)
flatFile = flatFitFileList['FILE-NAME'].iloc[0]
savePath = flatPath / 'masterFlat.fit'
data = flatData - darkData
hdr = flatHdr
hdr['HISTORY'] = 'BiasSub'
subbedCCD = CCDData(data=data, header=hdr, unit="adu")
subbedCCD.write(savePath, overwrite=True)
files = list(flatPath.glob("*.fit"))
fileInfo = fileOpener(files)
print(fileInfo)
fileInfo = np.hstack((fileInfo, np.zeros((fileInfo.shape[0], 1), str)))
flatFileList = pd.DataFrame(np.array(fileInfo),
columns=[
'FILE-NAME', 'DATE-OBS', 'EXPTIME',
'IMAGETYPE', 'OBJECT', 'REMARKS'
])
self.fitImageTableWidget.currentFileInfo.fitFileList = flatFileList
self.flatFileList = flatFileList
self.fitImageTableWidget.tableEdit()
self.darkSubstractionBtn.setEnabled(False)
self.preprocessingBtn.setEnabled(True)
def __init__(self,
fileName,
regFactor=None,
identificationMethod=None,
savePath=None):
super().__init__()
self.FWHM = 2 #Todo add method for getting FHWM, gain, readoutnoise from image hdr
self.fileName = fileName
self.hdr, self.flux = openFitData(self.fileName)
self.skyFit = linearSky
self.objFit = gaussian
self.apertureTracerOpen = False
self.isXcutterPicked = False
self.cropInfo = cropInfo(y0=0,
y1=len(self.flux),
x0=0,
x1=len(self.flux[0]),
filename=self.fileName)
self.norm = znorm(self.flux)
self.regFactor = regFactor
self.identificationMethod = identificationMethod
self.savePath = savePath
self.initUI()
def neonSpectrumDraw(self):
filePath = './NeonArcSpectrum.fit'
hdr, data = openFitData(filePath)
self.standardSpectrumDraw(data=data, arc='Neon')
def onCombine(self):
# combine bias, dark(per exposure), flat(per expoure)
self.fitImageTableWidget.currentFileInfo.currentFolderLocation = self.rawFolderLocation + '/combine'
combPath = Path(
self.fitImageTableWidget.currentFileInfo.currentFolderLocation)
Path.mkdir(combPath, mode=0o777, exist_ok=True)
grouped = self.rawFitFileList.groupby(["OBJECT", "EXPTIME"])
ngroup = grouped.ngroups
i = 0
for name, group in grouped:
# Todo 체크창을 띄워서 OBJECT에 있는것중에 합칠것만 선택할수 있게 하자
# Todo 보고 관측 이미지 합칠 수 있으면 합치자(따로 이미지 align을 안해서 지금 단계에선 합쳐도 될까 싶다.)
# 나중에 합칠거 생각해서 일단 /combine에 옮기고 합치는 과정만 나중에 추가해 주자
if name[0] in ["cali", "flat", "comp_10", "comp_15"]:
savePath = combPath / f"{name[0]}_{float(name[1]):.1f}.fit"
ccds = []
for fName in group['FILE-NAME']:
filename = self.rawFolderLocation + '/' + fName
hdr, data = openFitData(filename, fitInfo='SNUO')
if (self.fitImageTableWidget.currentFileInfo.flags.
isCropped):
ccds.append(
data[self.fitImageTableWidget.currentFileInfo.
cropInfo.y0:self.fitImageTableWidget.
currentFileInfo.cropInfo.y1,
self.fitImageTableWidget.currentFileInfo.
cropInfo.x0:self.fitImageTableWidget.
currentFileInfo.cropInfo.x1])
else:
ccds.append(data)
combined = np.median(ccds, axis=0)
hdr["NCOMBINE"] = (len(ccds), "Number of images combined")
combined_ccd = CCDData(data=combined, header=hdr, unit="adu")
combined_ccd.write(savePath, overwrite=True)
else:
j = 0
for fName in group['FILE-NAME']:
j = j + 1
savePath = combPath / f"{name[0]}_{float(name[1]):.1f}_{j}.fit"
filename = self.rawFolderLocation + '/' + fName
hdr, data = openFitData(filename, fitInfo='SNUO')
if (self.fitImageTableWidget.currentFileInfo.flags.
isCropped):
data = data[self.fitImageTableWidget.currentFileInfo.
cropInfo.y0:self.fitImageTableWidget.
currentFileInfo.cropInfo.y1,
self.fitImageTableWidget.currentFileInfo.
cropInfo.x0:self.fitImageTableWidget.
currentFileInfo.cropInfo.x1]
ccd = CCDData(data=data, header=hdr, unit="adu")
ccd.write(savePath, overwrite=True)
i = i + 1
self.step = i / ngroup * 100
self.onProgressChange()
files = list(combPath.glob("*.fit"))
fileInfo = fileOpener(files)
print(fileInfo)
fileInfo = np.hstack((fileInfo, np.zeros((fileInfo.shape[0], 1), str)))
combFileList = pd.DataFrame(np.array(fileInfo),
columns=[
'FILE-NAME', 'DATE-OBS', 'EXPTIME',
'IMAGETYPE', 'OBJECT', 'REMARKS'
])
self.fitImageTableWidget.currentFileInfo.fitFileList = combFileList
self.combFileList = combFileList
self.fitImageTableWidget.tableEdit()
self.fitImageTableWidget.currentFileInfo.flags.isReduced = True
self.combineBtn.setEnabled(False)
self.biasSubstractionBtn.setEnabled(True)