Catalogue_Instructions = CatalogueFolder + py_w.SpectraTreating + Fm.Extensions_dictionary['Reduction Instructions']
Fm.select_Table(Catalogue_Instructions, 1, loadheaders_check = True)
Objects, Redshifts = Fm.get_ColumnData(['CodeName', 'Dopcor'], 1, StringIndexes = True, datatype = str, unpack_check = True)
#Generate plot frame and colors
pv.FigFormat_One(ColorConf='Night1')
#Loop through files
for i in range(len(FilesList)):
#WARNING NOT THE BEST APPROACH... CLEAN:
if ('obj' in FilesList[i]):
CodeName, FileName, FileFolder = pv.FileAnalyzer(FilesList[i])
z = py_w.getTaskConfiguration(CodeName, Objects, Redshifts, ConfAttributes = {'Operation' : 'Redshift correction', 'FileFolder' : FileFolder})
#Calibration with the global curve
Object_DopcorCorrected = py_w.Dopcortask(FileName, None, FileFolder, z)
#Extract the data for the plots
Wave, Int, ExtraData_Single = pv.File2Data(FileFolder, Object_DopcorCorrected)
#Plotting the data
pv.DataPloter_One(Wave, Int, CodeName.upper() + ' spectrum', pv.Color_Vector[2][1])
#format of the graphs
pv.Labels_Legends_One(Plot_Title = 'Object '+ CodeName + ' Redshift corrected')
pv.SaveManager(SavingName = Object_DopcorCorrected.replace('.fits', '') , SavingFolder = FileFolder, ForceDisplay=False, ForceSave=True)
pv.ResetPlot()
)
# Find and organize files from terminal command or .py file
FilesList = Fm.Folder_Explorer(Pattern, CatalogueFolder, CheckComputer=False)
# Generate plot frame and colors
pv.FigFormat_One(ColorConf="Night1")
# Loop through files
for i in range(len(FilesList)):
CodeName, FileName, FileFolder = pv.FileAnalyzer(FilesList[i])
w_min, w_max = py_w.getTaskConfiguration(
CodeName,
Objects,
Trimming_wavelengths,
ConfAttributes={"Operation": "Wavelength trimming", "FileFolder": FileFolder},
)
print "-- The object", CodeName, "has new limits", w_min, w_max
# CTrim the spectrum
Object_TrimmedSpectrum = py_w.ScopyTask(FileName, None, Fits_Folder=FileFolder, Wmin=w_min, Wmax=w_max)
# Extract the data for the plots
Wave_Single, Int_Single, ExtraData_Single = pv.File2Data(FileFolder, Object_TrimmedSpectrum)
# Plotting the data
pv.DataPloter_One(Wave_Single, Int_Single, CodeName.upper() + " spectrum calibration", pv.Color_Vector[2][3])
# format of the graphs
Objects, TelluricStar_List = Fm.get_ColumnData(['CodeName', 'Telluric_Star'], 1, StringIndexes = True, datatype = str, unpack_check = True)
#Find and organize files from terminal command or .py file
FilesList = Fm.Folder_Explorer(Pattern, CatalogueFolder, CheckComputer = False)
#Generate plot frame and colors
pv.FigFormat_One(ColorConf='Night1')
#Loop through files
for i in range(len(FilesList)):
#WARNING NOT THE BEST APPROACH... CLEAN:
print FilesList[i]
if ('obj' in FilesList[i]) and ('Red' in FilesList[i]):
print 'hola'
CodeName, FileName, FileFolder = pv.FileAnalyzer(FilesList[i])
TelluricStar = py_w.getTaskConfiguration(CodeName, Objects, TelluricStar_List, ConfAttributes = {'Operation' : 'Telluric correction'})
StarNormalizedSpectrum = 'std' + TelluricStar + '_clean_N.fits'
#Calibration with the global curve
Object_TelluricCorrectedSectrum = py_w.SarithTask(FileName, StarNormalizedSpectrum, '/', OutputFile=None, FitsFolder=FileFolder)
print 'Object_TelluricCorrectedSectrum', Object_TelluricCorrectedSectrum
#Extract the data for the plots
Wave, Int, ExtraData_Single = pv.File2Data(FileFolder, Object_TelluricCorrectedSectrum)
#Plotting the data
pv.DataPloter_One(Wave, Int, CodeName.upper() + ' telluric correction', pv.Color_Vector[2][1])
#format of the graphs
