def main(night_name=None, files=None): # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) p = spirouStartup.LoadArguments(p, night_name, files, mainfitsdir='reduced') p = spirouStartup.InitialFileSetup(p, calibdb=True) # set up function name main_name = __NAME__ + '.main()' # ------------------------------------------------------------------ # Load first file # ------------------------------------------------------------------ loc = ParamDict() rd = spirouImage.ReadImage(p, p['FITSFILENAME']) loc['DATA'], loc['DATAHDR'], loc['YDIM'], loc['XDIM'] = rd loc.set_sources(['DATA', 'DATAHDR', 'XDIM', 'YDIM'], main_name) # ------------------------------------------------------------------ # Get the wave solution # ------------------------------------------------------------------ masterwavefile = spirouDB.GetDatabaseMasterWave(p) # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # read master wave map wout = spirouImage.GetWaveSolution(p, filename=masterwavefile, return_wavemap=True, quiet=True, return_header=True, fiber=wave_fiber) _, loc['WAVE'], loc['WAVEFILE'], _ = wout loc.set_sources(['WAVE', 'WAVEFILE'], main_name) # get the wave keys loc = spirouImage.GetWaveKeys(p, loc, loc['DATAHDR']) # ------------------------------------------------------------------ # Construct convolution kernels (used in GetMolecularTellLines) # ------------------------------------------------------------------ loc = spirouTelluric.ConstructConvKernel1(p, loc) # ------------------------------------------------------------------ # Get molecular telluric lines # ------------------------------------------------------------------ loc = spirouTelluric.GetMolecularTellLines(p, loc) # if TAPAS FNAME is not None we generated a new file so should add to tellDB if loc['TAPAS_FNAME'] is not None: # add to the telluric database spirouDB.UpdateDatabaseTellConv(p, loc['TAPAS_FNAME'], loc['DATAHDR']) # put file in telluDB spirouDB.PutTelluFile(p, loc['TAPAS_ABSNAME']) # ---------------------------------------------------------------------- # load the expected atmospheric transmission # ---------------------------------------------------------------------- # read filename from telluDB tapas_file_names = spirouDB.GetDatabaseTellConv(p) tapas_file_name = tapas_file_names[-1] # load atmospheric transmission sp_tapas = np.load(tapas_file_name) loc['TAPAS_ALL_SPECIES'] = sp_tapas # extract the water and other line-of-sight optical depths loc['TAPAS_WATER'] = sp_tapas[1, :] loc['TAPAS_OTHERS'] = np.prod(sp_tapas[2:, :], axis=0) loc.set_sources(['TAPAS_ALL_SPECIES', 'TAPAS_WATER', 'TAPAS_OTHERS'], main_name) # ------------------------------------------------------------------ # Get master wave solution map # ------------------------------------------------------------------ # get master wave map masterwavefile = spirouDB.GetDatabaseMasterWave(p) # log process wmsg1 = 'Shifting transmission map on to master wavelength grid' wmsg2 = '\tFile = {0}'.format(os.path.basename(masterwavefile)) WLOG(p, '', [wmsg1, wmsg2]) # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # read master wave map mout = spirouImage.GetWaveSolution(p, filename=masterwavefile, return_wavemap=True, quiet=True, return_header=True, fiber=wave_fiber) masterwavep, masterwave, masterwaveheader, mwsource = mout # get wave acqtimes master_acqtimes = spirouDB.GetTimes(p, masterwaveheader) # ------------------------------------------------------------------ # Loop around the files # ------------------------------------------------------------------ # construct extension tellu_ext = '{0}_{1}.fits' # get current telluric maps from telluDB # tellu_db_data = spirouDB.GetDatabaseTellMap(p, required=False) # tellu_db_files = tellu_db_data[0] # storage for valid output files loc['OUTPUTFILES'] = [] # loop around the files for basefilename in p['ARG_FILE_NAMES']: # ------------------------------------------------------------------ # Get absolute path of filename # ------------------------------------------------------------------ filename = os.path.join(p['ARG_FILE_DIR'], basefilename) # ------------------------------------------------------------------ # Read obj telluric file and correct blaze (per order) # ------------------------------------------------------------------ # get image sp, shdr, _, _ = spirouImage.ReadImage(p, filename) # ------------------------------------------------------------------ # check that file has valid DPRTYPE # ------------------------------------------------------------------ # get FP_FP DPRTYPE p = spirouImage.ReadParam(p, shdr, 'KW_DPRTYPE', 'DPRTYPE', dtype=str) # if dprtype is incorrect skip if p['DPRTYPE'] not in p['ALLOWED_TELLURIC_DPRTYPES']: wmsg1 = 'Skipping file (DPRTYPE incorrect)' wmsg2 = '\t DPRTYPE = {0}'.format(p['DPRTYPE']) WLOG(p, 'warning', [wmsg1, wmsg2]) continue # get blaze p, blaze = spirouImage.ReadBlazeFile(p, shdr) # get the blaze percentile blaze_p = p['MKTELLU_BLAZE_PERCENTILE'] # loop through blaze orders, normalize blaze by its peak amplitude for order_num in range(sp.shape[0]): # normalize the spectrum spo, bzo = sp[order_num], blaze[order_num] sp[order_num] = spo / np.nanpercentile(spo, blaze_p) # normalize the blaze blaze[order_num] = bzo / np.nanpercentile(bzo, blaze_p) # find where the blaze is bad with warnings.catch_warnings(record=True) as _: badblaze = blaze < p['MKTELLU_CUT_BLAZE_NORM'] # set bad blaze to NaN blaze[badblaze] = np.nan # set to NaN values where spectrum is zero zeromask = sp == 0 sp[zeromask] = np.nan # divide spectrum by blaze with warnings.catch_warnings(record=True) as _: sp = sp / blaze # add sp to loc loc['SP'] = sp loc.set_source('SP', main_name) # ---------------------------------------------------------------------- # Get object name, airmass and berv # ---------------------------------------------------------------------- # Get object name loc['OBJNAME'] = spirouImage.GetObjName(p, shdr) # Get the airmass loc['AIRMASS'] = spirouImage.GetAirmass(p, shdr) # Get the Barycentric correction from header p, loc = spirouImage.GetEarthVelocityCorrection(p, loc, shdr) # set sources source = main_name + '+ spirouImage.ReadParams()' loc.set_sources(['OBJNAME', 'AIRMASS'], source) # ------------------------------------------------------------------ # get output transmission filename outfile, tag1 = spirouConfig.Constants.TELLU_TRANS_MAP_FILE( p, filename) outfilename = os.path.basename(outfile) loc['OUTPUTFILES'].append(outfile) # ---------------------------------------------------------------------- # Load template (if available) # ---------------------------------------------------------------------- # read filename from telluDB template_file = spirouDB.GetDatabaseObjTemp(p, loc['OBJNAME'], required=False) # if we don't have a template flag it if template_file is None: loc['FLAG_TEMPLATE'] = False loc['TEMPLATE'] = None # construct progres string pstring = 'No template found.' else: loc['FLAG_TEMPLATE'] = True # load template template, _, _, _ = spirouImage.ReadImage(p, template_file) # add to loc loc['TEMPLATE'] = template # construct progres string template_bfile = os.path.basename(template_file) pstring = 'Using template {0}'.format(template_bfile) # set the source for flag and template loc.set_sources(['FLAG_TEMPLATE', 'TEMPLATE'], main_name) # ------------------------------------------------------------------ # log processing file wmsg = 'Processing file {0}. {1}' WLOG(p, '', [wmsg.format(outfilename, pstring)]) # ------------------------------------------------------------------ # Check that basefile is not in blacklist # ------------------------------------------------------------------ blacklist_check = spirouTelluric.CheckBlackList(loc['OBJNAME']) if blacklist_check: # log black list file found wmsg = 'File {0} is blacklisted (OBJNAME={1}). Skipping' wargs = [basefilename, loc['OBJNAME']] WLOG(p, 'warning', wmsg.format(*wargs)) # skip this file continue # ------------------------------------------------------------------ # deal with applying template to spectrum # ------------------------------------------------------------------ # Requires from loc: # TEMPLATE (None or template loaded from file) # FLAG_TEMPLATE # WAVE # SP # BERV # # Returns: # SP (modified if template was used) # TEMPLATE # WCONV loc = spirouTelluric.ApplyTemplate(p, loc) # ------------------------------------------------------------------ # calcullate telluric absorption (with a sigma clip loop) # ------------------------------------------------------------------ # Requires from loc: # AIRMASS # WAVE # SP # WCONV # Returns: # PASSED [Bool] True or False # SP_OUT # SED_OUT # RECOV_AIRMASS # RECOV_WATER loc = spirouTelluric.CalcTelluAbsorption(p, loc) # calculate tranmission map from sp and sed transmission_map = loc['SP_OUT'] / loc['SED_OUT'] # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- # set passed variable and fail message list passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # ---------------------------------------------------------------------- # if array is completely NaNs it shouldn't pass if np.sum(np.isfinite(transmission_map)) == 0: fail_msg.append('transmission map is all NaNs') passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append('NaN') qc_names.append('image') qc_logic.append('image is all NaN') # ---------------------------------------------------------------------- # get SNR for each order from header nbo = loc['DATA'].shape[0] snr_order = p['QC_MK_TELLU_SNR_ORDER'] snr = spirouImage.Read1Dkey(p, shdr, p['kw_E2DS_SNR'][0], nbo) # check that SNR is high enough if snr[snr_order] < p['QC_MK_TELLU_SNR_MIN']: fmsg = 'low SNR in order {0}: ({1:.2f} < {2:.2f})' fargs = [snr_order, snr[snr_order], p['QC_MK_TELLU_SNR_MIN']] fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(snr[snr_order]) qc_name_str = 'SNR[{0}]'.format(snr_order) qc_names.append(qc_name_str) qc_logic.append('{0} < {1:.2f}'.format(qc_name_str, p['QC_MK_TELLU_SNR_ORDER'])) # ---------------------------------------------------------------------- # check that the file passed the CalcTelluAbsorption sigma clip loop if not loc['PASSED']: fmsg = 'File {0} did not converge on a solution in function: {1}' fargs = [basefilename, 'spirouTelluric.CalcTelluAbsorption()'] fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(basefilename) qc_names.append('FILE') qc_logic.append('FILE did not converge') # ---------------------------------------------------------------------- # check that the airmass is not too different from input airmass airmass_diff = np.abs(loc['RECOV_AIRMASS'] - loc['AIRMASS']) fargs = [ loc['RECOV_AIRMASS'], loc['AIRMASS'], p['QC_MKTELLU_AIRMASS_DIFF'] ] if airmass_diff > p['QC_MKTELLU_AIRMASS_DIFF']: fmsg = ('Recovered airmass to de-similar than input airmass.' 'Recovered: {0:.3f}. Input: {1:.3f}. QC limit = {2}') fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(airmass_diff) qc_names.append('airmass_diff') qc_logic.append('airmass_diff > {0:.2f}' ''.format(p['QC_MKTELLU_AIRMASS_DIFF'])) # ---------------------------------------------------------------------- # check that the water vapor is within limits water_cond1 = loc['RECOV_WATER'] < p['MKTELLU_TRANS_MIN_WATERCOL'] water_cond2 = loc['RECOV_WATER'] > p['MKTELLU_TRANS_MAX_WATERCOL'] fargs = [ p['MKTELLU_TRANS_MIN_WATERCOL'], p['MKTELLU_TRANS_MAX_WATERCOL'] ] if water_cond1 or water_cond2: fmsg = ('Recovered water vapor optical depth not between {0:.3f} ' 'and {1:.3f}') fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(loc['RECOV_WATER']) qc_names.append('RECOV_WATER') qc_logic.append('RECOV_WATER not between {0:.3f} and {1:.3f}' ''.format(*fargs)) # ---------------------------------------------------------------------- # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') continue # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ------------------------------------------------------------------ # Save transmission map to file # ------------------------------------------------------------------ # get raw file name raw_in_file = os.path.basename(p['FITSFILENAME']) # copy original keys hdict = spirouImage.CopyOriginalKeys(loc['DATAHDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=os.path.basename(masterwavefile)) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=mwsource) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file', values=p['ARG_FILE_NAMES']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=master_acqtimes[0]) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=master_acqtimes[1]) # add wave solution number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=masterwavep.shape[0]) # add wave solution degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=masterwavep.shape[1] - 1) # add wave solution coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=masterwavep) # add telluric keys hdict = spirouImage.AddKey(p, hdict, p['KW_TELLU_AIRMASS'], value=loc['RECOV_AIRMASS']) hdict = spirouImage.AddKey(p, hdict, p['KW_TELLU_WATER'], value=loc['RECOV_WATER']) # write to file p = spirouImage.WriteImage(p, outfile, transmission_map, hdict) # ------------------------------------------------------------------ # Add transmission map to telluDB # ------------------------------------------------------------------ if p['QC']: # copy tellu file to the telluDB folder spirouDB.PutTelluFile(p, outfile) # update the master tellu DB file with transmission map targs = [ p, outfilename, loc['OBJNAME'], loc['RECOV_AIRMASS'], loc['RECOV_WATER'] ] spirouDB.UpdateDatabaseTellMap(*targs) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return a copy of locally defined variables in the memory return dict(locals())
def main(night_name=None, files=None, fiber_type=None, **kwargs): """ cal_DRIFT_E2DS_spirou.py main function, if night_name and files are None uses arguments from run time i.e.: cal_DRIFT_E2DS_spirou.py [night_directory] [files] :param night_name: string or None, the folder within data raw directory containing files (also reduced directory) i.e. /data/raw/20170710 would be "20170710" but /data/raw/AT5/20180409 would be "AT5/20180409" :param files: string, list or None, the list of files to use for arg_file_names and fitsfilename (if None assumes arg_file_names was set from run time) :param fiber_type: string, if None does all fiber types (defined in constants_SPIROU FIBER_TYPES (default is AB, A, B, C if defined then only does this fiber type (but must be in FIBER_TYPES) :param kwargs: any keyword to overwrite constant in param dict "p" :return ll: dictionary, containing all the local variables defined in main """ # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) p = spirouStartup.LoadArguments(p, night_name, files) p = spirouStartup.InitialFileSetup(p, calibdb=True) # deal with fiber type if fiber_type is None: fiber_type = p['FIBER_TYPES'] if type(fiber_type) == str: if fiber_type.upper() == 'ALL': fiber_type = p['FIBER_TYPES'] elif fiber_type in p['FIBER_TYPES']: fiber_type = [fiber_type] else: emsg = 'fiber_type="{0}" not understood' WLOG(p, 'error', emsg.format(fiber_type)) # set fiber type p['FIB_TYPE'] = fiber_type p.set_source('FIB_TYPE', __NAME__ + '__main__()') # Overwrite keys from source for kwarg in kwargs: p[kwarg] = kwargs[kwarg] # ---------------------------------------------------------------------- # Read image file # ---------------------------------------------------------------------- # read the image data p, data, hdr = spirouImage.ReadImageAndCombine(p, framemath='add') # ---------------------------------------------------------------------- # fix for un-preprocessed files # ---------------------------------------------------------------------- data = spirouImage.FixNonPreProcess(p, data) # ---------------------------------------------------------------------- # Get basic image properties # ---------------------------------------------------------------------- # get sig det value p = spirouImage.GetSigdet(p, hdr, name='sigdet') # get exposure time p = spirouImage.GetExpTime(p, hdr, name='exptime') # get gain p = spirouImage.GetGain(p, hdr, name='gain') # set sigdet and conad keywords (sigdet is changed later) p['KW_CCD_SIGDET'][1] = p['SIGDET'] p['KW_CCD_CONAD'][1] = p['GAIN'] # now change the value of sigdet if require if p['IC_EXT_SIGDET'] > 0: p['SIGDET'] = float(p['IC_EXT_SIGDET']) # get DPRTYPE from header (Will have it if valid) p = spirouImage.ReadParam(p, hdr, 'KW_DPRTYPE', required=False, dtype=str) # check the DPRTYPE is not None if (p['DPRTYPE'] == 'None') or (['DPRTYPE'] is None): emsg1 = 'Error: {0} is not set in header for file {1}' eargs = [p['KW_DPRTYPE'][0], p['FITSFILENAME']] emsg2 = '\tPlease run pre-processing on file.' emsg3 = ('\tIf pre-processing fails or skips file, file is not ' 'currrently as valid DRS fits file.') WLOG(p, 'error', [emsg1.format(*eargs), emsg2, emsg3]) else: p['DPRTYPE'] = p['DPRTYPE'].strip() # ---------------------------------------------------------------------- # Correction of DARK # ---------------------------------------------------------------------- p, datac = spirouImage.CorrectForDark(p, data, hdr) # ---------------------------------------------------------------------- # Resize image # ---------------------------------------------------------------------- # rotate the image and convert from ADU/s to ADU data = spirouImage.ConvertToADU(spirouImage.FlipImage(p, datac), p=p) # convert NaN to zeros data0 = np.where(~np.isfinite(data), np.zeros_like(data), data) # resize image bkwargs = dict(xlow=p['IC_CCDX_LOW'], xhigh=p['IC_CCDX_HIGH'], ylow=p['IC_CCDY_LOW'], yhigh=p['IC_CCDY_HIGH'], getshape=False) data1 = spirouImage.ResizeImage(p, data0, **bkwargs) # log change in data size wmsg = 'Image format changed to {1}x{0}' WLOG(p, '', wmsg.format(*data1.shape)) # ---------------------------------------------------------------------- # Correct for the BADPIX mask (set all bad pixels to zero) # ---------------------------------------------------------------------- p, data1 = spirouImage.CorrectForBadPix(p, data1, hdr) # ---------------------------------------------------------------------- # Log the number of dead pixels # ---------------------------------------------------------------------- # get the number of bad pixels n_bad_pix = np.sum(~np.isfinite(data1)) n_bad_pix_frac = n_bad_pix * 100 / np.product(data1.shape) # Log number wmsg = 'Nb dead pixels = {0} / {1:.4f} %' WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac)) # ---------------------------------------------------------------------- # Get the miny, maxy and max_signal for the central column # ---------------------------------------------------------------------- # get the central column y = data1[p['IC_CENT_COL'], :] # get the min max and max signal using box smoothed approach miny, maxy, max_signal, diff_maxmin = spirouBACK.MeasureMinMaxSignal(p, y) # Log max average flux/pixel wmsg = 'Maximum average flux/pixel in the spectrum: {0:.1f} [ADU]' WLOG(p, 'info', wmsg.format(max_signal / p['NBFRAMES'])) # ---------------------------------------------------------------------- # Background computation # ---------------------------------------------------------------------- if p['IC_DO_BKGR_SUBTRACTION']: # log that we are doing background measurement WLOG(p, '', 'Doing background measurement on raw frame') # get the bkgr measurement bargs = [p, data1, hdr] # background, xc, yc, minlevel = spirouBACK.MeasureBackgroundFF(*bargs) p, background = spirouBACK.MeasureBackgroundMap(*bargs) else: background = np.zeros_like(data1) p['BKGRDFILE'] = 'None' p.set_source('BKGRDFILE', __NAME__ + '.main()') # apply background correction to data (and set to zero where negative) data1 = data1 - background # ---------------------------------------------------------------------- # Read tilt slit angle # ---------------------------------------------------------------------- # define loc storage parameter dictionary loc = ParamDict() # get tilts (if the mode requires it) if p['IC_EXTRACT_TYPE'] not in EXTRACT_SHAPE_TYPES: p, loc['TILT'] = spirouImage.ReadTiltFile(p, hdr) loc.set_source('TILT', __NAME__ + '/main() + /spirouImage.ReadTiltFile') else: loc['TILT'] = None loc.set_source('TILT', __NAME__ + '/main()') # ---------------------------------------------------------------------- # Earth Velocity calculation # ---------------------------------------------------------------------- if p['IC_IMAGE_TYPE'] == 'H4RG': p, loc = spirouImage.GetEarthVelocityCorrection(p, loc, hdr) # ---------------------------------------------------------------------- # Get all fiber data (for all fibers) # ---------------------------------------------------------------------- # TODO: This is temp solution for options 5a and 5b loc_fibers = spirouLOCOR.GetFiberData(p, hdr) # ------------------------------------------------------------------ # Deal with debananafication # ------------------------------------------------------------------ # if mode 4a or 4b we need to straighten in x only if p['IC_EXTRACT_TYPE'] in ['4a', '4b']: # get the shape parameters p, shapem_x = spirouImage.GetShapeX(p, hdr) p, shape_local = spirouImage.GetShapeLocal(p, hdr) # log progress WLOG(p, '', 'Debananafying (straightening) image') # apply shape transforms targs = dict(lin_transform_vect=shape_local, dxmap=shapem_x) data2 = spirouImage.EATransform(data1, **targs) # if mode 5a or 5b we need to straighten in x and y using the # polynomial fits for location elif p['IC_EXTRACT_TYPE'] in ['5a', '5b']: # get the shape parameters p, shapem_x = spirouImage.GetShapeX(p, hdr) p, shapem_y = spirouImage.GetShapeY(p, hdr) p, shape_local = spirouImage.GetShapeLocal(p, hdr) p, fpmaster = spirouImage.GetFPMaster(p, hdr) # get the bad pixel map bkwargs = dict(return_map=True, quiet=True) p, badpix = spirouImage.CorrectForBadPix(p, data1, hdr, **bkwargs) # log progress WLOG(p, '', 'Cleaning image') # clean the image data1 = spirouEXTOR.CleanHotpix(data1, badpix) # log progress WLOG(p, '', 'Debananafying (straightening) image') # apply shape transforms targs = dict(lin_transform_vect=shape_local, dxmap=shapem_x, dymap=shapem_y) data2 = spirouImage.EATransform(data1, **targs) # in any other mode we do not straighten else: data2 = np.array(data1) # ---------------------------------------------------------------------- # Fiber loop # ---------------------------------------------------------------------- # loop around fiber types for fiber in p['FIB_TYPE']: # set fiber p['FIBER'] = fiber p.set_source('FIBER', __NAME__ + '/main()()') # ------------------------------------------------------------------ # Read wavelength solution # ------------------------------------------------------------------ # set source of wave file wsource = __NAME__ + '/main() + /spirouImage.GetWaveSolution' # Force A and B to AB solution if fiber in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = fiber # get wave image wkwargs = dict(hdr=hdr, return_wavemap=True, return_filename=True, return_header=True, fiber=wave_fiber) wout = spirouImage.GetWaveSolution(p, **wkwargs) loc['WAVEPARAMS'], loc['WAVE'], loc['WAVEFILE'] = wout[:3] loc['WAVEHDR'], loc['WSOURCE'] = wout[3:] source_names = ['WAVE', 'WAVEFILE', 'WAVEPARAMS', 'WAVEHDR'] loc.set_sources(source_names, wsource) # get dates loc['WAVE_ACQTIMES'] = spirouDB.GetTimes(p, loc['WAVEHDR']) loc.set_source('WAVE_ACQTIMES', __NAME__ + '.main()') # get the recipe that produced the wave solution if 'WAVECODE' in loc['WAVEHDR']: loc['WAVE_CODE'] = loc['WAVEHDR']['WAVECODE'] else: loc['WAVE_CODE'] = 'UNKNOWN' loc.set_source('WAVE_CODE', __NAME__ + '.main()') # ---------------------------------------------------------------------- # Get WFP keys # ---------------------------------------------------------------------- # Read the WFP keys - if they don't exist set to None and deal # with later p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_DRIFT', name='WFP_DRIFT', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_FWHM', name='WFP_FWHM', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_CONTRAST', name='WFP_CONTRAST', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_MAXCPP', name='WFP_MAXCPP', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_MASK', name='WFP_MASK', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_LINES', name='WFP_LINES', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_TARG_RV', name='WFP_TARG_RV', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_WIDTH', name='WFP_WIDTH', required=False) p = spirouImage.ReadParam(p, loc['WAVEHDR'], 'KW_WFP_STEP', name='WFP_STEP', required=False) # ---------------------------------------------------------------------- # Read Flat file # ---------------------------------------------------------------------- fout = spirouImage.ReadFlatFile(p, hdr, return_header=True) p, loc['FLAT'], flathdr = fout loc.set_source('FLAT', __NAME__ + '/main() + /spirouImage.ReadFlatFile') # get flat extraction mode if p['KW_E2DS_EXTM'][0] in flathdr: flat_ext_mode = flathdr[p['KW_E2DS_EXTM'][0]] else: flat_ext_mode = None # ------------------------------------------------------------------ # Check extraction method is same as flat extraction method # ------------------------------------------------------------------ # get extraction method and function extmethod, extfunc = spirouEXTOR.GetExtMethod(p, p['IC_EXTRACT_TYPE']) if not DEBUG: # compare flat extraction mode to extraction mode spirouEXTOR.CompareExtMethod(p, flat_ext_mode, extmethod, 'FLAT', 'EXTRACTION') # ------------------------------------------------------------------ # Read Blaze file # ------------------------------------------------------------------ p, loc['BLAZE'] = spirouImage.ReadBlazeFile(p, hdr) blazesource = __NAME__ + '/main() + /spirouImage.ReadBlazeFile' loc.set_source('BLAZE', blazesource) # ------------------------------------------------------------------ # Get fiber specific parameters from loc_fibers # ------------------------------------------------------------------ # get this fibers parameters p = spirouImage.FiberParams(p, p['FIBER'], merge=True) # get localisation parameters for key in loc_fibers[fiber]: loc[key] = loc_fibers[fiber][key] loc.set_source(key, loc_fibers[fiber].sources[key]) # get locofile source p['LOCOFILE'] = loc['LOCOFILE'] p.set_source('LOCOFILE', loc.sources['LOCOFILE']) # get the order_profile order_profile = loc_fibers[fiber]['ORDER_PROFILE'] # ------------------------------------------------------------------ # Set up Extract storage # ------------------------------------------------------------------ # Create array to store extraction (for each order and each pixel # along order) loc['E2DS'] = np.zeros((loc['NUMBER_ORDERS'], data2.shape[1])) loc['E2DSFF'] = np.zeros((loc['NUMBER_ORDERS'], data2.shape[1])) loc['E2DSLL'] = [] loc['SPE1'] = np.zeros((loc['NUMBER_ORDERS'], data2.shape[1])) loc['SPE3'] = np.zeros((loc['NUMBER_ORDERS'], data2.shape[1])) loc['SPE4'] = np.zeros((loc['NUMBER_ORDERS'], data2.shape[1])) loc['SPE5'] = np.zeros((loc['NUMBER_ORDERS'], data2.shape[1])) # Create array to store the signal to noise ratios for each order loc['SNR'] = np.zeros(loc['NUMBER_ORDERS']) # ------------------------------------------------------------------ # Extract orders # ------------------------------------------------------------------ # source for parameter dictionary source = __NAME__ + '/main()' # get limits of order extraction valid_orders = spirouEXTOR.GetValidOrders(p, loc) # loop around each order for order_num in valid_orders: # ------------------------------------------------------------- # IC_EXTRACT_TYPE decides the extraction routine # ------------------------------------------------------------- eargs = [p, loc, data2, order_num] ekwargs = dict(mode=p['IC_EXTRACT_TYPE'], order_profile=order_profile) with warnings.catch_warnings(record=True) as w: eout = spirouEXTOR.Extraction(*eargs, **ekwargs) # deal with different return if p['IC_EXTRACT_TYPE'] in EXTRACT_LL_TYPES: e2ds, e2dsll, cpt = eout else: e2ds, cpt = eout e2dsll = None # ------------------------------------------------------------- # calculate the noise range1, range2 = p['IC_EXT_RANGE1'], p['IC_EXT_RANGE2'] # set the noise noise = p['SIGDET'] * np.sqrt(range1 + range2) # get window size blaze_win1 = int(data2.shape[0] / 2) - p['IC_EXTFBLAZ'] blaze_win2 = int(data2.shape[0] / 2) + p['IC_EXTFBLAZ'] # get average flux per pixel flux = np.nansum( e2ds[blaze_win1:blaze_win2]) / (2 * p['IC_EXTFBLAZ']) # calculate signal to noise ratio = flux/sqrt(flux + noise^2) snr = flux / np.sqrt(flux + noise**2) # log the SNR RMS wmsg = 'On fiber {0} order {1}: S/N= {2:.1f} Nbcosmic= {3}' wargs = [p['FIBER'], order_num, snr, cpt] WLOG(p, '', wmsg.format(*wargs)) # add calculations to storage loc['E2DS'][order_num] = e2ds loc['E2DSFF'][order_num] = e2ds / loc['FLAT'][order_num] loc['SNR'][order_num] = snr # save the longfile if p['IC_EXTRACT_TYPE'] in EXTRACT_LL_TYPES: loc['E2DSLL'].append(e2dsll) # set sources loc.set_sources(['e2ds', 'SNR'], source) # Log if saturation level reached satvalue = (flux / p['GAIN']) / (range1 + range2) if satvalue > (p['QC_LOC_FLUMAX'] * p['NBFRAMES']): wmsg = 'SATURATION LEVEL REACHED on Fiber {0} order={1}' WLOG(p, 'warning', wmsg.format(fiber, order_num)) # ------------------------------------------------------------------ # Thermal correction # ------------------------------------------------------------------ # get fiber type if fiber in ['AB', 'A', 'B']: fibertype = p['DPRTYPE'].split('_')[0] else: fibertype = p['DPRTYPE'].split('_')[1] # apply thermal correction based on fiber type if fibertype in p['THERMAL_CORRECTION_TYPE1']: # log progress wmsg = 'Correcting thermal background for {0}={1} mode={2}' wargs = [fiber, fibertype, 1] WLOG(p, 'info', wmsg.format(*wargs)) # correct E2DS tkwargs = dict(image=loc['E2DS'], mode=1, fiber=fiber, hdr=hdr) p, loc['E2DS'] = spirouBACK.ThermalCorrect(p, **tkwargs) # correct E2DSFF tkwargs = dict(image=loc['E2DSFF'], mode=1, fiber=fiber, hdr=hdr, flat=loc['FLAT']) p, loc['E2DSFF'] = spirouBACK.ThermalCorrect(p, **tkwargs) elif fibertype in p['THERMAL_CORRECTION_TYPE2']: # log progress wmsg = 'Correcting thermal background for {0}={1} mode={2}' wargs = [fiber, fibertype, 2] WLOG(p, 'info', wmsg.format(*wargs)) # correct E2DS tkwargs = dict(image=loc['E2DS'], mode=2, fiber=fiber, hdr=hdr) p, loc['E2DS'] = spirouBACK.ThermalCorrect(p, **tkwargs) # correct E2DSFF tkwargs = dict(image=loc['E2DSFF'], mode=2, fiber=fiber, hdr=hdr, flat=loc['FLAT']) p, loc['E2DSFF'] = spirouBACK.ThermalCorrect(p, **tkwargs) else: # log progress wmsg = 'Not correcting thermal background for {0}={1}' wargs = [fiber, fibertype] WLOG(p, 'info', wmsg.format(*wargs)) # set filename for output outfile = 'THERMALFILE_{0}'.format(fiber) p[outfile] = 'None' p.set_source(outfile, __NAME__ + '.main()') # ------------------------------------------------------------------ # Plots # ------------------------------------------------------------------ if p['DRS_PLOT'] > 0: # start interactive session if needed sPlt.start_interactive_session(p) # plot all orders or one order if p['IC_FF_PLOT_ALL_ORDERS']: # plot image with all order fits (slower) sPlt.ext_aorder_fit(p, loc, data1, max_signal / 10.) else: # plot image with selected order fit and edge fit (faster) sPlt.ext_sorder_fit(p, loc, data1, max_signal / 10.) # plot e2ds against wavelength sPlt.ext_spectral_order_plot(p, loc) if p['IC_EXTRACT_TYPE'] in EXTRACT_SHAPE_TYPES: sPlt.ext_debanana_plot(p, loc, data2, max_signal / 10.) # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # ---------------------------------------------------------------------- # if array is completely NaNs it shouldn't pass if np.sum(np.isfinite(loc['E2DS'])) == 0: fail_msg.append('E2DS image is all NaNs') passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append('NaN') qc_names.append('image') qc_logic.append('image is all NaN') # ---------------------------------------------------------------------- # saturation check: check that the max_signal is lower than # qc_max_signal max_qcflux = p['QC_MAX_SIGNAL'] * p['NBFRAMES'] if max_signal > max_qcflux: fmsg = 'Too much flux in the image ({0:.2f} > {1:.2f})' fail_msg.append(fmsg.format(max_signal, max_qcflux)) passed = False # Question: Why is this test ignored? # For some reason this test is ignored in old code passed = True WLOG(p, 'info', fail_msg[-1]) qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(max_signal) qc_names.append('max_signal') qc_logic.append('QC_MAX_SIGNAL > {0:.3f}'.format(max_qcflux)) # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ------------------------------------------------------------------ # Store extraction in file(s) # ------------------------------------------------------------------ raw_ext_file = os.path.basename(p['FITSFILENAME']) # construct filename e2dsfits, tag1 = spirouConfig.Constants.EXTRACT_E2DS_FILE(p) e2dsfitsname = os.path.split(e2dsfits)[-1] e2dsfffits, tag2 = spirouConfig.Constants.EXTRACT_E2DSFF_FILE(p) e2dsfffitsname = os.path.split(e2dsfffits)[-1] e2dsllfits, tag4 = spirouConfig.Constants.EXTRACT_E2DSLL_FILE(p) e2dsfllitsname = os.path.split(e2dsllfits)[-1] # log that we are saving E2DS spectrum wmsg = 'Saving E2DS spectrum of Fiber {0} in {1}' WLOG(p, '', wmsg.format(p['FIBER'], e2dsfitsname)) wmsg = 'Saving E2DSFF spectrum of Fiber {0} in {1}' WLOG(p, '', wmsg.format(p['FIBER'], e2dsfffitsname)) # add keys from original header file hdict = spirouImage.CopyOriginalKeys(hdr) # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_FIBER'], value=p['FIBER']) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBDARK'], value=p['DARKFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBAD'], value=p['BADPFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBLOCO'], value=p['LOCOFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBACK'], value=p['BKGRDFILE']) if p['IC_EXTRACT_TYPE'] not in EXTRACT_SHAPE_TYPES: hdict = spirouImage.AddKey(p, hdict, p['KW_CDBTILT'], value=p['TILTFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBFLAT'], value=p['FLATFILE']) if p['IC_EXTRACT_TYPE'] in EXTRACT_SHAPE_TYPES: hdict = spirouImage.AddKey(p, hdict, p['KW_CDBSHAPEX'], value=p['SHAPEXFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBSHAPEY'], value=p['SHAPEYFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBSHAPE'], value=p['SHAPEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBFPMASTER'], value=p['FPMASTERFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBTHERMAL'], value=p['THERMALFILE_{0}'.format(fiber)]) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['WAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file', values=p['ARG_FILE_NAMES']) # construct loco filename locofile, _ = spirouConfig.Constants.EXTRACT_LOCO_FILE(p) locofilename = os.path.basename(locofile) # add barycentric keys to header hdict = spirouImage.AddKey(p, hdict, p['KW_BERV'], value=loc['BERV']) hdict = spirouImage.AddKey(p, hdict, p['KW_BJD'], value=loc['BJD']) hdict = spirouImage.AddKey(p, hdict, p['KW_BERV_MAX'], value=loc['BERV_MAX']) hdict = spirouImage.AddKey(p, hdict, p['KW_B_OBS_HOUR'], value=loc['BERVHOUR']) # add barycentric estimate keys to header hdict = spirouImage.AddKey(p, hdict, p['KW_BERV_EST'], value=loc['BERV_EST']) hdict = spirouImage.AddKey(p, hdict, p['KW_BJD_EST'], value=loc['BJD_EST']) hdict = spirouImage.AddKey(p, hdict, p['KW_BERV_MAX_EST'], value=loc['BERV_MAX_EST']) hdict = spirouImage.AddKey(p, hdict, p['KW_BERV_SOURCE'], value=loc['BERV_SOURCE']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # copy extraction method and function to header # (for reproducibility) hdict = spirouImage.AddKey(p, hdict, p['KW_E2DS_EXTM'], value=extmethod) hdict = spirouImage.AddKey(p, hdict, p['KW_E2DS_FUNC'], value=extfunc) # add localization file name to header hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_FILE'], value=locofilename) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=loc['WAVE_ACQTIMES'][0]) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=loc['WAVE_ACQTIMES'][1]) # add wave solution number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=loc['WAVEPARAMS'].shape[0]) # add wave solution degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=loc['WAVEPARAMS'].shape[1] - 1) # ------------------------------------------------------------------------- # add keys of the wave solution FP CCF hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_FILE'], value=loc['WAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_DRIFT'], value=p['WFP_DRIFT']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_FWHM'], value=p['WFP_FWHM']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_CONTRAST'], value=p['WFP_CONTRAST']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_MAXCPP'], value=p['WFP_MAXCPP']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_MASK'], value=p['WFP_MASK']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_LINES'], value=p['WFP_LINES']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_TARG_RV'], value=p['WFP_TARG_RV']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_WIDTH'], value=p['WFP_WIDTH']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_STEP'], value=p['WFP_STEP']) # write 1D list of the SNR hdict = spirouImage.AddKey1DList(p, hdict, p['KW_E2DS_SNR'], values=loc['SNR']) # add localization file keys to header root = p['KW_ROOT_DRS_LOC'][0] hdict = spirouImage.CopyRootKeys(p, hdict, locofile, root=root) # add wave solution coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['WAVEPARAMS']) # Save E2DS file hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) hdict = spirouImage.AddKey(p, hdict, p['KW_EXT_TYPE'], value=p['DPRTYPE']) p = spirouImage.WriteImage(p, e2dsfits, loc['E2DS'], hdict) # Save E2DSFF file hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2) hdict = spirouImage.AddKey(p, hdict, p['KW_EXT_TYPE'], value=p['DPRTYPE']) p = spirouImage.WriteImage(p, e2dsfffits, loc['E2DSFF'], hdict) # Save E2DSLL file hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag4) hdict = spirouImage.AddKey(p, hdict, p['KW_EXT_TYPE'], value=p['DPRTYPE']) if p['IC_EXTRACT_TYPE'] in EXTRACT_LL_TYPES: llstack = np.vstack(loc['E2DSLL']) p = spirouImage.WriteImage(p, e2dsllfits, llstack, hdict) # ------------------------------------------------------------------ # 1-dimension spectral S1D (uniform in wavelength) # ------------------------------------------------------------------ # get arguments for E2DS to S1D e2dsargs = [loc['WAVE'], loc['E2DSFF'], loc['BLAZE']] # get 1D spectrum xs1d1, ys1d1 = spirouImage.E2DStoS1D(p, *e2dsargs, wgrid='wave') # Plot the 1D spectrum if p['DRS_PLOT'] > 0: sPlt.ext_1d_spectrum_plot(p, xs1d1, ys1d1) # construct file name s1dfile1, tag3 = spirouConfig.Constants.EXTRACT_S1D_FILE1(p) s1dfilename1 = os.path.basename(s1dfile1) # add header keys # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3) hdict = spirouImage.AddKey(p, hdict, p['KW_EXT_TYPE'], value=p['DPRTYPE']) # log writing to file wmsg = 'Saving 1D spectrum (uniform in wavelength) for Fiber {0} in {1}' WLOG(p, '', wmsg.format(p['FIBER'], s1dfilename1)) # Write to file columns = ['wavelength', 'flux', 'eflux'] values = [xs1d1, ys1d1, np.zeros_like(ys1d1)] units = ['nm', None, None] s1d1 = spirouImage.MakeTable(p, columns, values, units=units) spirouImage.WriteTable(p, s1d1, s1dfile1, header=hdict) # ------------------------------------------------------------------ # 1-dimension spectral S1D (uniform in velocity) # ------------------------------------------------------------------ # get arguments for E2DS to S1D e2dsargs = [loc['WAVE'], loc['E2DSFF'], loc['BLAZE']] # get 1D spectrum xs1d2, ys1d2 = spirouImage.E2DStoS1D(p, *e2dsargs, wgrid='velocity') # Plot the 1D spectrum if p['DRS_PLOT'] > 0: sPlt.ext_1d_spectrum_plot(p, xs1d2, ys1d2) # construct file name s1dfile2, tag4 = spirouConfig.Constants.EXTRACT_S1D_FILE2(p) s1dfilename2 = os.path.basename(s1dfile2) # add header keys hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag4) hdict = spirouImage.AddKey(p, hdict, p['KW_EXT_TYPE'], value=p['DPRTYPE']) # log writing to file wmsg = 'Saving 1D spectrum (uniform in velocity) for Fiber {0} in {1}' WLOG(p, '', wmsg.format(p['FIBER'], s1dfilename2)) # Write to file columns = ['wavelength', 'flux', 'eflux'] values = [xs1d2, ys1d2, np.zeros_like(ys1d2)] units = ['nm', None, None] s1d2 = spirouImage.MakeTable(p, columns, values, units=units) spirouImage.WriteTable(p, s1d2, s1dfile2, header=hdict) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return a copy of locally defined variables in the memory return dict(locals())
def main(night_name=None, files=None): """ cal_loc_RAW_spirou.py main function, if night_name and files are None uses arguments from run time i.e.: cal_loc_RAW_spirou.py [night_name] [files] :param night_name: string or None, the folder within data raw directory containing files (also reduced directory) i.e. /data/raw/20170710 would be "20170710" but /data/raw/AT5/20180409 would be "AT5/20180409" :param files: string, list or None, the list of files to use for arg_file_names and fitsfilename (if None assumes arg_file_names was set from run time) :return ll: dictionary, containing all the local variables defined in main """ # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) p = spirouStartup.LoadArguments(p, night_name, files) p = spirouStartup.InitialFileSetup(p, calibdb=True) # ---------------------------------------------------------------------- # Read image file # ---------------------------------------------------------------------- # read the image data p, data, hdr = spirouImage.ReadImageAndCombine(p, framemath='add') # ---------------------------------------------------------------------- # fix for un-preprocessed files # ---------------------------------------------------------------------- data = spirouImage.FixNonPreProcess(p, data) # ---------------------------------------------------------------------- # Get basic image properties # ---------------------------------------------------------------------- # get sig det value p = spirouImage.GetSigdet(p, hdr, name='sigdet') # get exposure time p = spirouImage.GetExpTime(p, hdr, name='exptime') # get gain p = spirouImage.GetGain(p, hdr, name='gain') # ---------------------------------------------------------------------- # Correction of DARK # ---------------------------------------------------------------------- p, datac = spirouImage.CorrectForDark(p, data, hdr) # ---------------------------------------------------------------------- # Interpolation over bad regions (to fill in the holes) # ---------------------------------------------------------------------- # log process # wmsg = 'Interpolating over bad regions' # WLOG(p, '', wmsg) # run interpolation # datac = spirouImage.InterpolateBadRegions(p, datac) # ---------------------------------------------------------------------- # Resize image # ---------------------------------------------------------------------- # rotate the image and convert from ADU/s to e- data = spirouImage.ConvertToE(spirouImage.FlipImage(p, datac), p=p) # convert NaN to zeros data0 = np.where(~np.isfinite(data), np.zeros_like(data), data) # resize image bkwargs = dict(xlow=p['IC_CCDX_LOW'], xhigh=p['IC_CCDX_HIGH'], ylow=p['IC_CCDY_LOW'], yhigh=p['IC_CCDY_HIGH'], getshape=False) data2 = spirouImage.ResizeImage(p, data0, **bkwargs) # log change in data size WLOG(p, '', ('Image format changed to ' '{0}x{1}').format(*data2.shape)) # ---------------------------------------------------------------------- # Correct for the BADPIX mask (set all bad pixels to zero) # ---------------------------------------------------------------------- p, data2 = spirouImage.CorrectForBadPix(p, data2, hdr) # ---------------------------------------------------------------------- # Background computation # ---------------------------------------------------------------------- if p['IC_DO_BKGR_SUBTRACTION']: # log that we are doing background measurement WLOG(p, '', 'Doing background measurement on raw frame') # get the bkgr measurement bargs = [p, data2, hdr] # background, xc, yc, minlevel = spirouBACK.MeasureBackgroundFF(*bargs) p, background = spirouBACK.MeasureBackgroundMap(*bargs) else: background = np.zeros_like(data2) p['BKGRDFILE'] = 'None' p.set_source('BKGRDFILE', __NAME__ + '.main()') # apply background correction to data data2 = data2 - background # ---------------------------------------------------------------------- # Construct image order_profile # ---------------------------------------------------------------------- # log that we are doing background measurement WLOG(p, '', 'Creating Order Profile') order_profile = spirouLOCOR.BoxSmoothedImage(data2, p['LOC_BOX_SIZE']) # data 2 is now set to the order profile data2o = data2.copy() data2 = order_profile.copy() # ---------------------------------------------------------------------- # Write image order_profile to file # ---------------------------------------------------------------------- # Construct folder and filename rawfits, tag1 = spirouConfig.Constants.LOC_ORDER_PROFILE_FILE(p) rawfitsname = os.path.split(rawfits)[-1] # log saving order profile wmsg = 'Saving processed raw frame in {0}' WLOG(p, '', wmsg.format(rawfitsname)) # add keys from original header file hdict = spirouImage.CopyOriginalKeys(hdr) hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBDARK'], value=p['DARKFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBAD'], value=p['BADPFILE']) # write to file p = spirouImage.WriteImage(p, rawfits, order_profile, hdict) # ---------------------------------------------------------------------- # Move order_profile to calibDB and update calibDB # ---------------------------------------------------------------------- # set key for calibDB keydb = 'ORDER_PROFILE_{0}'.format(p['FIBER']) # copy dark fits file to the calibDB folder spirouDB.PutCalibFile(p, rawfits) # update the master calib DB file with new key spirouDB.UpdateCalibMaster(p, keydb, rawfitsname, hdr) # ###################################################################### # Localization of orders on central column # ###################################################################### # storage dictionary for localization parameters loc = ParamDict() # Plots setup: start interactive plot if p['DRS_PLOT'] > 0: sPlt.start_interactive_session(p) # ---------------------------------------------------------------------- # Measurement and correction of background on the central column # ---------------------------------------------------------------------- loc = spirouBACK.MeasureBkgrdGetCentPixs(p, loc, data2) # ---------------------------------------------------------------------- # Search for order center on the central column - quick estimation # ---------------------------------------------------------------------- # log progress WLOG(p, '', 'Searching order center on central column') # plot the minimum of ycc and ic_locseuil if in debug and plot mode if p['DRS_DEBUG'] > 0 and p['DRS_PLOT']: sPlt.debug_locplot_min_ycc_loc_threshold(p, loc['YCC']) # find the central positions of the orders in the central posc_all = spirouLOCOR.FindPosCentCol(loc['YCC'], p['IC_LOCSEUIL']) # depending on the fiber type we may need to skip some pixels and also # we need to add back on the ic_offset applied start = p['IC_FIRST_ORDER_JUMP'] posc = posc_all[start:] + p['IC_OFFSET'] # work out the number of orders to use (minimum of ic_locnbmaxo and number # of orders found in 'LocateCentralOrderPositions') number_of_orders = np.min([p['IC_LOCNBMAXO'], len(posc)]) # log the number of orders than have been detected wargs = [p['FIBER'], int(number_of_orders / p['NBFIB']), p['NBFIB']] WLOG(p, 'info', ('On fiber {0} {1} orders have been detected ' 'on {2} fiber(s)').format(*wargs)) # ---------------------------------------------------------------------- # Search for order center and profile on specific columns # ---------------------------------------------------------------------- # get fit polynomial orders for position and width fitpos, fitwid = p['IC_LOCDFITC'], p['IC_LOCDFITW'] # Create arrays to store position and width of order for each order loc['CTRO'] = np.zeros((number_of_orders, data2.shape[1]), dtype=float) loc['SIGO'] = np.zeros((number_of_orders, data2.shape[1]), dtype=float) # Create arrays to store coefficients for position and width loc['ACC'] = np.zeros((number_of_orders, fitpos + 1)) loc['ASS'] = np.zeros((number_of_orders, fitpos + 1)) # Create arrays to store rms values for position and width loc['RMS_CENTER'] = np.zeros(number_of_orders) loc['RMS_FWHM'] = np.zeros(number_of_orders) # Create arrays to store point to point max value for position and width loc['MAX_PTP_CENTER'] = np.zeros(number_of_orders) loc['MAX_PTP_FRACCENTER'] = np.zeros(number_of_orders) loc['MAX_PTP_FWHM'] = np.zeros(number_of_orders) loc['MAX_PTP_FRACFWHM'] = np.zeros(number_of_orders) # Create arrays to store rejected points loc['MAX_RMPTS_POS'] = np.zeros(number_of_orders) loc['MAX_RMPTS_WID'] = np.zeros(number_of_orders) # set the central col centers in the cpos_orders array loc['CTRO'][:, p['IC_CENT_COL']] = posc[0:number_of_orders] # set source for all locs loc.set_all_sources(__NAME__ + '/main()') # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # storage for plotting loc['XPLOT'], loc['YPLOT'] = [], [] # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # loop around each order rorder_num = 0 for order_num in range(number_of_orders): # find the row centers of the columns loc = spirouLOCOR.FindOrderCtrs(p, data2, loc, order_num) # only keep the orders with non-zero width mask = loc['SIGO'][order_num, :] != 0 loc['X'] = np.arange(data2.shape[1])[mask] loc.set_source('X', __NAME__ + '/main()') # check that we have enough data points to fit data if len(loc['X']) > (fitpos + 1): # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # initial fit params iofargs = [p, loc, mask, rorder_num] # initial fit for center positions for this order loc, cf_data = spirouLOCOR.InitialOrderFit(*iofargs, kind='center') # initial fit for widths for this order loc, wf_data = spirouLOCOR.InitialOrderFit(*iofargs, kind='fwhm') # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Log order number and fit at central pixel and width and rms wargs = [ rorder_num, cf_data['cfitval'], wf_data['cfitval'], cf_data['rms'] ] WLOG(p, '', ('ORDER: {0} center at pixel {1:.1f} width ' '{2:.1f} rms {3:.3f}').format(*wargs)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # sigma fit params sigfargs = [p, loc, cf_data, mask, order_num, rorder_num] # sigma clip fit for center positions for this order cf_data = spirouLOCOR.SigClipOrderFit(*sigfargs, kind='center') # load results into storage arrags for this order loc['ACC'][rorder_num] = cf_data['a'] loc['RMS_CENTER'][rorder_num] = cf_data['rms'] loc['MAX_PTP_CENTER'][rorder_num] = cf_data['max_ptp'] loc['MAX_PTP_FRACCENTER'][rorder_num] = cf_data['max_ptp_frac'] loc['MAX_RMPTS_POS'][rorder_num] = cf_data['max_rmpts'] # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # sigma fit params sigfargs = [p, loc, wf_data, mask, order_num, rorder_num] # sigma clip fit for width positions for this order wf_data = spirouLOCOR.SigClipOrderFit(*sigfargs, kind='fwhm') # load results into storage arrags for this order loc['ASS'][rorder_num] = wf_data['a'] loc['RMS_FWHM'][rorder_num] = wf_data['rms'] loc['MAX_PTP_FWHM'][rorder_num] = wf_data['max_ptp'] loc['MAX_PTP_FRACFWHM'][rorder_num] = wf_data['max_ptp_frac'] loc['MAX_RMPTS_WID'][rorder_num] = wf_data['max_rmpts'] # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # increase the roder_num iterator rorder_num += 1 # else log that the order is unusable else: WLOG(p, '', 'Order found too much incomplete, discarded') # ---------------------------------------------------------------------- # Plot the image (ready for fit points to be overplotted later) if p['DRS_PLOT'] > 0: # get saturation threshold satseuil = p['IC_SATSEUIL'] * p['GAIN'] * p['NBFRAMES'] # plot image above saturation threshold sPlt.locplot_im_sat_threshold(p, loc, data2, satseuil) # ---------------------------------------------------------------------- # Log that order geometry has been measured WLOG(p, 'info', ('On fiber {0} {1} orders geometry have been ' 'measured').format(p['FIBER'], rorder_num)) # Get mean rms mean_rms_center = np.nansum( loc['RMS_CENTER'][:rorder_num]) * 1000 / rorder_num mean_rms_fwhm = np.nansum(loc['RMS_FWHM'][:rorder_num]) * 1000 / rorder_num # Log mean rms values wmsg = 'Average uncertainty on {0}: {1:.2f} [mpix]' WLOG(p, 'info', wmsg.format('position', mean_rms_center)) WLOG(p, 'info', wmsg.format('width', mean_rms_fwhm)) # ---------------------------------------------------------------------- # Plot of RMS for positions and widths # ---------------------------------------------------------------------- if p['DRS_PLOT'] > 0: sPlt.locplot_order_number_against_rms(p, loc, rorder_num) # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # ---------------------------------------------------------------------- # check that max number of points rejected in center fit is below threshold if np.nansum(loc['MAX_RMPTS_POS']) > p['QC_LOC_MAXLOCFIT_REMOVED_CTR']: fmsg = 'abnormal points rejection during ctr fit ({0:.2f} > {1:.2f})' fail_msg.append( fmsg.format(np.nansum(loc['MAX_RMPTS_POS']), p['QC_LOC_MAXLOCFIT_REMOVED_CTR'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.nansum(loc['MAX_RMPTS_POS'])) qc_names.append('sum(MAX_RMPTS_POS)') qc_logic.append('sum(MAX_RMPTS_POS) > {0:.2f}' ''.format(p['QC_LOC_MAXLOCFIT_REMOVED_CTR'])) # ---------------------------------------------------------------------- # check that max number of points rejected in width fit is below threshold if np.nansum(loc['MAX_RMPTS_WID']) > p['QC_LOC_MAXLOCFIT_REMOVED_WID']: fmsg = 'abnormal points rejection during width fit ({0:.2f} > {1:.2f})' fail_msg.append( fmsg.format(np.nansum(loc['MAX_RMPTS_WID']), p['QC_LOC_MAXLOCFIT_REMOVED_WID'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.nansum(loc['MAX_RMPTS_WID'])) qc_names.append('sum(MAX_RMPTS_WID)') qc_logic.append('sum(MAX_RMPTS_WID) > {0:.2f}' ''.format(p['QC_LOC_MAXLOCFIT_REMOVED_WID'])) # ---------------------------------------------------------------------- # check that the rms in center fit is lower than qc threshold if mean_rms_center > p['QC_LOC_RMSMAX_CENTER']: fmsg = 'too high rms on center fitting ({0:.2f} > {1:.2f})' fail_msg.append(fmsg.format(mean_rms_center, p['QC_LOC_RMSMAX_CENTER'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(mean_rms_center) qc_names.append('mean_rms_center') qc_logic.append('mean_rms_center > {0:.2f}' ''.format(p['QC_LOC_RMSMAX_CENTER'])) # ---------------------------------------------------------------------- # check that the rms in center fit is lower than qc threshold if mean_rms_fwhm > p['QC_LOC_RMSMAX_FWHM']: fmsg = 'too high rms on profile fwhm fitting ({0:.2f} > {1:.2f})' fail_msg.append(fmsg.format(mean_rms_fwhm, p['QC_LOC_RMSMAX_CENTER'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(mean_rms_fwhm) qc_names.append('mean_rms_fwhm') qc_logic.append('mean_rms_fwhm > {0:.2f}' ''.format(p['QC_LOC_RMSMAX_CENTER'])) # ---------------------------------------------------------------------- # check for abnormal number of identified orders if rorder_num != p['QC_LOC_NBO']: fmsg = ('abnormal number of identified orders (found {0:.2f} ' 'expected {1:.2f})') fail_msg.append(fmsg.format(rorder_num, p['QC_LOC_NBO'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(rorder_num) qc_names.append('rorder_num') qc_logic.append('rorder_num != {0:.2f}'.format(p['QC_LOC_NBO'])) # ---------------------------------------------------------------------- # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ---------------------------------------------------------------------- # Save and record of image of localization with order center and keywords # ---------------------------------------------------------------------- raw_loco_file = os.path.basename(p['FITSFILENAME']) # construct filename locofits, tag2 = spirouConfig.Constants.LOC_LOCO_FILE(p) locofitsname = os.path.split(locofits)[-1] # log that we are saving localization file WLOG(p, '', ('Saving localization information ' 'in file: {0}').format(locofitsname)) # add keys from original header file hdict = spirouImage.CopyOriginalKeys(hdr) # define new keys to add hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBDARK'], value=p['DARKFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBAD'], value=p['BADPFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBLOCO'], value=raw_loco_file) hdict = spirouImage.AddKey(p, hdict, p['KW_CCD_SIGDET']) hdict = spirouImage.AddKey(p, hdict, p['KW_CCD_CONAD']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_BCKGRD'], value=loc['MEAN_BACKGRD']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_NBO'], value=rorder_num) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_DEG_C'], value=p['IC_LOCDFITC']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_DEG_W'], value=p['IC_LOCDFITW']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_DEG_E']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_DELTA']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_MAXFLX'], value=float(loc['MAX_SIGNAL'])) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_SMAXPTS_CTR'], value=np.nansum(loc['MAX_RMPTS_POS'])) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_SMAXPTS_WID'], value=np.nansum(loc['MAX_RMPTS_WID'])) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_RMS_CTR'], value=mean_rms_center) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_RMS_WID'], value=mean_rms_fwhm) # write 2D list of position fit coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_LOCO_CTR_COEFF'], values=loc['ACC'][0:rorder_num]) # write 2D list of width fit coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_LOCO_FWHM_COEFF'], values=loc['ASS'][0:rorder_num]) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # write center fits and add header keys (via hdict) center_fits = spirouLOCOR.CalcLocoFits(loc['ACC'], data2.shape[1]) p = spirouImage.WriteImage(p, locofits, center_fits, hdict) # ---------------------------------------------------------------------- # Save and record of image of sigma # ---------------------------------------------------------------------- # construct filename locofits2, tag3 = spirouConfig.Constants.LOC_LOCO_FILE2(p) locofits2name = os.path.split(locofits2)[-1] # log that we are saving localization file wmsg = 'Saving FWHM information in file: {0}' WLOG(p, '', wmsg.format(locofits2name)) # add keys from original header file hdict = spirouImage.CopyOriginalKeys(hdr) # define new keys to add hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBDARK'], value=p['DARKFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBAD'], value=p['BADPFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBACK'], value=p['BKGRDFILE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file', values=p['ARG_FILE_NAMES']) # add outputs hdict = spirouImage.AddKey(p, hdict, p['KW_CCD_SIGDET']) hdict = spirouImage.AddKey(p, hdict, p['KW_CCD_CONAD']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_NBO'], value=rorder_num) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_DEG_C'], value=p['IC_LOCDFITC']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_DEG_W'], value=p['IC_LOCDFITW']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOCO_DEG_E']) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_MAXFLX'], value=float(loc['MAX_SIGNAL'])) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_SMAXPTS_CTR'], value=np.nansum(loc['MAX_RMPTS_POS'])) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_SMAXPTS_WID'], value=np.nansum(loc['MAX_RMPTS_WID'])) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_RMS_CTR'], value=mean_rms_center) hdict = spirouImage.AddKey(p, hdict, p['KW_LOC_RMS_WID'], value=mean_rms_fwhm) # write 2D list of position fit coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_LOCO_CTR_COEFF'], values=loc['ACC'][0:rorder_num]) # write 2D list of width fit coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_LOCO_FWHM_COEFF'], values=loc['ASS'][0:rorder_num]) # add quality control hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) # write image and add header keys (via hdict) width_fits = spirouLOCOR.CalcLocoFits(loc['ASS'], data2.shape[1]) p = spirouImage.WriteImage(p, locofits2, width_fits, hdict) # ---------------------------------------------------------------------- # Save and Record of image of localization # ---------------------------------------------------------------------- if p['IC_LOCOPT1']: # construct filename locofits3, tag4 = spirouConfig.Constants.LOC_LOCO_FILE3(p) locofits3name = os.path.split(locofits3)[-1] # log that we are saving localization file wmsg1 = 'Saving localization image with superposition of orders in ' wmsg2 = 'file: {0}'.format(locofits3name) WLOG(p, '', [wmsg1, wmsg2]) # superpose zeros over the fit in the image data4 = spirouLOCOR.ImageLocSuperimp(data2o, loc['ACC'][0:rorder_num]) # save this image to file hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_FIBER'], value=p['FIBER']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag4) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBDARK'], value=p['DARKFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBAD'], value=p['BADPFILE']) p = spirouImage.WriteImage(p, locofits3, data4, hdict) # ---------------------------------------------------------------------- # Update the calibration database # ---------------------------------------------------------------------- if p['QC'] == 1: keydb = 'LOC_' + p['FIBER'] # copy localisation file to the calibDB folder spirouDB.PutCalibFile(p, locofits) # update the master calib DB file with new key spirouDB.UpdateCalibMaster(p, keydb, locofitsname, hdr) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return a copy of locally defined variables in the memory return dict(locals())
def part2test(p, loc): # ------------------------------------------------------------------ # Fit wavelength solution on identified lines (using Littrow) # ------------------------------------------------------------------ # log message wmsg = ('On fiber {0} fitting wavelength solution on identified ' 'lines: (second pass)') WLOG(p, '', wmsg.format(p['FIBER'])) # fit lines start, end = p['IC_HC_N_ORD_START_2'], p['IC_HC_N_ORD_FINAL_2'] lls = loc['LITTROW_EXTRAP_SOL_1'][start:end] loc = spirouTHORCA.Fit1DSolution(p, loc, lls, iteration=2) # ------------------------------------------------------------------ # Littrow test # ------------------------------------------------------------------ ckwargs = dict(ll=loc['LL_OUT_2'], iteration=2, log=True) loc = spirouTHORCA.CalcLittrowSolution(p, loc, **ckwargs) # ------------------------------------------------------------------ # Plot wave solution littrow check # ------------------------------------------------------------------ if p['DRS_PLOT'] > 0: # plot littrow x pixels against fitted wavelength solution sPlt.wave_littrow_check_plot(p, loc, iteration=2) # ------------------------------------------------------------------ # ------------------------------------------------------------------ # extrapolate Littrow solution # ------------------------------------------------------------------ ekwargs = dict(ll=loc['LL_OUT_2'], iteration=2) loc = spirouTHORCA.ExtrapolateLittrowSolution(p, loc, **ekwargs) # ------------------------------------------------------------------ # Plot littrow solution # ------------------------------------------------------------------ if p['DRS_PLOT'] > 0: # plot littrow x pixels against fitted wavelength solution sPlt.wave_littrow_extrap_plot(p, loc, iteration=2) # ------------------------------------------------------------------ # Join 0-24 and 25-36 solutions # ------------------------------------------------------------------ loc = spirouTHORCA.JoinOrders(p, loc) # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- # set passed variable and fail message list passed, fail_msg = True, [] # check for infinites and NaNs in X_MEAN_2 if ~np.isfinite(loc['X_MEAN_2']): # add failed message to the fail message list fmsg = 'NaN or Inf in X_MEAN_2' fail_msg.append(fmsg) passed = False # iterate through Littrow test cut values # for x_it in range(len(loc['X_CUT_POINTS_2'])): for x_it in range(1, len(loc['X_CUT_POINTS_2']), 2): # get x cut point x_cut_point = loc['X_CUT_POINTS_2'][x_it] # get the sigma for this cut point sig_littrow = loc['LITTROW_SIG_2'][x_it] # get the abs min and max dev littrow values min_littrow = abs(loc['LITTROW_MINDEV_2'][x_it]) max_littrow = abs(loc['LITTROW_MAXDEV_2'][x_it]) # check if sig littrow is above maximum rms_littrow_max = p['QC_RMS_LITTROW_MAX'] dev_littrow_max = p['QC_DEV_LITTROW_MAX'] if sig_littrow > rms_littrow_max: fmsg = ('Littrow test (x={0}) failed (sig littrow = ' '{1:.2f} > {2:.2f})') fargs = [x_cut_point, sig_littrow, rms_littrow_max] fail_msg.append(fmsg.format(*fargs)) passed = False # check if min/max littrow is out of bounds if np.max([max_littrow, min_littrow]) > dev_littrow_max: fmsg = ('Littrow test (x={0}) failed (min|max dev = ' '{1:.2f}|{2:.2f} > {3:.2f})') fargs = [x_cut_point, min_littrow, max_littrow, dev_littrow_max] fail_msg.append(fmsg.format(*fargs)) passed = False # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # ------------------------------------------------------------------ # archive result in e2ds spectra # ------------------------------------------------------------------ # get raw input file name raw_infile1 = os.path.basename(p['HCFILES'][0]) raw_infile2 = os.path.basename(p['FPFILE']) # get wave filename wavefits, tag1 = spirouConfig.Constants.WAVE_FILE_FP(p) wavefitsname = os.path.split(wavefits)[-1] WLOG(p, '', wavefits) # log progress wargs = [p['FIBER'], wavefitsname] wmsg = 'Write wavelength solution for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # write solution to fitsfilename header # copy original keys hdict = spirouImage.CopyOriginalKeys(loc['HCHDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBFLAT'], value=p['FLATFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) # add quality control hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) # add number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=loc['LL_PARAM_FINAL'].shape[0]) # add degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=loc['LL_PARAM_FINAL'].shape[1] - 1) # add wave solution hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['LL_PARAM_FINAL']) # write original E2DS file and add header keys (via hdict) # spirouImage.WriteImage(p['FITSFILENAME'], loc['HCDATA'], hdict) # write the wave "spectrum" p = spirouImage.WriteImage(p, wavefits, loc['LL_FINAL'], hdict) # get filename for E2DS calibDB copy of FITSFILENAME e2dscopy_filename, tag2 = spirouConfig.Constants.WAVE_E2DS_COPY(p) wargs = [p['FIBER'], os.path.split(e2dscopy_filename)[-1]] wmsg = 'Write reference E2DS spectra for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # make a copy of the E2DS file for the calibBD hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2) p = spirouImage.WriteImage(p, e2dscopy_filename, loc['HCDATA'], hdict) # ------------------------------------------------------------------ # Save to result table # ------------------------------------------------------------------ # calculate stats for table final_mean = 1000 * loc['X_MEAN_2'] final_var = 1000 * loc['X_VAR_2'] num_iterations = int(np.nansum(loc['X_ITER_2'][:, 2])) err = 1000 * np.sqrt(final_var / num_iterations) sig_littrow = 1000 * np.array(loc['LITTROW_SIG_2']) # construct filename wavetbl = spirouConfig.Constants.WAVE_TBL_FILE_FP(p) wavetblname = os.path.split(wavetbl)[-1] # construct and write table columnnames = [ 'night_name', 'file_name', 'fiber', 'mean', 'rms', 'N_lines', 'err', 'rms_L0', 'rms_L1', 'rms_L2' ] columnformats = [ '{:20s}', '{:30s}', '{:3s}', '{:7.4f}', '{:6.2f}', '{:3d}', '{:6.3f}', '{:6.2f}', '{:6.2f}', '{:6.2f}' ] columnvalues = [[p['ARG_NIGHT_NAME']], [p['ARG_FILE_NAMES'][0]], [p['FIBER']], [final_mean], [final_var], [num_iterations], [err], [sig_littrow[0]], [sig_littrow[1]], [sig_littrow[2]]] # make table table = spirouImage.MakeTable(p, columns=columnnames, values=columnvalues, formats=columnformats) # merge table wmsg = 'Global result summary saved in {0}' WLOG(p, '', wmsg.format(wavetblname)) spirouImage.MergeTable(p, table, wavetbl, fmt='ascii.rst') # ------------------------------------------------------------------ # Save line list table file # ------------------------------------------------------------------ # construct filename wavelltbl = spirouConfig.Constants.WAVE_LINE_FILE(p) wavelltblname = os.path.split(wavelltbl)[-1] # construct and write table columnnames = ['order', 'll', 'dv', 'w', 'xi', 'xo', 'dvdx'] columnformats = [ '{:.0f}', '{:12.4f}', '{:13.5f}', '{:12.4f}', '{:12.4f}', '{:12.4f}', '{:8.4f}' ] columnvalues = [] # construct column values (flatten over orders) for it in range(len(loc['X_DETAILS_2'])): for jt in range(len(loc['X_DETAILS_2'][it][0])): row = [ float(it), loc['X_DETAILS_2'][it][0][jt], loc['LL_DETAILS_2'][it][0][jt], loc['X_DETAILS_2'][it][3][jt], loc['X_DETAILS_2'][it][1][jt], loc['X_DETAILS_2'][it][2][jt], loc['SCALE_2'][it][jt] ] columnvalues.append(row) # log saving wmsg = 'List of lines used saved in {0}' WLOG(p, '', wmsg.format(wavelltblname)) # make table columnvalues = np.array(columnvalues).T table = spirouImage.MakeTable(p, columns=columnnames, values=columnvalues, formats=columnformats) # write table spirouImage.WriteTable(p, table, wavelltbl, fmt='ascii.rst') # ------------------------------------------------------------------ # Move to calibDB and update calibDB # ------------------------------------------------------------------ # if p['QC']: # # set the wave key # keydb = 'WAVE_{0}'.format(p['FIBER']) # # copy wave file to calibDB folder # spirouDB.PutFile(p, wavefits) # # update the master calib DB file with new key # spirouDB.UpdateMaster(p, keydb, wavefitsname, loc['HCHDR']) # # # set the hcref key # keydb = 'HCREF_{0}'.format(p['FIBER']) # # copy wave file to calibDB folder # spirouDB.PutFile(p, e2dscopy_filename) # # update the master calib DB file with new key # e2dscopyfits = os.path.split(e2dscopy_filename)[-1] # spirouDB.UpdateMaster(p, keydb, e2dscopyfits, loc['HCHDR']) # return p and loc return p, loc
def main(night_name=None, files=None): # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) p = spirouStartup.LoadArguments(p, night_name, files, mainfitsdir='reduced') p = spirouStartup.InitialFileSetup(p, calibdb=True) # set up function name main_name = __NAME__ + '.main()' # ---------------------------------------------------------------------- # load up first file # ---------------------------------------------------------------------- # construct loc parameter dictionary for storing data loc = ParamDict() # read first file and assign values rdata = spirouImage.ReadImage(p, p['FITSFILENAME']) loc['DATA'], loc['DATAHDR'] = rdata[:2] # Get object name and airmass loc['OBJNAME'] = spirouImage.GetObjName(p, loc['DATAHDR']) loc.set_source('OBJNAME', main_name) loc['AIRMASS'] = spirouImage.GetAirmass(p, loc['DATAHDR']) # set source source = main_name + '+ spirouImage.ReadParams()' loc.set_sources(['OBJNAME', 'AIRMASS'], source) # ---------------------------------------------------------------------- # Get and Normalise the blaze # ---------------------------------------------------------------------- p, loc = spirouTelluric.GetNormalizedBlaze(p, loc, loc['DATAHDR']) # ---------------------------------------------------------------------- # Get database files # ---------------------------------------------------------------------- # get current telluric maps from telluDB tellu_db_data = spirouDB.GetDatabaseTellObj(p, required=False) tellu_db_files, tellu_db_names = tellu_db_data[0], tellu_db_data[1] # sort files by name # sortmask = spirouImage.SortByName(tellu_db_files) # tellu_db_files = np.array(tellu_db_files)[sortmask] # tellu_db_names = np.array(tellu_db_names)[sortmask] # filter by object name (only keep OBJNAME objects) and only keep # unique filenames tell_files, tell_names = [], [] for it in range(len(tellu_db_files)): # check that objname is correct cond1 = loc['OBJNAME'] in tellu_db_names[it] # check that filename is not already used cond2 = tellu_db_files[it] not in tell_files # append to file list if criteria correct if cond1 and cond2: tell_files.append(tellu_db_files[it]) tell_names.append(tellu_db_names[it]) # log if we have no files if len(tell_files) == 0: wmsg = 'No "TELL_OBJ" files found for object ="{0}" skipping' WLOG(p, 'warning', wmsg.format(loc['OBJNAME'])) # End Message wmsg = 'Recipe {0} has been successfully completed' WLOG(p, 'info', wmsg.format(p['PROGRAM'])) # return a copy of locally defined variables in the memory return dict(locals()) else: # log how many found wmsg = 'N={0} "TELL_OBJ" files found for object ="{1}"' WLOG(p, 'info', wmsg.format(len(tell_files), loc['OBJNAME'])) # ---------------------------------------------------------------------- # Set up storage for cubes (NaN arrays) # ---------------------------------------------------------------------- # set up flat size dims = [loc['DATA'].shape[0], loc['DATA'].shape[1], len(tell_files)] flatsize = np.product(dims) # create NaN filled storage big_cube = np.repeat([np.nan], flatsize).reshape(*dims) big_cube0 = np.repeat([np.nan], flatsize).reshape(*dims) # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # get master wave map loc['MASTERWAVEFILE'] = spirouDB.GetDatabaseMasterWave(p) # read master wave map mout = spirouImage.GetWaveSolution(p, filename=loc['MASTERWAVEFILE'], return_wavemap=True, quiet=True, fiber=wave_fiber) loc['MASTERWAVEPARAMS'], loc['MASTERWAVE'], loc['WSOURCE'] = mout keys = ['MASTERWAVEPARAMS', 'MASTERWAVE', 'MASTERWAVEFILE', 'WSOURCE'] loc.set_sources(keys, main_name) # ---------------------------------------------------------------------- # Compile a median SNR for rejection of bad files # ---------------------------------------------------------------------- snr_all = [] # choose snr to check snr_order = p['QC_FIT_TELLU_SNR_ORDER'] # loop through files for it, filename in enumerate(tell_files): header = spirouImage.ReadHeader(p, filename) # get the SNR from header nbo = spirouImage.ReadParam(p, header, 'KW_WAVE_ORD_N', dtype=int, return_value=True) snr = spirouImage.Read1Dkey(p, header, p['kw_E2DS_SNR'][0], nbo) # append snr_all snr_all.append(snr[snr_order]) # work our bad snr (less than half the median SNR) snr_thres = np.nanmedian(snr_all) / 2.0 bad_snr_objects = np.where(snr_all < snr_thres)[0] # ---------------------------------------------------------------------- # Loop through input files # ---------------------------------------------------------------------- # empty lists for storage loc['BASE_ROWNUM'], loc['BASE_FILELIST'] = [], [] loc['BASE_OBJNAME'], loc['BASE_OBJECT'] = [], [] loc['BASE_BERVLIST'], loc['BASE_WAVELIST'] = [], [] loc['BASE_SNRLIST_{0}'.format(snr_order)] = [] loc['BASE_DATELIST'], loc['BASE_VERSION'] = [], [] loc['BASE_DARKFILE'], loc['BASE_BADFILE'] = [], [] loc['BASE_LOCOFILE'], loc['BASE_BLAZFILE'] = [], [] loc['BASE_FLATFILE'], loc['BASE_SHAPEFILE'] = [], [] # loop through files for it, filename in enumerate(tell_files): # get base filenmae basefilename = os.path.basename(filename) # ------------------------------------------------------------------ # skip if in bad snr objects if it in bad_snr_objects: wargs = [it + 1, len(tell_files), snr_all[it], snr_thres] wmsg1 = ('Skipping file {0} of {1} due to bad SNR ({2:.3f} < ' '{3:.3f})'.format(*wargs)) wmsg2 = '\tFile = {0}'.format(basefilename) WLOG(p, 'warning', [wmsg1, wmsg2]) continue # ------------------------------------------------------------------ # append basename to file list loc['BASE_FILELIST'].append(basefilename) # ------------------------------------------------------------------ # create image for storage image = np.repeat([np.nan], np.product(loc['DATA'].shape)) image = image.reshape(loc['DATA'].shape) # ------------------------------------------------------------------ # Load the data for this file tdata0, thdr, _, _ = spirouImage.ReadImage(p, filename) nbo, npix = tdata0.shape # Correct for the blaze tdata = tdata0 / loc['NBLAZE'] # get berv and add to list if p['KW_BERV'][0] in thdr: loc['BASE_BERVLIST'].append('{0}'.format(thdr[p['KW_BERV'][0]])) else: loc['BASE_BERVLIST'].append('UNKNOWN') # ------------------------------------------------------------------ # Get parameters from header snr = snr_all[it] dateobs = spirouImage.ReadParam(p, thdr, 'KW_DATE_OBS', dtype=str, return_value=True) utcobs = spirouImage.ReadParam(p, thdr, 'KW_UTC_OBS', dtype=str, return_value=True) tobjname = spirouImage.ReadParam(p, thdr, 'KW_OBJNAME', dtype=str, return_value=True) tobject = spirouImage.ReadParam(p, thdr, 'KW_OBJECT', dtype=str, return_value=True) tversion = spirouImage.ReadParam(p, thdr, 'KW_version', dtype=str, return_value=True) tdarkfile = spirouImage.ReadParam(p, thdr, 'KW_CDBDARK', dtype=str, return_value=True) tbadfile = spirouImage.ReadParam(p, thdr, 'KW_CDBBAD', dtype=str, return_value=True) tlocofile = spirouImage.ReadParam(p, thdr, 'KW_CDBLOCO', dtype=str, return_value=True) tblazfile = spirouImage.ReadParam(p, thdr, 'KW_CDBBLAZE', dtype=str, return_value=True) tflatfile = spirouImage.ReadParam(p, thdr, 'KW_CDBFLAT', dtype=str, return_value=True) tshapfile = spirouImage.ReadParam(p, thdr, 'KW_CDBSHAPE', dtype=str, return_value=True) # append to lists loc['BASE_ROWNUM'].append(it) loc['BASE_SNRLIST_{0}'.format(snr_order)].append(snr_all[it]) loc['BASE_DATELIST'].append('{0}_{1}'.format(dateobs, utcobs)) loc['BASE_OBJNAME'].append(tobjname) loc['BASE_OBJECT'].append(tobject) loc['BASE_VERSION'].append(tversion) loc['BASE_DARKFILE'].append(tdarkfile) loc['BASE_BADFILE'].append(tbadfile) loc['BASE_LOCOFILE'].append(tlocofile) loc['BASE_BLAZFILE'].append(tblazfile) loc['BASE_FLATFILE'].append(tflatfile) loc['BASE_SHAPEFILE'].append(tshapfile) # ------------------------------------------------------------------ # Get the wave solution for this file # ------------------------------------------------------------------ # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # need to reload calibDB # as we have custom arguments need to load the calibration database calib_db = dict(p['CALIBDB']) del p['CALIBDB'] p = spirouStartup.LoadCalibDB(p, header=thdr) # get wave solution wout = spirouImage.GetWaveSolution(p, image=tdata, hdr=thdr, return_wavemap=True, fiber=wave_fiber, return_filename=True) _, loc['WAVE'], loc['WAVEFILE'], _ = wout loc.set_sources(['WAVE', 'WAVEFILE'], main_name) # add wave to wave list loc['BASE_WAVELIST'].append(loc['WAVEFILE']) # readd original calibDB to p p['CALIBDB'] = dict(calib_db) # ------------------------------------------------------------------ # Get the Barycentric correction from header dv, _, _ = spirouImage.GetBERV(p, thdr) # ------------------------------------------------------------------ # log stats wmsg = 'Processing file {0} of {1} file={2} dv={3}' wargs = [it + 1, len(tell_files), basefilename, dv] WLOG(p, 'info', wmsg.format(*wargs)) # ------------------------------------------------------------------ # shift to correct berv dvshift = spirouMath.relativistic_waveshift(dv, units='km/s') image = spirouTelluric.Wave2Wave(p, tdata, loc['WAVE'] * dvshift, loc['MASTERWAVE']) # ------------------------------------------------------------------ # loop around orders for order_num in range(loc['DATA'].shape[0]): # normalise the tdata tdata[order_num, :] /= np.nanmedian(tdata[order_num, :]) image[order_num, :] /= np.nanmedian(image[order_num, :]) # ------------------------------------------------------------------ # add to cube storage big_cube[:, :, it] = image big_cube0[:, :, it] = tdata # ---------------------------------------------------------------------- # log if we have no files if len(loc['BASE_FILELIST']) == 0: wmsg = 'No good files found for object ="{0}" skipping' WLOG(p, 'warning', wmsg.format(loc['OBJNAME'])) # End Message wmsg = 'Recipe {0} has been successfully completed' WLOG(p, 'info', wmsg.format(p['PROGRAM'])) # return a copy of locally defined variables in the memory return dict(locals()) # ---------------------------------------------------------------------- # make median image with warnings.catch_warnings(record=True) as _: big_cube_med = np.nanmedian(big_cube, axis=2) # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- # set passed variable and fail message list passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # TODO: Needs doing # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # add to qc header lists qc_values.append('None') qc_names.append('None') qc_logic.append('None') qc_pass.append(1) # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ---------------------------------------------------------------------- # Write Cube median (the template) to file # ---------------------------------------------------------------------- # get raw file name raw_in_file = os.path.basename(p['FITSFILENAME']) # construct filename outfile, tag = spirouConfig.Constants.OBJTELLU_TEMPLATE_FILE(p, loc) outfilename = os.path.basename(outfile) # hdict is first file keys hdict = spirouImage.CopyOriginalKeys(loc['DATAHDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['MASTERWAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file', values=p['ARG_FILE_NAMES']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # add wave solution coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['MASTERWAVEPARAMS']) # write to file p = spirouImage.WriteImage(p, outfile, big_cube_med, hdict) # ---------------------------------------------------------------------- # Update the telluric database with the template # ---------------------------------------------------------------------- spirouDB.UpdateDatabaseObjTemp(p, outfilename, loc['OBJNAME'], loc['DATAHDR']) # put file in telluDB spirouDB.PutTelluFile(p, outfile) # ---------------------------------------------------------------------- # Save cubes to file # ---------------------------------------------------------------------- # make big cube table big_table = spirouTelluric.ConstructBigTable(p, loc) # construct file names outfile1, tag1 = spirouConfig.Constants.OBJTELLU_TEMPLATE_CUBE_FILE1( p, loc) outfile2, tag2 = spirouConfig.Constants.OBJTELLU_TEMPLATE_CUBE_FILE2( p, loc) # log big cube 1 wmsg1 = 'Saving bigcube to file {0}'.format(os.path.basename(outfile1)) # save big cube 1 hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) big_cube_s = np.swapaxes(big_cube, 1, 2) p = spirouImage.WriteImageTable(p, outfile1, image=big_cube_s, table=big_table, hdict=hdict) # log big cube 0 wmsg = 'Saving bigcube0 to file {0}'.format(os.path.basename(outfile2)) # save big cube 0 hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2) big_cube_s0 = np.swapaxes(big_cube0, 1, 2) p = spirouImage.WriteImageTable(p, outfile2, image=big_cube_s0, table=big_table, hdict=hdict) # # mega plot # nfiles = big_cube_s0.shape[1] # ncols = int(np.ceil(np.sqrt(nfiles))) # nrows = int(np.ceil(nfiles/ncols)) # fig, frames = plt.subplots(ncols=ncols, nrows=nrows) # for it in range(big_cube_s0.shape[1]): # jt, kt = it // ncols, it % ncols # frame = frames[jt][kt] # frame.imshow(big_cube_s0[:, it, :], origin='lower') # frame.set(xlim=(2030, 2060)) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return a copy of locally defined variables in the memory return dict(locals())
def main(night_name=None, files=None): # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) p = spirouStartup.LoadArguments(p, night_name, files, mainfitsdir='reduced') p = spirouStartup.InitialFileSetup(p, calibdb=True) # set up function name main_name = __NAME__ + '.main()' # ------------------------------------------------------------------ # Load first file # ------------------------------------------------------------------ loc = ParamDict() rd = spirouImage.ReadImage(p, p['FITSFILENAME']) loc['DATA'], loc['DATAHDR'], loc['YDIM'], loc['XDIM'] = rd loc.set_sources(['DATA', 'DATAHDR', 'XDIM', 'YDIM'], main_name) # ------------------------------------------------------------------ # Get the wave solution # ------------------------------------------------------------------ wout = spirouImage.GetWaveSolution(p, image=loc['DATA'], hdr=loc['DATAHDR'], return_wavemap=True, return_filename=True) _, loc['WAVE'], loc['WAVEFILE'], _ = wout loc.set_sources(['WAVE', 'WAVEFILE'], main_name) # get the wave keys loc = spirouImage.GetWaveKeys(p, loc, loc['DATAHDR']) # ------------------------------------------------------------------ # Get and Normalise the blaze # ------------------------------------------------------------------ p, loc = spirouTelluric.GetNormalizedBlaze(p, loc, loc['DATAHDR']) # ------------------------------------------------------------------ # Construct convolution kernels # ------------------------------------------------------------------ loc = spirouTelluric.ConstructConvKernel1(p, loc) loc = spirouTelluric.ConstructConvKernel2(p, loc, vsini=p['TELLU_VSINI']) # ------------------------------------------------------------------ # Get molecular telluric lines # ------------------------------------------------------------------ loc = spirouTelluric.GetMolecularTellLines(p, loc) # if TAPAS FNAME is not None we generated a new file so should add to tellDB if loc['TAPAS_FNAME'] is not None: # add to the telluric database spirouDB.UpdateDatabaseTellConv(p, loc['TAPAS_FNAME'], loc['DATAHDR']) # put file in telluDB spirouDB.PutTelluFile(p, loc['TAPAS_ABSNAME']) # ------------------------------------------------------------------ # Get master wave solution map # ------------------------------------------------------------------ # get master wave map masterwavefile = spirouDB.GetDatabaseMasterWave(p) # log process wmsg1 = 'Shifting transmission map on to master wavelength grid' wmsg2 = '\tFile = {0}'.format(os.path.basename(masterwavefile)) WLOG(p, '', [wmsg1, wmsg2]) # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # read master wave map mout = spirouImage.GetWaveSolution(p, filename=masterwavefile, return_wavemap=True, quiet=True, return_header=True, fiber=wave_fiber) masterwavep, masterwave, masterwaveheader, mwsource = mout # get wave acqtimes master_acqtimes = spirouDB.GetTimes(p, masterwaveheader) # ------------------------------------------------------------------ # Loop around the files # ------------------------------------------------------------------ # construct extension tellu_ext = '{0}_{1}.fits' # get current telluric maps from telluDB tellu_db_data = spirouDB.GetDatabaseTellMap(p, required=False) tellu_db_files = tellu_db_data[0] # storage for valid output files loc['OUTPUTFILES'] = [] # loop around the files for basefilename in p['ARG_FILE_NAMES']: # ------------------------------------------------------------------ # Get absolute path of filename # ------------------------------------------------------------------ filename = os.path.join(p['ARG_FILE_DIR'], basefilename) # ------------------------------------------------------------------ # Read obj telluric file and correct for blaze # ------------------------------------------------------------------ # get image sp, shdr, _, _ = spirouImage.ReadImage(p, filename) # divide my blaze sp = sp / loc['BLAZE'] # ------------------------------------------------------------------ # Get the wave solution # ------------------------------------------------------------------ wout = spirouImage.GetWaveSolution(p, image=sp, hdr=shdr, return_wavemap=True, return_filename=True) _, loc['WAVE_IT'], loc['WAVEFILE_IT'], _ = wout loc.set_sources(['WAVE_IT', 'WAVEFILE_IT'], main_name) # ------------------------------------------------------------------ # Shift data to master wave file # ------------------------------------------------------------------ # shift map wargs = [p, sp, loc['WAVE_IT'], masterwave] sp = spirouTelluric.Wave2Wave(*wargs) loc['SP'] = np.array(sp) loc.set_source('SP', main_name) # ------------------------------------------------------------------ # get output transmission filename outfile, tag1 = spirouConfig.Constants.TELLU_TRANS_MAP_FILE(p, filename) outfilename = os.path.basename(outfile) loc['OUTPUTFILES'].append(outfile) # if we already have the file skip it if outfile in tellu_db_files: wmsg = 'File {0} exists in telluDB, skipping' WLOG(p, '', wmsg.format(outfilename)) continue else: # log processing file wmsg = 'Processing file {0}' WLOG(p, '', wmsg.format(outfilename)) # Get object name and airmass loc['OBJNAME'] = spirouImage.GetObjName(p, shdr) loc['AIRMASS'] = spirouImage.GetAirmass(p, shdr) # set source source = main_name + '+ spirouImage.ReadParams()' loc.set_sources(['OBJNAME', 'AIRMASS'], source) # ------------------------------------------------------------------ # Check that basefile is not in blacklist # ------------------------------------------------------------------ blacklist_check = spirouTelluric.CheckBlackList(loc['OBJNAME']) if blacklist_check: # log black list file found wmsg = 'File {0} is blacklisted (OBJNAME={1}). Skipping' wargs = [basefilename, loc['OBJNAME']] WLOG(p, 'warning', wmsg.format(*wargs)) # skip this file continue # ------------------------------------------------------------------ # loop around the orders # ------------------------------------------------------------------ # define storage for the transmission map transmission_map = np.zeros_like(loc['DATA']) # define storage for measured rms within expected clean domains exp_clean_rms = np.zeros(loc['DATA'].shape[0]) # loop around the orders for order_num in range(loc['DATA'].shape[0]): # start and end start = order_num * loc['XDIM'] end = (order_num * loc['XDIM']) + loc['XDIM'] # get this orders combined tapas transmission trans = loc['TAPAS_ALL_SPECIES'][0, start:end] # keep track of the pixels that are considered valid for the SED # determination mask1 = trans > p['TRANSMISSION_CUT'] mask1 &= np.isfinite(loc['NBLAZE'][order_num, :]) # normalise the spectrum sp[order_num, :] /= np.nanmedian(sp[order_num, :]) # create a float mask fmask = np.array(mask1, dtype=float) # set up an SED to fill sed = np.ones(loc['XDIM']) # sigma clip until limit ww = None for it in range(p['N_ITER_SED_HOTSTAR']): # copy the spectrum sp2 = np.array(sp[order_num, :]) # flag Nans nanmask = ~np.isfinite(sp2) # set all NaNs to zero so that it does not propagate when # we convlve by KER2 - must set sp2[bad] to zero as # NaN * 0.0 = NaN and we want 0.0! sp2[nanmask] = 0.0 # trace the invalid points fmask[nanmask] = 0.0 # multiple by the float mask sp2 *= fmask # convolve with the second kernel sp2b = np.convolve(sp2 / sed, loc['KER2'], mode='same') # convolve with mask to get weights ww = np.convolve(fmask, loc['KER2'], mode='same') # normalise the spectrum by the weights with warnings.catch_warnings(record=True) as w: sp2bw = sp2b / ww # set zero pixels to 1 sp2bw[sp2b == 0] = 1 # recalculate the mask using the deviation from original with warnings.catch_warnings(record=True) as _: dev = (sp2bw - sp[order_num, :] / sed) dev /= np.nanmedian(np.abs(dev)) mask = mask1 * (np.abs(dev) < p['TELLU_SIGMA_DEV']) # update the SED with the corrected spectrum sed *= sp2bw # identify bad pixels with warnings.catch_warnings(record=True) as _: bad = (sp[order_num, :] / sed[:] > 1.2) sed[bad] = np.nan # debug plot if p['DRS_PLOT'] and (p['DRS_DEBUG'] > 1) and FORCE_PLOT_ON: # start non-interactive plot sPlt.plt.ioff() # plot the transmission map plot pargs = [order_num, mask1, sed, trans, sp, ww, outfilename] sPlt.tellu_trans_map_plot(p, loc, *pargs) # show and close sPlt.plt.show() sPlt.plt.close() # set all values below a threshold to NaN sed[ww < p['TELLU_NAN_THRESHOLD']] = np.nan # save the spectrum (normalised by the SED) to the tranmission map transmission_map[order_num, :] = sp[order_num, :] / sed # get expected clean rms fmaskb = np.array(fmask).astype(bool) with warnings.catch_warnings(record=True): zerotrans = np.abs(transmission_map[order_num, fmaskb]-1) ec_rms = np.nanmedian(zerotrans) exp_clean_rms[order_num] = ec_rms # log the rms wmsg = 'Order {0}: Fractional RMS in telluric free domain = {1:.3f}' wargs = [order_num, ec_rms] WLOG(p, '', wmsg.format(*wargs)) # --------------------------------------------------------------------- # Quality control # --------------------------------------------------------------------- # set passed variable and fail message list passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # ---------------------------------------------------------------------- # get SNR for each order from header nbo = loc['DATA'].shape[0] snr_order = p['QC_MK_TELLU_SNR_ORDER'] snr = spirouImage.Read1Dkey(p, shdr, p['kw_E2DS_SNR'][0], nbo) # check that SNR is high enough if snr[snr_order] < p['QC_MK_TELLU_SNR_MIN']: fmsg = 'low SNR in order {0}: ({1:.2f} < {2:.2f})' fargs = [snr_order, snr[snr_order], p['QC_MK_TELLU_SNR_MIN']] fail_msg.append(fmsg.format(*fargs)) passed = False # add to qc header lists qc_values.append(snr[snr_order]) qc_name_str = 'SNR[{0}]'.format(snr_order) qc_names.append(qc_name_str) qc_logic.append('{0} < {1:.2f}'.format(qc_name_str, p['QC_MK_TELLU_SNR_ORDER'])) qc_pass.append(0) else: qc_pass.append(1) # ---------------------------------------------------------------------- # check that the RMS is not too low if exp_clean_rms[snr_order] > p['QC_TELLU_CLEAN_RMS_MAX']: fmsg = ('Expected clean RMS is too high in order {0} ' '({1:.3f} > {2:.3f})') fargs = [snr_order, exp_clean_rms[snr_order], p['QC_TELLU_CLEAN_RMS_MAX']] fail_msg.append(fmsg.format(*fargs)) passed = False # add to qc header lists qc_values.append(exp_clean_rms[snr_order]) qc_name_str = 'exp_clean_rms[{0}]'.format(snr_order) qc_names.append(qc_name_str) qc_logic.append('{0} > {1:.2f}'.format(qc_name_str, p['QC_TELLU_CLEAN_RMS_MAX'])) qc_pass.append(0) else: qc_pass.append(1) # ---------------------------------------------------------------------- # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ------------------------------------------------------------------ # Save transmission map to file # ------------------------------------------------------------------ # get raw file name raw_in_file = os.path.basename(p['FITSFILENAME']) # copy original keys hdict = spirouImage.CopyOriginalKeys(loc['DATAHDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=os.path.basename(masterwavefile)) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=mwsource) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file', values=p['ARG_FILE_NAMES']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=master_acqtimes[0]) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=master_acqtimes[1]) # add wave solution number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=masterwavep.shape[0]) # add wave solution degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=masterwavep.shape[1] - 1) # add wave solution coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=masterwavep) # write to file p = spirouImage.WriteImage(p, outfile, transmission_map, hdict) # ------------------------------------------------------------------ # Generate the absorption map # ------------------------------------------------------------------ # set up storage for the absorption abso = np.array(transmission_map) # set values less than low threshold to low threshold # set values higher than high threshold to 1 low, high = p['TELLU_ABSO_LOW_THRES'], p['TELLU_ABSO_HIGH_THRES'] with warnings.catch_warnings(record=True) as w: abso[abso < low] = low abso[abso > high] = 1.0 # write to loc loc['RECON_ABSO'] = abso.reshape(np.product(loc['DATA'].shape)) loc.set_source('RECON_ABSO', main_name) # ------------------------------------------------------------------ # Get molecular absorption # ------------------------------------------------------------------ loc = spirouTelluric.CalcMolecularAbsorption(p, loc) # add molecular absorption to file for it, molecule in enumerate(p['TELLU_ABSORBERS'][1:]): # get molecule keyword store and key molkey = '{0}_{1}'.format(p['KW_TELLU_ABSO'][0], molecule.upper()) # add water col if molecule == 'h2o': loc['WATERCOL'] = loc[molkey] # set source loc.set_source('WATERCOL', main_name) # ------------------------------------------------------------------ # Add transmission map to telluDB # ------------------------------------------------------------------ if p['QC']: # copy tellu file to the telluDB folder spirouDB.PutTelluFile(p, outfile) # update the master tellu DB file with transmission map targs = [p, outfilename, loc['OBJNAME'], loc['AIRMASS'], loc['WATERCOL']] spirouDB.UpdateDatabaseTellMap(*targs) # ---------------------------------------------------------------------- # Optional Absorption maps # ---------------------------------------------------------------------- if p['TELLU_ABSO_MAPS']: # ------------------------------------------------------------------ # Generate the absorption map # ------------------------------------------------------------------ # get number of files nfiles = len(p['OUTPUTFILES']) # set up storage for the absorption abso = np.zeros([nfiles, np.product(loc['DATA'].shape)]) # loop around outputfiles and add them to abso for it, filename in enumerate(p['OUTPUTFILES']): # push data into array data_it, _, _, _ = spirouImage.ReadImage(p, filename) abso[it, :] = data_it.reshape(np.product(loc['DATA'].shape)) # set values less than low threshold to low threshold # set values higher than high threshold to 1 low, high = p['TELLU_ABSO_LOW_THRES'], p['TELLU_ABSO_HIGH_THRES'] abso[abso < low] = low abso[abso > high] = 1.0 # set values less than TELLU_CUT_BLAZE_NORM threshold to NaN abso[loc['NBLAZE'] < p['TELLU_CUT_BLAZE_NORM']] = np.nan # reshape data (back to E2DS) abso_e2ds = abso.reshape(nfiles, loc['YDIM'], loc['XDIM']) # get file name abso_map_file, tag2 = spirouConfig.Constants.TELLU_ABSO_MAP_FILE(p) # get raw file name raw_in_file = os.path.basename(p['FITSFILENAME']) # write the map to file hdict = spirouImage.CopyOriginalKeys(loc['DATAHDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['WAVEFILE']) # write to file p = spirouImage.WriteImage(p, abso_map_file, abso_e2ds, hdict) # ------------------------------------------------------------------ # Generate the median and normalized absorption maps # ------------------------------------------------------------------ # copy the absorption cube abso2 = np.array(abso) # log the absorption cube log_abso = np.log(abso) # get the threshold from p threshold = p['TELLU_ABSO_SIG_THRESH'] # calculate the abso_med abso_med = np.nanmedian(log_abso, axis=0) # sigma clip around the median for it in range(p['TELLU_ABSO_SIG_N_ITER']): # recalculate the abso_med abso_med = np.nanmedian(log_abso, axis=0) # loop around each file for jt in range(nfiles): # get this iterations row rowvalue = log_abso[jt, :] # get the mask of those values above threshold goodpix = (rowvalue > threshold) & (abso_med > threshold) # apply the mask of good pixels to work out ratio part1 = np.nansum(rowvalue[goodpix] * abso_med[goodpix]) part2 = np.nansum(abso_med[goodpix] ** 2) ratio = part1 / part2 # store normalised absol back on to log_abso log_abso[jt, :] = log_abso[jt, :] / ratio # unlog log_abso abso_out = np.exp(log_abso) # calculate the median of the log_abso abso_med_out = np.exp(np.nanmedian(log_abso, axis=0)) # reshape the median abso_map_n = abso_med_out.reshape(loc['DATA'].shape) # save the median absorption map to file abso_med_file, tag3 = spirouConfig.Constants.TELLU_ABSO_MEDIAN_FILE(p) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3) p = spirouImage.WriteImage(p, abso_med_file, abso_med_out, hdict) # save the normalized absorption map to file abso_map_file, tag4 = spirouConfig.Constants.TELLU_ABSO_NORM_MAP_FILE(p) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag4) p = spirouImage.WriteImage(p, abso_map_file, abso_map_n, hdict) # ------------------------------------------------------------------ # calculate dv statistic # ------------------------------------------------------------------ # get the order for dv calculation dvselect = p['TELLU_ABSO_DV_ORDER'] size = p['TELLU_ABSO_DV_SIZE'] threshold2 = p['TELLU_ABSO_DV_GOOD_THRES'] fitdeg = p['TELLU_ABSO_FIT_DEG'] ydim, xdim = loc['DATA'].shape # get the start and end points of this order start, end = xdim * dvselect + size, xdim * dvselect - size + xdim # get the median for selected order abso_med2 = np.exp(abso_med[start:end]) # get the dv pixels to extract dvpixels = np.arange(-np.floor(size / 2), np.ceil(size / 2), 1) # loop around files for it, filename in enumerate(p['OUTPUTFILES']): # storage for the extracted abso ratios for this file cc = np.zeros(size) # loop around a box of size="size" for jt, dv in dvpixels: # get the start and end position start = xdim * dvselect + size + dv end = xdim * dvselect + xdim - size + dv # get the log abso for this iteration rowvalue = np.exp(log_abso[it, start:end]) # find the good pixels goodpix = (rowvalue > threshold2) & (abso_med2 > threshold2) # get the ratio part1 = np.nansum(rowvalue[goodpix] * abso_med2[goodpix]) part2 = np.nansum(abso_med2[goodpix] ** 2) cc[jt] = part1 / part2 # fit the ratio across the points cfit = nanpolyfit(dvpixels, cc, fitdeg) # work out the dv pix dvpix = -0.5 * (cfit[1] / cfit[0]) # log stats wmsg = 'File: "{0}", dv={1}' WLOG(p, '', wmsg.format(filename, dvpix)) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return a copy of locally defined variables in the memory return dict(locals())
def main(night_name=None, files=None): """ cal_HC_E2DS.py main function, if night_name and files are None uses arguments from run time i.e.: cal_DARK_spirou.py [night_directory] [fitsfilename] :param night_name: string or None, the folder within data raw directory containing files (also reduced directory) i.e. /data/raw/20170710 would be "20170710" but /data/raw/AT5/20180409 would be "AT5/20180409" :param files: string, list or None, the list of files to use for arg_file_names and fitsfilename (if None assumes arg_file_names was set from run time) :return ll: dictionary, containing all the local variables defined in main """ # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) # get parameters from configuration files and run time arguments p = spirouStartup.LoadArguments(p, night_name, files, mainfitsdir='reduced') # setup files and get fiber p = spirouStartup.InitialFileSetup(p, calibdb=True) # set the fiber type p['FIB_TYP'] = [p['FIBER']] p.set_source('FIB_TYP', __NAME__ + '/main()') # ---------------------------------------------------------------------- # Read image file # ---------------------------------------------------------------------- # read and combine all files p, hcdata, hchdr = spirouImage.ReadImageAndCombine(p, 'add') # add data and hdr to loc loc = ParamDict() loc['HCDATA'], loc['HCHDR'] = hcdata, hchdr # set the source sources = ['HCDATA', 'HCHDR'] loc.set_sources(sources, 'spirouImage.ReadImageAndCombine()') # ---------------------------------------------------------------------- # Get basic parameters # ---------------------------------------------------------------------- # get sig det value p = spirouImage.GetSigdet(p, loc['HCHDR'], name='sigdet') # get exposure time p = spirouImage.GetExpTime(p, loc['HCHDR'], name='exptime') # get gain p = spirouImage.GetGain(p, loc['HCHDR'], name='gain') # get acquisition time p = spirouImage.GetAcqTime(p, loc['HCHDR'], name='ACQTIME', kind='julian') bjdref = p['ACQTIME'] # set sigdet and conad keywords (sigdet is changed later) p['KW_CCD_SIGDET'][1] = p['SIGDET'] p['KW_CCD_CONAD'][1] = p['GAIN'] # get lamp parameters p = spirouTHORCA.GetLampParams(p, loc['HCHDR']) # get number of orders # we always get fibre A number because AB is doubled in constants file loc['NBO'] = p['QC_LOC_NBO_FPALL']['A'] loc.set_source('NBO', __NAME__ + '.main()') # get number of pixels in x from hcdata size loc['NBPIX'] = loc['HCDATA'].shape[1] loc.set_source('NBPIX', __NAME__ + '.main()') # ---------------------------------------------------------------------- # Read blaze # ---------------------------------------------------------------------- # get tilts loc['BLAZE'] = spirouImage.ReadBlazeFile(p, hchdr) loc.set_source('BLAZE', __NAME__ + '/main() + /spirouImage.ReadBlazeFile') # ---------------------------------------------------------------------- # Read wave solution # ---------------------------------------------------------------------- # wavelength file; we will use the polynomial terms in its header, # NOT the pixel values that would need to be interpolated # getting header info with wavelength polynomials # set source of wave file wsource = __NAME__ + '/main() + /spirouImage.GetWaveSolution' # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # get wave image wout = spirouImage.GetWaveSolution(p, hdr=hchdr, return_wavemap=True, return_filename=True, fiber=wave_fiber) loc['WAVEPARAMS'], loc['WAVE_INIT'], loc['WAVEFILE'], loc['WSOURCE'] = wout loc.set_sources(['WAVE_INIT', 'WAVEFILE', 'WAVEPARAMS', 'WSOURCE'], wsource) # ---------------------------------------------------------------------- # Check that wave parameters are consistent with "ic_ll_degr_fit" # ---------------------------------------------------------------------- loc = spirouImage.CheckWaveSolConsistency(p, loc) # ---------------------------------------------------------------------- # Read UNe solution # ---------------------------------------------------------------------- wave_u_ne, amp_u_ne = spirouImage.ReadLineList(p) loc['LL_LINE'], loc['AMPL_LINE'] = wave_u_ne, amp_u_ne source = __NAME__ + '.main() + spirouImage.ReadLineList()' loc.set_sources(['ll_line', 'ampl_line'], source) # ---------------------------------------------------------------------- # Generate wave map from wave solution # ---------------------------------------------------------------------- loc = spirouWAVE.generate_wave_map(p, loc) # ---------------------------------------------------------------------- # Find Gaussian Peaks in HC spectrum # ---------------------------------------------------------------------- loc = spirouWAVE.find_hc_gauss_peaks(p, loc) # ---------------------------------------------------------------------- # Start plotting session # ---------------------------------------------------------------------- if p['DRS_PLOT'] > 0: # start interactive plot sPlt.start_interactive_session(p) # ---------------------------------------------------------------------- # Fit Gaussian peaks (in triplets) to # ---------------------------------------------------------------------- loc = spirouWAVE.fit_gaussian_triplets(p, loc) # ---------------------------------------------------------------------- # Generate Resolution map and line profiles # ---------------------------------------------------------------------- # log progress wmsg = 'Generating resolution map and ' # generate resolution map loc = spirouWAVE.generate_resolution_map(p, loc) # map line profile map if p['DRS_PLOT'] > 0: sPlt.wave_ea_plot_line_profiles(p, loc) # ---------------------------------------------------------------------- # End plotting session # ---------------------------------------------------------------------- # end interactive session if p['DRS_PLOT'] > 0: sPlt.end_interactive_session(p) # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # quality control on sigma clip (sig1 > qc_hc_wave_sigma_max if loc['SIG1'] > p['QC_HC_WAVE_SIGMA_MAX']: fmsg = 'Sigma too high ({0:.5f} > {1:.5f})' fail_msg.append(fmsg.format(loc['SIG1'], p['QC_HC_WAVE_SIGMA_MAX'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(loc['SIG1']) qc_names.append('SIG1') qc_logic.append('SIG1 > {0:.2f}'.format(p['QC_HC_WAVE_SIGMA_MAX'])) # ---------------------------------------------------------------------- # check the difference between consecutive orders is always positive # get the differences wave_diff = loc['WAVE_MAP2'][1:]-loc['WAVE_MAP2'][:-1] if np.min(wave_diff) < 0: fmsg = 'Negative wavelength difference between orders' fail_msg.append(fmsg) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.min(wave_diff)) qc_names.append('MIN WAVE DIFF') qc_logic.append('MIN WAVE DIFF < 0') # ---------------------------------------------------------------------- # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ---------------------------------------------------------------------- # log the global stats # ---------------------------------------------------------------------- # calculate catalog-fit residuals in km/s res_hc =[] sumres_hc = 0.0 sumres2_hc = 0.0 for order in range(loc['NBO']): # get HC line wavelengths for the order order_mask = loc['ORD_T'] == order hc_x_ord = loc['XGAU_T'][order_mask] hc_ll_ord = np.polyval(loc['POLY_WAVE_SOL'][order][::-1],hc_x_ord) hc_ll_cat = loc['WAVE_CATALOG'][order_mask] hc_ll_diff = hc_ll_ord - hc_ll_cat res_hc.append(hc_ll_diff*speed_of_light/hc_ll_cat) sumres_hc += np.nansum(res_hc[order]) sumres2_hc += np.nansum(res_hc[order] ** 2) total_lines_hc = len(np.concatenate(res_hc)) final_mean_hc = sumres_hc/total_lines_hc final_var_hc = (sumres2_hc/total_lines_hc) - (final_mean_hc ** 2) wmsg1 = 'On fiber {0} HC fit line statistic:'.format(p['FIBER']) wargs2 = [final_mean_hc * 1000.0, np.sqrt(final_var_hc) * 1000.0, total_lines_hc, 1000.0 * np.sqrt(final_var_hc / total_lines_hc)] wmsg2 = ('\tmean={0:.3f}[m/s] rms={1:.1f} {2} HC lines (error on mean ' 'value:{3:.4f}[m/s])'.format(*wargs2)) WLOG(p, 'info', [wmsg1, wmsg2]) # ---------------------------------------------------------------------- # Save wave map to file # ---------------------------------------------------------------------- # get base input filenames bfilenames = [] for raw_file in p['ARG_FILE_NAMES']: bfilenames.append(os.path.basename(raw_file)) # get wave filename wavefits, tag1 = spirouConfig.Constants.WAVE_FILE_EA(p) wavefitsname = os.path.basename(wavefits) # log progress WLOG(p, '', 'Saving wave map to {0}'.format(wavefitsname)) # log progress wargs = [p['FIBER'], wavefitsname] wmsg = 'Write wavelength solution for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # write solution to fitsfilename header # copy original keys hdict = spirouImage.CopyOriginalKeys(loc['HCHDR']) # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=p['MAX_TIME_HUMAN']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=p['MAX_TIME_UNIX']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_CODE'], value=__NAME__) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['WAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file', values=p['ARG_FILE_NAMES']) # add number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=loc['POLY_WAVE_SOL'].shape[0]) # add degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=loc['POLY_WAVE_SOL'].shape[1]-1) # add wave solution hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['POLY_WAVE_SOL']) # write the wave "spectrum" p = spirouImage.WriteImage(p, wavefits, loc['WAVE_MAP2'], hdict) # get filename for E2DS calibDB copy of FITSFILENAME e2dscopy_filename, tag2 = spirouConfig.Constants.WAVE_E2DS_COPY(p) wargs = [p['FIBER'], os.path.split(e2dscopy_filename)[-1]] wmsg = 'Write reference E2DS spectra for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # make a copy of the E2DS file for the calibBD hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2) p = spirouImage.WriteImage(p, e2dscopy_filename, loc['HCDATA'], hdict) # ---------------------------------------------------------------------- # Save resolution and line profiles to file # ---------------------------------------------------------------------- raw_infile = os.path.basename(p['FITSFILENAME']) # get wave filename resfits, tag3 = spirouConfig.Constants.WAVE_RES_FILE_EA(p) resfitsname = os.path.basename(resfits) WLOG(p, '', 'Saving wave resmap to {0}'.format(resfitsname)) # make a copy of the E2DS file for the calibBD # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3) # get res data in correct format resdata, hdicts = spirouTHORCA.GenerateResFiles(p, loc, hdict) # save to file p = spirouImage.WriteImageMulti(p, resfits, resdata, hdicts=hdicts) # ---------------------------------------------------------------------- # Update calibDB # ---------------------------------------------------------------------- if p['QC']: # set the wave key keydb = 'WAVE_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, wavefits) # update the master calib DB file with new key spirouDB.UpdateCalibMaster(p, keydb, wavefitsname, loc['HCHDR']) # set the hcref key keydb = 'HCREF_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, e2dscopy_filename) # update the master calib DB file with new key e2dscopyfits = os.path.split(e2dscopy_filename)[-1] spirouDB.UpdateCalibMaster(p, keydb, e2dscopyfits, loc['HCHDR']) # ---------------------------------------------------------------------- # Update header of current files # ---------------------------------------------------------------------- # only copy over if QC passed if p['QC']: rdir = os.path.dirname(wavefits) # loop around hc files and update header with for rawhcfile in p['ARG_FILE_NAMES']: hcfile = os.path.join(rdir, rawhcfile) raw_infilepath1 = os.path.join(p['ARG_FILE_DIR'], hcfile) p = spirouImage.UpdateWaveSolutionHC(p, loc, raw_infilepath1) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return a copy of locally defined variables in the memory return dict(locals())
def main(night_name=None, fpfile=None, hcfiles=None): """ cal_WAVE_E2DS.py main function, if night_name and files are None uses arguments from run time i.e.: cal_DARK_spirou.py [night_directory] [fpfile] [hcfiles] :param night_name: string or None, the folder within data raw directory containing files (also reduced directory) i.e. /data/raw/20170710 would be "20170710" but /data/raw/AT5/20180409 would be "AT5/20180409" :param fpfile: string, or None, the FP file to use for arg_file_names and fitsfilename (if None assumes arg_file_names was set from run time) :param hcfiles: string, list or None, the list of HC files to use for arg_file_names and fitsfilename (if None assumes arg_file_names was set from run time) :return ll: dictionary, containing all the local variables defined in main """ # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # test files TC2 # night_name = 'AT5/AT5-12/2018-05-29_17-41-44/' # fpfile = '2279844a_fp_fp_pp_e2dsff_AB.fits' # hcfiles = ['2279845c_hc_pp_e2dsff_AB.fits'] # test files TC3 # night_name = 'TC3/AT5/AT5-12/2018-07-24_16-17-57/' # fpfile = '2294108a_pp_e2dsff_AB.fits' # hcfiles = ['2294115c_pp_e2dsff_AB.fits'] # night_name = 'TC3/AT5/AT5-12/2018-07-25_16-49-50/' # fpfile = '2294223a_pp_e2dsff_AB.fits' # hcfiles = ['2294230c_pp_e2dsff_AB.fits'] # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) if hcfiles is None or fpfile is None: names, types = ['fpfile', 'hcfiles'], [str, str] customargs = spirouStartup.GetCustomFromRuntime(p, [0, 1], types, names, last_multi=True) else: customargs = dict(hcfiles=hcfiles, fpfile=fpfile) # get parameters from configuration files and run time arguments p = spirouStartup.LoadArguments(p, night_name, customargs=customargs, mainfitsdir='reduced', mainfitsfile='hcfiles') # ---------------------------------------------------------------------- # Construct reference filename and get fiber type # ---------------------------------------------------------------------- p, fpfitsfilename = spirouStartup.SingleFileSetup(p, filename=p['FPFILE']) fiber1 = str(p['FIBER']) p, hcfilenames = spirouStartup.MultiFileSetup(p, files=p['HCFILES']) fiber2 = str(p['FIBER']) # set the hcfilename to the first hcfilenames hcfitsfilename = hcfilenames[0] # ---------------------------------------------------------------------- # Once we have checked the e2dsfile we can load calibDB # ---------------------------------------------------------------------- # as we have custom arguments need to load the calibration database p = spirouStartup.LoadCalibDB(p) # ---------------------------------------------------------------------- # Have to check that the fibers match # ---------------------------------------------------------------------- if fiber1 == fiber2: p['FIBER'] = fiber1 fsource = __NAME__ + '/main() & spirouStartup.GetFiberType()' p.set_source('FIBER', fsource) else: emsg = 'Fiber not matching for {0} and {1}, should be the same' eargs = [hcfitsfilename, fpfitsfilename] WLOG(p, 'error', emsg.format(*eargs)) # set the fiber type p['FIB_TYP'] = [p['FIBER']] p.set_source('FIB_TYP', __NAME__ + '/main()') # ---------------------------------------------------------------------- # Read FP and HC files # ---------------------------------------------------------------------- # read and combine all HC files except the first (fpfitsfilename) rargs = [p, 'add', hcfitsfilename, hcfilenames[1:]] p, hcdata, hchdr = spirouImage.ReadImageAndCombine(*rargs) # read first file (fpfitsfilename) fpdata, fphdr, _, _ = spirouImage.ReadImage(p, fpfitsfilename) # TODO: ------------------------------------------------------------ # TODO remove to test NaNs # TODO: ------------------------------------------------------------ # hcmask = np.isfinite(hcdata) # fpmask = np.isfinite(fpdata) # hcdata[~hcmask] = 0.0 # fpdata[~fpmask] = 0.0 # TODO: ------------------------------------------------------------ # add data and hdr to loc loc = ParamDict() loc['HCDATA'], loc['HCHDR'] = hcdata, hchdr loc['FPDATA'], loc['FPHDR'] = fpdata, fphdr # set the source sources = ['HCDATA', 'HCHDR'] loc.set_sources(sources, 'spirouImage.ReadImageAndCombine()') sources = ['FPDATA', 'FPHDR'] loc.set_sources(sources, 'spirouImage.ReadImage()') # ---------------------------------------------------------------------- # Get basic image properties for reference file # ---------------------------------------------------------------------- # get sig det value p = spirouImage.GetSigdet(p, hchdr, name='sigdet') # get exposure time p = spirouImage.GetExpTime(p, hchdr, name='exptime') # get gain p = spirouImage.GetGain(p, hchdr, name='gain') # get acquisition time p = spirouImage.GetAcqTime(p, hchdr, name='acqtime', kind='julian') bjdref = p['ACQTIME'] # set sigdet and conad keywords (sigdet is changed later) p['KW_CCD_SIGDET'][1] = p['SIGDET'] p['KW_CCD_CONAD'][1] = p['GAIN'] # get lamp parameters p = spirouTHORCA.GetLampParams(p, hchdr) # get number of orders # we always get fibre A number because AB is doubled in constants file loc['NBO'] = p['QC_LOC_NBO_FPALL']['A'] loc.set_source('NBO', __NAME__ + '.main()') # get number of pixels in x from hcdata size loc['NBPIX'] = loc['HCDATA'].shape[1] loc.set_source('NBPIX', __NAME__ + '.main()') # ---------------------------------------------------------------------- # Read blaze # ---------------------------------------------------------------------- # get tilts p, loc['BLAZE'] = spirouImage.ReadBlazeFile(p, hchdr) loc.set_source('BLAZE', __NAME__ + '/main() + /spirouImage.ReadBlazeFile') # make copy of blaze (as it's overwritten later) loc['BLAZE2'] = np.copy(loc['BLAZE']) # ---------------------------------------------------------------------- # Read wave solution # ---------------------------------------------------------------------- # wavelength file; we will use the polynomial terms in its header, # NOT the pixel values that would need to be interpolated # set source of wave file wsource = __NAME__ + '/main() + /spirouImage.GetWaveSolution' # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # get wave image wout = spirouImage.GetWaveSolution(p, hdr=hchdr, return_wavemap=True, return_filename=True, fiber=wave_fiber) loc['WAVEPARAMS'], loc['WAVE_INIT'], loc['WAVEFILE'], loc['WSOURCE'] = wout loc.set_sources(['WAVE_INIT', 'WAVEFILE', 'WAVEPARAMS', 'WSOURCE'], wsource) poly_wave_sol = loc['WAVEPARAMS'] # ---------------------------------------------------------------------- # Check that wave parameters are consistent with "ic_ll_degr_fit" # ---------------------------------------------------------------------- loc = spirouImage.CheckWaveSolConsistency(p, loc) # ---------------------------------------------------------------------- # Read UNe solution # ---------------------------------------------------------------------- wave_u_ne, amp_u_ne = spirouImage.ReadLineList(p) loc['LL_LINE'], loc['AMPL_LINE'] = wave_u_ne, amp_u_ne source = __NAME__ + '.main() + spirouImage.ReadLineList()' loc.set_sources(['ll_line', 'ampl_line'], source) # ---------------------------------------------------------------------- # Generate wave map from wave solution # ---------------------------------------------------------------------- loc = spirouWAVE.generate_wave_map(p, loc) # ---------------------------------------------------------------------- # Find Gaussian Peaks in HC spectrum # ---------------------------------------------------------------------- loc = spirouWAVE.find_hc_gauss_peaks(p, loc) # ---------------------------------------------------------------------- # Start plotting session # ---------------------------------------------------------------------- if p['DRS_PLOT'] > 0: # start interactive plot sPlt.start_interactive_session(p) # ---------------------------------------------------------------------- # Fit Gaussian peaks (in triplets) to # ---------------------------------------------------------------------- loc = spirouWAVE.fit_gaussian_triplets(p, loc) # ---------------------------------------------------------------------- # Generate Resolution map and line profiles # ---------------------------------------------------------------------- # log progress wmsg = 'Generating resolution map and ' # generate resolution map loc = spirouWAVE.generate_resolution_map(p, loc) # map line profile map if p['DRS_PLOT'] > 0: sPlt.wave_ea_plot_line_profiles(p, loc) # ---------------------------------------------------------------------- # End plotting session # ---------------------------------------------------------------------- # end interactive session if p['DRS_PLOT'] > 0: sPlt.end_interactive_session(p) # ---------------------------------------------------------------------- # Set up all_lines storage # ---------------------------------------------------------------------- # initialise up all_lines storage all_lines_1 = [] # get parameters from p n_ord_start = p['IC_HC_N_ORD_START_2'] n_ord_final = p['IC_HC_N_ORD_FINAL_2'] pixel_shift_inter = p['PIXEL_SHIFT_INTER'] pixel_shift_slope = p['PIXEL_SHIFT_SLOPE'] # get values from loc xgau = np.array(loc['XGAU_T']) dv = np.array(loc['DV_T']) fit_per_order = np.array(loc['POLY_WAVE_SOL']) ew = np.array(loc['EW_T']) peak = np.array(loc['PEAK_T']) amp_catalog = np.array(loc['AMP_CATALOG']) wave_catalog = np.array(loc['WAVE_CATALOG']) ord_t = np.array(loc['ORD_T']) # loop through orders for iord in range(n_ord_start, n_ord_final): # keep relevant lines # -> right order # -> finite dv gg = (ord_t == iord) & (np.isfinite(dv)) nlines = np.nansum(gg) # put lines into ALL_LINES structure # reminder: # gparams[0] = output wavelengths # gparams[1] = output sigma(gauss fit width) # gparams[2] = output amplitude(gauss fit) # gparams[3] = difference in input / output wavelength # gparams[4] = input amplitudes # gparams[5] = output pixel positions # gparams[6] = output pixel sigma width (gauss fit width in pixels) # gparams[7] = output weights for the pixel position chebval = np.polynomial.chebyshev.chebval # dummy array for weights test = np.ones(np.shape(xgau[gg]), 'd') * 1e4 # get the final wavelength value for each peak in order output_wave_1 = np.polyval(fit_per_order[iord][::-1], xgau[gg]) # output_wave_1 = chebval(xgau[gg], fit_per_order[iord]) # convert the pixel equivalent width to wavelength units xgau_ew_ini = xgau[gg] - ew[gg] / 2 xgau_ew_fin = xgau[gg] + ew[gg] / 2 ew_ll_ini = np.polyval(fit_per_order[iord, :], xgau_ew_ini) ew_ll_fin = np.polyval(fit_per_order[iord, :], xgau_ew_fin) # ew_ll_ini = chebval(xgau_ew_ini, fit_per_order[iord]) # ew_ll_fin = chebval(xgau_ew_fin, fit_per_order[iord]) ew_ll = ew_ll_fin - ew_ll_ini # put all lines in the order into array gau_params = np.column_stack( (output_wave_1, ew_ll, peak[gg], wave_catalog[gg] - output_wave_1, amp_catalog[gg], xgau[gg], ew[gg], test)) # append the array for the order into a list all_lines_1.append(gau_params) # save dv in km/s and auxiliary order number # res_1 = np.concatenate((res_1,2.997e5*(input_wave - output_wave_1)/ # output_wave_1)) # ord_save = np.concatenate((ord_save, test*iord)) # add to loc loc['ALL_LINES_1'] = all_lines_1 loc['LL_PARAM_1'] = np.array(fit_per_order) loc['LL_OUT_1'] = np.array(loc['WAVE_MAP2']) loc.set_sources(['ALL_LINES_1', 'LL_PARAM_1'], __NAME__ + '/main()') # For compatibility w/already defined functions, I need to save # here all_lines_2 all_lines_2 = list(all_lines_1) loc['ALL_LINES_2'] = all_lines_2 # loc['LL_PARAM_2'] = np.fliplr(fit_per_order) # loc['LL_OUT_2'] = np.array(loc['WAVE_MAP2']) # loc.set_sources(['ALL_LINES_2', 'LL_PARAM_2'], __NAME__ + '/main()') # ------------------------------------------------------------------ # Littrow test # ------------------------------------------------------------------ start = p['IC_LITTROW_ORDER_INIT_1'] end = p['IC_LITTROW_ORDER_FINAL_1'] # calculate echelle orders o_orders = np.arange(start, end) echelle_order = p['IC_HC_T_ORDER_START'] - o_orders loc['ECHELLE_ORDERS'] = echelle_order loc.set_source('ECHELLE_ORDERS', __NAME__ + '/main()') # reset Littrow fit degree p['IC_LITTROW_FIT_DEG_1'] = 7 # Do Littrow check ckwargs = dict(ll=loc['LL_OUT_1'][start:end, :], iteration=1, log=True) loc = spirouTHORCA.CalcLittrowSolution(p, loc, **ckwargs) # Plot wave solution littrow check if p['DRS_PLOT'] > 0: # plot littrow x pixels against fitted wavelength solution sPlt.wave_littrow_check_plot(p, loc, iteration=1) # ------------------------------------------------------------------ # extrapolate Littrow solution # ------------------------------------------------------------------ ekwargs = dict(ll=loc['LL_OUT_1'], iteration=1) loc = spirouTHORCA.ExtrapolateLittrowSolution(p, loc, **ekwargs) # ------------------------------------------------------------------ # Plot littrow solution # ------------------------------------------------------------------ if p['DRS_PLOT'] > 0: # plot littrow x pixels against fitted wavelength solution sPlt.wave_littrow_extrap_plot(p, loc, iteration=1) # ------------------------------------------------------------------ # Incorporate FP into solution # ------------------------------------------------------------------ # Copy LL_OUT_1 and LL_PARAM_1 into new constants (for FP integration) loc['LITTROW_EXTRAP_SOL_1'] = np.array(loc['LL_OUT_1']) loc['LITTROW_EXTRAP_PARAM_1'] = np.array(loc['LL_PARAM_1']) # only use FP if switched on in constants file if p['IC_WAVE_USE_FP']: # ------------------------------------------------------------------ # Find FP lines # ------------------------------------------------------------------ # print message to screen wmsg = 'Identification of lines in reference file: {0}' WLOG(p, '', wmsg.format(fpfile)) # ------------------------------------------------------------------ # Get the FP solution # ------------------------------------------------------------------ loc = spirouTHORCA.FPWaveSolutionNew(p, loc) # ------------------------------------------------------------------ # FP solution plots # ------------------------------------------------------------------ if p['DRS_PLOT'] > 0: # Plot the FP extracted spectrum against wavelength solution sPlt.wave_plot_final_fp_order(p, loc, iteration=1) # Plot the measured FP cavity width offset against line number sPlt.wave_local_width_offset_plot(p, loc) # Plot the FP line wavelength residuals sPlt.wave_fp_wavelength_residuals(p, loc) # ------------------------------------------------------------------ # Create new wavelength solution # ------------------------------------------------------------------ # TODO: Melissa fault - fix later p['IC_HC_N_ORD_START_2'] = min(p['IC_HC_N_ORD_START_2'], p['IC_FP_N_ORD_START']) p['IC_HC_N_ORD_FINAL_2'] = max(p['IC_HC_N_ORD_FINAL_2'], p['IC_FP_N_ORD_FINAL']) start = p['IC_HC_N_ORD_START_2'] end = p['IC_HC_N_ORD_FINAL_2'] # recalculate echelle orders for Fit1DSolution o_orders = np.arange(start, end) echelle_order = p['IC_HC_T_ORDER_START'] - o_orders loc['ECHELLE_ORDERS'] = echelle_order loc.set_source('ECHELLE_ORDERS', __NAME__ + '/main()') # select the orders to fit lls = loc['LITTROW_EXTRAP_SOL_1'][start:end] loc = spirouTHORCA.Fit1DSolution(p, loc, lls, iteration=2) # from here, LL_OUT_2 wil be 0-47 # ------------------------------------------------------------------ # Repeat Littrow test # ------------------------------------------------------------------ start = p['IC_LITTROW_ORDER_INIT_2'] end = p['IC_LITTROW_ORDER_FINAL_2'] # recalculate echelle orders for Littrow check o_orders = np.arange(start, end) echelle_order = p['IC_HC_T_ORDER_START'] - o_orders loc['ECHELLE_ORDERS'] = echelle_order loc.set_source('ECHELLE_ORDERS', __NAME__ + '/main()') # Do Littrow check ckwargs = dict(ll=loc['LL_OUT_2'][start:end, :], iteration=2, log=True) loc = spirouTHORCA.CalcLittrowSolution(p, loc, **ckwargs) # Plot wave solution littrow check if p['DRS_PLOT'] > 0: # plot littrow x pixels against fitted wavelength solution sPlt.wave_littrow_check_plot(p, loc, iteration=2) # ------------------------------------------------------------------ # extrapolate Littrow solution # ------------------------------------------------------------------ ekwargs = dict(ll=loc['LL_OUT_2'], iteration=2) loc = spirouTHORCA.ExtrapolateLittrowSolution(p, loc, **ekwargs) # ------------------------------------------------------------------ # Plot littrow solution # ------------------------------------------------------------------ if p['DRS_PLOT'] > 0: # plot littrow x pixels against fitted wavelength solution sPlt.wave_littrow_extrap_plot(p, loc, iteration=2) # ------------------------------------------------------------------ # Join 0-47 and 47-49 solutions # ------------------------------------------------------------------ loc = spirouTHORCA.JoinOrders(p, loc) # ------------------------------------------------------------------ # Plot single order, wavelength-calibrated, with found lines # ------------------------------------------------------------------ if p['DRS_PLOT'] > 0: sPlt.wave_ea_plot_single_order(p, loc) # ---------------------------------------------------------------------- # Do correlation on FP spectra # ---------------------------------------------------------------------- # ------------------------------------------------------------------ # Compute photon noise uncertainty for FP # ------------------------------------------------------------------ # set up the arguments for DeltaVrms2D dargs = [loc['FPDATA'], loc['LL_FINAL']] dkwargs = dict(sigdet=p['IC_DRIFT_NOISE'], size=p['IC_DRIFT_BOXSIZE'], threshold=p['IC_DRIFT_MAXFLUX']) # run DeltaVrms2D dvrmsref, wmeanref = spirouRV.DeltaVrms2D(*dargs, **dkwargs) # save to loc loc['DVRMSREF'], loc['WMEANREF'] = dvrmsref, wmeanref loc.set_sources(['dvrmsref', 'wmeanref'], __NAME__ + '/main()()') # log the estimated RV uncertainty wmsg = 'On fiber {0} estimated RV uncertainty on spectrum is {1:.3f} m/s' WLOG(p, 'info', wmsg.format(p['FIBER'], wmeanref)) # Use CCF Mask function with drift constants p['CCF_MASK'] = p['DRIFT_CCF_MASK'] p['TARGET_RV'] = p['DRIFT_TARGET_RV'] p['CCF_WIDTH'] = p['DRIFT_CCF_WIDTH'] p['CCF_STEP'] = p['DRIFT_CCF_STEP'] p['RVMIN'] = p['TARGET_RV'] - p['CCF_WIDTH'] p['RVMAX'] = p['TARGET_RV'] + p['CCF_WIDTH'] + p['CCF_STEP'] # get the CCF mask from file (check location of mask) loc = spirouRV.GetCCFMask(p, loc) # TODO Check why Blaze makes bugs in correlbin loc['BLAZE'] = np.ones((loc['NBO'], loc['NBPIX'])) # set sources # loc.set_sources(['flat', 'blaze'], __NAME__ + '/main()') loc.set_source('blaze', __NAME__ + '/main()') # ---------------------------------------------------------------------- # Do correlation on FP # ---------------------------------------------------------------------- # calculate and fit the CCF loc['E2DSFF'] = np.array(loc['FPDATA']) loc.set_source('E2DSFF', __NAME__ + '/main()') p['CCF_FIT_TYPE'] = 1 loc['BERV'] = 0.0 loc['BERV_MAX'] = 0.0 loc['BJD'] = 0.0 # run the RV coravelation function with these parameters loc['WAVE_LL'] = np.array(loc['LL_FINAL']) loc['PARAM_LL'] = np.array(loc['LL_PARAM_FINAL']) loc = spirouRV.Coravelation(p, loc) # ---------------------------------------------------------------------- # Update the Correlation stats with values using fiber C (FP) drift # ---------------------------------------------------------------------- # get the maximum number of orders to use nbmax = p['CCF_NUM_ORDERS_MAX'] # get the average ccf loc['AVERAGE_CCF'] = np.nansum(loc['CCF'][:nbmax], axis=0) # normalize the average ccf normalized_ccf = loc['AVERAGE_CCF'] / np.nanmax(loc['AVERAGE_CCF']) # get the fit for the normalized average ccf ccf_res, ccf_fit = spirouRV.FitCCF(p, loc['RV_CCF'], normalized_ccf, fit_type=1) loc['CCF_RES'] = ccf_res loc['CCF_FIT'] = ccf_fit # get the max cpp loc['MAXCPP'] = np.nansum(loc['CCF_MAX']) / np.nansum( loc['PIX_PASSED_ALL']) # get the RV value from the normalised average ccf fit center location loc['RV'] = float(ccf_res[1]) # get the contrast (ccf fit amplitude) loc['CONTRAST'] = np.abs(100 * ccf_res[0]) # get the FWHM value loc['FWHM'] = ccf_res[2] * spirouCore.spirouMath.fwhm() # set the source keys = [ 'AVERAGE_CCF', 'MAXCPP', 'RV', 'CONTRAST', 'FWHM', 'CCF_RES', 'CCF_FIT' ] loc.set_sources(keys, __NAME__ + '/main()') # ---------------------------------------------------------------------- # log the stats wmsg = ('FP Correlation: C={0:.1f}[%] DRIFT={1:.5f}[km/s] ' 'FWHM={2:.4f}[km/s] maxcpp={3:.1f}') wargs = [loc['CONTRAST'], float(ccf_res[1]), loc['FWHM'], loc['MAXCPP']] WLOG(p, 'info', wmsg.format(*wargs)) # ---------------------------------------------------------------------- # rv ccf plot # ---------------------------------------------------------------------- if p['DRS_PLOT'] > 0: # Plot rv vs ccf (and rv vs ccf_fit) p['OBJNAME'] = 'FP' sPlt.ccf_rv_ccf_plot(p, loc['RV_CCF'], normalized_ccf, ccf_fit) # TODO : Add QC of the FP CCF # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- # get parameters ffrom p p['QC_RMS_LITTROW_MAX'] = p['QC_HC_RMS_LITTROW_MAX'] p['QC_DEV_LITTROW_MAX'] = p['QC_HC_DEV_LITTROW_MAX'] # set passed variable and fail message list passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # ---------------------------------------------------------------------- # quality control on sigma clip (sig1 > qc_hc_wave_sigma_max if loc['SIG1'] > p['QC_HC_WAVE_SIGMA_MAX']: fmsg = 'Sigma too high ({0:.5f} > {1:.5f})' fail_msg.append(fmsg.format(loc['SIG1'], p['QC_HC_WAVE_SIGMA_MAX'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(loc['SIG1']) qc_names.append('SIG1') qc_logic.append('SIG1 > {0:.2f}'.format(p['QC_HC_WAVE_SIGMA_MAX'])) # ---------------------------------------------------------------------- # check the difference between consecutive orders is always positive # get the differences wave_diff = loc['LL_FINAL'][1:] - loc['LL_FINAL'][:-1] if np.min(wave_diff) < 0: fmsg = 'Negative wavelength difference between orders' fail_msg.append(fmsg) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.min(wave_diff)) qc_names.append('MIN WAVE DIFF') qc_logic.append('MIN WAVE DIFF < 0') # ---------------------------------------------------------------------- # check for infinites and NaNs in mean residuals from fit if ~np.isfinite(loc['X_MEAN_2']): # add failed message to the fail message list fmsg = 'NaN or Inf in X_MEAN_2' fail_msg.append(fmsg) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(loc['X_MEAN_2']) qc_names.append('X_MEAN_2') qc_logic.append('X_MEAN_2 not finite') # ---------------------------------------------------------------------- # iterate through Littrow test cut values lit_it = 2 # checks every other value # TODO: This QC check (or set of QC checks needs re-writing it is # TODO: nearly impossible to understand for x_it in range(1, len(loc['X_CUT_POINTS_' + str(lit_it)]), 2): # get x cut point x_cut_point = loc['X_CUT_POINTS_' + str(lit_it)][x_it] # get the sigma for this cut point sig_littrow = loc['LITTROW_SIG_' + str(lit_it)][x_it] # get the abs min and max dev littrow values min_littrow = abs(loc['LITTROW_MINDEV_' + str(lit_it)][x_it]) max_littrow = abs(loc['LITTROW_MAXDEV_' + str(lit_it)][x_it]) # get the corresponding order min_littrow_ord = loc['LITTROW_MINDEVORD_' + str(lit_it)][x_it] max_littrow_ord = loc['LITTROW_MAXDEVORD_' + str(lit_it)][x_it] # check if sig littrow is above maximum rms_littrow_max = p['QC_RMS_LITTROW_MAX'] dev_littrow_max = p['QC_DEV_LITTROW_MAX'] if sig_littrow > rms_littrow_max: fmsg = ('Littrow test (x={0}) failed (sig littrow = ' '{1:.2f} > {2:.2f})') fargs = [x_cut_point, sig_littrow, rms_littrow_max] fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(sig_littrow) qc_names.append('sig_littrow') qc_logic.append('sig_littrow > {0:.2f}'.format(rms_littrow_max)) # ---------------------------------------------------------------------- # check if min/max littrow is out of bounds if np.max([max_littrow, min_littrow]) > dev_littrow_max: fmsg = ('Littrow test (x={0}) failed (min|max dev = ' '{1:.2f}|{2:.2f} > {3:.2f} for order {4}|{5})') fargs = [ x_cut_point, min_littrow, max_littrow, dev_littrow_max, min_littrow_ord, max_littrow_ord ] fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) # TODO: Should this be the QC header values? # TODO: it does not change the outcome of QC (i.e. passed=False) # TODO: So what is the point? # if sig was out of bounds, recalculate if sig_littrow > rms_littrow_max: # conditions check1 = min_littrow > dev_littrow_max check2 = max_littrow > dev_littrow_max # get the residuals respix = loc['LITTROW_YY_' + str(lit_it)][x_it] # check if both are out of bounds if check1 and check2: # remove respective orders worst_order = (min_littrow_ord, max_littrow_ord) respix_2 = np.delete(respix, worst_order) redo_sigma = True # check if min is out of bounds elif check1: # remove respective order worst_order = min_littrow_ord respix_2 = np.delete(respix, worst_order) redo_sigma = True # check if max is out of bounds elif check2: # remove respective order worst_order = max_littrow_ord respix_2 = np.delete(respix, max_littrow_ord) redo_sigma = True # else do not recalculate sigma else: redo_sigma, respix_2, worst_order = False, None, None wmsg = 'No outlying orders, sig littrow not recalculated' fail_msg.append(wmsg.format()) # if outlying order, recalculate stats if redo_sigma: mean = np.nansum(respix_2) / len(respix_2) mean2 = np.nansum(respix_2**2) / len(respix_2) rms = np.sqrt(mean2 - mean**2) if rms > rms_littrow_max: fmsg = ('Littrow test (x={0}) failed (sig littrow = ' '{1:.2f} > {2:.2f} removing order {3})') fargs = [ x_cut_point, rms, rms_littrow_max, worst_order ] fail_msg.append(fmsg.format(*fargs)) else: wargs = [ x_cut_point, rms, rms_littrow_max, worst_order ] wmsg = ('Littrow test (x={0}) passed (sig littrow = ' '{1:.2f} > {2:.2f} removing order {3})') fail_msg.append(wmsg.format(*wargs)) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.max([max_littrow, min_littrow])) qc_names.append('max or min littrow') qc_logic.append('max or min littrow > {0:.2f}' ''.format(dev_littrow_max)) # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ------------------------------------------------------------------ # archive result in e2ds spectra # ------------------------------------------------------------------ # get raw input file name(s) raw_infiles1 = [] for hcfile in p['HCFILES']: raw_infiles1.append(os.path.basename(hcfile)) raw_infile2 = os.path.basename(p['FPFILE']) # get wave filename wavefits, tag1 = spirouConfig.Constants.WAVE_FILE_EA_2(p) wavefitsname = os.path.split(wavefits)[-1] # log progress wargs = [p['FIBER'], wavefits] wmsg = 'Write wavelength solution for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # write solution to fitsfilename header # copy original keys hdict = spirouImage.CopyOriginalKeys(loc['HCHDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['WAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='fpfile', values=p['FPFILE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE2'], dim1name='hcfile', values=p['HCFILES']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=p['MAX_TIME_HUMAN']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=p['MAX_TIME_UNIX']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_CODE'], value=__NAME__) # add number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=loc['LL_PARAM_FINAL'].shape[0]) # add degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=loc['LL_PARAM_FINAL'].shape[1] - 1) # add wave solution hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['LL_PARAM_FINAL']) # add FP CCF drift # target RV and width hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_TARG_RV'], value=p['TARGET_RV']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_WIDTH'], value=p['CCF_WIDTH']) # the rv step # rvstep = np.abs(loc['RV_CCF'][0] - loc['RV_CCF'][1]) # hdict = spirouImage.AddKey(p, hdict, p['KW_CCF_CDELT'], value=rvstep) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_STEP'], value=p['CCF_STEP']) # add ccf stats hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_DRIFT'], value=loc['CCF_RES'][1]) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_FWHM'], value=loc['FWHM']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_CONTRAST'], value=loc['CONTRAST']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_MAXCPP'], value=loc['MAXCPP']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_MASK'], value=p['CCF_MASK']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_LINES'], value=np.nansum(loc['TOT_LINE'])) # write the wave "spectrum" hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) p = spirouImage.WriteImage(p, wavefits, loc['LL_FINAL'], hdict) # get filename for E2DS calibDB copy of FITSFILENAME e2dscopy_filename = spirouConfig.Constants.WAVE_E2DS_COPY(p)[0] wargs = [p['FIBER'], os.path.split(e2dscopy_filename)[-1]] wmsg = 'Write reference E2DS spectra for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # make a copy of the E2DS file for the calibBD p = spirouImage.WriteImage(p, e2dscopy_filename, loc['HCDATA'], hdict) # only copy over if QC passed if p['QC']: # loop around hc files and update header with for hcfile in p['HCFILES']: raw_infilepath1 = os.path.join(p['ARG_FILE_DIR'], hcfile) p = spirouImage.UpdateWaveSolution(p, loc, raw_infilepath1) # update fp file raw_infilepath2 = os.path.join(p['ARG_FILE_DIR'], raw_infile2) p = spirouImage.UpdateWaveSolution(p, loc, raw_infilepath2) # ------------------------------------------------------------------ # Save to result table # ------------------------------------------------------------------ # calculate stats for table final_mean = 1000 * loc['X_MEAN_2'] final_var = 1000 * loc['X_VAR_2'] num_lines = int(np.nansum(loc['X_ITER_2'][:, 2])) # loc['X_ITER_2'] err = 1000 * np.sqrt(loc['X_VAR_2'] / num_lines) sig_littrow = 1000 * np.array(loc['LITTROW_SIG_' + str(lit_it)]) # construct filename wavetbl = spirouConfig.Constants.WAVE_TBL_FILE_EA(p) wavetblname = os.path.basename(wavetbl) # construct and write table columnnames = [ 'night_name', 'file_name', 'fiber', 'mean', 'rms', 'N_lines', 'err', 'rms_L500', 'rms_L1000', 'rms_L1500', 'rms_L2000', 'rms_L2500', 'rms_L3000', 'rms_L3500' ] columnformats = [ '{:20s}', '{:30s}', '{:3s}', '{:7.4f}', '{:6.2f}', '{:3d}', '{:6.3f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}' ] columnvalues = [[p['ARG_NIGHT_NAME']], [p['ARG_FILE_NAMES'][0]], [p['FIBER']], [final_mean], [final_var], [num_lines], [err], [sig_littrow[0]], [sig_littrow[1]], [sig_littrow[2]], [sig_littrow[3]], [sig_littrow[4]], [sig_littrow[5]], [sig_littrow[6]]] # make table table = spirouImage.MakeTable(p, columns=columnnames, values=columnvalues, formats=columnformats) # merge table wmsg = 'Global result summary saved in {0}' WLOG(p, '', wmsg.format(wavetblname)) spirouImage.MergeTable(p, table, wavetbl, fmt='ascii.rst') # ---------------------------------------------------------------------- # Save resolution and line profiles to file # ---------------------------------------------------------------------- raw_infile = os.path.basename(p['FITSFILENAME']) # get wave filename resfits, tag3 = spirouConfig.Constants.WAVE_RES_FILE_EA(p) resfitsname = os.path.basename(resfits) WLOG(p, '', 'Saving wave resmap to {0}'.format(resfitsname)) # make a copy of the E2DS file for the calibBD # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3) # get res data in correct format resdata, hdicts = spirouTHORCA.GenerateResFiles(p, loc, hdict) # save to file p = spirouImage.WriteImageMulti(p, resfits, resdata, hdicts=hdicts) # ------------------------------------------------------------------ # Save line list table file # ------------------------------------------------------------------ # construct filename # TODO proper column values wavelltbl = spirouConfig.Constants.WAVE_LINE_FILE_EA(p) wavelltblname = os.path.split(wavelltbl)[-1] # construct and write table columnnames = ['order', 'll', 'dv', 'w', 'xi', 'xo', 'dvdx'] columnformats = [ '{:.0f}', '{:12.4f}', '{:13.5f}', '{:12.4f}', '{:12.4f}', '{:12.4f}', '{:8.4f}' ] columnvalues = [] # construct column values (flatten over orders) for it in range(len(loc['X_DETAILS_2'])): for jt in range(len(loc['X_DETAILS_2'][it][0])): row = [ float(it), loc['X_DETAILS_2'][it][0][jt], loc['LL_DETAILS_2'][it][0][jt], loc['X_DETAILS_2'][it][3][jt], loc['X_DETAILS_2'][it][1][jt], loc['X_DETAILS_2'][it][2][jt], loc['SCALE_2'][it][jt] ] columnvalues.append(row) # log saving wmsg = 'List of lines used saved in {0}' WLOG(p, '', wmsg.format(wavelltblname)) # make table columnvalues = np.array(columnvalues).T table = spirouImage.MakeTable(p, columns=columnnames, values=columnvalues, formats=columnformats) # write table spirouImage.WriteTable(p, table, wavelltbl, fmt='ascii.rst') # ------------------------------------------------------------------ # Move to calibDB and update calibDB # ------------------------------------------------------------------ if p['QC']: # set the wave key keydb = 'WAVE_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, wavefits) # update the master calib DB file with new key spirouDB.UpdateCalibMaster(p, keydb, wavefitsname, loc['HCHDR']) # set the hcref key keydb = 'HCREF_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, e2dscopy_filename) # update the master calib DB file with new key e2dscopyfits = os.path.split(e2dscopy_filename)[-1] spirouDB.UpdateCalibMaster(p, keydb, e2dscopyfits, loc['HCHDR']) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return p and loc return dict(locals())
def main(night_name=None): # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- main_name = __NAME__ + '.main()' # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) p = spirouStartup.LoadArguments(p, night_name, require_night_name=False) # ---------------------------------------------------------------------- # find all directories if night name was None (ARG_NIGHT_NAME = '') files, dirs = find_all_reduced_files(p) # ---------------------------------------------------------------------- # loop around each file # ---------------------------------------------------------------------- for it in range(len(files)): # get file name for this iteration basefilename = os.path.basename(files[it]) # Log process wmsg = 'Processing file {0} ({1}/{2})' wargs = [basefilename, it + 1, len(files)] WLOG(p, 'info', wmsg.format(*wargs)) # get this iteration values p['FITSFILENAME'] = files[it] p['ARG_NIGHT_NAME'] = dirs[it] p['REDUCED_DIR'] = os.path.join(p['DRS_DATA_REDUC'], dirs[it]) p['ARG_FILE_NAMES'] = [basefilename] p['DRS_TYPE'] = "REDUCED" # identify fiber if '_AB.fits' in os.path.basename(files[it]): p['FIBER'] = 'AB' elif '_AB.fits' in os.path.basename(files[it]): p['FIBER'] = 'A' elif '_AB.fits' in os.path.basename(files[it]): p['FIBER'] = 'B' elif '_C.fits' in os.path.basename(files[it]): p['FIBER'] = 'C' else: wmsg1 = 'Wrong fiber type found. This should happen. Skipping' wmsg2 = '\tFile = {0}'.format(files[it]) WLOG(p, 'warning', [wmsg1, wmsg2]) # skip continue # set sources source = ['FITSFILENAME', 'ARG_NIGHT_NAME', 'REDUCED_DIR', 'FIBER', 'ARG_FILE_NAMES'] p.set_sources(source, main_name) # define loc storage parameter dictionary loc = ParamDict() # --------------------------------------------------------------------- # Read image file # --------------------------------------------------------------------- # read the image data p, data, hdr = spirouImage.ReadImageAndCombine(p, framemath='add') # --------------------------------------------------------------------- # Load calibDB for this file # --------------------------------------------------------------------- # as we need a different calibDB for each file p = spirouStartup.LoadCalibDB(p, header=hdr) # ------------------------------------------------------------------ # Read wavelength solution # ------------------------------------------------------------------ # force reading from calibDB p['CALIB_DB_FORCE_WAVESOL'] = True p.set_source('CALIB_DB_FORCE_WAVESOL', main_name) # set source of wave file wsource = __NAME__ + '/main() + /spirouImage.GetWaveSolution' # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # get wave image wout = spirouImage.GetWaveSolution(p, hdr=hdr, return_wavemap=True, return_filename=True, return_header=True, fiber=wave_fiber) loc['WAVEPARAMS'], loc['WAVE'], loc['WAVEFILE'] = wout[:3] loc['WAVEHDR'], loc['WSOURCE'] = wout[3:] source_names = ['WAVE', 'WAVEFILE', 'WAVEPARAMS', 'WAVEHDR', 'WSOURCE'] loc.set_sources(source_names, wsource) # get dates loc['WAVE_ACQTIMES'] = spirouDB.GetTimes(p, loc['WAVEHDR']) loc.set_source('WAVE_ACQTIMES', __NAME__ + '.main()') # get the recipe that produced the wave solution if 'WAVECODE' in loc['WAVEHDR']: loc['WAVE_CODE'] = loc['WAVEHDR']['WAVECODE'] else: loc['WAVE_CODE'] = 'UNKNOWN' loc.set_source('WAVE_CODE', __NAME__ + '.main()') # ------------------------------------------------------------------ # Save file with new header # ------------------------------------------------------------------ # log that we are saving E2DS spectrum wmsg = 'Saving E2DS spectrum of Fiber {0} in {1}' WLOG(p, '', wmsg.format(p['FIBER'], basefilename)) # add keys from original header file hdict = spirouImage.CopyOriginalKeys(hdr) # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['WAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=loc['WAVE_ACQTIMES'][0]) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=loc['WAVE_ACQTIMES'][1]) # add wave solution number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=loc['WAVEPARAMS'].shape[0]) # add wave solution degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=loc['WAVEPARAMS'].shape[1] - 1) # add wave solution coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['WAVEPARAMS']) # write to file p = spirouImage.WriteImage(p, p['FITSFILENAME'], data, hdict) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p, outputs=None) # return a copy of locally defined variables in the memory return dict(locals())
def main(night_name=None, files=None): # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) p = spirouStartup.LoadArguments(p, night_name, files, mainfitsdir='reduced') p = spirouStartup.InitialFileSetup(p, calibdb=True) # set up function name main_name = __NAME__ + '.main()' # ---------------------------------------------------------------------- # load up first file # ---------------------------------------------------------------------- # construct loc parameter dictionary for storing data loc = ParamDict() # read first file and assign values rdata = spirouImage.ReadImage(p, p['FITSFILENAME']) loc['DATA'], loc['DATAHDR'] = rdata[:2] # Get output key loc['OUTPUT'] = loc['DATAHDR'][p['KW_OUTPUT'][0]] # set source source = main_name + '+ spirouImage.ReadParams()' loc.set_source('OUTPUT', source) # ---------------------------------------------------------------------- # Get database files # ---------------------------------------------------------------------- # get current telluric maps from telluDB files, output_codes = [], [] # filter by object name (only keep OBJNAME objects) and only keep # unique filenames use_files = [] for it in range(len(files)): # check that OUTPUT is correct cond1 = p['KW_OUTPUT'] in output_codes[it] # check that filename is not already used cond2 = files[it] not in use_files # append to file list if criteria correct if cond1 and cond2: use_files.append(files[it]) # log if we have no files if len(use_files) == 0: wmsg = 'No files found for OUTPUT ="{0}" skipping' WLOG(p, 'warning', wmsg.format(loc['KW_OUTPUT'])) # End Message wmsg = 'Recipe {0} has been successfully completed' WLOG(p, 'info', wmsg.format(p['PROGRAM'])) # return a copy of locally defined variables in the memory return dict(locals()) else: # log how many found wmsg = 'N={0} files found for OUTPUT="{1}"' WLOG(p, '', wmsg.format(len(use_files), loc['OUTPUT'])) # ---------------------------------------------------------------------- # Set up storage for cubes (NaN arrays) # ---------------------------------------------------------------------- # set up flat size dims = [loc['DATA'].shape[0], loc['DATA'].shape[1], len(use_files)] flatsize = np.product(dims) # create NaN filled storage big_cube = np.repeat([np.nan], flatsize).reshape(*dims) big_cube0 = np.repeat([np.nan], flatsize).reshape(*dims) # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # get master wave map loc['MASTERWAVEFILE'] = spirouDB.GetDatabaseMasterWave(p) # read master wave map mout = spirouImage.GetWaveSolution(p, filename=loc['MASTERWAVEFILE'], return_wavemap=True, quiet=True, fiber=wave_fiber) loc['MASTERWAVEPARAMS'], loc['MASTERWAVE'], loc['WSOURCE'] = mout keys = ['MASTERWAVEPARAMS', 'MASTERWAVE', 'MASTERWAVEFILE', 'WSOURCE'] loc.set_sources(keys, main_name) # ---------------------------------------------------------------------- # Loop through input files # ---------------------------------------------------------------------- base_filelist, berv_list = [], [] # loop through files for it, filename in enumerate(use_files): # get base filenmae basefilename = os.path.basename(filename) # append basename to file list base_filelist.append(basefilename) # ------------------------------------------------------------------ # Load the data for this file tdata, thdr, _, _ = spirouImage.ReadImage(p, filename) # ------------------------------------------------------------------ # log stats wmsg = 'Processing file {0} of {1} file={2}' wargs = [it + 1, len(use_files), basefilename] WLOG(p, '', wmsg.format(*wargs)) # ------------------------------------------------------------------ # add to cube storage big_cube0[:, :, it] = tdata # ---------------------------------------------------------------------- # Save cubes to file # ---------------------------------------------------------------------- # get raw file name raw_in_file = os.path.basename(p['FITSFILENAME']) # construct file names outfile = raw_in_file.replace('.fits', '_stack.fits') tag = loc['OUTPUT'] + '_STACK' # log big cube 1 wmsg1 = 'Saving bigcube to file {0}'.format(os.path.basename(outfile)) # hdict is first file keys hdict = spirouImage.CopyOriginalKeys(loc['DATAHDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_DATE'], value=p['DRS_DATE']) hdict = spirouImage.AddKey(p, hdict, p['KW_DATE_NOW'], value=p['DATE_NOW']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBLAZE'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['MASTERWAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) # add file list to header hdict = spirouImage.AddKey1DList(p, hdict, p['KW_OBJFILELIST'], values=base_filelist) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_OBJBERVLIST'], values=berv_list) # add wave solution coefficients hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['MASTERWAVEPARAMS']) # log big cube 0 wmsg = 'Saving bigcube0 to file {0}'.format(os.path.basename(outfile)) # save big cube 0 big_cube_s0 = np.swapaxes(big_cube0, 1, 2) p = spirouImage.WriteImageMulti(p, outfile, big_cube_s0, hdict) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return a copy of locally defined variables in the memory return dict(locals())
def main(night_name=None, fpfile=None, hcfiles=None): """ cal_wave_spirou.py main function, if night_name and files are None uses arguments from run time i.e.: cal_wave_spirou.py [night_directory] [fpfile] [hcfiles] :param night_name: string or None, the folder within data reduced directory containing files (also reduced directory) i.e. /data/reduced/20170710 would be "20170710" but /data/reduced/AT5/20180409 is "AT5/20180409" :param fpfile: string, or None, the FP file to use for arg_file_names and fitsfilename (if None assumes arg_file_names was set from run time) :param hcfiles: string, list or None, the list of HC files to use for arg_file_names and fitsfilename (if None assumes arg_file_names was set from run time) :return ll: dictionary, containing all the local variables defined in main """ # ---------------------------------------------------------------------- # Set up # ---------------------------------------------------------------------- # get parameters from config files/run time args/load paths + calibdb p = spirouStartup.Begin(recipe=__NAME__) if hcfiles is None or fpfile is None: names, types = ['fpfile', 'hcfiles'], [str, str] customargs = spirouStartup.GetCustomFromRuntime(p, [0, 1], types, names, last_multi=True) else: customargs = dict(hcfiles=hcfiles, fpfile=fpfile) # get parameters from configuration files and run time arguments p = spirouStartup.LoadArguments(p, night_name, customargs=customargs, mainfitsdir='reduced', mainfitsfile='hcfiles') # ---------------------------------------------------------------------- # Construct reference filename and get fiber type # ---------------------------------------------------------------------- p, fpfitsfilename = spirouStartup.SingleFileSetup(p, filename=p['FPFILE']) fiber1 = str(p['FIBER']) p, hcfilenames = spirouStartup.MultiFileSetup(p, files=p['HCFILES']) fiber2 = str(p['FIBER']) # set the hcfilename to the first hcfilenames hcfitsfilename = hcfilenames[0] # ---------------------------------------------------------------------- # Once we have checked the e2dsfile we can load calibDB # ---------------------------------------------------------------------- # as we have custom arguments need to load the calibration database p = spirouStartup.LoadCalibDB(p) # ---------------------------------------------------------------------- # Have to check that the fibers match # ---------------------------------------------------------------------- if fiber1 == fiber2: p['FIBER'] = fiber1 fsource = __NAME__ + '/main() & spirouStartup.GetFiberType()' p.set_source('FIBER', fsource) else: emsg = 'Fiber not matching for {0} and {1}, should be the same' eargs = [hcfitsfilename, fpfitsfilename] WLOG(p, 'error', emsg.format(*eargs)) # set the fiber type p['FIB_TYP'] = [p['FIBER']] p.set_source('FIB_TYP', __NAME__ + '/main()') # ---------------------------------------------------------------------- # Read FP and HC files # ---------------------------------------------------------------------- # read and combine all HC files except the first (fpfitsfilename) rargs = [p, 'add', hcfitsfilename, hcfilenames[1:]] p, hcdata, hchdr = spirouImage.ReadImageAndCombine(*rargs) # read first file (fpfitsfilename) fpdata, fphdr, _, _ = spirouImage.ReadImage(p, fpfitsfilename) # add data and hdr to loc loc = ParamDict() loc['HCDATA'], loc['HCHDR'], loc['HCCDR'] = hcdata, hchdr, hchdr.comments loc['FPDATA'], loc['FPHDR'], loc['FPCDR'] = fpdata, fphdr, fphdr.comments # set the source sources = ['HCDATA', 'HCHDR', 'HCCDR'] loc.set_sources(sources, 'spirouImage.ReadImageAndCombine()') sources = ['FPDATA', 'FPHDR', 'FPCDR'] loc.set_sources(sources, 'spirouImage.ReadImage()') # ---------------------------------------------------------------------- # Get basic image properties for reference file # ---------------------------------------------------------------------- # get sig det value p = spirouImage.GetSigdet(p, hchdr, name='sigdet') # get exposure time p = spirouImage.GetExpTime(p, hchdr, name='exptime') # get gain p = spirouImage.GetGain(p, hchdr, name='gain') # get acquisition time p = spirouImage.GetAcqTime(p, hchdr, name='acqtime', kind='julian') bjdref = p['ACQTIME'] # set sigdet and conad keywords (sigdet is changed later) p['KW_CCD_SIGDET'][1] = p['SIGDET'] p['KW_CCD_CONAD'][1] = p['GAIN'] # get lamp parameters p = spirouWAVE2.get_lamp_parameters(p, hchdr) # get number of orders # we always get fibre A number because AB is doubled in constants file loc['NBO'] = p['QC_LOC_NBO_FPALL']['A'] loc.set_source('NBO', __NAME__ + '.main()') # get number of pixels in x from hcdata size loc['NBPIX'] = loc['HCDATA'].shape[1] loc.set_source('NBPIX', __NAME__ + '.main()') # ---------------------------------------------------------------------- # Read blaze # ---------------------------------------------------------------------- # get tilts p, loc['BLAZE'] = spirouImage.ReadBlazeFile(p, hchdr) loc.set_source('BLAZE', __NAME__ + '/main() + /spirouImage.ReadBlazeFile') # make copy of blaze (as it's overwritten later in CCF part) # TODO is this needed? More sensible to make and set copy in CCF? loc['BLAZE2'] = np.copy(loc['BLAZE']) # ---------------------------------------------------------------------- # Read wave solution # ---------------------------------------------------------------------- # wavelength file; we will use the polynomial terms in its header, # NOT the pixel values that would need to be interpolated # set source of wave file wsource = __NAME__ + '/main() + /spirouImage.GetWaveSolution' # Force A and B to AB solution if p['FIBER'] in ['A', 'B']: wave_fiber = 'AB' else: wave_fiber = p['FIBER'] # get wave image wout = spirouImage.GetWaveSolution(p, hdr=hchdr, return_wavemap=True, return_filename=True, fiber=wave_fiber) loc['WAVEPARAMS'], loc['WAVE_INIT'], loc['WAVEFILE'], loc['WSOURCE'] = wout loc.set_sources(['WAVE_INIT', 'WAVEFILE', 'WAVEPARAMS', 'WSOURCE'], wsource) poly_wave_sol = loc['WAVEPARAMS'] # ---------------------------------------------------------------------- # Check that wave parameters are consistent with "ic_ll_degr_fit" # ---------------------------------------------------------------------- loc = spirouImage.CheckWaveSolConsistency(p, loc) # ---------------------------------------------------------------------- # HC wavelength solution # ---------------------------------------------------------------------- # log that we are running the HC part and the mode wmsg = 'Now running the HC solution, mode = {0}' WLOG(p, 'info', wmsg.format(p['WAVE_MODE_HC'])) # get the solution loc = spirouWAVE2.do_hc_wavesol(p, loc) # ---------------------------------------------------------------------- # Quality control - HC solution # ---------------------------------------------------------------------- # set passed variable and fail message list passed, fail_msg = True, [] qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # ---------------------------------------------------------------------- # quality control on sigma clip (sig1 > qc_hc_wave_sigma_max if loc['SIG1'] > p['QC_HC_WAVE_SIGMA_MAX']: fmsg = 'Sigma too high ({0:.5f} > {1:.5f})' fail_msg.append(fmsg.format(loc['SIG1'], p['QC_HC_WAVE_SIGMA_MAX'])) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(loc['SIG1']) qc_names.append('SIG1 HC') qc_logic.append('SIG1 > {0:.2f}'.format(p['QC_HC_WAVE_SIGMA_MAX'])) # ---------------------------------------------------------------------- # check the difference between consecutive orders is always positive # get the differences wave_diff = loc['WAVE_MAP2'][1:] - loc['WAVE_MAP2'][:-1] if np.min(wave_diff) < 0: fmsg = 'Negative wavelength difference between orders' fail_msg.append(fmsg) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.min(wave_diff)) qc_names.append('MIN WAVE DIFF HC') qc_logic.append('MIN WAVE DIFF < 0') # ---------------------------------------------------------------------- # check the difference between consecutive pixels along an order is # always positive # loop through the orders ord_check = np.zeros((loc['NBO']), dtype=bool) for order in range(loc['NBO']): oc = np.all(loc['WAVE_MAP2'][order, 1:] > loc['WAVE_MAP2'][order, :-1]) ord_check[order] = oc # TODO: Melissa Why is this here???? # ord_check[5] = False if np.all(ord_check): qc_pass.append(1) qc_values.append('None') else: fmsg = 'Negative wavelength difference along an order' fail_msg.append(fmsg) passed = False qc_pass.append(0) qc_values.append(np.ndarray.tolist(np.where(~ord_check)[0])) # add to qc header lists # vale: array of orders where it fails qc_names.append('WAVE DIFF ALONG ORDER HC') qc_logic.append('WAVE DIFF ALONG ORDER < 0') # ---------------------------------------------------------------------- # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ---------------------------------------------------------------------- # log the global stats # ---------------------------------------------------------------------- # calculate catalog-fit residuals in km/s res_hc = [] sumres_hc = 0.0 sumres2_hc = 0.0 for order in range(loc['NBO']): # get HC line wavelengths for the order order_mask = loc['ORD_T'] == order hc_x_ord = loc['XGAU_T'][order_mask] hc_ll_ord = np.polyval(loc['POLY_WAVE_SOL'][order][::-1], hc_x_ord) hc_ll_cat = loc['WAVE_CATALOG'][order_mask] hc_ll_diff = hc_ll_ord - hc_ll_cat res_hc.append(hc_ll_diff * speed_of_light / hc_ll_cat) sumres_hc += np.nansum(res_hc[order]) sumres2_hc += np.nansum(res_hc[order]**2) total_lines_hc = len(np.concatenate(res_hc)) final_mean_hc = sumres_hc / total_lines_hc final_var_hc = (sumres2_hc / total_lines_hc) - (final_mean_hc**2) wmsg1 = 'On fiber {0} HC fit line statistic:'.format(p['FIBER']) wargs2 = [ final_mean_hc * 1000.0, np.sqrt(final_var_hc) * 1000.0, total_lines_hc, 1000.0 * np.sqrt(final_var_hc / total_lines_hc) ] wmsg2 = ('\tmean={0:.3f}[m/s] rms={1:.1f} {2} HC lines (error on mean ' 'value:{3:.4f}[m/s])'.format(*wargs2)) WLOG(p, 'info', [wmsg1, wmsg2]) # ---------------------------------------------------------------------- # Save wave map to file # ---------------------------------------------------------------------- # TODO single file-naming function? Ask Neil # get base input filenames bfilenames = [] for raw_file in p['ARG_FILE_NAMES']: bfilenames.append(os.path.basename(raw_file)) # get wave filename wavefits, tag1 = spirouConfig.Constants.WAVE_FILE_EA(p) wavefitsname = os.path.basename(wavefits) # log progress WLOG(p, '', 'Saving wave map to {0}'.format(wavefitsname)) # log progress wargs = [p['FIBER'], wavefitsname] wmsg = 'Write wavelength solution for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # write solution to fitsfilename header # copy original keys hdict = spirouImage.CopyOriginalKeys(loc['HCHDR'], loc['HCCDR']) # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) # TODO add DRS_DATE and DRS_NOW hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBAD'], value=p['BLAZFILE']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=p['MAX_TIME_HUMAN']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=p['MAX_TIME_UNIX']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_CODE'], value=__NAME__) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['WAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file', values=p['ARG_FILE_NAMES']) # add number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=loc['POLY_WAVE_SOL'].shape[0]) # add degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=loc['POLY_WAVE_SOL'].shape[1] - 1) # add wave solution hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['POLY_WAVE_SOL']) # write the wave "spectrum" p = spirouImage.WriteImage(p, wavefits, loc['WAVE_MAP2'], hdict) # get filename for E2DS calibDB copy of FITSFILENAME e2dscopy_filename, tag2 = spirouConfig.Constants.WAVE_E2DS_COPY(p) wargs = [p['FIBER'], os.path.split(e2dscopy_filename)[-1]] wmsg = 'Write reference E2DS spectra for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # make a copy of the E2DS file for the calibBD hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2) p = spirouImage.WriteImage(p, e2dscopy_filename, loc['HCDATA'], hdict) # ---------------------------------------------------------------------- # Save resolution and line profiles to file # ---------------------------------------------------------------------- raw_infile = os.path.basename(p['FITSFILENAME']) # get wave filename resfits, tag3 = spirouConfig.Constants.WAVE_RES_FILE_EA(p) resfitsname = os.path.basename(resfits) WLOG(p, '', 'Saving wave resmap to {0}'.format(resfitsname)) # make a copy of the E2DS file for the calibBD # set the version hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) # TODO add DRS_DATE and DRS_NOW hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3) # get res data in correct format resdata, hdicts = spirouWAVE2.generate_res_files(p, loc, hdict) # save to file p = spirouImage.WriteImageMulti(p, resfits, resdata, hdicts=hdicts) # ---------------------------------------------------------------------- # Update calibDB # ---------------------------------------------------------------------- if p['QC']: # set the wave key keydb = 'WAVE_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, wavefits) # update the master calib DB file with new key spirouDB.UpdateCalibMaster(p, keydb, wavefitsname, loc['HCHDR']) # set the hcref key keydb = 'HCREF_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, e2dscopy_filename) # update the master calib DB file with new key e2dscopyfits = os.path.split(e2dscopy_filename)[-1] spirouDB.UpdateCalibMaster(p, keydb, e2dscopyfits, loc['HCHDR']) # ---------------------------------------------------------------------- # Update header of current files # ---------------------------------------------------------------------- # only copy over if QC passed if p['QC']: rdir = os.path.dirname(wavefits) # loop around hc files and update header with for rawhcfile in p['ARG_FILE_NAMES']: hcfile = os.path.join(rdir, rawhcfile) raw_infilepath1 = os.path.join(p['ARG_FILE_DIR'], hcfile) p = spirouImage.UpdateWaveSolutionHC(p, loc, raw_infilepath1) # ---------------------------------------------------------------------- # HC+FP wavelength solution # ---------------------------------------------------------------------- # check if there's a FP input and if HC solution passed QCs if has_fp and p['QC']: # log that we are doing the FP solution wmsg = 'Now running the combined FP-HC solution, mode = {}' WLOG(p, 'info', wmsg.format(p['WAVE_MODE_FP'])) # do the wavelength solution loc = spirouWAVE2.do_fp_wavesol(p, loc) # ---------------------------------------------------------------------- # Quality control # ---------------------------------------------------------------------- # get parameters ffrom p p['QC_RMS_LITTROW_MAX'] = p['QC_HC_RMS_LITTROW_MAX'] p['QC_DEV_LITTROW_MAX'] = p['QC_HC_DEV_LITTROW_MAX'] # set passed variable and fail message list # passed, fail_msg = True, [] # qc_values, qc_names, qc_logic, qc_pass = [], [], [], [] # ---------------------------------------------------------------------- # check the difference between consecutive orders is always positive # get the differences wave_diff = loc['LL_FINAL'][1:] - loc['LL_FINAL'][:-1] if np.min(wave_diff) < 0: fmsg = 'Negative wavelength difference between orders' fail_msg.append(fmsg) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.min(wave_diff)) qc_names.append('MIN WAVE DIFF FP-HC') qc_logic.append('MIN WAVE DIFF < 0') # ---------------------------------------------------------------------- # check for infinites and NaNs in mean residuals from fit if ~np.isfinite(loc['X_MEAN_2']): # add failed message to the fail message list fmsg = 'NaN or Inf in X_MEAN_2' fail_msg.append(fmsg) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(loc['X_MEAN_2']) qc_names.append('X_MEAN_2') qc_logic.append('X_MEAN_2 not finite') # ---------------------------------------------------------------------- # iterate through Littrow test cut values lit_it = 2 # checks every other value # TODO: This QC check (or set of QC checks needs re-writing it is # TODO: nearly impossible to understand for x_it in range(1, len(loc['X_CUT_POINTS_' + str(lit_it)]), 2): # get x cut point x_cut_point = loc['X_CUT_POINTS_' + str(lit_it)][x_it] # get the sigma for this cut point sig_littrow = loc['LITTROW_SIG_' + str(lit_it)][x_it] # get the abs min and max dev littrow values min_littrow = abs(loc['LITTROW_MINDEV_' + str(lit_it)][x_it]) max_littrow = abs(loc['LITTROW_MAXDEV_' + str(lit_it)][x_it]) # get the corresponding order min_littrow_ord = loc['LITTROW_MINDEVORD_' + str(lit_it)][x_it] max_littrow_ord = loc['LITTROW_MAXDEVORD_' + str(lit_it)][x_it] # check if sig littrow is above maximum rms_littrow_max = p['QC_RMS_LITTROW_MAX'] dev_littrow_max = p['QC_DEV_LITTROW_MAX'] if sig_littrow > rms_littrow_max: fmsg = ('Littrow test (x={0}) failed (sig littrow = ' '{1:.2f} > {2:.2f})') fargs = [x_cut_point, sig_littrow, rms_littrow_max] fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) else: qc_pass.append(1) # add to qc header lists qc_values.append(sig_littrow) qc_names.append('sig_littrow') qc_logic.append('sig_littrow > {0:.2f}'.format(rms_littrow_max)) # ---------------------------------------------------------------------- # check if min/max littrow is out of bounds if np.max([max_littrow, min_littrow]) > dev_littrow_max: fmsg = ('Littrow test (x={0}) failed (min|max dev = ' '{1:.2f}|{2:.2f} > {3:.2f} for order {4}|{5})') fargs = [ x_cut_point, min_littrow, max_littrow, dev_littrow_max, min_littrow_ord, max_littrow_ord ] fail_msg.append(fmsg.format(*fargs)) passed = False qc_pass.append(0) # TODO: Should this be the QC header values? # TODO: it does not change the outcome of QC (i.e. passed=False) # TODO: So what is the point? # if sig was out of bounds, recalculate if sig_littrow > rms_littrow_max: # conditions check1 = min_littrow > dev_littrow_max check2 = max_littrow > dev_littrow_max # get the residuals respix = loc['LITTROW_YY_' + str(lit_it)][x_it] # check if both are out of bounds if check1 and check2: # remove respective orders worst_order = (min_littrow_ord, max_littrow_ord) respix_2 = np.delete(respix, worst_order) redo_sigma = True # check if min is out of bounds elif check1: # remove respective order worst_order = min_littrow_ord respix_2 = np.delete(respix, worst_order) redo_sigma = True # check if max is out of bounds elif check2: # remove respective order worst_order = max_littrow_ord respix_2 = np.delete(respix, max_littrow_ord) redo_sigma = True # else do not recalculate sigma else: redo_sigma, respix_2, worst_order = False, None, None wmsg = 'No outlying orders, sig littrow not recalculated' fail_msg.append(wmsg.format()) # if outlying order, recalculate stats if redo_sigma: mean = np.nansum(respix_2) / len(respix_2) mean2 = np.nansum(respix_2**2) / len(respix_2) rms = np.sqrt(mean2 - mean**2) if rms > rms_littrow_max: fmsg = ( 'Littrow test (x={0}) failed (sig littrow = ' '{1:.2f} > {2:.2f} removing order {3})') fargs = [ x_cut_point, rms, rms_littrow_max, worst_order ] fail_msg.append(fmsg.format(*fargs)) else: wargs = [ x_cut_point, rms, rms_littrow_max, worst_order ] wmsg = ( 'Littrow test (x={0}) passed (sig littrow = ' '{1:.2f} > {2:.2f} removing order {3})') fail_msg.append(wmsg.format(*wargs)) else: qc_pass.append(1) # add to qc header lists qc_values.append(np.max([max_littrow, min_littrow])) qc_names.append('max or min littrow') qc_logic.append('max or min littrow > {0:.2f}' ''.format(dev_littrow_max)) # finally log the failed messages and set QC = 1 if we pass the # quality control QC = 0 if we fail quality control if passed: WLOG(p, 'info', 'QUALITY CONTROL SUCCESSFUL - Well Done -') p['QC'] = 1 p.set_source('QC', __NAME__ + '/main()') else: for farg in fail_msg: wmsg = 'QUALITY CONTROL FAILED: {0}' WLOG(p, 'warning', wmsg.format(farg)) p['QC'] = 0 p.set_source('QC', __NAME__ + '/main()') # store in qc_params qc_params = [qc_names, qc_values, qc_logic, qc_pass] # ------------------------------------------------------------------ # archive result in e2ds spectra # ------------------------------------------------------------------ # get raw input file name(s) raw_infiles1 = [] for hcfile in p['HCFILES']: raw_infiles1.append(os.path.basename(hcfile)) raw_infile2 = os.path.basename(p['FPFILE']) # get wave filename wavefits, tag1 = spirouConfig.Constants.WAVE_FILE_EA_2(p) wavefitsname = os.path.split(wavefits)[-1] # log progress wargs = [p['FIBER'], wavefits] wmsg = 'Write wavelength solution for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # write solution to fitsfilename header # copy original keys hdict = spirouImage.CopyOriginalKeys(loc['HCHDR'], loc['HCCDR']) # add version number hdict = spirouImage.AddKey(p, hdict, p['KW_VERSION']) hdict = spirouImage.AddKey(p, hdict, p['KW_PID'], value=p['PID']) # set the input files hdict = spirouImage.AddKey(p, hdict, p['KW_CDBBAD'], value=p['BLAZFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_CDBWAVE'], value=loc['WAVEFILE']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVESOURCE'], value=loc['WSOURCE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='fpfile', values=p['FPFILE']) hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE2'], dim1name='hcfile', values=p['HCFILES']) # add qc parameters hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC']) hdict = spirouImage.AddQCKeys(p, hdict, qc_params) # add wave solution date hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME1'], value=p['MAX_TIME_HUMAN']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_TIME2'], value=p['MAX_TIME_UNIX']) hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_CODE'], value=__NAME__) # add number of orders hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_ORD_N'], value=loc['LL_PARAM_FINAL'].shape[0]) # add degree of fit hdict = spirouImage.AddKey(p, hdict, p['KW_WAVE_LL_DEG'], value=loc['LL_PARAM_FINAL'].shape[1] - 1) # add wave solution hdict = spirouImage.AddKey2DList(p, hdict, p['KW_WAVE_PARAM'], values=loc['LL_PARAM_FINAL']) # add FP CCF drift # target RV and width hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_TARG_RV'], value=p['TARGET_RV']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_WIDTH'], value=p['CCF_WIDTH']) # the rv step # rvstep = np.abs(loc['RV_CCF'][0] - loc['RV_CCF'][1]) # hdict = spirouImage.AddKey(p, hdict, p['KW_CCF_CDELT'], value=rvstep) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_STEP'], value=p['CCF_STEP']) # add ccf stats hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_DRIFT'], value=loc['CCF_RES'][1]) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_FWHM'], value=loc['FWHM']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_CONTRAST'], value=loc['CONTRAST']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_MAXCPP'], value=loc['MAXCPP']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_MASK'], value=p['CCF_MASK']) hdict = spirouImage.AddKey(p, hdict, p['KW_WFP_LINES'], value=np.nansum(loc['TOT_LINE'])) # write the wave "spectrum" hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1) p = spirouImage.WriteImage(p, wavefits, loc['LL_FINAL'], hdict) # get filename for E2DS calibDB copy of FITSFILENAME e2dscopy_filename = spirouConfig.Constants.WAVE_E2DS_COPY(p)[0] wargs = [p['FIBER'], os.path.split(e2dscopy_filename)[-1]] wmsg = 'Write reference E2DS spectra for Fiber {0} in {1}' WLOG(p, '', wmsg.format(*wargs)) # make a copy of the E2DS file for the calibBD p = spirouImage.WriteImage(p, e2dscopy_filename, loc['HCDATA'], hdict) # only copy over if QC passed if p['QC']: # loop around hc files and update header with for hcfile in p['HCFILES']: raw_infilepath1 = os.path.join(p['ARG_FILE_DIR'], hcfile) p = spirouImage.UpdateWaveSolution(p, loc, raw_infilepath1) # update fp file raw_infilepath2 = os.path.join(p['ARG_FILE_DIR'], raw_infile2) p = spirouImage.UpdateWaveSolution(p, loc, raw_infilepath2) # ------------------------------------------------------------------ # Save to result table # ------------------------------------------------------------------ # calculate stats for table final_mean = 1000 * loc['X_MEAN_2'] final_var = 1000 * loc['X_VAR_2'] num_lines = loc['TOTAL_LINES_2'] err = 1000 * np.sqrt(loc['X_VAR_2'] / num_lines) sig_littrow = 1000 * np.array(loc['LITTROW_SIG_' + str(lit_it)]) # construct filename wavetbl = spirouConfig.Constants.WAVE_TBL_FILE_EA(p) wavetblname = os.path.basename(wavetbl) # construct and write table columnnames = [ 'night_name', 'file_name', 'fiber', 'mean', 'rms', 'N_lines', 'err', 'rms_L500', 'rms_L1000', 'rms_L1500', 'rms_L2000', 'rms_L2500', 'rms_L3000', 'rms_L3500' ] columnformats = [ '{:20s}', '{:30s}', '{:3s}', '{:7.4f}', '{:6.2f}', '{:3d}', '{:6.3f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}', '{:6.2f}' ] columnvalues = [[p['ARG_NIGHT_NAME']], [p['ARG_FILE_NAMES'][0]], [p['FIBER']], [final_mean], [final_var], [num_lines], [err], [sig_littrow[0]], [sig_littrow[1]], [sig_littrow[2]], [sig_littrow[3]], [sig_littrow[4]], [sig_littrow[5]], [sig_littrow[6]]] # make table table = spirouImage.MakeTable(p, columns=columnnames, values=columnvalues, formats=columnformats) # merge table wmsg = 'Global result summary saved in {0}' WLOG(p, '', wmsg.format(wavetblname)) spirouImage.MergeTable(p, table, wavetbl, fmt='ascii.rst') # ------------------------------------------------------------------ # Save line list table file # ------------------------------------------------------------------ # construct filename # TODO proper column values wavelltbl = spirouConfig.Constants.WAVE_LINE_FILE_EA(p) wavelltblname = os.path.split(wavelltbl)[-1] # construct and write table columnnames = ['order', 'll', 'dv', 'w', 'xi', 'xo', 'dvdx'] columnformats = [ '{:.0f}', '{:12.4f}', '{:13.5f}', '{:12.4f}', '{:12.4f}', '{:12.4f}', '{:8.4f}' ] columnvalues = [] # construct column values (flatten over orders) for it in range(len(loc['X_DETAILS_2'])): for jt in range(len(loc['X_DETAILS_2'][it][0])): row = [ float(it), loc['X_DETAILS_2'][it][0][jt], loc['LL_DETAILS_2'][it][0][jt], loc['X_DETAILS_2'][it][3][jt], loc['X_DETAILS_2'][it][1][jt], loc['X_DETAILS_2'][it][2][jt], loc['SCALE_2'][it][jt] ] columnvalues.append(row) # log saving wmsg = 'List of lines used saved in {0}' WLOG(p, '', wmsg.format(wavelltblname)) # make table columnvalues = np.array(columnvalues).T table = spirouImage.MakeTable(p, columns=columnnames, values=columnvalues, formats=columnformats) # write table spirouImage.WriteTable(p, table, wavelltbl, fmt='ascii.rst') # ------------------------------------------------------------------ # Move to calibDB and update calibDB # ------------------------------------------------------------------ if p['QC']: # set the wave key keydb = 'WAVE_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, wavefits) # update the master calib DB file with new key spirouDB.UpdateCalibMaster(p, keydb, wavefitsname, loc['HCHDR']) # set the hcref key keydb = 'HCREF_{0}'.format(p['FIBER']) # copy wave file to calibDB folder spirouDB.PutCalibFile(p, e2dscopy_filename) # update the master calib DB file with new key e2dscopyfits = os.path.split(e2dscopy_filename)[-1] spirouDB.UpdateCalibMaster(p, keydb, e2dscopyfits, loc['HCHDR']) # If the HC solution failed QCs we do not compute FP-HC solution elif has_fp and not p['QC']: wmsg = 'HC solution failed quality controls; FP not processed' WLOG(p, 'warning', wmsg) # If there is no FP file we log that elif not has_fp: wmsg = 'No FP file given; FP-HC combined solution cannot be generated' WLOG(p, 'warning', wmsg) # ---------------------------------------------------------------------- # End Message # ---------------------------------------------------------------------- p = spirouStartup.End(p) # return p and loc return dict(locals())