def main(night_name=None, key=None, filename=None):
"""
Manually add a file the the calibDB with "key"
i.e. adds
key night_name filename human-date unix-time
to the calibDB and copies "filename" from .../reduced_dir/night_name/ into
the calibDB
Note filename must be in .../reduced_dir/night_name/
"""
# ----------------------------------------------------------------------
# Set up
# ----------------------------------------------------------------------
# get parameters from config files/run time args/load paths + calibdb
p = spirouStartup.Begin(recipe=__NAME__)
# deal with arguments being None (i.e. get from sys.argv)
pos = [0, 1]
fmt = [str, str]
names = ['key', 'filename']
call = [key, filename]
# now get custom arguments
customargs = spirouStartup.GetCustomFromRuntime(p,
pos,
fmt,
names,
calls=call)
# get parameters from configuration files and run time arguments
p = spirouStartup.LoadArguments(p,
night_name,
customargs=customargs,
mainfitsfile='filename',
mainfitsdir='reduced')
# as we have custom arguments need to load the calibration database
p = spirouStartup.LoadCalibDB(p)
# ----------------------------------------------------------------------
# Read image file
# ----------------------------------------------------------------------
# read the image data
image, hdr, nbo, nx = spirouImage.ReadData(p, p['FITSFILENAME'])
# ----------------------------------------------------------------------
# Move to calibDB and update calibDB
# ----------------------------------------------------------------------
# set dark key
keydb = p['KEY']
# copy dark fits file to the calibDB folder
spirouDB.PutCalibFile(p, p['FITSFILENAME'])
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, p['FILENAME'], hdr)
# ----------------------------------------------------------------------
# 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_SLIT_spirou.py main function, if night_name and files are None uses
arguments from run time i.e.:
cal_SLIT_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)
: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)
# set the fiber type
p['FIB_TYP'] = 'AB'
p.set_source('FIB_TYP', __NAME__ + '/main()')
# ----------------------------------------------------------------------
# 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)
# ----------------------------------------------------------------------
# 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()')
# correct data2 with background
data2 = data2 - background
# ----------------------------------------------------------------------
# Log the number of dead pixels
# ----------------------------------------------------------------------
# get the number of bad pixels
n_bad_pix = np.nansum(~np.isfinite(data2))
n_bad_pix_frac = n_bad_pix * 100 / np.product(data2.shape)
# Log number
wmsg = 'Nb dead pixels = {0} / {1:.2f} %'
WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac))
# ----------------------------------------------------------------------
# Log the number of dead pixels
# ----------------------------------------------------------------------
loc = ParamDict()
# ----------------------------------------------------------------------
# Loop around fiber types
# ----------------------------------------------------------------------
# set fiber
p['FIBER'] = p['FIB_TYP']
# ------------------------------------------------------------------
# Get localisation coefficients
# ------------------------------------------------------------------
# original there is a loop but it is not used --> removed
p = spirouImage.FiberParams(p, p['FIBER'], merge=True)
# get localisation fit coefficients
p, loc = spirouLOCOR.GetCoeffs(p, hdr, loc)
# ------------------------------------------------------------------
# Calculating the tilt
# ------------------------------------------------------------------
# get the tilt by extracting the AB fibers and correlating them
loc = spirouImage.GetTilt(p, loc, data2)
# fit the tilt with a polynomial
loc = spirouImage.FitTilt(p, loc)
# log the tilt dispersion
wmsg = 'Tilt dispersion = {0:.3f} deg'
WLOG(p, 'info', wmsg.format(loc['RMS_TILT']))
# ------------------------------------------------------------------
# Plotting
# ------------------------------------------------------------------
if p['DRS_PLOT'] > 0:
# plots setup: start interactive plot
sPlt.start_interactive_session(p)
# plot image with selected order shown
sPlt.slit_sorder_plot(p, loc, data2)
# plot slit tilt angle and fit
sPlt.slit_tilt_angle_and_fit_plot(p, loc)
# end interactive section
sPlt.end_interactive_session(p)
# ------------------------------------------------------------------
# Replace tilt by the global fit
# ------------------------------------------------------------------
loc['TILT'] = loc['YFIT_TILT']
oldsource = loc.get_source('tilt')
loc.set_source('TILT', oldsource + '+{0}/main()'.format(__NAME__))
# ----------------------------------------------------------------------
# Quality control
# ----------------------------------------------------------------------
# set passed variable and fail message list
passed, fail_msg = True, []
qc_values, qc_names, qc_logic, qc_pass = [], [], [], []
# check that tilt rms is below required
if loc['RMS_TILT'] > p['QC_SLIT_RMS']:
# add failed message to fail message list
fmsg = 'abnormal RMS of SLIT angle ({0:.2f} > {1:.2f} deg)'
fail_msg.append(fmsg.format(loc['RMS_TILT'], p['QC_SLIT_RMS']))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
# add to qc header lists
qc_values.append(loc['RMS_TILT'])
qc_names.append('RMS_TILT')
qc_logic.append('RMS_TILT > {0:.2f}'.format(p['QC_SLIT_RMS']))
# ----------------------------------------------------------------------
# check that tilt is less than max tilt required
max_tilt = np.max(loc['TILT'])
if max_tilt > p['QC_SLIT_MAX']:
# add failed message to fail message list
fmsg = 'abnormal SLIT angle ({0:.2f} > {1:.2f} deg)'
fail_msg.append(fmsg.format(max_tilt, p['QC_SLIT_MAX']))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
# add to qc header lists
qc_values.append(max_tilt)
qc_names.append('max_tilt')
qc_logic.append('max_tilt > {0:.2f}'.format(p['QC_SLIT_MAX']))
# ----------------------------------------------------------------------
# check that tilt is greater than min tilt required
min_tilt = np.min(loc['TILT'])
if min_tilt < p['QC_SLIT_MIN']:
# add failed message to fail message list
fmsg = 'abnormal SLIT angle ({0:.2f} < {1:.2f} deg)'
fail_msg.append(fmsg.format(max_tilt, p['QC_SLIT_MIN']))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
# add to qc header lists
qc_values.append(min_tilt)
qc_names.append('min_tilt')
qc_logic.append('min_tilt > {0:.2f}'.format(p['QC_SLIT_MIN']))
# ----------------------------------------------------------------------
# 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 tilt table
# ----------------------------------------------------------------------
# copy the tilt along the orders
tiltima = np.ones((int(loc['NUMBER_ORDERS']/2), data2.shape[1]))
tiltima *= loc['TILT'][:, None]
# get the raw tilt file name
raw_tilt_file = os.path.basename(p['FITSFILENAME'])
# construct file name and path
tiltfits, tag = spirouConfig.Constants.SLIT_TILT_FILE(p)
tiltfitsname = os.path.basename(tiltfits)
# Log that we are saving tilt file
wmsg = 'Saving tilt information in file: {0}'
WLOG(p, '', wmsg.format(tiltfitsname))
# Copy keys from fits file
hdict = spirouImage.CopyOriginalKeys(hdr)
# 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)
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'])
hdict = spirouImage.AddKey1DList(p, hdict, p['KW_INFILE1'], dim1name='file',
values=p['ARG_FILE_NAMES'])
# add qc parameters
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
# add tilt parameters as 1d list
hdict = spirouImage.AddKey1DList(p, hdict, p['KW_TILT'], values=loc['TILT'])
# write tilt file to file
p = spirouImage.WriteImage(p, tiltfits, tiltima, hdict)
# ----------------------------------------------------------------------
# Update the calibration data base
# ----------------------------------------------------------------------
if p['QC']:
keydb = 'TILT'
# copy localisation file to the calibDB folder
spirouDB.PutCalibFile(p, tiltfits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, tiltfitsname, hdr)
# ----------------------------------------------------------------------
# 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 main(night_name=None, files=None):
"""
cal_DARK_spirou.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__)
p = spirouStartup.LoadArguments(p, night_name, files)
p = spirouStartup.InitialFileSetup(p)
# ----------------------------------------------------------------------
# Read image file
# ----------------------------------------------------------------------
# read the image data
p, data, hdr = spirouImage.ReadImageAndCombine(p, framemath='average')
# ----------------------------------------------------------------------
# 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')
# ----------------------------------------------------------------------
# Dark exposure time check
# ----------------------------------------------------------------------
# log the Dark exposure time
WLOG(p, 'info', 'Dark Time = {0:.3f} s'.format(p['EXPTIME']))
# Quality control: make sure the exposure time is longer than qc_dark_time
if p['EXPTIME'] < p['QC_DARK_TIME']:
emsg = 'Dark exposure time too short (< {0:.1f} s)'
WLOG(p, 'error', emsg.format(p['QC_DARK_TIME']))
# ----------------------------------------------------------------------
# Resize image
# ----------------------------------------------------------------------
# # rotate the image and conver from ADU/s to e-
# data = data[::-1, ::-1] * p['exptime'] * p['gain']
# convert NaN to zeros
nanmask = ~np.isfinite(data)
data0 = np.where(nanmask, np.zeros_like(data), data)
# resize blue image
bkwargs = dict(xlow=p['IC_CCDX_BLUE_LOW'],
xhigh=p['IC_CCDX_BLUE_HIGH'],
ylow=p['IC_CCDY_BLUE_LOW'],
yhigh=p['IC_CCDY_BLUE_HIGH'])
datablue, nx2, ny2 = spirouImage.ResizeImage(p, data, **bkwargs)
# Make sure we have data in the blue image
if nx2 == 0 or ny2 == 0:
WLOG(p, 'error', ('IC_CCD(X/Y)_BLUE_(LOW/HIGH) remove '
'all pixels from image.'))
# resize red image
rkwargs = dict(xlow=p['IC_CCDX_RED_LOW'],
xhigh=p['IC_CCDX_RED_HIGH'],
ylow=p['IC_CCDY_RED_LOW'],
yhigh=p['IC_CCDY_RED_HIGH'])
datared, nx3, ny3 = spirouImage.ResizeImage(p, data, **rkwargs)
# Make sure we have data in the red image
if nx3 == 0 or ny3 == 0:
WLOG(p, 'error', ('IC_CCD(X/Y)_RED_(LOW/HIGH) remove '
'all pixels from image.'))
# ----------------------------------------------------------------------
# Dark Measurement
# ----------------------------------------------------------------------
# Log that we are doing dark measurement
WLOG(p, '', 'Doing Dark measurement')
# measure dark for whole frame
p = spirouImage.MeasureDark(p, data, 'Whole det', 'full')
# measure dark for blue part
p = spirouImage.MeasureDark(p, datablue, 'Blue part', 'blue')
# measure dark for rede part
p = spirouImage.MeasureDark(p, datared, 'Red part', 'red')
# ----------------------------------------------------------------------
# Identification of bad pixels
# ----------------------------------------------------------------------
# get number of bad dark pixels (as a fraction of total pixels)
with warnings.catch_warnings(record=True) as w:
baddark = 100.0 * np.sum(data0 > p['DARK_CUTLIMIT'])
baddark /= np.product(data0.shape)
# log the fraction of bad dark pixels
wmsg = 'Frac pixels with DARK > {0:.2f} ADU/s = {1:.3f} %'
WLOG(p, 'info', wmsg.format(p['DARK_CUTLIMIT'], baddark))
# define mask for values above cut limit or NaN
with warnings.catch_warnings(record=True) as w:
datacutmask = ~((data0 > p['DARK_CUTLIMIT']) | (~np.isfinite(data)))
spirouCore.spirouLog.warninglogger(p, w)
# get number of pixels above cut limit or NaN
n_bad_pix = np.product(data.shape) - np.nansum(datacutmask)
# work out fraction of dead pixels + dark > cut, as percentage
p['DADEADALL'] = n_bad_pix * 100 / np.product(data.shape)
p.set_source('DADEADALL', __NAME__ + '/main()')
# log fraction of dead pixels + dark > cut
logargs = [p['DARK_CUTLIMIT'], p['DADEADALL']]
WLOG(p, 'info', ('Total Frac dead pixels (N.A.N) + DARK > '
'{0:.2f} ADU/s = {1:.3f} %').format(*logargs))
# ----------------------------------------------------------------------
# Plots
# ----------------------------------------------------------------------
if p['DRS_PLOT'] > 0:
# start interactive plot
sPlt.start_interactive_session(p)
# plot the image with blue and red regions
sPlt.darkplot_image_and_regions(p, data)
# plot histograms
sPlt.darkplot_histograms(p)
# end interactive session
sPlt.end_interactive_session(p)
# ----------------------------------------------------------------------
# Quality control
# ----------------------------------------------------------------------
# set passed variable and fail message list
passed, fail_msg = True, []
qc_values, qc_names, qc_logic, qc_pass = [], [], [], []
# ----------------------------------------------------------------------
# check that med < qc_max_darklevel
if p['MED_FULL'] > p['QC_MAX_DARKLEVEL']:
# add failed message to fail message list
fmsg = 'Unexpected Median Dark level ({0:5.2f} > {1:5.2f} ADU/s)'
fail_msg.append(fmsg.format(p['MED_FULL'], p['QC_MAX_DARKLEVEL']))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
# add to qc header lists
qc_values.append(p['MED_FULL'])
qc_names.append('MED_FULL')
qc_logic.append('MED_FULL > {0:.2f}'.format(p['QC_MAX_DARKLEVEL']))
# ----------------------------------------------------------------------
# check that fraction of dead pixels < qc_max_dead
if p['DADEADALL'] > p['QC_MAX_DEAD']:
# add failed message to fail message list
fmsg = 'Unexpected Fraction of dead pixels ({0:5.2f} > {1:5.2f} %)'
fail_msg.append(fmsg.format(p['DADEADALL'], p['QC_MAX_DEAD']))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
# add to qc header lists
qc_values.append(p['DADEADALL'])
qc_names.append('DADEADALL')
qc_logic.append('DADEADALL > {0:.2f}'.format(p['QC_MAX_DEAD']))
# ----------------------------------------------------------------------
# checl that the precentage of dark pixels < qc_max_dark
if baddark > p['QC_MAX_DARK']:
fmsg = ('Unexpected Fraction of dark pixels > {0:.2f} ADU/s '
'({1:.2f} > {2:.2f}')
fail_msg.append(
fmsg.format(p['DARK_CUTLIMIT'], baddark, p['QC_MAX_DARK']))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
# add to qc header lists
qc_values.append(baddark)
qc_names.append('baddark')
qc_logic.append('baddark > {0:.2f}'.format(p['QC_MAX_DARK']))
# ----------------------------------------------------------------------
# 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 dark to file
# ----------------------------------------------------------------------
# get raw dark filename
rawdarkfile = os.path.basename(p['FITSFILENAME'])
# construct folder and filename
darkfits, tag = spirouConfig.Constants.DARK_FILE(p)
darkfitsname = os.path.basename(darkfits)
# log saving dark frame
WLOG(p, '', 'Saving Dark frame in ' + darkfitsname)
# 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=tag)
hdict = spirouImage.AddKey1DList(p,
hdict,
p['KW_INFILE1'],
dim1name='file',
values=p['ARG_FILE_NAMES'])
hdict = spirouImage.AddKey(p,
hdict,
p['KW_DARK_DEAD'],
value=p['DADEAD_FULL'])
hdict = spirouImage.AddKey(p, hdict, p['KW_DARK_MED'], value=p['MED_FULL'])
hdict = spirouImage.AddKey(p,
hdict,
p['KW_DARK_B_DEAD'],
value=p['DADEAD_BLUE'])
hdict = spirouImage.AddKey(p,
hdict,
p['KW_DARK_B_MED'],
value=p['MED_BLUE'])
hdict = spirouImage.AddKey(p,
hdict,
p['KW_DARK_R_DEAD'],
value=p['DADEAD_RED'])
hdict = spirouImage.AddKey(p,
hdict,
p['KW_DARK_R_MED'],
value=p['MED_RED'])
hdict = spirouImage.AddKey(p,
hdict,
p['KW_DARK_CUT'],
value=p['DARK_CUTLIMIT'])
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
# Set to zero dark value > dark_cutlimit
cutmask = data0 > p['DARK_CUTLIMIT']
data0c = np.where(cutmask, np.zeros_like(data0), data0)
# write image and add header keys (via hdict)
p = spirouImage.WriteImage(p, darkfits, data0c, hdict)
# ----------------------------------------------------------------------
# Save bad pixel mask
# ----------------------------------------------------------------------
# construct bad pixel file name
badpixelfits, tag = spirouConfig.Constants.DARK_BADPIX_FILE(p)
badpixelfitsname = os.path.split(badpixelfits)[-1]
# log that we are saving bad pixel map in dir
WLOG(p, '', 'Saving Bad Pixel Map in ' + badpixelfitsname)
# 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=tag)
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
hdict['DACUT'] = (p['DARK_CUTLIMIT'],
'Threshold of dark level retain [ADU/s]')
# write to file
datacutmask = np.array(datacutmask, dtype=float)
p = spirouImage.WriteImage(p,
badpixelfits,
datacutmask,
hdict,
dtype='float64')
# ----------------------------------------------------------------------
# Move to calibDB and update calibDB
# ----------------------------------------------------------------------
if p['QC']:
# set dark key
if p['DPRTYPE'] == 'DARK_DARK':
keydb = 'DARK'
elif p['USE_SKYDARK_CORRECTION']:
keydb = 'SKYDARK'
else:
emsg = 'Error: Currently {0} only supports DARK_DARK and OBJ_DARK'
WLOG(p, 'error', emsg.format(__NAME__))
# copy dark fits file to the calibDB folder
spirouDB.PutCalibFile(p, darkfits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, darkfitsname, hdr)
# # set badpix key
# keydb = 'BADPIX_OLD'
# # copy badpix fits file to calibDB folder
# spirouDB.PutCalibFile(p, badpixelfits)
# # update the master calib DB file with new key
# spirouDB.UpdateCalibMaster(p, keydb, badpixelfitsname, hdr)
# ----------------------------------------------------------------------
# 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_SLIT_spirou.py main function, if night_name and files are None uses
arguments from run time i.e.:
cal_SLIT_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)
: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)
# set the fiber type
p['FIBER'] = 'AB'
p.set_source('FIBER', __NAME__ + '/main()')
# ----------------------------------------------------------------------
# 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)
datac = data
# ----------------------------------------------------------------------
# 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)
p, badpixmap = spirouImage.CorrectForBadPix(p, data2, hdr, return_map=True)
# ----------------------------------------------------------------------
# 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, badpixmap]
# 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
# save data to loc
loc = ParamDict()
loc['DATA'] = data2
loc.set_source('DATA', __NAME__ + '/main()')
# ----------------------------------------------------------------------
# Log the number of dead pixels
# ----------------------------------------------------------------------
# get the number of bad pixels
n_bad_pix = np.nansum(data2 <= 0)
n_bad_pix_frac = n_bad_pix * 100 / np.product(data2.shape)
# Log number
wmsg = 'Nb dead pixels = {0} / {1:.2f} %'
WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac))
# ------------------------------------------------------------------
# Get localisation coefficients
# ------------------------------------------------------------------
# original there is a loop but it is not used --> removed
p = spirouImage.FiberParams(p, p['FIBER'], merge=True)
# get localisation fit coefficients
p, loc = spirouLOCOR.GetCoeffs(p, hdr, loc)
# ------------------------------------------------------------------
# Calculate shape map
# ------------------------------------------------------------------
loc = spirouImage.GetShapeMap(p, loc)
# ------------------------------------------------------------------
# Plotting
# ------------------------------------------------------------------
if p['DRS_PLOT'] > 0:
# plots setup: start interactive plot
sPlt.start_interactive_session(p)
# plot the shape process for each order
sPlt.slit_shape_angle_plot(p, loc)
# end interactive section
sPlt.end_interactive_session(p)
# ------------------------------------------------------------------
# Writing to file
# ------------------------------------------------------------------
# get the raw tilt file name
raw_shape_file = os.path.basename(p['FITSFILENAME'])
# construct file name and path
shapefits, tag = spirouConfig.Constants.SLIT_XSHAPE_FILE(p)
shapefitsname = os.path.basename(shapefits)
# Log that we are saving tilt file
wmsg = 'Saving shape information in file: {0}'
WLOG(p, '', wmsg.format(shapefitsname))
# Copy keys from fits file
# Copy keys from fits file
hdict = spirouImage.CopyOriginalKeys(hdr)
# 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=tag)
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_CDBSHAPE'], value=raw_shape_file)
# write tilt file to file
p = spirouImage.WriteImage(p, shapefits, loc['DXMAP'], hdict)
# ----------------------------------------------------------------------
# Quality control
# ----------------------------------------------------------------------
# TODO: Decide on some quality control criteria?
# set passed variable and fail message list
passed, fail_msg = True, []
# 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()')
# ----------------------------------------------------------------------
# Move to calibDB and update calibDB
# ----------------------------------------------------------------------
if p['QC']:
keydb = 'SHAPE'
# copy shape file to the calibDB folder
spirouDB.PutCalibFile(p, shapefits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, shapefitsname, hdr)
# ----------------------------------------------------------------------
# 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_shape_spirou.py main function, if night_name and files are None uses
arguments from run time i.e.:
cal_shape_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__)
p = spirouStartup.LoadArguments(p, night_name, files)
p = spirouStartup.InitialFileSetup(p)
# ----------------------------------------------------------------------
# Read image file
# ----------------------------------------------------------------------
# read the image data
p, data, hdr = spirouImage.ReadImageAndCombine(p, framemath='add')
# ----------------------------------------------------------------------
# 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)
# ----------------------------------------------------------------------
# Get basic image properties for FP file
# ----------------------------------------------------------------------
# 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 reference FP
# ----------------------------------------------------------------------
# set the number of frames
p['NBFRAMES'] = 1
p.set_source('NBFRAMES', __NAME__ + '.main()')
# Correction of DARK
p, datac = spirouImage.CorrectForDark(p, data, hdr)
# Resize hc data
# rotate the image and convert from ADU/s to e-
data = spirouImage.ConvertToE(spirouImage.FlipImage(p, datac), p=p)
# 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, data, **bkwargs)
# log change in data size
WLOG(p, '',
('FPref Image format changed to {0}x{1}').format(*data1.shape))
# Correct for the BADPIX mask (set all bad pixels to zero)
bargs = [p, data1, hdr]
p, data1 = spirouImage.CorrectForBadPix(*bargs)
p, badpixmask = spirouImage.CorrectForBadPix(*bargs, return_map=True)
# log progress
WLOG(p, '', 'Cleaning FPref hot pixels')
# correct hot pixels
data1 = spirouEXTOR.CleanHotpix(data1, badpixmask)
# Log the number of dead pixels
# get the number of bad pixels
with warnings.catch_warnings(record=True) as _:
n_bad_pix = np.nansum(data1 <= 0)
n_bad_pix_frac = n_bad_pix * 100 / np.product(data1.shape)
# Log number
wmsg = 'Nb FPref dead pixels = {0} / {1:.2f} %'
WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac))
# ----------------------------------------------------------------------
# Get master FP file
# ----------------------------------------------------------------------
# log progress
WLOG(p, '', 'Getting FP Master from calibDB')
# get master fp
p, masterfp = spirouImage.GetFPMaster(p, hdr)
# ----------------------------------------------------------------------
# Get transform parameters
# ----------------------------------------------------------------------
# log progress
wargs = [p['ARG_FILE_NAMES'][0], p['FPMASTERFILE']]
WLOG(p, 'info', 'Calculating transforming for {0} onto {1}'.format(*wargs))
gout = spirouImage.GetLinearTransformParams(p, masterfp, data1)
transform, xres, yres = gout
# ------------------------------------------------------------------
# Need to straighten the fp data for debug
# ------------------------------------------------------------------
p, shapem_x = spirouImage.GetShapeX(p, hdr)
p, shapem_y = spirouImage.GetShapeY(p, hdr)
data2 = spirouImage.EATransform(data1, transform, dxmap=shapem_x,
dymap=shapem_y)
# ----------------------------------------------------------------------
# Quality control
# ----------------------------------------------------------------------
# set passed variable and fail message list
passed, fail_msg = True, []
qc_values, qc_names, qc_logic, qc_pass = [], [], [], []
# ----------------------------------------------------------------------
# if transform is None means the fp image quality was too poor
if transform is None:
fail_msg.append('FP Image quality too poor (sigma clip failed)')
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
qc_values.append('None')
qc_names.append('Image Quality')
qc_logic.append('Image too poor')
# ----------------------------------------------------------------------
# get residual qc parameter
qc_res = p['SHAPE_QC_LINEAR_TRANS_RES_THRES']
# assess quality of x residuals
if xres > qc_res:
fail_msg.append('x-resdiuals too high {0} > {1}'.format(xres, qc_res))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
qc_values.append(xres)
qc_names.append('XRES')
qc_logic.append('XRES > {0}'.format(qc_res))
# assess quality of x residuals
if yres > qc_res:
fail_msg.append('y-resdiuals too high {0} > {1}'.format(yres, qc_res))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
qc_values.append(yres)
qc_names.append('YRES')
qc_logic.append('YRES > {0}'.format(qc_res))
# ----------------------------------------------------------------------
# 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]
# ------------------------------------------------------------------
# Writing shape to file
# ------------------------------------------------------------------
# get the raw tilt file name
raw_shape_file = os.path.basename(p['FITSFILENAME'])
# construct file name and path
shapefits, tag = spirouConfig.Constants.SLIT_SHAPE_LOCAL_FILE(p)
shapefitsname = os.path.basename(shapefits)
# Log that we are saving tilt file
wmsg = 'Saving shape information in file: {0}'
WLOG(p, '', wmsg.format(shapefitsname))
# Copy keys from fits file
hdict = spirouImage.CopyOriginalKeys(hdr)
# 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)
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_CDBSHAPEX'],
value=p['SHAPEXFILE'])
hdict = spirouImage.AddKey(p, hdict, p['KW_CDBSHAPEY'],
value=p['SHAPEYFILE'])
hdict = spirouImage.AddKey(p, hdict, p['KW_CDBFPMASTER'],
value=p['FPMASTERFILE'])
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 the transform parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_SHAPE_DX'], value=transform[0])
hdict = spirouImage.AddKey(p, hdict, p['KW_SHAPE_DY'], value=transform[1])
hdict = spirouImage.AddKey(p, hdict, p['KW_SHAPE_A'], value=transform[2])
hdict = spirouImage.AddKey(p, hdict, p['KW_SHAPE_B'], value=transform[3])
hdict = spirouImage.AddKey(p, hdict, p['KW_SHAPE_C'], value=transform[4])
hdict = spirouImage.AddKey(p, hdict, p['KW_SHAPE_D'], value=transform[5])
# write tilt file to file
p = spirouImage.WriteImage(p, shapefits, [transform], hdict)
# ----------------------------------------------------------------------
# Move to calibDB and update calibDB
# ----------------------------------------------------------------------
if p['QC']:
# add shape
keydb = 'SHAPE'
# copy shape file to the calibDB folder
spirouDB.PutCalibFile(p, shapefits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, shapefitsname, hdr)
# ------------------------------------------------------------------
# Writing sanity check files
# ------------------------------------------------------------------
if p['SHAPE_DEBUG_OUTPUTS']:
# log
WLOG(p, '', 'Saving debug sanity check files')
# construct file names
dargs = [p, p['ARG_FILE_NAMES'][0]]
out1 = spirouConfig.Constants.SLIT_SHAPE_IN_FP_FILE(*dargs)
input_fp_file, tag1= out1
out2 = spirouConfig.Constants.SLIT_SHAPE_OUT_FP_FILE(*dargs)
output_fp_file, tag2 = out2
# write input fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1)
p = spirouImage.WriteImage(p, input_fp_file, data1, hdict)
# write output fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2)
p = spirouImage.WriteImage(p, output_fp_file, data2, 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_FF_RAW_spirou.py main function, if night_name and files are None uses
arguments from run time i.e.:
cal_FF_RAW_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)
: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)
# get the night name
night_name = p['ARG_NIGHT_NAME']
# ----------------------------------------------------------------------
# get the e2ds filenames (uses ARG_FILE_NAMES by default)
exists = False
e2ds_files, e2dsff_files = dict(), dict()
for fiber in p['FIBER_TYPES']:
# must set file type
p['FIBER'] = fiber
p.set_source('FIBER', __NAME__ + '.main()')
# construct this fibers file names
e2dsfits, tag1 = spirouConfig.Constants.EXTRACT_E2DS_FILE(p)
e2dsfffits, tag2 = spirouConfig.Constants.EXTRACT_E2DSFF_FILE(p)
# check whether e2ds file exists
if os.path.exists(e2dsfits):
exists = exists and True
else:
exists = exists and False
# append to files
e2ds_files[fiber] = e2dsfits
e2dsff_files[fiber] = e2dsfffits
# ----------------------------------------------------------------------
# extract the dark or read dark e2ds header
# ----------------------------------------------------------------------
# check if e2ds file exists - if not extract
if p['ALWAYS_EXTRACT'] or (not exists):
llout = cal_extract_RAW_spirou.main(night_name, files)
# get qc
passed = llout['p']['QC']
# get hdr
hdr = llout['hdr']
# else we need to get
else:
# read file header and push into outputs
hdr = spirouImage.ReadHeader(p, e2ds_files['AB'])
# get quality control parameters
passed = hdr['KW_DRS_QC']
# ------------------------------------------------------------------
# Update the calibration database
# ------------------------------------------------------------------
for fiber in p['FIBER_TYPES']:
if passed:
# copy THERMAL_{FIBER} to calibdb
keydb = 'THERMAL_' + fiber
# get absolute path
abspath = e2ds_files[fiber]
basename = os.path.basename(abspath)
# copy localisation file to the calibDB folder
spirouDB.PutCalibFile(p, abspath)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, basename, hdr)
# ----------------------------------------------------------------------
# End Message
# ----------------------------------------------------------------------
p = spirouStartup.End(p)
# return a copy of locally defined variables in the memory
return dict(locals())
def main(night_name=None, hcfile=None, fpfiles=None):
"""
cal_SLIT_spirou.py main function, if night_name and files are None uses
arguments from run time i.e.:
cal_SLIT_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)
: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 hcfile is None or fpfiles is None:
names, types = ['hcfile', 'fpfiles'], [str, str]
customargs = spirouStartup.GetCustomFromRuntime(p, [0, 1],
types,
names,
last_multi=True)
else:
customargs = dict(hcfile=hcfile, fpfiles=fpfiles)
# get parameters from configuration files and run time arguments
p = spirouStartup.LoadArguments(p,
night_name,
customargs=customargs,
mainfitsfile='fpfiles')
# ----------------------------------------------------------------------
# Construct reference filename and get fiber type
# ----------------------------------------------------------------------
p, hcfitsfilename = spirouStartup.SingleFileSetup(p, filename=p['HCFILE'])
p, fpfilenames = spirouStartup.MultiFileSetup(p, files=p['FPFILES'])
# set fiber (it doesn't matter with the 2D image but we need this to get
# the lamp type for FPFILES and HCFILES, AB == C
p['FIBER'] = 'AB'
p['FIB_TYP'] = [p['FIBER']]
fsource = __NAME__ + '/main()'
p.set_sources(['FIBER', 'FIB_TYP'], fsource)
# set the hcfilename to the first hcfilenames
fpfitsfilename = fpfilenames[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)
# add a force plot off
p['PLOT_PER_ORDER'] = PLOT_PER_ORDER
p.set_source('PLOT_PER_ORDER', __NAME__ + '.main()')
# ----------------------------------------------------------------------
# Read FP and HC files
# ----------------------------------------------------------------------
# read and combine all FP files except the first (fpfitsfilename)
rargs = [p, 'add', fpfitsfilename, fpfilenames[1:]]
p, fpdata, fphdr = spirouImage.ReadImageAndCombine(*rargs)
# read first file (hcfitsfilename)
hcdata, hchdr, _, _ = spirouImage.ReadImage(p, hcfitsfilename)
# 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()')
# ---------------------------------------------------------------------
# fix for un-preprocessed files
# ----------------------------------------------------------------------
hcdata = spirouImage.FixNonPreProcess(p, hcdata)
fpdata = spirouImage.FixNonPreProcess(p, fpdata)
# ----------------------------------------------------------------------
# Get basic image properties for reference file
# ----------------------------------------------------------------------
# get sig det value
p = spirouImage.GetSigdet(p, fphdr, name='sigdet')
# get exposure time
p = spirouImage.GetExpTime(p, fphdr, name='exptime')
# get gain
p = spirouImage.GetGain(p, fphdr, name='gain')
# get lamp parameters
p = spirouTHORCA.GetLampParams(p, hchdr)
# ----------------------------------------------------------------------
# Correction of DARK
# ----------------------------------------------------------------------
# p, hcdatac = spirouImage.CorrectForDark(p, hcdata, hchdr)
hcdatac = hcdata
p['DARKFILE'] = 'None'
# p, fpdatac = spirouImage.CorrectForDark(p, fpdata, fphdr)
fpdatac = fpdata
# ----------------------------------------------------------------------
# Resize hc data
# ----------------------------------------------------------------------
# rotate the image and convert from ADU/s to e-
hcdata = spirouImage.ConvertToE(spirouImage.FlipImage(p, hcdatac), p=p)
# convert NaN to zeros
hcdata0 = np.where(~np.isfinite(hcdata), np.zeros_like(hcdata), hcdata)
# 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)
hcdata2 = spirouImage.ResizeImage(p, hcdata0, **bkwargs)
# log change in data size
WLOG(p, '', ('HC Image format changed to '
'{0}x{1}').format(*hcdata2.shape))
# ----------------------------------------------------------------------
# Resize fp data
# ----------------------------------------------------------------------
# rotate the image and convert from ADU/s to e-
fpdata = spirouImage.ConvertToE(spirouImage.FlipImage(p, fpdatac), p=p)
# convert NaN to zeros
fpdata0 = np.where(~np.isfinite(fpdata), np.zeros_like(fpdata), fpdata)
# 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)
fpdata2 = spirouImage.ResizeImage(p, fpdata0, **bkwargs)
# log change in data size
WLOG(p, '', ('FP Image format changed to '
'{0}x{1}').format(*fpdata2.shape))
# ----------------------------------------------------------------------
# Correct for the BADPIX mask (set all bad pixels to zero)
# ----------------------------------------------------------------------
# p, hcdata2 = spirouImage.CorrectForBadPix(p, hcdata2, hchdr)
# p, fpdata2 = spirouImage.CorrectForBadPix(p, fpdata2, fphdr)
p['BADPFILE'] = 'None'
# save data to loc
loc['HCDATA'] = hcdata2
loc.set_source('HCDATA', __NAME__ + '/main()')
# save data to loc
loc['FPDATA'] = fpdata2
loc.set_source('FPDATA', __NAME__ + '/main()')
# ----------------------------------------------------------------------
# Log the number of dead pixels
# ----------------------------------------------------------------------
# get the number of bad pixels
n_bad_pix = np.nansum(hcdata2 <= 0)
n_bad_pix_frac = n_bad_pix * 100 / np.product(hcdata2.shape)
# Log number
wmsg = 'Nb HC dead pixels = {0} / {1:.2f} %'
WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac))
# ----------------------------------------------------------------------
# Log the number of dead pixels
# ----------------------------------------------------------------------
# get the number of bad pixels
n_bad_pix = np.nansum(fpdata2 <= 0)
n_bad_pix_frac = n_bad_pix * 100 / np.product(fpdata2.shape)
# Log number
wmsg = 'Nb FP dead pixels = {0} / {1:.2f} %'
WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac))
# ------------------------------------------------------------------
# Get localisation coefficients
# ------------------------------------------------------------------
# original there is a loop but it is not used --> removed
p = spirouImage.FiberParams(p, p['FIBER'], merge=True)
# get localisation fit coefficients
p, loc = spirouLOCOR.GetCoeffs(p, fphdr, loc)
# ------------------------------------------------------------------
# Get master wave solution map
# ------------------------------------------------------------------
# get master wave map
masterwavefile = spirouDB.GetDatabaseMasterWave(p)
# log process
wmsg1 = 'Getting 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
wout = spirouImage.GetWaveSolution(p,
filename=masterwavefile,
return_wavemap=True,
quiet=True,
return_header=True,
fiber=wave_fiber)
loc['MASTERWAVEP'], loc['MASTERWAVE'] = wout[:2]
loc['MASTERWAVEHDR'], loc['WSOURCE'] = wout[2:]
# set sources
wsource = ['MASTERWAVEP', 'MASTERWAVE', 'MASTERWAVEHDR']
loc.set_sources(wsource, 'spirouImage.GetWaveSolution()')
# ----------------------------------------------------------------------
# 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)
# ----------------------------------------------------------------------
# Read cavity length file
# ----------------------------------------------------------------------
loc['CAVITY_LEN_COEFFS'] = spirouImage.ReadCavityLength(p)
source = __NAME__ + '.main() + spirouImage.ReadCavityLength()'
loc.set_source('CAVITY_LEN_COEFFS', source)
# ------------------------------------------------------------------
# Calculate shape map
# ------------------------------------------------------------------
loc = spirouImage.GetShapeMap(p, loc)
# ------------------------------------------------------------------
# Plotting
# ------------------------------------------------------------------
if p['DRS_PLOT'] > 0:
# plots setup: start interactive plot
sPlt.start_interactive_session(p)
# plot the shape process for one order
sPlt.slit_shape_angle_plot(p, loc)
# end interactive section
sPlt.end_interactive_session(p)
# ----------------------------------------------------------------------
# Quality control
# ----------------------------------------------------------------------
# TODO: Decide on some quality control criteria?
# set passed variable and fail message list
passed, fail_msg = True, []
qc_values, qc_names, qc_logic, qc_pass = [], [], [], []
# 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]
# ------------------------------------------------------------------
# Writing DXMAP to file
# ------------------------------------------------------------------
# get the raw tilt file name
raw_shape_file = os.path.basename(p['FITSFILENAME'])
# construct file name and path
shapefits, tag = spirouConfig.Constants.SLIT_XSHAPE_FILE(p)
shapefitsname = os.path.basename(shapefits)
# Log that we are saving tilt file
wmsg = 'Saving shape information in file: {0}'
WLOG(p, '', wmsg.format(shapefitsname))
# Copy keys from fits file
hdict = spirouImage.CopyOriginalKeys(fphdr)
# 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)
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.AddKey1DList(p,
hdict,
p['KW_INFILE1'],
dim1name='hcfile',
values=p['HCFILE'])
hdict = spirouImage.AddKey1DList(p,
hdict,
p['KW_INFILE2'],
dim1name='fpfile',
values=p['FPFILES'])
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
# write tilt file to file
p = spirouImage.WriteImage(p, shapefits, loc['DXMAP'], hdict)
# ------------------------------------------------------------------
# Writing sanity check files
# ------------------------------------------------------------------
if p['SHAPE_DEBUG_OUTPUTS']:
# log
WLOG(p, '', 'Saving debug sanity check files')
# construct file names
input_fp_file, tag1 = spirouConfig.Constants.SLIT_SHAPE_IN_FP_FILE(p)
output_fp_file, tag2 = spirouConfig.Constants.SLIT_SHAPE_OUT_FP_FILE(p)
input_hc_file, tag3 = spirouConfig.Constants.SLIT_SHAPE_IN_HC_FILE(p)
output_hc_file, tag4 = spirouConfig.Constants.SLIT_SHAPE_OUT_HC_FILE(p)
overlap_file, tag5 = spirouConfig.Constants.SLIT_SHAPE_OVERLAP_FILE(p)
# write input fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1)
p = spirouImage.WriteImage(p, input_fp_file, loc['FPDATA'], hdict)
# write output fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2)
p = spirouImage.WriteImage(p, output_fp_file, loc['FPDATA2'], hdict)
# write input fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3)
p = spirouImage.WriteImage(p, input_hc_file, loc['HCDATA'], hdict)
# write output fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag4)
p = spirouImage.WriteImage(p, output_hc_file, loc['HCDATA2'], hdict)
# write overlap file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag5)
p = spirouImage.WriteImage(p, overlap_file, loc['ORDER_OVERLAP'],
hdict)
# ----------------------------------------------------------------------
# Move to calibDB and update calibDB
# ----------------------------------------------------------------------
if p['QC']:
keydb = 'SHAPE'
# copy shape file to the calibDB folder
spirouDB.PutCalibFile(p, shapefits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, shapefitsname, fphdr)
# ----------------------------------------------------------------------
# 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(filetype='DARK_DARK'):
"""
cal_DARK_spirou.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
# ----------------------------------------------------------------------
# set up function name
main_name = __NAME__ + '.main()'
# get parameters from config files/run time args/load paths + calibdb
p = spirouStartup.Begin(recipe=__NAME__)
# now get custom arguments
pos, fmt = [0], [str]
names, call = ['filetype'], [filetype]
customargs = spirouStartup.GetCustomFromRuntime(p, pos, fmt, names,
calls=call,
require_night_name=False)
p = spirouStartup.LoadArguments(p, None, customargs=customargs,
mainfitsdir='tmp',
require_night_name=False)
# set the DRS type (for file indexing)
p['DRS_TYPE'] = 'RAW'
p.set_source('DRS_TYPE', __NAME__ + '.main()')
# -------------------------------------------------------------------------
# find all "filetype" objects
filenames = spirouImage.FindFiles(p, filetype=p['FILETYPE'],
allowedtypes=p['ALLOWED_DARK_TYPES'])
# convert filenames to a numpy array
filenames = np.array(filenames)
# -------------------------------------------------------------------------
# julian date to know which file we need to
# process together
dark_time = np.zeros(len(filenames))
dark_exp, dark_pp_version, dark_wt_temp = [], [], []
basenames, nightnames, dark_cass_temp, dark_humidity = [], [], [], []
# log progress
WLOG(p, '', 'Reading all dark file headers')
# looping through the file headers
for it in range(len(filenames)):
# get night name
night_name = os.path.dirname(filenames[it]).split(p['TMP_DIR'])[-1]
# get header
hdr = spirouImage.ReadHeader(p, filepath=filenames[it])
# add MJDATE to dark times
dark_time[it] = float(hdr[p['KW_ACQTIME'][0]])
# add other keys (for tabular output)
basenames.append(os.path.basename(filenames[it]))
nightnames.append(night_name)
dark_exp.append(float(hdr[p['KW_EXPTIME'][0]]))
dark_pp_version.append(hdr[p['KW_PPVERSION'][0]])
dark_wt_temp.append(float(hdr[p['KW_WEATHER_TOWER_TEMP'][0]]))
dark_cass_temp.append(float(hdr[p['KW_CASS_TEMP'][0]]))
dark_humidity.append(float(hdr[p['KW_HUMIDITY'][0]]))
# -------------------------------------------------------------------------
# match files by date
# -------------------------------------------------------------------------
# log progress
wmsg = 'Matching dark files by observation time (+/- {0} hrs)'
WLOG(p, '', wmsg.format(p['DARK_MASTER_MATCH_TIME']))
# get the time threshold
time_thres = p['DARK_MASTER_MATCH_TIME']
# get items grouped by time
matched_id = spirouImage.GroupFilesByTime(p, dark_time, time_thres)
# -------------------------------------------------------------------------
# get the most recent position
lastpos = np.argmax(dark_time)
# load up the most recent dark
rout = spirouImage.ReadImage(p, filenames[lastpos], log=False)
data_ref, hdr_ref, nx, ny = rout
# set the night name and update the reduced directory
p['ARG_NIGHT_NAME'] = nightnames[lastpos]
p.set_source('ARG_NIGHT_NAME', __NAME__ + '.main()')
p['REDUCED_DIR'] = spirouConfig.Constants.REDUCED_DIR(p)
p.set_source('REDUCED_DIR', __NAME__ + '.main()')
# -------------------------------------------------------------------------
# Read individual files and sum groups
# -------------------------------------------------------------------------
# log process
WLOG(p, '', 'Reading Dark files and combining groups')
# Find all unique groups
u_groups = np.unique(matched_id)
# currently number of bins == number of groups
num_bins = len(u_groups)
# storage of dark cube
dark_cube = np.zeros([num_bins, ny, nx])
bin_cube = np.zeros(num_bins)
# loop through groups
for g_it, group_num in enumerate(u_groups):
# log progress
WLOG(p, '', '\tGroup {0} of {1}'.format(g_it + 1, len(u_groups)))
# find all files for this group
dark_ids = filenames[matched_id == group_num]
# load this groups files into a cube
cube = []
for filename in dark_ids:
# read data
data_it, _, _, _ = spirouImage.ReadImage(p, filename, log=False)
# add to cube
cube.append(data_it)
# median dark cube
groupdark = np.nanmedian(cube, axis=0)
# sum within each bin
dark_cube[g_it % num_bins] += groupdark
# record the number of cubes that are going into this bin
bin_cube[g_it % num_bins] += 1
# need to normalize if we have more than 1 cube per bin
for bin_it in range(num_bins):
dark_cube[bin_it] /= bin_cube[bin_it]
# -------------------------------------------------------------------------
# we perform a median filter over a +/- "med_size" pixel box
# -------------------------------------------------------------------------
# log process
WLOG(p, '', 'Performing median filter for {0} bins'.format(num_bins))
# get med_size from p
med_size = p['DARK_MASTER_MED_SIZE']
# storage of output dark cube
dark_cube1 = np.zeros([num_bins, ny, nx])
# loop around the bins
for bin_it in range(num_bins):
# get the dark for this bin
bindark = dark_cube[bin_it]
# performing a median filter of the image with [-med_size, med_size]
# box in x and 1 pixel wide in y. Skips the pixel considered,
# so this is equivalent of a 2*med_size boxcar
tmp = []
for jt in range(-med_size, med_size + 1):
if jt != 0:
tmp.append(np.roll(bindark, [0, jt]))
# low frequency image
lf_dark = np.nanmedian(tmp, axis=0)
# high frequency image
dark_cube1[bin_it] = bindark - lf_dark
# -------------------------------------------------------------------------
# median the dark cube to create the master dark
master_dark = np.nanmedian(dark_cube1, axis=0)
# ----------------------------------------------------------------------
# Quality control
# ----------------------------------------------------------------------
# set passed variable and fail message list
passed, fail_msg = True, []
qc_values, qc_names, qc_logic, qc_pass = [], [], [], []
# ----------------------------------------------------------------------
# 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]
# ----------------------------------------------------------------------
# Save master dark to file
# ----------------------------------------------------------------------
# set reference filename
reffile = filenames[lastpos]
# construct folder and filename
darkmasterfits, tag = spirouConfig.Constants.DARK_FILE_MASTER(p, reffile)
darkmasterfitsname = os.path.basename(darkmasterfits)
# log writing of file
WLOG(p, '', 'Saving master dark to {0}'.format(darkmasterfitsname))
# construct big dark table
colnames = ['FILENAME', 'NIGHT', 'MJDATE', 'EXPTIME', 'WEATHER_TEMP',
'CASS_TEMP', 'RELHUMID', 'PVERSION', 'GROUPID']
values = [basenames, nightnames, dark_time, dark_exp, dark_wt_temp,
dark_cass_temp, dark_humidity, dark_pp_version, matched_id]
darktable = spirouImage.MakeTable(p, colnames, values)
# add keys from original header file
hdict = spirouImage.CopyOriginalKeys(hdr_ref)
# 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=tag)
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
# write master dark + dark table to file
p = spirouImage.WriteImageTable(p, darkmasterfits, image=master_dark,
table=darktable, hdict=hdict)
# ----------------------------------------------------------------------
# Move to calibDB and update calibDB
# ----------------------------------------------------------------------
if p['QC']:
# set dark master key
keydb = 'DARKM'
# copy dark fits file to the calibDB folder
spirouDB.PutCalibFile(p, darkmasterfits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, darkmasterfitsname, hdr_ref)
# ----------------------------------------------------------------------
# End Message
# ----------------------------------------------------------------------
p = spirouStartup.End(p)
# return a copy of locally defined variables in the memory
return dict(locals())
def main(night_name=None, hcfile=None, fpfiles=None):
"""
cal_SLIT_spirou.py main function, if night_name and files are None uses
arguments from run time i.e.:
cal_SLIT_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)
: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 hcfile is None or fpfiles is None:
names, types = ['hcfile', 'fpfiles'], [str, str]
customargs = spirouStartup.GetCustomFromRuntime(p, [0, 1],
types,
names,
last_multi=True)
else:
customargs = dict(hcfile=hcfile, fpfile=fpfiles)
# get parameters from configuration files and run time arguments
p = spirouStartup.LoadArguments(p,
night_name,
customargs=customargs,
mainfitsfile='fpfiles')
# ----------------------------------------------------------------------
# Construct reference filename and get fiber type
# ----------------------------------------------------------------------
p, hcfitsfilename = spirouStartup.SingleFileSetup(p, filename=p['HCFILE'])
p, fpfitsfiles = spirouStartup.MultiFileSetup(p, files=p['FPFILES'])
# set fiber (it doesn't matter with the 2D image but we need this to get
# the lamp type for FPFILES and HCFILES, AB == C
p['FIBER'] = 'AB'
p['FIB_TYP'] = [p['FIBER']]
fsource = __NAME__ + '/main()'
p.set_sources(['FIBER', 'FIB_TYP'], fsource)
# ----------------------------------------------------------------------
# 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)
# add a force plot off
p['PLOT_PER_ORDER'] = PLOT_PER_ORDER
p.set_source('PLOT_PER_ORDER', __NAME__ + '.main()')
# ----------------------------------------------------------------------
# Read FP and HC files
# ----------------------------------------------------------------------
# read input fp and hc data
rkwargs = dict(filename=fpfitsfiles[0],
filenames=fpfitsfiles[1:],
framemath='add')
p, fpdata, fphdr = spirouImage.ReadImageAndCombine(p, **rkwargs)
hcdata, hchdr, _, _ = spirouImage.ReadImage(p, hcfitsfilename)
# 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.ReadImage()')
sources = ['FPDATA', 'FPHDR']
loc.set_sources(sources, 'spirouImage.ReadImage()')
# ---------------------------------------------------------------------
# fix for un-preprocessed files
# ----------------------------------------------------------------------
hcdata = spirouImage.FixNonPreProcess(p, hcdata)
fpdata = spirouImage.FixNonPreProcess(p, fpdata)
# ----------------------------------------------------------------------
# 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)
# add a force plot off
p['PLOT_PER_ORDER'] = PLOT_PER_ORDER
p.set_source('PLOT_PER_ORDER', __NAME__ + '.main()')
# ----------------------------------------------------------------------
# Get basic image properties for reference file
# ----------------------------------------------------------------------
# get sig det value
p = spirouImage.GetSigdet(p, fphdr, name='sigdet')
# get exposure time
p = spirouImage.GetExpTime(p, fphdr, name='exptime')
# get gain
p = spirouImage.GetGain(p, fphdr, name='gain')
# get lamp parameters
p = spirouTHORCA.GetLampParams(p, hchdr)
# get FP_FP DPRTYPE
p = spirouImage.ReadParam(p, fphdr, 'KW_DPRTYPE', 'DPRTYPE', dtype=str)
# ----------------------------------------------------------------------
# Correction of reference FP
# ----------------------------------------------------------------------
# set the number of frames
p['NBFRAMES'] = len(fpfitsfiles)
p.set_source('NBFRAMES', __NAME__ + '.main()')
# Correction of DARK
p, fpdatac = spirouImage.CorrectForDark(p, fpdata, fphdr)
# Resize hc data
# rotate the image and convert from ADU/s to e-
fpdata = spirouImage.ConvertToE(spirouImage.FlipImage(p, fpdatac), p=p)
# 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)
fpdata1 = spirouImage.ResizeImage(p, fpdata, **bkwargs)
# log change in data size
WLOG(p, '',
('FPref Image format changed to {0}x{1}').format(*fpdata1.shape))
# Correct for the BADPIX mask (set all bad pixels to zero)
bargs = [p, fpdata1, fphdr]
p, fpdata1 = spirouImage.CorrectForBadPix(*bargs)
p, badpixmask = spirouImage.CorrectForBadPix(*bargs, return_map=True)
# log progress
WLOG(p, '', 'Cleaning FPref hot pixels')
# correct hot pixels
fpdata1 = spirouEXTOR.CleanHotpix(fpdata1, badpixmask)
# add to loc
loc['FPDATA1'] = fpdata1
loc.set_source('FPDATA1', __NAME__ + '.main()')
# Log the number of dead pixels
# get the number of bad pixels
with warnings.catch_warnings(record=True) as _:
n_bad_pix = np.nansum(fpdata1 <= 0)
n_bad_pix_frac = n_bad_pix * 100 / np.product(fpdata1.shape)
# Log number
wmsg = 'Nb FPref dead pixels = {0} / {1:.2f} %'
WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac))
# ----------------------------------------------------------------------
# Correction of HC
# ----------------------------------------------------------------------
# set the number of frames
p['NBFRAMES'] = 1
p.set_source('NBFRAMES', __NAME__ + '.main()')
# Correction of DARK
p, hcdatac = spirouImage.CorrectForDark(p, hcdata, hchdr)
# Resize hc data
# rotate the image and convert from ADU/s to e-
hcdata = spirouImage.ConvertToE(spirouImage.FlipImage(p, hcdatac), p=p)
# 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)
hcdata1 = spirouImage.ResizeImage(p, hcdata, **bkwargs)
# log change in data size
WLOG(p, '', ('HC Image format changed to {0}x{1}').format(*hcdata1.shape))
# Correct for the BADPIX mask (set all bad pixels to zero)
bargs = [p, hcdata1, hchdr]
p, hcdata1 = spirouImage.CorrectForBadPix(*bargs)
p, badpixmask = spirouImage.CorrectForBadPix(*bargs, return_map=True)
# log progress
WLOG(p, '', 'Cleaning HC hot pixels')
# correct hot pixels
hcdata1 = spirouEXTOR.CleanHotpix(hcdata1, badpixmask)
# add to loc
loc['HCDATA1'] = hcdata1
loc.set_source('HCDATA1', __NAME__ + '.main()')
# Log the number of dead pixels
# get the number of bad pixels
with warnings.catch_warnings(record=True) as _:
n_bad_pix = np.nansum(hcdata1 <= 0)
n_bad_pix_frac = n_bad_pix * 100 / np.product(hcdata1.shape)
# Log number
wmsg = 'Nb HC dead pixels = {0} / {1:.2f} %'
WLOG(p, 'info', wmsg.format(int(n_bad_pix), n_bad_pix_frac))
# -------------------------------------------------------------------------
# get all FP_FP files
# -------------------------------------------------------------------------
fpfilenames = spirouImage.FindFiles(p,
filetype=p['DPRTYPE'],
allowedtypes=p['ALLOWED_FP_TYPES'])
# convert filenames to a numpy array
fpfilenames = np.array(fpfilenames)
# julian date to know which file we need to
# process together
fp_time = np.zeros(len(fpfilenames))
basenames, fp_exp, fp_pp_version, nightnames = [], [], [], []
# log progress
WLOG(p, '', 'Reading all fp file headers')
# looping through the file headers
for it in range(len(fpfilenames)):
# log progress
wmsg = '\tReading file {0} / {1}'
WLOG(p, 'info', wmsg.format(it + 1, len(fpfilenames)))
# get fp filename
fpfilename = fpfilenames[it]
# get night name
night_name = os.path.dirname(fpfilenames[it]).split(p['TMP_DIR'])[-1]
# read data
data_it, hdr_it, _, _ = spirouImage.ReadImage(p, fpfilename)
# get header
hdr = spirouImage.ReadHeader(p, filepath=fpfilenames[it])
# add MJDATE to dark times
fp_time[it] = float(hdr[p['KW_ACQTIME'][0]])
# add other keys (for tabular output)
basenames.append(os.path.basename(fpfilenames[it]))
nightnames.append(night_name)
fp_exp.append(float(hdr[p['KW_EXPTIME'][0]]))
fp_pp_version.append(hdr[p['KW_PPVERSION'][0]])
# -------------------------------------------------------------------------
# match files by date
# -------------------------------------------------------------------------
# log progress
wmsg = 'Matching FP files by observation time (+/- {0} hrs)'
WLOG(p, '', wmsg.format(p['DARK_MASTER_MATCH_TIME']))
# get the time threshold
time_thres = p['FP_MASTER_MATCH_TIME']
# get items grouped by time
matched_id = spirouImage.GroupFilesByTime(p, fp_time, time_thres)
# -------------------------------------------------------------------------
# construct the master fp file (+ correct for dark/badpix)
# -------------------------------------------------------------------------
cargs = [fpdata1, fpfilenames, matched_id]
fpcube, transforms = spirouImage.ConstructMasterFP(p, *cargs)
# log process
wmsg1 = 'Master FP construction complete.'
wmsg2 = '\tAdding {0} group images to form FP master image'
WLOG(p, 'info', [wmsg1, wmsg2.format(len(fpcube))])
# sum the cube to make fp data
masterfp = np.sum(fpcube, axis=0)
# add to loc
loc['MASTERFP'] = masterfp
loc.set_source('MASTERFP', __NAME__ + '.main()')
# ------------------------------------------------------------------
# Get localisation coefficients
# ------------------------------------------------------------------
# original there is a loop but it is not used --> removed
p = spirouImage.FiberParams(p, p['FIBER'], merge=True)
# get localisation fit coefficients
p, loc = spirouLOCOR.GetCoeffs(p, fphdr, loc)
# ------------------------------------------------------------------
# Get master wave solution map
# ------------------------------------------------------------------
# get master wave map
masterwavefile = spirouDB.GetDatabaseMasterWave(p)
# log process
wmsg1 = 'Getting 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
wout = spirouImage.GetWaveSolution(p,
filename=masterwavefile,
return_wavemap=True,
quiet=True,
return_header=True,
fiber=wave_fiber)
loc['MASTERWAVEP'], loc['MASTERWAVE'] = wout[:2]
loc['MASTERWAVEHDR'], loc['WSOURCE'] = wout[2:]
# set sources
wsource = ['MASTERWAVEP', 'MASTERWAVE', 'MASTERWAVEHDR']
loc.set_sources(wsource, 'spirouImage.GetWaveSolution()')
# ----------------------------------------------------------------------
# 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)
# ----------------------------------------------------------------------
# Read cavity length file
# ----------------------------------------------------------------------
loc['CAVITY_LEN_COEFFS'] = spirouImage.ReadCavityLength(p)
source = __NAME__ + '.main() + spirouImage.ReadCavityLength()'
loc.set_source('CAVITY_LEN_COEFFS', source)
# ----------------------------------------------------------------------
# Calculate shape map
# ----------------------------------------------------------------------
# calculate dx map
loc = spirouImage.GetXShapeMap(p, loc)
# if dx map is None we shouldn't continue
if loc['DXMAP'] is None:
fargs = [
loc['MAXDXMAPINFO'][0], loc['MAXDXMAPINFO'][1], loc['MAXDXMAPSTD'],
p['SHAPE_QC_DXMAP_STD']
]
fmsg = ('The std of the dxmap for order {0} y-pixel {1} is too large.'
' std = {2} (limit = {3})'.format(*fargs))
wmsg = 'QUALITY CONTROL FAILED: {0}'
WLOG(p, 'warning', wmsg.format(fmsg))
WLOG(p, 'warning', 'Cannot continue. Exiting.')
# End Message
p = spirouStartup.End(p)
# return a copy of locally defined variables in the memory
return dict(locals())
# calculate dymap
loc = spirouImage.GetYShapeMap(p, loc, fphdr)
# ------------------------------------------------------------------
# Need to straighten the dxmap
# ------------------------------------------------------------------
# copy it first
loc['DXMAP0'] = np.array(loc['DXMAP'])
# straighten it
loc['DXMAP'] = spirouImage.EATransform(loc['DXMAP'], dymap=loc['DYMAP'])
# ------------------------------------------------------------------
# Need to straighten the hc data and fp data for debug
# ------------------------------------------------------------------
# log progress
WLOG(p, '', 'Shape finding complete. Applying transforms.')
# apply very last update of the debananafication
tkwargs = dict(dxmap=loc['DXMAP'], dymap=loc['DYMAP'])
loc['HCDATA2'] = spirouImage.EATransform(loc['HCDATA1'], **tkwargs)
loc['FPDATA2'] = spirouImage.EATransform(loc['FPDATA1'], **tkwargs)
loc.set_sources(['HCDATA2', 'FPDATA2'], __NAME__ + '.main()')
# ------------------------------------------------------------------
# Plotting
# ------------------------------------------------------------------
if p['DRS_PLOT'] > 0:
# plots setup: start interactive plot
sPlt.start_interactive_session(p)
# plot the shape process for one order
sPlt.slit_shape_angle_plot(p, loc)
# end interactive section
sPlt.end_interactive_session(p)
# ----------------------------------------------------------------------
# Quality control
# ----------------------------------------------------------------------
# TODO: Decide on some quality control criteria?
# set passed variable and fail message list
passed, fail_msg = True, []
qc_values, qc_names, qc_logic, qc_pass = [], [], [], []
# 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(loc['MAXDXMAPSTD'])
qc_names.append('DXMAP STD')
qc_logic.append('DXMAP STD < {0}'.format(p['SHAPE_QC_DXMAP_STD']))
qc_pass.append(1)
# store in qc_params
qc_params = [qc_names, qc_values, qc_logic, qc_pass]
# ------------------------------------------------------------------
# Writing FP big table
# ------------------------------------------------------------------
# construct big fp table
colnames = [
'FILENAME', 'NIGHT', 'MJDATE', 'EXPTIME', 'PVERSION', 'GROUPID',
'DXREF', 'DYREF', 'A', 'B', 'C', 'D'
]
values = [
basenames, nightnames, fp_time, fp_exp, fp_pp_version, matched_id,
transforms[:, 0], transforms[:, 1], transforms[:, 2], transforms[:, 3],
transforms[:, 4], transforms[:, 5]
]
fptable = spirouImage.MakeTable(p, colnames, values)
# ------------------------------------------------------------------
# Writing DXMAP to file
# ------------------------------------------------------------------
# get the raw tilt file name
raw_shape_file = os.path.basename(p['FITSFILENAME'])
# construct file name and path
shapexfits, tag = spirouConfig.Constants.SLIT_XSHAPE_FILE(p)
shapexfitsname = os.path.basename(shapexfits)
# Log that we are saving tilt file
wmsg = 'Saving shape x information in file: {0}'
WLOG(p, '', wmsg.format(shapexfitsname))
# Copy keys from fits file
hdict = spirouImage.CopyOriginalKeys(fphdr)
# 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)
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.AddKey1DList(p,
hdict,
p['KW_INFILE1'],
dim1name='hcfile',
values=p['HCFILE'])
hdict = spirouImage.AddKey1DList(p,
hdict,
p['KW_INFILE2'],
dim1name='fpfile',
values=p['FPFILES'])
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
# write tilt file to file
p = spirouImage.WriteImageTable(p,
shapexfits,
image=loc['DXMAP'],
table=fptable,
hdict=hdict)
# ------------------------------------------------------------------
# Writing DYMAP to file
# ------------------------------------------------------------------
# get the raw tilt file name
raw_shape_file = os.path.basename(p['FITSFILENAME'])
# construct file name and path
shapeyfits, tag = spirouConfig.Constants.SLIT_YSHAPE_FILE(p)
shapeyfitsname = os.path.basename(shapeyfits)
# Log that we are saving tilt file
wmsg = 'Saving shape y information in file: {0}'
WLOG(p, '', wmsg.format(shapeyfitsname))
# Copy keys from fits file
hdict = spirouImage.CopyOriginalKeys(fphdr)
# 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)
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.AddKey1DList(p,
hdict,
p['KW_INFILE1'],
dim1name='hcfile',
values=p['HCFILE'])
hdict = spirouImage.AddKey1DList(p,
hdict,
p['KW_INFILE2'],
dim1name='fpfile',
values=p['FPFILES'])
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
# write tilt file to file
p = spirouImage.WriteImageTable(p,
shapeyfits,
image=loc['DYMAP'],
table=fptable,
hdict=hdict)
# ------------------------------------------------------------------
# Writing Master FP to file
# ------------------------------------------------------------------
# get the raw tilt file name
raw_shape_file = os.path.basename(p['FITSFILENAME'])
# construct file name and path
fpmasterfits, tag = spirouConfig.Constants.SLIT_MASTER_FP_FILE(p)
fpmasterfitsname = os.path.basename(fpmasterfits)
# Log that we are saving tilt file
wmsg = 'Saving master FP file: {0}'
WLOG(p, '', wmsg.format(fpmasterfitsname))
# Copy keys from fits file
hdict = spirouImage.CopyOriginalKeys(fphdr)
# 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)
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.AddKey1DList(p,
hdict,
p['KW_INFILE1'],
dim1name='hcfile',
values=p['HCFILE'])
hdict = spirouImage.AddKey1DList(p,
hdict,
p['KW_INFILE2'],
dim1name='fpfile',
values=p['FPFILES'])
# add qc parameters
hdict = spirouImage.AddKey(p, hdict, p['KW_DRS_QC'], value=p['QC'])
hdict = spirouImage.AddQCKeys(p, hdict, qc_params)
# write tilt file to file
p = spirouImage.WriteImageTable(p,
fpmasterfits,
image=masterfp,
table=fptable,
hdict=hdict)
# ------------------------------------------------------------------
# Writing sanity check files
# ------------------------------------------------------------------
if p['SHAPE_DEBUG_OUTPUTS']:
# log
WLOG(p, '', 'Saving debug sanity check files')
# construct file names
input_fp_file, tag1 = spirouConfig.Constants.SLIT_SHAPE_IN_FP_FILE(p)
output_fp_file, tag2 = spirouConfig.Constants.SLIT_SHAPE_OUT_FP_FILE(p)
input_hc_file, tag3 = spirouConfig.Constants.SLIT_SHAPE_IN_HC_FILE(p)
output_hc_file, tag4 = spirouConfig.Constants.SLIT_SHAPE_OUT_HC_FILE(p)
bdxmap_file, tag5 = spirouConfig.Constants.SLIT_SHAPE_BDXMAP_FILE(p)
# write input fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag1)
p = spirouImage.WriteImage(p, input_fp_file, loc['FPDATA1'], hdict)
# write output fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2)
p = spirouImage.WriteImage(p, output_fp_file, loc['FPDATA2'], hdict)
# write input fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag3)
p = spirouImage.WriteImage(p, input_hc_file, loc['HCDATA1'], hdict)
# write output fp file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag4)
p = spirouImage.WriteImage(p, output_hc_file, loc['HCDATA2'], hdict)
# write overlap file
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag5)
p = spirouImage.WriteImage(p, bdxmap_file, loc['DXMAP0'], hdict)
# ----------------------------------------------------------------------
# Move to calibDB and update calibDB
# ----------------------------------------------------------------------
if p['QC']:
# add shape x
keydb = 'SHAPEX'
# copy shape file to the calibDB folder
spirouDB.PutCalibFile(p, shapexfits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, shapexfitsname, fphdr)
# add shape y
keydb = 'SHAPEY'
# copy shape file to the calibDB folder
spirouDB.PutCalibFile(p, shapeyfits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, shapeyfitsname, fphdr)
# add fp master
keydb = 'FPMASTER'
# copy shape file to the calibDB folder
spirouDB.PutCalibFile(p, fpmasterfits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, fpmasterfitsname, fphdr)
# ----------------------------------------------------------------------
# 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, files=None):
"""
cal_FF_RAW_spirou.py main function, if night_name and files are None uses
arguments from run time i.e.:
cal_FF_RAW_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)
: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)
# run specific start up
p['FIB_TYPE'] = p['FIBER_TYPES']
p.set_source('FIB_TYPE', __NAME__ + '__main__()')
# ----------------------------------------------------------------------
# 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']
# ----------------------------------------------------------------------
# Correction of DARK
# ----------------------------------------------------------------------
p, datac = spirouImage.CorrectForDark(p, data, hdr)
# ----------------------------------------------------------------------
# Resize image
# ----------------------------------------------------------------------
# rotate the image and convert from ADU/s to e-
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
WLOG(p, '', ('Image format changed to '
'{0}x{1}').format(*data1.shape[::-1]))
# ----------------------------------------------------------------------
# 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 (95th percentile) /pixel in the spectrum: '
'{0:.1f} [ADU]')
WLOG(p, 'info', wmsg.format(max_signal / p['NBFRAMES']))
# ----------------------------------------------------------------------
# Background computation
# ----------------------------------------------------------------------
# p['ic_bkgr_percent'] = 3.0
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
data1 = data1 - background
# ----------------------------------------------------------------------
# Read tilt slit angle
# ----------------------------------------------------------------------
# define loc storage parameter dictionary
loc = ParamDict()
# get tilts
if p['IC_FF_EXTRACT_TYPE'] not in EXTRACT_SHAPE_TYPES:
p, loc['TILT'] = spirouImage.ReadTiltFile(p, hdr)
else:
loc['TILT'] = None
loc.set_source('TILT', __NAME__ + '/main()')
# ----------------------------------------------------------------------
# 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_FF_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_FF_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 in p
p['FIBER'] = fiber
p.set_source('FIBER', __NAME__ + '/main()')
# get fiber parameters
params2add = spirouImage.FiberParams(p, p['FIBER'])
for param in params2add:
p[param] = params2add[param]
p.set_source(param, __NAME__ + '.main()')
# ------------------------------------------------------------------
# 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['E2DSLL'] = []
# Create array to store the blaze (for each order and at each pixel
# along order)
loc['BLAZE'] = np.zeros((loc['NUMBER_ORDERS'], data2.shape[1]))
# Create array to store the flat (for each order and at each pixel
# along order)
loc['FLAT'] = 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'])
# Create array to store the rms for each order
loc['RMS'] = np.zeros(loc['NUMBER_ORDERS'])
# Manually set the sigdet to be used in extraction weighting
if p['IC_FF_SIGDET'] > 0:
p['SIGDET'] = float(p['IC_FF_SIGDET'])
# ------------------------------------------------------------------
# Extract orders
# old code time: 1 loop, best of 3: 22.3 s per loop
# new code time: 3.16 s ± 237 ms per loop
# ------------------------------------------------------------------
# get limits of order extraction
valid_orders = spirouFLAT.GetValidOrders(p, loc)
# loop around each order
for order_num in valid_orders:
# extract this order
eargs = [p, loc, data2, order_num]
ekwargs = dict(mode=p['IC_FF_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_FF_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']
noise = p['SIGDET'] * np.sqrt(range1 + range2)
# get window size
blaze_win1 = int(data2.shape[1] / 2) - p['IC_EXTFBLAZ']
blaze_win2 = int(data2.shape[1] / 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)
# remove edge of orders at low S/N
with warnings.catch_warnings(record=True) as _:
blazemask = e2ds < (flux / p['IC_FRACMINBLAZE'])
e2ds = np.where(blazemask, np.nan, e2ds)
# e2ds = np.where(e2ds < p['IC_MINBLAZE'], 0., e2ds)
# calcualte the blaze function
blaze = spirouFLAT.MeasureBlazeForOrder(p, e2ds)
# calculate the flat
flat = e2ds / blaze
# calculate the rms
rms = np.nanstd(flat)
# log the SNR RMS
wmsg = 'On fiber {0} order {1}: S/N= {2:.1f} - FF rms={3:.2f} %'
wargs = [fiber, order_num, snr, rms * 100.0]
WLOG(p, '', wmsg.format(*wargs))
# add calculations to storage
loc['E2DS'][order_num] = e2ds
loc['SNR'][order_num] = snr
loc['RMS'][order_num] = rms
loc['BLAZE'][order_num] = blaze
loc['FLAT'][order_num] = flat
# save the longfile
if p['IC_FF_EXTRACT_TYPE'] in EXTRACT_LL_TYPES:
loc['E2DSLL'].append(e2dsll)
# set sources
source = __NAME__ + '/main()()'
loc.set_sources(['e2ds', 'SNR', 'RMS', 'blaze', 'flat'], 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))
# ----------------------------------------------------------------------
# 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.ff_aorder_fit_edges(p, loc, data1)
else:
# plot image with selected order fit and edge fit (faster)
sPlt.ff_sorder_fit_edges(p, loc, data1)
# plot tilt adjusted e2ds and blaze for selected order
sPlt.ff_sorder_tiltadj_e2ds_blaze(p, loc)
# plot flat for selected order
sPlt.ff_sorder_flat(p, loc)
# plot the RMS for all but skipped orders
# sPlt.ff_rms_plot(p, loc)
if p['IC_FF_EXTRACT_TYPE'] in EXTRACT_SHAPE_TYPES:
sPlt.ff_debanana_plot(p, loc, data2)
# ------------------------------------------------------------------
# Quality control
# ------------------------------------------------------------------
passed, fail_msg = True, []
qc_values, qc_names, qc_logic, qc_pass = [], [], [], []
# saturation check: check that the max_signal is lower than
# qc_max_signal
# if max_signal > (p['QC_MAX_SIGNAL'] * p['nbframes']):
# fmsg = 'Too much flux in the image (max authorized={0})'
# fail_msg.append(fmsg.format(p['QC_MAX_SIGNAL'] * p['nbframes']))
# passed = False
# # For some reason this test is ignored in old code
# passed = True
# WLOG(p, 'info', fail_msg[-1])
# get mask for removing certain orders in the RMS calculation
remove_orders = np.array(p['FF_RMS_PLOT_SKIP_ORDERS'])
mask = np.in1d(np.arange(len(loc['RMS'])), remove_orders)
# apply mask and calculate the maximum RMS
max_rms = np.nanmax(loc['RMS'][~mask])
# apply the quality control based on the new RMS
if max_rms > p['QC_FF_RMS']:
fmsg = 'abnormal RMS of FF ({0:.3f} > {1:.3f})'
fail_msg.append(fmsg.format(max_rms, p['QC_FF_RMS']))
passed = False
qc_pass.append(0)
else:
qc_pass.append(1)
# add to qc header lists
qc_values.append(max_rms)
qc_names.append('max_rms')
qc_logic.append('max_rms > {0:.3f}'.format(p['QC_FF_RMS']))
# finally log the failed messages and set QC = 1 if we pass the
# quality control QC = 0 if we fail quality control
if passed:
wmsg = 'QUALITY CONTROL SUCCESSFUL - Well Done -'
WLOG(p, 'info', wmsg)
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 Blaze in file
# ----------------------------------------------------------------------
# get raw flat filename
raw_flat_file = os.path.basename(p['FITSFILENAME'])
e2dsllfits, tag4 = spirouConfig.Constants.EXTRACT_E2DSLL_FILE(p)
# get extraction method and function
efout = spirouEXTOR.GetExtMethod(p, p['IC_FF_EXTRACT_TYPE'])
extmethod, extfunc = efout
# construct filename
blazefits, tag1 = spirouConfig.Constants.FF_BLAZE_FILE(p)
blazefitsname = os.path.split(blazefits)[-1]
# log that we are saving blaze file
wmsg = 'Saving blaze spectrum for fiber: {0} in {1}'
WLOG(p, '', wmsg.format(fiber, blazefitsname))
# 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_FIBER'], value=p['FIBER'])
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'])
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_FF_EXTRACT_TYPE'] not in EXTRACT_SHAPE_TYPES:
hdict = spirouImage.AddKey(p,
hdict,
p['KW_CDBTILT'],
value=p['TILTFILE'])
if p['IC_FF_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.AddKey1DList(p,
hdict,
p['KW_INFILE1'],
dim1name='file',
values=p['ARG_FILE_NAMES'])
# add some properties back
hdict = spirouImage.AddKey(p, hdict, p['KW_CCD_SIGDET'])
hdict = spirouImage.AddKey(p, hdict, p['KW_CCD_CONAD'])
# 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)
# output keys
hdict = spirouImage.AddKey(p,
hdict,
p['KW_EXT_TYPE'],
value=p['DPRTYPE'])
# write 1D list of the SNR
hdict = spirouImage.AddKey1DList(p,
hdict,
p['KW_EXTRA_SN'],
values=loc['SNR'])
# write center fits and add header keys (via hdict)
p = spirouImage.WriteImage(p, blazefits, loc['BLAZE'], hdict)
# ----------------------------------------------------------------------
# Store Flat-field in file
# ----------------------------------------------------------------------
# construct filename
flatfits, tag2 = spirouConfig.Constants.FF_FLAT_FILE(p)
flatfitsname = os.path.split(flatfits)[-1]
# log that we are saving blaze file
wmsg = 'Saving FF spectrum for fiber: {0} in {1}'
WLOG(p, '', wmsg.format(fiber, flatfitsname))
# write 1D list of the RMS (add to hdict from blaze)
hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag2)
hdict = spirouImage.AddKey1DList(p,
hdict,
p['KW_FLAT_RMS'],
values=loc['RMS'])
# write center fits and add header keys (via same hdict as blaze)
p = spirouImage.WriteImage(p, flatfits, loc['FLAT'], 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_FF_EXTRACT_TYPE'] in EXTRACT_LL_TYPES:
llstack = np.vstack(loc['E2DSLL'])
p = spirouImage.WriteImage(p, e2dsllfits, llstack, hdict)
# ------------------------------------------------------------------
# Update the calibration database
# ------------------------------------------------------------------
if p['QC'] == 1:
# copy flatfits to calibdb
keydb = 'FLAT_' + p['FIBER']
# copy localisation file to the calibDB folder
spirouDB.PutCalibFile(p, flatfits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, flatfitsname, hdr)
# copy blazefits to calibdb
keydb = 'BLAZE_' + p['FIBER']
# copy localisation file to the calibDB folder
spirouDB.PutCalibFile(p, blazefits)
# update the master calib DB file with new key
spirouDB.UpdateCalibMaster(p, keydb, blazefitsname, hdr)
# ----------------------------------------------------------------------
# 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())
def part2(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 = p['IC_HC_N_ORD_START_2']
end = p['IC_HC_N_ORD_FINAL_2']
# redefine echelle orders
orderrange = np.arange(start, end)
loc['ECHELLE_ORDERS'] = p['IC_HC_T_ORDER_START'] - orderrange
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)
# ------------------------------------------------------------------
# Littrow test
# ------------------------------------------------------------------
start = p['IC_LITTROW_ORDER_INIT_2']
#end = p['IC_LITTROW_ORDER_FINAL_2']
# redefine echelle orders
orderrange = np.arange(start, end)
loc['ECHELLE_ORDERS'] = p['IC_HC_T_ORDER_START'] - orderrange
loc.set_source('ECHELLE_ORDERS', __NAME__ + '/main()')
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 0-24 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])
# get the corresponding order
min_littrow_ord = loc['LITTROW_MINDEVORD_2'][x_it]
max_littrow_ord = loc['LITTROW_MAXDEVORD_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} 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
# 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()')
# ----------------------------------------------------------------------
# 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_PID'], value=p['PID'])
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_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['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 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(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.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 p, loc