def match_redrock_zfit_to_spectra(redrockfile, spectra, Nfit=None) :
'''
Read Redrock file, and return astropy Table of best fits matched to the targetids of input spectra
- for each target, store arrays chi2[Nfit], coeff[Nfit], z[Nfit], spectype[Nfit], subtype[Nfit]
- if Nfit is None: take all available fits
'''
dummy, rr_table = redrock.results.read_zscan(redrockfile)
rr_targets = rr_table['targetid']
if Nfit is None :
ww, = np.where( (rr_targets == rr_targets[0]) )
Nfit = len(ww)
matched_redrock_cat = Table(dtype=[('TARGETID', '<i8'), ('CHI2', '<f8', (Nfit,)), ('DELTACHI2', '<f8', (Nfit,)), ('COEFF', '<f8', (Nfit,10,)), ('Z', '<f8', (Nfit,)), ('ZERR', '<f8', (Nfit,)), ('ZWARN', '<i8', (Nfit,)), ('SPECTYPE', '<U6', (Nfit,)), ('SUBTYPE', '<U2', (Nfit,))])
for i_spec in range(spectra.num_spectra()) :
ww, = np.where((rr_targets == spectra.fibermap['TARGETID'][i_spec]))
if len(ww)<Nfit :
raise RuntimeError("redrock table cannot match spectra with "+str(Nfit)+" best fits")
ind = np.argsort(rr_table[ww]['chi2'])[0:Nfit]
sub_table = rr_table[ww][ind]
the_entry = [ spectra.fibermap['TARGETID'][i_spec] ]
the_entry.append(sub_table['chi2'])
the_entry.append(sub_table['deltachi2'])
the_entry.append(sub_table['coeff'])
the_entry.append(sub_table['z'])
the_entry.append(sub_table['zerr'])
the_entry.append(sub_table['zwarn'])
the_entry.append(sub_table['spectype'])
the_entry.append(sub_table['subtype'])
matched_redrock_cat.add_row(the_entry)
return matched_redrock_cat
def match_redrock_zfit_to_spectra(redrockfile, spectra, num_best_fit=3):
'''
Read Redrock file, and return astropy Table of best fits matched to the targetids of input spectra
- for each target, returns arrays chi2[N], coeff[N], z[N], spectype[N], subtype[N]
- where N = num_best_fit
'''
matched_redrock_cat = Table(dtype=[(
'TARGETID',
'<i8'), ('chi2', '<f8',
(num_best_fit, )), ('coeff', '<f8', (
num_best_fit,
10,
)), ('z', '<f8',
(num_best_fit, )), ('spectype', '<U6', (
num_best_fit, )), ('subtype', '<U2',
(num_best_fit, ))])
dummy, rr_table = redrock.results.read_zscan(redrockfile)
for i_spec in range(spectra.num_spectra()):
ww, = np.where(
(rr_table['targetid'] == spectra.fibermap['TARGETID'][i_spec]))
if len(ww) < num_best_fit:
raise RuntimeError("redrock table cannot match spectra with " +
str(num_best_fit) + " best fits")
ind = np.argsort(rr_table[ww]['chi2'])[0:num_best_fit]
sub_table = rr_table[ww][ind][0:num_best_fit]
the_entry = [spectra.fibermap['TARGETID'][i_spec]]
the_entry.append(sub_table['chi2'])
the_entry.append(sub_table['coeff'])
the_entry.append(sub_table['z'])
the_entry.append(sub_table['spectype'])
the_entry.append(sub_table['subtype'])
matched_redrock_cat.add_row(the_entry)
return matched_redrock_cat
def main():
args = _parse()
log = get_logger()
specprod_dir = args.specprod_dir
if specprod_dir is None: specprod_dir = desispec.io.specprod_root()
webdir = args.webdir
if webdir is None:
webdir = os.environ[
"DESI_WWW"] + "/users/armengau/svdc2019c" # TMP, for test
nights = desispec.io.get_nights(specprod_dir=specprod_dir)
# TODO - Select night (eg. only last night)
nights = nights[8:11] # TMP, for test
for thenight in nights:
# Get spectra from tiles dir - To consolidate
specfiles = glob.glob(
os.path.join(specprod_dir,
"tiles/*/tilespectra-*-" + thenight + ".fits"))
for f in specfiles:
log.info("Working on file " + f)
file_label = f[
f.find("tilespectra-") + 12:f.find(thenight) -
1] # From tile-based file description - To consolidate
spectra = desispec.io.read_spectra(f)
zbfile = f.replace("tilespectra", "zbest")
zbest = Table.read(zbfile, 'ZBEST')
# Handle several html pages per pixel : sort by TARGETID
# NOTE : this way, individual spectra from the same target are together
# Does it make sense ? (they have the same fit)
nbpages = int(np.ceil((spectra.num_spectra() / args.nspecperfile)))
sort_indices = np.argsort(
spectra.fibermap["TARGETID"],
kind='mergesort') # keep order of equal elts
for i_page in range(1, 1 + nbpages):
log.info(" * Page " + str(i_page) + " / " + str(nbpages))
the_indices = sort_indices[(i_page - 1) *
args.nspecperfile:i_page *
args.nspecperfile]
thespec = myspecselect(spectra, indices=the_indices)
thezb, kk = match_zcat_to_spectra(zbest, thespec)
#model = plotframes.create_model(thespec, thezb)
### No VI results to display by default
# vifile = os.environ['HOME']+"/prospect/vilist_prototype.fits"
# vidata = match_vi_targets(vifile, thespec.fibermap["TARGETID"])
titlepage = "specviewer_night" + thenight + "_" + file_label + "_" + str(
i_page)
html_dir = webdir + "/nights/night" + thenight
if not os.path.exists(html_dir):
os.makedirs(html_dir)
os.mkdir(html_dir + "/vignettes")
plotspectra(thespec,
zcatalog=thezb,
title=titlepage,
html_dir=html_dir)
def page_subset_expo(fdir, exposure, frametype, petals, html_dir,
titlepage_prefix, mask, log, nspecperfile, snr_cut):
'''
Running prospect from frames : loop over petals for a given exposure
'''
nspec_done = 0
for petal_num in petals:
frames = [
desispec.io.read_frame(
os.path.join(
fdir, frametype + "-" + band + petal_num + "-" + exposure +
".fits")) for band in ['b', 'r', 'z']
]
spectra = utils_specviewer.frames2spectra(frames, with_scores=True)
if 'FIBERSTATUS' in spectra.fibermap.keys():
spectra = myspecselect.myspecselect(spectra,
clean_fiberstatus=True)
if spectra is None: return 0
# Selection
if (mask != None) or (snr_cut != None):
spectra = utils_specviewer.specviewer_selection(
spectra,
log=log,
mask=mask,
mask_type='CMX_TARGET',
snr_cut=snr_cut,
with_dirty_mask_merge=True)
if spectra == 0: continue
# Handle several html pages per exposure - sort by fiberid
nspec_expo = spectra.num_spectra()
log.info("Petal number " + petal_num + " : " + str(nspec_expo) +
" spectra")
sort_indices = np.argsort(spectra.fibermap["FIBER"])
nbpages = int(np.ceil((nspec_expo / nspecperfile)))
for i_page in range(1, 1 + nbpages):
log.info(" * Page " + str(i_page) + " / " + str(nbpages))
the_indices = sort_indices[(i_page - 1) * nspecperfile:i_page *
nspecperfile]
thespec = myspecselect.myspecselect(spectra, indices=the_indices)
titlepage = titlepage_prefix + "_petal" + petal_num + "_" + str(
i_page)
plotframes.plotspectra(thespec,
with_noise=True,
with_coaddcam=True,
is_coadded=False,
title=titlepage,
html_dir=html_dir,
mask_type='CMX_TARGET',
with_thumb_only_page=True)
nspec_done += nspec_expo
return nspec_done
def match_zcat_to_spectra(zcat_in, spectra):
'''
zcat_in : astropy Table from redshift fitter
creates a new astropy Table whose rows match the targetids of input spectra
also returns the corresponding list of indices
'''
zcat_out = Table(dtype=zcat_in.dtype)
index_list = list()
for i_spec in range(spectra.num_spectra()):
ww, = np.where(
(zcat_in['TARGETID'] == spectra.fibermap['TARGETID'][i_spec]))
if len(ww) < 1: raise RuntimeError("zcat table cannot match spectra.")
zcat_out.add_row(zcat_in[ww[0]])
index_list.append(ww[0])
return (zcat_out, index_list)
def match_zcat_to_spectra(zcat_in, spectra):
'''
zcat_in : astropy Table from redshift fitter
- creates a new astropy Table whose rows match the targetids of input spectra
- also returns the corresponding list of indices
- for each targetid, a unique row in zcat_in must exist.
'''
zcat_out = Table(dtype=zcat_in.dtype)
index_list = list()
for i_spec in range(spectra.num_spectra()):
ww, = np.where(
(zcat_in['TARGETID'] == spectra.fibermap['TARGETID'][i_spec]))
if len(ww) < 1:
raise RuntimeError("No zcat entry for target " +
str(spectra.fibermap['TARGETID'][i_spec]))
if len(ww) > 1:
raise RuntimeError("Several zcat entries for target " +
str(spectra.fibermap['TARGETID'][i_spec]))
zcat_out.add_row(zcat_in[ww[0]])
index_list.append(ww[0])
return (zcat_out, index_list)
def match_zcat_to_spectra(zcat_in, spectra) :
'''
zcat_in : astropy Table from redshift fitter
- creates a new astropy Table whose rows match the targetids of input spectra
- also returns the corresponding list of indices
- for each targetid, a unique row in zcat_in must exist.
TODO : maybe rename this fct ? match_table_to_spectra ?
=> it also works whatever kind of input zcat : just has to be a table with 'TARGETID' key
=> in particular it's useful for "redrock_cat" tables
'''
if zcat_in is None : return None
zcat_out = Table(dtype=zcat_in.dtype)
index_list = list()
for i_spec in range(spectra.num_spectra()) :
ww, = np.where((zcat_in['TARGETID'] == spectra.fibermap['TARGETID'][i_spec]))
if len(ww)<1 :
raise RuntimeError("No zcat entry for target "+str(spectra.fibermap['TARGETID'][i_spec]))
if len(ww)>1 :
raise RuntimeError("Several zcat entries for target "+str(spectra.fibermap['TARGETID'][i_spec]))
zcat_out.add_row(zcat_in[ww[0]])
index_list.append(ww[0])
return (zcat_out, index_list)
def plotspectra(spectra,
zcatalog=None,
redrock_cat=None,
notebook=False,
html_dir=None,
title=None,
with_imaging=True,
with_noise=True,
with_thumb_tab=True,
with_vi_widgets=True,
top_metadata=None,
vi_countdown=-1,
with_thumb_only_page=False,
with_coaddcam=True,
mask_type='DESI_TARGET',
model_from_zcat=True,
model=None,
num_approx_fits=None,
with_full_2ndfit=True,
template_dir=None,
archetype_fit=False,
archetypes_dir=None):
'''Main prospect routine. From a set of spectra, creates a bokeh document
used for VI, to be displayed as an HTML page or within a Jupyter notebook.
Parameters
----------
spectra : :class:`~desispec.spectra.Spectra` or :class:`~specutils.Spectrum1D` or :class:`~specutils.SpectrumList` or list of :class:`~desispec.frame.Frame`
Input spectra. :class:`~specutils.Spectrum1D` are assumed to be SDSS/BOSS/eBOSS.
Otherwise DESI spectra or frames is assumed.
zcatalog : :class:`~astropy.table.Table`, optional
Redshift values, matched one-to-one with the input spectra.
redrock_cat : :class:`~astropy.table.Table`, optional
Redrock output (as defined in :func:`~prospect.utilities.match_redrock_zfit_to_spectra`).
Entries must be matched one-by-one (in order) to spectra.
notebook : :class:`bool`, optional
If ``True``, bokeh outputs the viewer to a Jupyter notebook.
html_dir : :class:`str`, optional
Directory to store the HTML page if `notebook` is ``False``.
title : :class:`str`, optional
Title used to name the HTML page / the bokeh figure / the VI file.
with_imaging : :class:`bool`, optional
If ``False``, don't include thumb image from https://www.legacysurvey.org/viewer.
with_noise : :class:`bool`, optional
If ``False``, don't include uncertainty for each spectrum.
with_thumb_tab : :class:`bool`, optional
If ``False``, don't include a tab with spectra thumbnails.
with_vi_widgets : :class:`bool`, optional
Include widgets used to enter VI information. Set it to ``False`` if
you do not intend to record VI files.
top_metadata : :class:`list`, optional
List of metadata to be highlighted in the top (most visible) table.
Default values ['TARGETID', 'EXPID']
vi_countdown : :class:`int`, optional
If ``>0``, add a countdown widget in the VI panel, with a value in minutes given
by `vi_countdown``.
with_thumb_only_page : :class:`bool`, optional
When creating a static HTML (`notebook` is ``False``), a light HTML
page including only the thumb gallery will also be produced.
with_coaddcam : :class:`bool`, optional
Include camera-coaddition, only relevant for DESI.
mask_type : :class:`str`, optional (default: DESI_TARGET)
Bitmask type to identify target categories in the spectra. For DESI
these could be: DESI_TARGET, SV1_DESI_TARGET, SV1_BGS_TARGET, CMX_TARGET.
model_from_zcat : :class:`bool`, optional
If ``True``, model spectra will be computed from the input `zcatalog`.
model : :func:`tuple`, optional
If set, use this input set of model spectra instead of computing it from `zcatalog`.
model consists of (mwave, mflux); model must be entry-matched to `zcatalog`.
num_approx_fits : :class:`int`, optional
Number of best-fit models to display, if `redrock_cat` is provided.
By default, all best-fit models available in `redrock_cat` are diplayed.
with_full_2ndfit : :class:`bool`, optional
If ``True``, the second best-fit model from `redrock_cat` will be displayed
without approximation (no undersampling, full resolution).
template_dir : :class:`str`, optional
Redrock template directory.
archetype_fit : :class:`bool`, optional
If ``True``, assume `zcatalog` derived from :command:`redrock --archetypes`
and plot model accordingly.
archetypes_dir : :class:`str`, optional
Directory path for archetypes if not :envvar:`RR_ARCHETYPE_DIR`.
'''
#- Check input spectra.
#- Set masked bins to NaN for compatibility with bokeh.
if _specutils_imported and isinstance(spectra, Spectrum1D):
# We will assume this is from an SDSS/BOSS/eBOSS spPlate file.
survey = 'SDSS'
nspec = spectra.flux.shape[0]
bad = (spectra.uncertainty.array == 0.0) | spectra.mask
spectra.flux[bad] = np.nan
elif _specutils_imported and isinstance(spectra, SpectrumList):
# We will assume this is from a DESI spectra-64 file.
survey = 'DESI'
nspec = spectra[0].flux.shape[0]
for s in spectra:
bad = (s.uncertainty.array == 0.0) | s.mask
s.flux[bad] = np.nan
else:
# DESI object (Spectra or list of Frame)
survey = 'DESI'
if _desispec_imported and isinstance(spectra,
desispec.spectra.Spectra):
nspec = spectra.num_spectra()
elif _desispec_imported and isinstance(spectra, list) and isinstance(
spectra[0], desispec.frame.Frame):
# If inputs are frames, convert to a spectra object
spectra = frames2spectra(spectra)
nspec = spectra.num_spectra()
if title is None:
title = 'Night {} ExpID {} Spectrograph {}'.format(
spectra.meta['NIGHT'],
spectra.meta['EXPID'],
spectra.meta['CAMERA'][1],
)
else:
raise ValueError("Unsupported type for input spectra. \n" +
" _specutils_imported = " +
str(_specutils_imported) + "\n" +
" _desispec_imported = " +
str(_desispec_imported))
for band in spectra.bands:
bad = (spectra.ivar[band] == 0.0) | (spectra.mask[band] != 0)
spectra.flux[band][bad] = np.nan
#- No coaddition if spectra is already single-band
if len(spectra.bands) == 1: with_coaddcam = False
if title is None:
title = "specviewer"
#- Input zcatalog / model
if zcatalog is not None:
if survey == 'SDSS':
if len(zcatalog) != spectra.flux.shape[0]:
raise ValueError(
'zcatalog and spectra do not match (different lengths)')
else:
if np.any(zcatalog['TARGETID'] != spectra.fibermap['TARGETID']):
raise ValueError(
'zcatalog and spectra do not match (different targetids)')
if model is not None:
# SDSS spectra will supply the model.
assert not model_from_zcat
mwave, mflux = model
if len(mflux) != nspec:
raise ValueError(
"model fluxes do not match spectra (different nb of entries)"
)
if model_from_zcat:
# DESI spectra will obtain the model from templates.
model = create_model(spectra,
zcatalog,
archetype_fit=archetype_fit,
archetypes_dir=archetypes_dir,
template_dir=template_dir)
#-----
#- Gather information into ColumnDataSource objects for Bokeh
viewer_cds = ViewerCDS()
viewer_cds.load_spectra(spectra, with_noise)
if with_coaddcam:
viewer_cds.init_coaddcam_spec(spectra, with_noise)
if model is not None:
viewer_cds.init_model(model)
if redrock_cat is not None:
# TODO unhardcode delta_lambd_templates=3
if np.any(redrock_cat['TARGETID'] != spectra.fibermap['TARGETID']):
raise RuntimeError(
'redrock_cat and spectra do not match (different targetids)')
if zcatalog is None:
raise ValueError('Redrock_cat was provided but not zcatalog.')
with_fit_templates = False if num_approx_fits == 0 else True
template_dicts = make_template_dicts(
redrock_cat,
delta_lambd_templates=3,
with_fit_templates=with_fit_templates,
template_dir=template_dir)
nfits_redrock_cat = template_dicts[1]['Nfit']
if num_approx_fits is None: num_approx_fits = nfits_redrock_cat
if (num_approx_fits > nfits_redrock_cat):
raise ValueError("num_approx_fits too large wrt redrock_cat")
if with_full_2ndfit:
zcat_2ndfit = create_zcat_from_redrock_cat(redrock_cat, fit_num=1)
model_2ndfit = create_model(spectra,
zcat_2ndfit,
archetype_fit=archetype_fit,
archetypes_dir=archetypes_dir,
template_dir=template_dir)
viewer_cds.init_model(model_2ndfit, second_fit=True)
viewer_cds.init_othermodel(zcatalog)
else:
template_dicts = None
viewer_cds.load_metadata(spectra,
mask_type=mask_type,
zcatalog=zcatalog,
survey=survey)
#-------------------------
#-- Graphical objects --
#-------------------------
viewer_plots = ViewerPlots()
viewer_plots.create_mainfig(spectra,
title,
viewer_cds,
survey,
with_noise=with_noise,
with_coaddcam=with_coaddcam)
viewer_plots.create_zoomfig(viewer_cds,
with_noise=with_noise,
with_coaddcam=with_coaddcam)
if with_imaging:
viewer_plots.create_imfig(spectra)
#-----
#- Emission and absorption lines
z = zcatalog['Z'][0] if (zcatalog is not None) else 0.0
viewer_cds.load_spectral_lines(z)
viewer_plots.add_spectral_lines(viewer_cds, figure='main')
viewer_plots.add_spectral_lines(viewer_cds,
figure='zoom',
label_offsets=[50, 5])
#-------------------------
#-- Widgets and callbacks --
#-------------------------
viewer_widgets = ViewerWidgets(viewer_plots, nspec)
viewer_widgets.add_navigation(nspec)
viewer_widgets.add_resetrange(viewer_cds, viewer_plots)
viewer_widgets.add_redshift_widgets(z, viewer_cds, viewer_plots)
viewer_widgets.add_oii_widgets(viewer_plots)
viewer_plots.add_imfig_callback(viewer_widgets)
if viewer_cds.cds_coaddcam_spec is not None:
viewer_widgets.add_coaddcam(viewer_plots)
if zcatalog is not None:
show_zcat = True
else:
show_zcat = False
if top_metadata is None: top_metadata = ['TARGETID', 'EXPID']
viewer_widgets.add_metadata_tables(viewer_cds,
top_metadata=top_metadata,
show_zcat=show_zcat,
template_dicts=template_dicts)
viewer_widgets.add_specline_toggles(viewer_cds, viewer_plots)
if template_dicts is not None:
viewer_cds.compute_median_spectra(spectra)
viewer_widgets.add_model_select(viewer_cds,
template_dicts,
num_approx_fits,
with_full_2ndfit=with_full_2ndfit)
#-----
#- VI-related widgets
## TODO if with_vi_widgets (need to adapt update_plot.js..)
viewer_vi_widgets = ViewerVIWidgets(title, viewer_cds)
viewer_vi_widgets.add_filename()
viewer_vi_widgets.add_vi_issues(viewer_cds, viewer_widgets)
viewer_vi_widgets.add_vi_z(viewer_cds, viewer_widgets)
viewer_vi_widgets.add_vi_spectype(viewer_cds, viewer_widgets)
viewer_vi_widgets.add_vi_comment(viewer_cds, viewer_widgets)
viewer_vi_widgets.add_vi_quality(viewer_cds, viewer_widgets)
viewer_vi_widgets.add_vi_scanner(viewer_cds)
viewer_vi_widgets.add_guidelines()
viewer_vi_widgets.add_vi_storage(viewer_cds, viewer_widgets)
viewer_vi_widgets.add_vi_table(viewer_cds)
if (vi_countdown > 0):
viewer_vi_widgets.add_countdown(vi_countdown)
viewer_widgets.add_update_plot_callback(viewer_cds, viewer_plots,
viewer_vi_widgets, template_dicts)
#-----
#- Bokeh layout and output
bokeh_layout = ViewerLayout(viewer_plots,
viewer_widgets,
viewer_vi_widgets,
with_vi_widgets=with_vi_widgets)
if with_thumb_tab:
bokeh_layout.add_thumb_tab(spectra, viewer_plots, viewer_widgets,
nspec)
if notebook:
bk.output_notebook()
bk.show(bokeh_layout.full_viewer)
else:
if html_dir is None: raise RuntimeError("Need html_dir")
html_page = os.path.join(html_dir, "specviewer_" + title + ".html")
bk.output_file(html_page, title='DESI spectral viewer')
bk.save(bokeh_layout.full_viewer)
#-----
#- "Light" Bokeh layout including only the thumbnail gallery
if with_thumb_only_page:
assert not notebook
thumb_page = os.path.join(html_dir,
"thumbs_specviewer_" + title + ".html")
bk.output_file(thumb_page,
title='DESI spectral viewer - thumbnail gallery')
thumb_grid = StandaloneThumbLayout(spectra, viewer_plots, title)
bk.save(thumb_grid.thumb_viewer)
def main():
args = _parse()
log = get_logger()
specprod_dir = args.specprod_dir
if specprod_dir is None : specprod_dir = desispec.io.specprod_root()
webdir = args.webdir
if webdir is None : webdir = os.environ["DESI_WWW"]+"/users/armengau/svdc2019c" # TMP, for test
# TODO - Selection on pixels based on existing specviewer pages
if args.pixel_list is None :
pixels = glob.glob( os.path.join(specprod_dir,"spectra-64/*/*") )
pixels = [x[x.rfind("/")+1:] for x in pixels]
else :
pixels = np.loadtxt(args.pixel_list, dtype=str)
if args.random_pixels :
random.shuffle(pixels)
# Loop on pixels
nspec_done = 0
for pixel in pixels :
log.info("Working on pixel "+pixel)
thefile = desispec.io.findfile('spectra', groupname=int(pixel), specprod_dir=specprod_dir)
individual_spectra = desispec.io.read_spectra(thefile)
spectra = coadd_targets(individual_spectra)
zbfile = thefile.replace('spectra-64-', 'zbest-64-')
if os.path.isfile(zbfile) :
zbest = Table.read(zbfile, 'ZBEST')
else :
log.info("No associated zbest file found : skipping pixel")
continue
spectra = specviewer_selection(spectra, log=log,
mask=args.mask, mask_type=args.mask_type, gmag_cut=args.gcut, rmag_cut=args.rcut,
chi2cut=args.chi2cut, zbest=zbest)
if spectra == 0 : continue
# Handle several html pages per pixel : sort by TARGETID
# TODO - Find a more useful sort ?
nbpages = int(np.ceil((spectra.num_spectra()/args.nspecperfile)))
sort_indices = np.argsort(spectra.fibermap["TARGETID"])
for i_page in range(1,1+nbpages) :
log.info(" * Page "+str(i_page)+" / "+str(nbpages))
the_indices = sort_indices[(i_page-1)*args.nspecperfile:i_page*args.nspecperfile]
thespec = myspecselect(spectra, indices=the_indices)
thezb, kk = match_zcat_to_spectra(zbest,thespec)
### No VI results to display by default
# VI "catalog" - location to define later ..
# vifile = os.environ['HOME']+"/prospect/vilist_prototype.fits"
# vidata = match_vi_targets(vifile, thespec.fibermap["TARGETID"])
titlepage = "pix"+pixel+"_"+str(i_page)
if args.gcut is not None :
titlepage = "gcut-"+str(args.gcut[0])+"-"+str(args.gcut[1])+"_"+titlepage
if args.rcut is not None :
titlepage = "rcut-"+str(args.rcut[0])+"-"+str(args.rcut[1])+"_"+titlepage
if args.chi2cut is not None :
titlepage = "chi2cut-"+str(args.chi2cut[0])+"-"+str(args.chi2cut[1])+"_"+titlepage
if args.mask is not None :
titlepage = args.mask+"_"+titlepage
#model = create_model(thespec, thezb)
html_dir = os.path.join(webdir,"pix"+pixel)
if not os.path.exists(html_dir) :
os.makedirs(html_dir)
os.mkdir(html_dir+"/vignettes")
plotspectra(thespec, zcatalog=zbest, model_from_zcat=True, model=None, title=titlepage, html_dir=html_dir, mask_type=args.mask_type)
# for i_spec in range(thespec.num_spectra()) :
# saveplot = html_dir+"/vignettes/pix"+pixel+"_"+str(i_page)+"_"+str(i_spec)+".png"
# miniplot_spectrum(thespec, i_spec, model=model, saveplot=saveplot, smoothing = args.vignette_smoothing)
nspec_done += thespec.num_spectra()
# Stop running if needed, only once a full pixel is completed
if args.nmax_spectra is not None :
if nspec_done >= args.nmax_spectra :
log.info(str(nspec_done)+" spectra done : no other pixel will be processed")
break
def specviewer_selection(spectra,
log=None,
mask=None,
mask_type=None,
gmag_cut=None,
rmag_cut=None,
chi2cut=None,
zbest=None,
snr_cut=None,
with_dirty_mask_merge=False,
remove_scores=False):
'''
Simple sub-selection on spectra based on meta-data.
Implemented cuts based on : target mask ; photo mag (g, r) ; chi2 from fit ; SNR (in spectra.scores, BRZ)
- if chi2cut : a catalog zbest must be provided, with entries matching exactly those of spectra
'''
# SNR selection
if snr_cut is not None:
assert ((len(snr_cut) == 2) and (spectra.scores is not None))
for band in ['B', 'R', 'Z']:
w, = np.where(
(spectra.scores['MEDIAN_CALIB_SNR_' + band] > snr_cut[0])
& (spectra.scores['MEDIAN_CALIB_SNR_' + band] < snr_cut[1]))
if len(w) == 0:
if log is not None:
log.info(" * No spectra with MEDIAN_CALIB_SNR_" + band +
" in requested range")
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(
spectra, targets=targetids, remove_scores=remove_scores)
# Target mask selection
if mask is not None:
assert mask_type in ['SV1_DESI_TARGET', 'DESI_TARGET', 'CMX_TARGET']
if mask_type == 'SV1_DESI_TARGET':
assert (mask in sv1_desi_mask.names())
w, = np.where(
(spectra.fibermap['SV1_DESI_TARGET'] & sv1_desi_mask[mask]))
elif mask_type == 'DESI_TARGET':
assert (mask in desi_mask.names())
w, = np.where((spectra.fibermap['DESI_TARGET'] & desi_mask[mask]))
elif mask_type == 'CMX_TARGET':
assert (mask in cmx_mask.names())
mask2 = None
if with_dirty_mask_merge: # Self-explanatory... only for fast VI of minisv
if mask in ['SV0_QSO', 'SV0_ELG', 'SV0_LRG']:
mask2 = mask.replace('SV0', 'MINI_SV')
if mask == 'SV0_BGS': mask2 = 'MINI_SV_BGS_BRIGHT'
if mask in ['SV0_STD_FAINT', 'SV0_STD_BRIGHT']:
mask2 = mask.replace('SV0_', '')
if mask2 is None:
w, = np.where(
(spectra.fibermap['CMX_TARGET'] & cmx_mask[mask]))
else:
w, = np.where((spectra.fibermap['CMX_TARGET'] & cmx_mask[mask])
| (spectra.fibermap['CMX_TARGET']
& cmx_mask[mask2]))
if len(w) == 0:
if log is not None: log.info(" * No spectra with mask " + mask)
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra,
targets=targetids,
remove_scores=remove_scores)
# Photometry selection
if gmag_cut is not None:
assert len(gmag_cut) == 2 # Require range [gmin, gmax]
gmag = np.zeros(spectra.num_spectra())
w, = np.where((spectra.fibermap['FLUX_G'] > 0)
& (spectra.fibermap['MW_TRANSMISSION_G'] > 0))
gmag[w] = -2.5 * np.log10(
spectra.fibermap['FLUX_G'][w] /
spectra.fibermap['MW_TRANSMISSION_G'][w]) + 22.5
w, = np.where((gmag > gmag_cut[0]) & (gmag < gmag_cut[1]))
if len(w) == 0:
if log is not None:
log.info(" * No spectra with g_mag in requested range")
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra, targets=targetids)
if rmag_cut is not None:
assert len(rmag_cut) == 2 # Require range [rmin, rmax]
rmag = np.zeros(spectra.num_spectra())
w, = np.where((spectra.fibermap['FLUX_R'] > 0)
& (spectra.fibermap['MW_TRANSMISSION_R'] > 0))
rmag[w] = -2.5 * np.log10(
spectra.fibermap['FLUX_R'][w] /
spectra.fibermap['MW_TRANSMISSION_R'][w]) + 22.5
w, = np.where((rmag > rmag_cut[0]) & (rmag < rmag_cut[1]))
if len(w) == 0:
if log is not None:
log.info(" * No spectra with r_mag in requested range")
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra,
targets=targetids,
remove_scores=remove_scores)
# Chi2 selection
if chi2cut is not None:
assert len(chi2cut) == 2 # Require range [chi2min, chi2max]
if np.any(zbest['TARGETID'] != spectra.fibermap['TARGETID']):
raise RunTimeError(
'specviewer_selection : zbest and spectra do not match (different targetids)'
)
w, = np.where((zbest['DELTACHI2'] > chi2cut[0])
& (zbest['DELTACHI2'] < chi2cut[1]))
if len(w) == 0:
if log is not None:
log.info(
" * No target in this pixel with DeltaChi2 in requested range"
)
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra,
targets=targetids,
remove_scores=remove_scores)
return spectra
def create_model(spectra, zbest):
'''
Returns model_wave[nwave], model_flux[nspec, nwave], row matched to zbest,
which can be in a different order than spectra.
'''
import redrock.templates
from desispec.interpolation import resample_flux
nspec = spectra.num_spectra()
assert len(zbest) == nspec
#- Load redrock templates; redirect stdout because redrock is chatty
saved_stdout = sys.stdout
sys.stdout = open('/dev/null', 'w')
try:
templates = dict()
for filename in redrock.templates.find_templates():
tx = redrock.templates.Template(filename)
templates[(tx.template_type, tx.sub_type)] = tx
except Exception as err:
sys.stdout = saved_stdout
raise (err)
sys.stdout = saved_stdout
#- Empty model flux arrays per band to fill
model_flux = dict()
for band in spectra.bands:
model_flux[band] = np.zeros(spectra.flux[band].shape)
targetids = spectra.target_ids()
for i in range(len(zbest)):
zb = zbest[i]
j = np.where(targetids == zb['TARGETID'])[0][0]
tx = templates[(zb['SPECTYPE'], zb['SUBTYPE'])]
coeff = zb['COEFF'][0:tx.nbasis]
model = tx.flux.T.dot(coeff).T
for band in spectra.bands:
mx = resample_flux(spectra.wave[band], tx.wave * (1 + zb['Z']),
model)
model_flux[band][i] = spectra.R[band][j].dot(mx)
#- Now combine to a single wavelength grid across all cameras
#- TODO: assumes b,r,z all exist
br_split = 0.5 * (spectra.wave['b'][-1] + spectra.wave['r'][0])
rz_split = 0.5 * (spectra.wave['r'][-1] + spectra.wave['z'][0])
keep = dict()
keep['b'] = (spectra.wave['b'] < br_split)
keep['r'] = (br_split <= spectra.wave['r']) & (spectra.wave['r'] <
rz_split)
keep['z'] = (rz_split <= spectra.wave['z'])
model_wave = np.concatenate([
spectra.wave['b'][keep['b']],
spectra.wave['r'][keep['r']],
spectra.wave['z'][keep['z']],
])
mflux = np.concatenate([
model_flux['b'][:, keep['b']],
model_flux['r'][:, keep['r']],
model_flux['z'][:, keep['z']],
],
axis=1)
return model_wave, mflux
def plotspectra(spectra,
zcatalog=None,
model=None,
notebook=False,
title=None):
'''
TODO: document
'''
if notebook:
bk.output_notebook()
#- If inputs are frames, convert to a spectra object
if isinstance(spectra, list) and isinstance(spectra[0],
desispec.frame.Frame):
spectra = frames2spectra(spectra)
frame_input = True
else:
frame_input = False
if frame_input and title is None:
meta = spectra.meta
title = 'Night {} ExpID {} Spectrograph {}'.format(
meta['NIGHT'],
meta['EXPID'],
meta['CAMERA'][1],
)
#- Gather spectra into ColumnDataSource objects for Bokeh
nspec = spectra.num_spectra()
cds_spectra = list()
for band in spectra.bands:
#- Set masked bins to NaN so that Bokeh won't plot them
bad = (spectra.ivar[band] == 0.0) | (spectra.mask[band] != 0)
spectra.flux[band][bad] = np.nan
cdsdata = dict(
origwave=spectra.wave[band].copy(),
plotwave=spectra.wave[band].copy(),
)
for i in range(nspec):
key = 'origflux' + str(i)
cdsdata[key] = spectra.flux[band][i]
cdsdata['plotflux'] = cdsdata['origflux0']
cds_spectra.append(bk.ColumnDataSource(cdsdata, name=band))
#- Reorder zcatalog to match input targets
#- TODO: allow more than one zcatalog entry with different ZNUM per targetid
targetids = spectra.target_ids()
if zcatalog is not None:
ii = np.argsort(np.argsort(targetids))
jj = np.argsort(zcatalog['TARGETID'])
kk = jj[ii]
zcatalog = zcatalog[kk]
#- That sequence of argsorts may feel like magic,
#- so make sure we got it right
assert np.all(zcatalog['TARGETID'] == targetids)
assert np.all(zcatalog['TARGETID'] == spectra.fibermap['TARGETID'])
#- Also need to re-order input model fluxes
if model is not None:
mwave, mflux = model
model = mwave, mflux[kk]
#- Gather models into ColumnDataSource objects, row matched to spectra
if model is not None:
mwave, mflux = model
model_obswave = mwave.copy()
model_restwave = mwave.copy()
cds_model_data = dict(
origwave=mwave.copy(),
plotwave=mwave.copy(),
plotflux=np.zeros(len(mwave)),
)
for i in range(nspec):
key = 'origflux' + str(i)
cds_model_data[key] = mflux[i]
cds_model_data['plotflux'] = cds_model_data['origflux0']
cds_model = bk.ColumnDataSource(cds_model_data)
else:
cds_model = None
#- Subset of zcatalog and fibermap columns into ColumnDataSource
target_info = list()
for i, row in enumerate(spectra.fibermap):
target_bit_names = ' '.join(desi_mask.names(row['DESI_TARGET']))
txt = 'Target {}: {}'.format(row['TARGETID'], target_bit_names)
if zcatalog is not None:
txt += '<BR/>{} z={:.4f} ± {:.4f} ZWARN={}'.format(
zcatalog['SPECTYPE'][i],
zcatalog['Z'][i],
zcatalog['ZERR'][i],
zcatalog['ZWARN'][i],
)
target_info.append(txt)
cds_targetinfo = bk.ColumnDataSource(dict(target_info=target_info),
name='targetinfo')
if zcatalog is not None:
cds_targetinfo.add(zcatalog['Z'], name='z')
plot_width = 800
plot_height = 400
# tools = 'pan,box_zoom,wheel_zoom,undo,redo,reset,save'
tools = 'pan,box_zoom,wheel_zoom,reset,save'
fig = bk.figure(height=plot_height,
width=plot_width,
title=title,
tools=tools,
toolbar_location='above',
y_range=(-10, 20))
fig.toolbar.active_drag = fig.tools[1] #- box zoom
fig.toolbar.active_scroll = fig.tools[2] #- wheel zoom
fig.xaxis.axis_label = 'Wavelength [Å]'
fig.yaxis.axis_label = 'Flux'
fig.xaxis.axis_label_text_font_style = 'normal'
fig.yaxis.axis_label_text_font_style = 'normal'
colors = dict(b='#1f77b4', r='#d62728', z='maroon')
data_lines = list()
for spec in cds_spectra:
lx = fig.line('plotwave',
'plotflux',
source=spec,
line_color=colors[spec.name])
data_lines.append(lx)
if cds_model is not None:
model_lines = list()
lx = fig.line('plotwave',
'plotflux',
source=cds_model,
line_color='black')
model_lines.append(lx)
legend = Legend(items=[
("data",
data_lines[-1::-1]), #- reversed to get blue as lengend entry
("model", model_lines),
])
else:
legend = Legend(items=[
("data",
data_lines[-1::-1]), #- reversed to get blue as lengend entry
])
fig.add_layout(legend, 'center')
fig.legend.click_policy = 'hide' #- or 'mute'
#- Zoom figure around mouse hover of main plot
zoomfig = bk.figure(
height=plot_height // 2,
width=plot_height // 2,
y_range=fig.y_range,
x_range=(5000, 5100),
# output_backend="webgl",
toolbar_location=None,
tools=[])
for spec in cds_spectra:
zoomfig.line('plotwave',
'plotflux',
source=spec,
line_color=colors[spec.name],
line_width=1,
line_alpha=1.0)
if cds_model is not None:
zoomfig.line('plotwave',
'plotflux',
source=cds_model,
line_color='black')
#- Callback to update zoom window x-range
zoom_callback = CustomJS(args=dict(zoomfig=zoomfig),
code="""
zoomfig.x_range.start = cb_obj.x - 100;
zoomfig.x_range.end = cb_obj.x + 100;
""")
fig.js_on_event(bokeh.events.MouseMove, zoom_callback)
#-----
#- Emission and absorption lines
z = zcatalog['Z'][0] if (zcatalog is not None) else 0.0
line_data, lines, line_labels = add_lines(fig, z=z)
#-----
#- Add widgets for controling plots
z1 = np.floor(z * 100) / 100
dz = z - z1
zslider = Slider(start=0.0, end=4.0, value=z1, step=0.01, title='Redshift')
dzslider = Slider(start=0.0,
end=0.01,
value=dz,
step=0.0001,
title='+ Delta redshift')
dzslider.format = "0[.]0000"
#- Observer vs. Rest frame wavelengths
waveframe_buttons = RadioButtonGroup(labels=["Obs", "Rest"], active=0)
ifiberslider = Slider(start=0, end=nspec - 1, value=0, step=1)
if frame_input:
ifiberslider.title = 'Fiber'
else:
ifiberslider.title = 'Target'
zslider_callback = CustomJS(
args=dict(
spectra=cds_spectra,
model=cds_model,
targetinfo=cds_targetinfo,
ifiberslider=ifiberslider,
zslider=zslider,
dzslider=dzslider,
waveframe_buttons=waveframe_buttons,
line_data=line_data,
lines=lines,
line_labels=line_labels,
fig=fig,
),
#- TODO: reorder to reduce duplicated code
code="""
var z = zslider.value + dzslider.value
var line_restwave = line_data.data['restwave']
var ifiber = ifiberslider.value
var zfit = 0.0
if(targetinfo.data['z'] != undefined) {
zfit = targetinfo.data['z'][ifiber]
}
// Observer Frame
if(waveframe_buttons.active == 0) {
var x = 0.0
for(var i=0; i<line_restwave.length; i++) {
x = line_restwave[i] * (1+z)
lines[i].location = x
line_labels[i].x = x
}
for(var i=0; i<spectra.length; i++) {
var data = spectra[i].data
var origwave = data['origwave']
var plotwave = data['plotwave']
for (var j=0; j<plotwave.length; j++) {
plotwave[j] = origwave[j]
}
spectra[i].change.emit()
}
// Update model wavelength array
if(model) {
var origwave = model.data['origwave']
var plotwave = model.data['plotwave']
for(var i=0; i<plotwave.length; i++) {
plotwave[i] = origwave[i] * (1+z) / (1+zfit)
}
model.change.emit()
}
// Rest Frame
} else {
for(i=0; i<line_restwave.length; i++) {
lines[i].location = line_restwave[i]
line_labels[i].x = line_restwave[i]
}
for (var i=0; i<spectra.length; i++) {
var data = spectra[i].data
var origwave = data['origwave']
var plotwave = data['plotwave']
for (var j=0; j<plotwave.length; j++) {
plotwave[j] = origwave[j] / (1+z)
}
spectra[i].change.emit()
}
// Update model wavelength array
if(model) {
var origwave = model.data['origwave']
var plotwave = model.data['plotwave']
for(var i=0; i<plotwave.length; i++) {
plotwave[i] = origwave[i] / (1+zfit)
}
model.change.emit()
}
}
""")
zslider.js_on_change('value', zslider_callback)
dzslider.js_on_change('value', zslider_callback)
waveframe_buttons.js_on_click(zslider_callback)
plotrange_callback = CustomJS(args=dict(
zslider=zslider,
dzslider=dzslider,
waveframe_buttons=waveframe_buttons,
fig=fig,
),
code="""
var z = zslider.value + dzslider.value
// Observer Frame
if(waveframe_buttons.active == 0) {
fig.x_range.start = fig.x_range.start * (1+z)
fig.x_range.end = fig.x_range.end * (1+z)
} else {
fig.x_range.start = fig.x_range.start / (1+z)
fig.x_range.end = fig.x_range.end / (1+z)
}
""")
waveframe_buttons.js_on_click(plotrange_callback)
smootherslider = Slider(start=0,
end=31,
value=0,
step=1.0,
title='Gaussian Sigma Smooth')
target_info_div = Div(text=target_info[0])
#-----
#- Toggle lines
lines_button_group = CheckboxButtonGroup(labels=["Emission", "Absorption"],
active=[])
lines_callback = CustomJS(args=dict(line_data=line_data,
lines=lines,
line_labels=line_labels),
code="""
var show_emission = false
var show_absorption = false
if (cb_obj.active.indexOf(0) >= 0) { // index 0=Emission in active list
show_emission = true
}
if (cb_obj.active.indexOf(1) >= 0) { // index 1=Absorption in active list
show_absorption = true
}
for(var i=0; i<lines.length; i++) {
if(line_data.data['emission'][i]) {
lines[i].visible = show_emission
line_labels[i].visible = show_emission
} else {
lines[i].visible = show_absorption
line_labels[i].visible = show_absorption
}
}
""")
lines_button_group.js_on_click(lines_callback)
# lines_button_group.js_on_change('value', lines_callback)
#-----
update_plot = CustomJS(args=dict(
spectra=cds_spectra,
model=cds_model,
targetinfo=cds_targetinfo,
target_info_div=target_info_div,
ifiberslider=ifiberslider,
smootherslider=smootherslider,
zslider=zslider,
dzslider=dzslider,
lines_button_group=lines_button_group,
fig=fig,
),
code="""
var ifiber = ifiberslider.value
var nsmooth = smootherslider.value
target_info_div.text = targetinfo.data['target_info'][ifiber]
if(targetinfo.data['z'] != undefined) {
var z = targetinfo.data['z'][ifiber]
var z1 = Math.floor(z*100) / 100
zslider.value = z1
dzslider.value = (z - z1)
}
function get_y_minmax(pmin, pmax, data) {
// copy before sorting to not impact original, and filter out NaN
var dx = data.slice().filter(Boolean)
dx.sort()
var imin = Math.floor(pmin * dx.length)
var imax = Math.floor(pmax * dx.length)
return [dx[imin], dx[imax]]
}
// Smoothing kernel
var kernel = [];
for(var i=-2*nsmooth; i<=2*nsmooth; i++) {
kernel.push(Math.exp(-(i**2)/(2*nsmooth)))
}
var kernel_offset = Math.floor(kernel.length/2)
// Smooth plot and recalculate ymin/ymax
// TODO: add smoother function to reduce duplicated code
var ymin = 0.0
var ymax = 0.0
for (var i=0; i<spectra.length; i++) {
var data = spectra[i].data
var plotflux = data['plotflux']
var origflux = data['origflux'+ifiber]
for (var j=0; j<plotflux.length; j++) {
if(nsmooth == 0) {
plotflux[j] = origflux[j]
} else {
plotflux[j] = 0.0
var weight = 0.0
// TODO: speed could be improved by moving `if` out of loop
for (var k=0; k<kernel.length; k++) {
var m = j+k-kernel_offset
if((m >= 0) && (m < plotflux.length)) {
var fx = origflux[m]
if(fx == fx) {
plotflux[j] = plotflux[j] + fx * kernel[k]
weight += kernel[k]
}
}
}
plotflux[j] = plotflux[j] / weight
}
}
spectra[i].change.emit()
tmp = get_y_minmax(0.01, 0.99, plotflux)
ymin = Math.min(ymin, tmp[0])
ymax = Math.max(ymax, tmp[1])
}
// update model
if(model) {
var plotflux = model.data['plotflux']
var origflux = model.data['origflux'+ifiber]
for (var j=0; j<plotflux.length; j++) {
if(nsmooth == 0) {
plotflux[j] = origflux[j]
} else {
plotflux[j] = 0.0
var weight = 0.0
// TODO: speed could be improved by moving `if` out of loop
for (var k=0; k<kernel.length; k++) {
var m = j+k-kernel_offset
if((m >= 0) && (m < plotflux.length)) {
var fx = origflux[m]
if(fx == fx) {
plotflux[j] = plotflux[j] + fx * kernel[k]
weight += kernel[k]
}
}
}
plotflux[j] = plotflux[j] / weight
}
}
model.change.emit()
}
// update y_range
if(ymin<0) {
fig.y_range.start = ymin * 1.4
} else {
fig.y_range.start = ymin * 0.6
}
fig.y_range.end = ymax * 1.4
""")
smootherslider.js_on_change('value', update_plot)
ifiberslider.js_on_change('value', update_plot)
#-----
#- Add navigation buttons
navigation_button_width = 30
prev_button = Button(label="<", width=navigation_button_width)
next_button = Button(label=">", width=navigation_button_width)
prev_callback = CustomJS(args=dict(ifiberslider=ifiberslider),
code="""
if(ifiberslider.value>0) {
ifiberslider.value--
}
""")
next_callback = CustomJS(args=dict(ifiberslider=ifiberslider, nspec=nspec),
code="""
if(ifiberslider.value<nspec+1) {
ifiberslider.value++
}
""")
prev_button.js_on_event('button_click', prev_callback)
next_button.js_on_event('button_click', next_callback)
#-----
slider_width = plot_width - 2 * navigation_button_width
navigator = bk.Row(
widgetbox(prev_button, width=navigation_button_width),
widgetbox(next_button, width=navigation_button_width + 20),
widgetbox(ifiberslider, width=slider_width - 20))
bk.show(
bk.Column(
bk.Row(fig, zoomfig),
widgetbox(target_info_div, width=plot_width),
navigator,
widgetbox(smootherslider, width=plot_width // 2),
bk.Row(
widgetbox(waveframe_buttons, width=120),
widgetbox(zslider, width=plot_width // 2 - 60),
widgetbox(dzslider, width=plot_width // 2 - 60),
),
widgetbox(lines_button_group),
))
def specviewer_selection(spectra,
log=None,
mask=None,
mask_type=None,
gmag_cut=None,
rmag_cut=None,
chi2cut=None,
zbest=None,
snr_cut=None):
'''
Simple sub-selection on spectra based on meta-data.
Implemented cuts based on : target mask ; photo mag (g, r) ; chi2 from fit ; SNR (in spectra.scores, BRZ)
'''
# Target mask selection
if mask is not None:
assert mask_type in ['SV1_DESI_TARGET', 'DESI_TARGET', 'CMX_TARGET']
if mask_type == 'SV1_DESI_TARGET':
assert (mask in sv1_desi_mask.names())
w, = np.where(
(spectra.fibermap['SV1_DESI_TARGET'] & sv1_desi_mask[mask]))
elif mask_type == 'DESI_TARGET':
assert (mask in desi_mask.names())
w, = np.where((spectra.fibermap['DESI_TARGET'] & desi_mask[mask]))
elif mask_type == 'CMX_TARGET':
assert (mask in cmx_mask.names())
w, = np.where((spectra.fibermap['CMX_TARGET'] & cmx_mask[mask]))
if len(w) == 0:
if log is not None: log.info(" * No spectra with mask " + mask)
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra, targets=targetids)
# Photometry selection
if gmag_cut is not None:
assert len(gmag_cut) == 2 # Require range [gmin, gmax]
gmag = np.zeros(spectra.num_spectra())
w, = np.where((spectra.fibermap['FLUX_G'] > 0)
& (spectra.fibermap['MW_TRANSMISSION_G'] > 0))
gmag[w] = -2.5 * np.log10(
spectra.fibermap['FLUX_G'][w] /
spectra.fibermap['MW_TRANSMISSION_G'][w]) + 22.5
w, = np.where((gmag > gmag_cut[0]) & (gmag < gmag_cut[1]))
if len(w) == 0:
if log is not None:
log.info(" * No spectra with g_mag in requested range")
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra, targets=targetids)
if rmag_cut is not None:
assert len(rmag_cut) == 2 # Require range [rmin, rmax]
rmag = np.zeros(spectra.num_spectra())
w, = np.where((spectra.fibermap['FLUX_R'] > 0)
& (spectra.fibermap['MW_TRANSMISSION_R'] > 0))
rmag[w] = -2.5 * np.log10(
spectra.fibermap['FLUX_R'][w] /
spectra.fibermap['MW_TRANSMISSION_R'][w]) + 22.5
w, = np.where((rmag > rmag_cut[0]) & (rmag < rmag_cut[1]))
if len(w) == 0:
if log is not None:
log.info(" * No spectra with r_mag in requested range")
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra, targets=targetids)
# SNR selection ## TODO check it !! May not work ...
if snr_cut is not None:
assert ((len(snr_cut) == 2) and (spectra.scores is not None))
for band in ['B', 'R', 'Z']:
w, = np.where(
(spectra.scores['MEDIAN_CALIB_SNR_' + band] > snr_cut[0])
& (spectra.scores['MEDIAN_CALIB_SNR_' + band] < snr_cut[1]))
if len(w) == 0:
if log is not None:
log.info(" * No spectra with MEDIAN_CALIB_SNR_" + band +
" in requested range")
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra, targets=targetids)
# Chi2 selection
if chi2cut is not None:
assert len(chi2cut) == 2 # Require range [chi2min, chi2max]
assert (zbest is not None)
thezb, kk = match_zcat_to_spectra(zbest, spectra)
w, = np.where((thezb['DELTACHI2'] > chi2cut[0])
& (thezb['DELTACHI2'] < chi2cut[1]))
if len(w) == 0:
if log is not None:
log.info(
" * No target in this pixel with DeltaChi2 in requested range"
)
return 0
else:
targetids = spectra.fibermap['TARGETID'][w]
spectra = myspecselect.myspecselect(spectra, targets=targetids)
return spectra
def main():
args = _parse()
datadir = os.environ['DESI_ROOT'] + "/" + args.datadir
specfiles = glob.glob(datadir + "/*/*/spectra*")
specfiles = specfiles[0:2] # TMP
# Get list of exposures with associated files
# Todo : select exposures not yet processed
dict_exposures = {}
for thespecfile in specfiles:
spectra = desispec.io.read_spectra(thespecfile)
pp = np.unique(spectra.fibermap['EXPID'])
for the_expo in pp:
if the_expo not in dict_exposures.keys():
dict_exposures[the_expo] = [thespecfile]
else:
dict_exposures[the_expo].append(thespecfile)
# Loop on exposures
for exposure, thespecfiles in dict_exposures.items():
print("* Working on exposure " + str(exposure))
print("* ( Nb of files : " + str(len(thespecfiles)) + " )")
for ii, thefile in enumerate(thespecfiles):
thespec = desispec.io.read_spectra(thefile)
zbfile = thefile.replace('spectra-64-', 'zbest-64-')
thezb = Table.read(zbfile, 'ZBEST')
# First creation of spectra with metadata + zbest
if ii == 0:
spectra = desispec.spectra.Spectra(meta=thespec.meta,
extra=thespec.extra)
zbest = Table(dtype=thezb.dtype)
thespec = myspecselect(thespec, expids=[exposure])
spectra.update(thespec)
for i_zb in range(len(thezb)):
zbest.add_row(thezb[i_zb])
# Handle several html pages per exposure : sort by fibers
nbpages = int(np.ceil((spectra.num_spectra() / args.nspecperfile)))
fiberlist = np.unique(spectra.fibermap["FIBER"])
if len(fiberlist) != spectra.num_spectra():
print("!! Several times the same fiber in exposure ??")
for i_page in range(1, 1 + nbpages):
print("** Page " + str(i_page) + " / " + str(nbpages))
thespec = myspecselect(
spectra,
fibers=fiberlist[(i_page - 1) * args.nspecperfile:i_page *
args.nspecperfile])
thezb = match_zbest_to_spectra(zbest, thespec)
# VI "catalog" - location to define later ..
vifile = os.environ['HOME'] + "/prospect/vilist_prototype.fits"
vidata = match_vi_targets(vifile, thespec.fibermap["TARGETID"])
titlepage = "specviewer_expo" + str(exposure) + "_fiberset" + str(
i_page)
model = create_model(thespec, thezb)
savedir = args.webdir + "/exposures/expo" + str(exposure)
if not os.path.exists(savedir):
os.mkdir(savedir)
os.mkdir(savedir + "/vignettes")
plotspectra(thespec,
zcatalog=thezb,
vidata=vidata,
model=model,
title=titlepage,
savedir=savedir)
print("* ( Nb of files : "+str(len(thespecfiles))+" )")
for ii,thefile in enumerate(thespecfiles) :
thespec = desispec.io.read_spectra(thefile)
zbfile = thefile.replace('spectra-64-', 'zbest-64-')
thezb = Table.read(zbfile, 'ZBEST')
# First creation of spectra with metadata + zbest
if ii==0 :
spectra = desispec.spectra.Spectra(meta=thespec.meta, extra=thespec.extra)
zbest = Table(dtype=thezb.dtype)
thespec = myspecselect.myspecselect(thespec, expids=[exposure])
spectra.update(thespec)
for i_zb in range(len(thezb)) :
zbest.add_row(thezb[i_zb])
# Handle several html pages per exposure : sort by fibers
nbpages = int(np.ceil((spectra.num_spectra()/args.nspecperfile)))
fiberlist = np.unique(spectra.fibermap["FIBER"])
if len(fiberlist)!=spectra.num_spectra() : print("!! Several times the same fiber in exposure ??")
for i_page in range(1,1+nbpages) :
print("** Page "+str(i_page)+" / "+str(nbpages))
thespec = myspecselect.myspecselect(spectra, fibers=fiberlist[(i_page-1)*args.nspecperfile:i_page*args.nspecperfile])
thezb = utils_specviewer.match_zbest_to_spectra(zbest,thespec)
# VI "catalog" - location to define later ..
vifile = os.environ['HOME']+"/prospect/vilist_prototype.fits"
vidata = utils_specviewer.match_vi_targets(vifile, thespec.fibermap["TARGETID"])
titlepage = "specviewer_expo"+str(exposure)+"_fiberset"+str(i_page)
model = plotframes.create_model(thespec, thezb)
savedir=args.webdir+"/exposures/expo"+str(exposure)
if not os.path.exists(savedir) :
os.mkdir(savedir)
os.mkdir(savedir+"/vignettes")
