def phxFit(star='gj832', ax=None):
if ax is None: ax = plt.gca()
ax.autoscale(axis='x', tight=True)
ax.set_yscale('log')
pan = io.readpan(star)
bolo = utils.bolo_integral(star)
xf = db.findfiles('ir', 'phx', star, fullpaths=True)
phx = io.read(xf)[0]
phx['flux'] *= pan['normfac'][-1]
pan = pan[pan['instrument'] != pan['instrument'][-1]]
fmin = np.min(pan['error'][pan['error'] > 0]) / bolo
rng = [phx['w0'][0], 6000.]
phx, pan = [
utils.keepranges(spec, rng, ends='loose') for spec in [phx, pan]
]
phx, pan = [utils.fancyBin(s, mindw=10.0) for s in [phx, pan]]
pan['flux'][pan['flux'] < pan['error'] / 2.0] = np.nan
pan['normflux'] = pan['flux'] / bolo
phx['normflux'] = phx['flux'] / bolo
ymax = 10**np.ceil(np.log10(np.max(phx['normflux'])))
ymin = 10**np.floor(np.log10(fmin) - 3)
ax.set_ylim(ymin, ymax)
pline = plot.specstep(pan, ax=ax, color='k', err=False, key='normflux')
xline = plot.specstep(phx, ax=ax, color='gray', err=False, key='normflux')
ax.legend((pline, xline), ('$HST$ Data', 'PHOENIX Model'),
loc='lower right')
def plotnorm(spec_or_star, ax=plt.gca(), clip=1e-10):
if type(spec_or_star) is str:
spec = io.read(db.Rpanpath(spec_or_star, 10000))[0]
else:
spec = spec_or_star
spec = utils.add_normflux(spec)
bad = spec['normflux'] < 1e-10
spec['normflux'][bad] = np.nan
plot.specstep(spec, ax=ax)
def cyclespec(files):
plt.ioff()
for f in files:
specs = io.read(f)
for spec in specs:
specstep(spec)
plt.title(path.basename(f))
plt.xlabel('Wavelength [$\AA$]')
plt.ylabel('Flux [erg/s/cm$^2$/$\AA$]')
plt.show()
plt.ion()
def vetcoadd(star, config):
"""Plot the components of a coadded spectrum to check that the coadd agrees."""
coaddfile = db.coaddfile(star, config)
coadd = io.read(coaddfile)
assert len(coadd) == 1
coadd = coadd[0]
sourcefiles = coadd.meta['SOURCESPECS']
sourcespecs = io.read(sourcefiles)
for spec in sourcespecs:
specstep(spec)
specstep(coadd, lw=2.0, c='k', alpha=0.5)
plt.title(path.basename(coadd.meta['FILENAME']))
def examinedates(star):
"""Plot the min and max obs dates to make sure everything looks peachy."""
pans = io.readpans(star)
mindate = min([np.min(p['minobsdate'][p['minobsdate'] > 0]) for p in pans])
maxdate = max([np.max(p['maxobsdate'][p['maxobsdate'] > 0]) for p in pans])
d = (maxdate - mindate)
los = [specstep(p, key='minobsdate', linestyle='--') for p in pans]
colors = [l.get_color() for l in los]
his = [
specstep(p, key='maxobsdate', linestyle=':', color=c)
for p, c in zip(pans, colors)
]
plt.ylim(mindate - d / 10.0, maxdate + d / 10.0)
labels = [db.parse_paninfo(p.meta['FILENAME']) for p in pans]
plt.legend(los, labels)
def vetnormfacs(spec, panspec, normfac, normranges):
"""Check the normalization of files by plotting normalized and unnormalieze
versions. Called from reduce.panspec"""
name = spec.meta['NAME']
oranges = utils.overlap_ranges(spec, panspec)
if normranges is None:
normranges = oranges
else:
normranges = mnp.rangeset_intersect(normranges, oranges)
# construct bins from lower res in each normrange
overbins = []
for normrange in normranges:
getbins = lambda s: utils.wbins(utils.keepranges(s, normrange))
possible_bins = map(getbins, [spec, panspec])
bins = min(possible_bins, key=lambda b: len(b))
overbins.append(bins)
overbins = np.vstack(overbins)
# rebin each spec to the lowest res within the overlap
def coarsebin(s):
overspec = utils.rebin(s, overbins)
return red.splice(s, overspec)
spec, panspec = map(coarsebin, [spec, panspec])
# plot the spectrum being normalized, highlighting the normranges
plt.figure()
# ax = plt.axes()
inranges, _ = utils.argoverlap(spec, normranges)
specin = spec[inranges]
specout = spec[~inranges]
kwargs = {'err': False}
line1 = specstep(specout, alpha=0.3, linestyle='--', **kwargs)
kwargs['color'] = line1.get_color()
line2 = specstep(specin, alpha=0.3, **kwargs)
specin['flux'] *= normfac
specout['flux'] *= normfac
line3 = specstep(specout, linestyle='--', **kwargs)
line4 = specstep(specin, **kwargs)
plt.legend((line2, line4), ('original', 'normalized'))
# plot panspec
fullrange = spec['w0'][0], spec['w1'][-1]
panspec = utils.keepranges(panspec, fullrange)
# ymax = np.max(panspec['flux'])
piecespec(panspec, err=False)
normed = panspec['normfac'] != 1.0
if np.any(normed):
normspec = panspec[normed]
normspec['flux'] /= normspec['normfac']
specstep(normspec, color='k', linestyle='--', alpha=0.5, err=False)
# ax.set_ylim(-0.05 * ymax, 1.05 * ymax)
plt.title('{} normalization'.format(name))
def splitSpec(spec, ax=None, wsplit=3000., scale_fac=50.):
ax1 = plt.gca() if ax is None else ax
ax2 = ax1.twinx()
leftspec = utils.keepranges(spec, 0.0, wsplit)
rightspec = utils.keepranges(spec, wsplit, np.inf)
step = lambda spec, ax: plot.specstep(
spec, err=False, ax=ax, color='k', autolabel=False)
leftline = step(leftspec, ax1)
rightline = step(rightspec, ax2)
ax2.set_ylim(0.0, None)
ax1.set_ylim(0.0, ax2.get_ylim()[1] / scale_fac)
ax1.axvline(wsplit, ls=':')
return (ax1, leftline), (ax2, rightline)
def piecespec(spec, err=True):
"""Plot a spectrum color-coded by source instrument."""
insts = np.unique(spec['instrument'])
insts = filter(lambda i: np.log2(i) % 1 == 0, insts)
for i in insts:
keep = (spec['instrument'] == i)
thisspec = spec[keep]
configs_i = np.nonzero(np.array(rc.instvals) & i)[0]
configs = [rc.instruments[j] for j in configs_i]
configstr = ' + '.join(configs)
lines = specstep(thisspec, err=err)
color = lines[0].get_color() if err else lines.get_color()
w, f = [np.nanmean(thisspec[s]) for s in ['w0', 'flux']]
txtprops = dict(facecolor='white', alpha=0.5, color=color)
plt.text(w, f, configstr, bbox=txtprops, ha='center')
def lyavsdata(star):
data, = io.read(db.lyafile(star))
mod, = io.read(db.findfiles('u', 'lya', star, fullpaths=True)[0])
specstep(data)
specstep(mod)
plt.xlim(1208, 1222)
def plotfun(spec, **kwargs):
s = utils.rebin(spec, wbins)
return specstep(s, **kwargs)