def plot_time_course(self, data, mode='boolean', fontsize=16):
# TODO sort columnsi alphabetically
# FIXME: twiny labels are slightly shifted
# TODO flip
if mode == 'boolean':
cm = pylab.get_cmap('gray')
pylab.clf()
data = pd.DataFrame(data).fillna(0.5)
pylab.pcolor(data, cmap=cm, vmin=0, vmax=1,
shading='faceted')
pylab.colorbar()
ax1 = pylab.gca()
ax1.set_xticks([])
Ndata = len(data.columns)
ax1.set_xlim(0, Ndata)
ax = pylab.twiny()
ax.set_xticks(pylab.linspace(0.5, Ndata+0.5, Ndata ))
ax.set_xticklabels(data.columns, fontsize=fontsize, rotation=90)
times = list(data.index)
Ntimes = len(times)
ax1.set_yticks([x+0.5 for x in times])
ax1.set_yticklabels(times[::-1],
fontsize=fontsize)
pylab.sca(ax1)
else:
print('not implemented')
def plotsim(self, experiments=None, fontsize=16, vmin=0, vmax=1, cmap='gray'):
"""
:param experiments: if None, shows the steady state for each experiment and species
if provided, must be a valid experiment name (see midas.experiments attribute)
in which case, for that particular experiment, the steady state and all previous
states are shown for each species.
A simulation must be performed using :meth:`simulate`
::
# those 2 calls are identical
s.plotsim(experiments=8)
s.plotsim(experiments=8)
# This plot the steady states for all experiments
s.plotsim()
"""
# This is for all experiments is experiments is None
cm = pylab.get_cmap(cmap)
pylab.clf()
if experiments is None: # takes the latest (steady state) of each experiments
data = pd.DataFrame(self.debug_values[-1]).fillna(0.5)
else:
exp_name = self.midas.experiments.ix[experiments].name
index_exp = list(self.midas.experiments.index).index(exp_name)
data = [(k, [self.debug_values[i][k][index_exp] for i in range(0, len(self.debug_values))])
for k in self.debug_values[0].keys()]
data = dict(data)
data = pd.DataFrame(data).fillna(0.5)
data = data.ix[data.index[::-1]]
self.dummy = data
pylab.pcolor(data, cmap=cm, vmin=vmin, vmax=vmax,
shading='faceted', edgecolors='gray')
pylab.colorbar()
ax1 = pylab.gca()
ax1.set_xticks([])
Ndata = len(data.columns)
ax1.set_xlim(0, Ndata)
ax1.set_ylim(0, len(data))
ax = pylab.twiny()
# FIXME seems shifted. could not fix it xticklabels seems to reset the position of the ticks
xr = pylab.linspace(0.5, Ndata-1.5, Ndata)
ax.set_xticks(xr)
ax.set_xticklabels(data.columns, fontsize=fontsize, rotation=90)
times = list(data.index)
Ntimes = len(times)
ax1.set_yticks([x+0.5 for x in times])
ax1.set_yticklabels(times[::-1],
fontsize=fontsize)
pylab.sca(ax1)
#pylab.title("Steady state for all experiments(x-axis)\n\n\n\n")
pylab.tight_layout()
def xaxissfrlog(ax1, dy=0):
x1, x2 = ax1.get_xlim()
ax2 = pl.twiny()
#xa=getsfrfromlir(10.**x1)
#xb=getsfrfromlir(10.**x2)
#print x1, x2
#print xa, xb
ax2.set_xlim(getsfrfromlir(10.**x1), getsfrfromlir(10.**x2))
ax2.set_xscale('log')
s = shelve.open("AR_many_runs_shelf",flag='r')
data.append(s[m].__dict__['sigv50k_estimates'])
s.close()
plt.boxplot(data)
plt.axvline(2.5,color="grey")
plt.axvline(4.5,color="grey")
plt.gcf().subplots_adjust(left=0.12, bottom=0.2)
plt.ylabel(r"$\hat{\sigma}_{v,50k}$",rotation=90,fontsize=24)
plt.xticks([1,2,3,4], [r"$500$",r"$5k$",r"$0.6$",r"$0.9$"],fontsize=20,rotation=90)
plt.xlabel("tuning parameter value", fontsize=20)
plt.tick_params(axis='x', which='both', length=0)
plt.twiny()
plt.xlim(0,5)
plt.xticks([1,3,4.5], ["BEM","OEM","IOEM"], fontsize=20)
plt.tick_params(axis='x', which='both', length=0)
plt.savefig("sigv_50k.png")
plt.close()
#sigw
plt.rcParams.update({'font.size': 20})
plt.rcParams['xtick.major.pad']='12'
plt.axis([0,5,0,5])
plt.axhline(1)
data = []
def candplot1(self, plotcands={}, save=0):
""" Uses results of bispectrum results (calculated in do_search and find_candidates) to produce summary plot per candidate.
This is a "tier 1" plot, since it only uses info available to bispectrum search algorithm.
Requires do_search and find_candidates to be run first to build "master" objects.
Candidate should be dictionary with keys of beam location and value of a list of 3-element lists (dtind, int, dmbin).
save defines whether plots are also saved.
"""
print 'Building Tier 1 candidate plot.'
if n.array([len(plotcands[beam]) for beam in plotcands.keys()]).sum() == 0:
print 'No candidates available...'
return
figi = 1
for beam in plotcands.keys():
if len(plotcands[beam]) == 0:
continue
if (n.ndim(plotcands[beam]) != 2) | (n.shape(plotcands[beam])[1] != 3):
print 'Candidate does not seem to be in proper format. Skipping.'
continue
for cand in plotcands[beam]:
# find island containing candidate
for island in self.islands[beam]:
if n.any([n.all(member == cand) for member in island]): # if any member of island that has all three coords the same as plotcand, break
break
# next find snr for all candidates in island
islandind = []
for i in range(len(island)):
ww = n.where( (n.array(self.masterloc[beam])[:,0]==island[i,0]) & (n.array(self.masterloc[beam])[:,1]==island[i,1]) & (n.array(self.masterloc[beam])[:,2]==island[i,2]) )
islandind.append(ww[0][0])
loc = n.array(self.masterloc[beam])[islandind]
snr = n.array(self.masterprop[beam])[islandind, 0]
# then plot snr as function of dmind and dtind for island
fixed_dt = n.where(loc[:,0] == cand[0])
fixed_dm = n.where(loc[:,2] == cand[2])
dmdist = n.squeeze(n.array(self.dmarr)[loc[fixed_dt, 2]]) # seems to have superfluous axis...?
dmsnr = snr[fixed_dt]
dtdist = n.squeeze(n.array(self.timescales)[loc[fixed_dm][:, 0]]) # seems to have superfluous axis...?
dtsnr = snr[fixed_dm]
# find master index of candidate
ww = n.where([n.all(mastercand == cand) for mastercand in self.masterloc[beam]])[0][0]
# plot candidate info
p.figure(figi)
p.clf()
p.subplot(221, axisbg='white')
p.title('Candidate @ Tier 1')
p.xticks([])
p.yticks([])
p.text(0.1, 0.8, self.datafile, fontname='sans-serif')
beamra = n.round(beam, 3)[0]
beamdec = n.round(beam, 3)[1]
p.text(0.1, 0.6, 'Beam: ' + str(beamra) + ', ' + str(beamdec) + ', dt: ' + str(self.timescales[cand[0]]), fontname='sans-serif')
p.text(0.1, 0.4, 'Integration: ' + str(cand[1]) + ', DM: ' + str(self.dmarr[cand[2]]), fontname='sans-serif')
p.text(0.1, 0.2, 'SNR: ' + str(n.round(self.masterprop[beam][ww][0], 1)), fontname='sans-serif')
p.subplot(222)
p.plot(dmdist, dmsnr, 'b.', clip_on=False, label='DM at peak dt')
p.xlabel('DM (pc/cm3)')
p.ylabel('SNR')
p.twiny()
p.plot(dtdist, dtsnr, 'r+', clip_on=False, label='dt at peak DM')
p.xlabel('dt (ints)')
p.subplot(223)
p.plot(self.masterdata[beam][ww][0], 'b.', label='Mean B')
p.xlabel('Integration')
p.ylabel('Mean, Std of Bispectra')
p.twinx()
p.plot(self.masterdata[beam][ww][1], 'r.', label='Std B')
p.subplot(224)
dataph = n.rot90(self.masterdata[beam][ww][2])
sh = dataph.shape
im = p.imshow(dataph, aspect='auto', origin='upper', interpolation='nearest', extent=(0, sh[1], 0, sh[0]))
p.colorbar()
p.plot(self.obs.dmtrack0[cand[2]][0], self.obs.dmtrack0[cand[2]][1],'k.') # reference dispersed track
p.xlabel('Integration')
p.ylabel('Channel')
if self.datatype == 'mir': # casapy doesn't get along with recent matplotlib stuff
p.tight_layout()
if save:
p.savefig(self.fileroot + '_sc' + str(self.scan) + 'sp' + str(self.spw[0]) + 'i' + str(self.startints[0]) + '_tier1_' + str(figi) + '.png')
figi += 1
maxrange = max(mid - yl, yh - mid)
yl = mid - maxrange
yh = mid + maxrange*0.8
#
targettime = slope**(target_depth + diff)
print('Exposure time to hit nominal depth:', targettime)
plt.axvline(targettime, color='k', alpha=0.1)
plt.text(targettime, (yl+mid)/2, '%.1f s' % targettime, color='k', ha='left', va='center')
plt.xscale('log')
xt = [10, 20, 50, 100, 200, 300, 400, 500]
plt.xticks(xt, ['%i'%t for t in xt])
plt.axis([xl,xh,yl,yh])
plt.twiny()
bins = np.arange(20., 25., 0.05)
n,b,p = plt.hist(extdepth[notbad], bins=bins, orientation='horizontal',
histtype='step', color='b')
#plt.xlim(0, np.max(n)*4)
plt.xlim(np.max(n)*4, 0)
plt.ylim(yl,yh)
plt.xticks([])
plt.legend([p2[0]], ['+- 0.05 mag'], loc='lower right')
plt.title(tt)
ps.savefig()
plt.clf()
def plotPhotometry(imgsources,
refsources,
matches,
prefix,
band=None,
zp=None,
delta=False,
referrs=None,
refstargal=None,
title=None,
saveplot=True,
format='png'):
print('%i ref sources' % len(refsources))
print('%i image sources' % len(imgsources))
print('%i matches' % len(matches))
# In the "matches" list:
# m.first is catalog
# m.second is image
# In this function, the "m" prefix stands for "matched",
# "u" stands for "unmatched".
# *sigh*, turn these into Python lists, so we have the "index" function.
refsources = [s for s in refsources]
imgsources = [s for s in imgsources]
# Now we build numpy int arrays for indexing into the "refsources" and
# "imgsources" arrays.
MR = []
MI = []
for m in matches:
try:
i = refsources.index(m.first)
except ValueError:
print('Match list reference source ID', m.first.getSourceId(),
'was not in the list of reference stars')
continue
try:
j = imgsources.index(m.second)
except ValueError:
print('Match list source ID', m.second.getSourceId(),
'was not in the list of image sources')
continue
MR.append(i)
MI.append(j)
MR = np.array(MR)
MI = np.array(MI)
# Build numpy boolean arrays for indexing the unmatched stars.
UR = np.ones(len(refsources), bool)
UR[MR] = False
UI = np.ones(len(imgsources), bool)
UI[MI] = False
def flux2mag(f):
return -2.5 * np.log10(f)
refmag = np.array([flux2mag(s.getPsfInstFlux()) for s in refsources])
imgflux = np.array([s.getPsfInstFlux() for s in imgsources])
imgfluxerr = np.array([s.getPsfInstFluxErr() for s in imgsources])
# Cut to fluxes that aren't silly and get mags of matched sources.
okflux = (imgflux[MI] > 1)
MI = MI[okflux]
MR = MR[okflux]
mimgflux = imgflux[MI]
mimgmag = flux2mag(mimgflux)
mimgmagerr = abs(2.5 / np.log(10.) * imgfluxerr[MI] / mimgflux)
mrefmag = refmag[MR]
# Get mags of unmatched sources.
uimgflux = imgflux[UI]
okflux = (uimgflux > 1)
uimgmag = flux2mag(uimgflux[okflux])
urefmag = refmag[UR]
# Legend entries:
pp = []
pl = []
plt.clf()
imag = np.append(mimgmag, uimgmag)
mrefmagerr = None
if referrs is not None:
referrs = np.array(referrs)
mrefmagerr = referrs[MR]
if refstargal:
assert (len(refstargal) == len(refsources))
refstargal = np.array(refstargal).astype(bool)
ptsets = [(np.logical_not(refstargal[MR]), 'g', 'Matched galaxies',
10), (refstargal[MR], 'b', 'Matched stars', 12)]
else:
ptsets = [(np.ones_like(mrefmag).astype(bool), 'b', 'Matched sources',
10)]
for I, c, leg, zo in ptsets:
if delta:
dm = mimgmag[I] - mrefmag[I] + zp
xi = mrefmag[I]
yi = dm
dx = mrefmagerr
dy = mimgmagerr
else:
xi = mimgmag[I]
yi = mrefmag[I]
dx = mimgmagerr
dy = mrefmagerr
p1 = plt.plot(xi, yi, '.', color=c, mfc=c, mec=c, alpha=0.5, zorder=zo)
if dx is None or dy is None:
# errorbars
xerr, yerr = None, None
if dx is not None:
xerr = dx[I]
if dy is not None:
yerr = dy[I]
plt.errorbar(xi,
yi,
xerr=xerr,
yerr=yerr,
ecolor=c,
fmt=None,
zorder=zo)
else:
# get the current axis
ca = plt.gca()
# add error ellipses
for j, i in enumerate(np.flatnonzero(I)):
a = Ellipse(xy=np.array([xi[j], yi[j]]),
width=dx[i] / 2.,
height=dy[i] / 2.,
alpha=0.5,
fill=True,
ec=c,
fc=c,
zorder=zo)
ca.add_artist(a)
pp.append(p1)
pl.append(leg)
if delta:
m = max(abs(dm))
plt.axis([np.floor(min(refmag)) - 0.5, np.ceil(max(refmag)), -m, m])
else:
plt.axis([
np.floor(min(imag)) - 0.5,
np.ceil(max(imag)),
np.floor(min(refmag)) - 0.5,
np.ceil(max(refmag))
])
ax = plt.axis()
if not delta:
# Red tick marks show unmatched img sources
dy = (ax[3] - ax[2]) * 0.05
y1 = np.ones_like(uimgmag) * ax[3]
p2 = plt.plot(np.vstack((uimgmag, uimgmag)),
np.vstack((y1, y1 - dy)),
'r-',
alpha=0.5)
p2 = p2[0]
# Blue tick marks show matched img sources
y1 = np.ones_like(mimgmag) * ax[3]
p3 = plt.plot(np.vstack((mimgmag, mimgmag)),
np.vstack((y1 - (0.25 * dy), y1 - (1.25 * dy))),
'b-',
alpha=0.5)
p3 = p3[0]
# Red ticks for unmatched ref sources
dx = (ax[1] - ax[0]) * 0.05
x1 = np.ones_like(urefmag) * ax[1]
p4 = plt.plot(np.vstack((x1, x1 - dx)),
np.vstack((urefmag, urefmag)),
'r-',
alpha=0.5)
p4 = p4[0]
# Blue ticks for matched ref sources
x1 = np.ones_like(mrefmag) * ax[1]
p5 = plt.plot(np.vstack((x1 - (0.25 * dx), x1 - (1.25 * dx))),
np.vstack((mrefmag, mrefmag)),
'b-',
alpha=0.5)
p5 = p5[0]
if zp is not None:
if delta:
pzp = plt.axhline(0, linestyle='--', color='b')
else:
X = np.array([ax[0], ax[1]])
pzp = plt.plot(X, X + zp, 'b--')
pp.append(pzp)
pl.append('Zeropoint')
# reverse axis directions.
if delta:
plt.axis([ax[1], ax[0], ax[2], ax[3]])
else:
plt.axis([ax[1], ax[0], ax[3], ax[2]])
if band is not None:
reflabel = 'Reference catalog: %s band (mag)' % band
else:
reflabel = 'Reference catalog mag'
if delta:
plt.xlabel(reflabel)
plt.ylabel('Instrumental - Reference (mag)')
fn = prefix + '-dphotom.' + format
if zp is not None:
# Make the plot area smaller to fit the twin axis
pos = plt.gca().get_position()
ll = pos.min
sz = pos.size
plt.gca().set_position(pos=[ll[0], ll[1], sz[0], sz[1] - 0.05])
# Put the title up top (otherwise it follows the axis)
if title is not None:
plt.figtext(0.5,
0.96,
title,
ha='center',
va='top',
fontsize='large')
title = None
plt.twiny()
# Red tick marks show unmatched img sources
if zp is not None:
dy = (ax[3] - ax[2]) * 0.05
y1 = np.ones_like(uimgmag) * ax[3]
p2 = plt.plot(np.vstack((uimgmag, uimgmag)) + zp,
np.vstack((y1, y1 - dy)),
'r-',
alpha=0.5)
p2 = p2[0]
# Blue tick marks show matched img sources
y1 = np.ones_like(mimgmag) * ax[3]
p3 = plt.plot(np.vstack((mimgmag, mimgmag)) + zp,
np.vstack((y1 - (0.25 * dy), y1 - (1.25 * dy))),
'b-',
alpha=0.5)
p3 = p3[0]
# Red ticks for unmatched ref sources
y1 = np.ones_like(urefmag) * ax[2]
p4 = plt.plot(np.vstack((urefmag, urefmag)),
np.vstack((y1, y1 + dy)),
'r-',
alpha=0.5)
p4 = p4[0]
# Blue ticks for matched ref sources
y1 = np.ones_like(mrefmag) * ax[2]
p5 = plt.plot(np.vstack((mrefmag, mrefmag)),
np.vstack((y1 + (0.25 * dy), y1 + (1.25 * dy))),
'b-',
alpha=0.5)
p5 = p5[0]
plt.xlim(ax[1] - zp, ax[0] - zp)
plt.xlabel('Instrumental mag')
legloc = 'lower right'
else:
plt.ylabel(reflabel)
plt.xlabel('Image instrumental mag')
fn = prefix + '-photom.' + format
legloc = 'center right'
if title is not None:
plt.title(title)
pp += [p3, p2]
pl += ['Matched sources', 'Unmatched sources']
plt.figlegend(pp,
pl,
legloc,
numpoints=1,
prop=FontProperties(size='small'))
P1 = _output(fn, format, saveplot)
if delta:
plt.ylim(-0.5, 0.5)
fn = prefix + '-dphotom2.' + format
P2 = _output(fn, format, saveplot)
if not saveplot:
P1.update(P2)
return P1
l0 = ax.axvline(x=0.1, color='k', linestyle=':', linewidth=1.5)
ax.set_xlim(0., 0.635)
ax.set_xlabel('Frequency (Hz)')
# setup colorbar axes instance.
l,b,w,h = ax.get_position().bounds
cax = fig.add_axes([l+w+0.025, b-0.06, 1.0*w, 0.03])
cb = colorbar(pc, cax=cax, orientation='horizontal') # draw colorbar
cb.set_label('Spectral Density (m2/Hz/deg)')
cb.ax.xaxis.set_label_position('top')
# cb.ax.set_xticks([0.1, 0.3, 0.5, 0.7, 0.9])
# cb.ax.set_xticklabels([-0.4, -0.2, 0, 0.2, 0.4])
# top scale wave period
ax2 = twiny(ax)
ax2.set_xlim(0., 0.635)
ax2.xaxis.tick_top()
# convert (bottom) Hertz to (top scale) seconds (1/Hz)
Hertz = ax.get_xticks()
Hertz = [val for val in Hertz if val!=0 ]
ax2.set_xticks(Hertz)
s = [round(1./val,2) for val in Hertz]
ax2.set_xticklabels(s)
ax2.set_xlabel('Wave Period (sec)')
#######################################
# print ' ... all, swell, and wind labels'
ax = fig.add_axes((.1,.875,.4,.10))
axs.append(ax)
for i in xrange(40,600,chunk):
print i, n.average(lsts[i:i+chunk])
lst_hr = int(n.around(n.average(lsts[i:i+chunk])))
Tsys_avg = n.sqrt(n.average(Tsys[i:i+chunk]**2, axis=0))
valid = n.where(Tsys_avg > 213, 1, 0)
#p.plot(1e3*fqs.compress(valid), Tsys_avg.compress(valid), label='LST = %02dh00'%lst_hr)
p.plot(chans.compress(valid), Tsys_avg.compress(valid), label='LST = %02dh00'%lst_hr)
if VLINE:
nums=opts.chan.split('_')
vlines=[float(v) for v in nums]
p.vlines(vlines,200,2000,linestyles='dashed')
p.legend(loc='best', fontsize='medium')
p.xlabel(r'${\rm Channel}$', fontsize=16)
if FREQ_AXIS:
ax=p.twiny()
ax.plot(freqs,n.ones(len(freqs*1e3)))
ax.cla()
ax.set_xlabel(r'${\rm Frequency\ [MHz]}$', fontsize=16)
#p.xlim(125,175)
p.ylim(200,2000)
p.grid()
p.ylabel(r'${\rm T}_{\rm sys}$', fontsize=16)
p.subplots_adjust(0.12, 0.15, .95, .85)
fig_file='Tsys_chan_'+opts.chan
if not opts.output == '':
fig_file= opts.output + '/' + fig_file
p.savefig(fig_file)
p.show()
def plotPhotometry(imgsources, refsources, matches, prefix, band=None,
zp=None, delta=False, referrs=None, refstargal=None,
title=None,
saveplot=True, format='png'):
print '%i ref sources' % len(refsources)
print '%i image sources' % len(imgsources)
print '%i matches' % len(matches)
# In the "matches" list:
# m.first is catalog
# m.second is image
# In this function, the "m" prefix stands for "matched",
# "u" stands for "unmatched".
# *sigh*, turn these into Python lists, so we have the "index" function.
refsources = [s for s in refsources]
imgsources = [s for s in imgsources]
# Now we build numpy int arrays for indexing into the "refsources" and
# "imgsources" arrays.
MR = []
MI = []
for m in matches:
try:
i = refsources.index(m.first)
except ValueError:
print 'Match list reference source ID', m.first.getSourceId(), 'was not in the list of reference stars'
continue
try:
j = imgsources.index(m.second)
except ValueError:
print 'Match list source ID', m.second.getSourceId(), 'was not in the list of image sources'
continue
MR.append(i)
MI.append(j)
MR = np.array(MR)
MI = np.array(MI)
# Build numpy boolean arrays for indexing the unmatched stars.
UR = np.ones(len(refsources), bool)
UR[MR] = False
UI = np.ones(len(imgsources), bool)
UI[MI] = False
def flux2mag(f):
return -2.5*np.log10(f)
refmag = np.array([flux2mag(s.getPsfFlux()) for s in refsources])
imgflux = np.array([s.getPsfFlux() for s in imgsources])
imgfluxerr = np.array([s.getPsfFluxErr() for s in imgsources])
# Cut to fluxes that aren't silly and get mags of matched sources.
okflux = (imgflux[MI] > 1)
MI = MI[okflux]
MR = MR[okflux]
mimgflux = imgflux[MI]
mimgmag = flux2mag(mimgflux)
mimgmagerr = abs(2.5 / np.log(10.) * imgfluxerr[MI] / mimgflux)
mrefmag = refmag[MR]
# Get mags of unmatched sources.
uimgflux = imgflux[UI]
okflux = (uimgflux > 1)
uimgmag = flux2mag(uimgflux[okflux])
urefmag = refmag[UR]
if False:
unmatched = [imgsources[i] for i in np.flatnonzero(uimg)]
uflux = np.array([s.getPsfFlux() for s in unmatched])
I = np.argsort(-uflux)
print 'Unmatched image sources, by psf flux:'
print '# FLUX, X, Y, RA, DEC'
for i in I:
u = unmatched[i]
print u.getPsfFlux(), u.getXAstrom(), u.getYAstrom(), u.getRa(), u.getDec()
print 'Matched image sources, by psf flux:'
print '# FLUX, X, Y, RA, DEC'
for i in mimgi:
m = imgsources[i]
print m.getPsfFlux(), m.getXAstrom(), m.getYAstrom(), m.getRa(), m.getDec()
# Legend entries:
pp = []
pl = []
plt.clf()
imag = np.append(mimgmag, uimgmag)
mrefmagerr = None
if referrs is not None:
referrs = np.array(referrs)
mrefmagerr = referrs[MR]
if refstargal:
assert(len(refstargal) == len(refsources))
refstargal = np.array(refstargal).astype(bool)
ptsets = [ (np.logical_not(refstargal[MR]), 'g', 'Matched galaxies', 10),
(refstargal[MR], 'b', 'Matched stars', 12) ]
else:
ptsets = [ (np.ones_like(mrefmag).astype(bool), 'b', 'Matched sources', 10) ]
for I,c,leg,zo in ptsets:
if delta:
dm = mimgmag[I] - mrefmag[I] + zp
xi = mrefmag[I]
yi = dm
dx = mrefmagerr
dy = mimgmagerr
else:
xi = mimgmag[I]
yi = mrefmag[I]
dx = mimgmagerr
dy = mrefmagerr
p1 = plt.plot(xi, yi, '.', color=c, mfc=c, mec=c, alpha=0.5, zorder=zo)
if dx is None or dy is None:
# errorbars
xerr,yerr = None,None
if dx is not None:
xerr = dx[I]
if dy is not None:
yerr = dy[I]
plt.errorbar(xi, yi, xerr=xerr, yerr=yerr, ecolor=c, fmt=None, zorder=zo)
else:
# get the current axis
ca = plt.gca()
# add error ellipses
for j,i in enumerate(np.flatnonzero(I)):
a = Ellipse(xy=np.array([xi[j], yi[j]]),
width=dx[i]/2., height=dy[i]/2.,
alpha=0.5, fill=True, ec=c, fc=c, zorder=zo)
ca.add_artist(a)
pp.append(p1)
pl.append(leg)
if delta:
m = max(abs(dm))
plt.axis([np.floor(min(refmag))-0.5, np.ceil(max(refmag)),
-m, m])
else:
plt.axis([np.floor(min(imag))-0.5, np.ceil(max(imag)),
np.floor(min(refmag))-0.5, np.ceil(max(refmag))])
ax = plt.axis()
if not delta:
# Red tick marks show unmatched img sources
dy = (ax[3]-ax[2]) * 0.05
y1 = np.ones_like(uimgmag) * ax[3]
p2 = plt.plot(np.vstack((uimgmag, uimgmag)), np.vstack((y1, y1-dy)), 'r-', alpha=0.5)
p2 = p2[0]
# Blue tick marks show matched img sources
y1 = np.ones_like(mimgmag) * ax[3]
p3 = plt.plot(np.vstack((mimgmag, mimgmag)), np.vstack((y1-(0.25*dy), y1-(1.25*dy))), 'b-', alpha=0.5)
p3 = p3[0]
# Red ticks for unmatched ref sources
dx = (ax[1]-ax[0]) * 0.05
x1 = np.ones_like(urefmag) * ax[1]
p4 = plt.plot(np.vstack((x1, x1-dx)), np.vstack((urefmag, urefmag)), 'r-', alpha=0.5)
p4 = p4[0]
# Blue ticks for matched ref sources
x1 = np.ones_like(mrefmag) * ax[1]
p5 = plt.plot(np.vstack((x1-(0.25*dx), x1-(1.25*dx))), np.vstack((mrefmag, mrefmag)), 'b-', alpha=0.5)
p5 = p5[0]
if zp is not None:
if delta:
pzp = plt.axhline(0, linestyle='--', color='b')
else:
X = np.array([ax[0], ax[1]])
pzp = plt.plot(X, X+zp, 'b--')
pp.append(pzp)
pl.append('Zeropoint')
# reverse axis directions.
if delta:
plt.axis([ax[1],ax[0], ax[2], ax[3]])
else:
plt.axis([ax[1],ax[0], ax[3], ax[2]])
if band is not None:
reflabel = 'Reference catalog: %s band (mag)' % band
else:
reflabel = 'Reference catalog mag'
if delta:
plt.xlabel(reflabel)
plt.ylabel('Instrumental - Reference (mag)')
fn = prefix + '-dphotom.' + format
if zp is not None:
# Make the plot area smaller to fit the twin axis
pos = plt.gca().get_position()
ll = pos.min
sz = pos.size
plt.gca().set_position(pos=[ll[0], ll[1], sz[0], sz[1]-0.05])
# Put the title up top (otherwise it follows the axis)
if title is not None:
plt.figtext(0.5, 0.96, title, ha='center', va='top', fontsize='large')
title = None
ax2 = plt.twiny()
# Red tick marks show unmatched img sources
if zp is not None:
dy = (ax[3]-ax[2]) * 0.05
y1 = np.ones_like(uimgmag) * ax[3]
p2 = plt.plot(np.vstack((uimgmag, uimgmag)) + zp, np.vstack((y1, y1-dy)), 'r-', alpha=0.5)
p2 = p2[0]
# Blue tick marks show matched img sources
y1 = np.ones_like(mimgmag) * ax[3]
p3 = plt.plot(np.vstack((mimgmag, mimgmag)) + zp, np.vstack((y1-(0.25*dy), y1-(1.25*dy))), 'b-', alpha=0.5)
p3 = p3[0]
# Red ticks for unmatched ref sources
y1 = np.ones_like(urefmag) * ax[2]
p4 = plt.plot(np.vstack((urefmag, urefmag)), np.vstack((y1, y1+dy)), 'r-', alpha=0.5)
p4 = p4[0]
# Blue ticks for matched ref sources
y1 = np.ones_like(mrefmag) * ax[2]
p5 = plt.plot(np.vstack((mrefmag, mrefmag)), np.vstack((y1+(0.25*dy), y1+(1.25*dy))), 'b-', alpha=0.5)
p5 = p5[0]
plt.xlim(ax[1]-zp, ax[0]-zp)
plt.xlabel('Instrumental mag')
legloc = 'lower right'
else:
plt.ylabel(reflabel)
plt.xlabel('Image instrumental mag')
fn = prefix + '-photom.' + format
legloc = 'center right'
if title is not None:
plt.title(title)
pp += [p3, p2]
pl += ['Matched sources', 'Unmatched sources']
plt.figlegend(pp, pl, legloc, numpoints=1, prop=FontProperties(size='small'))
P1 = _output(fn, format, saveplot)
if delta:
plt.ylim(-0.5, 0.5)
fn = prefix + '-dphotom2.' + format
P2 = _output(fn, format, saveplot)
if not saveplot:
P1.update(P2)
return P1
def intensityRatio(self, wvlRange=None, wvlRanges=None, top=10):
"""
Plot the ratio of 2 lines or sums of lines.
Shown as a function of density and/or temperature.
For a single wavelength range, set wvlRange = [wMin, wMax]
For multiple wavelength ranges, set wvlRanges = [[wMin1,wMax1],[wMin2,wMax2], ...]
A plot of relative emissivities is shown and then a dialog appears for the user to
choose a set of lines.
"""
#
# self.Emiss={"temperature":temperature,"density":density,"wvl":wvl,"emiss":em,
# "plotLabels":plotLabels}
#
#
if not hasattr(self, 'Intensity'):
try:
self.intensity()
except:
print(
' intensities not calculated and emiss() is unable to calculate them'
)
print(' perhaps the temperature and/or eDensity are not set')
return
#
# everything in self.Intensity should be a numpy array
#
# intens = copy.copy(self.Intensity)
# intensity = intens['intensity']
#
#
fontsize = 14
#
# temperature = self.Temperature
eDensity = self.EDensity
temperature = self.Temperature
# intensity = intens['intensity']
#
# temp=np.asarray(temperature,'Float32')
ntemp = temperature.size
if ntemp > 0:
if temperature[0] == temperature[-1]:
ntemp = 1
#
ndens = eDensity.size
if ndens > 0:
if eDensity[0] == eDensity[-1]:
ndens = 1
#
print(' ndens = %5i ntemp = %5i' % (ndens, ntemp))
ionS = self.Intensity['ionS']
# see if we are dealing with more than a single ion
ionSet = set(ionS)
ionNum = len(ionSet)
wvl = self.Intensity["wvl"]
# plotLabels = intens["plotLabels"]
# xLabel = plotLabels["xLabel"]
# yLabel = plotLabels["yLabel"]
#
# find which lines are in the wavelength range if it is set
#
#
if wvlRange:
igvl = util.between(wvl, wvlRange)
if len(igvl) == 0:
print('no lines in wavelength range %12.2f - %12.2f' %
(wvlRange[0], wvlRange[1]))
return
elif wvlRanges:
igvl = []
for awvlRange in wvlRanges:
igvl.extend(util.between(wvl, awvlRange))
if len(igvl) == 0:
print('no lines in wavelength ranges specified ')
return
else:
igvl = range(len(wvl))
#
nlines = len(igvl)
#
# print ' nlines = ',nlines
# print ' iglv = ',igvl
igvl = np.take(igvl, wvl[igvl].argsort())
# find the top most intense lines
#
if top > nlines:
top = nlines
#
intensity = self.Intensity['intensity']
maxIntens = np.zeros(nlines, 'Float64')
for iline in range(nlines):
maxIntens[iline] = intensity[:, igvl[iline]].max()
for iline in range(nlines):
if maxIntens[iline] == maxIntens.max():
maxAll = intensity[:, igvl[iline]]
# line=range(nlines)
igvlsort = np.take(igvl, np.argsort(maxIntens))
# print 'igvlsort = ', igvlsort
topLines = igvlsort[-top:]
# print ' topLines = ', topLines
maxWvl = '%5.3f' % wvl[topLines[-1]]
# maxline=topLines[-1]
#
topLines = topLines[wvl[topLines].argsort()]
#
#
# need to make sure there are no negative values before plotting
good = intensity > 0.
intensMin = intensity[good].min()
bad = intensity <= 0.
intensity[bad] = intensMin
#
#
# ntemp=self.Temperature.size
#
# ndens=self.EDensity.size
#
ylabel = 'Intensity relative to ' + maxWvl
if ionNum == 1:
title = self.Spectroscopic
else:
title = ''
#
#
if ndens == 1 and ntemp == 1:
print(' only a single temperature and eDensity')
return
elif ndens == 1:
xlabel = 'Temperature (K)'
xvalues = self.Temperature
outTemperature = self.Temperature
# outDensity=np.zeros(ntemp,'Float64')
# outDensity.fill(self.EDensity[0])
outDensity = self.EDensity
desc_str = ' Density = %10.2e (cm)$^{-3}$' % self.EDensity[0]
elif ntemp == 1:
xvalues = self.EDensity
# outTemperature=np.zeros(ndens,'Float64')
# outTemperature.fill(self.Temperature[0])
outTemperature = self.Temperature
outDensity = self.EDensity
xlabel = r'$\rm{Electron Density (cm)^{-3}}$'
desc_str = ' Temp = %10.2e (K)' % self.Temperature[0]
else:
outTemperature = self.Temperature
outDensity = self.EDensity
xlabel = 'Temperature (K)'
xvalues = self.Temperature
desc_str = ' Variable Density'
#
# put all actual plotting here
#
pl.ion()
# if chInteractive:
# pl.ion()
# else:
# pl.ioff()
#
# maxAll is an array
ymax = np.max(intensity[:, topLines[0]] / maxAll)
ymin = ymax
pl.figure()
ax = pl.subplot(111)
nxvalues = len(xvalues)
for iline in range(top):
tline = topLines[iline]
pl.loglog(xvalues, intensity[:, tline] / maxAll)
if np.min(intensity[:, tline] / maxAll) < ymin:
ymin = np.min(intensity[:, tline] / maxAll)
if np.max(intensity[:, tline] / maxAll) > ymax:
ymax = np.max(intensity[:, tline] / maxAll)
skip = 2
start = divmod(iline, nxvalues)[1]
for ixvalue in range(start, nxvalues, nxvalues // skip):
if ionNum == 1:
text = '%10.4f' % (wvl[tline])
else:
text = '%s %10.4f' % (ionS[tline], wvl[tline])
pl.text(xvalues[ixvalue],
intensity[ixvalue, tline] / maxAll[ixvalue], text)
pl.xlim(xvalues.min(), xvalues.max())
# pl.ylim(ymin, ymax)
pl.xlabel(xlabel, fontsize=fontsize)
pl.ylabel(ylabel, fontsize=fontsize)
if ndens == ntemp and ntemp > 1:
pl.text(0.07,
0.5,
title,
horizontalalignment='left',
verticalalignment='center',
fontsize=fontsize,
transform=ax.transAxes)
#
ax2 = pl.twiny()
xlabelDen = r'Electron Density (cm$^{-3}$)'
pl.xlabel(xlabelDen, fontsize=fontsize)
pl.loglog(eDensity,
intensity[:, topLines[top - 1]] / maxAll,
visible=False)
ax2.xaxis.tick_top()
pl.ylim(ymin / 1.2, 1.2 * ymax)
else:
pl.ylim(ymin / 1.2, 1.2 * ymax)
pl.title(title + desc_str, fontsize=fontsize)
pl.draw()
# need time to let matplotlib finish plotting
time.sleep(0.5)
#
# get line selection
#
selectTags = []
for itop in topLines:
if ionNum == 1:
selectTags.append(str(wvl[itop]))
else:
selectTags.append(ionS[itop] + ' ' + str(wvl[itop]))
#
# numden = chGui.gui.choice2Dialog(wvl[topLines])
numden = chGui.gui.choice2Dialog(selectTags)
#
# num_idx and den_idx are tuples
#
num_idx = numden.numIndex
if len(num_idx) == 0:
print(' no numerator lines were selected')
return
#
den_idx = numden.denIndex
if len(den_idx) == 0:
print(' no denominator lines were selected')
return
#
numIntens = np.zeros(len(xvalues), 'Float64')
for aline in num_idx:
numIntens += intensity[:, topLines[aline]]
#
denIntens = np.zeros(len(xvalues), 'Float64')
for aline in den_idx:
denIntens += intensity[:, topLines[aline]]
#
# plot the desired ratio
# maxAll is an array
pl.figure()
ax = pl.subplot(111)
pl.loglog(xvalues, numIntens / denIntens)
pl.xlim(xvalues.min(), xvalues.max())
pl.xlabel(xlabel, fontsize=fontsize)
pl.ylabel('Ratio (' + self.Defaults['flux'] + ')', fontsize=fontsize)
if ionNum == 1:
desc = ionS[0]
else:
desc = ''
for aline in num_idx:
if ionNum == 1:
desc += ' ' + str(wvl[topLines[aline]])
else:
desc += ' ' + ionS[topLines[aline]] + ' ' + str(
wvl[topLines[aline]])
desc += ' / '
for aline in den_idx:
if ionNum == 1:
desc += ' ' + str(wvl[topLines[aline]])
else:
desc += ' ' + ionS[topLines[aline]] + ' ' + str(
wvl[topLines[aline]])
if ndens == ntemp and ntemp > 1:
pl.text(0.07,
0.5,
desc,
horizontalalignment='left',
verticalalignment='center',
fontsize=fontsize,
transform=ax.transAxes)
#
ax2 = pl.twiny()
xlabelDen = r'Electron Density (cm$^{-3}$)'
pl.xlabel(xlabelDen, fontsize=fontsize)
pl.loglog(eDensity, numIntens / denIntens, visible=False)
ax2.xaxis.tick_top()
else:
# pl.ylim(ymin, ymax)
pl.title(desc, fontsize=fontsize)
# desc=title+' '+str(wvl[num_line])+' / '+str(wvl[den_line])+' '+desc_str
# pl.title(desc, fontsize=fontsize)
# pl.title(title+' '+str(wvl[num_line])+' / '+str(wvl[den_line])+' '+desc_str,fontsize=fontsize)
# pl.draw()
# pl.ioff()
# pl.show()
#
# intensityRatioFileName=self.IonStr
# for aline in num_idx:
# intensityRatioFileName+= '_%3i'%(wvl[topLines[aline]])
# intensityRatioFileName+='_2'
# for aline in den_idx:
# intensityRatioFileName+= '_%3i'%(wvl[topLines[aline]])
cnt = desc.count(' ')
intensityRatioFileName = desc.replace(' ', '_', cnt) + '.rat'
intensityRatioFileName = intensityRatioFileName.lstrip('_').replace(
'_/_', '-')
self.IntensityRatio = {
'ratio': numIntens / denIntens,
'desc': desc,
'temperature': outTemperature,
'eDensity': outDensity,
'filename': intensityRatioFileName,
'numIdx': num_idx,
'denIdx': den_idx
}
def intensityRatio(self,wvlRange=None, wvlRanges=None,top=10):
"""
Plot the ratio of 2 lines or sums of lines.
Shown as a function of density and/or temperature.
For a single wavelength range, set wvlRange = [wMin, wMax]
For multiple wavelength ranges, set wvlRanges = [[wMin1,wMax1],[wMin2,wMax2], ...]
A plot of relative emissivities is shown and then a dialog appears for the user to
choose a set of lines.
"""
#
# self.Emiss={"temperature":temperature,"density":density,"wvl":wvl,"emiss":em,
# "plotLabels":plotLabels}
#
#
if not hasattr(self, 'Intensity'):
try:
self.intensity()
except:
print(' intensities not calculated and emiss() is unable to calculate them')
print(' perhaps the temperature and/or eDensity are not set')
return
#
# everything in self.Intensity should be a numpy array
#
# intens = copy.copy(self.Intensity)
# intensity = intens['intensity']
#
#
fontsize=14
#
# temperature = self.Temperature
eDensity = self.EDensity
temperature = self.Temperature
# intensity = intens['intensity']
#
# temp=np.asarray(temperature,'Float32')
ntemp = temperature.size
if ntemp > 0:
if temperature[0] == temperature[-1]:
ntemp = 1
#
ndens = eDensity.size
if ndens > 0:
if eDensity[0] == eDensity[-1]:
ndens = 1
#
print(' ndens = %5i ntemp = %5i'%(ndens, ntemp))
ionS = self.Intensity['ionS']
# see if we are dealing with more than a single ion
ionSet = set(ionS)
ionNum = len(ionSet)
wvl = self.Intensity["wvl"]
# plotLabels = intens["plotLabels"]
# xLabel = plotLabels["xLabel"]
# yLabel = plotLabels["yLabel"]
#
# find which lines are in the wavelength range if it is set
#
#
if wvlRange:
igvl=util.between(wvl,wvlRange)
if len(igvl) == 0:
print('no lines in wavelength range %12.2f - %12.2f'%(wvlRange[0], wvlRange[1]))
return
elif wvlRanges:
igvl = []
for awvlRange in wvlRanges:
igvl.extend(util.between(wvl,awvlRange))
if len(igvl) == 0:
print('no lines in wavelength ranges specified ')
return
else:
igvl=range(len(wvl))
#
nlines=len(igvl)
#
# print ' nlines = ',nlines
# print ' iglv = ',igvl
igvl=np.take(igvl,wvl[igvl].argsort())
# find the top most intense lines
#
if top > nlines:
top=nlines
#
intensity = self.Intensity['intensity']
maxIntens = np.zeros(nlines,'Float64')
for iline in range(nlines):
maxIntens[iline] = intensity[:, igvl[iline]].max()
for iline in range(nlines):
if maxIntens[iline]==maxIntens.max():
maxAll=intensity[:, igvl[iline]]
# line=range(nlines)
igvlsort=np.take(igvl,np.argsort(maxIntens))
# print 'igvlsort = ', igvlsort
topLines=igvlsort[-top:]
# print ' topLines = ', topLines
maxWvl='%5.3f' % wvl[topLines[-1]]
# maxline=topLines[-1]
#
topLines=topLines[wvl[topLines].argsort()]
#
#
# need to make sure there are no negative values before plotting
good = intensity > 0.
intensMin = intensity[good].min()
bad = intensity <= 0.
intensity[bad] = intensMin
#
#
# ntemp=self.Temperature.size
#
# ndens=self.EDensity.size
#
ylabel='Intensity relative to '+maxWvl
if ionNum == 1:
title=self.Spectroscopic
else:
title = ''
#
#
if ndens==1 and ntemp==1:
print(' only a single temperature and eDensity')
return
elif ndens == 1:
xlabel='Temperature (K)'
xvalues=self.Temperature
outTemperature=self.Temperature
# outDensity=np.zeros(ntemp,'Float64')
# outDensity.fill(self.EDensity[0])
outDensity = self.EDensity
desc_str=' Density = %10.2e (cm)$^{-3}$' % self.EDensity[0]
elif ntemp == 1:
xvalues=self.EDensity
# outTemperature=np.zeros(ndens,'Float64')
# outTemperature.fill(self.Temperature[0])
outTemperature = self.Temperature
outDensity=self.EDensity
xlabel=r'$\rm{Electron Density (cm)^{-3}}$'
desc_str=' Temp = %10.2e (K)' % self.Temperature[0]
else:
outTemperature=self.Temperature
outDensity=self.EDensity
xlabel='Temperature (K)'
xvalues=self.Temperature
desc_str=' Variable Density'
#
# put all actual plotting here
#
pl.ion()
# if chInteractive:
# pl.ion()
# else:
# pl.ioff()
#
# maxAll is an array
ymax = np.max(intensity[:, topLines[0]]/maxAll)
ymin = ymax
pl.figure()
ax = pl.subplot(111)
nxvalues=len(xvalues)
for iline in range(top):
tline=topLines[iline]
pl.loglog(xvalues,intensity[:, tline]/maxAll)
if np.min(intensity[:, tline]/maxAll) < ymin:
ymin = np.min(intensity[:, tline]/maxAll)
if np.max(intensity[:, tline]/maxAll) > ymax:
ymax = np.max(intensity[:, tline]/maxAll)
skip=2
start=divmod(iline,nxvalues)[1]
for ixvalue in range(start,nxvalues,nxvalues//skip):
if ionNum == 1:
text = '%10.4f'%(wvl[tline])
else:
text = '%s %10.4f'%(ionS[tline], wvl[tline])
pl.text(xvalues[ixvalue], intensity[ixvalue, tline]/maxAll[ixvalue], text)
pl.xlim(xvalues.min(),xvalues.max())
# pl.ylim(ymin, ymax)
pl.xlabel(xlabel,fontsize=fontsize)
pl.ylabel(ylabel,fontsize=fontsize)
if ndens == ntemp and ntemp > 1:
pl.text(0.07, 0.5,title, horizontalalignment='left', verticalalignment='center', fontsize=fontsize, transform = ax.transAxes)
#
ax2 = pl.twiny()
xlabelDen=r'Electron Density (cm$^{-3}$)'
pl.xlabel(xlabelDen, fontsize=fontsize)
pl.loglog(eDensity,intensity[:, topLines[top-1]]/maxAll, visible=False)
ax2.xaxis.tick_top()
pl.ylim(ymin/1.2, 1.2*ymax)
else:
pl.ylim(ymin/1.2, 1.2*ymax)
pl.title(title+desc_str,fontsize=fontsize)
pl.draw()
# need time to let matplotlib finish plotting
time.sleep(0.5)
#
# get line selection
#
selectTags = []
for itop in topLines:
if ionNum == 1:
selectTags.append(str(wvl[itop]))
else:
selectTags.append(ionS[itop]+ ' '+ str(wvl[itop]))
#
# numden = chGui.gui.choice2Dialog(wvl[topLines])
numden = chGui.gui.choice2Dialog(selectTags)
#
# num_idx and den_idx are tuples
#
num_idx=numden.numIndex
if len(num_idx) == 0:
print(' no numerator lines were selected')
return
#
den_idx=numden.denIndex
if len(den_idx) == 0:
print(' no denominator lines were selected')
return
#
numIntens=np.zeros(len(xvalues),'Float64')
for aline in num_idx:
numIntens += intensity[:, topLines[aline]]
#
denIntens = np.zeros(len(xvalues),'Float64')
for aline in den_idx:
denIntens += intensity[:, topLines[aline]]
#
# plot the desired ratio
# maxAll is an array
pl.figure()
ax = pl.subplot(111)
pl.loglog(xvalues,numIntens/denIntens)
pl.xlim(xvalues.min(),xvalues.max())
pl.xlabel(xlabel,fontsize=fontsize)
pl.ylabel('Ratio ('+self.Defaults['flux']+')',fontsize=fontsize)
if ionNum == 1:
desc = ionS[0]
else:
desc = ''
for aline in num_idx:
if ionNum == 1:
desc += ' ' + str(wvl[topLines[aline]])
else:
desc += ' ' + ionS[topLines[aline]] + ' ' + str(wvl[topLines[aline]])
desc += ' / '
for aline in den_idx:
if ionNum == 1:
desc += ' ' + str(wvl[topLines[aline]])
else:
desc += ' ' + ionS[topLines[aline]] + ' ' + str(wvl[topLines[aline]])
if ndens == ntemp and ntemp > 1:
pl.text(0.07, 0.5,desc, horizontalalignment='left', verticalalignment='center', fontsize=fontsize, transform = ax.transAxes)
#
ax2 = pl.twiny()
xlabelDen=r'Electron Density (cm$^{-3}$)'
pl.xlabel(xlabelDen, fontsize=fontsize)
pl.loglog(eDensity,numIntens/denIntens, visible=False)
ax2.xaxis.tick_top()
else:
# pl.ylim(ymin, ymax)
pl.title(desc,fontsize=fontsize)
# desc=title+' '+str(wvl[num_line])+' / '+str(wvl[den_line])+' '+desc_str
# pl.title(desc, fontsize=fontsize)
# pl.title(title+' '+str(wvl[num_line])+' / '+str(wvl[den_line])+' '+desc_str,fontsize=fontsize)
# pl.draw()
# pl.ioff()
# pl.show()
#
# intensityRatioFileName=self.IonStr
# for aline in num_idx:
# intensityRatioFileName+= '_%3i'%(wvl[topLines[aline]])
# intensityRatioFileName+='_2'
# for aline in den_idx:
# intensityRatioFileName+= '_%3i'%(wvl[topLines[aline]])
cnt = desc.count(' ')
intensityRatioFileName = desc.replace(' ', '_', cnt) + '.rat'
intensityRatioFileName = intensityRatioFileName.lstrip('_').replace('_/_','-')
self.IntensityRatio={'ratio':numIntens/denIntens,'desc':desc,
'temperature':outTemperature,'eDensity':outDensity,'filename':intensityRatioFileName, 'numIdx':num_idx, 'denIdx':den_idx}
lst_hr = int(n.around(n.average(lsts[i:i + chunk])))
Tsys_avg = n.sqrt(n.average(Tsys[i:i + chunk]**2, axis=0))
valid = n.where(Tsys_avg > 213, 1, 0)
#p.plot(1e3*fqs.compress(valid), Tsys_avg.compress(valid), label='LST = %02dh00'%lst_hr)
p.plot(chans.compress(valid),
Tsys_avg.compress(valid),
label='LST = %02dh00' % lst_hr)
if VLINE:
nums = opts.chan.split('_')
vlines = [float(v) for v in nums]
p.vlines(vlines, 200, 2000, linestyles='dashed')
p.legend(loc='best', fontsize='medium')
p.xlabel(r'${\rm Channel}$', fontsize=16)
if FREQ_AXIS:
ax = p.twiny()
ax.plot(freqs, n.ones(len(freqs * 1e3)))
ax.cla()
ax.set_xlabel(r'${\rm Frequency\ [MHz]}$', fontsize=16)
#p.xlim(125,175)
p.ylim(200, 2000)
p.grid()
p.ylabel(r'${\rm T}_{\rm sys}$', fontsize=16)
p.subplots_adjust(0.12, 0.15, .95, .85)
fig_file = 'Tsys_chan_' + opts.chan
if not opts.output == '':
fig_file = opts.output + '/' + fig_file
p.savefig(fig_file)
p.show()