def test_throw_no_filter_error(self):
"""Test throws error when filter name not in filter dictionary is supplied """
p = phot.PhotCalcs(self.testsed, self.testfilters)
z = 1.
self.assertRaises(LookupError, lambda: p.kCorrectionXY("junk", self.filterlist[0], z) )
self.assertRaises(LookupError, lambda: p.kCorrectionXY(self.filterlist[0], "junk", z) )
def test_convert_flux_to_mag(self):
"""Test cannot pass zero or negative flux to any flux to mag converter"""
p = phot.PhotCalcs(self.testsed, self.testfilters)
flux = 0.
self.assertRaises( ValueError, lambda: p.convertFluxToMag(flux, self.filterlist[0]) )
dFluxOverFlux = 0.1
self.assertRaises( ValueError, lambda: p.convertFluxAndErrorToMags(flux, dFluxOverFlux) )
flux = 1.
dFluxOverFlux = -0.1
self.assertRaises( ValueError, lambda: p.convertFluxAndErrorToMags(flux, dFluxOverFlux) )
def test_convert_mag_to_flux_positive(self):
"""Test converting any reasonable magnitude to a flux returns a positive flux"""
p = phot.PhotCalcs(self.testsed, self.testfilters)
minMag = -100.
maxMag = 100.
nMag = 100
dmag = (maxMag - minMag)/(nMag - 1.)
for i in xrange(nMag):
mag = minMag + i*dmag
self.assertGreater(p.convertMagToFlux(mag, self.filterlist[0]), 0.)
def main(argv):
save_stem = 'new_lsst' # files will be saved to filenames beginning `save_stem`
perf_lim = 3 # performance limit: min number of colors that should reach LSST sys err
color_file = "../tmp/brown_colors_lsst.txt" # File to contain colors or to read colors from
listOfFilters = 'LSST.filters' # Filter set to use
corr_type = 'cubic' # type of covariance function to use in GP
theta0 = 0.2 # parameters for GP covariance function
try:
opts, args = getopt.getopt(argv, "hs:p:c:f:g:")
except getopt.GetoptError as err: # if include option that's not there
usage(2)
for opt, arg in opts:
if opt == '-h':
usage(0)
elif opt in ("-s"):
save_stem = arg
elif opt in ("-p"):
perf_lim = int(arg)
elif opt in ("-c"):
color_file = arg
elif opt in ("-f"):
listOfFilters = arg
elif opt in ("-g"):
corr_type = arg.split(',')[0]
theta0 = float(arg.split(',')[1])
print '\n Command line arguments:'
print ' Saving to files ... ', save_stem
print ' Reading/saving colors from/to file', color_file
print ' Using', listOfFilters, 'filter set'
print ' At least', perf_lim, 'colors must meet LSST sys err to be `good`'
print ' Covariance function will be', corr_type, 'with parameter', theta0
print ''
### Read SEDs into a dictionary
listOfSeds = 'brown_masked.seds'
pathToSEDs = '../sed_data'
sedDict = sedFilter.createSedDict(listOfSeds, pathToSEDs)
nSED = len(sedDict)
print "Number of SEDs =", nSED
### Filter set to calculate colors
pathToFilters = '../filter_data/'
filterDict = sedFilter.createFilterDict(listOfFilters, pathToFilters)
filterList = sedFilter.orderFiltersByLamEff(filterDict)
nFilters = len(filterList)
print "Number of filters =", nFilters
### Wavelength grid to do PCA on
minWavelen = 1000.
maxWavelen = 12000.
nWavelen = 10000
### Do PCA and train GP
ncomp = nSED
nfit = -1
pcaGP = sedMapper.PcaGaussianProc(sedDict, filterDict, color_file, ncomp,
minWavelen, maxWavelen, nWavelen, nfit,
corr_type, theta0)
colors = pcaGP._colors
spectra = pcaGP._spectra
waveLen = pcaGP._waveLen
meanSpectrum = pcaGP.meanSpec
projected_all = pcaGP.eigenvalue_coeffs
print "... done\n"
### Leave out each SED in turn
delta_mag = np.zeros((nSED, nFilters))
perf = []
for i, (sedname, spec) in enumerate(sedDict.items()):
print "\nOn SED", i + 1, "of", nSED
### Retrain GP with SED removed
nc = nSED - 1
pcaGP.reTrainGP(nc, i)
### Reconstruct SED
sed_rec = pcaGP.generateSpectrum(colors[i, :])
### Calculate colors of reconstructed SED
pcalcs = phot.PhotCalcs(sed_rec, filterDict)
cnt = 0
isBad = False
for j in range(nFilters - 1):
cs = pcalcs.computeColor(filterList[j], filterList[j + 1])
delta_mag[i, j] = cs - colors[i, j]
if (j < 6):
print "(", cs, colors[i, j], delta_mag[i, j], ")"
if (abs(delta_mag[i, j]) < 0.005):
cnt += 1
if (abs(delta_mag[i, j]) > 0.05):
isBad = True
print ""
### Get array version of SED back
wl, spec_rec = sed_rec.getSedData(lamMin=minWavelen,
lamMax=maxWavelen,
nLam=nWavelen)
### Plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.plot(waveLen, spectra[i, :], color='blue', label='true')
ax.plot(wl,
spec_rec,
color='red',
linestyle='dashed',
label='estimated')
ax.plot(waveLen,
meanSpectrum,
color='black',
linestyle='dotted',
label='mean')
ax.set_xlabel('wavelength (angstroms)', fontsize=24)
ax.set_ylabel('flux', fontsize=24)
handles, labels = ax.get_legend_handles_labels()
ax.legend(loc='lower right', prop={'size': 12})
ax.set_title(sedname, fontsize=24)
annotate = "Mean $\Delta$ color = {0:.5f} \n".format(
np.mean(delta_mag[i, :]))
annotate += "Stdn $\Delta$ color = {0:.5f} ".format(
np.std(delta_mag[i, :]))
y1, y2 = ax.get_ylim()
ax.text(9000, 0.9 * y2, annotate, fontsize=12)
plt.savefig(save_stem + '_' + 'bad_' + sedname + '.png')
#plt.show(block=True)
### Performance check
print cnt, "colors within LSST systematic error"
perf.append(cnt)
perf = np.asarray(perf)
### Save results
np.savetxt(save_stem + '_deltamag.txt', delta_mag)
### Plot eigenvalue 1 vs eigenvalue 2
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.plot(projected_all[:, 0],
projected_all[:, 1],
linestyle='none',
marker='o',
color='blue',
label='good')
ax.plot(projected_all[np.where(perf < perf_lim), 0],
projected_all[np.where(perf < perf_lim), 1],
linestyle='none',
marker='o',
color='red',
label='bad')
ax.set_xlabel('eigenvalue 1', fontsize=24)
ax.set_ylabel('eigenvalue 2', fontsize=24)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[:4], labels[:4], loc='lower right', prop={'size': 12})
### Histogram of number of colors per SED better than LSST systematic error
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.hist(perf, 20, normed=False, histtype='stepfilled')
ax.set_xlabel('number of colors better than sys error', fontsize=24)
plt.savefig(save_stem + '_' + 'perf.png')
plt.show(block=True)
### Histogram of delta-mags
for j in range(nFilters - 1):
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
dmag_finite = delta_mag[np.where(abs(delta_mag[:, j]) < 50), j].T
ax.hist(dmag_finite, 20, normed=False, histtype='stepfilled')
ax.set_xlabel('$\Delta$color$_{' + str(j) + "}$", fontsize=24)
plt.savefig(save_stem + '_color' + str(j) + '.png')
plt.show(block=True)
def get_sed_colors(sedDict, filterDict, ipivot=-1, doPrinting=True):
"""Calculate the colors for all the SEDs in sedDict given the filters in filterDict, return as pandas
data frame
@param sedDict dictionary of SEDs
@param filterDict dictionary of filters
@param ipivot index of filter to reference ALL colors to (if -1 just does usual)
@param doPrinting
"""
nfilters = len(filterDict)
ncolors = nfilters - 1
nseds = len(sedDict)
if ipivot > ncolors:
raise ValueError("Error! pivot filter outside range")
# sort based upon effective wavelength
filter_order = sedFilter.orderFiltersByLamEff(filterDict)
# get names of colors
color_names = []
for i in range(ncolors):
color_names.append(
str(filter_order[i]) + "-" + str(filter_order[i + 1]))
# calculate SED colors
sed_colors = np.zeros((nseds, ncolors))
sed_names = []
i = 0
tot_time = 0.
for sedname, sed in sedDict.items():
if doPrinting:
print "Calculating colors for SED:", sedname
sed_names.append(sedname)
p = phot.PhotCalcs(sed, filterDict)
start_time = time.time()
if (ipivot >= 0):
# all colors in reference to a pivot filter, e.g. u-r, g-r, r-i, r-z, r-y
#ii = 0
colors = []
for j in range(nfilters):
if (j < ipivot):
colors.append(
p.computeColor(filter_order[j], filter_order[ipivot],
0.))
#sed_colors[i,ii] = p.computeColor(filter_order[j], filter_order[ipivot], 0.)
if (i < 1):
#print ii,
print "Doing", filter_order[j], "-", filter_order[
ipivot],
print p.computeColor(filter_order[j],
filter_order[ipivot], 0.)
#ii=+1
elif (j > ipivot):
#sed_colors[i,ii] = p.computeColor(filter_order[ipivot], filter_order[j], 0.)
colors.append(
p.computeColor(filter_order[ipivot], filter_order[j],
0.))
if (i < 1):
#print ii,
print "Doing", filter_order[ipivot], "-", filter_order[
j],
print p.computeColor(filter_order[ipivot],
filter_order[j], 0.)
#ii=+1
# note that nothing is done when filter index j = ipivot
if (i < 1):
print colors, len(colors)
for j in range(ncolors):
sed_colors[i, j] = colors[j]
else:
# traditional color definition: e.g. u-g, g-r, r-i etc
for j in range(ncolors):
sed_colors[i, j] = p.computeColor(filter_order[j],
filter_order[j + 1], 0.)
end_time = time.time()
if doPrinting:
print "Took", end_time - start_time, "to compute", ncolors, "colors"
tot_time += (end_time - start_time)
i += 1
if doPrinting:
print "Total time to compute colors for SEDs =", tot_time
# convert to dataframe and return
return pd.DataFrame(sed_colors, columns=color_names, index=sed_names)
def get_sed_array(sedDict,
minWavelen=2999.,
maxWavelen=12000.,
nWavelen=10000,
filterDict=None,
color_file=None):
"""Return array of SEDs on same wavelength grid (optionally along with colors as defined by filterDict)
If computing colors, first orders filters by effective wavelength, then a color is:
color_{i, i+1} = mag_filter_i - mag_filter_i+1
@param sedDict dictionary of SEDs
@param minWavelen minimum wavelength of wavelength grid
@param maxWavelen maximum wavelength of wavelength grid
@param nWavelen number of points in wavelength grid
@param filterDict dictionary of filters
@param color_file file to save SED colors to or read colors from (if exists)
"""
doColors = True
if (filterDict == None or color_file == None):
doColors = False
isFileExist = False
if doColors:
# sort based upon effective wavelength
filter_order = sedFilter.orderFiltersByLamEff(filterDict)
# check if file exists and need to calculate colors
isFileExist = os.path.isfile(color_file)
if (isFileExist):
print "\nColors already computed,",
else:
print "\nComputing colors,",
print "placing SEDs in array ..."
# loop over each SED
nSED = len(sedDict)
spectra = []
colors = []
for ised, (sedname, spec) in enumerate(sedDict.items()):
print "On SED", ised + 1, "of", nSED, sedname
# re-grid SEDs onto same wavelengths
waveLen, fl = spec.getSedData(lamMin=minWavelen,
lamMax=maxWavelen,
nLam=nWavelen)
# normalise so they sum to 1
norm = np.sum(fl)
spectra.append(fl / norm)
if doColors:
# calculate or read colors
cs = []
if (isFileExist):
# reading colors
colors_in_file = np.loadtxt(color_file)
cs = colors_in_file[ised, :]
else:
# calculating colors
spec = sedFilter.SED(waveLen, fl) #/norm)
pcalcs = phot.PhotCalcs(spec, filterDict)
# in each filter
for i in range(len(filterDict) - 1):
color = pcalcs.computeColor(filter_order[i],
filter_order[i + 1])
if (color == float('inf')):
color = 99.
cs.append(color)
# store colors for this SED
colors.append(cs)
# conver to np arrays for ease
spectra = np.array(spectra)
colors = np.array(colors)
# if had to calculate, save colors to file to re-use
if (not isFileExist and doColors):
print "Saving colors to file for future use"
np.savetxt(color_file, colors)
if doColors:
return waveLen, spectra, colors
else:
return waveLen, spectra
nz,
])
g_minus_r = np.zeros([
nz,
])
r_minus_i = np.zeros([
nz,
])
# load in elliptical CWW template
sedname = '../sed_data/El_B2004a.sed'
seddata = np.loadtxt(sedname)
sed = sedFilter.SED(seddata[:, 0], seddata[:, 1])
# instantiate photometry calculations
p = phot.PhotCalcs(sed, filterDict)
# loop over redshifts
for i in xrange(nz):
z = zmin + i * dz
zdata[i] = z
g_minus_r[i] = p.computeColor("LSST_g", "LSST_r", z)
r_minus_i[i] = p.computeColor("LSST_r", "LSST_i", z)
fig = plt.figure(figsize=(20, 10))
fig.suptitle('Elliptical galaxy', fontsize=24)
# plot of g-r vs z
ax = fig.add_subplot(131)
def test_kcorrection_zero(self):
"""Test k-correction = 0 when observed-frame filter = rest-frame filter and z=0 """
p = phot.PhotCalcs(self.testsed, self.testfilters)
self.assertEqual(p.kCorrectionXY(self.filterlist[0], self.filterlist[0], 0), 0.)
def test_throw_neg_z_error(self):
"""Test throws error with negative redshift """
p = phot.PhotCalcs(self.testsed, self.testfilters)
z = -1.
self.assertRaises(ValueError, lambda: p.kCorrectionXY(self.filterlist[0], self.filterlist[1], z) )
def test_convert_mag_error_to_flux(self):
"""Test cannot pass negative magnitude error to flux converter"""
p = phot.PhotCalcs(self.testsed, self.testfilters)
errorMag = -1.
self.assertRaises( ValueError, lambda: p.convertMagErrorToFracFluxError(errorMag) )