def bake(zgrid):
# get SED list
listOfSedsFile = "lsst.seds"
sedLib = sedFilter.createSedDict(listOfSedsFile, "../data/sed/")
sedList = sorted(sedLib.keys())
nSED = len(sedLib)
# get LSST filters
listOfFiltersFile = "lsst.filters"
filterLib = sedFilter.createFilterDict(listOfFiltersFile,
"../data/bandpass/")
filterList = sedFilter.orderFiltersByLamEff(filterLib)
nFilter = len(filterLib)
# instantiate photometric calculations
pcalcs = {}
for sedname, sed in sedLib.items():
pcalcs[sedname] = phot.PhotCalcs(sed, filterLib)
nz = len(zgrid)
# record array to return
n_rows = nSED*nz
dtype = np.dtype([('sedname', str, 300), ('redshift', np.float),
('ug', np.float), ('gr', np.float), ('ri', np.float),
('iz', np.float), ('zy', np.float), ('time', np.float)])
dummy = ('aaaa', 1.0, 1, 1, 1, 1, 1, 1.0)
records = np.array([dummy]*n_rows, dtype=dtype)
i = 0
# loop over redshifts
for z in zgrid:
# each SED in lib
for sedname in sedList:
# time calculation of *all* colors for this redshift+SED
start_time = time.time()
colors = []
for ifilt in range(nFilter-1):
mag = pcalcs[sedname].computeColor(filterList[ifilt],
filterList[ifilt+1], z)
colors.append(mag)
end_time = time.time()
rec = np.array([(sedname, z, colors[0], colors[1], colors[2],
colors[3], colors[4], end_time-start_time)],
dtype=records.dtype)
records[i] = rec
i += 1
return records
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 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)
""" import sedFilter import numpy as np import itertools import collections import math import matplotlib.pyplot as plt # File containing wavelength regions to mask out emission_lines_file = '../eml_data/emission_lines.dat' # Read in Brown SEDs listOfSedsFile = "brown.seds" pathToFile = "../sed_data/" brownSEDs = sedFilter.createSedDict(listOfSedsFile, pathToFile) # Class that does the masking (linear interpolation across wavelength region) msed = sedFilter.MaskSEDs(brownSEDs, emission_lines_file) msed.mask_SEDs() # return SEDs with lines masked masked_seds = msed.return_masked_SEDs() # Plot first 10 spectra with and without masking nMax = 10 # wavelength grid wlmin = 3000 wlmax = 12000 nlam = 10000
import sedFilter import numpy as np import itertools import collections import math import matplotlib.pyplot as plt # File containing wavelength regions to mask out emission_lines_file = '../eml_data/emission_lines.dat' # Read in Brown SEDs listOfSedsFile = "brown.seds" pathToFile = "../sed_data/" brownSEDs = sedFilter.createSedDict(listOfSedsFile, pathToFile) # Class that does the masking (linear interpolation across wavelength region) msed = sedFilter.MaskSEDs(brownSEDs, emission_lines_file) msed.mask_SEDs() # return SEDs with lines masked masked_seds = msed.return_masked_SEDs() # Plot first 10 spectra with and without masking nMax = 10 # wavelength grid