sel = random.randint(0, len(allmoadata) - 1)
datafiles.append(allmoadata[sel])
# Load OGLE magnitudes for given type of variable star.
oglemags = prep_data.load_ogle(sys.argv[1])
# Load MOA magnitudes contained in datafiles
#moamags_all, moaerrs_all = prep_data.load_nanmags(allmoadata) # Not enough memory to load all MOA data
moamags, moaerrs = prep_data.load_nanmags(
datafiles) # Use random subset instead
#datapoints = mkltcurve.LinkErrs(10) # Old code: idea was to create a class in order to link errors to magnitude values.
#for n in range(10): #
# datapoints.put_meas(moamags[n], moaerrs[n], n) #
#print datapoints.mag, datapoints.err #
ogle_hist, ogle_bins = mkltcurve.mk_hist(oglemags, 0.05)
moa_hist, moa_bins = mkltcurve.mk_hist(moamags, 0.01)
#moa_hist2, moa_bins2 = mkltcurve.mk_hist(othermags, 0.05)
moa_mean = mkltcurve.mk_hist_mean(moamags, 0.01)
print '-- Photometry --' + '\n', datafiles
print '# OGLE values:', len(oglemags), 'Range:', str(round(
min(oglemags), 3)) + '-' + str(round(max(oglemags), 3)), 'Median:', round(
numpy.median(oglemags), 3), 'Mean:', round(numpy.mean(oglemags), 3)
print '# MOA values:', len(moamags), 'Range:', str(round(
min(moamags), 3)) + '-' + str(round(max(moamags), 3)), 'Median:', round(
numpy.median(moamags), 3), 'Mean:', round(numpy.mean(moamags), 3)
corrctn_factor = 2.6
filename = '/projects/uoa00357/moa/training/' + str(sys.argv[1]) + '.arff'
sel = random.randint(0, len(allmoadata) - 1)
datafiles.append(allmoadata[sel])
# Load OGLE magnitudes for given type of variable star.
oglemags = prep_data.load_ogle(sys.argv[1])
# Load MOA magnitudes contained in datafiles
#moamags_all, moaerrs_all = prep_data.load_nanmags(allmoadata) # Not enough memory to load all MOA data
moamags, moaerrs = prep_data.load_nanmags(
datafiles) # Use random subset instead
#datapoints = mkltcurve.LinkErrs(10) # Old code: idea was to create a class in order to link errors to magnitude values.
#for n in range(10): #
# datapoints.put_meas(moamags[n], moaerrs[n], n) #
#print datapoints.mag, datapoints.err #
moa_hist, moa_bins = mkltcurve.mk_hist(moamags, 0.01)
#moa_hist2, moa_bins2 = mkltcurve.mk_hist(othermags, 0.05)
moa_mean = mkltcurve.mk_hist_mean(moamags, 0.01)
print '-- Photometry --' + '\n', datafiles
print '# OGLE values:', len(oglemags), 'Range:', str(
round(min(oglemags), 3)) + '-' + str(round(
max(oglemags), 3)), 'Median:', round(numpy.median(oglemags),
3), 'Mean:', round(
numpy.mean(oglemags), 3)
print '# MOA values:', len(moamags), 'Range:', str(round(
min(moamags),
3)) + '-' + str(round(max(moamags), 3)), 'Median:', round(
numpy.median(moamags), 3), 'Mean:', round(numpy.mean(moamags), 3)
mags, errs, times, star_type = mkltcurve.template(str(sys.argv[1]),
