def SExtractorSNR(catalog, bins=16, timeStamp=False):
"""
A simple plot showing the SNR SExtractor finds.
"""
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
snr = 1./catalog.magerr_aper
kde1 = KDE(snr)
kde1.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(snr, bins=bins, label='r=0.65 Aperture', normed=True, color='r', alpha=0.5)
ax1.axvline(x=np.mean(snr), c='g' ,ls='--', label='Mean', lw=1.6)
ax1.plot(kde1.support, kde1.density, 'b-', label='Gaussian KDE', lw=1.6)
print 'Sextractor:', np.mean(1./catalog.magerr_aper), np.mean(1./catalog.magerr_auto)
ax1.set_xlabel('SExtractor Signal-to-Noise Ratio')
ax1.set_ylabel('PDF')
if timeStamp:
ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)
ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0)
plt.savefig('SExtractorSNR.pdf')
plt.close()
def setupClass(cls):
cls.decimal_density = 2 # low accuracy because binning is different
res1 = KDE(Xi)
res1.fit(kernel="gau", fft=True, bw="silverman")
cls.res1 = res1
rfname2 = os.path.join(curdir,'results','results_kde_fft.csv')
cls.res_density = np.genfromtxt(open(rfname2, 'rb'))
def MagnitudeDistribution(catalog, mag=18., bins=16, timeStamp=False):
"""
A simple plot to compare input and extracted magnitudes for a fixed magnitude stars.
"""
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
kde1 = KDE(catalog.mag_aper - mag)
kde1.fit()
kde2 = KDE(catalog.mag_auto - mag)
kde2.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(catalog.mag_aper - mag,
bins=bins,
label='r=0.65 Aperture',
alpha=0.2,
normed=True,
color='b')
ax1.axvline(x=np.mean(catalog.mag_aper - mag),
c='b',
ls='--',
label='Mean')
ax1.hist(catalog.mag_auto - mag,
bins=bins,
label='Auto',
alpha=0.3,
normed=True,
color='r')
ax1.axvline(x=np.mean(catalog.mag_auto - mag),
c='r',
ls='--',
label='Mean')
ax1.plot(kde1.support, kde1.density, 'b-', label='Gaussian KDE')
ax1.plot(kde2.support, kde2.density, 'r-', label='Gaussian KDE')
#print np.std(catalog.mag_aper), np.std(catalog.mag_auto)
ax1.set_xlabel('SExtractor Magnitude - Input Catalog')
ax1.set_ylabel('PDF')
if timeStamp:
ax1.text(0.83,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax1.transAxes,
alpha=0.2)
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
scatterpoints=1,
markerscale=1.0)
plt.savefig('MagDistributionSExtractor.pdf')
plt.close()
def MagnitudeDistribution(catalog, mag=18., bins=16, timeStamp=False):
"""
A simple plot to compare input and extracted magnitudes for a fixed magnitude stars.
"""
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
kde1 = KDE(catalog.mag_aper-mag)
kde1.fit()
kde2 = KDE(catalog.mag_auto-mag)
kde2.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(catalog.mag_aper-mag, bins=bins, label='r=0.65 Aperture', alpha=0.2, normed=True, color='b')
ax1.axvline(x=np.mean(catalog.mag_aper-mag), c='b' ,ls='--', label='Mean')
ax1.hist(catalog.mag_auto-mag, bins=bins, label='Auto', alpha=0.3, normed=True, color='r')
ax1.axvline(x=np.mean(catalog.mag_auto-mag), c='r', ls='--', label='Mean')
ax1.plot(kde1.support, kde1.density, 'b-', label='Gaussian KDE')
ax1.plot(kde2.support, kde2.density, 'r-', label='Gaussian KDE')
#print np.std(catalog.mag_aper), np.std(catalog.mag_auto)
ax1.set_xlabel('SExtractor Magnitude - Input Catalog')
ax1.set_ylabel('PDF')
if timeStamp:
ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)
ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0)
plt.savefig('MagDistributionSExtractor.pdf')
plt.close()
def setupClass(cls):
cls.decimal_density = 2 # low accuracy because binning is different
res1 = KDE(Xi)
res1.fit(kernel="gau", fft=True, bw="silverman")
cls.res1 = res1
rfname2 = os.path.join(curdir, 'results', 'results_kde_fft.csv')
cls.res_density = np.genfromtxt(open(rfname2, 'rb'))
def setupClass(cls):
res1 = KDE(Xi)
weights = np.linspace(1,100,200)
res1.fit(kernel="gau", gridsize=50, weights=weights, fft=False,
bw="silverman")
cls.res1 = res1
rfname = os.path.join(curdir,'results','results_kde_weights.csv')
cls.res_density = np.genfromtxt(open(rfname, 'rb'), skip_header=1)
def setupClass(cls):
res1 = KDE(Xi)
weights = np.linspace(1, 100, 200)
res1.fit(kernel="gau",
gridsize=50,
weights=weights,
fft=False,
bw="silverman")
cls.res1 = res1
rfname = os.path.join(curdir, 'results', 'results_kde_weights.csv')
cls.res_density = np.genfromtxt(open(rfname, 'rb'), skip_header=1)
def SExtractorSNR(catalog, bins=16, timeStamp=False):
"""
A simple plot showing the SNR SExtractor finds.
"""
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
snr = 1. / catalog.magerr_aper
kde1 = KDE(snr)
kde1.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(snr,
bins=bins,
label='r=0.65 Aperture',
normed=True,
color='r',
alpha=0.5)
ax1.axvline(x=np.mean(snr), c='g', ls='--', label='Mean', lw=1.6)
ax1.plot(kde1.support, kde1.density, 'b-', label='Gaussian KDE', lw=1.6)
print 'Sextractor:', np.mean(1. / catalog.magerr_aper), np.mean(
1. / catalog.magerr_auto)
ax1.set_xlabel('SExtractor Signal-to-Noise Ratio')
ax1.set_ylabel('PDF')
if timeStamp:
ax1.text(0.83,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax1.transAxes,
alpha=0.2)
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
scatterpoints=1,
markerscale=1.0)
plt.savefig('SExtractorSNR.pdf')
plt.close()
def setupClass(cls):
res1 = KDE(Xi)
res1.fit(kernel="biw", fft=False, bw="silverman")
cls.res1 = res1
cls.res_density = KDEResults["biw_d"]
# 5-inch bins
bins5 = np.arange(heights.min(), heights.max(), 5.)
heights.hist(bins=bins5, fc='steelblue')
plt.savefig('height_hist_bins5.png')
# 0.001-inch bins
bins001 = np.arange(heights.min(), heights.max(), .001)
heights.hist(bins=bins001, fc='steelblue')
plt.savefig('height_hist_bins001.png')
# Kernel density estimators, from scipy.stats.
# Create a KDE ojbect
heights_kde = KDE(heights)
# Use fit() to estimate the densities. Default is gaussian kernel
# using fft. This will provide a "density" attribute.
heights_kde.fit()
# Plot the density of the heights
# Sort inside the plotting so the lines connect nicely.
fig = plt.figure()
plt.plot(heights_kde.support, heights_kde.density)
plt.savefig('heights_density.png')
# Pull out male and female heights as arrays over which to compute densities
heights_m = heights[heights_weights['Gender'] == 'Male'].values
heights_f = heights[heights_weights['Gender'] == 'Female'].values
heights_m_kde = KDE(heights_m)
heights_f_kde = KDE(heights_f)
heights_m_kde.fit()
heights_f_kde.fit()
def plotSourceFinderResults(file='objects.phot', mag=18., bins=14, apcorr=0.923, timeStamp=False):
"""
"""
data = sextutils.sextractor(file)
offs = data.magnitude-mag
xpos = np.mean(offs)
kde = KDE(offs)
kde.fit()
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(offs, bins=bins, label='r=0.65 Aperture', alpha=0.2, normed=True, color='b')
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE')
ax1.axvline(x=xpos, c='b' ,ls='-', label='Mean')
ax1.set_xlabel('Aperture Corrected Magnitude - Input Catalogue')
ax1.set_ylabel('PDF')
ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)
ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0, loc='upper left')
plt.savefig('MagDistributionSourceFinder.pdf')
plt.close()
if np.abs(mag - 18.) < 0.1:
counts = data.counts / apcorr - 608137.825681 #for 18mag
else:
counts = data.counts / apcorr - 1359.57331621 #for 24.5mag
xpos = np.mean(counts)
std = np.std(data.counts/apcorr)
snr = np.mean(data.counts/apcorr)/std
print 'SourceFinder:', snr, np.mean(data.counts)/std, 1359.57331621/std, np.mean(data.counts/apcorr), std
kde = KDE(counts)
kde.fit()
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(counts, bins=bins, label='r=0.65 Aperture', alpha=0.2, normed=True, color='b')
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE', lw=2)
ax1.axvline(x=xpos, c='g' ,ls='-', label='Mean', lw=2)
ax1.set_xlabel('Aperture Corrected Counts - Input Catalogue')
ax1.set_ylabel('PDF')
ax1.text(ax1.get_xlim()[0]*0.95, ax1.get_ylim()[1]*0.75, r'$SNR = \frac{\left < counts \right >}{\sigma} \sim %.2f$' % snr)
if timeStamp:
ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)
ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0, loc='upper left')
plt.savefig('CountDistributionSourceFinder.pdf')
plt.close()
kde = KDE(data.snr)
kde.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(data.snr, bins=bins, label='r=0.65 Aperture', alpha=0.2, normed=True, color='b')
ax1.axvline(x=np.mean(data.snr), c='g' ,ls='-', label='Mean', lw=2)
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE', lw=2)
ax1.set_xlabel('Derived Signal-to-Noise Ratio')
ax1.set_ylabel('PDF')
#ax1.text(ax1.get_xlim()[0]*1.02, ax1.get_ylim()[1]*0.9, r'$\left < SNR \right > = %.2f$' % np.mean(data.snr))
ax1.text(4.5, 0.33, r'$\left < SNR \right > = %.2f$' % np.mean(data.snr))
if timeStamp:
ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)
ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0, loc='upper left')
plt.savefig('SNRsSourceFinder.pdf')
plt.close()
#pick the ones with well recovered flux
msk = data.counts > 1223.6 #90% of 1369.57
kde = KDE(data.snr[msk])
kde.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(data.snr[msk], bins=bins, label='r=0.65 Aperture', alpha=0.2, normed=True, color='b')
ax1.axvline(x=np.mean(data.snr[msk]), c='g' ,ls='-', label='Mean', lw=2)
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE', lw=2)
ax1.set_xlabel('Derived Signal-to-Noise Ratio')
ax1.set_ylabel('PDF')
#ax1.text(ax1.get_xlim()[0]*1.02, ax1.get_ylim()[1]*0.9, r'$\left < SNR \right > = %.2f$' % np.mean(data.snr))
ax1.text(11., 0.5, r'$\left < SNR \right > = %.2f$' % np.mean(data.snr[msk]))
if timeStamp:
ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)
ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0)
plt.savefig('SNRsSourceFinder2.pdf')
plt.close()
avg = np.mean(data.ellipticity)
std = np.std(data.ellipticity)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(data.ellipticity, bins=bins, alpha=0.2, normed=True, color='b')
ax1.axvline(x=avg, c='b' ,ls='-')
ax1.text(ax1.get_xlim()[0]*1.02, ax1.get_ylim()[1]*0.95, r'$\bar{e} = %f$' % avg)
ax1.text(ax1.get_xlim()[0]*1.02, ax1.get_ylim()[1]*0.9, r'$\sigma = %f$' % std)
ax1.set_xlabel('Derived Ellipticity')
ax1.set_ylabel('PDF')
ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)
plt.savefig('EllipticityDistributionSourceFinder.pdf')
plt.close()
def plotSNR(deg=60, kdes=True, log=False):
CCDs = 1000
fudge = 47.0
#cumulative distribution of stars for different galactic latitudes
if deg == 30:
tmp = 1
sfudge = 0.79
elif deg == 60:
tmp = 2
sfudge = 0.79
else:
tmp = 3
sfudge = 0.78
#stars
d = np.loadtxt('data/stars.dat', usecols=(0, tmp))
stmags = d[:, 0]
stcounts = d[:, 1]
#fit a function and generate finer sample
z = np.polyfit(stmags, np.log10(stcounts), 4)
p = np.poly1d(z)
starmags = np.arange(1, 30.2, 0.2)
starcounts = 10**p(starmags)
cpdf = (starcounts - np.min(starcounts)) / (np.max(starcounts) -
np.min(starcounts))
starcounts /= 3600. #convert to square arcseconds
nstars = int(np.max(starcounts) * fudge * sfudge) * CCDs
magStars = cr.drawFromCumulativeDistributionFunction(
cpdf, starmags, nstars)
SNRsStars = ETC.SNR(ETC.VISinformation(),
magnitude=magStars,
exposures=1,
galaxy=False)
print 'Assuming Galactic Lattitude = %i deg' % deg
print 'Number of stars within a pointing (36CCDs) with 70 < SNR < 700 (single 565s exposure):', \
int((SNRsStars[(SNRsStars > 70) & (SNRsStars < 700)]).size * 36. / CCDs)
print 'Number of stars within a pointing (36CCDs) with 60 < SNR < 80 (single 565s exposure):', \
int((SNRsStars[(SNRsStars > 60) & (SNRsStars < 80)]).size * 36. / CCDs)
print 'Number of stars within a pointing (36CCDs) with 690 < SNR < 710 (single 565s exposure):', \
int((SNRsStars[(SNRsStars > 690) & (SNRsStars < 710)]).size * 36. / CCDs)
print 'Number of stars within a pointing (36CCDs) with 18 < mag < 22 (single 565s exposure):', \
int((SNRsStars[(magStars > 18) & (magStars < 22)]).size * 36. / CCDs)
print 'Number of stars within a pointing (36CCDs) with 18 < mag < 23 (single 565s exposure):', \
int((SNRsStars[(magStars > 18) & (magStars < 23)]).size * 36. / CCDs)
print 'Number of stars within a pointing (36CCDs) with 17.9 < mag < 18.1 (single 565s exposure):', \
int((SNRsStars[(magStars > 17.9) & (magStars < 18.1)]).size * 36. / CCDs)
print 'Number of stars within a pointing (36CCDs) with 21 < mag < 23 (single 565s exposure):', \
int((SNRsStars[(magStars > 21) & (magStars < 23)]).size * 36. / CCDs)
#calculate Gaussian KDE with statsmodels package (for speed)
if kdes:
kn = SNRsStars[SNRsStars < 1000]
kdeStars = KDE(kn)
kdeStars.fit(adjust=2)
nst = kn.size / 10. / 1.38
#galaxies
#cumulative distribution of galaxies
d = np.loadtxt('data/cdf_galaxies.dat', usecols=(0, 1))
gmags = d[:, 0]
gcounts = d[:, 1]
nums = int(np.max(gcounts) / 3600. * fudge * CCDs)
z = np.polyfit(gmags, np.log10(gcounts), 4)
p = np.poly1d(z)
galaxymags = np.arange(10.0, 30.2, 0.2)
galaxycounts = 10**p(galaxymags)
cumulative = (galaxycounts - np.min(galaxycounts)) / (
np.max(galaxycounts) - np.min(galaxycounts))
magGalaxies = cr.drawFromCumulativeDistributionFunction(
cumulative, galaxymags, nums)
SNRsGalaxies = ETC.SNR(VISinformation(),
magnitude=magGalaxies,
exposures=1)
#calculate Gaussian KDE, this time with scipy to save memory, and evaluate it
if kdes:
kn = SNRsGalaxies[SNRsGalaxies < 1000]
#pos = np.linspace(1, 810, num=70)
#kdegal = gaussian_kde(kn)
#gals = kdegal(pos)
#ngl = kn.size #/ df
kdeGalaxy = KDE(kn)
kdeGalaxy.fit(adjust=10)
ngl = kn.size / 10. / 1.38
#histogram binning and weighting
bins = np.linspace(0., 1000., 101)
df = bins[1] - bins[0]
weight = 1. / (2048 * 2 * 2066 * 2 * 0.1 * 0.1 * 7.71604938e-8 * CCDs) / df
weightsS = np.ones(magStars.size) * weight
weightsG = np.ones(magGalaxies.size) * weight
#simple magnitude distribution plot for stars
stars = np.loadtxt('data/stars.dat')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(magStars,
bins=30,
cumulative=True,
log=True,
alpha=0.3,
weights=weightsS * df,
label='Random Draws')
ax.semilogy(stars[:, 0], stars[:, 1], label='Stars (30deg)')
ax.semilogy(stars[:, 0], stars[:, 2], label='Stars (60deg)')
ax.semilogy(stars[:, 0], stars[:, 3], label='Stars (90deg)')
ax.set_xlabel(r'$M_{AB}$')
ax.set_ylabel(r'Cumulative Number of Objects [deg$^{-2}$]')
plt.legend(shadow=True, fancybox=True, loc='upper left')
plt.savefig('stars%ideg.pdf' % deg)
plt.close()
#make a plot
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
ax = host_subplot(111, axes_class=AA.Axes)
hist1 = ax.hist(SNRsStars,
bins=bins,
alpha=0.2,
log=True,
weights=weightsS,
label='Stars [%i deg]' % deg,
color='r')
hist2 = ax.hist(SNRsGalaxies,
bins=bins,
alpha=0.2,
log=True,
weights=weightsG,
label='Galaxies',
color='blue')
if kdes:
ax.plot(kdeStars.support,
kdeStars.density * nst,
'r-',
label='Gaussian KDE (stars)')
#ax.plot(pos, gals*ngl, 'b-', label='Gaussian KDE (galaxies)')
ax.plot(kdeGalaxy.support,
kdeGalaxy.density * ngl,
'b-',
label='Gaussian KDE (galaxies)')
#calculate magnitude scale, top-axis
if log:
mags = np.asarray([17, 18, 19, 20, 21, 22, 23, 24])
SNRs = ETC.SNR(VISinformation(),
magnitude=mags,
exposures=1,
galaxy=False)
else:
mags = np.asarray([17, 17.5, 18, 18.5, 19, 20, 21, 22.5])
SNRs = ETC.SNR(VISinformation(),
magnitude=mags,
exposures=1,
galaxy=False)
ax2 = ax.twin() # ax2 is responsible for "top" axis and "right" axis
ax2.set_xticks(SNRs)
ax2.set_xticklabels([str(tmp) for tmp in mags])
ax2.set_xlabel('$M(R+I)_{AB}$ [mag]')
ax2.axis['right'].major_ticklabels.set_visible(False)
ax.set_ylim(1e-1, 1e5)
ax.set_ylabel('Number of Objects [deg$^{-2}$ dex$^{-1}$]')
ax.set_xlabel('Signal-to-Noise Ratio [assuming a single 565s exposure]')
plt.text(0.8,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax.transAxes,
alpha=0.2)
plt.legend(shadow=True, fancybox=True)
if log:
ax.set_xscale('log')
plt.savefig('SNRtheoretical%ideglog.pdf' % deg)
else:
ax.set_xlim(1, 1e3)
plt.savefig('SNRtheoretical%ideglin.pdf' % deg)
plt.close()
#write output
if not log:
mid = df / 2.
#output to file
fh = open('SNRsSTARS%ideg.txt' % deg, 'w')
fh.write('#These values are for stars at %ideg (%s)\n' % (deg, txt))
fh.write('#SNR number_of_stars N\n')
fh.write('#bin_centre per_square_degree per_pointing\n')
for a, b in zip(hist1[0], hist1[1]):
fh.write('%i %f %f\n' % (b + mid, a * df, a * df * 0.496))
fh.close()
fh = open('SNRsGALAXIES.txt', 'w')
fh.write('#These values are for galaxies (%s)\n' % txt)
fh.write('#SNR number_of_galaxies N\n')
fh.write('#bin_centre per_square_degree per_pointing\n')
for a, b in zip(hist2[0], hist2[1]):
fh.write('%i %f %f\n' % (b + mid, a * df, a * df * 0.496))
fh.close()
def plotSourceFinderResults(file='objects.phot',
mag=18.,
bins=14,
apcorr=0.923,
timeStamp=False):
"""
"""
data = sextutils.sextractor(file)
offs = data.magnitude - mag
xpos = np.mean(offs)
kde = KDE(offs)
kde.fit()
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(offs,
bins=bins,
label='r=0.65 Aperture',
alpha=0.2,
normed=True,
color='b')
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE')
ax1.axvline(x=xpos, c='b', ls='-', label='Mean')
ax1.set_xlabel('Aperture Corrected Magnitude - Input Catalogue')
ax1.set_ylabel('PDF')
ax1.text(0.83,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax1.transAxes,
alpha=0.2)
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
scatterpoints=1,
markerscale=1.0,
loc='upper left')
plt.savefig('MagDistributionSourceFinder.pdf')
plt.close()
if np.abs(mag - 18.) < 0.1:
counts = data.counts / apcorr - 608137.825681 #for 18mag
else:
counts = data.counts / apcorr - 1359.57331621 #for 24.5mag
xpos = np.mean(counts)
std = np.std(data.counts / apcorr)
snr = np.mean(data.counts / apcorr) / std
print 'SourceFinder:', snr, np.mean(
data.counts) / std, 1359.57331621 / std, np.mean(data.counts /
apcorr), std
kde = KDE(counts)
kde.fit()
txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(counts,
bins=bins,
label='r=0.65 Aperture',
alpha=0.2,
normed=True,
color='b')
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE', lw=2)
ax1.axvline(x=xpos, c='g', ls='-', label='Mean', lw=2)
ax1.set_xlabel('Aperture Corrected Counts - Input Catalogue')
ax1.set_ylabel('PDF')
ax1.text(ax1.get_xlim()[0] * 0.95,
ax1.get_ylim()[1] * 0.75,
r'$SNR = \frac{\left < counts \right >}{\sigma} \sim %.2f$' % snr)
if timeStamp:
ax1.text(0.83,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax1.transAxes,
alpha=0.2)
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
scatterpoints=1,
markerscale=1.0,
loc='upper left')
plt.savefig('CountDistributionSourceFinder.pdf')
plt.close()
kde = KDE(data.snr)
kde.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(data.snr,
bins=bins,
label='r=0.65 Aperture',
alpha=0.2,
normed=True,
color='b')
ax1.axvline(x=np.mean(data.snr), c='g', ls='-', label='Mean', lw=2)
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE', lw=2)
ax1.set_xlabel('Derived Signal-to-Noise Ratio')
ax1.set_ylabel('PDF')
#ax1.text(ax1.get_xlim()[0]*1.02, ax1.get_ylim()[1]*0.9, r'$\left < SNR \right > = %.2f$' % np.mean(data.snr))
ax1.text(4.5, 0.33, r'$\left < SNR \right > = %.2f$' % np.mean(data.snr))
if timeStamp:
ax1.text(0.83,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax1.transAxes,
alpha=0.2)
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
scatterpoints=1,
markerscale=1.0,
loc='upper left')
plt.savefig('SNRsSourceFinder.pdf')
plt.close()
#pick the ones with well recovered flux
msk = data.counts > 1223.6 #90% of 1369.57
kde = KDE(data.snr[msk])
kde.fit()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(data.snr[msk],
bins=bins,
label='r=0.65 Aperture',
alpha=0.2,
normed=True,
color='b')
ax1.axvline(x=np.mean(data.snr[msk]), c='g', ls='-', label='Mean', lw=2)
ax1.plot(kde.support, kde.density, 'r-', label='Gaussian KDE', lw=2)
ax1.set_xlabel('Derived Signal-to-Noise Ratio')
ax1.set_ylabel('PDF')
#ax1.text(ax1.get_xlim()[0]*1.02, ax1.get_ylim()[1]*0.9, r'$\left < SNR \right > = %.2f$' % np.mean(data.snr))
ax1.text(11., 0.5,
r'$\left < SNR \right > = %.2f$' % np.mean(data.snr[msk]))
if timeStamp:
ax1.text(0.83,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax1.transAxes,
alpha=0.2)
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
scatterpoints=1,
markerscale=1.0)
plt.savefig('SNRsSourceFinder2.pdf')
plt.close()
avg = np.mean(data.ellipticity)
std = np.std(data.ellipticity)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.hist(data.ellipticity, bins=bins, alpha=0.2, normed=True, color='b')
ax1.axvline(x=avg, c='b', ls='-')
ax1.text(ax1.get_xlim()[0] * 1.02,
ax1.get_ylim()[1] * 0.95, r'$\bar{e} = %f$' % avg)
ax1.text(ax1.get_xlim()[0] * 1.02,
ax1.get_ylim()[1] * 0.9, r'$\sigma = %f$' % std)
ax1.set_xlabel('Derived Ellipticity')
ax1.set_ylabel('PDF')
ax1.text(0.83,
1.12,
txt,
ha='left',
va='top',
fontsize=9,
transform=ax1.transAxes,
alpha=0.2)
plt.savefig('EllipticityDistributionSourceFinder.pdf')
plt.close()
# 5-inch bins
bins5 = np.arange(heights.min(), heights.max(), 5.)
heights.hist(bins = bins5, fc = 'steelblue')
plt.savefig('height_hist_bins5.png')
# 0.001-inch bins
bins001 = np.arange(heights.min(), heights.max(), .001)
heights.hist(bins = bins001, fc = 'steelblue')
plt.savefig('height_hist_bins001.png')
# Kernel density estimators, from scipy.stats.
# Create a KDE ojbect
heights_kde = KDE(heights)
# Use fit() to estimate the densities. Default is gaussian kernel
# using fft. This will provide a "density" attribute.
heights_kde.fit()
# Plot the density of the heights
# Sort inside the plotting so the lines connect nicely.
fig = plt.figure()
plt.plot(heights_kde.support, heights_kde.density)
plt.savefig('heights_density.png')
# Pull out male and female heights as arrays over which to compute densities
heights_m = heights[heights_weights['Gender'] == 'Male'].values
heights_f = heights[heights_weights['Gender'] == 'Female'].values
heights_m_kde = KDE(heights_m)
heights_f_kde = KDE(heights_f)
heights_m_kde.fit()
heights_f_kde.fit()
def setupClass(cls):
res1 = KDE(Xi)
res1.fit(kernel="biw", fft=False, bw="silverman")
cls.res1 = res1
cls.res_density = KDEResults["biw_d"]
