def main():
"""Main script to prepare x-shooter observations for combination"""
from astropy.io import fits
import glob
import matplotlib.pyplot as pl
from methods import latexify
latexify()
import numpy as np
from xshoo.combine import inter_arm_cut
#Files
obj_name = 'SDSS1437-0147'
root_dir = '/Users/jselsing/Work/X-Shooter/CompositeRedQuasar/processed_data/'+obj_name
object_files = glob.glob(root_dir+'/OBJECT/*IDP*.fits')
transmission_files = glob.glob(root_dir+'/transmission*.fits')
arms = ['UVB', 'VIS', 'NIR']
wl_out = []
flux_out = []
flux_uncorr_out = []
err_out = []
start = []
end = []
for n in arms:
print('In arm: '+n)
#Read in object spectrum
obser = [k for k in object_files if n in k]
ob = fits.open(obser[0])
wl = 10.0*ob[1].data.field('WAVE')[0]
flux = ob[1].data.field('FLUX')[0]
err = ob[1].data.field('ERR')[0]
wl_tmp, flux_uncorr, err_tmp, start_tmp, end_tmp = inter_arm_cut(wl, flux, err, n, start, end)
if n== 'VIS' or n== 'NIR':
transmission = fits.open([k for k in transmission_files if n in k][0])[0].data
for j, k in enumerate(transmission):
if k <= 1e-10:
transmission[j] = 1
flux /= transmission
err /= transmission
wl, flux, err, start, end = inter_arm_cut(wl, flux, err, n, start, end)
wl_out.append(wl)
flux_out.append(flux)
err_out.append(err)
flux_uncorr_out.append(flux_uncorr)
wl_out = np.hstack(wl_out)
flux_out = np.hstack(flux_out)
err_out = np.hstack(err_out)
flux_uncorr_out = np.hstack(flux_uncorr_out)
bp_map = []
for j , (k, l) in enumerate(zip(flux_out[:-1],err_out[:-1])):
if k > 1.1 * flux_out[j-1] or k < 0:
bp_map.append(1)
elif k < 0.90 * flux_out[j-1] or k < 0:
bp_map.append(1)
else:
bp_map.append(0)
bp_map.append(1)
import json
import urllib2
query_terms = dict()
query_terms["ra"] = str(ob[0].header['RA'])+'d' #"185.1d"
query_terms["dec"] = str(ob[0].header['DEC']) #"56.78"
query_terms["radius"] = "5.0"
url = "http://api.sdss3.org/spectrumQuery?" + '&'.join(["{0}={1}".format(key, value) for key, value in query_terms.items()])
print(url)
# make call to API
response = urllib2.urlopen(url)
# read response, converting JSON format to Python list
matching_ids = json.loads(response.read())
print(json.dumps(matching_ids, indent=4))
# get the first id
spec_id = matching_ids[0]
url = "http://api.sdss3.org/spectrum?id={0}&format=json".format(spec_id)
response = urllib2.urlopen(url)
result = json.loads(response.read())
SDSS_spectrum = result[spec_id]
wl_sdss = np.array(SDSS_spectrum["wavelengths"])
flux_sdss = np.array(SDSS_spectrum["flux"])
z_sdss = (np.array(SDSS_spectrum["z"]))
z_sdss_err = ((np.array(SDSS_spectrum["z_err"])))
#Insert zeros
wl_sdss = np.concatenate([wl_sdss,np.zeros(len(wl_out) - len(wl_sdss))])
flux_sdss = np.concatenate([flux_sdss,np.zeros(len(flux_out) - len(flux_sdss))])
# Load linelist
fit_line_positions = np.genfromtxt('data/fitlinelist.txt', dtype=None)
linelist = []
for n in fit_line_positions:
linelist.append(n[1])
linelist = np.array(linelist)
from methods import wavelength_conversion
linelist = wavelength_conversion(linelist, conversion='vacuum_to_air')
#Cut out fitting region
mask = np.logical_and(wl_out > 11350, (wl_out < 11750))
wl_fit = wl_out[mask]
flux_fit = flux_out[mask]
fluxerr_fit = err_out[mask]
fluxerr_new = []
for j, (k, l) in enumerate(zip(flux_fit,fluxerr_fit)):
if k > 1.5 * flux_fit[j-2] and k > 0:
fluxerr_new.append(l*50)
elif k < 0.75 * flux_fit[j-2] and k > 0:
fluxerr_new.append(l*50)
else:
fluxerr_new.append(l)
from gen_methods import smooth
fluxerr_fit = smooth(np.array(fluxerr_new), window_len=15, window='hanning')
#Fit continuum and subtract
from methods import continuum_fit
from numpy.polynomial import chebyshev
cont, chebfit = continuum_fit(wl_fit, flux_fit, fluxerr_fit, edge_mask_len=20)
chebfitval = chebyshev.chebval(wl, chebfit)
#Define models to use
from methods import voigt,gauss
def model1(t, amp2, sig22g, sig22l, z):
tmp = voigt(t, abs(amp2), (1+z)*linelist[2], sig22g, sig22l)
return tmp
def model2(t, amp2, sig22g, z):
tmp = gauss(t, abs(amp2), (1+z)*linelist[2], sig22g)
return tmp
#Initial parameters
init_vals = [6e-12,100, z_sdss]
y_fit_guess = model2(wl_fit, *init_vals) + cont
#Fit
import scipy.optimize as op
np.random.seed(12345)
y_op = []
vals = []
for i in np.arange(10000):
print('Iteration: ', i)
resampled_spec = np.random.normal(flux_fit, abs(fluxerr_fit))
cont, chebfit = continuum_fit(wl_fit, resampled_spec, fluxerr_fit, edge_mask_len=20)
chebfitval = chebyshev.chebval(wl, chebfit)
best_vals, covar = op.curve_fit(model2, wl_fit, resampled_spec - cont, sigma=fluxerr_fit, absolute_sigma=True, p0=init_vals)
vals.append(best_vals)
up = (np.percentile(vals, 84, axis = 0)[2] - np.mean(vals, axis = 0)[2])
down = (np.percentile(vals, 16, axis = 0)[2] - np.mean(vals, axis = 0)[2])
v_bary = ob[0].header['HIERARCH ESO QC VRAD BARYCOR']
c_km = (2.99792458e8/1000.0)
print("""Curve_fit results:
Redshift = {0} + {1} - {2} (SDSS: {3} +- {4})
""".format(np.mean(vals, axis = 0)[2] + v_bary /c_km, up, down, z_sdss, z_sdss_err))
z_op = np.mean(vals, axis = 0)[2] + v_bary /c_km
#Correct for Lyman alpha forest absorption. Will only have data for objects with z > 3000 / 1216 -1 ~ 1.5
mask = (wl_out < (1 + z_op)*1216)
wave = wl_out[mask]
flux = flux_out[mask]
import continuum_mark.interactive
normalise = continuum_mark.interactive.continuum_mark(wl_out[mask], flux_out[mask], err_out[mask])
normalise.endpoint = 'n' #str(raw_input('Insert endpoint before interpolation(y/n)? '))
normalise.run()
pl.show()
cont_out = np.concatenate([normalise.continuum,flux_out[~mask]])
#Flag whether to use estimated redshift
flag = 1
from astroquery.irsa_dust import IrsaDust
import astropy.coordinates as coord
import astropy.units as u
C = coord.SkyCoord(ob[0].header['RA']*u.deg, ob[0].header['DEC']*u.deg, frame='fk5')
dust_image = IrsaDust.get_images(C, radius=2 *u.deg, image_type='ebv')[0]
ebv = np.mean(dust_image[0].data[40:42,40:42])
# Saving telluric uncorrected data to .dat file
dt = [("wl", np.float64), ("flux", np.float64), ("error", np.float64), ("bp map", np.float64),
("wl_sdss", np.float64), ("flux_sdss", np.float64) , ("flux_cont", np.float64) ]
data = np.array(zip(wl_out, flux_uncorr_out, err_out, bp_map, wl_sdss, flux_sdss, cont_out), dtype=dt)
file_name = "Telluric_uncorrected_science"
np.savetxt(root_dir+"/"+file_name+".dat", data, header="wl flux fluxerror bp_map wl_sdss flux_sdss cont")#, fmt = ['%5.1f', '%2.15E'] )
#Saving telluric corrected data to .dat file
dt = [("wl", np.float64), ("flux", np.float64), ("error", np.float64), ("bp map", np.float64),
("wl_sdss", np.float64), ("flux_sdss", np.float64) , ("flux_cont", np.float64) ]
data = np.array(zip(wl_out, flux_out, err_out, bp_map, wl_sdss, flux_sdss, cont_out), dtype=dt)
file_name = "Telluric_corrected_science"
np.savetxt(root_dir+"/"+file_name+".dat", data, header="wl flux fluxerror bp_map wl_sdss flux_sdss cont")#, fmt = ['%5.1f', '%2.15E'] )
#Saving info to .dat file
dt = [("z_op", np.float64), ("z_sdss", np.float64), ("flag", np.float64), ("ebv", np.float64)]
data = np.array(zip([z_op], [z_sdss], [flag], [ebv]), dtype=dt)
file_name = "Object_info"
np.savetxt(root_dir+"/"+file_name+".dat", data, header="z_op z_sdss flag ebv ") #, fmt = ['%5.1f', '%2.15E'] )
def main():
# latexify()
import numpy as np
root_dir = '/Users/jselsing/Work/X-Shooter/CompositeRedQuasar/processed_data/'
data_file = np.genfromtxt(root_dir+'Composite.dat')
wl = data_file[:,0]
mean = data_file[:,1]
err_mean = data_file[:,2]
wmean = data_file[:,3]
err_wmean = data_file[:,4]
geo_mean = data_file[:,5]
median = data_file[:,6]
n_spec = data_file[:,7]
std = data_file[:,8]
std_norm = data_file[:,9]
wmean_cont = data_file[:,10]
from scipy.interpolate import InterpolatedUnivariateSpline
mask = (np.where(err_wmean != 0))
f = InterpolatedUnivariateSpline(wl[mask], wmean_cont[mask], w=err_wmean[mask], k=5)
wmean_cont = f(wl)
f = InterpolatedUnivariateSpline(wl[mask], err_wmean[mask], k=5)
err_wmean = f(wl)
#Saving to .dat file
dt = [("wl", np.float64), ("wmean_cont", np.float64), ("err_wmean", np.float64) ]
data = np.array(zip(wl, wmean_cont, err_wmean), dtype=dt)
file_name = "data/templates/Selsing2015_interpolated.dat"
np.savetxt(file_name, data, header="wl weighted mean error of weighted mean", fmt = ['%5.1f', '%1.4f', '%1.4f' ])
pl.plot(wl,wmean_cont)
pl.semilogy()
pl.show()
exit()
#Fitting power laws
from scipy import optimize
def power_law(x_tmp, a_tmp, k_tmp):
tmp = a_tmp * x_tmp ** k_tmp
return tmp
def power_law2(x_tmp, a1_tmp, x_c, k1_tmp, k2_tmp):
tmp1 = power_law(x_tmp, a1_tmp,k1_tmp)[x_tmp<x_c]
scale2loc = np.argmin(np.abs(x_tmp - x_c))
a2_tmp = power_law(x_tmp[scale2loc], a1_tmp, (k1_tmp - k2_tmp))
tmp2 = power_law(x_tmp, a2_tmp,k2_tmp)[x_tmp>= x_c]
return np.concatenate((tmp1,tmp2))
par_guess = [1, -1.7]
par_guess2 = [1, 5000, -1.7, -1.7]
wmean[np.where(np.isnan(wmean) == True)] = 0
mask = (wl > 1300) & (wl < 1350) | (wl > 1425) & (wl < 1475) | (wl > 5500) & (wl < 5800) | (wl > 7300) & (wl < 7500) #| (wl > 9700) & (wl < 9900) | (wl > 10200) & (wl < 10600)
popt, pcov = optimize.curve_fit(power_law, wl[mask], wmean_cont[mask], p0=par_guess, sigma=err_wmean[mask], absolute_sigma=True)
popt2, pcov2 = optimize.curve_fit(power_law2, wl[mask], wmean_cont[mask], p0=par_guess2, sigma=err_wmean[mask], absolute_sigma=True)
print(*popt)
print(*popt2)
#Plotting
latexify(columns=2)
fig, ax = pl.subplots()
ax.plot(wl, medfilt(wmean_cont, 5),
lw = 0.5, alpha=1.0, linestyle = 'steps-mid', label='X-shooter mean composite', color=cmap[1])
ax.plot(wl, power_law(wl, *popt),
linestyle='dashed', label ='Pure power law fit', color=cmap[2])
sdss_compo = np.genfromtxt('/Users/jselsing/Work/X-Shooter/CompositeRedQuasar/processed_data/sdss_compo.dat')
sdss_wl = sdss_compo[:,0]
sdss_flux = sdss_compo[:, 1]
norm_reg = 1430
mask = (wl > norm_reg) & (wl < norm_reg + 20)
norm1 = np.median(wmean_cont[mask])
mask = (sdss_wl > norm_reg) & (sdss_wl < norm_reg + 20)
norm2 = np.median(sdss_flux[mask])
ax.plot(sdss_wl, sdss_flux * (norm1/norm2),
linestyle='solid', label ='Full sample SDSS composite', color=cmap[0])
#Overplot lines
fit_line_positions = np.genfromtxt('data/plotlinelist.txt', dtype=None)
linelist = []
linenames = []
for n in fit_line_positions:
linelist.append(n[1])
linenames.append(n[0])
pl.semilogy()
# Formatting axes
import matplotlib as mpl
ax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.get_xaxis().tick_bottom()
ax.xaxis.set_minor_locator(mpl.ticker.NullLocator())
ax.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_yticks([0.3, 1, 3, 10, 30, 100, 300])
ax.yaxis.set_minor_locator(mpl.ticker.NullLocator())
# pl.legend(loc=3)
ax.set_xlim((1000, 11500))
ax.set_ylim((0.2, 500))
format_axes(ax)
ax.set_xlabel(r'Wavelength [$\AA$]')
ax.set_ylabel(r'Rescaled flux density F$_\lambda$')
pl.tight_layout()
val = []
for p in range(len(linelist)):
xcoord = linelist[p]
mask = (wl > xcoord - 1) & (wl < xcoord + 1)
y_val = np.mean(wmean_cont[mask])
val.append(2 * y_val)
print(val)
arrow_tips = val
lineid_plot.plot_line_ids(wl, wmean_cont, linelist, linenames, arrow_tip=arrow_tips, ax=ax)
for i in ax.lines:
if '$' in i.get_label():
i.set_alpha(0.3)
a = ax.findobj(mpl.text.Annotation)
for i in a:
if '$' in i.get_label():
i.set_size(10)
pl.savefig('../documents/figs/compo_full_sample.pdf')
pl.show()
def main():
root_dir = '/Users/jselsing/Work/X-Shooter/CompositeRedQuasar/processed_data/'
data_file = np.genfromtxt(root_dir+'Composite.dat')
wl = data_file[:,0]
mean = data_file[:,1]
err_mean = data_file[:,2]
wmean = data_file[:,3]
err_wmean = data_file[:,4]
geo_mean = data_file[:,5]
median = data_file[:,6]
n_spec = data_file[:,7]
std = data_file[:,8]
std_norm = data_file[:,9]
wmean_cont = data_file[:,10]
#Fitting power laws
from scipy import optimize
def power_law(x_tmp, a_tmp, k_tmp):
tmp = a_tmp * x_tmp ** k_tmp
return tmp
def power_law2(x_tmp, a1_tmp, x_c, k1_tmp, k2_tmp):
tmp1 = power_law(x_tmp, a1_tmp,k1_tmp)[x_tmp<x_c]
scale2loc = np.argmin(np.abs(x_tmp - x_c))
a2_tmp = power_law(x_tmp[scale2loc], a1_tmp, (k1_tmp - k2_tmp))
tmp2 = power_law(x_tmp, a2_tmp,k2_tmp)[x_tmp>= x_c]
return np.concatenate((tmp1,tmp2))
def power_law3(x_tmp, a_tmp, k_tmp, b_tmp):
tmp = a_tmp * x_tmp ** (k_tmp + b_tmp * x_tmp)
return tmp
par_guess = [1, -1.70]
par_guess2 = [1, 5000, -1.7, -1.7]
wmean[np.where(np.isnan(wmean) == True)] = 0
mask = (wl > 1300) & (wl < 1350) | (wl > 1425) & (wl < 1475) | (wl > 5500) & (wl < 5800) | (wl > 7300) & (wl < 7500)
err = ((std)[std != 0])[mask]
popt, pcov = optimize.curve_fit(power_law, wl[mask], wmean_cont[mask], p0=par_guess, sigma=np.sqrt(err**2 + err_wmean[mask]**2), absolute_sigma=True, maxfev=5000)
popt2, pcov2 = optimize.curve_fit(power_law2, wl[mask], wmean_cont[mask], p0=par_guess2, sigma=np.sqrt(err**2 + err_wmean[mask]**2), absolute_sigma=True, maxfev=5000)
print(*popt)
print(*popt2)
par_guess = [1, -1.7]
wl_new = wl
wm = []
m = []
geo = []
med = []
#Fit
np.random.seed(12345)
mask = (wl_new > 1300) & (wl_new < 1350) | (wl_new > 1425) & (wl_new < 1475) | (wl_new > 5500) & (wl_new < 5800) | (wl_new > 7300) & (wl_new < 7500)
for i in np.arange(10):
print('Iteration: ', i)
err = ((std)[std != 0])[mask]
resampled_spec = np.random.normal((wmean_cont)[mask], np.sqrt(err**2 + err_wmean[mask]**2))
popt_wmean, pcov_wmean = optimize.curve_fit(power_law, wl_new[mask], resampled_spec, p0=par_guess,
sigma=np.sqrt(err**2 + err_wmean[mask]**2), absolute_sigma=True)
wm.append(popt_wmean)
err = ((std)[std != 0])[mask]
resampled_spec = np.random.normal((mean)[mask], np.sqrt(err**2 + err_mean[mask]**2))
popt_mean, pcov_mean = optimize.curve_fit(power_law, wl_new[mask], resampled_spec, p0=par_guess,
sigma=np.sqrt(err**2 + err_mean[mask]**2), absolute_sigma=True)
m.append(popt_mean)
err = ((std)[std != 0])[mask]
resampled_spec = np.random.normal((median[std != 0])[mask], err)
popt_median, pcov_median = optimize.curve_fit(power_law, wl_new[mask], resampled_spec, p0=par_guess,
sigma=err, absolute_sigma=True, maxfev=600)
med.append(popt_median)
err = ((std)[std != 0])[mask]
resampled_spec = np.random.normal((geo_mean[std != 0])[mask], err)
# pl.plot(wl_new[mask], resampled_spec)
popt_geo, pcov_geo = optimize.curve_fit(power_law, wl_new[mask], resampled_spec, p0=par_guess,
sigma=err, absolute_sigma=True, maxfev=600)
geo.append(popt_geo)
# pl.plot(wl, geo_mean)
# pl.show()
print("""Composite fit slope wmean...{0} +- {1}""".format(np.mean(wm, axis=0)[1],np.std(wm, axis=0)[1]))
print("""Composite fit slope mean...{0} +- {1}""".format(np.mean(m, axis=0)[1], np.std(m, axis=0)[1]))
print("""Composite fit slope median...{0} +- {1}""".format(np.mean(med, axis=0)[1], np.std(med, axis=0)[1]))
print("""Composite fit slope geo...{0} +- {1}""".format(np.mean(geo, axis=0)[1], np.std(geo, axis=0)[1]))
# Plotting
latexify(columns=2, fig_height=7)
import matplotlib.gridspec as gridspec
fig = pl.figure()
gs = gridspec.GridSpec(4, 1, height_ratios=[2,1,1,1])
ax1 = pl.subplot(gs[0])
ax2 = pl.subplot(gs[1])
ax3 = pl.subplot(gs[2])
ax4 = pl.subplot(gs[3])
#Plotting
# ax1.plot(wl, power_law2(wl, *popt2) , 'b--')
# ax1.plot(wl, power_law(wl, *popt) , 'b--')
ax1.plot(wl, wmean_cont, lw = 0.5, linestyle = 'steps-mid', label='X-shooter wmean composite')
nbins = len(wl)
from methods import hist
log_binned_wl = np.array(hist(wl,[min(wl),max(wl)], int(2*nbins),'log'))
from scipy.interpolate import InterpolatedUnivariateSpline
sps = InterpolatedUnivariateSpline(wl, std_norm)
std_plot = smooth(medfilt(sps(log_binned_wl) , 9), window='hanning', window_len=15)
wave_std = log_binned_wl
ax2.plot(wave_std, std_plot, lw = 0.5, linestyle = 'steps-mid', label='Normalised variability')
ax3.plot(wl, wmean_cont/medfilt(err_wmean, 5), lw = 0.5, linestyle = 'steps-mid', label = 'Signal-to-noise')
ax4.plot(wl,medfilt( n_spec, 1), label='Number of spectra', lw=0.5)
#Overplot lines
fit_line_positions = np.genfromtxt('data/plotlinelist.txt', dtype=None)
linelist = []
linenames = []
for n in fit_line_positions:
linelist.append(n[1])
linenames.append(n[0])
pl.xlabel(r'Rest Wavelength [$\AA$]')
ax1.set_ylabel(r'Normalised flux density F$_{\lambda}$')
ax2.set_ylabel(r'Normalised Variability $\delta$F$_{\lambda}$')
ax3.set_ylabel(r'S/N Ratio')
ax4.set_ylabel(r'Number of spectra')
ax1.semilogy()
ax1.semilogx()
ax1.set_xlim((1000, 11500 ))
ax1.set_ylim((0.1, 750))
ax2.semilogy()
ax2.semilogx()
ax2.set_xlim((1000, 11500 ))
ax2.set_ylim((0.001, 90))
ax3.semilogx()
ax3.set_xlim((1000, 11500 ))
ax4.semilogx()
ax4.set_xlim((1000, 11500 ))
ax4.set_ylim((0, 9))
# Formatting axes
import matplotlib as mpl
ax4.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax4.set_xticks([1000, 2000, 3000, 5000, 9000])
ax1.xaxis.set_major_locator(mpl.ticker.NullLocator())
ax2.xaxis.set_major_locator(mpl.ticker.NullLocator())
ax3.xaxis.set_major_locator(mpl.ticker.NullLocator())
ax1.yaxis.set_minor_locator(mpl.ticker.NullLocator())
ax2.yaxis.set_minor_locator(mpl.ticker.NullLocator())
ax1.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax1.set_yticks([0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500])
ax2.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax2.set_yticks([0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])
ax3.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax3.set_yticks([50, 100, 150, 200, 250, 300])
ax4.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax4.set_yticks([0, 1, 2, 3, 4, 5, 6, 7, 8])
pl.tight_layout()
fig.subplots_adjust(hspace=0)
format_axes(ax1)
format_axes(ax2)
format_axes(ax3)
format_axes(ax4)
val = []
for p in range(len(linelist)):
xcoord = linelist[p]
mask = (wl > xcoord - 1) & (wl < xcoord + 1)
y_val = np.mean(wmean_cont[mask])
val.append(1.5 * y_val)
arrow_tips = val
lineid_plot.plot_line_ids(wl, wmean_cont, linelist, linenames, arrow_tip=arrow_tips, ax=ax1)
for i in ax1.lines:
if '$' in i.get_label():
i.set_alpha(0.3)
i.set_linewidth(0.75)
for p in range(len(linelist)):
xcoord = linelist[p]
mask = (wl > xcoord - 1) & (wl < xcoord + 1)
y_val = np.mean(wmean_cont[mask])
ax1.vlines(xcoord, ax1.get_ylim()[0], y_val, color='black',linestyle='dashed', lw=0.75, alpha=0.3)
ax2.axvline(x=xcoord, color='black',linestyle='dashed', lw=0.75, alpha=0.3)
ax3.axvline(x=xcoord, color='black',linestyle='dashed', lw=0.75, alpha=0.3)
ax4.axvline(x=xcoord, color='black',linestyle='dashed', lw=0.75, alpha=0.3)
a = ax1.findobj(mpl.text.Annotation)
for i in a:
if '$' in i.get_label():
i.set_size(10)
fig.savefig("../documents/figs/Combined.pdf", rasterized=True, dpi=600)
pl.show()
if __name__ == '__main__':
dat = np.genfromtxt('data/regularised.dat')
print(np.shape(dat))
n_test = (dat[5800 : 5900,:])
# Checking for normality
from matplotlib import pyplot as plt
import seaborn as sns; sns.set_style('ticks')
cmap = sns.color_palette("cubehelix", 6)
import scipy.stats as stats
import statsmodels.api as sm
p_val = []
#Plotting
ratio = (1.0 + np.sqrt(5.0))/2.0
latexify(columns=2)
fig, ax = plt.subplots()
for i, k in enumerate(n_test):
p_val.append((stats.shapiro(k)[1]))
mtest = np.mean(n_test, axis = 0)
print(mtest)
sm.qqplot(mtest, fit=True, line='45', ax=ax)
print(np.mean(p_val))
format_axes(ax)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
def load_sdss_dr12(path):
data_file = fits.open(path)
# print(data_file[1].data.field)
sdss_nam = data_file[1].data.field('SDSS_NAME')
# f = 82900
# print(sdss_nam[f:f+100])
z_warning = data_file[1].data.field('zWarning')
z = data_file[1].data.field('z_vi')
# mask = np.logical_and(np.logical_and((z >= 1.0), (z <= 2.3)), (z_warning == 0))
# mask = np.ones(np.shape(z)).astype(bool)#(z_warning == 0)
mask = (z_warning == 0)
z = z[mask]
mi = data_file[1].data.field('MI')[mask]
# print(len(mi))
dgmi = data_file[1].data.field('DGMI')[mask]
bands = ['u', 'g', 'r', 'i', 'z']
data = {}
length_data = []
for i, k in enumerate(bands):
data[k] = (data_file[1].data.field('PSFMAG')[:,i])[mask] - (data_file[1].data.field('EXTINCTION_RECAL')[:,i])[mask]
length_data.append(len(data[k]))
nam = np.array(['082045.38+130618.9', '115043.86-002354.1', '121940.36-010007.4', '123602.33-033129.9', '135425.24-001357.9', '143148.09+053558.0', '143748.28-014710.7'])
# for n in nam:
# print(str(n))
# print([zip(i,k) for i,k in enumerate(sdss_nam) if str(n) == str(k)])
# [print(k) for i,k in enumerate(sdss_nam)]
u_obj = np.array([16.340282535250985, 16.868115642182865, 16.868490820682688, 16.829230761566329, 16.443006053710171, 16.86379118669786, 15.633876668255006 ])
u_obj_sdss = np.array([16.28, 17.10, 17.22, 17.09, 16.98, 16.99, 15.86])
g_obj = np.array([16.173628740863542, 16.942949312866595, 16.609497093992836, 16.763042601191465, 16.361315268986992, 16.878526911269127, 15.622724562677639])
g_obj_sdss = np.array([16.13, 17.06, 16.92, 16.99, 16.78, 16.88, 15.76])
r_obj = np.array([15.983908769640571, 16.910639809893361, 16.53028330223578, 16.606965809044731, 16.259465400302297, 16.763917281092667, 15.381490575686691])
r_obj_sdss = np.array([15.91, 16.99, 16.82, 16.90, 16.67, 16.75, 15.48])
i_obj = np.array([15.960828249585155, 16.647157166516017, 16.356895490345813, 16.375764327940693, 16.113968031003473, 16.585061165051222, 15.355882945611519])
i_obj_sdss = np.array([15.87, 16.78, 16.63, 16.66, 16.51, 16.52, 15.41])
z_obj = np.array([15.949328467438541, 16.501224893192735, 16.341537724690703, 16.366777188037226, 16.161932733774769, 16.349770828709673, 15.386318026049175])
z_obj_sdss = np.array([15.83, 16.60, 16.61, 16.71, 16.51, 16.25, 15.41])
mi_obj = np.array([-28.887216966079912, -29.492018441782825, -29.206175823944648, -29.521104883342353, -29.343074749485346, -29.759800580770957, -29.85016859018959])
zz_obj = np.array([1.1242971107973012, 1.9798694976693576, 1.5830422701934881, 1.8463767030959246, 1.5123220764878522, 2.0997959346967061, 1.3089485173836708])
K_corr = np.array([-0.048, -0.248, -0.268, -0.287, -0.258, -0.233, -0.143, -0.0])
Mz0 = np.array([-28.512270824785411, -29.337462849149809, -29.032421150963174, -29.423493429879706, -29.153275130431958, -29.555455539949492, -29.524955755590376, -29.523504957708496])
ric = Mz0 - K_corr
print(ric)
print(np.mean(mi), np.std(mi))
print(np.mean(ric), np.std(ric))
print(np.mean(1 - u_obj / u_obj_sdss), np.std(1 - u_obj / u_obj_sdss))
print(np.mean(1 - g_obj / g_obj_sdss), np.std(1 - g_obj / g_obj_sdss))
print(np.mean(1 - r_obj / r_obj_sdss), np.std(1 - r_obj / r_obj_sdss))
print(np.mean(1 - i_obj / i_obj_sdss), np.std(1 - i_obj / i_obj_sdss))
print(np.mean(1 - z_obj / z_obj_sdss), np.std(1 - z_obj / z_obj_sdss))
print(np.mean( u_obj - u_obj_sdss), np.std( u_obj - u_obj_sdss))
print(np.mean( g_obj - g_obj_sdss), np.std( g_obj - g_obj_sdss))
print(np.mean( r_obj - r_obj_sdss), np.std( r_obj - r_obj_sdss))
print(np.mean( i_obj - i_obj_sdss), np.std( i_obj - i_obj_sdss))
print(np.mean( z_obj - z_obj_sdss), np.std( z_obj - z_obj_sdss))
# pl.show()
colors = ['z', 'g - i', 'i']
gi = data['g'] - data['i']
# data_color = np.array(zip(mi , gi))
data_color = np.array(zip(z , gi, data['i']))
# data_color = data_color[(np.logical_and(np.logical_and(data_color[:,0] > -40.0, data_color[:,1] >= -5), data_color[:,0] < -28.0))]
data_color = data_color[(np.logical_and(data_color[:,0] > -40.0, data_color[:,1] >= -5))]
# data = data[(np.logical_and(data_color[:,0] > -40.0, data_color[:,1] >= -5))]
data_color = data_color[(data_color[:,1] >= -5)]
# data = data[(np.logical_and(data_color[:,0] > -40.0, data_color[:,1] >= -5))]
color_data = pd.DataFrame(data_color, columns=colors)
# latexify()
# Set up the subplot grid
ratio = 5
fig_width = 8
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_height = 1.0 * fig_width*golden_mean
latexify(columns=2)
fig = pl.figure()
gs = pl.GridSpec(ratio + 1, ratio + 1)
ax = fig.add_subplot(gs[1:, :-1])
# ax_marg_x = fig.add_subplot(gs[0, :-1], sharex=ax)
ax_marg_y = fig.add_subplot(gs[1:, -1], sharey=ax)
x = np.array(color_data['z'])
y = np.array(color_data['g - i'])
imag = np.array(color_data['i'])
import triangle
print(np.mean(x), np.std(x))
# print(np.shape(x))
mask = (imag < 17.0) & ((1 < x) & (x < 2.3))
# ax.scatter(mi_obj, g_obj - i_obj, s = 15, c=sns.xkcd_rgb["denim blue"], alpha = 0.7)
triangle.hist2d(x, y, bins=200, ax=ax, smooth=0.3)
ax.scatter(x[mask] , y[mask] , marker='o', s=10, facecolor=cmap[1], lw = 0, cmap=cmap, alpha= 1.0)
ax.scatter(zz_obj, g_obj - i_obj, s = 25, c=cmap[2], alpha = 1.0)
format_axes(ax)
format_axes(ax_marg_y)
pl.setp(ax_marg_y.get_yticklabels(), visible=False)
pl.setp(ax_marg_y.yaxis.get_majorticklines(), visible=False)
pl.setp(ax_marg_y.yaxis.get_minorticklines(), visible=False)
pl.setp(ax_marg_y.xaxis.get_majorticklines(), visible=False)
pl.setp(ax_marg_y.xaxis.get_minorticklines(), visible=False)
pl.setp(ax_marg_y.get_xticklabels(), visible=False)
ax_marg_y.xaxis.grid(False)
despine(ax=ax_marg_y, bottom=True)
sns.distplot(y, hist=False, kde=True, ax=ax_marg_y, kde_kws={"shade": True, "color": sns.xkcd_rgb["black"], "gridsize": 200, "alpha": 0.2},
vertical=True, axlabel=False)
sns.distplot(g_obj - i_obj, hist=False, rug=True, kde=False, ax=ax_marg_y, rug_kws={"height": 1.5, "color": cmap[2], "alpha": 1.0},
vertical=True, axlabel=False)
# sns.distplot(y[mask], hist=False, rug=True, kde=False, ax=ax_marg_y, rug_kws={"height": 0.5, "color": cmap[1], "alpha": 0.3},
# vertical=True, axlabel=False)
sns.distplot(y[mask], hist=False, kde=True, ax=ax_marg_y, kde_kws={"shade": True, "color": cmap[1], "gridsize": 200, "alpha": 0.3},
vertical=True, axlabel=False)
# sns.kdeplot(color_data, ax = ax, cmap='Greys', n_levels=50, norm=PowerNorm(gamma=0.3),
# shade=True, gridsize=100, linewidths = (0.5,), alpha=0.7)
# ax.set_xlabel(r"M$_i$(z=2)")
ax.set_xlabel(r"z")
ax.set_ylabel(r"$g - i$")
# ax.set_xlim((np.mean(x) - 5* np.std(x), np.mean(x) + 2* np.std(x)))
ax.set_xlim((0.0, 3.5))
ax.set_ylim((-0.5, 1.0))
format_axes(ax)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(16)
fig.tight_layout()
pl.savefig('../documents/figs/color_comparison2.pdf')
pl.show()
from __future__ import division, print_function __all__ = ["Module Name"] __version__ = "0.0.0" __author__ = "Jonatan Selsing ([email protected])" __copyright__ = "Copyright 2014 Jonatan Selsing" import numpy as np import glob from scipy import interpolate import matplotlib.pylab as pl from methods import latexify latexify() # use seaborn for nice default plot settings import seaborn; seaborn.set_style('ticks') # from unred import ccm_unred,cardelli_reddening from cardelli_unred import cardelli_reddening # from gen_methods import smooth,medfilt from methods import common_wavelength def main(): root_dir = '/Users/jselsing/Work/X-Shooter/CompositeRedQuasar/processed_data/' sdssobjects = glob.glob(root_dir+'*SDSS*/Telluric_corrected_science.dat') object_info_files = glob.glob(root_dir+'*SDSS*/Object_info.dat')