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plot-sbcontrast.py
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plot-sbcontrast.py
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#!/usr/bin/env python
"""
Generic python script.
"""
__author__ = "Alex Drlica-Wagner"
import numpy as np
import pylab as plt
import pandas as pd
import healpy as hp
import fitsio
import skymap
import matplotlib
matplotlib.use('Agg')
from mpl_toolkits.axisartist import Subplot
from matplotlib.ticker import MaxNLocator
from descolors import BAND_COLORS
matplotlib.rcParams['xtick.labelsize'] = 12
matplotlib.rcParams['ytick.labelsize'] = 12
matplotlib.rcParams['axes.labelsize'] = 14
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['font.serif'] = ['Computer Modern Roman']
matplotlib.rcParams['font.family'] = 'serif'
def set_defaults(kwargs,defaults):
for k,v in defaults.items():
kwargs.setdefault(k,v)
return kwargs
def draw_peak(peak,**kwargs):
kwargs.setdefault('ls','--')
kwargs.setdefault('label','%.1f '%(peak))
ax = plt.gca()
ax.axvline(peak,**kwargs)
def create_hpxmap_hist_figure():
#fig = plt.figure(figsize=(10.5,4))
#gridspec=plt.GridSpec(1, 3)
fig = plt.figure(figsize=(12.,4.))
gridspec = plt.GridSpec(1,3,wspace=1.0)
#gridspec.update(left=0.07,right=0.91,bottom=0.15,top=0.95,wspace=0.08)
return fig, gridspec
def draw_hist(hpxmap,label,color,**kwargs):
ax = plt.gca()
if isinstance(hpxmap,np.ma.MaskedArray):
pix = np.where(~hpxmap.mask)
else:
pix = np.where((np.isfinite(hpxmap)) & (hpxmap != hp.UNSEEN))
data = hpxmap[pix]
vmin = kwargs.pop('vmin',np.percentile(data,q=0.1))
vmax = kwargs.pop('vmax',np.percentile(data,q=99.9))
nbins = kwargs.pop('nbins',100)
defaults = dict(bins=np.linspace(vmin,vmax,nbins),
histtype='step',density=True,lw=2,
peak=False,quantiles=False,color=color,label=label)
set_defaults(kwargs,defaults)
do_peak = kwargs.pop('peak')
do_quantiles = kwargs.pop('quantiles')
do_overflow = kwargs.pop('overflow',False)
# Deal with bug: https://github.com/matplotlib/matplotlib/issues/6448/
if do_overflow:
data = np.clip(data,kwargs['bins'].min(),kwargs['bins'].max())
else:
data = data[(data > kwargs['bins'].min())&(data < kwargs['bins'].max())]
n,b,p = ax.hist(data,**kwargs)
ret = dict()
peak = ((b[1:]+b[:-1])/2.)[np.argmax(n)]
ret['peak'] = peak
if do_peak:
draw_peak(peak,color='k',label='%.1f'%(peak))
ret['mean'] = np.mean(data)
ret['std'] = np.std(data)
quantiles = [5,16,50,84,95]
percentiles = np.percentile(data,quantiles)
ret['quantiles'] = quantiles
ret['percentiles'] = percentiles
for p,q in zip(percentiles,quantiles):
ret['q%02d'%q] = p
if do_quantiles:
for q,p in zip(quantiles,percentiles):
draw_peak(p,color='r',label='%.1f (%g%%)'%(p,100-q))
ax.set_xlim(kwargs['bins'].min(),kwargs['bins'].max())
return ret
def change_sigma(sb_lim,sigma_out=10.0,sigma_in=1.0):
""" Convert from one detection significance to another
Parameters
----------
sb_lim : surface brightness limit
sigma_out : new significance
sigma_in : old significance
Returns
-------
new_sb_lim : surface brightness limit at new significance
"""
# sb_lim = zeropoint - 2.5 * np.log10(sig_adu / pixel_scale**2)
delta = -2.5*np.log10(sigma_out/sigma_in)
return sb_lim + delta
#tiles = pd.read_csv('y3a1_tilenames.csv')
#results = pd.DataFrame(np.load('y3a1_results.npy'))
#merge = results.merge(tiles,on='tilename')
merge = pd.DataFrame(fitsio.read('/Users/nsevilla/des/sp_maps/Y3A1_SURFACE_BRIGHTNESS-v1.fits').byteswap().newbyteorder())
nside=64
bands = ['g','r','i','z','Y']
#bands = ['g']
#sizes = [10,30,60]
sizes = [10]
sigma = 3.0
print(merge['sb'][0])
merge['sb'] = change_sigma(merge['sb'],sigma_out=sigma)
print(merge['sb'][0])
print(merge['size'][0])
print(merge['band'][0])
for i,(band,v) in enumerate(BAND_COLORS.items()):
print(f"{band}")
for size in sizes:
print(f" {size}")
if band == 'g':
sel = (merge['band'] == b'g') & (merge['size'] == size)
elif band == 'r':
sel = (merge['band'] == b'r') & (merge['size'] == size)
elif band == 'i':
sel = (merge['band'] == b'i') & (merge['size'] == size)
elif band == 'z':
sel = (merge['band'] == b'z') & (merge['size'] == size)
else:
sel = (merge['band'] == b'Y') & (merge['size'] == size)
#sel = (merge['band'] == band) & (merge['size'] == size)
x = merge[sel]
p16,p50,p84 = np.percentile(x['sb'],[16,50,84])
print(f" sb = {p50:.2f} [+{p84 - p50:.2f}, -{p50 - p16:.2f}]")
print(f" median(sb_err) = {x['sb_err'].median():.4f}")
hpxmap = hp.UNSEEN*np.ones(hp.nside2npix(nside))
pix = hp.ang2pix(64,x['ra_cent'],x['dec_cent'],lonlat=True)
hpxmap[pix] = x['sb']
vmin,vmax = np.percentile(x['sb'],[1,99])
fig,gridspec = create_hpxmap_hist_figure()
plt.gca().axis('off')
smap = skymap.DESSkymap(rect=gridspec[0:2])
#smap.draw_hpxmap(hpxmap,vmin=vmin,vmax=vmax)
smap.scatter(x['ra_cent'].values,x['dec_cent'].values,c=x['sb'].values,
s=4,marker='s',vmin=vmin,vmax=vmax,latlon=True,rasterized=True)
smap.draw_inset_colorbar(label=r'mag arcsec$^{-2}$')
ax1 = plt.gca()
#ax1.annotate(f"{band}-band",(0.05,0.9),xycoords='axes fraction',
# fontsize=14)
ax1.axis['right'].major_ticklabels.set_visible(False)
#ax1.axis['top'].major_ticklabels.set_visible(False)
# Plot histogram
ax2 = Subplot(fig,gridspec[2])
fig.add_subplot(ax2)
plt.sca(ax2)
#fig.add_subplot(gridspec[2])
bins = np.linspace(x['sb'].min(),x['sb'].max(),35)
ret = draw_hist(hpxmap,label=band,color=v,peak=False,bins=bins,density=True)
ax2.yaxis.set_major_locator(MaxNLocator(6,prune='both'))
ax2.axis['left'].major_ticklabels.set_visible(True)
ax2.axis['right'].major_ticklabels.set_visible(False)
ax2.axis['left'].label.set_visible(True)
#ax2.axis['right'].label.set_text(r'Number of Tiles')
ax2.axis['left'].label.set_text(r'PDF')
ax2.axis['bottom'].label.set_visible(True)
ax2.axis['bottom'].label.set_text(r'Surface Brightness Limit (mag arcsec$^{-2}$)')
if band == 'z':
ax2.set_aspect(1.5)
elif band == 'Y':
ax2.set_aspect(1.2)
else:
ax2.set_aspect(0.8)
#forceAspect(ax2,aspect=1)
#plt.suptitle(f'{band}-band ({size}" x {size}")',y=0.92)
plt.legend(loc='upper left')
outfile = f'sbcontrast_{band}_s{size:d}_v2.pdf'
print(f" {outfile}")
plt.savefig(outfile,bbox_inches='tight')
#outfile = outfile.replace('.pdf','.png')
#print(f" {outfile}")
#plt.savefig(outfile,bbox_inches='tight')