def col_mag(t1, col1, t2, col2): ids = np.where( (t1[col1] > 0.0) & (t1[col1] < 50.0) & (t2[col2] > 0.0) & (t2[col2] < 50.0) )[0] fig = plt.figure() ax = fig.add_subplot(111) plt.plot(t1[col1][ids]-t2[col2][ids], t1[col1][ids], "k.", ms = 2) plt.xlabel(col1 + "-" + col2, fontsize = "x-large") plt.ylabel(col1, fontsize = "x-large") plt.gca().invert_yaxis() plotid.plotid() info = "Number in table: " + str(len(t1)) + "\nNumber not in graph: " + str(len(t1) - len(ids)) plt.text(0.05, 0.80, info, transform = ax.transAxes, bbox = dict(facecolor = "white", alpha = 0.7)) plt.show()
def mag_hist(t, col, SG_col = "CLASS_STAR", SG_val = 1):
stars = np.where( (t[SG_col] == SG_val) )[0]
ids = np.where( (t[SG_col] == SG_val) & (t[col] > 0.0) & (t[col] < 50))[0]
print "Number in table:", len(t)
print "Number not in graph:", len(t) - len(ids)
print "Number that satisfy classifier:", len(stars)
print "Number that are junk:", - len(ids) + len(stars)
fig = plt.figure()
ax = fig.add_subplot(111)
info = "Number in table: " + str(len(t)) + "\nNumber not in graph: " + str(len(t) - len(ids)) + "\nNumber that satisfy classifier: " + str(len(stars)) + "\nNumber that are junk: " + str(len(stars)-len(ids))
plt.text(0.05, 0.80, info, transform = ax.transAxes, bbox = dict(facecolor = "white", alpha = 0.7))
plt.hist(t[col][ids], bins = 100)
plt.xlabel(col, fontsize = "x-large")
plt.ylabel("Number", fontsize = "x-large")
plotid.plotid()
plt.show()
def fxy(tS,tY,x,y,xlab,ylab,figname,yinvert=False, lim=False,frad=False):
plt.scatter(tS[x],tS[y],s=1,edgecolor='none',c='#2e64fe', label='SVA1')
plt.scatter(tY[x],tY[y],s=1,edgecolor='none',c='#ff00a5',label='Y1A1')
plt.xlabel(xlab,fontsize='17')
plt.ylabel(ylab,fontsize='17')
px=np.arange(min(tS[x]),max(tS[x])+1,0.1)
plt.plot(px,px,c='k')
if lim:
plt.xlim(min(tS[x]),np.median(tS[x])+5*(np.std(tS[x])))
plt.ylim(min(tS[x]),np.median(tS[x])+5*(np.std(tS[x])))
if frad:
#plt.xlim(np.median(t[x+'_G'])-(np.std(t[x+'_G'])),np.median(t[x+'_G'])+(np.std(t[x+'_G'])))
plt.xlim(1.0,12)
plt.ylim(1.0,12)
if yinvert:
plt.ylim(14,26)
plt.gca().invert_yaxis()
plt.title(tile)
plotid()
plt.legend(prop={'size':10})
plt.show()
#plt.savefig(figname)
plt.clf()
def make_cutouts_multiband(bands='r', detband='detr',
imagefiles=None,
listfile=None, format='region', width_arcsecs=10.0,
catfiles=None):
"""
"""
import os, sys
import time
import pyfits
import pyregion
import cutout
#import fits2png
import matplotlib.pyplot as plt
#if not interactive: matplotlib.use('Agg')
import aplpy
import plotid
t0=time.time()
base=os.path.basename(listfile)
id=os.path.splitext(base)[0]
weightmap = False
# open ds9 region
if format == 'region':
region = pyregion.open(listfile)
irow=-1
# print out the cordinates in degrees
for rows in region: # iterate over table rows
irow=irow+1
print irow+1, region[irow].coord_list[0], region[irow].coord_list[1]
nrows=len(region)
if format == 'ozdes':
table= pyfits.getdata(listfile,1)
ralist = table['RA']
declist = table['Dec']
object = table['object']
comment = table['comment']
redshift = table['z_fin']
print 'ralist range: ', min(ralist), max(ralist)
print 'declist range: ', min(declist), max(declist)
nrows=len(ralist)
# Read extension 2 of the catalog file
ext=2
catalog = pyfits.getdata(catalog_path+catalog_filename,ext)
ra_catalog = catalog['alphawin_j2000']
dec_catalog = catalog['deltawin_j2000']
title=imagefiles
#plt.text(.5, .95, title, horizontalalignment='center')
# leave some space to add annotation at the top of the page
plt.rc('figure.subplot', left=0.05, right=0.95, bottom=0.05, top=0.95,
wspace=0.0, hspace=0.0)
fig=plt.figure(figsize=(12.0, 10.0))
fig.text(0.1, 0.97, title, horizontalalignment='left',
color='blue', transform = plt.gcf().transFigure)
plt.xticks=[]
plt.yticks=[]
label=os.path.basename(__file__)
plotid.plotid(label=label)
nxplots=5
nyplots=6
irow=-1
iplot=0
#print plt.get_gca()
#print plt.get_gcf()
isource=0
ifig=1
#for rows in region: # iterate over table rows
for rows in ralist: # iterate over table rows
print 'Elpased time: %.2f seconds' %(time.time() - t0)
irow=irow+1
isource=isource+1
print irow, ' of ', nrows
#if irow > nxplots*nyplots: break
if format == 'region':
ra=region[irow].coord_list[0]
dec=region[irow].coord_list[1]
if format == 'ozdes':
ra=ralist[irow]
dec=declist[irow]
iband=-1
for band in bands: # iterate over the bands
iband=iband+1
iplot=iplot+1
outfile='cutout' + str(irow+1) + band + '.fits'
outfile=None
verbose=True
xc=ra
yc=dec
imagefile=imagefiles[iband]
print 'input image: ', imagefile
# width in arcsecs
width_arcsecs=10.0
# convert to semi-width in degrees
xw=(0.5*width_arcsecs)/3600.0
yw=(0.5*width_arcsecs)/3600.0
# create cutout FITS file
imagefile=imagefiles[iband]
image=cutout.cutout(imagefile, xc, yc, xw=xw, yw=yw, units='wcs',
outfile=outfile, clobber=True,
useMontage=False, coordsys='celestial', verbose=verbose)
image = image.data
ax=fig.add_subplot(nxplots,nyplots,iplot,
frameon=True, xticks=[], yticks=[])
ax.imshow(image)
if iband == 0: ax.text(0.1, 0.80, str(isource),
transform=plt.gca().transAxes)
ax.text(0.9, 0.80, band, transform=plt.gca().transAxes)
if format == 'ozdes':
annotation=object[isource-1].strip()
if iband == 0: ax.text(0.1, 0.02, annotation,
verticalalignment='baseline',
transform=plt.gca().transAxes)
annotation=comment[isource-1].strip()
if iband == 0: ax.text(0.1, 0.1, annotation,
transform=plt.gca().transAxes)
annotation=str(redshift[isource-1]).strip()
if iband == 0: ax.text(0.5, 0.1, annotation,
transform=plt.gca().transAxes)
if iplot == nxplots*nyplots:
print 'nxplots, nyplots, iplot, isource: ', \
nxplots, nyplots, iplot, isource
iplot=0
strfigno= '%4.4d' % (ifig)
#plt.savefig(id+'_cutouts_multiband_' + str(ifig) + '.png', dpi=150)
#plt.savefig(id+'_cutouts_multiband_' + strfigno + '.png', dpi=150)
plt.savefig(id+'_cutouts_multiband_' + strfigno + '.png', dpi=100)
if ifig == 1: plt.show()
ifig=ifig+1
fig=plt.figure(ifig,figsize=(12.0, 10.0))
plotid.plotid(label=label)
print 'nxplots, nyplots, iplot, isource: ', \
nxplots, nyplots, iplot, isource
iplot=0
strfigno= '%4.4d' % (ifig)
#plt.savefig(id+'_cutouts_multiband_' + str(ifig) + '.png', dpi=150)
#plt.savefig(id+'_cutouts_multiband_' + strfigno + '.png', dpi=150)
plt.savefig(id+'_cutouts_multiband_' + strfigno + '.png', dpi=100)
transform=plt.gcf().transFigure
transform=plt.gca().transAxes
info='Top Title'*5
def toptitle(text):
"""
Add some text to the top of the page
It is added as part of the first subplot
"""
plt.text(0.5, 1.10, text, transform=plt.gca().transAxes)
noticks=True
plotid.plotid()
nx=4
ny=5
iplot=0
for iy in range (ny):
for ix in range (nx):
iplot += 1
if noticks: plt.subplot(nx,ny,iplot,frameon=True, xticks=[], yticks=[])
#subplot(2,2,1)
if iplot == 1: toptitle(info)
if iplot == nx*ny: plotid.plotid()
plt.text(0.1, 0.9, str(iplot), transform=plt.gca().transAxes)
def multihist(table=None, colname_prefix=None, tile=None, release=None,
figname=None, lim=False, thetas=False, frad=False):
"""
"""
plt.subplots_adjust(bottom=0.2, wspace=0.25)
plt.title(tile + ' ' + release)
xmin = np.nanmin(table[colname_prefix + '_G'])
xmax = np.nanmax(table[colname_prefix + '_G'])
print('column name prefix:', colname_prefix)
print('minimum (G):', xmin)
print('maximum (G):', xmax)
print('range(G):', xmax - xmin)
print('mean(G): ', np.nanmean(table[colname_prefix + '_G']))
print('median(G): ', np.nanmedian(table[colname_prefix + '_G']))
binfactor = 20
if thetas:
bins = np.arange(np.nanmin(table[colname_prefix + '_G']),
np.nanmax(table[colname_prefix + '_G']),
np.abs(np.nanmean(table[colname_prefix + '_G'])))
else:
bins = np.arange(np.nanmin(table[colname_prefix + '_G']),
np.nanmax(table[colname_prefix + '_G']),
np.abs(np.nanmean(table[colname_prefix + '_G'])/binfactor))
bins = 100
xdata = table[colname_prefix + '_G']
xdata = xdata[~np.isnan(xdata)]
plt.hist(xdata, bins,
histtype='step', color='#2e64fe', label='g')
# lazy fix
t = table
x = colname_prefix
# bins=np.arange(min(t[x+'_R']),max(t[x+'_R']),np.mean(t[x+'_G'])/10)
print('Overplotting r')
xdata = table[colname_prefix + '_R']
xdata = xdata[~np.isnan(xdata)]
plt.hist(xdata, bins,
histtype='step', color='#92cd00',label='r')
# bins=np.arange(min(t[x+'_I']),max(t[x+'_I']),np.mean(t[x+'_G'])/10)
print('Overplotting i')
xdata = table[colname_prefix + '_I']
xdata = xdata[~np.isnan(xdata)]
plt.hist(xdata, bins,
histtype='step', color='#ffbf00', label='i')
# bins=np.arange(min(t[x+'_Z']),max(t[x+'_Z']),np.mean(t[x+'_G'])/10)
print('Overplotting z')
xdata = table[colname_prefix + '_Z']
xdata = xdata[~np.isnan(xdata)]
plt.hist(xdata, bins,
histtype='step', color='#eb7a3c', label='z')
# bins=np.arange(min(t[x+'_Y']),max(t[x+'_Y']),np.mean(t[x+'_Y'])/10)
print('Overplotting Y')
xdata = table[colname_prefix + '_Y']
xdata = xdata[~np.isnan(xdata)]
plt.hist(xdata, bins,
histtype='step', color='#cc0084',label='Y')
plt.xlabel(x)
plotid()
if lim:
plt.xlim(min(t[x+'_G']),np.median(t[x+'_G'])+5*(np.std(t[x+'_G'])))
if frad:
#plt.xlim(np.median(t[x+'_G'])-(np.std(t[x+'_G'])),np.median(t[x+'_G'])+(np.std(t[x+'_G'])))
plt.xlim(0.0,10)
plt.legend(prop={'size':10}, loc='upper center',
bbox_to_anchor=(0.5, -0.15), fancybox=True, shadow=False, ncol=5)
# plt.show()
print('Save:', figname)
plt.savefig(figname)
plt.clf()
