def show_kmap(klm=None,
fname=None,
lmin=200,
lmax=1024,
nside=1024,
v=.1,
lonra=[147, 244],
latra=[-3, 21],
output=False,
title=''):
import curvedsky
if fname is not None:
Flm, __ = pickle.load(open(fname, "rb"))
if klm is not None:
Flm = klm.copy()
Flm[:lmin, :] = 0.
kmap = curvedsky.utils.hp_alm2map(nside, lmax, lmax,
Flm[:lmax + 1, :lmax + 1])
print('max,min:', np.max(kmap), np.min(kmap))
hp.cartview(kmap,
lonra=lonra,
latra=latra,
min=-v,
max=v,
cbar=False,
title=title)
if output:
return kmap
def plot_sky_projection_healpy_count(Sliced_Halo_data,nside):
HEALPix_mode = read_data_bool(tag_name = 'HEALPix_Cartesian',file_name = 'parameters/Output_Parameters.xml')
HEALPix_grat = read_data_bool(tag_name = 'HEALPix_Graticule',file_name = 'parameters/Output_Parameters.xml')
fdir = './Output/plots/HEALPix/'
Sl_n = len(Sliced_Halo_data)
Z_max = max(Sliced_Halo_data[Sl_n-1].Z_red[:])
rc('text',usetex=True)
for k in range(Sl_n):
pix = zeros(12*nside**2)
n = len(Sliced_Halo_data[k].RA[:])
for i in range(n):
j = hp.ang2pix(nside,DtoR*(90.0-Sliced_Halo_data[k].DEC[i]),DtoR*Sliced_Halo_data[k].RA[i])
pix[j] += 1
clf()
if (HEALPix_mode):
hp.cartview(pix)
else:
hp.mollview(pix)
if (HEALPix_grat):
hp.graticule()
fname = 'sky_projection_HEALPix_Count_%i_%i.pdf'%(nside,(k+1))
title(r'sky projection for redshift between %0.2f and %0.2f'%(Sliced_Halo_data[k].z_min,Sliced_Halo_data[k].z_max),fontsize = 20)
print 'Saving plot', fname
savefig(fdir+fname,bbox_inches='tight')
rc('text',usetex=False)
close()
return 0
def plot_healpix(self, map_name, view='cart', **kwargs):
import healpy
import numpy as np
m, pix, nside = self.read_healpix(map_name, return_all=True)
lon, lat = healpy.pix2ang(nside, pix, lonlat=True)
npix = healpy.nside2npix(nside)
if len(pix) == 0:
print(f"Empty map {map_name}")
return
if len(pix) == len(m):
w = np.where((m != healpy.UNSEEN) & (m != 0))
else:
w = None
lon_range = [lon[w].min() - 0.1, lon[w].max() + 0.1]
lat_range = [lat[w].min() - 0.1, lat[w].max() + 0.1]
m[m == 0] = healpy.UNSEEN
title = kwargs.pop('title', map_name)
if view == 'cart':
healpy.cartview(m,
lonra=lon_range,
latra=lat_range,
title=title,
hold=True,
**kwargs)
elif view == 'moll':
healpy.mollview(m, title=title, hold=True, **kwargs)
else:
raise ValueError(f"Unknown Healpix view mode {mode}")
def footprint_area(cat,ngal=1,mask=None,nside=4096,nest=True,label=''):
import healpy as hp
import matplotlib
matplotlib.use ('agg')
import matplotlib.pyplot as plt
# plt.style.use('/home/troxel/SVA1/SVA1StyleSheet.mplstyle')
from matplotlib.colors import LogNorm
import pylab
mask=CatalogMethods.check_mask(cat.coadd,mask)
if not hasattr(cat, 'pix'):
cat.pix=CatalogMethods.radec_to_hpix(cat.ra,cat.dec,nside=nside,nest=True)
area=hp.nside2pixarea(nside)*(180./np.pi)**2
print 'pixel area (arcmin)', area*60**2
mask1=np.bincount(cat.pix[mask])>ngal
print 'footprint area (degree)', np.sum(mask1)*area
pix=np.arange(len(mask1))[mask1]
print pix
tmp=np.zeros((12*nside**2), dtype=[('hpix','int')])
tmp['hpix'][pix.astype(int)]=1
print tmp['hpix'][pix.astype(int)]
fio.write('footprint_hpix'+label+'.fits.gz',tmp,clobber=True)
tmp2=np.zeros(12*nside**2)
tmp2[tmp.astype(int)]=1
hp.cartview(tmp2,nest=True)
plt.savefig('footprint_hpix'+label+'.png')
plt.close()
return
def plot_footprint_base(cat, mask=None, label='', bins=100, part=''):
if not hasattr(cat, 'gdmask'):
cat.gdmask = hp.read_map(config.golddir +
'y1a1_gold_1.0.1_wide_footprint_4096.fit')
cat.badmask = hp.read_map(config.golddir +
'y1a1_gold_1.0.1_wide_badmask_4096.fit')
if not hasattr(cat, 'pix'):
cat.pix = hp.ang2pix(4096,
np.pi / 2. - np.radians(cat.dec),
np.radians(cat.ra),
nest=False)
mask0 = (cat.gdmask >= 1) & (cat.badmask == 0)
hpmap = np.ones((12 * 4096**2)) * hp.UNSEEN
hpmap[(cat.gdmask >= 1)] = 0
cnt = np.zeros(12 * 4096**2)
cnt[:np.max(cat.pix[mask]) + 1] = np.bincount(cat.pix[mask])
hpmap[mask0] = cnt[mask0]
hp.cartview(hpmap,
latra=config.dec_lim.get(part),
lonra=config.ra_lim.get(part),
xsize=10000,
title=label)
plt.savefig('plots/footprint/footprint_' + cat.name + '_' + label +
'_' + part + '.png',
dpi=1000,
bbox_inches='tight')
plt.close()
# dra=config.ra_lim.get(part)[1]-config.ra_lim.get(part)[0]
# ddec=config.dec_lim.get(part)[1]-config.dec_lim.get(part)[0]
# plt.figure()
# tmp=config.ra_lim.get(part),config.dec_lim.get(part)
# a=plt.hist2d(cat.ra[mask],cat.dec[mask],bins=(int(dra*bins),int(ddec*bins)),range=tmp,normed=True, cmap=plt.cm.afmhot)
# cb = plt.colorbar(orientation='horizontal')
# plt.gca().set_aspect('equal', 'box')
# plt.savefig('plots/footprint/footprint_'+cat.name+'_'+label+'_'+part+'.png', dpi=500, bbox_inches='tight')
# plt.close()
# plt.figure()
# plt.hist2d(cat.ra[mask],cat.dec[mask],bins=(int(dra*bins),int(ddec*bins)),range=((np.min(cat.ra[mask])-.1*dra,np.max(cat.ra[mask])+.1*dra),(np.min(cat.dec[mask])-.1*ddec,np.max(cat.dec[mask])+.1*ddec)),normed=True,norm=LogNorm(), cmap=plt.cm.afmhot)
# #cb = plt.colorbar()
# plt.gca().set_aspect('equal', 'box')
# plt.savefig('plots/footprint/footprint_'+cat.name+'_'+label+'_log.png', dpi=500, bbox_inches='tight')
# plt.close()
return
def display_healpix(self, map_name, **kwargs):
import healpy
import numpy as np
m, pix, nside = self.read_healpix(map_name, return_all=True)
lon, lat = healpy.pix2ang(nside, pix, lonlat=True)
npix = healpy.nside2npix(nside)
lon_range = [lon.min() - 0.1, lon.max() + 0.1]
lat_range = [lat.min() - 0.1, lat.max() + 0.1]
title = kwargs.pop('title', map_name)
healpy.cartview(m,
lonra=lon_range,
latra=lat_range,
title=title,
**kwargs)
def plot_sky(self,ra=None,dec=None,norm = 'log',unit='Kelvin',heapix_array=None):
""" plot_sky plots the sky tempreture and overlays pointing centers as red dots
The sky tempreture is the data that was loaded when the class was iniitated.
plot_sky takes in 3 optional parameters:
ra,dec are list/1D-array like values of right assension and declanation
returns matplotlib figure object that the plot is assosated with.
"""
#self.freq_map
fig = plt.figure(figsize=(16,9))
hp.cartview(self.freq_map,norm = norm,unit=unit,fig=fig.number)
c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
l = np.degrees(angle_wrap(-c.galactic.l.radian % (np.pi*2)) )
b = np.degrees(c.galactic.b.radian)
plt.plot(l,b,'ro')
#hp.graticule()
return fig
def plot_lotss_map(mp, title=None, cbar=True, mask=None, fname=None, **kwargs):
lonra = [159.5, 232.5]
latra = [44, 59]
if mask is not None:
mp2plot = mp.copy()
mp2plot[mask <= 0] = np.inf
else:
mp2plot = mp
arr = hp.cartview(mp2plot,
lonra=lonra,
latra=latra,
return_projected_map=True)
plt.cla()
plt.close(plt.gcf())
fig = plt.figure()
if title is not None:
plt.title(title, fontsize=14)
im = plt.imshow(arr,
origin='lower',
extent=(lonra[1], lonra[0], latra[0], latra[1]),
interpolation='none',
**kwargs)
plt.xlabel(r'${\rm R.A.}$', fontsize=15)
plt.ylabel(r'${\rm dec.}$', fontsize=15)
if cbar:
fig.colorbar(im, orientation='horizontal', aspect=40)
if fname is not None:
plt.savefig(fname, bbox_inches='tight')
def plot(self, ax=None, projection='mollweide', **kwargs):
"""
Plot healpix sky image using a global projection.
Parameters
----------
projection : ['mollweide', 'cartesian']
Which projection to use for plotting.
"""
#TODO: add other projections
if projection == 'mollweide':
hp.mollview(map=self.data, nest=self.wcs.nested, **kwargs)
elif projection == 'cartesian':
hp.cartview(map=self.data, nest=self.wcs.nested, **kwargs)
else:
raise ValueError("Projection must be 'cartesian' or 'mollweide'")
def plot_map(totCl, FileName=False, view='Mollweide'):
np.random.seed(42)
l = np.arange(len(totCl)) + 1
totCl_raw = totCl * 2 * np.pi / (l * (l + 1))
# --- create healpix map
map = hp.synfast(totCl_raw, nside=2048)
if view == 'Mollweide':
# --- Mollweide plot
m = hp.mollview(map, return_projected_map=True, cmap=cmap)
dpi = 600
figsize_inch = 6, 4
fig = plt.figure(figsize=figsize_inch, dpi=dpi)
ax = fig.add_axes([0, 0, 1, 1])
ax.imshow(m, cmap=cmap)
ax.axis('off')
if FileName:
fig.savefig(FileName, dpi=dpi, bbox_inches="tight") # save
else:
plt.show()
fig.clf()
if view == 'Cartesian':
# --- Cartesian view of a 20 deg2 region
# hp.cartview(map=None, fig=None, rot=None, zat=None, coord=None, unit='', xsize=800, ysize=None, lonra=None, latra=None, title='Cartesian view', nest=False, remove_dip=False, remove_mono=False, gal_cut=0, min=None, max=None, flip='astro', format='%.3g', cbar=True, cmap=None, badcolor='gray', bgcolor='white', norm=None, aspect=None, hold=False, sub=None, margins=None, notext=False, return_projected_map=False)
m = hp.cartview(map=map,
xsize=300,
lonra=[-5, 5],
latra=[-5, 5],
title=None,
return_projected_map=True)
plt.close()
dpi = 150
figsize_inch = 2, 2
fig = plt.figure(figsize=figsize_inch, dpi=dpi)
ax = fig.add_axes([0, 0, 1, 1])
ax.imshow(m, cmap=cmap, vmin=-500, vmax=500)
ax.axis('off')
if FileName:
fig.savefig(FileName, dpi=dpi) # save
else:
plt.show()
fig.clf()
def halo():
# Set stage
grid = hp.Harpix().adddisc(vec=(1, 0, 0), radius=2, nside=256)
#grid.addsingularity((0,0), 0.3, 2, n = 1000)
expo = 10
r = lambda l, b: np.sqrt(l**2 + b**2)
# Signal shape
sig_shape = hp.zeroslike(grid).addfunc(lambda l, b: 0 + 1. /
(r(l, b) + 10.)**1)
# Background shapes (and error)
bkg_shape = hp.zeroslike(grid).addfunc(lambda l, b: 1. /
(r(l, b) + 1.0)**2)
#err_shape = bkg_shape * 0.0001
#cov = hp.HarpixSigma1D(err_shape, corrlength = 1.)
print(bkg_shape.getintegral() * expo)
# Background component model
bkg_comp = BkgComponent(
lambda x: expo *
(bkg_shape.getdata(mul_sr=True) * x[0] + bkg_shape.getdata(mul_sr=True)
**3 * x[1] + bkg_shape.getdata(mul_sr=True)**2.5 * x[2] + x[3]),
x0=[1e8, 1e8, 1e8, 1.],
xerr=[0e3, 0e3, 0e3, 1.0],
cov=None)
SF = bkg_comp.getSwordfish()
# Signal model
#S = np.multiply.outer(sig_shape.getdata(mul_sr = True), sig_spec).flatten()
S = sig_shape.getdata(mul_sr=True)
#UL = SF.upperlimit(S, 0.05)
print('infoflux...')
F = SF.infoflux(S * expo, solver='direct')
print('...done')
grid.data = F
grid._div_sr()
m = grid.gethealpix(nside=256)
#healpy.mollview(m, nest=True)
healpy.cartview(m, nest=True, lonra=[-10, 10], latra=[-10, 10])
plt.show()
def Healpix2Cartesian(fname):
# Read from hdf5 file
f = h5py.File(fname,'r')
for t in f['templates/']:
print "Writing", t
if t=="energies":
continue
# Get the cartesian maps for each energy
hpixcube = f['templates/'+t][()]
cartcube = np.zeros((hpixcube.shape[0], 721,1440), dtype=np.float32)
for i in range(hpixcube.shape[0]):
cartcube[i] = healpy.cartview(hpixcube[i], hold=True, return_projected_map=True,
xsize=1440, lonra=[-179.875, 179.875],flip='geo')
plt.gcf()
# Generate new hdu object
hdu_new = pyfits.PrimaryHDU(cartcube.astype(np.float32))
# Copy galdef into header
galdef = dict(f['/galdef'].attrs.items())
hdu_new.header.add_comment("Diffuse model generated by Eric Carlson ([email protected])")
for key, val in galdef.items():
hdu_new.header.add_comment(key + "=" +val)
hdu_new.header['CRVAL1'] = 0.0
hdu_new.header['CRPIX1'] = 720
hdu_new.header['CDELT1'] = 0.25
hdu_new.header['CUNIT1']= 'deg'
hdu_new.header['CTYPE2']= 'GLON-CAR'
hdu_new.header['CRVAL2'] = 0
hdu_new.header['CRPIX2'] = 361
hdu_new.header['CDELT2'] = 0.25
hdu_new.header['CUNIT2']= 'deg'
hdu_new.header['CTYPE2']= 'GLAT-CAR'
hdu_new.header['CRVAL3'] = float(galdef['E_gamma_min'])
hdu_new.header['CRPIX3'] = 0
hdu_new.header['CDELT3'] = np.log10(float(galdef['E_gamma_factor']))
hdu_new.header['CTYPE2']= 'Energy'
hdu_new.header['CUNIT3']= 'MeV'
hdu_new.header['EXTEND']=True
hdu_new.header['CREATOR'] = ('Eric Carlson ([email protected])', '')
# Write energy extension table
energies = np.array([50*float(galdef['E_gamma_factor'])**i for i in range(cartcube.shape[0])])
tbhdu = pyfits.BinTableHDU.from_columns([
pyfits.Column(name='Energy', format='D', array=energies),])
tbhdu.header['EXTNAME']="ENERGIES"
hdulist = pyfits.HDUList([hdu_new,tbhdu])
# Write to file
fname_out = fname.split('.')[0]+"_"+t +'_mapcube.fits.gz'
hdulist.writeto(fname_out,clobber=True)
def test3():
# Set stage
grid = hp.Harpix().adddisc(vec=(1, 0, 0), radius=2, nside=1024)
#grid.addsingularity((0,0), 0.3, 2, n = 1000)
print(len(grid.data))
#quit()
E = np.linspace(1, 2, 2)
# Signal shape
sig_shape = hp.zeroslike(grid).addfunc(lambda l, b: 1. /
(l**2 + b**2 + 2.))
sig_spec = np.exp(-E)
# Background shapes (and error)
bkg_shape = hp.zeroslike(grid).addfunc(
lambda l, b: 1 + 0 * np.cos(l / 10) * np.cos(b / 10) + 0 / (b**2 + 1))
bkg_spec = lambda x: E**-x[0] * x[1]
err_shape = bkg_shape * 0.0001
cov = hp.HarpixSigma1D(err_shape, corrlength=1.)
# Background component model
bkg_comp = BkgComponent(lambda x: bkg_shape.getdata(mul_sr=True) * x[0],
x0=[1.],
xerr=[0.001],
cov=cov).tensordot(
BkgComponent(bkg_spec,
x0=[2., 1.],
xerr=[0.001, 0.001]))
SF = bkg_comp.getSwordfish()
# Signal model
S = np.multiply.outer(sig_shape.getdata(mul_sr=True), sig_spec).flatten()
#UL = SF.upperlimit(S, 0.05)
print('infoflux...')
F = SF.infoflux(S, solver='cg').reshape(-1, 2)
print('...done')
grid.data = F[:, 1]
grid._div_sr()
m = grid.gethealpix(nside=256)
#healpy.mollview(m, nest=True)
healpy.cartview(m, nest=True, lonra=[-10, 10], latra=[-10, 10])
plt.show()
def cut_map(self, rot, res=900, out_put_file = None):
"""
Returns map cut from desired region from the given longitudinal and latitudinal ranges (assuming small engough for flat sky limit)"
"""
map_cut=cartview(self.map, rot=rot,lonra=[-10,10],latra=[-5,5],xsize=res,ysize=res,return_projected_map=True)
close()
return map_cut
if out_put_file != None:
dump(map_cut,open(out_put_file,'wb'), protocol = -1)
def footprint_area(cat,
ngal=1,
mask=None,
nside=4096,
nest=True,
label=''):
import healpy as hp
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
# plt.style.use('/home/troxel/SVA1/SVA1StyleSheet.mplstyle')
from matplotlib.colors import LogNorm
import pylab
mask = CatalogMethods.check_mask(cat.coadd, mask)
if not hasattr(cat, 'pix'):
cat.pix = CatalogMethods.radec_to_hpix(cat.ra,
cat.dec,
nside=nside,
nest=True)
area = hp.nside2pixarea(nside) * (180. / np.pi)**2
print 'pixel area (arcmin)', area * 60**2
mask1 = np.bincount(cat.pix[mask]) > ngal
print 'footprint area (degree)', np.sum(mask1) * area
pix = np.arange(len(mask1))[mask1]
print pix
tmp = np.zeros((12 * nside**2), dtype=[('hpix', 'int')])
tmp['hpix'][pix.astype(int)] = 1
print tmp['hpix'][pix.astype(int)]
fio.write('footprint_hpix' + label + '.fits.gz', tmp, clobber=True)
tmp2 = np.zeros(12 * nside**2)
tmp2[tmp.astype(int)] = 1
hp.cartview(tmp2, nest=True)
plt.savefig('footprint_hpix' + label + '.png')
plt.close()
return
def show_tmap(Tlm,
ocl,
mask=1,
lmin=500,
lmax=3000,
v=3e11,
nside=512,
lonra=[148, 243],
latra=[-3, 20],
title=''):
import curvedsky
Flm = Tlm.copy()
Flm[:lmin, :] = 0.
Tmap = curvedsky.utils.hp_alm2map(
nside, lmax, lmax,
Flm[:lmax + 1, :lmax + 1] / (ocl[:lmax + 1, None] + 1e-30))
hp.cartview(mask * Tmap,
lonra=lonra,
latra=latra,
min=-v,
max=v,
cbar=False,
title=title)
def plot_full_sky_cart(Output_Para,pix,pix_bool):
rc('font',family='serif')
if (pix_bool):
plt.clf()
hp.cartview(pix, fig = 1)
hp.graticule()
plt.show()
plt.close()
else:
print "Please, first load a HEALPix map file ."
raw_input("Press enter to continue ... ")
return 0
save_ques = Read_YN_Input("Do you want to save its picture (please enter Y, y, N, or n)? ")
if save_ques :
rc('text',usetex=True)
plt.clf()
hp.cartview(pix, fig = 1)
hp.graticule()
print 'Saving plot', fname
plt.savefig(fdir+fname+'_cart.pdf',bbox_inches='tight')
# plt.savefig(fdir+fname+'.pdf')
rc('text',usetex=False)
plt.close()
def view(self, inclination=90*u.deg, phase=0.0, what='fluxes',
projection='mollweide', cmap='magma',
savefig=False, filename='star_surface.png',
dlat=30, dlon=30, **kwargs):
rot = (360*phase, 90-inclination.to(u.deg).value, 0)
if what == 'fluxes':
vals = self.tile_fluxes * self.tile_scales
vals = vals / vals.max()
elif what == 'areas':
vals = self.tile_areas / self.tile_areas.max()
if 'mollweide'.find(projection) == 0:
hp.mollview(vals, rot=rot, cmap=cmap, **kwargs)
elif 'cartesian'.find(projection) == 0:
hp.cartview(vals, rot=rot, cmap=cmap, **kwargs)
elif 'orthographic'.find(projection) == 0:
hp.orthview(vals, rot=rot, cmap=cmap, **kwargs)
else:
raise ValueError('Unrecognised projection')
hp.graticule(dlat, dlon)
if savefig:
plt.savefig(filename)
else:
plt.show()
def view_maps(output_maps, conds):
'''
Display the output maps and the relative condition number.
---------
output_maps : array-like
healpy maps array
conds: array-like
ondition number relative to the scan
'''
Freq = [90, 95, 100, 105, 110]
for maps, cond in zip(output_maps, conds):
cond[cond == np.inf] = hp.UNSEEN
cart_opts = dict(unit=r'[$\mu K_{\mathrm{CMB}}$]')
hp.cartview(cond, **cart_opts)
hp.cartview(maps[0], min=-250, max=250, **cart_opts)
hp.cartview(maps[1], min=-5, max=5, **cart_opts)
hp.cartview(maps[2], min=-5, max=5, **cart_opts)
plt.show()
def cart_proj(args):
"""Cartesian projection to get a square part of a full sky map.
"""
import numpy as np
import healpy
import h5py
import rotate as rot
import matplotlib
matplotlib.use('Agg')
# from matplotlib import pyplot as plt
# Read in maps data
hpmap = None
for mapname in args.mapfiles:
with h5py.File(mapname, 'r') as f:
if hpmap == None:
hpmap = f['map'][:]
else:
hpmap += f['map'][:]
lat_range = [args.clat - args.lat_ext, args.clat + args.lat_ext]
lon_range = [args.clon - args.lon_ext, args.clon + args.lon_ext]
# first rotate the map to let the point [clat, clon] be at the center
# must rotate clon and clat separately, and NOTE the negative sign
hpmap = rot.rotate_map(hpmap, rot=(-args.clon, 0.0, 0.0))
hpmap = rot.rotate_map(hpmap, rot=(0.0, -args.clat, 0.0))
# lat, lon value after rotation
rot_lat_range = [lat_range[0] - args.clat, lat_range[1] - args.clat]
rot_lon_range = [lon_range[0] - args.clon, lon_range[1] - args.clon]
# cartesian projection
cart_map = healpy.cartview(hpmap[args.ifreq, args.pol], latra=rot_lat_range, lonra=rot_lon_range, return_projected_map=True) # only T map
out_file = args.outfile or 'cart_%.1f_%.1f_%.1f_%.1f.hdf5' % (lat_range[0], lat_range[1], lon_range[0], lon_range[1])
with h5py.File(out_file, 'w') as f:
f.create_dataset('map', data=cart_map)
f.attrs['lat_min'] = lat_range[0]
f.attrs['lat_max'] = lat_range[1]
f.attrs['lon_min'] = lon_range[0]
f.attrs['lon_max'] = lon_range[1]
def plot_cart(map, min=None, max=None, title='', label=r'[$\#$ $deg^{-2}$]', savename=None, show=True, galactic_plane=False, ecliptic_plane=False, sgr_plane=False):
#attention au sens de l'axe en RA ! --> la on le prend normal et on le retourne à la fin :)
plt.figure(1)
m = hp.ma(map)
map_to_plot = hp.cartview(m, nest=True, flip='geo', rot=120, fig=1, return_projected_map=True)
plt.close()
fig, ax = plt.subplots(figsize=(11,7))
map_plotted = plt.imshow(map_to_plot, vmin=min, vmax=max, cmap='jet', origin='lower', extent=[-60, 300, -90, 90])
if label!=None:
cb = plt.colorbar(map_plotted, ax=ax, orientation='horizontal', shrink=0.8, aspect=40)
cb.set_label(label)
if galactic_plane:
ax.plot(galactic_plane_icrs.ra.wrap_at(300*u.deg).degree[index_galactic], galactic_plane_icrs.dec.degree[index_galactic], linestyle='-', color='black', label='Galactic plane')
if ecliptic_plane:
ax.plot(ecliptic_plane_icrs.ra.wrap_at(300*u.deg).degree[index_ecliptic], ecliptic_plane_icrs.dec.degree[index_ecliptic], linestyle=':', color='slategrey', label='Ecliptic plane')
if sgr_plane:
ax.plot(sgr_plane_icrs.ra.wrap_at(300*u.deg).degree[index_sgr], sgr_plane_icrs.dec.degree[index_sgr], linestyle='--', color='navy', label='Sgr. plane')
ax.set_xlim(-60, 300)
ax.xaxis.set_ticks(np.arange(-60, 330, 30))
plt.gca().invert_xaxis()
ax.set_xlabel('R.A. [deg]')
ax.set_ylim(-90, 90)
ax.yaxis.set_ticks(np.arange(-90, 120, 30))
ax.set_ylabel('Dec. [deg]')
if galactic_plane or ecliptic_plane:
ax.legend(loc='lower right')
if title!='':
plt.title(title)
if savename != None:
plt.savefig(savename)
if show:
plt.show()
else:
plt.close()
def gen_plots(self,lon,lat,width):
mapT_ol = np.zeros(self.nbpix,dtype='int')
# if self.indT[0] != 0: mapT_ol[self.indT[0]] = 0
h.cartview(self.T/self.stdT,max=5,min=-5,rot=[lon,lat],lonra=[-width/2.,width/2.],latra=[-width/2.,width/2.],title='T S/N')
h.graticule(dpar=10,dmer=10,coord='C')
py.savefig(self.outdir+'/png/'+self.dir_coadd+'/mapT_sn.png')
mapQ_ol = np.zeros(self.nbpix,dtype='int')
# if self.indQ_ol[0] != 0: mapQ_ol[self.indQ_ol[0]] = 1
h.cartview(self.Q/self.stdQ,max=5,min=-5,rot=[lon,lat],lonra=[-width/2.,width/2.],latra=[-width/2.,width/2.],title='Q S/N')
h.graticule(dpar=10,dmer=10,coord='C')
py.savefig(self.outdir+'/png/'+self.dir_coadd+'/mapQ_sn.png')
mapU_ol = np.zeros(self.nbpix,dtype='int')
# if self.indU_ol[0] != 0: mapU_ol[self.indU_ol[0]] = 1
h.cartview(self.U/self.stdU,max=5,min=-5,rot=[lon,lat],lonra=[-width/2.,width/2.],latra=[-width/2.,width/2.],title='U S/N')
h.graticule(dpar=10,dmer=10,coord='C')
py.savefig(self.outdir+'/png/'+self.dir_coadd+'/mapU_sn.png')
py.figure(10)
par = plot_hist(self.T[self.indT[0]], 40, init_auto=True, xtitle='T', \
fname=self.outdir+'/png/'+self.dir_coadd+'/histT.png')
py.figure(11)
par = plot_hist(self.T[self.indT[0]], 40, init_auto=True, xtitle='T', \
fname=self.outdir+'/png/'+self.dir_coadd+'/histT_log.png',option_ylog=True)
py.figure(12)
par = plot_hist(self.Q[self.indQ[0]], 40, init_auto=True, xtitle='Q', \
fname=self.outdir+'/png/'+self.dir_coadd+'/histQ.png')
py.figure(13)
par = plot_hist(self.Q[self.indQ[0]], 40, init_auto=True, xtitle='Q', \
fname=self.outdir+'/png/'+self.dir_coadd+'/histQ_log.png',option_ylog=True)
py.figure(14)
par = plot_hist(self.U[self.indU[0]], 40, init_auto=True, xtitle='U', \
fname=self.outdir+'/png/'+self.dir_coadd+'/histU.png')
py.figure(15)
par = plot_hist(self.U[self.indU[0]], 40, init_auto=True, xtitle='U', \
fname=self.outdir+'/png/'+self.dir_coadd+'/histU_log.png',option_ylog=True)
parser.add_option('-t','--targets',default=None)
parser.add_option('-c','--coord',default='GAL')
parser.add_option('-p','--proj',default='MOL',choices=['MOL','CAR'])
parser.add_option('-f','--field',default='LOG_LIKELIHOOD')
(opts, args) = parser.parse_args()
nside = pyfits.open(args[0])[1].header['NSIDE']
map = healpy.UNSEEN * numpy.ones( healpy.nside2npix(nside) )
pix,vals = ugali.utils.skymap.readSparseHealpixMap(args[0],opts.field,construct_map=False)
map[pix] = vals[0]
if opts.coord.upper() == "GAL":
coord = 'G'
elif opts.coord.upper() == "CEL":
coord = 'GC'
if opts.proj.upper() == "MOL":
healpy.mollview(map,coord=coord,xsize=1000)
elif opts.proj.upper() == "CAR":
healpy.cartview(map,coord=coord,xsize=1000)
else:
raise Exception("...")
healpy.graticule()
if opts.targets:
data = numpy.loadtxt(opts.targets,unpack=True,dtype='str')
coord = 'CG' # For RA/DEC input
healpy.projscatter(data[1].astype(float),data[2].astype(float),
lonlat=True,coord=coord,marker='o',c='w')
plt.savefig(opts.outfile)
return ret #================= MAIN ========================# info = read_info_no_co() # NSIDE = 2048 fact = 1.42144524614e-05 filename = 'HFI_CompMap_ThermalDustModel_2048_R1.20.fits' test_map = hp.read_map(filename, field = 0) #hp.mollview(test_map, title=filename, coord='G', unit='K', norm='hist', min=1e-7,max=1e-3, xsize=800) #hp.mollview(test_map) tmap = hp.cartview(test_map, title=filename, coord='G', rot=[0,0], unit='K', norm='hist', min=1e-7,max=1e-3, xsize=800, lonra=[-180,-160], latra=[63,90], return_projected_map=True, cbar=True) #hp.orthview(test_map) #hp.gnomview(test_map) # print hp.get_nside(test_map) # print hp.maptype(test_map) # print hp.get_map_size(test_map) # print len(test_map) # print test_map[0:10]*np.float(fact) # print test_map n = 16 rang = range(0,12*n**2) npix = hp.nside2npix(n) angs = hp.pix2ang(n,rang)
ymax = 1.0
xgrid, ygrid = NP.meshgrid(NP.linspace(xmin, xmax, backdrop_xsize), NP.linspace(ymin, ymax, backdrop_xsize))
nanind = (xgrid**2 + ygrid**2) > 1.0
goodind = (xgrid**2 + ygrid**2) <= 1.0
zgrid = NP.empty_like(xgrid)
zgrid[nanind] = NP.nan
zgrid[goodind] = NP.sqrt(1.0 - (xgrid[goodind]**2 + ygrid[goodind]**2))
xvect = xgrid.ravel()
yvect = ygrid.ravel()
zvect = zgrid.ravel()
xyzvect = NP.hstack((xvect.reshape(-1,1), yvect.reshape(-1,1), zvect.reshape(-1,1)))
if use_DSM or use_GSM:
backdrop = HP.cartview(fluxes_DSM.ravel(), coord=['G','E'], rot=[180,0,0], xsize=backdrop_xsize, return_projected_map=True)
elif use_GLEAM or use_SUMSS:
if backdrop_coords == 'radec':
backdrop = griddata(NP.hstack((ra_deg.reshape(-1,1), dec_deg.reshape(-1,1))), fpeak, NP.hstack((xvect.reshape(-1,1), yvect.reshape(-1,1))), method='cubic')
backdrop = backdrop.reshape(backdrop_xsize/2, backdrop_xsize)
elif backdrop_coords == 'dircos':
if (telescope == 'mwa_dipole') or (obs_mode == 'drift'):
backdrop = PB.primary_beam_generator(xyzvect, freq, telescope=telescope, freq_scale='Hz', skyunits='dircos', phase_center=[0.0,0.0,1.0])
backdrop = backdrop.reshape(backdrop_xsize, backdrop_xsize)
else:
if backdrop_coords == 'radec':
backdrop = griddata(NP.hstack((ra_deg.reshape(-1,1), dec_deg.reshape(-1,1))), fpeak, NP.hstack((xvect.reshape(-1,1), yvect.reshape(-1,1))), method='nearest')
backdrop = backdrop.reshape(backdrop_xsize/2, backdrop_xsize)
elif backdrop_coords == 'dircos':
if (telescope == 'mwa_dipole') or (obs_mode == 'drift'):
backdrop = PB.primary_beam_generator(xyzvect, freq, telescope=telescope, freq_scale='Hz', skyunits='dircos', phase_center=[0.0,0.0,1.0])
def plot_cartmap(lvc_healpix_file,
levels=[0.5, 0.9],
angsize=3.,
tile_ra=None,
tile_dec=None,
targ_ra=None,
targ_dec=None):
"""Plot the GW map with the DESI footprint in a Cartesian projection.
Parameters
----------
lvc_healpix_file : str
Relative or absolute path to LIGO/Virgo HEALPix angular reconstruction file.
levels : list
List of credible interval thresholds, e.g., 0.5, 0.9, etc.
angsize : float
Size of plot (-angsize, +angsize) in degrees about the center.
tile_ra : list or ndarray
List of RAs for DESI tiles (in deg).
tile_dec : list or ndarray
List of declinations for DESI tiles (in deg).
targ_ra : list or ndarray
List of RAs for DESI targets (in deg).
targ_dec : list or ndarray
List of declinations for DESI targets (in deg).
Returns
-------
fig : matplotlib.Figure
Figure object for accessing or saving a plot.
"""
gwmap = hp.read_map(lvc_healpix_file)
npix = len(gwmap)
nside = hp.npix2nside(npix)
# Compute contours.
if nside > 256:
_gwmap = hp.pixelfunc.ud_grade(gwmap, 256)
_gwmap = _gwmap / np.sum(_gwmap)
else:
_gwmap = gwmap
ra_contour, dec_contour = compute_contours(levels, _gwmap)
# Create a temporary plot to produce a nice image array.
# This code sets the size of the map around the maximum value.
maxpix = np.argmax(gwmap)
ra_c, dec_c = hp.pix2ang(nside, maxpix, lonlat=True)
xmin = np.round(ra_c - angsize)
xmax = np.round(ra_c + angsize)
if xmax < xmin:
xmin, xmax = xmax, xmin
cxmin, cxmax = xmin, xmax
frot = 0.
if xmax > 90 and xmax < -90:
frot, cxmin, cmax = 180., xmax - 180., xmax + 180.
ymin = np.round(dec_c - angsize)
ymax = np.round(dec_c + angsize)
faspect = np.abs(cxmax - cxmin) / np.abs(ymax - ymin)
fysize = 4
figsize = (fysize * faspect + 1, fysize + 2)
# Open and close the temporary plot.
tfig = plt.figure(num=2, figsize=figsize)
rotimg = hp.cartview(gwmap,
fig=2,
coord='C',
title="",
cbar=False,
flip='astro',
lonra=[cxmin, cxmax],
latra=[ymin, ymax],
rot=frot,
notext=True,
xsize=1000,
return_projected_map=True)
plt.close(tfig)
# Now make the real plot with the desired angular contours.
fig, ax = plt.subplots(1, 1, num=1, figsize=figsize)
img = ax.imshow(rotimg,
extent=[cxmax, cxmin, ymin, ymax],
origin='lower',
cmap='OrRd')
for i, (rc, dc, lstyle, clev) in enumerate(
zip(ra_contour, dec_contour, ['--', '-'], ['50', '90'])):
p = ax.plot(rc, dc, 'g-', ls=lstyle, lw=2, label='{}% CI'.format(clev))
ax.set(xlim=(cxmax, cxmin), xlabel='RA [deg]', ylabel='Dec [deg]')
ax.grid(ls=':')
_h, _l = ax.get_legend_handles_labels()
# Add DESI tile drawings, specified by central RA, Dec.
if tile_ra is not None and tile_dec is not None:
for _ra_c, _dec_c in zip(tile_ra, tile_dec):
circ = plt.Circle((_ra_c, _dec_c),
radius=1.6,
fc='None',
ec='b',
ls=':',
lw=2)
ax.add_artist(circ)
_h.append(circ)
_l.append('DESI FOV')
# Add DESI targets, specified by RA, Dec.
if targ_ra is not None and targ_dec is not None:
ax.plot(targ_ra, targ_dec, 'k.', alpha=0.1)
ax.legend(handles=_h, labels=_l, fontsize=10, ncol=2)
cb = fig.colorbar(img,
orientation='horizontal',
shrink=0.95,
fraction=0.04,
pad=0.2,
ax=ax)
cb.set_label(r'$dp/d\Omega$ [deg$^{-2}$]')
return fig
bmap.draw_parallels(); bmap.draw_meridians()
for band,sky in sum_skymaps.items():
outfile = outbase%(band,NSIDE)+'.fits.gz'
print "Writing %s..."%outfile
hp.write_map(outfile,sky)
outfile = outbase%(band,NSIDE)+'_mbt.png'
print "Writing %s..."%outfile
bmap.draw_hpxmap(np.log10(sky))
plt.savefig(outfile,bbox_inches='tight')
plt.clf()
outfile = outbase%(band,NSIDE)+'_car.png'
print "Writing %s..."%outfile
hp.cartview(np.log10(sky),title='DECam Coverage (%s-band)'%band,
unit='log10(TEFF*EXPTIME)',fig=1)
plt.savefig(outfile,bbox_inches='tight')
plt.clf()
outfile = outbase%(band,NSIDE)+'_hist.png'
plt.hist(sky,bins=np.linspace(1,1e3,50),color=COLORS['band'])
plt.title('%s-band'%band); plt.xlabel('sum(TEFF * EXPTIME)')
plt.savefig(outfile,bbox_inches='tight')
plt.clf()
fig = plt.figure(1)
bmap = DECamMcBride()
bmap.draw_parallels(); bmap.draw_meridians()
outbase = "decam_max_expmap_%s_n%s"
for band,sky in max_skymaps.items():
def convert(infile='empty', i_ext=-1, i_col=-1, coordsys_out='-1'):
"""Main function of makeFitsImage.py
Execute makeFitsImage.py --help for more details (it is not yet been fully implemented to be loaded within Python, also it should work already).
"""
warnings.warn(
'Attention: Conversion from HEALPix format to FITS image causes degradation.\n All information in the FITS header corresponds to the original HEALPix data.'
)
# read header:
hdulist_in = fits.open(infile)
type = hdulist_in[1].header['TTYPE1']
# detect type of input file
if type[:5] == 'PIXEL':
print(' ... detected Healpix part-sky map...')
fullsky = False
if (hp_version > 1.2 and hp_version < 1.9):
raise IOError(
'Handling of part-sky FITS files not supported by healpy versions < 1.9.0.'
)
else:
try:
scheme = hdulist_in[1].header['INDXSCHM']
if scheme[:8] == 'IMPLICIT':
print(
' ... detected Healpix full-sky map, e.g. created by Clumpy with the -o2 option.'
)
fullsky = True
elif scheme[:8] == 'EXPLICIT':
print(
' ... detected Healpix part-sky map, e.g. created by Clumpy with the -o2 option.'
)
fullsky = False
else:
raise IOError(
'FITS keyword "INDXSCHM" must be either "IMPLICIT" or "EXPLICIT".'
)
except:
warnings.warn(
'Possibly wrong input file format. Must contain Healpix sky map.\n'
)
fullsky = True
i_ext = 1
i_col = 1
if i_ext == -1:
warnings.warn(
'No input extension specified with --extension or -e. Set to first extension.'
)
i_ext = 1
if i_col == -1:
warnings.warn(
'No input column specified with --column or -c. Set to first column.'
)
i_col = 1
input_header = hdulist_in[i_ext].header
ext_name = input_header['EXTNAME']
columns = hdulist_in[i_ext].columns
if fullsky == False:
column = columns[i_col] # zeroth column is PIXEL
else:
column = columns[i_col - 1]
try:
map_center_psi_deg = input_header['PSI_0']
except:
raise IOError(
'Necessary input information "PSI_0" missing.\n Note: This script is tailored to read Healpix maps created by Clumpy > 2015.06_corr3.'
)
try:
map_center_theta_deg = input_header['THETA_0']
except:
raise IOError(
'Necessary input information "THETA_0" missing.\n Note: This script is tailored to read Healpix maps created by Clumpy > 2015.06_corr3.'
)
try:
map_diam_theta_deg = input_header['SIZE_Y']
except:
raise IOError(
'Necessary input information "SIZE_Y" missing.\n Note: This script is tailored to read Healpix maps created by Clumpy > 2015.06_corr3.'
)
try:
map_diam_theta_orth_deg = input_header['SIZE_X']
except:
map_diam_theta_orth_deg = map_diam_theta_deg
try:
coordsys_in = input_header['COORDSYS']
except:
raise IOError(
'Necessary input information "COORDSYS" missing.\n Note: This script is tailored to read Healpix maps created by Clumpy > 2015.06_corr3.'
)
try:
units = input_header['TUNIT' + str(i_col + 1)]
except:
units = 'not specified'
try:
fsky = input_header['F_SKY']
except:
fsky = 'not specified'
try:
map_mean = input_header['MEAN']
except:
map_mean = 'not specified'
try:
nside = input_header['NSIDE']
except:
raise IOError('Necessary input information "NSIDE" missing.')
try:
dtype_hpx = input_header['HPX_TYPE']
except:
raise IOError('Necessary input information "HPX_TYPE" missing.')
# special treatment for clumpy halo mode files:
map_center_psi_deg_out = map_center_psi_deg
map_center_theta_deg_out = map_center_theta_deg
try:
simumode = input_header['SIMUMODE']
if (simumode[0] == 'h'):
map_center_psi_deg = 0.
map_center_theta_deg = 0.
except:
warnings.warn(
'Could not read "SIMUMODE" keyword. Possibly wrong sky area exported.'
)
simumode = "-1"
tbdata = hdulist_in[i_ext].data
npix_fov = len(tbdata[column.name])
map_sum = tbdata[column.name].sum(dtype=np.float64)
if map_mean == 'not specified':
print('Calculate mean...')
map_mean = tbdata[column.name].mean(dtype=np.float64)
hdulist_in.close()
# read map:
dtype_map = np.float64
if dtype_hpx == 'FLOAT32':
dtype_map = np.float32
elif dtype_hpx == 'FLOAT64':
dtype_map = np.float64
else:
raise IOError(
'Healpix datatype (keyword "HPX_TYPE" in input file header) must be FLOAT32 or FLOAT64.'
)
print('Read input map...')
if fullsky == True:
map_in = hp.read_map(infile)
else:
map_in = hp.read_map(infile,
partial=True,
field=i_col - 1,
hdu=i_ext,
dtype=dtype_map)
if fsky == 'not specified':
npix = len(map_in)
fsky = float(npix_fov) / float(npix)
if map_diam_theta_orth_deg < 342.85:
map_diam_theta_orth_deg *= 1.05
if map_diam_theta_deg < 171.42:
map_diam_theta_deg *= 1.05
pixelnr = hp.nside2npix(nside)
resol_deg = hp.nside2resol(nside) * 180. / np.pi
# make sure that npix_x, npix_y are odd numbers:
npix_x_check = int(map_diam_theta_orth_deg / resol_deg) * 2 + 1
npix_y_check = int(map_diam_theta_deg / resol_deg) * 2 + 1
if npix_x_check > 1999:
npix_x_check = 1997
warnings.warn(
'Maximum number of 1997 pixels in x-dimension is reached.')
if npix_y_check > 1999:
npix_y_check = 1997
warnings.warn(
'Maximum number of 1997 pixels in y-dimension is reached.')
flip = 'astro'
## flip map for odd behavior at the gal. anticenter:
#if abs(map_center_psi_deg) == 180.0 and map_center_theta_deg == 0:
# flip='geo'
# transform to galactic coordinates:
if coordsys_in[:5] == 'G':
print('Detected galactic coordinate system')
if coordsys_out == '-1' or coordsys_out == 'G':
coord = 'G'
coordsys_out = coord
else:
coord = ['G', coordsys_out]
elif coordsys_in[:5] == 'C':
print('Detected equatorial coordinate system')
coord = ['C', 'G']
if coordsys_out == '-1' or coordsys_out == 'C':
coord = 'C'
coordsys_out = coord
else:
coord = ['C', coordsys_out]
elif coordsys_in[:5] == 'E':
print('Detected ecliptic coordinate system')
coord = ['E', 'G']
if coordsys_out == '-1' or coordsys_out == 'E':
coord = 'E'
coordsys_out = coord
else:
coord = ['E', coordsys_out]
else:
raise IOError(
'Input coordinate system not recognized. Must be either G, C, or E.'
)
if coordsys_out not in ['-1', 'G', 'C', 'E']:
raise IOError(
'Output coordinate system not recognized. Must be either G, C, or E.'
)
if len(coord) > 1:
map_center_theta_deg, map_center_psi_deg = hp.Rotator(
coord=coord,
deg=False)(np.pi / 2 - map_center_theta_deg / 180. * np.pi,
map_center_psi_deg / 180. * np.pi)
map_center_psi_deg *= 180. / np.pi
map_center_theta_deg = 90. - map_center_theta_deg * 180. / np.pi
# get projected map
image_array = hp.cartview(map_in, rot = [map_center_psi_deg,map_center_theta_deg], \
lonra = [- map_diam_theta_orth_deg/2, map_diam_theta_orth_deg/2], \
latra = [- map_diam_theta_deg/2, map_diam_theta_deg/2], \
xsize=npix_x_check, \
flip=flip, \
coord=coord, \
return_projected_map = True)
npix_x = image_array.shape[1]
npix_y = image_array.shape[0]
if npix_x_check != npix_x:
warnings.warn('Attention! npix_x_check =' + str(npix_x_check) +
'!= npix_x=' + str(npix_x))
if npix_y_check != npix_y:
warnings.warn('Attention! npix_y_check =' + str(npix_y_check) +
'!= npix_y=' + str(npix_y))
x_center = (npix_x + 1) / 2
y_center = (npix_y + 1) / 2
delta_x = map_diam_theta_orth_deg / npix_x
delta_y = map_diam_theta_deg / npix_y
print('Resolution of map ( delta_x , delta_y ): (' + str(delta_x) + ',' +
str(delta_y) + ')')
# rotate map for odd behavior at the gal. poles:
#if abs(map_center_theta_deg) == 90.0:
#map_center_psi_deg = map_center_psi_deg + 180
if map_center_psi_deg < 0:
map_center_psi_deg += 360.
map_out = np.zeros((npix_y, npix_x), dtype=dtype_map)
map_out[:][:] = image_array[:][:]
# replace 1e-40 values by HEALPIX blind value
map_out[map_out < 4e-40] = -1.6375e30
# write out total J-Factor/flux in map:
if 'sr^-1' in units:
totalflux = map_mean * 4. * np.pi * fsky
else:
totalflux = map_sum
# create fits header:
if coordsys_out == 'G':
coordlon = 'GLON-CAR'
coordlat = 'GLAT-CAR'
elif coordsys_out == 'C':
coordlon = 'RA---CAR'
coordlat = 'DEC--CAR'
elif coordsys_out == 'E':
coordlon = 'ELON-CAR'
coordlat = 'ELAT-CAR'
reference_header = input_header[41:]
reference_header.update({'EXTNAME' : ('Skymap'), \
'CDELT1' : (- delta_x, 'pixel size (approx.) in longitude-dir. [deg]'), \
'CDELT2' : (delta_y, 'pixel size (approx.) in latitude-dir. [deg]'), \
'CRPIX1' : (x_center, 'central pixel in longitude direction'), \
'CRPIX2' : (y_center, 'central pixel in latitude direction'), \
'CRVAL1' : (map_center_psi_deg_out, 'longitude coordinate of map center [deg]'), \
'CRVAL2' : (map_center_theta_deg_out, 'latitude coordinate of map center [deg]'), \
'CTYPE1' : (coordlon, 'longitude coord. system (cartesian projection)'), \
'CTYPE2' : (coordlat, 'latitude coord. system (cartesian projection)'), \
'CUNIT1' : ('deg', 'longitude axis unit'), \
'CUNIT2' : ('deg', 'latitude axis unit'), \
'NAXIS' : 2, \
'NAXIS1' : (npix_x, 'number of pixels in longitude direction'), \
'NAXIS2' : (npix_y, 'number of pixels in latitude direction'), \
'BUNIT' : (units, 'pixel value unit'),\
'F_SKY' : (fsky, 'fraction of sky covered by FOV'), \
'MEAN' : (map_mean, 'mean value in FOV (same unit as BUNIT)'), \
'FLUX_TOT': (totalflux, 'total integrated J-Factor (flux) in FOV'), \
'NSIDE' : (nside, 'HEALPix resolution of original image'), \
'AUTHOR' : 'file created by Clumpy, makeFitsImage.py'})
# write fits file:
if len(columns) > 1:
outfile = infile[:-5] + '-' + ext_name + '-' + column.name + '-image.fits'
else:
outfile = infile[:-5] + '-image.fits'
if astropy_version < 2:
fits.writeto(outfile, map_out, reference_header, clobber=True)
else:
fits.writeto(outfile, map_out, reference_header, overwrite=True)
print('Output file written to: ' + outfile)
def visualize_map(args):
"""Visualize sky maps in hdf5 files.
Arguments
---------
args : argparse namespace.
"""
import numpy as np
import h5py
import healpy
# import hpvisual
import matplotlib
matplotlib.use('Agg')
try:
# for new version matplotlib
import matplotlib.style as mstyle
mstyle.use('classic')
except ImportError:
pass
from matplotlib import pyplot as plt
# Read in maps data
hpmap = None
for mapname in args.mapfiles:
with h5py.File(mapname, 'r') as f:
if hpmap is None:
hpmap = f['map'][:]
else:
hpmap += f['map'][:]
# Check args validity
if args.ifreq < -(hpmap.shape)[0] or args.ifreq >= (hpmap.shape)[0]:
raise Exception('Invalid frequency channel %d, should be in range(-%d, %d).'%(args.ifreq, (hpmap.shape)[0], (hpmap.shape)[0]))
else:
ifreq = args.ifreq if args.ifreq >= 0 else args.ifreq + (hpmap.shape)[0]
if args.pol >= (hpmap.shape)[1]:
raise Exception('Invalid frequency channel %d, should be in range(0, %d).'%(args.pol, (hpmap.shape)[1]))
if args.figlength <= 0:
raise Exception('Figure length figlength (= %f) must greater than 0'%args.figlength)
if args.figwidth <= 0:
raise Exception('Figure width figwidth (= %f) must greater than 0'%args.figwidth)
# Create output image file name
if args.outfile:
out_file = args.outfile
else:
out_file = ((args.mapfiles[0].split('/')[-1]).split('.')[0] + '_' + str(ifreq) + '_{' + str(args.pol) + '}' + '.' + args.figfmt).format('T', 'Q', 'U', 'V')
# Plot and save image
if args.view == 'o':
fig = plt.figure(1, figsize=(8, 6))
else:
fig = plt.figure(1, figsize=(args.figlength,args.figwidth))
map_data = hpmap[ifreq][args.pol]
if args.sqrt:
map_data = map_data / np.sqrt(np.abs(map_data))
map_data = map_data / np.sqrt(np.abs(map_data))
# map_data = map_data / np.sqrt(np.abs(map_data))
# smoothing the map with a Gaussian symmetric beam
if args.fwhm is not None:
fwhm = np.radians(args.fwhm)
map_data = healpy.smoothing(map_data, fwhm=fwhm)
if args.view == 'm':
# set color map
if args.cmap is None:
cmap = None
else:
if args.cmap == 'jet09':
import colormap
cmap = colormap.jet09
else:
from pylab import cm
# cmap = cm.hot
cmap = getattr(cm, args.cmap)
cmap.set_under('w')
if args.abs:
healpy.mollview(np.abs(map_data), fig=1, title='', unit=args.unit, cmap=cmap, min=args.min, max=args.max)
else:
healpy.mollview(map_data, fig=1, title='', unit=args.unit, cmap=cmap, min=args.min, max=args.max)
# plot NVSS sources
if args.nvss is not None:
import aipy as a
flux = args.nvss
frequency = 750 # MHz
catalog = 'nvss'
# catalog = 'wenss'
src = '%f/%f' % (flux, frequency / 1.0e3)
srclist, cutoff, catalogs = a.scripting.parse_srcs(src, catalog)
cat = a.src.get_catalog(srclist, cutoff, catalogs)
nsrc = len(cat) # number of sources in cat
ras = [ np.degrees(cat.values()[i]._ra) for i in range(nsrc) ]
decs = [ np.degrees(cat.values()[i]._dec) for i in range(nsrc) ]
jys = [ cat.values()[i].get_jys() for i in range(nsrc) ]
# select sources
inds = np.where(np.array(decs)>-15.0)[0]
ras = np.array(ras)[inds]
decs = np.array(decs)[inds]
jys = np.array(jys)[inds]
# healpy.projscatter(ras, decs, lonlat=True, s=jys, facecolors='none', edgecolors='w', alpha=1.0, linewidth=1.0)
healpy.projscatter(ras, decs, lonlat=True, s=150, facecolors='none', edgecolors='w', alpha=1.0, linewidth=1.0)
elif args.view == 'c':
healpy.cartview(map_data, fig=1, title='', unit=args.unit, min=args.min, max=args.max)
elif args.view == 'o':
healpy.orthview(map_data, rot=(0, 90, 0), fig=1, title='', unit=args.unit, min=args.min, max=args.max, half_sky=True) # rot to make NCP at the center
# fig = plt.figure()
# ax = fig.add_axes()
# cbar.solids.set_rasterized(True)
if args.grid:
healpy.graticule()
if args.tight:
fig.savefig(out_file, bbox_inches='tight')
else:
fig.savefig(out_file)
fig.clf()
def plot_patches(src_num, info):
# Define constants #
deg2rad = np.pi/180.
# Define the width of area #
beam = 14.4 # Beam = 3.5'
dbeam = beam/60./2. # Beam = 3.5' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
# HI continuum map and resolution #
cont = hp.read_map(os.getenv("HOME")+'/hdata/hi/lambda_chipass_healpix_r10.fits', field = 0, h=False)
nside = hp.get_nside(cont)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/5.0
# OK - Go #
tb = {}
for i in range(0,src_num):
if(i ==2): continue
if(i ==3): continue
if(i ==6): continue
if(i ==11): continue
## Find the values of Continuum temperature #
tb[i] = []
## Longitude and latitude ##
l = info['l'][i]
b = info['b'][i]
# if(i != 14): continue
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
f = 10.
# if (i == sr):
proj = hp.cartview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')', coord='G', unit='',
norm=None, xsize=1920, lonra=[ll-0.5,ll+0.5], latra=[b-0.5,b+0.5],
return_projected_map=True)
# hp.cartview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')', coord='G', unit='',
# norm='hist', xsize=800, lonra=[ll-offset-f*offset,ll+offset+f*offset], latra=[b-offset-f*offset,b+offset+f*offset],
# return_projected_map=True)
# hp.orthview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')', coord='G', unit='',
# norm='hist', xsize=800, return_projected_map=True)
# hp.mollview(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')',
# coord='G', unit='', rot=[0,0,0], norm='hist')
# hp.mollzoom(cont, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+')',
# coord='G', unit='', rot=[0,0,0], norm=None, min=4599., max=4600.)
print proj
print proj.shape
# Cal. #
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
if (cont[pix] > -1.0e30) : # Some pixels not defined
tb[i].append(cont[pix])
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
cosb = np.cos(b*deg2rad)
cosy = np.cos(y*deg2rad)
if ( (((x-l)**2 + (y-b)**2) <= offset**2) ):
theta = (90.0 - y)*deg2rad
phi = x*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
# hp.projtext(x, y, '.'+str(pix)+str(cont[pix]), lonlat=True, coord='G')
# hp.projtext(x, y, '.'+str(cont[pix]), lonlat=True, coord='G')
hp.projtext(x, y, '.', lonlat=True, coord='G')
plt.show()
notext=False,
return_projected_map=False)
hp.cartview(map=eqdata,
fig=3,
rot=None,
zat=None,
coord=None,
unit='',
xsize=800,
ysize=None,
lonra=None,
latra=None,
title='Cartesian view',
nest=False,
remove_dip=False,
remove_mono=False,
gal_cut=0,
min=None,
max=None,
flip='astro',
format='%.3g',
cbar=True,
cmap=None,
norm=" ",
aspect=None,
hold=False,
sub=224,
margins=None,
notext=False,
return_projected_map=False)
plt.show()
def __call__(self, metricValueIn, slicer, userPlotDict, fignum=None):
"""
Plot the sky map of metricValue using healpy cartview plots in thin strips.
raMin: Minimum RA to plot (deg)
raMax: Max RA to plot (deg). Note raMin/raMax define the centers that will be plotted.
raLen: Length of the plotted strips in degrees
decMin: minimum dec value to plot
decMax: max dec value to plot
metricValueIn: metric values
"""
plotDict = {}
plotDict.update(self.defaultPlotDict)
plotDict.update(userPlotDict)
metricValue = applyZPNorm(metricValueIn, plotDict)
norm = None
if plotDict['logScale']:
norm = 'log'
clims = setColorLims(metricValue, plotDict)
cmap = setColorMap(plotDict)
racenters = np.arange(plotDict['raMin'], plotDict['raMax'],
plotDict['raLen'])
nframes = racenters.size
fig = plt.figure(fignum)
# Do not specify or use plotDict['subplot'] because this is done in each call to hp.cartview.
for i, racenter in enumerate(racenters):
if i == 0:
useTitle = plotDict['title'] + ' /n' + '%i < RA < %i' % (
racenter - plotDict['raLen'], racenter + plotDict['raLen'])
else:
useTitle = '%i < RA < %i' % (racenter - plotDict['raLen'],
racenter + plotDict['raLen'])
hp.cartview(metricValue.filled(slicer.badval),
title=useTitle,
cbar=False,
min=clims[0],
max=clims[1],
flip='astro',
rot=(racenter, 0, 0),
cmap=cmap,
norm=norm,
lonra=[-plotDict['raLen'], plotDict['raLen']],
latra=[plotDict['decMin'], plotDict['decMax']],
sub=(nframes + 1, 1, i + 1),
fig=fig)
hp.graticule(dpar=20, dmer=20, verbose=False)
# Add colorbar (not using healpy default colorbar because want more tickmarks).
ax = fig.add_axes([0.1, .15, .8, .075]) # left, bottom, width, height
# Add label.
if plotDict['label'] is not None:
plt.figtext(0.8, 0.9, '%s' % plotDict['label'])
# Make the colorbar as a seperate figure,
# from http: //matplotlib.org/examples/api/colorbar_only.html
cnorm = colors.Normalize(vmin=clims[0], vmax=clims[1])
# supress silly colorbar warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cb = mpl.colorbar.ColorbarBase(ax,
cmap=cmap,
norm=cnorm,
orientation='horizontal',
format=plotDict['cbarFormat'])
cb.set_label(plotDict['xlabel'])
cb.ax.tick_params(labelsize=plotDict['labelsize'])
if norm == 'log':
tick_locator = ticker.LogLocator(numticks=plotDict['nTicks'])
cb.locator = tick_locator
cb.update_ticks()
if (plotDict['nTicks'] is not None) & (norm != 'log'):
tick_locator = ticker.MaxNLocator(nbins=plotDict['nTicks'])
cb.locator = tick_locator
cb.update_ticks()
# If outputing to PDF, this fixes the colorbar white stripes
if plotDict['cbar_edge']:
cb.solids.set_edgecolor("face")
fig = plt.gcf()
return fig.number
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import healpy as hp
gauss_data = '/tigress/nrodd/NPTFWorking/FindPS/plots/nptf/IG_NDI/FD_check.txt.gz'
king_data = '/tigress/nrodd/NPTFWorking/FindPS/plots/nptf_king/IG_NDI/FD_check.txt.gz'
gaussload = np.loadtxt(gauss_data)
gaussmap = np.sum(gaussload[8:16,::],axis=0)
kingload = np.loadtxt(king_data)
kingmap = np.sum(kingload[8:16,::],axis=0)
hp.cartview(gaussmap,lonra=[-10,10], latra=[-10,10],title='Gauss NFW+Disk PS 10x10',max=30)
plt.savefig('./Plots/gauss10to10.pdf')
plt.close()
hp.cartview(kingmap,lonra=[-10,10], latra=[-10,10],title='King NFW+Disk PS 10x10',max=30)
plt.savefig('./Plots/king10to10.pdf')
plt.close()
hp.cartview(gaussmap,lonra=[-30,30], latra=[-30,30],title='Gauss NFW+Disk PS 30x30',max=30)
plt.savefig('./Plots/gauss30to30.pdf')
plt.close()
hp.cartview(kingmap,lonra=[-30,30], latra=[-30,30],title='King NFW+Disk PS 30x30',max=30)
plt.savefig('./Plots/king30to30.pdf')
plt.close()
plt.grid()
plt.show()
l = 51.641648
b = -9.6750019
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
offset = 1.
lonr = [51.25, 52.0]
latr = [-10., -9.35]
m = hp.cartview(ir_map, title=r'$CO$', coord='G', unit='Krj km/s', #min=-2.,max=3.,
norm=None, xsize=800, lonra=lonr, latra=latr, #lonra=[ll-offset,ll+offset], latra=[b-offset,b+offset],
return_projected_map=True)
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
hp.projplot(ll, b, 'bo', lonlat=True, coord='G')
hp.projtext(ll, b, ' (' + str(round(ll,2)) + ',' + str(round(b,2)) + ')', lonlat=True, coord='G', fontsize=18, weight='bold')
# theta = (90.0-b)*deg2rad
# phi = l*deg2rad
# pix = hp.ang2pix(nside, theta, phi, nest=False)
# for x in pl.frange(l-offset, l+offset, dd):
# for y in pl.frange(b-offset, b+offset, dd):
def plot_haslam_408mhz_map():
# map_file = 'data/haslam408_dsds_Remazeilles2014.fits'
# lambda_haslam408_nofilt.fits
# lambda_haslam408_dsds.fits
# lambda_zea_haslam408_dsds.fits
# haslam408_dsds_Remazeilles2014.fits
# haslam408_ds_Remazeilles2014.fits
# lambda_chipass_healpix_r10.fits
# Define constants #
map_file = os.getenv("HOME")+'/hdata/oh/haslam408_dsds_Remazeilles2014.fits'
deg2rad = np.pi/180.
factor = (408./1665.)**2.8
# Define the width of area #
beam = 51. # Beam for Haslam survey = 51'
dbeam = beam/120.0 # Beam = 51' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
edge = dbeam/40. # To plot a rectangle about the source
info = read_src_lb()
src = info['src']
gl = info['l']
gb = info['b']
il = info['il']
ib = info['ib']
tbg = info['tbg']
lid = info['l-idx']
bid = info['b-idx']
bg1 = info['tbg1']
bgh = info['tbg_hpy']
tb408 = hp.read_map(map_file,field=0, nest=False, hdu=1, h=False, verbose=False)
nside = hp.get_nside(tb408)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/10.0
hp.cartview(tb408, title='Continuum background at 408 MHz from Haslam et al. Survey', coord='G', unit='K',\
norm='hist', xsize=800) #min=19.9,max=20.6
# hp.mollview(tb408, title='Continuum background at 408 MHz from Haslam et al. Survey', coord='G', unit='K', norm='hist', xsize=800) #min=19.9,max=20.6
for i in range(len(src)):
sl = gl[i]
sb = gb[i]
if (sl > 180.) :
sl = sl - 360.
## Plot cartview a/o mollview #
# ll = sl
# if (sl>180):
# ll = ll-360.
# hp.cartview(tb408, title=src[i]+': ' + str(sl) + ',' + str(sb), coord='G', unit='',
# norm='hist', xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[sb-offset-0.1*offset,sb+offset+0.1*offset],
# return_projected_map=True)
## End Plot cartview a/o mollview ##
theta = (90.0 - sb)*deg2rad
phi = sl*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
tbg_i = []
if (tb408[pix] > 0.) : # Some pixels not defined
tbg_i.append(tb408[pix])
for x in pl.frange(sl-offset, sl+offset, dd):
for y in pl.frange(sb-offset, sb+offset, dd):
cosb = np.cos(sb*deg2rad)
cosy = np.cos(y*deg2rad)
if ( ((x-sl)**2 + (y-sb)**2) <= offset**2 ):
# hp.projtext(x, y, '.', lonlat=True, coord='G')
theta = (90.0 - y)*deg2rad
phi = x*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
if (tb408[pix] > 0.) :
tbg_i.append(tb408[pix])
# plt.show()
# tbg_i = list(set(tbg_i))
tbg_i = np.asarray(tbg_i, dtype = np.float32)
val = np.mean(tbg_i)
if(i<18):
print '{0}\t\t{1}\t{2}\t{3}\t{4}\t{5}\t{6}\t{7}\t{8}\t{9}'\
.format(src[i], gl[i], gb[i], il[i], ib[i], tbg[i], lid[i], bid[i], bg1[i], val)
else:
print '{0}\t{1}\t{2}\t{3}\t{4}\t{5}\t{6}\t{7}\t{8}\t{9}'\
.format(src[i], gl[i], gb[i], il[i], ib[i], tbg[i], lid[i], bid[i], bg1[i], val)
b_edge = 300.*edge
l_edge = b_edge/np.cos(sb*deg2rad)
equateur_lon = [sl-l_edge, sl+l_edge, sl+l_edge, sl-l_edge, sl-l_edge]
equateur_lat = [sb+b_edge, sb+b_edge, sb-b_edge, sb-b_edge, sb+b_edge]
# hp.projplot(equateur_lon, equateur_lat, lonlat=True, coord='G')
hp.projplot(sl, sb, 'ko', lonlat=True, coord='G')
# hp.projtext(sl, sb, src[i] +' ('+ str("{0:.4e}".format(val))+')', lonlat=True, coord='G')
hp.projtext(sl, sb, src[i], lonlat=True, coord='G', fontsize=14)
plt.show()
def plot_patches(map_file, info):
src = info['src']
# Define constants #
deg2rad = np.pi/180.
# Define the width of area #
beam = 4.3 # Beam = 4.3'
dbeam = beam/120.0 # Beam = 30' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
# TTYPE1 = 'TAU353 ' / Optical depth at 353GHz
# TTYPE2 = 'ERR_TAU ' / Error on optical depth
# TTYPE3 = 'EBV ' / E(B-V) color excess
# TTYPE4 = 'RADIANCE' / Integrated emission
# TTYPE5 = 'TEMP ' / Dust equilibrium temperature
# TTYPE6 = 'ERR_TEMP' / Error on T
# TTYPE7 = 'BETA ' / Dust emission spectral index
# TTYPE8 = 'ERR_BETA' / error on Beta
ir_map = hp.read_map(map_file, field = 0)
nside = hp.get_nside(ir_map)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/2.0
# OK - Go #
ebv = []
nh = []
nhi = []
for i in range(0,len(src)):
# if( i != 15):
# continue
l = info['l'][i]
b = info['b'][i]
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
# offset = 1.
hp.cartview(ir_map, title=info['src'][i], coord='G', unit='',
norm='hist', xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[b-offset-0.1*offset,b+offset+0.1*offset],
return_projected_map=True)
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
hp.projplot(ll, b, 'bo', lonlat=True, coord='G')
hp.projtext(ll, b, ' (' + str(round(ll,2)) + ',' + str(round(b,2)) + ')', lonlat=True, coord='G', fontsize=18, weight='bold')
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
cosb = np.cos(b*deg2rad)
cosy = np.cos(y*deg2rad)
if ( (((x-l)**2 + (y-b)**2) <= offset**2) ):
# hp.projtext(x, y, '.', lonlat=True, coord='G')
hp.projplot(x, y, 'kx', lonlat=True, coord='G')
mpl.rcParams.update({'font.size':30})
plt.show()
def plot_patches(map_file, src_num, info):
# Define constants #
deg2rad = np.pi/180.
fukui_cf = 2.10 #2.10e26
planck_cf = 0.84 #8.40e-27, 0.84e-26
pl_fact_err = 0.3 #3.0e-27
fk_fact_err = 0.0 #unknown
# Define the width of area #
beam = 5.0 # Beam = map_resolution = 5'
dbeam = beam/120.0 # Beam = 3.5' -> dbeam = beam/60/2 in degree
offset = dbeam # degree
# tau353 map, err_tau353 map and resolution #
tau_map = hp.read_map(map_file, field = 0)
err_map = hp.read_map(map_file, field = 1)
nside = hp.get_nside(tau_map)
res = hp.nside2resol(nside, arcmin=False)
dd = res/deg2rad/5.0
# OK - Go #
tau353 = []
nh = []
nhi = []
tau = {}
t_err = {}
rat_fk = 0.
rat_pl = 0.
for i in range(0,src_num):
# Find the values of Tau353 and Err_tau353 in small area #
tau[i] = []
t_err[i] = []
l = info['l'][i]
b = info['b'][i]
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
hp.cartview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file, coord='G', unit='',
norm='hist', xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[b-offset-0.1*offset,b+offset+0.1*offset],
return_projected_map=True)
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
if (tau_map[pix] > -1.0e30) : # Some pixels not defined
tau[i].append(tau_map[pix])
if (err_map[pix] >= 6.9e-11 and err_map[pix] <= 0.00081): # Checked Invalid error
t_err[i].append(err_map[pix])
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
cosb = np.cos(b*deg2rad)
cosy = np.cos(y*deg2rad)
if ( (((x-l)**2 + (y-b)**2) <= offset**2) ):
# hp.projtext(x, y, '.', lonlat=True, coord='G')
hp.projplot(x, y, 'kx', lonlat=True, coord='G')
plt.show()
mw=hp.ud_grade(hp.ma(hp.read_map(flw[1])),512,order_out='Ring')*1000.
m30=hp.ud_grade(hp.ma(hp.read_map(fll[0])),512)*1.e6
m44=hp.ud_grade(hp.ma(hp.read_map(fll[1])),512)*1.e6
m70=hp.ud_grade(hp.ma(hp.read_map(fll[2])),512)*1.e6
m100=hp.ud_grade(hp.ma(hp.read_map(flh[0])),512)*1.e6
m143=hp.ud_grade(hp.ma(hp.read_map(flh[1])),512)*1.e6
m217=hp.ud_grade(hp.ma(hp.read_map(flh[2])),512)*1.e6
mv.mask=mv.mask|psmask|galmask40
mw.mask=mw.mask|psmask|galmask40
m30.mask=m30.mask|psmask|galmask40
m44.mask=m44.mask|psmask|galmask40
m70.mask=m70.mask|psmask|galmask40
m100.mask=m100.mask|psmask|galmask40
m143.mask=m143.mask|psmask|galmask40
m217.mask=m217.mask|psmask|galmask40
hp.cartview(mw-np.mean(mw[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='WMAP W: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_w.png')
hp.cartview(mv-np.mean(mv[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='WMAP V: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_v.png')
hp.cartview(m30-np.mean(m30[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='Planck 30 GHz: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_30.png')
hp.cartview(m44-np.mean(m44[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='Planck 44 GHz: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_44.png')
hp.cartview(m70-np.mean(m70[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='Planck 70 GHz: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_70.png')
hp.cartview(m100-np.mean(m100[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='Planck 100 GHz: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_100.png')
hp.cartview(m143-np.mean(m143[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='Planck 143 GHz: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_143.png')
hp.cartview(m217-np.mean(m217[sgppix]),cmap=cmap,min=-250,max=250,lonra=[-20,20],latra=[-20,20],rot=[0,-90,0],remove_mono=True,title='Planck 217 GHz: SGP $\pm 10 \deg$',unit='$\mu K$')
savefig('/global/u1/p/peterm/plots/map_comparisons/sgp_217.png')
def main(argv):
'''
Main function: Takes input parameters, read observability map,
select best areas, and show and/or save areas at an output file.
Run program with -h for help.
'''
parser = OptionParser()
parser.add_option("-i",'--input',
help="Input file with masked healpy observability map (output of selectArea.py).",type="string")
parser.add_option("-m",'--mask',
help="Extra mask to be readed (field 0 of output from selectArea.py).",type="string")
parser.add_option("-f",'--field',default=0,
help="Field to be readed from input file. May be 0, 1 or 2, see selectArea.py for info.",type="int")
parser.add_option("--phi_min",
help="Inferior limit on phi as spacial cut (default = 0 graus).",type="float",default=0.)
parser.add_option("--phi_max",
help="Superior limit on phi as spacial cut (default = 180 graus).",type="float",default=180.)
parser.add_option("--theta_min",
help="Inferior limit on theta as spacial cut (default = 0 graus).",type="float",default=-180)
parser.add_option("--theta_max",
help="Superior limit on theta as spacial cut (default = 360).",type="float",default=360.)
parser.add_option("-o",'--output',
help="Output file. ",type="string")
parser.add_option('--save_mapped',
help="Output file for the masked map. Will store a healpy fits file with 1.0 for the marked areas and 0.0 otherwise.",type="string")
parser.add_option("-s", '--show',action="store_true", default=False,
help="Show map before quiting. If no output file is given, then consider show=True.")
parser.add_option("-c",'--coordinates',
help="Coordinates to use on spacial cut. Can be either 'G' (Galactic) or 'E' (Ecliptic). Default is 'G'.",type="string",default='G')
parser.add_option("-v", '--verbose',action="store_true", dest="verbose", default=False,
help="Don't print status messages to stdout")
opt,arg = parser.parse_args(argv)
if opt.input:
if opt.verbose:
print '(M):\n(M): Reading input file {0} ...\n(M):'.format(opt.input)
obsMap = H.read_map(opt.input,field=opt.field)
else:
print '(E):\n(E): Input map not given.\n(E):'
return -1
emask = np.zeros(len(obsMap)) == 0
if opt.mask:
emask = H.read_map(opt.mask) == 1.
if opt.verbose:
print '''(R):
(R): Reading extra mask from {0} (assuming field = 0)...
(R):'''.format(opt.mask)
nside = H.npix2nside(len(obsMap))
phi,theta = H.pix2ang(nside,np.arange(len(obsMap)))
if opt.coordinates == 'E':
print '''(R):
(R): Using ecliptic coordinates (J2000.). Convertion is made
(R): elementwise and may take some time.
(R):''',
sys.stdout.flush()
# rate = 0.
# sys.stdout.write('\r(R): {0:8.1f} %'.format(rate*10))
# sys.stdout.flush()
phi *= -180./np.pi
phi += 90.
theta *= 180./np.pi
for i in range(len(phi)):
# if rate != np.floor(i * 10. / len(phi)):
# rate = np.floor(i * 10. / len(phi))
# sys.stdout.write('\r(R): {0:8.1f} %'.format(rate*10))
# sys.stdout.flush()
phi[i],theta[i] = _skysub.gal2radec(theta[i],phi[i],2000.)
mask_phi = np.bitwise_and(phi > opt.phi_min , phi < opt.phi_max)
mask_theta = np.bitwise_and(theta > opt.theta_min ,theta < opt.theta_max )
print
else:
print '''(R):
(R): Using galactic coordinates.
(R):'''
mask_phi = np.bitwise_and(phi > opt.phi_min * np.pi / 180. , phi < opt.phi_max * np.pi / 180. )
mask_theta = np.bitwise_and(theta > opt.theta_min * np.pi / 180. ,theta < opt.theta_max * np.pi / 180. )
# print 'phi_min = {0}\nphi_max = {1}\ntheta_min = {2}\ntheta_max = {3}\n'.format(phi.min(),phi.max(),theta.min(),theta.max())
# mask = np.bitwise_and(np.bitwise_and(mask_phi,mask_theta),obsMap > 0.)
mask = np.bitwise_and(np.bitwise_and(mask_phi,mask_theta),emask)
print '''(R):
(R): Npix_select = {npixs}
(R): Npix_s/Npix_tot = {rnpix}
(R): selected Area = {sarea} square-degree
(R): pixel Area = {parea} square-degree
(R): Avg Observability = {avgobs}
(R):'''.format(npixs = len(obsMap[mask]),
sarea = len(obsMap[mask])*H.nside2pixarea(nside) * (180. / np.pi)**2.,
avgobs = np.mean(obsMap[mask]),
rnpix = len(obsMap[mask]) * 1.0 / len(obsMap),
parea = H.nside2pixarea(nside) * (180. / np.pi)**2. )
obsMap[np.bitwise_not(mask)] = 0.
if opt.output:
amask = np.bitwise_and(np.bitwise_and(mask,obsMap == 0.),emask)
fp = open(opt.output,'a')
fp.write('{avgtime} {avgarea} {avgobs}\n'.format(avgtime = (opt.phi_max+opt.phi_min) * 0.5,
avgarea = len(obsMap[amask])*H.nside2pixarea(nside) * (180. / np.pi)**2.,
avgobs = np.sum(obsMap[mask]) ) )
fp.close()
if opt.save_mapped:
H.write_map(opt.save_mapped,obsMap,fits_IDL=False)
if opt.show:
#
#H.cartview(phi,sub=(2,1,1),coord=['G','E'])
amask = np.bitwise_and(mask,emask)
obsMap[np.bitwise_not(amask)] = 0.
#obsMap[emask] = 1.
H.mollview(obsMap,sub=(2,1,1))
H.cartview(obsMap,coord=['G','E'],sub=(2,1,2))
py.show()
def superimpose_hpxmap(self, hpx_map, label, color='red', coord_in='C',
cbar=True):
'''Superimpose a Healpix map on the background map.
Parameters
----------
hpx_map : array-like
The hpx_map to superimpose upon the background.
label : string
The label to put on the colorbar for this footprint.
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
coord_in : 'C', 'G', or 'E'
The coordinate system of the input healpix map.
Notes
-----
The input healpix map will have zeros replaced with NaNs to make
those points completely transparent.
'''
idx_nan = (hpx_map == 0)
hpx_map /= np.max(hpx_map)
hpx_map[idx_nan] = np.NaN
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
if self.partialmap:
# Colorbar is added to this and then deleted to make sure there is
# room at the bottom of the map for the labels. Margins are to make
# sure the title is not partially off the figure for a square map
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
map_tmp = H.cartview(hpx_map, title='',
coord=coord, fig=self.fig.number, cmap=cm1,
notext=True, flip='astro', sub=sub,
margins=margins, return_projected_map=True,
**self.kwds)
idx = np.isfinite(map_tmp)
cbar = len(map_tmp[idx]) > 0
else:
self.mapview(hpx_map, title='', coord=coord,
cbar=True, fig=self.fig.number, cmap=cm1,
notext=True, flip='astro', **self.kwds)
self.fig.delaxes(self.fig.axes[-1])
if cbar:
# First add the new colorbar axis to the figure
im0 = self.fig.axes[-1].get_images()[0]
box = self.fig.axes[0].get_position()
ax_color = pl.axes([len(self.cbs), box.y0-0.1, 0.05, 0.05])
self.fig.colorbar(im0, cax=ax_color, orientation='horizontal',
label=label, values=[1, 1])
self.cbs.append(ax_color)
# Read just the location of every colorbar
ncb = len(self.cbs)
left = 1.0 / (2.0*ncb) - 0.025
for ax_tmp in self.cbs:
ax_tmp.set_position([left, box.y0-0.1, 0.05, 0.05])
left += 1.0 / ncb
def make_figure(moll=True, ecliptic=False, footprint=True, idr2=True, cmap='Greys', title='test', highlight=False, highlight_survey='HYDRA', highlight_tile='HYDRA-0011', path='./'):
alpha = 0.3
lw = 2
dust_map = 'data/lambda_sfd_ebv.fits'
bgmap = H.read_map(os.path.join(path, dust_map))
if highlight:
title = highlight_survey + '/' + highlight_tile
# plot the map projection
cmap = cm.get_cmap(cmap)
vmin = np.log10(np.percentile(bgmap, 20))
vmax = 1+np.log10(np.percentile(bgmap, 99))
cmap.set_under('w') # Set the color for low out-of-range values when norm.clip = False to white
if moll:
cart = H.mollview(np.log10(bgmap), coord=['G', 'C'], cmap=cmap, cbar=False, notext=False, title=title, min=vmin, max=vmax)
add_numbers_on_axes = 1
if add_numbers_on_axes == 1:
dra = -2
ddec = +1
H.projtext( 0+dra, 2+ddec,'0h', coord='C', lonlat=True)
H.projtext( 30+dra, 2+ddec,'2h', coord='C', lonlat=True)
H.projtext( 60+dra, 2+ddec,'4h', coord='C', lonlat=True)
H.projtext( 90+dra, 2+ddec,'6h', coord='C', lonlat=True)
H.projtext(120+dra, 2+ddec,'8h', coord='C', lonlat=True)
H.projtext(150+dra, 2+ddec,'10h', coord='C', lonlat=True)
H.projtext(210+dra, 2+ddec,'14h', coord='C', lonlat=True)
H.projtext(240+dra, 2+ddec,'16h', coord='C', lonlat=True)
H.projtext(270+dra, 2+ddec,'18h', coord='C', lonlat=True)
H.projtext(300+dra, 2+ddec,'20h', coord='C', lonlat=True)
H.projtext(330+dra, 2+ddec,'22h', coord='C', lonlat=True)
H.projtext( 0+dra,30+ddec,'30', coord='C', lonlat=True)
H.projtext( 0+dra,60+ddec,'60', coord='C', lonlat=True)
else:
cart = H.cartview(np.log10(bgmap), coord=['G', 'C'], cmap=cmap, cbar=False, notext=True, title=title, min=vmin, max=vmax)
# draw the ecliptic
if ecliptic:
ra = np.linspace(-np.pi,np.pi,100)
dec = np.zeros(100)+(90+23.5*np.sin(ra))*np.pi/180.
H.projplot(dec,ra,'--', color='k', lw=2.)
# draw the S-PLUS footprint
if footprint:
# read SPLUS pointings file
splus_tiles_file = os.path.join(path,'data/all_pointings.csv')
splus_tiles = Table.read(splus_tiles_file, format="csv")
splus_tiles['obs'] = 0 # add an extra column to mark if the pointing has been observed or not
tilesize = 1.4 # degrees
tilecol = ['b','g','r','m']
color_the_surveys = False
surveys = ['SPLUS', 'STRIPE82', 'HYDRA', 'MC']
for s in range(0,len(surveys)):
if color_the_surveys:
col = tilecol[s]
else:
col = '0.5'
idx = np.where(splus_tiles['PID'] == surveys[s])
tiledata = splus_tiles[idx]
ntiles = len(tiledata)
coords = SkyCoord(ra=tiledata['RA'], dec=tiledata['DEC'], unit=(u.hourangle,u.degree))
for j in range(0,ntiles):
plotEqTile([coords[j].ra.value,coords[j].dec.value],tilesize,col,Tile=True,CenterPoints=False,CornerPoints=False,lw=1)
if highlight:
idx = (splus_tiles['PID'] == highlight_survey) & (splus_tiles['NAME'] == highlight_tile)
tiledata = splus_tiles[idx]
coords = SkyCoord(ra=tiledata['RA'], dec=tiledata['DEC'], unit=(u.hourangle,u.degree))
lon = coords[0].ra.value
lat = coords[0].dec.value
plotEqTile([lon,lat],tilesize,'r',Tile=True,CenterPoints=True,CornerPoints=False,lw=1)
# draw the observed idr2 footprint
if idr2:
# read SPLUS pointings file
splus_tiles_file = os.path.join(path,'data/all_pointings.csv')
splus_tiles = Table.read(splus_tiles_file, format="csv")
splus_tiles_names = np.array(splus_tiles['NAME'])
# read IDR2 pointings file
idr2_tiles_file = os.path.join(path,'data/idr2_pointings.csv')
idr2_tiles = Table.read(idr2_tiles_file, format="csv")
idr2_tiles_names = np.array(idr2_tiles['FIELD'])
tilesize = 1.4 # degrees
for tile in range(0,len(splus_tiles)):
sel = (idr2_tiles_names == splus_tiles_names[tile])
if np.sum(sel) > 0:
tiledata = splus_tiles[tile]
coords = SkyCoord(ra=tiledata['RA'], dec=tiledata['DEC'], unit=(u.hourangle,u.degree))
plotEqTile([coords.ra.value,coords.dec.value],tilesize,'g',Tile=True,CenterPoints=False,CornerPoints=False,lw=1)
H.graticule() # draw the coordinate system axes
return 0
def almPlots(path, outDir, bundle,
nside=128, lmax=500, filterband='i',
raRange=[-50,50], decRange=[-65,5],
subsetsToConsider=[[130,165], [240, 300]],
showPlots=True):
"""
Plot the skymaps/cartview plots corresponding to alms with specified l-ranges.
Automatically creates the output directories and saves the plots.
Required Parameters
-------------------
* path: str: path to the main directory where output directory is saved
* outDir: str: name of the main output directory
* bundle: metricBundle object.
Optional Parameters
-------------------
* nside: int: HEALpix resolution parameter. Default: 128
* lmax: int: upper limit on the multipole. Default: 500
* filterBand: str: any one of 'u', 'g', 'r', 'i', 'z', 'y'. Default: 'i'
* raRange: float array: range of right ascention (in degrees) to consider in cartview plot; only useful when
cartview= True. Default: [-50,50]
* decRange: float array: range of declination (in degrees) to consider in cartview plot; only useful when
cartview= True. Default: [-65,5]
* subsetsToConsider: array of int arrays: l-ranges to consider, e.g. use [[50, 100]] to consider 50<l<100.
Currently built to handle five subsets (= number of colors built in).
Default: [[130,165], [240, 300]]
* showPlots: boolean: set to True if want to show figures. Default: True
"""
# set up the output directory
outDir2 = 'almAnalysisPlots_%s<RA<%s_%s<Dec<%s'%(raRange[0], raRange[1], decRange[0], decRange[1])
if not os.path.exists('%s%s/%s'%(path, outDir, outDir2)):
os.makedirs('%s%s/%s'%(path, outDir, outDir2))
outDir3 = 'almSkymaps'
if not os.path.exists('%s%s/%s/%s'%(path, outDir, outDir2, outDir3)):
os.makedirs('%s%s/%s/%s'%(path, outDir, outDir2, outDir3))
outDir4 = 'almCartviewMaps'
if not os.path.exists('%s%s/%s/%s'%(path, outDir, outDir2, outDir4)):
os.makedirs('%s%s/%s/%s'%(path, outDir, outDir2, outDir4))
# ------------------------------------------------------------------------
# In order to consider the out-of-survey area as with data=0, assign the masked region of the
# skymaps the median of the in-survey data, and then subtract the median off the entire survey.
# Add the median back later. This gets rid of the massive fake monopole and allows reconstructing
# the full skymap from components.
surveyMedianDict = {}
surveyStdDict = {}
for dither in bundle:
inSurvey = np.where(bundle[dither].metricValues.mask == False)[0]
outSurvey = np.where(bundle[dither].metricValues.mask == True)[0]
bundle[dither].metricValues.mask[outSurvey] = False
# data pixels
surveyMedian = np.median(bundle[dither].metricValues.data[inSurvey])
surveyStd = np.std(bundle[dither].metricValues.data[inSurvey])
# assign data[outOfSurvey]= medianData[inSurvey]
bundle[dither].metricValues.data[outSurvey] = surveyMedian
# subtract median off
bundle[dither].metricValues.data[:] = bundle[dither].metricValues.data[:]-surveyMedian
# save median for later use
surveyMedianDict[dither] = surveyMedian
surveyStdDict[dither] = surveyStd
# ------------------------------------------------------------------------
# now find the alms correponding to the map.
for dither in bundle:
array = hp.anafast(bundle[dither].metricValues.filled(bundle[dither].slicer.badval), alm=True, lmax=500)
cl = array[0]
alm = array[1]
l = np.arange(len(cl))
lsubsets = {}
colorArray = ['y', 'r', 'g', 'm', 'c']
color = {}
for case in range(len(subsetsToConsider)):
lsubsets[case] = ((l>subsetsToConsider[case][0]) & (l<subsetsToConsider[case][1]))
color[case] = colorArray[case]
# ------------------------------------------------------------------------
# plot things out
plt.clf()
plt.plot(l, (cl*l*(l+1))/(2.0*np.pi), color='b')
for key in list(lsubsets.keys()):
plt.plot(l[lsubsets[key]], (cl[lsubsets[key]]*l[lsubsets[key]]*(l[lsubsets[key]]+1))/(2.0*np.pi),
color=color[key])
plt.title(dither)
plt.xlabel('$\ell$')
plt.ylabel(r'$\ell(\ell+1)C_\ell/(2\pi)$')
filename = 'cls_%s.png'%(dither)
plt.savefig('%s%s/%s/%s'%(path, outDir, outDir2, filename), format='png', bbox_inches='tight')
if showPlots:
plt.show()
else:
plt.close()
surveyMedian = surveyMedianDict[dither]
surveyStd = surveyStdDict[dither]
# ------------------------------------------------------------------------
# plot full-sky-alm plots first
nTicks = 5
colorMin = surveyMedian-1.5*surveyStd
colorMax = surveyMedian+1.5*surveyStd
increment = (colorMax-colorMin)/float(nTicks)
ticks = np.arange(colorMin+increment, colorMax, increment)
# full skymap
hp.mollview(hp.alm2map(alm, nside=nside, lmax=lmax)+surveyMedian, flip='astro', rot=(0,0,0),
min=colorMin, max=colorMax, title='', cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('Full Map')
ax = plt.gca()
im = ax.get_images()[0]
fig = plt.gcf()
cbaxes = fig.add_axes([0.1, 0.015, 0.8, 0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im, orientation='horizontal', format='%.2f',
ticks=ticks, cax=cbaxes)
cb.set_label('$%s$-band Coadded Depth'%filterband)
filename = 'alm_FullMap_%s.png'%(dither)
plt.savefig('%s%s/%s/%s/%s'%(path, outDir, outDir2, outDir3, filename),
format='png', bbox_inches='tight')
# full cartview
hp.cartview(hp.alm2map(alm, nside=nside, lmax=lmax)+surveyMedian,
lonra=raRange, latra=decRange, flip='astro',
min=colorMin, max=colorMax, title='', cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('Full Map')
ax = plt.gca()
im = ax.get_images()[0]
fig= plt.gcf()
cbaxes = fig.add_axes([0.1, -0.05, 0.8, 0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im, orientation='horizontal', format='%.2f',
ticks=ticks, cax=cbaxes)
cb.set_label('$%s$-band Coadded Depth'%filterband)
filename = 'alm_Cartview_FullMap_%s.png'%(dither)
plt.savefig('%s%s/%s/%s/%s'%(path, outDir, outDir2, outDir4, filename),
format='png', bbox_inches='tight')
# prepare for the skymaps for l-range subsets
colorMin = surveyMedian-0.1*surveyStd
colorMax = surveyMedian+0.1*surveyStd
increment = (colorMax-colorMin)/float(nTicks)
increment = 1.15*increment
ticks = np.arange(colorMin+increment, colorMax, increment)
# ------------------------------------------------------------------------
# consider each l-range
for case in list(lsubsets.keys()):
index = []
lowLim = subsetsToConsider[case][0]
upLim = subsetsToConsider[case][1]
for ll in np.arange(lowLim, upLim+1):
for mm in np.arange(0,ll+1):
index.append(hp.Alm.getidx(lmax=lmax, l=ll, m=mm))
alms1 = alm.copy()
alms1.fill(0)
alms1[index] = alm[index] # an unmasked array
# plot the skymap
hp.mollview(hp.alm2map(alms1, nside=nside, lmax=lmax)+surveyMedian,
flip='astro', rot=(0,0,0),
min=colorMin, max=colorMax, title='', cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('%s<$\ell$<%s'%(lowLim, upLim))
ax = plt.gca()
im = ax.get_images()[0]
fig= plt.gcf()
cbaxes = fig.add_axes([0.1, 0.015, 0.8, 0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im, orientation='horizontal',format='%.3f',
ticks=ticks, cax = cbaxes)
cb.set_label('$%s$-band Coadded Depth'%filterband)
filename = 'almSkymap_%s<l<%s_%s.png'%(lowLim, upLim, dither)
plt.savefig('%s%s/%s/%s/%s'%(path, outDir, outDir2, outDir3, filename),
format='png', bbox_inches='tight')
# plot cartview
hp.cartview(hp.alm2map(alms1, nside=nside, lmax=lmax)+surveyMedian,
lonra=raRange, latra=decRange, flip='astro',
min=colorMin, max=colorMax, title='',cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('%s<$\ell$<%s'%(lowLim, upLim))
ax = plt.gca()
im = ax.get_images()[0]
fig= plt.gcf()
cbaxes = fig.add_axes([0.1, -0.05, 0.8, 0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im, orientation='horizontal',format='%.3f',
ticks=ticks, cax=cbaxes)
cb.set_label('$%s$-band Coadded Depth'%filterband)
filename = 'almCartview_%s<l<%s_%s.png'%(lowLim, upLim, dither)
plt.savefig('%s%s/%s/%s/%s'%(path, outDir, outDir2, outDir4, filename),
format='png', bbox_inches='tight')
if showPlots:
plt.show()
else:
plt.close('all')
def main():
p = argparse.ArgumentParser(
description="Plot map with Mercator projection")
p.add_argument("fitsfile",
nargs="*",
help="Input HEALPix FITS file. "
"To plot the weighted sum of multiple files, put a list of "
"file weight file weight pairs.")
p.add_argument("-a",
"--abthresh",
default=None,
type=float,
help="Apply an absolute (lower/upper) threshold to the map")
p.add_argument("-c",
"--coords",
default="C",
help="C=equatorial (default), G=galactic")
p.add_argument("-C",
"--column",
dest="col",
default=0,
type=int,
help="FITS column in which healpix map resides")
p.add_argument(
"--file-diff",
dest="diff",
default="",
type=str,
help=
"FITS File to take the difference from (optional) otherwise uses positional argument"
)
p.add_argument("-D",
"--coldiff",
default=-1,
type=int,
help="FITS column in which healpix map to be subtracted "
"(if any) resides")
p.add_argument(
"--mjd",
default=None,
type=float,
help=
"MJD of the map. Will be plotted in J2000. Supersedes info in header")
p.add_argument("-l",
"--threshold",
type=float,
default=None,
help="Apply a lower threshold to the plot.")
p.add_argument(
"--norm-sqdeg",
dest="sqdeg",
action="store_true",
default=False,
help="Normalize values to square degree, according to nSides"
" (makes sense only certain kinds of maps)")
p.add_argument("--milagro",
action="store_true",
help="Use Milagro color scale")
p.add_argument(
"--contours",
dest="contours",
type=float,
nargs='*',
default=None,
help="plot contours. If --contourmap is provided, it will be"
" used instead of plotted map. If no contours are"
" provided, it will plot confidence intervals"
" (1, 2, 3 sigma CI). Else, absolute sigmas are used,"
" e.g. --contours 1 2 3 4 [sigma] --contours")
p.add_argument("--contourscolor",
nargs='*',
default=None,
help="Draw options for contours")
p.add_argument("--contoursstyle",
nargs='*',
default=None,
help="Draw options for contours")
p.add_argument("--contourswidth",
type=float,
nargs='*',
default=None,
help="Draw options for contours")
p.add_argument("--contourfile",
nargs='*',
default=None,
help="Draw contours from file with RA/Dec pairs")
p.add_argument("--gamma",
action="store_true",
help="Use GeV/TeV gamma-ray color scale")
p.add_argument("-L", "--label", default=None, help="Color bar label")
p.add_argument("--cross",
action="store_true",
help="Plot a cross at the center of the plot")
p.add_argument("--moon",
action="store_true",
dest="isMoon",
help="is in moon centered coordinates")
p.add_argument("-m",
"--min",
type=float,
default=None,
help="Plot minimum value")
p.add_argument("-M",
"--max",
type=float,
default=None,
help="Plot maximum value")
p.add_argument("-n",
"--ncolors",
type=int,
default=256,
help="Number of contours to use in the colormap")
p.add_argument("--nocolorbar",
dest="colorbar",
action="store_false",
help="Do not draw color bar.")
p.add_argument("--nofill",
action="store_true",
help="Do not draw fill plot with colors. "
"Useful for contours.")
p.add_argument(
"-o",
"--output",
default=None,
help="Output file name (optional). "
"If file name ends in .fits or .fit, save as a healpix map with the pixels outside the plot area set to hp.UNSEEN."
)
p.add_argument("-s",
"--scale",
type=float,
default=1.,
help="scale up or down values in map")
p.add_argument("--sun",
action="store_true",
dest="isSun",
help="is in Sun centered coordinates")
p.add_argument("--dpar",
type=float,
default=1.,
help="Interval in degrees between parallels")
p.add_argument("--dmer",
type=float,
default=1.,
help="Interval in degrees between meridians.")
p.add_argument('--sexagesimal',
action='store_true',
help='Display RA in hh:mm.')
p.add_argument("--nogrid",
action="store_true",
help="Do not plot gridlines.")
p.add_argument("--interpolation",
action='store_true',
help="Uses bilinear interpolation in data map.")
p.add_argument("--xsize",
type=int,
default=1000,
help="Number of X pixels, Y will be scaled acordingly.")
p.add_argument("-t",
"--ticks",
nargs="+",
type=float,
help="List of ticks for color bar, space-separated")
p.add_argument("-T", "--title", help="title of map")
p.add_argument("--squareaspect",
action='store_true',
help="Better Mercator X/Y ratio.")
p.add_argument("--dpi",
type=int,
default=300,
help="Change the dpi of the output")
p.add_argument("--preliminary",
action="store_true",
help="Add a watermark indicating the plot as preliminary")
p.add_argument("--onlystats",
action="store_true",
help="Exits after returning min, max and value at center. "
"If map is called 'flux', it will give the error if there "
"is a map called 'flux_error'")
# Mutually exclusive option to specify plot xy range OR center + WxH
argRange = p.add_mutually_exclusive_group(required=False)
argRange.add_argument("--xyrange",
type=float,
nargs=4,
help="Xmin Xmax Ymin Ymax plot range [degree]")
argRange.add_argument(
"--origin",
type=float,
nargs=4,
help="Central (x,y) coords + width + height [degree]")
# Plot P-Value instead of sigma, expects location and scale of
# a right-skewed Gumble distribution
p.add_argument("--gumbel",
type=float,
nargs=2,
help="Plot P-Value instead of significance. "
"Arguments: location and scale of right-skewed "
"Gumble distribution.")
# Download/plotting options for Fermi catalog sources
p.add_argument(
"--download-fermi",
dest="dfermi",
default=None,
help="Download Fermi FITS data from NASA and exit. Enter version number"
)
p.add_argument("--fermicat",
default=None,
help="Fermi xFGL catalog FITS file name")
p.add_argument("--fermicat-labels",
dest="fermicatLabels",
action="store_true",
help="Draw Fermi sources with labels.")
# plotting options for Fermi Healpix file
p.add_argument("--contourmap",
default=None,
help="Healpix map from which to grab contours.")
p.add_argument(
"--contourmapcoord",
default="G",
help=
"Coordinate system of contourmap. C=equatorial, G=galactic (default)")
# Download/plotting options for TeVCat sources
p.add_argument("--download-tevcat",
dest="dtevcat",
action="store_true",
help="Download data from tevcat.uchicago.edu and exit")
p.add_argument("--tevcat",
default=None,
help="Draw TeVCat sources using TeVCat ASCII file")
p.add_argument("--tevcat-labels",
dest="tevcatLabels",
action="store_true",
help="Draw TeVCat sources with labels.")
p.add_argument("--cat-labels-angle",
dest="catLabelsAngle",
default=90,
help="Oriantation of catalog labels.")
p.add_argument("--cat-labels-size",
dest="catLabelsSize",
default=8,
help="Size of catalog labels.")
# Highlight hotspots listed in an ASCII file
p.add_argument("--hotspots",
default=None,
help="Hotspot coordinates in an ASCII file")
p.add_argument("--hotspot-labels",
dest="hotspotLabels",
action="store_true",
help="Draw hotspots sources with labels.")
# Plot 2D Gaussian fit result listed in an ASCII file from fitMap.py
p.add_argument("--gaussfit",
default=None,
help="2D-Gaussian fit result in an ASCII file")
args = p.parse_args()
#Sanity checks
if (args.mjd is not None) and (not canPrecess):
print("Missing necessary packages to make precession")
raise SystemExit
if args.nofill:
# If we don't fill with colors, we don't plot the colorbar either
args.colorbar = False
# Download TeVCat
if args.dtevcat:
if haveTeVCat:
print("Fetching data from tevcat.uchicago.edu.")
tevcat = TeVCat.TeVCat()
return None
else:
print("Sorry, AERIE TeVCat python module is not available.")
raise SystemExit
# Downlaod Fermi catalog
if args.dfermi:
if haveFermi:
print("Fetching 2FGL catalog, version %s" % args.dfermi)
FGLCatalog.fetch_catalog(args.dfermi)
return None
else:
print("Sorry, AERIE Fermi python module is not available.")
raise SystemExit
# Start normal processing
fitsfile = args.fitsfile
if fitsfile == []:
print("Please specify an FITS file to plot.")
raise SystemExit
# Fill 2D array
xmin = -180.
xmax = 180.
ymax = 90
ymin = -90
if (args.xyrange):
xmin, xmax, ymin, ymax = args.xyrange
xC = (xmin + xmax) / 2.
yC = (ymin + ymax) / 2.
elif (args.origin):
xC, yC, w, h = args.origin
xmin = xC - 0.5 * w
xmax = xC + 0.5 * w
ymin = yC - 0.5 * h
ymax = yC + 0.5 * h
else:
print("Using default zoom window. "
"To customize the zoom window you must specify either "
"(xyrange) or (origin and width and height).")
if args.isMoon or args.isSun:
xmin += 180.
xmax += 180.
# Move to range expected by healpy
while xmin < -180:
xmin += 360
while xmin > 180:
xmin -= 360
while xmax < -180:
xmax += 360
while xmax > 180:
xmax -= 360
if xmax < xmin:
tmax = xmax
tmin = xmin
xmin = tmax
xmax = tmin
cxmin = xmin
cxmax = xmax
frot = 0.
if xmax > 90. and xmin < -90.:
frot = 180.
cxmin = xmax - 180.
cxmax = xmin + 180.
if args.origin:
while xC > 180:
xC -= 360
# Read in the skymap and mask out empty pixels
skymap, skymapHeader = hp.read_map(fitsfile[0], args.col, h=True)
if len(fitsfile) > 1:
skymap *= float(fitsfile[1])
# If fitsfile has more parameters, they should be "mapfile weight" pairs
for i in range(2, len(fitsfile), 2):
skymap2 = hp.read_map(fitsfile[i], args.col)
skymap2 *= float(fitsfile[i + 1])
skymap += skymap2
# remove naughty values
skymap[np.isnan(skymap)] = hp.UNSEEN
skymap *= args.scale
nside1 = hp.get_nside(skymap)
npix = hp.nside2npix(nside1)
if args.coldiff > -1:
if os.path.isfile(args.diff):
print("Taking difference with {0}".format(args.diff))
skymap2 = hp.read_map(args.diff, args.coldiff)
else:
print("No extra file provided, using same file as input")
skymap2 = hp.read_map(fitsfile[0], args.coldiff)
if len(fitsfile) > 1:
skymap2 *= float(fitsfile[1])
print("Subtracting column {0} from skymap...".format(args.coldiff))
skymap -= skymap2
# If fitsfile has more parameters, they should be "mapfile weight" pairs
for i in range(2, len(fitsfile), 2):
skymap2 = hp.read_map(fitsfile[i], args.coldiff)
skymap2 *= float(fitsfile[i + 1])
skymap -= skymap2
if (args.gumbel):
assert haveGumbel
gumbel_location, gumbel_scale = args.gumbel
gumbel = gumbel_r(loc=gumbel_location, scale=gumbel_scale)
skymap = gumbel.logsf(skymap) / log(10)
def inf_suppressor(x):
return x if np.isfinite(x) else 0.
inf_suppressor = np.vectorize(inf_suppressor)
skymap = inf_suppressor(skymap)
# Normalize value to square degree
if args.sqdeg:
pixsizestr = 4 * np.pi / (12 * nside1**2)
str2sqdeg = (180 / np.pi)**2
pixsizesqdeg = pixsizestr * str2sqdeg
skymap /= pixsizesqdeg
# I did not find header handler, thats all I came up with...
toFind = 'TTYPE' + str(args.col + 1)
#print type(skymapHeader), type(skymapHeader[0]), skymapHeader, toFind, dict(skymapHeader)[toFind]
skymapName = dict(skymapHeader)[toFind]
#Check if it is flux
isFlux = False
hasFluxError = False
if skymapName == 'flux':
isFlux = True
keyname = dict((v, n) for n, v in skymapHeader).get("flux_error")
if keyname is not None:
if keyname.find('TTYPE') == 0 and len(keyname) == 6:
hasFluxError = True
fluxerrmap = hp.read_map(fitsfile[0], int(keyname[5]) - 1)
if not hasFluxError:
print("Map called 'flux_error' not present, will not print errors")
isFluxError = False
if skymapName == "flux_error":
isFluxError = True
# Find FOV
pxls = np.arange(skymap.size)
nZeroPix = pxls[(skymap != 0)]
pxTh, pxPh = hp.pix2ang(nside1, pxls)
# Mask outside FOV
values = np.ma.masked_where((pxTh > pxTh[nZeroPix].max())
| (pxTh < pxTh[nZeroPix].min())
| (skymap == hp.UNSEEN)
| (skymap == 1e20), skymap)
# Plot the skymap as an image, setting up the color palette on the way
mpl.rc("font", size=14, family="serif")
faspect = abs(cxmax - cxmin) / abs(ymax - ymin)
fysize = 4
# Set up the figure frame and coordinate rotation angle
coords = ["C", "C"]
gratcoord = "C"
if args.coords == "G":
coords = ["C", "G"]
gratcoord = "G"
if args.mjd is not None:
rotMap = precess.mjd2J2000ang(mjd=args.mjd, coord=coords, rot=frot)
rotVertex = precess.mjd2J2000ang(mjd=args.mjd, coord=coords)
elif not (args.isMoon or args.isSun):
mjd = False
if havePyfits:
hdulist = pf.open(fitsfile[0])
header = hdulist[0].header
if 'EPOCH' in header:
epoch = header['EPOCH']
if epoch == 'J2000':
pass
elif epoch == 'current':
if 'STARTMJD' in header and 'STOPMJD' in header:
startmjd = header['STARTMJD']
stopmjd = header['STOPMJD']
if (startmjd > 0
and stopmjd > 0) or startmjd > stopmjd:
mjd = (stopmjd + startmjd) / 2
else:
print(
"STARTMJD/STOPMJD are not well-definned, will not attempt to precess!"
)
else:
print(
"STARTMJD or STOPMJD not present in header, will not attempt to precess!"
)
else:
print(
"Currently EPOCH can be J2000 or current. Will not attempt to precess"
)
else:
print("Key EPOCH not in header, will not attempt to precess!")
else:
print(
"Pyfits not available -> Can't check map epoch -> Will not attempt to precess!"
)
if mjd:
print(
"Current epoch detected in header, will precess from MJD%g to J2000"
% mjd)
rotMap = precess.mjd2J2000ang(mjd=mjd, coord=coords, rot=frot)
rotVertex = precess.mjd2J2000ang(mjd=mjd, coord=coords)
else:
rotMap = frot
rotVertex = 0
else:
rotMap = frot
rotVertex = 0
# Get extrema
angverts = [[xmin,ymin],[xmax,ymin],\
[xmax,ymax],[xmin,ymax]]
vertlist = []
cRot = hp.Rotator(coord=coords, rot=rotVertex)
for x, y in angverts:
ctht, cph = np.deg2rad((y, x))
ctht = 0.5 * np.pi - ctht
if cph < 0:
cph = 2. * np.pi + cph
vertlist.append(cRot.I(hp.ang2vec(ctht, cph)))
# Get pixels in image
imgpix = hp.query_polygon(nside1, vertlist, inclusive=True)
seenpix = imgpix[values[imgpix] > hp.UNSEEN]
#if output is fits file: clip input healpy map by setting all pixels outside the plotted area to UNSEEN,
#then save the result.
if args.output and (args.output.endswith(".fits")
or args.output.endswith(".fit")):
pix = np.ones(npix, dtype=bool)
pix[seenpix] = False
values[pix] = hp.UNSEEN
print("Saving clipped healpy map to %s" % args.output)
hp.write_map(args.output,
values,
partial=True,
column_names=[skymapName])
exit()
#Get stats
precRot = hp.Rotator(coord=coords, rot=rotVertex)
pixMin, pixMax = seenpix[[
np.argmin(values[seenpix]),
np.argmax(values[seenpix])
]]
dMin, dMax = values[[pixMin, pixMax]]
[[thMin, thMax],
[phMin, phMax]] = np.rad2deg(precRot(hp.pix2ang(nside1,
[pixMin, pixMax])))
if args.coords == 'C':
while phMin < 0:
phMin += 360
while phMax < 0:
phMax += 360
th0, ph0 = precRot.I((90. - yC) * degree, xC * degree)
pix0 = hp.ang2pix(nside1, th0, ph0)
if isFlux:
if hasFluxError:
fluxerrMin, fluxerrMax, fluxerr0 = fluxerrmap[[
pixMin, pixMax, pix0
]]
else:
fluxerrMin, fluxerrMax, fluxerr0 = [0, 0, 0]
print("Flux units: TeV^-1 cm^-2 s^-1")
print("Coord units: deg")
print("Min: %11.2e +/- %11.2e (%6.2f, %5.2f)" %
(dMin, fluxerrMin, phMin, 90 - thMin))
print("Max: %11.2e +/- %11.2e (%6.2f, %5.2f)" %
(dMax, fluxerrMax, phMax, 90 - thMax))
print("Map value at origin: %11.2e +/- %11.2e" %
(skymap[pix0], fluxerr0))
elif isFluxError:
print("Min: %5.4e (%6.2f, %5.2f)" %
(dMin, phMin, 90 - thMin))
print("Max: %5.4e (%6.2f, %5.2f)" %
(dMax, phMax, 90 - thMax))
print("Map value at origin: %5.4e" % skymap[pix0])
else:
print("Min: %5.2f (%6.2f, %5.2f)" %
(dMin, phMin, 90 - thMin))
print("Max: %5.2f (%6.2f, %5.2f)" %
(dMax, phMax, 90 - thMax))
print("Map value at origin: %5.2f" % skymap[pix0])
if args.onlystats:
return 0
figsize = (fysize * faspect + 2, fysize + 2.75)
fig = plt.figure(num=1, figsize=figsize)
# Set min/max value of map
if args.min is not None:
dMin = args.min
values[(skymap < dMin) & (values > hp.UNSEEN)] = dMin
if args.max is not None:
dMax = args.max
values[(skymap > dMax) & (values > hp.UNSEEN)] = dMax
textcolor, colormap = MapPalette.setupDefaultColormap(args.ncolors)
# Use the Fermi/HESS/VERITAS purply-red-yellow color map
if args.gamma:
textcolor, colormap = MapPalette.setupGammaColormap(args.ncolors)
# Use the Milagro color map
if args.milagro:
dMin = -5
dMax = 15
dMin = args.min if args.min != None else -5
dMax = args.max if args.max != None else 15
thresh = args.threshold if args.threshold != None else 2.
textcolor, colormap = \
MapPalette.setupMilagroColormap(dMin, dMax, thresh, args.ncolors)
print("Milagro", dMin, dMax, thresh)
# Use a thresholded grayscale map with colors for extreme values
else:
if args.threshold != None:
textcolor, colormap = \
MapPalette.setupThresholdColormap(dMin, dMax, args.threshold,
args.ncolors)
elif args.abthresh != None:
textcolor, colormap = \
MapPalette.setupAbsThresholdColormap(dMin, dMax, args.abthresh,
args.ncolors)
if args.interpolation:
cRot = hp.Rotator(rot=rotMap, coord=coords)
phi = np.linspace(np.deg2rad(xmax), np.deg2rad(xmin), args.xsize)
if xmin < 0 and xmax > 0 and (xmax - xmin) > 180.:
phi = np.linspace(
np.deg2rad(xmin) + 2. * np.pi, np.deg2rad(xmax), args.xsize)
phi[(phi > 2. * np.pi)] -= 2. * np.pi
theta = 0.5 * np.pi - np.linspace(np.deg2rad(ymin), np.deg2rad(ymax),
args.xsize / faspect)
Phi, Theta = np.meshgrid(phi, theta)
rTheta,rPhi = cRot.I(Theta.reshape(phi.size*theta.size),\
Phi.reshape(phi.size*theta.size))
rotimg = hp.get_interp_val(values, rTheta.reshape(Phi.shape),\
rPhi.reshape(Theta.shape))
else:
tfig = plt.figure(num=2, figsize=figsize)
rotimg = hp.cartview(values, fig=2,coord=coords,title="",\
cmap=colormap, cbar=False,\
lonra=[cxmin,cxmax],latra=[ymin,ymax],rot=rotMap,
notext=True, xsize=args.xsize,
return_projected_map=True)
plt.close(tfig)
ax = fig.add_subplot(111)
ax.set_aspect(1.)
# if plotting contours
if args.contours != None:
if args.contourmap:
contourmap = args.contourmap
contourmapcoord = args.contourmapcoord
else:
contourmap = fitsfile[0]
contourmapcoord = 'C'
contourSkyMap, contourSkyMapHeader = hp.read_map(contourmap, h=True)
fnside1 = hp.get_nside(contourSkyMap)
ftoFind = 'TTYPE' + str(1)
contourSkyMapName = dict(contourSkyMapHeader)[ftoFind]
#<<<<<<< .mine
# fvalues = contourSkyMap #np.ma.masked_where(contourSkyMap == 0, contourSkyMap)
#=======
#>>>>>>> .r38901
if args.interpolation:
cRot = hp.Rotator(rot=rotMap, coord=[contourmapcoord, coords[-1]])
phi = np.linspace(np.deg2rad(xmax), np.deg2rad(xmin), args.xsize)
if xmin < 0 and xmax > 0 and (xmax - xmin) > 180.:
phi = np.linspace(
np.deg2rad(xmin) + 2. * np.pi, np.deg2rad(xmax),
args.xsize)
phi[(phi > 2. * np.pi)] -= 2. * np.pi
theta = 0.5 * np.pi - np.linspace(
np.deg2rad(ymin), np.deg2rad(ymax), args.xsize / faspect)
Phi, Theta = np.meshgrid(phi, theta)
rTheta,rPhi = cRot.I(Theta.reshape(phi.size*theta.size),\
Phi.reshape(phi.size*theta.size))
frotimg = hp.get_interp_val(contourSkyMap,rTheta.reshape(Phi.shape),\
rPhi.reshape(Theta.shape))
else:
tfig = plt.figure(num=3, figsize=figsize)
frotimg = hp.cartview(contourSkyMap,
fig=3,
coord=[contourmapcoord, coords[-1]],
title="",
cmap=colormap,
cbar=False,
lonra=[xmin, xmax],
latra=[ymin, ymax],
rot=rotMap,
notext=True,
xsize=1000,
min=dMin,
max=dMax,
return_projected_map=True)
plt.close(tfig)
if args.contours == []:
rMaxSig2 = (contourSkyMap[imgpix].max())**2
if rMaxSig2 < 11.83:
print(
"No spot detected with at least a 3sigma confidence contour"
)
contours = [-float("inf")]
else:
contours = [
sqrt(rMaxSig2 - 2.30),
sqrt(rMaxSig2 - 6.18),
sqrt(rMaxSig2 - 11.83)
]
else:
contours = args.contours
ccolor = args.contourscolor or 'g'
cstyle = args.contoursstyle or '-'
cwidth = args.contourswidth or 2.
print('Contours style: ', ccolor, cstyle, cwidth)
contp = ax.contour(frotimg,
levels=np.sort(contours),
colors=ccolor,
linestyles=cstyle,
linewidths=cwidth,
origin='upper',
extent=[cxmax, cxmin, ymax, ymin])
rotimg[(rotimg > dMax)] = dMax
rotimg[(rotimg < dMin)] = dMin
if not args.nofill:
imgp = ax.imshow(rotimg,extent=[cxmax, cxmin, ymax, ymin],\
vmin=dMin,vmax=dMax,cmap=colormap)
imgp.get_cmap().set_under('w', alpha=0.)
imgp.get_cmap().set_over('w', alpha=0.)
#User defined contour
if args.contourfile is not None:
ccolor = args.contourscolor or ['g']
cstyle = args.contoursstyle or ['-']
cwidth = args.contourswidth or [2.]
if len(ccolor) == 1:
ccolor = np.repeat(ccolor[0], len(args.contourfile))
if len(cstyle) == 1:
cstyle = np.repeat(cstyle[0], len(args.contourfile))
if len(cwidth) == 1:
cwidth = np.repeat(cwidth[0], len(args.contourfile))
for i, f in enumerate(args.contourfile):
ra, dec = np.loadtxt(f, unpack=True)
if xmax > 90. and xmin < -90.:
#Map at the discontinuity boundary
ra -= 180
ra[ra > 180] -= 360
ra[ra < -180] += 360
ax.plot(ra,
dec,
color=ccolor[i],
linestyle=cstyle[i],
linewidth=cwidth[i])
# Draw grid lines
xts = np.arange(np.floor(xmin), np.ceil(xmax + args.dmer), args.dmer)[1:-1]
xtlbs = [
'%g' % (xt + 360) if args.coords == 'C' and xt < 0 else '%g' % xt
for xt in xts
]
if xmin < 0. and xmax > 0. and (xmax - xmin) > 180.:
xts = np.arange(np.floor(cxmin), np.ceil(cxmax + args.dmer),
args.dmer)[1:-1]
xtlbs = []
for xt in xts:
cval = xt - 180.
if xt < 0:
cval = 180. + xt
if cval == -180:
cval = 180
if args.isMoon or args.isSun:
cval -= 180.
if cval < -180.:
cval += 360.
elif args.coords == 'C' and cval < 0:
cval += 360
xtlbs.append('%g' % (cval))
yts = np.arange(np.floor(ymin), np.ceil(ymax + args.dpar), args.dpar)[1:-1]
if args.nogrid == False:
ax.grid(color=textcolor)
ax.xaxis.set_ticks(xts)
ax.xaxis.set_ticklabels(xtlbs)
ax.yaxis.set_ticks(yts)
if args.preliminary:
plt.text((xmin + xmax) / 2.,
ymin + 0.85 * (ymax - ymin),
"PRELIMINARY",
color=textcolor,
alpha=0.8,
fontdict={
"family": "sans-serif",
"weight": "bold",
"size": 28
},
horizontalalignment='center')
# If TeVCat data are available, plot them
if args.tevcat or args.tevcatLabels:
if haveTeVCat:
try:
if args.tevcat:
tevcat = TeVCat.TeVCat(args.tevcat)
elif args.tevcatLabels:
tevcat = TeVCat.TeVCat(args.tevcatLabels)
except IOError as e:
print(e)
print("Downloading data from tevcat.uchicago.edu")
tevcat = TeVCat.TeVCat()
except:
print("Why caught here?")
print("Downloading data from tevcat.uchicago.edu")
tevcat = TeVCat.TeVCat()
cRot = hp.Rotator(coord=["C", coords[-1]])
tnside = 512
fpix = np.zeros(hp.nside2npix(tnside))
for cId in (1, 2):
catalog = tevcat.GetCatalog(cId)
ra = catalog.GetRA()
dec = catalog.GetDec()
assoc = catalog.GetCanonicalName()
cut = (assoc != 'Crab Pulsar')
ra = ra[cut]
dec = dec[cut]
assoc = assoc[cut]
cpix = hp.ang2pix(tnside, np.pi * 0.5 - dec, ra)
slpx = []
for sx, px in enumerate(cpix):
fpix[px] += 1
if fpix[px] != 1:
print("%s is a duplicate" % (assoc[sx]))
slpx.append(sx)
ra = np.delete(ra, slpx)
dec = np.delete(dec, slpx)
assoc = np.delete(assoc, slpx)
y, x = cRot(np.pi * 0.5 - dec, ra)
x = np.rad2deg(x) + frot
x[(x > 180.)] -= 360.
y = 90. - np.rad2deg(y)
cut = (x > xmin) & (x < xmax) & (y > ymin) & (y < ymax)
x = x[cut]
y = y[cut]
assoc = assoc[cut]
ax.scatter(x,
y,
color=textcolor,
facecolors="none",
marker="s")
if args.tevcatLabels:
for r, d, s in zip(x, y, assoc):
print(r, d, s)
ax.text(r,
d,
s + ' .',
color=textcolor,
rotation=args.catLabelsAngle,
fontdict={
'family': 'sans-serif',
'size': args.catLabelsSize,
'weight': 'bold'
})
else:
print("Sorry, TeVCat could not be loaded.")
# If Fermi data are available, plot them
if args.fermicat:
if haveFermi:
fcat = None
try:
fcat = FGLCatalog.FGLCatalog(args.fermicat)
aflag = fcat.GetAnalysisFlags()
acut = (aflag == 0) # cut analysis errors
flux = fcat.GetFlux1000() # 1-100 GeV flux
dflx = fcat.GetFlux1000Error() # flux uncertainty
fcut = dflx / flux < 0.5 # cut poorly measured srcs
cuts = np.logical_and(acut, fcut)
print('Using FGL')
except:
try:
fcat = FHLCatalog.FHLCatalog(args.fermicat)
cuts = (fcat.GetFlux() > 0.) # Dummy cut
print('Using FHL')
except:
print('Fermi catalog could not be loaded!')
if fcat != None:
# Don't show TeV associations if plotting from TeVCat
if args.tevcat or args.tevcatLabels:
tcfg = fcat.GetTeVCatFlag()
tcut = (tcfg == "N") | (tcfg == "C")
cuts = np.logical_and(cuts, tcut)
ra = fcat.GetRA()[cuts]
dec = fcat.GetDec()[cuts]
assoc = fcat.GetSourceName()[cuts]
catnms = fcat.GetCatalogName()[cuts]
for i in xrange(len(assoc)):
if assoc[i] == '':
assoc[i] = catnms[i]
cRot = hp.Rotator(coord=["C", coords[-1]])
y, x = cRot(np.pi * 0.5 - dec, ra)
x = np.rad2deg(x) + frot
x[(x > 180.)] -= 360.
y = 90. - np.rad2deg(y)
cut = (x > xmin) & (x < xmax) & (y > ymin) & (y < ymax)
x = x[cut]
y = y[cut]
assoc = assoc[cut]
ax.scatter(x, y, color=textcolor, facecolors="none", marker="o")
if args.fermicatLabels:
for r, d, s in zip(x, y, assoc):
ax.text(r,
d,
s,
color=textcolor,
rotation=args.catLabelsAngle,
fontdict={
'family': 'sans-serif',
'size': args.catLabelsSize,
'weight': 'bold'
})
else:
print("Sorry, the Fermi xFGL catalog could not be loaded.")
# If a hotspot list is given, plot it
if args.hotspots:
fhot = open(args.hotspots, "r")
ra = []
dec = []
assoc = []
for line in fhot:
if line.startswith("#"):
continue
larr = line.strip().split()
ra.append(float(larr[1]))
dec.append(float(larr[2]))
assoc.append(larr[4])
ra = np.deg2rad(ra)
dec = np.deg2rad(dec)
assoc = np.array(assoc)
cRot = hp.Rotator(coord=["C", coords[-1]])
y, x = cRot(np.pi * 0.5 - dec, ra)
x = np.rad2deg(x) + frot
x[(x > 180.)] -= 360.
y = 90. - np.rad2deg(y)
cut = (x > xmin) & (x < xmax) & (y > ymin) & (y < ymax)
x = x[cut]
y = y[cut]
assoc = assoc[cut]
ax.scatter(x, y, color=textcolor, facecolors="none", marker="o")
if args.hotspotLabels:
for r, d, s in zip(x, y, assoc):
print(r, d, s)
ax.text(r,
d,
s + ' .',
color=textcolor,
rotation=90,
fontdict={
'family': 'sans-serif',
'size': 8,
'weight': 'bold'
})
# If a gaussian fit file is given, plot it
if args.gaussfit:
gfit = open(args.gaussfit, "r")
gfit.next()
gfit.next()
ra, raErr = [float(i) for i in gfit.next().strip().split()]
dec, decErr = [float(i) for i in gfit.next().strip().split()]
raW, raWErr = [float(i) for i in gfit.next().strip().split()]
decW, decWErr = [float(i) for i in gfit.next().strip().split()]
gfit.close()
mrot = -180. if args.isMoon or args.isSun else 0.
cRot = hp.Rotator(coord=["C", coords[-1]])
y, x = cRot(np.pi * 0.5 - np.deg2rad(dec), np.deg2rad(ra + mrot))
x = np.rad2deg(x) + frot
x = x - 360. if x > 180. else x
y = 90. - np.rad2deg(y)
ellip0 = Ellipse((x, y),
width=2 * raW,
height=2 * decW,
edgecolor='black',
facecolor='None')
ax.add_patch(ellip0)
ax.scatter(x, y, s=20, color='black', facecolors="black", marker="o")
print(x, y, xmin, xmax, ymin, ymax)
ax.text(
x - 1,
y + 1,
"%s=%.02f$\pm$%.02f\n$\Delta\delta$=%.02f$\pm$%.02f\n%s=%.02f$\pm$%.02f\n$\sigma_\delta$=%.02f$\pm$%.02f"
% (r"$\Delta\alpha$", ra, raErr, dec, decErr, r"$\sigma_\alpha$",
raW, raWErr, decW, decWErr),
color=textcolor,
rotation=0,
fontdict={"size": 12})
# Set up the color bar
# Setup color tick marks
if args.colorbar:
cticks = args.ticks
if args.ticks == None:
if (dMax - dMin) > 3:
clrmin = np.floor(dMin)
clrmax = np.ceil(dMax)
ctspc = np.round((clrmax - clrmin) / 10.)
if ctspc == 0.:
ctspc = 1.
if clrmin < 0 and clrmax > 0:
cticks = -np.arange(0, -clrmin, ctspc)
cticks = np.sort(
np.unique(
np.append(cticks, np.arange(0, clrmax, ctspc))))
else:
cticks = np.arange(clrmin, clrmax + ctspc, ctspc)
else:
cticks = None
cb = fig.colorbar(
imgp,
orientation="horizontal",
shrink=0.85,
fraction=0.1,
#aspect=25,
pad=0.1,
ax=ax,
ticks=cticks)
if args.label:
skymapName = args.label
else:
if re.match("significance", skymapName):
skymapName = r"significance [$\sigma$]"
if args.gumbel:
skymapName = "log10(P-Value)"
if args.colorbar:
cb.set_label(skymapName)
xUnit = " [$^\circ$]"
if args.sexagesimal:
xUnit = ""
yUnit = " [$^\circ$]"
xlabel = r"$\alpha$%s" % xUnit
ylabel = r"$\delta$%s" % yUnit
# Set up the color bar and tick axis
if args.coords == "G":
xlabel = r"$l$%s" % xUnit
ylabel = r"$b$%s" % yUnit
if args.isMoon or args.isSun:
xlabel = r"$\Delta\alpha$%s" % xUnit
ylabel = r"$\Delta\delta$%s" % yUnit
# X axis label
ax.set_xlabel(xlabel, color='k')
# Y axis label
ax.set_ylabel(ylabel, color='k')
if args.sexagesimal:
if haveAstropy:
# Display RA with hh:mm nonsense.
ticksLocationsRA = plt.xticks()[0]
def degrees_to_hhmmss(ra):
return Angle(ra, u.degree).to_string(unit=u.hour, fields=2)
ticksLabelsRA = degrees_to_hhmmss(ticksLocationsRA)
plt.xticks(ticksLocationsRA, ticksLabelsRA)
# The same with DEC coordinates. Actually no, we never plot less than 1 degree...
if False:
ticksLocationsDEC = plt.yticks()[0]
def degrees_to_ddmmss(dec):
return Angle(dec, u.degree).to_string(unit=u.degree,
fields=2)
ticksLabelsDEC = degrees_to_ddmmss(ticksLocationsDEC)
plt.yticks(ticksLocationsDEC, ticksLabelsDEC)
else:
print('Error: "--sexagesimal" ignored (needs astropy module)')
# Title
if args.title != None:
ax.set_title(r"{0}".format(args.title.replace("\\n", "\n")), color='k')
if args.cross:
# Indicate the center of the plot with a thick black cross
ax.scatter(xC,
yC,
s=20**2,
marker="+",
facecolor="#000000",
color="#000000")
ax.set_ylim(ymin, ymax)
ax.set_xlim(cxmax, cxmin)
if args.squareaspect:
plt.axes().set_aspect(1. / cos((ymax + ymin) / 2 * pi / 180))
# Either output the image to a file and quit or plot it in a window
if args.output:
if not args.nofill:
fig.savefig(args.output, dpi=args.dpi)
else:
fig.savefig(args.output, dpi=args.dpi, transparent=True)
print("File %s created" % args.output)
else:
plt.show()
def Healpix2CartesianMerge(basedir, fname):
# Read from hdf5 file
f = h5py.File(fname,'r')
output = { 'pi0_brem':['pi0','brem'],
'ics_all':['ics_cmb','ics_opt','ics_fir'] }
for k, v in output.items():
print "Writing", k, v
# Get the cartesian maps for each energy
hpixcube=None
for t in v:
print 'Adding template:', t
if hpixcube is None:
hpixcube = f['templates/'+t][()]
else:
hpixcube += f['templates/'+t][()]
cartcube = np.zeros((hpixcube.shape[0], 721,1440), dtype=np.float32)
for i in range(hpixcube.shape[0]):
cartcube[i] = healpy.cartview(hpixcube[i], hold=True, return_projected_map=True,
xsize=1440, lonra=[-179.875, 179.875],flip='geo')
plt.gcf()
# Generate new hdu object
hdu_new = pyfits.PrimaryHDU(cartcube.astype(np.float32))
# Copy galdef into header
galdef = dict(f['/galdef'].attrs.items())
hdu_new.header.add_comment("Diffuse model generated by Eric Carlson ([email protected])")
for key, val in galdef.items():
hdu_new.header.add_comment(key + "=" +val)
hdu_new.header['CRVAL1'] = 0.0
hdu_new.header['CRPIX1'] = 720
hdu_new.header['CDELT1'] = 0.25
hdu_new.header['CUNIT1']= 'deg'
hdu_new.header['CTYPE1']= 'GLON-CAR'
hdu_new.header['CRVAL2'] = 0
hdu_new.header['CRPIX2'] = 361
hdu_new.header['CDELT2'] = 0.25
hdu_new.header['CUNIT2']= 'deg'
hdu_new.header['CTYPE2']= 'GLAT-CAR'
hdu_new.header['CRVAL3'] = float(galdef['E_gamma_min'])
hdu_new.header['CRPIX3'] = 0
hdu_new.header['CDELT3'] = np.log10(float(galdef['E_gamma_factor']))
hdu_new.header['CTYPE3']= 'Energy'
hdu_new.header['CUNIT3']= 'MeV'
hdu_new.header['EXTEND']=True
hdu_new.header['CREATOR'] = ('Eric Carlson ([email protected])', '')
# Write energy extension table
energies = np.array([float(galdef['E_gamma_min'])*float(galdef['E_gamma_factor'])**i for i in range(cartcube.shape[0])])
tbhdu = pyfits.BinTableHDU.from_columns([
pyfits.Column(name='Energy', format='D', array=energies),])
tbhdu.header['EXTNAME']="ENERGIES"
hdulist = pyfits.HDUList([hdu_new,tbhdu])
fname_out = fname.split('.')[0]+"_"+k+"_mapcube.fits"
hdulist.writeto(basedir+'/'+fname_out,clobber=True)
# For some reason on my platform, pyfits gzip compression was writing corrupt files.
p = subprocess.Popen(['gzip -f ' + basedir+'/' +fname_out ,], shell=True)
print 'File written to ', fname_out
zgrid[goodind] = NP.sqrt(1.0 - (xgrid[goodind]**2 + ygrid[goodind]**2))
xvect = xgrid.ravel()
yvect = ygrid.ravel()
zvect = zgrid.ravel()
xyzvect = NP.hstack((xvect.reshape(-1,1), yvect.reshape(-1,1), zvect.reshape(-1,1)))
if use_DSM or use_GSM:
dsm_file = '/data3/t_nithyanandan/project_MWA/foregrounds/gsmdata_{0:.1f}_MHz_nside_{1:0d}.fits'.format(freq/1e6,nside)
hdulist = fits.open(dsm_file)
dsm_table = hdulist[1].data
ra_deg = dsm_table['RA']
dec_deg = dsm_table['DEC']
temperatures = dsm_table['T_{0:.0f}'.format(freq/1e6)]
fluxes = temperatures
backdrop = HP.cartview(temperatures.ravel(), coord=['G','E'], rot=[0,0,0], xsize=backdrop_xsize, return_projected_map=True)
elif use_GLEAM or use_SUMSS or use_NVSS or use_CSM:
if use_GLEAM:
catalog_file = '/data3/t_nithyanandan/project_MWA/foregrounds/mwacs_b1_131016.csv' # GLEAM catalog
catdata = ascii.read(catalog_file, data_start=1, delimiter=',')
dec_deg = catdata['DEJ2000']
ra_deg = catdata['RAJ2000']
fpeak = catdata['S150_fit']
ferr = catdata['e_S150_fit']
freq_catalog = 1.4 # GHz
spindex = -0.83 + NP.zeros(fpeak.size)
fluxes = fpeak * (freq_catalog * 1e9 / freq)**spindex
elif use_SUMSS:
SUMSS_file = '/data3/t_nithyanandan/project_MWA/foregrounds/sumsscat.Mar-11-2008.txt'
catalog = NP.loadtxt(SUMSS_file, usecols=(0,1,2,3,4,5,10,12,13,14,15,16))
ra_deg = 15.0 * (catalog[:,0] + catalog[:,1]/60.0 + catalog[:,2]/3.6e3)
def plotBundleMaps(path,
outDir,
bundle,
dataLabel,
filterBand,
dataName=None,
skymap=True,
powerSpectrum=True,
cartview=False,
raRange=[-180, 180],
decRange=[-70, 10],
showPlots=True,
saveFigs=False,
outDirNameForSavedFigs='',
lmax=500,
nTicks=5,
numFormat='%.2f',
colorMin=None,
colorMax=None):
"""
Plot maps for the data in a metricBundle object without using MAF routines.
Required Parameters
-------------------
* path: str: path to the main directory where output directory is saved
* outDir: str: name of the main output directory
* bundle: metricBundle object.
* dataLabel: str: data type, e.g. 'counts', 'NumGal'. Will be the label for the colorbar in
in skymaps/cartview plots.
* filterBand: str: filter to consider, e.g. 'r'
Optional Parameters
-------------------
* dataName: str: dataLabel analog for filename. e.g. say for datalabel='c$_l$', a good dataName is 'cl'
Default: None
* skymap: boolean: set to True if want to plot skymaps. Default: True
* powerSpectrum: boolean: set to True if want to plot powerspectra; dipole is removed. Default: True
* cartview: boolean: set to True if want to plot cartview plots. Default: Fase
* raRange: float array: range of right ascention (in degrees) to consider in cartview plot; only useful when
cartview=True. Default: [-180,180]
* decRange: float array: range of declination (in degrees) to consider in cartview plot; only useful when
cartview=True. Default: [-70,10]
* showPlots: boolean: set to True if want to show figures. Default: True
* saveFigs: boolean: set to True if want to save figures. Default: False
* outDirNameForSavedFigs: str: name for the output directory if saveFigs=True. Default: ''
* lmax: int: upper limit on the multipole. Default: 500
* nTicks: int: (number of ticks - 1) on the skymap colorbar. Default: 5
* numFormat: str: number format for the labels on the colorbar. Default: '%.2f'
* colorMin: float: lower limit on the colorscale for skymaps. Default: None
* colorMax: float: upper limit on the colorscale for skymaps. Default: None
"""
if dataName is None:
dataName = dataLabel
if saveFigs:
# set up the subdirectory
outDirNew = outDirNameForSavedFigs
if not os.path.exists('%s%s/%s' % (path, outDir, outDirNew)):
os.makedirs('%s%s/%s' % (path, outDir, outDirNew))
if powerSpectrum:
# plot out the power spectrum
for dither in bundle:
plt.clf()
cl = hp.anafast(hp.remove_dipole(
bundle[dither].metricValues.filled(
bundle[dither].slicer.badval)),
lmax=lmax)
ell = np.arange(len(cl))
plt.plot(ell, (cl * ell * (ell + 1)) / 2.0 / np.pi)
plt.title('%s: %s' % (dataLabel, dither))
plt.xlabel(r'$\ell$')
plt.ylabel(r'$\ell(\ell+1)C_\ell/(2\pi)$')
plt.xlim(0, lmax)
if saveFigs:
# save power spectrum
filename = '%s_powerSpectrum_%s.png' % (dataName, dither)
plt.savefig('%s%s/%s/%s' % (path, outDir, outDirNew, filename),
bbox_inches='tight',
format='png')
if showPlots:
plt.show()
else:
plt.close('all')
if skymap:
# plot out the skymaps
for dither in bundle:
inSurveyIndex = np.where(
bundle[dither].metricValues.mask == False)[0]
median = np.median(bundle[dither].metricValues.data[inSurveyIndex])
stddev = np.std(bundle[dither].metricValues.data[inSurveyIndex])
if (colorMin == None):
colorMin = median - 1.5 * stddev
if (colorMax == None):
colorMax = median + 1.5 * stddev
increment = (colorMax - colorMin) / float(nTicks)
ticks = np.arange(colorMin + increment, colorMax, increment)
hp.mollview(bundle[dither].metricValues.filled(
bundle[dither].slicer.badval),
flip='astro',
rot=(0, 0, 0),
min=colorMin,
max=colorMax,
title='',
cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title(dither)
ax = plt.gca()
im = ax.get_images()[0]
fig = plt.gcf()
cbaxes = fig.add_axes([0.1, 0.03, 0.8,
0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im,
orientation='horizontal',
ticks=ticks,
format=numFormat,
cax=cbaxes)
cb.set_label(dataLabel)
if saveFigs:
# save skymap
filename = '%s_skymap_%s.png' % (dataName, dither)
plt.savefig('%s%s/%s/%s' % (path, outDir, outDirNew, filename),
bbox_inches='tight',
format='png')
if showPlots:
plt.show()
else:
plt.close('all')
if cartview:
# plot out the cartview plots
for dither in bundle:
inSurveyIndex = np.where(
bundle[dither].metricValues.mask == False)[0]
median = np.median(bundle[dither].metricValues.data[inSurveyIndex])
stddev = np.std(bundle[dither].metricValues.data[inSurveyIndex])
if (colorMin == None):
colorMin = median - 1.5 * stddev
if (colorMax == None):
colorMax = median + 1.5 * stddev
increment = (colorMax - colorMin) / float(nTicks)
ticks = np.arange(colorMin + increment, colorMax, increment)
hp.cartview(bundle[dither].metricValues.filled(
bundle[dither].slicer.badval),
flip='astro',
rot=(0, 0, 0),
lonra=raRange,
latra=decRange,
min=colorMin,
max=colorMax,
title='',
cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title(dither)
ax = plt.gca()
im = ax.get_images()[0]
fig = plt.gcf()
cbaxes = fig.add_axes([0.1, 0.25, 0.8,
0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im,
orientation='horizontal',
ticks=ticks,
format=numFormat,
cax=cbaxes)
cb.set_label(dataLabel)
if saveFigs:
# save cartview plot
filename = '%s_cartview_%s.png' % (dataName, dither)
plt.savefig('%s%s/%s/%s' % (path, outDir, outDirNew, filename),
bbox_inches='tight',
format='png')
if showPlots:
plt.show()
else:
plt.close('all')
moll_array = moll_array / (np.max(moll_array)) * 255.0
return moll_array
#######
# changing data
t_start = time.time()
for num, path_ in enumerate(all_images):
if not os.path.exists(destinationPath_data + 'msim_' + tag +
'%04i_data.tif' % num) or overwrite:
image_data = hp.read_map(path_)
image_data = np.array(image_data, np.float32)
moll_array = hp.cartview(image_data,
title=None,
xsize=x_size,
ysize=y_size,
return_projected_map=True)
if mod:
moll_array = modification(moll_array)
if norm:
moll_array = normalization(moll_array)
if rgb:
moll_array = toRGB(moll_array)
moll_image = Image.fromarray(moll_array)
print('Saving to ' + destinationPath_data + 'msim_' + tag +
'%04i_data.tif' % num)
moll_image.save(destinationPath_data + 'msim_' + tag +
'%04i_data.tif' % num)
print('Total elapsed time:', time.time() - t_start, 's')
plt.grid()
plt.show()
l = 51.641648
b = -9.6750019
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
offset = 1.
lonr = [51.25, 52.0]
latr = [-10., -9.35]
hp.cartview(ci_map, title='XXX', coord='G', unit='mag', min=0.1,max=0.4,
norm=None, xsize=800, lonra=lonr, latra=latr, #lonra=[ll-offset,ll+offset], latra=[b-offset,b+offset], min=0, max=0.4,
return_projected_map=True)
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
hp.projplot(ll, b, 'bo', lonlat=True, coord='G')
hp.projtext(ll, b, ' (' + str(round(ll,2)) + ',' + str(round(b,2)) + ')', lonlat=True, coord='G', fontsize=18, weight='bold')
# theta = (90.0-b)*deg2rad
# phi = l*deg2rad
# pix = hp.ang2pix(nside, theta, phi, nest=False)
# for x in pl.frange(l-offset, l+offset, dd):
# for y in pl.frange(b-offset, b+offset, dd):
def __init__(self, background, nside=None, fignum=None,
projection='mollweide', coord_bg='G', coord_plot='C',
partialmap=False, config='footprint.cfg',
map_path=None, download_config=False,
title='Survey Footprints', cbar=None, min=1.0,
max=5000.0, log=True, unit='', **kwds):
self.fig = pl.figure(fignum)
self.coord_plot = coord_plot
self.partialmap = partialmap
self.kwds = kwds
self.cbs = []
if projection == 'mollweide':
self.mapview = H.mollview
self.mapcontour = vf.mollcontour
elif projection == 'cartesian':
self.mapview = H.cartview
self.mapcontour = vf.cartcontour
elif projection == 'orthographic':
self.mapview = H.orthview
self.mapcontour = vf.orthcontour
elif projection == 'gnomonic':
self.mapview = H.gnomview
self.mapcontour = vf.gnomcontour
if map_path is None:
full_path = inspect.getfile(inspect.currentframe())
abs_path = os.path.split(full_path)[0]
map_path = os.path.join(abs_path, 'maps/')
self.config = ConfigHandler(config, map_path, nside=nside,
download_config=download_config)
# Could also just call load_survey which will call get_background
if isinstance(background, str):
bgmap, coord_bg, unit2 = self.config.load_survey(background,
get_unit=True)
background = bgmap[0]
if unit2 is not None:
unit = unit2
if nside is None:
nside = H.npix2nside(len(background))
self.nside = nside
coord = [coord_bg, coord_plot]
cm.Greys.set_under(alpha=0.0)
if log:
min = np.log(min)
max = np.log(max)
unit = r'$\log($' + unit + r'$)$'
background = np.log(background)
if self.partialmap:
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
H.cartview(background, title=title, coord=coord,
fig=self.fig.number, cmap=cm.Greys,
notext=True, flip='astro', min=min, max=max,
sub=sub, margins=margins, **kwds)
self.fig.delaxes(self.fig.axes[-1])
else:
self.mapview(background, title=title,
coord=coord, fig=self.fig.number, cmap=cm.Greys,
min=min, max=max, notext=True,
cbar=True, flip='astro', unit=unit, **kwds)
if not cbar:
self.fig.delaxes(self.fig.axes[-1])
H.graticule(dpar=30.0, dmer=30.0, coord='C', verbose=False)
weightsA = hp.fitsfunc.read_map(
"/Volumes/DataDavy/Foregrounds/RHT_mask_Equ_ch16_to_24_w75_s15_t70_galfapixcorr_UPSIDEDOWN_plusbgt30cut_plusstarmask_plusHFI_Mask_PointSrc_2048_R2.00_TempPol_allfreqs_RING_apodFWHM15arcmin_zerod.fits"
)
plotmaps = [Q353map, q_bamp1, q_bamp0p01, q_bamp0]
plotmaps = [U353map, u_bamp1, u_bamp0p01, u_bamp0]
plottitles = ["353 raw", "Z=1", "Z=1E-2", "Z=0"]
subnums = [221, 222, 223, 224]
fig = plt.figure()
for i, (plotmap, subnum,
plottitle) in enumerate(zip(plotmaps, subnums, plottitles)):
plotmap[weightsA < 0.5] = None
hp.cartview(plotmap,
latra=(5, 90),
lonra=(-30, 110),
sub=subnum,
title=plottitle)
# hp.mollview(plotmap, sub=subnum, title=plottitle)
RHT_TQU_Gal_fn = "../data/TQU_RHT_Planck_pol_ang_GALFA_HI_allsky_coadd_chS1004_1043_w75_s15_t70_Gal.fits"
RHT_T_Gal, QRHT, URHT = hp.fitsfunc.read_map(RHT_TQU_Gal_fn, field=(0, 1, 2))
psi_planck_flatprior_fn = maproot + "/psiMB_DR2_SC_241_353GHz_adaptivep0_True_new.fits"
psi_planck_flatprior = hp.fitsfunc.read_map(psi_planck_flatprior_fn)
psi_planck_flatprior_mask = copy.copy(psi_planck_flatprior)
psi_planck_flatprior_mask[weightsA < 0.5] = None
plotmaps = [thetarhtgal_mask, thet353_mask, thet1mmax]
subnums = [131, 132, 133]
plottitles = [r'$\psi_{RHT}$', r'$\psi_{353}$', r'1-R($\psi$) prior']
src = info['src']
for i in range(len(src)):
if( (src[i] != '3C409') ):
continue
l = info['l'][i]
b = info['b'][i]
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
hp.cartview(tau_map, title=src[i], coord='G', unit='',
norm=None, xsize=800, lonra=[ll-offset-0.1*offset,ll+offset+0.1*offset], latra=[b-offset-0.1*offset,b+offset+0.1*offset],
return_projected_map=True)
hp.projplot(ll, b, 'bo', lonlat=True, coord='G')
hp.projtext(ll, b, ' (' + str(round(ll,2)) + ',' + str(round(b,2)) + ')', lonlat=True, coord='G', fontsize=18, weight='bold', color='b')
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
theta = (90.0-b)*deg2rad
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
for x in pl.frange(l-offset, l+offset, dd):
for y in pl.frange(b-offset, b+offset, dd):
def main():
_path = os.path.dirname(os.path.abspath(__file__))
file_area = ['data/dcorr_smpas_obstime_15.fits', 'data/lambda_sfd_ebv.fits']
splus_tilles = os.path.join(_path,'data/splus_tiles.txt')
sn_tiles_file = os.path.expanduser('~/OneDrive/Documents/Documents/t80s/s-plus/sn2.txt')
#
# Reading data
#
map_area = np.array([H.read_map(os.path.join(_path, file_area[1])), ])
pt_splus = np.loadtxt(splus_tilles, unpack=True, usecols=(1, 2))
sn_tiles = np.loadtxt(sn_tiles_file, unpack=True)
for i in range(1, len(file_area)):
map_area = np.append(map_area, np.array([H.read_map(os.path.join(_path, file_area[i])), ]), axis=0)
#
# Plotting graph
#
# H.cartview(map_area[0], coord=['G', 'E'], cmap=cm.gray_r, cbar=False, notext=True,
# title='S-PLUS Survey Area', max=1)
H.cartview(map_area[0], coord=['G', 'C'], cmap=cm.gray_r, cbar=False, notext=True,
title='S-PLUS Survey Area', max=1)
####################################################################################################################
# START S-PLUS
#
# S-PLUS Southern part
#
# for line in np.arange(-5, 1, 0.25):
# # dec = np.zeros(100)+(90+20*np.sin(line))*np.pi/180.
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(35, -35, 100)) * np.pi / 180., '-',
# color='b', lw=1, alpha=0.2)
for line in np.arange(15, 45, 0.25):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(1, -35, 100)) * np.pi / 180., '-',
# color='m', lw=1) #, alpha=0.2)
H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(60., -60, 100)) * np.pi / 180., '-',
color='r', lw=1, alpha=1)
for line in np.arange(45, 70, 0.25):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(1, -35, 100)) * np.pi / 180., '-',
# color='m', lw=1) #, alpha=0.2)
H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(70., -70, 100)) * np.pi / 180., '-',
color='r', lw=1, alpha=1)
for line in np.arange(70, 75, 0.25):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(1, -35, 100)) * np.pi / 180., '-',
# color='m', lw=1) #, alpha=0.2)
H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(70., -90, 100)) * np.pi / 180., '-',
color='r', lw=1, alpha=1)
######
# for line in np.arange(15, 35, 0.25):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(1, -35, 100)) * np.pi / 180., '-',
# # color='m', lw=1) #, alpha=0.2)
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(0., -30, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
# for line in np.arange(35, 55, 0.25):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(1, -35, 100)) * np.pi / 180., '-',
# # color='m', lw=1) #, alpha=0.2)
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(0., -60, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
# for line in np.arange(20, 55, 0.25):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(1, -35, 100)) * np.pi / 180., '-',
# # color='m', lw=1) #, alpha=0.2)
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(0., 22.5, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
# for line in np.arange(20, 60, 0.25):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(1, -35, 100)) * np.pi / 180., '-',
# # color='m', lw=1) #, alpha=0.2)
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(22.5, 60, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
######
# for line in np.arange(15, 60, 0.25):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(45, -75, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
# for line in np.arange(30, 60, 0.25):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(40, -60, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
# for line in np.arange(60, 80, 0.25):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(80, -80, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
# ra = np.linspace(-180*np.pi/180.,-150*np.pi/180.,100.)
# dec = np.zeros(100)+(90+20*np.sin(ra))*np.pi/180.
# for line in np.arange(-4.,4.,0.25):
# H.projplot(dec+line * np.pi/180.,ra,'-',color='m',lw=2.)
#
# ra = np.linspace(150*np.pi/180.,180*np.pi/180.,100.)
# dec = np.zeros(100)+(90+20*np.sin(ra))*np.pi/180.
# for line in np.arange(-4.,4.,0.25):
# H.projplot(dec+line * np.pi/180.,ra,'-',color='m',lw=2.)
# for ra in np.linspace(-35 * np.pi / 180.,35 * np.pi / 180.,30.):
# dec = (90+20*np.sin(ra))*np.pi/180.
# H.projplot(np.array([dec, dec]) - 10. * np.pi / 180. ,[ra, -35 * np.pi /180.] ,'-',color='r',lw=2.,alpha=0.2)
# for line in np.arange(dec[0], dec[-1] , 0.25):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-35, 35, 100)) * np.pi / 180., '-',
# color='r', lw=1, alpha=0.2)
# for line in np.arange(30, 45, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-60, 91, 100)) * np.pi / 180., '-',
# color='r', lw=2, alpha=0.9)
# for line in np.arange(45, 80, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-90, 90, 100)) * np.pi / 180., '-',
# color='r', lw=2, alpha=0.9)
#
# S-PLUS Northern part
#
for line in np.arange(-10, 40, 0.5):
H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(150, 180, 100)) * np.pi / 180., '-',
color='r', lw=2, alpha=1)
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-157.5, -180, 100)) * np.pi / 180., '-',
# color='r', lw=2, alpha=0.2)
# for line in np.arange(-15, 0, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(157.5,180, 100)) * np.pi / 180., '-',
# color='r', lw=2, alpha=0.9)
# for line in np.arange(-5., 0, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-120,-160, 100)) * np.pi / 180., '-',
# color='r', lw=2, alpha=0.2)
for line in np.arange(-10., 40, 0.5):
H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-142.5,-180, 100)) * np.pi / 180., '-',
color='r', lw=2, alpha=1)
# for line in np.arange(30., 40, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-165,-120., 100)) * np.pi / 180., '-',
# color='r', lw=2, alpha=0.2)
l_start = [-12,210]
l_end = [12,260]
#
# # brange = [0.5,2,5,8,10,12]
brange = np.arange(-10, 10)
#
for b in brange:
for il in range(len(l_start)):
H.projplot(np.zeros(100) + (90.-b) * np.pi / 180.,
np.linspace(l_start[il] * np.pi / 180., l_end[il] * np.pi / 180., 100),
'g-',lw=2,alpha=1.,coord=['G','E'])
# H.projplot(np.zeros(100) + (90.-b) * np.pi / 180.,
# np.linspace(l_start[0] * np.pi / 180., l_end[0] * np.pi / 180., 100),
# 'r-',lw=1,alpha=0.8,coord=['G','E'])
#
# H.projplot(np.zeros(100) + (90.+b) * np.pi / 180.,
# np.linspace(l_start[0] * np.pi / 180., l_end[0] * np.pi / 180., 100),
# 'r-',lw=1,alpha=0.8,coord=['G','E'])
#
# H.projplot(np.zeros(100) + (90.-b) * np.pi / 180.,
# np.linspace(l_start[1] * np.pi / 180., l_end[1] * np.pi / 180., 100),
# 'r-',lw=1,alpha=0.8,coord=['G','E'])
#
# H.projplot(np.zeros(100) + (90.+b) * np.pi / 180.,
# np.linspace(l_start[1] * np.pi / 180., l_end[1] * np.pi / 180., 100),
# 'r-',lw=1,alpha=0.8,coord=['G','E'])
# H.projplot(np.zeros(100) + 85. * np.pi / 180.,
# np.linspace(-10. * np.pi / 180., -30. * np.pi / 180., 100),
# 'r-',lw=2,alpha=0.2,coord=['G','E'])
#
# H.projplot(np.zeros(100) + 95. * np.pi / 180.,
# np.linspace(-10. * np.pi / 180., -30. * np.pi / 180., 100),
# 'r-',lw=2,alpha=0.2,coord=['G','E'])
#
# H.projplot(np.zeros(100) + 82. * np.pi / 180.,
# np.linspace(-10. * np.pi / 180., -30. * np.pi / 180., 100),
# 'r-',lw=2,alpha=0.2,coord=['G','E'])
#
# H.projplot(np.zeros(100) + 98. * np.pi / 180.,
# np.linspace(-10. * np.pi / 180., -30. * np.pi / 180., 100),
# 'r-',lw=2,alpha=0.2,coord=['G','E'])
#
# H.projplot(np.zeros(100) + 90. * np.pi / 180.,
# np.linspace(210. * np.pi / 180., 230. * np.pi / 180., 100),
# 'r-',lw=2,alpha=0.2,coord=['G','E'])
# H.projplot(np.zeros(100) + 92. * np.pi / 180.,
# np.linspace(170. * np.pi / 180., 150. * np.pi / 180., 100),
# 'r-',lw=2,alpha=0.2,coord=['G','E'])
# H.projplot((90 - sn_tiles[1]) * np.pi / 180., sn_tiles[0] * np.pi / 180., 's', color='r', alpha=0.8,
# coord='E') #,coord=['E','G'])
H.graticule()
# py.show()
#
# return 0
# END S-PLUS
####################################################################################################################
# ATLAS
# for line in np.arange(10, 40, 1.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-37.5, 60, 100)) * np.pi / 180., ':',
# color='g', lw=2, alpha=0.9)
# H.projplot((90 - np.zeros(100) + 42) * np.pi / 180., (np.linspace(-37.5, 61, 100)) * np.pi / 180., '-', color='g',
# lw=3)
# H.projplot((90 - np.zeros(100) + 10) * np.pi / 180., (np.linspace(-37.5, 61, 100)) * np.pi / 180., '-', color='g',
# lw=2)
# H.projplot((90 - np.linspace(-40, -10, 100)) * np.pi / 180., (np.zeros(100) + 62) * np.pi / 180., '-', color='g',
# lw=3)
# H.projplot((90 - np.linspace(-40, -10, 100)) * np.pi / 180., (np.zeros(100) - 37.5) * np.pi / 180., '-', color='g',
# lw=2)
#
# for line in np.arange(2, 29, 1.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(150, 180, 100)) * np.pi / 180., ':',
# color='g', lw=2, alpha=0.9)
# if line < 20:
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-180, -127.5, 100)) * np.pi / 180., ':',
# color='g', lw=2, alpha=0.9)
# else:
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-180, -135, 100)) * np.pi / 180., ':',
# color='g', lw=2, alpha=0.9)
#
# H.projplot((90 - np.zeros(100) + 29) * np.pi / 180., (np.linspace(150, 180, 100)) * np.pi / 180., '-', color='g',
# lw=2)
# H.projplot((90 - np.zeros(100) + 20) * np.pi / 180., (np.linspace(-135, -127.5, 100)) * np.pi / 180., '-',
# color='g', lw=2)
# H.projplot((90 - np.zeros(100) + 29) * np.pi / 180., (np.linspace(-180, -135., 100)) * np.pi / 180., '-', color='g',
# lw=2)
#
# H.projplot((90 - np.zeros(100) + 2) * np.pi / 180., (np.linspace(150, 180, 100)) * np.pi / 180., '-', color='g',
# lw=2)
# H.projplot((90 - np.zeros(100) + 2) * np.pi / 180., (np.linspace(-180, -127.5, 100)) * np.pi / 180., '-', color='g',
# lw=2)
#
# H.projplot((90 - np.linspace(-29, -2, 100)) * np.pi / 180., (np.zeros(100) + 150) * np.pi / 180., '-', color='g',
# lw=2)
# H.projplot((90 - np.linspace(-20, -2, 100)) * np.pi / 180., (np.zeros(100) - 127.5) * np.pi / 180., '-', color='g',
# lw=2)
# H.projplot((90 - np.linspace(-29, -20, 100)) * np.pi / 180., (np.zeros(100) - 135) * np.pi / 180., '-', color='g',
# lw=2)
#
# # H.projplot((90 - pt_splus[1]) * np.pi / 180., pt_splus[0] * np.pi / 180., 's', color='r', alpha=0.8,
# # coord='E') #,coord=['E','G'])
#
# ra = np.linspace(-np.pi,np.pi,100.)
# dec = np.zeros(100)+(90+20*np.sin(ra))*np.pi/180.
# H.projplot(dec,ra,'-',color='w',lw=2.)
# H.projplot(dec,ra,'--',color='k',lw=2.)
#
# H.graticule()
# #
# # py.savefig(os.path.expanduser('~/Desktop/figure_01.pdf'))
# #
# # py.savefig(os.path.expanduser('~/Desktop/figure_01.pdf'))
# # py.show()
# #
# # return 0
#
# ####################################################################################################################
# # Vista
# #
# ## VVV Bulge
# #
#
# b = np.append(np.append(np.append(np.linspace(80. * np.pi / 180., 95. * np.pi / 180., 100),
# np.zeros(100) + 80. * np.pi / 180.),
# np.linspace(80. * np.pi / 180., 95. * np.pi / 180., 100)),
# np.zeros(100) + 96. * np.pi / 180.)
#
# l = np.append(np.append(np.append(np.zeros(100) + 10. * np.pi / 180.,
# np.linspace(-10. * np.pi / 180., 10. * np.pi / 180., 100)),
# np.zeros(100) - 10. * np.pi / 180.),
# np.linspace(-10. * np.pi / 180., 10. * np.pi / 180., 100))
# H.projplot(b,l,'-',color="0.5",lw=2,coord=['G','E'])
#
# #
# ## VVV Disk
# #
#
# b = np.append(np.append(np.append(np.linspace(88. * np.pi / 180., 92. * np.pi / 180., 100),
# np.zeros(100) + 88. * np.pi / 180.),
# np.linspace(88. * np.pi / 180., 92. * np.pi / 180., 100)),
# np.zeros(100) + 92. * np.pi / 180.)
#
# l = np.append(np.append(np.append(np.zeros(100) - 10. * np.pi / 180.,
# np.linspace(-10. * np.pi / 180., -65. * np.pi / 180., 100)),
# np.zeros(100) - 65. * np.pi / 180.),
# np.linspace(-65. * np.pi / 180., -10. * np.pi / 180., 100))
# H.projplot(b,l,'-',color="0.5",lw=2,coord=['G','E'])
#
#
# ##################################
# # VPHAS
#
# vb = np.append(np.append(np.append(np.linspace(80. * np.pi / 180., 100. * np.pi / 180., 100),
# np.zeros(100) + 80. * np.pi / 180.),
# np.linspace(80. * np.pi / 180., 100. * np.pi / 180., 100)),
# np.zeros(100) + 100. * np.pi / 180.)
#
# vl = np.append(np.append(np.append(np.zeros(100) + 10. * np.pi / 180.,
# np.linspace(-10. * np.pi / 180., 10. * np.pi / 180., 100)),
# np.zeros(100) - 10. * np.pi / 180.),
# np.linspace(-10. * np.pi / 180., 10. * np.pi / 180., 100))
# # H.projplot(vb,vl,'r-',lw=2,coord=['G','E'])
#
# #
# ## VPHAS Disk
# #
#
# vb = np.append(np.append(np.append(np.linspace(85. * np.pi / 180., 95. * np.pi / 180., 100),
# np.zeros(100) + 85. * np.pi / 180.),
# np.linspace(85. * np.pi / 180., 95. * np.pi / 180., 100)),
# np.zeros(100) + 95. * np.pi / 180.)
#
# vl = np.append(np.append(np.append(np.zeros(100) + 40. * np.pi / 180.,
# np.linspace(40. * np.pi / 180., -160. * np.pi / 180., 100)),
# np.zeros(100) - 160. * np.pi / 180.),
# np.linspace(-160. * np.pi / 180., 40. * np.pi / 180., 100))
# H.projplot(vb,vl,'m-',lw=2,coord=['G','E'])
#
# ##################################
# # Avoiding region
# vb = np.zeros(100) + 120. * np.pi / 180.
# vl = np.linspace(0. * np.pi / 180., 360. * np.pi / 180., 100)
# H.projplot(vb,vl,'w--',lw=2,coord=['G','E'])
#
# #
# ## DECAL
# #
# for line in np.arange(-30, 10, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(120, 240, 100)) * np.pi / 180., ':',
# color='k', lw=2, alpha=0.9)
#
# for line in np.arange(-30, -15, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-30, 40, 100)) * np.pi / 180., ':',
# color='k', lw=2, alpha=0.9)
#
# for line in np.arange(-15, -1, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-45, 40, 100)) * np.pi / 180., ':',
# color='k', lw=2, alpha=0.9)
#
# for line in np.arange(-10, 10, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(40, 60, 100)) * np.pi / 180., ':',
# color='k', lw=2, alpha=0.9)
#
# for line in np.arange(1, 10, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-0, -45, 100)) * np.pi / 180., ':',
# color='k', lw=2, alpha=0.9)
#
# ##
#
# a = [0,0,5,5]
# d = [-3,+3,+3,-3]
# for d in np.arange(-3,3,0.5):
# H.projplot((90 - np.zeros(100) + d) * np.pi / 180., (np.linspace(0, 5, 100)) * np.pi / 180., '-', color='c',
# lw=2)
#
# # H.projplot((90 + np.linspace(10, 30, 100)) * np.pi / 180., (np.zeros(100) + 61) * np.pi / 180., '-', color='r',
# # lw=2)
# # H.projplot((90 - np.zeros(100) + 30) * np.pi / 180., (np.linspace(60, 90, 100)) * np.pi / 180., '-', color='r',
# # lw=2)
# # H.projplot((90 + np.linspace(30, 80, 100)) * np.pi / 180., (np.zeros(100) + 90) * np.pi / 180., '-', color='r',
# # lw=2)
# # H.projplot((90 - np.zeros(100) + 80) * np.pi / 180., (np.linspace(-90, 90, 100)) * np.pi / 180., '-', color='r',
# # lw=2)
# # H.projplot((90 + np.linspace(45, 80, 100)) * np.pi / 180., (np.zeros(100) - 90) * np.pi / 180., '-', color='r',
# # lw=2)
# # H.projplot((90 - np.zeros(100) + 45) * np.pi / 180., (np.linspace(-90, -60, 100)) * np.pi / 180., '-', color='r',
# # lw=2)
# # H.projplot((90 + np.linspace(10, 45, 100)) * np.pi / 180., (np.zeros(100) -60) * np.pi / 180., '-', color='r',
# # lw=2)
#
# #
# # H.projplot((90 - nna_pt[1]) * np.pi / 180., nna_pt[0] * np.pi / 180., '.', color='r', alpha=0.2,
# # coord='E') #,coord=['E','G'])
# # H.projplot((90 - nsa_pt[1]) * np.pi / 180., nsa_pt[0] * np.pi / 180., '.', color='r', alpha=0.2,
# # coord='E') #,coord=['E','G'])
# #
# # H.projplot( (90-pt_kepler[1]) * np.pi/180. , pt_kepler[0] * np.pi /180., '-', color='c', alpha=1.0,
# # coord='E',lw=2) #,coord=['E','G'])
#
#
# # H.projplot((90 - pt_smaps_sul[1]) * np.pi / 180., pt_smaps_sul[0] * np.pi / 180., '.', color='r', alpha=0.2,
# # coord='E') #,coord=['E','G'])
# # H.projplot((90 - pt_smaps_nor[1]) * np.pi / 180., pt_smaps_nor[0] * np.pi / 180., '.', color='r', alpha=0.8,
# # coord='E') #,coord=['E','G'])
#
# # H.projplot((90 - pt_spp[1]) * np.pi / 180., pt_spp[0] * np.pi / 180., 'o',
# # color='b', coord='E') #,coord=['E','G'])
#
# #H.projplot((90-pt_smaps_lmc[1])*np.pi/180.,pt_smaps_lmc[0]*np.pi/180.,'.',color='k',alpha=1,coord='E')#,coord=['E','G'])
# #H.projplot((90-pt_smaps_smc[1])*np.pi/180.,pt_smaps_smc[0]*np.pi/180.,'.',color='k',alpha=1,coord='E')#,coord=['E','G'])
#
# H.graticule()
#
# # py.show()
# #
# # return 0
#
# # SPT
#
# H.projplot((90 - np.zeros(100) + 65) * np.pi / 180., (np.linspace(-60, 105, 100)) * np.pi / 180., '-', color='b',
# lw=2)
#
# # for line in np.arange(40, 65, 0.5):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-60, 105, 100)) * np.pi / 180., '-',
# # color='b', lw=2, alpha=0.5)
# H.projplot((90 - np.zeros(100) + 40) * np.pi / 180., (np.linspace(-60, 105, 100)) * np.pi / 180., '-', color='b',
# lw=2)
# # H.projplot((90 - np.zeros(100) + 40) * np.pi / 180., (np.linspace(-30, 105, 100)) * np.pi / 180., '--', color='b',
# # lw=2)
# # H.projplot((90 - np.zeros(100) + 40) * np.pi / 180., (np.linspace(+60, 105, 100)) * np.pi / 180., '-', color='b',
# # lw=2)
#
# H.projplot((90 - np.linspace(-65, -40, 100)) * np.pi / 180., (np.zeros(100) - 60) * np.pi / 180., '-', color='b',
# lw=2)
# H.projplot((90 - np.linspace(-65, -40, 100)) * np.pi / 180., (np.zeros(100) + 105) * np.pi / 180., '-', color='b',
# lw=2)
#
# # VIKING
# # for line in np.arange(25, 40, 0.5):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-30, 60, 100)) * np.pi / 180., '-',
# # color='b', lw=2, alpha=0.5)
# H.projplot((90 - np.linspace(-40, -25, 100)) * np.pi / 180., (np.zeros(100) - 30) * np.pi / 180., '-', color='b',
# lw=2)
# H.projplot((90 - np.linspace(-40, -25, 100)) * np.pi / 180., (np.zeros(100) + 60) * np.pi / 180., '-', color='b',
# lw=2)
# H.projplot((90 - np.zeros(100) + 25) * np.pi / 180., (np.linspace(-30, 60, 100)) * np.pi / 180., '-', color='b',
# lw=2)
#
# # ROUND 82
# # for line in np.arange(-3, 45, 0.5):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-25, 3, 100)) * np.pi / 180., '-',
# # color='b', lw=2, alpha=0.5)
# # H.projplot((90 - np.linspace(3, -45, 100)) * np.pi / 180., (np.zeros(100) - 25) * np.pi / 180., '-', color='b',
# # lw=2)
# # H.projplot((90 - np.linspace(3, -45, 100)) * np.pi / 180., (np.zeros(100) + 3) * np.pi / 180., '-', color='b', lw=2)
# # H.projplot((90 - np.zeros(100) + 45) * np.pi / 180., (np.linspace(-25, 3, 100)) * np.pi / 180., '-', color='b',
# # lw=2)
# # H.projplot((90 - np.zeros(100) - 3) * np.pi / 180., (np.linspace(-25, 3, 100)) * np.pi / 180., '-', color='b', lw=2)
#
# H.projplot((90 - np.linspace(3, -25, 100)) * np.pi / 180., (np.zeros(100) + 45) * np.pi / 180., '-', color='b',
# lw=2)
# H.projplot((90 - np.linspace(3, -25, 100)) * np.pi / 180., (np.zeros(100) - 3) * np.pi / 180., '-', color='b', lw=2)
# H.projplot((90 - np.zeros(100) + 25) * np.pi / 180., (np.linspace(+45, -3, 100)) * np.pi / 180., '-', color='b',
# lw=2)
# H.projplot((90 - np.zeros(100) - 3) * np.pi / 180., (np.linspace(+45, -3, 100)) * np.pi / 180., '-', color='b', lw=2)
#
# # STRIPE 82
# H.projplot((90 - np.linspace(-1, +1, 100)) * np.pi / 180., (np.zeros(100) + 3) * np.pi / 180., '-', color='b', lw=2)
# H.projplot((90 - np.linspace(-1, +1, 100)) * np.pi / 180., (np.zeros(100) + 43) * np.pi / 180., '-', color='b',
# lw=2)
# H.projplot((90 - np.zeros(100) + 1) * np.pi / 180., (np.linspace(-3, -43, 100)) * np.pi / 180., '-', color='b',
# lw=2)
# H.projplot((90 - np.zeros(100) - 1) * np.pi / 180., (np.linspace(-3, -43, 100)) * np.pi / 180., '-', color='b',
# lw=2)
#
# # KIDS
#
# H.projplot((90 - np.linspace(-5, +5, 100)) * np.pi / 180., (np.zeros(100) - 120) * np.pi / 180., '-', color='y',
# lw=2)
# H.projplot((90 - np.linspace(-5, +5, 100)) * np.pi / 180., (np.zeros(100) - 202.5) * np.pi / 180., '-', color='y',
# lw=2)
#
# for line in np.arange(-5, 5, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-120, -180, 100)) * np.pi / 180., '-',
# color='y', lw=2, alpha=0.5)
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(157, 180, 100)) * np.pi / 180., '-',
# color='y', lw=2, alpha=0.5)
# H.projplot((90 - np.zeros(100) - 5) * np.pi / 180., (np.linspace(157, 180, 100)) * np.pi / 180., '-', color='y',
# lw=2)
# H.projplot((90 - np.zeros(100) + 5) * np.pi / 180., (np.linspace(157, 180, 100)) * np.pi / 180., '-', color='y',
# lw=2)
#
# H.projplot((90 - np.zeros(100) - 5) * np.pi / 180., (np.linspace(-120, -180, 100)) * np.pi / 180., '-', color='y',
# lw=2)
# H.projplot((90 - np.zeros(100) + 5) * np.pi / 180., (np.linspace(-120, -180, 100)) * np.pi / 180., '-', color='y',
# lw=2)
#
# H.projplot((90 - np.linspace(-30, -25, 100)) * np.pi / 180., (np.zeros(100) - 30) * np.pi / 180., '-', color='y',
# lw=2)
# H.projplot((90 - np.linspace(-30, -25, 100)) * np.pi / 180., (np.zeros(100) + 53.5) * np.pi / 180., '-', color='y',
# lw=2)
#
# for line in np.arange(-30, -25, 0.5):
# H.projplot((90 - np.zeros(100) - line) * np.pi / 180., (np.linspace(-30.5, 53.5, 100)) * np.pi / 180., '-',
# color='y', lw=2, alpha=0.5)
#
# H.projplot((90 - np.zeros(100) + 30) * np.pi / 180., (np.linspace(-30, 30, 100)) * np.pi / 180., '-', color='y',
# lw=2)
# H.projplot((90 - np.zeros(100) + 25) * np.pi / 180., (np.linspace(-30, 30, 100)) * np.pi / 180., '-', color='y',
# lw=2)
#
# #
# ## GAMMA
# #
# # G02 30.2 : 38.8 / -10.25 : -3.72
# # G09 129.0 : 141.0 / -2 : +3
# # G12 174.0 : 186.0 / -3 : +2
# # G15 211.5 : 223.5 / -2 : +3
# # G23 339.0 : 351.0 / -35 : -30
#
# for line in np.arange(3.72, 10.25, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(30.2, 38.8, 100)) * np.pi / 180., '-',
# color='#66FF33', lw=2, alpha=0.9)
# for line in np.arange(-3, 2, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(129., 141., 100)) * np.pi / 180., '-',
# color='#66FF33', lw=2, alpha=0.9)
# for line in np.arange(-2, 3, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(174, 186, 100)) * np.pi / 180., '-',
# color='#66FF33', lw=2, alpha=0.9)
# for line in np.arange(-3, -2, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(211.5, 223.5, 100)) * np.pi / 180., '-',
# color='#66FF33', lw=2, alpha=0.9)
# for line in np.arange(30, 35, 0.5):
# H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(339, 351, 100)) * np.pi / 180., '-',
# color='#66FF33', lw=2, alpha=0.9)
#
# #
# ## VIKING
# #
#
# #SGP: 22h00 < RA < 03h30 , -36 < Dec < -26 deg.
# # H.projplot((90 - np.zeros(100) + 45.) * np.pi / 180., (np.linspace(-36, -26, 100)) * np.pi / 180., '-', color='k',
# # lw=2)
# # H.projplot((90 - np.linspace(-45, 30, 100)) * np.pi / 180., (np.zeros(100) - 36) * np.pi / 180., '-', color='k',
# # lw=2)
# # H.projplot((90 - np.zeros(100) - 30.) * np.pi / 180., (np.linspace(-36, -26, 100)) * np.pi / 180., '-', color='k',
# # lw=2)
# # H.projplot((90 - np.linspace(-45, 30, 100)) * np.pi / 180., (np.zeros(100) - 26) * np.pi / 180., '-', color='k',
# # lw=2)
# # # for line in np.arange(-45, 30, 0.5):
# # # H.projplot((90 - np.zeros(100) - line) * np.pi / 180., (np.linspace(-36, -26, 100)) * np.pi / 180., '-',
# # # color='k', lw=2, alpha=0.5)
# # #NGP: 10h00 < RA < 15h30, -5 < Dec < +4 deg
# # H.projplot((90 - np.zeros(100) + 150.) * np.pi / 180., (np.linspace(-5, 4, 100)) * np.pi / 180., '-', color='k',
# # lw=2)
# # H.projplot((90 - np.linspace(-150, -232.5, 100)) * np.pi / 180., (np.zeros(100) + 4) * np.pi / 180., '-', color='k',
# # lw=2)
# # H.projplot((90 - np.zeros(100) + 232.5) * np.pi / 180., (np.linspace(-5, +4, 100)) * np.pi / 180., '-', color='k',
# # lw=2)
# # H.projplot((90 - np.linspace(-150, -232.5, 100)) * np.pi / 180., (np.zeros(100) - 5) * np.pi / 180., '-', color='k',
# # lw=2)
# # for line in np.arange(150, 232.5, 0.5):
# # H.projplot((90 - np.zeros(100) + line) * np.pi / 180., (np.linspace(-5, +4, 100)) * np.pi / 180., '-',
# # color='k', lw=2, alpha=0.5)
#
# ####################################################################################################################
# # Ecliptic
# #
#
# # ra = np.linspace(-np.pi,np.pi,100.)
# # dec = np.zeros(100)+(90+20*np.sin(ra))*np.pi/180.
# # H.projplot(dec,ra,'-',color='w',lw=2.)
# # H.projplot(dec,ra,'--',color='k',lw=2.)
H.graticule()
py.savefig(os.path.expanduser('~/Desktop/figure_01.pdf'))
py.show()
def healpy_cartview(plot_map,
fig=None,
coord='G',
xsize=2000,
title='',
min_value=0.0,
max_value=0.0,
cbar=True,
notext=False,
norm=None,
name_out=None,
cmap=plt.cm.viridis,
return_projected=False):
"""
map - healpix map (1-d array),
fig - figure number to use,
coord - coordinate system of the map ('G', ['G', 'E'] or ['G', 'C']),
xsize - size of the image,
title - title of the plot,
min_value - minimum range value,
max_value - maximum range value,
cbar - display the colorbar,
notext - if True, no text is printed around the map,
norm - color normalization (None (linear), 'hist' or 'log'),
name_out - name of the output picture,
cmap - color map of the plot (jet, viridis, jet and other)
"""
cmap.set_under("w")
if fig is None:
fig = plt.figure(figsize=(20, 10))
if return_projected:
if min_value != 0.0 or max_value != 0.0:
plot_map_out = hp.cartview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
min=min_value,
max=max_value,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap,
return_projected_map=True)
else:
plot_map_out = hp.cartview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap,
return_projected_map=True)
if name_out is not None:
plt.savefig(name_out, dpi=400)
return plot_map_out
else:
if min_value != 0.0 or max_value != 0.0:
hp.cartview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
min=min_value,
max=max_value,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap)
else:
hp.cartview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap)
if name_out is not None:
plt.savefig(name_out, dpi=400)
def __call__(self, metricValueIn, slicer, userPlotDict, fignum=None, ):
"""
Plot the sky map of metricValue using healpy cartview plots in thin strips.
raMin: Minimum RA to plot (deg)
raMax: Max RA to plot (deg). Note raMin/raMax define the centers that will be plotted.
raLen: Length of the plotted strips in degrees
decMin: minimum dec value to plot
decMax: max dec value to plot
metricValueIn: metric values
"""
fig = plt.figure(fignum)
plotDict = {}
plotDict.update(self.defaultPlotDict)
plotDict.update(userPlotDict)
norm = None
if plotDict['logScale']:
norm = 'log'
if plotDict['cmap'] is None:
cmap = cm.cubehelix
else:
cmap = plotDict['cmap']
if type(cmap) == str:
cmap = getattr(cm, cmap)
# Make colormap compatible with healpy
cmap = colors.LinearSegmentedColormap('cmap', cmap._segmentdata, cmap.N)
cmap.set_over(cmap(1.0))
cmap.set_under('w')
cmap.set_bad('gray')
if plotDict['zp'] is not None:
metricValue = metricValueIn - plotDict['zp']
elif plotDict['normVal'] is not None:
metricValue = metricValueIn / plotDict['normVal']
else:
metricValue = metricValueIn
if plotDict['percentileClip'] is not None:
pcMin, pcMax = percentileClipping(metricValue.compressed(),
percentile=plotDict['percentileClip'])
if plotDict['colorMin'] is None:
plotDict['colorMin'] = pcMin
if plotDict['colorMax'] is None:
plotDict['colorMax'] = pcMax
if (plotDict['colorMin'] is not None) or (plotDict['colorMax'] is not None):
clims = [plotDict['colorMin'], plotDict['colorMax']]
else:
clims = None
# Make sure there is some range on the colorbar
if clims is None:
if metricValue.compressed().size > 0:
clims = [metricValue.compressed().min(), metricValue.compressed().max()]
else:
clims = [-1, 1]
if clims[0] == clims[1]:
clims[0] = clims[0] - 1
clims[1] = clims[1] + 1
racenters = np.arange(plotDict['raMin'], plotDict['raMax'], plotDict['raLen'])
nframes = racenters.size
for i, racenter in enumerate(racenters):
if i == 0:
useTitle = plotDict['title'] + ' /n' + '%i < RA < %i' % (racenter - plotDict['raLen'],
racenter + plotDict['raLen'])
else:
useTitle = '%i < RA < %i' % (racenter - plotDict['raLen'], racenter + plotDict['raLen'])
hp.cartview(metricValue.filled(slicer.badval), title=useTitle, cbar=False,
min=clims[0], max=clims[1], flip='astro', rot=(racenter, 0, 0),
cmap=cmap, norm=norm, lonra=[-plotDict['raLen'], plotDict['raLen']],
latra=[plotDict['decMin'], plotDict['decMax']], sub=(nframes + 1, 1, i + 1), fig=fig)
hp.graticule(dpar=20, dmer=20, verbose=False)
# Add colorbar (not using healpy default colorbar because want more tickmarks).
fig = plt.gcf()
ax1 = fig.add_axes([0.1, .15, .8, .075]) # left, bottom, width, height
# Add label.
if plotDict['label'] is not None:
plt.figtext(0.8, 0.9, '%s' % plotDict['label'])
# Make the colorbar as a seperate figure,
# from http: //matplotlib.org/examples/api/colorbar_only.html
cnorm = colors.Normalize(vmin=clims[0], vmax=clims[1])
# supress silly colorbar warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=cnorm,
orientation='horizontal', format=plotDict['cbarFormat'])
cb1.set_label(plotDict['xlabel'])
cb1.ax.tick_params(labelsize=plotDict['labelsize'])
# If outputing to PDF, this fixes the colorbar white stripes
if plotDict['cbar_edge']:
cb1.solids.set_edgecolor("face")
fig = plt.gcf()
return fig.number
plt.grid()
plt.show()
l = 51.641648
b = -9.6750019
# Plot cartview a/o mollview #
ll = l
if (l>180):
ll = ll-360.
offset = 1.
lonr = [51.25, 52.0]
latr = [-10., -9.35]
m = hp.cartview(ir_map, title='IRIS 100micron', coord='G', unit='MJy/sr', min=0.,max=26.,
norm=None, xsize=800, lonra=lonr, latra=latr, #lonra=[ll-offset,ll+offset], latra=[b-offset,b+offset],
return_projected_map=True)
# hp.mollview(tau_map, title=info['src'][i]+'('+str(info['l'][i])+','+str(info['b'][i])+') - '+map_file,
# coord='G', unit='', rot=[l,b,0], norm='hist', min=1e-7,max=1e-3, xsize=800)
# Cal. #
hp.projplot(ll, b, 'bo', lonlat=True, coord='G')
hp.projtext(ll, b, ' (' + str(round(ll,2)) + ',' + str(round(b,2)) + ')', lonlat=True, coord='G', fontsize=18, weight='bold')
# theta = (90.0-b)*deg2rad
# phi = l*deg2rad
# pix = hp.ang2pix(nside, theta, phi, nest=False)
# for x in pl.frange(l-offset, l+offset, dd):
# for y in pl.frange(b-offset, b+offset, dd):
def plotSkyMap(self, metricValueIn, xlabel=None, title='', raMin=-90, raMax=90,
raLen=45., decMin=-2., decMax=2.,
logScale=False, cbarFormat='%.2f', cmap=cm.jet,
percentileClip=None, colorMin=None, colorMax=None,
plotMaskedValues=False, zp=None, normVal=None,
cbar_edge=True, label=None, fignum=None, **kwargs):
"""
Plot the sky map of metricValue using healpy cartview plots in thin strips.
raMin: Minimum RA to plot (deg)
raMax: Max RA to plot (deg). Note raMin/raMax define the centers that will be plotted.
raLen: Length of the plotted strips in degrees
decMin: minimum dec value to plot
decMax: max dec value to plot
metricValueIn: metric values
xlabel: units for metric color-bar label
title: title for plot
cbarFormat: format for color bar numerals (i.e. '%.2g', etc) (default to matplotlib default)
plotMaskedValues: ignored, here to be consistent with OpsimFieldSlicer."""
if fignum:
fig = plt.figue(fignum)
else:
fig=plt.figure()
norm = None
if logScale:
norm = 'log'
if cmap is None:
cmap = cm.jet
if type(cmap) == str:
cmap = getattr(cm,cmap)
# Make colormap compatible with healpy
cmap = colors.LinearSegmentedColormap('cmap', cmap._segmentdata, cmap.N)
cmap.set_over(cmap(1.0))
cmap.set_under('w')
cmap.set_bad('gray')
if zp:
metricValue = metricValueIn - zp
elif normVal:
metricValue = metricValueIn/normVal
else:
metricValue = metricValueIn
if percentileClip:
pcMin, pcMax = percentileClipping(metricValue.compressed(), percentile=percentileClip)
if colorMin is None and percentileClip:
colorMin = pcMin
if colorMax is None and percentileClip:
colorMax = pcMax
if (colorMin is not None) or (colorMax is not None):
clims = [colorMin, colorMax]
else:
clims = None
# Make sure there is some range on the colorbar
if clims is None:
if metricValue.compressed().size > 0:
clims=[metricValue.compressed().min(), metricValue.compressed().max()]
else:
clims = [-1,1]
if clims[0] == clims[1]:
clims[0] = clims[0]-1
clims[1] = clims[1]+1
racenters=np.arange(raMin,raMax,raLen)
nframes = racenters.size
for i, racenter in enumerate(racenters):
if i == 0:
useTitle = title +' /n'+'%i < RA < %i'%(racenter-raLen, racenter+raLen)
else:
useTitle = '%i < RA < %i'%(racenter-raLen, racenter+raLen)
hp.cartview(metricValue.filled(self.badval), title=useTitle, cbar=False,
min=clims[0], max=clims[1], flip='astro', rot=(racenter,0,0),
cmap=cmap, norm=norm, lonra=[-raLen,raLen],
latra=[decMin,decMax], sub=(nframes+1,1,i+1), fig=fig)
hp.graticule(dpar=20, dmer=20, verbose=False)
# Add colorbar (not using healpy default colorbar because want more tickmarks).
fig = plt.gcf()
ax1 = fig.add_axes([0.1, .15,.8,.075]) #left, bottom, width, height
# Add label.
if label is not None:
plt.figtext(0.8, 0.9, '%s' %label)
# Make the colorbar as a seperate figure,
# from http://matplotlib.org/examples/api/colorbar_only.html
cnorm = colors.Normalize(vmin=clims[0], vmax=clims[1])
# supress silly colorbar warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=cnorm, orientation='horizontal', format=cbarFormat)
cb1.set_label(xlabel)
# If outputing to PDF, this fixes the colorbar white stripes
if cbar_edge:
cb1.solids.set_edgecolor("face")
fig = plt.gcf()
return fig.number
def almPlots(path,
outDir,
bundle,
nside=128,
lmax=500,
filterband='i',
raRange=[-50, 50],
decRange=[-65, 5],
subsetsToConsider=[[130, 165], [240, 300]],
showPlots=True):
"""
Plot the skymaps/cartview plots corresponding to alms with specified l-ranges.
Automatically creates the output directories and saves the plots.
Required Parameters
-------------------
* path: str: path to the main directory where output directory is saved
* outDir: str: name of the main output directory
* bundle: metricBundle object.
Optional Parameters
-------------------
* nside: int: HEALpix resolution parameter. Default: 128
* lmax: int: upper limit on the multipole. Default: 500
* filterBand: str: any one of 'u', 'g', 'r', 'i', 'z', 'y'. Default: 'i'
* raRange: float array: range of right ascention (in degrees) to consider in cartview plot; only useful when
cartview= True. Default: [-50,50]
* decRange: float array: range of declination (in degrees) to consider in cartview plot; only useful when
cartview= True. Default: [-65,5]
* subsetsToConsider: array of int arrays: l-ranges to consider, e.g. use [[50, 100]] to consider 50<l<100.
Currently built to handle five subsets (= number of colors built in).
Default: [[130,165], [240, 300]]
* showPlots: boolean: set to True if want to show figures. Default: True
"""
# set up the output directory
outDir2 = 'almAnalysisPlots_%s<RA<%s_%s<Dec<%s' % (
raRange[0], raRange[1], decRange[0], decRange[1])
if not os.path.exists('%s%s/%s' % (path, outDir, outDir2)):
os.makedirs('%s%s/%s' % (path, outDir, outDir2))
outDir3 = 'almSkymaps'
if not os.path.exists('%s%s/%s/%s' % (path, outDir, outDir2, outDir3)):
os.makedirs('%s%s/%s/%s' % (path, outDir, outDir2, outDir3))
outDir4 = 'almCartviewMaps'
if not os.path.exists('%s%s/%s/%s' % (path, outDir, outDir2, outDir4)):
os.makedirs('%s%s/%s/%s' % (path, outDir, outDir2, outDir4))
# ------------------------------------------------------------------------
# In order to consider the out-of-survey area as with data=0, assign the masked region of the
# skymaps the median of the in-survey data, and then subtract the median off the entire survey.
# Add the median back later. This gets rid of the massive fake monopole and allows reconstructing
# the full skymap from components.
surveyMedianDict = {}
surveyStdDict = {}
for dither in bundle:
inSurvey = np.where(bundle[dither].metricValues.mask == False)[0]
outSurvey = np.where(bundle[dither].metricValues.mask == True)[0]
bundle[dither].metricValues.mask[outSurvey] = False
# data pixels
surveyMedian = np.median(bundle[dither].metricValues.data[inSurvey])
surveyStd = np.std(bundle[dither].metricValues.data[inSurvey])
# assign data[outOfSurvey]= medianData[inSurvey]
bundle[dither].metricValues.data[outSurvey] = surveyMedian
# subtract median off
bundle[dither].metricValues.data[:] = bundle[
dither].metricValues.data[:] - surveyMedian
# save median for later use
surveyMedianDict[dither] = surveyMedian
surveyStdDict[dither] = surveyStd
# ------------------------------------------------------------------------
# now find the alms correponding to the map.
for dither in bundle:
array = hp.anafast(bundle[dither].metricValues.filled(
bundle[dither].slicer.badval),
alm=True,
lmax=500)
cl = array[0]
alm = array[1]
l = np.arange(len(cl))
lsubsets = {}
colorArray = ['y', 'r', 'g', 'm', 'c']
color = {}
for case in range(len(subsetsToConsider)):
lsubsets[case] = ((l > subsetsToConsider[case][0]) &
(l < subsetsToConsider[case][1]))
color[case] = colorArray[case]
# ------------------------------------------------------------------------
# plot things out
plt.clf()
plt.plot(l, (cl * l * (l + 1)) / (2.0 * np.pi), color='b')
for key in list(lsubsets.keys()):
plt.plot(l[lsubsets[key]],
(cl[lsubsets[key]] * l[lsubsets[key]] *
(l[lsubsets[key]] + 1)) / (2.0 * np.pi),
color=color[key])
plt.title(dither)
plt.xlabel('$\ell$')
plt.ylabel(r'$\ell(\ell+1)C_\ell/(2\pi)$')
filename = 'cls_%s.png' % (dither)
plt.savefig('%s%s/%s/%s' % (path, outDir, outDir2, filename),
format='png',
bbox_inches='tight')
if showPlots:
plt.show()
else:
plt.close()
surveyMedian = surveyMedianDict[dither]
surveyStd = surveyStdDict[dither]
# ------------------------------------------------------------------------
# plot full-sky-alm plots first
nTicks = 5
colorMin = surveyMedian - 1.5 * surveyStd
colorMax = surveyMedian + 1.5 * surveyStd
increment = (colorMax - colorMin) / float(nTicks)
ticks = np.arange(colorMin + increment, colorMax, increment)
# full skymap
hp.mollview(hp.alm2map(alm, nside=nside, lmax=lmax) + surveyMedian,
flip='astro',
rot=(0, 0, 0),
min=colorMin,
max=colorMax,
title='',
cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('Full Map')
ax = plt.gca()
im = ax.get_images()[0]
fig = plt.gcf()
cbaxes = fig.add_axes([0.1, 0.015, 0.8,
0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im,
orientation='horizontal',
format='%.2f',
ticks=ticks,
cax=cbaxes)
cb.set_label('$%s$-band Coadded Depth' % filterband)
filename = 'alm_FullMap_%s.png' % (dither)
plt.savefig('%s%s/%s/%s/%s' %
(path, outDir, outDir2, outDir3, filename),
format='png',
bbox_inches='tight')
# full cartview
hp.cartview(hp.alm2map(alm, nside=nside, lmax=lmax) + surveyMedian,
lonra=raRange,
latra=decRange,
flip='astro',
min=colorMin,
max=colorMax,
title='',
cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('Full Map')
ax = plt.gca()
im = ax.get_images()[0]
fig = plt.gcf()
cbaxes = fig.add_axes([0.1, -0.05, 0.8,
0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im,
orientation='horizontal',
format='%.2f',
ticks=ticks,
cax=cbaxes)
cb.set_label('$%s$-band Coadded Depth' % filterband)
filename = 'alm_Cartview_FullMap_%s.png' % (dither)
plt.savefig('%s%s/%s/%s/%s' %
(path, outDir, outDir2, outDir4, filename),
format='png',
bbox_inches='tight')
# prepare for the skymaps for l-range subsets
colorMin = surveyMedian - 0.1 * surveyStd
colorMax = surveyMedian + 0.1 * surveyStd
increment = (colorMax - colorMin) / float(nTicks)
increment = 1.15 * increment
ticks = np.arange(colorMin + increment, colorMax, increment)
# ------------------------------------------------------------------------
# consider each l-range
for case in list(lsubsets.keys()):
index = []
lowLim = subsetsToConsider[case][0]
upLim = subsetsToConsider[case][1]
for ll in np.arange(lowLim, upLim + 1):
for mm in np.arange(0, ll + 1):
index.append(hp.Alm.getidx(lmax=lmax, l=ll, m=mm))
alms1 = alm.copy()
alms1.fill(0)
alms1[index] = alm[index] # an unmasked array
# plot the skymap
hp.mollview(hp.alm2map(alms1, nside=nside, lmax=lmax) +
surveyMedian,
flip='astro',
rot=(0, 0, 0),
min=colorMin,
max=colorMax,
title='',
cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('%s<$\ell$<%s' % (lowLim, upLim))
ax = plt.gca()
im = ax.get_images()[0]
fig = plt.gcf()
cbaxes = fig.add_axes([0.1, 0.015, 0.8,
0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im,
orientation='horizontal',
format='%.3f',
ticks=ticks,
cax=cbaxes)
cb.set_label('$%s$-band Coadded Depth' % filterband)
filename = 'almSkymap_%s<l<%s_%s.png' % (lowLim, upLim, dither)
plt.savefig('%s%s/%s/%s/%s' %
(path, outDir, outDir2, outDir3, filename),
format='png',
bbox_inches='tight')
# plot cartview
hp.cartview(hp.alm2map(alms1, nside=nside, lmax=lmax) +
surveyMedian,
lonra=raRange,
latra=decRange,
flip='astro',
min=colorMin,
max=colorMax,
title='',
cbar=False)
hp.graticule(dpar=20, dmer=20, verbose=False)
plt.title('%s<$\ell$<%s' % (lowLim, upLim))
ax = plt.gca()
im = ax.get_images()[0]
fig = plt.gcf()
cbaxes = fig.add_axes([0.1, -0.05, 0.8,
0.04]) # [left, bottom, width, height]
cb = plt.colorbar(im,
orientation='horizontal',
format='%.3f',
ticks=ticks,
cax=cbaxes)
cb.set_label('$%s$-band Coadded Depth' % filterband)
filename = 'almCartview_%s<l<%s_%s.png' % (lowLim, upLim, dither)
plt.savefig('%s%s/%s/%s/%s' %
(path, outDir, outDir2, outDir4, filename),
format='png',
bbox_inches='tight')
if showPlots:
plt.show()
else:
plt.close('all')
def plot_Total_Sky_Halos_Surface_Brightness(Halo_data,Input_Para,Output_Para):
from XCat_Objects import DtoR, RtoD
rc('font',family='serif')
fdir = './Output/plots/HEALPixSurfaceBrightness/'
nside = Output_Para.nside
Sl_n = len(Halo_data)
from XCat_Objects import Halo_Brightness_Surface_Sample_Object
for k in range(Sl_n):
pix = zeros(12*nside**2)
if (len(Halo_data[k].RA[:]) == 0):
pass
else:
for halo_n in range(len(Halo_data[k].RA[:])):
Halo_Sample = Halo_Brightness_Surface_Sample_Object(Halo_data[k],Input_Para,Output_Para,halo_n)
for i in range(len(Halo_Sample.sRA[:])):
j = hp.ang2pix(nside,DtoR*(90.0-Halo_Sample.sDEC[i]),DtoR*(Halo_Sample.sRA[i]))
pix[j] += Halo_Sample.sSB[i]
if (Output_Para.log_scale):
pix_min = max(pix[:])/10.0**4
pix = log10((pix+pix_min)*4.0*pi/(12.0*nside**2))
else:
pix = pix/(12.0*nside**2)
plt.clf()
if (Output_Para.HEALpix_cart):
hp.cartview(pix, fig = 1)
else:
hp.mollview(pix, fig = 1)
if (Output_Para.HEALpix_grat):
hp.graticule()
plt.show()
plt.close()
save_ques = Read_YN_Input("Do you want to save the plot (please enter Y, y, N, or n)? ")
if save_ques :
rc('text',usetex=True)
plt.clf()
if (Output_Para.HEALpix_cart):
hp.cartview(pix, fig = 1)
else:
hp.mollview(pix, fig = 1)
if (Output_Para.HEALpix_grat):
hp.graticule()
if (Output_Para.log_scale):
fname = 'Full_Sky_Surface_Brightness_Profile_log_%i_%i.pdf'%(nside,(k+1))
else:
fname = 'Full_Sky_Surface_Brightness_Profile_%i_%i.pdf'%(nside,(k+1))
plt.title(r'Surface Brightness Profile at redshift between %0.3f and %0.3f'%(Halo_data[k].z_min,Halo_data[k].z_max),fontsize = 20)
print 'Saving plot', fname
# savefig(fdir+fname,bbox_inches='tight')
plt.savefig(fdir+fname)
rc('text',usetex=False)
plt.close()
save_ques = Read_YN_Input("Do you want to save the map data (please enter Y, y, N, or n)? ")
if save_ques :
if (Output_Para.log_scale):
fname = 'Full_Sky_Surface_Brightness_Profile_log_HEALPix_map_%i_%i.txt'%(nside,(k+1))
else:
fname = 'Full_Sky_Surface_Brightness_Profile_HEALPix_map_%i_%i.txt'%(nside,(k+1))
print 'Saving data', fname
f = open(fdir+fname,'w')
f.write("# Pix_Value \n")
savetxt(f, array([pix]).T)
f.close()
