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_model1(qu, QU, mod, R_mod, mask, lab=''):
plt.figure()
plt.title('Data vs model {}'.format(lab))
plt.scatter(qu[0, mask], QU[0, mask], c='grey', marker='^', label='data')
plt.scatter(qu[1, mask], QU[1, mask], c='skyblue', marker='^')
plt.scatter(qu[0, mask], mod[0, mask], c='k', marker='.', label='model')
plt.scatter(qu[1, mask], mod[1, mask], c='b', marker='.')
plt.legend()
plt.xlabel(r'$q, u$')
plt.ylabel(r'$Q, U$ [MJy/sr]')
plt.title('Plot data vs model')
plt.savefig('Figures/Sampling/model_vs_data_sampler.png')
# Residuals:
res = residual(QU, mod, mask)
print(np.shape(res))
hp.gnomview(res[0,:], title=r'Residuals, $\frac{Q_s - Q_m}{|Q_s|}$',\
cmap='bwr', min=-1, max=1, rot=[104,22.2], xsize=150)
hp.graticule()
plt.savefig('Figures/Sampling/Q_residuals.png')
hp.gnomview(res[1,:], title=r'Residuals, $\frac{U_s - U_m}{|U_s|}$',\
cmap='bwr', min=-1, max=1, rot=[104, 22.2], xsize=150)
hp.graticule()
plt.savefig('Figures/Sampling/U_residuals.png')
def plot_zoom_sky(Output_Para,pix,pix_bool):
rc('font',family='serif')
from XCat_Objects import DtoR
if (pix_bool):
lon = Read_Float_Input("Please enter long (-180<long<180) : ")
lat = Read_Float_Input("Please enter lati (-90<long<90) : ")
rot = Read_Float_Input("Please enter rot (0<rot<360) : ")
res = Read_Float_Input("Please enter resolution (in arcmin) : ")
else:
print "Please, first load a HEALPix map file ."
raw_input("Press enter to continue ... ")
return 0
plt.clf()
hp.gnomview(pix, fig = 1, rot=(lon,lat,rot),reso = res)
hp.graticule()
plt.show()
plt.close()
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.gnomview(pix, fig = 1, rot=(lon,lat,rot),reso = res)
hp.graticule()
print 'Saving plot', fname
plt.savefig(fdir+fname+'_zoom.pdf',bbox_inches='tight')
# plt.savefig(fdir+fname+'.pdf')
rc('text',usetex=False)
plt.close()
def animation_path(sched,rot,title,*args, **kwargs):
vid_title = "videos/"+title + ".mp4"
hpy.mollview(title=title,rot=rot)
hpy.graticule(verbose=0)
points = hpy.projscatter(np.pi/2-sched[:,1],sched[:,0],lonlat=False,cmap="hsv",c=np.arange(np.size(sched,0)),*args, **kwargs)
offto = points.get_offsets()
arrayto = points.get_array()
climto = points.get_clim()
cmapto = points.get_cmap()
plt.clf()
hpy.mollview(title=title,rot=rot)
hpy.graticule(verbose=0)
points = hpy.projscatter([],[],lonlat=False,*args, **kwargs)
fig = points.get_figure()
points.set_clim(climto)
points.set_cmap(cmapto)
def animate(i,data,color):
start_data = max(0,i-300)
# start_data = 0
points.set_offsets(data[start_data:i,:])
points.set_array(color[start_data:i])
return points,
anim = animation.FuncAnimation(fig, animate,fargs=(offto,arrayto),frames=np.size(offto,0), interval=20, blit=True)
anim.save(vid_title)
def plot_mask(ra, dec, nside=2**10):
pix = hp.pixelfunc.ang2pix(nside, dec, dec, lonlat=True)
hp.visufunc.mollview(map=np.bincount(pix,minlength=hp.nside2npix(nside)),
cbar=False, notext=True, max=1, min=0, title="",
cmap="binary")
hp.graticule()
plt.show()
def draw_function(l, b, value, max, folder=None, title=None):
## s310, t211, v
nside = 8
if folder is not None:
folder = f"{folder}/{nside}"
pix = compute_pixel(nside, np.degrees(l.values), np.degrees(b.values))
npix = hp.nside2npix(nside)
bin_means, bin_edges, binnumber = scipy.stats.binned_statistic(
pix, value, bins=npix, statistic='median')
# hist, _ = np.histogram(bin_means, range=(0, npix), bins=(npix))
plt.clf()
hp.mollview(bin_means,
title=title,
unit='медианное значение в пикселе',
coord='G',
min=-max,
max=+max)
hp.graticule()
if folder is not None and title is not None:
Path(folder).mkdir(parents=True, exist_ok=True)
plt.savefig(f'{folder}/{title}.png')
else:
plt.show()
def plot(self, title='MOC', coord='C'):
"""
plot current instance using matplotlib
coord can be 'C', 'G' or 'E' (respectively celestial, galactic or ecliptic)
"""
from matplotlib import pyplot as plt
import healpy as hp
# degrade to NORDER 8 if norder is greater
if self.max_order > 8:
plotted_moc = self.degrade_to_order(8)
else:
plotted_moc = self
m = {} # partial HEALPix map
for val in plotted_moc.best_res_pixels_iterator():
m[val] = 1
# ensure the map is at the correct nside
max_ipix = hp.nside2npix(2**plotted_moc.max_order) - 1
if not max_ipix in m:
m[max_ipix] = hp.pixelfunc.UNSEEN
hp.mollview(m, nest=True, coord=['C', coord], title=title, cbar=False)
hp.graticule()
plt.show()
def test_calc_healpixels_for_region():
ahp = HEALPix(nside=NSIDE, order='ring', frame=TETE())
NPIX = hp.nside2npix(NSIDE)
map = np.zeros(NPIX)
test_regions = [NGC288_params]
for region in test_regions:
r = generate_sky_maps.CelestialRegion(region)
r.calc_healpixels_for_region(ahp)
map[r.pixels] += 1.0
# Approximate test: Compare the combined area of the HEALpixels to the
# minimum (cartesian) area that the region should cover.
# True area will always be greater.
min_expected_area = np.pi * (NGC288_params['l_width'] * u.deg / 2.0)**2
healpix_area = len(r.pixels) * ahp.pixel_area
assert healpix_area > min_expected_area.to(u.steradian)
fig = plt.figure(1, (10, 10))
hp.mollview(map, title='Test of HEALpix regions')
hp.graticule()
plt.tight_layout()
plt.savefig('test_cone_search_regions.png')
plt.close(1)
def compute_visibility(self):
# coord=SkyCoord(ra.flatten(),dec.flatten(),unit=u.deg)
# m=Visibility().for_time("2016-10-09T17:31:06",coord=coord)
# figure(figsize=(20,10))
# scatter(ra,dec,s=100,c=m,lw=0,alpha=0.4)
visibility = Visibility()
gwm = healpy.read_map(rootd + "/" + self.skymap)
nsides = healpy.npix2nside(gwm.shape[0])
vmap = visibility.for_time(self.utc, nsides=nsides)
healpy.mollview(
gwm * (vmap * 2 - 1),
title="visibility for INTEGRAL due to sun constrains\n" + self.utc)
healpy.projscatter(self.ra, self.dec, lonlat=True)
healpy.graticule()
plot.plot(self.dir + "/visibility.png")
healpy.write_map(self.dir + "/visibility.fits", vmap)
source_theta = (90 - self.dec) / 180 * pi
source_phi = (self.ra) / 180 * pi
visibility = dict(probability_visible=sum(gwm * vmap),
source_visible=vmap[healpy.ang2pix(
nsides, source_theta, source_phi)])
json.dump(visibility, open(self.dir + "/visibility.json", "w"))
def display_stacked_image(self, smoothing_kernel_sigma=0.5):
"""
Display a map with all active analysis bins stacked together.
:param smoothing_kernel_sigma: sigma for the Gaussian smoothing kernel to apply
:return: a matplotlib.Figure instance
"""
# This is the resolution (i.e., the size of one pixel) of the image in arcmin
resolution = 3.0
# The image is going to cover the diameter plus 20% padding
xsize = self._get_optimal_xsize(resolution)
active_planes_bins = [self._maptree[x] for x in self._active_planes]
# Get the center of the projection for this plane
this_ra, this_dec = self._roi.ra_dec_center
# Healpix uses longitude between -180 and 180, while R.A. is between 0 and 360. We need to fix that:
longitude = ra_to_longitude(this_ra)
# Declination is already between -90 and 90
latitude = this_dec
total = None
for i, data_analysis_bin in enumerate(active_planes_bins):
# Plot data
background_map = data_analysis_bin.background_map.as_dense()
this_data = data_analysis_bin.observation_map.as_dense(
) - background_map
idx = np.isnan(this_data)
# this_data[idx] = hp.UNSEEN
if i == 0:
total = this_data
else:
# Sum only when there is no UNSEEN, so that the UNSEEN pixels will stay UNSEEN
total[~idx] += this_data[~idx]
delta_coord = (self._roi.data_radius.to("deg").value * 2.0) / 15.0
fig, sub = plt.subplots(1, 1)
proj = self._represent_healpix_map(fig, total, longitude, latitude,
xsize, resolution,
smoothing_kernel_sigma)
cax = sub.imshow(proj, origin='lower')
fig.colorbar(cax)
sub.axis('off')
hp.graticule(delta_coord, delta_coord)
return fig
def plot_zoom(scan,
ra,
dec,
title,
reso=3,
var="pVal",
range=[0, 6],
cmap=None):
if cmap is None:
pdf_palette = sns.color_palette("Blues", 500)
cmap = mpl.colors.ListedColormap(pdf_palette)
hp.gnomview(scan,
rot=(np.degrees(ra), np.degrees(dec), 0),
cmap=cmap,
max=max(scan),
reso=reso,
title=title,
notext=True,
cbar=False
#unit=r""
)
plt.plot(4.95 / 3. * reso * np.radians([-1, 1, 1, -1, -1]),
4.95 / 3. * reso * np.radians([1, 1, -1, -1, 1]),
color="k",
ls="-",
lw=3)
hp.graticule(verbose=False)
plot_labels(dec, ra, reso)
def make_healpix_image(plt, img, title, num_bins, source_json=None):
"""
Writes out an image as a healpy image
"""
nside = hp.pixelfunc.get_min_valid_nside(num_bins*num_bins*3/4)
npix = hp.nside2npix(nside)
pixels = np.arange(npix)
m = np.zeros(npix) + hp.UNSEEN
window_d = np.degrees(hp.nside2resol(nside))
for i in pixels:
theta, phi = hp.pix2ang(nside, i)
if (theta < np.pi / 2):
el = np.degrees(np.pi/2 - theta)
az = np.degrees(phi)
s = elaz.ElAz(el, az)
x_min, x_max, y_min, y_max, area = s.get_px_window(num_bins, window_deg=window_d)
s_px = img[y_min:y_max, x_min:x_max]
m[i] = np.sum(s_px)/area
hp.orthview(m, rot=(0, 90, 180), title=title, xsize=3000, cbar=False, half_sky=True)
hp.graticule()
if source_json is not None:
src_list = elaz.from_json(source_json, el_limit=20.0, jy_limit=1e4)
output_list = []
for s in src_list:
l, m = s.get_lm()
output_list.append(plt.Circle([-l, m], 0.03, color=(0.9, 0.2, 0.3), fill=False))
ax = plt.gca()
for circle in output_list:
ax.add_artist(circle)
def _generatePlot(self, table):
coords, proj = self.__getCoordinatesAndProjection()
fignum = 1
self.Plot.figure(num = fignum, tight_layout = True)
pixels = self.__getPixelsData(table)
#pixels = self.__getMaskedPixels(pixels)
cmap = self.Plot.get_cmap(self.Configuration.getValueOf(ChartHealpixOptions.COLOURMAP, "jet"))
cmap.set_under('w')
healpy.mollview(pixels, fig = fignum, coord = [coords, proj], unit = 'mas', title = "",
min = self.__Min, max = self.__Max, flip = 'astro', cbar = False,
cmap = cmap, norm = self.__getNormalization(), nest = self.__nestedPixels())
healpy.graticule(dpar = 30, dmer = 30, coord = proj, local = None, linestyle = ':',
color = 'white')
healpy.projtext(0, 90, "90", lonlat = True, coord = proj,
verticalalignment = 'bottom', horizontalalignment = 'center',
fontsize = 12)
healpy.projtext(0, 270, "-90", lonlat = True, coord = proj,
verticalalignment = 'top', horizontalalignment = 'center',
linespacing = 2.0, fontsize = 12)
healpy.projtext( 179, 0, "180 ", lonlat = True, coord = proj,
verticalalignment = 'center', horizontalalignment = 'right',
fontsize = 12)
healpy.projtext(-179, 0, " -180", lonlat = True, coord = proj,
verticalalignment = 'center', horizontalalignment = 'left',
fontsize = 12)
def mollview(hp_map,
filename=None,
lim=None,
coord='C',
verbose=True,
return_projected_map=False,
xsize=1200,
grat_deg=None,
**kwargs):
'''
mollview plot for healpix wrapper
'''
import healpy as hp
if lim is None:
cmin = cmax = None
elif type(lim) is list or type(lim) is tuple:
cmin, cmax = lim
else:
cmin = -lim
cmax = lim
retimg = hp.mollview(hp_map,
min=cmin,
max=cmax,
coord=coord,
return_projected_map=return_projected_map,
xsize=xsize,
**kwargs)
if grat_deg is not None:
hp.graticule(dpar=grat_deg, dmer=grat_deg, coord=coord)
if filename is not None:
plt.savefig(filename)
if verbose: cprint("Saved healpix plot to " + filename, color="g")
if return_projected_map: return retimg
def graphtargets(info, targets, skymap):
"Generate a graph with the targets generated over the sky map"
import healpy as hp
from io import BytesIO
import matplotlib.pyplot as plt
aligo_banana = hp.read_map(skymap)
numfig = 1
fig = plt.figure(numfig, figsize=(10, 5))
graph_title = "{} -{}-{}\nBayestar Prob. Sky Map with Targets".format(
info["graceid"], info["pkt_ser_num"], info["alerttype"])
hp.mollview(
aligo_banana,
title=graph_title,
flip="astro",
unit="$\Delta$",
fig=numfig,
cmap=plt.cm.gist_heat_r,
)
fig.axes[1].texts[0].set_fontsize(8)
for obs in targets:
ra_pointings = np.array(obs["targets"]["RA"])
dec_pointings = np.array(obs["targets"]["Dec"])
hp.projscatter(ra_pointings,
dec_pointings,
lonlat=True,
color="green",
marker=".")
hp.graticule()
graphIO = BytesIO()
plt.savefig(graphIO)
return graphIO.getvalue()
def tsmaps(self):
"""TS maps for the spectral templates
Each has 12*512**2 = 3.2M pixels. The following table has the number of pixels above the TS value labeling the column.
%(tsmap_info)s
"""
z = dict()
cuts = (10, 16, 25, 100)
keys = self.keys
for key in keys:
z[key] = [np.sum(self.tables[key] > x) for x in cuts]
df = pd.DataFrame(z, index=cuts).T
self.tsmap_info = html_table(
df,
href=False,
)
fig, axx = plt.subplots(2, 3, figsize=(18, 15))
plt.subplots_adjust(hspace=-0.4, wspace=0.05, left=0.05)
for ax, key in zip(axx.flatten(), keys):
plt.sca(ax)
healpy.mollview(self.tables[key],
hold=True,
min=10,
max=25,
title=key,
cbar=True,
cmap=plt.get_cmap('YlOrRd'))
healpy.graticule(dmer=180, dpar=90, color='lightgrey')
axx[1, 2].set_visible(False)
return fig
def plot_tsfile(file,skiprows=32,column=22):
data = np.loadtxt(file,skiprows=skiprows,unpack=True)
print len(data[0])
nside=int(round(mt.sqrt(len(data[9])/12)))
tsarray=np.zeros(nside*nside*12)
index=np.array(data[0]).astype(int)
'''
data2=np.transpose(0*data)
data2[index]=np.transpose(data)
data2=np.transpose(data2)
print(data2[0])
print(data2[10])
return
'''
tsarray[index]=data[column]
tsarray_s = hp.sphtfunc.smoothing(tsarray,sigma = 1.023/nside)
hp.mollview(tsarray,coord=['C','G'], title='TS Map', unit='prob',xsize = 2048)
hp.graticule()
plt.savefig("%s_c%d.png" % (file,column))
plt.show()
hp.mollview(tsarray_s,coord=['C','G'], title='TS Map Smoothed', unit='prob',xsize = 2048)
hp.graticule()
plt.savefig("%s_c%d__s.png" % (file,column))
plt.show()
def plot_sky_projection_healpy_simple_zoom(Sliced_Halo_data,Output_Para):
rc('font',family='serif')
fdir = './Output/plots/HEALPixZoom/'
nside = Output_Para.nside
Sl_n = len(Sliced_Halo_data)
Z_max = max(Sliced_Halo_data[Sl_n-1].Z_red[:])
ques = True
while (ques):
k = Read_Integer_Input("We have %i redshift slice which one you want exctract the halo ? "%Sl_n)
k = int(k) - 1
while(k > (Sl_n-1) or k < 0 ):
print "Invalid Number."
k = Read_Integer_Input("Please choose an integer between 1 and %i : "%Sl_n)
k = int(k) - 1
pix = zeros(12*nside**2)
n = len(Sliced_Halo_data[k].RA[:])
if (n == 0):
print "There is no halo in this piece of redshift, please choos another one ..."
raw_input("Press enter to continue ... ")
break
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] += 10.0**Sliced_Halo_data[k].lgFx[i]
halo_n = Read_Integer_Input("We have %i halos in this slice which one you want to exctract ? "%n)
halo_n = halo_n - 1
while(halo_n > (n-1) or halo_n < 0 ):
print "Invalid Number."
halo_n = Read_Integer_Input("Please choose an integer between 1 and %i : "%n)
halo_n = halo_n - 1
clf()
hp.gnomview(pix, fig = 1 ,rot=(Sliced_Halo_data[k].RA[halo_n],Sliced_Halo_data[k].DEC[halo_n],0.0) , xsize=Output_Para.xsize , reso=Output_Para.resolution , degree=Output_Para.resol_degree)
hp.graticule()
show()
close()
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)
clf()
hp.gnomview(pix, fig=1 , rot=(Sliced_Halo_data[k].RA[halo_n],Sliced_Halo_data[k].DEC[halo_n],0.0) , xsize=Output_Para.xsize , reso=Output_Para.resolution , degree=Output_Para.resol_degree)
hp.graticule()
fname = 'sky_projection_HEALPix_Simple_%i_%i_%i.pdf'%(nside,(halo_n+1),(k+1))
title(r'Redshift is 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')
savefig(fdir+fname)
rc('text',usetex=False)
close()
ques = False
return 0
def plots(maps, surveys, clf=True, save=True, title=None, pure_noise=True):
import pylab as pl
from utils import latexify
latexify(columns=2, equal=False, fontsize=10, ratio=None, ggplot=True)
for kk, survey in enumerate(surveys):
if survey in ['Planck', 'SO', 'AdvACT']:
fmap = 1.e6 * cmb_map(survey, pure_noise=pure_noise)
else:
fmap = np.ones_like(maps[kk])
if clf:
pl.clf()
## C equates to equatorial. ## min=-150., max=150.
hp.visufunc.mollview(maps[kk] * fmap, coord=['C', 'C'], flip='astro', rot=(0., 0., 0.), hold=False, format='%.1lf', cbar=True,\
unit=r'$\mu K$', cmap='RdBu_r')
hp.graticule(dpar=15., dmer=30.)
if save:
if title is None:
pl.title('%s in Mollweide' % survey)
pl.savefig('plots/footprint/%s.pdf' % survey)
else:
pl.title('%s' % title)
pl.savefig('plots/footprint/%s.pdf' % ('_' + title))
def plot(self, title='MOC', coord='C'):
"""
plot current instance using matplotlib
coord can be 'C', 'G' or 'E' (respectively celestial, galactic or ecliptic)
"""
from matplotlib import pyplot as plt
import healpy as hp
# degrade to NORDER 8 if norder is greater
if self.max_order > 8:
plotted_moc = self.degrade_to_order(8)
else:
plotted_moc = self
m = np.zeros(hp.nside2npix(2**plotted_moc.max_order))
for val in plotted_moc.best_res_pixels_iterator():
m[val] = 1
from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list('w2r', ['#ffffff', '#ff0000'])
cmap.set_under('w')
cmap.set_bad('gray')
hp.mollview(m,
nest=True,
coord=['C', coord],
title=title,
cbar=False,
cmap=cmap)
hp.graticule()
plt.show()
def plot_healpix(file_name="ALL_SKY.FITS", field=0, max_v=60, min_v=0):
#file_names = glob.glob("*_healpix.fits")
haslam = hp.read_map(file_name, field=field)
for x in xrange(len(haslam)):
if np.allclose(haslam[x], 0.0):
haslam[x] = hp.UNSEEN
#print "haslam = ",haslam
cmap = cm.jet
cmap.set_over(cmap(1.0))
cmap.set_under('w')
cmap.set_bad('gray')
proj_map = hp.mollview(haslam,
coord=['C'],
xsize=2000,
return_projected_map=True,
title='',
max=max_v,
min=min_v,
cmap=cmap) #max=0.4
#proj_map = hp.mollview(haslam,coord=['C'], xsize=2000,return_projected_map=True,title='',cmap=cmap)
hp.graticule()
f = file_name[:-5]
plt.savefig(f + str(field) + ".png")
plt.show()
def parse_patches(args):
# Parse the patch definitions
patches = []
total_weight = 0
for patch_def in args.patch:
parts = patch_def.split(',')
name = parts[0]
weight = float(parts[1])
total_weight += weight
print('Adding patch "{}" {} '.format(name, weight), end='')
if len(parts[2:]) == 3:
corners = parse_patch_center_and_width(args, parts)
elif len(parts[2:]) == 4:
corners = parse_patch_rectangular(args, parts)
else:
corners = parse_patch_explicit(args, parts)
print('')
patches.append([name, weight, corners])
if args.debug:
import matplotlib.pyplot as plt
import healpy as hp
plt.figure(figsize=[18, 12])
for iplot, coord in enumerate('CEG'):
hp.mollview(np.zeros(12),
coord=coord,
cbar=False,
title='Patch locations',
sub=[2, 2, 1 + iplot])
hp.graticule(30)
for name, weight, corners in patches:
lon = [corner._ra / degree for corner in corners]
lat = [corner._dec / degree for corner in corners]
lon.append(lon[0])
lat.append(lat[0])
print('{} corners:\n lon = {}\n lat= {}'.format(
name, lon, lat),
flush=True)
hp.projplot(lon,
lat,
'r-',
threshold=1,
lonlat=True,
coord='C',
lw=2)
hp.projtext(lon[0],
lat[0],
name,
lonlat=True,
coord='C',
fontsize=14)
plt.savefig('patches.png')
plt.close()
# Normalize the weights
for i in range(len(patches)):
patches[i][1] /= total_weight
return patches
def main(argv):
_path = '/home/tiago/Develop/SMAPs/'
file15 = 'smpas_obstime_15.fits'
file2 = 'smpas_obstime_2.fits'
extMapFile = 'extintion_at3800.fits'
map15 = H.read_map(os.path.join(_path,file15))
map2 = H.read_map(os.path.join(_path,file2))
extMap = H.read_map(os.path.join(_path,extMapFile))
fig = py.figure(1,figsize=(8,3))
H.mollview(map2*10**(-extMap),fig=1,coord=['G','E'],title='secz < 2.0',sub=(1,2,1),max=1296,cbar=False,notext=True)#,unit='hours z < 1.3')
H.graticule()
H.mollview(map15*10**(-extMap),fig=1,coord=['G','E'],title='secz < 1.5',sub=(1,2,2),max=1296,cbar=False,notext=True)#,unit='hours z < 1.3')
H.graticule()
#H.write_map(os.path.join(_path,'dcorr_'+file15),map15*10**(-extMap))
#H.write_map(os.path.join(_path,'dcorr_'+file2),map2*10**(-extMap))
#H.write_map(os.path.join(_path,'dcorr_'+file3),map3*10**(-extMap))
py.savefig(os.path.join(_path,'Figures/fig2.png'))
py.show()
def plot(self, plt, src_list):
"""
Modified plot to deal with the reduced number of pixels
"""
all_npix = hp.nside2npix(self.nside)
all_pixels = np.zeros(all_npix) + hp.UNSEEN
all_pixels[self.pixel_indices] = self.pixels
rot = (0, 90, 0)
plt.figure() # (figsize=(6,6))
logger.info("self.pixels: {}".format(self.pixels.shape))
if True:
hp.orthview(all_pixels,
rot=rot,
xsize=1000,
cbar=True,
half_sky=True,
hold=False)
hp.graticule(verbose=False)
else:
hp.mollview(all_pixels, rot=rot, xsize=1000, cbar=True)
hp.graticule(verbose=True)
if src_list is not None:
for s in src_list:
self.plot_x(plt, s.el_r, s.az_r)
def _grid(self, **kwargs):
"""generate background grids for figure
Parameters
----------
Healpix graticule Parameters
"""
hp.graticule(**self.getkeys(kwargs, 'graticule'))
def main():
"""
This is the main routine.
"""
h_nside=512 # The same value a bayesstar.fits
smoothing=0.01 # Smooth the resulting map to 0.01 radians with a Gaussian
hubbleconstant=72
speedoflight=3E5
filenameCat = 'XSC_Completed.tbl.gz'
RA,DEC,JMAG,HMAG,KMAG = np.loadtxt(filenameCat,skiprows= 50,
usecols = (0,1,3,4,5),
dtype=[('f0',float),
('f1',float),
('f2',float),
('f3',float),
('f4',float)], unpack = True)
MK_Star = -24.0
DIST = np.power(10,(KMAG-MK_Star)/5)*1E-05
REDSHIFT = (DIST*hubbleconstant)/speedoflight
print("Interval of K MAG = ",min(KMAG)," - ",max(KMAG))
print("Interval of distances [Mpc] = ",min(DIST)," - ",max(DIST))
print("Interval of redshift = ",min(REDSHIFT)," - ",max(REDSHIFT))
# Select Galaxies by redshift under the assumption of MK=MK_Star
for rmin in np.arange(4)*0.01:
rmax=rmin+0.01
if rmin == 0:
kmin=0
else:
kmin=5*np.log10(rmin/hubbleconstant*speedoflight*1E5)+MK_Star
kmax=5*np.log10(rmax/hubbleconstant*speedoflight*1E5)+MK_Star
galpixels_Range= np.zeros(hp.nside2npix(h_nside))
include_me = np.logical_and((REDSHIFT > rmin),
np.logical_and((REDSHIFT<rmax),
(KMAG != 0.0)))
ra_Range = RA[include_me]
dec_Range = DEC[include_me]
pix_num_Range = (DeclRaToIndex(dec_Range,ra_Range,h_nside))
galpixels_Range[pix_num_Range]+=1
print("Number of objects with %g < z < %g : %d" % (rmin,rmax,len(ra_Range)))
map = hp.sphtfunc.smoothing(galpixels_Range,sigma = smoothing)
hp.write_map("%g-%g_raw.fits" % (rmin,rmax),galpixels_Range)
hp.write_map("%g-%g.fits" % (rmin,rmax),map)
hp.mollview(map,coord='C',rot = [0,0.3], title='Relative Surface Density of Galaxies: %0.1f < K < %0.1f (%g < z < %g)' % (kmin,kmax,rmin,rmax), unit='prob',xsize = 2048)
hp.graticule()
plt.savefig("%g-%g.png" % (rmin,rmax))
plt.show()
def plot_Single_Halo_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)
k = Read_Integer_Input("We have %i redshift slice which one you want exctract the halo ? "%Sl_n)
k = int(k) - 1
while(k > (Sl_n-1) or k < 0 ):
print "Invalid Number."
k = Read_Integer_Input("Please choose an integer between 1 and %i : "%Sl_n)
k = int(k) - 1
n = len(Halo_data[k].RA[:])
if (n == 0):
print "There is no halo in this piece of redshift, please choos another one ..."
raw_input("Press enter to continue ... ")
return
halo_n = Read_Integer_Input("We have %i halos in this slice which one you want to exctract ? "%n)
halo_n = halo_n - 1
from XCat_Objects import Halo_Brightness_Surface_Sample_Object
Halo_Sample = Halo_Brightness_Surface_Sample_Object(Halo_data[k],Input_Para,Output_Para,halo_n)
pix = zeros(12*nside**2)
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()
hp.gnomview(pix, fig=1 ,rot=(Halo_Sample.RA,Halo_Sample.DEC,0.0), xsize=Output_Para.xsize , reso=Output_Para.resolution , degree=Output_Para.resol_degree)
hp.graticule()
plt.show()
plt.close()
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.gnomview(pix, fig=1 , rot=(Halo_Sample.RA,Halo_Sample.DEC,0.0) , xsize=Output_Para.xsize , reso=Output_Para.resolution , degree=Output_Para.resol_degree)
hp.graticule()
if (Output_Para.log_scale):
fname = 'sky_projection_HEALPix_Surface_Brightness_Profile_log_%i_%i_%i.pdf'%(nside,(halo_n+1),(k+1))
else:
fname = 'sky_projection_HEALPix_Surface_Brightness_Profile_%i_%i_%i.pdf'%(nside,(halo_n+1),(k+1))
plt.title(r'Surface Brightness Profile',fontsize = 20)
print 'Saving plot', fname
# savefig(fdir+fname,bbox_inches='tight')
plt.savefig(fdir+fname)
rc('text',usetex=False)
plt.close()
def PlotMap(Map, NsideMap, MapName):
hp.mollview(Map,
coord='C',
rot=[0, 0.3],
title='Histogram-Equalized Probability Density Map',
unit='prob',
xsize=NsideMap)
hp.graticule()
plt.savefig(MapName)
def draw_map(m, cmap=pl.cm.jet, filename=None):
mm = m.copy()
mm[mm <= 0] = hp.UNSEEN
cmap.set_under('w')
hp.mollview(mm, nest=True, cmap=cmap)
hp.graticule()
if filename is not None:
fig = pl.gcf()
fig.savefig(filename + '.png', bbox_inches='tight')
def plot_map_data(map, file_name, range=None):
fig = plt.figure(1,(10,10))
if range == None:
hp.mollview(map)
else:
hp.mollview(map, min=range[0], max=range[1])
hp.graticule()
plt.tight_layout()
plt.savefig(file_name)
plt.close(1)
def plot_skymap(skymap, smooth=None, decmax=None, scale=None, color_bins=40,
color_palette='viridis', symmetric=False, cbar_min=None,
cbar_max=None, cbar_title='Skymap', llabel=None, polar=False,
fig=None, sub=None):
cpalette = sns.color_palette(color_palette, color_bins)
cmap = ListedColormap(cpalette.as_hex())
cmap.set_under('white')
cmap.set_bad('gray')
# if cbar_max and cbar_max and symmetric and (cbar_max != -cbar_min):
# raise ValueError('The max/min colorbar values can\'t be symmetric')
# elif cbar_max and cbar_max:
# pass
# elif:
# skymap_min = skymap.min()
# skymap_max = skymap.max()
# maximum = np.max(np.abs([skymap_min, skymap_max]))
# cbar_min = np.sign(skymap_min) * maximum
# cbar_max = np.sign(skymap_max) * maximum
# else:
# cbar_min = cbar_min if cbar_min else skymap.min()
# cbar_max = cbar_max if cbar_max else skymap.max()
if polar:
shrink = 0.6
rot = [0,-90,180]
hp.orthview(skymap, half_sky=True, rot=rot, coord='C', title='',
min=cbar_min, max=cbar_max, cbar=False, cmap=cmap,
fig=fig, sub=sub)
else:
shrink = 1.0
hp.mollview(skymap, rot=180, coord='C', title='', min=cbar_min,
max=cbar_max, cbar=False, cmap=cmap, fig=fig, sub=sub)
hp.graticule(verbose=False)
fig = plt.gcf()
ax = plt.gca()
image = ax.get_images()[0]
cbar = fig.colorbar(image, orientation='horizontal', aspect=50,
pad=0.01, fraction=0.1, ax=ax,
format=FormatStrFormatter('%g'),
shrink=shrink)
if cbar_title:
cbar.set_label(cbar_title, size=14)
if not polar:
ax.set_ylim(-1, 0.005)
ax.annotate('0$^\circ$', xy=(1.8, -0.75), size=14)
ax.annotate('360$^\circ$', xy=(-1.99, -0.75), size=14)
if llabel:
ax.annotate(llabel, xy=(-1.85,-0.24), size=20, color='white')
return fig, ax
def plotMap(map, unit, title):
min_map = max(0., np.min(map))
max_map = np.max(map)
hp.mollview(map,
min=min_map,
max=max_map,
cmap=plt.cm.rainbow,
norm="hist",
title=title,
unit=unit)
hp.graticule()
def plot_efield_beams(xt,xp,yt,yp,rot=[0,0],minv = [-1,-1,-1,-1],maxv=[1,1,1,1],figno=1):
A = [xt,xp,yt,yp]
plt.figure(figno)
plt.clf()
for a in range(4):
hp.orthview(A[a],half_sky=True,sub=(2,2,a+1),min=minv[a],max=maxv[a],rot=rot)
#plt.title(beam_labels[a,b])
hp.graticule()
# plt.show()
return
def plot_footprint_object(self, footprint_obj):
"""sfd is what footprint_wSFD() returns"""
temp = np.log10(footprint_obj.temp)
temp[footprint_obj.ipix_legsurvey] = 2.
hp.mollview(temp,
nest=True,
flip='geo',
title='Mollweide Projection, Galactic Coordinates',
unit='',
max=-0.5)
hp.graticule(c='k', lw=1)
plt.savefig('footprint_wSFD.png', dpi=150)
def add_edges():
hp.graticule(verbose=False)
plt.grid(True)
lons = np.arange(-150.0, 180, 30.0)
lats = np.zeros(lons.shape)
for lon, lat in zip(lons, lats):
hp.projtext(lon, lat, "%.0f" % lon, lonlat=True)
lats = np.arange(-60.0, 90, 30.0)
lons = np.zeros(lons.shape)
for lon, lat in zip(lons, lats):
hp.projtext(lon, lat, "%.0f" % lat, lonlat=True)
def plot_zoom(ind, LLH=False, reso=1., cmap=None, draw_contour=True, ax=None):
"""Plot skymap of an alert event
Args:
ind (int): Alert event index
LLH (bool, default=False): plot LLH vs. scaled probs.
"""
skymap, header = hp.read_map(skymap_files[ind], h=True, verbose=False,
dtype=None)
header = {name: val for name, val in header}
nside = hp.get_nside(skymap)
area = np.count_nonzero(skymap < 64.2) * hp.nside2pixarea(nside) * 180.**2. / (np.pi**2.)
reso *= int(np.sqrt(area))
reso = np.max([reso, 1.])
original_LLH = None
if not LLH:
original_LLH = np.copy(skymap)
skymap = np.exp(-1. * skymap / 2.)
skymap = np.where(skymap > 1e-20, skymap, 0.0)
skymap = skymap / np.sum(skymap)
ra = np.radians(header['RA'])
dec = np.radians(header['DEC'])
title = skymap_files[ind][l_ind:r_ind].replace('Run', 'Run ').replace('_', ', Event ')
if cmap is None:
pdf_palette = sns.color_palette("Blues", 500)
cmap = mpl.colors.ListedColormap(pdf_palette)
max_color = 100 if LLH else max(skymap)
hp.gnomview(skymap, rot=(np.degrees(ra), np.degrees(dec), 0),
cmap=cmap,
max=max_color,
min=0,
reso=reso,
title=title,
notext=True,
cbar=False
#unit=r""
)
plt.plot(4.95/3.*reso*np.radians([-1, 1, 1, -1, -1]), 4.95/3.*reso*np.radians([1, 1, -1, -1, 1]), color="k", ls="-", lw=3)
hp.graticule(verbose=False)
plot_labels(dec, ra, reso)
original_LLH = skymap if original_LLH is None else original_LLH
con_nside = 256 if area < 5. else 128
if draw_contour:
sample_points = np.array(hp.pix2ang(nside,np.arange(len(original_LLH)))).T
msk = original_LLH < 110.
contours = plot_contours(None, original_LLH[msk],
sample_points[msk], levels=[22.2, 64.2]) #, nside = con_nside)
for contour in np.array(contours).T:
hp.projplot(contour[0],contour[1],linewidth=1.5,c='k')
col_label=r'$-2\Delta \mathrm{LLH}$' if LLH else "Prob."
plot_color_bar(cmap = cmap, labels = [0, max_color], col_label = col_label)
def plot_skymap(ind, LLH=True):
"""Plot skymap of an alert event
Args:
ind (int): Alert event index
LLH (bool, default=False): plot LLH vs. scaled probs.
"""
skymap, header = hp.read_map(skymap_files[ind], h=True, verbose=False,
dtype=None)
header = {name: val for name, val in header}
original_LLH = None
if not LLH:
original_LLH = np.copy(skymap)
skymap = np.exp(-1. * skymap / 2.)
skymap = np.where(skymap > 1e-20, skymap, 0.0)
skymap = skymap / np.sum(skymap)
if LLH:
inf_msk = np.isinf(skymap)
other_max = np.max(skymap[~inf_msk])
skymap[inf_msk] = other_max
pdf_palette = sns.color_palette("Blues", 500)
cmap = mpl.colors.ListedColormap(pdf_palette)
hp.mollview(skymap, title="", cbar=True,
notext=True, rot=180, hold=False,
cmap=cmap,
unit = r'$-2 \Delta \mathrm{LLH}$')
hp.graticule()
# add some labels
plt.text(2.0,0., r"$0^\circ$", ha="left", va="center")
plt.text(1.9,0.45, r"$30^\circ$", ha="left", va="center")
plt.text(1.4,0.8, r"$60^\circ$", ha="left", va="center")
plt.text(1.9,-0.45, r"$-30^\circ$", ha="left", va="center")
plt.text(1.4,-0.8, r"$-60^\circ$", ha="left", va="center")
plt.text(2.0, -0.15, r"$0\,\mathrm{h}$", ha="center", va="center")
plt.text(1.333, -0.15, r"$4\,\mathrm{h}$", ha="center", va="center")
plt.text(.666, -0.15, r"$8\,\mathrm{h}$", ha="center", va="center")
plt.text(0.0, -0.15, r"$12\,\mathrm{h}$", ha="center", va="center")
plt.text(-.666, -0.15, r"$16\,\mathrm{h}$", ha="center", va="center")
plt.text(-1.333, -0.15, r"$20\,\mathrm{h}$", ha="center", va="center")
plt.text(-2.0, -0.15, r"$0\,\mathrm{h}$", ha="center", va="center")
original_LLH = skymap if original_LLH is None else original_LLH
try:
sample_points = np.array(hp.pix2ang(nside,np.arange(len(original_LLH)))).T
msk = original_LLH < 110.
contours = plot_contours(None, original_LLH[msk], sample_points[msk],
levels=[22.2, 64.2])
for contour in np.array(contours).T:
hp.projplot(contour[0],contour[1],linewidth=1.5,c='k')
except:
pass
def plot(self, plt, sphere, src_list):
rot = (0, 90, 0)
plt.figure() # (figsize=(6,6))
logger.info('sphere.pixels: {}'.format(sphere.pixels.shape))
if True:
hp.orthview(sphere.pixels, rot=rot, xsize=1000, cbar=True, half_sky=True, hold=True)
hp.graticule(verbose=False)
plt.tight_layout()
else:
hp.mollview(sphere.pixels, rot=rot, xsize=1000, cbar=True)
hp.graticule(verbose=True)
if src_list is not None:
for s in src_list:
sphere.plot_x(s.el_r, s.az_r)
def draw_map(self, fig, norm=None):
print("Data type are:")
for i, key in enumerate(self.data_type):
print("{}: {}".format(i, key))
dtype = int(input("Which data do you want to show? "))
hp.mollview(self.data[dtype],
coord='G',
title=self.data_type[dtype],
norm=norm,
fig=fig,
xsize=8192)
hp.graticule()
logger.info("Sky map created, field loaded: {}".format(
self.data_type[dtype]))
def main(argv):
_path = os.path.expanduser('~/Develop/SMAPs/')
nna_file = os.path.expanduser('~/Documents/SMAPs/norpointT80.dat')
nsa_file = os.path.expanduser('~/Documents/SMAPs/surpointT80.dat')
sna_file = os.path.expanduser('~/Develop/SMAPs/coordinatesystemandtiling/smaps_pointT80norte.dat')
ssa_file = os.path.expanduser('~/Develop/SMAPs/coordinatesystemandtiling/smaps_pointsulT80.dat')
nna_pt = np.loadtxt(nna_file,unpack=True,usecols=(4,5))
nsa_pt = np.loadtxt(nsa_file,unpack=True,usecols=(4,5))
sna_pt = np.loadtxt(sna_file,unpack=True,usecols=(4,5))
ssa_pt = np.loadtxt(ssa_file,unpack=True,usecols=(4,5))
file1 = 'lambda_sfd_ebv.fits'
file2 = 'smpas_obstime_2.fits'
file3 = 'smpas_obstime_15.fits'
extMapFile = 'extintion_at3800.fits'
map1 = H.read_map(os.path.join(_path,file1))
map2 = H.read_map(os.path.join(_path,file2))
map3 = H.read_map(os.path.join(_path,file3))
extMap = H.read_map(os.path.join(_path,extMapFile))
H.mollview(map1,fig=1,coord=['G','E'],max=1.0,title='',sub=(2,2,3),cbar=False,notext=True)#,unit='hours z < 1.3')
H.projplot((90-nna_pt[1])*np.pi/180.,nna_pt[0]*np.pi/180.,'r.')#,coord=['E','G'])
H.projplot((90-nsa_pt[1])*np.pi/180.,nsa_pt[0]*np.pi/180.,'r.')#,coord=['E','G'])
H.projplot((90-sna_pt[1])*np.pi/180.,sna_pt[0]*np.pi/180.,'w.')#,coord=['E','G'])
H.projplot((90-ssa_pt[1])*np.pi/180.,ssa_pt[0]*np.pi/180.,'w.')#,coord=['E','G'])
H.graticule()
H.mollview(map2,fig=1,coord=['G','E'],title='',sub=(2,2,1),cbar=False,notext=True,max=1800)
#,unit='hours z < 1.3')
H.graticule()
H.mollview(map3,fig=1,coord='G',title='',sub=(2,2,2),cbar=False,notext=True,max=1800)#,unit='hours z < 1.3')
H.graticule()
H.mollview(map2*10**(-extMap),fig=1,coord='G',title='',sub=(2,2,4),cbar=False,notext=True)#,unit='hours z < 1.3')
H.graticule()
py.savefig(os.path.join(_path,'Figures/fig1.png'))
py.show()
def main():
"""
This is the main routine.
"""
'''
# Parse the command line.
parser = OptionParser(usage=usage)
parser.disable_interspersed_args()
parser.add_option('-q','--quiet',dest='quiet',action='store_true',
default=False,help='quiet output')
parser.add_option('-v','--verbose',dest='verbose',action='store_true',
default=False,help='verbose output')
(options,args) = parser.parse_args()
nargs = len(args)
'''
filenameCat = '/Users/Elisa/c/EAntolini/Healpix/NedMAP/NED_z_0.1_0.11.txt'
RA,DEC = np.loadtxt(filenameCat,skiprows= 26,usecols = (2,3), delimiter = '|',dtype=[('f0',float),('f1',float)], unpack = True)
pix = DeclRaToIndex(DEC,RA,512)
galpixels= np.zeros(hp.nside2npix(512))
galpixels[pix]=galpixels[pix]+1
print(len(pix))
print(hp.npix2nside(len(pix)))
#print(hp.nside2npix(64))
hp.mollview(galpixels,coord='C',rot = [0,0.3], title='Histogram equalized Ecliptic', unit='prob',xsize = 64)
hp.graticule()
plt.show()
# Generate my own Map
'''
def plot_leakage_beams(nom_vis,rot=[0,0],mn=-2,mx=0,log=True,figno=2):
if log:
nom_vis_to_plot = np.log10(np.abs(nom_vis.copy()))
else:
nom_vis_to_plot = nom_vis.copy()
fig = plt.figure(figno)
plt.clf()
for a in range(4):
for b in range(4):
if (a==b) and (not log):
mn_plot = 0
mx_plot = 1
else:
mn_plot = mn
mx_plot = mx
print mn_plot, mx_plot
hp.orthview(nom_vis_to_plot[a,b,:],half_sky=True,title='',sub=(4,4,4*a+b+1),min=mn_plot,max=mx_plot,rot=rot)#,margins=[0.1,0.9,0.9,0.1])
hp.graticule()
#fig.tight_layout()
# plt.show()
return
def visualize_beam(args):
angpos = hputil.ang_positions(args.nside)
zenith = latlon_to_sphpol([args.lat, args.lon])
beamx_resp = beamx(args.feed, args.freq, angpos, zenith, args.dish_width)
beamy_resp = beamy(args.feed, args.freq, angpos, zenith, args.dish_width)
# Create output image file name
if args.outfile:
out_file = args.outfile
else:
freq = ('%s'%args.freq).replace('.', '_')
diameter = ('%s'%args.dish_width).replace('.', '_')
out_file = 'beam_resp_%s__%s.%s'%(freq, diameter, args.figfmt)
# Plot and save image
fig = plt.figure(1, figsize=(args.figlength, args.figwidth))
title1 = 'X beam pattern, f= %sMHz, d = %sm'%(args.freq, args.dish_width)
healpy.mollview(beamx_resp[:, 1], fig=1, sub=121, title=title1, min=args.min, max=args.max)
title2 = 'Y beam pattern, f= %sMHz, d = %sM'%(args.freq, args.dish_width)
healpy.mollview(beamy_resp[:, 0], fig=1, sub=122, title=title2, min=args.min, max=args.max)
if args.grid:
healpy.graticule()
fig.savefig(out_file)
fig.clf()
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()
phi = l*deg2rad
pix = hp.ang2pix(nside, theta, phi, nest=False)
val = ir_map[pix]
print src, l,b,pix, val
hp.projplot(l, b, 'bo', lonlat=True, coord='G')
# hp.projtext(l, b, src+','+str(l)+','+str(b), lonlat=True, coord='G')
if (b<60):
hp.projtext(l, b, src, lonlat=True, coord='G', fontsize=13, weight='bold')
else:
hp.projtext(l, b, src, lonlat=True, coord='G', fontsize=13, color='r', weight='bold')
if(src == '3C109'):
hp.projtext(l, b, src, lonlat=True, coord='G', fontsize=13, color='b', weight='bold')
mpl.rcParams.update({'font.size':30})
hp.graticule()
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.,
sys.exit()
# ====================================================================
# To make Table.3 and associated fig.7; run it three times with inc_haze/inc_bubbles on/off
from onestep_fit_flux import onestep_fit_flux
onestep_fit_flux( write_png=False, inc_disc=False, inc_haze=True, inc_bubbles=True, raw_sync=False,
mfile='ebvh_mask_top-half_disc_40_ns0128.fits', model_list=['12132h'], plot_moll=False )
sys.exit()
# ====================================================================
maskfile = 'ebvh_mask_ns0128.fits'
mask = hp.read_map( dir + maskfile )
mask[mask == 0.] = hp.UNSEEN
hp.mollview(mask, cbar=False, title='Galactic mask')
hp.graticule(dpar=20,dmer=20)
reg_maskfile = 'south_haze_ptsrc_mask_ns0128.fits'
reg_mask = hp.read_map( dir + reg_maskfile )
reg_mask[reg_mask == 0.] = hp.UNSEEN
hp.mollview(reg_mask, title='Southern region mask')
hp.graticule(dpar=20,dmer=20)
pl.show()
sys.exit()
# ====================================================================
raw_sync = True
files = {}
files['cmb'] = dir + 'planck_hfi_ilc_ns'+snside+'_1deg_mK.fits'
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 __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)
#!/usr/local/bin/python3
def run_final_mcmc( model=None,
dir = '/global/scratch2/sd/dpietrob/DM-haze/data/maps/ns0128/',
mfile = 'south_haze_ptsrc_mask_ns0128.fits', # mask used for the region fit
sig_scaling = 8,
haze_fit_mask = 'fit_mask_p-20_20_t-10-35_ns0128.fits',
fix_cmb = True,
fix_ff = False, # Does/does not assign freefree parameter
inc_disc = False,
## inc_bubbles = True,
## :DP May 2015 to get Col.4 of Tab.3 running with no bubbles
inc_bubbles = False,
inc_mono = True,
inc_dipo = True,
plot_gnom = False,
seed=0,
regression = 0,
tag = '',
nsamples = 750000
):
if model == None:
info_message( 'ERROR: dm_model not provided. Stop' )
sys.exit()
tag = tag + '_' + model + '_2stp_'+str(sig_scaling).zfill(2)+'x'
tag = tag + '_' + str(seed)
print tag
print model
# --- Templates converted to mK_CMB
fname = dir + 'haze_model_54_'+model+'_ns0128_K_norm.fits'
if not os.path.isfile(fname):
info_message( ' >>> making DM model templates...' )
make_dmhaze_templates( model=model )
#
#
#
# --- Making a more suitable mask ---
# 30 deg radius from [0,-22.5]
# radius = -1
# if False:
# m = hp.read_map( dir + 'south_haze_ptsrc_mask_ns0128.fits' )
# # hp.mollview(m)
# print np.sum(m)/len(m)
#
# x,y,z = hp.ang2vec(np.pi/2+22.5/180.*np.pi ,0.)
# d = make_disc(128, radius, direction=[x,y,z])
# # hp.mollview(d)
# # hp.mollview(d*m)
# print np.sum(m*d)/len(m)
#
# hp.write_map( dir + 'south'+str(radius)+'_haze_ptsrc_mask_ns0128.fits', d*m*ptsrc*ptsrc1 )
# hp.gnomview( d*m*ptsrc*ptsrc1, rot=[0,-22.5], reso=25, min=-1, max=1)
# hp.graticule(dpar=5,dmer=5)
# # hp.gnomview( ptsrc, rot=[0,-22.5], reso=25, min=-1, max=1)
# # hp.graticule(dpar=5,dmer=5)
#
# m = hp.read_map( dir + 'fit_mask_p-35_35_t-10-35_ns0128.fits' )
# # hp.mollview(m)
# print np.sum(m)/len(m)
#
# pl.show()
# # sys.exit()
#
# if radius>0:
# tag = 'southern'+str(radius)
# mfile = 'south'+str(radius)+'_haze_ptsrc_mask_ns0128.fits'
# else:
# tag = 'southern'
# mfile = 'south_haze_ptsrc_mask_ns0128.fits'
#
# # tag = tag + '_2stp'
# # tag = tag + '_2stp_10x'
# sig_scaling = 10
#
# tag = tag + '_2stp_6freq_fxFF_'+str(sig_scaling).zfill(2)+'x'
mask = hp.read_map( dir + mfile )
mask_npix = np.sum( mask )
fsky = np.sum( mask ) / len(mask)
gpix = np.array( (mask == 1.) )
bpix = np.array( (mask == 0.) )
# --- Define haze flux computation region
d = hp.read_map( dir + haze_fit_mask )
haze_region = (mask*d == 1)
not_haze_region = (mask*d == 0)
# --- What to do
find_minimum = True
run_mcmc = True
# ------
plot_min_sed = False
do_write = False
# ------ Run MCMC ------
# setup = Data( DMmodel=model, maskfile='ebvh_mask_ns0128.fits', raw_sync=True )
# setup = Data( DMmodel=model, maskfile=mfile, raw_sync=True )
setup = Data( DMmodel=model, maskfile=mfile, raw_sync=True )
data = setup.data
gmodel = setup.gmodel
freq_min = OrderedDict()
# --- Set parameter values from full sky for some components ---
if find_minimum:
info_message( 'Running regression' )
fx_mnpnt = OrderedDict()
fx_mnpnt_er = OrderedDict()
mnpnt = OrderedDict()
mnpnt_er = OrderedDict()
chisq = []
fx_c2 = 0.
c2 = 0.
flux = {}
cfreq = data.cfreq
mKt2cgs = conversionfactor( cfreq, option='mKthermo2cgs' )
haze_flux = []
haze_flux_er = []
haze_templ_flux = []
haze_templ_flux_er = []
# --- Find minimum for each frequency
for ifreq, freq in enumerate( data.frequencies ):
h = getattr( gmodel.haze, 'f'+freq )
# --- Full run for starting point determination
# Thin disc component removed: chisq gets worse, but global fit does
# not change drmatically.
# tfiles = [ gmodel.galaxy.cmb.filename, gmodel.galaxy.dust.filename, gmodel.galaxy.freefree.filename,
tfiles = [ gmodel.galaxy.cmb.filename, gmodel.galaxy.dust.filename,
gmodel.galaxy.freefree.filename,
gmodel.galaxy.sync.filename, h.filename ]
icmb = 0
idust = 1
iff = 2
isync = 3
ihaze = 4
if inc_mono:
tfiles.append( dir+'map_monopole_ns0128.fits' )
imono = len(tfiles)-1
if inc_dipo:
tfiles.append( dir+'map_xdipole_ns0128.fits' )
idipox = len(tfiles)-1
tfiles.append( dir+'map_ydipole_ns0128.fits' )
idipoy = len(tfiles)-1
tfiles.append( dir+'map_zdipole_ns0128.fits' )
idipoz = len(tfiles)-1
if inc_disc:
tfiles.append( gmodel.galaxy.disc.filename )
idisc = len(tfiles)-1
if inc_bubbles:
tfiles.append( gmodel.galaxy.bubbles.filename )
ibubbles = len(tfiles)-1
map = getattr( data, 'f'+freq )
r = map_regression( map.imap.map, tfiles, rms=map.irms.map,
maskfile=dir+'ebvh_mask_ns0128.fits', return_res=True, return_chi2=True )
# maskfile=dir+'map_monopole_ns0128.fits', return_res=True, return_chi2=True )
chisq.append( str("%.3f" % r[3]) )
fx_c2 += r[3]
# hp.mollview( r[2], title='FS '+freq, min=-0.3, max=0.3)
fx_mnpnt['f'+freq+'_cmb'] = r[0][icmb]
fx_mnpnt['f'+freq+'_dust'] = r[0][idust]
fx_mnpnt['f'+freq+'_freefree'] = r[0][iff]
fx_mnpnt['f'+freq+'_sync'] = r[0][isync]
fx_mnpnt['f'+freq+'_haze'] = r[0][ihaze]
if inc_mono:
fx_mnpnt['f'+freq+'_monopole'] = r[0][imono]
if inc_dipo:
fx_mnpnt['f'+freq+'_dipole_x'] = r[0][idipox]
fx_mnpnt['f'+freq+'_dipole_y'] = r[0][idipoy]
fx_mnpnt['f'+freq+'_dipole_z'] = r[0][idipoz]
if inc_disc:
fx_mnpnt['f'+freq+'_disc'] = r[0][idisc]
if inc_bubbles:
fx_mnpnt['f'+freq+'_bubbles'] = r[0][ibubbles]
fx_mnpnt_er['f'+freq+'_cmb'] = r[1][icmb]
fx_mnpnt_er['f'+freq+'_dust'] = r[1][idust]
fx_mnpnt_er['f'+freq+'_freefree'] = r[1][iff]
fx_mnpnt_er['f'+freq+'_sync'] = r[1][isync]
fx_mnpnt_er['f'+freq+'_haze'] = r[1][ihaze]
if inc_mono:
fx_mnpnt_er['f'+freq+'_monopole'] = r[1][imono]
if inc_dipo:
fx_mnpnt_er['f'+freq+'_dipole_x'] = r[1][idipox]
fx_mnpnt_er['f'+freq+'_dipole_z'] = r[1][idipoz]
fx_mnpnt_er['f'+freq+'_dipole_y'] = r[1][idipoy]
if inc_disc:
fx_mnpnt_er['f'+freq+'_disc'] = r[1][idisc]
if inc_bubbles:
fx_mnpnt_er['f'+freq+'_bubbles'] = r[1][ibubbles]
# --- Remove monopole, dipole and CMB from the map
if inc_mono:
flat_comp = r[0][imono]
if inc_dipo:
flat_comp += gmodel.galaxy.dipole_x.map * r[0][idipox] + \
gmodel.galaxy.dipole_y.map * r[0][idipoy] + \
gmodel.galaxy.dipole_z.map * r[0][idipoz]
if fix_cmb:
flat_comp += gmodel.galaxy.cmb.map * r[0][icmb]
# gmodel.galaxy.freefree.map * r[0][2]
# --- Run on small patch after flat component removed
tfiles = [ gmodel.galaxy.dust.filename,
gmodel.galaxy.sync.filename,
h.filename,
gmodel.galaxy.freefree.filename ]
if inc_disc:
tfiles.append( gmodel.galaxy.disc.filename )
idisc = len( tfiles ) -1
if inc_bubbles:
tfiles.append( gmodel.galaxy.bubbles.filename )
ibubbles = len( tfiles ) -1
if not fix_cmb:
tfiles.append( gmodel.galaxy.cmb.filename )
icmb = len( tfiles ) -1
#if inc_mono:
# tfiles.append( dir+'map_monopole_ns0128.fits' )
# imono = len( tfiles ) -1
# map = getattr( data, 'f'+freq )
# --- Setting up the smaller mask in the southern region
imap = getattr( map, 'imap' )
setattr(imap, 'map', map.imap.map-flat_comp )
setattr(map, 'imap', imap )
r = map_regression( map.imap.map, tfiles, rms=map.irms.map,
maskfile=dir+mfile, return_res=True, return_chi2=True )
chisq.append( str("%.3f" % r[3]) )
c2 += r[3]
# --- Plot gnomview projections
if plot_gnom:
ccc = map.imap.map
ccc[ bpix] = -1.6375e30
hp.gnomview( ccc, title=freq, rot=[0,-22.5], reso=25 )
hp.graticule(dpar=5,dmer=5)
if freq == '070':
ccc = gmodel.galaxy.cmb.map
ccc[ bpix] = -1.6375e30
hp.gnomview( ccc, min=-0.3, max=0.3, title='CMB', rot=[0,-22.5], reso=25 )
hp.graticule(dpar=5,dmer=5)
if freq == 'K':
ccc = gmodel.galaxy.sync.map
ccc[ bpix] = -1.6375e30
hp.gnomview( ccc, title='Sync', rot=[0,-22.5], reso=25 )
hp.graticule(dpar=5,dmer=5)
ccc = gmodel.galaxy.dust.map
ccc[ bpix] = -1.6375e30
hp.gnomview( ccc, title='Dust', rot=[0,-22.5], reso=25 )
hp.graticule(dpar=5,dmer=5)
ccc = gmodel.galaxy.freefree.map
ccc[ bpix] = -1.6375e30
hp.gnomview( ccc, title='FF', rot=[0,-22.5], reso=25 )
hp.graticule(dpar=5,dmer=5)
ccc = r[2]*mask
ccc[ bpix] = -1.6375e30
hp.gnomview( ccc, min=-0.05, max=0.05, title='Residuals: '+freq,
rot=[0,-22.5], reso=25 )
hp.graticule(dpar=5,dmer=5)
ccc = (r[2]+r[0][2]*h.map)*mask
ccc[ bpix] = -1.6375e30
hp.gnomview( ccc, title='Haze: '+freq, rot=[0,-22.5], reso=25 )
hp.graticule(dpar=5,dmer=5)
# ccc = (r[0][2]*h.map)*mask
# ccc[ bpix] = -1.6375e30
# hp.gnomview( ccc, min=0., title='Haze template: '+freq, rot=[0,-22.5], reso=25 )
# hp.graticule(dpar=5,dmer=5)
pl.show()
# --- Flux with residuals
# haze_flux.append( np.mean( (r[2] + r[0][2]*h.map )[gpix] ) * mKt2cgs[ifreq] )
haze_flux.append( np.mean( (r[2] + r[0][2]*h.map )[haze_region] ) * mKt2cgs[ifreq] )
# haze_flux_er.append( np.std( (r[2] + r[0][2]*h.map)[gpix] ) * mKt2cgs[ifreq] )
# --- Trying to be independent of the haze template
# haze_flux_er.append( np.std( r[2][gpix] ) * mKt2cgs[ifreq] )
haze_flux_er.append( np.sqrt( np.var( r[2][haze_region] ) + (haze_flux[-1]*r[1][2])**2)* mKt2cgs[ifreq] )
freq_min[ freq ] = r
mnpnt['f'+freq+'_dust'] = r[0][0]
mnpnt['f'+freq+'_sync'] = r[0][1]
mnpnt['f'+freq+'_haze'] = r[0][2]
mnpnt['f'+freq+'_freefree'] = r[0][3]
if inc_disc:
mnpnt['f'+freq+'_disc'] = r[0][idisc]
if inc_bubbles:
mnpnt['f'+freq+'_bubbles'] = r[0][ibubbles]
if not fix_cmb:
mnpnt['f'+freq+'_cmb'] = r[0][icmb]
#if inc_mono:
# mnpnt['f'+freq+'_monopole'] = r[0][imono]
mnpnt_er['f'+freq+'_dust'] = r[1][0]
mnpnt_er['f'+freq+'_sync'] = r[1][1]
mnpnt_er['f'+freq+'_haze'] = r[1][2]
mnpnt_er['f'+freq+'_freefree'] = r[1][3]
if inc_disc:
mnpnt_er['f'+freq+'_disc'] = r[1][idisc]
if inc_bubbles:
mnpnt_er['f'+freq+'_bubbles'] = r[1][ibubbles]
if not fix_cmb:
mnpnt_er['f'+freq+'_cmb'] = r[1][icmb]
#if inc_mono:
# mnpnt_er['f'+freq+'_monopole'] = r[1][imono]
flux[model+'_haze'] = ( np.array( haze_flux )*1.e20,
np.array( haze_flux_er )*1.e20 )
if do_write:
figtag = '../sed_dm_' + model + '_2step_' + tag
if fix_cmb:
figtag = figtag + '_fixCMB'
if not inc_disc:
figtag = figtag + '_noDISC'
if inc_mono:
figtag = figtag + '_MONO'
file = open(figtag + '.txt', 'w')
file.write( '# cfreq, res_haze, res_haze err\n' )
for ifreq,freq in enumerate(data.cfreq):
# print ifreq, freq
file.write( '%f \t %f \t %f \n' %(freq, flux[model+'_haze'][0][ifreq],
flux[model+'_haze'][1][ifreq] ) )
file.close()
# print flux
info_message(' total chi2 = '+str(c2)+','+str(c2*np.sum(mask)))
sed = get_sed( mnpnt )
sed_er = get_sed( mnpnt_er )
fx_sed = get_sed( fx_mnpnt )
fx_sed_er = get_sed( fx_mnpnt_er )
if plot_min_sed:
f = data.cfreq
cv = conversionfactor( f, option='antenna2thermo' )
fig = plt.figure(30+imodel,(18,7.5), dpi=80)
# --- SED plot based on regression coefficients
ax = plt.subplot(121)
plt.errorbar( f, sed['dust'], yerr=sed_er['dust'], label='dust', color='b', fmt='s:' )
plt.errorbar( f, sed['sync'], yerr=sed_er['sync'], label='sync', color='g', fmt='s:' )
plt.errorbar( f, sed['haze'], yerr=sed_er['haze'], label='haze', color='m', fmt='s:' )
plt.errorbar( f, sed['freefree'], yerr=sed_er['freefree'], label='freefree', color='r', fmt='s:' )
if not fix_cmb:
plt.errorbar( f, sed['cmb'], yerr=sed_er['cmb'], label='cmb', color='k', fmt='s:' )
if inc_disc:
plt.errorbar( f, sed['disc'], yerr=sed_er['disc'], label='disc', color='y', fmt='s:' )
plt.errorbar( f, fx_sed['cmb'], yerr=fx_sed_er['cmb'], label='cmb FS', color='k', fmt='.-' )
plt.errorbar( f, fx_sed['dust'], yerr=fx_sed_er['dust'], label='dust FS', color='b', fmt='.-' )
plt.errorbar( f, fx_sed['sync'], yerr=fx_sed_er['sync'], label='sync FS', color='g', fmt='.-' )
plt.errorbar( f, fx_sed['haze'], yerr=fx_sed_er['haze'], label='haze FS', color='m', fmt='.-' )
plt.errorbar( f, fx_sed['freefree'], yerr=fx_sed_er['freefree'], label='freefree FS', color='r', fmt='.-' )
if inc_disc:
plt.errorbar( f, fx_sed['disc'], yerr=fx_sed_er['disc'], label='disc FS', color='y', fmt='s-' )
plt.plot( f, (f/f[0])**(-3.1)*cv*fx_sed['sync'][0], ':', label='-3.1', color='g' )
plt.plot( f, (f/f[0])**(-2.15)*cv*fx_sed['freefree'][0], ':', label='-2.15', color='r' )
plt.plot( f, f*0.+sed['haze'][0], ':', color='m', label='same amplitude' )
plt.title('Dark matter model: '+model)
plt.xlabel(r'$\nu$ [GHz]')
plt.ylabel('Template amplitudes')
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim([5e-3,10])
ax.set_xlim([8,100])
plt.legend( loc='upper left',prop={'size':10})
# --- Monopole dipole plot
# ax = plt.subplot(122)
#
# plt.errorbar( f, sed['monopole'], yerr=sed_er['monopole'], label='monopole', color='k' )
# plt.errorbar( f, sed['dipole_x'], yerr=sed_er['dipole_x'], label='dipole_x', color='b' )
# plt.errorbar( f, sed['dipole_y'], yerr=sed_er['dipole_y'], label='dipole_y', color='r' )
# plt.errorbar( f, sed['dipole_z'], yerr=sed_er['dipole_z'], label='dipole_z', color='m' )
# plt.plot( f, f*0., '--', color='g' )
# plt.errorbar( f, sed_noh['monopole'], yerr=sed_er_noh['monopole'], label='monopole no-haze', color='k', fmt='--' )
# plt.errorbar( f, sed_noh['dipole_x'], yerr=sed_er_noh['dipole_x'], label='dipole_x no-haze', color='b', fmt='--' )
# plt.errorbar( f, sed_noh['dipole_y'], yerr=sed_er_noh['dipole_y'], label='dipole_y no-haze', color='r', fmt='--' )
# plt.errorbar( f, sed_noh['dipole_z'], yerr=sed_er_noh['dipole_z'], label='dipole_z no-haze', color='m', fmt='--' )
#
# plt.errorbar( f, sed_nod['monopole'], yerr=sed_er_nod['monopole'], label='monopole no-disc', color='k', fmt='s-' )
# plt.errorbar( f, sed_nod['dipole_x'], yerr=sed_er_nod['dipole_x'], label='dipole_x no-disc', color='b', fmt='s-' )
# plt.errorbar( f, sed_nod['dipole_y'], yerr=sed_er_nod['dipole_y'], label='dipole_y no-disc', color='r', fmt='s-' )
# plt.errorbar( f, sed_nod['dipole_z'], yerr=sed_er_nod['dipole_z'], label='dipole_z no-disc', color='m', fmt='s-' )
#
# plt.title(r'$^{\rm red}\chi^2=$'+', '.join(chisq)+r'$\pm0.0028$'+' vs \n'+', '.join(chisq_noh)+' vs \n'+', '.join(chisq_nod), fontsize=12)
# # plt.title('Dark matter model: '+model+' - Southern region')
# plt.xlabel(r'$\nu$ [GHz]')
# plt.ylabel(r'Mono/dipole amplitudes [mK_CMB]')
#
# ax.set_xlim([10,100])
# ax.set_xscale('log')
# hd = np.reshape([sed['monopole'],sed['dipole_x'],sed['dipole_y'],sed['dipole_z'],sed_noh['monopole'],sed_noh['dipole_x'],sed_noh['dipole_y'],sed_noh['dipole_z']], 8*len(sed['monopole']) )
# thrs = 1.1 * np.max( abs( hd ) )
# ax.set_ylim([-thrs,thrs])
# plt.legend( loc='lower right',prop={'size':10})
# --- SED plot based on average flux in the region.
# Mind, this is region dependent!!!
ax = plt.subplot(122)
plt.errorbar( f, flux[model+'_haze'][0], yerr=flux[model+'_haze'][1], label='Haze flux', color='m', fmt='s-' )
plt.plot( f, f*0., '--', color='k')
ax.set_ylim([-.75,2])
ax.set_xlim([8,100])
plt.xlabel(r'$\nu$ [GHz]')
plt.ylabel(r'Haze flux [erg/(cm$^2$ s Hz sr)] $\times 10^{20}$')
plt.legend( loc='upper left',prop={'size':10})
ax.annotate(', '.join(data.frequencies), xy=(10, -0.5) )
ax.annotate(', '.join([ str("%0.3f" %s) for s in flux[model+'_haze'][0] ]), xy=(10, -0.6) )
ax.annotate(r'$\pm$'+', '.join([ str("%0.3f" %s) for s in flux[model+'_haze'][1] ]), xy=(10, -0.7) )
figtag = '../sed_dm_'+model+'_2step_'+tag
if fix_cmb:
figtag = figtag + '_fixCMB'
if not inc_disc:
figtag = figtag + '_noDISC'
if inc_mono:
figtag = figtag + '_MONO'
fig.savefig(figtag + '.png')
pl.show()
# --- Setting starting point
p = OrderedDict()
limits = {}
limits['dust'] = (0.,11.)
limits['freefree'] = (0.,50)
limits['sync'] = (0.,50)
limits['cmb'] = (0.97,1.03)
# limits['monopole'] = (-0.06,0.06)
# limits['disc'] = (0,100)
# limits['monopole'] = (-0.06,0.06)
limits['disc'] = (0.,1)
limits['bubbles'] = (0.,10.) #This is actually the Haze, not DM
# sig_scaling = 6.
# p['global_haze'] = {'center_value':np.min( sed['haze'] ), 'sigma':np.min(sed_er['haze'])/sig_scaling, 'min':0., 'max':1000}
p['global_haze'] = {'center_value':sed['haze'][0], 'sigma':np.min(sed_er['haze'])/sig_scaling, 'min':0., 'max':1000.}
for ifreq, freq in enumerate( data.frequencies ):
m = getattr( data, 'f'+freq )
# hp.mollview( m.imap.map )
r = freq_min[ freq ]
if not fix_cmb:
p['f'+freq+'_cmb'] = {'center_value':np.max( np.array([0.,sed['cmb'][ifreq]])), 'sigma':np.min(sed_er['cmb'])/sig_scaling, 'min':limits['cmb'][0], 'max':limits['cmb'][1]}
p['f'+freq+'_dust'] = {'center_value':np.max( np.array([0.,sed['dust'][ifreq]])), 'sigma':np.min(sed_er['dust'])/sig_scaling, 'min':limits['dust'][0], 'max':limits['dust'][1]}
if not fix_ff:
p['f'+freq+'_freefree'] = {'center_value':np.max( np.array([0.,sed['freefree'][ifreq]])), 'sigma':np.min(sed_er['freefree'])/sig_scaling, 'min':limits['freefree'][0], 'max':limits['freefree'][1]}
p['f'+freq+'_sync'] = {'center_value':np.max( np.array([0.,sed['sync'][ifreq]])), 'sigma':np.min(sed_er['sync'])/sig_scaling, 'min':limits['sync'][0], 'max':limits['sync'][1]}
# p['f'+freq+'_monopole'] = {'center_value':r[0][5], 'sigma':np.min(sed_er['monopole'])/sig_scaling, 'min':limits['monopole'][0], 'max':limits['monopole'][1]}
# p['f'+freq+'_dipole_x'] = {'center_value':r[0][6], 'sigma':np.min(sed_er['dipole_x'])/sig_scaling, 'min':limits['monopole'][0], 'max':limits['monopole'][1]}
# p['f'+freq+'_dipole_y'] = {'center_value':r[0][7], 'sigma':np.min(sed_er['dipole_y'])/sig_scaling, 'min':limits['monopole'][0], 'max':limits['monopole'][1]}
# p['f'+freq+'_dipole_z'] = {'center_value':r[0][8], 'sigma':np.min(sed_er['dipole_z'])/sig_scaling, 'min':limits['monopole'][0], 'max':limits['monopole'][1]}
if inc_disc:
p['f'+freq+'_disc'] = {'center_value':np.max( np.array([0.,r[0][9]])), 'sigma':np.min(sed_er['disc'])/sig_scaling, 'min':limits['disc'][0], 'max':limits['disc'][1]}
if inc_bubbles:
p['f'+freq+'_bubbles'] = {'center_value':np.max( np.array([0.,sed['bubbles'][ifreq]])), 'sigma':np.min(sed_er['bubbles'])/sig_scaling, 'min':limits['bubbles'][0], 'max':limits['bubbles'][1]}
ppp = {}
for key in p.keys():
print key, '\t', p[key]['center_value'], '\t', p[key]['sigma']
ppp[key] = p[key]['center_value']
info_message( 'Starting point chi2: '+str(setup.fg_chisq(ppp) ) )
# sys.exit()
# ------ Run MCMC ------
mcmc = MCMC()
if run_mcmc:
bf, ch = mcmc.sampler( p, setup.fg_chisq, nsamples=nsamples, seed=seed, output_tag=tag,
temperature=1., accept_first=False, update_covmat=False )
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()
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)
def go(mcFile, npix, ntrials, plottrial=0, plotlog=False, olddisks=False):
#healpixTable = open(mcFile, 'rb')
nside = healpy.npix2nside(npix)
#healpix = healpixTable[:,plottrial]
#hp = healpixTable.read()
#healpix = np.fromstring(hp, dtype=np.float64)
#start_arr = plottrial*npix
#end_arr = plottrial*npix+npix
#healpix = healpix[start_arr:end_arr]
healpix = np.fromfile(mcFile, dtype=float)
healpix = healpix.reshape((ntrials, npix))
healpix = healpix[plottrial]
if plotlog == True:
healpix = np.log(healpix)
# all sky pixels sorted with highest pixels first
idx = (np.argsort(healpix))[::-1]
sortHP = healpix[idx]
# Cumulatively sum up the pixels
cumHP = np.cumsum(sortHP)
totHP = sortHP.sum()
# Make contours
pixIdx = np.arange(npix)
theta, phi = healpy.pix2ang(nside, pixIdx)
# Peak disk value
peakPixVal = healpix.max()
peakPixIdx = healpix.argmax()
peakIncl = np.degrees(theta[peakPixIdx])
peakOmeg = np.degrees(phi[peakPixIdx])
print 'Disk candidate at i = %6.2f, O = %6.2f' % (peakIncl, peakOmeg)
# Determine the background (avg and stddev)
avgiter = 2
idx = np.arange(len(healpix))
for ii in range(avgiter):
avgval = healpix[idx].mean()
stddev = healpix[idx].std()
hicut = avgval + 5.0 * stddev
locut = avgval - 5.0 * stddev
idx = np.where((healpix > locut) & (healpix < hicut))[0]
print 'BKG: iter = %d, rejecting %d out of %d pixels' % \
(ii, (npix - len(idx)), npix)
print ''
print 'Background mean = %f, stddev = %f' % (avgval, stddev)
print 'Peak = %f, significance = %f' % \
(healpix.max(), (healpix.max() - avgval) / stddev)
# Find pixels 1, 2, 3 sigma below the peak
idx1 = np.where(healpix > (peakPixVal - (1.0 * stddev)))[0]
idx2 = np.where(healpix > (peakPixVal - (2.0 * stddev)))[0]
idx3 = np.where(healpix > (peakPixVal - (3.0 * stddev)))[0]
cnt1 = len(idx1)
cnt2 = len(idx2)
cnt3 = len(idx3)
inclRng1 = np.degrees([min(theta[idx1]), max(theta[idx1])])
inclRng2 = np.degrees([min(theta[idx2]), max(theta[idx2])])
inclRng3 = np.degrees([min(theta[idx3]), max(theta[idx3])])
omegRng1 = np.degrees([min(phi[idx1]), max(phi[idx1])])
omegRng2 = np.degrees([min(phi[idx2]), max(phi[idx2])])
omegRng3 = np.degrees([min(phi[idx3]), max(phi[idx3])])
fmt = '%d sigma ranges: i = [%5.1f - %5.1f] o = [%5.1f - %5.1f] (N=%d)'
print fmt % (1, inclRng1[0], inclRng1[1], omegRng1[0], omegRng1[1], cnt1)
print fmt % (2, inclRng2[0], inclRng2[1], omegRng2[0], omegRng2[1], cnt2)
print fmt % (3, inclRng3[0], inclRng3[1], omegRng3[0], omegRng3[1], cnt3)
# Now display gaussian fits along inclination and Omega to get
# estimate of the disk thickness
iidx = np.where((np.degrees(theta) > (peakIncl - 45.0)) &
(np.degrees(theta) < (peakIncl + 45.0)) &
(np.degrees(phi) > (peakOmeg - 1.0)) &
(np.degrees(phi) < (peakOmeg + 1.0)))[0]
oidx = np.where((np.degrees(phi) > (peakOmeg - 45.0)) &
(np.degrees(phi) < (peakOmeg + 45.0)) &
(np.degrees(theta) > (peakIncl - 1.0)) &
(np.degrees(theta) < (peakIncl + 1.0)))[0]
#py.clf()
#py.plot(np.degrees(theta[iidx]),healpix[iidx],'r.')
#py.plot(np.degrees(phi[oidx]),healpix[oidx],'b.')
#py.show()
# Plot the previously proposed disks
if olddisks == True:
i = [129.0, 30.0] # clockwise (Bartko+09); CCW (Paumard+06)
i_err = [3.0, 4.0]
i_thick = [9.0, 9.5]
Om = [98.0, 167.0]
Om_err = [3.0, 9.0]
Om_thick = [9.0, 9.5]
disks = {'s1': ellipseOutline(i[0], Om[0], i_err[0], Om_err[0]),
's2': ellipseOutline(i[1], Om[1], i_err[1], Om_err[1]),
's3': ellipseOutline(i[0], Om[0], i_thick[0], Om_thick[0], line='--'),
's4': ellipseOutline(i[1], Om[1], i_thick[1], Om_thick[1], line='--')}
s1 = ellipseOutline(i[0], Om[0], i_err[0], Om_err[0])
s2 = ellipseOutline(i[1], Om[1], i_err[1], Om_err[1])
s3 = ellipseOutline(i[0], Om[0], i_thick[0], Om_thick[0])
s4 = ellipseOutline(i[1], Om[1], i_thick[1], Om_thick[1])
# Plot up the orientation of the orbital plane for this star
py.clf()
healpy.mollview(healpix, 1, coord=['C','C'], flip='geo', rot=[0,180],
#cmap=py.cm.gist_stern_r,title='i=180',
cmap=py.cm.Blues,title='i=180',
fontsize=12,notext=True)
healpy.graticule(dpar=30,dmer=45)
# old disk plotting not functional yet:
#py.plot(s1['dec'], s1['ra'], 'k-')
#py.plot(s2['dec'], s2['ra'], 'k-')
#py.plot(s3['dec'], s3['ra'], 'k--')
#py.plot(s4['dec'], s4['ra'], 'k--')
fs = 12
py.text(0.02, -0.98, '0', fontsize=fs)
py.text(0.02, -0.73, '30', fontsize=fs)
py.text(0.02, -0.38, '60', fontsize=fs)
py.text(0.02, 0.02, '90', fontsize=fs)
py.text(0.00, 0.43, '120', fontsize=fs)
py.text(0.00, 0.80, '150', fontsize=fs)
py.text(-1.07, 0.02, 'N', fontsize=fs)
py.text(-0.07, 0.02, 'W', fontsize=fs)
py.text(0.93, 0.02, 'S', fontsize=fs)
py.text(1.93, 0.02, 'E', fontsize=fs)
py.savefig(mcFile + '.png')
py.close()
def plotSkyMap(self, metricValueIn, xlabel=None, title='',
logScale=False, cbarFormat='%.2f', cmap=cm.jet,
percentileClip=None, colorMin=None, colorMax=None,
zp=None, normVal=None,
cbar_edge=True, label=None, nTicks=None, rot1=0, rot2=0, rot3=0, **kwargs):
"""
Plot the sky map of metricValue using healpy Mollweide plot.
metricValue = metric values
units = units for metric color-bar label
title = title for plot
cbarFormat = format for color bar numerals (i.e. '%.2g', etc) (default to matplotlib default)
rot1,2,3 = rotations passed to mollview.
"""
# Generate a Mollweide full-sky plot.
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
rot = (rot1,rot2,rot3)
hp.mollview(metricValue.filled(self.badval), title=title, cbar=False,
min=clims[0], max=clims[1], rot=rot, flip='astro',
cmap=cmap, norm=norm)
# This graticule call can fail with old versions of healpy and matplotlib 1.4.0.
# Make sure the latest version of healpy in the stack is setup
hp.graticule(dpar=20, dmer=20, verbose=False)
# Add colorbar (not using healpy default colorbar because want more tickmarks).
ax = plt.gca()
im = ax.get_images()[0]
# Add label.
if label is not None:
plt.figtext(0.8, 0.8, '%s' %label)
# supress silly colorbar warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cb = plt.colorbar(im, shrink=0.75, aspect=25, orientation='horizontal',
extend='both', extendrect=True, format=cbarFormat)
cb.set_label(xlabel)
if nTicks is not None:
tick_locator = ticker.MaxNLocator(nbins=nTicks)
cb.locator = tick_locator
cb.update_ticks()
# If outputing to PDF, this fixes the colorbar white stripes
if cbar_edge:
cb.solids.set_edgecolor("face")
fig = plt.gcf()
return fig.number
def plot(data, showdisks=False, unit='stars/deg^2'):
_fontsize = rcParams['font.size']
_fontweight = rcParams['font.weight']
rcParams['font.size'] = 14
rcParams['font.weight'] = 'normal'
# Draw the map
healpy.mollview(map=data, rot=[0, 180],
cmap=cm.gist_stern_r, title='',
unit=unit)
healpy.graticule(dpar=30.0, dmer=45.0, local=True)
# Handle colorbar labels
try:
fig = py.gcf()
cbar = fig.axes[-1]
cbarLabels = cbar.xaxis.get_ticklabels()
for cc in range(len(cbarLabels)):
cbarLabels[cc] = '%.5f' % float(cbarLabels[cc].get_text())
cbar.xaxis.set_ticklabels(cbarLabels, fontweight='bold', fontsize=14)
except UnicodeEncodeError:
pass
# Draw contours for the old disk positions
if showdisks:
incl = [124.0, 30.0]
incl_err = [2.0, 4.0]
incl_thick = [7.0, 9.5]
Om = [100.0, 167.0]
Om_err = [3.0, 9.0]
Om_thick = [7.0, 9.5]
for jj in range(len(incl)):
x1, y1 = ellipseOutline(incl[jj], Om[jj],
incl_err[jj], Om_err[jj])
x2, y2 = ellipseOutline(incl[jj], Om[jj],
incl_thick[jj], Om_thick[jj])
healpy.projplot(x1, y1, lonlat=True, color='k',
linestyle='-', linewidth=2)
healpy.projplot(x2, y2, lonlat=True, color='k',
linestyle='--', linewidth=2)
# Flip so that the axes go in the right direction
foo = py.axis()
py.xlim(2.01, -2.01)
# Make axis labels.
healpy.projtext(178, 90, '0', lonlat=True)
healpy.projtext(178, 60, '30', lonlat=True)
healpy.projtext(178, 30, '60', lonlat=True)
healpy.projtext(178, 0, '90', lonlat=True)
healpy.projtext(178, -30, '120', lonlat=True)
healpy.projtext(178, -60, '150', lonlat=True)
healpy.projtext(178, -90, 'i = 180', lonlat=True,
horizontalalignment='center')
healpy.projtext(92, 1, 'S', lonlat=True,
horizontalalignment='right', verticalalignment='top')
healpy.projtext(182, 1, 'W', lonlat=True,
horizontalalignment='right', verticalalignment='top')
healpy.projtext(272, 1, 'N', lonlat=True,
horizontalalignment='right', verticalalignment='top')
healpy.projtext(362, 1, 'E', lonlat=True,
horizontalalignment='right', verticalalignment='top')
rcParams['font.size'] = _fontsize
rcParams['font.weight'] = _fontweight
def main():
"""Simple test unit
"""
from scipy.interpolate import interp1d
cat_file = os.path.join(GRATOOLS_CONFIG,'catalogs/gll_psc_v16.fit')
src_cat = pf.open(cat_file)
CAT = src_cat['LAT_Point_Source_Catalog']
SOURCES = CAT.data
src_cat.close()
flux = np.sort(SOURCES.field('Flux1000'))
flux_min, flux_max = min(flux), max(flux)
"""
rad_min, rad_max = 0.5, 5.
RADdeg = rad_min + flux*((rad_max - rad_min)/(flux_max - flux_min)) -\
flux_min*((rad_max - rad_min)/(flux_max - flux_min))
plt.figure(facecolor='white')
plt.title('Disk Radius as a function of $\Phi_{src}$')
plt.plot(flux, RADdeg, '*-', color='cornflowerblue', linewidth=1,
ms=5, alpha=0.2)
plt.xlabel('Flux [photon/cm$^{2}$/s]')
plt.ylabel('mask disk Radius [$^{\circ}$]')
plt.yscale('log')
plt.xscale('log')
plt.ylim(1.5, 6)
plt.grid(which='both', linestyle='--', linewidth=1)
"""
"""
from GRATools.utils.gWindowFunc import get_psf_ref
psf_ref_file = os.path.join(GRATOOLS_CONFIG, 'ascii/PSF_UCV_custom.txt')
psf_ref = get_psf_ref(psf_ref_file)
energy = np.logspace(1, 6, 10)
energy = [301.99,524.81,1000.00,2754.23,
8317.64,22908.68,331131.12]
lab = ['PSF3','PSF3','PSF1+2+3', 'PSF1+2+3', 'PSF1+2+3','PSF1+2+3',
'PSF1+2+3']
plt.figure(facecolor='white')
for i, en in enumerate(energy):
r_min = 2*psf_ref(en)
r_max = 5*psf_ref(en)
print 'R min', r_min, 'R max', r_max
print 'F min', flux_min, 'F max', flux_max
RADdeg = r_min + np.log10(flux)*((r_max - r_min)/(np.log10(flux_max) - np.log10(flux_min))) -\
np.log10(flux_min)*((r_max - r_min)/(np.log10(flux_max) - np.log10(flux_min)))
#plt.figure(facecolor='white')
plt.title('Disk Radius as a function of $\Phi_{src}$')
plt.plot(np.log10(flux), RADdeg, 'o-', linewidth=1,
ms=3, alpha=0.4, label="%.1f GeV (%s)"%(en/1000., lab[i]))
plt.xlabel('Flux [photon/cm$^{2}$/s]')
plt.ylabel('Mask disk Radius [$^{\circ}$]')
#plt.yscale('log')
#plt.xscale('log')
#plt.ylim(1.5, 6)
plt.grid(which='both', linestyle='--', linewidth=1)
plt.legend(fontsize=11, loc=2)
"""
nside = 512
SRC_CATALOG_FILE = os.path.join(GRATOOLS_CONFIG,'catalogs/gll_psc_v16.fit')
bad_pix = mask_src_weighted(SRC_CATALOG_FILE, SRC_CATALOG_FILE,8317, nside)
bad_pix += mask_bat_gp(2.5e-7, nside)
npix = hp.nside2npix(nside)
mask = np.ones(npix)
for bpix in np.unique(bad_pix):
mask[bpix] = 0
fsky = 1-(len(np.unique(bad_pix))/float(npix))
#title = 'Energy = 100 GeV, F$_{sky}$ = %.3f'%(fsky)
title = 'F$_{sky}$ = %.3f'%(fsky)
hp.mollview(mask, title=title, coord='G', cmap='bone')
hp.graticule(color='silver')
plt.show()
def main():
"""
This is the main routine.
"""
'''
# Parse the command line.
parser = OptionParser(usage=usage)
parser.disable_interspersed_args()
parser.add_option('-q','--quiet',dest='quiet',action='store_true',
default=False,help='quiet output')
parser.add_option('-v','--verbose',dest='verbose',action='store_true',
default=False,help='verbose output')
(options,args) = parser.parse_args()
nargs = len(args)
'''
# Read Galaxy Catalog Parameters
filenameCat1 = '/Users/Elisa/c/EAntolini/Healpix/Catalogs/Complete/Tully_Magnitude_Completed_shape.txt'
outfilename = '/Users/Elisa/c/EAntolini/Healpix/IpacTableFromSource.fits'
#Name,Morphology,GAL_RA,GAL_DEC,r_k20fe,j_m_k20fe,k_m_k20fe,k_ba,k_pa,distance = np.loadtxt(filenameCat1,dtype=[('f0',str),('f1',str),('f2',float),('f3',float),('f4',float),('f5',float),('f6',float),('f7',float),('f8',float),('f9',float)], unpack = True)
# 2MASS MAP
#GAL_RA,GAL_DEC,r_k20fe,j_m_k20fe,k_m_k20fe,k_ba,k_pa, vel, distance = np.loadtxt(filenameCat1,dtype=[('f0',float),('f1',float),('f2',float),('f3',float),('f4',float),('f5',float),('f6',float),('f7',float),('f8',float)], unpack = True)
#TULLY MAP 1794
RA,DEC,radius,frac_diameter,distance,mag,incl= np.loadtxt(filenameCat1,dtype=[('f0',float),('f1',float),('f2',float),('f3',float),('f4',float),('f5',float),('f6',float)], unpack = True)
GAL_RA = RA
GAL_DEC =DEC
r_k20fe = radius
k_ba = frac_diameter
k_pa = np.cos(np.absolute(incl-3)) #degree
k_m_k20fe = mag
# Generate my own Map
'''
subplot(221, projection="aitoff")
title("Aitoff")
grid(True)
show()
'''
# Open LIGO map
# By default, input maps are converted to RING ordering, if they are in NESTED ordering.
map_dir = '/Users/Elisa/Documents/Robotic_Telescope/LIGO/Healpix/'
map_name = 'bayestar.fits'
#wmap_map_Nested = hp.read_map(map_dir+map_name, nest=True) #Remains NESTED
wmap_map_Ring = hp.read_map(map_dir+map_name,0) #Change to RING (Default), read the 0 columns of the file
hp.mollview(np.log10(wmap_map_Ring),coord='C',rot = [0,0.3], title='Histogram equalized Ecliptic', unit='prob', min=-8,max=-6, xsize=4096)
hp.graticule()
plt.show()
#Get RA and DEC values from LIGO map
mypixels = np.asarray(np.log10(wmap_map_Ring))
galpixels=0*mypixels
print(len(mypixels))
#2)
#LIGO_RA=[]
#LIGO_DEC=[]
# Convert Pixels to RA and DEC in LIGO map
'''
for i in range(len(mypixels)):
dec, ra = IndexToDeclRa(512,i)
LIGO_RA.append(ra)
LIGO_DEC.append(dec)
with open('/Users/Elisa/c/EAntolini/Healpix/LigoRADEC.txt', 'w') as fLigofile:
for i in range(len(LIGO_RA)):
fLigofile.write(str(LIGO_RA[i])+" "+str(LIGO_DEC[i])+"\n")
'''
#Load File with M31 and Andromeda (LIGO data set)
LIGO_RA,LIGO_DEC = np.loadtxt('/Users/Elisa/c/EAntolini/Healpix/LigoRADEC.txt',dtype=[('f0',float),('f1',float)], unpack = True)
pos = (mt.pi/180.0)
arcsec_to_radians = 4.84813681e-06
cosdec_c= np.cos((LIGO_DEC)*pos) #cos(dec_c) radians
sindec_c= np.sin((LIGO_DEC)*pos) #sin(dec_c) radians
#2) Take RA and DEC from GALAXY Catalog and convert to Index
#for r, d, radius in zip(GAL_RA[Name=='M31'],GAL_DEC[Name=='M31'],r_k20fe[Name=='M31']):
#for r, d,radius, semi_mayor,polar_angle in zip(GAL_RA,GAL_DEC,r_k20fe,k_ba,k_pa):
#2MASS
#for r, d,semi_mayor,K_mag,ba,polar_angle,dist in zip(GAL_RA,GAL_DEC,r_k20fe,k_m_k20fe,k_ba,k_pa,distance):
#Tully
for r, d,semi_mayor,K_mag,ba,polar_angle,dist in zip(GAL_RA,GAL_DEC,r_k20fe,k_m_k20fe,k_ba,k_pa,distance):
#polar_angle = 0
# Distance of the galaxy from the center [radians]
dumy=np.arccos(np.cos(d*pos)*cosdec_c*np.cos((r-LIGO_RA)*pos)+np.sin(d*pos)*sindec_c)
# Polar Angle (between North-South directions) [radians]
dumx=np.arctan2(np.sin(d*pos)-np.cos(dumy)*sindec_c,np.cos(d*pos)*np.sin((r-LIGO_RA)*pos)*cosdec_c);
dumx +=(polar_angle+90)*pos
'''
dumx -=(polar_angle+90)*pos
dumx +=(90-polar_angle)*pos
dumx -=(90-polar_angle)*pos
'''
semi_minor=ba*semi_mayor
#Compute the semi-minor axes of the Glaxy from Catalog
f_dumx = (semi_mayor * semi_minor)/np.sqrt(np.square(semi_minor*np.cos(dumx))+np.square(semi_mayor*np.sin(dumx)))
LumK = np.power(10,(-0.4*(K_mag-5*np.log10(dist*1e5)-6.35))) # lUMINOSTY OF THE GALAXY IN SOLAR LUMINOSITY
radius = f_dumx*pos/3600
#Utilizzare dumy se non ho tutte le distanze delle galassie
# Quando ho tutte le galassie posso usare Lumk
#galpixels += np.exp(-dumy/radius)
galpixels += (LumK/(semi_mayor * semi_minor))*np.exp(-dumy/radius)
#galpixels += (1/(semi_mayor * semi_minor))*np.exp(-dumy/radius)
hp.mollview(np.log10(galpixels),coord='C',rot = [0,0.3], title='Histogram equalized Ecliptic', unit='prob', xsize=4096)
#hp.mollview(galpixels,coord='C',rot = [0,0.3], title='Histogram equalized Ecliptic', unit='prob', xsize=4096)
hp.graticule()
plt.show()
def main(argv):
_path = os.path.expanduser('~/Develop/SMAPs/')
file_area = [ 'dcorr_smpas_obstime_2_area12.fits'
,'dcorr_smpas_obstime_2_area18.fits'
,'dcorr_smpas_obstime_2_area6.fits']
nna_file = os.path.expanduser('~/Develop/SMAPs/coordinatesystemandtiling/norpointT80.dat')
nsa_file = os.path.expanduser('~/Develop/SMAPs/coordinatesystemandtiling/surpointT80.dat')
smaps_sul = os.path.expanduser('~/Develop/SMAPs/smaps_pointsulT80.dat')
smaps_nor = os.path.expanduser('~/Develop/SMAPs/smaps_pointT80norte.dat')
#
# Reading data
#
map_area = np.array([H.read_map(os.path.join(_path,file_area[0])),])
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
#
##
## This is best area cut observability
##
plmap = np.zeros(map_area.shape[1])
#for i in range(3):
# plmap += map_area[i]
#
#H.mollview(plmap,fig=1,title='secz < 1.5',coord=['G','E'],sub=(1,2,4),cbar=False,notext=True,max=3.5)
#
#plmap = np.zeros(map_area.shape[1])
for i in range(len(map_area)):
plmap += map_area[i]
# make a color map of fixed colors
cmap = colors.ListedColormap(['grey', 'blue', 'green', 'red'])
H.mollview(plmap,coord=['G','E'],cmap=cmap,cbar=False,notext=True,title='S-MAPS Survey Area Selection')#,rot=(0,-90,90))#, norm=norm)
#H.cartview(plmap,coord='G',cmap=cmap,cbar=False,notext=True,title='S-MAPS Survey Area Selection')#, norm=norm)
H.graticule()
nna_pt = np.loadtxt(nna_file,unpack=True,usecols=(4,5))
nsa_pt = np.loadtxt(nsa_file,unpack=True,usecols=(4,5))
pt_smaps_sul = np.loadtxt(smaps_sul,unpack=True,usecols=(4,5))
pt_smaps_nor = np.loadtxt(smaps_nor,unpack=True,usecols=(4,5))
#myPatches=[]
#for i in range(len(nna_pt[0])):
# r2 = patches.RegularPolygon(((90-nna_pt[1][i])*np.pi/180.,nna_pt[0][i]*np.pi/180.),4,0.573*np.pi,orientation=45*np.pi/180.)
# myPatches.append(r2)
#collection = PatchCollection(myPatches,alpha=0.5)
#H.add_collection(collection)
H.projplot((90-nna_pt[1])*np.pi/180.,nna_pt[0]*np.pi/180.,'.',color='w',alpha=0.5)#,coord=['E','G'])
H.projplot((90-nsa_pt[1])*np.pi/180.,nsa_pt[0]*np.pi/180.,'.',color='w',alpha=0.5)#,coord=['E','G'])
H.projplot((90-pt_smaps_sul[1])*np.pi/180.,pt_smaps_sul[0]*np.pi/180.,'.',color='k',alpha=0.5)#,coord=['E','G'])
H.projplot((90-pt_smaps_nor[1])*np.pi/180.,pt_smaps_nor[0]*np.pi/180.,'.',color='k',alpha=0.5)#,coord=['E','G'])
#
# Area 1 - SGH
#
#sgh_x = [ [135,180] , [135, 120] , [ 120 , 105] , [ 105 , 80] , [80 , 105] , [105] , [105 , 180] ]
#sgh_y = [ [100] , [100 , 40] , [ 40 ] , [ 40 , -55] , [-55, -60] , [-60 , -85] , [-85 ]
sgh_x = [ [135,180] , [135, 120] , [ 120 , 113.] , [ 113. , 100] , [100 , 112] , [112] , [112 , 180] ]
sgh_y = [ [85] , [85 , 25] , [ 25 ] , [ 25 , -40] , [ -40] , [-40 , -65] , [-65 ] ]
#plotArea(sgh_x,sgh_y)
#ngh_x1 = [ [135, 125] , [125,100] , [55,100]]
#ngh_y1 = [ [180,130] , [130, 110] , [179,110]]
ngh_x1 = [ [115] , [115,80] , [80]]
ngh_y1 = [ [180,160] , [160] , [179.9,160]]
#plotArea(ngh_x1,ngh_y1)
#ngh_x2 = [ [135, 115] , [115,55] , [55]]
#ngh_y2 = [ [-180,-130] , [-130, -115] , [-115,-180]]
ngh_x2 = [ [115] , [90,115] , [90] , [90,65] , [65]]
ngh_y2 = [ [-165,-180] , [-165] , [-165,-150] , [-150] , [-150,-180]]
#plotArea(ngh_x2,ngh_y2)
x1 = np.zeros(100)+np.pi/2. #np.linspace(0,np.pi/2.,100)
y1 = np.linspace(-np.pi,np.pi,100)
H.projplot(x1,y1,'w',lw=2.,coord=['G','E'])#,rot=(0,-90,90),coord=['G','E'])
#H.projplot([0],[np.pi/2.],'ko',lw=2.,coord=['G','E'])#,rot=(0,-90,90),coord=['G','E'])
H.projplot([192.859508],[27.128336],'ko',lw=2.,coord=['E'],lonlat=True)#,rot=(0,-90,90),coord=['G','E'])
H.projplot([192.859508-180],[-27.128336],'wo',lw=2.,coord=['E'],lonlat=True)#,rot=(0,-90,90),coord=['G','E'])
#H.projplot([-np.pi],[np.pi/2.],'wo',lw=2.,coord=['G','E'])#,rot=(0,-90,90),coord=['G','E'])
#H.projplot([-np.pi],[0],'wo',lw=2.,coord=['G','E'])#,rot=(0,-90,90),coord=['G','E'])
# ngh_x2 = [ [135, 115] , [90, 115], [90,55] , [55]]
# ngh_y2 = [ [-180,-120] , [-140, -120 ], [-140, -115] , [-115,-180]]
# plotArea(ngh_x2,ngh_y2)
py.savefig(os.path.join(_path,'Figures/areaSelection.pdf'))
py.show()
