def plotSkyMap(raSample, decSample, nside=64, contours=None, colorbar=True, \
inj=None, psrs=None, cmap='YlOrBr', outfile='skymap.pdf'):
"""
Plot Skymap of chain samples on Mollwiede projection.
@param raSample: Array of right ascension samples
@param decSample: Array of declination samples
@param nside: Number of pixels across equator [default = 64]
@param contours: Confidence contours to draw eg. 68%, 95% etc
By default this is set to none and no contours
will be drawn.
@param colorbar: Boolean option to draw colorbar [default = True]
@param inj: list of injected values [ra, dec] in radians to plot
[default = None]
@param psrs: Stacked array of pulsar sky locations [ra, dec] in radians
[default=None] Will plot as white diamonds
"""
# clear figures
plt.clf()
# create stacked array of ra and dec
skypos = np.column_stack([raSample, decSample])
npix = hp.nside2npix(nside) # number of pixels total
# initialize theta and phi map coordinantes
skycarts = []
for ii in range(npix):
skycarts.append(np.array(hp.pix2vec(nside, ii)))
# get skymap values from greedy binning algorithm
skymap = greedy_bin_sky(skypos, skycarts)
# smooth skymap
skymap = hp.smoothing(skymap, sigma=0.02)
# make plot
ax = plt.subplot(111, projection='astro mollweide')
# Add contours
if contours is not None:
for percent in contours:
indices = np.argsort(-skymap)
sky = skymap[indices]
region = np.zeros(skymap.shape)
ind = np.min(ml.find(np.cumsum(sky) >= 0.01 * percent))
region[indices[0:ind]] = 1.0
cs = plot.contour(lambda lon, lat: region[hp.ang2pix(nside, 0.5*np.pi - lat, lon)], \
colors='k', linewidths=1.0, levels=[0.5])
#plt.clabel(cs, [0.5], fmt={0.5: '$\mathbf{%d\%%}$' % percent}, fontsize=8, inline=True)
# plot map
ax.grid()
plot.outline_text(ax)
plot.healpix_heatmap(skymap, cmap=cmap)
# add injection
if inj:
ax.plot(inj[0], inj[1], 'x', color='k', markersize=8, mew=2, mec='k')
# add pulsars
if np.all(psrs):
ax.plot(psrs[:, 0],
psrs[:, 1],
'*',
color='lime',
markersize=8,
mew=1,
mec='k')
# add colorbar and title
if colorbar:
plt.colorbar(orientation='horizontal')
plt.suptitle(r'$p(\alpha,\delta|d)$', y=0.1)
# save skymap
plt.savefig(outfile, bbox_inches='tight')
def makeSkyMap(samples,
lmax,
nside=32,
cmap=None,
strain=None,
tex=True,
psrs=None,
axis=None):
if tex == True:
plt.rcParams['text.usetex'] = True
npix = hp.nside2npix(nside) # number of pixels total
# initialize theta and phi map coordinantes
skypos = []
for ii in range(npix):
skypos.append(np.array(hp.pix2ang(nside, ii)))
skypos = np.array(skypos)
harmvals = utils.SetupSkymapPlottingGrid(lmax, skypos)
if np.atleast_2d(samples).shape[0] > 1:
if strain is None:
samples = np.mean(samples, axis=0)
samples = np.append(2. * np.sqrt(np.pi), samples)
elif strain is not None:
samples = np.mean((samples.T * 10**(2.0 * strain)).T, axis=0)
samples = np.append(
np.mean(10**(2.0 * strain) * (2.0 * np.sqrt(np.pi))), samples)
else:
#### !!!!!
samples = np.append(2. * np.sqrt(np.pi), samples)
pwr = utils.GWpower(samples, harmvals)
print pwr, samples
if axis is None:
ax = plt.subplot(111, projection='astro mollweide')
ax.grid()
plot.outline_text(ax)
if cmap is not None:
if strain is None:
plot.healpix_heatmap(pwr, cmap=cmap)
elif strain is not None:
plot.healpix_heatmap(pwr, cmap=cmap)
else:
plot.healpix_heatmap(pwr)
plt.colorbar(orientation='horizontal')
if strain is None:
plt.suptitle(r'$\langle P_{\mathrm{GWB}}(\hat\Omega)\rangle$',
y=0.1)
elif strain is not None:
plt.suptitle(
r'$\langle A_h^2 P_{\mathrm{GWB}}(\hat\Omega)\rangle$', y=0.1)
# add pulsars locations
if psrs is not None:
ax.plot(psrs[:, 0],
np.pi / 2. - psrs[:, 1],
'*',
color='w',
markersize=15,
mew=1,
mec='k')
elif axis is not None:
axis.grid()
plot.outline_text(axis)
if cmap is not None:
plot.healpix_heatmap(pwr, cmap=cmap)
else:
plot.healpix_heatmap(pwr)
# add pulsars locations
if psrs is not None:
axis.plot(psrs[:, 0],
np.pi / 2. - psrs[:, 1],
'*',
color='w',
markersize=6,
mew=1,
mec='w')
def plotSkyMap(raSample, decSample, nside=64, contours=None, colorbar=True, \
inj=None, psrs=None, cmap='YlOrBr', outfile='skymap.pdf'):
"""
Plot Skymap of chain samples on Mollwiede projection.
@param raSample: Array of right ascension samples
@param decSample: Array of declination samples
@param nside: Number of pixels across equator [default = 64]
@param contours: Confidence contours to draw eg. 68%, 95% etc
By default this is set to none and no contours
will be drawn.
@param colorbar: Boolean option to draw colorbar [default = True]
@param inj: list of injected values [ra, dec] in radians to plot
[default = None]
@param psrs: Stacked array of pulsar sky locations [ra, dec] in radians
[default=None] Will plot as white diamonds
"""
# clear figures
plt.clf()
# create stacked array of ra and dec
skypos = np.column_stack([raSample, decSample])
npix = hp.nside2npix(nside) # number of pixels total
# initialize theta and phi map coordinantes
skycarts=[]
for ii in range(npix):
skycarts.append(np.array(hp.pix2vec(nside,ii)))
# get skymap values from greedy binning algorithm
skymap = greedy_bin_sky(skypos, skycarts)
# smooth skymap
skymap = hp.smoothing(skymap, sigma=0.02)
# make plot
ax = plt.subplot(111, projection='astro mollweide')
# Add contours
if contours is not None:
for percent in contours:
indices = np.argsort(-skymap)
sky = skymap[indices]
region = np.zeros(skymap.shape)
ind = np.min(ml.find(np.cumsum(sky) >= 0.01*percent))
region[indices[0:ind]] = 1.0
cs = plot.contour(lambda lon, lat: region[hp.ang2pix(nside, 0.5*np.pi - lat, lon)], \
colors='k', linewidths=1.0, levels=[0.5])
#plt.clabel(cs, [0.5], fmt={0.5: '$\mathbf{%d\%%}$' % percent}, fontsize=8, inline=True)
# plot map
ax.grid()
plot.outline_text(ax)
plot.healpix_heatmap(skymap, cmap=cmap)
# add injection
if inj:
ax.plot(inj[0], inj[1], 'x', color='k', markersize=8, mew=2, mec='k')
# add pulsars
if np.all(psrs):
ax.plot(psrs[:,0], psrs[:,1], '*', color='lime', markersize=8, mew=1, mec='k')
# add colorbar and title
if colorbar:
plt.colorbar(orientation='horizontal')
plt.suptitle(r'$p(\alpha,\delta|d)$', y=0.1)
# save skymap
plt.savefig(outfile, bbox_inches='tight')
def makeSkyMap(samples, lmax, nside=32, cmap=None, strain=None, tex=True,
psrs=None, axis=None):
if tex == True:
plt.rcParams['text.usetex'] = True
npix = hp.nside2npix(nside) # number of pixels total
# initialize theta and phi map coordinantes
skypos=[]
for ii in range(npix):
skypos.append(np.array(hp.pix2ang(nside,ii)))
skypos = np.array(skypos)
harmvals = utils.SetupSkymapPlottingGrid(lmax,skypos)
if np.atleast_2d(samples).shape[0]>1:
if strain is None:
samples = np.mean(samples, axis=0)
samples = np.append(2.*np.sqrt(np.pi), samples)
elif strain is not None:
samples = np.mean((samples.T * 10**(2.0*strain)).T, axis=0)
samples = np.append(np.mean(10**(2.0*strain) * (2.0*np.sqrt(np.pi))), samples)
else:
#### !!!!!
samples = np.append(2.*np.sqrt(np.pi), samples)
pwr = utils.GWpower(samples, harmvals)
print pwr, samples
if axis is None:
ax = plt.subplot(111, projection='astro mollweide')
ax.grid()
plot.outline_text(ax)
if cmap is not None:
if strain is None:
plot.healpix_heatmap(pwr,cmap=cmap)
elif strain is not None:
plot.healpix_heatmap(pwr,cmap=cmap)
else:
plot.healpix_heatmap(pwr)
plt.colorbar(orientation='horizontal')
if strain is None:
plt.suptitle(r'$\langle P_{\mathrm{GWB}}(\hat\Omega)\rangle$', y=0.1)
elif strain is not None:
plt.suptitle(r'$\langle A_h^2 P_{\mathrm{GWB}}(\hat\Omega)\rangle$', y=0.1)
# add pulsars locations
if psrs is not None:
ax.plot(psrs[:,0], np.pi/2. - psrs[:,1], '*', color='w',
markersize=15, mew=1, mec='k')
elif axis is not None:
axis.grid()
plot.outline_text(axis)
if cmap is not None:
plot.healpix_heatmap(pwr,cmap=cmap)
else:
plot.healpix_heatmap(pwr)
# add pulsars locations
if psrs is not None:
axis.plot(psrs[:,0], np.pi/2. - psrs[:,1], '*', color='w',
markersize=6, mew=1, mec='w')
kind='cubic')
result_ds = round(float(conf_interp(result_strain)), 4)
ax = plt.subplot(111, projection="astro mollweide")
ax.grid()
plot.outline_text(ax)
#TODO this is just temporary
def find_nearest(array, value):
idx = (np.abs(array - value)).argmin()
return array[idx]
closest_freq = find_nearest(freq_array, orbital_f)
closest_conf = find_nearest(confidence_array, result_ds)
plot.healpix_heatmap(np.log10(
freqdict[closest_freq].map_dictionary[closest_conf]),
cmap='viridis_r')
#---------------------------
ax.scatter(math.radians(input_phi),
math.radians(input_theta),
marker='o',
s=40,
color='orangered',
linewidths=1.0,
edgecolors='k',
zorder=8)
#TOD make colorbar an axes
cb = plt.colorbar(orientation='horizontal')
cb.ax.tick_params(labelcolor='w', color='w')
plt.suptitle(
'{0}% Characteristic Strain Upper Limit at {1}Hz, '.format(