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 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 show_CMB_T_map(self,Tmap=None, max=100, title = "CMB graviational potential fluctuations as seen from inside the LSS", from_perspective_of = "observer", cmap=None):
if Tmap is None:
self.NSIDE = 256
self.Tmap = hp.alm2map(self.alm,self.NSIDE)
else:
self.Tmap = Tmap
if from_perspective_of == "observer":
dpi = 300
figsize_inch = 60, 40
fig = plt.figure(figsize=figsize_inch, dpi=dpi)
# Sky map:
hp.mollview(self.Tmap, rot=(-90,0,0), min=-max, max=max, title=title + ", $\ell_{max}=$%d " % self.truncated_lmax, cmap=cmap, unit="$\mu$K")
plt.savefig(title+".png", dpi=dpi, bbox_inches="tight")
else:
# Interactive "external" view ([like this](http://zonca.github.io/2013/03/interactive-3d-plot-of-sky-map.html)) pass
# beatbox.zoncaview(self.Tmap)
# This did not work, sadly. Maybe we can find a 3D
# spherical surface plot routine using matplotlib? For
# now, just use the healpix vis.
R = (0.0,0.0,0.0) # (lon,lat,psi) to specify center of map and rotation to apply
hp.orthview(self.Tmap,rot=R,flip='geo',half_sky=True,title="CMB graviational potential fluctuations as seen from outside the LSS, $\ell_{max}$=%d" % self.truncated_lmax)
print "Ahem - we can't visualize maps on the surface of the sphere yet, sorry."
return
def orth(i, j, panel, ttl=None):
if ttl is None:
ttl = str(i) + ', ' + str(j)
hp.orthview(func(A[:, i, j]),
rot=[0, 90],
half_sky=True,
sub=[5, 4, panel],
title=ttl)
def update(val):
latitude = latitude_slider.val
longitude = longitude_slider.val
angle = angle_slider.val
hp.orthview(data,
fig=1,
sub=211,
half_sky=True,
rot=(longitude, latitude, angle),
norm='hist')
def view_healpix(self, n_side=32, show=True):
""" View the beam pattern as a healpix map
Shows orthographic view of beam pattern, using healpy plotting routines.
"""
hmap = self.to_healpix(n_side)
hp.orthview(hmap)
if show:
plt.show()
def displaymaps(zup,cen=[45.,-44.],cmap='inferno'):
z=zup[::-1]
i=0
for (z2,z1) in zip(z[:-1],np.roll(z,-1)):
myMap=hp.read_map("map{:02d}.fits".format(i))
fig=plt.figure(figsize=(10,10))
hp.orthview(myMap,fig=1,half_sky=True,cbar=False,rot=cen,cmap=cmap,title=r"{:.1f}<z<{:.1f}".format(z1,z2),min=-0.4,max=1,margins=(0,0,0,0))
plt.savefig("map{:02d}.png".format(i))
plt.close(1)
i=i+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 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 show_CMB_T_map(
self,
Tmap=None,
max=100,
title="CMB graviational potential fluctuations as seen from inside the LSS",
from_perspective_of="observer",
cmap=None):
if Tmap is None:
self.NSIDE = 256
self.Tmap = hp.alm2map(self.alm, self.NSIDE)
else:
self.Tmap = Tmap
if from_perspective_of == "observer":
dpi = 300
figsize_inch = 60, 40
fig = plt.figure(figsize=figsize_inch, dpi=dpi)
# Sky map:
hp.mollview(self.Tmap,
rot=(-90, 0, 0),
min=-max,
max=max,
title=title +
", $\ell_{max}=$%d " % self.truncated_lmax,
cmap=cmap,
unit="$\mu$K")
plt.savefig(title + ".png", dpi=dpi, bbox_inches="tight")
else:
# Interactive "external" view ([like this](http://zonca.github.io/2013/03/interactive-3d-plot-of-sky-map.html)) pass
# beatbox.zoncaview(self.Tmap)
# This did not work, sadly. Maybe we can find a 3D
# spherical surface plot routine using matplotlib? For
# now, just use the healpix vis.
R = (
0.0, 0.0, 0.0
) # (lon,lat,psi) to specify center of map and rotation to apply
hp.orthview(
self.Tmap,
rot=R,
flip='geo',
half_sky=True,
title=
"CMB graviational potential fluctuations as seen from outside the LSS, $\ell_{max}$=%d"
% self.truncated_lmax)
print "Ahem - we can't visualize maps on the surface of the sphere yet, sorry."
return
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 view(self, logged=False, show=False, **kwargs):
""" View the local sky, in orthographic projection.
Parameters
----------
logged: bool
Default False, return the log2 image
"""
sky = self.observed_sky
if logged:
sky = np.log2(sky)
hp.orthview(sky, half_sky=True, **kwargs)
if show:
show_plt()
return sky
def plot_polarisation(sky,
fig=3,
title="",
mode=3,
sub=(1, 2, 1),
show=False):
import matplotlib.pyplot as plt
assert (isinstance(mode, int) and 0 <= mode < 4) or isinstance(mode, str), \
"Mode should be an integer between 0 and 3, or a string of the form 'bb' where b is for binary."
if isinstance(mode, str):
mode = (int(mode[0]) << 1) + (int(mode[1]) << 0)
sub2 = sub[2]
lon, lat = sun2lonlat(sky.sun)
f = plt.figure(fig, figsize=(15, 5))
if (mode >> 1) % 2 == 1:
hp.orthview(sky.DOP,
rot=SkyModel.VIEW_ROT,
min=0,
max=1,
flip="geo",
cmap="Greys",
half_sky=True,
title="Degree of (linear) Polarisation",
unit=r'',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 0) % 2 == 1:
hp.orthview(sky.AOP,
rot=SkyModel.VIEW_ROT,
min=0,
max=np.pi,
flip="geo",
cmap="Greys",
half_sky=True,
title="Angle of (linear) Polarisation",
unit=r'rad',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
hp.projplot(lat, lon, 'yo')
f.suptitle(title)
if show:
plt.show()
return f
def view(self, logged=False, show=False, **kwargs):
""" View the local sky, in orthographic projection.
Parameters
----------
logged: bool
Default False, return the log2 image
"""
sky = self.observed_sky
if logged:
sky = np.log2(sky)
hp.orthview(sky, half_sky=True, **kwargs)
if show:
plt.show()
return sky
def orth(base_UT22, start_UT22, unit, show=False):
UT22_interp = np.zeros_like(base_UT22)
c = 0
for i, val in enumerate(start_UT22):
if np.isnan(val):
c += 1
theta, phi = hp.pix2ang(nside, i)
neybs = hp.get_neighbours(nside, theta, phi=phi)
UT22_interp[i] = np.nanmean(start_UT22[neybs[0]])
else:
UT22_interp[i] = val
print(c, 'NaN vals')
hp.orthview(UT22_interp, rot=[0, 90], min=0, max=2, unit=unit, title='SAST 00:00 2012-02-13', half_sky=True)
if show:
pylab.show()
return UT22_interp
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 mapplot(self, mapstr=None, coord="C", norm="hist", **kwargs):
"""
Plots a molliweide projection of a map using healpy.mollview
Parameters
----------
mapstr : str, {"mapname", "all"} or None
Name of the map that should be plotted. Running with no
arguments will list the available maps for plotting.
kwargs
Optional arguments passed to healpy.mollview
Default coord="C", norm="hist"
"""
if isnone(mapstr):
print("Here's the available keys:")
print(list(self.maps.keys()))
elif mapstr == 'all':
[
hp.orthview(self.maps[ms].values,
title=ms,
coord=coord,
norm=norm,
**kwargs) for ms in self.maps.keys()
]
else:
try:
hp.orthview(self.maps[mapstr],
title=mapstr,
coord=coord,
norm=norm,
**kwargs)
hp.graticule()
except KeyError:
print("The map ''" + mapstr + "'' does not exist!")
print("Here's the available keys:")
print(list(self.maps.keys()))
return 0
def view(self, logged=False, show=False, **kwargs):
""" View the local sky, in orthographic projection.
Parameters
----------
logged: bool
Default False, return the log2 image
"""
import healpy as hp
import pylab as plt
sky = self.observed_sky
if logged:
import numpy as np
sky = np.log2(sky)
hp.orthview(sky, half_sky=True, **kwargs)
if show:
plt.show()
return sky
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 create_background(self):
# hp.orthview(map=self.background, rot=[0.0,270.0,0.0], coord=['G','C'], half_sky=True, notext=True, min=-400, max=400, xsize=3000)
m = self.background * self.factor + self.offset * self.factor
msinh = np.arcsinh(m * 0.5)/np.log(10.0)
cbticks = np.array([-500.0,0.0,100.0,1000.0, 1e6])
cbticklabels = np.array([r'$-500$',r'$0$',r'$100$',r'$1000$',r'$10^6$'])
cbticks = np.arcsinh( (cbticks-80.0) * 0.5) / np.log(10.0)
if not (self.bad_pixels is None):
msinh[self.bad_pixels] = -1.6375e30
if (self.cbar):
orth = hp.orthview(map=msinh, rot=[0.0,270.0,0.0], coord=[self.input_coord,'C'], half_sky=True, notext=True, xsize=3000, cmap=self.planck_color_map(), min=-3.1, max=7, cbticks=cbticks, cbticklabels=cbticklabels, title=self.title, cbtitle=r'$\mathrm{\mu K}$', sub=self.sub, return_projected_map=True)
else:
orth = hp.orthview(map=msinh, rot=[0.0,270.0,0.0], coord=[self.input_coord,'C'], half_sky=True, notext=True, xsize=3000, cmap=self.planck_color_map(), min=-3.1, max=7, cbar=False, title=self.title, sub=self.sub, return_projected_map=True)
# self.create_spt_area()
# self.create_graticule([160,140,120,100], np.arange(0,360,45))
# plt.savefig('test.png', format='png', dpi=600)
return orth
def view(self, inclination=90*u.deg, phase=0.0, what='fluxes',
projection='mollweide', cmap='magma',
savefig=False, filename='star_surface.png',
dlat=30, dlon=30, **kwargs):
rot = (360*phase, 90-inclination.to(u.deg).value, 0)
if what == 'fluxes':
vals = self.tile_fluxes * self.tile_scales
vals = vals / vals.max()
elif what == 'areas':
vals = self.tile_areas / self.tile_areas.max()
if 'mollweide'.find(projection) == 0:
hp.mollview(vals, rot=rot, cmap=cmap, **kwargs)
elif 'cartesian'.find(projection) == 0:
hp.cartview(vals, rot=rot, cmap=cmap, **kwargs)
elif 'orthographic'.find(projection) == 0:
hp.orthview(vals, rot=rot, cmap=cmap, **kwargs)
else:
raise ValueError('Unrecognised projection')
hp.graticule(dlat, dlon)
if savefig:
plt.savefig(filename)
else:
plt.show()
def plot_healpix_projection(file,
nside,
outside=False,
rotp=True,
multiple=False,
**kwargs):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
try:
mesh = ToMesh(file, rot=rotp, tr=0.)
data = ProjectOnSphere(nside, mesh, outside, multiple)
except:
print("Exception during transform of {}".format(file))
raise
im1 = data[:, 0]
id_im = os.path.splitext(os.path.basename(file))[0]
cm = plt.cm.RdBu_r
cm.set_under('w')
cmin = np.min(im1)
cmax = np.max(im1)
im1[im1 > cmax] = np.nan
#norm = colors.LogNorm(vmin=cmin, vmax=cmax)
#norm = colors.PowerNorm(gamma=4)
hp.orthview(im1,
title='',
nest=True,
cmap=cm,
min=cmin,
max=cmax,
**kwargs)
plt.plot()
if multiple:
hp.orthview(data[:, 1],
title=id_im,
nest=True,
cmap=cm,
min=cmin,
max=cmax,
norm=norm)
plt.plot()
hp.orthview(data[:, 2],
title=id_im,
nest=True,
cmap=cm,
min=cmin,
max=cmax,
norm=norm)
return im1
def test(nested=False):
# test map previously masked and smoothed by gauss_beam 30'
#testMap = '/Data/planckMaskedSmoothed.fits'
# test map previously smoothed by gauss_beam 30'
testMap = '/Data/planckSmoothed.fits'
# test mask previously smoothed by gauss_beam 30'
testMask = '/Data/pMaskSmoothed.fits'
# test get_temps
#result = get_temps(testMap,radius=3.5,maskFile=testMask,nside=2048,nested=nested)
#print 'get_temps result: ',result
# test getSDSSrnd
result = getSDSSrnd()
print 'getSDSSrnd result: ',result
# 2d histogram in healpix map; check uniformity
nside = 32
nrnd = 1000#0
title = 'test of random coordinate generator'
mapArray = np.zeros(hp.nside2npix(nside))
for drawNum in range(nrnd):
print 'draw ',drawNum+1,' of ',nrnd
randomCoord = getSDSSrnd()
print 'random coord: ',randomCoord
randomVec = mm.glgb2vec(randomCoord[0],randomCoord[1]) #returns array of unit vectors
randomPix = hp.query_disc(nside,randomVec,1e-5,nest=nested,inclusive=True)
print 'random pixel: ',randomPix[0]
mapArray[randomPix[0]] += 1.0
projected = hp.orthview(mapArray,rot=[180,35],coord=['C'],half_sky=True, #,coord=['G','C']
nest=nested,flip='geo',title=title)
hp.graticule(dpar=30,dmer=30)
plt.show()
# test checknoise
cSN,vSN = checknoise(testMap,radius=3.5,ntries=100,maskFile=testMask)
print 'Signal to Noise ratios: ', cSN, vSN
def showMap(mapFile,
nested=False,
return_projected_map=False,
title='ISW map over SDSS region'):
"""
Purpose:
make orthographic plots of maps or masks over the SDSS region
Note:
This is translated from IDL function sdss_plot.pro. The IDL
graphics are superior to the ones available in python, and
the plotting functions have more options.
Args:
mapFile:
nested:
return_projected_map: pass to orthview, get back numpy map
note: I don't know how to use this now 2016.01.19
title: the title of the plot
Returns:
if return_projected_map is set: the projected map in a numpy array
"""
#subTitle = 'equitorial coordinates with RA flipped'
map = hp.read_map(mapFile, nest=nested)
projected = hp.orthview(map,
rot=[180, 35],
coord=['G', 'C'],
half_sky=True,
nest=nested,
flip='geo',
title=title,
return_projected_map=return_projected_map) #,
#max=60,min=-60,unit='microK')
hp.graticule(dpar=30, dmer=30)
plt.show()
return projected
ras,decs = [],[]
for p in ipix:
dec,ra = IndexToDeclRa(p)
ras.append(ra)
decs.append(dec)
######
#dates = ['2012-02-10', '2012-02-11', '2012-02-13', '2012-02-14', '2012-02-15', '2012-02-16', '2012-02-17','2012-02-18', '2012-02-19', '2012-02-20', '2012-02-21', '2012-02-22']
dates = ['2011-12-06', '2011-12-07', '2011-12-08', '2011-12-09', '2011-12-10', '2011-12-11', '2011-12-12', '2011-12-13', '2011-12-14', '2011-12-15', '2011-12-16', '2011-12-17', '2011-12-18', '2011-12-19', '2011-12-20', '2011-12-21', '2011-12-22', '2011-12-23', '2011-12-24', '2011-12-25', '2011-12-26', '2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30', '2011-12-31', '2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04', '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08', '2012-01-09', '2012-01-10', '2012-01-11', '2012-01-12', '2012-01-13', '2012-01-14', '2012-01-15', '2012-01-16', '2012-01-17', '2012-01-18', '2012-01-19', '2012-01-20', '2012-01-21', '2012-01-22', '2012-01-23', '2012-01-24', '2012-01-25', '2012-01-26', '2012-01-27', '2012-01-28', '2012-01-29', '2012-01-30', '2012-01-31', '2012-02-01', '2012-02-02', '2012-02-03', '2012-02-04', '2012-02-05', '2012-02-06', '2012-02-07', '2012-02-08', '2012-02-09', '2012-02-10', '2012-02-11', '2012-02-12', '2012-02-13', '2012-02-14', '2012-02-15', '2012-02-16', '2012-02-17', '2012-02-18', '2012-02-19', '2012-02-20', '2012-02-21', '2012-02-22', '2012-02-23', '2012-02-24', '2012-02-25', '2012-02-26', '2012-02-27', '2012-02-28']
if not os.path.exists('%s_to_%s.pkl'%(dates[0],dates[-1])):
IM = rm.IonoMap('30d43m17.5ss','21d25m41.9se',dates)
IM.get_radec_RM(ras,decs)
datadict = {}
bm = beamPAPER(150.,nside=16)
bm_map = np.ma.masked_invalid(hp.orthview(bm,return_projected_map=True))[:,:400]
plt.close() #negate orthview launching xwindow
datadict['bm'] = bm_map
for dayindex in range(IM.RMs.shape[0]):
datadict[dates[dayindex]] = {}
#TODO convert keys to JDs? would be neater for everything to be numeric
for ut in range(IM.RMs.shape[1]):
map = hp.orthview(IM.RMs[dayindex,ut,:],rot=[0,90],return_projected_map=True)
map = np.ma.masked_invalid(map)[:,:400] #only get above horizon, mask elsewhere
datadict[dates[dayindex]][ut] = map
plt.close() #negate orthview launching xwindow
output = open('%s_to_%s.pkl'%(dates[0],dates[-1]),'wb')
pickle.dump(datadict,output)
output.close()
else: print 'Opening %s_to_%s.pkl'%(dates[0],dates[-1])
def plot_sun(sky, fig=1, title="", mode=15, sub=(1, 4, 1), show=False):
assert (isinstance(mode, int) and 0 <= mode < 16) or isinstance(mode, str), \
"Mode should be an integer between 0 and 15, or a string of the form 'bbbb' where b is for binary."
if isinstance(mode, str):
mode = (int(mode[0]) << 3) + (int(mode[1]) << 2) + (
int(mode[2]) << 1) + (int(mode[3]) << 0)
sub2 = sub[2]
lon, lat = sun2lonlat(sky.sun)
f = plt.figure(fig, figsize=(15, 5))
if (mode >> 3) % 2 == 1:
hp.orthview(sky.theta_z,
rot=SkyModel.VIEW_ROT,
min=0,
max=np.pi,
flip="geo",
cmap="Greys",
half_sky=True,
title="Elevation",
unit=r'rad',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 2) % 2 == 1:
hp.orthview(sky.phi_z,
rot=SkyModel.VIEW_ROT,
min=0,
max=2 * np.pi,
flip="geo",
cmap="Greys",
half_sky=True,
title="Azimuth",
unit=r'rad',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 1) % 2 == 1:
hp.orthview(sky.theta_s,
rot=SkyModel.VIEW_ROT,
min=0,
max=np.pi,
flip="geo",
cmap="Greys",
half_sky=True,
title="Elevation",
unit=r'rad',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 0) % 2 == 1:
hp.orthview(sky.phi_s,
rot=SkyModel.VIEW_ROT,
min=0,
max=2 * np.pi,
flip="geo",
cmap="Greys",
half_sky=True,
title="Azimuth",
unit=r'rad',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
hp.projplot(lat, lon, 'yo')
f.suptitle(title)
if show:
plt.show()
return f
def get_map():
# Get current time
now = Time.now()
T = datetime.utcnow() + timedelta(hours=0)
T = Time(T, format="datetime")
loc = astropy.coordinates.EarthLocation(lon=22.13303, lat=-31.58)
ra = (T.sidereal_time("mean", longitude=22.13303)) / u.hourangle
lon = ra * 15 - 360
rot = [(lon), -31.58]
sid_time = T.sidereal_time("mean", longitude=22.13303)
sidstr = sid_time.to_string()
print(sidstr)
moon = astropy.coordinates.get_moon(T, location=loc, ephemeris=None)
sun = astropy.coordinates.get_sun(T)
ssbodies = ["mercury", "venus", "mars", "jupiter", "saturn", "neptune", "uranus"]
colors = ["grey", "pink", "red", "orange", "yellow", "blue", "blue", "blue"]
pic = astropy.coordinates.SkyCoord(
ra="05h19m49.7230919028", dec="-45d 46m 44s"
) # Pictor
forn = astropy.coordinates.SkyCoord(ra="03h23m25.1s", dec="-37d 08m")
cass = astropy.coordinates.SkyCoord(ra="23h 23m 24s", dec="+58d 48.9m")
crab = astropy.coordinates.SkyCoord(ra="05h 34m 31s", dec="+22d 00m 52.2s")
lmc = astropy.coordinates.SkyCoord(ra="05h 40m 05s", dec="-69d 45m 51s")
smc = astropy.coordinates.SkyCoord(ra="00h 52m 44.8s", dec="-72d 49m 43s")
cenA = astropy.coordinates.SkyCoord(ra="13h 25m 27.6s", dec="-43d 01m 09s")
callibrator1 = astropy.coordinates.SkyCoord(
ra=109.32351 * u.degree, dec=-25.0817 * u.degree
)
callibrator2 = astropy.coordinates.SkyCoord(
ra=30.05044 * u.degree, dec=-30.89106 * u.degree
)
callibrator3 = astropy.coordinates.SkyCoord(
ra=6.45484 * u.degree, dec=-26.0363 * u.degree
)
source_list = [
[moon, "moon", "slategrey"],
[sun, "sun", "y"],
[pic, "pictor", "w"],
[forn, "fornax", "w"],
[cass, "Cass A", "w"],
[crab, "Crab", "w"],
[lmc, "LMC", "w"],
[cenA, "Cen A", "w"],
[smc, "SMC", "w"],
[callibrator1, "J071717.6-250454", "r"],
[callibrator2, "J020012.1-305327", "r"],
[callibrator3, " J002549.1-260210", "r"],
]
healpy.orthview(
np.log10(mappy),
title=sidstr,
coord=["G", "C"],
rot=rot,
return_projected_map=True,
min=0,
max=2,
half_sky=1,
)
for item in source_list:
if item[1] == "sun":
name = item[1]
healpy.projscatter(
item[0].ra, item[0].dec, lonlat=True, s=1000, c=item[2], label=name
)
healpy.projtext(item[0].ra, item[0].dec, lonlat=True, color="k", s=name)
if item[1] == "moon":
name = item[1]
healpy.projscatter(
item[0].ra, item[0].dec, lonlat=True, s=200, c=item[2], label=name
)
healpy.projtext(item[0].ra, item[0].dec, lonlat=True, color="k", s=name)
else:
name = item[1]
healpy.projscatter(
item[0].ra, item[0].dec, lonlat=True, s=50, color=item[2], label=name
)
healpy.projtext(item[0].ra, item[0].dec, lonlat=True, color="k", s=name)
count = 0
for body in ssbodies:
name = body
body = astropy.coordinates.get_body(body, T)
healpy.projscatter(
body.ra, body.dec, lonlat=True, s=50, color=colors[count], label=name
)
healpy.projtext(body.ra, body.dec, lonlat=True, color="k", s=name)
count += 1
nom_vis = np.zeros([4,4,npix],dtype='complex64')
S = 1./2*np.array([[1.,1,0,0],[0,0,1,1j],[0,0,1,-1j],[1,-1,0,0]])
Sinv = np.array([[1.,0,0,1],[1,0,0,-1],[0,1,1,0],[0,-1j,1j,0]])
for i in range(npix):
J = A[:,:,i]
AA[:,:,i] = np.kron(J,J)
nom_vis[:,:,i] = np.dot(Sinv,np.dot(AA[:,:,i],S))
nom_vis = nom_vis.real
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
beam_labels = np.array([[r'A_x^{\theta}',r'A_x^\phi'],[r'A_y^{\theta}',r'A_y^\phi']])
plt.figure(1)
for a in range(2):
for b in range(2):
hp.orthview(A[a,b,:],rot=[0,90],half_sky=True,sub=(2,2,2*a+b+1),min=-1,max=1)
plt.title(beam_labels[a,b])
hp.graticule()
plt.figure(2)
for a in range(4):
for b in range(4):
hp.orthview(nom_vis[a,b,:],rot=[0,90],half_sky=True,title='',sub=(4,4,4*a+b+1),min=0,max=1)
hp.graticule()
plt.show()
for p in ipix:
dec,ra = IndexToDeclRa(p)
ras.append(ra)
decs.append(dec)
tec,rmstec,ionh = IM.ionex_data(YYYY,MM,DD)
IM.get_radec_RM(ras,decs)
mc = Basemap(projection='cyl', resolution='c')
mo = Basemap(projection='ortho',lon_0=Plon,lat_0=Plat,resolution='c')
for ut in range(24):
print 'UT=%i'%ut
tecUT=hp.cartview(0.1*tec[ut,:],flip='geo',max=90,min=0,return_projected_map=True)
mc.drawcoastlines()
_tec = mc.imshow(tecUT, alpha=0.6,vmax=90,vmin=0)
plt.title('UT %i hr, %s-%s-%s, TEC'%(ut,YYYY,MM,DD))
mc.colorbar(location='bottom')
#plt.show()
plt.savefig('TEC_%s-%s-%s_UT%i.png'%(YYYY,MM,DD,ut))
plt.close()
hp.orthview(IM.RMs[0,ut,:],half_sky=True,rot=[0,90],flip='astro',max=5,min=-2,unit=r'${\rm rad\,m^{-2}}$',return_projected_map=True)
#mo.drawcoastlines()
#_rm = mo.imshow(rmUT,alpha=0.6,vmax=3,vmin=-3)
plt.title('UT %i hr, %s-%s-%s'%(ut,YYYY,MM,DD))
#mo.colorbar(location='bottom')
plt.savefig('RM_%s-%s-%s_UT%i.png'%(YYYY,MM,DD,ut))
plt.close()
trot,prot = r(t,p)
ipix_rot = hp.ang2pix(nside,trot,prot)
rotmap = map[ipix_rot]
return rotmap
beam = np.load('XX_beam_maps.npz')['maps']
freqs = np.linspace(0.117,0.182,num=131) #aipy likes GHz units. avoiding band edges
nside = 128
npix = hp.nside2npix(nside)
beam_167 = beam[100,:]
#beam_167 += 1e-9
def brp(beam,rotarr,log=False):
a1 = np.sqrt(beam)
a2 = np.sqrt(rotate_hmap(beam_167,rotarr))
if not log: return a1*a2
else: return np.log10(a1*a2)
L = True
lb = '\n'
#f,axarr = pylab.subplots(4,1)
for i,rot in enumerate([0,10,20,30]):
R = hp.orthview(brp(beam_167,[rot,0],log=L), rot=[180,0], half_sky=True, max=0, min=-3, title='Rotation=%i'%rot, unit=lb+r'$\log_{10}({\rm Normalized Response})$')#,hold=True)
pylab.show()
pixels = hp.ang2pix(nside, thetas, j)
n[pixels] = func[pixels]
n[n < 0] = 0
filter_array = n # This is the filter to apply to GSM
(latitude, longitude, elevation) = ('-32.998370', '148.263659', 100
) # Near EDGES site
delta_t = 60 # EDGES antenna takes a number of measurements in 24 hours; this is the time between measurements in minutes
sky_array = []
timer = datetime(2018, 1, 1, 0, 0)
while timer.hour < 23:
gsm = GSMObserver()
gsm.lon = longitude
gsm.lat = latitude
gsm.elev = elevation
gsm.date = timer
g = hp.ud_grade(
gsm.generate(78),
nside) # 78MHz is the frequency of the 21cm signal absorption feature
g_filt = g * n
#g_filt = g_filt*(np.amax(g)/np.amax(g_filt)) # normalisation
hp.orthview(g_filt, half_sky=True)
plt.savefig(timer.strftime("%H:%M:%S") + "_filtered.png")
g_int = np.trapz(g_filt)
sky_array.append(g_int)
timer = timer + timedelta(minutes=delta_t)
sky_array = np.array(sky_array)
sky = np.trapz(sky_array)
print(sky)
def visualize_map(args):
"""Visualize sky maps in hdf5 files.
Arguments
---------
args : argparse namespace.
"""
import numpy as np
import h5py
import healpy
# import hpvisual
import matplotlib
matplotlib.use('Agg')
try:
# for new version matplotlib
import matplotlib.style as mstyle
mstyle.use('classic')
except ImportError:
pass
from matplotlib import pyplot as plt
# Read in maps data
hpmap = None
for mapname in args.mapfiles:
with h5py.File(mapname, 'r') as f:
if hpmap is None:
hpmap = f['map'][:]
else:
hpmap += f['map'][:]
# Check args validity
if args.ifreq < -(hpmap.shape)[0] or args.ifreq >= (hpmap.shape)[0]:
raise Exception('Invalid frequency channel %d, should be in range(-%d, %d).'%(args.ifreq, (hpmap.shape)[0], (hpmap.shape)[0]))
else:
ifreq = args.ifreq if args.ifreq >= 0 else args.ifreq + (hpmap.shape)[0]
if args.pol >= (hpmap.shape)[1]:
raise Exception('Invalid frequency channel %d, should be in range(0, %d).'%(args.pol, (hpmap.shape)[1]))
if args.figlength <= 0:
raise Exception('Figure length figlength (= %f) must greater than 0'%args.figlength)
if args.figwidth <= 0:
raise Exception('Figure width figwidth (= %f) must greater than 0'%args.figwidth)
# Create output image file name
if args.outfile:
out_file = args.outfile
else:
out_file = ((args.mapfiles[0].split('/')[-1]).split('.')[0] + '_' + str(ifreq) + '_{' + str(args.pol) + '}' + '.' + args.figfmt).format('T', 'Q', 'U', 'V')
# Plot and save image
if args.view == 'o':
fig = plt.figure(1, figsize=(8, 6))
else:
fig = plt.figure(1, figsize=(args.figlength,args.figwidth))
map_data = hpmap[ifreq][args.pol]
if args.sqrt:
map_data = map_data / np.sqrt(np.abs(map_data))
map_data = map_data / np.sqrt(np.abs(map_data))
# map_data = map_data / np.sqrt(np.abs(map_data))
# smoothing the map with a Gaussian symmetric beam
if args.fwhm is not None:
fwhm = np.radians(args.fwhm)
map_data = healpy.smoothing(map_data, fwhm=fwhm)
if args.view == 'm':
# set color map
if args.cmap is None:
cmap = None
else:
if args.cmap == 'jet09':
import colormap
cmap = colormap.jet09
else:
from pylab import cm
# cmap = cm.hot
cmap = getattr(cm, args.cmap)
cmap.set_under('w')
if args.abs:
healpy.mollview(np.abs(map_data), fig=1, title='', unit=args.unit, cmap=cmap, min=args.min, max=args.max)
else:
healpy.mollview(map_data, fig=1, title='', unit=args.unit, cmap=cmap, min=args.min, max=args.max)
# plot NVSS sources
if args.nvss is not None:
import aipy as a
flux = args.nvss
frequency = 750 # MHz
catalog = 'nvss'
# catalog = 'wenss'
src = '%f/%f' % (flux, frequency / 1.0e3)
srclist, cutoff, catalogs = a.scripting.parse_srcs(src, catalog)
cat = a.src.get_catalog(srclist, cutoff, catalogs)
nsrc = len(cat) # number of sources in cat
ras = [ np.degrees(cat.values()[i]._ra) for i in range(nsrc) ]
decs = [ np.degrees(cat.values()[i]._dec) for i in range(nsrc) ]
jys = [ cat.values()[i].get_jys() for i in range(nsrc) ]
# select sources
inds = np.where(np.array(decs)>-15.0)[0]
ras = np.array(ras)[inds]
decs = np.array(decs)[inds]
jys = np.array(jys)[inds]
# healpy.projscatter(ras, decs, lonlat=True, s=jys, facecolors='none', edgecolors='w', alpha=1.0, linewidth=1.0)
healpy.projscatter(ras, decs, lonlat=True, s=150, facecolors='none', edgecolors='w', alpha=1.0, linewidth=1.0)
elif args.view == 'c':
healpy.cartview(map_data, fig=1, title='', unit=args.unit, min=args.min, max=args.max)
elif args.view == 'o':
healpy.orthview(map_data, rot=(0, 90, 0), fig=1, title='', unit=args.unit, min=args.min, max=args.max, half_sky=True) # rot to make NCP at the center
# fig = plt.figure()
# ax = fig.add_axes()
# cbar.solids.set_rasterized(True)
if args.grid:
healpy.graticule()
if args.tight:
fig.savefig(out_file, bbox_inches='tight')
else:
fig.savefig(out_file)
fig.clf()
def test_radec_multip(self):
self.rm_map.get_radec_RM(testRAs,testDecs)
assert(self.rm_map.RMs.shape == (1,24,npix))
assert(self.rm_map.dRMs.shape == (1,24,npix))
hp.orthview(self.rm_map.RMs[0,0,:],half_sky=True,rot=[0,90])
plt.close()
# Set up the observer
(latitude, longitude, elevation) = ('-30.7224', '21.4278', 1100)
ov = GSMObserver()
ov.lon = longitude
ov.lat = latitude
ov.elev = elevation
fig = plt.figure("Tsky calc")
for poli, pol in enumerate(pols):
for fi, freq in enumerate(freqs):
print 'Forming HERA Tsky for frequency ' + str(freq) + ' MHz.'
# Rotate and project hera beam (Need to figure out how to do this w/o making figure)
pol_ang = 90 * (1 - poli) # Extra rotation for X
hbeam = hp.orthview(hera_beam[pol][:, fi], rot=[pol_ang, 90], fig=fig.number,
xsize=400, return_projected_map=True, half_sky=True)
hbeam[np.isinf(hbeam)] = np.nan
if calc_paper:
pbeam = hp.orthview(paper_beam[pol][:, fi], rot=[pol_ang, 90], fig=fig.number,
xsize=400, return_projected_map=True, half_sky=True)
pbeam[np.isinf(pbeam)] = np.nan
for ti, t in enumerate(hours):
plt.clf()
dt = datetime(2013, 1, 1, np.int(t), np.int(60.0 * (t - np.floor(t))),
np.int(60.0 * (60.0 * t - np.floor(t * 60.0))))
lsts[ti] = Time(dt).sidereal_time('apparent', longitude).hour
ov.date = dt
ov.generate(freq)
d = ov.view(fig=fig.number)
sky = hp.orthview(d, fig=fig.number, xsize=400, return_projected_map=True,
half_sky=True)
#!/opt/local/bin/python2.7 import smallfield import healmap import healpy as hp import numpy as np if __name__ == "__main__": use_split = 'D' use_nside = 512 h1,h2 = healmap.get_input_maps(use_split,use_nside=use_nside,dir='..') sfac = 0.0395 squ = 10 st = 200 rot=[0,90,0] # hp.orthview(h1.map[0,:]*sfac,rot=rot,nest=False,title="Split 1 T",min=0,max=st) hp.orthview(h1.map[1,:]*sfac,rot=rot,nest=False,title="Split 1 Q",min=-squ,max=squ) hp.orthview(h1.map[2,:]*sfac,rot=rot,nest=False,title="Split 1 U",min=-squ,max=squ) # hp.orthview(h2.map[0,:]*sfac,rot=rot,nest=False,title="Split 2 T",min=0,max=st) hp.orthview(h2.map[1,:]*sfac,rot=rot,nest=False,title="Split 2 Q",min=-squ,max=squ) hp.orthview(h2.map[2,:]*sfac,rot=rot,nest=False,title="Split 2 U",min=-squ,max=squ) # plt.show()
def plot_luminance(cls,
sky,
fig=2,
title="",
mode=31,
sub=(1, 5, 1),
show=False):
import matplotlib.pyplot as plt
assert (isinstance(mode, int) and 0 <= mode < 32) or isinstance(mode, str), \
"Mode should be an integer between 0 and 15, or a string of the form 'bbbb' where b is for binary."
if isinstance(mode, str):
mode = (int(mode[0]) << 3) + (int(mode[1]) << 2) + (
int(mode[2]) << 1) + (int(mode[3]) << 0)
sub2 = sub[2]
lon, lat = sun2lonlat(sky.sun)
f = plt.figure(fig,
figsize=(5 + 3 * (sub[1] - 1), 5 + 3 * (sub[0] - 1)))
if (mode >> 4) % 2 == 1:
hp.orthview(sky.si,
rot=cls.VIEW_ROT,
min=0,
max=12,
flip="geo",
cmap="Greys",
half_sky=True,
title="Scattering indicatrix",
unit=r'',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 3) % 2 == 1:
hp.orthview(sky.lg,
rot=cls.VIEW_ROT,
min=0,
max=3,
flip="geo",
cmap="Greys",
half_sky=True,
title="Luminance gradation",
unit=r'cd/m^2',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 2) % 2 == 1:
hp.orthview(sky.L,
rot=cls.VIEW_ROT,
min=0,
max=30,
flip="geo",
cmap="Greys",
half_sky=True,
title="Luminance",
unit=r'K cd/m^2',
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 1) % 2 == 1:
hp.orthview(sky.C_x,
rot=cls.VIEW_ROT,
min=0,
max=1.5,
flip="geo",
cmap="Greys",
half_sky=True,
title="Chromaticity x",
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
if (mode >> 0) % 2 == 1:
hp.orthview(sky.C_y,
rot=cls.VIEW_ROT,
min=0,
max=1.5,
flip="geo",
cmap="Greys",
half_sky=True,
title="Chromaticity y",
sub=(sub[0], sub[1], sub2),
fig=fig)
sub2 += 1
# hp.projplot(lat, lon, 'yo')
f.suptitle(title)
if show:
plt.show()
return f
def plot_healpix(data_map=None,
sub=None,
title=None,
vmin=None,
vmax=None,
cmap=None,
labels=True):
'''Y'know, plot healpix maps'''
rot = (-45, 90, 0)
if vmin == None:
if cmap == None:
hp.orthview(map=data_map,
coord='E',
half_sky=True,
xsize=400,
title=title,
rot=rot,
sub=sub,
notext=True,
unit='dB',
cbar=None)
else:
hp.orthview(map=data_map,
coord='E',
half_sky=True,
xsize=400,
title=title,
rot=rot,
sub=sub,
cmap=cmap,
notext=True,
unit='dB',
cbar=None)
else:
if cmap == None:
hp.orthview(map=data_map,
coord='E',
half_sky=True,
xsize=400,
title=title,
rot=rot,
sub=sub,
min=vmin,
max=vmax,
notext=True,
unit='dB',
cbar=None)
else:
hp.orthview(map=data_map,
coord='E',
half_sky=True,
xsize=400,
title=title,
rot=rot,
sub=sub,
min=vmin,
max=vmax,
cmap=cmap,
notext=True,
unit='dB',
cbar=None)
hp.graticule(dpar=10, coord='E', color='k', alpha=0.1, dmer=45)
if labels == True:
hp.projtext(0.0 * (pi / 180.0), 0.0, r'$0^\circ$', coord='E')
hp.projtext(30.0 * (pi / 180.0), 0.0, r'$30^\circ$', coord='E')
hp.projtext(60.0 * (pi / 180.0), 0.0, r'$60^\circ$', coord='E')
hp.projtext(80.0 * (pi / 180.0), (0.0 + rot[0]) * (pi / 180.0),
"S",
coord='E',
color='w',
verticalalignment='bottom',
weight='bold')
hp.projtext(80.0 * (pi / 180.0), (90.0 + rot[0]) * (pi / 180.0),
"W",
coord='E',
color='w',
horizontalalignment='left',
weight='bold')
hp.projtext(80.0 * (pi / 180.0), (180.0 + rot[0]) * (pi / 180.0),
"N",
coord='E',
color='w',
verticalalignment='top',
weight='bold')
hp.projtext(80.0 * (pi / 180.0), (270.0 + rot[0]) * (pi / 180.0),
'E',
coord='E',
color='w',
horizontalalignment='right',
weight='bold')
plt.title(title)
plt.subplot(233)
plt.semilogy(tau,Ptau_T_nu)
plt.semilogy(-tau0.value*np.array([1.,1.]),[1e-2,1e5],'r--')
plt.semilogy(tau0.value*np.array([1.,1.]),[1e-2,1e5],'r--')
plt.subplot(234)
plt.plot(C_f/C_f.max(),label='C_l,fringe')
plt.plot(C_a/C_a.max(),label='C_l,beam')
plt.xlim([0,150])
#plt.ylim([1e-8,1e1])
plt.legend()
plt.subplot(235)
plt.plot(ell,C_fa/C_fa.max())
plt.xlim([0,30])
hp.orthview((beam_nu*fringe)[:,100].real,rot=[0,90],half_sky=True,sub=236)
hp.graticule()
if ut == 4.: UT4[p] = RM[i]
if ut == 6.: UT6[p] = RM[i]
if ut == 22.:
UT22[p] = RM[i]
eUT22[p] = eRM[i]
except TypeError:
continue
except IOError:
print 'issue with %s' % filename
UT22[p] = np.nan
c += 1
continue
print c, 'IOErrors'
hp.orthview(UT22,
rot=[0, 90],
max=2,
unit=r'rad m$^{-1}$',
title='SAST 00:00 2012-02-13',
half_sky=True)
#pylab.show()
print UT22
print 'Interpolating NaNs'
UT22_interp = np.zeros_like(UT22)
c = 0
for i, val in enumerate(UT22):
if np.isnan(val):
c += 1
theta, phi = hp.pix2ang(nside, i)
neybs = hp.get_interp_weights(nside, theta, phi=phi)
v = np.nanmean(UT22[neybs[0]])
UT22_interp[i] = v
import healpy as hp
import astropy.io.fits as ap
import astropy.table as table
import numpy as np
import matplotlib.pyplot as plt
import pylab
from matplotlib.pyplot import Slider, Button
filename = 'commander_synch.fits'
data = hp.fitsfunc.read_map(filename)
b = table.Table.read(filename, hdu=1)
fig1 = plt.figure(1, figsize=(16, 9))
hp.orthview(data, fig=1, sub=211, norm='hist', half_sky=True)
axcolor = 'lightgoldenrodyellow'
axlatitude = plt.axes([0.2, 0.35, 0.65, 0.03], axisbg=axcolor)
axlongitude = plt.axes([0.2, 0.4, 0.65, 0.03], axisbg=axcolor)
axangle = plt.axes([0.2, 0.45, 0.65, 0.03], axisbg=axcolor)
latitude_slider = Slider(axlatitude, 'Latitude', -90, 90, valinit=0)
longitude_slider = Slider(axlongitude, 'Longitude', -90, 90, valinit=0)
angle_slider = Slider(axangle, 'Angle', -90, 90, valinit=0)
def update(val):
latitude = latitude_slider.val
longitude = longitude_slider.val
angle = angle_slider.val
hp.orthview(data,
def plot_healpix(
data_map=None,
fig=None,
sub=None,
title=None,
vmin=None,
vmax=None,
cmap=None,
cbar=True,
):
"""Yeesh do some healpix magic to plot the thing"""
# Disable cryptic healpy warnings. Can't figure out where they originate
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
hp.delgraticules()
hp.orthview(
map=data_map,
coord="E",
fig=fig,
half_sky=True,
rot=(0, 90, 180),
xsize=1200,
title=title,
sub=sub,
min=vmin,
max=vmax,
cmap=cmap,
notext=True,
hold=True,
cbar=cbar,
return_projected_map=False,
)
hp.graticule(dpar=10,
coord="E",
color="k",
alpha=0.7,
dmer=45,
lw=0.4,
ls=":")
# Altitude grid
hp.projtext(
00.0 * (np.pi / 180.0),
225.0 * (np.pi / 180),
"0",
color="k",
coord="E",
fontsize=6,
fontweight="light",
)
hp.projtext(
30.0 * (np.pi / 180.0),
225.0 * (np.pi / 180),
"30",
color="k",
coord="E",
fontsize=6,
fontweight="light",
)
hp.projtext(
60.0 * (np.pi / 180.0),
225.0 * (np.pi / 180),
"60",
color="k",
coord="E",
fontsize=6,
fontweight="light",
)
# NSEW
hp.projtext(
80.0 * (np.pi / 180.0),
000.0 * (np.pi / 180.0),
r"$N $",
coord="E",
color="w",
fontweight="light",
verticalalignment="top",
)
hp.projtext(
80.0 * (np.pi / 180.0),
090.0 * (np.pi / 180.0),
r"$E $",
coord="E",
color="w",
fontweight="light",
horizontalalignment="right",
)
hp.projtext(
80.0 * (np.pi / 180.0),
180.0 * (np.pi / 180.0),
r"$S $",
coord="E",
color="w",
fontweight="light",
verticalalignment="bottom",
)
hp.projtext(
80.0 * (np.pi / 180.0),
270.0 * (np.pi / 180.0),
r"$W $",
coord="E",
color="w",
fontweight="light",
horizontalalignment="left",
)
def myPlot(x, xerr, args):
# General setup
npix = len(x)
nside = hp.npix2nside(npix)
theta, phi = hp.pix2ang(nside, range(npix))
degree = np.pi / 180.
fDict = {'chi2':getChi2, 'pvals':getChi2, 'bayes':bayesFactor, 'test':getTest}
f = fDict[args.maptype]
# Compare each pixel to ensemble distribution for given declination
map = np.zeros(npix, dtype=np.double)
if args.decbands:
thetaRings = np.array(sorted(list(set(theta))))
thetaRings = thetaRings[thetaRings > (180-args.thetaMax)*degree]
for i, th in enumerate(thetaRings):
c0 = (theta == th)
map[c0] += f(x[c0], xerr[c0], args)
else:
map = f(x, xerr, args)
# Option to calculate p-values
#if args.maptype == 'pvals':
# pvals = np.zeros(len(chi2), dtype=np.double)
# for i, chi in enumerate(chi2):
# if chi == 0:
# continue
# pvals[i] = stats.chisqprob(chi2[i], ndof[i])
# map = np.log10(pvals)
# Mask
map = mf.maskMap(map, args.thetaMin-90, args.thetaMax-90)
if args.scale:
map[map!=hp.UNSEEN] *= (10**args.scale)
temp = {'Excess':(150,185)}
title = makeTitle(args)
### SETUP FROM PLOTFITS ###
unmasked = np.array([i for i in map if (i!=hp.UNSEEN and i!=np.inf)])
min = float(args.min) if args.min else unmasked.min()
max = float(args.max) if args.max else unmasked.max()
if not args.min:
args.min = '%.2f' % min
if not args.max:
args.max = '%.2f' % max
# Setup colormap with option for threshold
colormap = plt.get_cmap('jet')
colormap.set_under('white')
colormap.set_bad('gray')
pltParams = {'fig':1, 'rot':[0, -90, 180], 'title':'', 'min':min, \
'max':max, 'cbar':False, 'notext':True, 'coord':'C', \
'cmap':colormap}
###
if args.polar:
#hp.orthview(map, rot=(0,90,180), title=title, half_sky=True)
hp.orthview(map, half_sky=True, **pltParams)
for key in temp.keys():
hp.projtext(temp[key][0]*degree, temp[key][1]*degree, key)
else:
#hp.mollview(map, rot=(0,0,180), title=title)
#hp.mollview(map, rot=(0,0,180), title='')
hp.mollview(map, rot=180, title='')
#for key in temp.keys():
# hp.projtext(temp[key][0]*degree, temp[key][1]*degree, key)
hp.graticule(verbose=False)
### MORE SETUP FROM PLOTFITS ###
labelDict = {'bayes':r'Bayes Factor [$\ln(B_{21})$]'}
labelDict['test'] = r'Difference from Mean [$\overline{D_i} - \overline{D_{tot}}$]'
label = labelDict[args.maptype]
if args.scale:
label += ' [x 10$^{-%d}$]' % args.scale
cbarParams = {'label':label, 'min':args.min, 'max':args.max, \
'coord':'C', 'fontsize':'small', \
'projaxis':hp.projaxes.HpxOrthographicAxes}
SetupColorBar(**cbarParams)
if args.out != None:
plt.savefig(args.out, dpi=300, bbox_inches='tight')
if not args.batch:
plt.show()
def healpy_orthview(plot_map,
fig=None,
coord='G',
xsize=2000,
title='',
min_value=0.0,
max_value=0.0,
cbar=True,
notext=False,
norm=None,
name_out=None,
cmap=plt.cm.viridis,
return_projected=False):
"""
map - healpix map (1-d array),
fig - figure number to use,
coord - coordinate system of the map ('G', ['G', 'E'] or ['G', 'C']),
xsize - size of the image,
title - title of the plot,
min_value - minimum range value,
max_value - maximum range value,
cbar - display the colorbar,
notext - if True, no text is printed around the map,
norm - color normalization (None (linear), 'hist' or 'log'),
name_out - name of the output picture,
cmap - color map of the plot (jet, viridis, jet and other)
"""
cmap.set_under("w")
if fig is None:
fig = plt.figure(figsize=(20, 10))
if return_projected:
if min_value != 0.0 or max_value != 0.0:
plot_map_out = hp.orthview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
min=min_value,
max=max_value,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap,
return_projected_map=True)
else:
plot_map_out = hp.orthview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap,
return_projected_map=True)
if name_out is not None:
plt.savefig(name_out, dpi=400)
return plot_map_out
else:
if min_value != 0.0 or max_value != 0.0:
hp.orthview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
min=min_value,
max=max_value,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap)
else:
hp.orthview(plot_map,
fig.number,
coord=coord,
xsize=xsize,
title=title,
cbar=cbar,
notext=notext,
norm=norm,
cmap=cmap)
if name_out is not None:
plt.savefig(name_out, dpi=400)
try:
for i, ut in enumerate(UT):
if ut==0.: UT0[p]=RM[i]
if ut==4.: UT4[p]=RM[i]
if ut==6.: UT6[p]=RM[i]
if ut==22.:
UT22[p]=RM[i]
eUT22[p]=eRM[i]
except TypeError: continue
except IOError:
print 'issue with %s'%filename
UT22[p] = np.nan
c+=1
continue
print c,'IOErrors'
hp.orthview(UT22,rot=[0,90],max=2,unit=r'rad m$^{-1}$',title='SAST 00:00 2012-02-13',half_sky=True)
#pylab.show()
print UT22
print 'Interpolating NaNs'
UT22_interp = np.zeros_like(UT22)
c=0
for i,val in enumerate(UT22):
if np.isnan(val):
c+=1
theta,phi = hp.pix2ang(nside,i)
neybs = hp.get_neighbours(nside,theta,phi=phi)
v = np.nanmean(UT22[neybs[0]])
UT22_interp[i] = v
else: UT22_interp[i]=val
def Orth(map):
hp.orthview(map,rot=[0,90],half_sky=True)
return
def Orth(map):
hp.orthview(map,half_sky=True)
return
norm=" ",
sub=221,
xsize=2000) #, min=-5e-7, max=5e7, xsize=2000)
hp.orthview(map=eqdata,
fig=3,
rot=None,
coord=None,
unit='',
xsize=800,
half_sky=False,
title='Orthographic view',
nest=False,
min=None,
max=None,
flip='astro',
remove_dip=False,
remove_mono=False,
gal_cut=0,
format='%g',
format2='%g',
cbar=True,
cmap=None,
notext=False,
norm=" ",
hold=False,
margins=None,
sub=222,
return_projected_map=False)
hp.gnomview(map=eqdata,
fig=3,
rot=None,
for i in ip: map[i]+=1
map = hp.smoothing(map, fwhm=np.radians(14))
map = map/np.max(map)
fsky = map.sum()/len(map)
hp.mollview(map, title='Fractime: {0:4.1f}% - fsky: {1:4.1f}%'.format(fractime, fsky*100))
nside = 8
map = np.arange(12*8**2)
idbicep2 = hp.ang2pix(nside, np.radians(90-galbicep2[1]), np.radians(galbicep2[0]))
map[idbicep2]+=100
idnew = hp.ang2pix(nside, np.radians(90-galnew[1]), np.radians(galnew[0]))
map[idnew]+=100
hp.orthview(map, rot=[0,90])
############# Tower
time, azimuth, elevation = observe_onespot(spot_bicep2, domec, nt=1000, DEFAULT_DATE_OBS='2016-01-01 00:00:00')
time2, azimuth2, elevation2 = observe_onespot(newspot, domec, nt=1000, DEFAULT_DATE_OBS='2016-01-01 00:00:00')
htower = 45.
dtower = np.array([45., 70.])
thetatower = np.degrees(arctan2(htower, dtower))
clf()
plot(azimuth, elevation)
hpxidx = np.arange(npix)
theta, phi = hp.pix2ang(nside, hpxidx)
phi = 2. * np.pi - phi
RotAngle = np.radians(30.)
RotAxis = np.array([0, -1, 0])
R = rotation_matrix(RotAxis, RotAngle)
cosX, sinX = spherical_basis_transformation_components(theta, phi, R)
fig = plt.figure(figsize=(16, 8))
hp.orthview(cosX,
rot=[-45, 45],
cmap='RdBu_r',
sub=(1, 2, 1),
half_sky=True,
min=-1,
max=1)
ax = plt.gca()
ax.set_aspect('equal')
ax.set_title(r'$\cos \chi$', fontsize=32)
hp.orthview(sinX,
rot=[-45, 45],
cmap='RdBu_r',
sub=(1, 2, 2),
half_sky=True,
min=-1,
max=1)
ax = plt.gca()
def Orth90(map):
hp.orthview(map,half_sky=True,rot=[0,90])
return
plt.plot(nu, T_nu.real, 'b')
plt.plot(nu, T_nu.real - T_nu.mean(), 'b--')
plt.plot(nu, window * T_nu.real, 'g')
plt.plot(nu, T_nu.imag, 'r')
plt.subplot(232)
plt.plot(nu, T_nu.real / omega_nu)
plt.title(title)
plt.subplot(233)
plt.semilogy(tau, Ptau_T_nu)
plt.semilogy(-tau0.value * np.array([1., 1.]), [1e-2, 1e5], 'r--')
plt.semilogy(tau0.value * np.array([1., 1.]), [1e-2, 1e5], 'r--')
plt.subplot(234)
plt.plot(C_f / C_f.max(), label='C_l,fringe')
plt.plot(C_a / C_a.max(), label='C_l,beam')
plt.xlim([0, 150])
#plt.ylim([1e-8,1e1])
plt.legend()
plt.subplot(235)
plt.plot(ell, C_fa / C_fa.max())
plt.xlim([0, 30])
hp.orthview((beam_nu * fringe)[:, 100].real,
rot=[0, 90],
half_sky=True,
sub=236)
hp.graticule()
m[:ring_max] = hp.pixelfunc.UNSEEN
# replace nan values with zero
if opts.fill: m = np.nan_to_num(m)
fig = plt.figure(1, figsize=(12,8))
if opts.mode.lower()=='h':
norm = 'hist'
elif opts.mode.lower()=='i':
norm = None
elif opts.mode.lower()=='l':
norm = 'log'
elif opts.mode.lower()=='s':
norm = None
if opts.visu.lower().startswith('moll'): # Mollweide projection
hp.mollview(m, coord=coord_proj, norm=norm, max=opts.max, min=opts.min, cmap=plt.get_cmap(opts.cmap), cbar=cbar, fig=1, unit=unit, title=opts.title, xsize=xsize, rot=rot)
elif opts.visu.lower().startswith('cart'): # Cartesian
hp.cartview(m, coord=coord_proj, norm=norm, max=opts.max, min=opts.min, cmap=plt.get_cmap(opts.cmap), cbar=cbar, fig=1, unit=unit, title=opts.title, xsize=xsize, rot=rot)
elif opts.visu.lower().startswith('ortho'): # Orthographic
hp.orthview(m, coord=coord_proj, norm=norm, max=opts.max, min=opts.min, cmap=plt.get_cmap(opts.cmap), cbar=cbar, fig=1, unit=unit, title=opts.title, xsize=xsize, rot=rot)
# set grid lines
hp.graticule(dpar=5, dmer=5)
if not(opts.savefig is None):
plt.savefig(opts.savefig)
else: plt.show()