def plot(a, b=None, **kw):
ind = kw.get('ind')
if ind is None:
idlplot.plot(a, b, **kw)
else:
del kw['ind']
if b is not None:
idlplot.plot(a[ind], b[ind], **kw)
else:
idlplot.plot(a[ind], None, **kw)
def plot(a, b=None, **kw):
ind = kw.get('ind')
if ind is None:
idlplot.plot(a,b,**kw)
else:
del kw['ind']
if b is not None:
idlplot.plot(a[ind], b[ind], **kw)
else:
idlplot.plot(a[ind], None, **kw)
def healmap(ras, decs, ramin=0, ramax=360, decmin=-90, decmax=90, nside=64, xflip=False, vmax=None, vmin=None, cmap=plt.cm.gray_r, linewidth=1, weights=None, wrap_angle=None, skip_empty=True, weight_norm=False, rasterized=True): """ Make the 2D histogram of the datapoints on the sky using the Healpix pixels """ plt.ioff() nel = 12 * nside**2 ipix = healpy.ang2pix(nside, numpy.deg2rad(decs)+numpy.pi*0.5, numpy.deg2rad(ras)) hh = quick_hist.quick_hist((ipix,), nbins=[nel], range=[[0, nel]],weights=weights) hhsum = quick_hist.quick_hist((ipix,), nbins=[nel], range=[[0, nel]]) pixarea = (4*numpy.pi*(180/numpy.pi)**2)/nel # in sq. deg hh = hh / pixarea hhsum = hhsum / pixarea xloc = numpy.arange(nel) verts = [bovy_healpy.pix2vert(nside, xx) for xx in xloc] collist = [] fac = 180/numpy.pi if wrap_angle is None: wrap_angle = 2 * numpy.pi else: wrap_angle = numpy.deg2rad(wrap_angle) for ii, v in enumerate(verts): if skip_empty and hhsum[ii] == 0: continue b,a = v.T if (a.max()-a.min())>(numpy.pi): a = ((a + (numpy.pi - a[0])) % (2 * numpy.pi)) - (numpy.pi-a[0]) if a.mean() < 0: a = (a + 2 * numpy.pi) if a.mean() > wrap_angle: a -= 2 * numpy.pi xys = numpy.array([a * fac, b * fac - 90]).T curpoly = matplotlib.patches.Polygon(xys, closed=True) collist.append(curpoly) raranges = [ramin,ramax] if xflip: raranges = raranges[::-1] plot([-1], xr=raranges, yr=[decmin,decmax]) ax = plt.gca() tmppol = matplotlib.patches.Polygon(numpy.array(([0, 360, 360, 0], [-90, -90, 90, 90])).T,closed=True) tmpcoll = matplotlib.collections.PatchCollection([tmppol], linewidths=0.01, edgecolors='black', facecolors='black') tmpcoll.set_array(numpy.array([0])) coll = matplotlib.collections.PatchCollection(collist, linewidths=linewidth) if vmin is None: vmin = 0 if vmax is None: vmax = hh.max() if weight_norm: hh = hh * 1. / (hhsum + 1 * (hhsum == 0)) if skip_empty: hh1 = hh[hhsum > 0] else: hh1 = hh coll.set_array(hh1) coll.set_cmap(cmap) coll.set_clim(vmin, vmax) coll.set_edgecolors(coll.cmap(coll.norm(hh1))) coll.set_rasterized(rasterized) tmpcoll.set_clim(vmin, vmax) ax.add_collection(coll) return coll
def healmap(ras,
decs,
ramin=0,
ramax=360,
decmin=-90,
decmax=90,
nside=64,
xflip=False,
vmax=None,
vmin=None,
cmap=plt.cm.gray_r,
linewidth=1,
weights=None,
wrap_angle=None,
skip_empty=True,
weight_norm=False,
rasterized=True):
"""
Make the 2D histogram of the datapoints on the sky using the Healpix
pixels
"""
plt.ioff()
nel = 12 * nside**2
ipix = healpy.ang2pix(nside,
numpy.deg2rad(decs) + numpy.pi * 0.5,
numpy.deg2rad(ras))
hh = quick_hist.quick_hist((ipix, ),
nbins=[nel],
range=[[0, nel]],
weights=weights)
hhsum = quick_hist.quick_hist((ipix, ), nbins=[nel], range=[[0, nel]])
pixarea = (4 * numpy.pi * (180 / numpy.pi)**2) / nel # in sq. deg
hh = hh / pixarea
hhsum = hhsum / pixarea
xloc = numpy.arange(nel)
verts = [bovy_healpy.pix2vert(nside, xx) for xx in xloc]
collist = []
fac = 180 / numpy.pi
if wrap_angle is None:
wrap_angle = 2 * numpy.pi
else:
wrap_angle = numpy.deg2rad(wrap_angle)
for ii, v in enumerate(verts):
if skip_empty and hhsum[ii] == 0:
continue
b, a = v.T
if (a.max() - a.min()) > (numpy.pi):
a = ((a + (numpy.pi - a[0])) % (2 * numpy.pi)) - (numpy.pi - a[0])
if a.mean() < 0:
a = (a + 2 * numpy.pi)
if a.mean() > wrap_angle:
a -= 2 * numpy.pi
xys = numpy.array([a * fac, b * fac - 90]).T
curpoly = matplotlib.patches.Polygon(xys, closed=True)
collist.append(curpoly)
raranges = [ramin, ramax]
if xflip:
raranges = raranges[::-1]
plot([-1], xr=raranges, yr=[decmin, decmax])
ax = plt.gca()
tmppol = matplotlib.patches.Polygon(numpy.array(
([0, 360, 360, 0], [-90, -90, 90, 90])).T,
closed=True)
tmpcoll = matplotlib.collections.PatchCollection([tmppol],
linewidths=0.01,
edgecolors='black',
facecolors='black')
tmpcoll.set_array(numpy.array([0]))
coll = matplotlib.collections.PatchCollection(collist,
linewidths=linewidth)
if vmin is None:
vmin = 0
if vmax is None:
vmax = hh.max()
if weight_norm:
hh = hh * 1. / (hhsum + 1 * (hhsum == 0))
if skip_empty:
hh1 = hh[hhsum > 0]
else:
hh1 = hh
coll.set_array(hh1)
coll.set_cmap(cmap)
coll.set_clim(vmin, vmax)
coll.set_edgecolors(coll.cmap(coll.norm(hh1)))
coll.set_rasterized(rasterized)
tmpcoll.set_clim(vmin, vmax)
ax.add_collection(coll)
return coll
