def map2minfo(m): """Given an enmap with constant-latitude rows and constant longitude intervals, return a corresponding sharp map_info.""" theta = np.pi/2 - m[...,:,:1].posmap()[0,:,0] # Slice to make calculation faster. Could have just queried m.wcs cirectly here # instead. Offset by 1 away from bottom to avoid any pole-related problems. phi0 = m[...,1:2,0:1].posmap()[1,0,0] nphi = m.shape[-1] return sharp.map_info(theta, nphi, phi0)
def match_predefined_minfo(m, rtol=None, atol=None):
"""Given an enmapwith constant-latitude rows and constant longitude
intervals, return the libsharp predefined minfo with ringweights that's
the closest match to our pixelization."""
if rtol is None: rtol = 1e-3*utils.arcmin
if atol is None: atol = 1.0*utils.arcmin
# Make sure the colatitude ascends
flipy = m.wcs.wcs.cdelt[1] > 0
if flipy: m = m[...,::-1,:]
theta = np.pi/2 - m[...,:,:1].posmap()[0,:,0]
phi0 = m[...,1:2,0:1].posmap()[1,0,0]
ntheta, nphi = m.shape[-2:]
# First find out how many lat rings there are in the whole sky.
# Find the first and last pixel center inside bounds
y1 = int(np.round(m.sky2pix([np.pi/2,0])[0]))
y2 = int(np.round(m.sky2pix([-np.pi/2,0])[0]))
phi0 = m.pix2sky([0,0])[1]
ny = utils.nint(y2-y1+1)
nx = utils.nint(np.abs(360./m.wcs.wcs.cdelt[0]))
# Define our candidate pixelizations
minfos = []
for i in range(-1,2):
#minfos.append(sharp.map_info_gauss_legendre(ny+i, nx, phi0))
minfos.append(sharp.map_info_clenshaw_curtis(ny+i, nx, phi0))
minfos.append(sharp.map_info_fejer1(ny+i, nx, phi0))
minfos.append(sharp.map_info_fejer2(ny+i, nx, phi0))
minfos.append(sharp.map_info_mw(ny+i, nx, phi0))
# For each pixelization find the first ring in the map
aroffs, scores, minfos2 = [], [], []
for minfo in minfos:
# Find theta closest to our first theta
i1 = np.argmin(np.abs(theta[0]-minfo.theta))
# If we're already on the full sky, the the +1
# pixel alternative will not work.
if i1+len(theta) > minfo.theta.size: continue
# Find the largest theta offset for all y in our input map
offs = theta-minfo.theta[i1:i1+len(theta)]
aoff = np.max(np.abs(offs))
# Find the largest offset after applying a small pointing offset
roff = np.max(np.abs(offs-np.mean(offs)))
aroffs.append([aoff,roff,i1])
scores.append(aoff/atol + roff/rtol)
minfos2.append(minfo)
# Choose the one with the lowest score (lowest mismatch)
best = np.argmin(scores)
aoff, roff, i1 = aroffs[best]
i2 = i1+ntheta
if not aoff < atol: raise ShapeError("Could not find a map_info with predefined weights matching input map (abs offset %e >= %e)" % (aoff, atol))
if not roff < rtol: raise ShapeError("Could not find a map_info with predefined weights matching input map (%rel offset e >= %e)" % (aoff, atol))
minfo = minfos2[best]
# Modify the minfo to restrict it to only the rows contained in m
minfo_cut = sharp.map_info(
minfo.theta[i1:i2], minfo.nphi[i1:i2], minfo.phi0[i1:i2],
minfo.offsets[i1:i2]-minfo.offsets[i1], minfo.stride[i1:i2],
minfo.weight[i1:i2])
if flipy:
# Actual map is flipped in y relative to the one we computed the map info
minfo_cut = sharp.map_info(
minfo_cut.theta[::-1], minfo_cut.nphi[::-1], minfo_cut.phi0[::-1],
minfo_cut.offsets[:], minfo_cut.stride[:], minfo_cut.weight[::-1])
# Phew! Return the result
return minfo_cut