thininsulatinglayer_max_y, thininsulatinglayer_min_x,
thininsulatinglayer_max_x)
Tnoisy = T + np.random.randn(*T.shape) * .022 # 22 mK NETD
# To plot:
# loglog(trange+dt/2,T[20,20,:])
# imshow(T[:,:,200]
# Saturation check not technically necessary on simulated data
(saturation_fraction,
saturation_map) = greensinversion.saturationcheck(Tnoisy, 0)
(inversioncoeffs, residual, errs,
tikparams) = greensinversion.performinversionsteps(rowselects, inversions,
inversionsfull, inverses,
nresults, Tnoisy, tikparam)
(fig, subplots, images) = greensinversion.plotabstractinverse(
5, 2, 3, inversioncoeffs, reflectors, -10000.0, 20000.0, y_bnd, x_bnd,
num_sources_y, num_sources_x)
concreteinverse = greensinversion.buildconcreteinverse(
inversioncoeffs, reflectors, ygrid[0, :, :], xgrid[0, :, :], y_bnd, x_bnd,
ny, nx, num_sources_y, num_sources_x)
(cfig, csubplots, cimages) = greensinversion.plotconcreteinverse(
6, 2, 3, saturation_map, concreteinverse, reflectors, -10000.0, 20000.0,
y_bnd, x_bnd, num_sources_y, num_sources_x)
(ss_inversioncoeffs, ss_residual,
ss_errs, ss_tikparams) = greensinversion.performinversionsteps(
ny, nx, dy, dx, reflectors, wfmdict[channel].data)
# Create a data cube for storing the inversion
fullinverse = np.zeros((len(reflectors) + 1, wfmdict[channel].data.shape[1],
wfmdict[channel].data.shape[0]),
dtype='d')
# Iterate through the tiles
for tile_idx in range(len(minyminx_corners)):
(yidx, xidx) = minyminx_corners[tile_idx]
# Perform the inversion on this tile
(inversioncoeffs, residual, errs,
tikparams) = greensinversion.performinversionsteps(
rowselects, inversions, inversionsfull, inverses, nresults,
wfmdict[channel].data[xidx:(xidx + nx), yidx:(yidx + ny),
startframe:].transpose((2, 1, 0)), tikparam
) # transpose to convert dataguzzler axis ordering (x,y,t) to greensinversion ordering (t,y,x)
# Build a concrete reconstruction of the buried heat sources from the inversion coefficients
concreteinverse = greensinversion.buildconcreteinverse(
inversioncoeffs, reflectors, ygrid, xgrid, y_bnd, x_bnd, ny, nx)
# concreteinverse is (len(reflectors)+1,ny,nx)... first layer is surface
# accumulate weighted contributions of this tile to full inverse
fullinverse[:, yidx:(yidx + ny),
xidx:(xidx +
nx)] += concreteinverse * contributionprofiles[tile_idx]
pass
# Create a data cube for storing the single-step inversion