def make_map(mb_dissolve_df=None,
glac_df_mb=None,
agg_df=None,
col=('mb_mwea', 'mean'),
border_df=None,
crs=crs,
extent=None,
hs=None,
hs_extent=None,
clim=None,
labels='val',
title=None):
fig, ax = plt.subplots(figsize=(10, 8))
ax.set_aspect('equal')
legend = add_legend(ax, sf=scaling_f)
if title is not None:
ax.set_title(title)
if clim is None:
#clim = (glac_df_mb[col].min(), glac_df_mb[col].max())
clim = malib.calcperc_sym(mb_dissolve_df[col], perc=(1, 99))
cmap = 'RdBu'
if 'mb_mwea' in col:
label = 'Mass Balance (m we/yr)'
elif 'mb_Gta' in col:
label = 'Mass Balance (Gt/yr)'
elif 'meltwater' in col:
label = 'Excess Meltwater Runoff (Gt/yr)'
#Reverse, as these are negative values
cmap = 'YlOrRd_r'
#cmap = 'inferno'
clim = malib.calcperc(mb_dissolve_df[col], perc=(0, 99))
elif 't1' in col:
cmap = 'inferno'
label = 'Source Date (year)'
#This is cartopy-enabled axes
#ax = plt.axes(projection=crs)
#Currently unsupported for AEA
#gl = ax.gridlines(draw_labels=True, linewidth=0.5, color='gray', alpha=0.5, linestyle='--')
if hs is not None:
print("Plotting image")
hs_style = {
'cmap': 'gray',
'origin': 'upper',
'extent': cartopy_extent(hs_extent),
'transform': crs
}
ax.imshow(hs, **hs_style)
if border_df is not None:
print("Plotting borders")
border_style = {
'facecolor': '0.65',
'edgecolor': 'k',
'linewidth': 0.7
}
border_df.plot(ax=ax, **border_style)
if agg_df is not None:
print("Plotting agg boundaries")
#This was to get colored regions
#agg_style = {'cmap':'cpt_rainbow', 'edgecolor':'none', 'linewidth':0, 'alpha':0.05}
agg_style = {
'cmap': 'summer',
'edgecolor': 'none',
'linewidth': 0,
'alpha': 0.05
}
#agg_style = {'facecolor':'0.95','edgecolor':'k', 'linewidth':0.3, 'alpha':0.2}
agg_df.plot(ax=ax, **agg_style)
if glac_df_mb is not None:
print("Plotting glacier polygons")
glac_style = {'edgecolor': 'k', 'linewidth': 0.1, 'alpha': 0.2}
#This plots mb color ramp for each glacier polygon
#glac_ax = glac_df_mb.plot(ax=ax, column=col[0], cmap=cmap, vmin=clim[0], vmax=clim[1], **glac_style)
#This plots outlines
glac_ax = glac_df_mb.plot(ax=ax, facecolor='none', **glac_style)
if agg_df is not None:
agg_style = {'facecolor': 'none', 'edgecolor': 'w', 'linewidth': 0.5}
agg_df.plot(ax=ax, **agg_style)
#https://stackoverflow.com/questions/36008648/colorbar-on-geopandas
# fake up the array of the scalar mappable so we can plot colorbar. Urgh...
sc = plt.cm.ScalarMappable(cmap=cmap,
norm=plt.Normalize(vmin=clim[0], vmax=clim[1]))
sc._A = []
if mb_dissolve_df is not None:
print("Plotting scatterplot of %s values" % (col, ))
#Plot single values for region or basin
x = mb_dissolve_df['centroid_x']
y = mb_dissolve_df['centroid_y']
#Scale by total glacier area in each polygon
s = scaling_f * mb_dissolve_df[('Area_all', 'sum')]
c = mb_dissolve_df[col]
sc_style = {
'cmap': cmap,
'edgecolor': 'k',
'linewidth': 0.5,
'alpha': 0.8
}
sc = ax.scatter(x, y, s, c, vmin=clim[0], vmax=clim[1], **sc_style)
#Add labels
text_kw = {'family': 'sans-serif', 'fontsize': 8, 'color': 'k'}
if labels is not None:
print("Adding annotations")
for k, v in mb_dissolve_df.iterrows():
#lbl = '%0.2f +/- %0.2f' % (v[col], v[(col[0]+'_sigma',col[1])])
if labels == 'name+val':
lbl = '%s\n%+0.2f' % (k, v[col])
else:
lbl = '%+0.2f' % v[col]
#ax.annotate(lbl, xy=(v['centroid_x'],v['centroid_y']), xytext=(1,0), textcoords='offset points', family='sans-serif', fontsize=6, color='darkgreen')
txt = ax.annotate(lbl,
xy=(v['centroid_x'], v['centroid_y']),
ha='center',
va='center',
**text_kw)
txt.set_path_effects([
path_effects.Stroke(linewidth=0.75, foreground='w'),
path_effects.Normal()
])
#This is minx, miny, maxx, maxy
if extent is None:
#if glac_df_mb is not None:
# extent = glac_df_mb.total_bounds
#else:
extent = mb_dissolve_df.total_bounds
#For cartopy axes
#ax.set_extent(cartopy_extent(extent), crs=crs)
#Pad extent so labels fit within map
#extent = geolib.pad_extent(extent, perc=0.01, uniform=True)
ax.set_xlim(extent[0], extent[2])
ax.set_ylim(extent[1], extent[3])
#Adding colorbar doesn't work with the cartopy axes
pltlib.add_cbar(ax, sc, label=label)
pltlib.add_scalebar(ax, res=1)
pltlib.hide_ticks(ax)
plt.tight_layout()
return fig
def main(argv=None):
parser = getparser()
args = parser.parse_args()
#Should check that files exist
ref_dem_fn = args.ref_fn
src_dem_fn = args.src_fn
mode = args.mode
mask_list = args.mask_list
max_offset = args.max_offset
max_dz = args.max_dz
slope_lim = tuple(args.slope_lim)
tiltcorr = args.tiltcorr
polyorder = args.polyorder
res = args.res
#Maximum number of iterations
max_iter = args.max_iter
#These are tolerances (in meters) to stop iteration
tol = args.tol
min_dx = tol
min_dy = tol
min_dz = tol
outdir = args.outdir
if outdir is None:
outdir = os.path.splitext(src_dem_fn)[0] + '_dem_align'
if tiltcorr:
outdir += '_tiltcorr'
tiltcorr_done = False
#Relax tolerance for initial round of co-registration
#tiltcorr_tol = 0.1
#if tol < tiltcorr_tol:
# tol = tiltcorr_tol
if not os.path.exists(outdir):
os.makedirs(outdir)
outprefix = '%s_%s' % (os.path.splitext(os.path.split(src_dem_fn)[-1])[0], \
os.path.splitext(os.path.split(ref_dem_fn)[-1])[0])
outprefix = os.path.join(outdir, outprefix)
print("\nReference: %s" % ref_dem_fn)
print("Source: %s" % src_dem_fn)
print("Mode: %s" % mode)
print("Output: %s\n" % outprefix)
src_dem_ds = gdal.Open(src_dem_fn)
ref_dem_ds = gdal.Open(ref_dem_fn)
#Get local cartesian coordinate system
#local_srs = geolib.localtmerc_ds(src_dem_ds)
#Use original source dataset coordinate system
#Potentially issues with distortion and xyz/tiltcorr offsets for DEM with large extent
local_srs = geolib.get_ds_srs(src_dem_ds)
#local_srs = geolib.get_ds_srs(ref_dem_ds)
#Resample to common grid
ref_dem_res = float(geolib.get_res(ref_dem_ds, t_srs=local_srs, square=True)[0])
#Create a copy to be updated in place
src_dem_ds_align = iolib.mem_drv.CreateCopy('', src_dem_ds, 0)
src_dem_res = float(geolib.get_res(src_dem_ds, t_srs=local_srs, square=True)[0])
src_dem_ds = None
#Resample to user-specified resolution
ref_dem_ds, src_dem_ds_align = warplib.memwarp_multi([ref_dem_ds, src_dem_ds_align], \
extent='intersection', res=args.res, t_srs=local_srs, r='cubic')
res = float(geolib.get_res(src_dem_ds_align, square=True)[0])
print("\nReference DEM res: %0.2f" % ref_dem_res)
print("Source DEM res: %0.2f" % src_dem_res)
print("Resolution for coreg: %s (%0.2f m)\n" % (args.res, res))
#Iteration number
n = 1
#Cumulative offsets
dx_total = 0
dy_total = 0
dz_total = 0
#Now iteratively update geotransform and vertical shift
while True:
print("*** Iteration %i ***" % n)
dx, dy, dz, static_mask, fig = compute_offset(ref_dem_ds, src_dem_ds_align, src_dem_fn, mode, max_offset, \
mask_list=mask_list, max_dz=max_dz, slope_lim=slope_lim, plot=True)
xyz_shift_str_iter = "dx=%+0.2fm, dy=%+0.2fm, dz=%+0.2fm" % (dx, dy, dz)
print("Incremental offset: %s" % xyz_shift_str_iter)
dx_total += dx
dy_total += dy
dz_total += dz
xyz_shift_str_cum = "dx=%+0.2fm, dy=%+0.2fm, dz=%+0.2fm" % (dx_total, dy_total, dz_total)
print("Cumulative offset: %s" % xyz_shift_str_cum)
#String to append to output filenames
xyz_shift_str_cum_fn = '_%s_x%+0.2f_y%+0.2f_z%+0.2f' % (mode, dx_total, dy_total, dz_total)
#Should make an animation of this converging
if n == 1:
#static_mask_orig = static_mask
if fig is not None:
dst_fn = outprefix + '_%s_iter%02i_plot.png' % (mode, n)
print("Writing offset plot: %s" % dst_fn)
fig.gca().set_title("Incremental: %s\nCumulative: %s" % (xyz_shift_str_iter, xyz_shift_str_cum))
fig.savefig(dst_fn, dpi=300)
#Apply the horizontal shift to the original dataset
src_dem_ds_align = coreglib.apply_xy_shift(src_dem_ds_align, dx, dy, createcopy=False)
#Should
src_dem_ds_align = coreglib.apply_z_shift(src_dem_ds_align, dz, createcopy=False)
n += 1
print("\n")
#If magnitude of shift in all directions is less than tol
#if n > max_iter or (abs(dx) <= min_dx and abs(dy) <= min_dy and abs(dz) <= min_dz):
#If magnitude of shift is less than tol
dm = np.sqrt(dx**2 + dy**2 + dz**2)
dm_total = np.sqrt(dx_total**2 + dy_total**2 + dz_total**2)
if dm_total > max_offset:
sys.exit("Total offset exceeded specified max_offset (%0.2f m). Consider increasing -max_offset argument" % max_offset)
#Stop iteration
if n > max_iter or dm < tol:
if fig is not None:
dst_fn = outprefix + '_%s_iter%02i_plot.png' % (mode, n)
print("Writing offset plot: %s" % dst_fn)
fig.gca().set_title("Incremental:%s\nCumulative:%s" % (xyz_shift_str_iter, xyz_shift_str_cum))
fig.savefig(dst_fn, dpi=300)
#Compute final elevation difference
if True:
ref_dem_clip_ds_align, src_dem_clip_ds_align = warplib.memwarp_multi([ref_dem_ds, src_dem_ds_align], \
res=res, extent='intersection', t_srs=local_srs, r='cubic')
ref_dem_align = iolib.ds_getma(ref_dem_clip_ds_align, 1)
src_dem_align = iolib.ds_getma(src_dem_clip_ds_align, 1)
ref_dem_clip_ds_align = None
diff_align = src_dem_align - ref_dem_align
src_dem_align = None
ref_dem_align = None
#Get updated, final mask
static_mask_final = get_mask(src_dem_clip_ds_align, mask_list, src_dem_fn)
static_mask_final = np.logical_or(np.ma.getmaskarray(diff_align), static_mask_final)
#Final stats, before outlier removal
diff_align_compressed = diff_align[~static_mask_final]
diff_align_stats = malib.get_stats_dict(diff_align_compressed, full=True)
#Prepare filtered version for tiltcorr fit
diff_align_filt = np.ma.array(diff_align, mask=static_mask_final)
diff_align_filt = outlier_filter(diff_align_filt, f=3, max_dz=max_dz)
#diff_align_filt = outlier_filter(diff_align_filt, perc=(12.5, 87.5), max_dz=max_dz)
slope = get_filtered_slope(src_dem_clip_ds_align)
diff_align_filt = np.ma.array(diff_align_filt, mask=np.ma.getmaskarray(slope))
diff_align_filt_stats = malib.get_stats_dict(diff_align_filt, full=True)
#Fit 2D polynomial to residuals and remove
#To do: add support for along-track and cross-track artifacts
if tiltcorr and not tiltcorr_done:
print("\n************")
print("Calculating 'tiltcorr' 2D polynomial fit to residuals with order %i" % polyorder)
print("************\n")
gt = src_dem_clip_ds_align.GetGeoTransform()
#Need to apply the mask here, so we're only fitting over static surfaces
#Note that the origmask=False will compute vals for all x and y indices, which is what we want
vals, resid, coeff = geolib.ma_fitpoly(diff_align_filt, order=polyorder, gt=gt, perc=(0,100), origmask=False)
#vals, resid, coeff = geolib.ma_fitplane(diff_align_filt, gt, perc=(12.5, 87.5), origmask=False)
#Should write out coeff or grid with correction
vals_stats = malib.get_stats_dict(vals)
#Want to have max_tilt check here
#max_tilt = 4.0 #m
#Should do percentage
#vals.ptp() > max_tilt
#Note: dimensions of ds and vals will be different as vals are computed for clipped intersection
#Need to recompute planar offset for full src_dem_ds_align extent and apply
xgrid, ygrid = geolib.get_xy_grids(src_dem_ds_align)
valgrid = geolib.polyval2d(xgrid, ygrid, coeff)
#For results of ma_fitplane
#valgrid = coeff[0]*xgrid + coeff[1]*ygrid + coeff[2]
src_dem_ds_align = coreglib.apply_z_shift(src_dem_ds_align, -valgrid, createcopy=False)
if True:
print("Creating plot of polynomial fit to residuals")
fig, axa = plt.subplots(1,2, figsize=(8, 4))
dz_clim = malib.calcperc_sym(vals, (2, 98))
ax = pltlib.iv(diff_align_filt, ax=axa[0], cmap='RdBu', clim=dz_clim, \
label='Residual dz (m)', scalebar=False)
ax = pltlib.iv(valgrid, ax=axa[1], cmap='RdBu', clim=dz_clim, \
label='Polyfit dz (m)', ds=src_dem_ds_align)
#if tiltcorr:
#xyz_shift_str_cum_fn += "_tiltcorr"
tiltcorr_fig_fn = outprefix + '%s_polyfit.png' % xyz_shift_str_cum_fn
print("Writing out figure: %s\n" % tiltcorr_fig_fn)
fig.savefig(tiltcorr_fig_fn, dpi=300)
print("Applying tilt correction to difference map")
diff_align -= vals
#Should iterate until tilts are below some threshold
#For now, only do one tiltcorr
tiltcorr_done=True
#Now use original tolerance, and number of iterations
tol = args.tol
max_iter = n + args.max_iter
else:
break
if True:
#Write out aligned difference map for clipped extent with vertial offset removed
align_diff_fn = outprefix + '%s_align_diff.tif' % xyz_shift_str_cum_fn
print("Writing out aligned difference map with median vertical offset removed")
iolib.writeGTiff(diff_align, align_diff_fn, src_dem_clip_ds_align)
if True:
#Write out fitered aligned difference map
align_diff_filt_fn = outprefix + '%s_align_diff_filt.tif' % xyz_shift_str_cum_fn
print("Writing out filtered aligned difference map with median vertical offset removed")
iolib.writeGTiff(diff_align_filt, align_diff_filt_fn, src_dem_clip_ds_align)
#Extract final center coordinates for intersection
center_coord_ll = geolib.get_center(src_dem_clip_ds_align, t_srs=geolib.wgs_srs)
center_coord_xy = geolib.get_center(src_dem_clip_ds_align)
src_dem_clip_ds_align = None
#Write out final aligned src_dem
align_fn = outprefix + '%s_align.tif' % xyz_shift_str_cum_fn
print("Writing out shifted src_dem with median vertical offset removed: %s" % align_fn)
#Open original uncorrected dataset at native resolution
src_dem_ds = gdal.Open(src_dem_fn)
src_dem_ds_align = iolib.mem_drv.CreateCopy('', src_dem_ds, 0)
#Apply final horizontal and vertial shift to the original dataset
#Note: potentially issues if we used a different projection during coregistration!
src_dem_ds_align = coreglib.apply_xy_shift(src_dem_ds_align, dx_total, dy_total, createcopy=False)
src_dem_ds_align = coreglib.apply_z_shift(src_dem_ds_align, dz_total, createcopy=False)
if tiltcorr:
xgrid, ygrid = geolib.get_xy_grids(src_dem_ds_align)
valgrid = geolib.polyval2d(xgrid, ygrid, coeff)
#For results of ma_fitplane
#valgrid = coeff[0]*xgrid + coeff[1]*ygrid + coeff[2]
src_dem_ds_align = coreglib.apply_z_shift(src_dem_ds_align, -valgrid, createcopy=False)
#Might be cleaner way to write out MEM ds directly to disk
src_dem_full_align = iolib.ds_getma(src_dem_ds_align)
iolib.writeGTiff(src_dem_full_align, align_fn, src_dem_ds_align)
if True:
#Output final aligned src_dem, masked so only best pixels are preserved
#Useful if creating a new reference product
#Can also use apply_mask.py
print("Applying filter to shiftec src_dem")
align_diff_filt_full_ds = warplib.memwarp_multi_fn([align_diff_filt_fn,], res=src_dem_ds_align, extent=src_dem_ds_align, \
t_srs=src_dem_ds_align)[0]
align_diff_filt_full = iolib.ds_getma(align_diff_filt_full_ds)
align_diff_filt_full_ds = None
align_fn_masked = outprefix + '%s_align_filt.tif' % xyz_shift_str_cum_fn
iolib.writeGTiff(np.ma.array(src_dem_full_align, mask=np.ma.getmaskarray(align_diff_filt_full)), \
align_fn_masked, src_dem_ds_align)
src_dem_full_align = None
src_dem_ds_align = None
#Compute original elevation difference
if True:
ref_dem_clip_ds, src_dem_clip_ds = warplib.memwarp_multi([ref_dem_ds, src_dem_ds], \
res=res, extent='intersection', t_srs=local_srs, r='cubic')
src_dem_ds = None
ref_dem_ds = None
ref_dem_orig = iolib.ds_getma(ref_dem_clip_ds)
src_dem_orig = iolib.ds_getma(src_dem_clip_ds)
#Needed for plotting
ref_dem_hs = geolib.gdaldem_mem_ds(ref_dem_clip_ds, processing='hillshade', returnma=True, computeEdges=True)
src_dem_hs = geolib.gdaldem_mem_ds(src_dem_clip_ds, processing='hillshade', returnma=True, computeEdges=True)
diff_orig = src_dem_orig - ref_dem_orig
#Only compute stats over valid surfaces
static_mask_orig = get_mask(src_dem_clip_ds, mask_list, src_dem_fn)
#Note: this doesn't include outlier removal or slope mask!
static_mask_orig = np.logical_or(np.ma.getmaskarray(diff_orig), static_mask_orig)
#For some reason, ASTER DEM diff have a spike near the 0 bin, could be an issue with masking?
diff_orig_compressed = diff_orig[~static_mask_orig]
diff_orig_stats = malib.get_stats_dict(diff_orig_compressed, full=True)
#Prepare filtered version for comparison
diff_orig_filt = np.ma.array(diff_orig, mask=static_mask_orig)
diff_orig_filt = outlier_filter(diff_orig_filt, f=3, max_dz=max_dz)
#diff_orig_filt = outlier_filter(diff_orig_filt, perc=(12.5, 87.5), max_dz=max_dz)
slope = get_filtered_slope(src_dem_clip_ds)
diff_orig_filt = np.ma.array(diff_orig_filt, mask=np.ma.getmaskarray(slope))
diff_orig_filt_stats = malib.get_stats_dict(diff_orig_filt, full=True)
#Write out original difference map
print("Writing out original difference map for common intersection before alignment")
orig_diff_fn = outprefix + '_orig_diff.tif'
iolib.writeGTiff(diff_orig, orig_diff_fn, ref_dem_clip_ds)
src_dem_clip_ds = None
ref_dem_clip_ds = None
if True:
align_stats_fn = outprefix + '%s_align_stats.json' % xyz_shift_str_cum_fn
align_stats = {}
align_stats['src_fn'] = src_dem_fn
align_stats['ref_fn'] = ref_dem_fn
align_stats['align_fn'] = align_fn
align_stats['res'] = {}
align_stats['res']['src'] = src_dem_res
align_stats['res']['ref'] = ref_dem_res
align_stats['res']['coreg'] = res
align_stats['center_coord'] = {'lon':center_coord_ll[0], 'lat':center_coord_ll[1], \
'x':center_coord_xy[0], 'y':center_coord_xy[1]}
align_stats['shift'] = {'dx':dx_total, 'dy':dy_total, 'dz':dz_total, 'dm':dm_total}
#This tiltcorr flag gets set to false, need better flag
if tiltcorr:
align_stats['tiltcorr'] = {}
align_stats['tiltcorr']['coeff'] = coeff.tolist()
align_stats['tiltcorr']['val_stats'] = vals_stats
align_stats['before'] = diff_orig_stats
align_stats['before_filt'] = diff_orig_filt_stats
align_stats['after'] = diff_align_stats
align_stats['after_filt'] = diff_align_filt_stats
import json
with open(align_stats_fn, 'w') as f:
json.dump(align_stats, f)
#Create output plot
if True:
print("Creating final plot")
kwargs = {'interpolation':'none'}
#f, axa = plt.subplots(2, 4, figsize=(11, 8.5))
f, axa = plt.subplots(2, 4, figsize=(16, 8))
for ax in axa.ravel()[:-1]:
ax.set_facecolor('k')
pltlib.hide_ticks(ax)
dem_clim = malib.calcperc(ref_dem_orig, (2,98))
axa[0,0].imshow(ref_dem_hs, cmap='gray', **kwargs)
im = axa[0,0].imshow(ref_dem_orig, cmap='cpt_rainbow', clim=dem_clim, alpha=0.6, **kwargs)
pltlib.add_cbar(axa[0,0], im, arr=ref_dem_orig, clim=dem_clim, label=None)
pltlib.add_scalebar(axa[0,0], res=res)
axa[0,0].set_title('Reference DEM')
axa[0,1].imshow(src_dem_hs, cmap='gray', **kwargs)
im = axa[0,1].imshow(src_dem_orig, cmap='cpt_rainbow', clim=dem_clim, alpha=0.6, **kwargs)
pltlib.add_cbar(axa[0,1], im, arr=src_dem_orig, clim=dem_clim, label=None)
axa[0,1].set_title('Source DEM')
#axa[0,2].imshow(~static_mask_orig, clim=(0,1), cmap='gray')
axa[0,2].imshow(~static_mask, clim=(0,1), cmap='gray', **kwargs)
axa[0,2].set_title('Surfaces for co-registration')
dz_clim = malib.calcperc_sym(diff_orig_compressed, (5, 95))
im = axa[1,0].imshow(diff_orig, cmap='RdBu', clim=dz_clim)
pltlib.add_cbar(axa[1,0], im, arr=diff_orig, clim=dz_clim, label=None)
axa[1,0].set_title('Elev. Diff. Before (m)')
im = axa[1,1].imshow(diff_align, cmap='RdBu', clim=dz_clim)
pltlib.add_cbar(axa[1,1], im, arr=diff_align, clim=dz_clim, label=None)
axa[1,1].set_title('Elev. Diff. After (m)')
#tight_dz_clim = (-1.0, 1.0)
tight_dz_clim = (-2.0, 2.0)
#tight_dz_clim = (-10.0, 10.0)
#tight_dz_clim = malib.calcperc_sym(diff_align_filt, (5, 95))
im = axa[1,2].imshow(diff_align_filt, cmap='RdBu', clim=tight_dz_clim)
pltlib.add_cbar(axa[1,2], im, arr=diff_align_filt, clim=tight_dz_clim, label=None)
axa[1,2].set_title('Elev. Diff. After (m)')
#Tried to insert Nuth fig here
#ax_nuth.change_geometry(1,2,1)
#f.axes.append(ax_nuth)
bins = np.linspace(dz_clim[0], dz_clim[1], 128)
axa[1,3].hist(diff_orig_compressed, bins, color='g', label='Before', alpha=0.5)
axa[1,3].hist(diff_align_compressed, bins, color='b', label='After', alpha=0.5)
axa[1,3].set_xlim(*dz_clim)
axa[1,3].axvline(0, color='k', linewidth=0.5, linestyle=':')
axa[1,3].set_xlabel('Elev. Diff. (m)')
axa[1,3].set_ylabel('Count (px)')
axa[1,3].set_title("Source - Reference")
before_str = 'Before\nmed: %0.2f\nnmad: %0.2f' % (diff_orig_stats['med'], diff_orig_stats['nmad'])
axa[1,3].text(0.05, 0.95, before_str, va='top', color='g', transform=axa[1,3].transAxes, fontsize=8)
after_str = 'After\nmed: %0.2f\nnmad: %0.2f' % (diff_align_stats['med'], diff_align_stats['nmad'])
axa[1,3].text(0.65, 0.95, after_str, va='top', color='b', transform=axa[1,3].transAxes, fontsize=8)
#This is empty
axa[0,3].axis('off')
suptitle = '%s\nx: %+0.2fm, y: %+0.2fm, z: %+0.2fm' % (os.path.split(outprefix)[-1], dx_total, dy_total, dz_total)
f.suptitle(suptitle)
f.tight_layout()
plt.subplots_adjust(top=0.90)
fig_fn = outprefix + '%s_align.png' % xyz_shift_str_cum_fn
print("Writing out figure: %s" % fig_fn)
f.savefig(fig_fn, dpi=300)
def main2(args):
#Should check that files exist
dem1_fn = args.ref_fn
dem2_fn = args.src_fn
mode = args.mode
apply_mask = not args.nomask
max_offset_m = args.max_offset
tiltcorr = args.tiltcorr
#These are tolerances (in meters) to stop iteration
tol = args.tol
min_dx = tol
min_dy = tol
min_dz = tol
#Maximum number of iterations
max_n = 10
outdir = args.outdir
if outdir is None:
outdir = os.path.splitext(dem2_fn)[0] + '_dem_align'
if not os.path.exists(outdir):
os.makedirs(outdir)
outprefix = '%s_%s' % (os.path.splitext(os.path.split(dem2_fn)[-1])[0], \
os.path.splitext(os.path.split(dem1_fn)[-1])[0])
outprefix = os.path.join(outdir, outprefix)
print("\nReference: %s" % dem1_fn)
print("Source: %s" % dem2_fn)
print("Mode: %s" % mode)
print("Output: %s\n" % outprefix)
dem2_ds = gdal.Open(dem2_fn, gdal.GA_ReadOnly)
#Often the "ref" DEM is high-res lidar or similar
#This is a shortcut to resample to match "source" DEM
dem1_ds = warplib.memwarp_multi_fn([
dem1_fn,
],
res=dem2_ds,
extent=dem2_ds,
t_srs=dem2_ds)[0]
#dem1_ds = gdal.Open(dem1_fn, gdal.GA_ReadOnly)
#Create a copy to be updated in place
dem2_ds_align = iolib.mem_drv.CreateCopy('', dem2_ds, 0)
#dem2_ds_align = dem2_ds
#Iteration number
n = 1
#Cumulative offsets
dx_total = 0
dy_total = 0
dz_total = 0
#Now iteratively update geotransform and vertical shift
while True:
print("*** Iteration %i ***" % n)
dx, dy, dz, static_mask, fig = compute_offset(dem1_ds,
dem2_ds_align,
dem2_fn,
mode,
max_offset_m,
apply_mask=apply_mask)
if n == 1:
static_mask_orig = static_mask
xyz_shift_str_iter = "dx=%+0.2fm, dy=%+0.2fm, dz=%+0.2fm" % (dx, dy,
dz)
print("Incremental offset: %s" % xyz_shift_str_iter)
#Should make an animation of this converging
if fig is not None:
dst_fn = outprefix + '_%s_iter%i_plot.png' % (mode, n)
print("Writing offset plot: %s" % dst_fn)
fig.gca().set_title(xyz_shift_str_iter)
fig.savefig(dst_fn, dpi=300, bbox_inches='tight', pad_inches=0.1)
#Apply the horizontal shift to the original dataset
dem2_ds_align = coreglib.apply_xy_shift(dem2_ds_align,
dx,
dy,
createcopy=False)
dem2_ds_align = coreglib.apply_z_shift(dem2_ds_align,
dz,
createcopy=False)
dx_total += dx
dy_total += dy
dz_total += dz
print("Cumulative offset: dx=%+0.2fm, dy=%+0.2fm, dz=%+0.2fm" %
(dx_total, dy_total, dz_total))
#Fit plane to residuals and remove
#Might be better to do this after converging
"""
if tiltcorr:
print("Applying planar tilt correction")
gt = dem2_ds_align.GetGeoTransform()
#Need to compute diff_euler here
#Copy portions of compute_offset, create new function
vals, resid, coeff = geolib.ma_fitplane(diff_euler_align, gt, perc=(4, 96))
dem2_ds_align = coreglib.apply_z_shift(dem2_ds_align, -vals, createcopy=False)
"""
n += 1
print("\n")
#If magnitude of shift in all directions is less than tol
#if n > max_n or (abs(dx) <= min_dx and abs(dy) <= min_dy and abs(dz) <= min_dz):
#If magnitude of shift is less than tol
dm = np.sqrt(dx**2 + dy**2 + dz**2)
if n > max_n or dm < tol:
break
#String to append to output filenames
xyz_shift_str_cum = '_%s_x%+0.2f_y%+0.2f_z%+0.2f' % (mode, dx_total,
dy_total, dz_total)
if tiltcorr:
xyz_shift_str_cum += "_tiltcorr"
#Compute original elevation difference
if True:
dem1_clip_ds, dem2_clip_ds = warplib.memwarp_multi([dem1_ds, dem2_ds], \
res='max', extent='intersection', t_srs=dem2_ds)
dem1_orig = iolib.ds_getma(dem1_clip_ds, 1)
dem2_orig = iolib.ds_getma(dem2_clip_ds, 1)
diff_euler_orig = dem2_orig - dem1_orig
if not apply_mask:
static_mask_orig = np.ma.getmaskarray(diff_euler_orig)
diff_euler_orig_compressed = diff_euler_orig[~static_mask_orig]
diff_euler_orig_stats = np.array(
malib.print_stats(diff_euler_orig_compressed))
#Write out original eulerian difference map
print(
"Writing out original euler difference map for common intersection before alignment"
)
dst_fn = outprefix + '_orig_dz_eul.tif'
iolib.writeGTiff(diff_euler_orig, dst_fn, dem1_clip_ds)
#Compute final elevation difference
if True:
dem1_clip_ds_align, dem2_clip_ds_align = warplib.memwarp_multi([dem1_ds, dem2_ds_align], \
res='max', extent='intersection', t_srs=dem2_ds_align)
dem1_align = iolib.ds_getma(dem1_clip_ds_align, 1)
dem2_align = iolib.ds_getma(dem2_clip_ds_align, 1)
diff_euler_align = dem2_align - dem1_align
if not apply_mask:
static_mask = np.ma.getmaskarray(diff_euler_align)
diff_euler_align_compressed = diff_euler_align[~static_mask]
diff_euler_align_stats = np.array(
malib.print_stats(diff_euler_align_compressed))
#Fit plane to residuals and remove
if tiltcorr:
print("Applying planar tilt correction")
gt = dem1_clip_ds_align.GetGeoTransform()
#Need to apply the mask here, so we're only fitting over static surfaces
#Note that the origmask=False will compute vals for all x and y indices, which is what we want
vals, resid, coeff = geolib.ma_fitplane(np.ma.array(diff_euler_align, mask=static_mask), \
gt, perc=(4, 96), origmask=False)
#Remove planar offset from difference map
diff_euler_align -= vals
#Remove planar offset from aligned dem2
#Note: dimensions of ds and vals will be different as vals are computed for clipped intersection
#Recompute planar offset for dem2_ds_align extent
xgrid, ygrid = geolib.get_xy_grids(dem2_ds_align)
vals = coeff[0] * xgrid + coeff[1] * ygrid + coeff[2]
dem2_ds_align = coreglib.apply_z_shift(dem2_ds_align,
-vals,
createcopy=False)
if not apply_mask:
static_mask = np.ma.getmaskarray(diff_euler_align)
diff_euler_align_compressed = diff_euler_align[~static_mask]
diff_euler_align_stats = np.array(
malib.print_stats(diff_euler_align_compressed))
print("Creating fitplane plot")
fig, ax = plt.subplots(figsize=(6, 6))
fitplane_clim = malib.calcperc(vals, (2, 98))
im = ax.imshow(vals, cmap='cpt_rainbow', clim=fitplane_clim)
res = float(geolib.get_res(dem2_clip_ds, square=True)[0])
pltlib.add_scalebar(ax, res=res)
pltlib.hide_ticks(ax)
pltlib.add_cbar(ax, im, label='Fit plane residuals (m)')
fig.tight_layout()
dst_fn1 = outprefix + '%s_align_dz_eul_fitplane.png' % xyz_shift_str_cum
print("Writing out figure: %s" % dst_fn1)
fig.savefig(dst_fn1, dpi=300, bbox_inches='tight', pad_inches=0.1)
#Compute higher-order fits?
#Could also attempt to model along-track and cross-track artifacts
#Write out aligned eulerian difference map for clipped extent with vertial offset removed
dst_fn = outprefix + '%s_align_dz_eul.tif' % xyz_shift_str_cum
print(
"Writing out aligned difference map with median vertical offset removed"
)
iolib.writeGTiff(diff_euler_align, dst_fn, dem1_clip_ds)
#Write out aligned dem_2 with vertial offset removed
if True:
dst_fn2 = outprefix + '%s_align.tif' % xyz_shift_str_cum
print(
"Writing out shifted dem2 with median vertical offset removed: %s"
% dst_fn2)
#Might be cleaner way to write out MEM ds directly to disk
dem2_align = iolib.ds_getma(dem2_ds_align)
iolib.writeGTiff(dem2_align, dst_fn2, dem2_ds_align)
dem2_ds_align = None
#Create output plot
if True:
print("Creating final plot")
dem1_hs = geolib.gdaldem_mem_ma(dem1_orig, dem1_clip_ds, returnma=True)
dem2_hs = geolib.gdaldem_mem_ma(dem2_orig, dem2_clip_ds, returnma=True)
f, axa = plt.subplots(2, 3, figsize=(11, 8.5))
for ax in axa.ravel()[:-1]:
ax.set_facecolor('k')
pltlib.hide_ticks(ax)
dem_clim = malib.calcperc(dem1_orig, (2, 98))
axa[0, 0].imshow(dem1_hs, cmap='gray')
axa[0, 0].imshow(dem1_orig,
cmap='cpt_rainbow',
clim=dem_clim,
alpha=0.6)
res = float(geolib.get_res(dem1_clip_ds, square=True)[0])
pltlib.add_scalebar(axa[0, 0], res=res)
axa[0, 0].set_title('Reference DEM')
axa[0, 1].imshow(dem2_hs, cmap='gray')
axa[0, 1].imshow(dem2_orig,
cmap='cpt_rainbow',
clim=dem_clim,
alpha=0.6)
axa[0, 1].set_title('Source DEM')
axa[0, 2].imshow(~static_mask_orig, clim=(0, 1), cmap='gray')
axa[0, 2].set_title('Surfaces for co-registration')
dz_clim = malib.calcperc_sym(diff_euler_orig_compressed, (5, 95))
im = axa[1, 0].imshow(diff_euler_orig, cmap='RdBu', clim=dz_clim)
pltlib.add_cbar(axa[1, 0], im, label=None)
axa[1, 0].set_title('Elev. Diff. Before (m)')
im = axa[1, 1].imshow(diff_euler_align, cmap='RdBu', clim=dz_clim)
pltlib.add_cbar(axa[1, 1], im, label=None)
axa[1, 1].set_title('Elev. Diff. After (m)')
#Tried to insert Nuth fig here
#ax_nuth.change_geometry(1,2,1)
#f.axes.append(ax_nuth)
bins = np.linspace(dz_clim[0], dz_clim[1], 128)
axa[1, 2].hist(diff_euler_orig_compressed,
bins,
color='g',
label='Before',
alpha=0.5)
axa[1, 2].hist(diff_euler_align_compressed,
bins,
color='b',
label='After',
alpha=0.5)
axa[1, 2].axvline(0, color='k', linewidth=0.5, linestyle=':')
axa[1, 2].set_xlabel('Elev. Diff. (m)')
axa[1, 2].set_ylabel('Count (px)')
axa[1, 2].set_title("Source - Reference")
#axa[1,2].legend(loc='upper right')
#before_str = 'Before\nmean: %0.2f\nstd: %0.2f\nmed: %0.2f\nnmad: %0.2f' % tuple(diff_euler_orig_stats[np.array((3,4,5,6))])
#after_str = 'After\nmean: %0.2f\nstd: %0.2f\nmed: %0.2f\nnmad: %0.2f' % tuple(diff_euler_align_stats[np.array((3,4,5,6))])
before_str = 'Before\nmed: %0.2f\nnmad: %0.2f' % tuple(
diff_euler_orig_stats[np.array((5, 6))])
axa[1, 2].text(0.05,
0.95,
before_str,
va='top',
color='g',
transform=axa[1, 2].transAxes)
after_str = 'After\nmed: %0.2f\nnmad: %0.2f' % tuple(
diff_euler_align_stats[np.array((5, 6))])
axa[1, 2].text(0.65,
0.95,
after_str,
va='top',
color='b',
transform=axa[1, 2].transAxes)
suptitle = '%s\nx: %+0.2fm, y: %+0.2fm, z: %+0.2fm' % (
os.path.split(outprefix)[-1], dx_total, dy_total, dz_total)
f.suptitle(suptitle)
f.tight_layout()
plt.subplots_adjust(top=0.90)
dst_fn = outprefix + '%s_align.png' % xyz_shift_str_cum
print("Writing out figure: %s" % dst_fn)
f.savefig(dst_fn, dpi=300, bbox_inches='tight', pad_inches=0.1)
#Removing residual planar tilt can introduce additional slope/aspect dependent offset
#Want to run another round of main dem_align after removing planar tilt
if tiltcorr:
print("\n Rerunning after applying tilt correction \n")
#Create copy of original arguments
import copy
args2 = copy.copy(args)
#Use aligned, tilt-corrected DEM as input src_fn for second round
args2.src_fn = dst_fn2
#Assume we've already corrected most of the tilt during first round (also prevents endless loop)
args2.tiltcorr = False
main2(args2)