def makefig(dem, hs, anomaly, ds, title=None):
f,axa = plt.subplots(1,2,figsize=(10,5))
#dem_clim = (2300, 4200)
dem_clim = (1600, 2100)
hs_clim = (1, 255)
anomaly_clim = (-15, 15)
hs_im = axa[0].imshow(hs, vmin=hs_clim[0], vmax=hs_clim[1], cmap='gray')
dem_im = axa[0].imshow(dem, vmin=dem_clim[0], vmax=dem_clim[1], cmap='cpt_rainbow', alpha=0.5)
res = 8
pltlib.add_scalebar(axa[0], res=res)
pltlib.add_cbar(axa[0], dem_im, label='Elevation (m WGS84)')
anomaly_im = axa[1].imshow(anomaly, vmin=anomaly_clim[0], vmax=anomaly_clim[1], cmap='RdBu')
pltlib.add_cbar(axa[1], anomaly_im, label='Elevation Anomaly (m)')
if shp_fn is not None:
pltlib.shp_overlay(axa[1], ds, shp_fn, color='darkgreen')
plt.tight_layout()
for ax in axa:
pltlib.hide_ticks(ax)
ax.set_facecolor('k')
if title is not None:
ax.set_title(title)
return f
def main():
parser = getparser()
#Create dictionary of arguments
args = vars(parser.parse_args())
#Want to enable -full when -of is specified, probably a fancy way to do this with argparse
if args['of']:
args['full'] = True
#Note, imshow has many interpolation types:
#'none', 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming',
#'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos'
#{'interpolation':'bicubic', 'aspect':'auto'}
#args['imshow_kwargs']={'interpolation':'bicubic'}
args['imshow_kwargs']={'interpolation':'none'}
if args['clipped'] and args['overlay'] is None:
sys.exit("Must specify an overlay filename with option 'clipped'")
#Set this as the background numpy array
args['bg'] = None
if args['shp'] is not None:
print args['shp']
if args['link']:
fig = plt.figure(0)
n_ax = len(args['filelist'])
src_ds_list = [gdal.Open(fn) for fn in args['filelist']]
t_srs = geolib.get_ds_srs(src_ds_list[0])
res_stats = geolib.get_res_stats(src_ds_list, t_srs=t_srs)
#Use min res
res = res_stats[0]
extent = geolib.ds_geom_union_extent(src_ds_list, t_srs=t_srs)
#print res, extent
for n,fn in enumerate(args['filelist']):
if not iolib.fn_check(fn):
print 'Unable to open input file: %s' % fn
continue
#Note: this won't work if img1 has 1 band and img2 has 3 bands
#Hack for now
if not args['link']:
fig = plt.figure(n)
n_ax = 1
#fig.set_facecolor('black')
fig.set_facecolor('white')
fig.canvas.set_window_title(os.path.split(fn)[1])
#fig.suptitle(os.path.split(fn)[1], fontsize=10)
#Note: warplib SHOULD internally check to see if extent/resolution/projection are identical
#This eliminates the need for a clipped flag
#If user has already warped the background and source data
if args['overlay']:
if args['clipped']:
src_ds = gdal.Open(fn, gdal.GA_ReadOnly)
#Only load up the bg array once
if args['bg'] is None:
#Need to check that background fn exists
print "%s background" % args['overlay']
bg_ds = gdal.Open(args['overlay'], gdal.GA_ReadOnly)
#Check image dimensions
args['bg'] = get_bma(bg_ds, 1, args['full'])
else:
#Clip/warp background dataset to match overlay dataset
#src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']], extent='union')
src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']], extent='first')
#src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']], res='min', extent='first')
#Want to load up the unique bg array for each input
args['bg'] = get_bma(bg_ds, 1, args['full'])
else:
src_ds = gdal.Open(fn, gdal.GA_ReadOnly)
if args['link']:
#Not sure why, but this still warps all linked ds, even when identical res/extent/srs
#src_ds = warplib.warp(src_ds, res=res, extent=extent, t_srs=t_srs)
src_ds = warplib.memwarp_multi([src_ds,], res=res, extent=extent, t_srs=t_srs)[0]
cbar_kwargs={'extend':'both', 'orientation':'vertical', 'shrink':0.7, 'fraction':0.12, 'pad':0.02}
nbands = src_ds.RasterCount
b = src_ds.GetRasterBand(1)
dt = gdal.GetDataTypeName(b.DataType)
#Eventually, check dt of each band
print
print "%s (%i bands)" % (fn, nbands)
#Singleband raster
if (nbands == 1):
if args['cmap'] is None:
#Special case to handle ASP float32 grayscale data
if '-L_sub' in fn or '-R_sub' in fn:
args['cmap'] = 'gray'
else:
if (dt == 'Float64') or (dt == 'Float32') or (dt == 'Int32'):
args['cmap'] = 'cpt_rainbow'
#This is for WV images
elif (dt == 'UInt16'):
args['cmap'] = 'gray'
elif (dt == 'Byte'):
args['cmap'] = 'gray'
else:
args['cmap'] = 'cpt_rainbow'
"""
if 'count' in fn:
args['clim_perc'] = (0,100)
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'cpt_rainbow'
if 'mask' in fn:
args['clim'] = (0, 1)
#Could be (0, 255)
#args['clim_perc'] = (0,100)
#Want absolute clim of 0, then perc of 100
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'gray'
"""
args['cbar_kwargs'] = cbar_kwargs
bma = get_bma(src_ds, 1, args['full'])
#Note n+1 here ensures we're assigning subplot correctly here (n is 0-relative, subplot is 1)
bma_fig(fig, bma, n_subplt=n_ax, subplt=n+1, ds=src_ds, **args)
#3-band raster, likely disparity map
#This doesn't work when alpha band is present
elif (nbands == 3) and (dt == 'Byte'):
#For some reason, tifs are vertically flipped
if (os.path.splitext(fn)[1] == '.tif'):
args['imshow_kwargs']['origin'] = 'lower'
#Use gdal dataset here instead of imread(fn)?
imgplot = plt.imshow(plt.imread(fn), **args['imshow_kwargs'])
pltlib.hide_ticks(imgplot.axes)
#Handle the 3-band disparity map case here
#elif ((dt == 'Float32') or (dt == 'Int32')):
else:
if args['cmap'] is None:
args['cmap'] = 'cpt_rainbow'
bn = 1
while bn <= nbands:
bma = get_bma(src_ds, bn, args['full'])
bma_fig(fig, bma, n_subplt=nbands, subplt=bn, ds=src_ds, **args)
bn += 1
#Want to be better about this else case - lazy for now
#else:
# bma = get_bma(src_ds, 1, args['full'])
# bma_fig(fig, bma, **args)
ts = timelib.fn_getdatetime_list(fn)
if ts:
print "Timestamp list: ", ts
"""
if len(ts) == 1:
plt.title(ts[0].date())
elif len(ts) == 2:
plt.title("%s to %s" % (ts[0].date(), ts[1].date()))
"""
plt.tight_layout()
#Write out the file
#Note: make sure display is local for savefig
if args['of']:
outf = str(os.path.splitext(fn)[0])+'_fig.'+args['of']
#outf = str(os.path.splitext(fn)[0])+'_'+str(os.path.splitext(args['overlay'])[0])+'_fig.'+args['of']
#Note: need to account for colorbar (12%) and title - some percentage of axes beyond bma dimensions
#Should specify minimum text size for output
max_size = np.array((10.0,10.0))
max_dpi = 300.0
#If both outsize and dpi are specified, don't try to change, just make the figure
if (args['outsize'] is None) and (args['dpi'] is None):
args['dpi'] = 150.0
#Unspecified out figure size for a given dpi
if (args['outsize'] is None) and (args['dpi'] is not None):
args['outsize'] = np.array(bma.shape[::-1])/args['dpi']
if np.any(np.array(args['outsize']) > max_size):
args['outsize'] = max_size
#Specified output figure size, no specified dpi
elif (args['outsize'] is not None) and (args['dpi'] is None):
args['dpi'] = np.min([np.max(np.array(bma.shape[::-1])/np.array(args['outsize'])), max_dpi])
print
print "Saving output figure:"
print "Filename: ", outf
print "Size (in): ", args['outsize']
print "DPI (px/in): ", args['dpi']
print "Input dimensions (px): ", bma.shape[::-1]
print "Output dimensions (px): ", tuple(np.array(args['outsize'])*args['dpi'])
print
fig.set_size_inches(args['outsize'])
#fig.set_size_inches(54.427, 71.87)
#fig.set_size_inches(40, 87)
fig.savefig(outf, dpi=args['dpi'], bbox_inches='tight', pad_inches=0, facecolor=fig.get_facecolor(), edgecolor='none')
#Show the plot - want to show all at once
if not args['of']:
plt.show()
def bma_fig(fig, bma, cmap='cpt_rainbow', clim=None, clim_perc=(2,98), bg=None, bg_perc=(2,98), n_subplt=1, subplt=1, label=None, title=None, cint=None, alpha=0.5, ticks=False, scalebar=None, ds=None, shp=None, imshow_kwargs={'interpolation':'nearest'}, cbar_kwargs={'extend':'both', 'orientation':'vertical', 'shrink':0.7, 'fraction':0.12, 'pad':0.02}, **kwargs):
#We don't use the kwargs, just there to save parsing in main
if clim is None:
clim = malib.calcperc(bma, clim_perc)
#Deal with masked cases
if clim[0] == clim[1]:
if clim[0] > bma.fill_value:
clim = (bma.fill_value, clim[0])
else:
clim = (clim[0], bma.fill_value)
print "Colorbar limits (%0.1f-%0.1f%%): %0.3f %0.3f" % (clim_perc[0], clim_perc[1], clim[0], clim[1])
else:
print "Colorbar limits: %0.3f %0.3f" % (clim[0], clim[1])
#Link all subplots for zoom/pan
sharex = sharey = None
if len(fig.get_axes()) > 0:
sharex = sharey = fig.get_axes()[0]
#Hack to catch situations with only 1 subplot, but a subplot number > 1
if n_subplt == 1:
subplt = 1
#One row, multiple columns
ax = fig.add_subplot(1, n_subplt, subplt, sharex=sharex, sharey=sharey)
#This occupies the full figure
#ax = fig.add_axes([0., 0., 1., 1., ])
#ax.patch.set_facecolor('black')
ax.patch.set_facecolor('white')
cmap_name = cmap
cmap = plt.get_cmap(cmap_name)
if 'inferno' in cmap_name:
#Use a gray background
cmap.set_bad('0.5', alpha=1)
else:
#This sets the nodata background to opaque black
cmap.set_bad('k', alpha=1)
#cmap.set_bad('w', alpha=1)
#ax.set_title("Band %i" % subplt, fontsize=10)
if title is not None:
ax.set_title(title)
#If a background image is provided, plot it first
if bg is not None:
#Note, 1 is opaque, 0 completely transparent
#alpha = 0.6
#bg_perc = (4,96)
bg_perc = (0.05, 99.95)
#bg_perc = (1, 99)
bg_alpha = 1.0
#bg_alpha = 0.5
bg_clim = malib.calcperc(bg, bg_perc)
bg_cmap_name = 'gray'
bg_cmap = plt.get_cmap(bg_cmap_name)
if 'inferno' in cmap_name:
bg_cmap.set_bad('0.5', alpha=1)
else:
bg_cmap.set_bad('k', alpha=1)
#Set the overlay bad values to completely transparent, otherwise darkens the bg
cmap.set_bad(alpha=0)
bgplot = ax.imshow(bg, cmap=bg_cmap, clim=bg_clim, alpha=bg_alpha)
imgplot = ax.imshow(bma, alpha=alpha, cmap=cmap, clim=clim, **imshow_kwargs)
else:
imgplot = ax.imshow(bma, cmap=cmap, clim=clim, **imshow_kwargs)
gt = None
if ds is not None:
gt = np.array(ds.GetGeoTransform())
gt_scale_factor = min(np.array([ds.RasterYSize, ds.RasterXSize])/np.array(bma.shape,dtype=float))
gt[1] *= gt_scale_factor
gt[5] *= gt_scale_factor
ds_srs = geolib.get_ds_srs(ds)
if ticks:
scale_ticks(ax, ds)
else:
pltlib.hide_ticks(ax)
xres = geolib.get_res(ds)[0]
else:
pltlib.hide_ticks(ax)
#This forces the black line outlining the image subplot to snap to the actual image dimensions
ax.set_adjustable('box-forced')
cbar = True
if cbar:
#Had to turn off the ax=ax for overlay to work
#cbar = fig.colorbar(imgplot, ax=ax, extend='both', shrink=0.5)
#Should set the format based on dtype of input data
#cbar_kwargs['format'] = '%i'
#cbar_kwargs['format'] = '%0.1f'
#cbar_kwargs['orientation'] = 'horizontal'
#cbar_kwargs['shrink'] = 0.8
cbar = pltlib.add_cbar(ax, imgplot, label=label, cbar_kwargs=cbar_kwargs)
#Plot contours every cint interval and update colorbar appropriately
if cint is not None:
if bma_c is not None:
bma_clim = malib.calcperc(bma_c)
#PIG bed ridge contours
#bma_clim = (-1300, -300)
#Jak front shear margin contours
#bma_clim = (2000, 4000)
cstart = int(np.floor(bma_clim[0] / cint)) * cint
cend = int(np.ceil(bma_clim[1] / cint)) * cint
else:
#cstart = int(np.floor(bma.min() / cint)) * cint
#cend = int(np.ceil(bma.max() / cint)) * cint
cstart = int(np.floor(clim[0] / cint)) * cint
cend = int(np.ceil(clim[1] / cint)) * cint
#Turn off dashed negative (beds are below sea level)
#matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
clvl = np.arange(cstart, cend+1, cint)
#contours = ax.contour(bma_c, colors='k', levels=clvl, alpha=0.5)
contours = ax.contour(bma_c, cmap='gray', linestyle='--', levels=clvl, alpha=1.0)
#Update the cbar with contour locations
cbar.add_lines(contours)
cbar.set_ticks(contours.levels)
#Plot shape overlay, moved code to pltlib
if shp is not None:
pltlib.shp_overlay(ax, ds, shp, gt=gt)
if scalebar:
scale_ticks(ax, ds)
pltlib.add_scalebar(ax, xres)
if not ticks:
pltlib.hide_ticks(ax)
#imgplot.set_cmap(cmap)
#imgplot.set_clim(clim)
global gbma
gbma = bma
global ggt
ggt = gt
#Clicking on a subplot will make it active for z-coordinate display
fig.canvas.mpl_connect('button_press_event', onclick)
fig.canvas.mpl_connect('axes_enter_event', enter_axis)
#Add support for interactive z-value display
ax.format_coord = format_coord
dem_hs = iolib.fn_getma_sub(dem_hs_fn)
else:
dem_hs = geolib.gdaldem_mem_ds(dem_ds, 'hillshade', returnma=True)
dt = timelib.fn_getdatetime(dem_fn)
if dt is not None:
title = dt.strftime('%Y-%m-%d')
t = ax.set_title(title, fontdict={'fontsize': 6})
t.set_position([0.5, 0.95])
hs_im = ax.imshow(dem_hs, vmin=hs_clim[0], vmax=hs_clim[1], cmap='gray')
dem_im = ax.imshow(dem,
vmin=dem_clim[0],
vmax=dem_clim[1],
cmap='cpt_rainbow',
alpha=0.5)
ax.set_facecolor('k')
pltlib.hide_ticks(ax)
for ax in grid[i + 1:]:
ax.axis('off')
#for i in range(nrows*ncols):
# ax = grid[i]
if add_cbar:
cbar_lbl = 'Elevation (m WGS84)'
cbar_kwargs = {'extend': 'both', 'alpha': 1.0}
cbar = grid.cbar_axes[0].colorbar(dem_im, **cbar_kwargs)
cbar.update_bruteforce(dem_im)
cbar.set_label_text(cbar_lbl)
#res = geolib.get_res(dem_ds)[0]
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 main():
parser = getparser()
#Create dictionary of arguments
args = vars(parser.parse_args())
#Want to enable -full when -of is specified, probably a fancy way to do this with argparse
if args['of']:
args['full'] = True
args['imshow_kwargs'] = pltlib.imshow_kwargs
#Need to implement better extent handling for link and overlay
#Can use warplib extent parsing
extent = 'first'
#extent = 'union'
#Should accept 'ts' or 'fn' or string here, default is 'ts'
#Can also accept list for subplots
title = args['title']
if args['link']:
fig = plt.figure(0)
n_ax = len(args['filelist'])
src_ds_list = [gdal.Open(fn) for fn in args['filelist']]
t_srs = geolib.get_ds_srs(src_ds_list[0])
res_stats = geolib.get_res_stats(src_ds_list, t_srs=t_srs)
#Use min res
res = res_stats[0]
extent = 'intersection'
extent = geolib.ds_geom_union_extent(src_ds_list, t_srs=t_srs)
#extent = geolib.ds_geom_intersection_extent(src_ds_list, t_srs=t_srs)
#print(res, extent)
for n, fn in enumerate(args['filelist']):
if not iolib.fn_check(fn):
print('Unable to open input file: %s' % fn)
continue
if title == 'ts':
ts = timelib.fn_getdatetime_list(fn)
if ts:
print("Timestamp list: ", ts)
if len(ts) == 1:
args['title'] = ts[0].date()
elif len(ts) > 1:
args['title'] = "%s to %s" % (ts[0].date(), ts[1].date())
else:
print("Unable to extract timestamp")
args['title'] = None
elif title == 'fn':
args['title'] = fn
#if title is not None:
# plt.title(title, fontdict={'fontsize':12})
#Note: this won't work if img1 has 1 band and img2 has 3 bands
#Hack for now
if not args['link']:
fig = plt.figure(n)
n_ax = 1
#fig.set_facecolor('black')
fig.set_facecolor('white')
fig.canvas.set_window_title(os.path.split(fn)[1])
#fig.suptitle(os.path.split(fn)[1], fontsize=10)
if args['overlay']:
#Should automatically search for shaded relief with same base fn
#bg_fn = os.path.splitext(fn)[0]+'_hs_az315.tif'
#Clip/warp background dataset to match overlay dataset
src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']],
extent=extent,
res='max')
#Want to load up the unique bg array for each input
args['bg'] = get_bma(bg_ds, 1, args['full'])
else:
src_ds = gdal.Open(fn, gdal.GA_ReadOnly)
if args['link']:
src_ds = warplib.memwarp_multi([
src_ds,
],
res=res,
extent=extent,
t_srs=t_srs)[0]
args['cbar_kwargs'] = pltlib.cbar_kwargs
if args['no_cbar']:
args['cbar_kwargs'] = None
nbands = src_ds.RasterCount
b = src_ds.GetRasterBand(1)
dt = gdal.GetDataTypeName(b.DataType)
#Eventually, check dt of each band
print("%s (%i bands)" % (fn, nbands))
#Singleband raster
if (nbands == 1):
if args['cmap'] is None:
#Special case to handle ASP float32 grayscale data
if '-L_sub' in fn or '-R_sub' in fn:
args['cmap'] = 'gray'
else:
if (dt == 'Float64') or (dt == 'Float32') or (dt
== 'Int32'):
args['cmap'] = 'cpt_rainbow'
#This is for WV images
elif (dt == 'UInt16'):
args['cmap'] = 'gray'
elif (dt == 'Byte'):
args['cmap'] = 'gray'
else:
args['cmap'] = 'cpt_rainbow'
"""
if 'count' in fn:
args['clim_perc'] = (0,100)
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'cpt_rainbow'
if 'mask' in fn:
args['clim'] = (0, 1)
#Could be (0, 255)
#args['clim_perc'] = (0,100)
#Want absolute clim of 0, then perc of 100
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'gray'
"""
bma = get_bma(src_ds, 1, args['full'])
if args['invert']:
bma *= -1
#Note n+1 here ensures we're assigning subplot correctly here (n is 0-relative, subplot is 1)
bma_fig(fig, bma, n_subplt=n_ax, subplt=n + 1, ds=src_ds, **args)
#3-band raster, likely disparity map
#This doesn't work when alpha band is present
elif (nbands == 3) and (dt == 'Byte'):
#For some reason, tifs are vertically flipped
if (os.path.splitext(fn)[1] == '.tif'):
args['imshow_kwargs']['origin'] = 'lower'
#Use gdal dataset here instead of imread(fn)?
imgplot = plt.imshow(plt.imread(fn), **args['imshow_kwargs'])
pltlib.hide_ticks(imgplot.axes)
#Handle the 3-band disparity map case here
#elif ((dt == 'Float32') or (dt == 'Int32')):
else:
if args['cmap'] is None:
args['cmap'] = 'cpt_rainbow'
bn = 1
while bn <= nbands:
bma = get_bma(src_ds, bn, args['full'])
bma_fig(fig,
bma,
n_subplt=nbands,
subplt=bn,
ds=src_ds,
**args)
bn += 1
#Want to be better about this else case - lazy for now
#else:
# bma = get_bma(src_ds, 1, args['full'])
# bma_fig(fig, bma, **args)
plt.tight_layout()
#Write out the file
#Note: make sure display is local for savefig
if args['of']:
outf = str(os.path.splitext(fn)[0]) + '_fig.' + args['of']
#outf = str(os.path.splitext(fn)[0])+'_'+str(os.path.splitext(args['overlay'])[0])+'_fig.'+args['of']
#Note: need to account for colorbar (12%) and title - some percentage of axes beyond bma dimensions
#Should specify minimum text size for output
max_size = np.array((10.0, 10.0))
max_dpi = 300.0
#If both outsize and dpi are specified, don't try to change, just make the figure
if (args['outsize'] is None) and (args['dpi'] is None):
args['dpi'] = 150.0
#Unspecified out figure size for a given dpi
if (args['outsize'] is None) and (args['dpi'] is not None):
args['outsize'] = np.array(bma.shape[::-1]) / args['dpi']
if np.any(np.array(args['outsize']) > max_size):
args['outsize'] = max_size
#Specified output figure size, no specified dpi
elif (args['outsize'] is not None) and (args['dpi'] is None):
args['dpi'] = np.min([
np.max(
np.array(bma.shape[::-1]) / np.array(args['outsize'])),
max_dpi
])
print()
print("Saving output figure:")
print("Filename: ", outf)
print("Size (in): ", args['outsize'])
print("DPI (px/in): ", args['dpi'])
print("Input dimensions (px): ", bma.shape[::-1])
print("Output dimensions (px): ",
tuple(np.array(args['outsize']) * args['dpi']))
print()
fig.set_size_inches(args['outsize'])
#fig.set_size_inches(54.427, 71.87)
#fig.set_size_inches(40, 87)
fig.savefig(outf,
dpi=args['dpi'],
bbox_inches='tight',
pad_inches=0,
facecolor=fig.get_facecolor(),
edgecolor='none')
#fig.savefig(outf, dpi=args['dpi'], facecolor=fig.get_facecolor(), edgecolor='none')
#Show the plot - want to show all at once
if not args['of']:
plt.show()
def bma_fig(fig,
bma,
cmap='cpt_rainbow',
clim=None,
clim_perc=(2, 98),
bg=None,
bg_perc=(2, 98),
n_subplt=1,
subplt=1,
label=None,
title=None,
contour_int=None,
contour_fn=None,
alpha=0.5,
ticks=False,
scalebar=None,
ds=None,
shp=None,
imshow_kwargs={'interpolation': 'nearest'},
cbar_kwargs={'orientation': 'vertical'},
**kwargs):
#We don't use the kwargs, just there to save parsing in main
if clim is None:
clim = pltlib.get_clim(bma, clim_perc=clim_perc)
print("Colorbar limits: %0.3f %0.3f" % (clim[0], clim[1]))
#Link all subplots for zoom/pan
sharex = sharey = None
if len(fig.get_axes()) > 0:
sharex = sharey = fig.get_axes()[0]
#Hack to catch situations with only 1 subplot, but a subplot number > 1
if n_subplt == 1:
subplt = 1
#One row, multiple columns
ax = fig.add_subplot(1, n_subplt, subplt, sharex=sharex, sharey=sharey)
#This occupies the full figure
#ax = fig.add_axes([0., 0., 1., 1., ])
#ax.patch.set_facecolor('black')
ax.patch.set_facecolor('white')
#Set appropriate nodata value color
cmap_name = cmap
cmap = pltlib.cmap_setndv(cmap_name)
#ax.set_title("Band %i" % subplt, fontsize=10)
if title is not None:
ax.set_title(title)
#If a background image is provided, plot it first
if bg is not None:
#Note, alpha=1 is opaque, 0 completely transparent
#alpha = 0.6
bg_perc = (4, 96)
bg_alpha = 1.0
#bg_clim = malib.calcperc(bg, bg_perc)
bg_clim = (1, 255)
bg_cmap_name = 'gray'
bg_cmap = pltlib.cmap_setndv(bg_cmap_name, cmap_name)
#bg_cmap = plt.get_cmap(bg_cmap_name)
#if 'inferno' in cmap_name:
# bg_cmap.set_bad('0.5', alpha=1)
#else:
# bg_cmap.set_bad('k', alpha=1)
#Set the overlay bad values to completely transparent, otherwise darkens the bg
cmap.set_bad(alpha=0)
bgplot = ax.imshow(bg, cmap=bg_cmap, clim=bg_clim, alpha=bg_alpha)
imgplot = ax.imshow(bma,
alpha=alpha,
cmap=cmap,
clim=clim,
**imshow_kwargs)
else:
imgplot = ax.imshow(bma, cmap=cmap, clim=clim, **imshow_kwargs)
gt = None
if ds is not None:
gt = np.array(ds.GetGeoTransform())
gt_scale_factor = min(
np.array([ds.RasterYSize, ds.RasterXSize]) /
np.array(bma.shape, dtype=float))
gt[1] *= gt_scale_factor
gt[5] *= gt_scale_factor
ds_srs = geolib.get_ds_srs(ds)
if ticks:
scale_ticks(ax, ds)
else:
pltlib.hide_ticks(ax)
xres = geolib.get_res(ds)[0]
else:
pltlib.hide_ticks(ax)
#This forces the black line outlining the image subplot to snap to the actual image dimensions
#depreciated in 2.2
#ax.set_adjustable('box-forced')
if cbar_kwargs:
#Should set the format based on dtype of input data
#cbar_kwargs['format'] = '%i'
#cbar_kwargs['format'] = '%0.1f'
#cbar_kwargs['orientation'] = 'horizontal'
#Determine whether we need to add extend triangles to colorbar
cbar_kwargs['extend'] = pltlib.get_cbar_extend(bma, clim)
#Add the colorbar to the axes
cbar = pltlib.add_cbar(ax,
imgplot,
label=label,
cbar_kwargs=cbar_kwargs)
#Plot contours every contour_int interval and update colorbar appropriately
if contour_int is not None:
if contour_fn is not None:
contour_bma = iolib.fn_getma(contour_fn)
contour_bma_clim = malib.calcperc(contour_bma)
else:
contour_bma = bma
contour_bma_clim = clim
#PIG bed ridge contours
#bma_clim = (-1300, -300)
#Jak front shear margin contours
#bma_clim = (2000, 4000)
contour_bma_clim = (100, 250)
cstart = int(np.floor(contour_bma_clim[0] / contour_int)) * contour_int
cend = int(np.ceil(contour_bma_clim[1] / contour_int)) * contour_int
#Turn off dashed negative (beds are below sea level)
#matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
clvl = np.arange(cstart, cend + 1, contour_int)
contour_prop = {
'levels': clvl,
'linestyle': '-',
'linewidths': 0.5,
'alpha': 1.0
}
#contours = ax.contour(contour_bma, colors='k', **contour_prop)
#contour_cmap = 'gray'
contour_cmap = 'gray_r'
#This prevents white contours
contour_cmap_clim = (0, contour_bma_clim[-1])
contours = ax.contour(contour_bma, cmap=contour_cmap, vmin=contour_cmap_clim[0], \
vmax=contour_cmap_clim[-1], **contour_prop)
#Add labels
ax.clabel(contours,
inline=True,
inline_spacing=0,
fontsize=4,
fmt='%i')
#Update the cbar with contour locations
#cbar.add_lines(contours)
#cbar.set_ticks(contours.levels)
#Plot shape overlay, moved code to pltlib
if shp is not None:
pltlib.shp_overlay(ax, ds, shp, gt=gt, color='k')
if scalebar:
scale_ticks(ax, ds)
sb_loc = pltlib.best_scalebar_location(bma)
#Force scalebar position
#sb_loc = 'lower right'
pltlib.add_scalebar(ax, xres, location=sb_loc)
if not ticks:
pltlib.hide_ticks(ax)
#Set up interactive display
global gbma
gbma = bma
global ggt
ggt = gt
#Clicking on a subplot will make it active for z-coordinate display
fig.canvas.mpl_connect('button_press_event', onclick)
fig.canvas.mpl_connect('axes_enter_event', enter_axis)
#Add support for interactive z-value display
ax.format_coord = format_coord
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 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)
def map_plot(gf, z_bin_edges, outdir, hs=True):
#print("Generating map plot")
f, axa = plt.subplots(1, 3, figsize=(10, 7.5))
#f.suptitle(gf.feat_fn)
alpha = 1.0
if hs:
#z1_hs = geolib.gdaldem_wrapper(gf.out_z1_fn, product='hs', returnma=True, verbose=False)
#z2_hs = geolib.gdaldem_wrapper(gf.out_z2_fn, product='hs', returnma=True, verbose=False)
z1_hs = gf.z1_hs
z2_hs = gf.z2_hs
hs_clim = malib.calcperc(z2_hs, (2, 98))
z1_hs_im = axa[0].imshow(z1_hs,
cmap='gray',
vmin=hs_clim[0],
vmax=hs_clim[1])
z2_hs_im = axa[1].imshow(z2_hs,
cmap='gray',
vmin=hs_clim[0],
vmax=hs_clim[1])
alpha = 0.5
z1_im = axa[0].imshow(gf.z1,
cmap='cpt_rainbow',
vmin=z_bin_edges[0],
vmax=z_bin_edges[-1],
alpha=alpha)
z2_im = axa[1].imshow(gf.z2,
cmap='cpt_rainbow',
vmin=z_bin_edges[0],
vmax=z_bin_edges[-1],
alpha=alpha)
axa[0].contour(gf.z1, [
gf.z1_ela,
],
linewidths=0.5,
linestyles=':',
colors='w')
axa[1].contour(gf.z2, [
gf.z2_ela,
],
linewidths=0.5,
linestyles=':',
colors='w')
#t1_title = int(np.round(gf.t1))
#t2_title = int(np.round(gf.t2))
t1_title = '%0.2f' % gf.t1
t2_title = '%0.2f' % gf.t2
#t1_title = gf.t1.strftime('%Y-%m-%d')
#t2_title = gf.t2.strftime('%Y-%m-%d')
axa[0].set_title(t1_title)
axa[1].set_title(t2_title)
axa[2].set_title('%s to %s (%0.2f yr)' % (t1_title, t2_title, gf.dt))
#dz_clim = (-10, 10)
dz_clim = (-2.0, 2.0)
dz_im = axa[2].imshow(gf.dhdt,
cmap='RdBu',
vmin=dz_clim[0],
vmax=dz_clim[1])
for ax in axa:
pltlib.hide_ticks(ax)
ax.set_facecolor('k')
sb_loc = pltlib.best_scalebar_location(gf.z1)
pltlib.add_scalebar(axa[0], gf.res[0], location=sb_loc)
pltlib.add_cbar(axa[0], z1_im, label='Elevation (m WGS84)')
pltlib.add_cbar(axa[1], z2_im, label='Elevation (m WGS84)')
pltlib.add_cbar(axa[2], dz_im, label='dh/dt (m/yr)')
plt.tight_layout()
#Make room for suptitle
#plt.subplots_adjust(top=0.90)
#print("Saving map plot")
fig_fn = os.path.join(outdir, gf.feat_fn + '_mb_map.png')
plt.savefig(fig_fn, bbox_inches='tight', dpi=300)
plt.close(f)
def main():
if len(sys.argv) != 2:
sys.exit("Usage: %s stack.npz" % os.path.basename(sys.argv[0]))
stack_fn = sys.argv[1]
print "Loading stack"
s = malib.DEMStack(stack_fn=stack_fn, stats=True, trend=True, save=False)
global d
d = s.date_list_o
d_ptp = d[-1] - d[0]
d_pad = 0.03*d_ptp
global min_dt
min_dt = d[0]-d_pad
global max_dt
max_dt = d[-1]+d_pad
#Use these to set bounds to hardcode min/max of all stacks
#import pytz
#min_dt = datetime(1999,1,1)
#min_dt = datetime(2007,1,1, tzinfo=pytz.utc)
#max_dt = datetime(2015,12,31, tzinfo=pytz.utc)
global source
source = np.ma.array(s.source)
global source_dict
source_dict = get_source_dict()
global error
error = s.error
global gt
gt = s.gt
global m
m = s.ma_stack
val = s.stack_mean
count = s.stack_count
std = s.stack_std
trend = s.stack_trend
detrended_std = s.stack_detrended_std
stack_type = 'dem'
global filter_outliers
filter_outliers = False
global pad
global geoid_offset
global plot_trend
global plot_resid
global errorbars
if 'TSX' in source or 'ALOS' in source or 'RS1' in source or 'RS2' in source:
stack_type = 'velocity'
if 'zs' in stack_fn:
stack_type = 'racmo'
if 'meltrate' in stack_fn:
stack_type = 'meltrate'
if stack_type == 'velocity':
#pad = 3
#Use this for Jak stack with RADARSAT data
pad = 0
ylabel = 'Velocity (m/yr)'
ylabel_rel = 'Relative Velocity (m/yr)'
ylabel_resid = 'Detrended Velocity (m/yr)'
plot4_label = 'Detrended std (m/yr)'
hs = None
alpha = 1.0
geoid_offset = False
plot_trend = False
plot_resid = False
errorbars = False
if 'RS' in source:
filter_outliers = True
elif stack_type == 'racmo':
pad = 0
ylabel = 'RACMOFDM zs (m)'
ylabel_rel = 'Relative RACMOFDM zs (m)'
ylabel_resid = 'Detrended RACMOFDM zs (m)'
plot4_label = 'Detrended std (m)'
hs = None
alpha = 1.0
geoid_offset = False
plot_trend = True
plot_resid = True
errorbars = False
elif stack_type == 'meltrate':
pad = 3
ylabel = 'Melt Rate (m/yr)'
ylabel_rel = 'Relative Melt Rate (m/yr)'
ylabel_resid = 'Detrended Melt Rate (m/yr)'
plot4_label = 'Detrended std (m/yr)'
hs = None
alpha = 1.0
geoid_offset = False
plot_trend = True
plot_resid = False
errorbars = False
else:
#pad = 5
#pad = 1
pad = 3
ylabel = 'Elevation (m EGM2008)'
ylabel_rel = 'Relative Elevation (m)'
ylabel_resid = 'Detrended Elevation (m)'
#plot4_label = 'Detrended std (m)'
plot4_label = 'Elevation std (m)'
s.mean_hillshade()
hs = s.stack_mean_hs
hs_clim = malib.calcperc(hs, (2,98))
alpha = 0.6
geoid_offset = False
plot_trend = True
plot_resid = True
errorbars = True
#Set color cycle
reset_colors()
global ms
ms = 5
#fig = plt.figure(0, figsize=(14,12), facecolor='white')
fig = plt.figure(0, figsize=(14,12))
#These record all points plotted on the context plots
global ax_pt_list
ax_pt_list = [[], [], [], []]
interp = 'none'
#interp = 'bicubic'
#Overlay on mean_hs
#Add colorbars
imshow_kwargs = {'interpolation':interp}
val_clim = malib.calcperc(val, (2,98))
ax0 = fig.add_subplot(221)
if hs is not None:
ax0.imshow(hs, cmap='gray', clim=hs_clim, **imshow_kwargs)
im0 = ax0.imshow(val, cmap=cpt_rainbow, clim=val_clim, alpha=alpha, **imshow_kwargs)
#This was used for Stanton et al figure
#val_clim = (0, 50)
#im0 = ax0.imshow(val, cmap=cmaps.inferno, clim=val_clim, alpha=alpha, **imshow_kwargs)
ax0.set_adjustable('box-forced')
pltlib.hide_ticks(ax0)
pltlib.add_cbar(ax0, im0, ylabel)
count_clim = malib.calcperc(count, (2,98))
#count_clim = malib.calcperc(count, (4,100))
ax1 = fig.add_subplot(222, sharex=ax0, sharey=ax0)
if hs is not None:
ax1.imshow(hs, cmap='gray', clim=hs_clim, **imshow_kwargs)
im1 = ax1.imshow(count, cmap=cmaps.inferno, clim=count_clim, alpha=alpha, **imshow_kwargs)
ax1.set_adjustable('box-forced')
pltlib.hide_ticks(ax1)
pltlib.add_cbar(ax1, im1, 'Count')
#clim=(-20, 20)
#trend_clim = malib.calcperc(trend, (1,99))
#trend_clim = malib.calcperc(trend, (2,98))
trend_clim = malib.calcperc(trend, (4,96))
#trend_clim = malib.calcperc(trend, (10,90))
max_abs_clim = max(np.abs(trend_clim))
trend_clim = (-max_abs_clim, max_abs_clim)
ax2 = fig.add_subplot(223, sharex=ax0, sharey=ax0)
#ax0.set_title("Trend")
if hs is not None:
ax2.imshow(hs, cmap='gray', clim=hs_clim, **imshow_kwargs)
im2 = ax2.imshow(trend, cmap='RdBu', clim=trend_clim, alpha=alpha, **imshow_kwargs)
ax2.set_adjustable('box-forced')
pltlib.hide_ticks(ax2)
pltlib.add_cbar(ax2, im2, 'Linear Trend (m/yr)')
dstd_clim = (0, malib.calcperc(std, (0,95))[1])
#dstd_clim = (0, malib.calcperc(detrended_std, (0,98))[1])
ax3 = fig.add_subplot(224, sharex=ax0, sharey=ax0)
if hs is not None:
ax3.imshow(hs, cmap='gray', clim=hs_clim, **imshow_kwargs)
im3 = ax3.imshow(detrended_std, cmap=cpt_rainbow, clim=dstd_clim, alpha=alpha, **imshow_kwargs)
#im3 = ax3.imshow(std, cmap=cpt_rainbow, clim=dstd_clim, alpha=alpha, **imshow_kwargs)
ax3.set_adjustable('box-forced')
pltlib.hide_ticks(ax3)
#pltlib.add_cbar(ax3, im3, 'Detrended Std (m)')
pltlib.add_cbar(ax3, im3, plot4_label)
global ax_list
ax_list = [ax0, ax1, ax2, ax3]
plt.autoscale(tight=True)
plt.tight_layout()
cid = fig.canvas.mpl_connect('button_press_event', onclick)
fig1 = plt.figure(1)
global ax_rel
ax_rel = fig1.add_subplot(111)
fmt_ax(ax_rel, ylabel=ylabel_rel, legend_source=source)
fig2 = plt.figure(2)
global ax_abs
ax_abs = fig2.add_subplot(111)
fmt_ax(ax_abs, ylabel=ylabel, legend_source=source)
fig3 = plt.figure(3)
global ax_resid
ax_resid = fig3.add_subplot(111)
fmt_ax(ax_resid, ylabel=ylabel_resid, legend_source=source)
plt.axhline(0, color='k', linestyle='-', linewidth=0.6)
"""
#print "Saving figure"
#fig_fn = os.path.splitext(s.stack_fn)[0] + '_context_maps.pdf'
fig_fn = os.path.splitext(s.stack_fn)[0] + '_context_maps.png'
plt.figure(0)
plt.tight_layout()
plt.savefig(fig_fn, dpi=300)
fig_fn = os.path.splitext(s.stack_fn)[0] + '.png'
plt.figure(2)
#plt.ylim(70, 350)
plt.tight_layout()
plt.savefig(fig_fn, dpi=300)
"""
plt.show()
def make_map(mb_dissolve_df=None, glac_df_mb=None, region_df=None, col='mb_mwea', border_df=None, \
basin_df=None, crs=crs, extent=None, hs=None, hs_extent=None, clim=None):
fig, ax = plt.subplots(figsize=(10, 8))
ax.set_aspect('equal')
cmap = 'RdBu'
label = 'Mass Balance (m we/yr)'
if 't1' in col:
cmap = 'inferno'
label = 'Source Date (year)'
if clim is None:
clim = (glac_df_mb[col].min(), glac_df_mb[col].max())
#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': '0.95',
'edgecolor': 'k',
'linewidth': 0.7
}
border_df.plot(ax=ax, **border_style)
if region_df is not None:
#This is original region_col
#region_style = {'column':col_name, 'cmap':'cpt_rainbow', 'edgecolor':'k', 'linewidth':0.5, 'alpha':0.2}
#region_style = {'column':'Name', 'cmap':'gray', 'edgecolor':'k', 'linewidth':0.5, 'alpha':0.3}
#region_style = {'cmap':'cpt_rainbow', 'edgecolor':'k', 'linewidth':0.5, 'alpha':0.05}
region_style = {
'facecolor': 'none',
'edgecolor': 'k',
'linewidth': 0.3,
'alpha': 0.4
}
region_df.plot(ax=ax, **region_style)
if basin_df is not None:
basin_style = {
'facecolor': 'none',
'edgecolor': 'k',
'linewidth': 0.3,
'alpha': 0.4
}
basin_df.plot(ax=ax, **basin_style)
#https://stackoverflow.com/questions/36008648/colorbar-on-geopandas
# fake up the array of the scalar mappable. 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:
#Plot single values for region or basin
#This was HMA
#scaling_f = 0.2
#CONUS
scaling_f = 3
x = mb_dissolve_df['centroid_x']
y = mb_dissolve_df['centroid_y']
#s = scaling_f*mb_dissolve_df[('area_m2_sum')]/1E6
#s = scaling_f*mb_dissolve_df[('Area_sum')]
s = scaling_f * mb_dissolve_df[('Area_all', '')]
#c = mb_dissolve_df[('mb_mwea_mean')]
c = mb_dissolve_df['mb_mwea', 'mean']
sc_style = {'cmap': cmap, 'edgecolor': 'k', 'linewidth': 0.5}
sc = ax.scatter(x, y, s, c, vmin=clim[0], vmax=clim[1], **sc_style)
#Add labels
for k, v in mb_dissolve_df.iterrows():
#lbl = '%0.2f +/- %0.2f' % (v[('mb_mwea_mean')], v[('mb_mwea_sigma_mean')])
lbl = '%0.2f +/- %0.2f' % (v[col, 'mean'], v[col + '_sigma',
'mean'])
ax.annotate(lbl,
xy=(v['centroid_x'], v['centroid_y']),
xytext=(1, 0),
textcoords='offset points',
family='sans-serif',
fontsize=8,
color='k')
if glac_df_mb is not None:
print("Plotting glacier polygons")
glac_style = {'edgecolor': 'k', 'linewidth': 0.5}
glac_ax = glac_df_mb.plot(ax=ax,
column=col,
cmap=cmap,
vmin=clim[0],
vmax=clim[1],
**glac_style)
#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)
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
