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