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
#t2_title = int(t2)
t1_title = z1_date.strftime('%Y-%m-%d')
t2_title = z2_date.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, dt))
#dz_clim = (-10, 10)
dz_clim = (-2.0, 2.0)
dz_im = axa[2].imshow(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(z1)
pltlib.add_scalebar(axa[0], ds_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, feat_fn + '_mb_map.png')
plt.savefig(fig_fn, dpi=300)
print("Generating aed plot")
f, axa = plt.subplots(1, 2, figsize=(6, 6))
f.suptitle(feat_fn)
axa[0].plot(z1_bin_areas, z_bin_centers, label='%0.2f' % t1)
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)