def _distribute_thickness_per_interp(glacier_mask,
topo,
cls,
fls,
grid,
smooth=True,
add_slope=True):
"""Where the job is actually done."""
# Thick to interpolate
dx = grid.dx
thick = np.where(glacier_mask, np.NaN, 0)
# Along the lines
vs = []
for cl, fl in zip(cls, fls):
# TODO: here one should see if parabola is always the best choice
vs.extend(cl['volume'])
x, y = utils.tuple2int(fl.line.xy)
thick[y, x] = cl['thick']
vol = np.sum(vs)
# Interpolate
xx, yy = grid.ij_coordinates
pnan = np.nonzero(~np.isfinite(thick))
pok = np.nonzero(np.isfinite(thick))
points = np.array((np.ravel(yy[pok]), np.ravel(xx[pok]))).T
inter = np.array((np.ravel(yy[pnan]), np.ravel(xx[pnan]))).T
thick[pnan] = griddata(points, np.ravel(thick[pok]), inter, method='cubic')
# Smooth
if smooth:
gsize = np.rint(cfg.PARAMS['smooth_window'] / dx)
thick = gaussian_blur(thick, np.int(gsize))
thick = np.where(glacier_mask, thick, 0.)
# Slope
slope = 1.
if add_slope:
sy, sx = np.gradient(topo, dx, dx)
slope = np.arctan(np.sqrt(sy**2 + sx**2))
slope = np.clip(slope, np.deg2rad(6.), np.pi / 2.)
slope = 1 / slope**(cfg.N / (cfg.N + 2))
# Conserve volume
tmp_vol = np.nansum(thick * slope * dx**2)
final_t = thick * slope * vol / tmp_vol
# Add to grids
final_t = np.where(np.isfinite(final_t), final_t, 0.)
assert np.allclose(np.sum(final_t * dx**2), vol)
return final_t
def _distribute_thickness_per_interp(glacier_mask, topo, cls, fls, grid,
smooth=True, add_slope=True):
"""Where the job is actually done."""
# Thick to interpolate
dx = grid.dx
thick = np.where(glacier_mask, np.NaN, 0)
# Along the lines
vs = []
for cl, fl in zip(cls, fls):
# TODO: here one should see if parabola is always the best choice
vs.extend(cl['volume'])
x, y = utils.tuple2int(fl.line.xy)
thick[y, x] = cl['thick']
vol = np.sum(vs)
# Interpolate
xx, yy = grid.ij_coordinates
pnan = np.nonzero(~ np.isfinite(thick))
pok = np.nonzero(np.isfinite(thick))
points = np.array((np.ravel(yy[pok]), np.ravel(xx[pok]))).T
inter = np.array((np.ravel(yy[pnan]), np.ravel(xx[pnan]))).T
thick[pnan] = griddata(points, np.ravel(thick[pok]), inter, method='cubic')
# Smooth
if smooth:
gsize = np.rint(cfg.PARAMS['smooth_window'] / dx)
thick = gaussian_blur(thick, np.int(gsize))
thick = np.where(glacier_mask, thick, 0.)
# Slope
slope = 1.
if add_slope:
sy, sx = np.gradient(topo, dx, dx)
slope = np.arctan(np.sqrt(sy**2 + sx**2))
slope = np.clip(slope, np.deg2rad(6.), np.pi/2.)
slope = 1 / slope**(cfg.N / (cfg.N+2))
# Conserve volume
tmp_vol = np.nansum(thick * slope * dx**2)
final_t = thick * slope * vol / tmp_vol
# Add to grids
final_t = np.where(np.isfinite(final_t), final_t, 0.)
assert np.allclose(np.sum(final_t * dx**2), vol)
return final_t
def _distribute_thickness_per_altitude(glacier_mask, topo, cls, fls, grid,
add_slope=True,
smooth=True):
"""Where the job is actually done."""
# Along the lines
dx = grid.dx
hs, ts, vs, xs, ys = [], [], [], [] ,[]
for cl, fl in zip(cls, fls):
# TODO: here one should see if parabola is always the best choice
hs = np.append(hs, cl['hgt'])
ts = np.append(ts, cl['thick'])
vs = np.append(vs, cl['volume'])
x, y = fl.line.xy
xs = np.append(xs, x)
ys = np.append(ys, y)
vol = np.sum(vs)
# very inefficient inverse distance stuff
to_compute = np.nonzero(glacier_mask)
thick = topo * np.NaN
for (y, x) in np.asarray(to_compute).T:
assert glacier_mask[y, x] == 1
phgt = topo[y, x]
# take the ones in a 100m range
starth = 100.
while True:
starth += 10
pok = np.nonzero(np.abs(phgt - hs) <= starth)[0]
if len(pok) != 0:
break
dis_w = 1 / np.sqrt((xs[pok]-x)**2 + (ys[pok]-y)**2)
thick[y, x] = np.average(ts[pok], weights=dis_w)
# Smooth
if smooth:
thick = np.where(np.isfinite(thick), thick, 0.)
gsize = np.rint(cfg.PARAMS['smooth_window'] / dx)
thick = gaussian_blur(thick, np.int(gsize))
thick = np.where(glacier_mask, thick, 0.)
# Distance
dis = distance_transform_edt(glacier_mask)
dis = np.where(glacier_mask, dis, np.NaN)**0.5
# Slope
slope = 1.
if add_slope:
sy, sx = np.gradient(topo, dx, dx)
slope = np.arctan(np.sqrt(sy**2 + sx**2))
slope = np.clip(slope, np.deg2rad(6.), np.pi/2.)
slope = 1 / slope**(cfg.N / (cfg.N+2))
# Conserve volume
tmp_vol = np.nansum(thick * dis * slope * dx**2)
final_t = thick * dis * slope * vol / tmp_vol
# Done
final_t = np.where(np.isfinite(final_t), final_t, 0.)
assert np.allclose(np.sum(final_t * dx**2), vol)
return final_t
def glacier_masks_itmix(gdir):
"""Converts the glacier vector geometries to grids.
Uses where possible the ITMIX DEM
Parameters
----------
gdir : oggm.GlacierDirectory
"""
# open srtm tif-file:
dem_ds = gdal.Open(gdir.get_filepath('dem'))
dem = dem_ds.ReadAsArray().astype(float)
# Correct the DEM (ASTER...)
# Currently we just do a linear interp -- ASTER is totally shit anyway
min_z = -999.
if np.min(dem) <= min_z:
xx, yy = gdir.grid.ij_coordinates
pnan = np.nonzero(dem <= min_z)
pok = np.nonzero(dem > min_z)
if len(pok[0]) > 0:
points = np.array((np.ravel(yy[pok]), np.ravel(xx[pok]))).T
inter = np.array((np.ravel(yy[pnan]), np.ravel(xx[pnan]))).T
dem[pnan] = griddata(points, np.ravel(dem[pok]), inter)
msg = gdir.rgi_id + ': DEM needed interpolation'
msg += '({:.1f}% missing).'.format(
len(pnan[0]) / len(dem.flatten()) * 100)
log.warning(msg)
else:
dem = dem * np.NaN
# Replace DEM values with ITMIX ones where possible
# Open DEM
dem_f = None
n_g = gdir.name.split(':')[-1]
searchf = os.path.join(DATA_DIR, 'itmix', 'glaciers_sorted', '*')
searchf = os.path.join(searchf, '02_surface_' + n_g + '_*.asc')
for dem_f in glob.glob(searchf):
pass
if dem_f is None:
# try synth
n_g = gdir.rgi_id
searchf = os.path.join(DATA_DIR, 'itmix', 'glaciers_synth', '*')
searchf = os.path.join(searchf, '02_surface_' + n_g + '*.asc')
for dem_f in glob.glob(searchf):
pass
if dem_f is not None:
log.info('%s: ITMIX DEM file: %s', gdir.rgi_id, dem_f)
it_dem_ds = EsriITMIX(dem_f)
it_dem = it_dem_ds.get_vardata()
it_dem = np.where(it_dem < -999., np.NaN, it_dem)
# for some glaciers, trick
if n_g in ['Academy', 'Devon']:
it_dem = np.where(it_dem <= 0, np.NaN, it_dem)
it_dem = np.where(np.isfinite(it_dem), it_dem, np.nanmin(it_dem))
if n_g in ['Brewster', 'Austfonna']:
it_dem = np.where(it_dem <= 0, np.NaN, it_dem)
# Transform to local grid
it_dem = gdir.grid.map_gridded_data(it_dem,
it_dem_ds.grid,
interp='linear')
# And update values where possible
if n_g in ['Synthetic2', 'Synthetic1']:
dem = np.where(~it_dem.mask, it_dem, np.nanmin(it_dem))
else:
dem = np.where(~it_dem.mask, it_dem, dem)
else:
if 'Devon' in n_g:
raise RuntimeError('Should have found DEM for Devon')
# Disallow negative
dem = dem.clip(0)
# Grid
nx = dem_ds.RasterXSize
ny = dem_ds.RasterYSize
assert nx == gdir.grid.nx
assert ny == gdir.grid.ny
# Proj
geot = dem_ds.GetGeoTransform()
x0 = geot[0] # UL corner
y0 = geot[3] # UL corner
dx = geot[1]
dy = geot[5] # Negative
assert dx == -dy
assert dx == gdir.grid.dx
assert y0 == gdir.grid.corner_grid.y0
assert x0 == gdir.grid.corner_grid.x0
dem_ds = None # to be sure...
# Smooth SRTM?
if cfg.PARAMS['smooth_window'] > 0.:
gsize = np.rint(cfg.PARAMS['smooth_window'] / dx)
smoothed_dem = gaussian_blur(dem, np.int(gsize))
else:
smoothed_dem = dem.copy()
# Make entity masks
log.debug('%s: glacier mask, divide %d', gdir.rgi_id, 0)
_mask_per_divide(gdir, 0, dem, smoothed_dem)
# Glacier divides
nd = gdir.n_divides
if nd == 1:
# Optim: just make links
linkname = gdir.get_filepath('gridded_data', div_id=1)
sourcename = gdir.get_filepath('gridded_data')
# overwrite as default
if os.path.exists(linkname):
os.remove(linkname)
# TODO: temporary suboptimal solution
try:
# we are on UNIX
os.link(sourcename, linkname)
except AttributeError:
# we are on windows
copyfile(sourcename, linkname)
linkname = gdir.get_filepath('geometries', div_id=1)
sourcename = gdir.get_filepath('geometries')
# overwrite as default
if os.path.exists(linkname):
os.remove(linkname)
# TODO: temporary suboptimal solution
try:
# we are on UNIX
os.link(sourcename, linkname)
except AttributeError:
# we are on windows
copyfile(sourcename, linkname)
else:
# Loop over divides
for i in gdir.divide_ids:
log.debug('%s: glacier mask, divide %d', gdir.rgi_id, i)
_mask_per_divide(gdir, i, dem, smoothed_dem)
def glacier_masks_itmix(gdir):
"""Converts the glacier vector geometries to grids.
Uses where possible the ITMIX DEM
Parameters
----------
gdir : oggm.GlacierDirectory
"""
# open srtm tif-file:
dem_ds = gdal.Open(gdir.get_filepath('dem'))
dem = dem_ds.ReadAsArray().astype(float)
# Correct the DEM (ASTER...)
# Currently we just do a linear interp -- ASTER is totally shit anyway
min_z = -999.
if np.min(dem) <= min_z:
xx, yy = gdir.grid.ij_coordinates
pnan = np.nonzero(dem <= min_z)
pok = np.nonzero(dem > min_z)
if len(pok[0]) > 0:
points = np.array((np.ravel(yy[pok]), np.ravel(xx[pok]))).T
inter = np.array((np.ravel(yy[pnan]), np.ravel(xx[pnan]))).T
dem[pnan] = griddata(points, np.ravel(dem[pok]), inter)
msg = gdir.rgi_id + ': DEM needed interpolation'
msg += '({:.1f}% missing).'.format(len(pnan[0])/len(dem.flatten())*100)
log.warning(msg)
else:
dem = dem*np.NaN
# Replace DEM values with ITMIX ones where possible
# Open DEM
dem_f = None
n_g = gdir.name.split(':')[-1]
searchf = os.path.join(DATA_DIR, 'itmix', 'glaciers_sorted', '*')
searchf = os.path.join(searchf, '02_surface_' + n_g + '_*.asc')
for dem_f in glob.glob(searchf):
pass
if dem_f is None:
# try synth
n_g = gdir.rgi_id
searchf = os.path.join(DATA_DIR, 'itmix', 'glaciers_synth', '*')
searchf = os.path.join(searchf, '02_surface_' + n_g + '*.asc')
for dem_f in glob.glob(searchf):
pass
if dem_f is not None:
log.info('%s: ITMIX DEM file: %s', gdir.rgi_id, dem_f)
it_dem_ds = EsriITMIX(dem_f)
it_dem = it_dem_ds.get_vardata()
it_dem = np.where(it_dem < -999., np.NaN, it_dem)
# for some glaciers, trick
if n_g in ['Academy', 'Devon']:
it_dem = np.where(it_dem <= 0, np.NaN, it_dem)
it_dem = np.where(np.isfinite(it_dem), it_dem, np.nanmin(it_dem))
if n_g in ['Brewster', 'Austfonna']:
it_dem = np.where(it_dem <= 0, np.NaN, it_dem)
# Transform to local grid
it_dem = gdir.grid.map_gridded_data(it_dem, it_dem_ds.grid,
interp='linear')
# And update values where possible
if n_g in ['Synthetic2', 'Synthetic1']:
dem = np.where(~ it_dem.mask, it_dem, np.nanmin(it_dem))
else:
dem = np.where(~ it_dem.mask, it_dem, dem)
else:
if 'Devon' in n_g:
raise RuntimeError('Should have found DEM for Devon')
# Disallow negative
dem = dem.clip(0)
# Grid
nx = dem_ds.RasterXSize
ny = dem_ds.RasterYSize
assert nx == gdir.grid.nx
assert ny == gdir.grid.ny
# Proj
geot = dem_ds.GetGeoTransform()
x0 = geot[0] # UL corner
y0 = geot[3] # UL corner
dx = geot[1]
dy = geot[5] # Negative
assert dx == -dy
assert dx == gdir.grid.dx
assert y0 == gdir.grid.corner_grid.y0
assert x0 == gdir.grid.corner_grid.x0
dem_ds = None # to be sure...
# Smooth SRTM?
if cfg.PARAMS['smooth_window'] > 0.:
gsize = np.rint(cfg.PARAMS['smooth_window'] / dx)
smoothed_dem = gaussian_blur(dem, np.int(gsize))
else:
smoothed_dem = dem.copy()
# Make entity masks
log.debug('%s: glacier mask, divide %d', gdir.rgi_id, 0)
_mask_per_divide(gdir, 0, dem, smoothed_dem)
# Glacier divides
nd = gdir.n_divides
if nd == 1:
# Optim: just make links
linkname = gdir.get_filepath('gridded_data', div_id=1)
sourcename = gdir.get_filepath('gridded_data')
# overwrite as default
if os.path.exists(linkname):
os.remove(linkname)
# TODO: temporary suboptimal solution
try:
# we are on UNIX
os.link(sourcename, linkname)
except AttributeError:
# we are on windows
copyfile(sourcename, linkname)
linkname = gdir.get_filepath('geometries', div_id=1)
sourcename = gdir.get_filepath('geometries')
# overwrite as default
if os.path.exists(linkname):
os.remove(linkname)
# TODO: temporary suboptimal solution
try:
# we are on UNIX
os.link(sourcename, linkname)
except AttributeError:
# we are on windows
copyfile(sourcename, linkname)
else:
# Loop over divides
for i in gdir.divide_ids:
log.debug('%s: glacier mask, divide %d', gdir.rgi_id, i)
_mask_per_divide(gdir, i, dem, smoothed_dem)
def _distribute_thickness_per_altitude(glacier_mask, topo, cls, fls, grid,
add_slope=True,
smooth=True):
"""Where the job is actually done."""
# Along the lines
dx = grid.dx
hs, ts, vs, xs, ys = [], [], [], [] ,[]
for cl, fl in zip(cls, fls):
# TODO: here one should see if parabola is always the best choice
hs = np.append(hs, fl.surface_h)
ts = np.append(ts, cl['thick'])
vs = np.append(vs, cl['volume'])
x, y = fl.line.xy
xs = np.append(xs, x)
ys = np.append(ys, y)
vol = np.sum(vs)
# very inefficient inverse distance stuff
to_compute = np.nonzero(glacier_mask)
thick = topo * np.NaN
for (y, x) in np.asarray(to_compute).T:
assert glacier_mask[y, x] == 1
phgt = topo[y, x]
# take the ones in a 100m range
starth = 100.
while True:
starth += 10
pok = np.nonzero(np.abs(phgt - hs) <= starth)[0]
if len(pok) != 0:
break
sqr = np.sqrt((xs[pok]-x)**2 + (ys[pok]-y)**2)
pzero = np.where(sqr == 0)
if len(pzero[0]) == 0:
thick[y, x] = np.average(ts[pok], weights=1 / sqr)
elif len(pzero[0]) == 1:
thick[y, x] = ts[pzero]
else:
raise RuntimeError('We should not be there')
# Smooth
if smooth:
thick = np.where(np.isfinite(thick), thick, 0.)
gsize = np.rint(cfg.PARAMS['smooth_window'] / dx)
thick = gaussian_blur(thick, np.int(gsize))
thick = np.where(glacier_mask, thick, 0.)
# Distance
dis = distance_transform_edt(glacier_mask)
dis = np.where(glacier_mask, dis, np.NaN)**0.5
# Slope
slope = 1.
if add_slope:
sy, sx = np.gradient(topo, dx, dx)
slope = np.arctan(np.sqrt(sy**2 + sx**2))
slope = np.clip(slope, np.deg2rad(6.), np.pi/2.)
slope = 1 / slope**(cfg.N / (cfg.N+2))
# Conserve volume
tmp_vol = np.nansum(thick * dis * slope * dx**2)
final_t = thick * dis * slope * vol / tmp_vol
# Done
final_t = np.where(np.isfinite(final_t), final_t, 0.)
assert np.allclose(np.sum(final_t * dx**2), vol)
return final_t
