def test_smooth(self):
a = np.array([1., 4, 7, 7, 4, 1])
b = utils.smooth1d(a, 3, kernel='mean')
assert_allclose(b, [3, 4, 6, 6, 4, 3])
kernel = [0.60653066, 1., 0.60653066]
b = utils.smooth1d(a, 3, kernel=kernel)
c = utils.smooth1d(a, 3)
assert_allclose(b, c)
def test_smooth(self):
a = np.array([1., 4, 7, 7, 4, 1])
b = utils.smooth1d(a, 3, kernel='mean')
assert_allclose(b, [3, 4, 6, 6, 4, 3])
kernel = [0.60653066, 1., 0.60653066]
b = utils.smooth1d(a, 3, kernel=kernel)
c = utils.smooth1d(a, 3)
assert_allclose(b, c)
def filter_inversion_output(gdir):
"""Filters the last few grid points after the physically-based inversion.
For various reasons (but mostly: the equilibrium assumption), the last few
grid points on a glacier flowline are often noisy and create unphysical
depressions. Here we try to correct for that. It is not volume conserving,
but area conserving.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
"""
if gdir.is_tidewater:
# No need for filter in tidewater case
return
if not gdir.has_file('downstream_line'):
raise InvalidWorkflowError('filter_inversion_output now needs a '
'previous call to the '
'compute_dowstream_line and '
'compute_downstream_bedshape tasks')
dic_ds = gdir.read_pickle('downstream_line')
bs = np.average(dic_ds['bedshapes'][:3])
n = -5
cls = gdir.read_pickle('inversion_output')
cl = cls[-1]
# First guess thickness based on width
w = cl['width'][n:]
s = w**3 * bs / 6
h = 3 / 2 * s / w
# Smoothing things out a bit
hts = np.append(np.append(cl['thick'][n - 3:n], h), 0)
h = utils.smooth1d(hts, 3)[n - 1:-1]
# Recompute bedshape based on that
bs = utils.clip_min(4 * h / w**2, cfg.PARAMS['mixed_min_shape'])
# OK, done
s = w**3 * bs / 6
cl['thick'][n:] = 3 / 2 * s / w
cl['volume'][n:] = s * cl['dx']
cl['is_trapezoid'][n:] = False
cl['is_rectangular'][n:] = False
gdir.write_pickle(cls, 'inversion_output')
# output the volume here - this simplifies code for some downstream funcs
return np.sum([np.sum(cl['volume']) for cl in cls])
def catchment_width_correction(gdir, div_id=None):
"""Corrects for NaNs and inconsistencies in the geometrical widths.
Interpolates missing values, ensures consistency of the
surface-area distribution AND with the geometrical area of the glacier
polygon, avoiding errors due to gridded representation.
Updates the 'inversion_flowlines' save file.
Parameters
----------
gdir : oggm.GlacierDirectory
"""
# The code below makes of this task a "special" divide task.
# We keep it as is and remove the divide task decorator
if div_id is None:
# This is the original call
# This time instead of just looping over the divides we add a test
# to check for the conservation of the shapefile's area.
area = 0.
divides = []
for i in gdir.divide_ids:
log.info('%s: width correction, divide %d', gdir.rgi_id, i)
fls = catchment_width_correction(gdir, div_id=i, reset=True)
for fl in fls:
area += np.sum(fl.widths) * fl.dx
divides.append(fls)
# Final correction - because of the raster, the gridded area of the
# glacier is not that of the actual geometry. correct for that
fac = gdir.rgi_area_km2 / (area * gdir.grid.dx**2 * 10**-6)
log.debug('%s: corrected widths with a factor %.2f', gdir.rgi_id, fac)
for i in gdir.divide_ids:
fls = divides[i-1]
for fl in fls:
fl.widths *= fac
# Overwrite centerlines
gdir.write_pickle(fls, 'inversion_flowlines', div_id=i)
return None
# variables
flowlines = gdir.read_pickle('inversion_flowlines', div_id=div_id)
catchment_indices = gdir.read_pickle('catchment_indices', div_id=div_id)
# Topography for altitude-area distribution
# I take the non-smoothed topography and remove the borders
fpath = gdir.get_filepath('gridded_data', div_id=div_id)
with netCDF4.Dataset(fpath) as nc:
topo = nc.variables['topo'][:]
ext = nc.variables['glacier_ext'][:]
topo[np.where(ext==1)] = np.NaN
# Param
nmin = int(cfg.PARAMS['min_n_per_bin'])
smooth_ws = int(cfg.PARAMS['smooth_widths_window_size'])
# Per flowline (important so that later, the indices can be moved)
catchment_heights = []
for ci in catchment_indices:
_t = topo[tuple(ci.T)][:]
catchment_heights.append(list(_t[np.isfinite(_t)]))
# Loop over lines in a reverse order
for fl, catch_h in zip(flowlines, catchment_heights):
# Interpolate widths
widths = utils.interp_nans(fl.widths)
widths = np.clip(widths, 0.1, np.max(widths))
# Get topo per catchment and per flowline point
fhgt = fl.surface_h
# Sometimes, the centerline does not reach as high as each pix on the
# glacier. (e.g. RGI40-11.00006)
catch_h = np.clip(catch_h, 0, np.max(fhgt))
# Max and mins for the histogram
maxh = np.max(fhgt)
if fl.flows_to is None:
minh = np.min(fhgt)
catch_h = np.clip(catch_h, minh, np.max(catch_h))
else:
minh = np.min(fhgt) # Min just for flowline (this has reasons)
# Now decide on a binsize which ensures at least N element per bin
bsize = cfg.PARAMS['base_binsize']
while True:
maxb = utils.nicenumber(maxh, 1)
minb = utils.nicenumber(minh, 1, lower=True)
bins = np.arange(minb, maxb+bsize+0.01, bsize)
minb = np.min(bins)
# Ignore the topo pixels below the last bin
tmp_ght = catch_h[np.where(catch_h >= minb)]
topo_digi = np.digitize(tmp_ght, bins) - 1 # I prefer the left
fl_digi = np.digitize(fhgt, bins) - 1 # I prefer the left
if nmin == 1:
# No need for complicated count
_c = set(topo_digi)
_fl = set(fl_digi)
else:
# Keep indexes with at least n counts
_c = Counter(topo_digi.tolist())
_c = set([k for (k, v) in _c.items() if v >= nmin])
_fl = Counter(fl_digi.tolist())
_fl = set([k for (k, v) in _fl.items() if v >= nmin])
ref_set = set(range(len(bins)-1))
if (_c == ref_set) and (_fl == ref_set):
# For each bin, the width(s) have to represent the "real" area
new_widths = widths.copy()
for bi in range(len(bins) - 1):
bintopoarea = len(np.where(topo_digi == bi)[0])
wherewiths = np.where(fl_digi == bi)
binflarea = np.sum(new_widths[wherewiths]) * fl.dx
new_widths[wherewiths] = (bintopoarea / binflarea) * \
new_widths[wherewiths]
break
bsize += 5
# Add a security for infinite loops
if bsize > 500:
nmin -= 1
bsize = cfg.PARAMS['base_binsize']
log.warning('%s: reduced min n per bin to %d', gdir.rgi_id,
nmin)
if nmin == 0:
raise RuntimeError('NO binsize could be chosen for: '
'{}'.format(gdir.rgi_id))
if bsize > 150:
log.warning('%s: chosen binsize %d', gdir.rgi_id, bsize)
else:
log.debug('%s: chosen binsize %d', gdir.rgi_id, bsize)
# Now keep the good topo pixels and send the unattributed ones to the
# next flowline
tosend = list(catch_h[np.where(catch_h < minb)])
if (len(tosend) > 0) and (fl.flows_to is not None):
ide = flowlines.index(fl.flows_to)
catchment_heights[ide] = np.append(catchment_heights[ide], tosend)
if (len(tosend) > 0) and (fl.flows_to is None):
raise RuntimeError('This should not happen')
# Now we have a width which is the "best" representation of our
# tributary according to the altitude area distribution.
# This sometimes leads to abrupt changes in the widths from one
# grid point to another. I think it's not too harmful to smooth them
# here, at the cost of a less perfect altitude area distribution
if smooth_ws != 0:
if smooth_ws == 1:
new_widths = utils.smooth1d(new_widths)
else:
new_widths = utils.smooth1d(new_widths, window_size=smooth_ws)
# Write it
fl.widths = new_widths
return flowlines
def catchment_width_correction(gdir, div_id=None):
"""Corrects for NaNs and inconsistencies in the geometrical widths.
Interpolates missing values, ensures consistency of the
surface-area distribution AND with the geometrical area of the glacier
polygon, avoiding errors due to gridded representation.
Updates the 'inversion_flowlines' save file.
Parameters
----------
gdir : oggm.GlacierDirectory
"""
# The code below makes of this task a "special" divide task.
# We keep it as is and remove the divide task decorator
if div_id is None:
# This is the original call
# This time instead of just looping over the divides we add a test
# to check for the conservation of the shapefile's area.
area = 0.
divides = []
for i in gdir.divide_ids:
log.info('%s: width correction, divide %d', gdir.rgi_id, i)
fls = catchment_width_correction(gdir, div_id=i, reset=True)
for fl in fls:
area += np.sum(fl.widths) * fl.dx
divides.append(fls)
# Final correction - because of the raster, the gridded area of the
# glacier is not that of the actual geometry. correct for that
fac = gdir.rgi_area_km2 / (area * gdir.grid.dx**2 * 10**-6)
log.debug('%s: corrected widths with a factor %.2f', gdir.rgi_id, fac)
for i in gdir.divide_ids:
fls = divides[i - 1]
for fl in fls:
fl.widths *= fac
# Overwrite centerlines
gdir.write_pickle(fls, 'inversion_flowlines', div_id=i)
return None
# variables
flowlines = gdir.read_pickle('inversion_flowlines', div_id=div_id)
catchment_indices = gdir.read_pickle('catchment_indices', div_id=div_id)
# Topography for altitude-area distribution
# I take the non-smoothed topography and remove the borders
fpath = gdir.get_filepath('gridded_data', div_id=div_id)
with netCDF4.Dataset(fpath) as nc:
topo = nc.variables['topo'][:]
ext = nc.variables['glacier_ext'][:]
topo[np.where(ext == 1)] = np.NaN
# Param
nmin = int(cfg.PARAMS['min_n_per_bin'])
smooth_ws = int(cfg.PARAMS['smooth_widths_window_size'])
# Per flowline (important so that later, the indices can be moved)
catchment_heights = []
for ci in catchment_indices:
_t = topo[tuple(ci.T)][:]
catchment_heights.append(list(_t[np.isfinite(_t)]))
# Loop over lines in a reverse order
for fl, catch_h in zip(flowlines, catchment_heights):
# Interpolate widths
widths = utils.interp_nans(fl.widths)
widths = np.clip(widths, 0.1, np.max(widths))
# Get topo per catchment and per flowline point
fhgt = fl.surface_h
# Sometimes, the centerline does not reach as high as each pix on the
# glacier. (e.g. RGI40-11.00006)
catch_h = np.clip(catch_h, 0, np.max(fhgt))
# Max and mins for the histogram
maxh = np.max(fhgt)
if fl.flows_to is None:
minh = np.min(fhgt)
catch_h = np.clip(catch_h, minh, np.max(catch_h))
else:
minh = np.min(fhgt) # Min just for flowline (this has reasons)
# Now decide on a binsize which ensures at least N element per bin
bsize = cfg.PARAMS['base_binsize']
while True:
maxb = utils.nicenumber(maxh, 1)
minb = utils.nicenumber(minh, 1, lower=True)
bins = np.arange(minb, maxb + bsize + 0.01, bsize)
minb = np.min(bins)
# Ignore the topo pixels below the last bin
tmp_ght = catch_h[np.where(catch_h >= minb)]
topo_digi = np.digitize(tmp_ght, bins) - 1 # I prefer the left
fl_digi = np.digitize(fhgt, bins) - 1 # I prefer the left
if nmin == 1:
# No need for complicated count
_c = set(topo_digi)
_fl = set(fl_digi)
else:
# Keep indexes with at least n counts
_c = Counter(topo_digi.tolist())
_c = set([k for (k, v) in _c.items() if v >= nmin])
_fl = Counter(fl_digi.tolist())
_fl = set([k for (k, v) in _fl.items() if v >= nmin])
ref_set = set(range(len(bins) - 1))
if (_c == ref_set) and (_fl == ref_set):
# For each bin, the width(s) have to represent the "real" area
new_widths = widths.copy()
for bi in range(len(bins) - 1):
bintopoarea = len(np.where(topo_digi == bi)[0])
wherewiths = np.where(fl_digi == bi)
binflarea = np.sum(new_widths[wherewiths]) * fl.dx
new_widths[wherewiths] = (bintopoarea / binflarea) * \
new_widths[wherewiths]
break
bsize += 5
# Add a security for infinite loops
if bsize > 500:
nmin -= 1
bsize = cfg.PARAMS['base_binsize']
log.warning('%s: reduced min n per bin to %d', gdir.rgi_id,
nmin)
if nmin == 0:
raise RuntimeError('NO binsize could be chosen for: '
'{}'.format(gdir.rgi_id))
if bsize > 150:
log.warning('%s: chosen binsize %d', gdir.rgi_id, bsize)
else:
log.debug('%s: chosen binsize %d', gdir.rgi_id, bsize)
# Now keep the good topo pixels and send the unattributed ones to the
# next flowline
tosend = list(catch_h[np.where(catch_h < minb)])
if (len(tosend) > 0) and (fl.flows_to is not None):
ide = flowlines.index(fl.flows_to)
catchment_heights[ide] = np.append(catchment_heights[ide], tosend)
if (len(tosend) > 0) and (fl.flows_to is None):
raise RuntimeError('This should not happen')
# Now we have a width which is the "best" representation of our
# tributary according to the altitude area distribution.
# This sometimes leads to abrupt changes in the widths from one
# grid point to another. I think it's not too harmful to smooth them
# here, at the cost of a less perfect altitude area distribution
if smooth_ws != 0:
if smooth_ws == 1:
new_widths = utils.smooth1d(new_widths)
else:
new_widths = utils.smooth1d(new_widths, window_size=smooth_ws)
# Write it
fl.widths = new_widths
return flowlines
