def calculate_volume_changes(dDEM,
glacier_shapes,
burn_handle=None,
ind_glac_vals=None):
if type(glacier_shapes) is str:
ind_glac_mask, ind_glac_vals = it.rasterize_polygons(
dDEM, glacier_shapes, burn_handle=burn_handle)
elif type(glacier_shapes) is np.array:
if ind_glac_vals is None:
ind_glac_vals = np.unique(glacier_shapes)
elif type(ind_glac_vals) is list:
ind_glac_vals = np.array(ind_glac_vals)
ind_glac_mask = glacier_shapes
ind_vol_chgs = np.nan * np.zeros(ind_glac_vals.shape)
for i, ind in enumerate(ind_glac_vals):
glac_inds = np.where(ind_glac_mask == ind)
if glac_inds[0].size == 0:
continue
glac_chgs = dDEM.img[glac_inds]
# get the volume change by summing dh/dt, multiplying by cell
ind_vol_chgs[i] = np.nansum(glac_chgs) * np.abs(dDEM.dx) * np.abs(
dDEM.dy)
return ind_glac_vals, ind_vol_chgs
def fill_holes_individually(dDEM,
glacshapes,
functype,
burn_handle=None,
**kwargs):
# first, get the individual glacier mask.
ind_glac_mask, ind_glac_vals = it.rasterize_polygons(
dDEM, glacshapes, burn_handle)
filled_ddem = dDEM.copy()
# if we already have a glacier mask defined, remove it.
if 'glacier_mask' in kwargs:
del kwargs['glacier_mask']
for glac in ind_glac_vals:
tmp_mask = ind_glac_mask == glac
try:
tmp_dem = fill_holes(dDEM,
elevation_function,
glacier_mask=tmp_mask,
functype=functype,
**kwargs)
filled_ddem.img[tmp_mask] = tmp_dem.img[tmp_mask]
except:
continue
return filled_ddem
def normalize_glacier_elevations(DEM, glacshapes, burn_handle=None):
ind_glac_mask, raw_inds = it.rasterize_polygons(DEM, glacshapes,
burn_handle)
normed_els = DEM.copy().img
ind_glac_vals = clean_glacier_indices(DEM, ind_glac_mask, raw_inds)
for i in ind_glac_vals:
glac_inds = np.where(ind_glac_mask == i)
glac_els = DEM.img[glac_inds]
if glac_els.size > 0:
max_el = np.nanmax(glac_els)
min_el = np.nanmin(glac_els)
normed_els[glac_inds] = (glac_els - min_el) / (max_el - min_el)
normDEM = DEM.copy(new_raster=normed_els)
return normDEM, ind_glac_mask, ind_glac_vals
def main():
parser = _argparser()
args = parser.parse_args()
if args.plot_curves:
# set font stuff
font = {'family': 'sans',
'weight': 'normal',
'size': 22}
# legend_font = {'family': 'sans',
# 'weight': 'normal',
# 'size': '16'}
matplotlib.rc('font', **font)
# load base dem
print('Loading DEM {}'.format(args.basedem))
basedem = GeoImg(args.basedem)
print('DEM loaded.')
# get glacier masks
if args.glac_mask is None:
print('Rasterizing glacier polygons to DEM extent.')
master_mask, master_glacs = it.rasterize_polygons(basedem, args.glac_outlines, burn_handle='fid')
master_mask[master_mask < 0] = np.nan
else:
print('Loading raster of glacier polygons {}'.format(args.glac_mask))
master_mask_geo = GeoImg(args.glac_mask)
master_mask = master_mask_geo.img
master_glacs = np.unique(master_mask[np.isfinite(master_mask)])
# master_mask = np.logical_and(master_mask, np.isfinite(basedem.img))
# get names
gshp = gpd.read_file(args.glac_outlines)
print('Glacier masks loaded.')
# create output folder if it doesn't already exist
os.system('mkdir -p {}'.format(args.out_folder))
# create folders to store glacier dH curve figures
for g in gshp[args.namefield]:
os.system('mkdir -p {}'.format(os.path.sep.join([args.out_folder, g])))
print('Getting glacier AADs.')
# get aad
aad_bins, aads = area_alt_dist(basedem, master_mask, glacier_inds=master_glacs)
# initialize pd dataframes for dH_curves
df_list = [pd.DataFrame(aad_bin, columns=['elevation']) for aad_bin in aad_bins]
g_list = [str(gshp[args.namefield][gshp['fid'] == glac].values[0]) for glac in master_glacs]
df_dict = dict(zip(g_list, df_list))
# turn aad_bins, aads into dicts with RGIId as keys
bin_dict = dict(zip(g_list, aad_bins))
aad_dict = dict(zip(g_list, aads))
for i, df in enumerate(df_list):
df['area'] = pd.Series(aads[i], index=df.index)
# now that we have the AADs, make sure we preserve that distribution when we reproject.
bin_widths = [np.diff(b)[0] for b in aad_bins]
basedem.img[np.isnan(master_mask)] = np.nan # remove all elevations outside of the glacier mask
for i, g in enumerate(master_glacs):
basedem.img[master_mask == g] = np.floor(basedem.img[master_mask == g] / bin_widths[i]) * bin_widths[i]
# get a list of all dH
dH_list = glob('{}/*.tif'.format(args.dH_folder))
# initialize ur_dataframe
ur_df = pd.DataFrame([os.path.basename(x) for x in dH_list], columns=['filename'])
ur_df['dem1'] = [nice_split(x)[0] for x in ur_df['filename']]
ur_df['dem2'] = [nice_split(x)[1] for x in ur_df['filename']]
date1 = [parse_filename(x) for x in ur_df['dem1']]
date2 = [parse_filename(x) for x in ur_df['dem2']]
ur_df['date1'] = date1
ur_df['date2'] = date2
ur_df['delta_t'] = [(x - y).days / 365.2425 for x, y in list(zip(date1, date2))]
ur_df['centerdate'] = [(y + dt.timedelta((x - y).days / 2)) for x, y in list(zip(date1, date2))]
print('Found {} files in {}'.format(len(dH_list), args.dH_folder))
print('Getting dH curves.')
for i, dHfile in enumerate(dH_list):
dH = GeoImg(dHfile)
print('{} ({}/{})'.format(dH.filename, i+1, len(dH_list)))
if args.glac_mask is None:
dh_mask, dh_glacs = it.rasterize_polygons(dH, args.glac_outlines, burn_handle='fid')
else:
tmp_dh_mask = master_mask_geo.reproject(dH, method=GRA_NearestNeighbour)
dh_mask = tmp_dh_mask.img
dh_glacs = np.unique(dh_mask[np.isfinite(dh_mask)])
tmp_basedem = basedem.reproject(dH, method=GRA_NearestNeighbour)
deltat = ur_df.loc[i, 'delta_t']
this_fname = ur_df.loc[i, 'filename']
for i, glac in enumerate(dh_glacs):
this_name = str(gshp[args.namefield][gshp['fid'] == glac].values[0])
this_dem = tmp_basedem.img[dh_mask == glac]
this_ddem = dH.img[dh_mask == glac]
this_ddem[np.abs(this_ddem) > args.outlier] = np.nan
if np.count_nonzero(np.isfinite(this_ddem)) / this_ddem.size < 0.25:
continue
# these_bins = get_bins(this_dem, dh_mask)
filtered_ddem = outlier_filter(bin_dict[this_name], this_dem, this_ddem)
# _, odH_curve = get_dH_curve(this_dem, this_ddem, dh_mask, bins=aad_bins)
_, fdH_curve, fbin_area = get_dH_curve(this_dem, filtered_ddem, dh_mask, bins=bin_dict[this_name])
_, fdH_median, _ = get_dH_curve(this_dem, filtered_ddem, dh_mask, bins=bin_dict[this_name], mode='median')
fbin_area = 100 * fbin_area * np.abs(dH.dx) * np.abs(dH.dy) / aad_dict[this_name]
if args.plot_curves:
plot_dH_curve(this_ddem, this_dem, bin_dict[this_name], fdH_curve,
fdH_median, fbin_area, dH.filename.strip('.tif'))
plt.savefig(os.path.join(args.out_folder, this_name, dH.filename.strip('.tif') + '.png'),
bbox_inches='tight', dpi=200)
plt.close()
# write dH curve in units of dH/dt (so divide by deltat)
this_fname = this_fname.rsplit('.tif', 1)[0]
df_dict[this_name][this_fname + '_mean'] = pd.Series(fdH_curve / deltat, index=df_dict[this_name].index)
df_dict[this_name][this_fname + '_med'] = pd.Series(fdH_median / deltat, index=df_dict[this_name].index)
df_dict[this_name][this_fname + '_pct'] = pd.Series(fbin_area, index=df_dict[this_name].index)
print('Writing dH curves to {}'.format(args.out_folder))
# write all dH_curves
for g in df_dict.keys():
print(g)
df_dict[g].to_csv(os.path.sep.join([args.out_folder, '{}_dH_curves.csv'.format(g)]), index=False)