def test_download_demo_files(self):
f = utils.get_demo_file('Hintereisferner.shp')
self.assertTrue(os.path.exists(f))
sh = salem.read_shapefile(f)
self.assertTrue(hasattr(sh, 'geometry'))
# Data files
cfg.initialize()
lf, df = utils.get_wgms_files()
self.assertTrue(os.path.exists(df))
lf = utils.get_glathida_file()
self.assertTrue(os.path.exists(lf))
def test_download_demo_files(self):
f = utils.get_demo_file('Hintereisferner.shp')
self.assertTrue(os.path.exists(f))
sh = salem.read_shapefile(f)
self.assertTrue(hasattr(sh, 'geometry'))
# Data files
cfg.initialize()
lf, df = utils.get_wgms_files()
self.assertTrue(os.path.exists(lf))
lf = utils.get_glathida_file()
self.assertTrue(os.path.exists(lf))
def _prepare_inv(gdirs):
# Get test glaciers (all glaciers with thickness data)
fpath = utils.get_glathida_file()
try:
gtd_df = pd.read_csv(fpath).sort_values(by=["RGI_ID"])
except AttributeError:
gtd_df = pd.read_csv(fpath).sort(columns=["RGI_ID"])
dfids = gtd_df["RGI_ID"].values
print("GTD Glac before", len(dfids))
ref_gdirs = []
for gdir in gdirs:
if gdir.rgi_id not in dfids:
continue
if gdir.glacier_type == "Ice cap":
continue
if gdir.terminus_type in [
"Marine-terminating",
"Lake-terminating",
"Dry calving",
"Regenerated",
"Shelf-terminating",
]:
continue
ref_gdirs.append(gdir)
print("GTD Glac after", len(ref_gdirs))
ref_rgiids = [gdir.rgi_id for gdir in ref_gdirs]
gtd_df = gtd_df.set_index("RGI_ID").loc[ref_rgiids]
# Account for area differences between glathida and rgi
ref_area_km2 = np.asarray([gdir.rgi_area_km2 for gdir in ref_gdirs])
gtd_df.VOLUME = gtd_df.MEAN_THICKNESS * gtd_df.GTD_AREA * 1e-3
ref_cs = gtd_df.VOLUME.values / (gtd_df.GTD_AREA.values ** 1.375)
ref_volume_km3 = ref_cs * ref_area_km2 ** 1.375
ref_thickness_m = ref_volume_km3 / ref_area_km2 * 1000.0
gtd_df["ref_area_km2"] = ref_area_km2
gtd_df["ref_volume_km3"] = ref_volume_km3
gtd_df["ref_thickness_m"] = ref_thickness_m
gtd_df["ref_gdirs"] = ref_gdirs
return gtd_df
def _prepare_inv(gdirs):
# Get test glaciers (all glaciers with thickness data)
fpath = utils.get_glathida_file()
try:
gtd_df = pd.read_csv(fpath).sort_values(by=['RGI_ID'])
except AttributeError:
gtd_df = pd.read_csv(fpath).sort(columns=['RGI_ID'])
dfids = gtd_df['RGI_ID'].values
print('GTD Glac before', len(dfids))
ref_gdirs = []
for gdir in gdirs:
if gdir.rgi_id not in dfids:
continue
if gdir.glacier_type == 'Ice cap':
continue
if gdir.terminus_type in [
'Marine-terminating', 'Lake-terminating', 'Dry calving',
'Regenerated', 'Shelf-terminating'
]:
continue
ref_gdirs.append(gdir)
print('GTD Glac after', len(ref_gdirs))
ref_rgiids = [gdir.rgi_id for gdir in ref_gdirs]
gtd_df = gtd_df.set_index('RGI_ID').loc[ref_rgiids]
# Account for area differences between glathida and rgi
ref_area_km2 = np.asarray([gdir.rgi_area_km2 for gdir in ref_gdirs])
gtd_df.VOLUME = gtd_df.MEAN_THICKNESS * gtd_df.GTD_AREA * 1e-3
ref_cs = gtd_df.VOLUME.values / (gtd_df.GTD_AREA.values**1.375)
ref_volume_km3 = ref_cs * ref_area_km2**1.375
ref_thickness_m = ref_volume_km3 / ref_area_km2 * 1000.
gtd_df['ref_area_km2'] = ref_area_km2
gtd_df['ref_volume_km3'] = ref_volume_km3
gtd_df['ref_thickness_m'] = ref_thickness_m
gtd_df['ref_gdirs'] = ref_gdirs
return gtd_df
def optimize_inversion_params(gdirs):
"""Optimizes fs and fd based on GlaThiDa thicknesses.
We use the glacier averaged thicknesses provided by GlaThiDa and correct
them for differences in area with RGI, using a glacier specific volume-area
scaling formula.
Parameters
----------
gdirs: list of oggm.GlacierDirectory objects
"""
# Do we even need to do this?
if not cfg.PARAMS['optimize_inversion_params']:
log.info('User did not want to optimize the inversion params')
return
# Get test glaciers (all glaciers with thickness data)
fpath = utils.get_glathida_file()
try:
gtd_df = pd.read_csv(fpath).sort_values(by=['RGI_ID'])
except AttributeError:
gtd_df = pd.read_csv(fpath).sort(columns=['RGI_ID'])
dfids = gtd_df['RGI_ID'].values
ref_gdirs = [gdir for gdir in gdirs if gdir.rgi_id in dfids]
ref_rgiids = [gdir.rgi_id for gdir in ref_gdirs]
gtd_df = gtd_df.set_index('RGI_ID').loc[ref_rgiids]
# Account for area differences between glathida and rgi
gtd_df['RGI_AREA'] = [gdir.rgi_area_km2 for gdir in ref_gdirs]
ref_area_km2 = gtd_df.RGI_AREA.values
gtd_df.VOLUME = gtd_df.MEAN_THICKNESS * gtd_df.GTD_AREA * 1e-3
ref_cs = gtd_df.VOLUME.values / (gtd_df.GTD_AREA.values**1.375)
ref_volume_km3 = ref_cs * ref_area_km2**1.375
ref_thickness_m = ref_volume_km3 / ref_area_km2 * 1000.
# Minimize volume or thick RMSD?
optim_t = cfg.PARAMS['optimize_thick']
if optim_t:
ref_data = ref_thickness_m
tol = 0.1
else:
ref_data = ref_volume_km3
tol = 1.e-4
if cfg.PARAMS['invert_with_sliding']:
# Optimize with both params
log.info('Compute the inversion parameters.')
def to_optimize(x):
tmp_ref = np.zeros(len(ref_gdirs))
glen_a = cfg.A * x[0]
fs = cfg.FS * x[1]
for i, gdir in enumerate(ref_gdirs):
v, a = invert_parabolic_bed(gdir, glen_a=glen_a,
fs=fs, write=False)
if optim_t:
tmp_ref[i] = v / a
else:
tmp_ref[i] = v * 1e-9
return utils.rmsd(tmp_ref, ref_data)
opti = optimization.minimize(to_optimize, [1., 1.],
bounds=((0.01, 10), (0.01, 10)),
tol=tol)
# Check results and save.
glen_a = cfg.A * opti['x'][0]
fs = cfg.FS * opti['x'][1]
else:
# Optimize without sliding
log.info('Compute the inversion parameter.')
def to_optimize(x):
tmp_ref = np.zeros(len(ref_gdirs))
glen_a = cfg.A * x[0]
for i, gdir in enumerate(ref_gdirs):
v, a = invert_parabolic_bed(gdir, glen_a=glen_a,
fs=0., write=False)
if optim_t:
tmp_ref[i] = v / a
else:
tmp_ref[i] = v * 1e-9
return utils.rmsd(tmp_ref, ref_data)
opti = optimization.minimize(to_optimize, [1.],
bounds=((0.01, 10), ),
tol=tol)
# Check results and save.
glen_a = cfg.A * opti['x'][0]
fs = 0.
# This is for the stats
oggm_volume_m3 = np.zeros(len(ref_gdirs))
rgi_area_m2 = np.zeros(len(ref_gdirs))
for i, gdir in enumerate(ref_gdirs):
v, a = invert_parabolic_bed(gdir, glen_a=glen_a, fs=fs,
write=False)
oggm_volume_m3[i] = v
rgi_area_m2[i] = a
assert np.allclose(rgi_area_m2 * 1e-6, ref_area_km2)
# This is for each glacier
out = dict()
out['glen_a'] = glen_a
out['fs'] = fs
out['factor_glen_a'] = opti['x'][0]
try:
out['factor_fs'] = opti['x'][1]
except IndexError:
out['factor_fs'] = 0.
for gdir in gdirs:
gdir.write_pickle(out, 'inversion_params')
# This is for the working dir
# Simple stats
out['vol_rmsd'] = utils.rmsd(oggm_volume_m3 * 1e-9, ref_volume_km3)
out['thick_rmsd'] = utils.rmsd(oggm_volume_m3 * 1e-9 / ref_area_km2 / 1000,
ref_thickness_m)
log.info('Optimized glen_a and fs with a factor {factor_glen_a:.2f} and '
'{factor_fs:.2f} for a thick RMSD of '
'{thick_rmsd:.1f} and a volume RMSD of '
'{vol_rmsd:.3f}'.format(**out))
df = pd.DataFrame(out, index=[0])
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_params.csv')
df.to_csv(fpath)
# All results
df = dict()
df['ref_area_km2'] = ref_area_km2
df['ref_volume_km3'] = ref_volume_km3
df['oggm_volume_km3'] = oggm_volume_m3 * 1e-9
df['vas_volume_km3'] = 0.034*(df['ref_area_km2']**1.375)
rgi_id = [gdir.rgi_id for gdir in ref_gdirs]
df = pd.DataFrame(df, index=rgi_id)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df.to_csv(fpath)
# return value for tests
return out
def get_rgi_df(reset=False):
"""This function prepares a kind of `fake` RGI file, with the updated
geometries for ITMIX.
"""
# This makes an RGI dataframe with all ITMIX + WGMS + GTD glaciers
RGI_DIR = utils.get_rgi_dir()
df_rgi_file = os.path.join(DATA_DIR, 'itmix', 'itmix_rgi_shp.pkl')
if os.path.exists(df_rgi_file) and not reset:
rgidf = pd.read_pickle(df_rgi_file)
else:
linkf = os.path.join(DATA_DIR, 'itmix', 'itmix_rgi_links.pkl')
df_itmix = pd.read_pickle(linkf)
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
f = utils.get_glathida_file()
gtd_df = pd.read_csv(f)
divides = []
rgidf = []
_rgi_ids_for_overwrite = []
for i, row in df_itmix.iterrows():
log.info('Prepare RGI df for ' + row.name)
# read the rgi region
rgi_shp = find_path(RGI_DIR, row['rgi_reg'] + '_rgi50_*.shp')
rgi_df = salem.read_shapefile(rgi_shp, cached=True)
rgi_parts = row.T['rgi_parts_ids']
sel = rgi_df.loc[rgi_df.RGIId.isin(rgi_parts)].copy()
# use the ITMIX shape where possible
if row.name in [
'Hellstugubreen', 'Freya', 'Aqqutikitsoq', 'Brewster',
'Kesselwandferner', 'NorthGlacier', 'SouthGlacier',
'Tasman', 'Unteraar', 'Washmawapta', 'Columbia'
]:
shf = find_path(SEARCHD, '*_' + row.name + '*.shp')
shp = salem.read_shapefile(shf)
if row.name == 'Unteraar':
shp = shp.iloc[[-1]]
if 'LineString' == shp.iloc[0].geometry.type:
shp.loc[shp.index[0],
'geometry'] = shpg.Polygon(shp.iloc[0].geometry)
if shp.iloc[0].geometry.type == 'MultiLineString':
# Columbia
geometry = shp.iloc[0].geometry
parts = list(geometry)
for p in parts:
assert p.type == 'LineString'
exterior = shpg.Polygon(parts[0])
# let's assume that all other polygons are in fact interiors
interiors = []
for p in parts[1:]:
assert exterior.contains(p)
interiors.append(p)
geometry = shpg.Polygon(parts[0], interiors)
assert 'Polygon' in geometry.type
shp.loc[shp.index[0], 'geometry'] = geometry
assert len(shp) == 1
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
sel = sel.iloc[[0]]
sel.loc[sel.index[0], 'geometry'] = shp
sel.loc[sel.index[0], 'Area'] = area_km2
elif row.name == 'Urumqi':
# ITMIX Urumqi is in fact two glaciers
shf = find_path(SEARCHD, '*_' + row.name + '*.shp')
shp2 = salem.read_shapefile(shf)
assert len(shp2) == 2
for k in [0, 1]:
shp = shp2.iloc[[k]].copy()
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
assert sel.loc[sel.index[k],
'geometry'].contains(shp.centroid)
sel.loc[sel.index[k], 'geometry'] = shp
sel.loc[sel.index[k], 'Area'] = area_km2
assert len(sel) == 2
elif len(rgi_parts) > 1:
# Ice-caps. Make divides
# First we gather all the parts:
sel = rgi_df.loc[rgi_df.RGIId.isin(rgi_parts)].copy()
# Make the multipolygon for the record
multi = shpg.MultiPolygon([g for g in sel.geometry])
# update the RGI attributes. We take a dummy rgi ID
new_area = np.sum(sel.Area)
found = False
for i in range(len(sel)):
tsel = sel.iloc[[i]].copy()
if 'Multi' in tsel.loc[tsel.index[0], 'geometry'].type:
continue
else:
found = True
sel = tsel
break
if not found:
raise RuntimeError()
inif = 0.
add = 1e-5
if row.name == 'Devon':
inif = 0.001
add = 1e-4
while True:
buff = multi.buffer(inif)
if 'Multi' in buff.type:
inif += add
else:
break
x, y = multi.centroid.xy
if 'Multi' in buff.type:
raise RuntimeError
sel.loc[sel.index[0], 'geometry'] = buff
sel.loc[sel.index[0], 'Area'] = new_area
sel.loc[sel.index[0], 'CenLon'] = np.asarray(x)[0]
sel.loc[sel.index[0], 'CenLat'] = np.asarray(y)[0]
# Divides db
div_sel = dict()
for k, v in sel.iloc[0].iteritems():
if k == 'geometry':
div_sel[k] = multi
elif k == 'RGIId':
div_sel['RGIID'] = v
else:
div_sel[k] = v
divides.append(div_sel)
else:
pass
# add glacier name to the entity
name = ['I:' + row.name] * len(sel)
add_n = sel.RGIId.isin(wgms_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'W-' + name[z]
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'G-' + name[z]
sel.loc[:, 'Name'] = name
rgidf.append(sel)
# Add divides to the original one
adf = pd.DataFrame(divides)
adf.to_pickle(cfg.PATHS['itmix_divs'])
log.info('N glaciers ITMIX: {}'.format(len(rgidf)))
# WGMS glaciers which are not already there
# Actually we should remove the data of those 7 to be honest...
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
wgms_df = wgms_df.loc[~wgms_df.RGI_ID.isin(_rgi_ids_for_overwrite)]
log.info('N glaciers WGMS: {}'.format(len(wgms_df)))
for i, row in wgms_df.iterrows():
rid = row.RGI_ID
reg = rid.split('-')[1].split('.')[0]
# read the rgi region
rgi_shp = find_path(RGI_DIR, reg + '_rgi50_*.shp')
rgi_df = salem.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_cor = row.NAME.replace('/', 'or').replace('.',
'').replace(' ', '-')
name = ['W:' + _cor] * len(sel)
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'G-' + name[z]
for n in name:
if len(n) > 48:
raise
sel.loc[:, 'Name'] = name
rgidf.append(sel)
_rgi_ids_for_overwrite.extend(wgms_df.RGI_ID.values)
# GTD glaciers which are not already there
# Actually we should remove the data of those 2 to be honest...
gtd_df = gtd_df.loc[~gtd_df.RGI_ID.isin(_rgi_ids_for_overwrite)]
log.info('N glaciers GTD: {}'.format(len(gtd_df)))
for i, row in gtd_df.iterrows():
rid = row.RGI_ID
reg = rid.split('-')[1].split('.')[0]
# read the rgi region
rgi_shp = find_path(RGI_DIR, reg + '_rgi50_*.shp')
rgi_df = salem.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_corname = row.NAME.replace('/',
'or').replace('.',
'').replace(' ', '-')
name = ['G:' + _corname] * len(sel)
for n in name:
if len(n) > 48:
raise
sel.loc[:, 'Name'] = name
rgidf.append(sel)
# Save for not computing each time
rgidf = pd.concat(rgidf)
rgidf.to_pickle(df_rgi_file)
return rgidf
def get_rgi_df(reset=False):
"""This function prepares a kind of `fake` RGI file, with the updated
geometries for ITMIX.
"""
# This makes an RGI dataframe with all ITMIX + WGMS + GTD glaciers
RGI_DIR = utils.get_rgi_dir()
df_rgi_file = os.path.join(DATA_DIR, 'itmix', 'itmix_rgi_shp.pkl')
if os.path.exists(df_rgi_file) and not reset:
rgidf = pd.read_pickle(df_rgi_file)
else:
linkf = os.path.join(DATA_DIR, 'itmix', 'itmix_rgi_links.pkl')
df_itmix = pd.read_pickle(linkf)
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
f = utils.get_glathida_file()
gtd_df = pd.read_csv(f)
divides = []
rgidf = []
_rgi_ids_for_overwrite = []
for i, row in df_itmix.iterrows():
log.info('Prepare RGI df for ' + row.name)
# read the rgi region
rgi_shp = find_path(RGI_DIR, row['rgi_reg'] + '_rgi50_*.shp')
rgi_df = salem.read_shapefile(rgi_shp, cached=True)
rgi_parts = row.T['rgi_parts_ids']
sel = rgi_df.loc[rgi_df.RGIId.isin(rgi_parts)].copy()
# use the ITMIX shape where possible
if row.name in ['Hellstugubreen', 'Freya', 'Aqqutikitsoq',
'Brewster', 'Kesselwandferner', 'NorthGlacier',
'SouthGlacier', 'Tasman', 'Unteraar',
'Washmawapta', 'Columbia']:
shf = find_path(SEARCHD, '*_' + row.name + '*.shp')
shp = salem.read_shapefile(shf)
if row.name == 'Unteraar':
shp = shp.iloc[[-1]]
if 'LineString' == shp.iloc[0].geometry.type:
shp.loc[shp.index[0], 'geometry'] = shpg.Polygon(shp.iloc[0].geometry)
if shp.iloc[0].geometry.type == 'MultiLineString':
# Columbia
geometry = shp.iloc[0].geometry
parts = list(geometry)
for p in parts:
assert p.type == 'LineString'
exterior = shpg.Polygon(parts[0])
# let's assume that all other polygons are in fact interiors
interiors = []
for p in parts[1:]:
assert exterior.contains(p)
interiors.append(p)
geometry = shpg.Polygon(parts[0], interiors)
assert 'Polygon' in geometry.type
shp.loc[shp.index[0], 'geometry'] = geometry
assert len(shp) == 1
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
sel = sel.iloc[[0]]
sel.loc[sel.index[0], 'geometry'] = shp
sel.loc[sel.index[0], 'Area'] = area_km2
elif row.name == 'Urumqi':
# ITMIX Urumqi is in fact two glaciers
shf = find_path(SEARCHD, '*_' + row.name + '*.shp')
shp2 = salem.read_shapefile(shf)
assert len(shp2) == 2
for k in [0, 1]:
shp = shp2.iloc[[k]].copy()
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
assert sel.loc[sel.index[k], 'geometry'].contains(shp.centroid)
sel.loc[sel.index[k], 'geometry'] = shp
sel.loc[sel.index[k], 'Area'] = area_km2
assert len(sel) == 2
elif len(rgi_parts) > 1:
# Ice-caps. Make divides
# First we gather all the parts:
sel = rgi_df.loc[rgi_df.RGIId.isin(rgi_parts)].copy()
# Make the multipolygon for the record
multi = shpg.MultiPolygon([g for g in sel.geometry])
# update the RGI attributes. We take a dummy rgi ID
new_area = np.sum(sel.Area)
found = False
for i in range(len(sel)):
tsel = sel.iloc[[i]].copy()
if 'Multi' in tsel.loc[tsel.index[0], 'geometry'].type:
continue
else:
found = True
sel = tsel
break
if not found:
raise RuntimeError()
inif = 0.
add = 1e-5
if row.name == 'Devon':
inif = 0.001
add = 1e-4
while True:
buff = multi.buffer(inif)
if 'Multi' in buff.type:
inif += add
else:
break
x, y = multi.centroid.xy
if 'Multi' in buff.type:
raise RuntimeError
sel.loc[sel.index[0], 'geometry'] = buff
sel.loc[sel.index[0], 'Area'] = new_area
sel.loc[sel.index[0], 'CenLon'] = np.asarray(x)[0]
sel.loc[sel.index[0], 'CenLat'] = np.asarray(y)[0]
# Divides db
div_sel = dict()
for k, v in sel.iloc[0].iteritems():
if k == 'geometry':
div_sel[k] = multi
elif k == 'RGIId':
div_sel['RGIID'] = v
else:
div_sel[k] = v
divides.append(div_sel)
else:
pass
# add glacier name to the entity
name = ['I:' + row.name] * len(sel)
add_n = sel.RGIId.isin(wgms_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'W-' + name[z]
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'G-' + name[z]
sel.loc[:, 'Name'] = name
rgidf.append(sel)
# Add divides to the original one
adf = pd.DataFrame(divides)
adf.to_pickle(cfg.PATHS['itmix_divs'])
log.info('N glaciers ITMIX: {}'.format(len(rgidf)))
# WGMS glaciers which are not already there
# Actually we should remove the data of those 7 to be honest...
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
wgms_df = wgms_df.loc[~ wgms_df.RGI_ID.isin(_rgi_ids_for_overwrite)]
log.info('N glaciers WGMS: {}'.format(len(wgms_df)))
for i, row in wgms_df.iterrows():
rid = row.RGI_ID
reg = rid.split('-')[1].split('.')[0]
# read the rgi region
rgi_shp = find_path(RGI_DIR, reg + '_rgi50_*.shp')
rgi_df = salem.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_cor = row.NAME.replace('/', 'or').replace('.', '').replace(' ', '-')
name = ['W:' + _cor] * len(sel)
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'G-' + name[z]
for n in name:
if len(n) > 48:
raise
sel.loc[:, 'Name'] = name
rgidf.append(sel)
_rgi_ids_for_overwrite.extend(wgms_df.RGI_ID.values)
# GTD glaciers which are not already there
# Actually we should remove the data of those 2 to be honest...
gtd_df = gtd_df.loc[~ gtd_df.RGI_ID.isin(_rgi_ids_for_overwrite)]
log.info('N glaciers GTD: {}'.format(len(gtd_df)))
for i, row in gtd_df.iterrows():
rid = row.RGI_ID
reg = rid.split('-')[1].split('.')[0]
# read the rgi region
rgi_shp = find_path(RGI_DIR, reg + '_rgi50_*.shp')
rgi_df = salem.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_corname = row.NAME.replace('/', 'or').replace('.', '').replace(' ', '-')
name = ['G:' + _corname] * len(sel)
for n in name:
if len(n) > 48:
raise
sel.loc[:, 'Name'] = name
rgidf.append(sel)
# Save for not computing each time
rgidf = pd.concat(rgidf)
rgidf.to_pickle(df_rgi_file)
return rgidf
def get_rgi_df(reset=False):
# This makes an RGI dataframe with all ITMIX + WGMS + GTD glaciers
df_rgi_file = os.path.expanduser('~/itmix_rgi_shp.pkl')
if os.path.exists(df_rgi_file) and not reset:
rgidf = pd.read_pickle(df_rgi_file)
else:
linkf = os.path.join(DATA_DIR, 'itmix', 'itmix_rgi_links.pkl')
df_itmix = pd.read_pickle(linkf)
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
f = utils.get_glathida_file()
gtd_df = pd.read_csv(f)
rgidf = []
_rgi_ids = []
for i, row in df_itmix.iterrows():
# read the rgi region
rgi_shp = os.path.join(RGI_DIR, "*",
row['rgi_reg'] + '_rgi50_*.shp')
rgi_shp = list(glob.glob(rgi_shp))[0]
rgi_df = salem.utils.read_shapefile(rgi_shp, cached=True)
rgi_parts = row.T['rgi_parts_ids']
sel = rgi_df.loc[rgi_df.RGIId.isin(rgi_parts)].copy()
_rgi_ids.extend(rgi_parts)
# use the ITMIX shape where possible
if row.name in ['Hellstugubreen', 'Freya', 'Aqqutikitsoq',
'Brewster', 'Kesselwandferner', 'NorthGlacier',
'SouthGlacier', 'Tasman', 'Unteraar',
'Washmawapta']:
for shf in glob.glob(itmix_cfg.itmix_data_dir + '*/*/*_' +
row.name + '*.shp'):
pass
shp = salem.utils.read_shapefile(shf)
if row.name == 'Unteraar':
shp = shp.iloc[[-1]]
if 'LineString' == shp.iloc[0].geometry.type:
shp.loc[shp.index[0], 'geometry'] = shpg.Polygon(shp.iloc[0].geometry)
assert len(shp) == 1
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
sel = sel.iloc[[0]]
sel.loc[sel.index[0], 'geometry'] = shp
sel.loc[sel.index[0], 'Area'] = area_km2
elif row.name == 'Urumqi':
# ITMIX Urumqi is in fact two glaciers
for shf in glob.glob(itmix_cfg.itmix_data_dir + '*/*/*_' +
row.name + '*.shp'):
pass
shp2 = salem.utils.read_shapefile(shf)
assert len(shp2) == 2
for k in [0, 1]:
shp = shp2.iloc[[k]].copy()
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
assert sel.loc[sel.index[k], 'geometry'].contains(shp.centroid)
sel.loc[sel.index[k], 'geometry'] = shp
sel.loc[sel.index[k], 'Area'] = area_km2
assert len(sel) == 2
else:
pass
# add glacier name to the entity
name = ['I:' + row.name] * len(sel)
add_n = sel.RGIId.isin(wgms_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'W-' + name[z]
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'G-' + name[z]
sel.loc[:, 'Name'] = name
rgidf.append(sel)
# WGMS glaciers which are not already there
# Actually we should remove the data of those 7 to be honest...
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
print('N WGMS before: {}'.format(len(wgms_df)))
wgms_df = wgms_df.loc[~ wgms_df.RGI_ID.isin(_rgi_ids)]
print('N WGMS after: {}'.format(len(wgms_df)))
for i, row in wgms_df.iterrows():
rid = row.RGI_ID
reg = rid.split('-')[1].split('.')[0]
# read the rgi region
rgi_shp = os.path.join(RGI_DIR, "*",
reg + '_rgi50_*.shp')
rgi_shp = list(glob.glob(rgi_shp))[0]
rgi_df = salem.utils.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_cor = row.NAME.replace('/', 'or').replace('.', '').replace(' ', '-')
name = ['W:' + _cor] * len(sel)
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = 'G-' + name[z]
for n in name:
if len(n) > 48:
raise
sel.loc[:, 'Name'] = name
rgidf.append(sel)
_rgi_ids.extend(wgms_df.RGI_ID.values)
# GTD glaciers which are not already there
# Actually we should remove the data of those 2 to be honest...
print('N GTD before: {}'.format(len(gtd_df)))
gtd_df = gtd_df.loc[~ gtd_df.RGI_ID.isin(_rgi_ids)]
print('N GTD after: {}'.format(len(gtd_df)))
for i, row in gtd_df.iterrows():
rid = row.RGI_ID
reg = rid.split('-')[1].split('.')[0]
# read the rgi region
rgi_shp = os.path.join(RGI_DIR, "*",
reg + '_rgi50_*.shp')
rgi_shp = list(glob.glob(rgi_shp))[0]
rgi_df = salem.utils.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_corname = row.NAME.replace('/', 'or').replace('.', '').replace(' ', '-')
name = ['G:' + _corname] * len(sel)
for n in name:
if len(n) > 48:
raise
sel.loc[:, 'Name'] = name
rgidf.append(sel)
# Save for not computing each time
rgidf = pd.concat(rgidf)
rgidf.to_pickle(df_rgi_file)
return rgidf
def optimize_inversion_params(gdirs):
"""Optimizes fs and fd based on GlaThiDa thicknesses.
We use the glacier averaged thicknesses provided by GlaThiDa and correct
them for differences in area with RGI, using a glacier specific volume-area
scaling formula.
Parameters
----------
gdirs: list of oggm.GlacierDirectory objects
"""
# Do we even need to do this?
if not cfg.PARAMS['optimize_inversion_params']:
log.info('User did not want to optimize the inversion params')
return
# Get test glaciers (all glaciers with thickness data)
fpath = utils.get_glathida_file()
try:
gtd_df = pd.read_csv(fpath).sort_values(by=['RGI_ID'])
except AttributeError:
gtd_df = pd.read_csv(fpath).sort(columns=['RGI_ID'])
dfids = gtd_df['RGI_ID'].values
ref_gdirs = [gdir for gdir in gdirs if gdir.rgi_id in dfids]
if len(ref_gdirs) == 0:
raise RuntimeError('No reference GlaThiDa glaciers. Maybe something '
'went wrong with the link list?')
ref_rgiids = [gdir.rgi_id for gdir in ref_gdirs]
gtd_df = gtd_df.set_index('RGI_ID').loc[ref_rgiids]
# Account for area differences between glathida and rgi
gtd_df['RGI_AREA'] = [gdir.rgi_area_km2 for gdir in ref_gdirs]
ref_area_km2 = gtd_df.RGI_AREA.values
ref_area_m2 = ref_area_km2 * 1e6
gtd_df.VOLUME = gtd_df.MEAN_THICKNESS * gtd_df.GTD_AREA * 1e-3
ref_cs = gtd_df.VOLUME.values / (gtd_df.GTD_AREA.values**1.375)
ref_volume_km3 = ref_cs * ref_area_km2**1.375
ref_thickness_m = ref_volume_km3 / ref_area_km2 * 1000.
# Minimize volume or thick RMSD?
optim_t = cfg.PARAMS['optimize_thick']
if optim_t:
ref_data = ref_thickness_m
tol = 0.1
else:
ref_data = ref_volume_km3
tol = 1.e-4
if cfg.PARAMS['invert_with_sliding']:
# Optimize with both params
log.info('Compute the inversion parameters.')
def to_optimize(x):
tmp_ref = np.zeros(len(ref_gdirs))
glen_a = cfg.A * x[0]
fs = cfg.FS * x[1]
for i, gdir in enumerate(ref_gdirs):
v, a = mass_conservation_inversion(gdir, glen_a=glen_a,
fs=fs, write=False)
if optim_t:
tmp_ref[i] = v / a
else:
tmp_ref[i] = v * 1e-9
return utils.rmsd(tmp_ref, ref_data)
opti = optimization.minimize(to_optimize, [1., 1.],
bounds=((0.01, 10), (0.01, 10)),
tol=tol)
# Check results and save.
glen_a = cfg.A * opti['x'][0]
fs = cfg.FS * opti['x'][1]
else:
# Optimize without sliding
log.info('Compute the inversion parameter.')
def to_optimize(x):
tmp_ref = np.zeros(len(ref_gdirs))
glen_a = cfg.A * x[0]
for i, gdir in enumerate(ref_gdirs):
v, a = mass_conservation_inversion(gdir, glen_a=glen_a,
fs=0., write=False)
if optim_t:
tmp_ref[i] = v / a
else:
tmp_ref[i] = v * 1e-9
return utils.rmsd(tmp_ref, ref_data)
opti = optimization.minimize(to_optimize, [1.],
bounds=((0.01, 10),),
tol=tol)
# Check results and save.
glen_a = cfg.A * opti['x'][0]
fs = 0.
# This is for the stats
oggm_volume_m3 = np.zeros(len(ref_gdirs))
rgi_area_m2 = np.zeros(len(ref_gdirs))
for i, gdir in enumerate(ref_gdirs):
v, a = mass_conservation_inversion(gdir, glen_a=glen_a, fs=fs,
write=False)
oggm_volume_m3[i] = v
rgi_area_m2[i] = a
assert np.allclose(rgi_area_m2 * 1e-6, ref_area_km2)
# This is for each glacier
out = dict()
out['glen_a'] = glen_a
out['fs'] = fs
out['factor_glen_a'] = opti['x'][0]
try:
out['factor_fs'] = opti['x'][1]
except IndexError:
out['factor_fs'] = 0.
for gdir in gdirs:
gdir.write_pickle(out, 'inversion_params')
# This is for the working dir
# Simple stats
out['vol_rmsd'] = utils.rmsd(oggm_volume_m3 * 1e-9, ref_volume_km3)
out['thick_rmsd'] = utils.rmsd(oggm_volume_m3 / ref_area_m2,
ref_thickness_m)
log.info('Optimized glen_a and fs with a factor {factor_glen_a:.2f} and '
'{factor_fs:.2f} for a thick RMSD of '
'{thick_rmsd:.1f} m and a volume RMSD of '
'{vol_rmsd:.3f} km3'.format(**out))
df = pd.DataFrame(out, index=[0])
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_params.csv')
df.to_csv(fpath)
# All results
df = dict()
df['ref_area_km2'] = ref_area_km2
df['ref_volume_km3'] = ref_volume_km3
df['oggm_volume_km3'] = oggm_volume_m3 * 1e-9
df['vas_volume_km3'] = 0.034*(df['ref_area_km2']**1.375)
rgi_id = [gdir.rgi_id for gdir in ref_gdirs]
df = pd.DataFrame(df, index=rgi_id)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df.to_csv(fpath)
# return value for tests
return out
def get_rgi_df(reset=False):
"""This function prepares a kind of `fake` RGI file, with the updated
geometries for ITMIX.
"""
# This makes an RGI dataframe with all ITMIX + WGMS + GTD glaciers
RGI_DIR = utils.get_rgi_dir()
df_rgi_file = os.path.join(DATA_DIR, "itmix", "itmix_rgi_shp.pkl")
if os.path.exists(df_rgi_file) and not reset:
rgidf = pd.read_pickle(df_rgi_file)
else:
linkf = os.path.join(DATA_DIR, "itmix", "itmix_rgi_links.pkl")
df_itmix = pd.read_pickle(linkf)
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
f = utils.get_glathida_file()
gtd_df = pd.read_csv(f)
divides = []
rgidf = []
_rgi_ids_for_overwrite = []
for i, row in df_itmix.iterrows():
log.info("Prepare RGI df for " + row.name)
# read the rgi region
rgi_shp = find_path(RGI_DIR, row["rgi_reg"] + "_rgi50_*.shp")
rgi_df = salem.utils.read_shapefile(rgi_shp, cached=True)
rgi_parts = row.T["rgi_parts_ids"]
sel = rgi_df.loc[rgi_df.RGIId.isin(rgi_parts)].copy()
# use the ITMIX shape where possible
if row.name in [
"Hellstugubreen",
"Freya",
"Aqqutikitsoq",
"Brewster",
"Kesselwandferner",
"NorthGlacier",
"SouthGlacier",
"Tasman",
"Unteraar",
"Washmawapta",
"Columbia",
]:
shf = find_path(SEARCHD, "*_" + row.name + "*.shp")
shp = salem.utils.read_shapefile(shf)
if row.name == "Unteraar":
shp = shp.iloc[[-1]]
if "LineString" == shp.iloc[0].geometry.type:
shp.loc[shp.index[0], "geometry"] = shpg.Polygon(shp.iloc[0].geometry)
if shp.iloc[0].geometry.type == "MultiLineString":
# Columbia
geometry = shp.iloc[0].geometry
parts = list(geometry)
for p in parts:
assert p.type == "LineString"
exterior = shpg.Polygon(parts[0])
# let's assume that all other polygons are in fact interiors
interiors = []
for p in parts[1:]:
assert exterior.contains(p)
interiors.append(p)
geometry = shpg.Polygon(parts[0], interiors)
assert "Polygon" in geometry.type
shp.loc[shp.index[0], "geometry"] = geometry
assert len(shp) == 1
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
sel = sel.iloc[[0]]
sel.loc[sel.index[0], "geometry"] = shp
sel.loc[sel.index[0], "Area"] = area_km2
elif row.name == "Urumqi":
# ITMIX Urumqi is in fact two glaciers
shf = find_path(SEARCHD, "*_" + row.name + "*.shp")
shp2 = salem.utils.read_shapefile(shf)
assert len(shp2) == 2
for k in [0, 1]:
shp = shp2.iloc[[k]].copy()
area_km2 = shp.iloc[0].geometry.area * 1e-6
shp = salem.gis.transform_geopandas(shp)
shp = shp.iloc[0].geometry
assert sel.loc[sel.index[k], "geometry"].contains(shp.centroid)
sel.loc[sel.index[k], "geometry"] = shp
sel.loc[sel.index[k], "Area"] = area_km2
assert len(sel) == 2
elif len(rgi_parts) > 1:
# Ice-caps. Make divides
# First we gather all the parts:
sel = rgi_df.loc[rgi_df.RGIId.isin(rgi_parts)].copy()
# Make the multipolygon for the record
multi = shpg.MultiPolygon([g for g in sel.geometry])
# update the RGI attributes. We take a dummy rgi ID
new_area = np.sum(sel.Area)
found = False
for i in range(len(sel)):
tsel = sel.iloc[[i]].copy()
if "Multi" in tsel.loc[tsel.index[0], "geometry"].type:
continue
else:
found = True
sel = tsel
break
if not found:
raise RuntimeError()
inif = 0.0
add = 1e-5
if row.name == "Devon":
inif = 0.001
add = 1e-4
while True:
buff = multi.buffer(inif)
if "Multi" in buff.type:
inif += add
else:
break
x, y = multi.centroid.xy
if "Multi" in buff.type:
raise RuntimeError
sel.loc[sel.index[0], "geometry"] = buff
sel.loc[sel.index[0], "Area"] = new_area
sel.loc[sel.index[0], "CenLon"] = np.asarray(x)[0]
sel.loc[sel.index[0], "CenLat"] = np.asarray(y)[0]
# Divides db
div_sel = dict()
for k, v in sel.iloc[0].iteritems():
if k == "geometry":
div_sel[k] = multi
elif k == "RGIId":
div_sel["RGIID"] = v
else:
div_sel[k] = v
divides.append(div_sel)
else:
pass
# add glacier name to the entity
name = ["I:" + row.name] * len(sel)
add_n = sel.RGIId.isin(wgms_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = "W-" + name[z]
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = "G-" + name[z]
sel.loc[:, "Name"] = name
rgidf.append(sel)
# Add divides to the original one
adf = pd.DataFrame(divides)
adf.to_pickle(cfg.PATHS["itmix_divs"])
log.info("N glaciers ITMIX: {}".format(len(rgidf)))
# WGMS glaciers which are not already there
# Actually we should remove the data of those 7 to be honest...
f, d = utils.get_wgms_files()
wgms_df = pd.read_csv(f)
wgms_df = wgms_df.loc[~wgms_df.RGI_ID.isin(_rgi_ids_for_overwrite)]
log.info("N glaciers WGMS: {}".format(len(wgms_df)))
for i, row in wgms_df.iterrows():
rid = row.RGI_ID
reg = rid.split("-")[1].split(".")[0]
# read the rgi region
rgi_shp = find_path(RGI_DIR, reg + "_rgi50_*.shp")
rgi_df = salem.utils.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_cor = row.NAME.replace("/", "or").replace(".", "").replace(" ", "-")
name = ["W:" + _cor] * len(sel)
add_n = sel.RGIId.isin(gtd_df.RGI_ID.values)
for z, it in enumerate(add_n.values):
if it:
name[z] = "G-" + name[z]
for n in name:
if len(n) > 48:
raise
sel.loc[:, "Name"] = name
rgidf.append(sel)
_rgi_ids_for_overwrite.extend(wgms_df.RGI_ID.values)
# GTD glaciers which are not already there
# Actually we should remove the data of those 2 to be honest...
gtd_df = gtd_df.loc[~gtd_df.RGI_ID.isin(_rgi_ids_for_overwrite)]
log.info("N glaciers GTD: {}".format(len(gtd_df)))
for i, row in gtd_df.iterrows():
rid = row.RGI_ID
reg = rid.split("-")[1].split(".")[0]
# read the rgi region
rgi_shp = find_path(RGI_DIR, reg + "_rgi50_*.shp")
rgi_df = salem.utils.read_shapefile(rgi_shp, cached=True)
sel = rgi_df.loc[rgi_df.RGIId.isin([rid])].copy()
assert len(sel) == 1
# add glacier name to the entity
_corname = row.NAME.replace("/", "or").replace(".", "").replace(" ", "-")
name = ["G:" + _corname] * len(sel)
for n in name:
if len(n) > 48:
raise
sel.loc[:, "Name"] = name
rgidf.append(sel)
# Save for not computing each time
rgidf = pd.concat(rgidf)
rgidf.to_pickle(df_rgi_file)
return rgidf
