def test_add_consensus(self, class_case_dir, monkeypatch):
# Init
cfg.initialize()
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['working_dir'] = class_case_dir
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
entity = gpd.read_file(get_demo_file('Hintereisferner_RGI5.shp'))
entity['RGIId'] = 'RGI60-11.00897'
gdir = workflow.init_glacier_directories(entity)[0]
tasks.define_glacier_region(gdir)
tasks.glacier_masks(gdir)
ft = utils.get_demo_file('RGI60-11.00897_thickness.tif')
monkeypatch.setattr(utils, 'file_downloader', lambda x: ft)
bedtopo.add_consensus_thickness(gdir)
# Check with rasterio
cfg.add_to_basenames('consensus', 'consensus.tif')
gis.rasterio_to_gdir(gdir, ft, 'consensus', resampling='bilinear')
with xr.open_dataset(gdir.get_filepath('gridded_data')) as ds:
mine = ds.consensus_ice_thickness
with xr.open_rasterio(gdir.get_filepath('consensus')) as ds:
ref = ds.isel(band=0)
# Check area
my_area = np.sum(np.isfinite(mine.data)) * gdir.grid.dx**2
np.testing.assert_allclose(my_area, gdir.rgi_area_m2, rtol=0.07)
rio_area = np.sum(ref.data > 0) * gdir.grid.dx**2
np.testing.assert_allclose(rio_area, gdir.rgi_area_m2, rtol=0.15)
np.testing.assert_allclose(my_area, rio_area, rtol=0.15)
# They are not same:
# - interpolation not 1to1 same especially at borders
# - we preserve total volume
np.testing.assert_allclose(mine.sum(), ref.sum(), rtol=0.01)
assert utils.rmsd(ref, mine) < 2
# Check vol
cdf = pd.read_hdf(utils.get_demo_file('rgi62_itmix_df.h5'))
ref_vol = cdf.loc[gdir.rgi_id].vol_itmix_m3
my_vol = mine.sum() * gdir.grid.dx**2
np.testing.assert_allclose(my_vol, ref_vol)
# Now check the rest of the workflow
# Check that no error when var not there
vn = 'consensus_ice_thickness'
centerlines.elevation_band_flowline(gdir, bin_variables=[vn, 'foo'])
# Check vol
df = pd.read_csv(gdir.get_filepath('elevation_band_flowline'),
index_col=0)
my_vol = (df[vn] * df['area']).sum()
np.testing.assert_allclose(my_vol, ref_vol)
centerlines.fixed_dx_elevation_band_flowline(gdir,
bin_variables=[vn, 'foo'])
fdf = pd.read_csv(gdir.get_filepath('elevation_band_flowline',
filesuffix='_fixed_dx'),
index_col=0)
# Check vol
my_vol = (fdf[vn] * fdf['area_m2']).sum()
np.testing.assert_allclose(my_vol, ref_vol)
def debris_to_gdir(gdir,
debris_dir=pygem_prms.debris_fp,
add_to_gridded=True,
hd_max=5,
hd_min=0,
ed_max=10,
ed_min=0):
"""Reproject the debris thickness and enhancement factor files to the given glacier directory
Variables are exported as new files in the glacier directory.
Reprojecting debris data from one map proj to another is done.
We use bilinear interpolation to reproject the velocities to the local glacier map.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
where to write the data
"""
assert os.path.exists(
debris_dir), "Error: debris directory does not exist."
hd_dir = debris_dir + 'hd_tifs/' + gdir.rgi_region + '/'
ed_dir = debris_dir + 'ed_tifs/' + gdir.rgi_region + '/'
glac_str_nolead = str(int(
gdir.rgi_region)) + '.' + gdir.rgi_id.split('-')[1].split('.')[1]
# If debris thickness data exists, then write to glacier directory
if os.path.exists(hd_dir + glac_str_nolead + '_hdts_m.tif'):
hd_fn = hd_dir + glac_str_nolead + '_hdts_m.tif'
elif os.path.exists(hd_dir + glac_str_nolead + '_hdts_m_extrap.tif'):
hd_fn = hd_dir + glac_str_nolead + '_hdts_m_extrap.tif'
else:
hd_fn = None
if hd_fn is not None:
rasterio_to_gdir(gdir, hd_fn, 'debris_hd', resampling='bilinear')
if add_to_gridded and hd_fn is not None:
output_fn = gdir.get_filepath('debris_hd')
# append the debris data to the gridded dataset
with rasterio.open(output_fn) as src:
grids_file = gdir.get_filepath('gridded_data')
with ncDataset(grids_file, 'a') as nc:
# Mask values
glacier_mask = nc['glacier_mask'][:]
data = src.read(1) * glacier_mask
data[data > hd_max] = 0
data[data < hd_min] = 0
# Write data
vn = 'debris_hd'
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f8', (
'y',
'x',
), zlib=True)
v.units = 'm'
v.long_name = 'Debris thicknness'
v[:] = data
# If debris enhancement factor data exists, then write to glacier directory
if os.path.exists(ed_dir + glac_str_nolead + '_meltfactor.tif'):
ed_fn = ed_dir + glac_str_nolead + '_meltfactor.tif'
elif os.path.exists(ed_dir + glac_str_nolead + '_meltfactor_extrap.tif'):
ed_fn = ed_dir + glac_str_nolead + '_meltfactor_extrap.tif'
else:
ed_fn = None
if ed_fn is not None:
rasterio_to_gdir(gdir, ed_fn, 'debris_ed', resampling='bilinear')
if add_to_gridded and ed_fn is not None:
output_fn = gdir.get_filepath('debris_ed')
# append the debris data to the gridded dataset
with rasterio.open(output_fn) as src:
grids_file = gdir.get_filepath('gridded_data')
with ncDataset(grids_file, 'a') as nc:
# Mask values
glacier_mask = nc['glacier_mask'][:]
data = src.read(1) * glacier_mask
data[data > ed_max] = 1
data[data < ed_min] = 1
# Write data
vn = 'debris_ed'
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f8', (
'y',
'x',
), zlib=True)
v.units = '-'
v.long_name = 'Debris enhancement factor'
v[:] = data
def test_repro_to_glacier(self, class_case_dir, monkeypatch):
# Init
cfg.initialize()
cfg.PATHS['working_dir'] = class_case_dir
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_Columbia.tif')
cfg.PARAMS['border'] = 10
entity = gpd.read_file(get_demo_file('RGI60-01.10689.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity)
tasks.define_glacier_region(gdir)
tasks.glacier_masks(gdir)
# use our files
region_files = {
'ALA': {
'vx': get_demo_file('crop_ALA_G0120_0000_vx.tif'),
'vy': get_demo_file('crop_ALA_G0120_0000_vy.tif')
}
}
monkeypatch.setattr(its_live, 'region_files', region_files)
monkeypatch.setattr(utils, 'file_downloader', lambda x: x)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
its_live.velocity_to_gdir(gdir)
with xr.open_dataset(gdir.get_filepath('gridded_data')) as ds:
mask = ds.glacier_mask.data.astype(bool)
vx = ds.obs_icevel_x.where(mask).data
vy = ds.obs_icevel_y.where(mask).data
vel = np.sqrt(vx**2 + vy**2)
assert np.nanmax(vel) > 2900
assert np.nanmin(vel) < 2
# We reproject with rasterio and check no big diff
cfg.BASENAMES['its_live_vx'] = ('its_live_vx.tif', '')
cfg.BASENAMES['its_live_vy'] = ('its_live_vy.tif', '')
gis.rasterio_to_gdir(gdir,
region_files['ALA']['vx'],
'its_live_vx',
resampling='bilinear')
gis.rasterio_to_gdir(gdir,
region_files['ALA']['vy'],
'its_live_vy',
resampling='bilinear')
with xr.open_rasterio(gdir.get_filepath('its_live_vx')) as da:
_vx = da.where(mask).data.squeeze()
with xr.open_rasterio(gdir.get_filepath('its_live_vy')) as da:
_vy = da.where(mask).data.squeeze()
_vel = np.sqrt(_vx**2 + _vy**2)
np.testing.assert_allclose(utils.rmsd(vel[mask], _vel[mask]),
0,
atol=40)
np.testing.assert_allclose(utils.md(vel[mask], _vel[mask]), 0, atol=8)
if DO_PLOT:
import matplotlib.pyplot as plt
smap = salem.Map(gdir.grid.center_grid, countries=False)
smap.set_shapefile(gdir.read_shapefile('outlines'))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
smap.set_topography(gdir.get_filepath('dem'))
vel = np.sqrt(vx**2 + vy**2)
smap.set_data(vel)
smap.set_plot_params(cmap='Blues', vmin=None, vmax=None)
xx, yy = gdir.grid.center_grid.xy_coordinates
xx, yy = smap.grid.transform(xx, yy, crs=gdir.grid.proj)
yy = yy[2::5, 2::5]
xx = xx[2::5, 2::5]
vx = vx[2::5, 2::5]
vy = vy[2::5, 2::5]
f, ax = plt.subplots()
smap.visualize(ax=ax,
title='ITS_LIVE velocity',
cbar_title='m yr-1')
ax.quiver(xx, yy, vx, vy)
plt.show()
def consensus_gridded(gdir,
h_consensus_fp=pygem_prms.h_consensus_fp,
add_mass=True,
add_to_gridded=True):
"""Bin consensus ice thickness and add total glacier mass to the given glacier directory
Updates the 'inversion_flowlines' save file and creates new consensus_mass.pkl
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
where to write the data
"""
# If binned mb data exists, then write to glacier directory
h_fn = h_consensus_fp + 'RGI60-' + gdir.rgi_region + '/' + gdir.rgi_id + '_thickness.tif'
assert os.path.exists(
h_fn
), 'Error: h_consensus_fullfn for ' + gdir.rgi_id + ' does not exist.'
# open consensus ice thickness estimate
h_dr = rasterio.open(h_fn, 'r', driver='GTiff')
h = h_dr.read(1).astype(rasterio.float32)
# Glacier mass [kg]
glacier_mass_raw = (h * h_dr.res[0] *
h_dr.res[1]).sum() * pygem_prms.density_ice
# print(glacier_mass_raw)
if add_mass:
# Pickle data
consensus_fn = gdir.get_filepath('consensus_mass')
with open(consensus_fn, 'wb') as f:
pickle.dump(glacier_mass_raw, f)
if add_to_gridded:
rasterio_to_gdir(gdir, h_fn, 'consensus_h', resampling='bilinear')
output_fn = gdir.get_filepath('consensus_h')
# append the debris data to the gridded dataset
with rasterio.open(output_fn) as src:
grids_file = gdir.get_filepath('gridded_data')
with ncDataset(grids_file, 'a') as nc:
# Mask values
glacier_mask = nc['glacier_mask'][:]
data = src.read(1) * glacier_mask
# Pixel area
pixel_m2 = abs(gdir.grid.dx * gdir.grid.dy)
# Glacier mass [kg] reprojoected (may lose or gain mass depending on resampling algorithm)
glacier_mass_reprojected = (
data * pixel_m2).sum() * pygem_prms.density_ice
# Scale data to ensure conservation of mass during reprojection
data_scaled = data * glacier_mass_raw / glacier_mass_reprojected
# glacier_mass = (data_scaled * pixel_m2).sum() * pygem_prms.density_ice
# print(glacier_mass)
# Write data
vn = 'consensus_h'
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f8', (
'y',
'x',
), zlib=True)
v.units = 'm'
v.long_name = 'Consensus ice thicknness'
v[:] = data_scaled
