def test_run_random_climate(self):
""" Test the run_random_climate task for a climate based on the
equilibrium period centred around t*. Additionally a positive and a
negative temperature bias are tested.
Returns
-------
"""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# define some parameters for the random climate model
nyears = 300
seed = 1
temp_bias = 0.5
# read the equilibirum year used for the mass balance calibration
t_star = gdir.read_json('vascaling_mustar')['t_star']
# run model with random climate
_ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star,
seed=seed)
# run model with positive temperature bias
_ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star,
seed=seed, temperature_bias=temp_bias,
output_filesuffix='_bias_p')
# run model with negative temperature bias
_ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star,
seed=seed,
temperature_bias=-temp_bias,
output_filesuffix='_bias_n')
# compile run outputs
ds = utils.compile_run_output([gdir], input_filesuffix='')
ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p')
ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n')
# the glacier should not change much under a random climate
# based on the equilibirum period centered around t*
assert abs(1 - ds.volume.mean() / ds.volume[0]) < 0.015
# higher temperatures should result in a smaller glacier
assert ds.volume.mean() > ds_p.volume.mean()
# lower temperatures should result in a larger glacier
assert ds.volume.mean() < ds_n.volume.mean()
def test_run_constant_climate(self):
""" Test the run_constant_climate task for a climate based on the
equilibrium period centred around t*. Additionally a positive and a
negative temperature bias are tested.
"""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# define some parameters for the constant climate model
nyears = 500
temp_bias = 0.5
_ = vascaling.run_constant_climate(gdir, nyears=nyears,
output_filesuffix='')
_ = vascaling.run_constant_climate(gdir, nyears=nyears,
temperature_bias=+temp_bias,
output_filesuffix='_bias_p')
_ = vascaling.run_constant_climate(gdir, nyears=nyears,
temperature_bias=-temp_bias,
output_filesuffix='_bias_n')
# compile run outputs
ds = utils.compile_run_output([gdir], input_filesuffix='')
ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p')
ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n')
# the glacier should not change under a constant climate
# based on the equilibirum period centered around t*
assert abs(1 - ds.volume.mean() / ds.volume[0]) < 1e-7
# higher temperatures should result in a smaller glacier
assert ds.volume.mean() > ds_p.volume.mean()
# lower temperatures should result in a larger glacier
assert ds.volume.mean() < ds_n.volume.mean()
# compute volume change from one year to the next
dV_p = (ds_p.volume[1:].values - ds_p.volume[:-1].values).flatten()
dV_n = (ds_n.volume[1:].values - ds_n.volume[:-1].values).flatten()
# compute relative volume change, with respect to the final volume
rate_p = abs(dV_p / float(ds_p.volume.values[-1]))
rate_n = abs(dV_n / float(ds_n.volume.values[-1]))
# the glacier should be in a new equilibirum for last 300 years
assert max(rate_p[-300:]) < 0.001
assert max(rate_n[-300:]) < 0.001
def _set_up_VAS_model(self):
"""Avoiding a chunk of code duplicate. Set's up a running volume/area
scaling model, including all needed prepo tasks.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# --------------------
# MASS BALANCE TASKS
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# instance the mass balance models
mbmod = vascaling.VAScalingMassBalance(gdir)
# ----------------
# DYNAMICAL PART
# ----------------
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
model = vascaling.VAScalingModel(year_0=y0,
area_m2_0=a0,
min_hgt=h0,
max_hgt=h1,
mb_model=mbmod)
return gdir, model
def test_run_until_equilibrium(self):
""""""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# instance a constant mass balance model, centred around t*
mb_model = vascaling.ConstantVASMassBalance(gdir)
# add a positive temperature bias
mb_model.temp_bias = 0.5
# create a VAS model: start with year 0 since we are using a constant
# massbalance model, other values are read from RGI
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
model = vascaling.VAScalingModel(year_0=0,
area_m2_0=gdir.rgi_area_m2,
min_hgt=min_hgt,
max_hgt=max_hgt,
mb_model=mb_model)
# run glacier with new mass balance model
model.run_until_equilibrium(rate=1e-4)
# equilibrium should be reached after a couple of 100 years
assert model.year <= 300
# new equilibrium glacier should be smaller (positive temperature bias)
assert model.volume_m3 < model.volume_m3_0
# run glacier for another 100 years and check volume again
v_eq = model.volume_m3
model.run_until(model.year + 100)
assert abs(1 - (model.volume_m3 / v_eq)) < 0.01
def _setup_mb_test(self):
"""Avoiding a chunk of code duplicate. Performs needed prepo tasks and
returns the oggm.GlacierDirectory.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and the glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = vascaling.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# run OGGM mu* calibration
climate.local_t_star(gdir, tstar=t_star, bias=bias)
climate.mu_star_calibration(gdir)
# pass the GlacierDirectory
return gdir
def test_local_t_star(self):
# set parameters for climate file and mass balance calibration
cfg.PARAMS['baseline_climate'] = 'CUSTOM'
cfg.PARAMS['baseline_y0'] = 1850
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['run_mb_calibration'] = False
# read the Hintereisferner
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and the glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = vascaling.t_star_from_refmb(gdir)
t_star, bias = res['t_star'], res['bias']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# read calibration results
vas_mustar_refmb = gdir.read_json('vascaling_mustar')
# get reference t* list
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, ref_df=ref_df)
# read calibration results
vas_mustar_refdf = gdir.read_json('vascaling_mustar')
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# read calibration results
vas_mustar = gdir.read_json('vascaling_mustar')
# compare with each other
assert vas_mustar_refdf == vas_mustar
# TODO: this test is failing currently
# np.testing.assert_allclose(vas_mustar_refmb['bias'],
# vas_mustar_refdf['bias'], atol=1)
vas_mustar_refdf.pop('bias')
vas_mustar_refmb.pop('bias')
# end of workaround
assert vas_mustar_refdf == vas_mustar_refmb
def test_run_until_and_store(self):
"""Test the volume/area scaling model against the oggm.FluxBasedModel.
Both models run the Hintereisferner over the entire HistAlp climate
period, initialized with the 2003 RGI outline without spin up.
The following two parameters for length, area and volume are tested:
- correlation coefficient
- relative RMSE, i.e. RMSE/mean(OGGM). Whereby the results from the
VAS model are offset with the average differences to the OGGM
results.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# --------------------
# SCALING MODEL
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# instance the mass balance models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0,
min_hgt=h0, max_hgt=h1,
mb_model=vas_mbmod)
# let model run over entire HistAlp climate period
vas_ds = vas_model.run_until_and_store(2003)
# ------
# OGGM
# ------
# compute local t* and the corresponding mu*
climate.local_t_star(gdir, tstar=t_star, bias=bias)
climate.mu_star_calibration(gdir)
# instance the mass balance models
mb_mod = massbalance.PastMassBalance(gdir)
# perform ice thickness inversion
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
# initialize present time glacier
flowline.init_present_time_glacier(gdir)
# instance flowline model
fls = gdir.read_pickle('model_flowlines')
y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0']
fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0)
# run model and store output as xarray data set
_, oggm_ds = fl_mod.run_until_and_store(2003)
# temporal indices must be equal
assert (vas_ds.time == oggm_ds.time).all()
# specify which parameters to compare and their respective correlation
# coefficients and rmsd values
params = ['length_m', 'area_m2', 'volume_m3']
corr_coeffs = np.array([0.96, 0.90, 0.93])
rmsds = np.array([0.43e3, 0.14e6, 0.03e9])
# compare given parameters
for param, cc, rmsd in zip(params, corr_coeffs, rmsds):
# correlation coefficient
assert corrcoef(oggm_ds[param].values, vas_ds[param].values) >= cc
# root mean squared deviation
rmsd_an = rmsd_bc(oggm_ds[param].values, vas_ds[param].values)
assert rmsd_an <= rmsd
climate.process_cru_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# --------------------
# MASS BALANCE TASKS
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# instance the mass balance models
mbmod = vascaling.VAScalingMassBalance(gdir)
# ----------------
# DYNAMICAL PART
# ----------------
# get reference area (from RGI entry)
a0 = gdir.rgi_area_m2
# get reference year (start of climate records)
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation (based on RGI outline)
h0, h1 = vascaling.get_min_max_elevation(gdir)
# initialize iteration counter variable
k = 1
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# --------------------
# MASS BALANCE TASKS
# --------------------
# compute local t* and the corresponding mu*
tstar = 1927
vascaling.local_t_star(gdir, tstar=tstar)
# instance the mass balance models
mbmod = vascaling.VAScalingMassBalance(gdir)
# ----------------
# DYNAMICAL PART
# ----------------
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
climate.process_histalp_data(gdir) # run center line preprocessing tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # compute the reference t* for the glacier # given the reference of mass balance measurements res = vascaling.t_star_from_refmb(gdir) vascaling.local_t_star(gdir) t_star, bias = res['t_star'], res['bias'] # -------------------- # SCALING MODEL # -------------------- # compute local t* and the corresponding mu* vascaling.local_t_star(gdir, tstar=t_star, bias=bias) # instance the mass balance models ben_mbmod = vascaling.VAScalingMassBalance(gdir) # get reference area a0 = gdir.rgi_area_m2 # get reference year
def compare(rgi_id, glacier_name):
"""
:param rgi_id:
:param glacier_name:
:return:
"""
# ---------------------
# PREPROCESSING TASKS
# ---------------------
# create test directory
wdir = os.path.join(os.path.abspath('.'), 'comparison_wdir')
if not os.path.exists(wdir):
os.makedirs(wdir)
shutil.rmtree(wdir)
os.makedirs(wdir)
# load default parameter file
cfg.initialize()
# RGI entity
# get/downlaod the rgi entity including the outline shapefile
rgi_df = utils.get_rgi_glacier_entities([rgi_id])
# set name, since not delivered with RGI
if rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] is None:
rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] = glacier_name
# select single entry
rgi_entity = rgi_df.iloc[0]
# GlacierDirectory
# specify the working directory and define the glacier directory
cfg.PATHS['working_dir'] = wdir
gdir = oggm.GlacierDirectory(rgi_entity)
# DEM and GIS tasks
# get the path to the DEM file (will download if necessary)
dem = utils.get_topo_file(gdir.cenlon, gdir.cenlat)
# set path in config file
cfg.PATHS['dem_file'] = dem[0][0]
cfg.PARAMS['border'] = 10
cfg.PARAMS['use_intersects'] = False
# run GIS tasks
gis.define_glacier_region(gdir, entity=rgi_entity)
gis.glacier_masks(gdir)
# Climate data
# using HistAlp
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# climate records before 1850 are hardly reliable, which is not so drastic for
# qualitative experiments (could be driven with random climate anyway)
# cfg.PARAMS['baseline_y0'] = 1850
# change hyper parameters for HistAlp
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
# run climate task
climate.process_histalp_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# --------------------
# SCALING MODEL
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# instance the mass balance models
vas_mb_mod = vascaling.VAScalingMassBalance(gdir)
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
# instance VAS model
vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0,
min_hgt=h0, max_hgt=h1,
mb_model=vas_mb_mod)
# run model over all HistAlp climate period
vas_df = vas_model.run_and_store(y1, reset=True)
# get relevant parameters
years_vas = vas_df.index.values
length_m_vas = vas_df.length_m.values
area_m2_vas = vas_df.area_m2.values
volume_m3_vas = vas_df.volume_m3.values
# ------
# OGGM
# ------
# compute local t* and the corresponding mu*
climate.local_t_star(gdir)
climate.mu_star_calibration(gdir)
# instance the mass balance models
mb_mod = massbalance.PastMassBalance(gdir)
# run inversion tasks
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
# initialize present time glacier
flowline.init_present_time_glacier(gdir)
# instance flowline model
fls = gdir.read_pickle('model_flowlines')
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1']
fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0)
# run model and store output as xarray data set
_, oggm_ds = fl_mod.run_until_and_store(y1)
years_oggm = oggm_ds.hydro_year.values
# annual index must be equal
np.testing.assert_array_equal(years_oggm, years_vas)
length_m_oggm = oggm_ds.length_m.values
area_m2_oggm = oggm_ds.area_m2.values
volume_m3_oggm = oggm_ds.volume_m3.values
# define column names for DataFrame
names = ['length_vas', 'length_oggm',
'area_vas', 'area_oggm',
'volume_vas', 'volume_oggm']
# combine glacier geometries into DataFrame
df = pd.DataFrame(np.array([length_m_vas, length_m_oggm,
area_m2_vas, area_m2_oggm,
volume_m3_vas, volume_m3_oggm]).T,
index=years_vas, columns=names)
# save to file
store = True
if store:
# define path and file names
folder = '/Users/oberrauch/work/master/data/'
df.to_csv(folder+'run_comparison.csv')
def plot_both(vas_df, oggm_df, ref=None, correct_bias=False,
title='', ylabel='', file_path='', exp=0):
""" Plot geometric parameters of both models.
If a `file_path` is given, the figure will be saved.
:param vas_df: (pandas.Series) geometric glacier parameter of the VAS model
:param oggm_df: (pandas.Series) geometric glacier parameter of the OGGM
:param ref: (pandas.Series) measured glacier parameter, optional
:param title: (string) figure title, optional
:param ylabel: (string) label for y-axis, optional
:param file_path: (string) where to store the figure, optional
:param exp: (int) exponent for labels in scientific notation, optional
"""
beamer = True
if beamer:
mpl.rc('axes', titlesize=18)
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=14)
mpl.rc('ytick', labelsize=14)
mpl.rc('legend', fontsize=10)
# create figure and first axes
fig = plt.figure(figsize=[6, 4])
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8])
# define colors
c1 = 'C0'
c2 = 'C1'
c3 = 'C3'
# plot vas and OGGM parameters
ax.plot(oggm_df.index, oggm_df.values, c=c2, label='OGGM')
ax.plot(vas_df.index, vas_df.values, c=c1, label='VAS')
if ref:
# plot reference parameter if given
ax.plot(ref.index, ref.values, c=c3, label='measurements')
if correct_bias:
# plot bias corrected vas
df_ = pd.DataFrame([oggm_df, vas_df]).T
bias = vas_df.values - df_.mean().diff().iloc[1]
ax.plot(vas_df.index, bias, c=c1, ls='--',
label='VAS, bias corrected')
# add RMSD as text
ax.text(0.05, 0.05,
'RMSD: {:.1e}'.format(utils.rmsd(oggm_df, bias)),
transform=plt.gca().transAxes)
# add correlation coefficient as text
ax.text(0.05, 0.11, 'Corr. Coef.: {:.2f}'.format(
utils.corrcoef(oggm_df, vas_df)),
transform=plt.gca().transAxes)
# add title, labels, legend
ax.set_title(title)
ax.set_ylabel(ylabel)
ax.legend()
import matplotlib.ticker
class OOMFormatter(matplotlib.ticker.ScalarFormatter):
def __init__(self, order=0, fformat="%1.1f", offset=False, mathText=False):
self.oom = order
self.fformat = fformat
matplotlib.ticker.ScalarFormatter.__init__(self, useOffset=offset, useMathText=mathText)
def _set_orderOfMagnitude(self, nothing):
self.orderOfMagnitude = self.oom
def _set_format(self, vmin, vmax):
self.format = self.fformat
if self._useMathText:
self.format = '$%s$' % matplotlib.ticker._mathdefault(self.format)
# use scientific notation with fixed exponent according
ax.yaxis.set_major_formatter(OOMFormatter(exp, "%1.2f"))
# store to file
if file_path:
plt.savefig(file_path, bbox_inches='tight',
format=file_path.split('.')[-1])
# specify plot directory
folder = '/Users/oberrauch/work/master/plots/'
# plot length
plot_both(df.length_vas, df.length_oggm, correct_bias=True,
title='Glacier length - {}'.format(glacier_name),
ylabel=r'Length [m]',
file_path=os.path.join(folder, '{}_length.pdf'.format(rgi_id)),
exp=3)
# plot area
plot_both(df.area_vas, df.area_oggm, correct_bias=True,
title='Surface area - {}'.format(glacier_name),
ylabel=r'Area [m$^2$]',
file_path=os.path.join(folder, '{}_area.pdf'.format(rgi_id)),
exp=6)
# plot volume
plot_both(df.volume_vas, df.volume_oggm, correct_bias=True,
title='Glacier volume - {}'.format(glacier_name),
ylabel=r'Volume [m$^3$]',
file_path=os.path.join(folder, '{}_volume.pdf'.format(rgi_id)),
exp=9)
