def _run_to_present(tupel, gdir, ys, ye, bias):
"""
Run glacier candidates forwards.
"""
suffix = tupel[0]
#path = gdir.get_filepath('model_run', filesuffix=suffix)
path = os.path.join(gdir.dir, str(ys), 'model_run' + suffix + '.nc')
# does file already exists?
if not os.path.exists(path):
try:
tasks.run_from_climate_data(gdir,
ys=ys,
ye=ye,
bias=bias,
output_filesuffix=suffix,
init_model_fls=copy.deepcopy(
tupel[1].fls))
return suffix
# oggm failed --> probaly "glacier exeeds boundaries"
except:
return None
else:
# does file contain a model?
try:
fmod = FileModel(path)
return suffix
except:
return None
def _run_to_present(array, gdir, ys, ye, mb_offset):
"""
Run glacier candidates forwards.
"""
init_yr = array[0]
init_filesuffix = array[1]
s = init_filesuffix.split('_random')[-1]
output_filesuffix = str(ys) + '_past' + s + '_' + str(int(init_yr))
path = os.path.join(gdir.dir, str(ys),
'model_geometry' + output_filesuffix + '.nc')
# does file already exists?
if not os.path.exists(path):
try:
tasks.run_from_climate_data(gdir,
ys=ys,
ye=ye,
bias=mb_offset,
init_model_filesuffix=init_filesuffix,
init_model_yr=init_yr,
output_filesuffix=output_filesuffix)
return output_filesuffix
# oggm failed --> probaly "glacier exeeds boundaries"
except:
return None
else:
# does file contain a model?
try:
fmod = FileModel(path)
return suffix
except:
return None
def _run_parallel_experiment(gdir, t0, te):
"""
Creates the synthetic experiment for one glacier. model_run_synthetic_experiment.nc
will be saved in working directory.
"""
try:
fls = gdir.read_pickle('model_flowlines')
# try to run random climate with temperature bias -1
model = tasks.run_random_climate(gdir,
nyears=600,
y0=t0,
bias=0,
seed=1,
temperature_bias=-1,
init_model_fls=fls)
# construct observed glacier, previous glacier will be run forward from
# t0 - te with past climate file
b = fls[-1].bed_h
fls = copy.deepcopy(model.fls)
tasks.run_from_climate_data(gdir,
ys=t0,
ye=te,
init_model_fls=fls,
output_filesuffix='_synthetic_experiment')
except:
print('experiment failed : ' + str(gdir.rgi_id))
def plot_lenght(gdir, candidates_df, experiment):
plot_dir = '/home/juliaeis/Dokumente/OGGM/work_dir/find_initial_state/past_state_information/plots'
fig, ax = plt.subplots(figsize=(20, 10))
list = range(len(candidates_df))
pool = Pool()
present_models = pool.map(
partial(run_and_store_to_present,
gdir=gdir,
ys=1850,
ye=2000,
flowlines=candidates_df.model), list)
pool.close()
pool.join()
first1 = 0
first2 = 0
for i in list:
if candidates_df.iloc[i].objective <= 100:
color = 'green'
first1 = first1 + 1
else:
color = 'red'
first2 = first2 + 1
path = gdir.get_filepath('model_run', filesuffix='_past_' + str(i))
fmod = FileModel(path)
if first1 == 1:
p1 = fmod.volume_m3_ts().plot(ax=ax, color=color, label='possible')
first1 = 10
elif first2 == 1:
p2 = fmod.volume_m3_ts().plot(ax=ax,
color=color,
label='not possible')
first2 = 10
else:
fmod.volume_m3_ts().plot(ax=ax, color=color, label='')
tasks.run_from_climate_data(gdir,
ys=1850,
ye=2000,
init_model_fls=deepcopy(
experiment['y_t0'].fls),
output_filesuffix='_past_experiment')
path = gdir.get_filepath('model_run', filesuffix='_past_experiment')
fmod = FileModel(path)
fmod.volume_m3_ts().plot(ax=ax, style='k:', linewidth=3, label='')
plt.tick_params(axis='both', which='major', labelsize=25)
plt.xlabel('time', fontsize=30)
plt.ylabel(r'volume $(m^3)$', fontsize=30)
plt.title(gdir.rgi_id, fontsize=30)
plt.legend(fontsize=20)
plt.savefig(os.path.join(plot_dir, 'lenght_' + str(gdir.rgi_id) + '.png'),
dpi=200)
plt.show()
def plot_issue(gdir, plot_dir):
#plt.style.use('ggplot')
workflow.gis_prepro_tasks([gdir])
workflow.climate_tasks([gdir])
workflow.inversion_tasks([gdir])
tasks.init_present_time_glacier(gdir)
# Observed length changes
df = gdir.get_ref_length_data()
df = df.loc[1855:2003]['dL']
df = df - df.iloc[-1]
tasks.run_from_climate_data(gdir,
ys=1855,
ye=2003,
output_filesuffix='hist_from_current')
ds = xr.open_dataset(
gdir.get_filepath('model_diagnostics', filesuffix='hist_from_current'))
(ds.length_m.to_series().rolling(36, center=True).mean() -
ds.length_m.to_series().iloc[0]).plot(c='C0', label='OGGM')
#s = s - s.iloc[-1]
#print(s)
ax = df.plot(c='k', label='Observations')
#s.plot(c='C0', label='OGGM');
plt.legend()
ax.set_ylabel('Glacier Length Change [m]')
plt.title('Hintereisferner length changes Experiment 2')
plt.tight_layout()
plt.show()
'''
fls = gdir.read_pickle('model_flowlines')
x = np.arange(fls[-1].nx) *fls[-1].dx * fls[-1].map_dx
plt.figure(figsize=(13,10))
rc('axes', linewidth=3)
plt.plot(x,fls[-1].surface_h,linewidth=3, label='Surface Elevation')
plt.plot(x,fls[-1].bed_h,'k',linewidth=3,label='Bed Topography')
plt.ylabel('Altitude (m)',size=30)
plt.xlabel('Distance along the Flowline (m)',size=30)
plt.legend(loc='best',fontsize=30)
#plt.annotate('?', xy=(5000, 2700), fontsize=40)
plt.tick_params(axis='both', which='major', labelsize=30)
plt.title(gdir.rgi_id+ ': '+gdir.name,size=35)
plt.savefig(os.path.join(plot_dir, 'issue_today.png'),dpi=200)
'''
#plt.savefig(os.path.join(plot_dir, 'issue_1850.pdf'),dpi=200)
plt.show()
return
def _run_experiment(gdir, temp_bias, bias, ys, ye):
"""
Creates the synthetic experiment for one glacier. model_run_experiment.nc
will be saved in working directory.
"""
# check, if this experiment already exists
try:
rp = gdir.get_filepath('model_run',
filesuffix='_advanced_experiment_' +
str(temp_bias) + '_' + str(bias))
model = FileModel(rp)
# otherwise create experiment
except:
try:
fls = gdir.read_pickle('model_flowlines')
model = tasks.run_random_climate(
gdir,
nyears=600,
y0=ys,
bias=bias,
seed=1,
temperature_bias=temp_bias,
init_model_fls=fls,
output_filesuffix='_random_experiment_' + str(temp_bias) +
'_' + str(bias))
# construct observed glacier, previous glacier will be run forward from
# 1917 - rgi_date with past climate file
fls = copy.deepcopy(model.fls)
tasks.run_from_climate_data(
gdir,
ys=ys,
ye=ye,
init_model_fls=fls,
bias=bias,
output_filesuffix='_advanced_experiment_' + str(temp_bias) +
'_' + str(bias))
# to return FileModel
rp = gdir.get_filepath('model_run',
filesuffix='_advanced_experiment_' +
str(temp_bias) + '_' + str(bias))
model = FileModel(rp)
except:
with open(os.path.join(gdir.dir, 'log.txt')) as log:
error = list(log)[-1].split(';')[-1]
return error
return model
def _single_calibration_run(gdir, mb_offset, ys, ye):
"""
Creates the synthetic experiment for one glacier. model_geometry_experiment.nc
will be saved in working directory.
"""
# check, if this model_geometry already exists
try:
rp = gdir.get_filepath('model_geometry',
filesuffix='_calibration_past_' +
str(mb_offset))
model = FileModel(rp)
# otherwise create calibration_run with mb_offset
except:
try:
fls = gdir.read_pickle('model_flowlines')
# run a 600 years random run with mb_offset
tasks.run_random_climate(gdir,
nyears=600,
y0=ys,
bias=mb_offset,
seed=1,
init_model_fls=fls,
output_filesuffix='_calibration_random_' +
str(mb_offset))
# construct s_OGGM --> previous glacier will be run forward from
# ys - ye with past climate file
tasks.run_from_climate_data(
gdir,
ys=ys,
ye=ye,
init_model_filesuffix='_calibration_random_' + str(mb_offset),
bias=mb_offset,
init_model_yr=600,
output_filesuffix='_calibration_past_' + str(mb_offset))
# return FileModel
rp = gdir.get_filepath('model_geometry',
filesuffix='_calibration_past_' +
str(mb_offset))
model = FileModel(rp)
except:
with open(os.path.join(gdir.dir, 'log.txt')) as log:
error = list(log)[-1].split(';')[-1]
return error
return model
def plot_volume_dif_time(gdir, dict, experiment):
fig, axs = plt.subplots(len(dict), 1)
try:
rp = gdir.get_filepath('model_run', filesuffix='experiment')
ex_mod = FileModel(rp)
except:
# create volume plot from experiment
model = experiment['y_t0']
tasks.run_from_climate_data(gdir,
ys=1850,
ye=2000,
init_model_fls=model.fls,
output_filesuffix='experiment')
rp = gdir.get_filepath('model_run', filesuffix='experiment')
ex_mod = FileModel(rp)
if gdir.name != '':
plt.suptitle(gdir.rgi_id + ':' + gdir.name, fontsize=20)
else:
plt.suptitle(gdir.rgi_id, fontsize=20)
import matplotlib as mpl
import matplotlib.cm as cm
norm = mpl.colors.LogNorm(vmin=0.1, vmax=1e5)
cmap = matplotlib.cm.get_cmap('RdYlGn_r')
for i, ax in enumerate(fig.axes):
yr = list(dict.keys())[i]
df = dict.get(yr)
df = df.sort_values('objective', ascending=False)
for i, model in df['model_t0'].iteritems():
color = cmap(norm(df.loc[i, 'objective']))
model.volume_m3_ts().plot(ax=ax, color=color, linewidth=2)
ex_mod.volume_m3_ts().plot(ax=ax,
color='k',
linestyle=':',
linewidth=3)
def _run_parallel_experiment(gdir, t0, te):
"""
Creates the synthetic experiment for one glacier. model_geometry_synthetic_experiment.nc
will be saved in working directory.
"""
try:
fls = gdir.read_pickle('model_flowlines')
# try to run random climate with temperature bias -1
tasks.run_random_climate(
gdir,
nyears=600,
y0=t0,
bias=0,
seed=1,
temperature_bias=-1,
init_model_fls=fls,
output_filesuffix='_random_synthetic_experiment')
# construct observed glacier, previous glacier will be run forward from
# t0 - te with past climate file
tasks.run_from_climate_data(
gdir,
ys=t0,
ye=te,
init_model_filesuffix='_random_synthetic_experiment',
init_model_yr=600,
output_filesuffix='_synthetic_experiment')
# remove output file from random run
os.remove(
gdir.get_filepath('model_geometry',
filesuffix='_random_synthetic_experiment'))
os.remove(
gdir.get_filepath('model_diagnostics',
filesuffix='_random_synthetic_experiment'))
except:
print('experiment failed : ' + str(gdir.rgi_id))
def _run_experiment(gdir, temp_bias, bias, ys, ye):
"""
Creates the synthetic experiment for one glacier. model_run_experiment.nc
will be saved in working directory.
"""
# check, if this experiment already exists
try:
rp = gdir.get_filepath('model_run',
filesuffix='_advanced_experiment_' +
str(temp_bias) + '_' + str(bias))
model = FileModel(rp)
# otherwise create experiment
except:
fls = gdir.read_pickle('model_flowlines')
try:
model = tasks.run_random_climate(gdir,
nyears=400,
y0=ys,
bias=bias,
seed=1,
temperature_bias=temp_bias,
init_model_fls=fls)
# construct observed glacier, previous glacier will be run forward from
# 1917 - 2000 with past climate file
fls = deepcopy(model.fls)
model = tasks.run_from_climate_data(
gdir,
ys=ys,
ye=ye,
init_model_fls=fls,
bias=bias,
output_filesuffix='_advanced_experiment_' + str(temp_bias) +
'_' + str(bias))
except:
pass
return model
def run_to_present(tupel,gdir,ys,ye):
suffix = tupel[0]
path = gdir.get_filepath('model_run',filesuffix=suffix)
# does file already exists?
if not os.path.exists(path):
try:
model = tasks.run_from_climate_data(gdir, ys=ys, ye=ye,
output_filesuffix=suffix,
init_model_fls=copy.deepcopy(
tupel[1].fls))
return suffix
# oggm failed --> probaly "glacier exeeds boundaries"
except:
return None
else:
# does file contain a model?
try:
fmod = FileModel(path)
return suffix
except:
return None
def plot_surface_col(gdir, df, experiment, ys):
#df = df[df['objective']<=100]
x = np.arange(experiment['y_t'].fls[-1].nx) * \
experiment['y_t'].fls[-1].dx * experiment['y_t'].fls[-1].map_dx
fig = plt.figure(figsize=(20, 15))
grid = plt.GridSpec(2, 2, hspace=0.2, wspace=0.2)
ax1 = plt.subplot(grid[0, 0])
ax2 = plt.subplot(grid[0, 1])
ax3 = plt.subplot(grid[1, :])
p2 = ax2.get_position()
if gdir.name != '':
plt.suptitle(gdir.rgi_id + ':' + gdir.name, x=p2.x1 / 2, fontsize=20)
else:
plt.suptitle(gdir.rgi_id, x=p2.x1 / 2, fontsize=20)
import matplotlib as mpl
import matplotlib.cm as cm
norm = mpl.colors.LogNorm(vmin=0.1, vmax=1e5)
cmap = matplotlib.cm.get_cmap('RdYlGn_r')
df = df.sort_values('objective', ascending=False)
for i, model in df['model_t0'].iteritems():
color = cmap(norm(df.loc[i, 'objective']))
ax1.plot(x, model.fls[-1].surface_h, color=color, linewidth=2)
model.volume_m3_ts().plot(ax=ax3, color=color, linewidth=2)
for i, model in df['model_t'].iteritems():
color = cmap(norm(df.loc[i, 'objective']))
ax2.plot(x, model.fls[-1].surface_h, color=color, linewidth=2)
ax2.plot(x, experiment['y_t'].fls[-1].surface_h, 'k:', linewidth=3)
ax2.plot(x, model.fls[-1].bed_h, 'k', linewidth=3)
# create volume plot from experiment
model = experiment['y_t0']
tasks.run_from_climate_data(gdir,
ys=1850,
ye=2000,
init_model_fls=model.fls,
output_filesuffix='experiment')
rp = gdir.get_filepath('model_run', filesuffix='experiment')
ex_mod = FileModel(rp)
ex_mod.volume_m3_ts().plot(ax=ax3, color='k', linestyle=':', linewidth=3)
ex_mod.run_until(ys)
ax1.plot(x, ex_mod.fls[-1].surface_h, 'k:', linewidth=3)
ax1.plot(x, ex_mod.fls[-1].bed_h, 'k', linewidth=3)
ax1.annotate(r'$t = ' + str(ys) + '$',
xy=(0.8, 0.9),
xycoords='axes fraction',
fontsize=15)
ax2.annotate(r'$t = 2000$',
xy=(0.8, 0.9),
xycoords='axes fraction',
fontsize=15)
ax1.set_ylabel('Altitude (m)', fontsize=15)
ax1.set_xlabel('Distance along the main flowline (m)', fontsize=15)
ax2.set_ylabel('Altitude (m)', fontsize=15)
ax2.set_xlabel('Distance along the main flowline (m)', fontsize=15)
ax3.set_ylabel(r'Volume ($m^3$)', fontsize=15)
ax3.set_xlabel('Time (years)', fontsize=15)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cax, kw = mpl.colorbar.make_axes([ax1, ax2, ax3])
cbar = fig.colorbar(sm, cax=cax, **kw)
cbar.ax.tick_params(labelsize=15)
cbar.set_label('objective', fontsize=15)
ax1.tick_params(axis='both', which='major', labelsize=15)
ax2.tick_params(axis='both', which='major', labelsize=15)
ax3.tick_params(axis='both', which='major', labelsize=15)
ax3.yaxis.offsetText.set_fontsize(15)
plot_dir = '/home/juliaeis/Dokumente/OGGM/work_dir/find_initial_state/past_state_information/plots'
plt.savefig(os.path.join(plot_dir, 'surface_' + str(ys) + '_' +
gdir.rgi_id + '.pdf'),
dpi=200)
plt.savefig(os.path.join(plot_dir, 'surface_' + str(ys) + '_' +
gdir.rgi_id + '.png'),
dpi=200)
def run_and_store_to_present(i, gdir, ys, ye, flowlines):
tasks.run_from_climate_data(gdir,
ys=1850,
ye=2000,
init_model_fls=deepcopy(flowlines.iloc[i].fls),
output_filesuffix='_past_' + str(i))
