def plot_advanced_experiment(gdir):
fig = plt.figure(figsize=(15, 14))
grid = plt.GridSpec(1, 2, hspace=0.2, wspace=0.2)
ax1 = plt.subplot(grid[0])
ax2 = plt.subplot(grid[1], sharey=ax1)
ax2.plot(gdir.rgi_date,gdir.rgi_area_km2,'o', label='RGI area '+ str(gdir.rgi_date))
mod = FluxBasedModel(flowlines=gdir.read_pickle('model_flowlines'))
ax2.plot(gdir.rgi_date, mod.area_km2, 'o',
label='RGI area ' + str(gdir.rgi_date))
for f in os.listdir(gdir.dir):
if f.startswith('model_run_'):
rp = os.path.join(gdir.dir,f)
model = FileModel(rp)
model.area_km2_ts().plot(ax=ax2, label=f)
#ax2.set_xlim((1915,2005))
#ax2.legend()
plt.show()
def find_residual(gdir, temp_bias_list, ys,a=-2000,b=2000):
best_df = pd.DataFrame()
fls = gdir.read_pickle('model_flowlines')
mod = FluxBasedModel(flowlines=fls)
for temp_bias in temp_bias_list:
try:
ye = gdir.rgi_date
max_it = 15
i = 0
bounds = [a,b]
df = pd.DataFrame()
while i < max_it:
bias = round((bounds[0] + bounds[1]) / 2,1)
ex_mod2 = _run_experiment(gdir, temp_bias, bias, ys, ye)
diff = mod.area_km2 - ex_mod2.area_km2_ts()[ye]
df = df.append(pd.Series({'bias':bias,'area_diff':diff}),ignore_index=True)
if (abs(diff)<1e-4) or bounds[1]-bounds[0]<=1:
break
elif ex_mod2.area_km2_ts()[ye] > mod.area_km2:
bounds[0] = bias
else:
bounds[1] = bias
i +=1
# best bias found
bias = df.iloc[df.area_diff.abs().idxmin()].bias
rp = gdir.get_filepath('model_run', filesuffix='_advanced_experiment_'+str(temp_bias)+'_'+str(bias))
model = FileModel(rp)
diff = gdir.rgi_area_km2 - model.area_km2_ts()[gdir.rgi_date]
series = pd.Series({'rgi_id':gdir.rgi_id,'bias':bias,'iterations':i, 'area_diff':diff, 'model':model, 'temp_bias':temp_bias})
except:
series = pd.Series({'rgi_id':gdir.rgi_id, 'temp_bias':temp_bias})
best_df = best_df.append(series, ignore_index=True)
return best_df
# plot_random climate
if exp_df.loc[i, 'temp_bias'] == -1.0:
temp_bias = '-1'
if exp_df.loc[i, 'temp_bias'] == 0.0:
temp_bias = '0'
if exp_df.loc[i, 'temp_bias'] == -0.5:
temp_bias = '-0.5'
name = 'model_run_random_experiment_' + str(
exp_df.loc[i, 'temp_bias']) + '_' + str(exp_df.loc[i,
'bias']) + '.nc'
file = os.path.join(cfg.PATHS['working_dir'], 'temp_' + temp_bias,
'per_glacier', 'RGI60-11', 'RGI60-11.00',
'RGI60-11.00897', name)
model = FileModel(file)
model.area_km2_ts().plot(ax=ax1, color=color)
add_at(ax1, 'a')
add_at(ax2, 'b')
ax1.grid()
ax2.grid()
ax1.set_xticks([0, 100, 200, 300, 400, 500])
ax1.set_xlabel('Time(years)')
ax2.set_xlabel('Time(years)')
ax1.set_ylabel(r'Area (km$^2$)')
ax1.set_title('Random climate forcing')
ax2.set_title(r'CRU 1917-2003 (no $\widetilde{\beta}$)')
plt.suptitle('RGI60-11.00897: Hintereisferner')
plt.legend(title='\t' + r'temp.bias $\widetilde{\beta}$' + r'$\qquad$' +
def find_residual(gdir, temp_bias_list, ys, a=-2000, b=2000):
best_df = pd.DataFrame()
fls = gdir.read_pickle('model_flowlines')
mod = FluxBasedModel(flowlines=fls)
for temp_bias in temp_bias_list:
print(temp_bias)
try:
ye = gdir.rgi_date
max_it = 15
i = 0
bounds = [a, b]
df = pd.DataFrame()
while i < max_it:
bias = round((bounds[0] + bounds[1]) / 2, 1)
ex_mod2 = _run_experiment(gdir, temp_bias, bias, ys, ye)
fit = fitness_function(ye, ex_mod2, mod)
df = df.append(pd.Series({
'bias': bias,
'fitness': fit
}),
ignore_index=True)
if (abs(mod.area_km2 - ex_mod2.area_km2) < 1e-4
and fit < 125) or bounds[1] - bounds[0] <= 1:
break
elif ex_mod2.area_km2 > mod.area_km2:
bounds[0] = bias
else:
bounds[1] = bias
i += 1
# best bias found
bias = df.iloc[df.fitness.idxmin()].bias
rp = gdir.get_filepath('model_run',
filesuffix='_advanced_experiment_' +
str(temp_bias) + '_' + str(bias))
model = FileModel(rp)
model.run_until(ye)
rp = gdir.get_filepath('model_run',
filesuffix='_advanced_experiment_' +
str(temp_bias) + '_' + str(0.0))
ex_mod = FileModel(rp)
ex_mod.run_until(ye)
'''
plt.figure(figsize=(15,10))
plt.plot(model.fls[-1].surface_h,'r',label='best')
plt.plot(mod.fls[-1].surface_h, 'orange', label='original')
plt.plot(ex_mod.fls[-1].surface_h, 'r:', label='old experiment')
plt.plot(model.fls[-1].bed_h,'k', label='bed')
plt.legend()
utils.mkdir(os.path.join(cfg.PATHS['plot_dir'],'bias_test'))
plt.savefig(os.path.join(cfg.PATHS['plot_dir'],'bias_test',gdir.rgi_id+'.png'),dpi=200)
'''
diff = mod.area_km2 - model.area_km2_ts()[gdir.rgi_date]
model.reset_y0(ys)
series = pd.Series({
'rgi_id': gdir.rgi_id,
'bias': bias,
'iterations': i,
'fitness': df.fitness.min(),
'area_diff': diff,
'model': model,
'temp_bias': temp_bias
})
except:
series = pd.Series({'rgi_id': gdir.rgi_id, 'temp_bias': temp_bias})
best_df = best_df.append(series, ignore_index=True)
plt.figure()
x = np.arange(mod.fls[-1].nx) * mod.fls[-1].dx * mod.fls[-1].map_dx
for temp_bias, model in zip(best_df.temp_bias, best_df.model):
model.run_until(model.length_m_ts().index[-1])
plt.plot(x, model.fls[-1].surface_h, label=str(temp_bias))
#model.volume_km3_ts().plot()
plt.plot(x, mod.fls[-1].surface_h, 'r:')
plt.plot(x, mod.fls[-1].bed_h, 'k')
plt.legend(title='temp_bias')
plt.xlabel('Distance along the main flowline (m)')
plt.ylabel('Altitude (m)')
plt.title(gdir.rgi_id)
utils.mkdir(os.path.join(cfg.PATHS['plot_dir'], 'bias_test'))
plt.savefig(os.path.join(cfg.PATHS['plot_dir'], 'bias_test',
gdir.rgi_id + '.png'),
dpi=200)
plt.show()
return best_df