def run_parallel(i, gdir, y_t):
try:
random_climate2 = RandomMassBalance(gdir, y0=1865, halfsize=14)
experiment = gdir.read_pickle('synthetic_experiment')
# ensure that not the same climate as in the experiments is used
if experiment['climate'] == random_climate2:
random_climate2 = RandomMassBalance(gdir, y0=1865, halfsize=14)
res = minimize(
objfunc,
[0],
args=(gdir, y_t, random_climate2),
method='COBYLA',
#constraints={'type':'ineq','fun':con},
tol=1e-04,
options={
'maxiter': 100,
'rhobeg': 1
})
result_model_t0, result_model_t = run_model(res.x, gdir, y_t,
random_climate2)
return result_model_t0, result_model_t
except:
return None, None
def _run_parallel_experiment(gdir):
# read flowlines from pre-processing
fls = gdir.read_pickle('model_flowlines')
try:
# construct searched glacier
# TODO: y0 in random mass balance?
random_climate1 = RandomMassBalance(gdir, y0=1850, bias=0, seed=[1])
random_climate1.temp_bias = -0.75
commit_model = FluxBasedModel(fls, mb_model=random_climate1,
glen_a=cfg.A, y0=1850)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# try different seed of mass balance, if equilibrium could not be found
except:
# construct searched glacier
random_climate1 = RandomMassBalance(gdir, y0=1850, bias=0,seed=[1])
commit_model = FluxBasedModel(fls, mb_model=random_climate1,
glen_a=cfg.A, y0=1850)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# construct observed glacier
past_climate = PastMassBalance(gdir)
commit_model2 = FluxBasedModel(commit_model.fls, mb_model=past_climate,
glen_a=cfg.A, y0=1850)
commit_model2.run_until(2000)
y_t = deepcopy(commit_model2)
# save output in gdir_dir
experiment = {'y_t0': y_t0, 'y_t': y_t,
'climate': random_climate1}
gdir.write_pickle(experiment, 'synthetic_experiment')
def _run_parallel_experiment(gdir, t0, te):
'''
:param gdir:
:param t0:
:param te:
:return:
'''
# read flowlines from pre-processing
fls = gdir.read_pickle('model_flowlines')
try:
# construct searched glacier
random_climate1 = RandomMassBalance(gdir, y0=t0, halfsize=14)
#set temp bias negative to force a glacier retreat later
random_climate1.temp_bias = -0.75
commit_model = FluxBasedModel(fls,
mb_model=random_climate1,
glen_a=cfg.A,
y0=t0)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# try different seed of mass balance, if equilibrium could not be found
except:
# construct searched glacier
random_climate1 = RandomMassBalance(gdir, y0=t0, halfsize=14)
commit_model = FluxBasedModel(fls,
mb_model=random_climate1,
glen_a=cfg.A,
y0=t0)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# construct observed glacier
past_climate = PastMassBalance(gdir)
commit_model2 = FluxBasedModel(commit_model.fls,
mb_model=past_climate,
glen_a=cfg.A,
y0=t0)
commit_model2.run_until(te)
y_t = deepcopy(commit_model2)
# save output in gdir_dir
experiment = {'y_t0': y_t0, 'y_t': y_t, 'climate': random_climate1}
gdir.write_pickle(experiment, 'synthetic_experiment')
def run_parallel(i, gdir, y_2000):
try:
random_climate2 = RandomMassBalance(gdir, y0=1875, halfsize=14)
res = minimize(objfunc, [0],
args=(gdir, y_2000.fls, random_climate2),
method='COBYLA',
tol=1e-04,
options={
'maxiter': 100,
'rhobeg': 1
})
# try:
result_model_1880, result_model_2000 = run_model(
res.x, gdir, y_2000.fls, random_climate2)
return result_model_1880, result_model_2000
except:
return None, None
def run_parallel(i, gdir, y_t, t0, te):
try:
random_climate2 = RandomMassBalance(gdir, y0=t0, halfsize=14)
res = minimize(
objfunc,
[0],
args=(gdir, y_t, random_climate2, t0, te),
method='COBYLA',
#constraints={'type':'ineq','fun':con},
tol=1e-04,
options={
'maxiter': 100,
'rhobeg': 1
})
result_model_t0, result_model_t = run_model(res.x, gdir, y_t,
random_climate2, t0, te)
return result_model_t0, result_model_t
except:
return None, None
def find_initial_state(gdir):
global past_climate, random_climate2
global y_2000
global x
global ax1, ax2, ax3
fls = gdir.read_pickle('model_flowlines')
past_climate = PastMassBalance(gdir)
random_climate1 = RandomMassBalance(gdir, y0=1865, halfsize=14)
#construct searched glacier
commit_model = FluxBasedModel(fls,
mb_model=random_climate1,
glen_a=cfg.A,
y0=1850)
commit_model.run_until_equilibrium()
y_1880 = copy.deepcopy(commit_model)
#construct observed glacier
commit_model2 = FluxBasedModel(commit_model.fls,
mb_model=past_climate,
glen_a=cfg.A,
y0=1880)
commit_model2.run_until(2000)
y_2000 = copy.deepcopy(commit_model2)
results = pd.DataFrame(columns=['1880', '2000', 'length_1880'])
pool = mp.Pool()
result_list = pool.map(partial(run_parallel, gdir=gdir, y_2000=y_2000),
range(300))
pool.close()
pool.join()
result_list = [x for x in result_list if x != [None, None]]
# create plots
for i in range(len(result_list[0][0].fls)):
plt.figure(i, figsize=(20, 10))
#add subplot in the corner
fig, ax1 = plt.subplots(figsize=(20, 10))
ax2 = fig.add_axes([0.55, 0.66, 0.3, 0.2])
ax1.set_title(gdir.rgi_id + ' flowline ' + str(i))
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.95, box.height])
x = np.arange(
y_2000.fls[i].nx) * y_2000.fls[i].dx * y_2000.fls[i].map_dx
for j in range(len(result_list)):
if result_list[j][0] != None:
ax1.plot(
x,
result_list[j][0].fls[i].surface_h,
alpha=0.8,
)
ax2.plot(
x,
result_list[j][1].fls[i].surface_h,
alpha=0.8,
)
ax1.plot(x, y_1880.fls[i].surface_h, 'k:')
ax2.plot(x, y_2000.fls[i].surface_h, 'k')
ax1.plot(x, y_1880.fls[i].bed_h, 'k')
ax2.plot(x, y_2000.fls[i].bed_h, 'k')
plot_dir = os.path.join(cfg.PATHS['working_dir'], 'plots',
'Ben_idea_parallel_without_length')
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
plt.savefig(
os.path.join(plot_dir, gdir.rgi_id + '_fls' + str(i) + '.png'))
pickle.dump(result_list,
open(os.path.join(plot_dir, gdir.rgi_id + '.pkl'), 'wb'))
solution = [y_1880, y_2000]
pickle.dump(
solution,
open(os.path.join(plot_dir, gdir.rgi_id + '_solution.pkl'), 'wb'))
ice_thick[:, -1] = 0
return ice_thick
# The model run function
def run_task(gdir, grid=None, mb_model=None, glen_a=cfg.A, outf=None,
ice_thick_filter=None, print_stdout=None):
dmodel = Upstream2D(bed_topo, dx=grid.dx, mb_model=mb_model,
y0=0., glen_a=glen_a,
ice_thick_filter=ice_thick_filter)
dmodel.run_until_and_store(800, run_path=outf, grid=grid,
print_stdout=print_stdout)
# Multiprocessing
tasks = []
mbmod = RandomMassBalance(gdir, seed=0)
outf = db_dir + 'out_def_100m.nc'
tasks.append((run_task, {'grid': grid, 'mb_model': mbmod, 'outf': outf,
'ice_thick_filter':filter_ice_border,
'print_stdout':'Task1'}))
mbmod = RandomMassBalance(gdir, seed=0)
mbmod.temp_bias = 0.5
outf = db_dir + 'out_tbias_100m.nc'
tasks.append((run_task, {'grid': grid, 'mb_model': mbmod, 'outf': outf,
'ice_thick_filter':filter_ice_border,
'print_stdout': 'Task2'}))
mbmod = RandomMassBalance(gdir, seed=0)
outf = db_dir + 'out_filter_100m.nc'
tasks.append((run_task, {'grid': grid, 'mb_model': mbmod, 'outf': outf,
def find_initial_state(gdir):
global past_climate
global y_1900, y_1850
global y_start
global x
global ax1, ax2, ax3
fls = gdir.read_pickle('model_flowlines')
past_climate = PastMassBalance(gdir)
random_climate = RandomMassBalance(gdir)
commit_model = FluxBasedModel(fls,
mb_model=random_climate,
glen_a=cfg.A,
y0=1850)
commit_model.run_until_equilibrium()
y_1850 = copy.deepcopy(commit_model)
commit_model = FluxBasedModel(commit_model.fls,
mb_model=past_climate,
glen_a=cfg.A,
y0=1850)
commit_model.run_until(2000)
y_1900 = copy.deepcopy(commit_model)
x = np.arange(
y_1900.fls[-1].nx) * y_1900.fls[-1].dx * y_1900.fls[-1].map_dx
plt.figure(figsize=(20, 10))
fig, ax1 = plt.subplots()
ax2 = fig.add_axes([0.55, 0.66, 0.3, 0.2])
ax1.set_title(gdir.rgi_id)
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.95, box.height])
# Put a legend to the right of the current axis
#plt.setp(ax1.get_xticklabels(), visible=False)
#plt.plot(x, y_1850.fls[-1].surface_h, 'k:', label='solution')
#plt.plot(x, y_1850.fls[-1].bed_h, 'k', label='bed')
#plt.legend(loc='best')
#ax2 = plt.subplot(412, sharex=ax1)
#plt.setp(ax2.get_xticklabels(), visible=False)
'''
ax3 = plt.subplot(413,sharex=ax1)
ax3.plot(x, np.zeros(len(x)), 'k--')
ax4 = plt.subplot(414, sharex=ax1)
ax4.plot(x, np.zeros(len(x)), 'k--')
'''
growing_model = FluxBasedModel(fls,
mb_model=past_climate,
glen_a=cfg.A,
y0=1850)
y_start = copy.deepcopy(growing_model)
colors = [
pylab.cm.Blues(np.linspace(0.3, 1, 2)),
pylab.cm.Reds(np.linspace(0.3, 1, 2)),
pylab.cm.Greens(np.linspace(0.3, 1, 2))
]
#for i in [0,0.2,0.4,0.6,0.8,1,5,10,15,20,25,30,35,40,45,50]:
j = 0
for i in [1]:
k = 0
col = colors[j]
j = j + 1
for t in [20, 40, 60, 80, 100, 120, 140, 150]:
res = minimize(objfunc, [0],
args=(
gdir,
y_1900.fls,
t,
i,
),
method='COBYLA',
tol=1e-04,
options={
'maxiter': 500,
'rhobeg': 2
})
try:
result_model_1850, result_model_1900 = run_model(
res.x, gdir, y_1900.fls, t, i)
f = np.sum(abs(result_model_1900.fls[-1].surface_h-y_1900.fls[-1].surface_h) ** 2) + \
np.sum(abs(y_1900.fls[-1].widths - result_model_1900.fls[-1].widths) ** 2)
dif_s = result_model_1900.fls[-1].surface_h - y_1900.fls[
-1].surface_h
dif_w = result_model_1900.fls[-1].widths - y_1900.fls[-1].widths
#if np.max(dif_s)<40 and np.max(dif_w)<15:
ax1.plot(x,
result_model_1850.fls[-1].surface_h,
alpha=0.8,
color=col[k],
label='t=' + str(t))
ax2.plot(x,
result_model_1900.fls[-1].surface_h,
alpha=0.8,
color=col[k])
except:
pass
k = k + 1
ax1.plot(x, y_1850.fls[-1].surface_h,
'k:') #, label='surface elevation (not known)')
ax1.plot(x, y_1850.fls[-1].bed_h, 'k') #, label='bed topography')
ax2.plot(x,
y_1900.fls[-1].surface_h,
'k',
label='surface elevation (observed)')
ax2.plot(x, y_1900.fls[-1].bed_h, 'k', label='bed')
#ax3.plot(x,np.zeros(len(x)),'k:')
ax1.annotate('t = 1850',
xy=(0.1, 0.95),
xycoords='axes fraction',
fontsize=13)
ax2.annotate('t = 2000',
xy=(0.1, 0.9),
xycoords='axes fraction',
fontsize=9)
ax1.set_xlabel('Distance along the Flowline (m)')
ax1.set_ylabel('Altitude (m)')
ax2.set_xlabel('Distance along the Flowline (m)')
ax2.set_ylabel('Altitude (m)')
ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax2.legend(loc='best')
plot_dir = os.path.join(cfg.PATHS['working_dir'], 'plots')
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
plt.savefig(os.path.join(plot_dir, gdir.rgi_id + '.png'))
plt.show()
def find_initial_state(gdir):
global past_climate,random_climate2
global y_2000
global x
global ax1,ax2,ax3
fls = gdir.read_pickle('model_flowlines')
past_climate = PastMassBalance(gdir)
random_climate1 = RandomMassBalance(gdir,y0=1865,halfsize=14)
#construct searched glacier
commit_model = FluxBasedModel(fls, mb_model=random_climate1,
glen_a=cfg.A, y0=1850)
commit_model.run_until_equilibrium()
y_1880 = copy.deepcopy(commit_model)
#construct observed glacier
commit_model2 = FluxBasedModel(commit_model.fls, mb_model=past_climate,
glen_a=cfg.A, y0=1880)
commit_model2.run_until(2000)
y_2000 = copy.deepcopy(commit_model2)
results = pd.DataFrame(columns=['1880','2000','length_1880'])
for i in range(4):
random_climate2 = RandomMassBalance(gdir, y0=1875, halfsize=14)
res = minimize(objfunc, [0], args=(gdir, y_2000.fls),
method='COBYLA',
tol=1e-04, options={'maxiter': 100, 'rhobeg': 1})
#try:
result_model_1880, result_model_2000 = run_model(res.x, gdir,
y_2000.fls)
results = results.append({'1880':result_model_1880,'2000':result_model_2000,'length_1880':result_model_1880.length_m,'length_2000':result_model_2000.length_m}, ignore_index=True)
#except:
# pass
# create plots
for i in range(len(fls)):
plt.figure(i,figsize=(20,10))
#add subplot in the corner
fig, ax1 = plt.subplots(figsize=(20, 10))
ax2 = fig.add_axes([0.55, 0.66, 0.3, 0.2])
ax1.set_title(gdir.rgi_id+' flowline '+str(i))
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.95, box.height])
x = np.arange(y_2000.fls[i].nx) * y_2000.fls[i].dx * y_2000.fls[i].map_dx
for j in range(len(results)):
ax1.plot(x, results.get_value(j, '1880').fls[i].surface_h, alpha=0.8, )
ax2.plot(x, results.get_value(j, '2000').fls[i].surface_h, alpha=0.8, )
ax1.plot(x,y_1880.fls[i].surface_h,'k:')
ax2.plot(x, y_2000.fls[i].surface_h, 'k')
ax1.plot(x, y_1880.fls[i].bed_h, 'k')
ax2.plot(x, y_2000.fls[i].bed_h, 'k')
plot_dir = os.path.join(cfg.PATHS['working_dir'], 'plots')
plt.savefig(os.path.join(plot_dir, gdir.rgi_id +'_fls'+str(i)+ '.png'))
#plt.show()
results.to_csv(os.path.join(plot_dir,str(gdir.rgi_id)+'.csv'))
'''