def grey_madness(glcdict, pout, y_len=5):
for glid, df in glcdict.items():
# take care of merged glaciers
rgi_id = glid.split('_')[0]
fig, ax1 = plt.subplots(figsize=[20, 7])
# OGGM standard
for run in df.columns:
if run == 'obs':
continue
para = ast.literal_eval('{' + run + '}')
if ((np.abs(para['prcp_scaling_factor'] - 1.75) < 0.01) and
(para['mbbias'] == 0) and
(para['glena_factor'] == 1)):
oggmdefault = run
break
nolbl = df.loc[:, df.columns != 'obs'].\
rolling(y_len, center=True).mean().copy()
nolbl.columns = ['' for i in range(len(nolbl.columns))]
nolbl.plot(ax=ax1, linewidth=0.8, color='0.7')
df.loc[:, oggmdefault].rolling(y_len, center=True).mean().plot(
ax=ax1, linewidth=0.8, color='0.7',
label='Every possible calibration parameter combination')
df.loc[:, oggmdefault].rolling(y_len, center=True).mean().\
plot(ax=ax1, color='k', linewidth=2,
label='OGGM default parameters')
df.loc[:, 'obs'].plot(ax=ax1, color='k', marker='o',
label='Observations')
name = name_plus_id(rgi_id)
ax1.set_title('%s' % name, fontsize=28)
ax1.set_ylabel('relative length change [m]', fontsize=26)
ax1.set_xlabel('Year', fontsize=26)
ax1.set_xlim([1850, 2014])
ax1.set_ylim([-7500, 4000])
ax1.tick_params(axis='both', which='major', labelsize=22)
ax1.grid(True)
ax1.legend(bbox_to_anchor=(-0.0, -0.15), loc='upper left',
fontsize=18, ncol=2)
fig.subplots_adjust(left=0.09, right=0.99, bottom=0.24, top=0.93,
wspace=0.5)
fn1 = os.path.join(pout, 'all_%s.png' % glid)
fig.savefig(fn1)
def elevation_profiles(rgi, meta, histalp_storage, pout):
name = name_plus_id(rgi)
df1850 = pd.DataFrame()
df2003 = pd.DataFrame()
df2003b = pd.DataFrame()
dfbed = pd.DataFrame()
for i in np.arange(999):
# Local working directory (where OGGM will write its output)
rgipath = os.path.join(histalp_storage, rgi, '{:02d}'.format(i),
rgi[:8], rgi[:11], rgi)
fn = os.path.join(rgipath, 'model_run_histalp_{:02d}.nc'.format(i))
try:
tmpmod = FileModel(fn)
except FileNotFoundError:
break
df1850.loc[:, i] = tmpmod.fls[-1].surface_h
# get bed surface
dfbed.loc[:, i] = tmpmod.fls[-1].bed_h
# HISTALP surface
tmpmod.run_until(2003)
df2003.loc[:, i] = tmpmod.fls[-1].surface_h
df2003b.loc[:, i] = tmpmod.fls[-1].thick
# RGI init surface, once is enough
fn2 = os.path.join(histalp_storage, rgi, '00', rgi[:8], rgi[:11],
rgi, 'model_run_spinup_00.nc')
tmpmod2 = FileModel(fn2)
initsfc = tmpmod2.fls[-1].surface_h
# get distance on line
dx_meter = tmpmod.fls[-1].dx_meter
meanbed = dfbed.mean(axis=1).values
maxbed = dfbed.max(axis=1).values
minbed = dfbed.min(axis=1).values
# 1850
mean1850 = df1850.mean(axis=1).values
# where is mean glacier thinner than 1m
ix50 = np.where(mean1850-meanbed < 1)[0][0]
mean1850[ix50:] = np.nan
min1850 = df1850.min(axis=1).values
min1850[ix50:] = np.nan
min1850[min1850 <= meanbed] = meanbed[min1850 <= meanbed]
max1850 = df1850.max(axis=1).values
max1850[max1850 <= meanbed] = meanbed[max1850 <= meanbed]
# 2003
mean2003 = df2003.mean(axis=1).values
# where is mean glacier thinner than 1m
ix03 = np.where(mean2003-meanbed < 1)[0][0]
mean2003[ix03:] = np.nan
min2003 = df2003.min(axis=1).values
min2003[ix03:] = np.nan
min2003[min2003 <= meanbed] = meanbed[min2003 <= meanbed]
max2003 = df2003.max(axis=1).values
max2003[max2003 <= meanbed] = meanbed[max2003 <= meanbed]
lastx = np.where(initsfc-meanbed < 1)[0][0]
initsfc[lastx:] = np.nan
initsfc[lastx] = meanbed[lastx]
dis = np.arange(len(meanbed)) * dx_meter / 1000
xmax = sum(np.isfinite(mean1850))
ymax = np.nanmax(mean1850) + 50
ymin = minbed[np.where(np.isfinite(mean1850))].min() - 50
fig, ax = plt.subplots(1, figsize=[15, 9])
ax.fill_between(dis[:xmax+1], dis[:xmax+1] * 0 + ymin, minbed[:xmax+1],
color='0.7', alpha=0.5)
ax.fill_between(dis[:xmax+1], minbed[:xmax+1], maxbed[:xmax+1],
color='xkcd:tan', alpha=0.5)
ax.plot(dis[:xmax+1], meanbed[:xmax+1], 'k-', color='xkcd:tan',
linewidth=3, label='Glacier bed elevation [m]')
ax.fill_between(dis, min1850, max1850, color='xkcd:azure', alpha=0.5)
ax.plot(dis, mean1850, 'k-', color='xkcd:azure', linewidth=4,
label=('Surface elevation [m] year {:d}\n'
'(initialization state after spinup)'.
format(meta['first'])))
ax.fill_between(dis, min2003, max2003, color='xkcd:teal', alpha=0.5)
ax.plot(dis, mean2003, 'k-', color='xkcd:teal', linewidth=4,
label=('Surface elevation [m] year 2003\n'
'(from HISTALP ensemble simulations)'))
ax.plot(dis, initsfc, 'k-', color='xkcd:crimson', linewidth=4,
label=('Surface elevation [m] year 2003\n'
'(from RGI initialization)'))
ax.legend(loc=1, fontsize=20)
ax.set_ylim(ymin, ymax)
ax.set_xlim(0, dis[xmax])
ax.set_xlabel('Distance along major flowline [km]', fontsize=28)
ax.set_ylabel('Elevation [m a.s.l.]', fontsize=28)
ax.tick_params(axis='both', which='major', labelsize=26)
ax.grid(True)
ax.set_title(name, fontsize=30)
fig.tight_layout()
fn = os.path.join(pout, 'profile_%s' % rgi)
if ('3643' in rgi) or ('1450' in rgi) or ('2051' in rgi) or ('897' in rgi):
fig.savefig('{}.svg'.format(fn))
fig.savefig('{}.png'.format(fn))
def past_simulation_and_projection(rgi, allobs, allmeta, histalp_storage,
proj_storage, comit_storage,
pout, y_len=5,):
cols = ['xkcd:teal',
'xkcd:azure',
'xkcd:lime',
'xkcd:orange',
'xkcd:magenta',
'xkcd:tomato',
'xkcd:blue',
'xkcd:green'
]
obs = allobs.loc[rgi.split('_')[0]]
meta = allmeta.loc[rgi.split('_')[0]]
dfall = pd.DataFrame([], index=np.arange(1850, 2101))
dfallstd = pd.DataFrame([], index=np.arange(1850, 2101))
for rcp in ['rcp26', 'rcp45', 'rcp60', 'rcp85']:
dfrcp = get_rcp_ensemble_length(rgi, histalp_storage, proj_storage,
rcp, meta)
ensmean = dfrcp.mean(axis=1)
dfall.loc[:, rcp] = ensmean.rolling(y_len, center=True).mean()
dfallstd.loc[:, rcp] = dfrcp.std(axis=1).\
rolling(y_len, center=True).mean()
# plot
fig, ax1 = plt.subplots(1, figsize=[20, 7])
obs.plot(ax=ax1, color='k', marker='o',
label='Observations')
# past
ax1.fill_between(dfall.loc[:2015, rcp].index,
dfall.loc[:2015, rcp] - dfallstd.loc[:2015, rcp],
dfall.loc[:2015, rcp] + dfallstd.loc[:2015, rcp],
color=cols[0], alpha=0.5)
dfall.loc[:2015, rcp].plot(ax=ax1, linewidth=4.0, color=cols[0],
label='HISTALP climate')
# dummy
ax1.plot(0, 0, 'w-', label=' ')
# projections
# rcp26
ax1.fill_between(dfall.loc[2015:, 'rcp26'].index,
dfall.loc[2015:, 'rcp26'] - dfallstd.loc[2015:, 'rcp26'],
dfall.loc[2015:, 'rcp26'] + dfallstd.loc[2015:, 'rcp26'],
color=cols[1], alpha=0.5)
dfall.loc[2015:, 'rcp26'].plot(ax=ax1, linewidth=4.0, color=cols[1],
label='RCP 2.6 climate')
# rcp45
dfall.loc[2015:, 'rcp45'].plot(ax=ax1, linewidth=4.0, color=cols[2],
label='RCP 4.5 climate')
# dummy
ax1.plot(0, 0, 'w-', label=' ')
# rcp60
dfall.loc[2015:, 'rcp60'].plot(ax=ax1, linewidth=4.0, color=cols[3],
label='RCP 6.0 climate')
# rcp85
ax1.fill_between(dfall.loc[2015:, 'rcp85'].index,
dfall.loc[2015:, 'rcp85'] - dfallstd.loc[2015:, 'rcp85'],
dfall.loc[2015:, 'rcp85'] + dfallstd.loc[2015:, 'rcp85'],
color=cols[4], alpha=0.5)
dfall.loc[2015:, 'rcp85'].plot(ax=ax1, linewidth=4.0, color=cols[4],
label='RCP 8.5 climate')
# dummy
ax1.plot(0, 0, 'w-', label=' ')
# plot commitment length
# 1984
fn99 = 'model_diagnostics_commitment1999_{:02d}.nc'
df99 = get_ensemble_length(rgi, histalp_storage, comit_storage, fn99, meta)
ensmean = df99.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = df99.std(axis=1).rolling(y_len, center=True).mean()
postlength = ensmeanmean.dropna().iloc[-30:].mean()
poststd = ensstdmean.dropna().iloc[-30:].mean()
ax1.fill_between([2105, 2111],
postlength + poststd, postlength - poststd,
color=cols[5], alpha=0.5)
ax1.plot([2105.5, 2110.5], [postlength, postlength], linewidth=4.0,
color=cols[5],
label=('Random climate (1984-2014) '
'equilibrium length'))
# 1970
fn70 = 'model_diagnostics_commitment1970_{:02d}.nc'
df70 = get_ensemble_length(rgi, histalp_storage, comit_storage, fn70, meta)
ensmean = df70.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = df70.std(axis=1).rolling(y_len, center=True).mean()
prelength = ensmeanmean.dropna().iloc[-30:].mean()
prestd = ensstdmean.dropna().iloc[-30:].mean()
ax1.fill_between([2105, 2111],
prelength + prestd, prelength - prestd,
color=cols[7], alpha=0.5)
ax1.plot([2105.5, 2110.5], [prelength, prelength], linewidth=4.0,
color=cols[7],
label=('Random climate (1960-1980) '
'equilibrium length'))
# 1885
fn85 = 'model_diagnostics_commitment1885_{:02d}.nc'
df85 = get_ensemble_length(rgi, histalp_storage, comit_storage, fn85, meta)
ensmean = df85.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = df85.std(axis=1).rolling(y_len, center=True).mean()
prelength = ensmeanmean.dropna().iloc[-30:].mean()
prestd = ensstdmean.dropna().iloc[-30:].mean()
ax1.fill_between([2105, 2111],
prelength + prestd, prelength - prestd,
color=cols[6], alpha=0.5)
ax1.plot([2105.5, 2110.5], [prelength, prelength], linewidth=4.0,
color=cols[6],
label=('Random climate (1870-1900) '
'equilibrium length'))
ylim = ax1.get_ylim()
ax1.set_xlim([1850, 2112])
ax2 = ax1.twinx()
ax2.set_ylabel('apporixmate\n absolute glacier length [m]', fontsize=26)
y1, y2 = get_absolute_length(ylim[0], ylim[1], rgi, df99, histalp_storage)
ax2.tick_params(axis='both', which='major', labelsize=22)
ax2.set_ylim([y1, y2])
name = name_plus_id(rgi)
ax1.set_title('%s' % name, fontsize=28)
ax1.set_ylabel('relative length change [m]', fontsize=26)
ax1.set_xlabel('Year', fontsize=26)
ax1.tick_params(axis='both', which='major', labelsize=22)
ax1.grid(True)
ax1.legend(bbox_to_anchor=(0.0, -0.17), loc='upper left', fontsize=18,
ncol=4)
fig.subplots_adjust(left=0.09, right=0.9, bottom=0.3, top=0.93,
wspace=0.5)
fn1 = os.path.join(pout, 'proj_%s.png' % rgi)
fig.savefig(fn1)
def past_simulation_and_commitment(rgi, allobs, allmeta, histalp_storage,
comit_storage, comit_storage_noseed,
pout, y_len=5, comyears=300):
cols = ['xkcd:teal',
'xkcd:orange',
'xkcd:azure',
'xkcd:tomato',
'xkcd:blue',
'xkcd:chartreuse',
'xkcd:green'
]
obs = allobs.loc[rgi.split('_')[0]]
meta = allmeta.loc[rgi.split('_')[0]]
fn99 = 'model_diagnostics_commitment1999_{:02d}.nc'
df99 = get_ensemble_length(rgi, histalp_storage, comit_storage, fn99, meta)
fn85 = 'model_diagnostics_commitment1885_{:02d}.nc'
df85 = get_ensemble_length(rgi, histalp_storage, comit_storage, fn85, meta)
fn70 = 'model_diagnostics_commitment1970_{:02d}.nc'
df70 = get_ensemble_length(rgi, histalp_storage, comit_storage, fn70, meta)
# plot
fig, ax1 = plt.subplots(1, figsize=[20, 7])
obs.plot(ax=ax1, color='k', marker='o',
label='Observations')
# past
ensmean = df99.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = df99.std(axis=1).rolling(y_len, center=True).mean()
ax1.fill_between(ensmeanmean.loc[:2015].index,
ensmeanmean.loc[:2015] - ensstdmean.loc[:2015],
ensmeanmean.loc[:2015] + ensstdmean.loc[:2015],
color=cols[0], alpha=0.5)
ensmeanmean.loc[:2015].plot(ax=ax1, linewidth=4.0, color=cols[0],
label='HISTALP climate')
# dummy
ax1.plot(0, 0, 'w-', label=' ')
# 1999
ax1.fill_between(ensmeanmean.loc[2015:].index,
ensmeanmean.loc[2015:] - ensstdmean.loc[2015:],
ensmeanmean.loc[2015:] + ensstdmean.loc[2015:],
color=cols[1], alpha=0.5)
ensmeanmean.loc[2015:].plot(ax=ax1, linewidth=4.0, color=cols[1],
label='Random climate (1984-2014)')
# 1970
ensmean = df70.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = df70.std(axis=1).rolling(y_len, center=True).mean()
ax1.fill_between(ensmeanmean.loc[2015:].index,
ensmeanmean.loc[2015:] - ensstdmean.loc[2015:],
ensmeanmean.loc[2015:] + ensstdmean.loc[2015:],
color=cols[5], alpha=0.5)
ensmeanmean.loc[2015:].plot(ax=ax1, linewidth=4.0, color=cols[5],
label='Random climate (1960-1980)')
# 1885
ensmean = df85.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = df85.std(axis=1).rolling(y_len, center=True).mean()
ax1.fill_between(ensmeanmean.loc[2015:].index,
ensmeanmean.loc[2015:] - ensstdmean.loc[2015:],
ensmeanmean.loc[2015:] + ensstdmean.loc[2015:],
color=cols[2], alpha=0.5)
ensmeanmean.loc[2015:].plot(ax=ax1, linewidth=4.0, color=cols[2],
label='Random climate (1870-1900)')
# ---------------------------------------------------------------------
# plot commitment ensemble length
# 1984
efn99 = 'model_diagnostics_commitment1999_{:02d}.nc'
edf99 = get_ensemble_length(rgi, histalp_storage, comit_storage_noseed,
efn99, meta)
ensmean = edf99.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = edf99.std(axis=1).rolling(y_len, center=True).mean()
postlength = ensmeanmean.dropna().iloc[-30:].mean()
poststd = ensstdmean.dropna().iloc[-30:].mean()
ax1.fill_between([2014+comyears+10, 2014+comyears+25],
postlength + poststd, postlength - poststd,
color=cols[3], alpha=0.5)
ax1.plot([2014+comyears+10.5, 2014+comyears+24.5], [postlength, postlength], linewidth=4.0,
color=cols[3],
label=('Random climate (1984-2014) '
'equlibrium length'))
# 1970
efn70 = 'model_diagnostics_commitment1970_{:02d}.nc'
edf70 = get_ensemble_length(rgi, histalp_storage, comit_storage_noseed,
efn70, meta)
ensmean = edf70.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = edf70.std(axis=1).rolling(y_len, center=True).mean()
prelength = ensmeanmean.dropna().iloc[-30:].mean()
prestd = ensstdmean.dropna().iloc[-30:].mean()
ax1.fill_between([2014+comyears+10, 2014+comyears+25],
prelength + prestd, prelength - prestd,
color=cols[6], alpha=0.5)
ax1.plot([2014+comyears+10.5, 2014+comyears+24.5], [prelength, prelength],
linewidth=4.0,
color=cols[6],
label=('Random climate (1960-1980) '
'equlibrium length'))
# 1885
efn85 = 'model_diagnostics_commitment1885_{:02d}.nc'
edf85 = get_ensemble_length(rgi, histalp_storage, comit_storage_noseed,
efn85, meta)
ensmean = edf85.mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = edf85.std(axis=1).rolling(y_len, center=True).mean()
prelength = ensmeanmean.dropna().iloc[-30:].mean()
prestd = ensstdmean.dropna().iloc[-30:].mean()
ax1.fill_between([2014+comyears+10, 2014+comyears+25],
prelength + prestd, prelength - prestd,
color=cols[4], alpha=0.5)
ax1.plot([2014+comyears+10.5, 2014+comyears+24.5], [prelength, prelength],
linewidth=4.0,
color=cols[4],
label=('Random climate (1870-1900) '
'equlibrium length'))
# ---------------------------------------------------------------------
ylim = ax1.get_ylim()
#ax1.plot([2015, 2015], ylim, 'k-', linewidth=2)
ax1.set_xlim([1850, 2014+comyears+30])
#ax1.set_ylim(ylim)
ax2 = ax1.twinx()
ax2.set_ylabel('approximate\n absolute glacier length [m]', fontsize=26)
y1, y2 = get_absolute_length(ylim[0], ylim[1], rgi, df99, histalp_storage)
ax2.tick_params(axis='both', which='major', labelsize=22)
ax2.set_ylim([y1, y2])
name = name_plus_id(rgi)
ax1.set_title('%s' % name, fontsize=28)
ax1.set_ylabel('relative length change [m]', fontsize=26)
ax1.set_xlabel('Year', fontsize=26)
ax1.tick_params(axis='both', which='major', labelsize=22)
ax1.set_xticks([1850, 1950, 2014, 2114, 2214, 2314])
ax1.set_xticklabels(['1850', '1950', '2014/0', '100', '200', '300'])
ax1.grid(True)
ax1.legend(bbox_to_anchor=(-0.0, -0.17), loc='upper left', fontsize=18,
ncol=3)
fig.subplots_adjust(left=0.09, right=0.9, bottom=0.3, top=0.93,
wspace=0.5)
fn1 = os.path.join(pout, 'commit_%s.png' % rgi)
fig.savefig(fn1)
def paramplots(df, glid, pout, y_len=None):
# take care of merged glaciers
rgi_id = glid.split('_')[0]
fig1, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=[20, 7])
allvars = ['prcp_scaling_factor', 'mbbias', 'glena_factor']
varcols = {'mbbias': np.array([-1400, -1200, -1000, -800, -600, -400, -200,
-100, 0, 100, 200, 400, 600, 800, 1000]),
'prcp_scaling_factor': np.arange(0.5, 4.1, 0.25),
'glena_factor': np.arange(1, 4.1, 0.5)}
for var, ax in zip(allvars, [ax1, ax2, ax3]):
notvars = allvars.copy()
notvars.remove(var)
# lets use OGGM HISTALP default
papar = {'glena_factor': 1.0, 'mbbias': 0, 'prcp_scaling_factor': 1.75}
# store specific runs
dfvar = pd.DataFrame([], columns=varcols[var], index=df.index)
# OGGM standard
for run in df.columns:
if run == 'obs':
continue
para = ast.literal_eval('{' + run + '}')
if ((np.isclose(para[notvars[0]],
papar[notvars[0]], atol=0.01)) and
(np.isclose(para[notvars[1]],
papar[notvars[1]], atol=0.01))):
dfvar.loc[:, para[var]] = df.loc[:, run]
if var == 'prcp_scaling_factor':
lbl = 'Precip scaling factor'
cmap = LinearSegmentedColormap('lala', cmocean.tools.get_dict(
cmocean.cm.deep))
normalize = mcolors.Normalize(vmin=0,
vmax=4.5)
bounds = np.arange(0.375, 4.2, 0.25)
cbarticks = np.arange(1, 4.1, 1)
elif var == 'glena_factor':
lbl = 'Glen A factor'
cmap = LinearSegmentedColormap('lala', cmocean.tools.get_dict(
cmocean.cm.matter))
normalize = mcolors.Normalize(vmin=0,
vmax=4.5)
bounds = np.arange(0.75, 4.3, 0.5)
cbarticks = np.arange(1, 4.1, 1)
elif var == 'mbbias':
cmap = LinearSegmentedColormap('lala', cmocean.tools.get_dict(
cmocean.cm.balance))
cmaplist = [cmap(i) for i in range(cmap.N)]
cmaplist[128] = (0.412, 0.847, 0.655, 1.0)
cmap = mcolors.LinearSegmentedColormap.from_list('mcm', cmaplist,
cmap.N)
cbarticks = np.array([-1400, -1000, -600, -200,
0, 200, 600, 1000])
bounds = np.array([-1500, -1300, -1100, -900, -700, -500, -300,
-150, -50, 50, 100, 300, 500, 700, 900, 1100])
normalize = mcolors.Normalize(vmin=-1600,
vmax=1600)
lbl = 'MB bias [mm w.e.]'
colors = [cmap(normalize(n)) for n in varcols[var]]
scalarmappaple = cm.ScalarMappable(norm=normalize, cmap=cmap)
cbaxes = inset_axes(ax, width="3%", height="40%", loc=3)
cbar = plt.colorbar(scalarmappaple, cax=cbaxes,
label=lbl,
boundaries=bounds)
cbar.set_ticks(cbarticks)
cbaxes.tick_params(axis='both', which='major', labelsize=16)
cbar.set_label(label=lbl, size=16)
# plot observations
df.loc[:, 'obs'].rolling(1, min_periods=1).mean(). \
plot(ax=ax, color='k', style='.',
marker='o', label='Observed length change',
markersize=6)
dfvar = dfvar.sort_index(axis=1)
# default parameter column
dc = np.where(dfvar.columns == papar[var])[0][0]
dfvar.loc[:, varcols[var][dc]].rolling(y_len, center=True).mean(). \
plot(ax=ax, color=colors[dc], linewidth=5,
label='{}: {} (OGGM default)'.
format(lbl, str(varcols[var][dc])))
# all parameters
nolbl = ['' for i in np.arange(len(dfvar.columns))]
dfvar.columns = nolbl
dfvar.rolling(y_len, center=True).mean().plot(ax=ax, color=colors,
linewidth=2)
ax.set_xlabel('Year', fontsize=26)
ax.set_xlim([1850, 2010])
ax.set_ylim([-4000, 2000])
ax.tick_params(axis='both', which='major', labelsize=22)
if not ax == ax1:
ax.set_yticklabels([])
ax.grid(True)
ax.set_xticks(np.arange(1880, 2010, 40))
ax.legend(fontsize=16, loc=2)
ax1.set_ylabel('relative length change [m]', fontsize=26)
name = name_plus_id(rgi_id)
fig1.suptitle('%s' % name, fontsize=28)
fig1.subplots_adjust(left=0.09, right=0.99, bottom=0.12, top=0.89,
wspace=0.05)
fn1 = os.path.join(pout, 'calibration_%s.png' % glid)
fig1.savefig(fn1)
def past_simulation_and_params(glcdict, pout, y_len=5):
for glid, df in glcdict.items():
# take care of merged glaciers
rgi_id = glid.split('_')[0]
fig = plt.figure(figsize=[20, 7])
gs = GridSpec(1, 4) # 1 rows, 4 columns
ax1 = fig.add_subplot(gs[0, 0:3])
ax2 = fig.add_subplot(gs[0, 3])
df.loc[:, 'obs'].plot(ax=ax1, color='k', marker='o',
label='Observations')
# OGGM standard
for run in df.columns:
if run == 'obs':
continue
para = ast.literal_eval('{' + run + '}')
if ((np.abs(para['prcp_scaling_factor'] - 1.75) < 0.01) and
(para['mbbias'] == 0) and
(para['glena_factor'] == 1)):
df.loc[:, run].rolling(y_len, center=True). \
mean().plot(ax=ax1, linewidth=2, color='k',
label='OGGM default parameter run')
oggmdefault = run
maes = mae_weighted(df).sort_values()
idx2plot = optimize_cov(df.loc[:, maes.index[:150]],
df.loc[:, 'obs'], glid, minuse=5)
ensmean = df.loc[:, idx2plot].mean(axis=1)
ensmeanmean = ensmean.rolling(y_len, center=True).mean()
ensstdmean = df.loc[:, idx2plot].std(axis=1).rolling(y_len,
center=True).mean()
# coverage
cov = calc_coverage(df, idx2plot, df['obs'])
ax1.fill_between(ensmeanmean.index, ensmeanmean - ensstdmean,
ensmeanmean + ensstdmean, color='xkcd:teal', alpha=0.5)
# nolbl = df.loc[:, idx2plot2].rolling(y_len, center=True).mean().copy()
# nolbl.columns = ['' for i in range(len(nolbl.columns))]
#df.loc[:, idx2plot2].rolling(y_len, center=True).mean().plot(
# ax=ax1, linewidth=0.8)
# plot ens members
ensmeanmean.plot(ax=ax1, linewidth=4.0, color='xkcd:teal',
label='ensemble parameters runs')
# reference run (basically min mae)
df.loc[:, maes.index[0]].rolling(y_len, center=True).mean(). \
plot(ax=ax1, linewidth=3, color='xkcd:lavender',
label='minimum wMAE parameter run')
name = name_plus_id(rgi_id)
mae_ens = mae_weighted(pd.concat([ensmean, df['obs']], axis=1))[0]
mae_best = maes[0]
ax1.set_title('%s' % name, fontsize=28)
ax1.text(2030, -4900, 'wMAE ensemble mean = %.2f m\n'
'wMAE minimum run = %.2f m' %
(mae_ens, mae_best), fontsize=18,
horizontalalignment='right')
ax1.text(2040, -4900, '%d ensemble members\n'
'coverage = %.2f' %
(len(idx2plot), cov), fontsize=18)
ax1.set_ylabel('relative length change [m]', fontsize=26)
ax1.set_xlabel('Year', fontsize=26)
ax1.set_xlim([1850, 2020])
ax1.set_ylim([-3500, 1000])
ax1.tick_params(axis='both', which='major', labelsize=22)
ax1.grid(True)
ax1.legend(bbox_to_anchor=(-0.1, -0.15), loc='upper left',
fontsize=18, ncol=2)
# parameter plots
from colorspace import sequential_hcl
col = sequential_hcl('Blue-Yellow').colors(len(idx2plot) + 3)
for i, run in enumerate(idx2plot):
para = ast.literal_eval('{' + run + '}')
psf = para['prcp_scaling_factor']
gla = para['glena_factor']
mbb = para['mbbias']
mbb = (mbb - -1400) * (4-0.5) / (1000 - -1400) + 0.5
ax2.plot([1, 2, 3], [psf, gla, mbb], color=col[i], linewidth=2)
ax2.set_xlabel('calibration parameters', fontsize=18)
ax2.set_ylabel('Precipitation scaling factor\nGlen A factor',
fontsize=18)
ax2.set_xlim([0.8, 3.2])
ax2.set_ylim([0.3, 4.2])
ax2.set_xticks([1, 2, 3])
ax2.set_xticklabels(['Psf', 'GlenA', 'MB bias'], fontsize=16)
ax2.tick_params(axis='y', which='major', labelsize=16)
ax2.grid(True)
ax3 = ax2.twinx()
# scale to same y lims
scale = (4.2-0.3)/(4.0-0.5)
dy = (2400*scale-2400)/2
ax3.set_ylim([-1400-dy, 1000+dy])
ax3.set_ylabel('mass balance bias [m w.e. ]', fontsize=18)
ax3.set_yticks(np.arange(-1400, 1100, 400))
ax3.set_yticklabels(['-1.4', '-1.0', '-0.6', '-0.2',
'0.2', '0.6', '1.0'])
ax3.tick_params(axis='both', which='major', labelsize=16)
fig.subplots_adjust(left=0.08, right=0.95, bottom=0.24, top=0.93,
wspace=0.5)
fn1 = os.path.join(pout, 'histalp_%s.png' % glid)
fig.savefig(fn1)
used = dict()
used['oggmdefault'] = oggmdefault
used['minmae'] = idx2plot[0]
used['ensemble'] = idx2plot
pickle.dump(used, open(os.path.join(pout, 'runs_%s.p' % glid), 'wb'))