def set_oggm_cmaps(use_hcl=None):
# Set global colormaps
global OGGM_CMAPS
if use_hcl is None:
use_hcl = HAS_HCL_CMAP
OGGM_CMAPS['terrain'] = colormap.terrain
if HAS_HCL_CMAP and use_hcl:
cm_divs = 100 # number of discrete colours from continuous colormaps
tcmap = sequential_hcl("Blue-Yellow", rev=True).cmap(cm_divs)
OGGM_CMAPS['section_thickness'] = tcmap
OGGM_CMAPS['glacier_thickness'] = tcmap
else:
OGGM_CMAPS['section_thickness'] = plt.cm.get_cmap('YlOrRd')
OGGM_CMAPS['glacier_thickness'] = plt.get_cmap('viridis')
def setColorConf(ngroups, colors="tab20", alternative="grey") -> list:
"""
Parameters:
----------
ngroups:int
Number of tags need to be colored.
colors:str
Built-in colormaps accessible in the matplotlib.
See more:https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
alternative:
If <ngroups> is greater than the maximum number of colorPalette, the rest tags would be colored with <alternative>.
Returns:
--------
colors_list:list
a list of colors(hex).
"""
if colors == "hcl":
try:
from colorspace import sequential_hcl
color_repo = sequential_hcl(h=[15, 375], l=65, c=70)
colors_list = color_repo.colors(ngroups + 1)
except ImportError:
print('hcl colorspace package has not being installed.')
print('please try the following command:')
print(
'pip install git+https://github.com/retostauffer/python-colorspace'
)
else:
colors = list(plt.get_cmap(colors).colors)
colors_list = [to_hex(color) for color in colors]
colors_list = colors_list[:ngroups]
# if len of colors_list less than ngroups, use grey to fullfill.
if len(colors_list) < ngroups:
for i in range(ngroups - len(colors_list)):
colors_list.append(to_hex(alternative))
return colors_list
from colorspace import sequential_hcl
USE_HCL_CMAP = True
except ImportError:
USE_HCL_CMAP = False
from oggm.core.flowline import FileModel
from oggm import cfg, utils
# Module logger
log = logging.getLogger(__name__)
# Set global colormaps
ALTITUDE_CMAP = colormap.terrain
if USE_HCL_CMAP:
CMAP_DIVS = 100 # number of discrete colours from continuous colormaps
THICKNESS_CMAP = sequential_hcl("Blue-Yellow", rev=True).cmap(CMAP_DIVS)
SECTION_THICKNESS_CMAP = THICKNESS_CMAP
GLACIER_THICKNESS_CMAP = THICKNESS_CMAP
else:
SECTION_THICKNESS_CMAP = plt.cm.get_cmap('YlOrRd')
GLACIER_THICKNESS_CMAP = plt.get_cmap('viridis')
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=256):
"""Remove extreme colors from a colormap."""
new_cmap = colors.LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
from colorspace import sequential_hcl
USE_HCL_CMAP = True
except ImportError:
USE_HCL_CMAP = False
from oggm.core.flowline import FileModel
from oggm import cfg, utils
# Module logger
log = logging.getLogger(__name__)
# Set global colormaps
ALTITUDE_CMAP = colormap.terrain
if USE_HCL_CMAP:
CMAP_DIVS = 100 # number of discrete colours from continuous colormaps
THICKNESS_CMAP = sequential_hcl("Blue-Yellow", rev=True).cmap(CMAP_DIVS)
SECTION_THICKNESS_CMAP = THICKNESS_CMAP
GLACIER_THICKNESS_CMAP = THICKNESS_CMAP
else:
SECTION_THICKNESS_CMAP = plt.cm.get_cmap('YlOrRd')
GLACIER_THICKNESS_CMAP = plt.get_cmap('viridis')
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=256):
"""Remove extreme colors from a colormap."""
new_cmap = colors.LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
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'))