def lineplot_scenarios(incubes, outpath, region):
"""
Line plot showing how the metric evolves over the years for all models and all scenarios combined
:param incubes: wildcard path to all tseries multi-model cubes
:param outpath: the path where the plot is saved
:param region: the region dictionary as defined in constants
:return: plot
"""
ano_list = glob.glob(incubes + '_tseries.nc')
ano_list = atlas_utils.order(ano_list)[::-1]
f = plt.figure(figsize=(8, 5), dpi=300)
ax = f.add_subplot(111)
colors = ['gray', 'gold', 'green', 'mediumblue'][::-1]
tag = cnst.SCENARIO[::-1]
scenarios = []
for ano, co, ta in zip(ano_list, colors, tag):
fdict = atlas_utils.split_filename_path(ano)
scen = fdict['scenario']
metric = fdict['metric']
variable = fdict['variable']
season = fdict['season']
bc = fdict['bc_res']
scenarios.append(scen)
cube = iris.load_cube(ano)
time = cube.coord('year').points
cube = cube.data
ax.plot(time[0], 0, color=co, label=ta)
for nb in range(cube.shape[0]):
ax.plot(time, cube[nb, :], color=co, alpha=0.5, lw=0.75)
bottom, top = ax.get_ylim()
plt.vlines(2005, bottom, top, linestyle='--', linewidth=1.5, zorder=10)
plt.ylabel(lblr.getYlab(metric, variable, anom=""))
plt.title(lblr.getTitle(metric,
variable,
season,
scenarios,
bc,
region[1],
anom=''),
fontsize=10)
plt.legend()
plt.savefig(outpath + os.sep + fdict['metric'] + '_' + fdict['variable'] +
'_' + fdict['bc_res'] + '_' + fdict['season'] + '_' +
region[0] + '_lineplot_allscen_' + '.png')
plt.close(f)
def nbModels_histogram_scenarios(incubes, outpath, region, anomaly=False):
"""
Histogram plot showing the number of models within different ranges of the metric (anomaly) value for ALL scenarios
:param incubes: wildcard path to all tseries multi-model cubes
:param outpath: the path where the plot is saved
:param region: the region dictionary as defined in constants
:param anomaly: boolean, switch for anomaly calculation
:return: plot
"""
fname = incubes + '_tseries.nc'
fdict = atlas_utils.split_filename_path(fname)
if fdict['aggregation'] not in cnst.METRIC_AGGS[fdict['metric']]:
return
ano_list = glob.glob(fname)
ano_list = atlas_utils.order(ano_list)
ldata = []
scen = []
perc = []
for ano in ano_list:
toplot = None
fdict = atlas_utils.split_filename_path(ano)
scenario = fdict['scenario']
metric = fdict['metric']
variable = fdict['variable']
season = fdict['season']
bc = fdict['bc_res']
if (anomaly == True) & (fdict['scenario'] == 'historical'):
continue
vname = cnst.VARNAMES[fdict['variable']]
cube = iris.load_cube(ano)
cube = atlas_utils.time_slicer(cube, fdict['scenario'])
cube = cube.collapsed('year', iris.analysis.MEDIAN)
if anomaly:
ano_hist = ano.replace(fdict['scenario'], 'historical')
hist = iris.load_cube(ano_hist)
hist = atlas_utils.time_slicer(hist, 'historical')
hist = hist.collapsed('year', iris.analysis.MEDIAN)
data = atlas_utils.anomalies(hist, cube, percentage=False)
data_perc = atlas_utils.anomalies(hist, cube, percentage=True)
data = data.data
data_perc = data_perc.data
hhisto, bi = np.histogram(data,
bins=np.linspace(data.min(), data.max(),
10))
try:
hhistop, bip = np.histogram(data_perc,
bins=np.linspace(
data_perc.min(),
data_perc.max(), 10))
except:
continue
an = 'anomaly'
ylabel = lblr.getYlab(metric, variable, anom="anomaly")
an_p = 'percentageAnomaly'
ylabel_p = lblr.getYlab(metric, variable, anom="percentageAnomaly")
ldata.append((hhisto, bi))
perc.append((hhistop, bip))
else:
cube = cube.data
hhisto, bi = np.histogram(cube,
bins=np.linspace(cube.min(), cube.max(),
10))
ldata.append((hhisto, bi))
ylabel = lblr.getYlab(metric, variable, anom="")
an = 'scenarios'
scen.append(fdict['scenario'])
plot_dic1 = {
'data': ldata,
'ftag': an,
'ylabel': ylabel,
}
toplot = [plot_dic1]
if anomaly and not 'tas' in variable:
plot_dic2 = {
'data': perc,
'ftag': an_p,
'ylabel': ylabel_p,
}
toplot = [plot_dic1, plot_dic2]
if toplot:
# This will only fail if both the historical and future are zero
for p in toplot:
if len(p['data']) < 4:
xp = len(p['data'])
yp = 1
xx = 8
yy = 6
else:
xp = 2
yp = 2
xx = 9
yy = 5
f = plt.figure(figsize=(xx, yy))
for id in range(len(p['data'])):
ax = f.add_subplot(xp, yp, id + 1)
bin = p['data'][id][1]
ax.bar(bin[0:-1] + ((bin[1::] - bin[0:-1]) / 2),
p['data'][id][0],
edgecolor='black',
width=(bin[1::] - bin[0:-1]),
align='edge',
color='darkseagreen')
ax.set_xlabel(p['ylabel'])
if cnst.LANGUAGE == 'ENGLISH':
ax.set_ylabel('Number of models')
else:
ax.set_ylabel(u'Nombre de modèles')
ax.set_title(lblr.getTitle(metric,
variable,
season,
scenario,
bc,
region[1],
anom=p['ftag']),
fontsize=10)
plt.tight_layout()
plt.savefig(outpath + os.sep + fdict['metric'] + '_' +
fdict['variable'] + '_' + fdict['bc_res'] + '_' +
fdict['season'] + '_' + region[0] +
'_MultiNbModelHistogram_' + p['ftag'] + '.png')
plt.close(f)
def barplot_scenarios(incubes, outpath, region, anomaly=False):
"""
Barplot showing the average of the (anomaly) metric over the climatological period for all
individual models and scenarios.
:param incubes: wildcard path to all tseries multi-model cubes
:param outpath: the path where the plot is saved
:param region: the region dictionary as defined in constants
:param anomaly: boolean, switch for anomaly calculation
:return: plot
"""
fname = incubes + '_tseries.nc'
fdict = atlas_utils.split_filename_path(fname)
if fdict['aggregation'] not in cnst.METRIC_AGGS[fdict['metric']]:
return
# This creates a list of '*allModels_tseries.nc' files, one element for each scenario
ano_list = glob.glob(fname)
ano_list = atlas_utils.order(ano_list)
ldata = []
scen = []
perc = []
lmodels = []
for ano in ano_list:
fdict = atlas_utils.split_filename_path(ano)
scenario = fdict['scenario']
metric = fdict['metric']
variable = fdict['variable']
season = fdict['season']
bc = fdict['bc_res']
if (anomaly == True) & (fdict['scenario'] == 'historical'):
continue
cube = iris.load_cube(ano)
cube = atlas_utils.time_slicer(cube, fdict['scenario'])
cube = cube.collapsed('year', iris.analysis.MEDIAN)
lmodels.append(np.ndarray.tolist(cube.coord('model_name').points))
if anomaly:
ano_hist = ano.replace(fdict['scenario'], 'historical')
hist = iris.load_cube(ano_hist)
hist = atlas_utils.time_slicer(hist, 'historical')
hist = hist.collapsed('year', iris.analysis.MEDIAN)
data = atlas_utils.anomalies(hist, cube, percentage=False)
data_perc = atlas_utils.anomalies(hist, cube, percentage=True)
an = 'anomaly'
ylabel = lblr.getYlab(metric, variable, anom="anomaly")
an_p = 'percentageAnomaly'
ylabel_p = lblr.getYlab(metric, variable, anom="percentageAnomaly")
dat_perc = np.ndarray.tolist(data_perc.data)
perc.append(dat_perc)
else:
data = cube
an = 'scenarios'
ylabel = lblr.getYlab(metric, variable, anom="")
dat = data.data
dat = np.ndarray.tolist(dat)
ldata.append(dat)
scen.append(scenario)
plot_dic1 = {
'data': ldata,
'xlabel': scen,
'ftag': an,
'ylabel': ylabel,
'models': lmodels
}
toplot = [plot_dic1]
if anomaly and not 'tas' in variable:
plot_dic2 = {
'data': perc,
'xlabel': scen,
'ftag': an_p,
'ylabel': ylabel_p,
'models': lmodels
}
toplot = [plot_dic1, plot_dic2]
for p in toplot:
if len(p['data']) < 4:
xp = len(p['data'])
yp = 1
xx = 8
yy = 7
else:
xp = 2
yp = 2
xx = 13
yy = 8
f = plt.figure(figsize=(xx, yy))
for id in range(len(p['data'])):
ax = f.add_subplot(xp, yp, id + 1)
b = plt.bar(range(len(p['models'][id])),
p['data'][id],
align='edge',
label=p['models'][id],
color='darkseagreen')
# plt.subplots_adjust(bottom=0.8)
xticks_pos = [
0.65 * patch.get_width() + patch.get_xy()[0] for patch in b
]
plt.xticks(xticks_pos, p['models'][id], ha='right', rotation=45)
plt.ylabel(p['ylabel'])
plt.title(p['xlabel'][id])
plt.tight_layout(h_pad=0.2, rect=(0, 0, 1, 0.92))
plt.suptitle(lblr.getTitle(metric,
variable,
season,
scen,
bc,
region[1],
anom=p['ftag']),
fontsize=10)
plt.savefig(outpath + os.sep + fdict['metric'] + '_' +
fdict['variable'] + '_' + fdict['bc_res'] + '_' +
fdict['season'] + '_' + region[0] + '_allModelHisto_' +
p['ftag'] + '.png')
plt.close(f)
def boxplot_scenarios(incubes, outpath, region, anomaly=False):
"""
Produces a boxplot with a box for each scenario, indicating the model spread.
:param incubes: wildcard path to all tseries multi-model cubes
:param outpath: the path where the plot is saved
:param region: the region dictionary as defined in constants
:param anomaly: boolean, switch for anomaly calculation
:return: plot
"""
fname = incubes + '_tseries.nc'
fdict = atlas_utils.split_filename_path(fname)
if fdict['aggregation'] not in cnst.METRIC_AGGS[fdict['metric']]:
return
ano_list = glob.glob(fname)
ano_list = atlas_utils.order(ano_list)
ldata = []
scen = []
perc = []
for ano in ano_list:
print ano
fdict = atlas_utils.split_filename_path(ano)
metric = fdict['metric']
variable = fdict['variable']
season = fdict['season']
scenario = fdict['scenario']
bc = fdict['bc_res']
if (anomaly == True) & (fdict['scenario'] == 'historical'):
continue
cube = iris.load_cube(ano)
cube = atlas_utils.time_slicer(cube, fdict['scenario'])
cube = cube.collapsed('year', iris.analysis.MEDIAN)
if anomaly:
ano_hist = ano.replace(fdict['scenario'], 'historical')
hist = iris.load_cube(ano_hist)
hist = atlas_utils.time_slicer(hist, 'historical')
hist = hist.collapsed('year', iris.analysis.MEDIAN)
data = atlas_utils.anomalies(hist, cube, percentage=False)
data_perc = atlas_utils.anomalies(hist, cube, percentage=True)
an = 'anomaly'
ylabel = lblr.getYlab(metric, variable, anom='absolute')
an_p = 'percentageAnomaly'
ylabel_p = lblr.getYlab(metric, variable, anom='percentage')
dat_perc = np.ndarray.tolist(data_perc.data)
perc.append(dat_perc)
else:
data = cube
an = 'scenarios'
ylabel = lblr.getYlab(metric, variable, anom=None)
dat = data.data
dat = np.ndarray.tolist(dat)
ldata.append(dat)
scen.append(fdict['scenario'])
plot_dic1 = {'data': ldata, 'xlabel': scen, 'ftag': an, 'ylabel': ylabel}
toplot = [plot_dic1]
if anomaly and not 'tas' in variable:
plot_dic2 = {
'data': perc,
'xlabel': scen,
'ftag': an_p,
'ylabel': ylabel_p
}
toplot = [plot_dic1, plot_dic2]
for p in toplot:
f = plt.figure(figsize=(8, 7))
try:
bp = plt.boxplot(p['data'],
labels=p['xlabel'],
sym='',
patch_artist=True,
notch=False,
zorder=9,
whis=[10, 90])
except ValueError:
print('Boxplot data has a problem, please check. Cannot continue')
pdb.set_trace()
plt.title(lblr.getTitle(metric,
variable,
season,
scenario,
bc,
region[1],
anom=p['ftag']),
fontsize=10)
if cnst.LANGUAGE == 'ENGLISH':
plt.xlabel('Scenario')
plt.ylabel(p['ylabel'])
else:
plt.xlabel(u'Scénario')
plt.ylabel(p['ylabel'])
for median, box, lab in zip(bp['medians'], bp['boxes'], p['xlabel']):
box.set(facecolor='none')
median.set(linewidth=0) #, color='steelblue')
for id, d in enumerate(p['data']):
try:
plt.scatter([id + 1] * len(d),
d,
marker='_',
color='firebrick',
s=60,
zorder=9)
except:
pdb.set_trace()
if np.nanmax(d) - np.nanmin(d) > np.nanmax(d):
plt.hlines(0, 0, len(d), linestyle='dashed', color='grey')
plt.savefig(outpath + os.sep + fdict['metric'] + '_' +
fdict['variable'] + '_' + fdict['bc_res'] + '_' +
fdict['season'] + '_' + region[0] + '_allModelBoxplot_' +
p['ftag'] + '.png')
plt.close(f)