def plot_tseries(dayrel, ind, std, clr, key, xlabel, ylabel):
plt.plot(dayrel, ind, clr, label=key)
plt.fill_between(dayrel, ind-std, ind+std, color=clr, alpha=0.2)
plt.title(key, fontsize=12)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
plt.autoscale(tight=True)
plt.figure(figsize=(9, 12))
nrow, ncol = 3, 2
clr = 'k'
for i, key in enumerate(keys):
ind = tseries_rel[key].mean(dim='year')
std = tseries_rel[key].std(dim='year')
row, col = atm.subplot_index(nrow, ncol, i + 1)
plt.subplot(nrow, ncol, i + 1)
if row == nrow:
xlabel = 'Relative Day'
else:
xlabel = ''
if col == 1:
ylabel = 'Daily Index'
else:
ylabel = ''
plot_tseries(dayrel, ind, std, clr, key, xlabel, ylabel)
plt.suptitle('1979-2014 Climatological Composites Relative to Onset Day')
saveclose('ind_tseries_composites', isave, exts)
def plot_tseries_together(data, onset=None, years=None, suptitle='',
figsize=(14,10), legendsize=10,
legendloc='lower right', nrow=3, ncol=4,
yearnm='year', daynm='day', standardize=True,
label_attr=None, data_style=None, onset_style=None,
show_days=False):
"""Plot multiple daily timeseries together each year.
Parameters
----------
data : xray.Dataset
Dataset of timeseries variables to plot together.
onset : ndarray or dict of ndarrays, optional
Array of onset day for each year, or dict of onset arrays (e.g.
to compare onset days from different methods).
years : ndarray, optional
Subset of years to include. If omitted, all years are included.
suptitle : str, optional
Supertitle for plot.
figsize : 2-tuple, optional
Size of each figure.
legendsize : int, optional
Font size for legend
legendloc : str, optional
Legend location
nrow, ncol : int, optional
Number of rows, columns in each figure.
yearnm, daynm : str, optional
Name of year and day dimensions in data.
standardize : bool, optional
If True, standardize each timeseries by dividing by its
standard deviation.
label_attr : str, optional
Attribute of each data variable to use for labels. If omitted,
then the variable name is used.
data_style, onset_style : list or dict, optional
Matlab-style strings for each data variable or onset index.
show_days : bool, optional
If True, annotate each subplot with a textbox showing the
onset days.
"""
if years is None:
# All years
years = data[yearnm].values
data = atm.subset(data, {yearnm : (years, None)})
if label_attr is not None:
labels = {nm : data[nm].attrs[label_attr] for nm in data.data_vars}
if onset is not None:
if isinstance(onset, dict):
if onset_style is None:
onset_style = {key : 'k' for key in onset.keys()}
else:
onset = {'onset' : onset}
if onset_style is None:
onset_style = {'onset' : 'k'}
textpos = {key : (0.05, 0.9 - 0.1*i) for i, key in enumerate(onset)}
# Plot each year
for y, year in enumerate(years):
df = atm.subset(data, {yearnm : (year, None)}).to_dataframe()
df.drop(yearnm, axis=1, inplace=True)
if label_attr is not None:
df.rename(columns=labels, inplace=True)
if standardize:
for key in df.columns:
df[key] = (df[key] - np.nanmean(df[key])) / np.nanstd(df[key])
ylabel = 'Standardized Timeseries'
else:
ylabel = 'Timeseries'
if y % (nrow * ncol) == 0:
fig, axes = plt.subplots(nrow, ncol, figsize=figsize, sharex=True)
plt.subplots_adjust(left=0.08, right=0.95, wspace=0.2, hspace=0.2)
plt.suptitle(suptitle)
yplot = 1
else:
yplot += 1
i, j = atm.subplot_index(nrow, ncol, yplot)
ax = axes[i-1, j-1]
df.plot(ax=ax, style=data_style)
ax.grid()
if yplot == 1:
ax.legend(fontsize=legendsize, loc=legendloc)
else:
ax.legend_.remove()
if onset is not None:
for key in onset:
d0 = onset[key][y]
ax.plot([d0, d0], ax.get_ylim(), onset_style[key])
if show_days:
atm.text(d0, textpos[key], ax=ax, color=onset_style[key])
if j == 1:
ax.set_ylabel(ylabel)
if i == nrow:
ax.set_xlabel('Day')
else:
ax.set_xlabel('')
ax.set_title(year)
ylims = {"MFC": (-350, 400), "precip": (0, 1700)}
for varnm in varnms:
suptitle = "Onset/Retreat from Accum. %s Changepoints (Merged)" % varnm.upper()
for y, year in enumerate(years):
if y % (nrow * ncol) == 0:
fig, axes = plt.subplots(nrow, ncol, figsize=figsize, sharex=True)
plt.subplots_adjust(left=0.08, right=0.95, wspace=0.0, hspace=0.2)
plt.suptitle(suptitle)
yplot = 1
else:
yplot += 1
plt.subplot(nrow, ncol, yplot)
plotyear(chp[varnm], y, xlims, ylims[varnm])
row, col = atm.subplot_index(nrow, ncol, yplot)
if row == nrow:
plt.xlabel("Day")
else:
plt.gca().set_xticklabels("")
if col > 1:
plt.gca().set_yticklabels("")
if isavefigs:
atm.savefigs("onset_changepoint_merged_" + varnm.upper(), "pdf")
plt.close("all")
df = pd.DataFrame()
for key in ["onset", "retreat"]:
for varnm in varnms:
df["%s_%s" % (key, varnm.upper())] = chp[varnm][key].to_series()
atm.scatter_matrix(df, incl_p=True, incl_line=True, annotation_pos=(0.05, 0.7))
