def plot_jma(num=None, fill=True, full_year=False, fill_neutral=False):
if num is None:
fig = csfigure(series=JMA)
else:
fig = csfigure(num=num, series=JMA)
fig.clear()
fsp = fig.add_csplot(111)
fill_colors = ENSOcolors['fill']
polygon_colors = ENSOcolors['polygons']
marker_colors = ENSOcolors['markers']
line_colors = ENSOcolors['lines']
#
enso_indices = JMA.set_indices(full_year=full_year)
xxthcentury = (JMA.years >= 1900)
imask = enso_indices
cold = JMA[((enso_indices == -1) & xxthcentury).filled(False)]
neutral = JMA[((enso_indices == 0) & xxthcentury).filled(False)]
warm = JMA[((enso_indices == +1) & xxthcentury).filled(False)]
#
_JMA = JMA[xxthcentury]
#
xdata = enso_indices._dates.tovalue()
#
fsp.tsplot(_JMA)
if fill:
cold = _JMA.view(type(_JMA))
cold.unshare_mask()
cold.__setmask__((enso_indices != -1), copy=True)
fsp.fill(xdata, cold._series.filled(_JMA.thresholds[0]),
ec=fill_colors['C'], fc=fill_colors['C'])
warm = _JMA.view(type(_JMA))
warm.unshare_mask()
warm.__setmask__((enso_indices != 1), copy=True)
fsp.fill(xdata, warm._series.filled(JMA.thresholds[-1]),
ec=fill_colors['W'], fc=fill_colors['W'])
if fill_neutral:
neutral = _JMA.view(type(_JMA))
neutral.unshare_mask()
neutral.__setmask__((enso_indices != 0).filled(True), copy=True)
fsp.fill(xdata, neutral._series.filled(0),
ec=fill_colors['N'], fc=fill_colors['N'])
else:
fsp.tsplot(cold, marker='o', c=marker_colors['C'], ls='')
fsp.tsplot(neutral, marker='o', c=marker_colors['N'], ls='')
fsp.tsplot(warm, marker='o', c=marker_colors['W'], ls='')
#
fsp.axhline(0, ls='-', c='k')
fsp.axhline(_JMA.thresholds[-1], ls='-.', c=line_colors['W'])
fsp.axhline(_JMA.thresholds[0], ls='-.', c=line_colors['C'])
fsp.format_dateaxis()
fsp.set_datelimits(end_date='2010')
#
fsp.set_ylabel("5-m Averaged SST Anomalies (oC)", fontweight='bold')
fsp.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(0.5))
fsp.set_xlabel("Years", fontweight='bold')
cpl.show()
return fig
fig = cpl.hydrograph(rainfall, flowdata, figsize=(12,6))
"""
The handle of the hyetograph subplot can be accessed through the :attr:`hyeto`
attribute of the figure, and the handle of hydrograph through the :attr:`hydro`
attribute.
We can set the labels of the y-axis:
"""
fig.hyeto.set_ylabel("Rainfall (mm)", fontweight='bold')
fig.hydro.set_ylabel("Flows (cfs)", fontweight='bold')
fig.suptitle("Hydrograph for the North Oconee River at Athens, GA",
fontweight="bold", fontsize=12)
fig.savefig("athens_hydrograph.png")
"""
As the hydrograph is a subclass of :class:`~scikits.timeseries.lib.plotlib.TimeSeriesFigure`,
the ticks on the x axis of each subplot are automatically adjusted with the level
of zoom.
Moreover, as the two subplots share the same x-axis, any modification on one
subplot is reflected on the other.
As an example, let's focus on the year 2005.
"""
fig.hyeto.set_datelimits('2005-01-01', '2005-12-31')
fig.savefig("athens_hydrograph_zoomed.png")
cpl.show()
def plot_jma_vs_oni_comparison(freq='M'):
"""
Plots the comparison of ONI vs JMAI ENSO indices at the given frequency.
"""
# Check that we have a valid frequency
freq = climpy.check_freq(freq)
# Get the ENSO indices: annual for 12 months period, monthly on a monthly basis
JMAi = JMA.indices(full_year=False, reference_season='NDJ', minimum_size=6)
ONIi = ONI.indices(full_year=False, reference_season=None, minimum_size=5)
# Convert to the new frequency
if freq != climpy._c.FR_MTH:
JMAi = np.round_(JMAi.convert(freq, func=ma.mean))
ONIi = np.round_(ONIi.convert(freq, func=ma.mean))
# Convert the indices to a Nx12 series (N the nb of years)
JMAi = JMAi.convert('A')
ONIi = ONIi.convert('A')
#
JMAi = JMAi[JMAi.year >= 1900]
ONIi = ONIi[ONIi.year >= 1900]
#
j_colors = get_colormatrix(JMAi)
o_colors = get_colormatrix(ONIi)
#
fig = cpl.figure(num=1, figsize=(13, 5))
fig.clear()
fsp = fig.add_axes([0.05, 0.1, 0.85, 0.85])
(nrows, ncols) = j_colors.shape
edgecolors = 'none'
#
j_vert = np.array([[((i, j + 1), (i, j), (i + 1, j)) for i in range(ncols)]
for j in range(nrows)]).reshape(-1, 3, 2)
j_coll = matplotlib.collections.PolyCollection(j_vert,
facecolors=j_colors.ravel(),
edgecolors=edgecolors)
fsp.add_collection(j_coll)
#............................................
o_vert = np.array([[((i, j + 1), (i + 1, j + 1), (i + 1, j)) for i in range(ncols)]
for j in range(nrows)]).reshape(-1, 3, 2)
o_coll = matplotlib.collections.PolyCollection(o_vert,
facecolors=o_colors.ravel(),
edgecolors=edgecolors)
fsp.add_collection(o_coll)
#............................................
xy_vert = [((i, 0), (i, nrows)) for i in range(0, ncols, 12)]
xy_vert += [((0, j), (ncols, j)) for j in range(nrows)]
xy_coll = matplotlib.collections.LineCollection(xy_vert,
colors='k')
fsp.add_collection(xy_coll)
#............................................
fsp.xaxis.set_major_locator(cpl.MultipleLocator(12))
fsp.set_xticks(np.arange(0, ncols, 12) + 6)
fsp.set_xticklabels(["%02i" % i for i in range(12)])
fsp.set_xlim(0, ncols)
#............................................
fsp.set_yticks(np.arange(0, nrows) + 0.5)
fsp.set_yticklabels(["%i" % (i * 10 + 1900) for i in range(nrows + 1)])
fsp.set_ylim(0, nrows)
fsp.set_ylim(fsp.get_ylim()[::-1])
add_plot_legend(fig, lableft='JMA', labright='ONI')
cpl.show()
return fig
"""
fig = cpl.hydrograph(rainfall, flowdata, figsize=(12, 6))
"""
The handle of the hyetograph subplot can be accessed through the :attr:`hyeto`
attribute of the figure, and the handle of hydrograph through the :attr:`hydro`
attribute.
We can set the labels of the y-axis:
"""
fig.hyeto.set_ylabel("Rainfall (mm)", fontweight='bold')
fig.hydro.set_ylabel("Flows (cfs)", fontweight='bold')
fig.suptitle("Hydrograph for the North Oconee River at Athens, GA",
fontweight="bold",
fontsize=12)
fig.savefig("athens_hydrograph.png")
"""
As the hydrograph is a subclass of :class:`~scikits.timeseries.lib.plotlib.TimeSeriesFigure`,
the ticks on the x axis of each subplot are automatically adjusted with the level
of zoom.
Moreover, as the two subplots share the same x-axis, any modification on one
subplot is reflected on the other.
As an example, let's focus on the year 2005.
"""
fig.hyeto.set_datelimits('2005-01-01', '2005-12-31')
fig.savefig("athens_hydrograph_zoomed.png")
cpl.show()