def testFancyErrorPlot(self):
''' test a plot with error bands with separate upper and lower limits '''
fig, ax = getFigAx(1,
name=sys._getframe().f_code.co_name[4:],
**figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,
(list, tuple)) # should return a "naked" axes
var1 = self.var1
var2 = self.var2
err1 = self.err1
err2 = self.err2
# create plot
plts = ax.linePlot([var1, var2],
errorband=[err1, err2],
edgecolor=('k', 1),
expand_list=('edgecolor', ),
ylabel='Variables with Error Bands [{1:s}]',
llabel=True,
ylim=var1.limits(),
legend=2,
hline=2.,
vline=(2, 3))
assert len(plts) == 2
# add label
ax.addLabel(label=0,
loc=4,
lstroke=False,
lalphabet=True,
size=None,
prop=None)
def testBasicTaylorPlot(self):
''' test a simple Taylor plot with two variables/timeseries and a reference '''
fig,ax = getFigAx(1, lTaylor=True, axes_args=dict(std=1.2, leps=True),
name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'TaylorAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var0 = self.var0; var1 = self.var1; var2 = self.var2
# set up reference
# print(ax.setReference(var0))
# ax.showRefLines()
# add some dots...
plts, = ax.taylorPlot([var1, var2], reference=var0, rmse_lines=6)
assert len(plts) == 2
plts, = ax.taylorPlot([var1*1.5, var2/2., var0], reference='Reference', loverride=True)
assert len(plts) == 2
# add a negative correlation
negvar1 = var1 * -1
plts, = ax.taylorPlot(negvar1, legend=1, lprint=True, label_ext=' (neg.)')
assert len(plts) == 1
# add a random variable (should have no correlation
plts, = ax.taylorPlot(self.var3, legend=1, pval=0.01, lprint=True, linsig=True)
assert len(plts) == 1
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testFancyErrorPlot(self):
""" test a plot with error bands with separate upper and lower limits """
fig, ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == "MyFigure"
assert fig.axes_class.__name__ == "MyAxes"
assert not isinstance(ax, (list, tuple)) # should return a "naked" axes
var1 = self.var1
var2 = self.var2
err1 = self.err1
err2 = self.err2
# create plot
plts = ax.linePlot(
[var1, var2],
errorband=[err1, err2],
edgecolor=("k", 1),
expand_list=("edgecolor",),
ylabel="Variables with Error Bands [{UNITS:s}]",
llabel=True,
ylim=var1.limits(),
legend=2,
hline=2.0,
vline=(2, 3),
)
assert len(plts) == 2
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testFancyBandPlot(self):
""" test a fancy error plot with two lines and their errors in transparent bands """
fig, ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == "MyFigure"
assert fig.axes_class.__name__ == "MyAxes"
assert not isinstance(ax, (list, tuple)) # should return a "naked" axes
var1 = self.var1
var2 = self.var2
lowvar1 = self.var1 - 1.0
lowvar2 = None # self.var2 / 2.
upvar1 = self.var1 * 2.0
upvar2 = self.var2 # + 1.
# create plot
bnds = ax.bandPlot(upper=[upvar1, upvar2], lower=[lowvar1, lowvar2], edgecolor=0.1)
assert len(bnds) == 2
plts = ax.linePlot(
[var1, var2],
ylabel="Variables with Difference Bands [{UNITS:s}]",
llabel=True,
ylim=var1.limits(),
legend=2,
)
assert len(plts) == 2
# add label
ax.addLabel(label="Fancy\nError\nBands", loc=4, lstroke=False, size=None, prop=None)
def testMeanAxisPlot(self):
""" test a simple errorbar plot with a parasitic axes showing the means """
fig, ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == "MyFigure"
assert fig.axes_class.__name__ == "MyAxes"
assert not isinstance(ax, (list, tuple)) # should return a "naked" axes
var1 = self.var1
var2 = self.var2
err1 = self.err1
err2 = self.err2
# create plot
plts = ax.linePlot(
[var1, var2],
errorbar=[err1, err2],
lparasiteMeans=True,
errorevery=[1, 3],
expand_list=["errorevery"], # expand skip interval
ylabel="Variables with Errors [{UNITS:s}]",
llabel=True,
ylim=var1.limits(),
legend=2,
hline=2.0,
vline=(2, 3),
)
assert len(plts) == 2
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testBootstrapCI(self):
''' test a line plot with confidence intervals from bootstrapping '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
# settings
varlist = [self.var1, self.var2]
nbins = 15
# add regular histogram for comparison
bins, ptchs = ax.histogram(varlist, bins=nbins, legend=2, alpha=0.5, rwidth=0.8, histtype='bar')
# histtype = 'bar' | 'barstacked' | 'step' | 'stepfilled'
assert len(ptchs) == 2
assert len(bins) == nbins
vmin = np.min([var.min() for var in varlist])
vmax = np.max([var.max() for var in varlist])
#print bins[0], vmin; print bins[-1], vmax
assert bins[0] == vmin and bins[-1] == vmax
# add bootstrap plot with errorbars
support = np.linspace(vmin, vmax, 100)
fitvars = [var.fitDist(dist=self.dist,lflatten=True, lbootstrap=True, nbs=1000) for var in varlist]
ax.bootPlot(fitvars[0], support=support, errorscale=0.5, linewidth=2, lsmooth=False,
percentiles=None, lvar=True, lvarBand=False, lmean=True, reset_color=True)
ax.bootPlot(fitvars[1], support=support, errorscale=0.5, linewidth=2, lsmooth=False,
percentiles=(0.25,0.75), lvar=False, lvarBand=False, lmedian=True, reset_color=False)
# add the actual distribution
ax.linePlot(self.distVar, support=support, linewidth=1, marker='^')
# add statistical info
pstr = "p-values for '{:s}':\n".format(self.dist)
for var in varlist:
pstr += ' {:<9s} {:3.2f}\n'.format(var.name, var.kstest(dist=self.dist, asVar=False))
pstr += ' 2-samples {:3.2f}\n'.format(kstest(varlist[0], varlist[1], lflatten=True))
ax.addLabel(label=pstr, loc=1, lstroke=False, lalphabet=True, size=None, prop=None)
def testCombinedLinePlot(self):
''' test a two panel line plot with combined legend '''
fig, axes = getFigAx(4,
sharey=True,
sharex=True,
name=sys._getframe().f_code.co_name[4:],
**figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert isinstance(axes, np.ndarray) # should return a list of axes
var1 = self.var1
var2 = self.var2
# create plot
for i, ax in enumerate(axes.ravel()):
plts = ax.linePlot(
[var1, var2],
ylim=var1.limits(),
legend=0,
)
ax.addTitle('Panel {:d}'.format(i + 1))
assert len(plts) == 2
# add common legend
fig.addSharedLegend(plots=plts)
# add labels
fig.addLabels(labels=None,
loc=4,
lstroke=False,
lalphabet=True,
size=None,
prop=None)
# add a line
ax.addHline(3)
def testBasicHistogram(self):
''' a simple bar plot of two normally distributed samples '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
# settings
varlist = [self.var1, self.var2]
nbins = 15
# create regular histogram
bins, ptchs = ax.histogram(varlist, bins=nbins, legend=2, alpha=0.5, rwidth=0.8, histtype='bar')
# histtype = 'bar' | 'barstacked' | 'step' | 'stepfilled'
assert len(ptchs) == 2
assert len(bins) == nbins
vmin = np.min([var.min() for var in varlist])
vmax = np.max([var.max() for var in varlist])
#print bins[0], vmin; print bins[-1], vmax
assert bins[0] == vmin and bins[-1] == vmax
# add a KDE plot
support = np.linspace(vmin, vmax, 100)
kdevars = [var.kde(lflatten=True, lbootstrap=True, nbs=10) for var in varlist]
# N.B.: the bootstrapping is just to test bootstrap tolerance in regular plotting methods
ax.linePlot(kdevars, support=support, linewidth=2)
# add label
pval = kstest(varlist[0], varlist[1], lflatten=True)
pstr = 'p-value = {:3.2f}'.format(pval)
ax.addLabel(label=pstr, loc=1, lstroke=False, lalphabet=True, size=None, prop=None)
def testBootstrapCI(self):
''' test a line plot with confidence intervals from bootstrapping '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
# settings
varlist = [self.var1, self.var2]
nbins = 15
# add regular histogram for comparison
bins, ptchs = ax.histogram(varlist, bins=nbins, legend=2, alpha=0.5, rwidth=0.8, histtype='bar')
# histtype = 'bar' | 'barstacked' | 'step' | 'stepfilled'
assert len(ptchs) == 2
assert len(bins) == nbins
vmin = np.min([var.min() for var in varlist])
vmax = np.max([var.max() for var in varlist])
#print bins[0], vmin; print bins[-1], vmax
assert bins[0] == vmin and bins[-1] == vmax
# add bootstrap plot with errorbars
support = np.linspace(vmin, vmax, 100)
fitvars = [var.fitDist(dist=self.dist,lflatten=True, lbootstrap=True, nbs=1000) for var in varlist]
ax.bootPlot(fitvars[0], support=support, errorscale=0.5, linewidth=2, lsmooth=False,
percentiles=None, lvar=True, lvarBand=False, lmean=True, reset_color=True)
ax.bootPlot(fitvars[1], support=support, errorscale=0.5, linewidth=2, lsmooth=False,
percentiles=(0.25,0.75), lvar=False, lvarBand=False, lmedian=True, reset_color=False)
# add the actual distribution
ax.linePlot(self.distVar, support=support, linewidth=1, marker='^')
# add statistical info
pstr = "p-values for '{:s}':\n".format(self.dist)
for var in varlist:
pstr += ' {:<9s} {:3.2f}\n'.format(var.name, var.kstest(dist=self.dist, asVar=False))
pstr += ' 2-samples {:3.2f}\n'.format(kstest(varlist[0], varlist[1], lflatten=True))
ax.addLabel(label=pstr, loc=1, lstroke=False, lalphabet=True, size=None, prop=None)
def testBasicHistogram(self):
''' a simple bar plot of two normally distributed samples '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
# settings
varlist = [self.var1, self.var2]
nbins = 15
# create regular histogram
bins, ptchs = ax.histogram(varlist, bins=nbins, legend=2, alpha=0.5, rwidth=0.8, histtype='bar')
# histtype = 'bar' | 'barstacked' | 'step' | 'stepfilled'
assert len(ptchs) == 2
assert len(bins) == nbins
vmin = np.min([var.min() for var in varlist])
vmax = np.max([var.max() for var in varlist])
#print bins[0], vmin; print bins[-1], vmax
assert bins[0] == vmin and bins[-1] == vmax
# add a KDE plot
support = np.linspace(vmin, vmax, 100)
kdevars = [var.kde(lflatten=True, lbootstrap=True, nbs=10) for var in varlist]
# N.B.: the bootstrapping is just to test bootstrap tolerance in regular plotting methods
ax.linePlot(kdevars, support=support, linewidth=2)
# add label
pval = kstest(varlist[0], varlist[1], lflatten=True)
pstr = 'p-value = {:3.2f}'.format(pval)
ax.addLabel(label=pstr, loc=1, lstroke=False, lalphabet=True, size=None, prop=None)
def testAdvancedLinePlot(self):
''' test more advanced options of the line plot function '''
var1 = self.var1
var2 = self.var2
varatts = dict() # set some default values
for var in var1, var2:
varatts[var.name] = dict(color=var.name, marker='1', markersize=15)
fig, ax = getFigAx(1,
title='Fancy Plot Styles',
name=sys._getframe().f_code.co_name[4:],
variable_plotargs=varatts,
**figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(
ax, (list, tuple, np.ndarray)) # should return a "naked" axes
assert isinstance(ax.variable_plotargs, dict)
# define fancy attributes
plotatts = dict() # override some defaults
plotatts[var1.name] = dict(color='red', marker='*', markersize=5)
# define fancy legend
legend = dict(loc=2,
labelspacing=0.125,
handlelength=2.5,
handletextpad=0.5,
fancybox=True)
# create plot
plts = ax.linePlot([var1, var2],
linestyles=('--', '-.'),
plotatts=plotatts,
legend=legend)
assert len(plts) == 2
def testFancyBandPlot(self):
''' test a fancy error plot with two lines and their errors in transparent bands '''
fig, ax = getFigAx(1,
name=sys._getframe().f_code.co_name[4:],
**figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,
(list, tuple)) # should return a "naked" axes
var1 = self.var1
var2 = self.var2
lowvar1 = self.var1 - 1.
lowvar2 = None # self.var2 / 2.
upvar1 = self.var1 * 2.
upvar2 = self.var2 # + 1.
# create plot
bnds = ax.bandPlot(upper=[upvar1, upvar2],
lower=[lowvar1, lowvar2],
edgecolor=0.1)
assert len(bnds) == 2
plts = ax.linePlot(
[var1, var2],
ylabel='Variables with Difference Bands [{1:s}]',
llabel=True,
ylim=var1.limits(),
legend=2,
)
assert len(plts) == 2
# add label
ax.addLabel(label='Fancy\nError\nBands',
loc=4,
lstroke=False,
size=None,
prop=None)
def testBasicLinePlot(self):
""" test a simple line plot with two lines """
fig, ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == "MyFigure"
assert fig.axes_class.__name__ == "MyAxes"
assert not isinstance(ax, (list, tuple)) # should return a "naked" axes
var0 = self.var0
var1 = self.var1
var2 = self.var2
# create plot
plts = ax.linePlot(
[var1, var2],
ylabel="custom label [{UNITS:s}]",
llabel=True,
ylim=var1.limits(),
legend=2,
hline=2.0,
vline=(2, 3),
)
assert len(plts) == 2
# add rescaled plot
plts = ax.linePlot(var0, lrescale=True, scalefactor=2, offset=-1, llabel=True, legend=2, linestyle=":")
assert len(plts) == 1
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testSamplePlot(self):
""" test a line and band plot showing the mean/median and given percentiles """
fig, ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == "MyFigure"
assert fig.axes_class.__name__ == "MyAxes"
assert not isinstance(ax, (list, tuple)) # should return a "naked" axes
# settings
varlist = [self.var1, self.var2]
nbins = 15
# add regular histogram for comparison
bins, ptchs = ax.histogram(varlist, bins=nbins, legend=2, alpha=0.5, rwidth=0.8, histtype="bar")
# histtype = 'bar' | 'barstacked' | 'step' | 'stepfilled'
assert len(ptchs) == 2
assert len(bins) == nbins
vmin = np.min([var.min() for var in varlist])
vmax = np.max([var.max() for var in varlist])
# print bins[0], vmin; print bins[-1], vmax
assert bins[0] == vmin and bins[-1] == vmax
# add bootstrap plot with errorbars
support = np.linspace(vmin, vmax, 100)
fitvars = [var.fitDist(dist=self.dist, lflatten=True, lbootstrap=True, nbs=1000) for var in varlist]
# add the actual distribution
ax.linePlot(self.distVar, support=support, linewidth=1, marker="^")
# add simple sample (using bootstrap axis as sample
ax.samplePlot(
fitvars[0],
support=support,
linewidth=2,
lsmooth=False,
sample_axis="bootstrap",
percentiles=(0.05, 0.95),
lmedian=True,
reset_color=True,
)
# replicate axis and add random noise to mean
rndfit = fitvars[1].insertAxis(axis="sample", iaxis=0, length=100)
rndfit.data_array += np.random.randn(*rndfit.shape) / 100.0
ax.samplePlot(
rndfit,
support=support,
linewidth=2,
lsmooth=False,
bootstrap_axis="bootstrap",
sample_axis=("no_axis", "sample"),
percentiles=(0.25, 0.75),
lmean=True,
reset_color=False,
)
# add statistical info
pstr = "p-values for '{:s}':\n".format(self.dist)
for var in varlist:
pstr += " {:<9s} {:3.2f}\n".format(var.name, var.kstest(dist=self.dist, asVar=False))
pstr += " 2-samples {:3.2f}\n".format(kstest(varlist[0], varlist[1], lflatten=True))
ax.addLabel(label=pstr, loc=1, lstroke=False, lalphabet=True, size=None, prop=None)
def testAxesGridLinePlot(self):
''' test a two panel line plot with combined legend '''
fig,axes = getFigAx(4, AxesGrid=True, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
#assert grid.__class__.__name__ == 'ImageGrid'
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyLocatableAxes'
assert isinstance(axes,np.ndarray) # should return a list of axes
var1 = self.var1; var2 = self.var2
# create plot
for ax in axes.ravel():
plts = ax.linePlot([var1, var2], ylim=var1.limits(), legend=0)
assert len(plts) == 2
def testAxesGridLinePlot(self):
''' test a two panel line plot with combined legend '''
fig,axes = getFigAx(4, lAxesGrid=True, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
#assert grid.__class__.__name__ == 'ImageGrid'
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyLocatableAxes'
assert isinstance(axes,np.ndarray) # should return a list of axes
var1 = self.var1; var2 = self.var2
# create plot
for ax in axes.ravel():
plts = ax.linePlot([var1, var2], ylim=var1.limits(), legend=0)
assert len(plts) == 2
def testBasicScatterPlot(self):
''' test a simple scatter plot with two variables '''
fig,ax = getFigAx(1, lTaylor=True, axes_args=dict(std=1.5),
name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
# assert fig.axes_class.__name__ == 'TaylorAxes' # in this case, just regular rectilinear axes
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var0 = self.var0; var1 = self.var1; # var2 = self.var2
# create plot
plts = ax.scatterPlot(xvars=var0, yvars=var1, llabel=True, legend=0, lprint=True)
assert len(plts) == 1
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def evaFigAx(subplot,
dataset_plotargs=None,
variable_plotargs=None,
plot_labels=None,
**kwargs):
if dataset_plotargs is None: dataset_plotargs = evads_plotargs_rc
if variable_plotargs is None: variable_plotargs = None
if plot_labels is None: plot_labels = plot_labels_rc
return figure.getFigAx(subplot,
dataset_plotargs=dataset_plotargs,
variable_plotargs=variable_plotargs,
plot_labels=plot_labels,
**kwargs)
def evaFigAx(subplot, dataset_plotargs=None, variable_plotargs=None, plot_labels=None, **kwargs):
if dataset_plotargs is None:
dataset_plotargs = evads_plotargs_rc
if variable_plotargs is None:
variable_plotargs = None
if plot_labels is None:
plot_labels = plot_labels_rc
return figure.getFigAx(
subplot,
dataset_plotargs=dataset_plotargs,
variable_plotargs=variable_plotargs,
plot_labels=plot_labels,
**kwargs
)
def testBasicSurfacePlot(self):
''' test a simple color/surface plot '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var0 = self.var0
# create plot
vline = (2,3)
plt = ax.surfacePlot(var0, ylabel='custom label [{UNITS:s}]', llabel=True, lprint=True,
ylim=self.yax.limits(), clim=var0.limits(), hline=2., vline=vline)
assert plt
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testBasicLinePlot(self):
''' test a simple line plot with two lines '''
fig,ax = getFigAx(1, lPolarAxes=True,
name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyPolarAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var0 = self.var0; var1 = self.var1
# create plot
plts = ax.linePlot([var0, var1], ylabel='custom label [{UNITS:s}]', llabel=True,
ylim=(self.Rmin,self.Rmax), legend=2, hline=1., vline=(np.pi/4.,np.pi/2.,np.pi))
assert len(plts) == 2
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testSharedColorbar(self):
''' test a simple shared colorbar between to surface plots '''
name = sys._getframe().f_code.co_name[4:]
fig,axes = getFigAx(2, name=name, title=name, **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert isinstance(axes,(np.ndarray,list,tuple)) # should return a "naked" axes
# create plot
for ax,var in zip(axes,self.vars):
ax.surfacePlot(var, lprint=True, clim=var.limits(), centroids=dict(color='black'))
# add labels
fig.addLabels(loc=4, lstroke=False, lalphabet=True,)
fig.updateSubplots(left=0.02, bottom=0.03)
# add shared colorbar
cbar = fig.addSharedColorbar(ax=axes[1], location='bottom', clevs=3, lunits=True, length=0.8)
assert cbar
def testBasicErrorPlot(self):
''' test a simple errorbar plot with two lines and their standard deviations '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var1 = self.var1; var2 = self.var2
err1 = self.err1; err2 = self.err2
# create plot
plts = ax.linePlot([var1, var2], errorbar=[err1, err2],
errorevery=[1, 3,], expand_list=['errorevery'], # expand skip interval
ylabel='Variables with Errors [{1:s}]', llabel=True,
ylim=var1.limits(), legend=2, hline=2., vline=(2,3))
assert len(plts) == 2
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testMeanAxisPlot(self):
''' test a simple errorbar plot with a parasitic axes showing the means '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var1 = self.var1; var2 = self.var2
err1 = self.err1; err2 = self.err2
# create plot
plts = ax.linePlot([var1, var2], errorbar=[err1, err2], lparasiteMeans=True,
errorevery=[1, 3,], expand_list=['errorevery'], # expand skip interval
ylabel='Variables with Errors [{UNITS:s}]', llabel=True,
ylim=var1.limits(), legend=2, hline=2., vline=(2,3))
assert len(plts) == 2
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testLogSurfacePlot(self): ''' test a surface plot with one logarithmic and one linear panel and a shared colorbar ''' name = sys._getframe().f_code.co_name[4:] fig,axes = getFigAx(2, name=name, title=name, **figargs) # use test method name as title assert fig.__class__.__name__ == 'MyFigure' assert fig.axes_class.__name__ == 'MyAxes' assert isinstance(axes,(np.ndarray,list,tuple)) # should return a "naked" axes # create plot expvar = ( self.var0 * 10 ).exp() # large exponents for large effects axes[0].surfacePlot(expvar, lprint=True, clog=False, ) axes[1].surfacePlot(expvar, lprint=True, clog=True, ) # add labels fig.addLabels(loc=4, lstroke=False, lalphabet=True,) fig.updateSubplots(left=0.02, bottom=0.03) # add shared colorbar cbar = fig.addSharedColorbar(ax=axes[0], location='bottom', clevs=3, lunits=True, length=0.8) assert cbar
def testBasicLinePlot(self):
''' test a simple line plot with two lines '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var0 = self.var0; var1 = self.var1; var2 = self.var2
# create plot
vline = np.datetime64('1981-05-16') if self.ldatetime else (2,3)
plts = ax.linePlot([var1, var2], ylabel='custom label [{UNITS:s}]', llabel=True, lprint=True,
ylim=var1.limits(), legend=2, hline=2., vline=vline)
assert len(plts) == 2
# add rescaled plot
plts = ax.linePlot(var0, lrescale=True, scalefactor=2, offset=-1, llabel=True, legend=2, linestyle=':')
assert len(plts) == 1
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def climFigAx(subplot,
dataset_plotargs=None,
variable_plotargs=None,
plot_labels=None,
xtop=None,
yright=None,
**kwargs):
if dataset_plotargs is None: dataset_plotargs = climds_plotargs_rc
if variable_plotargs is None: variable_plotargs = variable_plotargs_rc
if plot_labels is None: plot_labels = plot_labels_rc
return figure.getFigAx(subplot,
dataset_plotargs=dataset_plotargs,
variable_plotargs=variable_plotargs,
plot_labels=plot_labels,
xtop=xtop,
yright=yright,
**kwargs)
def testCombinedLinePlot(self):
''' test a two panel line plot with combined legend '''
fig,axes = getFigAx(4, sharey=True, sharex=True, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert isinstance(axes,np.ndarray) # should return a list of axes
var1 = self.var1; var2 = self.var2
# create plot
for i,ax in enumerate(axes.ravel()):
plts = ax.linePlot([var1, var2], ylim=var1.limits(), legend=0,)
ax.addTitle('Panel {:d}'.format(i+1))
assert len(plts) == 2
# add common legend
fig.addSharedLegend(plots=plts)
# add labels
fig.addLabels(labels=None, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
# add a line
ax.addHline(3)
def climFigAx(
subplot, dataset_plotargs=None, variable_plotargs=None, plot_labels=None, xtop=None, yright=None, **kwargs
):
if dataset_plotargs is None:
dataset_plotargs = climds_plotargs_rc
if variable_plotargs is None:
variable_plotargs = variable_plotargs_rc
if plot_labels is None:
plot_labels = plot_labels_rc
return figure.getFigAx(
subplot,
dataset_plotargs=dataset_plotargs,
variable_plotargs=variable_plotargs,
plot_labels=plot_labels,
xtop=xtop,
yright=yright,
**kwargs
)
def testIrregularSurfacePlot(self):
''' test a color/surface plot with irregular coordiante variables '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
var0 = self.var0
# create coordiante variables
xax,yax = var0.axes
xx,yy = np.meshgrid(xax[:],yax[:], indexing='ij')
xax = Variable(name='X Coordinate', units='X Units', data=xx, axes=var0.axes)
yax = Variable(name='Y Coordinate', units='Y Units', data=yy, axes=var0.axes)
# create plot
plt = ax.surfacePlot(var0, flipxy=False, clog=False, xax=xax, yax=yax,
llabel=True, lprint=True, clim=var0.limits(),)
assert plt
# add label
ax.addLabel(label=0, loc=4, lstroke=False, lalphabet=True, size=None, prop=None)
def testAdvancedLinePlot(self):
''' test more advanced options of the line plot function '''
var1 = self.var1; var2 = self.var2
varatts = dict() # set some default values
for var in var1,var2: varatts[var.name] = dict(color=var.name, marker='1', markersize=15)
fig,ax = getFigAx(1, title='Fancy Plot Styles', name=sys._getframe().f_code.co_name[4:],
variable_plotargs=varatts, **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple,np.ndarray)) # should return a "naked" axes
assert isinstance(ax.variable_plotargs, dict)
# define fancy attributes
plotatts = dict() # override some defaults
plotatts[var1.name] = dict(color='red', marker='*', markersize=5)
# define fancy legend
legend = dict(loc=2, labelspacing=0.125, handlelength=2.5, handletextpad=0.5, fancybox=True)
# create plot
plts = ax.linePlot([var1, var2], linestyles=('--','-.'), plotatts=plotatts, legend=legend)
assert len(plts) == 2
def testSamplePlot(self):
''' test a line and band plot showing the mean/median and given percentiles '''
fig,ax = getFigAx(1, name=sys._getframe().f_code.co_name[4:], **figargs) # use test method name as title
assert fig.__class__.__name__ == 'MyFigure'
assert fig.axes_class.__name__ == 'MyAxes'
assert not isinstance(ax,(list,tuple)) # should return a "naked" axes
# settings
varlist = [self.var1, self.var2]
nbins = 15
# add regular histogram for comparison
bins, ptchs = ax.histogram(varlist, bins=nbins, legend=2, alpha=0.5, rwidth=0.8, histtype='bar')
# histtype = 'bar' | 'barstacked' | 'step' | 'stepfilled'
assert len(ptchs) == 2
assert len(bins) == nbins
vmin = np.min([var.min() for var in varlist])
vmax = np.max([var.max() for var in varlist])
#print bins[0], vmin; print bins[-1], vmax
assert bins[0] == vmin and bins[-1] == vmax
# add bootstrap plot with errorbars
support = np.linspace(vmin, vmax, 100)
fitvars = [var.fitDist(dist=self.dist,lflatten=True, lbootstrap=True, nbs=1000) for var in varlist]
# add the actual distribution
ax.linePlot(self.distVar, support=support, linewidth=1, marker='^')
# add simple sample (using bootstrap axis as sample
ax.samplePlot(fitvars[0], support=support, linewidth=2, lsmooth=False,
sample_axis='bootstrap', percentiles=(0.05,0.95), lmedian=True, reset_color=True)
# replicate axis and add random noise to mean
rndfit = fitvars[1].insertAxis(axis='sample',iaxis=0,length=100)
rndfit.data_array += np.random.randn(*rndfit.shape)/100.
ax.samplePlot(rndfit, support=support, linewidth=2, lsmooth=False,
bootstrap_axis='bootstrap', sample_axis=('no_axis','sample'),
percentiles=(0.25,0.75), lmean=True, reset_color=False)
# add statistical info
pstr = "p-values for '{:s}':\n".format(self.dist)
for var in varlist:
pstr += ' {:<9s} {:3.2f}\n'.format(var.name, var.kstest(dist=self.dist, asVar=False))
pstr += ' 2-samples {:3.2f}\n'.format(kstest(varlist[0], varlist[1], lflatten=True))
ax.addLabel(label=pstr, loc=1, lstroke=False, lalphabet=True, size=None, prop=None)
evads_plotargs_rc['MEns'] = AttrDict(color='royalblue',)
evads_plotargs_rc['EC (1940)'] = AttrDict(color='blue')
lgage = True
ldisc = False # scale sfroff to discharge
lprecip = True # scale sfroff by precip bias
# figure parameters for saving
# sf, figformat, margins, subplot, figsize = getFigureSettings(2, cbar=False, sameSize=False)
# make figure and axes
# fig, axes = pyl.subplots(*subplot, sharex=True, sharey=False, facecolor='white', figsize=figsize)
# margins = dict(bottom=0.11, left=0.11, right=.975, top=.95, hspace=0.05, wspace=0.05)
# fig.subplots_adjust(**margins) # hspace, wspace
nax = len(basins)
paper_folder = '/home/data/Figures/Basins/'
# fig = pyl.figure(1, figsize=(6.25,3.75))
# axes = ImageGrid(fig, (0.07,0.11,0.91,0.82), nrows_ncols = (1, nax), axes_pad = 0.2, aspect=False, label_mode = "L")
fig, axes = getFigAx((1,2), name=None, title='IC Ensemble Average (Hist., Mid-, End-Century)',
title_font='x-large', figsize=(6.25,3.75),
stylesheet='myggplot', lpublication=True, yright=False, xtop=False,
variable_plotargs=None, dataset_plotargs=None, plot_labels=None,
sharex=True, AxesGrid=False, direction='row',
axes_pad = 0., aspect=False, margins=(0.075,0.11,0.95,0.81),)
# loop over panels/basins
for n,ax,basin in zip(xrange(nax),axes,basins):
# for basin in basins:
# load meteo data
if lfield:
print ' - loading Data'
unity = loadUnity_ShpTS(varlist=['precip'], shape='shpavg')
unity = unity(shape_name=basin).load().climMean()
# unity['precip'][:] *= 86400. # scale with basin area
wrf = loadWRF_ShpEns(name=exp, domains=2, shape='shpavg', filetypes=['srfc','lsm'],
varlist=variables[:]) # WRF
wrf = wrf(shape_name=basin).load().climMean()
