def test_ScatterPlot_InvalidShape(self):
x = self.D
S = ScatterPlot(x)
y = self.D.copy()
y.data = np.random.random((10, 20, 30, 40))
with self.assertRaises(ValueError):
S.plot(y, fldmean=False)
def test_ScatterPlot_InvalidShape(self):
x = self.D
S = ScatterPlot(x)
y = self.D.copy()
y.data = np.random.random((10,20,30,40))
with self.assertRaises(ValueError):
S.plot(y, fldmean=False)
print 'Temporal stdv. ...'
t = D.timstd(return_object=True)
map_plot(t,use_basemap=True,title='Temporal stdv.',show_stat=True)
print 'Some LinePlot'
L=LinePlot(regress=True, title='This is a LinePlot with regression')
L.plot(D, label='2m air temperature')
L.plot(P, label='Precipitable water', ax=L.ax.twinx(), color='green') # use secondary axis for plotting here
L.legend()
print 'Scatterplot between different variables ...'
#here we just generate some random second variable
D1 = D.copy()
D1.data += np.random.random(D.shape)*50.
S=ScatterPlot(D) # scatterplot is initialized with definition of X-axis object
S.plot(D1)
S.legend()
print 'Temporal trend ...'
f=plt.figure()
ax1=f.add_subplot(221)
ax2=f.add_subplot(222)
ax3=f.add_subplot(223)
ax4=f.add_subplot(224)
R,S,I,P = D.temporal_trend(return_object=True)
map_plot(R, use_basemap=True, ax=ax1)
map_plot(S, use_basemap=True, ax=ax2)
map_plot(I, use_basemap=True, ax=ax3)
map_plot(P, use_basemap=True, ax=ax4)
f.suptitle('Example of temporal correlation analysis results', size=20)
def test_ScatterPlot_FldemeanFalse(self):
x = self.D
S = ScatterPlot(x)
S.plot(x, fldmean=False)
S.legend()
def test_ScatterPlot_GeneralWithNormalization(self):
x = self.D
S = ScatterPlot(x, normalize_data=True)
S.plot(x)
S.legend()
def test_ScatterPlot_General(self):
x = self.D
S = ScatterPlot(x)
S.plot(x)
S.legend()
def test_ScatterPlot_FldemeanFalse(self):
x = self.D
S = ScatterPlot(x)
S.plot(x, fldmean=False)
S.legend()
def test_ScatterPlot_GeneralWithNormalization(self):
x = self.D
S = ScatterPlot(x, normalize_data=True)
S.plot(x)
S.legend()
def test_ScatterPlot_General(self):
x = self.D
S = ScatterPlot(x)
S.plot(x)
S.legend()
print 'Map difference between datasets ...'
map_difference(D,P)
print 'ZonalPlot ...'
Z=ZonalPlot()
Z.plot(D)
print 'Some LinePlot'
L=LinePlot(regress=True, title='This is a LinePlot with regression')
L.plot(D, label='2m air temperature')
L.plot(P, label='Precipitable water', ax=L.ax.twinx(), color='green') # use secondary axis for plotting here
L.legend()
print 'Scatterplot between different variables ...'
S=ScatterPlot(D) # scatterplot is initialized with definition of X-axis object
S.plot(P)
S.legend()
print 'Hovmoeller diagrams ...'
hm = HovmoellerPlot(D)
hm.plot(climits=[-20.,30.])
print '... generate Hovmoeller plot from deseasonalized anomalies'
ha=HovmoellerPlot(D.get_deseasonalized_anomaly(base='all'))
ha.plot(climits=[-2.,2.], cmap='RdBu_r')
plt.show()
r=raw_input("Press Enter to continue...")
plt.close('all')
print 'Temporal stdv. ...'
t = D.timstd(return_object=True)
map_plot(t, use_basemap=True, title='Temporal stdv.', show_stat=True)
print 'Some LinePlot'
L = LinePlot(regress=True, title='This is a LinePlot with regression')
L.plot(D, label='2m air temperature')
L.plot(P, label='Precipitable water', ax=L.ax.twinx(),
color='green') # use secondary axis for plotting here
L.legend()
print 'Scatterplot between different variables ...'
#here we just generate some random second variable
D1 = D.copy()
D1.data += np.random.random(D.shape) * 50.
S = ScatterPlot(
D) # scatterplot is initialized with definition of X-axis object
S.plot(D1)
S.legend()
print 'Temporal trend ...'
f = plt.figure()
ax1 = f.add_subplot(221)
ax2 = f.add_subplot(222)
ax3 = f.add_subplot(223)
ax4 = f.add_subplot(224)
R, S, I, P = D.temporal_trend(return_object=True)
map_plot(R, use_basemap=True, ax=ax1)
map_plot(S, use_basemap=True, ax=ax2)
map_plot(I, use_basemap=True, ax=ax3)
map_plot(P, use_basemap=True, ax=ax4)
f.suptitle('Example of temporal correlation analysis results', size=20)
