# -*- coding: utf-8 -*- """ This file is part of pyCMBS. (c) 2012- Alexander Loew For COPYING and LICENSE details, please refer to the LICENSE files """ from pycmbs.data import Data from pycmbs.plots import map_difference import matplotlib.pyplot as plt file_name = '../../../pycmbs/examples/example_data/air.mon.mean.nc' A = Data(file_name, 'air', lat_name='lat', lon_name='lon', read=True, label='air temperature') B = A.copy() B.mulc(2.3, copy=False) a = A.get_climatology(return_object=True) b = B.get_climatology(return_object=True) # a quick plot as well as a projection plot f1 = map_difference(a, b, show_stat=False, vmin=-30., vmax=30., dmin=-60., dmax=60.) # unprojected plt.show()
"""
This file is part of pyCMBS.
(c) 2012- Alexander Loew
For COPYING and LICENSE details, please refer to the LICENSE files
"""
from pycmbs.data import Data
from pycmbs.plots import map_difference
import matplotlib.pyplot as plt
file_name = '../../../pycmbs/examples/example_data/air.mon.mean.nc'
A = Data(file_name,
'air',
lat_name='lat',
lon_name='lon',
read=True,
label='air temperature')
B = A.copy()
B.mulc(2.3, copy=False)
a = A.get_climatology(return_object=True)
b = B.get_climatology(return_object=True)
# a quick plot as well as a projection plot
f1 = map_difference(a,
b,
show_stat=False,
vmin=-30.,
vmax=30.,
dmin=-60.,
dmax=60.) # unprojected
plt.show()
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)
print 'Calculate climatology and plot ...'
# get_climatology() returns 12 values which are then used for plotting
map_season(D.get_climatology(return_object=True), use_basemap=True, vmin=-20., vmax=30.)
print 'Map difference between datasets ...'
map_difference(D, P)
print 'ZonalPlot ...'
Z=ZonalPlot()
Z.plot(D)
#~ 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...")
def test_map_difference_General(self):
map_difference(self.D, self.D)
def test_map_difference_General(self):
map_difference(self.D, self.D)
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)
print 'Calculate climatology and plot ...'
# get_climatology() returns 12 values which are then used for plotting
map_season(D.get_climatology(return_object=True),
use_basemap=True,
vmin=-20.,
vmax=30.)
print 'Map difference between datasets ...'
map_difference(D, P)
print 'ZonalPlot ...'
Z = ZonalPlot()
Z.plot(D)
#~ 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...")
