def test_HstackTimeSeries_inconsistent_data_geometries(self):
HT = HstackTimeseries()
x = np.random.random(100)*2.-1.
y = np.random.random(1000)*2.-1.
HT.add_data(x, 'A')
with self.assertRaises(ValueError):
HT.add_data(y, 'B')
def test_HstackTimeSeries_inconsistent_data_geometries(self):
HT = HstackTimeseries()
x = np.random.random(100) * 2. - 1.
y = np.random.random(1000) * 2. - 1.
HT.add_data(x, 'A')
with self.assertRaises(ValueError):
HT.add_data(y, 'B')
def test_HstackTimeSeries(self):
HT = HstackTimeseries()
for i in xrange(15):
x = np.random.random(100) * 2. - 1.
HT.add_data(x, 'model' + str(i).zfill(3))
HT.plot(cmap='RdBu_r',
interpolation='nearest',
vmin=-1.,
vmax=1.,
nclasses=15,
title='Testtitle')
def test_HstackTimeSeries_noplotdone(self):
HT = HstackTimeseries()
with self.assertRaises(ValueError):
HT._add_colorbar()
def test_HstackTimeSeries_monthly_ticks(self):
HT = HstackTimeseries()
f = plt.figure()
ax = f.add_subplot(111)
ax.plot(np.arange(12))
HT._set_monthly_xtick_labels(ax)
def test_HstackTimeSeries_duplicate_keys(self):
HT = HstackTimeseries()
x = np.random.random(100) * 2. - 1.
HT.add_data(x, 'A')
with self.assertRaises(ValueError):
HT.add_data(x, 'A')
def test_HstackTimeSeries_invalidData(self):
HT = HstackTimeseries()
x = np.random.random((50, 60)) * 2. - 1.
with self.assertRaises(ValueError):
HT.add_data(x, 'test')
def test_HstackTimeSeries_noplotdone(self):
HT = HstackTimeseries()
with self.assertRaises(ValueError):
HT._add_colorbar()
def test_HstackTimeSeries_monthly_ticks(self):
HT = HstackTimeseries()
f = plt.figure()
ax = f.add_subplot(111)
ax.plot(np.arange(12))
HT._set_monthly_xtick_labels(ax)
def test_HstackTimeSeries_duplicate_keys(self):
HT = HstackTimeseries()
x = np.random.random(100)*2.-1.
HT.add_data(x, 'A')
with self.assertRaises(ValueError):
HT.add_data(x, 'A')
def test_HstackTimeSeries_invalidData(self):
HT = HstackTimeseries()
x = np.random.random((50, 60))*2.-1.
with self.assertRaises(ValueError):
HT.add_data(x, 'test' )
def test_HstackTimeSeries(self):
HT = HstackTimeseries()
for i in xrange(15):
x = np.random.random(100)*2.-1.
HT.add_data(x, 'model' + str(i).zfill(3) )
HT.plot(cmap='RdBu_r', interpolation='nearest', vmin=-1., vmax=1., nclasses=15, title='Testtitle')
"""
This file is part of pyCMBS. (c) 2012-2014
For COPYING and LICENSE details, please refer to the file
COPYRIGHT.md
"""
"""
Hstackplotting
"""
from pycmbs.plots import HstackTimeseries
import numpy as np
import matplotlib.pyplot as plt
plt.close('all')
ht = HstackTimeseries()
for i in xrange(50):
x = np.random.random(12) * 2. - 1.
ht.add_data(x, 'model' + str(i).zfill(3))
ht.plot(cmap='RdBu_r',
interpolation='nearest',
vmin=-1.,
vmax=1.,
nclasses=15,
title='Testtitle',
maxheight=0.3,
monthly_clim_ticks=True)
plt.show()
This file is part of pyCMBS. (c) 2012-2014
For COPYING and LICENSE details, please refer to the file
COPYRIGHT.md
"""
"""
Hstackplotting
"""
from pycmbs.plots import HstackTimeseries
import numpy as np
import matplotlib.pyplot as plt
plt.close('all')
ht = HstackTimeseries()
for i in xrange(50):
x = np.random.random(12)*2.-1.
ht.add_data(x, 'model' + str(i).zfill(3) )
ht.plot(cmap='RdBu_r', interpolation='nearest', vmin=-1., vmax=1., nclasses=15, title='Testtitle', maxheight=0.3, monthly_clim_ticks=True)
plt.show()
#~ set_size_inches(18.5,10.5)
