def test_mean_model():
#The following code provides a routine that allows to validate the MeanModel() class
print ('Jetzt gehts los')
# generate some sample data ---
x = Data(None, None)
x.data = np.random.random((10,20,30))
x.label='nothing'
y = x.mulc(0.3)
z = x.mulc(0.5)
m = x.add(y).add(z).divc(3.)
r = m.div(x) # gives 0.6 as reference solution
# generate Model instances and store Data objects as 'variables' ---
dic_variables = ['var1', 'var2']
X = Model(None, dic_variables, name='x', intervals='season')
X.variables = {'var1': x, 'var2': x}
Y = Model(None, dic_variables, name='y', intervals='season')
Y.variables = {'var1': y, 'var2': y}
Z = Model(None, dic_variables, name='z', intervals='season')
Z.variables={'var1': z, 'var2': z}
#... now try multimodel ensemble
M=MeanModel(dic_variables,intervals='season')
M.add_member(X)
M.add_member(Y)
M.add_member(Z)
M.ensmean() # calculate ensemble mean
# print M.variables['var2'].div(x).data #should give 0.6
npt.assert_equal(np.all(np.abs(1. - M.variables['var2'].div(x).data/0.6) < 0.00000001), True)
def test_mean_model():
#The following code provides a routine that allows to validate the MeanModel() class
print('Jetzt gehts los')
# generate some sample data ---
x = Data(None, None)
x.data = np.random.random((10, 20, 30))
x.label = 'nothing'
y = x.mulc(0.3)
z = x.mulc(0.5)
m = x.add(y).add(z).divc(3.)
r = m.div(x) # gives 0.6 as reference solution
# generate Model instances and store Data objects as 'variables' ---
dic_variables = ['var1', 'var2']
X = Model(None, dic_variables, name='x', intervals='season')
X.variables = {'var1': x, 'var2': x}
Y = Model(None, dic_variables, name='y', intervals='season')
Y.variables = {'var1': y, 'var2': y}
Z = Model(None, dic_variables, name='z', intervals='season')
Z.variables = {'var1': z, 'var2': z}
#... now try multimodel ensemble
M = MeanModel(dic_variables, intervals='season')
M.add_member(X)
M.add_member(Y)
M.add_member(Z)
M.ensmean() # calculate ensemble mean
# print M.variables['var2'].div(x).data #should give 0.6
npt.assert_equal(
np.all(
np.abs(1. - M.variables['var2'].div(x).data / 0.6) < 0.00000001),
True)
def xxxxtest_median_model():
x = Data(None, None)
x.label = 'nothing'
d = np.random.random((100, 1, 1))
x.data = np.ma.array(d, mask= d!=d)
# odd number and no masked values
a = x.copy()
a.data[:, 0, 0] = 1.
b = x.copy()
b.data[:, 0, 0] = 3.
c = x.copy()
c.data[:, 0, 0] = 2.
d = x.copy()
d.data[:, 0, 0] = 5.
e = x.copy()
e.data[:, 0, 0] = 4.
m = MedianModel()
m.add_member(a)
m.add_member(b)
m.add_member(c)
m.add_member(d)
m.add_member(e)
m.ensmedian()
# should give the value of 3. for all timesteps
del m
# even number and no masked values
a = x.copy()
a.data[:, 0, 0] = 1.
b = x.copy()
b.data[:, 0, 0] = 3.
c = x.copy()
c.data[:, 0, 0] = 2.
d = x.copy()
c.data[:, 0, 0] = 4.
m = MedianModel()
m.add_member(a)
m.add_member(b)
m.add_member(c)
m.add_member(d)
m.ensmedian()
# should give the value of 2.5 for all timesteps
del m
def xxxxtest_median_model():
x = Data(None, None)
x.label = 'nothing'
d = np.random.random((100, 1, 1))
x.data = np.ma.array(d, mask=d != d)
# odd number and no masked values
a = x.copy()
a.data[:, 0, 0] = 1.
b = x.copy()
b.data[:, 0, 0] = 3.
c = x.copy()
c.data[:, 0, 0] = 2.
d = x.copy()
d.data[:, 0, 0] = 5.
e = x.copy()
e.data[:, 0, 0] = 4.
m = MedianModel()
m.add_member(a)
m.add_member(b)
m.add_member(c)
m.add_member(d)
m.add_member(e)
m.ensmedian()
# should give the value of 3. for all timesteps
del m
# even number and no masked values
a = x.copy()
a.data[:, 0, 0] = 1.
b = x.copy()
b.data[:, 0, 0] = 3.
c = x.copy()
c.data[:, 0, 0] = 2.
d = x.copy()
c.data[:, 0, 0] = 4.
m = MedianModel()
m.add_member(a)
m.add_member(b)
m.add_member(c)
m.add_member(d)
m.ensmedian()
# should give the value of 2.5 for all timesteps
del m
