def test_rasterize_init(self):
x = Data(None, None)
x._init_sample_object(ny=1, nx=272)
x.lon = np.random.random(272) * 10. + 5. # 5 ... 15
x.lat = np.random.random(272) * 20. + 0. # 0 ... 20
lon = np.random.random((10, 20))
lat = np.random.random((30, 20))
with self.assertRaises(ValueError):
x._rasterize(lon, lat, radius=0.1)
lon = np.random.random((10, 20))
lat = np.random.random((10, 20))
with self.assertRaises(ValueError):
x._rasterize(lon, lat, radius=None)
def test_rasterize_init(self):
x = Data(None, None)
x._init_sample_object(ny=1, nx=272)
x.lon = np.random.random(272)*10. + 5. # 5 ... 15
x.lat = np.random.random(272)*20. + 0. # 0 ... 20
lon = np.random.random((10,20))
lat = np.random.random((30,20))
with self.assertRaises(ValueError):
x._rasterize(lon, lat, radius=0.1)
lon = np.random.random((10,20))
lat = np.random.random((10,20))
with self.assertRaises(ValueError):
x._rasterize(lon, lat, radius=None)
def test_rasterize_data(self):
"""
testdataset
+---+---+---+
|1.2|2.3| |
+---+---+---+
| | |0.7|
+---+---+---+
| |5.2| |
+---+---+---+
"""
x = Data(None, None)
x._init_sample_object(ny=1, nx=272)
x.lon = np.asarray([2.25, 2.45, 1.8, 3.6])
x.lat = np.asarray([11.9, 10.1, 10.2, 11.3])
x.data = np.asarray([5.2, 2.3, 1.2, 0.7])
# target grid
lon = np.asarray([1.5, 2.5, 3.5])
lat = np.asarray([10., 11., 12.])
LON, LAT = np.meshgrid(lon, lat)
# rasterize data
# no valid data
res = x._rasterize(LON, LAT, radius=0.000001, return_object=True)
self.assertEqual(res.data.mask.sum(), np.prod(LON.shape))
with self.assertRaises(ValueError):
res = x._rasterize(LON, LAT, radius=0.000001, return_object=False)
# check valid results
res = x._rasterize(LON, LAT, radius=0.5, return_object=True)
self.assertEqual(res.data[0,0], 1.2)
self.assertEqual(res.data[0,1], 2.3)
self.assertEqual(res.data[1,2], 0.7)
self.assertEqual(res.ny*res.nx - res.data.mask.sum(), 4)
def test_rasterize_data(self):
"""
testdataset
+---+---+---+
|1.2|2.3| |
+---+---+---+
| | |0.7|
+---+---+---+
| |5.2| |
+---+---+---+
"""
x = Data(None, None)
x._init_sample_object(ny=1, nx=272)
x.lon = np.asarray([2.25, 2.45, 1.8, 3.6])
x.lat = np.asarray([11.9, 10.1, 10.2, 11.3])
x.data = np.asarray([5.2, 2.3, 1.2, 0.7])
# target grid
lon = np.asarray([1.5, 2.5, 3.5])
lat = np.asarray([10., 11., 12.])
LON, LAT = np.meshgrid(lon, lat)
# rasterize data
# no valid data
res = x._rasterize(LON, LAT, radius=0.000001, return_object=True)
self.assertEqual(res.data.mask.sum(), np.prod(LON.shape))
with self.assertRaises(ValueError):
res = x._rasterize(LON, LAT, radius=0.000001, return_object=False)
# check valid results
res = x._rasterize(LON, LAT, radius=0.5, return_object=True)
self.assertEqual(res.data[0, 0], 1.2)
self.assertEqual(res.data[0, 1], 2.3)
self.assertEqual(res.data[1, 2], 0.7)
self.assertEqual(res.ny * res.nx - res.data.mask.sum(), 4)
def test_lomb_basic(self):
def _sample_data(t, w, A, B):
e = np.random.random(len(t))*0.
y = A * np.cos(w*self.t + B)
return y, e
def _test_ratio(x,y, thres=0.05):
r = np.abs(1. - x / y)
print r, x/y
self.assertTrue(r <= thres) # accuracy of ration by 5%
# test with single frequency
p_ref = 10.
w = 2.*np.pi / p_ref
y, e = _sample_data(self.t, w, 5., 0.1)
P = np.arange(2., 20., 2.) # target period [days]
Ar, Br = lomb_scargle_periodogram(self.t, P, y+e, corr=False)
_test_ratio(Ar[4], 5.)
_test_ratio(Br[4], 0.1)
Ar, Br, Rr, Pr = lomb_scargle_periodogram(self.t, P, y)
_test_ratio(Ar[4], 5.)
_test_ratio(Br[4], 0.1)
#~ self.assertEqual(Rr[4], 1.)
#~ self.assertEqual(Pr[4], 0.)
# test for functions with overlapping frequencies
p_ref1 = 365.
p_ref2 = 365.
w1 = 2.*np.pi / p_ref1
w2 = 2.*np.pi / p_ref2
y1, e1 = _sample_data(self.t, w1, 4., 0.1)
y2, e2 = _sample_data(self.t, w2, 3.6, 0.1)
P = np.arange(1., 366., 1.) # target period [days]
Ar, Br = lomb_scargle_periodogram(self.t, P, y1+e1+y2+e2, corr=False)
_test_ratio(Ar[-1], 7.6)
_test_ratio(Br[-1], 0.1)
# overlapping frequencies 2
p_ref1 = 100.
p_ref2 = 200.
w1 = 2.*np.pi / p_ref1
w2 = 2.*np.pi / p_ref2
y1, e1 = _sample_data(self.t, w1, 2., np.pi*0.3) # don't choose pi for phase, as this will result in an optimization with negative amplitude and zero phase (= sin)
y2, e2 = _sample_data(self.t, w2, 3., np.pi*0.5)
P = np.arange(1., 366., 1.) # target period [days]
hlp = y1+e1+y2+e2
Ar, Br = lomb_scargle_periodogram(self.t, P, hlp, corr=False)
# sample data object
D = Data(None, None)
D._init_sample_object(nt=len(y), ny=1, nx=1)
D.data[:,0,0] = np.ma.array(hlp, mask=hlp!=hlp)
D.time = self.t
D_dummy = Data(None, None)
D_dummy._init_sample_object(nt=len(y), ny=1, nx=1)
with self.assertRaises(ValueError):
D_dummy.time_str = 'hours since 2001-01-01' # only days currently supported!
xx, yy = D_dummy.lomb_scargle_periodogram(P, return_object=False)
AD, BD = D.lomb_scargle_periodogram(P, return_object=False, corr=False)
AD1, BD1 = D.lomb_scargle_periodogram(P, return_object=True, corr=False)
self.assertEqual(AD.shape, BD.shape)
self.assertEqual(D.ny, AD.shape[1])
self.assertEqual(D.nx, AD.shape[2])
_test_ratio(Ar[99], 2.)
_test_ratio(AD[99,0,0], 2.)
_test_ratio(AD1.data[99, 0,0], 2.)
_test_ratio(Ar[199], 3.)
_test_ratio(AD[199,0,0], 3.)
_test_ratio(AD1.data[199,0,0], 3.)
_test_ratio(Br[99], np.pi*0.3)
_test_ratio(BD[99,0,0], np.pi*0.3)
_test_ratio(BD1.data[99,0,0], np.pi*0.3)
_test_ratio(Br[199], np.pi*0.5)
_test_ratio(BD[199,0,0], np.pi*0.5)
_test_ratio(BD1.data[199,0,0], np.pi*0.5)
# test for data with gaps
# tests are not very robust yet as results depend on noise applied!
p_ref1 = 100.
p_ref2 = 200.
w1 = 2.*np.pi / p_ref1
w2 = 2.*np.pi / p_ref2
y1, e1 = _sample_data(self.t, w1, 2., np.pi*0.3) # don't choose pi for phase, as this will result in an optimization with negative amplitude and zero phase (= sin)
y2, e2 = _sample_data(self.t, w2, 3., np.pi*0.5)
P = np.arange(1., 366., 1.) # target period [days]
ran = np.random.random(len(self.t))
msk = ran > 0.1
tmsk = self.t[msk]
yref = y1+e1+y2+e2
ymsk = yref[msk]
Ar, Br = lomb_scargle_periodogram(tmsk, P, ymsk, corr=False)
class TestPycmbsPlots(unittest.TestCase):
def setUp(self):
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=1, nx=1)
self._tmpdir = tempfile.mkdtemp()
def test_ReichlerPlotGeneral(self):
RP = ReichlerPlot()
for i in xrange(10):
RP.add([i * 12.], 'test' + str(i))
RP.simple_plot()
RP.bar(title='some title', vmin=-10., vmax=10.)
#~ RP.circle_plot()
def test_ScatterPlot_General(self):
x = self.D
S = ScatterPlot(x)
S.plot(x)
S.legend()
def test_rotate_ticks(self):
f = plt.figure()
ax = f.add_subplot(111)
ax.plot(np.random.random(1000))
rotate_ticks(ax, 20.)
def test_correlation_analysis(self):
x = self.D
y = self.D
C = CorrelationAnalysis(x, y)
C.do_analysis()
def test_ScatterPlot_GeneralWithNormalization(self):
x = self.D
S = ScatterPlot(x, normalize_data=True)
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_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_LinePlot_General(self):
x = self.D
L = LinePlot()
L1 = LinePlot(regress=True)
L.plot(x)
L1.plot(x)
def test_LinePlot_WithAxis(self):
x = self.D
f = plt.figure()
ax = f.add_subplot(111)
L = LinePlot(ax=ax)
L.plot(x)
def test_HistogrammPlot_General(self):
H = HistogrammPlot(normalize=True)
H.plot(self.D, bins=10, shown=True)
def test_ZonalPlot(self):
Z = ZonalPlot()
Z.plot(self.D)
def test_map_difference_General(self):
map_difference(self.D, self.D)
def test_GlecklerPlot_InvalidNumberOfObservations(self):
G = GlecklerPlot()
G.add_model('echam5')
G.add_model('mpi-esm')
G.add_variable('ta')
G.add_data('ta', 'echam5', 0.5, pos=1)
G.add_data('ta', 'echam5', 0.25, pos=2)
G.add_data('ta', 'echam5', -0.25, pos=3)
G.add_data('ta', 'mpi-esm', -0.25, pos=4)
G.add_data('ta', 'mpi-esm', -0.25, pos=5)
with self.assertRaises(ValueError):
G.plot()
def test_GlecklerPlot_4obs(self):
G = GlecklerPlot()
G.add_model('echam5')
G.add_model('mpi-esm')
G.add_variable('ta')
G.add_variable('P')
G.add_data('P', 'echam5', 0.5, pos=1)
G.add_data('P', 'echam5', 0.25, pos=2)
G.add_data('P', 'echam5', -0.25, pos=3)
G.add_data('P', 'mpi-esm', -0.25, pos=4)
G.plot()
def test_GlecklerPlot(self):
G = GlecklerPlot()
G.add_model('echam5')
G.add_model('mpi-esm')
G.add_variable('ta')
G.add_variable('P')
G.add_data('ta', 'echam5', 0.5, pos=1)
G.add_data('P', 'echam5', 0.25, pos=1)
G.add_data('P', 'echam5', -0.25, pos=2)
G.add_data('P', 'mpi-esm', -0.25, pos=1)
G.plot()
G.plot_model_error('ta')
G.plot_model_ranking('ta')
G.write_ranking_table('ta',
self._tmpdir + os.sep + 'nix.tex',
fmt='latex')
self.assertTrue(os.path.exists(self._tmpdir + os.sep + 'nix.tex'))
if os.path.exists(self._tmpdir + os.sep + 'nix.tex'):
os.remove(self._tmpdir + os.sep + 'nix.tex')
G.write_ranking_table('ta',
self._tmpdir + os.sep + 'nix1',
fmt='latex')
self.assertTrue(os.path.exists(self._tmpdir + os.sep + 'nix1.tex'))
if os.path.exists(self._tmpdir + os.sep + 'nix1.tex'):
os.remove(self._tmpdir + os.sep + 'nix1.tex')
def test_old_map_plot(self):
xx_map_plot(self.D)
#~ xx_map_plot(self.D, use_basemap=True)
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_invalidData(self):
HT = HstackTimeseries()
x = np.random.random((50, 60)) * 2. - 1.
with self.assertRaises(ValueError):
HT.add_data(x, 'test')
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_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_violin_plot(self):
Violin_example()
def test_globalmeanplot(self):
G = GlobalMeanPlot()
with self.assertRaises(ValueError):
G.plot(self.D, stat_type='no_stat_type')
G.plot(self.D, show_std=True)
G.plot_mean_result()
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_noplotdone(self):
HT = HstackTimeseries()
with self.assertRaises(ValueError):
HT._add_colorbar()
def test_GlobalMean(self):
GM1 = GlobalMeanPlot(climatology=False)
self.assertEqual(GM1.nplots, 1)
f = plt.figure()
ax1 = f.add_subplot(111)
axA = f.add_subplot(211)
axB = f.add_subplot(212)
with self.assertRaises(ValueError):
GM2 = GlobalMeanPlot(climatology=True, ax=ax1)
GM2 = GlobalMeanPlot(climatology=True, ax=axA, ax1=axB)
self.assertEqual(GM2.nplots, 2)
def test_Hovmoeller(self):
H = HovmoellerPlot(self.D)
with self.assertRaises(ValueError):
H.plot()
H.plot(climits=[0., 1.])
class TestPycmbsBenchmarkingModels(unittest.TestCase):
def setUp(self):
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=1, nx=1)
# generate dummy Model object
data_dir = './test/'
varmethods = {'albedo':'get_albedo()', 'sis': 'get_sis()'}
self.model = models.Model(data_dir, varmethods, name='testmodel', intervals='monthly')
sis = self.D.copy()
sis.mulc(5., copy=False)
sis.label='sisdummy'
alb = self.D.copy()
alb.label='albedodummy'
# add some dummy data variable
self.model.variables = {'albedo':alb, 'sis':sis}
def test_save_prefix_missing(self):
m = self.model
odir = tempfile.mkdtemp() + os.sep
with self.assertRaises(ValueError):
m.save(odir)
def test_save_create_odir(self):
m = self.model
odir = tempfile.mkdtemp() + os.sep
if os.path.exists(odir):
os.system('rm -rf ' + odir)
m.save(odir, prefix='test')
self.assertTrue(os.path.exists(odir))
os.system('rm -rf ' + odir)
def test_save(self):
m = self.model
odir = tempfile.mkdtemp() + os.sep
sisfile = odir + 'testoutput_SIS.nc'
albfile = odir + 'testoutput_ALBEDO.nc'
if os.path.exists(sisfile):
os.remove(sisfile)
if os.path.exists(albfile):
os.remove(albfile)
m.save(odir, prefix='testoutput')
self.assertTrue(os.path.exists(sisfile))
self.assertTrue(os.path.exists(albfile))
if os.path.exists(sisfile):
os.remove(sisfile)
if os.path.exists(albfile):
os.remove(albfile)
os.system('rm -rf ' + odir)
def test_cmip5_init_singlemember(self):
data_dir = tempfile.mkdtemp()
# invalid model identifier
with self.assertRaises(ValueError):
M = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR1', 'amip', {}, intervals='monthly')
with self.assertRaises(ValueError):
M = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#1#2', 'amip', {}, intervals='monthly')
M1 = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#1', 'amip', {}, intervals='monthly')
M2 = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#728', 'amip', {}, intervals='monthly')
self.assertEqual(M1.ens_member, 1)
self.assertEqual(M2.ens_member, 728)
def test_cmip5_singlemember_filename(self):
data_dir = tempfile.mkdtemp()
# generate testfile
testfile = data_dir + os.sep + 'MPI-M' + os.sep + 'MPI-ESM-LR' + os.sep + 'amip' + os.sep + 'mon' + os.sep + 'atmos' + os.sep + 'Amon' + os.sep + 'r1i1p1' + os.sep + 'ta' + os.sep + 'ta_Amon_MPI-ESM-LR_amip_r1i1p1_197901-200812.nc'
os.makedirs(os.path.dirname(testfile))
os.system('touch ' + testfile)
self.assertTrue(os.path.exists(testfile))
M = models.CMIP5RAW_SINGLE(data_dir, 'MPI-M:MPI-ESM-LR#1', 'amip', {}, intervals='monthly')
f = M.get_single_ensemble_file('ta', mip='Amon', realm='atmos')
self.assertTrue(os.path.exists(f))
self.assertEqual(f, testfile)
class TestPycmbsPlots(unittest.TestCase):
def setUp(self):
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=1, nx=1)
self._tmpdir = tempfile.mkdtemp()
def test_ReichlerPlotGeneral(self):
RP = ReichlerPlot()
for i in xrange(10):
RP.add([i*12.], 'test'+str(i))
RP.simple_plot()
RP.bar(title='some title', vmin=-10., vmax=10.)
#~ RP.circle_plot()
def test_ScatterPlot_General(self):
x = self.D
S = ScatterPlot(x)
S.plot(x)
S.legend()
def test_rotate_ticks(self):
f = plt.figure()
ax=f.add_subplot(111)
ax.plot(np.random.random(1000))
rotate_ticks(ax, 20.)
def test_correlation_analysis(self):
x = self.D
y = self.D
C = CorrelationAnalysis(x, y)
C.do_analysis()
def test_ScatterPlot_GeneralWithNormalization(self):
x = self.D
S = ScatterPlot(x, normalize_data=True)
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_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_LinePlot_General(self):
x = self.D
L = LinePlot()
L1 = LinePlot(regress=True)
L.plot(x)
L1.plot(x)
def test_LinePlot_WithAxis(self):
x = self.D
f = plt.figure()
ax = f.add_subplot(111)
L = LinePlot(ax=ax)
L.plot(x)
def test_HistogrammPlot_General(self):
H = HistogrammPlot(normalize=True)
H.plot(self.D, bins=10, shown=True)
def test_ZonalPlot(self):
Z = ZonalPlot()
Z.plot(self.D)
def test_map_difference_General(self):
map_difference(self.D, self.D)
def test_GlecklerPlot_InvalidNumberOfObservations(self):
G = GlecklerPlot()
G.add_model('echam5')
G.add_model('mpi-esm')
G.add_variable('ta')
G.add_data('ta', 'echam5', 0.5,pos=1)
G.add_data('ta', 'echam5',0.25,pos=2)
G.add_data('ta', 'echam5',-0.25,pos=3)
G.add_data('ta', 'mpi-esm',-0.25,pos=4)
G.add_data('ta', 'mpi-esm',-0.25,pos=5)
with self.assertRaises(ValueError):
G.plot()
def test_GlecklerPlot_4obs(self):
G = GlecklerPlot()
G.add_model('echam5')
G.add_model('mpi-esm')
G.add_variable('ta')
G.add_variable('P')
G.add_data('P', 'echam5', 0.5,pos=1)
G.add_data('P', 'echam5',0.25,pos=2)
G.add_data('P', 'echam5',-0.25,pos=3)
G.add_data('P', 'mpi-esm',-0.25,pos=4)
G.plot()
def test_GlecklerPlot(self):
G = GlecklerPlot()
G.add_model('echam5')
G.add_model('mpi-esm')
G.add_variable('ta')
G.add_variable('P')
G.add_data('ta', 'echam5', 0.5,pos=1)
G.add_data('P', 'echam5',0.25,pos=1)
G.add_data('P', 'echam5',-0.25,pos=2)
G.add_data('P', 'mpi-esm',-0.25,pos=1)
G.plot()
G.plot_model_error('ta')
G.plot_model_ranking('ta')
G.write_ranking_table('ta', self._tmpdir + os.sep + 'nix.tex', fmt='latex')
self.assertTrue(os.path.exists(self._tmpdir + os.sep + 'nix.tex'))
if os.path.exists(self._tmpdir + os.sep + 'nix.tex'):
os.remove(self._tmpdir + os.sep + 'nix.tex')
G.write_ranking_table('ta', self._tmpdir + os.sep + 'nix1', fmt='latex')
self.assertTrue(os.path.exists(self._tmpdir + os.sep + 'nix1.tex'))
if os.path.exists(self._tmpdir + os.sep + 'nix1.tex'):
os.remove(self._tmpdir + os.sep + 'nix1.tex')
def test_old_map_plot(self):
xx_map_plot(self.D)
#~ xx_map_plot(self.D, use_basemap=True)
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_invalidData(self):
HT = HstackTimeseries()
x = np.random.random((50, 60))*2.-1.
with self.assertRaises(ValueError):
HT.add_data(x, 'test' )
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_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_violin_plot(self):
Violin_example()
def test_globalmeanplot(self):
G = GlobalMeanPlot()
with self.assertRaises(ValueError):
G.plot(self.D, stat_type='no_stat_type')
G.plot(self.D, show_std=True)
G.plot_mean_result()
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_noplotdone(self):
HT = HstackTimeseries()
with self.assertRaises(ValueError):
HT._add_colorbar()
def test_GlobalMean(self):
GM1 = GlobalMeanPlot(climatology=False)
self.assertEqual(GM1.nplots, 1)
f = plt.figure()
ax1 = f.add_subplot(111)
axA = f.add_subplot(211)
axB = f.add_subplot(212)
with self.assertRaises(ValueError):
GM2 = GlobalMeanPlot(climatology=True, ax=ax1)
GM2 = GlobalMeanPlot(climatology=True, ax=axA, ax1=axB)
self.assertEqual(GM2.nplots, 2)
def test_Hovmoeller(self):
H = HovmoellerPlot(self.D)
with self.assertRaises(ValueError):
H.plot()
H.plot(climits=[0., 1.])
class TestMapPlotGeneric(unittest.TestCase):
def setUp(self):
self.map_plot = mapping.MapPlotGeneric()
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=10, nx=20)
self._tmpdir = tempfile.mkdtemp()
def test_SingleMap_Init(self):
try:
import cartopy.crs as ccrs
except:
return True #no testing if cartopy not installed
# just test if things pass
SM1 = mapping.SingleMap(self.D)
SM2 = mapping.SingleMap(self.D, stat_type='sum')
proj_prop = {'projection': 'robin'}
SM3 = mapping.SingleMap(self.D, backend='basemap', stat_type='median')
SM4 = mapping.SingleMap(self.D, backend='cartopy')
SM1.plot(show_zonal=True)
SM2.plot(show_zonal=True, colorbar_orientation='horizontal')
SM3.plot(show_zonal=True, colorbar_orientation='horizontal', proj_prop=proj_prop)
SM4.plot(show_zonal=True, colorbar_orientation='horizontal', proj_prop=proj_prop)
def test_SingleMap_WithoutColorbar(self):
SM = mapping.SingleMap(self.D)
SM.plot(show_colorbar=False)
def test_invalid_colorbar_orientation(self):
SM = mapping.SingleMap(self.D)
with self.assertRaises(ValueError):
SM.plot(colorbar_orientation='something')
def test_SingleMap_WithPredefinedAxis(self):
f = plt.figure()
ax = f.add_subplot(2,1,1)
SM1 = mapping.SingleMap(self.D, ax=ax)
def test_SingleMap_WithPredefinedAxisButWhichIsNone(self):
ax = None
SM1 = mapping.SingleMap(self.D, ax=ax)
def test_SingleMap_InitWithInvalidBackend(self):
# just test if things pass
with self.assertRaises(ValueError):
SM = mapping.SingleMap(self.D, backend='some_invalid_backend')
def test_SingleMap_InitWithMissingProjectionProperties(self):
# just test if things pass
with self.assertRaises(ValueError):
SM = mapping.SingleMap(self.D, backend='cartopy')
SM.plot()
def test_SingleMap_Save(self):
SM = mapping.SingleMap(self.D, savefile=self._tmpdir + os.sep + 'my_test_save_file.nc')
SM.save(save_mean=True, save_all=True)
self.assertTrue(os.path.exists(self._tmpdir + os.sep + 'my_test_save_file.nc_timmean.nc'))
self.assertTrue(os.path.exists(self._tmpdir + os.sep + 'my_test_save_file.nc_timmean.nc_all.nc'))
if os.path.exists(self._tmpdir + os.sep + 'my_test_save_file.nc_timmean.nc'):
os.remove(self._tmpdir + os.sep + 'my_test_save_file.nc_timmean.nc')
if os.path.exists(self._tmpdir + os.sep + 'my_test_save_file.nc_timmean.nc_all.nc'):
os.remove(self._tmpdir + os.sep + 'my_test_save_file.nc_timmean.nc_all.nc')
@unittest.skip('skip as only for local testing')
def test_SingleMap_add_cyclic(self):
file='/home/m300028/shared/data/SEP/variables/land/Ta_2m/cru_ts_3_00.1901.2006.tmp_miss_t63.nc'
ofile = 'world.png'
if os.path.exists(ofile):
os.remove(ofile)
d=Data(file,'tmp',read=True)
map_plot(d, use_basemap=True, savegraphicfile=ofile)
if os.path.exists(ofile):
os.remove(ofile)
class TestData(unittest.TestCase):
def setUp(self):
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=1, nx=1)
def test_is_closed(self):
poly = [(150., 20.), (-160., 30.), (-170., 10.), (170., 10.)]
P = Polygon(3, poly)
self.assertFalse(P.is_closed())
poly1 = [(150., 20.), (-160., 30.), (-170., 10.), (170., 10.),
(150., 20.)]
P1 = Polygon(3, poly1)
self.assertTrue(P1.is_closed())
@unittest.skip(
'OGR import causes trouble in import locally, therefore currently skipping this test'
)
def test_convertOGR(self):
poly = [(150., 20.), (-160., 30.), (-170., 10.), (170., 10.)]
P = Polygon(3, poly)
A = P.convertToOGRPolygon()
B = P.convertToOGRPolygon(ensure_positive=True)
def test_shift(self):
poly3 = [(150., 20.), (-160., 30.), (-170., 10.), (170., 10.)]
P3 = Polygon(3, poly3)
P3._shift_coordinates() # shift longitudes by 200 degree
self.assertEqual(P3.poly[0][0], 150.)
self.assertEqual(P3.poly[1][0], 200.)
self.assertEqual(P3.poly[2][0], 190.)
self.assertEqual(P3.poly[3][0], 170.)
def test_point_in_polygon(self):
x = 1
y = 1
poly = [(0, 0), (2, 0), (2, 2), (0, 2)]
P = Polygon(1, poly)
self.assertTrue(P.point_in_poly(x, y))
x = 4
y = 4
self.assertFalse(P.point_in_poly(x, y))
@unittest.skip(
'OGR import causes trouble in import locally, therefore currently skipping this test'
)
def test_point_in_polygon_latlon(self):
# test for point in polygon across dateline
x1 = -175.
y1 = 50.
poly1 = [(150., 60.), (-160., 60.), (-170., 45.), (170., 45.)]
P1 = Polygon(1, poly1)
self.assertTrue(P1.point_in_poly_latlon(x1, y1))
def test_polygon_min_max(self):
x = 1
y = 1
poly = [(-5, 0), (2, 0), (2, 2), (0, 6)]
P = Polygon(1, poly)
bbox = P.bbox()
self.assertEqual(bbox[0], -5.)
self.assertEqual(bbox[1], 2.)
self.assertEqual(bbox[2], 0.)
self.assertEqual(bbox[3], 6.)
def test_raster_wrong_geometry(self):
lon = np.random.random((10, 20))
lat = np.random.random((11, 20))
with self.assertRaises(ValueError):
R = Raster(lon, lat)
def test_raster_wrong_latlon(self):
lon = np.random.random(10)
lat = np.random.random(10)
print lon.ndim
with self.assertRaises(ValueError):
R = Raster(lon, lat)
def test_raster_no_Polygon(self):
lon = np.random.random((10, 20))
lat = np.random.random((10, 20))
R = Raster(lon, lat)
with self.assertRaises(ValueError):
P = np.arange(10)
R._rasterize_single_polygon(P)
def test_raster_single_polygon(self):
lon = np.linspace(-180., 180., 361)
lat = np.linspace(-90., 90., 181)
LON, LAT = np.meshgrid(lon, lat)
# test a single polygon
poly = [(-10., -10.), (-10., 20), (15., 0.), (0., -25.)]
P = Polygon(5, poly)
R = Raster(LON, LAT)
R.mask = np.zeros(LON.shape) * np.nan
R._rasterize_single_polygon(P)
print np.unique(R.mask)
R.mask = np.ma.array(R.mask, mask=np.isnan(R.mask))
u = np.unique(R.mask[~R.mask.mask])
print R.mask
print u
self.assertTrue(len(u) == 1)
self.assertTrue(5. in u)
#~ xxxxxxxxdef test_raster_single_polygon_fast(self):
#~ lon = np.linspace(-180., 180., 361)
#~ lat = np.linspace(-90., 90., 181)
#~ LON,LAT=np.meshgrid(lon, lat)
#~
#~ # test a single polygon
#~ poly = [(-10.,-10.), (-10.,20), (15.,0.), (0.,-25.)]
#~ P = Polygon(5, poly)
#~ R=Raster(LON,LAT)
#~ R.mask = np.zeros(LON.shape)*np.nan
#~ R._rasterize_single_polygon(P, method='fast')
#~ R.mask = np.ma.array(R.mask, mask=np.isnan(R.mask))
#~
#~ u = np.unique(R.mask[~R.mask.mask])
#~ self.assertTrue(len(u) == 1)
#~ self.assertTrue(5. in u)
def test_raster_multiple_polygon(self): # this is quite slow!
lon = np.linspace(-180., 180., 361)
lat = np.linspace(-90., 90., 181)
LON, LAT = np.meshgrid(lon, lat)
#~ #~
# test a single polygon
poly = []
poly1 = [(-10., -10.), (-10., 20), (15., 0.), (0., -15.)]
poly.append(Polygon(1, poly1))
#~ #~
poly2 = [(-50., -80.), (-50., -70.), (-40., -70.), (-40., -75.)]
poly.append(Polygon(2, poly2))
#~ #~
R = Raster(LON, LAT)
R.rasterize_polygons(poly)
#~ #~
u = np.unique(R.mask[~R.mask.mask])
self.assertTrue(len(u) == 2)
self.assertTrue(1 in u)
self.assertTrue(2 in u)
class TestPycmbsBenchmarkingModels(unittest.TestCase):
def setUp(self):
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=1, nx=1)
# generate dummy Model object
data_dir = '.' + os.sep + 'test' + os.sep
varmethods = {'albedo': 'get_albedo()', 'sis': 'get_sis()'}
self.model = models.Model(data_dir,
varmethods,
name='testmodel',
intervals='monthly')
sis = self.D.copy()
sis.mulc(5., copy=False)
sis.label = 'sisdummy'
alb = self.D.copy()
alb.label = 'albedodummy'
# add some dummy data variable
self.model.variables = {'albedo': alb, 'sis': sis}
def test_save_prefix_missing(self):
m = self.model
odir = tempfile.mkdtemp() + os.sep
with self.assertRaises(ValueError):
m.save(odir)
def test_save_create_odir(self):
m = self.model
odir = tempfile.mkdtemp() + os.sep
if os.path.exists(odir):
os.system('rm -rf ' + odir)
m.save(odir, prefix='test')
self.assertTrue(os.path.exists(odir))
os.system('rm -rf ' + odir)
def test_save(self):
m = self.model
odir = tempfile.mkdtemp() + os.sep
sisfile = odir + 'testoutput_SIS.nc'
albfile = odir + 'testoutput_ALBEDO.nc'
if os.path.exists(sisfile):
os.remove(sisfile)
if os.path.exists(albfile):
os.remove(albfile)
m.save(odir, prefix='testoutput')
self.assertTrue(os.path.exists(sisfile))
self.assertTrue(os.path.exists(albfile))
if os.path.exists(sisfile):
os.remove(sisfile)
if os.path.exists(albfile):
os.remove(albfile)
os.system('rm -rf ' + odir)
def test_cmip5_init_singlemember(self):
data_dir = tempfile.mkdtemp()
# invalid model identifier
with self.assertRaises(ValueError):
M = models.CMIP5RAW_SINGLE(data_dir,
'MPI-M:MPI-ESM-LR1',
'amip', {},
intervals='monthly')
with self.assertRaises(ValueError):
M = models.CMIP5RAW_SINGLE(data_dir,
'MPI-M:MPI-ESM-LR#1#2',
'amip', {},
intervals='monthly')
M1 = models.CMIP5RAW_SINGLE(data_dir,
'MPI-M:MPI-ESM-LR#1',
'amip', {},
intervals='monthly')
M2 = models.CMIP5RAW_SINGLE(data_dir,
'MPI-M:MPI-ESM-LR#728',
'amip', {},
intervals='monthly')
self.assertEqual(M1.ens_member, 1)
self.assertEqual(M2.ens_member, 728)
def test_cmip5_singlemember_filename(self):
data_dir = tempfile.mkdtemp()
# generate testfile
testfile = data_dir + os.sep + 'MPI-M' + os.sep + 'MPI-ESM-LR' + os.sep + 'amip' + os.sep + 'mon' + os.sep + 'atmos' + os.sep + 'Amon' + os.sep + 'r1i1p1' + os.sep + 'ta' + os.sep + 'ta_Amon_MPI-ESM-LR_amip_r1i1p1_197901-200812.nc'
os.makedirs(os.path.dirname(testfile))
os.system('touch ' + testfile)
self.assertTrue(os.path.exists(testfile))
M = models.CMIP5RAW_SINGLE(data_dir,
'MPI-M:MPI-ESM-LR#1',
'amip', {},
intervals='monthly')
kwargs = {
'CMIP5RAWSINGLE': {
'mip': 'Amon',
'realm': 'atmos',
'temporal_resolution': 'mon'
}
}
f = M.get_raw_filename('ta', **kwargs)
self.assertTrue(os.path.exists(f))
self.assertEqual(f, testfile)
class TestData(unittest.TestCase):
def setUp(self):
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=1, nx=1)
def test_is_closed(self):
poly = [(150.,20.), (-160.,30.), (-170.,10.), (170.,10.)]
P = Polygon(3, poly)
self.assertFalse(P.is_closed())
poly1 = [(150.,20.), (-160.,30.), (-170.,10.), (170.,10.), (150.,20.)]
P1 = Polygon(3, poly1)
self.assertTrue(P1.is_closed())
@unittest.skip('OGR import causes trouble in import locally, therefore currently skipping this test')
def test_convertOGR(self):
poly = [(150.,20.), (-160.,30.), (-170.,10.), (170.,10.)]
P = Polygon(3, poly)
A = P.convertToOGRPolygon()
B = P.convertToOGRPolygon(ensure_positive=True)
def test_shift(self):
poly3 = [(150.,20.), (-160.,30.), (-170.,10.), (170.,10.)]
P3 = Polygon(3, poly3)
P3._shift_coordinates() # shift longitudes by 200 degree
self.assertEqual(P3.poly[0][0], 150.)
self.assertEqual(P3.poly[1][0], 200.)
self.assertEqual(P3.poly[2][0], 190.)
self.assertEqual(P3.poly[3][0], 170.)
def test_point_in_polygon(self):
x = 1
y = 1
poly = [(0,0), (2,0), (2,2), (0,2)]
P = Polygon(1, poly)
self.assertTrue(P.point_in_poly(x,y))
x = 4
y = 4
self.assertFalse(P.point_in_poly(x,y))
@unittest.skip('OGR import causes trouble in import locally, therefore currently skipping this test')
def test_point_in_polygon_latlon(self):
# test for point in polygon across dateline
x1 = -175.
y1 = 50.
poly1= [(150.,60.), (-160.,60.), (-170.,45.), (170.,45.)]
P1 = Polygon(1, poly1)
self.assertTrue(P1.point_in_poly_latlon(x1,y1))
def test_polygon_min_max(self):
x = 1
y = 1
poly = [(-5,0), (2,0), (2,2), (0,6)]
P = Polygon(1, poly)
bbox = P.bbox()
self.assertEqual(bbox[0], -5.)
self.assertEqual(bbox[1], 2.)
self.assertEqual(bbox[2], 0.)
self.assertEqual(bbox[3], 6.)
def test_raster_wrong_geometry(self):
lon = np.random.random((10,20))
lat = np.random.random((11,20))
with self.assertRaises(ValueError):
R = Raster(lon, lat)
def test_raster_wrong_latlon(self):
lon = np.random.random(10)
lat = np.random.random(10)
print lon.ndim
with self.assertRaises(ValueError):
R = Raster(lon, lat)
def test_raster_no_Polygon(self):
lon = np.random.random((10,20))
lat = np.random.random((10,20))
R = Raster(lon, lat)
with self.assertRaises(ValueError):
P = np.arange(10)
R._rasterize_single_polygon(P)
def test_raster_single_polygon(self):
lon = np.linspace(-180., 180., 361)
lat = np.linspace(-90., 90., 181)
LON,LAT=np.meshgrid(lon, lat)
# test a single polygon
poly = [(-10.,-10.), (-10.,20), (15.,0.), (0.,-25.)]
P = Polygon(5, poly)
R=Raster(LON,LAT)
R.mask = np.zeros(LON.shape)*np.nan
R._rasterize_single_polygon(P)
print np.unique(R.mask)
R.mask = np.ma.array(R.mask, mask=np.isnan(R.mask))
u = np.unique(R.mask[~R.mask.mask])
print R.mask
print u
self.assertTrue(len(u)==1)
self.assertTrue(5. in u)
#~ xxxxxxxxdef test_raster_single_polygon_fast(self):
#~ lon = np.linspace(-180., 180., 361)
#~ lat = np.linspace(-90., 90., 181)
#~ LON,LAT=np.meshgrid(lon, lat)
#~
#~ # test a single polygon
#~ poly = [(-10.,-10.), (-10.,20), (15.,0.), (0.,-25.)]
#~ P = Polygon(5, poly)
#~ R=Raster(LON,LAT)
#~ R.mask = np.zeros(LON.shape)*np.nan
#~ R._rasterize_single_polygon(P, method='fast')
#~ R.mask = np.ma.array(R.mask, mask=np.isnan(R.mask))
#~
#~ u = np.unique(R.mask[~R.mask.mask])
#~ self.assertTrue(len(u) == 1)
#~ self.assertTrue(5. in u)
def test_raster_multiple_polygon(self): # this is quite slow!
lon = np.linspace(-180., 180., 361)
lat = np.linspace(-90., 90., 181)
LON,LAT=np.meshgrid(lon, lat)
#~ #~
# test a single polygon
poly=[]
poly1 = [(-10.,-10.), (-10.,20), (15.,0.), (0.,-15.)]
poly.append(Polygon(1, poly1))
#~ #~
poly2 = [(-50.,-80.), (-50.,-70.), (-40.,-70.), (-40.,-75.)]
poly.append(Polygon(2, poly2))
#~ #~
R=Raster(LON,LAT)
R.rasterize_polygons(poly)
#~ #~
u = np.unique(R.mask[~R.mask.mask])
self.assertTrue(len(u)==2)
self.assertTrue(1 in u)
self.assertTrue(2 in u)
class TestMapPlotGeneric(unittest.TestCase):
def setUp(self):
self.map_plot = mapping.MapPlotGeneric()
self.D = Data(None, None)
self.D._init_sample_object(nt=1000, ny=10, nx=20)
self._tmpdir = tempfile.mkdtemp()
def test_SingleMap_Init(self):
try:
import cartopy.crs as ccrs
except:
return True #no testing if cartopy not installed
# just test if things pass
SM1 = mapping.SingleMap(self.D)
SM2 = mapping.SingleMap(self.D, stat_type='sum')
proj_prop = {'projection': 'robin'}
SM3 = mapping.SingleMap(self.D, backend='basemap', stat_type='median')
SM4 = mapping.SingleMap(self.D, backend='cartopy')
SM1.plot(show_zonal=True)
SM2.plot(show_zonal=True, colorbar_orientation='horizontal')
SM3.plot(show_zonal=True,
colorbar_orientation='horizontal',
proj_prop=proj_prop)
SM4.plot(show_zonal=True,
colorbar_orientation='horizontal',
proj_prop=proj_prop)
def test_SingleMap_WithoutColorbar(self):
SM = mapping.SingleMap(self.D)
SM.plot(show_colorbar=False)
def test_invalid_colorbar_orientation(self):
SM = mapping.SingleMap(self.D)
with self.assertRaises(ValueError):
SM.plot(colorbar_orientation='something')
def test_SingleMap_WithPredefinedAxis(self):
f = plt.figure()
ax = f.add_subplot(2, 1, 1)
SM1 = mapping.SingleMap(self.D, ax=ax)
def test_SingleMap_WithPredefinedAxisButWhichIsNone(self):
ax = None
SM1 = mapping.SingleMap(self.D, ax=ax)
def test_SingleMap_InitWithInvalidBackend(self):
# just test if things pass
with self.assertRaises(ValueError):
SM = mapping.SingleMap(self.D, backend='some_invalid_backend')
def test_SingleMap_InitWithMissingProjectionProperties(self):
# just test if things pass
with self.assertRaises(ValueError):
SM = mapping.SingleMap(self.D, backend='cartopy')
SM.plot()
def test_SingleMap_Save(self):
SM = mapping.SingleMap(self.D,
savefile=self._tmpdir + os.sep +
'my_test_save_file.nc')
SM.save(save_mean=True, save_all=True)
self.assertTrue(
os.path.exists(self._tmpdir + os.sep +
'my_test_save_file.nc_timmean.nc'))
self.assertTrue(
os.path.exists(self._tmpdir + os.sep +
'my_test_save_file.nc_timmean.nc_all.nc'))
if os.path.exists(self._tmpdir + os.sep +
'my_test_save_file.nc_timmean.nc'):
os.remove(self._tmpdir + os.sep +
'my_test_save_file.nc_timmean.nc')
if os.path.exists(self._tmpdir + os.sep +
'my_test_save_file.nc_timmean.nc_all.nc'):
os.remove(self._tmpdir + os.sep +
'my_test_save_file.nc_timmean.nc_all.nc')
@unittest.skip('skip as only for local testing')
def test_SingleMap_add_cyclic(self):
file = '/home/m300028/shared/data/SEP/variables/land/Ta_2m/cru_ts_3_00.1901.2006.tmp_miss_t63.nc'
ofile = 'world.png'
if os.path.exists(ofile):
os.remove(ofile)
d = Data(file, 'tmp', read=True)
map_plot(d, use_basemap=True, savegraphicfile=ofile)
if os.path.exists(ofile):
os.remove(ofile)
def test_lomb_basic(self):
def _sample_data(t, w, A, B):
e = np.random.random(len(t)) * 0.
y = A * np.cos(w * self.t + B)
return y, e
def _test_ratio(x, y, thres=0.05):
r = np.abs(1. - x / y)
print r, x / y
self.assertTrue(r <= thres) # accuracy of ration by 5%
# test with single frequency
p_ref = 10.
w = 2. * np.pi / p_ref
y, e = _sample_data(self.t, w, 5., 0.1)
P = np.arange(2., 20., 2.) # target period [days]
Ar, Br = lomb_scargle_periodogram(self.t, P, y + e, corr=False)
_test_ratio(Ar[4], 5.)
_test_ratio(Br[4], 0.1)
Ar, Br, Rr, Pr = lomb_scargle_periodogram(self.t, P, y)
_test_ratio(Ar[4], 5.)
_test_ratio(Br[4], 0.1)
#~ self.assertEqual(Rr[4], 1.)
#~ self.assertEqual(Pr[4], 0.)
# test for functions with overlapping frequencies
p_ref1 = 365.
p_ref2 = 365.
w1 = 2. * np.pi / p_ref1
w2 = 2. * np.pi / p_ref2
y1, e1 = _sample_data(self.t, w1, 4., 0.1)
y2, e2 = _sample_data(self.t, w2, 3.6, 0.1)
P = np.arange(1., 366., 1.) # target period [days]
Ar, Br = lomb_scargle_periodogram(self.t,
P,
y1 + e1 + y2 + e2,
corr=False)
_test_ratio(Ar[-1], 7.6)
_test_ratio(Br[-1], 0.1)
# overlapping frequencies 2
p_ref1 = 100.
p_ref2 = 200.
w1 = 2. * np.pi / p_ref1
w2 = 2. * np.pi / p_ref2
y1, e1 = _sample_data(
self.t, w1, 2., np.pi * 0.3
) # don't choose pi for phase, as this will result in an optimization with negative amplitude and zero phase (= sin)
y2, e2 = _sample_data(self.t, w2, 3., np.pi * 0.5)
P = np.arange(1., 366., 1.) # target period [days]
hlp = y1 + e1 + y2 + e2
Ar, Br = lomb_scargle_periodogram(self.t, P, hlp, corr=False)
# sample data object
D = Data(None, None)
D._init_sample_object(nt=len(y), ny=1, nx=1)
D.data[:, 0, 0] = np.ma.array(hlp, mask=hlp != hlp)
D.time = self.t
D_dummy = Data(None, None)
D_dummy._init_sample_object(nt=len(y), ny=1, nx=1)
with self.assertRaises(ValueError):
D_dummy.time_str = 'hours since 2001-01-01' # only days currently supported!
xx, yy = D_dummy.lomb_scargle_periodogram(P, return_object=False)
AD, BD = D.lomb_scargle_periodogram(P, return_object=False, corr=False)
AD1, BD1 = D.lomb_scargle_periodogram(P,
return_object=True,
corr=False)
self.assertEqual(AD.shape, BD.shape)
self.assertEqual(D.ny, AD.shape[1])
self.assertEqual(D.nx, AD.shape[2])
_test_ratio(Ar[99], 2.)
_test_ratio(AD[99, 0, 0], 2.)
_test_ratio(AD1.data[99, 0, 0], 2.)
_test_ratio(Ar[199], 3.)
_test_ratio(AD[199, 0, 0], 3.)
_test_ratio(AD1.data[199, 0, 0], 3.)
_test_ratio(Br[99], np.pi * 0.3)
_test_ratio(BD[99, 0, 0], np.pi * 0.3)
_test_ratio(BD1.data[99, 0, 0], np.pi * 0.3)
_test_ratio(Br[199], np.pi * 0.5)
_test_ratio(BD[199, 0, 0], np.pi * 0.5)
_test_ratio(BD1.data[199, 0, 0], np.pi * 0.5)
# test for data with gaps
# tests are not very robust yet as results depend on noise applied!
p_ref1 = 100.
p_ref2 = 200.
w1 = 2. * np.pi / p_ref1
w2 = 2. * np.pi / p_ref2
y1, e1 = _sample_data(
self.t, w1, 2., np.pi * 0.3
) # don't choose pi for phase, as this will result in an optimization with negative amplitude and zero phase (= sin)
y2, e2 = _sample_data(self.t, w2, 3., np.pi * 0.5)
P = np.arange(1., 366., 1.) # target period [days]
ran = np.random.random(len(self.t))
msk = ran > 0.1
tmsk = self.t[msk]
yref = y1 + e1 + y2 + e2
ymsk = yref[msk]
Ar, Br = lomb_scargle_periodogram(tmsk, P, ymsk, corr=False)
class TestData(unittest.TestCase):
def setUp(self):
self.nx = 20
self.ny = 10
self.tempfile = tempfile.mktemp(suffix='.nc')
self.gfile1 = tempfile.mktemp(suffix='.nc')
self.gfile2 = tempfile.mktemp(suffix='.nc')
self.gfile3 = tempfile.mktemp(suffix='.nc')
self.x = Data(None, None)
self.x._init_sample_object(nt=10, ny=self.ny, nx=self.nx)
self.x.save(self.tempfile, varname='myvar')
# generate some arbitrary geometry file
F = NetCDFHandler()
F.open_file(self.gfile1, 'w')
F.create_dimension('ny', size=self.ny)
F.create_dimension('nx', size=self.nx)
F.create_variable('lat', 'd', ('ny', 'nx'))
F.create_variable('lon', 'd', ('ny', 'nx'))
F.assign_value('lat', np.ones((self.ny, self.nx)) * 5.)
F.assign_value('lon', np.ones((self.ny, self.nx)) * 3.)
F.close()
F = NetCDFHandler()
F.open_file(self.gfile2, 'w')
F.create_dimension('ny', size=self.ny)
F.create_dimension('nx', size=self.nx)
F.create_variable('latitude', 'd', ('ny', 'nx'))
F.create_variable('longitude', 'd', ('ny', 'nx'))
F.assign_value('latitude', np.ones((self.ny, self.nx)) * 7.)
F.assign_value('longitude', np.ones((self.ny, self.nx)) * 8.)
F.close()
F = NetCDFHandler()
F.open_file(self.gfile3, 'w')
F.create_dimension('ny', size=self.ny * 2)
F.create_dimension('nx', size=self.nx * 3)
F.create_variable('latitude', 'd', ('ny', 'nx'))
F.create_variable('longitude', 'd', ('ny', 'nx'))
F.assign_value('latitude', np.ones((self.ny * 2, self.nx * 3)) * 7.)
F.assign_value('longitude', np.ones((self.ny * 2, self.nx * 3)) * 8.)
F.close()
def test_read_coordinates(self):
# read data normal
x1 = Data(self.tempfile, 'myvar', read=True)
self.assertEqual(x1.nx, self.nx)
self.assertEqual(x1.ny, self.ny)
# read data with separate geometry file 'lat', 'lon' names
x2 = Data(self.tempfile, 'myvar', read=True, geometry_file=self.gfile1)
self.assertTrue(np.all(x2.lat == 5.))
self.assertTrue(np.all(x2.lon == 3.))
# read data with separate geometry file 'latitude', 'longitude' names
x3 = Data(self.tempfile, 'myvar', read=True, geometry_file=self.gfile2)
self.assertTrue(np.all(x3.lat == 7.))
self.assertTrue(np.all(x3.lon == 8.))
# read data with separate geometry file 'lat', 'lon' names, invalid geometry
with self.assertRaises(ValueError):
x4 = Data(self.tempfile,
'myvar',
read=True,
geometry_file=self.gfile3)
class TestData(unittest.TestCase):
def setUp(self):
self.nx = 20
self.ny = 10
self.tempfile = tempfile.mktemp(suffix='.nc')
self.gfile1 = tempfile.mktemp(suffix='.nc')
self.gfile2 = tempfile.mktemp(suffix='.nc')
self.gfile3 = tempfile.mktemp(suffix='.nc')
self.x = Data(None, None)
self.x._init_sample_object(nt=10, ny=self.ny, nx=self.nx)
self.x.save(self.tempfile, varname='myvar')
# generate some arbitrary geometry file
F = NetCDFHandler()
F.open_file(self.gfile1, 'w')
F.create_dimension('ny', size=self.ny)
F.create_dimension('nx', size=self.nx)
F.create_variable('lat', 'd', ('ny', 'nx'))
F.create_variable('lon', 'd', ('ny', 'nx'))
F.assign_value('lat', np.ones((self.ny,self.nx)) * 5.)
F.assign_value('lon', np.ones((self.ny,self.nx)) * 3.)
F.close()
F = NetCDFHandler()
F.open_file(self.gfile2, 'w')
F.create_dimension('ny', size=self.ny)
F.create_dimension('nx', size=self.nx)
F.create_variable('latitude', 'd', ('ny', 'nx'))
F.create_variable('longitude', 'd', ('ny', 'nx'))
F.assign_value('latitude', np.ones((self.ny,self.nx)) * 7.)
F.assign_value('longitude', np.ones((self.ny,self.nx)) * 8.)
F.close()
F = NetCDFHandler()
F.open_file(self.gfile3, 'w')
F.create_dimension('ny', size=self.ny*2)
F.create_dimension('nx', size=self.nx*3)
F.create_variable('latitude', 'd', ('ny', 'nx'))
F.create_variable('longitude', 'd', ('ny', 'nx'))
F.assign_value('latitude', np.ones((self.ny*2,self.nx*3)) * 7.)
F.assign_value('longitude', np.ones((self.ny*2,self.nx*3)) * 8.)
F.close()
def test_read_coordinates(self):
# read data normal
x1 = Data(self.tempfile, 'myvar', read=True)
self.assertEqual(x1.nx,self.nx)
self.assertEqual(x1.ny,self.ny)
# read data with separate geometry file 'lat', 'lon' names
x2 = Data(self.tempfile, 'myvar', read=True, geometry_file=self.gfile1)
self.assertTrue(np.all(x2.lat == 5.))
self.assertTrue(np.all(x2.lon == 3.))
# read data with separate geometry file 'latitude', 'longitude' names
x3 = Data(self.tempfile, 'myvar', read=True, geometry_file=self.gfile2)
self.assertTrue(np.all(x3.lat == 7.))
self.assertTrue(np.all(x3.lon == 8.))
# read data with separate geometry file 'lat', 'lon' names, invalid geometry
with self.assertRaises(ValueError):
x4 = Data(self.tempfile, 'myvar', read=True, geometry_file=self.gfile3)
