def test_bin_data_type(self):
"""Test failure when a non array-like is given to median2D
"""
avg.median2D(self.testInst, ['1', 'a', '23', '10'],
'longitude', ['0', 'd', '24', 'c'],
'mlt', ['dummy1', 'dummy2', 'dummy3'],
auto_bin=False)
def test_bin_data_depth(self):
"""Test failure when an array-like of length 1 is given to median2D
"""
avg.median2D(self.testInst,
1,
'longitude',
24,
'mlt', ['dummy1', 'dummy2', 'dummy3'],
auto_bin=False)
def test_nonmonotonic_bins(self):
"""Test 2D median failure when provided with a non-monotonic bins
"""
avg.median2D(self.testInst,
np.array([0., 300., 100.]),
'longitude',
np.array([0., 24., 13.]),
'mlt', ['dummy1', 'dummy2', 'dummy3'],
auto_bin=False)
def test_median2D_deprecation_warning(self):
"""Test generation of deprecation warning for median1D"""
with warnings.catch_warnings(record=True) as war:
try:
avg.median2D(None, [0., 360., 24.], 'longitude',
[0., 24., 24.], 'mlt', ['dummy1'])
except ValueError:
# Setting inst to None should produce a ValueError after
# warning is generated
pass
assert len(war) >= 1
assert war[0].category == DeprecationWarning
def test_constellation_median2D(self):
""" Test constellation implementation of 2D median"""
for i in self.testC.instruments:
i.bounds = self.bounds
self.testI.bounds = self.bounds
resultsC = avg.median2D(self.testC, [0., 360., 24.], 'longitude',
[0., 24, 24], 'mlt',
['dummy1', 'dummy2', 'dummy3'])
resultsI = avg.median2D(self.testI, [0., 360., 24.], 'longitude',
[0., 24, 24], 'mlt',
['dummy1', 'dummy2', 'dummy3'])
medC1 = resultsC['dummy1']['median']
medI1 = resultsI['dummy1']['median']
medC2 = resultsC['dummy2']['median']
medI2 = resultsI['dummy2']['median']
medC3 = resultsC['dummy3']['median']
medI3 = resultsI['dummy3']['median']
assert np.array_equal(medC1, medI1)
assert np.array_equal(medC2, medI2)
assert np.array_equal(medC3, medI3)
def test_basic_seasonal_median2D(self):
""" Test basic seasonal 2D median"""
results = avg.median2D(self.testInst, [0., 360., 24.], 'longitude',
[0., 24, 24], 'mlt', [self.dname])
# iterate over all
# no variation in the median, all values should be the same
test_vals = np.arange(50) * 1.2
for i, row in enumerate(results[self.dname]['median']):
for j, item in enumerate(row):
assert np.all(item == test_vals)
for i, row in enumerate(results[self.dname]['avg_abs_dev']):
for j, item in enumerate(row):
assert np.all(item == 0)
def test_basic_seasonal_2Dmedian(self):
""" Test the basic seasonal 2D median"""
results = avg.median2D(self.testInst, [0., 360., 24.], 'longitude',
[0., 24, 24], 'mlt', [self.dname])
# iterate over all
# no variation in the median, all values should be the same
for i, row in enumerate(results[self.dname]['median']):
for j, item in enumerate(row):
assert np.all(item['density'] == self.test_vals)
assert np.all(item['fraction'] == self.test_fracs)
for i, row in enumerate(results[self.dname]['avg_abs_dev']):
for j, item in enumerate(row):
assert np.all(item['density'] == 0)
assert np.all(item['fraction'] == 0)
def test_2D_median(self):
""" Test a 2D median calculation with a constellation"""
for i in self.testC.instruments:
i.bounds = self.bounds
results = avg.median2D(self.testC, [0., 360., 24], 'longitude',
[0., 24, 24], 'slt', ['uts'])
dummy_val = results['uts']['median']
dummy_dev = results['uts']['avg_abs_dev']
dummy_y = results['uts']['bin_y']
# iterate over all y rows
# value should be equal to integer value of mlt
# no variation in the median, all values should be the same
check = []
for i, y in enumerate(dummy_y[:-1]):
check.append(np.all(dummy_val[i, :] == y.astype(int)))
check.append(np.all(dummy_dev[i, :] == 0))
def test_seasonal_average_uneven_bins(self):
""" Test seasonal 2D median with uneven bins"""
results = avg.median2D(self.testInst,
np.linspace(0., 360., 25),
'longitude',
np.linspace(0., 24., 25),
'mlt', ['dummy1', 'dummy2', 'dummy3'],
auto_bin=False)
dummy_val = results['dummy1']['median']
dummy_dev = results['dummy1']['avg_abs_dev']
dummy2_val = results['dummy2']['median']
dummy2_dev = results['dummy2']['avg_abs_dev']
dummy3_val = results['dummy3']['median']
dummy3_dev = results['dummy3']['avg_abs_dev']
dummy_x = results['dummy1']['bin_x']
dummy_y = results['dummy1']['bin_y']
# iterate over all y rows
# value should be equal to integer value of mlt
# no variation in the median, all values should be the same
check = []
for i, y in enumerate(dummy_y[:-1]):
assert np.all(dummy_val[i, :] == y.astype(int))
assert np.all(dummy_dev[i, :] == 0)
for i, x in enumerate(dummy_x[:-1]):
assert np.all(dummy2_val[:, i] == x / 15.0)
assert np.all(dummy2_dev[:, i] == 0)
for i, x in enumerate(dummy_x[:-1]):
check.append(
np.all(dummy3_val[:, i] == x / 15.0 * 1000.0 + dummy_y[:-1]))
check.append(np.all(dummy3_dev[:, i] == 0))
# holds here because there are 32 days, no data is discarded,
# each day holds same amount of data
assert (self.testInst.data['dummy1'].size * 3 == sum(
[sum(i) for i in results['dummy1']['count']]))
assert np.all(check)
def test_heterogenous_constellation_median2D(self):
""" Test the seasonal 2D median of a heterogeneous constellation """
for inst in self.testC:
inst.bounds = self.bounds
results = avg.median2D(self.testC, [0., 360., 24.], 'longitude',
[0., 24, 24], 'mlt',
['dummy1', 'dummy2', 'dummy3'])
dummy_val = results['dummy1']['median']
dummy_dev = results['dummy1']['avg_abs_dev']
dummy2_val = results['dummy2']['median']
dummy2_dev = results['dummy2']['avg_abs_dev']
dummy3_val = results['dummy3']['median']
dummy3_dev = results['dummy3']['avg_abs_dev']
dummy_x = results['dummy1']['bin_x']
dummy_y = results['dummy1']['bin_y']
# iterate over all y rows
# value should be equal to integer value of mlt
# no variation in the median, all values should be the same
check = []
for i, y in enumerate(dummy_y[:-1]):
check.append(np.all(dummy_val[i, :] == y.astype(int)))
check.append(np.all(dummy_dev[i, :] == 0))
for i, x in enumerate(dummy_x[:-1]):
check.append(np.all(dummy2_val[:, i] == x/15.0))
check.append(np.all(dummy2_dev[:, i] == 0))
for i, x in enumerate(dummy_x[:-1]):
check.append(np.all(dummy3_val[:, i] == x/15.0 * 1000.0
+ dummy_y[:-1]))
check.append(np.all(dummy3_dev[:, i] == 0))
assert np.all(check)
