def test_nonmonotonic_bins(self):
"""Test median1D failure when provided with a non-monotonic bins
"""
avg.median1D(self.testInst, [0, 13, 5],
self.test_label,
self.test_data,
auto_bin=False)
def test_bin_data_depth(self):
"""Test failure when array-like of length 1 is given to median1D
"""
avg.median1D(self.testInst,
24,
self.test_label,
self.test_data,
auto_bin=False)
def test_median1D_deprecation_warning(self):
"""Test generation of deprecation warning for median1D"""
with warnings.catch_warnings(record=True) as war:
try:
avg.median1D(None, [0., 360., 24.],
'longitude', ['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_median1D_default(self):
"""Test success of median1D with default options"""
med_dict = avg.median1D(self.testInst, self.test_bins, self.test_label,
self.test_data)
# Test output type
assert isinstance(med_dict, dict)
assert len(med_dict.keys()) == len(self.test_data)
# Test output keys
for kk in med_dict.keys():
assert kk in self.test_data
assert np.all([jj in self.out_keys
for jj in med_dict[kk].keys()])
# Test output values
for jj in self.out_keys[:-1]:
assert len(med_dict[kk][jj]) == self.test_bins[-1]
assert np.all(med_dict[kk][jj] == self.out_data[kk][jj])
jj = self.out_keys[-1]
assert len(med_dict[kk][jj]) == self.test_bins[-1]+1
assert np.all(med_dict[kk][jj] == np.linspace(self.test_bins[0],
self.test_bins[1],
self.test_bins[2]+1))
del med_dict, kk, jj
def test_constellation_median1D(self):
""" Test constellation implementation of 1D median"""
for i in self.testC.instruments:
i.bounds = self.bounds
self.testI.bounds = self.bounds
resultsC = avg.median1D(self.testC, [0., 24, 24], 'mlt',
['dummy1', 'dummy2', 'dummy3'])
resultsI = avg.median1D(self.testI, [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_median1D(self):
""" Test basic seasonal 1D median"""
results = avg.median1D(self.testInst, [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']):
assert np.all(row == test_vals)
for i, row in enumerate(results[self.dname]['avg_abs_dev']):
assert np.all(row == 0)
def test_basic_seasonal_1Dmedian(self):
""" Test the basic seasonal 1D median"""
results = avg.median1D(self.testInst, [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']):
assert np.all(row['density'] == self.test_vals)
assert np.all(row['fraction'] == self.test_fracs)
for i, row in enumerate(results[self.dname]['avg_abs_dev']):
assert np.all(row['density'] == 0)
assert np.all(row['fraction'] == 0)
def test_1D_median(self):
""" Test a 1D median calculation with a constellation"""
for i in self.testC.instruments:
i.bounds = self.bounds
results = avg.median1D(self.testC, [0., 24, 24], 'slt', ['uts'])
dummy_val = results['uts']['median']
dummy_dev = results['uts']['avg_abs_dev']
dummy_x = results['uts']['bin_x']
# iterate over all x rows
# value should be equal to integer value of slt
# no variation in the median, all values should be the same
check = []
for i, x in enumerate(dummy_x[:-1]):
check.append(np.all(dummy_val[i] == x.astype(int)))
check.append(np.all(dummy_dev[i] == 0))
def test_heterogenous_constellation_median1D(self):
""" Test the seasonal 1D median of a heterogeneous constellation """
for inst in self.testC:
inst.bounds = self.bounds
results = avg.median1D(self.testC, [0., 24, 24], 'mlt', ['dummy1'])
# Extract the results
dummy_val = results['dummy1']['median']
dummy_dev = results['dummy1']['avg_abs_dev']
# iterate over all x rows
# value should be equal to integer value of mlt
# no variation in the median, all values should be the same
check = []
for i, x in enumerate(results['dummy1']['bin_x'][:-1]):
check.append(np.all(dummy_val[i] == x.astype(int)))
check.append(np.all(dummy_dev[i] == 0))
assert np.all(check)
def test_median1D_bad_label(self):
"""Test failure of median1D with unknown label"""
avg.median1D(self.testInst, self.test_bins, "bad_label",
self.test_data)
def test_median1D_bad_data(self):
"""Test failure of median1D with string data instead of list"""
avg.median1D(self.testInst, self.test_bins, self.test_label,
self.test_data[0])
