def test_environmental_few_points():
"""outliers - environmental few points"""
points, duplicate_indices = t.rand_xy_list(1, -1, 1, -1, 1)
qc = outliers.environmental(points, duplicate_indices, None, None)
for grid in ['bathymetry', 'sssalinity', 'sstemperature']:
g = qc[grid]
assert len(g['ok_mad']) == len(points) and np.all(g['ok_mad'])
assert len(g['ok_iqr']) == len(points) and np.all(g['ok_iqr'])
assert isinstance(g['median'], float) and g['median'] != 0
for k in ['mad', 'q1', 'q3']:
assert g[k] is None
xy, duplicate_indices = t.rand_xy_list(10, -1, 1, -1, 1)
qc = outliers.environmental(xy, duplicate_indices, mad_coef=1, iqr_coef=0.1)
for grid in ['bathymetry', 'sssalinity', 'sstemperature']:
g = qc[grid]
assert len(g['ok_mad']) == len(xy) and 0 < np.sum(g['ok_mad']) < len(xy)
assert len(g['ok_iqr']) == len(xy) and np.all(g['ok_iqr'])
assert isinstance(g['median'], float) and g['median'] != 0
assert isinstance(g['mad'], float) and g['mad'] != 0
assert g['q1'] is None
assert g['q3'] is None
random.seed(42)
points, duplicate_indices = [(random.uniform(-1, 1), random.uniform(-1, 1)) for _ in range(21)], list(range(21))
qc = outliers.environmental(points, duplicate_indices, mad_coef=1, iqr_coef=0.1)
for grid in ['bathymetry', 'sssalinity', 'sstemperature']:
g = qc[grid]
assert len(g['ok_mad']) == len(points) and 0 < np.sum(g['ok_mad']) < len(points)
assert len(g['ok_iqr']) == len(points) and 0 < np.sum(g['ok_iqr']) < len(points)
for k in ['median', 'mad', 'q1', 'q3']:
assert isinstance(g[k], float) and g[k] != 0
def _qc_taxon(req):
points, aphiaid, mad_coef, iqr_coef, return_values = QcResource._parse_request(
req)
try:
qcstats = None
if aphiaid is not None:
qcstats = taxoninfo.qc_stats(aphiaid)
points, duplicate_indices = np.unique(points,
return_inverse=True,
axis=0)
qc = outliers.environmental(points, duplicate_indices, mad_coef,
iqr_coef, qcstats, return_values)
qc['spatial'] = outliers.spatial(points, duplicate_indices,
mad_coef, iqr_coef, qcstats,
return_values)
if qcstats is not None:
qc['count'] = qcstats['count']
qc['id'] = qcstats['id']
else:
qc['count'] = len(points)
qc['id'] = None
return qc
except Exception as ex:
raise falcon.HTTPError(
falcon.HTTP_400, 'Error looking up data for provided points',
str(ex))
def test_environmental():
"""outliers - environmental"""
points, duplicate_indices = t.rand_xy_list(150)
qc = outliers.environmental(points, duplicate_indices, None, None)
for grid in ['bathymetry', 'sssalinity', 'sstemperature']:
g = qc[grid]
assert len(g['ok_mad']) == len(points)
assert len(g['ok_iqr']) == len(points)
for k in ['median', 'mad', 'q1', 'q3']:
assert isinstance(g[k], float) and g[k] != 0
def test_environmental_qcstats():
"""outliers - environmental qc stats"""
qcstats = taxoninfo.qc_stats(aphiaid=141433)
points, duplicate_indices = t.rand_xy_list(200)
qc = outliers.environmental(points, duplicate_indices, 12, 6, qcstats=qcstats)
for grid in ['bathymetry', 'sssalinity', 'sstemperature']:
g = qc[grid]
print(grid)
print(np.sum(g['ok_mad']))
print(np.sum(g['ok_iqr']))
assert len(g['ok_mad']) == len(points) and 0 < np.sum(g['ok_mad']) < len(points)
assert len(g['ok_iqr']) == len(points) and 0 < np.sum(g['ok_iqr']) < len(points)
for i, k in enumerate(['median', 'mad', 'q1', 'q3']):
assert isinstance(g[k], float) and g[k] == qcstats[grid][i]
def test_environmental_no_valid_points():
"""outliers environmental - no valid salinity"""
qc = outliers.environmental([[15, 2]], [0], mad_coef=6, iqr_coef=3)
for grid in ['sssalinity', 'sstemperature']:
g = qc[grid]
assert g['median'] is None