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_spatial_qcstats():
"""outliers - spatial qc stats"""
qcstats = taxoninfo.qc_stats(aphiaid=141433)
points, duplicate_indices = t.rand_xy_list(200)
qc = outliers.spatial(points, duplicate_indices, None, None, qcstats=qcstats)
assert len(qc['ok_mad']) == len(points) and 0 < np.sum(qc['ok_mad']) < len(points)
assert len(qc['ok_iqr']) == len(points) and 0 < np.sum(qc['ok_iqr']) < len(points)
for i, k in enumerate(['median', 'mad', 'q1', 'q3']):
assert isinstance(qc[k], float) and qc[k] == qcstats['spatial'][i+1]
def test_fetch_taxon_qc():
"""taxoninfo - qc stats"""
stats = taxoninfo.qc_stats(test_aphiaid)
assert stats['id'] == 141433
for env in ['bathymetry', 'sssalinity', 'sstemperature']:
assert env in stats and len(stats[env]) == 4
assert 'count' in stats and stats['count'] > 0
assert 'spatial' in stats and len(stats['spatial']) == 5
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_fetch_taxon_qc_missing_species():
"""taxoninfo - qc stats data poor taxon"""
stats = taxoninfo.qc_stats(145552) # Sargassum baccularia: 7 records
assert stats['count'] > 0
centroid, median, mad, q1, q3 = stats['spatial']
assert len(centroid) == 2
assert median > 0
assert mad > 0
assert q1 is None
assert q3 is None
for env in ['sssalinity', 'sstemperature']:
median, mad, q1, q3 = stats[env]
assert median is not None
assert mad > 0
assert q1 is None
assert q3 is None
def test_fetch_taxon_qc_missing_species():
"""taxoninfo - qc stats missing species"""
stats = taxoninfo.qc_stats(-1)
assert stats is None