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_spatial():
"""outliers - spatial"""
points, duplicate_indices = t.rand_xy_list(150)
qc = outliers.spatial(points, duplicate_indices, None, None)
assert len(qc['ok_mad']) == len(points)
assert len(qc['ok_iqr']) == len(points)
assert len(qc['centroid']) > 0 and "SRID=4326;POINT(" in qc['centroid']
assert 16000e3 > qc['median'] > 4000e3
assert 6000e3 > qc['mad'] > 1500e3
assert 12000e3 > qc['q1'] > 2000e3
assert 24000e3 > qc['q3'] > 6000e3
xy = [(random.uniform(-0.001, 0.001), random.uniform(-0.001, 0.001)) for _ in range(100)]
duplicate_indices = list(range(100))
qc = outliers.spatial(xy, duplicate_indices, None, None)
assert len(qc['ok_mad']) == len(xy)
assert len(qc['ok_iqr']) == len(xy)
assert qc['centroid'].startswith('SRID=4326;POINT(')
assert round(qc['median']/1000) == 0
assert round(qc['mad']/1000) == 0
assert round(qc['q1']/1000) == 0
assert round(qc['q3']/1000) == 0
def test_spatial_few_points():
""" outliers - spatial few points """
points, duplicate_indices = [[1,2]], [0]
qc = outliers.spatial(points, duplicate_indices, None, None)
assert len(qc['ok_mad']) == len(points) and np.all(qc['ok_mad'])
assert len(qc['ok_iqr']) == len(points) and np.all(qc['ok_iqr'])
print(qc)
assert qc['centroid'].startswith('SRID=4326;POINT(')
x, y = [float(x) for x in qc['centroid'].replace('SRID=4326;POINT(', '').replace(')', '').split(' ')]
assert abs(round(x) - 1) < 0.00001 and abs(round(y) - 2) < 0.00001
assert abs(round(qc['median'])) < 0.00001
for k in ['mad', 'q1', 'q3']:
assert qc[k] is None
def _qc_dataset(req):
points, _, mad_coef, iqr_coef, return_values = QcResource._parse_request(
req)
try:
points, duplicate_indices = np.unique(points,
return_inverse=True,
axis=0)
return {
'spatial':
outliers.spatial(points,
duplicate_indices,
mad_coef,
iqr_coef,
return_values=return_values),
'count':
len(points)
}
except Exception as ex:
raise falcon.HTTPError(
falcon.HTTP_400, 'Error looking up data for provided points',
str(ex))
def test_spatial():
"""outliers - spatial return values"""
points, duplicate_indices = t.rand_xy_list(150)
qc = outliers.spatial(points, duplicate_indices, None, None, return_values=True)
assert len(qc['values']) == len(points)