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 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_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_api_dataset_post_works():
""" api dataset - post works """
points, _ = t.rand_xy_list(150)
points = points.tolist()
body = json.dumps({'points': points}, ignore_nan=True)
result1 = client.simulate_post('/outliersdataset', body=body)
_check_outliersdataset_result(result1)
body = msgpack.dumps({'points': points})
result2 = client.simulate_post('/outliersdataset', body=body, headers={'Content-Type': falcon.MEDIA_MSGPACK})
content = msgpack.loads(result2.content)
_check_outliersdataset_result(result2, content)
assert result1.json == content
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_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_api_taxon_post_works():
""" api taxon - post works """
points, _ = t.rand_xy_list(150)
points = points.tolist()
body = json.dumps({'points': points}, ignore_nan=True)
result1 = client.simulate_post('/outlierstaxon', body=body)
_check_outlierstaxon_result(result1)
body = msgpack.dumps({'points': points})
result2 = client.simulate_post('/outlierstaxon', body=body, headers={'Content-Type': falcon.MEDIA_MSGPACK})
content = msgpack.loads(result2.content)
_check_outlierstaxon_result(result2, content)
assert result1.json == content
for return_values in [True, False]:
body = json.dumps({'points': points, 'returnvalues': return_values}, ignore_nan=True)
result1 = client.simulate_post('/outlierstaxon', body=body)
_check_outlierstaxon_result(result1, return_values=return_values)
body = msgpack.dumps({'points': points, 'returnvalues': return_values})
result2 = client.simulate_post('/outlierstaxon', body=body, headers={'Content-Type': falcon.MEDIA_MSGPACK})
content = msgpack.loads(result2.content)
_check_outlierstaxon_result(result2, content, return_values=return_values)
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)