def test_get_platform_tracks(self):
result = q.get_platform_tracks(self.session,
quantum="hour",
platform_types=["drifter"])
self.assertEqual(len(result), 101)
array = np.array(result)
self.assertEqual(np.unique(array[:, 0]), 1)
self.assertEqual(np.unique(array[0, 1]), Platform.Type.drifter)
def observation_track_v1_0(query: str):
"""
API Format: /api/v1.0/observation/track/<string:query>.json
<string:query> : List of key=value pairs, seperated by ;
valid query keys are: start_date, end_date, datatype, platform_type,
meta_key, meta_value, mindepth, maxdepth, area, radius, quantum
Observational query for tracks
**Used in ObservationSelector**
"""
query_dict = {
key: value
for key, value in [q.split('=') for q in query.split(';')]
}
data = []
max_age = 86400
params = {}
MAPPING = {
'start_date': 'starttime',
'end_date': 'endtime',
'platform_type': 'platform_types',
'meta_key': 'meta_key',
'meta_value': 'meta_value',
}
for k, v in query_dict.items():
if k not in MAPPING:
continue
if k in ['start_date', 'end_date']:
params[MAPPING[k]] = dateparse(v)
elif k in ['datatype', 'meta_key', 'meta_value']:
if k == 'meta_key' and v == 'Any':
continue
if k == 'meta_value' and query_dict.get('meta_key') == 'Any':
continue
params[MAPPING[k]] = v
elif k == 'platform_type':
params[MAPPING[k]] = v.split(',')
else:
params[MAPPING[k]] = float(v)
if 'area' in query_dict:
area = json.loads(query_dict.get('area'))
if len(area) > 1:
lats = [c[0] for c in area]
lons = [c[1] for c in area]
params['minlat'] = min(lats)
params['minlon'] = min(lons)
params['maxlat'] = max(lats)
params['maxlon'] = max(lons)
else:
params['latitude'] = area[0][0]
params['longitude'] = area[0][1]
params['radius'] = float(query_dict.get('radius', 10))
platforms = ob_queries.get_platforms(DB.session, **params)
for param in [
'minlat', 'maxlat', 'minlon', 'maxlon', 'latitude',
'longitude', 'radius'
]:
if param in params:
del params[param]
params['platforms'] = platforms
coordinates = ob_queries.get_platform_tracks(
DB.session, query_dict.get("quantum", "day"), **params)
if len(coordinates) > 1:
df = pd.DataFrame(np.array(coordinates),
columns=['id', 'type', 'lon', 'lat'])
df['id'] = df.id.astype(int)
vc = df.id.value_counts()
for p_id in vc.where(vc > 1).dropna().index:
d = {
'type': "Feature",
'geometry': {
'type':
"LineString",
'coordinates':
df[['lon', 'lat']][df.id == p_id].values.tolist()
},
'properties': {
'id': int(p_id),
'type': df.type[df.id == p_id].values[0].name,
'class': 'observation',
}
}
data.append(d)
result = {
'type': "FeatureCollection",
'features': data,
}
resp = jsonify(result)
resp.cache_control.max_age = max_age
return resp
def observation_track_v1_0(query: str):
"""
API Format: /api/v1.0/observation/track/<string:query>.json
<string:query> : List of key=value pairs, seperated by ;
valid query keys are: start_date, end_date, datatype, platform_type,
meta_key, meta_value, mindepth, maxdepth, area, radius, quantum
Observational query for tracks
**Used in ObservationSelector**
"""
query_dict = {key: value for key, value in [q.split("=") for q in query.split(";")]}
data = []
max_age = 86400
params = {}
MAPPING = {
"start_date": "starttime",
"end_date": "endtime",
"platform_type": "platform_types",
"meta_key": "meta_key",
"meta_value": "meta_value",
}
for k, v in query_dict.items():
if k not in MAPPING:
continue
if k in ["start_date", "end_date"]:
params[MAPPING[k]] = dateparse(v)
elif k in ["datatype", "meta_key", "meta_value"]:
if k == "meta_key" and v == "Any":
continue
if k == "meta_value" and query_dict.get("meta_key") == "Any":
continue
params[MAPPING[k]] = v
elif k == "platform_type":
params[MAPPING[k]] = v.split(",")
else:
params[MAPPING[k]] = float(v)
if "area" in query_dict:
area = json.loads(query_dict.get("area"))
if len(area) > 1:
lats = [c[0] for c in area]
lons = [c[1] for c in area]
params["minlat"] = min(lats)
params["minlon"] = min(lons)
params["maxlat"] = max(lats)
params["maxlon"] = max(lons)
else:
params["latitude"] = area[0][0]
params["longitude"] = area[0][1]
params["radius"] = float(query_dict.get("radius", 10))
platforms = ob_queries.get_platforms(DB.session, **params)
for param in [
"minlat",
"maxlat",
"minlon",
"maxlon",
"latitude",
"longitude",
"radius",
]:
if param in params:
del params[param]
params["platforms"] = platforms
coordinates = ob_queries.get_platform_tracks(
DB.session, query_dict.get("quantum", "day"), **params
)
if len(coordinates) > 1:
df = pd.DataFrame(np.array(coordinates), columns=["id", "type", "lon", "lat"])
df["id"] = df.id.astype(int)
df["lon"] = (df["lon"] + 360) % 360
vc = df.id.value_counts()
for p_id in vc.where(vc > 1).dropna().index:
d = {
"type": "Feature",
"geometry": {
"type": "LineString",
"coordinates": df[["lon", "lat"]][df.id == p_id].values.tolist(),
},
"properties": {
"id": int(p_id),
"type": df.type[df.id == p_id].values[0].name,
"class": "observation",
},
}
data.append(d)
result = {
"type": "FeatureCollection",
"features": data,
}
resp = jsonify(result)
resp.cache_control.max_age = max_age
return resp