);
- let observationVariableSelector = null;
+ let observationInputs = null;
if (plotData.observation) {
- if (typeof plotData.id === "number") {
- observationVariableSelector = (
-
setObservationVariable(value)}
- alwaysShow={true}
- />
- );
- } else {
- const data = plotData.coordinates[0][2].datatypes.map((o, i) => ({
- id: i,
- value: o.replace(/ \[.*\]/, ""),
- }));
- observationVariableSelector = (
- setObservationVariable(value)}
- alwaysShow={true}
- />
- );
- }
+ let obsOptions =
+ typeof plotData.id === "number"
+ ? observationVariables.data
+ : plotData.coordinates[0][2].datatypes.map((o, i) => ({
+ id: i,
+ value: o.replace(/ \[.*\]/, ""),
+ }));
+ observationInputs = (
+
+ {_("Observation Settings")}
+
+ setObservationVariable(value)}
+ alwaysShow={true}
+ />
+
+
+ );
}
const inputs = (
<>
- {observationVariableSelector}
- setShowMap(value)}
- title={_("Show Location")}
- >
- {_("showmap_help")}
-
+ {selected !== TabEnum.OBSERVATION && (
+ setShowMap(value)}
+ title={_("Show Location")}
+ >
+ {_("showmap_help")}
+
+ )}
{/* {plotData.coordinates.length === 1 && (
v.id)
+ : plotDataset.variable.id,
observation: [plotData.id],
observation_variable: observationVariable,
};
@@ -365,22 +367,34 @@ const PointWindow = ({
@@ -402,6 +418,7 @@ const PointWindow = ({
{_("Global Settings")}
{inputs}
+ {observationInputs}
GetComboBoxQuery(url),
- });
-
- return { data, status };
-}
-
export function prefetchAllVariables() {
const queryClient = useQueryClient();
queryClient.prefetchQuery({
diff --git a/plotting/observation.py b/plotting/observation.py
index 5f817942a..8c0ef6fba 100644
--- a/plotting/observation.py
+++ b/plotting/observation.py
@@ -8,7 +8,7 @@
import pint
import pytz
from babel.dates import format_datetime
-from sqlalchemy import func
+from sqlalchemy import func, select
from sqlalchemy.orm import Session
from data import open_dataset
@@ -71,17 +71,19 @@ def load_data(self):
data = []
for dt in datatypes:
- data.append(
- self.db.query(Sample.depth, Sample.value)
- .filter(Sample.station == station, Sample.datatype == dt)
- .all()
+ query = (
+ select(Sample.depth, Sample.value)
+ .where(Sample.station_id == station.id)
+ .where(Sample.datatype_key == dt.key)
)
+ dt_values = self.db.execute(query).all()
+ data.append(np.ma.array(dt_values))
if idx == 0:
self.observation_variable_names.append(dt.name)
self.observation_variable_units.append(dt.unit)
- observation["data"] = np.ma.array(data) # .transpose()
+ observation["data"] = data # .transpose()
self.observation[idx] = observation
self.points = [
@@ -176,10 +178,11 @@ def plot(self):
data = []
for o in self.observation:
- d = np.ma.MaskedArray(o["data"])
- d[np.where(d == "")] = np.ma.masked
- d = np.ma.masked_invalid(d.filled(np.nan).astype(np.float32))
- data.append(d)
+ for obs_data in o["data"]:
+ d = np.ma.MaskedArray(obs_data)
+ d[np.where(d == "")] = np.ma.masked
+ d = np.ma.masked_invalid(d.filled(np.nan).astype(np.float32))
+ data.append(d)
ureg = pint.UnitRegistry()
ax_idx = -1
@@ -187,13 +190,13 @@ def plot(self):
unit_map = {}
for idx in self.observation_variable:
ax_idx += 1
- for d in data:
- if d.shape[1] == 1:
- style = "."
- else:
- style = "-"
+ d = data[idx]
+ if d.shape[0] == 1:
+ style = "."
+ else:
+ style = "-"
- ax[ax_idx].plot(d[idx, :, 1], d[idx, :, 0], style)
+ ax[ax_idx].plot(d[:, 1], d[:, 0], style)
ax[ax_idx].xaxis.set_label_position("top")
ax[ax_idx].xaxis.set_ticks_position("top")
ax[ax_idx].set_xlabel(
@@ -212,7 +215,7 @@ def plot(self):
u = self.observation_variable_units[idx].lower()
unit_map[self.observation_variable_names[idx]] = ureg.parse_units(u)
- except:
+ except Exception:
unit_map[self.observation_variable_names[idx]] = ureg.dimensionless
for k, v in list(unit_map.items()):
@@ -233,7 +236,7 @@ def plot(self):
if destunit == ureg.speed_of_light:
destunit = ureg.celsius
- except:
+ except Exception:
destunit = ureg.dimensionless
for j in range(0, self.data.shape[0]):
@@ -243,7 +246,7 @@ def plot(self):
u = ureg.celsius
quan = ureg.Quantity(self.data[j, idx, :], u)
- except:
+ except Exception:
quan = ureg.Quantity(self.data[j, idx, :], ureg.dimensionless)
axis.plot(quan.to(destunit).magnitude, self.depths[j, idx, :])
@@ -260,7 +263,7 @@ def plot(self):
)
)
else:
- l = []
+ leg_items = []
for j in [
(
"Observed",
@@ -277,9 +280,9 @@ def plot(self):
else:
name = name + " "
- l.append("%s%s (%s)" % (name, j[0], format_datetime(j[1][i])))
+ leg_items.append("%s%s (%s)" % (name, j[0], format_datetime(j[1][i])))
- leg = axis.legend(l, loc="best")
+ leg = axis.legend(leg_items, loc="best")
for legobj in leg.legend_handles:
legobj.set_linewidth(4.0)
diff --git a/routes/api_v2_0.py b/routes/api_v2_0.py
index 568a864f1..b9d900472 100644
--- a/routes/api_v2_0.py
+++ b/routes/api_v2_0.py
@@ -1383,25 +1383,36 @@ def observation_track(
db, 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 = pd.DataFrame(np.array(coordinates), columns=["id", "type", "lon", "lat"])
+ df["id"] = df.id.astype(int)
+ df = df.groupby("id").filter(lambda id: len(id) > 1)
+
+ if len(df) > 0:
+ df["coordinates"] = df[["lon", "lat"]].values.tolist()
+ df["type"] = df["type"].apply(lambda t: t.name)
+ df = (
+ df[["id", "type", "coordinates"]]
+ .groupby(["id", "type"])
+ .agg(list)
+ .reset_index()
+ )
- vc = df.id.value_counts()
- for p_id in vc.where(vc > 1).dropna().index:
- d = {
+ data = [
+ {
"type": "Feature",
"geometry": {
"type": "LineString",
- "coordinates": df[["lon", "lat"]][df.id == p_id].values.tolist(),
+ "coordinates": row[2],
},
"properties": {
- "id": int(p_id),
- "type": df.type[df.id == p_id].values[0].name,
+ "id": int(row[0]),
+ "type": row[1],
"class": "observation",
},
}
- data.append(d)
+ for row in df.values
+ if len(row[2]) > 1
+ ]
result = {
"type": "FeatureCollection",
@@ -1488,23 +1499,38 @@ def observation_point(
if len(stations) > 500:
stations = stations[:: round(len(stations) / 500)]
- for s in stations:
- if checkpoly and not poly.contains(Point(s.latitude, s.longitude)):
- continue
+ df = pd.DataFrame(
+ np.array(stations),
+ columns=["type", "id", "name", "lat", "lon"],
+ )
+ df["id"] = df.id.astype(int)
- d = {
- "type": "Feature",
- "geometry": {"type": "Point", "coordinates": [s.longitude, s.latitude]},
- "properties": {
- "type": s.platform.type.name,
- "id": s.id,
- "class": "observation",
- },
- }
- if s.name:
- d["properties"]["name"] = s.name
+ if len(df) > 0:
+ df["coordinates"] = df[["lon", "lat"]].values.tolist()
+ df["type"] = df["type"].apply(lambda t: t.name)
+
+ if checkpoly:
+ df["in_poly"] = df["coordinates"].apply(
+ lambda c: poly.contains(Point(c[1], c[0]))
+ )
+ df = df.loc[df["in_poly"]]
- data.append(d)
+ data = [
+ {
+ "type": "Feature",
+ "geometry": {
+ "type": "Point",
+ "coordinates": row["coordinates"],
+ },
+ "properties": {
+ "id": int(row.id),
+ "type": row["type"],
+ "class": "observation",
+ **({"name": row["name"]} if row["name"] is not None else {}),
+ },
+ }
+ for idx, row in df.iterrows()
+ ]
result = {
"type": "FeatureCollection",
diff --git a/tests/test_api_v_2_0.py b/tests/test_api_v_2_0.py
index feb8d5f75..101f6fadc 100644
--- a/tests/test_api_v_2_0.py
+++ b/tests/test_api_v_2_0.py
@@ -98,7 +98,7 @@ def test_variables_depth_only(self) -> None:
"interp": None,
"two_dimensional": False,
"vector_variable": False,
- "data_categories": False
+ "data_categories": False,
}
]
@@ -393,27 +393,32 @@ def test_observation_meta_values(self, patch_get_meta_values):
data = json.loads(response.content)
assert data[0] == "this is a test"
- @patch("data.observational.queries.get_stations")
+ @patch("data.observational.queries.get_platform_tracks")
def test_observation_track(
self,
- patch_get_stations,
+ patch_get_platform_tracks,
):
platform_type = PropertyMock()
platform_type.name = "platform_type"
- station = PropertyMock(
- platform=PropertyMock(type=platform_type),
- latitude=0,
- longitude=0,
- id=0,
- )
- station.name = "myname"
- patch_get_stations.return_value = [station]
- response = self.client.get(self.api_links["observation_point"])
+ platform = PropertyMock()
+ platform.name = "platform_name"
+
+ coordinates = [
+ (1, platform, -120.4, 33.1),
+ (1, platform, -120.3, 32.9),
+ (1, platform, -120.1, 32.7),
+ ]
+ patch_get_platform_tracks.return_value = coordinates
+ response = self.client.get(self.api_links["observation_track"])
assert response.status_code == 200
data = json.loads(response.content)
assert len(data["features"]) == 1
- assert data["features"][0]["geometry"]["coordinates"] == [0, 0]
+ assert data["features"][0]["geometry"]["coordinates"] == [
+ [-120.4, 33.1],
+ [-120.3, 32.9],
+ [-120.1, 32.7],
+ ]
@patch("sqlalchemy.orm.session.Session.query")
def test_observation_variables(self, patch_query):