Black edits

Signed-off-by: bvandekerkhof <[email protected]>

src/pyelq/component/background.py

@@ -176,7 +176,7 @@ def initialise(self, sensor_object: SensorGroup, meteorology: MeteorologyGroup,
         """
         self.n_obs = sensor_object.nof_observations
         self.time, unique_inverse = np.unique(sensor_object.time, return_inverse=True)
-        self.time = pd.array(self.time, dtype='datetime64[ns]')
+        self.time = pd.array(self.time, dtype="datetime64[ns]")
         self.n_parameter = len(self.time)
         self.basis_matrix = sparse.csr_array((np.ones(self.n_obs), (np.array(range(self.n_obs)), unique_inverse)))
         self.precision_matrix = gmrf.precision_temporal(time=self.time)
@@ -300,12 +300,13 @@ def make_temporal_knots(self, sensor_object: SensorGroup):
 
         """
         if self.n_time is None:
-            self.time = pd.array(np.unique(sensor_object.time), dtype='datetime64[ns]')
+            self.time = pd.array(np.unique(sensor_object.time), dtype="datetime64[ns]")
             self.n_time = len(self.time)
         else:
             self.time = pd.array(
                 pd.date_range(start=np.min(sensor_object.time), end=np.max(sensor_object.time), periods=self.n_time),
-                dtype='datetime64[ns]')
+                dtype="datetime64[ns]",
+            )
 
     def make_spatial_knots(self, sensor_object: SensorGroup):
         """Create the spatial grid for the model.

src/pyelq/sensor/sensor.py

@@ -137,7 +137,7 @@ def concentration(self) -> np.ndarray:
     @property
     def time(self) -> pd.arrays.DatetimeArray:
         """DatetimeArray: Column vector of time values across all sensors."""
-        return pd.array(np.concatenate([sensor.time for sensor in self.values()]), dtype='datetime64[ns]')
+        return pd.array(np.concatenate([sensor.time for sensor in self.values()]), dtype="datetime64[ns]")
 
     @property
     def location(self) -> Coordinate:

tests/conftest.py

@@ -60,7 +60,8 @@ def fix_sensor_group(request, ref_longitude, ref_latitude):
         sensor[device_name] = Sensor()
         sensor[device_name].time = pd.array(
             pd.date_range(start=datetime.now(), end=datetime.now() + timedelta(hours=1.0), periods=n_time),
-            dtype='datetime64[ns]')
+            dtype="datetime64[ns]",
+        )
         sensor[device_name].concentration = np.random.random_sample(size=(n_time,))
         sensor[device_name].location = ENU(
             east=locations[k, 0],
@@ -77,7 +78,8 @@ def fix_sensor_group(request, ref_longitude, ref_latitude):
     sensor[device_name] = Beam()
     sensor[device_name].time = pd.array(
         pd.date_range(start=datetime.now(), end=datetime.now() + timedelta(hours=1.0), periods=n_time),
-        dtype='datetime64[ns]')
+        dtype="datetime64[ns]",
+    )
     sensor[device_name].concentration = np.random.random_sample(size=(n_time,))
     sensor[device_name].location = ENU(
         east=np.array([0, locations[k, 0]]),

tests/sensor/test_sensorgroup.py

@@ -28,7 +28,7 @@ def test_sensorgroup():
         nof_observations = np.random.randint(1, 10)
         total_observations += nof_observations
         sensor.concentration = np.random.rand(nof_observations, 1)
-        sensor.time = pd.array(pd.date_range(start="1/1/2022", periods=nof_observations), dtype='datetime64[ns]')
+        sensor.time = pd.array(pd.date_range(start="1/1/2022", periods=nof_observations), dtype="datetime64[ns]")
         sensor.location = LLA(
             latitude=0.01 * np.random.rand(), longitude=0.01 * np.random.rand(), altitude=0.01 * np.random.rand()
         )
@@ -57,7 +57,7 @@ def test_plotting():
         nof_observations = np.random.randint(5, 10)
         total_observations += nof_observations
         sensor.concentration = np.random.rand(nof_observations, 1)
-        sensor.time = pd.array(pd.date_range(start="1/1/2022", periods=nof_observations), dtype='datetime64[ns]')
+        sensor.time = pd.array(pd.date_range(start="1/1/2022", periods=nof_observations), dtype="datetime64[ns]")
         location = LLA()
         location.latitude = np.array(idx)
         location.longitude = np.array(idx)

tests/support_functions/test_spatio_temporal_interpolation.py

@@ -30,7 +30,9 @@ def test_default_returns():
     same."""
 
     loc_in = np.array([[0, 0, 0], [1, 1, 1]])
-    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=loc_in.shape[0], freq="h"), dtype='datetime64[ns]')[:, None]
+    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=loc_in.shape[0], freq="h"), dtype="datetime64[ns]")[
+        :, None
+    ]
     vals = np.random.random((loc_in.shape[0], 1))
     # check if same input/output locations and time give the same answer
     return_vals = sti.interpolate(
@@ -47,7 +49,7 @@ def test_single_value():
     """Tests if all interpolated values are set to the same value when 1 input value is provided."""
     loc_in = np.array([[0, 0, 0], [1, 1, 1]])
     n_obs = loc_in.shape[0]
-    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=n_obs, freq="h"), dtype='datetime64[ns]')[:, None]
+    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=n_obs, freq="h"), dtype="datetime64[ns]")[:, None]
     vals = np.random.random((loc_in.shape[0], 1))
 
     # Check if we get the same output for all values when 1 value is provided
@@ -73,7 +75,7 @@ def test_temporal_interpolation():
     interpolation in 1d) Also checks if we get the same values when an array of integers (representing seconds) is
     supplied instead of an array of datetimes."""
     periods = 10
-    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=periods, freq="s"), dtype='datetime64[ns]')[:, None]
+    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=periods, freq="s"), dtype="datetime64[ns]")[:, None]
     time_in_array = np.array(range(periods))[:, None]
     vals = np.random.random(time_in.size)
     random_index = np.random.randint(0, periods - 1)
@@ -133,13 +135,17 @@ def test_fill_value():
 def test_consistent_shapes():
     """Test if output shapes are consistent with provided input."""
     loc_in = np.array([[0, 0, 0], [1, 1, 1]])
-    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=loc_in.shape[0] - 1, freq="h"), dtype='datetime64[ns]')[:, None]
+    time_in = pd.array(
+        pd.date_range(pd.Timestamp.now(), periods=loc_in.shape[0] - 1, freq="h"), dtype="datetime64[ns]"
+    )[:, None]
     vals = np.random.random((loc_in.shape[0], 1))
     with pytest.raises(ValueError):
         sti.interpolate(location_in=loc_in, time_in=time_in, values_in=vals, location_out=loc_in, time_out=time_in)
 
     loc_in = np.array([[0, 0, 0], [0, 1, 0], [1, 0.5, 0], [0.5, 0.5, 1]])
-    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=loc_in.shape[0], freq="h"), dtype='datetime64[ns]')[:, None]
+    time_in = pd.array(pd.date_range(pd.Timestamp.now(), periods=loc_in.shape[0], freq="h"), dtype="datetime64[ns]")[
+        :, None
+    ]
     vals = np.random.random((loc_in.shape[0], 1))
     return_vals = sti.interpolate(
         location_in=loc_in, time_in=time_in, values_in=vals, location_out=loc_in, time_out=time_in
@@ -174,16 +180,18 @@ def test_temporal_resampling():
     time_in = [datetime(2000, 1, 1, 0, 0, 1) + timedelta(minutes=i) for i in range(n_values_in)]
     time_bin_edges = pd.array(
         pd.to_datetime([datetime(2000, 1, 1) + timedelta(minutes=i * 10) for i in range(n_time_out + 1)]),
-        dtype='datetime64[ns]')
+        dtype="datetime64[ns]",
+    )
 
     correct_values_out_mean = np.array([np.mean(i) for i in np.split(values_in, n_time_out)])
     correct_values_out_max = np.array([np.max(i) for i in np.split(values_in, n_time_out)])
     correct_values_out_min = np.array([np.min(i) for i in np.split(values_in, n_time_out)])
 
     time_bin_edges_non_monotonic = pd.array(
-        pd.Series(list(time_bin_edges)[:-1] + [datetime(1999, 1, 1)]), dtype='datetime64[ns]')
+        pd.Series(list(time_bin_edges)[:-1] + [datetime(1999, 1, 1)]), dtype="datetime64[ns]"
+    )
 
-    time_in = pd.array(pd.to_datetime(time_in + [datetime(2001, 1, 1)]), dtype='datetime64[ns]')
+    time_in = pd.array(pd.to_datetime(time_in + [datetime(2001, 1, 1)]), dtype="datetime64[ns]")
     values_in = np.append(values_in, 1000000)
 
     p = np.random.permutation(len(time_in))

tests/test_gaussian_plume.py

@@ -33,7 +33,8 @@ def fixture_met_object():
     location.from_array(loc_in)
     time = pd.array(
         pd.date_range(pd.Timestamp.fromisoformat("2022-01-01 00:00:00"), periods=loc_in.shape[0], freq="s"),
-        dtype='datetime64[ns]')[:, None]
+        dtype="datetime64[ns]",
+    )[:, None]
     met_object = Meteorology()
     met_object.location = location
     met_object.time = time
@@ -56,7 +57,8 @@ def fixture_met_object_single():
     location.from_array(loc_in)
     time = pd.array(
         pd.date_range(pd.Timestamp.fromisoformat("2022-01-01 00:00:00"), periods=loc_in.shape[0], freq="s"),
-        dtype='datetime64[ns]')[:, None]
+        dtype="datetime64[ns]",
+    )[:, None]
     met_object = Meteorology()
     met_object.location = location
     met_object.time = time
@@ -79,8 +81,8 @@ def fixture_sensor_object():
     location.from_array(np.array([[25, 0, 0]]))
     sensor_object.location = location
     time = pd.array(
-        pd.date_range(pd.Timestamp.fromisoformat("2022-01-01 00:00:00"), periods=5, freq="ns"),
-        dtype='datetime64[ns]')[:, None]
+        pd.date_range(pd.Timestamp.fromisoformat("2022-01-01 00:00:00"), periods=5, freq="ns"), dtype="datetime64[ns]"
+    )[:, None]
     sensor_object.time = time
     sensor_object.concentration = np.zeros(time.size)
     sensor_object.label = "Generic"
@@ -97,7 +99,8 @@ def fixture_drone_object():
     sensor_object.location = location
     time = pd.array(
         pd.date_range(pd.Timestamp.fromisoformat("2022-01-01 00:00:00"), periods=loc_in.shape[0], freq="s"),
-        dtype='datetime64[ns]')[:, None]
+        dtype="datetime64[ns]",
+    )[:, None]
     sensor_object.time = time
     sensor_object.concentration = np.zeros(time.size)
     sensor_object.label = "Generic"
@@ -112,8 +115,8 @@ def fixture_beam_object():
     beam_object = Beam()
     beam_object.location = beam_location
     time = pd.array(
-        pd.date_range(pd.Timestamp.fromisoformat("2022-01-01 00:00:00"), periods=4, freq="ns"),
-        dtype='datetime64[ns]')[:, None]
+        pd.date_range(pd.Timestamp.fromisoformat("2022-01-01 00:00:00"), periods=4, freq="ns"), dtype="datetime64[ns]"
+    )[:, None]
     beam_object.time = time
     beam_object.concentration = np.zeros(time.size)
     beam_object.label = "Beam"

tests/test_meteorology.py

@@ -209,13 +209,15 @@ def test_calculate_wind_turbulence_horizontal():
     met = Meteorology()
     met.time = pd.array(
         np.array([dt.datetime(2023, 1, 1), dt.datetime(2023, 1, 1), dt.datetime(2023, 1, 1)]).astype("datetime64[ns]"),
-        dtype='datetime64[ns]')
+        dtype="datetime64[ns]",
+    )
     met.wind_direction = np.linspace(0, 360, met.time.shape[0])
 
     sigma = 3
 
-    met.time = pd.array(pd.date_range(dt.datetime(2023, 1, 1), dt.datetime(2023, 1, 2), freq="5s"),
-                        dtype='datetime64[ns]')
+    met.time = pd.array(
+        pd.date_range(dt.datetime(2023, 1, 1), dt.datetime(2023, 1, 2), freq="5s"), dtype="datetime64[ns]"
+    )
     met.wind_direction = np.random.normal(180, sigma, met.time.shape[0])
 
     met.calculate_wind_turbulence_horizontal(window="300s")

tests/test_preprocessing.py

@@ -29,7 +29,7 @@ def fix_time_bin_edges(sensor_group):
     """Fix the time bin edges to be used for aggregation."""
     min_time, max_time = get_time_lims(sensor_group=sensor_group)
     min_time, max_time = min_time - timedelta(seconds=60), max_time + timedelta(seconds=60)
-    time_bin_edges = pd.array(pd.date_range(min_time, max_time, freq="120s"), dtype='datetime64[ns]')
+    time_bin_edges = pd.array(pd.date_range(min_time, max_time, freq="120s"), dtype="datetime64[ns]")
     return time_bin_edges
 
 
@@ -38,7 +38,7 @@ def fix_block_times(sensor_group):
     """Fix the time bin edges for re-blocking the processed data."""
     min_time, max_time = get_time_lims(sensor_group=sensor_group)
     min_time, max_time = min_time - timedelta(hours=1), max_time + timedelta(hours=1)
-    block_times = pd.array(pd.date_range(min_time, max_time, freq="1200s"), dtype='datetime64[ns]')
+    block_times = pd.array(pd.date_range(min_time, max_time, freq="1200s"), dtype="datetime64[ns]")
     return block_times
 
 
@@ -77,7 +77,7 @@ def fix_meteorology(request, sensor_group):
     with_nans = request.param
     min_time, max_time = get_time_lims(sensor_group=sensor_group)
     meteorology = Meteorology()
-    meteorology.time = pd.array(pd.date_range(min_time, max_time, freq="1s"), dtype='datetime64[ns]')
+    meteorology.time = pd.array(pd.date_range(min_time, max_time, freq="1s"), dtype="datetime64[ns]")
     meteorology.wind_speed = 1.9 + 0.2 * np.random.random_sample(size=meteorology.time.shape)
     meteorology.wind_direction = np.mod(358.0 + 4.0 * np.random.random_sample(size=meteorology.time.shape), 360)
     meteorology.wind_turbulence_horizontal = 10.0 * np.ones(shape=meteorology.time.shape)