Merge branch 'master' into validation_particle

Author: ludwigVonKoopa

examples/02_eddy_identification/pet_eddy_detection_ACC.py

@@ -65,8 +65,7 @@ def set_fancy_labels(fig, ticklabelsize=14, labelsize=14, labelweight="semibold"
     y_name="latitude",
     # Manual area subset
     indexs=dict(
-        latitude=slice(100 - margin, 220 + margin),
-        longitude=slice(0, 230 + margin),
+        latitude=slice(100 - margin, 220 + margin), longitude=slice(0, 230 + margin),
     ),
 )
 g_raw = RegularGridDataset(**kw_data)
@@ -188,16 +187,10 @@ def set_fancy_labels(fig, ticklabelsize=14, labelsize=14, labelweight="semibold"
     ax.set_ylabel("With filter")
 
     ax.plot(
-        a_[field][i_a] * factor,
-        a[field][j_a] * factor,
-        "r.",
-        label="Anticyclonic",
+        a_[field][i_a] * factor, a[field][j_a] * factor, "r.", label="Anticyclonic",
     )
     ax.plot(
-        c_[field][i_c] * factor,
-        c[field][j_c] * factor,
-        "b.",
-        label="Cyclonic",
+        c_[field][i_c] * factor, c[field][j_c] * factor, "b.", label="Cyclonic",
     )
     ax.set_aspect("equal"), ax.grid()
     ax.plot((0, 1000), (0, 1000), "g")

examples/06_grid_manipulation/pet_lavd.py

@@ -142,8 +142,9 @@ def update(i_frame):
 
 kw_video = dict(frames=arange(nb_time), interval=1000.0 / step_by_day / 2, blit=True)
 fig, ax, txt = start_ax(dpi=60)
-x_g_, y_g_ = arange(0 - step / 2, 36 + step / 2, step), arange(
-    28 - step / 2, 46 + step / 2, step
+x_g_, y_g_ = (
+    arange(0 - step / 2, 36 + step / 2, step),
+    arange(28 - step / 2, 46 + step / 2, step),
 )
 # pcolorfast will be faster than pcolormesh, we could use pcolorfast due to x and y are regular
 pcolormesh = ax.pcolorfast(x_g_, y_g_, lavd, **kw_vorticity)
@@ -158,11 +159,7 @@ def update(i_frame):
 # Format LAVD data
 lavd = RegularGridDataset.with_array(
     coordinates=("lon", "lat"),
-    datas=dict(
-        lavd=lavd.T,
-        lon=x_g,
-        lat=y_g,
-    ),
+    datas=dict(lavd=lavd.T, lon=x_g, lat=y_g,),
     centered=True,
 )
 

examples/07_cube_manipulation/pet_fsle_med.py

@@ -27,7 +27,7 @@
 # ADT in med
 # ----------
 # :py:meth:`~py_eddy_tracker.dataset.grid.GridCollection.from_netcdf_cube` method is
-# made for data stores in time cube, you could use also 
+# made for data stores in time cube, you could use also
 # :py:meth:`~py_eddy_tracker.dataset.grid.GridCollection.from_netcdf_list` method to
 # load data-cube from multiple file.
 c = GridCollection.from_netcdf_cube(

examples/16_network/pet_replay_segmentation.py

@@ -149,13 +149,7 @@ def get_obs(dataset):
 n_.median_filter(15, "time", "latitude")
 kw["s"] = (n_.radius_e * 1e-3) ** 2 / 30 ** 2 * 20
 m = n_.scatter_timeline(
-    ax,
-    "shape_error_e",
-    vmin=14,
-    vmax=70,
-    **kw,
-    yfield="lon",
-    method="all",
+    ax, "shape_error_e", vmin=14, vmax=70, **kw, yfield="lon", method="all",
 )
 ax.set_ylabel("Longitude")
 cb = update_axes(ax, m["scatter"])

examples/16_network/pet_segmentation_anim.py

@@ -96,8 +96,7 @@ def update(i_frame):
 
     indices_frames = INDICES[i_frame]
     mappable_CONTOUR.set_data(
-        e.contour_lon_e[indices_frames],
-        e.contour_lat_e[indices_frames],
+        e.contour_lon_e[indices_frames], e.contour_lat_e[indices_frames],
     )
     mappable_CONTOUR.set_color(cmap.colors[tr[indices_frames] % len(cmap.colors)])
     return (mappable_tracks,)

src/py_eddy_tracker/__init__.py

@@ -407,20 +407,14 @@ def parse_args(self, *args, **kwargs):
         nc_name="previous_cost",
         nc_type="float32",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Previous cost for previous observation",
-            comment="",
-        ),
+        nc_attr=dict(long_name="Previous cost for previous observation", comment="",),
     ),
     next_cost=dict(
         attr_name=None,
         nc_name="next_cost",
         nc_type="float32",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Next cost for next observation",
-            comment="",
-        ),
+        nc_attr=dict(long_name="Next cost for next observation", comment="",),
     ),
     n=dict(
         attr_name=None,
@@ -631,8 +625,7 @@ def parse_args(self, *args, **kwargs):
         nc_type="f4",
         nc_dims=("obs",),
         nc_attr=dict(
-            long_name="Log base 10 background chlorophyll",
-            units="Log(Chl/[mg/m^3])",
+            long_name="Log base 10 background chlorophyll", units="Log(Chl/[mg/m^3])",
         ),
     ),
     year=dict(

src/py_eddy_tracker/appli/eddies.py

@@ -367,8 +367,7 @@ def track(
 
     logger.info("Longer track saved have %d obs", c.nb_obs_by_tracks.max())
     logger.info(
-        "The mean length is %d observations for long track",
-        c.nb_obs_by_tracks.mean(),
+        "The mean length is %d observations for long track", c.nb_obs_by_tracks.mean(),
     )
 
     long_track.write_file(**kw_write)
@@ -378,14 +377,7 @@ def track(
 
 
 def get_group(
-    dataset1,
-    dataset2,
-    index1,
-    index2,
-    score,
-    invalid=2,
-    low=10,
-    high=60,
+    dataset1, dataset2, index1, index2, score, invalid=2, low=10, high=60,
 ):
     group1, group2 = dict(), dict()
     m_valid = (score * 100) >= invalid
@@ -494,8 +486,7 @@ def get_values(v, dataset):
             ]
 
     labels = dict(
-        high=f"{high:0.0f} <= high",
-        low=f"{invalid:0.0f} <= low < {low:0.0f}",
+        high=f"{high:0.0f} <= high", low=f"{invalid:0.0f} <= low < {low:0.0f}",
     )
 
     keys = [labels.get(key, key) for key in list(gr_ref.values())[0].keys()]

src/py_eddy_tracker/appli/network.py

@@ -76,9 +76,7 @@ def subset_network():
         help="Remove short dead end, first is for minimal obs number and second for minimal segment time to keep",
     )
     parser.add_argument(
-        "--remove_trash",
-        action="store_true",
-        help="Remove trash (network id == 0)",
+        "--remove_trash", action="store_true", help="Remove trash (network id == 0)",
     )
     parser.add_argument(
         "-p",

src/py_eddy_tracker/observations/network.py

@@ -677,13 +677,7 @@ def display_timeline(
         """
         self.only_one_network()
         j = 0
-        line_kw = dict(
-            ls="-",
-            marker="+",
-            markersize=6,
-            zorder=1,
-            lw=3,
-        )
+        line_kw = dict(ls="-", marker="+", markersize=6, zorder=1, lw=3,)
         line_kw.update(kwargs)
         mappables = dict(lines=list())
 
@@ -916,10 +910,7 @@ def event_map(self, ax, **kwargs):
         """Add the merging and splitting events to a map"""
         j = 0
         mappables = dict()
-        symbol_kw = dict(
-            markersize=10,
-            color="k",
-        )
+        symbol_kw = dict(markersize=10, color="k",)
         symbol_kw.update(kwargs)
         symbol_kw_split = symbol_kw.copy()
         symbol_kw_split["markersize"] += 4
@@ -948,13 +939,7 @@ def event_map(self, ax, **kwargs):
         return mappables
 
     def scatter(
-        self,
-        ax,
-        name="time",
-        factor=1,
-        ref=None,
-        edgecolor_cycle=None,
-        **kwargs,
+        self, ax, name="time", factor=1, ref=None, edgecolor_cycle=None, **kwargs,
     ):
         """
         This function scatters the path of each network, with the merging and splitting events

src/py_eddy_tracker/observations/observation.py

@@ -701,11 +701,7 @@ def load_file(cls, filename, **kwargs):
         .. code-block:: python
 
             kwargs_latlon_300 = dict(
-                include_vars=[
-                    "longitude",
-                    "latitude",
-                ],
-                indexs=dict(obs=slice(0, 300)),
+                include_vars=["longitude", "latitude",], indexs=dict(obs=slice(0, 300)),
             )
             small_dataset = TrackEddiesObservations.load_file(
                 filename, **kwargs_latlon_300
@@ -1677,7 +1673,8 @@ def write_file(
             handler = zarr.open(filename, "w")
             self.to_zarr(handler, **kwargs)
         else:
-            with Dataset(filename, "w", format="NETCDF4") as handler:
+            nc_format = kwargs.pop("format", "NETCDF4")
+            with Dataset(filename, "w", format=nc_format) as handler:
                 self.to_netcdf(handler, **kwargs)
 
     @property
@@ -1968,11 +1965,7 @@ def bins_stat(self, xname, bins=None, yname=None, method=None, mask=None):
 
     def format_label(self, label):
         t0, t1 = self.period
-        return label.format(
-            t0=t0,
-            t1=t1,
-            nb_obs=len(self),
-        )
+        return label.format(t0=t0, t1=t1, nb_obs=len(self),)
 
     def display(self, ax, ref=None, extern_only=False, intern_only=False, **kwargs):
         """Plot the speed and effective (dashed) contour of the eddies
@@ -2331,14 +2324,7 @@ def grid_count_pixel_in(
         x_, y_ = reduce_size(x_, y_)
         v = create_vertice(x_, y_)
         (x_start, x_stop), (y_start, y_stop) = bbox_indice_regular(
-            v,
-            x_bounds,
-            y_bounds,
-            xstep,
-            ystep,
-            N,
-            is_circular,
-            x_size,
+            v, x_bounds, y_bounds, xstep, ystep, N, is_circular, x_size,
         )
         i, j = get_pixel_in_regular(v, x_c, y_c, x_start, x_stop, y_start, y_stop)
         grid_count_(grid, i, j)