Merge pull request AntSimi#101 from CoriPegliasco/advection

modify coherence_analysis and basic changes

Author: ludwigVonKoopa

examples/02_eddy_identification/pet_eddy_detection_ACC.py

@@ -65,7 +65,8 @@ 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)
@@ -187,10 +188,16 @@ 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

@@ -159,7 +159,11 @@ 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/16_network/pet_atlas.py

@@ -153,33 +153,33 @@ def update_axes(ax, mappable=None):
 update_axes(ax, m).set_label("Pixel used in % all atlas")
 
 # %%
-# All Spliting
-# ------------
-# Display the occurence of spliting events
+# All splitting
+# -------------
+# Display the occurence of splitting events
 ax = start_axes("")
-g_all_spliting = n.spliting_event().grid_count(bins)
-m = g_all_spliting.display(ax, **kw_time, vmin=0, vmax=1)
+g_all_splitting = n.splitting_event().grid_count(bins)
+m = g_all_splitting.display(ax, **kw_time, vmin=0, vmax=1)
 update_axes(ax, m).set_label("Pixel used in % of time")
 
 # %%
-# Ratio spliting events / eddy presence
+# Ratio splitting events / eddy presence
 ax = start_axes("")
-g_ = g_all_spliting.vars["count"] * 100.0 / g_all.vars["count"]
-m = g_all_spliting.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
+g_ = g_all_splitting.vars["count"] * 100.0 / g_all.vars["count"]
+m = g_all_splitting.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
 update_axes(ax, m).set_label("Pixel used in % all atlas")
 
 # %%
-# Spliting in networks longer than 10 days
-# ----------------------------------------
+# splitting in networks longer than 10 days
+# -----------------------------------------
 ax = start_axes("")
-g_10_spliting = n10.spliting_event().grid_count(bins)
-m = g_10_spliting.display(ax, **kw_time, vmin=0, vmax=1)
+g_10_splitting = n10.splitting_event().grid_count(bins)
+m = g_10_splitting.display(ax, **kw_time, vmin=0, vmax=1)
 update_axes(ax, m).set_label("Pixel used in % of time")
 # %%
 ax = start_axes("")
 g_ = ma.array(
-    g_10_spliting.vars["count"] * 100.0 / g_10.vars["count"],
+    g_10_splitting.vars["count"] * 100.0 / g_10.vars["count"],
     mask=g_10.vars["count"] < 365,
 )
-m = g_10_spliting.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
+m = g_10_splitting.display(ax, **kw_ratio, vmin=0, vmax=5, name=g_)
 update_axes(ax, m).set_label("Pixel used in % all atlas")

examples/16_network/pet_follow_particle.py

@@ -125,9 +125,11 @@ def update(frame):
 # %%
 # Particle advection
 # ^^^^^^^^^^^^^^^^^^
+# Advection from speed contour to speed contour (default)
+
 step = 1 / 60.0
 
-t_start, t_end = n.period
+t_start, t_end = int(n.period[0]), int(n.period[1])
 dt = 14
 
 shape = (n.obs.size, 2)

examples/16_network/pet_relative.py

@@ -292,13 +292,13 @@
 m1
 
 # %%
-# Get spliting event
-# ------------------
+# Get splitting event
+# -------------------
 # Display the position of the eddies before a splitting
 fig = plt.figure(figsize=(15, 8))
 ax = fig.add_axes([0.04, 0.06, 0.90, 0.88], projection=GUI_AXES)
 n.plot(ax, color_cycle=n.COLORS)
-s0, s1, s1_start = n.spliting_event(triplet=True)
+s0, s1, s1_start = n.splitting_event(triplet=True)
 s0.display(ax, color="violet", lw=2, label="Eddies before splitting")
 s1.display(ax, color="blueviolet", lw=2, label="Eddies after splitting")
 s1_start.display(ax, color="black", lw=2, label="Eddies starting by splitting")

examples/16_network/pet_replay_segmentation.py

@@ -149,7 +149,13 @@ 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

@@ -96,7 +96,8 @@ 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,)

requirements.txt

@@ -1,7 +1,7 @@
 matplotlib
 netCDF4
 numba>=0.53
-numpy
+numpy<1.21
 opencv-python
 pint
 polygon3

src/py_eddy_tracker/__init__.py

@@ -404,7 +404,7 @@ def identify_time(str_date):
         nc_dims=("obs",),
         nc_attr=dict(
             long_name="Previous observation index",
-            comment="Index of previous observation in a spliting case",
+            comment="Index of previous observation in a splitting case",
         ),
     ),
     next_obs=dict(
@@ -422,14 +422,20 @@ def identify_time(str_date):
         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,
@@ -640,7 +646,8 @@ def identify_time(str_date):
         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

@@ -243,7 +243,8 @@ def browse_dataset_in(
         filenames = bytes_(glob(full_path))
 
     dataset_list = empty(
-        len(filenames), dtype=[("filename", "S500"), ("date", "datetime64[s]")],
+        len(filenames),
+        dtype=[("filename", "S500"), ("date", "datetime64[s]")],
     )
     dataset_list["filename"] = filenames
 
@@ -371,7 +372,8 @@ 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)
@@ -381,7 +383,14 @@ 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
@@ -490,7 +499,8 @@ 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()]