english correction

examples/02_eddy_identification/pet_statistics_on_identification.py

@@ -40,7 +40,7 @@ def update_axes(ax, mappable=None):
 )[:365]
 
 # %%
-# Merge all identification dataset in one object
+# Merge all identification datasets in one object
 all_a = EddiesObservations.concatenate(
     [EddiesObservations.load_file(i) for i in file_objects]
 )
@@ -50,14 +50,14 @@ def update_axes(ax, mappable=None):
 x0, x1, y0, y1 = 15, 20, 33, 38
 xs = np.array([[x0, x1, x1, x0, x0]], dtype="f8")
 ys = np.array([[y0, y0, y1, y1, y0]], dtype="f8")
-# Polygon object is create to be usable by match function.
+# Polygon object created for the match function use.
 polygon = dict(contour_lon_e=xs, contour_lat_e=ys, contour_lon_s=xs, contour_lat_s=ys)
 
 # %%
 # Geographic frequency of eddies
 step = 0.125
 ax = start_axes("")
-# Count pixel used for each contour
+# Count pixel encompassed in each contour
 g_a = all_a.grid_count(bins=((-10, 37, step), (30, 46, step)), intern=True)
 m = g_a.display(
     ax, cmap="terrain_r", vmin=0, vmax=0.75, factor=1 / all_a.nb_days, name="count"
@@ -66,7 +66,7 @@ def update_axes(ax, mappable=None):
 update_axes(ax, m)
 
 # %%
-# We use match function to count number of eddies which intersect the polygon defined previously.
+# We use the match function to count the number of eddies that intersect the polygon defined previously
 # `p1_area` option allow to get in c_e/c_s output, precentage of area occupy by eddies in the polygon.
 i_e, j_e, c_e = all_a.match(polygon, p1_area=True, intern=False)
 i_s, j_s, c_s = all_a.match(polygon, p1_area=True, intern=True)
@@ -79,22 +79,22 @@ def update_axes(ax, mappable=None):
 # translate julian day in datetime64
 t = all_a.time.astype("datetime64") - dt
 # %%
-# Count how many are in polygon
+# Number of eddies within a polygon
 ax = plt.figure(figsize=(12, 6)).add_subplot(111)
-ax.set_title("Different way to count eddies presence in a polygon")
+ax.set_title("Different ways to count eddies within a polygon")
 ax.set_ylabel("Count")
 m = all_a.mask_from_polygons(((xs, ys),))
-ax.hist(t[m], label="center in polygon", **kw_hist)
-ax.hist(t[i_s[c_s > 0]], label="intersect speed contour with polygon", **kw_hist)
-ax.hist(t[i_e[c_e > 0]], label="intersect extern contour with polygon", **kw_hist)
+ax.hist(t[m], label="Eddy Center in polygon", **kw_hist)
+ax.hist(t[i_s[c_s > 0]], label="Intersection Speed contour and polygon", **kw_hist)
+ax.hist(t[i_e[c_e > 0]], label="Intersection Effective contour and polygon", **kw_hist)
 ax.legend()
 ax.set_xlim(np.datetime64("2010"), np.datetime64("2011"))
 ax.grid()
 
 # %%
-# Percent of are of interest occupy by eddies
+# Percent of the area of interest occupied by eddies.
 ax = plt.figure(figsize=(12, 6)).add_subplot(111)
-ax.set_title("Percent of polygon occupy by an anticyclonic eddy")
+ax.set_title("Percent of polygon occupied by an anticyclonic eddy")
 ax.set_ylabel("Percent of polygon")
 ax.hist(t[i_s[c_s > 0]], weights=c_s[c_s > 0] * 100.0, label="speed contour", **kw_hist)
 ax.hist(