Solve docstring python example issue

Author: AntSimi

examples/02_eddy_identification/pet_display_id.py

@@ -14,18 +14,18 @@
 c = EddiesObservations.load_file(data.get_path("Cyclonic_20190223.nc"))
 
 # %%
-# Fill effective contour with amplitude 
+# Fill effective contour with amplitude
 fig = plt.figure(figsize=(15, 8))
 ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])
 ax.set_aspect("equal")
 ax.set_xlim(0, 140)
 ax.set_ylim(-80, 0)
 kwargs = dict(extern_only=True, color="k", lw=1)
 a.display(ax, **kwargs), c.display(ax, **kwargs)
-a.filled(ax, "amplitude", cmap="magma_r", vmin=0, vmax=.5)
-m = c.filled(ax, "amplitude", cmap="magma_r", vmin=0, vmax=.5)
+a.filled(ax, "amplitude", cmap="magma_r", vmin=0, vmax=0.5)
+m = c.filled(ax, "amplitude", cmap="magma_r", vmin=0, vmax=0.5)
 colorbar = plt.colorbar(m, cax=ax.figure.add_axes([0.95, 0.03, 0.02, 0.94]))
-colorbar.set_label('Amplitude (m)')
+colorbar.set_label("Amplitude (m)")
 
 # %%
 # Draw speed contours

examples/02_eddy_identification/pet_eddy_detection.py

@@ -20,7 +20,7 @@ def start_axes(title):
     ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])
     ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
     ax.set_aspect("equal")
-    ax.set_title(title, weight='bold')
+    ax.set_title(title, weight="bold")
     return ax
 
 
@@ -113,7 +113,11 @@ def update_axes(ax, mappable=None):
 g.contours.label_contour_unused_which_contain_eddies(a)
 g.contours.label_contour_unused_which_contain_eddies(c)
 g.contours.display(
-    ax, only_contain_eddies=True, color="k", lw=1, label="Could be a contour of interaction"
+    ax,
+    only_contain_eddies=True,
+    color="k",
+    lw=1,
+    label="Could be a contour of interaction",
 )
 a.display(ax, color="r", linewidth=0.75, label="Anticyclonic", ref=-10)
 c.display(ax, color="b", linewidth=0.75, label="Cyclonic", ref=-10)
@@ -123,7 +127,8 @@ def update_axes(ax, mappable=None):
 # %%
 # Output
 # ------
-# When displaying the detected eddies, dashed lines are for effective contour, solide lines for the contour of the maximum mean speed. See figure 1 of https://doi.org/10.1175/JTECH-D-14-00019.1
+# When displaying the detected eddies, dashed lines are for effective contour, solide lines for the contour of
+# the maximum mean speed. See figure 1 of https://doi.org/10.1175/JTECH-D-14-00019.1
 
 ax = start_axes("Detected Eddies")
 a.display(ax, color="r", linewidth=0.75, label="Anticyclonic", ref=-10)
@@ -135,7 +140,9 @@ def update_axes(ax, mappable=None):
 # Display the speed radius of the detected eddies
 ax = start_axes("Speed Radius (km)")
 a.scatter(ax, "radius_s", vmin=10, vmax=50, s=80, ref=-10, cmap="magma_r", factor=0.001)
-m = c.scatter(ax, "radius_s", vmin=10, vmax=50, s=80, ref=-10, cmap="magma_r", factor=0.001)
+m = c.scatter(
+    ax, "radius_s", vmin=10, vmax=50, s=80, ref=-10, cmap="magma_r", factor=0.001
+)
 update_axes(ax, m)
 
 # %%

examples/02_eddy_identification/pet_eddy_detection_gulf_stream.py

@@ -54,7 +54,7 @@ def update_axes(ax, mappable=None):
 
 # %%
 # Get geostrophic speed u,v
-# -------
+# -------------------------
 # U/V are deduced from ADT, this algortihm is not ok near the equator (~+- 2°)
 g.add_uv("adt")
 
@@ -91,7 +91,7 @@ def update_axes(ax, mappable=None):
 
 # %%
 # Post analysis
-# ------------
+# -------------
 # Contours can be rejected for several reasons (shape error to high, several extremum in contour, ...)
 ax = start_axes("ADT rejected contours")
 g.contours.display(ax, only_unused=True, lw=0.25)
@@ -123,7 +123,11 @@ def update_axes(ax, mappable=None):
 g.contours.label_contour_unused_which_contain_eddies(a)
 g.contours.label_contour_unused_which_contain_eddies(c)
 g.contours.display(
-    ax, only_contain_eddies=True, color="k", lw=1, label="Could be a contour of interaction"
+    ax,
+    only_contain_eddies=True,
+    color="k",
+    lw=1,
+    label="Could be a contour of interaction",
 )
 a.display(ax, color="r", linewidth=0.75, label="Anticyclonic", ref=-10)
 c.display(ax, color="b", linewidth=0.75, label="Cyclonic", ref=-10)
@@ -133,7 +137,8 @@ def update_axes(ax, mappable=None):
 # %%
 # Output
 # ------
-# When displaying the detected eddies, dashed lines are for effective contour, solide lines for the contour of the maximum mean speed. See figure 1 of https://doi.org/10.1175/JTECH-D-14-00019.1
+# When displaying the detected eddies, dashed lines are for effective contour, solide lines for the contour of the
+# maximum mean speed. See figure 1 of https://doi.org/10.1175/JTECH-D-14-00019.1
 
 ax = start_axes("Eddies detected")
 a.display(ax, color="r", linewidth=0.75, label="Anticyclonic", ref=-10)

examples/02_eddy_identification/pet_filter_and_detection.py

@@ -57,7 +57,7 @@ def update_axes(ax, mappable=None):
 
 # %%
 # Amplitude and Speed Radius distributions
-# -----------------------
+# ----------------------------------------
 fig = plt.figure(figsize=(12, 5))
 ax_a = fig.add_subplot(121, xlabel="Amplitude (cm)")
 ax_r = fig.add_subplot(122, xlabel="Speed Radius (km)")

examples/02_eddy_identification/pet_radius_vs_area.py

@@ -32,7 +32,7 @@
 ax.grid()
 ax.set_xlabel("Speed radius computed with fit_circle")
 ax.set_ylabel("Radius deduced from area\nof contour_lon_s/contour_lat_s")
-ax.set_title('Area vs radius')
+ax.set_title("Area vs radius")
 ax.plot(a["radius_s"] / 1000.0, (areas / pi) ** 0.5 / 1000.0, ".")
 ax.plot((0, 250), (0, 250), "r")
 
@@ -46,4 +46,4 @@
 ax.set_xlabel("Radius ratio")
 ax.set_ylabel("Shape error")
 ax.set_title("err = f(radius_ratio)")
-ax.plot(a["radius_s"] / (areas / pi) ** .5 , a['shape_error_s'], ".")
+ax.plot(a["radius_s"] / (areas / pi) ** 0.5, a["shape_error_s"], ".")

examples/02_eddy_identification/pet_sla_and_adt.py

@@ -133,8 +133,18 @@ def update_axes(ax, mappable=None):
     ax.set_xlabel("Absolute Dynamic Topography")
     ax.set_ylabel("Sea Level Anomaly")
 
-    ax.plot(a_adt[field][i_a_adt] * factor, a_sla[field][i_a_sla] * factor, "r.", label='Anticyclonic')
-    ax.plot(c_adt[field][i_c_adt] * factor, c_sla[field][i_c_sla] * factor, "b.", label='Cyclonic')
+    ax.plot(
+        a_adt[field][i_a_adt] * factor,
+        a_sla[field][i_a_sla] * factor,
+        "r.",
+        label="Anticyclonic",
+    )
+    ax.plot(
+        c_adt[field][i_c_adt] * factor,
+        c_sla[field][i_c_sla] * factor,
+        "b.",
+        label="Cyclonic",
+    )
     ax.set_aspect("equal"), ax.grid()
     ax.plot((0, 1000), (0, 1000), "g")
     ax.set_xlim(0, stop), ax.set_ylim(0, stop)

examples/06_grid_manipulation/pet_hide_pixel_out_eddies.py

@@ -34,7 +34,7 @@
 # We will used the outter contour
 x_name, y_name = a.intern(False)
 adt = g.grid("adt")
-mask = ones(adt.shape, dtype='bool')
+mask = ones(adt.shape, dtype="bool")
 for eddy in a:
     i, j = Path(create_vertice(eddy[x_name], eddy[y_name])).pixels_in(g)
     mask[i, j] = False

examples/10_tracking_diagnostics/pet_center_count.py

@@ -58,7 +58,9 @@
 cb.set_label("Eddies by 1°^2 by day")
 
 g_c.vars["count"] = ratio
-m = g_c.display(ax_ratio, name="count", vmin=0.1, vmax=10, norm=LogNorm(), cmap='coolwarm_r')
+m = g_c.display(
+    ax_ratio, name="count", vmin=0.1, vmax=10, norm=LogNorm(), cmap="coolwarm_r"
+)
 plt.colorbar(m, cax=fig.add_axes([0.94, 0.02, 0.01, 0.2]))
 
 for ax in (ax_a, ax_c, ax_all, ax_ratio):

examples/10_tracking_diagnostics/pet_pixel_used.py

@@ -54,7 +54,9 @@
 plt.colorbar(m, cax=fig.add_axes([0.95, 0.27, 0.01, 0.7]))
 
 g_c.vars["count"] = ratio
-m = g_c.display(ax_ratio, name="count", vmin=0.1, vmax=10, norm=LogNorm(), cmap='coolwarm_r')
+m = g_c.display(
+    ax_ratio, name="count", vmin=0.1, vmax=10, norm=LogNorm(), cmap="coolwarm_r"
+)
 plt.colorbar(m, cax=fig.add_axes([0.95, 0.02, 0.01, 0.2]))
 
 for ax in (ax_a, ax_c, ax_all, ax_ratio):

notebooks/python_module/02_eddy_identification/pet_display_id.ipynb

@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Fill effective contour with amplitude \n\n"
+        "Fill effective contour with amplitude\n\n"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "fig = plt.figure(figsize=(15, 8))\nax = fig.add_axes([0.03, 0.03, 0.90, 0.94])\nax.set_aspect(\"equal\")\nax.set_xlim(0, 140)\nax.set_ylim(-80, 0)\nkwargs = dict(extern_only=True, color=\"k\", lw=1)\na.display(ax, **kwargs), c.display(ax, **kwargs)\na.filled(ax, \"amplitude\", cmap=\"magma_r\", vmin=0, vmax=.5)\nm = c.filled(ax, \"amplitude\", cmap=\"magma_r\", vmin=0, vmax=.5)\ncolorbar = plt.colorbar(m, cax=ax.figure.add_axes([0.95, 0.03, 0.02, 0.94]))\ncolorbar.set_label('Amplitude (m)')"
+        "fig = plt.figure(figsize=(15, 8))\nax = fig.add_axes([0.03, 0.03, 0.90, 0.94])\nax.set_aspect(\"equal\")\nax.set_xlim(0, 140)\nax.set_ylim(-80, 0)\nkwargs = dict(extern_only=True, color=\"k\", lw=1)\na.display(ax, **kwargs), c.display(ax, **kwargs)\na.filled(ax, \"amplitude\", cmap=\"magma_r\", vmin=0, vmax=0.5)\nm = c.filled(ax, \"amplitude\", cmap=\"magma_r\", vmin=0, vmax=0.5)\ncolorbar = plt.colorbar(m, cax=ax.figure.add_axes([0.95, 0.03, 0.02, 0.94]))\ncolorbar.set_label(\"Amplitude (m)\")"
       ]
     },
     {
@@ -129,7 +129,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.6"
+      "version": "3.7.7"
     }
   },
   "nbformat": 4,