Merge remote-tracking branch 'upstream/master'

# Conflicts:
#	examples/02_eddy_identification/pet_display_id.py
#	examples/02_eddy_identification/pet_eddy_detection.py
#	examples/02_eddy_identification/pet_eddy_detection_gulf_stream.py
#	examples/02_eddy_identification/pet_filter_and_detection.py
#	notebooks/python_module/02_eddy_identification/pet_display_id.ipynb
#	notebooks/python_module/02_eddy_identification/pet_eddy_detection.ipynb
#	notebooks/python_module/02_eddy_identification/pet_eddy_detection_gulf_stream.ipynb
#	src/py_eddy_tracker/observations/observation.py

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
 
 
@@ -42,7 +42,7 @@ 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")
 ax = start_axes("U/V deduce from ADT (m)")
@@ -82,7 +82,7 @@ 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)
@@ -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)")

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,

notebooks/python_module/02_eddy_identification/pet_eddy_detection.ipynb

@@ -37,7 +37,7 @@
       },
       "outputs": [],
       "source": [
-        "def start_axes(title):\n    fig = plt.figure(figsize=(13, 5))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])\n    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight='bold')\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid()\n    if mappable:\n        plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
+        "def start_axes(title):\n    fig = plt.figure(figsize=(13, 5))\n    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])\n    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n    ax.set_aspect(\"equal\")\n    ax.set_title(title, weight=\"bold\")\n    return ax\n\n\ndef update_axes(ax, mappable=None):\n    ax.grid()\n    if mappable:\n        plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))"
       ]
     },
     {
@@ -217,14 +217,14 @@
       },
       "outputs": [],
       "source": [
-        "ax = start_axes(\"ADT rejected contours containing eddies\")\ng.contours.label_contour_unused_which_contain_eddies(a)\ng.contours.label_contour_unused_which_contain_eddies(c)\ng.contours.display(\n    ax, only_contain_eddies=True, color=\"k\", lw=1, label=\"Could be a contour of interaction\"\n)\na.display(ax, color=\"r\", linewidth=0.75, label=\"Anticyclonic\", ref=-10)\nc.display(ax, color=\"b\", linewidth=0.75, label=\"Cyclonic\", ref=-10)\nax.legend()\nupdate_axes(ax)"
+        "ax = start_axes(\"ADT rejected contours containing eddies\")\ng.contours.label_contour_unused_which_contain_eddies(a)\ng.contours.label_contour_unused_which_contain_eddies(c)\ng.contours.display(\n    ax,\n    only_contain_eddies=True,\n    color=\"k\",\n    lw=1,\n    label=\"Could be a contour of interaction\",\n)\na.display(ax, color=\"r\", linewidth=0.75, label=\"Anticyclonic\", ref=-10)\nc.display(ax, color=\"b\", linewidth=0.75, label=\"Cyclonic\", ref=-10)\nax.legend()\nupdate_axes(ax)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Output\nWhen 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\n\n"
+        "## Output\nWhen displaying the detected eddies, dashed lines are for effective contour, solide lines for the contour of\nthe maximum mean speed. See figure 1 of https://doi.org/10.1175/JTECH-D-14-00019.1\n\n"
       ]
     },
     {
@@ -253,7 +253,7 @@
       },
       "outputs": [],
       "source": [
-        "ax = start_axes(\"Speed Radius (km)\")\na.scatter(ax, \"radius_s\", vmin=10, vmax=50, s=80, ref=-10, cmap=\"magma_r\", factor=0.001)\nm = c.scatter(ax, \"radius_s\", vmin=10, vmax=50, s=80, ref=-10, cmap=\"magma_r\", factor=0.001)\nupdate_axes(ax, m)"
+        "ax = start_axes(\"Speed Radius (km)\")\na.scatter(ax, \"radius_s\", vmin=10, vmax=50, s=80, ref=-10, cmap=\"magma_r\", factor=0.001)\nm = c.scatter(\n    ax, \"radius_s\", vmin=10, vmax=50, s=80, ref=-10, cmap=\"magma_r\", factor=0.001\n)\nupdate_axes(ax, m)"
       ]
     },
     {
@@ -271,7 +271,7 @@
       },
       "outputs": [],
       "source": [
-        "ax = start_axes(\"Effective Radius (km)\")\nkwargs = dict(vmin=10, vmax=80, cmap=\"magma_r\", factor=0.001, lut=14, ref=-10)\na.filled(ax, \"radius_e\", **kwargs)\nm = c.filled(\n    ax, \"radius_e\", vmin=10, vmax=80, cmap=\"magma_r\", factor=0.001, lut=14, ref=-10\n)\nupdate_axes(ax, m)"
+        "ax = start_axes(\"Effective Radius (km)\")\nkwargs = dict(vmin=10, vmax=80, cmap=\"magma_r\", factor=0.001, lut=14, ref=-10)\na.filled(ax, \"effective_radius\", **kwargs)\nm = c.filled(\n    ax, \"radius_e\", vmin=10, vmax=80, cmap=\"magma_r\", factor=0.001, lut=14, ref=-10\n)\nupdate_axes(ax, m)"
       ]
     }
   ],
@@ -291,7 +291,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.6"
+      "version": "3.7.7"
     }
   },
   "nbformat": 4,

notebooks/python_module/02_eddy_identification/pet_eddy_detection_gulf_stream.ipynb

@@ -217,14 +217,14 @@
       },
       "outputs": [],
       "source": [
-        "ax = start_axes(\"ADT rejected contours containing eddies\")\ng.contours.label_contour_unused_which_contain_eddies(a)\ng.contours.label_contour_unused_which_contain_eddies(c)\ng.contours.display(\n    ax, only_contain_eddies=True, color=\"k\", lw=1, label=\"Could be a contour of interaction\"\n)\na.display(ax, color=\"r\", linewidth=0.75, label=\"Anticyclonic\", ref=-10)\nc.display(ax, color=\"b\", linewidth=0.75, label=\"Cyclonic\", ref=-10)\nax.legend()\nupdate_axes(ax)"
+        "ax = start_axes(\"ADT rejected contours containing eddies\")\ng.contours.label_contour_unused_which_contain_eddies(a)\ng.contours.label_contour_unused_which_contain_eddies(c)\ng.contours.display(\n    ax,\n    only_contain_eddies=True,\n    color=\"k\",\n    lw=1,\n    label=\"Could be a contour of interaction\",\n)\na.display(ax, color=\"r\", linewidth=0.75, label=\"Anticyclonic\", ref=-10)\nc.display(ax, color=\"b\", linewidth=0.75, label=\"Cyclonic\", ref=-10)\nax.legend()\nupdate_axes(ax)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Output\nWhen 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\n\n"
+        "## Output\nWhen displaying the detected eddies, dashed lines are for effective contour, solide lines for the contour of the\nmaximum mean speed. See figure 1 of https://doi.org/10.1175/JTECH-D-14-00019.1\n\n"
       ]
     },
     {
@@ -273,7 +273,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.6"
+      "version": "3.7.7"
     }
   },
   "nbformat": 4,

src/py_eddy_tracker/observations/observation.py

@@ -425,7 +425,7 @@ def reset(self):
 
     @property
     def obs(self):
-        """Return an array observations.
+        """Return observations.
         """
         return self.observations
 
@@ -986,13 +986,13 @@ def mask_function(self, other, distance):
 
     @staticmethod
     def cost_function(records_in, records_out, distance):
-        """Return the cost function between two obs
+        """Return the cost function between two obs.
 
         .. math::
 
-            cost = \sqrt{ ( {Amp_{_{in}} - Amp_{_{out}} \over Amp_{_{in}} } ) ^2 +
-              ( {Rspeed_{_{in}} - Rspeed_{_{out}} \over Rspeed_{_{in}} }) ^2 + 
-              ( {distance \over 125} ) ^2
+            cost = \sqrt{({Amp_{_{in}} - Amp_{_{out}} \over Amp_{_{in}}}) ^2 +
+              ({Rspeed_{_{in}} - Rspeed_{_{out}} \over Rspeed_{_{in}}}) ^2 + 
+              ({distance \over 125}) ^2
             }
 
         :param records_in: starting observations
@@ -1480,15 +1480,14 @@ def extract_with_area(self, area, **kwargs):
         Extract geographically with a bounding box.
 
         :param dict area: 4 coordinates in a dictionary to specify bounding box (lower left corner and upper right corner)
+        :param dict kwargs: look at :py:meth:`extract_with_mask`
+        :return: Return all eddy tracks which are in bounds
+        :rtype: EddiesObservations
+
         .. code-block:: python
 
             area = dict(llcrnrlon=x0, llcrnrlat=y0, urcrnrlon=x1, urcrnrlat=y1)
 
-	:param dict kwargs: look at :py:meth:`extract_with_mask`
-        :return: Return all eddy tracks which are in bounds
-        :rtype: EddiesObservations
-
-
         .. minigallery:: py_eddy_tracker.EddiesObservations.extract_with_area
         """
         mask = (self.latitude > area["llcrnrlat"]) * (self.latitude < area["urcrnrlat"])