Give direct access to variable for eddies dataset i.e. : obj.amplitude return amplitude

Could use extern and intern name

Author: AntSimi

examples/02_eddy_identification/pet_eddy_detection.py

@@ -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)
@@ -149,7 +149,7 @@ def update_axes(ax, mappable=None):
 # Filling the effective radius contours with the effective radius values
 ax = start_axes("Effective Radius (km)")
 kwargs = dict(vmin=10, vmax=80, cmap="magma_r", factor=0.001, lut=14, ref=-10)
-a.filled(ax, "radius_e", **kwargs)
+a.filled(ax, "effective_radius", **kwargs)
 m = c.filled(
     ax, "radius_e", vmin=10, vmax=80, cmap="magma_r", factor=0.001, lut=14, ref=-10
 )

examples/06_grid_manipulation/pet_okubo_weiss.py

@@ -62,7 +62,6 @@ def update_axes(axes, mappable=None):
 v_y = g.compute_stencil(g.grid("vgos"), vertical=True)
 ow = g.vars["ow"] = (u_x - v_y) ** 2 + (v_x + u_y) ** 2 - (v_x - u_y) ** 2
 
-
 ax = start_axes("Okubo weis")
 m = g.display(ax, "ow", vmin=-1e-10, vmax=1e-10, cmap="bwr")
 update_axes(ax, m)
@@ -84,7 +83,6 @@ def update_axes(axes, mappable=None):
 a.display(ax, **kw_ed), c.display(ax, **kw_ed)
 update_axes(ax, m)
 
-
 # %%
 # Get okubo-weiss mean/min/center in eddies
 plt.figure(figsize=(8, 6))

examples/10_tracking_diagnostics/pet_histo.py

@@ -25,7 +25,7 @@
 
 for x0, name, title, xmax, factor, bins in zip(
     (0.4, 0.72, 0.08),
-    ("radius_s", "speed_average", "amplitude"),
+    ("speed_radius", "speed_average", "amplitude"),
     ("Speed radius (km)", "Speed average (cm/s)", "Amplitude (cm)"),
     (100, 50, 20),
     (0.001, 100, 100),

notebooks/python_module/02_eddy_identification/pet_eddy_detection.ipynb

@@ -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)"
       ]
     }
   ],

notebooks/python_module/06_grid_manipulation/pet_okubo_weiss.ipynb

@@ -91,7 +91,7 @@
       },
       "outputs": [],
       "source": [
-        "u_x = g.compute_stencil(g.grid(\"ugos\"))\nu_y = g.compute_stencil(g.grid(\"ugos\"), vertical=True)\nv_x = g.compute_stencil(g.grid(\"vgos\"))\nv_y = g.compute_stencil(g.grid(\"vgos\"), vertical=True)\now = g.vars[\"ow\"] = (u_x - v_y) ** 2 + (v_x + u_y) ** 2 - (v_x - u_y) ** 2\n\n\nax = start_axes(\"Okubo weis\")\nm = g.display(ax, \"ow\", vmin=-1e-10, vmax=1e-10, cmap=\"bwr\")\nupdate_axes(ax, m)"
+        "u_x = g.compute_stencil(g.grid(\"ugos\"))\nu_y = g.compute_stencil(g.grid(\"ugos\"), vertical=True)\nv_x = g.compute_stencil(g.grid(\"vgos\"))\nv_y = g.compute_stencil(g.grid(\"vgos\"), vertical=True)\now = g.vars[\"ow\"] = (u_x - v_y) ** 2 + (v_x + u_y) ** 2 - (v_x - u_y) ** 2\n\nax = start_axes(\"Okubo weis\")\nm = g.display(ax, \"ow\", vmin=-1e-10, vmax=1e-10, cmap=\"bwr\")\nupdate_axes(ax, m)"
       ]
     },
     {

notebooks/python_module/10_tracking_diagnostics/pet_histo.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "fig = plt.figure(figsize=(12, 7))\n\nfor x0, name, title, xmax, factor, bins in zip(\n    (0.4, 0.72, 0.08),\n    (\"radius_s\", \"speed_average\", \"amplitude\"),\n    (\"Speed radius (km)\", \"Speed average (cm/s)\", \"Amplitude (cm)\"),\n    (100, 50, 20),\n    (0.001, 100, 100),\n    (arange(0, 2000, 1), arange(0, 1000, 0.5), arange(0.0005, 1000, 0.2)),\n):\n    ax_hist = fig.add_axes((x0, 0.24, 0.27, 0.35))\n    nb_a, _, _ = ax_hist.hist(a[name] * factor, bins=bins, **kwargs_a)\n    nb_c, _, _ = ax_hist.hist(c[name] * factor, bins=bins, **kwargs_c)\n    ax_hist.set_xticklabels([])\n    ax_hist.set_xlim(0, xmax)\n    ax_hist.grid()\n\n    ax_cum = fig.add_axes((x0, 0.62, 0.27, 0.35))\n    ax_cum.hist(a[name] * factor, bins=bins, cumulative=-1, **kwargs_a)\n    ax_cum.hist(c[name] * factor, bins=bins, cumulative=-1, **kwargs_c)\n    ax_cum.set_xticklabels([])\n    ax_cum.set_title(title)\n    ax_cum.set_xlim(0, xmax)\n    ax_cum.set_ylim(0, 1)\n    ax_cum.grid()\n\n    ax_ratio = fig.add_axes((x0, 0.06, 0.27, 0.15))\n    ax_ratio.set_xlim(0, xmax)\n    ax_ratio.set_ylim(0, 2)\n    ax_ratio.plot((bins[1:] + bins[:-1]) / 2, nb_c / nb_a)\n    ax_ratio.axhline(1, color=\"k\")\n    ax_ratio.grid()\n    ax_ratio.set_xlabel(title)\n\nax_cum.set_ylabel(\"Cumulative\\npercent distribution\")\nax_hist.set_ylabel(\"Percent of observations\")\nax_ratio.set_ylabel(\"Ratio percent\\nCyc/Acyc\")\nax_cum.legend()"
+        "fig = plt.figure(figsize=(12, 7))\n\nfor x0, name, title, xmax, factor, bins in zip(\n    (0.4, 0.72, 0.08),\n    (\"speed_radius\", \"speed_average\", \"amplitude\"),\n    (\"Speed radius (km)\", \"Speed average (cm/s)\", \"Amplitude (cm)\"),\n    (100, 50, 20),\n    (0.001, 100, 100),\n    (arange(0, 2000, 1), arange(0, 1000, 0.5), arange(0.0005, 1000, 0.2)),\n):\n    ax_hist = fig.add_axes((x0, 0.24, 0.27, 0.35))\n    nb_a, _, _ = ax_hist.hist(a[name] * factor, bins=bins, **kwargs_a)\n    nb_c, _, _ = ax_hist.hist(c[name] * factor, bins=bins, **kwargs_c)\n    ax_hist.set_xticklabels([])\n    ax_hist.set_xlim(0, xmax)\n    ax_hist.grid()\n\n    ax_cum = fig.add_axes((x0, 0.62, 0.27, 0.35))\n    ax_cum.hist(a[name] * factor, bins=bins, cumulative=-1, **kwargs_a)\n    ax_cum.hist(c[name] * factor, bins=bins, cumulative=-1, **kwargs_c)\n    ax_cum.set_xticklabels([])\n    ax_cum.set_title(title)\n    ax_cum.set_xlim(0, xmax)\n    ax_cum.set_ylim(0, 1)\n    ax_cum.grid()\n\n    ax_ratio = fig.add_axes((x0, 0.06, 0.27, 0.15))\n    ax_ratio.set_xlim(0, xmax)\n    ax_ratio.set_ylim(0, 2)\n    ax_ratio.plot((bins[1:] + bins[:-1]) / 2, nb_c / nb_a)\n    ax_ratio.axhline(1, color=\"k\")\n    ax_ratio.grid()\n    ax_ratio.set_xlabel(title)\n\nax_cum.set_ylabel(\"Cumulative\\npercent distribution\")\nax_hist.set_ylabel(\"Percent of observations\")\nax_ratio.set_ylabel(\"Ratio percent\\nCyc/Acyc\")\nax_cum.legend()"
       ]
     }
   ],

src/py_eddy_tracker/observations/observation.py

@@ -159,27 +159,11 @@ def __init__(
                 raise Exception("Unknown element : %s" % elt)
         self.observations = zeros(size, dtype=self.dtype)
         self.sign_type = None
-
-    @property
-    def longitude(self):
-        return self.observations["lon"]
-
-    @property
-    def latitude(self):
-        return self.observations["lat"]
-
-    @property
-    def time(self):
-        return self.observations["time"]
-
+ 
     @property
     def tracks(self):
         return self.observations["track"]
 
-    @property
-    def observation_number(self):
-        return self.observations["n"]
-
     @property
     def sign_legend(self):
         return "Cyclonic" if self.sign_type != 1 else "Anticyclonic"
@@ -267,11 +251,35 @@ def __repr__(self):
         Mean effective radius (km): {self.box_display(self.hist('radius_e', 'lat', bins_lat, mean=True) / 1000.)}
         Mean amplitude (cm)       : {self.box_display(self.hist('amplitude', 'lat', bins_lat, mean=True) * 100.)}"""
 
+    def __dir__(self):
+        """Provide method name lookup and completion.
+        """
+        base = set(dir(type(self)))
+        intern_name = set(self.elements)
+        extern_name = set([VAR_DESCR[k]['nc_name'] for k in intern_name])
+        # Must be check in init not here
+        if base & intern_name:
+            logger.warning("Some variable name have a common name with class attrs: %s", base & intern_name)
+        if base & extern_name:
+            logger.warning("Some variable name have a common name with class attrs: %s", base & extern_name)
+        return sorted(base.union(intern_name).union(extern_name))
+
     def __getitem__(self, attr):
         if attr in self.elements:
             return self.observations[attr]
+        elif attr in VAR_DESCR_inv:
+            return self.observations[VAR_DESCR_inv[attr]]
         raise KeyError("%s unknown" % attr)
 
+    def __getattr__(self, attr):
+        if attr in self.elements:
+            return self.observations[attr]
+        elif attr in VAR_DESCR_inv:
+            return self.observations[VAR_DESCR_inv[attr]]
+        raise AttributeError(
+            "{!r} object has no attribute {!r}".format(type(self).__name__, attr)
+        )
+
     @classmethod
     def needed_variable(cls):
         return None