add filter to grid_stat and grid_count

examples/02_eddy_identification/pet_filter_and_detection.py

@@ -33,9 +33,7 @@ def update_axes(ax, mappable=None):
 # Add a new filed to store the high-pass filtered ADT
 
 g = RegularGridDataset(
-    data.get_path("dt_med_allsat_phy_l4_20160515_20190101.nc"),
-    "longitude",
-    "latitude",
+    data.get_path("dt_med_allsat_phy_l4_20160515_20190101.nc"), "longitude", "latitude",
 )
 g.add_uv("adt")
 g.copy("adt", "adt_high")

examples/02_eddy_identification/pet_sla_and_adt.py

@@ -31,9 +31,7 @@ def update_axes(ax, mappable=None):
 # Load Input grid, ADT will be used to detect eddies
 
 g = RegularGridDataset(
-    data.get_path("dt_med_allsat_phy_l4_20160515_20190101.nc"),
-    "longitude",
-    "latitude",
+    data.get_path("dt_med_allsat_phy_l4_20160515_20190101.nc"), "longitude", "latitude",
 )
 g.add_uv("adt", "ugos", "vgos")
 g.add_uv("sla", "ugosa", "vgosa")

examples/06_grid_manipulation/pet_filter.py

@@ -32,9 +32,7 @@ def update_axes(ax, mappable=None):
 # %%
 # All information will be for regular grid
 g = RegularGridDataset(
-    data.get_path("dt_med_allsat_phy_l4_20160515_20190101.nc"),
-    "longitude",
-    "latitude",
+    data.get_path("dt_med_allsat_phy_l4_20160515_20190101.nc"), "longitude", "latitude",
 )
 # %%
 # Kernel

examples/08_tracking_manipulation/pet_select_track_across_area.py

@@ -32,20 +32,8 @@
 c.plot(ax, color="gray", lw=0.1, ref=-10, label="all tracks")
 c_subset.plot(ax, color="red", lw=0.2, ref=-10, label="selected tracks")
 ax.plot(
-    (
-        x0,
-        x0,
-        x1,
-        x1,
-        x0,
-    ),
-    (
-        y0,
-        y1,
-        y1,
-        y0,
-        y0,
-    ),
+    (x0, x0, x1, x1, x0,),
+    (y0, y1, y1, y0, y0,),
     color="green",
     lw=1.5,
     label="Box of selection",

examples/10_tracking_diagnostics/pet_center_count.py

@@ -68,3 +68,21 @@
     ax.set_aspect("equal")
     ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
     ax.grid()
+
+# %%
+# Count Anticyclones as a function of lifetime
+# --------------------------------------------
+# Count at the center's position
+
+fig = plt.figure(figsize=(12, 10))
+ax = fig.add_subplot(211)
+g_a = a.grid_count(bins, center=True, filter=a.lifetime >= 30)
+m = g_a.display(ax, **kwargs_pcolormesh)
+ax.set_aspect("equal")
+ax.set_title("Anticyclones with lifetime >= 30 days")
+cb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.1, 0.015, 0.8]))
+ax = fig.add_subplot(212)
+g_a = a.grid_count(bins, center=True, filter=a.lifetime < 30)
+m = g_a.display(ax, **kwargs_pcolormesh)
+ax.set_aspect("equal")
+ax.set_title("Anticyclones with lifetime < 30 days")

examples/10_tracking_diagnostics/pet_pixel_used.py

@@ -64,3 +64,20 @@
     ax.set_aspect("equal")
     ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
     ax.grid()
+
+# %%
+# Count Anticyclones as a function of lifetime
+# --------------------------------------------
+
+fig = plt.figure(figsize=(12, 10))
+ax = fig.add_subplot(211)
+g_a = a.grid_count(bins, intern=True, filter=a.lifetime >= 30)
+m = g_a.display(ax, **kwargs_pcolormesh)
+ax.set_aspect("equal")
+ax.set_title("Anticyclones with lifetime >= 30 days")
+cb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.1, 0.015, 0.8]))
+ax = fig.add_subplot(212)
+g_a = a.grid_count(bins, intern=True, filter=a.lifetime < 30)
+m = g_a.display(ax, **kwargs_pcolormesh)
+ax.set_aspect("equal")
+ax.set_title("Anticyclones with lifetime < 30 days")

notebooks/python_module/10_tracking_diagnostics/pet_center_count.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from matplotlib import pyplot as plt\nfrom matplotlib.colors import LogNorm\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations\nimport py_eddy_tracker_sample"
+        "import py_eddy_tracker_sample\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import LogNorm\n\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations"
       ]
     },
     {
@@ -64,6 +64,24 @@
       "source": [
         "fig = plt.figure(figsize=(12, 18.5))\nax_a = fig.add_axes([0.03, 0.75, 0.90, 0.25])\nax_a.set_title(\"Anticyclonic center frequency\")\nax_c = fig.add_axes([0.03, 0.5, 0.90, 0.25])\nax_c.set_title(\"Cyclonic center frequency\")\nax_all = fig.add_axes([0.03, 0.25, 0.90, 0.25])\nax_all.set_title(\"All eddies center frequency\")\nax_ratio = fig.add_axes([0.03, 0.0, 0.90, 0.25])\nax_ratio.set_title(\"Ratio cyclonic / Anticyclonic\")\n\n# Count pixel used for each center\ng_a = a.grid_count(bins, intern=True, center=True)\ng_a.display(ax_a, **kwargs_pcolormesh)\ng_c = c.grid_count(bins, intern=True, center=True)\ng_c.display(ax_c, **kwargs_pcolormesh)\n# Compute a ratio Cyclonic / Anticyclonic\nratio = g_c.vars[\"count\"] / g_a.vars[\"count\"]\n\n# Mask manipulation to be able to sum the 2 grids\nm_c = g_c.vars[\"count\"].mask\nm = m_c & g_a.vars[\"count\"].mask\ng_c.vars[\"count\"][m_c] = 0\ng_c.vars[\"count\"] += g_a.vars[\"count\"]\ng_c.vars[\"count\"].mask = m\n\nm = g_c.display(ax_all, **kwargs_pcolormesh)\ncb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.27, 0.01, 0.7]))\ncb.set_label(\"Eddies by 1\u00b0^2 by day\")\n\ng_c.vars[\"count\"] = ratio\nm = g_c.display(\n    ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm(), cmap=\"coolwarm_r\"\n)\nplt.colorbar(m, cax=fig.add_axes([0.94, 0.02, 0.01, 0.2]))\n\nfor ax in (ax_a, ax_c, ax_all, ax_ratio):\n    ax.set_aspect(\"equal\")\n    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n    ax.grid()"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Count Anticyclones as a function of lifetime\nCount at the center's position\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(figsize=(12, 10))\nax = fig.add_subplot(211)\ng_a = a.grid_count(bins, center=True, filter=a.lifetime >= 30)\nm = g_a.display(ax, **kwargs_pcolormesh)\nax.set_aspect(\"equal\")\nax.set_title(\"Anticyclones with lifetime >= 30 days\")\ncb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.1, 0.015, 0.8]))\nax = fig.add_subplot(212)\ng_a = a.grid_count(bins, center=True, filter=a.lifetime < 30)\nm = g_a.display(ax, **kwargs_pcolormesh)\nax.set_aspect(\"equal\")\nax.set_title(\"Anticyclones with lifetime < 30 days\")"
+      ]
     }
   ],
   "metadata": {
@@ -82,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.7"
+      "version": "3.7.6"
     }
   },
   "nbformat": 4,

notebooks/python_module/10_tracking_diagnostics/pet_pixel_used.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from matplotlib import pyplot as plt\nfrom matplotlib.colors import LogNorm\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations\nimport py_eddy_tracker_sample"
+        "import py_eddy_tracker_sample\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import LogNorm\n\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations"
       ]
     },
     {
@@ -64,6 +64,24 @@
       "source": [
         "fig = plt.figure(figsize=(12, 18.5))\nax_a = fig.add_axes([0.03, 0.75, 0.90, 0.25])\nax_a.set_title(\"Anticyclonic frequency\")\nax_c = fig.add_axes([0.03, 0.5, 0.90, 0.25])\nax_c.set_title(\"Cyclonic frequency\")\nax_all = fig.add_axes([0.03, 0.25, 0.90, 0.25])\nax_all.set_title(\"All eddies frequency\")\nax_ratio = fig.add_axes([0.03, 0.0, 0.90, 0.25])\nax_ratio.set_title(\"Ratio cyclonic / Anticyclonic\")\n\n# Count pixel used for each contour\ng_a = a.grid_count(bins, intern=True)\ng_a.display(ax_a, **kwargs_pcolormesh)\ng_c = c.grid_count(bins, intern=True)\ng_c.display(ax_c, **kwargs_pcolormesh)\n# Compute a ratio Cyclonic / Anticyclonic\nratio = g_c.vars[\"count\"] / g_a.vars[\"count\"]\n\n# Mask manipulation to be able to sum the 2 grids\nm_c = g_c.vars[\"count\"].mask\nm = m_c & g_a.vars[\"count\"].mask\ng_c.vars[\"count\"][m_c] = 0\ng_c.vars[\"count\"] += g_a.vars[\"count\"]\ng_c.vars[\"count\"].mask = m\n\nm = g_c.display(ax_all, **kwargs_pcolormesh)\nplt.colorbar(m, cax=fig.add_axes([0.95, 0.27, 0.01, 0.7]))\n\ng_c.vars[\"count\"] = ratio\nm = g_c.display(\n    ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm(), cmap=\"coolwarm_r\"\n)\nplt.colorbar(m, cax=fig.add_axes([0.95, 0.02, 0.01, 0.2]))\n\nfor ax in (ax_a, ax_c, ax_all, ax_ratio):\n    ax.set_aspect(\"equal\")\n    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)\n    ax.grid()"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Count Anticyclones as a function of lifetime\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(figsize=(12, 10))\nax = fig.add_subplot(211)\ng_a = a.grid_count(bins, intern=True, filter=a.lifetime >= 30)\nm = g_a.display(ax, **kwargs_pcolormesh)\nax.set_aspect(\"equal\")\nax.set_title(\"Anticyclones with lifetime >= 30 days\")\ncb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.1, 0.015, 0.8]))\nax = fig.add_subplot(212)\ng_a = a.grid_count(bins, intern=True, filter=a.lifetime < 30)\nm = g_a.display(ax, **kwargs_pcolormesh)\nax.set_aspect(\"equal\")\nax.set_title(\"Anticyclones with lifetime < 30 days\")"
+      ]
     }
   ],
   "metadata": {
@@ -82,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.7"
+      "version": "3.7.6"
     }
   },
   "nbformat": 4,

src/py_eddy_tracker/__init__.py

@@ -149,10 +149,7 @@ def parse_args(self, *args, **kwargs):
         output_type="uint16",
         scale_factor=50.0,
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Distance to next position",
-            units="m",
-        ),
+        nc_attr=dict(long_name="Distance to next position", units="m",),
     ),
     virtual=dict(
         attr_name=None,
@@ -304,18 +301,14 @@ def parse_args(self, *args, **kwargs):
         nc_name="i",
         nc_type="uint16",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Longitude index in the grid of the detection",
-        ),
+        nc_attr=dict(long_name="Longitude index in the grid of the detection",),
     ),
     j=dict(
         attr_name="j",
         nc_name="j",
         nc_type="uint16",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Latitude index in the grid of the detection",
-        ),
+        nc_attr=dict(long_name="Latitude index in the grid of the detection",),
     ),
     eke=dict(
         attr_name="eke",
@@ -364,8 +357,7 @@ def parse_args(self, *args, **kwargs):
         nc_type="uint32",
         nc_dims=("obs",),
         nc_attr=dict(
-            long_name="Trajectory number",
-            comment="Trajectory identification number",
+            long_name="Trajectory number", comment="Trajectory identification number",
         ),
     ),
     sub_track=dict(
@@ -374,8 +366,7 @@ def parse_args(self, *args, **kwargs):
         nc_type="uint32",
         nc_dims=("obs",),
         nc_attr=dict(
-            long_name="Segment Number",
-            comment="Segment number inside a group",
+            long_name="Segment Number", comment="Segment number inside a group",
         ),
     ),
     n=dict(
@@ -498,11 +489,7 @@ def parse_args(self, *args, **kwargs):
         output_type="u1",
         scale_factor=0.4,
         nc_dims=("obs",),
-        nc_attr=dict(
-            units="%",
-            comment="score",
-            long_name="Score",
-        ),
+        nc_attr=dict(units="%", comment="score", long_name="Score",),
     ),
     index_other=dict(
         attr_name=None,
@@ -571,10 +558,7 @@ def parse_args(self, *args, **kwargs):
         old_nc_name=["Chl"],
         nc_type="f4",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Log base 10 chlorophyll",
-            units="Log(Chl/[mg/m^3])",
-        ),
+        nc_attr=dict(long_name="Log base 10 chlorophyll", units="Log(Chl/[mg/m^3])",),
     ),
     dchl=dict(
         attr_name=None,
@@ -594,8 +578,7 @@ def parse_args(self, *args, **kwargs):
         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(
@@ -604,32 +587,23 @@ def parse_args(self, *args, **kwargs):
         old_nc_name=["Year"],
         nc_type="u2",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Year",
-            units="year",
-        ),
+        nc_attr=dict(long_name="Year", units="year",),
     ),
     month=dict(
         attr_name=None,
         nc_name="month",
         old_nc_name=["Month"],
         nc_type="u1",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Month",
-            units="month",
-        ),
+        nc_attr=dict(long_name="Month", units="month",),
     ),
     day=dict(
         attr_name=None,
         nc_name="day",
         old_nc_name=["Day"],
         nc_type="u1",
         nc_dims=("obs",),
-        nc_attr=dict(
-            long_name="Day",
-            units="day",
-        ),
+        nc_attr=dict(long_name="Day", units="day",),
     ),
     nb_contour_selected=dict(
         attr_name=None,

src/py_eddy_tracker/appli/network.py

@@ -21,8 +21,7 @@
 def build_network():
     parser = EddyParser("Merge eddies")
     parser.add_argument(
-        "identification_regex",
-        help="Give an expression which will use with glob",
+        "identification_regex", help="Give an expression which will use with glob",
     )
     parser.add_argument("out", help="output file")
     parser.add_argument(
@@ -294,11 +293,7 @@ def display_network(x, y, tr, t, c):
             lw=0.5,
         )
         ax_time.plot(
-            t[sl],
-            tr[s].repeat(e - s),
-            color=color,
-            lw=1,
-            marker="+",
+            t[sl], tr[s].repeat(e - s), color=color, lw=1, marker="+",
         )
         ax_time.text(t[s], tr[s] + 0.15, f"{x[s].mean():.2f}, {y[s].mean():.2f}")
         ax_time.axvline(t[s], color=".75", lw=0.5, ls="--", zorder=-10)

src/py_eddy_tracker/dataset/grid.py

@@ -301,8 +301,7 @@ def __init__(
         self.interpolators = dict()
         if centered is None:
             logger.warning(
-                "We assume pixel position of grid is center for %s",
-                filename,
+                "We assume pixel position of grid is center for %s", filename,
             )
         if not unset:
             self.load_general_features()
@@ -344,10 +343,7 @@ def load_general_features(self):
             self.variables_description = dict()
             for i, v in h.variables.items():
                 args = (i, v.datatype)
-                kwargs = dict(
-                    dimensions=v.dimensions,
-                    zlib=True,
-                )
+                kwargs = dict(dimensions=v.dimensions, zlib=True,)
                 if hasattr(v, "_FillValue"):
                     kwargs["fill_value"] = (v._FillValue,)
                 attrs = dict()

src/py_eddy_tracker/eddy_feature.py

@@ -431,8 +431,8 @@ def __init__(self, x, y, z, levels, wrap_x=False, keep_unclose=False):
         closed_contours = 0
         # Count level and contour
         for i, collection in enumerate(self.contours.collections):
-            collection.get_nearest_path_bbox_contain_pt = (
-                lambda x, y, i=i: self.get_index_nearest_path_bbox_contain_pt(i, x, y)
+            collection.get_nearest_path_bbox_contain_pt = lambda x, y, i=i: self.get_index_nearest_path_bbox_contain_pt(
+                i, x, y
             )
             nb_level += 1