add function to get circle instead of contour

Author: AntSimi

examples/02_eddy_identification/pet_contour_circle.py

@@ -0,0 +1,27 @@
+"""
+Display contour & circle
+========================
+
+"""
+
+from matplotlib import pyplot as plt
+from py_eddy_tracker.observations.observation import EddiesObservations
+from py_eddy_tracker import data
+
+# %%
+# Load detection files
+a = EddiesObservations.load_file(data.get_path("Anticyclonic_20190223.nc"))
+
+# %%
+# Plot
+fig = plt.figure(figsize=(15, 8))
+ax = fig.add_axes((0.05, 0.05, 0.9, 0.9))
+ax.set_aspect("equal")
+ax.set_xlim(10, 70)
+ax.set_ylim(-50, -25)
+a.display(ax, label="Anticyclonic contour", color="r", lw=1)
+
+# Replace contour by circle
+a.circle_contour()
+a.display(ax, label="Anticyclonic circle", color="g", lw=1)
+ax.legend(loc="upper right")

notebooks/python_module/02_eddy_identification/pet_contour_circle.ipynb

@@ -0,0 +1,90 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\nDisplay contour & circle\n========================\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from matplotlib import pyplot as plt\nfrom py_eddy_tracker.observations.observation import EddiesObservations\nfrom py_eddy_tracker import data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Load detection files\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "a = EddiesObservations.load_file(data.get_path(\"Anticyclonic_20190223.nc\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(figsize=(15, 8))\nax = fig.add_axes((0.05, 0.05, 0.9, 0.9))\nax.set_aspect(\"equal\")\nax.set_xlim(10, 70)\nax.set_ylim(-50, -25)\na.display(ax, label=\"Anticyclonic contour\", color=\"r\", lw=1)\n\n# Replace contour by circle\na.circle_contour()\na.display(ax, label=\"Anticyclonic circle\", color=\"g\", lw=1)\nax.legend(loc=\"upper right\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/python_module/08_tracking_manipulation/pet_track_anim.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\nimport matplotlib.transforms as mt\nfrom numpy import arange\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations\nfrom py_eddy_tracker.poly import create_vertice\nimport py_eddy_tracker_sample"
+        "from matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\nfrom numpy import arange\nfrom py_eddy_tracker.observations.tracking import TrackEddiesObservations\nfrom py_eddy_tracker.poly import create_vertice\nimport py_eddy_tracker_sample"
       ]
     },
     {

notebooks/python_module/10_tracking_diagnostics/pet_center_count.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "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)\nm = g_a.display(ax_a, **kwargs_pcolormesh)\ng_c = c.grid_count(bins, intern=True, center=True)\nm = g_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(ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm())\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()"
+        "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(ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm())\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()"
       ]
     }
   ],

notebooks/python_module/10_tracking_diagnostics/pet_pixel_used.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "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)\nm = g_a.display(ax_a, **kwargs_pcolormesh)\ng_c = c.grid_count(bins, intern=True)\nm = g_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(ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm())\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()"
+        "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(ax_ratio, name=\"count\", vmin=0.1, vmax=10, norm=LogNorm())\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()"
       ]
     }
   ],

src/py_eddy_tracker/observations/observation.py

@@ -48,6 +48,8 @@
     ceil,
     arange,
     histogram2d,
+    linspace,
+    sin,
 )
 from netCDF4 import Dataset
 from datetime import datetime
@@ -58,7 +60,13 @@
 from tokenize import TokenError
 from matplotlib.path import Path as BasePath
 from .. import VAR_DESCR, VAR_DESCR_inv
-from ..generic import distance_grid, distance, flatten_line_matrix, wrap_longitude
+from ..generic import (
+    distance_grid,
+    distance,
+    flatten_line_matrix,
+    wrap_longitude,
+    local_to_coordinates,
+)
 from ..poly import bbox_intersection, common_area, create_vertice
 
 logger = logging.getLogger("pet")
@@ -247,6 +255,23 @@ def add_rotation_type(self):
         new.observations["type_cyc"] = self.sign_type
         return new
 
+    def circle_contour(self):
+        angle = radians(linspace(0, 360, self.track_array_variables))
+        x_norm, y_norm = cos(angle), sin(angle)
+        for i, obs in enumerate(self):
+            r_s, r_e, x, y = (
+                obs["radius_s"],
+                obs["radius_e"],
+                obs["lon"],
+                obs["lat"],
+            )
+            obs["contour_lon_s"], obs["contour_lat_s"] = local_to_coordinates(
+                x_norm * r_s, y_norm * r_s, x, y
+            )
+            obs["contour_lon_e"], obs["contour_lat_e"] = local_to_coordinates(
+                x_norm * r_e, y_norm * r_e, x, y
+            )
+
     @property
     def dtype(self):
         """Return dtype to build numpy array