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Add example to compare radius and area
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‎examples/02_eddy_identification/pet_filter_and_detection.py‎

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@@ -58,8 +58,8 @@ def update_axes(ax, mappable=None):
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# Parameters distribution
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# -----------------------
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fig = plt.figure(figsize=(12, 5))
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ax_a = plt.subplot(121, xlabel="amplitdue(cm)")
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ax_r = plt.subplot(122, xlabel="speed radius (km)")
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ax_a = fig.add_subplot(121, xlabel="amplitdue(cm)")
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ax_r = fig.add_subplot(122, xlabel="speed radius (km)")
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ax_a.hist(
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merge_f["amplitude"] * 100,
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bins=arange(0.0005, 100, 1),

‎examples/02_eddy_identification/pet_radius_vs_area.py‎

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"""
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Radius vs area
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==============
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"""
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from matplotlib import pyplot as plt
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from py_eddy_tracker.observations.observation import EddiesObservations
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from py_eddy_tracker import data
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from py_eddy_tracker.poly import poly_area
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from py_eddy_tracker.generic import coordinates_to_local
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from numpy import pi, array
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# %%
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# Load detection files
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a = EddiesObservations.load_file(data.get_path("Anticyclonic_20190223.nc"))
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areas = list()
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# For each contour area will be compute in local reference
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for i in a:
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x, y = coordinates_to_local(
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i["contour_lon_s"], i["contour_lat_s"], i["lon"], i["lat"]
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)
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areas.append(poly_area(x, y))
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areas = array(areas)
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# %%
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# Radius provided by eddy detection is computed with :func:`~py_eddy_tracker.poly.fit_circle` method.
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# This radius will be compared with an equivalent radius deduced from polygon area.
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ax = plt.subplot(111)
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ax.set_aspect("equal")
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ax.grid()
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ax.set_xlabel("Speed radius computed with fit_circle")
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ax.set_ylabel("Radius deduced from area\nof contour_lon_s/contour_lat_s")
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ax.set_title('Area vs radius')
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ax.plot(a["radius_s"] / 1000.0, (areas / pi) ** 0.5 / 1000.0, ".")
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ax.plot((0, 250), (0, 250), "r")
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# %%
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# Fit circle give a radius bigger than polygon area
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# %%
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# When error is tiny, radius are very close.
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ax = plt.subplot(111)
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ax.grid()
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ax.set_xlabel("Radius ratio")
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ax.set_ylabel("Shape error")
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ax.set_title("err = f(radius_ratio)")
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ax.plot(a["radius_s"] / (areas / pi) ** .5 , a['shape_error_s'], ".")

‎notebooks/python_module/02_eddy_identification/pet_filter_and_detection.ipynb‎

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@@ -15,7 +15,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\nEddy detection and filter\n=========================\n"
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"\n# Eddy detection and filter\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Parameters distribution\n-----------------------\n\n"
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"## Parameters distribution\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"fig = plt.figure(figsize=(12, 5))\nax_a = plt.subplot(121, xlabel=\"amplitdue(cm)\")\nax_r = plt.subplot(122, xlabel=\"speed radius (km)\")\nax_a.hist(\n merge_f[\"amplitude\"] * 100,\n bins=arange(0.0005, 100, 1),\n label=\"Eddy from filtered grid\",\n histtype=\"step\",\n)\nax_a.hist(\n merge_r[\"amplitude\"] * 100,\n bins=arange(0.0005, 100, 1),\n label=\"Eddy from raw grid\",\n histtype=\"step\",\n)\nax_a.set_xlim(0, 10)\nax_r.hist(merge_f[\"radius_s\"] / 1000.0, bins=arange(0, 300, 5), histtype=\"step\")\nax_r.hist(merge_r[\"radius_s\"] / 1000.0, bins=arange(0, 300, 5), histtype=\"step\")\nax_r.set_xlim(0, 100)\nax_a.legend()"
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"fig = plt.figure(figsize=(12, 5))\nax_a = fig.add_subplot(121, xlabel=\"amplitdue(cm)\")\nax_r = fig.add_subplot(122, xlabel=\"speed radius (km)\")\nax_a.hist(\n merge_f[\"amplitude\"] * 100,\n bins=arange(0.0005, 100, 1),\n label=\"Eddy from filtered grid\",\n histtype=\"step\",\n)\nax_a.hist(\n merge_r[\"amplitude\"] * 100,\n bins=arange(0.0005, 100, 1),\n label=\"Eddy from raw grid\",\n histtype=\"step\",\n)\nax_a.set_xlim(0, 10)\nax_r.hist(merge_f[\"radius_s\"] / 1000.0, bins=arange(0, 300, 5), histtype=\"step\")\nax_r.hist(merge_r[\"radius_s\"] / 1000.0, bins=arange(0, 300, 5), histtype=\"step\")\nax_r.set_xlim(0, 100)\nax_a.legend()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Match detection and compare\n---------------------------\n\n"
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"## Match detection and compare\n\n"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"i_, j_, c = merge_f.match(merge_r, 0.1)"
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"i_, j_, c = merge_f.match(merge_r, cmin=0.1)"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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"version": "3.7.0"
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}
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},
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"nbformat": 4,

‎notebooks/python_module/02_eddy_identification/pet_radius_vs_area.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Radius vs area\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\nfrom py_eddy_tracker.observations.observation import EddiesObservations\nfrom py_eddy_tracker import data\n\nfrom py_eddy_tracker.poly import poly_area\nfrom py_eddy_tracker.generic import coordinates_to_local\nfrom numpy import pi, array"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Load detection files\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"a = EddiesObservations.load_file(data.get_path(\"Anticyclonic_20190223.nc\"))\nareas = list()\n# For each contour area will be compute in local reference\nfor i in a:\n x, y = coordinates_to_local(\n i[\"contour_lon_s\"], i[\"contour_lat_s\"], i[\"lon\"], i[\"lat\"]\n )\n areas.append(poly_area(x, y))\nareas = array(areas)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Radius provided by eddy detection is computed with :func:`~py_eddy_tracker.poly.fit_circle` method.\nThis radius will be compared with an equivalent radius deduced from polygon area.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"ax = plt.subplot(111)\nax.set_aspect(\"equal\")\nax.grid()\nax.set_xlabel(\"Speed radius computed with fit_circle\")\nax.set_ylabel(\"Radius deduced from area\\nof contour_lon_s/contour_lat_s\")\nax.set_title('Area vs radius')\nax.plot(a[\"radius_s\"] / 1000.0, (areas / pi) ** 0.5 / 1000.0, \".\")\nax.plot((0, 250), (0, 250), \"r\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Fit circle give a radius bigger than polygon area\n\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When error is tiny, radius are very close.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"ax = plt.subplot(111)\nax.grid()\nax.set_xlabel(\"Radius ratio\")\nax.set_ylabel(\"Shape error\")\nax.set_title(\"err = f(radius_ratio)\")\nax.plot(a[\"radius_s\"] / (areas / pi) ** .5 , a['shape_error_s'], \".\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎src/py_eddy_tracker/poly.py‎

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@@ -86,6 +86,8 @@ def poly_area_vertice(v):
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@njit(cache=True)
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def poly_area(x, y):
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"""
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Must be call with local coordinates (in m, to get an area in m²)
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:param array x:
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:param array y:
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:return: area of polygon in coordinates unit

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