{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Stastics on identification files\n\nSome statistics on raw identification without any tracking\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib import pyplot as plt\nfrom matplotlib.dates import date2num\nimport numpy as np\n\nfrom py_eddy_tracker import start_logger\nfrom py_eddy_tracker.data import get_remote_demo_sample\nfrom py_eddy_tracker.observations.observation import EddiesObservations\n\nstart_logger().setLevel(\"ERROR\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "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)\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([0.95, 0.05, 0.01, 0.9]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load demo sample and take only first year.\n\nReplace by a list of filename to apply on your own dataset.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "file_objects = get_remote_demo_sample(\n \"eddies_med_adt_allsat_dt2018/Anticyclonic_2010_2011_2012\"\n)[:365]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Merge all identification datasets in one object\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_a = EddiesObservations.concatenate(\n [EddiesObservations.load_file(i) for i in file_objects]\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define polygon bound\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x0, x1, y0, y1 = 15, 20, 33, 38\nxs = np.array([[x0, x1, x1, x0, x0]], dtype=\"f8\")\nys = np.array([[y0, y0, y1, y1, y0]], dtype=\"f8\")\n# Polygon object created for the match function use.\npolygon = dict(contour_lon_e=xs, contour_lat_e=ys, contour_lon_s=xs, contour_lat_s=ys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Geographic frequency of eddies\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "step = 0.125\nax = start_axes(\"\")\n# Count pixel encompassed in each contour\ng_a = all_a.grid_count(bins=((-10, 37, step), (30, 46, step)), intern=True)\nm = g_a.display(\n ax, cmap=\"terrain_r\", vmin=0, vmax=0.75, factor=1 / all_a.nb_days, name=\"count\"\n)\nax.plot(polygon[\"contour_lon_e\"][0], polygon[\"contour_lat_e\"][0], \"r\")\nupdate_axes(ax, m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the match function to count the number of eddies that intersect the polygon defined previously\n`p1_area` option allow to get in c_e/c_s output, precentage of area occupy by eddies in the polygon.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i_e, j_e, c_e = all_a.match(polygon, p1_area=True, intern=False)\ni_s, j_s, c_s = all_a.match(polygon, p1_area=True, intern=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dt = np.datetime64(\"1970-01-01\") - np.datetime64(\"1950-01-01\")\nkw_hist = dict(\n bins=date2num(np.arange(21900, 22300).astype(\"datetime64\") - dt), histtype=\"step\"\n)\n# translate julian day in datetime64\nt = all_a.time.astype(\"datetime64\") - dt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number of eddies within a polygon\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ax = plt.figure(figsize=(12, 6)).add_subplot(111)\nax.set_title(\"Different ways to count eddies within a polygon\")\nax.set_ylabel(\"Count\")\nm = all_a.mask_from_polygons(((xs, ys),))\nax.hist(t[m], label=\"Eddy Center in polygon\", **kw_hist)\nax.hist(t[i_s[c_s > 0]], label=\"Intersection Speed contour and polygon\", **kw_hist)\nax.hist(t[i_e[c_e > 0]], label=\"Intersection Effective contour and polygon\", **kw_hist)\nax.legend()\nax.set_xlim(np.datetime64(\"2010\"), np.datetime64(\"2011\"))\nax.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Percent of the area of interest occupied by eddies.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ax = plt.figure(figsize=(12, 6)).add_subplot(111)\nax.set_title(\"Percent of polygon occupied by an anticyclonic eddy\")\nax.set_ylabel(\"Percent of polygon\")\nax.hist(t[i_s[c_s > 0]], weights=c_s[c_s > 0] * 100.0, label=\"speed contour\", **kw_hist)\nax.hist(\n t[i_e[c_e > 0]], weights=c_e[c_e > 0] * 100.0, label=\"effective contour\", **kw_hist\n)\nax.legend(), ax.set_ylim(0, 25)\nax.set_xlim(np.datetime64(\"2010\"), np.datetime64(\"2011\"))\nax.grid()" ] } ], "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 0 }