crop_AVISO_to_edward.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crop AVISO dataset to LBE\n",
    "Data file from AVISO: 'Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc'\n",
    "\n",
    "Lexi Jones-Kellett\n",
    "\n",
    "Date created: 08/06/24\n",
    "\n",
    "Last edited: 08/07/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, pylab, csv\n",
    "import xarray as xr\n",
    "import netCDF4\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "from itertools import groupby\n",
    "from collections import Counter\n",
    "from matplotlib.path import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fontsize = 18\n",
    "\n",
    "plt.rc('font', size=fontsize)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=fontsize)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=fontsize)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=fontsize)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=fontsize)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=fontsize)    # legend fontsize\n",
    "plt.rc('figure', titlesize=fontsize)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno -51] NetCDF: Unknown file format: '/home/jovyan/GO-SWACE/Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/home/jovyan/GO-SWACE/Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), '7584eeee-1915-44d1-ada7-8f7c848571df']",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnetcdf4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m ds\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/api.py:588\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    576\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    577\u001b[0m     decode_cf,\n\u001b[1;32m    578\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    584\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    585\u001b[0m )\n\u001b[1;32m    587\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 588\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    589\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    590\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    594\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    595\u001b[0m     backend_ds,\n\u001b[1;32m    596\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    606\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    607\u001b[0m )\n\u001b[1;32m    608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:645\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    624\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]  # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m    625\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    626\u001b[0m     filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    642\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    643\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m    644\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 645\u001b[0m     store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    650\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    652\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    653\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    654\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    655\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    657\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    658\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:408\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    402\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    403\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m    404\u001b[0m )\n\u001b[1;32m    405\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    406\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m    407\u001b[0m )\n\u001b[0;32m--> 408\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:355\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 355\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m    357\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:417\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    415\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 417\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:411\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 411\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    412\u001b[0m \u001b[43m        \u001b[49m\u001b[43mds\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m_nc4_require_group\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_group\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    413\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen)\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2470\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2107\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno -51] NetCDF: Unknown file format: '/home/jovyan/GO-SWACE/Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc'"
     ]
    }
   ],
   "source": [
    "ds = xr.open_dataset('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc', engine=\"netcdf4\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_min,lat_max = 34.5,37\n",
    "lon_min,lon_max = -73+360,-68+360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_inds = np.where(((ds.latitude > lat_min) & (ds.latitude < lat_max)) & \n",
    "                       ((ds.longitude > lon_min) & (ds.longitude < lon_max)) & \n",
    "                       ((ds.time) >= np.datetime64('2024-04-01')))[0]\n",
    "print(len(subset_inds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat,lon,time,track = [],[],[],[]\n",
    "for i in subset_inds: \n",
    "    this_track_id = int(ds.track[i].values)\n",
    "    if ds.longitude[i] > 360:\n",
    "        lon.append(float((ds.longitude[i] - 360).values))\n",
    "    else:\n",
    "        lon.append(float(ds.longitude[i].values))\n",
    "    lat.append(float(ds.latitude[i].values))\n",
    "    time.append(np.datetime64(ds.time[i].values))\n",
    "    track.append(this_track_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(8,6))\n",
    "\n",
    "ax.scatter(lon,lat,s=3,c=time)\n",
    "scat = ax.set_title('Eddy Centers in Box (N = %s)'%(len(track)))\n",
    "ax.set_xlabel('Longitude ($^{\\circ}$E)');\n",
    "ax.set_ylabel('Latitude ($^{\\circ}$N)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(track)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_inds = np.where((ds.track==174440))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat,lon,time,track = [],[],[],[]\n",
    "for i in subset_inds: \n",
    "    this_track_id = int(ds.track[i].values)\n",
    "    if ds.longitude[i] > 360:\n",
    "        lon.append(float((ds.longitude[i] - 360).values))\n",
    "    else:\n",
    "        lon.append(float(ds.longitude[i].values))\n",
    "    lat.append(float(ds.latitude[i].values))\n",
    "    time.append(np.datetime64(ds.time[i].values))\n",
    "    track.append(this_track_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import Normalize\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(10,5))\n",
    "\n",
    "ax.scatter(lon,lat,s=3,c=time)\n",
    "scat = ax.set_title('Edward Eddy Centers')\n",
    "ax.set_xlabel('Longitude ($^{\\circ}$E)');\n",
    "ax.set_ylabel('Latitude ($^{\\circ}$N)');\n",
    "\n",
    "# Create a colorbar\n",
    "norm = Normalize(vmin=0, vmax=len(time))\n",
    "sm = plt.cm.ScalarMappable(cmap='viridis', norm=norm)\n",
    "sm.set_array([])  # Dummy array for the colorbar\n",
    "cbar = plt.colorbar(sm, ax=ax)\n",
    "cbar.set_label('Days', rotation=270,labelpad=20)\n",
    "\n",
    "\n",
    "# Format colorbar ticks as dates\n",
    "#cb.ax.yaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_eddy_by_ID_date(ds,track_id,eddy_date):\n",
    "    \"\"\"\n",
    "    ds: netCDF AVISO format\n",
    "    track_id: id of eddy to extract\n",
    "    eddy_date: date in format 'YYYY-MM-DD'\n",
    "\n",
    "    Returns contour lons, contour lats, center lon, center lat\n",
    "    \"\"\"\n",
    "    try:\n",
    "        ind = np.where((ds.track == track_id) & (ds.time == np.datetime64(eddy_date)))[0][0]\n",
    "        contour_lons = np.array(ds.effective_contour_longitude[ind])\n",
    "        contour_lats = np.array(ds.effective_contour_latitude[ind])\n",
    "    except:\n",
    "        print('No eddy data available with that request ... :(')        \n",
    "    \n",
    "    return contour_lons,contour_lats,ds.longitude[ind],ds.latitude[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_to_plot = '2024-05-08'\n",
    "\n",
    "############################\n",
    "contour_lons,contour_lats,center_lon,center_lat = get_eddy_by_ID_date(ds,174440,date_to_plot)\n",
    "\n",
    "# Plot\n",
    "plt.plot(contour_lons,contour_lats)\n",
    "plt.scatter(center_lon,center_lat,marker='*',s=100)\n",
    "\n",
    "# Labels\n",
    "plt.title('Edward on %s'%(date_to_plot))\n",
    "plt.xlabel('Longitude ($^{\\circ}$E)');\n",
    "plt.ylabel('Latitude ($^{\\circ}$N)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test plotting first 10 days of the contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(8,6))\n",
    "\n",
    "for t in time[0:10]:\n",
    "    ind = np.where((ds.track == 174440) & (ds.time == np.datetime64(t)))[0][0]\n",
    "    contour_lons = np.array(ds.effective_contour_longitude[ind])\n",
    "    contour_lats = np.array(ds.effective_contour_latitude[ind])\n",
    "    ax.plot(contour_lons,contour_lats)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(time))\n",
    "print(min(time))\n",
    "print(max(time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How old is Edward on May 8th, 2024?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(np.datetime64('2024-05-08') - time[0]).astype('timedelta64[D]')/ np.timedelta64(1, 'D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Edward Subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ds_Edward = ds.isel(obs=subset_inds)\n",
    "ds_Edward.to_netcdf('Edward_Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test opening Edward subset of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('Edward_Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test checking if a float is in or out of the eddy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def in_eddy(ds,float_lat,float_lon,float_time):\n",
    "    \"\"\"\n",
    "    float_lat: degrees north\n",
    "    float_lon: degrees east\n",
    "    float_time: should be in format 'YYYY-MM-DD'\n",
    "    \"\"\"\n",
    "\n",
    "    def all_equal(iterable):\n",
    "        g = groupby(iterable)\n",
    "        return next(g, True) and not next(g, False)\n",
    "\n",
    "    in_eddy_flag = False \n",
    "\n",
    "    float_time = np.datetime64(float_time)\n",
    "    if float_time in ds.time: # some dates not in dateset        \n",
    "        for i in np.where(ds.time == float_time)[0]: # usually will only be 1 eddy, but sometimes there are 2 after a split\n",
    "            contour_lons = np.array(ds.effective_contour_longitude[i]) # eddy lons\n",
    "            contour_lats = np.array(ds.effective_contour_latitude[i]) # eddy lats\n",
    "            \n",
    "            if all_equal(contour_lons): # eddy break\n",
    "                pass\n",
    "            else:\n",
    "                poly = Path([(contour_lats[j],contour_lons[j]) for j in np.arange(0,len(contour_lats))]) # set up the polygon\n",
    "                if poly.contains_points([(float_lat,float_lon)]): #find if point is inside the polygon\n",
    "                    in_eddy_flag = True \n",
    "\n",
    "    return in_eddy_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_eddy(ds,35,289.5,'2024-05-09')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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