{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial on how to use `timestaps` in Field construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from parcels import Field\n",
    "from glob import glob\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some NetCDF files, such as for example those from the [World Ocean Atlas](https://www.nodc.noaa.gov/OC5/woa18/), have time calendars that can't be parsed by `xarray`. These result in a `ValueError: unable to decode time units`, for example when the calendar is in 'months since' a particular date.\n",
    "\n",
    "In these cases, a workaround in Parcels is to use the `timestamps` argument in `Field` (or `FieldSet`) creation. Here, we show how this works for example temperature data from the World Ocean Atlas in the Pacific Ocean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell raises an error, since the calendar of the World Ocean Atlas data is in \"months since 1955-01-01 00:00:00\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: File WOA_data/woa18_decav_t10_04.nc could not be decoded properly by xarray (version 0.20.1).\n",
      "         It will be opened with no decoding. Filling values might be wrongly parsed.\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Xarray could not convert the calendar. If youre using from_netcdf, try using the timestamps keyword in the construction of your Field. See also the tutorial at https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_timestamps.ipynb",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:240\u001b[0m, in \u001b[0;36mdecode_cf_datetime\u001b[0;34m(num_dates, units, calendar, use_cftime)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 240\u001b[0m     dates \u001b[38;5;241m=\u001b[39m \u001b[43m_decode_datetime_with_pandas\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflat_num_dates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalendar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    241\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, OutOfBoundsDatetime, \u001b[38;5;167;01mOverflowError\u001b[39;00m):\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:186\u001b[0m, in \u001b[0;36m_decode_datetime_with_pandas\u001b[0;34m(flat_num_dates, units, calendar)\u001b[0m\n\u001b[1;32m    185\u001b[0m delta, ref_date \u001b[38;5;241m=\u001b[39m _unpack_netcdf_time_units(units)\n\u001b[0;32m--> 186\u001b[0m delta \u001b[38;5;241m=\u001b[39m \u001b[43m_netcdf_to_numpy_timeunit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelta\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    187\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:83\u001b[0m, in \u001b[0;36m_netcdf_to_numpy_timeunit\u001b[0;34m(units)\u001b[0m\n\u001b[1;32m     82\u001b[0m     units \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00munits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m{\u001b[49m\n\u001b[1;32m     84\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnanoseconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     85\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmicroseconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mus\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     86\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmilliseconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mms\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     87\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ms\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mminutes\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mm\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     89\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhours\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mh\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdays\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mD\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m\u001b[49m\u001b[43m}\u001b[49m\u001b[43m[\u001b[49m\u001b[43munits\u001b[49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'months'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:153\u001b[0m, in \u001b[0;36m_decode_cf_datetime_dtype\u001b[0;34m(data, units, calendar, use_cftime)\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_cf_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexample_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalendar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:242\u001b[0m, in \u001b[0;36mdecode_cf_datetime\u001b[0;34m(num_dates, units, calendar, use_cftime)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, OutOfBoundsDatetime, \u001b[38;5;167;01mOverflowError\u001b[39;00m):\n\u001b[0;32m--> 242\u001b[0m     dates \u001b[38;5;241m=\u001b[39m \u001b[43m_decode_datetime_with_cftime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    243\u001b[0m \u001b[43m        \u001b[49m\u001b[43mflat_num_dates\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalendar\u001b[49m\n\u001b[1;32m    244\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    246\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    247\u001b[0m         dates[np\u001b[38;5;241m.\u001b[39mnanargmin(num_dates)]\u001b[38;5;241m.\u001b[39myear \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1678\u001b[39m\n\u001b[1;32m    248\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m dates[np\u001b[38;5;241m.\u001b[39mnanargmax(num_dates)]\u001b[38;5;241m.\u001b[39myear \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2262\u001b[39m\n\u001b[1;32m    249\u001b[0m     ):\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:174\u001b[0m, in \u001b[0;36m_decode_datetime_with_cftime\u001b[0;34m(num_dates, units, calendar)\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mModuleNotFoundError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo module named \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcftime\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    173\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(\n\u001b[0;32m--> 174\u001b[0m     \u001b[43mcftime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum2date\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_dates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalendar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43monly_use_cftime_datetimes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    175\u001b[0m )\n",
      "File \u001b[0;32msrc/cftime/_cftime.pyx:498\u001b[0m, in \u001b[0;36mcftime._cftime.num2date\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/cftime/_cftime.pyx:99\u001b[0m, in \u001b[0;36mcftime._cftime._dateparse\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: 'months since' units only allowed for '360_day' calendar",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/Codes/parcels/parcels/tools/converters.py:233\u001b[0m, in \u001b[0;36mconvert_xarray_time_units\u001b[0;34m(ds, time)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 233\u001b[0m     da2 \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_cf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mda2\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/conventions.py:646\u001b[0m, in \u001b[0;36mdecode_cf\u001b[0;34m(obj, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m    644\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcan only decode Dataset or DataStore objects\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 646\u001b[0m \u001b[38;5;28mvars\u001b[39m, attrs, coord_names \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_cf_variables\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mvars\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconcat_characters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    650\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    652\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_coords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    653\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    654\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    655\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_timedelta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    656\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    657\u001b[0m ds \u001b[38;5;241m=\u001b[39m Dataset(\u001b[38;5;28mvars\u001b[39m, attrs\u001b[38;5;241m=\u001b[39mattrs)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/conventions.py:512\u001b[0m, in \u001b[0;36mdecode_cf_variables\u001b[0;34m(variables, attributes, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m    506\u001b[0m stack_char_dim \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    507\u001b[0m     concat_characters\n\u001b[1;32m    508\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m v\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mS1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    509\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m v\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    510\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m stackable(v\u001b[38;5;241m.\u001b[39mdims[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m    511\u001b[0m )\n\u001b[0;32m--> 512\u001b[0m new_vars[k] \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_cf_variable\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    513\u001b[0m \u001b[43m    \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[43m    \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    515\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconcat_characters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcat_characters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask_and_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    517\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    518\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstack_char_dim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstack_char_dim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    519\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    520\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_timedelta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decode_coords \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoordinates\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/conventions.py:360\u001b[0m, in \u001b[0;36mdecode_cf_variable\u001b[0;34m(name, var, concat_characters, mask_and_scale, decode_times, decode_endianness, stack_char_dim, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decode_times:\n\u001b[0;32m--> 360\u001b[0m     var \u001b[38;5;241m=\u001b[39m \u001b[43mtimes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCFDatetimeCoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    362\u001b[0m dimensions, data, attributes, encoding \u001b[38;5;241m=\u001b[39m variables\u001b[38;5;241m.\u001b[39munpack_for_decoding(var)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:526\u001b[0m, in \u001b[0;36mCFDatetimeCoder.decode\u001b[0;34m(self, variable, name)\u001b[0m\n\u001b[1;32m    525\u001b[0m calendar \u001b[38;5;241m=\u001b[39m pop_to(attrs, encoding, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalendar\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 526\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[43m_decode_cf_datetime_dtype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalendar\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[43muse_cftime\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    527\u001b[0m transform \u001b[38;5;241m=\u001b[39m partial(\n\u001b[1;32m    528\u001b[0m     decode_cf_datetime,\n\u001b[1;32m    529\u001b[0m     units\u001b[38;5;241m=\u001b[39munits,\n\u001b[1;32m    530\u001b[0m     calendar\u001b[38;5;241m=\u001b[39mcalendar,\n\u001b[1;32m    531\u001b[0m     use_cftime\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_cftime,\n\u001b[1;32m    532\u001b[0m )\n",
      "File \u001b[0;32m/opt/anaconda3/envs/py3_parcels/lib/python3.8/site-packages/xarray/coding/times.py:163\u001b[0m, in \u001b[0;36m_decode_cf_datetime_dtype\u001b[0;34m(data, units, calendar, use_cftime)\u001b[0m\n\u001b[1;32m    158\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munable to decode time units \u001b[39m\u001b[38;5;132;01m{\u001b[39;00munits\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m with \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcalendar_msg\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m. Try \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopening your dataset with decode_times=False or installing cftime \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    161\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it is not installed.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    162\u001b[0m     )\n\u001b[0;32m--> 163\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mValueError\u001b[0m: unable to decode time units 'months since 1955-01-01 00:00:00' with 'the default calendar'. Try opening your dataset with decode_times=False or installing cftime if it is not installed.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tempfield \u001b[38;5;241m=\u001b[39m \u001b[43mField\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mglob\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mWOA_data/woa18_decav_*_04.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_an\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                              \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlon\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlon\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Codes/parcels/parcels/field.py:390\u001b[0m, in \u001b[0;36mField.from_netcdf\u001b[0;34m(cls, filenames, variable, dimensions, indices, grid, mesh, timestamps, allow_time_extrapolation, time_periodic, deferred_load, **kwargs)\u001b[0m\n\u001b[1;32m    385\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;124mMultiple files given but no time dimension specified\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    387\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grid \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;66;03m# Concatenate time variable to determine overall dimension\u001b[39;00m\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;66;03m# across multiple files\u001b[39;00m\n\u001b[0;32m--> 390\u001b[0m     time, time_origin, timeslices, dataFiles \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollect_timeslices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimestamps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_filenames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    391\u001b[0m \u001b[43m                                                                      \u001b[49m\u001b[43m_grid_fb_class\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdimensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    392\u001b[0m \u001b[43m                                                                      \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnetcdf_engine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnetcdf_decodewarning\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    393\u001b[0m     grid \u001b[38;5;241m=\u001b[39m Grid\u001b[38;5;241m.\u001b[39mcreate_grid(lon, lat, depth, time, time_origin\u001b[38;5;241m=\u001b[39mtime_origin, mesh\u001b[38;5;241m=\u001b[39mmesh)\n\u001b[1;32m    394\u001b[0m     grid\u001b[38;5;241m.\u001b[39mtimeslices \u001b[38;5;241m=\u001b[39m timeslices\n",
      "File \u001b[0;32m~/Codes/parcels/parcels/field.py:248\u001b[0m, in \u001b[0;36mField.collect_timeslices\u001b[0;34m(timestamps, data_filenames, _grid_fb_class, dimensions, indices, netcdf_engine, netcdf_decodewarning)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fname \u001b[38;5;129;01min\u001b[39;00m data_filenames:\n\u001b[1;32m    246\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m _grid_fb_class(fname, dimensions, indices, netcdf_engine\u001b[38;5;241m=\u001b[39mnetcdf_engine,\n\u001b[1;32m    247\u001b[0m                         netcdf_decodewarning\u001b[38;5;241m=\u001b[39mnetcdf_decodewarning) \u001b[38;5;28;01mas\u001b[39;00m filebuffer:\n\u001b[0;32m--> 248\u001b[0m         ftime \u001b[38;5;241m=\u001b[39m \u001b[43mfilebuffer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\n\u001b[1;32m    249\u001b[0m         timeslices\u001b[38;5;241m.\u001b[39mappend(ftime)\n\u001b[1;32m    250\u001b[0m         dataFiles\u001b[38;5;241m.\u001b[39mappend([fname] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(ftime))\n",
      "File \u001b[0;32m~/Codes/parcels/parcels/fieldfilebuffer.py:171\u001b[0m, in \u001b[0;36mNetcdfFileBuffer.time\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtime\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 171\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[43mtime_access\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Codes/parcels/parcels/fieldfilebuffer.py:181\u001b[0m, in \u001b[0;36mNetcdfFileBuffer.time_access\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;28;01mNone\u001b[39;00m])\n\u001b[1;32m    180\u001b[0m time_da \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdimensions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m--> 181\u001b[0m \u001b[43mconvert_xarray_time_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime_da\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[43mdimensions\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    182\u001b[0m time \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([time_da[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdimensions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m]]\u001b[38;5;241m.\u001b[39mdata]) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(time_da\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(time_da[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdimensions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m]])\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(time[\u001b[38;5;241m0\u001b[39m], datetime\u001b[38;5;241m.\u001b[39mdatetime):\n",
      "File \u001b[0;32m~/Codes/parcels/parcels/tools/converters.py:235\u001b[0m, in \u001b[0;36mconvert_xarray_time_units\u001b[0;34m(ds, time)\u001b[0m\n\u001b[1;32m    233\u001b[0m     da2 \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mdecode_cf(da2)\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[0;32m--> 235\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;124mXarray could not convert the calendar. If you\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mre using from_netcdf, \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    236\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtry using the timestamps keyword in the construction of your Field. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    237\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee also the tutorial at https://nbviewer.jupyter.org/github/OceanParcels/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    238\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparcels/blob/master/parcels/examples/tutorial_timestamps.ipynb\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    239\u001b[0m ds[time] \u001b[38;5;241m=\u001b[39m da2[time]\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Xarray could not convert the calendar. If youre using from_netcdf, try using the timestamps keyword in the construction of your Field. See also the tutorial at https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_timestamps.ipynb"
     ]
    }
   ],
   "source": [
    "tempfield = Field.from_netcdf(glob('WOA_data/woa18_decav_*_04.nc'), 't_an', \n",
    "                              {'lon': 'lon', 'lat': 'lat', 'time': 'time'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we can create our own numpy array of timestamps associated with each of the 12 snapshots in the netcdf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamps = np.expand_dims(np.array([np.datetime64('2001-%.2d-15' %m) for m in range(1,13)]), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can add the `timestamps` as an extra argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tempfield = Field.from_netcdf(glob('WOA_data/woa18_decav_*_04.nc'), 't_an',\n",
    "                              {'lon': 'lon', 'lat': 'lat', 'time': 'time'},\n",
    "                              netcdf_decodewarning=False,\n",
    "                              timestamps=timestamps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, by the way, that adding the `time_periodic=True` argument to `Field.from_netcdf()` will also mean that the climatology can be cycled for multiple years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, note that we used `netcdf_decodewarning=False` in the `FieldSet.from_netcdf()` call above. This is to silence an expected warning because the time dimension in the `coordinates.nc` file can't be decoded by `xarray`."
   ]
  }
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