notebooks/2-plot-aims-ereefs-time-plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot AIMS eReefs time data with Python\n",
    "\n",
    "In this notebook we use OpeNDAP to extract time series data at a single location of interest, then plot this data. This extraction process can also be done with the [AIMS eReefs data extraction tool](https://extraction.ereefs.aims.gov.au/). If you which to perform bigger extractions then we recommend using this tool instead of this process outlined in this example. \n",
    "\n",
    "> ***Note***: This script has no error checking and so changing the date ranges or locations might result in out of bounds errors.\n",
    "\n",
    "## Load the required Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset, num2date\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import os\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "cartopy.config['data_dir'] = os.getenv('CARTOPY_DIR', cartopy.config.get('data_dir'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose OPeNDAP end point\n",
    "The first part of the process is to choose the OPeNDAP end point on the AIMS eReefs THREDD server. You can view the products here: http://thredds.ereefs.aims.gov.au/thredds/s3catalogue/aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/catalog.html.\n",
    "At this stage there is no grouped OPeNDAP service for the entire time series and so this script only works for looking at a single month of data. Hopefully this can be improved in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-03'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Connect to the OpeNDAP endpoint for the specified month.  \n",
    "month = 3\n",
    "year = 2020\n",
    "netCDF_datestr = str(year)+'-'+format(month, '02')\n",
    "netCDF_datestr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_cur': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 mean_cur(time, k, latitude, longitude)\n",
       "     coordinates: time zc latitude longitude\n",
       "     substanceOrTaxon_id: http://environment.data.gov.au/def/feature/ocean_current\n",
       "     units: ms-1\n",
       "     medium_id: http://environment.data.gov.au/def/feature/ocean\n",
       "     unit_id: http://qudt.org/vocab/unit#MeterPerSecond\n",
       "     short_name: mean_cur\n",
       "     aggregation: mean_speed\n",
       "     standard_name: mean_current_speed\n",
       "     long_name: mean_current_speed\n",
       "     _ChunkSizes: [  1   1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 17, 723, 491)\n",
       " filling off,\n",
       " 'salt': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 salt(time, k, latitude, longitude)\n",
       "     coordinates: time zc latitude longitude\n",
       "     substanceOrTaxon_id: http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater\n",
       "     scaledQuantityKind_id: http://environment.data.gov.au/def/property/practical_salinity\n",
       "     short_name: salt\n",
       "     aggregation: Daily\n",
       "     units: PSU\n",
       "     medium_id: http://environment.data.gov.au/def/feature/ocean\n",
       "     unit_id: http://environment.data.gov.au/water/quality/def/unit/PSU\n",
       "     long_name: Salinity\n",
       "     _ChunkSizes: [  1   1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 17, 723, 491)\n",
       " filling off,\n",
       " 'temp': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 temp(time, k, latitude, longitude)\n",
       "     coordinates: time zc latitude longitude\n",
       "     substanceOrTaxon_id: http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater\n",
       "     scaledQuantityKind_id: http://environment.data.gov.au/def/property/sea_water_temperature\n",
       "     short_name: temp\n",
       "     aggregation: Daily\n",
       "     units: degrees C\n",
       "     medium_id: http://environment.data.gov.au/def/feature/ocean\n",
       "     unit_id: http://qudt.org/vocab/unit#DegreeCelsius\n",
       "     long_name: Temperature\n",
       "     _ChunkSizes: [  1   1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 17, 723, 491)\n",
       " filling off,\n",
       " 'u': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 u(time, k, latitude, longitude)\n",
       "     vector_components: u v\n",
       "     coordinates: time zc latitude longitude\n",
       "     substanceOrTaxon_id: http://environment.data.gov.au/def/feature/ocean_current\n",
       "     scaledQuantityKind_id: http://environment.data.gov.au/def/property/sea_water_velocity_eastward\n",
       "     short_name: u\n",
       "     vector_name: Currents\n",
       "     standard_name: eastward_sea_water_velocity\n",
       "     aggregation: Daily\n",
       "     units: ms-1\n",
       "     medium_id: http://environment.data.gov.au/def/feature/ocean\n",
       "     unit_id: http://qudt.org/vocab/unit#MeterPerSecond\n",
       "     long_name: Eastward current\n",
       "     _ChunkSizes: [  1   1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 17, 723, 491)\n",
       " filling off,\n",
       " 'v': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 v(time, k, latitude, longitude)\n",
       "     vector_components: u v\n",
       "     coordinates: time zc latitude longitude\n",
       "     substanceOrTaxon_id: http://environment.data.gov.au/def/feature/ocean_current\n",
       "     scaledQuantityKind_id: http://environment.data.gov.au/def/property/sea_water_velocity_northward\n",
       "     short_name: v\n",
       "     vector_name: Currents\n",
       "     standard_name: northward_sea_water_velocity\n",
       "     aggregation: Daily\n",
       "     units: ms-1\n",
       "     medium_id: http://environment.data.gov.au/def/feature/ocean\n",
       "     unit_id: http://qudt.org/vocab/unit#MeterPerSecond\n",
       "     long_name: Northward current\n",
       "     _ChunkSizes: [  1   1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 17, 723, 491)\n",
       " filling off,\n",
       " 'zc': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 zc(k)\n",
       "     positive: up\n",
       "     coordinate_type: Z\n",
       "     units: m\n",
       "     long_name: Z coordinate\n",
       "     axis: Z\n",
       "     _CoordinateAxisType: Height\n",
       "     _CoordinateZisPositive: up\n",
       " unlimited dimensions: \n",
       " current shape = (17,)\n",
       " filling off,\n",
       " 'time': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 time(time)\n",
       "     units: days since 1990-01-01 00:00:00 +10\n",
       "     long_name: Time\n",
       "     standard_name: time\n",
       "     coordinate_type: time\n",
       "     calendar: gregorian\n",
       "     _CoordinateAxisType: Time\n",
       "     _ChunkSizes: 1024\n",
       " unlimited dimensions: time\n",
       " current shape = (31,)\n",
       " filling off,\n",
       " 'latitude': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 latitude(latitude)\n",
       "     long_name: Latitude\n",
       "     standard_name: latitude\n",
       "     units: degrees_north\n",
       "     coordinate_type: latitude\n",
       "     projection: geographic\n",
       "     _CoordinateAxisType: Lat\n",
       " unlimited dimensions: \n",
       " current shape = (723,)\n",
       " filling off,\n",
       " 'longitude': <class 'netCDF4._netCDF4.Variable'>\n",
       " float64 longitude(longitude)\n",
       "     standard_name: longitude\n",
       "     long_name: Longitude\n",
       "     units: degrees_east\n",
       "     coordinate_type: longitude\n",
       "     projection: geographic\n",
       "     _CoordinateAxisType: Lon\n",
       " unlimited dimensions: \n",
       " current shape = (491,)\n",
       " filling off,\n",
       " 'mean_wspeed': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 mean_wspeed(time, latitude, longitude)\n",
       "     coordinates: time latitude longitude\n",
       "     units: ms-1\n",
       "     short_name: mean_wspeed\n",
       "     aggregation: mean_speed\n",
       "     standard_name: mean_wind_speed\n",
       "     long_name: mean_wind_speed\n",
       "     _ChunkSizes: [  1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 723, 491)\n",
       " filling off,\n",
       " 'eta': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 eta(time, latitude, longitude)\n",
       "     coordinates: time latitude longitude\n",
       "     substanceOrTaxon_id: http://environment.data.gov.au/def/feature/ocean_near_surface\n",
       "     scaledQuantityKind_id: http://environment.data.gov.au/def/property/sea_surface_elevation\n",
       "     short_name: eta\n",
       "     standard_name: sea_surface_height_above_sea_level\n",
       "     aggregation: Daily\n",
       "     units: metre\n",
       "     positive: up\n",
       "     medium_id: http://environment.data.gov.au/def/feature/ocean\n",
       "     unit_id: http://qudt.org/vocab/unit#Meter\n",
       "     long_name: Surface elevation\n",
       "     _ChunkSizes: [  1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 723, 491)\n",
       " filling off,\n",
       " 'wspeed_u': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 wspeed_u(time, latitude, longitude)\n",
       "     coordinates: time latitude longitude\n",
       "     short_name: wspeed_u\n",
       "     aggregation: Daily\n",
       "     units: ms-1\n",
       "     long_name: eastward_wind\n",
       "     _ChunkSizes: [  1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 723, 491)\n",
       " filling off,\n",
       " 'wspeed_v': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 wspeed_v(time, latitude, longitude)\n",
       "     coordinates: time latitude longitude\n",
       "     short_name: wspeed_v\n",
       "     aggregation: Daily\n",
       "     units: ms-1\n",
       "     long_name: northward_wind\n",
       "     _ChunkSizes: [  1 133 491]\n",
       " unlimited dimensions: time\n",
       " current shape = (31, 723, 491)\n",
       " filling off}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OPeNDAP URL to file \"EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-YYYY-MM.nc\". Hydrodynamic 4km model, daily data for the month specified\n",
    "inputFile = \"http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-\"+netCDF_datestr+\".nc\"\n",
    "\n",
    "nc_data = Dataset(inputFile, 'r')\n",
    "print(nc_data.title)\n",
    "\n",
    "# To find a list of the variables uncomment the next line: \n",
    "nc_data.variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the point location\n",
    "Work out the bounds of the gridded data. We can then use this to find out which grid cell best matches our location of interest. \n",
    "> ***Note***: This only works because the AIMS eReefs aggregate datasets are regridded onto a regularly spaced grid. The original raw model data is on a curvilinear grid and this approach would not work for that data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid bounds, Lon: 142.168788 - 156.868788 Lat:-28.696022 - -7.036022\n",
      "Grid size is: 491 x 723\n"
     ]
    }
   ],
   "source": [
    "lons = nc_data.variables['longitude'][:]\n",
    "max_lon = max(lons)\n",
    "min_lon = min(lons) \n",
    "lats = nc_data.variables['latitude'][:]\n",
    "max_lat = max(lats)\n",
    "min_lat = min(lats)\n",
    "grid_lon = lons.size\n",
    "grid_lat = lats.size\n",
    "print(\"Grid bounds, Lon: \"+str(min_lon)+\" - \"+str(max_lon)+\" Lat:\"+str(min_lat)+\" - \"+str(max_lat))\n",
    "print(\"Grid size is: \"+str(grid_lon)+\" x \"+str(grid_lat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the closest index to the location of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid position of location: 330, 183\n"
     ]
    }
   ],
   "source": [
    "# Davies reef\n",
    "lat = -18.82\n",
    "lon = 147.64\n",
    "selectedLatIndex = round((lat-min_lat)/(max_lat-min_lat)*grid_lat)\n",
    "selectedLonIndex = round((lon-min_lon)/(max_lon-min_lon)*grid_lon)\n",
    "print(\"Grid position of location: \"+str(selectedLatIndex)+\", \"+str(selectedLonIndex))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract values\n",
    "Extract the values over time at this location. Note that because we are access the underlying data here this results in an OpeNDAP call to get the data from the remote server. As a result this call can take a while (~10 sec)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[29.586527 27.764503]\n",
      " [29.513176 27.867159]\n",
      " [29.505758 28.179167]\n",
      " [29.782936 28.216429]\n",
      " [30.008278 28.062304]]\n"
     ]
    }
   ],
   "source": [
    "selectedDepthIndex = 15 # -1.5m\n",
    "selectedDepthIndex2 = 10 # -17.75m\n",
    "\n",
    "# Time, Depth, Lat, Lon\n",
    "dailyTemp1 = nc_data.variables['temp'][:,[selectedDepthIndex,selectedDepthIndex2], selectedLatIndex, selectedLonIndex]\n",
    "print(dailyTemp1[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the wind for the same location. The wind variable doesn't have any depth dimension and so our indexing into the data is different. The wind is a vector measurement, with an x and y component. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wspeed_v = nc_data.variables['wspeed_v'][:, selectedLatIndex, selectedLonIndex]\n",
    "wspeed_u = nc_data.variables['wspeed_v'][:, selectedLatIndex, selectedLonIndex]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the wind speed we need to calculate the magnitude of this vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "wspeed = np.sqrt(wspeed_v**2 + wspeed_u**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the time series. Note that the time values are stored as the number of days since 1990-01-01 00:00:00 +10. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11017. 11018. 11019. 11020. 11021.]\n"
     ]
    }
   ],
   "source": [
    "times = nc_data.variables['time'][:]\n",
    "print(times[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert the days since the 1990 origin into Pandas dates for plotting\n",
    "t = pd.to_datetime(times,unit='D',origin=pd.Timestamp('1990-01-01'))\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "fig.set_size_inches(8, 7)\n",
    "\n",
    "\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Temperature (deg C)')\n",
    "ax1.plot(t, dailyTemp1[:,0], color='tab:red', label='Temp (-1.5 m)')\n",
    "ax1.plot(t, dailyTemp1[:,1], color='tab:orange', label='Temp (-17.75 m)')\n",
    "#ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Wind speed (m/s)', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(t, wspeed, color=color, label='Wind')\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.legend()\n",
    "# Set the axes formating to show the dates on an angle on the current figure (gcf)\n",
    "plt.gcf().autofmt_xdate()\n",
    "\n",
    "#fig.tight_layout()  # otherwise the right y-label is slightly clipped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this graph we can see that the surface water at Davies Reef was very warm during March 2020. There was a strong stratification of the temperature profile with cool water at -18 m. Around the 10th March the wind picked up for a few days, mixing the water, cooling the surface down rapidly."
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}