{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gridded Data Renderer\n",
    "\n",
    "PAVICS uses ncWMS2 to render netCDF data on a map canvas. ncWMS2 use the OGC's WMS standard to serve images rendered from a netCDF file. The WMS `GetMap` operation specifies a layer (<netCDF file path>/<variable name>), styles, figure size and format, projection and color range and the server returns an image that can be displayed in a figure or a map canvas. \n",
    "   \n",
    "   \n",
    "Note that with the current configuration, the path for ncWMS layers starts with `outputs` and not `birdhouse` as in the THREDDS server. Note also that the image we obtain below has the North down. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from owslib.wms import WebMapService\n",
    "from IPython.display import Image\n",
    "\n",
    "url = \"https://pavics.ouranos.ca/twitcher/ows/proxy/ncWMS2/wms\"\n",
    "wms = WebMapService(url, version='1.3.0')\n",
    "link = 'outputs/testdata/flyingpigeon/cmip5/tasmax_Amon_MPI-ESM-MR_rcp45_r1i1p1_200601-200612.nc'\n",
    "resp = wms.getmap(\n",
    "          layers=[\"{}/{}\".format(link, \"tasmax\")], \n",
    "          format='image/png', \n",
    "          colorscalerange='{},{}'.format(250, 350), \n",
    "          size=[256,256], \n",
    "          srs='CRS:84', \n",
    "          bbox=(150, 30, 250, 80),\n",
    "          time='2006-02',\n",
    "          transparent=True)\n",
    "Image(resp.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One issue with the figure above is the colorscale range and the colormap, which do not provide a lot of contrast. In PAVICS, the data crawler while parsing the netCDF metadata also computes the min and max, so that the frontend knows for a given file the data range and can include it in its WMS request. Here, we'll do the same using the OPeNDAP protocol. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(241.01828002929688, 308.3053894042969)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "dap = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/'\n",
    "ncfile = 'birdhouse/testdata/flyingpigeon/cmip5/tasmax_Amon_MPI-ESM-MR_rcp45_r1i1p1_200601-200612.nc'\n",
    "ds = xr.open_dataset(dap+ncfile)\n",
    "subtas = ds.tasmax.sel(time=slice('2006-02-01', '2006-03-01'), lon=slice(188,330), lat=slice(6, 70))\n",
    "mn, mx = subtas.min().values.tolist(), subtas.max().values.tolist()\n",
    "mn, mx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll simply pass those min/max to `getmap` with the colorscalerange parameter, and change the palette in the same go using the `styles` parameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resp = wms.getmap(\n",
    "          layers=[\"{}/{}\".format(link, \"tasmax\")], \n",
    "          styles=['default/div-Spectral'],\n",
    "          format='image/png', \n",
    "          colorscalerange='{},{}'.format(mn, mx), \n",
    "          size=[256,256], \n",
    "          srs='CRS:84', \n",
    "          bbox=(150, 30, 250, 80),\n",
    "          time='2006-02',\n",
    "          transparent=True)\n",
    "Image(resp.read())"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}