docs/source/notebooks/README.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to the PAVICS's JupyterLab\n",
    "\n",
    "This is Ouranos' JupyterLab server for the PAVICS platform. PAVICS is a climate science gateway hosting both climate data and analytical services:\n",
    "- NetCDF datasets: [THREDDS Data Server](https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/)\n",
    "- Geospatial layers: [GeoServer](https://pavics.ouranos.ca/geoserver)\n",
    "- Climate indicators: [Finch](https://pavics.ouranos.ca/twitcher/ows/proxy/finch/)\n",
    "- Hydrological modeling: [Raven](https://pavics.ouranos.ca/twitcher/ows/proxy/raven/)\n",
    "- Experimental utilities: [FlyingPigeon](https://pavics.ouranos.ca/twitcher/ows/proxy/flyingpigeon/)\n",
    "\n",
    "These servers are intended to be accessed through a programming environment. More precisely, analytical services can be accessed through a Web Processing Services (WPS) Application programming interface (API). To facilitate the integration of these WPS processes into typical scientific workflows, we suggest using the high-level Python client library (`birdy.WPSClient`). \n",
    "\n",
    "The public demo account on the login page of this JupyterLab is not meant as a production environment. It is a public account with no private directory and limited computing resources. Your files are public, can be seen by the rest of the world and can be reset at anytime. Use it to explore the system's capability and see whether it may be useful to your work.\n",
    "\n",
    "You can request personal account by contacting the email on the login page.  With a personal account, you will have a production environment with private workspace and no computing resource limitation.\n",
    "\n",
    "## Example: Computing a climate index at a given coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# Open the connection to the WPS server\n",
    "from birdy import WPSClient\n",
    "wps = WPSClient('https://pavics.ouranos.ca/twitcher/ows/proxy/finch/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Launch the process extracting the daily time series from a grid point\n",
    "url = \"https://pavics.ouranos.ca/thredds/dodsC/birdhouse/\"\\\n",
    "      \"cmip5/MRI/rcp85/day/atmos/r1i1p1/pr/pr_day_MRI-CGCM3_rcp85_r1i1p1_20060101-20151231.nc\"\n",
    "resp = wps.subset_gridpoint(url, -108, 45, variable='pr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://pavics.ouranos.ca/wpsoutputs/a1652284-c03d-11e9-a6ff-0242ac12000c/pr_day_MRI-CGCM3_rcp85_r1i1p1_20060101-20151231_sub.nc\n"
     ]
    }
   ],
   "source": [
    "# Inspect the response\n",
    "reference, meta = resp.get()\n",
    "print(reference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the process is stored on the server. You may reuse this link as an input to another process, or open it locally. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading to /tmp/tmpphgljtuh/pr_day_MRI-CGCM3_rcp85_r1i1p1_20060101-20151231_sub.nc.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 3652)\n",
       "Coordinates:\n",
       "    lon      float64 ...\n",
       "  * time     (time) datetime64[ns] 2006-01-01T12:00:00 ... 2015-12-31T12:00:00\n",
       "    lat      float64 ...\n",
       "Data variables:\n",
       "    pr       (time) float32 ...\n",
       "Attributes:\n",
       "    institution:                     MRI (Meteorological Research Institute, ...\n",
       "    institute_id:                    MRI\n",
       "    experiment_id:                   rcp85\n",
       "    source:                          MRI-CGCM3 2011 atmosphere: GSMUV (gsmuv-...\n",
       "    model_id:                        MRI-CGCM3\n",
       "    forcing:                         GHG, SA, Oz, LU, Sl, Vl, BC, OC (GHG inc...\n",
       "    parent_experiment_id:            historical\n",
       "    parent_experiment_rip:           r1i1p1\n",
       "    branch_time:                     56978.0\n",
       "    contact:                         Seiji Yukimoto (yukimoto@mri-jma.go.jp)\n",
       "    history:                         Output from /sharex2/cmip5/rcp85/run-C3_...\n",
       "    references:                      Model described by Yukimoto et al. (Tech...\n",
       "    initialization_method:           1\n",
       "    physics_version:                 1\n",
       "    tracking_id:                     0fd95a33-a805-4e18-8821-3c4e51fcf800\n",
       "    product:                         output\n",
       "    experiment:                      RCP8.5\n",
       "    frequency:                       day\n",
       "    creation_date:                   2011-06-09T19:59:26Z\n",
       "    Conventions:                     CF-1.4\n",
       "    project_id:                      CMIP5\n",
       "    table_id:                        Table day (27 April 2011) 86d1558d99b6ed...\n",
       "    title:                           MRI-CGCM3 model output prepared for CMIP...\n",
       "    parent_experiment:               historical\n",
       "    modeling_realm:                  atmos\n",
       "    realization:                     1\n",
       "    cmor_version:                    2.6.0\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr, meta = resp.get(asobj=True)\n",
    "pr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use the precipitation time series stored on the server to compute the maximum annual daily precipitation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp_rx1 = wps.rx1day(reference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rx1, meta = resp_rx1.get(asobj=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd3a7286a20>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k8dCHZqPA46v3ux3WkBLojmMtsC7A1i1VLVfVrwBTgKXA74B9IvItEelpkqYLgfeKyF7gcWChiPxJVQ+rVyvwCDA3wHUfUtXZqjo7Ly8vwK8VWWWVHpIT4phZODxk5zzVs8qaq8wQ9PDyPRyqb+Er10yjMDeVS6bk8fiafXR0nt5YYcKlx+K4qj7a35OKyAzgduA9wFPAEmAB8Cows5trfQnv3QUicinwWVX9oIjkq+ph547lemBLf2NyS1mFh9nF2SQnhK7nR9awRMZm29ocZuipbmjll8vKeXfJKOY5d/GLS4v4zz+s5ZUdx7hy+miXIxwaeu1VJSJ5eJuISoAU3+OqurCH/dcBx4GHgS86dwsAq0Tkwj7Gt8S5vgAbgDv7eLyrPI2t7DjSwOeuPCvk57YCuRmKfvzPXbR2dPHFq6eeeuyys/IYnZnCklX7LHFESDDF8SV4u8iOB74F7AXWBNj/FlW9XFWX+iUNAFS110GCqvqaql7rfL9QVc9R1bNV9YOq2hhEvFFj1Z5agFOfjEKppCCTPTVNNLd1hPzc0WRVpYcvPrWJLptiZcjbeaSBJ9bs44PzipjgN/g1IT6OW+eO49+7q9nnaXYxwqEjmMSRq6oPA+2q+rozVmNeTzuraqWIXCMinxeRr/u2kEUcQ8oqPKQmxTNjbFbIz12Sn4kq7DgyuNfm+ENZFY+v2c/y8hq3QzEu++6L20lPTuBTl08+47lb5xQSJ8Jja/a5ENnQE0ziaHe+HnYSwnnA2J52FpFfAx8APom3iekWoGiggcaiskoPc4pzSIwP/ZRgJUNgbY6uLmVFhTdhLFlV5XI0xk1v7Krm9V3VfHLhZLLTks54fnRWCpdPHcmf1+ynrcOK5OEWzDvad0QkC/gM8Fm8A/TuC7D/fFX9MFCnqt8CLgDGDTjSGHOsoYXyY40hHb/hb8zwYWQNSxzUU6xvO3yC483tFOWm8q/txzh6wsatDEWdXcp3X9xOYU4qH57f82fQxfOK8DS18fetRyIY3dAUTOKoU9V6Vd2iqpep6iygNsD+vpE4zSJSgPeOZfxAA401Kyu9L1Eox2/4E5FBXyD3NU/94OZz6exSnlhjffWHor+s3c+OIw184aqpAXsnXjRpBONyhtndaQQEkzh+HuRjPi+IyHDgB8B6vMX0x/seWmwrq/CQkZxwarnXcCgpyGTH4RODtv/6ivIapoxKZ+74HC6aPILHV++zdUiGmKbWDn70z13MKsrmPecE7jEVFycsmlvEyspayo/FVD+amBNo5PgFIvIZIE9E7vfbvgn0mPZV9duqelxVn8Jb25iqql8LeeRRbmWlh7njc0gIQ33DpyQ/k9aOrkG5NkdLeyer99RyoTN53aK5hRyqb+G1ncdcjsxE0m9er6C6oZWvXDONXiagAOCW2WNJjBeWrrIieTgFGseRBKQ7+2T4PX4CuPn0nQOtxyEiqOrT/Q0y1hypb2FPTROLSwvDeh1fgXzroRNMGpnRy96xZX1VHa0dXSxwEscVJaPIy0hm6ap9XD5tlMvRmUg4XH+Sh/5dybUz8jm/MLip70akJ3Pl9NE8tf4An7/qLJtyPUwCjRx/HXhdRH6vqlUikuF9uMexFNc5X0cC8/GOEge4DHgNGDKJo6zS2zYfjvEb/ibmpZMUH8e2wyd438wxYb1WpC0vryE+Tih1XsPE+Dg+MHscv3itnAN1zYzNTnU5QhNuP/z7Lrq64AtXTe19Zz+LS4t4YdNh/rbpMDfN6rEDqBmAYNpRMkTkLbzTfWwVkXUicvbpO6nq7ap6O6BAiarepKo3AdNDG3L0K6vwkDUs8dRkhOGSlBDH5FHpg7JL7oryGs4bN5z05Lc/29w619s5z4rkg9+Wg/U8/dYBbr+wmHE5ffuQMG9CDhPy0qxIHkbBJI6HgPtVtUhVi/B2y30owP7FqnrY7+ejeCc8HDLKKj2Ujs8hLq73NtmBKsnPZNuhE6gOnqJxfXM7mw7Wn6pv+IzNTuXSKXk8sWY/7YO0Q4ABVeW//7ad4cMSueuySX0+XkRYXFrE+n3HB+WHqmgQTOJIU9Vlvh9U9TUgLcD+r4nI30XkoyLyEeBvwLIA+w8qB+qa2V97MmzjN05XUpCJp6mNYw2tve8cI8oqa1CFBZPPXNVtcWkRxxpaeWX7URciM5HwyvZjlFV6uO+KKWQNS+zXOW46fwzJCXEsXW13HeEQTOKoFJGviUixs30V2NPTzqp6D/Ab4Fy8M+E+pKqfDE240a+swgMQscQxvcA7nclg+mS1vLyGtKR4Zo47cyr6y6aOpCDLO6GdGXzaO7v47kvbmZCXxqIBdC4ZnprEtTMKeGb9QRpbB/d8bm4IJnH8B5CHt7j9NDAC+OjpOzl3GZ8Wkamq+rSqftrZnglpxFGurMJDTloSUyLUy2lqvvc6g2kg4IpyD6UTcrudqiU+TvjAnEL+vbuGqkHYDXmoe2z1Piqrm/jS1dMGPFXPotJCmto6eW7DoRBFZ3yC+Ze5QlXvVdXzne0+4F3d7PcRoA74poisF5Fficj7RCS9m30HJVWlrNLDvAmRqW8AZKYkUpiTOmjuOA7UNbOnpumM+oa/D8wZR3yc8Jit+jaonGhp5yf/2s28CTlcMW3kgM93fuFwpo7OYMmqqkFVA4wGwSSOLwXzmKoeUdXfq+qtwGzgD8As4O8i8i8R+fzAQo1+VZ5mDte3cMHEnt/0wmEwTT3yZrm3qW9BgMThm9DuL2ttQrvB5BfLyqlrbuOr15QENdivNyLC4nlFbD10gk0H6kMQofEJNHL8ahH5OTDmtPXGfw8EbDRU1S5VLVPVr6vqhXjXIT8Y0sijUFmlU98I8/iN05UUZLLX0zQo2nJXVNQwIj2ZKaMC36jahHaDy/7aZh5ZvpcbzhvD2WNCtwzB9TMLSE2Kt665IRbojuMQ3nXHW3jnWuPPAVf2dJCIjBeRB0TkaRF5TkSeA36nqktCGHdUKqvwkJeRzMS8QJ3OQs+3NsfOI7F916GqrCivYcGk3F4/cdqEdoPL9/++k7g4Qr5aZkZKIu+bOYbnNh6i/mR77weYoAQaOb4R2CgiS1W1L6/4X/EuG/scMGTaEXz1jQsm9P6mF2r+U4/MKsqJ6LVDaefRBmoa2wLWN3zi4oTb5hby/Zd3Un6skUkjh0wpbdB5a18dz288xL0LJ5GfNSzk519cWshjq/fxzPoDfPTCITdRd1j0WuPoY9IAaFHVn6nqMmfFwNed6UsGtYrqJqobWiPWDddfflYK2amJMV8gX77bO1VLMIkD4JZZ40iIEx5bbV1zY5Wq8p2/bScvI5n/d8nEsFzj7DFZnDtuOEtW7bMieYiEY+rWn4rIN5zZdc/3bWG4TlRxq74BztocBbFfIF9RXsOEEWkUDA/uU2deRjJXnj2aJ9cdoKW9M8zRmXB4acsR1lXV8Zl3TSEtOdCcqwOzeG4hu481smZvXdiuMZT0mji6m5eqF+cA/wl8D/iRs/2w76HFlpUVHvKzUijKdWfyvZL8THYcaYjZtTnaOrpY5TeNerAWlxZSf7KdFzcf7n1nE1VaOzr53ks7mDo6g1tmh3eR0GvPzScjJYGlVhMLiWDuOH4tIqtF5C5ngabe3ABMUNVLnBUDL1PVhQOMM6qpKitdqm/4lBRk0tbRRWVNbA6K27D/OM1tnX1OHBdMyGXCiDQbSR6D/vBmFftqm/nye6YRH+ZxT6lJCdx0/lhe3HyE2qa2sF5rKAimxrEAWIx33fC1IrJURLobAOizEQgmwQwau4424mlqY54L9Q2fkvzYnnpkeXkNcdL3pj4RYVFpIeuq6tgR473KhpK6pjZ+/upuLpmSx8VT8iJyzUWlhbR1dvHkOhs4OlBB1ThUdTfwVeALwCXAz0RkRw+LN40CdjhTkDzn1yV30Cqr8BZ13ahv+EzISyMpIS5m6xwryms4Z+xwslL7PqndTeePJSkhzlZ9iyE/fWU3ja0dfOWaaRG75pRRGcwtzmHpqn102RLEAxJMjWOGiDwAbAcWAtep6jTn+we6OeQbeJurvsvbNY4fhSziKFRW6WFs9rA+rxsQSonxcZw1KoOth2JvhGxDSzsb9h9nwaT+Jd7stCSuOSefZ9YfpLkt9gdBDnaV1Y38aWUVH5hTyJRRkV25cvG8QvZ6mnnTmYzU9E8wdxwPAuuBc1X1blVdD6Cqh/DehbyDX/fbt4DNftug1NWlrNpT6+rdhk+srs2xqrKWzi7tc33D36LSQhpaO3h+o01oF+2+99IOkhPiuP9dkV+m56qzR5OTlmQDRwcomBrHxar6R1U92c1zfzz9MRG5Q0SOApvwjjxf53wdlLYfOcHx5nZXxm+cbvqYTOqa2zlyosXtUPpkeXkNKYlxQa8r3Z3ZRdlMGZVuRfIot7LSwz+2HeUTl04kLyM54tdPTojn5llj+ce2oxyLsb+TaBJMU9VkEXlSRLaJSKVvC3DI54DpqlqsqhNUdbyqTghdyNEl0utvBOJbqjbWCuQrymuYU5xDSmJ8v8/hW/Vt04F6NtuEdlGpq8u7sl9+VgofW+DeW8Jtcwvp7FL+vNaK5P0VTFPVI8Cv8E5seBneWW/PuNPwUwE0Dzy02FBW4aE4NzUsUyX01dQYTBxHT7Sw+1hjwNlwg3X9eWNISbRV36LVsxsPsvlgPZ+78iyGJfX/Q8JAjR+RxoJJI3hs9X46rUjeL8EkjmGq+gogqlqlqt/EWxjvyZeAN0XkN/6z6oYi2GjT0dnF6j21EZ9GvSfpyQkU56bGVM+qFeV9m2YkkKxhibz33AKe3XCIhhab0C6atLR38oOXd3LOmCyunznG7XBYXFrIweMneX3XMbdDiUnBJI4WEYkDdovIPSJyAxBolZXfAK8CK3nnrLq9EpF4EXlLRF5wfh4vIqtEZLeIPCEiScGcJ1K2HjpBQ2tHVDRT+cTa1CPLy2vITk081cw2UItKi2hu6+SvtupbVHl4+R4O1bfwlWumRWyRs0CuKBlFXkYyS1ZaTaw/gkkc9wGpwL14F2b6EN7V/nrSoar3q+ojqvqobwsynk/h7fbr87/AA6o6Ge/qgh8L8jwR4Zufat6E6JmRtiQ/kypPc0x84vZNoz5/0oiQvZmcOzaL6QWZLLUJ7aJGdUMrv1xWzrtKRjEvCnofgrf7+q1zxvHqzmMcqBsyLeshE0yvqjWq2qiqB1T1dlW9UVVXBjhkmdOzKl9Ecnxbb9cRkbHANcD/OT8L3iaxJ51dHgWu7/U3iqCyCg+TRqYzMiPF7VBO8U2xvv1wg8uR9K6iupGjJ1pDUt/w8Y0k3374BG/tPx6y85r+e+Bfu2jt6OJLV091O5R3uHVuIQI8scaK5H0VaAXA5/1Hfp++BTjnIpw6B283UwXTHfcnwOd5ew2PXOC4qvpGdB0AemwcdZLVWhFZW11dHcTlBqa9s4s1e6Nj/Ia/6QW+qUeiv2eRbxr1UCYOgPfNHENaUrw1Q0SBXUcbeHz1Pj44r4gJedG1ZsqY4cO49KyRPL5mP+0xOjmoWwLdcfwQ74jvPcBJ4LfO1ghs6ekgp/vt6VvAvncici1wTFX9ayHdtV302Pagqg+p6mxVnZ2XF/65bzYdqKe5rTOq6hsAIzOSyU1Liok6x/JyD4U5qSEfcZ+enMD1543hhU2HqG+O/ia7wey7L24nPTmBT10+2e1QurW4tJDqhlZe2X7U7VBiSo+Jw28E+Hmq+gFVfd7ZFgELTt8/mDU3AuxzIfBeEdkLPI63ieonwHAR8U3SPxbvcrZRYeWp+kZ0JY5YWZujo7OLlZWekPSm6s6i0kJaO7p4av2BsJzf9O7fu6t5bWc1n1w4mey0qOrXcsqlZ42kICvFBo72UTDF8TwROXXHICLjge4+0j8iItn+dY3TN7xLyp5BVb+kqmNVtRi4FXhVVRcDy4Cbnd0+Ajzbh98trMoqPEwdnUFOFP5BlORnsutIY1Tffm88UE9ja0fIm6l8phdkMXPccJautiK5GzqdwX6FOal8eH6R2+H0KN5Zgvjfu2vYG6NLErghmMTxaeA1EXlNRF7D+2b+qW72y+Kd3W+72/rabvAF4H4RKcdb8+g28URaa0cna6tqo+5uw6ekIJO2zi4qqhvtcLRoAAAgAElEQVTdDqVHK8prEAnviPtFpYWUH2tk9Z7asF3DdO/JdfvZcaSBL1w1leQE9wb7BeMDc8YRb0sQ90mvazWq6ssiMhnwdYnYoaqt3exXHIqAVPU14DXn+0pgbijOG0ob99fT0t4VdfUNH/+pR6aODs34iFBbXl7D9ILMsN6xXTejgG+/sI2lq/dRGqVJfjBqau3gh//YxayibN5zzmi3w+nVyMwU3l0yij+v3c/9754S9YkuGgS7Hkerqm4E7u4uaQw1ZRUeRGDe+Oh8Mxo/Io3khDi2RunUI81tHby1ry5s9Q2fYUnx3HT+WF7afARP45D/bxsxv3mjkuqGVr5yzTTXVsTsq8WlRdQ1t/PyliNuhxITgkocfmaHJYoYU1ZZQ0l+Zr8WHYqEhPg4po7OiNo5q1bvqaW9U8NW3/D39qpvViSPhCP1LTz0RgXXzsgf0GzHkTZ/Yi5FuanWhTtIfU0cQ35il5b2TtbvOx514zdOV1KQxbbD0bk2x4ryGpIS4phTHP4R91NGZTCnOJvHVtuqb5Hww3/spKsLvnBVdA32601cnLBobiGr99ay62j0D551W58Sh6peFcx+IjJGROaLyMW+rX/hRZ/1++po64je+oZPSUEm9SfbOVQffWsOLC/3MLsoe0DTqPfF4tIiW/UtArYequep9Qe4/cJiV1fD7K+bZ40lKd6WIA5GX+84ABCRhwI897/ACryrA37O2T7br+iiUFmFhziBOeOjZ36q7kTr2hw1ja1sP3wi7PUNf1edPZrs1ESbbj2MVL3db4cPS+Suyya5HU6/5KYnc/U5o3lq/QFOtnW6HU5UCzTlSE/jMXKB9wQ45/XAWar6HlW9ztneG/LIXVJW4eGcscPJTInO+obP1NEZiERf4vB96o9k4khJdFZ923qUYw3Rdwc2GLyy/RhvVni474opZA2L7r+NQBaXFtHQ0sHzm6JmrHFUCnTHUc3bS7/6zzm1lsDTqlcCsfs/J4Dmtg42Hoj++gZAWnIC43PT2HY4uuasWrG7hoyUBM4ZkxXR6942t5COLuUva61IHmrtnV1896XtTMhLY1FpodvhDMic4mwmj7QliHsTKHFUApeePueUqo4HAk3s0gxsGIwLOa3dW0d7p0Z9fcNnWpRNPaKqLC+vYf7EXOIjvCbDhLx05k/MZemqfbbqW4g9tnofldVNfOnqaSTG96v1O2p4lyAuZOP+42w5GF0fuqJJoH/lnwA99af7foDjngO+zTtnxw1qIadoV1bpISFOmF0UG90MS/Iz2V97kvqT0THRX5WnmYPHT0akG253FpcWcfD4Sd7YHf7Zk4eKEy3t/ORfu5k3IYcrpgVqiIgdN5w/lpTEOLvrCCDQJIe/cAb9dffczwMc9yjwGG8njKV9WMgpqpVVeDh33HDSknsdcB8Vpp9amyM67jqWh3CZ2P54V8koRqQnWV/9EPrFsnLqmtv46jUlMTPYrzdZwxK5bkYBz244GBMLorkh5PeVInIpsBv4BfBLYNdg6I7b2NrB5oP1MVHf8PEt6hQtBfIV5TUUZKUwfkSaK9dPSojj/bPH8eqOoxw6ftKVGAaT/bXNPLJ8LzecN4azI1yzCrfF87xLED9rSxB3KxwNkj8C3q2ql6jqxcCVwANhuE5ErdlTS2dX7NQ3AEZmpDAiPTkq6hydXcqbFd5p1N38ZHrb3EIUW/UtFL7/953ExcHnrjzL7VBCzrcE8RJbgrhb4Ugciaq60/eDqu5iEPSyKqv0kBQfx6wYqW/4lBRkRsUdx9ZD9dSfbGfBZHeaqXzG5aRy8eQ8Hl+zj44onnY+2r21r47nNx7iPy+aQH7WMLfDCTlvkbzIliDuQVCJQ0RmiMh7ReRG3xZg97Ui8rCIXOpsv2UQFMfLKjzMLBwesdHOoVKSn8nuYw20dbj7Jumrb8yf6G7iAO+qb0dPtPLqjiE/g06/tHV08Z2/bScvI5k7L5nodjhh896ZBaQnJ1hNrBu9Jg4R+R3wO+Am4DpnuzbAIZ8AtgL34l23Yxtw54AjdVH9yXa2Hoqt+oZPSUEm7Z1K+TF31+ZYUV7D1NEZ5GUkuxoHwMKpIxmdaau+9cfG/ce57ufLWVdVxxeumhozHUX6Iz05gffNLOCFTYc43tzmdjhRJZh/9XmqWhLsCZ1p13/sbIPC6j21dGl4Fx0KF9/UI1sP1Z8qlkdaS3sna/bW8aF50bESXEJ8HB+YM46fvbqb/bXNMTmvUqS1tHfyk3/t5qE3KsjLSObhj8zm8mmj3A4r7BaXFrFk1T6eWn+Qjy0Y73Y4USOYpqoyEek1cYjIn52vm0Vk0+nbgCN1UVmFh+SEOM4rHO52KH02fkQawxLjXS2Qr93rnRjSrfEb3bl17jgEbNW3IKyrquU9P/s3v369gltmjeMfn75kSCQN8N6xn1c4nCWrqqxI7ieYO45H8SaPI0ArIICq6ozT9vMtJxuoGSsmlVV6mFWUHZMrg8XHCVPz3V2bY3l5DQlxwtwomhgyP2sYC6d6V32774opJCXE9ojncDjZ1skP/r6TR97cQ0HWMP74sblcNDnP7bAibnFpEZ/9y0ZW7Yne5aIjLZi/lt8BHwKu4u36xnWn76Sqh51v71LVKv8NuCtUAUdaXVMb2w+fiMn6hk9Jfqara3OsKK/h/MLsqGsPX1xaSE1jG//cFmgGnaGprMLDVT99g9+t2MOH5hXx909fPCSTBsC1M/LJTEmwmpifYBLHPlV9TlX3nJYMevKubh67up/xuW5lpXc211isb/iUFGTS0NLBgbrID3qra2pjy6F610aLB3LxlDzGDB/GklU23bpPY2sHX/vrFm777UoAHr9jHv/1vrNJj7KkH0ne2ZXH8fKWw9TYEsRAcIljh4gsFZHbAnXHFZFPiMhm4KzT6ht7gJitcZRVekhNimfG2Nirb/icWpvDhTpHWaUHVVgwOfoSb3ycsKi0kDcrPFRWu9vrLBq8sauaKx94gz+tquJjC8bz8qcutqYZx6LSQto7bXZln2ASxzC8tY13E7g77lLnuef89rsOmKWqHwxJtC4oq/AwuzgnptvAp47OJM6ltTmWl9eQnpwQtYn3ltljSYiTIV0krz/Zzuef3MiHf7ealMQ4nrxzPl+7toRhSbFX0wuXSSPTKR2fw9LVVbYEMUEUx1X19mBOpKr1QD1wG4CIjARSgHQRSVfVmPvLrG5oZfexRm48f6zboQzIsKR4xo9Ic+WOY0V5DfMm5ETtdNsjM1J49/RRPLnuAJ9591kxN8BzoF7ZfpQvP7OZ6oZWPnHpRD51+eQh9xoEa/G8Iu597C3+XV7DJVOGZr3Hp9fEISKPAGekWFX9jx72vw7vGI4C4BhQBGwHpg8oUhcMhvqGT0lBFuur6iJ6zf21zVR5mvno/OKIXrevFs0t4sXNR3h5yxGuP2+M2+FERF1TG//1wjaeeesgZ43K4Lcfnh21d4XR4srpo8hNS2LJyqohnziC+Rj4AvA3Z3sFyAQCNQh/B5gH7HIWfboc7xrkMaes0kN6cgJnuzRwLpSmF2Ry8PjJiI6AXeFMMxJN4ze6M39iLsW5qSwdIr1mXt5ymHc98AbPbzzEvZdP5vlPLrCkEYTkhHhumT2OV3Yc40j90F6CuNfEoapP+W1LgPcDZwc4pF1VPUCciMSp6jJgZojijaiVFR7mjs8hIUqbWfrCjQL5igoPIzOSmTQyPWLX7I+4OOG2uYWs3lvLrqMNbocTNjWNrdy9ZD13/mk9ozKTee6eBdz/LhvD0heL5hbS2aVDfnbl/vyPmQwEWlj4uIikA28AS0Tkp0BHf4Jz09ETLVTWNMX0+A1/0/IjuzZHV5fyZnkNC1yeRj1YN88aS1J83KC861BVnt1wkHf9+HX+ue0on7vyLP5694WuTUETywpzU7l4is2uHMwkhw0icsL3FXge+EKAQ96Hd93xTwMvAxXE4GjysorBU98AyMtIZmRG5Nbm2HGkAU9TW1SO3+hObnoyV58zmqfWH+BkW6fb4YTMsRMt3PHHdXzq8Q0U5qbxwr0LuPuySVHbWSEWLC4t5HB9C8t2Dt0liINpqspQ1Uy/r1NU9akAh3xdVbtUtUNVH1XVnxE40USlsgoPmSkJpz6pDwaRXJtjhcvLxPbHormFNLR08Pym2F/1TVX5y9r9XPHj13ljVzVffs9Unv7EfKaMynA7tJh3+dSRjMpMHtIDR8OxHke/Ro6LSIqIrBaRjSKyVUS+5Tz+exHZIyIbnC0i9ZKySg+lE3KJj4v+ZpZgleRnUn6skdaO8H+iXl5ew6SR6YzOSgn7tUJl7vgcJo1Mj/nmqoPHT/LRR9bwuSc3cdboDF761EXccfHEQfV/2U3e2ZULeX1XNftrm90OxxUhW48jBCPHW4GFqnou3mL6VSIyz3nuc6o609k2BPer9d/B4yfZV9s8aOobPiUFmXR0KbuPhneUdGtHJ6v31HJhjDXziQiL5hayYf9xth6qdzucPlNVlqyq4soH3mD1nlq+eV0JT9xxARPyortzQiy6dc7Qnl05lOtxLAVeAv4H+KLf4w2qWtvbweqdgc/3jpbobK4M0Rxs9Q2fEr8C+dljssJ2nbf2Hedke2dMNVP53HT+WP735R0sXbWP/77hHLfDCdo+TzNffHoTb1Z4mD8xl+/dOIPCXFtnJFwKhvtmVz4wJGdXDtl6HHjf+/cCdwMNfhsiEtR82iISLyIb8A4c/KeqrnKe+m/n7uUBEQn7EnJlFR6yUxM5a5C1BxfnppGaFP61OVaU1xAnMC8GE29WaiLXzijgr28dpLE1+jsDdnUpv1+xhyt/8gabDtTz3RvOYcnHSy1pRMDieYXUNLYOydmVg0kcvvU4djpv3pt7WJhpqfN1HbDW+brO7+deqWqnqs4ExgJzReRs4EvAVGAOkEMPhXYRuUNE1orI2urq/vd2UFVWVnqYNyGXuEHWJhwXJ0zLD3+BfHl5DeeOG05mSmJYrxMui+cV0tTWybMbDrodSkCV1Y184KEyvvn8NuaMz+Hvn76YRaWFMdH9eTC4eHIeY7OH5uzKoVyP41rn63hVneB89W0T+hKUqh4HXgOuUtXD6tUKPALM7eGYh1R1tqrOzsvr/3QA+2qbOXj85KBrpvLxrc0RronaTrS0s3H/8agfLR7IeeOGM3V0BktX7YvKVd86u5SH3qjg6p/+m51HGvjhLefy6O1zGDN8mNuhDSnxzsDRNys8VAyx2ZXDsR4HTs+rH4vIj0Tk+mACEZE8ERnufD8MuALvlO75zmMCXA9sCeZ8/eWrb8wfrImjIJPG1vCtzbGywkOXxlY33NOJCIvnFbH10Ak2HoieIrmqsvlAPTf+6k2+++IOLpqcxz/vv4SbZ421uwyXnJpdOcZ74vVVMMXxHSKyFO/Av1OrmKjq093tLCK/BCYBjzkP3Ski71LVu3u5Tj7wqIjE401of1bVF0TkVRHJw7tk7QbgziBi7reySg95GclMHKQ9Ud6eeqQ+LO3gK8prGJYYH5Prs/u7fmYB//PidpauqmLmOHd+l/bOLrYeOsGaPbWs2VvL2qo6apvayE5N5Ke3zuS95xZYwnDZyIwUrpw+mr84sysPlanog0kc/utx+CjQbeIALgHOdnpJISKPApt7u4iqbgLO6+bxhUHEGBKqSlmFt74xWP8gzxqdQZzA1kMnuOrs/JCff3l5DXPH58Tk+uz+MlISed/MAp556yBfuaaErGHhr9c0tXbw1r7jrN5by9q9tad6pwEU5aZy2VkjmTs+m3eVjCYnLSns8ZjgfPTCYv62+TCPr9nH7ReOdzuciAjZehx+duKdy8rXnDWOGFkBsLKmiWMNrYNu/Ia/lMR4Jualh6VAfrj+JBXVTdw6J9BUZrFj0dwiHlu9n7++dZCPhGFq+OqGVtZV1bJ6Tx1rq2rZeugEnV1KnHjnFvvAnHHMKc5hdnE2ozJjZyDlUDOnOIe543P4zeuVLCotjPkPTcHoMXGIyOdV9fsi8nO6X4/j3h4OzQW2i8hq5+c5eHtlPecc994Bxhw2g3X8xummF2Syak+vQ2v6bEW59/WL5fqGv3PGZjFjbBZLVlXx4QuKBnQXqqpUeZpP3U2s2VvHnpomAJIT4pg5bjh3XTqR2cU5nF84nIwY7ZE2VN27cDIffHgVT647wOLSIrfDCbtAdxzbna9BdaX18/V+xuK6skoPozNTKB7kfeBLCjL564ZD1Da1hbTJY0V5DblpSUwdPXjGvywuLeQLT21mXVUds4uDGo4EQEdnFzuONLB6Ty1rq7yJorrBWyLMGpbInOJsbp0zjtnFOZwzJmvIDSAbbC6clMvMccP51WsVvH/2uEE/iWSPiUNVn3e+fUJV37FqiYj0+JFSVV8PUWwRpaqsqvRw0eS8QVvf8CnJ944a3374RMjuDlSV5eU1zJ80YlCNf7nu3AK+88J2lqzaFzBxnGzrZMP+46zZ6y1kv7Xv+KkBhGOGD+PCibnMGZ/DnOIcJuWlD6rXyHh74n1y4SQ+9uhant1wiJtnxfZy070Jpji+WkTuUNWVACJyE95pRab47yQiy1V1gYg08M6mLcE7qjyqp5k92d7JVWeP5qLJg39JyGn53juCbYdClzh2H2ukuqGVBZMGVzNfalICN5w/hsfX7Ofr15aQ7dyh1TW1sbaq7lSi2HKwnvZORQTOGpXB9ecVMKfYmygKbHzFkLBw6khK8jP55bJybjhvzKCeVDKYxLEY+J2IvIZ3HfFc4IyeTqq6wPkak+0UqUkJfOf62JmbaCBy05MZnZkS0qlHlu+OvWnUg7WotJA/lFXx7b9tIzkhnjV7ayk/5h3wlRQfx4yxWXxswQTmjs9mVmEOWalWnxiKfHcdn1iynr9tPsx7zy1wO6SwCaZX1WYR+W/gj3jnnrpYVQ/0tL8zo+1WVfXNU5UOTPebd8pEgVCvzbGivIbi3FTGZg+++tDU0ZnMLc7h6fUHyUhOYFZxNjecN4Y5xTnMGJtFSuLg70VjgnPl9NFMGpnOL14t59pz8gdtk2SviUNEHgYmAjPwNk89LyIPquovejjkV8D5fj83d/OYcVlJfiav76qmpb1zwG987Z1drKz0cP15Y0IUXfT59YdmcayhhckjMwZ1E4QZmLg44Z7LJnHfExv45/ajXDl9tNshhUUwpf8twGXOlCN/B+YROAmIb/AfgKp2EVyTmImgkoJMOruUXUcbBnyujfuP09TWGdPzU/UmJy2JqaMzLWmYXl07I5/i3FR+/uruqJzrLBSCWTr2gdMSQb2qfizAIZUicq+IJDrbp4DKUARrQmd6wdtrcwzU8vIaRAb/+BdjgpEQH8ddl05iy8ETvLZrcK5LHswKgJNF5EkR2SYilb4twCF3AvOBg8ABoBS4IzThmlAZl51KenJCSArkK8prOGdMFsNTbRoMYwCuP28MY4YP4+evDM67jmCaqh7BW6PoAC4D/oC3UN4tVT2mqreq6khVHaWqi1T1WGjCNaHiXZsjY8B3HL75lQZjbypj+ispIY47L53I+n3HT81IMZgEkziGqeoreGsXVar6TbrpjusjIlNE5BUR2eL8PENEvhqacE0oleRnsn2Aa3Os3lNLR5cO6vqGMf1xy6yxjMxI5uevlrsdSsgFkzhaRCQO2C0i94jIDcDIAPv/Fu+qfe1watbbWwccqQm5koJMmto62Vfb3O9zLC+vITkhjllF2SGMzJjYl5IYzx0XT6Cs0sPavaGfG85NwSSO+4BU4F5gFt7VAD8SYP9UVV192mPRv3jzEOSbemTrAJqrVpTXMKc4x8YyGNONRaWF5KQl8eCywXXXEUyvqjWq2qiqB1T1dlW90Tf9SA9qRGQizrQjInIzcDhE8ZoQmjwqnfg4Ydvh/q1yd6yhhR1HGqy+YUwPUpMS+PhF43ltZzWbDhx3O5yQCaZX1WwReUZE1ovIJt8W4JC7gd8AU0XkIN47lrCu2mf6JyUxnskj+782h6/oZ/UNY3r2oXlFZKYk8OAgqnUEMzBvCfA5vKv4dQXa0amFzFbVK0QkDYjzTT1iolNJfiYrKmr6dezy3TVkDUukpCCq5680xlUZKYncfuF4fvrKbnYcOcHU0bH/9xJMjaNaVZ9zRo5X+bbudnRGid/jfN9kSSP6lRRkcvREKzWNrb3v7EdVWVFew/yJuTaa2phe3H5hMenJg+euI5jE8Q0R+T8RuU1EbvRtAfb/p4h8VkTGiUiObwtVwCa0SvK9n36293Eg4J6aJg7Vt1h9w5ggDE9N4kMXFPG3zYepqG50O5wBCyZx3A7MBK4CrnO2awPs/x946xxvAOucra+rCJoImZbfv6lHVpR7m7esvmFMcD62YDzJCXH8clmF26EMWDA1jnNVNeiFKlR1/ADiMRGWnZZEQVbf1+ZYXl7DmOHDKBrky+waEyoj0pNZNLeIR8v2ct8VkxmXE7t/O8HccawUkZJgTygiKSJyv4g8LSJPich9IpIygBhNmJUUZPZpLEdnl/JmhYcFk0YM+mV2jQml/3fJBOJF+OVrsX3XEUziWABsEJGdTlfczb10x/0DMB34OfAgUEKAua2M+0ryM6msbuRkW2dQ+28+WE9DSwcXTrZmKmP6YlRmCu+fM5Yn1+3ncP1Jt8Ppt2Caqq7q4znPUtVz/X5eJiIb+3gOE0ElBVl0Kew82sDMccN73d9X35hv06gb02d3XjKRx1fv5zevV/LN9053O5x+CWbkeFV3W4BD3nKWjwVAREqBFaEI1oRHX9fmWL67hmn5mYxITw5nWMYMSmOzU7nx/DE8tnofxxpa3A6nX4JpquqrUuBNEdkrInuBMuCSIJq4jEvGZg8jIzkhqKlHTrZ1sq6qjgWT7G7DmP76xKWTaO/s4uF/73E7lH4Jx5KufW3aMi4TEaYVZAZ1x7Fmby1tnV02fsOYARg/Io3rzi3gjyuruPOSiWSnxdYiaCG/4+ipaSuIJi7jopL8THYcaaCzl7U5VpTXkBgvzB1vYzqNGYh7LptEc1snj6yIvbuOcDRV9YvTjXe1iGwUka0i8i3n8fEiskpEdovIEyISW6k5RpQUZNLc1sleT1PA/ZaX13B+YTapSeG4WTVm6Jg8KoOrzx7NI2/u5URLu9vh9EnUJA6gFVjo9MiaCVzlFNn/F3hAVScDdcDHXIxx0CoJYgR5bVMbWw+dsNHixoTI3ZdNoqGlgz+8udftUPokahKHevkmcUl0NsW7TO2TzuOPAte7EN6gN2VUBonxEnAE+ZvOLLo2fsOY0Dh7TBYLp47k4eV7aGqNnfXuoiZxAIhIvIhsAI4B/wQqgOOq6ntFDwBj3IpvMEtKiGPSyIyAdxwrymvISE5gxpisCEZmzOB2z8JJ1DW3s2RV7JSAoypxqGqnqs4ExgJzgWnd7dbdsSJyh4isFZG11dXV4Qxz0CrJzwx4x7G8vIZ5E3NJiI+q/zbGxLTzC7NZMGkED72xh5b24GZvcFtUvgOo6nHgNWAeMFxEfJXYscChHo55SFVnq+rsvLy8yAQ6yJQUZFLd0NrtoKR9nmb21560+oYxYXDPwknUNLbyxJr9bocSlKhJHCKSJyLDne+HAVcA24FlwM3Obh8BnnUnwsHv7bU5zlx/a7kzzYiN3zAm9OZNyGVucQ6/fr2C1o7ov+uImsQB5OOd12oTsAb4p6q+AHwBuF9EyoFc4GEXYxzUAvWsWlFRw+jMFCbmpUU6LGOGhHsWTuJwfQtPrz/odii9iprO+Kq6CTivm8cr8dY7TJhlpSYyZvgwth5659QjXV3Km+U1LJw6yqZRNyZMLpo8gnPHZvHL18q5ZdbYqK4lRm9kxhUlBWcWyLcdPkFdczsLJtv8VMaEi4hwz8LJ7K89yXMbuy3lRg1LHOYdphdksqemiea2t/uU+6ZRv3Ci1TeMCacrpo1kWn4mDy4r73X6HzdZ4jDvUJKfiSrsOPJ2gXx5eQ1TRqUzMtMWcjQmnESEey6bRGV1Ey9tOex2OD2yxGHeoeS0tTla2jtZs7fWelMZEyFXnT2aiXlpPPhqOV1RetdhicO8w5jhw8hMSThV51i/r46W9i5rpjImQuLjhHsWTmLHkQb+tf2o2+F0yxKHeQcR8RbInTuOFeU1xMcJpRNsGnVjIuW6GQUU5qTy4LJyVKPvrsMShzlDSX4WO46coLNLWV7uYea44WSkJLodljFDRkJ8HHddOpFNB+p5Y3eN2+GcwRKHOUNJQSYt7V1s2F/H5gPHrb5hjAtuPH8sBVkp/PyV3VF312GJw5xhulMg/93yvXQpNj+VMS5ISojjzksnsraqjpWVtW6H8w6WOMwZJualkxQfx0tbDpOaFM/MccPdDsmYIen9s8eRl5HMg8t2ux3KO1jiMGdISohj8qh0uhRKx+eQlGD/TYxxQ0piPHdcNIEV5R7WVdW5Hc4p9o5guuWb8NDqG8a4a/G8QrJTE3nw1ei567DEYbp1zljvKn8XTba1TYxxU2pSAh+/aALLdlaz5WB97wdEgCUO0633zx7H0v8s5azRGW6HYsyQ96ELishMSeDBV8vdDgWwxGF6kJIYz3wbLW5MVMhMSeSjF47n5a1H2HnkzIXWIs0ShzHGxIDb5xeTlhTPL5a5f9dhicMYY2JAdloSH7ygiBc2HaKyutHVWCxxGGNMjPj4ggkkJcTxq9cqXI3DEocxxsSIvIxkbptbyDNvHWR/bbNrcVjiMMaYGHLHxROIE+HXr7t312GJwxhjYkh+1jBunj2Wv6w9wJH6FldisMRhjDEx5hOXTKRTld+84c5dhyUOY4yJMeNyUrnhvDE8tnof1Q2tEb++JQ5jjIlBd106kbaOLh5evifi17bEYYwxMWhCXjrXzijgj2V7Od7cFtFrW+IwxpgYdfdlk2hq6+R3K/ZG9LqWOIwxJkadNTqDK6eP4vcr9nCipT1i17XEYYwxMeyey/ajwqgAAAyCSURBVCZzoqWDP5ZVReyaljiMMSaGnTM2i8vOyuPh5XtobuuIyDWjJnGIyDgRWSYi20Vkq4h8ynn8myJyUEQ2ONt73I7VGGOiyT0LJ1Pb1MbSVfsicr2oSRxAB/AZVZ0GzAPuFpES57kHVHWms73oXojGGBN9ZhVlM39iLr95o5K2jq6wXy8h7FcIkqoeBg473zeIyHZgjLtRGWNMbPjye6bR2tFFUkL47wei6Y7jFBEpBs4DVjkP3SMim0TkdyKS7VpgxhgTpc4ek8Wsosi8PUZd4hCRdOAp4D5VPQH8CpgIzMR7R/KjHo67Q0TWisja6urqiMVrjDFDTVQlDhFJxJs0lqjq0wCqelRVO1W1C/gtMLe7Y1X1IVWdraqz8/LyIhe0McYMMVGTOEREgIeB7ar6Y7/H8/12uwHYEunYjDHGvC1qiuPAhcCHgM0issF57MvAbSIyE1BgL/D/3AnPGGMMRFHiUNXlgHTzlHW/NcaYKBI1TVXGGGNigyUOY4wxfSKq6nYMISci1UB/Z/waAdSEMJz+ioY4oiEGsDhOZ3G8UzTEEQ0xwMDiKFLVoLqkDsrEMRAislZVZ1sc0RGDxWFxxEIc0RBDJOOwpipjjDF9YonDGGNMn1jiONNDbgfgiIY4oiEGsDhOZ3G8UzTEEQ0xQITisBqHMcaYPrE7DmOMMX1iicMYY0yfDMnEISIToyCG80UkNwriSHQ7Bn/OZJduXj8+GuJwYnD17zMaXgN4+9/EbSKS5Xx1+99ltPPVtX+fIZU4nDfrN4DviUimSzGcJyL/wrtIlWtzhYnIPBF5HPiBiJztYhwXiMjPROSjAOpS0U1ELhSRR4GvikiOi3HMFZF7AZylBNyIoVREfgt8QURcW6NARGaLyB+Br7v1YU9E4kQkU0ReAH4Grv67nCcirwDfduJwrUA9ZBKHiCQB3wGeUNVbnEWiIpa1RSRZRH6Nd02RXwJvANdEMga/WG7Bu0DWC0AKcL9LcdwMPAisAa4Qke+4kcREZALef5NlQBHwbRG5xoU47gOewZu8rnYei9inbRGJF5H/wdszZwVwPvANERkVqRicOOJE5EHgN8ArQD7wTRFJjWQccCpJNACJwBgR+YAvxkjFIF4PAH8AHlXV/4zUtXsyZBIH3j8Cj6r+Ak590k2OYNbOB9YBC5xFqv4B5IqIuPDJYTLwvKr+CXgAvE1WLsQxHXhaVf8IfBYoBW4RkeERjmMW3nVgfg98BtgAXCsi4yIcRzlwLfAJ4EsAqtoZwYQeB+wDbnFei/uAecCwCF0fOPVm/SpwuRPH9/Euq9ARyTj8TMU7jcdPgMUikqGqXZH6d3H+LtOBt1T1D+BtbnezyWzQJg4Reb+I3C8iFzgPVQFnich1IvJP4BvAb0XktjDH8FkRmauqe1X1t6ra4jydDoxTVQ33p8puXoudwI0i8nn+f3tnH2RVWcfxzw9MDHZRmt4GCU2IlwmIbUksC8aKiWpSCyOUkXSYphF5yejtH4vejBkCZ3SoGcvKqUkXiWpMy1F7hbSkRG0rKIYZRdAwRHdjRGW//fF7rt52dolz7zn33Lv8PjNn9txzz7n3s8895/ye5/c85xy4FxgLbDSztzTY4yAwwsxOlfQ48AQwHj9ZFelxjplNqlp0PzDOzF4n6Sm8tn0If3BYIz1uBx5Kf3srKSugsP2jn0MfcLOkXalStQ/Yi9//qFD6l4WkLZIOmdk8YDte8brGzKY2yqMqMPwTeA7Yk6aPmtn4IitaA+wbq4HZZna1mW0D1gHfM7POohyOxZALHKm5/Xngs2nRDWa2ADgA3IanZdZKmo+nJt5pZlMKdOgDbjSzD6X3KmX+E+B8Mxsp6Wie3z+IB3igPB/YAqwC5gBLUlkcABZY6nhrgMd7gD8CrwG+bWab8BNkL1BI55+ZnWZmtwN3AQvNn28P8CywFViYXu8E/oq3CE/J02EQj1GVt9Jjkp8F1gNLzeyVknKvaQ9UFum7DwFIOmJm7cDrgX15f/8xPEal5ZXf/ingEknzgMP4STv31NlAHlWBYRbwjKRuoBuvdH7TzF6Wd61/sPJIqfWNwAK8NXoxsB8/ZhveDzXkAkc6CU8GVqdH0K7Bm/5TgAfx9EjlZPBLoB34T8EOXwCWm9nUqo61A+n7cw1ax+FxFTBJ0j34CXNnWv2nwAxyLotBPNbgNage/CDYDPxC0sX4oIH3pu3yrtGNAu4EVqT5OWn5AeA+YHpqHR4FHgPOrWohFu7Rr9P118lpBXinecEO7xhgndlAt6R9ZtZmZm/I2WEgj0pZKP3dLqnyMLc7gA48gDTEI/EI0G5mXcBn8JTzLknPF9BRPqiHpOuA8yT9VtIRvPI5i2LK45gMicBhZkvMbG5VbvwJYIyZnSTpR8Au4APA7/B86apUU5gHvAI/gRbpsAWvwS6sqqH0AhPx3G1utevjKItuYFFqWewGLkrrdZBDORynx2bgH8BHJB2U1CXpO2m9yfgBkbfHaEmP4R2/m/D/9WwzOz0FivuAB4BrU0vkjcAjllOH7P/xmG1mY9N6Bi8G26/gI5ueBt5c7z6SwaEy2u804FEzuxxP582s5/uzegxAJ17LzqUFlsFjDPAq4HH8OLkCT3vnkjbLUh4plVqhE08lFpKxOBYte8uRdBC9Fvghng7ajUfojwMr8aGu16U86RSgC5gvab+ZrcXz+uOAKyX9rQEOk/GdYb6k/Wn7HwNbJa2v5ftr9JgC3IIHzRnAlXhZ9ALLJf29QR7/Ux5m9i58uOMe4ApJjxbgsUrSk2mdc/HU1PbUOV/ZdgO+X5yBp/F2UiMZPe5PgxUq6cyzgO/iufVPSHq4kQ5p+feBxcBNwLWSHqrFoR4P82Hzs4Fr8BP3akm7GuTx4r6RUoaV99uAkyUdbJBHdXmMAN4KfB0PonWVR81IarkJGJ7+TgJ+kOZPwodU3ojXlO7Em3kj0/tdwCfTvAFtJTmsrPqM0SWVxa3AsjTfBkwv8TdZleYnAB8s0ON6fARX9bpX4bX6U4H2yvaV+ZI8KmXzajwt0WiH0ZVjA1gEXFRiWZySlr0buKBEj1FV+8awEj1enpa9DbiwXo96ptIuQKuF1IT+EjDczO7Ad/KjAJJeMLPleK1kAx7JF+GjMbrw5u22tK7wGnYZDn+ofJbStSQleDyH52mR1AvUVJvNweMFPEWEpN14rasoj5XAPjObK+k3abNv4QflXcAZZtYhH0nUU7JHp6S9wL9KcLgHGG9mMyXdUsv35+Rxd/LokHR3iR79942yPKrL4/f1eORBy/RxmNlc/GQ3Bh8e92XgeeA8Sx2H8o6qLwLrJN2EXyuxxMwewCN6zSfIZnEIj5o9hB+0a6o2fT+wDB80MT2HE0NeHntLdNiRHPbX6pCzR7P8JmV75FIeuVFmcyfLhI/6uLTq9TfwTqrLgD+lZcPwvOFm/BoJ0uuzhopDeNTtsQk4My27AJgzlDyawSE8mtcjt/+nbIEMBT8SGMFL+cHFwNfS/A5gRZqfhV/ENCQdwiM8mt0hPJrXI6+pZVJVkg5LOqKXLpabh4+/B7gcmGp+I7KbgT8PVYfwyMcjjWoZch7N4BAezeuRG2VHrhoi93C8SfdzYGJaNhEftfN24PQTwSE8wqPZHcKjeT3qnVqmxVFFH36nyieBGSlKXw30Sdoqv4DmRHAIj/BodofwaF6P+ig7ctUYtc/Bf4CtwNIT1SE8wqPZHcKjeT3qmVryynEzGwdcCmyQ37PlhHQIj/BodofwaF6PemjJwBEEQRCURyv2cQRBEAQlEoEjCIIgyEQEjiAIgiATETiCIAiCTETgCIIgCDIRgSMI6sT8OdHL0vxYM9tctlMQFEkMxw2COjGzM4GfSZpWskoQNISWepBTEDQpa4EJZrYDf5b6VEnTzOwy4EL8/kTTgPXAyfjFX0eA90k6aGYTgI34c60PAx9THY/wDYKiiVRVENTP54DdkmYCn+733jTgEuBs4KvAYUkdwL3AkrTODfhttTuBT+HPagiCpiVaHEFQLL+S1AP0mNnTwG1p+cP4Te7a8GdI31p1F+0RjdcMguMnAkcQFEv1vYj6ql734cffMOBQaq0EQUsQqaogqJ8eoL2WDSU9A+wxsw+DP7zHzN6Up1wQ5E0EjiCoE0n/BraZ2V+AdTV8xGJgqZk9CHTjz5kOgqYlhuMGQRAEmYgWRxAEQZCJCBxBEARBJiJwBEEQBJmIwBEEQRBkIgJHEARBkIkIHEEQBEEmInAEQRAEmYjAEQRBEGTiv6hn9FX7Sew6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "xr.plot.utils.register_pandas_datetime_converter_if_needed()\n",
    "rx1.rx1day.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "Want to check out what other neat stuff it can do ? The `help` function will get you the description of each process available (`help(wps.rx1day)`). The code in this notebook will work from your laptop, provided you install the following: \n",
    "```\n",
    "pip install birdhouse-birdy xarray matplotlib\n",
    "```\n",
    "\n",
    "If you have any problem, check out the [documentation](https://pavics-sdi.readthedocs.io/en/latest/) or open an [issue] (https://github.com/Ouranosinc/pavics-sdi/issues).\n",
    "\n",
    "Note that you may use the services over any netCDF file, whether it sits on your computer or another server. It will simply be downloaded to the PAVICS server before the process is launched. \n",
    "\n",
    "We're diligently working on improving the services offered and growing the data co-located with the services. Some aspects of the gateway are mature, while others are in development, so be aware that you might hit roadblocks along the way. Don't hesitate to provide feedback at pavics@ouranos.ca. \n",
    "\n",
    "## Credits\n",
    "\n",
    "The PAVICS platform is developed and maintained by Ouranos, the Centre de Recherche Informatique de Montréal and the German Climate Computing Center (DKRZ). It is part of [birdhouse](https://birdhouse.readthedocs.io), a software stack built by an international community motivated by free public access to climate data and services. The platform's development was funded by the [CANARIE](https://canarie.ca) research software program and the [Canadian Center for Climate Services](https://www.canada.ca/en/environment-climate-change/services/climate-change/canadian-centre-climate-services.html).  "
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
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}