{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cmocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import os\n",
    "HINDCAST_PATH = '/results/SalishSea/nowcast-green/'\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jan17 = pickle.load(open('2017test/jan17depths.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feb17 = pickle.load(open('2017test/feb17depths.pkl', 'rb'))\n",
    "mar17 = pickle.load(open('2017test/mar17depths.pkl', 'rb'))\n",
    "apr17 = pickle.load(open('2017test/apr17depths.pkl', 'rb'))\n",
    "may17 = pickle.load(open('2017test/may17depths.pkl', 'rb'))\n",
    "jun17 = pickle.load(open('2017test/jun17depths.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jul17 = pickle.load(open('2017test/jul17depths.pkl', 'rb'))\n",
    "aug17 = pickle.load(open('2017test/aug17depths.pkl', 'rb'))\n",
    "sep17 = pickle.load(open('2017test/sep17depths.pkl', 'rb'))\n",
    "oct17 = pickle.load(open('2017test/oct17depths.pkl', 'rb'))\n",
    "nov17 = pickle.load(open('2017test/nov17depths.pkl', 'rb'))\n",
    "dec17 = pickle.load(open('2017test/dec17depths.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jan17b = np.reshape(np.ma.masked_array(jan17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((jan17.shape[0],1,1)))).compressed(), \n",
    "                   ((jan17.shape[0], -1)))\n",
    "feb17b = np.reshape(np.ma.masked_array(feb17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((feb17.shape[0],1,1)))).compressed(), \n",
    "                   ((feb17.shape[0], -1)))\n",
    "mar17b = np.reshape(np.ma.masked_array(mar17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5, 100::5]\n",
    "                             *np.ones((mar17.shape[0],1,1)))).compressed(), \n",
    "                   ((mar17.shape[0], -1)))\n",
    "apr17b = np.reshape(np.ma.masked_array(apr17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((apr17.shape[0],1,1)))).compressed(), \n",
    "                   ((apr17.shape[0], -1)))\n",
    "may17b = np.reshape(np.ma.masked_array(may17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((may17.shape[0],1,1)))).compressed(), \n",
    "                   ((may17.shape[0], -1)))\n",
    "jun17b = np.reshape(np.ma.masked_array(jun17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((jun17.shape[0],1,1)))).compressed(), \n",
    "                   ((jun17.shape[0], -1)))\n",
    "jul17b = np.reshape(np.ma.masked_array(jul17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((jul17.shape[0],1,1)))).compressed(), \n",
    "                   ((jul17.shape[0], -1)))\n",
    "aug17b = np.reshape(np.ma.masked_array(aug17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((aug17.shape[0],1,1)))).compressed(), \n",
    "                   ((aug17.shape[0], -1)))\n",
    "sep17b = np.reshape(np.ma.masked_array(sep17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((sep17.shape[0],1,1)))).compressed(), \n",
    "                   ((sep17.shape[0], -1)))\n",
    "oct17b = np.reshape(np.ma.masked_array(oct17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((oct17.shape[0],1,1)))).compressed(), \n",
    "                   ((oct17.shape[0], -1)))\n",
    "nov17b = np.reshape(np.ma.masked_array(nov17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((nov17.shape[0],1,1)))).compressed(), \n",
    "                   ((nov17.shape[0], -1)))\n",
    "dec17b = np.reshape(np.ma.masked_array(dec17,\n",
    "                            mask = 1 - \n",
    "                            (mesh.variables['tmask'][0,0,350:750:5,100::5]\n",
    "                             *np.ones((dec17.shape[0],1,1)))).compressed(), \n",
    "                   ((dec17.shape[0], -1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8760, 1554)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "together = np.append(jan17b, feb17b, axis=0)\n",
    "for f in [mar17b, apr17b, may17b, jun17b, jul17b, aug17b, sep17b, \n",
    "          oct17b, nov17b, dec17b]:\n",
    "    together = np.append(together, f, axis=0)\n",
    "together.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A_prime, sqrtL, E_T = np.linalg.svd(together - together.mean(axis=0)[np.newaxis, :], \n",
    "                                    full_matrices=False)\n",
    "A = A_prime.dot(np.diag(sqrtL))\n",
    "PercentVar = sqrtL**2/(sqrtL**2).sum()\n",
    "Z_0 = A[:, 0, np.newaxis].dot(E_T[0, np.newaxis, :])\n",
    "Z_1 = A[:, 1, np.newaxis].dot(E_T[1, np.newaxis, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q65u28bMqRnZ6yk2P/fX0EOMESBaByVsiIiKyGLsuXUfwOztwIi4XAFBYLkdMZglWPTFI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\nBMt2FeO+WSmdPWqPVjRiaVYR/vhZluDoqC+YvCXqo4XXj8Hb144SHYb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clxbqx+Iq0g79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\n+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa45ae28b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (24,7))\n",
    "ax.plot([datetime.date(2017,1,1) + datetime.timedelta(hours = n) for n in range(8760)],\n",
    "         A[:,0].T)\n",
    "ax.plot((datetime.datetime(2017,1,1) + datetime.timedelta(hours = 8213), \n",
    "         datetime.datetime(2017,1,1) + datetime.timedelta(hours = 8213)), \n",
    "        (-350,150), '--', color = 'grey')\n",
    "ax.plot((datetime.datetime(2017,1,1) + datetime.timedelta(hours = 2337), \n",
    "         datetime.datetime(2017,1,1) + datetime.timedelta(hours = 2337)), \n",
    "        (-350,150), '--', color = 'grey');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PercentVar[0] =  0.20218913182660772\n"
     ]
    }
   ],
   "source": [
    "print('PercentVar[0] = ',PercentVar[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PercentVar[1] =  0.06585124098585858\n"
     ]
    }
   ],
   "source": [
    "print('PercentVar[1] = ',PercentVar[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from salishsea_tools import timeseries_tools, geo_tools, tidetools, viz_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n",
    "bathy, X, Y = tidetools.get_bathy_data(grid)\n",
    "lats = grid.variables['nav_lat'][350:750:5,100::5]\n",
    "lons = grid.variables['nav_lon'][350:750:5,100::5]\n",
    "\n",
    "compressed_lats0 = np.ma.masked_array(lats, \n",
    "                                     mask = 1 - \n",
    "                                      mesh.variables['tmask'][0,0,350:750:5,100::5]).compressed()\n",
    "compressed_lons0 = np.ma.masked_array(lons, \n",
    "                                     mask = 1 \n",
    "                                      - mesh.variables['tmask'][0,0,350:750:5,100::5]).compressed()\n",
    "Yinds = np.array([])\n",
    "Xinds = np.array([])\n",
    "for lon, lat in zip(compressed_lons0, compressed_lats0):\n",
    "    Yind, Xind = geo_tools.find_closest_model_point(lon, lat, X[350:750:5,100::5],\n",
    "                                                    Y[350:750:5,100::5], \n",
    "                                                    land_mask = bathy.mask[350:750:5,100::5])\n",
    "    Yinds = np.append(Yinds, Yind)\n",
    "    Xinds = np.append(Xinds, Xind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cmocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q1S1uCdBSq83s7mnPt1f7l+mi/qfh8n46/6i7P2VmayXdZmYPTX/R3d0srqxc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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa43bdd4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gridded = np.zeros((80,60))\n",
    "for Yind, Xind, data in zip(Yinds, Xinds, Z_0[8213,:]):\n",
    "    gridded[int(Yind), int(Xind)] = data\n",
    "fig, ax = plt.subplots(figsize = ((15,9)))\n",
    "z = ax.pcolormesh(np.ma.masked_array(gridded, \n",
    "                                     mask = 1 - \n",
    "                                     mesh.variables['tmask'][0,0,350:750:5, 100::5]),\n",
    "                  vmin = -20, vmax = 20, \n",
    "                  cmap = cmocean.cm.balance)\n",
    "ax.set_title(datetime.datetime(2017,1,1) + datetime.timedelta(hours = 8213))\n",
    "fig.colorbar(z, ax=ax)\n",
    "viz_tools.set_aspect(ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fscvdtja7bpXcZVNmmthjfYdL0JPJY/1nP2BiMy7xE6aIKhELdZe5qil+geMh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\niCjyOBgSEVHkcTAkIqLI42BIRESR9/8BBIxzNNGwqeoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa43bdd4b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gridded = np.zeros((80,60))\n",
    "for Yind, Xind, data in zip(Yinds, Xinds, Z_0[2337,:]):\n",
    "    gridded[int(Yind), int(Xind)] = data\n",
    "fig, ax = plt.subplots(figsize = ((15,9)))\n",
    "z = ax.pcolormesh(np.ma.masked_array(gridded, \n",
    "                                     mask = 1 - \n",
    "                                     mesh.variables['tmask'][0,0,350:750:5, 100::5]),\n",
    "                  vmin = -20, vmax = 20, \n",
    "                  cmap = cmocean.cm.balance)\n",
    "ax.set_title(datetime.datetime(2017,1,1) + datetime.timedelta(hours = 2337))\n",
    "fig.colorbar(z, ax=ax)\n",
    "viz_tools.set_aspect(ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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