{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io as sio\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import xarray as xr\n",
    "from salishsea_tools import tidetools, geo_tools, viz_tools\n",
    "import os\n",
    "import cmocean\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": 2,
     "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>''')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "HINDCAST_PATH= '/results/SalishSea/nowcast-green/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n",
    "bathy, X, Y = tidetools.get_bathy_data(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = sio.loadmat('/ocean/rchappel/MATLAB/PSFc2017.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctd_data = mat[list(mat.keys())[3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctd_dtype = ctd_data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctddata = {n: ctd_data[n][0, 0] for n in ctd_dtype.names}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1181)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctddata['mtime'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1181,)\n"
     ]
    }
   ],
   "source": [
    "base = datetime.datetime(2000, 1, 1)\n",
    "py_ctd_times = np.array([base for i in range(1181)])\n",
    "print(py_ctd_times.shape)\n",
    "for n in range(1181):\n",
    "    py_ctd_times[n] = ((datetime.datetime.fromordinal(int(ctddata['mtime'][0,n])))\n",
    "                       + datetime.timedelta(days=ctddata['mtime'][0,n]%1)\n",
    "                       - datetime.timedelta(days = 366))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['pr', 'temp', 'sal', 'cond', 'depth', 'patrol', 'mtime', 'lat', 'long', 'stn'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctddata.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lats = ctddata['lat'][0,:]\n",
    "lons = ctddata['long'][0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctddata['depth'] = np.ma.masked_invalid(ctddata['depth'])\n",
    "ctddata['temp'] = np.ma.masked_invalid(ctddata['temp'])\n",
    "sals = np.ma.masked_invalid(ctddata['sal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hour = np.array([])\n",
    "for n in py_ctd_times:\n",
    "    hour = np.append(hour, n.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = nc.Dataset('/results/SalishSea/nowcast-green/01feb18/SalishSea_1d_20180201_20180201_dia2_T.nc')\n",
    "deptht = f.variables['deptht'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')\n",
    "tmask = mesh.variables['tmask'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_Yinds = np.array([])\n",
    "list_of_Xinds = np.array([])\n",
    "for m in range(1181):\n",
    "    Yind, Xind = geo_tools.find_closest_model_point(lons[m],\n",
    "                                                    lats[m], \n",
    "                                                    X, Y, land_mask = bathy.mask)\n",
    "    list_of_Yinds = np.append(list_of_Yinds, Yind)\n",
    "    list_of_Xinds = np.append(list_of_Xinds, Xind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1081"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ma.count(np.ma.masked_invalid(list_of_Yinds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_Yinds2 = np.ma.masked_invalid(list_of_Yinds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_model_sal = np.ones((200,1181)) * 9999\n",
    "list_of_model_temp = np.ones((200,1181)) * 9999\n",
    "for m in range(1181):\n",
    "    if list_of_Yinds2.mask[m] == False:\n",
    "        Yind = int(list_of_Yinds[m])\n",
    "        Xind = int(list_of_Xinds[m])\n",
    "        date = py_ctd_times[m]\n",
    "        sub_dir = date.strftime('%d%b%y').lower()\n",
    "        datestr = date.strftime('%Y%m%d')\n",
    "        fname = 'SalishSea_1h_{}_{}_grid_T.nc'.format(datestr, datestr)\n",
    "        nuts = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir, fname))\n",
    "        if date.minute < 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, \n",
    "                                       day = date.day, hour = (date.hour), \n",
    "                                       minute = 30) - datetime.timedelta(hours=1)\n",
    "        if date.minute >= 30:\n",
    "            before = datetime.datetime(year = date.year, month = date.month, \n",
    "                                       day = date.day, hour = (date.hour), \n",
    "                                       minute = 30)\n",
    "        after = before + datetime.timedelta(hours=1)\n",
    "        sub_dir2 = after.strftime('%d%b%y').lower()\n",
    "        datestr2 = after.strftime('%Y%m%d')\n",
    "        fname2 = 'SalishSea_1h_{}_{}_grid_T.nc'.format(datestr2, datestr2)\n",
    "        nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))\n",
    "        delta = (date.minute + 30) / 60\n",
    "        for n in range(200):\n",
    "            if sals.mask[n,m] == False:\n",
    "                depth = np.abs((deptht - ctddata['depth'][n, m])).argmin()\n",
    "                if tmask[0,depth,Yind,Xind] == 1:\n",
    "                    t_val = ((1-delta)*(nuts.variables['votemper'][before.hour, depth, Yind, Xind] ) + \n",
    "                              (delta)*(nuts2.variables['votemper'][after.hour, depth, Yind, Xind] ))\n",
    "                    s_val = ((1-delta)*(nuts.variables['vosaline'][before.hour, depth, Yind, Xind] ) + \n",
    "                               (delta)*(nuts2.variables['vosaline'][after.hour, depth, Yind, Xind] ))\n",
    "                    list_of_model_sal[n, m] = s_val\n",
    "                    list_of_model_temp[n, m] = t_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 1181)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_model_sal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_model_sal2 = np.ma.masked_greater(list_of_model_sal, 900)\n",
    "list_of_model_temp2 = np.ma.masked_greater(list_of_model_temp, 900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.26150894165039"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_model_sal2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118059"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ma.count(list_of_model_sal2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126009"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ma.count(ctddata['sal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctddata['sal'].mask = list_of_model_sal2.mask\n",
    "ctddata['temp'].mask = list_of_model_temp2.mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118059"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ma.count(ctddata['sal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_model_sal2.mask = ctddata['sal'].mask \n",
    "list_of_model_temp2.mask = ctddata['temp'].mask "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.26150894165039"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_model_sal2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.139666"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctddata['sal'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.26150894165039"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plottable_cs_sals =ctddata['sal'].compressed()\n",
    "plottable_cs_temps = ctddata['temp'].compressed()\n",
    "plottable_model_sals = list_of_model_sal2.compressed()\n",
    "plottable_model_temps = list_of_model_temp2.compressed()\n",
    "plottable_model_sals.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.size'] = 12\n",
    "mpl.rcParams['axes.titlesize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118059,)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plottable_cs_sals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9b9++FWMolCpVigDYrFkz9u/fnz///DNr1apFAJ9l1pGfFEvBd3FxYZMmTbh+/XrxB3Lm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62d9faf6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "viz_tools.plot_coastline(ax, grid)\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.set_ylim(300, 800)\n",
    "ax.set_xlim(100, 375)\n",
    "ax.plot(list_of_Xinds, list_of_Yinds, '.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bias =  0.39233559227915293\n",
      "RMSE = 0.8003981811760156\n",
      "Willmott = 0.9348053317503713\n"
     ]
    },
    {
     "data": {
      "image/png": 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xhUbxp99IlT+FJ0ktfqfyhMu5AFre7OOn6bCXBgH6yPU75MnFhQBJhCiAFjf7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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62dbfeb710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 8))\n",
    "c, xedge, yedge, im = ax.hist2d(plottable_cs_sals, plottable_model_sals, \n",
    "                                     bins = 100, norm=LogNorm());\n",
    "ax.set_title('Reference Salinity (g/kg)')\n",
    "ax.set_xlabel('PSF Citizen Science Observations')\n",
    "ax.set_ylabel('Model Results')\n",
    "ax.set_aspect('equal')\n",
    "ax.plot(np.arange(0,34), np.arange(0,34), color = 'grey')\n",
    "print('bias =  ' + str(-np.mean(plottable_cs_sals) + np.mean(plottable_model_sals)))\n",
    "print('RMSE = ' + str(np.sqrt(np.sum((plottable_model_sals - plottable_cs_sals)**2) /\n",
    "                              118059)))\n",
    "xbar = np.mean(plottable_cs_sals)\n",
    "print('Willmott = ' + str(1-(np.sum((plottable_model_sals - plottable_cs_sals)**2)  / \n",
    "                             np.sum((np.abs(plottable_model_sals - xbar) \n",
    "                                     + np.abs(plottable_cs_sals - xbar))**2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bias =  0.030829213671029976\n",
      "RMSE = 0.5323857620830894\n",
      "Willmott = 0.9735495669157884\n"
     ]
    },
    {
     "data": {
      "image/png": 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Qa5ve3+bVrO197nseNMG1N68X9PNN+RkNuO+LzREGna+YebhqzPnNFK8Aiags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\nyf8ABWUdMwC7udQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62dbc7fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 8))\n",
    "c, xedge, yedge, im = ax.hist2d(plottable_cs_temps, plottable_model_temps, \n",
    "                                     bins = 100, norm=LogNorm());\n",
    "ax.set_title('Conservative Temperature ($^{\\circ}$C)')\n",
    "ax.set_xlabel('PSF Citizen Science Observations')\n",
    "ax.set_ylabel('Model Results')\n",
    "ax.set_aspect('equal')\n",
    "ax.plot(np.arange(4,34), np.arange(4,34), color = 'grey')\n",
    "print('bias =  ' + str(-np.mean(plottable_cs_temps) + np.mean(plottable_model_temps)))\n",
    "print('RMSE = ' + str(np.sqrt(np.sum((plottable_model_temps - plottable_cs_temps)**2) /\n",
    "                              118059)))\n",
    "xbar = np.mean(plottable_cs_temps)\n",
    "print('Willmott = ' + str(1-(np.sum((plottable_model_temps - plottable_cs_temps)**2)  / \n",
    "                             np.sum((np.abs(plottable_model_temps - xbar) \n",
    "                                     + np.abs(plottable_cs_temps - xbar))**2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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