{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import rasterio as rio\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from rasterio.plot import show\n",
    "import random\n",
    "from salishsea_tools import geo_tools, nc_tools, tidetools, viz_tools\n",
    "import netCDF4 as nc"
   ]
  },
  {
   "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",
       "\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",
    "\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sep_data2 = rio.open('/home/vdo/Downloads/September_2018_All_Tugs_Hours_Geographic.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Sep_data2.read(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12,12))\n",
    "show(Sep_data2, ax=ax, cmap = \"Greens_r\")\n",
    "ax.set_xlim(-124.5, -122)\n",
    "ax.set_ylim(47.9, 50);\n",
    "#fig.savefig(\"/home/vdo/Pictures/tif_file.png\", bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundingBox(left=-128.6649765716981, bottom=47.930315884019635, right=-121.91792896978994, top=51.02318999694867)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sep_data2.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.011455089307144591, 0.011455089307144587)\n"
     ]
    }
   ],
   "source": [
    "res = Sep_data2.res\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fb0b6db16a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 488)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.argmax(data)\n",
    "np.unravel_index(m, data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-123.53309656209733 49.30492660087698\n"
     ]
    }
   ],
   "source": [
    "lon, lat = Sep_data2.transform * (448,150)\n",
    "print(lon, lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13225\n"
     ]
    }
   ],
   "source": [
    "lonlat_list = []\n",
    "n=0\n",
    "for x in np.arange(lon - res[0]/2, lon + res[0]/2, 0.0001):\n",
    "    for y in np.arange(lat - res[1]/2, lat + res[1]/2, 0.0001):\n",
    "        lonlat_list.append([x,y])\n",
    "        n = n + 1\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13225"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lonlat_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = random.choice(np.arange(0,13225,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-123.53432410675074, 49.30899905622373]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lonlat_list[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = random.choices(np.arange(0,13225,1),k=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-123.536 49.3071\n",
      "-123.5297 49.3082\n",
      "-123.5358 49.3029\n",
      "-123.5305 49.3093\n",
      "-123.5323 49.3058\n",
      "-123.5346 49.3049\n",
      "-123.5283 49.3026\n",
      "-123.5285 49.3013\n",
      "-123.5317 49.3025\n",
      "-123.5364 49.3026\n",
      "-123.5296 49.3041\n",
      "-123.5382 49.3028\n",
      "-123.5319 49.3096\n",
      "-123.5287 49.3048\n",
      "-123.5364 49.3018\n",
      "-123.5365 49.3087\n",
      "-123.5379 49.3018\n",
      "-123.5276 49.3012\n",
      "-123.5385 49.3028\n",
      "-123.5336 49.3097\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in index:\n",
    "    print(np.round(lonlat_list[i][0], 4), np.round(lonlat_list[i][1], 4))\n",
    "    plt.plot(lonlat_list[i][0], lonlat_list[i][1], 'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "483 284\n",
      "483 285\n",
      "483 283\n",
      "483 285\n",
      "483 284\n",
      "483 284\n",
      "482 284\n",
      "482 284\n",
      "482 284\n",
      "483 283\n",
      "482 284\n",
      "483 283\n",
      "484 285\n",
      "482 284\n",
      "482 283\n",
      "484 284\n",
      "482 283\n",
      "482 284\n",
      "483 283\n",
      "484 284\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in index:\n",
    "    y,x = geo_tools.find_closest_model_point(lonlat_list[i][0], lonlat_list[i][1], \n",
    "                                             X, Y, land_mask = bathy.mask)\n",
    "    print(y,x)\n",
    "    plt.plot(y,x,'ro',alpha = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}