{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import itertools\n",
    "import pathlib\n",
    "\n",
    "import geojson\n",
    "import scipy.stats\n",
    "import toolz\n",
    "import toolz.curried\n",
    "import pydash\n",
    "import numpy as np\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy\n",
    "import cartopy.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/Users/baart_f/Downloads/BOX099_transects_rates.json') as f:\n",
    "    features = geojson.load(f)\n",
    "with open('/Users/baart_f/Downloads/BOX187_transects_rates.json') as f:\n",
    "    features = geojson.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"coordinates\": [[17.966394475619165, 54.8245417781275], [17.96443101967979, 54.834828544733064]], \"type\": \"LineString\"}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features['features'][0]['properties'].keys()\n",
    "features['features'][0]['geometry']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sand_features = list(toolz.filter(lambda x: pydash.get(x, 'properties.flag_sandy') == 'True', features['features']))\n",
    "grouped = toolz.groupby(\n",
    "    lambda x: pydash.get(x, 'properties.country_id'),\n",
    "    sand_features\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for country, country_features in grouped.items():\n",
    "    path = pathlib.Path(country).with_suffix('.json')\n",
    "    if path.exists():\n",
    "        with path.open() as f:\n",
    "            data = json.load(f)\n",
    "    else:\n",
    "        data = {}\n",
    "    change_rates = [\n",
    "        pydash.get(feature, 'properties.change_rate')\n",
    "        for feature\n",
    "        in country_features\n",
    "    ]\n",
    "    coordinates = [\n",
    "        pydash.get(feature, ['geometry', 'coordinates'])\n",
    "        for feature\n",
    "        in country_features\n",
    "    ]\n",
    "    rates = np.ma.masked_equal(change_rates, -999)\n",
    "    freqs, bins = np.histogram(rates[~rates.mask], bins=np.linspace(-20, 20, num=51))\n",
    "    summed_freqs = data.get('freqs', np.zeros_like(freqs)) + freqs\n",
    "    data['freqs'] = summed_freqs.tolist()\n",
    "    data['coordinates'] = data.get('coordinates', []) + coordinates\n",
    "    # just overwrite verytime\n",
    "    data['bins'] = bins.tolist()\n",
    "    data['country'] = country\n",
    "    with path.open('w') as f:\n",
    "        json.dump(data, f)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "path = pathlib.Path('GAB').with_suffix('.json')\n",
    "path.exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x1152b9710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TT5i6zouRlpam7du365dfflFWVpZrFulUzPn7+7uioXXr1rriiisUHh5+0THQqFEjtWvX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\nAMB0BAYAADAdgQEAAExHYAAAANP9f5MsrDDvtwVjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115285da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    subplot_kw=dict(\n",
    "        projection=cartopy.crs.PlateCarree()\n",
    "    ), \n",
    "    figsize=(13, 8)\n",
    ")\n",
    "ax.set_extent([2, 8, 50, 55], cartopy.crs.PlateCarree())\n",
    "ax.coastlines('10m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.61718773,  52.56825875],\n",
       "       [  4.59940751,  52.56982636]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = pathlib.Path('NLD.json')\n",
    "data = json.load(path.open())\n",
    "# find plot point\n",
    "xy_line = np.array(data['coordinates'])\n",
    "x, y = xy_line[:, 0, 0], xy_line[:, 1, 0]\n",
    "\n",
    "x_gt_y = (x.max() - x.min()) > (y.max() - y.min())\n",
    "if x_gt_y:\n",
    "    1 + 1\n",
    "    \n",
    "median = np.median(x)\n",
    "sort_idx = np.argsort(x)\n",
    "# np.searchsorted(x, median, sorter=np.sort)\n",
    "sorted_idx = np.searchsorted(x[sort_idx], median)\n",
    "idx = sort_idx[sorted_idx]\n",
    "xy_line[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.436197954948 0.513651749562\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/baart_f/.virtualenvs/main/lib/python3.5/site-packages/matplotlib/figure.py:1743: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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zj1KHw6G3335bx48f16hRoyq8FA/gLm3btlXbtm0lSadOndK+ffu0Zs0aJSYm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\njEGcAgAAwBj/B7TQPTbLzAs0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127182208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    subplot_kw=dict(\n",
    "        projection=cartopy.crs.PlateCarree()\n",
    "    ), \n",
    "    figsize=(13, 8)\n",
    ")\n",
    "extent = [2, 8, 50, 55]\n",
    "ax.set_extent(extent, cartopy.crs.PlateCarree())\n",
    "ax.coastlines('10m')\n",
    "transect = xy_line[idx]\n",
    "lon, lat = transect[0, 0], transect[0, 1]\n",
    "\n",
    "ax.plot(transect[:, 0], transect[:, 1], 'r-')\n",
    "# does not show up\n",
    "# import matplotlib.transforms\n",
    "# import mpl_toolkits.axisartist.floating_axes\n",
    "# helper = mpl_toolkits.axisartist.floating_axes.GridHelperCurveLinear(transform, extremes=(0, 10, 0, 10))\n",
    "# ax1 = mpl_toolkits.axisartist.floating_axes.FloatingSubplot(fig, 111, grid_helper=helper)\n",
    "\n",
    "x = (lon - extent[0])/(extent[1] - extent[0])\n",
    "y = (lat - extent[2])/(extent[3] - extent[2])\n",
    "print(x, y)\n",
    "ax1 = fig.add_axes([x, y, 0.1, 0.1])\n",
    "transform = matplotlib.transforms.Affine2D().rotate(30)\n",
    "ax1.get_transform()\n",
    "ax1.set_transform(transform)\n",
    "ax1.plot(0.0, 0.0, 'k.')\n",
    "ax1.axis('off')\n",
    "\n",
    "x = np.array(data['bins'])[:-1]\n",
    "freqs = np.array(data['freqs'])\n",
    "x_erosion = x[x<=0]\n",
    "y1_erosion = freqs[x<=0]\n",
    "y0_erosion = np.zeros_like(y1_erosion)\n",
    "ax1.fill_between(x_erosion, y0_erosion, y1_erosion, color='red')\n",
    "x_accretion = x[x>=0]\n",
    "y1_accretion = freqs[x>=0]\n",
    "y0_accretion = np.zeros_like(y1_accretion)\n",
    "ax1.fill_between(x_accretion, y0_accretion, y1_accretion, color='blue')\n",
    "\n",
    "# ax1.bar(data['bins'][:-1], data['freqs'])\n",
    "# ax1.bar(data['bins'][:-1], -np.array(data['freqs']))\n",
    "ax.set_xlim()\n",
    "ax.set_ylim()\n",
    "fig.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "kde = scipy.stats.gaussian_kde(rates[~rates.mask])\n",
    "x = np.linspace(-20, 20, num=51)\n",
    "plt.plot(x, kde.evaluate(x))\n",
    "\n",
    "x_erosion = x[x<=0]\n",
    "y1_erosion = kde.evaluate(x_erosion)\n",
    "y0_erosion = np.zeros_like(y1_erosion)\n",
    "plt.fill_between(x_erosion, y0_erosion, y1_erosion, color='red')\n",
    "x_accretion = x[x>=0]\n",
    "y1_accretion = kde.evaluate(x_accretion)\n",
    "y0_accretion = np.zeros_like(y1_accretion)\n",
    "plt.fill_between(x_accretion, y0_accretion, y1_accretion, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# separated countries\n",
    "# - files per country\n",
    "# - attached countries, update country file per box\n",
    "# - ignore separated islands for first version\n",
    "\n",
    "# missing countries\n",
    "# - first version, put in unknown countries\n",
    "# - second version: extend countries manually \n",
    "\n",
    "\n",
    "# store what per country\n",
    "# - option1: binned frequency + density estimate (min, max)\n",
    "# - chosen: np.histogram update per file, fixed bins (-20, 20)\n",
    "\n",
    "\n",
    "# skewed distributions\n",
    "# - option1: 95 percentile\n",
    "# - chosen: -20, 20 \n",
    "\n",
    "\n",
    "# location for the chart\n",
    "# - 80% automatic (pick a middle transect, biggest range y or x)\n",
    "# - 20% manual \n",
    "\n",
    "# countries with too long coastlines (only VS, Australia, Russia?)\n",
    "# - manual\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
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