{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "triskele_path = Path('../triskele/python/')\n",
    "sys.path.append(str(triskele_path.resolve()))\n",
    "import triskele\n",
    "\n",
    "def DFC_filter(raster):\n",
    "    raster[raster > 1e4] = raster[raster < 1e4].max()\n",
    "\n",
    "    \n",
    "def show(im, im_size=1, save=None):\n",
    "    plt.figure(figsize=(16*im_size,3*im_size))\n",
    "    plt.imshow(im)\n",
    "    plt.colorbar()\n",
    "    \n",
    "    if save is not None:\n",
    "        plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "def mshow(Xs, titles=None, im_size=1, save=None):\n",
    "    s = len(Xs)\n",
    "\n",
    "    plt.figure(figsize=(16*im_size,3*im_size*s))\n",
    "\n",
    "    for i in range(s):\n",
    "        plt.subplot(s,1,i+1)\n",
    "        plt.imshow(Xs[i])\n",
    "        \n",
    "        if titles is not None:\n",
    "            plt.title(titles[i])\n",
    "            \n",
    "        plt.colorbar()\n",
    "    \n",
    "    if save is not None:\n",
    "        plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers_files = [\n",
    "    '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif',\n",
    "    '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = list()\n",
    "\n",
    "for file in layers_files:\n",
    "    print('Loading {}'.format(file))\n",
    "    layer = triskele.read(file)\n",
    "    DFC_filter(layer)\n",
    "    layers.append(layer)\n",
    "\n",
    "layers_stack = np.stack(layers, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = triskele.Triskele(layers_stack, verbose=False)\n",
    "\n",
    "attributes = t.filter(tree='min-tree',\n",
    "                      area=[1e3,1e5], \n",
    "                      standard_deviation=[.3,.9],\n",
    "                      moment_of_inertia=[.1,.4]\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes.shape"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
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 "nbformat": 4,
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}