venation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Venation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import image\n",
    "from matplotlib import pyplot\n",
    "\n",
    "from matplotlib import _contour as contour\n",
    "\n",
    "from skimage import measure\n",
    "\n",
    "from shapely.geometry import Polygon, MultiPolygon, box, MultiLineString, LineString, Point, MultiPoint, GeometryCollection\n",
    "from shapely.affinity import rotate, translate, scale as af_scale\n",
    "from shapely.ops import unary_union, split\n",
    "\n",
    "from scipy.interpolate import splprep, splev\n",
    "\n",
    "import sys\n",
    "\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors as KNN\n",
    "from scipy.spatial.distance import pdist, cdist\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "gs = gpd.GeoSeries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Various helper functions and settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DBS, DBV = 0.03, 0.03\n",
    "DNEI = 2\n",
    "DKILL = 0.0\n",
    "\n",
    "DGROWTH = 0.03\n",
    "DGROWTH_MIN = DGROWTH / 4\n",
    "\n",
    "RHO = 0.3\n",
    "\n",
    "MAX_STUCK = 50\n",
    "\n",
    "\n",
    "def get_contours(f, vals):\n",
    "\n",
    "    polygons = []\n",
    "    for v in vals: \n",
    "        for c in measure.find_contours(f.T, v):\n",
    "            polygons.append(Polygon(c))\n",
    "            \n",
    "    multipolygon = MultiPolygon(polygons)\n",
    "    \n",
    "    return multipolygon\n",
    "\n",
    "def get_polygons(file):\n",
    "    \n",
    "    m = image.imread(file)[:,:,0]\n",
    "    m = np.where(m > 0.5, 1, 0)\n",
    "    m = m[::-1,:]\n",
    "    \n",
    "    mpoly = get_contours(m, [0.5])\n",
    "    \n",
    "    mpoly = mpoly.buffer(-100).buffer(+100).simplify(5)\n",
    "    \n",
    "    return mpoly\n",
    "\n",
    "\n",
    "def get_extreme_pt(poly, th):\n",
    "\n",
    "    if type(poly) not in [Polygon, MultiPolygon]: \n",
    "        return -np.inf\n",
    "    \n",
    "    vec = np.array([np.cos(np.deg2rad(th)), np.sin(np.deg2rad(th))])\n",
    "    \n",
    "    max_vec, max_prod = None, -np.inf\n",
    "    for xy in poly.convex_hull.exterior.coords:\n",
    "\n",
    "        prod = np.dot(vec, np.array(xy))\n",
    "        if prod > max_prod:\n",
    "            max_prod = prod\n",
    "            max_vec = np.array(xy)\n",
    "\n",
    "    return max_vec\n",
    "\n",
    "def get_closest(poly, pt):\n",
    "\n",
    "    line = poly.boundary\n",
    "    closest = line.interpolate(line.project(Point(pt)))\n",
    "    \n",
    "    closest = np.array(closest.coords[0])\n",
    "    \n",
    "    return closest\n",
    "\n",
    "def random_points(p, rho = 100, dist = np.inf):\n",
    "\n",
    "    N = rho * p.area\n",
    "    \n",
    "    minx, miny, maxx, maxy = p.bounds\n",
    "    \n",
    "    pts = []\n",
    "    while len(pts) < N:\n",
    "                \n",
    "        pt = np.random.uniform(minx, maxx), np.random.uniform(miny, maxy)\n",
    "        if p.contains(Point(pt)):  pts.append(pt)\n",
    "      \n",
    "    pts = np.array(pts)\n",
    "            \n",
    "    pdist = np.triu(cdist(pts, pts))\n",
    "    pdist = np.ma.array(pdist, mask = pdist == 0)\n",
    "\n",
    "    pdist_select = ~(pdist < dist).any(axis = 0).data\n",
    "\n",
    "    pts = pts[pdist_select]\n",
    "\n",
    "    return pts\n",
    "\n",
    "def min_dist_select(new, existing, distance):\n",
    "    \n",
    "    if existing is None:\n",
    "        return np.ones(new.shape[0]) == 1\n",
    "    \n",
    "    pdist = cdist(new, existing)\n",
    "    mask = (pdist > distance).all(axis = 1)\n",
    "    \n",
    "    return mask\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize the shape (an _M_) and the stem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = get_polygons(\"m.png\")\n",
    "M = af_scale(M, 0.001, 0.001, origin = (0, 0))\n",
    "\n",
    "start = get_extreme_pt(M, 135)\n",
    "# start = get_closest(M, (6, 1))\n",
    "# start = np.array((6, 3.5))\n",
    "\n",
    "veins = np.array([start])\n",
    "sources = None\n",
    "\n",
    "G = nx.Graph()\n",
    "vein_lines = []\n",
    "\n",
    "stuck = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is the loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 4 4 4 4 4 4 4 4 4 4 5 5 4 3 3 5 5 4 4 5 5 7 5 5 6 6 7 7 7 8 9 10 11 13 15 12 13 9 12 11 11 11 8 7 10 9 9 10 10 11 12 14 15 13 15 16 12 13 12 10 9 12 12 11 8 11 12 13 14 14 17 17 16 12 15 13 20 19 16 15 17 16 17 19 20 20 14 15 17 18 18 21 20 21 20 18 19 17 19 21 17 17 16 17 16 19 17 16 17 17 20 21 22 22 23 22 23 24 24 22 22 22 20 22 22 16 17 18 23 20 18 15 18 16 17 19 19 17 18 25 22 25 26 24 24 23 20 23 27 25 27 25 24 25 24 24 27 26 28 25 23 21 19 21 17 20 24 28 25 22 25 31 28 26 26 21 21 27 29 27 29 29 30 31 29 33 33 35 29 23 18 19 22 26 22 26 24 26 23 24 21 21 22 24 23 27 26 25 24 24 31 33 28 29 22 20 22 28 26 26 19 20 23 25 28 26 27 25 22 22 25 25 24 23 30 33 28 25 14 15 17 21 18 20 17 18 19 19 23 27 23 25 21 20 22 21 22 20 24 23 27 31 25 22 27 23 21 20 24 22 25 25 31 27 32 29 27 29 28 28 28 25 30 26 21 31 32 32 30 31 35 35 37 35 37 37 39 35 31 34 31 34 36 39 35 36 36 37 35 35 36 36 45 44 39 35 35 37 29 26 33 30 33 30 27 24 31 32 33 32 27 29 34 31 27 30 28 24 26 20 19 20 17 17 21 22 21 24 23 23 18 20 18 21 18 19 19 20 20 20 17 20 18 16 16 19 16 17 16 14 12 14 14 11 15 13 16 12 17 13 12 12 15 18 14 10 13 14 13 13 12 16 15 16 14 16 18 16 16 15 12 13 15 17 13 14 8 12 13 13 12 12 14 16 12 11 13 15 17 17 16 15 11 14 11 12 11 13 11 12 17 14 11 15 15 11 12 9 12 9 12 14 12 10 14 16 10 12 10 10 7 8 12 8 11 14 9 13 15 14 17 21 11 15 19 15 13 14 15 12 5 8 13 10 13 13 15 16 13 13 9 13 13 13 11 11 11 7 11 8 9 15 14 10 9 11 9 13 11 8 11 8 8 11 6 11 11 10 14 10 14 13 12 10 14 11 9 11 12 14 12 11 10 7 9 13 11 10 10 7 13 11 9 9 12 15 17 13 8 8 7 9 7 6 8 10 17 9 6 8 13 11 8 9 8 10 10 15 11 12 13 11 11 11 11 13 10 8 10 4 9 10 13 10 8 9 8 6 4 7 8 11 11 10 10 10 7 10 10 13 11 8 5 6 11 8 12 9 12 8 9 7 12 13 10 7 8 6 5 9 13 11 10 7 5 7 5 9 10 5 7 12 11 8 9 7 6 10 13 11 5 8 8 10 11 9 8 6 8 11 16 12 5 7 6 6 9 10 5 11 10 7 8 12 8 8 8 7 8 13 11 10 7 11 15 16 10 10 9 14 13 14 12 11 8 9 7 6 10 7 3 5 7 5 7 7 12 11 7 9 7 12 9 9 8 7 7 5 8 7 9 7 5 8 5 6 7 7 11 12 12 8 8 13 8 7 7 7 8 8 10 8 11 5 6 7 8 12 9 8 10 7 5 9 10 6 5 12 6 10 9 12 11 6 3 5 9 8 11 10 8 9 9 11 5 5 9 9 8 3 8 9 11 7 7 8 6 8 7 7 10 11 3 8 9 6 7 8 5 7 10 7 9 8 7 7 7 9 7 10 8 2 8 7 10 11 5 9 8 5 10 12 8 3 7 4 7 2 7 10 7 8 7 10 8 7 12 4 9 8 6 5 5 7 7 7 8 9 5 6 6 10 7 9 9 7 6 9 5 8 8 5 6 6 4 6 6 8 9 5 6 9 5 9 9 8 8 9 8 7 4 8 7 4 5 7 7 5 11 11 10 7 6 6 8 10 8 5 6 8 3 5 6 7 7 6 8 6 5 4 10 11 8 7 6 7 7 7 8 6 6 8 5 8 6 7 4 7 2 2 4 6 10 4 7 5 7 12 10 7 5 7 5 5 6 5 9 3 8 7 5 5 6 6 7 9 6 6 6 7 5 8 5 5 5 7 6 8 6 6 5 8 8 2 3 6 6 11 5 5 7 3 10 8 4 3 8 2 5 7 7 9 7 3 9 7 6 2 3 3 6 10 8 "
     ]
    }
   ],
   "source": [
    "for xi in range(1000):\n",
    "        \n",
    "    # Create new sources with a certain density per turn...\n",
    "    # Do not allow these sources to be closer than DBS to eachother.\n",
    "    new_sources = random_points(M, RHO, DBS)\n",
    "\n",
    "    # Select only those sources that are far enough from existing sources.\n",
    "    source_sel  = min_dist_select(new_sources, sources, DBS)\n",
    "    new_sources = new_sources[source_sel]\n",
    "\n",
    "    # Add new sources to old.\n",
    "    if sources is None: sources = new_sources\n",
    "    else: sources = np.concatenate([new_sources, sources])\n",
    "\n",
    "    # Check that they are not too close to existing veins.\n",
    "    vein_sel = min_dist_select(sources, veins, DBV)\n",
    "\n",
    "    #  ... and optionally that they are, \n",
    "    #    notwithstanding, within some vicinity of them.\n",
    "    if DNEI:\n",
    "        vein_nei_sel = ~min_dist_select(sources, veins, DNEI)\n",
    "        vein_sel = vein_sel & vein_nei_sel\n",
    "\n",
    "    # Now we have our new sources.\n",
    "    sources = sources[vein_sel]\n",
    "\n",
    "    closest_vein = np.argmin(cdist(sources, veins), axis = 1)\n",
    "    vectors = sources - veins[closest_vein]\n",
    "\n",
    "    # Normalize them\n",
    "    vlen = np.linalg.norm(vectors, axis = 1)\n",
    "    vectors /= vlen[:,np.newaxis]\n",
    "\n",
    "    new_veins, old_nodes = [], []\n",
    "    for v in np.unique(closest_vein):\n",
    "\n",
    "        closest_vectors = vectors[closest_vein == v]\n",
    "\n",
    "        # Get out, if there aren't any.\n",
    "        if not closest_vectors.size: continue\n",
    "\n",
    "        # Average that...\n",
    "        direction = closest_vectors.mean(axis = 0)\n",
    "        direction /= np.linalg.norm(direction)\n",
    "\n",
    "        # The new one, a distance DGROWTH from the reference.\n",
    "        new_node = veins[v] + direction * DGROWTH\n",
    "        \n",
    "        if not Point(new_node).within(M): continue\n",
    "\n",
    "        # And create a new node:\n",
    "        new_veins.append(new_node)\n",
    "        old_nodes.append(v)\n",
    "\n",
    "    new_veins = np.array(new_veins)\n",
    "    old_nodes = np.array(old_nodes)\n",
    "       \n",
    "    if not len(new_veins): \n",
    "        stuck += 1\n",
    "        if stuck > MAX_STUCK: break\n",
    "        continue\n",
    "\n",
    "    # Check with respect to existing veins....\n",
    "    min_dist_old_sel = (cdist(new_veins, veins) > DGROWTH_MIN).all(axis = 1)\n",
    "    \n",
    "    # And compare the new veins to eachother\n",
    "    vdist = cdist(new_veins, new_veins)\n",
    "    veto = np.triu(np.ones(vdist.shape) * np.inf)\n",
    "    vdist += veto\n",
    "\n",
    "    min_dist_new_sel = (vdist > DGROWTH_MIN).all(axis = 1)\n",
    "    \n",
    "    # Veto those on both the new and old nodes.\n",
    "    new_veins = new_veins[min_dist_old_sel & min_dist_new_sel]\n",
    "    old_nodes = old_nodes[min_dist_old_sel & min_dist_new_sel]\n",
    "\n",
    "    if not len(new_veins): \n",
    "        stuck += 1\n",
    "        if stuck > MAX_STUCK: break\n",
    "        continue\n",
    "    \n",
    "    stuck = 0\n",
    "    \n",
    "    for ni, new_vein in enumerate(new_veins):\n",
    "        \n",
    "        new_node = len(veins) + ni\n",
    "        old_node = old_nodes[ni]\n",
    "        old_vein = veins[old_node]\n",
    "        \n",
    "        vein_lines.append([old_node, new_node, LineString([old_vein, new_vein])])\n",
    "        \n",
    "        G.add_node(new_node)\n",
    "        G.add_edge(old_node, new_node)\n",
    "    \n",
    "    veins = np.concatenate([veins, new_veins])\n",
    "\n",
    "    print(len(new_veins), end = \" \")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use networkx to figure out the number of length of the veins that are connected to the stem, through each other vein segment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "paths = nx.shortest_path(G, source = 0)\n",
    "bcounts = sum(list([p[1:] for p in paths.values()]), [])\n",
    "\n",
    "flow = pd.Series(bcounts).value_counts().reset_index(name = \"flow\")\n",
    "flow.rename(columns = {\"index\" : \"B\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a dataframe of all this, for plotting..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vein_lines_df = pd.DataFrame(vein_lines, columns = [\"A\", \"B\", \"lines\"])\n",
    "vein_lines_gdf = gpd.GeoDataFrame(data = vein_lines_df[[\"A\", \"B\"]], geometry = vein_lines_df.lines)\n",
    "vein_lines_gdf = vein_lines_gdf.merge(flow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the alpha levels based on the length of the downstream veins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vein_lines_gdf[\"diam\"] = np.sqrt(vein_lines_gdf[\"flow\"] / vein_lines_gdf[\"flow\"].max())\n",
    "vein_lines_gdf[\"lw\"] = 5 * vein_lines_gdf[\"diam\"]\n",
    "\n",
    "fa = 0.7\n",
    "vein_lines_gdf[\"alpha\"] = (1 - fa) + fa * vein_lines_gdf[\"diam\"]\n",
    "\n",
    "get_5d7_alpha = lambda a : \"#55DD77{:02x}\".format(int(a * 255))\n",
    "vein_lines_gdf[\"color\"] = vein_lines_gdf[\"alpha\"].apply(get_5d7_alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (4, 3), subplot_kw = {\"aspect\" : 1})\n",
    "\n",
    "vein_lines_gdf.plot(lw = vein_lines_gdf[\"lw\"], \n",
    "                    # alpha = vein_lines_gdf[\"alpha\"], \n",
    "                    color = vein_lines_gdf[\"color\"],\n",
    "                    ax = ax, capstyle = \"round\")\n",
    "gs(M).plot(color = \"#008800\", ax = ax)\n",
    "\n",
    "map_format(ax)\n",
    "\n",
    "f.savefig(\"m_venation.pdf\", pad_inches = 0.1, bbox_inches='tight')\n",
    "f.savefig(\"m_venation.png\", dpi = 300, pad_inches = 0.1, bbox_inches='tight')"
   ]
  }
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