examples/synthetic_network_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------\n",
    "\n",
    "**If any part of this notebook is used in your research, please cite with the reference found in [README.md](https://github.com/jGaboardi/tigernet#citations)**.\n",
    "\n",
    "-------------------------------------------------------------\n",
    "\n",
    "# Example usage: synthetic lattice network\n",
    "\n",
    "\n",
    "### Author: James D. Gaboardi jgaboardi@gmail.com\n",
    "\n",
    "\n",
    "-------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:13.659118Z",
     "start_time": "2021-07-08T14:03:13.614617Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2021-07-08T10:03:13.640178-04:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.9.5\n",
      "IPython version      : 7.25.0\n",
      "\n",
      "Compiler    : Clang 11.1.0 \n",
      "OS          : Darwin\n",
      "Release     : 20.5.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 8\n",
      "Architecture: 64bit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%config InlineBackend.figure_format = \"retina\"\n",
    "%load_ext watermark\n",
    "%watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:14.806630Z",
     "start_time": "2021-07-08T14:03:13.661352Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watermark: 2.2.0\n",
      "\n",
      "json    : 2.0.9\n",
      "tigernet: 0.2.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tigernet\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%watermark -w\n",
    "%watermark -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:14.843206Z",
     "start_time": "2021-07-08T14:03:14.809706Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Network', 'Observations', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', 'generate_data', 'generate_lattice', 'generate_obs', 'generate_sine_lines', 'get_discard_mtfcc_by_desc', 'get_discard_segms', 'get_mtfcc_types', 'info', 'obs2obs_cost_matrix', 'stats', 'testing_data', 'tigernet', 'utils']\n"
     ]
    }
   ],
   "source": [
    "print(dir(tigernet))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate a synthetic lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.007272Z",
     "start_time": "2021-07-08T14:03:14.845464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
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       "      <td>3</td>\n",
       "      <td>S1400</td>\n",
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      ],
      "text/plain": [
       "                                        geometry  SegID  MTFCC\n",
       "0  LINESTRING (4.50000 0.00000, 4.50000 4.50000)      1  S1400\n",
       "1  LINESTRING (4.50000 4.50000, 4.50000 9.00000)      2  S1400\n",
       "2  LINESTRING (0.00000 4.50000, 4.50000 4.50000)      3  S1400\n",
       "3  LINESTRING (4.50000 4.50000, 9.00000 4.50000)      4  S1400"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 245
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lattice = tigernet.generate_lattice(n_hori_lines=1, n_vert_lines=1)\n",
    "lattice.plot()\n",
    "lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.052011Z",
     "start_time": "2021-07-08T14:03:15.009024Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Network in module tigernet.tigernet:\n",
      "\n",
      "class Network(builtins.object)\n",
      " |  Network(s_data, from_raw=False, sid_name='SegID', nid_name='NodeID', geo_col='geometry', len_col='length', xyid='xyid', tnid='TNID', tnidf='TNIDF', tnidt='TNIDT', attr1=None, attr2=None, mtfcc_types=None, mtfcc_discard=None, discard_segs=None, mtfcc_split=None, mtfcc_intrst=None, mtfcc_ramp=None, mtfcc_serv=None, mtfcc_split_by=None, mtfcc_split_grp=None, skip_restr=False, calc_len=False, record_components=False, record_geom=False, largest_component=False, calc_stats=False, def_graph_elems=False)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, s_data, from_raw=False, sid_name='SegID', nid_name='NodeID', geo_col='geometry', len_col='length', xyid='xyid', tnid='TNID', tnidf='TNIDF', tnidt='TNIDT', attr1=None, attr2=None, mtfcc_types=None, mtfcc_discard=None, discard_segs=None, mtfcc_split=None, mtfcc_intrst=None, mtfcc_ramp=None, mtfcc_serv=None, mtfcc_split_by=None, mtfcc_split_grp=None, skip_restr=False, calc_len=False, record_components=False, record_geom=False, largest_component=False, calc_stats=False, def_graph_elems=False)\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      s_data : geopandas.GeoDataFrame\n",
      " |          Segments dataframe.\n",
      " |      from_raw : bool\n",
      " |          Input ``s_data`` is raw TIGER/Line Edge data. Default is ``False``.\n",
      " |      sid_name : str\n",
      " |          Segment column name. Default is ``'SegID'``.\n",
      " |      nid_name : str\n",
      " |          Node column name. Default is ``'NodeID'``.\n",
      " |      geo_col : str\n",
      " |          Geometry column name. Default is ``'geometry'``.\n",
      " |      len_col : str\n",
      " |          Length column name. Default is ``'length'``.\n",
      " |      xyid : str\n",
      " |          Combined x-coord + y-coords string ID. Default is ``'xyid'``.\n",
      " |      tnid : str\n",
      " |          TIGER/Line node ID variable used for working with\n",
      " |          TIGER/Line edges. Default is ``'TNID'``.\n",
      " |      tnidf : str\n",
      " |          TIGER/Line 'From Node' variable used for building topology\n",
      " |          in TIGER/Line edges. Default is ``'TNIDF'``.\n",
      " |      tnidt : str\n",
      " |           TIGER/Line 'To Node' variable used for building topology in\n",
      " |           TIGER/Line edges. Default is ``'TNIDT'``.\n",
      " |      attr1 : str\n",
      " |          Auxillary variable being used. Default is ``None``.\n",
      " |      attr2 : str\n",
      " |          Auxillary variable being used. Either ``'TLID'`` for tiger edges\n",
      " |          or ``'LINEARID'`` for tiger roads. Default is ``None``.\n",
      " |      mtfcc_types : dict\n",
      " |          MTFCC road type descriptions. Default is ``None``.\n",
      " |          from [utils.get_mtfcc_types()]\n",
      " |      mtfcc_discard : list\n",
      " |          MTFCC types (by code) to discard. Default is ``None``.\n",
      " |          from [utils.get_discard_mtfcc_by_desc()]\n",
      " |      discard_segs : list\n",
      " |          specifc segment ids to discard. Default is ``None``.\n",
      " |          from [utils.discard_troublemakers()]\n",
      " |      mtfcc_split : str\n",
      " |          MTFCC codes for segments to weld and then split during the\n",
      " |          line splitting process. Default is ``None``.\n",
      " |      mtfcc_intrst : str\n",
      " |          MTFCC codes for interstates. Default is ``None``.\n",
      " |      mtfcc_ramp : str\n",
      " |          MTFCC codes for on ramps. Default is ``None``.\n",
      " |      mtfcc_serv : str\n",
      " |          MTFCC codes for service drives. Default is ``None``.\n",
      " |      mtfcc_split_by : list\n",
      " |          MTFCC codes to eventually split the segments of\n",
      " |          `mtfcc_no_split` with. Default is ``None``.\n",
      " |      mtfcc_split_grp : str\n",
      " |          After subseting this road type, group by this attribute\n",
      " |          before welding. Default is ``None``.\n",
      " |      skip_restr : bool\n",
      " |          Skip re-welding restricted segments. Default is ``False``.\n",
      " |      calc_len : bool\n",
      " |          Calculate length and add column. Default is ``False``.\n",
      " |      record_components : bool\n",
      " |          Record connected components in graph. This is used for teasing out the\n",
      " |          largest connected component. Default is ``False``.\n",
      " |      record_geom : bool\n",
      " |          Create associated between IDs and shapely geometries.\n",
      " |          Default is ``False``.\n",
      " |      largest_component : bool\n",
      " |          Keep only the largest connected component in the graph. Default is ``False``.\n",
      " |      calc_stats : bool\n",
      " |          Calculate network stats. Default is ``False``.\n",
      " |      def_graph_elems : bool\n",
      " |          Define graph elements. Default is ``False``.\n",
      " |      \n",
      " |      Attributes\n",
      " |      ----------\n",
      " |      segm2xyid : dict\n",
      " |          Segment to xyID lookup.\n",
      " |      node2xyid : dict\n",
      " |          Node to xyID lookup.\n",
      " |      segm2node : dict\n",
      " |          Segment to node lookup.\n",
      " |      node2segm : dict\n",
      " |          Node to segment lookup.\n",
      " |      segm2segm : dict\n",
      " |          Segment to segment lookup.\n",
      " |      node2node : dict\n",
      " |          Node to node lookup.\n",
      " |      segm_cc : dict\n",
      " |          Root segment ID to connected component segment IDs lookup.\n",
      " |      cc_lens : dict\n",
      " |          Root segment ID to connected component length lookup.\n",
      " |      node_cc : dict\n",
      " |          Root node ID to connected component node IDs lookup.\n",
      " |      largest_segm_cc : dict\n",
      " |          Root segment ID to largest connected component segment IDs lookup.\n",
      " |      largest_node_cc : dict\n",
      " |          Root node ID to largest connected component node IDs lookup.\n",
      " |      n_ccs : int\n",
      " |          The number of connected components.\n",
      " |      s_ids : list\n",
      " |          Segment IDs.\n",
      " |      n_ids : list\n",
      " |          Node IDs.\n",
      " |      n_segm : int\n",
      " |          Network segment count.\n",
      " |      n_node : int\n",
      " |          Network node count.\n",
      " |      segm2len : dict\n",
      " |          Segment to segment length lookup.\n",
      " |      network_length : float\n",
      " |          Full network length.\n",
      " |      node2degree : dict\n",
      " |          Node to node degree lookup.\n",
      " |      segm2tlid : dict\n",
      " |          Segment to TIGER/Line ID lookup.\n",
      " |      segm2elem : dict\n",
      " |          Segment to network element lookup.\n",
      " |      node2elem : dict\n",
      " |          Node to network element lookup.\n",
      " |      diameter : float\n",
      " |          The longest shortest path between two nodes in the network.\n",
      " |      radius : float\n",
      " |          The shortest path between two nodes in the network.\n",
      " |      d_net : float\n",
      " |          Cumulative network diameter.\n",
      " |      d_euc : float\n",
      " |          Cumulative euclidean diameter.\n",
      " |      circuity : float\n",
      " |          Network circuity. See ``stats.circuity()``.\n",
      " |      n2n_matrix : numpy.array\n",
      " |          All node-to-node shortest path lengths in the network.\n",
      " |      n2n_paths : dict\n",
      " |          All node-to-node shortest paths in the network.\n",
      " |      max_sinuosity : float\n",
      " |          Maximum segment sinuosity.\n",
      " |      min_sinuosity : float\n",
      " |          Minimum segment sinuosity.\n",
      " |      net_mean_sinuosity : float\n",
      " |          Network segment sinuosity mean.\n",
      " |      net_std_sinuosity : float\n",
      " |          Network segment sinuosity standard deviation.\n",
      " |      max_node_degree : int\n",
      " |          Maximum node degree.\n",
      " |      min_node_degree : int\n",
      " |          Minimum node degree.\n",
      " |      mean_node_degree : float\n",
      " |          Network node degree mean.\n",
      " |      std_node_degree : float\n",
      " |          Network node degree standard deviation.\n",
      " |      alpha : float\n",
      " |          Network alpha measure. See ``stats.connectivity()``.\n",
      " |      beta : float\n",
      " |          Network beta measure. See ``stats.connectivity()``.\n",
      " |      gamma : float\n",
      " |          Network gamma measure. See ``stats.connectivity()``.\n",
      " |      eta : float\n",
      " |          Network eta measure. See ``stats.connectivity()``.\n",
      " |      entropies_{} : dict\n",
      " |          Segment/Node ID to {variable/attribute} entropies.\n",
      " |      network_entropy_{} : float\n",
      " |          Network {variable/attribute} entropy.\n",
      " |      corrected_rings : int\n",
      " |          Number of corrected rings in the network.\n",
      " |      lines_split : int\n",
      " |          Number of split lines in the network.\n",
      " |      welded_mls : int\n",
      " |          Number of welded multilinestrings in the network.\n",
      " |      segm2geom : dict\n",
      " |          Segment to geometry lookup.\n",
      " |      node2geom : dict\n",
      " |          Node to geometry lookup.\n",
      " |      segm2coords : dict\n",
      " |          Segment to endpoint coordinates lookup.\n",
      " |      node2coords : dict\n",
      " |          Node to coordinates lookup.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      \n",
      " |      >>> import tigernet\n",
      " |      >>> lat = tigernet.generate_lattice(n_hori_lines=1, n_vert_lines=1)\n",
      " |      >>> net = tigernet.Network(s_data=lat)\n",
      " |      >>> net.s_data[[\"SegID\", \"MTFCC\", \"length\", \"xyid\", \"s_neigh\", \"n_neigh\"]]\n",
      " |         SegID  MTFCC  length                      xyid    s_neigh n_neigh\n",
      " |      0      0  S1400     4.5  ['x4.5y0.0', 'x4.5y4.5']  [1, 2, 3]  [0, 1]\n",
      " |      1      1  S1400     4.5  ['x4.5y4.5', 'x4.5y9.0']  [0, 2, 3]  [1, 2]\n",
      " |      2      2  S1400     4.5  ['x0.0y4.5', 'x4.5y4.5']  [0, 1, 3]  [1, 3]\n",
      " |      3      3  S1400     4.5  ['x4.5y4.5', 'x9.0y4.5']  [0, 1, 2]  [1, 4]\n",
      " |      \n",
      " |      >>> net.n_data[[\"NodeID\", \"xyid\", \"s_neigh\", \"n_neigh\", \"degree\"]]\n",
      " |         NodeID          xyid       s_neigh       n_neigh  degree\n",
      " |      0       0  ['x4.5y0.0']           [0]           [1]       1\n",
      " |      1       1  ['x4.5y4.5']  [0, 1, 2, 3]  [0, 2, 3, 4]       4\n",
      " |      2       2  ['x4.5y9.0']           [1]           [1]       1\n",
      " |      3       3  ['x0.0y4.5']           [2]           [1]       1\n",
      " |      4       4  ['x9.0y4.5']           [3]           [1]       1\n",
      " |      \n",
      " |      >>> net.segm2xyid[0]\n",
      " |      ['x4.5y0.0', 'x4.5y4.5']\n",
      " |      \n",
      " |      >>> net.node2xyid[0]\n",
      " |      ['x4.5y0.0']\n",
      " |      \n",
      " |      >>> net.segm2node[3]\n",
      " |      [1, 4]\n",
      " |      \n",
      " |      >>> net.node2segm[4]\n",
      " |      [3]\n",
      " |      \n",
      " |      >>> net.segm2segm[3]\n",
      " |      [0, 1, 2]\n",
      " |      \n",
      " |      >>> net.node2node[4]\n",
      " |      [1]\n",
      " |  \n",
      " |  build_associations(self, record_geom=False)\n",
      " |      Associate graph elements with geometries, coordinates,\n",
      " |      segment lengths, node degrees, and other information.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      record_geom : bool\n",
      " |          Create an ID-to-geometry lookup (``True``). Default is ``False``.\n",
      " |  \n",
      " |  build_base(self, s_data)\n",
      " |      Extract nodes from segment endpoints and relate\n",
      " |      segments and nodes to a location ID (``xyid``).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      s_data : geopandas.GeoDataFrame\n",
      " |          Segments data.\n",
      " |  \n",
      " |  build_components(self, largest_cc=False)\n",
      " |      Find the rooted connected components of the graph (either largest or longest).\n",
      " |      *** Must choose either largest or longest. If both ``largest_cc`` and\n",
      " |      ``longest_cc`` are ``True``, ``largest_cc`` will be selected by default. ***\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      largest_cc : bool\n",
      " |          Keep only the largest connected component (the most\n",
      " |          edges/nodes) in the graph. Default is ``False``.\n",
      " |  \n",
      " |  build_network(self, s_data, record_components=False, record_geom=False, largest_component=False, def_graph_elems=False)\n",
      " |      Top-level method for full network object creation from a\n",
      " |      geopandas.GeoDataFrame of lines.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      s_data : geopandas.GeoDataFrame\n",
      " |          Segments data.\n",
      " |      record_components : bool\n",
      " |          Find rooted connected components in the network (``True``),\n",
      " |          or ignore (``False``). Default is ``False``.\n",
      " |      largest_component : bool\n",
      " |          Keep only the largest connected compnent of the network\n",
      " |          (``True``), or keep all components (``False``). Default is ``False``.\n",
      " |      record_geom : bool\n",
      " |          Create an id to geometry lookup (``True``), or ignore (``False``).\n",
      " |          Default is ``False``.\n",
      " |      def_graph_elems : bool\n",
      " |          Define each element of the graph as either a branch\n",
      " |          [connected to two or more other elements], or a leaf\n",
      " |          [connected to only one other element] (``True``), or ignore\n",
      " |          (``False``). Default is ``False``.\n",
      " |  \n",
      " |  build_topology(self)\n",
      " |      Relate all graph elements.\n",
      " |  \n",
      " |  calc_entropy(self, ent_col, frame_name)\n",
      " |      Network entropy statistics. For descriptions see ``stats.entropies()``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      ent_col : str\n",
      " |          The column name in ``frame_name`` to calculate entropy on.\n",
      " |      frame_name : str\n",
      " |          The name of the network element dataframe.\n",
      " |  \n",
      " |  calc_net_stats(self, conn_stat=None)\n",
      " |      Calculate network analyis descriptive statistics.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      conn_stat : {None, str}\n",
      " |          Either ``'alpha'``, ``'beta'``, ``'gamma'``, ``'eta'``.\n",
      " |          Set to ``'all'`` toc calculate all available statistics.\n",
      " |          For descriptions see ``stats.connectivity()``.\n",
      " |  \n",
      " |  cost_matrix(self, wpaths=False, asattr=True)\n",
      " |      Network node-to-node cost matrix calculation with options for generating\n",
      " |      shortest paths along tree. For best results the network should be simplified\n",
      " |      prior to running this method.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      wpaths : bool\n",
      " |          Generate shortest paths tree. Default is ``False``.\n",
      " |      asattr : bool\n",
      " |          Set ``n2n_matrix`` and ``paths`` as attributes of ``Network`` if ``True``,\n",
      " |          otherwise return them. Default is ``True``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      n2n_matrix : numpy.ndarray\n",
      " |          Shortest path costs between all nodes.\n",
      " |      paths : dict\n",
      " |          Graph traveral paths.\n",
      " |  \n",
      " |  define_graph_elements(self)\n",
      " |      Define all segments and nodes as either a leaf (incident with one other\n",
      " |      element) or a branch (incident with more than one other graph element).\n",
      " |  \n",
      " |  nodes_kdtree(self, only_coords=False)\n",
      " |      Build a kdtree from the network node coords for observations lookup.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      only_coords : bool\n",
      " |          Flag for only coordinated being passed in.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      kdtree : scipy.spatial.kdtree.KDTree\n",
      " |          All network nodes lookup.\n",
      " |  \n",
      " |  simplify_network(self, record_components=False, largest_component=False, record_geom=False, def_graph_elems=False, inplace=False)\n",
      " |      Remove all non-articulation points in the network.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      record_components : bool\n",
      " |          Record connected components in graph. This is used for teasing out the\n",
      " |          largest connected component. Default is ``False``.\n",
      " |      largest_component : bool\n",
      " |          Keep only the largest connected component in the graph. Default is ``False``.\n",
      " |      record_geom : bool\n",
      " |          Create associated between IDs and shapely geometries. Default is ``False``.\n",
      " |      def_graph_elems : bool\n",
      " |          Define graph elements. Default is ``False``.\n",
      " |      inplace : bool\n",
      " |          Overwrite the original network with the simplified. Default is ``False``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      simp_net : geopandas.GeoDataFrame\n",
      " |          The simplified network (if ``inplace`` is set to ``False``).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(help(tigernet.Network))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a network instance & examine its attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.134675Z",
     "start_time": "2021-07-08T14:03:15.054714Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'attr1', 'attr2', 'build_associations', 'build_base', 'build_components', 'build_network', 'build_topology', 'calc_entropy', 'calc_net_stats', 'cc_lens', 'cost_matrix', 'define_graph_elements', 'discard_segs', 'from_raw', 'geo_col', 'len_col', 'mtfcc_discard', 'mtfcc_types', 'n_ccs', 'n_data', 'n_ids', 'n_node', 'n_segm', 'network_length', 'nid_name', 'node2coords', 'node2degree', 'node2elem', 'node2geom', 'node2node', 'node2segm', 'node2xyid', 'node_cc', 'nodes_kdtree', 's_data', 's_ids', 'segm2coords', 'segm2elem', 'segm2geom', 'segm2len', 'segm2node', 'segm2segm', 'segm2xyid', 'segm_cc', 'sid_name', 'simplify_network', 'tlid', 'tnid', 'tnidf', 'tnidt', 'xyid']\n"
     ]
    }
   ],
   "source": [
    "network = tigernet.Network(\n",
    "    s_data=lattice,\n",
    "    record_components=True,\n",
    "    record_geom=True,\n",
    "    largest_component=False,\n",
    "    def_graph_elems=True\n",
    ")\n",
    "print(dir(network))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.178159Z",
     "start_time": "2021-07-08T14:03:15.136599Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>SegID</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>length</th>\n",
       "      <th>xyid</th>\n",
       "      <th>s_neigh</th>\n",
       "      <th>n_neigh</th>\n",
       "      <th>CC</th>\n",
       "      <th>ccLength</th>\n",
       "      <th>graph_elem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LINESTRING (4.50000 0.00000, 4.50000 4.50000)</td>\n",
       "      <td>0</td>\n",
       "      <td>S1400</td>\n",
       "      <td>4.5</td>\n",
       "      <td>['x4.5y0.0', 'x4.5y4.5']</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LINESTRING (4.50000 4.50000, 4.50000 9.00000)</td>\n",
       "      <td>1</td>\n",
       "      <td>S1400</td>\n",
       "      <td>4.5</td>\n",
       "      <td>['x4.5y4.5', 'x4.5y9.0']</td>\n",
       "      <td>[0, 2, 3]</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LINESTRING (0.00000 4.50000, 4.50000 4.50000)</td>\n",
       "      <td>2</td>\n",
       "      <td>S1400</td>\n",
       "      <td>4.5</td>\n",
       "      <td>['x0.0y4.5', 'x4.5y4.5']</td>\n",
       "      <td>[0, 1, 3]</td>\n",
       "      <td>[1, 3]</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LINESTRING (4.50000 4.50000, 9.00000 4.50000)</td>\n",
       "      <td>3</td>\n",
       "      <td>S1400</td>\n",
       "      <td>4.5</td>\n",
       "      <td>['x4.5y4.5', 'x9.0y4.5']</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>[1, 4]</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        geometry  SegID  MTFCC  length  \\\n",
       "0  LINESTRING (4.50000 0.00000, 4.50000 4.50000)      0  S1400     4.5   \n",
       "1  LINESTRING (4.50000 4.50000, 4.50000 9.00000)      1  S1400     4.5   \n",
       "2  LINESTRING (0.00000 4.50000, 4.50000 4.50000)      2  S1400     4.5   \n",
       "3  LINESTRING (4.50000 4.50000, 9.00000 4.50000)      3  S1400     4.5   \n",
       "\n",
       "                       xyid    s_neigh n_neigh  CC  ccLength graph_elem  \n",
       "0  ['x4.5y0.0', 'x4.5y4.5']  [1, 2, 3]  [0, 1]   1      18.0       leaf  \n",
       "1  ['x4.5y4.5', 'x4.5y9.0']  [0, 2, 3]  [1, 2]   1      18.0       leaf  \n",
       "2  ['x0.0y4.5', 'x4.5y4.5']  [0, 1, 3]  [1, 3]   1      18.0       leaf  \n",
       "3  ['x4.5y4.5', 'x9.0y4.5']  [0, 1, 2]  [1, 4]   1      18.0       leaf  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.s_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.218181Z",
     "start_time": "2021-07-08T14:03:15.179926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>NodeID</th>\n",
       "      <th>xyid</th>\n",
       "      <th>s_neigh</th>\n",
       "      <th>n_neigh</th>\n",
       "      <th>CC</th>\n",
       "      <th>degree</th>\n",
       "      <th>graph_elem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (4.50000 0.00000)</td>\n",
       "      <td>0</td>\n",
       "      <td>['x4.5y0.0']</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (4.50000 4.50000)</td>\n",
       "      <td>1</td>\n",
       "      <td>['x4.5y4.5']</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 2, 3, 4]</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>branch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (4.50000 9.00000)</td>\n",
       "      <td>2</td>\n",
       "      <td>['x4.5y9.0']</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (0.00000 4.50000)</td>\n",
       "      <td>3</td>\n",
       "      <td>['x0.0y4.5']</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (9.00000 4.50000)</td>\n",
       "      <td>4</td>\n",
       "      <td>['x9.0y4.5']</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>leaf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  geometry  NodeID          xyid       s_neigh       n_neigh  \\\n",
       "0  POINT (4.50000 0.00000)       0  ['x4.5y0.0']           [0]           [1]   \n",
       "1  POINT (4.50000 4.50000)       1  ['x4.5y4.5']  [0, 1, 2, 3]  [0, 2, 3, 4]   \n",
       "2  POINT (4.50000 9.00000)       2  ['x4.5y9.0']           [1]           [1]   \n",
       "3  POINT (0.00000 4.50000)       3  ['x0.0y4.5']           [2]           [1]   \n",
       "4  POINT (9.00000 4.50000)       4  ['x9.0y4.5']           [3]           [1]   \n",
       "\n",
       "   CC  degree graph_elem  \n",
       "0   1       1       leaf  \n",
       "1   1       4     branch  \n",
       "2   1       1       leaf  \n",
       "3   1       1       leaf  \n",
       "4   1       1       leaf  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.n_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.252640Z",
     "start_time": "2021-07-08T14:03:15.222224Z"
    }
   },
   "outputs": [],
   "source": [
    "skws = {\"color\": \"k\", \"alpha\": .5, \"zorder\": 0}\n",
    "nkws = {\"color\": \"r\", \"markersize\": 90, \"alpha\": .5, \"ec\":\"k\", \"zorder\": 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.421268Z",
     "start_time": "2021-07-08T14:03:15.256047Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 353
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = network.s_data.plot(figsize=(6,6), **skws)\n",
    "network.n_data.plot(ax=ax, **nkws);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network element IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.458107Z",
     "start_time": "2021-07-08T14:03:15.423140Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3], [0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.s_ids, network.n_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network segment count and length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.494453Z",
     "start_time": "2021-07-08T14:03:15.460089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 18.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.n_segm, network.network_length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network node count and degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.531345Z",
     "start_time": "2021-07-08T14:03:15.496381Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, {0: 1, 1: 4, 2: 1, 3: 1, 4: 1})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.n_node, network.node2degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network connected components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.567492Z",
     "start_time": "2021-07-08T14:03:15.532911Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, {1: [0, 1, 2, 3]}, {1: [0, 1, 2, 3, 4]})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.n_ccs, network.segm_cc, network.node_cc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network element topology\n",
    "#### x->x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.605029Z",
     "start_time": "2021-07-08T14:03:15.569878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0: [1, 2, 3], 1: [0, 2, 3], 2: [0, 1, 3], 3: [0, 1, 2]},\n",
       " {0: [1], 1: [0, 2, 3, 4], 2: [1], 3: [1], 4: [1]})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.segm2segm, network.node2node"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### x->y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.641720Z",
     "start_time": "2021-07-08T14:03:15.606571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0: [0, 1], 1: [1, 2], 2: [1, 3], 3: [1, 4]},\n",
       " {0: [0], 1: [0, 1, 2, 3], 2: [1], 3: [2], 4: [3]})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.segm2node, network.node2segm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network element type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.678152Z",
     "start_time": "2021-07-08T14:03:15.643814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0: 'leaf', 1: 'leaf', 2: 'leaf', 3: 'leaf'},\n",
       " {0: 'leaf', 1: 'branch', 2: 'leaf', 3: 'leaf', 4: 'leaf'})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.segm2elem, network.node2elem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------\n",
    "\n",
    "## Simply the network (no effect in this case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.750108Z",
     "start_time": "2021-07-08T14:03:15.679853Z"
    }
   },
   "outputs": [],
   "source": [
    "network.simplify_network(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------\n",
    "\n",
    "## Generate a network cost matrix with shortest path trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.786372Z",
     "start_time": "2021-07-08T14:03:15.752036Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. , 4.5, 9. , 9. , 9. ],\n",
       "       [4.5, 0. , 4.5, 4.5, 4.5],\n",
       "       [9. , 4.5, 0. , 9. , 9. ],\n",
       "       [9. , 4.5, 9. , 0. , 9. ],\n",
       "       [9. , 4.5, 9. , 9. , 0. ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.cost_matrix(asattr=True, wpaths=True)\n",
    "network.n2n_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.824009Z",
     "start_time": "2021-07-08T14:03:15.788186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {0: [0], 1: [0], 2: [1, 0], 3: [1, 0], 4: [1, 0]},\n",
       " 1: {0: [1], 1: [1], 2: [1], 3: [1], 4: [1]},\n",
       " 2: {0: [1, 2], 1: [2], 2: [2], 3: [1, 2], 4: [1, 2]},\n",
       " 3: {0: [1, 3], 1: [3], 2: [1, 3], 3: [3], 4: [1, 3]},\n",
       " 4: {0: [1, 4], 1: [4], 2: [1, 4], 3: [1, 4], 4: [4]}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.n2n_paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------\n",
    "## Generate synthetic observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.866498Z",
     "start_time": "2021-07-08T14:03:15.826798Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>obs_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (6.01943 3.15640)</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (8.03795 2.50232)</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (0.47238 8.26102)</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (4.24946 0.05623)</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (4.39206 7.57014)</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  geometry obs_id\n",
       "0  POINT (6.01943 3.15640)      a\n",
       "1  POINT (8.03795 2.50232)      b\n",
       "2  POINT (0.47238 8.26102)      c\n",
       "3  POINT (4.24946 0.05623)      d\n",
       "4  POINT (4.39206 7.57014)      e"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observations = tigernet.generate_obs(5, network.s_data, seed=404)\n",
    "observations[\"obs_id\"] = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n",
    "observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:15.900583Z",
     "start_time": "2021-07-08T14:03:15.868386Z"
    }
   },
   "outputs": [],
   "source": [
    "okws = {\"color\": \"b\", \"markersize\": 60, \"alpha\": .75, \"ec\":\"k\", \"zorder\": 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.098782Z",
     "start_time": "2021-07-08T14:03:15.902320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 353
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = network.s_data.plot(figsize=(6,6), **skws)\n",
    "network.n_data.plot(ax=ax, **nkws)\n",
    "observations.plot(ax=ax, **okws);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------\n",
    "## Associate the observations with the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.151283Z",
     "start_time": "2021-07-08T14:03:16.100780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'df', 'df_key', 'df_name', 'geo_col', 'k', 'kd_tree', 'obs2coords', 'obs2segm', 'sid_name', 'snap_to', 'snapped_points', 'tol', 'xyid']\n"
     ]
    }
   ],
   "source": [
    "network_observations = tigernet.Observations(\n",
    "    network,\n",
    "    observations.copy(),\n",
    "    df_name=\"obs1\",\n",
    "    df_key=\"obs_id\",\n",
    ")\n",
    "print(dir(network_observations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.196160Z",
     "start_time": "2021-07-08T14:03:16.153486Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs_id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>assoc_segm</th>\n",
       "      <th>dist_a</th>\n",
       "      <th>node_a</th>\n",
       "      <th>dist_b</th>\n",
       "      <th>node_b</th>\n",
       "      <th>dist2line</th>\n",
       "      <th>xyid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>POINT (6.01943 4.50000)</td>\n",
       "      <td>3</td>\n",
       "      <td>1.519426</td>\n",
       "      <td>1</td>\n",
       "      <td>2.980574</td>\n",
       "      <td>4</td>\n",
       "      <td>1.343598</td>\n",
       "      <td>['x6.019425770680618y4.5']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>POINT (8.03795 4.50000)</td>\n",
       "      <td>3</td>\n",
       "      <td>3.537952</td>\n",
       "      <td>1</td>\n",
       "      <td>0.962048</td>\n",
       "      <td>4</td>\n",
       "      <td>1.997682</td>\n",
       "      <td>['x8.03795164668808y4.5']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>POINT (0.47238 4.50000)</td>\n",
       "      <td>2</td>\n",
       "      <td>0.472383</td>\n",
       "      <td>3</td>\n",
       "      <td>4.027617</td>\n",
       "      <td>1</td>\n",
       "      <td>3.761022</td>\n",
       "      <td>['x0.472383396581738y4.5']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>POINT (4.50000 0.05623)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.056225</td>\n",
       "      <td>0</td>\n",
       "      <td>4.443775</td>\n",
       "      <td>1</td>\n",
       "      <td>0.250542</td>\n",
       "      <td>['x4.5y0.05622526079105139']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>POINT (4.50000 7.57014)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.070140</td>\n",
       "      <td>1</td>\n",
       "      <td>1.429860</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107943</td>\n",
       "      <td>['x4.5y7.5701402617992954']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  obs_id                 geometry  assoc_segm    dist_a  node_a    dist_b  \\\n",
       "0      a  POINT (6.01943 4.50000)           3  1.519426       1  2.980574   \n",
       "1      b  POINT (8.03795 4.50000)           3  3.537952       1  0.962048   \n",
       "2      c  POINT (0.47238 4.50000)           2  0.472383       3  4.027617   \n",
       "3      d  POINT (4.50000 0.05623)           0  0.056225       0  4.443775   \n",
       "4      e  POINT (4.50000 7.57014)           1  3.070140       1  1.429860   \n",
       "\n",
       "   node_b  dist2line                          xyid  \n",
       "0       4   1.343598    ['x6.019425770680618y4.5']  \n",
       "1       4   1.997682     ['x8.03795164668808y4.5']  \n",
       "2       1   3.761022    ['x0.472383396581738y4.5']  \n",
       "3       1   0.250542  ['x4.5y0.05622526079105139']  \n",
       "4       2   0.107943   ['x4.5y7.5701402617992954']  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network_observations.snapped_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.232067Z",
     "start_time": "2021-07-08T14:03:16.198529Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 3, 'b': 3, 'c': 2, 'd': 0, 'e': 1}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network_observations.obs2segm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.269690Z",
     "start_time": "2021-07-08T14:03:16.238442Z"
    }
   },
   "outputs": [],
   "source": [
    "bkws = {\"color\": \"y\", \"markersize\": 30, \"alpha\": .85, \"ec\":\"k\", \"zorder\": 3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.477751Z",
     "start_time": "2021-07-08T14:03:16.272517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 353
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = network.s_data.plot(figsize=(6,6), **skws)\n",
    "network.n_data.plot(ax=ax, **nkws)\n",
    "network_observations.df.plot(ax=ax, **okws)\n",
    "network_observations.snapped_points.plot(ax=ax, **bkws);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------\n",
    "## Generate a cost matrix between all observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.511769Z",
     "start_time": "2021-07-08T14:03:16.479685Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function obs2obs_cost_matrix in module tigernet.tigernet:\n",
      "\n",
      "obs2obs_cost_matrix(origin_observations, network, destination_observations=None, snap_dist=True, distance_type='network')\n",
      "    Calculate a cost matrix from (n) observations to (m) observations.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    origin_observations : tigernet.Observations\n",
      "    network : tigernet.Network\n",
      "    destination_observations : tigernet.Observations\n",
      "        Destination observations. Default is ``None``.\n",
      "    snap_dist : str\n",
      "        Include the distance to observations from the network. Default is ``True``.\n",
      "    distance_type : str\n",
      "        Type of distance cost matrix. Default is ``'network'``.\n",
      "        Option is ``'euclidean'``.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    n2m_matrix : numpy.ndarray\n",
      "        'nXm' cost matrix.\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(help(tigernet.obs2obs_cost_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T14:03:16.546787Z",
     "start_time": "2021-07-08T14:03:16.513944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  5.35980629, 10.65166293,  7.55734019,  6.04110705],\n",
       "       [ 5.35980629,  0.        , 13.3242729 , 10.22995016,  8.71371701],\n",
       "       [10.65166293, 13.3242729 ,  0.        , 12.48295526, 10.96672212],\n",
       "       [ 7.55734019, 10.22995016, 12.48295526,  0.        ,  7.87239938],\n",
       "       [ 6.04110705,  8.71371701, 10.96672212,  7.87239938,  0.        ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tigernet.obs2obs_cost_matrix(network_observations, network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T20:20:19.545813Z",
     "start_time": "2020-11-02T20:20:12.812Z"
    }
   },
   "source": [
    "------------------"
   ]
  }
 ],
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