jGaboardi/resolved-unconnected_ring.ipynb

## resolved-unconnected_ring.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "-------------------"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-12-04T04:35:29.294163Z",
          "end_time": "2018-12-04T04:35:30.509914Z"
        },
        "trusted": true,
        "extensions": {
          "jupyter_dashboards": {
            "views": {
              "report_default": {},
              "grid_default": {
                "hidden": true
              }
            },
            "version": 1
          }
        }
      },
      "cell_type": "code",
      "source": "import spaghetti as spgh\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-12-04T04:35:31.104529Z",
          "end_time": "2018-12-04T04:35:31.175119Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ntw = spgh.Network(in_data='unconnected_ring/unconnected_ring.shp')",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/Users/jgaboardi/miniconda3/envs/py3_spgh_dev/lib/python3.6/site-packages/libpysal/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 4 components\n  warnings.warn(\"The weights matrix is not fully connected. There are %d components\" % self.n_components)\n/Users/jgaboardi/miniconda3/envs/py3_spgh_dev/lib/python3.6/site-packages/libpysal/weights/weights.py:168: UserWarning: There are 2 disconnected observations \n  Island ids: (44, 73), (87, 96)\n  \" Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### New attribute for the tracking connected components and whether or not it is an isolated loop"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-12-04T04:35:31.845042Z",
          "end_time": "2018-12-04T04:35:31.852376Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ntw.network_component_is_ring",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "{0: True, 1: False, 2: False, 3: False}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-12-04T04:35:32.240675Z",
          "end_time": "2018-12-04T04:35:32.249157Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ntw.network_component2arc[3]",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "[(87, 88),\n (88, 89),\n (89, 90),\n (90, 91),\n (91, 92),\n (92, 93),\n (93, 94),\n (94, 95),\n (95, 96)]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Extract network elements (vertices and arcs) as `geopandas.GeoDataFrame` objects"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-12-04T04:35:33.179838Z",
          "end_time": "2018-12-04T04:35:33.299693Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "vertices, arcs = spgh.element_as_gdf(ntw, vertices=True, arcs=True)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Extract only `graph_nodes` from `network_vertices`. These are non-articulation points"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-12-04T04:35:33.920830Z",
          "end_time": "2018-12-04T04:35:33.926711Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "graph_nodes = vertices[~vertices.id.isin(ntw.non_articulation_points)]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Plot connected components in `tab20`, network vertices in large red, and graph nodes in small black"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2018-12-04T04:35:34.732023Z",
          "end_time": "2018-12-04T04:35:35.139085Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "base = arcs.plot(cmap='tab20', column='comp_label', alpha=.5,\n                 figsize=(12,12), linewidth=4, zorder=0)\nvertices.plot(ax=base, color='k', markersize=4, alpha=1., zorder=1)\ngraph_nodes.plot(ax=base, markersize=20, color='r', alpha=1., zorder=2)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a27ded2b0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x864 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "markdown",
      "source": "------------------------"
    }
  ],
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "version": "3.6.6",
      "mimetype": "text/x-python",
      "file_extension": ".py",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "name": "python"
    },
    "kernelspec": {
      "name": "conda-env-py3_spgh_dev-py",
      "display_name": "Python [conda env:py3_spgh_dev]",
      "language": "python"
    },
    "anaconda-cloud": {},
    "extensions": {
      "jupyter_dashboards": {
        "activeView": "grid_default",
        "views": {
          "report_default": {
            "type": "report",
            "name": "report"
          },
          "grid_default": {
            "maxColumns": 12,
            "defaultCellHeight": 20,
            "cellMargin": 10,
            "type": "grid",
            "name": "grid"
          }
        },
        "version": 1
      }
    },
    "gist": {
      "id": "3ce1e7b0fb31b8bb169c8a476b1ceec1",
      "data": {
        "description": "resolved-unconnected_ring",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/3ce1e7b0fb31b8bb169c8a476b1ceec1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}