{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Point Sampling\n",
    "\n",
    "MATSim requires point locations for all activities and trips. More generally it can be useful to assign geometries to activity location and/or trip origin and destinations. This notebook shows how to sample geometric locations from ozone/dzone regions using a simple random point sampler.\n",
    "\n",
    "More advanced samplers are available in `pam.samplers.spatial`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-15T14:22:45.434401Z",
     "start_time": "2020-04-15T14:22:43.811833Z"
    },
    "execution": {
     "iopub.execute_input": "2023-09-22T10:52:14.981747Z",
     "iopub.status.busy": "2023-09-22T10:52:14.981649Z",
     "iopub.status.idle": "2023-09-22T10:52:15.737134Z",
     "shell.execute_reply": "2023-09-22T10:52:15.736771Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import geopandas as gp\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from pam import read\n",
    "from pam.samplers.spatial import RandomPointSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-15T14:23:03.601994Z",
     "start_time": "2020-04-15T14:23:03.510974Z"
    },
    "execution": {
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     "iopub.status.busy": "2023-09-22T10:52:15.739016Z",
     "iopub.status.idle": "2023-09-22T10:52:15.756356Z",
     "shell.execute_reply": "2023-09-22T10:52:15.756044Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>hid</th>\n",
       "      <th>seq</th>\n",
       "      <th>hzone</th>\n",
       "      <th>ozone</th>\n",
       "      <th>dzone</th>\n",
       "      <th>purp</th>\n",
       "      <th>mode</th>\n",
       "      <th>tst</th>\n",
       "      <th>tet</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>census_0</td>\n",
       "      <td>census_0</td>\n",
       "      <td>0</td>\n",
       "      <td>Harrow</td>\n",
       "      <td>Harrow</td>\n",
       "      <td>Camden</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>444</td>\n",
       "      <td>473</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>census_0</td>\n",
       "      <td>census_0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harrow</td>\n",
       "      <td>Camden</td>\n",
       "      <td>Harrow</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>890</td>\n",
       "      <td>919</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>census_1</td>\n",
       "      <td>census_1</td>\n",
       "      <td>0</td>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>507</td>\n",
       "      <td>528</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>census_1</td>\n",
       "      <td>census_1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>Greenwich</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>1065</td>\n",
       "      <td>1086</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>census_2</td>\n",
       "      <td>census_2</td>\n",
       "      <td>0</td>\n",
       "      <td>Croydon</td>\n",
       "      <td>Croydon</td>\n",
       "      <td>Croydon</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>422</td>\n",
       "      <td>425</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>census_2</td>\n",
       "      <td>census_2</td>\n",
       "      <td>1</td>\n",
       "      <td>Croydon</td>\n",
       "      <td>Croydon</td>\n",
       "      <td>Croydon</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>917</td>\n",
       "      <td>920</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>census_3</td>\n",
       "      <td>census_3</td>\n",
       "      <td>0</td>\n",
       "      <td>Haringey</td>\n",
       "      <td>Haringey</td>\n",
       "      <td>Redbridge</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>428</td>\n",
       "      <td>447</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>census_3</td>\n",
       "      <td>census_3</td>\n",
       "      <td>1</td>\n",
       "      <td>Haringey</td>\n",
       "      <td>Redbridge</td>\n",
       "      <td>Haringey</td>\n",
       "      <td>work</td>\n",
       "      <td>pt</td>\n",
       "      <td>1007</td>\n",
       "      <td>1026</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>census_4</td>\n",
       "      <td>census_4</td>\n",
       "      <td>0</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>Westminster,City of London</td>\n",
       "      <td>work</td>\n",
       "      <td>car</td>\n",
       "      <td>483</td>\n",
       "      <td>516</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>census_4</td>\n",
       "      <td>census_4</td>\n",
       "      <td>1</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>Westminster,City of London</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>work</td>\n",
       "      <td>car</td>\n",
       "      <td>1017</td>\n",
       "      <td>1050</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pid       hid  seq      hzone                       ozone  \\\n",
       "uid                                                                   \n",
       "0    census_0  census_0    0     Harrow                      Harrow   \n",
       "1    census_0  census_0    1     Harrow                      Camden   \n",
       "2    census_1  census_1    0  Greenwich                   Greenwich   \n",
       "3    census_1  census_1    1  Greenwich               Tower Hamlets   \n",
       "4    census_2  census_2    0    Croydon                     Croydon   \n",
       "5    census_2  census_2    1    Croydon                     Croydon   \n",
       "6    census_3  census_3    0   Haringey                    Haringey   \n",
       "7    census_3  census_3    1   Haringey                   Redbridge   \n",
       "8    census_4  census_4    0   Hounslow                    Hounslow   \n",
       "9    census_4  census_4    1   Hounslow  Westminster,City of London   \n",
       "\n",
       "                          dzone  purp mode   tst   tet  freq  \n",
       "uid                                                           \n",
       "0                        Camden  work   pt   444   473  1000  \n",
       "1                        Harrow  work   pt   890   919  1000  \n",
       "2                 Tower Hamlets  work   pt   507   528  1000  \n",
       "3                     Greenwich  work   pt  1065  1086  1000  \n",
       "4                       Croydon  work   pt   422   425  1000  \n",
       "5                       Croydon  work   pt   917   920  1000  \n",
       "6                     Redbridge  work   pt   428   447  1000  \n",
       "7                      Haringey  work   pt  1007  1026  1000  \n",
       "8    Westminster,City of London  work  car   483   516  1000  \n",
       "9                      Hounslow  work  car  1017  1050  1000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips = pd.read_csv(\n",
    "    os.path.join(\"data\", \"example_data\", \"example_travel_diaries.csv\"), index_col=\"uid\"\n",
    ")\n",
    "attributes = pd.read_csv(\n",
    "    os.path.join(\"data\", \"example_data\", \"example_attributes.csv\"), index_col=\"pid\"\n",
    ")\n",
    "trips.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-22T10:52:15.758024Z",
     "iopub.status.busy": "2023-09-22T10:52:15.757909Z",
     "iopub.status.idle": "2023-09-22T10:52:15.781331Z",
     "shell.execute_reply": "2023-09-22T10:52:15.781042Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using tour based purpose parser (recommended)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding pid->hh mapping to persons_attributes from trips.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding home locations to persons attributes using trips attributes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using freq of 'None' for all persons.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'num_households': 51,\n",
       " 'num_people': 51,\n",
       " 'num_activities': 152,\n",
       " 'num_legs': 101}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = read.load_travel_diary(trips, attributes)\n",
    "population.stats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Geometries\n",
    "Transform trip ozone/dzone to geographically sampled points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-22T10:52:15.782772Z",
     "iopub.status.busy": "2023-09-22T10:52:15.782674Z",
     "iopub.status.idle": "2023-09-22T10:52:15.985738Z",
     "shell.execute_reply": "2023-09-22T10:52:15.985449Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HECTARES</th>\n",
       "      <th>NONLD_AREA</th>\n",
       "      <th>ONS_INNER</th>\n",
       "      <th>SUB_2009</th>\n",
       "      <th>SUB_2006</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kingston upon Thames</th>\n",
       "      <td>3726.117</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((516401.600 160201.800, 516407.300 16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croydon</th>\n",
       "      <td>8649.441</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((535009.200 159504.700, 535005.500 15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromley</th>\n",
       "      <td>15013.487</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((540373.600 157530.400, 540361.200 15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hounslow</th>\n",
       "      <td>5658.541</td>\n",
       "      <td>60.755</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((521975.800 178100.000, 521967.700 17...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ealing</th>\n",
       "      <td>5554.428</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((510253.500 182881.600, 510249.900 18...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       HECTARES  NONLD_AREA ONS_INNER SUB_2009 SUB_2006  \\\n",
       "NAME                                                                      \n",
       "Kingston upon Thames   3726.117       0.000         F     None     None   \n",
       "Croydon                8649.441       0.000         F     None     None   \n",
       "Bromley               15013.487       0.000         F     None     None   \n",
       "Hounslow               5658.541      60.755         F     None     None   \n",
       "Ealing                 5554.428       0.000         F     None     None   \n",
       "\n",
       "                                                               geometry  \n",
       "NAME                                                                     \n",
       "Kingston upon Thames  POLYGON ((516401.600 160201.800, 516407.300 16...  \n",
       "Croydon               POLYGON ((535009.200 159504.700, 535005.500 15...  \n",
       "Bromley               POLYGON ((540373.600 157530.400, 540361.200 15...  \n",
       "Hounslow              POLYGON ((521975.800 178100.000, 521967.700 17...  \n",
       "Ealing                POLYGON ((510253.500 182881.600, 510249.900 18...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zones = gp.read_file(os.path.join(\"data\", \"example_data\", \"geometry.geojson\")).set_index(\"NAME\")\n",
    "zones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-22T10:52:15.987154Z",
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     "iopub.status.idle": "2023-09-22T10:52:15.989123Z",
     "shell.execute_reply": "2023-09-22T10:52:15.988760Z"
    }
   },
   "outputs": [],
   "source": [
    "random_sampler = RandomPointSampler(geoms=zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-22T10:52:15.990899Z",
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     "shell.execute_reply": "2023-09-22T10:52:16.093705Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x16b52aad0>"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test\n",
    "fig, ax = plt.subplots(1)\n",
    "zones.plot(ax=ax, color=\"grey\")\n",
    "zones.loc[[\"Camden\"]].plot(ax=ax)\n",
    "point = random_sampler.sample(\"Camden\", activity=None)\n",
    "ax.scatter(point.x, point.y, c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-22T10:52:16.112794Z",
     "iopub.status.busy": "2023-09-22T10:52:16.112572Z",
     "iopub.status.idle": "2023-09-22T10:52:16.156005Z",
     "shell.execute_reply": "2023-09-22T10:52:16.154613Z"
    }
   },
   "outputs": [],
   "source": [
    "population.sample_locs(random_sampler)  # apply to whole population"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-09T14:32:22.432201Z",
     "start_time": "2020-04-09T14:32:15.568791Z"
    }
   },
   "source": [
    "Let's check out an example Activity Plan and Attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-15T14:23:58.041512Z",
     "start_time": "2020-04-15T14:23:58.002047Z"
    },
    "execution": {
     "iopub.execute_input": "2023-09-22T10:52:16.166690Z",
     "iopub.status.busy": "2023-09-22T10:52:16.166082Z",
     "iopub.status.idle": "2023-09-22T10:52:16.257728Z",
     "shell.execute_reply": "2023-09-22T10:52:16.257432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person: census_0\n",
      "{'gender': 'female', 'job': 'work', 'occ': 'white', 'inc': 'low', 'hzone': 'Harrow'}\n",
      "0:\tActivity(act:home, location:POINT (513495.3484428172 186270.18744166623), time:00:00:00 --> 07:24:00, duration:7:24:00)\n",
      "1:\tLeg(mode:pt, area:POINT (513495.3484428172 186270.18744166623) --> POINT (528597.1882704783 183610.4704643505), time:07:24:00 --> 07:53:00, duration:0:29:00)\n",
      "2:\tActivity(act:work, location:POINT (528597.1882704783 183610.4704643505), time:07:53:00 --> 14:50:00, duration:6:57:00)\n",
      "3:\tLeg(mode:pt, area:POINT (528597.1882704783 183610.4704643505) --> POINT (513495.3484428172 186270.18744166623), time:14:50:00 --> 15:19:00, duration:0:29:00)\n",
      "4:\tActivity(act:home, location:POINT (513495.3484428172 186270.18744166623), time:15:19:00 --> 00:00:00, duration:8:41:00)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x175 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "person = population[\"census_0\"][\"census_0\"]\n",
    "person.print()\n",
    "person.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pam",
   "language": "python",
   "name": "pam"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "248.333px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "c2a90f01ee7329c0dc072323d04ab57047490a620cb7d1fe86527d3a3edaf28c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}