{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Marked Point Pattern\n",
    "\n",
    "In addition to the [unmarked point pattern](pointpattern.ipynb), non-binary attributes might be associated with each point, leading to the so-called marked point pattern. The charactertistics of a marked point pattern are:\n",
    "\n",
    "* Location pattern of the events are of interest\n",
    "* Stochastic attribute attached to the events is of interest\n",
    "\n",
    "Unmarked point pattern can be modified to be a marked point pattern using the method **add_marks** while the method **explode** could decompose a marked point pattern into a sequence of unmarked point patterns. Both methods belong to the class **PointPattern**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pointpats import PoissonPointProcess, PoissonClusterPointProcess, Window, poly_from_bbox, PointPattern\n",
    "import libpysal as ps\n",
    "from libpysal.cg import shapely_ext\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the virginia polygon shapefile\n",
    "va = ps.io.open(ps.examples.get_path(\"virginia.shp\"))\n",
    "polys = [shp for shp in va]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the exterior polygons for VA from the union of the county shapes\n",
    "state = shapely_ext.cascaded_union(polys)\n",
    "# create window from virginia state boundary\n",
    "window = Window(state.parts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-83.67526245117188, 36.541481018066406, -75.24258422851562, 39.45690155029297]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window.bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-78.85183583334933, 37.51851209850039)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window.centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = PoissonPointProcess(window, 200, 1, conditioning=False, asPP=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "csr = PointPattern(samples.realizations[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cx, cy = window.centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-78.85183583334933"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.51851209850039"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "west = csr.points.x < cx\n",
    "south = csr.points.y < cy\n",
    "east = 1 - west\n",
    "north = 1 - south"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create an attribute named quad which has a value for each event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "quad = 1 * east * north +  2 * west * north + 3 * west * south + 4 * east * south"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(quad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "1      3\n",
       "2      3\n",
       "3      4\n",
       "4      1\n",
       "5      3\n",
       "6      1\n",
       "7      4\n",
       "8      2\n",
       "9      3\n",
       "10     4\n",
       "11     4\n",
       "12     3\n",
       "13     1\n",
       "14     1\n",
       "15     1\n",
       "16     4\n",
       "17     1\n",
       "18     1\n",
       "19     3\n",
       "20     4\n",
       "21     3\n",
       "22     1\n",
       "23     3\n",
       "24     1\n",
       "25     1\n",
       "26     4\n",
       "27     1\n",
       "28     3\n",
       "29     3\n",
       "      ..\n",
       "170    2\n",
       "171    2\n",
       "172    3\n",
       "173    3\n",
       "174    1\n",
       "175    4\n",
       "176    3\n",
       "177    3\n",
       "178    4\n",
       "179    1\n",
       "180    1\n",
       "181    3\n",
       "182    3\n",
       "183    1\n",
       "184    2\n",
       "185    3\n",
       "186    1\n",
       "187    2\n",
       "188    4\n",
       "189    1\n",
       "190    4\n",
       "191    1\n",
       "192    3\n",
       "193    3\n",
       "194    4\n",
       "195    3\n",
       "196    1\n",
       "197    1\n",
       "198    4\n",
       "199    4\n",
       "Length: 200, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Attach the attribute quad to the point pattern "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "csr.add_marks([quad], mark_names=['quad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>quad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-79.603948</td>\n",
       "      <td>37.791190</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-80.079205</td>\n",
       "      <td>37.396681</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-79.464397</td>\n",
       "      <td>36.625981</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-76.437205</td>\n",
       "      <td>36.884895</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-78.545956</td>\n",
       "      <td>37.992603</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-80.241928</td>\n",
       "      <td>36.991135</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-77.898430</td>\n",
       "      <td>37.716846</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-76.498353</td>\n",
       "      <td>37.321863</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-79.900184</td>\n",
       "      <td>37.854658</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-81.102672</td>\n",
       "      <td>36.633735</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-77.281811</td>\n",
       "      <td>36.976553</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-77.083152</td>\n",
       "      <td>37.303132</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-83.035154</td>\n",
       "      <td>36.618248</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-77.997824</td>\n",
       "      <td>38.483939</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-76.576682</td>\n",
       "      <td>37.932985</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-77.048965</td>\n",
       "      <td>37.697935</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-78.330254</td>\n",
       "      <td>37.421786</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-78.420495</td>\n",
       "      <td>38.125428</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-77.379883</td>\n",
       "      <td>38.801099</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-82.102351</td>\n",
       "      <td>36.838275</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-77.139652</td>\n",
       "      <td>36.681456</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-81.411197</td>\n",
       "      <td>37.009286</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-78.377109</td>\n",
       "      <td>38.110156</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-82.348030</td>\n",
       "      <td>37.260970</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-78.540305</td>\n",
       "      <td>37.759264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-78.708559</td>\n",
       "      <td>38.778273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-77.119341</td>\n",
       "      <td>37.441159</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-77.532402</td>\n",
       "      <td>37.827257</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-81.025392</td>\n",
       "      <td>36.963752</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-81.234484</td>\n",
       "      <td>37.187202</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>-79.175687</td>\n",
       "      <td>38.412747</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>-79.466336</td>\n",
       "      <td>37.677601</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>-80.503082</td>\n",
       "      <td>36.784620</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>-79.654007</td>\n",
       "      <td>36.620456</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>-78.283744</td>\n",
       "      <td>38.436626</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>-76.917112</td>\n",
       "      <td>36.713404</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-82.234255</td>\n",
       "      <td>36.978863</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>-81.012936</td>\n",
       "      <td>37.186895</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>-77.979839</td>\n",
       "      <td>36.758554</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>-77.979268</td>\n",
       "      <td>38.366633</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-75.550882</td>\n",
       "      <td>37.940486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-81.031949</td>\n",
       "      <td>37.194569</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>-80.957128</td>\n",
       "      <td>37.150309</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>-77.766785</td>\n",
       "      <td>37.647643</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>-79.411096</td>\n",
       "      <td>37.705193</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>-80.691844</td>\n",
       "      <td>36.673324</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>-78.752009</td>\n",
       "      <td>38.137110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>-79.160647</td>\n",
       "      <td>38.047687</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>-78.244262</td>\n",
       "      <td>37.137316</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>-77.641999</td>\n",
       "      <td>38.612349</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>-77.961376</td>\n",
       "      <td>37.257348</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>-77.664101</td>\n",
       "      <td>37.612087</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>-82.073349</td>\n",
       "      <td>37.151563</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>-80.029721</td>\n",
       "      <td>36.650720</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-77.015099</td>\n",
       "      <td>36.892940</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-81.938727</td>\n",
       "      <td>36.759000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-78.653617</td>\n",
       "      <td>38.637913</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-78.353159</td>\n",
       "      <td>38.009389</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-76.652473</td>\n",
       "      <td>36.918815</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-76.806542</td>\n",
       "      <td>37.477773</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             x          y  quad\n",
       "0   -79.603948  37.791190     2\n",
       "1   -80.079205  37.396681     3\n",
       "2   -79.464397  36.625981     3\n",
       "3   -76.437205  36.884895     4\n",
       "4   -78.545956  37.992603     1\n",
       "5   -80.241928  36.991135     3\n",
       "6   -77.898430  37.716846     1\n",
       "7   -76.498353  37.321863     4\n",
       "8   -79.900184  37.854658     2\n",
       "9   -81.102672  36.633735     3\n",
       "10  -77.281811  36.976553     4\n",
       "11  -77.083152  37.303132     4\n",
       "12  -83.035154  36.618248     3\n",
       "13  -77.997824  38.483939     1\n",
       "14  -76.576682  37.932985     1\n",
       "15  -77.048965  37.697935     1\n",
       "16  -78.330254  37.421786     4\n",
       "17  -78.420495  38.125428     1\n",
       "18  -77.379883  38.801099     1\n",
       "19  -82.102351  36.838275     3\n",
       "20  -77.139652  36.681456     4\n",
       "21  -81.411197  37.009286     3\n",
       "22  -78.377109  38.110156     1\n",
       "23  -82.348030  37.260970     3\n",
       "24  -78.540305  37.759264     1\n",
       "25  -78.708559  38.778273     1\n",
       "26  -77.119341  37.441159     4\n",
       "27  -77.532402  37.827257     1\n",
       "28  -81.025392  36.963752     3\n",
       "29  -81.234484  37.187202     3\n",
       "..         ...        ...   ...\n",
       "170 -79.175687  38.412747     2\n",
       "171 -79.466336  37.677601     2\n",
       "172 -80.503082  36.784620     3\n",
       "173 -79.654007  36.620456     3\n",
       "174 -78.283744  38.436626     1\n",
       "175 -76.917112  36.713404     4\n",
       "176 -82.234255  36.978863     3\n",
       "177 -81.012936  37.186895     3\n",
       "178 -77.979839  36.758554     4\n",
       "179 -77.979268  38.366633     1\n",
       "180 -75.550882  37.940486     1\n",
       "181 -81.031949  37.194569     3\n",
       "182 -80.957128  37.150309     3\n",
       "183 -77.766785  37.647643     1\n",
       "184 -79.411096  37.705193     2\n",
       "185 -80.691844  36.673324     3\n",
       "186 -78.752009  38.137110     1\n",
       "187 -79.160647  38.047687     2\n",
       "188 -78.244262  37.137316     4\n",
       "189 -77.641999  38.612349     1\n",
       "190 -77.961376  37.257348     4\n",
       "191 -77.664101  37.612087     1\n",
       "192 -82.073349  37.151563     3\n",
       "193 -80.029721  36.650720     3\n",
       "194 -77.015099  36.892940     4\n",
       "195 -81.938727  36.759000     3\n",
       "196 -78.653617  38.637913     1\n",
       "197 -78.353159  38.009389     1\n",
       "198 -76.652473  36.918815     4\n",
       "199 -76.806542  37.477773     4\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explode a marked point pattern into a sequence of individual point patterns. Since the mark quad has 4 unique values, the sequence will be of length 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "csr_q = csr.explode('quad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(csr_q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.pointpattern.PointPattern at 0x1b2058a320>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point Pattern\n",
      "200 points\n",
      "Bounding rectangle [(-83.5775552379073,36.58477411642467), (-75.55088173741038,39.23835955804836)]\n",
      "Area of window: 21.299463945585302\n",
      "Intensity estimate for window: 9.389907676125041\n",
      "           x          y  quad\n",
      "0 -79.603948  37.791190     2\n",
      "1 -80.079205  37.396681     3\n",
      "2 -79.464397  36.625981     3\n",
      "3 -76.437205  36.884895     4\n",
      "4 -78.545956  37.992603     1\n"
     ]
    }
   ],
   "source": [
    "csr.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the 4 individual sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.xlim?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim()\n",
    "for ppn in csr_q:\n",
    "    ppn.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the 4 unmarked point patterns using the same axes for a convenient comparison of locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0, y0, x1, y1 = csr.mbb\n",
    "ylim = (y0, y1)\n",
    "xlim = (x0, x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ppn in csr_q:\n",
    "    ppn.plot()\n",
    "    plt.xlim(xlim)\n",
    "    plt.ylim(ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}