{
 "metadata": {
  "name": "",
  "signature": "sha256:86728bd1f81b030b0152580821dbc6554431bd42b99cb9368907a6506fff70f1"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Geospatial Data in Python: Database, Desktop, and the Web\n",
      "## Tutorial (Part 4)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Creating a simple GeoSeries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from shapely.geometry import Polygon\n",
      "from geopandas import GeoSeries, GeoDataFrame\n",
      "\n",
      "# Create three simple polygons\n",
      "p1 = Polygon([(0, 0), (1, 0), (1, 1)])\n",
      "p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])\n",
      "p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)])\n",
      "\n",
      "s = GeoSeries([p1, p2, p3])\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some geographic operations return a normal pandas object. The `area` property of a `GeoSeries` will return a pandas `Series` containing the area of each item in the `GeoSeries`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(s.area)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Simple file I/O"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Specify file name\n",
      "import os\n",
      "boro_file = os.path.join(\"..\", \"data\", \"nybb\", \"nybb.shp\")\n",
      "\n",
      "# Create from file (one line)\n",
      "boros = GeoDataFrame.from_file(boro_file)\n",
      "\n",
      "# Do some pandas stuff\n",
      "boros.set_index('BoroCode', inplace=True)\n",
      "boros.sort()\n",
      "boros"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Plotting a simple GeoSeries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5), sharey=True)\n",
      "s.plot(axes=ax1)\n",
      "ax1.set_title(\"Original Polygons\")\n",
      "ax1.set_xlim(-0.5, 3.5)\n",
      "s.buffer(0.4).plot(axes=ax2)\n",
      "ax2.set_title(\"Buffered Polygons\")\n",
      "ax2.set_ylim(-0.5, 1.5)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Plotting a GeoDataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8, 8))\n",
      "ax = boros.plot()  # Regular plot\n",
      "# We can access and plot the geometries directly as well\n",
      "ax = boros.geometry.convex_hull.plot(axes=ax)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## GeoPandas makes databases easy\n",
      "\n",
      "You can access `PostGIS` and other spatially aware databases with a similar `API` to Pandas. The added bonus here is that GeoPandas *understands* geospatial data (such as `WKB`):\n",
      "\n",
      "It automatically converts `WKB` to the appropriate Shapely `geometry` type.\n",
      "\n",
      "*Note: If you're following along, you can use the included shapefile instead of a database*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import geopandas as gpd\n",
      "\n",
      "wifi_file = os.path.join(\"..\", \"data\", \"wifi\", \"wifi.shp\")\n",
      "\n",
      "df_geo = gpd.read_file(wifi_file)\n",
      "df_geo.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Now that we have a GeoDataFrame...\n",
      "\n",
      "### We can do all sorts of fun things!\n",
      "\n",
      "Like compute **summaries** of the geometries:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What is the total area of all 5 NYC boroughs?\n",
      "print(boros.area.sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "...change the **CRS**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Convert from NY State Plane to WGS84 (long/lat)\n",
      "df_wgs84 = df_geo.to_crs(epsg=4326) # 4326 is the EPSG code for WGS84\n",
      "print(df_geo.crs)\n",
      "print(df_wgs84.crs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "...and **much** more... plus anything else that Pandas can do"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Super quick plotting for the web\n",
      "\n",
      "Here's a super simple map, which shows `Free` and `Fee-based` WiFi hotspots in NYC:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = df_wgs84.plot() # This is all that's needed, but...\n",
      "# ...here's a hack to plot things a bit more nicely (GeoPandas is still new!)\n",
      "free = df_wgs84[\"type\"] == \"Free\"  # Is it free?\n",
      "col = {True: \"green\", False: \"blue\"}  # If free, green, otherwise blue\n",
      "for i, l in enumerate(ax.lines):\n",
      "    l.set_markersize(6)\n",
      "    l.set_color(col[free[i]])\n",
      "ax.axis('off')  # Turn off axes\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "### Let's turn this map into a 'slippy' web-map"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import mplleaflet\n",
      "mplleaflet.display(fig=ax.figure, crs=df_wgs8.crs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "#### Want that as a standalone HTML page?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mplleaflet.show(fig=ax.figure, crs=df_wgs84.crs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "#### Or perhaps you want to share something with others online? Use `geojson.io`.\n",
      "\n",
      "Note, your map should really be reprojected to EPSG:4326 (WGS84 lon/lat) for this to align properly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import geojsonio\n",
      "res = geojsonio.display(boros.to_crs(epsg=4326).to_json(), force_gist=True)\n",
      "print(res)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Don't forget to pop on over to geojson.io and play around/edit your GeoJSON layer..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "### Time to work on Notebook:\n",
      "\n",
      "[`Working with Vectors in Python`](../exercises/Working with Vectors in Python.ipynb)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}