#!/usr/bin/env python
# coding: utf-8

# ## Choropleth classification with PySAL and GeoPandas - With legend
# 
# This is a modified version of http://nbviewer.ipython.org/github/geopandas/geopandas/blob/master/examples/choropleths.ipynb
# with the patch to add a legend to those figures.

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


import geopandas as gp


# In[3]:


# we use PySAL for loading a test shapefile
# replace this cell if you have a local shapefile and want to use GeoPandas readers
import pysal as ps 
pth = ps.examples.get_path("columbus.shp")
tracts = gp.GeoDataFrame.from_file(pth)


# In[4]:


ax = tracts.plot(column='CRIME', scheme='QUANTILES', k=3, colormap='OrRd')


# In[5]:


tracts.head()


# Slightly adapted functions:

# In[6]:


def __pysal_choro(values, scheme, k=5):
    """ Wrapper for choropleth schemes from PySAL for use with plot_dataframe

        Parameters
        ----------

        values
            Series to be plotted

        scheme
            pysal.esda.mapclassify classificatin scheme ['Equal_interval'|'Quantiles'|'Fisher_Jenks']

        k
            number of classes (2 <= k <=9)

        Returns
        -------

        values
            Series with values replaced with class identifier if PySAL is available, otherwise the original values are used
    """

    try:
        from pysal.esda.mapclassify import Quantiles, Equal_Interval, Fisher_Jenks
        schemes = {}
        schemes['equal_interval'] = Equal_Interval
        schemes['quantiles'] = Quantiles
        schemes['fisher_jenks'] = Fisher_Jenks
        s0 = scheme
        scheme = scheme.lower()
        if scheme not in schemes:
            scheme = 'quantiles'
            print('Unrecognized scheme: ', s0)
            print('Using Quantiles instead')
        if k < 2 or k > 9:
            print('Invalid k: ', k)
            print('2<=k<=9, setting k=5 (default)')
            k = 5
        binning = schemes[scheme](values, k)
        values = binning.yb
    except ImportError:
        print('PySAL not installed, setting map to default')

    return binning


# In[7]:


# adding linewidth parameter

def plot_polygon(ax, poly, facecolor='red', edgecolor='black', alpha=0.5, linewidth=1):
    """ Plot a single Polygon geometry """
    from descartes.patch import PolygonPatch
    a = np.asarray(poly.exterior)
    # without Descartes, we could make a Patch of exterior
    ax.add_patch(PolygonPatch(poly, facecolor=facecolor, alpha=alpha))
    ax.plot(a[:, 0], a[:, 1], color=edgecolor, linewidth=linewidth)
    for p in poly.interiors:
        x, y = zip(*p.coords)
        ax.plot(x, y, color=edgecolor, linewidth=linewidth)

def plot_multipolygon(ax, geom, facecolor='red', edgecolor='black', alpha=0.5, linewidth=1):
    """ Can safely call with either Polygon or Multipolygon geometry
    """
    if geom.type == 'Polygon':
        plot_polygon(ax, geom, facecolor=facecolor, edgecolor=edgecolor, alpha=alpha, linewidth=linewidth)
    elif geom.type == 'MultiPolygon':
        for poly in geom.geoms:
            plot_polygon(ax, poly, facecolor=facecolor, edgecolor=edgecolor, alpha=alpha, linewidth=linewidth)


# In[8]:


import numpy as np
from geopandas.plotting import (plot_linestring, plot_point, norm_cmap)


def plot_dataframe(s, column=None, colormap=None, alpha=0.5,
                   categorical=False, legend=False, axes=None, scheme=None,
                   k=5, linewidth=1):
    """ Plot a GeoDataFrame

        Generate a plot of a GeoDataFrame with matplotlib.  If a
        column is specified, the plot coloring will be based on values
        in that column.  Otherwise, a categorical plot of the
        geometries in the `geometry` column will be generated.

        Parameters
        ----------

        GeoDataFrame
            The GeoDataFrame to be plotted.  Currently Polygon,
            MultiPolygon, LineString, MultiLineString and Point
            geometries can be plotted.

        column : str (default None)
            The name of the column to be plotted.

        categorical : bool (default False)
            If False, colormap will reflect numerical values of the
            column being plotted.  For non-numerical columns (or if
            column=None), this will be set to True.

        colormap : str (default 'Set1')
            The name of a colormap recognized by matplotlib.

        alpha : float (default 0.5)
            Alpha value for polygon fill regions.  Has no effect for
            lines or points.

        legend : bool (default False)
            Plot a legend (Experimental; currently for categorical
            plots only)

        axes : matplotlib.pyplot.Artist (default None)
            axes on which to draw the plot

        scheme : pysal.esda.mapclassify.Map_Classifier
            Choropleth classification schemes

        k   : int (default 5)
            Number of classes (ignored if scheme is None)


        Returns
        -------

        matplotlib axes instance
    """
    import matplotlib.pyplot as plt
    from matplotlib.lines import Line2D
    from matplotlib.colors import Normalize
    from matplotlib import cm

    if column is None:
        return plot_series(s.geometry, colormap=colormap, alpha=alpha, axes=axes)
    else:
        if s[column].dtype is np.dtype('O'):
            categorical = True
        if categorical:
            if colormap is None:
                colormap = 'Set1'
            categories = list(set(s[column].values))
            categories.sort()
            valuemap = dict([(k, v) for (v, k) in enumerate(categories)])
            values = [valuemap[k] for k in s[column]]
        else:
            values = s[column]
        if scheme is not None:
            binning = __pysal_choro(values, scheme, k=k)
            values = binning.yb
            # set categorical to True for creating the legend
            categorical = True
            binedges = [binning.yb.min()] + binning.bins.tolist()
            categories = ['{0:.2f} - {1:.2f}'.format(binedges[i], binedges[i+1]) for i in range(len(binedges)-1)]
        cmap = norm_cmap(values, colormap, Normalize, cm)
        if axes == None:
            fig = plt.gcf()
            fig.add_subplot(111, aspect='equal')
            ax = plt.gca()
        else:
            ax = axes
        for geom, value in zip(s.geometry, values):
            if geom.type == 'Polygon' or geom.type == 'MultiPolygon':
                plot_multipolygon(ax, geom, facecolor=cmap.to_rgba(value), alpha=alpha, linewidth=linewidth)
            elif geom.type == 'LineString' or geom.type == 'MultiLineString':
                plot_multilinestring(ax, geom, color=cmap.to_rgba(value))
            # TODO: color point geometries
            elif geom.type == 'Point':
                plot_point(ax, geom, color=cmap.to_rgba(value))
        if legend:
            if categorical:
                patches = []
                for value, cat in enumerate(categories):
                    patches.append(Line2D([0], [0], linestyle="none",
                                          marker="o", alpha=alpha,
                                          markersize=10, markerfacecolor=cmap.to_rgba(value)))
                ax.legend(patches, categories, numpoints=1, loc='best')
            else:
                # TODO: show a colorbar
                raise NotImplementedError

    plt.draw()
    return ax


# In[9]:


ax = plot_dataframe(tracts, column='CRIME', scheme='QUANTILES', k=3, colormap='OrRd', legend=True)


# In[10]:


# thicker linewidth for polygons
ax = plot_dataframe(tracts, column='CRIME', scheme='QUANTILES', k=3, colormap='OrRd', legend=True, linewidth=2.0)


# In[11]:


tracts.plot(column='CRIME', scheme='equal_interval', k=7, colormap='OrRd')


# In[12]:


plot_dataframe(tracts, column='CRIME', scheme='equal_interval', k=7, colormap='OrRd', legend=True)


# In[ ]: