import string
import unicodedata

import geopandas

from pandas import read_csv


def remove_accents(s):
    """Python 3 please!"""
    return ''.join(x for x in unicodedata.normalize('NFKD', s)
                   if x in string.ascii_letters).lower()


geo_path = './data/br_states.json'
gdf = geopandas.read_file(geo_path)
gdf['name'] = gdf['name'].str.encode('utf-8')
gdf['name'] = [remove_accents(s.decode('utf-8')) for s in gdf['name']]


df = read_csv('./data/pfas.csv')
df['name'] = [remove_accents(s.decode('utf-8')) for s in df['name']]


gdf.sort('name', inplace=True)
df.sort('name', inplace=True)
gdf['2013'] = df['2013'].values

kw = dict(column='2013', k=6, colormap='YlGn')
ax = gdf.plot(scheme='QUANTILES', **kw)

ax = gdf.plot(scheme='fisher_jenks', **kw)

ax = gdf.plot(scheme='equal_interval', **kw)

import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from pysal.esda.mapclassify import Fisher_Jenks


def norm_cmap(values, cmap, vmin=None, vmax=None):
    """
    Normalize and set colormap
    
    Parameters
    ----------
    values : Series or array to be normalized
    cmap : matplotlib Colormap
    normalize : matplotlib.colors.Normalize
    cm : matplotlib.cm
    vmin : Minimum value of colormap. If None, uses min(values).
    vmax : Maximum value of colormap. If None, uses max(values).
    
    Returns
    -------
    n_cmap : mapping of normalized values to colormap (cmap)
    
    """
    mn = vmin or min(values)
    mx = vmax or max(values)
    norm = Normalize(vmin=mn, vmax=mx)
    n_cmap = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
    return n_cmap


fj = Fisher_Jenks(df['2013'].values, k=6)
cmap = norm_cmap(fj.yb, cmap='YlGn')
df['color'] = [cmap.to_rgba(value) for value in df['2013'].values]

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from cartopy.io import shapereader


states = df['name'].str.decode('utf-8').tolist()

kw = dict(resolution='50m', category='cultural',
          name='admin_1_states_provinces')

states_shp = shapereader.natural_earth(**kw)
shp = shapereader.Reader(states_shp)


subplot_kw = dict(projection=ccrs.PlateCarree())

fig, ax = plt.subplots(figsize=(5, 5),
                       subplot_kw=subplot_kw)
ax.set_extent([-82, -32, -45, 10])

ax.background_patch.set_visible(False)
ax.outline_patch.set_visible(False)

for record, state in zip(shp.records(), shp.geometries()):
    name = record.attributes['name'].decode('latin-1')
    name = remove_accents(name)
    if name in states:
        facecolor = df[df['name'] == name]['color']
        ax.add_geometries([state], ccrs.PlateCarree(),
                          facecolor=facecolor, edgecolor='black')

cmap = plt.cm.ScalarMappable(cmap='YlGn')
cmap.set_array([])
cmap.set_clim(-0.5, 6+0.5)

bounds = [0, 1, 2, 3, 4, 5, 6]
norm = plt.cm.colors.BoundaryNorm(bounds, cmap.cmap.N)

kw = dict(orientation='vertical', extend='both', cmap=cmap,
          norm=norm, boundaries=[0]+bounds+[13],ticks=bounds,
          spacing='proportional')

cax = fig.add_axes([0.4, 0.35, 0.015, 0.2])

cbar = plt.colorbar(cmap, cax=cax, **kw)

fig.savefig('choropleth.png', dpi=150, transparent=True)
plt.close(fig)

import numpy as np
from PIL import Image, ImageChops


def trim(img):
    border = Image.new(img.mode, img.size, img.getpixel((0, 0)))
    diff = ImageChops.difference(img, border)
    diff = ImageChops.add(diff, diff, 2.0, -100)
    bbox = diff.getbbox()
    if bbox:
        img = img.crop(bbox)
    return img

img = Image.open('choropleth.png')
img = trim(img)

img

import json
import folium
import numpy as np


geo_str = json.dumps(json.load(open(geo_path, 'r')))
threshold_scale = np.linspace(df['2013'].min(),
                              df['2013'].max(), 6, dtype=int).tolist()


mapa = folium.Map(location=[-15.80, -47.88],
                  tiles="Mapbox Bright",
                  zoom_start=3)

mapa.geo_json(geo_str=geo_str,
              data=df,
              columns=['state', '2013'],
              fill_color='YlGn',
              key_on='feature.id',
              threshold_scale=threshold_scale)

mapa

HTML(html)