import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
from matplotlib.collections import LineCollection
from matplotlib.patches import Polygon
from matplotlib.patches import Polygon
from mpl_toolkits.basemap import Basemap

%matplotlib inline

import shapefile

def draw_portugal(islands = True):
    """
    This functions draws and returns a map of Portugal, either just of the mainland or including the Azores and Madeira islands.
    """
    
    fig = plt.figure(figsize=(15.7,12.3))
    
    projection='merc'
    llcrnrlat=-80
    urcrnrlat=90
    llcrnrlon=-180
    urcrnrlon=180
    resolution='i'

    if islands:
        x1 = -32.
        x2 = 5.
        y1 = 30.
        y2 = 45.
    else:
        x1 = -12.
        x2 = -5.
        y1 = 36.
        y2 = 44.
    
    m = Basemap(projection=projection, llcrnrlat=y1, urcrnrlat=y2, llcrnrlon=x1,
                urcrnrlon=x2, resolution=resolution)
    m.drawcoastlines()
    m.drawmapboundary()
    m.drawcountries()
    m.fillcontinents(color = '#C0C0C0')
    
    return m

draw_portugal()
plt.show()

r = shapefile.Reader(r"maps\prt_adm1")
shapes = r.shapes()
records = r.records()

print r.fields

print "\n----------------------------------------------------------------------------------\n"

for record in records:
    print record

populacao = {"Aveiro": 735790,
             "Azores": 246746,
             "Beja": 150287,
             "Braga": 848185,
             "Bragança": 139344,
             "Castelo Branco": 225916,
             "Coimbra": 429714,
             "Évora": 168034,
             "Faro": 434023,
             "Guarda": 168898,
             "Leiria": 470895,
             "Lisboa": 2244799,
             "Madeira": 267785,
             "Portalegre": 118952,
             "Porto": 2027191,
             "Santarém": 465701,
             "Setúbal": 866794,
             "Viana do Castelo": 250390,
             "Vila Real": 213775,
             "Viseu": 391215
             }

max_pop = np.max(populacao.values())+0.0

for record in records:
    record.append(populacao[record[4].decode('Latin1').encode('UTF-8')]/max_pop)
    
#print a record to make sure that the information was added
print records[0]

m = draw_portugal(islands=False)

ax = plt.subplot(111)

for record, shape in zip(records,shapes):
    #read shape
    lons,lats = zip(*shape.points)
    data = np.array(m(lons, lats)).T

    #each shape may have different segments
    if len(shape.parts) == 1:
        segs = [data,]
    else:
        segs = []
        for i in range(1,len(shape.parts)):
            index = shape.parts[i-1]
            index2 = shape.parts[i]
            segs.append(data[index:index2])
        segs.append(data[index2:])

    #draws the segments, and sets its properties. A colormap is used to get the gradient effect.
    lines = LineCollection(segs,antialiaseds=(1,))
    lines.set_facecolors(cm.YlGn(record[-1]))
    lines.set_edgecolors('k')
    lines.set_linewidth(0.1)
    ax.add_collection(lines)

plt.show()

fig = plt.figure(figsize=(15.7,12.3))
ax = plt.subplot(111)

map = Basemap(projection='cyl',llcrnrlat=35,urcrnrlat=72,\
            llcrnrlon=-17,urcrnrlon=42,resolution='c')

map.drawmapboundary(fill_color='aqua')

map.readshapefile('maps\world_by_country', 'countries_sf', drawbounds=True)

#the readshapefile method allow you to call the shapefile's shapes and info.
#Both are lists, the first containing a list of tuples (coordinates), and the second containig a dictionary with associated metadata

print len(map.countries_sf) #number of shapes
print map.countries_sf_info[0].keys() #metadata

#plots the shapes as Polygons with a random facecolor (if European), or gray (if not)
for shape, country in zip(map.countries_sf, map.countries_sf_info):
    if country['REGION'] == 'Europe':
        poly = Polygon(shape, facecolor=cm.YlGn(np.random.rand()))
        plt.gca().add_patch(poly)
    else:
        poly = Polygon(shape, facecolor="gray")
        plt.gca().add_patch(poly)

#adds the colormap legend
cmleg = np.zeros((1,20))
for i in range(20):
    cmleg[0,i] = (i*5)/100.0
plt.imshow(cmleg, cmap=plt.get_cmap("YlGn"), aspect=1)
plt.colorbar()


plt.show()