# http://matplotlib.org/basemap/users/examples.html

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

# set up orthographic map projection with
# perspective of satellite looking down at 50N, 100W.
# use low resolution coastlines.
map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='l')

# draw coastlines, country boundaries, fill continents.
map.drawcoastlines(linewidth=0.25)
map.drawcountries(linewidth=0.25)
map.fillcontinents(color='coral',lake_color='aqua')

# draw the edge of the map projection region (the projection limb)
map.drawmapboundary(fill_color='aqua')

# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(np.arange(0,360,30))
map.drawparallels(np.arange(-90,90,30))

plt.title('contour lines over filled continent background')
plt.show()

# http://matplotlib.org/basemap/users/merc.html

from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# llcrnrlat,llcrnrlon,urcrnrlat,urcrnrlon
# are the lat/lon values of the lower left and upper right corners
# of the map.
# lat_ts is the latitude of true scale.
# resolution = 'c' means use crude resolution coastlines.
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,\
            llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')

# draw parallels and meridians.
m.drawparallels(np.arange(-90.,91.,30.))
m.drawmeridians(np.arange(-180.,181.,60.))
m.drawmapboundary(fill_color='aqua')
plt.title("Mercator Projection")
plt.show()


# http://matplotlib.org/basemap/users/laea.html

from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# setup lambert azimuthal equal area basemap.
# lat_ts is latitude of true scale.
# lon_0,lat_0 is central point.
m = Basemap(width=12000000,height=8000000,
            resolution='l',projection='laea',\
            lat_ts=50,lat_0=50,lon_0=-107.)
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-80.,81.,20.))
m.drawmeridians(np.arange(-180.,181.,20.))
m.drawmapboundary(fill_color='aqua')
# draw tissot's indicatrix to show distortion.
ax = plt.gca()
for y in np.linspace(m.ymax/20,19*m.ymax/20,9):
    for x in np.linspace(m.xmax/20,19*m.xmax/20,12):
        lon, lat = m(x,y,inverse=True)
        poly = m.tissot(lon,lat,1.5,100,\
                        facecolor='green',zorder=10,alpha=0.5)
plt.title("Lambert Azimuthal Equal Area Projection")
plt.show()


# http://matplotlib.org/basemap/users/lcc.html

from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# setup lambert conformal basemap.
# lat_1 is first standard parallel.
# lat_2 is second standard parallel (defaults to lat_1).
# lon_0,lat_0 is central point.
# rsphere=(6378137.00,6356752.3142) specifies WGS4 ellipsoid
# area_thresh=1000 means don't plot coastline features less
# than 1000 km^2 in area.
m = Basemap(width=12000000,height=9000000,
            rsphere=(6378137.00,6356752.3142),\
            resolution='l',area_thresh=1000.,projection='lcc',\
            lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-80.,81.,20.))
m.drawmeridians(np.arange(-180.,181.,20.))
m.drawmapboundary(fill_color='aqua')
# draw tissot's indicatrix to show distortion.
ax = plt.gca()
for y in np.linspace(m.ymax/20,19*m.ymax/20,9):
    for x in np.linspace(m.xmax/20,19*m.xmax/20,12):
        lon, lat = m(x,y,inverse=True)
        poly = m.tissot(lon,lat,1.5,100,\
                        facecolor='green',zorder=10,alpha=0.5)
plt.title("Lambert Conformal Projection")
plt.show()


from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# setup lambert azimuthal equal area basemap.
# lat_ts is latitude of true scale.
# lon_0,lat_0 is central point.
m = Basemap(width=16200000,height=19500000,
            resolution='l',projection='laea',\
            lat_ts=0, lat_0=0,lon_0=0.)
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-80.,81.,20.))
m.drawmeridians(np.arange(-180.,181.,20.))
m.drawmapboundary(fill_color='aqua')
# draw tissot's indicatrix to show distortion.
ax = plt.gca()
for y in np.linspace(m.ymax/20,19*m.ymax/20,9):
    for x in np.linspace(m.xmax/20,19*m.xmax/20,12):
        lon, lat = m(x,y,inverse=True)
        poly = m.tissot(lon,lat,1.5,100,\
                        facecolor='green',zorder=10,alpha=0.5)
plt.title("Lambert Azimuthal Equal Area Projection")
plt.show()

# http://matplotlib.org/basemap/users/geography.html

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# setup Lambert Conformal basemap.
m = Basemap(width=12000000,height=9000000,projection='lcc',
            resolution='c',lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
# draw coastlines.
m.drawcoastlines()
# draw a boundary around the map, fill the background.
# this background will end up being the ocean color, since
# the continents will be drawn on top.
m.drawmapboundary(fill_color='aqua')
# fill continents, set lake color same as ocean color.
m.fillcontinents(color='coral',lake_color='aqua')
plt.show()

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# setup Lambert Conformal basemap.
# set resolution=None to skip processing of boundary datasets.
m = Basemap(width=12000000,height=9000000,projection='lcc',
            resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
# draw a land-sea mask for a map background.
# lakes=True means plot inland lakes with ocean color.

try:
    m.drawlsmask(land_color='coral',ocean_color='aqua',lakes=True)
    plt.show()
except Exception, e:
    print e

# for some reason -- this doesn't work for me
# bluemarble doesn't look right

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# setup Lambert Conformal basemap.
# set resolution=None to skip processing of boundary datasets.
#m = Basemap(width=12000000,height=9000000,projection='lcc',
#            resolution=None,lat_1=45.,lat_2=55.,lat_0=45.,lon_0=-107.)

m = Basemap(width=12000000,height=9000000,projection='lcc',
            resolution='c',lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)

m.bluemarble()
plt.show()

# for some reason -- this doesn't work for me
# shadedrelief doesn't look right


from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# setup Lambert Conformal basemap.
# set resolution=None to skip processing of boundary datasets.
#m = Basemap(width=12000000,height=9000000,projection='lcc',
#            resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)

m = Basemap(width=12000000,height=9000000,
            rsphere=(6378137.00,6356752.3142),\
            resolution='l',area_thresh=1000.,projection='lcc',\
            lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)

try:
    m.shadedrelief()
    plt.show()
except Exception as e:
    print e

# for some reason -- this doesn't work for me
# etopo doesn't look right


from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# setup Lambert Conformal basemap.
# set resolution=None to skip processing of boundary datasets.
m = Basemap(width=12000000,height=9000000,projection='lcc',
            resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)
m.etopo()
plt.show()

# http://matplotlib.org/basemap/users/mapcoords.html

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

# setup Lambert Conformal basemap.
m = Basemap(width=12000000,height=9000000,projection='lcc',
            resolution='c',lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.)

# draw a boundary around the map, fill the background.
# this background will end up being the ocean color, since
# the continents will be drawn on top.
m.drawmapboundary(fill_color='aqua')
# fill continents, set lake color same as ocean color.
m.fillcontinents(color='coral',lake_color='aqua')

# draw state boundaries too
# http://matplotlib.org/basemap/api/basemap_api.html#mpl_toolkits.basemap.Basemap.drawstates
m.drawstates(linewidth=0.1)


# draw parallels and meridians.
# label parallels on right and top
# meridians on bottom and left
parallels = np.arange(0.,81,10.)
# labels = [left,right,top,bottom]
m.drawparallels(parallels,labels=[False,True,True,False])
meridians = np.arange(10.,351.,20.)
m.drawmeridians(meridians,labels=[True,False,False,True])


# plot blue dot on Boulder, colorado and label it as such.
lon, lat = -104.237, 40.125 # Location of Boulder
# convert to map projection coords.
# Note that lon,lat can be scalars, lists or numpy arrays.
xpt,ypt = m(lon,lat)
# convert back to lat/lon
lonpt, latpt = m(xpt,ypt,inverse=True)
m.plot(xpt,ypt,'bo')  # plot a blue dot there
# put some text next to the dot, offset a little bit
# (the offset is in map projection coordinates)
plt.text(xpt+100000,ypt+100000,'Boulder (%5.1fW,%3.1fN)' % (lonpt,latpt))
plt.show()

# https://github.com/matplotlib/basemap/blob/master/examples/fillstates.py



import zipfile
import os


from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap as Basemap
from matplotlib.colors import rgb2hex
from matplotlib.patches import Polygon

shp_info_path = os.path.join(os.pardir, "data/census/st99_d00")

# Lambert Conformal map of lower 48 states.
m = Basemap(llcrnrlon=-119,llcrnrlat=22,urcrnrlon=-64,urcrnrlat=49,
            projection='lcc',lat_1=33,lat_2=45,lon_0=-95)
# draw state boundaries.
# data from U.S Census Bureau
# http://www.census.gov/geo/www/cob/st2000.html


shp_info = m.readshapefile(shp_info_path,'states',drawbounds=True)
# population density by state from
# http://en.wikipedia.org/wiki/List_of_U.S._states_by_population_density
popdensity = {
'New Jersey':  438.00,
'Rhode Island':   387.35,
'Massachusetts':   312.68,
'Connecticut':	  271.40,
'Maryland':   209.23,
'New York':    155.18,
'Delaware':    154.87,
'Florida':     114.43,
'Ohio':	 107.05,
'Pennsylvania':	 105.80,
'Illinois':    86.27,
'California':  83.85,
'Hawaii':  72.83,
'Virginia':    69.03,
'Michigan':    67.55,
'Indiana':    65.46,
'North Carolina':  63.80,
'Georgia':     54.59,
'Tennessee':   53.29,
'New Hampshire':   53.20,
'South Carolina':  51.45,
'Louisiana':   39.61,
'Kentucky':   39.28,
'Wisconsin':  38.13,
'Washington':  34.20,
'Alabama':     33.84,
'Missouri':    31.36,
'Texas':   30.75,
'West Virginia':   29.00,
'Vermont':     25.41,
'Minnesota':  23.86,
'Mississippi':	 23.42,
'Iowa':	 20.22,
'Arkansas':    19.82,
'Oklahoma':    19.40,
'Arizona':     17.43,
'Colorado':    16.01,
'Maine':  15.95,
'Oregon':  13.76,
'Kansas':  12.69,
'Utah':	 10.50,
'Nebraska':    8.60,
'Nevada':  7.03,
'Idaho':   6.04,
'New Mexico':  5.79,
'South Dakota':	 3.84,
'North Dakota':	 3.59,
'Montana':     2.39,
'Wyoming':      1.96,
'Alaska':     0.42}
print(shp_info)
# choose a color for each state based on population density.
colors={}
statenames=[]
cmap = plt.cm.hot # use 'hot' colormap
vmin = 0; vmax = 450 # set range.
print(m.states_info[0].keys())
for shapedict in m.states_info:
    statename = shapedict['NAME']
    # skip DC and Puerto Rico.
    if statename not in ['District of Columbia','Puerto Rico']:
        pop = popdensity[statename]
        # calling colormap with value between 0 and 1 returns
        # rgba value.  Invert color range (hot colors are high
        # population), take sqrt root to spread out colors more.
        colors[statename] = cmap(1.-np.sqrt((pop-vmin)/(vmax-vmin)))[:3]
    statenames.append(statename)
# cycle through state names, color each one.
ax = plt.gca() # get current axes instance
for nshape,seg in enumerate(m.states):
    # skip DC and Puerto Rico.
    if statenames[nshape] not in ['District of Columbia','Puerto Rico']:
        color = rgb2hex(colors[statenames[nshape]]) 
        poly = Polygon(seg,facecolor=color,edgecolor=color)
        ax.add_patch(poly)
# draw meridians and parallels.
m.drawparallels(np.arange(25,65,20),labels=[1,0,0,0])
m.drawmeridians(np.arange(-120,-40,20),labels=[0,0,0,1])
plt.title('Filling State Polygons by Population Density')
plt.show()

import os

HAITI_PATH = os.path.join(os.pardir, "pydata-book", "ch08", "Haiti.csv")

assert os.path.exists(HAITI_PATH)

import os
import pandas as pd
from pandas import DataFrame, Series

data = pd.read_csv(HAITI_PATH)

data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][:10]

data['CATEGORY'][:6]

data.describe()

data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) &
            (data.LONGITUDE > -75) & (data.LONGITUDE < -70)
            & data.CATEGORY.notnull()]

def to_cat_list(catstr):
    stripped = (x.strip() for x in catstr.split(','))
    return [x for x in stripped if x]

def get_all_categories(cat_series):
    cat_sets = (set(to_cat_list(x)) for x in cat_series)
    return sorted(set.union(*cat_sets))

def get_english(cat):
	code, names = cat.split('.')
	if '|' in names:
		names = names.split(' | ')[1]
	return code, names.strip()


get_english('2. Urgences logistiques | Vital Lines')

all_cats = get_all_categories(data.CATEGORY)

english_mapping = dict(get_english(x) for x in all_cats)

def get_code(seq):
    return [x.split('.')[0] for x in seq if x]

all_codes = get_code(all_cats)
code_index = pd.Index(np.unique(all_codes))
dummy_frame = DataFrame(np.zeros((len(data), len(code_index))),
                        index=data.index, columns=code_index)

dummy_frame.ix[:, :6]

for row, cat in zip(data.index, data.CATEGORY):
    codes = get_code(to_cat_list(cat))
    dummy_frame.ix[row, codes] = 1

data = data.join(dummy_frame.add_prefix('category_'))


data.CATEGORY.isnull().value_counts()

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt

def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,
                    lllon=-75, urlon=-71):
    # create polar stereographic Basemap instance.
    m = Basemap(ax=ax, projection='stere',
                lon_0=(urlon + lllon) / 2,
                lat_0=(urlat + lllat) / 2,
                llcrnrlat=lllat, urcrnrlat=urlat,
                llcrnrlon=lllon, urcrnrlon=urlon,
                resolution='f')
    # draw coastlines, state and country boundaries, edge of map.
    m.drawcoastlines()
    m.drawstates()
    m.drawcountries()
    return m


fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
fig.subplots_adjust(hspace=0.05, wspace=0.05)

to_plot = ['2a', '1', '3c', '7a']

lllat=17.25; urlat=20.25; lllon=-75; urlon=-71

for code, ax in zip(to_plot, axes.flat):
	m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,
						lllon=lllon, urlon=urlon)

	cat_data = data[data['category_%s' % code] == 1]

    # compute map proj coordinates.
	x, y = m(cat_data.LONGITUDE, cat_data.LATITUDE)

	m.plot(x, y, 'k.', alpha=0.5)
	ax.set_title('%s: %s' % (code, english_mapping[code]))
    
plt.show()

# high res data
# http://magician.ucsd.edu/Software/PmagPy/hires.html
from mpl_toolkits.basemap import basemap_datadir; print basemap_datadir