"""Draw a basic map of Toronto.

:param axis: where to draw the map

:returns: Basemap with Toronto

"""

# These are the latitudes of the City of Toronto.

# (Fiona can be better to find them, e.g., from the 'icitw_wgs84' Shapefile

# below.)

low_left_corner_longitude = -79.75

low_left_corner_latitude = 43.40

up_right_corner_longitude = -79.10

up_right_corner_latitude = 43.95

to_map = Basemap(llcrnrlon=low_left_corner_longitude,

llcrnrlat=low_left_corner_latitude,

urcrnrlon=up_right_corner_longitude,

urcrnrlat=up_right_corner_latitude,

ellps='WGS84',

resolution='h', area_thresh=0.1,

ax=axis)

to_map.drawmapboundary(fill_color='white')

"""Draw a basic map of Toronto with also the division among

its city wards.

:param axis: where to draw the map

:returns: Basemap with Toronto

"""

to_map = draw_basic_map_of_toronto(axis)

# Plot the City Wards in Toronto. The borders of these polygons are plot as

# they are in the Shapefile

dummy = to_map.readshapefile(shapefile='./shp_dir/icitw_wgs84',

name='city_wards',

drawbounds=True, color='green')

"""Function to visualize the investment in the neighborhoods of

Toronto.

This function is too big, it will be splitted in several parts according

to the visualization they do.

It plots these ESRI Shapefiles from Toronto using matplotlib/basemap/etc.

(These shapefiles are available as Open Data from the City of Toronto):

. The City Wards in Toronto;

. The Neighborhoods defined as Priority Investment by the City of Toronto

. The Business Improvement Areas defined by the City of Toronto

. The Current Value Assessment on Tax Impact for Residential Neighborhoods

in the City of Toronto (last year available, 2011)

"""

fig = plt.figure()

axes = [None, None, None]

# grid of 2x2 rows and colums for the subplots with differ. visualizations

axes[0] = plt.subplot2grid((2, 2), (0, 0))

axes[1] = plt.subplot2grid((2, 2), (0, 1))

axes[2] = plt.subplot2grid((2, 2), (1, 0), colspan=2)

# axes = fig.add_subplot(111)

# First map is the Priority investment by the City of Toronto

axes[0].set_title("Priority investment by the City of Toronto")

to_map = draw_toronto_and_city_wards(axis=axes[0])

# Read the Shapefile of the Priority Investment Neighborhoods in Toronto.

# The Shapefile is read first and its polygons are filled with the color

# 'facecolor' in a for-loop below

dummy = to_map.readshapefile(shapefile='./shp_dir/TO_priority_inv_neighb',

name='prio_investm',

drawbounds=False)

patches = []

for info, shape in zip(to_map.prio_investm_info, to_map.prio_investm):

# print info

patches.append(Polygon(np.array(shape), True))

axes[0].add_collection(PatchCollection(patches, facecolor='m',

edgecolor='k', linewidths=1.,

zorder=3))

# Read the Shapefile of the Business Improvement Areas in Toronto.

# The Shapefile is read first and its polygons are filled with the color

# 'facecolor' in a for-loop below

axes[1].set_title("Business Improvement Areas of Toronto")

to_map = draw_toronto_and_city_wards(axis=axes[1])

dummy = to_map.readshapefile(shapefile='./shp_dir/TO_busin_improv_area',

name='busin_improv',

drawbounds=False)

patches = []

for info, shape in zip(to_map.busin_improv_info, to_map.busin_improv):

# print info

patches.append(Polygon(np.array(shape), True))

axes[1].add_collection(PatchCollection(patches, facecolor='g',

edgecolor='k', linewidths=1.,

zorder=2))

# Read the Shapefile of the Estimated Tax Impact in Toronto.

# The way to plot this Shapefile is different than the previous one, since

# this shapefile has an Avg Tax Impact value [field 'avgtaximpa']

# associated to each of its polygons, so the different tonalities of

# 'facecolor' in its polygons represent the Avg Tax Impact value in each

# polygon

axes[2].set_title("Current Value and Assessed Tax Impact per Sub-Ward")

to_map = draw_basic_map_of_toronto(axis=axes[2])

dummy = to_map.readshapefile(shapefile='shp_dir/CVA_2011_Tax_Impact_WGS84',

name='tax_assesm_impact',

drawbounds=False)

patches = []

taxes = []

# Note that the taxes impact (in taxes[]) is different inside a same ward

# in the city of Toronto. I.e., a same ward can have different subpolygons

# with different tax-impact. Example, for 'ward' = 2:

# {'subdiv': '19041', 'ward': 2.0, 'avgtaximpa': -56.0554, ...}

# {'subdiv': '19042', 'ward': 2.0, 'avgtaximpa': -58.6994, ...}

for info, shape in zip(to_map.tax_assesm_impact_info,

to_map.tax_assesm_impact):

# print info

# The estimated taxes in the polygon is the gradient which gives the

# tonality of red to the polygon

taxes.append(float(info['avgtaximpa']))

patches.append(Polygon(np.array(shape), True))

cmap = plt.get_cmap('Reds')

patch_collection = PatchCollection(patches, match_original=True)

min_taxes = min(taxes)

max_taxes = max(taxes)

norm = Normalize(min_taxes, max_taxes)

patch_collection.set_facecolor(cmap(norm(taxes)))

axes[2].add_collection(patch_collection)

# Add a colour bar

delta_gradient_taxes = max_taxes - min_taxes

color_bar_taxes = [min_taxes]

for i in range(1, 6):

color_bar_taxes.append(min_taxes + (i/6.0) * delta_gradient_taxes)

color_bar_taxes.append(max_taxes)

clor_bar = colorbar_index(ncolors=len(color_bar_taxes), cmap=cmap,

shrink=0.7, labels=color_bar_taxes,

format='%.2f', ax=axes[2])

# Set the font-size of the tick labels in the color bar

clor_bar.ax.tick_params(labelsize=7)

clor_bar.set_label(label='Tax Impact')

# Show highest densities, in descending order

# highest = '\n'.join(

# value[1] for _, value in df_map[(df_map['jenks_bins'] == 4)][:10].sort().iterrows())

# highest = 'Most Dense Wards:\n\n' + highest

# Subtraction is necessary for precise y coordinate alignment

# details = clor_bar.ax.text(

# -1., 0 - 0.007,

# highest,

# ha='right', va='bottom',

# size=5,

# color='#555555')

# Add a small legend

# ( http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.text )

# dummy = axes[2].text(

# 0.98, 0.05,

# 'This is a map of taxes and investment per subwards in Toronto\n' +

# 'Obtained from Open Data of the City of Toronto\n' +

# 'See README of this project for URLs\n',

# horizontalalignment='right', verticalalignment='bottom',

# size=6,

# color='#555555',

# bbox=dict(facecolor='red', alpha=0.2),

# transform=axes[2].transAxes)

# Draw a map scale

# to_map.drawmapscale(

# coords[0] + 0.08, coords[1] + 0.015,

# coords[0], coords[1],

# 10.,

# barstyle='fancy', labelstyle='simple',

# fillcolor1='w', fillcolor2='#555555',

# fontcolor='#555555',

# zorder=5)

fig.set_tight_layout(True)

fig.set_size_inches(9, 7)

# plt.title('Toronto Neighborhoods: Priority Investment, Business ' +

# 'Improvement Areas,\nand Current Value Assessment of Tax ' +

# 'Impact on Residential Properties')

# plt.legend()

fig.savefig('TO_developm_neighborhoods.png', dpi=600)

"""Main function on the program.

What it does, for now, is just to visualize the investment and taxes

in the neighborhoods in Toronto,

"""

"""

This is a convenience function to stop you making off-by-one errors

Takes a standard colour ramp, and discretizes it,

then draws a colour bar with correctly aligned labels

"""

cmap = cmap_discretize(cmap, ncolors)

mappable = cm.ScalarMappable(cmap=cmap)

mappable.set_array([])

mappable.set_clim(-0.5, ncolors+0.5)

colorbar = plt.colorbar(mappable, **kwargs)

colorbar.set_ticks(np.linspace(0, ncolors, ncolors))

colorbar.set_ticklabels(range(ncolors))

if labels:

colorbar.set_ticklabels(labels)

"""

Return a discrete colormap from the continuous colormap cmap.

cmap: colormap instance, eg. cm.jet.

num: number of colors.

Example

x = resize(arange(100), (5,100))

djet = cmap_discretize(cm.jet, 5)

imshow(x, cmap=djet)

"""

if isinstance(cmap, str):

cmap = plt.get_cmap(cmap)

colors_i = np.concatenate((np.linspace(0, 1., num), (0., 0., 0., 0.)))

colors_rgba = cmap(colors_i)

indices = np.linspace(0, 1., num + 1)

cdict = {}

for c_i, key in enumerate(('red', 'green', 'blue')):

cdict[key] = [(indices[i], colors_rgba[i - 1, c_i], colors_rgba[i, c_i])

for i in xrange(num + 1)]