def create_grid_squares_shapefile(outfile, side_length=250):
mpoly = get_boundary()
ipoly, fpoly, full = spatial.create_spatial_grid(mpoly, side_length)
field_description = {'id': {'fieldType': 'N'}}
id = range(1, len(fpoly) + 1)
full_polys = [spatial.shapely_rectangle_from_vertices(*t) for t in fpoly]
spatial.write_polygons_to_shapefile(outfile,
full_polys,
field_description=field_description,
id=id)
def set_sample_units(self, side_length, *args, **kwargs):
'''
Set the ROC grid.
:param length_or_arr: Either a scalar, interpreted as the side length of the grid square, OR an array of
shapely Polygon or shapely MultiPolygon objects
:param args: Passed to set_sample_points
:param kwargs: Passed to set_sample_points
:return: None
'''
# reset prediction values
self.prediction_values = None
if not self.poly:
# find minimal bounding rectangle
self.poly = self.generate_bounding_poly(self.data)
self.side_length = side_length
self.grid_polys, self.sample_units, self.full_grid_square = create_spatial_grid(
self.poly, self.side_length)
self.set_centroids()
self.set_sample_points(*args, **kwargs)
def set_sample_units(self, side_length, interval, *args, **kwargs):
"""
Set the ROC grid.
:param side_length: side length of grid squares
:param interval: The length interval between sample points
:param args: Passed to set_sample_points
:param kwargs: Passed to set_sample_points.
:return: None
"""
# reset prediction values
self.prediction_values = None
if not self.poly:
# find minimal bounding rectangle
self.poly = self.generate_bounding_poly(self.data)
# set sample grid
self.side_length = side_length
self.grid_polys, self.sample_units, _ = create_spatial_grid(
self.poly, self.side_length)
# set network sampling points
self.set_sample_points(interval, *args, **kwargs)
ax = ax or plt.gca()
y = [len(t) for t in aggregate_data.values()]
spatial.plot_shaded_regions(aggregate_data.keys(),
y,
ax=ax,
cmap=plt.get_cmap('autumn_r'),
vmin=1e-6,
domain=domain,
scale_bar_loc=scale_bar_loc)
if __name__ == "__main__":
chic_s_domain = chicago.get_chicago_side_polys(as_shapely=True)['South']
camden_domain = cad.get_camden_region(as_shapely=True)
chic_s_grid = create_spatial_grid(chic_s_domain, 250)[0]
cad_grid = [t.mpoly for t in models.Division.objects.filter(type='monsuru_250m_grid')]
## Camden
nicl_types = (
(1, 'Violence'),
(3, 'Burglary'),
(13, 'Shoplifting')
)
cad_data = {}
cad_spatial_data = {}
for k, v in nicl_types:
a, b, c = cad.get_crimes_by_type(k, convert_dates=False)
ax = fig.add_subplot(111)
cm = utils.transparent_colour_map()
net.plot_network(ax=ax)
pred_t = 180.
arr_fun = lambda x, y: plots.txy_to_cartesian_data_array(np.ones_like(x) * pred_t, x, y)
pred_fun = lambda x, y: r.predict(arr_fun(x, y))
xx, yy, zz = spatial.plot_surface_function_on_polygon(domain, pred_fun, ax=ax, fmax=0.98, cmap=cm, dx=50)
ax.axis('off')
plt.tight_layout()
fig.savefig('continuous_heatmap.png', dpi=300)
fig.savefig('continuous_heatmap.pdf', dpi=300)
# (2) top 10 % grid squares
ipolys, full_extents, full_grid_square = create_spatial_grid(domain, 200)
grid_values = []
for xmin, ymin, xmax, ymax in full_extents:
i, j = np.where((xx >= xmin) & (xx < xmax) & (yy >= ymin) & (yy < ymax))
grid_values.append(zz[i, j].sum())
sort_idx = np.argsort(grid_values)[::-1]
top_10_pct = sort_idx[:int(len(sort_idx) * 0.1)]
top_10_pct_grids = [shapely_rectangle_from_vertices(*full_extents[i]) for i in top_10_pct]
fig = plt.figure(figsize=(5, 6))
ax = fig.add_subplot(111)
net.plot_network(ax=ax)
spatial.plot_shapely_geos(domain, ax=ax)
spatial.plot_shapely_geos(top_10_pct_grids, ax=ax, fc='r', ec='none', lw=1.5, alpha=0.7)
ax.axis('off')
def get_chicago_grid(poly):
ipoly, fpoly, full = spatial.create_spatial_grid(poly, 250)
return fpoly
def create_roc_grid(poly, grid_length=250):
ipoly, fpoly, full = spatial.create_spatial_grid(poly, grid_length)
return fpoly