def _geojs_filename_changed(self, name):
data = file(name).read()
polys = process_raw(data.replace('\r\n', ''))
# Generate compiled path from the polygons
paths = []
for poly in polys:
path = CompiledPath()
for p in poly:
path.lines(p)
paths.append(path)
self._paths = paths
red = np.array([202, 0, 32])/255.
blue = np.array([5, 113, 176])/255.
colors = red * np.random.random_integers(0,1,len(paths)).reshape(-1,1)
colors[np.sum(colors,axis=-1)==0] = blue
self._colors = colors
# Store the polygons just in case we need to regenerate the path
self._polys = polys
self.request_redraw()
def _geojs_filename_changed(self, name):
data = file(name).read()
polys = process_raw(data.replace('\r\n', ''))
# Generate compiled path from the polygons
paths = []
for poly in polys:
path = CompiledPath()
for p in poly:
path.lines(p)
paths.append(path)
self._paths = paths
red = np.array([202, 0, 32]) / 255.
blue = np.array([5, 113, 176]) / 255.
colors = red * np.random.random_integers(0, 1, len(paths)).reshape(
-1, 1)
colors[np.sum(colors, axis=-1) == 0] = blue
self._colors = colors
# Store the polygons just in case we need to regenerate the path
self._polys = polys
self.request_redraw()
def _data_columns_default(self):
return list(self.dataframe.columns[1:][::-1])
if __name__ == "__main__":
populations = pandas.read_csv('state_populations.csv')
from mapping.enable.geojson_overlay import process_raw
polys = process_raw(file("states.geojs").read().replace('\r\n', ''))
# generate compiled paths from polys
paths = []
coords = numpy.zeros((len(polys), 2))
for poly, coord in zip(polys, coords):
path = CompiledPath()
total = numpy.sum((numpy.sum(p, axis=0) for p in poly), axis=0)
coord[:] = total / sum(map(len, poly))
for p in poly:
path.lines(p - coord) # recenter on origin
paths.append(path)
with enaml.imports():
from choropleth_view import MapView
view = MapView(model=Demo(title="State population from 1900 to 2010",
index_ds=ArrayDataSource(coords[:, 0]),
value_ds=ArrayDataSource(coords[:, 1]),
paths=paths,
dataframe=populations), )
view.show()
if __name__ == "__main__":
populations = pandas.read_csv('example/state_populations.csv')
from mapping.enable.geojson_overlay import process_raw
polys = process_raw(file("example/states.geojs").read().replace('\r\n',''))
# generate compiled paths from polys
paths = []
coords = numpy.zeros((len(polys), 2))
for poly, coord in zip(polys, coords):
path = CompiledPath()
total = numpy.sum((numpy.sum(p, axis=0) for p in poly), axis=0)
coord[:] = total/sum(map(len,poly))
for p in poly:
path.lines(p - coord) # recenter on origin
paths.append(path)
with enaml.imports():
from choropleth_view import MapView
view = MapView(
model = Demo(title = "State population from 1900 to 2010",
index_ds = ArrayDataSource(coords[:,0]),
value_ds = ArrayDataSource(coords[:,1]),
paths = paths,
dataframe = populations),
)
view.show()
