def main():
n_points = 100
mass = np.random.rand(n_points)
xy = np.random.randn(n_points, 2)
accretor = EqualMassAccretor(xy, mass, 5.)
accretor.accrete()
node_xy = accretor.nodes()
print node_xy.shape
def main():
img_shape = (1024, 1024)
mean = (512., 512.)
cov = 50000. * np.array(((1., 0.5), (0.5, 1.)))
point_xy = np.random.multivariate_normal(mean, cov, 10000)
s = np.where((point_xy[:, 0] >= 0.) & (point_xy[:, 1] >= 0.)
& (point_xy[:, 0] < img_shape[0] - 1.)
& (point_xy[:, 1] < img_shape[1] - 1.))[0]
point_xy = point_xy[s, :]
point_mass = np.ones(point_xy.shape[0])
plot_points(point_xy, img_shape, "density_demo_points")
# Bin points with a CVT
accretor = EqualMassAccretor(point_xy, point_mass, 100.)
accretor.accrete()
generator_xy = accretor.nodes()
cvt = CVTessellation(point_xy[:, 0],
point_xy[:, 1],
point_mass,
node_xy=generator_xy)
node_xy = cvt.nodes
# FIXME issue with this rendering
plot_voronoi_tessellation(cvt, img_shape, "density_demo_voronoi_cells")
# Density estimate
delaunay = DelaunayTessellation(node_xy[:, 0], node_xy[:, 1])
dtfe = DelaunayDensityEstimator(delaunay)
dens = dtfe.estimate_density((0., img_shape[1]), (0., img_shape[0]),
cvt.node_weights)
del_dens_map = delaunay.render_delaunay_field(
dens,
(0., img_shape[1]),
(0., img_shape[0]),
1.,
1.,
)
# FIXME this is broken too
vor_dens_map = delaunay.render_voronoi_field(
dens,
(0., img_shape[1]),
(0., img_shape[0]),
1.,
1.,
)
plot_density_map(del_dens_map, "density_demo_delaunay_field")
plot_density_map(vor_dens_map, "density_demo_voronoi_field")
def main():
img_shape = (1024, 1024)
mean = (512., 512.)
cov = 50000. * np.array(((1., 0.5), (0.5, 1.)))
point_xy = np.random.multivariate_normal(mean, cov, 10000)
s = np.where((point_xy[:, 0] >= 0.) &
(point_xy[:, 1] >= 0.) &
(point_xy[:, 0] < img_shape[0] - 1.) &
(point_xy[:, 1] < img_shape[1] - 1.))[0]
point_xy = point_xy[s, :]
point_mass = np.ones(point_xy.shape[0])
plot_points(point_xy, img_shape, "density_demo_points")
# Bin points with a CVT
accretor = EqualMassAccretor(point_xy, point_mass, 100.)
accretor.accrete()
generator_xy = accretor.nodes()
cvt = CVTessellation(point_xy[:, 0], point_xy[:, 1], point_mass,
node_xy=generator_xy)
node_xy = cvt.nodes
# FIXME issue with this rendering
plot_voronoi_tessellation(cvt, img_shape, "density_demo_voronoi_cells")
# Density estimate
delaunay = DelaunayTessellation(node_xy[:, 0], node_xy[:, 1])
dtfe = DelaunayDensityEstimator(delaunay)
dens = dtfe.estimate_density((0., img_shape[1]),
(0., img_shape[0]),
cvt.node_weights)
del_dens_map = delaunay.render_delaunay_field(dens,
(0., img_shape[1]),
(0., img_shape[0]),
1., 1.,)
# FIXME this is broken too
vor_dens_map = delaunay.render_voronoi_field(dens,
(0., img_shape[1]),
(0., img_shape[0]),
1., 1.,)
plot_density_map(del_dens_map, "density_demo_delaunay_field")
plot_density_map(vor_dens_map, "density_demo_voronoi_field")
def main():
# Set up the size of the mock image
xRange = [0, 500]
yRange = [0, 500]
# We'll generate data distributed in a 2D guassian space
x, y = guassian_point_process(250, 250, 100, 50, 20000)
inRange = np.where((x > 0) & (y > 0)
& (x < xRange[1]) & (y < yRange[1]))[0]
x = x[inRange]
y = y[inRange]
nStars = len(x)
weight = np.random.uniform(0.1, 1., size=(nStars, ))
# Centroidal Voronoi Tessellation, meeting a target cell mass
target_mass = 20.
xy = np.column_stack((x, y))
gen = EqualMassAccretor(xy, weight, target_mass)
gen.accrete()
node_xy = gen.nodes()
print "Original node_xy.shape", node_xy.shape
gen.cleanup() # re-allocate failed bins
node_xy = gen.nodes()
print "Cleaned node_xy.shape", node_xy.shape
cvt = CVTessellation(x, y, weight, node_xy=node_xy)
# Set up the pixel grid (or use set_fits_grid if using a FITS image)
cvt.set_pixel_grid(xRange, yRange)
# Map of Voronoi cell IDs (saving to FITS)
cvt.save_segmap("voronoi_segmap.fits")
# Compute Density of Points and save to FITS
cvt.compute_cell_areas()
cellDensity = cvt.cell_point_density(x, y, mass=weight)
densityField = cvt.render_voronoi_field(cellDensity)
astropy.io.fits.writeto("voronoi_densitymap.fits",
densityField,
clobber=True)
# Save CSV table of Voronoi bins, areas, and densities
save_cell_table(cvt, cellDensity)
# Save a list of points with assignment to Voronoi cells
save_cell_membership(cvt, x, y)
def main():
# Set up the size of the mock image
xRange = [0, 500]
yRange = [0, 500]
# We'll generate data distributed in a 2D guassian space
x, y = guassian_point_process(250, 250, 100, 50, 20000)
inRange = np.where((x > 0) & (y > 0)
& (x < xRange[1]) & (y < yRange[1]))[0]
x = x[inRange]
y = y[inRange]
nStars = len(x)
weight = np.random.uniform(0.1, 1., size=(nStars,))
# Centroidal Voronoi Tessellation, meeting a target cell mass
target_mass = 20.
xy = np.column_stack((x, y))
gen = EqualMassAccretor(xy, weight, target_mass)
gen.accrete()
node_xy = gen.nodes()
print "Original node_xy.shape", node_xy.shape
gen.cleanup() # re-allocate failed bins
node_xy = gen.nodes()
print "Cleaned node_xy.shape", node_xy.shape
cvt = CVTessellation(x, y, weight, node_xy=node_xy)
# Set up the pixel grid (or use set_fits_grid if using a FITS image)
cvt.set_pixel_grid(xRange, yRange)
# Map of Voronoi cell IDs (saving to FITS)
cvt.save_segmap("voronoi_segmap.fits")
# Compute Density of Points and save to FITS
cvt.compute_cell_areas()
cellDensity = cvt.cell_point_density(x, y, mass=weight)
densityField = cvt.render_voronoi_field(cellDensity)
astropy.io.fits.writeto("voronoi_densitymap.fits", densityField, clobber=True)
# Save CSV table of Voronoi bins, areas, and densities
save_cell_table(cvt, cellDensity)
# Save a list of points with assignment to Voronoi cells
save_cell_membership(cvt, x, y)
