def plot_sepp_bg_surface(sepp_obj, domain=None, **kwargs):
k = sepp_obj.bg_kde
# if x_range is None:
# x_range = [min(sepp_obj.data.toarray(1)), max(sepp_obj.data.toarray(1))]
# y_range = [min(sepp_obj.data.toarray(2)), max(sepp_obj.data.toarray(2))]
# loc = DataArray.from_meshgrid(*np.meshgrid(
# np.linspace(x_range[0], x_range[1], npt),
# np.linspace(y_range[0], y_range[1], npt),
# ))
# z = k.partial_marginal_pdf(loc)
# if ax is None:
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.contourf(loc.toarray(0), loc.toarray(1), z, 50)
# ax.set_aspect('equal')
# if domain is not None:
func = lambda x, y: k.partial_marginal_pdf(DataArray.from_meshgrid(x, y))
if domain is None:
xmin, xmax = min(sepp_obj.data.toarray(1)), max(
sepp_obj.data.toarray(1))
ymin, ymax = min(sepp_obj.data.toarray(2)), max(
sepp_obj.data.toarray(2))
domain = shapely_rectangle_from_vertices(xmin, ymin, xmax, ymax)
plot_surface_function_on_polygon(domain, func=func, **kwargs)
plt.axis('equal')
def test_pdf_values(self):
for i in range(self.min_nd, 4):
x = DataArray.from_meshgrid(
*np.meshgrid(
*[np.linspace(-5, 5, 50)] * i
)
)
y = self.kernels[i].pdf(x)
ye = self.expected_pdf(x)
self.assertTrue(np.all(np.abs(y - ye) < self.tol))
def test_cutoff(self):
x = DataArray(np.linspace(-5, 5, 100))
self.assertTrue(np.all(self.kernels[1].pdf(x)[x.toarray(0) < 0] == 0))
x = DataArray.from_meshgrid(
*np.meshgrid(
np.linspace(-5, 5, 50),
np.linspace(-5, 5, 50)
)
)
res = self.kernels[2].pdf(x)
self.assertTrue(np.all(res[x.toarray(0) < 0] == 0))
self.assertTrue(np.all(res[x.toarray(0) >= 0] > 0.))
def plot_xy_kde(k, x_range, y_range, npt_1d=50, **kwargs):
x, y = np.meshgrid(np.linspace(x_range[0], x_range[1], npt_1d),
np.linspace(y_range[0], y_range[1], npt_1d))
loc = DataArray.from_meshgrid(x, y)
z = k.pdf(loc, normed=False)
fig = plt.figure()
ax = fig.add_subplot(111)
n_contours = kwargs.pop('n_contours', 40)
cax = ax.contourf(x, y, z, n_contours, cmap='binary')
if 'clim' in kwargs:
clim = kwargs.pop('clim')
cax.set_clim(clim)
ax.set_xlabel('X (m)')
ax.set_ylabel('Y (m)')
if kwargs.pop('colorbar', True):
fig.colorbar(cax)
return fig
l_target = k.pdf(res_all.getrows(this_idx_target), normed=False)
l_sources.append(l_source)
l_targets.append(l_target)
kst[i,
j] = n / (nv * S * T) * np.sum(1 / (omega * l_source * l_target))
# plots
plt.figure()
plt.contourf(uu, vv, kst - np.pi * uu**2 * vv, 50)
plt.colorbar()
xy = DataArray.from_meshgrid(*np.meshgrid(
np.linspace(res_all.toarray(1).min(),
res_all.toarray(1).max(), 50),
np.linspace(res_all.toarray(2).min(),
res_all.toarray(2).max(), 50)))
zz = k.partial_marginal_pdf(xy, normed=False)
plt.figure()
plt.contourf(xy.toarray(0), xy.toarray(1), zz, 50)
plt.colorbar()
t = np.linspace(res_all.toarray(0).min(), res_all.toarray(0).max(), 500)
plt.figure()
plt.plot(t, k.marginal_pdf(t, dim=0, normed=False))
# simulation ONLY
if b_sim:
true_int = lambda xyz: 250 / (
(1 - np.exp(-1)) *
def weighted_bg_rel_range(x, y):
xy = DataArray.from_meshgrid(x, y)
fxy = np.array(
[a.partial_marginal_pdf(xy) for a in res['weighted_backgrounds']])
fxy_mean = fxy.mean(axis=0)
return (fxy.ptp(axis=0) / fxy_mean).reshape(xy.original_shape)
def weighted_bg_mean_density(x, y):
xy = DataArray.from_meshgrid(x, y)
fxy = np.array(
[a.partial_marginal_pdf(xy) for a in res['weighted_backgrounds']])
return fxy.mean(axis=0).reshape(xy.original_shape)
def radial_spatial_triggering_plots(ppobj,
simobj=None,
xmax=None,
ymax=None,
cbar=True,
fmax=None,
vmax=None):
npt = 500
ci = 0.99
fig_kwargs = {
'figsize': (10, 5),
'dpi': 100,
'facecolor': 'w',
}
assert fmax is None or vmax is None, "Can specify EITHER vmax OR fmax"
xmax = xmax or ppobj.trigger_kde.marginal_icdf(ci, dim=1)
ymax = ymax or xmax
xy = DataArray.from_meshgrid(*np.meshgrid(np.linspace(-xmax, xmax, npt),
np.linspace(-ymax, ymax, npt)))
zxy1 = ppobj.trigger_kde.partial_marginal_pdf(xy,
normed=False) / ppobj.ndata
# zxy1 = ppobj.trigger_kde.partial_marginal_pdf(xy, normed=True)
if fmax is not None:
vmax = sorted(zxy1.flat)[int(zxy1.size * fmax)]
elif vmax is not None:
pass
else:
vmax = zxy1.max()
if simobj:
sx = simobj.trigger_params['sigma'][0]
sy = simobj.trigger_params['sigma'][1]
th = simobj.trigger_params['intensity']
# zxy2 = th / (np.sqrt(2 * np.pi) * sx * sy) * np.exp(
# -(xy.toarray(0) ** 2) / (2 * sx**2) - xy.toarray(1) ** 2 / (2 * sy ** 2)
# )
zxy2 = 1 / (2 * np.pi * sx *
sy) * np.exp(-(xy.toarray(0)**2) /
(2 * sx**2) - xy.toarray(1)**2 / (2 * sy**2))
vmax = max(zxy2.max(), vmax)
vmax *= 1.02
fig = plt.figure(**fig_kwargs)
if simobj:
ax1 = fig.add_subplot(121)
else:
ax1 = fig.add_subplot(111)
cont1 = ax1.contourf(xy.toarray(0),
xy.toarray(1),
zxy1,
50,
cmap=cm.coolwarm,
vmin=0,
vmax=vmax)
ax1.set_xlim([-xmax, xmax])
ax1.set_ylim([-ymax, ymax])
ax1.set_xlabel('X (metres)')
ax1.set_ylabel('Y (metres)')
ax1.set_aspect('equal')
# cax1 = plt.colorbar(cont, ax=ax1)
hbar = None
if simobj:
ax2 = fig.add_subplot(122)
cont2 = ax2.contourf(xy.toarray(0),
xy.toarray(1),
zxy2,
50,
cmap=cm.coolwarm,
vmin=0,
vmax=vmax)
ax2.yaxis.set_ticklabels([])
ax2.set_xlabel('X (metres)')
ax2.set_xlim([-xmax, xmax])
ax2.set_ylim([-ymax, ymax])
ax2.set_aspect('equal')
if cbar:
hbar = fig.colorbar(cont2, ax=[ax1, ax2])
else:
if cbar:
hbar = fig.colorbar(cont1, ax=ax1)
if cbar:
return hbar
