def test_constraint_global_noisy(self):
hard_radius = 1.
constraints = dimer_global(1, self.im.ndim)
self.im.clear()
self.im.draw_clusters(10, 2, hard_radius, 2*self.separation,
2*self.diameter)
f0 = self.im.f(noise=self.pos_err)
f0['signal'] = 180
f0['size'] = 6.
result = refine_leastsq(f0, self.im.noisy_image(0.2*self.signal),
self.diameter, self.separation,
constraints=constraints,
param_mode=dict(signal='global',
size='global'),
options=dict(maxiter=1000))
dists = []
for _, f_cl in result.groupby('cluster'):
pos = result[['y', 'x']].values
dists.append(np.sqrt(np.sum(((pos[0] - pos[1])/np.array(self.im.size))**2)))
assert_allclose(dists, 2*hard_radius, atol=0.1)
assert_coordinates_close(result[self.im.pos_columns].values,
self.im.coords, 0.1)
assert_allclose(result['signal'].values, 200, atol=2)
assert_allclose(result['size'].values, 5.25, atol=1)
def test_constraint_global_noisy(self):
hard_radius = 1.
constraints = dimer_global(1, self.im.ndim)
self.im.clear()
self.im.draw_clusters(10, 2, hard_radius, 2 * self.separation,
2 * self.diameter)
f0 = self.im.f(noise=self.pos_err)
f0['signal'] = 180
f0['size'] = 6.
result = refine_leastsq(f0,
self.im.noisy_image(0.2 * self.signal),
self.diameter,
self.separation,
constraints=constraints,
param_mode=dict(signal='global',
size='global'),
options=dict(maxiter=1000))
dists = []
for _, f_cl in result.groupby('cluster'):
pos = result[['y', 'x']].values
dists.append(
np.sqrt(np.sum(
((pos[0] - pos[1]) / np.array(self.im.size))**2)))
assert_allclose(dists, 2 * hard_radius, atol=0.1)
assert_coordinates_close(result[self.im.pos_columns].values,
self.im.coords, 0.1)
assert_allclose(result['signal'].values, 200, atol=2)
assert_allclose(result['size'].values, 5.25, atol=1)
def test_constraint_global_dimer(self):
hard_radius = 1.
constraints = dimer_global(1, self.im.ndim)
self.im.clear()
self.im.draw_clusters(10, 2, hard_radius, 2 * self.separation,
2 * self.diameter)
f0 = self.im.f(noise=self.pos_err)
result = refine_leastsq(f0,
self.im(),
self.diameter,
self.separation,
constraints=constraints,
options=dict(maxiter=1000))
dists = []
for _, f_cl in result.groupby('cluster'):
pos = result[['y', 'x']].values
dists.append(
np.sqrt(np.sum(
((pos[0] - pos[1]) / np.array(self.im.size))**2)))
assert_allclose(dists, 2 * hard_radius, atol=0.01)
assert_coordinates_close(result[self.im.pos_columns].values,
self.im.coords, 0.1)
def test_constraint_global_dimer(self):
hard_radius = 1.
constraints = dimer_global(1, self.im.ndim)
self.im.clear()
self.im.draw_clusters(10, 2, hard_radius, 2*self.separation,
2*self.diameter)
f0 = self.im.f(noise=self.pos_err)
result = refine_leastsq(f0, self.im(), self.diameter,
self.separation, constraints=constraints,
options=dict(maxiter=1000))
dists = []
for _, f_cl in result.groupby('cluster'):
pos = result[['y', 'x']].values
dists.append(np.sqrt(np.sum(((pos[0] - pos[1])/np.array(self.im.size))**2)))
assert_allclose(dists, 2*hard_radius, atol=0.01)
assert_coordinates_close(result[self.im.pos_columns].values,
self.im.coords, 0.1)
