Commit f44d8758 authored by Martin Reinecke's avatar Martin Reinecke
Browse files

start adjusting demos

parent d166c586
Pipeline #64980 failed with stages
in 8 minutes and 36 seconds
......@@ -31,9 +31,9 @@ for ii in range(10, 26):
vis = ift.from_global_data(visspace, vis)
op = GM.getFull().adjoint
t1 = time()
op(img).to_global_data()
op(img).val
t2 = time()
op.adjoint(vis).to_global_data()
op.adjoint(vis).val
t3 = time()
print(t2-t1, t3-t2)
N0s.append(N)
......
......@@ -42,7 +42,7 @@ def make_random_mask():
# Random mask for spherical mode
mask = ift.from_random('pm1', position_space)
mask = (mask + 1)/2
return mask.to_global_data()
return mask.val
if __name__ == '__main__':
......@@ -95,7 +95,7 @@ if __name__ == '__main__':
# and harmonic transformaion
# Masking operator to model that parts of the field have not been observed
mask = ift.Field.from_global_data(position_space, mask)
mask = ift.Field.from_arr(position_space, mask)
Mask = ift.MaskOperator(mask)
# The response operator consists of
......
......@@ -40,7 +40,7 @@ def exposure_2d():
exposure[:, x_shape*4//5:x_shape] *= .1
exposure[:, x_shape//2:x_shape*3//2] *= 3.
return ift.Field.from_global_data(position_space, exposure)
return ift.Field.from_arr(position_space, exposure)
if __name__ == '__main__':
......@@ -94,8 +94,8 @@ if __name__ == '__main__':
lamb = R(sky)
mock_position = ift.from_random('normal', domain)
data = lamb(mock_position)
data = np.random.poisson(data.to_global_data().astype(np.float64))
data = ift.Field.from_global_data(d_space, data)
data = np.random.poisson(data.val.astype(np.float64))
data = ift.Field.from_arr(d_space, data)
likelihood = ift.PoissonianEnergy(data)(lamb)
# Settings for minimization
......
......@@ -40,7 +40,7 @@ class SingleDomain(ift.LinearOperator):
def apply(self, x, mode):
self._check_input(x, mode)
return ift.from_global_data(self._tgt(mode), x.to_global_data())
return ift.from_global_data(self._tgt(mode), x.val)
def random_los(n_los):
......
......@@ -9,12 +9,12 @@ def testAmplitudesConsistency(seed, sspace):
sc = ift.StatCalculator()
for s in samples:
sc.add(op(s.extract(op.domain)))
return sc.mean.to_global_data(), sc.var.sqrt().to_global_data()
return sc.mean.val, sc.var.sqrt().val
np.random.seed(seed)
offset_std = .1
intergated_fluct_std0 = .003
intergated_fluct_std1 = 0.1
nsam = 1000
......@@ -32,7 +32,7 @@ def testAmplitudesConsistency(seed, sspace):
offset_std,_ = stats(fa.amplitude_total_offset,samples)
intergated_fluct_std0,_ = stats(fa.average_fluctuation(0),samples)
intergated_fluct_std1,_ = stats(fa.average_fluctuation(1),samples)
slice_fluct_std0,_ = stats(fa.slice_fluctuation(0),samples)
slice_fluct_std1,_ = stats(fa.slice_fluctuation(1),samples)
......@@ -54,7 +54,7 @@ def testAmplitudesConsistency(seed, sspace):
print("Expected integrated fluct. frequency Std: " +
str(intergated_fluct_std1))
print("Estimated integrated fluct. frequency Std: " + str(fluct_freq))
print("Expected slice fluct. space Std: " +
str(slice_fluct_std0))
print("Estimated slice fluct. space Std: " + str(sl_fluct_space))
......@@ -65,8 +65,8 @@ def testAmplitudesConsistency(seed, sspace):
print("Expected total fluct. Std: " + str(tot_flm))
print("Estimated total fluct. Std: " + str(fluct_total))
np.testing.assert_allclose(offset_std, zm_std_mean, rtol=0.5)
np.testing.assert_allclose(intergated_fluct_std0, fluct_space, rtol=0.5)
np.testing.assert_allclose(intergated_fluct_std1, fluct_freq, rtol=0.5)
......@@ -74,7 +74,7 @@ def testAmplitudesConsistency(seed, sspace):
np.testing.assert_allclose(slice_fluct_std0, sl_fluct_space, rtol=0.5)
np.testing.assert_allclose(slice_fluct_std1, sl_fluct_freq, rtol=0.5)
fa = ift.CorrelatedFieldMaker.make(offset_std, .1, '')
fa.add_fluctuations(fsspace, intergated_fluct_std1, 1., 3.1, 1., .5, .1,
-4, 1., 'freq')
......@@ -87,7 +87,7 @@ def testAmplitudesConsistency(seed, sspace):
print("Forced slice fluct. space Std: "+str(m))
print("Expected slice fluct. Std: " + str(em))
np.testing.assert_allclose(m, em, rtol=0.5)
assert op.target[0] == sspace
assert op.target[1] == fsspace
......
......@@ -36,7 +36,7 @@ def polynomial(coefficients, sampling_points):
if not (isinstance(coefficients, ift.Field)
and isinstance(sampling_points, np.ndarray)):
raise TypeError
params = coefficients.to_global_data()
params = coefficients.val
out = np.zeros_like(sampling_points)
for ii in range(len(params)):
out += params[ii] * sampling_points**ii
......@@ -71,7 +71,7 @@ class PolynomialResponse(ift.LinearOperator):
def apply(self, x, mode):
self._check_input(x, mode)
val = x.to_global_data_rw()
val = x.val.copy()
if mode == self.TIMES:
# FIXME Use polynomial() here
out = self._mat.dot(val)
......@@ -136,9 +136,8 @@ plt.savefig('fit.png')
plt.close()
# Print parameters
mean = sc.mean.to_global_data()
sigma = np.sqrt(sc.var.to_global_data())
if ift.dobj.master:
for ii in range(len(mean)):
print('Coefficient x**{}: {:.2E} +/- {:.2E}'.format(ii, mean[ii],
mean = sc.mean.val
sigma = np.sqrt(sc.var.val)
for ii in range(len(mean)):
print('Coefficient x**{}: {:.2E} +/- {:.2E}'.format(ii, mean[ii],
sigma[ii]))
......@@ -3,7 +3,7 @@
# 2) we can import it in setup.py for the same reason
# 3) we can import it into your module module
__version__ = '5.0.0'
__version__ = '6.0.0'
def gitversion():
......
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