start adjusting demos

demos/bench_gridder.py

@@ -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)

demos/getting_started_1.py

@@ -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

demos/getting_started_2.py

@@ -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

demos/getting_started_mf.py

@@ -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):

demos/multi_amplitudes_consistency.py

@@ -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
 

demos/polynomial_fit.py

@@ -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]))

nifty6/version.py

@@ -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():