Merge branch 'NIFTy_5' into plottest

.gitlab-ci.yml

@@ -73,9 +73,6 @@ run_ipynb:
   stage: demo_runs
   script:
     - jupyter nbconvert --execute --ExecutePreprocessor.timeout=None demos/Wiener_Filter.ipynb
-  artifacts:
-    paths:
-      - '*.png'
 
 run_getting_started_1:
   stage: demo_runs
@@ -83,6 +80,9 @@ run_getting_started_1:
     - python demos/getting_started_1.py
     - python3 demos/getting_started_1.py
     - mpiexec -n 2 --bind-to none python3 demos/getting_started_1.py 2> /dev/null
+  artifacts:
+    paths:
+      - '*.png'
 
 run_getting_started_2:
   stage: demo_runs
@@ -90,12 +90,18 @@ run_getting_started_2:
     - python demos/getting_started_2.py
     - python3 demos/getting_started_2.py
     - mpiexec -n 2 --bind-to none python3 demos/getting_started_2.py 2> /dev/null
+  artifacts:
+    paths:
+      - '*.png'
 
 run_getting_started_3:
   stage: demo_runs
   script:
     - python demos/getting_started_3.py
     - python3 demos/getting_started_3.py
+  artifacts:
+    paths:
+      - '*.png'
 
 run_bernoulli:
   stage: demo_runs

demos/getting_started_2.py

@@ -103,4 +103,4 @@ if __name__ == '__main__':
 
     # Plot results
     result_sky = sky.at(H.position).value
-    # PLOTTING
+    # FIXME PLOTTING

nifty5/library/correlated_fields.py

@@ -44,13 +44,10 @@ def make_correlated_field(s_space, amplitude_model):
     ht = HartleyOperator(h_space, s_space)
     p_space = amplitude_model.value.domain[0]
     power_distributor = PowerDistributor(h_space, p_space)
-    # FIXME Remove tau and phi stuff from here. Should not be necessary
-    position = MultiField.from_dict({
-        'xi': Field.from_random('normal', h_space),
-        'tau': amplitude_model.position['tau'],
-        'phi': amplitude_model.position['phi']})
 
+    position = MultiField.from_dict({'xi': Field.full(h_space, 0.)})
     xi = Variable(position)['xi']
+
     A = power_distributor(amplitude_model)
     correlated_field_h = A * xi
     correlated_field = ht(correlated_field_h)
@@ -79,10 +76,11 @@ def make_mf_correlated_field(s_space_spatial, s_space_energy,
     pd_energy = PowerDistributor(pd_spatial.domain, p_space_energy, 1)
     pd = pd_spatial*pd_energy
 
-    dom_distr_0 = DomainDistributor(pd.domain, 0)
-    dom_distr_1 = DomainDistributor(pd.domain, 1)
-    a_spatial = dom_distr_1(amplitude_model_spatial)
-    a_energy = dom_distr_0(amplitude_model_energy)
+    dom_distr_spatial = DomainDistributor(pd.domain, 0)
+    dom_distr_energy = DomainDistributor(pd.domain, 1)
+
+    a_spatial = dom_distr_spatial(amplitude_model_spatial)
+    a_energy = dom_distr_energy(amplitude_model_energy)
     a = a_spatial*a_energy
     A = pd(a)
 

nifty5/library/gaussian_energy.py

@@ -61,7 +61,7 @@ class GaussianEnergy(Energy):
     @property
     @memo
     def gradient(self):
-        return self._inp.jacobian.adjoint_times(self._icovres)
+        return self._inp.jacobian.adjoint_times(self._icovres).real
 
     @property
     @memo

nifty5/minimization/energy_sum.py

@@ -37,9 +37,11 @@ class EnergySum(Energy):
         eout = []
         fout = []
         EnergySum._unpackEnergies(energies, factors, 1., eout, fout)
-        for e in eout[1:]:
-            if not e.position.isEquivalentTo(eout[0].position):
-                raise ValueError("position mismatch")
+        # FIXME we need to reach an agreement about how we want to deal
+        # with domain and field compatibility. Until then, we relax te check.
+        # for e in eout[1:]:
+        #     if not e.position.isEquivalentTo(eout[0].position):
+        #         raise ValueError("position mismatch")
         return EnergySum(eout[0].position, eout, fout)
 
     @staticmethod

test/test_energies/test_energy_sum.py

@@ -53,5 +53,5 @@ class EnergySum_Tests(unittest.TestCase):
             E1+2
         with assert_raises(TypeError):
             E1-"hello"
-        with assert_raises(ValueError):
-            E1+E2.at(2*pos)
+        # with assert_raises(ValueError):
+        #     E1+E2.at(2*pos)