test_sky_models: skip polarisation consistency check

test/test_sky_models.py

@@ -37,30 +37,48 @@ from .common import setup_function, teardown_function
 pmp = pytest.mark.parametrize
 np.seterr(all="raise")
 
-obs = rve.ms2observations("/data/CYG-D-6680-64CH-10S.ms", "DATA", True, 0, polarizations="all")
+obs = rve.ms2observations(
+    "/data/CYG-D-6680-64CH-10S.ms", "DATA", True, 0, polarizations="all"
+)
+
 
 class StokesAdder(jft.Model):
     def __init__(self, correlated_field_dict):
         self.cfs = correlated_field_dict
-        
-        super().__init__(init=reduce(operator.or_,
-                                     [value.init for value in self.cfs.values()]))
+
+        super().__init__(
+            init=reduce(operator.or_, [value.init for value in self.cfs.values()])
+        )
 
     def __call__(self, x):
         def get_stokes(pre_stokes):
-            pol_int = jnp.sqrt(sum(pre_stokes[i]**2 for i in range(1,4)))
-            return jnp.concatenate([jnp.exp(pre_stokes[:1])*jnp.cosh(pol_int),
-                                    (jnp.exp(pre_stokes[:1])*jnp.sinh(pol_int)/pol_int)*pre_stokes[1:]])
+            pol_int = jnp.sqrt(sum(pre_stokes[i] ** 2 for i in range(1, 4)))
+            return jnp.concatenate(
+                [
+                    jnp.exp(pre_stokes[:1]) * jnp.cosh(pol_int),
+                    (jnp.exp(pre_stokes[:1]) * jnp.sinh(pol_int) / pol_int)
+                    * pre_stokes[1:],
+                ]
+            )
 
         pre_stokes = jnp.stack([cf(x) for cf in self.cfs.values()])
         dims_remaining = pre_stokes.shape[1:]
-        pre_stokes = pre_stokes.reshape((4,-1))
+        pre_stokes = pre_stokes.reshape((4, -1))
 
         stokes = jax.vmap(get_stokes, in_axes=1, out_axes=-1)(pre_stokes)
         return stokes.reshape((4,) + dims_remaining)
 
-@pmp("fname", ["cfg/cygnusa.cfg", "cfg/cygnusa_polarization.cfg", "cfg/mf.cfg",
-               "cfg/cygnusa_mf.cfg", "cfg/cygnusa_mf_cfm.cfg"])
+
+@pmp(
+    "fname",
+    [
+        "cfg/cygnusa.cfg",
+        "cfg/cygnusa_polarization.cfg",
+        "cfg/mf.cfg",
+        "cfg/cygnusa_mf.cfg",
+        "cfg/cygnusa_mf_cfm.cfg",
+    ],
+)
 def test_build_multi_frequency_skymodel(fname):
     tmp = TemporaryDirectory()
     direc = tmp.name
@@ -69,7 +87,7 @@ def test_build_multi_frequency_skymodel(fname):
     op, _ = rve.sky_model_diffuse(cfg["sky"], obs)
     out = op(ift.from_random(op.domain))
 
-    if not fname == "cfg/cygnusa_mf_cfm.cfg": # FIXME: overflow in float32 conversion
+    if not fname == "cfg/cygnusa_mf_cfm.cfg":  # FIXME: overflow in float32 conversion
         rve.ubik_tools.field2fits(out, join(direc, "tmp.fits"))
 
     key1 = op.domain.keys()
@@ -82,35 +100,56 @@ def test_build_multi_frequency_skymodel(fname):
         key2 = op.domain.keys()
         assert len(set(key1) & set(key2)) == 0
 
+
+@pytest.mark.skip(reason="pol_test.cfg not on funk")
 def test_jax_skymodel_consistency():
     cfg = configparser.ConfigParser()
-    cfg.read('pol_test.cfg')
+    cfg.read("pol_test.cfg")
 
-    diffuse, additional = rve.sky_model_diffuse(cfg['sky'])
+    diffuse, additional = rve.sky_model_diffuse(cfg["sky"])
     pols_dict = dict()
 
-    for pol_lbl in ('i', 'q', 'u', 'v'):
+    for pol_lbl in ("i", "q", "u", "v"):
         pols_dict |= {pol_lbl: dict()}
-        for key in cfg['sky'].keys():
-            if 'stokes'+pol_lbl in key:
-                pols_dict[pol_lbl] |= {key.removeprefix('stokes'+pol_lbl+' diffuse space i0 '): cfg['sky'][key]}
+        for key in cfg["sky"].keys():
+            if "stokes" + pol_lbl in key:
+                pols_dict[pol_lbl] |= {
+                    key.removeprefix("stokes" + pol_lbl + " diffuse space i0 "): cfg[
+                        "sky"
+                    ][key]
+                }
 
-    sky_shape = (int(cfg['sky']['space npix x']),int(cfg['sky']['space npix y']))
-    distances = tuple(1/npix for npix in sky_shape)
+    sky_shape = (int(cfg["sky"]["space npix x"]), int(cfg["sky"]["space npix y"]))
+    distances = tuple(1 / npix for npix in sky_shape)
 
     correlated_field_dict = dict()
 
     for key, val in pols_dict.items():
-        cfm = jft.CorrelatedFieldMaker('stokes'+key.upper()+' diffuse')
-        cfm.set_amplitude_total_offset(offset_mean=float(val['zero mode offset']), offset_std=(float(val['zero mode mean']), float(val['zero mode stddev'])))
-        cfm.add_fluctuations(sky_shape,
-                             distances=distances,
-                             asperity=(float(val['asperity mean']), float(val['asperity stddev'])),
-                             loglogavgslope=(float(val['loglogavgslope mean']), float(val['loglogavgslope stddev'])),
-                             flexibility=(float(val['flexibility mean']), float(val['flexibility stddev'])),
-                             fluctuations=(float(val['fluctuations mean']), float(val['fluctuations stddev'])),
-                            prefix='space i0',non_parametric_kind="power")
-        correlated_field_dict |= {'prestokes '+key: cfm.finalize()}
+        cfm = jft.CorrelatedFieldMaker("stokes" + key.upper() + " diffuse")
+        cfm.set_amplitude_total_offset(
+            offset_mean=float(val["zero mode offset"]),
+            offset_std=(float(val["zero mode mean"]), float(val["zero mode stddev"])),
+        )
+        cfm.add_fluctuations(
+            sky_shape,
+            distances=distances,
+            asperity=(float(val["asperity mean"]), float(val["asperity stddev"])),
+            loglogavgslope=(
+                float(val["loglogavgslope mean"]),
+                float(val["loglogavgslope stddev"]),
+            ),
+            flexibility=(
+                float(val["flexibility mean"]),
+                float(val["flexibility stddev"]),
+            ),
+            fluctuations=(
+                float(val["fluctuations mean"]),
+                float(val["fluctuations stddev"]),
+            ),
+            prefix="space i0",
+            non_parametric_kind="power",
+        )
+        correlated_field_dict |= {"prestokes " + key: cfm.finalize()}
 
     stokes_sky = StokesAdder(correlated_field_dict)
 
@@ -118,10 +157,10 @@ def test_jax_skymodel_consistency():
     re_pos_init = pos_init.val
 
     for key in re_pos_init.keys():
-        if 'spectrum' in key:
+        if "spectrum" in key:
             re_pos_init[key] = re_pos_init[key].transpose()
 
-    diffuse_field = diffuse(pos_init).val.reshape((4,)+sky_shape)
+    diffuse_field = diffuse(pos_init).val.reshape((4,) + sky_shape)
     stokes_field = stokes_sky(re_pos_init)
 
-    np.testing.assert_allclose(diffuse_field, stokes)
+    np.testing.assert_allclose(diffuse_field, stokes_field)
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