allow from_pymc3 args to override model values

CHANGELOG.md

@@ -6,8 +6,7 @@
 * loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The HDI is computed analitically (#1215)
 * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217)
 * Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219)
-* `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228
-  and #1240)
+* `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228, #1240 and #1249)
 
 ### Maintenance and fixes
 * Include data from `MultiObservedRV` to `observed_data` when using

arviz/data/io_pymc3.py

@@ -147,13 +147,14 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray:
             aelem = arbitrary_element(get_from)
             self.ndraws = aelem.shape[0]
 
-        self.coords = coords
-        if coords is None and hasattr(self.model, "coords"):
-            self.coords = self.model.coords
-
-        self.dims = dims
-        if dims is None and hasattr(self.model, "RV_dims"):
-            self.dims = {k: list(v) for k, v in self.model.RV_dims.items()}
+        self.coords = {} if coords is None else coords
+        if hasattr(self.model, "coords"):
+            self.coords = {**self.model.coords, **self.coords}
+
+        self.dims = {} if dims is None else dims
+        if hasattr(self.model, "RV_dims"):
+            model_dims = {k: list(v) for k, v in self.model.RV_dims.items()}
+            self.dims = {**model_dims, **self.dims}
 
         self.observations, self.multi_observations = self.find_observations()
 

arviz/tests/external_tests/test_data_pymc.py

@@ -244,6 +244,33 @@ def test_autodetect_coords_from_model(self, use_context):
         np.testing.assert_array_equal(idata.observed_data.coords["date"], coords["date"])
         np.testing.assert_array_equal(idata.observed_data.coords["city"], coords["city"])
 
+    def test_ovewrite_model_coords_dims(self):
+        """Check coords and dims from model object can be partially overwrited."""
+        dim1 = ["a", "b"]
+        new_dim1 = ["c", "d"]
+        coords = {"dim1": dim1, "dim2": ["c1", "c2"]}
+        x_data = np.arange(4).reshape((2, 2))
+        y = x_data + np.random.normal(size=(2, 2))
+        with pm.Model(coords=coords):
+            x = pm.Data("x", x_data, dims=("dim1", "dim2"))
+            beta = pm.Normal("beta", 0, 1, dims="dim1")
+            _ = pm.Normal("obs", x * beta, 1, observed=y, dims=("dim1", "dim2"))
+            trace = pm.sample(100, tune=100)
+            idata1 = from_pymc3(trace)
+            idata2 = from_pymc3(trace, coords={"dim1": new_dim1}, dims={"beta": ["dim2"]})
+
+        test_dict = {"posterior": ["beta"], "observed_data": ["obs"], "constant_data": ["x"]}
+        fails1 = check_multiple_attrs(test_dict, idata1)
+        assert not fails1
+        fails2 = check_multiple_attrs(test_dict, idata2)
+        assert not fails2
+        assert "dim1" in list(idata1.posterior.beta.dims)
+        assert "dim2" in list(idata2.posterior.beta.dims)
+        assert np.all(idata1.constant_data.x.dim1.values == np.array(dim1))
+        assert np.all(idata1.constant_data.x.dim2.values == np.array(["c1", "c2"]))
+        assert np.all(idata2.constant_data.x.dim1.values == np.array(new_dim1))
+        assert np.all(idata2.constant_data.x.dim2.values == np.array(["c1", "c2"]))
+
     def test_missing_data_model(self):
         # source pymc3/pymc3/tests/test_missing.py
         data = ma.masked_values([1, 2, -1, 4, -1], value=-1)