update properties when update method is used

pesummary/core/plots/plot.py

@@ -490,8 +490,8 @@ def _1d_histogram_plot(
     elif ax is None:
         ax = fig.gca()
 
-    if len(set(samples)) <= max_vline:
-        for _ind, _sample in enumerate(set(samples)):
+    if len(set(samples.to_numpy())) <= max_vline:
+        for _ind, _sample in enumerate(set(samples.to_numpy())):
             _label = None
             if _ind == 0:
                 _label = label
@@ -531,7 +531,6 @@ def _1d_histogram_plot(
                     prior, color=conf.prior_color, ax=ax, linestyle=linestyle,
                     **kwargs
                 )
-
     if set_labels:
         ax.set_xlabel(latex_label)
         ax.set_ylabel("Probability Density")

pesummary/tests/utils_test.py

@@ -325,7 +325,24 @@ class TestSamplesDict(object):
         assert dataset.mean["a"] == np.mean(self.samples[0])
         assert dataset.mean["b"] == np.mean(self.samples[1])
         assert dataset.number_of_samples == len(self.samples[1])
-        assert len(dataset.downsample(10)["a"]) == 10
+        p = dataset.to_pandas()
+        assert isinstance(p, pd.core.frame.DataFrame)
+
+    def test_downsample_and_discard(self):
+        """Test that the downsample and discard methods of the SamplesDict class
+        are correct
+        """
+        dataset = SamplesDict(self.parameters, self.samples)
+        np.testing.assert_almost_equal(
+            dataset["a"].confidence_interval([5, 95]),
+            np.percentile(dataset["a"], [5, 95])
+        )
+        dataset.downsample(10)
+        assert len(dataset["a"]) == 10
+        np.testing.assert_almost_equal(
+            dataset["a"].confidence_interval([5, 95]),
+            np.percentile(dataset["a"], [5, 95])
+        )
         dataset = SamplesDict(self.parameters, self.samples)
         assert len(dataset.discard_samples(10)["a"]) == len(self.samples[0]) - 10
         np.testing.assert_almost_equal(
@@ -334,8 +351,6 @@ class TestSamplesDict(object):
         np.testing.assert_almost_equal(
             dataset.discard_samples(10)["b"], self.samples[1][10:]
         )
-        p = dataset.to_pandas()
-        assert isinstance(p, pd.core.frame.DataFrame)
         remove = dataset.pop("a")
         assert list(dataset.keys()) == ["b"]
 

pesummary/utils/samples_dict.py

@@ -133,7 +133,10 @@ class SamplesDict(Dict):
             _value = Array(value)
         super(SamplesDict, self).__setitem__(key, _value)
         try:
-            if key not in self.parameters:
+            if key in self.parameters:
+                ind = self.parameters.index(key)
+                self.samples[ind] = value
+            else:
                 self.parameters.append(key)
                 try:
                     cond = (
@@ -300,6 +303,14 @@ class SamplesDict(Dict):
             return Parameters([key for key in original if key[0] != "_"])
         return Parameters(original)
 
+    def update(self, *args, **kwargs):
+        super(SamplesDict, self).update(*args, **kwargs)
+        try:
+            for key, item in self.items():
+                self.__setitem__(key, item)
+        except ValueError:
+            self = self.__init__(self)
+
     def write(self, **kwargs):
         """Save the stored posterior samples to file