Calculate hist properties with new stats info

Comparisons are made to ROOT to ensure that they're done properly

pachyderm/histogram.py

@@ -236,6 +236,67 @@ def x(self) -> np.ndarray:
 
         return self._x
 
+    @property
+    def mean(self) -> float:
+        """ Mean of values filled into the histogram.
+
+        Calculated in the same way as ROOT and physt.
+
+        Args:
+            None.
+        Returns:
+            Mean of the histogram.
+        """
+        # Validation
+        if "total_sum_wx" not in self.metadata:
+            raise ValueError("Sum of weights * x is not available, so we cannot calculate the mean.")
+        # Calculate the mean.
+        total_sum_w = np.sum(self.y)
+        if total_sum_w > 0:
+            return cast(float, self.metadata["total_sum_wx"] / total_sum_w)
+        # Can't divide, so return NaN
+        return np.nan  # type: ignore
+
+    @property
+    def std_dev(self) -> float:
+        """ Standard deviation of the values filled into the histogram.
+
+        Calculated in the same way as ROOT and physt.
+
+        Args:
+            None.
+        Returns:
+            Standard deviation of the histogram.
+        """
+        return cast(float, np.sqrt(self.variance))
+
+    @property
+    def variance(self) -> float:
+        """ Variance of the values filled into the histogram.
+
+        Calculated in the same way as ROOT and physt.
+
+        Args:
+            None.
+        Returns:
+            Variance of the histogram.
+        """
+        # Validation
+        if "total_sum_wx" not in self.metadata:
+            raise ValueError("Sum of weights * x is not available, so we cannot calculate the variance.")
+        # Validation
+        if "total_sum_wx2" not in self.metadata:
+            raise ValueError("Sum of weights * x * x is not available, so we cannot calculate the variance.")
+        # Calculate the variance.
+        total_sum_w = np.sum(self.y)
+        if total_sum_w > 0:
+            return cast(
+                float,
+                (self.metadata["total_sum_wx2"] - (self.metadata["total_sum_wx"] ** 2 / total_sum_w)) / total_sum_w
+            )
+        # Can't divide, so return NaN
+        return np.nan  # type: ignore
+
     def find_bin(self, value: float) -> int:
         """ Find the bin corresponding to the specified value.
 

tests/test_histogram.py

@@ -298,6 +298,19 @@ def test_uproot_hist_to_histogram(setup_histogram_conversion: Any) -> None:
     # Cleanup
     del uproot_file
 
+@pytest.mark.ROOT  # type: ignore
+def test_derived_properties(logging_mixin: Any, test_root_hists: Any) -> None:
+    """ Test derived histogram properties (mean, std. dev, variance, etc). """
+    h_root = test_root_hists.hist1D
+    h = histogram.Histogram1D.from_existing_hist(h_root)
+
+    # Mean
+    assert np.isclose(h.mean, h_root.GetMean())
+    # Standard deviation
+    assert np.isclose(h.std_dev, h_root.GetStdDev())
+    # Variance
+    assert np.isclose(h.variance, h_root.GetStdDev() ** 2)
+
 @pytest.mark.parametrize("bin_edges, y, errors_squared", [  # type: ignore
     (np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2, 3])),
     (np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2])),