Merge branch 'finalise-v0.13.10' into 'release/0.13.x'

Update 0.13.x release branch for 0.13.10

See merge request !724

pesummary/core/file/meta_file.py

@@ -5,7 +5,8 @@ import os
 import numpy as np
 import json
 import copy
-
+from getpass import getuser
+import pandas as pd
 import pesummary
 from pesummary import __version__
 from pesummary.core.inputs import PostProcessing
@@ -47,6 +48,8 @@ def recursively_save_dictionary_to_hdf5_file(
         current_path = []
 
     for k, v in dictionary.items():
+        if isinstance(v, pd.DataFrame):
+            v = v.to_dict(orient="list")
         if isinstance(v, dict):
             if k not in f["/" + "/".join(current_path)].keys():
                 f["/".join(current_path)].create_group(k)

pesummary/gw/p_astro.py

@@ -67,8 +67,10 @@ class PAstro(object):
                 "because not all required parameters have been provided."
             )
         M_rem = computeDiskMass(
-            samples["mass_1_source"], samples["mass_2_source"], samples["a_1"],
-            samples["a_2"]
+            samples["mass_1_source"].to_numpy(),
+            samples["mass_2_source"].to_numpy(),
+            samples["a_1"].to_numpy(),
+            samples["a_2"].to_numpy()
         )
         prediction_ns = float(
             np.sum(samples["mass_2_source"] <= 3.0) / len(samples["mass_2_source"])

pesummary/utils/array.py

@@ -261,3 +261,18 @@ class Array(np.ndarray):
         self.maxP = getattr(obj, 'maxP', None)
         self.weights = getattr(obj, 'weights', None)
         self.key_data = getattr(obj, 'key_data', None)
+
+    def to_numpy(self):
+        """Convert Array object to a numpy.ndarray
+
+        Returns
+        -------
+        data: np.ndarray/tuple
+            return stored data as a np.ndarray. If weights are stored in the
+            Array object, return a tuple containing the stored data as a
+            np.ndarray and the weights as a np.ndarray
+        """
+        _array = np.asarray(self)
+        if self.weights is None:
+            return _array
+        return _array, np.asarray(self.weights)