Update mean and sum functions

.all-contributorsrc

@@ -595,15 +595,6 @@
       "contributions": [
         "doc"
       ]
-    },
-    {
-      "login": "E105D104U125",
-      "name": "E69D68U85",
-      "avatar_url": "https://avatars.githubusercontent.com/u/72515278?v=4",
-      "profile": "https://github.com/E105D104U125",
-      "contributions": [
-        "code"
-      ]
     }
   ],
   "contributorsPerLine": 7,

CONTRIBUTORS.md

@@ -1,6 +1,6 @@
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-52-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-51-orange.svg?style=flat-square)](#contributors-)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 ## Contributors ✨
 
@@ -76,7 +76,6 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/psotom"><img src="https://avatars.githubusercontent.com/u/166627986?v=4?s=100" width="100px;" alt="psotom"/><br /><sub><b>psotom</b></sub></a><br /><a href="https://github.com/GAA-UAM/scikit-fda/commits?author=psotom" title="Code">💻</a> <a href="#example-psotom" title="Examples">💡</a> <a href="#ideas-psotom" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/GAA-UAM/scikit-fda/commits?author=psotom" title="Tests">⚠️</a> <a href="#research-psotom" title="Research">🔬</a> <a href="https://github.com/GAA-UAM/scikit-fda/commits?author=psotom" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/luisheb"><img src="https://avatars.githubusercontent.com/u/24703335?v=4?s=100" width="100px;" alt="Luis Hebrero"/><br /><sub><b>Luis Hebrero</b></sub></a><br /><a href="https://github.com/GAA-UAM/scikit-fda/commits?author=luisheb" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="14.28%"><a href="https://github.com/E105D104U125"><img src="https://avatars.githubusercontent.com/u/72515278?v=4?s=100" width="100px;" alt="E69D68U85"/><br /><sub><b>E69D68U85</b></sub></a><br /><a href="https://github.com/GAA-UAM/scikit-fda/commits?author=E105D104U125" title="Code">💻</a></td>
     </tr>
   </tbody>
   <tfoot>

skfda/representation/basis/_fdatabasis.py

@@ -428,9 +428,15 @@ def sum(  # noqa: WPS125
         super().sum(axis=axis, out=out, keepdims=keepdims, skipna=skipna)
 
         valid_functions = ~self.isna()
+        valid_counts = np.sum(valid_functions, axis=0)
         valid_coefficients = self.coefficients[valid_functions]
 
-        coefs = np.sum(valid_coefficients, axis=0)
+        coefs = (
+            np.nansum(valid_coefficients, axis=0) if skipna 
+            else np.sum(valid_coefficients, axis=0)
+        )
+
+        coefs = np.where(valid_counts >= min_count, coefs, np.nan)
 
         return self.copy(
             coefficients=coefs,
@@ -455,8 +461,9 @@ def mean(  # noqa: WPS125
             out: Used for compatibility with numpy. Must be None.
             keepdims: Used for compatibility with numpy. Must be False.
             skipna: Wether the NaNs are ignored or not.
-            min_count: Ignored, used for compatibility with FDataGrid
-                and FDataIrregular.
+            min_count: Number of valid (non NaN) data to have in order
+                for the a variable to not be NaN when `skipna` is
+                `True`.
 
         Returns:
             A FDataBasis object with just one sample representing
@@ -471,6 +478,7 @@ def mean(  # noqa: WPS125
                 out=out,
                 keepdims=keepdims, 
                 skipna=skipna,
+                min_count=min_count,
             )
             / np.sum(~self.isna()),
         )

skfda/representation/irregular.py

@@ -762,7 +762,7 @@ def mean(  # noqa: WPS125
         if skipna:
             count_values = np.sum(~np.isnan(common_values), axis=0)
         else:
-            count_values = np.full(sum_values.shape, self.n_samples)
+            count_values = self.n_samples
 
         if min_count > 0 and skipna:
             count_values[count_values < min_count] = np.nan