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/_functional_data.py

@@ -882,6 +882,7 @@ def mean(
         out: None = None,
         keepdims: bool = False,
         skipna: bool = False,
+        min_count: int = 0,
     ) -> T:
         """Compute the mean of all the samples.
 
@@ -891,6 +892,9 @@ def mean(
             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: Number of valid (non NaN) data to have in order
+                for the a variable to not be NaN when `skipna` is
+                `True`.
 
         Returns:
             A FData object with just one sample representing
@@ -902,10 +906,7 @@ def mean(
                 "Not implemented for that parameter combination",
             )
 
-        return (
-            self.sum(axis=axis, out=out, keepdims=keepdims, skipna=skipna)
-            / self.n_samples
-        )
+        return self
 
     @abstractmethod
     def to_grid(

skfda/representation/basis/_fdatabasis.py

@@ -427,20 +427,61 @@ 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.nansum(self.coefficients, axis=0) if skipna
-            else np.sum(self.coefficients, axis=0)
+            np.nansum(valid_coefficients, axis=0) if skipna 
+            else np.sum(valid_coefficients, axis=0)
         )
-
-        if min_count > 0:
-            valid = ~np.isnan(self.coefficients)
-            n_valid = np.sum(valid, axis=0)
-            coefs[n_valid < min_count] = np.nan
+
+        coefs = np.where(valid_counts >= min_count, coefs, np.nan)
 
         return self.copy(
             coefficients=coefs,
             sample_names=(None,),
         )
+
+    def mean(  # noqa: WPS125
+        self: T,
+        *,
+        axis: Optional[int] = None,
+        dtype: None = None,
+        out: None = None,
+        keepdims: bool = False,
+        skipna: bool = False,
+        min_count: int = 0,
+    ) -> T:
+        """Compute the mean of all the samples.
+
+        Args:
+            axis: Used for compatibility with numpy. Must be None or 0.
+            dtype: Used for compatibility with numpy. Must be None.
+            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: 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
+            the mean of all the samples in the original object.
+        """
+        super().mean(axis=axis, dtype=dtype, out=out, keepdims=keepdims, 
+                     skipna=skipna)
+
+        return (
+            self.sum(
+                axis=axis,
+                out=out,
+                keepdims=keepdims, 
+                skipna=skipna,
+                min_count=min_count,
+            )
+            / np.sum(~self.isna()),
+        )
 
     def var(
         self: T,
@@ -998,7 +1039,7 @@ def isna(self) -> NDArrayBool:
         Returns:
             na_values (np.ndarray): Positions of NA.
         """
-        return np.all(  # type: ignore[no-any-return]
+        return np.any(  # type: ignore[no-any-return]
             np.isnan(self.coefficients),
             axis=1,
         )

skfda/representation/grid.py

@@ -544,6 +544,60 @@ def _get_points_and_values(self: T) -> Tuple[NDArrayFloat, NDArrayFloat]:
 
     def _get_input_points(self: T) -> GridPoints:
         return self.grid_points
+
+    def _compute_aggregate(
+        self: T,
+        operation: str,
+        *,
+        skipna: bool = False,
+        min_count: int = 0,
+    ) -> T:
+        """Compute a defined aggregation operation of the samples.
+
+        Args:
+            operation: Operation to be performed. Can be 'mean', 'sum' or 
+                'var'.
+            axis: Used for compatibility with numpy. Must be None or 0.
+            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: Number of valid (non NaN) data to have in order
+                for the a variable to not be NaN when `skipna` is
+                `True`.
+
+        Returns:
+            An FDataGrid object with just one sample representing
+            the aggregation of all the samples in the original object.
+
+        """
+        if operation not in {'sum', 'mean', 'var'}:
+            raise ValueError("Invalid operation."
+                             "Must be one of 'sum', 'mean', or 'var'.")
+
+        if skipna:
+            agg_func = {
+                'sum': np.nansum, 
+                'mean': np.nanmean, 
+                'var': np.nanvar
+            }[operation]
+        else:
+            agg_func = {
+                'sum': np.sum, 
+                'mean': np.mean, 
+                'var': np.var
+            }[operation]
+
+        data = agg_func(self.data_matrix, axis=0, keepdims=True)
+
+        if min_count > 0 and skipna:
+            valid = ~np.isnan(self.data_matrix)
+            n_valid = np.sum(valid, axis=0)
+            data[n_valid < min_count] = np.nan
+
+        return self.copy(
+            data_matrix=data,
+            sample_names=(None,),
+        )
 
     def sum(  # noqa: WPS125
         self: T,
@@ -583,19 +637,50 @@ def sum(  # noqa: WPS125
         """
         super().sum(axis=axis, out=out, keepdims=keepdims, skipna=skipna)
 
-        data = (
-            np.nansum(self.data_matrix, axis=0, keepdims=True) if skipna
-            else np.sum(self.data_matrix, axis=0, keepdims=True)
+        return self._compute_aggregate(
+            operation='sum',
+            skipna=skipna,
+            min_count=min_count,
         )
 
-        if min_count > 0:
-            valid = ~np.isnan(self.data_matrix)
-            n_valid = np.sum(valid, axis=0)
-            data[n_valid < min_count] = np.nan
+    def mean(  # noqa: WPS125
+        self: T,
+        *,
+        axis: Optional[int] = None,
+        dtype: None = None,
+        out: None = None,
+        keepdims: bool = False,
+        skipna: bool = False,
+        min_count: int = 0,
+    ) -> T:
+        """Compute the mean of all the samples.
 
-        return self.copy(
-            data_matrix=data,
-            sample_names=(None,),
+        Args:
+            axis: Used for compatibility with numpy. Must be None or 0.
+            dtype: Used for compatibility with numpy. Must be None.
+            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: Number of valid (non NaN) data to have in order
+                for the a variable to not be NaN when `skipna` is
+                `True`.
+
+        Returns:
+            A FDataGrid object with just one sample representing
+            the mean of all the samples in the original object.
+        """
+        super().mean(
+            axis=axis,
+            dtype=dtype,
+            out=out,
+            keepdims=keepdims,
+            skipna=skipna,
+        )
+
+        return self._compute_aggregate(
+            operation='mean',
+            skipna=skipna,
+            min_count=min_count,
         )
 
     def var(self: T, correction: int = 0) -> T:

skfda/representation/irregular.py

@@ -716,6 +716,65 @@ def sum(  # noqa: WPS125
             values=sum_values,
             sample_names=(None,),
         )
+
+    def mean(  # noqa: WPS125
+        self: T,
+        *,
+        axis: Optional[int] = None,
+        dtype: None = None,
+        out: None = None,
+        keepdims: bool = False,
+        skipna: bool = False,
+        min_count: int = 0,
+    ) -> T:
+        """Compute the mean of all the samples.
+
+        Args:
+            axis: Used for compatibility with numpy. Must be None or 0.
+            dtype: Used for compatibility with numpy. Must be None.
+            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: Number of valid (non NaN) data to have in order
+                for the a variable to not be NaN when `skipna` is
+                `True`.
+
+        Returns:
+            An FDataIrregular object with just one sample representing
+            the mean of all the samples in the original object.
+        """
+        super().mean(
+            axis=axis,
+            dtype=dtype,
+            out=out,
+            keepdims=keepdims,
+            skipna=skipna,
+        )
+
+        common_points, common_values = self._get_common_points_and_values()
+
+        if len(common_points) == 0:
+            raise ValueError("No common points in FDataIrregular object")
+
+        sum_function = np.nansum if skipna else np.sum
+        sum_values = sum_function(common_values, axis=0)
+
+        if skipna:
+            count_values = np.sum(~np.isnan(common_values), axis=0)
+        else:
+            count_values = self.n_samples
+
+        if min_count > 0 and skipna:
+            count_values[count_values < min_count] = np.nan
+
+        mean_values = sum_values / count_values
+
+        return FDataIrregular(
+            start_indices=np.array([0]),
+            points=common_points,
+            values=mean_values,
+            sample_names=(None,),
+        )
 
     def var(self: T, correction: int = 0) -> T:
         """Compute the variance of all the samples.