Docs: Add links to external documentations

epyt_flow/data/benchmarks/batadal.py

@@ -91,7 +91,7 @@ def load_data(download_dir: str = None, return_X_y: bool = False,
     `dict`
         Dictionary of the loaded benchmark data. The dictionary contains the two training
         data sets ("train_1" and "train_2"), as well as the test data set ("test").
-        If `return_X_y` is False, each dictionary entry is a Pandas dataframe.
+        If `return_X_y` is False, each dictionary entry is a `Pandas dataframe <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_.
         Otherwise, it is a tuple of sensor readings and labels (except for the test set) --
         if `return_ground_truth` is True or `return_features_desc` is True, the corresponding
         data is appended to the tuple.

epyt_flow/data/benchmarks/battledim.py

@@ -286,7 +286,7 @@ def load_data(return_test_scenario: bool, download_dir: str = None, return_X_y:
 
     Returns
     -------
-    Either a `pandas.DataFrame` instance or a tuple of Numpy arrays.
+    Either a `pandas.DataFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ instance or a tuple of `Numpy arrays <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_.
         Benchmark data set.
     """
     # Download data files if necessary
@@ -389,8 +389,9 @@ def load_scada_data(return_test_scenario: bool, download_dir: str = None,
     :class:`~epyt_flow.simulation.scada.scada_data.ScadaData` or `list[tuple[numpy.ndarray, numpy.ndarray]]`
         The simulated benchmark scenario as either a
         :class:`~epyt_flow.simulation.scada.scada_data.ScadaData` instance or as a tuple of
-        (X, y) Numpy arrays. If 'return_leak_locations' is True, the leak locations are included
-        as an instance of `scipy.sparse.bsr_array` as well.
+        (X, y) `Numpy arrays <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_.
+        If 'return_leak_locations' is True, the leak locations are included
+        as an instance of `scipy.sparse.bsr_array <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.bsr_array.html>`_ as well.
     """
     download_dir = download_dir if download_dir is not None else get_temp_folder()
 

epyt_flow/data/benchmarks/gecco_water_quality.py

@@ -31,9 +31,9 @@ def compute_evaluation_score(y_pred: np.ndarray, y: np.ndarray) -> float:
 
     Parameters
     ----------
-    y_pred : `numpy.ndarray`
+    y_pred : `numpy.ndarray <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_
         Event indication prediction over time
-    y : `numpy.ndarray`
+    y : `numpy.ndarray <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_
         Ground truth event indication over time.
 
     Returns
@@ -85,7 +85,7 @@ def load_gecco2017_water_quality_data(download_dir: str = None, return_X_y: bool
 
     Returns
     -------
-    `pandas.DataFrame` or `tuple[numpy.ndarray, numpy.ndarray]`
+    `pandas.DataFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ or `tuple[numpy.ndarray, numpy.ndarray] <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_
         The benchmark data set as either a Pandas data frame or as a pair of (X, y) Numpy arrays.
     """
     url_data = "https://zenodo.org/records/3884465/files/1_gecco2017_water_quality.csv?download=1"
@@ -154,7 +154,7 @@ def load_gecco2018_water_quality_data(download_dir: str = None, return_X_y: bool
 
     Returns
     -------
-    `pandas.DataFrame` or `tuple[numpy.ndarray, numpy.ndarray]`
+    `pandas.DataFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ or `tuple[numpy.ndarray, numpy.ndarray] <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_
         The benchmark data set as either a Pandas data frame or as a pair of (X, y) Numpy arrays.
     """
     # Download data if necessary

epyt_flow/data/benchmarks/leakdb.py

@@ -97,7 +97,7 @@ def compute_evaluation_score(scenarios_id: list[int], use_net1: bool,
         List of scenarios ID that are to be evaluated -- there is a total number of 1000 scenarios.
     use_net1 : `bool`
         If True, Net1 LeakDB will be used for evaluation, otherwise the Hanoi LeakDB will be used.
-    y_pred_labels_per_scenario : `list[numpy.ndarray]`
+    y_pred_labels_per_scenario : `list[numpy.ndarray] <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_
         Predicted binary labels (over time) for each scenario in `scenarios_id`.
 
     Returns
@@ -201,7 +201,7 @@ def load_data(scenarios_id: list[int], use_net1: bool, download_dir: str = None,
         The default is False.
     return_leak_locations : `bool`
         If True and if `return_X_y` is True, the leak locations are returned as well --
-        as an instance of `scipy.sparse.bsr_array`.
+        as an instance of `scipy.sparse.bsr_array <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.bsr_array.html>`_.
 
         The default is False.
     verbose : `bool`, optional
@@ -327,7 +327,7 @@ def load_scada_data(scenarios_id: list[int], use_net1: bool = True, download_dir
         The default is False.
     return_leak_locations : `bool`
         If True, the leak locations are returned as well --
-        as an instance of `scipy.sparse.bsr_array`.
+        as an instance of `scipy.sparse.bsr_array <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.bsr_array.html>`_.
 
         The default is False.
     verbose : `bool`, optional
@@ -341,7 +341,7 @@ def load_scada_data(scenarios_id: list[int], use_net1: bool = True, download_dir
         The simulated benchmark scenarios as either a list of
         :class:`~epyt_flow.simulation.scada.scada_data.ScadaData` instances or as a list of
         (X, y) Numpy arrays. If 'return_leak_locations' is True, the leak locations are included
-        as an instance of `scipy.sparse.bsr_array` as well.
+        as an instance of `scipy.sparse.bsr_array <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.bsr_array.html>`_ as well.
     """
     download_dir = download_dir if download_dir is not None else get_temp_folder()
 

epyt_flow/data/benchmarks/water_usage.py

@@ -22,9 +22,9 @@ def compute_evaluation_score(y_pred: np.ndarray, y: np.ndarray) -> dict:
 
     Parameters
     ----------
-    y_pred : `numpy.ndarray`
+    y_pred : `numpy.ndarray <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_
         Event indication prediction over time
-    y : `numpy.ndarray`
+    y : `numpy.ndarray <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`_
         Ground truth event indication over time.
 
     Returns