change to readthedocs documentation file to test how build works

pyrates-neuroscience · Feb 6, 2023 · 2115a69 · 2115a69
1 parent b828ab0
commit 2115a69
Show file tree

Hide file tree

Showing 3 changed files with 6 additions and 10 deletions.
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -8,6 +8,8 @@ Changelog
 -----
 
 - added a background input parameter to the izhikevich population template
+- updated the documentation example for parameter sweeps to account for recent changes in the keyword arguments to the `grid_search` function
+- changed keyword argument `vectorization` of the function `grid_search` to `vectorize`, to be consistent with the naming of the same argument in `CircuitTemplate.run`
 
 1.0.0
 -----

diff --git a/documentation/model_analysis/parameter_sweeps.py b/documentation/model_analysis/parameter_sweeps.py
@@ -84,7 +84,8 @@
                                    sampling_step_size=dts,
                                    inputs={'jrc/jrc_op/u': noise},
                                    outputs={'V_pce': 'jrc/jrc_op/V_e', 'V_pci': 'jrc/jrc_op/V_i'},
-                                   init_kwargs={'backend': 'default', 'solver': 'euler'},
+                                   backend="default",
+                                   solver="euler",
                                    cutoff=cutoff
                                    )
 

diff --git a/pyrates/utility.py b/pyrates/utility.py
@@ -226,7 +226,7 @@ def grid_search(circuit_template: Union[CircuitTemplate, str], param_grid: Union
     # argument pre-processing
     #########################
 
-    vectorization = kwargs.pop('vectorization', True)
+    vectorize = kwargs.pop('vectorize', True)
 
     # linearize parameter grid if necessary
     if type(param_grid) is dict:
@@ -268,16 +268,9 @@ def grid_search(circuit_template: Union[CircuitTemplate, str], param_grid: Union
                           sampling_step_size=sampling_step_size,
                           inputs=inputs,
                           outputs=outputs_new,
-                          vectorization=vectorization,
+                          vectorize=vectorize,
                           **kwargs)    # type: pd.DataFrame
 
-    # # create dataframe that maps between output names and parameter sets
-    # data, index = [], []
-    # for key in results.keys():
-    #     param_key = key[1].split('/')[0]
-    #     data.append(param_grid.loc[param_key, :].values)
-    # param_map = pd.DataFrame(data=np.asarray(data).T, columns=results.columns, index=param_grid.columns)
-
     # return results
     return results, param_grid