get_figure() for ax in axes.values()] if len(set(figs)) == 1: return figs[0] Expand Down Expand Up @@ -1171,7 +1171,7 @@ def plot_evoked_topo( scalings=None, title=None, proj=False, vline=[0.0], vline=(0.0,), fig_background=None, merge_grads=False, legend=True, ...
Additionally, it's important to check the data types of all the numerical values in the dataset to ensure they are compatible. Additionally, you can check the swagger for datatype checks and artifacts to see whether slicing is being done really or is it considering the "time" part...
So on the whole, NumPy is certainly in a much better position than pandas: there are only a handful of functions where the return type depends on a literal values (although they are widely used). I'm not going to bother going through SciPy as the API is larger and more varied, and I...
Thus, as can be seen in Table3, it is enough to apply thek-anonymityprocess (which requires little computational cost and, depending on the size of the dataset, inARXis done in a few seconds), to obtain better values for the parameters of other anonymization techniques, such as increasing ...
The bounds check in minimize, https://github.com/scipy/scipy/blame/master/scipy/optimize/_numdiff.py#L392, seems to be raising for valid input. This is showing up using pre-release NumPy 1.19 and SciPy 1.5. We have a test run failure her...
"check_estimators_nan_inf": "FIXME", "check_classifiers_one_label_sample_weights": "FIXME", "check_fit2d_1feature": "FIXME", } ) return tags class HalvingGridSearchCV(BaseSuccessiveHalving): """Search over specified parameter values with successive halving. Expand Down 6 changes: 0 additi...