e0d48a0558f12cc6b206c6235c4c432f6c88bd07,GPy/plotting/gpy_plot/latent_plots.py,,plot_steepest_gradient_map,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,284

Before Change

    :param scatter_kwargs: the kwargs for the scatter plots
    
    input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
    canvas, imshow_kwargs = pl.get_new_canvas(**imshow_kwargs)
    X, _, _, _, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, plot_limits, which_indices, None, resolution)    
    if (data_labels is not None):
        legend = find_best_layout_for_subplots(len(np.unique(data_labels)))[1]
    else:
        data_labels = np.ones(self.num_data)
        legend = False
    plots = dict(scatter=_plot_latent_scatter(canvas, X, which_indices, data_labels, marker, num_samples, **scatter_kwargs or {}))
    plots.update(_plot_steepest_gradient_map(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, output_labels, kern, annotation_kwargs=annotation_kwargs, **imshow_kwargs))
    pl.show_canvas(canvas, plots, legend=legend, 
                           xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]),
                           xlabel="latent dimension %i" % input_1, ylabel="latent dimension %i" % input_2)
    _wait_for_updates(plots["annotation"], updates)
    return plots

After Change

    
    input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
    X, _, _, _, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, plot_limits, which_indices, None, resolution)    
    canvas, imshow_kwargs = pl.new_canvas(xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]),
                           xlabel="latent dimension %i" % input_1, ylabel="latent dimension %i" % input_2, **imshow_kwargs)
    if (data_labels is not None):
        legend = find_best_layout_for_subplots(len(np.unique(data_labels)))[1]
    else:
        data_labels = np.ones(self.num_data)
        legend = False
    plots = dict(scatter=_plot_latent_scatter(canvas, X, which_indices, data_labels, marker, num_samples, **scatter_kwargs or {}))
    plots.update(_plot_steepest_gradient_map(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, output_labels, updates, kern, annotation_kwargs=annotation_kwargs, **imshow_kwargs))
    show = pl.show_canvas(canvas, plots, legend=legend)
    _wait_for_updates(plots["annotation"], updates)
    return show

Instances