It is used to automatically adjust subplot parameters to give specified padding. The tight_layout() is a method available in the pyplot module of the matplotlib library. Method 1: tight_layout for matplotlib subplot spacing: Let us now discuss all these methods in detail. Method 4: Achieving Subplot spacing Using constrained_layout parameterĭifferent methods to add matplotlib subplot spacing:.Method 3: plt.subplot_adjust() for matplotlib subplot spacing:.Method 2: plt.subplot_tool() for matplotlib subplot spacing:.Method 1: tight_layout for matplotlib subplot spacing:.Different methods to add matplotlib subplot spacing:.# plt.bar(np.arange(len(top_labels)), probs, w, color=colors, alpha=. # ['lightcoral', 'steelblue', 'forestgreen', 'darkviolet', 'sienna', 'dimgrey', # color_dict = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS) t_ylabel('count accuracy', fontsize=14)ĭef att_plot(top_labels, gt_ind, probs, fn): ot(air_bins, air_means, linewidth=2.0, label='AIR') Plot.fill_between(air_bins, air_means + air_stdev, air_means - air_stdev, alpha=0.5) ot(nobg_bins, nobg_means, linewidth=2.0, label='SuPAIR w/o bg') Plot.fill_between(nobg_bins, nobg_means + nobg_stdev, nobg_means - nobg_stdev, alpha=0.5) Nobg_bins, nobg_means, nobg_stdev = process_perf(nobg_data, max_t + 20) Nobg_data = rows_to_matrix(read_csv(no_bg_file)) No_bg_file = os.path.join(directory, no_bg_file) ot(spn_bins, spn_means, linewidth=2.0, label='SuPAIR') Plot.fill_between(spn_bins, spn_means + spn_stdev, spn_means - spn_stdev, alpha=0.5) Print(spair_file, 'AIR:', air_means, 'SuPAIR', spn_means)įig, plot = plt.subplots(figsize=) Spn_bins, spn_means, spn_stdev = process_perf(spn_data, max_t + 20) Spn_data = rows_to_matrix(read_csv(spair_file))Īir_data = rows_to_matrix(read_csv(air_file))Īir_bins, air_means, air_stdev = process_perf(air_data, max_t + 20) Output_file = os.path.join(directory, output_file) ax.setxlabel('xlabel') I would then like to clear a specific subplot completely, as described in When to use cla(), clf() or close() for clearing a plot in matplotlib. ax fig1.addsubplot(221) I then plot stuff in each of the subplots via. Spair_file = os.path.join(directory, spair_file)Īir_file = os.path.join(directory, air_file) I have a number of subplots in a figure fig1, created via. Max_abs_edit_value=10.0, epoch=0, batch_id = 0, save_dir="results"):Įdit_dim_values = np.linspace(-1.0 *max_abs_edit_value, max_abs_edit_value, num_sweeps)į, axarr = plt.subplots(args.latent_dim, len(edit_dim_values), sharex=True, sharey=True)į.set_size_inches(10, 10* args.latent_dim / len(edit_dim_values))Įdited_images = convert_and_reshape(explainer.explain(input_images=input_images,Įdit_dim_value = edit_dim_values,edit_z_sample=False), dataset_obj)Īxarr.imshow(edited_images, cmap="gray", aspect='auto')Īxarr.imshow(edited_images*0.5 + 0.5, aspect='auto')Īt_xlabel("z:" + str(np.round(edit_dim_values, 1)))į.savefig(os.path.join(save_dir, 'traversal_epoch_.png'.format(epoch, batch_id)))ĭef make_perf(spair_file, air_file, output_file, max_t,Īdd_annotation=None, directory='./plots', no_bg_file=None): Yticklabels=list(reversed(range(12, 22))))įig.set_ylabel('player sum', fontsize=30)įig.set_xlabel('dealer showing', fontsize=30)ĭef plot_latent_traversal(explainer, input_images, args, dataset_obj, image_id_to_plot=0, num_sweeps=15, Plt.subplots_adjust(wspace=0.1, hspace=0.2)įor image, title, axis in zip(images, titles, axes):įig = sns.heatmap(np.flipud(image), cmap="YlGnBu", ax=axis, xticklabels=range(1, 11), Titles = ['Optimal policy with usable Ace', State_value_usable_ace = np.max(state_action_values, axis=-1)Īction_no_usable_ace = np.argmax(state_action_values, axis=-1)Īction_usable_ace = np.argmax(state_action_values, axis=-1) State_value_no_usable_ace = np.max(state_action_values, axis=-1) State_action_values = monte_carlo_es(500000) Plt.savefig(os.path.join(save_to, "activation_%s.png" % str(step))) Matrix = matrix.argsort()]įig, axes = plt.subplots(ncols=1, nrows=num_label, figsize=(15,12))įig.suptitle("The probability of entity presence (step %s)"%str(step), fontsize=20)Īx.t_major_locator(ticker.NullLocator())Īx.t_major_locator(ticker.NullLocator())Īx.t_major_locator(ticker.IndexLocator(base=500,offset=0))Īx_color('none') Raise ValueError('Input "matrix" should have 2 rank, but it is',str(len(matrix.shape))) Save_to = os.path.join(".", "activations") if save_to is None else save_to Neural network - one achieving over 93 percent accuracy - andīad_image_indices = īad_images = for j in bad_image_indices]Īx.matshow(bad_images, cmap = matplotlib.cm.binary)Īx.set_title(str(bad_image_indices))ĭef plot_activation(matrix, step, save_to=None): """This takes a list of images misclassified by a pretty good
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