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| Original file line number | Diff line number | Diff line change |
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| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| import random | ||
| from itertools import product | ||
| import porespy as ps | ||
| from representativity.validation import validation | ||
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| if __name__ == '__main__': | ||
| num_generators = 50 | ||
| num_images = 5 | ||
| generators_chosen = np.random.choice(num_generators, num_images, replace=False) | ||
| images = [] | ||
| large_img_size = np.array([1000, 1000]) | ||
| img_size = np.array([200, 200]) | ||
| alpha = 1 | ||
| ps_generators = validation.get_ps_generators() | ||
| rand_iter = 0 | ||
| for generator, params in ps_generators.items(): | ||
| for value_comb in product(*params.values()): | ||
| if rand_iter in generators_chosen: | ||
| args = {key: value for key, value in zip(params.keys(), value_comb)} | ||
| args = validation.factors_to_params(args, im_shape=large_img_size) | ||
| image = validation.get_large_im_stack(generator, large_img_size, 1, args) | ||
| image = image[0] | ||
| image = image[:img_size[0], :img_size[1]] | ||
| images.append(image) | ||
| rand_iter += 1 | ||
| random.shuffle(images) | ||
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| layers = num_images # How many images should be stacked. | ||
| x_offset, y_offset = img_size[0]-25, 30 # Number of pixels to offset each image. | ||
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| new_shape = ((layers - 1)*y_offset + images[0].shape[0], | ||
| (layers - 1)*x_offset + images[0].shape[1] | ||
| ) # the last number, i.e. 4, refers to the 4 different channels, being RGB + alpha | ||
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| stacked = np.zeros(new_shape) | ||
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| for layer in range(layers): | ||
| cur_im = images[layer] | ||
| stacked[layer*y_offset:layer*y_offset + cur_im.shape[0], | ||
| layer*x_offset:layer*x_offset + cur_im.shape[1] | ||
| ] += cur_im | ||
| stacked = 1 - stacked | ||
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| # Create the PoreSpy images: | ||
| ax_porespy_im = fig.add_subplot(gs[0, 0]) | ||
| ax_porespy_im.imshow(stacked, vmin=0, vmax=1, cmap='gray', interpolation='nearest') | ||
| ax_porespy_im.set_title('(a)') | ||
| ax_porespy_im.axis('off') | ||
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| pos1 = ax_porespy_im.get_position() # get the original position | ||
| pos2 = [pos1.x0 - 0.15, pos1.y0+0, pos1.width+0.1, pos1.height+0.1] | ||
| ax_porespy_im.set_position(pos2) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,140 @@ | ||
| import json | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| from functools import reduce | ||
| from matplotlib.gridspec import GridSpec | ||
| from matplotlib.font_manager import FontProperties | ||
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| def get_in_bounds_results(dims, name="porespy"): | ||
| # Load the data | ||
| validation_data_dir = 'representativity/validation/validation_w_real.json' | ||
| with open(validation_data_dir, "r") as file: | ||
| validation_data = json.load(file) | ||
| # Get the in-bounds results | ||
| in_bound_results = {} | ||
| for dim in dims: | ||
| dim_results = { | ||
| "one_im": [], "model_with_gmm": [], "model_wo_gmm": []} | ||
| for gen_name in validation_data[f"validation_{dim}"].keys(): | ||
| # if there are more generators, they need to be added here: | ||
| if name == "porespy": | ||
| if not gen_name.startswith("blob") and not gen_name.startswith("frac"): | ||
| continue | ||
| else: | ||
| if dim == "2D": | ||
| if not gen_name.startswith("anode"): | ||
| continue | ||
| else: | ||
| if name == 'Targray': # 3D | ||
| if not gen_name.startswith("separator_Targray"): | ||
| continue | ||
| else: | ||
| if not gen_name.startswith("separator_PP1615"): | ||
| continue | ||
| gen_data = validation_data[f"validation_{dim}"][gen_name] | ||
| for run in gen_data.keys(): | ||
| if not run.startswith("run"): | ||
| continue | ||
| run_data = gen_data[run] | ||
| dim_results["one_im"].append(run_data["in_bounds_one_im"]) | ||
| dim_results["model_with_gmm"].append(run_data["model_in_bounds"]) | ||
| dim_results["model_wo_gmm"].append(run_data["model_wo_gmm_in_bounds"]) | ||
| n_trials = len(dim_results["one_im"]) | ||
| for res in dim_results.keys(): | ||
| dim_results[res] = np.array([n_trials, np.array(dim_results[res]).sum()]) | ||
| in_bound_results[dim] = dim_results | ||
| return in_bound_results | ||
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| def make_data(dim_res, order): | ||
| data = [] | ||
| for key in order: | ||
| num_trials, num_in_bounds = dim_res[key] | ||
| row = [ | ||
| f"{num_in_bounds}/{num_trials} = {num_in_bounds/num_trials*100:.2f}%", | ||
| "95%", | ||
| f"{np.abs(0.95-num_in_bounds/num_trials)*100:.2f}%" | ||
| ] | ||
| data.append(row) | ||
| return data | ||
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| def bold_min_value(table_data, table1, start_idx = 0): | ||
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| absolute_errors = np.array([float(table_data[i][2][:-1]) for i in range(start_idx, start_idx+3)]) | ||
| min_indices = np.where(absolute_errors==absolute_errors.min())[0] | ||
| for min_idx in min_indices: | ||
| imagerep_2d_cell_right = table1[(start_idx+min_idx+1, 2)] | ||
| imagerep_2d_cell_right.set_text_props(fontproperties=FontProperties(weight='bold')) | ||
| imagerep_2d_cell_left = table1[(start_idx+min_idx+1, -1)] | ||
| imagerep_2d_cell_left.set_text_props(fontproperties=FontProperties(weight='bold')) | ||
| # imagerep_2d_cell.set_facecolor('lightgreen') | ||
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| def make_table(dims, ax_table, in_bounds_res, title): | ||
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| order = ["one_im", "model_wo_gmm", "model_with_gmm"] | ||
| dim_data = [make_data(in_bounds_res[dim], order) for dim in dims] | ||
| table_data = reduce(lambda x, y: x + y, dim_data) | ||
| # plt.figtext(0.415, 0.485, '(b)', ha='center', va='bottom', fontsize=12) | ||
| ax_table.axis('off') | ||
| colWidths = np.array([0.31, 0.14, 0.14]) | ||
| colWidths /= colWidths.sum() | ||
| column_labels = ["Material's true phase fraction in the predicted bounds", "Confidence goal", "Absolute error"] | ||
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| general_row_labels = ["Classical subdivision method", "ImageRep only std", "ImageRep"] | ||
| dim_row_labels = [[f"{general_row_labels[i]} ({dim})" for i in range(len(general_row_labels))] for dim in dims] | ||
| row_labels = reduce(lambda x, y: x + y, dim_row_labels) | ||
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| table1 = ax_table.table(cellText=table_data, colLabels=column_labels, rowLabels=row_labels, loc='center', colWidths=colWidths) | ||
| for key, cell in table1.get_celld().items(): | ||
| cell.set_text_props(ha='center', va='center') | ||
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| title_len_addition = len(title) * 0.003 | ||
| y_pos = 0.905 if len(dims) == 1 else 0.885 | ||
| ax_table.text(-0.11-title_len_addition, y_pos, title, ha='left', va='top', transform=ax_table.transAxes) | ||
| # Find minimum error and highlight the corresponding cell in bold: | ||
| bold_min_value(table_data, table1) | ||
| if len(dims) > 1: | ||
| bold_min_value(table_data, table1, start_idx=3) | ||
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| def join_all_data(all_data): | ||
| dims = ["2D", "3D"] | ||
| res = {dim: {key: np.array([0, 0]) for key in all_data[0]["2D"].keys()} for dim in dims} | ||
| for dim in dims: | ||
| # add the reults of the dimension together: | ||
| for i in range(len(all_data)): | ||
| if dim in all_data[i]: | ||
| for key in all_data[i][dim].keys(): | ||
| res[dim][key] += all_data[i][dim][key] | ||
| return res | ||
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| if __name__ == '__main__': | ||
| dims = ["2D", "3D"] | ||
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| # make a fig with 2 subplots, one for each table: | ||
| # Create a figure with 2 subplots | ||
| col_width = 16 | ||
| fig = plt.figure(figsize=(col_width, col_width/2.1)) | ||
| gs = GridSpec(5, 1, height_ratios=[2, 1, 1, 1, 2]) | ||
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| subplot_args = ( | ||
| [["2D", "3D"], "porespy", 'PoreSpy simulated materials'], | ||
| [["2D"], "anode", 'Solid Oxide Fuel Cell anode'], | ||
| [["3D"], "Targray", 'Targray separator'], | ||
| [["3D"], "PP1615", 'PP1615 separator'] | ||
| ) | ||
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| all_data = [] | ||
| for i, (dims, name, title) in enumerate(subplot_args): | ||
| in_bounds_res = get_in_bounds_results(dims=dims, name=name) | ||
| all_data.append(in_bounds_res) | ||
| make_table(dims, fig.add_subplot(gs[i]), in_bounds_res, title=title) | ||
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| # Join all the data together: | ||
| all_data = join_all_data(all_data) | ||
| make_table(["2D", "3D"], fig.add_subplot(gs[4]), all_data, title="All materials") | ||
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| # Adjust layout | ||
| plt.tight_layout() | ||
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| # Save the figure, with high dpi | ||
| plt.savefig("paper_figures/output/SI_validation_tables.pdf", format='pdf', dpi=300) | ||
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