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import matplotlib.pyplot as plt |
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import pandas as pd |
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import h5py as h5 |
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import numpy as np |
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class StatsUtils(object): |
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_pattern_dict = {"projection": ["SINOGRAM", "PROJECTION", "TANGENTOGRAM", "4D_SCAN", "SINOMOVIE"], |
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"reconstruction": ["VOLUME_YZ", "VOLUME_XZ", "VOLUME_XY", "VOLUME_3D"]} |
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_stats_list = ["max", "min", "mean", "mean_std_dev", "median_std_dev", "RMSD"] |
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def generate_figures(self, filepath, savepath): |
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f = h5.File(filepath, 'r') |
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stats_dict, index_dict = self._get_dicts_for_graphs(f) |
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f.close() |
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p_stats = pd.DataFrame(stats_dict["projection"], index_dict["projection"]["max"]) |
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r_stats = pd.DataFrame(stats_dict["reconstruction"], index_dict["reconstruction"]["max"]) |
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all_stats = pd.concat([p_stats, r_stats], keys=["Projection", "Reconstruction"]) |
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all_stats.to_html(f"{savepath}/stats_table.html") # create table of all stats for all plugins |
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stats_dict, array_plugins = self._remove_arrays(stats_dict, index_dict) |
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p_stats_new = pd.DataFrame(stats_dict["projection"], index_dict["projection"]["max"]) |
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r_stats_new = pd.DataFrame(stats_dict["reconstruction"], index_dict["reconstruction"]["max"]) |
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colours = ["red", "blue", "green", "black", "purple", "brown"] |
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# creating projection stats figure |
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new_p_index = [] |
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p_legend = "" |
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for ind in p_stats_new.index: |
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new_p_index.append(ind[0]) # change x ticks to only be plugin numbers rather than names (for space) |
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p_legend += f"{ind}\n" |
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p_stats_new.index = new_p_index |
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fig, ax = plt.subplots(3, 2, figsize=(11, 9), dpi=320, facecolor="azure") |
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i = 0 |
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for row in ax: |
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for axis in row: |
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stat = self._stats_list[i] |
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axis.plot(p_stats_new[stat], "x-", color=colours[i]) |
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for plugin in array_plugins["projection"]: # adding 'error' bars for plugins with different values due to parameter changes |
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my_max = max(p_stats[stat][plugin]) |
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my_min = min(p_stats[stat][plugin]) |
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middle = (my_max + my_min) / 2 |
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my_range = my_max - my_min |
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axis.errorbar(int(plugin[0]) - len(p_stats_new) + 1, middle, yerr=[my_range], capsize=5) |
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if i == 1: |
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maxx = len(p_stats_new[stat]) * 1.08 - 1 |
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maxy = max(p_stats_new[stat]) |
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axis.text(maxx, maxy, p_legend, ha="left", va="top", |
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bbox=dict(boxstyle="round", facecolor="red", alpha=0.4)) |
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stat.replace("_", " ") |
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axis.set_title(stat) |
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axis.grid(True) |
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i += 1 |
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fig.suptitle("Projection Stats", fontsize="x-large") |
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plt.savefig(f"{savepath}/projection_stats.png", bbox_inches="tight") |
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# creating reconstruction stats figure |
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new_r_index = [] |
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r_legend = "" |
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for ind in r_stats_new.index: |
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new_r_index.append(ind[0]) # change x ticks to only be plugin numbers rather than names (for space) |
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r_legend += f"{ind}\n" |
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r_stats_new.index = new_r_index |
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fig, ax = plt.subplots(3, 2, figsize=(11, 9), dpi=320, facecolor="lavender") |
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i = 0 |
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for row in ax: |
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for axis in row: |
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stat = self._stats_list[i] |
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axis.plot(r_stats_new[stat], "x-", color=colours[i]) |
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for plugin in array_plugins["reconstruction"]: # adding 'error' bars for plugins with different values due to parameter changes |
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my_max = max(r_stats[stat][plugin]) |
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my_min = min(r_stats[stat][plugin]) |
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middle = (my_max + my_min) / 2 |
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my_range = my_max - my_min |
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axis.errorbar(int(plugin[0]) - len(r_stats_new) + 1, middle, yerr=[my_range], capsize=5) |
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if i == 1: |
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maxx = len(r_stats_new[stat]) * 1.08 - 1 |
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maxy = max(r_stats_new[stat]) |
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axis.text(maxx, maxy, r_legend, ha="left", va="top", |
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bbox=dict(boxstyle="round", facecolor="red", alpha=0.4)) |
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stat = stat.replace("_", " ") |
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axis.set_title(stat) |
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axis.grid(True) |
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i += 1 |
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fig.suptitle("Reconstruction Stats", fontsize="x-large") |
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plt.savefig(f"{savepath}/reconstruction_stats.png", bbox_inches="tight") |
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@staticmethod |
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def _get_dicts_for_graphs(file): |
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stats_dict = {} |
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index_dict = {} |
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stats_dict["projection"] = {"max": [], "min": [], "mean": [], "mean_std_dev": [], "median_std_dev": [], |
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"RMSD": []} |
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stats_dict["reconstruction"] = {"max": [], "min": [], "mean": [], "mean_std_dev": [], "median_std_dev": [], |
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"RMSD": []} |
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index_dict["projection"] = {"max": [], "min": [], "mean": [], "mean_std_dev": [], "median_std_dev": [], |
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"RMSD": []} |
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index_dict["reconstruction"] = {"max": [], "min": [], "mean": [], "mean_std_dev": [], "median_std_dev": [], |
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"RMSD": []} |
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group = file["stats"] |
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for space in ("projection", "reconstruction"): |
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for index, stat in enumerate(["max", "min", "mean", "mean_std_dev", "median_std_dev", "RMSD"]): |
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for key in list(group.keys()): |
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if group[key].attrs.get("pattern") in StatsUtils._pattern_dict[space]: |
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index_dict[space][stat].append(f"{key}: {group[key].attrs.get('plugin_name')}") |
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if group[key].ndim == 1: |
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if len(group[key]) > index: |
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stats_dict[space][stat].append(group[key][index]) |
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else: |
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stats_dict[space][stat].append(None) |
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elif group[key].ndim == 2: |
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if len(group[key][0]) > index: |
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stats_dict[space][stat].append(group[key][:, index]) |
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else: |
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stats_dict[space][stat].append(None) |
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return stats_dict, index_dict |
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@staticmethod |
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def _remove_arrays(stats_dict, index_dict): |
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array_plugins = {"projection": set(()), "reconstruction": set(())} |
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for space in list(stats_dict.keys()): |
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for stat in list(stats_dict[space].keys()): |
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for index, value in enumerate(stats_dict[space][stat]): |
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if isinstance(value, np.ndarray): |
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stats_dict[space][stat][index] = stats_dict[space][stat][index][0] |
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array_plugins[space].add(index_dict[space][stat][index]) |
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return stats_dict, array_plugins |
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DiamondLightSource /
Savu
