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import matplotlib.pyplot as plt |
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from matplotlib.ticker import MaxNLocator |
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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", "NRMSD"] |
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plt.set_loglevel('WARNING') |
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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_list = self._get_dicts_for_graphs(f) |
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loop_stats, loop_plugins = self._get_dicts_for_loops(f) |
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f.close() |
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#self.make_loop_graphs(loop_stats, loop_plugins, savepath) |
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table_index_list = index_list |
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for i in range(len(loop_plugins)): |
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for space in list(table_index_list.keys()): |
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for j, plugin in enumerate(table_index_list[space]): |
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for loop_plugin in loop_plugins[i]: |
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if loop_plugin == plugin[3::]: |
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table_index_list[space][j] = f"{table_index_list[space][j]} (loop{i})" |
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self.make_stats_table(stats_dict, table_index_list, f"{savepath}/stats_table.html") |
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if len(stats_dict["projection"]["max"]): |
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self.make_stats_graphs(stats_dict["projection"], index_list["projection"], "Projection Stats", |
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f"{savepath}/projection_stats.png") |
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if len(stats_dict["reconstruction"]["max"]): |
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self.make_stats_graphs(stats_dict["reconstruction"], index_list["reconstruction"], "Reconstruction Stats", |
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f"{savepath}/reconstruction_stats.png") |
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@staticmethod |
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def make_stats_table(stats_dict, index_list, savepath): |
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p_stats = pd.DataFrame(stats_dict["projection"], index_list["projection"]) |
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r_stats = pd.DataFrame(stats_dict["reconstruction"], index_list["reconstruction"]) |
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all_stats = pd.concat([p_stats, r_stats], keys=["Projection", "Reconstruction"]) |
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all_stats.to_html(savepath) # create table of stats for all plugins |
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def make_loop_graphs(self, loop_stats, loop_plugins, savepath): |
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for i in range(len(loop_stats)): |
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y = loop_stats[i]["NRMSD"] |
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#x = list(range(1, len(loop_stats[i]["RMSD"]) + 1)) |
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x = [None]*len(y) |
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for j in range(len(loop_stats[i]["NRMSD"])): |
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x[j] = f"{j}-{j+1}" |
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ax = plt.figure(figsize=(11, 9), dpi=320).gca() |
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ax.xaxis.set_major_locator(MaxNLocator(integer=True)) |
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#ax.locator_params(axis='x', nbins=j + 1) |
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ax.grid(True) |
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plt.plot(x, y) |
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maxx = j |
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maxy = max(y) |
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plt.title("NRMSD over loop 0") |
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text = f"Loop 0 iterates {maxx + 2} times over:\n" |
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for plugin in loop_plugins[i]: |
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text += f"{plugin}\n" |
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plt.xlabel("Iteration") |
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plt.ylabel("NRMSD") |
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plt.text(maxx, maxy, text, ha="right", va="top", bbox=dict(boxstyle="round", facecolor="red", alpha=0.4)) |
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plt.savefig(f"{savepath}/loop_stats{i}.png", bbox_inches="tight") |
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def make_stats_graphs(self, stats_dict, index_list, title, savepath): |
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stats_df = pd.DataFrame(stats_dict, index_list) |
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stats_dict, array_plugins = self._remove_arrays(stats_dict, index_list) |
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stats_df_new = pd.DataFrame(stats_dict, index_list) |
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colours = ["red", "blue", "green", "black", "purple", "brown"] #max, min, mean, mean std dev, median std dev, NRMSD |
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new_index = [] |
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legend = "" |
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for ind in stats_df_new.index: |
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new_index.append(ind[0]) # change x ticks to only be plugin numbers rather than names (for space) |
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legend += f"{ind}\n" # This will form a key showing the plugin names corresponding to plugin numbers |
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stats_df_new.index = new_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(stats_df_new[stat], "x-", color=colours[i]) |
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for plugin in array_plugins: # adding 'error' bars for plugins with multiple values |
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if stats_df[stat][plugin] is not None: |
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if not np.isnan(stats_df[stat][plugin]).any(): |
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my_max = max(stats_df[stat][plugin]) |
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my_min = min(stats_df[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(list(stats_df_new.index).index(plugin[0]), middle, yerr=[my_range / 2], capsize=5) |
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if i == 1: |
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maxx = len(stats_df_new[stat]) * 1.08 - 1 |
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maxy = max(stats_df_new[stat]) |
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axis.text(maxx, maxy, 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(title, fontsize="x-large") |
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plt.savefig(savepath, 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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stats_dict["projection"] = {"max": [], "min": [], "mean": [], "mean_std_dev": [], "median_std_dev": [], |
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"NRMSD": []} |
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stats_dict["reconstruction"] = {"max": [], "min": [], "mean": [], "mean_std_dev": [], "median_std_dev": [], |
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"NRMSD": []} |
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index_list = {"projection": [], "reconstruction": []} |
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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", "NRMSD"]): |
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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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if f"{key}: {group[key].attrs.get('plugin_name')}" not in index_list[space]: |
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index_list[space].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_list |
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@staticmethod |
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def _get_dicts_for_loops(file): |
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if "iterative" in list(file.keys()): |
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group = file["iterative"] |
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loop_stats = [] |
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loop_plugins = [] |
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for key in list(group.keys()): |
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loop_stats.append({"NRMSD": list(group[key])}) |
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loop_plugins.append(group[key].attrs.get("loop_plugins")) |
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return loop_stats, loop_plugins |
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else: |
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return [], [] |
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@staticmethod |
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def _remove_arrays(stats_dict, index_list): |
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array_plugins = set(()) |
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for stat in list(stats_dict.keys()): |
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for index, value in enumerate(stats_dict[stat]): |
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if isinstance(value, np.ndarray): |
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stats_dict[stat][index] = stats_dict[stat][index][0] |
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array_plugins.add(index_list[index]) |
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return stats_dict, array_plugins |
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DiamondLightSource /
Savu
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