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plot.py
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/
plot.py
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import pathlib
import re
import json
from collections import defaultdict
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from collections import namedtuple
from scipy.stats import norm
Measurement = namedtuple('Measurement', ['mean', 'std'])
def get_2sigma_filtered(data):
mu, std = norm.fit(data)
lower = mu - 2*std
upper = mu + 2*std
data = data[(data < upper) & (lower < data)]
return data
# Mean absolute deviation https://en.wikipedia.org/wiki/Average_absolute_deviation#Mean_absolute_deviation_around_the_mean
def get_MAD(data):
mean = np.mean(data)
return np.sum(np.abs(data-mean)) / data.size
def get_times_from_txt(filename, ignore_count):
ms_regex = re.compile(r'TotalMilliseconds\s?:\s?(\d*[,.]\d*)')
with open(filename) as file:
times = np.zeros(shape=(0))
for line in file.readlines():
match = ms_regex.match(line.strip())
if match is None:
continue
if ignore_count > 0:
ignore_count -= 1
continue
ms_str = match.group(1).replace(",", ".")
ms = float(ms_str)
times = np.append(times, ms)
times = get_2sigma_filtered(times)
return times
def get_time_and_std_from_txt(filename, ignore_count):
times = get_times_from_txt(filename, ignore_count)
return Measurement(np.mean(times), np.std(times, dtype=np.float64))
def get_file_data(ignore_count=1):
measurement_fn_regex = re.compile(r'([\w_]+)-([\w_]+)-(\d+)\.txt')
file_data = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(int))))
for path in pathlib.Path('measurements').iterdir():
if not path.is_file():
continue
filename = path.name
match = measurement_fn_regex.match(filename)
if match is None:
continue
category, lib, tu_count = match.group(1, 2, 3)
if category == "std_modules":
lib = lib.replace("std_", "std.")
tu_count = int(tu_count)
file_data[category][lib] = get_time_and_std_from_txt(path, ignore_count)
# print(json.dumps(file_data, indent=4))
return file_data
def get_pretty_name(description):
replacements = {
"windows": "windows.h",
"windows_mal": "windows.h [LAM]",
"tracy": "Tracy.hpp",
"doctest": "doctest/doctest.h",
"spdlog": "spdlog/spdlog.h",
"fmt": "fmt/core.h",
"nl_json": "nlohmann/json.hpp",
"nl_json_fwd": "nlohmann/json_fwd.hpp",
"glm": "glm/glm.hpp",
"vulkan": "vulkan/vulkan.h",
"vulkanhpp": "vulkan/vulkan.hpp",
"boost_json": "boost/json.hpp",
"boost_variant": "boost/variant.hpp",
"boost_variant2": "boost/variant2/variant.hpp",
"stb_image": "stb_image.h",
"stb_image_write": "stb_image_write.h",
"entt": "entt/entt.hpp"
}
if description in replacements.keys():
return replacements[description]
else:
return description
def get_raw_labels(categories, file_data):
labels = []
for category in categories:
for name in file_data[category].keys():
labels.append(name)
return labels
def get_labels(raw_labels):
labels = []
is_std = True
for raw_label in raw_labels:
if is_std:
raw_label = "<{}>".format(raw_label)
labels.append(get_pretty_name(raw_label))
if raw_label == "<version>":
is_std = False
max_len = max([len(label) for label in labels])
labels = [(label+" ").ljust(max_len+1, '—') for label in labels]
return labels
def get_positions(categories, file_data):
current_pos = 0.0
positions = []
for category in categories:
for _ in file_data[category].keys():
positions.append(current_pos)
current_pos -= 1.0
current_pos -= 1.0
return np.array(positions)
def get_addition_error(a, b):
return np.sqrt(a*a + b*b)
tu_count = 10
cpp_20_headers = ["concepts", "coroutines", "compare", "version", "source_location", "format", "semaphore", "span", "ranges", "bit", "numbers", "syncstream", "stop_token", "latch", "barrier"]
def get_worst(category, file_data):
sort_data = np.empty([0, 2])
for res in file_data[category].values():
worst_time = (res.mean - file_data["special"]["baseline"].mean) / tu_count
worst_time_std = get_addition_error(res.std/tu_count, file_data["special"]["baseline"].std/tu_count)
sort_data = np.append(sort_data, np.array([[worst_time, worst_time_std]]), axis=0)
return sort_data
def main_plot():
file_data = get_file_data()
categories = ["std", "std_modules", "third_party"]
raw_labels = get_raw_labels(categories, file_data)
labels = get_labels(raw_labels)
positions = get_positions(categories, file_data)
worst_data = np.empty([0, 2])
for category in categories:
worst_data = np.append(worst_data, get_worst(category, file_data), axis=0)
max_pos = np.max(np.abs(positions))
fig = plt.figure(figsize=(10, 2 + 0.12 * max_pos))
ax = fig.add_subplot()
bar_height = 0.3
_ = ax.barh(y=positions, width=worst_data[:, 0], height=bar_height, color="tab:orange")
_ = ax.barh(y=positions, left=worst_data[:, 0] - worst_data[:, 1], width=2 * worst_data[:, 1], height=bar_height/2, color="blue", alpha=0.5)
_ = plt.yticks(positions, labels, fontfamily="monospace", horizontalalignment='left')
for i, label in enumerate(raw_labels):
if label in cpp_20_headers:
ax.get_yticklabels()[i].set_color("red")
ax.grid(axis='x', alpha=0.2)
ax.get_yaxis().set_tick_params(pad=160)
ax.get_yaxis().set_tick_params(length=0)
ax.set_axisbelow(True)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
_ = ax.axvline(x=0, linewidth=0.5, color="black", zorder=1)
_ = ax.set_xlabel("Include time [ms]")
ax.margins(0)
_ = ax.set_ylim(ax.get_ylim()[0]-1, ax.get_ylim()[1]+1)
ax2 = ax.twiny()
_ = ax2.set_xlabel("Include time [ms]")
ax2.set_xlim(ax.get_xlim())
ax2.spines = ax.spines
fig.tight_layout()
fig.savefig("lit.png")
def disable_top_right_spines(ax):
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
if __name__ == "__main__":
main_plot()