import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import time
from time import strftime, gmtime


def get_name() -> str:

    curr = time.time()
    s = strftime("%d_%b_%Y_%H%M%S", gmtime(curr))
    return s


width = 0.4


kernels = ["Gmail", "WhatsApp", "Youtube", "Twitter", "Facebook"]

x = np.arange(5)
values = (8.13, 4.15, 10.06, 16.75, 18.84)

print(values)

# Standard deviations (error values)
# errors = [np.std(poly), np.std(rbf), np.std(sigmoid)]
# errors2 = [np.std(baseline_poly), np.std(baseline_rbf), np.std(baseline_sigmoid)]
# print(errors)
# print(errors2)

# Create bar chart with error bars
fig, ax = plt.subplots()
# plt.bar(x-0.22, values, width, yerr=errors, capsize=8, color='moccasin', edgecolor='black', label='Sunfish')
# plt.bar(x+0.22,values2, width, yerr=errors2, capsize=8, color='slategrey', edgecolor='black', label='OpenDC')


plt.bar(
    x,
    values,
    width,
    capsize=8,
    color="thistle",
    edgecolor="black",
    label="Red + Yellow Alarms / Total Failures",
)

# Add labels and title
plt.xlabel("Mean Failure Intensity (Trace)", fontweight="bold", size=10)
plt.ylabel("Failures Detection Rate", fontweight="bold", size=10)
loc = range(len(kernels))
labels = kernels
plt.xticks(loc, kernels, size=10)
plt.grid(True, color="lightgray", linewidth=1, linestyle="-.")


plt.legend(fontsize=10, loc="upper left")
plt.yticks(size=10)
# ax.set_yscale("log")

# Show plot
location: str = "figures/%s.pdf" % get_name()
plt.savefig(location, dpi=300, bbox_inches="tight")