with open('./results_n.txt') as f:

y_pts_n = f.read().splitlines()

with open('./results_n_2.txt') as f:

y_pts_n_2 = f.read().splitlines()

with open('./results_log_p.txt') as f:

y_pts_log_p = f.read().splitlines()

line_n, _ = scipy.optimize.curve_fit(lambda t,a,b: a b*numpy.log2(t), range(2, 25, 2), y_pts_n)

line_n_2, _ = scipy.optimize.curve_fit(lambda t,a,b: a b*numpy.log2(t), range(2, 49, 2), y_pts_n_2)

line_log_p, _ = scipy.optimize.curve_fit(lambda t,a,b: a b*numpy.log2(t), range(2, 111, 2), y_pts_log_p)

fig, ax = plt.subplots()

# ax.plot(range(2, 25, 2), y_pts_n, linestyle='None', marker='o', color='b', label=r"$p(n) = n$")

ax.plot(range(2, 111, 2), func(range(2, 111, 2), *line_n),

linewidth=2.0, linestyle='-', color='b', label=r"$Fitted Curve: n$"

)

# ax.plot(range(2, 49, 2), y_pts_n_2,

# linestyle='None', marker='o', color='r', label=r"$p(n) = \frac{n}{2}$"

# )

ax.plot(range(2, 111, 2), func(range(2, 111, 2), *line_n_2),

linewidth=2.0, linestyle='-', color='r', label=r"$Fitted Curve: \frac{n}{2}$"

)

# ax.plot(range(2, 111, 2), y_pts_log_p,

# linestyle='None', marker='o', color='g', label=r"$p(n) = \frac{n}{p} \geq \log \, p$"

# )

ax.plot(range(2, 111, 2), func(range(2, 111, 2), *line_log_p),

linewidth=2.0, linestyle='-', color='g', label=r"$Fitted Curve: \frac{n}{p} \geq \log \, p$"

)

ax.set(xlabel='n - points count', ylabel=r'time $(\mu s)$',

title='Line-of-Sight')

ax.grid()

ax.set_ylim(ymin=0)

ax.legend(loc="upper left")

fig.savefig("common_graph.pdf", format="pdf")