# -*- coding: utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.axislines import Subplot


def get_mean_std(data, size):
    s = data[np.where(data[:,0] == size)][:,1]

    # remove 2x std outliers
    std = np.std(s)
    outliers = s[np.abs(s - np.mean(s)) > 4 * np.std(s)]
    s = s[np.abs(s - np.mean(s)) < 4 * np.std(s)]
    return np.mean(s), np.std(s)


def read_data(fname):
    data = np.loadtxt(fname)
    ymean = []
    ystd = []

    for i in range(128, 4096 + 128, 128):
        m, s = get_mean_std(data, i)
        ymean.append(m)
        ystd.append(s)

    return ymean, ystd


xs = np.arange(128, 4096 + 128, 128)
dgma_cpu_mean, dgma_cpu_std = read_data('ipedirectgma.cpu.txt')
#dgma_gpu_mean, dgma_gpu_std = read_data('ipedirectgma.gpu.txt')
cam2_cpu_mean, cam2_cpu_std = read_data('ipecamera2.cpu.txt')
#cam2_gpu_mean, cam2_gpu_std = read_data('ipecamera2.gpu.txt')

print dgma_cpu_mean[8]
print cam2_cpu_mean[8]

#plt.rc('font', **dict(family='serif'))

fig = plt.figure(1, (4,3))
ax = Subplot(fig, 111)
fig.add_subplot(ax)

plt.xlabel('Packet size (B)')
plt.ylabel('Latency (us)')

#ax.plot(xs, dgma_gpu_mean, 'x-', label='GPU memory (embedded)', color='#3b5b92')

#ax.plot(xs, cam2_gpu_mean, '.-', label='GPU memory (workstation)', color='#3b5b92')
ax.plot(xs, cam2_cpu_mean, '.-', markersize=4, label='Setup1', color='#028e2c')
ax.plot(xs, dgma_cpu_mean, 'x-', markersize=4, label='Setup2', color='#77DD77')

plt.xticks([128, 1024, 2048, 2048+1024, 4096])
# plt.yticks([2,4,6,8])
plt.xlim(0, 4200)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
plt.legend(loc='upper left', frameon=False)
plt.savefig('latency-cpu.pdf', dpi=300, bbox_inches='tight')


# A = np.vstack([xs, np.ones(len(xs))]).T
# mc, cc = np.linalg.lstsq(A, yscm)[0]

# mg, cg = np.linalg.lstsq(A, ysgm)[0]

# print 100000000/ (100000000 * mc + cc)
# print 100000000/ (100000000 * mg + cg)