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test_cmp_mpms_pms.py
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import os
import numpy as np
import matplotlib.pyplot as plt
from hamming import HammingCode
from utility import h, BSC_capacity, BSC_Hamming_capacity, hamming_err_prob, hamming_LOEP
Px = 0.2
Pe = 0.01
def test_cmp_code_len_against_tranx_rate(Px, Pe, msgL=4, est=False):
""" Read logs, plot message length against transmission rate and capacity
This function will read log files recording average transmission rate and
channel use for each message length and compare them with channel complexity.
"""
min_msg_len = 1
max_msg_len = 80
sample_size = 1000
# hamming code
hmsg_len = msgL
hblk_len = HammingCode.calc_redundant_bits(hmsg_len) + hmsg_len
hrate = "{}/{}".format(hmsg_len,hblk_len)
name = 'Hamming({},{})'.format(hblk_len,hmsg_len)
# read log files
fn_pms = 'pms_len_tranx_rate.txt'
fn_HEP = 'mpms_len_tranx_rate_({},{})_Ps=HEP.txt'.format(hblk_len,hmsg_len)
fn_pms = os.path.join('log', fn_pms)
fn_HEP = os.path.join('log', fn_HEP)
with open(fn_pms, 'r') as f:
rate_list_pms = f.readlines()
rate_pms = np.array([np.float32(r.strip()) for r in rate_list_pms])
with open(fn_HEP, 'r') as f:
rate_list_HEP = f.readlines()
rate_HEP = np.array([np.float32(r.strip()) for r in rate_list_HEP])
if est: # estimate scaled probability
fn_Px = 'mpms_len_tranx_rate_({},{})_Ps=Px.txt'.format(hblk_len,hmsg_len)
fn_LOEP = 'mpms_len_tranx_rate_({},{})_Ps=LOEP.txt'.format(hblk_len,hmsg_len)
fn_Px = os.path.join('log', fn_Px)
fn_LOEP = os.path.join('log', fn_LOEP)
with open(fn_Px, 'r') as f:
rate_list_Px = f.readlines()
rate_Px = np.array([np.float32(r.strip()) for r in rate_list_Px])
with open(fn_LOEP, 'r') as f:
rate_list_LOEP = f.readlines()
rate_LOEP = np.array([np.float32(r.strip()) for r in rate_list_LOEP])
# capacity
capacity = BSC_capacity(Px)
rho1 = hamming_err_prob(Px, hmsg_len, hblk_len)
capacity_with_HEP = BSC_Hamming_capacity(hblk_len, hmsg_len, rho1)
if est:
rho2 = hamming_LOEP(Px, hblk_len)
capacity_with_LOEP = BSC_Hamming_capacity(hblk_len, hmsg_len, rho2)
# data to be plotted
x = np.array(range(min_msg_len, max_msg_len+1))
y1 = rate_pms
y2 = rate_HEP
y3 = rate_pms * hmsg_len / hblk_len
c1 = capacity
c2 = capacity_with_HEP
c3 = capacity * hmsg_len / hblk_len
if est:
y4 = rate_Px
y5 = rate_LOEP
c5 = capacity_with_LOEP
# plot
figname = "cmp_len_tranx_rate_{}.png".format(name)
plt.plot(x, y1, label="PMS", color='tab:orange')
plt.plot(x, y2, label=name, color='tab:blue')
plt.plot(x, y3, label="PMS x {}".format(hrate), color='tab:green')
plt.hlines(c1, 0, max_msg_len+1, colors='red', label="capacity")
plt.hlines(c2, 0, max_msg_len+1, colors='m', label="cap of HEP")
if est:
plt.plot(x, y4, label="Crossover", color='saddlebrown')
plt.plot(x, y5, label="LOEP", color='black')
# plt.hlines(c5, 0, max_msg_len+1, colors='cyan', label="cap of LOEP")
figname = "cmp_len_tranx_rate_{}_EST.png".format(name)
plt.xlim([0, max_msg_len+1])
plt.ylim([0, c1 * 1.1])
plt.xlabel("Message length")
plt.ylabel("Transmission rate")
plt.legend(loc='lower right')
plt.title("Comparison: Pe={},Px={},size={},{}".format(Pe,Px,sample_size,name))
plt.savefig(os.path.join("graph", figname))
plt.show()
def test_cmp_hamming_7_4_diff_Px(Pe):
min_msg_len = 1
max_msg_len = 34
sample_size = 700
# read log files
fn_pms_1 = 'pms_len_tranx_rate_Px=0.1.txt'
fn_pms_2 = 'pms_len_tranx_rate.txt'
fn_pms_3 = 'pms_len_tranx_rate_Px=0.3.txt'
fn_HEP_1 = 'mpms_len_tranx_rate_(7,4)_Px=0.1.txt'
fn_HEP_2 = 'mpms_len_tranx_rate_(7,4)_Ps=HEP.txt'
fn_HEP_3 = 'mpms_len_tranx_rate_(7,4)_Px=0.3.txt'
fn_pms_1 = os.path.join('log', fn_pms_1)
fn_pms_2 = os.path.join('log', fn_pms_2)
fn_pms_3 = os.path.join('log', fn_pms_3)
fn_HEP_1 = os.path.join('log', fn_HEP_1)
fn_HEP_2 = os.path.join('log', fn_HEP_2)
fn_HEP_3 = os.path.join('log', fn_HEP_3)
with open(fn_pms_1, 'r') as f:
rate_list_pms_1 = f.readlines()
rate_pms_1 = np.array([np.float32(r.strip()) for r in rate_list_pms_1])
with open(fn_pms_2, 'r') as f:
rate_list_pms_2 = f.readlines()
rate_pms_2 = np.array([np.float32(r.strip()) for r in rate_list_pms_2])
with open(fn_pms_3, 'r') as f:
rate_list_pms_3 = f.readlines()
rate_pms_3 = np.array([np.float32(r.strip()) for r in rate_list_pms_3])
with open(fn_HEP_1, 'r') as f:
rate_list_HEP_1 = f.readlines()
rate_HEP_1 = np.array([np.float32(r.strip()) for r in rate_list_HEP_1])
with open(fn_HEP_2, 'r') as f:
rate_list_HEP_2 = f.readlines()
rate_HEP_2 = np.array([np.float32(r.strip()) for r in rate_list_HEP_2])
with open(fn_HEP_3, 'r') as f:
rate_list_HEP_3 = f.readlines()
rate_HEP_3 = np.array([np.float32(r.strip()) for r in rate_list_HEP_3])
# capacity
capacity_1 = BSC_capacity(0.1)
capacity_2 = BSC_capacity(0.2)
capacity_3 = BSC_capacity(0.3)
rho1 = hamming_err_prob(0.1, 4, 7)
rho2 = hamming_err_prob(0.2, 4, 7)
rho3 = hamming_err_prob(0.3, 4, 7)
capacity_with_HEP_1 = BSC_Hamming_capacity(7, 4, rho1)
capacity_with_HEP_2 = BSC_Hamming_capacity(7, 4, rho2)
capacity_with_HEP_3 = BSC_Hamming_capacity(7, 4, rho3)
# data to be plotted
x = np.array(range(min_msg_len, max_msg_len+1))
y1 = rate_pms_1[min_msg_len:max_msg_len+1]
y2 = rate_pms_2[min_msg_len:max_msg_len+1]
y3 = rate_pms_3[min_msg_len:max_msg_len+1]
y4 = rate_HEP_1[min_msg_len:max_msg_len+1]
y5 = rate_HEP_2[min_msg_len:max_msg_len+1]
y6 = rate_HEP_3[min_msg_len:max_msg_len+1]
c1 = capacity_1
c2 = capacity_2
c3 = capacity_3
c4 = capacity_with_HEP_1
c5 = capacity_with_HEP_2
c6 = capacity_with_HEP_3
# plot
figname = "cmp_len_tranx_rate_diff_Px.png"
fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
ax1.plot(x, y1, label="PMS .1", color='tab:blue')
ax1.plot(x, y4, label="MPMS .1", color='tab:orange')
ax1.hlines(c1, 0, max_msg_len+1, colors='red', label="cap PMS .1")
ax1.set_xlim([0, max_msg_len+1])
ax1.set_ylim([0, c1 * 1.1])
ax1.set_xlabel("Message length")
ax1.legend(loc='lower right')
ax1.spines['bottom'].set_linewidth(0.5)
ax2.plot(x, y2, label="PMS .2", color='tab:blue')
ax2.plot(x, y5, label="MPMS .2", color='tab:orange')
ax2.hlines(c2, 0, max_msg_len+1, colors='red', label="cap PMS .2")
ax2.set_xlim([0, max_msg_len+1])
ax2.set_ylim([0, c1 * 1.1])
ax2.set_xlabel("Message length")
ax2.legend(loc='upper right')
ax3.plot(x, y3, label="PMS .3", color='tab:blue')
ax3.plot(x, y6, label="MPMS .3", color='tab:orange')
ax3.hlines(c3, 0, max_msg_len+1, colors='red', label="cap PMS .3")
ax3.set_xlim([0, max_msg_len+1])
ax3.set_ylim([0, c1 * 1.1])
ax3.set_xlabel("Message length")
ax3.legend(loc='upper right')
# plt.hlines(c4, 0, max_msg_len+1, colors='m', label="cap MPMS .1")
# plt.hlines(c5, 0, max_msg_len+1, colors='m', label="cap MPMS .2")
# plt.hlines(c6, 0, max_msg_len+1, colors='m', label="cap MPMS .3")
fig.suptitle("Comparison: Pe={}, size={}, diff Px".format(Pe,Px,sample_size))
plt.savefig(os.path.join("graph", figname))
plt.show()
def test_mismatched_Px(Px, Pe, msgL=4):
min_msg_len = 1
max_msg_len = 39
sample_size = "700/1000"
# hamming code
hmsg_len = msgL
hblk_len = HammingCode.calc_redundant_bits(hmsg_len) + hmsg_len
hrate = "{}/{}".format(hmsg_len,hblk_len)
name = 'Hamming({},{})'.format(hblk_len,hmsg_len)
# read log files
fn_pms = 'pms_len_tranx_rate.txt'
fn_HEP = 'mpms_len_tranx_rate_({},{})_Ps=HEP.txt'.format(hblk_len,hmsg_len)
fn_mm = 'mpms_len_tranx_rate_({},{})_mismatched_Px.txt'.format(hblk_len,hmsg_len)
fn_pms = os.path.join('log', fn_pms)
fn_HEP = os.path.join('log', fn_HEP)
fn_mm = os.path.join('log', fn_mm)
with open(fn_pms, 'r') as f:
rate_list_pms = f.readlines()
rate_pms = np.array([np.float32(r.strip()) for r in rate_list_pms])
with open(fn_HEP, 'r') as f:
rate_list_HEP = f.readlines()
rate_HEP = np.array([np.float32(r.strip()) for r in rate_list_HEP])
with open(fn_mm, 'r') as f:
rate_list_mm = f.readlines()
rate_mm = np.array([np.float32(r.strip()) for r in rate_list_mm])
# capacity
capacity = BSC_capacity(Px)
rho1 = hamming_err_prob(Px, hmsg_len, hblk_len)
capacity_with_HEP = BSC_Hamming_capacity(hblk_len, hmsg_len, rho1)
# data to be plotted
x = np.array(range(min_msg_len, max_msg_len+1))
y1 = rate_pms[min_msg_len:max_msg_len+1]
y2 = rate_HEP[min_msg_len:max_msg_len+1]
y3 = rate_mm[min_msg_len:max_msg_len+1]
c1 = capacity
c2 = capacity_with_HEP
# plot
figname = "cmp_len_tranx_rate_{}_with_mismatched_Px.png".format(name)
plt.plot(x, y1, label="PMS", color='tab:orange')
plt.plot(x, y2, label=name, color='tab:blue')
plt.plot(x, y3, label="mismatched", color='tab:green')
plt.hlines(c1, 0, max_msg_len+1, colors='red', label="capacity")
plt.hlines(c2, 0, max_msg_len+1, colors='m', label="cap of HEP")
plt.xlim([0, max_msg_len+1])
plt.ylim([0, c1 * 1.1])
plt.xlabel("Message length")
plt.ylabel("Transmission rate")
plt.legend(loc='lower right')
plt.title("Comparison: Pe={},Px={},size={},{}".format(Pe,Px,sample_size,name))
plt.savefig(os.path.join("graph", figname))
plt.show()
if __name__ == '__main__':
estimate = True
# test_cmp_code_len_against_tranx_rate(Px, Pe, msgL=4, est=estimate)
# test_cmp_hamming_7_4_diff_Px(Pe)
test_mismatched_Px(Px, Pe, msgL=4)