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interactive.py
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interactive.py
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# Copyright (c) 2017 Elias Riedel Gårding
# Licensed under the MIT License
import sys, os
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(__file__), 'code')))
from itertools import islice, count
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
from scipy.integrate import quad
from collections import defaultdict
from simulation import Simulation, Parameters
from measurements import Measurement
from plotting import plot_lloyd_max, plot_lloyd_max_tracker, \
plot_lloyd_max_hikmet, plot_lloyd_max_tracker_hikmet, \
plot_spiral, plot_spiral_decode
import separate.coding.source.lloyd_max as lm
from separate.coding.convolutional import ConvolutionalCode, Node, \
NaiveMLDecoder, StackDecoder
import separate.coding.PAM as PAM
from utilities import *
from joint.coding import SpiralMap
n_runs = 1 << 0
T = 1 << 7
params = None
# SNR_dB = 4.5
# SNR = 10**(SNR_dB / 10)
# params = Parameters(
# T = T,
# alpha = 1.2,
# W = 1, V = 0, # Lloyd-Max paper assumes no observation noise
# Q = 1, R = 0, F = 1)
# params.setRates(KC = 2, KS = 1)
# params.setAnalog(SNR)
# params.setScheme('joint')
# print("SDR0 = {}".format(params.SDR0))
# params.setDigital(quantizer_bits = 1)
# params.setScheme('lloyd-max')
# code_blocklength = None
# params.setDigital(quantizer_bits = 1, p = 0.001)
# params.setBlocklength(2)
# params.setScheme('noisy-lloyd-max')
# params.set_random_code()
# params.setRates(KC = 2, KS = 1)
# params.setAnalog(SNR)
# params.quantizer_bits = 1
# params.setBlocklength(2)
# params.set_PAM()
# params.setScheme('separate')
# params.set_random_code()
def generate_filename(SNR_dB, alpha, i, quantizer_bits=1, code_blocklength=1):
if SNR_dB == 'noiseless':
filename_pattern = \
'data/separate/varying-SNR/alpha{}/noiseless/noiseless--{{}}.p' \
.format(alpha)
else:
filename_pattern = 'data/separate/varying-SNR/alpha{}/{}:{}/{}dB--{{}}.p' \
.format(alpha, quantizer_bits, code_blocklength, SNR_dB)
return filename_pattern.format(i)
def load_measurements(SNR_dB, alpha=1.2, quantizer_bits=1, code_blocklength=2):
results = []
for i in count(1):
filename = generate_filename(SNR_dB, alpha, i, quantizer_bits,
code_blocklength)
if os.path.isfile(filename):
results.append(Measurement.load(filename))
else:
break
return results
def simulate_and_record(params):
# Take measurement
bad = False
if SNR_dB != 'noiseless':
params.set_random_code() # Use different codes each time
try:
simulate(params)
except (ValueError, TypeError):
input("Bad! :( ")
bad = True
# Generate filename
for i in count(1):
filename = 'bad-' if bad else ''
filename += generate_filename(SNR_dB, params.alpha, i,
params.quantizer_bits,
params.code_blocklength)
if not os.path.isfile(filename):
break
print("Saving to {}".format(filename))
measurements[-1].save(filename)
measurements = []
def simulate(params=params, get_noise_record=lambda: None, plots=False):
global sim, measurements
for i in range(n_runs):
sim = Simulation(params, get_noise_record())
measurement = Measurement(params)
measurements.append(measurement)
if plots:
tracker = sim.encoder.get_tracker().clone()
prev_distr = tracker.distr
prev_lm_encoder = tracker.lm_encoder
prev_lm_decoder = tracker.lm_decoder
try:
for t in sim.simulate(T):
measurement.record(sim)
if plots:
if t == 1:
if hasattr(prev_distr, 'is_hikmet'):
plot_lloyd_max_hikmet(prev_distr,
prev_lm_encoder.boundaries,
prev_lm_decoder.levels,
x_hit=sim.plant.x)
else:
plot_lloyd_max(prev_distr,
prev_lm_encoder,
prev_lm_decoder,
x_hit=sim.plant.x)
else:
if hasattr(prev_distr, 'is_hikmet'):
plot_lloyd_max_tracker_hikmet(prev_distr,
prev_lm_encoder.boundaries,
prev_lm_decoder.levels,
tracker.d1, tracker.fw, x_hit=sim.plant.x)
else:
plot_lloyd_max_tracker(prev_distr,
prev_lm_encoder,
prev_lm_decoder,
tracker, x_hit=sim.plant.x)
tracker = sim.encoder.get_tracker().clone()
prev_distr = tracker.distr
prev_lm_encoder = tracker.lm_encoder
prev_lm_decoder = tracker.lm_decoder
print("Run {:d}, t = {:d} done".format(i, t))
except KeyboardInterrupt:
print("Keyboard interrupt!")
print(" Average power over channel: {:.4f}".format(
sim.channel.average_power()))
globals().update(params.all()) # Bring parameters into scope
def plot(average=True):
figure = plt.figure()
for measurement in measurements if not average \
else [Measurement.average(measurements)]:
plt.figure(figure.number)
measurement.plot_setup()
measurement.plot_LQG()
measurement.plot_bounds()
def generate_plot_lloyd_max(n_levels):
distr = st.norm
enc, dec = lm.generate(n_levels, distr)
plot_lloyd_max(distr, enc, dec)
def test_update(i=4):
global tracker
from separate.coding.source import DistributionTracker
tracker = DistributionTracker(sim, 10)
plot_lloyd_max(tracker.distr, tracker.lm_encoder, tracker.lm_decoder)
tracker.update(i, debug_globals=globals())
plot_lloyd_max(tracker.distr, tracker.lm_encoder, tracker.lm_decoder)
def plot_compare():
jscc = Measurement.load('data/joint/alpha_1.001_SNR_2_KC_32-runs.p')
separate1 = Measurement.load('data/separate/alpha_1.001_SNR_2_KC_2--1.p')
separate2 = Measurement.load('data/separate/alpha_1.001_SNR_2_KC_2--2.p')
plt.figure()
jscc.plot_setup()
# Plot in the right order so that the legend reads top-down
separate1.plot_LQG("Separation, single run")
separate2.plot_LQG("Separation, single run")
jscc.plot_LQG("Spiral JSCC, 32-run average")
jscc.plot_bounds(upper_label="Theoretical prediction (spiral JSCC)")
plt.legend()
plt.text(25, 5, jscc.params.text_description(),
bbox={'facecolor': 'white', 'edgecolor': 'gray'})
def plot_compare_2():
jscc_avg = Measurement.load('data/joint/alpha_1.5_SNR_2_KC_2_256-runs.p')
jscc = Measurement.load('data/comparison/alpha_1.5_SNR_2_KC_2--1-joint.p')
sep = Measurement.load('data/comparison/alpha_1.5_SNR_2_KC_2--1-separate.p')
jscc.plot_setup()
sep.plot_LQG("Tandem with (2-PAM)$^2$")
sep.plot_correctly_decoded()
jscc.plot_LQG("Spiral JSCC, same noise sequences")
jscc_avg.plot_LQG("Spiral JSCC, 256-run average")
jscc.plot_bounds(upper_label="Theoretical prediction (spiral JSCC)")
plt.legend(loc=(.55, .48))
plt.text(40, 1.6, jscc.params.text_description(),
bbox={'facecolor': 'white', 'edgecolor': 'gray'})
def plot_compare_3():
import matplotlib
matplotlib.rcParams.update({'font.size': 20, 'lines.linewidth': 3})
jscc = Measurement.load('data/comparison/alpha_1.2_SNR_4.5dB_KC_2--1-joint.p')
sep = Measurement.load('data/comparison/alpha_1.2_SNR_4.5dB_KC_2--1-separate.p')
jscc.plot_setup(label="t")
sep.plot_LQG("2-PAM", ':')
sep.plot_correctly_decoded(y=-15)
jscc.plot_LQG("Spiral", '-')
jscc.plot_bounds(upper_label="Spiral: analytic", lower_label="OPTA",
upper_args=['-.'], lower_args=['--'])
plt.legend(loc=(.4, .1))
def plot_varying_SNR(alpha, multi=False, log_outside=True):
plt.figure()
def closest(n):
return min(256, 512, 1024, 1536, 2048, key=lambda x: abs(x - n))
# SNR_dBs = [9, 10, 10.5, 11, 11.5, 12, 13, 13.5, 14, 15, 17, 20, 23, 25]
# SNR_dBs = [7.5, 8, 8.5, 9, 10, 10.25, 10.5, 10.75, 11, 11.25, 11.5, 11.75, 12, 13,
# 14, 15, 16, 'noiseless']
if not multi:
# SNR_dBs = sorted([
# # 10, 10.25, 10.5, 10.75,
# 11, 11.25, 11.5, 11.75,
# 12, 12.25, 12.5, 12.75,
# 13, 13.5, 13.75,
# 14, 15, 16
# ])
SNR_dBs = sorted(
# [1, 1.5, 2, 2.5, 3, 3.5] +
[4, 4.25, 4.5, 5, 5.5, 6, 6.5, 7, 15, 25])
ms = {SNR_dB: load_measurements(SNR_dB, alpha) for SNR_dB in SNR_dBs}
else:
# SNR_dBs = sorted([30, 25, 24.5, 24, 23.5, 23, 22.5, 22, 20, 17.5, 15, 12.5, 10])
# SNR_dBs = sorted([8, 25, 24.5, 24, 23.5, 23, 22.75, 22.5, 22])
SNR_dBs = sorted([7, 8, 8.5, 9, 9.5, 10, 11, 12, 15, 25])
ms = {SNR_dB: load_measurements(SNR_dB, alpha, 2, 4)
for SNR_dB in SNR_dBs}
if log_outside:
LQGlog10s = [10 * np.log10(np.mean([m.LQG[-1] for m in ms[SNR_dB]]))
for SNR_dB in SNR_dBs]
else:
LQGlog10s = [np.mean([10 * np.log10(m.LQG[-1]) for m in ms[SNR_dB]])
for SNR_dB in SNR_dBs]
plt.grid()
SNR_dBs += [None]
LQGlog10s += [None]
plt.scatter(SNR_dBs[:-1], LQGlog10s[:-1]) # (without noiseless)
plt.xlabel("SNR [dB]")
plt.ylabel("Average final average cost [dB]")
del SNR_dBs[-1], LQGlog10s[-1]
for SNR_dB, LQGlog10 in zip(SNR_dBs, LQGlog10s):
print("{:5}dB: {} ({} runs)".format(SNR_dB, LQGlog10,
len(ms[SNR_dB]) ))#,
# closest(len(ms[SNR_dB]))))
def show(delay=0):
if delay != 0:
from time import sleep
sleep(delay)
plt.show(block=False)
def take_data():
global SNR_dB
for alpha in [1.2]: #, 1.2]:
for SNR_dB in [
# 11, 12
# 8.5, 9.5
15, 25
]:
SNR = 10**(SNR_dB / 10)
params = Parameters(
T = T,
alpha = alpha,
W = 1, V = 0, # Lloyd-Max paper assumes no observation noise
Q = 1, R = 0, F = 1)
params.setRates(KC = 2, KS = 1)
params.setAnalog(SNR)
params.quantizer_bits = 1
params.setBlocklength(2)
params.set_PAM()
params.setScheme('separate')
params.set_random_code()
N = 256
n = defaultdict(lambda: N, {3: 2 * 256 - 280})
for _ in range(n[SNR_dB]):
try:
simulate_and_record(params)
except KeyboardInterrupt:
break
except (ValueError, TypeError):
pass
del measurements[:]