simulations: refactor parse script to plot script

utils/simulations/awgn/parse.py

@@ -1,78 +0,0 @@
-#!/usr/bin/python
-
-import glob, os, re, sys
-import numpy as np
-import matplotlib.pyplot as plt
-import scipy as sp
-import scipy.stats as ss
-
-log_info = []
-table = {}
-
-enc_table = ["BPSK 1/2", "BPSK 3/4", "QPSK 1/2",
-			 "QPSK 3/4", "16QAM 1/2", "16QAM 3/4", 
-			 "64QAM 2/3", "64QAM 3/4"]
-
-path = sys.argv[1]
-ntrials = int(sys.argv[2])
-
-snr_list = []
-
-def mean_confidence_interval(data, confidence=0.95):
-	a = 1.0 * np.array(data)
-	n = len(a)
-	m, se = np.mean(a), ss.sem(a)
-	h = se * ss.t._ppf((1 + confidence)/2., n-1)
-	return m, h
-
-
-for filename in glob.glob(os.path.join(path, "*.log")):
-	params = filename.split("/")[1].split("_")
-	print params
-
-	try:
-		trial = int(params[1])
-		npkts = int(params[2])
-		snr = float(params[3])
-		encode = int(params[4])
-	except AssertionError:
-		print "Log filename parse failed; exiting!"
-		sys.exit()		
-
-	pdrate = sum(1 for line in open(filename))/float(npkts)
-
-	if snr not in snr_list:
-		snr_list.append(snr)
-
-	if encode not in table:
-		table[encode] = {}
-	if snr not in table[encode]:
-		table[encode][snr] = []
-	table[encode][snr].append(pdrate)  
-
-snr_list.sort()
-
-for enc in sorted(table.keys()):  # sort so legend labels are in order
-	# Check first that we got all datapoints for each trial
-	for snr in table[enc].keys():
-		try:
-			assert len(table[enc][snr]) == ntrials
-		except AssertionError:
-			print "You don't have enough datapoints; exiting!"
-			sys.exit()
-
-	y = [mean_confidence_interval(table[enc][snr]) for snr in snr_list]
-
-	# print enc_table[enc], snr_list, y
-	plt.errorbar(snr_list, [m for m,h in y], 
-		yerr=[h for m,h in y], 
-		marker='o', 
-		label="%s" % enc_table[enc])
-
-plt.ylabel("PDR")
-plt.xlabel("SNR")
-plt.title("Delivery rate vs. SNR for different encoding schemes")
-plt.legend(loc="upper left")
-plt.savefig(os.path.join(path,"plot.png"))
-plt.show()
-

utils/simulations/awgn/plot.py

@@ -0,0 +1,23 @@
+import numpy as np
+import pandas as pd
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+pdr = pd.read_csv('./results/all.csv', sep=';')
+
+pdr['encoding'] = pdr.encoding.astype("category", categories=range(8), ordered=True)
+
+pdr.encoding.cat.categories = [
+        "BPSK 1/2", "BPSK 3/4",
+        "QPSK 1/2", "QPSK 3/4",
+        "16-QAM 1/2", "16-QAM 3/4",
+        "64-QAM 2/3", "64-QAM 3/4"]
+
+
+a = pdr.groupby(['encoding', 'snr'])
+
+b = a.agg({'received': np.mean}).reset_index()
+c = b.pivot(index='snr', columns='encoding', values='received')
+c.plot()
+plt.show()
+