From 9596ce932bf6c2b55932bbb4adc6e8d6f6745bed Mon Sep 17 00:00:00 2001
From: giomba <giomba@glgprograms.it>
Date: Sun, 15 Dec 2019 10:58:32 +0100
Subject: [PATCH] [simulation] minor fix for plots

---
 simulation/main.sh   |  8 ++--
 simulation/plot.py   |  2 +-
 simulation/pretty.py | 87 +++-----------------------------------------
 3 files changed, 11 insertions(+), 86 deletions(-)

diff --git a/simulation/main.sh b/simulation/main.sh
index 8a9d2ae..a83fb24 100755
--- a/simulation/main.sh
+++ b/simulation/main.sh
@@ -3,15 +3,15 @@
 REPOSITORY=$(pwd)
 CONTIKI=/home/giomba/workspace/uni/contiki/
 SIMULATION=/home/giomba/workspace/uni/anaws-proj/cooja/simulation-prng.csc
-REPEAT=10
+REPEAT=20
 
 # Setup environment
 mkdir -p "$REPOSITORY/simulation/results"
 
 # Setup simulation parameters
-for KAPPA in 10 7 5 3 2 1; do
-    for I_MIN in 12 14 16 18; do
-        I_MAX=$((20 - I_MIN))   # 20 =~ 17 minutes
+for KAPPA in 5; do
+    for I_MIN in 20 18 16; do
+        I_MAX=$((22 - I_MIN))   # 22 =~ 70 minutes
 
         for PROJECTCONF in "$REPOSITORY/oracle/project-conf.h" "$CONTIKI/examples/ipv6/rpl-border-router/project-conf.h"; do
             sed -i "s/^\#define RPL_CONF_DIO_REDUNDANCY *[0-9]*$/\#define RPL_CONF_DIO_REDUNDANCY $KAPPA/g" "$PROJECTCONF"
diff --git a/simulation/plot.py b/simulation/plot.py
index 8b5ba07..93127a9 100644
--- a/simulation/plot.py
+++ b/simulation/plot.py
@@ -4,7 +4,7 @@ import numpy as numpy
 import pandas as pd
 
 def getcolor(n):
-    return ['#aa0000', '#00aa00', '#0000aa', '#aaaa00'][int((i - 12) / 2)]
+    return ['#aa0000', '#00aa00', '#0000aa', '#aaaa00', '#aa00aa'][int((i - 12) / 2)]
 
 df = pd.read_csv('pretty.csv', delimiter='\s', engine='python')
 
diff --git a/simulation/pretty.py b/simulation/pretty.py
index 16d306a..039ba1e 100644
--- a/simulation/pretty.py
+++ b/simulation/pretty.py
@@ -29,11 +29,12 @@ for filename in os.listdir(RESULTSDIR):
 
     kappa = int(df['KAPPA'].to_string().split()[1])
     i_min = int(df['I_MIN'].to_string().split()[1])
-    i_max = int(df['I_MAX'].to_string().split()[1])
-    key = str(kappa) + ':' + str(i_min) + ':' + str(i_max)
+    key = str(kappa) + ':' + str(i_min)
 
     if (not key in all_route_discovery):
-        all_route_discovery[key] = { 'samples': [], 'k': kappa, 'i_min': i_min }
+        all_route_discovery[key] = { 'samples': [], 'k': kappa, 'i_min': i_min, 'count': 0 }
+
+    all_route_discovery[key]['count'] += 1
 
     with open(RESULTSDIR + '/' + filename) as file:
         for line in file:
@@ -45,94 +46,18 @@ for filename in os.listdir(RESULTSDIR):
 # now, for every k, i_min, i_max
 # all_route_discovery contains an array with all samples of the measured value
 
-print('k i_min mean ci')
+print('k i_min count mean ci')
 for simulation in all_route_discovery:
     all_route_discovery[simulation]['mean'] = np.mean(all_route_discovery[simulation]['samples'])
     all_route_discovery[simulation]['ci']   = compute_confidence_interval(all_route_discovery[simulation]['samples'])
 
     print(
-        #simulation,
         all_route_discovery[simulation]['k'],
         all_route_discovery[simulation]['i_min'],
+        all_route_discovery[simulation]['count'],
         all_route_discovery[simulation]['mean'],
         all_route_discovery[simulation]['ci']
     )
 
 exit(0)
 
-'''
-d = []
-f = []
-
-# compute fd in our interval of interest
-d = np.arange(0, (M * 2**0.5 / 2))
-for i in d:
-    f.append(fd(i))
-
-# compute integral of fd
-integral = []
-last = 0
-for i in range(0, len(f)):
-    part = f[i]
-    last = last + part
-    integral.append(last)
-
-print("normalization fd: ", np.trapz(f))
-
-# make plots
-fig, ax1 = plt.subplots()
-
-color = 'tab:red'
-ax1.set_xlabel('d [m]')
-ax1.set_ylabel('fD(d)')
-ax1.plot(d, f, 'c,', label='fD')
-
-ax2 = ax1.twinx()
-
-color = 'tab:blue'
-ax2.set_ylabel('FD(d)')
-ax2.plot(d, integral, 'b,', label="FD")
-
-plt.legend()
-plt.show()
-
-def fs(s):
-    if s >= 0 and s <= (T * M**2 ) / 4:
-        return np.pi / ( T * M**2 )
-    elif s >= (T * M**2 ) / 4 and s <= (T * M**2) / 2:
-        return (np.pi / (T * M**2)) - (4 / (T * M**2)) * np.arccos(M/2 * ((T / s)**0.5))
-    else:
-        return 0
-
-### Distribution of Service Time
-s = T * d**2
-f = []
-for i in s:
-    f.append(fs(i))
-
-print("normalization fs: ", np.trapz(f, s))
-
-# compute integral of fs
-integral = [ 0 ]
-last = 0
-for i in range(1, len(f)):
-    part = f[i] * (s[i] - s[i - 1])
-    last = last + part
-    integral.append(last)
-
-# make plots
-fig, ax1 = plt.subplots()
-color = 'tab:red'
-ax1.set_xlabel('s [s]')
-ax1.set_ylabel('fS(s)')
-ax1.plot(s, f, 'c,', label='fS')
-
-ax2 = ax1.twinx()
-
-color = 'tab:blue'
-ax2.set_ylabel('FD(d)')
-ax2.plot(s, integral, 'b,', label='FS')
-
-plt.legend()
-plt.show()
-'''
\ No newline at end of file