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[simulation] minor fix for plots

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This commit is contained in:
giomba 2019-12-15 10:58:32 +01:00
parent 12139d07c4
commit 9596ce932b
3 changed files with 11 additions and 86 deletions
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8
simulation/main.sh
View File

@ -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"

2
simulation/plot.py
View File

@ -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')

87
simulation/pretty.py
View File

@ -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()
'''