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utils.py
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from core.global_params import *
from matplotlib.lines import Line2D
from shutil import copyfile
import os
import matplotlib.pyplot as plt
from time import gmtime, strftime
import plotly.graph_objects as go
def plot_cdf(data, xlabel: str, dirstr: str, xlim=0):
_, ax = plt.subplots(figsize=(8,4))
ax.grid(linestyle='--')
ax.set_xlabel(xlabel)
step = STEP/10
maxval = 0
for NodeID in range(NUM_NODES):
val = np.max(data[NodeID][0])
if val>maxval:
maxval = val
maxval = max(maxval, xlim)
Lines = [[] for NodeID in range(NUM_NODES)]
mal = False
iot = False
for NodeID in range(NUM_NODES):
if MODE[NodeID]>0:
bins = np.arange(0, round(maxval*1/step), 1)*step
pdf = np.zeros(len(bins))
i = 0
if data[NodeID][0].size>1:
lats = sorted(data[NodeID][0])
for lat in lats:
while i<len(bins):
if lat>bins[i]:
i += 1
else:
break
pdf[i-1] += 1
pdf = pdf/sum(pdf) # normalise
cdf = np.cumsum(pdf)
if IOT[NodeID]:
iot = True
marker = 'x'
else:
marker = None
if MODE[NodeID]==1:
Lines[NodeID] = ax.plot(bins, cdf, color='tab:blue', linewidth=4*REP[NodeID]/REP[0], marker=marker, markevery=0.1)
if MODE[NodeID]==2:
Lines[NodeID] = ax.plot(bins, cdf, color='tab:red', linewidth=4*REP[NodeID]/REP[0], marker=marker, markevery=0.1)
if MODE[NodeID]==3:
mal = True
Lines[NodeID] = ax.plot(bins, cdf, color='tab:green', linewidth=4*REP[NodeID]/REP[0], marker=marker, markevery=0.1)
if mal:
ModeLines = [Line2D([0],[0],color='tab:red', lw=4), Line2D([0],[0],color='tab:blue', lw=4), Line2D([0],[0],color='tab:green', lw=4)]
ax.legend(ModeLines, ['Best-effort', 'Content','Malicious'], loc='lower right')
elif iot:
ModeLines = [Line2D([0],[0],color='tab:blue'), Line2D([0],[0],color='tab:red'), Line2D([0],[0],color='tab:blue', marker='x'), Line2D([0],[0],color='tab:red', marker='x')]
ax.legend(ModeLines, ['Content value node','Best-effort value node', 'Content IoT node', 'Best-effort IoT node'], loc='lower right')
else:
ModeLines = [Line2D([0],[0],color='tab:blue', lw=4), Line2D([0],[0],color='tab:red', lw=4)]
ax.legend(ModeLines, ['Content','Best-effort'], loc='lower right')
plt.savefig(dirstr, bbox_inches='tight')
def plot_cdf_exp(data, ax):
step = STEP/10
maxval = 0
for NodeID in range(NUM_NODES):
if len(data[NodeID][0])>0:
val = np.max(data[NodeID][0])
else:
val = 0
if val>maxval:
maxval = val
for NodeID in range(len(data)):
if MODE[NodeID]>0:
bins = np.arange(0, round(maxval*1/step), 1)*step
pdf = np.zeros(len(bins))
i = 0
lats = sorted(data[NodeID][0])
for lat in lats:
while i<len(bins):
if lat>bins[i]:
i += 1
else:
break
pdf[i-1] += 1
pdf = pdf/sum(pdf) # normalise
cdf = np.cumsum(pdf)
ax.plot(bins, cdf, color='tab:red')
lmd = np.mean(data[1][0])
ax.axvline(lmd, linestyle='--', color='tab:red')
ax.set_title('rho = ' + str(1/(lmd*NU)))
ax.plot(bins, np.ones(len(bins))-np.exp(-(1/lmd)*bins), color='black')
#ax.plot(bins, np.ones(len(bins))-np.exp(-0.95*NU*bins), linestyle='--', color='tab:red')
ModeLines = [Line2D([0],[0],color='tab:red', lw=2), Line2D([0],[0],linestyle='--',color='black', lw=2)]
ax.legend(ModeLines, ['Measured',r'$1-e^{-\lambda t}$'], loc='lower right')
def plot_ratesetter_comp(dir1, dir2, dir3):
fig, ax = plt.subplots(figsize=(8,4))
ax.grid(linestyle='--')
ax.set_xlabel('Time (sec)')
axt = ax.twinx()
ax.tick_params(axis='y', labelcolor='black')
axt.tick_params(axis='y', labelcolor='tab:gray')
ax.set_ylabel(r'$DR/\nu \quad (\%)$', color='black')
axt.set_ylabel('Mean Latency (sec)', color='tab:gray')
avgTP1 = np.loadtxt(dir1+'/avgTP.csv', delimiter=',')
avgMeanDelay1 = np.loadtxt(dir1+'/avgMeanDelay.csv', delimiter=',')
avgTP2 = np.loadtxt(dir2+'/avgTP.csv', delimiter=',')
avgMeanDelay2 = np.loadtxt(dir2+'/avgMeanDelay.csv', delimiter=',')
avgTP3 = np.loadtxt(dir3+'/avgTP.csv', delimiter=',')
avgMeanDelay3 = np.loadtxt(dir3+'/avgMeanDelay.csv', delimiter=',')
markerevery = 500
ax.plot(np.arange(10, SIM_TIME, STEP), 100*np.sum(avgTP1[1000:,:]/NU, axis=1), color = 'black', marker = 'o', markevery=markerevery)
axt.plot(np.arange(0, SIM_TIME, 1), avgMeanDelay1, color='tab:gray', marker = 'o', markevery=int(markerevery*STEP))
ax.plot(np.arange(10, SIM_TIME, STEP), 100*np.sum(avgTP2[1000:,:]/NU, axis=1), color = 'black', marker = 'x', markevery=markerevery)
axt.plot(np.arange(0, SIM_TIME, 1), avgMeanDelay2, color='tab:gray', marker = 'x', markevery=int(markerevery*STEP))
ax.plot(np.arange(10, SIM_TIME, STEP), 100*np.sum(avgTP3[1000:,:]/NU, axis=1), color = 'black', marker = '^', markevery=markerevery)
axt.plot(np.arange(0, SIM_TIME, 1), avgMeanDelay3, color='tab:gray', marker = '^', markevery=int(markerevery*STEP))
ax.set_ylim([0,110])
axt.set_ylim([0,9])
ModeLines = [Line2D([0],[0],color='black', linestyle=None, marker='o'), Line2D([0],[0],color='black', linestyle=None, marker='x'), Line2D([0],[0],color='black', linestyle=None, marker='^')]
#ax.legend(ModeLines, [r'$A=0.05$', r'$A=0.075$', r'$A=0.1$'], loc='lower right')
#ax.set_title(r'$\beta=0.7, \quad W=2$')
#ax.legend(ModeLines, [r'$\beta=0.5$', r'$\beta=0.7$', r'$\beta=0.9$'], loc='lower right')
#ax.set_title(r'$A=0.075, \quad W=2$')
ax.legend(ModeLines, [r'$W=1$', r'$W=2$', r'$W=3$'], loc='lower right')
ax.set_title(r'$A=0.075, \quad \beta=0.7$')
#ax.legend(ModeLines, ['Our algorithm', 'PoW case 1', 'PoW case 2'], loc='right')
#ax.set_title('Our algorithm vs. PoW')
#ax.legend(ModeLines, [r'$|\mathcal{M}|=25$', r'$|\mathcal{M}|=50$', r'$|\mathcal{M}|=75$'], loc='lower right', ncol=1)
#ax.set_title(r'$A=0.075, \quad \beta=0.7, \quad W=2$')
#ax.legend(ModeLines, ['PoW case 1', 'PoW case 2', 'PoW case 3'], loc='right')
dirstr = os.path.dirname(os.path.realpath(__file__)) + '/results/'+ strftime("%Y-%m-%d_%H%M%S", gmtime())
os.makedirs(dirstr, exist_ok=True)
copyfile(dir1+'/aaconfig.txt', dirstr+'/config1.txt')
copyfile(dir2+'/aaconfig.txt', dirstr+'/config2.txt')
copyfile(dir3+'/aaconfig.txt', dirstr+'/config3.txt')
fig.tight_layout()
plt.savefig(dirstr+'/Throughput.png', bbox_inches='tight')
def plot_scheduler_comp(dir1, dir2):
latencies1 = []
for NodeID in range(NUM_NODES):
if os.stat(dir1+'/latencies'+str(NodeID)+'.csv').st_size != 0:
lat = [np.loadtxt(dir1+'/latencies'+str(NodeID)+'.csv', delimiter=',')]
else:
lat = [0]
latencies1.append(lat)
latencies2 = []
for NodeID in range(NUM_NODES):
if os.stat(dir2+'/latencies'+str(NodeID)+'.csv').st_size != 0:
lat = [np.loadtxt(dir2+'/latencies'+str(NodeID)+'.csv', delimiter=',')]
else:
lat = [0]
latencies2.append(lat)
fig, ax = plt.subplots(2,1, sharex=True, figsize=(8,4))
ax[0].grid(linestyle='--')
ax[1].grid(linestyle='--')
ax[1].set_xlabel('Latency (sec)')
ax[0].set_title('DRR')
ax[1].set_title('DRR-')
xlim = plot_cdf(latencies1, ax[0])
plot_cdf(latencies2, ax[1], xlim)
dirstr = os.path.dirname(os.path.realpath(__file__)) + '/results/'+ strftime("%Y-%m-%d_%H%M%S", gmtime())
os.makedirs(dirstr, exist_ok=True)
copyfile(dir1+'/aaconfig.txt', dirstr+'/config1.txt')
copyfile(dir2+'/aaconfig.txt', dirstr+'/config2.txt')
plt.savefig(dirstr+'/LatencyComp.png', bbox_inches='tight')
def per_node_barplot(data, xlabel: str, ylabel: str, title: str, dirstr: str, legend_loc: str = 'upper right', modes=None):
fig, ax = plt.subplots(figsize=(8,4))
ax.grid(linestyle='--')
ax.set_xlabel(xlabel)
ax.title.set_text(title)
ax.set_ylabel(ylabel)
if modes is None:
modes = list(set(MODE))
mode_names = ['Inactive', 'Content','Best-effort', 'Malicious', 'Multi-rate']
colors = ['tab:gray', 'tab:blue', 'tab:red', 'tab:green', 'tab:olive']
for NodeID in range(NUM_NODES):
ax.bar(NodeID, data[NodeID], color=colors[MODE[NodeID]])
ModeLines = [Line2D([0],[0],color=colors[mode], lw=4) for mode in modes]
if len(modes)>1:
fig.legend(ModeLines, [mode_names[i] for i in modes], loc=legend_loc)
plt.savefig(dirstr, bbox_inches='tight')
def per_node_plot(data: np.ndarray, xlabel: str, ylabel: str, title: str, dirstr: str, avg_window: int = 100, legend_loc: str = 'upper right', modes = None, step=STEP, figtxt = ''):
fig, ax = plt.subplots(figsize=(8,4))
ax.grid(linestyle='--')
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.title.set_text(title)
if modes is None:
modes = list(set(MODE))
mode_names = ['Inactive', 'Content','Best-effort', 'Malicious', 'Multi-rate']
colors = ['tab:gray', 'tab:blue', 'tab:red', 'tab:green', 'tab:olive']
for NodeID in range(NUM_NODES):
if MODE[NodeID] in modes and np.any(data[:, NodeID]):
ax.plot(np.arange((avg_window-1)*step, SIM_TIME, step), np.convolve(np.ones(avg_window)/avg_window, data[:,NodeID], 'valid'), color=colors[MODE[NodeID]])
ax.set_xlim(0, SIM_TIME)
ModeLines = [Line2D([0],[0],color=colors[mode], lw=4) for mode in modes]
if len(modes)>1:
fig.legend(ModeLines, [mode_names[i] for i in modes], loc=legend_loc)
plt.figtext(0.5, 0.01, figtxt, wrap=True, horizontalalignment='center', fontsize=12)
plt.savefig(dirstr+'/plots/'+ylabel+'.png', bbox_inches='tight')
def scaled_rate_plot(data: np.ndarray, xlabel: str, ylabel: str, title: str, dirstr: str, avg_window: int = 1000, legend_loc: str = 'right', modes = None, step=STEP, figtxt = ''):
fig, ax = plt.subplots(2,1, sharex=True, figsize=(8,8))
ax[0].grid(linestyle='--')
ax[1].grid(linestyle='--')
ax[1].set_xlabel(xlabel)
ax[0].set_ylabel(ylabel)
ax[1].set_ylabel('Scaled '+ylabel)
if modes is None:
modes = list(set(MODE))
mode_names = ['Inactive', 'Content','Best-effort', 'Malicious', 'Multi-rate']
colors = ['tab:gray', 'tab:blue', 'tab:red', 'tab:green', 'tab:olive']
for NodeID in range(NUM_NODES):
if MODE[NodeID] in modes and np.any(data[:, NodeID]):
ax[0].plot(np.arange(avg_window*STEP, SIM_TIME, STEP), (data[avg_window:,NodeID]-data[:-avg_window,NodeID])/(avg_window*STEP), linewidth=5*REP[NodeID]/REP[0], color=colors[MODE[NodeID]])
ax[1].plot(np.arange(avg_window*STEP, SIM_TIME, STEP), (data[avg_window:,NodeID]-data[:-avg_window,NodeID])*sum(REP)/(NU*REP[NodeID]*avg_window*STEP), linewidth=5*REP[NodeID]/REP[0], color=colors[MODE[NodeID]])
ax[0].set_xlim(0, SIM_TIME)
ax[1].set_xlim(0, SIM_TIME)
ModeLines = [Line2D([0],[0],color=colors[mode], lw=4) for mode in modes]
if len(modes)>1:
fig.legend(ModeLines, [mode_names[i] for i in modes], loc=legend_loc)
plt.figtext(0.5, 0.01, figtxt, wrap=True, horizontalalignment='center', fontsize=12)
plt.savefig(dirstr+'/plots/Scaled '+ylabel+'.png', bbox_inches='tight')
def per_node_rate_plot(data: np.ndarray, xlabel: str, ylabel: str, title: str, dirstr: str, avg_window: int = 1000, legend_loc: str = 'upper right', modes = None, step=STEP, figtxt = ''):
fig, ax = plt.subplots(figsize=(8,4))
ax.grid(linestyle='--')
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.title.set_text(title)
if modes is None:
modes = list(set(MODE))
mode_names = ['Inactive', 'Content','Best-effort', 'Malicious', 'Multi-rate']
colors = ['tab:gray', 'tab:blue', 'tab:red', 'tab:green', 'tab:olive']
for NodeID in range(NUM_NODES):
if MODE[NodeID] in modes and np.any(data[:, NodeID]):
ax.plot(np.arange(avg_window*STEP, SIM_TIME, STEP), (data[avg_window:,NodeID]-data[:-avg_window,NodeID])/(avg_window*STEP), color=colors[MODE[NodeID]])
ax.set_xlim(0, SIM_TIME)
ModeLines = [Line2D([0],[0],color=colors[mode], lw=4) for mode in modes]
if len(modes)>1:
fig.legend(ModeLines, [mode_names[i] for i in modes], loc=legend_loc)
plt.figtext(0.5, 0.01, figtxt, wrap=True, horizontalalignment='center', fontsize=12)
plt.savefig(dirstr+'/plots/'+ylabel+'.png', bbox_inches='tight')
def per_node_plotly_plot(time, data: np.ndarray, xlabel: str, ylabel: str, title: str, avg_window: int = 2000, legend_loc: str = 'upper right', modes = None, step=STEP):
fig = go.Figure()
max_val = np.amax(data)
fig.update_layout(title=title,
xaxis_title=xlabel,
yaxis_title=ylabel,
yaxis_range=[0, 1.1*max_val])
if modes is None:
modes = list(set(MODE))
colors = ['gray', 'blue', 'red', 'green']
for NodeID in range(NUM_NODES):
if np.any(data[:, NodeID]):
fig.add_trace(go.Scatter(x=np.arange((avg_window-1)*step-SIM_TIME+time, time, step),
y=np.convolve(np.ones(avg_window)/avg_window, data[:,NodeID], 'valid'),
mode='lines',
name="Node " + str(NodeID+1)))
"""ModeLines = [Line2D([0],[0],color=colors[mode], lw=4) for mode in modes]
if len(modes)>1:
fig.legend(ModeLines, [mode_names[i] for i in modes], loc=legend_loc)"""
return fig
def all_node_plot(data: np.ndarray, xlabel: str, ylabel: str, title: str, dirstr: str):
_, ax = plt.subplots(figsize=(8,4))
ax.grid(linestyle='--')
ax.plot(np.arange(0, SIM_TIME, STEP), data, color='black')
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
plt.savefig(dirstr, bbox_inches='tight')