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Arnaud WATLET authoredbabafcb3
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import matplotlib.pyplot as plt
import numpy as np
from utils import parse_log
from datetime import datetime
import matplotlib
def plot_exec_log(exec_log,names=None,last_session=True):
time, process_id, tag, msg, session = parse_log(exec_log)
print(session)
if last_session:
time, process_id, tag, msg = time[session==max(session)], process_id[session==max(session)], tag[session==max(session)], msg[session==max(session)]
events = msg[tag == 'EVENT']
print(time)
category, name, state, time = np.empty(events.shape[0]).astype(str), np.empty(events.shape[0]).astype(str), \
np.empty(events.shape[0]).astype(str), np.empty(events.shape[0]).astype(str)
for i, event in enumerate(events):
# print(event.split("\t")[3])
# print('o',datetime.strptime('2023-06-16 10:04:54.222336','%Y-%m-%d %H:%M:%S.%f'))
category[i] = event.split("\t")[0]
name[i] = event.split("\t")[1]
state[i] = event.split("\t")[2]
time[i] = event.split("\t")[3].replace('\n','') #datetime.strptime(event.split("\t")[3],'%Y-%m-%d %H:%M:%S.%f')
time = time.astype(np.datetime64)
state = state[time.argsort()]
category = category[time.argsort()]
name = name[time.argsort()]
time = np.sort(time)
if names is None:
names = dict.fromkeys(np.unique(category))
for cat in np.unique(category):
names[cat] = np.array(np.unique(name[category == cat]))
fig, axarr = plt.subplots(len(names.keys()),sharex=True)
print(axarr)
if not isinstance(axarr,np.ndarray):
print('no')
axarr = np.array([axarr])
for i,cat in enumerate(names.keys()):
print(cat)
y=0
for j,n in enumerate(names[cat]):
cmap = matplotlib.cm.get_cmap('tab20')
colors = [cmap(c/len(names[cat])) for c in range(len(names[cat]))]
event_ids = np.where((name==n) & (category==cat))[0]
y+=1
axarr[i].set_title(cat)
label=True
for k,id in enumerate(event_ids[:-1]):
# print(state[event_ids[k]])
if state[event_ids[k]] == 'begin' and state[event_ids[k+1]] == 'end':
if label:
axarr[i].fill_betweenx([y,y+1],time[event_ids[k]],time[event_ids[k+1]],color=colors[j],label=n)
label=False
else:
axarr[i].fill_betweenx([y, y + 1], time[event_ids[k]], time[event_ids[k + 1]], color=colors[j])
ylabels = names[cat]
ylabelpos = np.arange(len(names[cat]))+1.5
axarr[i].set_yticks(ylabelpos)
axarr[i].set_yticklabels(ylabels)
axarr[i].legend()
plt.show()
plot_exec_log('logs/exec.log')