def stop_test(request, running_test_id):
running_test = TestRunning.objects.get(id=running_test_id)
workspace = running_test.workspace
jris = json.loads(
json.dumps(
running_test.jmeter_remote_instances, indent=4, sort_keys=True))
if jris is not None:
for jri in jris:
hostname = jri.get('hostname')
pid = int(jri.get('pid'))
ssh_key_id = int(LoadGeneratorServer.objects.get(address=hostname).ssh_key_id)
ssh_key = SSHKey.objects.get(id=ssh_key_id).path
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect(hostname, key_filename=ssh_key)
cmds = ['kill -9 {0}'.format(str(pid))]
stdin, stdout, stderr = ssh.exec_command(' ; '.join(cmds))
response = []
generate_data(running_test.id)
try:
proxy_process = psutil.Process(running_test.pid)
proxy_process.terminate()
response = [{
"message": "test was stopped",
"test_id": running_test_id,
"pid": running_test.pid
}]
running_test.delete()
except psutil.NoSuchProcess:
response = [{
"message": "test does not exist",
"test_id": running_test_id
}]
running_test.delete()
#post-test script execution:
project_id = running_test.project_id
header = script_header(project_id)
project = Project.objects.get(id=project_id)
body = project.script_post
script = header + body
with open(workspace + '/logs/' + "script_pre.log", 'w') as f:
rc = call(script, shell=True, stdout=f)
return JsonResponse(response, safe=False)
def main():
print("----------------------")
print("| codedrome.com |")
print("| Logarithmic Plots |")
print("----------------------\n")
data = datagenerator.generate_data()
print_data(data)
logarithmicplot.draw_logarithmic_plot(720, 540, "Logarithmic Plot", data,
6, "logarithmicplot1.svg")
import datagenerator
import matrix as m
matplotlib.rcParams.update({'font.size': 16})
# data_groundtruth() has the following inputs:
# Generates Data
# Input variables are:
# initial position meters
# initial velocity km/h
# final velocity (should be a negative number) km/h
# acceleration (should be a negative number) m/s^2
# how long the vehicle should idle
# how long the vehicle should drive in reverse at constant velocity
# time between lidar measurements in milliseconds
time_groundtruth, distance_groundtruth, velocity_groundtruth, acceleration_groundtruth = datagenerator.generate_data(
5, 100, -10, -10, 5000, 5000, 50)
data_groundtruth = pd.DataFrame({
'time': time_groundtruth,
'distance': distance_groundtruth,
'velocity': velocity_groundtruth,
'acceleration': acceleration_groundtruth
})
# ### Visualizing the Tracked Object Distance
#
# The next cell visualizes the simulating data. The first visualization shows
# the object distance over time. You can see that the car is moving forward
# although decelerating. Then the car stops for 5 seconds and then drives
# backwards for 5 seconds.
# In[2]:
def fetch_new_values(socketio, data):
while True:
generate_data(data)
socketio.emit("car_data", data)
sleep(0.5)
