How do I display solution in GEKKO GUI?
Question:
I would like to explore the functionality of the GEKKO web GUI for visualising results as described in https://www.researchgate.net/publication/326740143_GEKKO_optimization_suite.
I have enabled m.options.WEB=1 and solved with m.solve(GUI=True).
The GUI spawns but with no trends as expected (see below). How do I show the variable content and how are the web interface html files generated used by the GUI?
Answers:
The Gekko display version is with GUI=True as is shown by the plot in your question. Only FV, MV, SV, and CV values display in the web interface.
Edit: As noted in your comment, you resolved the lack of trend display by upgrading flask.
pip install flask --upgrade
The option WEB is the APMonitor version of the web-interface that uses AJAX and Flash plots (deprecated, to be replaced in a future version) to display the web interface. This can remain as m.options.WEB=0 to use the Gekko GUI.
Below is an example that generates the web-interface. The flask package is a dependency for the web-interface and the web-page needs to be refreshed the first time it is launched. Select the variables on the plot to display or add a new plot.
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
m = GEKKO()
m.time = np.linspace(0,20,41)
# Parameters
mass = 500
b = m.Param(value=50)
K = m.Param(value=0.8)
# Manipulated variable
p = m.MV(value=0, lb=0, ub=100)
p.STATUS = 1 # allow optimizer to change
p.DCOST = 0.1 # smooth out gas pedal movement
p.DMAX = 20 # slow down change of gas pedal
# Controlled Variable
v = m.CV(value=0)
v.STATUS = 1 # add the SP to the objective
m.options.CV_TYPE = 2 # squared error
v.SP = 40 # set point
v.TR_INIT = 1 # set point trajectory
v.TAU = 5 # time constant of trajectory
# Process model
m.Equation(mass*v.dt() == -v*b + K*b*p)
m.options.IMODE = 6 # control
m.solve(disp=False,GUI=True)
Calling m.solve(GUI=True) generates (or updates) the web-interface with each call. It is also possible to display the MPC solution with Matplotlib.
# get additional solution information
import json
with open(m.path+'//results.json') as f:
results = json.load(f)
plt.figure()
plt.subplot(2,1,1)
plt.plot(m.time,p.value,'b-',label='MV Optimized')
plt.legend()
plt.ylabel('Input')
plt.subplot(2,1,2)
plt.plot(m.time,results['v1.tr'],'k-',label='Reference Trajectory')
plt.plot(m.time,v.value,'r--',label='CV Response')
plt.ylabel('Output')
plt.xlabel('Time')
plt.legend(loc='best')
plt.show()
More complicated Matplotlib plots can also be generated to show a future prediction horizon, moving horizon estimation, or other customized features of the MPC solution.
I’m having a similar issue with the variables not being in the GUI. Flask is up to date, version 2.2.2…
I’ve found to ways that make them disappear would like to understand ways to work around this that don’t require all the objects to be in the global scope.
Running the example above works the GUI shows the variables and their results. But when init is separated into a function call (see 1st code block below) and the call to solve outside of it in python the GUI doesn’t work. I’ve also observed if I place the variables under the model using the dot notation (2nd code block) so I can pass the model and all of it’s variables around example m.u = m.MV() instead of u=m.MV() the GUI also stops showing the variables.
Passing model from function
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
def init():
m = GEKKO()
m.time = np.linspace(0,20,41)
# Parameters
mass = 500
b = m.Param(value=50)
K = m.Param(value=0.8)
# Manipulated variable
p = m.MV(value=0, lb=0, ub=100)
p.STATUS = 1 # allow optimizer to change
p.DCOST = 0.1 # smooth out gas pedal movement
p.DMAX = 20 # slow down change of gas pedal
# Controlled Variable
v = m.CV(value=0)
v.STATUS = 1 # add the SP to the objective
m.options.CV_TYPE = 2 # squared error
v.SP = 40 # set point
v.TR_INIT = 1 # set point trajectory
v.TAU = 5 # time constant of trajectory
# Process model
m.Equation(mass*v.dt() == -v*b + K*b*p)
m.options.IMODE = 6 # control
return m
m=init()
m.solve(disp=False,GUI=True)
Using dot notation to group variables with model
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
m = GEKKO()
m.time = np.linspace(0,20,41)
# Parameters
mass = 500
b = m.Param(value=50)
K = m.Param(value=0.8)
# Manipulated variable
m.p = m.MV(value=0, lb=0, ub=100)
m.p.STATUS = 1 # allow optimizer to change
m.p.DCOST = 0.1 # smooth out gas pedal movement
m.p.DMAX = 20 # slow down change of gas pedal
# Controlled Variable
m.v = m.CV(value=0)
m.v.STATUS = 1 # add the SP to the objective
m.options.CV_TYPE = 2 # squared error
m.v.SP = 40 # set point
m.v.TR_INIT = 1 # set point trajectory
m.v.TAU = 5 # time constant of trajectory
# Process model
m.Equation(mass*m.v.dt() == -m.v*b + K*b*m.p)
m.options.IMODE = 6 # control
m.solve(disp=False,GUI=True)
Param show but variables are missing!
I would like to explore the functionality of the GEKKO web GUI for visualising results as described in https://www.researchgate.net/publication/326740143_GEKKO_optimization_suite.
I have enabled m.options.WEB=1 and solved with m.solve(GUI=True).
The GUI spawns but with no trends as expected (see below). How do I show the variable content and how are the web interface html files generated used by the GUI?
The Gekko display version is with GUI=True as is shown by the plot in your question. Only FV, MV, SV, and CV values display in the web interface.
Edit: As noted in your comment, you resolved the lack of trend display by upgrading flask.
pip install flask --upgrade
The option WEB is the APMonitor version of the web-interface that uses AJAX and Flash plots (deprecated, to be replaced in a future version) to display the web interface. This can remain as m.options.WEB=0 to use the Gekko GUI.
Below is an example that generates the web-interface. The flask package is a dependency for the web-interface and the web-page needs to be refreshed the first time it is launched. Select the variables on the plot to display or add a new plot.
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
m = GEKKO()
m.time = np.linspace(0,20,41)
# Parameters
mass = 500
b = m.Param(value=50)
K = m.Param(value=0.8)
# Manipulated variable
p = m.MV(value=0, lb=0, ub=100)
p.STATUS = 1 # allow optimizer to change
p.DCOST = 0.1 # smooth out gas pedal movement
p.DMAX = 20 # slow down change of gas pedal
# Controlled Variable
v = m.CV(value=0)
v.STATUS = 1 # add the SP to the objective
m.options.CV_TYPE = 2 # squared error
v.SP = 40 # set point
v.TR_INIT = 1 # set point trajectory
v.TAU = 5 # time constant of trajectory
# Process model
m.Equation(mass*v.dt() == -v*b + K*b*p)
m.options.IMODE = 6 # control
m.solve(disp=False,GUI=True)
Calling m.solve(GUI=True) generates (or updates) the web-interface with each call. It is also possible to display the MPC solution with Matplotlib.
# get additional solution information
import json
with open(m.path+'//results.json') as f:
results = json.load(f)
plt.figure()
plt.subplot(2,1,1)
plt.plot(m.time,p.value,'b-',label='MV Optimized')
plt.legend()
plt.ylabel('Input')
plt.subplot(2,1,2)
plt.plot(m.time,results['v1.tr'],'k-',label='Reference Trajectory')
plt.plot(m.time,v.value,'r--',label='CV Response')
plt.ylabel('Output')
plt.xlabel('Time')
plt.legend(loc='best')
plt.show()
More complicated Matplotlib plots can also be generated to show a future prediction horizon, moving horizon estimation, or other customized features of the MPC solution.
I’m having a similar issue with the variables not being in the GUI. Flask is up to date, version 2.2.2…
I’ve found to ways that make them disappear would like to understand ways to work around this that don’t require all the objects to be in the global scope.
Running the example above works the GUI shows the variables and their results. But when init is separated into a function call (see 1st code block below) and the call to solve outside of it in python the GUI doesn’t work. I’ve also observed if I place the variables under the model using the dot notation (2nd code block) so I can pass the model and all of it’s variables around example m.u = m.MV() instead of u=m.MV() the GUI also stops showing the variables.
Passing model from function
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
def init():
m = GEKKO()
m.time = np.linspace(0,20,41)
# Parameters
mass = 500
b = m.Param(value=50)
K = m.Param(value=0.8)
# Manipulated variable
p = m.MV(value=0, lb=0, ub=100)
p.STATUS = 1 # allow optimizer to change
p.DCOST = 0.1 # smooth out gas pedal movement
p.DMAX = 20 # slow down change of gas pedal
# Controlled Variable
v = m.CV(value=0)
v.STATUS = 1 # add the SP to the objective
m.options.CV_TYPE = 2 # squared error
v.SP = 40 # set point
v.TR_INIT = 1 # set point trajectory
v.TAU = 5 # time constant of trajectory
# Process model
m.Equation(mass*v.dt() == -v*b + K*b*p)
m.options.IMODE = 6 # control
return m
m=init()
m.solve(disp=False,GUI=True)
Using dot notation to group variables with model
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
m = GEKKO()
m.time = np.linspace(0,20,41)
# Parameters
mass = 500
b = m.Param(value=50)
K = m.Param(value=0.8)
# Manipulated variable
m.p = m.MV(value=0, lb=0, ub=100)
m.p.STATUS = 1 # allow optimizer to change
m.p.DCOST = 0.1 # smooth out gas pedal movement
m.p.DMAX = 20 # slow down change of gas pedal
# Controlled Variable
m.v = m.CV(value=0)
m.v.STATUS = 1 # add the SP to the objective
m.options.CV_TYPE = 2 # squared error
m.v.SP = 40 # set point
m.v.TR_INIT = 1 # set point trajectory
m.v.TAU = 5 # time constant of trajectory
# Process model
m.Equation(mass*m.v.dt() == -m.v*b + K*b*m.p)
m.options.IMODE = 6 # control
m.solve(disp=False,GUI=True)
Param show but variables are missing!