What is causing this Gekko syntax error: Error in syntax of function string: Missing opening parenthesis?

Question

I am using a function to create a dictionary containing gekko variables. Please can someone tell me what is causing the syntax error?

Here is the code I am running:

from gekko import GEKKO
m = GEKKO(remote = False)
def tank(tank_id, area, gm): #gm = gekko model
    tank_dict = {}
    tank_dict['id'] = tank_id
    tank_dict['area'] = gm.Param(value = area, name = tank_id + '_area')
    tank_dict['volume'] = gm.FV(value = 10, name = tank_id + '_volume')
    tank_dict['height'] = gm.Var(name = tank_id + '_height')
    
    gm.Equation(tank_dict['height'] == tank_dict['volume']/tank_dict['area'])
    
    return(tank_dict)

tank_1 = tank('tank_1', area = 5, gm = m)
m.solve(disp = True)

This is the error message I get:

 ----------------------------------------------------------------
 APMonitor, Version 1.0.0
 APMonitor Optimization Suite
 ----------------------------------------------------------------
 
 
 --------- APM Model Size ------------
 Each time step contains
   Objects      :  0
   Constants    :  0
   Variables    :  3
   Intermediates:  0
   Connections  :  0
   Equations    :  1
   Residuals    :  1
 
 @error: Model Expression
 *** Error in syntax of function string: Missing opening parenthesis

Position: 4                   
 tank_1_height-(((tank_1_volume)/(tank_1_area)))
    ?

I'm expecting the 'height' key in the tank_1 dictionary to contain a value of 2.

Answer 1

There are reserved keywords for naming variables in gekko because of how the underlying APMonitor model is written and compiled to byte-code. Those keywords include the tan() function that is used in the name tank in the variable definition. Naming the variables is optional, but does help if you need to read the gk0_model.apm file in the run directory m._path. Prepending something like x_ to the variable names overcomes this error.

tank_1 = tank('x_tank_1', area = 5, gm = m)

Here is the full script.

from gekko import GEKKO
m = GEKKO(remote = False)
def tank(tank_id, area, gm): #gm = gekko model
    tank_dict = {}
    tank_dict['id'] = tank_id
    tank_dict['area'] = gm.Param(value = area, name = tank_id + '_area')
    tank_dict['volume'] = gm.FV(value = 10, name = tank_id + '_volume')
    tank_dict['height'] = gm.Var(name = tank_id + '_height')
    
    gm.Equation(tank_dict['height'] == tank_dict['volume']/tank_dict['area'])
    
    return(tank_dict)

tank_1 = tank('x_tank_1', area = 5, gm = m)
m.solve(disp = False)

print('Height: ', tank_1['height'].value[0])

Alternatively, just let gekko handle the internal variable naming:

from gekko import GEKKO
m = GEKKO(remote = False)
def tank(tank_id, area, gm):
    tank_dict = {}
    tank_dict['id'] = tank_id
    tank_dict['area'] = gm.Param(value = area)
    tank_dict['volume'] = gm.FV(value = 10)
    tank_dict['height'] = gm.Var()
    
    gm.Equation(tank_dict['height'] == tank_dict['volume']/tank_dict['area'])
    
    return(tank_dict)

tank_1 = tank('tank_1', area = 5, gm = m)
m.solve(disp = False)

print('Height: ', tank_1['height'].value[0])

Here is a list of other reserved keywords in gekko for naming variables:

abs(x) absolute value |x|
abs2(x) absolute value with MPCC
abs3(x) absolute value with binary variable for switch
acos(x) inverse cosine, cos^-1(x)
acosh(x) inverse hyperbolic cosine, cosh^-1(x)
Array(type,size) array of GEKKO objects
arx auto-regressive exogenous input (time series) model
asin(x) inverse sine, sin^-1(x)
asinh(x) inverse hyperbolic sine, sinh^-1(x)
atan(x) inverse tangent, tan^-1(x)
atanh(x) inverse hyperbolic tangent, tanh^-1(x)
bspline bspline for 2D data
cos(x) cosine
cspline cubic spline for 1D data
erf(x) error function
erfc(x) complementary error function
exp(x) e^x
if3(cond,x1,x2) switch between x1 (cond<0) and x2 (cond>=0)
log(x) log_e (x), natural log
log10(x) log_10 (x), log base 10
max2(x1,x2) maximum value with MPCC
max3(x1,x2) maximum value with binary variable for switch
min2(x1,x2) minimum value with MPCC
min3(x1,x2) minimum value with binary variable for switch
periodic periodic (initial=final) for dynamic problems
pwl piece-wise linear function
sign2(x) signum operator with MPCC
sign3(x) signum operator with binary variable for switch
sin(x) sine
sinh(x) hyperbolic sine
sqrt(x) square root
state_space continuous/discrete and dense/sparse state space
sum summation of elements in a list or numpy array
tan(x) tangent
tanh(x) hyperbolic tangent
vsum(x) vertical sum of a single variable in the data direction

This list is also found in the APMonitor Documentation.