# Using numpy functions and updating with each time step

**URL:** <https://fenicsproject.discourse.group/t/using-numpy-functions-and-updating-with-each-time-step/7172>\
**Category:** I/O\
**Created:** [November 27, 2021, 10:51am UTC](https://fenicsproject.discourse.group/t/using-numpy-functions-and-updating-with-each-time-step/7172 "2021-11-27T10:51:23Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![CoE2845](https://yyz2.discourse-cdn.com/free1/user_avatar/fenicsproject.discourse.group/coe2845/32/1210_2.png) [@CoE2845](https://fenicsproject.discourse.group/u/CoE2845)\
**Post date:** [November 27, 2021, 10:51am UTC](https://fenicsproject.discourse.group/t/using-numpy-functions-and-updating-with-each-time-step/7172/1 "2021-11-27T10:51:23Z")

</div>

Hi everyone,

I currently have a function `Evap(T)` that is a function of my solution `T`, I wish to include this in my equation that is solved in a time dependently, and have the value of this function update for every time step. Currently I have this as the function I wish to use:

```auto
def vp_function(x): # x is the values of the solution at each node
	a = 5.73623
	b = 13204.109
	c = -24.306
	return 10**(a - (b/(x + c)))

tempspace = np.linspace(200,10000,10000)
vp_f = vp_function(tempspace)

mesh = UnitSquareMesh(10,10)
Space = FunctionSpace(mesh, 'P', 1) 
T = Function(Space)
vp_new_array = vp_f(T.vector().get_local())
vp_space = FunctionSpace(mesh, 'CG',1)
vp = Function(vp_space)
vp.vector()[:] = vp_new_array

def Evap(T):
	return vp*(1/T)

```

Where `Evap(T)` is the function I wish to use in my final form. However when I run what I have so farm I get this:

```auto
  File "test.py", line 54, in <module>
    vp_new_array = vp_f(T.vector().get_local())
TypeError: 'numpy.ndarray' object is not callable

```

Is there a way that I can set `vp` up such that it is usable in Fenics and its value will update for every time step, such that the value of `Evap(T)` will change due to its `1/T` dependence and its implicit `T` dependence from `vp` ? My current code is below:

```auto

from __future__ import print_function
import numpy as np
import scipy as scipy
import matplotlib.pyplot as plt
from dolfin import *
import ufl
from ufl import as_tensor
from ufl import Index
import math
from scipy.optimize import curve_fit
from mpi4py import MPI
from scipy.interpolate import interp1d
from scipy import interp
from scipy.interpolate import UnivariateSpline

parameters["allow_extrapolation"] = True
parameters["form_compiler"]["cpp_optimize"] = True
parameters["form_compiler"]["optimize"] = True
set_log_level(50)
hfont = {'fontname':'Liberation Sans'}

#----------------
t_start = 0.0
t_end = 100
nstep = 200
dtime = (t_end - t_start)/ nstep #time step

#---------IMPORT EVAPORATION DATA--------------
def vp_function(x):
	a = 5.73623
	b = 13204.109
	c = -24.306
	return 10**(a - (b/(x + c)))

tempspace = np.linspace(200,10000,10000)
vp_f = vp_function(tempspace)

#---------------------------------------
mesh = UnitSquareMesh(10,10)
Space = FunctionSpace(mesh, 'P', 1) 
T = Function(Space)
T0 = Function(Space)
T_init = Expression('Tambient', degree=1, Tambient=1000.)
T = project(T_init, Space)
assign(T0,T)

vp_new_array = vp_f(T.vector().get_local())
vp_space = FunctionSpace(mesh, 'CG',1)
vp = Function(vp_space)
vp.vector()[:] = vp_new_array

def Evap(T):
	return vp*(1/T)

#-------------------------------------------

V = TestFunction(Space) # Test Function used for FEA
dT = TrialFunction(Space) # Temperature Derivative

i = Index()
G = as_tensor(T.dx(i), (i)) #gradient of T
G0 = as_tensor(T0.dx(i), (i)) # gradient of T at previous time step 

q = -G
F = (1/dtime*(T-T0)*V - q[i]*V.dx(i)) * dx + Evap(T)*V*ds #final form to solve
Gain = derivative(F, T, dT) # Gain will be used as the Jacobian required to determine the evolution of a dynamic system 

problem = NonlinearVariationalProblem(F, T, [], Gain)
solver = NonlinearVariationalSolver(problem)

solver.parameters["newton_solver"]["relative_tolerance"] = 1E-4
solver.parameters["newton_solver"]["absolute_tolerance"] = 1E-3
solver.parameters["newton_solver"]["convergence_criterion"] = "residual"
solver.parameters["newton_solver"]["error_on_nonconvergence"] = True
solver.parameters["newton_solver"]["linear_solver"] = "cg"
solver.parameters["newton_solver"]["maximum_iterations"] = 10000
solver.parameters["newton_solver"]["preconditioner"] = "hypre_euclid"
solver.parameters["newton_solver"]["report"] = True

solver.parameters["newton_solver"]["krylov_solver"]["nonzero_initial_guess"] = False
solver.parameters["newton_solver"]["krylov_solver"]["relative_tolerance"] = 1E-4
solver.parameters["newton_solver"]["krylov_solver"]["absolute_tolerance"] = 1E-3
solver.parameters["newton_solver"]["krylov_solver"]["monitor_convergence"] = False
solver.parameters["newton_solver"]["krylov_solver"]["maximum_iterations"] = 100000

t = 0
for t in np.arange(t_start + dtime,t_end + dtime,dtime):
	print( "Time", t)
	solver.solve()
	assign(T0, T)
	print(assemble(Evap(T)*da))
	print(vp(max(T.vector())))

```

---

<div class="post-metadata">

**Author:** ![conpierce8](https://yyz2.discourse-cdn.com/free1/user_avatar/fenicsproject.discourse.group/conpierce8/32/2464_2.png) [@conpierce8](https://fenicsproject.discourse.group/u/conpierce8)\
**Post date:** [November 29, 2021, 2:50pm UTC](https://fenicsproject.discourse.group/t/using-numpy-functions-and-updating-with-each-time-step/7172/2 "2021-11-29T14:50:50Z")

</div>

Did you mean to use

```python
vp_new_array = vp_function(T.vector().get_local())

```

?

As the error states, `vp_f` is a `numpy.ndarray`, not a function.