Exact solution used in MES runs

We would like to MES the operation where we extrapolate the y boundaries



In [5]:

    
%matplotlib notebook

from sympy import init_printing
from sympy import S
from sympy import sin, cos, tanh, exp, pi, sqrt

from boutdata.mms import x, y, z, t
from boutdata.mms import DDX

import os, sys
# If we add to sys.path, then it must be an absolute path
common_dir = os.path.abspath('./../../../../common')
# Sys path is a list of system paths
sys.path.append(common_dir)
from CELMAPy.MES import make_plot, BOUT_print

init_printing()

Initialize



In [6]:

    
folder = '../gaussianWSinAndParabola/'

Define the variables



In [7]:

    
# Initialization
the_vars = {}



In [8]:

    
# We need Lx
from boututils.options import BOUTOptions
myOpts = BOUTOptions(folder)
Lx = eval(myOpts.geom['Lx'])
Ly = eval(myOpts.geom['Ly'])



In [9]:

    
# Gaussian with sinus and parabola

# The skew sinus
# In cartesian coordinates we would like a sinus with with a wave-vector in the direction
# 45 degrees with respect to the first quadrant. This can be achieved with a wave vector
# k = [1/sqrt(2), 1/sqrt(2)]
# sin((1/sqrt(2))*(x + y))
# We would like 2 nodes, so we may write
# sin((1/sqrt(2))*(x + y)*(2*pi/(2*Lx)))

# The gaussian
# In cartesian coordinates we would like
# f = exp(-(1/(2*w^2))*((x-x0)^2 + (y-y0)^2))

# A parabola
# In cartesian coordinates, we have
# ((x-x0)/Lx)^2


wx = 0.5*Lx
wy = 0.5*Ly
x0 = 0.3*Lx
y0 = 0.5*Ly
the_vars['f'] = sin((1/sqrt(2))*(x + y)*(2*pi/(2*Lx)))*\
                exp(-(((x-x0)**2/(2*wx**2)) + ((y-y0)**2/(2*wy**2))))*\
                ((x-x0)/Lx)**2

Plot



In [6]:

    
make_plot(folder=folder, the_vars=the_vars, plot2d=True, include_aux=False, direction='y')

Print the variables in BOUT++ format



In [7]:

    
BOUT_print(the_vars, rational=False)