def draw(name,zshift_tol = 0.25, path = None):
# Set the work_dir
if path == None:
work_dir = os.getcwd()
if path != None:
work_dir = os.chdir(path)
work_dir = os.getcwd()
grid_file = visual.get("BOUT.inp","grid") # Import grid file
eigs = collect("t_array") # Import Eigen values
var = collect(name) # Import variable
nx,ny,nz = visual.dimd(name) # Set the shape of the variable
#Import the R,Z values from the grid file
r = visual.nc_var(grid_file,'Rxy') # R
z = visual.nc_var(grid_file,'Zxy') # Z
zshift = visual.nc_var(grid_file, 'zShift') # zShift
z_tol = visual.z_shift_tol(nx,ny,zshift,zshift_tol) # set the z_tolerance value
#Interpolate r, z, zshift values
r2, ny2 = visual.zshift_interp2d(nx,ny,zshift,z_tol,r)
z2, ny2 = visual.zshift_interp2d(nx,ny,zshift,z_tol,z)
zshift2, ny2 = visual.zshift_interp2d(nx,ny,zshift,z_tol,zshift)
pts2 = visual.elm(r2,z2,zshift2,nx,ny2,nz) # Write the vtk points
# Plot eigen values and write the associated vtk file
plot_eigenvals(eigs, nx, ny, nz ,zshift, z_tol, ny2, name, pts2, var)
def onclick(event):
# Check if user clicked inside the plot
if event.xdata is None:
return
# Find closest data point, but stretch axes so
# real and imaginary components are weighted equally
dist = ((eigs_r - event.xdata)/range_r)**2 + ((eigs_i - event.ydata)/range_i)**2
ind = argmin(dist)
# Update the highlight plot
overplot.set_data([eigs_r[ind]], [eigs_i[ind]])
print("Eigenvalue number: %d (%e,%e)" % (ind, eigs_r[ind], eigs_i[ind]))
if data is not None:
# Draw the vtk file
# Set the variables up
var_r = data[2*ind,:]
var_i = data[2*ind+1,:]
nx,ny,nz = visual.dimd(name)
# Interpolate the variables
var_r2,ny3 = visual.zshift_interp3d(nx,ny,nz,zshift,z_tol,var_r)
var_i2,ny3 = visual.zshift_interp3d(nx,ny,nz,zshift,z_tol,var_i)
#c reate the vtk variables
vrbl_r = visual.vtk_var(var_r2,nx,ny2,nz)
vrbl_i = visual.vtk_var(var_i2,nx,ny2,nz)
#W rite the vtk file
vtk_path = visual.write_vtk_2(name,pts,vrbl_r,vrbl_i,ind)
print "Eigenvalue %d written to vtk" % ind
fig.canvas.draw()