plankton wrote:

Greetings all,

I rotate a vector field and than I tried to interpolate it to a new grid

using griddata.

CODE:

x_grid_unique = unique(x_grid)

y_grid_unique = unique(y_grid)

x_meshgrid, y_meshgrid = meshgrid(x_grid_unique, y_grid_unique)

x_rot_meshgrid = reshape(x_rot, [ len(x_meshgrid[:, 0]),

len(x_meshgrid[0, :])] )

y_rot_meshgrid = reshape(y_rot, [ len(x_meshgrid[:, 0]),

len(x_meshgrid[0, :])] )

u_rot_meshgrid = reshape(u_rot, [ len(x_meshgrid[:, 0]),

len(x_meshgrid[0, :])] )

v_rot_meshgrid = reshape(v_rot, [ len(x_meshgrid[:, 0]),

len(x_meshgrid[0, :])] )

u_interpolate = griddata(x_rot, y_rot, u_rot, x_rot_meshgrid,

y_rot_meshgrid)

v_interpolate = griddata(x_rot, y_rot, v_rot, x_rot_meshgrid,

y_rot_meshgrid)

I unfortunately griddata returns some nan (It seems that there are

multiple occurrences of the same [X,Y] pair in the data). In matlab you

can use griddata with additional options e.g. ru =

griddata(nx,ny,nu,rx,ry,'linear', {'QJ'}) to fix this, but this seems to

be not possible using the griddata function in matplotlib. Is there any

other way to avoid a return of nan?

For any help many thanks in advance

Andreas

Problem solved more or less. Griddata produces only at the boundary of the

vector field nan, which seems to be the result of my data matrixes u and v.

They contain serveral colums and rows with zeros at the boundary, which

leads to nan at the boundary. Finaly this is not a great problem and can be

fixed easily by adding zeros at the boundary after using griddata.

But maybe this can be fixed otherwise e.g. using griddata with parameters,

so that it is not necessary to fix the matrix by rewriting the boundary.

Therefore, following a sample script, which demonstrates the problem.

SAMPLE SCRIPT

--CODE--

from pylab import *

def rotate(x, y, angle):

x_rot = x * cos(angle) - y * sin(angle)

y_rot = x * sin(angle) + y * cos(angle)

return x_rot, y_rot

def generate_new_grid(x_rot, y_rot, x_elements, y_elements):

xmin = min(x_rot)

xmax = max(x_rot)

ymin = min(y_rot)

ymax = max(y_rot)

x = linspace(xmin, xmax, x_elements)

y = linspace(ymin, ymax, y_elements)

x_tecplot_vector = zeros(25, float)

y_tecplot_vector = zeros(25, float)

for i in range(5):

first = i *5

last = (i+1) * 5

x_tecplot_vector[first:last] = x

y_tecplot_vector[first:last] = y[i]

return x_tecplot_vector, y_tecplot_vector

# ###################################

u = zeros(25, float)

u[12] = 1

v = zeros(25, float)

v[11] = 2

x = zeros(5, float)

y = zeros(5, float)

x = linspace(1, 5, 5)

y = linspace(1, 5, 5)

x_grid, y_grid= generate_new_grid(x, y, 5, 5)

print y_grid

# ###################################

angle = 0.1

x_rot, y_rot = rotate(x_grid, y_grid, angle)

x_elements = 5

y_elements = 5

x_grid, y_grid= generate_new_grid(x_rot, y_rot, x_elements, y_elements)

u_rot, v_rot = rotate(u, v, angle)

x_meshgrid, y_meshgrid = meshgrid(x_grid, y_grid)

x_rot_meshgrid = reshape(x_grid, [ 5, 5] )

y_rot_meshgrid = reshape(y_grid, [ 5, 5] )

u_rot_meshgrid = reshape(u_rot, [ 5, 5] )

u_interpolate = griddata(x_rot, y_rot, u_rot, x_rot_meshgrid,

y_rot_meshgrid)

#save('test.dat', u_interpolate)

print u_interpolate

--CODE--

## ···

--

View this message in context: http://www.nabble.com/griddata-returns-nan-tp24537481p24623042.html

Sent from the matplotlib - users mailing list archive at Nabble.com.