test_filters.py

from openpiv import filters
from openpiv.lib import replace_nans
import numpy as np
from skimage.util import random_noise
from skimage import img_as_ubyte


def test_import():
    """ test of the simplest PIV run """
    try:
        from openpiv import filters
        from openpiv import lib
    except:
        raise ImportError
    
def test_gaussian_kernel():
    """ test of a _gaussian_kernel """
    
    assert(np.allclose(filters._gaussian_kernel(1), 
    np.array([[ 0.04491922,  0.12210311,  0.04491922],
       [ 0.12210311,  0.33191066,  0.12210311],
       [ 0.04491922,  0.12210311,  0.04491922]])))
       
    # assert(np.isnan(filters._gaussian_kernel(0))) # issues a Warning
    
    
def test_gaussian():
    """ test of a Gaussian filter """
    u = np.ones((3,3))
    v = np.eye(3)
    uf,vf = filters.gaussian(u,v,1)
    assert(np.allclose(uf,np.array([[ 0.62103611,  0.78805844,  0.62103611],
        [ 0.78805844,  1.        ,  0.78805844],
        [ 0.62103611,  0.78805844,  0.62103611]])))
    assert(np.allclose(vf,np.array([[ 0.37682989,  0.24420622,  0.04491922],
        [ 0.24420622,  0.42174911,  0.24420622],
        [ 0.04491922,  0.24420622,  0.37682989]])))
        
def test_replace_nans():
    """ test of NaNs inpainting """
    u = np.nan*np.ones((5,5))
    u[2,2] = 1
    replace_nans(u,2,1e-3)
    assert(~np.all(np.isnan(u)))
    v = np.ones((9,9))
    v[1:-1,1:-1] = np.nan
    u = v.copy()
    replace_nans(u,1,1e-3,method='disk')
    assert(np.sum(np.isnan(u)) == 9) # central core is nan
    u = v.copy()
    replace_nans(u,2,1e-3,method='disk')
    assert(np.allclose(np.ones((9,9)),u))
    
    
def test_replace_outliers():
    """ test of replacing outliers """
    v = np.ones((9,9))
    v[1:-1,1:-1] = np.nan
    u = v.copy()
    uf,vf = filters.replace_outliers(u,v)
    assert(np.sum(np.isnan(u))==7**2)
    assert(np.allclose(np.ones((9,9)),uf))