68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import pyqtgraph as pg
|
|
import numpy as np
|
|
from numpy.testing import assert_array_almost_equal, assert_almost_equal
|
|
|
|
np.random.seed(12345)
|
|
|
|
def testSolve3D():
|
|
p1 = np.array([[0,0,0,1],
|
|
[1,0,0,1],
|
|
[0,1,0,1],
|
|
[0,0,1,1]], dtype=float)
|
|
|
|
# transform points through random matrix
|
|
tr = np.random.normal(size=(4, 4))
|
|
tr[3] = (0,0,0,1)
|
|
p2 = np.dot(tr, p1.T).T[:,:3]
|
|
|
|
# solve to see if we can recover the transformation matrix.
|
|
tr2 = pg.solve3DTransform(p1, p2)
|
|
|
|
assert_array_almost_equal(tr[:3], tr2[:3])
|
|
|
|
|
|
def test_interpolateArray():
|
|
data = np.array([[ 1., 2., 4. ],
|
|
[ 10., 20., 40. ],
|
|
[ 100., 200., 400.]])
|
|
|
|
x = np.array([[ 0.3, 0.6],
|
|
[ 1. , 1. ],
|
|
[ 0.5, 1. ],
|
|
[ 0.5, 2.5],
|
|
[ 10. , 10. ]])
|
|
|
|
result = pg.interpolateArray(data, x)
|
|
|
|
#import scipy.ndimage
|
|
#spresult = scipy.ndimage.map_coordinates(data, x.T, order=1)
|
|
spresult = np.array([ 5.92, 20. , 11. , 0. , 0. ]) # generated with the above line
|
|
|
|
assert_array_almost_equal(result, spresult)
|
|
|
|
# test mapping when x.shape[-1] < data.ndim
|
|
x = np.array([[ 0.3, 0],
|
|
[ 0.3, 1],
|
|
[ 0.3, 2]])
|
|
|
|
r1 = pg.interpolateArray(data, x)
|
|
r2 = pg.interpolateArray(data, x[0,:1])
|
|
assert_array_almost_equal(r1, r2)
|
|
|
|
|
|
# test mapping 2D array of locations
|
|
x = np.array([[[0.5, 0.5], [0.5, 1.0], [0.5, 1.5]],
|
|
[[1.5, 0.5], [1.5, 1.0], [1.5, 1.5]]])
|
|
|
|
r1 = pg.interpolateArray(data, x)
|
|
#r2 = scipy.ndimage.map_coordinates(data, x.transpose(2,0,1), order=1)
|
|
r2 = np.array([[ 8.25, 11. , 16.5 ], # generated with the above line
|
|
[ 82.5 , 110. , 165. ]])
|
|
|
|
assert_array_almost_equal(r1, r2)
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_interpolateArray() |