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WIP: adding new tests and fixing bugs in pg.interpolateArray

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This commit is contained in:
Luke Campagnola 2015-02-16 11:04:58 -05:00
parent 1d7cbca64d
commit accafcce36
3 changed files with 54 additions and 27 deletions
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15
pyqtgraph/functions.py
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@ -460,7 +460,7 @@ def affineSlice(data, shape, origin, vectors, axes, order=1, returnCoords=False,
ind = (Ellipsis,) + inds
output[ind] = scipy.ndimage.map_coordinates(data[ind], x, order=order, **kargs)
else:
# map_coordinates expects the indexes as the first axis, whereas
# map_coordinates expects the indexes as the first axis, whereas
# interpolateArray expects indexes at the last axis.
tr = tuple(range(1,x.ndim)) + (0,)
output = interpolateArray(data, x.transpose(tr))
@ -483,7 +483,7 @@ def affineSlice(data, shape, origin, vectors, axes, order=1, returnCoords=False,
def interpolateArray(data, x, default=0.0):
"""
N-dimensional interpolation similar scipy.ndimage.map_coordinates.
N-dimensional interpolation similar to scipy.ndimage.map_coordinates.
This function returns linearly-interpolated values sampled from a regular
grid of data.
@ -492,7 +492,7 @@ def interpolateArray(data, x, default=0.0):
*x* is an array with (shape[-1] <= data.ndim) containing the locations
within *data* to interpolate.
Returns array of shape (x.shape[:-1] + data.shape)
Returns array of shape (x.shape[:-1] + data.shape[x.shape[-1]:])
For example, assume we have the following 2D image data::
@ -535,7 +535,7 @@ def interpolateArray(data, x, default=0.0):
This is useful for interpolating from arrays of colors, vertexes, etc.
"""
print "x:\n", x.shape, x
prof = debug.Profiler()
nd = data.ndim
@ -564,7 +564,7 @@ def interpolateArray(data, x, default=0.0):
axisIndex[axisIndex >= data.shape[ax]] = 0
fieldInds.append(axisIndex)
prof()
print "fieldInds:\n", fieldInds
# Get data values surrounding each requested point
# fieldData[..., i] contains all 2**nd values needed to interpolate x[i]
fieldData = data[tuple(fieldInds)]
@ -585,8 +585,11 @@ def interpolateArray(data, x, default=0.0):
result = result.sum(axis=0)
prof()
totalMask.shape = totalMask.shape + (1,) * (nd - md)
#totalMask.shape = totalMask.shape + (1,) * (nd - md)
print "mask:\n", totalMask.shape, totalMask
print "result:\n", result.shape, result
result[~totalMask] = default
print "masked:\n", result.shape, result
prof()
return result

20
pyqtgraph/graphicsItems/ROI.py
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@ -1061,8 +1061,8 @@ class ROI(GraphicsObject):
=================== ====================================================
This method uses :func:`affineSlice <pyqtgraph.affineSlice>` to generate
the slice from *data* and uses :func:`getAffineSliceParams <pyqtgraph.ROI.getAffineSliceParams>` to determine the parameters to
pass to :func:`affineSlice <pyqtgraph.affineSlice>`.
the slice from *data* and uses :func:`getAffineSliceParams <pyqtgraph.ROI.getAffineSliceParams>`
to determine the parameters to pass to :func:`affineSlice <pyqtgraph.affineSlice>`.
If *returnMappedCoords* is True, then the method returns a tuple (result, coords)
such that coords is the set of coordinates used to interpolate values from the original
@ -1079,24 +1079,16 @@ class ROI(GraphicsObject):
else:
kwds['returnCoords'] = True
result, coords = fn.affineSlice(data, shape=shape, vectors=vectors, origin=origin, axes=axes, **kwds)
#tr = fn.transformToArray(img.transform())[:2] ## remove perspective transform values
### separate translation from scale/rotate
#translate = tr[:,2]
#tr = tr[:,:2]
#tr = tr.reshape((2,2) + (1,)*(coords.ndim-1))
#coords = coords[np.newaxis, ...]
### map coordinates and return
#mapped = (tr*coords).sum(axis=0) ## apply scale/rotate
#mapped += translate.reshape((2,1,1))
mapped = fn.transformCoordinates(img.transform(), coords)
return result, mapped
def getAffineSliceParams(self, data, img, axes=(0,1)):
"""
Returns the parameters needed to use :func:`affineSlice <pyqtgraph.affineSlice>` to
extract a subset of *data* using this ROI and *img* to specify the subset.
Returns the parameters needed to use :func:`affineSlice <pyqtgraph.affineSlice>`
(shape, vectors, origin) to extract a subset of *data* using this ROI
and *img* to specify the subset.
See :func:`getArrayRegion <pyqtgraph.ROI.getArrayRegion>` for more information.
"""
@ -1138,8 +1130,6 @@ class ROI(GraphicsObject):
relativeTo['scale'] = relativeTo['size']
st['scale'] = st['size']
t1 = SRTTransform(relativeTo)
t2 = SRTTransform(st)
return t2/t1

44
pyqtgraph/tests/test_functions.py
View File

@ -22,18 +22,39 @@ def testSolve3D():
def test_interpolateArray():
def interpolateArray(data, x):
result = pg.interpolateArray(data, x)
assert result.shape == x.shape[:-1] + data.shape[x.shape[-1]:]
return result
data = np.array([[ 1., 2., 4. ],
[ 10., 20., 40. ],
[ 100., 200., 400.]])
# test various x shapes
interpolateArray(data, np.ones((1,)))
interpolateArray(data, np.ones((2,)))
interpolateArray(data, np.ones((1, 1)))
interpolateArray(data, np.ones((1, 2)))
interpolateArray(data, np.ones((5, 1)))
interpolateArray(data, np.ones((5, 2)))
interpolateArray(data, np.ones((5, 5, 1)))
interpolateArray(data, np.ones((5, 5, 2)))
with pytest.raises(TypeError):
interpolateArray(data, np.ones((3,)))
with pytest.raises(TypeError):
interpolateArray(data, np.ones((1, 3,)))
with pytest.raises(TypeError):
interpolateArray(data, np.ones((5, 5, 3,)))
x = np.array([[ 0.3, 0.6],
[ 1. , 1. ],
[ 0.5, 1. ],
[ 0.5, 2.5],
[ 10. , 10. ]])
result = pg.interpolateArray(data, x)
result = 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
@ -44,9 +65,10 @@ def test_interpolateArray():
x = np.array([[ 0.3, 0],
[ 0.3, 1],
[ 0.3, 2]])
r1 = interpolateArray(data, x)
x = np.array([0.3]) # should broadcast across axis 1
r2 = interpolateArray(data, x)
r1 = pg.interpolateArray(data, x)
r2 = pg.interpolateArray(data, x[0,:1])
assert_array_almost_equal(r1, r2)
@ -54,13 +76,25 @@ def test_interpolateArray():
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)
r1 = 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)
# test interpolate where data.ndim > x.shape[1]
data = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) # 2x2x3
x = np.array([[1, 1], [0, 0.5], [5, 5]])
r1 = interpolateArray(data, x)
assert np.all(r1[0] == data[1, 1])
assert np.all(r1[1] == 0.5 * (data[0, 0] + data[0, 1]))
assert np.all(r1[2] == 0)
def test_subArray():
a = np.array([0, 0, 111, 112, 113, 0, 121, 122, 123, 0, 0, 0, 211, 212, 213, 0, 221, 222, 223, 0, 0, 0, 0])
b = pg.subArray(a, offset=2, shape=(2,2,3), stride=(10,4,1))