a6971c768d
To reduce complexity, and make it easier to add more images and tests, the images in the `test-data` repository should be merged with the main repository. Furthermore, we can remove a lot of the subprocess work in the image_testing.py file, as we no longer need to have it interact with git. The images are not the same. Images were regenerated with Qt6, and now have proper big and little endian handling thanks to @pijyoi Second commit is a slightly modified variant of 2e135ab282d6007b34a3854921be54d0e9efb241 authored by @pijyoi it is to convert qimages to RGBA8888 for testing. Image files were regenerated images for the big/little handling Fixed issue with bogus test from test_NonUniformImage and generated a new image
248 lines
8.0 KiB
Python
248 lines
8.0 KiB
Python
# -*- coding: utf-8 -*-
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from collections import OrderedDict
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from copy import deepcopy
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import numpy as np
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import pytest
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from numpy.testing import assert_array_almost_equal
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import pyqtgraph as pg
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np.random.seed(12345)
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def testSolve3D():
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p1 = np.array([[0,0,0,1],
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[1,0,0,1],
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[0,1,0,1],
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[0,0,1,1]], dtype=float)
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# transform points through random matrix
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tr = np.random.normal(size=(4, 4))
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tr[3] = (0,0,0,1)
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p2 = np.dot(tr, p1.T).T[:,:3]
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# solve to see if we can recover the transformation matrix.
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tr2 = pg.solve3DTransform(p1, p2)
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assert_array_almost_equal(tr[:3], tr2[:3])
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def test_interpolateArray_order0():
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check_interpolateArray(order=0)
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def test_interpolateArray_order1():
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check_interpolateArray(order=1)
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def check_interpolateArray(order):
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def interpolateArray(data, x):
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result = pg.interpolateArray(data, x, order=order)
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assert result.shape == x.shape[:-1] + data.shape[x.shape[-1]:]
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return result
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data = np.array([[ 1., 2., 4. ],
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[ 10., 20., 40. ],
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[ 100., 200., 400.]])
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# test various x shapes
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interpolateArray(data, np.ones((1,)))
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interpolateArray(data, np.ones((2,)))
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interpolateArray(data, np.ones((1, 1)))
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interpolateArray(data, np.ones((1, 2)))
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interpolateArray(data, np.ones((5, 1)))
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interpolateArray(data, np.ones((5, 2)))
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interpolateArray(data, np.ones((5, 5, 1)))
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interpolateArray(data, np.ones((5, 5, 2)))
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with pytest.raises(TypeError):
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interpolateArray(data, np.ones((3,)))
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with pytest.raises(TypeError):
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interpolateArray(data, np.ones((1, 3,)))
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with pytest.raises(TypeError):
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interpolateArray(data, np.ones((5, 5, 3,)))
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x = np.array([[ 0.3, 0.6],
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[ 1. , 1. ],
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[ 0.501, 1. ], # NOTE: testing at exactly 0.5 can yield different results from map_coordinates
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[ 0.501, 2.501], # due to differences in rounding
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[ 10. , 10. ]])
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result = interpolateArray(data, x)
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# make sure results match ndimage.map_coordinates
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import scipy.ndimage
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spresult = scipy.ndimage.map_coordinates(data, x.T, order=order)
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#spresult = np.array([ 5.92, 20. , 11. , 0. , 0. ]) # generated with the above line
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assert_array_almost_equal(result, spresult)
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# test mapping when x.shape[-1] < data.ndim
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x = np.array([[ 0.3, 0],
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[ 0.3, 1],
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[ 0.3, 2]])
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r1 = interpolateArray(data, x)
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x = np.array([0.3]) # should broadcast across axis 1
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r2 = interpolateArray(data, x)
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assert_array_almost_equal(r1, r2)
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# test mapping 2D array of locations
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x = np.array([[[0.501, 0.501], [0.501, 1.0], [0.501, 1.501]],
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[[1.501, 0.501], [1.501, 1.0], [1.501, 1.501]]])
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r1 = interpolateArray(data, x)
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r2 = scipy.ndimage.map_coordinates(data, x.transpose(2,0,1), order=order)
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#r2 = np.array([[ 8.25, 11. , 16.5 ], # generated with the above line
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#[ 82.5 , 110. , 165. ]])
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assert_array_almost_equal(r1, r2)
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def test_subArray():
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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])
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b = pg.subArray(a, offset=2, shape=(2,2,3), stride=(10,4,1))
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c = np.array([[[111,112,113], [121,122,123]], [[211,212,213], [221,222,223]]])
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assert np.all(b == c)
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# operate over first axis; broadcast over the rest
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aa = np.vstack([a, a/100.]).T
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cc = np.empty(c.shape + (2,))
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cc[..., 0] = c
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cc[..., 1] = c / 100.
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bb = pg.subArray(aa, offset=2, shape=(2,2,3), stride=(10,4,1))
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assert np.all(bb == cc)
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def test_rescaleData():
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dtypes = map(np.dtype, ('ubyte', 'uint16', 'byte', 'int16', 'int', 'float'))
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for dtype1 in dtypes:
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for dtype2 in dtypes:
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data = (np.random.random(size=10) * 2**32 - 2**31).astype(dtype1)
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for scale, offset in [(10, 0), (10., 0.), (1, -50), (0.2, 0.5), (0.001, 0)]:
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if dtype2.kind in 'iu':
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lim = np.iinfo(dtype2)
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lim = lim.min, lim.max
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else:
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lim = (-np.inf, np.inf)
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s1 = np.clip(float(scale) * (data-float(offset)), *lim).astype(dtype2)
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s2 = pg.rescaleData(data, scale, offset, dtype2)
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assert s1.dtype == s2.dtype
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if dtype2.kind in 'iu':
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assert np.all(s1 == s2)
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else:
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assert np.allclose(s1, s2)
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def test_eq():
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eq = pg.functions.eq
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zeros = [0, 0.0, np.float64(0), np.float32(0), np.int32(0), np.int64(0)]
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for i,x in enumerate(zeros):
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for y in zeros[i:]:
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assert eq(x, y)
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assert eq(y, x)
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assert eq(np.nan, np.nan)
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# test
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class NotEq(object):
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def __eq__(self, x):
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return False
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noteq = NotEq()
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assert eq(noteq, noteq) # passes because they are the same object
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assert not eq(noteq, NotEq())
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# Should be able to test for equivalence even if the test raises certain
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# exceptions
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class NoEq(object):
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def __init__(self, err):
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self.err = err
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def __eq__(self, x):
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raise self.err
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noeq1 = NoEq(AttributeError())
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noeq2 = NoEq(ValueError())
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noeq3 = NoEq(Exception())
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assert eq(noeq1, noeq1)
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assert not eq(noeq1, noeq2)
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assert not eq(noeq2, noeq1)
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with pytest.raises(Exception):
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eq(noeq3, noeq2)
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# test array equivalence
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# note that numpy has a weird behavior here--np.all() always returns True
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# if one of the arrays has size=0; eq() will only return True if both arrays
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# have the same shape.
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a1 = np.zeros((10, 20)).astype('float')
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a2 = a1 + 1
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a3 = a2.astype('int')
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a4 = np.empty((0, 20))
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assert not eq(a1, a2) # same shape/dtype, different values
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assert not eq(a1, a3) # same shape, different dtype and values
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assert not eq(a1, a4) # different shape (note: np.all gives True if one array has size 0)
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assert not eq(a2, a3) # same values, but different dtype
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assert not eq(a2, a4) # different shape
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assert not eq(a3, a4) # different shape and dtype
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assert eq(a4, a4.copy())
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assert not eq(a4, a4.T)
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# test containers
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assert not eq({'a': 1}, {'a': 1, 'b': 2})
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assert not eq({'a': 1}, {'a': 2})
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d1 = {'x': 1, 'y': np.nan, 3: ['a', np.nan, a3, 7, 2.3], 4: a4}
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d2 = deepcopy(d1)
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assert eq(d1, d2)
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d1_ordered = OrderedDict(d1)
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d2_ordered = deepcopy(d1_ordered)
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assert eq(d1_ordered, d2_ordered)
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assert not eq(d1_ordered, d2)
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items = list(d1.items())
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assert not eq(OrderedDict(items), OrderedDict(reversed(items)))
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assert not eq([1,2,3], [1,2,3,4])
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l1 = [d1, np.inf, -np.inf, np.nan]
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l2 = deepcopy(l1)
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t1 = tuple(l1)
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t2 = tuple(l2)
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assert eq(l1, l2)
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assert eq(t1, t2)
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assert eq(set(range(10)), set(range(10)))
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assert not eq(set(range(10)), set(range(9)))
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@pytest.mark.parametrize("s,suffix,expected", [
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# usual cases
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("100 uV", "V", ("100", "u", "V")),
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("100 µV", "V", ("100", "µ", "V")),
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("4.2 nV", None, ("4.2", "n", "V")),
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("1.2 m", "m", ("1.2", "", "m")),
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("1.2 m", None, ("1.2", "", "m")),
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("5.0e9", None, ("5.0e9", "", "")),
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("2 units", "units", ("2", "", "units")),
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# siPrefix with explicit empty suffix
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("1.2 m", "", ("1.2", "m", "")),
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("5.0e-9 M", "", ("5.0e-9", "M", "")),
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# weirder cases that should return the reasonable thing
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("4.2 nV", "nV", ("4.2", "", "nV")),
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("4.2 nV", "", ("4.2", "n", "")),
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("1.2 j", "", ("1.2", "", "")),
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("1.2 j", None, ("1.2", "", "j")),
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# expected error cases
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("100 uV", "v", ValueError),
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])
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def test_siParse(s, suffix, expected):
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if isinstance(expected, tuple):
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assert pg.siParse(s, suffix=suffix) == expected
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else:
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with pytest.raises(expected):
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pg.siParse(s, suffix=suffix)
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