from collections import OrderedDict from copy import deepcopy import numpy as np import pytest from numpy.testing import assert_array_almost_equal import pyqtgraph as pg from pyqtgraph.functions import arrayToQPath, eq, SignalBlock from pyqtgraph.Qt import QtCore, QtGui 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_order0(): check_interpolateArray(order=0) def test_interpolateArray_order1(): check_interpolateArray(order=1) def check_interpolateArray(order): pytest.importorskip("scipy") def interpolateArray(data, x): result = pg.interpolateArray(data, x, order=order) 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. ], [ 0.501, 1. ], # NOTE: testing at exactly 0.5 can yield different results from map_coordinates [ 0.501, 2.501], # due to differences in rounding [ 10. , 10. ]]) result = interpolateArray(data, x) # make sure results match ndimage.map_coordinates import scipy.ndimage spresult = scipy.ndimage.map_coordinates(data, x.T, order=order) #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 = interpolateArray(data, x) x = np.array([0.3]) # should broadcast across axis 1 r2 = interpolateArray(data, x) assert_array_almost_equal(r1, r2) # test mapping 2D array of locations x = np.array([[[0.501, 0.501], [0.501, 1.0], [0.501, 1.501]], [[1.501, 0.501], [1.501, 1.0], [1.501, 1.501]]]) r1 = interpolateArray(data, x) r2 = scipy.ndimage.map_coordinates(data, x.transpose(2,0,1), order=order) #r2 = np.array([[ 8.25, 11. , 16.5 ], # generated with the above line #[ 82.5 , 110. , 165. ]]) assert_array_almost_equal(r1, r2) 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)) c = np.array([[[111,112,113], [121,122,123]], [[211,212,213], [221,222,223]]]) assert np.all(b == c) # operate over first axis; broadcast over the rest aa = np.vstack([a, a/100.]).T cc = np.empty(c.shape + (2,)) cc[..., 0] = c cc[..., 1] = c / 100. bb = pg.subArray(aa, offset=2, shape=(2,2,3), stride=(10,4,1)) assert np.all(bb == cc) def test_rescaleData(): rng = np.random.default_rng(12345) dtypes = map(np.dtype, ('ubyte', 'uint16', 'byte', 'int16', 'int', 'float')) for dtype1 in dtypes: for dtype2 in dtypes: if dtype1.kind in 'iu': lim = np.iinfo(dtype1) data = rng.integers(lim.min, lim.max, size=10, dtype=dtype1, endpoint=True) else: data = (rng.random(size=10) * 2**32 - 2**31).astype(dtype1) for scale, offset in [(10, 0), (10., 0.), (1, -50), (0.2, 0.5), (0.001, 0)]: if dtype2.kind in 'iu': lim = np.iinfo(dtype2) lim = lim.min, lim.max else: lim = (-np.inf, np.inf) s1 = np.clip(float(scale) * (data-float(offset)), *lim).astype(dtype2) s2 = pg.rescaleData(data, scale, offset, dtype2) assert s1.dtype == s2.dtype if dtype2.kind in 'iu': assert np.all(s1 == s2) else: assert np.allclose(s1, s2) def test_eq(): eq = pg.functions.eq zeros = [0, 0.0, np.float64(0), np.float32(0), np.int32(0), np.int64(0)] for i,x in enumerate(zeros): for y in zeros[i:]: assert eq(x, y) assert eq(y, x) assert eq(np.nan, np.nan) # test class NotEq(object): def __eq__(self, x): return False noteq = NotEq() assert eq(noteq, noteq) # passes because they are the same object assert not eq(noteq, NotEq()) # Should be able to test for equivalence even if the test raises certain # exceptions class NoEq(object): def __init__(self, err): self.err = err def __eq__(self, x): raise self.err noeq1 = NoEq(AttributeError()) noeq2 = NoEq(ValueError()) noeq3 = NoEq(Exception()) assert eq(noeq1, noeq1) assert not eq(noeq1, noeq2) assert not eq(noeq2, noeq1) with pytest.raises(Exception): eq(noeq3, noeq2) # test array equivalence # note that numpy has a weird behavior here--np.all() always returns True # if one of the arrays has size=0; eq() will only return True if both arrays # have the same shape. a1 = np.zeros((10, 20)).astype('float') a2 = a1 + 1 a3 = a2.astype('int') a4 = np.empty((0, 20)) assert not eq(a1, a2) # same shape/dtype, different values assert not eq(a1, a3) # same shape, different dtype and values assert not eq(a1, a4) # different shape (note: np.all gives True if one array has size 0) assert not eq(a2, a3) # same values, but different dtype assert not eq(a2, a4) # different shape assert not eq(a3, a4) # different shape and dtype assert eq(a4, a4.copy()) assert not eq(a4, a4.T) # test containers assert not eq({'a': 1}, {'a': 1, 'b': 2}) assert not eq({'a': 1}, {'a': 2}) d1 = {'x': 1, 'y': np.nan, 3: ['a', np.nan, a3, 7, 2.3], 4: a4} d2 = deepcopy(d1) assert eq(d1, d2) d1_ordered = OrderedDict(d1) d2_ordered = deepcopy(d1_ordered) assert eq(d1_ordered, d2_ordered) assert not eq(d1_ordered, d2) items = list(d1.items()) assert not eq(OrderedDict(items), OrderedDict(reversed(items))) assert not eq([1,2,3], [1,2,3,4]) l1 = [d1, np.inf, -np.inf, np.nan] l2 = deepcopy(l1) t1 = tuple(l1) t2 = tuple(l2) assert eq(l1, l2) assert eq(t1, t2) assert eq(set(range(10)), set(range(10))) assert not eq(set(range(10)), set(range(9))) @pytest.mark.parametrize("s,suffix,expected", [ # usual cases ("100 uV", "V", ("100", "u", "V")), ("100 µV", "V", ("100", "µ", "V")), ("4.2 nV", None, ("4.2", "n", "V")), ("1.2 m", "m", ("1.2", "", "m")), ("1.2 m", None, ("1.2", "", "m")), ("5.0e9", None, ("5.0e9", "", "")), ("2 units", "units", ("2", "", "units")), # siPrefix with explicit empty suffix ("1.2 m", "", ("1.2", "m", "")), ("5.0e-9 M", "", ("5.0e-9", "M", "")), # weirder cases that should return the reasonable thing ("4.2 nV", "nV", ("4.2", "", "nV")), ("4.2 nV", "", ("4.2", "n", "")), ("1.2 j", "", ("1.2", "", "")), ("1.2 j", None, ("1.2", "", "j")), # expected error cases ("100 uV", "v", ValueError), ]) def test_siParse(s, suffix, expected): if isinstance(expected, tuple): assert pg.siParse(s, suffix=suffix) == expected else: with pytest.raises(expected): pg.siParse(s, suffix=suffix) def test_CIELab_reconversion(): color_list = [ pg.Qt.QtGui.QColor('#100235') ] # known problematic values for _ in range(20): qcol = pg.Qt.QtGui.QColor() qcol.setRgbF( *np.random.random((3)) ) color_list.append(qcol) for qcol1 in color_list: vec_Lab = pg.functions.colorCIELab( qcol1 ) qcol2 = pg.functions.CIELabColor(*vec_Lab) for val1, val2 in zip( qcol1.getRgb(), qcol2.getRgb() ): assert abs(val1-val2)<=1, f'Excess CIELab reconversion error ({qcol1.name() } > {vec_Lab } > {qcol2.name()})' MoveToElement = pg.QtGui.QPainterPath.ElementType.MoveToElement LineToElement = pg.QtGui.QPainterPath.ElementType.LineToElement _dtypes = [] for bits in 32, 64: for base in 'int', 'float', 'uint': _dtypes.append(f'{base}{bits}') _dtypes.extend(['uint8', 'uint16']) def _handle_underflow(dtype, *elements): """Wrapper around path description which converts underflow into proper points""" out = [] dtype = np.dtype(dtype) # get the signed integer type of the same width dtype_int = np.dtype(f'i{dtype.itemsize}') for el in elements: newElement = [el[0]] for ii in range(1, 3): coord = el[ii] if dtype.kind == 'u' and coord < 0: # coord is a float with a negative integral value. # for unsigned integer types, we want negative values to # wrap-around. to get consistent wrap-around behavior # across different numpy versions and machine platforms, # we first convert coord to a signed integer. coord = np.array(coord, dtype=dtype_int).astype(dtype) newElement.append(float(coord)) out.append(tuple(newElement)) return out @pytest.mark.parametrize( "xs, ys, connect, expected", [ *( ( np.arange(6, dtype=dtype), np.arange(0, -6, step=-1).astype(dtype), 'all', _handle_underflow(dtype, (MoveToElement, 0.0, 0.0), (LineToElement, 1.0, -1.0), (LineToElement, 2.0, -2.0), (LineToElement, 3.0, -3.0), (LineToElement, 4.0, -4.0), (LineToElement, 5.0, -5.0) ) ) for dtype in _dtypes ), *( ( np.arange(6, dtype=dtype), np.arange(0, -6, step=-1).astype(dtype), 'pairs', _handle_underflow(dtype, (MoveToElement, 0.0, 0.0), (LineToElement, 1.0, -1.0), (MoveToElement, 2.0, -2.0), (LineToElement, 3.0, -3.0), (MoveToElement, 4.0, -4.0), (LineToElement, 5.0, -5.0), ) ) for dtype in _dtypes ), *( ( np.arange(5, dtype=dtype), np.arange(0, -5, step=-1).astype(dtype), 'pairs', _handle_underflow(dtype, (MoveToElement, 0.0, 0.0), (LineToElement, 1.0, -1.0), (MoveToElement, 2.0, -2.0), (LineToElement, 3.0, -3.0), (MoveToElement, 4.0, -4.0) ) ) for dtype in _dtypes ), # NaN types don't coerce to integers, don't test for all types since that doesn't make sense ( np.arange(5), np.array([0, -1, np.nan, -3, -4]), 'finite', ( (MoveToElement, 0.0, 0.0), (LineToElement, 1.0, -1.0), (LineToElement, 1.0, -1.0), (MoveToElement, 3.0, -3.0), (LineToElement, 4.0, -4.0) ) ), ( np.array([0, 1, np.nan, 3, 4]), np.arange(0, -5, step=-1), 'finite', ( (MoveToElement, 0.0, 0.0), (LineToElement, 1.0, -1.0), (LineToElement, 1.0, -1.0), (MoveToElement, 3.0, -3.0), (LineToElement, 4.0, -4.0) ) ), *( ( np.arange(5, dtype=dtype), np.arange(0, -5, step=-1).astype(dtype), np.array([0, 1, 0, 1, 0]), _handle_underflow(dtype, (MoveToElement, 0.0, 0.0), (MoveToElement, 1.0, -1.0), (LineToElement, 2.0, -2.0), (MoveToElement, 3.0, -3.0), (LineToElement, 4.0, -4.0) ) ) for dtype in _dtypes ), # Empty path with all types of connection *( ( np.arange(0), np.arange(0, dtype=dtype), conn, () ) for conn in ['all', 'pairs', 'finite', np.array([])] for dtype in _dtypes ), ] ) def test_arrayToQPath(xs, ys, connect, expected): path = arrayToQPath(xs, ys, connect=connect) element = None for i in range(path.elementCount()): # nan elements add two line-segments, for simplicity of test config # we can ignore the second segment if element is not None and (eq(element.x, np.nan) or eq(element.y, np.nan)): continue element = path.elementAt(i) assert eq(expected[i], (element.type, element.x, element.y)) def test_ndarray_from_qpolygonf(): # test that we get an empty ndarray from an empty QPolygonF poly = pg.functions.create_qpolygonf(0) arr = pg.functions.ndarray_from_qpolygonf(poly) assert isinstance(arr, np.ndarray) def test_ndarray_from_qimage(): # for QImages created w/o specifying bytesPerLine, Qt will pad # each line to a multiple of 4-bytes. # test that we can handle such QImages. h = 10 fmt = QtGui.QImage.Format.Format_RGB888 for w in [5, 6, 7, 8]: qimg = QtGui.QImage(w, h, fmt) qimg.fill(0) arr = pg.functions.ndarray_from_qimage(qimg) assert arr.shape == (h, w, 3) fmt = QtGui.QImage.Format.Format_Grayscale8 for w in [5, 6, 7, 8]: qimg = QtGui.QImage(w, h, fmt) qimg.fill(0) arr = pg.functions.ndarray_from_qimage(qimg) assert arr.shape == (h, w) def test_colorDistance(): pg.colorDistance([pg.Qt.QtGui.QColor(0,0,0), pg.Qt.QtGui.QColor(255,0,0)]) pg.colorDistance([]) @pytest.mark.parametrize( "test_input,expected", [ (["r"], [255, 0, 0, 255]), (["g"], [0, 255, 0, 255]), (["b"], [0, 0, 255, 255]), (["c"], [0, 255, 255, 255]), (["m"], [255, 0, 255, 255]), (["y"], [255, 255, 0, 255]), (["k"], [0, 0, 0, 255]), (["w"], [255, 255, 255, 255]), (["d"], [150, 150, 150, 255]), (["l"], [200, 200, 200, 255]), (["s"], [100, 100, 150, 255]), ([0.75], [191, 191, 191, 255]), ([11, 22, 33], [11, 22, 33, 255]), ([11, 22, 33, 44], [11, 22, 33, 44]), ([(11, 22, 33)], [11, 22, 33, 255]), ([(11, 22, 33, 44)], [11, 22, 33, 44]), ([0], [255, 0, 0, 255]), ([1], [255, 170, 0, 255]), ([2], [170, 255, 0, 255]), ([3], [0, 255, 0, 255]), ([4], [0, 255, 170, 255]), ([5], [0, 170, 255, 255]), ([9], [255, 0, 0, 255]), ([(0, 2)], [255, 0, 0, 255]), ([(1, 2)], [0, 255, 255, 255]), ([(2, 2)], [255, 0, 0, 255]), (["#89a"], [136, 153, 170, 255]), (["#89ab"], [136, 153, 170, 187]), (["#4488cc"], [68, 136, 204, 255]), (["#4488cc00"], [68, 136, 204, 0]), ([QtGui.QColor(1, 2, 3, 4)], [1, 2, 3, 4]), (["steelblue"], [70, 130, 180, 255]), (["lawngreen"], [124, 252, 0, 255]), ], ) def test_mkColor(test_input, expected): qcol: QtGui.QColor = pg.functions.mkColor(*test_input) assert list(qcol.getRgb()) == expected def test_signal_block_unconnected(): """Test that SignalBlock does not end up connecting an unconnected slot""" class Sender(QtCore.QObject): signal = QtCore.Signal() class Receiver: def __init__(self): self.counter = 0 def slot(self): self.counter += 1 sender = Sender() receiver = Receiver() with SignalBlock(sender.signal, receiver.slot): pass sender.signal.emit() assert receiver.counter == 0