7d41e8a878
* extended color map functions * assertion string, documentation pass, comment purge * fix some documentation links * simplify assert statement * removed comments and redundancy, renamed modulated bar * include modulatedBarData in documentation * test running matplotlib with dummy backend * skip color maps example on OSX/PySide6.1.1 * removed skipping of tests * reverted some accidental whitespace, removed unneeded numpy import * removed unneded alpha parameter
336 lines
11 KiB
Python
336 lines
11 KiB
Python
# -*- coding: utf-8 -*-
|
|
from collections import OrderedDict
|
|
from copy import deepcopy
|
|
from contextlib import suppress
|
|
from pyqtgraph.functions import arrayToQPath, eq
|
|
|
|
import numpy as np
|
|
import pytest
|
|
from numpy.testing import assert_array_almost_equal
|
|
|
|
import pyqtgraph as pg
|
|
|
|
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):
|
|
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():
|
|
dtypes = map(np.dtype, ('ubyte', 'uint16', 'byte', 'int16', 'int', 'float'))
|
|
for dtype1 in dtypes:
|
|
for dtype2 in dtypes:
|
|
data = (np.random.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
|
|
@pytest.mark.parametrize(
|
|
"xs, ys, connect, expected", [
|
|
(
|
|
np.arange(6), np.arange(0, -6, step=-1), 'all', (
|
|
(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),
|
|
)
|
|
),
|
|
(
|
|
np.arange(6), np.arange(0, -6, step=-1), 'pairs', (
|
|
(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),
|
|
)
|
|
),
|
|
(
|
|
np.arange(5), np.arange(0, -5, step=-1), 'pairs', (
|
|
(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)
|
|
)
|
|
),
|
|
(
|
|
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), np.arange(0, -5, step=-1), np.array([0, 1, 0, 1, 0]), (
|
|
(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)
|
|
)
|
|
)
|
|
]
|
|
)
|
|
def test_arrayToQPath(xs, ys, connect, expected):
|
|
path = arrayToQPath(xs, ys, connect=connect)
|
|
for i in range(path.elementCount()):
|
|
with suppress(NameError):
|
|
# nan elements add two line-segments, for simplicity of test config
|
|
# we can ignore the second segment
|
|
if (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))
|