def test_crop(self, use_indices):
axis = UniformDataAxis(size=10, scale=0.1, offset=10)
start = 10.2
if use_indices:
start = axis.value2index(start)
axis.crop(start)
assert axis.size == 8
np.testing.assert_almost_equal(axis.axis[0], 10.2)
np.testing.assert_almost_equal(axis.axis[-1], 10.9)
np.testing.assert_almost_equal(axis.offset, 10.2)
np.testing.assert_almost_equal(axis.scale, 0.1)
axis = UniformDataAxis(size=10, scale=0.1, offset=10)
end = 10.4
if use_indices:
end = axis.value2index(end)
axis.crop(start, end)
assert axis.size == 2
np.testing.assert_almost_equal(axis.axis[0], 10.2)
np.testing.assert_almost_equal(axis.axis[-1], 10.3)
np.testing.assert_almost_equal(axis.offset, 10.2)
np.testing.assert_almost_equal(axis.scale, 0.1)
axis = UniformDataAxis(size=10, scale=0.1, offset=10)
axis.crop(None, end)
assert axis.size == 4
np.testing.assert_almost_equal(axis.axis[0], 10.0)
np.testing.assert_almost_equal(axis.axis[-1], 10.3)
np.testing.assert_almost_equal(axis.offset, 10.0)
np.testing.assert_almost_equal(axis.scale, 0.1)
def test_rounding_consistency_axis_type_half(shift):
axis = UniformDataAxis(size=20, scale=0.1, offset=-1.0);
test_vals = axis.axis[:-1] + shift
uaxis_indices = axis.value2index(test_vals)
nuaxis_indices = super(type(axis), axis).value2index(test_vals)
np.testing.assert_allclose(uaxis_indices, nuaxis_indices)
def test_rounding_consistency_axis_type():
scales = [0.1, -0.1, 0.1, -0.1]
offsets = [-11.0, -10.9, 10.9, 11.0]
values = [-10.95, -10.95, 10.95, 10.95]
for i, (scale, offset, value) in enumerate(zip(scales, offsets, values)):
ax = UniformDataAxis(scale=scale, offset=offset, size=3)
ua_idx = ax.value2index(value)
nua_idx = super(type(ax), ax).value2index(value)
print('scale', scale)
print('offset', offset)
print('Axis values:', ax.axis)
print(f"value: {value} --> uniform: {ua_idx}, non-uniform: {nua_idx}")
print("\n")
assert nua_idx == ua_idx
class TestUniformDataAxis:
def setup_method(self, method):
self.axis = UniformDataAxis(size=10, scale=0.1, offset=10)
def _test_initialisation_parameters(self, axis):
assert axis.scale == 0.1
assert axis.offset == 10
def func(x): return axis.scale * x + axis.offset
np.testing.assert_allclose(axis.axis, func(np.arange(10)))
def test_initialisation_parameters(self):
self._test_initialisation_parameters(self.axis)
def test_create_axis(self):
axis = create_axis(**self.axis.get_axis_dictionary())
assert isinstance(axis, UniformDataAxis)
self._test_initialisation_parameters(axis)
def test_value_range_to_indices_in_range(self):
assert self.axis.is_uniform
assert (
self.axis.value_range_to_indices(
10.1, 10.8) == (1, 8))
def test_value_range_to_indices_endpoints(self):
assert self.axis.value_range_to_indices(10, 10.9) == (0, 9)
def test_value_range_to_indices_out(self):
assert self.axis.value_range_to_indices(9, 11) == (0, 9)
def test_value_range_to_indices_None(self):
assert self.axis.value_range_to_indices(None, None) == (0, 9)
def test_value_range_to_indices_v1_greater_than_v2(self):
with pytest.raises(ValueError):
self.axis.value_range_to_indices(2, 1)
def test_deepcopy(self):
ac = copy.deepcopy(self.axis)
ac.offset = 100
assert self.axis.offset != ac.offset
assert self.axis.navigate == ac.navigate
assert self.axis.is_binned == ac.is_binned
def test_deepcopy_on_trait_change(self):
ac = copy.deepcopy(self.axis)
ac.offset = 100
assert ac.axis[0] == ac.offset
def test_uniform_value2index(self):
#Tests for value2index
#Works as intended
assert self.axis.value2index(10.15) == 1
assert self.axis.value2index(10.17, rounding=math.floor) == 1
assert self.axis.value2index(10.13, rounding=math.ceil) == 2
# Test that output is integer
assert isinstance(self.axis.value2index(10.15), (int, np.integer))
#Endpoint left
assert self.axis.value2index(10.) == 0
#Endpoint right
assert self.axis.value2index(10.9) == 9
#Input None --> output None
assert self.axis.value2index(None) == None
#NaN in --> error out
with pytest.raises(ValueError):
self.axis.value2index(np.nan)
#Values in out of bounds --> error out (both sides of axis)
with pytest.raises(ValueError):
self.axis.value2index(-2)
with pytest.raises(ValueError):
self.axis.value2index(111)
#str without unit in --> error out
with pytest.raises(ValueError):
self.axis.value2index("69")
#Empty str in --> error out
with pytest.raises(ValueError):
self.axis.value2index("")
#Value with unit when axis is unitless --> Error out
with pytest.raises(ValueError):
self.axis.value2index("0.0101um")
#Tests with array Input
#Arrays work as intended
arval = np.array([[10.15, 10.15], [10.24, 10.28]])
assert np.all(self.axis.value2index(arval) \
== np.array([[1, 1], [2, 3]]))
assert np.all(self.axis.value2index(arval, rounding=math.floor) \
== np.array([[1, 1], [2, 2]]))
assert np.all(self.axis.value2index(arval, rounding=math.ceil)\
== np.array([[2, 2], [3, 3]]))
#List in --> array out
assert np.all(self.axis.value2index(arval.tolist()) \
== np.array([[1, 1], [2, 3]]))
#One value out of bound in array in --> error out (both sides)
arval[1, 1] = 111
with pytest.raises(ValueError):
self.axis.value2index(arval)
arval[1, 1] = -0.3
with pytest.raises(ValueError):
self.axis.value2index(arval)
#One NaN in array in --> error out
if platform.machine() != 'aarch64':
# Skip aarch64 platform because it doesn't raise error
arval[1, 1] = np.nan
with pytest.raises(ValueError):
self.axis.value2index(arval)
#Copy of axis with units
axis = copy.deepcopy(self.axis)
axis.units = 'nm'
#Value with unit in --> OK out
assert axis.value2index("0.0101um") == 1
#Value with relative units in --> OK out
assert self.axis.value2index("rel0.5") == 4
#Works with arrays of values with units in
np.testing.assert_allclose(
axis.value2index(['0.01um', '0.0101um', '0.0103um']),
np.array([0, 1, 3])
)
#Raises errors if a weird unit is passed in
with pytest.raises(BaseException):
axis.value2index(["0.01uma", '0.0101uma', '0.0103uma'])
#Values
np.testing.assert_allclose(
self.axis.value2index(["rel0.0", "rel0.5", "rel1.0"]),
np.array([0, 4, 9])
)
def test_slice_me(self):
assert (
self.axis._slice_me(slice(np.float32(10.2), 10.4, 2)) ==
slice(2, 4, 2)
)
def test_update_from(self):
ax2 = UniformDataAxis(size=2, units="nm", scale=0.5)
self.axis.update_from(ax2, attributes=("units", "scale"))
assert ((ax2.units, ax2.scale) ==
(self.axis.units, self.axis.scale))
def test_value_changed_event(self):
ax = self.axis
m = mock.Mock()
ax.events.value_changed.connect(m.trigger_me)
ax.value = ax.value
assert not m.trigger_me.called
ax.value = ax.value + ax.scale * 0.3
assert not m.trigger_me.called
ax.value = ax.value + ax.scale
assert m.trigger_me.called
def test_index_changed_event(self):
ax = self.axis
m = mock.Mock()
ax.events.index_changed.connect(m.trigger_me)
ax.index = ax.index
assert not m.trigger_me.called
ax.index += 1
assert m.trigger_me.called
def test_convert_to_non_uniform_axis(self):
axis = np.copy(self.axis.axis)
is_binned = self.axis.is_binned
navigate = self.axis.navigate
self.axis.name = "parrot"
self.axis.units = "plumage"
s = Signal1D(np.arange(10), axes=[self.axis])
index_in_array = s.axes_manager[0].index_in_array
s.axes_manager[0].convert_to_non_uniform_axis()
assert isinstance(s.axes_manager[0], DataAxis)
assert s.axes_manager[0].name == "parrot"
assert s.axes_manager[0].units == "plumage"
assert s.axes_manager[0].size == 10
assert s.axes_manager[0].low_value == 10
assert s.axes_manager[0].high_value == 10 + 0.1 * 9
np.testing.assert_allclose(s.axes_manager[0].axis, axis)
with pytest.raises(AttributeError):
s.axes_manager[0].offset
with pytest.raises(AttributeError):
s.axes_manager[0].scale
assert index_in_array == s.axes_manager[0].index_in_array
assert is_binned == s.axes_manager[0].is_binned
assert navigate == s.axes_manager[0].navigate
def test_convert_to_functional_data_axis(self):
axis = np.copy(self.axis.axis)
is_binned = self.axis.is_binned
navigate = self.axis.navigate
self.axis.name = "parrot"
self.axis.units = "plumage"
s = Signal1D(np.arange(10), axes=[self.axis])
index_in_array = s.axes_manager[0].index_in_array
s.axes_manager[0].convert_to_functional_data_axis(expression = 'x**2')
assert isinstance(s.axes_manager[0], FunctionalDataAxis)
assert s.axes_manager[0].name == "parrot"
assert s.axes_manager[0].units == "plumage"
assert s.axes_manager[0].size == 10
assert s.axes_manager[0].low_value == 10**2
assert s.axes_manager[0].high_value == (10 + 0.1 * 9)**2
assert s.axes_manager[0]._expression == 'x**2'
assert isinstance(s.axes_manager[0].x, UniformDataAxis)
np.testing.assert_allclose(s.axes_manager[0].axis, axis**2)
with pytest.raises(AttributeError):
s.axes_manager[0].offset
with pytest.raises(AttributeError):
s.axes_manager[0].scale
assert index_in_array == s.axes_manager[0].index_in_array
assert is_binned == s.axes_manager[0].is_binned
assert navigate == s.axes_manager[0].navigate
@pytest.mark.parametrize("use_indices", (False, True))
def test_crop(self, use_indices):
axis = UniformDataAxis(size=10, scale=0.1, offset=10)
start = 10.2
if use_indices:
start = axis.value2index(start)
axis.crop(start)
assert axis.size == 8
np.testing.assert_almost_equal(axis.axis[0], 10.2)
np.testing.assert_almost_equal(axis.axis[-1], 10.9)
np.testing.assert_almost_equal(axis.offset, 10.2)
np.testing.assert_almost_equal(axis.scale, 0.1)
axis = UniformDataAxis(size=10, scale=0.1, offset=10)
end = 10.4
if use_indices:
end = axis.value2index(end)
axis.crop(start, end)
assert axis.size == 2
np.testing.assert_almost_equal(axis.axis[0], 10.2)
np.testing.assert_almost_equal(axis.axis[-1], 10.3)
np.testing.assert_almost_equal(axis.offset, 10.2)
np.testing.assert_almost_equal(axis.scale, 0.1)
axis = UniformDataAxis(size=10, scale=0.1, offset=10)
axis.crop(None, end)
assert axis.size == 4
np.testing.assert_almost_equal(axis.axis[0], 10.0)
np.testing.assert_almost_equal(axis.axis[-1], 10.3)
np.testing.assert_almost_equal(axis.offset, 10.0)
np.testing.assert_almost_equal(axis.scale, 0.1)
@pytest.mark.parametrize("mixed", (False, True))
def test_crop_reverses_indexing(self, mixed):
axis = UniformDataAxis(size=10, scale=0.1, offset=10)
if mixed:
i1, i2 = 2, -6
else:
i1, i2 = -8, -6
axis.crop(i1, i2)
assert axis.size == 2
np.testing.assert_almost_equal(axis.axis[0], 10.2)
np.testing.assert_almost_equal(axis.axis[-1], 10.3)
np.testing.assert_almost_equal(axis.offset, 10.2)
np.testing.assert_almost_equal(axis.scale, 0.1)
def test_parse_value(self):
ax = copy.deepcopy(self.axis)
ax.units = 'nm'
# slicing by index
assert ax._parse_value(5) == 5
assert type(ax._parse_value(5)) is int
# slicing by calibrated value
assert ax._parse_value(10.5) == 10.5
assert type(ax._parse_value(10.5)) is float
# slicing by unit
assert ax._parse_value('10.5nm') == 10.5
np.testing.assert_almost_equal(ax._parse_value('10500pm'), 10.5)
def test_parse_value_from_relative_string(self):
ax = self.axis
assert ax._parse_value_from_string('rel0.0') == 10.0
assert ax._parse_value_from_string('rel0.5') == 10.45
assert ax._parse_value_from_string('rel1.0') == 10.9
def test_slice_empty_string(self):
ax = self.axis
with pytest.raises(ValueError):
ax._parse_value("")
def test_calibrate(self):
offset, scale = self.axis.calibrate(value_tuple=(11,12), \
index_tuple=(0,5), modify_calibration=False)
assert scale == 0.2
assert offset == 11
self.axis.calibrate(value_tuple=(11,12), index_tuple=(0,5))
assert self.axis.scale == 0.2
assert self.axis.offset == 11