def __init__(self,
data,
plot_axis_index=-1,
axis_ranges=None,
ylabel=None,
xlabel=None,
xlim=None,
ylim=None,
aspect='auto',
**kwargs):
# Check inputs.
self.plot_axis_index = int(plot_axis_index)
if self.plot_axis_index not in range(-data.ndim, data.ndim):
raise ValueError(
"plot_axis_index must be within range of number of data dimensions"
" (or equivalent negative indices).")
if data.ndim < 2:
raise ValueError(
"data must have at least two dimensions. One for data "
"for each single plot and at least one for time/iteration.")
# Define number of slider axes.
self.naxis = data.ndim
self.num_sliders = self.naxis - 1
# Attach data to class.
if axis_ranges is not None and all(axis_range is None
for axis_range in axis_ranges):
axis_ranges = None
if axis_ranges is None or axis_ranges[self.plot_axis_index] is None:
self.xdata = np.arange(data.shape[self.plot_axis_index])
# Else derive the xdata as pixel centers from the pixel edges supplied by
# the user in axis_ranges[plot_axis_index]
else:
# If the shape of the array is a 1D array, get the centers about axis=0
if np.asarray(axis_ranges[self.plot_axis_index]).ndim == 1:
self.xdata = edges_to_centers_nd(
np.asarray(axis_ranges[self.plot_axis_index]), 0)
# Else derive the xdata as pixel centers from the pixel edges supplied by
# the user in axis_ranges[plot_axis_index] along axis=plot_axis_index
else:
self.xdata = edges_to_centers_nd(
np.asarray(axis_ranges[self.plot_axis_index]),
plot_axis_index)
if ylim is None:
ylim = (data.min(), data.max())
if xlim is None:
xlim = (self.xdata.min(), self.xdata.max())
self.ylim = ylim
self.xlim = xlim
self.xlabel = xlabel
self.ylabel = ylabel
self.aspect = aspect
# Run init for base class
super().__init__(data,
image_axes=[self.plot_axis_index],
axis_ranges=axis_ranges,
**kwargs)
def __init__(self, data, plot_axis_index=-1, axis_ranges=None, ylabel=None, xlabel=None,
xlim=None, ylim=None, aspect='auto', **kwargs):
# Check inputs.
self.plot_axis_index = int(plot_axis_index)
if self.plot_axis_index not in range(-data.ndim, data.ndim):
raise ValueError("plot_axis_index must be within range of number of data dimensions"
" (or equivalent negative indices).")
if data.ndim < 2:
raise ValueError("data must have at least two dimensions. One for data "
"for each single plot and at least one for time/iteration.")
# Define number of slider axes.
self.naxis = data.ndim
self.num_sliders = self.naxis-1
# Attach data to class.
if axis_ranges is not None and all(axis_range is None for axis_range in axis_ranges):
axis_ranges = None
if axis_ranges is None or axis_ranges[self.plot_axis_index] is None:
self.xdata = np.arange(data.shape[self.plot_axis_index])
else:
# Else derive the xdata as the centers of the pixel/bin edges
# supplied by the user for the plotted axis.
self.xdata = edges_to_centers_nd(np.asarray(axis_ranges[self.plot_axis_index]),
plot_axis_index)
if ylim is None:
ylim = (data.min(), data.max())
if xlim is None:
xlim = (self.xdata.min(), self.xdata.max())
self.ylim = ylim
self.xlim = xlim
self.xlabel = xlabel
self.ylabel = ylabel
self.aspect = aspect
# Run init for base class
super().__init__(data, image_axes=[self.plot_axis_index], axis_ranges=axis_ranges,
**kwargs)
def test_edges_to_centers_nd():
edges_axis = 0
axis_range = np.zeros((10, 2))
axis_range[:, 0] = np.arange(10, 20)
expected = np.zeros((9, 2))
expected[:, edges_axis] = np.arange(10.5, 19)
output = base.edges_to_centers_nd(axis_range, edges_axis)
assert np.array_equal(output, expected)
def test_edges_to_centers_nd():
edges_axis = 0
axis_range = np.zeros((10, 2))
axis_range[:, 0] = np.arange(10, 20)
expected = np.zeros((9,2))
expected[:, edges_axis] = np.arange(10.5, 19)
output = base.edges_to_centers_nd(axis_range, edges_axis)
assert np.array_equal(output, expected)
super().update_plot(val, artist, slider)
axis_ranges1 = np.tile(np.linspace(0, 100, 21), (10, 1))
@pytest.mark.parametrize(
'axis_ranges, exp_extent, exp_axis_ranges',
[([None, None], [-0.5, 19.5], [np.arange(10),
np.array([-0.5, 19.5])]),
([[0, 10], [0, 20]], [0, 20], [np.arange(0.5, 10.5),
np.asarray([0, 20])]),
([np.arange(0, 11), np.arange(0, 21)], [0, 20],
[np.arange(0.5, 10.5), np.arange(0.5, 20.5)]),
([None, axis_ranges1], [0.0, 100.0],
[np.arange(10), base.edges_to_centers_nd(axis_ranges1, 1)])])
def test_sanitize_axis_ranges(axis_ranges, exp_extent, exp_axis_ranges):
data_shape = (10, 20)
data = np.random.rand(*data_shape)
aanim = ArrayAnimatorTest(data=data)
out_axis_ranges, out_extent = aanim._sanitize_axis_ranges(
axis_ranges=axis_ranges, data_shape=data_shape)
assert exp_extent == out_extent
assert np.array_equal(exp_axis_ranges[1], out_axis_ranges[1])
assert callable(out_axis_ranges[0])
assert np.array_equal(exp_axis_ranges[0],
out_axis_ranges[0](np.arange(10)))
XDATA = np.tile(np.linspace(0, 100, 11), (5, 5, 1))
unit=u.pix).value.astype(int)
new_x_axis_coords3_shape[-1] = 1
none_axis_ranges_axis3 = [
np.arange(0, len(seq.data)),
np.array([0., 1.]),
np.arange(0, 3),
np.tile(np.array(x_axis_coords3), new_x_axis_coords3_shape)
]
none_axis_ranges_axis0 = [
np.arange(len(seq.data)),
np.array([0., 1.]),
np.arange(0, 3),
np.arange(0, int(seq.dimensions[-1].value))
]
distance0_none_axis_ranges_axis0 = \
[edges_to_centers_nd(_get_extra_coord_edges(seq.sequence_axis_extra_coords["distance"].value), 0),
np.array([0., 1.]), np.arange(0, 3),
np.arange(0, int(seq.dimensions[-1].value))]
distance0_none_axis_ranges_axis0_mm = \
[edges_to_centers_nd(_get_extra_coord_edges(seq.sequence_axis_extra_coords["distance"].to(
"mm").value), 0), np.array([0., 1.]), np.arange(0, 3), np.arange(0, int(seq.dimensions[-1].value))]
userrangequantity_none_axis_ranges_axis0 = [
np.arange(int(seq.dimensions[0].value)),
np.array([0., 1.]),
np.arange(0, 3),
np.arange(0, int(seq.dimensions[-1].value))
]
userrangequantity_none_axis_ranges_axis0_1e7 = [
(np.arange(int(seq.dimensions[0].value)) * u.J).to(u.erg).value,
np.array([0., 1.]),
axis_ranges1 = np.tile(np.linspace(0, 100, 21), (10, 1))
@pytest.mark.parametrize('axis_ranges, exp_extent, exp_axis_ranges',
[([None, None], [-0.5, 19.5],
[np.arange(10), np.array([-0.5, 19.5])]),
([[0, 10], [0, 20]], [0, 20],
[np.arange(0.5, 10.5), np.asarray([0, 20])]),
([np.arange(0, 11), np.arange(0, 21)], [0, 20],
[np.arange(0.5, 10.5), np.arange(0.5, 20.5)]),
([None, axis_ranges1], [0.0, 100.0],
[np.arange(10), base.edges_to_centers_nd(axis_ranges1, 1)])])
def test_sanitize_axis_ranges(axis_ranges, exp_extent, exp_axis_ranges):
data_shape = (10, 20)
data = np.random.rand(*data_shape)
aanim = ArrayAnimatorTest(data=data)
out_axis_ranges, out_extent = aanim._sanitize_axis_ranges(axis_ranges=axis_ranges,
data_shape=data_shape)
assert exp_extent == out_extent
assert np.array_equal(exp_axis_ranges[0], out_axis_ranges[0])
assert np.array_equal(exp_axis_ranges[1], out_axis_ranges[1])
xdata = np.tile(np.linspace(0, 100, 11), (5, 5, 1))
@pytest.mark.parametrize('plot_axis_index, axis_ranges, xlabel, xlim',