def test_any_with_dim():
var = sc.Variable(['x', 'y'],
values=np.array([True, True, False,
False]).reshape(2, 2))
assert sc.is_equal(sc.any(var, 'x'),
sc.Variable(dims=['y'], values=[True, True]))
assert sc.is_equal(sc.any(var, 'y'),
sc.Variable(dims=['x'], values=[True, False]))
def mask_from_adj_pixels(mask):
"""
Checks if the adjacent pixels (in 8 directions) are masked to remove
any noisy pixels which are erroneously masked or unmasked compared to
it's neighbours
If all adj. pixels are then the pixel considered is set to True
If no adj. pixels are then the pixel considered is set to False
If surrounding pixels have a mix of True/False the val is left as-is
This function handles border pixels as if they aren't there. So that
the following happens:
------------------------
|F|T| -> |T|T|
|T|T| |T|T|
-----------------------
Parameters
----------
mask: Existing mask with some positions masked
Returns
-------
mask: Mask copy after completing the op. described above
"""
mask = mask.copy()
def make_flip(fill):
flip = sc.empty(dims=['neighbor', 'y', 'x'],
shape=[
8,
] + mask.shape,
dtype=bool)
flip['neighbor', 0] = _shift(mask, "x", True, fill)
flip['neighbor', 1] = _shift(mask, "x", False, fill)
flip['neighbor', 2] = _shift(mask, "y", True, fill)
flip['neighbor', 3] = _shift(mask, "y", False, fill)
flip['neighbor', 4:6] = _shift(flip['neighbor', 0:2], "y", True, fill)
flip['neighbor', 6:8] = _shift(flip['neighbor', 0:2], "y", False, fill)
return flip
# mask if all neighbors masked
mask = mask | sc.all(make_flip(True), 'neighbor')
# unmask if no neighbor masked
mask = mask & sc.any(make_flip(False), 'neighbor')
return mask
def convert_Workspace2D_to_data_array(ws,
load_run_logs=True,
advanced_geometry=False,
**ignored):
dim, unit = validate_and_get_unit(ws.getAxis(0).getUnit())
spec_dim, spec_coord = init_spec_axis(ws)
coords_labs_data = _convert_MatrixWorkspace_info(
ws, advanced_geometry=advanced_geometry, load_run_logs=load_run_logs)
_, data_unit = validate_and_get_unit(ws.YUnit(), allow_empty=True)
if ws.id() == 'MaskWorkspace':
coords_labs_data["data"] = sc.Variable(dims=[spec_dim],
unit=data_unit,
values=ws.extractY().flatten(),
dtype=sc.DType.bool)
else:
stddev2 = ws.extractE()
np.multiply(stddev2, stddev2, out=stddev2) # much faster than np.power
coords_labs_data["data"] = sc.Variable(dims=[spec_dim, dim],
unit=data_unit,
values=ws.extractY(),
variances=stddev2)
array = sc.DataArray(**coords_labs_data)
if ws.hasAnyMaskedBins():
bin_mask = sc.zeros(dims=array.dims,
shape=array.shape,
dtype=sc.DType.bool)
for i in range(ws.getNumberHistograms()):
# maskedBinsIndices throws instead of returning empty list
if ws.hasMaskedBins(i):
set_bin_masks(bin_mask, dim, i, ws.maskedBinsIndices(i))
common_mask = sc.all(bin_mask, spec_dim)
if sc.identical(common_mask, sc.any(bin_mask, spec_dim)):
array.masks["bin"] = common_mask
else:
array.masks["bin"] = bin_mask
# Avoid creating dimensions that are not required since this mostly an
# artifact of inflexible data structures and gets in the way when working
# with scipp.
if len(spec_coord.values) == 1:
if 'position' in array.coords:
array.coords['position'] = array.coords['position'][spec_dim, 0]
array = array[spec_dim, 0].copy()
return array
def test_any():
var = sc.Variable(['x'], values=[True, False, True])
assert sc.any(var, 'x') == sc.Variable(value=True)
def test_any():
var = sc.Variable(['x', 'y'],
values=np.array([True, True, True, False]).reshape(2, 2))
assert sc.is_equal(sc.any(var), sc.Variable(value=True))
def test_any():
var = sc.Variable([Dim.X], values=[True, False, True])
assert sc.any(var, Dim.X) == sc.Variable(value=True)