def test_dataset_histogram():
var = sc.Variable(dims=['x'], shape=[2], dtype=sc.dtype.event_list_float64)
var['x', 0].values = np.arange(3)
var['x', 0].values.append(42)
var['x', 0].values.extend(np.ones(3))
var['x', 1].values = np.ones(6)
ds = sc.Dataset()
s = sc.DataArray(data=sc.Variable(dims=['x'],
values=np.ones(2),
variances=np.ones(2)),
coords={'y': var})
s1 = sc.DataArray(data=sc.Variable(dims=['x'],
values=np.ones(2),
variances=np.ones(2)),
coords={'y': var * 5.0})
realign_coords = {
'y': sc.Variable(values=np.arange(5, dtype=np.float64), dims=['y'])
}
ds['s'] = sc.realign(s, realign_coords)
ds['s1'] = sc.realign(s1, realign_coords)
h = sc.histogram(ds)
assert np.array_equal(
h['s'].values, np.array([[1.0, 4.0, 1.0, 0.0], [0.0, 6.0, 0.0, 0.0]]))
assert np.array_equal(
h['s1'].values, np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]))
def test_realign():
base = make_dataarray()
y = sc.Variable(dims=['y'], values=[0.0, 4.0])
realigned = sc.realign(base, coords={'y': y})
assert realigned.data is None
assert sc.is_equal(realigned.unaligned, base)
expected = sc.DataArray(data=sc.Variable(dims=['y'], values=[4.0]),
coords={'y': y})
assert sc.is_equal(sc.histogram(realigned), expected)
def project_xy(data, nx=100, ny=20):
z = sc.geometry.z(sc.neutron.position(data))
x = sc.geometry.x(sc.neutron.position(data)) / z
y = sc.geometry.y(sc.neutron.position(data)) / z
data.coords['x/z'] = x
data.coords['y/z'] = y
x = sc.Variable(dims=['x/z'],
values=np.linspace(sc.min(x).value,
sc.max(x).value,
num=nx))
y = sc.Variable(dims=['y/z'],
values=np.linspace(sc.min(y).value,
sc.max(y).value,
num=ny))
return sc.realign(data, coords={'y/z': y, 'x/z': x})
def test_realign():
co = sc.Variable(['x'], shape=[1], dtype=sc.dtype.event_list_float64)
co.values[0].append(1.0)
co.values[0].append(2.0)
co.values[0].append(2.0)
data = sc.Variable(['y'],
dtype=sc.dtype.float64,
values=np.array([1]),
variances=np.array([1]))
da = sc.DataArray(data=data, coords={'x': co})
assert not da.unaligned
da_r = sc.realign(
da, {'x': sc.Variable(['x'], values=np.array([0.0, 1.0, 3.0]))})
assert da_r.shape == [1, 2]
assert da_r.unaligned == da
assert not da_r.data
assert np.allclose(sc.histogram(da_r).values, np.array([0, 3]), atol=1e-9)
da.realign({'x': sc.Variable(['x'], values=np.array([0.0, 1.0, 3.0]))})
assert da.shape == [1, 2]
def test_data_array_unaligned_to_dict():
N = 50
values = 10 * np.random.rand(N)
da = sc.DataArray(data=sc.Variable(dims=['position'],
unit=sc.units.counts,
values=values,
variances=values),
coords={
'position':
sc.Variable(
dims=['position'],
values=['site-{}'.format(i) for i in range(N)]),
'x':
sc.Variable(dims=['position'],
unit=sc.units.m,
values=np.random.rand(N)),
'y':
sc.Variable(dims=['position'],
unit=sc.units.m,
values=np.random.rand(N))
})
xbins = sc.Variable(dims=['x'], unit=sc.units.m, values=[0.1, 0.5, 0.9])
ybins = sc.Variable(dims=['y'],
unit=sc.units.m,
values=[0.1, 0.3, 0.5, 0.7, 0.9])
realigned = sc.realign(da, {'y': ybins, 'x': xbins})
da_dict = sc.to_dict(realigned)
assert "unaligned" in da_dict
assert "data" not in da_dict
assert sc.from_dict(da_dict["coords"]["x"]) == xbins
assert sc.from_dict(da_dict["coords"]["y"]) == ybins
assert sc.from_dict(
da_dict["unaligned"]["coords"]["x"]) == realigned.unaligned.coords["x"]
assert sc.from_dict(
da_dict["unaligned"]["coords"]["y"]) == realigned.unaligned.coords["y"]
def test_plot_realigned_1d():
d = make_events_dataset(ndim=1)
tbins = sc.Variable(dims=['tof'], unit=sc.units.us, values=np.arange(100.))
r = sc.realign(d, {'tof': tbins})
plot(r['x', 25])