def test_index(self):
a = np.array([1, 2, 3])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2])
a = np.array([1, 2, 3, 4])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 3])
a = np.array([1, 2, 3, 6, 5])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 5, 4])
def test_index(self):
a = np.array([1, 2, 3])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2])
a = np.array([1, 2, 3, 4])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 3])
a = np.array([1, 2, 3, 6, 5])
v = values_to_linspace(a)
iv = index_values(a, v)
np.testing.assert_equal(iv, [0, 1, 2, 5, 4])
def update_view(self):
self.img.clear()
self.img.setSelection(None)
self.lsx = None
self.lsy = None
self.wavenumbers = None
self.data_xs = None
self.data_imagepixels = None
if self.data and len(self.data.domain.attributes):
if self.attr_x is not None:
xat = self.data.domain[self.attr_x]
ndom = Domain([xat])
datam = Table(ndom, self.data)
coorx = datam.X[:, 0]
else:
coorx = np.arange(len(self.data))
self.lsx = lsx = values_to_linspace(coorx)
self.data_xs = coorx
self.wavenumbers = wavenumbers = getx(self.data)
self.lsy = lsy = values_to_linspace(wavenumbers)
# set data
imdata = np.ones((lsy[2], lsx[2])) * float("nan")
xindex = index_values(coorx, lsx)
yindex = index_values(wavenumbers, lsy)
for xind, d in zip(xindex, self.data.X):
imdata[yindex, xind] = d
self.data_imagepixels = xindex
self.img.setImage(imdata, autoLevels=False)
self.img.setLevels([0, 1])
self.update_levels()
self.update_color_schema()
# shift centres of the pixels so that the axes are useful
shiftx = _shift(lsx)
shifty = _shift(lsy)
left = lsx[0] - shiftx
bottom = lsy[0] - shifty
width = (lsx[1]-lsx[0]) + 2*shiftx
height = (lsy[1]-lsy[0]) + 2*shifty
self.img.setRect(QRectF(left, bottom, width, height))
self.refresh_img_selection()
def update_view(self):
self.img.clear()
self.img.setSelection(None)
self.legend.set_colors(None)
self.lsx = None
self.lsy = None
self.wavenumbers = None
self.data_xs = None
self.data_imagepixels = None
if self.data and len(self.data.domain.attributes):
if self.attr_x is not None:
xat = self.data.domain[self.attr_x]
ndom = Domain([xat])
datam = self.data.transform(ndom)
coorx = datam.X[:, 0]
else:
coorx = np.arange(len(self.data))
self.lsx = lsx = values_to_linspace(coorx)
self.data_xs = coorx
self.wavenumbers = wavenumbers = getx(self.data)
self.lsy = lsy = values_to_linspace(wavenumbers)
# set data
imdata = np.ones((lsy[2], lsx[2])) * float("nan")
xindex = index_values(coorx, lsx)
yindex = index_values(wavenumbers, lsy)
for xind, d in zip(xindex, self.data.X):
imdata[yindex, xind] = d
self.data_imagepixels = xindex
self.img.setImage(imdata, autoLevels=False)
self.update_levels()
self.update_color_schema()
# shift centres of the pixels so that the axes are useful
shiftx = _shift(lsx)
shifty = _shift(lsy)
left = lsx[0] - shiftx
bottom = lsy[0] - shifty
width = (lsx[1] - lsx[0]) + 2 * shiftx
height = (lsy[1] - lsy[0]) + 2 * shifty
self.img.setRect(QRectF(left, bottom, width, height))
self.refresh_img_selection()
def test_hypercube_roundtrip(self):
d = self.mosaic
xat = [v for v in d.domain.metas if v.name == "map_x"][0]
yat = [v for v in d.domain.metas if v.name == "map_y"][0]
hypercube, lsx, lsy = get_hypercube(d, xat, yat)
features = getx(d)
ndom = Orange.data.Domain([xat, yat])
datam = Orange.data.Table(ndom, d)
coorx = datam.X[:, 0]
coory = datam.X[:, 1]
coords = np.ones((lsx[2], lsy[2], 2))
coords[index_values(coorx, lsx), index_values(coory, lsy)] = datam.X
x_locs = coords[:, 0, 0]
y_locs = coords[0, :, 1]
features, spectra, data = _spectra_from_image(hypercube, features,
x_locs, y_locs)
nd = build_spec_table(features, spectra, data)
np.testing.assert_equal(d.X, nd.X)
np.testing.assert_equal(d.Y, nd.Y)
np.testing.assert_equal(d.metas, nd.metas)
self.assertEqual(d.domain, nd.domain)
def update_view(self):
self.img.clear()
self.img.setSelection(None)
self.lsx = None
self.lsy = None
self.data_points = None
self.data_values = None
self.data_imagepixels = None
if self.data and self.attr_x and self.attr_y:
xat = self.data.domain[self.attr_x]
yat = self.data.domain[self.attr_y]
ndom = Orange.data.Domain([xat, yat])
datam = Orange.data.Table(ndom, self.data)
coorx = datam.X[:, 0]
coory = datam.X[:, 1]
self.data_points = datam.X
self.lsx = lsx = values_to_linspace(coorx)
self.lsy = lsy = values_to_linspace(coory)
if lsx[-1] * lsy[-1] > IMAGE_TOO_BIG:
self.parent.Error.image_too_big(lsx[-1], lsy[-1])
return
else:
self.parent.Error.image_too_big.clear()
di = {}
if self.parent.value_type == 0: # integrals
imethod = self.parent.integration_methods[self.parent.integration_method]
if imethod != Integrate.PeakAt:
datai = Integrate(methods=imethod,
limits=[[self.parent.lowlim, self.parent.highlim]])(self.data)
else:
datai = Integrate(methods=imethod,
limits=[[self.parent.choose, self.parent.choose]])(self.data)
if np.any(self.parent.curveplot.selection_group):
# curveplot can have a subset of curves on the input> match IDs
ind = np.flatnonzero(self.parent.curveplot.selection_group)[0]
dind = self.data_ids[self.parent.curveplot.data[ind].id]
di = datai.domain.attributes[0].compute_value.draw_info(self.data[dind:dind+1])
d = datai.X[:, 0]
else:
dat = self.data.domain[self.parent.attr_value]
ndom = Orange.data.Domain([dat])
d = Orange.data.Table(ndom, self.data).X[:, 0]
self.refresh_markings(di)
# set data
imdata = np.ones((lsy[2], lsx[2])) * float("nan")
xindex = index_values(coorx, lsx)
yindex = index_values(coory, lsy)
imdata[yindex, xindex] = d
self.data_values = d
self.data_imagepixels = np.vstack((yindex, xindex)).T
self.img.setImage(imdata, autoLevels=False)
self.img.setLevels([0, 1])
self.update_levels()
self.update_color_schema()
# shift centres of the pixels so that the axes are useful
shiftx = _shift(lsx)
shifty = _shift(lsy)
left = lsx[0] - shiftx
bottom = lsy[0] - shifty
width = (lsx[1]-lsx[0]) + 2*shiftx
height = (lsy[1]-lsy[0]) + 2*shifty
self.img.setRect(QRectF(left, bottom, width, height))
self.refresh_img_selection()