def transformed(self, data):
if data.X.shape[0] == 0:
return data.X
data = data.copy()
if self.method == Normalize.Vector:
nans = np.isnan(data.X)
nan_num = nans.sum(axis=1, keepdims=True)
ys = data.X
if np.any(nan_num > 0):
# interpolate nan elements for normalization
x = getx(data)
ys = interp1d_with_unknowns_numpy(x, ys, x)
ys = np.nan_to_num(ys) # edge elements can still be zero
data.X = sknormalize(ys, norm='l2', axis=1, copy=False)
if np.any(nan_num > 0):
# keep nans where they were
data.X[nans] = float("nan")
elif self.method == Normalize.Area:
norm_data = Integrate(methods=self.int_method,
limits=[[self.lower, self.upper]])(data)
data.X /= norm_data.X
replace_infs(data.X)
elif self.method == Normalize.Attribute:
if self.attr in data.domain and isinstance(
data.domain[self.attr], Orange.data.ContinuousVariable):
ndom = Orange.data.Domain([data.domain[self.attr]])
factors = data.transform(ndom)
data.X /= factors.X
replace_infs(data.X)
nd = data.domain[self.attr]
else: # invalid attribute for normalization
data.X *= float("nan")
return data.X
def transformed(self, data):
if data.X.shape[0] == 0:
return data.X
data = data.copy()
if self.method == Normalize.Vector:
nans = np.isnan(data.X)
nan_num = nans.sum(axis=1, keepdims=True)
ys = data.X
if np.any(nan_num > 0):
# interpolate nan elements for normalization
x = getx(data)
ys = interp1d_with_unknowns_numpy(x, ys, x)
ys = np.nan_to_num(ys) # edge elements can still be zero
data.X = sknormalize(ys, norm='l2', axis=1, copy=False)
if np.any(nan_num > 0):
# keep nans where they were
data.X[nans] = float("nan")
elif self.method == Normalize.Area:
norm_data = Integrate(methods=self.int_method,
limits=[[self.lower, self.upper]])(data)
data.X /= norm_data.X
replace_infs(data.X)
elif self.method == Normalize.Attribute:
if self.attr in data.domain and isinstance(data.domain[self.attr], Orange.data.ContinuousVariable):
ndom = Orange.data.Domain([data.domain[self.attr]])
factors = data.transform(ndom)
data.X /= factors.X
replace_infs(data.X)
nd = data.domain[self.attr]
else: # invalid attribute for normalization
data.X *= float("nan")
return data.X
def transformed(self, data):
if len(data):
ref_X = self.interpolate_extend_to(self.reference, getx(data))
return replace_infs(
np.angle(np.exp(data.X * 1j) / np.exp(ref_X * 1j)))
else:
return data
def transformed(self, data):
if self.reference is not None:
# Calculate from single-channel data
if len(data): # numpy does not like to divide shapes (0, b) by (a, b)
ref_X = self.interpolate_extend_to(self.reference, getx(data))
transd = data.X / ref_X
else:
transd = data
else:
# Calculate from absorbance data
transd = data.X.copy()
transd *= -1
np.power(10, transd, transd)
# Replace infs from either np.true_divide or np.log10
return replace_infs(transd)
def transformed(self, data):
if self.reference is not None:
# Calculate from single-channel data
ref_X = self.interpolate_extend_to(self.reference, getx(data))
if len(data): # numpy does not like to divide shapes (0, b) by (a, b)
absd = ref_X / data.X
np.log10(absd, absd)
else:
absd = data
else:
# Calculate from transmittance data
absd = np.log10(data.X)
absd *= -1
# Replace infs from either np.true_divide or np.log10
return replace_infs(absd)
def transformed(self, data):
if len(data): # numpy does not like to divide shapes (0, b) by (a, b)
ref_X = self.interpolate_extend_to(self.reference, getx(data))
return replace_infs(data.X / ref_X)
else:
return data
def transformed(self, data):
if len(data): # numpy does not like to divide shapes (0, b) by (a, b)
ref_X = self.interpolate_extend_to(self.reference, getx(data))
return replace_infs(data.X / ref_X)
else:
return data
