pca_local: fix long lines
@@ -51,12 +51,12 @@ def pca_adi_annular(cube, angle_list, radius_int=0, fwhm=4, asize=2,
it is used for all annuli. When set to 'auto', the number of segments is
automatically determined for every annulus, based on the annulus width.
delta_rot : scalar, optional
- Factor for increasing the parallactic angle threshold, expressed in FWHM.
- Default is 1 (excludes 1 FHWM on each side of the considered frame).
- According to Absil+13, a slightly better contrast can be reached for the
- innermost annuli if we consider a ``delta_rot`` condition as small as
- 0.1 lambda/D. This is because at very small separation, the effect of
- speckle correlation is more significant than self-subtraction.
+ Factor for increasing the parallactic angle threshold, expressed in
+ FWHM. Default is 1 (excludes 1 FHWM on each side of the considered
+ frame). According to Absil+13, a slightly better contrast can be reached
+ for the innermost annuli if we consider a ``delta_rot`` condition as
+ small as 0.1 lambda/D. This is because at very small separation, the
+ effect of speckle correlation is more significant than self-subtraction.
ncomp : int or list or 1d numpy array, optional
How many PCs are kept. If none it will be automatically determined. If a
list is provided and it matches the number of annuli then a different
@@ -90,8 +90,8 @@ def pca_adi_annular(cube, angle_list, radius_int=0, fwhm=4, asize=2,
10*FWHM. The more distant/decorrelated frames are removed from the
library.
tol : float, optional
- Stopping criterion for choosing the number of PCs when ``ncomp`` is None.
- Lower values will lead to smaller residuals and more PCs.
+ Stopping criterion for choosing the number of PCs when ``ncomp``
+ is None. Lower values will lead to smaller residuals and more PCs.
scaling : {None, 'temp-mean', 'spat-mean', 'temp-standard', 'spat-standard'}
With None, no scaling is performed on the input data before SVD. With
"temp-mean" then temporal px-wise mean subtraction is done, with
@@ -372,8 +372,8 @@ def pca_rdi_annular(cube, angle_list, cube_ref, radius_int=0, asize=1, ncomp=1,
nfrslib = data_ref.shape[0]
if nfrslib < 5:
- msg = 'Too few frames left (<5) fulfill the given correlation level.'
- msg += 'Try decreasing it'
+ msg = 'Too few frames left (<5) fulfill the given correlation'
+ msg += ' level. Try decreasing it'
raise RuntimeError(msg)
matrix = matrix_scaling(matrix, scaling)
