updating SVDecomposer
@@ -45,15 +45,6 @@ class SVDecomposer:
"""
Class for SVD decomposition of 2d, 3d or 4d HCI arrays.
- Notes
- -----
- For info on CEVR search: # Get variance explained by singular values in
- https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/pca.py
- """
- def __init__(self, data, mode='fullfr', inrad=None, outrad=None,
- svd_mode='lapack', scaling='temp-standard', wavelengths=None,
- verbose=True):
- """
Parameters
----------
data : numpy ndarray
@@ -123,6 +114,15 @@ class SVDecomposer:
verbose : bool, optional
If True intermediate messages and timing are printed.
+ Notes
+ -----
+ For info on CEVR search: # Get variance explained by singular values in
+ https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/pca.py
+ """
+ def __init__(self, data, mode='fullfr', inrad=None, outrad=None,
+ svd_mode='lapack', scaling='temp-standard', wavelengths=None,
+ verbose=True):
+ """
"""
check_array(data, (2, 3, 4), msg='data')
self.data = data
@@ -179,16 +179,9 @@ class SVDecomposer:
cube_ = big_cube.reshape(z * n_frames, y_in, x_in)
self.cube4d_shape = cube_.shape
- if self.mode == 'annular':
- self.ann_width = int(np.ceil(self.outrad - self.inrad))
- self.cent_ann = self.inrad + int(np.round(self.ann_width / 2.))
- else:
- self.cent_ann = None
- self.ann_width = None
-
result = prepare_matrix(cube_, self.scaling, mode=self.mode,
- annulus_radius=self.cent_ann,
- annulus_width=self.ann_width,
+ inner_radius=self.inrad,
+ outer_radius=self.outrad,
verbose=self.verbose)
if self.mode == 'annular':
self.matrix = result[0]
