Author: bugman
Date: Wed Jul 21 11:46:03 2010
New Revision: 11325
URL: http://svn.gna.org/viewcvs/relax?rev=11325&view=rev
Log:
Removed the docstring linewrapping in the frame order maths_fns module.
Modified:
1.3/maths_fns/frame_order.py
Modified: 1.3/maths_fns/frame_order.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/maths_fns/frame_order.py?rev=11325&r1=11324&r2=11325&view=diff
==============================================================================
--- 1.3/maths_fns/frame_order.py (original)
+++ 1.3/maths_fns/frame_order.py Wed Jul 21 11:46:03 2010
@@ -46,23 +46,15 @@
@type model: str
@keyword init_params: The initial parameter values.
@type init_params: numpy float64 array
- @keyword full_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all full
- alignment tensors. The format is [Sxx1,
Syy1, Sxy1, Sxz1, Syz1,
- Sxx2, Syy2, Sxy2, Sxz2, Syz2, ..., Sxxn,
Syyn, Sxyn, Sxzn,
- Syzn].
+ @keyword full_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all full alignment tensors. The format is [Sxx1, Syy1, Sxy1,
Sxz1, Syz1, Sxx2, Syy2, Sxy2, Sxz2, Syz2, ..., Sxxn, Syyn, Sxyn, Sxzn, Syzn].
@type full_tensors: numpy nx5D, rank-1 float64 array
- @keyword red_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all reduced
- tensors. The array format is the same
as for full_tensors.
+ @keyword red_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all reduced tensors. The array format is the same as for
full_tensors.
@type red_tensors: numpy nx5D, rank-1 float64 array
- @keyword red_errors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} errors for all reduced
- tensors. The array format is the same
as for full_tensors.
+ @keyword red_errors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} errors for all reduced tensors. The array format is the same as for
full_tensors.
@type red_errors: numpy nx5D, rank-1 float64 array
- @keyword full_in_ref_frame: An array of flags specifying if the
tensor in the reference
- frame is the full or reduced tensor.
+ @keyword full_in_ref_frame: An array of flags specifying if the
tensor in the reference frame is the full or reduced tensor.
@type full_in_ref_frame: numpy rank-1 array
- @keyword frame_order_2nd: The numerical values of the 2nd degree
Frame Order matrix. If
- supplied, the target functions will
optimise directly to these
- values.
+ @keyword frame_order_2nd: The numerical values of the 2nd degree
Frame Order matrix. If supplied, the target functions will optimise directly
to these values.
@type frame_order_2nd: None or numpy 9D, rank-2 array
"""
@@ -80,7 +72,6 @@
# Alias the target function.
self.func = self.func_rigid
-
# Isotropic cone model.
if model == 'iso cone':
@@ -108,16 +99,11 @@
def __init_tensors(self, full_tensors, red_tensors, red_errors,
full_in_ref_frame):
"""Set up isotropic cone optimisation against the alignment tensor
data.
- @keyword full_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all full
- alignment tensors. The format is [Sxx1,
Syy1, Sxy1, Sxz1, Syz1,
- Sxx2, Syy2, Sxy2, Sxz2, Syz2, ..., Sxxn,
Syyn, Sxyn, Sxzn,
- Syzn].
+ @keyword full_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all full alignment tensors. The format is [Sxx1, Syy1, Sxy1,
Sxz1, Syz1, Sxx2, Syy2, Sxy2, Sxz2, Syz2, ..., Sxxn, Syyn, Sxyn, Sxzn, Syzn].
@type full_tensors: numpy nx5D, rank-1 float64 array
- @keyword red_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all reduced
- tensors. The array format is the same
as for full_tensors.
+ @keyword red_tensors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all reduced tensors. The array format is the same as for
full_tensors.
@type red_tensors: numpy nx5D, rank-1 float64 array
- @keyword red_errors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} errors for all reduced
- tensors. The array format is the same
as for full_tensors.
+ @keyword red_errors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} errors for all reduced tensors. The array format is the same as for
full_tensors.
@type red_errors: numpy nx5D, rank-1 float64 array
"""
@@ -153,9 +139,7 @@
def __init_iso_cone_elements(self, frame_order_2nd):
"""Set up isotropic cone optimisation against the 2nd degree Frame
Order matrix elements.
- @keyword frame_order_2nd: The numerical values of the 2nd degree
Frame Order matrix. If
- supplied, the target functions will
optimise directly to these
- values.
+ @keyword frame_order_2nd: The numerical values of the 2nd degree
Frame Order matrix. If supplied, the target functions will optimise directly
to these values.
@type frame_order_2nd: numpy 9D, rank-2 array
"""
@@ -182,11 +166,9 @@
def func_rigid(self, params):
"""Target function for rigid model optimisation using the alignment
tensors.
- This function optimises against alignment tensors. The Euler angles
for the tensor rotation
- are the 3 parameters optimised in this model.
-
- @param params: The vector of parameter values. These are the
tensor rotation angles
- {alpha, beta, gamma}.
+ This function optimises against alignment tensors. The Euler angles
for the tensor rotation are the 3 parameters optimised in this model.
+
+ @param params: The vector of parameter values. These are the
tensor rotation angles {alpha, beta, gamma}.
@type params: list of float
@return: The chi-squared or SSE value.
@rtype: float
@@ -276,9 +258,7 @@
super matrix. The cone axis spherical angles theta and phi and the
cone angle theta are the
3 parameters optimised in this model.
- @param params: The vector of parameter values {theta, phi,
theta_cone} where the first two
- are the polar and azimuthal angles of the cone axis
theta_cone is the
- isotropic cone angle.
+ @param params: The vector of parameter values {theta, phi,
theta_cone} where the first two are the polar and azimuthal angles of the
cone axis theta_cone is the isotropic cone angle.
@type params: list of float
@return: The chi-squared or SSE value.
@rtype: float
r11325 - /1.3/maths_fns/frame_order.py