Author: bugman
Date: Sun Jul 12 23:49:33 2009
New Revision: 9192
URL: http://svn.gna.org/viewcvs/relax?rev=9192&view=rev
Log:
Modified the N-state model code to be more like the Frame Order matrix code.
Modified:
branches/frame_order/maths_fns/n_state_model.py
branches/frame_order/specific_fns/n_state_model.py
Modified: branches/frame_order/maths_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order/maths_fns/n_state_model.py?rev=9192&r1=9191&r2=9192&view=diff
==============================================================================
--- branches/frame_order/maths_fns/n_state_model.py (original)
+++ branches/frame_order/maths_fns/n_state_model.py Sun Jul 12 23:49:33 2009
@@ -176,7 +176,7 @@
# Tensor set up.
self.full_tensors = array(full_tensors, float64)
- self.num_tensors = len(self.full_tensors)
+ self.num_tensors = len(self.full_tensors) / 5
self.red_data = red_data
self.red_errors = red_errors
self.full_in_ref_frame = full_in_ref_frame
Modified: branches/frame_order/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order/specific_fns/n_state_model.py?rev=9192&r1=9191&r2=9192&view=diff
==============================================================================
--- branches/frame_order/specific_fns/n_state_model.py (original)
+++ branches/frame_order/specific_fns/n_state_model.py Sun Jul 12 23:49:33
2009
@@ -26,7 +26,7 @@
# Python module imports.
from math import acos, cos, pi, sqrt
from minfx.generic import generic_minimise
-from numpy import array, dot, float64, identity, zeros
+from numpy import array, dot, float64, identity, ones, zeros
from numpy.linalg import inv, norm
from re import search
from warnings import warn
@@ -669,69 +669,106 @@
return rdcs_numpy, rdc_err_numpy, xh_vect_numpy, array(dj, float64)
- def __minimise_setup_tensors(self):
+ def __minimise_setup_tensors(self, sim_index=None):
"""Set up the data structures for optimisation using alignment
tensors as base data sets.
- @return: The assembled data structures for using alignment
tensors as the base data for
- optimisation. These include:
- - full_tensors, the data of the full alignment
tensors.
- - red_tensor_elem, the tensors as concatenated
rank-1 5D arrays.
- - red_tensor_err, the tensor errors as concatenated
rank-1 5D arrays.
- - full_in_ref_frame, flags specifying if the tensor
in the reference frame
- is the full or reduced tensor.
- @rtype: tuple of (list, numpy rank-1 array, numpy rank-1 array,
numpy rank-1 array)
+ @keyword sim_index: The index of the simulation to optimise. This
should be None if
+ normal optimisation is desired.
+ @type sim_index: None or int
+ @return: The assembled data structures for using
alignment tensors as the base
+ data for optimisation. These include:
+ - full_tensors, the data of the full
alignment tensors.
+ - red_tensor_elem, the tensors as
concatenated rank-1 5D arrays.
+ - red_tensor_err, the tensor errors as
concatenated rank-1 5D
+ arrays.
+ - full_in_ref_frame, flags specifying if the
tensor in the reference
+ frame is the full or reduced tensor.
+ @rtype: tuple of (list, numpy rank-1 array, numpy rank-1
array, numpy rank-1
+ array)
"""
# Alias the current data pipe.
cdp = pipes.get_pipe()
# Initialise.
- full_tensors = []
- red_tensor_elem = []
- red_tensor_err = []
- full_in_ref_frame = []
-
- # Loop over all tensors.
- for tensor in cdp.align_tensors:
+ n = len(cdp.align_tensors.reduction)
+ full_tensors = zeros(n*5, float64)
+ red_tensors = zeros(n*5, float64)
+ red_err = ones(n*5, float64) * 1e-5
+ full_in_ref_frame = zeros(n, float64)
+
+ # Loop over the full tensors.
+ for i, tensor in self.__tensor_loop(red=False):
# The full tensor.
- if not tensor.red:
- # The full tensor corresponds to the frame of reference.
- if cdp.ref_domain == tensor.domain:
- full_in_ref_frame.append(1)
- else:
- full_in_ref_frame.append(0)
-
- # Create a list of matrices consisting of all the full
alignment tensors.
- full_tensors.append(tensor.tensor)
-
- # Create a list of all the reduced alignment tensor elements and
their errors (for the chi-squared function).
- elif tensor.red:
- # Append the 5 unique elements.
- red_tensor_elem.append(tensor.Axx)
- red_tensor_elem.append(tensor.Ayy)
- red_tensor_elem.append(tensor.Axy)
- red_tensor_elem.append(tensor.Axz)
- red_tensor_elem.append(tensor.Ayz)
-
- # Append the 5 unique error elements (if they exist).
- if hasattr(tensor, 'Axx_err'):
- red_tensor_err.append(tensor.Axx_err)
- red_tensor_err.append(tensor.Ayy_err)
- red_tensor_err.append(tensor.Axy_err)
- red_tensor_err.append(tensor.Axz_err)
- red_tensor_err.append(tensor.Ayz_err)
-
- # Otherwise append errors of 1.0 to convert the chi-squared
equation to the ASE equation (for the tensors without errors).
- else:
- red_tensor_err = red_tensor_err + [1.0, 1.0, 1.0, 1.0,
1.0]
-
- # Convert the reduced alignment tensor element lists into numpy
arrays (for the chi-squared function maths).
- red_tensor_elem = array(red_tensor_elem, float64)
- red_tensor_err = array(red_tensor_err, float64)
- full_in_ref_frame = array(full_in_ref_frame, float64)
+ full_tensors[5*i + 0] = tensor.Axx
+ full_tensors[5*i + 1] = tensor.Ayy
+ full_tensors[5*i + 2] = tensor.Axy
+ full_tensors[5*i + 3] = tensor.Axz
+ full_tensors[5*i + 4] = tensor.Ayz
+
+ # The full tensor corresponds to the frame of reference.
+ if cdp.ref_domain == tensor.domain:
+ full_in_ref_frame[i] = 1
+
+ # Loop over the reduced tensors.
+ for i, tensor in self.__tensor_loop(red=True):
+ # The reduced tensor (simulation data).
+ if sim_index != None:
+ red_tensors[5*i + 0] = tensor.Axx_sim[sim_index]
+ red_tensors[5*i + 1] = tensor.Ayy_sim[sim_index]
+ red_tensors[5*i + 2] = tensor.Axy_sim[sim_index]
+ red_tensors[5*i + 3] = tensor.Axz_sim[sim_index]
+ red_tensors[5*i + 4] = tensor.Ayz_sim[sim_index]
+
+ # The reduced tensor.
+ else:
+ red_tensors[5*i + 0] = tensor.Axx
+ red_tensors[5*i + 1] = tensor.Ayy
+ red_tensors[5*i + 2] = tensor.Axy
+ red_tensors[5*i + 3] = tensor.Axz
+ red_tensors[5*i + 4] = tensor.Ayz
+
+ # The reduced tensor errors.
+ if hasattr(tensor, 'Axx_err'):
+ red_err[5*i + 0] = tensor.Axx_err
+ red_err[5*i + 1] = tensor.Ayy_err
+ red_err[5*i + 2] = tensor.Axy_err
+ red_err[5*i + 3] = tensor.Axz_err
+ red_err[5*i + 4] = tensor.Ayz_err
# Return the data structures.
- return full_tensors, red_tensor_elem, red_tensor_err,
full_in_ref_frame
+ return full_tensors, red_tensors, red_err, full_in_ref_frame
+
+
+ def __tensor_loop(self, red=False):
+ """Generator method for looping over the full or reduced tensors.
+
+ @keyword red: A flag which if True causes the reduced tensors to
be returned, and if False
+ the full tensors are returned.
+ @type red: bool
+ @return: The tensor index and the tensor.
+ @rtype: (int, AlignTensorData instance)
+ """
+
+ # Alias the current data pipe.
+ cdp = pipes.get_pipe()
+
+ # Number of tensor pairs.
+ n = len(cdp.align_tensors.reduction)
+
+ # Alias.
+ data = cdp.align_tensors
+ list = data.reduction
+
+ # Full or reduced index.
+ if red:
+ index = 1
+ else:
+ index = 0
+
+ # Loop over the reduction list.
+ for i in range(n):
+ yield i, data[list[i][index]]
def __q_factors_rdc(self):
@@ -1406,7 +1443,7 @@
# Get the data structures for optimisation using the tensors as base
data sets.
full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame =
None, None, None, None
if 'tensor' in data_types:
- full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame
= self.__minimise_setup_tensors()
+ full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame
= self.__minimise_setup_tensors(sim_index=sim_index)
# Get the data structures for optimisation using PCSs as base data
sets.
pcs, pcs_err, pcs_vect, pcs_dj = None, None, None, None
r9192 - in /branches/frame_order: maths_fns/n_state_model.py specific_fns/n_state_model.py