Author: bugman
Date: Fri Aug 8 11:26:18 2008
New Revision: 7118
URL: http://svn.gna.org/viewcvs/relax?rev=7118&view=rev
Log:
Shifted the __update_model() method for fixing the alphabetical ordering of
methods.
Modified:
branches/rdc_analysis/specific_fns/n_state_model.py
Modified: branches/rdc_analysis/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/specific_fns/n_state_model.py?rev=7118&r1=7117&r2=7118&view=diff
==============================================================================
--- branches/rdc_analysis/specific_fns/n_state_model.py (original)
+++ branches/rdc_analysis/specific_fns/n_state_model.py Fri Aug 8 11:26:18
2008
@@ -263,6 +263,90 @@
gamma[i] = param_vector[cdp.N-1 + 3*i + 2]
+ def __linear_constraints(self, data_types=None, scaling_matrix=None):
+ """Function for setting up the linear constraint matrices A and b.
+
+ Standard notation
+ =================
+
+ The N-state model constraints are:
+
+ 0 <= pc <= 1,
+
+ where p is the probability and c corresponds to state c.
+
+
+ Matrix notation
+ ===============
+
+ In the notation A.x >= b, where A is an matrix of coefficients, x is
an array of parameter
+ values, and b is a vector of scalars, these inequality constraints
are:
+
+ | 1 0 0 | | 0 |
+ | | | |
+ |-1 0 0 | | -1 |
+ | | | p0 | | |
+ | 0 1 0 | | | | 0 |
+ | | . | p1 | >= | |
+ | 0 -1 0 | | | | -1 |
+ | | | p2 | | |
+ | 0 0 1 | | 0 |
+ | | | |
+ | 0 0 -1 | | -1 |
+
+ This example is for a 4-state model, the last probability pn is not
included as this
+ parameter does not exist (because the sum of pc is equal to 1). The
Euler angle parameters
+ have been excluded here but will be included in the returned A and b
objects. These
+ parameters simply add columns of zero to the A matrix and have no
effect on b.
+
+
+ @keyword data_types: The base data types used in the
optimisation. This list can
+ contain the elements 'rdc', 'pcs' or
'tensor'.
+ @type data_types: list of str
+ @keyword scaling_matrix: The diagonal scaling matrix.
+ @type scaling_matrx: numpy rank-2 square matrix
+ @return: The matrices A and b.
+ @rtype: tuple of len 2 of a numpy rank-2, size
NxM matrix and numpy
+ rank-1, size N array
+ """
+
+ # Alias the current data pipe.
+ cdp = ds[ds.current_pipe]
+
+ # Starting point of the populations.
+ pop_start = 0
+ if 'rdc' in data_types or 'pcs' in data_types:
+ pop_start = pop_start + 5*len(cdp.align_tensors)
+
+ # Initialisation (0..j..m).
+ A = []
+ b = []
+ zero_array = zeros(self.param_num(), float64)
+ i = pop_start
+ j = 0
+
+ # Loop over the prob parameters (N - 1, because the sum of pc is 1).
+ for k in xrange(cdp.N - 1):
+ # 0 <= pc <= 1.
+ A.append(zero_array * 0.0)
+ A.append(zero_array * 0.0)
+ A[j][i] = 1.0
+ A[j+1][i] = -1.0
+ b.append(0.0)
+ b.append(-1.0)
+ j = j + 2
+
+ # Increment i.
+ i = i + 1
+
+ # Convert to numpy data structures.
+ A = array(A, float64)
+ b = array(b, float64)
+
+ # Return the contraint objects.
+ return A, b
+
+
def __update_model(self):
"""Update the model parameters as necessary."""
@@ -334,90 +418,6 @@
# Initialise the tensor.
if not exists:
generic_fns.align_tensor.init(tensor=id, params=[0.0, 0.0,
0.0, 0.0, 0.0])
-
-
- def __linear_constraints(self, data_types=None, scaling_matrix=None):
- """Function for setting up the linear constraint matrices A and b.
-
- Standard notation
- =================
-
- The N-state model constraints are:
-
- 0 <= pc <= 1,
-
- where p is the probability and c corresponds to state c.
-
-
- Matrix notation
- ===============
-
- In the notation A.x >= b, where A is an matrix of coefficients, x is
an array of parameter
- values, and b is a vector of scalars, these inequality constraints
are:
-
- | 1 0 0 | | 0 |
- | | | |
- |-1 0 0 | | -1 |
- | | | p0 | | |
- | 0 1 0 | | | | 0 |
- | | . | p1 | >= | |
- | 0 -1 0 | | | | -1 |
- | | | p2 | | |
- | 0 0 1 | | 0 |
- | | | |
- | 0 0 -1 | | -1 |
-
- This example is for a 4-state model, the last probability pn is not
included as this
- parameter does not exist (because the sum of pc is equal to 1). The
Euler angle parameters
- have been excluded here but will be included in the returned A and b
objects. These
- parameters simply add columns of zero to the A matrix and have no
effect on b.
-
-
- @keyword data_types: The base data types used in the
optimisation. This list can
- contain the elements 'rdc', 'pcs' or
'tensor'.
- @type data_types: list of str
- @keyword scaling_matrix: The diagonal scaling matrix.
- @type scaling_matrx: numpy rank-2 square matrix
- @return: The matrices A and b.
- @rtype: tuple of len 2 of a numpy rank-2, size
NxM matrix and numpy
- rank-1, size N array
- """
-
- # Alias the current data pipe.
- cdp = ds[ds.current_pipe]
-
- # Starting point of the populations.
- pop_start = 0
- if 'rdc' in data_types or 'pcs' in data_types:
- pop_start = pop_start + 5*len(cdp.align_tensors)
-
- # Initialisation (0..j..m).
- A = []
- b = []
- zero_array = zeros(self.param_num(), float64)
- i = pop_start
- j = 0
-
- # Loop over the prob parameters (N - 1, because the sum of pc is 1).
- for k in xrange(cdp.N - 1):
- # 0 <= pc <= 1.
- A.append(zero_array * 0.0)
- A.append(zero_array * 0.0)
- A[j][i] = 1.0
- A[j+1][i] = -1.0
- b.append(0.0)
- b.append(-1.0)
- j = j + 2
-
- # Increment i.
- i = i + 1
-
- # Convert to numpy data structures.
- A = array(A, float64)
- b = array(b, float64)
-
- # Return the contraint objects.
- return A, b
def CoM(self, pivot_point=None, centre=None):
r7118 - /branches/rdc_analysis/specific_fns/n_state_model.py