Author: bugman
Date: Fri Sep 10 15:32:46 2010
New Revision: 11542
URL: http://svn.gna.org/viewcvs/relax?rev=11542&view=rev
Log:
This change is related to the task #6397 (https://gna.org/task/?6397).
kada _at_ chemi _dot_ muni _dot_ cz
https://mail.gna.org/public/relax-devel/2010-07/msg00029.html
https://gna.org/task/download.php?file_id=9687
This patch includes change in __init__ function of class Mf in a file
maths_fns/mf.py. Function was
modified in order to handle data for more interactions. Also documentation
string was updated
according to the changes of function arguments.
Modified:
branches/cst/maths_fns/mf.py
Modified: branches/cst/maths_fns/mf.py
URL:
http://svn.gna.org/viewcvs/relax/branches/cst/maths_fns/mf.py?rev=11542&r1=11541&r2=11542&view=diff
==============================================================================
--- branches/cst/maths_fns/mf.py (original)
+++ branches/cst/maths_fns/mf.py Fri Sep 10 15:32:46 2010
@@ -39,7 +39,7 @@
class Mf:
- def __init__(self, init_params=None, model_type=None, diff_type=None,
diff_params=None, scaling_matrix=None, num_spins=None, equations=None,
param_types=None, param_values=None, relax_data=None, errors=None,
bond_length=None, csa=None, num_frq=0, frq=None, num_ri=None,
remap_table=None, noe_r1_table=None, ri_labels=None, gx=0, gh=0, h_bar=0,
mu0=0, num_params=None, vectors=None):
+ def __init__(self, init_params=None, model_type=None, diff_type=None,
diff_params=None, scaling_matrix=None, num_spins=None, equations=None,
param_types=None, param_values=None, relax_data=None, errors=None,
num_interactions=None, interactions=None, internuclei_distance=None,
csa=None, num_frq=0, frq=None, num_ri=None, remap_table=None,
noe_r1_table=None, ri_labels=None, gx=0, gh=0, gratio_ext=0, h_bar=0, mu0=0,
num_params=None, unit_vector=None):
"""The model-free minimisation class.
This class should be initialised before every calculation.
@@ -56,9 +56,18 @@
errors: An array containing the experimental errors.
- bond_length: The fixed bond length in meters.
-
- csa: The fixed CSA value.
+ interactions: An array containing the types of interactions (either
dipole-dipole or CSA).
+
+ gratio_ext: An array containing the gyromagnetic ratios of
interacting nuclei (None is included
+ in case of CSA interaction)
+
+ internuclei_distance: An array containing the fixed
internuclei_distances in meters.
+ (None is included in case of CSA interaction)
+
+ csa: An array containing the fixed CSA value. (None is included in
case of dipole-dipole
+ interaction)
+
+ unit_vector: An array containing the fixed unit eigenvectors of
interactions
diff_type: The diffusion tensor string which should be either
'sphere', 'spheroid', or
'ellipsoid'.
@@ -244,98 +253,107 @@
# Total number of ri.
self.total_num_ri = self.total_num_ri + num_ri[i]
+ self.data.append([])
+
# The ratio of gyromagnetic ratios.
g_ratio = gh[i] / gx[i]
- # Append the class instance Data to the data array.
- self.data.append(Data())
-
- # Number of indices.
- self.data[i].num_indices = self.diff_data.num_indices
-
- # Calculate the five frequencies per field strength which cause
R1, R2, and NOE relaxation.
- self.data[i].frq_list = zeros((num_frq[i], 5), float64)
- self.data[i].frq_list_ext = zeros((num_frq[i], 5,
self.diff_data.num_indices), float64)
- self.data[i].frq_sqrd_list_ext = zeros((num_frq[i], 5,
self.diff_data.num_indices), float64)
- for j in xrange(num_frq[i]):
- frqH = 2.0 * pi * frq[i][j]
- frqX = frqH / g_ratio
- self.data[i].frq_list[j, 1] = frqX
- self.data[i].frq_list[j, 2] = frqH - frqX
- self.data[i].frq_list[j, 3] = frqH
- self.data[i].frq_list[j, 4] = frqH + frqX
- self.data[i].frq_sqrd_list = self.data[i].frq_list ** 2
- for j in xrange(self.diff_data.num_indices):
- self.data[i].frq_list_ext[:,:, j] = self.data[i].frq_list
- self.data[i].frq_sqrd_list_ext[:,:, j] =
self.data[i].frq_sqrd_list
-
- # Store supplied data in self.data
- self.data[i].gh = gh[i]
- self.data[i].gx = gx[i]
- self.data[i].g_ratio = g_ratio
- self.data[i].h_bar = h_bar
- self.data[i].mu0 = mu0
- self.data[i].equations = equations[i]
- self.data[i].param_types = param_types[i]
- self.data[i].relax_data = relax_data[i]
- self.data[i].errors = errors[i]
- self.data[i].bond_length = bond_length[i]
- self.data[i].csa = csa[i]
- self.data[i].num_ri = num_ri[i]
- self.data[i].num_frq = num_frq[i]
- self.data[i].frq = frq[i]
- self.data[i].remap_table = remap_table[i]
- self.data[i].noe_r1_table = noe_r1_table[i]
- self.data[i].ri_labels = ri_labels[i]
- self.data[i].num_params = num_params[i]
- self.data[i].xh_unit_vector = vectors[i]
-
- # Parameter values for minimising solely the diffusion tensor
parameters.
- if self.model_type == 'diff':
- self.data[i].param_values = param_values[i]
-
- # Indices for constructing the global generic model-free
gradient and Hessian kite.
- if i == 0:
- self.data[i].start_index = self.diff_data.num_params
- else:
- self.data[i].start_index = self.data[i-1].end_index
- self.data[i].end_index = self.data[i].start_index +
self.data[i].num_params
-
- # Total number of parameters.
- if self.model_type == 'mf' or self.model_type == 'local_tm':
- self.data[i].total_num_params = self.data[i].num_params
- elif self.model_type == 'diff':
- self.data[i].total_num_params = self.diff_data.num_params
- else:
- self.data[i].total_num_params = self.data[i].num_params +
self.diff_data.num_params
-
- # Initialise the residue specific data.
- self.init_res_data(self.data[i], self.diff_data)
-
- # Setup the residue specific equations.
- if not self.setup_equations(self.data[i]):
- raise RelaxError("The model-free equations could not be
setup.")
-
- # Diffusion tensor parameters.
- if self.model_type == 'local_tm':
- self.diff_data.params = self.params[0:1]
- elif self.model_type == 'diff' or self.model_type == 'all':
- self.diff_data.params = self.params[0:self.diff_end_index]
-
- # Calculate the correlation times ti.
- self.diff_data.calc_ti(self.data[i], self.diff_data)
-
- # ti spectral density components.
- self.data[i].w_ti_sqrd = self.data[i].frq_sqrd_list_ext *
self.data[i].ti ** 2
- self.data[i].fact_ti = 1.0 / (1.0 + self.data[i].w_ti_sqrd)
-
- # Initialise the R1 data class. This is used only if an NOE
data set is collected but the R1 data of the same frequency has not.
- missing_r1 = 0
- for j in xrange(self.data[i].num_ri):
- if self.data[i].ri_labels[j] == 'NOE' and
self.data[i].noe_r1_table[j] == None:
- missing_r1 = 1
- if missing_r1:
- self.init_res_r1_data(self.data[i])
+
+ # loop over interactions
+ for j in xrange(self.num_interactions[i]):
+ self.data[i].append(Data())
+ self.data[i][j].interactions=interactions[i][j]
+
self.data[i][j].internuclei_distance=internuclei_distance[i][j]
+ self.data[i][j].gratio_ext=gratio_ext[i][j]
+ self.data[i][j].csa=csa[i][j]
+ self.data[i][j].unit_vector=unit_vector[i][j]
+
+ # Number of indices.
+ self.data[i][j].num_indices = self.diff_data.num_indices
+
+ # Calculate the five frequencies per field strength which
cause R1, R2, and NOE relaxation.
+ self.data[i][j].frq_list = zeros((num_frq[i], 5), float64)
+ self.data[i][j].frq_list_ext = zeros((num_frq[i], 5,
self.diff_data.num_indices), float64)
+ self.data[i][j].frq_sqrd_list_ext = zeros((num_frq[i], 5,
self.diff_data.num_indices), float64)
+ for k in xrange(num_frq[i]):
+ frqH = 2.0 * pi * frq[i][k]
+ frqX = frqH / g_ratio
+ frqY = frqH * gratio_ext[i][j] / gh[i]
+ self.data[i][j].frq_list[k, 1] = frqX
+ self.data[i][j].frq_list[k, 2] = frqY - frqX
+ self.data[i][j].frq_list[k, 3] = frqY
+ self.data[i][j].frq_list[k, 4] = frqY + frqX
+ self.data[i][j].frq_sqrd_list = self.data[i].frq_list ** 2
+ for k in xrange(self.diff_data.num_indices):
+ self.data[i][j].frq_list_ext[:,:, k] =
self.data[i][j].frq_list
+ self.data[i][j].frq_sqrd_list_ext[:,:, k] =
self.data[i][j].frq_sqrd_list
+
+
+
+ # Store supplied data in self.data
+ self.data[i][j].gh = gh[i]
+ self.data[i][j].gx = gx[i]
+ self.data[i][j].g_ratio = g_ratio
+ self.data[i][j].h_bar = h_bar
+ self.data[i][j].mu0 = mu0
+ self.data[i][j].equations = equations[i]
+ self.data[i][j].param_types = param_types[i]
+ self.data[i][j].relax_data = relax_data[i]
+ self.data[i][j].errors = errors[i]
+ self.data[i][j].num_ri = num_ri[i]
+ self.data[i][j].num_frq = num_frq[i]
+ self.data[i][j].frq = frq[i]
+ self.data[i][j].remap_table = remap_table[i]
+ self.data[i][j].noe_r1_table = noe_r1_table[i]
+ self.data[i][j].ri_labels = ri_labels[i]
+ self.data[i][j].num_params = num_params[i]
+
+ # Parameter values for minimising solely the diffusion
tensor parameters.
+ if self.model_type == 'diff':
+ self.data[i][j].param_values = param_values[i]
+
+ # Indices for constructing the global generic model-free
gradient and Hessian kite.
+ if i == 0:
+ self.data[i][j].start_index = self.diff_data.num_params
+ else:
+ self.data[i][j].start_index = self.data[i-1][0].end_index
+ self.data[i][j].end_index = self.data[i][j].start_index +
self.data[i][j].num_params
+
+ # Total number of parameters.
+ if self.model_type == 'mf' or self.model_type == 'local_tm':
+ self.data[i][j].total_num_params =
self.data[i][j].num_params
+ elif self.model_type == 'diff':
+ self.data[i][j].total_num_params =
self.diff_data.num_params
+ else:
+ self.data[i][j].total_num_params =
self.data[i][j].num_params + self.diff_data.num_params
+
+ # Initialise the residue specific data.
+ self.init_res_data(self.data[i][j], self.diff_data)
+
+ # Setup the residue specific equations.
+ if not self.setup_equations(self.data[i][j]):
+ raise RelaxError("The model-free equations could not be
setup.")
+
+ # Diffusion tensor parameters.
+ if self.model_type == 'local_tm':
+ self.diff_data.params = self.params[0:1]
+ elif self.model_type == 'diff' or self.model_type == 'all':
+ self.diff_data.params =
self.params[0:self.diff_end_index]
+
+ # Calculate the correlation times ti.
+ self.diff_data.calc_ti(self.data[i][j], self.diff_data)
+
+ # ti spectral density components.
+ self.data[i][j].w_ti_sqrd =
self.data[i][j].frq_sqrd_list_ext * self.data[i][j].ti ** 2
+ self.data[i][j].fact_ti = 1.0 / (1.0 +
self.data[i][j].w_ti_sqrd)
+
+ # Initialise the R1 data class. This is used only if an NOE
data set is collected but the R1 data of the same frequency has not.
+ missing_r1 = 0
+ for k in xrange(self.data[i][j].num_ri):
+ if self.data[i][j].ri_labels[k] == 'NOE' and
self.data[i][j].noe_r1_table[k] == None:
+ missing_r1 = 1
+ if missing_r1:
+ self.init_res_r1_data(self.data[i][j])
# Scaling initialisation.
if self.scaling_matrix != None:
r11542 - /branches/cst/maths_fns/mf.py