Author: bugman
Date: Tue Jul 17 16:49:30 2012
New Revision: 17273
URL: http://svn.gna.org/viewcvs/relax?rev=17273&view=rev
Log:
Merged revisions 17272 via svnmerge from
svn+ssh://bugman@xxxxxxxxxxx/svn/relax/trunk
........
r17272 | bugman | 2012-07-17 16:46:28 +0200 (Tue, 17 Jul 2012) | 3 lines
Import clean ups for the N-state model specific module.
........
Modified:
branches/frame_order_testing/ (props changed)
branches/frame_order_testing/specific_fns/n_state_model.py
Propchange: branches/frame_order_testing/
------------------------------------------------------------------------------
--- svnmerge-integrated (original)
+++ svnmerge-integrated Tue Jul 17 16:49:30 2012
@@ -1,1 +1,1 @@
-/trunk:1-17256
+/trunk:1-17272
Modified: branches/frame_order_testing/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order_testing/specific_fns/n_state_model.py?rev=17273&r1=17272&r2=17273&view=diff
==============================================================================
--- branches/frame_order_testing/specific_fns/n_state_model.py (original)
+++ branches/frame_order_testing/specific_fns/n_state_model.py Tue Jul 17
16:49:30 2012
@@ -23,7 +23,7 @@
"""Module for the specific analysis of the N-state dynamic model."""
# Python module imports.
-from math import acos, cos, pi, sqrt
+from math import acos, cos, pi
from minfx.generic import generic_minimise
from minfx.grid import grid
from numpy import array, dot, float64, identity, ones, zeros
@@ -32,26 +32,25 @@
from warnings import warn
# relax module imports.
-from api_base import API_base
-from api_common import API_common
import arg_check
from float import isNaN, isInf
import generic_fns
-from generic_fns.align_tensor import all_tensors_fixed, get_tensor_object,
num_tensors, return_tensor
+from generic_fns import align_tensor, pcs, pipes, rdc
from generic_fns.interatomic import interatomic_loop
from generic_fns.mol_res_spin import return_spin, spin_loop
-from generic_fns import pcs, pipes, rdc
+from generic_fns.structure import geometric
from generic_fns.structure.cones import Iso_cone
-import generic_fns.structure.geometric
from generic_fns.structure.internal import Internal
-import generic_fns.structure.mass
+from generic_fns.structure.mass import centre_of_mass
from maths_fns.n_state_model import N_state_opt
from maths_fns.potential import quad_pot
from maths_fns.rotation_matrix import two_vect_to_R, euler_to_R_zyz
from physical_constants import dipolar_constant, g1H,
return_gyromagnetic_ratio
-from relax_errors import RelaxError, RelaxInfError, RelaxModelError,
RelaxNaNError, RelaxNoModelError, RelaxNoTensorError, RelaxNoValueError,
RelaxSpinTypeError
+from relax_errors import RelaxError, RelaxInfError, RelaxNaNError,
RelaxNoModelError, RelaxNoValueError, RelaxSpinTypeError
from relax_io import open_write_file
-from relax_warnings import RelaxWarning, RelaxDeselectWarning
+from relax_warnings import RelaxWarning
+from specific_fns.api_base import API_base
+from specific_fns.api_common import API_common
from user_functions.data import Uf_tables; uf_tables = Uf_tables()
from user_functions.objects import Desc_container
@@ -101,7 +100,7 @@
param_vector = []
# A RDC or PCS data type requires the alignment tensors to be at the
start of the parameter vector (unless the tensors are fixed).
- if ('rdc' in data_types or 'pcs' in data_types) and not
all_tensors_fixed():
+ if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
# Loop over the alignments, adding the alignment tensor
parameters to the parameter vector.
for i in xrange(len(cdp.align_tensors)):
# No alignment ID, so skip the tensor as it will not be
optimised.
@@ -192,7 +191,7 @@
# Starting point of the populations.
pop_start = 0
- if ('rdc' in data_types or 'pcs' in data_types) and not
all_tensors_fixed():
+ if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
# Loop over the alignments.
tensor_num = 0
for i in range(len(cdp.align_tensors)):
@@ -344,7 +343,7 @@
# Calculate from the structure file.
else:
- cdp.CoM = generic_fns.structure.mass.centre_of_mass()
+ cdp.CoM = centre_of_mass()
# Calculate the vector between the pivot and CoM points.
cdp.pivot_CoM = array(cdp.CoM, float64) - array(cdp.pivot_point,
float64)
@@ -514,19 +513,19 @@
sim_vectors = None
if hasattr(cdp, 'ave_pivot_CoM_sim'):
sim_vectors = cdp.ave_pivot_CoM_sim
- res_num =
generic_fns.structure.geometric.generate_vector_residues(mol=mol,
vector=cdp.ave_pivot_CoM, atom_name='Ave', res_name_vect='AVE',
sim_vectors=sim_vectors, res_num=2, origin=cdp.pivot_point, scale=scale)
+ res_num = geometric.generate_vector_residues(mol=mol,
vector=cdp.ave_pivot_CoM, atom_name='Ave', res_name_vect='AVE',
sim_vectors=sim_vectors, res_num=2, origin=cdp.pivot_point, scale=scale)
# Generate the cone outer edge.
print("\nGenerating the cone outer edge.")
cap_start_atom = mol.atom_num[-1]+1
- generic_fns.structure.geometric.cone_edge(mol=mol, cone=cone,
res_name='CON', res_num=3, apex=cdp.pivot_point, R=R,
scale=norm(cdp.pivot_CoM), inc=inc)
+ geometric.cone_edge(mol=mol, cone=cone, res_name='CON', res_num=3,
apex=cdp.pivot_point, R=R, scale=norm(cdp.pivot_CoM), inc=inc)
# Generate the cone cap, and stitch it to the cone edge.
if cone_type == 'diff in cone':
print("\nGenerating the cone cap.")
cone_start_atom = mol.atom_num[-1]+1
- generic_fns.structure.geometric.generate_vector_dist(mol=mol,
res_name='CON', res_num=3, centre=cdp.pivot_point, R=R,
limit_check=cone.limit_check, scale=norm(cdp.pivot_CoM), inc=inc)
- generic_fns.structure.geometric.stitch_cone_to_edge(mol=mol,
cone=cone, dome_start=cone_start_atom, edge_start=cap_start_atom+1, inc=inc)
+ geometric.generate_vector_dist(mol=mol, res_name='CON',
res_num=3, centre=cdp.pivot_point, R=R, limit_check=cone.limit_check,
scale=norm(cdp.pivot_CoM), inc=inc)
+ geometric.stitch_cone_to_edge(mol=mol, cone=cone,
dome_start=cone_start_atom, edge_start=cap_start_atom+1, inc=inc)
# Create the PDB file.
print("\nGenerating the PDB file.")
@@ -553,7 +552,7 @@
raise RelaxNoModelError
# Unpack and strip off the alignment tensor parameters.
- if ('rdc' in data_types or 'pcs' in data_types) and not
all_tensors_fixed():
+ if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
# Loop over the alignments, adding the alignment tensor
parameters to the tensor data container.
tensor_num = 0
for i in xrange(len(cdp.align_tensors)):
@@ -732,7 +731,7 @@
# Starting point of the populations.
pop_start = 0
- if ('rdc' in data_types or 'pcs' in data_types) and not
all_tensors_fixed():
+ if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
# Loop over the alignments.
for i in range(len(cdp.align_tensors)):
# Fixed tensor.
@@ -1226,7 +1225,7 @@
"""
# Initialise.
- n = num_tensors(skip_fixed=False) - num_tensors(skip_fixed=True)
+ n = align_tensor.num_tensors(skip_fixed=False) -
align_tensor.num_tensors(skip_fixed=True)
tensors = zeros(n*5, float64)
# Nothing to do.
@@ -1361,7 +1360,7 @@
num = 0
# Alignment tensor params.
- if ('rdc' in data_types or 'pcs' in data_types) and not
all_tensors_fixed():
+ if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
# Loop over the alignments.
for i in xrange(len(cdp.align_tensors)):
# No alignment ID, so skip the tensor as it is not part of
the parameter set.
@@ -1618,7 +1617,7 @@
for id in cdp.align_ids:
# No tensors initialised.
if not hasattr(cdp, 'align_tensors'):
- generic_fns.align_tensor.init(tensor=id, align_id=id,
params=[0.0, 0.0, 0.0, 0.0, 0.0])
+ align_tensor.init(tensor=id, align_id=id, params=[0.0,
0.0, 0.0, 0.0, 0.0])
# Find if the tensor corresponding to the id exists.
exists = False
@@ -1628,7 +1627,7 @@
# Initialise the tensor.
if not exists:
- generic_fns.align_tensor.init(tensor=id, align_id=id,
params=[0.0, 0.0, 0.0, 0.0, 0.0])
+ align_tensor.init(tensor=id, align_id=id, params=[0.0,
0.0, 0.0, 0.0, 0.0])
def base_data_loop(self):
@@ -2314,7 +2313,7 @@
tensor_index = (index - index % 5) / 5
# Set the error.
- tensor = return_tensor(index=tensor_index, skip_fixed=True)
+ tensor = align_tensor.return_tensor(index=tensor_index,
skip_fixed=True)
return setattr(tensor, names[param_index]+'_err', error)
@@ -2482,11 +2481,11 @@
names = ['Axx', 'Ayy', 'Axy', 'Axz', 'Ayz']
# Alignment tensor parameters.
- if index < num_tensors(skip_fixed=True)*5:
+ if index < align_tensor.num_tensors(skip_fixed=True)*5:
# The tensor and parameter index.
param_index = index % 5
tensor_index = (index - index % 5) / 5
# Return the simulation parameter array.
- tensor = return_tensor(index=tensor_index, skip_fixed=True)
+ tensor = align_tensor.return_tensor(index=tensor_index,
skip_fixed=True)
return getattr(tensor, names[param_index]+'_sim')
r17273 - in /branches/frame_order_testing: ./ specific_fns/n_state_model.py