Author: bugman
Date: Mon Jan 25 19:21:05 2010
New Revision: 10323
URL: http://svn.gna.org/viewcvs/relax?rev=10323&view=rev
Log:
The dAuvergne_protocol class now calls the user functions from the
self.interpreter instance directly.
Modified:
1.3/auto_analyses/dauvergne_protocol.py
Modified: 1.3/auto_analyses/dauvergne_protocol.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/auto_analyses/dauvergne_protocol.py?rev=10323&r1=10322&r2=10323&view=diff
==============================================================================
--- 1.3/auto_analyses/dauvergne_protocol.py (original)
+++ 1.3/auto_analyses/dauvergne_protocol.py Mon Jan 25 19:21:05 2010
@@ -148,12 +148,13 @@
from float import floatAsByteArray
from generic_fns.mol_res_spin import generate_spin_id, spin_index_loop,
spin_loop
from generic_fns import pipes
+from prompt.interpreter import Interpreter
from relax_errors import RelaxError
class dAuvergne_protocol:
- def __init__(self, diff_model=None, mf_models=['m0', 'm1', 'm2', 'm3',
'm4', 'm5', 'm6', 'm7', 'm8', 'm9'], local_tm_models=['tm0', 'tm1', 'tm2',
'tm3', 'tm4', 'tm5', 'tm6', 'tm7', 'tm8', 'tm9'], pdb_file=None,
seq_args=None, het_name=None, relax_data=None, unres=None, exclude=None,
bond_length=None, csa=None, hetnuc=None, proton='1H', grid_inc=11,
min_algor='newton', mc_num=500, conv_loop=True):
+ def __init__(self, diff_model=None, mf_models=['m0', 'm1', 'm2', 'm3',
'm4', 'm5', 'm6', 'm7', 'm8', 'm9'], local_tm_models=['tm0', 'tm1', 'tm2',
'tm3', 'tm4', 'tm5', 'tm6', 'tm7', 'tm8', 'tm9'], pdb_file=None,
seq_args=None, het_name=None, relax_data=None, unres=None, exclude=None,
bond_length=None, csa=None, hetnuc=None, proton='1H', grid_inc=11,
min_algor='newton', mc_num=500, user_fns=None, conv_loop=True):
"""Perform the full model-free analysis protocol of d'Auvergne and
Gooley, 2008b.
@keyword diff_model: The global diffusion model to optimise.
This can be one of 'local_tm', 'sphere', 'oblate', 'prolate', 'ellipsoid', or
'final'.
@@ -188,6 +189,8 @@
@type min_algor: str
@keyword mc_num: The number of Monte Carlo simulations to
be used for error analysis at the end of the analysis.
@type mc_num: int
+ @keyword user_fns: A dictionary of replacement user
functions. These will overwrite the standard user functions. The key should
be the name of the user function or user function class and the value should
be the function or class instance.
+ @type user_fns: dict
@keyword conv_loop: Automatic looping over all rounds until
convergence.
@type conv_loop: bool
"""
@@ -214,6 +217,16 @@
# User variable checks.
self.check_vars()
+ # Load the interpreter.
+ self.interpreter = Interpreter(show_script=False, quit=False,
raise_relax_error=True)
+ self.interpreter.populate_self()
+ self.interpreter.on(verbose=False)
+
+ # Replacement user functions.
+ if user_fns:
+ for name in user_fns:
+ setattr(self.interpreter, name, user_fns[name])
+
# MI - Local tm.
################
@@ -248,44 +261,44 @@
name = self.diff_model
# Create the data pipe.
- pipe.create(name, 'mf')
+ self.interpreter.pipe.create(name, 'mf')
# Load the local tm diffusion model MI results.
- results.read(file='results', dir='local_tm'+sep+'aic')
+ self.interpreter.results.read(file='results',
dir='local_tm'+sep+'aic')
# Remove the tm parameter.
- model_free.remove_tm()
+ self.interpreter.model_free.remove_tm()
# Deselect the spins in the exclude list.
if self.exclude:
- deselect.read(file=self.exclude)
+ self.interpreter.deselect.read(file=self.exclude)
# Load the PDB file and calculate the unit vectors
parallel to the XH bond.
if self.pdb_file:
- structure.read_pdb(self.pdb_file)
- structure.vectors(attached='H')
+ self.interpreter.structure.read_pdb(self.pdb_file)
+ self.interpreter.structure.vectors(attached='H')
# Add an arbitrary diffusion tensor which will be
optimised.
if self.diff_model == 'sphere':
- diffusion_tensor.init(10e-9, fixed=False)
+ self.interpreter.diffusion_tensor.init(10e-9,
fixed=False)
inc = 11
elif self.diff_model == 'prolate':
- diffusion_tensor.init((10e-9, 0, 0, 0),
spheroid_type='prolate', fixed=False)
+ self.interpreter.diffusion_tensor.init((10e-9, 0, 0,
0), spheroid_type='prolate', fixed=False)
inc = 11
elif self.diff_model == 'oblate':
- diffusion_tensor.init((10e-9, 0, 0, 0),
spheroid_type='oblate', fixed=False)
+ self.interpreter.diffusion_tensor.init((10e-9, 0, 0,
0), spheroid_type='oblate', fixed=False)
inc = 11
elif self.diff_model == 'ellipsoid':
- diffusion_tensor.init((10e-09, 0, 0, 0, 0, 0),
fixed=False)
+ self.interpreter.diffusion_tensor.init((10e-09, 0,
0, 0, 0, 0), fixed=False)
inc = 6
# Minimise just the diffusion tensor.
- fix('all_spins')
- grid_search(inc=inc)
- minimise(self.min_algor)
+ self.interpreter.fix('all_spins')
+ self.interpreter.grid_search(inc=inc)
+ self.interpreter.minimise(self.min_algor)
# Write the results.
- results.write(file='results', dir=self.base_dir,
force=True)
+ self.interpreter.results.write(file='results',
dir=self.base_dir, force=True)
# Normal round of optimisation for diffusion models MII to
MV.
@@ -303,14 +316,14 @@
self.model_selection(modsel_pipe='aic',
dir=self.base_dir + 'aic')
# Final optimisation of all diffusion and model-free
parameters.
- fix('all', fixed=False)
+ self.interpreter.fix('all', fixed=False)
# Minimise all parameters.
- minimise(self.min_algor)
+ self.interpreter.minimise(self.min_algor)
# Write the results.
dir = self.base_dir + 'opt'
- results.write(file='results', dir=dir, force=True)
+ self.interpreter.results.write(file='results', dir=dir,
force=True)
# Test for convergence.
converged = self.convergence()
@@ -331,10 +344,10 @@
self.pipes = ['local_tm', 'sphere', 'prolate', 'oblate',
'ellipsoid']
# Create the local_tm data pipe.
- pipe.create('local_tm', 'mf')
+ self.interpreter.pipe.create('local_tm', 'mf')
# Load the local tm diffusion model MI results.
- results.read(file='results', dir='local_tm'+sep+'aic')
+ self.interpreter.results.read(file='results',
dir='local_tm'+sep+'aic')
# Loop over models MII to MV.
for model in ['sphere', 'prolate', 'oblate', 'ellipsoid']:
@@ -352,10 +365,10 @@
raise RelaxError("Multiple rounds of optimisation of the
" + name + " (between 8 to 15) are required for the proper execution of this
script.")
# Create the data pipe.
- pipe.create(model, 'mf')
+ self.interpreter.pipe.create(model, 'mf')
# Load the diffusion model results.
- results.read(file='results', dir=model +
sep+'round_'+repr(self.round)+sep+'opt')
+ self.interpreter.results.read(file='results', dir=model +
sep+'round_'+repr(self.round)+sep+'opt')
# Model selection between MI to MV.
self.model_selection(modsel_pipe='final', write_flag=False)
@@ -366,21 +379,21 @@
# Fix the diffusion tensor, if it exists.
if hasattr(pipes.get_pipe('final'), 'diff_tensor'):
- fix('diff')
+ self.interpreter.fix('diff')
# Simulations.
- monte_carlo.setup(number=self.mc_num)
- monte_carlo.create_data()
- monte_carlo.initial_values()
- minimise(self.min_algor)
- eliminate()
- monte_carlo.error_analysis()
+ self.interpreter.monte_carlo.setup(number=self.mc_num)
+ self.interpreter.monte_carlo.create_data()
+ self.interpreter.monte_carlo.initial_values()
+ self.interpreter.minimise(self.min_algor)
+ self.interpreter.eliminate()
+ self.interpreter.monte_carlo.error_analysis()
# Write the final results.
##########################
- results.write(file='results', dir='final', force=True)
+ self.interpreter.results.write(file='results', dir='final',
force=True)
# Unknown script behaviour.
@@ -679,16 +692,16 @@
# Create the data pipe for the previous data (deleting the old data
pipe first if necessary).
if pipes.has_pipe('previous'):
- pipe.delete('previous')
- pipe.create('previous', 'mf')
+ self.interpreter.pipe.delete('previous')
+ self.interpreter.pipe.create('previous', 'mf')
# Load the optimised diffusion tensor from the initial round.
if self.round == 1:
- results.read('results', self.diff_model + sep+'init')
+ self.interpreter.results.read('results', self.diff_model +
sep+'init')
# Load the optimised diffusion tensor from the previous round.
else:
- results.read('results', self.diff_model +
sep+'round_'+repr(self.round-1)+sep+'opt')
+ self.interpreter.results.read('results', self.diff_model +
sep+'round_'+repr(self.round-1)+sep+'opt')
def model_selection(self, modsel_pipe=None, dir=None, write_flag=True):
@@ -696,16 +709,16 @@
# Model elimination.
if modsel_pipe != 'final':
- eliminate()
+ self.interpreter.eliminate()
# Model selection (delete the model selection pipe if it already
exists).
if pipes.has_pipe(modsel_pipe):
- pipe.delete(modsel_pipe)
- model_selection(method='AIC', modsel_pipe=modsel_pipe,
pipes=self.pipes)
+ self.interpreter.pipe.delete(modsel_pipe)
+ self.interpreter.model_selection(method='AIC',
modsel_pipe=modsel_pipe, pipes=self.pipes)
# Write the results.
if write_flag:
- results.write(file='results', dir=dir, force=True)
+ self.interpreter.results.write(file='results', dir=dir,
force=True)
def multi_model(self, local_tm=False):
@@ -721,49 +734,50 @@
for name in self.pipes:
# Create the data pipe.
if pipes.has_pipe(name):
- pipe.delete(name)
- pipe.create(name, 'mf')
+ self.interpreter.pipe.delete(name)
+ self.interpreter.pipe.create(name, 'mf')
# Load the sequence.
- sequence.read(self.seq_args[0], self.seq_args[1],
self.seq_args[2], self.seq_args[3], self.seq_args[4], self.seq_args[5],
self.seq_args[6], self.seq_args[7])
+ print self.seq_args
+ self.interpreter.sequence.read(self.seq_args[0],
self.seq_args[1], self.seq_args[2], self.seq_args[3], self.seq_args[4],
self.seq_args[5], self.seq_args[6], self.seq_args[7])
# Name the spins if necessary.
if self.seq_args[6] == None:
- spin.name(name=self.het_name)
+ self.interpreter.spin.name(name=self.het_name)
# Load the PDB file and calculate the unit vectors parallel to
the XH bond.
if not local_tm and self.pdb_file:
- structure.read_pdb(self.pdb_file)
- structure.vectors(attached='H')
+ self.interpreter.structure.read_pdb(self.pdb_file)
+ self.interpreter.structure.vectors(attached='H')
# Load the relaxation data.
for data in self.relax_data:
- relax_data.read(data[0], data[1], data[2], data[3], data[4],
data[5], data[6], data[7], data[8], data[9], data[10], data[11], data[12])
+ self.interpreter.relax_data.read(data[0], data[1], data[2],
data[3], data[4], data[5], data[6], data[7], data[8], data[9], data[10],
data[11], data[12])
# Deselect spins to be excluded (including unresolved and
specifically excluded spins).
if self.unres:
- deselect.read(file=self.unres)
+ self.interpreter.deselect.read(file=self.unres)
if self.exclude:
- deselect.read(file=self.exclude)
+ self.interpreter.deselect.read(file=self.exclude)
# Copy the diffusion tensor from the 'opt' data pipe and prevent
it from being minimised.
if not local_tm:
- diffusion_tensor.copy('previous')
- fix('diff')
+ self.interpreter.diffusion_tensor.copy('previous')
+ self.interpreter.fix('diff')
# Set all the necessary values.
- value.set(self.bond_length, 'bond_length')
- value.set(self.csa, 'csa')
- value.set(self.hetnuc, 'heteronucleus')
- value.set(self.proton, 'proton')
+ self.interpreter.value.set(self.bond_length, 'bond_length')
+ self.interpreter.value.set(self.csa, 'csa')
+ self.interpreter.value.set(self.hetnuc, 'heteronucleus')
+ self.interpreter.value.set(self.proton, 'proton')
# Select the model-free model.
- model_free.select_model(model=name)
+ self.interpreter.model_free.select_model(model=name)
# Minimise.
- grid_search(inc=self.grid_inc)
- minimise(self.min_algor)
+ self.interpreter.grid_search(inc=self.grid_inc)
+ self.interpreter.minimise(self.min_algor)
# Write the results.
dir = self.base_dir + name
- results.write(file='results', dir=dir, force=True)
+ self.interpreter.results.write(file='results', dir=dir,
force=True)
r10323 - /1.3/auto_analyses/dauvergne_protocol.py