Author: bugman
Date: Thu Jan 17 15:08:15 2008
New Revision: 4815
URL: http://svn.gna.org/viewcvs/relax?rev=4815&view=rev
Log:
Updated the generic_fns.value module functions set() and partition_params()
to be more general.
The updating is not complete, but works for most of the current codebase.
The assumption of a data
analysis type having a diffusion or other tensor has been removed. This has
been replaced by the
concept that the params are now partitioned into those that are spin specific
and those that are
not. The diffusion tensor stuff will be moved into the model-free code.
Modified:
1.3/generic_fns/value.py
Modified: 1.3/generic_fns/value.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/generic_fns/value.py?rev=4815&r1=4814&r2=4815&view=diff
==============================================================================
--- 1.3/generic_fns/value.py (original)
+++ 1.3/generic_fns/value.py Thu Jan 17 15:08:15 2008
@@ -34,105 +34,86 @@
from specific_fns import get_specific_fn
-def partition_params(val, param, return_data_name):
+def partition_params(val, param):
"""Function for sorting and partitioning the parameters and their values.
The two major partitions are the tensor parameters and the spin specific
parameters.
- @return: A tuple, of length 4, of arrays.
- @return type: tuple of arrays
+ @param val: The parameter values.
+ @type val: None, number, or list of numbers
+ @param param: The parameter names.
+ @type param: None, str, or list of str
+ @return: A tuple, of length 4, of lists. The first and second
elements are the lists of
+ spin specific parameters and values respectively. The
third and forth elements
+ are the lists of all other parameters and their values.
+ @return type: tuple of 4 lists
"""
+ # Alias the current data pipe.
+ cdp = relax_data_store[relax_data_store.current_pipe]
+
+ # Specific functions.
+ return_data_name = get_specific_fn('return_data_name', cdp.pipe_type)
+
# Initialise.
- tensor_params = []
- tensor_values = []
spin_params = []
spin_values = []
-
- # Separate the residue specific parameters from the diffusion tensor
parameters.
+ other_params = []
+ other_values = []
+
+ # The parameter has been specified.
if param:
# Single parameter.
if type(param) == str:
- # Get the diffusion tensor parameter name.
- tensor_name = diffusion_tensor.return_data_name(param)
-
- # The parameter is a diffusion parameter.
- if tensor_name:
- # List of values.
- if type(val) == list or type(val) == ArrayType:
- # Parameter name.
- for i in xrange(len(val)):
- tensor_params.append(tensor_name)
-
- # Parameter value.
- tensor_values = val
-
- # Single value.
- else:
- # Parameter name.
- tensor_params.append(param)
-
- # Parameter value.
- tensor_values.append(val)
-
- # The parameter is not a diffusion parameter.
- elif return_data_name(param):
- # List of values.
- if type(val) == list or type(val) == ArrayType:
- # Parameter name.
- for i in xrange(len(val)):
- spin_params.append(param)
-
- # Parameter value.
- spin_values = val
-
- # Single value.
- else:
- # Parameter name.
- spin_params.append(param)
-
- # Parameter value.
- spin_values.append(val)
-
- # Unknown parameter
- else:
- raise RelaxUnknownParamError, param
+ # Spin specific parameter.
+ if return_data_name(param):
+ params = spin_params
+ values = spin_values
+
+ # Other parameters.
+ else:
+ params = other_params
+ values = other_values
+
+ # List of values.
+ if type(val) == list or type(val) == ArrayType:
+ # Parameter name.
+ for i in xrange(len(val)):
+ params.append(param)
+
+ # Parameter value.
+ values = val
+
+ # Single value.
+ else:
+ # Parameter name.
+ params.append(param)
+
+ # Parameter value.
+ values.append(val)
# Multiple parameters.
elif type(param) == list:
# Loop over all parameters.
for i in xrange(len(param)):
- # Get the diffusion tensor parameter name.
- try:
- tensor_name = diffusion_tensor.return_data_name(param[i])
- except RelaxUnknownParamError:
- tensor_name = None
-
- # The parameter is a diffusion parameter.
- if tensor_name:
- # Parameter name.
- tensor_params.append(tensor_name)
-
- # Parameter value.
- if type(val) == list or type(val) == ArrayType:
- tensor_values.append(val[i])
- else:
- tensor_values.append(val)
-
- # The parameter is not a diffusion parameter.
- elif return_data_name(param[i]):
- # Parameter name.
- spin_params.append(param[i])
-
- # Parameter value.
- if type(val) == list or type(val) == ArrayType:
- spin_values.append(val[i])
- else:
- spin_values.append(val)
-
- # Unknown parameter
+ # Spin specific parameter.
+ if return_data_name(param[i]):
+ params = spin_params
+ values = spin_values
+
+ # Other parameters.
else:
- raise RelaxUnknownParamError, param[i]
+ params = other_params
+ values = other_values
+
+ # Parameter name.
+ params.append(param[i])
+
+ # Parameter value.
+ if type(val) == list or type(val) == ArrayType:
+ values.append(val[i])
+ else:
+ values.append(val)
# All other parameters.
@@ -170,15 +151,15 @@
spin_values.append(val)
# Debugging.
- if len(tensor_params) != len(tensor_values) or len(spin_params) !=
len(spin_values):
- print "Diff params: " + `tensor_params`
- print "Diff values: " + `tensor_values`
- print "Res params: " + `spin_params`
- print "Res values: " + `spin_values`
+ if len(other_params) != len(other_values) or len(spin_params) !=
len(spin_values):
+ print "Spin params: " + `spin_params`
+ print "Spin values: " + `spin_values`
+ print "Other params: " + `other_params`
+ print "Other values: " + `other_values`
raise RelaxError, "Bug in the code."
# Return the partitioned parameters and values.
- return tensor_params, tensor_values, spin_params, spin_values
+ return spin_params, spin_values, other_params, other_values
def set(val=None, param=None, spin_id=None, force=False):
@@ -187,7 +168,7 @@
@param val: The parameter values.
@type val: None, number, or list of numbers
@param param: The parameter names.
- @type val: None, str, or list of str
+ @type param: None, str, or list of str
@param spin_id: The spin identification string.
@type spin_id: str
@param force: A flag forcing the overwriting of current values.
@@ -202,24 +183,15 @@
cdp = relax_data_store[relax_data_store.current_pipe]
# Specific functions.
- return_data_name = get_specific_fn('return_data_name', cdp.pipe_type)
return_value = get_specific_fn('return_value', cdp.pipe_type)
set = get_specific_fn('set', cdp.pipe_type)
- # Sort the parameters and their values.
- tensor_params, tensor_values, spin_params, spin_values =
partition_params(val, param, return_data_name)
-
-
- # Diffusion tensor parameters.
- ##############################
-
- if tensor_params:
- # Set the diffusion parameters.
- diffusion_tensor.set(value=tensor_values, param=tensor_params)
-
-
- # Residue specific parameters.
- ##############################
+ # Partition the parameters into those which are spin specific and those
which are not.
+ spin_params, spin_values, other_params, other_values =
partition_params(val, param)
+
+
+ # Spin specific parameters.
+ ###########################
if spin_params:
# Test if the sequence data is loaded.
@@ -252,6 +224,15 @@
# Go to the specific code.
for j in xrange(len(spin_params)):
set(value=spin_values[j], error=None, spin=spin,
param=spin_params[j])
+
+
+ # All other parameters.
+ #######################
+
+ if other_params:
+ # Set the diffusion parameters.
+ diffusion_tensor.set(value=other_values, param=other_params)
+
# Reset all minimisation statistics.
reset_min_stats()
r4815 - /1.3/generic_fns/value.py