Author: bugman
Date: Mon Dec 12 11:45:36 2011
New Revision: 15076
URL: http://svn.gna.org/viewcvs/relax?rev=15076&view=rev
Log:
Added the ability for setting up MC simulations with the new frame order
input data.
This includes randomised RDCs and PCSs as well as randomised full alignment
tensor components. The
tensor components adds an additional error which contributes to the real
fitting error.
Modified:
branches/frame_order_testing/specific_fns/frame_order.py
Modified: branches/frame_order_testing/specific_fns/frame_order.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order_testing/specific_fns/frame_order.py?rev=15076&r1=15075&r2=15076&view=diff
==============================================================================
--- branches/frame_order_testing/specific_fns/frame_order.py (original)
+++ branches/frame_order_testing/specific_fns/frame_order.py Mon Dec 12
11:45:36 2011
@@ -40,7 +40,7 @@
from float import isNaN, isInf
from generic_fns import align_tensor, pipes
from generic_fns.angles import wrap_angles
-from generic_fns.mol_res_spin import spin_loop
+from generic_fns.mol_res_spin import return_spin, spin_loop
from generic_fns.structure.cones import Iso_cone, Pseudo_elliptic
from generic_fns.structure.geometric import create_cone_pdb,
generate_vector_dist, generate_vector_residues
from generic_fns.structure.internal import Internal
@@ -1379,15 +1379,49 @@
def base_data_loop(self):
- """Generator method for looping nothing.
-
- The loop essentially consists of a single element.
-
- @return: Nothing.
- @rtype: None
- """
-
- yield None
+ """Generator method for looping over the base data - alignment
tensors, RDCs, PCSs.
+
+ This loop first yields the string 'A' representing the alignment
tensors, and then iterates for each data point (RDC, PCS) for each spin,
returning the identification information.
+
+ @return: The alignment tensor or a list of the spin ID string,
the data type ('rdc', 'pcs') and the alignment ID.
+ @rtype: string or list of str
+ """
+
+ # First the tensors.
+ yield 'A'
+
+ # Then the spin IDs.
+ for spin, spin_id in spin_loop(return_id=True):
+ # Re-initialise the data structure.
+ base_ids = [spin_id, None, None]
+
+ # Skip deselected spins.
+ if not spin.select:
+ continue
+
+ # RDC data.
+ if hasattr(spin, 'rdc'):
+ base_ids[1] = 'rdc'
+
+ # Loop over the alignment IDs.
+ for id in cdp.rdc_ids:
+ # Add the ID.
+ base_ids[2] = id
+
+ # Yield the set.
+ yield base_ids
+
+ # PCS data.
+ if hasattr(spin, 'pcs'):
+ base_ids[1] = 'pcs'
+
+ # Loop over the alignment IDs.
+ for id in cdp.pcs_ids:
+ # Add the ID.
+ base_ids[2] = id
+
+ # Yield the set.
+ yield base_ids
def calculate(self, spin_id=None, verbosity=1, sim_index=None):
@@ -1423,11 +1457,55 @@
@rtype: list of floats
"""
- # Back calculate the tensors.
- red_tensors_bc = self._back_calc()
+ # Initialise the MC data structure.
+ mc_data = []
+
+ # Alignment tensor data.
+ if data_id == 'A':
+ # Back calculate the tensors.
+ red_tensors_bc = self._back_calc()
+
+ # Append the data.
+ for i in range(len(red_tensors_bc)):
+ mc_data.append(red_tensors_bc[i])
+
+ # The spin specific data.
+ else:
+ # Get the spin container.
+ spin = return_spin(data_id[0])
+
+ # RDC data.
+ if data_id[1] == 'rdc' and hasattr(spin, 'rdc'):
+ # Does back-calculated data exist?
+ if not hasattr(spin, 'rdc_bc'):
+ self.calculate()
+
+ # The data.
+ if not hasattr(spin, 'rdc_bc') or not
spin.rdc_bc.has_key(data_id[2]):
+ data = None
+ else:
+ data = spin.rdc_bc[data_id[2]]
+
+ # Append the data.
+ mc_data.append(data)
+
+ # PCS data.
+ elif data_id[1] == 'pcs' and hasattr(spin, 'pcs'):
+ # Does back-calculated data exist?
+ if not hasattr(spin, 'pcs_bc'):
+ self.calculate()
+
+ # The data.
+ if not hasattr(spin, 'pcs_bc') or not
spin.pcs_bc.has_key(data_id[2]):
+ data = None
+ else:
+ data = spin.pcs_bc[data_id[2]]
+
+ # Append the data.
+ mc_data.append(data)
# Return the data.
- return red_tensors_bc
+ return mc_data
def data_names(self, set='all', error_names=False, sim_names=False):
@@ -1870,11 +1948,43 @@
@rtype: list of float
"""
- # Get the tensor data structures.
- full_tensors, red_tensors, red_tensor_err, full_in_ref_frame =
self._minimise_setup_tensors()
+ # Initialise the MC data structure.
+ mc_errors = []
+
+ # Alignment tensor data.
+ if data_id == 'A':
+ # Get the tensor data structures.
+ full_tensors, red_tensors, red_tensor_err, full_in_ref_frame =
self._minimise_setup_tensors()
+
+ # Return the errors.
+ for i in range(len(red_tensor_err)):
+ mc_errors.append(red_tensor_err[i])
+
+ # The spin specific data.
+ else:
+ # Get the spin container.
+ spin = return_spin(data_id[0])
+
+ # RDC data.
+ if data_id[1] == 'rdc' and hasattr(spin, 'rdc'):
+ # Do errors exist?
+ if not hasattr(spin, 'rdc_err'):
+ raise RelaxError("The RDC errors are missing for spin
'%s'." % data_id[0])
+
+ # Append the data.
+ mc_errors.append(spin.rdc_err[data_id[2]])
+
+ # PCS data.
+ elif data_id[1] == 'pcs' and hasattr(spin, 'pcs'):
+ # Do errors exist?
+ if not hasattr(spin, 'pcs_err'):
+ raise RelaxError("The PCS errors are missing for spin
'%s'." % data_id[0])
+
+ # Append the data.
+ mc_errors.append(spin.pcs_err[data_id[2]])
# Return the errors.
- return red_tensor_err
+ return mc_errors
def return_units(self, param, spin=None, spin_id=None):
@@ -2011,30 +2121,65 @@
def sim_pack_data(self, data_id, sim_data):
"""Pack the Monte Carlo simulation data.
- @param data_id: The spin identification string, as yielded by
the base_data_loop() generator method.
- @type data_id: None
+ @param data_id: The identification data as yielded by the
base_data_loop() generator method.
+ @type data_id: string or list of str
@param sim_data: The Monte Carlo simulation data.
@type sim_data: list of float
"""
- # Transpose the data.
- sim_data = transpose(sim_data)
-
- # Loop over the reduced tensors.
- for i, tensor in self._tensor_loop(red=True):
- # Set the reduced tensor simulation data.
- tensor.Axx_sim = sim_data[5*i + 0]
- tensor.Ayy_sim = sim_data[5*i + 1]
- tensor.Axy_sim = sim_data[5*i + 2]
- tensor.Axz_sim = sim_data[5*i + 3]
- tensor.Ayz_sim = sim_data[5*i + 4]
-
-
- def sim_return_param(self, model_info, index):
+ # Alignment tensor data.
+ if data_id == 'A':
+ # Loop over the full tensors.
+ for j, tensor in self._tensor_loop(red=False):
+ # Initialise the data if needed.
+ if not hasattr(tensor, 'Axx_sim'):
+ tensor.Axx_sim = []
+ tensor.Ayy_sim = []
+ tensor.Axy_sim = []
+ tensor.Axz_sim = []
+ tensor.Ayz_sim = []
+
+ # Set the full tensor simulation data.
+ for i in range(cdp.sim_number):
+ tensor.Axx_sim.append(sim_data[i][5*j + 0])
+ tensor.Ayy_sim.append(sim_data[i][5*j + 1])
+ tensor.Axy_sim.append(sim_data[i][5*j + 2])
+ tensor.Axz_sim.append(sim_data[i][5*j + 3])
+ tensor.Ayz_sim.append(sim_data[i][5*j + 4])
+
+ # The spin specific data.
+ else:
+ # Get the spin container.
+ spin = return_spin(data_id[0])
+
+ # RDC data.
+ if data_id[1] == 'rdc' and hasattr(spin, 'rdc'):
+ # Initialise.
+ if not hasattr(spin, 'rdc_sim'):
+ spin.rdc_sim = {}
+
+ # Store the data structure.
+ spin.rdc_sim[data_id[2]] = []
+ for i in range(cdp.sim_number):
+ spin.rdc_sim[data_id[2]].append(sim_data[i][0])
+
+ # PCS data.
+ if data_id[1] == 'pcs' and hasattr(spin, 'pcs'):
+ # Initialise.
+ if not hasattr(spin, 'pcs_sim'):
+ spin.pcs_sim = {}
+
+ # Store the data structure.
+ spin.pcs_sim[data_id[2]] = []
+ for i in range(cdp.sim_number):
+ spin.pcs_sim[data_id[2]].append(sim_data[i][0])
+
+
+ def sim_return_param(self, data_id, index):
"""Return the array of simulation parameter values.
- @param model_info: The model information originating from
model_loop() (unused).
- @type model_info: None
+ @param data_id: The identification data as yielded by the
base_data_loop() generator method.
+ @type data_id: string or list of str
@param index: The index of the parameter to return the array
of values for.
@type index: int
"""
r15076 - /branches/frame_order_testing/specific_fns/frame_order.py