Package auto_analyses :: Module frame_order :: Class Frame_order_analysis
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Class Frame_order_analysis

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The frame order auto-analysis protocol.

Instance Methods [hide private]
 
__init__(self, data_pipe_full=None, data_pipe_subset=None, pipe_bundle=None, results_dir=None, grid_inc=11, grid_inc_rigid=21, min_algor='simplex', num_int_pts_grid=50, num_int_pts_subset=[20, 100], func_tol_subset=[0.01, 0.01], num_int_pts_full=[100, 1000, 200000], func_tol_full=[0.01, 0.001, 0.0001], mc_sim_num=500, mc_int_pts=1000, mc_func_tol=0.001, models=['rigid', 'free rotor', 'rotor', 'iso cone, free rotor', 'iso ...)
Perform the full frame order analysis.
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check_vars(self)
Check that the user has set the variables correctly.
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list of int and None
custom_grid_incs(self, model)
Set up a customised grid search increment number for each model.
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nested_params(self, model)
Copy the parameters from the simpler nested models for faster optimisation.
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nested_models(self)
Protocol for the nested optimisation of the frame order models.
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optimise_rigid(self)
Optimise the rigid frame order model.
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print_results(self)
Print out the optimisation results for the current data pipe.
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bool
read_results(self, model=None, pipe_name=None)
Attempt to read old results files.
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visualisation(self, model=None)
Create visual representations of the frame order results for the given model.
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Method Details [hide private]

__init__(self, data_pipe_full=None, data_pipe_subset=None, pipe_bundle=None, results_dir=None, grid_inc=11, grid_inc_rigid=21, min_algor='simplex', num_int_pts_grid=50, num_int_pts_subset=[20, 100], func_tol_subset=[0.01, 0.01], num_int_pts_full=[100, 1000, 200000], func_tol_full=[0.01, 0.001, 0.0001], mc_sim_num=500, mc_int_pts=1000, mc_func_tol=0.001, models=['rigid', 'free rotor', 'rotor', 'iso cone, free rotor', 'iso ...)
(Constructor)

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Perform the full frame order analysis.

Parameters:
  • data_pipe_full (str) - The name of the data pipe containing all of the RDC and PCS data.
  • data_pipe_subset (str) - The name of the data pipe containing all of the RDC data but only a small subset of ~5 PCS points.
  • pipe_bundle (str) - The data pipe bundle to associate all spawned data pipes with.
  • results_dir (str) - The directory where files are saved in.
  • grid_inc (int) - The number of grid increments to use in the grid search of certain models.
  • grid_inc_rigid (int) - The number of grid increments to use in the grid search of the initial rigid model.
  • min_algor (str) - The minimisation algorithm (in most cases this should not be changed).
  • num_int_pts_grid (int) - The number of Sobol' points for the PCS numerical integration in the grid searches.
  • num_int_pts_subset (list of int) - The list of the number of Sobol' points for the PCS numerical integration to use iteratively in the optimisations after the grid search (for the PCS data subset).
  • func_tol_subset (list of float) - The minimisation function tolerance cutoff to terminate optimisation (for the PCS data subset, see the minimise user function).
  • num_int_pts_full (list of int) - The list of the number of Sobol' points for the PCS numerical integration to use iteratively in the optimisations after the grid search (for all PCS and RDC data).
  • func_tol_full (list of float) - The minimisation function tolerance cutoff to terminate optimisation (for all PCS and RDC data, see the minimise user function).
  • mc_sim_num (int) - The number of Monte Carlo simulations to be used for error analysis at the end of the analysis.
  • mc_int_num (int) - The number of Sobol' points for the PCS numerical integration during Monte Carlo simulations.
  • mc_func_tol (float) - The minimisation function tolerance cutoff to terminate optimisation during Monte Carlo simulations.
  • models (list of str) - The frame order models to use in the analysis. The 'rigid' model must be included as this is essential for the analysis.

custom_grid_incs(self, model)

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Set up a customised grid search increment number for each model.

Parameters:
  • model (str) - The frame order model.
Returns: list of int and None
The list of increment values.

nested_params(self, model)

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Copy the parameters from the simpler nested models for faster optimisation.

Parameters:
  • model (str) - The frame order model.

optimise_rigid(self)

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Optimise the rigid frame order model.

The Sobol' integration is not used here, so the algorithm is different to the other frame order models.

read_results(self, model=None, pipe_name=None)

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Attempt to read old results files.

Parameters:
  • model (str) - The frame order model.
  • pipe_name (str) - The name of the data pipe to use for this model.
Returns: bool
True if the file exists and has been read, False otherwise.

visualisation(self, model=None)

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Create visual representations of the frame order results for the given model.

This includes a PDB representation of the motions (the 'cone.pdb' file located in each model directory) together with a relax script for displaying the average domain positions together with the cone/motion representation in PyMOL (the 'pymol_display.py' file, also created in the model directory).

Parameters:
  • model (str) - The frame order model to visualise. This should match the model of the current data pipe, unless the special value of 'final' is used to indicate the visualisation of the final results.