Source Code for Module specific_analyses.model_free.multi_processor_commands

  1  ############################################################################### 
  2  #                                                                             # 
  3  # Copyright (C) 2007 Gary S Thompson (https://gna.org/users/varioustoxins)    # 
  4  # Copyright (C) 2008-2013 Edward d'Auvergne                                   # 
  5  #                                                                             # 
  6  # This file is part of the program relax (http://www.nmr-relax.com).          # 
  7  #                                                                             # 
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  9  # it under the terms of the GNU General Public License as published by        # 
 10  # the Free Software Foundation, either version 3 of the License, or           # 
 11  # (at your option) any later version.                                         # 
 12  #                                                                             # 
 13  # This program is distributed in the hope that it will be useful,             # 
 14  # but WITHOUT ANY WARRANTY; without even the implied warranty of              # 
 15  # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               # 
 16  # GNU General Public License for more details.                                # 
 17  #                                                                             # 
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 19  # along with this program.  If not, see <http://www.gnu.org/licenses/>.       # 
 20  #                                                                             # 
 21  ############################################################################### 
 22   
 23  # Module docstring. 
 24  """Module for the multi-processor command system.""" 
 25   
 26  # Python module imports. 
 27  from minfx.generic import generic_minimise 
 28  from minfx.grid import grid, grid_point_array 
 29   
 30  # relax module imports. 
 31  from multi import Memo, Result_command, Slave_command 
 32  from target_functions.mf import Mf 
 33   
 34   
 35   
 36 -def spin_print(spin_id, verbosity): 
 37      """Print out some header text for the spin. 
 38   
 39      @param spin_id:     The spin ID string. 
 40      @type spin_id:      str 
 41      @param verbosity:   The amount of information to print.  The higher the value, the greater the verbosity. 
 42      @type verbosity:    int 
 43      """ 
 44   
 45      # Some extra spacing for verbose printouts. 
 46      if verbosity >= 2: 
 47          print("\n\n") 
 48   
 49      # The header. 
 50      string = "Fitting to spin " + repr(spin_id) 
 51      print("\n\n" + string) 
 52      print(len(string) * '~') 
 53   
 54   
 55   
 56 -class MF_memo(Memo): 
 57      """The model-free memo class. 
 58   
 59      Not quite a momento so a memo. 
 60      """ 
 61   
 62 -    def __init__(self, model_free=None, model_type=None, spin=None, sim_index=None, scaling=None, scaling_matrix=None): 
 63          """Initialise the model-free memo class. 
 64   
 65          This memo stores the model-free class instance so that the _disassemble_result() method can be called to store the optimisation results.  The other args are those required by this method but not generated through optimisation. 
 66   
 67          @keyword model_free:        The model-free class instance. 
 68          @type model_free:           specific_analyses.model_free.Model_free instance 
 69          @keyword spin:              The spin data container.  If this argument is supplied, then the spin_id argument will be ignored. 
 70          @type spin:                 SpinContainer instance 
 71          @keyword sim_index:         The optional MC simulation index. 
 72          @type sim_index:            int 
 73          @keyword scaling:           If True, diagonal scaling is enabled. 
 74          @type scaling:              bool 
 75          @keyword scaling_matrix:    The diagonal, square scaling matrix. 
 76          @type scaling_matrix:       numpy diagonal matrix 
 77          """ 
 78   
 79          # Execute the base class __init__() method. 
 80          super(MF_memo, self).__init__() 
 81   
 82          # Store the arguments. 
 83          self.model_free = model_free 
 84          self.model_type = model_type 
 85          self.spin = spin 
 86          self.sim_index = sim_index 
 87          self.scaling = scaling 
 88          self.scaling_matrix = scaling_matrix 
 89   
 90   
 91   
 92 -class MF_minimise_command(Slave_command): 
 93      """Command class for standard model-free minimisation.""" 
 94   
 95 -    def __init__(self): 
 96          """Initialise the base class.""" 
 97   
 98          # Execute the base class __init__() method. 
 99          super(MF_minimise_command, self).__init__() 
100   
101   
102 -    def optimise(self): 
103          """Model-free optimisation. 
104   
105          @return:    The optimisation results consisting of the parameter vector, function value, iteration count, function count, gradient count, Hessian count, and warnings. 
106          @rtype:     tuple of numpy array, float, int, int, int, int, str 
107          """ 
108   
109          # Minimisation. 
110          results = generic_minimise(func=self.mf.func, dfunc=self.mf.dfunc, d2func=self.mf.d2func, args=(), x0=self.opt_params.param_vector, min_algor=self.opt_params.min_algor, min_options=self.opt_params.min_options, func_tol=self.opt_params.func_tol, grad_tol=self.opt_params.grad_tol, maxiter=self.opt_params.max_iterations, A=self.opt_params.A, b=self.opt_params.b, full_output=True, print_flag=self.opt_params.verbosity) 
111   
112          # Return the minfx results unmodified. 
113          return results 
114   
115   
116 -    def run(self, processor, completed): 
117          """Setup and perform the model-free optimisation.""" 
118   
119          # Initialise the function to minimise. 
120          self.mf = Mf(init_params=self.opt_params.param_vector, model_type=self.data.model_type, diff_type=self.data.diff_type, diff_params=self.data.diff_params, scaling_matrix=self.data.scaling_matrix, num_spins=self.data.num_spins, equations=self.data.equations, param_types=self.data.param_types, param_values=self.data.param_values, relax_data=self.data.ri_data, errors=self.data.ri_data_err, bond_length=self.data.r, csa=self.data.csa, num_frq=self.data.num_frq, frq=self.data.frq, num_ri=self.data.num_ri, remap_table=self.data.remap_table, noe_r1_table=self.data.noe_r1_table, ri_labels=self.data.ri_types, gx=self.data.gx, gh=self.data.gh, h_bar=self.data.h_bar, mu0=self.data.mu0, num_params=self.data.num_params, vectors=self.data.xh_unit_vectors) 
121   
122          # Print out. 
123          if self.opt_params.verbosity >= 1 and (self.data.model_type == 'mf' or self.data.model_type == 'local_tm'): 
124              spin_print(self.data.spin_id, self.opt_params.verbosity) 
125   
126          # Preform optimisation. 
127          results = self.optimise() 
128   
129          # Disassemble the results list. 
130          param_vector, func, iter, fc, gc, hc, warning = results 
131   
132          processor.return_object(MF_result_command(processor, self.memo_id, param_vector, func, iter, fc, gc, hc, warning, completed=False)) 
133   
134   
135 -    def store_data(self, data, opt_params): 
136          """Store all the data required for model-free optimisation. 
137   
138          @param data:        The data used to initialise the model-free target function class. 
139          @type data:         class instance 
140          @param opt_params:  The parameters and data required for optimisation using minfx. 
141          @type opt_params:   class instance 
142          """ 
143   
144          # Store the data. 
145          self.data = data 
146          self.opt_params = opt_params 
147   
148   
149   
150 -class MF_grid_command(MF_minimise_command): 
151      """Command class for the model-free grid search.""" 
152   
153 -    def __init__(self): 
154          """Initialise all the data.""" 
155   
156          # Execute the base class __init__() method. 
157          super(MF_grid_command, self).__init__() 
158   
159   
160 -    def optimise(self): 
161          """Model-free grid search. 
162   
163          @return:    The optimisation results consisting of the parameter vector, function value, iteration count, function count, gradient count, Hessian count, and warnings. 
164          @rtype:     tuple of numpy array, float, int, int, int, int, str 
165          """ 
166   
167          # Normal grid search. 
168          if not hasattr(self.opt_params, 'subdivision'): 
169              results = grid(func=self.mf.func, args=(), num_incs=self.opt_params.inc, lower=self.opt_params.lower, upper=self.opt_params.upper, A=self.opt_params.A, b=self.opt_params.b, verbosity=self.opt_params.verbosity) 
170   
171          # Subdivided grid. 
172          else: 
173              results = grid_point_array(func=self.mf.func, args=(), points=self.opt_params.subdivision, verbosity=self.opt_params.verbosity) 
174   
175          # Unpack the results. 
176          param_vector, func, iter, warning = results 
177          fc = iter 
178          gc = 0.0 
179          hc = 0.0 
180   
181          # Return everything. 
182          return param_vector, func, iter, fc, gc, hc, warning 
183   
184   
185   
186 -class MF_result_command(Result_command): 
187      """Class for processing the model-free results.""" 
188   
189 -    def __init__(self, processor, memo_id, param_vector, func, iter, fc, gc, hc, warning, completed): 
190          """Set up the class, placing the minimisation results here.""" 
191   
192          # Execute the base class __init__() method. 
193          super(MF_result_command, self).__init__(processor=processor, completed=completed) 
194   
195          # Store the arguments. 
196          self.memo_id = memo_id 
197          self.param_vector = param_vector 
198          self.func = func 
199          self.iter = iter 
200          self.fc = fc 
201          self.gc = gc 
202          self.hc = hc 
203          self.warning = warning 
204   
205   
206 -    def run(self, processor, memo): 
207          """Disassemble the model-free optimisation results. 
208   
209          @param processor:   Unused! 
210          @type processor:    None 
211          @param memo:        The model-free memo. 
212          @type memo:         memo 
213          """ 
214   
215          # Disassemble the results. 
216          memo.model_free._disassemble_result(param_vector=self.param_vector, func=self.func, iter=self.iter, fc=self.fc, gc=self.gc, hc=self.hc, warning=self.warning, spin=memo.spin, sim_index=memo.sim_index, model_type=memo.model_type, scaling=memo.scaling, scaling_matrix=memo.scaling_matrix) 
217