MeqSolver.py

# demo_template.py:

# Demonstrates the following MeqTree features:
# Simple Tree to solve a parameter 

# Tips:

#For more paramter options, see demo_parm.py


 
#********************************************************************************
# Initialisation:
#********************************************************************************

from Timba.TDL import *
from Timba.Meq import meq
# from qt import *
# from numarray import *

# from Timba.Contrib.JEN.util import JEN_bookmarks

# Make sure that all nodes retain their results in their caches,
# for your viewing pleasure.
Settings.forest_state.cache_policy = 100
Settings.forest_state.bookmarks = []



#********************************************************************************
# The function under the 'blue button':
#********************************************************************************

def _define_forest (ns, **kwargs):
   """Definition of a 'forest' of one or more trees"""

   # Make a Parm  node, initialize it with constant 1.
   # The node_groups='Parm' options is needed to be recognized by the solver
   parm = ns['parm'] << Meq.Parm(1.,node_groups='Parm')

   #We are going to fit the constant to a node varying in frequency
   b = ns['b'] << Meq.Freq()

   # The Condeq has exactly 2 children: the 'model' (in this case the parm)
   # and the 'data' on which you want to fit the 'model'.
   # solvable parm can be on both sides, so there is no real distinction between 'model' and 'data' here.
   condeq = ns['condeq'] << Meq.Condeq(children=(parm,b))

   # Now create a solver node. A solver can have several children, but they all must be condeqs.
   solver = ns['solver'] << Meq.Solver(children=(condeq),
                                       solvable = ['parm'], #list of solvable parameters 
                                       num_iter = 10,       #max number of iterations
                                       epsilon = 1e-4       #convergence limit, good default
                                       )
   # Make a bookmark of the result node, for easy viewing:
   bm = record(name='result',page=
               [record(viewer='Result Plotter',udi='/node/solver', publish=True, pos=(0,0)),
                record(viewer='Result Plotter',udi='/node/condeq', publish=True, pos=(0,1)),
                record(viewer='Result Plotter',udi='/node/parm', publish=True, pos=(1,0)),
                record(viewer='Result Plotter',udi='/node/b', publish=True, pos=(1,1))])
   Settings.forest_state.bookmarks.append(bm)

   # Finished:
   return True



#********************************************************************************
# The function under the TDL Exec button:
#********************************************************************************

def _tdl_job_execute (mqs, parent):
    """Execute the forest, starting at the named node"""
    domain = meq.domain(1,10,1,10)                            # (f1,f2,t1,t2)
    cells = meq.cells(domain, num_freq=10, num_time=11)
    request = meq.request(cells, rqtype='ev')
    result = mqs.meq('Node.Execute',record(name='solver', request=request))
    return result
       
#********************************************************************************
#********************************************************************************