pyflakes (static code analysis) error fixes

Brent_minimization.py

@@ -1,5 +1,5 @@
+# -*- coding: utf-8 -*-
 
-# 
 # This program is free software; you can redistribute it and/or
 # modify it under the terms of the GNU General Public License as
 # published by the Free Software Foundation; either version 3, or
@@ -40,12 +40,10 @@
 '''
 
 from __future__ import print_function
-import numpy
 from scipy import optimize
 import argparse
 import os
 import sys
-import argparse
 import findSim
 from multiprocessing import Pool
 

basicParameterSweep.py

@@ -39,11 +39,9 @@
 '''
 
 from __future__ import print_function
-import numpy
 import argparse
 import os
 import sys
-import argparse
 import findSim
 from multiprocessing import Pool
 

findSim.py

@@ -45,7 +45,6 @@
 import time
 import imp      # This is apparently deprecated in Python 3.4 and up
 import matplotlib.pyplot as pyplot
-import mpld3
 
 #mpld3 hack
 # suggested: https://github.com/mpld3/mpld3/issues/434
@@ -378,14 +377,14 @@ def doScore( self, scoringFormula ):
         assert( len(self.data) == len( self.simData ) )
         score = 0.0
         numScore = 0.0
-        dvals = [i[1] for i in self.data]
-        datarange = max( dvals ) - min( dvals )
+        #  dvals = [i[1] for i in self.data]
+        #  datarange = max( dvals ) - min( dvals )
         for i,sim in zip( self.data, self.simData ):
-            t = i[0]
-            expt = i[1]
-            sem = i[2]
-            #print t, expt, sem, sim, datarange
-            #print "Formula = ", scoringFormula, eval( scoringFormula )
+            #  t = i[0]
+            #  expt = i[1]
+            #  sem = i[2]
+            #  print t, expt, sem, sim, datarange
+            #  print "Formula = ", scoringFormula, eval( scoringFormula )
             score += eval( scoringFormula )
             numScore += 1.0
 
@@ -399,16 +398,16 @@ def directParamScore( readouts, modelLookup, scoringFormula ):
         for rd in readouts:
             for d in rd.data:
                 entity = d[0]
-                expt = d[1]*rd.quantityScale
-                sem = d[2]*rd.quantityScale
+                #  expt = d[1]*rd.quantityScale
+                #  sem = d[2]*rd.quantityScale
                 if not entity in modelLookup:
                     raise SimError( "Readout::directParamScore: Entity {} not found".format( entity ) )
                 elmList = modelLookup[entity]
                 if len( elmList ) != 1:
                     raise SimError( "Readout::directParamScore: Should only have 1 object, found {} ".format( len( elmList ) ) )
                 # We use a utility function because findSim may permit
                 # parameters like Kd that need to be evaluated.
-                sim = getObjParam( elmList[0], rd.field )
+                #  sim = getObjParam( elmList[0], rd.field )
                 #sim = elmList[0].getField( rd.field )
                 score += eval( scoringFormula )
                 numScore += 1.0
@@ -717,15 +716,15 @@ def isNotDescendant( elm, ancestorSet ):
 def getObjParam( elm, field ):
     if field == 'Kd':
         if not elm.isA['ReacBase']:
-            raise SimError( "getObjParam: can only get Kd on a Reac, was: '{}'".format( obj.className ) )
+            raise SimError( "getObjParam: can only get Kd on a Reac, was: '{}'".format( elm.className ) )
         return elm.Kb/elm.Kf
     elif field == 'tau':
         # This is a little dubious, because order 1 reac has 1/conc.time
         # units. Suppose Kf = x / mM.sec. Then Kf = 0.001x/uM.sec
         # This latter is the Kf we want to use, assuming typical concs are
         # around 1 uM.
         if not elm.isA['ReacBase']:
-            raise SimError( "getObjParam: can only get tau on a Reac, was: '{}'".format( obj.className ) )
+            raise SimError( "getObjParam: can only get tau on a Reac, was: '{}'".format( elm.className ) )
         scaleKf = 0.001 ** (elm.numSubstrates-1)
         scaleKb = 0.001 ** (elm.numProducts-1)
         #print( "scaleKf={}; scaleKb={}, numsu ={}, numPrd={},Kb={},Kf={}".format( scaleKf, scaleKb, elm.numSubstrates, elm.numProducts, elm.Kb, elm.Kf ) )
@@ -867,7 +866,7 @@ def pruneDanglingObj( kinpath, erSPlist):
     for i in erlist:
         isub = i.neighbors["sub"]
         iprd = i.neighbors["prd"]
-        tobedelete = False
+        #  tobedelete = False
         if moose.exists(i.path):
             if len(isub) == 0 or len(iprd) == 0 :
                 subprdNotfound = True
@@ -954,8 +953,8 @@ def makeReadoutPlots( readouts, modelLookup ):
             moose.connect( plot, 'requestOut', elm, fieldname )
 
 def putReadoutsInQ( q, readouts, pauseHsolve ):
-    stdError  = []
-    plotLookup = {}
+    #  stdError  = []
+    #  plotLookup = {}
     for i in readouts:
         if i.field in (epspFields + epscFields):
             for j in range( len( i.data ) ):
@@ -1016,7 +1015,7 @@ def doReadout( qe, model ):
     '''
     readout = qe.entry
     val = int(round( ( qe.val ) ) )
-    ratioReference = 0.0
+    #  ratioReference = 0.0
     if readout.field in (epspFields + epscFields):
         doEpspReadout( readout, model.modelLookup )
     elif val == -1: # This is a special event to get RatioReferenceValue
@@ -1109,7 +1108,7 @@ def parseAndRunDoser( model, stims, readouts, modelId ):
             exactly one stimulus block, {} defined".format( len(stims)) )
     if len( readouts ) != 1:
         raise SimError( "parseAndRunDoser: Dose response run needs \
-            exactly one readout block, {} defined".format( len(readout) ) )
+            exactly one readout block, {} defined".format( len(readouts) ) )
     numLevels = len( readouts[0].data )
 
     if numLevels == 0:
@@ -1131,10 +1130,10 @@ def parseAndRunDoser( model, stims, readouts, modelId ):
 
 ##########################################################################
 def runDoser( model, stim, readout, doseMol ):
-    responseScale = readout.quantityScale
+    #  responseScale = readout.quantityScale
     doseScale = stim.quantityScale
     referenceDose = readout.ratioReferenceDose * stim.quantityScale
-    sim = 0.0
+    #  sim = 0.0
     for dose, response, sem in readout.data:
         doseMol.concInit = dose * doseScale
         moose.reinit()
@@ -1164,7 +1163,7 @@ def doBarChartStim( multiStimLine, doseMol, dose, field ):
 
 def doBarChartReference( readout, stim, modelLookup ):
     if readout.useRatio and readout.ratioReferenceTime >= 0.0:
-        responseScale = readout.quantityScale
+        #  responseScale = readout.quantityScale
         referenceDose = readout.ratioReferenceDose * stim.quantityScale
         referenceMol = modelLookup[readout.ratioReferenceEntities[0] ]
 
@@ -1206,7 +1205,7 @@ def parseAndRunBarChart( model, stims, readouts, modelId ):
             one stimulus block, {} defined".format( len( stims ) ) )
     if len( readouts ) != 1:
         raise SimError( "parseAndRunBarChart: BarChart run needs exactly \
-            one readout block, {} defined".format( len( readout ) ) )
+            one readout block, {} defined".format( len( readouts ) ) )
     numLevels = len( readouts[0].data )
 
     if numLevels == 0:
@@ -1268,9 +1267,9 @@ def convertBarChartLabels( self, readout, stim ):
 
     def plotbar( self, readout, stim, scriptName ):
         barpos = np.arange( len( self.sim ) )
-        width = 0.35 # A reasonable looking bar width
-        exptBar = pyplot.bar(barpos - width/2, self.expt, width, yerr=self.yerror, color='SkyBlue', label='Experiment')
-        simBar = pyplot.bar(barpos + width/2, self.sim, width, color='IndianRed', label='Simulation')
+        #  width = 0.35 # A reasonable looking bar width
+        #  exptBar = pyplot.bar(barpos - width/2, self.expt, width, yerr=self.yerror, color='SkyBlue', label='Experiment')
+        #  simBar = pyplot.bar(barpos + width/2, self.sim, width, color='IndianRed', label='Simulation')
         pyplot.xlabel( "Stimulus combinations" )
         pyplot.ylabel( self.ylabel )
         pyplot.title(scriptName)
@@ -1480,7 +1479,7 @@ def innerMain( script, modelFile = "model/synSynth7.g", dumpFname = "", paramFna
     '''
 
     global pause
-    solver = "gsl"  # Pick any of gsl, gssa, ee..
+    #  solver = "gsl"  # Pick any of gsl, gssa, ee..
     modelWarning = ""
     modelId = ""
     expt, stims, readouts, model = loadTsv( script )

models/loadhh.py

@@ -1,4 +1,3 @@
-import moose
 import rdesigneur as rd
 def load():
     rdes = rd.rdesigneur(

multi_param_minimization.py

@@ -42,7 +42,6 @@
 from __future__ import print_function
 import numpy as np
 from scipy import optimize
-import argparse
 import os
 import sys
 import argparse

runAllParallel.py

@@ -37,11 +37,9 @@
 '''
 from __future__ import print_function, division
 
-import numpy
 import argparse
 import os
 import sys
-import argparse
 import time
 from multiprocessing import Pool
 

setup.py

@@ -7,8 +7,6 @@
 __maintainer__       = "Dilawar Singh"
 __email__            = "[email protected]"
 
-import os
-import sys
 import setuptools
 
 with open("README.md") as f: