Merge pull request #25 from EmoryUniversityTheoreticalBiophysics/develop

Update master branch

.gitignore

@@ -55,6 +55,26 @@ stdout_stderr_*
 *.txt
 # but include the simple example data file
 !simpleExample_data.txt
+!requirements.txt
 
 # other code
-othercode/*
+othercode/*
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+

Installation.md

@@ -8,82 +8,51 @@ SirIsaac depends on the following packages:
 - Python 2.6 or later (not Python 3)
 - Scipy
 - Matplotlib
-- SloppyCell (http://sloppycell.sourceforge.net)
+- SloppyCell (https://github.com/GutenkunstLab/SloppyCell)
 
 There exist several relatively simple ways to install
 the first three packages above at once, including
 
-- Anaconda: http://store.continuum.io/cshop/anaconda/
+- Anaconda: http://www.anaconda.com
 - Sage: http://www.sagemath.org
 
 These systems also have the added benefit of coming
 prepackaged with other useful software such as
 iPython and Jupyter.
 
-### Install SloppyCell
-
-As of 2018, SirIsaac depends on the latest
-developer version of SloppyCell.  This software is
-available through a git repository.
-We describe here one typical way to download and 
-install SloppyCell.
-More information is available here: 
-http://sloppycell.sourceforge.net/
-http://sourceforge.net/p/sloppycell/git/ci/master/tree/
-
-Typically you will want to install SloppyCell in Python's
-site-packages folder, usually found within a directory such as
-
-* /lib/python2.7/site-packages/ for Linux
-* /Library/Python/2.7/site-packages/ for Mac (using built-in Python)
-* /sw/lib/python2.7/site-packages/ for Mac using Fink
-* ~/anaconda/lib/python2.7/site-packages/ using Anaconda
-* ~/sage-x.x/lib/python2.6/site-packages/ using Sage
-
-In the simplest case, you can anonymously 'clone' the latest version 
-by changing to the above directory and running
-
-    git clone https://github.com/GutenkunstLab/SloppyCell.git
-
-You should see a bunch of files being downloaded.  Next,
-build the Fortran libraries by changing into the new 'SloppyCell' 
-directory and running setup.py:
+Note: As of June 2020, SirIsaac does not support Python 3 (we are working on it).  Installing Python 2 using the above package managers is likely no longer the default option as it is now officially out of date.
 
-    cd SloppyCell
-    sudo python setup.py build install --install-lib=..
+### Install SloppyCell
 
-(prefacing by sudo if necessary for write privileges).  (If you
-do not build the Fortran libraries, you will
-get the error 'No module named _daskr' when trying to
-import SirIsaac.FittingProblem.)  If this is successful, you'll 
-eventually see something like 
+SirIsaac depends on the latest
+version of SloppyCell, available on GitHub.
+In the simplest case, you can install with two steps.  First download the git repository by running
 
-    Finished processing dependencies for SloppyCell==1.1.0.dev1
+    https://github.com/GutenkunstLab/SloppyCell.git
+
+which will create a folder named `SloppyCell` in the current directory.  Next, install SloppyCell by running setup.py.  The easiest way to do this is using `pip`:
 
+    pip install -e SloppyCell/
 
 ## Install SirIsaac
 
-SirIsaac is available as a git repository hosted on GitHub.  
-Typically you will want to install SirIsaac in the same Python
-site-packages folder as SloppyCell.  
-
-GitHub offers many ways to download ("clone") software, including
-GUI apps for Windows and Mac (perhaps easiest for those unfamiliar with git) 
-and command line access using git.  To clone from 
-the command line, change to the site-packages folder and run
+SirIsaac is similarly available as a git repository on GitHub.   To install, first download by running
 
     git clone https://github.com/EmoryUniversityTheoreticalBiophysics/SirIsaac.git
 
-This will create a 'SirIsaac' directory in the current
-location containing the SirIsaac software.
+which will create a folder named `SirIsaac` in the current directory.  Next, install SirIsaac by running setup.py.   The easiest way to do this is using `pip`: 
+
+	pip install -e SirIsaac/
 
 ## Test installation
 
 A basic test of the SirIsaac and SloppyCell installation can be
-run by descending into the SirIsaac directory and running
+run by descending into the `SirIsaac/SirIsaac/` directory and running
 
     python SloppyCellTest.py
 
+More comprehensive tests are found in the `test` subfolder, and can be run using, e.g., `nosetests`.
+
 To further help you get up and running, 
 code to fit and analyze a simple example dataset 
 using SirIsaac is provided in two formats: 
@@ -100,3 +69,4 @@ To run the .py file in iPython at the command line, run:
     ipython --pylab
     %run simpleExample.py
     show()
+

README.md

@@ -33,13 +33,13 @@ Scipy
 Matplotlib  
 (One way to install the above is with Anaconda or Sage.  See Installation.md.)
 
-SloppyCell (http://sloppycell.sourceforge.net)  
+SloppyCell (https://github.com/GutenkunstLab/SloppyCell)  
 
 
 Optional dependencies
 =====================
 
-Pypar (for running on multiple processors)  
+mpi4py (for running on multiple processors)  
 SBML (systems biology markup language)  
 BioNetGen  
 Pygraphviz (for creating network diagrams)  

__init__.py → SirIsaac/__init__.py

@@ -5,5 +5,3 @@
 #
 # This file makes SirIsaac importable as a Python package.
 #
-
-from FittingProblem import *

analyzeFittingProblemDict.py → SirIsaac/analyzeFittingProblemDict.py

@@ -4,7 +4,7 @@
 # 5.10.2012
 #
 
-from FittingProblem import *
+from fittingProblem import *
 from plotMatrix import plotMatrix
 
 # the general scheme for calculating over all fpds

CTSNNetwork.py → SirIsaac/ctsnNetwork.py

@@ -4,100 +4,100 @@
 # 7.29.2009
 #
 # A SloppyCell implementation of CTSNs (continuous-time sigmoidal networks).
-# 
-# (modeled after PowerLawNetwork.py)
+#
+# (modeled after powerLawNetwork.py)
 
 
 from SloppyCell.ReactionNetworks import *
 from scipy import random, log
 import copy
-import GaussianPrior
+import gaussianPrior
 
 def CTSN_List(networkList,speciesNames=None,                                    \
     logParams=True,netid='CTSN',switchSigmoid=False,xiNegative=False):
     """
     Defines a CTSN based on a connection list.
-    
+
     A SloppyCell implementation of CTSNs:
-    
-    d X_i / d t  =  
+
+    d X_i / d t  =
       1/tau_i * ( -X_i + sum_j=1^n w_i_j xi(y_j+theta_j) )
-    
+
     tau is given the default value of 1, and xinit, theta, and w
     parameters are by default 0.
-    
+
     Right now, inputs come into the sum as w_i_j*input_j.
     (Not sure if this is "correct"...)
-    
+
     networkList  : list of the form
-  
+
     [ [nodeType, { connectFrom: connectType, connectFrom: connectType, ...}], ... ]
-        nodeType    : integer between 0 and 4 (the number of optimizable 
+        nodeType    : integer between 0 and 4 (the number of optimizable
                       parameters specifying the node's behavior; 0 for input node)
         connectFrom : integer index of node to get connection from
         connectType : integer, either 1 or 2 (the number of parameters specifying
                       the connection)
-                      
+
     speciesNames    : list of species names (length n+m).  If None,
                       species are named X_i for i in range(n+m).
-    logParams       : if True, the time constants tau are written 
+    logParams       : if True, the time constants tau are written
                       as log_tau (to facilitate parameter searches)
     switchSigmoid   : If True, use sigmoid(sum) instead of sum(sigmoid) in
                       each node's ODE rate rule.  See notes 7.8.2013.
-    
+
     (The X_js beyond n (up to n+m) are constant inputs.)
     """
-    
+
     n = len(networkList)
     #m = 0
-    
+
     # the order in which to add parameters
     order = dict( zip(['xinit','wself','tau','theta'], range(5)) )
     orderConnect = dict( zip(['w'], range(1)) )
-    
+
     net = Network(netid, name='CTSN')
     net.addCompartment('Comp',name='Compartment')
-    
+
     net.addParameter('n', n, isOptimizable=False)
     #net.addParameter('m', m, isOptimizable=False)
-    
+
     defaultParam = 0.
     defaultLogParam = 1.
     defaultW = 0.
     #defaultExpParam = 0.
-    
+
     if speciesNames is None:
         speciesNames = [ 'X_'+str(i) for i in range(n) ]
-    
+
     # add parameters
     for i in range(n):
-      
+
       nodeType, connectionDict = networkList[i]
-      
+
       if nodeType != 0: # if it's not an input node
-      
+
         notLog = not logParams
-        
+
         net.addParameter('wself_'+str(i), defaultW,                             \
             isOptimizable=order['wself']<nodeType)
         net.addParameter('theta_'+str(i), defaultParam,                         \
             isOptimizable=order['theta']<nodeType)
         net.addParameter('tau_'+str(i), defaultLogParam,                        \
             isOptimizable=(notLog and order['tau']<nodeType))
-        
+
         if logParams:
             net.addParameter('log_tau_'+str(i), log(defaultLogParam),           \
                 isOptimizable=order['tau']<nodeType,typicalValue=1.)
             net.addAssignmentRule('tau_'+str(i),'exp(log_tau_'+str(i)+')')
-        
+
         # connect to others
         for j in connectionDict.keys():
             net.addParameter('w_'+str(i)+'_'+str(j), defaultW,                  \
                 isOptimizable=orderConnect['w']<connectionDict[j])
-        
+
         net.addParameter(speciesNames[i]+'_init', defaultParam,                 \
             isOptimizable=order['xinit']<nodeType)
-            
+
     # add species
     for i in range(n):
       nodeType, connectionDict = networkList[i]
@@ -107,7 +107,7 @@ def CTSN_List(networkList,speciesNames=None,
         # add as a parameter if it's not already there
         if speciesNames[i] not in net.parameters.keys():
           net.addParameter( speciesNames[i], 0., isOptimizable=False )
-    
+
     # reaction rate rules
     for i in range(n):
       nodeType, connectionDict = networkList[i]
@@ -122,7 +122,7 @@ def CTSN_List(networkList,speciesNames=None,
                 +' / (1. + exp('+speciesNames[i]+' + theta_'+str(i)+')) + '
         for j in connectionDict.keys():
           if networkList[j][0] != 0: # the connection is not from an input node
-            if xiNegative: 
+            if xiNegative:
                 sum += 'w_'+str(i)+'_'+str(j)                                       \
                   +' / (1. + exp(-'+speciesNames[j]+' - theta_'+str(j)+')) + '
             else: # prior to 12.19.2013
@@ -154,8 +154,7 @@ def CTSN_List(networkList,speciesNames=None,
 
     return net
 
-
-
+
 def setRandomParameters(net,seed=None,randFunc=random.random):
     """
     Sets parameters to random values given by the function randFunc (by
@@ -164,6 +163,3 @@ def setRandomParameters(net,seed=None,randFunc=random.random):
     random.seed(seed)
     net.setOptimizables( randFunc(len(net.GetParameters())) )
     return net.GetParameters()
-
-
-

fitAllParallel.py → SirIsaac/fitAllParallel.py

@@ -9,9 +9,9 @@
 
 import scipy
 import time, copy, os
-from SirIsaac.simplePickle import load,save
-from SirIsaac.SloppyCellTest import testCcompiling
-from SirIsaac.FittingProblemMultipleCondition import *
+from simplePickle import load,save
+from sloppyCellTest import testCcompiling
+from fittingProblemMultipleCondition import *
 
 def directoryPrefix(fileNumString,conditioni,numTimepoints):
     return fileNumString+'_fitProbs/N'+str(numTimepoints)+'/condition'+str(conditioni)+'/'