Fixed a couple small bugs in the swarm_it module, picked up when trying to run with models with no swarm_param instance. Ticked up the patch version number: 0.1.0->0.1.1

Author: blakeaw

README.md

@@ -2,7 +2,7 @@
 
 ![Python version badge](https://img.shields.io/badge/python-3.6-blue.svg)
 [![license](https://img.shields.io/github/license/LoLab-VU/swarm_it.svg)](LICENSE)
-![version](https://img.shields.io/badge/version-0.1.0-orange.svg)
+![version](https://img.shields.io/badge/version-0.1.1-orange.svg)
 [![release](https://img.shields.io/github/release-pre/LoLab-VU/swarm_it.svg)](https://github.com/LoLab-VU/swarm_it/releases/tag/v0.1.0)
 
 **swarm_it** is a python utility designed to abstract away some of the effort in setting up Particle Swarm Optimization (PSO)-based calibrations of biological models in the [PySB](http://pysb.org/) format using [simplePSO](https://github.com/LoLab-VU/ParticleSwarmOptimization).

setup.py

@@ -1,7 +1,7 @@
 from distutils.core import setup
 
 setup(name='swarm_it',
-      version='0.1.0',
+      version='0.1.1',
       description='Utility to help setup PSO runs for PySB models.',
       author='Blake A. Wilson',
       author_email='[email protected]',

swarm_it.py

@@ -251,11 +251,11 @@ def __init__(self, model, observable_data, timespan,
                 if rule.rate_reverse:
                      swarm_param(rule.rate_reverse)
             parm_mask = swarm_param.mask(model.parameters)
-            self._sampled_parameters = [SampledParameter(parm.name, *swarm_param[parm.name]) for i,parm in enumerate(model.parameters) if parm_mask[i]]
+#            self._sampled_parameters = [SampledParameter(parm.name, *swarm_param[parm.name]) for i,parm in enumerate(model.parameters) if parm_mask[i]]
             self._rate_mask = parm_mask
             self._starting_position = swarm_param.centers()
             self._lower = swarm_param.lower()
-            self._upper = self.upper()
+            self._upper = swarm_param.upper()
         self._param_values = np.array([param.value for param in model.parameters])
         return