RMG-Py v2.1.1 release

Author: mliu49

README.md

@@ -17,7 +17,7 @@ Gaussian, MOPAC, QChem, and MOLPRO.
 - [RMG-database Github Repository](https://github.com/ReactionMechanismGenerator/RMG-database): contains source code for the latest version of the database
 
 ## Latest Stable Release
-**RMG v2.1.0**
+**RMG v2.1.1**
 [![Anaconda-Server Badge](https://anaconda.org/rmg/rmg/badges/version.svg)](https://anaconda.org/rmg/rmg)
 
 View the [Release Notes](http://reactionmechanismgenerator.github.io/RMG-Py/users/rmg/releaseNotes.html).

documentation/source/users/rmg/releaseNotes.rst

@@ -4,6 +4,36 @@
 Release Notes
 *************
 
+RMG-Py Version 2.1.1
+====================
+Date: April 07, 2017
+
+- Uncertainty analysis:
+    - Local and global uncertainty analysis now available for RMG-generated models
+    - Global uncertainty analysis uses MIT Uncertainty Quantification library, currently only supported on Linux systems
+    - Examples for each module are available in localUncertainty.ipynb and globalUncertainty.ipynb
+
+- Fixes:
+    - Clar structure generation no longer intercepts signals
+    - Fixes to SMILES generation
+    - Fix default spin state of [CH]
+
+RMG-database Version 2.1.1
+==========================
+Date: April 07, 2017
+
+- Additions:
+    - More species added to FFCM1(-) thermo library
+
+- Changes:
+    - Improved handling of excited species in FFCM1(-) kinetics library
+    - Replaced Klippenstein H2O2 kinetics and thermo libraries with BurkeH2O2inN2 and BurkeH2O2inArHe
+
+- Fixes:
+    - Corrected adjlists for some species in JetSurf2.0 kinetics and thermo libraries (also renamed from JetSurf0.2)
+    - Correct multiplicities for [C] and [CH] in multiple libraries ([C] from 5 to 3, [CH] from 4 to 2)
+
+
 RMG-Py Version 2.1.0
 ====================
 Date: March 07, 2017

environment_linux.yml

@@ -37,3 +37,5 @@ dependencies:
   - ffmpeg
   - jupyter
   - networkx
+  - mock
+  - muq

environment_mac.yml

@@ -36,3 +36,5 @@ dependencies:
   - ffmpeg
   - jupyter
   - networkx
+  - mock
+  # - muq

environment_windows.yml

@@ -24,8 +24,8 @@ dependencies:
   - nose
   - coverage
   - gprof2dot
-  #- cairo
-  #- cairocffi
+  - cairo
+  - cairocffi
   - openbabel
   - psutil
   - symmetry
@@ -35,3 +35,5 @@ dependencies:
   - ffmpeg
   - jupyter
   - networkx
+  - mock
+  # - muq

examples/rmg/commented/input.py

@@ -121,7 +121,7 @@
 #all relative values are normalized by a characteristic flux at that time point
 model(
 	#determines the relative flux to put a species into the core.  
-	#A higher value will result in a larger, more complex model
+	#A smaller value will result in a larger, more complex model
 	#when running a new model, it is recommended to start with higher values and then decrease to converge on the model
     toleranceMoveToCore=0.1,
     #comment out the next three terms to disable pruning

ipython/findParameterSourcesAndAssignUncertainies.ipynb

@@ -11,13 +11,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
-    "from rmgpy.tools.uncertainty import Uncertainty\n",
+    "from rmgpy.tools.uncertainty import Uncertainty, ThermoParameterUncertainty, KineticParameterUncertainty\n",
     "from IPython.display import display\n",
-    "import copy"
+    "import copy\n",
+    "import numpy"
    ]
   },
   {
@@ -73,7 +74,8 @@
    },
    "outputs": [],
    "source": [
-    "uncertainty.extractSourcesFromModel()"
+    "uncertainty.extractSourcesFromModel()\n",
+    "uncertainty.compileAllSources()"
    ]
   },
   {
@@ -114,7 +116,7 @@
     "                print '\\t\\t', entry\n",
     "    else:\n",
     "        print sourceType\n",
-    "        raise Exception('Kinetics source mut be Library, PDep, Rate Rules, or Training')"
+    "        raise Exception('Kinetics source must be Library, PDep, Rate Rules, or Training')"
    ]
   },
   {
@@ -175,6 +177,18 @@
     "P = 1e5  # Pa "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "gParamEngine = ThermoParameterUncertainty()\n",
+    "kParamEngine = KineticParameterUncertainty()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -189,8 +203,10 @@
     "    display(rxn)\n",
     "    if 'Library' in source:\n",
     "        print 'Library reaction'\n",
+    "        print source['Library']\n",
     "    elif 'PDep' in source:\n",
     "        print 'PDep reaction'\n",
+    "        print source['PDep']\n",
     "    elif 'Rate Rules' in source:\n",
     "        print 'Rate rule estimate'\n",
     "        family = source['Rate Rules'][0]\n",
@@ -233,7 +249,29 @@
     "#     print reconstructedKinetics\n",
     "\n",
     "    rxnIndex = uncertainty.reactionList.index(rxn)\n",
-    "    print 'Uncertainty dln(k) = ', uncertainty.kineticInputUncertainties[rxnIndex]"
+    "    print 'Uncertainty dln(k) = ', uncertainty.kineticInputUncertainties[rxnIndex]\n",
+    "    \n",
+    "#     # Test that the partial uncertainty calculation is working\n",
+    "#     dlnk = 0.0\n",
+    "#     if 'Rate Rules' in source:\n",
+    "#         family = source['Rate Rules'][0]\n",
+    "#         sourceDict = source['Rate Rules'][1]\n",
+    "#         rules = sourceDict['rules']\n",
+    "#         training = sourceDict['training']\n",
+    "#         for ruleEntry, weight in rules:\n",
+    "#             dlnk += kParamEngine.getPartialUncertaintyValue(source, 'Rate Rules', corrParam=ruleEntry, corrFamily=family)\n",
+    "#         for ruleEntry, trainingEntry, weight in training:\n",
+    "#             dlnk += kParamEngine.getPartialUncertaintyValue(source, 'Rate Rules', corrParam=ruleEntry, corrFamily=family)\n",
+    "#         dlnk += kParamEngine.getPartialUncertaintyValue(source, 'Estimation')\n",
+    "#     elif 'PDep' in source:\n",
+    "#         dlnk += kParamEngine.getPartialUncertaintyValue(source, 'PDep', source['PDep'])\n",
+    "#     elif 'Library' in source:\n",
+    "#         dlnk += kParamEngine.getPartialUncertaintyValue(source, 'Library', source['Library'])\n",
+    "#     elif 'Training' in source:\n",
+    "#         dlnk += kParamEngine.getPartialUncertaintyValue(source, 'Training', source['Training'])\n",
+    "#     print 'Uncertainty dlnk calculated using sum of partial values = ', dlnk\n",
+    "    \n",
+    "    "
    ]
   },
   {
@@ -259,9 +297,62 @@
     "            for group, weight in groupList:\n",
     "                print '\\t\\t', group, '=', weight\n",
     "                \n",
+    "    \n",
+    "                \n",
     "    spcIndex = uncertainty.speciesList.index(species)    \n",
     "    print ''\n",
-    "    print 'Uncertainty dG = ', uncertainty.thermoInputUncertainties[spcIndex], ' kcal/mol'"
+    "    print 'Uncertainty dG = ', uncertainty.thermoInputUncertainties[spcIndex], ' kcal/mol'\n",
+    "    \n",
+    "    \n",
+    "    # Test that the partial uncertainty calculation is working\n",
+    "    dG = 0.0\n",
+    "    if 'Library' in source:\n",
+    "        dG += gParamEngine.getPartialUncertaintyValue(source, 'Library', corrParam=source['Library'])\n",
+    "    if 'QM' in source:\n",
+    "        dG += gParamEngine.getPartialUncertaintyValue(source, 'QM',corrParam=source['QM'])\n",
+    "    if 'GAV' in source:\n",
+    "        for groupType, groupList in source['GAV'].iteritems():\n",
+    "            for group, weight in groupList:\n",
+    "                dG += gParamEngine.getPartialUncertaintyValue(source, 'GAV', group, groupType)\n",
+    "        dG += gParamEngine.getPartialUncertaintyValue(source, 'Estimation')\n",
+    "    print 'Uncertainty dG calculated using sum of partial values = ', dG, ' kcal/mol'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Assign correlated parameter uncertainties \n",
+    "uncertainty.assignParameterUncertainties(correlated=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# See the thermo correlated parameter partial uncertainties\n",
+    "uncertainty.thermoInputUncertainties"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# See the kinetics correlated parameter partial uncertainties\n",
+    "uncertainty.kineticInputUncertainties"
    ]
   }
  ],
@@ -281,7 +372,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,