Merge pull request #162 from kwmcbride/package_dev

Previous update plus new changes targeting package development
salvadorgarciamunoz · Feb 16, 2021 · c151fdf · c151fdf
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diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
diff --git a/README.md b/README.md
@@ -1,64 +1,75 @@
-KIPET: Kinetic Parameter Estimation Toolkit
-===========================================
+# <img alt="KIPET" src="branding/kipetlogo_full.svg" height="60">
+
+[![](https://img.shields.io/pypi/v/kipet.svg?style=flat)](https://pypi.org/pypi/kipet/)
+[![](https://img.shields.io/github/license/salvadorgarciamunoz/kipet)](https://github.com/salvadorgarciamunoz/kipet/blob/master/LICENSE)
+[![](https://img.shields.io/github/last-commit/salvadorgarciamunoz/kipet)](https://github.com/salvadorgarciamunoz/kipet/)
+[![](https://img.shields.io/pypi/wheel/kipet)](https://pypi.org/manage/project/kipet/release/0.1.1/)
+
 
 KIPET is a Python package designed to simulate, and estimate parameters from 
 chemical reaction systems through the use of maximum likelihood principles,
-large-scale nonlinear programming and discretization methods. The software 
-has the following functionality:
-
-* Simulate a reactive system described with DAEs
-* Solve the DAE system with collocation methods
-* Pre-process data
-* Estimate variances of noise from the model and measurements
-* Estimate kinetic parameters from spectra or concentration data across 1 or 
+large-scale nonlinear programming and discretization methods. 
+
+- **Documentation:** - https://kipet.readthedocs.io
+- **Examples and Tutorials** - https://github.com/kwmcbride/kipet_examples
+- **Source code:** - https://github.com/salvadorgarciamunoz/kipet
+- **Bug reports:** - https://github.com/salvadorgarciamunoz/kipet/issues
+
+It has the following functionality:
+
+ - Simulate a reactive system described with DAEs
+ - Solve the DAE system with collocation methods
+ - Pre-process data
+ - Estimate variances of noise from the model and measurements
+ - Estimate kinetic parameters from spectra or concentration data across 1 or 
   multiple experiments with different conditions
-* Estimate confidence intervals of the estimated parameters
-* Able to estimate variances and parameters for problems where there is dosing / inputs into the system
-* Provide a set of tools for estimability analysis
-* Allows for wavelength selection of most informative wavelengths from a dataset
-* Visualize results
+ - Estimate confidence intervals of the estimated parameters
+ - Able to estimate variances and parameters for problems where there is dosing / inputs into the system
+ - Provide a set of tools for estimability analysis
+ - Allows for wavelength selection of most informative wavelengths from a dataset
+ - Visualize results
+
 
-For more information and detailed tutorials go to our readthedocs website:
+Installation
+------------
+
+A packaged version of KIPET can be installed using:
+
+    pip install kipet
 
-https://kipet.readthedocs.io
+Additionally, KIPET may be installed directly from the repository (if you want the latest version, simply install the desired branch (#branch)):
 
+    poetry add git+http://github.com/salvadorgarciamunoz/kipet#master
+
+Naturally you can simply clone or download the repository.
 
 License
 ------------
 
 GPL-3
 
-Organization
-------------
-
-Directories
-  * kipet - The root directory for Kipet source code
-  * documentation - user manual
-  * kipet/examples - tutorial examples and data files
-  * kipet/library - all libraries and functions
-  * kipet/validation - validation/test scripts
 
 Authors
 ----------
 
-   * Kevin McBride - Carnegie Mellon University
-   * Kuan-Han Lin - Carnegie Mellon University
-   * Christina Schenk - Basque Center for Applied Mathematics
-   * Michael Short - University of Surrey
-   * Jose Santiago Rodriguez - Purdue University
-   * David M. Thierry - Carnegie Mellon University
-   * Salvador García-Muñoz - Eli Lilly
-   * Lorenz T. Biegler - Carnegie Mellon University
+    - Kevin McBride - Carnegie Mellon University
+    - Kuan-Han Lin - Carnegie Mellon University
+    - Christina Schenk - Basque Center for Applied Mathematics
+    - Michael Short - University of Surrey
+    - Jose Santiago Rodriguez - Purdue University
+    - David M. Thierry - Carnegie Mellon University
+    - Salvador García-Muñoz - Eli Lilly
+    - Lorenz T. Biegler - Carnegie Mellon University
 
 Please cite
 ------------
-C. Schenk, M. Short, J.S. Rodriguez, D. Thierry, L.T. Biegler, S. García-Muñoz, W. Chen (2020)
+ - C. Schenk, M. Short, J.S. Rodriguez, D. Thierry, L.T. Biegler, S. García-Muñoz, W. Chen (2020)
 Introducing KIPET: A novel open-source software package for kinetic parameter estimation from experimental datasets including spectra, Computers & Chemical Engineering, 134, 106716. https://doi.org/10.1016/j.compchemeng.2019.106716
 
-M. Short, L.T. Biegler, S. García-Muñoz, W. Chen (2020)
+ - M. Short, L.T. Biegler, S. García-Muñoz, W. Chen (2020)
 Estimating variances and kinetic parameters from spectra across multiple datasets using KIPET, Chemometrics and Intelligent Laboratory Systems, https://doi.org/10.1016/j.chemolab.2020.104012
 
-M. Short, C. Schenk, D. Thierry, J.S. Rodriguez, L.T. Biegler, S. García-Muñoz (2019)
+ - M. Short, C. Schenk, D. Thierry, J.S. Rodriguez, L.T. Biegler, S. García-Muñoz (2019)
 KIPET–An Open-Source Kinetic Parameter Estimation Toolkit, Computer Aided Chemical Engineering, 47, 299-304.
 
 

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diff --git a/examples/example_1/Ex_1_ode_sim.py b/examples/example_1/Ex_1_ode_sim.py
@@ -0,0 +1,54 @@
+"""Example 1: ODE Simulation with new KipetModel"""
+
+# Standard library imports
+import sys
+
+# Third party imports
+
+# Kipet library imports
+from kipet import KipetModel
+
+if __name__ == "__main__":
+
+    with_plots = True
+    if len(sys.argv)==2:
+        if int(sys.argv[1]):
+            with_plots = False
+
+    kipet_model = KipetModel()
+
+    r1 = kipet_model.new_reaction('reaction-1')
+
+    # Add the model parameters
+    k1 = r1.parameter('k1', value=2, units='1/s')
+    k2 = r1.parameter('k2', value=0.2, units='1/s')
+
+    # Declare the components and give the initial values
+    A = r1.component('A', value=1.0, units='M')
+    B = r1.component('B', value=0.0, units='M')
+    C = r1.component('C', value=0.0, units='M')
+
+    rA = r1.add_expression('rA', k1*A)
+    rB = r1.add_expression('rB', k2*B)
+
+    r1.add_ode('A', -rA )
+    r1.add_ode('B', rA - rB )
+    r1.add_ode('C', rB )
+
+    # # Option to check the units of your models
+    r1.check_model_units()
+    # # Add dosing points 
+    #r1.add_dosing_point('A', 3, 0.3)
+
+    # # Create the model - simulations require times
+    r1.set_times(0, 10)
+    # r1.create_pyomo_model()
+
+    r1.settings.collocation.ncp = 3
+    r1.settings.collocation.nfe = 50
+
+    #Simulate with default options
+    r1.simulate()
+
+    if with_plots:
+        r1.plot()