Add thirty models

.flake8

@@ -9,6 +9,8 @@ ignore =
     W504,
     # missing whitespace around arithmetic operator
     E226,
+    # Line length
+    E501
 exclude=
     .git,
-    venv,
+    venv,

.github/workflows/pytest.yml

@@ -23,14 +23,16 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
+        sudo apt-get install libsundials-dev -y
         python -m pip install --upgrade pip
         python -m pip install .[test]
     - name: Lint with flake8
       run: |
         python -m flake8
     - name: Import sorting with isort
       run: |
-        python -m isort --verbose --check-only --diff markov_builder tests setup.py
+        python -m isort . --show-config
+        python -m isort --verbose --check-only --diff . --skip .venv
     - name: Test with pytest
       run: |
         python -m pytest --cov --cov-config=.coveragerc

.gitignore

@@ -25,6 +25,8 @@ wheels/
 *.egg
 MANIFEST
 
+markov_builder/_version.py
+
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
@@ -106,3 +108,8 @@ venv.bak/
 # MacOS extensions
 **.DS_Store
 
+# Test output
+test_output/*
+
+# output
+output/*

README.md

@@ -2,15 +2,15 @@
 
 
 ## Description
-`markov_builder` is a Python package for constructing Markov models of ion-channel kinetics. These models may be thought of as chemical reaction networks where the transition rate matrix depends on the cell's transmembrane potential (and sometimes the concentration of a drug). This package uses 'networkx' to allow the user to specify the graph associated with the model (model topology). Many models of ion channel kinetics can be expressed in this way. For example, Markov models equivalent to Hodgkin-Huxley style conductance models are straightforward to generate. The transition rates may then be parameterised with any choice of equation, and depend on any number of 'shared variables' such as transmembrane potential, drug concentration, temperature, etc... 
+`markov_builder` is a Python package for constructing Markov models of ion-channel kinetics. These models may be thought of as chemical reaction networks where the transition rate matrix depends on the cell's transmembrane potential (and sometimes the concentration of a drug). This package uses 'networkx' to allow the user to specify the graph associated with the model (model topology). Many models of ion channel kinetics can be expressed in this way. For example, Markov models equivalent to Hodgkin-Huxley style conductance models are straightforward to generate. The transition rates may then be parameterised with any choice of equation, and depend on any number of 'shared variables' such as transmembrane potential, drug concentration, temperature, etc...
 
 Given the graph for a model and a parameterisation, you may then:
 1. Output latex describing the model
 2. Visualise the model using pyvis
 3. Output the system of equations as `sympy` expressions for use in other code
 4. Model drug trapping by composing models together
 5. Simulate a small number of channels using the Gillespie algorithm
-6. Check the model satisfies properties such as microscopic reversibility and connectedness 
+6. Check the model satisfies properties such as microscopic reversibility and connectedness
 
 
 ## Getting Started