DOCS: add script as tutorial for equilibrator integration

tutorials/tutorial_equilibrator.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""Tutorial for usage of equilibrator with pyTFA.
+
+Instead of using a local thermodynamic database, pyTFA can use eQuilibrator
+(https://gitlab.com/elad.noor/equilibrator-api) to build this structure and use
+it to compute the thermodynamics MILP problem.
+
+Requirements
+------------
+
+On `setup.cfg`, you can see that equilibrator-api and equilibrator-cache are
+extra dependencies of the project. You can check the required versions on this
+file or install them directly with pip from the project directory:
+
+.. code:: shell
+
+    pip install .[equilibrator]
+
+"""
+
+import os
+import pytfa
+import pytfa.io
+
+from pytfa.thermo.equilibrator import build_thermo_from_equilibrator
+
+
+this_directory = os.path.dirname(os.path.realpath(__file__))
+
+# 1. Load the cobra_model
+cobra_model = pytfa.io.import_matlab_model(
+    this_directory + "/../models/small_ecoli.mat"
+)
+cobra_model.optimizer = "glpk"
+
+# 2. Normalize annotation ids (but don't overwrite them)
+for met in cobra_model.metabolites:
+    met.annotation["seed.compound"] = met.annotation["seed_id"]
+
+# 3. Build the thermodb strucutre and initialize the model
+# this process can take some time
+thermo_data = build_thermo_from_equilibrator(cobra_model)
+tmodel = pytfa.ThermoModel(thermo_data, cobra_model)
+tmodel.solver = "optlang-glpk"
+
+# 4. Now, the process follows the same steps as usual
+tmodel.prepare()
+tmodel.convert()
+
+# 5. Finally, we can optimize the simulation and print some summary
+solution = tmodel.optimize()
+print("Some fluxes")
+print(solution.fluxes.head())
+print("\nOnjective solution:", str(solution.objective_value))
+print("\nTotal sum of fluxes:", str(solution.fluxes.sum()))