Solution for pytfa.relax_dgo (sympy)

CLAI

@@ -0,0 +1,134 @@
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README.rst

@@ -183,8 +183,8 @@ Here is an example script :
    :target: http://pytfa.readthedocs.io/en/latest/?badge=latest
 .. |license| image:: http://img.shields.io/badge/license-APACHE2-blue.svg
    :target: https://github.com/EPFL-LCSB/pytfa/blob/master/LICENSE.txt
-.. |Build Status| image:: https://travis-ci.org/EPFL-LCSB/pytfa.svg?branch=master
-   :target: https://travis-ci.org/EPFL-LCSB/pytfa
+.. |Build Status| image:: https://travis-ci.com/EPFL-LCSB/pytfa.svg?branch=master
+   :target: https://travis-ci.com/EPFL-LCSB/pytfa
 .. |Codecov| image:: https://img.shields.io/codecov/c/github/EPFL-LCSB/pytfa.svg
    :target: https://codecov.io/gh/EPFL-LCSB/pytfa
 .. |Codacy branch grade| image:: https://img.shields.io/codacy/grade/d8fd67ee134d46a69115c9b39c19be26/master.svg

doc/conf.py

@@ -95,7 +95,7 @@ def __getattr__(cls, name):
 version = '0.9'
 # The full version, including alpha/beta/rc tags.
 
-release = '0.9.1'
+release = '0.9.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docker/requirements.txt

@@ -1,5 +1,7 @@
 bokeh>=0.12.1
 cobra>0.13
+equilibrator-api
+equilibrator-cache
 ipdb
 lxml
 networkx

models/README.rst

@@ -46,12 +46,12 @@ Publications
 
 Some models have been published:
 
-+-----------------+-------------------------------------------------------------------------+
-| small_ecoli.mat | Ataman, M., Gardiol, D. F. H., Fengos, G., & Hatzimanikatis, V. (2017). |
-|                 | redGEM: Systematic reduction and analysis of genome-scale metabolic     |
-|                 | reconstructions for development of consistent core metabolic models.    |
-|                 | PLoS computational biology, 13(7), e1005444.                            |
-+-----------------+-------------------------------------------------------------------------+
++-----------------+--------------------------------------------------------------------------------+
+| small_ecoli.mat | Ataman, M., Hernandez Gardiol, D. F., Fengos, G., & Hatzimanikatis, V. (2017). |
+|                 | redGEM: Systematic reduction and analysis of genome-scale metabolic            |
+|                 | reconstructions for development of consistent core metabolic models.           |
+|                 | PLoS computational biology, 13(7), e1005444.                                   |
++-----------------+--------------------------------------------------------------------------------+
 
 
 

pytfa/io/base.py

@@ -189,8 +189,7 @@ def import_matlab_model(path, variable_name=None):
         comp = comps[i]
         comp['membranePot'] = {}
         for j in range(num_comp):
-            comp['membranePot'][comps[j]['symbol']] = \
-            CompartmentDB['membranePot'][0][i, j]
+            comp['membranePot'][comps[j]['symbol']] = float(CompartmentDB['membranePot'][0][i, j])
 
     cobra_model.compartments = Compartments
 

pytfa/io/dict.py

@@ -18,6 +18,8 @@
 from ..optim.constraints import ReactionConstraint, MetaboliteConstraint, ModelConstraint
 from ..optim.utils import symbol_sum
 
+from ..thermo.metabolite import MetaboliteThermo
+
 from optlang.util import expr_to_json, parse_expr
 
 from copy import copy
@@ -215,7 +217,7 @@ def model_to_dict(model):
 
         if is_thermo and not is_peptide: # peptides have no thermo
             the_met = model.metabolites.get_by_id(the_met_id)
-            _add_thermo_metabolite_info(the_met, rxn_dict)
+            _add_thermo_metabolite_info(the_met, met_dict)
             met_dict['kind'] = 'Metabolite'
 
     # Relaxation info
@@ -425,6 +427,9 @@ def init_thermo_model_from_dict(new, obj):
 
         if 'thermo' in met_dict:
             new._prepare_metabolite(met)
+            met.thermo = MetaboliteThermo(metData=None, pH=7,ionicStr=0)
+            for k,v in met_dict['thermo'].items():
+                setattr(met.thermo,k,v)
     return new
 
 def rebuild_compositions(new, compositions_dict):

pytfa/io/json.py

@@ -54,3 +54,27 @@ def load_json_model(filepath):
 
     model = model_from_dict(obj)
     return model
+
+
+def json_dumps_model(model):
+    """
+    Returns a JSON dump as a string
+
+    :param model:
+    :return:
+    """
+
+    obj = model_to_dict(model)
+
+    return json.dumps(obj,cls=MyEncoder)
+
+
+def json_loads_model(s):
+    """
+    Loads a model from a string JSON dump
+
+    :param s: JSON string
+    :return:
+    """
+    obj = json.loads(s)
+    return model_from_dict(obj)

pytfa/optim/utils.py

@@ -16,6 +16,8 @@
 import sympy
 from cobra.core.solution import Solution
 
+from sympy.core.numbers import Zero
+
 from numbers import Number
 
 from .constraints import GenericConstraint
@@ -107,7 +109,7 @@ def symbol_sum(variables):
 
     k=0
     # If we encounter a zero, which is a special type, increase k
-    while isinstance(variables[k], sympy.numbers.Zero) and k<len(variables):
+    while isinstance(variables[k], Zero) and k<len(variables):
         k+=1
         if k == len(variables):
             # everything is 0

pytfa/optim/variables.py

@@ -568,7 +568,6 @@ class NegSlackLC(MetaboliteVariable):
 
     prefix = 'NegSlackLC_'
 
-
 class LinearizationVariable(ModelVariable):
     """
     Class to represent a product A*B when performing linearization of the

pytfa/thermo/equilibrator.py

@@ -40,7 +40,8 @@ def build_thermo_from_equilibrator(model, T=TEMPERATURE_0):
         ccache = create_compound_cache_from_quilt()
         logger.debug("eQuilibrator compound cache is loaded.")
 
-    cc = ComponentContribution(temperature=Q_(str(T) + "K"))
+    cc = ComponentContribution()
+    cc.temperature=Q_(str(T) + "K")
 
     thermo_data = {"name": "eQuilibrator", "units": "kJ/mol", "cues": {}}
     met_to_comps = compat.map_cobra_metabolites(ccache, model.metabolites)

pytfa/thermo/utils.py

@@ -76,7 +76,7 @@ def check_reaction_balance(reaction, proton = None):
                 'Cd', 'W', 'Hg', 'R', 'Mo', 'X'].index(atom[0])
             except ValueError:
                 # Jorge Carrasco: Can we use warnings here?
-                reaction.model.logger.warn(
+                reaction.model.logger.warning(
                     'Warning : ' + metabolite.formula + '/' + atom[0]
                 )
                 continue

setup.py

@@ -20,7 +20,7 @@
 #     return reqs
 
 
-version_tag = '0.9.1'
+version_tag = '0.9.3'
 
 setup(name='pytfa',
       version=version_tag,

tests/test_equilibrator.py

@@ -3,48 +3,56 @@
 """Tests for the equilibrator integration."""
 import pytest
 import sys
+from pytfa.io.json import json_dumps_model, json_loads_model
 
 
-t_model = None
-
 
 # previous version aren't compatible for the required equlibrator_cache version
 # this annotation is not necessary since this file shoud be ignored in the test
 # collection phase for previous versions, but it makes it more clear.
 @pytest.mark.skipif(sys.version_info < (3, 6), reason="requires >= python3.6")
-def test_load_with_equi_thermo():
+def test_load_with_equi_thermo(request):
     """Build thermo data structure with equilibrator."""
+    t_model = request.config.cache.get('model', None)
     from pytfa.thermo.equilibrator import build_thermo_from_equilibrator
     from pytfa import ThermoModel
     from settings import cobra_model
-
-    global t_model
     cmodel = cobra_model.copy()
     for met in cmodel.metabolites:
         # normalize but don't overwrite
         met.annotation["seed.compound"] = met.annotation["seed_id"]
     thermo_data = build_thermo_from_equilibrator(cmodel)
     t_model = ThermoModel(thermo_data, cmodel)
     t_model.solver = "optlang-glpk"
+    request.config.cache.set('model', json_dumps_model(t_model))
+
 
 
+@pytest.mark.dependency(depends=['test_load_with_equi_thermo'])
 @pytest.mark.skipif(sys.version_info < (3, 6), reason="requires >= python3.6")
-def test_equilibrator_preparation():
+def test_equilibrator_preparation(request):
     """Prepare using equilibrator_api."""
-    global t_model
+    t_model = json_loads_model(request.config.cache.get('model',None))
+    assert t_model is not None
     t_model.prepare()
+    request.config.cache.set('model', json_dumps_model(t_model))
 
 
+@pytest.mark.dependency(depends=['test_equilibrator_preparation'])
 @pytest.mark.skipif(sys.version_info < (3, 6), reason="requires >= python3.6")
-def test_equilibrator_conversion():
+def test_equilibrator_conversion(request):
     """Convert by the usual method in `ThermoModel`."""
-    global t_model
+    t_model = json_loads_model(request.config.cache.get('model',None))
+    assert t_model is not None
     t_model.convert()
+    request.config.cache.set('model', json_dumps_model(t_model))
 
 
+@pytest.mark.dependency(depends=['test_equilibrator_conversion'])
 @pytest.mark.skipif(sys.version_info < (3, 6), reason="requires >= python3.6")
-def test_equilibrator_optim():
+def test_equilibrator_optim(request):
     """LP optimization by the usual method in `ThermoModel`."""
-    global t_model
+    t_model = json_loads_model(request.config.cache.get('model',None))
+    assert t_model is not None
     solution = t_model.optimize()
     return sum(solution.fluxes)