Merge pull request #61 from weilandtd/master

FIX: Fixing dependencies / getting ready for a new release

Author: weilandtd

README.rst

@@ -36,17 +36,6 @@ properly. The installer should take care of that for you. You might also
 want to install a dedicated solver. GLPK, CPLEX and Gurobi are
 supported.
 
-Container-based install
------------------------
-
-You might want to use this program inside of a container. The
-|docker|_
-subfolder has all the necessary information and source files to set it
-up.
-
-.. |docker| replace:: ``docker/``
-.. _docker: https://github.com/EPFL-LCSB/pytfa/tree/master/docker
-
 Setup
 =====
 
@@ -109,6 +98,19 @@ You can then generate the documentation with this command:
 
 The resulting HTML files will be located in ``work/pytfa/doc/_build``.
 
+
+DEPRECATEDL: Container-based install
+-----------------------
+
+You might want to use this program inside of a container. The
+|docker|_
+subfolder has all the necessary information and source files to set it
+up.
+
+.. |docker| replace:: ``docker/``
+.. _docker: https://github.com/EPFL-LCSB/pytfa/tree/master/docker
+
+
 Testing the code
 ----------------
 

pytfa/analysis/manipulation.py

@@ -18,8 +18,18 @@ def apply_reaction_variability(tmodel, va, inplace = True):
         if this_reaction.id not in va.index:
             continue
 
-        this_reaction.lower_bound = va.loc[this_reaction.id,'minimum']
-        this_reaction.upper_bound = va.loc[this_reaction.id,'maximum']
+        min_bound = va.loc[this_reaction.id,'minimum']
+        max_bound = va.loc[this_reaction.id,'maximum']
+        
+        # Handle numerical precision issues where min > max by a tiny amount
+        if min_bound > max_bound:
+            # Use the average of the two values
+            avg_bound = (min_bound + max_bound) / 2
+            min_bound = avg_bound
+            max_bound = avg_bound
+
+        this_reaction.lower_bound = min_bound
+        this_reaction.upper_bound = max_bound
 
     return _tmodel
 

pytfa/io/base.py

@@ -260,7 +260,7 @@ def create_thermo_dict(tmodel):
                 continue
 
         if column == 'metSEEDID':
-            mat[column] = np.array(the_data, dtype=np.object)
+            mat[column] = np.array(the_data, dtype=object)
         else:
             mat[column] = np.array(the_data)
 
@@ -294,8 +294,8 @@ def create_thermo_dict(tmodel):
     CompartmentDB['compMinConc'] = np.array([x['c_min'] for x in compartments])
 
     #Write symbols and names in collumn cell arrays
-    CompartmentDB['compSymbolList'] = np.zeros((1, len(compartments)), dtype=np.object)
-    CompartmentDB['compNameList'] =  np.zeros((1, len(compartments)), dtype=np.object)
+    CompartmentDB['compSymbolList'] = np.zeros((1, len(compartments)), dtype=object)
+    CompartmentDB['compNameList'] =  np.zeros((1, len(compartments)), dtype=object)
 
     mat_to_python_string = [('compSymbolList', 'symbol'),
                             ('compNameList', 'name')]
@@ -365,8 +365,8 @@ def create_problem_dict(tmodel):
 
     mat['var_lb'] = np.array([x.lb for x in tmodel.variables]) * 1.
     mat['var_ub'] = np.array([x.ub for x in tmodel.variables]) * 1.
-    vname = np.full(len(tmodel.variables),'', dtype=np.object)
-    vtype = np.full(len(tmodel.variables),'', dtype=np.object)
+    vname = np.full(len(tmodel.variables),'', dtype=object)
+    vtype = np.full(len(tmodel.variables),'', dtype=object)
 
     for e, this_var in enumerate(tmodel.variables):
         this_name = this_var.name
@@ -389,8 +389,8 @@ def create_problem_dict(tmodel):
     # Constraints
 
     rhs = np.empty(len(tmodel.constraints))
-    ctype = np.full(len(tmodel.constraints),'', dtype=np.object)
-    cname = np.full(len(tmodel.constraints),'', dtype=np.object)
+    ctype = np.full(len(tmodel.constraints),'', dtype=object)
+    cname = np.full(len(tmodel.constraints),'', dtype=object)
 
     for e,this_cons in enumerate(tmodel.constraints):
         if   this_cons.lb is None and this_cons.ub is not None:

pytfa/io/json.py

@@ -76,5 +76,7 @@ def json_loads_model(s):
     :param s: JSON string
     :return:
     """
+    if s is None:
+        return None
     obj = json.loads(s)
     return model_from_dict(obj)

pytfa/redgem/redgem.py

@@ -189,7 +189,7 @@ def _extract_inorganics(self):
 
 def add_lump(model, lump_object, id_suffix=''):
     new = Reaction(id = lump_object.id_+id_suffix)
-    model.add_reaction(new)
+    model.add_reactions([new])
     new.add_metabolites(lump_object.metabolites)
     new.gene_reaction_rule = lump_object.gene_reaction_rule
     new.subnetwork = lump_object.subnetwork

pytfa/thermo/equilibrator.py

@@ -14,7 +14,7 @@
 # triggers the loading of eQuilbrator
 from equilibrator_api import ComponentContribution, Q_
 from equilibrator_api.phased_reaction import PhasedReaction
-from equilibrator_cache import create_compound_cache_from_quilt
+from equilibrator_cache import create_compound_cache_from_zenodo
 from .std import TEMPERATURE_0
 from ..utils.numerics import BIGM_DG
 from ..utils.logger import get_bistream_logger
@@ -37,7 +37,7 @@ def build_thermo_from_equilibrator(model, T=TEMPERATURE_0):
     """
     global ccache
     if ccache is None:
-        ccache = create_compound_cache_from_quilt()
+        ccache = create_compound_cache_from_zenodo()
         logger.debug("eQuilibrator compound cache is loaded.")
 
     cc = ComponentContribution()

requirements.txt

@@ -7,5 +7,4 @@ networkx
 pytest
 scipy
 tqdm
-equilibrator-cache==0.2.6
 equilibrator_api

tests/settings.py

@@ -14,7 +14,13 @@
 
 from pytfa.io.enrichment import read_lexicon, annotate_from_lexicon, \
     read_compartment_data, apply_compartment_data
-from cobra.test import create_test_model
+try:
+    from cobra.test import create_test_model
+except ImportError:
+    # For newer versions of cobra
+    from cobra.io import load_model
+    def create_test_model(model_name):
+        return load_model(model_name)
 
 ############
 # SETTINGS #
@@ -46,7 +52,6 @@
 
 
 # Small model for simpler tests
-
 small_model = create_test_model('textbook')
 
 # Make your computations on it

tests/test_equilibrator.py

@@ -4,6 +4,9 @@
 import pytest
 import sys
 from pytfa.io.json import json_dumps_model, json_loads_model
+from pytfa.thermo.equilibrator import build_thermo_from_equilibrator
+from pytfa import ThermoModel
+from settings import small_model
 
 
 
@@ -14,13 +17,11 @@
 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
-    cmodel = cobra_model.copy()
+    cmodel = small_model.copy()
     for met in cmodel.metabolites:
         # normalize but don't overwrite
-        met.annotation["seed.compound"] = met.annotation["seed_id"]
+        if "seed_id" in met.annotation:
+            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"
@@ -50,6 +51,45 @@ def test_equilibrator_conversion(request):
 
 @pytest.mark.dependency(depends=['test_equilibrator_conversion'])
 @pytest.mark.skipif(sys.version_info < (3, 6), reason="requires >= python3.6")
+def test_equilibrator_deltag_loaded(request):
+    """Test that deltaG values are actually loaded from equilibrator."""
+    # Get the original cobra model and rebuild thermo_data to check it directly
+    
+    cmodel = small_model.copy()
+    for met in cmodel.metabolites:
+        # normalize but don't overwrite
+        if "seed_id" in met.annotation:
+            met.annotation["seed.compound"] = met.annotation["seed_id"]
+    
+    # Build thermo data from equilibrator
+    thermo_data = build_thermo_from_equilibrator(cmodel)
+    
+    print(f"Thermo data structure: {thermo_data['name']}")
+    print(f"Units: {thermo_data['units']}")
+    
+    # Check metabolites in thermo_data
+    metabolites_with_deltag = 0
+    sample_mets = []
+    
+    for met_data in thermo_data['metabolites']:
+        if 'DeltaGf_tr' in met_data:
+            metabolites_with_deltag += 1
+            if len(sample_mets) < 5:  # Collect some samples
+                sample_mets.append((met_data['id'], met_data['DeltaGf_tr']))
+    
+    print(f"Found {metabolites_with_deltag} metabolites with deltaG formation values from equilibrator")
+    print("Sample metabolites with deltaG values:")
+    for met_id, deltag in sample_mets:
+        print(f"  {met_id}: {deltag} {thermo_data['units']}")
+    
+    # There should be at least some metabolites with deltaG values
+    assert metabolites_with_deltag > 0, f"No deltaG formation values found in thermo_data! Expected > 0, got {metabolites_with_deltag}"
+    
+    return metabolites_with_deltag
+
+
+@pytest.mark.dependency(depends=['test_equilibrator_deltag_loaded'])
+@pytest.mark.skipif(sys.version_info < (3, 6), reason="requires >= python3.6")
 def test_equilibrator_optim(request):
     """LP optimization by the usual method in `ThermoModel`."""
     t_model = json_loads_model(request.config.cache.get('model',None))

tests/test_optim.py

@@ -10,7 +10,13 @@
 
 """
 
-from cobra.test import create_test_model
+try:
+    from cobra.test import create_test_model
+except ImportError:
+    # For newer versions of cobra
+    from cobra.io import load_model
+    def create_test_model(model_name):
+        return load_model(model_name)
 import os
 import sys
 import pytfa