test: updating methionine example

tests/data/methionine_pldf_grad.json

@@ -0,0 +1 @@
+{"log_kcat": {"MAT1": -8.515730409411995, "MAT3": -2.5685756593727778, "METH-Gen": -27.620460850911506, "GNMT1": -4.548438844490129, "AHC1": -11.75344338177346, "MS1": -16.03214664980906, "BHMT1": 12.038008848152892, "CBS1": -27.970086306108342, "MTHFR1": 21.633517021058395, "PROT1": -11.72862587853564}, "log_enzyme": {"MAT1": 6.136798020189005, "MAT3": 12.083952770228223, "METH-Gen": -12.96793242131051, "GNMT1": 10.10408958511087, "AHC1": 2.8990850478275423, "MS1": -1.379618220208057, "BHMT1": 26.690537277753894, "CBS1": -13.317557876507342, "MTHFR1": 36.286045450659394, "PROT1": 2.9239025510653605}, "log_drain": {"the_drain": 77.11394277227623}, "log_km": {"met-L MAT1": 6.190853781501735, "atp MAT1": 5.292693046521437, "met-L MAT3": 2.4814738123803086, "atp MAT3": 1.705092962802143, "amet METH-Gen": 6.601750014600864, "amet GNMT1": 4.590545886354766, "gly GNMT1": 4.820977250097301, "ahcys AHC1": 11.683829123279876, "hcys-L AHC1": -2.9137920929850405, "adn AHC1": -1.769215504424688, "hcys-L MS1": 5.240078482919715, "5mthf MS1": 14.652528429601952, "hcys-L BHMT1": -10.22016805310029, "glyb BHMT1": -10.688987009618199, "hcys-L CBS1": 25.82504526708683, "ser-L CBS1": 0.049935152823941564, "mlthf MTHFR1": -20.851184438848044, "nadph MTHFR1": -2.4216899858227414, "met-L PROT1": 5.971084568691083}, "dgf": {"met-L": 0.0, "atp": 0.0, "pi": 0.0, "ppi": 0.0, "amet": 0.0, "ahcys": -1.3226601224426768, "gly": 0.0, "sarcs": 0.0, "hcys-L": 1.3226601224426768, "adn": 1.3226601224426768, "thf": 0.0, "5mthf": 0.0, "mlthf": 0.0, "glyb": 0.0, "dmgly": 0.0, "ser-L": 0.0, "nadp": 0.0, "nadph": 0.0, "cyst-L": 0.0}, "log_ki": {"MAT1": -0.3185780584014895, "METH-Gen": -0.24799879824208654, "GNMT1": 0.0018358603202088403}, "log_conc_unbalanced": {"atp": 7.654742420277419, "pi": 14.652528429601, "ppi": 14.652528429601, "gly": 9.831551179503698, "sarcs": 14.652528429601988, "adn": 19.700379108345924, "thf": 14.652528429601986, "mlthf": 35.57574270245104, "glyb": 25.3415154392192, "dmgly": 14.652528429601988, "ser-L": 14.60259327677706, "nadp": 14.652528429601988, "nadph": 17.074218415424728, "cyst-L": 14.652528429601988}, "log_transfer_constant": {"METAT": 0.07549016742205726, "GNMT": 1.3246972471203298, "CBS": 7.813277693021312e-05, "MTHFR": -0.3918664031389874}, "log_dissociation_constant": {"met-L MAT3": 0.009116267028546848, "amet MAT3": 0.012649140053254744, "amet GNMT1": 3.233378796396484, "mlthf GNMT1": -0.0720298340010019, "amet CBS1": 0.0, "amet MTHFR1": 0.5327826505992013, "ahcys MTHFR1": -0.0637729300752556}}

tests/test_lp_grad.py

@@ -1,30 +1,30 @@
 import json
-import pytest
 import jax
 from jax import numpy as jnp
 
 from enzax.examples import methionine
 from enzax.mcmc import (
     ObservationSet,
     AllostericMichaelisMentenPriorSet,
-    get_idata,
     ind_prior_from_truth,
     posterior_logdensity_amm,
-    run_nuts,
 )
 from enzax.steady_state import get_kinetic_model_steady_state
-from enzax.examples import linear, methionine
 
-import functools
+import importlib.resources
+from tests import data
 
+import functools
 
-SEED = 1234
 jax.config.update("jax_enable_x64", True)
+SEED = 1234
 
+methionine_pldf_grad_file = (
+    importlib.resources.files(data) / "methionine_pldf_grad.json"
+)
 
 
 def test_lp_grad():
-    """Demonstrate How to make a Bayesian kinetic model with enzax."""
     model = methionine
     structure = methionine.structure
     rate_equations = methionine.rate_equations
@@ -39,8 +39,8 @@ def test_lp_grad():
         log_enzyme=ind_prior_from_truth(true_parameters.log_enzyme, 0.1),
         log_drain=ind_prior_from_truth(true_parameters.log_drain, 0.1),
         dgf=(
-            ind_prior_from_truth(true_parameters.dgf, 0.1),
-            jnp.diag(ind_prior_from_truth(true_parameters.dgf, 0.1)[1]),
+            ind_prior_from_truth(true_parameters.dgf, 0.1)[0],
+            jnp.diag(jnp.square(ind_prior_from_truth(true_parameters.dgf, 0.1)[1])),
         ),
         log_km=ind_prior_from_truth(true_parameters.log_km, 0.1),
         log_conc_unbalanced=ind_prior_from_truth(
@@ -100,11 +100,8 @@ def test_lp_grad():
             } 
         for p in model.dims.keys()
         }
-    with open('methionine_pldf_grad.json', 'r') as file:
+    with open(methionine_pldf_grad_file, 'r') as file:
         saved_pldf_grad = file.read()
     true_gradient = json.loads(saved_pldf_grad)
     assert index_pldf_grad == true_gradient
 
-if __name__ == "__main__":
-    test_lp_grad()
-