Allow global setting of random number seed + better RepartitionedHybridTopologyFactory test

perses/tests/test_coordinate_numba.py

@@ -14,6 +14,7 @@
 CARBON_MASS = 12.01 # float (implicitly in units of AMU)
 REFERENCE_PLATFORM = openmm.Platform.getPlatformByName("Reference")
 running_on_github_actions = os.environ.get('GITHUB_ACTIONS', None) == 'true'
+rng = np.random.RandomState(42)
 #########################################
 # Tests
 #########################################
@@ -27,7 +28,7 @@ def test_coordinate_conversion():
     geometry_engine = geometry.FFAllAngleGeometryEngine({'test': 'true'})
     #try to transform random coordinates to and from cartesian
     for i in range(200):
-        indices = np.random.randint(100, size=4)
+        indices = rng.randint(100, size=4)
         atom_position = unit.Quantity(np.array([ 0.80557722 ,-1.10424644 ,-1.08578826]), unit=unit.nanometers)
         bond_position = unit.Quantity(np.array([ 0.0765,  0.1  ,  -0.4005]), unit=unit.nanometers)
         angle_position = unit.Quantity(np.array([ 0.0829 , 0.0952 ,-0.2479]) ,unit=unit.nanometers)
@@ -134,7 +135,7 @@ def test_try_random_itoc():
     angle_position = unit.Quantity(np.array([ 0.0829 , 0.0952 ,-0.2479]) ,unit=unit.nanometers)
     torsion_position = unit.Quantity(np.array([-0.057 ,  0.0951 ,-0.1863] ) ,unit=unit.nanometers)
     for i in range(1000):
-        atom_position += unit.Quantity(np.random.normal(size=3), unit=unit.nanometers)
+        atom_position += unit.Quantity(rng.normal(size=3), unit=unit.nanometers)
         r, theta, phi = _get_internal_from_omm(atom_position, bond_position, angle_position, torsion_position)
         recomputed_xyz, _ = geometry_engine._internal_to_cartesian(bond_position, angle_position, torsion_position, r, theta, phi)
         new_r, new_theta, new_phi = _get_internal_from_omm(recomputed_xyz,bond_position, angle_position, torsion_position)

perses/tests/test_relative.py

@@ -5,7 +5,6 @@
 from simtk import unit, openmm
 import numpy as np
 import os
-import random
 from nose.tools import nottest
 from pkg_resources import resource_filename
 
@@ -36,6 +35,7 @@
 ENERGY_THRESHOLD = 1e-1
 REFERENCE_PLATFORM = openmm.Platform.getPlatformByName("CPU")
 aminos = ['ALA','ARG','ASN','ASP','CYS','GLN','GLU','GLY','HIS','ILE','LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL']
+rng = np.random.RandomState(42)
 
 def run_hybrid_endpoint_overlap(topology_proposal, current_positions, new_positions):
     """
@@ -809,7 +809,7 @@ def flattenedHybridTopologyFactory_energies(topology, chain, system, positions,
 
     # Create point mutation engine to mutate residue at id 2 to a random amino acid
     aminos_updated = [amino for amino in aminos if amino not in ['ALA', 'PRO']]
-    mutant = random.choice(aminos_updated)
+    mutant = rng.choice(aminos_updated)
     print(f'Making mutation ALA->{mutant}')
     point_mutation_engine = PointMutationEngine(wildtype_topology=topology,
                                                 system_generator=system_generator,

perses/tests/test_smc.py

@@ -34,6 +34,7 @@
 external_parallelism = None
 internal_parallelism = {'library': ('dask', 'LSF'), 'num_processes': 2}
 os.system(f"mkdir {trajectory_directory}")
+rng = np.random.RandomState(42)
 #######################
 
 
@@ -180,7 +181,7 @@ def dummy_ancestry_generator_function():
         """
         ancestries = [np.arange(10)]
         for i in range(10):
-            new_ancestries = np.random.choice(ancestries[-1], 10)
+            new_ancestries = rng.choice(ancestries[-1], 10)
             ancestries.append(new_ancestries)
         return ancestries
 
@@ -195,7 +196,7 @@ def test_multinomial_resample():
     """
     test the multinomial resampler
     """
-    total_works = np.random.rand(10)
+    total_works = rng.rand(10)
     num_resamples = 10
     resampled_works, resampled_indices = multinomial_resample(total_works, num_resamples)
     assert all(_val == np.average(total_works) for _val in resampled_works), f"the returned resampled works are not a uniform average"
@@ -207,22 +208,22 @@ def test_ESS():
     test the effective sample size computation
     """
     #the ESS already passes with a normalization assertion
-    dummy_prev_works, dummy_works_incremental = np.random.rand(10), np.random.rand(10)
+    dummy_prev_works, dummy_works_incremental = rng.rand(10), rng.rand(10)
     normalized_ESS = ESS(dummy_prev_works, dummy_works_incremental)
 
 def test_CESS():
     """
     test the conditional effective sample size computation
     """
     #the CESS must be guaranteed to be between 0 and 1
-    dummy_prev_works, dummy_works_incremental = np.random.rand(10), np.random.rand(10)
+    dummy_prev_works, dummy_works_incremental = rng.rand(10), rng.rand(10)
     _CESS = CESS(dummy_prev_works, dummy_works_incremental)
 
 def test_compute_timeseries():
     """
     test the compute_timeseries function
     """
-    reduced_potentials = np.random.rand(100)
+    reduced_potentials = rng.rand(100)
     data = compute_timeseries(reduced_potentials)
     assert len(data[3]) <= len(reduced_potentials), f"the length of uncorrelated data is at most the length of the raw data"
 

perses/tests/test_storage.py

@@ -81,13 +81,15 @@ def test_write_array():
     view1 = NetCDFStorageView(storage, 'envname1', 'modname')
     view2 = NetCDFStorageView(storage, 'envname2', 'modname')
 
-    from numpy.random import random
+    import numpy as np
+    rng = np.random.RandomState(42)
+
     shape = (10,3)
-    array = random(shape)
+    array = rng.random(shape)
     view1.write_array('singleton', array)
 
     for iteration in range(10):
-        array = random(shape)
+        array = rng.random(shape)
         view1.write_array('varname', array, iteration=iteration)
         view2.write_array('varname', array, iteration=iteration)
 

perses/tests/test_topology_proposal.py

@@ -36,6 +36,7 @@
 beta = 1.0 / kT
 ENERGY_THRESHOLD = 1e-6
 PROHIBITED_RESIDUES = ['CYS']
+rng = np.random.RandomState(42)
 
 running_on_github_actions = os.environ.get('GITHUB_ACTIONS', None) == 'true'
 
@@ -515,7 +516,7 @@ def test_specify_allowed_mutants():
     for chain in modeller.topology.chains():
         if chain.id == chain_id:
             residues = chain._residues
-    mutant_res = np.random.choice(residues[1:-1])
+    mutant_res = rng.choice(residues[1:-1])
 
     pm_top_engine = topology_proposal.PointMutationEngine(modeller.topology, system_generator, chain_id, allowed_mutations=allowed_mutations)
 
@@ -558,7 +559,7 @@ def test_propose_self():
     for chain in modeller.topology.chains():
         if chain.id == chain_id:
             residues = [res for res in chain._residues if res.name not in PROHIBITED_RESIDUES]
-    mutant_res = np.random.choice(residues[1:-1])
+    mutant_res = rng.choice(residues[1:-1])
     allowed_mutations = [(mutant_res.id,mutant_res.name)]
 
     pm_top_engine = topology_proposal.PointMutationEngine(modeller.topology, system_generator, chain_id, allowed_mutations=allowed_mutations)