obtain initial concentrations from truncated normal dist. that doesn'…

…t oversample 0

tests/test_dynamic_components.py

@@ -560,7 +560,7 @@ def test_initialize_mass_and_density(self):
             self.assertIn("initial accounted ratio (", str(w[-1].message))
             self.assertIn(") greater than 1.0", str(w[-1].message))
 
-        dynamic_compartment = self.specify_init_accounted_fraction(2)
+        dynamic_compartment = self.specify_init_accounted_fraction(4)
         with self.assertRaises(MultialgorithmError):
             dynamic_compartment.initialize_mass_and_density(self.local_species_pop)
 

wc_sim/dynamic_components.py

@@ -20,6 +20,7 @@
 from obj_tables.math.expression import Expression, ObjTablesTokenCodes
 from wc_lang import Species, Compartment
 from wc_onto import onto
+from wc_sim.model_utilities import ModelUtilities
 from wc_sim.multialgorithm_errors import MultialgorithmError, MultialgorithmWarning
 from wc_sim.species_populations import LocalSpeciesPopulation
 from wc_utils.util.enumerate import CaseInsensitiveEnum
@@ -29,7 +30,6 @@
 import wc_sim.config
 import wc_sim.submodels
 
-
 # mapping from wc_lang Models to DynamicComponents
 WC_LANG_MODEL_TO_DYNAMIC_MODEL = {}
 
@@ -464,7 +464,7 @@ def __init__(self, dynamic_model, random_state, wc_lang_compartment, species_ids
                 config_multialgorithm = wc_sim.config.core.get_config()['wc_sim']['multialgorithm']
                 MEAN_TO_STD_DEV_RATIO = config_multialgorithm['mean_to_std_dev_ratio']
                 std = mean / MEAN_TO_STD_DEV_RATIO
-            self.init_volume = max(0., random_state.normal(mean, std))
+            self.init_volume = ModelUtilities.non_neg_normal_sample(random_state, mean, std)
         else:
             raise MultialgorithmError('Initial volume must be normally distributed')
 

wc_sim/model_utilities.py

@@ -121,7 +121,7 @@ def sample_copy_num_from_concentration(species, volume, random_state):
             std = dist_conc.std
             if numpy.isnan(std):
                 std = mean / MEAN_TO_STD_DEV_RATIO
-            conc = max(0., random_state.normal(mean, std))
+            conc = ModelUtilities.non_neg_normal_sample(random_state, mean, std)
 
             if not isinstance(dist_conc.units, unit_registry.Unit):
                 raise ValueError('Unsupported unit type "{}"'.format(type(dist_conc.units)))
@@ -183,7 +183,7 @@ def parse_species_id(species_id):
         return species_id[0:comp_start], species_id[comp_start+1:-1]
 
     @staticmethod
-    def non_neg_normal_sample(random_state, mean, std, max_iters=1000):
+    def non_neg_normal_sample(random_state, mean, std, max_iters=100):
         """ Obtain a non-negative sample from a normal distribution
 
         The distribution returned is 0 for x < 0 and normal for 0 <= x