Added test_run_results.py :: TestExponentialGrowth_M test case.

tests/multialgorithm/test_dynamic_components.py

@@ -26,6 +26,7 @@
 from wc_sim.multialgorithm.make_models import MakeModels
 from wc_sim.multialgorithm.submodels.dynamic_submodel import DynamicSubmodel
 
+
 def prepare_model(model):
     PrepareModel(model).run()
     CheckModel(model).run()

tests/multialgorithm/test_run_results.py

@@ -6,13 +6,16 @@
 :License: MIT
 """
 
+import os
 import unittest
 import shutil
 import tempfile
 import pandas
 import numpy
+from scipy.optimize import curve_fit
 from capturer import CaptureOutput
 
+from wc_lang.io import Reader
 from wc_lang.core import SpeciesType
 from wc_sim.multialgorithm.multialgorithm_errors import MultialgorithmError
 from wc_sim.multialgorithm.simulation import Simulation
@@ -66,3 +69,31 @@ def test_run_results_errors(self):
         run_results = RunResults(self.results_dir)
         with self.assertRaisesRegexp(MultialgorithmError, "component '.*' is not an element of "):
             run_results.get('not_a_component')
+
+class TestExponentialGrowth_M(unittest.TestCase):
+
+    MODEL_FILENAME = os.path.join(os.path.dirname(__file__), 'fixtures', 'test_model_for_exponential_growth_in_M.xlsx')
+
+    def setUp(self):
+        self.model = Reader().run(self.MODEL_FILENAME, strict=False)
+        self.temp_dir = tempfile.mkdtemp()
+        simulation = Simulation(self.model)
+        self.checkpoint_period = 60
+        self.end_time = 108000
+        with CaptureOutput(relay=False) as capturer:
+            _, self.results_dir = simulation.run(end_time=self.end_time, results_dir=self.temp_dir,
+                checkpoint_period=self.checkpoint_period)
+
+    def tearDown(self):
+        shutil.rmtree(self.temp_dir)
+
+    def test_exponential_growth_in_M(self):
+        time = pandas.Float64Index(numpy.linspace(0, self.end_time, 1 + self.end_time/self.checkpoint_period))
+        run_results = RunResults(self.results_dir)
+        populations = run_results.get('populations')
+        M_abundance = populations['M[c]']
+        cf_result = curve_fit(lambda t,a,b: a*numpy.exp(b*t), time, M_abundance, p0=(M_abundance[time[0]], 1e-6))
+        cf_M_0 = cf_result[0][0]  # target is 1e4
+        cf_exp = cf_result[0][1]  # target is 8.3713e-06 = ln(2)/23 * 1/3600
+        self.assertTrue(abs(cf_M_0 - 1e4) < 100)
+        self.assertTrue(abs(cf_exp -  8.3713e-06) < 5e-7)