test mixture id

tests/test_mixture_id.py

@@ -0,0 +1,54 @@
+import unittest
+import numpy as np
+import pandas as pd
+from pyphenopop.mixpopid import mixture_id
+import os
+
+
+class TestMixtureId(unittest.TestCase):
+
+    def setUp(self):
+        # Create a temporary CSV file with mock data
+        self.data_file = 'test_data.csv'
+        data = np.random.rand(10*2, 3)  # 10 timepoints, 3 concentrations
+        pd.DataFrame(data).to_csv(self.data_file, header=False, index=False)
+
+        self.max_subpop = 3
+        self.timepoints = np.linspace(0, 48, 10)  # 10 timepoints from 0 to 48 hours
+        self.concentrations = np.linspace(0.01, 10, 3)  # 3 concentrations from 0.01 to 10
+        self.num_replicates = 2
+        self.model = 'expo'
+        self.bounds_model = {'alpha': (0.0, 0.1), 'b': (0.0, 1.0), 'E': (1e-06, 15), 'n': (0.01, 10)}
+        self.bounds_sigma_high = (1e-05, 10000.0)
+        self.bounds_sigma_low = (1e-05, 5000.0)
+        self.optimizer_options = {'method': 'L-BFGS-B', 'options': {'disp': False, 'ftol': 1e-12}}
+        self.num_optim = 5
+        self.selection_method = 'BIC'
+
+    def test_mixture_id(self):
+        results = mixture_id(
+            self.max_subpop,
+            self.data_file,
+            self.timepoints,
+            self.concentrations,
+            self.num_replicates,
+            self.model,
+            self.bounds_model,
+            self.bounds_sigma_high,
+            self.bounds_sigma_low,
+            self.optimizer_options,
+            self.num_optim,
+            self.selection_method
+        )
+
+        self.assertIn('summary', results)
+        self.assertIn('estimated_num_populations', results['summary'])
+        self.assertIn('final_neg_log_likelihood', results['summary'])
+        self.assertIn('final_parameters', results['summary'])
+
+    def tearDown(self):
+        os.remove(self.data_file)
+
+
+if __name__ == '__main__':
+    unittest.main()