fixed failing tests after the new nmi calculation algo was implemented

mdpath/tests/test_cluster.py

@@ -75,7 +75,7 @@ def test_pathways_cluster(setup_clustering):
     clustering = setup_clustering
 
     with patch("matplotlib.pyplot.savefig") as mock_savefig:
-        clusters = clustering.pathways_cluster(n_top_clust=0)
+        clusters = clustering.pathways_cluster(n_top_clust=3)
 
     assert isinstance(clusters, dict)
     assert all(isinstance(k, int) and isinstance(v, list) for k, v in clusters.items())

mdpath/tests/test_mdpath.py

@@ -87,14 +87,14 @@ def test_mdpath_output_files():
 
     topology = os.path.join(script_dir, "test_topology.pdb")
     trajectory = os.path.join(script_dir, "test_trajectory.dcd")
-    numpath = "25"
+    numpath = "10" 
     bootstrap = "1"
     assert os.path.exists(topology), f"Topology file {topology} does not exist."
     assert os.path.exists(trajectory), f"Trajectory file {trajectory} does not exist."
 
     expected_files = [
         os.path.join(script_dir, "first_frame.pdb"),
-        os.path.join(script_dir, "nmi_df.csv.csv"),
+        os.path.join(script_dir, "nmi_df.csv"),
         os.path.join(script_dir, "output.txt"),
         os.path.join(script_dir, "residue_coordinates.pkl"),
         os.path.join(script_dir, "cluster_pathways_dict.pkl"),

mdpath/tests/test_mdpath_tools.py

@@ -283,7 +283,7 @@ def test_multitraj_analysis():
             topology,
             "-multitraj",
             multitraj_1,
-            multitraj_1,
+            multitraj_1
         ]
         with pytest.raises(SystemExit) as exc_info:
             multitraj_analysis()

mdpath/tests/test_mutual_information.py

@@ -1,17 +1,12 @@
 import sys
 import pandas as pd
 import numpy as np
-from tqdm import tqdm
-from sklearn.metrics import mutual_info_score
-from scipy.stats import entropy
-import pytest
-import tempfile
-from unittest.mock import MagicMock, Mock, patch, call
+
 
 from mdpath.src.mutual_information import NMICalculator
 
 
-def test_nmi_calculator(mocker):
+def test_nmi_calculator_invert():
     np.random.seed(0)  # Set a fixed seed for reproducibility
     data = {
         "Residue1": np.random.uniform(-180, 180, size=100),
@@ -20,7 +15,7 @@ def test_nmi_calculator(mocker):
     }
     df_all_residues = pd.DataFrame(data)
 
-    calculator = NMICalculator(df_all_residues)
+    calculator = NMICalculator(df_all_residues, invert=True)
     result = calculator.nmi_df
 
     assert isinstance(result, pd.DataFrame), "Result should be a DataFrame"
@@ -57,3 +52,51 @@ def test_nmi_calculator(mocker):
         assert np.isclose(
             actual, expected, atol=1e-5
         ), f"For pair {pair}, expected {expected} but got {actual}"
+
+
+def test_nmi_calculator():
+    np.random.seed(0)  # Set a fixed seed for reproducibility
+    data = {
+        "Residue1": np.random.uniform(-180, 180, size=100),
+        "Residue2": np.random.uniform(-180, 180, size=100),
+        "Residue3": np.random.uniform(-180, 180, size=100),
+    }
+    df_all_residues = pd.DataFrame(data)
+
+    calculator = NMICalculator(df_all_residues)
+    result = calculator.nmi_df
+
+    assert isinstance(result, pd.DataFrame), "Result should be a DataFrame"
+
+    assert (
+        "Residue Pair" in result.columns
+    ), "DataFrame should contain 'Residue Pair' column"
+    assert (
+        "MI Difference" in result.columns
+    ), "DataFrame should contain 'MI Difference' column"
+
+    assert not result.empty, "DataFrame should not be empty"
+
+    expected_shape = (
+        len(df_all_residues.columns) * (len(df_all_residues.columns) - 1),
+        2,
+    )
+    assert (
+        result.shape == expected_shape
+    ), f"Expected shape {expected_shape}, but got {result.shape}"
+
+    assert (
+        result["MI Difference"] >= 0
+    ).all(), "MI Difference values should be non-negative"
+
+    # Add your expected values here
+    expected_values = {
+        ("Residue1", "Residue2"): 0.6729976399990001,
+        ("Residue1", "Residue3"): 0.6667438140756161,
+        ("Residue2", "Residue3"): 0.6667102733802279,
+    }
+    for pair, expected in expected_values.items():
+        actual = result.loc[result["Residue Pair"] == pair, "MI Difference"].values[0]
+        assert np.isclose(
+            actual, expected, atol=1e-5
+        ), f"For pair {pair}, expected {expected} but got {actual}"