Refactor fit_gmm to support weighted particle resampling

src/flekspy/amrex/particle_data.py

@@ -557,6 +557,17 @@ def fit_gmm(
         if rdata.size == 0:
             raise ValueError("No particles to fit GMM.")
 
+        # Check for weights and resample if necessary
+        resample_indices = None
+        if "weight" in self.header.real_component_names:
+            weight_idx = self.header.real_component_names.index("weight")
+            weights = rdata[:, weight_idx]
+            # Normalize weights to sum to 1
+            total_weight = np.sum(weights)
+            if total_weight > 0:
+                p = weights / total_weight
+                resample_indices = np.random.choice(len(rdata), size=len(rdata), p=p)
+
         # --- 2. Apply transformation if provided ---
         component_names = self.header.real_component_names
         if transform:
@@ -565,6 +576,9 @@ def fit_gmm(
         # --- 3 & 4. Extract data columns ---
         data = self._extract_variable_columns(rdata, variables, component_names)
 
+        if resample_indices is not None:
+            data = data[resample_indices]
+
         from sklearn.mixture import GaussianMixture
 
         # --- 5. Fit GMM ---

tests/test_weighted_gmm.py

@@ -0,0 +1,87 @@
+import numpy as np
+import pytest
+from unittest.mock import MagicMock
+from flekspy.amrex.particle_data import AMReXParticle
+
+class MockAMReXParticle(AMReXParticle):
+    def __init__(self, rdata, header):
+        self._rdata = rdata
+        self.header = header
+        self.output_dir = "mock_dir" # Dummy path
+        # Mock other attributes needed by fit_gmm
+        self._idata = np.empty((0, 0)) # Prevent load trigger if checked
+
+    @property
+    def rdata(self):
+        return self._rdata
+
+    def _extract_variable_columns(self, rdata, variables, component_names=None):
+        # Determine column indices
+        if component_names is None:
+            component_names = self.header.real_component_names
+
+        indices = [component_names.index(var) for var in variables]
+        return rdata[:, indices]
+
+    def select_particles_in_region(self, x_range=None, y_range=None, z_range=None):
+        # For this test, we assume no region selection is actually performed or needed
+        # fit_gmm calls this if ranges are provided.
+        # If fit_gmm calls this, we just return all data for simplicity unless
+        # we specifically test range selection (which we aren't here).
+        return self._rdata
+
+
+@pytest.fixture
+def mock_weighted_data():
+    """
+    Creates a MockAMReXParticle with weighted data.
+    Two populations:
+    1. Center 0, Weight 1
+    2. Center 10, Weight 100
+    Equal number of particles.
+    """
+    rng = np.random.default_rng(42)
+    n_per_group = 1000
+
+    # Group 1: Center 0, weight 1
+    g1_x = rng.normal(0, 0.1, n_per_group)
+    g1_w = np.ones(n_per_group)
+
+    # Group 2: Center 10, weight 100
+    g2_x = rng.normal(10, 0.1, n_per_group)
+    g2_w = np.full(n_per_group, 100.0)
+
+    x = np.concatenate([g1_x, g2_x])
+    w = np.concatenate([g1_w, g2_w])
+
+    # Dummy y for 2D requirement of fit_gmm if needed, though we fit 1D "x" mostly
+    y = np.zeros_like(x)
+
+    # Create rdata: columns [x, y, weight]
+    rdata = np.column_stack([x, y, w])
+
+    header = MagicMock()
+    header.real_component_names = ["x", "y", "weight"]
+
+    return MockAMReXParticle(rdata, header)
+
+def test_fit_gmm_weighted(mock_weighted_data):
+    """
+    Tests that fit_gmm respects particle weights.
+    Without weighting: Mean should be ~5 (average of 0 and 10).
+    With weighting (1 vs 100): Mean should be close to 10.
+    """
+    # Fit GMM on 'x'
+    # We pass variables=['x'] to fit 1D
+    gmm = mock_weighted_data.fit_gmm(n_components=1, variables=['x'])
+
+    mean = gmm.means_[0][0]
+
+    # If weights are ignored, mean is (0 + 10) / 2 = 5
+    # If weights are respected, mean is (1*0 + 100*10) / 101 ~= 9.9
+
+    print(f"GMM Mean: {mean}")
+
+    # Assert that the mean is significantly higher than 5, indicating weights were used.
+    # We use a loose bound to account for randomness, but 5 vs 9.9 is huge.
+    assert mean > 8.0, f"Mean {mean} is too low, weights likely ignored."