Backmerge

pyproject.toml

@@ -34,6 +34,7 @@ dependencies = [
     "refnx",
     "refl1d>=1.0.0rc0",
     "orsopy",
+    "svglib<1.6 ; platform_system=='Linux'",
     "xhtml2pdf",
     "bumps",
 ]

src/easyreflectometry/__version__.py

@@ -1 +1 @@
-__version__ = '1.3.3'
+__version__ = '1.4.1'

src/easyreflectometry/fitting.py

@@ -1,5 +1,7 @@
 __author__ = 'github.com/arm61'
 
+import warnings
+
 import numpy as np
 import scipp as sc
 from easyscience.fitting import AvailableMinimizers
@@ -36,11 +38,42 @@ def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
 
         :param data: DataGroup to be fitted to and populated
         :param method: Optimisation method
+
+        :note: Points with zero variance in the data will be automatically masked
+               out during fitting. A warning will be issued if any such points
+               are found, indicating the number of points masked per reflectivity.
         """
         refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
-        x = [data['coords'][f'Qz_{i}'].values for i in refl_nums]
-        y = [data['data'][f'R_{i}'].values for i in refl_nums]
-        dy = [1 / np.sqrt(data['data'][f'R_{i}'].variances) for i in refl_nums]
+        x = []
+        y = []
+        dy = []
+
+        # Process each reflectivity dataset
+        for i in refl_nums:
+            x_vals = data['coords'][f'Qz_{i}'].values
+            y_vals = data['data'][f'R_{i}'].values
+            variances = data['data'][f'R_{i}'].variances
+
+            # Find points with non-zero variance
+            zero_variance_mask = (variances == 0.0)
+            num_zero_variance = np.sum(zero_variance_mask)
+
+            if num_zero_variance > 0:
+                warnings.warn(
+                    f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
+                    UserWarning
+                )
+
+            # Keep only points with non-zero variances
+            valid_mask = ~zero_variance_mask
+            x_vals_masked = x_vals[valid_mask]
+            y_vals_masked = y_vals[valid_mask]
+            variances_masked = variances[valid_mask]
+
+            x.append(x_vals_masked)
+            y.append(y_vals_masked)
+            dy.append(1 / np.sqrt(variances_masked))
+
         result = self.easy_science_multi_fitter.fit(x, y, weights=dy)
         new_data = data.copy()
         for i, _ in enumerate(result):

tests/_static/ref_zero_var.txt

@@ -0,0 +1,193 @@
+# q(A-1), ref, err
+7.6135e-03, 9.7447e-01, 2.5462e-02
+7.7658e-03, 1.0139e+00, 2.5015e-02
+7.9211e-03, 1.0429e+00, 2.4555e-02
+8.0796e-03, 9.7839e-01, 2.2475e-02
+8.2411e-03, 9.7563e-01, 2.1450e-02
+8.4060e-03, 9.8295e-01, 2.0815e-02
+8.5741e-03, 1.0347e+00, 2.0846e-02
+8.7456e-03, 9.9588e-01, 1.9555e-02
+8.9205e-03, 9.8956e-01, 1.8711e-02
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