add pybaselines in dependencies, pump to v26.36.1

Author: h0anle

pyproject.toml

@@ -40,6 +40,7 @@ dependencies = [
     "openpyxl >=3.1.5",
     "pandas",
     "pyarrow >= 15.0.0",
+    "pybaselines >= 1.2.1",
     "matplotlib >=3.6.2, <3.10.9",
     "pyside6",
     "scipy",

requirements.txt

@@ -2,6 +2,7 @@ matplotlib>=3.6.2,<3.10.9
 openpyxl>=3.1.2
 pandas>=3.0.1
 pyarrow>=15.0.0
+pybaselines>=1.2.1
 pyqt_toast_notification==1.3.3
 PySide6==6.10.2
 PySide6_Addons==6.10.2

spectroview/__init__.py

@@ -6,7 +6,7 @@
 # The app uses PySide6 throughout, but superqt (used for QLabeledDoubleRangeSlider)
 os.environ.setdefault("QT_API", "pyside6")
 
-VERSION = "26.35.4"
+VERSION = "26.36.1"
 
 
 TEXT_EXPIRE = (

spectroview/fit_engine/baseline.py

@@ -1,5 +1,22 @@
 import numpy as np
 
+
+class BaselineEvaluationError(RuntimeError):
+    """Raised when an automatic baseline method cannot be evaluated."""
+
+
+def _evaluation_error(mode: str, exc: Exception) -> BaselineEvaluationError:
+    if isinstance(exc, ImportError):
+        detail = (
+            "the required 'pybaselines' package is not installed correctly"
+        )
+    else:
+        detail = str(exc) or exc.__class__.__name__
+    return BaselineEvaluationError(
+        f"Automatic baseline method '{mode}' failed: {detail}."
+    )
+
+
 _INTERNAL_METHODS = {
     None: {'label': 'None', 'use_points': False}, 
     'Linear': {'label': 'Linear Interpolation', 'use_points': True, 'sigma_kwarg': 'sigma', 'category': 'Manual'}, 
@@ -90,17 +107,17 @@ def eval_baseline(x: np.ndarray, y: np.ndarray, config: dict) -> np.ndarray:
             lam = 10 ** config.get("coef", 5.0)
             b, _ = baseline_fitter.arpls(y, lam=lam)
             return b
-        except Exception:
-            return np.zeros_like(x)
+        except Exception as exc:
+            raise _evaluation_error(mode, exc) from exc
     elif mode == 'sonneveld_vesser':
         try:
             from pybaselines.classification import Classification
             baseline_fitter = Classification(x_data=x)
             niter = config.get("coef", 100)
             b, _ = baseline_fitter.dietrich(y, num_iter=int(niter)) # Just an approximation for Sonneveld-Vesser
             return b
-        except Exception:
-            return np.zeros_like(x)
+        except Exception as exc:
+            raise _evaluation_error(mode, exc) from exc
     else:
         try:
             from pybaselines import Baseline
@@ -122,9 +139,9 @@ def eval_baseline(x: np.ndarray, y: np.ndarray, config: dict) -> np.ndarray:
                 b, _ = func(y, **kwargs)
                 return b
             else:
-                return np.zeros_like(x)
-        except Exception:
-            return np.zeros_like(x)
+                raise ValueError(f"unsupported automatic baseline method '{mode}'")
+        except Exception as exc:
+            raise _evaluation_error(mode, exc) from exc
 
 def eval_baseline_batch(x: np.ndarray, Y: np.ndarray, config: dict) -> np.ndarray:
     """Evaluate baseline for a batch of spectra (N, M)."""

tests/unit/fit_engine/test_baseline.py

@@ -1,9 +1,13 @@
 """Unit tests for fit_engine/baseline.py - single and batched baseline evaluation."""
+import builtins
+from pathlib import Path
+
 import numpy as np
 import pytest
 
 from spectroview.fit_engine.baseline import (
-    eval_baseline, eval_baseline_batch, get_baseline_method_meta,
+    BaselineEvaluationError, eval_baseline, eval_baseline_batch,
+    get_baseline_method_meta,
 )
 
 
@@ -86,6 +90,46 @@ def test_order_capped_by_number_of_points(self, x, linear_y):
         np.testing.assert_allclose(baseline, linear_y, atol=1e-6)
 
 
+class TestEvalBaselineAutomatic:
+    @pytest.mark.parametrize("mode", ["airpls", "arpls", "asls", "modpoly"])
+    def test_supported_method_returns_a_real_baseline(self, x, mode):
+        background = 8.0 + 0.02 * x + 0.0005 * (x - 50.0) ** 2
+        peak = 30.0 * np.exp(-0.5 * ((x - 55.0) / 4.0) ** 2)
+        y = background + peak
+        config = {"mode": mode, "coef": 5.0, "order_max": 2}
+
+        baseline = eval_baseline(x, y, config)
+
+        assert baseline.shape == y.shape
+        assert np.all(np.isfinite(baseline))
+        assert np.any(baseline != 0)
+
+    def test_missing_pybaselines_is_not_silently_treated_as_zero(
+            self, x, linear_y, monkeypatch):
+        real_import = builtins.__import__
+
+        def reject_pybaselines(name, *args, **kwargs):
+            if name == "pybaselines" or name.startswith("pybaselines."):
+                raise ModuleNotFoundError("No module named 'pybaselines'")
+            return real_import(name, *args, **kwargs)
+
+        monkeypatch.setattr(builtins, "__import__", reject_pybaselines)
+
+        with pytest.raises(BaselineEvaluationError, match="pybaselines"):
+            eval_baseline(x, linear_y, {"mode": "airpls", "coef": 5.0})
+
+    def test_pybaselines_is_declared_as_a_direct_dependency(self):
+        project_root = Path(__file__).parents[3]
+        pyproject = (project_root / "pyproject.toml").read_text(encoding="utf-8")
+        requirements = (project_root / "requirements.txt").read_text(encoding="utf-8")
+
+        assert '"pybaselines ' in pyproject
+        assert any(
+            line.lower().startswith("pybaselines")
+            for line in requirements.splitlines()
+        )
+
+
 class TestEvalBaselineBatchMatchesPerSpectrumLoop:
     """eval_baseline_batch has a fully-vectorized fast path for Linear and
     Polynomial; it must match calling eval_baseline() row-by-row."""