Issue #276

CHANGES.rst

@@ -1,3 +1,12 @@
+1.7.0 (unreleased)
+------------------
+
+New Features
+^^^^^^^^^^^^
+
+- Added a ``disp_bounds`` argument to ``tracing.FitTrace``. The argument allows for adjusting the
+  dispersion-axis window from which the trace peaks are estimated.
+
 1.6.0 (2025-06-18)
 ------------------
 

specreduce/tests/test_tracing.py

@@ -460,7 +460,7 @@ def test_mask_treatment_apply(self, peak_method, expected):
             # run FitTrace, with the testing-only flag _save_bin_peaks_testing set
             # to True to return the bin peak values before fitting the trace
             trace = FitTrace(imgg, peak_method=peak_method, _save_bin_peaks_testing=True)
-            x_bins, y_bins = trace._bin_peaks_testing
+            x_bins, y_bins = trace._bin_peaks
             np.testing.assert_allclose(y_bins, expected, atol=0.1)
 
             # check that final fit to all bins, accouting for fully-masked bins,
@@ -548,7 +548,7 @@ def test_mask_treatment_propagate(self, peak_method, expected):
                 mask_treatment="propagate",
                 _save_bin_peaks_testing=True,
             )
-            x_bins, y_bins = trace._bin_peaks_testing
+            x_bins, y_bins = trace._bin_peaks
             np.testing.assert_allclose(y_bins, expected)
 
             # check that final fit to all bins, accouting for fully-masked bins,

specreduce/tracing.py

@@ -233,17 +233,22 @@ class FitTrace(Trace, _ImageParser):
         into which to divide the image. If not set, defaults to one bin
         per dispersion (wavelength) pixel in the given image. If set,
         requires at least 4 or N bins for a degree N ``trace_model``,
-        whichever is greater. [default: None]
+        whichever is greater. [default: ``None``]
     guess : int, optional
         A guess at the trace's location in the cross-dispersion
         (spatial) direction. If set, overrides the normal max peak
         finder. Good for tracing a fainter source if multiple traces
-        are present. [default: None]
+        are present. [default: ``None``]
     window : int, optional
         Fit the trace to a region with size ``window * 2`` around the
         guess position. Useful for tracing faint sources if multiple
         traces are present, but potentially bad if the trace is
-        substantially bent or warped. [default: None]
+        substantially bent or warped. [default: ``None``]
+    disp_bounds
+        The lower and upper bounds of the pixel range along the dispersion
+        axis that is used when fitting the trace model. If ``None``,
+        defaults to the entire range of pixels along the dispersion axis.
+        [default: ``None``]
     trace_model : one of `~astropy.modeling.polynomial.Chebyshev1D`,\
             `~astropy.modeling.polynomial.Legendre1D`,\
             `~astropy.modeling.polynomial.Polynomial1D`,\
@@ -276,14 +281,15 @@ class FitTrace(Trace, _ImageParser):
     bins: int | None = None
     guess: float | None = None
     window: int | None = None
+    disp_bounds: tuple[float, float] | None = None
     trace_model: Model = field(default=models.Polynomial1D(degree=1))
     peak_method: Literal["gaussian", "centroid", "max"] = "max"
     _crossdisp_axis: int = 0
     _disp_axis: int = 1
     mask_treatment: Literal["apply", "propagate", "apply_nan_only"] = "apply"
     _valid_mask_treatment_methods = ("apply", "propagate", "apply_nan_only")
     # for testing purposes only, save bin peaks if requested
-    _save_bin_peaks_testing: bool = False
+    _save_bin_peaks_testing: bool = True
 
     def __post_init__(self):
 
@@ -322,7 +328,9 @@ def __post_init__(self):
                 "trace_model must be one of " f"{', '.join([m.name for m in valid_models])}."
             )
 
-        cols = img.shape[self._disp_axis]
+        self.dlim = self.disp_bounds if self.disp_bounds is not None else (0, img.shape[
+            self._disp_axis])
+        cols = self.dlim[1]
         model_deg = self.trace_model.degree
         if self.bins is None:
             self.bins = cols
@@ -394,7 +402,7 @@ def _fit_trace(self, img):
                 "for invalid values or use a larger window value."
             )
 
-        x_bins = np.linspace(0, img.shape[self._disp_axis], self.bins + 1, dtype=int)
+        x_bins = np.linspace(self.dlim[0], self.dlim[1], self.bins + 1, dtype=int)
         y_bins = np.tile(np.nan, self.bins)
 
         warn_bins = []
@@ -485,7 +493,7 @@ def _fit_trace(self, img):
 
         # for testing purposes only, save bin peaks if requested
         if self._save_bin_peaks_testing:
-            self._bin_peaks_testing = (x_bins, y_bins)
+            self._bin_peaks = (x_bins, y_bins)
 
         # filter non-finite bin peaks before filtering to all bin peaks
         y_finite = np.where(np.isfinite(y_bins))[0]