zairving · zairving · Sep 30, 2025 · Sep 28, 2025 · Sep 28, 2025 · Sep 28, 2025
diff --git a/docs/tutorials/applying_corrections.ipynb b/docs/tutorials/applying_corrections.ipynb
diff --git a/docs/tutorials/backgrounds.ipynb b/docs/tutorials/backgrounds.ipynb
diff --git a/docs/tutorials/finders.ipynb b/docs/tutorials/finders.ipynb
diff --git a/docs/tutorials/local_backgrounds.ipynb b/docs/tutorials/local_backgrounds.ipynb
diff --git a/docs/tutorials/reduction.ipynb b/docs/tutorials/reduction.ipynb
diff --git a/docs/tutorials/timing_methods.ipynb b/docs/tutorials/timing_methods.ipynb
diff --git a/opticam_new/align.py b/opticam_new/align.py
@@ -169,7 +169,7 @@ def align_batch(
             output_shape=reference_image_shape,
             order=3,
             mode='constant',
-            cval=float(np.nanmedian(data)),
+            cval=0.,
             clip=True,
             preserve_range=True,
             )

diff --git a/opticam_new/analysis/analyzer.py b/opticam_new/analysis/analyzer.py
@@ -486,6 +486,11 @@ def compute_power_spectra(
 
             lc = self._convert_lc_time_to_seconds(self.light_curves[fltr])
 
+            dt = np.diff(lc.time)
+            if not np.allclose(dt, dt[0]):
+                print(f'[OPTICAM] Unable to compute periodogram for {fltr} light curve due to gaps. Consider using either the compute_lomb_scargle_periodograms() or compute_averaged_power_spectra() methods instead.')
+                continue
+
             ps = Powerspectrum.from_lightcurve(
                 lc,
                 norm=norm,
@@ -494,7 +499,7 @@ def compute_power_spectra(
 
             results[fltr] = ps
 
-        if self.show_plots:
+        if self.show_plots and len(results) > 0:
             fig = _plot(results, scale)
             fig.savefig(f'{self.out_directory}/plots/{self.prefix}_{self.phot_label}_periodograms.png')
             plt.show()
@@ -645,22 +650,18 @@ def compute_averaged_crossspectra(
             of filter names and the values are the cross-spectra.
         """
 
-        segment_size = segment_size.to(u.day).value  # convert from given units to days
+        segment_size = segment_size.to_value(u.s)  # convert from given units to seconds
 
         results = {}
 
         for fltr1, lc1 in self.light_curves.items():
             for fltr2, lc2 in self.light_curves.items():
-                if fltr1 == fltr2:
+                if fltr1 == fltr2 or (fltr1, fltr2) in results or (fltr2, fltr1) in results:
                     continue
 
-                # convert light curves from days to seconds
-                lc1 = self._convert_lc_time_to_seconds(lc1)
-                lc2 = self._convert_lc_time_to_seconds(lc2)
-
                 cs = AveragedCrossspectrum.from_lightcurve(
-                    lc1,
-                    lc2,
+                    self._convert_lc_time_to_seconds(lc1),
+                    self._convert_lc_time_to_seconds(lc2),
                     segment_size=segment_size,
                     norm=norm,
                     silent=True,
@@ -852,10 +853,15 @@ def _plot(
             x_label = 'Time lag [s]'
             y_label = 'Correlation'
 
+        if key in colors:
+            color = colors[key]
+        else:
+            color = 'k'
+
         axes[i].step(
             x,
             y,
-            color='k',
+            color=color,
             lw=1,
             where='mid',
         )

diff --git a/opticam_new/catalog.py b/opticam_new/catalog.py
@@ -49,7 +49,7 @@ def __init__(
         finder: None | Callable = None,
         threshold: float = 5,
         aperture_selector: Callable = np.median,
-        remove_cosmic_rays: bool = True,
+        remove_cosmic_rays: bool = False,
         number_of_processors: int = cpu_count() // 2,
         show_plots: bool = True,
         verbose: bool = True
@@ -95,9 +95,9 @@ def __init__(
             photometry. If a callable is provided, it should take a list of source sizes (`List[float]`) as input and
             return a single value.
         remove_cosmic_rays: bool, optional
-            Whether to remove cosmic rays from images, by default True. Cosmic rays are removed using the LACosmic
+            Whether to remove cosmic rays from images, by default False. Cosmic rays are removed using the LACosmic
             algorithm as implemented in `astroscrappy`. Note: this can be computationally expensive, particularly for
-            large images (i.e., low binning factors).
+            large images.
         number_of_processors: int, optional
             The number of processors to use for parallel processing, by default half the number of available processors.
         show_plots: bool, optional
@@ -398,7 +398,6 @@ def create_catalogs(
                 reference_coords = self.get_source_coords_from_image(
                     reference_image,
                     background=self.background,
-                    away_from_edge=True,
                     n_sources=n_alignment_sources,
                     )
             except Exception as e:

diff --git a/opticam_new/finders.py b/opticam_new/finders.py
@@ -51,7 +51,6 @@ def get_source_coords_from_image(
     finder: DefaultFinder,
     threshold: float | int,
     bkg: Background2D | None = None,
-    away_from_edge: bool | None = False,
     n_sources: int | None = None,
     background: BaseBackground | None = None,
     ) -> NDArray:
@@ -64,8 +63,6 @@ def get_source_coords_from_image(
         The **non-background-subtracted** image from which to extract source coordinates.
     bkg : Background2D, optional
         The background of the image, by default None. If None, the background is estimated from the image.
-    away_from_edge : bool, optional
-        Whether to exclude sources near the edge of the image, by default False.
     n_sources : int, optional
         The number of source coordinates to return, by default `None` (all sources will be returned).
 
@@ -88,12 +85,6 @@ def get_source_coords_from_image(
 
     coords = np.array([tbl["xcentroid"], tbl["ycentroid"]]).T
 
-    if away_from_edge:
-        edge = background.box_size
-        for coord in coords:
-            if coord[0] < edge or coord[0] > image.shape[1] - edge or coord[1] < edge or coord[1] > image.shape[0] - edge:
-                coords = np.delete(coords, np.where(np.all(coords == coord, axis=1)), axis=0)
-
     if n_sources is not None:
         coords = coords[:n_sources]
 

diff --git a/opticam_new/utils/generate.py b/opticam_new/utils/generate.py
@@ -452,7 +452,7 @@ def generate_gappy_observations(
 
         # randomly skip some images to create gaps
         if rng.random() < gap_probability:
-            # if an image is skipped, increase the probability of skipping the next one to creaate larger gaps
+            # if an image is skipped, increase the probability of skipping the next one to create larger gaps
             gap_probability = .95
             continue
         else:

diff --git a/pyproject.toml b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "opticam_new"
-version = "0.2.1"
+version = "0.2.2"
 description = "A Python package for reducing OPTICAM data."
 readme = "README.md"
 authors = [