- Replaced percentile with nanpercentile to handle NaN values in residuals calculation.

- Added a `CHANGELOG.md` to document changes, adhering to semantic versioning.

Author: hpparvi

CHANGELOG.md

@@ -0,0 +1,39 @@
+# Changelog
+
+All notable changes to ExoIris will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+
+### Added
+
+### Changed
+
+### Removed
+
+## [0.16.0] - 2025-02-17
+
+### Added
+- **New Data Mask:** Introduced a general `mask` attribute in the `TSData` class to automatically flag valid data points (based on finite fluxes and errors).
+- **Uncertainty Estimation:** Added the `estimate_average_uncertainties` method in `TSData` to compute per-wavelength uncertainties using first differences.
+- **White Light Curve Processing:** Updated the `WhiteLPF` class to use `nanmean` and `isfinite` checks when computing the white light curve, ensuring only valid flux values are averaged.
+
+### Changed
+- **Transit Mask Renaming:** Renamed the old `ootmask` attribute to `transit_mask` throughout the codebase for clarity. This change affects plotting, normalization, binning, and file I/O.
+- **Method Renaming:** Renamed `calculate_ootmask` in the `TSDataSet` class to `mask_transit` to reflect the updated naming convention.
+- **Normalization Enhancements:** Updated normalization methods (`normalize_to_poly` and `normalize_to_median`) to utilize the new `transit_mask` and `mask` attributes, improving the reliability of baseline fits.
+- **Cropping Flexibility:** Added an `inplace` parameter to both `crop_wavelength` and `crop_time` methods, allowing users to choose between modifying the existing data or returning a new cropped instance.
+- **Likelihood Function Update:** Modified the `lnlike_normal` function in `TSLPF` to accept an additional `mask` parameter and process only valid data points during likelihood calculations.
+- **Outlier Handling:** Refined the `remove_outliers` method to flag outliers by setting affected fluxes and errors to NaN, rather than replacing them with median-filtered values.
+
+### Removed
+- **Removed Deprecated Method: `calculate_ootmask`**  
+  This method (deprecated since v0.9) has been removed in favor of the new transit masking functionality.
+- **Removed Deprecated Method: `normalize_baseline`**  
+  The `normalize_baseline` method (deprecated since v0.9) has been removed; users should now use `normalize_to_poly`.
+- **Removed Deprecated Method: `normalize_median`**  
+  The deprecated `normalize_median` method has been removed. Its functionality is now available via `normalize_to_median`.
+- **Removed Deprecated Method: `split_time`**  
+  The `split_time` method, deprecated since v0.9, has been removed. Use `partition_time` instead.

exoiris/exoiris.py

@@ -33,7 +33,7 @@
 from emcee import EnsembleSampler
 from matplotlib.pyplot import subplots, setp, figure, Figure, Axes
 from numpy import (where, sqrt, clip, percentile, median, squeeze, floor, ndarray,
-                   array, inf, newaxis, arange, tile, sort, argsort, concatenate, full, nan, r_)
+                   array, inf, newaxis, arange, tile, sort, argsort, concatenate, full, nan, r_, nanpercentile)
 from numpy.random import normal, permutation
 from pytransit import UniformPrior, NormalPrior
 from pytransit.orbits import epoch
@@ -912,17 +912,12 @@ def plot_residuals(self, result: Optional[str] = None, ax: None | Axes | Sequenc
         for ids, data in enumerate(self.data):
             ax = axs[ids]
             residuals = data.fluxes - squeeze(fmodel[ids])
-            pp = percentile(residuals, [pmin, pmax])
+            pp = nanpercentile(residuals, [pmin, pmax])
             data.plot(ax=ax, data=residuals, vmin=pp[0], vmax=pp[1], cmap=cmap)
 
             tc = pv[1] + pv[2]*epoch(data.time.mean(), pv[1], pv[2])
             td = self.transit_duration
 
-            #for i in range(2):
-            #    ax.axvline(tc + (-1) ** i * 0.5 * td - self._tref, c='w', ymax=0.05, lw=5)
-            #    ax.axvline(tc + (-1) ** i * 0.5 * td - self._tref, c='w', ymin=0.95, lw=5)
-            #    ax.axvline(tc + (-1) ** i * 0.5 * td - self._tref, c='k', ymax=0.05, lw=1)
-            #    ax.axvline(tc + (-1) ** i * 0.5 * td - self._tref, c='k', ymin=0.95, lw=1)
             if not show_names:
                 ax.set_title("")