- Updated all instances of TSDataSet to TSDataGroup in class definitions, methods, and documentation.

- Modified related imports and property references to align with the new naming.
- Updated CHANGES.md and Sphinx documentation to reflect the renaming.
- Refactored functionality dependent on the `TSDataSet` class without altering behavior.

Author: hpparvi

CHANGELOG.md

@@ -22,6 +22,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
+- Renamed `TSDataSet` to `TSDataGroup` 
+
 ### Removed
 
 ## [0.18.0] - 2025-02-20
@@ -53,7 +55,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### 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.
+- **Method Renaming:** Renamed `calculate_ootmask` in the `TSDataGroup` 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.

doc/source/api/exoiris.rst

@@ -11,7 +11,7 @@ Constructor
 -----------
 
 The `ExoIris` class is initialized by providing a name for the analysis, selecting a limb darkening model, and passing the
-spectroscopic light curves as either a `~exoiris.tsdata.TSData` or `~exoiris.tsdata.TSDataSet` object.
+spectroscopic light curves as either a `~exoiris.tsdata.TSData` or `~exoiris.tsdata.TSDataGroup` object.
 Additional parameters can also be supplied to the initializer to refine the analysis, although these can be set
 later if preferred.
 

doc/source/api/tsdata.rst

@@ -4,9 +4,10 @@ Data handling
 =============
 .. currentmodule:: exoiris.tsdata
 
-The spectroscopic light curves are given to ExoIris as a `TSData` (Transmission Spectroscopy Data) or `TSDataSet`
-object. `TSData` is a utility class that provides methods for data cleanup, binning, and cropping, while `TSDataSet`
-is a container class that can contain many `TSData` objects.
+The spectroscopic light curves are given to ExoIris as a `TSData` (Transmission Spectroscopy Data) or `TSDataGroup`
+object. `TSData` is a utility class that provides methods for data cleanup, binning, and cropping, and uncertainty
+estimation, while `TSDataGroup` is a container class that can hold multiple `TSData` objects and provides aggregate
+properties and methods.
 
 Main classes
 ------------
@@ -15,7 +16,7 @@ Main classes
     :toctree: api/
 
     TSData
-    TSDataSet
+    TSDataGroup
 
 Data wrangling
 --------------
@@ -32,13 +33,22 @@ Data wrangling
     TSData.normalize_to_poly
     TSData.partition_time
 
+Uncertainty estimation
+----------------------
+
+.. autosummary::
+    :toctree: api/
+
+    TSData.estimate_average_uncertainties
+
 Masking
 -------
 
 .. autosummary::
     :toctree: api/
 
     TSData.mask_transit
+    TSDataGroup.mask_transit
 
 I/O
 ---
@@ -48,8 +58,8 @@ I/O
 
     TSData.export_fits
     TSData.import_fits
-    TSDataSet.export_fits
-    TSDataSet.import_fits
+    TSDataGroup.export_fits
+    TSDataGroup.import_fits
 
 
 Plotting
@@ -60,4 +70,4 @@ Plotting
 
     TSData.plot
     TSData.plot_white
-    TSDataSet.plot
+    TSDataGroup.plot

exoiris/__init__.py

@@ -15,7 +15,7 @@
 #  along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 from .exoiris import ExoIris, load_model # noqa
-from .tsdata import TSData, TSDataSet # noqa
+from .tsdata import TSData, TSDataGroup # noqa
 from .binning import Binning # noqa
 from .ldtkld import LDTkLD # noqa
 from .tslpf import clean_knots # noqa

exoiris/exoiris.py

@@ -41,7 +41,7 @@
 from uncertainties import UFloat
 
 from .ldtkld import LDTkLD
-from .tsdata import TSData, TSDataSet
+from .tsdata import TSData, TSDataGroup
 from .tslpf import TSLPF
 from .wlpf import WhiteLPF
 
@@ -70,7 +70,7 @@ def load_model(fname: Path | str, name: str | None = None):
         If the file format is invalid or does not match the expected format.
     """
     with pf.open(fname) as hdul:
-        data = TSDataSet.import_fits(hdul)
+        data = TSDataGroup.import_fits(hdul)
 
         if hdul[0].header['LDMODEL'] == 'ldtk':
             filters, teff, logg, metal, dataset = pickle.loads(codecs.decode(json.loads(hdul[0].header['LDTKLD']).encode(), "base64"))
@@ -117,7 +117,7 @@ class ExoIris:
     """The core ExoIris class providing tools for exoplanet transit spectroscopy.
     """
 
-    def __init__(self, name: str, ldmodel, data: TSDataSet | TSData, nk: int = 50, nldc: int = 10, nthreads: int = 1,
+    def __init__(self, name: str, ldmodel, data: TSDataGroup | TSData, nk: int = 50, nldc: int = 10, nthreads: int = 1,
                  tmpars: dict | None = None, noise_model: Literal["white", "fixed_gp", "free_gp"] = 'white',
                  interpolation: Literal['bspline', 'pchip', 'makima'] = 'bspline'):
         """
@@ -140,7 +140,7 @@ def __init__(self, name: str, ldmodel, data: TSDataSet | TSData, nk: int = 50, n
         noise_model
             The noise model to use. Should be either "white" for white noise or "fixed_gp" for Gaussian Process.
         """
-        data = TSDataSet([data]) if isinstance(data, TSData) else data
+        data = TSDataGroup([data]) if isinstance(data, TSData) else data
 
         for d in data:
             if any(~isfinite(d.fluxes[d.mask])):
@@ -161,7 +161,7 @@ def __init__(self, name: str, ldmodel, data: TSDataSet | TSData, nk: int = 50, n
         if not ((egs.min() == 0) and (egs.max() + 1 == unique(egs).size)):
             raise ValueError("The epoch groups must start from 0 and be consecutive.")
 
-        self._tsa: TSLPF = TSLPF(name, ldmodel, data, nk=nk, nldc=nldc, nthreads=nthreads, tmpars=tmpars,
+        self._tsa: TSLPF = TSLPF(self, name, ldmodel, data, nk=nk, nldc=nldc, nthreads=nthreads, tmpars=tmpars,
                                  noise_model=noise_model, interpolation=interpolation)
         self._wa: WhiteLPF | None = None
         self.nthreads: int = nthreads
@@ -201,15 +201,15 @@ def set_noise_model(self, noise_model: Literal['white', 'fixed_gp', 'free_gp'])
         """
         self._tsa.set_noise_model(noise_model)
 
-    def set_data(self, data: TSData | TSDataSet) -> None:
+    def set_data(self, data: TSData | TSDataGroup) -> None:
         """Set the model data.
 
         Parameters
         ----------
         data
            The spectroscopic transit light curve.
         """
-        data = TSDataSet([data]) if isinstance(data, TSData) else data
+        data = TSDataGroup([data]) if isinstance(data, TSData) else data
         self._tsa.set_data(data)
 
     def set_prior(self, parameter: Literal['radius ratios', 'baselines', 'wn multipliers'] | str,
@@ -334,7 +334,7 @@ def name(self, name: str):
         self._tsa.name = name
 
     @property
-    def data(self) -> TSDataSet:
+    def data(self) -> TSDataGroup:
         """Analysis data set."""
         return self._tsa.data
 
@@ -516,7 +516,7 @@ def plot_setup(self, figsize: tuple[float, float] | None = None,
         if xticks is not None:
             ax.set_xticks(xticks, labels=xticks)
 
-        setp(ax, yticks=[], xlim=(self.data.wlmin-side_margin, self.data.wlmax+side_margin), xlabel='Wavelength [$\mu$m]')
+        setp(ax, yticks=[], xlim=(self.data.wlmin-side_margin, self.data.wlmax+side_margin), xlabel=r'Wavelength [$\mu$m]')
         ax.set_yticks(concatenate([arange(ndata), arange(ndata+1, ndata+4, 2)])*yshift+0.5*mh, labels=[n.replace("_", " ") for n in self.data.names] + ["Limb darkening knots", "Radius ratio knots"])
         return fig
 

exoiris/ldtkld.py

@@ -23,18 +23,18 @@
 from pytransit import LDTkLD as PTLDTkLD
 from ldtk import BoxcarFilter
 
-from .tsdata import TSData, TSDataSet
+from .tsdata import TSData, TSDataGroup
 
 
 class LDTkLD(PTLDTkLD):
-    def __init__(self, data: TSDataSet | TSData,
+    def __init__(self, data: TSDataGroup | TSData,
                  teff: tuple[float, float],
                  logg: tuple[float, float],
                  metal: tuple[float, float],
                  cache: str | Path | None = None,
                  dataset: str = 'vis-lowres') -> None:
 
-        data = TSDataSet([data]) if isinstance(data, TSData) else data
+        data = TSDataGroup([data]) if isinstance(data, TSData) else data
         wl_edges = concatenate([array([d._wl_l_edges, d._wl_r_edges]) for d in data], axis=1).T
         filters = [BoxcarFilter(f"{0.5*(wla+wlb):08.5f}", wla*1e3, wlb*1e3) for wla, wlb in wl_edges]
         super().__init__(filters, teff, logg, metal, cache, dataset)