Fix issue with Rainbow scaler for list-inputs

CHANGELOG.md

@@ -26,7 +26,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
---
+- Rainbow multi-band scaler didn't work with list inputs https://github.com/light-curve/light-curve-python/issues/492 https://github.com/light-curve/light-curve-python/pull/493
 
 ### Security
 

light-curve/light_curve/light_curve_py/features/rainbow/_scaler.py

@@ -23,6 +23,11 @@ class Scaler:
     Either a single value or an array of the same shape as the input array
     """
 
+    def __eq__(self, other):
+        if not isinstance(other, Scaler):
+            return False
+        return np.array_equal(self.shift, other.shift) and np.array_equal(self.scale, other.scale)
+
     @classmethod
     def from_time(cls, t) -> "Scaler":
         """Create a Scaler from a time array
@@ -55,13 +60,21 @@ class MultiBandScaler(Scaler):
     per_band_shift: Dict[str, float]
     """Shift to apply to each band"""
 
+    def __eq__(self, other):
+        if not isinstance(other, MultiBandScaler):
+            return False
+        return super().__eq__(other) and self.per_band_shift == other.per_band_shift
+
     @classmethod
     def from_flux(cls, flux, band, *, with_baseline: bool) -> "MultiBandScaler":
         """Create a Scaler from a flux array.
 
         It uses standard deviation for the scale. For the shift, it is either
         zero (`with_baseline=False`) or the mean of each band otherwise.
         """
+        flux = np.asarray(flux)
+        band = np.asarray(band)
+
         uniq_bands = np.unique(band)
         per_band_shift = dict.fromkeys(uniq_bands, 0.0)
         shift_array = np.zeros(len(flux))

light-curve/tests/light_curve_py/features/test_rainbow.py

@@ -1,6 +1,7 @@
 import numpy as np
 
 from light_curve.light_curve_py import RainbowFit
+from light_curve.light_curve_py.features.rainbow._scaler import MultiBandScaler
 
 
 def test_noisy_with_baseline():
@@ -113,3 +114,14 @@ def test_noisy_all_functions_combination():
             # plt.show()
 
             np.testing.assert_allclose(actual[:-1], expected[:-1], rtol=0.1)
+
+
+def test_scaler_from_flux_list_input():
+    "https://github.com/light-curve/light-curve-python/issues/492"
+    # Was failing
+    scaler1 = MultiBandScaler.from_flux(
+        flux=[1.0, 2.0, 3.0, 4.0], band=np.array(["g", "r", "g", "r"]), with_baseline=True
+    )
+    # Was not failing, but was wrong
+    scaler2 = MultiBandScaler.from_flux(flux=[1.0, 2.0, 3.0, 4.0], band=["g", "r", "g", "r"], with_baseline=True)
+    assert scaler1 == scaler2