WIP red dwarf flares fitting

Author: anlava

light-curve/light_curve/light_curve_py/__init__.py

@@ -23,6 +23,7 @@
 from .features.otsusplit import *
 from .features.pdiffmperc import *
 from .features.percampl import *
+from .features.red_dwarf_fit import *
 from .features.redchi2 import *
 from .features.skew import *
 from .features.stdev import *

light-curve/light_curve/light_curve_py/features/red_dwarf_fit.py

@@ -0,0 +1,89 @@
+import numpy as np
+from scipy.special import erfc
+from scipy.stats import chisquare, sigmaclip
+
+from ._base import BaseFeature
+
+
+class RedDwarfFit(BaseFeature):
+    """Red dwarf flares fitting function.
+
+    - Depends on:  **time**, **magnitude**
+    - Minimum number of observations: **2**
+    - Number of features: **4**
+
+    Note, that the function is developed to be used with fluxes, not magnitudes.
+
+    Guadalupe Tovar Mendoza et al. 2022 [DOI:10.3847/1538-3881/ac6fe6](https://doi.org/10.3847/1538-3881/ac6fe6)
+    """
+
+    def _eval(self, t, m, sigma=None):
+        amplitude, fwhm, tpeak = RedDwarfFit._flare_params(t, m)
+        model = self.fit(t, amplitude, fwhm, tpeak)
+        chi2 = np.sum((m - model) ** 2) / (len(m) - 1)
+
+        return np.array([amplitude, fwhm, tpeak, chi2])
+
+    @staticmethod
+    def _flare_params(t, m):
+        clipped = sigmaclip(m, low=3.0, high=3.0)[0]
+        background = np.mean(clipped)
+        clean_flux = m - background
+        peak = np.argmax(clean_flux)
+        amplitude = clean_flux[peak]
+        tpeak = t[peak]
+
+        left_idx = np.where(clean_flux[:peak] < 0.5 * clean_flux[peak])[0][-1]
+        left_t = t[left_idx] + (t[left_idx + 1] - t[left_idx]) * (0.5 * clean_flux[peak] - clean_flux[left_idx]) / (
+            clean_flux[left_idx + 1] - clean_flux[left_idx]
+        )
+
+        right_idx = (np.where(clean_flux[peak:] < 0.5 * clean_flux[peak]) + peak)[0][0]
+        right_t = t[right_idx] + (t[right_idx + 1] - t[right_idx]) * (
+            0.5 * clean_flux[peak] - clean_flux[right_idx]
+        ) / (clean_flux[right_idx + 1] - clean_flux[right_idx])
+
+        fwhm = right_t - left_t
+
+        return amplitude, fwhm, tpeak
+
+    @staticmethod
+    def fit(t, amplitude, fwhm, tpeak):
+        A, B, C, D1, D2, f1 = [
+            0.9687734504375167,
+            -0.251299705922117,
+            0.22675974948468916,
+            0.15551880775110513,
+            1.2150539528490194,
+            0.12695865022878844,
+        ]
+
+        t_new = (t - tpeak) / fwhm
+        f2 = 1 - f1
+
+        eq = (
+            (1 / 2)
+            * np.sqrt(np.pi)
+            * A
+            * C
+            * f1
+            * np.exp(-D1 * t_new + ((B / C) + (D1 * C / 2)) ** 2)
+            * erfc(((B - t_new) / C) + (C * D1 / 2))
+        ) + (
+            (1 / 2)
+            * np.sqrt(np.pi)
+            * A
+            * C
+            * f2
+            * np.exp(-D2 * t_new + ((B / C) + (D2 * C / 2)) ** 2)
+            * erfc(((B - t_new) / C) + (C * D2 / 2))
+        )
+
+        return eq * amplitude
+
+    @property
+    def size(self):
+        return 4
+
+
+__all__ = ("RedDwarfFit",)