Merge pull request #167 from COINtoolbox/Malanchev

add malanchev light curve feature extractors

resspect/feature_extractors/malanchev.py

@@ -0,0 +1,174 @@
+# Copyright 2020 resspect software
+# Author: Emille E. O. Ishida
+#
+# created on 9 April 2023
+#
+# Licensed GNU General Public License v3.0;
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.gnu.org/licenses/gpl-3.0.en.html
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+## This modules uses features from https://github.com/light-curve/light-curve
+
+import numpy as np
+import light_curve as licu
+from resspect.feature_extractors.light_curve import LightCurve
+
+__all__ = ['MalanchevFeatureExtractor']
+
+class MalanchevFeatureExtractor(LightCurve):
+    def __init__(self):
+        super().__init__()
+        self.features_names = ['anderson_darling_normal',
+                               'inter_percentile_range_5',
+                               'chi2',
+                               'stetson_K',
+                               'weighted_mean',
+                               'duration',
+                               'otsu_mean_diff',
+                               'otsu_std_lower',
+                               'otsu_std_upper',
+                               'otsu_lower_to_all_ratio',
+                               'linear_fit_slope',
+                               'linear_fit_slope_sigma',
+                               'linear_fit_reduced_chi2']
+
+
+    def fit(self, band: str) -> np.ndarray:
+        """
+        Extracts malanchev-light-curve features for one filter.
+
+        Parameters
+        ----------
+        band: str
+            Choice of broad band filter
+
+        Returns
+        -------
+        mlc_param: np.ndarray
+            Features from malanchev-light-curve:
+            ['anderson_darling_normal',
+                               'inter_percentile_range_5',
+                               'chi2',
+                               'stetson_K',
+                               'weighted_mean',
+                               'duration',
+                               'otsu_mean_diff',
+                               'otsu_std_lower',
+                               'otsu_std_upper',
+                               'otsu_lower_to_all_ratio',
+                               'linear_fit_slope',
+                               'linear_fit_slope_sigma',
+                               'linear_fit_reduced_chi2'].
+        """
+
+        # build filter flag
+        band_indices = self.photometry['band'] == band
+        if not sum(band_indices) > (len(self.features_names) - 1):
+            return np.array([])
+
+        extractor = licu.Extractor(licu.AndersonDarlingNormal(),
+                                   licu.InterPercentileRange(0.05),
+                                   licu.ReducedChi2(),
+                                   licu.StetsonK(),
+                                   licu.WeightedMean(),
+                                   licu.Duration(),
+                                   licu.OtsuSplit(),
+                                   licu.LinearFit())
+
+        # get info for this filter
+        time = self.photometry['mjd'].values[band_indices]
+        idx = np.argsort(time)
+        flux = self.photometry['flux'].values[band_indices]
+        flux_error = self.photometry['fluxerr'].values[band_indices]
+
+        time = time[idx].astype(float)
+        flux = flux[idx].astype(float)
+        flux_error = flux_error[idx].astype(float)
+
+        return extractor(time, flux, flux_error,
+                                  fill_value=-999,
+                                  sorted=True,
+                                  check=False)
+
+
+
+    def fit_all_points(self):
+        """
+        Extracts Malanchev's light_curve features for all data points in all filters together.
+        
+        Populates self.photometry with features:
+            AndersonDarlingNormal, InterPercentileRange(0.05),
+            ReducedChi2, StetsonK, WeightedMean, Duration, OtsuSplit,
+            LinearFit.
+
+        Returns
+        -------
+        mlc_param: np.ndarray
+            Features from malanchev-light-curve:
+            ['anderson_darling_normal',
+                               'inter_percentile_range_5',
+                               'chi2',
+                               'stetson_K',
+                               'weighted_mean',
+                               'duration',
+                               'otsu_mean_diff',
+                               'otsu_std_lower',
+                               'otsu_std_upper',
+                               'otsu_lower_to_all_ratio',
+                               'linear_fit_slope',
+                               'linear_fit_slope_sigma',
+                               'linear_fit_reduced_chi2'].
+        """
+
+        extractor = licu.Extractor(licu.AndersonDarlingNormal(),
+                                   licu.InterPercentileRange(0.05), 
+                                   licu.ReducedChi2(),
+                                   licu.StetsonK(), 
+                                   licu.WeightedMean(), 
+                                   licu.Duration(), 
+                                   licu.OtsuSplit(), 
+                                   licu.LinearFit())
+
+        # get info for this filter
+        time = self.photometry['mjd'].values
+        idx = np.argsort(time)
+
+        time = time[idx].astype(float)
+        flux = self.photometry['flux'].values[idx].astype(float)
+        flux_error = self.photometry['fluxerr'].values[idx].astype(float)
+
+
+        return extractor(time, flux, flux_error,
+                             fill_value = -999, 
+                             sorted = True, 
+                             check = False)
+
+    def fit_all(self):
+        """
+        Performs malanchev-light-curve feature extraction for all filters independently and concatenate results.
+        Populates the attributes: mlcfeatures.
+        """
+        default_mlcfeatures = ['None'] * len(self.features_names)
+
+        if self.photometry.shape[0] < 1:
+            self.features = ['None'] * len(
+                self.features_names) * len(self.filters)
+
+        elif 'None' not in self.features:
+            self.features = []
+            for each_band in self.filters:
+                best_fit = self.fit(band=each_band)
+                if (best_fit.size > 0) and (not np.isnan(np.sum(best_fit))):
+                    self.features.extend(best_fit.tolist())
+                else:
+                    self.features.extend(default_mlcfeatures)
+        else:
+            self.features.extend(default_mlcfeatures)