fix a recursive import issue and complete the interface to

build features as a list of pairs (metadata, features) for
a list of light curve objects passed as input.

resspect/feature_extractors/bazin.py

@@ -13,8 +13,8 @@
 
 
 class BazinFeatureExtractor(LightCurve):
-    def __init__(self):
-        super().__init__()
+    def __init__(self, lc=None):
+        super().__init__(lc)
         self.features_names = ['a', 'b', 't0', 'tfall', 'trise']
 
     def evaluate(self, time: np.array) -> dict:

resspect/feature_extractors/bump.py

@@ -11,8 +11,8 @@
 
 
 class BumpFeatureExtractor(LightCurve):
-    def __init__(self):
-        super().__init__()
+    def __init__(self, lc=None):
+        super().__init__(lc)
         self.features_names = ['p1', 'p2', 'p3', 'time_shift', 'max_flux']
 
     def evaluate(self, time: np.array) -> dict:

resspect/feature_extractors/light_curve.py

@@ -141,7 +141,7 @@ def _copy_constructor(self, lc):
         self.last_mag = lc.last_mag
         self.photometry = lc.photometry
         self.redshift = lc.redshift
-        self.sample = ls.sample
+        self.sample = lc.sample
         self.sim_peakmag = lc.sim_peakmag
         self.sim_pkmjd = lc.sim_pkmjd
         self.sncode = lc.sncode
@@ -166,18 +166,6 @@ def from_file(filename: str) -> list:
                 light_curves.append(lc)
         return light_curves
 
-    @staticmethod
-    def compute_feature(feature_type: str, allLC: list) -> list:
-        feature_list = []
-        if feature_type == 'bazin':
-            extractor = BazinFeatureExtractor
-        elif feature_extractor == 'bump':
-            extractor = BumpFeatureExtractor
-        for lc in allLC:
-            ex = extractor(lc)
-            ex.fit_all()
-            feature_list.appen(ex)
-
     def _get_snpcc_photometry_raw_and_header(
             self, lc_data: np.ndarray,
             sntype_test_value: str = "-9") -> Tuple[np.ndarray, list]:

resspect/fit_lightcurves.py

@@ -43,6 +43,17 @@
 }
 
 
+
+def compute_features(feature_type: str, allLC: list) -> list:
+    feature_list = []
+    extractor = FEATURE_EXTRACTOR_MAPPING[feature_type]
+    for lc in allLC:
+        ex = extractor(lc)
+        ex.fit_all()
+        ex.metadata = [ex.id, ex.redshift, ex.sntype, ex.sncode, ex.sample]
+        feature_list.append((ex.metadata, ex.features))
+    return feature_list
+
 def _get_features_to_write(light_curve_data) -> list:
     """
     Returns features list to write