diff --git a/resspect/feature_extractors/light_curve.py b/resspect/feature_extractors/light_curve.py
index 431f9f77..d947cf05 100644
--- a/resspect/feature_extractors/light_curve.py
+++ b/resspect/feature_extractors/light_curve.py
@@ -18,6 +18,7 @@
 import logging
 from typing import Tuple
 import warnings
+import os
 
 import numpy as np
 import pandas as pd
@@ -30,7 +31,6 @@
 from resspect.lightcurves_utils import load_plasticc_photometry_df
 from resspect.lightcurves_utils import get_snpcc_sntype
 
-
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 logging.basicConfig(level=logging.INFO)
 
@@ -102,7 +102,15 @@ class LightCurve:
 
     """
 
-    def __init__(self):
+    def __init__(self, lc=None):
+        if lc is None:
+            self._non_copy_constructor()
+        else:
+            if not isinstance(lc, LightCurve):
+                raise RuntimeError("argument is not a LightCurve object: "+type(lc))
+            self._copy_constructor(lc)
+
+    def _non_copy_constructor(self):
         self.queryable = None
         self.features = []
         #self.features_names = ['p1', 'p2', 'p3', 'time_shift', 'max_flux']
@@ -121,6 +129,55 @@ def __init__(self):
         self.sncode = 0
         self.sntype = ' '
 
+    def _copy_constructor(self, lc):
+        self.queryable = lc.queryable
+        self.features = lc.features
+        self.dataset_name = lc.dataset_name
+        self.exp_time = lc.exp_time
+        self.filters = lc.filters
+        self.full_photometry = lc.full_photometry
+        self.id = lc.id
+        self.id_name = lc.id_name
+        self.last_mag = lc.last_mag
+        self.photometry = lc.photometry
+        self.redshift = lc.redshift
+        self.sample = ls.sample
+        self.sim_peakmag = lc.sim_peakmag
+        self.sim_pkmjd = lc.sim_pkmjd
+        self.sncode = lc.sncode
+        self.sntype = lc.sntype
+    
+    @staticmethod
+    def from_file(filename: str) -> list:
+        light_curves = []
+        with open(filename, 'r') as f:
+            for ff in f.readlines():
+                survey, path = ff.split()
+                if not os.path.exists(path):
+                    raise FileNotFoundError('File Not found: '+path)
+                survey = survey.strip().upper()
+                lc = LightCurve()
+                if survey == "SNPCC":
+                    lc.load_snpcc_lc(path)
+                elif survey == "PLASTICC":
+                    lc.load_plasticc_lc(path)
+                else:
+                    raise NameError("survey argument not recognized: "+survey)
+                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]: