Repository: igmhub/picca (language: python)

Issue:
Merge master into fix rebin



Discussion / hints:

Fix patch:
diff --git a/docs/delta_extraction/data_model/data_model-Data.png b/docs/delta_extraction/data_model/data_model-Data.png
index 8a4c8d978..3c7a86d4c 100644
Binary files a/docs/delta_extraction/data_model/data_model-Data.png and b/docs/delta_extraction/data_model/data_model-Data.png differ
diff --git a/docs/delta_extraction/data_model/data_model-ExpectedFlux.png b/docs/delta_extraction/data_model/data_model-ExpectedFlux.png
index 9cc47fb1c..c07f4e276 100644
Binary files a/docs/delta_extraction/data_model/data_model-ExpectedFlux.png and b/docs/delta_extraction/data_model/data_model-ExpectedFlux.png differ
diff --git a/docs/delta_extraction/data_model/data_model-FullDataModel.png b/docs/delta_extraction/data_model/data_model-FullDataModel.png
index ecae83abd..c13561ca6 100644
Binary files a/docs/delta_extraction/data_model/data_model-FullDataModel.png and b/docs/delta_extraction/data_model/data_model-FullDataModel.png differ
diff --git a/docs/delta_extraction/data_model/data_model-Main.png b/docs/delta_extraction/data_model/data_model-Main.png
index 2d95ad232..5f376a7e8 100644
Binary files a/docs/delta_extraction/data_model/data_model-Main.png and b/docs/delta_extraction/data_model/data_model-Main.png differ
diff --git a/docs/delta_extraction/data_model/data_model-Simplified Main.png b/docs/delta_extraction/data_model/data_model-Simplified Main.png
index 10743248b..580c56045 100644
Binary files a/docs/delta_extraction/data_model/data_model-Simplified Main.png and b/docs/delta_extraction/data_model/data_model-Simplified Main.png differ
diff --git a/docs/delta_extraction/data_model/data_model.drawio b/docs/delta_extraction/data_model/data_model.drawio
index 374e3acdd..eb93fd17d 100644
--- a/docs/delta_extraction/data_model/data_model.drawio
+++ b/docs/delta_extraction/data_model/data_model.drawio
@@ -1,1 +1,1 @@
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</diagram><diagram name="Simplified Main" id="joHP-pMOU07-pkZl2VE5">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</diagram><diagram id="je2KSbhj7LrZFA1j369E" name="ExpectedFlux">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id="iPTn10C6GmmupZz-RSug" 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</diagram></mxfile>
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</diagram><diagram name="Simplified Main" 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</diagram><diagram id="je2KSbhj7LrZFA1j369E" name="ExpectedFlux">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</diagram></mxfile>
\ No newline at end of file
diff --git a/py/picca/delta_extraction/astronomical_objects/desi_forest.py b/py/picca/delta_extraction/astronomical_objects/desi_forest.py
index 65f589234..b2841b112 100644
--- a/py/picca/delta_extraction/astronomical_objects/desi_forest.py
+++ b/py/picca/delta_extraction/astronomical_objects/desi_forest.py
@@ -19,48 +19,22 @@ class DesiForest(Forest):
 
     Class Attributes
     ----------------
-    delta_lambda: float or None (from Forest)
-    Variation of the wavelength (in Angs) between two pixels. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    delta_log_lambda: float or None (from Forest)
-    Variation of the logarithm of the wavelength (in Angs) between two pixels.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    lambda_max: float or None (from Forest)
-    Maximum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_max_rest_frame: float or None (from Forest)
-    As wavelength_max but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min: float or None (from Forest)
-    Minimum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min_rest_frame: float or None (from Forest)
-    As wavelength_min but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    log_lambda_max: float or None (from Forest)
-    Logarithm of the maximum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_max_rest_frame: float or None (from Forest)
-    As log_lambda_max but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
-
-    log_lambda_min: float or None (from Forest)
-    Logarithm of the minimum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_min_rest_frame: float or None (from Forest)
-    As log_lambda_min but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
+    blinding: str (from Forest)
+    Name of the blinding strategy used
+
+    lambda_grid: array of float or None (from Forest)
+    Common grid in lambda_ based on the specified minimum and maximum
+    wavelengths, and delta_lambda.
+
+    lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as lambda_grid but for rest-frame wavelengths
+
+    log_lambda_grid: array of float or None (from Forest)
+    Common grid in log_lambda based on the specified minimum and maximum
+    wavelengths, and delta_log_lambda.
+
+    log_lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as log_lambda_grid but for rest-frame wavelengths.
 
     mask_fields: list of str (from Forest)
     Names of the fields that are affected by masking. In general it will
diff --git a/py/picca/delta_extraction/astronomical_objects/desi_pk1d_forest.py b/py/picca/delta_extraction/astronomical_objects/desi_pk1d_forest.py
index 4870f6c48..ec7396387 100644
--- a/py/picca/delta_extraction/astronomical_objects/desi_pk1d_forest.py
+++ b/py/picca/delta_extraction/astronomical_objects/desi_pk1d_forest.py
@@ -23,48 +23,22 @@ class DesiPk1dForest(DesiForest, Pk1dForest):
 
     Class Attributes
     ----------------
-    delta_lambda: float or None (from Forest)
-    Variation of the wavelength (in Angs) between two pixels. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    delta_log_lambda: float or None (from Forest)
-    Variation of the logarithm of the wavelength (in Angs) between two pixels.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    lambda_max: float or None (from Forest)
-    Maximum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_max_rest_frame: float or None (from Forest)
-    As wavelength_max but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min: float or None (from Forest)
-    Minimum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min_rest_frame: float or None (from Forest)
-    As wavelength_min but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    log_lambda_max: float or None (from Forest)
-    Logarithm of the maximum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_max_rest_frame: float or None (from Forest)
-    As log_lambda_max but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
-
-    log_lambda_min: float or None (from Forest)
-    Logarithm of the minimum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_min_rest_frame: float or None (from Forest)
-    As log_lambda_min but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
+    blinding: str (from Forest)
+    Name of the blinding strategy used
+    
+    lambda_grid: array of float or None (from Forest)
+    Common grid in lambda_ based on the specified minimum and maximum
+    wavelengths, and delta_lambda.
+
+    lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as lambda_grid but for rest-frame wavelengths
+
+    log_lambda_grid: array of float or None (from Forest)
+    Common grid in log_lambda based on the specified minimum and maximum
+    wavelengths, and delta_log_lambda.
+
+    log_lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as log_lambda_grid but for rest-frame wavelengths.
 
     mask_fields: list of str (from Forest)
     Names of the fields that are affected by masking. In general it will
diff --git a/py/picca/delta_extraction/astronomical_objects/forest.py b/py/picca/delta_extraction/astronomical_objects/forest.py
index 706538473..1d3556e1e 100644
--- a/py/picca/delta_extraction/astronomical_objects/forest.py
+++ b/py/picca/delta_extraction/astronomical_objects/forest.py
@@ -5,6 +5,7 @@
 
 from picca.delta_extraction.astronomical_object import AstronomicalObject
 from picca.delta_extraction.errors import AstronomicalObjectError
+from picca.delta_extraction.utils import find_bins
 
 defaults = {
     "mask fields log": ["flux", "ivar", "transmission_correction", "log_lambda"],
@@ -25,54 +26,26 @@ class Forest(AstronomicalObject):
     get_data
     get_header
     rebin
+    set_class_variables
 
     Class Attributes
     ----------------
     blinding: str
     Name of the blinding strategy used
 
-    delta_lambda: float or None
-    Variation of the wavelength (in Angs) between two pixels. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
+    lambda_grid: array of float or None
+    Common grid in lambda_ based on the specified minimum and maximum
+    wavelengths, and delta_lambda.
 
-    delta_log_lambda: float or None
-    Variation of the logarithm of the wavelength (in Angs) between two pixels.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
+    lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as lambda_grid but for rest-frame wavelengths
 
-    lambda_max: float or None
-    Maximum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
+    log_lambda_grid: array of float or None
+    Common grid in log_lambda based on the specified minimum and maximum
+    wavelengths, and delta_log_lambda.
 
-    lambda_max_rest_frame: float or None
-    As wavelength_max but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min: float or None
-    Minimum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min_rest_frame: float or None
-    As wavelength_min but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    log_lambda_max: float or None
-    Logarithm of the maximum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_max_rest_frame: float or None
-    As log_lambda_max but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
-
-    log_lambda_min: float or None
-    Logarithm of the minimum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_min_rest_frame: float or None
-    As log_lambda_min but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
+    log_lambda_rest_frame_grid: array of float or None
+    Same as log_lambda_grid but for rest-frame wavelengths.
 
     mask_fields: list of str
     Names of the fields that are affected by masking. In general it will
@@ -134,17 +107,11 @@ class Forest(AstronomicalObject):
     Weights associated to the delta field. None for no information
     """
     blinding = "none"
-    delta_lambda = None
-    delta_log_lambda = None
-    lambda_max = None
-    lambda_max_rest_frame = None
-    lambda_min = None
-    lambda_min_rest_frame = None
-    log_lambda_max = None
-    log_lambda_max_rest_frame = None
-    log_lambda_min = None
-    log_lambda_min_rest_frame = None
-    mask_fields = []
+    lambda_grid = None
+    lambda_rest_frame_grid = None
+    log_lambda_grid = None
+    log_lambda_rest_frame_grid = None
+    mask_fields = None
     wave_solution = None
 
     def __init__(self, **kwargs):
@@ -220,71 +187,54 @@ def class_variable_check(cls):
                                           "Class variable 'wave_solution' "
                                           "must be set prior to initialize "
                                           "instances of this type")
-        if not isinstance(cls.mask_fields, list):
+        if cls.mask_fields is None:
             raise AstronomicalObjectError(
                 "Error constructing Forest. "
                 "Expected list in class variable 'mask fields'. "
                 f"Found {cls.mask_fields}.")
+
         if cls.wave_solution == "log":
-            if cls.delta_log_lambda is None:
+            if cls.log_lambda_grid is None:
                 raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'delta_log_lambda' "
+                                              "Class variable 'log_lambda_grid' "
                                               "must be set prior to initialize "
                                               "instances of this type")
-            if cls.log_lambda_max is None:
+            if cls.log_lambda_rest_frame_grid is None:
                 raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'log_lambda_max' "
+                                              "Class variable 'log_lambda_rest_frame_grid' "
                                               "must be set prior to initialize "
                                               "instances of this type")
-            if cls.log_lambda_max_rest_frame is None:
+            if cls.lambda_grid is not None:
                 raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'log_lambda_max_rest_frame' "
-                                              "must be set prior to initialize "
-                                              "instances of this type")
-            if cls.log_lambda_min is None:
-                raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'log_lambda_min' "
-                                              "must be set prior to initialize "
-                                              "instances of this type")
-            if cls.log_lambda_min_rest_frame is None:
+                                              "Class variable 'lambda_grid' "
+                                              "should be None. Found: "
+                                              f"{cls.lambda_grid}")
+            if cls.lambda_rest_frame_grid is not None:
                 raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'log_lambda_min_rest_frame' "
-                                              "must be set prior to initialize "
-                                              "instances of this type")
-            if len(cls.mask_fields) == 0:
-                Forest.mask_fields = defaults.get("mask fields log").copy()
-
+                                              "Class variable 'lambda_rest_frame_grid' "
+                                              "should be None. Found: "
+                                              f"{cls.lambda_rest_frame_grid}")
         elif cls.wave_solution == "lin":
-            if cls.delta_lambda is None:
+            if cls.log_lambda_grid is not None:
                 raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'delta_lambda' "
-                                              "must be set prior to initialize "
-                                              "instances of this type")
-            if cls.lambda_max is None:
+                                              "Class variable 'log_lambda_grid' "
+                                              "should be None. Found: "
+                                              f"{cls.log_lambda_grid}")
+            if cls.log_lambda_rest_frame_grid is not None:
                 raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'lambda_max' "
+                                              "Class variable 'log_lambda_rest_frame_grid' "
+                                              "should be None. Found: "
+                                              f"{cls.log_lambda_rest_frame_grid}")
+            if cls.lambda_grid is None:
+                raise AstronomicalObjectError("Error constructing Forest. "
+                                              "Class variable 'ambda_grid' "
                                               "must be set prior to initialize "
                                               "instances of this type")
-            if cls.lambda_max_rest_frame is None:
-                raise AstronomicalObjectError(
-                    "Error constructing Forest. "
-                    "Class variable 'lambda_max_rest_frame' "
-                    "must be set prior to initialize "
-                    "instances of this type")
-            if cls.lambda_min is None:
+            if cls.lambda_rest_frame_grid is None:
                 raise AstronomicalObjectError("Error constructing Forest. "
-                                              "Class variable 'lambda_min' "
+                                              "Class variable 'lambda_rest_frame_grid' "
                                               "must be set prior to initialize "
                                               "instances of this type")
-            if cls.lambda_min_rest_frame is None:
-                raise AstronomicalObjectError(
-                    "Error constructing Forest. "
-                    "Class variable 'lambda_min_rest_frame' "
-                    "must be set prior to initialize "
-                    "instances of this type")
-
-            if len(cls.mask_fields) == 0:
-                Forest.mask_fields = defaults.get("mask fields lin").copy()
         else:
             raise AstronomicalObjectError("Error constructing Forest. "
                                           "Class variable 'wave_solution' "
@@ -491,15 +441,18 @@ def rebin(self):
         orig_ivar = self.ivar.copy()
         # compute bins
         if Forest.wave_solution == "log":
-            bins = (np.floor((self.log_lambda - Forest.log_lambda_min) /
-                             Forest.delta_log_lambda + 0.5).astype(int))
-            self.log_lambda = Forest.log_lambda_min + bins * Forest.delta_log_lambda
-            w1 = (self.log_lambda >= Forest.log_lambda_min)
-            w1 = w1 & (self.log_lambda < Forest.log_lambda_max)
-            w1 = w1 & (self.log_lambda - np.log10(1. + self.z) >
-                       Forest.log_lambda_min_rest_frame)
+            delta_log_lambda = Forest.log_lambda_grid[1] - Forest.log_lambda_grid[0]
+            half_delta_log_lambda = delta_log_lambda / 2.
+
+            half_delta_log_lambda_rest_frame = (Forest.log_lambda_rest_frame_grid[1] -
+                                                Forest.log_lambda_rest_frame_grid[0]) / 2.
+
+            w1 = (self.log_lambda >= Forest.log_lambda_grid[0] - half_delta_log_lambda)
+            w1 = w1 & (self.log_lambda < Forest.log_lambda_grid[-1] + half_delta_log_lambda)
+            w1 = w1 & (self.log_lambda - np.log10(1. + self.z) >=
+                       Forest.log_lambda_rest_frame_grid[0] - half_delta_log_lambda_rest_frame)
             w1 = w1 & (self.log_lambda - np.log10(1. + self.z) <
-                       Forest.log_lambda_max_rest_frame)
+                       Forest.log_lambda_rest_frame_grid[-1] + half_delta_log_lambda_rest_frame)
             w1 = w1 & (self.ivar > 0.)
             if w1.sum() == 0:
                 self.log_lambda = np.array([])
@@ -507,20 +460,24 @@ def rebin(self):
                 self.ivar = np.array([])
                 self.transmission_correction = np.array([])
                 return [], [], [], [], []
-            bins = bins[w1]
             self.log_lambda = self.log_lambda[w1]
             self.flux = self.flux[w1]
             self.ivar = self.ivar[w1]
             self.transmission_correction = self.transmission_correction[w1]
 
+            bins = find_bins(self.log_lambda, Forest.log_lambda_grid)
+            self.log_lambda = Forest.log_lambda_grid[0] + bins * delta_log_lambda
+
         elif Forest.wave_solution == "lin":
-            bins = (np.floor((self.lambda_ - Forest.lambda_min) /
-                             Forest.delta_lambda + 0.5).astype(int))
-            self.lambda_ = Forest.lambda_min + bins * Forest.delta_lambda
-            w1 = (self.lambda_ >= Forest.lambda_min)
-            w1 = w1 & (self.lambda_ < Forest.lambda_max)
-            w1 = w1 & (self.lambda_ / (1. + self.z) > Forest.lambda_min_rest_frame)
-            w1 = w1 & (self.lambda_ / (1. + self.z) < Forest.lambda_max_rest_frame)
+            delta_lambda = Forest.lambda_grid[1] - Forest.lambda_grid[0]
+            half_delta_lambda = delta_lambda / 2.
+
+            half_delta_lambda_rest_frame = (Forest.lambda_rest_frame_grid[1] -
+                                            Forest.lambda_rest_frame_grid[0]) / 2.
+            w1 = (self.lambda_ >= Forest.lambda_grid[0] - half_delta_lambda)
+            w1 = w1 & (self.lambda_ < Forest.lambda_grid[-1] + half_delta_lambda)
+            w1 = w1 & (self.lambda_ / (1. + self.z) >= Forest.lambda_rest_frame_grid[0] - half_delta_lambda_rest_frame)
+            w1 = w1 & (self.lambda_ / (1. + self.z) < Forest.lambda_rest_frame_grid[-1] + half_delta_lambda_rest_frame)
             w1 = w1 & (self.ivar > 0.)
             if w1.sum() == 0:
                 self.lambda_ = np.array([])
@@ -528,11 +485,13 @@ def rebin(self):
                 self.ivar = np.array([])
                 self.transmission_correction = np.array([])
                 return [], [], [], [], []
-            bins = bins[w1]
             self.lambda_ = self.lambda_[w1]
             self.flux = self.flux[w1]
             self.ivar = self.ivar[w1]
             self.transmission_correction = self.transmission_correction[w1]
+
+            bins = find_bins(self.lambda_, Forest.lambda_grid)
+            self.lambda_ = Forest.lambda_grid[0] + bins * delta_lambda
         else:
             raise AstronomicalObjectError("Error in rebinning Forest. "
                                           "Class variable 'wave_solution' "
@@ -566,12 +525,12 @@ def rebin(self):
 
         # then rebin wavelength
         if self.wave_solution == "log":
-            rebin_lambda = (Forest.log_lambda_min +
-                            np.arange(bins.max() + 1) * Forest.delta_log_lambda)
+            rebin_lambda = (Forest.log_lambda_grid[0] +
+                            np.arange(bins.max() + 1) * delta_log_lambda)
             self.log_lambda = rebin_lambda[w2]
         elif self.wave_solution == "lin":
-            rebin_lambda = (Forest.lambda_min +
-                            np.arange(bins.max() + 1) * Forest.delta_lambda)
+            rebin_lambda = (Forest.lambda_grid[0] +
+                            np.arange(bins.max() + 1) * delta_lambda)
             self.lambda_ = rebin_lambda[w2]
         else:
             raise AstronomicalObjectError("wavelength solution must be either "
@@ -584,3 +543,55 @@ def rebin(self):
         # return weights and binning solution to be used by child classes if
         # required
         return bins, rebin_ivar, orig_ivar, w1, w2
+
+    @classmethod
+    def set_class_variables(cls, lambda_min, lambda_max,
+                            lambda_min_rest_frame,
+                            lambda_max_rest_frame,
+                            pixel_step, wave_solution):
+        """Set class variables
+        Arguments
+        ---------
+        lambda_min: float
+        Logarithm of the minimum wavelength (in Angs) to be considered in a forest.
+        lambda_max: float
+        Logarithm of the maximum wavelength (in Angs) to be considered in a forest.
+        lambda_min_rest_frame: float or None
+        As lambda_min but for rest-frame wavelength.
+        lambda_max_rest_frame: float
+        As lambda_max but for rest-frame wavelength.
+        pixel_step: float
+        Wavelength cahnge between two pixels. If pixel_step is "log" this is in
+        units of the logarithm of the wavelength (in Angs). If pixel_step is "lin"
+        this is in units of the wavelength (in Angs).
+        wave_solution: "log" or "lin"
+        Specifies whether we want to construct a wavelength grid that is evenly
+        spaced on wavelength (lin) or on the logarithm of the wavelength (log)
+        """
+        if wave_solution == "log":
+            cls.log_lambda_grid = np.arange(
+                np.log10(lambda_min),
+                np.log10(lambda_max) + pixel_step/2,
+                pixel_step)
+            cls.log_lambda_rest_frame_grid = np.arange(
+                np.log10(lambda_min_rest_frame) + pixel_step/2,
+                np.log10(lambda_max_rest_frame),
+                pixel_step)
+            cls.mask_fields = defaults.get("mask fields log").copy()
+        elif wave_solution == "lin":
+            cls.lambda_grid = np.arange(
+                lambda_min,
+                lambda_max + pixel_step/2,
+                pixel_step)
+            cls.lambda_rest_frame_grid = np.arange(
+                lambda_min_rest_frame + pixel_step/2,
+                lambda_max_rest_frame,
+                pixel_step)
+            cls.mask_fields = defaults.get("mask fields lin").copy()
+        else:
+            raise AstronomicalObjectError("Error in setting Forest class "
+                                          "variables. 'wave_solution' "
+                                          "must be either 'lin' or 'log'. "
+                                          f"Found: {wave_solution}")
+
+        cls.wave_solution = wave_solution
diff --git a/py/picca/delta_extraction/astronomical_objects/pk1d_forest.py b/py/picca/delta_extraction/astronomical_objects/pk1d_forest.py
index b7036d93a..adc5d5074 100644
--- a/py/picca/delta_extraction/astronomical_objects/pk1d_forest.py
+++ b/py/picca/delta_extraction/astronomical_objects/pk1d_forest.py
@@ -21,48 +21,22 @@ class Pk1dForest(Forest):
 
     Class Attributes
     ----------------
-    delta_lambda: float or None (from Forest)
-    Variation of the wavelength (in Angs) between two pixels. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    delta_log_lambda: float or None (from Forest)
-    Variation of the logarithm of the wavelength (in Angs) between two pixels.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    lambda_max: float or None (from Forest)
-    Maximum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_max_rest_frame: float or None (from Forest)
-    As wavelength_max but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min: float or None (from Forest)
-    Minimum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min_rest_frame: float or None (from Forest)
-    As wavelength_min but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    log_lambda_max: float or None (from Forest)
-    Logarithm of the maximum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_max_rest_frame: float or None (from Forest)
-    As log_lambda_max but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
-
-    log_lambda_min: float or None (from Forest)
-    Logarithm of the minimum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_min_rest_frame: float or None (from Forest)
-    As log_lambda_min but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
+    blinding: str (from Forest)
+    Name of the blinding strategy used
+    
+    lambda_grid: array of float or None (from Forest)
+    Common grid in lambda_ based on the specified minimum and maximum
+    wavelengths, and delta_lambda.
+
+    lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as lambda_grid but for rest-frame wavelengths
+
+    log_lambda_grid: array of float or None (from Forest)
+    Common grid in log_lambda based on the specified minimum and maximum
+    wavelengths, and delta_log_lambda.
+
+    log_lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as log_lambda_grid but for rest-frame wavelengths.
 
     mask_fields: list of str (from Forest)
     Names of the fields that are affected by masking. In general it will
diff --git a/py/picca/delta_extraction/astronomical_objects/sdss_forest.py b/py/picca/delta_extraction/astronomical_objects/sdss_forest.py
index d3bb7b326..3e078d1a5 100644
--- a/py/picca/delta_extraction/astronomical_objects/sdss_forest.py
+++ b/py/picca/delta_extraction/astronomical_objects/sdss_forest.py
@@ -19,48 +19,22 @@ class SdssForest(Forest):
 
     Class Attributes
     ----------------
-    delta_lambda: float or None (from Forest)
-    Variation of the wavelength (in Angs) between two pixels. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    delta_log_lambda: float or None (from Forest)
-    Variation of the logarithm of the wavelength (in Angs) between two pixels.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    lambda_max: float or None (from Forest)
-    Maximum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_max_rest_frame: float or None (from Forest)
-    As wavelength_max but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min: float or None (from Forest)
-    Minimum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min_rest_frame: float or None (from Forest)
-    As wavelength_min but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    log_lambda_max: float or None (from Forest)
-    Logarithm of the maximum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_max_rest_frame: float or None (from Forest)
-    As log_lambda_max but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
-
-    log_lambda_min: float or None (from Forest)
-    Logarithm of the minimum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_min_rest_frame: float or None (from Forest)
-    As log_lambda_min but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
+    blinding: str (from Forest)
+    Name of the blinding strategy used
+    
+    lambda_grid: array of float or None (from Forest)
+    Common grid in lambda_ based on the specified minimum and maximum
+    wavelengths, and delta_lambda.
+
+    lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as lambda_grid but for rest-frame wavelengths
+
+    log_lambda_grid: array of float or None (from Forest)
+    Common grid in log_lambda based on the specified minimum and maximum
+    wavelengths, and delta_log_lambda.
+
+    log_lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as log_lambda_grid but for rest-frame wavelengths.
 
     mask_fields: list of str (from Forest)
     Names of the fields that are affected by masking. In general it will
diff --git a/py/picca/delta_extraction/astronomical_objects/sdss_pk1d_forest.py b/py/picca/delta_extraction/astronomical_objects/sdss_pk1d_forest.py
index 8ed1cdd97..2baac9546 100644
--- a/py/picca/delta_extraction/astronomical_objects/sdss_pk1d_forest.py
+++ b/py/picca/delta_extraction/astronomical_objects/sdss_pk1d_forest.py
@@ -21,48 +21,22 @@ class SdssPk1dForest(SdssForest, Pk1dForest):
 
     Class Attributes
     ----------------
-    delta_lambda: float or None (from Forest)
-    Variation of the wavelength (in Angs) between two pixels. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    delta_log_lambda: float or None (from Forest)
-    Variation of the logarithm of the wavelength (in Angs) between two pixels.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    lambda_max: float or None (from Forest)
-    Maximum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_max_rest_frame: float or None (from Forest)
-    As wavelength_max but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min: float or None (from Forest)
-    Minimum wavelength (in Angs) to be considered in a forest. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    lambda_min_rest_frame: float or None (from Forest)
-    As wavelength_min but for rest-frame wavelength. This should not
-    be None if wave_solution is "lin". Ignored if wave_solution is "log".
-
-    log_lambda_max: float or None (from Forest)
-    Logarithm of the maximum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_max_rest_frame: float or None (from Forest)
-    As log_lambda_max but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
-
-    log_lambda_min: float or None (from Forest)
-    Logarithm of the minimum wavelength (in Angs) to be considered in a forest.
-    This should not be None if wave_solution is "log". Ignored if wave_solution
-    is "lin".
-
-    log_lambda_min_rest_frame: float or None (from Forest)
-    As log_lambda_min but for rest-frame wavelength. This should not be None if
-    wave_solution is "log". Ignored if wave_solution is "lin".
+    blinding: str (from Forest)
+    Name of the blinding strategy used
+    
+    lambda_grid: array of float or None (from Forest)
+    Common grid in lambda_ based on the specified minimum and maximum
+    wavelengths, and delta_lambda.
+
+    lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as lambda_grid but for rest-frame wavelengths
+
+    log_lambda_grid: array of float or None (from Forest)
+    Common grid in log_lambda based on the specified minimum and maximum
+    wavelengths, and delta_log_lambda.
+
+    log_lambda_rest_frame_grid: array of float or None (from Forest)
+    Same as log_lambda_grid but for rest-frame wavelengths.
 
     mask_fields: list of str (from Forest)
     Names of the fields that are affected by masking. In general it will
diff --git a/py/picca/delta_extraction/data.py b/py/picca/delta_extraction/data.py
index 65905f69d..455d889c3 100644
--- a/py/picca/delta_extraction/data.py
+++ b/py/picca/delta_extraction/data.py
@@ -7,17 +7,26 @@
 import fitsio
 import healpy
 
+from picca.delta_extraction.astronomical_objects.forest import Forest
 from picca.delta_extraction.astronomical_objects.pk1d_forest import Pk1dForest
 from picca.delta_extraction.errors import DataError
 from picca.delta_extraction.utils import ABSORBER_IGM
 
-accepted_options = ["analysis type", "lambda abs IGM",
+accepted_options = ["analysis type", "delta lambda", "delta log lambda",
+                    "input directory",
+                    "lambda abs IGM",
+                    "lambda max", "lambda max rest frame",
+                    "lambda min", "lambda min rest frame",
                     "minimum number pixels in forest",
                     "out dir", "rejection log file"]
 
 defaults = {
     "analysis type": "BAO 3D",
     "lambda abs IGM": "LYA",
+    "lambda max": 5500.0,
+    "lambda max rest frame": 1200.0,
+    "lambda min": 3600.0,
+    "lambda min rest frame": 1040.0,
     "minimum number pixels in forest": 50,
     "rejection log file": "rejection_log.fits.gz",
 }
@@ -78,6 +87,73 @@ def __init__(self, config):
         self.logger = logging.getLogger('picca.delta_extraction.data.Data')
         self.forests = []
 
+        self.analysis_type = None
+        self.input_directory = None
+        self.min_num_pix = None
+        self.out_dir = None
+        self.rejection_log_file = None
+        self.__parse_config(config)
+
+        # rejection log arays
+        self.rejection_log_initialized = False
+        self.rejection_log_cols = []
+        self.rejection_log_names = []
+        self.rejection_log_comments = []
+
+    def __parse_config(self, config):
+        """Parse the configuration options
+
+        Arguments
+        ---------
+        config: configparser.SectionProxy
+        Parsed options to initialize class
+
+        Raise
+        -----
+        DataError upon missing required variables
+        """
+        # setup SdssForest class variables
+        wave_solution = config.get("wave solution")
+
+        if wave_solution is None:
+            raise DataError("Missing argument 'wave solution' required by Data")
+        if wave_solution not in ["lin", "log"]:
+            raise DataError("Unrecognised value for 'wave solution'. Expected either "
+                            f"'lin' or 'lof'. Found {wave_solution}")
+
+        if wave_solution == "log":
+            pixel_step = config.getfloat("delta log lambda")
+            if pixel_step is None:
+                raise DataError("Missing argument 'delta log lambda' required by "
+                                "Data when 'wave solution' is set to 'log'")
+        elif wave_solution == "lin":
+            pixel_step = config.getfloat("delta lambda")
+            if pixel_step is None:
+                raise DataError("Missing argument 'delta lambda' required by "
+                                "Data when 'wave solution' is set to 'lin'")
+        else:
+            raise DataError("Forest.wave_solution must be either "
+                            "'log' or 'lin'")
+
+        lambda_max = config.getfloat("lambda max")
+        if lambda_max is None:
+            raise DataError("Missing argument 'lambda max' required by Data")
+        lambda_max_rest_frame = config.getfloat("lambda max rest frame")
+        if lambda_max_rest_frame is None:
+            raise DataError("Missing argument 'lambda max rest frame' required by Data")
+        lambda_min = config.getfloat("lambda min")
+        if lambda_min is None:
+            raise DataError("Missing argument 'lambda min' required by Data")
+        lambda_min_rest_frame = config.getfloat("lambda min rest frame")
+        if lambda_min_rest_frame is None:
+            raise DataError("Missing argument 'lambda min rest frame' required by Data")
+
+        Forest.set_class_variables(lambda_min, lambda_max,
+                                   lambda_min_rest_frame,
+                                   lambda_max_rest_frame,
+                                   pixel_step, wave_solution)
+
+        # instance variables
         self.analysis_type = config.get("analysis type")
         if self.analysis_type is None:
             raise DataError("Missing argument 'analysis type' required by Data")
@@ -92,6 +168,10 @@ def __init__(self, config):
                 raise DataError("Missing argument 'lambda abs IGM' required by Data "
                                 "when 'analysys type' is 'BAO 3D'")
 
+        self.input_directory = config.get("input directory")
+        if self.input_directory is None:
+            raise DataError(
+                "Missing argument 'input directory' required by Data")
 
         self.min_num_pix = config.getint("minimum number pixels in forest")
         if self.min_num_pix is None:
@@ -111,12 +191,6 @@ def __init__(self, config):
                             "should en with '.fits' or '.fits.gz'. Found "
                             f"'{self.rejection_log_file}'")
 
-        # rejection log arays
-        self.rejection_log_initialized = False
-        self.rejection_log_cols = []
-        self.rejection_log_names = []
-        self.rejection_log_comments = []
-
     def add_to_rejection_log(self, header, size, rejection_status):
         """Adds to the rejection log arrays.
         In the log forest headers will be saved along with the forest size and
diff --git a/py/picca/delta_extraction/data_catalogues/desi_data.py b/py/picca/delta_extraction/data_catalogues/desi_data.py
index 60f60f304..4a69d25f3 100644
--- a/py/picca/delta_extraction/data_catalogues/desi_data.py
+++ b/py/picca/delta_extraction/data_catalogues/desi_data.py
@@ -20,20 +20,13 @@
 from picca.delta_extraction.utils_pk1d import spectral_resolution_desi
 
 accepted_options = sorted(list(set(accepted_options + accepted_options_quasar_catalogue + [
-    "blinding", "delta lambda", "input directory", "lambda max",
-    "lambda max rest frame", "lambda min", "lambda min rest frame",
-    "rebin", "wave solution"])))
+    "blinding", "wave solution"])))
 
 defaults.update({
     "delta lambda": 0.8,
-    "lambda max": 5500.0,
-    "lambda max rest frame": 1200.0,
-    "lambda min": 3600.0,
-    "lambda min rest frame": 1040.0,
+    "delta log lambda": 3e-4,
     "blinding": "corr_yshift",
-    # TODO: update this to "lin" when we are sure that the linear binning work
-    "wave solution": "log",
-    "rebin": 3,
+    "wave solution": "lin",
 })
 defaults.update(defaults_quasar_catalogue)
 
@@ -45,7 +38,7 @@ class DesiData(Data):
     -------
     filter_forests (from Data)
     __init__
-    _parse_config
+    __parse_config
     read_data
     set_blinding
 
@@ -86,9 +79,8 @@ def __init__(self, config):
         super().__init__(config)
 
         # load variables from config
-        self.input_directory = None
         self.blinding = None
-        self._parse_config(config)
+        self.__parse_config(config)
 
         # load z_truth catalogue
         self.catalogue = DesiQuasarCatalogue(config).catalogue
@@ -99,7 +91,7 @@ def __init__(self, config):
         # set blinding
         self.set_blinding(is_mock, is_sv)
 
-    def _parse_config(self, config):
+    def __parse_config(self, config):
         """Parse the configuration options
 
         Arguments
@@ -111,59 +103,6 @@ def _parse_config(self, config):
         -----
         DataError upon missing required variables
         """
-        # setup Forest class variables
-        wave_solution = config.get("wave solution")
-        if wave_solution is None:
-            raise DataError("Missing argument 'wave solution' required by DesiData")
-        if wave_solution not in ["lin", "log"]:
-            raise DataError("Unrecognised value for 'wave solution'. Expected either "
-                            f"'lin' or 'lof'. Found {wave_solution}")
-        Forest.wave_solution = wave_solution
-
-        if Forest.wave_solution == "log":
-            rebin = config.getint("rebin")
-            if rebin is None:
-                raise DataError("Missing argument 'rebin' required by DesiData when "
-                                "'wave solution' is set to 'log'")
-            Forest.delta_log_lambda = rebin * 1e-4
-
-            lambda_max = config.getfloat("lambda max")
-            if lambda_max is None:
-                raise DataError("Missing argument 'lambda max' required by DesiData")
-            Forest.log_lambda_max = np.log10(lambda_max)
-            lambda_max_rest_frame = config.getfloat("lambda max rest frame")
-            if lambda_max_rest_frame is None:
-                raise DataError("Missing argument 'lambda max rest frame' required by DesiData")
-            Forest.log_lambda_max_rest_frame = np.log10(lambda_max_rest_frame)
-            lambda_min = config.getfloat("lambda min")
-            if lambda_min is None:
-                raise DataError("Missing argument 'lambda min' required by DesiData")
-            Forest.log_lambda_min = np.log10(lambda_min)
-            lambda_min_rest_frame = config.getfloat("lambda min rest frame")
-            if lambda_min_rest_frame is None:
-                raise DataError("Missing argument 'lambda min rest frame' required by DesiData")
-            Forest.log_lambda_min_rest_frame = np.log10(lambda_min_rest_frame)
-
-        elif Forest.wave_solution == "lin":
-            Forest.delta_lambda = config.getfloat("delta lambda")
-            if Forest.delta_lambda is None:
-                raise DataError("Missing argument 'delta lambda' required by DesiData")
-            Forest.lambda_max = config.getfloat("lambda max")
-            if Forest.lambda_max is None:
-                raise DataError("Missing argument 'lambda max' required by DesiData")
-            Forest.lambda_max_rest_frame = config.getfloat("lambda max rest frame")
-            if Forest.lambda_max_rest_frame is None:
-                raise DataError("Missing argument 'lambda max rest frame' required by DesiData")
-            Forest.lambda_min = config.getfloat("lambda min")
-            if Forest.lambda_min is None:
-                raise DataError("Missing argument 'lambda min' required by DesiData")
-            Forest.lambda_min_rest_frame = config.getfloat("lambda min rest frame")
-            if Forest.lambda_min_rest_frame is None:
-                raise DataError("Missing argument 'lambda min rest frame' required by DesiData")
-        else:
-            raise DataError("Forest.wave_solution must be either "
-                            "'log' or 'lin'")
-
         # instance variables
         self.blinding = config.get("blinding")
         if self.blinding is None:
@@ -172,11 +111,6 @@ def _parse_config(self, config):
             raise DataError("Unrecognized blinding strategy. Accepted strategies "
                             f"are {ACCEPTED_BLINDING_STRATEGIES}. Found {self.blinding}")
 
-        self.input_directory = config.get("input directory")
-        if self.input_directory is None:
-            raise DataError(
-                "Missing argument 'input directory' required by DesiData")
-
     # pylint: disable=no-self-use
     # this method should use self in child classes
     def read_data(self):
diff --git a/py/picca/delta_extraction/data_catalogues/desi_healpix.py b/py/picca/delta_extraction/data_catalogues/desi_healpix.py
index 155c52172..cb9ac29f7 100644
--- a/py/picca/delta_extraction/data_catalogues/desi_healpix.py
+++ b/py/picca/delta_extraction/data_catalogues/desi_healpix.py
@@ -196,7 +196,7 @@ def read_file(self, filename, catalogue, forests_by_targetid):
                 w_t = w_t[0]
             # Construct DesiForest instance
             # Fluxes from the different spectrographs will be coadded
-            for spec in spectrographs_data.values():
+            for spec_name, spec in spectrographs_data.items():
                 ivar = spec['IVAR'][w_t].copy()
                 flux = spec['FLUX'][w_t].copy()
 
diff --git a/py/picca/delta_extraction/data_catalogues/desi_tile.py b/py/picca/delta_extraction/data_catalogues/desi_tile.py
index b475eaf11..c7ba3e3f6 100644
--- a/py/picca/delta_extraction/data_catalogues/desi_tile.py
+++ b/py/picca/delta_extraction/data_catalogues/desi_tile.py
@@ -32,7 +32,7 @@ class DesiTile(DesiData):
     filter_forests (from Data)
     set_blinding (from Data)
     __init__
-    _parse_config
+    __parse_config
     read_data
 
     Attributes
@@ -78,11 +78,11 @@ def __init__(self, config):
         # load variables from config
         self.use_all = None
         self.use_single_nights = None
-        self._parse_config(config)
+        self.__parse_config(config)
 
         super().__init__(config)
 
-    def _parse_config(self, config):
+    def __parse_config(self, config):
         """Parse the configuration options
 
         Arguments
diff --git a/py/picca/delta_extraction/data_catalogues/sdss_data.py b/py/picca/delta_extraction/data_catalogues/sdss_data.py
index 5209a70d3..d187e3981 100644
--- a/py/picca/delta_extraction/data_catalogues/sdss_data.py
+++ b/py/picca/delta_extraction/data_catalogues/sdss_data.py
@@ -17,14 +17,9 @@
 from picca.delta_extraction.utils_pk1d import exp_diff, spectral_resolution
 
 accepted_options = sorted(list(set(accepted_options + accepted_options_quasar_catalogue +[
-    "input directory", "mode", "lambda max", "lambda max rest frame", "lambda min",
-    "lambda min rest frame", "rebin"])))
+    "rebin", "mode"])))
 
 defaults.update({
-    "lambda max": 5500.0,
-    "lambda max rest frame": 1200.0,
-    "lambda min": 3600.0,
-    "lambda min rest frame": 1040.0,
     "mode": "spplate",
     "rebin": 3,
 })
@@ -38,7 +33,7 @@ class SdssData(Data):
     -------
     filter_forests (from Data)
     __init__
-    _parse_config
+    __parse_config
     read_from_spec
     read_from_spplate
 
@@ -77,12 +72,11 @@ def __init__(self, config):
         """
         self.logger = logging.getLogger(__name__)
 
-        super().__init__(config)
-
         # load variables from config
-        self.input_directory = None
         self.mode = None
-        self._parse_config(config)
+        self.__parse_config(config)
+
+        super().__init__(config)
 
         # load DRQ Catalogue
         catalogue = DrqCatalogue(config).catalogue
@@ -96,7 +90,7 @@ def __init__(self, config):
             raise DataError(f"Error reading data in SdssData. Mode {self.mode} "
                             "is not supported.")
 
-    def _parse_config(self, config):
+    def __parse_config(self, config):
         """Parse the configuration options
 
         Arguments
@@ -108,41 +102,20 @@ def _parse_config(self, config):
         -----
         DataError upon missing required variables
         """
-        # setup SdssForest class variables
-        Forest.wave_solution = "log"
-
-        rebin = config.getint("rebin")
-        if rebin is None:
-            raise DataError("Missing argument 'rebin' required by SdssData")
-        Forest.delta_log_lambda = rebin * 1e-4
-
-        lambda_max = config.getfloat("lambda max")
-        if lambda_max is None:
-            raise DataError("Missing argument 'lambda max' required by SdssData")
-        Forest.log_lambda_max = np.log10(lambda_max)
-        lambda_max_rest_frame = config.getfloat("lambda max rest frame")
-        if lambda_max_rest_frame is None:
-            raise DataError("Missing argument 'lambda max rest frame' required by SdssData")
-        Forest.log_lambda_max_rest_frame = np.log10(lambda_max_rest_frame)
-        lambda_min = config.getfloat("lambda min")
-        if lambda_min is None:
-            raise DataError("Missing argument 'lambda min' required by SdssData")
-        Forest.log_lambda_min = np.log10(lambda_min)
-        lambda_min_rest_frame = config.getfloat("lambda min rest frame")
-        if lambda_min_rest_frame is None:
-            raise DataError("Missing argument 'lambda min rest frame' required by SdssData")
-        Forest.log_lambda_min_rest_frame = np.log10(lambda_min_rest_frame)
-
         # instance variables
-        self.input_directory = config.get("input directory")
-        if self.input_directory is None:
-            raise DataError(
-                "Missing argument 'input directory' required by SdssData")
-
         self.mode = config.get("mode")
         if self.mode is None:
             raise DataError("Missing argument 'mode' required by SdssData")
 
+        rebin = config.getint("rebin")
+        if rebin is None:
+            raise DataError("Missing argument 'delta log lambda' required by "
+                            "Data when 'wave solution' is set to 'log'")
+        config["delta log lambda"] = str(rebin*1e-4)
+        del config["rebin"]
+
+        config["wave solution"] = "log"
+
     def read_from_spec(self, catalogue):
         """Read the spectra and formats its data as Forest instances.
 
@@ -239,7 +212,6 @@ def read_from_spplate(self, catalogue):
         for (plate, mjd), group in zip(grouped_catalogue.groups.keys,
                                        grouped_catalogue.groups):
             spplate = f"{self.input_directory}/{plate}/spPlate-{plate:04d}-{mjd}.fits"
-
             try:
                 hdul = fitsio.FITS(spplate)
                 header = hdul[0].read_header()
diff --git a/py/picca/delta_extraction/expected_fluxes/dr16_expected_flux.py b/py/picca/delta_extraction/expected_fluxes/dr16_expected_flux.py
index 70974bbdd..3255f613b 100644
--- a/py/picca/delta_extraction/expected_fluxes/dr16_expected_flux.py
+++ b/py/picca/delta_extraction/expected_fluxes/dr16_expected_flux.py
@@ -11,6 +11,7 @@
 from picca.delta_extraction.astronomical_objects.pk1d_forest import Pk1dForest
 from picca.delta_extraction.errors import ExpectedFluxError
 from picca.delta_extraction.expected_flux import ExpectedFlux
+from picca.delta_extraction.utils import find_bins
 
 accepted_options = ["iter out prefix", "limit eta", "limit var lss",
                     "num bins variance", "num iterations", "num processors",
@@ -40,7 +41,7 @@ class Dr16ExpectedFlux(ExpectedFlux):
     __init__
     _initialize_arrays_lin
     _initialize_arrays_log
-    _parse_config
+    __parse_config
     compute_continuum
         get_cont_model
         chi2
@@ -106,10 +107,6 @@ class Dr16ExpectedFlux(ExpectedFlux):
     Wavelengths where the variance functions and statistics are
     computed. None (and unused) for a logarithmic wavelength solution.
 
-    lambda_rest_frame: array of float or None
-    Rest-frame wavelengths where the unabsorbed mean quasar continua is are
-    computed. None (and unused) for a logarithmic wavelength solution.
-
     limit_eta: tuple of floats
     Limits on the correction factor to the contribution of the pipeline estimate
     of the instrumental noise to the variance.
@@ -121,10 +118,6 @@ class Dr16ExpectedFlux(ExpectedFlux):
     Logarithm of the rest frame wavelengths where the variance functions and
     statistics are computed. None (and unused) for a linear wavelength solution.
 
-    log_lambda_rest_frame: array of float or None
-    Logarithm of the rest-frame wavelengths where the unabsorbed mean quasar
-    continua is are computed. None (and unused) for a linear wavelength solution.
-
     num_bins_variance: int
     Number of bins to be used to compute variance functions and statistics as
     a function of wavelength.
@@ -172,7 +165,7 @@ def __init__(self, config):
         self.num_processors = None
         self.use_constant_weight = None
         self.use_ivar_as_weight = None
-        self._parse_config(config)
+        self.__parse_config(config)
 
         self.get_eta = None
         self.get_fudge = None
@@ -182,9 +175,7 @@ def __init__(self, config):
         self.get_valid_fit = None
         self.get_var_lss = None
         self.lambda_ = None
-        self.lambda_rest_frame = None
         self.log_lambda = None
-        self.log_lambda_rest_frame = None
         if Forest.wave_solution == "log":
             self._initialize_variables_log()
         elif Forest.wave_solution == "lin":
@@ -207,31 +198,24 @@ def _initialize_variables_lin(self):
         - self.get_num_pixels
         - self.get_var_lss
         - self.lambda_
-        - self.lambda_rest_frame
         """
         # initialize the mean quasar continuum
         # TODO: maybe we can drop this and compute first the mean quasar
         # continuum on compute_mean_expected_flux
-        num_bins = (int(
-            (Forest.lambda_max_rest_frame - Forest.lambda_min_rest_frame) /
-            Forest.delta_lambda) + 1)
-        self.lambda_rest_frame = (
-            Forest.lambda_min_rest_frame + (np.arange(num_bins) + .5) *
-            (Forest.lambda_max_rest_frame - Forest.lambda_min_rest_frame) /
-            num_bins)
-        self.get_mean_cont = interp1d(self.lambda_rest_frame,
-                                      np.ones_like(self.lambda_rest_frame),
+        self.get_mean_cont = interp1d(Forest.lambda_rest_frame_grid,
+                                      np.ones_like(Forest.lambda_rest_frame_grid),
                                       fill_value="extrapolate")
-        self.get_mean_cont_weight = interp1d(self.lambda_rest_frame,
+        self.get_mean_cont_weight = interp1d(Forest.lambda_rest_frame_grid,
                                              np.zeros_like(
-                                                 self.lambda_rest_frame),
+                                                 Forest.lambda_rest_frame_grid),
                                              fill_value="extrapolate")
 
         # initialize the variance-related variables (see equation 4 of
         # du Mas des Bourboux et al. 2020 for details on these variables)
         self.lambda_ = (
-            Forest.lambda_min + (np.arange(self.num_bins_variance) + .5) *
-            (Forest.lambda_max - Forest.lambda_min) / self.num_bins_variance)
+            Forest.lambda_grid[0] + (np.arange(self.num_bins_variance) + .5) *
+            (Forest.lambda_grid[-1] - Forest.lambda_grid[0]) /
+            self.num_bins_variance)
         # if use_ivar_as_weight is set, eta, var_lss and fudge will be ignored
         # print a message to inform the user
         if self.use_ivar_as_weight:
@@ -292,33 +276,24 @@ def _initialize_variables_log(self):
         - self.get_valid_fit
         - self.get_var_lss
         - self.log_lambda
-        - self.log_lambda_rest_frame
         """
         # initialize the mean quasar continuum
         # TODO: maybe we can drop this and compute first the mean quasar
         # continuum on compute_mean_expected_flux
-        num_bins = (int(
-            (Forest.log_lambda_max_rest_frame -
-             Forest.log_lambda_min_rest_frame) / Forest.delta_log_lambda) + 1)
-        self.log_lambda_rest_frame = (
-            Forest.log_lambda_min_rest_frame + (np.arange(num_bins) + 0.5) *
-            (Forest.log_lambda_max_rest_frame -
-             Forest.log_lambda_min_rest_frame) / num_bins)
-
-        self.get_mean_cont = interp1d(self.log_lambda_rest_frame,
-                                      np.ones_like(self.log_lambda_rest_frame),
+        self.get_mean_cont = interp1d(Forest.log_lambda_rest_frame_grid,
+                                      np.ones_like(Forest.log_lambda_rest_frame_grid),
                                       fill_value="extrapolate")
-        self.get_mean_cont_weight = interp1d(self.log_lambda_rest_frame,
+        self.get_mean_cont_weight = interp1d(Forest.log_lambda_rest_frame_grid,
                                              np.zeros_like(
-                                                 self.log_lambda_rest_frame),
+                                                 Forest.log_lambda_rest_frame_grid),
                                              fill_value="extrapolate")
 
 
         # initialize the variance-related variables (see equation 4 of
         # du Mas des Bourboux et al. 2020 for details on these variables)
-        self.log_lambda = (Forest.log_lambda_min +
+        self.log_lambda = (Forest.log_lambda_grid[0] +
                            (np.arange(self.num_bins_variance) + .5) *
-                           (Forest.log_lambda_max - Forest.log_lambda_min) /
+                           (Forest.log_lambda_grid[-1] - Forest.log_lambda_grid[0]) /
                            self.num_bins_variance)
 
         # if use_ivar_as_weight is set, eta, var_lss and fudge will be ignored
@@ -371,7 +346,7 @@ def _initialize_variables_log(self):
                                       fill_value="extrapolate",
                                       kind='nearest')
 
-    def _parse_config(self, config):
+    def __parse_config(self, config):
         """Parse the configuration options
 
         Arguments
@@ -495,15 +470,15 @@ def compute_continuum(self, forest):
 
         if Forest.wave_solution == "log":
             mean_cont_kwargs["log_lambda_max"] = (
-                Forest.log_lambda_max_rest_frame + np.log10(1 + forest.z))
+                Forest.log_lambda_rest_frame_grid[-1] + np.log10(1 + forest.z))
             mean_cont_kwargs["log_lambda_min"] = (
-                Forest.log_lambda_min_rest_frame + np.log10(1 + forest.z))
+                Forest.log_lambda_rest_frame_grid[0] + np.log10(1 + forest.z))
 
         elif Forest.wave_solution == "lin":
             mean_cont_kwargs["lambda_max"] = (
-                Forest.lambda_max_rest_frame * (1 + forest.z))
+                Forest.lambda_rest_frame_grid[-1] * (1 + forest.z))
             mean_cont_kwargs["lambda_min"] = (
-                Forest.lambda_min_rest_frame * (1 + forest.z))
+                Forest.lambda_rest_frame_grid[0] * (1 + forest.z))
 
         leasts_squares = LeastsSquaresContModel(
             forest=forest,
@@ -564,22 +539,15 @@ def compute_delta_stack(self, forests, stack_from_deltas=False):
         # TODO: move this to _initialize_variables_lin and
         # _initialize_variables_log (after tests are done)
         if Forest.wave_solution == "log":
-            num_bins = int((Forest.log_lambda_max - Forest.log_lambda_min) /
-                           Forest.delta_log_lambda) + 1
-            stack_log_lambda = (Forest.log_lambda_min +
-                                np.arange(num_bins) * Forest.delta_log_lambda)
+            stack_delta = np.zeros_like(Forest.log_lambda_grid)
+            stack_weight = np.zeros_like(Forest.log_lambda_grid)
         elif Forest.wave_solution == "lin":
-            num_bins = int((Forest.lambda_max - Forest.lambda_min) /
-                           Forest.delta_lambda) + 1
-            stack_lambda = (Forest.lambda_min +
-                            np.arange(num_bins) * Forest.delta_lambda)
+            stack_delta = np.zeros_like(Forest.lambda_grid)
+            stack_weight = np.zeros_like(Forest.lambda_grid)
         else:
             raise ExpectedFluxError("Forest.wave_solution must be either "
                                     "'log' or 'linear'")
 
-        stack_delta = np.zeros(num_bins)
-        stack_weight = np.zeros(num_bins)
-
         for forest in forests:
             if stack_from_deltas:
                 delta = forest.delta
@@ -606,11 +574,10 @@ def compute_delta_stack(self, forests, stack_from_deltas=False):
                 weights = 1. / variance
 
             if Forest.wave_solution == "log":
-                bins = ((forest.log_lambda - Forest.log_lambda_min) /
-                        Forest.delta_log_lambda + 0.5).astype(int)
+                bins = find_bins(forest.log_lambda, Forest.log_lambda_grid)
+
             elif Forest.wave_solution == "lin":
-                bins = ((forest.lambda_ - Forest.lambda_min) /
-                        Forest.delta_lambda + 0.5).astype(int)
+                bins = find_bins(forest.lambda_, Forest.lambda_grid)
             else:
                 raise ExpectedFluxError("Forest.wave_solution must be either "
                                         "'log' or 'linear'")
@@ -623,23 +590,23 @@ def compute_delta_stack(self, forests, stack_from_deltas=False):
         stack_delta[w] /= stack_weight[w]
 
         if Forest.wave_solution == "log":
-            self.get_stack_delta = interp1d(stack_log_lambda[stack_weight > 0.],
+            self.get_stack_delta = interp1d(Forest.log_lambda_grid[stack_weight > 0.],
                                             stack_delta[stack_weight > 0.],
                                             kind="nearest",
                                             fill_value="extrapolate")
             self.get_stack_delta_weights = interp1d(
-                stack_log_lambda[stack_weight > 0.],
+                Forest.log_lambda_grid[stack_weight > 0.],
                 stack_weight[stack_weight > 0.],
                 kind="nearest",
                 fill_value=0.0,
                 bounds_error=False)
         elif Forest.wave_solution == "lin":
-            self.get_stack_delta = interp1d(stack_lambda[stack_weight > 0.],
+            self.get_stack_delta = interp1d(Forest.lambda_grid[stack_weight > 0.],
                                             stack_delta[stack_weight > 0.],
                                             kind="nearest",
                                             fill_value="extrapolate")
             self.get_stack_delta_weights = interp1d(
-                stack_lambda[stack_weight > 0.],
+                Forest.lambda_grid[stack_weight > 0.],
                 stack_weight[stack_weight > 0.],
                 kind="nearest",
                 fill_value=0.0,
@@ -658,9 +625,8 @@ def compute_mean_cont_lin(self, forests):
         forests: List of Forest
         A list of Forest from which to compute the deltas.
         """
-        num_bins = self.lambda_rest_frame.size
-        mean_cont = np.zeros(num_bins)
-        mean_cont_weight = np.zeros(num_bins)
+        mean_cont = np.zeros_like(Forest.lambda_rest_frame_grid)
+        mean_cont_weight = np.zeros_like(Forest.lambda_rest_frame_grid)
 
         # first compute <F/C> in bins. C=Cont_old*spectrum_dependent_fitting_fct
         # (and Cont_old is constant for all spectra in a bin), thus we actually
@@ -669,11 +635,10 @@ def compute_mean_cont_lin(self, forests):
         for forest in forests:
             if forest.bad_continuum_reason is not None:
                 continue
-            bins = (
-                (forest.lambda_ /
-                 (1 + forest.z) - Forest.lambda_min_rest_frame) /
-                (Forest.lambda_max_rest_frame - Forest.lambda_min_rest_frame) *
-                num_bins).astype(int)
+            bins = find_bins(
+                forest.lambda_ / (1 + forest.z),
+                Forest.lambda_rest_frame_grid
+            )
 
             var_lss = self.get_var_lss(forest.lambda_)
             eta = self.get_eta(forest.lambda_)
@@ -690,7 +655,7 @@ def compute_mean_cont_lin(self, forests):
         w = mean_cont_weight > 0
         mean_cont[w] /= mean_cont_weight[w]
         mean_cont /= mean_cont.mean()
-        lambda_cont = self.lambda_rest_frame[w]
+        lambda_cont = Forest.lambda_rest_frame_grid[w]
 
         # the new mean continuum is multiplied by the previous one to recover
         # <F/spectrum_dependent_fitting_function>
@@ -713,9 +678,8 @@ def compute_mean_cont_log(self, forests):
         forests: List of Forest
         A list of Forest from which to compute the deltas.
         """
-        num_bins = self.log_lambda_rest_frame.size
-        mean_cont = np.zeros(num_bins)
-        mean_cont_weight = np.zeros(num_bins)
+        mean_cont = np.zeros_like(Forest.log_lambda_rest_frame_grid)
+        mean_cont_weight = np.zeros_like(Forest.log_lambda_rest_frame_grid)
 
         # first compute <F/C> in bins. C=Cont_old*spectrum_dependent_fitting_fct
         # (and Cont_old is constant for all spectra in a bin), thus we actually
@@ -724,10 +688,10 @@ def compute_mean_cont_log(self, forests):
         for forest in forests:
             if forest.bad_continuum_reason is not None:
                 continue
-            bins = ((forest.log_lambda - Forest.log_lambda_min_rest_frame -
-                     np.log10(1 + forest.z)) /
-                    (Forest.log_lambda_max_rest_frame -
-                     Forest.log_lambda_min_rest_frame) * num_bins).astype(int)
+            bins = find_bins(
+                forest.log_lambda - np.log10(1 + forest.z),
+                Forest.log_lambda_rest_frame_grid
+            )
 
             var_lss = self.get_var_lss(forest.log_lambda)
             eta = self.get_eta(forest.log_lambda)
@@ -744,7 +708,7 @@ def compute_mean_cont_log(self, forests):
         w = mean_cont_weight > 0
         mean_cont[w] /= mean_cont_weight[w]
         mean_cont /= mean_cont.mean()
-        log_lambda_cont = self.log_lambda_rest_frame[w]
+        log_lambda_cont = Forest.log_lambda_rest_frame_grid[w]
 
         # the new mean continuum is multiplied by the previous one to recover
         # <F/spectrum_dependent_fitting_function>
@@ -876,18 +840,19 @@ def compute_var_stats(self, forests):
 
             # select the wavelength bins
             if Forest.wave_solution == "log":
-                log_lambda_bins = (
-                    (forest.log_lambda - Forest.log_lambda_min) /
-                    (Forest.log_lambda_max - Forest.log_lambda_min) *
-                    self.num_bins_variance).astype(int)
+                log_lambda_bins = find_bins(
+                    forest.log_lambda,
+                    self.log_lambda
+                    )
                 # filter the values with a pipeline variance out of range
                 log_lambda_bins = log_lambda_bins[w]
                 # compute overall bin
                 bins = var_pipe_bins + num_var_bins * log_lambda_bins
             elif Forest.wave_solution == "lin":
-                lambda_bins = ((forest.lambda_ - Forest.lambda_min) /
-                               (Forest.lambda_max - Forest.lambda_min) *
-                               self.num_bins_variance).astype(int)
+                lambda_bins = find_bins(
+                    forest.lambda_,
+                    self.lambda_,
+                    )
                 # filter the values with a pipeline variance out of range
                 lambda_bins = lambda_bins[w]
                 # compute overall bin
@@ -1289,15 +1254,10 @@ def save_iteration_step(self, iteration):
             header["FITORDER"] = self.order
             if Forest.wave_solution == "log":
                 # TODO: update this once the TODO in compute continua is fixed
-                num_bins = int((Forest.log_lambda_max - Forest.log_lambda_min) /
-                               Forest.delta_log_lambda) + 1
-                stack_log_lambda = (
-                    Forest.log_lambda_min +
-                    np.arange(num_bins) * Forest.delta_log_lambda)
                 results.write([
-                    stack_log_lambda,
-                    self.get_stack_delta(stack_log_lambda),
-                    self.get_stack_delta_weights(stack_log_lambda)
+                    Forest.log_lambda_grid,
+                    self.get_stack_delta(Forest.log_lambda_grid),
+                    self.get_stack_delta_weights(Forest.log_lambda_grid)
                 ],
                               names=['loglam', 'stack', 'weight'],
                               header=header,
@@ -1316,23 +1276,18 @@ def save_iteration_step(self, iteration):
                               extname='VAR_FUNC')
 
                 results.write([
-                    self.log_lambda_rest_frame,
-                    self.get_mean_cont(self.log_lambda_rest_frame),
-                    self.get_mean_cont_weight(self.log_lambda_rest_frame),
+                    Forest.log_lambda_rest_frame_grid,
+                    self.get_mean_cont(Forest.log_lambda_rest_frame_grid),
+                    self.get_mean_cont_weight(Forest.log_lambda_rest_frame_grid),
                 ],
                               names=['loglam_rest', 'mean_cont', 'weight'],
                               extname='CONT')
             elif Forest.wave_solution == "lin":
                 # TODO: update this once the TODO in compute continua is fixed
-                num_bins = int((Forest.lambda_max - Forest.lambda_min) /
-                               Forest.delta_lambda) + 1
-                stack_lambda = (Forest.lambda_min +
-                                np.arange(num_bins) * Forest.delta_lambda)
-
                 results.write([
-                    stack_lambda,
-                    self.get_stack_delta(stack_lambda),
-                    self.get_stack_delta_weights(stack_lambda)
+                    Forest.lambda_grid,
+                    self.get_stack_delta(Forest.lambda_grid),
+                    self.get_stack_delta_weights(Forest.lambda_grid)
                 ],
                               names=['lambda', 'stack', 'weight'],
                               header=header,
@@ -1351,9 +1306,9 @@ def save_iteration_step(self, iteration):
                               extname='VAR_FUNC')
 
                 results.write([
-                    self.lambda_rest_frame,
-                    self.get_mean_cont(self.lambda_rest_frame),
-                    self.get_mean_cont_weight(self.lambda_rest_frame),
+                    Forest.lambda_rest_frame_grid,
+                    self.get_mean_cont(Forest.lambda_rest_frame_grid),
+                    self.get_mean_cont_weight(Forest.lambda_rest_frame_grid),
                 ],
                               names=['lambda_rest_frame', 'mean_cont', 'weight'],
                               extname='CONT')
diff --git a/py/picca/delta_extraction/expected_fluxes/true_continuum.py b/py/picca/delta_extraction/expected_fluxes/true_continuum.py
index 73f8c6e73..ef69540c7 100644
--- a/py/picca/delta_extraction/expected_fluxes/true_continuum.py
+++ b/py/picca/delta_extraction/expected_fluxes/true_continuum.py
@@ -33,7 +33,7 @@ class TrueContinuum(ExpectedFlux):
     -------
     extract_deltas (from ExpectedFlux)
     __init__
-    _parse_config
+    __parse_config
     compute_expected_flux
     compute_mean_cont_lin
     compute_mean_cont_log
@@ -92,16 +92,16 @@ def __init__(self, config):
         self.input_directory = None
         self.iter_out_prefix = None
         self.num_processors = None
-        self._parse_config(config)
+        self.__parse_config(config)
+
 
-        
         # read large scale structure variance and mean flux
         self.get_var_lss = None
         self.get_mean_flux = None
         self.read_raw_statistics()
 
 
-    def _parse_config(self, config):
+    def __parse_config(self, config):
         """Parse the configuration options
 
         Arguments
@@ -147,31 +147,6 @@ def compute_expected_flux(self, forests):
         -----
         ExpectedFluxError if Forest.wave_solution is not 'lin' or 'log'
         """
-
-        if Forest.wave_solution == "log":
-            num_bins = (int(
-            (Forest.log_lambda_max_rest_frame -
-             Forest.log_lambda_min_rest_frame) / Forest.delta_log_lambda) + 1)
-
-            self.log_lambda_rest_frame = (
-            Forest.log_lambda_min_rest_frame + (np.arange(num_bins) + 0.5) *
-            (Forest.log_lambda_max_rest_frame -
-             Forest.log_lambda_min_rest_frame) / num_bins)
-
-        elif Forest.wave_solution == "lin":
-            num_bins = (int(
-            (Forest.lambda_max_rest_frame -
-             Forest.lambda_min_rest_frame) / Forest.delta_lambda) + 1)
-
-            self.lambda_rest_frame = (
-            Forest.lambda_min_rest_frame + (np.arange(num_bins) + 0.5) *
-            (Forest.lambda_max_rest_frame -
-             Forest.lambda_min_rest_frame) / num_bins)
-        else:
-            raise ExpectedFluxError("Forest.wave_solution must be "
-                                            "either 'log' or 'linear'")
-
-
         context = multiprocessing.get_context('fork')
         for iteration in range(1):
             pool = context.Pool(processes=self.num_processors)
@@ -204,18 +179,16 @@ def compute_mean_cont_lin(self, forests):
         forests: List of Forest
         A list of Forest from which to compute the deltas.
         """
-        num_bins = self.lambda_rest_frame.size
-        mean_cont = np.zeros(num_bins)
-        mean_cont_weight = np.zeros(num_bins)
+        mean_cont = np.zeros_like(Forest.lambda_rest_frame_grid.size)
+        mean_cont_weight = np.zeros_like(Forest.lambda_rest_frame_grid.size)
 
         for forest in forests:
             if forest.bad_continuum_reason is not None:
                 continue
-            bins = (
-                (forest.lambda_ /
-                 (1 + forest.z) - Forest.lambda_min_rest_frame) /
-                (Forest.lambda_max_rest_frame - Forest.lambda_min_rest_frame) *
-                num_bins).astype(int)
+            bins = find_bins(
+                forest.lambda_ / (1 + forest.z),
+                Forest.lambda_rest_frame_grid
+                )
 
             var_lss = self.get_var_lss(forest.lambda_)
             var_pipe = 1. / forest.ivar / forest.continuum**2
@@ -230,7 +203,7 @@ def compute_mean_cont_lin(self, forests):
         w = mean_cont_weight > 0
         mean_cont[w] /= mean_cont_weight[w]
         mean_cont /= mean_cont.mean()
-        lambda_cont = self.lambda_rest_frame[w]
+        lambda_cont = Forest.lambda_rest_frame_grid[w]
 
         self.get_mean_cont = interp1d(lambda_cont,
                                       mean_cont,
@@ -250,17 +223,16 @@ def compute_mean_cont_log(self, forests):
         forests: List of Forest
         A list of Forest from which to compute the deltas.
         """
-        num_bins = self.log_lambda_rest_frame.size
-        mean_cont = np.zeros(num_bins)
-        mean_cont_weight = np.zeros(num_bins)
+        mean_cont = np.zeros_like(Forest.log_lambda_rest_frame_grid.size)
+        mean_cont_weight = np.zeros_like(Forest.log_lambda_rest_frame_grid.size)
 
         for forest in forests:
             if forest.bad_continuum_reason is not None:
                 continue
-            bins = ((forest.log_lambda - Forest.log_lambda_min_rest_frame -
-                     np.log10(1 + forest.z)) /
-                    (Forest.log_lambda_max_rest_frame -
-                     Forest.log_lambda_min_rest_frame) * num_bins).astype(int)
+            bins = find_bins(
+                forest.log_lambda -  np.log10(1 + forest.z),
+                Forest.log_lambda_rest_frame_grid
+            )
 
             var_lss = self.get_var_lss(forest.log_lambda)
             var_pipe = 1. / forest.ivar / forest.continuum**2
@@ -275,7 +247,7 @@ def compute_mean_cont_log(self, forests):
         w = mean_cont_weight > 0
         mean_cont[w] /= mean_cont_weight[w]
         mean_cont /= mean_cont.mean()
-        log_lambda_cont = self.log_lambda_rest_frame[w]
+        log_lambda_cont = Forest.log_lambda_rest_frame_grid[w]
 
         self.get_mean_cont = interp1d(log_lambda_cont,
                                       mean_cont,
@@ -358,31 +330,31 @@ def read_raw_statistics(self):
         header = hdul[1].header
         if Forest.wave_solution == "log":
             if (
-                header['LINEAR'] 
-                or not np.isclose(header['L_MIN'], 10**Forest.log_lambda_min, rtol=1e-3) 
-                or not np.isclose(header['L_MAX'], 10**Forest.log_lambda_max, rtol=1e-3)  
+                header['LINEAR']
+                or not np.isclose(header['L_MIN'], 10**Forest.log_lambda_min, rtol=1e-3)
+                or not np.isclose(header['L_MAX'], 10**Forest.log_lambda_max, rtol=1e-3)
                 or not np.isclose(header['LR_MIN'], 10**Forest.log_lambda_min_rest_frame, rtol=1e-3)
                 or not np.isclose(header['LR_MAX'], 10**Forest.log_lambda_max_rest_frame, rtol=1e-3)
                 or not np.isclose(header['DEL_LL'], Forest.delta_log_lambda, rtol=1e-3)
             ):
-                raise ExpectedFluxError(f'''raw statistics file pixelization scheme does not match input pixelization scheme. 
+                raise ExpectedFluxError(f'''raw statistics file pixelization scheme does not match input pixelization scheme.
                 \t\tL_MIN\tL_MAX\tLR_MIN\tLR_MAX\tDEL_LL
                 raw\t{header['L_MIN']}\t{header['L_MAX']}\t{header['LR_MIN']}\t{header['LR_MAX']}\t{header['DEL_LL']}
                 input\t{10**Forest.log_lambda_min}\t{10**Forest.log_lambda_max}\t{10**Forest.log_lambda_min_rest_frame}\t{10**Forest.log_lambda_max_rest_frame}\t{Forest.delta_log_lambda}
                 provide a custom file in 'raw statistics file' field matching input pixelization scheme''')
         elif Forest.wave_solution == "lin":
             if (
-                not header['LINEAR'] 
+                not header['LINEAR']
                 or not np.isclose(header['L_MIN'], Forest.lambda_min , rtol=1e-3)
                 or not np.isclose(header['L_MAX'], Forest.lambda_max , rtol=1e-3)
                 or not np.isclose(header['LR_MIN'], Forest.lambda_min_rest_frame, rtol=1e-3)
                 or not np.isclose(header['LR_MAX'], Forest.lambda_max_rest_frame, rtol=1e-3)
                 or not np.isclose(header['DEL_L'], Forest.delta_lambda, rtol=1e-3)
             ):
-                raise ExpectedFluxError(f'''raw statistics file pixelization scheme does not match input pixelization scheme. 
-                \tL_MIN\tL_MAX\tLR_MIN\tLR_MAX\tDEL_LL
+                raise ExpectedFluxError(f'''raw statistics file pixelization scheme does not match input pixelization scheme.
+                \t\tL_MIN\tL_MAX\tLR_MIN\tLR_MAX\tDEL_LL
                 raw\t{header['L_MIN']}\t{header['L_MAX']}\t{header['LR_MIN']}\t{header['LR_MAX']}\t{header['DEL_LL']}
-                input\t{Forest.lambda_min}\t{Forest.lambda_max}\t{Forest.lambda_min_rest_frame}\t{Forest.lambda_max_rest_frame}\t{Forest.delta_lambda}
+                input\t{10**Forest.log_lambda_min}\t{10**Forest.log_lambda_max}\t{10**Forest.log_lambda_min_rest_frame}\t{10**Forest.log_lambda_max_rest_frame}\t{Forest.delta_log_lambda}
                 provide a custom file in 'raw statistics file' field matching input pixelization scheme''')
 
         lambda_ = hdul[1].data['LAMBDA']
@@ -462,9 +434,9 @@ def save_delta_attributes(self):
                                Forest.delta_log_lambda) + 1
 
                 results.write([
-                    self.log_lambda_rest_frame,
-                    self.get_mean_cont(self.log_lambda_rest_frame),
-                    self.get_mean_cont_weight(self.log_lambda_rest_frame),
+                    Forest.log_lambda_rest_frame_grid,
+                    self.get_mean_cont(Forest.log_lambda_rest_frame_grid),
+                    self.get_mean_cont_weight(Forest.log_lambda_rest_frame_grid),
                 ],
                               names=['loglam_rest', 'mean_cont', 'weight'],
                               extname='CONT')
@@ -473,9 +445,9 @@ def save_delta_attributes(self):
                                Forest.delta_lambda) + 1
 
                 results.write([
-                    self.lambda_rest_frame,
-                    self.get_mean_cont(self.lambda_rest_frame),
-                    self.get_mean_cont_weight(self.lambda_rest_frame),
+                    Forest.lambda_rest_frame_grid,
+                    self.get_mean_cont(Forest.lambda_rest_frame_grid),
+                    self.get_mean_cont_weight(Forest.lambda_rest_frame_grid),
                 ],
                               names=['lambda_rest_frame', 'mean_cont', 'weight'],
                               extname='CONT')
diff --git a/py/picca/delta_extraction/quasar_catalogues/desi_quasar_catalogue.py b/py/picca/delta_extraction/quasar_catalogues/desi_quasar_catalogue.py
index 407bec922..902dd0faf 100644
--- a/py/picca/delta_extraction/quasar_catalogues/desi_quasar_catalogue.py
+++ b/py/picca/delta_extraction/quasar_catalogues/desi_quasar_catalogue.py
@@ -26,7 +26,7 @@ class DesiQuasarCatalogue(QuasarCatalogue):
     -------
     trim_catalogue (from QuasarCatalogue)
     __init__
-    _parse_config
+    __parse_config
     filter_surveys
     read_catalogue
 
@@ -66,7 +66,7 @@ def __init__(self, config):
 
         # load variables from config
         self.filename = None
-        self._parse_config(config)
+        self.__parse_config(config)
 
         # read quasar catalogue
         self.read_catalogue()
@@ -79,7 +79,7 @@ def __init__(self, config):
         if self.max_num_spec is not None:
             super().trim_catalogue()
 
-    def _parse_config(self, config):
+    def __parse_config(self, config):
         """Parse the configuration options
 
         Arguments
diff --git a/py/picca/delta_extraction/quasar_catalogues/drq_catalogue.py b/py/picca/delta_extraction/quasar_catalogues/drq_catalogue.py
index 8f587f582..1befc858a 100644
--- a/py/picca/delta_extraction/quasar_catalogues/drq_catalogue.py
+++ b/py/picca/delta_extraction/quasar_catalogues/drq_catalogue.py
@@ -25,7 +25,7 @@ class DrqCatalogue(QuasarCatalogue):
     -------
     trim_catalogue (from QuasarCatalogue)
     __init__
-    _parse_config
+    __parse_config
     read_drq
     read_spall
 
@@ -84,7 +84,7 @@ def __init__(self, config):
         self.drq_filename = None
         self.keep_bal = None
         self.spall = None
-        self._parse_config(config)
+        self.__parse_config(config)
 
         # read DRQ Catalogue
         catalogue = self.read_drq()
@@ -100,7 +100,7 @@ def __init__(self, config):
         if self.max_num_spec is not None:
             super().trim_catalogue()
 
-    def _parse_config(self, config):
+    def __parse_config(self, config):
         """Parse the configuration options
 
         Arguments
diff --git a/py/picca/delta_extraction/utils.py b/py/picca/delta_extraction/utils.py
index dc576fb07..a72e824fa 100644
--- a/py/picca/delta_extraction/utils.py
+++ b/py/picca/delta_extraction/utils.py
@@ -4,6 +4,7 @@
 import logging
 import os
 
+import numpy as np
 from scipy.constants import speed_of_light as speed_light
 
 module_logger = logging.getLogger(__name__)
@@ -126,6 +127,25 @@ def class_from_string(class_name, module_name):
         accepted_options = []
     return class_object, default_args, accepted_options
 
+def find_bins(original_array, grid_array):
+    """For each element in original_array, find the corresponding bin in grid_array
+
+    Arguments
+    ---------
+    original_array: array of float
+    Read array, e.g. forest.log_lambda
+
+    grid_array: array of float
+    Common array, e.g. Forest.log_lambda_grid
+
+    Return
+    ------
+    found_bin: array of int
+    An array of size original_array.size filled with values smaller than
+    grid_array.size with the bins correspondance
+    """
+    found_bin = (np.abs(grid_array - original_array[:,np.newaxis])).argmin(axis=1)
+    return found_bin
 
 PROGRESS_LEVEL_NUM = 15
 logging.addLevelName(PROGRESS_LEVEL_NUM, "PROGRESS")
diff --git a/tutorials/delta_extraction/picca_delta_extraction_configuration_tutorial.ipynb b/tutorials/delta_extraction/picca_delta_extraction_configuration_tutorial.ipynb
index a5e3e50f2..6ea44b535 100644
--- a/tutorials/delta_extraction/picca_delta_extraction_configuration_tutorial.ipynb
+++ b/tutorials/delta_extraction/picca_delta_extraction_configuration_tutorial.ipynb
@@ -158,8 +158,10 @@
     "\n",
     "### DesiHealpix\n",
     "- `analysis type`: Selected analysis type. Current options are \"BAO 3D\" or \"PK 1D\" **Type: str, Required: no, Default: \"BAO 3D\"\n",
+    "- `blinding`: Selected blinding strategy (\"none\" for no blinding). Available strategies listed in `py/picca/delta_extraction/utils.py` in variable `ACCEPTED_BLINDING_STRATEGIES`. For the moment, blinding strategy \"corr_yshift\" is automatically engaged for main survey data, and blinding strategy \"none\" is automatically engaged for SV data or mocks. **Type: str, Required: no, Default: \"corr_yshift\"**\n",
     "- `catalogue`: Name the z_truth catalogue. **Type: str, Required: yes**\n",
-    "- `delta lambda`: Variation of the wavelength (in Angs) between two pixels. **Type: float, Required: no, Default: 1.0**\n",
+    "- `delta lambda`: Variation of the wavelength (in Angs) between two pixels. Ignored if `wave solution` is \"lin\" **Type: float, Required: no, Default: 1.0**\n",
+    "- `delta log lambda`: Variation of the logarithm of the wavelength (in Angs) between two pixels. Ignored if `wave solution` is \"log\"  **Type: float, Required: no, Default: 1e-4**\n",
     "- `input directory`: Directory to spectra files. **Type: str, Required: yes**\n",
     "- `keep surveys`: Only keep the entries in the catalogue that have a \"SURVEY\" specified in field. Ignored if \"SURVEY\" column is not present in the catalogue. Multiple values can be added spearated by white spaces. Specifying \"all\" is equivalent to specifying \"sv1 sv2 sv3 main\". Accepted values are \"sv1\", \"sv2\", \"sv3\", \"main\", \"all\" and \"special\" **Type: str, Required: no, Default: \"all\"**\n",
     "- `max num spec`: Maximum number of spectra to read. None for no maximum. **Type: int or None, Required: no, Default: None**\n",
@@ -170,14 +172,16 @@
     "- `lambda min rest frame`: Lower limit on rest frame wavelength [Angstrom] **Type: float, Required: no, Default: 1040.0**\n",
     "- `minimum number pixels in forest`: Minimum number of pixels in a forest. Forests with less pixels will be dropped. **Type: int, Required: no, Default: 50**\n",
     "- `rejection log file`: Filelame of the rejection log. Must have extension .fits or .fits.gz **Type: str, Required: no, Default: \"rejection_log.fits.gz\"**\n",
-    "- `wave solution`: Use linear (lin) or logarithmic wavelength solution. **Type: str\n",
+    "- `wave solution`: Use linear (lin) or logarithmic wavelength solution. **Type: str, Required: no, Default: \"lin\"**\n",
     "- `z max`: Maximum redshift. Quasars with redshifts higher than or equal to z_max will be discarded. If not specified will be computed at runtime base on the values of `lambda max` and `lambda min rest frame` as $z_{\\rm max} = \\max\\left(0, \\lambda_{\\rm max} / \\lambda_{\\rm min, rf} - 1\\right)$\n",
     "- `z min`: Minimum redshift. Quasars with redshifts lower than z_min will be discarded. If not specified will be computed at runtime base on the values of `lambda min` and `lambda max rest frame` as $z_{\\rm min} = \\max\\left(0, \\lambda_{\\rm min} / \\lambda_{\\rm max, rf} - 1\\right)$ **Type: float, Required: False**\n",
     "\n",
     "### DesiTile\n",
     "- `analysis type`: Selected analysis type. Current options are \"BAO 3D\" or \"PK 1D\" **Type: str, Required: no, Default: \"BAO 3D\"\n",
+    "- `blinding`: Selected blinding strategy (\"none\" for no blinding). Available strategies listed in `py/picca/delta_extraction/utils.py` in variable `ACCEPTED_BLINDING_STRATEGIES`. For the moment, blinding strategy \"corr_yshift\" is automatically engaged for main survey data, and blinding strategy \"none\" is automatically engaged for SV data or mocks. **Type: str, Required: no, Default: \"corr_yshift\"**\n",
     "- `catalogue`: Name the z_truth catalogue. **Type: str, Required: yes**\n",
-    "- `delta lambda`: Variation of the wavelength (in Angs) between two pixels. **Type: float, Required: no, Default: 1.0**\n",
+    "- `delta lambda`: Variation of the wavelength (in Angs) between two pixels. Ignored if `wave solution` is \"lin\" **Type: float, Required: no, Default: 1.0**\n",
+    "- `delta log lambda`: Variation of the logarithm of the wavelength (in Angs) between two pixels. Ignored if `wave solution` is \"log\"  **Type: float, Required: no, Default: 1e-4**\n",
     "- `input directory`: Directory to spectra files. **Type: str, Required: yes**\n",
     "- `keep surveys`: Only keep the entries in the catalogue that have a \"SURVEY\" specified in field. Ignored if \"SURVEY\" column is not present in the catalogue. Multiple values can be added spearated by white spaces. Specifying \"all\" is equivalent to specifying \"sv1 sv2 sv3 main\". Accepted values are \"sv1\", \"sv2\", \"sv3\", \"main\", \"all\" and \"special\" **Type: str, Required: no, Default: \"all\"**\n",
     "- `max num spec`: Maximum number of spectra to read. None for no maximum. **Type: int or None, Required: no, Default: None**\n",
@@ -190,14 +194,16 @@
     "- `rejection log file`: Filelame of the rejection log. Must have extension .fits or .fits.gz **Type: str, Required: no, Default: \"rejection_log.fits.gz\"**\n",
     "- `use all`: Rread using the all directory. **Type: bool, Required: no, Default: False**\n",
     "- `use single nights`: Read using only nights specified within the cat **Type: bool, Required: no, Default: False**\n",
-    "- `wave solution`: Use linear (lin) or logarithmic wavelength solution. **Type: str\n",
+    "- `wave solution`: Use linear (lin) or logarithmic wavelength solution. **Type: str, Required: no, Default: \"lin\"**\n",
     "- `z max`: Maximum redshift. Quasars with redshifts higher than or equal to z_max will be discarded. If not specified will be computed at runtime base on the values of `lambda max` and `lambda min rest frame` as $z_{\\rm max} = \\max\\left(0, \\lambda_{\\rm max} / \\lambda_{\\rm min, rf} - 1\\right)$\n",
     "- `z min`: Minimum redshift. Quasars with redshifts lower than z_min will be discarded. If not specified will be computed at runtime base on the values of `lambda min` and `lambda max rest frame` as $z_{\\rm min} = \\max\\left(0, \\lambda_{\\rm min} / \\lambda_{\\rm max, rf} - 1\\right)$ **Type: float, Required: False**\n",
     "\n",
     "### DesisimMocks\n",
     "- `analysis type`: Selected analysis type. Current options are \"BAO 3D\" or \"PK 1D\" **Type: str, Required: no, Default: \"BAO 3D\"\n",
+    "- `blinding`: Selected blinding strategy (\"none\" for no blinding). Available strategies listed in `py/picca/delta_extraction/utils.py` in variable `ACCEPTED_BLINDING_STRATEGIES`. For the moment, blinding strategy \"corr_yshift\" is automatically engaged for main survey data, and blinding strategy \"none\" is automatically engaged for SV data or mocks. **Type: str, Required: no, Default: \"corr_yshift\"**\n",
     "- `catalogue`: Name the z_truth catalogue. **Type: str, Required: yes**\n",
-    "- `delta lambda`: Variation of the wavelength (in Angs) between two pixels. **Type: float, Required: no, Default: 1.0**\n",
+    "- `delta lambda`: Variation of the wavelength (in Angs) between two pixels. Ignored if `wave solution` is \"lin\" **Type: float, Required: no, Default: 1.0**\n",
+    "- `delta log lambda`: Variation of the logarithm of the wavelength (in Angs) between two pixels. Ignored if `wave solution` is \"log\"  **Type: float, Required: no, Default: 1e-4**\n",
     "- `input directory`: Directory to spectra files. **Type: str, Required: yes**\n",
     "- `keep surveys`: Only keep the entries in the catalogue that have a \"SURVEY\" specified in field. Ignored if \"SURVEY\" column is not present in the catalogue. Multiple values can be added spearated by white spaces. Specifying \"all\" is equivalent to specifying \"sv1 sv2 sv3 main\". Accepted values are \"sv1\", \"sv2\", \"sv3\", \"main\", \"all\" and \"special\" **Type: str, Required: no, Default: \"all\"**\n",
     "- `max num spec`: Maximum number of spectra to read. None for no maximum. **Type: int or None, Required: no, Default: None**\n",
@@ -208,7 +214,7 @@
     "- `lambda min rest frame`: Lower limit on rest frame wavelength [Angstrom] **Type: float, Required: no, Default: 1040.0**\n",
     "- `rejection log file`: Filelame of the rejection log. Must have extension .fits or .fits.gz **Type: str, Required: no, Default: \"rejection_log.fits.gz\"**\n",
     "- `minimum number pixels in forest`: Minimum number of pixels in a forest. Forests with less pixels will be dropped. **Type: int, Required: no, Default: 50**\n",
-    "- `wave solution`: Use linear (lin) or logarithmic wavelength solution. **Type: str\n",
+    "- `wave solution`: Use linear (lin) or logarithmic wavelength solution. **Type: str, Required: no, Default: \"lin\"**\n",
     "- `z max`: Maximum redshift. Quasars with redshifts higher than or equal to z_max will be discarded. If not specified will be computed at runtime base on the values of `lambda max` and `lambda min rest frame` as $z_{\\rm max} = \\max\\left(0, \\lambda_{\\rm max} / \\lambda_{\\rm min, rf} - 1\\right)$\n",
     "- `z min`: Minimum redshift. Quasars with redshifts lower than z_min will be discarded. If not specified will be computed at runtime base on the values of `lambda min` and `lambda max rest frame` as $z_{\\rm min} = \\max\\left(0, \\lambda_{\\rm min} / \\lambda_{\\rm max, rf} - 1\\right)$ **Type: float, Required: False**"
    ]


Test patch:
diff --git a/py/picca/tests/delta_extraction/abstract_test.py b/py/picca/tests/delta_extraction/abstract_test.py
index 4b11034ee..8c7047db3 100644
--- a/py/picca/tests/delta_extraction/abstract_test.py
+++ b/py/picca/tests/delta_extraction/abstract_test.py
@@ -89,7 +89,7 @@ def compare_fits(self, orig_file, new_file):
                     self.assertTrue(key in new_header)
                     if not key in ["CHECKSUM", "DATASUM"]:
                         if orig_header[key] != new_header[key]:
-                            print(f"Original file: {orig_file}")
+                            print(f"\nOriginal file: {orig_file}")
                             print(f"New file: {new_file}")
                             print(f"Different values found for key {key}: "
                                   f"orig: {orig_header[key]}, new: {new_header[key]}")
@@ -108,11 +108,27 @@ def compare_fits(self, orig_file, new_file):
                     self.assertTrue(new_data is None)
                 else:
                     for col in orig_data.dtype.names:
+                        if not col in new_data.dtype.names:
+                            print(f"\nOriginal file: {orig_file}")
+                            print(f"New file: {new_file}")
+                            print(f"Column {col} in HDU {orig_header['EXTNAME']} "
+                                  "missing in new file")
                         self.assertTrue(col in new_data.dtype.names)
-                        self.assertTrue(((orig_data[col] == new_data[col]).all()) or
-                                        (np.allclose(orig_data[col],
-                                                     new_data[col],
-                                                     equal_nan=True)))
+                        if not np.allclose(orig_data[col],
+                                     new_data[col],
+                                     equal_nan=True):
+                            print(f"\nOriginal file: {orig_file}")
+                            print(f"New file: {new_file}")
+                            print(f"Different values found for column {col} in"
+                                  f"HDU {orig_header['EXTNAME']}")
+                            print("original new isclose original-new\n")
+                            for new, orig in zip(new_data[col], orig_data[col]):
+                                print(f"{orig} {new} "
+                                      f"{np.isclose(orig, new, equal_nan=True)} "
+                                      f"{orig-new}")
+                        self.assertTrue(np.allclose(orig_data[col],
+                                                    new_data[col],
+                                                    equal_nan=True))
                     for col in new_data.dtype.names:
                         if col not in orig_data.dtype.names:
                             print(f"Column {col} missing in orig header")
diff --git a/py/picca/tests/delta_extraction/astronomical_object_tests.py b/py/picca/tests/delta_extraction/astronomical_object_tests.py
index 990ce0fa4..7123861db 100644
--- a/py/picca/tests/delta_extraction/astronomical_object_tests.py
+++ b/py/picca/tests/delta_extraction/astronomical_object_tests.py
@@ -33,13 +33,13 @@
 RESO_REBIN = np.ones(5)
 RESO_COADD = np.ones(5) * 2
 LOG_LAMBDA = np.array([
-    3.5565, 3.55655, 3.5567, 3.55675, 3.5569, 3.55695, 3.5571, 3.55715, 3.5573,
-    3.55735
+    3.5562825, 3.5563225, 3.5565825, 3.5566225, 3.5568825, 3.5569225,
+    3.5571825, 3.5572225, 3.5574825, 3.5575225
 ])
 LOG_LAMBDA_REBIN = np.array(
-    [3.5565025, 3.5567025, 3.5569025, 3.5571025, 3.5573025])
+    [3.5563025, 3.5566025, 3.5569025, 3.5572025, 3.5575025])
 LAMBDA_ = np.array(
-    [3610, 3610.4, 3650, 3650.4, 3670, 3670.4, 3680, 3680.4, 3700, 3700.4])
+    [3610.0, 3610.4, 3650.0, 3650.4, 3670.0, 3670.4, 3680.0, 3680.4, 3700.0, 3700.4])
 LAMBDA_REBIN = np.array([3610, 3650, 3670, 3680, 3700])
 
 THINGID = 100000000
@@ -955,7 +955,7 @@ def test_forest(self):
             Forest(**kwargs_forest_log)
 
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a Forest
         test_obj = Forest(**kwargs_forest_log)
@@ -998,7 +998,7 @@ def test_forest(self):
 
     def test_forest_comparison(self):
         """Test comparison is properly inheried in Forest."""
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         test_obj = Forest(**kwargs_forest_log)
         test_obj.rebin()
@@ -1038,7 +1038,7 @@ def test_forest_comparison(self):
     def test_forest_rebin(self):
         """Test the rebin function in Forest."""
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a Forest
         test_obj = Forest(**kwargs_forest_log)
@@ -1061,7 +1061,7 @@ def test_forest_rebin(self):
     def test_forest_coadd(self):
         """Test the coadd function in Forest."""
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a Forest
         test_obj = Forest(**kwargs_forest_log)
@@ -1104,7 +1104,7 @@ def test_forest_coadd(self):
     def test_forest_get_data(self):
         """Test method get_data for Forest."""
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a Forest
         test_obj = Forest(**kwargs_forest_log)
@@ -1123,7 +1123,7 @@ def test_forest_get_data(self):
     def test_forest_get_header(self):
         """Test method get_header for Forest."""
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a Forest
         test_obj = Forest(**kwargs_forest_log)
@@ -1133,6 +1133,7 @@ def test_forest_get_header(self):
         self.assert_get_header(test_obj)
 
         # set class variables; case: linear wavelength solution
+        reset_forest()
         setup_forest(wave_solution="lin")
 
         # create a Forest
@@ -1149,7 +1150,7 @@ def test_pk1d_forest(self):
             Pk1dForest(**kwargs_pk1d_forest_log)
 
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a Pk1dForest with missing Pk1dForest variables
         with self.assertRaises(AstronomicalObjectError):
@@ -1178,7 +1179,7 @@ def test_pk1d_forest(self):
     def test_pk1d_forest_coadd(self):
         """Test the coadd function in Pk1dForest"""
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
         setup_pk1d_forest("LYA")
 
         # create a Pk1dForest
@@ -1223,7 +1224,7 @@ def test_pk1d_forest_coadd(self):
     def test_pk1d_forest_get_data(self):
         """Test method get_data for Pk1dForest."""
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
         setup_pk1d_forest("LYA")
 
         # create a Pk1dForest
@@ -1244,7 +1245,7 @@ def test_pk1d_forest_get_data(self):
     def test_pk1d_forest_get_header(self):
         """Test method get_header for Pk1dForest."""
         # set class variables; case: logarithmic wavelength solution
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
         setup_pk1d_forest("LYA")
 
         # create a Pk1dForest
@@ -1255,7 +1256,9 @@ def test_pk1d_forest_get_header(self):
         self.assert_get_header(test_obj)
 
         # set class variables; case: linear wavelength solution
+        reset_forest()
         setup_forest(wave_solution="lin")
+        setup_pk1d_forest("LYA")
 
         # create a Pk1dForest
         test_obj = Pk1dForest(**kwargs_pk1d_forest_lin)
@@ -1273,7 +1276,7 @@ def test_sdss_forest(self):
             SdssForest(**kwargs_sdss_forest)
 
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a SdssForest
         test_obj = SdssForest(**kwargs_sdss_forest)
@@ -1320,7 +1323,7 @@ def test_sdss_forest(self):
     def test_sdss_forest_coadd(self):
         """Test the coadd function in SdssForest"""
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a SdssForest
         test_obj = SdssForest(**kwargs_sdss_forest)
@@ -1347,7 +1350,7 @@ def test_sdss_forest_coadd(self):
     def test_sdss_forest_get_data(self):
         """Test method get_data for SdssForest."""
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create an SdssForest
         test_obj = SdssForest(**kwargs_sdss_forest)
@@ -1357,7 +1360,7 @@ def test_sdss_forest_get_data(self):
     def test_sdss_forest_get_header(self):
         """Test method get_header for SdssForest."""
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create an SdssForest
         test_obj = SdssForest(**kwargs_sdss_forest)
@@ -1390,7 +1393,7 @@ def test_sdss_pk1d_forest(self):
             SdssPk1dForest(**kwargs_sdss_pk1d_forest)
 
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
 
         # create a SdssPk1dForest
         test_obj = SdssPk1dForest(**kwargs_sdss_pk1d_forest)
@@ -1469,7 +1472,7 @@ def test_sdss_pk1d_forest(self):
     def test_sdss_pk1d_forest_coadd(self):
         """Test the coadd function in SdssPk1dForest"""
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
         setup_pk1d_forest("LYA")
 
         # create a SdssPk1dForest
@@ -1497,7 +1500,7 @@ def test_sdss_pk1d_forest_coadd(self):
     def test_sdss_pk1d_forest_get_data(self):
         """Test method get_data for SdssPk1dForest."""
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
         setup_pk1d_forest("LYA")
 
         # create an SdssPk1dForest
@@ -1507,7 +1510,7 @@ def test_sdss_pk1d_forest_get_data(self):
     def test_sdss_pk1d_forest_get_header(self):
         """Test method get_header for SdssPk1dForest."""
         # set class variables
-        setup_forest(wave_solution="log")
+        setup_forest(wave_solution="log", rebin=3)
         setup_pk1d_forest("LYA")
 
         # create an SdssPk1dForest
diff --git a/py/picca/tests/delta_extraction/data/continua_log.txt b/py/picca/tests/delta_extraction/data/continua_log.txt
index 11b16350d..54009df74 100644
--- a/py/picca/tests/delta_extraction/data/continua_log.txt
+++ b/py/picca/tests/delta_extraction/data/continua_log.txt
@@ -1,44 +1,44 @@
 # thingid cont[0] ... cont[N]
-434932660 2.609058456300303 2.6203825676014914 2.6317066789026633 2.643030790203852 2.654354901505024 2.665679012806213 2.677003124107385 2.6883272354085737 2.699651346709762 2.710975458010934 2.722299569312123 2.7336236806132947 2.744947791914483 2.756271903215655 2.767596014516844 2.7789201258180327 2.7902442371192047 2.8015683484203935 2.8128924597215654 2.824216571022754 2.8355406823239258 2.8468647936251146 2.858188904926303 2.869513016227475 2.8808371275286637 2.8921612388298357 2.9034853501310245 2.9148094614322133 2.9261335727333853 2.937457684034574 2.948781795335746 2.9601059066369344 2.9714300179381063 2.982754129239295 2.9940782405404835 3.0054023518416555 3.0167264631428443 3.0280505744440163 3.039374685745205 3.050698797046377 3.062022908347566 3.0733470196487542 3.084671130949926 3.095995242251115 3.107319353552287 3.1186434648534753 3.1299675761546473 3.141291687455836 3.152615798757025 3.163939910058197 3.1752640213593857 3.1865881326605576 3.1979122439617464 3.2092363552629184 3.2205604665641068 3.2318845778652956 3.2432086891664675 3.254532800467656 3.265856911768828 3.2771810230700167 3.2885051343711886 3.2998292456723775 3.3111533569735663 3.322477468274738 3.333801579575927 3.345125690877099 3.3564498021782874 3.367773913479459 3.3790980247806477 3.3904221360818365 3.4017462473830085 3.4130703586841973 3.4243944699853692 3.435718581286558 3.447042692587747 3.458366803888919 3.469690915190107 3.481015026491279 3.492339137792468 3.50366324909364 3.5149873603948283 3.526311471696017 3.537635582997189 3.548959694298378 3.56028380559955 3.5716079169007386 3.5829320282019106 3.5942561395030994 3.605580250804288 3.6169043621054597 3.6282284734066486 3.6395525847078205 3.650876696009009 3.662200807310181 3.6735249186113696 3.6848490299125585 3.6961731412137304 3.7074972525149192 3.718821363816091 3.7301454751172796 3.7414695864184515 3.75279369771964 3.7641178090208287 3.7754419203220007 3.7867660316231895 3.7980901429243614 3.8094142542255502 3.820738365526722 3.8320624768279106 3.8433865881290994 3.8547106994302713 3.86603481073146 3.877358922032632 3.8886830333338205 3.900007144634993 3.9113312559361817 3.92265536723737 3.933979478538542 3.9453035898397304 3.956627701140903 3.9679518124420916 3.97927592374328 3.990600035044452 4.00192414634564 4.013248257646813 4.024572368948002 4.035896480249173 4.047220591550362 4.058544702851551 4.069868814152723 4.081192925453911 4.092517036755083 4.103841148056272 4.115165259357443 4.126489370658632 4.137813481959821 4.149137593260994 4.160461704562182
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+442100624 0.8129387327459383 0.8125648829471793 0.8121910331484203 0.8118171833496614 0.8114433335509024 0.8110694837521434 0.8106956339533844 0.8103217841546254 0.8099479343558664 0.8095740845571074 0.8092002347583485 0.8088263849595895 0.8084525351608305 0.8080786853620715 0.8077048355633125 0.8073309857645535 0.8069571359657945 0.8065832861670356 0.8062094363682766 0.8058355865695176 0.8054617367707586 0.8050878869719996 0.8047140371732406 0.8043401873744817 0.8039663375757227 0.8035924877769637 0.8032186379782047 0.8028447881794457 0.8024709383806867 0.8020970885819277 0.8017232387831688 0.8013493889844098 0.8009755391856508 0.8006016893868918 0.8002278395881328 0.7998539897893738 0.7994801399906148 0.7991062901918559 0.7987324403930969 0.7983585905943379 0.7979847407955789 0.7976108909968199 0.7972370411980609 0.796863191399302 0.796489341600543 0.796115491801784 0.795741642003025 0.795367792204266 0.794993942405507 0.794620092606748 0.7942462428079891 0.7938723930092301 0.7934985432104711 0.7931246934117121 0.7927508436129531 0.7923769938141941 0.7920031440154351 0.7916292942166762 0.7912554444179172 0.7908815946191582 0.7905077448203992 0.7901338950216402 0.7897600452228812 0.7893861954241223 0.7890123456253633 0.7886384958266043 0.7882646460278453 0.7878907962290863 0.7875169464303273 0.7871430966315683 0.7867692468328094 0.7863953970340504 0.7860215472352914 0.7856476974365324 0.7852738476377734 0.7848999978390144 0.7845261480402554 0.7841522982414965 0.7837784484427375 0.7834045986439785 0.7830307488452195 0.7826568990464605 0.7822830492477015 0.7819091994489425 0.7815353496501836 0.7811614998514246 0.7807876500526656 0.7804138002539066 0.7800399504551476 0.7796661006563886 0.7792922508576297 0.7789184010588707 0.7785445512601117 0.7781707014613527 0.7777968516625937 0.7774230018638347 0.7770491520650757 0.7766753022663168 0.7763014524675578 0.7759276026687988 0.7755537528700398 0.7751799030712807 0.7748060532725218 0.7744322034737627 0.7740583536750039 0.7736845038762448 0.7733106540774859 0.7729368042787268 0.7725629544799679 0.7721891046812088 0.7718152548824498 0.7714414050836909 0.7710675552849319 0.7706937054861729 0.7703198556874139 0.7699460058886549 0.769572156089896 0.769198306291137 0.7688244564923781 0.768450606693619 0.7680767568948601 0.767702907096101 0.7673290572973421 0.766955207498583 0.766581357699824 0.7662075079010651 0.7658336581023061 0.7654598083035471 0.7650859585047881 0.7647121087060291 0.7643382589072701 0.7639644091085112 0.7635905593097522 0.7632167095109932 0.7628428597122342 0.7624690099134752 0.7620951601147162 0.7617213103159572 0.7613474605171983 0.7609736107184393 0.7605997609196803 0.7602259111209213 0.7598520613221623 0.7594782115234033 0.7591043617246443 0.7587305119258854 0.7583566621271264 0.7579828123283674 0.7576089625296084 0.7572351127308494 0.7568612629320904 0.7564874131333315 0.7561135633345725 0.7557397135358135 0.7553658637370545 0.7549920139382955 0.7546181641395365 0.7542443143407775 0.7538704645420186 0.7534966147432596 0.7531227649445006 0.7527489151457416 0.7523750653469826 0.7520012155482236 0.7516273657494646 0.7512535159507057 0.7508796661519467 0.7505058163531877 0.7501319665544287 0.7497581167556697 0.7493842669569107 0.7490104171581518 0.7486365673593928 0.7482627175606338 0.7478888677618748 0.7475150179631158 0.7471411681643568 0.7467673183655978 0.7463934685668389 0.7460196187680799 0.7456457689693209 0.7452719191705619 0.7448980693718029 0.7445242195730439 0.744150369774285 0.743402670176767 0.743028820378008 0.742654970579249 0.74228112078049 0.741907270981731 0.741533421182972 0.7411595713842131 0.7407857215854541 0.7404118717866951 0.7400380219879361 0.7396641721891771 0.7392903223904181 0.7389164725916592 0.7385426227929002 0.7381687729941412 0.7377949231953822 0.7374210733966232 0.7370472235978642 0.7366733737991052 0.7362995240003463 0.7359256742015873 0.7355518244028283 
diff --git a/py/picca/tests/delta_extraction/data/data_filter_forests_print.txt b/py/picca/tests/delta_extraction/data/data_filter_forests_print.txt
index bfb880aa4..06ff9ad5f 100644
--- a/py/picca/tests/delta_extraction/data/data_filter_forests_print.txt
+++ b/py/picca/tests/delta_extraction/data/data_filter_forests_print.txt
@@ -2,6 +2,6 @@
 [PROGRESS]: Removed forest that are too short
 [PROGRESS]: Remaining sample has 1 forests
 [PROGRESS]: Input sample has 1 forests
-[PROGRESS]: Rejected forest with los_id 10000 due to forest being too short (59)
+[PROGRESS]: Rejected forest with los_id 10000 due to forest being too short (195)
 [PROGRESS]: Removed forest that are too short
 [PROGRESS]: Remaining sample has 0 forests
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-28.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-28.fits.gz
index e38ca422c..56fe110b9 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-28.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-28.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-45.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-45.fits.gz
index d85446187..859c886b3 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-45.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta-45.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes.fits.gz
index c8fa6ec02..4b51f0487 100644
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation1.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation1.fits.gz
deleted file mode 100644
index 61ad70aa4..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation2.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation2.fits.gz
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index 90db6a689..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation3.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation3.fits.gz
deleted file mode 100644
index 606c137d9..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation4.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation4.fits.gz
deleted file mode 100644
index 05a6cc6ce..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation5.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation5.fits.gz
deleted file mode 100644
index c19d46ae8..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation6.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation6.fits.gz
deleted file mode 100644
index 3bb5062a7..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation7.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation7.fits.gz
deleted file mode 100644
index 2bd01498c..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation8.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation8.fits.gz
deleted file mode 100644
index c71084910..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation9.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib/delta_attributes_delta_attributesation9.fits.gz
deleted file mode 100644
index f19b672f9..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib/update_fits.py b/py/picca/tests/delta_extraction/data/delta_extraction_calib/update_fits.py
deleted file mode 100644
index 4768171f7..000000000
--- a/py/picca/tests/delta_extraction/data/delta_extraction_calib/update_fits.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import astropy.io.fits as fits
-import numpy as np
-
-
-filenames = ["delta-28.fits.gz", "delta-45.fits.gz"]
-
-for filename in filenames:
-    hdul = fits.open(filename)
-
-    for hdu in hdul[1:]:
-
-        hdu.header["TUNIT4"] = 'Flux units'
-        hdu.header["LOS_ID"] = hdu.header["THING_ID"]
-
-        if "PMF"in hdu.header:
-            pmf = hdu.header["PMF"]
-            hdu.header["PLATE"] = f"{int(pmf.split('-')[0]):04d}"
-            hdu.header["MJD"] = f"{int(pmf.split('-')[1]):05d}"
-            hdu.header["FIBERID"] = f"{int(pmf.split('-')[2]):04d}"
-            del hdu.header["PMF"]
-
-        if "ORDER" in hdu.header:
-            del hdu.header["ORDER"]
-
-        if hdu.header["THING_ID"] in [428690499, 429522561]:
-            hdu.header["PLATE"] = "3655-3657"
-            hdu.header["MJD"] = "55240-55244"
-            if hdu.header["THING_ID"] == 428690499:
-                hdu.header["FIBERID"] = "0054-0660"
-            elif hdu.header["THING_ID"] == 429522561:
-                hdu.header["FIBERID"] = "0179-0540"
-
-    names = sorted([hdul[i].header["EXTNAME"] for i in range(1, len(hdul))])
-    new_hdul = fits.HDUList([hdul[0]]+[hdul[name] for name in names])
-
-    new_hdul.writeto(filename, overwrite=True)
-    hdul.close()
-
-
-prefix = "iter"
-new_prefix = "delta_attributes"
-filenames = [prefix+".fits.gz"]+[prefix+f"_iteration{i}.fits.gz" for i in range(1, 10)]
-new_filenames = [filename.replace(prefix, new_prefix) for filename in filenames]
-
-for filename, new_filename in zip(filenames, new_filenames):
-    hdul = fits.open(filename)
-
-    hdul = hdul[:-1]
-
-    del hdul[1].header["PIXORDER"]
-    del hdul[1].header["NSIDE"]
-    data = hdul[1].data
-    for index in range(data["stack"].size):
-        if data["stack"][index] != 0.0:
-            data["stack"][:index] = data["stack"][index]
-            break
-    for index in range(data["stack"].size - 1, 0, -1):
-        if data["stack"][index] != 0.0:
-            data["stack"][index:] = data["stack"][index]
-            break
-    hdu = fits.BinTableHDU(data, name="STACK_DELTAS", header=hdul[1].header)
-    hdul[1] = hdu
-
-
-    data = np.array(hdul[2].data)[["loglam", "eta", "var_lss", "fudge"]]
-    hdu = fits.BinTableHDU(data, name="VAR_FUNC", header=hdul[2].header)
-    hdul[2] = hdu
-
-    #data = np.array(hdul[3].data)[["loglam_rest", "mean_cont"]]
-    #hdu = fits.BinTableHDU(data, name=hdul[3].name, header=hdul[3].header)
-    #hdul[3] = hdu
-
-    hdul.writeto(new_filename, overwrite=True)
-
-    hdul.close()
-    os.remove(filename)
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-28.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-28.fits.gz
index 3f26b2c17..369fe8807 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-28.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-28.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-45.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-45.fits.gz
index 15cde555e..582164475 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-45.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta-45.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes.fits.gz
index a3bfd8215..4ec779e98 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation1.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation1.fits.gz
deleted file mode 100644
index 0809455fd..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation1.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation2.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation2.fits.gz
deleted file mode 100644
index cf7864ba6..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation2.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation3.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation3.fits.gz
deleted file mode 100644
index 5db9106dd..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation3.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation4.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation4.fits.gz
deleted file mode 100644
index 1bc404755..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation4.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation5.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation5.fits.gz
deleted file mode 100644
index 15a841f0d..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation5.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation6.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation6.fits.gz
deleted file mode 100644
index a4492561a..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation6.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation7.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation7.fits.gz
deleted file mode 100644
index 3ae3146cf..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation8.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation8.fits.gz
deleted file mode 100644
index 61fb2ab71..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation9.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation9.fits.gz
deleted file mode 100644
index 016de5118..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/delta_attributes_delta_attributesation9.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/update_fits.py b/py/picca/tests/delta_extraction/data/delta_extraction_calib2/update_fits.py
deleted file mode 100644
index 4768171f7..000000000
--- a/py/picca/tests/delta_extraction/data/delta_extraction_calib2/update_fits.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import astropy.io.fits as fits
-import numpy as np
-
-
-filenames = ["delta-28.fits.gz", "delta-45.fits.gz"]
-
-for filename in filenames:
-    hdul = fits.open(filename)
-
-    for hdu in hdul[1:]:
-
-        hdu.header["TUNIT4"] = 'Flux units'
-        hdu.header["LOS_ID"] = hdu.header["THING_ID"]
-
-        if "PMF"in hdu.header:
-            pmf = hdu.header["PMF"]
-            hdu.header["PLATE"] = f"{int(pmf.split('-')[0]):04d}"
-            hdu.header["MJD"] = f"{int(pmf.split('-')[1]):05d}"
-            hdu.header["FIBERID"] = f"{int(pmf.split('-')[2]):04d}"
-            del hdu.header["PMF"]
-
-        if "ORDER" in hdu.header:
-            del hdu.header["ORDER"]
-
-        if hdu.header["THING_ID"] in [428690499, 429522561]:
-            hdu.header["PLATE"] = "3655-3657"
-            hdu.header["MJD"] = "55240-55244"
-            if hdu.header["THING_ID"] == 428690499:
-                hdu.header["FIBERID"] = "0054-0660"
-            elif hdu.header["THING_ID"] == 429522561:
-                hdu.header["FIBERID"] = "0179-0540"
-
-    names = sorted([hdul[i].header["EXTNAME"] for i in range(1, len(hdul))])
-    new_hdul = fits.HDUList([hdul[0]]+[hdul[name] for name in names])
-
-    new_hdul.writeto(filename, overwrite=True)
-    hdul.close()
-
-
-prefix = "iter"
-new_prefix = "delta_attributes"
-filenames = [prefix+".fits.gz"]+[prefix+f"_iteration{i}.fits.gz" for i in range(1, 10)]
-new_filenames = [filename.replace(prefix, new_prefix) for filename in filenames]
-
-for filename, new_filename in zip(filenames, new_filenames):
-    hdul = fits.open(filename)
-
-    hdul = hdul[:-1]
-
-    del hdul[1].header["PIXORDER"]
-    del hdul[1].header["NSIDE"]
-    data = hdul[1].data
-    for index in range(data["stack"].size):
-        if data["stack"][index] != 0.0:
-            data["stack"][:index] = data["stack"][index]
-            break
-    for index in range(data["stack"].size - 1, 0, -1):
-        if data["stack"][index] != 0.0:
-            data["stack"][index:] = data["stack"][index]
-            break
-    hdu = fits.BinTableHDU(data, name="STACK_DELTAS", header=hdul[1].header)
-    hdul[1] = hdu
-
-
-    data = np.array(hdul[2].data)[["loglam", "eta", "var_lss", "fudge"]]
-    hdu = fits.BinTableHDU(data, name="VAR_FUNC", header=hdul[2].header)
-    hdul[2] = hdu
-
-    #data = np.array(hdul[3].data)[["loglam_rest", "mean_cont"]]
-    #hdu = fits.BinTableHDU(data, name=hdul[3].name, header=hdul[3].header)
-    #hdul[3] = hdu
-
-    hdul.writeto(new_filename, overwrite=True)
-
-    hdul.close()
-    os.remove(filename)
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-28.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-28.fits.gz
index 63ba2cea0..8cf7e15cd 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-28.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-28.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-45.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-45.fits.gz
index bf2e7d2e8..dc2274a8f 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-45.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta-45.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes.fits.gz
index 95cadc172..8bfc0f444 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation1.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation1.fits.gz
deleted file mode 100644
index e553502ac..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation1.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation2.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation2.fits.gz
deleted file mode 100644
index 4851793c8..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation2.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation3.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation3.fits.gz
deleted file mode 100644
index 1671afd80..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation3.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation4.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation4.fits.gz
deleted file mode 100644
index ae86c781a..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation4.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation5.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation5.fits.gz
deleted file mode 100644
index 97c34529e..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation5.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation6.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation6.fits.gz
deleted file mode 100644
index 74ff9c90b..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation6.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation7.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation7.fits.gz
deleted file mode 100644
index 75d13939a..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation7.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation8.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation8.fits.gz
deleted file mode 100644
index 8f31deaa4..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation8.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation9.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation9.fits.gz
deleted file mode 100644
index a0b636aaa..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/delta_attributes_delta_attributesation9.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/update_fits.py b/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/update_fits.py
deleted file mode 100644
index 4768171f7..000000000
--- a/py/picca/tests/delta_extraction/data/delta_extraction_calib2_nomask/update_fits.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import astropy.io.fits as fits
-import numpy as np
-
-
-filenames = ["delta-28.fits.gz", "delta-45.fits.gz"]
-
-for filename in filenames:
-    hdul = fits.open(filename)
-
-    for hdu in hdul[1:]:
-
-        hdu.header["TUNIT4"] = 'Flux units'
-        hdu.header["LOS_ID"] = hdu.header["THING_ID"]
-
-        if "PMF"in hdu.header:
-            pmf = hdu.header["PMF"]
-            hdu.header["PLATE"] = f"{int(pmf.split('-')[0]):04d}"
-            hdu.header["MJD"] = f"{int(pmf.split('-')[1]):05d}"
-            hdu.header["FIBERID"] = f"{int(pmf.split('-')[2]):04d}"
-            del hdu.header["PMF"]
-
-        if "ORDER" in hdu.header:
-            del hdu.header["ORDER"]
-
-        if hdu.header["THING_ID"] in [428690499, 429522561]:
-            hdu.header["PLATE"] = "3655-3657"
-            hdu.header["MJD"] = "55240-55244"
-            if hdu.header["THING_ID"] == 428690499:
-                hdu.header["FIBERID"] = "0054-0660"
-            elif hdu.header["THING_ID"] == 429522561:
-                hdu.header["FIBERID"] = "0179-0540"
-
-    names = sorted([hdul[i].header["EXTNAME"] for i in range(1, len(hdul))])
-    new_hdul = fits.HDUList([hdul[0]]+[hdul[name] for name in names])
-
-    new_hdul.writeto(filename, overwrite=True)
-    hdul.close()
-
-
-prefix = "iter"
-new_prefix = "delta_attributes"
-filenames = [prefix+".fits.gz"]+[prefix+f"_iteration{i}.fits.gz" for i in range(1, 10)]
-new_filenames = [filename.replace(prefix, new_prefix) for filename in filenames]
-
-for filename, new_filename in zip(filenames, new_filenames):
-    hdul = fits.open(filename)
-
-    hdul = hdul[:-1]
-
-    del hdul[1].header["PIXORDER"]
-    del hdul[1].header["NSIDE"]
-    data = hdul[1].data
-    for index in range(data["stack"].size):
-        if data["stack"][index] != 0.0:
-            data["stack"][:index] = data["stack"][index]
-            break
-    for index in range(data["stack"].size - 1, 0, -1):
-        if data["stack"][index] != 0.0:
-            data["stack"][index:] = data["stack"][index]
-            break
-    hdu = fits.BinTableHDU(data, name="STACK_DELTAS", header=hdul[1].header)
-    hdul[1] = hdu
-
-
-    data = np.array(hdul[2].data)[["loglam", "eta", "var_lss", "fudge"]]
-    hdu = fits.BinTableHDU(data, name="VAR_FUNC", header=hdul[2].header)
-    hdul[2] = hdu
-
-    #data = np.array(hdul[3].data)[["loglam_rest", "mean_cont"]]
-    #hdu = fits.BinTableHDU(data, name=hdul[3].name, header=hdul[3].header)
-    #hdul[3] = hdu
-
-    hdul.writeto(new_filename, overwrite=True)
-
-    hdul.close()
-    os.remove(filename)
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-28.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-28.fits.gz
index 7b70dc9e3..0bcb63096 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-28.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-28.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-45.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-45.fits.gz
index 7f7bd1b92..34cd6cdbb 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-45.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta-45.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes.fits.gz
index 4b0a8c812..6c5c7edc3 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation1.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation1.fits.gz
deleted file mode 100644
index 92dafadb3..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation2.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation2.fits.gz
deleted file mode 100644
index 070fd3668..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation3.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation3.fits.gz
deleted file mode 100644
index 96da3438d..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation4.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation4.fits.gz
deleted file mode 100644
index f85fe6f4f..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation5.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation5.fits.gz
deleted file mode 100644
index a6e36d257..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation5.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation6.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation6.fits.gz
deleted file mode 100644
index e885bf566..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation7.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation7.fits.gz
deleted file mode 100644
index 7435d8ecc..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation8.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation8.fits.gz
deleted file mode 100644
index 3601b42a4..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation8.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation9.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation9.fits.gz
deleted file mode 100644
index 3f20e1f32..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/delta_attributes_delta_attributesation9.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/update_fits.py b/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/update_fits.py
deleted file mode 100644
index 4768171f7..000000000
--- a/py/picca/tests/delta_extraction/data/delta_extraction_calib_nomask/update_fits.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import astropy.io.fits as fits
-import numpy as np
-
-
-filenames = ["delta-28.fits.gz", "delta-45.fits.gz"]
-
-for filename in filenames:
-    hdul = fits.open(filename)
-
-    for hdu in hdul[1:]:
-
-        hdu.header["TUNIT4"] = 'Flux units'
-        hdu.header["LOS_ID"] = hdu.header["THING_ID"]
-
-        if "PMF"in hdu.header:
-            pmf = hdu.header["PMF"]
-            hdu.header["PLATE"] = f"{int(pmf.split('-')[0]):04d}"
-            hdu.header["MJD"] = f"{int(pmf.split('-')[1]):05d}"
-            hdu.header["FIBERID"] = f"{int(pmf.split('-')[2]):04d}"
-            del hdu.header["PMF"]
-
-        if "ORDER" in hdu.header:
-            del hdu.header["ORDER"]
-
-        if hdu.header["THING_ID"] in [428690499, 429522561]:
-            hdu.header["PLATE"] = "3655-3657"
-            hdu.header["MJD"] = "55240-55244"
-            if hdu.header["THING_ID"] == 428690499:
-                hdu.header["FIBERID"] = "0054-0660"
-            elif hdu.header["THING_ID"] == 429522561:
-                hdu.header["FIBERID"] = "0179-0540"
-
-    names = sorted([hdul[i].header["EXTNAME"] for i in range(1, len(hdul))])
-    new_hdul = fits.HDUList([hdul[0]]+[hdul[name] for name in names])
-
-    new_hdul.writeto(filename, overwrite=True)
-    hdul.close()
-
-
-prefix = "iter"
-new_prefix = "delta_attributes"
-filenames = [prefix+".fits.gz"]+[prefix+f"_iteration{i}.fits.gz" for i in range(1, 10)]
-new_filenames = [filename.replace(prefix, new_prefix) for filename in filenames]
-
-for filename, new_filename in zip(filenames, new_filenames):
-    hdul = fits.open(filename)
-
-    hdul = hdul[:-1]
-
-    del hdul[1].header["PIXORDER"]
-    del hdul[1].header["NSIDE"]
-    data = hdul[1].data
-    for index in range(data["stack"].size):
-        if data["stack"][index] != 0.0:
-            data["stack"][:index] = data["stack"][index]
-            break
-    for index in range(data["stack"].size - 1, 0, -1):
-        if data["stack"][index] != 0.0:
-            data["stack"][index:] = data["stack"][index]
-            break
-    hdu = fits.BinTableHDU(data, name="STACK_DELTAS", header=hdul[1].header)
-    hdul[1] = hdu
-
-
-    data = np.array(hdul[2].data)[["loglam", "eta", "var_lss", "fudge"]]
-    hdu = fits.BinTableHDU(data, name="VAR_FUNC", header=hdul[2].header)
-    hdul[2] = hdu
-
-    #data = np.array(hdul[3].data)[["loglam_rest", "mean_cont"]]
-    #hdu = fits.BinTableHDU(data, name=hdul[3].name, header=hdul[3].header)
-    #hdul[3] = hdu
-
-    hdul.writeto(new_filename, overwrite=True)
-
-    hdul.close()
-    os.remove(filename)
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-28.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-28.fits.gz
index 9e36f0a34..c33bcb0b0 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-28.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-28.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-45.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-45.fits.gz
index 5ece71fc4..e0a695550 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-45.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta-45.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes.fits.gz
index d2daeb855..5b3e35d94 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation1.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation1.fits.gz
deleted file mode 100644
index d2fd616a9..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation1.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation2.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation2.fits.gz
deleted file mode 100644
index 22e83836c..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation2.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation3.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation3.fits.gz
deleted file mode 100644
index ca81fccf2..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation3.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation4.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation4.fits.gz
deleted file mode 100644
index 804562425..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation4.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation5.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation5.fits.gz
deleted file mode 100644
index 09d49d514..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation5.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation6.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation6.fits.gz
deleted file mode 100644
index 65fd06137..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation6.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation7.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation7.fits.gz
deleted file mode 100644
index 7764d6b73..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation8.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation8.fits.gz
deleted file mode 100644
index bf02da882..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation8.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation9.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation9.fits.gz
deleted file mode 100644
index ab27aa04f..000000000
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/delta_attributes_delta_attributesation9.fits.gz and /dev/null differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/update_fits.py b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/update_fits.py
deleted file mode 100644
index fd45e5665..000000000
--- a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nodla/update_fits.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-import astropy.io.fits as fits
-import numpy as np
-
-
-filenames = ["delta-28.fits.gz", "delta-45.fits.gz"]
-
-for filename in filenames:
-    hdul = fits.open(filename)
-
-    for hdu in hdul[1:]:
-
-        hdu.header["TUNIT4"] = 'Flux units'
-        hdu.header["LOS_ID"] = hdu.header["THING_ID"]
-
-        if "PMF"in hdu.header:
-            pmf = hdu.header["PMF"]
-            hdu.header["PLATE"] = f"{int(pmf.split('-')[0]):04d}"
-            hdu.header["MJD"] = f"{int(pmf.split('-')[1]):05d}"
-            hdu.header["FIBERID"] = f"{int(pmf.split('-')[2]):04d}"
-            del hdu.header["PMF"]
-
-        if "ORDER" in hdu.header:
-            del hdu.header["ORDER"]
-
-        if hdu.header["THING_ID"] in [428690499, 429522561, 434205493]:
-            hdu.header["PLATE"] = "3655-3657"
-            hdu.header["MJD"] = "55240-55244"
-            if hdu.header["THING_ID"] == 428690499:
-                hdu.header["FIBERID"] = "0054-0660"
-            elif hdu.header["THING_ID"] == 429522561:
-                hdu.header["FIBERID"] = "0179-0540"
-            elif hdu.header["THING_ID"] == 434205493:
-                hdu.header["FIBERID"] = "0890-0552"
-        elif hdu.header["THING_ID"] in [428035498, 430151253, 430805392,
-                                        431936632, 427513857, 427878017,
-                                        428707431, 429231814, 429522957]:
-            hdu.header["PLATE"] = "3655-9367"
-            hdu.header["MJD"] = "55240-57758"
-            if hdu.header["THING_ID"] == 428035498:
-                hdu.header["FIBERID"] = "0344-0482"
-            elif hdu.header["THING_ID"] == 430151253:
-                hdu.header["FIBERID"] = "0370-0500"
-            elif hdu.header["THING_ID"] == 430805392:
-                hdu.header["FIBERID"] = "0273-0601"
-            elif hdu.header["THING_ID"] == 431936632:
-                hdu.header["FIBERID"] = "0368-0538"
-            elif hdu.header["THING_ID"] == 427513857:
-                hdu.header["FIBERID"] = "0310-0457"
-            elif hdu.header["THING_ID"] == 427878017:
-                hdu.header["FIBERID"] = "0228-0446"
-            elif hdu.header["THING_ID"] == 428707431:
-                hdu.header["FIBERID"] = "0186-0386"
-            elif hdu.header["THING_ID"] == 429231814:
-                hdu.header["FIBERID"] = "0100-0345"
-            elif hdu.header["THING_ID"] == 429522957:
-                hdu.header["FIBERID"] = "0198-0396"
-        elif hdu.header["THING_ID"] in [434932660]:
-            hdu.header["PLATE"] = "3655-9367-10656"
-            hdu.header["MJD"] = "55240-57758-58163"
-            if hdu.header["THING_ID"] == 434932660:
-                hdu.header["FIBERID"] = "0990-0836-0366"
-
-    names = sorted([hdul[i].header["EXTNAME"] for i in range(1, len(hdul))])
-    new_hdul = fits.HDUList([hdul[0]]+[hdul[name] for name in names])
-
-    new_hdul.writeto(filename, overwrite=True)
-    hdul.close()
-
-
-prefix = "iter"
-new_prefix = "delta_attributes"
-filenames = [prefix+".fits.gz"]+[prefix+f"_iteration{i}.fits.gz" for i in range(1, 10)]
-new_filenames = [filename.replace(prefix, new_prefix) for filename in filenames]
-
-for filename, new_filename in zip(filenames, new_filenames):
-    hdul = fits.open(filename)
-
-    hdul = hdul[:-1]
-
-    del hdul[1].header["PIXORDER"]
-    del hdul[1].header["NSIDE"]
-    data = hdul[1].data
-    for index in range(data["stack"].size):
-        if data["stack"][index] != 0.0:
-            data["stack"][:index] = data["stack"][index]
-            break
-    for index in range(data["stack"].size - 1, 0, -1):
-        if data["stack"][index] != 0.0:
-            data["stack"][index:] = data["stack"][index]
-            break
-    hdu = fits.BinTableHDU(data, name="STACK_DELTAS", header=hdul[1].header)
-    hdul[1] = hdu
-
-
-    data = np.array(hdul[2].data)[["loglam", "eta", "var_lss", "fudge"]]
-    hdu = fits.BinTableHDU(data, name="VAR_FUNC", header=hdul[2].header)
-    hdul[2] = hdu
-
-    #data = np.array(hdul[3].data)[["loglam_rest", "mean_cont"]]
-    #hdu = fits.BinTableHDU(data, name=hdul[3].name, header=hdul[3].header)
-    #hdul[3] = hdu
-
-    hdul.writeto(new_filename, overwrite=True)
-
-    hdul.close()
-    os.remove(filename)
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-28.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-28.fits.gz
index 336522763..c23b4964a 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-28.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-28.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-45.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-45.fits.gz
index 49f029df5..617bdfee1 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-45.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta-45.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes.fits.gz
index b26a4bf1a..6a9d9b7e2 100644
Binary files a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes.fits.gz and b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation1.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation1.fits.gz
deleted file mode 100644
index cd167be69..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation2.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation2.fits.gz
deleted file mode 100644
index 0702bf514..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation3.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation3.fits.gz
deleted file mode 100644
index 0ce351d1f..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation4.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation4.fits.gz
deleted file mode 100644
index 8b17a895a..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation5.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation5.fits.gz
deleted file mode 100644
index 1ed113a18..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation6.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation6.fits.gz
deleted file mode 100644
index 989a7553e..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation7.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation7.fits.gz
deleted file mode 100644
index 25b02fd2a..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation8.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation8.fits.gz
deleted file mode 100644
index 886529751..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation9.fits.gz b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/delta_attributes_delta_attributesation9.fits.gz
deleted file mode 100644
index e65e8fbbf..000000000
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diff --git a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/update_fits.py b/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/update_fits.py
deleted file mode 100644
index fd45e5665..000000000
--- a/py/picca/tests/delta_extraction/data/delta_extraction_lya_nomask_nodla/update_fits.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-import astropy.io.fits as fits
-import numpy as np
-
-
-filenames = ["delta-28.fits.gz", "delta-45.fits.gz"]
-
-for filename in filenames:
-    hdul = fits.open(filename)
-
-    for hdu in hdul[1:]:
-
-        hdu.header["TUNIT4"] = 'Flux units'
-        hdu.header["LOS_ID"] = hdu.header["THING_ID"]
-
-        if "PMF"in hdu.header:
-            pmf = hdu.header["PMF"]
-            hdu.header["PLATE"] = f"{int(pmf.split('-')[0]):04d}"
-            hdu.header["MJD"] = f"{int(pmf.split('-')[1]):05d}"
-            hdu.header["FIBERID"] = f"{int(pmf.split('-')[2]):04d}"
-            del hdu.header["PMF"]
-
-        if "ORDER" in hdu.header:
-            del hdu.header["ORDER"]
-
-        if hdu.header["THING_ID"] in [428690499, 429522561, 434205493]:
-            hdu.header["PLATE"] = "3655-3657"
-            hdu.header["MJD"] = "55240-55244"
-            if hdu.header["THING_ID"] == 428690499:
-                hdu.header["FIBERID"] = "0054-0660"
-            elif hdu.header["THING_ID"] == 429522561:
-                hdu.header["FIBERID"] = "0179-0540"
-            elif hdu.header["THING_ID"] == 434205493:
-                hdu.header["FIBERID"] = "0890-0552"
-        elif hdu.header["THING_ID"] in [428035498, 430151253, 430805392,
-                                        431936632, 427513857, 427878017,
-                                        428707431, 429231814, 429522957]:
-            hdu.header["PLATE"] = "3655-9367"
-            hdu.header["MJD"] = "55240-57758"
-            if hdu.header["THING_ID"] == 428035498:
-                hdu.header["FIBERID"] = "0344-0482"
-            elif hdu.header["THING_ID"] == 430151253:
-                hdu.header["FIBERID"] = "0370-0500"
-            elif hdu.header["THING_ID"] == 430805392:
-                hdu.header["FIBERID"] = "0273-0601"
-            elif hdu.header["THING_ID"] == 431936632:
-                hdu.header["FIBERID"] = "0368-0538"
-            elif hdu.header["THING_ID"] == 427513857:
-                hdu.header["FIBERID"] = "0310-0457"
-            elif hdu.header["THING_ID"] == 427878017:
-                hdu.header["FIBERID"] = "0228-0446"
-            elif hdu.header["THING_ID"] == 428707431:
-                hdu.header["FIBERID"] = "0186-0386"
-            elif hdu.header["THING_ID"] == 429231814:
-                hdu.header["FIBERID"] = "0100-0345"
-            elif hdu.header["THING_ID"] == 429522957:
-                hdu.header["FIBERID"] = "0198-0396"
-        elif hdu.header["THING_ID"] in [434932660]:
-            hdu.header["PLATE"] = "3655-9367-10656"
-            hdu.header["MJD"] = "55240-57758-58163"
-            if hdu.header["THING_ID"] == 434932660:
-                hdu.header["FIBERID"] = "0990-0836-0366"
-
-    names = sorted([hdul[i].header["EXTNAME"] for i in range(1, len(hdul))])
-    new_hdul = fits.HDUList([hdul[0]]+[hdul[name] for name in names])
-
-    new_hdul.writeto(filename, overwrite=True)
-    hdul.close()
-
-
-prefix = "iter"
-new_prefix = "delta_attributes"
-filenames = [prefix+".fits.gz"]+[prefix+f"_iteration{i}.fits.gz" for i in range(1, 10)]
-new_filenames = [filename.replace(prefix, new_prefix) for filename in filenames]
-
-for filename, new_filename in zip(filenames, new_filenames):
-    hdul = fits.open(filename)
-
-    hdul = hdul[:-1]
-
-    del hdul[1].header["PIXORDER"]
-    del hdul[1].header["NSIDE"]
-    data = hdul[1].data
-    for index in range(data["stack"].size):
-        if data["stack"][index] != 0.0:
-            data["stack"][:index] = data["stack"][index]
-            break
-    for index in range(data["stack"].size - 1, 0, -1):
-        if data["stack"][index] != 0.0:
-            data["stack"][index:] = data["stack"][index]
-            break
-    hdu = fits.BinTableHDU(data, name="STACK_DELTAS", header=hdul[1].header)
-    hdul[1] = hdu
-
-
-    data = np.array(hdul[2].data)[["loglam", "eta", "var_lss", "fudge"]]
-    hdu = fits.BinTableHDU(data, name="VAR_FUNC", header=hdul[2].header)
-    hdul[2] = hdu
-
-    #data = np.array(hdul[3].data)[["loglam_rest", "mean_cont"]]
-    #hdu = fits.BinTableHDU(data, name=hdul[3].name, header=hdul[3].header)
-    #hdul[3] = hdu
-
-    hdul.writeto(new_filename, overwrite=True)
-
-    hdul.close()
-    os.remove(filename)
diff --git a/py/picca/tests/delta_extraction/data/delta_stack_log.txt b/py/picca/tests/delta_extraction/data/delta_stack_log.txt
index 5358b3c75..afd290141 100644
--- a/py/picca/tests/delta_extraction/data/delta_stack_log.txt
+++ b/py/picca/tests/delta_extraction/data/delta_stack_log.txt
@@ -1,522 +1,522 @@
-#log_lambda delta
-3.5563025007672873 1.083779666797419
-3.5566025007672875 1.4259744857889003
-3.556902500767287 1.3185932955989494
-3.5572025007672874 1.213545779752848
-3.557502500767287 1.1064112084271467
-3.5578025007672873 1.2025996227196583
-3.558102500767287 1.1891763771161574
-3.5584025007672873 1.1943707979793032
-3.5587025007672874 1.2192654804833252
-3.559002500767287 1.32092888850951
-3.5593025007672874 1.18917526374498
-3.559602500767287 1.313043113443692
-3.5599025007672873 1.3788548401773821
-3.560202500767287 1.1590688459644098
-3.5605025007672872 1.0566274469902752
-3.5608025007672874 1.1238651100257144
-3.561102500767287 1.0533619257900662
-3.5614025007672874 0.8623664288552697
-3.561702500767287 1.017722473666433
-3.5620025007672873 1.104004663940706
-3.562302500767287 1.2632911918683516
-3.5626025007672872 1.2015596494234304
-3.5629025007672874 1.1977755407474966
-3.563202500767287 1.2420194644697633
-3.5635025007672874 1.2719194372629183
-3.563802500767287 1.1782498234747432
-3.5641025007672873 1.2823008003881686
-3.5644025007672875 1.196922979151318
-3.5647025007672872 0.9114017632472406
-3.5650025007672874 1.009476584668009
-3.565302500767287 1.0796630481426475
-3.5656025007672874 1.0993027111236129
-3.565902500767287 1.0747879862446212
-3.5662025007672873 1.1680731798564488
-3.5665025007672875 1.173443995189743
-3.566802500767287 1.254300268854448
-3.5671025007672874 1.0429587736442028
-3.567402500767287 1.19393944614633
-3.5677025007672873 1.2418476692684417
-3.568002500767287 1.0959997501038825
-3.5683025007672873 1.2221497797515335
-3.5686025007672875 1.0687809970634465
-3.568902500767287 1.0513796452690212
-3.5692025007672874 1.1733510370753881
-3.569502500767287 1.0253418312705995
-3.5698025007672873 0.8722627457464253
-3.570102500767287 1.0323725015494478
-3.5704025007672873 1.0008483121805398
-3.5707025007672875 1.019780945405698
-3.571002500767287 1.0272219048464104
-3.5713025007672874 1.1647634686452262
-3.571602500767287 1.0412146062544014
-3.5719025007672873 1.0809369792338606
-3.572202500767287 1.1063079792980808
-3.5725025007672873 1.0582352635038255
-3.5728025007672874 1.1430417616508153
-3.573102500767287 1.1007151060834603
-3.5734025007672874 1.0369842620456575
-3.573702500767287 1.0241151530526948
-3.5740025007672873 0.9377561720883836
-3.574302500767287 0.928990726099811
-3.5746025007672872 1.010305322335128
-3.5749025007672874 1.0041464770523731
-3.575202500767287 1.0403968161299482
-3.5755025007672874 1.009807653936573
-3.575802500767287 1.047627799021038
-3.5761025007672873 1.0058063063426996
-3.576402500767287 1.0810682705557295
-3.5767025007672872 1.022960098101701
-3.5770025007672874 0.9729824160730695
-3.577302500767287 1.0393432589186604
-3.5776025007672874 1.0218985805002008
-3.577902500767287 0.9809990771794913
-3.5782025007672873 0.968151924845718
-3.5785025007672875 1.061579337605434
-3.5788025007672872 0.986273662244439
-3.5791025007672874 1.0988762313655445
-3.579402500767287 1.017815502354803
-3.5797025007672874 1.0678374904893455
-3.580002500767287 1.0805808155103565
-3.5803025007672873 0.9778523482914175
-3.5806025007672875 0.8784056967260341
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-3.581502500767287 1.0395637019417978
-3.5818025007672873 1.0217129606694837
-3.582102500767287 1.1133411482145672
-3.5824025007672873 1.0549942918235191
-3.5827025007672875 1.0605534188929067
-3.583002500767287 1.002958387825857
-3.5833025007672874 1.054146422557425
-3.583602500767287 1.0456064700703172
-3.5839025007672873 1.039109265592182
-3.584202500767287 0.9124290525245758
-3.5845025007672873 1.088853079390024
-3.5848025007672875 1.0303530169233877
-3.585102500767287 1.0218035104325651
-3.5854025007672874 0.9955211193574927
-3.585702500767287 1.0036760788692696
-3.5860025007672873 1.0172533095103076
-3.586302500767287 1.0427147752481405
-3.5866025007672873 1.031221782358388
-3.5869025007672874 1.0311494904780785
-3.587202500767287 1.069810583566588
-3.5875025007672874 1.1018270796289884
-3.587802500767287 1.1734549210277259
-3.5881025007672873 1.1434766990385707
-3.588402500767287 1.1412228052734164
-3.5887025007672873 1.1077128390633864
-3.5890025007672874 1.0320240181145062
-3.589302500767287 1.054142036890344
-3.5896025007672874 1.0073132631406165
-3.589902500767287 1.1355673557984458
-3.5902025007672873 1.0834074639283227
-3.590502500767287 1.059019067893645
-3.5908025007672872 1.0190268726631737
-3.5911025007672874 1.0035456082075571
-3.591402500767287 0.9981473841920449
-3.5917025007672874 0.9993030392999086
-3.592002500767287 1.0362606408334867
-3.5923025007672873 0.9815127856703694
-3.5926025007672875 1.1288089245679307
-3.5929025007672872 1.1069696042497745
-3.5932025007672874 1.183584018334555
-3.593502500767287 0.9484853973250142
-3.5938025007672874 1.1746599863805571
-3.594102500767287 1.0753455903483942
-3.5944025007672873 1.1915130147887565
-3.5947025007672875 1.189507062947262
-3.5950025007672872 1.0632031250517255
-3.5953025007672874 1.1351984533205206
-3.595602500767287 1.121310127541083
-3.5959025007672873 1.120328770758063
-3.596202500767287 1.1318227684136335
-3.5965025007672873 1.1590395701361582
-3.5968025007672875 1.0613151405741899
-3.597102500767287 1.0021958711923338
-3.5974025007672874 1.2306216217462087
-3.597702500767287 1.0474100520361163
-3.5980025007672873 1.054532004441134
-3.598302500767287 0.9678196798092584
-3.5986025007672873 1.0179416004308024
-3.5989025007672875 1.220054177984968
-3.599202500767287 1.2235846319198576
-3.5995025007672874 1.1335043148185955
-3.599802500767287 1.0287948571569716
-3.6001025007672873 1.0191418493381827
-3.600402500767287 1.051915364483535
-3.6007025007672873 1.0865971581816607
-3.6010025007672875 1.0372703370363514
-3.601302500767287 1.0743890096503113
-3.6016025007672874 0.9423533573299617
-3.601902500767287 0.9828705309926978
-3.6022025007672873 1.0765778663682548
-3.602502500767287 1.054100272845366
-3.6028025007672873 1.0930853537477954
-3.6031025007672874 0.844823392582333
-3.603402500767287 0.816735124957373
-3.6037025007672874 1.03962687121813
-3.604002500767287 0.9796892440820263
-3.6043025007672873 1.0051846140675265
-3.604602500767287 0.9328115996901869
-3.6049025007672872 0.8629171079865567
-3.6052025007672874 0.999982812909427
-3.605502500767287 1.0444186987157373
-3.6058025007672874 0.9918843859616493
-3.606102500767287 0.9955471156709631
-3.6064025007672873 0.9163874645202545
-3.6067025007672875 0.8752230716745156
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-3.6100025007672873 1.116365527410411
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-3.6109025007672875 0.9696453785088206
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-3.613302500767287 1.032722123852847
-3.6136025007672874 1.042566320329628
-3.613902500767287 1.042551400292472
-3.6142025007672873 0.8436325918745529
-3.614502500767287 0.9125990452559894
-3.6148025007672873 0.88128569314326
-3.6151025007672875 1.014808755693715
-3.615402500767287 0.9845036797423146
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-3.6163025007672873 0.826303544830307
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-3.617502500767287 0.961432026700112
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-3.6184025007672873 0.993952185003923
-3.618702500767287 1.0619125026026082
-3.6190025007672872 1.040888719560617
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-3.6199025007672874 1.161586049235826
-3.620202500767287 1.0493803933003916
-3.6205025007672873 0.9261093885125073
-3.6208025007672875 1.1099728551796184
-3.6211025007672872 1.1943997765220529
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-3.621702500767287 0.8857829619337636
-3.6220025007672874 0.990011070152389
-3.622302500767287 1.116541473301533
-3.6226025007672873 1.0321899570528452
-3.6229025007672875 0.9127403969821621
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-3.624402500767287 1.1500904775000853
-3.6247025007672873 1.2474138866844897
-3.6250025007672875 1.1071361956503194
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-3.6256025007672874 0.9302277648136876
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-3.6262025007672873 1.1226366414890392
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diff --git a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log.fits.gz b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log.fits.gz
index cce30d0c8..fbe856233 100644
Binary files a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log.fits.gz and b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration1.fits.gz b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration1.fits.gz
index c22abb939..9851012dd 100644
Binary files a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration1.fits.gz and b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration1.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration2.fits.gz b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration2.fits.gz
index 7881f5685..b94839ec2 100644
Binary files a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration2.fits.gz and b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration2.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration3.fits.gz b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration3.fits.gz
index 6707fc653..0ce0e164c 100644
Binary files a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration3.fits.gz and b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration3.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration4.fits.gz b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration4.fits.gz
index bcdceef1d..e4ddcaf6f 100644
Binary files a/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration4.fits.gz and b/py/picca/tests/delta_extraction/data/iter_out_prefix_compute_expected_flux_log_iteration4.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/iter_out_prefix_log.fits.gz b/py/picca/tests/delta_extraction/data/iter_out_prefix_log.fits.gz
index dc29c0da7..721c17d68 100644
Binary files a/py/picca/tests/delta_extraction/data/iter_out_prefix_log.fits.gz and b/py/picca/tests/delta_extraction/data/iter_out_prefix_log.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/iter_out_prefix_log_iteration1.fits.gz b/py/picca/tests/delta_extraction/data/iter_out_prefix_log_iteration1.fits.gz
index dc29c0da7..721c17d68 100644
Binary files a/py/picca/tests/delta_extraction/data/iter_out_prefix_log_iteration1.fits.gz and b/py/picca/tests/delta_extraction/data/iter_out_prefix_log_iteration1.fits.gz differ
diff --git a/py/picca/tests/delta_extraction/data/mean_cont_log.txt b/py/picca/tests/delta_extraction/data/mean_cont_log.txt
index 2bcc8ae73..7085fcdf9 100644
--- a/py/picca/tests/delta_extraction/data/mean_cont_log.txt
+++ b/py/picca/tests/delta_extraction/data/mean_cont_log.txt
@@ -1,209 +1,209 @@
 # log_lambda mean_cont
-3.017182733305388 1.6544659983430479
-3.0174815213186035 1.5160753691168605
-3.0177803093318194 1.369826840325626
-3.018079097345035 1.2564266775099295
-3.0183778853582504 1.1354406219829871
-3.018676673371466 1.1101610449737447
-3.0189754613846818 1.214093504146901
-3.0192742493978972 1.344074796782075
-3.0195730374111127 1.2126895038170444
-3.0198718254243286 1.167542249528433
-3.020170613437544 1.2038877536456845
-3.0204694014507596 1.147315342419476
-3.020768189463975 1.0909711394896222
-3.021066977477191 1.0622789716239465
-3.0213657654904065 0.9252376722137859
-3.021664553503622 1.1932637091682077
-3.0219633415168374 1.1524436559974744
-3.0222621295300534 1.129237455057739
-3.022560917543269 1.1325246004329284
-3.0228597055564843 1.1274797363736613
-3.0231584935697002 1.077065971625745
-3.0234572815829157 1.034152035359352
-3.023756069596131 1.0378208214178042
-3.0240548576093467 1.0133345760004968
-3.0243536456225626 1.1791062547885276
-3.024652433635778 1.1391669080968583
-3.0249512216489935 1.0115205775524394
-3.025250009662209 1.0450334943654565
-3.025548797675425 1.184693294516171
-3.0258475856886404 1.2166039051816797
-3.026146373701856 1.208522652170133
-3.0264451617150714 1.1253029314422818
-3.0267439497282873 1.0826612844178194
-3.027042737741503 1.1690903131358616
-3.0273415257547183 1.1491282145131234
-3.027640313767934 1.1077691547656143
-3.0279391017811497 0.8851590983423596
-3.028237889794365 0.9363497229393448
-3.0285366778075806 1.0083755853733463
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diff --git a/py/picca/tests/delta_extraction/data_tests.py b/py/picca/tests/delta_extraction/data_tests.py
index 02b3b2429..11c62ed4d 100644
--- a/py/picca/tests/delta_extraction/data_tests.py
+++ b/py/picca/tests/delta_extraction/data_tests.py
@@ -43,6 +43,9 @@ def test_data(self):
         config.read_dict({"data": {
                             "out dir": f"{THIS_DIR}/results/",
                             "rejection log file": "rejection_log.fits.gz",
+                            "wave solution": "log",
+                            "delta log lambda": 3e-4,
+                            "input directory": f"{THIS_DIR}/data/",
                          }})
         for key, value in defaults_data.items():
             if key not in config["data"]:
@@ -57,6 +60,9 @@ def test_data(self):
         config.read_dict({"data": {"minimum number pixels in forest": 40,
                                    "out dir": f"{THIS_DIR}/results/",
                                    "rejection log file": "rejection_log.fits.gz",
+                                   "wave solution": "log",
+                                   "delta log lambda": 3e-4,
+                                   "input directory": f"{THIS_DIR}/data/",
                          }})
         for key, value in defaults_data.items():
             if key not in config["data"]:
@@ -80,6 +86,9 @@ def test_data_filter_forests(self):
         config.read_dict({"data": {
                             "out dir": f"{THIS_DIR}/results/",
                             "rejection log file": "rejection_log.fits.gz",
+                            "wave solution": "log",
+                            "delta log lambda": 3e-4,
+                            "input directory": f"{THIS_DIR}/data/",
                         }})
         for key, value in defaults_data.items():
             if key not in config["data"]:
@@ -99,6 +108,9 @@ def test_data_filter_forests(self):
         config.read_dict({"data": {"minimum number pixels in forest": 10000,
                                    "out dir": f"{THIS_DIR}/results/",
                                    "rejection log file": "rejection_log.fits.gz",
+                                   "wave solution": "log",
+                                   "delta log lambda": 3e-4,
+                                   "input directory": f"{THIS_DIR}/data/",
                          }})
         for key, value in defaults_data.items():
             if key not in config["data"]:
diff --git a/py/picca/tests/delta_extraction/expected_flux_tests.py b/py/picca/tests/delta_extraction/expected_flux_tests.py
index f353804ec..96f9e6a6a 100644
--- a/py/picca/tests/delta_extraction/expected_flux_tests.py
+++ b/py/picca/tests/delta_extraction/expected_flux_tests.py
@@ -88,10 +88,7 @@ def test_dr16_expected_flux(self):
         self.assertTrue(isinstance(expected_flux.get_mean_cont, interp1d))
         self.assertTrue(isinstance(expected_flux.get_var_lss, interp1d))
         self.assertTrue(expected_flux.lambda_ is None)
-        self.assertTrue(expected_flux.lambda_rest_frame is None)
         self.assertTrue(isinstance(expected_flux.log_lambda, np.ndarray))
-        self.assertTrue(
-            isinstance(expected_flux.log_lambda_rest_frame, np.ndarray))
 
         # setup Forest variables; case: linear wavelength solution
         reset_forest()
@@ -103,15 +100,14 @@ def test_dr16_expected_flux(self):
         self.assertTrue(isinstance(expected_flux.get_mean_cont, interp1d))
         self.assertTrue(isinstance(expected_flux.get_var_lss, interp1d))
         self.assertTrue(isinstance(expected_flux.lambda_, np.ndarray))
-        self.assertTrue(isinstance(expected_flux.lambda_rest_frame, np.ndarray))
         self.assertTrue(expected_flux.log_lambda is None)
-        self.assertTrue(expected_flux.log_lambda_rest_frame is None)
 
     def test_dr16_expected_flux_compute_continuum(self):
         """Test method compute_continuum for class Dr16ExpectedFlux"""
         # setup Forest variables; case: logarithmic wavelength solution
         setup_forest("log")
 
+        out_file = f"{THIS_DIR}/results/continua_log.txt"
         test_file = f"{THIS_DIR}/data/continua_log.txt"
 
         # initialize Data and Dr16ExpectedFlux instances
@@ -136,6 +132,16 @@ def test_dr16_expected_flux_compute_continuum(self):
         for forest in data.forests:
             expected_flux.compute_continuum(forest)
 
+        # save the results
+        f = open(out_file, "w")
+        f.write("# thingid cont[0] ... cont[N]\n")
+        for forest in data.forests:
+            f.write(f"{forest.los_id} ")
+            for item in forest.continuum:
+                f.write(f"{item} ")
+            f.write("\n")
+        f.close()
+
         # load expected forest continua
         continua = {}
         f = open(test_file)
@@ -151,6 +157,12 @@ def test_dr16_expected_flux_compute_continuum(self):
         # compare the results
         correct_forests = 0
         for forest in data.forests:
+            if not np.allclose(forest.continuum, continua.get(forest.los_id)):
+                print("Difference found in forest.continuum")
+                print(f"forest.los_id: {forest.los_id}")
+                print(f"result test are_close result-test")
+                for i1, i2 in zip(forest.continuum, continua.get(forest.los_id)):
+                    print(i1, i2, np.isclose(i1, i2), i1-i2)
             self.assertTrue(
                 np.allclose(forest.continuum, continua.get(forest.los_id)))
             correct_forests += 1
@@ -168,6 +180,7 @@ def test_dr16_expected_flux_compute_delta_stack(self):
         # setup Forest variables; case: logarithmic wavelength solution
         setup_forest("log")
 
+        out_file = f"{THIS_DIR}/results/delta_stack_log.txt"
         test_file = f"{THIS_DIR}/data/delta_stack_log.txt"
 
         # initialize Data and Dr16ExpectedFlux instances
@@ -195,11 +208,25 @@ def test_dr16_expected_flux_compute_delta_stack(self):
         # compute variance functions and statistics
         expected_flux.compute_delta_stack(data.forests)
 
+        # save results
+        f = open(out_file, "w")
+        f.write("# log_lambda delta\n")
+        for log_lambda in np.arange(3.5563025, 3.7123025 + 3e-4, 3e-4):
+            f.write(f"{log_lambda} {expected_flux.get_stack_delta(log_lambda)}\n")
+        f.close()
+
         # load expected delta stack
         expectations = np.genfromtxt(test_file, names=True)
 
         # compare with obtained results
         stack_delta = expected_flux.get_stack_delta(expectations["log_lambda"])
+        if not np.allclose(stack_delta, expectations["delta"]):
+            print(f"\nOriginal file: {test_file}")
+            print(f"New file: {out_file}")
+            print("Difference found in delta stack")
+            print(f"result test are_close result-test")
+            for i1, i2 in zip(stack_delta, expectations["delta"]):
+                print(i1, i2, np.isclose(i1, i2), i1-i2)
         self.assertTrue(np.allclose(stack_delta, expectations["delta"]))
 
         # setup Forest variables; case: linear wavelength solution
@@ -237,6 +264,7 @@ def test_dr16_expected_flux_compute_expected_flux(self):
         # compute the expected flux
         expected_flux.compute_expected_flux(data.forests)
 
+        # check the results
         for iteration in range(1, 5):
             self.compare_fits(
                 test_file.replace(".fits", f"_iteration{iteration}.fits"),
@@ -262,6 +290,7 @@ def test_dr16_expected_flux_compute_mean_cont_log(self):
         # setup Forest variables; case: logarithmic wavelength solution
         setup_forest("log")
 
+        out_file = f"{THIS_DIR}/results/mean_cont_log.txt"
         test_file = f"{THIS_DIR}/data/mean_cont_log.txt"
 
         # initialize Data and Dr16ExpectedFlux instances
@@ -289,11 +318,30 @@ def test_dr16_expected_flux_compute_mean_cont_log(self):
         # compute mean quasar continuum
         expected_flux.compute_mean_cont_log(data.forests)
 
+        # save results
+        f = open(out_file, "w")
+        f.write("# log_lambda delta\n")
+        for log_lambda in np.arange(3.0171, 3.079 + 3e-4, 3e-4):
+            f.write(f"{log_lambda} {expected_flux.get_mean_cont(log_lambda)}\n")
+        f.close()
+
         # load the expected results
         expectations = np.genfromtxt(test_file, names=True)
+        f = open(f"{THIS_DIR}/results/mean_cont_log.txt", "w")
+        f.write("# log_lambda mean_cont\n")
+        for item in expectations["log_lambda"]:
+            f.write(f"{item} {expected_flux.get_mean_cont(item)}\n")
+        f.close()
 
         # compare with obtained results
         mean_cont = expected_flux.get_mean_cont(expectations["log_lambda"])
+        if not np.allclose(mean_cont, expectations["mean_cont"]):
+            print(f"\nOriginal file: {test_file}")
+            print(f"New file: {out_file}")
+            print("Difference found in mean_cont")
+            print(f"result test are_close result-test")
+            for i1, i2 in zip(mean_cont, expectations["mean_cont"]):
+                print(i1, i2, np.isclose(i1, i2), i1-i2)
         self.assertTrue(np.allclose(mean_cont, expectations["mean_cont"]))
 
     def test_dr16_expected_flux_compute_var_stats(self):
diff --git a/py/picca/tests/delta_extraction/test_utils.py b/py/picca/tests/delta_extraction/test_utils.py
index 41e22b024..bf8a8b105 100644
--- a/py/picca/tests/delta_extraction/test_utils.py
+++ b/py/picca/tests/delta_extraction/test_utils.py
@@ -13,22 +13,16 @@
 def reset_forest():
     """Reset the class variables of Forest and Pk1dForest"""
     Forest.wave_solution = None
-    Forest.delta_log_lambda = None
-    Forest.delta_log_lambda = None
-    Forest.lambda_max = None
-    Forest.lambda_max_rest_frame = None
-    Forest.lambda_min = None
-    Forest.lambda_min_rest_frame = None
-    Forest.log_lambda_max = None
-    Forest.log_lambda_max_rest_frame = None
-    Forest.log_lambda_min = None
-    Forest.log_lambda_min_rest_frame = None
+    Forest.lambda_grid = None
+    Forest.lambda_rest_frame_grid = None
+    Forest.log_lambda_grid = None
+    Forest.log_lambda_rest_frame_grid = None
     Forest.mask_fields = []
     Pk1dForest.lambda_abs_igm = None
 
 
 # setup Forest class variables
-def setup_forest(wave_solution):
+def setup_forest(wave_solution, rebin=1):
     """Set Forest class variables
 
     Arguments
@@ -36,25 +30,21 @@ def setup_forest(wave_solution):
     wave_solution: "log" or "lin"
     Determines whether the wavelength solution has linear spacing ("lin") or
     logarithmic spacing ("log").
+
+    rebin: int
+    If wave_solution is "log", number of pixels that will be rebinned
     """
     assert wave_solution in ["log", "lin"]
 
     if wave_solution == "log":
-        Forest.wave_solution = "log"
-        Forest.delta_log_lambda = 1e-4
-        Forest.log_lambda_max = np.log10(5500.0)
-        Forest.log_lambda_max_rest_frame = np.log10(1200.0)
-        Forest.log_lambda_min = np.log10(3600.0)
-        Forest.log_lambda_min_rest_frame = np.log10(1040.0)
+        pixel_step = 1e-4 * rebin
     elif wave_solution == "lin":
-        Forest.wave_solution = "lin"
-        Forest.delta_lambda = 1.
-        Forest.lambda_max = 5500.0
-        Forest.lambda_max_rest_frame = 1200.0
-        Forest.lambda_min = 3600.0
-        Forest.lambda_min_rest_frame = 1040.0
+        pixel_step = 1.
+
+    Forest.set_class_variables(3600.0, 5500.0, 1040.0, 1200.0, pixel_step,
+                               wave_solution)
 
-setup_forest("log")
+setup_forest("log", rebin=3)
 
 # setup Pk1dForest class variables
 def setup_pk1d_forest(absorber):
@@ -73,7 +63,7 @@ def setup_pk1d_forest(absorber):
 # has:
 # * 1 DLA in dummy_absorbers_cat.fits.gz
 # * 1 absorber in dummy_absorbers_cat.fits.gz
-forest1_log_lambda = np.arange(3.562, 3.62, 1e-3)
+forest1_log_lambda = np.arange(3.5617025, 3.6200525, 3e-4)
 kwargs1 = {
     "los_id": 9999,
     "ra": 0.15,
@@ -100,7 +90,7 @@ def setup_pk1d_forest(absorber):
 # has:
 # * 1 DLA in dummy_absorbers_cat.fits.gz
 # * 1 absorber in dummy_absorbers_cat.fits.gz
-forest2_log_lambda = np.arange(3.562, 3.62, 1e-3)
+forest2_log_lambda = np.arange(3.5617025, 3.6200525, 3e-4)
 kwargs2 = {
     "los_id": 9999,
     "ra": 0.15,
@@ -127,7 +117,7 @@ def setup_pk1d_forest(absorber):
 # has:
 # * 0 DLA in dummy_absorbers_cat.fits.gz
 # * 0 absorber in dummy_absorbers_cat.fits.gz
-forest3_log_lambda = np.arange(3.562, 3.62, 1e-3)
+forest3_log_lambda = np.arange(3.5617025, 3.6200525, 3e-4)
 kwargs3 = {
     "los_id": 9999,
     "ra": 0.15,