diff --git a/pyspectools/spectra/analysis.py b/pyspectools/spectra/analysis.py
index 8d03731..864ecc8 100644
--- a/pyspectools/spectra/analysis.py
+++ b/pyspectools/spectra/analysis.py
@@ -282,6 +282,68 @@ def search_center_frequency(frequency, width=0.5):
         return None
 
 
+def calc_line_weighting(
+        self, frequency, catalog_df, prox=0.00005,
+        abs=True, freq_col="Frequency", int_col="Intensity"
+):
+    """
+        Function for calculating the weighting factor for determining
+        the likely hood of an assignment. The weighting factor is
+        determined by the proximity of the catalog frequency to the
+        observed frequency, as well as the theoretical intensity if it
+        is available.
+        Parameters
+        ----------------
+         frequency : float
+            Observed frequency in MHz
+         catalog_df : dataframe
+            Pandas dataframe containing the catalog data entries
+         prox: float, optional
+            Frequency proximity threshold
+         abs: bool
+            Specifies whether argument prox is taken as the absolute value
+        Returns
+        ---------------
+        None
+            If nothing matches the frequency, returns None.
+        dataframe
+            If matches are found, calculate the weights and return the
+            candidates in a dataframe.
+    """
+    if abs is False:
+        lower_freq = frequency * (1 - prox)
+        upper_freq = frequency * (1 + prox)
+    else:
+        lower_freq = frequency - prox
+        upper_freq = frequency + prox
+    sliced_catalog = catalog_df.loc[
+        catalog_df[freq_col].between(lower_freq, upper_freq)
+    ]
+    nentries = len(sliced_catalog)
+    if nentries > 0:
+        if int_col in sliced_catalog:
+            column = sliced_catalog[int_col]
+        elif "CDMS/JPL Intensity" in sliced_catalog:
+            column = sliced_catalog["CDMS/JPL Intensity"]
+        else:
+            column = None
+        # Vectorized function for calculating the line weighting
+        sliced_catalog["Weighting"] = line_weighting(
+            frequency, sliced_catalog[freq_col], column
+        )
+        # Normalize and sort the weights only if there are more than one
+        # candidates
+        if nentries > 1:
+            sliced_catalog.loc[:, "Weighting"] /= sliced_catalog[
+                "Weighting"].max()
+            # Sort by obs-calc
+            sliced_catalog.sort_values(["Weighting"], ascending=False,
+                                       inplace=True)
+        sliced_catalog.reset_index(drop=True, inplace=True)
+        return sliced_catalog
+    else:
+        return None
+
 def brute_harmonic_search(frequencies, maxJ=10, dev_thres=5., prefilter=False):
     """
         Function that will search for possible harmonic candidates
diff --git a/pyspectools/spectra/assignment.py b/pyspectools/spectra/assignment.py
index 2947f17..3bd2e4d 100644
--- a/pyspectools/spectra/assignment.py
+++ b/pyspectools/spectra/assignment.py
@@ -973,7 +973,7 @@ def process_splatalogue(self, auto=True, progressbar=True):
                         splat_df.drop(index, inplace=True)
                 nitems = len(splat_df)
 
-                splat_df = self.calc_line_weighting(
+                splat_df = analysis.calc_line_weighting(
                     frequency, splat_df, prox=self.session.freq_prox, abs=self.session.freq_abs
                 )
                 if splat_df is not None:
@@ -1014,7 +1014,7 @@ def process_splatalogue(self, auto=True, progressbar=True):
                             "deviation": frequency - ass_df["Frequency"][0]
                         }
                         # Update the Transition entry
-                        uline.update(**ass_dict)
+                        uline.__dict__.update(**ass_dict)
                     except ValueError:
                         # If nothing matches, keep in the U-line
                         # pile.
@@ -1024,66 +1024,6 @@ def process_splatalogue(self, auto=True, progressbar=True):
                     self.logger.info("No species known for {:,.4f}".format(frequency))
         self.logger.info("Splatalogue search finished.")
 
-    def calc_line_weighting(
-            self, frequency, catalog_df, prox=0.00005,
-            abs=True, freq_col="Frequency", int_col="Intensity"
-    ):
-        """
-            Function for calculating the weighting factor for determining
-            the likely hood of an assignment. The weighting factor is
-            determined by the proximity of the catalog frequency to the
-            observed frequency, as well as the theoretical intensity if it
-            is available.
-
-            Parameters
-            ----------------
-             frequency : float
-                Observed frequency in MHz
-             catalog_df : dataframe
-                Pandas dataframe containing the catalog data entries
-             prox: float, optional
-                Frequency proximity threshold
-             abs: bool
-                Specifies whether argument prox is taken as the absolute value
-
-            Returns
-            ---------------
-            None
-                If nothing matches the frequency, returns None.
-            dataframe
-                If matches are found, calculate the weights and return the candidates in a dataframe.
-        """
-        if abs is False:
-            lower_freq = frequency * (1 - prox)
-            upper_freq = frequency * (1 + prox)
-        else:
-            lower_freq = frequency - prox
-            upper_freq = frequency + prox
-        sliced_catalog = catalog_df.loc[
-                catalog_df[freq_col].between(lower_freq, upper_freq)
-            ]
-        nentries = len(sliced_catalog)
-        if nentries > 0:
-            if int_col in sliced_catalog:
-                column = sliced_catalog[int_col]
-            elif "CDMS/JPL Intensity" in sliced_catalog:
-                column = sliced_catalog["CDMS/JPL Intensity"]
-            else:
-                column = None
-            # Vectorized function for calculating the line weighting
-            sliced_catalog["Weighting"] = analysis.line_weighting(
-                frequency, sliced_catalog[freq_col], column
-            )
-            # Normalize and sort the weights only if there are more than one candidates
-            if nentries > 1:
-                sliced_catalog.loc[:, "Weighting"] /= sliced_catalog["Weighting"].max()
-                # Sort by obs-calc
-                sliced_catalog.sort_values(["Weighting"], ascending=False, inplace=True)
-            sliced_catalog.reset_index(drop=True, inplace=True)
-            return sliced_catalog
-        else:
-            return None
-
     def process_linelist(self, name=None, formula=None, filepath=None, linelist=None, auto=True, thres=-10.,
                          progressbar=True, tol=None, **kwargs,):
         """
diff --git a/setup.py b/setup.py
index 3b537e7..c7b93c5 100644
--- a/setup.py
+++ b/setup.py
@@ -159,7 +159,7 @@ def run(self):
 
 setup(
     name="pyspectools",
-    version="4.0.1",
+    version="4.0.2",
     description="A set of Python tools/routines for spectroscopy",
     author="Kelvin Lee",
     packages=find_packages(),