diff --git a/fink_science/common/__init__.py b/fink_science/common/__init__.py
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diff --git a/fink_science/common/processor.py b/fink_science/common/processor.py
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+# Copyright 2022 AstroLab Software
+# Author: Julien Peloton
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pyspark.sql.functions import pandas_udf, PandasUDFType
+from pyspark.sql.types import ArrayType, FloatType
+
+import pandas as pd
+import numpy as np
+
+import os
+
+from fink_science import __file__
+from fink_utils.photometry.vect_conversion import vect_dc_mag
+
+from fink_science.tester import spark_unit_tests
+
+@pandas_udf(ArrayType(ArrayType(FloatType())), PandasUDFType.SCALAR)
+def dcmag(fid, magpsf, sigmapsf, magnr, sigmagnr, magzpsci, isdiffpos):
+    """ Correct lightcurves (DC magnitude)
+
+    Parameters
+    ----------
+    fid: ints
+        filter, 1 for green and 2 for red
+    magpsf, sigmapsf; floats
+        magnitude from PSF-fit photometry, and 1-sigma error
+    magnr, sigmagnr: floats
+        magnitude of nearest source in reference image PSF-catalog
+        within 30 arcsec and 1-sigma error
+    magzpsci: floats
+        Magnitude zero point for photometry estimates
+    isdiffpos: strs
+        t or 1 => candidate is from positive (sci minus ref) subtraction;
+        f or 0 => candidate is from negative (ref minus sci) subtraction
+
+    Returns
+    ----------
+    pd.Series
+        Series with [dcmag, dcmagerr] rows
+
+    Examples
+    ----------
+    >>> from fink_science.xmatch.processor import xmatch_cds
+    >>> from fink_utils.spark.utils import concat_col
+    >>> from pyspark.sql import functions as F
+
+    >>> df = spark.read.load(ztf_alert_sample)
+
+    >>> df = xmatch_cds(df)
+
+    # Required alert columns
+    >>> what = ['jd', 'fid', 'magpsf', 'sigmapsf', 'magnr', 'sigmagnr', 'magzpsci', 'isdiffpos']
+
+    # Use for creating temp name
+    >>> prefix = 'c'
+    >>> what_prefix = [prefix + i for i in what]
+
+    # Append temp columns with historical + current measurements
+    >>> for colname in what:
+    ...    df = concat_col(df, colname, prefix=prefix)
+
+    >>> df = df.withColumn(
+    ...     'dc',
+    ...     dcmag('cfid', 'cmagpsf', 'csigmapsf', 'cmagnr', 'csigmagnr', 'cmagzpsci', 'cisdiffpos')
+    ... ).withColumn(
+    ...     'dcmag',
+    ...     F.col('dc').getItem(0)
+    ... ).withColumn(
+    ...     'dcmagerr',
+    ...     F.col('dc').getItem(1)
+    ... ).drop('dc', 'cmagpsf', 'csigmapsf', 'cmagnr', 'csigmagnr', 'cmagzpsci', 'cisdiffpos')
+    """
+    out = []
+    for args in zip(fid, magpsf, sigmapsf, magnr, sigmagnr, magzpsci, isdiffpos):
+        dcmag, err = vect_dc_mag(*args)
+        out.append([dcmag, err])
+
+    return pd.Series(out)
+
+@pandas_udf(FloatType(), PandasUDFType.SCALAR)
+def rate(magpsf, jd, fid, filt, absolute):
+    """ Compute (g(t + dt) - g(t)) / (dt)
+    """
+    filt = filt.values[0]
+    absolute = absolute.values[0]
+
+    delta = []
+    for m, t, f in zip(magpsf.values, jd.values, fid.values):
+        mask = m == m
+
+        maskfilt = f[mask] == filt
+
+        if len(m[mask][maskfilt]) > 1:
+            dm = m[mask][maskfilt][-1] - m[mask][maskfilt][-2]
+            if not absolute:
+                dt = t[mask][maskfilt][-1] - t[mask][maskfilt][-2]
+            else:
+                dt = 1.
+            if dt > 0:
+                delta.append(dm / dt)
+            else:
+                delta.append(None)
+        else:
+            delta.append(None)
+    return pd.Series(delta)
+
+@pandas_udf(FloatType(), PandasUDFType.SCALAR)
+def color(magpsf, jd, fid, absolute):
+    """ Compute (g-r)(t + dt) - (g-r)(t)
+    """
+    absolute = absolute.values[0]
+    delta = []
+    for m, t, f in zip(magpsf.values, jd.values, fid.values):
+        mask = m == m
+
+        n = len(m[mask])
+        ng = np.sum(f[mask] == 1)
+        nr = np.sum(f[mask] == 2)
+
+        if (n < 4) or (ng < 2) or (nr < 2):
+            delta.append(None)
+        else:
+            maskg = f[mask] == 1
+            maskr = f[mask] == 2
+
+            pdfg = pd.DataFrame(
+                {
+                    'mag': m[mask][maskg],
+                    'filt': f[mask][maskg],
+                    'time': t[mask][maskg]
+                }
+            )
+            pdfr = pd.DataFrame(
+                {
+                    'mag': m[mask][maskr],
+                    'filt': f[mask][maskr],
+                    'time': t[mask][maskr]
+                }
+            )
+
+            dgr0 = pdfg['mag'].values[-1] - pdfr['mag'].values[-1]
+            dgr1 = pdfg['mag'].values[-2] - pdfr['mag'].values[-2]
+
+            if absolute:
+                delta.append(dgr0 - dgr1)
+            else:
+                t0 = (pdfg['time'].values[-1] + pdfr['time'].values[-1]) / 2
+                t1 = (pdfg['time'].values[-2] + pdfr['time'].values[-2]) / 2
+
+                dt = t0 - t1
+                delta.append((dgr0 - dgr1) / dt)
+
+    return pd.Series(delta)
+
+
+if __name__ == "__main__":
+    """ Execute the test suite """
+
+    globs = globals()
+    path = os.path.dirname(__file__)
+
+    ztf_alert_sample = 'file://{}/data/alerts/datatest'.format(path)
+    globs["ztf_alert_sample"] = ztf_alert_sample
+
+    # Run the test suite
+    spark_unit_tests(globs)