Low state blazar

fink_science/blazar_low_state/README.md

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+# Quiescent state of blazar detection
+
+This module adds a new column containing quantities that indicate if the last observation of a given blazar can classify it as a **quiescent state**.
+The available blazar set comes from the list of blazars that are planned to be monitored by the CTAO collaboration from the start of the South and North sites.
+
+These quantities are:
+* The sliding mean of standardized flux of the last but new alert over a specific threshold computed for the source beforehand
+* The sliding mean of standardized flux of the new alert over the same threshold computed for the source beforehand
+* The standardized flux of the last alert over the same threshold
+
+If the last two ratio are below one, the source is considered to be in a quiescent state.
+If the source is in a quiescent state but the first ratio is above one, this quiescent state is considered to have just started.

fink_science/blazar_low_state/processor.py

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+# Copyright 2025 AstroLab Software
+# Author: Julian Hamo
+#
+# 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 line_profiler import profile
+
+import pandas as pd
+
+from pyspark.sql.functions import pandas_udf
+from pyspark.sql.types import ArrayType, DoubleType
+from fink_science.blazar_low_state.utils import quiescent_state_
+
+from fink_science.tester import spark_unit_tests
+from fink_science import __file__
+import os
+
+RELEASE = 22
+
+
+@pandas_udf(ArrayType(DoubleType()))
+@profile
+def quiescent_state(
+        candid: pd.Series,
+        objectId: pd.Series,
+        cstd_flux: pd.Series,
+        cjd: pd.Series) -> pd.Series:
+    """Returns an array containing:
+            The mean over threshold ratio of the last but one alert
+            The mean over threshold ratio of the last alert
+            The standardized flux over threshold ratio of the last alert
+
+    Parameters
+    ----------
+    pdf: pd.core.frame.DataFrame
+        Pandas DataFrame of the alert history containing:
+        candid, ojbectId, cdistnr, cmagpsf, csigmapsf, cmagnr,
+        csigmagnr, cisdiffpos, cfid, cjd, cstd_flux, csigma_std_flux
+    CTAO_blazar: pd.core.frame.DataFrame
+        Pandas DataFrame of the monitored sources containing:
+        3FGL Name, ZTF Name, Arrays of Medians, Computed Threshold,
+        Observed Threshold, Redshift, Final Threshold
+    Returns
+    -------
+    out: pd.Series of np.ndarray of np.float64
+        Array of ratios for:
+        Mean over threshold of the last but one alert
+        Mean over threshold of the last alert
+        Measurement over threshold of the last alert
+        All the default values are set to -1 as it is an unphysical value
+
+    Examples
+    --------
+    >>> import os
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from fink_utils.spark.utils import concat_col
+    >>> import pyspark.sql.functions as F
+    >>> from fink_science.standardized_flux.processor import standardized_flux
+
+    >>> parDF = spark.read.parquet(ztf_alert_sample)
+
+    # Required alert columns
+    >>> what = [
+    ...     'distnr',
+    ...     'magpsf',
+    ...     'sigmapsf',
+    ...     'magnr',
+    ...     'sigmagnr',
+    ...     'isdiffpos',
+    ...     'fid',
+    ...     'jd'
+    ... ]
+
+    # Concatenation
+    >>> prefix = 'c'
+    >>> for key in what:
+    ...     parDF = concat_col(parDF, colname=key, prefix=prefix)
+
+    # Preliminary module run
+    >>> args = [
+    ...     'candid',
+    ...     'objectId',
+    ...     'cdistnr',
+    ...     'cmagpsf',
+    ...     'csigmapsf',
+    ...     'cmagnr',
+    ...     'csigmagnr',
+    ...     'cisdiffpos',
+    ...     'cfid',
+    ...     'cjd'
+    ... ]
+    >>> parDF = parDF.withColumn(
+    ...     'container',
+    ...     standardized_flux(*args)
+    ... )
+    >>> parDF = parDF.withColumn(
+    ...     'cstd_flux',
+    ...     parDF['container'].getItem('flux')
+    ... )
+    >>> parDF = parDF.withColumn(
+    ...     'csigma_std_flux',
+    ...     parDF['container'].getItem('sigma')
+    ... )
+
+    # Drop temporary columns
+    >>> what_prefix = [prefix + key for key in what]
+    >>> parDF = parDF.drop('container')
+
+    # Test the module
+    >>> args = ['candid', 'objectId', 'cstd_flux', 'cjd']
+    >>> parDF = parDF.withColumn('blazar_stats', quiescent_state(*args))
+
+    # Test
+    >>> stats = parDF.select('blazar_stats').toPandas()['blazar_stats']
+    >>> stats = np.array(stats.to_list())
+    >>> stats[pd.isnull(stats)] = np.nan
+    >>> (stats.sum(axis=-1) == -3).sum()
+    320
+    """
+
+    path = os.path.dirname(os.path.abspath(__file__))
+    CTAO_PATH = os.path.join(path, 'data/catalogs')
+    CTAO_filename = 'CTAO_blazars_ztf_dr{}.parquet'.format(RELEASE)
+    CTAO_blazar = pd.read_parquet(os.path.join(CTAO_PATH, CTAO_filename))
+
+    pdf = pd.DataFrame(
+        {
+            "candid": candid,
+            "objectId": objectId,
+            "cstd_flux": cstd_flux,
+            "cjd": cjd
+        }
+    )
+    out = []
+    for candid_ in pdf["candid"]:
+        tmp = pdf[pdf["candid"] == candid_]
+        if len(tmp["cstd_flux"].to_numpy()[0]) == 0:
+            out.append([-1., -1., -1.])
+            continue
+        sub = pd.DataFrame(
+            {
+                "candid": tmp["candid"].to_numpy()[0],
+                "objectId": tmp["objectId"].to_numpy()[0],
+                "cstd_flux": tmp["cstd_flux"].to_numpy()[0],
+                "cjd": tmp["cjd"].to_numpy()[0],
+            }
+        )
+        out.append(quiescent_state_(sub, CTAO_blazar))
+
+    return pd.Series(out)
+
+
+if __name__ == "__main__":
+    """Execute the test suite"""
+
+    globs = globals()
+    path = os.path.join(os.path.dirname(__file__), 'data/alerts/datatest')
+    filename = 'CTAO_blazar_datatest_v20-12-24.parquet'
+    ztf_alert_sample = "file://{}/{}".format(path, filename)
+    globs["ztf_alert_sample"] = ztf_alert_sample
+
+    # Run the test suite
+    spark_unit_tests(globs)

fink_science/blazar_low_state/utils.py

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+import numpy as np
+import pandas as pd
+
+
+def instantness_criterion(
+        pdf: pd.DataFrame,
+        CTAO_blazar: pd.DataFrame) -> np.float64:
+    """Returns the standardized flux of the last measurement
+       over the precomputed threshold ratio
+
+    Parameters
+    ----------
+    pdf: pd.core.frame.DataFrame
+        Pandas DataFrame of the alert history containing:
+        candid, ojbectId, cdistnr, cmagpsf, csigmapsf, cmagnr,
+        csigmagnr, cisdiffpos, cfid, cjd, cstd_flux, csigma_std_flux
+    CTAO_blazar: pd.core.frame.DataFrame
+        Pandas DataFrame of the monitored sources containing:
+        3FGL Name, ZTF Name, Arrays of Medians, Computed Threshold,
+        Observed Threshold, Redshift, Final Threshold
+
+    Returns
+    -------
+    out: np.float64
+        Ratio of the standardized flux coming from the last measurement alert
+        over precomputed threshold
+    """
+
+    name = pdf['objectId'].values[0]
+
+    try:
+        threshold = np.array(
+            CTAO_blazar.loc[
+                CTAO_blazar['ZTF Name'] == name,
+                'Final Threshold'
+            ].values[0]
+        )
+    except IndexError:
+        threshold = np.nan
+
+    try:
+        return pdf['cstd_flux'].iloc[-1] / threshold
+    except KeyError:
+        return np.nan
+
+
+def robustness_criterion(
+        pdf: pd.DataFrame,
+        CTAO_blazar: pd.DataFrame) -> np.float64:
+    """Returns the sliding mean over 30 days of the standardized flux
+       over the precomputed threshold ratio
+
+    Parameters
+    ----------
+    pdf: pd.core.frame.DataFrame
+        Pandas DataFrame of the alert history containing:
+        candid, ojbectId, cdistnr, cmagpsf, csigmapsf, cmagnr,
+        csigmagnr, cisdiffpos, cfid, cjd, cstd_flux, csigma_std_flux
+    CTAO_blazar: pd.core.frame.DataFrame
+        Pandas DataFrame of the monitored sources containing:
+        3FGL Name, ZTF Name, Arrays of Medians, Computed Threshold,
+        Observed Threshold, Redshift, Final Threshold
+
+    Returns
+    -------
+    out: np.float64
+        Ratio of the sliding mean over 30 days of the standardized flux over
+        the precomputed threshold
+    """
+
+    integration_period = 30
+    name = pdf['objectId'].values[0]
+
+    try:
+        threshold = np.array(
+            CTAO_blazar.loc[
+                CTAO_blazar['ZTF Name'] == name,
+                'Final Threshold'
+            ].values[0]
+        )
+    except IndexError:
+        threshold = np.nan
+
+    try:
+        full_time = pdf['cjd']
+        maskTime = full_time >= full_time.iloc[-1] - integration_period
+        time = pdf.loc[maskTime, 'cjd']
+        flux = pdf.loc[maskTime, 'cstd_flux']
+    except KeyError:
+        return np.nan
+
+    maskNan = ~pd.isnull(flux)
+    mtime = time[maskNan]
+    if maskNan.sum() > 1:
+        mtimestart = mtime.iloc[0]
+        mtimestop = mtime.iloc[-1]
+        integral = np.trapz(flux[maskNan], x=mtime)
+        return integral / (mtimestop - mtimestart) / threshold
+    else:
+        return np.nan
+
+
+def quiescent_state_(
+        pdf: pd.DataFrame,
+        CTAO_blazar: pd.DataFrame) -> np.ndarray:
+    """Returns an array containing:
+            The mean over threshold ratio of the last but one alert
+            The mean over threshold ratio of the last alert
+            The standardized flux over threshold ratio of the last alert
+
+    Parameters
+    ----------
+    pdf: pd.core.frame.DataFrame
+        Pandas DataFrame of the alert history containing:
+        candid, ojbectId, cdistnr, cmagpsf, csigmapsf, cmagnr,
+        csigmagnr, cisdiffpos, cfid, cjd, cstd_flux, csigma_std_flux
+    CTAO_blazar: pd.core.frame.DataFrame
+        Pandas DataFrame of the monitored sources containing:
+        3FGL Name, ZTF Name, Arrays of Medians, Computed Threshold,
+        Observed Threshold, Redshift, Final Threshold
+    Returns
+    -------
+    out: np.ndarray of np.float64
+        Array of ratios for:
+        Mean over threshold of the last but one alert
+        Mean over threshold of the last alert
+        Measurement over threshold of the last alert
+    """
+
+    name = pdf['objectId'].values[0]
+
+    if not CTAO_blazar.loc[CTAO_blazar['ZTF Name'] == name].empty:
+        return np.array(
+            [
+                robustness_criterion(pdf[:-1], CTAO_blazar),
+                robustness_criterion(pdf, CTAO_blazar),
+                instantness_criterion(pdf, CTAO_blazar)
+            ]
+        )
+
+    else:
+        return np.full(3, np.nan)

fink_science/standardized_flux/README.md

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+ # Flux standardization module
+
+ This module adds two new columns for a set of sources defined beforehand. 
+ These sources are blazars that the CTAO collaboration wants to monitor starting the launch of the South and North sites. 
+ The new columns are the **standardized flux** (which is the flux over the median flux computed beforhand using archival light curves of ZTF) and the **uncertainties** over this standardized flux.