Add ELEPHANT hostless detection as science module

Author: utthishtastro

bin/ztf/archive_hostless.py

@@ -151,7 +151,7 @@ def main():
             cutout_template = get_cutout(cutout=alert["cutoutTemplate"])
 
             text = """
-*Object ID*: [{}](https://fink-portal.org/{})
+*Object ID*: [{}](https://ztf.fink-portal.org/{})
 *Scores:*\n- Science: {:.2f}\n- Template: {:.2f}
 *Fink class*: {}
             """.format(

fink_broker/rubin/science.py

@@ -23,6 +23,8 @@
 
 from fink_broker.common.tester import spark_unit_tests
 
+from fink_science.rubin.hostless_detection.processor import run_potential_hostless
+
 # Import of science modules
 from fink_science.rubin.snn.processor import snn_ia_elasticc
 from fink_science.rubin.cats.processor import predict_nn
@@ -374,6 +376,11 @@ def apply_science_modules(df: DataFrame, tns_raw_output: str = "") -> DataFrame:
     df = df.withColumn("misc", F.struct(*misc_struct))
     df = df.drop(*misc_struct)
 
+    _LOG.info("New processor: ELEPHANT Hostless module")
+    df = df.withColumn(
+        "elephant_kstest",
+        run_potential_hostless(df["cutoutScience"], df["cutoutTemplate"]),
+    )
     # Drop temp columns
     df = df.drop(*expanded)
 

fink_broker/ztf/science.py

@@ -23,6 +23,9 @@
 
 from fink_broker.common.tester import spark_unit_tests
 
+from fink_filters.ztf.classification import extract_fink_classification
+from fink_science.ztf.hostless_detection.processor import run_potential_hostless
+
 # Import of science modules
 from fink_science.ztf.random_forest_snia.processor import rfscore_sigmoid_full
 
@@ -347,7 +350,6 @@ def apply_science_modules(df: DataFrame, tns_raw_output: str = "") -> DataFrame:
         .withColumn("lc_features_r", df["lc_features"].getItem("2"))
         .drop("lc_features")
     )
-
     # Apply level one processor: fast transient
     _LOG.info("New processor: magnitude rate for fast transient")
     mag_rate_args = [
@@ -421,6 +423,47 @@ def apply_science_modules(df: DataFrame, tns_raw_output: str = "") -> DataFrame:
 
     df = df.withColumn("slsn_score", superluminous_score(*args))
 
+    _LOG.info("New processor: ELEPHANT Hostless module")
+    fink_classifier_cols = [
+        "cdsxmatch",
+        "roid",
+        "mulens",
+        "snn_snia_vs_nonia",
+        "snn_sn_vs_all",
+        "rf_snia_vs_nonia",
+        "candidate.ndethist",
+        "candidate.drb",
+        "candidate.classtar",
+        "candidate.jd",
+        "candidate.jdstarthist",
+        "rf_kn_vs_nonkn",
+        "tracklet",
+    ]
+    df = df.withColumn("finkclass", extract_fink_classification(*fink_classifier_cols))
+    df = df.withColumn("tnsclass", F.lit("Unknown"))
+    df = df.withColumn(
+        "elephant_kstest",
+        F.slice(
+            run_potential_hostless(
+                df["cmagpsf"],
+                df["cutoutScience.stampData"],
+                df["cutoutTemplate.stampData"],
+                df["snn_snia_vs_nonia"],
+                df["snn_sn_vs_all"],
+                df["rf_snia_vs_nonia"],
+                df["rf_kn_vs_nonkn"],
+                df["finkclass"],
+                df["tnsclass"],
+                df["candidate.jd"] - df["candidate.jdstarthist"],
+                df["roid"],
+            ),
+            1,
+            2,
+        ),
+    )
+    expanded.extend(["finkclass", "tnsclass"])
+    df = df.drop(*expanded)
+
     # Drop temp columns
     df = df.drop(*expanded)