Fix missing import (#189)

* Fix missing import

* Update filter test (#190)

fink_filters/filter_anomaly_notification/filter.py

@@ -25,10 +25,16 @@
 
 
 def anomaly_notification_(
-        df_proc, threshold=10,
-        send_to_tg=False, channel_id=None,
-        send_to_slack=False, channel_name=None,
-        trick_par=10, cut_coords=False, history_period=90):
+    df_proc,
+    threshold=10,
+    send_to_tg=False,
+    channel_id=None,
+    send_to_slack=False,
+    channel_name=None,
+    trick_par=10,
+    cut_coords=False,
+    history_period=90,
+):
     """ Create event notifications with a high `anomaly_score` value
 
     Notes
@@ -115,7 +121,7 @@ def anomaly_notification_(
     ...     send_to_tg=False, channel_id=None,
     ...     send_to_slack=False, channel_name=None)
     >>> print(sorted(pdf_anomalies['objectId'].values))
-    ['ZTF18aaakhsv', 'ZTF18aabeyfi', 'ZTF18aapgymv', 'ZTF18aapoack', 'ZTF18abbtxsx', 'ZTF18abgjtxx', 'ZTF18abzvnya', 'ZTF19aboujyi', 'ZTF19acevxhv', 'ZTF21acoshvy']
+    ['ZTF18aabeyfi', 'ZTF18aapgymv', 'ZTF18aaypnnd', 'ZTF18abbtxsx', 'ZTF18abgjtxx', 'ZTF18abhxigz', 'ZTF18abjuixy', 'ZTF18abtrvkm', 'ZTF18acaksuq', 'ZTF21acoshvy']
 
     # Check cut_coords
     >>> pdf_anomalies = anomaly_notification_(df_proc, threshold=10,
@@ -127,71 +133,85 @@ def anomaly_notification_(
     """
     # Filtering by coordinates
     if cut_coords:
-        df_proc = df_proc.filter('dec <= 20 AND (ra >= 160 AND ra <= 240)')
+        df_proc = df_proc.filter("dec <= 20 AND (ra >= 160 AND ra <= 240)")
         # We need to know the total number of objects per night which satisfy the condition on coordinates
         cut_count = df_proc.count()
         if cut_count == 0:
             return pd.DataFrame()
 
     # Compute the median for the night
-    med = df_proc.select('anomaly_score').approxQuantile('anomaly_score', [0.5], 0.05)
+    med = df_proc.select("anomaly_score").approxQuantile("anomaly_score", [0.5], 0.05)
     med = round(med[0], 2)
 
     # Extract anomalous objects
 
-    pdf_anomalies_ext = df_proc.sort(['anomaly_score'], ascending=True).limit(trick_par * threshold).toPandas()
-    pdf_anomalies_ext = pdf_anomalies_ext.drop_duplicates(['objectId'])
-    upper_bound = np.max(pdf_anomalies_ext['anomaly_score'].values[:threshold])
-    pdf_anomalies = pdf_anomalies_ext[pdf_anomalies_ext['anomaly_score'] <= upper_bound]
+    pdf_anomalies_ext = (
+        df_proc.sort(["anomaly_score"], ascending=True)
+        .limit(trick_par * threshold)
+        .toPandas()
+    )
+    pdf_anomalies_ext = pdf_anomalies_ext.drop_duplicates(["objectId"])
+    upper_bound = np.max(pdf_anomalies_ext["anomaly_score"].values[:threshold])
+    pdf_anomalies = pdf_anomalies_ext[pdf_anomalies_ext["anomaly_score"] <= upper_bound]
 
     history_objects = filter_utils.get_an_history(history_period)
 
     tg_data, slack_data = [], []
     for _, row in pdf_anomalies.iterrows():
-        gal = SkyCoord(ra=row.ra * u.degree, dec=row.dec * u.degree, frame='icrs').galactic
+        gal = SkyCoord(
+            ra=row.ra * u.degree, dec=row.dec * u.degree, frame="icrs"
+        ).galactic
         oid = filter_utils.get_OID(row.ra, row.dec)
-        t1a = f'ID: [{row.objectId}](https://fink-portal.org/{row.objectId})'
-        t1b = f'ID: <https://fink-portal.org/{row.objectId}|{row.objectId}>'
+        t1a = f"ID: [{row.objectId}](https://fink-portal.org/{row.objectId})"
+        t1b = f"ID: <https://fink-portal.org/{row.objectId}|{row.objectId}>"
         t_oid_1a = f"DR OID (<1''): [{oid}](https://ztf.snad.space/view/{oid})"
         t_oid_1b = f"DR OID (<1''): <https://ztf.snad.space/view/{oid}|{oid}>"
-        t2_ = f'GAL coordinates: {round(gal.l.deg, 6)},   {round(gal.b.deg, 6)}'
-        t_ = f'''
-EQU: {row.ra},   {row.dec}'''
+        t2_ = f"GAL coordinates: {round(gal.l.deg, 6)},   {round(gal.b.deg, 6)}"
+        t_ = f"""
+EQU: {row.ra},   {row.dec}"""
         t2_ += t_
-        t3_ = f'UTC: {str(row.timestamp)[:-3]}'
-        t4_ = f'Real bogus: {round(row.rb, 2)}'
-        t5_ = f'Anomaly score: {round(row.anomaly_score, 2)}'
+        t3_ = f"UTC: {str(row.timestamp)[:-3]}"
+        t4_ = f"Real bogus: {round(row.rb, 2)}"
+        t5_ = f"Anomaly score: {round(row.anomaly_score, 2)}"
         if row.objectId in history_objects:
-            t5_ += f'''
-Detected as top-{threshold} in the last {history_period} days: {history_objects[row.objectId]} {'times' if history_objects[row.objectId] > 1 else 'time'}.'''
-        cutout, curve, cutout_perml, curve_perml = filter_utils.get_data_permalink_slack(row.objectId)
+            t5_ += f"""
+Detected as top-{threshold} in the last {history_period} days: {history_objects[row.objectId]} {'times' if history_objects[row.objectId] > 1 else 'time'}."""
+        cutout, curve, cutout_perml, curve_perml = (
+            filter_utils.get_data_permalink_slack(row.objectId)
+        )
         curve.seek(0)
         cutout.seek(0)
         cutout_perml = f"<{cutout_perml}|{' '}>"
         curve_perml = f"<{curve_perml}|{' '}>"
-        tg_data.append((f'''{t1a}
+        tg_data.append(
+            (
+                f"""{t1a}
 {t_oid_1a}
 {t2_}
 {t3_}
 {t4_}
-{t5_}''', cutout, curve))
-        slack_data.append(f'''==========================
+{t5_}""",
+                cutout,
+                curve,
+            )
+        )
+        slack_data.append(f"""==========================
 {t1b}
 {t_oid_1b}
 {t2_}
 {t3_}
 {t4_}
 {t5_}
-{cutout_perml}{curve_perml}''')
-    init_msg = f'Median anomaly score overnight: {med}.'
+{cutout_perml}{curve_perml}""")
+    init_msg = f"Median anomaly score overnight: {med}."
     if cut_coords:
-        init_msg += f'''
+        init_msg += f"""
 (of the objects in the sky area)
 Sky area:
     1) delta <= 20°
     2) alpha ∈ (160°, 240°)
 Total number of objects per night in the area: {cut_count}.
-'''
+"""
     if send_to_slack:
         filter_utils.msg_handler_slack(slack_data, channel_name, init_msg)
     if send_to_tg:

fink_filters/filter_dwarf_agn/filter.py

@@ -18,6 +18,8 @@
 from pyspark.sql.types import StringType
 
 from fink_science.xmatch.utils import cross_match_astropy
+from fink_filters import __file__
+
 from astropy.coordinates import SkyCoord
 from astropy import units as u