All necessary methods to apply a transformation function have been im…

…plemented.

.tmp/config.yaml

@@ -6,34 +6,76 @@ columns:
       -
         name: lsst.sal.ATAOS.logevent_focusOffsetSummary
         fields: ["total"]
-        window: 0.0
 
-  # - name: ATAOS_focusOffsetSummary_userApplied
-  #   function: mean
-  #   topics:
-  #     -
-  #       topic: lsst.sal.ATAOS.logevent_focusOffsetSummary
-  #       fields: ["userApplied"]
-  #       window: 0.0
+  - name: ATPtg_warning
+    function: mean
+    topics:
+      -
+        name: lsst.sal.ATPtg.logevent_airmassWarning
+        fields: ["level"]
+      -
+        name: lsst.sal.ATPtg.logevent_moonProximityWarning
+        fields: ["level"]
+      -
+        name: lsst.sal.ATPtg.logevent_mountDataWarning
+        fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_azWrapWarning
+      #   fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_blindSpotWarning
+      #   fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_elLimitWarning
+      #   fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_focusNameInconsistentWarning
+      #   fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_objectSetWarning
+      #   fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_rotWrapWarning
+      #   fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_sunProximityWarning
+      #   fields: ["level"]
+      # -
+      #   name: lsst.sal.ATPtg.logevent_objectSetWarning
+      #   fields: ["level"]
 
-  # - name: ATAOS_focusOffsetSummary_filter
-  #   function: mean
-  #   topics:
-  #     -
-  #       topic: lsst.sal.ATAOS.logevent_focusOffsetSummary
-  #       fields: ["filter"]
-  #       window: 0.0
+  - name: ATAOS_focusOffsetSummary_userApplied
+    function: min
+    topics:
+      -
+        name: lsst.sal.ATAOS.logevent_focusOffsetSummary
+        fields: ["userApplied"]
 
+  - name: ATAOS_focusOffsetSummary_filter
+    function: max
+    topics:
+      -
+        name: lsst.sal.ATAOS.logevent_focusOffsetSummary
+        fields: ["filter"]
 
-# Exemplo retorna 0 Rows
-  # - name: ESS_pressure
-  #   type: double
-  #   function: mean
-  #   window: 0.0
-  #   topics:
-  #     -
-  #       topic: lsst.sal.ESS.pressure
-  #       fields: ["pressureItem"]
+  - name: ATAOS_focusOffsetSummary_userApplied_min
+    function: min
+    topics:
+      -
+        name: lsst.sal.ATAOS.logevent_focusOffsetSummary
+        fields: ["userApplied"]
+  - name: ATAOS_focusOffsetSummary_userApplied_mean
+    function: mean
+    topics:
+      -
+        name: lsst.sal.ATAOS.logevent_focusOffsetSummary
+        fields: ["userApplied"]
+  - name: ATAOS_focusOffsetSummary_userApplied_max
+    function: max
+    topics:
+      -
+        name: lsst.sal.ATAOS.logevent_focusOffsetSummary
+        fields: ["userApplied"]
 
 # topics:
 #   # other optional information

python/lsst/consdb/efd_transform/butler_dao.py

@@ -1,6 +1,7 @@
+import astropy.time
+import pandas
 from lsst.daf.butler import Butler
 
-import astropy.time
 
 class ButlerDao:
 
@@ -10,23 +11,32 @@ def __init__(self, butler: Butler):
     def query_dimension_to_list(self, resultset):
         return [r.toDict() for r in resultset]
 
-    def exposures_by_period(self,
+    def query_dimension_to_dataframe(self, resultset):
+        return pandas.DataFrame([q.toDict() for q in resultset])
+
+    def exposures_by_period(
+        self,
         instrument: str,
         start_time: astropy.time.Time,
         end_time: astropy.time.Time,
     ) -> list:
         where_clause = f"instrument=instr and exposure.timespan OVERLAPS (T'{start_time}', T'{end_time}')"
 
-        return self.butler.registry.queryDimensionRecords(
-            "exposure", where=where_clause, bind=dict(instr=instrument))
-
-    def visits_by_period(self,
+        resultset = self.butler.registry.queryDimensionRecords(
+            "exposure", where=where_clause, bind=dict(instr=instrument)
+        )
+        return self.query_dimension_to_list(resultset)
+
+    def visits_by_period(
+        self,
         instrument: str,
         start_time: astropy.time.Time,
         end_time: astropy.time.Time,
     ) -> list:
         where_clause = f"instrument=instr and visit.timespan OVERLAPS (T'{start_time}', T'{end_time}')"
 
-        return self.butler.registry.queryDimensionRecords(
-            "visit", where=where_clause, bind=dict(instr=instrument))
-
+        resultset = self.butler.registry.queryDimensionRecords(
+            "visit", where=where_clause, bind=dict(instr=instrument)
+        )
+
+        return self.query_dimension_to_list(resultset)

python/lsst/consdb/efd_transform/transform.py

@@ -2,6 +2,7 @@
 
 import numpy
 
+
 class Transform:
     """
     A class that performs various transformations on a list or numpy
@@ -20,13 +21,32 @@ def __init__(self, values: Union[List[Union[float, int, bool]], numpy.ndarray]):
         """
         self.values = numpy.array(values)
 
+    def apply(self, method_name: str) -> Union[float, None]:
+        """
+        Apply a transformation method specified by method_name.
+
+        Args:
+            method_name: Name of the method to apply.
+
+        Returns:
+            The result of the transformation method or None if the method is not found.
+        """
+        method = getattr(self, method_name, None)
+        if method:
+            return method()
+        else:
+            return None
+
     def mean(self) -> float:
         """
         Calculate the mean of the values.
 
         Returns:
             The mean value as a float.
         """
+        if numpy.size(self.values) == 0:
+            return numpy.nan
+
         return numpy.nanmean(self.values)
 
     def std(self, ddof: Optional[int] = 1) -> float:
@@ -48,6 +68,9 @@ def max(self) -> Union[float, int, bool]:
         Returns:
             The maximum value as a float, int, or bool.
         """
+        if numpy.size(self.values) == 0:
+            return numpy.nan
+
         return numpy.nanmax(self.values)
 
     def min(self) -> Union[float, int, bool]:
@@ -57,6 +80,9 @@ def min(self) -> Union[float, int, bool]:
         Returns:
             The minimum value as a float, int, or bool.
         """
+        if numpy.size(self.values) == 0:
+            return numpy.nan
+
         return numpy.nanmin(self.values)
 
     def logical_and(self) -> Union[bool, numpy.ndarray]: