adds class Transform

python/lsst/consdb/transform_efd.py

@@ -1,19 +1,127 @@
 import argparse
 import asyncio
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Optional, Union
 
 import astropy.time
 import lsst_efd_client
 import pandas
 import yaml
 from lsst.daf.butler import Butler, DimensionRecord
 from sqlalchemy import Engine, create_engine
-
+import numpy
 
 class Summary:
     # TODO define summary
     pass
 
+class Transform:
+    """
+    A class that performs various transformations on a list or numpy array of values.
+    """
+
+    def __init__(self, values: Union[List[Union[float, int, bool]], numpy.ndarray]):
+        """
+        Initialize the Transform object.
+
+        Args:
+            values: A list or numpy array of values.
+
+        Returns:
+            None
+        """
+        self.values = np.array(values)
+
+    def mean(self) -> float:
+        """
+        Calculate the mean of the values.
+
+        Returns:
+            The mean value as a float.
+        """
+        return numpy.nanmean(self.values)
+
+    def std(self, ddof: Optional[int] = 1) -> float:
+        """
+        Calculate the standard deviation of the values.
+
+        Args:
+            ddof: Delta degrees of freedom.
+
+        Returns:
+            The standard deviation as a float.
+        """
+        return numpy.nanstd(self.values, ddof=ddof)
+
+    def max(self) -> Union[float, int, bool]:
+        """
+        Find the maximum value in the values.
+
+        Returns:
+            The maximum value as a float, int, or bool.
+        """
+        return numpy.nanmax(self.values)
+
+    def min(self) -> Union[float, int, bool]:
+        """
+        Find the minimum value in the values.
+
+        Returns:
+            The minimum value as a float, int, or bool.
+        """
+        return numpy.nanmin(self.values)
+
+    def logical_and(self) -> Union[bool, numpy.ndarray]:
+        """
+        Perform element-wise logical AND operation on the values.
+
+        Returns:
+            The result of the logical AND operation as a bool or numpy array.
+        """
+        return numpy.logical_and(self.values)
+
+    def logical_or(self) -> Union[bool, numpy.ndarray]:
+        """
+        Perform element-wise logical OR operation on the values.
+
+        Returns:
+            The result of the logical OR operation as a bool or numpy array.
+        """
+        return numpy.logical_or(self.values)
+
+    def logical_not(self) -> numpy.ndarray:
+        """
+        Perform element-wise logical NOT operation on the values.
+
+        Returns:
+            The result of the logical NOT operation as a numpy array.
+        """
+        return numpy.logical_not(self.values)
+
+    def logical_xor(self, other_values: Union[List[Union[float, int, bool]], numpy.ndarray]) -> numpy.ndarray:
+        """
+        Perform element-wise logical XOR operation between the values and other values.
+
+        Args:
+            other_values: A list or numpy array of other values.
+
+        Returns:
+            The result of the logical XOR operation as a numpy array.
+        """
+        other_values = numpy.array(other_values)
+        return numpy.logical_xor(self.values, other_values)
+
+    def percentile(self, q: float) -> Union[float, int, bool]:
+        """
+        Calculate the q-th percentile of the values.
+
+        Args:
+            q: The percentile value.
+
+        Returns:
+            The q-th percentile value as a float, int, or bool.
+        """
+        return numpy.nanpercentile(self.values, q)
+
 
 # TODO add all summarizing functions
 def gen_mean(
@@ -157,7 +265,7 @@ def get_visits_by_period(
 def butler_query_results_to_pandas(query):
     return pandas.DataFrame([q.toDict() for q in query])
 
-def get_topic_correponding_indexes(butler_table: pandas.DataFrame, topic_time_array: Callable[[list],numpy.ndarray]) -> List:
+def get_topic_correponding_indexes(butler_table: pandas.DataFrame, topic_time_array: Union[[list],numpy.ndarray]) -> List:
     """
     Returns a list of topic indexes corresponding to each row in the butler_table. It is assumed that the butler_table and the topic_time_array are sorted in time ascending order.