[Unitary-Hack][Task-Base] Docstring for "tasks/base"

src/bloqade/task/base.py

@@ -22,6 +22,14 @@
 @Serializer.register
 @dataclass(frozen=True)
 class Geometry:
+    """Class representing geometry of an atom arrangement.
+
+    Attributes:
+        sites (List[Tuple[float, float]]): Atom site arrangement
+        filling (List[int]): Which sites are filled
+        parallel_decoder: Decoder object for decoding Geometry object
+    """
+
     sites: List[Tuple[float, float]]
     filling: List[int]
     parallel_decoder: Optional[ParallelDecoder] = None
@@ -48,6 +56,10 @@ def geometry(self) -> Geometry:
 
 
 class RemoteTask(Task):
+    """`Task` to use for remote executions to run the program on Quera
+    Quantum Computers.
+    """
+
     def validate(self) -> None:
         raise NotImplementedError
 
@@ -86,6 +98,8 @@ def _result_exists(self) -> bool:
 
 
 class LocalTask(Task):
+    """`Task` to use for local executions for simulation purposes.."""
+
     def result(self):
         # need a new results type
         # for emulator jobs
@@ -95,9 +109,9 @@ def run(self, **kwargs):
         raise NotImplementedError
 
 
-# Report is now just a helper class for
-# organize and analysis data:
 class Report:
+    """Report is a helper class for organizing and analysing data"""
+
     dataframe: pd.DataFrame
     metas: List[Dict]
     geos: List[Geometry]
@@ -133,6 +147,7 @@ def cast(x):
 
     @property
     def markdown(self) -> str:
+        """Get the markdown representation of the dataframe"""
         return self.dataframe.to_markdown()
 
     def _filter(
@@ -172,16 +187,16 @@ def bitstrings(
 
         Args:
             filter_perfect_filling (bool): whether return will
-            only contain perfect filling shots. Defaults to True.
+                only contain perfect filling shots. Defaults to True.
             clusters: (tuple[int, int], Sequence[Tuple[int, int]]):
-            cluster index to filter shots from. If none are provided
-            all clusters are used, defaults to [].
+                cluster index to filter shots from. If none are provided
+                all clusters are used, defaults to [].
 
         Returns:
             bitstrings (list of ndarray): list corresponding to each
-            task in the report. Each element is an ndarray of shape
-            (nshots, nsites) where nshots is the number of shots for
-            the task and nsites is the number of sites in the task.
+                task in the report. Each element is an ndarray of shape
+                (nshots, nsites) where nshots is the number of shots for
+                the task and nsites is the number of sites in the task.
 
         Note:
             Note that nshots may vary between tasks if filter_perfect_filling
@@ -216,24 +231,24 @@ def counts(
 
         Args:
             filter_perfect_filling (bool): whether return will
-            only contain perfect filling shots. Defaults to True.
+                only contain perfect filling shots. Defaults to True.
             clusters: (tuple[int, int], Sequence[Tuple[int, int]]):
-            cluster index to filter shots from. If none are provided
-            all clusters are used, defaults to [].
+                cluster index to filter shots from. If none are provided
+                all clusters are used, defaults to [].
 
         Returns:
             bitstrings (list of ndarray): list corresponding to each
-            task in the report. Each element is an ndarray of shape
-            (nshots, nsites) where nshots is the number of shots for
-            the task and nsites is the number of sites in the task.
+                task in the report. Each element is an ndarray of shape
+                (nshots, nsites) where nshots is the number of shots for
+                the task and nsites is the number of sites in the task.
 
         Note:
             Note that nshots may vary between tasks if filter_perfect_filling
             is set to True.
 
         """
 
-        def generate_counts(bitstring):
+        def _generate_counts(bitstring):
             output = np.unique(bitstring, axis=0, return_counts=True)
 
             count_list = [
@@ -246,7 +261,7 @@ def generate_counts(bitstring):
             return count
 
         return list(
-            map(generate_counts, self.bitstrings(filter_perfect_filling, clusters))
+            map(_generate_counts, self.bitstrings(filter_perfect_filling, clusters))
         )
 
     @beartype
@@ -259,7 +274,10 @@ def rydberg_densities(
 
         Args:
             filter_perfect_filling (bool, optional): whether return will
-            only contain perfect filling shots. Defaults to True.
+                only contain perfect filling shots. Defaults to True.
+            clusters: (tuple[int, int], Sequence[Tuple[int, int]]):
+                cluster index to filter shots from. If none are provided
+                all clusters are used, defaults to [].
 
         Return:
             per-site rydberg density for each task as a pandas DataFrame or Series.