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3 Jun 2024 14:33:54 -0700 Subject: [PATCH 2/2] [Unitary-Hack][Task-Base] Docstring for "tasks/base" (#970) * Init * Update src/bloqade/task/base.py Co-authored-by: Xiu-zhe (Roger) Luo * PR feedback * Convert to internal function * Add example for Report * more examples * Update src/bloqade/task/base.py Co-authored-by: Xiu-zhe (Roger) Luo * PR feedback: combine docstrings * minor edits * Update src/bloqade/task/base.py Co-authored-by: Xiu-zhe (Roger) Luo * PR feedback --------- Co-authored-by: Xiu-zhe (Roger) Luo --- src/bloqade/task/base.py | 120 ++++++++++++++++++++++++++++++++------- 1 file changed, 99 insertions(+), 21 deletions(-) diff --git a/src/bloqade/task/base.py b/src/bloqade/task/base.py index 7f770c7d0..76a9d975d 100644 --- a/src/bloqade/task/base.py +++ b/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 +109,49 @@ 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 + + ### Analyzing Results + When you've retrieved your results from either emulation + or hardware you can generate a `.report()`: + + ```python + report = results.report() + ``` + + For the examples below we analyze the results of a two atom program. + + The report contains useful information such as: + + The raw bitstrings measured per each execution of the program + ```python + >>> report.bitstrings() + [array([[1, 1], + [1, 1], + [1, 1], + ..., + [1, 1], + [1, 1], + ``` + + The number of times each unique bitstring occurred: + ```python + >>> report.counts() + + [OrderedDict([('11', 892), ('10', 59), ('01', 49)])] + ``` + + The Rydberg Density for each atom + ```python + >>> report.rydberg_densities() + + 0 1 + task_number + 0 0.053 0.054 + ``` + """ + dataframe: pd.DataFrame metas: List[Dict] geos: List[Geometry] @@ -133,6 +187,7 @@ def cast(x): @property def markdown(self) -> str: + """Get the markdown representation of the dataframe""" return self.dataframe.to_markdown() def _filter( @@ -172,16 +227,26 @@ 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. + For example: + ```python3 + [array([[1, 1], + [1, 1], + [1, 1], + ..., + [1, 1], + [1, 1], + [1, 0]], dtype=int8)] + ``` Note: Note that nshots may vary between tasks if filter_perfect_filling @@ -216,16 +281,20 @@ 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. + counts (list of OrderedDict[str, int]): 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. + For example: + ```python + [OrderedDict([('11', 892), ('10', 59), ('01', 49)])] + ``` Note: Note that nshots may vary between tasks if filter_perfect_filling @@ -233,7 +302,7 @@ def counts( """ - def generate_counts(bitstring): + def _generate_counts(bitstring): output = np.unique(bitstring, axis=0, return_counts=True) count_list = [ @@ -246,7 +315,7 @@ 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 @@ -259,11 +328,20 @@ def rydberg_densities( Args: filter_perfect_filling (bool, optional): whether return will - only contain perfect filling shots. Defaults to True. - - Return: - per-site rydberg density for each task as a pandas DataFrame or Series. + 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 []. + Returns: + rydberg_densities (Union[pd.Series, pd.DataFrame]): + per-site rydberg density for each task as a pandas DataFrame or Series. + For example: + ```python + 0 1 + task_number + 0 0.053 0.054 + ``` """ mask = self._filter( filter_perfect_filling=filter_perfect_filling, clusters=clusters