Add compute_mapped

docs/api-reference/index.md

@@ -21,6 +21,17 @@
    HandleAsComputeTimeException
 ```
 
+## Top-level functions
+
+```{eval-rst}
+.. autosummary::
+   :toctree: ../generated/functions
+   :recursive:
+
+   compute_series
+   get_mapped_node_names
+```
+
 ## Exceptions
 
 ```{eval-rst}

docs/user-guide/parameter-tables.ipynb

@@ -146,8 +146,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then we can compute `Result` for each index in the parameter table.\n",
-    "Currently there is no convenient way of accessing these, instead we manually define the target nodes to compute:"
+    "We can use the [compute_series](https://scipp.github.io/sciline/generated/functions/sciline.compute_series.html) function to compute `Result` for each index in the parameter table:"
    ]
   },
   {
@@ -156,10 +155,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from cyclebane.graph import NodeName, IndexValues\n",
-    "\n",
-    "targets = [NodeName(Result, IndexValues(('run_id',), (i,))) for i in run_ids]\n",
-    "pipeline.compute(targets)"
+    "results = sciline.compute_series(pipeline, Result)\n",
+    "pd.DataFrame(results)  # DataFrame for HTML rendering"
    ]
   },
   {
@@ -168,7 +165,22 @@
    "source": [
     "Note the use of the `run_id` index.\n",
     "If the index axis of the DataFrame has no name then a default of `dim_0`, `dim_1`, etc. is used.\n",
-    "We can also visualize the task graph for computing the series of `Result` values:"
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**Note**\n",
+    "\n",
+    "[compute_series](https://scipp.github.io/sciline/generated/functions/sciline.compute_series.html) depends on Pandas, which is not a dependency of Sciline and must be installed separately, e.g., using pip:\n",
+    "\n",
+    "```bash\n",
+    "pip install pandas\n",
+    "```\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "We can also visualize the task graph for computing the series of `Result` values.\n",
+    "For this, we need to get all the node names derived from `Result` via the `map` operation.\n",
+    "The [get_mapped_node_names](https://scipp.github.io/sciline/generated/functions/sciline.get_mapped_node_names.html) function can be used to get a `pandas.Series` of these node names, which we can then visualize:"
    ]
   },
   {
@@ -177,6 +189,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "targets = sciline.get_mapped_node_names(pipeline, Result)\n",
     "pipeline.visualize(targets)"
    ]
   },

requirements/base.in

@@ -2,4 +2,4 @@
 # will not be touched by ``make_base.py``
 # --- END OF CUSTOM SECTION ---
 # The following was generated by 'tox -e deps', DO NOT EDIT MANUALLY!
-cyclebane >= 24.06.0
+cyclebane>=24.06.0

requirements/base.txt

@@ -1,4 +1,4 @@
-# SHA1:44b03b700447e95874de7cd4d8b11558c0c972e5
+# SHA1:1b4246f703135629f3fb69e65829cfb99abca695
 #
 # This file is autogenerated by pip-compile-multi
 # To update, run:

requirements/basetest.in

@@ -4,4 +4,5 @@
 graphviz
 jsonschema
 numpy
+pandas
 pytest

requirements/basetest.txt

@@ -1,4 +1,4 @@
-# SHA1:f7d11f6aab1600c37d922ffb857a414368b800cd
+# SHA1:fa9ebd4f58fe57db20baa224ad46fac634f3f046
 #
 # This file is autogenerated by pip-compile-multi
 # To update, run:
@@ -19,14 +19,22 @@ jsonschema==4.22.0
     # via -r basetest.in
 jsonschema-specifications==2023.12.1
     # via jsonschema
-numpy==1.26.4
-    # via -r basetest.in
+numpy==2.0.0
+    # via
+    #   -r basetest.in
+    #   pandas
 packaging==24.1
     # via pytest
+pandas==2.2.2
+    # via -r basetest.in
 pluggy==1.5.0
     # via pytest
 pytest==8.2.2
     # via -r basetest.in
+python-dateutil==2.9.0.post0
+    # via pandas
+pytz==2024.1
+    # via pandas
 referencing==0.35.1
     # via
     #   jsonschema
@@ -35,5 +43,9 @@ rpds-py==0.18.1
     # via
     #   jsonschema
     #   referencing
+six==1.16.0
+    # via python-dateutil
 tomli==2.0.1
     # via pytest
+tzdata==2024.1
+    # via pandas

requirements/ci.txt

@@ -17,7 +17,7 @@ colorama==0.4.6
     # via tox
 distlib==0.3.8
     # via virtualenv
-filelock==3.14.0
+filelock==3.15.1
     # via
     #   tox
     #   virtualenv
@@ -50,7 +50,7 @@ tomli==2.0.1
     #   tox
 tox==4.15.1
     # via -r ci.in
-urllib3==2.2.1
+urllib3==2.2.2
     # via requests
 virtualenv==20.26.2
     # via tox

requirements/dev.txt

@@ -92,7 +92,7 @@ prometheus-client==0.20.0
     # via jupyter-server
 pycparser==2.22
     # via cffi
-pydantic==2.7.3
+pydantic==2.7.4
     # via copier
 pydantic-core==2.18.4
     # via pydantic

requirements/docs.txt

@@ -48,7 +48,7 @@ exceptiongroup==1.2.1
     # via ipython
 executing==2.0.1
     # via stack-data
-fastjsonschema==2.19.1
+fastjsonschema==2.20.0
     # via nbformat
 graphviz==0.20.3
     # via -r docs.in
@@ -120,7 +120,7 @@ nbsphinx==0.9.4
     # via -r docs.in
 nest-asyncio==1.6.0
     # via ipykernel
-numpy==1.26.4
+numpy==2.0.0
     # via pandas
 packaging==24.1
     # via
@@ -241,7 +241,7 @@ typing-extensions==4.12.2
     #   pydata-sphinx-theme
 tzdata==2024.1
     # via pandas
-urllib3==2.2.1
+urllib3==2.2.2
     # via requests
 wcwidth==0.2.13
     # via prompt-toolkit

requirements/static.txt

@@ -9,7 +9,7 @@ cfgv==3.4.0
     # via pre-commit
 distlib==0.3.8
     # via virtualenv
-filelock==3.14.0
+filelock==3.15.1
     # via virtualenv
 identify==2.5.36
     # via pre-commit

requirements/test-dask.txt

@@ -10,7 +10,7 @@ click==8.1.7
     # via dask
 cloudpickle==3.0.0
     # via dask
-dask==2024.5.2
+dask==2024.6.0
     # via -r test-dask.in
 fsspec==2024.6.0
     # via dask

src/sciline/__init__.py

@@ -17,7 +17,7 @@
     HandleAsComputeTimeException,
     UnsatisfiedRequirement,
 )
-from .pipeline import Pipeline
+from .pipeline import Pipeline, compute_series, get_mapped_node_names
 from .task_graph import TaskGraph
 
 __all__ = [
@@ -30,6 +30,8 @@
     "UnsatisfiedRequirement",
     "HandleAsBuildTimeException",
     "HandleAsComputeTimeException",
+    "compute_series",
+    "get_mapped_node_names",
 ]
 
 del importlib

src/sciline/pipeline.py

@@ -5,7 +5,7 @@
 from collections.abc import Callable, Iterable
 from itertools import chain
 from types import UnionType
-from typing import Any, TypeVar, get_args, get_type_hints, overload
+from typing import TYPE_CHECKING, Any, TypeVar, get_args, get_type_hints, overload
 
 from ._provider import Provider, ToProvider
 from .data_graph import DataGraph, to_task_graph
@@ -15,6 +15,11 @@
 from .task_graph import TaskGraph
 from .typing import Key
 
+if TYPE_CHECKING:
+    import graphviz
+    import pandas
+
+
 T = TypeVar('T')
 KeyType = TypeVar('KeyType', bound=Key)
 
@@ -84,7 +89,7 @@ def compute(self, tp: type | Iterable[type] | UnionType, **kwargs: Any) -> Any:
         """
         return self.get(tp, **kwargs).compute()
 
-    def visualize(self, tp: type | Iterable[type], **kwargs: Any) -> graphviz.Digraph:  # type: ignore[name-defined] # noqa: F821
+    def visualize(self, tp: type | Iterable[type], **kwargs: Any) -> graphviz.Digraph:
         """
         Return a graphviz Digraph object representing the graph for the given keys.
 
@@ -194,3 +199,66 @@ def bind_and_call(
     def _repr_html_(self) -> str:
         nodes = ((key, data) for key, data in self._graph.nodes.items())
         return pipeline_html_repr(nodes)
+
+
+def get_mapped_node_names(graph: Pipeline, key: type) -> pandas.Series:
+    """
+    Given a graph with key depending on mapped nodes, return a series corresponding
+    mapped keys.
+
+    This is meant to be used in combination with :py:func:`Pipeline.map`.
+    If the key depends on multiple indices, the series will be a multi-index series.
+
+    Note that Pandas is not a dependency of Sciline and must be installed separately.
+
+    Parameters
+    ----------
+    graph:
+        The pipeline to get the mapped key names from.
+    key:
+        The key to get the mapped key names for. This key must depend on mapped nodes.
+
+    Returns
+    -------
+    :
+        The series of mapped key names.
+    """
+    import pandas as pd
+    from cyclebane.graph import IndexValues, NodeName
+
+    graph = graph[key]  # Drops unrelated indices
+    indices = graph._cbgraph.indices
+    if len(indices) == 0:
+        raise ValueError(f"'{key}' does not depend on any mapped nodes.")
+    index_names = tuple(indices)
+    index = pd.MultiIndex.from_product(indices.values(), names=index_names)
+    keys = tuple(NodeName(key, IndexValues(index_names, idx)) for idx in index)
+    if index.nlevels == 1:  # Avoid more complicate MultiIndex if unnecessary
+        index = index.get_level_values(0)
+    return pd.Series(keys, index=index, name=key)
+
+
+def compute_series(graph: Pipeline, key: type) -> pandas.Series:
+    """
+    Given a graph with key depending on mapped nodes, compute a series for the key.
+
+    This is meant to be used in combination with :py:func:`Pipeline.map`.
+    If the key depends on multiple indices, the series will be a multi-index series.
+
+    Note that Pandas is not a dependency of Sciline and must be installed separately.
+
+    Parameters
+    ----------
+    graph:
+        The pipeline to compute the series from.
+    key:
+        The key to compute the series for. This key must depend on mapped nodes.
+
+    Returns
+    -------
+    :
+        The computed series.
+    """
+    key_series = get_mapped_node_names(graph, key)
+    results = graph.compute(key_series)
+    return key_series.apply(lambda x: results[x])