Fix doc link warnings/errors

doc/lsst.faro/using.rst

@@ -26,31 +26,31 @@ Running faro
 Shared Gen3 Data Repositories
 -----------------------------
 
-Information on shared Gen3 data repositories for data managed at NCSA can be found in `/repo/README.md`. See `DMTN-167 <https://dmtn-167.lsst.io/>`_ for more information on the organization of Gen3 data repositories.
+Information on shared Gen3 data repositories for data managed at NCSA can be found in `/repo/README.md`. See `DMTN-167 <https://dmtn-167.lsst.io/>`__ for more information on the organization of Gen3 data repositories.
 
-.. warning:: When developing metrics in ``faro``, particular care should be taken when creating a new dataset type name associated with a metric. As noted in `DMTN-167 <https://dmtn-167.lsst.io/#naming-conventions-for-dataset-types>`_, the dataset type names are *global* with no implicit name spacing. This may change in the future; see `DM-29817 <https://jira.lsstcorp.org/browse/DM-29817>`_. When developing metrics, it is recommended to run on a local data repository rather than a shared Gen3 data repository in case metrics need to renamed or the `dimensions <https://pipelines.lsst.io/modules/lsst.daf.butler/dimensions.html>`_ associated with metric calculation need to be changed.
+.. warning:: When developing metrics in ``faro``, particular care should be taken when creating a new dataset type name associated with a metric. As noted in `DMTN-167 <https://dmtn-167.lsst.io/#naming-conventions-for-dataset-types>`__, the dataset type names are *global* with no implicit name spacing. This may change in the future; see `DM-29817 <https://jira.lsstcorp.org/browse/DM-29817>`_. When developing metrics, it is recommended to run on a local data repository rather than a shared Gen3 data repository in case metrics need to renamed or the `dimensions <https://pipelines.lsst.io/modules/lsst.daf.butler/dimensions.html>`_ associated with metric calculation need to be changed.
 
 Example: rc2_subset
 -------------------
 
 Running ``faro`` on a small local dataset. The `rc2_subset <[email protected]:lsst/rc2_subset.git>`_ is the smallest CI dataset for which all ``faro`` metrics can be run without error and produce meaingful results.
 
-1. Set up ``rc2_subset`` following the instructions `here <https://pipelines.lsst.io/v/daily/getting-started/data-setup.html#downloading-the-sample-hsc-data>`_.
+1. Set up ``rc2_subset`` following the instructions `here <https://pipelines.lsst.io/v/daily/getting-started/data-setup.html#downloading-the-sample-hsc-data>`__.
 
 2. Set up ``faro`` package; see :ref:`setting up <lsst.faro.setting_up>`.
 
 3. An example command (update the command)::
 
      pipetask run -b $RC2_SUBSET_DIR/SMALL_HSC/butler.yaml -p $FARO_DIR/pipelines/metrics_pipeline_matched.yaml -i u/$USER/single_frame -o u/$USER/faro_matched_visits_r --register-dataset-types -d "instrument='HSC' AND detector=42 AND band='r'"
 
-Documentation for using the ``pipetask run`` command and various options can be found `here <https://pipelines.lsst.io/modules/lsst.ctrl.mpexec/pipetask.html#pipetask-run>`_. Briefly, the example command above uses the ``-b`` option to specify the Butler repository, ``-p`` to specify the pipeline, ``-i`` to specify the input collection, ``-o`` to specify the output collection (this should almost always be a user collection prefixed with ``u/username/`` unless you are running in production), and ``-d`` to provide a query to select a subset of data on which to compute metrics.
+Documentation for using the ``pipetask run`` command and various options can be found `here <https://pipelines.lsst.io/modules/lsst.ctrl.mpexec/pipetask.html#pipetask-run>`__. Briefly, the example command above uses the ``-b`` option to specify the Butler repository, ``-p`` to specify the pipeline, ``-i`` to specify the input collection, ``-o`` to specify the output collection (this should almost always be a user collection prefixed with ``u/username/`` unless you are running in production), and ``-d`` to provide a query to select a subset of data on which to compute metrics.
 
 .. warning:: The ``--register-dataset-types`` option should be used with caution as this will allow the registration of new dataset types that are global across the repository.
 
 Example: HSC RC2 dataset
 ------------------------
 
-Running ``faro`` on a Gen3 repository at NCSA. The HSC RC2 data that is reprocessed monthly with the latest version of the Science Pipelines is a good example, see `DMTN-091<https://dmtn-091.lsst.io>`_. Information on the current status of HSC RC2 re-processing and latest runs can be found `here <https://jira.lsstcorp.org/browse/DM-26911>`_.
+Running ``faro`` on a Gen3 repository at NCSA. The HSC RC2 data that is reprocessed monthly with the latest version of the Science Pipelines is a good example, see `DMTN-091 <https://dmtn-091.lsst.io>`_. Information on the current status of HSC RC2 re-processing and latest runs can be found `here <https://jira.lsstcorp.org/browse/DM-26911>`__.
 
 1. Set up ``lsst.faro`` package; see :ref:`setting_up <lsst.faro.setting_up>`.
 
@@ -65,7 +65,7 @@ Example: DRP processing
 
 ``lsst.faro`` can be run together with other processing steps in a pipeline, e.g., as part of DRP processing.
 
-  Examples of this functionality can be found in the `rc2_subset <https://github.com/lsst/drp_pipe/blob/main/pipelines/HSC/DRP-RC2_subset.yaml>`_.
+  Examples of this functionality can be found in the `rc2_subset <https://github.com/lsst/drp_pipe/blob/main/pipelines/HSC/DRP-RC2_subset.yaml>`__.
   One could follow the steps `in this tutorial <https://pipelines.lsst.io/v/daily/getting-started/singleframe.html#running-single-frame-processing>`_ for more information.
 
 .. _lsst.faro.adding_a_metric:
@@ -131,15 +131,15 @@ All development should happen on ticket branches (and should have associated JIR
 Adding a Metric
 ---------------
 
-1. Identify the analysis context. Review the associated connections, config, and task base classes for that analysis context to understand the in-memory python objects that will be passed to the ``run`` method of the metric measurement task and the configuration options. See :ref:`design concepts <lsst.faro.design_concepts>` for more information. Currently implemented analysis contexts are listed :ref:`here<lsst.faro.currently_implemented_analysis_contexts>`.
+1. Identify the analysis context. Review the associated connections, config, and task base classes for that analysis context to understand the in-memory python objects that will be passed to the ``run`` method of the metric measurement task and the configuration options. See :ref:`design concepts <lsst.faro.design_concepts>` for more information. Currently implemented analysis contexts are listed :ref:`here <lsst.faro.currently_implemented_analysis_contexts>`.
 
 2. Implement Measurement task. This will be an instance of ``lsst.pipe.base.Task`` that performs the specific operations of a given metric. See ``NumSourcesTask`` defined in `BaseSubTasks.py <https://github.com/lsst/faro/blob/master/python/lsst/faro/base/BaseSubTasks.py>`_ for a simple example metric that returns the number of rows in an input source/object catalog. Additional examples of measurement tasks can be found in the ``python/lsst/faro/measurement`` directory of the package.
 
 3. Implement unit tests. All algorithmic code used for metric computation should have associated unit tests. Examples can be found in the package ``tests`` directory.
 
 4. Add metric to a pipeline yaml file. The pipeline yaml contains the configuration information to execute metrics. See `measurement_visit_table.yaml <https://github.com/lsst/faro/blob/master/pipelines/measurement/measurement_visit_table.yaml>` for an example that uses ``VisitTableMeasurementTask`` to count the number of rows in an input source/object catalog. Additional examples of pipeline files can be found in ``pipelines/measurement`` directory of the package.
 
-5. Name the metric. Currently each metric is associated with separately named dataset type that is global (more info :ref:`here<lsst.faro.shared>`). To date, metric names have followed the pattern "metricvalue_{package}_{metric}" where the "package" and "metric" are given in the yaml configuration file. Metric naming conventions is an area of active development and it is recommended to contact the ``faro`` development team for up-to-date guidance.
+5. Name the metric. Currently each metric is associated with separately named dataset type that is global (more info :ref:`here <lsst.faro.shared>`). To date, metric names have followed the pattern "metricvalue_{package}_{metric}" where the "package" and "metric" are given in the yaml configuration file. Metric naming conventions is an area of active development and it is recommended to contact the ``faro`` development team for up-to-date guidance.
 
 Review
 ------