Few small formatting/layout fixes (for the PDF version)

docs/basics/101-130-yodaproject.rst

@@ -18,95 +18,95 @@ a Python API for DataLad's functionality that you can read about in :ref:`a Find
    :name: fom-pythonapi
    :float:
 
-    .. _python:
+   .. _python:
 
-    Whatever you can do with DataLad from the command line, you can also do it with
-    DataLad's Python API.
-    Thus, DataLad's functionality can also be used within interactive Python sessions
-    or Python scripts.
-    All of DataLad's user-oriented commands are exposed via ``datalad.api``.
-    Thus, any command can be imported as a stand-alone command like this:
+   Whatever you can do with DataLad from the command line, you can also do it with
+   DataLad's Python API.
+   Thus, DataLad's functionality can also be used within interactive Python sessions
+   or Python scripts.
+   All of DataLad's user-oriented commands are exposed via ``datalad.api``.
+   Thus, any command can be imported as a stand-alone command like this:
 
-    .. code-block:: python
+   .. code-block:: python
 
-       >>> from datalad.api import <COMMAND>
+      >>> from datalad.api import <COMMAND>
 
-    Alternatively, to import all commands, one can use
+   Alternatively, to import all commands, one can use
 
-    .. code-block:: python
+   .. code-block:: python
 
-       >>> import datalad.api as dl
+      >>> import datalad.api as dl
 
-    and subsequently access commands as ``dl.get()``, ``dl.clone()``, and so forth.
+   and subsequently access commands as ``dl.get()``, ``dl.clone()``, and so forth.
 
 
-    The `developer documentation <https://docs.datalad.org/en/latest/modref.html>`_
-    of DataLad lists an overview of all commands, but naming is congruent to the
-    command line interface. The only functionality that is not available at the
-    command line is ``datalad.api.Dataset``, DataLad's core Python data type.
-    Just like any other command, it can be imported like this:
+   The `developer documentation <https://docs.datalad.org/en/latest/modref.html>`_
+   of DataLad lists an overview of all commands, but naming is congruent to the
+   command line interface. The only functionality that is not available at the
+   command line is ``datalad.api.Dataset``, DataLad's core Python data type.
+   Just like any other command, it can be imported like this:
 
-    .. code-block:: python
+   .. code-block:: python
 
-       >>> from datalad.api import Dataset
+      >>> from datalad.api import Dataset
 
-    or like this:
+   or like this:
 
-    .. code-block:: python
+   .. code-block:: python
 
-       >>> import datalad.api as dl
-       >>> dl.Dataset()
+      >>> import datalad.api as dl
+      >>> dl.Dataset()
 
-    A ``Dataset`` is a `class <https://docs.python.org/3/tutorial/classes.html>`_
-    that represents a DataLad dataset. In addition to the
-    stand-alone commands, all of DataLad's functionality is also available via
-    `methods <https://docs.python.org/3/tutorial/classes.html#method-objects>`_
-    of this class. Thus, these are two equally valid ways to create a new
-    dataset with DataLad in Python:
+   A ``Dataset`` is a `class <https://docs.python.org/3/tutorial/classes.html>`_
+   that represents a DataLad dataset. In addition to the
+   stand-alone commands, all of DataLad's functionality is also available via
+   `methods <https://docs.python.org/3/tutorial/classes.html#method-objects>`_
+   of this class. Thus, these are two equally valid ways to create a new
+   dataset with DataLad in Python:
 
-    .. code-block:: python
+   .. code-block:: python
 
-       >>> from datalad.api import create, Dataset
-       # create as a stand-alone command
-       >>> create(path='scratch/test')
-       [INFO   ] Creating a new annex repo at /.../scratch/test
-       Out[3]: <Dataset path=/home/me/scratch/test>
-       # create as a dataset method
-       >>> ds = Dataset(path='scratch/test')
-       >>> ds.create()
-       [INFO   ] Creating a new annex repo at /.../scratch/test
-       Out[3]: <Dataset path=/home/me/scratch/test>
+      >>> from datalad.api import create, Dataset
+      # create as a stand-alone command
+      >>> create(path='scratch/test')
+      [INFO   ] Creating a new annex repo at /.../scratch/test
+      Out[3]: <Dataset path=/home/me/scratch/test>
+      # create as a dataset method
+      >>> ds = Dataset(path='scratch/test')
+      >>> ds.create()
+      [INFO   ] Creating a new annex repo at /.../scratch/test
+      Out[3]: <Dataset path=/home/me/scratch/test>
 
-    As shown above, the only required parameter for a Dataset is the ``path`` to
-    its location, and this location may or may not exist yet.
+   As shown above, the only required parameter for a Dataset is the ``path`` to
+   its location, and this location may or may not exist yet.
 
-    Stand-alone functions have a ``dataset=`` argument, corresponding to the
-    ``-d/--dataset`` option in their command-line equivalent. You can specify
-    the ``dataset=`` argument with a path (string) to your dataset (such as
-    ``dataset='.'`` for the current directory, or ``dataset='path/to/ds'`` to
-    another location). Alternatively, you can pass a ``Dataset`` instance to it:
+   Stand-alone functions have a ``dataset=`` argument, corresponding to the
+   ``-d/--dataset`` option in their command-line equivalent. You can specify
+   the ``dataset=`` argument with a path (string) to your dataset (such as
+   ``dataset='.'`` for the current directory, or ``dataset='path/to/ds'`` to
+   another location). Alternatively, you can pass a ``Dataset`` instance to it:
 
-    .. code-block:: python
+   .. code-block:: python
 
-        >>> from datalad.api import save, Dataset
-        # use save with dataset specified as a path
-        >>> save(dataset='path/to/dataset/')
-        # use save with dataset specified as a dataset instance
-        >>> ds = Dataset('path/to/dataset')
-        >>> save(dataset=ds, message="saving all modifications")
-        # use save as a dataset method (no dataset argument)
-        >>> ds.save(message="saving all modifications")
+       >>> from datalad.api import save, Dataset
+       # use save with dataset specified as a path
+       >>> save(dataset='path/to/dataset/')
+       # use save with dataset specified as a dataset instance
+       >>> ds = Dataset('path/to/dataset')
+       >>> save(dataset=ds, message="saving all modifications")
+       # use save as a dataset method (no dataset argument)
+       >>> ds.save(message="saving all modifications")
 
 
-    **Use cases for DataLad's Python API**
+   **Use cases for DataLad's Python API**
 
-    Using the command line or the Python API of DataLad are both valid ways to accomplish the same results.
-    Depending on your workflows, using the Python API can help to automate dataset operations, provides an alternative
-    to the command line, or could be useful for scripting reproducible data analyses.
-    One unique advantage of the Python API is the ``Dataset``:
-    As the Python API does not suffer from the startup time cost of the command line,
-    there is the potential for substantial speed-up when doing many calls to the API,
-    and using a persistent Dataset object instance.
+   Using the command line or the Python API of DataLad are both valid ways to accomplish the same results.
+   Depending on your workflows, using the Python API can help to automate dataset operations, provides an alternative
+   to the command line, or could be useful for scripting reproducible data analyses.
+   One unique advantage of the Python API is the ``Dataset``:
+   As the Python API does not suffer from the startup time cost of the command line,
+   there is the potential for substantial speed-up when doing many calls to the API,
+   and using a persistent Dataset object instance.
 
 .. importantnote:: Use DataLad in languages other than Python
 
@@ -151,7 +151,6 @@ independent dataset from scratch in a :ref:`dedicated Findoutmore <fom-iris>`.
 
 .. find-out-more:: Creating an independent input dataset
    :name: fom-iris
-   :float:
 
    If you acquire your own data for a data analysis, you will have
    to turn it into a DataLad dataset in order to install it as a subdataset.