fix more references

doc/source/analyzing/astropy_integrations.rst

@@ -33,8 +33,8 @@ Fixed-resolution two-dimensional images generated from datasets using yt (such a
 slices or projections) and fixed-resolution three-dimensional grids can be written
 to FITS files using yt's :class:`~yt.visualization.fits_image.FITSImageData` class
 and its subclasses. Multiple images can be combined into a single file, operations
-can be performed on the images and their coordinates, etc. See :ref:`writing_fits_images`
-for more information.
+can be performed on the images and their coordinates, etc. See
+`this notebook <../visualizing/FITSImageData>`_ for more information.
 
 Converting Field Container and 1D Profile Data to AstroPy Tables
 ----------------------------------------------------------------

doc/source/cookbook/index.rst

@@ -42,9 +42,9 @@ Example Notebooks
    custom_colorbar_tickmarks
    yt_gadget_analysis
    yt_gadget_owls_analysis
-   ../visualizing/transfer_function_helper
+   ../visualizing/TransferFunctionHelper_Tutorial
    fits_radio_cubes
    fits_xray_images
    geographic_xforms_and_projections
    tipsy_and_yt
-   ../visualizing/volume_rendering_tutorial
+   ../visualizing/Volume_Rendering_Tutorial

doc/source/examining/index.rst

@@ -6,8 +6,8 @@ Loading and Examining Data
 Nominally, one should just be able to run ``yt.load()`` on a dataset and start
 computing; however, there may be additional notes associated with different
 data formats as described below.  Furthermore, we provide methods for loading
-data from unsupported data formats in :ref:`loading-numpy-array`,
-:ref:`generic-particle-data`, and :ref:`loading-spherical-data`.  Lastly, if
+data from unsupported data formats in `<Loading_Generic_Array_Data>`_,
+`<Loading_Generic_Particle_Data>`_, and `<Loading_Spherical_Data>`_.  Lastly, if
 you want to examine the raw data for your particular dataset, visit
 :ref:`low-level-data-inspection`.
 

doc/source/examining/loading_data.rst

@@ -70,7 +70,7 @@ Simple HDF5 Data
 .. note::
 
    This wrapper takes advantage of the functionality described in
-   :ref:`loading-via-functions` but the basics of setting up function handlers,
+   `<Loading_Data_via_Functions>`_ but the basics of setting up function handlers,
    guessing fields, etc, are handled by yt.
 
 Using the function :func:`yt.loaders.load_hdf5_file`, you can load a generic
@@ -1013,8 +1013,8 @@ produced from slices, projections, and 3D covering grids. The resulting
 FITS images are fully-describing in that unit, parameter, and coordinate
 information is passed from the original dataset. These can be created via the
 :class:`~yt.visualization.fits_image.FITSImageData` class and its subclasses.
-For information about how to use these special classes, see
-:ref:`writing_fits_images`.
+For information about how to use these special classes, see `this notebook
+<../visualizing/FITSImageData>`_.
 
 Once you have produced a FITS file in this fashion, you can load it using
 yt and it will be detected as a ``YTFITSDataset`` object, and it can be analyzed
@@ -1056,7 +1056,7 @@ particle fields in yt, but a grid will be constructed from the WCS
 information in the FITS header. There is a helper function,
 ``setup_counts_fields``, which may be used to make deposited image fields
 from the event data for different energy bands (for an example see
-:ref:`xray_fits`).
+`<../cookbook/fits_xray_images>`_).
 
 Generic FITS Images
 """""""""""""""""""
@@ -1296,9 +1296,9 @@ Examples of Using FITS Data
 The following Jupyter notebooks show examples of working with FITS data in yt,
 which we recommend you look at in the following order:
 
-* :ref:`radio_cubes`
-* :ref:`xray_fits`
-* :ref:`writing_fits_images`
+* `<../cookbook/fits_radio_cubes>`_
+* `<../cookbook/fits_xray_images>`_
+* `Writing FITS Images <../visualizing/FITSImageData>`_
 
 .. _loading-flash-data:
 
@@ -1385,9 +1385,9 @@ yt has support for reading Gadget data in both raw binary and HDF5 formats.  It
 is able to access the particles as it would any other particle dataset, and it
 can apply smoothing kernels to the data to produce both quantitative analysis
 and visualization. See :ref:`loading-sph-data` for more details and
-:ref:`gadget-notebook` for a detailed example of loading, analyzing, and
+`<../cookbook/yt_gadget_analysis>`_ for a detailed example of loading, analyzing, and
 visualizing a Gadget dataset.  An example which makes use of a Gadget snapshot
-from the OWLS project can be found at :ref:`owls-notebook`.
+from the OWLS project can be found at `<../cookbook/yt_gadget_owls_analysis>`_.
 
 .. note::
 
@@ -1882,14 +1882,14 @@ to avoid catastrophic cancellations.
 Generic AMR Data
 ----------------
 
-See :ref:`loading-numpy-array` and
+See `<Loading_Generic_Array_Data>`_ and
 :func:`~yt.frontends.stream.data_structures.load_amr_grids` for more detail.
 
 .. note::
 
    It is now possible to load data using *only functions*, rather than using the
    fully-in-memory method presented here.  For more information and examples,
-   see :ref:`loading-via-functions`.
+   see `<Loading_Data_via_Functions>`_.
 
 It is possible to create native yt dataset from Python's dictionary
 that describes set of rectangular patches of data of possibly varying
@@ -1946,7 +1946,7 @@ Particle fields are supported by adding 1-dimensional arrays to each
 Generic Array Data
 ------------------
 
-See :ref:`loading-numpy-array` and
+See `<Loading_Generic_Array_Data>`_ and
 :func:`~yt.frontends.stream.data_structures.load_uniform_grid` for more detail.
 
 Even if your data is not strictly related to fields commonly used in
@@ -2010,7 +2010,7 @@ Semi-Structured Grid Data
 
    See :ref:`loading-stretched-grids` for more information.
 
-See :ref:`loading-numpy-array`,
+See `<Loading_Generic_Array_Data>`_,
 :func:`~yt.frontends.stream.data_structures.hexahedral_connectivity`,
 :func:`~yt.frontends.stream.data_structures.load_hexahedral_mesh` for
 more detail.
@@ -2124,7 +2124,7 @@ fewer) cells.
 Unstructured Grid Data
 ----------------------
 
-See :ref:`loading-numpy-array`,
+See `<Loading_Generic_Array_Data>`_,
 :func:`~yt.frontends.stream.data_structures.load_unstructured_mesh` for
 more detail.
 
@@ -2250,7 +2250,7 @@ Generic Particle Data
    For more information about how yt indexes and reads particle data, set the
    section :ref:`demeshening`.
 
-See :ref:`generic-particle-data` and
+See `<Loading_Generic_Particle_Data>`_ and
 :func:`~yt.frontends.stream.data_structures.load_particles` for more detail.
 
 You can also load generic particle data using the same ``stream`` functionality
@@ -3202,7 +3202,7 @@ Tipsy Data
    For more information about how yt indexes and reads particle data, set the
    section :ref:`demeshening`.
 
-See :ref:`tipsy-notebook` and :ref:`loading-sph-data` for more details.
+See `<../cookbook/tipsy_and_yt>`_ and :ref:`loading-sph-data` for more details.
 
 yt also supports loading Tipsy data.  Many of its characteristics are similar
 to how Gadget data is loaded.

doc/source/reference/code_support.rst

@@ -92,6 +92,6 @@ each supported output format using yt.
          CFRadial coordinates will be gridded on load, see :ref:`loading-cfradial-data`.
 
 If you have a dataset that uses an output format not yet supported by yt, you
-can either input your data following :ref:`loading-numpy-array` or
-:ref:`generic-particle-data`, or help us by :ref:`creating_frontend` for this
+can either input your data following `<../examining/Loading_Generic_Array_Data>`_ or
+`<../examining/Loading_Generic_Particle_Data>`_, or help us by :ref:`creating_frontend` for this
 new format.