diff --git a/doc/source/analyzing/astropy_integrations.rst b/doc/source/analyzing/astropy_integrations.rst index f7ac4e0c49d..f74419de95c 100644 --- a/doc/source/analyzing/astropy_integrations.rst +++ b/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 ---------------------------------------------------------------- diff --git a/doc/source/cookbook/index.rst b/doc/source/cookbook/index.rst index a8bc99b7e42..7838f150932 100644 --- a/doc/source/cookbook/index.rst +++ b/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 diff --git a/doc/source/examining/index.rst b/doc/source/examining/index.rst index 5928ce9075c..3d9cfc15b73 100644 --- a/doc/source/examining/index.rst +++ b/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 ``_, +``_, and ``_. Lastly, if you want to examine the raw data for your particular dataset, visit :ref:`low-level-data-inspection`. diff --git a/doc/source/examining/loading_data.rst b/doc/source/examining/loading_data.rst index c8eaebbb107..d5bf6b6c3a0 100644 --- a/doc/source/examining/loading_data.rst +++ b/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, + ``_ 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 ``_ 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 ``_. 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 ``_ 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 ``_, :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 ``_, :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 ``_ 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. diff --git a/doc/source/reference/code_support.rst b/doc/source/reference/code_support.rst index 558a13fa8d6..87a91f2130c 100644 --- a/doc/source/reference/code_support.rst +++ b/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.