Quick Overview docs page (#62)

* wrote quick overview page

* extremely basic intallation instructions

* version 0.0.1

* updated with .from_dict constructor change

* linting

docs/source/api.rst

@@ -11,7 +11,6 @@ DataTree
    :toctree: generated/
 
    DataTree
-   DataNode
 
 Attributes
 ----------
@@ -51,6 +50,7 @@ Methods
 .. autosummary::
    :toctree: generated/
 
+   DataTree.from_dict
    DataTree.load
    DataTree.compute
    DataTree.persist

docs/source/conf.py

@@ -45,6 +45,8 @@
     "sphinx.ext.intersphinx",
     "sphinx.ext.extlinks",
     "sphinx.ext.napoleon",
+    "IPython.sphinxext.ipython_console_highlighting",
+    "IPython.sphinxext.ipython_directive",
 ]
 
 extlinks = {
@@ -76,9 +78,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = "0.0.0"  # datatree.__version__
+version = "0.0.1"  # datatree.__version__
 # The full version, including alpha/beta/rc tags.
-release = "0.0.0"  # datatree.__version__
+release = "0.0.1"  # datatree.__version__
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/source/index.rst

@@ -1,17 +1,21 @@
 Datatree
 ========
 
-**Datatree is a WIP implementation of a tree-like hierarchical data structure for xarray.**
+**Datatree is a prototype implementation of a tree-like hierarchical data structure for xarray.**
 
 
 .. toctree::
    :maxdepth: 2
    :caption: Documentation Contents
 
-   installation
-   tutorial
-   api
-   contributing
+   Installation <installation>
+   Quick Overview <quick-overview>
+   Tutorial <tutorial>
+   API Reference <api>
+   How do I ... <howdoi>
+   Contributing Guide <contributing>
+   Development Roadmap <roadmap>
+   GitHub repository <https://github.com/TomNicholas/datatree>
 
 Feedback
 --------

docs/source/installation.rst

@@ -2,4 +2,21 @@
 Installation
 ============
 
-Coming soon!
+Datatree is not yet available on pypi or via conda, so for now you will have to install it from source.
+
+``git clone https://github.com/TomNicholas/datatree.git```
+
+``pip install -e ./datatree/``
+
+The main branch will be kept up-to-date, so if you clone main and run the test suite with ``pytest datatree`` and get no failures,
+then you have the most up-to-date version.
+
+You will need xarray and `anytree <https://github.com/c0fec0de/anytree>`_
+as dependencies, with netcdf4, zarr, and h5netcdf as optional dependencies to allow file I/O.
+
+.. note::
+
+    Datatree is very much still in the early stages of development. There may be functions that are present but whose
+    internals are not yet implemented, or significant changes to the API in future.
+    That said, if you try it out and find some behaviour that looks like a bug to you, please report it on the
+    `issue tracker <https://github.com/TomNicholas/datatree/issues>`_!

docs/source/quick-overview.rst

@@ -0,0 +1,83 @@
+##############
+Quick overview
+##############
+
+DataTrees
+---------
+
+:py:class:`DataTree` is a tree-like container of ``DataArray`` objects, organised into multiple mutually alignable groups.
+You can think of it like a (recursive) ``dict`` of ``Dataset`` objects.
+
+Let's first make some example xarray datasets (following on from xarray's
+`quick overview <https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html>`_ page):
+
+.. ipython:: python
+
+    import numpy as np
+    import xarray as xr
+
+    data = xr.DataArray(np.random.randn(2, 3), dims=("x", "y"), coords={"x": [10, 20]})
+    ds = xr.Dataset(dict(foo=data, bar=("x", [1, 2]), baz=np.pi))
+    ds
+
+    ds2 = ds.interp(coords={"x": [10, 12, 14, 16, 18, 20]})
+    ds2
+
+    ds3 = xr.Dataset(
+        dict(people=["alice", "bob"], heights=("people", [1.57, 1.82])),
+        coords={"species": "human"},
+    )
+    ds3
+
+Now we'll put this data into a multi-group tree:
+
+.. ipython:: python
+
+    from datatree import DataTree
+
+    dt = DataTree.from_dict(
+        {"root/simulation/coarse": ds, "root/simulation/fine": ds2, "root": ds3}
+    )
+    print(dt)
+
+This creates a datatree with various groups. We have one root group (named ``root``), containing information about individual people.
+The root group then has one subgroup ``simulation``, which contains no data itself but does contain another two subgroups,
+named ``fine`` and ``coarse``.
+
+The (sub-)sub-groups ``fine`` and ``coarse`` contain two very similar datasets.
+They both have an ``"x"`` dimension, but the dimension is of different lengths in each group, which makes the data in each group unalignable.
+In (``root``) we placed some completely unrelated information, showing how we can use a tree to store heterogenous data.
+
+The constraints on each group are therefore the same as the constraint on dataarrays within a single dataset.
+
+We created the sub-groups using a filesystem-like syntax, and accessing groups works the same way.
+We can access individual dataarrays in a similar fashion
+
+.. ipython:: python
+
+    dt["simulation/coarse/foo"]
+
+and we can also pull out the data in a particular group as a ``Dataset`` object using ``.ds``:
+
+.. ipython:: python
+
+    dt["simulation/coarse"].ds
+
+Operations map over subtrees, so we can take a mean over the ``x`` dimension of both the ``fine`` and ``coarse`` groups just by
+
+.. ipython:: python
+
+    avg = dt["simulation"].mean(dim="x")
+    print(avg)
+
+Here the ``"x"`` dimension used is always the one local to that sub-group.
+
+You can do almost everything you can do with ``Dataset`` objects with ``DataTree`` objects
+(including indexing and arithmetic), as operations will be mapped over every sub-group in the tree.
+This allows you to work with multiple groups of non-alignable variables at once.
+
+.. note::
+
+    If all of your variables are mutually alignable
+    (i.e. they live on the same grid, such that every common dimension name maps to the same length),
+    then you probably don't need :py:class:`DataTree`, and should consider just sticking with ``xarray.Dataset``.