DOC: Add getting started page (#37)

docs/getting_started.rst

@@ -0,0 +1,42 @@
+Getting Started
+================
+
+`geocube` combines the interfaces of:
+
+- `geopandas <https://github.com/geopandas/geopandas>`__
+- `xarray <https://github.com/pydata/xarray>`__
+- `rioxarray <https://github.com/corteva/rioxarrau>`__
+
+and is powered by `GDAL <https://github.com/osgeo/gdal>`__ using:
+
+- `rasterio <https://github.com/mapbox/rasterio>`__
+- `fiona <https://github.com/toblerity/fiona>`__
+
+
+When getting started, the API documentation to start reading would be :func:`geocube.api.core.make_geocube`.
+
+The simplest example would be to rasterize a single column:
+
+.. code-block:: python
+
+    from geocube.api.core import make_geocube
+    
+    out_grid = make_geocube(
+        vector_data="path_to_file.gpkg",
+        measurements=["column_name"].
+        resolution=(-0.0001, 0.0001),
+    )
+    out_grid["column_name"].rio.to_raster("my_rasterized_column.tif")
+
+
+You can also rasterize a `GeoDataFrame <https://geopandas.readthedocs.io/en/latest/docs/user_guide/data_structures.html#geodataframe>`__
+directly in the `vector_data` argument. This enables you to `load in subsets of data <https://geopandas.readthedocs.io/en/latest/docs/user_guide/io.html#reading-subsets-of-the-data>`__
+or perform various operations before rasterization.
+
+Once finished, you can write to anything supported by `rasterio <https://github.com/mapbox/rasterio>`__ 
+using `rioxarray`'s `rio.to_raster() <https://corteva.github.io/rioxarray/stable/examples/convert_to_raster.html>`__ method.
+You can also write to a netCDF file using `xarray`'s `to_netcdf() <http://xarray.pydata.org/en/stable/generated/xarray.Dataset.to_netcdf.html>`__.
+
+However, life is only this simple when your data is perfectly clean, geospatially unique, and numeric.
+The good news is that geocube supports a variety of use cases and custom rasterization functions if your dataset
+does not meet these criteria (see: :ref:`usage_examples`).

docs/index.rst

@@ -9,6 +9,7 @@ GitHub: http://github.com/corteva/geocube
 
    readme
    installation
+   getting_started
    examples/examples
    modules
    contributing

docs/installation.rst

@@ -11,41 +11,37 @@ Stable release
 Use pip to install from `PyPI <https://pypi.org/project/geocube/>`__:
 
 
-Step 1: Install python GDAL version associated with your GDAL version.
+Install from pip:
 
-Here is a Linux example with GDAL installed in your system:
+.. code-block:: bash
 
-  .. code-block:: bash
+    pip install geocube
 
-      export CPLUS_INCLUDE_PATH=/usr/include/gdal
-      export C_INCLUDE_PATH=/usr/include/gdal
-      pip install GDAL~=$(gdal-config --version | awk -F'[.]' '{print $1"."$2}').0
 
-Step 2: Install from pip:
+Use `conda <https://conda.io/en/latest/>`__ with the `conda-forge <https://conda-forge.org/>`__ channel:
 
-  .. code-block:: bash
+.. code-block:: bash
 
-      pip install geocube
+    conda install -c conda-forge geocube
 
 
-2. Use `conda <https://conda.io/en/latest/>`__ with the `conda-forge <https://conda-forge.org/>`__ channel:
-
-  .. code-block:: bash
+From source
+-----------
 
-      conda install -c conda-forge geocube
+The source for geocube can be installed from the `GitHub repo`_.
 
+.. code-block:: bash
 
-From source
------------
+    python -m pip install git+git://github.com/corteva/geocube.git#egg=geocube
 
-The source for geocube can be downloaded from the `GitHub repo`_.
 
-.. code-block:: console
+To install for local development:
 
-    $ git clone git@github.com:corteva/geocube.git
+.. code-block:: bash
 
-    $ cd geocube
-    $ python setup.py install
+    git clone git@github.com:corteva/geocube.git
+    cd geocube
+    python -m pip install -e .[dev]
 
 
 .. _GitHub repo: https://github.com/corteva/geocube