add quickstart guide

docs/content/user/quickstart.rst

@@ -4,4 +4,100 @@
 Quickstart Guide
 ################
 
-Under development.
+Installation
+------------
+
+**obsarray** is installable via pip.
+
+.. code-block::
+
+   pip install obsarray
+
+
+Dependencies
+------------
+
+**obsarray** is an extension to `xarray <https://docs.xarray.dev/en/stable/>`_ to support defining, storing and interfacing with measurement data. It is designed to work well with `netCDF <https://www.unidata.ucar.edu/software/netcdf/>`_ files, using the `netcdf4 <https://unidata.github.io/netcdf4-python/>`_ library.
+
+The pip installation will also automatically install any dependencies.
+
+
+Example Usage
+-------------
+
+First we build an example dataset that represents a time series of temperatures (for more on how do this see the `xarray <https://docs.xarray.dev/en/stable/>`_ documentation).
+
+.. ipython:: python
+
+   import numpy as np
+   import xarray as xr
+   import obsarray
+
+   # build an xarray to represents a time series of temperatures
+   temps = np.array([20.2, 21.1, 20.8])
+   times = np.array([0, 30, 60])
+   ds = xr.Dataset(
+      {"temperature": (["time"], temps, {"units": "degC"})},
+      coords = {"time": (["time"], times, {"units": "s"})}
+   )
+
+Uncertainty and error-covariance information for observation variables can be defined using the dataset's ``unc`` accessor, which is provided by **obsarray**.
+
+.. ipython:: python
+
+   # add random component uncertainty
+   ds.unc["temperature"]["u_r_temperature"] = (
+      ["time"],
+      np.array([0.5, 0.5, 0.6]),
+      {"err_corr": [{"dim": "time", "form": "random"}]}
+   )
+   # add systematic component uncertainty
+   ds.unc["temperature"]["u_s_temperature"] = (
+      ["time"],
+      np.array([0.3, 0.3, 0.3]),
+      {"err_corr": [{"dim": "time", "form": "systematic"}]}
+   )
+
+Dataset structures can be defined separately using **obsarray**'s :ref:`templating <template>` functionality. This is helpful for processing chains where you want to write files to a defined format.
+
+The defined uncertainty information then can be interfaced with, for example:
+
+.. ipython:: python
+
+   # get total combined uncertainty of all components
+   ds.unc["temperature"].total_unc()
+   # get total error-covariance matrix for all components
+   ds.unc["temperature"].total_err_cov_matrix()
+
+This information is preserved in metadata when written to netCDF files
+
+.. ipython:: python
+
+   # show uncertainty components
+   ds.unc["temperature"]
+   # write file
+   ds.to_netcdf("~/temp_ds.nc")
+   # reopen file
+   ds = xr.open_dataset("~/temp_ds.nc")
+   # show uncertainty components
+   ds.unc["temperature"]
+
+Similarly, data flags can be defined using the dataset’s ``flag`` accessor, which again is provided by **obsarray**. These flags are defined following the `CF Convention <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.html#flags>`_ metadata standard.
+
+A flag variable can be created to store data for a set of flags with defined meanings
+
+.. ipython:: python
+
+    ds.flag["quality_flags"] = (
+        ["time"],
+        {"flag_meanings": ["dubious", "invalid", "saturated"]}
+    )
+    print(ds.flag)
+
+These flag meanings can be indexed, to get and set their value
+
+.. ipython:: python
+
+    print(ds.flag["quality_flags"]["dubious"].value)
+    ds.flag["quality_flags"]["dubious"][0] = True
+    print(ds.flag["quality_flags"]["dubious"].value)

docs/content/user/templater.rst

@@ -1,3 +1,5 @@
+.. _template:
+
 =================================
 Creating Datasets Using Templates
 =================================