Minor terminology fixes

Author: Joel Collins

README.md

@@ -15,7 +15,7 @@ A Python implementation of the LabThings API structure, based on the Flask micro
 
 ## Quickstart example
 
-This example assumes a `PretendSpectrometer` class, which already has `data` and `integration_time` properties, as well as an `average_data(n)` method. LabThings allows you to easily convert this existing instrument control code into a fully documented, standardised web API complete with auto-discovery and automatic background task threading.
+This example assumes a `PretendSpectrometer` class, which already has `data` and `integration_time` attributes, as well as an `average_data(n)` method. LabThings allows you to easily convert this existing instrument control code into a fully documented, standardised web API complete with auto-discovery and automatic background task threading.
 
 ```python
 from labthings import fields, create_app

docs/quickstart.rst

@@ -3,7 +3,7 @@ Quickstart
 
 The easiest way to get started with Python-LabThings is via the :meth:`labthings.create_app` function, and the :class:`labthings.LabThing` builder methods.
 
-We will assume that for basic usage you already have some basic instrument control code. In our example, this is in the form of a ``PretendSpectrometer`` class, which will generate some data like your instrument control code might. Our ``PretendSpectrometer`` class has a ``data`` property which quickly returns a spectrum, an ``x_range`` property which determines the range of data we'll return, an ``integration_time`` property for cleaning up our signal, and a slow ``average_data(n)`` method to average ``n`` individual data measurements.
+We will assume that for basic usage you already have some basic instrument control code. In our example, this is in the form of a ``PretendSpectrometer`` class, which will generate some data like your instrument control code might. Our ``PretendSpectrometer`` class has a ``data`` attribute which quickly returns a spectrum, an ``x_range`` attribute which determines the range of data we'll return, an ``integration_time`` attribute for cleaning up our signal, and a slow ``average_data(n)`` method to average ``n`` individual data measurements.
 
 Building an API from this class requires a few extra considerations. In order to tell our API what data to expect from users, we need to construct a schema for each of our interactions. This schema simply maps variable names to JSON-compatible types, and is made simple via the :mod:`labthings.fields` module.