tweaked docs

docs/source/conf.py

@@ -107,10 +107,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "sphinx_rtd_theme"
-import sphinx_rtd_theme
-
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+html_theme = "furo"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -125,15 +122,6 @@
 
 # Custom sidebar templates, must be a dictionary that maps document names
 # to template names.
-#
-# This is required for the alabaster theme
-# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
-html_sidebars = {
-    "**": [
-        "relations.html",  # needs 'show_related': True theme option to display
-        "searchbox.html",
-    ]
-}
 
 
 # -- Options for HTMLHelp output ------------------------------------------

docs/source/tutorials/introduction.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "This tutorial is an introduction to the syntax used by the optimizer, as well as the principles of Bayesian optimization in general.\n",
     "\n",
-    "We'll start by minimizing the Rastrigin function in one dimension, which looks like this:"
+    "We'll start by minimizing the Styblinski-Tang function in one dimension, which looks like this:"
    ]
   },
   {

docs/source/usage.rst

@@ -26,32 +26,17 @@ Degrees of freedom (DOFs) are passed as an iterable of dicts, each containing at
 Here ``some_motor`` and ``another_motor`` are ``ophyd`` objects.
 
 
-Detectors
-+++++++++
-
-Detectors are triggered for each input.
-
-.. code-block:: python
-
-    my_dets = [some_detector, some_other_detector]
-
-
-
-Acquisition
-+++++++++++
-
-We run this plan for each set of DOF inputs. By default, this just moves the active DOFs to the desired points and triggers the supplied detectors.
-
 
 Tasks
 +++++
 
-Degrees of freedom (DOFs) are passed as an iterable of dicts, each containing at least the device and set of limits.
+Tasks are what we want our agent to try to optimize (either maximize or minimize). We can pass as many as we'd like:
 
 .. code-block:: python
 
     my_tasks = [
-        {"key": "value_to_maximize"}
+        {"key": "something_to_maximize", "kind": "maximize"}
+        {"key": "something_to_minimize", "kind": "minimize"}
         ]
 
 
@@ -73,11 +58,29 @@ The digestion function is how we go from what is spit out by the acquisition to
             raw_output_1 = entry.raw_output_1
             raw_output_2 = entry.raw_output_2
 
-            entry.loc[index, "value_to_maximize"] = some_fitness_function(raw_output_1, raw_output_2)
+            entry.loc[index, "thing_to_maximize"] = some_fitness_function(raw_output_1, raw_output_2)
 
         return products
 
 
+Detectors
++++++++++
+
+Detectors are triggered for each input.
+
+.. code-block:: python
+
+    my_dets = [some_detector, some_other_detector]
+
+
+
+Acquisition
++++++++++++
+
+We run this plan for each set of DOF inputs. By default, this just moves the active DOFs to the desired points and triggers the supplied detectors.
+
+
+
 
 Building the agent
 ++++++++++++++++++