Doc updates

docs/bufferedstreaming.rst

@@ -3,7 +3,7 @@
 Buffered Streaming
 ==================
 
-In a machine learning setting, it is common to train a model with multiple input datapoints simultaneously, in what are commonly referred to as "minibatches". To achieve this, pescador provides the :ref:`pescador.maps.buffer_stream` map transformer, which will "buffer" a data stream into fixed batch sizes.
+In a machine learning setting, it is common to train a model with multiple input datapoints simultaneously, in what are commonly referred to as "minibatches". To achieve this, pescador provides the `pescador.maps.buffer_stream` map transformer, which will "buffer" a data stream into fixed batch sizes.
 
 Following up on the first example, we use the `noisy_samples` generator.
 
@@ -17,17 +17,17 @@ Following up on the first example, we use the `noisy_samples` generator.
 
     minibatch_size = 128
     # Wrap your streamer
-    buffered_sample_gen = pescador.buffer_stream(streamer, minibatch_size)
+    buffered_sample_gen = pescador.maps.buffer_stream(streamer, minibatch_size)
 
     # Generate batches in exactly the same way as you would from the base streamer
     for batch in buffered_sample_gen:
         ...
 
 
 
-A few important points to note about using :ref:`pescador.maps.buffer_stream`:
+A few important points to note about using `pescador.maps.buffer_stream`:
 
-    - :ref:`pescador.maps.buffer_stream` will concatenate your arrays, adding a new sample dimension such that the first dimension contains the number of batches (`minibatch_size` in the above example). e.g. if your samples are shaped (4, 5), a batch size of 10 will produce arrays shaped (10, 4, 5)
+    - `pescador.maps.buffer_stream` will concatenate your arrays, adding a new sample dimension such that the first dimension contains the number of batches (`minibatch_size` in the above example). e.g. if your samples are shaped (4, 5), a batch size of 10 will produce arrays shaped (10, 4, 5)
 
     - Each key in the batches generated will be concatenated (across all the samples buffered).
 

docs/conf.py

@@ -110,7 +110,7 @@
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.
-#default_role = None
+default_role = "autolink"
 
 # If true, '()' will be appended to :func: etc. cross-reference text.
 #add_function_parentheses = True
@@ -146,7 +146,9 @@
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-html_theme = 'default'
+import sphinx_rtd_theme
+html_theme = 'sphinx_rtd_theme'
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 
 # 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

docs/example2.rst

@@ -21,8 +21,6 @@ cleaner syntax.
 .. code-block:: python
     :linenos:
 
-    from __future__ import print_function
-
     import numpy as np
 
     from sklearn.datasets import load_breast_cancer

docs/index.rst

@@ -45,7 +45,7 @@ To make this all possible, Pescador provides the following utilities:
     - :ref:`Mux` objects allow flexible sampling from multiple streams
     - :ref:`ZMQStreamer` provides parallel processing with low communication overhead
     - Transform or modify streams with Maps (see :ref:`processing-data-streams`)
-    - Buffering of sampled data into fixed-size batches (see :ref:`pescador.maps.buffer_stream`)
+    - Buffering of sampled data into fixed-size batches (see `pescador.maps.buffer_stream`)
 
 ************
 Installation

examples/mux/epoch.py

@@ -10,7 +10,7 @@
 
 - If presentation order does not matter, and a deterministic
   sequence is acceptable, then this can be achieved with
-  `ChainMux` as demonstrated in :ref:`Using ChainMux for repeatable streams`.
+  `ChainMux` as demonstrated in :ref:`sphx_glr_auto_examples_mux_chain_example.py`.
   This is typically a good approach for validation or evaluation,
   but not training, since the deterministic sequence order could bias
   the model.