changes made while working on manuscript

docs/_build_html/_sources/clustergrammer_js.txt

@@ -5,7 +5,7 @@ Clustergrammer-JS
 |npm-version|
 |license|
 
-Clustergrammer-JS is the front-end JavaScript library that builds the interactive heatmap visualization in `SVG`_ using the visualization library `D3.js`_. The library is free and open-source and can be found on `GitHub`_.
+Clustergrammer-JS is the front end JavaScript library that builds the interactive clustergram visualization in `SVG`_ using the visualization library `D3.js`_. The library is free and open-source and can be found on `GitHub`_.
 
 .. _clustergrammer_js_dependencies:
 
@@ -232,7 +232,7 @@ Clustergrammer-JS API
 
 Visualization-JSON
 ==================
-The visualization-JSON is calculated by :ref:`clustergrammer_py` and encodes everything needed for the front-end Clustergrammer-JS to produce the visualization. The visualization-JSON format is described here (see `clustergrammer_example.json`_ for an example file). An overview of the format is shown below (note that the group arrays are not shown):
+The visualization-JSON is calculated by :ref:`clustergrammer_py` and encodes everything needed for the front end Clustergrammer-JS to produce the visualization. The visualization-JSON format is described here (see `clustergrammer_example.json`_ for an example file). An overview of the format is shown below (note that the group arrays are not shown):
 ::
 
   {

docs/_build_html/_sources/clustergrammer_py.txt

@@ -4,7 +4,7 @@ Clustergrammer-PY
 -----------------
 |pypi-version|
 
-Clustergrammer-PY is the back-end Python library that is used to hierarchically cluster the data and generate the :ref:`visualization_json` for the front end :ref:`clustergrammer_js` visualization library. Clustergrammer-PY is compatible with Python 2 and 3. The library is free and open-source and can be found on `GitHub`_.
+Clustergrammer-PY is the back end Python library that is used to hierarchically cluster the data and generate the :ref:`visualization_json` for the front end :ref:`clustergrammer_js` visualization library. Clustergrammer-PY is compatible with Python 2 and 3. The library is free and open-source and can be found on `GitHub`_.
 
 .. _clustergrammer_py_dependencies:
 
@@ -40,10 +40,10 @@ This workflow shows how to cluster a matrix of data from a file (see :ref:`matri
   # calculate clustering using default parameters
   net.cluster()
 
-  # save visualization JSON to file for use by front-end
+  # save visualization JSON to file for use by front end
   net.write_json_to_file('viz', 'mult_view.json')
 
-The file ``mult_view.json`` will be loaded by the front-end and used to build the interactive visualization. See `clusterergrammer.py`_ for an additional example.
+The file ``mult_view.json`` will be loaded by the front end and used to build the interactive visualization. See `clusterergrammer.py`_ for an additional example.
 
 Clustergrammer can also load data from a Pandas DataFrame and perform normalization and filtering. In this example, we will load data from a DataFrame, normalize the rows, and filter the columns:
 ::
@@ -61,16 +61,16 @@ Clustergrammer can also load data from a Pandas DataFrame and perform normalizat
   # cluster using default parameters
   net.cluster()
 
-  # save visualization JSON to file for use by front-end
+  # save visualization JSON to file for use by front end
   net.write_json_to_file('viz', 'mult_view.json')
 
-Note that filtering done on the ``Network`` object before clustering is permanent, unlike the filtering done within ``cluster`` which can be toggled on and off in the front-end visualization. The ``keep_orig`` parameter in the ``normalize`` function allows us to show un-normalized data a user mouses over a matrix-cell in the visualization. See the :ref:`clustergrammer_py_api` documentation below for more information.
+Note that filtering done on the ``Network`` object before clustering is permanent, unlike the filtering done within ``cluster`` which can be toggled on and off in the front end visualization. The ``keep_orig`` parameter in the ``normalize`` function allows us to show un-normalized data a user mouses over a matrix-cell in the visualization. See the :ref:`clustergrammer_py_api` documentation below for more information.
 
 .. _clustergrammer_py_api:
 
 Clustergrammer-PY API
 =====================
-Clustergrammer-PY generates a Network object (see `Network class definition`_), which is used to load a matrix (e.g. from a Pandas `DataFrame`_), optionally normalize or filter the matrix, cluster the matrix, and finally generate the visualization JSON for the front-end Clustergrammer.js.
+Clustergrammer-PY generates a Network object (see `Network class definition`_), which is used to load a matrix (e.g. from a Pandas `DataFrame`_), optionally normalize or filter the matrix, cluster the matrix, and finally generate the visualization JSON for the front end Clustergrammer.js.
 
 When a matrix is loaded into an instance of ``Network`` (e.g. ``net.load_file('your_file.txt')``) it is stored in the data, ``dat``, attribute. Normalization and filtering will permanently modify the ``dat`` representation of the matrix. When the matrix is clustered (by calling ``cluster``) this produces the :ref:`visualization_json`, which is stored in the ``viz`` attribute. This JSON can then be exported as a string using ``net.export_net_json('viz')`` or saved to a file using ``net.write_json_to_file('viz', filename)``.
 

docs/_build_html/_sources/index.txt

@@ -18,7 +18,7 @@ The easiest ways to use Clustergrammer to produce an interactive visualization o
 
 The :ref:`clustergrammer_web` is the quickest way to generate an interactive and shareable visualization (see `example visualization`_ and :ref:`getting started Web-app<getting_started_web_app>`). For users who want to visualize their data within a Jupyter notebook, the :ref:`clustergrammer_widget` enables visualizations to be embedded into shareable Jupyter notebooks (see `example notebook`_ and :ref:`Getting Started Widget <getting_started_widget>`).
 
-Web developers can use Clustergrammer's core libraries (:ref:`clustergrammer_js` and :ref:`clustergrammer_py`) or the :ref:`clustergrammer_web_api` to dynamically generate visualizations for their own web applications (see examples in :ref:`app_integration`).
+Web developers can use Clustergrammer's core libraries, :ref:`clustergrammer_js` and :ref:`clustergrammer_py`, or the :ref:`clustergrammer_web_api` to dynamically generate visualizations for their own web applications (see examples in :ref:`app_integration`).
 
 Please read the :doc:`getting_started` guide for more information.
 

docs/_build_html/_sources/interacting_with_viz.txt

@@ -26,7 +26,7 @@ Press play to take a quick tour of some of Clustergrammer's interactive features
 
 Zooming and Panning
 ===================
-Clustergrammer allows users to zoom into and pan across their heatmap by scrolling and dragging. Double-clicking the visualization resets zooming and panning. This is useful for working with large datasets, where labels are not readable without zooming, and for closely investigating regions of interest. Users can also increase the size of the visualization using the Expand button to hide the sidebar, see :ref:`expanding` and, when in full-screen mode by adjusting the size of their window (see :ref:`clustergrammer_web_viz` for information about full-screen mode).
+Clustergrammer allows users to zoom into and pan across their heatmap by scrolling and dragging. Double-clicking the heatmap resets zooming and panning. This is useful for working with large datasets where labels are not readable without zooming and for closely investigating regions of interest. Users can also increase the size of the visualization using the Expand button to hide the sidebar, see :ref:`expanding` and, when in full-screen mode by adjusting the size of their window (see :ref:`clustergrammer_web_viz` for information about full-screen mode).
 
 **Zooming and Panning Detailed Behavior**
 
@@ -80,7 +80,7 @@ Clustergrammer's sidebar reordering-buttons allows users to order rows and colum
 - hierarchical clustering order
 - label order
 
-This can be useful for identifying broad patterns in your data. Users can also reorder their matrix based on the values in a single row/column by double-clicking the row/column labels. Similarly, users can reorder based on categorical information by double-clicking the category labels (see :ref:`interactive_categories`). For small matrices reordering events are animated to help users visually track the effects of this transformation.
+This can be useful for identifying broad patterns in the data. Users can also reorder their matrix based on the values in a single row/column by double-clicking the row/column labels. Similarly, users can reorder based on categorical information by double-clicking category labels (see :ref:`interactive_categories`). For small matrices reordering events are animated to help users visually track the results of this transformation.
 
 .. _interactive_dim_reduction:
 

docs/_build_html/clustergrammer_js.html

@@ -154,7 +154,7 @@
 <span id="id1"></span><h1>Clustergrammer-JS<a class="headerlink" href="#clustergrammer-js" title="Permalink to this headline">¶</a></h1>
 <p><a class="reference external" href="https://www.npmjs.com/package/clustergrammer"><img alt="version" src="https://img.shields.io/npm/v/clustergrammer.svg" /></a>
 <a class="reference external" href="https://github.com/MaayanLab/clustergrammer/blob/master/LICENSES/LICENSE"><img alt="license" src="https://img.shields.io/npm/l/clustergrammer.svg" /></a></p>
-<p>Clustergrammer-JS is the front-end JavaScript library that builds the interactive heatmap visualization in <a class="reference external" href="https://en.wikipedia.org/wiki/Scalable_Vector_Graphics">SVG</a> using the visualization library <a class="reference external" href="https://d3js.org/">D3.js</a>. The library is free and open-source and can be found on <a class="reference external" href="https://github.com/MaayanLab/clustergrammer">GitHub</a>.</p>
+<p>Clustergrammer-JS is the front end JavaScript library that builds the interactive clustergram visualization in <a class="reference external" href="https://en.wikipedia.org/wiki/Scalable_Vector_Graphics">SVG</a> using the visualization library <a class="reference external" href="https://d3js.org/">D3.js</a>. The library is free and open-source and can be found on <a class="reference external" href="https://github.com/MaayanLab/clustergrammer">GitHub</a>.</p>
 <div class="section" id="clustergrammer-js-dependencies">
 <span id="id2"></span><h2>Clustergrammer-JS Dependencies<a class="headerlink" href="#clustergrammer-js-dependencies" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
@@ -472,7 +472,7 @@ <h2>Installation<a class="headerlink" href="#installation" title="Permalink to t
 </div>
 <div class="section" id="visualization-json">
 <span id="id6"></span><h2>Visualization-JSON<a class="headerlink" href="#visualization-json" title="Permalink to this headline">¶</a></h2>
-<p>The visualization-JSON is calculated by <a class="reference internal" href="clustergrammer_py.html#clustergrammer-py"><span class="std std-ref">Clustergrammer-PY</span></a> and encodes everything needed for the front-end Clustergrammer-JS to produce the visualization. The visualization-JSON format is described here (see <a class="reference external" href="https://github.com/MaayanLab/clustergrammer-json/blob/master/clustergrammer_example.json">clustergrammer_example.json</a> for an example file). An overview of the format is shown below (note that the group arrays are not shown):</p>
+<p>The visualization-JSON is calculated by <a class="reference internal" href="clustergrammer_py.html#clustergrammer-py"><span class="std std-ref">Clustergrammer-PY</span></a> and encodes everything needed for the front end Clustergrammer-JS to produce the visualization. The visualization-JSON format is described here (see <a class="reference external" href="https://github.com/MaayanLab/clustergrammer-json/blob/master/clustergrammer_example.json">clustergrammer_example.json</a> for an example file). An overview of the format is shown below (note that the group arrays are not shown):</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
   <span class="s2">&quot;row_nodes&quot;</span><span class="p">:[</span>
      <span class="p">{</span>