Support for streaming data

doc/user_guide/Dashboards.rst

@@ -1,6 +1,6 @@
 Creating interactive dashboards
 _______________________________
 
-.. notebook:: holoviews ../../examples/user_guide/15-Dashboards.ipynb
+.. notebook:: holoviews ../../examples/user_guide/16-Dashboards.ipynb
     :skip_execute: True
     :offset: 1

doc/user_guide/Streaming_Data.rst

@@ -0,0 +1,6 @@
+Working with Streaming Data
+___________________________
+
+.. notebook:: holoviews ../../examples/user_guide/15-Streaming_Data.ipynb
+    :skip_execute: True
+    :offset: 1

doc/user_guide/index.rst

@@ -57,6 +57,9 @@ concepts in HoloViews:
 * `Working with large data <Large_Data.html>`_
    Leverage Datashader to interactively explore millions or billions of datapoints.
 
+* `Working with Streaming Data <Streaming_Data.html>`_
+   Demonstrates how to leverage the streamz library with HoloViews to work with streaming datasets.
+
 * `Creating interactive dashboards <Dashboards.html>`_
    Use external widget libraries to build custom, interactive dashboards.
 
@@ -111,6 +114,7 @@ These guides provide detail about specific additional features in HoloViews:
     Custom Interactivity <Custom_Interactivity>
     Data Processing Pipelines <Data_Pipelines>
     Working with large data <Large_Data>
+    Working with streaming data <Streaming_Data>
     Creating interactive dashboards <Dashboards>
     Plotting with Bokeh <Plotting_with_Bokeh>
     Deploying Bokeh Apps <Deploying_Bokeh_Apps>

examples/gallery/apps/bokeh/streaming_psutil.py

@@ -0,0 +1,62 @@
+import datetime as dt
+
+import psutil
+import pandas as pd
+import holoviews as hv
+
+hv.extension('bokeh')
+
+# Define functions to get memory and CPU usage
+def get_mem_data():
+    vmem = psutil.virtual_memory()
+    df = pd.DataFrame(dict(free=vmem.free/vmem.total,
+                           used=vmem.used/vmem.total),
+                      index=[pd.Timestamp.now()])
+    return df*100
+
+def get_cpu_data():
+    cpu_percent = psutil.cpu_percent(percpu=True)
+    df = pd.DataFrame(list(enumerate(cpu_percent)),
+                      columns=['CPU', 'Utilization'])
+    df['time'] = pd.Timestamp.now()
+    return df
+
+
+# Define DynamicMap callbacks returning Elements
+
+def mem_stack(data):
+    data = pd.melt(data, 'index', var_name='Type', value_name='Usage')
+    areas = hv.Dataset(data).to(hv.Area, 'index', 'Usage')
+    return hv.Area.stack(areas.overlay()).relabel('Memory')
+
+def cpu_box(data):
+    return hv.BoxWhisker(data, 'CPU', 'Utilization').relabel('CPU Usage')
+
+
+# Set up StreamingDataFrame and add async callback
+
+cpu_stream = hv.streams.Buffer(get_cpu_data(), 800, index=False)
+mem_stream = hv.streams.Buffer(get_mem_data())
+
+def cb():
+    cpu_stream.send(get_cpu_data())
+    mem_stream.send(get_mem_data())
+
+
+# Define DynamicMaps and display plot
+
+cpu_dmap = hv.DynamicMap(cpu_box, streams=[cpu_stream])
+mem_dmap = hv.DynamicMap(mem_stack, streams=[mem_stream])
+
+cpu_opts = {'plot': dict(width=500, height=400, color_index='CPU'),
+        'style': dict(box_fill_color=hv.Cycle('Category20'))}
+mem_opts = dict(height=400, width=400)
+
+plot = (cpu_dmap.redim.range(Utilization=(0, 100)).opts(**cpu_opts) +
+        mem_dmap.redim.range(Usage=(0, 100)).opts(plot=mem_opts))
+
+
+# Render plot and attach periodic callback
+
+doc = hv.renderer('bokeh').server_doc(plot)
+doc.add_periodic_callback(cb, 0.05)