Streaming Psutil

Download this script from GitHub (right-click to download).

---

import pandas as pd
import panel as pn
import psutil

import holoviews as hv
from holoviews import dim, opts

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", label="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())

# Define DynamicMaps and display plot
cpu_dmap = hv.DynamicMap(cpu_box, streams=[cpu_stream])
mem_dmap = hv.DynamicMap(mem_stack, streams=[mem_stream])

plot = (cpu_dmap + mem_dmap).opts(
    opts.Area(height=400, width=400, ylim=(0, 100), framewise=True),
    opts.BoxWhisker(
        box_fill_color=dim("CPU").str(), cmap="Category20", width=500, height=400, ylim=(0, 100)
    ),
)


# Create PeriodicCallback which run every 500 milliseconds
def cb():
    cpu_stream.send(get_cpu_data())
    mem_stream.send(get_mem_data())


callback = pn.io.PeriodicCallback(callback=cb, period=500)
callback.start()

# Show plot inside notebook
plot  # noqa: B018

---

Download this script from GitHub (right-click to download).