# 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])
plot = (cpu_dmap + mem_dmap).opts(
opts.Area(height=400, width=400, ylim=(0, 100)),
opts.BoxWhisker(box_fill_color=dim('CPU').str(),
cmap='Category20',
width=500,
height=400,
ylim=(0, 100)))
# Render plot and attach periodic callback
doc = renderer.server_doc(plot)
doc.add_periodic_callback(cb, 0.05)
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())
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])
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))
)
# Render plot and attach periodic callback
doc = renderer.server_doc(plot)
doc.add_periodic_callback(cb, 0.05)
"Some time varying function"
return np.exp(np.sin(xs + np.pi / time))
def integral(limit_a, limit_b, y, time):
limit_a = -3 if limit_a is None else np.clip(limit_a, -3, 3)
limit_b = 3 if limit_b is None else np.clip(limit_b, -3, 3)
curve = hv.Curve((xs, function(xs, time)))
area = hv.Area((xs, function(xs, time)))[limit_a:limit_b]
summed = area.dimension_values('y').sum() * 0.015 # Numeric approximation
return (area * curve * hv.VLine(limit_a) * hv.VLine(limit_b) *
hv.Text(limit_b - 0.8, 2.0, '%.2f' % summed))
integral_streams = [
streams.Stream.define('Time', time=1.0)(),
streams.PointerX().rename(x='limit_b'),
streams.Tap().rename(x='limit_a')
]
integral_dmap = hv.DynamicMap(integral, streams=integral_streams)
integral_dmap.opts(opts.Area(color='#fff8dc', line_width=2),
opts.Curve(color='black'), opts.VLine(color='red'))
image.dimensions()
dim('charge').min().apply(image)
dim('charge').max().apply(image)
clr_inSample = 'steelblue'
clr_5cv = 'tomato'
clr_20x5cv = 'gold'
clr_5cvMean = 'maroon'
clr_20x5cvMean = 'darkgoldenrod'
default_fontsizes = dict(title=8, labels=8, ticks=7, minor_ticks=7, legend=7)
fig_opts = [
opts.Layout(aspect_weight=1,
fig_inches=(3.42, None),
sublabel_size=10,
fontsize=8),
opts.Overlay(fontsize=default_fontsizes, ),
opts.Area(fontsize=default_fontsizes),
opts.Arrow(textsize=default_fontsizes),
opts.Curve(fontsize=default_fontsizes),
opts.HexTiles(fontsize=default_fontsizes),
opts.Histogram(fontsize=default_fontsizes),
opts.Raster(fontsize=default_fontsizes),
opts.Scatter(fontsize=default_fontsizes),
opts.Text(fontsize=default_fontsizes),
opts.QuadMesh(fontsize=default_fontsizes),
opts.Violin(fontsize=default_fontsizes),
opts.VLine(fontsize=default_fontsizes),
]
# hv hooks
e_min = histogram.edges[0]
limit_a = e_min if limit_a is None else np.clip(limit_a, e_min, e_max)
limit_b = e_max if limit_b is None else np.clip(limit_b, e_min, e_max)
area = hv.Area((curve.dimension_values('energy'),
curve.dimension_values('frequency')))[limit_a:limit_b]
charge = np.sum(
np.diff(histogram[limit_a:limit_b].edges) *
histogram[limit_a:limit_b].values)
return curve * area * hv.VLine(limit_a) * hv.VLine(limit_b) * hv.Text(
limit_b - 2., 5, 'Q = %.0f pC' % charge)
integral_dmap = hv.DynamicMap(integrated_charge, streams=integral_streams)
integral_dmap.opts(
opts.Area(color='#fff8dc', line_width=2),
opts.Curve(color='black',
height=300,
responsive=True,
show_grid=True,
xlim=(None, e_max),
ylim=(None, 10)), opts.VLine(color='red'))
integral_dmap.event(iteration=800)
print(time_series.iterations)