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Holoviews: how to customize histogram for linked time series Curve plots

I am just getting started with Holoviews. My questions are on customizing histograms, but also I am sharing a complete example as it may be helpful for other newbies to look at, since the documentation for Holoviews is very thorough but can be overwhelming.

I have a number of time series in text files loaded as Pandas DataFrames where:

each file is for a specific location at each location about 10 time series were collected, each with about 15,000 points I am building a small interactive tool where a Selector can be used to choose the location / DataFrame, and then another Selector to pick 3 of 10 of the time series to be plotted together.

My goal is to allow linked zooms (both x and y scales). The questions and code will focus on this aspect of the tool. I cannot share the actual data I am using, unfortunately, as it is proprietary, but I have created 3 random walks with specific data ranges that are consistent with the actual data.

## preliminaries ##

import pandas as pd
import numpy as np
import holoviews as hv
from holoviews.util.transform import dim
from holoviews.selection import link_selections
from holoviews import opts
from holoviews.operation.datashader import shade, rasterize
import hvplot.pandas
hv.extension('bokeh', width=100)

## create random walks (one location) ##
data_df = pd.DataFrame()
npoints=15000
np.random.seed(71)
x = np.arange(npoints)
y1 = 1300+2.5*np.random.randn(npoints).cumsum()
y2 = 1500+2*np.random.randn(npoints).cumsum()
y3 = 3+np.random.randn(npoints).cumsum()
data_df.loc[:,'x'] = x
data_df.loc[:,'rand1'] = y1
data_df.loc[:,'rand2'] = y2
data_df.loc[:,'rand3'] = y3

This first block is just to plot the data and show how, by design, one of the random walks have different range from the other two:

data_df.hvplot(x='x',y=['rand1','rand2','rand3'],value_label='y',width=800,height=400)

hvplot

As a result, although hvplot subplots work out of the box (for linking), ranges are different so the scaling is not quite there:

data_df.hvplot(x='x',y=['rand1','rand2','rand3'], 
               value_label='y',subplots=True,width=800,height=200).cols(1)

subplots

So, my first attempt was to adapt the Python-based Points example from Linked brushing in the documentation:

colors = hv.Cycle('Category10').values
dims   = ['rand1', 'rand2', 'rand3']
layout = hv.Layout([
    hv.Points(data_df, dim).opts(color=c)
    for c, dim in zip(colors, [['x', d] for d in dims])
])
link_selections(layout).opts(opts.Points(width=1200, height=300)).cols(1)

Points

That is already an amazing result for a 20 minutes effort!

However, what I would really like is to plot a curve rather than points, and also see a histogram, so I adapted the comprehension syntax to work with Curve (after reading the documentation pages Applying customization, and Composing elements):

colors = hv.Cycle('Category10').values
dims   = ['rand1', 'rand2', 'rand3']
layout = hv.Layout([hv.Curve(data_df,'x',dim).opts(height=300,width=1200, 
                                                     color=c).hist(dim) for c, 
                    dim in zip(colors,[d for d in dims])])
link_selections(layout).cols(1)

lines

Which is almost exactly what I want. But I still struggle with the different layers of opts syntax. Question 1: with the comprehension from the last code block, how would I make the histogram share color with the curves?

Now, suppose I want to rasterize the plots (although I do not think is quite yet necessary with 15,000 points like in this case), I tried to adapt the first example with Points:

cmaps = ['Blues', 'Greens', 'Reds']
dims   = ['rand1', 'rand2', 'rand3']
layout = hv.Layout([
    shade(rasterize(hv.Points(data_df, dims), 
                    cmap=c)).opts(width=1200, height = 400).hist(dims[1])
    for c, dims in zip(cmaps, [['x', d] for d in dims])
])
link_selections(layout).cols(1)

rasterized This is a decent start, but again I struggle with the options/customization.

Question 2: in the above cod block, how would I pass the colormaps (it does not work as it is now), and how do I make the histogram reflect data values as in the previous case (and also have the right colormap)?

Thank you!

Solution

  • To answer your first question to make the histogram share the color of the curve, I've added .opts(opts.Histogram(color=c)) to your code.
    When you have a layout you can specify the options of an element inside the layout like that.

    colors = hv.Cycle('Category10').values
dims   = ['rand1', 'rand2', 'rand3']
layout = hv.Layout(
    [hv.Curve(data_df,'x',dim)
         .opts(height=300,width=600, color=c)
         .hist(dim)
         .opts(opts.Histogram(color=c)) 
     for c, dim in zip(colors,[d for d in dims])]
)
link_selections(layout).cols(1)