Plot limitation on number of Curves?

The code below plots creates 3 plots with 6 parameters each. However, it seems to not allow more than 10 parameters to be plotted on a plot. Why would this be?

Change the second arg of create_generic_data() from 6 to anything greater than 10, and it will only plot ten curves per plot:

import holoviews as hv
import panel as pn
from holoviews.operation.datashader import datashade, rasterize, dynspread
import datashader as ds
from math import sin


class MultiPlot:
    def __init__(self, data, max_plots=6, plot_width=800, plot_height=150, cnorm="eq_hist", line_width=2,

        # Create a dictionary of all Curves.  Top-level keys define number of subplots.  Secondary-level keys point to
        # each parameter to be plotted in each subplot.  Time indices are aligned to start time.
        curves_dict = {top_key: {sec_key: hv.Curve(( x: (x-x[0]).total_seconds()),
                                                    sec_value.values), kdims=['Time'], vdims=['Value'])
                                 for sec_key, sec_value in top_value.items()
                       for top_key, top_value in data.items()}

        # Create list of overlay objects; one per top-level key, with each containing one Curve per secondary-key
        self.overlay = [datashade(hv.NdOverlay(curves), line_width=line_width, pixel_ratio=pixel_ratio,
                                   cnorm=cnorm, aggregator=ds.count()
                                   ).opts(width=plot_width, tools=['hover']).relabel(top_key)
                        for top_key, curves in curves_dict.items()]
        # Create Layout to plot each Overlay figure in a single column
        self.layout = hv.Layout(self.overlay).cols(1)

    def show(self):

def create_generic_data(level1_parm_count, level2_parm_count, parm_sample_count):
    """Generates generic data dictionary"""
    import pandas as pd
    l1_parms = [f'L1P_{str(i)}' for i in range(level1_parm_count)]
    l2_parms = [f'L2P_{str(i)}' for i in range(level2_parm_count)]
    return {l1p: {l2p: pd.Series([sin(j/(parm_sample_count*.01)) * int(l2p[-1]) for j in range(parm_sample_count)],
                                 index=pd.to_timedelta(range(parm_sample_count), unit='s'))
                  for l2p in l2_parms} for l1p in l1_parms}

if __name__ == '__main__':

    # Optional creation of generic data set for troubleshooting. Args are (# of top keys, # of sec. keys, and # of
    # points per Curve
    data = create_generic_data(3, 6, 100000)

    myplot = MultiPlot(data)

There’s no hard limitation. Here your code l2p[-1] is selecting a single digit from your label, which will thus always be between 0 and 9, so you only end up with 10 plots. If you replace that with l2p[4:] you’ll get all the digits needed to keep these plots distinct.

good catch. Thanks!