Hi there,
I was looking at the following example from the documentation on DynamicMap :
# credit: https://holoviews.org/user_guide/Dashboards.html
def load_symbol(symbol, variable, **kwargs):
df = pd.DataFrame(getattr(stocks, symbol))
df['date'] = df.date.astype('datetime64[ns]')
return hv.Curve(df, ('date', 'Date'), variable).opts(framewise=True)
stock_symbols = ['AAPL', 'IBM', 'FB', 'GOOG', 'MSFT']
variables = ['open', 'high', 'low', 'close', 'volume', 'adj_close']
dmap = hv.DynamicMap(load_symbol, kdims=['Symbol','Variable'])
dmap = dmap.redim.values(Symbol=stock_symbols, Variable=variables)
dmap.opts(framewise=True)
rolling(dmap, rolling_window=2)
Here, the assumption is that all the possible key combinations define scenarios to plot:
- What if we only had a sparse subset of the entire combinations?
- Is there anyway we could specify the subset of key combinations that we care about?
- Of course, the range of successive keys would depend on the previously selected values, but the ranges can be structured in a hierarchical way, and updated upon user interactions.
The reason for this is that I have about 8 key dimensions, defining more than 10B cartesian combinations if we consider each key’s unique values, but I only have 1000 combinations to show and care about. Could you please point me in the right direction?
Thanks in advance for your valuable help and support. Looking forward to hearing from you.
Sincerely, Ehsan