Without a doubt, the most requested Deephaven feature has been more powerful moving averages.
In version 0.15.0, Deephaven improved aggregations through
update_by.
Now, you can compute exponential moving averages (EMAs) that are more efficient and properly update when values in the middle of your table change.
Let's look at an example:
from deephaven import time_table
from deephaven.updateby import ema_time
t1 = time_table('PT00:00:00.1') \
.update(["Label = ii%2 == 0 ? `cos` : `sin`", "X = ii%2 == 0 ? cos(0.05*ii)+0.7*random() : sin(0.01*ii)+0.7*random()"])
t2 = t1.update_by([ema_time("Timestamp", "PT00:00:01", "XEma=X")], by="Label")
from deephaven.plot import Figure
p = Figure() \
.plot_xy("Value", t=t2, x="Timestamp", y="X", by=["Label"]) \
.plot_xy("EMA", t=t2, x="Timestamp", y="XEma", by=["Label"]) \
.show()
Future releases will support Simple Moving Averages (SMAs), moving standard deviations, and more.
See the updateby Python module for the latest list of
supported update_by aggregations.
If there is a new aggregation you want to see, tell us on Slack.