I came up with a Python solution to this using the new FigureWidget class. The basic idea is the register a Python callback function to run whenever the xaxis range changes. In response, the callback function computes and applies a new yaxis range.
First construct a simple scatter plot line plot with a range slider. Make sure to set the
import plotly.graph_objs as go
from datetime import datetime
import pandas_datareader.data as web
df = web.DataReader('AAPL.US', 'quandl',
datetime(2007, 10, 1),
datetime(2009, 4, 1))
# Make sure dates are in ascending order
# We need this for slicing in the callback below
df.sort_index(ascending=True, inplace=True)
trace = go.Scatter(x=list(df.index),
y=list(df.High))
data = [trace]
layout = dict(
title='Time series with range slider and selectors',
xaxis=dict(
rangeselector=dict(
buttons=list([
dict(count=1,
label='1m',
step='month',
stepmode='backward'),
dict(count=6,
label='6m',
step='month',
stepmode='backward'),
dict(count=1,
label='YTD',
step='year',
stepmode='todate'),
dict(count=1,
label='1y',
step='year',
stepmode='backward'),
dict(step='all')
])
),
rangeslider=dict(
visible = True
),
type='date'
)
)
fig = go.FigureWidget(data=data, layout=layout)
fig
Then install a property change callback to update the yaxis range
def zoom(layout, xrange):
in_view = df.loc[fig.layout.xaxis.range[0]:fig.layout.xaxis.range[1]]
fig.layout.yaxis.range = [in_view.High.min() - 10, in_view.High.max() + 10]
fig.layout.on_change(zoom, 'xaxis.range')
Now, when you manipulate the range slider, the yaxis will automatically adjust to the data in view (plus a 10 unit buffer on each side, you should adjust that to what looks right for your data.
Hope that helps!
-Jon