Hi @batdan,
Based on reading your code, I would have expected the plotly.offline.plot call to be taking up most of the time. In my experience the go.Figure constructor call can get slow with lots of traces, but if you have only a few traces with lots of data then the long pole is typically the call to *.plot/*.iplot. Could you time go.Figure(...) and plotly.offline.plot separately?
The main reason that plotly.offline.plot gets slow for large arrays is that the array gets serialized to a JSON list. If you’re working in the Jupyter notebook you can display the figure as a go.FigureWidget instance (https://plot.ly/python/figurewidget/), in which case the large arrays are transfered to the JavaScript library as binary buffers, which is a lot faster.
If the go.Figure call by itself is the slow part, you can bypass the validation work that the graph_objs objects do by defining you figure in terms of raw dict and list instances. Then you can set the validate=False argument to plotly.offline.plot to skip validation. Something like
trace1 = dict(x=alldata['datetime'], y=alldata['a_gpib_alt_power_w'],
name='A Alt Power', yaxis='y2', mode = 'lines+markers',
line = dict(width = 1, color = '#1f77b4'),
marker = dict(size = 2, color = '#1f77b4')) #muted blue
trace2 = dict(x=alldata['datetime'], y=alldata['b_gpib_alt_power_w'],
name='B Alt Power', yaxis='y2', mode = 'lines+markers',
line = dict(width = 1, color = '#17becf'),
marker = dict(size= 2, color = '#17becf')) #blue-teal
trace3 = dict(x=alldata['datetime'], y=alldata['ambient_tc_c'],
name='Ambient Temp', yaxis='y3', mode = 'lines+markers',
line = dict(width = 1, color = '#ff7f0e'),
marker = dict(size = 2, color = '#ff7f0e')) # safety orange
data = [trace1, trace2, trace3]
layout = dict(
xaxis=dict(
autorange=False,
range=[alldata['datetime'].min(), alldata['datetime'].max()]
)
#this is the function that I profiled in the original post.
fig = dict(data=data, layout=layout)
plotly.offline.plot(fig, filename=convertorname+'_weekly.html', auto_open=False, validate=False)
-Jon