Consider the example below:

import numpy as np
import pandas=
import statsmodels.api as sm
from statsmodels.tsa.api import VAR

mdata = sm.datasets.macrodata.load_pandas().data

# prepare the dates index
dates = mdata[['year', 'quarter']].astype(int).astype(str)

quarterly = dates["year"] + "Q" + dates["quarter"]

from statsmodels.tsa.base.datetools import dates_from_str

quarterly = dates_from_str(quarterly)

mdata = mdata[['realgdp','realcons','realinv']]

mdata.index = pandas.DatetimeIndex(quarterly)

data = np.log(mdata).diff().dropna()

# make a VAR model
model = VAR(data).fit()

model.irf().plot()

This gives:

However, as you can see, the Y axis are different across the charts. I would like to have the same Y axis for all IRFs, so that side-by-side comparisons are more sensible. How can I do that (while keeping the confidence interval bands for each chart)?