Is this your desired plot?
If that’s the case then you’ll have to inset a shape to each subplot defined
by the positions
row=i,col=j. The following snippet will do that for you. If
you change the total numbers of subplots You’ll just need to have som
oversight of how your grid will look with regards to the numbers of rows and
columns.
from plotly.subplots import make_subplotsimport plotly.graph_objects as goimport numpy as npfig = make_subplots( rows=2, cols=2, subplot_titles=list(map(str, range(4))), shared_xaxes=True, shared_yaxes=False,)time = np.linspace(-np.pi, np.pi, 1000)for i in range(4): data = np.sin((i+1) * time) fig.add_trace( go.Scatter(y=data,x=time, name=str(i)), row=1 if i in [0, 1] else 2, col=1 if i in [0, 2] else 2, )colors = ['blue', 'firebrick', 'green', 'purple']rows = 2cols = 2# add tracescounter=0 # for colorsfor i in range(1,3): for j in range(1,3): fig.add_shape(go.layout.Shape(type="line", yref="paper", xref="x", x0=1, y0=-2, x1=1, y1=2, #line=dict(color="RoyalBlue", width=3),), line=dict(color=colors[counter], width=3),),row=i,col=j) counter = counter + 1fig.show()
Edit after comments:
To my knowledge, you can’t define an unbounded directly. But what you’re
trying to accomplish will work pretty good as long as you define axis limits
well beyond the data you want to visualize. Because contrary to your comment,
you can set the axis limits of each subplot like this:
# Set y ranges for each subplotfor i in range(1,3): for j in range(1,3): fig.update_yaxes(range=[-4, 4], row=i, col=j)
And instead of predefined limits you can find the proper max and min values
from your datasets.
Plot 2: Unzoomed
Plot 3: Zoomed out
I hope this is a bit more helpful!
