Smith Charts in Python

How to make Smith Charts with plotly.

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A Smith Chart is a specialized chart for visualizing complex numbers: numbers with both a real and imaginary part.

Smith Charts with Plotly Graph Objects¶

In [1]:

import plotly.graph_objects as go

fig = go.Figure(go.Scattersmith(imag=[0.5, 1, 2, 3], real=[0.5, 1, 2, 3]))
fig.show()

Smith Chart Subplots and Styling¶

In [2]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scattersmith(
    imag=[1],
    real=[1],
    marker_symbol='x',
    marker_size=30,
    marker_color="green",
    subplot="smith1"
))

fig.add_trace(go.Scattersmith(
    imag=[1],
    real=[1],
    marker_symbol='x',
    marker_size=30,
    marker_color="pink",
    subplot="smith2"
))

fig.update_layout(
    smith=dict(
        realaxis_gridcolor='red',
        imaginaryaxis_gridcolor='blue',
        domain=dict(x=[0,0.45])
    ),
    smith2=dict(
        realaxis_gridcolor='blue',
        imaginaryaxis_gridcolor='red',
        domain=dict(x=[0.55,1])
    )
)

fig.update_smiths(bgcolor="lightgrey")

fig.show()

Reference¶

See https://plotly.com/python/reference/scattersmith/ and https://plotly.com/python/reference/layout/smith/ for more information and chart attribute options!

In [ ]:

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter