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# Carpet Contour Plot in Python

How to make carpet contour plots in Python with Plotly.

If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a Workspace Jupyter notebook.
Alternatively, download this entire tutorial as a Jupyter notebook and import it into your Workspace.
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New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

### Basic Carpet Plot¶

Set the x and y coorindates, using x and y attributes. If x coorindate values are ommitted a cheater plot will be created. To save parameter values use a and b attributes. To make changes to the axes, use aaxis or baxis attributes. For a more detailed list of axes attributes refer to python reference.

In [1]:
import plotly.graph_objects as go

fig = go.Figure(go.Carpet(
a = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3],
b = [4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6],
x = [2, 3, 4, 5, 2.2, 3.1, 4.1, 5.1, 1.5, 2.5, 3.5, 4.5],
y = [1, 1.4, 1.6, 1.75, 2, 2.5, 2.7, 2.75, 3, 3.5, 3.7, 3.75],
aaxis = dict(
tickprefix = 'a = ',
smoothing = 0,
minorgridcount = 9,
type = 'linear'
),
baxis = dict(
tickprefix = 'b = ',
smoothing = 0,
minorgridcount = 9,
type = 'linear'
)
))

fig.show()


In [2]:
import plotly.graph_objects as go

fig = go.Figure()

a = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3],
b = [4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6],
z = [1, 1.96, 2.56, 3.0625, 4, 5.0625, 1, 7.5625, 9, 12.25, 15.21, 14.0625],
autocontour = False,
contours = dict(
start = 1,
end = 14,
size = 1
),
line = dict(
width = 2,
smoothing = 0
),
colorbar = dict(
len = 0.4,
y = 0.25
)
))

a = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3],
b = [4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6],
x = [2, 3, 4, 5, 2.2, 3.1, 4.1, 5.1, 1.5, 2.5, 3.5, 4.5],
y = [1, 1.4, 1.6, 1.75, 2, 2.5, 2.7, 2.75, 3, 3.5, 3.7, 3.75],
aaxis = dict(
tickprefix = 'a = ',
smoothing = 0,
minorgridcount = 9,
type = 'linear'
),
baxis = dict(
tickprefix = 'b = ',
smoothing = 0,
minorgridcount = 9,
type = 'linear'
)
))

fig.show()


### Add Multiple Traces¶

In [3]:
import plotly.graph_objects as go
import json
from urllib.request import urlopen

url = "https://raw.githubusercontent.com/bcdunbar/datasets/master/airfoil_data.json"

fig=go.Figure()

a = data[0]['a'],
b = data[0]['b'],
x = data[0]['x'],
y = data[0]['y'],
baxis = dict(
startline = False,
endline = False,
showticklabels = "none",
smoothing = 0,
showgrid = False
),
aaxis = dict(
startlinewidth = 2,
startline = True,
showticklabels = "none",
endline = True,
showgrid = False,
endlinewidth = 2,
smoothing = 0
)
))

z = data[1]['z'],
autocolorscale = False,
zmax = 1,
name = "Pressure",
colorscale = "Viridis",
zmin = -8,
colorbar = dict(
y = 0,
yanchor = "bottom",
titleside = "right",
len = 0.75,
title = "Pressure coefficient, c<sub>p</sub>"
),
contours = dict(
start = -1,
size = 0.025,
end = 1.000,
showlines = False
),
line = dict(
smoothing = 0
),
autocontour = False,
zauto = False
))

z = data[2]['z'],
opacity = 0.300,
showlegend = True,
name = "Streamlines",
autocontour = True,
ncontours = 50,
contours = dict(
coloring = "none"
),
line = dict(
color = "white",
width = 1
)
))

z = data[3]['z'],
showlegend = True,
name = "Pressure<br>contours",
autocontour = False,
line = dict(
color = "rgba(0, 0, 0, 0.5)",
smoothing = 1
),
contours = dict(
size = 0.250,
start = -4,
coloring = "none",
end = 1.000,
showlines = True
)
))

x = data[4]['x'],
y = data[4]['y'],
legendgroup = "g1",
name = "Surface<br>pressure",
mode = "lines",
hoverinfo = "skip",
line = dict(
color = "rgba(255, 0, 0, 0.5)",
width = 1,
shape = "spline",
smoothing = 1
),
fill = "toself",
fillcolor = "rgba(255, 0, 0, 0.2)"
))

x = data[5]['x'],
y = data[5]['y'],
showlegend = False,
legendgroup = "g1",
mode = "lines",
hoverinfo = "skip",
line = dict(
color = "rgba(255, 0, 0, 0.3)",
width = 1
)
))

x = data[6]['x'],
y = data[6]['y'],
showlegend = False,
legendgroup = "g1",
name = "cp",
text = data[6]['text'],
hoverinfo = "text",
mode = "lines",
line = dict(
color = "rgba(255, 0, 0, 0.2)",
width = 0
)
))

fig.update_layout(
yaxis = dict(
zeroline = False,
range = [-1.800,1.800],
showgrid = False
),
dragmode = "pan",
height = 700,
xaxis = dict(
zeroline = False,
scaleratio = 1,
scaleanchor = 'y',
range = [-3.800,3.800],
showgrid = False
),
title = "Flow over a Karman-Trefftz airfoil",
hovermode = "closest",
margin = dict(
r = 60,
b = 40,
l = 40,
t = 80
),
width = 900
)

fig.show()


### Reference¶

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

### 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( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

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

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