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Streamline Plots in Python

How to make a streamline plot in Python. A streamline plot displays vector field data.

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.

A Streamline plot is a representation based on a 2-D vector field interpreted as a velocity field, consisting of closed curves tangent to the velocity field. In the case of a stationary velocity field, streamlines coincide with trajectories (see also the Wikipedia page on streamlines, streaklines and pathlines).

For the streamline figure factory, one needs to provide

  • uniformly spaced ranges of x and y values (1D)
  • 2-D velocity values u and v defined on the cross-product (np.meshgrid(x, y)) of x and y.

Velocity values are interpolated when determining the streamlines. Streamlines are initialized on the boundary of the x-y domain.

Basic Streamline Plot

Streamline plots can be made with a figure factory as detailed in this page.

In [1]:
import plotly.figure_factory as ff

import numpy as np

x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
Y, X = np.meshgrid(x, y)
u = -1 - X**2 + Y
v = 1 + X - Y**2

# Create streamline figure
fig = ff.create_streamline(x, y, u, v, arrow_scale=.1)

Streamline and Source Point Plot

In [2]:
import plotly.figure_factory as ff
import plotly.graph_objects as go

import numpy as np

N = 50
x_start, x_end = -2.0, 2.0
y_start, y_end = -1.0, 1.0
x = np.linspace(x_start, x_end, N)
y = np.linspace(y_start, y_end, N)
X, Y = np.meshgrid(x, y)
source_strength = 5.0
x_source, y_source = -1.0, 0.0

# Compute the velocity field on the mesh grid
u = (source_strength/(2*np.pi) *
     (X - x_source)/((X - x_source)**2 + (Y - y_source)**2))
v = (source_strength/(2*np.pi) *
     (Y - y_source)/((X - x_source)**2 + (Y - y_source)**2))

# Create streamline figure
fig = ff.create_streamline(x, y, u, v,

# Add source point
fig.add_trace(go.Scatter(x=[x_source], y=[y_source],
                          name='source point'))

See also

For a 3D version of streamlines, use the trace go.Streamtube documented here.

For representing the 2-D vector field as arrows, see the quiver plot tutorial.


For more info on ff.create_streamline(), see the full function reference

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

Everywhere in this page that you see, 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 as px
fig = go.Figure() # or any Plotly Express function e.g.
# 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([

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