"""

==============

CanvasAgg demo

==============

This example shows how to use the agg backend directly to create images, which

may be of use to web application developers who want full control over their

code without using the pyplot interface to manage figures, figure closing etc.

.. note::

It is not necessary to avoid using the pyplot interface in order to

create figures without a graphical front-end - simply setting

the backend to "Agg" would be sufficient.

In this example, we show how to save the contents of the agg canvas to a file,

and how to extract them to a string, which can in turn be passed off to PIL or

put in a numpy array. The latter functionality allows e.g. to use Matplotlib

inside a cgi-script *without* needing to write a figure to disk.

"""

from matplotlib.backends.backend_agg import FigureCanvasAgg

from matplotlib.figure import Figure

import numpy as np

fig = Figure(figsize=(5, 4), dpi=100)

# A canvas must be manually attached to the figure (pyplot would automatically

# do it). This is done by instantiating the canvas with the figure as

# argument.

canvas = FigureCanvasAgg(fig)

# Do some plotting.

ax = fig.add_subplot()

ax.plot([1, 2, 3])

# Option 1: Save the figure to a file; can also be a file-like object (BytesIO,

# etc.).

fig.savefig("test.png")

# Option 2: Retrieve a view on the renderer buffer...

canvas.draw()

buf = canvas.buffer_rgba()

# ... convert to a NumPy array ...

X = np.asarray(buf)

# ... and pass it to PIL.

from PIL import Image

im = Image.fromarray(X)

# Uncomment this line to display the image using ImageMagick's `display` tool.

# im.show()

#############################################################################

#

# .. admonition:: References

#

# The use of the following functions, methods, classes and modules is shown

# in this example:

#

# - `matplotlib.backends.backend_agg.FigureCanvasAgg`

# - `matplotlib.figure.Figure`

# - `matplotlib.figure.Figure.add_subplot`

# - `matplotlib.figure.Figure.savefig` / `matplotlib.pyplot.savefig`

# - `matplotlib.axes.Axes.plot` / `matplotlib.pyplot.plot`