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# History {#project_history}

::: note
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Note
:::

The following introductory text was written in 2008 by John D. Hunter
(1968-2012), the original author of Matplotlib.
:::

Matplotlib is a library for making 2D plots of arrays in
[Python](https://www.python.org). Although it has its origins in
emulating the MATLAB graphics commands, it is independent of MATLAB, and
can be used in a Pythonic, object-oriented way. Although Matplotlib is
written primarily in pure Python, it makes heavy use of
[NumPy](https://numpy.org) and other extension code to provide good
performance even for large arrays.

Matplotlib is designed with the philosophy that you should be able to
create simple plots with just a few commands, or just one! If you want
to see a histogram of your data, you shouldn\'t need to instantiate
objects, call methods, set properties, and so on; it should just work.

For years, I used to use MATLAB exclusively for data analysis and
visualization. MATLAB excels at making nice looking plots easy. When I
began working with EEG data, I found that I needed to write applications
to interact with my data, and developed an EEG analysis application in
MATLAB. As the application grew in complexity, interacting with
databases, http servers, manipulating complex data structures, I began
to strain against the limitations of MATLAB as a programming language,
and decided to start over in Python. Python more than makes up for all
of MATLAB\'s deficiencies as a programming language, but I was having
difficulty finding a 2D plotting package (for 3D
[VTK](http://www.vtk.org/) more than exceeds all of my needs).

When I went searching for a Python plotting package, I had several
requirements:

-   Plots should look great - publication quality. One important
    requirement for me is that the text looks good (antialiased, etc.)
-   Postscript output for inclusion with TeX documents
-   Embeddable in a graphical user interface for application development
-   Code should be easy enough that I can understand it and extend it
-   Making plots should be easy

Finding no package that suited me just right, I did what any
self-respecting Python programmer would do: rolled up my sleeves and
dived in. Not having any real experience with computer graphics, I
decided to emulate MATLAB\'s plotting capabilities because that is
something MATLAB does very well. This had the added advantage that many
people have a lot of MATLAB experience, and thus they can quickly get up
to steam plotting in python. From a developer\'s perspective, having a
fixed user interface (the pylab interface) has been very useful, because
the guts of the code base can be redesigned without affecting user code.

The Matplotlib code is conceptually divided into three parts: the *pylab
interface* is the set of functions provided by `pylab`{.interpreted-text
role="mod"} which allow the user to create plots with code quite similar
to MATLAB figure generating code (`pyplot_tutorial`{.interpreted-text
role="ref"}). The *Matplotlib frontend* or *Matplotlib API* is the set
of classes that do the heavy lifting, creating and managing figures,
text, lines, plots and so on (`artists_tutorial`{.interpreted-text
role="ref"}). This is an abstract interface that knows nothing about
output. The *backends* are device-dependent drawing devices, aka
renderers, that transform the frontend representation to hardcopy or a
display device (`what-is-a-backend`{.interpreted-text role="ref"}).
Example backends: PS creates
[PostScript®](https://www.adobe.com/products/postscript.html) hardcopy,
SVG creates [Scalable Vector Graphics](https://www.w3.org/Graphics/SVG/)
hardcopy, Agg creates PNG output using the high quality [Anti-Grain
Geometry](http://agg.sourceforge.net/antigrain.com/) library that ships
with Matplotlib, GTK embeds Matplotlib in a [Gtk+](https://www.gtk.org/)
application, GTKAgg uses the Anti-Grain renderer to create a figure and
embed it in a Gtk+ application, and so on for
[PDF](https://acrobat.adobe.com/us/en/acrobat/about-adobe-pdf.html),
[WxWidgets](https://www.wxpython.org/),
[Tkinter](https://docs.python.org/3/library/tkinter.html), etc.

Matplotlib is used by many people in many different contexts. Some
people want to automatically generate PostScript files to send to a
printer or publishers. Others deploy Matplotlib on a web application
server to generate PNG output for inclusion in dynamically-generated web
pages. Some use Matplotlib interactively from the Python shell in
Tkinter on Windows. My primary use is to embed Matplotlib in a Gtk+ EEG
application that runs on Windows, Linux and Macintosh OS X.

------------------------------------------------------------------------

Matplotlib\'s original logo (2003 \-- 2008).

::: plot
from matplotlib import cbook, pyplot as plt, style import numpy as np

style.use(\"classic\")

datafile = cbook.get_sample_data(\'membrane.dat\', asfileobj=False)

\# convert data to mV x = 1000 \* 0.1 \* np.fromstring(open(datafile,
\'rb\').read(), np.float32) \# 0.0005 is the sample interval t = 0.0005
\* np.arange(len(x)) plt.figure(1, figsize=(7, 1), dpi=100) ax =
plt.subplot(111, facecolor=\'y\') plt.plot(t, x) plt.text(0.5, 0.5,
\'matplotlib\', color=\'r\', fontsize=40, fontname=\[\'Courier\',
\'DejaVu Sans Mono\'\], horizontalalignment=\'center\',
verticalalignment=\'center\', transform=ax.transAxes, ) plt.axis(\[1,
1.72, -60, 10\]) plt.gca().set_xticklabels(\[\])
plt.gca().set_yticklabels(\[\])
:::

Matplotlib logo (2008 - 2015).

::: plot
import numpy as np import matplotlib as mpl import matplotlib.pyplot as
plt import matplotlib.cm as cm

mpl.rcParams\[\'xtick.labelsize\'\] = 10
mpl.rcParams\[\'ytick.labelsize\'\] = 12
mpl.rcParams\[\'axes.edgecolor\'\] = \'gray\'

axalpha = 0.05 figcolor = \'white\' dpi = 80 fig =
plt.figure(figsize=(6, 1.1), dpi=dpi) fig.patch.set_edgecolor(figcolor)
fig.patch.set_facecolor(figcolor)

def add_math_background():

:   ax = fig.add_axes((0., 0., 1., 1.))

    text = \[\] text.append( (r\"\$W\^{3beta}\_{delta_1 rho_1 sigma_2} =
    \" r\"U\^{3beta}\_{delta_1 rho_1} + frac{1}{8 pi 2}\"
    r\"int\^{alpha_2}\_{alpha_2} d alpha\^prime_2 \" r\"left\[frac{
    U\^{2beta}\_{delta_1 rho_1} - \" r\"alpha\^prime_2U\^{1beta}\_{rho_1
    sigma_2} \" r\"}{U\^{0beta}\_{rho_1 sigma_2}}right\]\$\", (0.7,
    0.2), 20)) text.append((r\"\$frac{drho}{d t} + rho
    vec{v}cdotnablavec{v} \" r\"= -nabla p + munabla\^2 vec{v} + rho
    vec{g}\$\", (0.35, 0.9), 20))
    text.append((r\"\$i[nt](){-infty}\^infty e\^{-x\^2}dx=sqrt{pi}\$\",
    (0.15, 0.3), 25)) text.append((r\"\$F_G = Gfrac{m_1m_2}{r\^2}\$\",
    (0.85, 0.7), 30)) for eq, (x, y), size in text: ax.text(x, y, eq,
    ha=\'center\', va=\'center\', color=\"#11557c\", alpha=0.25,
    transform=ax.transAxes, fontsize=size) ax.set_axis_off() return ax

def add_matplotlib_text(ax):

:   

    ax.text(0.95, 0.5, \'matplotlib\', color=\'#11557c\', fontsize=65,

    :   ha=\'right\', va=\'center\', alpha=1.0, transform=ax.transAxes)

def add_polar_bar():

:   ax = fig.add_axes((0.025, 0.075, 0.2, 0.85), projection=\'polar\')

    ax.patch.set_alpha(axalpha) ax.set_axisbelow(True) N = 7 arc = 2. \*
    np.pi theta = np.arange(0.0, arc, arc/N) radii = 10 \*
    np.array(\[0.2, 0.6, 0.8, 0.7, 0.4, 0.5, 0.8\]) width = np.pi / 4 \*
    np.array(\[0.4, 0.4, 0.6, 0.8, 0.2, 0.5, 0.3\]) bars = ax.bar(theta,
    radii, width=width, bottom=0.0) for r, bar in zip(radii, bars):
    bar.set_facecolor(cm.jet(r/10.)) bar.set_alpha(0.6)

    ax.tick_params(labelbottom=False, labeltop=False,

    :   labelleft=False, labelright=False)

    ax.grid(lw=0.8, alpha=0.9, ls=\'-\', color=\'0.5\')

    ax.set_yticks(np.arange(1, 9, 2)) ax.set_rmax(9)

main_axes = add_math_background() add_polar_bar()
add_matplotlib_text(main_axes)
:::