Ploting digital signals


I want to plot some digital signals using matplotlib.
I want to plot some 6 digital signals (or say any arbitary number of
signals) in one figure.
For each signal I want a y-label displayed.

One way to plot digital signals in one plot is that I add an offset to
other signals and then plot. But I thought if anyone knows a better
idea then it will be good.
Also how can I have different y-labels for each signal. I don't want
to use legends because with too many signals it will be dificult to
match the legend with where the signal is.

I am attaching a picture which is generated by some other tools but
now I want to genrate somewhat similar way in matplotlib.
Can anyone give me some small hints?

There are a few ways to do it. You could make each signal a separate
axes and make the y label horizontal. This works fine for a small
number of signals (4-10 say) except the extra horizontal lines and
ticks around the axes may be annoying. It's on our list of things to
change the way these axes lines are draw, but it isn't done yet.

    from pylab import figure, show, setp
    from matplotlib.numerix import sin, cos, exp, pi, arange

    yprops = dict(rotation=0,

    axprops = dict(yticks=)
    fig = figure()
    t = arange(0.0, 2.0, 0.01)

    ax1 =fig.add_axes([0.1, 0.7, 0.8, 0.2], **axprops)
    ax1.plot(t, sin(2*pi*t))
    ax1.set_ylabel('S1', **yprops)

    # force x axes to remain in register, even with toolbar navigation
    ax2 = fig.add_axes([0.1, 0.5, 0.8, 0.2], sharex=ax1, **axprops)
    ax2.plot(t, exp(-t))
    ax2.set_ylabel('S2', **yprops)

    ax3 = fig.add_axes([0.1, 0.3, 0.8, 0.2], sharex=ax1, **axprops)
    ax3.plot(t, sin(2*pi*t)*exp(-t))
    ax3.set_ylabel('S3', **yprops)

    ax4 = fig.add_axes([0.1, 0.1, 0.8, 0.2], sharex=ax1, **axprops)
    ax4.plot(t, sin(2*pi*t)*cos(4*pi*t))
    ax4.set_ylabel('S4', **yprops)


It turns out that I lot signals in the way you suggest all the time
(EEG viewer), see a screenshot of the application that gave birth to
matplotlib at . I've
been meaning to refactor the EEG viewer into a "multiline" viewer and
put it into matplotlib but haven't gotten around to it (the source
code is at

Here is an example of how I do it in my app with additional comments.
Note that this will break the y behavior of the toolbar because we
have changed all the default transforms. In my application I have a
custom toolbar to increase or decrease the y scale. In this example,
I bind the plus/minus keys to a function which increases or decreases
the y gain. Perhaps I will take this and wrap it up into a function
called plot_signals or something like that because the code is a bit
hairy since it makes heavy use of the somewhat arcane matplotlib
transforms. I suggest reading the header of before
trying to understand this example.

from pylab import figure, show, setp, connect, draw
from matplotlib.numerix import sin, cos, exp, pi, arange
from matplotlib.numerix.mlab import mean
from matplotlib.transforms import Bbox, Value, Point, \
     get_bbox_transform, unit_bbox
# load the data

t = arange(0.0, 2.0, 0.01)
s1 = sin(2*pi*t)
s2 = exp(-t)
s3 = sin(2*pi*t)*exp(-t)
s4 = sin(2*pi*t)*cos(4*pi*t)
s5 = s1*s2
s6 = s1-s4
s7 = s3*s4-s1

signals = s1, s2, s3, s4, s5, s6, s7
for sig in signals:
    sig = sig-mean(sig)
lineprops = dict(linewidth=1, color='black', linestyle='-')
fig = figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

# The normal matplotlib transformation is the view lim bounding box
# (ax.viewLim) to the axes bounding box (ax.bbox). Where are going to
# define a new transform by defining a new input bounding box. See the
# matplotlib.transforms module helkp for more information on
# transforms

# This bounding reuses the x data of the viewLim for the xscale -10 to
# 10 on the y scale. -10 to 10 means that a signal with a min/max
# amplitude of 10 will span the entire vertical extent of the axes
scale = 10
boxin = Bbox(
    Point(ax.viewLim.ll().x(), Value(-scale)),
    Point(ax.viewLim.ur().x(), Value(scale)))

# height is a lazy value
height = ax.bbox.ur().y() - ax.bbox.ll().y()

boxout = Bbox(
    Point(ax.bbox.ll().x(), Value(-0.5) * height),
    Point(ax.bbox.ur().x(), Value( 0.5) * height))

# matplotlib transforms can accepts an offset, which is defined as a
# point and another transform to map that point to display. This
# transform maps x as identity and maps the 0-1 y interval to the
# vertical extent of the yaxis. This will be used to offset the lines
# and ticks vertically
transOffset = get_bbox_transform(
    Bbox( Point( Value(0), ax.bbox.ll().y()),
          Point( Value(1), ax.bbox.ur().y())

# now add the signals, set the transform, and set the offset of each
# line
ticklocs =
for i, s in enumerate(signals):
    trans = get_bbox_transform(boxin, boxout)
    offset = (i+1.)/(len(signals)+1.)
    trans.set_offset( (0, offset), transOffset)

    ax.plot(t, s, transform=trans, **lineprops)

ax.set_yticklabels(['S%d'%(i+1) for i in range(len(signals))])

# place all the y tick attributes in axes coords
all =
labels =
for tick in ax.yaxis.get_major_ticks():
    all.extend(( tick.label1, tick.label2, tick.tick1line,
                 tick.tick2line, tick.gridline))
setp(all, transform=ax.transAxes)
setp(labels, x=-0.01)

ax.set_xlabel('time (s)')

# Because we have hacked the transforms, you need a special method to
# set the voltage gain; this is a naive implementation of how you
# might want to do this in real life (eg make the scale changes
# exponential ranther than linear) but it gives you the idea
def set_ygain(direction):
    set_ygain.scale += direction
    if set_ygain.scale <=0:
        set_ygain.scale -= direction

    for line in ax.lines:
        trans = line.get_transform()
        box1 = trans.get_bbox1()
        box1.intervaly().set_bounds(-set_ygain.scale, set_ygain.scale)
set_ygain.scale = scale
def keypress(event):
    if event.key in ('+', '='): set_ygain(-1)
    elif event.key in ('-', '_'): set_ygain(1)

connect('key_press_event', keypress)
ax.set_title('Use + / - to change y gain')

Also one more point is if I want all the signal colors in plot to be
same say black then how can I do it?. The default plot function varies
the colors of the signal plotted.

pass the color='black' argument to plot

  plot(x, y, color='black')

For marker plots, you may also want to set the markerfacecolor and
markeredgecolor attributes.