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I have a situation where sometimes, a whole series of data is not available. I'm real-time plotting values from sensors, and these can be turned on and off via user interaction, and thus I cannot be sure the values are always in a series. A user can start a sensor and later turn it off and on again, but In this case, matplotlib draws a line from the last end point and the new start point.

The data I plotted was as follows:

[[  5.          22.57011604]
 [  6.          22.57408142]
 [  7.          22.56350136]
 [  8.          22.56394005]
 [  9.          22.56790352]
 [ 10.          22.56451225]
 [ 11.          22.56481743]
 [ 12.          22.55789757]
  #Missing x vals. Still plots straight line..
 [ 29.          22.55654716]
 [ 29.          22.56066513]
 [ 30.          22.56110382]
 [ 31.          22.55050468]
 [ 32.          22.56550789]
 [ 33.          22.56213379]
 [ 34.          22.5588932 ]
 [ 35.          22.54829407]
 [ 35.          22.56697655]
 [ 36.          22.56005478]
 [ 37.          22.5568161 ]
 [ 38.          22.54621696]
 [ 39.          22.55033493]
 [ 40.          22.55079269]
 [ 41.          22.55475616]
 [ 41.          22.54783821]
 [ 42.          22.55195618]]

my plot function looks a lot simplified like this:

def plot(self, data)
    for name, xy_dict in data.iteritems():
        x_vals = xy_dict['x_values']
        y_vals = xy_dict['y_values']
        line_to_plot = xy_dict['line_number']
        self.lines[line_to_plot].set_xdata(x_vals)
        self.lines[line_to_plot].set_ydata(y_vals)

Does anyone know why it does like that? And do I have to take care of non-serial x and y values when plotting? It seems matplotlib should take care of this on its own.. Otherwise i have to split lists into smaller lists and plot these?

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3 Answers 3

Reset to default
4

One option would be to add dummy items wherever data is missing (in your case apparently when x changes by more than 1), and set them as masked elements. That way matplotlib skips the line segments. For example:

import numpy as np
import matplotlib.pylab as pl

# Your data, with some additional elements deleted...
data = np.array(
[[  5., 22.57011604],
 [  6., 22.57408142],
 [  9., 22.56790352],
 [ 10., 22.56451225],
 [ 11., 22.56481743],
 [ 12., 22.55789757],
 [ 29., 22.55654716],
 [ 33., 22.56213379],
 [ 34., 22.5588932 ],
 [ 35., 22.54829407],
 [ 40., 22.55079269],
 [ 41., 22.55475616],
 [ 41., 22.54783821],
 [ 42., 22.55195618]])

x = data[:,0]
y = data[:,1]

# Difference from element to element in x
dx = x[1:]-x[:-1]

# Wherever dx > 1, insert a dummy item equal to -1
x2 = np.insert(x, np.where(dx>1)[0]+1, -1)
y2 = np.insert(y, np.where(dx>1)[0]+1, -1)

# As discussed in the comments, another option is to use e.g.:
#x2 = np.insert(x, np.where(dx>1)[0]+1, np.nan)
#y2 = np.insert(y, np.where(dx>1)[0]+1, np.nan)
# and skip the masking step below.

# Mask elements which are -1
x2 = np.ma.masked_where(x2 == -1, x2)
y2 = np.ma.masked_where(y2 == -1, y2)

pl.figure()
pl.subplot(121)
pl.plot(x,y)
pl.subplot(122)
pl.plot(x2,y2)

enter image description here

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10 Comments

Real nice! Thanks for the tip. I'm currently looking into using np.nan to also discontinue a line, which apparently works. I will try that first and yours later.
Masking seems to be relatively slow, for example on an array with random numbers, np.ma.masked_where(x<0, x) is about 5x slower as x[x<0]=np.nan for a large size of x.
I can't reproduce that, but given this: stackoverflow.com/questions/12708807/numpy-integer-nan answer, it seems that your x2 array is interpreted as an array with integers. You could try something like x2.astype(np.float64)
Yes, you need numpy arrays for my solution, so if you start of from Python lists, you need to do a cast first (where you can pass the dtype keyword, e.g. x=np.array(x, dtype=np.float64))
Ah, real nice. Tried it and it works like it should. Lots of thanks for your answers! <3
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3

Another option is to include None or numpy.nan as values for y.

This, for example, shows a disconnected line:

import matplotlib.pyplot as plt
plt.plot([1,2,3,4,5],[5,6,None,7,8])
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1 Comment

Found this out as well. Am currently trying to incorporate @Bart s answer to use np.nan, but getting some errors. Regards!
1

Matplotlib will connect all your consequetive datapoints with lines.

If you want to avoid this you could split your data at the missing x-values, and plot the two splitted lists separately.

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1 Comment

Can I still set the x and y-data as before or do I need to plot them on new lines? (That would not be very good, since they would change colors etc). I would prefer if the data points were on the same matplotlib line

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