python - Change matplotlib line style mid-graph

11
votes

I'm graphing some data (two lines) and I'd like to change the line style for the portions of the lines where the difference between them is statistically significant. So, in the below image (now a link b/c anti-spam policies don't allow me to post an image) I'd like the lines to look different (i.e. dashed perhaps) up until they start converging at about 35 on the x axis.

line plot

Is there a way to do this easily? I have the values for the x axis where the differences are significant, I'm just not clear how to change line styles at certain x-axis locations.

2
pythonstylesmatplotliblinegraphing
What about making two plots per curve? One with the first set of points (before the values start converging) and then another one with the second set. Just set the plots to have same style (color, marker, etc), except for the line style! :) - Ricardo Cárdenes
Thanks, I'll try this. I have a few other plots where the lines diverge again later on cycling into and out of significance. This'll mean I'll have a bunch of plot commands, but hopefully I'll be able to sort it out. - Sceeerutinizer
@RicardoCardenes- I suggest you put your comment as an answer so it can be upvoted/accepted. It is the right way to do it :-) - David Robinson
You can automate it a great deal. See my answer. - Ricardo Cárdenes
@DavidRobinson: I know, I know, I didn't got +2k for just commenting ;). It's just that sometimes I don't feel like my comment is worth an answer :) - Ricardo Cárdenes

2 Answers

15
votes

Edit: I'd had this open and left, so I didn't notice @Ricardo's answer. Because matplotlib will convert things to numpy arrays regardless, there are more efficient ways to do it.

As an example:

Just plot two different lines, one with a dashed linestyle and another with a solid linestyle.

E.g.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y1 = 2 * x
y2 = 3 * x

xthresh = 4.5
diff = np.abs(y1 - y2)
below = diff < xthresh
above = diff >= xthresh

# Plot lines below threshold as dotted...
plt.plot(x[below], y1[below], 'b--')
plt.plot(x[below], y2[below], 'g--')

# Plot lines above threshold as solid...
plt.plot(x[above], y1[above], 'b-')
plt.plot(x[above], y2[above], 'g-')

plt.show()

enter image description here

For the case where they're cyclic, use masked arrays:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y1 = 2 * np.cos(x)
y2 = 3 * np.sin(x)

xthresh = 2.0
diff = np.abs(y1 - y2)
below = diff < xthresh
above = diff >= xthresh

# Plot lines below threshold as dotted...
plt.plot(np.ma.masked_where(below, x), np.ma.masked_where(below, y1), 'b--')
plt.plot(np.ma.masked_where(below, x), np.ma.masked_where(below, y2), 'g--')

# Plot lines above threshold as solid...
plt.plot(np.ma.masked_where(above, x), np.ma.masked_where(above, y1), 'b-')
plt.plot(np.ma.masked_where(above, x), np.ma.masked_where(above, y2), 'g-')

plt.show()

enter image description here

3
votes

Let's say that your data is on NumPy arrays dataset1 and dataset2 and you've defined threshold as your significance

def group(data):
    """Assumes that len(data) > 0"""
    prev = 0
    index = 1
    value = data[0]

    while (index < len(data)):
        if data[index] != value:
            yield (value, prev, index)

            value = not value
            prev = index
        index += 1

    yield (value, prev, index)

diff = np.abs(dataset1 - dataset2)
for significant, start, end in group(diff < threshold):
   # Plot data from dataset1[start:end] and dataset2[start:end]
   # Use the value in "significant" (True/False) to figure out
   # The style