Here's some template code for plotting histograms that don't look like bar charts, but instead have only outlines (like IDL creates).
First define a function that does the bulk of the heavy lifting.
1 import numpy as np
2
3 def histOutline(dataIn, *args, **kwargs):
4 (histIn, binsIn) = np.histogram(dataIn, *args, **kwargs)
5
6 stepSize = binsIn[1] - binsIn[0]
7
8 bins = np.zeros(len(binsIn)*2 + 2, dtype=np.float)
9 data = np.zeros(len(binsIn)*2 + 2, dtype=np.float)
10 for bb in range(len(binsIn)):
11 bins[2*bb + 1] = binsIn[bb]
12 bins[2*bb + 2] = binsIn[bb] + stepSize
13 if bb < len(histIn):
14 data[2*bb + 1] = histIn[bb]
15 data[2*bb + 2] = histIn[bb]
16
17 bins[0] = bins[1]
18 bins[-1] = bins[-2]
19 data[0] = 0
20 data[-1] = 0
21
22 return (bins, data)
Now we can make plots:
1
2 # Make some data to plot
3 data = randn(500)
4
5 figure(2, figsize=(10, 5))
6 clf()
7
8 ##########
9 #
10 # First make a normal histogram
11 #
12 ##########
13 subplot(1, 2, 1)
14 (n, bins, patches) = hist(data)
15
16 # Boundaries
17 xlo = -max(abs(bins))
18 xhi = max(abs(bins))
19 ylo = 0
20 yhi = max(n) * 1.1
21
22 axis([xlo, xhi, ylo, yhi])
23
24 ##########
25 #
26 # Now make a histogram in outline format
27 #
28 ##########
29 (bins, n) = histOutline(data)
30
31 subplot(1, 2, 2)
32 plot(bins, n, 'k-')
33 axis([xlo, xhi, ylo, yhi])
Here you can find this functionality packaged up into histOutline.py
