|
7 | 7 | """ |
8 | 8 | importnumpyasnp |
9 | 9 | importmatplotlib.pyplotasplt |
10 | | -frommatplotlibimportmlab |
11 | 10 |
|
12 | 11 |
|
13 | 12 | mu=200 |
14 | 13 | sigma=25 |
15 | | -n_bins=50 |
16 | 14 | x=mu+sigma*np.random.randn(10000) |
17 | 15 |
|
18 | 16 | fig, (ax0,ax1)=plt.subplots(ncols=2,figsize=(8,4)) |
19 | 17 |
|
20 | | -n,bins,patches=ax0.hist(x,n_bins,normed=1,histtype='stepfilled', |
21 | | -facecolor='g',alpha=0.75) |
22 | | -# Add a line showing the expected distribution. |
23 | | -y=mlab.normpdf(bins,mu,sigma) |
24 | | -ax0.plot(bins,y,'k--',linewidth=1.5) |
| 18 | +ax0.hist(x,20,normed=1,histtype='stepfilled',facecolor='g',alpha=0.75) |
25 | 19 | ax0.set_title('stepfilled') |
26 | 20 |
|
27 | 21 | # Create a histogram by providing the bin edges (unequally spaced). |
28 | 22 | bins= [100,150,180,195,205,220,250,300] |
29 | | -n,bins,patches=ax1.hist(x,bins,normed=1,histtype='bar',rwidth=0.8) |
| 23 | +ax1.hist(x,bins,normed=1,histtype='bar',rwidth=0.8) |
30 | 24 | ax1.set_title('unequal bins') |
31 | 25 |
|
32 | 26 | plt.tight_layout() |
|