Friends,
I created a histogram plot using data files that have discrete values
(sample file attached 'test.dat').However when i view the plot, i see that
the bars are not located exactly over the values. For example in the
attached figure (test.png), i see a bar (gray) placed between values 1 and
2, while there is no such value between 1 and 2. Precisely i would like to
know how to make histogram for discrete data values.

The following is my code.

 #!/usr/bin/env python
 import matplotlib.pyplot as plt
 import numpy as np
 import matplotlib as mpl
 import matplotlib.mlab as mlb

 #Creating input file list
 flist=open('list').read().split()

 #Assignments
 FIG=plt.figure(figsize=(4.,2.2),dpi=300)
 FIG.subplots_adjust(hspace=0.04,wspace=0.06)
 NROW=1;NCOL=1
 mpl.rcParams['font.size']=10
 mpl.rcParams['lines.linewidth']=0.8
 mpl.rcParams['axes.linewidth']=1.2
 mpl.rcParams['legend.handletextpad']=0.05
 mpl.rcParams['legend.fontsize']=10
 mpl.rcParams['legend.labelspacing']=0.009

 pattern=['k','r']
 color=['black','green','red']
 ax1=FIG.add_subplot(111)

 for value in range(len(flist)):
     data=np.loadtxt(flist[value])
     n, bins, patches = ax1.hist(data,facecolor=color[value],
alpha=0.60,visible=True,histtype='bar',align='mid')
 ax1.grid(True,alpha=1.5)
 ax1.set_ylabel('Absolute no.',size=10)
 plt.savefig('test.png',dpi=100)
 ax1.set_xlim([0,9])
 plt.show()

Attachment: test.dat
Description: Binary data

<<attachment: test.png>>

------------------------------------------------------------------------------
The ultimate all-in-one performance toolkit: Intel(R) Parallel Studio XE:
Pinpoint memory and threading errors before they happen.
Find and fix more than 250 security defects in the development cycle.
Locate bottlenecks in serial and parallel code that limit performance.
http://p.sf.net/sfu/intel-dev2devfeb
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Reply via email to