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# pphisto.SortHistogram

All Samples(8)  |  Call(6)  |  Derive(0)  |  Import(2)

```        def SortHistogram(histogram,key=True,doreverse=False):

if type(histogram)!=type({}):
sys.stderr.writelines("error: SortHistogram(h)...h isn't a histogram; h="+str(histogram)+"\n")
return []
sortedhistogram={}
if key==False:
items = histogram.items()
items.sort(key=itemgetter(1), reverse=doreverse)
else:
titems=[]
for k in histogram:
titems.append([histogram[k],k])
titems.sort(key=itemgetter(1), reverse=doreverse)
#reverse v,k->k,v
items=[]
for vk in titems:
items.append((vk[1],vk[0]))

sortedhistogram=items
return sortedhistogram
```

```import csv
from numpy import *
from pphisto import Histogram,SortHistogram
from ppdist import *
from __init__ import extract_features
```
```	# print sorted corr
if verbose:
scorr=SortHistogram(corr,False,True)
for el in scorr:
print el[0],el[1]*100,"% "
```
```            #get highest info value (first one)...Yes we are computing 1-entropy
if len(infov)>=1:
[vi,info]=SortHistogram(infov,False,False)[0]

if 0:
```
```                #get max classification value (first one)
if len(infov)>=1:
[vc,classprob]=SortHistogram(classprobv,False,True)[0]

if 0:
```
```            thresholdph[field]=vc

sinfoh=SortHistogram(infoh,False,True)

# print info
```

```# simple fct to display an histogram sorted
from pylab import *
from pphisto import SortHistogram
from numpy import arange,zeros,array,where
from ppcorr import probs2corr
```
```
# sort histogram
shm=SortHistogram(hm,False,True)
x=arange(n,dtype=float)
y=zeros(n,float)
```