Normalize the values of d by setting the largest value in d.values to normalizeTo. The return dict will have it's values spreading between 0~normalizeTo
(d, normalizeTo=1)
| 90 | #======================================================================== |
| 91 | |
| 92 | def normDict(d, normalizeTo=1): |
| 93 | ''' Normalize the values of d by setting the largest value in d.values |
| 94 | to normalizeTo. The return dict will have it's values spreading |
| 95 | between 0~normalizeTo''' |
| 96 | ks = d.keys() |
| 97 | vs = d.values() |
| 98 | vMax = max(vs) |
| 99 | vs= [ x/(vMax*1.0)*normalizeTo for x in vs] |
| 100 | return dict(zip(ks, vs)) |
| 101 | |
| 102 | #======================================================================== |
| 103 |