I'm plotting some histograms with hist() --- well, actually with ax.hist(), where ax is an axis --- and the "normed=1" isn't working the way I would expect.
from pylab import *
data = sin(arange(0.0,100,.01))
fig = figure()
ax = fig.add_subplot(111)
ax.hist(data,bins=50,normed=1,align='center')
show()
If I do not include normed=1, then the Y scale is an actual count inside each bin. (The scale goes from 1-1000).
If I include normed=1, the Y scale goes from 1 - 7. What does that mean? normed is supposed to make the first result from ax.hist be a normalized probability distribution. But I would think that it would change the Y axis to be a probability as well, and it doesn't do that.
The docstrings do not give any insight, so I looked at the source code. It certainly *looks* like it's plotting the probability distribution. But why does the above example give a Y scale going from 1 to 7? Perhaps I'm showing my lack of statistics here, but I would think that a strict probability distribution would have the value of all of the bars adding to 1,
Sorry to send out so many messages today. I really am trying to figure this out on my own...
Simson,
Using your example I get most of the values around 0.5, and the ends near 2.3. This is correct for a probability density function; the integral of the pdf over the range of the bins should be 1. This way the pdf values as a function of x don't change with changes in the number of bins, apart from the change in resolution. The probability of a datum appearing in any subrange is the integral of the pdf over that subrange.
Having the sum of the bars add to 1 would be a different sort of normalization. Undoubtedly it has a name, but I don't know what it is. And I don't know why you were getting a y-axis up to 7.
Eric
Simson Garfinkel wrote:
ยทยทยท
I'm plotting some histograms with hist() --- well, actually with ax.hist(), where ax is an axis --- and the "normed=1" isn't working the way I would expect.
from pylab import *
data = sin(arange(0.0,100,.01))
fig = figure()
ax = fig.add_subplot(111)
ax.hist(data,bins=50,normed=1,align='center')
show()
If I do not include normed=1, then the Y scale is an actual count inside each bin. (The scale goes from 1-1000).
If I include normed=1, the Y scale goes from 1 - 7. What does that mean? normed is supposed to make the first result from ax.hist be a normalized probability distribution. But I would think that it would change the Y axis to be a probability as well, and it doesn't do that.
The docstrings do not give any insight, so I looked at the source code. It certainly *looks* like it's plotting the probability distribution. But why does the above example give a Y scale going from 1 to 7? Perhaps I'm showing my lack of statistics here, but I would think that a strict probability distribution would have the value of all of the bars adding to 1,
Sorry to send out so many messages today. I really am trying to figure this out on my own...
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