Hi Chris,
I think I understand what you are asking. I think the key point is I have used
"np.histogram" where you are using "np.hist"
When I make my plots, I use np.hist, but then to access the data, I use
np.histogram.
Just to demonstrate, incase this is not what you want, I have found, if I
create a bin
> bin =
> np.histogram(binData,range=(ymin,ymax),weights=binQ,bins=np.arange(ymin,ymax,dm0/4))
>
where
> ind = np.argsort(my_data) # list to order the data from low to high
> binDat = my_data[ind]
> binQ = weights[ind] / np.sum(weights) #ordered list of weight factors
> for the data (for a weighted distribution. example, if you have data with
> uncertainties, the weights are given by the inverse uncertainties)
and ymin, ymax and dm0 are params I have specified (based on the data) to set
the bin size and range of bins
The pdf, in this case, is given by pdf[i] = binQ[i].
I can then access this with
> bin[0][i] #this is the i'th weight (the pdf at i)
also, the data (the x values) can be accessed by
> bin[1][i]
At the very least, this gives a poor-working man's solution. I couldn't figure
out how to get it from np.hist.
Andre
On Mar 24, 2011, at 8:47 PM, Chris Edwards wrote:
> Hi,
>
> I would like to access values in the bins of a matplotlib histogram. The
> following example script is an attempt to do this. Clearly pdf contains
> floating point numbers, but I am unable to access them.
>
> Help with this problem would be much appreciated.
>
> Chris
>
> --------------------------------------------------------------------------------------------------------------
> import numpy as np
> import matplotlib.pyplot as plt
> fig = plt.figure()
> ax = fig.add_subplot(111)
>
> mu, sigma = 100, 15
> x = mu + sigma * np.random.randn(20)
>
> #Generate the histogram of the data. Example from Matplotlib documentation
>
> n, bins, patches = plt.hist(x, 50, normed=True, facecolor='g', alpha=0.75)
> plt.xlabel('Smarts')
> plt.ylabel('Probability')
> plt.title('Histogram of IQ')
> plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
> plt.axis([40, 160, 0, 0.03])
> plt.grid(True)
>
> #From Matplotlib documentation.
> #normed: If True, the first element of the return tuple will be the counts
> normalized
> #to form a probability density, i.e., n/(len(x)*dbin). In a probability
> density,
> #the integral of the histogram should be 1; you can verify that with a
> trapezoidal
> #integration of the probability density function.
>
> pdf, bins, patches = ax.hist(x, 50, normed=True, facecolor='g', alpha=0.75)
>
> #print pdf shows pdf contains the value in each bin of the normed histogram
>
> print "pdf = ", pdf
>
> print " Integration of PDF = ", np.sum(pdf * np.diff(bins))
>
> #How to access values in pdf? Various tries made but none successful. Example
> attempt shown
>
> count=0
> for line in open(pdf,'r+'):
> x=pdf.readline()
> z=('%.10f' % float(x))
> count=count+1
> print "count = ", count
>
> ----------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------
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