scientimate.probability1d#
fdensity, fdensitycumulative, bincenter, xmean, xstd = scientimate.probability1d(x, binedge=None, dispout='no')
Description#
Calculate 1D probability density distribution for a given dataset
Inputs#
- x
Input data
- binedge
- Bin edgeslength(binedge)=number of bin +1If there are N bins in histogram/distribution, then values in binedge are as:1st value: left edge of 1st bin, 2nd value: left edge of 2nd bin, …(N)th value: left edge of last bin, (N+1)th value: right edge of last bin
- dispout=’no’
Define to display outputs or not (‘yes’: display, ‘no’: not display)
Outputs#
- fdensity
Probability density distribution
- fdensitycumulative
Cumulative probability density distribution
- bincenter
Bin center
- xmean
Mean value of input data
- xstd
Standard deviation of input data
Examples#
import scientimate as sm
import numpy as np
x=(-0.1+(0.1-(-0.1)))*np.random.randn(1024*2)
binedge=np.linspace(min(x),max(x),11)
fdensity,fdensitycumulative,bincenter,xmean,xstd=sm.probability1d(x,binedge,'yes')