# Fron Python to Matlab

7 views (last 30 days)
Pouyan Msgn on 14 Nov 2018
Commented: Nour Sd on 8 Dec 2018
I got a code in Python that I will write it again in Matlab and get the same plot:
The code is :
npts = 1000 # number of points in uncorrelated data set
Emax = 10. # energy goes from zero to Emax
Ec = 0.1 # correlation energy
kT = 0.25
# create discrete values of Energy
E = np.linspace(0, Emax, npts)
E_extra = np.linspace(0, Emax, 2*npts-1) #sometimes we need this size to convolve
#PLOT 1: Tbar correlated
Tbar = np.random.normal(loc=0., scale=1.0, size=2*npts-1 )
y = np.exp(- ((E-Emax/2.)/Ec)**2)
#convolution between Tbar and correlation energy
Tbar_c = np.convolve(y, Tbar, mode='valid') #valid = no edge effects
# normalize the data (maximum=1, minimum=0)
Tbar_c = Tbar_c - Tbar_c.min()
Tbar_c = Tbar_c/Tbar_c.max()
# plot the correlated data set
ax1.plot(E, Tbar_c)
The plot is (I need the first plot (the one at the top T(E))) Here is what I have done yet:
clc
clear all
Emax=10;
Ec=0.01;
E=0:0.01:10;
g=exp(-0.5.*((E-Emax).^2)./Ec);
y=rand(1,length(E));
%y=rand(size(E))
T1=conv(y,g,'same');
T=T1./norm(T1);
subplot(3,1,1);
plot(E,T)
The problem is for me here:
Draw random samples from a normal (Gaussian) distribution. in Python we can do it by :
Tbar = np.random.normal(loc=0., scale=1.0, size=2*npts-1 )
Nour Sd on 8 Dec 2018
Okay thank you very much :)

Adam Danz on 14 Nov 2018
Edited: Adam Danz on 14 Nov 2018
I can't run your code right now in python so I can't compare the results but most of these lines should be the correct conversion.
See help normrnd to pull numbers from a given gaussian distribution.
npts = 1000; % number of points in uncorrelated data set
Emax = 10; % energy goes from zero to Emax
Ec = 0.1; % correlation energy
kT = 0.25;
% create discrete values of Energy
E = linspace(0, Emax, npts);
E_extra = linspace(0, Emax, 2*npts-1); %sometimes we need this size to convolve
%PLOT 1: Tbar correlated
Tbar = normrnd(0, 1, 1, 2*npts-1)
y = exp(- ((E-Emax/2.)/Ec).*2);
%convolution between Tbar and correlation energy
Tbar_c = conv(y, Tbar, 'same'); %valid = no edge effects
% normalize the data (maximum=1, minimum=0)
Tbar_c = Tbar_c - min(Tbar_c);
Tbar_c = Tbar_c/max(Tbar_c);
% plot the correlated data set
plot(E, Tbar_c)
Adam Danz on 14 Nov 2018
good catch!

dpb on 14 Nov 2018
See
doc randn
When looking for something you don't know function name but have a clue about what is,
lookfor keyword
is useful; in this case either
lookfor random
lookfor normal
would lead you there...also just the venerable old
help
can show you what areas are in base Matlab plus the installed toolboxes available...

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