I am trying to plot a best fit line on a probability density function with logarithmic axes. The Y-axis (PDF) is 10^-12 to 10^-28, while the X-axis is 10^10 to 10^20. I've tried polyfit, with no luck. Any ideas? Attached is my code.
Thanks,
Kevin
clc;
clear all;
load Aug2005_basin_variables.mat
% Initialize
j_len = length(W_SH);
prob_dens_all = zeros(j_len,30);
ii = 1 : j_len;
count(1:30) = 0;
bin(1:30) = 0;
for i = 1 : 30
bin(i) = 10^(11 + (0.3*i));
end
% Bin the Watts
for i = 1 : j_len
if((log10(W_SH(i)) >= 11) && (log10(W_SH(i)) < 11.3))
count(1) = count(1) + 1;
end
if((log10(W_SH(i)) >= 11.3) && (log10(W_SH(i)) < 11.6))
count(2) = count(2) + 1;
end
if((log10(W_SH(i)) >= 11.6) && (log10(W_SH(i)) < 11.9))
count(3) = count(3) + 1;
end
if((log10(W_SH(i)) >= 11.9) && (log10(W_SH(i)) < 12.2))
count(4) = count(4) + 1;
end
if((log10(W_SH(i)) >= 12.2) && (log10(W_SH(i)) < 12.5))
count(5) = count(5) + 1;
end
if((log10(W_SH(i)) >= 12.5) && (log10(W_SH(i)) < 12.8))
count(6) = count(6) + 1;
end
if((log10(W_SH(i)) >= 12.8) && (log10(W_SH(i)) < 13.1))
count(7) = count(7) + 1;
end
if((log10(W_SH(i)) >= 13.1) && (log10(W_SH(i)) < 13.4))
count(8) = count(8) + 1;
end
if((log10(W_SH(i)) >= 13.4) && (log10(W_SH(i)) < 13.7))
count(9) = count(9) + 1;
end
if((log10(W_SH(i)) >= 13.7) && (log10(W_SH(i)) < 14.0))
count(10) = count(10) + 1;
end
if((log10(W_SH(i)) >= 14.0) && (log10(W_SH(i)) < 14.3))
count(11) = count(11) + 1;
end
if((log10(W_SH(i)) >= 14.3) && (log10(W_SH(i)) < 14.6))
count(12) = count(12) + 1;
end
if((log10(W_SH(i)) >= 14.6) && (log10(W_SH(i)) < 14.9))
count(13) = count(13) + 1;
end
if((log10(W_SH(i)) >= 14.9) && (log10(W_SH(i)) < 15.2))
count(14) = count(14) + 1;
end
if((log10(W_SH(i)) >= 15.2) && (log10(W_SH(i)) < 15.5))
count(15) = count(15) + 1;
end
if((log10(W_SH(i)) >= 15.5) && (log10(W_SH(i)) < 15.8))
count(16) = count(16) + 1;
end
if((log10(W_SH(i)) >= 15.8) && (log10(W_SH(i)) < 16.1))
count(17) = count(17) + 1;
end
if((log10(W_SH(i)) >= 16.1) && (log10(W_SH(i)) < 16.4))
count(18) = count(18) + 1;
end
if((log10(W_SH(i)) >= 16.4) && (log10(W_SH(i)) < 16.7))
count(19) = count(19) + 1;
end
if((log10(W_SH(i)) >= 16.7) && (log10(W_SH(i)) < 17.0))
count(20) = count(20) + 1;
end
if((log10(W_SH(i)) >= 17.3) && (log10(W_SH(i)) < 17.6))
count(21) = count(21) + 1;
end
if((log10(W_SH(i)) >= 17.6) && (log10(W_SH(i)) < 17.9))
count(22) = count(22) + 1;
end
if((log10(W_SH(i)) >= 17.9) && (log10(W_SH(i)) < 18.2))
count(23) = count(23) + 1;
end
if((log10(W_SH(i)) >= 18.2) && (log10(W_SH(i)) < 18.5))
count(24) = count(24) + 1;
end
if((log10(W_SH(i)) >= 18.5) && (log10(W_SH(i)) < 18.8))
count(25) = count(25) + 1;
end
if((log10(W_SH(i)) >= 18.8) && (log10(W_SH(i)) < 19.1))
count(26) = count(26) + 1;
end
if((log10(W_SH(i)) >= 19.1) && (log10(W_SH(i)) < 19.4))
count(27) = count(27) + 1;
end
if((log10(W_SH(i)) >= 19.4) && (log10(W_SH(i)) < 19.7))
count(28) = count(28) + 1;
end
if((log10(W_SH(i)) >= 19.7) && (log10(W_SH(i)) < 20.0))
count(29) = count(29) + 1;
end
if((log10(W_SH(i)) >= 20.0) && (log10(W_SH(i)) < 20.3))
count(30) = count(30) + 1;
end
end
for i=1:30
prob(i) = count(i)/sum(count);
prob_dens(i) = prob(i)/bin(i);
end
% Check
sum(prob_dens.*bin);
prob_dens_all(i,:) = prob_dens(:);
%end
prob_dens_mean = zeros(1,30);
for i = 1 : 30
prob_dens_mean(1,i) = mean(prob_dens_all(:,i));
%prob_dens_std(1,i) = std(prob_dens_all(:,i));
end
% Plot
best_fit = polyfit(bin,log10(prob_dens_mean),11)
h = figure;
loglog(bin,prob_dens_mean,'ro','MarkerSize',10)
hold on;
plot(best_fit,'b')
t = title('Event Power Distribution, SHem, August 2005');
set(t, 'FontWeight', 'bold', 'FontSize', 12)
set(gca, 'FontWeight', 'bold', 'FontSize', 12)
xlabel('Event Power (W)');
ylabel('Probability Density');
print -dpng SHem_Wattage_PDF_AUG2005.png
I don't have your data, but here is an example using some random normally-distributed random data
x=randn(1000,1)+5; % create some data, keep numbers positive by adding 5
[n,xb]=hist(x); % Create the histogram
n = n/sum(n); % convert counts to a pdf
p=polyfit(log(xb), log(n), 3); % Do a 3rd order fit
loglog(xb,n, '*-', xb, exp(polyval(p, log(xb))), 'r')
grid on
legend('PDF', 'Fit', 0)
Related
I am trying to calculate normal direction within a grayscale image using pixel-neighborhood like this:
Suppose I have a matrix
|148 141 145|
|144 140 148|
|146 147 148|
The normal direction that I want to calculate is the maximum grayscale difference from the center pixel. if the grayscale difference is more than one value, the final normal direction of that plurality is a summation of normal direction.
How to implement that method efficiently in MATLAB?
Edit
I tried to implement it, but I doubt the result is correct or not.
function [resVectorDirection, magnitudeNormal, actualNormal] = estSurfNorm(newImg)
% Define empty array
resVectorDirection = [];
magnitudeNormal = [];
actualNormal = [];
for i = 2:size(newImg, 1) - 1
for j = 2:size(newImg, 2) - 1
% Obtain value from 8-neighborhood matrix
imgNeighborhood(1) = newImg(i-1,j-1);
imgNeighborhood(2) = newImg(i-1,j);
imgNeighborhood(3) = newImg(i-1,j+1);
imgNeighborhood(4) = newImg(i,j-1);
imgNeighborhood(5) = newImg(i,j+1);
imgNeighborhood(6) = newImg(i+1,j-1);
imgNeighborhood(7) = newImg(i+1,j);
imgNeighborhood(8) = newImg(i+1,j+1);
% Perform sort operation for array
[value, index] = sort(imgNeighborhood, 'descend');
% Obtain total largest value in array
totalLargestValue = sum(ismember(imgNeighborhood, value(1)));
% Obtain all largest value in array
largestValue = value(1:totalLargestValue);
% Obtain all index largest value in array
indexLargestValue = index(1:totalLargestValue);
% Check if there are multiple value from 8-neighborhood matrix
if (totalLargestValue > 1)
for k = 1:totalLargestValue
if (indexLargestValue(k) == 1)
multiVectorDirection(k, 1) = (i-1) - i;
multiVectorDirection(k, 2) = (j-1) - j;
elseif (indexLargestValue(k) == 2)
multiVectorDirection(k, 1) = (i-1) - i;
multiVectorDirection(k, 2) = j - j;
elseif (indexLargestValue(k) == 3)
multiVectorDirection(k, 1) = (i-1) - i;
multiVectorDirection(k, 2) = (j+1) - j;
elseif (indexLargestValue(k) == 4)
multiVectorDirection(k, 1) = i - i;
multiVectorDirection(k, 2) = (j-1) - j;
elseif (indexLargestValue(k) == 5)
multiVectorDirection(k, 1) = i - i;
multiVectorDirection(k, 2) = (j+1) - j;
elseif (indexLargestValue(k) == 6)
multiVectorDirection(k, 1) = (i+1) - i;
multiVectorDirection(k, 2) = (j-1) - j;
elseif (indexLargestValue(k) == 7)
multiVectorDirection(k, 1) = (i+1) - i;
multiVectorDirection(k, 2) = j - j;
elseif (indexLargestValue(k) == 8)
multiVectorDirection(k, 1) = (i+1) - i;
multiVectorDirection(k, 2) = (j+1) - j;
end
end
% Calculate suface normal direction and magnitude for multiple
% direction
tempVectorDirection = sum(multiVectorDirection, 1);
resVectorDirection = [resVectorDirection; tempVectorDirection];
actualNormal = [actualNormal; i j tempVectorDirection(1)+i tempVectorDirection(2)+j];
magnitudeNormal = [magnitudeNormal; largestValue(1)];
elseif (totalLargestValue == 1)
if (indexLargestValue == 1)
singleVectorDirection(1) = (i-1) - i;
singleVectorDirection(2) = (j-1) - j;
elseif (indexLargestValue == 2)
singleVectorDirection(1) = (i-1) - i;
singleVectorDirection(2) = j - j;
elseif (indexLargestValue == 3)
singleVectorDirection(1) = (i-1) - i;
singleVectorDirection(2) = (j+1) - j;
elseif (indexLargestValue == 4)
singleVectorDirection(1) = i - i;
singleVectorDirection(2) = (j-1) - j;
elseif (indexLargestValue == 5)
singleVectorDirection(1) = i - i;
singleVectorDirection(2) = (j+1) - j;
elseif (indexLargestValue == 6)
singleVectorDirection(1) = (i+1) - i;
singleVectorDirection(2) = (j-1) - j;
elseif (indexLargestValue == 7)
singleVectorDirection(1) = (i+1) - i;
singleVectorDirection(2) = j - j;
elseif (indexLargestValue == 8)
singleVectorDirection(1) = (i+1) - i;
singleVectorDirection(2) = (j+1) - j;
end
% If surface direction is only one direction, then assign it to
% actual surface normal and magnitude
resVectorDirection = [resVectorDirection; singleVectorDirection];
actualNormal = [actualNormal; i j singleVectorDirection(1)+i singleVectorDirection(2)+j];
magnitudeNormal = [magnitudeNormal; largestValue];
end
end
end
end
New to programing in matlab. I am currently trying to make a MATLAB program that will find the critical values of a multi-variable function and tell me whether each are a minimum, maximum, or saddle point. Unfortunately I always get the error : An array for multiple LHS assignment cannot contain LEX_TS_STRING
Any help will be very appreciated.
here's the code:
function [c,d] = critcalpoints(f)
syms x y
f(x,y)=x^3-3*x^2+5*x*y-7*y^2;
gradf = jacobian(f(x,y));
hessmatf = jacobian(gradf,[x,y]);
[xcr,ycr]=solve(gradf(1),gradf(2));
H1=subs(hessmatf,[x,y],[xcr(1),ycr(1)]);
H2=subs(hessmatf,[x,y],[xcr(2),ycr(2)]);
eig(H1);
eig(H2);
c = double(eig(H1));
d = double(eig(H2));
if (c(1) > 0 && d(1) > 0) || (c(2) > 0 && d(2) > 0)
print([xcr,ycr],' is a minimum')
elseif (c(1) < 0 && d(1) < 0) || (c(2) < 0 && d(2) < 0)
print( [xcr, ycr], ' is a maximum')
elseif (c(1) < 0 && d(1) > 0) || (c(1) > 0 && d(1) < 0)
print( [xcr, ycr], ' is a saddle point')
elseif (c(2) < 0 && d(2) > 0) || (c(2) > 0 && d(2) < 0)
print( [xcr, ycr], ' is a saddle point')
elseif (c(1)==0 || d(1)==0)
print( [xcr, ycr], ' is degenerate')
elseif (c(2)==0 || d(2)==0)
print( [xcr, ycr], ' is degenerate')
end
Cannot reproduce your error. The code can work but you need to change print to something else as print doesn't do what you think it does. The print function prints a figure that is open to file. Change it so that you display xcr and ycr first then display the right condition to satisfy after. Use disp instead:
syms x y
f(x,y)=x^3-3*x^2+5*x*y-7*y^2;
gradf = jacobian(f(x,y));
hessmatf = jacobian(gradf,[x,y]);
[xcr,ycr]=solve(gradf(1),gradf(2));
H1=subs(hessmatf,[x,y],[xcr(1),ycr(1)]);
H2=subs(hessmatf,[x,y],[xcr(2),ycr(2)]);
eig(H1);
eig(H2);
c = double(eig(H1));
d = double(eig(H2));
disp([xcr, ycr]); % Display the solutions first
if (c(1) > 0 && d(1) > 0) || (c(2) > 0 && d(2) > 0)
disp('is a minimum')
elseif (c(1) < 0 && d(1) < 0) || (c(2) < 0 && d(2) < 0)
disp('is a maximum')
elseif (c(1) < 0 && d(1) > 0) || (c(1) > 0 && d(1) < 0)
disp('is a saddle point')
elseif (c(2) < 0 && d(2) > 0) || (c(2) > 0 && d(2) < 0)
disp('is a saddle point')
elseif (c(1)==0 || d(1)==0)
disp('is degenerate')
elseif (c(2)==0 || d(2)==0)
disp('is degenerate')
end
I get:
[ 0, 0]
[ 59/42, 295/588]
is a maximum
I have a for loop code which I want to vectorize. Below is the initial for loop code, and the vectorized version of the code. The vectorized code isn't giving the same result as that of the parfor loop, hence I know something is wrong with the code. I would appreciate it if any member of the forum can help me review the vectorized code and see if they can point out my errors to me. Thank you in advance.
% Initialization and precomputations
% w is an n x 1 vector
% beta: any number larger than 0. Usually set to 1.
Here is the for-loop code I need to vectorize:
f = zeros(n,1);
x = w;
y = w;
rho = 1;
v = f – (rho*y);
rhow = rho*w;
n = length(w);
parfor i = 1 : n
if w(i) >= 0
if v(i) < -rhow(i) – beta – 1
x(i) = (-beta -1 -v(i))/rho;
elseif (-rhow(i) – beta – 1 <= v(i)) && (v(i) <= -rhow(i) + beta – 1)
x(i) = w(i);
elseif (-rhow(i) + beta – 1 < v(i)) && (v(i) < beta – 1)
x(i) = (beta – 1 -v(i))/rho;
elseif (beta – 1 <= v(i)) && (v(i) <= beta + 1)
x(i) = 0;
else
x(i) = (beta + 1 – v(i))/rho;
end
else
if v(i) < -beta -1
x(i) = (-beta -1 – v(i))/rho;
elseif (-beta – 1 <= v(i) )&& (v(i) <= -beta + 1)
x(i) = 0;
elseif (-beta + 1 < v(i)) && (v(i) < -rhow(i) – beta + 1)
x(i) = (-beta + 1 – v(i))/rho;
elseif (-rhow(i) – beta + 1 <= v(i)) && (v(i) <= -rhow(i) + beta + 1)
x(i) = w(i);
else
x(i) = (beta + 1 – v(i))/rho;
end
end
end
======================================================================
And here is my vectorized version of the code above:
cond1 = (w >= 0);
cond2 = (w >= 0) & (v < -rhow-beta-1);
x(cond2) = (-beta-1-v(cond2))/rho;
cond3 = (w>=0)&(-rhow - beta -1 <= v) & (v <= -rhow + beta - 1);
x(cond3) = w(cond3);
cond4 = (w>=0) & (-rhow +beta - 1 < v) & (v < beta - 1);
x(cond4) = (beta - 1 - v(cond4))/rho;
cond5 = (w>=0) & (beta - 1 <= v) & (v <= beta + 1);
x(cond5) = 0;
cond6 = (~cond2);
x(cond6) = (beta + 1 - v(cond6))/rho;
cond7 = ((~cond1) & v < -beta -1);
x(cond7) = (-beta -1 - v(cond7))/rho;
cond8 = ((~cond1) & (-beta - 1 <= v) & (v <= -beta + 1));
x(cond8) = 0;
cond9 = ((~cond1) & (-beta + 1 < v) & (v < -rhow - beta + 1));
x(cond9) = (-beta + 1 - v(cond9))/rho;
cond10 = ((~cond1) & (-rhow - beta + 1 <= v) & (v <= -rhow + beta + 1));
x(cond10) = w(cond10);
cond11 = (~cond1);
x(cond11) = (beta + 1 - v(cond11))/rho;
I'm adding another answer with all the conditionals checked:
cond1 = (w >= 0);
cond2 = cond1 & (v < -rhow – beta – 1);
cond3 = cond1 & ((-rhow – beta – 1 <= v) && (v <= -rhow + beta – 1));
cond4 = cond1 & ((-rhow + beta – 1 < v) && (v < beta – 1));
cond5 = cond1 & ((beta – 1 <= v) && (v <= beta + 1));
cond6 = cond1 & (v > beta + 1)
cond7 = ~cond1 & (v < -beta -1);
cond8 = ~cond1 & ((-beta – 1 <= v ) && (v <= -beta + 1));
cond9 = ~cond1 & ((-beta + 1 < v) && (v < -rhow – beta + 1));
cond10 = ~cond1 & ((-rhow – beta + 1 <= v) && (v <= -rhow + beta + 1));
cond11 = ~cond1 & (v > -rhow + beta + 1);
x(cond2)=... to x(cond11)=... remain the same.
Hope this works.
Here is one mistake, cond6 is not equivalent to the original first else
cond2 = (w >= 0) & (v < -rhow-beta-1);
cond6 = (~cond2);
x(cond6) = (beta + 1 - v(cond6))/rho;
in the original this is:
if w(i) >= 0
if v(i) < -rhow(i) – beta – 1
...
else
x(i) = (beta + 1 – v(i))/rho; %this should be cond6
end
end
The else should be evaluated like this (If I'm not mistaken)
x(cond1) = (beta + 1 - v(cond6))/rho;
and before all the others up to cond5.
I did not check all the code, so if this doesn't solve your problem let me know.
This is really basic question. I have an array "relevant_IDs". I need to store 1 to 100 values in it when variable category is 1. Similarly, 101 to 200 when category is 2. So on till 901 to 1000 when category is 10.
I have written code for it but it is not inserting 100 values in it.
Code:
for i=1: 1000
if(category==1 && i>0 && i< 101)
relevant_IDs(i) = i;
end
if(category==2 && i>100 && i< 201)
relevant_IDs(i) = i;
end
if(category==3 && i>200 && i< 301)
relevant_IDs(i) = i;
end
if(category==4 && i>300 && i< 401)
relevant_IDs(i) = i;
end
if(category==5 && i>400 && i< 501)
relevant_IDs(i) = i;
end
if(category==6 && i>500 && i< 601)
relevant_IDs(i) = i;
end
if(category==7 && i>600 && i< 701)
relevant_IDs(i) = i;
end
if(category==8 && i>700 && i< 801)
relevant_IDs(i) = i;
end
if(category==9 && i>800 && i< 901)
relevant_IDs(i) = i;
end
if(category==10 && i>900 && i< 1001)
relevant_IDs(i) = i;
end
end
Something like this should work and be much quicker:
relevant_IDs = (category - 1) * 100 + (1:100);
You could also just generate the whole thing (numbers from 1 to 1000), then index into the matrix using category value as index to get the desired relevant_IDs:
relevant_IDs = reshape(1:1000, [100,10]).';
relevant_IDs(category,:) % this will return a 1x100 row vector
% (category is a number from 1 to 10)
I am plotting a series of probability density functions (PDFs) with log scales for both axes. Problem is, they keep changing depending on which data I am analyzing. I'd like to fix the Y-axis from 10^-12 to 10^-28, and X-axis from 10^10 to 10^20. Any ideas?
Thanks!
clc;
clear all;
load Aug2005_basin_variables.mat
% Initialize
j_len = length(W_SH);
prob_dens_all = zeros(j_len,30);
ii = 1 : j_len;
count(1:30) = 0;
bin(1:30) = 0;
for i = 1 : 30
bin(i) = 10^(11 + (0.3*i));
end
% Bin the Watts
for i = 1 : j_len
if((log10(W_SH(i)) >= 11) && (log10(W_SH(i)) < 11.3))
count(1) = count(1) + 1;
end
if((log10(W_SH(i)) >= 11.3) && (log10(W_SH(i)) < 11.6))
count(2) = count(2) + 1;
end
if((log10(W_SH(i)) >= 11.6) && (log10(W_SH(i)) < 11.9))
count(3) = count(3) + 1;
end
if((log10(W_SH(i)) >= 11.9) && (log10(W_SH(i)) < 12.2))
count(4) = count(4) + 1;
end
if((log10(W_SH(i)) >= 12.2) && (log10(W_SH(i)) < 12.5))
count(5) = count(5) + 1;
end
if((log10(W_SH(i)) >= 12.5) && (log10(W_SH(i)) < 12.8))
count(6) = count(6) + 1;
end
if((log10(W_SH(i)) >= 12.8) && (log10(W_SH(i)) < 13.1))
count(7) = count(7) + 1;
end
if((log10(W_SH(i)) >= 13.1) && (log10(W_SH(i)) < 13.4))
count(8) = count(8) + 1;
end
if((log10(W_SH(i)) >= 13.4) && (log10(W_SH(i)) < 13.7))
count(9) = count(9) + 1;
end
if((log10(W_SH(i)) >= 13.7) && (log10(W_SH(i)) < 14.0))
count(10) = count(10) + 1;
end
if((log10(W_SH(i)) >= 14.0) && (log10(W_SH(i)) < 14.3))
count(11) = count(11) + 1;
end
if((log10(W_SH(i)) >= 14.3) && (log10(W_SH(i)) < 14.6))
count(12) = count(12) + 1;
end
if((log10(W_SH(i)) >= 14.6) && (log10(W_SH(i)) < 14.9))
count(13) = count(13) + 1;
end
if((log10(W_SH(i)) >= 14.9) && (log10(W_SH(i)) < 15.2))
count(14) = count(14) + 1;
end
if((log10(W_SH(i)) >= 15.2) && (log10(W_SH(i)) < 15.5))
count(15) = count(15) + 1;
end
if((log10(W_SH(i)) >= 15.5) && (log10(W_SH(i)) < 15.8))
count(16) = count(16) + 1;
end
if((log10(W_SH(i)) >= 15.8) && (log10(W_SH(i)) < 16.1))
count(17) = count(17) + 1;
end
if((log10(W_SH(i)) >= 16.1) && (log10(W_SH(i)) < 16.4))
count(18) = count(18) + 1;
end
if((log10(W_SH(i)) >= 16.4) && (log10(W_SH(i)) < 16.7))
count(19) = count(19) + 1;
end
if((log10(W_SH(i)) >= 16.7) && (log10(W_SH(i)) < 17.0))
count(20) = count(20) + 1;
end
if((log10(W_SH(i)) >= 17.3) && (log10(W_SH(i)) < 17.6))
count(21) = count(21) + 1;
end
if((log10(W_SH(i)) >= 17.6) && (log10(W_SH(i)) < 17.9))
count(22) = count(22) + 1;
end
if((log10(W_SH(i)) >= 17.9) && (log10(W_SH(i)) < 18.2))
count(23) = count(23) + 1;
end
if((log10(W_SH(i)) >= 18.2) && (log10(W_SH(i)) < 18.5))
count(24) = count(24) + 1;
end
if((log10(W_SH(i)) >= 18.5) && (log10(W_SH(i)) < 18.8))
count(25) = count(25) + 1;
end
if((log10(W_SH(i)) >= 18.8) && (log10(W_SH(i)) < 19.1))
count(26) = count(26) + 1;
end
if((log10(W_SH(i)) >= 19.1) && (log10(W_SH(i)) < 19.4))
count(27) = count(27) + 1;
end
if((log10(W_SH(i)) >= 19.4) && (log10(W_SH(i)) < 19.7))
count(28) = count(28) + 1;
end
if((log10(W_SH(i)) >= 19.7) && (log10(W_SH(i)) < 20.0))
count(29) = count(29) + 1;
end
if((log10(W_SH(i)) >= 20.0) && (log10(W_SH(i)) < 20.3))
count(30) = count(30) + 1;
end
end
for i=1:30
prob(i) = count(i)/sum(count);
prob_dens(i) = prob(i)/bin(i);
end
% Check
sum(prob_dens.*bin);
prob_dens_all(i,:) = prob_dens(:);
%end
prob_dens_mean = zeros(1,30);
for i = 1 : 30
prob_dens_mean(1,i) = mean(prob_dens_all(:,i));
%prob_dens_std(1,i) = std(prob_dens_all(:,i));
end
% Plot
best_fit = polyfit(bin,log10(prob_dens_mean),11)
h = figure;
loglog(bin,prob_dens_mean,'ro','MarkerSize',10)
hold on;
plot(best_fit,'b')
t = title('Event Power Distribution, SHem, August 2005');
set(t, 'FontWeight', 'bold', 'FontSize', 12)
set(gca, 'FontWeight', 'bold', 'FontSize', 12)
xlabel('Event Power (W)');
ylabel('Probability Density');
print -dpng SHem_Wattage_PDF_AUG2005.png
axis([1e10 1e20 1e-28 1e-12]) after hold on