I know how to calculate the line parameter defined as x below for one layer, considering the given wavelength range 50 to 550 um. Now I want to repeat this calculation for all 10 layers. all the other parameters remain as a constant while temperature varies from layer 1 to 10.Any suggestion would be greatly appreciated.
wl=[100 200 300 400 500]; %5 wavelengths, 5 spectral lines
br=[0.12 0.56 0.45 0.67 0.89]; % broadening parameter for each wavelength
T=[101 102 103 104 105 106 107 108 109 110];% temperature for 10 layers
wlall=linspace(50,550,40);%all the wavelength in 50um to 550 um range
% x is defined as,
%(br*wl/(br*br + (wlall-wl)^2))*br;
%If I do a calculation for the first line
((br(1)*T(1)*wl(1))./(br(1)*br(1)*(T(1)) + (wlall(:)-wl(1)).^2))*br(1)*T(1)
%Now I'm going to calculate it for all the lines in the first layer
k= repmat(wlall,5,1);
for i=1:5;
kn(i,:)=(br(i)*T(1)* wl(i)./(br(i)*br(i)*T(1) + (k(i,:)-
wl(i)).^2))*br(i)*T(1);
end
%Above code gives me x parameter for all the wavelengths in the
%given range( 50 to 550 um) in the first layer, dimension is (5,40)
% I need only the maximum value of each column
an=(kn(:,:)');
[ll,mm]=sort(an,2,'descend');
vn=(ll(:,1))'
%Now my output has the dimension , (1,40) one is for the first layer, 40 is
%for the maximum x parameter corresponding to each wavelength in first layer
%Now I want to calculate the x parameter in all 10 layers,So T should vary
%from T(1) to T(10) and get the
%maximum in each column, so my output should have the dimension ( 10, 40)
You just need to run an extra 'for' loop for each value of 'T'. Here is an example:
clc; close all; clear all;
wl=[100 200 300 400 500]; %5 wavelengths, 5 spectral lines
br=[0.12 0.56 0.45 0.67 0.89]; % broadening parameter for each wavelength
T=[101 102 103 104 105 106 107 108 109 110];% temperature for 10 layers
wlall=linspace(50,550,40);%all the wavelength in 50um to 550 um range
% x is defined as,
%(br*wl/(br*br + (wlall-wl)^2))*br;
%If I do a calculation for the first line
((br(1)*T(1)*wl(1))./(br(1)*br(1)*(T(1)) + (wlall(:)-wl(1)).^2))*br(1)*T(1)
%Now I'm going to calculate it for all the lines in the first layer
k= repmat(wlall,5,1);
for index = 1:numel(T)
for i=1:5
kn(i,:, index)=(br(i)*T(index)* wl(i)./(br(i)*br(i)*T(index) + (k(i,:)- wl(i)).^2))*br(i)*T(index);
end
an(:, :, index) = transpose(kn(:, :, index));
vn(:, index) = max(an(:, :, index), [], 2);
end
vn = transpose(vn);
Related
I need to calculate a parameter defined as x,( this is defined in my code below) for the given spectral lines in each layer. My atmospheric profile has 10 layers. I know how to calculate x for just one layer. Then I get 5 values for x corresponding to each spectral line ( or wavelength) .
Suppose I want to do this for all 10 layers. Then my output should have 10 rows and 5 columns , size should be (10,5) , 10 represent number of the layer and 5 represent the spectral line. Any suggestion would be greatly appreciated
wl=[100 200 300 400 500]; %5 wavelengths, 5 spectral lines
br=[0.12 0.56 0.45 0.67 0.89]; % broadening parameter for each wavelength
p=[1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ]; % pressure for 10 layers
T=[101 102 103 104 105 106 107 108 109 110]; % temperature for 10 layers
%suppose I want to caculate a parameter, x for all the layers
% x is defined as,( wavelength*br*T)/p
%when I do the calculation for the first layer,I have to consider all the
%wavelengths , all the broadening parameters and only the first value of
%pressure and only the first value of temperature
for i=1:5;
x(i)= (wl(i)*br(i)*T(1))/p(1);
end
% x is the x parameter for all the wavelengths in the first layer
%Now I want to calculate the x parameter for all the wavelengths in all 10
%layers
%my output should have 10 rows for 10 layers and 5 columns , size= (10,5)
you don't need loops for this case
>> (T./p)'*(wl.*br)
ans =
1.0e+05 *
0.0121 0.1131 0.1364 0.2707 0.4495
0.0136 0.1269 0.1530 0.3037 0.5043
0.0155 0.1442 0.1738 0.3451 0.5729
0.0178 0.1664 0.2006 0.3982 0.6611
0.0210 0.1960 0.2362 0.4690 0.7788
0.0254 0.2374 0.2862 0.5682 0.9434
0.0321 0.2996 0.3611 0.7169 1.1904
0.0432 0.4032 0.4860 0.9648 1.6020
0.0654 0.6104 0.7358 1.4606 2.4253
0.1320 1.2320 1.4850 2.9480 4.8950
I have a vector that has values, say a=[10 20 42 90] and what I am trying to do is to find the neighbors in the range of 30 and replace these values with their means. For example, for the a vector, the value of 20 is a neighbor of 10. Additionally, 42 is also a neighbor of 10 through 20, because it is a neighbor's neighbor but 90 is not a neighboring value and it is not reachable from 10 with a neighborhood size of 30.
So I want to replace all 10, 20 and 42 with their means and obtain the vector a=[24 90].
If a=[10 20 42 66 155], then the resulting vector would be a=[34.5 155].
How do I achieve that?
a=[10 20 42 66 155]; % sample data
r = 30; % sample range
a = accumarray(cumsum([r+1 abs(diff(a))]>r).',a,[],#mean).';
Ungolfed and commented version:
a=[10 20 42 66 155]; % sample data
r = 30; % range
% difference between subsequent groupmembers. First difference is set to be higher than r
d = [r+1 abs(diff(a))];
% each group one label
L = cumsum(d>r);
% calculate mean of each group
a = accumarray(L.',a,[],#mean).';
I have a series of ordered points (X, Y, Z) and I want to plot a 3D histogram, any suggestions?
I'm trying to do it by this tutorial http://www.mathworks.com/help/stats/hist3.html , but points are random here and presented as a function. My example is easier, since i already know the points.
Furthermore, depending on the number value of Z coordinate, i'd like to colour it differently. E.g. Max value - green, min value - red. Similar as in this case Conditional coloring of histogram graph in MATLAB, only in 3D.
So, if I have a series of points:
X = [32 64 32 12 56 76 65]
Y = [160 80 70 48 90 80 70]
Z = [80 70 90 20 45 60 12]
Can you help me with the code for 3D histogram with conditional coloring?
So far the code looks like this:
X = [32 64 32 12 56 76 65];
Y= [160 80 70 48 90 80 70];
Z= [80 70 90 20 45 60 12];
A = full( sparse(X',Y',Z'));
figure;
h = bar3(A); % get handle to graphics
for k=1:numel(h),
z=get(h(k),'ZData'); % old data - need for its NaN pattern
nn = isnan(z);
nz = kron( A(:,k),ones(6,4) ); % map color to height 6 faces per data point
nz(nn) = NaN; % used saved NaN pattern for transparent faces
set(h(k),'CData', nz); % set the new colors
end
colorbar;
Now I just have to clear the lines and design the chart to make it look useful. But how would it be possible to make a bar3 without the entire mesh on 0 level?
Based on this answer, all you need to do is rearrange your data to match the Z format of that answer. After than you might need to remove edgelines and possibly clear the zero height bars.
% Step 1: rearrange your data
X = [32 64 32 12 56 76 65];
Y= [160 80 70 48 90 80 70];
Z= [80 70 90 20 45 60 12];
A = full( sparse(X',Y',Z'));
% Step 2: Use the code from the link to plot the 3D histogram
figure;
h = bar3(A); % get handle to graphics
set(h,'edgecolor','none'); % Hopefully this will remove the lines (from https://www.mathworks.com/matlabcentral/newsreader/view_thread/281581)
for k=1:numel(h),
z=get(h(k),'ZData'); % old data - need for its NaN pattern
nn = isnan(z);
nz = kron( A(:,k),ones(6,4) ); % map color to height 6 faces per data point
nz(nn) = NaN; % used saved NaN pattern for transparent faces
nz(nz==0) = NaN; % This bit makes all the zero height bars have no colour
set(h(k),'CData', nz); % set the new colors. Note in later versions you can do h(k).CData = nz
end
colorbar;
The plot in MATLAB looks like this:
The code to generate this is very simple:
y = [0 18 450];
x = [0 5.3 6.575];
plot(x,y);
How could I know the values of 119 equally spaced discrete points on this plot?
In simple MATLAB plots, the points are connected together by simple linear interpolation. Simply put, a straight line is drawn between each pair of points. You can't physically get these points from the graph other than those you used to plot the points (at least not easily...).
If you however do desire 119 points at equally spaced intervals that would theoretically be obtained from the above set of 4 points, you can use the interp1 function to do so:
y = [0 18 450];
x = [0 5.3 6.575]
yy = interp1(x, y, linspace(min(x),max(x),119), 'linear');
interp1 performs linear (note the 'linear' flag at the end...) interpolation given a set of key points defined by x and y points and a set of x points to use to interpolate between the key x points to generate the interpolated y points stored in yy. linspace in this case generates a linearly increasing array from the smallest value in x to the largest value in x with 119 of these points.
Here's a running example with your data:
>> format compact;
>> y = [0 18 450];
>> x = [0 5.3 6.575];
>> yy = interp1(x, y, linspace(min(x),max(x),119), 'linear');
>> yy
yy =
Columns 1 through 8
0 0.1892 0.3785 0.5677 0.7570 0.9462 1.1354 1.3247
Columns 9 through 16
1.5139 1.7031 1.8924 2.0816 2.2709 2.4601 2.6493 2.8386
Columns 17 through 24
3.0278 3.2171 3.4063 3.5955 3.7848 3.9740 4.1633 4.3525
Columns 25 through 32
4.5417 4.7310 4.9202 5.1094 5.2987 5.4879 5.6772 5.8664
Columns 33 through 40
6.0556 6.2449 6.4341 6.6234 6.8126 7.0018 7.1911 7.3803
Columns 41 through 48
7.5696 7.7588 7.9480 8.1373 8.3265 8.5157 8.7050 8.8942
Columns 49 through 56
9.0835 9.2727 9.4619 9.6512 9.8404 10.0297 10.2189 10.4081
Columns 57 through 64
10.5974 10.7866 10.9759 11.1651 11.3543 11.5436 11.7328 11.9220
Columns 65 through 72
12.1113 12.3005 12.4898 12.6790 12.8682 13.0575 13.2467 13.4360
Columns 73 through 80
13.6252 13.8144 14.0037 14.1929 14.3822 14.5714 14.7606 14.9499
Columns 81 through 88
15.1391 15.3283 15.5176 15.7068 15.8961 16.0853 16.2745 16.4638
Columns 89 through 96
16.6530 16.8423 17.0315 17.2207 17.4100 17.5992 17.7885 17.9777
Columns 97 through 104
34.6540 53.5334 72.4128 91.2921 110.1715 129.0508 147.9302 166.8096
Columns 105 through 112
185.6889 204.5683 223.4477 242.3270 261.2064 280.0857 298.9651 317.8445
Columns 113 through 119
336.7238 355.6032 374.4826 393.3619 412.2413 431.1206 450.0000
I have implemented "normalized cut" segmentation. The change here is that, instead of giving an image as input, I have given an matrix of image. As my output looks odd to me. I need to know whether my implementation is correct or not.
Code:
clear all
tic;
% im = imread('lena.pgm');
im =[94 122 99 101 111 101;
99 92 103 87 107 116;
93 109 113 84 86 106;
5 17 6 54 56 53;
13 11 5 56 44 50;
0 10 5 49 42 51];
% resizing to avoid out of memory error
% cim=imresize(im,[100 100]);
cim=im;
[r, c]=size(cim);
ind=find(cim);
lind=length(ind);
[I,J]=ind2sub([r,c],ind);
% % I've used linear indexing to speed up the partitioning
% vectoring the pixel nodes
for i=1:lind
V1(i)=double(cim(ind(i)));
end
% normalizing to [0-1] scale
V=(V1./255);
% w is the weight matrix (similarity matrix or adjacency matrix)
w=zeros(lind,lind);
% r, sigmaI, sigmaX values
rad=4.5;
sigi=10;
sigx=25;
% computing the weight matrix
for i=1:lind
x1=I(i,1);
y1=J(i,1);
for j=1:lind
if (i==j)
w(i,j)=1;
else
x2=I(j,1);
y2=J(j,1);
dist=((x1-x2)^2 + (y1-y2)^2);
if sqrt(dist)>=rad
dx=0;
else
dx=exp(-((dist)/(sigx^2)));
end
pdiff=(V(i)-V(j))^2;
di=exp(-((pdiff)/(sigi)^2));
w(i,j)=di*dx;
end
end
end
d=zeros(lind,lind);
s=sum(w,2);
% the diagonal matrix for computing the laplacian matrix
for i=1:lind
d(i,i)=s(i);
end
A=zeros(lind,lind);
A=(d-w); % A is the laplacian matrix
% vt has the eigen vectors corresponding to eigen values in vl
% other eigs / eig functions in matlab can be used but I'm using the
% function to compute the 5 smallest eigenvectors
[vt,vl]=eigs(A,d,5,'sm');
% se has the second smallest eigen vector, third and so on
se=vt(:,2:4);
% % % % % Simultaneous 'k' partitions
k=6;
id=kmeans(se,k);
imp=cell(1,k);
pic=cell(1,k);
for i=1:k
imp{1,i}= find(id(:,1)==i);
mat=zeros(100,100);
in=imp{1,i};
mat(in)=cim(in);
pic{1,i}=uint8(mat);
% figure,imshow(pic{1,i});
end
% pic has the sub graphs or partitiond sub images
figure;
subplot(2,4,1);imshow(uint8(im));title('Original image');
subplot(2,4,2);imshow(uint8(cim));title('Preprocessed image');
subplot(2,4,3);imshow(pic{1,1});title('Partition 1');
subplot(2,4,4);imshow(pic{1,2});title('Partition 2');
subplot(2,4,5);imshow(pic{1,3});title('Partition 3');
subplot(2,4,6);imshow(pic{1,4});title('Partition 4');
subplot(2,4,7);imshow(pic{1,5});title('Partition 5');
subplot(2,4,8);imshow(pic{1,6});title('Partition 6');
toc;
Output:
Thanks in advance.