I have 2 columns like this:
0.0 1.2
0.0 2.3
0.0 1.5
0.1 1.0
0.1 1.2
0.1 1.4
0.1 1.7
0.4 1.1
0.4 1.3
0.4 1.5
In the 1st column, 0.0 is repeated 3 times. I want to sum corresponding elements
(1.2 + 2.3 + 1.5) in the 2nd column. Similarly, 0.1 is repeated 4 times in the 1st
column. I want to sum the corresponding elements (1.0 + 1.2 + 1.4 + 1.7) in the 2nd
column and so on.
I am trying like this
for i = 1:length(col1)
for j = 1:length(col2)
% if col2(j) == col1(i)
% to do
end
end
end
This is a classical use of unique and accumarray:
x = [0.0 1.2
0.0 2.3
0.0 1.5
0.1 1.0
0.1 1.2
0.1 1.4
0.1 1.7
0.4 1.1
0.4 1.3
0.4 1.5]; % data
[~, ~, w] = unique(x(:,1)); % labels of unique elements
result = accumarray(w, x(:,2)); % sum using the above as grouping variable
You can also use the newer splitapply function instead of accumarray:
[~, ~, w] = unique(x(:,1)); % labels of unique elements
result = splitapply(#sum, x(:,2), w); % sum using the above as grouping variable
a=[0.0 1.2
0.0 2.3
0.0 1.5
0.1 1.0
0.1 1.2
0.1 1.4
0.1 1.7
0.4 1.1
0.4 1.3
0.4 1.5]
% Get unique col1 values, and indices
[uniq,~,ib]=unique(a(:,1));
% for each unique value in col1
for ii=1:length(uniq)
% sum all col2 values that correspond to the current index of the unique value
s(ii)=sum(a(ib==ii,2));
end
Gives:
s =
5.0000 5.3000 3.9000
Related
Say I have the following dataframe:
df = pd.DataFrame({'A' : [0, 0.3, 0.8, 1, 1.5, 2.3, 2.3, 2.9], 'B' : randn(8)})
df
Out[86]:
A B
0 0.0 0.130471
1 0.3 0.029251
2 0.8 0.790972
3 1.0 -0.870462
4 1.5 -0.700132
5 2.3 -0.361464
6 2.3 -1.100923
7 2.9 -1.003341
How could I split this dataframe based on the range of Col A values as in the following (0<=A<1, 1<=A<2, etc.)?:
A B
0 0.0 0.130471
1 0.3 0.029251
2 0.8 0.790972
A B
3 1.0 -0.870462
4 1.5 -0.700132
A B
5 2.3 -0.361464
6 2.3 -1.100923
7 2.9 -1.003341
I know that np.array could be used if this were to be split based on equal number of rows:
np.array_split(df, 3)
but does something similar exist that allows me to apply my condition here?
Try with groupby and floored division:
for k, d in df.groupby(df['A']//1):
print(d)
Output:
A B
0 0.0 0.130471
1 0.3 0.029251
2 0.8 0.790972
A B
3 1.0 -0.870462
4 1.5 -0.700132
A B
5 2.3 -0.361464
6 2.3 -1.100923
7 2.9 -1.003341
I have an array of doubles img which I use to multiple with a mask mask.*img where the mask will have values of 1 in the middle but go linearly to 0 at the borders e.g. for a 5x5 mask it would be something like
0.1 0.1 0.1 0.1 0.1
0 0.5 0.5 0.5 0.1
0.1 0.5 1 0.5 0.1
0.1 0.5 0.5 0.5 0.1
0.1 0.1 0.1 0.1 0.1
My idea for this currently is to create the center using x = ones(M)
and then create a sequence of decreasing values y = [0.9 0.5 0.3 0.1]
and then do
for k = 1: size(y)
x = padarray(x,[1 1], y(k))
which will add the values of y as a perimeter around x multiple times, one at a time. Is there a more clever way to create this kind of mask that tapers off at the perimeter?
An interesting way to do something similar might be. Where vector Taper is the same as the centre row of the 5 by 5 matrix. The rows are generated by comparing the corresponding element in the transpose with the vector Taper which is Taper.'.
Broken down into steps:
Row 1: min([0.1 0.5 1 0.5 0.1],[0.1]); → [0.1 0.1 0.1 0.1 0.1]
Row 2: min([0.1 0.5 1 0.5 0.1],[0.5]); → [0.1 0.5 0.5 0.5 0.1]
Row 3: min([0.1 0.5 1 0.5 0.1],[1]); → [0.1 0.5 1 0.5 0.1]
Row 4: min([0.1 0.5 1 0.5 0.1],[0.5]); → [0.1 0.5 0.5 0.5 0.1]
Row 5: min([0.1 0.5 1 0.5 0.1],[0.1]); → [0.1 0.1 0.1 0.1 0.1]
Taper = [0.1 0.5 1 0.5 0.1];
Result = min(Taper, Taper.');
Result
MATLAB software
i=[0 1.264241 1.729329 1.900426 1.963369 1.986524 1.995042 1.998176 1.999329 1.999753 1.999909];
t=[0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2];
How can I call the value of i if the user input is from the t array as the position for both array is the same?
For example, if I call the value 0.2 the program will call the value 1.264341 from array i.
You can use input to get the user to enter a number, and ismembertol to find the number's index in t. Once you have the index, you can get the corresponding value in i. You could even throw an error if the number entered is not found in t. Here's an example:
i=[0 1.264241 1.729329 1.900426 1.963369 1.986524 1.995042 1.998176 1.999329 1.999753 1.999909];
t=[0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2];
x = input('Enter number:\n');
[~,ind] = ismembertol(x,t);
if ind > 0
fprintf('Corresponding number in i is %g\n', i(ind))
else
error('Number not found in i')
end
I have a matrix A in Matlab of dimension hxk where element ik reports an index from {1,2,...,s<=h}. The indices can be repeated across rows. I want to obtain B of dimension sx(k-1) where element j is the sum of the rows of A(:,1:k-1) with index j. For example if
A = [0.4 5 6 0.3 1;
0.6 -0.7 3 2 2;
0.3 4.5 6 8.9 1;
0.9 0.8 0.7 3 3;
0.7 0.8 0.9 0.5 2]
the result shoud be
B = [0.7 9.5 12 9.2;
1.3 0.1 3.9 2.5;
0.9 0.8 0.7 3]
You'd need a multi-column version of accumarray. Failing that, you can use sparse as follows:
[m n] = size(A);
rows = ceil(1/(n-1):1/(n-1):m);
cols = repmat(1:n-1,1,m);
B = full(sparse(A(rows,end), cols, A(:,1:end-1).'));
cell2mat(arrayfun(#(x) sum(A(A(:,end)==x,1:end-1),1), unique(A(:,end)), 'UniformOutput', false))
The key point is selecting rows A(A(:,end)==x,1:end-1) where x is a unique element of A(:,end)
I am trying to make an array Z that have indexes of the most frequent occurring difference between two elements in the array X. So if the most frequent occurring difference between two elements in X is 3 then I would get all the indexes in X that have that difference into array Z.
x = [ 0.2 0.4 0.6 0.4 0.1 0.2 0.2 0.3 0.4 0.3 0.6];
ct = 0;
difference_x = diff(x);
unique_x = unique(difference_x);
for i = 1:length(unique_x)
for j = 1:length(x)
space_between_elements = abs(x(i)-x(i+1));
if space_between_elements == difference_x
ct = ct + 1;
space_set(i,ct) = j;
end
end
end
I Don´t get the indexes of X containing the most frequent difference from this code.
It appears you want to find how many unique differences there are, with "difference" interpreted in an absoulte-value sense; and also find how many times each difference occurs.
You can do that as follows:
x = [ 0.2 0.4 0.6 0.4 0.1 0.2 0.2 0.3 0.4 0.3 0.6]; %// data
difference_x = abs(diff(x));
unique_x = unique(difference_x); %// all differences
counts = histc(difference_x, unique_x); %// count for each difference
However, comparing reals for uniqueness (or equality) is problematic because of finite precision. You should rather apply a tolerance to declare two values as "equal":
x = [ 0.2 0.4 0.6 0.4 0.1 0.2 0.2 0.3 0.4 0.3 0.6]; %// data
tol = 1e-6; %// tolerance
difference_x = abs(diff(x));
difference_x = round(difference_x/tol)*tol; %// apply tolerance
unique_x = unique(difference_x); %// all differences
counts = histc(difference_x, unique_x); %// count for each difference
With your example x, the second approach gives
>> unique_x
unique_x =
0 0.1000 0.2000 0.3000
>> counts
counts =
1 4 3 2