I want to iterate over all row nodes but in the current sorting order.
In the API (https://www.ag-grid.com/javascript-grid/accessing-data/#accessing-row-node-api-methods) there are methods for iterating over all rows (forEachNode()) and over all filtered rows in sorted order (forEachNodeAfterFilterAndSort())
Basically what I would need is a forEachNodeAfterSort() :)
Related
I have a table (L-arrival) of 279 rows and 252 columns. Only the first column has values while others are just NaN. The cells in the first column have multiple values (i.e. some have 1, some have 4 number of values). First of all, I am trying to select a single maximum value from each cell of the first column so that I can have a column of a single value for each cell only. Then I want to do this in a loop so that for every new value that I get, they are sorted and only the maximum values are chosen. Finally, I want to make a collection of these values obtained from multiple runs for each cell. Can anyone suggest to me how it can be approached in MatLab?I tried using the following code but didn't work well.
for b=1:279
m = numel(cell2mat(L_arrival(b,1)));
g(b)=mat2cell([cell2mat(g(b)); cell(L_arrival(b,1))]',[1 2]);
end
I have an array with many rows and two columns. I wish to find the first row for which both entries are zero in the array. I have read this post but am unsure how to adapt the find command for a 2 column array.
Easiest way is to show you through excel:
Unsorted:
Sorted:
This example is with excel, but I would need to do the same thing in matlab with thousands of entries (with 2 rows if possible).
Here is my code so far:
%At are random numbers between 0 and 2, 6000 entries.
[sorted]=sort(At);
max=sorted(end);
min=sorted(1);
%need the position of the min and max
But this is only 1 row that's being sorted and it has no numbers in the second row, and no index. How would I add one and keep it following my first row?
Thank you!
I don't have access to Matlab, but try
[sorted, I] = sort(At);
Where I will be a corresponding vector of indices of At. See the Matlab Documentation for details.
You have a couple of options here. For the simple case where you just need the indices, the fourth form of sort listed in the docs already does this for you:
[sorted, indices] = sort(At);
In this case, At(indices) is the same as sorted.
If your "indices" are actually another distinct array, you can use sortrows:
toSort = [At(:) some_other_array(:)];
sorted = sortrows(toSort);
In this case sorted(:, 1) will be the sorted array from the first example and sorted(:, 2) will be the other array sorted according to At.
sortrows accepts a second parameter which tells you the column to sort by. This can be a single column or a list of columns, like in Excel. It can also provide a second output argument, the indices, just like regular sort.
I am new to Spark Scala. I am having a situation where I have to compare values of a particular column in a data set, for example:
Source Data
Source Destination Distance
Austin Houston 200
Dallas Houston 400
Kansas Dallas 700
Resultant
Source1 Destination1 Distance1 Source2 Destination2 Distance2 DistDiff
Dallas Houston 400 Kansas Dallas 700 300
As per the situation, I have to compare the distance of subsequent rows and if the difference is more than or equals to 300 then save the records in the Resultant data set
700 - 400 = 300
The examples which I have encountered are having functions which are executing on per row basis on any particular data set, however my scenario is to play with consecutive rows.
You mentioned you can sort rows by datetime. So, assuming it's sorted using sortBy or sortByKey to create an ordered rdd, and also assuming you have an even number of rows (so each row has another one to calculate difference with) you can:
Give each row an index using zipWithIndex.
Split the RDD into two RDDs, one with even-numbered indices and one with odd-numbered indices, by filtering on the index created.
zip the split RDDs together, creating a new RDD of Tuple2 with even-indexed rows on the left and odd-indexed rows on the right.
map the result to calculate the difference between left/right of each row.
This may be done in the following way:
Add an index column to your sorted rdd
Make sure the rdd has an even number of rows N
Make an rdd rdd_even1 to contain the even rows with indices [0, N-2]
Make an rdd rdd_odd1 to contain the odd rows [1, N-1]
Make an rdd rdd_even2 to contain the even rows [2, N-2]
Make an rdd rdd_odd2 to contain the odd rows [1, N-3]
Now you need to repartition rdd_even1 and *rdd_odd1 before zipping, because zipping won't work if both rdd's do not have the same number of elements in all partitions (in pyspark at least). You can do it in memory using collect and parallelize, but most likely you have to write the rdd's to HDFS and re-read them, controlling for the paritioning
Do the same for rdd_even2 and rdd_odd2
zip the rdd's from step 7 to rdd_zip1
zip the rdd's from step 8 to rdd_zip2
Call rdd_zip1.union(rdd_zip2)
Now you can call map() on the union to get your "resultant" with the required differences
Good luck.
I am using the Cobra Toolbox in MATLAB to perform a double gene knockout study and output for growth ratios is a 100 by 100 matrix called grRatioDble. I need to find the row and column index for elements of this matrix which are <0.001, excluding the rows which were essential on single gene knockout. I have a one-column matrix of the row indexes that I want to exclude. Is there an easy way to do this?
(NB: I cannot just remove the unwanted rows from the matrix as then row, column index changes for the remaining cells)
This piece of code should do the work:
Get all row/col indexes where grRatioDble<0.001:
[row,col] = find(grRatioDble<0.001);
Exclude unwanted rows (say the vector containing unwanted rows is rows2exclude):
row = row(~ismember(row, rows2exclude));
col = col(~ismember(row, rows2exclude));