The problem is not to render them. You could switch to canvas or webgl for the rendering part. You can find some examples of using canvas and X3DOM with D3 data-binding. But it will be slow because of the number of DOM objects, so it’s better to keep them separated, as in this parallel coordinates … Read more
Solution: header = True for chunk in chunks: chunk.to_csv(os.path.join(folder, new_folder, “new_file_” + filename), header=header, cols=[[‘TIME’,’STUFF’]], mode=”a”) header = False Notes: The mode=”a” tells pandas to append. We only write a column header on the first chunk.
Performance depends wether or not the data fits in RAM. If it does, then you should use NSData writeToURL with the atomically feature turned on, which is what you’re doing. Apple’s notes about this being dangerous when “writing to a public directory” are completely irrelevant on iOS because there are no public directories. That section … Read more
A red/black tree is more or less equivalent to a 2-3-4 tree, which is just a type of B-tree. The worst-case performance is identical, provided you do a binary search of the B-tree node values. The obvious disadvantage of a B-tree is wasted space, but depending on the language/memory allocator used, you may find that … Read more
You can try Sublime. Its quite good in handling large files:
The default options for Parallel.ForEach only work well when the task is CPU-bound and scales linearly. When the task is CPU-bound, everything works perfectly. If you have a quad-core and no other processes running, then Parallel.ForEach uses all four processors. If you have a quad-core and some other process on your computer is using one … Read more
Have you tried using the write function? line=”blah text blah blah etc etc” write(line,file=”myfile.txt”,append=TRUE)