Cutoff | source code for the Cutoff data augmentation approach | Natural Language Processing library

Here's a way in dplyr:

If you have two images that are similar (or the same) and you want to align them, you can do it using both functions rotate and shift :

You can use lapply to move through the "Outer" lists, and Map to compare each inner list to the corresponding cutoff:

As per Santiago Squarzon request this was his answer. Just adding it here since it was the answer to my question.

"Seems like the text doesn't fit in the size of your label, have you tried tweaking."

$UserInfoLabel.Size = New-Object System.Drawing.Size(280,20)

Q : " Writing to a file parallely while processing in a loop in python ... "

A :
Frankly speaking, the file-I/O is not your performance-related enemy.

_{"With all due respect to the colleagues, Python (since ever) used GIL-lock to avoid any level of concurrent execution ( actually re-SERIAL-ising the code-execution flow into dancing among any amount of threads, lending about 100 [ms] of code-interpretation time to one-AFTER-another-AFTER-another, thus only increasing the interpreter's overhead times ( and devastating all pre-fetches into CPU-core caches on each turn ... paying the full mem-I/O costs on each next re-fetch(es) ). So threading is ANTI-pattern in python (except, I may accept, for network-(long)-transport latency masking ) – user3666197 44 mins ago "}

Given about the 65k files, listed in CSV, ought get processed ASAP, the performance-tuned orchestration is the goal, file-I/O being just a negligible ( and by-design well latency-maskable ) part thereof _{( which does not mean, we can't screw it even more ( if trying to organise it in another performance-devastating ANTI-pattern ), can we? )}

Tip #1 : avoid & resist to use any low-hanging fruit SLOCs if The Performance is the goal

If the code starts with a cheapest-ever iterator-clause,
be it a mock-up for aRow in aCsvDataSET: ...
or the real-code for i in range( len( queries ) ): ... - these (besides being known for ages to be awfully slow part of the python code-interpretation capabilites, the second one being even an iterator-on-range()-iterator in Py3 and even a silent RAM-killer in Py2 ecosystem for any larger sized ranges) look nice in "structured-programming" evangelisation, as they form a syntax-compliant separation of a deeper-level part of the code, yet it does so at an awfully high costs impacts due to repetitively paid overhead-costs accumulation. A finally injected need to "coordinate" unordered concurrent file-I/O operations, not necessary in principle at all, if done smart, are one such example of adverse performance impacts if such a trivial SLOC's ( and similarly poor design decisions' ) are being used.

Better way?

• a ) avoid the top-level (slow & overhead-expensive) looping
• b ) "split" the 65k-parameter space into not much more blocks than how many memory-I/O-channels are present on your physical device ( the scoring process, I can guess from the posted text, is memory-I/O intensive, as some model has to go through all the texts for scoring to happen )
• c ) spawn n_jobs-many process workers, that will joblib.Parallel( n_jobs = ... )( delayed( <_scoring_fun_> )( block_start, block_end, ...<_params_>... ) ) and run the scoring_fun(...) for such distributed block-part of the 65k-long parameter space.
• d ) having computed the scores and related outputs, each worker-process can and shall file-I/O its own results in its private, exclusively owned, conflicts-prevented output file
• e ) having finished all partial block-parts' processing, the main-Python process can just join the already ( just-[CONCURRENTLY] created, smoothly & non-blocking-ly O/S-buffered / interleaved-flow, real-hardware-deposited ) stored outputs, if such a need is ...,
  and
  finito - we are done ( knowing there is no faster way to compute the same block-of-tasks, that are principally embarrasingly independent, besides the need to orchestrate them collision-free with minimised-add-on-costs).

If interested in tweaking a real-system End-to-End processing-performance,
start with lstopo-map
next verify the number of physical memory-I/O-channels
and
may a bit experiment with Python joblib.Parallel()-process instantiation, under-subscribing or over-subscribing the n_jobs a bit lower or a bit above the number of physical memory-I/O-channels. If the actual processing has some, hidden to us, maskable latencies, there might be chances to spawn more n_jobs-workers, until the End-to-End processing performance keeps steadily growing, until a system-noise hides any such further performance-tweaking effects

A Bonus part - why un-managed sources of latency kill The Performance

The problem seems to be the × in the intermediate .tex file.

You can avoid the problem by choosing an unicode aware engine, like lualatex or xelatex, to compile your document:

I see that the comments of @Cris Luengo have already developed your solution into the right direction. The last thing you're missing now is that the spectrum you obtain from np.fft.fft is composed of the positive frequency components in the first half and the 'mirrored' negative frequency components in the second half.

If you now set all components beyond your bandlimit_index to zero, you're erradicating these negative frequency components. That explains the drop in signal amplitude of 6dB, you're eliminating half the signal power (+ as you already noticed every real signal has to have conjugate symmetric frequency spectrum). The np.fft.ifft function documentation (ifft documentation) explains the expected format quite nicely. It states:

"The input should be ordered in the same way as is returned by fft, i.e.,"

• a[0] should contain the zero frequency term,
• a[1:n//2] should contain the positive-frequency terms,
• a[n//2 + 1:] should contain the negative-frequency terms, in increasing order starting from the most negative frequency.

That's essentially the symmetry you have to preserve. So in order to preserve these components just set the components between bandlimit_index + 1 -> (len(fsig) - bandlimit_index) to zero.

get_multimir is used to retrieve predicted and validated miRNA-target interactions and their disease and drug associations from the multiMiR package.

It returns an object of class mmquery_bioc

The error:

Error in as.data.frame.default(x[[i]], optional = TRUE) : cannot coerce class ‘structure("mmquery_bioc", package = "multiMiR")’ to a data.frame

is informing you that R does not know how to convert this object into a data frame.

It is not completely clear what you are trying to achieve but if I was to take a guess, perhaps you just need the data slot from this object:

You could do something like this.