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Is anyone aware of a pure python implementation of BLAST alignment? I am trying to study this algorithm...
In fact a complete implementation of the BLAST algorithm is a quite hard. It has a lot of steps and optimizations. What could you do is: take a look of the BLAST Book from O'Reilly, for a very good explanation, take a look of the NCBI Blast code base, that it is big and hard to understand at the first glace, or, I sugest you to take a look at other BLAST implementation or may be, others algorithms like BLAT and Genoogle (http://genoogle.pih.bio.br/)
Try looking into BioPython:
http://biopython.org/
http://github.com/JoaoRodrigues/biopython/tree/GSOC2010
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Is there any Hierarchical Agglomerative Clustering implementation (in Python) available that preserves the order of data points? For example, I want the output something like this.
(((seg1, seg2), (seg3, seg4)), seg5)
but not like this
(((seg1, seg5), (seg2, seg3)), seg4)
E.g., Actual output with existing implementation
Expected output (any implementation?)
Vijaya, from what I know, there is only one public library that does order preserving hierarchical clustering (ophac), but that will only return a trivial hierarchy if your data is totally ordered (which is the case with the sections of a book).
There is a theory that may offer a theoretical reply to your answer, but no industry-strength algorithms currently exist: https://arxiv.org/abs/2109.04266. I have an implementation of this theory that can deal with up to 20 elements, so if this is interesting, give me a hint, and I will share the code.
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I'm trying to get some approximation ratios for the Maximum Independent Set Problem and so I need some exact solutions !
I've found libraries written in C++ (i.e https://github.com/iPapatsoris/Maximum-Independent-Set)
but wondered if there were any directly in Python. I know of the `networkx' maximal indepedent set function but these are only approximations.
I realise it's far from the most efficient language to use but I'm only solving small Erdős–Rényi graphs (N<20).
In addition to this, are there any libraries that solve this for the weighted problem, where some nodes matter more than others?
This is the only python library I could find:
https://github.com/pchervi/Graph-Coloring/blob/master/Coloring_MWIS_heuristics.py
I haven't checked that it works correctly however.
I've been using KaMIS instead, which is a C++ implementation.
https://github.com/KarlsruheMIS/KaMIS
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I note that Matlab has a straightforward function for getting the entropy of an image. I need something similar for python. Scikit image has an entropy filter, which outputs the image using the least amount of bits needed to do so (at least, I think it does). I assume that to do this it calculates the entropy, but I can't seem to access it as a scalar value.
Before I code a function to do this manually, does anyone know if already exists and I'm somehow missing it? Or for that matter, some existing code that they'd recommend?
If you don't mind shelling out to ImageMagick you can do it like this:
convert someImage.png -format '%[entropy]' info:
0.907238
Not sure how you do it with the ImageMagick Python bindings, but it is probably possible.
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I have been using the Simple Blob Detection algorithm from the OpenCV library (for Python) for a research project. I would like to reference this particular method algorithm in my paper.
Does anyone know from where this method is from and indicate me a good to reference to cite? The openCV source code does not refer to any particular literature.
Thanks
It uses the Connected-component labeling algorithm.
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I am looking for an implementation of Continuous Wavelet Transform for Python that includes Haar Wavelet.
I would like to reproduce the experiment given by MathWorks for Matlab, at this link.
I tried with Pyscellania but I obtain completely different coefficients.
Is there a Python implementation of the CWT out there that includes the Haar Wavalet apart from Pyscellania?
Your request is clear.
Have you tried Pyscellania's normalised or standard Haar Wavelet?
Maybe you are just using the wrong one.