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I am trying to find a good 3D geometry library for Python that has similar operations and functionality to Shapely.
http://toblerity.org/shapely/manual.html
Shapely is great, and has exactly what I need, especially around the creation and manipulation of Linestring objects. Unfortunately, it only supports operations on 2D objects, even though 3D points can be created.
Does anybody know of any a similar module that operates in full 3D? It would be greatly appreciated. Thanks.
Have a look at Pymesh:
http://pymesh.readthedocs.io/en/latest/
Its a new CSG wrapper for basic 3d geometry applications.
Is that the type of thing you're looking for?
Have a look at d3g package in PyPI.
Visit https://pypi.org/project/d3g
pip install d3g
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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 am currently doing natural language processing using python NLTK. I want to generate some beautiful graphics of the representation of input. What package can I do to get something like this?
Bokeh is the go-to visualization library for Python. Have a look at its gallery to see what it can do. I actually don't know if it can generate the kind of images you've shown though.
Altair is another capable plotting library which kindly includes a few links to other libraries in its README:
Matplotlib
Bokeh
Seaborn
Lightning
Plotly
Pandas built-in plotting
HoloViews
VisPy
pygg
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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.