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0

Take a look at this library : https://physt.readthedocs.io/ I guess what you need is here : https://physt.readthedocs.io/en/latest/2d_histograms.html


0

I tried implementing the same approach and got the following solution: Here is my code after pip install scikit-fem==4.0.0: import numpy as np from scipy.sparse import bmat, csr_matrix from skfem import * from skfem.models import laplace from skfem.visuals.matplotlib import plot m = MeshLine().refined(5) basis = Basis(m, ElementLineP1()) A = laplace....


3

To get the filaments drawn in such contrast, a (inverse?) distance estimator is used. This is based on the derivative $dz_n/dc$, see, e.g., http://mrob.com/pub/muency/distanceestimator.html for details and the links there for application ideas. That is, instead of only iterating $z_{n+1}=z_n^2+c$, you now iterate dz = 2*z*dz + 1 z = z*z + c and return in ...


1

Also possibly of interest (as of Sep-21) is the software package Drake and its python bindings pydrake, which give interfaces to a pretty wide range of solvers.


5

You can examine the sol object to see why the integration failed. It provides the message 'Required step size is less than spacing between numbers.' This usually indicates an implementation error in the right-hand side function or a singularity in the ODE. Your ODE is simple enough to find the exact solution. We can consider the scalar case because each ...


1

As suggested in Kindlmann the curvature of a surface is defined by the relationship between positional changes in the neighborhood of a point placed on the surface and the change in the surface normal. Given a level-set $\Phi(\mathbf{x})$, we consider that the value of the level-set is positive inside the object, negative outside. Hence, we define the ...


5

The discrete Fourier transform for a signal of period $T$ with $N$ samples reads in its inverse or reconstruction form as $$ y(t)=\frac1{N}\sum_{k=-N/2}^{N/2}c_k e^{i2\pi k\frac{t}{T}} $$ with redundancy in $c_N$ and $c_{-N}$ if $N$ is even. Sampling this at points $t_m=\frac{mT}{N}$ gives a completely determined linear system whose solution is given by the ...


-2

Use a deconvolutional neural network. There are ways to build a deconvolutional neural network that takes a source image, and then uses the neural network to build a higher-resolution version of that image. This is basically the reverse of using a convolutional neural network to simplify images into their low-resolution features. Depending on what your data ...


1

To repeat the answers you got at your cross-post https://stackoverflow.com/questions/69292456/logistic-growth-curve-using-scipy-is-not-quite-right The logistic curve is rather rigid in its symmetry. Forcing the initial value to be some prescribed constant effectively removes a degree of freedom in the family of curves that are available to the fitting ...


4

Since you are on a uniform $x-y-z$ grid, you are in luck that others have had similar issues before. Specifically, I would suggest that you take a look at HDF5 for a low-level way of storing this kind of data as arrays. But in the end, you want to not just store an array, but actually do something with it: interpolate. For this, a higher-level approach would ...


7

Shoutout to Kyle Mandli and Endulum who each contributed to this answer in the comments. First, I took Endulum's suggestion and removed the redundant reshapes. After this change the Fortran version was beating Python on small scale examples, but at scale the Python version was still faster. Then I implemented Kyle Mandli's suggestion and replaced all of the ...


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