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Overflow error with maxdim>0 #120
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This is concerning. Thanks for notifying us. I'll try to take a look within the next week, but am totally swamped with work, so no guarantees. In the meantime if you're able to find the bug and fix, a PR would be immensely appreciated. |
I second what Nathaniel is saying, though I think this may just be an issue
beyond the scope of ripser.py. The lower star image filtrations were not
designed to support homology above 0D. For homology above that, it may
just be blowing up the complex because of how many simplices there are in
an image, particularly one in 3D.
…On Wed, Apr 28, 2021 at 11:55 AM Nathaniel Saul ***@***.***> wrote:
This is concerning. Thanks for notifying us. I'll try to take a look
within the next week, but am totally swamped with work, so no guarantees.
In the meantime if you're able to find the bug and fix, a PR would be
immensely appreciated.
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Thank you for answering me about this issue. For reference, here is the Python script I used to trigger this overflow (the import numpy as np
import PIL.Image
from ripser import ripser
from scipy import sparse
def lower_star_img(img):
m, n = img.shape
idxs = np.arange(m * n).reshape((m, n))
I = idxs.flatten()
J = idxs.flatten()
V = img.flatten()
# Connect 8 spatial neighbors
tidxs = np.ones((m + 2, n + 2), dtype=np.int64) * np.nan
tidxs[1:-1, 1:-1] = idxs
tD = np.ones_like(tidxs) * np.nan
tD[1:-1, 1:-1] = img
for di in [-1, 0, 1]:
for dj in [-1, 0, 1]:
if di == 0 and dj == 0:
continue
thisJ = np.roll(np.roll(tidxs, di, axis=0), dj, axis=1)
thisD = np.roll(np.roll(tD, di, axis=0), dj, axis=1)
thisD = np.maximum(thisD, tD)
# Deal with boundaries
boundary = ~np.isnan(thisD)
thisI = tidxs[boundary]
thisJ = thisJ[boundary]
thisD = thisD[boundary]
I = np.concatenate((I, thisI.flatten()))
J = np.concatenate((J, thisJ.flatten()))
V = np.concatenate((V, thisD.flatten()))
sparseDM = sparse.coo_matrix((V, (I, J)), shape=(idxs.size, idxs.size))
return ripser(sparseDM, distance_matrix=True, maxdim=1)["dgms"][0]
if __name__ == "__main__":
cells_grey = np.asarray(PIL.Image.open("Cells.jpg").convert("L"))
dgm = lower_star_img(-cells_grey)
print(dgm) I copied the In the meantime, I've looked into the C++ code and I'm trying to check if raising Line 83 in ff3f017
|
Hello and thank you for your work!
I am trying to use ripser to compute the persistence diagrams of 3D images. Looking at the Python code in ripser/ripser.py and especially at the
ripser.lower_star_img
function, I have been able to feed ripser distance matrices corresponding to my datasets. However, when I call theripser.ripser
function withmaxdim=1
or above (ideally 2), the program aborts with an overflow error.This problem also occurs using the
Cell.jpg
2D image from the Lower Star Image Filtrations tutorial. When I usemaxdim=1
when callingripser.ripser
in theripser.lower_star_img
function (or remove it since the default value is 1), the overflow exception happens here too.If I understand correctly the underlaying C++ code in ripser/ripser.cpp, this overflow exception is caused by the computation of binomial coefficients that depend on the size of the distance matrix and the
maxdim
parameter.Is this an inherent limitation of ripser? What's the right way to compute higher dimension persistence pairs?
Thank you in advance for your help,
Best regards,
Pierre
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