从https://github.com/Ecogenomics/GroopM运行groopm程序时，遇到以下错误：
Unexpected error: <type 'exceptions.FloatingPointError'>
Traceback (most recent call last):
File "/home/mathed/virtualenv/groopm_env/bin/groopm", line 381, in <module>
GM_parser.parseOptions(args)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/groopm.py", line 138, in parseOptions
gf=gf)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/cluster.py", line 217, in makeCores
self.RE.refineBins(self.timer, auto=True, saveBins=False, plotFinal=prfx, gf=gf)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/refine.py", line 158, in refineBins
graph = self.autoRefineBins(timer, makeGraph=gf!="")
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/refine.py", line 474, in autoRefineBins
self.mergeSimilarBins(graph=graph, verbose=False)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/refine.py", line 571, in mergeSimilarBins
mergers = self.findMergeGroups(kCutMedian, kCutStd, cCutMedian, cCutStd, verbose=verbose)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/refine.py", line 631, in findMergeGroups
retA=True)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/bin.py", line 352, in getBoundingKEllipseArea
(A, center, radii, _rotation) = ET.getMinVolEllipse(KPCAs, retA=True)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/ellipsoid.py", line 112, in getMinVolEllipse
(A, center, radii, rotation) = self.getMinVolEllipse(PP, retA=True)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/ellipsoid.py", line 112, in getMinVolEllipse
(A, center, radii, rotation) = self.getMinVolEllipse(PP, retA=True)
File "/home/mathed/virtualenv/groopm_env/local/lib/python2.7/site-packages/groopm/ellipsoid.py", line 153, in getMinVolEllipse
radii = 1.0/np.sqrt(s)
FloatingPointError: divide by zero encountered in divide
在151行的ellipsoid.py中，我有：
try:
U, s, rotation = linalg.svd(A)
radii = 1.0/np.sqrt(s)
except np.linalg.linalg.LinAlgError:
# hack -> better than crashing...
rotation = np.eye(3)
radii = np.ones(3)
ellipsoidtool的整个代码如下：
class EllipsoidTool:
"""Some stuff for playing with ellipsoids"""
def __init__(self): pass
def getMinVolEllipse(self, P, tolerance=0.01, retA=False):
""" Find the minimum volume ellipsoid which holds all the points
Based on work by Nima Moshtagh
http://www.mathworks.com/matlabcentral/fileexchange/9542
and also by looking at:
http://cctbx.sourceforge.net/current/python/scitbx.math.minimum_covering_ellipsoid.html
Which is based on the first reference anyway!
Here, P is a numpy array of 3D points like this:
P = [[x,y,z],
[x,y,z],
[x,y,z]]
Returns:
(center, radii, rotation)
"""
(N, d) = np.shape(P)
# Q will be out working array
Q = np.copy(P.T)
Q = np.vstack([Q, np.ones(N)])
QT = Q.T
# initializations
err = 1 + tolerance
u = np.array([1.0 / N for i in range(N)]) # first iteration
# Khachiyan Algorithm
singular = False
while err > tolerance:
V = np.dot(Q, np.dot(np.diag(u), QT))
try:
M = np.diag(np.dot(QT , np.dot(linalg.inv(V), Q))) # M the diagonal vector of an NxN matrix
except linalg.linalg.LinAlgError:
# most likely a singular matrix
# permute the values a little and then we'll try again
from random import random, randint
PP = np.copy(P)
for i in range(N):
if randint(0,3) == 0:
j = randint(0,2)
if randint(0,1) != 0:
PP[i,j] += random()
else:
PP[i,j] -= random()
(A, center, radii, rotation) = self.getMinVolEllipse(PP, retA=True)
singular = True
break
j = np.argmax(M)
maximum = M[j]
step_size = (maximum - d - 1.0) / ((d + 1.0) * (maximum - 1.0))
new_u = (1.0 - step_size) * u
new_u[j] += step_size
err = np.linalg.norm(new_u - u)
u = new_u
if not singular:
# center of the ellipse
center = np.dot(P.T, u)
# the A matrix for the ellipse
try:
A = linalg.inv(
np.dot(P.T, np.dot(np.diag(u), P)) -
np.array([[a * b for b in center] for a in center])
) / d
except linalg.linalg.LinAlgError:
# the matrix is singular so we need to return a degenerate ellipse
#print '[Notice] Degenerate ellipse constructed indicating a bin with extremely small coverage divergence.'
center = np.mean(P, axis=0)
radii = np.max(P,axis=0) - np.min(P, axis=0)
if len(P[0]) == 3:
rotation = [[0,0,0],[0,0,0],[0,0,0]]
else:
rotation = [[0,0],[0,0]]
if retA:
return (None, center, radii, rotation)
else:
return (center, radii, rotation)
# Get the values we'd like to return
try:
U, s, rotation = linalg.svd(A)
radii = 1.0/np.sqrt(s)
except np.linalg.linalg.LinAlgError:
# hack -> better than crashing...
rotation = np.eye(3)
radii = np.ones(3)
else:
# hack -> better than crashing...
rotation = np.eye(3)
radii = np.ones(3)
if retA:
return (A, center, radii, rotation)
else:
return (center, radii, rotation)
即使代码在try块中，我也不知道为什么会给我一个错误。有没有办法来解决这个问题？
谢谢。
答案 0 :(得分：1)
您的except块无法处理出现的错误。它设置为处理np.linalg.linalg.LinAlgError，但引发的错误是FloatingPointError。
更改代码以捕获任一异常：
try:
U, s, rotation = linalg.svd(A)
radii = 1.0/np.sqrt(s)
except (np.linalg.linalg.LinAlgError, FloatingPointError):
# hack -> better than crashing...
rotation = np.eye(3)
radii = np.ones(3)
如果存在except或np.linalg.linalg.LinAlgError的 ，FloatingPointError块现在将运行。