Catching exception in numpy array element or row
I am performing a row-wise operation on a numpy array. How can I catch possible exceptions so that I can still obtain the other valid rows?
As an example, I'll take the sum of the array's row and divide it through the first or second element in the row. If I catch the created ZeroDivisionError and try to replace it by the number zero, the whole array is replaced. I would like only the excepting element to be replaced.
The actual function is different and the actual error is an OverflowError, this is just for illustrative purposes.
import numpy as np
c = np.array([[1,2,3],[0, 4, 5]])
def catch(func, handle=lambda e : e, *args, **kwargs):
with np.errstate(divide='raise'):
try:
return func(*args, **kwargs)
except Exception as e:
return 0
#divide by second column
def div_arr1(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,1] )
return ans
#divide by first column, create ZeroDivisionError
def div_arr0(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,0] )
return ans
foo = div_arr1(c)
bar = div_arr0(c) #ZeroDivisionError
print('Divide by second column:' , foo)
print('Divide by first column:' , bar, ' , expected: [6. 0]')
#output
#Divide by second column: [3. 2.25]
#Divide by first column: 0 , expected: [6. 0]
python numpy error-handling
asked Nov 12 at 13:06
Cliff
1257
add a comment |
I am performing a row-wise operation on a numpy array. How can I catch possible exceptions so that I can still obtain the other valid rows?
As an example, I'll take the sum of the array's row and divide it through the first or second element in the row. If I catch the created ZeroDivisionError and try to replace it by the number zero, the whole array is replaced. I would like only the excepting element to be replaced.
The actual function is different and the actual error is an OverflowError, this is just for illustrative purposes.
import numpy as np
c = np.array([[1,2,3],[0, 4, 5]])
def catch(func, handle=lambda e : e, *args, **kwargs):
with np.errstate(divide='raise'):
try:
return func(*args, **kwargs)
except Exception as e:
return 0
#divide by second column
def div_arr1(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,1] )
return ans
#divide by first column, create ZeroDivisionError
def div_arr0(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,0] )
return ans
foo = div_arr1(c)
bar = div_arr0(c) #ZeroDivisionError
print('Divide by second column:' , foo)
print('Divide by first column:' , bar, ' , expected: [6. 0]')
#output
#Divide by second column: [3. 2.25]
#Divide by first column: 0 , expected: [6. 0]
python numpy error-handling
asked Nov 12 at 13:06
Cliff
1257
add a comment |
I am performing a row-wise operation on a numpy array. How can I catch possible exceptions so that I can still obtain the other valid rows?
As an example, I'll take the sum of the array's row and divide it through the first or second element in the row. If I catch the created ZeroDivisionError and try to replace it by the number zero, the whole array is replaced. I would like only the excepting element to be replaced.
The actual function is different and the actual error is an OverflowError, this is just for illustrative purposes.
import numpy as np
c = np.array([[1,2,3],[0, 4, 5]])
def catch(func, handle=lambda e : e, *args, **kwargs):
with np.errstate(divide='raise'):
try:
return func(*args, **kwargs)
except Exception as e:
return 0
#divide by second column
def div_arr1(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,1] )
return ans
#divide by first column, create ZeroDivisionError
def div_arr0(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,0] )
return ans
foo = div_arr1(c)
bar = div_arr0(c) #ZeroDivisionError
print('Divide by second column:' , foo)
print('Divide by first column:' , bar, ' , expected: [6. 0]')
#output
#Divide by second column: [3. 2.25]
#Divide by first column: 0 , expected: [6. 0]
python numpy error-handling
asked Nov 12 at 13:06
Cliff
1257
I am performing a row-wise operation on a numpy array. How can I catch possible exceptions so that I can still obtain the other valid rows?
As an example, I'll take the sum of the array's row and divide it through the first or second element in the row. If I catch the created ZeroDivisionError and try to replace it by the number zero, the whole array is replaced. I would like only the excepting element to be replaced.
The actual function is different and the actual error is an OverflowError, this is just for illustrative purposes.
import numpy as np
c = np.array([[1,2,3],[0, 4, 5]])
def catch(func, handle=lambda e : e, *args, **kwargs):
with np.errstate(divide='raise'):
try:
return func(*args, **kwargs)
except Exception as e:
return 0
#divide by second column
def div_arr1(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,1] )
return ans
#divide by first column, create ZeroDivisionError
def div_arr0(x):
ans = catch(lambda : np.sum(x, axis = 1) / x[:,0] )
return ans
foo = div_arr1(c)
bar = div_arr0(c) #ZeroDivisionError
print('Divide by second column:' , foo)
print('Divide by first column:' , bar, ' , expected: [6. 0]')
#output
#Divide by second column: [3. 2.25]
#Divide by first column: 0 , expected: [6. 0]
python numpy error-handling
python numpy error-handling
asked Nov 12 at 13:06
Cliff
1257
asked Nov 12 at 13:06
Cliff
1257
edited Nov 12 at 13:18
asked Nov 12 at 13:06
Cliff
1257
asked Nov 12 at 13:06
Cliff
1257
asked Nov 12 at 13:06
Cliff
1257
1257
add a comment |
add a comment |
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