Numpy Divide By Zero Error
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Numpy Seterr
Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a python avoid divide by zero community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up NumPy: Return 0 with divide by zero up vote 10 down vote favorite 4 I'm numpy errstate trying to perform an element wise divide in python, but if a zero is encountered, I need the quotient to just be zero. For example: array1 = np.array([0, 1, 2]) array2 = np.array([0, 1, 1]) array1 / array2 # should be np.array([0, 1, 2]) I could always just use a for-loop through my data, but to really utilize numpy's optimizations, I need the divide function to return 0 upon divide by
Numpy Divide Array By Scalar
zero errors instead of ignoring the error. Unless I'm missing something, it doesn't seem numpy.seterr() can return values upon errors. Does anyone have any other suggestions on how I could get the best out of numpy while setting my own divide by zero error handling? python arrays numpy error-handling divide-by-zero share|improve this question edited Aug 20 '15 at 5:04 asked Oct 8 '14 at 3:13 hlin117 1,40922135 In my python version (Python 2.7.11 |Continuum Analytics, Inc.) that is exactly the output that you get. With a warning. –Ramon Martinez Aug 9 at 16:15 add a comment| 7 Answers 7 active oldest votes up vote 11 down vote accepted Building on @Franck Dernoncourt's answer, fixing -1 / 0: def div0( a, b ): """ ignore / 0, div0( [-1, 0, 1], 0 ) -> [0, 0, 0] """ with np.errstate(divide='ignore', invalid='ignore'): c = np.true_divide( a, b ) c[ ~ np.isfinite( c )] = 0 # -inf inf NaN return c div0( [-1, 0, 1], 0 ) array([0, 0, 0]) share|improve this answer answered Feb 29 at 9:34 denis 10.6k53856 Thanks, I didn't even catch that bug with @Frank Dernoncourt's code. –hlin117 Feb 29 at 17:38 Hi, I'm trying to do array math and I w
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Runtimewarning: Divide By Zero Encountered In Divide
Ignore divide by 0 warning in python up vote 2 down vote favorite I have a function for statistic issues: import numpy as np from scipy.special import gamma as Gamma def Foo(xdata): ... return x1 * ( ( #R is a http://stackoverflow.com/questions/26248654/numpy-return-0-with-divide-by-zero numpy vector ( ((R - x2)/beta) ** (x3 -1) ) * ( np.exp( - ((R - x2) / x4) ) ) / ( x4 * Gamma(x3)) ).real ) Sometimes I get from the shell the following warning: RuntimeWarning: divide by zero encountered in... I use the numpy isinf function to correct the results of the function in other files where I need to do. so I do not need to be warning. There is a way to ignore the message? In other words, I http://stackoverflow.com/questions/29950557/ignore-divide-by-0-warning-in-python do not want that the shell print this message. I do not want to disable all the python warning, just this one. python numpy suppress-warnings divide-by-zero share|improve this question edited May 1 '15 at 1:31 asked Apr 29 '15 at 17:27 overcomer 12616 You could just catch it and ignore it if you don't need to recover from it. –Carcigenicate Apr 29 '15 at 17:30 you can always use try... except ZeroDivisionError (or whatever error is being thrown) –letsc Apr 29 '15 at 17:30 2 possible duplicate of How to disable python warnings –marsh Apr 29 '15 at 17:34 @letsc no error is being thrown. the OP is getting a warning. –dbliss Apr 29 '15 at 17:35 The problem is that I correct the value, after, where I need. I don't want to catch in every execution. –overcomer Apr 29 '15 at 17:37 | show 1 more comment 2 Answers 2 active oldest votes up vote 7 down vote accepted You can disable the warning with numpy.seterr. Put this before the possible division by zero: np.seterr(divide='ignore') That'll disable zero division warnings globally. If you just want to disable them for a little bit, you can use numpy.errstate in a with clause: with np.errstate(divide='ignore'): # some code here share|improve this answer answered Apr 29 '15 at 17:38 dddsnn 43625 add a comment| up vote 2 down vote You could also use numpy.divide for division. That way you don't have to explicitly disabl
instance of errstate as a context manager allows statements in that context to execute with a known error handling behavior. Upon https://docs.scipy.org/doc/numpy/reference/generated/numpy.errstate.html entering the context the error handling is set with seterr and seterrcall, and upon exiting it is reset to what it was before. Parameters:kwargs : {divide, over, under, invalid} Keyword arguments. The valid keywords are the possible floating-point exceptions. Each keyword should have a string value that by zero defines the treatment for the particular error. Possible values are {‘ignore', ‘warn', ‘raise', ‘call', ‘print', ‘log'}. See also seterr, geterr, seterrcall, geterrcall Notes The with statement was introduced in Python 2.5, and can only be used there by importing it: from __future__ import with_statement. In earlier Python divide by zero versions the with statement is not available. For complete documentation of the types of floating-point exceptions and treatment options, see seterr. Examples >>> from __future__ import with_statement # use 'with' in Python 2.5 >>> olderr = np.seterr(all='ignore') # Set error handling to known state. >>> np.arange(3) / 0. array([ NaN, Inf, Inf]) >>> with np.errstate(divide='warn'): ... np.arange(3) / 0. ... __main__:2: RuntimeWarning: divide by zero encountered in divide array([ NaN, Inf, Inf]) >>> np.sqrt(-1) nan >>> with np.errstate(invalid='raise'): ... np.sqrt(-1) Traceback (most recent call last): File "