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Parameters Return value Examples Basic usage Specifying an absolute tolerance parameter Specifying a relative tolerance parameter Comparing NaNs

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NumPy | isclose method

schedule Aug 12, 2023

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Parameters Return value Examples Basic usage Specifying an absolute tolerance parameter Specifying a relative tolerance parameter Comparing NaNs

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Numpy's isclose(~) performs an element-wise comparison given two arrays, and for each comparison, returns True if the difference between the two values falls within the specified tolerance.

Parameters

1. x1 | array-like

The first input array.

2. x2 | array-like

The second input array.

3. rtol | float | optional

The relative tolerance parameter. By default, rtol=0.

4. atol | float | optional

The absolute tolerance parameter. By default, atol is set to a small number (~1e-8), and is thus not suitable when comparing numbers that are far smaller than 1.

5. equal_nan | boolean | optional

If True, then element-wise comparisons that involve two NaNs will evaluate to True. By default, equal_nan=False.

Here, the element-wise comparison evaluates to True if:


        
        
            
                
                
                    absolute(a - b) <= (atol + rtol * absolute(b))

Return value

A single boolean that indicates whether or not two arrays are "close" enough.

Examples

Basic usage


        
        
            
                
                
                    np.isclose([1,2], [3,2])
                
            
            array([False,  True])

Here, the first element-wise comparison 2 != 5, so the method returns False.

Specifying an absolute tolerance parameter


        
        
            
                
                
                    np.isclose([6,4], [8,3], atol=2)
                
            
            array([ True,  True])

Here, absolute(6,8) <= 2 and absolute(4,3) <= 2.

Specifying a relative tolerance parameter


        
        
            
                
                
                    np.isclose([6,3], [4,6], rtol=0.5)
                
            
            array([ True,  True])

Here, absolute(6,4) <= 4*0.5 and absolute(3,6) <= 6*0.5.

Comparing NaNs


        
        
            
                
                
                    np.isclose(np.NaN, np.NaN)
                
            
            False


        
        
            
                
                
                    np.isclose(np.NaN, np.NaN, equal_nan=True)
                
            
            True

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Published by Isshin Inada

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