issue_comments: 906634816
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/pull/5571#issuecomment-906634816 | https://api.github.com/repos/pydata/xarray/issues/5571 | 906634816 | IC_kwDOAMm_X842CiZA | 14371165 | 2021-08-26T18:16:51Z | 2021-08-27T16:56:35Z | MEMBER | Seems to be a problem in numpy I wonder if the docstring can be ignored if it is copied from somewhere else? I think an easy workaround is simply replacing the docstring: https://github.com/pydata/xarray/blob/4fd81b51101aceaad08570f1368ad4b50a946da5/xarray/core/duck_array_ops.py#L75
```python
around = _dask_or_eager_func("around")
# np.around has failing doctests, overwrite it so they pass:
around.__doc__ = """
Evenly round to the given number of decimals.
Parameters
----------
a : array_like
Input data.
decimals : int, optional
Number of decimal places to round to (default: 0). If
decimals is negative, it specifies the number of positions to
the left of the decimal point.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary. See :ref:`ufuncs-output-type` for more
details.
Returns
-------
rounded_array : ndarray
An array of the same type as `a`, containing the rounded values.
Unless `out` was specified, a new array is created. A reference to
the result is returned.
The real and imaginary parts of complex numbers are rounded
separately. The result of rounding a float is a float.
See Also
--------
ndarray.round : equivalent method
ceil, fix, floor, rint, trunc
Notes
-----
For values exactly halfway between rounded decimal values, NumPy
rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
-0.5 and 0.5 round to 0.0, etc.
``np.around`` uses a fast but sometimes inexact algorithm to round
floating-point datatypes. For positive `decimals` it is equivalent to
``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has
error due to the inexact representation of decimal fractions in the IEEE
floating point standard [1]_ and errors introduced when scaling by powers
of ten. For instance, note the extra "1" in the following:
>>> np.round(56294995342131.5, 3)
56294995342131.51
If your goal is to print such values with a fixed number of decimals, it is
preferable to use numpy's float printing routines to limit the number of
printed decimals:
>>> np.format_float_positional(56294995342131.5, precision=3)
'56294995342131.5'
The float printing routines use an accurate but much more computationally
demanding algorithm to compute the number of digits after the decimal
point.
Alternatively, Python's builtin `round` function uses a more accurate
but slower algorithm for 64-bit floating point values:
>>> round(56294995342131.5, 3)
56294995342131.5
>>> np.round(16.055, 2), round(16.055, 2) # equals 16.0549999999999997
(16.06, 16.05)
References
----------
.. [1] "Lecture Notes on the Status of IEEE 754", William Kahan,
https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
.. [2] "How Futile are Mindless Assessments of
Roundoff in Floating-Point Computation?", William Kahan,
https://people.eecs.berkeley.edu/~wkahan/Mindless.pdf
Examples
--------
>>> np.around([0.37, 1.64])
array([0., 2.])
>>> np.around([0.37, 1.64], decimals=1)
array([0.4, 1.6])
>>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
array([0., 2., 2., 4., 4.])
>>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
array([ 1, 2, 3, 11])
>>> np.around([1,2,3,11], decimals=-1)
array([ 0, 0, 0, 10])
"""
```
|
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