issues

Reduce number of imports inside functions. I think it helps making the code easier to read as well, as now you know that plt is always available.

• [x] Tests added
• [x] Passes pre-commit run --all-files
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst

Seems to not be a major difference in initial imports from (my small sample of) repeated tests:

This branch: ```python %timeit -n1 -r1 import xarray

3.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.83 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.87 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.7 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.77 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.91 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.8 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

np.mean([3.81, 3.83, 3.87, 3.7, 3.77, 3.91, 3.8]) Out[3]: 3.812857142857143 ```

Main: ```python %timeit -n1 -r1 import xarray

3.93 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.69 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.64 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.76 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.79 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.68 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

np.mean([3.93, 3.69, 3.64, 3.76, 3.79, 3.81, 3.68]) Out[4]: 3.7571428571428567 ```

There's some error in the minimum environments (for example ubuntu-latest py37-bare-minimum) introduced in #5682 but was hidden in all other errors because of #5654: =========================== short test summary info ============================ ERROR xarray/tests/test_plot.py::test_latex_name_isnt_split - NameError: name... = 7908 passed, 4163 skipped, 202 xfailed, 42 xpassed, 89 warnings, 1 error in 279.31s (0:04:39) =

But when looking further in the details it mentions a completely different function crashing because matplotlib isn't installed, which is true but the function doesn't test any mpl-specific things. ``` ==================================== ERRORS ==================================== ___ ERROR at teardown of test_latex_name_isnt_split ____ [gw0] linux -- Python 3.7.10 /usr/share/miniconda/envs/xarray-tests/bin/python

@pytest.fixture(scope="function", autouse=True)
def test_all_figures_closed():
    """meta-test to ensure all figures are closed at the end of a test

    Notes:  Scope is kept to module (only invoke this function once per test
    module) else tests cannot be run in parallel (locally). Disadvantage: only
    catches one open figure per run. May still give a false positive if tests
    are run in parallel.
    """
    yield None

  open_figs = len(plt.get_fignums())

E NameError: name 'plt' is not defined

/home/runner/work/xarray/xarray/xarray/tests/test_plot.py:72: NameError ``` Might be something that pytest runs on every function. Not sure if there's a clean way of turning it off, but requiring matplotlib to be installed works of course.

• [x] Passes pre-commit run --all-files

Attempt to fix errors found in #5365. I've changed it to integers because when does it ever make sense to input 1.5 or a complex number? It will always be forced to integers anyway: https://github.com/dask/dask/blob/8aea537d925b794a94f828d35211a5da05ad9dce/dask/array/core.py#L2815

Integers don't work with Numbers: xarray/core/computation.py:1576: error: Dict entry 0 has incompatible type "Hashable": "int"; expected "Hashable": "Union[None, Number, str, Tuple[Number, ...]]" [dict-item] xarray/core/computation.py:1576: error: Dict entry 0 has incompatible type "Hashable": "int"; expected "Hashable": "Union[None, Number, Tuple[Number, ...]]" [dict-item] xarray/core/computation.py:1536: error: Dict entry 0 has incompatible type "Hashable": "int"; expected "Hashable": "Union[None, Number, str, Tuple[Number, ...]]" [dict-item]

Floats don't work with Numbers: xarray/core/computation.py:1536: error: Dict entry 0 has incompatible type "Hashable": "int"; expected "Hashable": "Union[None, Number, str, Tuple[Number, ...]]" [dict-item] xarray/core/computation.py:1578: error: Dict entry 0 has incompatible type "Hashable": "float"; expected "Hashable": "Union[None, Number, str, Tuple[Number, ...]]" [dict-item] xarray/core/computation.py:1578: error: Dict entry 0 has incompatible type "Hashable": "float"; expected "Hashable": "Union[None, Number, Tuple[Number, ...]]" [dict-item] This workaround seems related to: https://github.com/python/mypy/issues/3186

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] Passes pre-commit run --all-files
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

Is your feature request related to a problem? Please describe. When creating docs with examples it would be nice if you could simply use print(ds) without being concerned about line lengths.

Describe the solution you'd like Limit line length so that a method docstring in a class can use print(ds) without breaking PEP8 conventions. So maximum line length for the ds.__repr__/ds.__str__ would be 79 - 4 - 4 = 71.

Example code Example where print(ds) creates lines longer than 79: ```python import numpy as np import pandas as pd import xarray as xr

class foo(): """ Test class.

(...)
"""

def bar():
    """
    Return 1.

    Examples
    --------
    Create data:

    >>> np.random.seed(0)
    >>> temperature = 15 + 8 * np.random.randn(2, 2, 3)
    >>> precipitation = 10 * np.random.rand(2, 2, 3)
    >>> lon = [[-99.83, -99.32], [-99.79, -99.23]]
    >>> lat = [[42.25, 42.21], [42.63, 42.59]]
    >>> time = pd.date_range("2014-09-06", periods=3)
    >>> reference_time = pd.Timestamp("2014-09-05")

    Initialize a dataset with multiple dimensions:

    >>> ds = xr.Dataset(
    ...     {
    ...         "temperature": (["x", "y", "time"], temperature),
    ...         "precipitation": (["x", "y", "time"], precipitation),
    ...     },
    ...     coords={
    ...         "lon": (["x", "y"], lon),
    ...         "lat": (["x", "y"], lat),
    ...         "time": time,
    ...         "reference_time": reference_time,
    ...     },
    ... )

    Print results:

    >>> print(ds.temperature.values[0])
    [[29.11241877 18.20125767 22.82990387]
     [32.92714559 29.94046392  7.18177696]]
    >>> print(ds)
    <xarray.Dataset>
    Dimensions:         (time: 3, x: 2, y: 2)
    Coordinates:
        lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
        lat             (x, y) float64 42.25 42.21 42.63 42.59
      * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
        reference_time  datetime64[ns] 2014-09-05
    Dimensions without coordinates: x, y
    Data variables:
        temperature     (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63
        precipitation   (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805
    >>> ds
    <xarray.Dataset>
    Dimensions:         (time: 3, x: 2, y: 2)
    Coordinates:
        lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
        lat             (x, y) float64 42.25 42.21 42.63 42.59
      * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
        reference_time  datetime64[ns] 2014-09-05
    Dimensions without coordinates: x, y
    Data variables:
        temperature     (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63
        precipitation   (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805
    """
    return 1

if name == 'main': import doctest doctest.testmod()

```