本文整理汇总了Python中xarray.DataArray.mean方法的典型用法代码示例。如果您正苦于以下问题:Python DataArray.mean方法的具体用法?Python DataArray.mean怎么用?Python DataArray.mean使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类xarray.DataArray
的用法示例。
在下文中一共展示了DataArray.mean方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_cftime_datetime_mean
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import mean [as 别名]
def test_cftime_datetime_mean():
times = cftime_range('2000', periods=4)
da = DataArray(times, dims=['time'])
assert da.isel(time=0).mean() == da.isel(time=0)
expected = DataArray(times.date_type(2000, 1, 2, 12))
result = da.mean()
assert_equal(result, expected)
da_2d = DataArray(times.values.reshape(2, 2))
result = da_2d.mean()
assert_equal(result, expected)
示例2: _pearsonr
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import mean [as 别名]
def _pearsonr(x: xr.DataArray, y: xr.DataArray, monitor: Monitor) -> xr.Dataset:
"""
Calculate Pearson correlation coefficients and p-values for testing
non-correlation of lon/lat/time xarray datasets for each lon/lat point.
Heavily influenced by scipy.stats.pearsonr
The Pearson correlation coefficient measures the linear relationship
between two datasets. Strictly speaking, Pearson's correlation requires
that each dataset be normally distributed, and not necessarily zero-mean.
Like other correlation coefficients, this one varies between -1 and +1
with 0 implying no correlation. Correlations of -1 or +1 imply an exact
linear relationship. Positive correlations imply that as x increases, so
does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Pearson correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
:param x: lon/lat/time xr.DataArray
:param y: xr.DataArray of the same spatiotemporal extents and resolution as x.
:param monitor: Monitor to use for monitoring the calculation
:return: A dataset containing the correlation coefficients and p_values on
the lon/lat grid of x and y.
References
----------
http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation
"""
with monitor.starting("Calculate Pearson correlation", total_work=6):
n = len(x['time'])
xm, ym = x - x.mean(dim='time'), y - y.mean(dim='time')
xm.time.values = [i for i in range(0, len(xm.time))]
ym.time.values = [i for i in range(0, len(ym.time))]
xm_ym = xm * ym
r_num = xm_ym.sum(dim='time')
xm_squared = np.square(xm)
ym_squared = np.square(ym)
r_den = np.sqrt(xm_squared.sum(dim='time') * ym_squared.sum(dim='time'))
r_den = r_den.where(r_den != 0)
r = r_num / r_den
# Presumably, if abs(r) > 1, then it is only some small artifact of floating
# point arithmetic.
# At this point r should be a lon/lat dataArray, so it should be safe to
# load it in memory explicitly. This may take time as it will kick-start
# deferred processing.
# Comparing with NaN produces warnings that can be safely ignored
default_warning_settings = np.seterr(invalid='ignore')
with monitor.child(1).observing("task 1"):
negativ_r = r.values < -1.0
with monitor.child(1).observing("task 2"):
r.values[negativ_r] = -1.0
with monitor.child(1).observing("task 3"):
positiv_r = r.values > 1.0
with monitor.child(1).observing("task 4"):
r.values[positiv_r] = 1.0
np.seterr(**default_warning_settings)
r.attrs = {'description': 'Correlation coefficients between'
' {} and {}.'.format(x.name, y.name)}
df = n - 2
t_squared = np.square(r) * (df / ((1.0 - r.where(r != 1)) * (1.0 + r.where(r != -1))))
prob = df / (df + t_squared)
with monitor.child(1).observing("task 5"):
prob_values_in = prob.values
with monitor.child(1).observing("task 6"):
prob.values = betainc(0.5 * df, 0.5, prob_values_in)
prob.attrs = {'description': 'Rough indicator of probability of an'
' uncorrelated system producing datasets that have a Pearson'
' correlation at least as extreme as the one computed from'
' these datsets. Not entirely reliable, but reasonable for'
' datasets larger than 500 or so.'}
retset = xr.Dataset({'corr_coef': r,
'p_value': prob})
return retset
示例3: test_cftime_datetime_mean_dask_error
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import mean [as 别名]
def test_cftime_datetime_mean_dask_error():
times = cftime_range('2000', periods=4)
da = DataArray(times, dims=['time']).chunk()
with pytest.raises(NotImplementedError):
da.mean()