沿XArray的时间维度应用函数 [英] Apply function along time dimension of XArray

问题描述

我有一个图像堆栈存储在XArray数据数组中，其尺寸为时间x，y，我想沿每个像素的时间轴在其上应用自定义函数，以使输出为尺寸为x的单个图像， y.

I have an image stack stored in an XArray DataArray with dimensions time, x, y on which I'd like to apply a custom function along the time axis of each pixel such that the output is a single image of dimensions x,y.

我尝试过:apply_ufunc，但是函数失败，说明我需要先将数据加载到RAM中(即不能使用Dask数组).理想情况下，我想在内部将DataArray保留为Dask Array，因为不可能将整个堆栈加载到RAM中.确切的错误消息是:

I have tried: apply_ufunc but the function fails stating that I need to first load the data into RAM (i.e. cannot use a Dask Array). Ideally, I'd like to keep the DataArray as Dask Arrays internally as it isn't possible to load the entire stack into RAM. The exact error message is:

ValueError:apply_ufunc在参数上遇到了dask数组，但是尚未启用对dask数组的处理.设置dask参数或使用.load()或.compute()

我的代码当前如下所示:

My code currently looks like this:

import numpy as np
import xarray as xr
import pandas as pd 

def special_mean(x, drop_min=False):
    s = np.sum(x)
    n = len(x)
    if drop_min:
    s = s - x.min()
    n -= 1
    return s/n

times = pd.date_range('2019-01-01', '2019-01-10', name='time')

data = xr.DataArray(np.random.rand(10, 8, 8), dims=["time", "y", "x"], coords={'time': times})
data = data.chunk({'time':10, 'x':1, 'y':1})

res = xr.apply_ufunc(special_mean, data, input_core_dims=[["time"]], kwargs={'drop_min': True})

如果我确实使用.compute将数据加载到RAM中，那么我仍然会遇到以下错误:

If I do load the data into RAM using .compute then I still end up with an error which states:

ValueError:应用的函数返回的数据具有意外的维数:0与2，维数('y'，'x')

ValueError: applied function returned data with unexpected number of dimensions: 0 vs 2, for dimensions ('y', 'x')

我不确定我到底缺少什么/做错了什么.

I'm not sure entirely what I am missing/doing wrong.

推荐答案

def special_mean(x, drop_min=False):
    s = np.sum(x)
    n = len(x)
    if drop_min:
        s = s - x.min()
    n -= 1
    return s/n

times = pd.date_range('2019-01-01', '2019-01-10', name='time')

data = xr.DataArray(np.random.rand(10, 8, 8), dims=["time", "y", "x"], coords={'time': times})
data = data.chunk({'time':10, 'x':1, 'y':1})

res = xr.apply_ufunc(special_mean, data, input_core_dims=[["time"]], kwargs={'drop_min': True}, dask = 'allowed', vectorize = True)

上面的代码应该可以工作.

The code above should work.

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