英文:
Taking differences along time axis for the data with irregular overpass time
问题
我想知道如何使用Xarray在具有不规则过境时间的数据的时间轴上计算差异。
数据:
- NASA SMAP L3土壤湿度数据，规则网格
- 不规则的过境时间。每两天或三天一次，取决于过境情况。缺少的每日数值用NaN填充。
我想做的事情:
计算土壤湿度数据在时间轴上的差异，忽略NaN数据。例如，在pandas数据框中，我可以通过删除NaN数据并使用diff()来实现。现在我想在Xarray中做同样的事情；然而，Xarray不支持逐元素删除，所以我不知道该怎么办。
我尝试过的方法:
- 简单地使用diff()。由于缺少的每日数值被NaN填充，结果值最终充满NaN。
- 向前填充NaN数据并使用diff()。这成功地计算了我想要的差异。但我失去了时间信息；与数据对应的原始时间变得不清楚，而这是我下一步需要的。
- 查看稀疏数组包... 但我没有找到任何有助于计算diff()的东西。
我正在寻找任何算法或解决方法！
英文:
I wonder how to take differences along the time axis of data with the irregular overpass time with Xarray.
Data:
- NASA SMAP L3 soil moisture data, regularly gridded
- Irregular overpass time. Two- or three-daily, depending on the overpass. Missing daily values are filled with NaNl.
What I want to do:
Calculate the difference of the soil moisture data along the time axis, ignoring NaN data. For example, in pandas dataframes, I can achieve it by dropping NaN data and take the diff(). Now I want to do it in Xarray; however, an element-wise drop is not supported in Xarray, so I am at a loss.
What I tried:
- Simply taking diff(). The resulting values end up in full of NaN because missing daily values are filled with NaN.
- Forward-filling the NaN data and taking diff(). This successfully calculates the differences that I want. However, I lose time information; the original time corresponding to the data becomes unclear, which I need for the next step.
- Looking into Sparse Array package ... but I didn't find anything helpful to calculate diff().
I am looking for any algorithms or workaround!
答案1
得分: 1
感谢 @MichaelDelgado，以下是答案。
da.bfill(dim="time").diff(dim="time").where(da.notnull().shift(time=+1))
另外一点：我也研究了稀疏数组，但没有找到好的解决方案。
英文:
Thanks to @MichaelDelgado here is the answer.
da.bfill(dim="time").diff(dim="time").where(da.notnull().shift(time=+1))
Another note: I also dug into sparse array, but there was no good solution to it.
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