子集 xarray.Dataset 关于多个坐标

问题描述

假设我使用 xarray.open_dataset(..., decode_times=False) 加载了一个 xarray.Dataset 对象，打印时看起来像这样:

Say I have an xarray.Dataset object loaded in using xarray.open_dataset(..., decode_times=False) that looks like this when printed:

<xarray.Dataset>
Dimensions:    (bnds: 2, lat: 15, lon: 34, plev: 8, time: 3650)
Coordinates:
  * time       (time) float64 3.322e+04 3.322e+04 3.322e+04 3.322e+04 ...
  * plev       (plev) float64 1e+05 8.5e+04 7e+04 5e+04 2.5e+04 1e+04 5e+03 ...
  * lat        (lat) float64 40.46 43.25 46.04 48.84 51.63 54.42 57.21 60.0 ...
  * lon        (lon) float64 216.6 219.4 222.2 225.0 227.8 230.6 233.4 236.2 ...
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) float64 3.322e+04 3.322e+04 3.322e+04 3.322e+04 ...
    lat_bnds   (lat, bnds) float64 39.07 41.86 41.86 44.65 44.65 47.44 47.44 ...
    lon_bnds   (lon, bnds) float64 215.2 218.0 218.0 220.8 220.8 223.6 223.6 ...
    hus        (time, plev, lat, lon) float64 0.006508 0.007438 0.008751 ...

对 lat、lon 和 time 给定的多个范围进行子集化的最佳方法是什么?我尝试链接一系列条件并使用 xarray.Dataset.where，但我收到一条错误消息:

What would be the best way to subset this given multiple ranges for lat, lon, and time? I've tried chaining a series of conditions and used xarray.Dataset.where, but I get an error saying:

IndexError: The indexing operation you are attempting to perform is not valid on netCDF4.Variable object. Try loading your data into memory first by calling .load().

我无法将整个数据集加载到内存中，那么执行此操作的典型方法是什么?

I can't load the entire dataset into memory, so what would be the typical way to do this?

推荐答案

NetCDF4 不支持 NumPy 支持的所有多维索引操作.但是确实支持切片(非常快)和一维索引(有点慢).

NetCDF4 doesn't support all of the multi-dimensional indexing operations supported by NumPy. But does support slicing (which is very fast) and one dimensional indexing (somewhat slower).

一些值得尝试的事情:

• 用切片索引(例如，.sel(time=slice(start, end)))before 用一维数组索引.这应该将基于数组的索引从 netCDF4 卸载到 Dask/NumPy.
• 将您的索引操作拆分为更多的中间操作，这些操作一次沿更少的维度进行索引.听起来您已经尝试过这个，但也许值得多探索一下.
• 要优化性能，请使用 .chunk() 尝试不同的 Dask 分块方案.

• Index with slices (e.g., .sel(time=slice(start, end))) before indexing with 1-dimensional arrays. This should offload the array-based indexing from netCDF4 to Dask/NumPy.
• Split up your indexing operations into more intermediate operations that index along fewer dimensions at once. It sounds like you've already tried this one, but maybe it's worth exploring a little more.
• To optimize performance, try different Dask chunking schemes using the .chunk().

如果这不起作用，请将完整的独立示例发布到 GitHub 上的 xarray 问题跟踪器，我们可以更详细地研究它.

If that doesn't work, post a full self-contained example to the xarray issue tracker on GitHub and we can take a look into it in more detail.

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