使用xarray open_mfdataset函数时出错 [英] Error on using xarray open_mfdataset function

问题描述

我正在尝试合并具有相同尺寸的多个netCDF文件，它们的尺寸如下:

OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])

但是，在使用open_mfdataset时，出现此错误:

xr.open_mfdataset(path_file, decode_times=False)

*** ValueError: cannot infer dimension to concatenate: supply the ``concat_dim`` argument explicitly

如何解决此错误?我的尺寸在所有文件中都一样

解决方案

出现此错误消息的原因可能是因为您有两个具有相同变量和坐标值的文件，并且xarray不知道是否应将它们沿着一个方向堆叠在一起.新维度或只需检查以确保所有值都不冲突.

如果用concat_dim=None显式调用open_mfdataset禁用了所有级联尝试，那就太好了. 此更改应该使它进入xarray的下一版本(v0.9.0). >

同时，您可以通过单独打开文件并明确合并文件来解决此问题，例如

def open_mfdataset_merge_only(paths, **kwargs):
    if isinstance(paths, basestring):
        paths = sorted(glob(paths))
    return xr.merge([xr.open_dataset(path, **kwargs) for path in paths])

在幕后，基本上这就是open_mfdataset的全部工作.

I am trying to combine multiple netCDF files with the same dimensions, their dimensions are as follows:

OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])
OrderedDict([(u'lat', <type 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 720
), (u'lon', <type 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 1440
), (u'time', <type 'netCDF4._netCDF4.Dimension'>: name = 'time', size = 96
), (u'nv', <type 'netCDF4._netCDF4.Dimension'>: name = 'nv', size = 2
)])

However, on using open_mfdataset, I get this error:

xr.open_mfdataset(path_file, decode_times=False)

*** ValueError: cannot infer dimension to concatenate: supply the ``concat_dim`` argument explicitly

How to fix this error? My dimensions are the same in all the files

解决方案

This error message is probably arising because you have two files with the same variables and coordinate values, and xarray doesn't know whether it should stack them together along a new dimension or simply check to make sure none of the values conflict.

It would be nice if explicitly calling open_mfdataset with concat_dim=None disabled all attempts at concatenation. This change should make it into the next release of xarray (v0.9.0).

In the meantime, you can work around this by opening the files individually and merging them explicitly, e.g.,

def open_mfdataset_merge_only(paths, **kwargs):
    if isinstance(paths, basestring):
        paths = sorted(glob(paths))
    return xr.merge([xr.open_dataset(path, **kwargs) for path in paths])

Under the covers, this is basically all that open_mfdataset is doing.

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