Question

我正在尝试将多个netCDF文件组合在一起，尺寸相同，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，我收到此错误：

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相同

Answer 1

http://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html

xarray.open_mfdataset(paths, chunks=None, concat_dim=None, preprocess=None, engine=None, lock=None, **kwargs)

看起来需要您提供concat_dim参数。从数据中推断它有问题。

连接文件的维度。此参数与数据集对象一起传递给xarray.auto_combine（）。如果要连接的维度不是原始数据集中的维度，则只需要提供此参数，例如，如果要沿第三维堆叠2D数组的集合。

您想要沿着新的第4维度堆叠这些3d数组吗？

Answer 2

可能会出现此错误消息，因为您有两个具有相同变量和坐标值的文件，并且xarray不知道它是否应该沿着新维度将它们堆叠在一起，或者只是检查以确保没有值冲突。

如果open_mfdataset显式调用concat_dim=None禁用所有连接尝试，那将是很好的。 This change应该进入下一版xarray（v0.9.0）。

与此同时，您可以通过单独打开文件并明确合并它们来解决此问题，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，这基本上就是open_mfdataset正在做的所有事情。

使用xarray open_mfdataset函数时出错

2 个答案: