我有一个用xarray加载的三维气候数据集
climate = xr.open_dataset(data_file)
climate
<xarray.Dataset>
Dimensions: (lat: 621, lon: 1405, time: 424)
Coordinates:
* time (time) datetime64[ns] 2017-11-01 2017-11-02 2017-11-03 ...
* lon (lon) float64 -125.0 -125.0 -124.9 -124.9 -124.8 -124.8 -124.7 ...
* lat (lat) float64 49.92 49.87 49.83 49.79 49.75 49.71 49.67 49.62 ...
Data variables:
tmean (time, lat, lon) float64 nan nan nan nan nan nan nan nan nan ...
status (time) object 'provisional' 'provisional' 'provisional' ...
我有一个模型,它应用于时间维度,并返回一个仅包含lat,lon的二维数组。
apply_model(climate.tmean.values).shape
(621, 1405)
我如何在
xr.apply_ufunc()
?我尝试了几种不同的方法,但它总是抱怨一些尺寸错误。
例如:
def apply_model_ufunc(climate):
return xr.apply_ufunc(
apply_model, climate,
input_core_dims=[['lat','lon']])
apply_model_ufunc(climate)
ValueError: dimensions ('time',) must have the same length as the number of data dimensions, ndim=2
或
def apply_model_ufunc(climate):
return xr.apply_ufunc(
apply_model, climate,
input_core_dims=[['time','lat','lon']],
output_core_dims=[['lat','lon']])
apply_model_ufunc(climate)
ValueError: operand to apply_ufunc has required core dimensions ['time', 'lat', 'lon'], but some of these are missing on the input variable: ['lat', 'lon']
