xarray新手在这里。非常简单的情况是，我有一个降水量类型数组（ntim x nlat x nlon）和一个总降水量数组（相同尺寸）。两者都在单独的netCDF文件中。我想掩盖降水阵列，其中A）降水下降（> 1e-8 m / s速率），B）降水类型为降雪（maskvar = 0.0）。因此，输出数组是“在哪里下雪？”数组。
在将xarray where（）与来自两个不同（但大小相同）的数组的多个条件一起使用时，在生成的蒙版数组中仅存在两个纬度（北极和南极）。
但是，如果我使用预遮罩的数组（来自NCL，写为netCDF，具有相同的暗点）作为测试，它的表现将达到预期的效果（即，返回ntim x nlat x nlon）。
向我展示的唯一显而易见的事情是，两个数组之间的纬度坐标输入方式不同，尽管目前尚不清楚为什么会导致这种方式失败。
任何帮助表示赞赏。
示例代码：
ensnum='001'
indir = '/glade/u/home/zarzycki/scratch/LENS-snow/'
files = [indir+'/b.e11.B20TRC5CNBDRD.f09_g16.'+ensnum+'.cam.h2.PTYPE.1990010100Z-2005123118Z.nc']
indir2 = '/glade/p_old/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/hourly6/PRECT/'
files2 = [indir2+'/b.e11.B20TRC5CNBDRD.f09_g16.'+ensnum+'.cam.h2.PRECT.1990010100Z-2005123118Z.nc']
indir3 = indir
files3 = [indir3+'/b.e11.B20TRC5CNBDRD.f09_g16.'+ensnum+'.cam.h2.PRECT_SNOW.1990010100Z-2005123118Z.nc']
for idx, val in enumerate(files):
ds = xr.open_dataset(files[idx])
ds2 = xr.open_dataset(files2[idx])
ds3 = xr.open_dataset(files3[idx])
ptype = ds.PTYPE[1:11,:,:] # 10 time x 192 lat x 288 lon
prect1 = ds2.PRECT[1:11,:,:] # 10 time x 192 lat x 288 lon
prect2 = ds3.PRECT_SNOW[1:11,:,:] # 10 time x 192 lat x 288 lon
print('---------')
print(ptype)
print(prect1)
print(prect2)
ptype1 = ptype.where((ptype > -0.1) & (ptype < 0.1) & (prect1 > 1e-8))
ptype2 = ptype.where((ptype > -0.1) & (ptype < 0.1) & (prect2 > 1e-8))
print('---------')
print(ptype1)
print(ptype2)
示例输出显示所有读取的变量为（时间：10，纬度：192，lon：288），但返回的掩码变量为（时间：10，lat：2，lon：288）和（时间：10，lat： 192，lon：288）
---------
<xarray.DataArray 'PTYPE' (time: 10, lat: 192, lon: 288)>
[552960 values with dtype=float32]
Coordinates:
* lat (lat) float32 -90.0 -89.0576 -88.1152 -87.1728 -86.2304 -85.288 ...
* lon (lon) float32 0.0 1.25 2.5 3.75 5.0 6.25 7.5 8.75 10.0 11.25 ...
* time (time) datetime64[ns] 1990-01-01T12:00:00 1990-01-01T18:00:00 ...
<xarray.DataArray 'PRECT' (time: 10, lat: 192, lon: 288)>
[552960 values with dtype=float32]
Coordinates:
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 -86.23 -85.29 -84.35 ...
* lon (lon) float64 0.0 1.25 2.5 3.75 5.0 6.25 7.5 8.75 10.0 11.25 ...
* time (time) datetime64[ns] 1990-01-01T12:00:00 1990-01-01T18:00:00 ...
Attributes:
units: m/s
long_name: Total (convective and large-scale) precipitation rate (liq...
cell_methods: time: mean
<xarray.DataArray 'PRECT_SNOW' (time: 10, lat: 192, lon: 288)>
[552960 values with dtype=float32]
Coordinates:
* lat (lat) float32 -90.0 -89.0576 -88.1152 -87.1728 -86.2304 -85.288 ...
* lon (lon) float32 0.0 1.25 2.5 3.75 5.0 6.25 7.5 8.75 10.0 11.25 ...
* time (time) datetime64[ns] 1990-01-01T12:00:00 1990-01-01T18:00:00 ...
Attributes:
units: m/s
---------
<xarray.DataArray (time: 10, lat: 2, lon: 288)>
array([[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
...,
[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]]], dtype=float32)
Coordinates:
* lat (lat) float64 -90.0 90.0
* lon (lon) float32 0.0 1.25 2.5 3.75 5.0 6.25 7.5 8.75 10.0 11.25 ...
* time (time) datetime64[ns] 1990-01-01T12:00:00 1990-01-01T18:00:00 ...
<xarray.DataArray (time: 10, lat: 192, lon: 288)>
array([[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan],
...,
[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan],
...,
[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
...,
[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan],
...,
[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]],
[[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan],
...,
[ nan, nan, ..., nan, nan],
[ nan, nan, ..., nan, nan]]], dtype=float32)
Coordinates:
* lat (lat) float32 -90.0 -89.0576 -88.1152 -87.1728 -86.2304 -85.288 ...
* lon (lon) float32 0.0 1.25 2.5 3.75 5.0 6.25 7.5 8.75 10.0 11.25 ...
* time (time) datetime64[ns] 1990-01-01T12:00:00 1990-01-01T18:00:00 ...