data.py里的各个文件的生成
Load forcing data
ERA5_LAND_label_4_1990？？？哪生成的
ERA5-Land_forcing {sr} spatial resolution {year}.npy？？哪来的
lat_{s}.npy #109
lon_{s}.npy#110
filter Glacial region
Mask with {sr} spatial resolution.npy#204
Determine whether to perform memmap mapping
# Memmap is to deal with the problem of excessive amount of data
forcing_memmap.npy？？？#120通过flush
Load land_surface data
ERA5-Land_land_surface {sr} spatial resolution {year}.npy？？哪来的
land_surface_memmap.npy？哪生成的！#172通过flush
Load label data
# filter Glacial region
Mask with {sr} spatial resolution.npy#204
Load static_norm data
static_norm.npy#224
Partition and create datasets
Create a training dataset；
The default training data is 1990 to 2019.
x_train.npy？？？哪生成？通过memmap里的flush
y_train.npy#265
x_test.npy#275 通过flush
normalize
# Adopt maximum and minimum normalization
# There are two forms of normalization： region and gloabl
scaler_x.npy#303，flush
scaler_y.npy#304，flush
Save the normalized dataset
x_train_norm_shape.npy#383
x_test_norm_shape.npy#384
x_test_norm.npy#396
x_train_norm.npy#385,memmap
y_test_norm.npy#401
y_train_norm.npy#402