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159 lines (123 loc) · 4.99 KB
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import numpy as np
import torch
import netCDF4 as nC
from sympy.physics.units import year
class Data_Reader:
@staticmethod
def get_data_path(info: tuple) -> tuple[str, str]:
if info[0]=="dec" and info[1]=="t2m":
data_path = {'sample': "../dataset/SEAS5_ERA5_12_init_t2m/seas5_12_t2m.nc",
'label': "../dataset/SEAS5_ERA5_12_init_t2m/era5_12_t2m.nc"}
elif info[0]=="dec" and info[1]=="tp":
data_path ={'sample': "../dataset/SEAS5_ERA5_12_init_tp/seas5_12_tp.nc",
'label': "../dataset/SEAS5_ERA5_12_init_tp/era5_12_tp.nc"}
elif info[0]=="jun" and info[1]=="t2m":
data_path ={'sample': "../dataset/SEAS5_ERA5_6_init_t2m/seas5_6_t2m.nc",
'label': "../dataset/SEAS5_ERA5_6_init_t2m/era5_6_t2m.nc"}
elif info[0]=="jun" and info[1]=="tp":
data_path ={'sample': "../dataset/SEAS5_ERA5_6_init_tp/seas5_6_tp.nc",
'label': "../dataset/SEAS5_ERA5_6_init_tp/era5_6_tp.nc"}
else:
raise
return data_path['sample'], data_path['label']
@staticmethod
def choice_obj(init, variable) -> dict:
obj = {}
if init == "dec":
obj["slice"] = slice(11,257)
obj["end_inx"] = -1
elif init == "jun":
obj["slice"] = slice(6,258)
obj["end_inx"] = -2
else:
raise
if variable == "t2m":
obj["var"] = "t2m"
elif variable == "tp":
obj["var"] = "tp"
else:
raise
return obj
@staticmethod
def read_nc(nwp_path, era5_path, obj:dict):
nwp = nC.Dataset(nwp_path)
era5 = nC.Dataset(era5_path)
if obj["var"] == "t2m":
nwp_tp = nwp.variables["t2m"][:25]
era5_tp = era5.variables["t2m"][obj["slice"]]
elif obj["var"] == "tp":
nwp_tp = nwp.variables['tprate'][:25] * 86400 * 1000
era5_tp = era5.variables['tp'][obj["slice"]] * 1000
else:
raise
nwp_tp = nwp_tp[:, :, :, 35:71, 70:138]
era5_tp = era5_tp[:, 37:69, 72:136]
era5_tp = era5_tp.reshape(-1, 6, 32, 64)
nwp_tp = nwp_tp[:, :obj["end_inx"]]
era5_tp = era5_tp[:obj["end_inx"]]
return nwp_tp.astype(np.float32), era5_tp.astype(np.float32)
@staticmethod
def read_nc_validate(nwp_path, era5_path,obj:dict):
nwp = nC.Dataset(nwp_path)
era5 = nC.Dataset(era5_path)
if obj["var"] == "t2m":
nwp_tp = nwp.variables["t2m"][:25]
era5_tp = era5.variables["t2m"][obj["slice"]]
elif obj["var"] == "tp":
nwp_tp = nwp.variables['tprate'][:25] * 86400 * 1000
era5_tp = era5.variables['tp'][obj["slice"]] * 1000
else:
raise
nwp_tp = nwp_tp[:, :, :, 37:69, 72:136]
era5_tp = era5_tp[:, 37:69, 72:136]
era5_tp = era5_tp.reshape(-1, 6, 32, 64)
nwp_tp = nwp_tp[:, :obj["end_inx"]]
era5_tp = era5_tp[:obj["end_inx"]]
return nwp_tp.astype(np.float32), era5_tp.astype(np.float32)
class Data_Reshape:
@staticmethod
def reshape_normalize(data, jump_year_num, train=True):
data = data.transpose(1, 2, 0, 3, 4)
if train:
data = np.delete(data, jump_year_num, axis=0)
else:
data = data[jump_year_num]
mean_return = np.nanmean(data, axis=(2, 3, 4), keepdims=True)
std_return = np.nanstd(data, axis=(2, 3, 4), keepdims=True)
mean = np.nanmean(data, axis=(3, 4), keepdims=True)
std = np.nanstd(data, axis=(3, 4), keepdims=True)
data = (data - mean) / std
# ###
return data, mean_return, std_return
@staticmethod
def reshape_normalize_obs(data, jump_year_num, train=True):
if train:
data = data[:, :, np.newaxis, :, :]
data = np.delete(data, jump_year_num, axis=0)
else:
data = data[jump_year_num, :, np.newaxis, :, :]
mean = np.nanmean(data, axis=(3, 4), keepdims=True)
std = np.nanstd(data, axis=(3, 4), keepdims=True)
data = (data - mean) / std
return data, mean, std
@staticmethod
def unet_shape_case(data, jump_year, train=True):
data, mean, std = Data_Reshape.reshape_normalize(data, jump_year, train)
data_tensor = torch.from_numpy(data)
# ###
return data_tensor, mean, std
@staticmethod
def unet_shape_obs(data, jump_year, train=True):
data, mean, std = Data_Reshape.reshape_normalize_obs(data, jump_year, train)
data = np.tile(data, (1, 1, 25, 1, 1))
mean = np.tile(mean, (1, 1, 1, 1, 1))
std = np.tile(std, (1, 1, 1, 1, 1))
data_tensor = torch.from_numpy(data)
return data_tensor, mean, std
class Assess:
@staticmethod
def cal_CC(cases, obses, use_anomaly=True):
cases = cases.flatten()
obses = obses.flatten()
acc = np.corrcoef(cases, obses)[0, 1]
return acc