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SA.py
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SA.py
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import os
import pandas as pd
import numpy as np
import shutil
import mpi4py
import sys
import multiprocessing
from pySWATPlus.TxtinoutReader import TxtinoutReader
from pySWATPlus.FileReader import FileReader
from datetime import datetime
from SALib.sample.morris import morris as sample
from SALib.analyze import morris as analyze
from functools import partial
from tqdm import tqdm
def swat(params_list):
# 这里是不是只运行一组参数就行
cwd, reader, swat_params, tpl_params, copy_path, show_output, delete_copy = params_list
result = reader.copy_and_run(dir=copy_path,
params=swat_params,
tpl_params=tpl_params,
show_output=show_output
)
# todo 路径直接输入，不要用os拼
reader = FileReader(os.path.join(result, "basin_aqu_mon.txt"),
has_units=True,
index=None,
usecols=["mon", "day", "yr", "unit", "no3_lat"],
filter_by={"unit": 1}
)
res = reader.df
res["Date"] = pd.to_datetime(pd.DataFrame({"year": res["yr"],
"month": res["mon"],
"day": res["day"]}))
# res.drop(columns=["Date", "mon", "day", "yr", "unit"], inplace=True)
# todo deprecated
if delete_copy:
shutil.rmtree(result, ignore_errors=True)
os.chdir(cwd) # 改回当前路径
return res["no3_lat"].mean()
if __name__ == "__main__":
problem = {
"num_vars": 68,
"names": [
"lat_ttime",
"can_max",
"esco",
"epco",
"cn3_swf",
"perco",
"pet_co",
"latq_co",
"cn_a",
"cn_b",
"cn_c",
"cn_d",
"gw_flo",
"dep_bot",
"dep_wt",
"no3_n",
"flo_dist",
"bf_max",
"alpha_bf",
"revap",
"rchg_dp",
"spec_yld",
"hl_no3n",
"flo_min",
"revap_min",
"fall_tmp",
"melt_tmp",
"melt_max_min",
"tmp_lag",
"snow_h2o",
"cov50",
"snow_init",
"dp",
"t_fc",
"lag",
"rad",
"dist",
"drain",
"pump",
"lat_ksat",
"lai_noevap",
"sw_init",
"surq_lag",
"orgn_min",
"n_uptake",
"n_perc",
"rsd_decomp",
"msk_co1",
"msk_co2",
"msk_x",
"nperco_lchtile",
"evap_adj",
"scoef",
"denit_exp",
"denit_frac",
"n_fix_max",
"rsd_decay",
"rsd_cover",
"surq_exp",
"exp_co",
"nitrate",
"fr_hum_act",
"hum_c_n",
"ovn",
"awc",
"soil_k",
"rsd_init",
"fert",
],
"bounds": [
[0.51, 179.0],
[0.01, 99.9],
[0.01, 0.99],
[0.01, 0.99],
[0.01, 0.99],
[0.01, 0.99],
[0.71, 1.29],
[0.01, 0.99],
[30.0, 70.0],
[50.0, 80.0],
[70.0, 90.0],
[80.0, 95.0],
[0.0, 2.0],
[5.0, 50.0],
[2.0, 20.0],
[0.1, 999.9],
[10.0, 200.0],
[0.11, 1.99],
[0.01, 0.99],
[0.021, 0.199], # revap
[0.01, 0.99],
[0.01, 0.49], # spec_yld
[0.1, 199.9],
[0.1, 49.0],
[0.1, 49.0],
[-4.9, 4.9],
[-4.9, 4.9],
[0.1, 9.9], # melt_max_min
[0.01, 0.99],
[0.1, 499.9],
[0.01, 0.90],
[0, 1000], # snow_init
[0.1, 5999.9],
[0.1, 99.9],
[0.1, 99.9],
[3.1, 39.9],
[7601, 29999],
[10.1, 50.9],
[0.1, 9.9],
[0.02, 3.99],
[0.1, 9.9],
[0.01, 0.99],
[1.1, 23.9],
[0.0011, 0.0029], # orgn_min
[0.1, 99.9],
[0.01, 0.99],
[0.021, 0.099],
[0.1, 9.9],
[0.1, 9.9],
[0.01, 0.29],
[0.01, 0.99], # nperco_lchtile
[0.51, 0.99],
[0.01, 0.99],
[0.01, 2.99], # denit_exp
[0.01, 0.99],
[1.1, 19.9],
[0.001, 0.049],
[0.11, 0.49],
[1.1, 2.9],
[0.01, 0.99], # exp_co
[0.1, 99.9],
[0.01, 0.99],
[8.1, 11.9],
[0.011, 0.69], # ovn_mean, ovn_min, ovn_max
[0.01, 0.99],
[0.1, 1999], # soil_k
[1, 9999],
[1, 1000]
]
}
par = sample.sample(problem,
N=100,
num_levels=6,
optimal_trajectories=20,
seed=1,
)
np.savetxt("SA_X.txt", par, fmt="%.4f")
# 源文件路径和复制文件路径
cwd = "E:\\SPOTPY-and-pySWATPlus"
proj_path = os.path.join(cwd, "SA_TxtInOut")
copy_path = os.path.join(cwd, "SA_copy")
# 设置SWAT模拟时间范围
start_sim = "2017-01-01"
end_sim = "2020-12-31"
# 设置SWAT输出时间范围
start_print = "2018-01-01"
end_print = "2020-12-31"
warmup = 1
# 输出选项
show_output = False
delete_copy = True
reader = TxtinoutReader(proj_path)
reader.enable_object_in_print_prt("basin_aqu", False, True, False, False)
reader.set_simulation_time(start_sim, end_sim)
reader.set_print_time(start_print, end_print, warmup)
num_runs = par.shape[0]
params_list = []
# 将模型运行一次的参数进行封装,存入一个变量如a
# 创建列表，将很多类似a的变量放入，比如叫a_list
# 注意深复制和浅复制
for i in range(num_runs):
swat_params = {"hydrology.hyd": ("name", [(None, "lat_ttime", par[i, 0]),
(None, "can_max", par[i, 1]),
(None, "esco", par[i, 2]),
(None, "epco", par[i, 3]),
(None, "cn3_swf", par[i, 4]),
(None, "perco", par[i, 5]),
(None, "pet_co", par[i, 6]),
(None, "latq_co", par[i, 7]),
],
),
"cntable.lum": ("description", [(None, "cn_a", par[i, 8]),
(None, "cn_b", par[i, 9]),
(None, "cn_c", par[i, 10]),
(None, "cn_d", par[i, 11]),
],
),
"aquifer.aqu": ("name", [(None, "gw_flo", par[i, 12]),
(None, "dep_bot", par[i, 13]),
(None, "dep_wt", par[i, 14]),
(None, "no3_n", par[i, 15]),
(None, "flo_dist", par[i, 16]),
(None, "bf_max", par[i, 17]),
(None, "alpha_bf", par[i, 18]),
(None, "revap", par[i, 19]),
(None, "rchg_dp", par[i, 20]),
(None, "spec_yld", par[i, 21]),
(None, "hl_no3n", par[i, 22]),
(None, "flo_min", par[i, 23]),
(None, "revap_min", par[i, 24]),
],
),
"snow.sno": ("name", [(None, "fall_tmp", par[i, 25]),
(None, "melt_tmp", par[i, 26]),
(None, "melt_max", par[i, 27]),
(None, "melt_min", par[i, 27]),
(None, "tmp_lag", par[i, 28]),
(None, "snow_h2o", par[i, 29]),
(None, "cov50", par[i, 30]),
(None, "snow_init", par[i, 31])
],
),
"tiledrain.str": ("name", [(None, "dp", par[i, 32]),
(None, "t_fc", par[i, 33]),
(None, "lag", par[i, 34]),
(None, "rad", par[i, 35]),
(None, "dist", par[i, 36]),
(None, "drain", par[i, 37]),
(None, "pump", par[i, 38]),
(None, "lat_ksat", par[i, 39]),
],
),
"parameters.bsn": ("igen", [(None, "lai_noevap", par[i, 40]),
(None, "sw_init", par[i, 41]),
(None, "surq_lag", par[i, 42]),
(None, "orgn_min", par[i, 43]),
(None, "n_uptake", par[i, 44]),
(None, "n_perc", par[i, 45]),
(None, "rsd_decomp", par[i, 46]),
(None, "msk_co1", par[i, 47]),
(None, "msk_co2", par[i, 48]),
(None, "msk_x", par[i, 49]),
(None, "nperco_lchtile", par[i, 50]),
(None, "evap_adj", par[i, 51]),
(None, "scoef", par[i, 52]),
(None, "denit_exp", par[i, 53]),
(None, "denit_frac", par[i, 54]),
(None, "n_fix_max", par[i, 55]),
(None, "rsd_decay", par[i, 56]),
(None, "rsd_cover", par[i, 57]),
(None, "surq_exp", par[i, 58]),
],
),
"nutrients.sol": ("name", [(None, "exp_co", par[i, 59]),
(None, "nitrate", par[i, 60]),
(None, "fr_hum_act", par[i, 61]),
(None, "hum_c_n", par[i, 62]),
],
),
"ovn_table.lum": ("name", [(None, "ovn_mean", par[i, 63]),
(None, "ovn_min", par[i, 63]),
(None, "ovn_max", par[i, 63]),
],
),
}
tpl_params = {"soils.sol.tpl": {"awc": par[i, 64],
"soil_k": par[i, 65],
},
"plant.ini.tpl": {"rsd_init": par[i, 66]},
"lum.dtl.tpl": {"fert": par[i, 67]}
}
params_list.append((cwd, reader, swat_params, tpl_params, copy_path, show_output, delete_copy))
print("=================== Start ===================")
p = multiprocessing.Pool()
# 第二个参数为alist
result = list(tqdm(p.imap(swat, params_list), total=num_runs))
p.close()
p.join()
np.savetxt("SA_Y.txt", result, fmt="%.4f")
print("=============== Successfully Done =================")