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Cal.py
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Cal.py
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#!/usr/bin/env python
# coding: utf-8
# In[9]:
"""
一、TxtinoutReader
1.必须指定工作路径，否则工作路径会变成copy里的路径
2.必须使用绝对路径
3.网页教程的参数格式不对
4.改变参数，不指定行id时，用 None
二、FileReader
1.读取文件不指定index时，必须用None,不可省略
2.usecols省略的话，读取的df为空
"""
# %reset -f
import os
import pandas as pd
import numpy as np
import spotpy as sp
from spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as gauss
import shutil
import mpi4py
import sys
from pySWATPlus.TxtinoutReader import TxtinoutReader
from pySWATPlus.FileReader import FileReader
from matplotlib import pyplot as plt
from datetime import datetime
from tqdm import tqdm
# cwd = "E:/SPOTPY-and-pySWATPlus"
cwd = "E:/BaiduSyncdisk/Code/Python/SPOTPY-and-pySWATPlus"
# # 用pySWATPlus定义huron_swat函数
# In[10]:
def huron_swat(reader, params, tpl_params, copy_path, output_scale="day", show_output=False, delete_copy=True):
result = reader.copy_and_run(dir=copy_path,
params=params,
tpl_params=tpl_params,
show_output=show_output
)
reader = FileReader(os.path.join(result, "basin_aqu_day.txt"),
has_units=True,
index=None,
usecols=["mon", "day", "yr", "unit", "no3_lat"],
filter_by={"unit": 1}
)
res = reader.df
if output_scale == "mon":
res = (res.
groupby(["yr", "mon"]).
agg({"no3_lat": np.sum}).
reset_index())
res["Date"] = pd.to_datetime(pd.DataFrame({"year": res["yr"],
"month": res["mon"],
"day": 1}))
res.drop(columns=["yr", "mon"], inplace=True)
elif output_scale == "day":
res["Date"] = pd.to_datetime(pd.DataFrame({"year": res["yr"],
"month": res["mon"],
"day": res["day"]}))
res.drop(columns=["mon", "day", "yr", "unit"], inplace=True)
if delete_copy:
shutil.rmtree(result, ignore_errors=True)
os.chdir(cwd) #改回当前路径
return res
# # 定义SPOTPY类
# In[11]:
class spot_swat():
def __init__(self, TxtInOut_abspath, copy_path, start_print, end_print,
output_scale="day", prior=sp.parameter.Uniform, obj_func=None,
show_output=False, delete_copy=True):
self.reader = TxtinoutReader(TxtInOut_abspath)
self.copy_path = copy_path
self.start = start_print
self.end = end_print
self.output_scale = output_scale
self.obj_func = obj_func
self.show_output = show_output
self.delete_copy = delete_copy
# self.params = [prior('alpha_bf', 0.0001, 0.9999), #0
# prior('bf_max', 0.0001, 1.9999),
# prior('dep_bot', 0.0001, 9.9999),
# prior('dep_wt', 0.0001, 9.9999),
# prior('flo_dist', 0.0001, 199.9999),
# prior('flo_min', 0.0001, 49.9999),
# prior('gw_flo', 0.0001, 1.9999),
# prior('no3_n', 0.0001, 999.9999),
# prior('rchg_dp', 0.0001, 0.9999),
# prior('revap', 0.0201, 0.1999), #9
# prior('revap_min', 0.0001, 49.9999),
# prior('spec_yld', 0.0001, 0.4999),
# prior('hl_no3n', 0.0001, 199.9999),
# prior('cn_a', 30.0001, 69.9999),
# prior('cn_b', 50.0001, 79.9999),
# prior('cn_c', 70.0001, 89.9999),
# prior('cn_d', 80.0001, 94.9999),
# prior('can_max', 0.0001, 99.9999),
# prior('cn3_swf', 0.0001, 0.9999),
# prior('epco', 0.0001, 0.9999), #19
# prior('esco', 0.0001, 0.9999),
# prior('lat_ttime', 0.5001, 179.9999),
# prior('latq_co', 0.0001, 0.9999),
# prior('perco', 0.0001, 0.9999),
# prior('pet_co', 0.7001, 1.2999),
# prior('exp_co', 0.0001, 0.9999),
# prior('fr_hum_act', 0.0001, 0.9999),
# prior('hum_c_n', 8.0001, 11.9999),
# prior('nitrate', 0.0001, 99.9999),
# prior('ovn', 0.0101, 0.6999), #29
# prior('denit_exp', 0.0001, 2.9999),
# prior('denit_frac', 0.0001, 0.9999),
# prior('evap_adj', 0.5001, 0.9999),
# prior('lai_noevap', 0.0001, 9.9999),
# prior('msk_co1', 0.0001, 9.9999),
# prior('msk_co2', 0.0001, 9.9999),
# prior('msk_x', 0.0001, 0.2999),
# prior('n_fix_max', 1.0001, 19.9999),
# prior('n_perc', 0.0001, 0.9999),
# prior('n_uptake', 0.0001, 99.9999), #39
# prior('nperco_lchtile', 0.0001, 0.9999),
# prior('orgn_min', 0.0011, 0.0029),
# prior('rsd_cover', 0.1001, 0.4999),
# prior('rsd_decay', 0.0001, 0.0499),
# prior('rsd_decomp', 0.0201, 0.0999),
# prior('scoef', 0.0001, 0.9999),
# prior('surq_exp', 1.0001, 2.9999),
# prior('surq_lag', 1.0001, 23.9999),
# prior('sw_init', 0.0001, 0.9999),
# prior('cov50', 0.0001, 0.90), #49
# prior('fall_tmp', -4.9999, 4.9999),
# prior('melt_max_min', 0.0001, 9.9999),
# prior('melt_tmp', -4.9999, 4.9999),
# prior('snow_h2o', 0.0001, 499.9999),
# prior('snow_init', 0.0001, 999.9999),
# prior('tmp_lag', 0.0001, 0.9999),
# prior('dist', 7600.0001, 29999.9999),
# prior('dp', 0.0001, 5999.9999),
# prior('drain', 10.0001, 50.9999),
# prior('lag', 0.0001, 99.9999), #59
# prior('lat_kast', 0.0101, 3.9999),
# prior('pump', 0.0001, 9.9999),
# prior('rad', 3.0001, 39.9999),
# prior('t_fc', 0.0001, 99.9999),
# prior('fert_1', 0.0001, 999.9999),
# prior('fert_2', 0.0001, 999.9999),
# prior('fert_3', 0.0001, 999.9999),
# prior('fert_4', 0.0001, 999.9999),
# prior('fert_5', 0.0001, 999.9999),
# prior('fert_6', 0.0001, 999.9999),
# prior('fert_7', 0.0001, 999.9999),
# prior('fert_8', 0.0001, 999.9999),
# prior('fert_9', 0.0001, 999.9999),
# prior('fert_10', 0.0001, 999.9999),
# prior('fert_11', 0.0001, 999.9999),
# prior('fert_12', 0.0001, 999.9999),
# prior('rsd_init', 0.0001, 9999.9999),
# prior('awc', 0.0001, 0.9999),
# prior('soil_k', 0.0001, 1999.9999),
# ]
self.params = [prior('alpha_bf', 0.0001, 0.9999), #0
prior('dep_bot', 0.0001, 9.9999),
prior('dep_wt', 0.0001, 9.9999),
prior('flo_min', 0.0001, 49.9999),
prior('gw_flo', 0.0001, 1.9999),
prior('no3_n', 0.0001, 999.9999),
prior('rchg_dp', 0.0001, 0.9999),
prior('revap_min', 0.0001, 49.9999),
prior('spec_yld', 0.0001, 0.4999),
prior('hl_no3n', 0.0001, 199.9999), #9
prior('esco', 0.0001, 0.9999),
prior('latq_co', 0.0001, 0.9999),
prior('perco', 0.0001, 0.9999),
prior('cov50', 0.0001, 0.90),
prior('fall_tmp', -4.9999, 4.9999),
prior('melt_max_min', 0.0001, 9.9999),
prior('melt_tmp', -4.9999, 4.9999),
prior('snow_h2o', 0.0001, 499.9999),
prior('snow_init', 0.0001, 999.9999),
prior('dp', 0.0001, 5999.9999), # 19
prior('fert_1', 0.0001, 999.9999),
prior('fert_2', 0.0001, 999.9999),
prior('fert_3', 0.0001, 999.9999),
prior('fert_4', 0.0001, 999.9999),
prior('fert_5', 0.0001, 999.9999),
prior('fert_6', 0.0001, 999.9999),
prior('fert_7', 0.0001, 999.9999),
prior('fert_8', 0.0001, 999.9999),
prior('fert_9', 0.0001, 999.9999),
prior('fert_10', 0.0001, 999.9999), #29
prior('fert_11', 0.0001, 999.9999),
prior('fert_12', 0.0001, 999.9999),
prior('awc', 0.0001, 0.9999),
prior('soil_k', 0.0001, 1999.9999),
]
def parameters(self):
return sp.parameter.generate(self.params)
def simulation(self, vector):
par = np.array(vector)
# params = {"aquifer.aqu":("name", [(None, 'alpha_bf', par[0]), # 17
# (None, 'bf_max', 0.041), # par[0]
# (None, 'dep_bot', par[2]), # 2
# (None, 'dep_wt', par[3]), # 10
# (None, 'flo_dist', 132.0), # par[4]
# (None, 'flo_min', par[5]), # 3
# (None, 'gw_flo', par[6]), # 22
# (None, 'no3_n', par[7]), # 5
# (None, 'rchg_dp', par[8]), # 23
# (None, 'revap', 0.194), # par[9]
# (None, 'revap_min', par[10]), # 15
# (None, 'spec_yld', par[11]), # 16
# (None, 'hl_no3n', par[12]), # 1
# ],
# ),
# "cntable.lum":("description", [(None,"cn_a", 54.0), # par[13]
# (None,"cn_b", 63.0), # par[14]
# (None,"cn_c", 86.0), # par[15]
# (None,"cn_d", 89.0), # par[16]
# ],
# ),
# "hydrology.hyd":("name", [(None, 'can_max', 30.3), # par[17]
# (None, 'cn3_swf', 0.911), # par[18]
# (None, 'epco', 0.068), # par[19]
# (None, 'esco', par[20]), # 20
# (None, 'lat_ttime', 81.7), # par[21]
# (None, 'latq_co', par[22]), # 12
# (None, 'perco', par[23]), # 11
# (None, 'pet_co', 1.25), # par[24]
# ],
# ),
# "nutrients.sol":("name", [(None, 'exp_co', par[25]), # par[25]
# (None, 'fr_hum_act', par[26]), # par[26]
# (None, 'hum_c_n', par[27]), # par[27]
# (None, 'nitrate', par[28]), # par[28]
# ],
# ),
# "ovn_table.lum":("name", [(None, 'ovn_mean', par[29]), # par[29]
# (None, 'ovn_min', par[29]), # par[29]
# (None, 'ovn_max', par[29]) # par[29]
# ],
# ),
# "parameters.bsn":("igen", [(None, 'denit_exp', par[30]), # par[30]
# (None, 'denit_frac', par[31]), # par[31]
# (None, 'evap_adj', par[32]), # par[32]
# (None, 'lai_noevap', par[33]), # par[33]
# (None, 'msk_co1', par[34]), # par[34]
# (None, 'msk_co2', par[35]), # par[35]
# (None, 'msk_x', par[36]), # par[36]
# (None, 'n_fix_max', par[37]), # par[37]
# (None, 'n_perc', par[38]), # par[38]
# (None, 'n_uptake', par[39]), # par[39]
# (None, 'nperco_lchtile', par[40]), # par[40]
# (None, 'orgn_min', par[41]), # par[41]
# (None, 'rsd_cover', par[42]), # par[42]
# (None, 'rsd_decay', par[43]), # par[43]
# (None, 'rsd_decomp', par[44]), # par[44]
# (None, 'scoef', par[45]), # par[45]
# (None, 'surq_exp', par[46]), # par[46]
# (None, 'surq_lag', par[47]), # par[47]
# (None, 'sw_init', par[48]), # par[48]
# ],
# ),
# "snow.sno":("name", [(None, 'cov50', par[49]), # 6
# (None, 'fall_tmp', par[50]), # 19
# (None, 'melt_max', par[51]), # 13
# (None, 'melt_min', par[51]), # 14
# (None, 'melt_tmp', par[52]), # 7
# (None, 'snow_h2o', par[53]), # 18
# (None, 'snow_init', par[54]), # 21
# (None, 'tmp_lag', par[55]), # par[55]
# ],
# ),
# "tiledrain.str":("name", [(None, 'dist', par[56]), # par[56]
# (None, 'dp', par[57]), # 8
# (None, 'drain', par[58]), # par[58]
# (None, 'lag', par[59]), # par[59]
# (None, 'lat_ksat', par[60]), # par[60]
# (None, 'pump', par[61]), # par[61]
# (None, 'rad', par[62]), # par[62]
# (None, 't_fc', par[63]), # par[63]
# ],
# ),
#
# }
# tpl_params = {"management.sch.tpl": {"fert_1": par[64],
# "fert_2": par[65],
# "fert_3": par[66],
# "fert_4": par[67],
# "fert_5": par[68],
# "fert_6": par[69],
# "fert_7": par[70],
# "fert_8": par[71],
# "fert_9": par[72],
# "fert_10": par[73],
# "fert_11": par[74],
# "fert_12": par[75],
# },
# "plant.ini.tpl": {"rsd_init": par[76]}, # par[76]
# "soils.sol.tpl": {"awc": par[77], # 9
# "soil_k": par[78]}, # 4
# }
params = {"aquifer.aqu":("name", [(None, 'alpha_bf', par[0]), # 17
(None, 'dep_bot', par[1]), # 2
(None, 'dep_wt', par[2]), # 10
(None, 'flo_min', par[3]), # 3
(None, 'gw_flo', par[4]), # 22
(None, 'no3_n', par[5]), # 5
(None, 'rchg_dp', par[6]), # 23
(None, 'revap_min', par[7]), # 15
(None, 'spec_yld', par[8]), # 16
(None, 'hl_no3n', par[9]), # 1
],
),
"hydrology.hyd":("name", [
(None, 'esco', par[10]), # 20
(None, 'latq_co', par[11]), # 12
(None, 'perco', par[12]), # 11
],
),
"snow.sno":("name", [(None, 'cov50', par[13]), # 6
(None, 'fall_tmp', par[14]), # 19
(None, 'melt_max', par[15]), # 13
(None, 'melt_min', par[15]), # 14
(None, 'melt_tmp', par[16]), # 7
(None, 'snow_h2o', par[17]), # 18
(None, 'snow_init', par[18]), # 21
],
),
"tiledrain.str":("name", [
(None, 'dp', par[19]), # 8
],
),
}
tpl_params = {"management.sch.tpl": {"fert_1": par[20],
"fert_2": par[21],
"fert_3": par[22],
"fert_4": par[23],
"fert_5": par[24],
"fert_6": par[25],
"fert_7": par[26],
"fert_8": par[27],
"fert_9": par[28],
"fert_10": par[29],
"fert_11": par[30],
"fert_12": par[31],
},
"soils.sol.tpl": {"awc": par[32], # 9
"soil_k": par[33]}, # 4
}
sim = huron_swat(self.reader, params, tpl_params, self.copy_path,
output_scale=self.output_scale,
show_output=self.show_output,
delete_copy=self.delete_copy)
return sim["no3_lat"]*90643.0
def evaluation(self):
if self.output_scale == "day":
obs = pd.read_csv(os.path.join(cwd,'TimeSeries\\daily_load.csv'))
elif self.output_scale == "mon":
obs = pd.read_csv(os.path.join(cwd,'TimeSeries\\monthly_load.csv'))
obs["Date"] = pd.to_datetime(obs["Date"])
obs = obs.loc[((obs["Date"] >= datetime.strptime(self.start, "%Y-%m-%d")) & (obs["Date"] <= datetime.strptime(self.end, "%Y-%m-%d"))),("Load_COND_min", "Load_COND_max")]
return obs
def objectivefunction(self, simulation, evaluation):
if not self.obj_func:
like = sp.objectivefunctions.nashsutcliffe(evaluation, simulation)
else:
like = self.obj_func(evaluation, simulation)
return like
# In[12]:
if __name__ == "__main__":
# 源文件路径和复制文件路径
proj_path = os.path.join(cwd, "Cal_TxtInOut")
copy_path = os.path.join(cwd, "Cal_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 = "2018-02-01"
end_print = "2020-12-31"
warmup = 1
# 设置输出时间尺度
output_scale = "mon"
# 输出选项
show_output = False
delete_copy = True
# 目标函数
def obj_func(evaluation, simulation):
evaluation = np.array(evaluation)
simulation = np.array(simulation)
e = np.where(simulation < evaluation[:, 0],
evaluation[:, 0] - simulation,
np.where(simulation > evaluation[:, 1],
evaluation[:, 1] - simulation,
0)
)
# o = np.zeros_like(e)
return np.sqrt(np.mean(e**2))
# 实例化及采样
spot_setup = spot_swat(proj_path, copy_path, start_print, end_print,
obj_func=obj_func, output_scale=output_scale,
show_output=show_output, delete_copy=delete_copy)
# spot_setup.reader.set_simulation_time(start_sim, end_sim)
# spot_setup.reader.set_print_time(start_print, end_print, warmup)
# spot_setup.reader.enable_object_in_print_prt("basin_aqu", True, False, False, False)
sampler = sp.algorithms.mc(spot_setup,
dbname="Cal",
dbformat="csv",
parallel="mpc",
)
# print(describe(sampler))
rep = 14
ngs = 80
sampler.sample(rep,
# ngs,
)
print("============= Successfully done! =================")