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interpolation.py
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import numpy as np
import pandas as pd
from scipy import interpolate
from scipy.optimize import fsolve
from matplotlib import pyplot as plt
from datetime import datetime
# %%
class DataInterp:
def __init__(self, t_sim_start, t_sim_end, t_interp_start,
method='slinear', date_format='%Y/%m/%d'):
if method not in ["nearest", "zero", "slinear",
"quadratic", "cubic"]:
raise ValueError('No such interpolation type.')
self.t_sim_start = datetime.strptime(t_sim_start, date_format)
self.t_sim_end = datetime.strptime(t_sim_end, date_format)
self.t_interp_start = datetime.strptime(t_interp_start, date_format)
self.method = method
self.date_format = date_format
def _date_transformer(self, date):
if isinstance(date, str):
date = datetime.strptime(date, self.date_format)
return (date - self.t_sim_start).days
def data_timelim(self, dat_in):
t_start = self.t_interp_start
t_end = self.t_sim_end
if np.dtype('<M8[ns]') not in dat_in.dtypes.to_list():
raise ValueError('No date data.')
time_idx = dat_in.dtypes == np.dtype('<M8[ns]')
time_colname = [x for x, y in time_idx.to_dict().items() if y is True]
time = dat_in.loc[:, time_colname[0]]
time.apply(lambda x: self._date_transformer(x))
dat_out = dat_in[((time <= t_end) & (time >= t_start)).values].copy()
return dat_out.reset_index()
def array_interp(self, x_p, y_p):
if x_p.dtype == np.dtype('<M8[ns]'):
if x_p[0] > self.t_interp_start:
raise ValueError('Interpolation start time should not be earlier than the first observed data.')
else:
x_start = x_p.iloc[0]
x_p = (x_p - x_start).dt.total_seconds() / 86400
func_interp = interpolate.interp1d(x_p, y_p, kind=self.method)
return func_interp
def df_interp(self, df_in, sim_var, method='slinear'):
# 调整参数method的值
if method != 'slinear':
self.method = method
# 先对输入数据框作裁剪
df_in = self.data_timelim(df_in)
# 再对数据框作插值
time_idx = df_in.dtypes == np.dtype('<M8[ns]')
time_name = [x for x, y in time_idx.to_dict().items() if y is True]
# 如果有nan值，去掉后插值
df = df_in.loc[:, [time_name[0], sim_var]].dropna(subset=[sim_var])
output = self.array_interp(df.loc[:, time_name[0]], df.loc[:, sim_var])
return output
class SpatialInterp:
def __init__(self, delta_x, r_df, q_in, reach_runoff, reach_loads, rough):
self.delta_x = delta_x
self.r_df = r_df
self.q_in = q_in
self.reach_runoff = reach_runoff
self.reach_loads = reach_loads
self.rough = rough
self.x_grids = np.concatenate([np.array([0]), r_df.L.values.cumsum()])
def get_qx(self, time):
qx0 = self.q_in(time)
runoff_ = self.reach_runoff.values.cumsum().ravel() * 1e4 / 86400 / 365
func_interp = interpolate.interp1d(
self.x_grids,
np.concatenate([np.array([0]), runoff_]),
kind='next'
)
return lambda x: func_interp(x) + qx0
def get_bx(self):
b = self.r_df.B.values
bx = interpolate.interp1d(
self.x_grids,
np.concatenate([np.array([b[0]]), b]),
kind='next'
)
return bx
def get_jx(self):
j = self.r_df.J.values
jx = interpolate.interp1d(
self.x_grids,
np.concatenate([np.array([j[0]]), j]),
kind='next'
)
return jx
def get_haux(self, time):
q = self.get_qx(time)
b = self.get_bx()
j = self.get_jx()
# h = np.vectorize(lambda x: self.solve_manning(q(x), b(x), j(x)))
h = lambda x: (self.rough * q(x) / b(x) / j(x) ** (1.0 / 2)) ** (3.0 / 5)
area = lambda x: b(x) * h(x)
velocity = lambda x: q(x) / area(x)
return h, area, velocity
def solve_manning(self, q, b, j):
def func(h):
return (b * h / (b + 2 * h)) ** (2.0 / 3) * j ** (1.0 / 2) * b * h / self.rough - q
depth = fsolve(func, np.array([0.1]))
return depth
def get_sx(self, time):
h, a, u = self.get_haux(time)
x_ = self.x_grids[1:] - self.delta_x
areas_ = np.zeros_like(x_)
for i in range(len(areas_)):
areas_[i] = a(x_[i])
loads_ = self.reach_loads.values / (0.001 * areas_)
# sx = interpolate.interp1d(
# self.x_grids,
# np.concatenate([np.array([loads_[0]]), loads_]),
# kind='next'
# )
return np.column_stack([self.x_grids[:-1], loads_])
def load_interp(loads, u_to_r, r_df, sim_var):
var_loads = loads.reset_index().loc[:, ['UID', sim_var]]
merged = pd.merge(u_to_r, r_df, on='RID', how='left')
merged.loc[:, sim_var] = 0
for uid in var_loads.UID:
loads_uid = var_loads.loc[var_loads.UID == uid, sim_var]
reach_len_rid = merged.loc[merged.UID == uid, 'L'].values
loads_rid = loads_uid.values / reach_len_rid.sum() * reach_len_rid
merged.loc[merged.UID == uid, sim_var] = loads_rid
output = merged.groupby('RID').sum().loc[:, [sim_var]]
return output
if __name__ == '__main__':
Q = pd.read_csv('data/Q.csv', index_col=0, parse_dates=['Date'], header=0)
WQ = pd.read_excel('data/WQ.xlsx', index_col=0, parse_dates=['time'], header=0)
R = pd.read_excel('data/Reaches.xlsx', header=0, sheet_name='Reaches')
U_to_R = pd.read_excel('data/Reaches.xlsx', header=0, sheet_name='HRU_to_Reaches')
loads = pd.read_csv('data/Loads.csv', header=0)
runoff = pd.read_csv('data/PrecipRunoff.csv', header=0)
t_sim_start = '2019/1/26'
t_sim_end = '2019/10/22'
t_interp_start = '2019/1/26'
di = DataInterp(t_sim_start, t_sim_end, t_interp_start)
reach_loads = load_interp(loads, U_to_R, R, 'TP')
reach_runoff = load_interp(runoff, U_to_R, R, 'Runoff')