simulation.py
# This file was autogenerated from the template: simulation.py.template (2022-10-10 15:54:03).
import numpy as np
import system_model
from scipy.integrate import solve_ivp, odeint
from ackrep_core import ResultContainer
from ackrep_core.system_model_management import save_plot_in_dir
import matplotlib.pyplot as plt
import os
from ipydex import Container
# link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html
def simulate():
"""
simulate the system model with scipy.integrate.solve_ivp
:return: result of solve_ivp, might contains input function
"""
model = system_model.Model()
rhs_xx_pp_symb = model.get_rhs_symbolic()
rhs = model.get_rhs_func()
# initial state values
xx0 = np.ones(model.sys_dim)
t_end = 10
tt = np.linspace(0, t_end, 1000)
simulation_data = solve_ivp(rhs, (0, t_end), xx0, t_eval=tt)
# using odeint for models with large state vectors
# res = odeint(rhs, y0=xx0, t=tt, tfirst=True)
# simulation_data = Container()
# simulation_data.y = res.transpose()
# simulation_data.t = tt
# postprocessing: calc output
ny = 4
C = model.get_parameter_value("C")
D21 = model.get_parameter_value("D21")
output = np.zeros((ny, len(tt)))
for i in range(len(tt)):
output[:,i] = np.matmul(C, simulation_data.y[:,i]) # + np.matmul(D21, w)
simulation_data.output = output
save_plot(simulation_data)
return simulation_data
def save_plot(simulation_data):
"""
plot your data and save the plot
access to data via: simulation_data.t array of time values
simulation_data.y array of data components
simulation_data.uu array of input values
:param simulation_data: simulation_data of system_model
:return: None
"""
for i in range(simulation_data.output.shape[0]):
plt.plot(simulation_data.t, simulation_data.output[i], label=f"$y_{i}$")
plt.legend()
plt.tight_layout()
save_plot_in_dir()
def evaluate_simulation(simulation_data):
"""
assert that the simulation results are as expected
:param simulation_data: simulation_data of system_model
:return:
"""
expected_final_state = np.array([-3.68463210e+10, -1.00601123e+11, 5.27960755e+11, 1.44148117e+12,
4.65888711e-15, -4.25919464e+09, -1.16287973e+10, -1.60167853e+08,
-4.25638580e+08])
rc = ResultContainer(score=1.0)
simulated_final_state = simulation_data.y[:, -1]
rc.final_state_errors = [
simulated_final_state[i] - expected_final_state[i] for i in np.arange(0, len(simulated_final_state))
]
rc.success = np.allclose(expected_final_state, simulated_final_state, rtol=0, atol=1e-2)
return rc
system_model.py
# This file was autogenerated from the template: system_model.py.template (2022-10-10 15:54:03).
import sympy as sp
import numpy as np
import symbtools as st
import importlib
import sys, os
#from ipydex import IPS, activate_ips_on_exception
from ackrep_core.system_model_management import GenericModel, import_parameters
# Import parameter_file
params = import_parameters()
#link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html
class Model(GenericModel):
def initialize(self):
"""
this function is called by the constructor of GenericModel
:return: None
"""
# Define number of inputs -- MODEL DEPENDENT
self.u_dim = 1
# Set "sys_dim" to constant value, if system dimension is constant
self.sys_dim = 9
# check existence of params file
self.has_params = True
self.params = params
# ----------- SET DEFAULT INPUT FUNCTION ---------- #
def uu_default_func(self):
"""
define input function
:return:(function with 2 args - t, xx_nv) default input function
"""
def uu_rhs(t, xx_nv):
"""
sequence of numerical input values
:param t:(scalar or vector) time
:param xx_nv:(vector or array of vectors) numeric state vector
:return:(list) numeric inputs
"""
u = np.zeros(self.u_dim)
return u
return uu_rhs
# ----------- SYMBOLIC RHS FUNCTION ---------- #
def get_rhs_symbolic(self):
"""
define symbolic rhs function
:return: matrix of symbolic rhs-functions
"""
if self.dxx_dt_symb is not None:
return self.dxx_dt_symb
x = self.xx_symb
A, B, B1, C1, C, D11, D12, D21 = self.pp_symb # parameters
w = np.zeros(5) # noise
u = self.uu_symb # inputs
# define symbolic rhs functions
self.dxx_dt_symb = np.matmul(A,x) + np.matmul(B1,w) + np.matmul(B,u)
return self.dxx_dt_symb
parameters.py
# This file was autogenerated from the template: parameters.py.template (2022-10-10 15:54:03).
import sys
import os
import numpy as np
import sympy as sp
import tabulate as tab
#link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html
# set model name
model_name = 'NN6'
# ---------- create symbolic parameters
A = sp.MatrixSymbol('A', 9, 9)
B = sp.MatrixSymbol('B', 9, 1)
B1 = sp.MatrixSymbol('B1', 9, 5)
C1 = sp.MatrixSymbol('C1', 3, 9)
C = sp.MatrixSymbol('C', 4, 9)
D11 = sp.MatrixSymbol('D11', 3, 5)
D12 = sp.MatrixSymbol('D12', 3, 1)
D21 = sp.MatrixSymbol('D21', 4, 5)
pp_symb = [A, B, B1, C1, C, D11, D12, D21]
# ---------- create auxiliary symbolic parameters
# set numerical values of auxiliary parameters
# trailing "_nv" stands for "numerical value"
A_nv = sp.Matrix(np.array([[ 0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
0.00e+00, 0.00e+00, 0.00e+00],
[ 0.00e+00, -2.00e+01, -4.20e+00, 0.00e+00, 4.45e+00, 1.25e+01,
0.00e+00, 1.00e+02, 0.00e+00],
[ 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, 0.00e+00,
0.00e+00, 0.00e+00, 0.00e+00],
[ 0.00e+00, 4.70e+00, 8.35e+00, 0.00e+00, -1.10e+00, 0.00e+00,
0.00e+00, 0.00e+00, 0.00e+00],
[ 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -3.30e+00, 0.00e+00,
0.00e+00, 0.00e+00, 0.00e+00],
[ 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
1.00e+00, 0.00e+00, 0.00e+00],
[ 0.00e+00, 1.09e+01, 0.00e+00, 0.00e+00, -2.55e+00, -2.50e+02,
0.00e+00, 0.00e+00, 0.00e+00],
[ 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
0.00e+00, 0.00e+00, 1.00e+00],
[ 0.00e+00, 5.90e+00, 0.00e+00, 0.00e+00, -1.39e+00, 0.00e+00,
0.00e+00, -3.70e+03, 0.00e+00]]))
B_nv = sp.Matrix(np.array([[0. ],
[0. ],
[0. ],
[0. ],
[3.3],
[0. ],
[0. ],
[0. ],
[0. ]]))
B1_nv = sp.Matrix(np.array([[0. ],
[0. ],
[0. ],
[0. ],
[3.3],
[0. ],
[0. ],
[0. ],
[0. ]]))
C1_nv = sp.Matrix(np.array([[1., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0.]]))
C_nv = sp.Matrix(np.array([[1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 1. , 0. , 0. , 0.66, 0. , 1.2 , 0. ],
[0. , 0. , 0. , 1. , 0. , 0. , 0.66, 0. , 1.2 ]]))
D11_nv = sp.Matrix(np.array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]]))
D12_nv = sp.Matrix(np.array([[0.],
[0.],
[1.]]))
D21_nv = sp.Matrix(np.array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]]))
# ---------- create symbolic parameter functions
# parameter values can be constant/fixed values OR set in relation to other parameters (for example: a = 2*b)
# list of symbolic parameter functions
# tailing "_sf" stands for "symbolic parameter function"
pp_sf = [A_nv, B_nv, B1_nv, C1_nv, C_nv, D11_nv, D12_nv, D21_nv]
# ---------- list for substitution
# -- entries are tuples like: (independent symbolic parameter, numerical value)
pp_subs_list = []
# OPTONAL: Dictionary which defines how certain variables shall be written
# in the table - key: Symbolic Variable, Value: LaTeX Representation/Code
# useful for example for complex variables: {Z: r"\underline{Z}"}
latex_names = {}
# ---------- Define LaTeX table
# Define table header
# DON'T CHANGE FOLLOWING ENTRIES: "Symbol", "Value"
tabular_header = ["Symbol", "Value"]
# Define column text alignments
col_alignment = ["center", "left"]
# Define Entries of all columns before the Symbol-Column
# --- Entries need to be latex code
col_1 = []
# contains all lists of the columns before the "Symbol" Column
# --- Empty list, if there are no columns before the "Symbol" Column
start_columns_list = []
# Define Entries of the columns after the Value-Column
# --- Entries need to be latex code
col_4 = []
# contains all lists of columns after the FIX ENTRIES
# --- Empty list, if there are no columns after the "Value" column
end_columns_list = []