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Details for: "Linear cable mass problem of order 20"

Steps for local reproduction
Name: Linear cable mass problem of order 20 (Key: CO142)
Path: ackrep_data/system_models/compleib_models/CM5 View on GitHub
Type: system_model
Short Description: CM5 To CM1 corresponding example of order 480.
Created: 2022-10-10 15:54:19
Compatible Environment: default_conda_environment (Key: CDAMA)
Source Code [ / ] simulation.py 
# This file was autogenerated from the template: simulation.py.template (2022-10-10 15:55:22).

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 = 2
    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.zeros(480)

    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 15:55:22).

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 = 480

        # 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(1) # 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 15:55:22).

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 = 'Linear cable mass problem of order 20'


# ---------- create symbolic parameters
A = sp.MatrixSymbol('A', 480, 480)
B = sp.MatrixSymbol('B', 480, 1)
B1 = sp.MatrixSymbol('B1', 480, 1)
C1 = sp.MatrixSymbol('C1', 3, 480)
C = sp.MatrixSymbol('C', 2, 480)
D11 = sp.MatrixSymbol('D11', 3, 1)
D12 = sp.MatrixSymbol('D12', 3, 1)
D21 = sp.MatrixSymbol('D21', 2, 1)

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([[ 0.00000000e+000,  0.00000000e+000,  0.00000000e+000,
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        -1.72036344e-003,  6.42048376e-003, -5.06367590e-003]]))
B_nv = sp.Matrix(np.array([[ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 5.96329979e-090],
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       [-6.01339144e-086],
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       [-8.37557381e-085],
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       [-1.62481815e-082],
       [ 6.06390387e-082],
       [-2.26307973e-081],
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       [-3.15206345e-080],
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       [ 3.28669281e-060],
       [-1.22661045e-059],
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       [ 8.88064275e-057],
       [-3.31430099e-056],
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       [-2.41970553e-049],
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       [ 3.39852481e-044],
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       [ 6.59296295e-042],
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       [ 9.18281279e-041],
       [-3.42707239e-040],
       [ 1.27900083e-039],
       [-4.77329607e-039],
       [ 1.78141835e-038],
       [-6.64834378e-038],
       [ 2.48119568e-037],
       [-9.25994833e-037],
       [ 3.45585976e-036],
       [-1.28974442e-035],
       [ 4.81339171e-035],
       [-1.79638224e-034],
       [ 6.70418980e-034],
       [-2.50203770e-033],
       [ 9.33773180e-033],
       [-3.48488895e-032],
       [ 1.30057826e-031],
       [-4.85382415e-031],
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       [-6.76050493e-030],
       [ 2.52305479e-029],
       [-9.41616866e-029],
       [ 3.51416198e-028],
       [-1.31150311e-027],
       [ 4.89459623e-027],
       [-1.82668818e-026],
       [ 6.81729310e-026],
       [-2.54424842e-025],
       [ 9.49526438e-025],
       [-3.54368091e-024],
       [ 1.32251972e-023],
       [-4.93571079e-023],
       [ 1.84203234e-022],
       [-6.87455830e-022],
       [ 2.56562008e-021],
       [-9.57502451e-021],
       [ 3.57344779e-020],
       [-1.33362887e-019],
       [ 4.97717071e-019],
       [-1.85750540e-018],
       [ 6.93230452e-018],
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       [ 9.65545462e-017],
       [-3.60346472e-016],
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       [ 3.63373379e-012],
       [-1.35612791e-011],
       [ 5.06113827e-011],
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       [-7.10846992e-006],
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       [-1.92070831e-002],
       [ 7.16818100e-002],
       [-2.67520157e-001],
       [ 9.98398817e-001]]))
B1_nv = sp.Matrix(np.array([[ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
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       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [-1.86845771e-137],
       [ 7.47383084e-137],
       [-2.80268657e-136],
       [ 1.04633632e-135],
       [-3.90507662e-135],
       [ 1.45739701e-134],
       [-5.43908040e-134],
       [ 2.02989246e-133],
       [-7.57566179e-133],
       [ 2.82727547e-132],
       [-1.05515357e-131],
       [ 3.93788673e-131],
       [-1.46963934e-130],
       [ 5.48476867e-130],
       [-2.04694354e-129],
       [ 7.63929727e-129],
       [-2.85102456e-128],
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       [-3.97096494e-127],
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       [-5.53084067e-126],
       [ 2.06413784e-125],
       [-7.70346729e-125],
       [ 2.87497313e-124],
       [-1.07295458e-123],
       [ 4.00432101e-123],
       [-1.49443295e-122],
       [ 5.57729968e-122],
       [-2.08147658e-121],
       [ 7.76817634e-121],
       [-2.89912288e-120],
       [ 1.08196739e-119],
       [-4.03795727e-119],
       [ 1.50698617e-118],
       [-5.62414894e-118],
       [ 2.09896096e-117],
       [-7.83342895e-117],
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       [-1.09105590e-115],
       [ 4.07187607e-115],
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       [ 7.89922967e-113],
       [-2.94803265e-112],
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       [-4.10607979e-111],
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       [-5.71903137e-110],
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       [-7.96558312e-109],
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       [ 4.14057081e-107],
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       [ 5.76707118e-106],
       [-2.15230027e-105],
       [ 8.03249394e-105],
       [-2.99776755e-104],
       [ 1.11878208e-103],
       [-4.17535157e-103],
       [ 1.55826242e-102],
       [-5.81551452e-102],
       [ 2.17037957e-101],
       [-8.09996681e-101],
       [ 3.02294877e-100],
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       [ 5.96329979e-090],
       [-2.22553378e-089],
       [ 8.30580513e-089],
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       [-4.31742081e-087],
       [ 1.61128338e-086],
       [-6.01339144e-086],
       [ 2.24422824e-085],
       [-8.37557381e-085],
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       [-1.62481815e-082],
       [ 6.06390387e-082],
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       [-2.37956105e-057],
       [ 8.88064275e-057],
       [-3.31430099e-056],
       [ 1.23691397e-055],
       [-4.61622578e-055],
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       [ 6.48358143e-050],
       [-2.41970553e-049],
       [ 9.03046399e-049],
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       [ 1.75186346e-046],
       [-6.53804345e-046],
       [ 2.44003103e-045],
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       [ 3.39852481e-044],
       [-1.26834673e-043],
       [ 4.73353443e-043],
       [-1.76657910e-042],
       [ 6.59296295e-042],
       [-2.46052727e-041],
       [ 9.18281279e-041],
       [-3.42707239e-040],
       [ 1.27900083e-039],
       [-4.77329607e-039],
       [ 1.78141835e-038],
       [-6.64834378e-038],
       [ 2.48119568e-037],
       [-9.25994833e-037],
       [ 3.45585976e-036],
       [-1.28974442e-035],
       [ 4.81339171e-035],
       [-1.79638224e-034],
       [ 6.70418980e-034],
       [-2.50203770e-033],
       [ 9.33773180e-033],
       [-3.48488895e-032],
       [ 1.30057826e-031],
       [-4.85382415e-031],
       [ 1.81147184e-030],
       [-6.76050493e-030],
       [ 2.52305479e-029],
       [-9.41616866e-029],
       [ 3.51416198e-028],
       [-1.31150311e-027],
       [ 4.89459623e-027],
       [-1.82668818e-026],
       [ 6.81729310e-026],
       [-2.54424842e-025],
       [ 9.49526438e-025],
       [-3.54368091e-024],
       [ 1.32251972e-023],
       [-4.93571079e-023],
       [ 1.84203234e-022],
       [-6.87455830e-022],
       [ 2.56562008e-021],
       [-9.57502451e-021],
       [ 3.57344779e-020],
       [-1.33362887e-019],
       [ 4.97717071e-019],
       [-1.85750540e-018],
       [ 6.93230452e-018],
       [-2.58717127e-017],
       [ 9.65545462e-017],
       [-3.60346472e-016],
       [ 1.34483134e-015],
       [-5.01897890e-015],
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       [-6.99053581e-014],
       [ 2.60890348e-013],
       [-9.73656034e-013],
       [ 3.63373379e-012],
       [-1.35612791e-011],
       [ 5.06113827e-011],
       [-1.88884252e-010],
       [ 7.04925624e-010],
       [-2.63081824e-009],
       [ 9.81834735e-009],
       [-3.66425712e-008],
       [ 1.36751937e-007],
       [-5.10365178e-007],
       [ 1.90470877e-006],
       [-7.10846992e-006],
       [ 2.65291709e-005],
       [-9.90082137e-005],
       [ 3.69503684e-004],
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       [ 5.14652240e-003],
       [-1.92070831e-002],
       [ 7.16818100e-002],
       [-2.67520157e-001],
       [ 9.98398817e-001]]))
C1_nv = sp.Matrix(np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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C_nv = sp.Matrix(np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]))
D11_nv = sp.Matrix(np.array([[0.],
       [0.],
       [0.]]))
D12_nv = sp.Matrix(np.array([[0.        ],
       [0.        ],
       [1.22474487]]))
D21_nv = sp.Matrix(np.array([[0.],
       [1.]]))


# ---------- 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 = []
Related Problems:
Extensive Material:
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Result: Script Error.
Last Build: Checkout CI Build
Runtime: 181.1 (estimated: 10s)
Debug: 
Checking <SystemModel (pk: 40, key: CO142)> "(Linear cable mass problem of order 20, 10s)"
12:34:25 - ackrep - INFO -   ... Creating exec-script ... 
12:34:25 - ackrep - INFO - execscript-path: /code/ackrep/ackrep_data/execscript.py
12:34:25 - ackrep - INFO -   ... running exec-script /code/ackrep/ackrep_data/execscript.py ... 
12:37:25 - ackrep - ERROR - Entity calculation reached timeout (180s).
Fail.