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Details for: "Perturbetd linear cable mass problem of order 20"

Name: Perturbetd linear cable mass problem of order 20 (Key: CO149)
Path: ackrep_data/system_models/compleib_models/CM6_IS View on GitHub
Type: system_model
Short Description: CM6_IS To CM1_IS corresponding example of order 960, sd=1.72485e-001
Created: 2022-10-10 15:57:36
Compatible Environment: default_conda_environment (Key: CDAMA)
Source Code [ / ] simulation.py
# This file was autogenerated from the template: simulation.py.template (2022-10-10 15:57:36).

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(960)

    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 15:57:36).

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 = 960

        # 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 15:57:36).

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 = 'Perturbetd linear cable mass problem of order 20'


# ---------- create symbolic parameters
A = sp.MatrixSymbol('A', 960, 960)
B = sp.MatrixSymbol('B', 960, 1)
B1 = sp.MatrixSymbol('B1', 960, 1)
C1 = sp.MatrixSymbol('C1', 3, 960)
C = sp.MatrixSymbol('C', 2, 960)
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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B_nv = sp.Matrix(np.array([[ 0.00000000e+000],
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       [-1.67596795e-225],
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       [-1.00430704e-155],
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       [-3.90821295e-135],
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       [-7.58173166e-133],
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       [-1.47081686e-130],
       [ 5.48916325e-130],
       [-2.04858361e-129],
       [ 7.64541813e-129],
       [-2.85330889e-128],
       [ 1.06486937e-127],
       [-3.97414661e-127],
       [ 1.48317171e-126],
       [-5.53527217e-126],
       [ 2.06579170e-125],
       [-7.70963957e-125],
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       [-1.07381427e-123],
       [ 4.00752940e-123],
       [-1.49563033e-122],
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       [-2.90144575e-120],
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       [-7.97196541e-109],
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       [-1.11035154e-107],
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       [-2.15402476e-105],
       [ 8.03892984e-105],
       [-3.00016946e-104],
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       [-4.17869700e-103],
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       [-5.82017410e-102],
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       [ 5.86906351e-098],
       [-2.19036432e-097],
       [ 8.17455093e-097],
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       [-4.24919387e-095],
       [ 1.58582074e-094],
       [-5.91836358e-094],
       [ 2.20876336e-093],
       [-8.24321708e-093],
       [ 3.07641049e-092],
       [-1.14813203e-091],
       [ 4.28488706e-091],
       [-1.59914162e-090],
       [ 5.96807778e-090],
       [-2.22731695e-089],
       [ 8.31246002e-089],
       [-3.10225231e-088],
       [ 1.15777632e-087],
       [-4.32088007e-087],
       [ 1.61257439e-086],
       [-6.01820957e-086],
       [ 2.24602639e-085],
       [-8.38228460e-085],
       [ 3.12831120e-084],
       [-1.16750163e-083],
       [ 4.35717542e-083],
       [-1.62612000e-082],
       [ 6.06876247e-082],
       [-2.26489299e-081],
       [ 8.45269571e-081],
       [-3.15458898e-080],
       [ 1.17730864e-079],
       [-4.39377565e-079],
       [ 1.63977940e-078],
       [-6.11974002e-078],
       [ 2.28391807e-077],
       [-8.52369827e-077],
       [ 3.18108750e-076],
       [-1.18719802e-075],
       [ 4.43068332e-075],
       [-1.65355353e-074],
       [ 6.17114577e-074],
       [-2.30310296e-073],
       [ 8.59529725e-073],
       [-3.20780860e-072],
       [ 1.19717047e-071],
       [-4.46790102e-071],
       [ 1.66744336e-070],
       [-6.22298334e-070],
       [ 2.32244900e-069],
       [-8.66749766e-069],
       [ 3.23475416e-068],
       [-1.20722669e-067],
       [ 4.50543134e-067],
       [-1.68144987e-066],
       [ 6.27525633e-066],
       [-2.34195755e-065],
       [ 8.74030455e-065],
       [-3.26192607e-064],
       [ 1.21736738e-063],
       [-4.54327692e-063],
       [ 1.69557403e-062],
       [-6.32796842e-062],
       [ 2.36162997e-061],
       [-8.81372302e-061],
       [ 3.28932621e-060],
       [-1.22759326e-059],
       [ 4.58144040e-059],
       [-1.70981683e-058],
       [ 6.38112330e-058],
       [-2.38146764e-057],
       [ 8.88775821e-057],
       [-3.31695652e-056],
       [ 1.23790503e-055],
       [-4.61992445e-055],
       [ 1.72417928e-054],
       [-6.43472467e-054],
       [ 2.40147194e-053],
       [-8.96241529e-053],
       [ 3.34481892e-052],
       [-1.24830342e-051],
       [ 4.65873177e-051],
       [-1.73866237e-050],
       [ 6.48877629e-050],
       [-2.42164428e-049],
       [ 9.03769949e-049],
       [-3.37291537e-048],
       [ 1.25878915e-047],
       [-4.69786507e-047],
       [ 1.75326711e-046],
       [-6.54328195e-046],
       [ 2.44198607e-045],
       [-9.11361608e-045],
       [ 3.40124782e-044],
       [-1.26936297e-043],
       [ 4.73732709e-043],
       [-1.76799454e-042],
       [ 6.59824545e-042],
       [-2.46249873e-041],
       [ 9.19017036e-041],
       [-3.42981827e-040],
       [ 1.28002561e-039],
       [-4.77712059e-039],
       [ 1.78284568e-038],
       [-6.65367065e-038],
       [ 2.48318369e-037],
       [-9.26736770e-037],
       [ 3.45862871e-036],
       [-1.29077781e-035],
       [ 4.81724836e-035],
       [-1.79782156e-034],
       [ 6.70956142e-034],
       [-2.50404241e-033],
       [ 9.34521350e-033],
       [-3.48768116e-032],
       [ 1.30162033e-031],
       [-4.85771320e-031],
       [ 1.81292325e-030],
       [-6.76592167e-030],
       [ 2.52507634e-029],
       [-9.42371320e-029],
       [ 3.51697765e-028],
       [-1.31255393e-027],
       [ 4.89851794e-027],
       [-1.82815178e-026],
       [ 6.82275534e-026],
       [-2.54628696e-025],
       [ 9.50287230e-025],
       [-3.54652022e-024],
       [ 1.32357937e-023],
       [-4.93966544e-023],
       [ 1.84350824e-022],
       [-6.88006642e-022],
       [ 2.56767574e-021],
       [-9.58269633e-021],
       [ 3.57631096e-020],
       [-1.33469742e-019],
       [ 4.98115858e-019],
       [-1.85899369e-018],
       [ 6.93785891e-018],
       [-2.58924419e-017],
       [ 9.66319088e-017],
       [-3.60635193e-016],
       [ 1.34590886e-015],
       [-5.02300027e-015],
       [ 1.87460922e-014],
       [-6.99613685e-014],
       [ 2.61099382e-013],
       [-9.74436159e-013],
       [ 3.63664525e-012],
       [-1.35721449e-011],
       [ 5.06519342e-011],
       [-1.89035592e-010],
       [ 7.05490433e-010],
       [-2.63292614e-009],
       [ 9.82621413e-009],
       [-3.66719304e-008],
       [ 1.36861507e-007],
       [-5.10774099e-007],
       [ 1.90623489e-006],
       [-7.11416546e-006],
       [ 2.65504269e-005],
       [-9.90875423e-005],
       [ 3.69799742e-004],
       [-1.38011143e-003],
       [ 5.15064597e-003],
       [-1.92224724e-002],
       [ 7.17392438e-002],
       [-2.67734503e-001],
       [ 9.99198767e-001]]))
B1_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],
       [ 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],
       [ 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],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
       [ 0.00000000e+000],
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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.,
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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.,
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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.]]))
C_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., 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.,
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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.,
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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., 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:
Download pdf
Result: Script Error.
Last Build: Checkout CI Build
Runtime: 181.1 (estimated: 10s)
Debug:
Checking <SystemModel (pk: 43, key: CO149)> "(Perturbetd linear cable mass problem of order 20, 10s)"
12:43:28 - ackrep - INFO -   ... Creating exec-script ... 
12:43:28 - ackrep - INFO - execscript-path: /code/ackrep/ackrep_data/execscript.py
12:43:28 - ackrep - INFO -   ... running exec-script /code/ackrep/ackrep_data/execscript.py ... 
12:46:28 - ackrep - ERROR - Entity calculation reached timeout (180s).
Fail.