def test_force_model(force_scale_factor, Km, tau_1, tau_2):
if not isinstance(force_scale_factor, numbers.Number) or not isinstance(Km, numbers.Number) \
or not isinstance(tau_1, numbers.Number) or not isinstance(tau_2, numbers.Number):
with pytest.raises(TypeError):
ForceModel(force_scale_factor, Km, tau_1, tau_2)
elif force_scale_factor < 0 or Km < 0 or tau_1 < 0 or tau_2 <= 0:
with pytest.raises(Exception):
ForceModel(force_scale_factor, Km, tau_1, tau_2)
import pytest
import numbers
from model.force_model import ForceModel
from model.fatigue_model import dynamics
@pytest.mark.parametrize("t, Y, force_model, tau_fat, alpha_scale_factor, "
"alpha_Km, alpha_tau_1, train, tau_c, result", [
(1, [1, 1, 1, 1, 1], ForceModel(
1, 2, 1, 1), 1, 1, 1, 1, [1], 1, [1, 2, 1]),
(1, [1, 1, 1, 1], ForceModel(
1, 2, 1, 1), 1, 1, 1, 1, [1], 1, [1, 1, 1]),
(1, [1, 1, 1, 1, 1], ForceModel(
1, 2, 1, 1), -1, 1, 1, 1, [1], 1, [1, 1, 1]),
("1", ["1", 1, 1, 1, 1], ForceModel(1, 2, 1, 1),
"1", "1", 1, "1", [1], "1", [1, 1, 1]),
])
def test_dynamics(t, Y, force_model, tau_fat, alpha_scale_factor, alpha_Km,
alpha_tau_1, train, tau_c, result):
if not isinstance(t, numbers.Number) or not isinstance(tau_fat, numbers.Number) \
or not isinstance(alpha_scale_factor, numbers.Number) or not isinstance(alpha_Km, numbers.Number) \
or not isinstance(alpha_tau_1, numbers.Number) or not isinstance(tau_c, numbers.Number) \
or not hasattr(Y, '__len__'):
with pytest.raises(TypeError):
dynamics(t, Y, force_model, tau_fat, alpha_scale_factor, alpha_Km,
alpha_tau_1, train, tau_c)
elif len(Y) != 5 or tau_fat < 0:
with pytest.raises(Exception):
dynamics(t, Y, force_model, tau_fat, alpha_scale_factor, alpha_Km,
alpha_tau_1, train, tau_c)
else:
import os from scipy.integrate import cumulative_trapezoid, simps CONFIG_PATH = 'configuration/vft_cft33_configuration.csv' config = load_model_configuration(CONFIG_PATH) tau_c, tau_2, tau_fat, alpha_scale_factor, alpha_Km, alpha_tau_1, \ CN0, F0, scale_factor_rest, tau_1_rest, Km_rest = config.values() CONFIG_PATH = 'dft155_configuration.csv' config = load_model_configuration(CONFIG_PATH) tau_c, tau_2, tau_fat, alpha_scale_factor, alpha_Km, alpha_tau_1, \ CN0, F0, scale_factor_rest, tau_1_rest, Km_rest = config.values() total_time = (0, 60 * 1000) force_model = ForceModel(scale_factor_rest, Km_rest, tau_1_rest, tau_2) initial_state = [CN0, F0, scale_factor_rest, Km_rest, tau_1_rest] simulation_trains = [0, 0, 0, 0, 0, 0, 0, 0, 0] simulation_trains[0] = generate_constant_train(20, 10 * 1000) simulation_trains[1] = generate_constant_train(50, 10 * 1000) simulation_trains[2] = generate_constant_train(80, 10 * 1000) simulation_trains[3] = generate_variable_train(20, 10 * 1000) simulation_trains[4] = generate_variable_train(50, 10 * 1000) simulation_trains[5] = generate_variable_train(80, 10 * 1000) simulation_trains[6] = generate_doublet_train(20, 10 * 1000) simulation_trains[7] = generate_doublet_train(50, 10 * 1000) simulation_trains[8] = generate_doublet_train(80, 10 * 1000) for i in range(9):
def force_model():
"""Returns a ForceModel object"""
return ForceModel(1, 2, 1, 1)