def test_fatigue_disabled():
motor_unit_count = 120
p = Pool(motor_unit_count, apply_fatigue=False)
max_input = 67.0
first_output = p.step(np.full(motor_unit_count, max_input), 1.0)
first_output_sum = np.sum(first_output)
# Advance the simulation 10 seconds
for _ in range(10):
next_output = p.step(np.full(motor_unit_count, max_input), 1.0)
next_output_sum = np.sum(next_output)
# Should NOT have changed
assert next_output_sum == pytest.approx(first_output_sum)
def test_precacl_values():
# Pre calculated values and on-the-fly values should be the same
motor_unit_count = 120
p1 = Pool(motor_unit_count)
p2 = Pool(motor_unit_count, pre_calc_firing_rates=True)
moderate_input = 40.0
input_array = np.full(motor_unit_count, moderate_input)
output1 = p1.step(input_array, 1.0)
output1_sum = np.sum(output1)
output2 = p2.step(input_array, 1.0)
output2_sum = np.sum(output2)
assert output1_sum == pytest.approx(output2_sum)
def test_fatigue_values():
motor_unit_count = 120
p = Pool(motor_unit_count)
max_input = 67.0
first_output = p.step(np.full(motor_unit_count, max_input), 1.0)
first_output_sum = np.sum(first_output)
# Advance the simulation 10 seconds
for _ in range(10):
adapted_output = p.step(np.full(motor_unit_count, max_input), 1.0)
adapted_output_sum = np.sum(adapted_output)
# Should have changed
assert adapted_output_sum != pytest.approx(first_output_sum)
# To this value
expected_adapted_output = 3749.91061
assert adapted_output_sum == pytest.approx(expected_adapted_output)
def test_step():
motor_unit_count = 120
p = Pool(motor_unit_count)
# Missing arguments
with pytest.raises(TypeError):
p.step()
# Bad type
with pytest.raises(TypeError):
p.step(33.0, 1)
# Wrong shape
with pytest.raises(AssertionError):
p.step(np.ones(3), 1)
# No excitation
output = p.step(np.zeros(motor_unit_count), 1.0)
output_sum = sum(output)
assert output_sum == pytest.approx(0.0)
# Moderate
p = Pool(motor_unit_count)
moderate_input = 40.0
moderate_output = 3503.58881
output = p.step(np.full(motor_unit_count, moderate_input), 1.0)
output_sum = np.sum(output)
assert output_sum == pytest.approx(moderate_output)
# Max
p = Pool(motor_unit_count)
max_input = 67.0
max_output = 3915.06787
output = p.step(np.full(motor_unit_count, max_input), 1.0)
output_sum = np.sum(output)
assert output_sum == pytest.approx(max_output)
# Above
p = Pool(motor_unit_count)
output = p.step(np.full(motor_unit_count, max_input + 40), 1.0)
output_sum = np.sum(output)
assert output_sum == pytest.approx(max_output)
import numpy as np
import plotly.graph_objs as go
from plotly.offline import plot
# from pymuscle import PotvinFuglevand2017MuscleFibers as Fibers
from pymuscle import PyMuscleFibers as Fibers
from pymuscle import PotvinFuglevand2017MotorNeuronPool as Pool
motor_unit_count = 120
motor_unit_indices = np.arange(1, motor_unit_count + 1)
# Motor Neuron Pool
pool = Pool(motor_unit_count)
# Fibers
fibers = Fibers(motor_unit_count)
# excitations = np.full(motor_unit_count, 10.0)
# firing_rates = pool._calc_firing_rates(excitations)
# normalized_firing_rates = fibers._normalize_firing_rates(firing_rates)
# normalized_forces = fibers._calc_normalized_forces(normalized_firing_rates)
# forces = fibers._calc_inst_forces(normalized_forces)
# Motor Neuron Pool Charts
if False:
# Recruitment Thresholds
if True:
fig = go.Figure(
data=[go.Scatter(
x=motor_unit_indices,