def test_box_convolution1(self):
for box in [
BoxConvolution('CONV-1', [1]),
BoxConvolution('CONV-1', [1], True)
]:
box.add(10)
self.assertEqual(box.input(), 10)
self.assertEqual(box.size(), 0)
self.assertEqual(box.output(), 0)
box.step()
self.assertEqual(box.input(), 0)
self.assertEqual(box.size(), 0)
self.assertEqual(box.output(), 10)
box.step()
self.assertEqual(box.input(2), 10)
self.assertEqual(box.size(2), 0)
self.assertEqual(box.output(2), 0)
self.assertEqual(box.input(1), 0)
self.assertEqual(box.size(1), 0)
self.assertEqual(box.output(1), 10)
self.assertEqual(box.input(), 0)
self.assertEqual(box.size(), 0)
self.assertEqual(box.output(), 10) # nothing has been removed
def test_box_force_output10(self):
for box in [
BoxConvolution('CONV-10', 9 * [0] + [1]),
BoxConvolution('CONV-10', 9 * [0] + [1], True)
]:
inputs = [
1, 2, 4, 8, 10, 12, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0,
0, 0, 0, 0
]
r = [
1, 3, 7, 15, 25, 37, 47, 56, 64, 70, 74, 75, 71, 64, 54, 45,
36, 28, 21, 15, 10, 6, 3, 1, 0, 0
]
for i in range(4):
box.add(inputs[i])
box.step()
box.remove(box.output())
self.assertAlmostEqual(box.size(), r[3])
self.assertAlmostEqual(box.output(), 0)
box.force_output(10)
self.assertAlmostEqual(box.size(), 15 - 10)
box.force_output(10)
self.assertAlmostEqual(box.size(), 0)
def test_box_convolution_delay1(self):
for box in [
BoxConvolution('CONV-1', [1]),
BoxConvolution('CONV-1', [1], True)
]:
inputs = [1, 2, 4, 8, 10, 12, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
r = [0 for x in inputs]
outputs = [1, 2, 4, 8, 10, 12, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
self.check_input_output(box, inputs, r, outputs)
def test_box_convolution_delay10(self):
for box in [
BoxConvolution('CONV-10', 9 * [0] + [1]),
BoxConvolution('CONV-10', 9 * [0] + [1], True)
]:
inputs = [
1, 2, 4, 8, 10, 12, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0,
0, 0, 0, 0
]
r = [
1, 3, 7, 15, 25, 37, 47, 56, 64, 70, 74, 75, 71, 64, 54, 45,
36, 28, 21, 15, 10, 6, 3, 1, 0, 0
]
outputs = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 8, 10, 12, 10, 9, 8, 7, 6,
5, 4, 3, 2, 1, 0
]
self.check_input_output(box, inputs, r, outputs)
def __init__(self, parameters):
self._parameters = dict(parameters) # to not modify parameters
self._integer = parameters['integer_flux']
self._boxes = {
'SE':
BoxSource('SE'),
'INCUB':
BoxConvolution('INCUB', compute_khi_delay(self.delay('dm_incub')),
self._integer),
'IR':
BoxConvolution('IR', compute_khi_exp(self.delay('dm_r')),
self._integer),
'IH':
BoxConvolution('IH', compute_khi_exp(self.delay('dm_h')),
self._integer),
'SM':
BoxConvolution('SM', compute_khi_exp(self.delay('dm_sm')),
self._integer),
'SI':
BoxConvolution('SI', [
0, 0.03, 0.03, 0.04, 0.05, 0.05, 0.05, 0.05, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.05, 0.03, 0.02
], self._integer),
'SS':
BoxConvolution('SS', compute_khi_exp(self.delay('dm_ss')),
self._integer),
'R':
BoxTarget('R'),
'DC':
BoxTarget('DC')
}
# src -> [targets]
def lambda_coefficient(a, b=None):
if isinstance(a, int):
return lambda: a
elif b == None:
return lambda: self.coefficient(a)
else:
return lambda: self.coefficient(a) * self.coefficient(b)
self._moves = {
'INCUB': [('IH', lambda_coefficient('pc_ih')),
('IR', lambda_coefficient('pc_ir'))],
'IR': [('R', lambda_coefficient(1))],
'IH': [('SM', lambda_coefficient('pc_sm')),
('SI', lambda_coefficient('pc_si'))],
'SM': [('SI', lambda_coefficient('pc_sm_si')),
('DC', lambda_coefficient('pc_sm_dc')),
('SS', lambda_coefficient('pc_sm_out', 'pc_h_ss')),
('R', lambda_coefficient('pc_sm_out', 'pc_h_r'))],
'SI': [('DC', lambda_coefficient('pc_si_dc')),
('SS', lambda_coefficient('pc_si_out', 'pc_h_ss')),
('R', lambda_coefficient('pc_si_out', 'pc_h_r'))],
'SS': [('R', lambda_coefficient(1))]
}
self.time = -1 # first step should be t=0
self.e0 = int(self.constant('population') * self.coefficient('kpe'))
self.box('SE').add(self.e0 - self.constant('patient0'))
self.box('INCUB').add(self.constant('patient0'))
def test_box_int_convolution2(self):
box = BoxConvolution('CONV-2', [0.4, 0.6], True)
box.add(10)
box.step()
self.assertEqual(box.input(), 0)
self.assertEqual(box.size(), int(0.6 * 10))
self.assertEqual(box.output(), int(0.4 * 10))
box.remove(int(0.4 * 10))
box.add(20)
box.step()
self.assertEqual(box.input(), 0)
self.assertEqual(box.size(),
int(0.6 * 10) + 20 - int(0.6 * 10) - int(0.4 * 20))
self.assertEqual(box.output(), int(0.6 * 10) + int(0.4 * 20))
def test_box_convolution2(self):
box = BoxConvolution('CONV-2', [0.4, 0.6])
box.add(10)
box.step()
self.assertEqual(box.input(), 0)
self.assertEqual(box.size(), 0.6 * 10)
self.assertEqual(box.output(), 0.4 * 10)
box.remove(0.4 * 10)
box.add(20)
box.step()
self.assertEqual(box.input(), 0)
self.assertEqual(box.size(), (0.6 * 10) + 20 - (0.6 * 10) - (0.4 * 20))
self.assertEqual(box.output(), 0.6 * 10 + 0.4 * 20)
def test_box_queue_decrease_duration(self):
box = BoxConvolution('DELAY', [0, 0, 0, 0, 1])
inputs = [5] * 5 + [0] * 5
r = [5, 10, 15, 20, 20, 15, 10, 5, 0, 0]
outputs = [0, 0, 0, 0, 5, 5, 5, 5, 5, 0]
self.check_input_output(box, inputs, r, outputs)
inputs = [5] * 5 + [0] * 5
r = [5, 10, 10, 10, 10, 5, 0, 0, 0, 0]
outputs = [0, 0, 5, 5, 5, 5, 5, 0, 0, 0]
for i in range(len(inputs)):
if i == 2:
box.set_output_coefficients([0, 0, 1])
box.add(inputs[i])
self.assertEqual(box.input(), inputs[i])
box.step()
self.assertEqual(box.size(), r[i])
self.assertEqual(box.output(), outputs[i])
box.remove(box.output())