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Auto-format code using Clang-Format (#198)
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Co-authored-by: GitHub Actions <actions@github.com>
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github-actions[bot] and actions-user authored Aug 13, 2024
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259 changes: 144 additions & 115 deletions python/test/test_analytical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,88 +3,94 @@
import musica
import random


def TestSingleGridCell(self, solver):
num_grid_cells = 1
time_step = 200.0
temperature = 272.5
pressure = 101253.3
GAS_CONSTANT = 8.31446261815324
air_density = pressure / (GAS_CONSTANT * temperature)


rates = musica.user_defined_reaction_rates(solver)
species_ordering = musica.species_ordering(solver)

rate_constants = {
"USER.reaction 1": 0.001,
"USER.reaction 2": 0.002
}

concentrations = {
"A": 0.75,
"B": 0,
"C": 0.4,
"D": 0.8,
"E": 0,
"F": 0.1
}

ordered_rate_constants = len(rate_constants.keys()) * [0.0]
num_grid_cells = 1
time_step = 200.0
temperature = 272.5
pressure = 101253.3
GAS_CONSTANT = 8.31446261815324
air_density = pressure / (GAS_CONSTANT * temperature)

for key, value in rate_constants.items():
ordered_rate_constants[rates[key]] = value
rates = musica.user_defined_reaction_rates(solver)
species_ordering = musica.species_ordering(solver)

ordered_concentrations = len(concentrations.keys()) * [0.0]
rate_constants = {
"USER.reaction 1": 0.001,
"USER.reaction 2": 0.002
}

for key, value in concentrations.items():
ordered_concentrations[species_ordering[key]] = value

initial_concentrations = ordered_concentrations


time_step = 1
sim_length = 100

curr_time = time_step
initial_A = initial_concentrations[species_ordering["A"]]
initial_C = initial_concentrations[species_ordering["C"]]
initial_D = initial_concentrations[species_ordering["D"]]
initial_F = initial_concentrations[species_ordering["F"]]

# Gets analytical concentrations
while curr_time <= sim_length:
musica.micm_solve(
solver,
time_step,
temperature,
pressure,
air_density,
ordered_concentrations,
ordered_rate_constants)

k1 = ordered_rate_constants[rates["USER.reaction 1"]]
k2 = ordered_rate_constants[rates["USER.reaction 2"]]
k3 = 0.004 * np.exp(50.0 / temperature)
k4 = 0.012 * np.exp(75.0 / temperature) * (temperature / 50.0)**(-2) * (1.0 + 1.0e-6 * pressure)
A_conc = initial_A * np.exp(-(k3) * curr_time)
B_conc = initial_A * \
(k3 / (k4 - k3)) * (np.exp(-k3 * curr_time) - np.exp(-k4 * curr_time))
C_conc = initial_C + initial_A * \
(1.0 + (k3 * np.exp(-k4 * curr_time) - k4 * np.exp(-k3 * curr_time)) / (k4 - k3))
D_conc = initial_D * np.exp(-(k1) * curr_time)
E_conc = initial_D * \
(k1 / (k2 - k1)) * (np.exp(-k1 * curr_time) - np.exp(-k2 * curr_time))
F_conc = initial_F + initial_D * \
(1.0 + (k1 * np.exp(-k2 * curr_time) - k2 * np.exp(-k1 * curr_time)) / (k2 - k1))

self.assertAlmostEqual(ordered_concentrations[species_ordering["A"]], A_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[species_ordering["B"]], B_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[species_ordering["C"]], C_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[species_ordering["D"]], D_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[species_ordering["E"]], E_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[species_ordering["F"]], F_conc, places=5)

curr_time += time_step
concentrations = {
"A": 0.75,
"B": 0,
"C": 0.4,
"D": 0.8,
"E": 0,
"F": 0.1
}

ordered_rate_constants = len(rate_constants.keys()) * [0.0]

for key, value in rate_constants.items():
ordered_rate_constants[rates[key]] = value

ordered_concentrations = len(concentrations.keys()) * [0.0]

for key, value in concentrations.items():
ordered_concentrations[species_ordering[key]] = value

initial_concentrations = ordered_concentrations

time_step = 1
sim_length = 100

curr_time = time_step
initial_A = initial_concentrations[species_ordering["A"]]
initial_C = initial_concentrations[species_ordering["C"]]
initial_D = initial_concentrations[species_ordering["D"]]
initial_F = initial_concentrations[species_ordering["F"]]

# Gets analytical concentrations
while curr_time <= sim_length:
musica.micm_solve(
solver,
time_step,
temperature,
pressure,
air_density,
ordered_concentrations,
ordered_rate_constants)

k1 = ordered_rate_constants[rates["USER.reaction 1"]]
k2 = ordered_rate_constants[rates["USER.reaction 2"]]
k3 = 0.004 * np.exp(50.0 / temperature)
k4 = 0.012 * np.exp(75.0 / temperature) * \
(temperature / 50.0)**(-2) * (1.0 + 1.0e-6 * pressure)
A_conc = initial_A * np.exp(-(k3) * curr_time)
B_conc = initial_A * (k3 / (k4 - k3)) * \
(np.exp(-k3 * curr_time) - np.exp(-k4 * curr_time))
C_conc = initial_C + initial_A * \
(1.0 + (k3 * np.exp(-k4 * curr_time) - k4 * np.exp(-k3 * curr_time)) / (k4 - k3))
D_conc = initial_D * np.exp(-(k1) * curr_time)
E_conc = initial_D * (k1 / (k2 - k1)) * \
(np.exp(-k1 * curr_time) - np.exp(-k2 * curr_time))
F_conc = initial_F + initial_D * \
(1.0 + (k1 * np.exp(-k2 * curr_time) - k2 * np.exp(-k1 * curr_time)) / (k2 - k1))

self.assertAlmostEqual(
ordered_concentrations[species_ordering["A"]], A_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[species_ordering["B"]], B_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[species_ordering["C"]], C_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[species_ordering["D"]], D_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[species_ordering["E"]], E_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[species_ordering["F"]], F_conc, places=5)

curr_time += time_step


class TestAnalyticalRosenbrock(unittest.TestCase):
Expand All @@ -105,19 +111,18 @@ def test_simulation(self):
TestSingleGridCell(self, solver)



def TestMultipleGridCell(self, solver, num_grid_cells):
time_step = 200.0
temperatures = []
pressures = []
air_densities = []
concentrations = {
"A" : [],
"B" : [],
"C" : [],
"D" : [],
"E" : [],
"F" : []
"A": [],
"B": [],
"C": [],
"D": [],
"E": [],
"F": []
}
rate_constants = {
"USER.reaction 1": [],
Expand All @@ -126,28 +131,37 @@ def TestMultipleGridCell(self, solver, num_grid_cells):
for i in range(num_grid_cells):
temperatures.append(275.0 + random.uniform(-10.0, 10.0))
pressures.append(101253.3 + random.uniform(-500.0, 500.0))
air_densities.append(pressures[i] / (8.31446261815324 * temperatures[i]))
air_densities.append(
pressures[i] / (8.31446261815324 * temperatures[i]))
concentrations["A"].append(0.75 + random.uniform(-0.05, 0.05))
concentrations["B"].append(0)
concentrations["C"].append(0.4 + random.uniform(-0.05, 0.05))
concentrations["D"].append(0.8 + random.uniform(-0.05, 0.05))
concentrations["E"].append(0)
concentrations["F"].append(0.1 + random.uniform(-0.05, 0.05))
rate_constants["USER.reaction 1"].append(0.001 + random.uniform(-0.0001, 0.0001))
rate_constants["USER.reaction 2"].append(0.002 + random.uniform(-0.0001, 0.0001))
rate_constants["USER.reaction 1"].append(
0.001 + random.uniform(-0.0001, 0.0001))
rate_constants["USER.reaction 2"].append(
0.002 + random.uniform(-0.0001, 0.0001))

rates_ordering = musica.user_defined_reaction_rates(solver)
species_ordering = musica.species_ordering(solver)

ordered_rate_constants = (len(rate_constants.keys()) * num_grid_cells) * [0.0]
ordered_rate_constants = (
len(rate_constants.keys()) * num_grid_cells) * [0.0]
for i in range(num_grid_cells):
for key, value in rate_constants.items():
ordered_rate_constants[i * len(rate_constants.keys()) + rates_ordering[key]] = value[i]
ordered_rate_constants[i *
len(rate_constants.keys()) +
rates_ordering[key]] = value[i]

ordered_concentrations = (len(concentrations.keys()) * num_grid_cells) * [0.0]
ordered_concentrations = (
len(concentrations.keys()) * num_grid_cells) * [0.0]
for i in range(num_grid_cells):
for key, value in concentrations.items():
ordered_concentrations[i * len(concentrations.keys()) + species_ordering[key]] = value[i]
ordered_concentrations[i *
len(concentrations.keys()) +
species_ordering[key]] = value[i]

initial_concentrations = ordered_concentrations

Expand All @@ -160,20 +174,29 @@ def TestMultipleGridCell(self, solver, num_grid_cells):
initial_D = num_grid_cells * [0.0]
initial_F = num_grid_cells * [0.0]
for i in range(num_grid_cells):
initial_A[i] = initial_concentrations[i * len(concentrations.keys()) + species_ordering["A"]]
initial_C[i] = initial_concentrations[i * len(concentrations.keys()) + species_ordering["C"]]
initial_D[i] = initial_concentrations[i * len(concentrations.keys()) + species_ordering["D"]]
initial_F[i] = initial_concentrations[i * len(concentrations.keys()) + species_ordering["F"]]
initial_A[i] = initial_concentrations[i * \
len(concentrations.keys()) + species_ordering["A"]]
initial_C[i] = initial_concentrations[i * \
len(concentrations.keys()) + species_ordering["C"]]
initial_D[i] = initial_concentrations[i * \
len(concentrations.keys()) + species_ordering["D"]]
initial_F[i] = initial_concentrations[i * \
len(concentrations.keys()) + species_ordering["F"]]

k1 = num_grid_cells * [0.0]
k2 = num_grid_cells * [0.0]
k3 = num_grid_cells * [0.0]
k4 = num_grid_cells * [0.0]
for i in range(num_grid_cells):
k1[i] = ordered_rate_constants[i * len(rate_constants.keys()) + rates_ordering["USER.reaction 1"]]
k2[i] = ordered_rate_constants[i * len(rate_constants.keys()) + rates_ordering["USER.reaction 2"]]
k1[i] = ordered_rate_constants[i *
len(rate_constants.keys()) +
rates_ordering["USER.reaction 1"]]
k2[i] = ordered_rate_constants[i *
len(rate_constants.keys()) +
rates_ordering["USER.reaction 2"]]
k3[i] = 0.004 * np.exp(50.0 / temperatures[i])
k4[i] = 0.012 * np.exp(75.0 / temperatures[i]) * (temperatures[i] / 50.0)**(-2) * (1.0 + 1.0e-6 * pressures[i])
k4[i] = 0.012 * np.exp(75.0 / temperatures[i]) * \
(temperatures[i] / 50.0)**(-2) * (1.0 + 1.0e-6 * pressures[i])

while curr_time <= sim_length:
musica.micm_solve(
Expand All @@ -184,25 +207,31 @@ def TestMultipleGridCell(self, solver, num_grid_cells):
air_densities,
ordered_concentrations,
ordered_rate_constants)

for i in range(num_grid_cells):
A_conc = initial_A[i] * np.exp(-(k3[i]) * curr_time)
B_conc = initial_A[i] * \
(k3[i] / (k4[i] - k3[i])) * (np.exp(-k3[i] * curr_time) - np.exp(-k4[i] * curr_time))
C_conc = initial_C[i] + initial_A[i] * \
(1.0 + (k3[i] * np.exp(-k4[i] * curr_time) - k4[i] * np.exp(-k3[i] * curr_time)) / (k4[i] - k3[i]))
B_conc = initial_A[i] * (k3[i] / (k4[i] - k3[i])) * \
(np.exp(-k3[i] * curr_time) - np.exp(-k4[i] * curr_time))
C_conc = initial_C[i] + initial_A[i] * (1.0 + (
k3[i] * np.exp(-k4[i] * curr_time) - k4[i] * np.exp(-k3[i] * curr_time)) / (k4[i] - k3[i]))
D_conc = initial_D[i] * np.exp(-(k1[i]) * curr_time)
E_conc = initial_D[i] * \
(k1[i] / (k2[i] - k1[i])) * (np.exp(-k1[i] * curr_time) - np.exp(-k2[i] * curr_time))
F_conc = initial_F[i] + initial_D[i] * \
(1.0 + (k1[i] * np.exp(-k2[i] * curr_time) - k2[i] * np.exp(-k1[i] * curr_time)) / (k2[i] - k1[i]))

self.assertAlmostEqual(ordered_concentrations[i * len(concentrations.keys()) + species_ordering["A"]], A_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[i * len(concentrations.keys()) + species_ordering["B"]], B_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[i * len(concentrations.keys()) + species_ordering["C"]], C_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[i * len(concentrations.keys()) + species_ordering["D"]], D_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[i * len(concentrations.keys()) + species_ordering["E"]], E_conc, places=5)
self.assertAlmostEqual(ordered_concentrations[i * len(concentrations.keys()) + species_ordering["F"]], F_conc, places=5)
E_conc = initial_D[i] * (k1[i] / (k2[i] - k1[i])) * \
(np.exp(-k1[i] * curr_time) - np.exp(-k2[i] * curr_time))
F_conc = initial_F[i] + initial_D[i] * (1.0 + (
k1[i] * np.exp(-k2[i] * curr_time) - k2[i] * np.exp(-k1[i] * curr_time)) / (k2[i] - k1[i]))

self.assertAlmostEqual(
ordered_concentrations[i * len(concentrations.keys()) + species_ordering["A"]], A_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[i * len(concentrations.keys()) + species_ordering["B"]], B_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[i * len(concentrations.keys()) + species_ordering["C"]], C_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[i * len(concentrations.keys()) + species_ordering["D"]], D_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[i * len(concentrations.keys()) + species_ordering["E"]], E_conc, places=5)
self.assertAlmostEqual(
ordered_concentrations[i * len(concentrations.keys()) + species_ordering["F"]], F_conc, places=5)

curr_time += time_step

Expand Down
11 changes: 7 additions & 4 deletions python/test/test_chapman.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ def test_micm_solve(self):
"configs/chapman",
musica.micmsolver.rosenbrock,
num_grid_cells)

rate_constant_ordering = musica.user_defined_reaction_rates(solver)
species_ordering = musica.species_ordering(solver)

Expand Down Expand Up @@ -51,10 +51,13 @@ def test_micm_solve(self):
ordered_concentrations,
ordered_rate_constants)

self.assertAlmostEqual(ordered_concentrations[species_ordering["O2"]], 0.75, places=5)
self.assertAlmostEqual(
ordered_concentrations[species_ordering["O2"]], 0.75, places=5)
self.assertGreater(ordered_concentrations[species_ordering["O"]], 0.0)
self.assertGreater(ordered_concentrations[species_ordering["O1D"]], 0.0)
self.assertNotEqual(ordered_concentrations[species_ordering["O3"]], 0.0000081)
self.assertGreater(
ordered_concentrations[species_ordering["O1D"]], 0.0)
self.assertNotEqual(
ordered_concentrations[species_ordering["O3"]], 0.0000081)


if __name__ == '__main__':
Expand Down

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