def test_multidimensional_power_law_fitting():
np.random.seed(0) # Set a seed for repeatability.
### Make some data z(x,y)
x = np.logspace(0, 3)
y = np.logspace(0, 3)
X, Y = np.meshgrid(x, y, indexing="ij")
noise = np.random.lognormal(mean=0, sigma=0.05)
Z = 0.5 * X**0.75 * Y**1.25 * noise
### Fit data
def model(x, p):
return (p["multiplier"] * x["X"]**p["X_power"] * x["Y"]**p["Y_power"])
x_data = {
"X": X.flatten(),
"Y": Y.flatten(),
}
fitted_model = FittedModel(
model=model,
x_data=x_data,
y_data=Z.flatten(),
parameter_guesses={
"multiplier": 1,
"X_power": 1,
"Y_power": 1,
},
parameter_bounds={
"multiplier": (None, None),
"X_power": (None, None),
"Y_power": (None, None),
},
put_residuals_in_logspace=True
# Putting residuals in logspace minimizes the norm of log-error instead of absolute error
)
### Check that the fit is right
assert fitted_model.parameters["multiplier"] == pytest.approx(0.546105,
abs=1e-3)
assert fitted_model.parameters["X_power"] == pytest.approx(0.750000,
abs=1e-3)
assert fitted_model.parameters["Y_power"] == pytest.approx(1.250000,
abs=1e-3)
volume_insulator = area_insulator * wire_length
mass_insulator = insulator_density * volume_insulator
else:
mass_insulator = 0
# Total them up
return mass_conductor + mass_insulator
if __name__ == '__main__':
print(motor_electric_performance(rpm=100, current=3))
print(motor_electric_performance(rpm=4700, torque=0.02482817))
print(mass_battery_pack(100))
pows = np.logspace(2, 5, 300)
mass_mot_burton = mass_motor_electric(pows, method="burton")
mass_mot_hobbyking = mass_motor_electric(pows, method="hobbyking")
mass_mot_astroflight = mass_motor_electric(pows, method="astroflight")
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(palette=sns.color_palette("husl"))
fig, ax = plt.subplots(1, 1, figsize=(6.4, 4.8), dpi=200)
plt.loglog(pows, np.array(mass_mot_burton), "-", label="Burton Model")
plt.plot(pows, np.array(mass_mot_hobbyking), "--", label="Hobbyking Model")
plt.plot(pows,
np.array(mass_mot_astroflight),
"-.",
import plotly.express as px
import pandas as pd
# # Oxamide Function tests
# oxamides = np.linspace(-0.3,0.5,200)
# burn_rate_coefficients = burn_rate_coefficient(oxamides).toarray()
# c_stars = c_star(oxamides)
# min_combustion_pressures = dubious_min_combustion_pressure(oxamides)
# gammas = gamma(oxamides)
# px.scatter(x=oxamides,y=burn_rate_coefficients,labels={"x": "Oxamide", "y": "Burn Rate Coeff"}).show()
# px.scatter(x=oxamides,y=c_stars,labels={"x": "Oxamide", "y": "c_star"}).show()
# px.scatter(x=oxamides,y=min_combustion_pressures,labels={"x": "Oxamide", "y": "Min. Combustion Pressure"}).show()
# px.scatter(x=oxamides,y=gammas,labels={"x": "Oxamide", "y": "Gamma"}).show()
# # ER_from_P test
chamber_pressure_inputs = np.logspace(5, 6, 200)
exit_pressure_inputs = np.logspace(4, 5, 200)
ox_for_test = 0
chamber_pressures = []
exit_pressures = []
ers = []
for chamber_pressure in chamber_pressure_inputs:
for exit_pressure in exit_pressure_inputs:
chamber_pressures.append(chamber_pressure)
exit_pressures.append(exit_pressure)
ers.append(
expansion_ratio_from_pressure(chamber_pressure, exit_pressure,
gamma(ox_for_test), ox_for_test))
data = pd.DataFrame({
'chamber_pressure': chamber_pressures,
'exit_pressure': exit_pressures,