import sys
sys.path.append(os.path.abspath(os.path.dirname(__file__) + '/../..'))
import numpy as np
from gpkit import ureg
from matplotlib import pyplot as plt
from noise_models import noise_weighting
#Computations
f_peak_array = np.logspace(np.log10(100), np.log10(40000),
100) * ureg.turn / ureg.s
A_weighting_response_function = np.zeros(np.size(f_peak_array))
vortex_dBA_offset = np.zeros(np.size(f_peak_array))
A_weighting_response_function = noise_weighting(f_peak_array,
A_weighting_response_function,
type="A")
for i, f_peak in enumerate(f_peak_array):
spectrum = {}
spectrum["f"] = f_peak * [0.5, 1, 2, 4, 8, 16]
spectrum["SPL"] = -np.array([7.92, 4.17, 8.33, 8.75, 12.92, 13.33])
spectrum["SPL"] = noise_weighting(spectrum["f"], spectrum["SPL"], type="A")
fr = (spectrum["f"] / f_peak).to(
ureg.dimensionless) #frequency ratio array
weighted_p_ratio_squared = 0
plt.rc('axes', axisbelow=True)
plt.show()
#Find value of y closest to that desired
y_desired = 1000 * ureg.feet
idx = (np.abs(y_array - y_desired)).argmin()
y_selected = y_array[idx]
for i, config in enumerate(configs):
c = configs[config]
#Rotational noise (1st harmonic only)
rotational_f = c["y"]["rotational"]["spectrum"][idx]["f"][0]
rotational_SPL = c["y"]["rotational"]["spectrum"][idx]["SPL"][0]
rotational_SPL_A = noise_weighting(rotational_f, rotational_SPL)
#Vortex noise
vortex_f_spectrum = c["y"]["vortex"]["spectrum"][idx]["f"]
vortex_SPL_spectrum = c["y"]["vortex"]["spectrum"][idx]["SPL"]
vortex_SPL_A_spectrum = noise_weighting(vortex_f_spectrum,
vortex_SPL_spectrum)
#A-weighting spectrum
f_min_rev_per_s = rotational_f.to(ureg.turn / ureg.s).magnitude
f_max_rev_per_s = np.max(
vortex_f_spectrum.to(ureg.turn / ureg.s).magnitude)
f_dBA_offset = np.linspace(f_min_rev_per_s, f_max_rev_per_s,
100) * ureg.turn / ureg.s
dBA_offset = noise_weighting(f_dBA_offset,
np.zeros(np.shape(f_dBA_offset)))
import os
import sys
sys.path.append(os.path.abspath(os.path.dirname(__file__) + '/../../models'))
import numpy as np
from gpkit import ureg
from matplotlib import pyplot as plt
from noise_models import noise_weighting
#Computations
f_peak_array = np.logspace(np.log10(100), np.log10(40000),
100) * ureg.turn / ureg.s
A_weighting_response_function = np.zeros(np.size(f_peak_array))
vortex_dBA_offset = np.zeros(np.size(f_peak_array))
A_weighting_response_function = noise_weighting(
f=f_peak_array, SPL=A_weighting_response_function, weighting="A")
for i, f_peak in enumerate(f_peak_array):
spectrum = {}
spectrum["f"] = f_peak * [0.5, 1, 2, 4, 8, 16]
spectrum["SPL"] = -np.array([7.92, 4.17, 8.33, 8.75, 12.92, 13.33])
spectrum["SPL"] = noise_weighting(f=spectrum["f"],
SPL=spectrum["SPL"],
weighting="A")
fr = (spectrum["f"] / f_peak).to(
ureg.dimensionless) # Frequency ratio array
weighted_p_ratio_squared = 0