def test_hysteresis(self):
freq = 100
n = np.arange(_N_)
x = np.sin(2 * np.pi * n * freq / _FS_) * (n / _N_)
hysteresis = adsp.Hysteresis(1.0, 1.0, 1.0, _FS_, mode='RK4')
y = hysteresis.process_block(x)
hysteresis2 = adsp.Hysteresis(1.0, 1.0, 1.0, _FS_, mode='RK2')
y2 = hysteresis2.process_block(x)
hysteresis3 = adsp.Hysteresis(1.0, 1.0, 1.0, _FS_, mode='NR10')
y3 = hysteresis3.process_block(x)
self.assertTrue(
np.sum(np.abs(y - y2)) / _N_ < 5.0e-6,
'RK2 Hysteresis response is incorrect!')
self.assertTrue(
np.sum(np.abs(y - y3)) / _N_ < 5.0e-6,
'NR10 Hysteresis response is incorrect!')
# %%
# look at file
idx = 4
plt.plot(clean_data[idx])
# %%
hyst_data = []
drive_data = []
sat_data = []
width_data = []
for x in tqdm(clean_data):
drive = np.random.uniform()
sat = np.random.uniform()
width = np.random.uniform()
hyst = adsp.Hysteresis(drive, sat, width, FS, mode='RK4')
y = hyst.process_block(x)
drive_data.append(np.ones_like(x) * drive)
sat_data.append(np.ones_like(x) * sat)
width_data.append(np.ones_like(x) * width)
hyst_data.append(y.astype(np.float32))
# %%
idx = 4
plt.figure()
plt.plot(clean_data[idx])
plt.plot(hyst_data[idx])
plt.plot(drive_data[idx])
plt.plot(sat_data[idx])
plt.plot(width_data[idx])
# %%
# look at file
idx = 8
plt.plot(clean_data[idx])
# %%
hyst_data = []
drive_data = []
sat_data = []
width_data = []
for i, x in tqdm(enumerate(clean_data)):
fs = 1.0 / fs_data[i][0]
drive = np.random.uniform()
sat = np.random.uniform()
width = np.random.uniform()
hyst = adsp.Hysteresis(drive, sat, width, fs, dAlpha=0.95, mode='RK4')
y = hyst.process_block(x)
drive_data.append(np.ones_like(x) * drive)
sat_data.append(np.ones_like(x) * sat)
width_data.append(np.ones_like(x) * width)
hyst_data.append(y.astype(np.float32))
# %%
idx = 4
plt.figure()
plt.plot(clean_data[idx])
plt.plot(hyst_data[idx])
plt.figure()
freqs, x_fft = plot_fft(clean_data[idx], 1.0 / fs_data[idx][0])
clean_data.append(x)
clean_data = np.asarray(clean_data)
print(np.shape(clean_data))
# %%
# look at file
idx = 4
plt.plot(clean_data[idx])
# %%
hyst_data = []
drive_data = []
for x in tqdm(clean_data):
drive = random.choice([0.05, 0.25, 0.5, 0.75, 1.0])
hyst = adsp.Hysteresis(drive, 1.0, 1.0, FS, mode='RK4')
y = hyst.process_block(x)
drive_data.append(np.ones_like(x) * drive)
hyst_data.append(y.astype(np.float32))
# %%
idx = 4
plt.figure()
plt.plot(clean_data[idx])
plt.plot(hyst_data[idx])
plt.plot(drive_data[idx])
plt.figure()
freqs, x_fft = plot_fft(clean_data[idx], FS)
freqs, y_fft = plot_fft(hyst_data[idx], FS)
clean_data.append(x)
clean_data = np.asarray(clean_data)
# print(np.shape(clean_data))
# %%
# look at file
idx = 4
plt.plot(clean_data[idx])
# %%
NUM_FILES = len(clean_data)
hyst_data = []
for i, x in tqdm(enumerate(clean_data)):
fs = 1.0 / fs_data[i][0]
hyst = adsp.Hysteresis(1.0, 1.0, 1.0, fs, mode='RK4')
y = hyst.process_block(x)
hyst_data.append(y.astype(np.float32))
# %%
idx = 4
plt.figure()
plt.plot(clean_data[idx])
plt.plot(hyst_data[idx])
plt.figure()
freqs, x_fft = plot_fft(clean_data[idx], 1.0 / fs_data[idx][0])
freqs, y_fft = plot_fft(hyst_data[idx], 1.0 / fs_data[idx][0])
plt.semilogx(freqs, x_fft)
plt.semilogx(freqs, y_fft)
# %%
from audio_dspy.hysteresis import Hysteresis
import numpy as np
import matplotlib.pyplot as plt
import audio_dspy as adsp
FS = 96000
# %%
# drive plot
WIDTH = 1.0
SAT = 0.5
legend = []
for DRIVE in [0.0, 0.25, 1.0]:
hyst = adsp.Hysteresis(DRIVE, SAT, WIDTH, FS, mode='NR4')
adsp.plot_dynamic_curve(hyst.process_block, freq=10, fs=FS, gain=3, num=10000)
legend.append(f'Drive = {DRIVE}')
plt.legend(legend)
plt.grid()
plt.title('Hysteresis Drive')
plt.tight_layout()
plt.savefig('drive.png')
# %%
# saturation plot
WIDTH = 1.0
DRIVE= 1.0
legend = []
for SAT in [0.0, 0.4, 0.8]: