def main():
stock = FrequencyResponse.read_from_csv('data/HE400S stock.csv')
focus = FrequencyResponse.read_from_csv('data/HE400S focus.csv')
base = FrequencyResponse.read_from_csv(
'innerfidelity/data/HiFiMAN HE400S/HiFiMAN HE400S.csv')
stock.interpolate(f_min=20, f_max=20000)
stock.center()
focus.interpolate(f_min=20, f_max=20000)
focus.center()
base.interpolate(f=stock.frequency)
base.center()
diff = FrequencyResponse(name='Diff',
frequency=stock.frequency,
raw=focus.raw - stock.raw)
fr = FrequencyResponse(name='HE400S with Focus Pads',
frequency=stock.frequency,
raw=base.raw + diff.raw)
_fr = FrequencyResponse(name='debug',
frequency=stock.frequency,
raw=base.raw,
smoothed=diff.raw,
equalization=fr.raw)
_fr.plot_graph()
fr.smoothen(window_size=1 / 5,
iterations=10,
treble_window_size=1 / 2,
treble_iterations=100)
#stock.plot_graph()
#focus.plot_graph()
#diff.plot_graph()
#base.plot_graph()
#fr.plot_graph()
os.makedirs('innerfidelity/data/HiFiMAN HE400S with Focus Pads',
exist_ok=True)
fr.write_to_csv(
file_path=
'innerfidelity/data/HiFiMAN HE400S with Focus Pads/HiFiMAN HE400S with Focus Pads ORIG.csv'
)
fr.equalize(max_gain=12, smoothen=True, window_size=1 / 5, bass_target=4)
fr.write_to_csv(
file_path=
'innerfidelity/data/HiFiMAN HE400S with Focus Pads/HiFiMAN HE400S with Focus Pads.csv'
)
fig, ax = fr.plot_graph(
show=False,
file_path=
'innerfidelity/data/HiFiMAN HE400S with Focus Pads/HiFiMAN HE400S with Focus Pads.png'
)
plt.close(fig)
fr.write_eqapo_graphic_eq(
'innerfidelity/data/HiFiMAN HE400S with Focus Pads/HiFiMAN HE400S with Focus Pads EqAPO.txt'
)
def main():
fig, ax = plt.subplots()
diffs = []
# Calculate differences for all models
for file in glob(os.path.join('compensation', 'compensated', '**',
'*.csv'),
recursive=True):
file = os.path.abspath(file)
comp = FrequencyResponse.read_from_csv(file)
comp.interpolate()
comp.center()
raw_data_path = file.replace('compensated', 'raw')
raw = FrequencyResponse.read_from_csv(raw_data_path)
raw.interpolate()
raw.center()
diff = FrequencyResponse(name=comp.name,
frequency=comp.frequency,
raw=raw.raw - comp.raw)
plt.plot(diff.frequency, diff.raw)
diffs.append(diff.raw)
# Average and smoothen difference
f = FrequencyResponse.generate_frequencies()
diffs = np.vstack(diffs)
diff = np.mean(diffs, axis=0)
diff = FrequencyResponse(name='Headphone.com Compensation',
frequency=f,
raw=diff)
diff.smoothen(window_size=1 / 9, iterations=10)
diff.raw = diff.smoothed
diff.smoothed = np.array([])
plt.xlabel('Frequency (Hz)')
plt.semilogx()
plt.xlim([20, 20000])
plt.ylabel('Amplitude (dBr)')
plt.ylim([-15, 15])
plt.grid(which='major')
plt.grid(which='minor')
plt.title('Headphone.com Compensation Function')
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))
plt.show()
diff.write_to_csv('headphonecom_compensation.csv')
diff.plot_graph(f_min=10,
f_max=20000,
file_path='headphonecom_compensation.png')
def main():
dt770 = FrequencyResponse.read_from_csv('headphonecom/data/onear/Beyerdynamic DT770/Beyerdynamic DT770.csv')
dt770.interpolate()
he400s = FrequencyResponse.read_from_csv('innerfidelity/data/onear/HiFiMAN HE400S/HiFiMAN HE400S.csv')
he400s.interpolate()
calibration = FrequencyResponse.read_from_csv('calibration/headphonecom_raw_to_innerfidelity_raw.csv')
calibration.interpolate()
compensation = FrequencyResponse.read_from_csv('innerfidelity/resources/innerfidelity_compensation_2017.csv')
compensation.interpolate()
dt770_calibrated = FrequencyResponse(name='DT 770 calibrated', frequency=dt770.frequency, raw=dt770.raw)
dt770_calibrated.calibrate(calibration)
dt770_calibrated.compensate(he400s)
#dt770_calibrated.compensate(compensation)
dt770_calibrated.center()
dt770_calibrated.smoothen()
dt770_calibrated.equalize(smoothen=True)
dt770_calibrated.plot_graph(a_min=-20, a_max=20)
def main():
# Filenames
if_files = list(
glob(os.path.join('innerfidelity', 'data', '**', '*.csv'),
recursive=True))
if_file_names = [os.path.split(os.path.abspath(f))[-1] for f in if_files]
normalized_if_files = [normalize(s) for s in if_file_names]
hp_files = list(
glob(os.path.join('headphonecom', 'data', '**', '*.csv'),
recursive=True))
# Find matching files
matching_if_files = []
matching_hp_files = []
for hp_file in hp_files:
file_name = os.path.split(os.path.abspath(hp_file))[-1]
for i in range(len(normalized_if_files)):
if normalized_if_files[i] == normalize(file_name):
matching_hp_files.append(hp_file)
matching_if_files.append(if_files[i])
# Write mathces to file for manual inspection
df = pd.DataFrame(
np.array([matching_hp_files, matching_if_files]).transpose())
df.to_csv('matches.csv', index=False, header=False)
fig, ax = plt.subplots()
diffs = []
# Calculate differences for all models
if_compensation = FrequencyResponse.read_from_csv(
os.path.join('innerfidelity', 'resources',
'innerfidelity_compensation_2017.csv'))
if_compensation.interpolate()
hp_compensation = FrequencyResponse.read_from_csv(
os.path.join('headphonecom', 'resources',
'headphonecom_compensation.csv'))
hp_compensation.interpolate()
for i in range(len(matching_if_files)):
if_fr = FrequencyResponse.read_from_csv(matching_if_files[i])
if_fr.interpolate()
if_fr.center()
#if_fr.compensate(if_compensation)
hp_fr = FrequencyResponse.read_from_csv(matching_hp_files[i])
hp_fr.interpolate()
hp_fr.center()
#hp_fr.compensate(hp_compensation)
#diff = FrequencyResponse(name=if_fr.name, frequency=if_fr.frequency, raw=hp_fr.error - if_fr.error)
diff = FrequencyResponse(name=if_fr.name,
frequency=if_fr.frequency,
raw=hp_fr.raw - if_fr.raw)
plt.plot(diff.frequency, diff.raw)
diffs.append(diff.raw)
# Average and smoothen difference
f = FrequencyResponse.generate_frequencies()
diffs = np.vstack(diffs)
diff = np.mean(diffs, axis=0)
std = np.std(diffs, axis=0)
diff = FrequencyResponse(name='Headphone.com Raw to Innerfidelity Raw',
frequency=f,
raw=diff)
diff.smoothen(window_size=1 / 9, iterations=10)
diff.raw = diff.smoothed
diff.smoothed = np.array([])
plt.xlabel('Frequency (Hz)')
plt.semilogx()
plt.xlim([20, 20000])
plt.ylabel('Amplitude (dBr)')
plt.ylim([-15, 15])
plt.grid(which='major')
plt.grid(which='minor')
plt.title('Headphone.com Raw to Innerfidelity Raw')
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))
plt.show()
fig, ax = diff.plot_graph(f_min=10, f_max=20000, show=False)
ax.fill_between(diff.frequency,
diff.raw + std,
diff.raw - std,
facecolor='lightblue')
plt.legend(
['Headphone.com Raw to Innerfidelity Raw', 'Standard Deviation'])
fig.savefig(os.path.join('calibration',
'headphonecom_raw_to_innerfidelity_raq.png'),
dpi=240)
plt.show()
diff.write_to_csv(
os.path.join('calibration',
'headphonecom_raw_to_innerfidelity_raw.csv'))
diff.raw *= -1
diff.name = 'Innerfidelity Raw to Headphone.com Raw'
fig, ax = diff.plot_graph(f_min=10, f_max=20000, show=False)
ax.fill_between(diff.frequency,
diff.raw + std,
diff.raw - std,
facecolor='lightblue')
plt.legend(
['Innerfidelity Raw to Headphone.com Raw', 'Standard Deviation'])
fig.savefig(os.path.join('calibration',
'innerfidelity_raw_to_headphonecom_raw.png'),
dpi=240)
plt.show()
diff.write_to_csv(
os.path.join('calibration',
'innerfidelity_raw_to_headphonecom_raw.csv'))
