def average_measurements(input_dir=None, output_dir=None):
if input_dir is None:
raise TypeError('Input directory path is required!')
if output_dir is None:
output_dir = os.path.abspath(input_dir)
input_dir = os.path.abspath(input_dir)
output_dir = os.path.abspath(output_dir)
models = {}
for file_path in glob(os.path.join(input_dir, '*')):
model = os.path.split(file_path)[-1]
if not re.search(MOD_REGEX, model, re.IGNORECASE):
continue
norm = re.sub(MOD_REGEX, '', model, 0, flags=re.IGNORECASE)
try:
models[norm].append(model)
except KeyError as err:
models[norm] = [model]
for norm, origs in models.items():
if len(origs) > 1:
f = FrequencyResponse.generate_frequencies()
avg = np.zeros(len(f))
for model in origs:
fr = FrequencyResponse.read_from_csv(os.path.join(input_dir, model, model + '.csv'))
fr.interpolate()
fr.center()
avg += fr.raw
avg /= len(origs)
fr = FrequencyResponse(name=norm, frequency=f, raw=avg)
d = os.path.join(output_dir, norm)
os.makedirs(d, exist_ok=True)
file_path = os.path.join(d, norm + '.csv')
fr.write_to_csv(file_path)
def main():
models = {}
for file_path in glob(os.path.join(DIR, '*')):
model = os.path.split(file_path)[-1]
if not (re.search(' sample [a-zA-Z0-9]$', model, re.IGNORECASE)
or re.search(' sn[a-zA-Z0-9]+$', model, re.IGNORECASE)):
# Skip measurements with sample or serial number, those have averaged results
continue
norm = re.sub(' sample [a-zA-Z0-9]$', '', model, 0, re.IGNORECASE)
norm = re.sub(' sn[a-zA-Z0-9]+$', '', norm, 0, re.IGNORECASE)
try:
models[norm].append(model)
except KeyError as err:
models[norm] = [model]
for norm, origs in models.items():
if len(origs) > 1:
print(norm, origs)
avg = np.zeros(613)
f = FrequencyResponse.generate_frequencies()
for model in origs:
fr = FrequencyResponse.read_from_csv(
os.path.join(DIR, model, model + '.csv'))
fr.interpolate()
fr.center()
avg += fr.raw
avg /= len(origs)
fr = FrequencyResponse(name=norm, frequency=f, raw=avg)
d = os.path.join(OUT_DIR, norm)
if not os.path.isdir(d):
os.makedirs(d)
fr.write_to_csv(os.path.join(d, norm + '.csv'))
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_fractional_octave(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(show=True,
f_min=10,
f_max=20000,
file_path='headphonecom_compensation.png')
def main(input_dir=None, output_dir=None):
if input_dir is None:
raise TypeError('Input directory path is required!')
if output_dir is None:
raise TypeError('Output directory path is required!')
input_dir = os.path.abspath(input_dir)
output_dir = os.path.abspath(output_dir)
models = {}
for file_path in glob(os.path.join(input_dir, '*')):
model = os.path.split(file_path)[-1]
if not (re.search(' sample [a-zA-Z0-9]$', model, re.IGNORECASE)
or re.search(' sn[a-zA-Z0-9]+$', model, re.IGNORECASE)):
continue
norm = re.sub(' sample [a-zA-Z0-9]$', '', model, 0, re.IGNORECASE)
norm = re.sub(' sn[a-zA-Z0-9]+$', '', norm, 0, re.IGNORECASE)
try:
models[norm].append(model)
except KeyError as err:
models[norm] = [model]
for norm, origs in models.items():
if len(origs) > 1:
print(norm, origs)
avg = np.zeros(613)
f = FrequencyResponse.generate_frequencies()
for model in origs:
fr = FrequencyResponse.read_from_csv(
os.path.join(input_dir, model, model + '.csv'))
fr.interpolate()
fr.center()
avg += fr.raw
avg /= len(origs)
fr = FrequencyResponse(name=norm, frequency=f, raw=avg)
d = os.path.join(output_dir, norm)
if not os.path.isdir(d):
os.makedirs(d)
fr.write_to_csv(os.path.join(d, norm + '.csv'))
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('rtings', '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('rtings', 'resources', 'rtings_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='Rtings Raw to Innerfidelity Raw',
frequency=f,
raw=diff)
#diff.smoothen(window_size=1/7, iterations=10)
diff.smoothen_fractional_octave(window_size=1 / 5, iterations=100)
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('Rtings Raw to Innerfidelity Raw')
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))
plt.show()
fig, ax = diff.plot_graph(f_min=20, f_max=20000, show=False, color=None)
ax.fill_between(diff.frequency,
diff.raw + std,
diff.raw - std,
facecolor='lightblue')
plt.legend(['Rtings Raw to Innerfidelity Raw', 'Standard Deviation'])
plt.ylim([-10, 10])
fig.savefig(os.path.join('calibration', 'rtings_to_innerfidelity.png'),
dpi=240)
plt.show()
diff.write_to_csv(
os.path.join('calibration', 'rtings_to_innerfidelity.csv'))
diff.raw *= -1
diff.name = 'Innerfidelity Raw to Rtings Raw'
fig, ax = diff.plot_graph(f_min=20, f_max=20000, show=False, color=None)
ax.fill_between(diff.frequency,
diff.raw + std,
diff.raw - std,
facecolor='lightblue')
plt.legend(['Innerfidelity Raw to Rtings Raw', 'Standard Deviation'])
plt.ylim([-10, 10])
fig.savefig(os.path.join('calibration', 'innerfidelity_to_rtings.png'),
dpi=240)
plt.show()
diff.write_to_csv(
os.path.join('calibration', 'innerfidelity_to_rtings.csv'))
