def write_hesuvi_zip():
manufacturers = ManufacturerIndex()
zip_object = ZipFile(os.path.join(DIR_PATH, 'hesuvi.zip'), 'w')
dir_paths = [
os.path.join(DIR_PATH, 'oratory1990'),
os.path.join(DIR_PATH, 'crinacle', 'gras_43ag-7_harman_over-ear_2018'),
os.path.join(DIR_PATH, 'crinacle', 'ears-711_harman_over-ear_2018'),
os.path.join(DIR_PATH, 'innerfidelity'),
os.path.join(DIR_PATH, 'rtings'),
os.path.join(DIR_PATH, 'headphonecom'),
os.path.join(DIR_PATH, 'referenceaudioanalyzer'),
]
zip_files = set()
for dir_path in dir_paths:
for fp in glob(os.path.join(dir_path, '**', '* GraphicEQ.txt'),
recursive=True):
_, name = os.path.split(fp)
name = name.replace(' GraphicEQ.txt', '')
if re.search(MOD_REGEX, name, flags=re.IGNORECASE):
# Skip samples, there are averaged results available
continue
manufacturer, _ = manufacturers.find(name)
if manufacturer is None:
print(f'Manufacturer could not be found for {name}')
continue
name = manufacturers.model(name)
arcname = f'eq/{manufacturer}/{name}.txt'
if arcname in zip_files:
# Skip duplicates
continue
with open(fp, 'r', encoding='utf-8') as fh:
s = fh.read()
data = np.array(
[x.split() for x in s.split(': ')[1].split('; ')],
dtype='float')
sl = np.logical_and(data[:, 0] > 100, data[:, 0] < 10000)
data[:, 1] -= np.mean(data[sl, 1])
s = 'GraphicEQ: '
s += '; '.join([f'{x[0]:.0f} {x[1]:.1f}' for x in data])
zip_object.writestr(arcname, s)
zip_files.add(arcname)
zip_object.close()
def write_hesuvi_index():
os.makedirs(os.path.join(DIR_PATH, 'hesuvi'), exist_ok=True)
manufacturers = ManufacturerIndex()
zip_object = ZipFile(os.path.join(DIR_PATH, 'hesuvi.zip'), 'w')
dir_paths = [
os.path.join(DIR_PATH, 'oratory1990'),
os.path.join(DIR_PATH, 'crinacle', 'harman_in-ear_2019v2'),
os.path.join(DIR_PATH, 'crinacle', 'crinacl_over-ear'),
os.path.join(DIR_PATH, 'innerfidelity'),
os.path.join(DIR_PATH, 'rtings'),
os.path.join(DIR_PATH, 'headphonecom'),
]
zip_files = set()
for dir_path in dir_paths:
for fp in glob(os.path.join(dir_path, '**', '* GraphicEQ.txt'),
recursive=True):
_, name = os.path.split(fp)
name = name.replace(' GraphicEQ.txt', '')
if re.search(r' \(?(sample |sn)[a-zA-Z0-9]+\)?$',
name,
flags=re.IGNORECASE):
# Skip samples, there are averaged results available
continue
manufacturer, _ = manufacturers.find(name)
name = manufacturers.model(name)
arcname = f'eq/{manufacturer}/{name}.txt'
if arcname in zip_files:
# Skip duplicates
continue
with open(fp, 'r', encoding='utf-8') as fh:
s = fh.read()
data = np.array(
[x.split() for x in s.split(': ')[1].split('; ')],
dtype='float')
sl = np.logical_and(data[:, 0] > 100, data[:, 0] < 10000)
data[:, 1] -= np.mean(data[sl, 1])
s = 'GraphicEQ: '
s += '; '.join([f'{x[0]:.0f} {x[1]:.1f}' for x in data])
zip_object.writestr(arcname, s)
zip_files.add(arcname)
zip_object.close()