def load(fname, filter=None, abr_window=8.5e-3):
with open(fname) as fh:
line = fh.readline()
if not line.startswith('Identifier:'):
raise IOError('Unsupported file format')
info = load_metadata(fname)
info = info[info.channel == 1]
fs = 1 / (info.iloc[0]['smp. period'] * 1e-6)
series = []
for frequency, f_info in info.groupby('stim. freq.'):
signal = load_waveforms(fname, f_info)
signal = signal[signal.index >= 0]
waveforms = []
min_latency = latencies.get(frequency)
for i, row in f_info.iterrows():
s = signal[i].values[np.newaxis]
waveform = ABRWaveform(fs,
s,
row['level'],
min_latency=min_latency,
filter=filter)
waveforms.append(waveform)
s = ABRSeries(waveforms, frequency / 1e3)
s.filename = fname
series.append(s)
return series
def load(base_directory, filter_settings=None, frequencies=None):
filename = get_filename(base_directory, filter_settings)
if frequencies is not None and np.isscalar(frequencies):
frequencies = [frequencies]
data = pd.io.parsers.read_csv(filename, header=[0, 1], index_col=0).T
fs = np.mean(np.diff(data.columns.values)**-1)
data.columns *= 1e3
waveforms = {}
for (frequency, level), w in data.iterrows():
frequency = float(frequency)
level = float(level)
if frequencies is not None:
if frequency not in frequencies:
continue
frequency = float(frequency)
level = float(level)
waveform = ABRWaveform(fs, w, level)
waveforms.setdefault(frequency, []).append(waveform)
series = []
for frequency, stack in waveforms.items():
s = ABRSeries(stack, frequency)
s.filename = filename
s.id = filename.parent.name
series.append(s)
return series
def load(filename, filter=None, frequencies=None):
filename = Path(filename)
with filename.open(encoding='ISO-8859-1') as f:
line = f.readline()
if not line.startswith(':RUN-'):
raise IOError('Unsupported file format')
p_level = re.compile(':LEVELS:([0-9;]+)')
p_fs = re.compile('SAMPLE \(.sec\): ([0-9]+)')
p_freq = re.compile('FREQ: ([0-9\.]+)')
abr_window = 8500 # usec
try:
with filename.open(encoding='ISO-8859-1') as f:
header, data = f.read().split('DATA')
# Extract data from header
levelstring = p_level.search(header).group(1).strip(';').split(';')
levels = np.array(levelstring).astype(np.float32)
sampling_period = float(p_fs.search(header).group(1))
frequency = float(p_freq.search(header).group(1))
# Convert text representation of data to Numpy array
fs = 1e6 / sampling_period
cutoff = int(abr_window / sampling_period)
data = np.array(data.split()).astype(np.float32)
data.shape = -1, len(levels)
data = data.T[:, :cutoff]
t = np.arange(data.shape[-1]) / fs * 1e3
t = pd.Index(t, name='time')
if filter is not None:
Wn = filter['highpass'] / (0.5 *
fs), filter['lowpass'] / (0.5 * fs)
N = filter['order']
b, a = signal.iirfilter(N, Wn)
data = signal.filtfilt(b, a, data, axis=-1)
waveforms = []
for s, level in zip(data, levels):
# Checks for a ABR I-O bug that sometimes saves zeroed waveforms
if not (s == 0).all():
w = pd.Series(s, index=t)
waveform = ABRWaveform(fs, w, level)
waveforms.append(waveform)
series = ABRSeries(waveforms, frequency)
series.filename = filename
return [series]
except (AttributeError, ValueError):
msg = 'Could not parse %s. Most likely not a valid ABR file.' % fname
raise IOError(msg)
def load(base_directory, filter_settings=None, frequencies=None):
filename = get_filename(base_directory)
with filename.open() as fh:
fh.readline() # frequency
fh.readline() # level
has_metadata = fh.readline().startswith('epoch_n')
if frequencies is not None and np.isscalar(frequencies):
frequencies = [frequencies]
if has_metadata:
data = pd.io.parsers.read_csv(filename,
header=[0, 1, 2, 3],
index_col=0).T
data = data.reset_index(['epoch_n', 'epoch_reject_ratio'], drop=True)
settings_file = filename.parent / 'ABR processing settings.json'
fs = json.loads(settings_file.read_text())['actual_fs']
print(fs)
else:
data = pd.io.parsers.read_csv(filename, header=[0, 1], index_col=0).T
fs = np.mean(np.diff(data.columns.values)**-1)
if filter_settings is not None:
Wn = filter_settings['highpass'], filter_settings['lowpass']
N = filter_settings['order']
b, a = signal.iirfilter(N, Wn, fs=fs)
data_filt = signal.filtfilt(b, a, data.values, axis=-1)
data = pd.DataFrame(data_filt, columns=data.columns, index=data.index)
data.columns *= 1e3
waveforms = {}
for (frequency, level), w in data.iterrows():
frequency = float(frequency)
level = float(level)
if frequencies is not None:
if frequency not in frequencies:
continue
frequency = float(frequency)
level = float(level)
waveform = ABRWaveform(fs, w, level)
waveforms.setdefault(frequency, []).append(waveform)
series = []
for frequency, stack in waveforms.items():
s = ABRSeries(stack, frequency)
s.filename = filename
s.id = filename.parent.name
series.append(s)
return series
def load(fname, filter=None, frequencies=None):
with io.open(fname, encoding='ISO-8859-1') as f:
line = f.readline()
if not line.startswith(':RUN-'):
raise IOError('Unsupported file format')
p_level = re.compile(':LEVELS:([0-9;]+)')
p_fs = re.compile('SAMPLE \(.sec\): ([0-9]+)')
p_freq = re.compile('FREQ: ([0-9\.]+)')
abr_window = 8500 # usec
try:
with io.open(fname, encoding='ISO-8859-1') as f:
header, data = f.read().split('DATA')
# Extract data from header
levelstring = p_level.search(header).group(1).strip(';').split(';')
levels = np.array(levelstring).astype(np.float32)
sampling_period = float(p_fs.search(header).group(1))
frequency = float(p_freq.search(header).group(1))
# Convert text representation of data to Numpy array
fs = 1e6/sampling_period
cutoff = int(abr_window / sampling_period)
data = np.array(data.split()).astype(np.float32)
data.shape = -1, len(levels)
data = data.T[:, :cutoff]
waveforms = []
for signal, level in zip(data, levels):
# Checks for a ABR I-O bug that sometimes saves zeroed waveforms
if not (signal == 0).all():
# Add new dimension to signal since the updated ABR program
# now takes individual waveforms.
waveform = ABRWaveform(fs, signal[np.newaxis], level,
filter=filter)
waveforms.append(waveform)
series = ABRSeries(waveforms, frequency)
series.filename = fname
return [series]
except (AttributeError, ValueError):
msg = 'Could not parse %s. Most likely not a valid ABR file.' % fname
raise IOError(msg)
def load(fname, filter=None, abr_window=8.5e-3, frequencies=None):
with tables.open_file(fname) as fh:
fs = fh.root.waveforms._v_attrs['fs']
cutoff = int(abr_window*fs)
signal = fh.root.waveforms[:, :, :cutoff]*1e6
levels = fh.root.trial_log.read(field='level')
available_frequencies = fh.root.trial_log.read(field='frequency')
# Load all frequencies by default
if frequencies is None:
frequencies = np.unique(available_frequencies)
series = []
for frequency in frequencies:
mask = available_frequencies == frequency
waveforms = [ABRWaveform(fs, s, l, filter=filter, min_latency=2)
for s, l in zip(signal[mask], levels[mask])]
s = ABRSeries(waveforms, frequency/1e3)
s.filename = fname
series.append(s)
return series