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Update edc.py #154

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29 changes: 14 additions & 15 deletions pyrato/edc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -912,13 +912,17 @@ def intersection_time_lundeby(
sampling_rate = np.round(1/np.diff(data.times).mean(), decimals=4)
energy_data = energy_data.time

# number of frequency bands given by first channel axis
n_bands = data.cshape[0]

if smoothing_parameter == "broadband":
# broadband: use 30 ms windows sizes
freq_dependent_window_time = 0.03
freq_dependent_window_time = np.atleast_1d([0.03] * n_bands)
elif isinstance(smoothing_parameter, (int, list, tuple, np.ndarray)):
smoothing_parameter = np.asarray(smoothing_parameter)
if (smoothing_parameter.ndim > 0) and (
smoothing_parameter.size != data.cshape[0]):
smoothing_parameter = np.atleast_1d(smoothing_parameter)
if smoothing_parameter.size == 1:
smoothing_parameter = np.tile(smoothing_parameter, n_bands)
elif smoothing_parameter.size != n_bands:
raise ValueError(
"The length of smoothing_parameter must match data.cshape[0].")
freq_dependent_window_time = (800 / smoothing_parameter + 10) / 1000
Expand All @@ -944,18 +948,13 @@ def intersection_time_lundeby(
noise_peak_level = np.zeros(data.cshape, data.time.dtype)

for ch in np.ndindex(data.cshape):
if len(np.atleast_1d(smoothing_parameter)) > 1:
output = _intersection_time_lundby(
time_window_data[ch], noise_estimation[ch], energy_data[ch],
np.squeeze(np.atleast_2d(time_vector_window)[ch, :]),
dB_above_noise, n_intervals_per_10dB,
use_dyn_range_for_regression, sampling_rate,
ch, failure_policy)
else:
output = _intersection_time_lundby(

output = _intersection_time_lundby(
time_window_data[ch], noise_estimation[ch], energy_data[ch],
time_vector_window, dB_above_noise, n_intervals_per_10dB,
use_dyn_range_for_regression, sampling_rate, ch, failure_policy)
np.squeeze(np.atleast_2d(time_vector_window)[ch, :]),
dB_above_noise, n_intervals_per_10dB,
use_dyn_range_for_regression, sampling_rate,
ch, failure_policy)

if output is None:
reverberation_time[ch] = np.nan
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