too_long = too_long * fs_mic
mic = mic.squeeze()
if normalize_mic: # because our mic channel has leak current / noise
i = 0
previous_i = 0
while i < len(mic):
i = i + np.round(
fs_mic / 2) # filter by the half second, not optimized
if i > len(mic):
i = len(mic)
mic[previous_i:i] = mic[previous_i:i] - np.mean(mic[previous_i:i])
previous_i = i
if len(mic.shape) > 1:
mic = mic[:, 0] # discards any other mic channels beyond the first.
vocal_band = lowpass_filter(mic, fs_mic, vb_lowpass)
vocal_band = highpass_filter(vocal_band, fs_mic, vb_highpass)
unfiltered_mic = mic
mic = lowpass_filter(mic, fs_mic, mic_lowpass)
mic = highpass_filter(mic, fs_mic, mic_highpass)
mic_env = lowpass_filter(np.abs(mic), fs_mic, 250.0)
vocal_band = np.abs(vocal_band)
vocal_band = lowpass_filter(vocal_band, fs_mic, vb_low)
# find periods of sound on the mic channel that is likely vocalizations
# sound_onset is in data points, not time
sound_onset, sound_offset = find_vocal_periods(vocal_band, vb_stds, vd_stds,
vd_win, fs_mic, onset_shift)
if shorten: # if you're troubleshooting just deal with the beginning of the data
sound_onset = sound_onset[0:18]
sound_offset = sound_offset[0:18]
# Chose the electrode to plot
ielec = 4
# This is the entire neural recording (amp) and microphone recording (mic)
amp = [
asig for asig in segment.analogsignalarrays if asig.name == "Amplifier"
][0]
mic = [
asig for asig in segment.analogsignalarrays if asig.name == "Board ADC"
][0]
# High pass the microphone
sample_rate = float(mic.sampling_rate)
micvals = np.asarray(mic).squeeze()
micvals -= micvals.mean()
micfilt = highpass_filter(micvals, sample_rate, 400.0)
mic_env = lowpass_filter(np.abs(micfilt), float(sample_rate), 125.0)
max_mic = np.std(mic_env)
# Find good plot boundaries
sample_rate = float(amp.sampling_rate)
amp_all = np.asarray(amp)[:, ielec]
low_amp = lowpass_filter(amp_all, sample_rate, 400.0)
low_maxabs = np.std(low_amp)
high_amp = highpass_filter(amp_all, sample_rate, 400.0)
high_maxabs = np.std(high_amp)
neural_signal_env = lowpass_filter(np.abs(high_amp), float(sample_rate), 50.0)
maxS = np.max(neural_signal_env)
# Find all the stims
all_stims = []
os.path.basename(experiment_file).replace(".rhd", "_stimuli.h5"))
sm = sound_manager.SoundManager(HDF5Store, stimulus_h5_filename)
# This is the entire microphone recording
mic = [
asig for asig in block.segments[0].analogsignalarrays
if asig.name == "Board ADC"
][0]
fs_mic = np.int(mic.sampling_rate)
t_mic = np.asarray(mic.times)
mic = mic.squeeze()
if normalize_mic:
mic = mic - np.mean(
mic
) # because our mic channel often has 1.652 v of dc leak from somewhere!
vocal_band = lowpass_filter(mic, fs_mic, low_power)
vocal_band = highpass_filter(vocal_band, fs_mic, high_power)
vocal_band = np.abs(vocal_band)
vocal_band = lowpass_filter(vocal_band, fs_mic, vb_low)
sound_onset, sound_offset = find_vocal_periods(vocal_band, vb_stds, vd_stds,
vd_win, fs_mic, onset_shift)
#==============================================================================
# for i in range(len(sound_onset)):
# t, freq, timefreq, rms = spectrogram(mic[sound_onset[i]:sound_offset[i]], fs_mic, 1000, 50)
# plot_spectrogram(t, freq, timefreq, dBNoise=80, colorbar = False)
# pause(.05)
#==============================================================================
# just a temporary template, ~ 1 motif, maybe even a playback but who cares
# TODO make a template finding function
i = 17
# Chose the electrode to plot
ielec = 10
# This is the entire neural recording (amp) and microphone recording (mic)
amp = [
asig for asig in segment.analogsignalarrays if asig.name == "Amplifier"
][0]
mic = [
asig for asig in segment.analogsignalarrays if asig.name == "Board ADC"
][0]
# High pass the microphone
sample_rate = float(mic.sampling_rate)
micvals = np.asarray(mic).squeeze()
micvals -= micvals.mean()
micfilt = highpass_filter(micvals, sample_rate, 400.0)
mic_env = lowpass_filter(np.abs(micfilt), float(sample_rate), 125.0)
max_mic = np.std(mic_env)
# Find good plot boundaries
sample_rate = float(amp.sampling_rate)
amp_all = np.asarray(amp)[:, ielec]
low_amp = lowpass_filter(amp_all, sample_rate, 400.0)
low_maxabs = np.std(low_amp)
high_amp = highpass_filter(amp_all, sample_rate, 400.0)
high_maxabs = np.std(high_amp)
neural_signal_env = lowpass_filter(np.abs(high_amp), float(sample_rate), 50.0)
maxS = np.max(neural_signal_env)
# Find all the stims
all_stims = []