start_sample = sample_start_clip
num_samples = clip_sample_lenght
# Specify data paths
raw_path = raw_recording
# Specify sample range for clip
start_sample = 32837802
num_samples = 657331
# Load raw data and convert to microvolts
raw_uV = ephys.get_raw_clip_from_amplifier(raw_path, start_sample, num_samples)
# Compute mean and standard deviation for each channel
raw_mean = np.mean(raw_uV, axis=1)
raw_std = np.std(raw_uV, axis=1)
# Z-score each channel
raw_Z = np.zeros(raw_uV.shape)
for ch in range(128):
raw_Z[ch,:] = (raw_uV[ch,:] - raw_mean[ch]) / raw_std[ch]
# Specify channels to exclude
#exlcude_channels = np.array([12, 13, 18, 19, 108, 109 ,115])
exlcude_channels = np.array([12, 13, 18, 54, 108, 109 ,115])
include_channels = np.delete(np.arange(128), exlcude_channels)
if idx == 0:
count = +1
else:
start_sample = end_samples[idx - 1]
num_samples = samples_lenght_end_to_end[idx]
trial_end_and_lenght.append((start_sample, num_samples))
#iterating on each trial
#trial =21
for trial in np.arange(len(trial_end_and_lenght)):
# Load raw data and convert to microvolts
raw_uV = ephys.get_raw_clip_from_amplifier(raw_path,
trial_end_and_lenght[trial][0],
trial_end_and_lenght[trial][1])
# Compute mean and standard deviation for each channel
raw_mean = np.mean(raw_uV, axis=1)
raw_std = np.std(raw_uV, axis=1)
# Z-score each channel
raw_Z = np.zeros(raw_uV.shape)
for ch in range(128):
raw_Z[ch, :] = (raw_uV[ch, :] - raw_mean[ch]) / raw_std[ch]
# Store raw Z-scored as raw
raw = np.copy(raw_Z)