#plt.show()
# Measure threshold crossings
signal = ephys.apply_probe_map_to_amplifier(clean)
num_channels = len(signal)
spike_times = [[] for _ in range(num_channels)]
spike_peaks = [[] for _ in range(num_channels)]
for channel in np.arange(num_channels):
try:
# Extract data for single channel
channel_data = signal[channel,:]
# FILTERS (one ch at the time)
channel_data_highpass = ephys.highpass(channel_data,BUTTER_ORDER=3, F_HIGH=14250,sampleFreq=30000.0,passFreq=500)
# Determine high and low threshold
abs_channel_data_highpass = np.abs(channel_data_highpass)
sigma_n = np.median(abs_channel_data_highpass) / 0.6745
#adaptive th depending of ch noise
spike_threshold_hard = -3.0 * sigma_n
spike_threshold_soft = -1.0 * sigma_n
# Find threshold crossings
spike_start_times, spike_stop_times = threshold_crossing(channel_data_highpass,spike_threshold_hard,spike_threshold_soft)
# Find peak voltages and times
spike_peak_voltages = []
spike_peak_times = []
# Measure stats for each channel
for ch in range(128):
# Report
print("Starting channel {0}".format(ch))
# Extract channel data and convert to uV (float32)
data_ch = data[ch, :]
data_ch_uV = (data_ch.astype(np.float32) - 32768) * 0.195
print("- converted to uV")
# High-pass filter at 500 Hz
highpass_ch_uV = ephys.highpass(data_ch_uV,
BUTTER_ORDER=3,
F_HIGH=14250,
sampleFreq=30000.0,
passFreq=500)
print("- highpassed")
# Determine RMS noise after filtering
abs_highpass_ch_uV = np.abs(highpass_ch_uV)
sigma_n = np.median(abs_highpass_ch_uV) / 0.6745
print("- Noise sigma measured")
# Measure RMS of each channel (in uV) and save
# Make example plots...of unfiltered and high-pass filtered data
# Should compare this with both "cleaned" and raw data
#FIN
highcut= 52 wo50 = ephys.butter_bandstop(raw_uV,lowcut, highcut, fs=30000, order=3, btype='bandstop') plt.plot(wo50[:150000],alpha = 0.4) raw_diff = raw_uV - wo50 cleaned_diff = cleaned_uV - wo50 plt.plot(raw_diff[:150000],alpha = 0.4) plt.plot(cleaned_diff[:150000],alpha = 0.4) # highpass highpass_cleaned = ephys.highpass(wo50,BUTTER_ORDER=3, F_HIGH=14250,sampleFreq=30000.0,passFreq=500) plt.plot(highpass_cleaned[:150000],alpha = 0.4) #lowpass lowcut = 250 lowpass_cleaned = ephys.butter_filter_lowpass(wo50,lowcut, fs=30000, order=3, btype='lowpass') plt.plot(lowpass_cleaned[:150000],alpha = 0.4)
