fish_names = helpers.get_all_fish(helpers.RECORDING_PATH16) # CHANGE TO RECORDING_PATH8 for 8 bits files
indexes = []
fish = fish_names[1]
levels = 3
mat_files = np.array(helpers.get_mat_files(fish, helpers.RECORDING_PATH16))
fft_files = helpers.get_npy_files(fish, helpers.SAVE_PATH, 'fft')
counter = 0
shift = 0
set = 1
for ind in range(len(fft_files)):
if counter <= levels:
counter += 1
file_name = helpers.path_to_name(fft_files[0])
xf, power = helpers.load_npy(fft_files[0])
plt.plot(xf, shift + power / np.max(power))
shift = shift + 1.2
fft_files.pop(0)
if counter == levels:
shift = 0
counter = 0
plt.title('FFT ' + fish + str(set))
plt.grid()
plt.xlim(0, 6000)
plt.ylabel('Amplitude')
plt.xlabel('Frequency ')
helpers.save_figure(join(helpers.SAVE_PATH, fish), 'FFT for set ' + str(set), fish, file_name, 'FFT_graphs')
plt.close()
threshold = 0
f_estimate = 800
frequencies = calculate_frequency(EOD,
sampling_frequency,
estimated_frequency=f_estimate,
crossing_threshold=threshold,
temporal_threshold=0.05,
method='median',
ascending=False)
cv = '{:.2e}'.format(np.std(frequencies) / np.mean(frequencies))
print(cv)
print(np.mean(frequencies))
file_name = helpers.path_to_name(file)
frequency_info = [frequencies, cv, threshold]
helpers.save_results(frequency_info, fish, file_name, 'frequency')
npy_files = helpers.get_npy_files(fish, helpers.SAVE_PATH, 'frequency')
frequencies = []
for i in range(len(npy_files)):
[frequency, cv, threshold] = helpers.load_npy(npy_files[i])
frequency = frequency.tolist()
frequencies.extend(frequency)
size = np.size(frequencies) / len(npy_files)
frequencies = []
npy_files = helpers.get_npy_files(fish, helpers.SAVE_PATH, 'frequency')
fft_files = helpers.get_npy_files(fish, helpers.SAVE_PATH, 'fft')
mat_files = helpers.get_mat_files(fish, helpers.RECORDING_PATH16)
[lowrez_index, highrez_index] = helpers.sort_files(mat_files)
file_number = highrez_index
for numb in file_number:
index = file_number.index(numb)
raw_data = helpers.load_mat(mat_files[numb])
data = np.array(raw_data)
data = data[np.isfinite(data)]
EOD = data - np.mean(data)
file_name = helpers.path_to_name(mat_files[numb])
[frequency, cv, threshold] = helpers.load_npy(npy_files[numb])
time_array = helpers.create_time(len(raw_data)-1, style='MAT')
cycle_time = np.cumsum(1. / frequency)
[xf, power] = helpers.load_npy(fft_files[index])
fft_main = 2*power.argmax()
fig = plt.figure('Explore V1 ' + file_name )
ax1 = fig.add_subplot(221)
ax1.plot(time_array[::50] , EOD[1::50])
ax1.set_title('Full data')
ax2 = fig.add_subplot(222)
ax2.plot(time_array[0:675000] , EOD[0:675000]) # shows 5 cycles
marker_ind = []
lowrez_index = []
highrez_index = []
mat_files = helpers.get_mat_files(fish, helpers.RECORDING_PATH16)
[lowrez_index ,highrez_index ] = helpers.sort_files(mat_files)
if lowrez is True:
file_number = lowrez_index
else:
file_number = highrez_index
for index in file_number:
npy_files = helpers.get_npy_files(fish, helpers.SAVE_PATH, 'frequency')
file_name = helpers.path_to_name(npy_files[index])
[frequency, cv, threshold] = helpers.load_npy(npy_files[index])
cycle_time = np.cumsum(1. / frequency)
plt.figure('Frequency with time')
plt.plot(cycle_time, frequency, '.')
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [s]')
plt.title('Frequency variation in time for ' + file_name + ' CV = ' + str(cv))
helpers.save_figure(join(helpers.SAVE_PATH, fish), 'Frequency in time for ', fish, file_name, 'Frequency_graphs')
plt.close()
if lowrez is False:
plt.figure('Histogram of frequency')
plt.hist(frequency, bins = 35)
plt.xlabel('Frequency [Hz]')
plt.ylabel('Occurrence')