def __init__(self, rt_list_three_values_csv, i_list_csv):
if rt_list_three_values_csv != 'NA':
rt_list = map(
float,
peaks.rt_three_values_to_full_list_string(
rt_list_three_values_csv).split(','))
i_list = map(float, i_list_csv.split(','))
# i_list_smoothed = smooth_chromatogram_using_Savitzky_Golay(i_list)
self.rt_list = rt_list
self.i_list = i_list
# self.i_list_smoothed = i_list_smoothed
max_peaks, __ = peaks.peakdetect(i_list, rt_list, 2.0, 0.3)
# max_peaks_smoothed, __ = peaks.peakdetect(i_list_smoothed, rt_list, 6.0, 0.3)
if len(max_peaks) > 0:
# max_peaks_smoothed = filter_smoothed_peaks_based_on_raw_peaks(max_peaks, max_peaks_smoothed)
max_peaks_all = filter_peaks_based_on_peak_shape(
max_peaks, i_list, rt_list)
self.peak_apex_rt_list = [rt for (rt, i) in max_peaks_all]
self.peak_apex_i_list = [i for (rt, i) in max_peaks_all]
else:
# if no peak found. Most likely there is no signal. Use looser criteria to detect peaks
max_peaks, __ = peaks.peakdetect(i_list, rt_list, 1.0, 0.3)
# max_peaks_smoothed, __ = peaks.peakdetect(i_list_smoothed, rt_list, 1.0, 0.3)
# max_peaks_smoothed = filter_smoothed_peaks_based_on_raw_peaks(max_peaks, max_peaks_smoothed)
max_peaks_all = filter_peaks_based_on_peak_shape(
max_peaks, i_list, rt_list)
if len(max_peaks) > 0:
self.peak_apex_rt_list = [rt for (rt, i) in max_peaks_all]
self.peak_apex_i_list = [i for (rt, i) in max_peaks_all]
else:
# if still no peak found, most likely it is an empty chrom.
# use the point in the median value as peak
self.peak_apex_rt_list = [np.median(np.array(rt_list))]
self.peak_apex_i_list = [np.median(np.array(i_list))]
else:
self.rt_list = 'NA'
self.i_list = 'NA'
self.peak_apex_rt_list = 'NA'
self.peak_apex_i_list = 'NA'
def __init__(self, rt_list_three_values_csv, i_list_csv):
if rt_list_three_values_csv != 'NA':
rt_list = map(float, peaks.rt_three_values_to_full_list_string(rt_list_three_values_csv).split(','))
i_list = map(float, i_list_csv.split(','))
# i_list_smoothed = smooth_chromatogram_using_Savitzky_Golay(i_list)
self.rt_list = rt_list
self.i_list = i_list
# self.i_list_smoothed = i_list_smoothed
max_peaks, __ = peaks.peakdetect(i_list, rt_list, 2.0, 0.3)
# max_peaks_smoothed, __ = peaks.peakdetect(i_list_smoothed, rt_list, 6.0, 0.3)
if len(max_peaks) > 0:
# max_peaks_smoothed = filter_smoothed_peaks_based_on_raw_peaks(max_peaks, max_peaks_smoothed)
max_peaks_all = filter_peaks_based_on_peak_shape(max_peaks, i_list, rt_list)
self.peak_apex_rt_list = [rt for (rt, i) in max_peaks_all]
self.peak_apex_i_list = [i for (rt, i) in max_peaks_all]
else:
# if no peak found. Most likely there is no signal. Use looser criteria to detect peaks
max_peaks, __ = peaks.peakdetect(i_list, rt_list, 1.0, 0.3)
# max_peaks_smoothed, __ = peaks.peakdetect(i_list_smoothed, rt_list, 1.0, 0.3)
# max_peaks_smoothed = filter_smoothed_peaks_based_on_raw_peaks(max_peaks, max_peaks_smoothed)
max_peaks_all = filter_peaks_based_on_peak_shape(max_peaks, i_list, rt_list)
if len(max_peaks) > 0:
self.peak_apex_rt_list = [rt for (rt, i) in max_peaks_all]
self.peak_apex_i_list = [i for (rt, i) in max_peaks_all]
else:
# if still no peak found, most likely it is an empty chrom.
# use the point in the median value as peak
self.peak_apex_rt_list = [np.median(np.array(rt_list))]
self.peak_apex_i_list = [np.median(np.array(i_list))]
else:
self.rt_list = 'NA'
self.i_list = 'NA'
self.peak_apex_rt_list = 'NA'
self.peak_apex_i_list = 'NA'
def jcamp2wav(fh, wavenme=None, rate=44100, secs=5):
' convert a fh to a .dx file to a .wav file'
def sound_func(amp, hz, t): # sound generation
return amp * sin(hz * t)
def mean(l):
return sum(l) / len(l)
dct = jcamp.jcamp_read(fh)
x, y = dct['x'], dct['y']
dif = (y.max() - y.min())
pk = peakdetect(y, x, lookahead=1, delta=dif / 10)
max_peaks = pk[0] # [ [x0,y0] , ...., [xn,yn] ]
waves = [[(_y - y.min()) / dif,
MusicFreq.freq2octave(_x, 0)]
for _x, _y in max_peaks] # amp(0..1), freq oct '0'
waves.sort(reverse=True) # get <= 10 most powerful
waves = waves[:10]
pi2 = pi * 2 # -> evaluate waves average for each sample
data = np.asarray([
mean([sound_func(amp, hz, t) for amp, hz in waves])
for t in np.arange(0, secs * pi2, pi2 / rate)
],
dtype=np.float32)
if wavenme is None or not wavenme:
wavenme = fh.name.replace('.dx', '.wav')
wav_write(wavenme, rate, data)
def star_sound(fnme, wavenme=None, rate=44100, secs=5): # from data[1]
def sound_func(amp, hz, t): # sound generation
return amp * sin(hz * t) * sin(MusicFreq.freq2octave(hz, -7) * t)
def mean(l):
return sum(l) / len(l)
irms, lrms, header, data = read_dat(fnme)
y = data.T[1]
x = data.T[0]
dif = (y.max() - y.min())
pk = peakdetect(y, x, lookahead=1, delta=dif / 10)
max_peaks = pk[0] # [ [x0,y0] , ...., [xn,yn] ]
waves = [[(_y - y.min()) / dif, MusicFreq.freq2octave(_x, 0)] for _x, _y in max_peaks] # amp(0..1), freq oct '0'
waves.sort(reverse=True) # get <= 10 most powerful
waves = waves[:10]
pi2 = pi * 2 # -> evaluate waves average for each sample
datawav = np.asarray(
[mean([sound_func(amp, hz, t) for amp, hz in waves]) for t in np.arange(0, secs * pi2, pi2 / rate)],
dtype=np.float32)
if wavenme is None or not wavenme:
wavenme = fnme.replace('.dat', '.wav')
wav_write(wavenme, rate, datawav)
def find_peaks(y_axis, x_axis=None, lookahead=200, delta=0):
r1, r2 = pks.peakdetect(y_axis, x_axis=None, lookahead=200, delta=0)
return np.array(r1), np.array(r2)