def midpoint(specdata):
"""Finds major/minor peaks according to threshold values; Threshold
value is divided by max peak height"""
if not isinstance(specdata, pd.DataFrame): # check datatype
specdata = main(specdata)
specdata.set_index('0', inplace = True) #reset index as time-series
else:
pass
midpoint = (len(specdata.iloc[0,:]))/2 # find the mid-timepoint
midpoint_spectrum = specdata.iloc[:, midpoint] # find the midpoint spectrum
return midpoint_spectrum
def peakfind(specdata, threshold = 0.02):
"""Finds major/minor peaks according to threshold values; Threshold
value is divided by max peak height"""
if not isinstance(specdata, pd.DataFrame): # check datatype
specdata = main(specdata)
specdata.set_index('0', inplace = True) #reset index as time-series
else:
pass
# exectue the peak finding on this spectrum
indices = peakutils.indexes(midpoint_spectrum, thres=threshold/max(midpoint_spectrum))
return indices
def series_peakfind(specdata, threshold = 0.2, min_dist=50):
"Produces a list of lists containing the peaks at each timepoint"
if not isinstance(specdata, pd.DataFrame): # check datatype
specdata = main(specdata)
specdata.set_index('0', inplace = True) #reset index as time-series
print 'Warning: This series is not baseline corrected!'
else:
pass
indexes=[] # initialize the empty array
for i in xrange(len(specdata.iloc[0,:])): # execute peakfinding algorithm by column
individual_spectrum = specdata.iloc[:,i]
indexes.append(peakutils.indexes(individual_spectrum, min_dist= min_dist, thres = threshold/max(individual_spectrum)))
return indexes
def series_baseline(seriesdata):
"""For a spectral series: Performs a baseline-identifying algorithm
from -m peakutils, and corrects"""
df = main(seriesdata)
df.set_index('0', inplace = True)
baseline_series = df.copy() # make a deep copy to the dataframe
corrected_spectra = df.copy() # make a deep copy to the dataframe
for i in xrange(len(df.iloc[0,:])): # execute baseline-identifier algorithm by column
baseline_series.iloc[:,i] = peakutils.baseline(df.iloc[:,i])
corrected_spectra = df - baseline_series # subtract from original spectra to find correction
return baseline_series, corrected_spectra
def series_peakfind(specdata, threshold = 0.05):
if not isinstance(specdata, pd.DataFrame): # check datatype
specdata = main(specdata)
specdata.set_index('0', inplace = True) #reset index as time-series
else:
pass
indexes=[]
for i in xrange(len(specdata.iloc[0,:])): # execute baseline-identifier algorithm by column
individual_spectrum = specdata.iloc[:,i]
indexes.append(peakutils.indexes(individual_spectrum, thres = threshold/max(individual_spectrum)))
# exectue the peak finding on this spectrum
#indexes = peakutils.indexes(midpoint_spectrum, thres=threshold/max(midpoint_spectrum))
return indexes
#def series_peakshow(specdata, indexes): # Next step
"""Plots peaks found in peakfind"""
def midpoint_peakfind(specdata):
"""Finds major peaks according to threshold values; Threshold
value is divided by max peak height"""
if not isinstance(specdata, pd.DataFrame): # check datatype
specdata = main(specdata)
specdata.set_index('0', inplace = True) #reset index as time-series
else:
pass
midpoint = (len(specdata.iloc[0,:]))/2 # find the mid-timepoint
midpoint_spectrum = specdata.iloc[:, midpoint] # find the midpoint spectrum
# exectue the peak finding on this spectrum
indices = signal.find_peaks_cwt(midpoint_spectrum)
# trace the midpoint spectrum
spectrum = go.Scatter(
x=[j for j in range(len(midpoint_spectrum))],
y=midpoint_spectrum,
mode='lines',
name='Abs spectrum of the midpoint-time in kinetic series'
)
# mark the peaks
peaks = go.Scatter(
x=indices,
y=[midpoint_spectrum.iloc[j] for j in indices],
mode='markers',
marker=dict(
size=8,
color='rgb(255,0,0)',
symbol='cross'
),
name='Detected Peaks'
)
data = [spectrum, peaks]
return py.plot(data)