Using peak picking with PPM scale about nmrglue HOT 5 CLOSED

I have solved this problem by doing the following:

dic, data = ng.bruker.read_pdata('JE-9-27-02/1/pdata/1/') #Import Data

SW = 6393.862                                   #Sweep Width in Hz
SF01M = 400.1324008                             #Transmitter Frequency offset in MHz
O1 = 2400.78                                    #Frequency Offset in Hz

uc = ng.fileiobase.unit_conversion(data.size,True,SW,SF01M,O1) #Convert spectra to PPM
frq = uc.ppm_scale()

peaks = ng.peakpick.pick(data,28000)            #Pick Peaks
npeaks = len(peaks)

SF01 = 400132400.8                              #Transmitter Frequency offset in Hz
SF = 400130003.6                                #Spectrometer Frequency (INCLUDES SR VALUE)
SFMhz = 400.1300036                             #Converted SF to Mhz
SW = 6393.862                                   #Sweep Width in Hz
FIDRES = 0.195125                               #Fid Resolution
SR = 3.56                                       #SR value in Hz
SI = 32768                                      #Size of FID. Number of points collected. 
startpoint = ((SF01+(0.5*6393.862)))            #The maximum value of Hz collected. Upper limmit. 


DeltaStart = (startpoint - SF)                  # Maximum change in Hz that may be observed at collection point 1. 
PeaksHZ = (DeltaStart -((peaks.X_AXIS)*FIDRES)) #List of observed delta in Hz procession Frequency 
PeaksPPM = ((PeaksHZ+SR)/SFMhz)                    # Converts the deltaHz to PPM based on Frequency of the spectrometer
print(PeaksPPM)

fig = plt.figure(figsize=(20,10))               #Plot figure
ax = fig.add_subplot(111)

ax.plot(frq,data, 'k-')
ax.invert_xaxis()
ax.plot(PeaksPPM,data[peaks['X_AXIS'].astype('int')], 'ro')#This displays picked peaks
ax.axhline(y=28000, color='k', ls='--')#This shows the cutoff for the peak picking
ax.set_xlabel('PPM')
plt.show()

However, I am now having an issue with integration, or rather, how to do it from a peak list that is generated from this peak picking. This makes sense based on the way that I had to get around this problem of converting it to Hz. I do not have a good way to extract the peak start and stop autonomously.

Any suggestions?

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Please allow me to start off with an apology. As there isn't a "subreddit" like place that I can post general questions, which I am mostly used to.

nmrglue is not big enough to have a forum based question site. Posting questions here is fine. The mailing list is also an option although there has not been much traffic there recently.

Peak picking and all other function in nmrglue operate by default in units of points. To convert to PPM or other units you should use a unit conversion object which deals with all of the organization of the carrier frequencies, offsets and spectral widths for you. For Bruker data these can be created from a universal dictionary which can be created from the file metadata (dic) and data (data). For example to pick all the peaks in the 1D capistruin spectrum and plot them on a PPM scale one could use the following script:

import matplotlib.pyplot as plt
import nmrglue as ng

# read in the data
dic, data = ng.bruker.read_pdata('./Capistruin/10/pdata/1')

# peak pick the data
peak_table = ng.peakpick.pick(data, pthres=1e6, algorithm='downward')

# make a universal parameter dictionary from the data
udic = ng.bruker.guess_udic(dic, data)

# create a unit conversion object
uc = ng.fileiobase.uc_from_udic(udic)

# convert the peak locations from unit of points to PPM
# using the unit conversion object
peak_locations_ppm = [uc.ppm(i) for i in peak_table['X_AXIS']]

# Find the peak amplitudes
peak_amplitudes = data[peak_table['X_AXIS'].astype('int')]

# plot the spectrum and peak locations on a PPM scale
fig = plt.figure()
ax = fig.add_subplot(111)

ppm_scale = uc.ppm_scale()
ax.plot(ppm_scale, data, 'k-')

ax.plot(peak_locations_ppm, peak_amplitudes, 'ro')

ax.invert_xaxis()
ax.set_xlim(7.8, 6.8)
ax.set_xlabel('PPM')
plt.show()

Which produces the following plot:

The peak table from the above script also records peak line width estimates which could be used as limits for integration as follows:

import numpy as np
for peak_number in range(len(peak_table)):
    loc_ppm = peak_locations_ppm[peak_number]
    loc_pts = int(peak_table['X_AXIS'][peak_number])
    fwhm = peak_table['X_LW'][peak_number]
    hwhm_int = int(np.floor(fwhm / 2.))
    peak_area = data[loc_pts - hwhm_int: loc_pts+hwhm_int+1].sum()
    print(loc_ppm, peak_area)

More details on the outputs of the pick function can be found in the documentation

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Your timing could not possibly be better. I was just mulling over how to use the peak line widths to define the parameters for integration.

Thanks for the followup. I can use a lot of things here to clean up my code. I will update some if I can on the questions I'm coming to form.

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How does the integration protocol above actually work? Does it integrate the regions above the peak picking cutoff, or is it integrating down to the baseline?

I noticed that the integration values that I am getting using this is very far off from the integration that I get when using something like MNOVA. I double checked by processing a spectrum I knew to be have a methyl group as well as an internal standard. In MNOVA, the ratios of the integration areas came out to be a value around 0.22xxx, but when I used the values from the above code, I get 0.03xxxx. Although the phasing could be better, I wouldn't think this would cause this large a difference, would it?

Any thoughts on this? Is something else going on?

Also, I was curious about the fitting examples. Does the fitting function allow for integration of only real peaks by smoothing out any fluctuations that it might see as peaks? Or is there another function of this?

Cheers,
Joe

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@Jmegan042 The above integration does a sum of all spectral points within one half width half max, there is not baseline identification done. These results may be very different than those found by more sophisticated methods.

The fitting function in nmrglue performs a constrained least squares fit of the spectra to the peak model specified by the function. False peaks should either be included in the model or removed by other means (i.e. pre-processing).

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