1D ^{1}H NMR is a commonly applied to metabolomic studies, being well suited
to untargeted analysis of complex biofluids. It has been successfully applied to
the classification and diagnosis of a number of diseases including [ref].

There are a number of important steps that must be applied prior 1D ^{1}H NMR data
to statistical analysis, all gearded towards ensuring that the variation observed
in the data is representative of real biological variation and not a artifact of
sample handling, metabolite extraction or NMR acquisition. These steps are
described briefly below and approached in turn in the following method. It is
important to familiarise yourself with both the purpose of these steps, and
any associated caveats or limits, so that you can correctly apply them to your
own data.

## Setting up

To process raw NMR data we will be making use of of the `nmrglue`

library. In
addition we will make use of features from the `numpy`

, `scipy`

and `matplotlib`

libraries, which you should already have installed.
To make sure you can run the following command on the terminal:

```
pip install numpy scipy nmrglue matplotlib
```

The demo data for this example is available for download.

Download and unzip the file into your data folder.

## Loading Bruker FID spectra

Create a new Jupyter notebook using the Python 3 kernel, and in the first cell
enter and run the following. This will import all the neccessary libraries, as
well as using the `%matplotlib`

magic to display output figures in the notebook.

```
%matplotlib inline
import matplotlib.pyplot as plt
import nmrglue as ng
import numpy as np
import scipy as sp
import os
plt.style.use('ggplot')
```

Bruker-format data is processed using a digital filter before saving as FID, this
filter must be removed for subsequent analysis to work. To open a given spectra,
pass the parent folder of the `fid`

file.

```
dic, data = ng.fileio.bruker.read('87')
data = ng.bruker.remove_digital_filter(dic, data)
```

We can plot the spectra using `matplotlib`

.

```
fig = plt.figure(figsize=(12,4))
ax = fig.add_subplot(1,1,1)
ax.plot(np.arange(0, data.shape[0]), data) # <1>
```

- We don't have the x-axis
*ppm*scale yet, so we generate a linear scale of the same size as the data. Here`.shape[0]`

is the length of the fid along the first (and only) axis of the 1D spectra.

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## Fourier transform (FT)

A *fourier transform* is a signal transformation that decomposes a signal
into it's constituent frequencies. In the case of NMR data, this decomposition
is to a series of peaks, that represent the resonance of chemical subgroups. These
peaks contain both our identification (frequency) and quantification (amplitude)
information.

The `nmrglue`

package provides a *fast fourier transform* (FFT) for this purpose

```
data_fft = ng.proc_base.fft(data)
fig = plt.figure(figsize=(12,4))
ax = fig.add_subplot(1,1,1)
ax.plot(np.arange(0, data_fft.shape[0]), data_fft)
```

You'll notice that the spectra is out of *phase*, i.e. the peaks are not
symmetrical and well defined. We will fix this shortly, but first lets load
a complete set of spectra so we can view them all at once.

## Batch loading + FFT for multiple spectra

```
root_folder = '.'
zero_fill_size = 32768
data = [] # <1>
for folder in os.listdir(root_folder):
dic, fid = ng.fileio.bruker.read(os.path.join(root_folder,folder))
fid = ng.bruker.remove_digital_filter(dic, fid)
fid = ng.proc_base.zf_size(fid, zero_fill_size) # <2>
fid = ng.proc_base.rev(fid) # <3>
fid = ng.proc_base.fft(fid)
data.append(fid)
data = np.array(data)
```

- We build as a list, then transform to an array for speed.
- Zero-filling to a power of 2 speeds up FFT.
- Reverse the data to match common representation (for convenience only).

The data is output in a 2D array, with samples along the first axis.

```
data.shape
(17, 32768)
```

We want to be able to plot multiple spectra on the same plot, so we'll write a simple function to do this.

```
def plotspectra(ppms, data, start=None, stop=None):
if start: # <1>
ixs = list(ppms).index(start)
ppms = ppms[ixs:]
data = data[:,ixs:]
if stop:
ixs = list(ppms).index(stop)
ppms = ppms[:ixs]
data = data[:,:ixs]
fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1,1,1)
for n in range(data.shape[0]):
ax.plot(ppms, data[n,:])
ax.set_xlabel('ppm')
ax.invert_xaxis()
return fig
```

- This block allows us to select regions of the spectra to view by filtering on the supplied ppms.

We can use this function to look at all spectra following fourier transform:

```
ppms = np.arange(0, data.shape[1])
fig = plotspectra(ppms, data)
```

We can take a close up view of the TMSP peak, which is currently on the left hand side of the spectra in the region 28000-30000.

```
ppms = np.arange(0, data.shape[1])
fig = plotspectra(ppms, data, start=28000, stop=30000)
```

Clearly the spectra are all out of phase and poorly aligned. We will fix the phasing problem first, but lets start off by getting the correct ppm values for the spectra.

## Calculating ppm values

The method for calculating ppm values is rather complicted for Bruker format files. However, the following will give the correct output:

```
offset = (float(dic['acqus']['SW']) / 2) - (float(dic['acqus']['O1']) / float(dic['acqus']['BF1']))
start = float(dic['acqus']['SW']) - offset
end = -offset
step = float(dic['acqus']['SW']) / zero_fill_size
ppms = np.arange(start, end, -step)[:zero_fill_size]
```

We can now plot the spectra with the correct ppms.

```
fig = plotspectra(ppms, data)
```

## Phase correction

The next step is to phase correct all the spectra. The package `nmrglue`

provides
a few automated algorithms that do a reasonable job with most normal, good quality
spectra. Thankfully, that's what we have here.

```
for n in range(0, data.shape[0]):
data[n, :] = ng.proc_autophase.autops(data[n,:], 'acme')
fig = plotspectra(ppms, data)
```

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