Easy plots

The current version of SpinPlots only works with processed Bruker data. If you’re dealing with unprocessed data or using data from a different brand, we recommend using NMRglue instead. Also, if you want more control over the looks of your plot, SpinPlots might feel a bit limited since it mostly sticks to the default matplotlib style for now.

from spinplots.plot import bruker1d, bruker1d_grid, bruker2d

Plot 1D spectra

The easiest way to plot a spectrum is as follows

bruker1d(['../../data/1D/8/pdata/1'], xlim=(250, -20))

SpinPlots uses Matplotlib's standard style by default. If you want to customize the appearance of your spectra, you can use the option return_fig=True. This allows you to return the figure object and modify it as needed before displaying or saving it.

fig, ax = bruker1d(['../../data/1D/8/pdata/1'], return_fig=True)

ax.set_title('Sample')
ax.set_xlim(200, 90)
ax.lines[0].set_color('red')
ax.lines[0].set_linewidth(2)
ax.lines[0].set_linestyle('--')
fig.savefig('../../data/1D/modified.png')

The bruker1d function has a bunch of useful features beyond just making a simple plot. Some of the key ones include:

• Plotting several plots together
• Plot with/without frame
• Labelling of spectra
• Normalization of data
• Select colors for each spectrum using matplotlib's colors
• Saving the plot directly to a file

Here’s an example that highlights most of these features:

path = '../../data/1D/'
bruker1d([path + '8/pdata/1', path + '12/pdata/1', path + '33/pdata/1', path + '39/pdata/1'],
              labels=['Sample A', 'Sample B', 'Sample C', 'Sample D'],
              color=['brown', 'teal', 'skyblue', 'sandybrown'],
              xlim=(250, -20),
              frame=False,
              normalized=False,
              save=True,
              filename='../../data/1D/overlapped',
              format='png')

Stacked 1D spectra

You can also opt by stacking the plots, using the stacked keyword, as shown below:

path = '../../data/1D/'
bruker1d([path + '8/pdata/1', path + '12/pdata/1', path + '33/pdata/1', path + '39/pdata/1'],
              labels=['Sample A', 'Sample B', 'Sample C', 'Sample D'],
              xlim=(250, -20),
              frame=False,
              normalized=True,
              stacked=True,
              save=True,
              filename='../../data/1D/stacked',
              format='png')

The bruker1d_grid function works similarly to bruker1d, but instead of overlapping the spectra, it lets you arrange them in a grid (subplots).

Here’s an example of how it works:

path = '../../data/1D/'
bruker1d_grid([path + '8/pdata/1', path + '12/pdata/1', path + '33/pdata/1', path + '39/pdata/1'],
                labels=['Sample A', 'Sample B', 'Sample C', 'Sample D'],
                xlim=(250, -20),
                subplot_dims=(2, 2),
                frame=False,
                normalized=True,
                save=True,
                filename='../../data/1D/grid',
                format='png')

Plot 2D spectra

Lastly, the bruker2d function makes it super easy to plot 2D NMR spectra. You just need to provide a bit more info to customize the plot. Here’s what you can specify: - countour_start: the minimum value for the contour plot - countour_num: how many contour levels you want - countour_factor: the factor between each contour level - cmap: the matplotlib colormap for countour (default is 'black') - xlim: limits for the x-axis, i.e. direct dimension (F2) - ylim: limits for the y-axis, i.e. indirect dimension (F1) - save: whether you want to save the plot - filename: the name to save the plot as - format: the file format to save it

bruker2d('../../data/2D/36/pdata/1',
        contour_start=5e5,
        contour_num=15,
        contour_factor=1.2,
        cmap='viridis',
        xlim=(180, -10),
        ylim=(16, -5),
        save=True,
        filename='../../data/2D/2d_spectrum',
        format='png',)

Some NMR spectra, like double-quantum (DQ) experiments, are often visualized with a diagonal line representing y=2x. You can add this diagonal line for any line of the form y=nx using the keyword diag=n, where n sets the desired quantum order. Here’s an example of a ¹³C-¹³C DQ-SQ experiment. The option homo=True is only needed if the y-axis label isn’t displaying correctly, such as when the DQ-SQ is acquired through CPMAS.

bruker2d('../../data/2D/16/pdata/1',
        contour_start=7e6,
        contour_num=25,
        contour_factor=1.4,
        xlim=(185, 15),
        ylim=(305, 55),
        diag=2,
        save=True,
        homo=True,
        filename='../../data/2D/dq_sq_spectrum',
        format='png',)