Read and Export data

The functions in the previous tutorial make plotting pretty automatic, using matplotlib's standard style. But sometimes, you might just want to read the data without immediately plotting it—maybe to style it your own way or to convert it into a different format.

from __future__ import annotations
import warnings
warnings.filterwarnings("ignore", category=UserWarning)

import matplotlib.pyplot as plt

from spinplots.io import read_nmr
from spinplots.utils import nmr_df

Customize spinplots

In the previous tutorial, we used bruker1d(['../../data/1D/glycine/pdata/1'], xlim=(250, -20)) to create a simple ¹³C NMR plot. By adding the option return_fig=True, you can return the figure object, which allows you to further customize the appearance of the spectrum as you like.

gly = read_nmr("../../data/1D/glycine/pdata/1")
fig, ax = gly.plot(xlim=(250, -20), return_fig=True)
ax.set_title("Customized plot")
ax.set_xlabel("13-Carbon NMR in ppm")
ax.lines[0].set_color("purple")
ax.lines[0].set_linewidth(2)
ax.lines[0].set_linestyle("-.")
fig.savefig("../../data/1D/customized_plot.png")

Export data to Pandas and CSV

A different option is to use the nmr_df function from spinplots.utils to get a Pandas DataFrame. This way, you can manipulate the data however you like before plotting.

df_nmr = nmr_df("../../data/1D/glycine/pdata/1")
df_nmr

	hz	ppm	intensity	norm_intensity	nuclei
0	62320.002580	619.310059	36928800.0	0.004269	13C
1	62318.476701	619.294895	40210336.0	0.004648	13C
2	62316.950822	619.279732	39049024.0	0.004514	13C
3	62315.424943	619.264568	33449568.0	0.003867	13C
4	62313.899064	619.249404	23894656.0	0.002762	13C
...	...	...	...	...	...
65531	-37672.368025	-374.372200	-20542656.0	-0.002375	13C
65532	-37673.893904	-374.387363	-8086976.0	-0.000935	13C
65533	-37675.419783	-374.402527	5093344.0	0.000589	13C
65534	-37676.945662	-374.417690	17696256.0	0.002046	13C
65535	-37678.471541	-374.432854	28418208.0	0.003285	13C

65536 rows × 5 columns

plt.plot(df_nmr["ppm"], df_nmr["intensity"], color="green")
plt.xlim(250, -20)
plt.xlabel("$^{31}$C (ppm)")
plt.ylabel("Intensity (a.u.)")
plt.show()

Convert data in the terminal

Pandas DataFrames can be easily exported to other formats such as .csv. You can do this directly in your code with df.to_csv('exported_data.csv'), or use the bruker2csv function from the terminal for a quick export.

bruker2csv ../../data/1D/glycine/pdata/1 ../../data/1D/exported_data.csv