Getting Started

This page walks you through the first steps with SpinLab: loading a real data file, inspecting the result, processing the data, and plotting the spectrum. The entire workflow takes less than 10 minutes.

Note

Before starting, make sure SpinLab is installed. See Installation for instructions. SpinLab requires Python 3.10 or higher.

Step 1 — Import SpinLab

Open a terminal (e.g. Windows PowerShell, macOS Terminal, or a Jupyter notebook) and import SpinLab:

import spinlab as sl

SpinLab is imported under the alias sl by convention. All SpinLab functions are accessible via this alias (e.g. sl.load(), sl.plot()).

Step 2 — Load a Data File

SpinLab can load many different spectrometer formats. In most cases the file format is detected automatically from the file extension:

data = sl.load("data/bes3t/1D_CW.DTA")

The result is a The SpinData Object — SpinLab's central data container. It holds the spectral values together with the axis coordinates and experimental parameters.

To see a summary of what was loaded:

print(data)

Example output:

values:
    2250 ndarray (float64)
dims:
    ['B0']
coords:
    array([342.055, 342.062, ..., 357.048], shape=(2250,))
attrs:
    'experiment_type': 'epr_spectrum'
    'mw_frequency': 9.804448
    'center_field': 3495.55
    'power': 1.002
    +12 attrs

The object contains:

  • values — the spectral data as a NumPy array (2250 points)

  • dims — the dimension label ('B0', the magnetic field axis)

  • coords — the field values in mT

  • attrs — experimental parameters read directly from the file

See Loading Data for examples of every supported file format.

Step 3 — Inspect the Data

You can access any part of the object directly:

print(data.values.shape)          # (2250,)
print(data.dims)                  # ['B0']
print(data.coords["B0"])          # field axis array in mT
print(data.attrs["mw_frequency"]) # 9.804448 GHz

To see all experimental parameters in a formatted table:

data.exp_info()

Step 4 — Plot the Spectrum

Use sl.plot() to quickly visualize the data:

sl.plt.figure()
sl.plot(data)
sl.plt.tight_layout()
sl.plt.show()

SpinLab imports Matplotlib internally and exposes the full pyplot module as sl.plt. Any Matplotlib command works:

sl.plt.figure()
sl.plot(data)
sl.plt.xlabel("Magnetic Field (mT)")
sl.plt.ylabel("Intensity (a.u.)")
sl.plt.title("CW EPR Spectrum")
sl.plt.tight_layout()
sl.plt.show()

For publication-ready figures with automatic axis labels and formatting based on the experiment type, use sl.fancy_plot():

sl.plt.figure()
sl.fancy_plot(data)
sl.plt.show()

See Plotting Data for the full list of supported experiment types and plot options.

Step 5 — Process the Data

SpinLab processing functions always take a SpinData object as input and return a new one — the original is never modified.

A typical NMR processing sequence (load → apodize → Fourier transform → phase):

# Load a raw NMR FID from TopSpin
data = sl.load("experiment/1/")

# Apply exponential line broadening (5 Hz)
data = sl.apodize(data, dim="t2", kind="exponential", lw=5)

# Fourier transform with 2x zero-filling
data = sl.fourier_transform(data, zero_fill_factor=2)

# Phase correction
data = sl.phase(data, p0=12.5)

# Plot the spectrum
sl.plt.figure()
sl.plot(data)
sl.plt.show()

Every processing step is automatically recorded in the audit log. To inspect it:

data.proc_info()

See Processing Data for the complete processing reference.

Step 6 — Save the Result

Save the processed data to SpinLab's HDF5 format, which preserves the full object including axes and the processing audit log:

sl.save(data, "processed_spectrum.h5")

Reload it at any time with:

data = sl.load("processed_spectrum.h5")

Next Steps