sig

A module for manipulating and processing NMR signals.

nmrespy.sig.add_noise(fid: numpy.ndarray, snr: float, decibels: bool = True) → numpy.ndarray[source]

Add Gaussian white noise noise to an FID.

Parameters:

  • fid – Noiseless FID.

  • snr – The signal-to-noise ratio. The smaller this value, the greater the variance of the noise.

  • decibels – If True, the snr is taken to be in units of decibels. If False, it is taken to be simply the ratio of the singal power and noise power.

See also

make_noise()

nmrespy.sig.baseline_correction(spectrum: numpy.ndarray, mask: numpy.ndarray | None = None, min_length: int = 50) → Tuple[numpy.ndarray, dict][source]

Apply baseline correction to a 1D dataset.

The algorithm applied is desribed in [1]. This uses an implementation provided by pybaselines.

Parameters:

  • spectrum – Spectrum to apply baseline correction to.

  • mask –

    Should be either:

    • None: the points which comprise noise are predicted automatically

    • A boolean array with the same size as spectrum.

      • True indicates that a particular point comprises baseline.

      • False indicates that a point comprises a peak.

  • min_length – from pybaselines: Any region of consecutive baseline points less than min_length is considered to be a false positive and all points in the region are converted to peak points. A higher min_length ensures less points are falsely assigned as baseline points.

Returns:

  • fixed_spectrum – The baseline-corrected spectrum.

  • params – A dictionary with the items:

    • "mask" A boolean array designating baseline points as True and peak points as False.

    • "baseline" The computed baseline. Note that fixed_spectrum is computed via spectrum - baseline.

References

nmrespy.sig.convdta(data: numpy.ndarray, grpdly: float) → numpy.ndarray[source]

Remove the digital filter from time-domain Bruker data.

This function is inspired by nmrglue.fileio.bruker.rm_dig_filter.

Parameters:

  • data – Time-domain data to process.

  • grpdly – Group delay.

nmrespy.sig.exp_apodisation(fid: numpy.ndarray, k: float, axes: Iterable[int] | None = None) → numpy.ndarray[source]

Apply exponential apodisation to an FID.

The FID is multiplied by np.exp(-k * np.linspace(0, 1, n)) in each dimension specified by axes, where n is the size of each dimension.

Parameters:

  • fid – FID to process.

  • k – Line-broadening factor.

  • axes – The axes to apply the apodiisation over. If None, all axes are apodised.

nmrespy.sig.ft(fid: numpy.ndarray, axes: Iterable[int] | int | None = None, flip: bool = True) → numpy.ndarray[source]

Fourier transformation with optional spectrum flipping.

It is conventional in NMR to plot spectra from high to low going left to right/down to up. This function utilises the numpy.fft module.

Parameters:

  • fid – Time-domain data.

  • axes – The axes to apply Fourier Transformation to. By default (None), FT is applied to all axes. If an int, FT will only be applied to the relevant axis. If a list of ints, FT will be applied to this subset of axes.

  • flip – Whether or not to flip the Fourier Transform of fid in each dimension.

nmrespy.sig.ift(spectrum: numpy.ndarray, axes: Iterable[int] | int | None = None, flip: bool = True) → numpy.ndarray[source]

Inverse Fourier Transform a spectrum.

This function utilises the numpy.fft module.

Parameters:

  • spectrum (numpy.ndarray) – Spectrum

  • axes – The axes to apply IFT to. By default (None), IFT is applied to all axes. If an int, IFT will only be applied to the relevant axis. If a list of ints, IFT will be applied to this subset of axes.

  • flip (bool, default: True) – Whether or not to flip spectrum in each dimension prior to Inverse Fourier Transform.

nmrespy.sig.make_noise(fid: numpy.ndarray, snr: float, decibels: bool = True) → numpy.ndarray[source]

Generate an array of white Guassian complex noise.

The noise will be created with zero mean and a variance that abides by the desired SNR, in accordance with the FID provided.

Parameters:

  • fid – Noiseless FID.

  • snr – The signal-to-noise ratio.

  • decibels – If True, the snr is taken to be in units of decibels. If False, it is taken to be simply the ratio of the singal power and noise power.

Return type:

noise

Notes

Noise variance is given by:

\rho = \frac{\sum_{n=0}^{N-1} \left(x_n - \mu_x\right)^2} {N \cdot 20 \log_10 \left(\mathrm{SNR}_{\mathrm{dB}}\right)}

See also

add_noise()

nmrespy.sig.make_virtual_echo(data: numpy.ndarray, twodim_dtype: str | None = None) → numpy.ndarray[source]

Generate a virtual echo [2] from a time-domain signal.

A vitrual echo is a signal with a purely real Fourier-Tranform.

Parameters:

  • data – The data to construct the virtual echo from. If the data comprises a pair of amplitude/phase modulated signals, these should be stored in a single 3D array with shape[0] == 2, such that data[0] is the cos/p signal, and data[1] is the sin/n signal.

  • twodim_dtype –

    If the data is 2D, this parameter specifies the way to process the data. Allowed options are:

    • "hyper": The data is hypercomplex. Virtual echo is constructed

      along the second axis.

    • "amp": The data comprises an amplitude-modulated pair.

    • "phase": The data comprises a phase-modulated pair.

References

nmrespy.sig.manual_phase_data(spectrum: numpy.ndarray, max_p1: Iterable[float] | None = None) → Tuple[Iterable[float] | None, Iterable[float] | None][source]

Manual phase correction using a Graphical User Interface.

Note

Only 1D spectral data is currently supported.

Parameters:

  • spectrum – Spectral data of interest.

  • max_p1 – Specifies the range of first-order phases permitted. Bounds are set as [-max_p1, max_p1].

Returns:

  • p0 – Zero-order phase correction in each dimension, in radians. If the user chooses to cancel rather than save, this is set to None.

  • p1 – First-order phase correction in each dimension, in radians. If the user chooses to cancel rather than save, this is set to None.

nmrespy.sig.phase(data: numpy.ndarray, p0: Iterable[float], p1: Iterable[float], pivot: Iterable[float | int] | None = None) → numpy.ndarray[source]

Apply a linear phase correction to a signal.

Parameters:

  • data – Data to be phased.

  • p0 – Zero-order phase correction in each dimension, in radians.

  • p1 – First-order phase correction in each dimension, in radians.

  • pivot – Index of the pivot in each dimension. If None, the pivot will be 0 in each dimension.

nmrespy.sig.proc_amp_modulated(data: numpy.ndarray) → numpy.ndarray[source]

Generate a frequency-discrimiated signal from amplitude-modulated 2D FIDs.

Parameters:

data – cos-modulated signal and sin-modulated signal, stored in a 3D numpy array, such that data[0] is the the cos signal and data[1] is the sin signal.

Returns:

spectrum – Frequency-discrimiated spectrum.

Return type:

np.ndarray

nmrespy.sig.proc_phase_modulated(data: numpy.ndarray) → numpy.ndarray[source]

Process phase modulated 2D FIDs.

This function generates the set of spectra corresponding to the processing protocol outlined in [3].

Parameters:

data – P-type signal and N-type signal, stored in a 3D numpy array, such that data[0] is the the P signal and data[1] is the N signal.

Returns:

3D array with spectra.shape[0] == 4. The sub-arrays in axis 0 correspond to the following signals:

  • spectra[0]: RR

  • spectra[1]: RI

  • spectra[2]: IR

  • spectra[3]: II

Return type:

spectra

References

nmrespy.sig.sinebell_apodisation(fid: numpy.ndarray, axes: Iterable[int] | None = None) → numpy.ndarray[source]

Apply sine-bell apodisation to an FID.

The FID is multiplied by np.exp(-k * np.linspace(0, 1, n)) in each dimension specified by axes, where n is the size of each dimension.

Warning

This is not intended for manipulating the FID prior to estimation.

Parameters:

  • fid – FID to process.

  • axes – The axes to apply the apodiisation over. If None, all axes are apodised.

nmrespy.sig.zf(data: numpy.ndarray) → numpy.ndarray[source]

Zero-fill data to the next power of 2 in each dimension.

Parameters:

data – Signal to zero-fill.