Reading & Creating Data

Reading .DSC Data

dive.deerload.deerload(name: str, scaling: str | None = None, plot: bool = False) → tuple[ndarray, ndarray, dict]

Loads DEER data from a .DSC or .DTA file.

Loads file in BES3T format (Bruker EPR Standard for Spectrum Storage and Transfer)

.DSC: description file .DTA: data file

which is used on Bruker ELEXSYS and EMX machines. Code based on BES3T version 1.2 (Xepr >=2.1).

Parameters:

  • name (str) – The full filename, including the .DTA or .DSC extension.

  • scaling (str, optional) – The scaling to use. Options are ‘n’ (number of scans), ‘G’ (receiver gain), ‘C’ (conversion time), ‘P’ (power), and ‘T’ (temperature).

  • plot (bool, default=False) – Whether or not to plot the data once loaded.

Returns:

(abcissa, data, parameters) – The x-axis values, y-axis values, and additional parameters, respectively.

Return type:

tuple of np.ndarray, np.ndarray, dict

dive.deerload.read_description_file(name: str) → dict

Retrieves the parameters from a .DSC files as a dictionary.

Parameters:

name (str) – The filename to be read, including the .DSC extension.

Returns:

Parameters – A dictionary of the parameters in the .DSC file.

Return type:

dict

Creating Synthetic Data

dive.test_data.generate_multi_gauss(r0: tuple[float, ...] = [4, 4.8], w: tuple[float, ...] = [0.6, 1.2], a: tuple[float, ...] = [0.6, 0.4], lamb: float = 0.5, k: float = 0.1, V0: float = 1, sigma: float = 0.01, seed: int = 0, nr: int = 800, nt: int = 150, r_lim: tuple[float, float] = [1, 10], t_lim: tuple[float, float] = [-0.1, 2.5]) → tuple[dict, dict]

Generates a multi-gauss P(r) and associated V(t).

Parameters:

  • r0 (tuple of float, default=[4,4.8]) – The means of the Gaussians.

  • w (tuple of flaot, default=[0.3,0.6]) – The full widths at half maximum of the Gaussians.

  • a (tuple of float, default=[0.6,0.4]) – The amplitudes of the Gaussians.

  • lamb (float, default=0.5) – The modulation depth.

  • k (float, default=0.1) – The background decay rate.

  • V0 (float, default=1) – The signal amplitude.

  • sigma (float, default=0.01) – The noise level.

  • seed (int, default=0) – The random seed to use.

  • nr (int, default=800) – The number of points to use for the distance axis.

  • nt (int, default=150) – The number of points to use for the time axis.

  • r_lim (tuple of float, float) – The minimum and maximum values of the distance axis.

  • t_lim (tuple of float, float) – The minimum and maximum values of the time axis.

Returns:

(data, pars) – The data dictionary contains the P(r) and V(t) data. The ‘pars’ dictionary stores the parameters used for data generation.

Return type:

tuple of dict, dict

See also

dl.dd_gauss2

dive.test_data.generate_single_gauss(r0: float = 4, w: float = 0.4, lamb: float = 0.5, k: float = 0.1, V0: float = 1, sigma: float = 0.01, seed: int = 0, nr: int = 800, nt: int = 150, r_lim: tuple[float, float] = [1, 10], t_lim: tuple[float, float] = [-0.1, 2.5]) → tuple[dict, dict]

Generates a single-gauss P(r) and associated V(t).

Parameters:

  • r0 (float, default=4) – The mean of the Gaussian.

  • w (float, default=0.4) – The full width at half-maximum of the Gaussian.

  • lamb (float, default=0.5) – The modulation depth.

  • k (float, default=0.1) – The background decay rate.

  • V0 (float, default=1) – The signal amplitude.

  • sigma (float, default=0.01) – The noise level.

  • seed (int, default=0) – The random seed to use.

  • nr (int, default=800) – The number of points to use for the distance axis.

  • nt (int, default=150) – The number of points to use for the time axis.

  • r_lim (tuple of float, float) – The minimum and maximum values of the distance axis.

  • t_lim (tuple of float, float) – The minimum and maximum values of the time axis.

Returns:

(data, pars) – The data dictionary contains the P(r) and V(t) data. The ‘pars’ dictionary stores the parameters used for data generation.

Return type:

tuple of dict, dict

See also

dl.dd_gauss

Creating P(r) and B(t)

dive.deer.bg_exp(t: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], k: float) → ndarray

Generates an exponential background decay vector from k.

Parameters:

  • t (ArrayLike) – The time axis to generate the background decay on.

  • k (float) – The exponent of the exponential decay.

Returns:

B – The background decay vector.

Return type:

np.ndarray

dive.deer.bg_hom3d(t: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], conc: float, lamb: float) → ndarray

Generates an exponential decay vector from conc and lamb.

Assumes a homogeneous 3D distribution of background spins.

Parameters:

  • t (ArrayLike) – The time axis to generate the background decay on.

  • conc (float) – The spin concentration in micromolars.

  • lamb (float) – The modulation depth.

Returns:

B – The background decay vector.

Return type:

np.ndarray

dive.deer.dd_gauss(r: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], r0: float | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], fwhm: float | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], a: float | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] = 1) → ndarray

Calculates a multi-Gauss distance distribution.

Requires the distance vector r in nanometers and the mean, width, and amplitude of each gaussian distribution. The distance distribution will be normalized such that integrates to 1 over r.

Parameters:

  • r (ArrayLike) – The distance axis to generate P on.

  • r0 (float or ArrayLike of float) – The mean(s) of the Gaussian(s).

  • fwhm (float or ArrayLike of float) – The full width(s) at half maximum of the Gaussian(s).

  • a (float or ArrayLike of float, default=1) – The amplitudes of the Gaussians, for a multi-Gauss distribution.

Returns:

P – A normalized vector of the distance distribution.

Return type:

np.ndarray

dive.deer.gauss(r: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], r0: float, sig: float) → ndarray

Calculates a single-Gauss distance distribution.

Requires the distance vector r in nanometers and the mean and standard deviation of the Gaussian. The distance distribution will be normalized such that it integrates to 1 over r.

Parameters:

  • r (ArrayLike) – The distance axis to generate P on.

  • r0 (float) – The mean of the Gaussian.

  • sig (float) – The standard deviation of the Gaussian.

Returns:

P – A normalized vector of the distance distribution.

Return type:

np.ndarray