pyepr¶
Submodules¶
Attributes¶
Classes¶
Represents an experimental pulse sequence. |
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Represents a Hahn-Echo sequence. |
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Represents a T1 Inversion Recovery Sequence. |
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Represents a T2 relaxation sequence. A Hahn Echo where the interpulse delay increases |
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Represents a Field Sweep (EDFS) sequence. |
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Represents a reptime scan of a Hahn Echo Sequence. |
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Represents a Carr-Purcell sequence. |
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Builds nutation based Resonator Profile sequence. |
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Builds TWT based Resonator Profile sequence. |
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A class for defining criteria for terminating experiments. This should |
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A class for defining criteria for terminating experiments. This should |
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A class for defining criteria for terminating experiments. This should |
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Represents an experimental pulse sequence. |
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Analysis and calculation of FieldSweep Experiment. |
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Represents an experimental pulse sequence. |
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Analysis and calculation of Carr Purcell decay. |
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Analysis, fitting and plotting for the HahnEchoRelaxation Sequence. |
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Analysis and calculation of Reptime based saturation recovery. |
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Analysis and calculation of resonator profiles. |
Functions¶
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A general versions of eprload |
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Spin Operator Matricies. |
Turns a dictionary of lists into a list of dictionaries. |
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Turns a list of dictionaries into a dictionary of lists. |
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Generates the greatest common dividor on a list of floats |
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Returns the value or axis of a parameter in microseconds |
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Returns the value or axis of a parameter in nanoseconds |
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Adds a phase shift to the data |
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Add an ESEEM modulation to a time domain signal |
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Returns the value or axis of a parameter in nanoseconds |
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Returns the value or axis of a parameter in microseconds |
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Adds a phase shift to the data |
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Add an ESEEM modulation to a time domain signal |
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Create the field sweep model for a Nitroxide spin system. |
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Create an xarray dataset from a numpy array and a list of axes. |
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Spin Operator Matricies. |
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Create the field sweep model for a Nitroxide spin system. |
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Passive rotation matrix. |
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Calculates the Kronecker product of the identity matrix with a matrix M. |
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Computes the Kronecker product of a matrix with the identity matrix of the same size. |
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Build a field sweep spectrum |
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Detect if the dataset is an ESEEM experiment. |
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Create a superimposed plot of relaxation data and fits. |
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Create an xarray dataset from a numpy array and a list of axes. |
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Phase corrects a resonator profile dataset, by making sure the first point of each frequnecy is real |
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Calcuates the overlap between two functions. |
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Package Contents¶
- pyepr.eprload(path, experiment=None, type=None, **kwargs)[source]¶
A general versions of eprload
- Parameters:
- pathstr
The file path of the data that should be loaded.
- experimentstr, optional
_description_, by default None
- typestr, optional
_description_, by default None
- Returns:
- xarray.Dataarray
_description_
- Raises:
- ValueError
_description_
- RuntimeError
_description_
- Parameters:
path (str)
experiment (str)
type (str)
- pyepr.sop(spins, comps)[source]¶
Spin Operator Matricies.
This function is ported from EasySpin (https://easyspin.org/easyspin/documentation/sop.html)
References:¶
[1] Stefan Stoll, Arthur Schweiger EasySpin, a comprehensive software package for spectral simulation and analysis in EPR J. Magn. Reson. 178(1), 42-55 (2006)
[2] Stefan Stoll, R. David Britt General and efficient simulation of pulse EPR spectra Phys. Chem. Chem. Phys. 11, 6614-6625 (2009)
- Parameters:
- spinslist
A list of each spin and its spin qunatum number
- compsstr
The type of spin operator matrix to create. Options are: x,y,z,+,-,e
- pyepr.gcd(values)[source]¶
Generates the greatest common dividor on a list of floats
- Parameters:
- valueslist
_description_
- Parameters:
values (list)
- pyepr.val_in_us(Param, axis=True)[source]¶
Returns the value or axis of a parameter in microseconds
- Parameters:
- Paramautodeer.Parameter
The parameter to be converted
- Returns:
- float or numpy.ndarray
- pyepr.val_in_ns(Param)[source]¶
Returns the value or axis of a parameter in nanoseconds
- Parameters:
- Paramautodeer.Parameter
The parameter to be converted
- Returns:
- float or numpy.ndarray
- pyepr.add_phaseshift(data, phase)[source]¶
Adds a phase shift to the data
- Parameters:
- datanumpy.ndarray
The data to be phase shifted
- phasefloat
The phase shift in degrees
- Returns:
- numpy.ndarray
- pyepr._gen_ESEEM(t, freq, depth)[source]¶
Add an ESEEM modulation to a time domain signal
- Parameters:
- tnumpy.ndarray
The time domain signal
- freqfloat
The modulation frequency
- depthfloat
The modulation depth
- Returns:
- numpy.ndarray
The
- pyepr.val_in_ns(Param)[source]¶
Returns the value or axis of a parameter in nanoseconds
- Parameters:
- Paramautodeer.Parameter
The parameter to be converted
- Returns:
- float or numpy.ndarray
- pyepr.val_in_us(Param, axis=True)[source]¶
Returns the value or axis of a parameter in microseconds
- Parameters:
- Paramautodeer.Parameter
The parameter to be converted
- Returns:
- float or numpy.ndarray
- pyepr.add_phaseshift(data, phase)[source]¶
Adds a phase shift to the data
- Parameters:
- datanumpy.ndarray
The data to be phase shifted
- phasefloat
The phase shift in degrees
- Returns:
- numpy.ndarray
- pyepr._gen_ESEEM(t, freq, depth)[source]¶
Add an ESEEM modulation to a time domain signal
- Parameters:
- tnumpy.ndarray
The time domain signal
- freqfloat
The modulation frequency
- depthfloat
The modulation depth
- Returns:
- numpy.ndarray
The
- pyepr.create_Nmodel(mwFreq)[source]¶
Create the field sweep model for a Nitroxide spin system.
- Parameters:
- mwFreqfloat
The microwave frequency in MHz
- pyepr.create_dataset_from_sequence(data, sequence, extra_params={})[source]¶
- Parameters:
sequence (pyepr.sequences.Sequence)
- pyepr.create_dataset_from_axes(data, axes, params={}, axes_labels=None)[source]¶
Create an xarray dataset from a numpy array and a list of axes.
- Parameters:
- datanp.ndarray
The data to be stored in the dataset.
- axeslist
A list of numpy arrays containing the axes for each dimension of the data.
- paramsdict, optional
A dictionary containing any additional parameters to be stored in the dataset, by default None
- axes_labelslist, optional
A list of labels for each axis, by default None
- Parameters:
params (dict)
- pyepr.sop(spins, comps)[source]¶
Spin Operator Matricies.
This function is ported from EasySpin (https://easyspin.org/easyspin/documentation/sop.html)
References:¶
[1] Stefan Stoll, Arthur Schweiger EasySpin, a comprehensive software package for spectral simulation and analysis in EPR J. Magn. Reson. 178(1), 42-55 (2006)
[2] Stefan Stoll, R. David Britt General and efficient simulation of pulse EPR spectra Phys. Chem. Chem. Phys. 11, 6614-6625 (2009)
- Parameters:
- spinslist
A list of each spin and its spin qunatum number
- compsstr
The type of spin operator matrix to create. Options are: x,y,z,+,-,e
- pyepr.create_Nmodel(mwFreq)[source]¶
Create the field sweep model for a Nitroxide spin system.
- Parameters:
- mwFreqfloat
The microwave frequency in MHz
- pyepr.eyekron(M)[source]¶
Calculates the Kronecker product of the identity matrix with a matrix M.
Parameters: M (np.ndarray): The matrix to be multiplied with the identity matrix.
Returns: np.ndarray: The Kronecker product of the identity matrix with M.
- Parameters:
M (numpy.ndarray)
- pyepr.kroneye(M)[source]¶
Computes the Kronecker product of a matrix with the identity matrix of the same size.
- Args:
M (numpy.ndarray): The matrix to compute the Kronecker product with.
- Returns:
numpy.ndarray: The Kronecker product of M with the identity matrix of the same size.
- pyepr.resfields(system, Orientations, mwFreq, computeIntensities=True, RejectionRatio=1e-08, Range=(0, 100000000.0), Threshold=0, computeFreq2Field=True)[source]¶
- pyepr.build_spectrum(system, mwFreq, Range, knots=19, npoints=1000, Guass_broadening=0.25)[source]¶
Build a field sweep spectrum
- Parameters:
- systemSpinSystem
The spin system it must include: I & S spins, g, A, gn
- mwFreqfloat
The microwave frequency in MHz
- Rangefloat
The field range in mT
- knotsint, optional
The number of knots of orientation averaging, by default 19
- npointsint, optional
The number of points in the spectrum, by default 1000
- Returns:
- xAxis: np.ndarray
The xAxis in mT
- y: np.ndarray
The spectrum intensities normalised to 1
- pyepr.detect_ESEEM(dataset, type='deuteron', threshold=1.5)[source]¶
Detect if the dataset is an ESEEM experiment.
- Parameters:
- datasetxr.DataArray
The dataset to be analyzed.
- typestr, optional
The type of ESEEM experiment, either deuteron or proton, by default ‘deuteron’
- thresholdfloat, optional
The SNR threshold for detection, by default 1.5
- Returns:
- bool
True if ESEEM is detected, False if not.
- pyepr.plot_1Drelax(*args, fig=None, axs=None, cmap=cmap, labels=None)[source]¶
Create a superimposed plot of relaxation data and fits.
- Parameters:
- argsad.Analysis
The 1D relaxation data to be plotted.
- figFigure, optional
The figure to plot to, by default None
- axsAxes, optional
The axes to plot to, by default None
- cmaplist, optional
The color map to use, by default ad.cmap
- pyepr.create_dataset_from_axes(data, axes, params={}, axes_labels=None)[source]¶
Create an xarray dataset from a numpy array and a list of axes.
- Parameters:
- datanp.ndarray
The data to be stored in the dataset.
- axeslist
A list of numpy arrays containing the axes for each dimension of the data.
- paramsdict, optional
A dictionary containing any additional parameters to be stored in the dataset, by default None
- axes_labelslist, optional
A list of labels for each axis, by default None
- Parameters:
params (dict)
- pyepr.phase_correct_respro(dataset)[source]¶
Phase corrects a resonator profile dataset, by making sure the first point of each frequnecy is real