pyepr.relaxation_analysis.CarrPurcellAnalysis¶
- class pyepr.relaxation_analysis.CarrPurcellAnalysis(dataset, sequence=None)¶
Analysis and calculation of Carr Purcell decay.
- Parameters:
- dataset
_description_
- Attributes:
- axisxr.DataArray
The time axis representing the interpulse delay.
- Parameters:
sequence (pyepr.sequences.Sequence)
- data¶
- dataset¶
- noise¶
- fit(type='mono', **kwargs)¶
Fit the experimental CP decay
- Parameters:
- typestr, optional
Either a mono or double exponential decay model, by default “mono”
- Parameters:
type (str)
- plot(norm=True, ci=50, axs=None, fig=None)¶
Plot the carr purcell decay with fit, if avaliable.
- Parameters:
- normbool, optional
Normalise the fit to a maximum of 1, by default True
- ciint, optional
The percentage confidence interval to plot, by default 50
- Returns:
- Figure
The figure.
- Parameters:
norm (bool)
- Return type:
matplotlib.figure.Figure
- check_decay(level=0.1)¶
Checks that the data has decayed by over 90% in the first half, and less than 90% in the first quarter.
- Parameters:
- levelfloat, optional
The level to check the decay, by default 0.05
- Returns:
- int
0 if both conditions are met, 1 if a longer decay is needed, and -1 if the decay is too long.
- find_optimal(SNR_target, target_time, target_step, averages=None)¶
Calculate the optimal inter pulse delay for a given total measurment time.
- Parameters:
- SNR_target: float,
The Signal to Noise ratio target.
- target_timefloat
The target time in hours
- target_shrtfloat
The shot repettition time of target in seconds
- target_step: float
The target step size in ns.
- averagesint, optional
The total number of shots taken, by default None. If None, the number of shots will be calculated from the dataset.
- Returns:
- float
The calculated optimal time in us
- Parameters:
target_time (float)
- Return type:
float
- __call__(x, norm=True, SNR=False, source=None)¶
Evaluate the fit or data at a given x value.
- Parameters:
- xfloat
The x value to evaluate the data at.
- normbool, optional
Normalise the data to the maximum, by default True
- SNRbool, optional
Return the SNR_per_sqrt(shot) for this data point, by default False If True, the data is normalised to the maximum of the data.
- sourcestr, optional
The source of the data, either ‘fit’ or ‘data’, by default None If None, the source is determined by the presence of a fit result.