def create_radial_fourier_analysis(
self,
dataset: DataSet,
cx: float = None,
cy: float = None,
ri: float = None,
ro: float = None,
n_bins: int = None,
max_order: int = None,
use_sparse: bool = None) -> RadialFourierAnalysis:
"""
Create an Analysis that calculates the Fourier transform of rings around the center.
See :ref:`radialfourier app` for details on the method!
Parameters
----------
dataset
the dataset to work on
cx
center x value
cy
center y value
ri
inner radius
ro
outer radius
n_bins
number of bins
max_order
maximum order of calculated Fourier component
Returns
-------
RadialFourierAnalysis : libertem.analysis.base.Analysis
When run by the Context, this Analysis generates a
:class:`libertem.analysis.radialfourier.RadialFourierResultSet`.
"""
if dataset.shape.sig.dims != 2:
raise ValueError(
"incompatible dataset: need two signal dimensions")
loc = locals()
parameters = {
name: loc[name]
for name in
['cx', 'cy', 'ri', 'ro', 'n_bins', 'max_order', 'use_sparse']
if loc[name] is not None
}
analysis = RadialFourierAnalysis(dataset=dataset,
parameters=parameters)
return analysis
def create_radial_fourier_analysis(self,
dataset,
cx: float = None,
cy: float = None,
ri: float = None,
ro: float = None,
n_bins: int = None,
max_order: int = None,
use_sparse: bool = None):
"""
Calculate the Fourier transform of rings around the center.
See :class:`~libertem.analysis.radialfourier.RadialFourierAnalysis` for details!
Parameters
----------
dataset
the dataset to work on
cx
center x value
cy
center y value
ri
inner radius
ro
outer radius
n_bins
number of bins
max_order
maximum order of calculated Fourier component
"""
if dataset.shape.sig.dims != 2:
raise ValueError(
"incompatible dataset: need two signal dimensions")
loc = locals()
parameters = {
name: loc[name]
for name in
['cx', 'cy', 'ri', 'ro', 'n_bins', 'max_order', 'use_sparse']
if loc[name] is not None
}
analysis = RadialFourierAnalysis(dataset=dataset,
parameters=parameters)
return analysis