pyepr.dataset¶

Classes¶

`get_all_axes`(sequence)
`get_all_fixed_param`(sequence)
`create_dataset_from_sequence`(data, sequence[, ...])
`create_dataset_from_axes`(data, axes[, params, ...])	Create an xarray dataset from a numpy array and a list of axes.
`create_dataset_from_bruker`(filepath)
`downconvert_dataset`(dataset[, filter_type, IF, ...])	Downconvert a dataset to baseband using a filter
`find_peak`(dataset, freq[, freq_axis, search_range])

pyepr.dataset.create_dataset_from_sequence(data, sequence, extra_params={})[source]¶

pyepr.dataset.create_dataset_from_axes(data, axes, params={}, extra_coords=None, 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:

pyepr.dataset.downconvert_dataset(dataset, filter_type='boxcar', IF=None, reduce=True, sampling_rate=None, **kwargs)[source]¶

Downconvert a dataset to baseband using a filter Parameters ———- dataset : xr.DataArray

The dataset to downconvert

filter_typestr, optional: The type of filter to use, by default ‘boxcar’. Other options are ‘cheby’ and a Pulse object
filter_widthfloat, optional: The width of the filter in MHz or ns depending on filter tyre, by default 20 ns for boxcar and 50 MHz for cheby.
IFfloat, optional: The intermediate frequency to use, by default 0.15
reducebool, optional: If True, the dataset is reduced to a single point, by default True
**kwargsdict: Extra arguments to pass to the filter function

Returns:

pyepr.dataset.find_peak(dataset, freq, freq_axis=None, search_range=4)[source]¶