The measured data is stored as a dictionary of
AcquiredResult objects and accessed through the
acquired_results = my_results.acquired_results
The dictionary keys are the handles specified for the different acquire statements in the experiment.
AcquiredResult object contains the measured data itself as well as one or multiple measurement axis in case the data is the result of a parameter sweep and the name for the measurement axis, as defined in the definition of the
They can be accessed through
my_acquired_data = acquired_results[handle] my_data = my_acquired_data.data my_axis = my_acquired_data.axis my_axis_name = my_acquired_data.axis_name
There are also convenience functions to access this data directly from the
my_data = my_results.get_data(handle) my_axis = my_results.get_axis(handle) my_axis_name = my_results.get_axis_name(handle)
The data and associated axes are returned directly as a numpy arrays for quick re-use. The dimensionality of the data array directly reflects the dimensionality of the experimental sweep, i.e., a simple sweep returns a one dimensional array whereas concatenated sweeps will return multi-dimensional arrays. The axes and axes names are returned as a list of arrays and strings respectively, with the number of entries corresponding to the number of dimensions in the data array.
For a one dimensional sweep, simple data visualization with Python is possible for example through
import matplotlib.plotlib as plt my_data = my_results.get_data(handle) my_axis = my_results.get_axis(handle) my_axis_name = my_results.get_axis_name(handle) plt.plot(my_axis, my_data) plt.x_label(my_axis_name) plt.y_label('acquired data')
AcquiredResult object also contains the
last_nt_step property, which indicates the index of the last acquired data point within a near-time sweep. This information is useful when accessing partial results e.g.in a user function context, for processing during the near-time loop.
Again, there is a convenience function to access this property through the
Results object directly.
last_result_index = my_results.get_last_nt_step(handle) last_result = my_results.get_data(handle)[last_result_index]