hann_series_stf
- signals.hann_series_stf(coefficients, end_time, start_time=0.0)
Create an Stf representing a sum of Hann window functions.
- Parameters:
coefficients (np.ndarray or Tensor) – The coefficients for the different Hann window functions, \(c_n\). It must be a 1D array or Tensor.
end_time (float) – The time at which the Hann series ends, \(t_\mathrm{end}\).
start_time (float, optional) – The time at which the Hann series starts, \(t_\mathrm{start}\). Defaults to 0.
- Returns:
The sampleable Hann window functions series.
- Return type:
See also
boulderopal.signals.hann_series
Create a Signal object representing a sum of Hann window functions.
Graph.signals.hann_series_pwc
Corresponding operation with Pwc output.
Graph.signals.sinusoid_stf
Create an Stf representing a sinusoidal oscillation.
Notes
The series is defined as
\[\mathop{\mathrm{Hann}}(t) = \sum_{n=1}^N c_n \sin^2 \left( \frac{\pi n (t - t_\mathrm{start})}{t_\mathrm{end} - t_\mathrm{start}} \right) ,\]where \(N\) is the number of coefficients.
Note that the function values outside the \((t_\mathrm{start}, t_\mathrm{end})\) range will not be zero.
Examples
Define a simple sampleable Hann series.
>>> hann = graph.signals.hann_series_stf( ... coefficients=np.array([0.5, 1, 0.25]), end_time=1.0 ... ) >>> graph.discretize_stf(hann, duration=1, segment_count=5, name="hann") <Pwc: name="hann", operation_name="discretize_stf", value_shape=(), batch_shape=()> >>> result = bo.execute_graph(graph=graph, output_node_names="hann") >>> result["output"]["hann"] {'durations': array([0.2, 0.2, 0.2, 0.2, 0.2]), 'values': array([0.5568644 , 1.25563559, 0.75 , 1.25563569, 0.55686415]), 'time_dimension': 0}
Define a sampleable Hann series with optimizable coefficients.
>>> coefficients = graph.optimization_variable( ... count=8, lower_bound=-3.5e6, upper_bound=3.5e6, name="coefficients" ... ) >>> graph.signals.hann_series_stf(coefficients=coefficients, end_time=2.0e-6) <Stf: operation_name="stf_sum", value_shape=(), batch_shape=()>