def test_interpolate_to_keys():
grid, mid_point = numeric_series()
interpolated = TimeSeries.interpolate_to_keys(grid, mid_point)
assert interpolated == mid_point
assert interpolated._first == mid_point._first
assert interpolated._last == mid_point._last
assert interpolated.first == mid_point.first
assert interpolated.last == mid_point.last
interpolated = TimeSeries.interpolate_to_keys(mid_point, grid)
assert interpolated._first == grid.first
assert interpolated._last == grid.last
assert interpolated.first == 0.0
assert interpolated.last == 0.0
def wrapped(self, state):
continuous_gradient = function(self, state)
gradient_time_series = TimeSeries.from_list(
continuous_gradient, self.midpoint_grid
)
du = TimeSeries.interpolate_to_keys(gradient_time_series, self.time_grid)
discrete_grad = self.basis.discretize(du.values())
gradient_norm = (gradient_time_series * du).integrate()
log.info(f"gradient norm: {gradient_norm}")
discrete_grad_norm = (
gradient_time_series
* TimeSeries.from_list(
self.basis.extrapolate(discrete_grad), self.time_grid
)
).integrate()
log.info("discrete gradient norm: {}".format(discrete_grad_norm))
return discrete_grad