pyfan.amto.array.geomspace.grid_to_geom

pyfan.amto.array.geomspace.grid_to_geom(choice_grid, choice_grid_max, choice_grid_min, start, stop, num, geom_ratio, a)

the code now is under the assumption that initial start and end were 0 and 1

Given geom_grid results, how do we go back to actual data grid. So for interpolation. interpolate not on actual K and B scales, but on any even grid, as long as the grid count is right.

interp_K_grid = np.linspace(0,1,n)

but then there is a vector of actual choices kn_vec, how to map kn_vec to interp_K_grid?

Parameters

choice_grid:

this is the choice grid, on the actual choice scale

start: float

from gen_geom_grid

stop: float

from gen_geom_grid

num: int

from gen_geom_grid

geom_ratio: float

from gen_geom_grid