This interpolation method aims to maintain conservatism across a set of data. The strategy used is to assume exponential behaviour from a certain point, however until the interpolation gets to this point, other forms of interpolation are used and interconnected, creating two functions that complement each other.
Use the package manager pip to install climbinterp.
pip install climbinterpfrom climbinterp.data_arrange import ArrangeData
from climbinterp.adv_interpolation import ClimbInterp
from random import sample
# Random set of x and y data.
random_numbers_x = sample(range(1, 51), 50)
random_numbers_y = sample(range(1, 51), 50)
# Will arrange the data in ascending order.
arrange_data = ArrangeData(random_numbers_x, random_numbers_y)
random_numbers_x, random_numbers_y = arrange_data.get_arranged_points()
climb_interp = ClimbInterp(random_numbers_x, random_numbers_y)