cytonormpy._normalization.Spline.fit#
- Spline.fit(current_distribution, goal_distribution)#
Interpolates a function between the current expression values and the goal expression values. First, limits are appended and the arrays are regularized in order to prevent duplicate entries. Next, interpolants are selected using the Fritsch-Carlson algorithm. The Cubic Hermite Spline is fitted and extrapolated linearly outside of the data range.
- Parameters:
current_distribution (
Optional[ndarray]) – A numpy array containing the expression values at the quantilesgoal_distribution (
Optional[ndarray]) – A numpy array containing the goal expression values at the quantiles
- Return type:
None