# Data preparation for NeurEco Discrete Dynamic with the Python API

# Data preparation for NeurEco Discrete Dynamic with the Python API#

The python API expects the data for model construction or evaluation in form of a list of NumPy arrays containing the data.

allowed types of arrays: int, float, double

each

**time**array contains one column corresponding to a time variable, it is a finite arithmetic sequence with spacing equal to time-stepeach

**input (excitation)**array contains a table:number of columns is the number of input features (excitations)

number of line is the same as in corresponding

**time**arrayeach line contains: the values of input features (excitations) at point of time found at the same line number of the corresponding

**time**array

each

**output**array contains a table:number of lines is the same as in the corresponding

**time**and**input (excitation)**arraysnumber of columns is equal to the number of output features

each line contains: the values of output features at point of time found at the same line number of the corresponding

**time**array

The

**time-step**must be the**same**for all tablesWhen data represent multiple experiences, they are passed as multiple

**time**,**input (excitation)**and**output**arrays. In this case pay attention to preserving the correspondence between**time**,**input (excitation)**and**output**arrays.The

**time**array in different set of**time/input (excitation)/output**arrays are not required to be the same, they can have different length and/or initial time-point, but the time-step must stay the same for all experiences.

There is no need to normalize the data, as the normalization is handled by NeurEco, Data normalization for Discrete Dynamic.