FunctionPatternDecomposition

2022년 1월 25일

Library "FunctionPatternDecomposition"
Methods for decomposing price into common grid/matrix patterns.

series_to_array(source, length) Helper for converting series to array.
  Parameters:
    source: float, data series.
    length: int, size.
  Returns: float array.

smooth_data_2d(data, rate) Smooth data sample into 2d points.
  Parameters:
    data: float array, source data.
    rate: float, default=0.25, the rate of smoothness to apply.
  Returns: tuple with 2 float arrays.

thin_points(data_x, data_y, rate) Thin the number of points.
  Parameters:
    data_x: float array, points x value.
    data_y: float array, points y value.
    rate: float, default=2.0, minimum threshold rate of sample stdev to accept points.
  Returns: tuple with 2 float arrays.

extract_point_direction(data_x, data_y) Extract the direction each point faces.
  Parameters:
    data_x: float array, points x value.
    data_y: float array, points y value.
  Returns: float array.

find_corners(data_x, data_y, rate) ...
  Parameters:
    data_x: float array, points x value.
    data_y: float array, points y value.
    rate: float, minimum threshold rate of data y stdev.
  Returns: tuple with 2 float arrays.

grid_coordinates(data_x, data_y, m_size) transforms points data to a constrained sized matrix format.
  Parameters:
    data_x: float array, points x value.
    data_y: float array, points y value.
    m_size: int, default=10, size of the matrix.
  Returns: flat 2d pseudo matrix.