PINE LIBRARY
FunctionPatternDecomposition
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.
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.
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