FirstGeneration¶
- class ompy.FirstGeneration[source]¶
Bases:
object
First generation method from Guttormsen et al. (NIM 1987).
Note
Attributes need to be set only if the respective method (statistical / total) multiplicity estimation is used.
- Variables:
num_iterations (int) – Number of iterations the first generations method is applied. Defaults to 10.
multiplicity_estimation (str) – Selects which method should be used for the multiplicity estimation. Can be either “statistical”, or “total”. Default is “statistical”.
statistical_upper (float) – Threshold for upper limit in statistical multiplicity estimation. Defaults to 430 keV.
statistical_lower (float) – Threshold for lower limit in statistical multiplicity estimation. Defaults to 200 keV.
statistical_ratio (float) – Ratio in statistical multiplicity estimation. Defaults to 0.3.
Ex_entry_shift (float) – Shift applied to the energy in statistical multiplicity estimation. Defaults to 200 keV. TODO: Unknown how to pick. Magne described a manual method by looking at the known low energy states.
Ex_entry_statistical (float) – Average entry point in ground band for statistical multiplicity in statistical multiplicity estimation. Defaults to 300 keV.
Ex_entry_total (float) – Average entry point in ground band for total multiplicity estimation. Defaults to 0 keV.
valley_correction (Vector) – See step method. Default: None.
use_slide (bool) – Use sliding Ex ratio (?). Default: False.
action (Action) – Placeholder if an Action should be applied. This cut for example be a “cut” of the Ex bins to consider.
Todo
Clean up where attributes are set for the respective methods.
Attributes Summary
Methods Summary
__call__
(matrix)Wrapper for self.apply()
allgen_from_primary
(fg[, xs])Create all generation matrix from first generations matrix
apply
(unfolded)Apply the first generation method to a matrix
cut_valley_correction
(matrix)Cut valley correction Ex axis if neccessary.
multiplicity
(matrix)Dispatch method returning statistical or total multiplicity
multiplicity_normalization
(matrix)Generate multiplicity normalization
multiplicity_statistical
(matrix)Finds the multiplicties using Ex above yrast
multiplicity_total
(matrix)Finds the multiplicties using all of Ex
remove_negative
(matrix)Wrapper for Matrix.remove_negative()
row_normalized
(matrix)Set up a diagonal array with constant Ex rows
setup
(matrix)Set up initial first generation matrix with normalized Ex rows
step
(iteration, H_old, W_old, N, matrix[, ...])An iteration step in the first generation method
Attributes Documentation
Methods Documentation
- static allgen_from_primary(fg, xs=None)[source]¶
Create all generation matrix from first generations matrix
AG(Ex, Eg) = FG(Ex, Eg) + ∑ σ[Ex] weight(Ex->Ex') AG(Ex', Eg) / σ[Ex'],
where the sum runs over all excitation energies Ex’ < Ex.
- cut_valley_correction(matrix)[source]¶
Cut valley correction Ex axis if neccessary.
Ensures valley correction has the same Ex axis as the matrix it will be used with.
- Parameters:
matrix (Matrix) – Matrix that the valley correction will be used with.
- Returns:
- None if
self.valley_correction is None. Otherwise a np.ndarray with the same length as matrix.Ex.
- Return type:
valley_correction (None or np.ndarray)
- remove_negative(matrix)[source]¶
Wrapper for Matrix.remove_negative()
Put in as an extra method to facilitate replacing this by eg. fill_and_remove_negatve
- Parameters:
matrix (
Matrix
) – Input matrix
- step(iteration, H_old, W_old, N, matrix, valley_correction=None)[source]¶
An iteration step in the first generation method
The most interesting part of the first generation method. Implementation of a single step in the first generation method.
- Parameters:
iteration (
int
) – the current iteration stepH_old (
ndarray
) – The previous H matrixW_old (
ndarray
) – The previous weightsN (
ndarray
) – The normalizationmatrix (
Matrix
) – The matrix the method is applied tovalley_correction (np.ndarray, optional) – Array of weight factors for each Ex bin that can be used to manually “turn off” /decrease the influence of very large peaks in the method.
- Return type: