gcubed.projections.gcubed_parameters

class GCubedParameters(typing.NamedTuple):

GCubedParameters(diagnostics_path, intertemporal_constants_count, itc_decay_rate, x1_target_mask, j1_target_mask, ze_target_mask, z1_target_mask, x1_target_indices, j1_target_indices, ze_target_indices, z1_target_indices, len_x1, len_j1, len_ze, len_z1, x1r_difference_from_ssf, j1r_difference_from_ssf, zer_difference_from_ssf, z1r_difference_from_ssf, delta_x1l_yjr, delta_x1l_exz, delta_j1l_exz, delta_zel_yxr, delta_zel_yjr, delta_zel_exz, delta_zel_exo, delta_z1l_yxr, delta_z1l_yjr, delta_z1l_exz, delta_z1l_exo, M1_lead, M2, H1, H2, mu1, mu2, Gamma_jt, Gamma_st, Gamma_jT, Gamma_rT, psi_rj, tau_sjt, common_factor, Znew, Anew)

GCubedParameters( diagnostics_path: pathlib.Path, intertemporal_constants_count: int, itc_decay_rate: float, x1_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]], j1_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]], ze_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]], z1_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]], x1_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]], j1_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]], ze_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]], z1_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]], len_x1: int, len_j1: int, len_ze: int, len_z1: int, x1r_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], j1r_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], zer_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], z1r_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_x1l_yjr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_x1l_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_j1l_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_zel_yxr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_zel_yjr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_zel_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_zel_exo: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_z1l_yxr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_z1l_yjr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_z1l_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], delta_z1l_exo: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], M1_lead: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], M2: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], H1: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], H2: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], mu1: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], mu2: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], Gamma_jt: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], Gamma_st: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], Gamma_jT: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], Gamma_rT: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], psi_rj: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], tau_sjt: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], common_factor: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], Znew: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]], Anew: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]])

Create new instance of GCubedParameters(diagnostics_path, intertemporal_constants_count, itc_decay_rate, x1_target_mask, j1_target_mask, ze_target_mask, z1_target_mask, x1_target_indices, j1_target_indices, ze_target_indices, z1_target_indices, len_x1, len_j1, len_ze, len_z1, x1r_difference_from_ssf, j1r_difference_from_ssf, zer_difference_from_ssf, z1r_difference_from_ssf, delta_x1l_yjr, delta_x1l_exz, delta_j1l_exz, delta_zel_yxr, delta_zel_yjr, delta_zel_exz, delta_zel_exo, delta_z1l_yxr, delta_z1l_yjr, delta_z1l_exz, delta_z1l_exo, M1_lead, M2, H1, H2, mu1, mu2, Gamma_jt, Gamma_st, Gamma_jT, Gamma_rT, psi_rj, tau_sjt, common_factor, Znew, Anew)

diagnostics_path: pathlib.Path

Alias for field number 0

intertemporal_constants_count: int

Alias for field number 1

itc_decay_rate: float

Alias for field number 2

x1_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]]

Alias for field number 3

j1_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]]

Alias for field number 4

ze_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]]

Alias for field number 5

z1_target_mask: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.bool]]

Alias for field number 6

x1_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]]

Alias for field number 7

j1_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]]

Alias for field number 8

ze_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]]

Alias for field number 9

z1_target_indices: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.int32]]

Alias for field number 10

len_x1: int

Alias for field number 11

len_j1: int

Alias for field number 12

len_ze: int

Alias for field number 13

len_z1: int

Alias for field number 14

x1r_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 15

j1r_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 16

zer_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 17

z1r_difference_from_ssf: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 18

delta_x1l_yjr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 19

delta_x1l_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 20

delta_j1l_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 21

delta_zel_yxr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 22

delta_zel_yjr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 23

delta_zel_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 24

delta_zel_exo: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 25

delta_z1l_yxr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 26

delta_z1l_yjr: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 27

delta_z1l_exz: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 28

delta_z1l_exo: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 29

M1_lead: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 30

M2: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 31

H1: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 32

H2: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 33

mu1: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 34

mu2: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 35

Gamma_jt: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 36

Gamma_st: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 37

Gamma_jT: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 38

Gamma_rT: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 39

psi_rj: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 40

tau_sjt: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 41

common_factor: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 42

Znew: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 43

Anew: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.float64]]

Alias for field number 44