.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/kinematics/plotEMvsGAcomparisonForagingMouse.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_kinematics_plotEMvsGAcomparisonForagingMouse.py: Comparison between EM and gradient ascent for tracking a foraging mouse ======================================================================= The code below compares the expectation maximization (EM) and gradient ascent algorithm for tracking a foraging mouse. .. GENERATED FROM PYTHON SOURCE LINES 11-28 .. code-block:: default import sys import os.path import argparse import configparser import math import random import pickle import numpy as np import pandas as pd import torch import scipy import plotly.graph_objects as go import ssm.learning .. GENERATED FROM PYTHON SOURCE LINES 29-31 Define parameters for estimation -------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 31-51 .. code-block:: default skip_estimation_sigma_a = False skip_estimation_R = False skip_estimation_m0 = False skip_estimation_V0 = False start_position = 0 # number_positions = 10000 # number_positions = 7500 number_positions = 50 lbfgs_max_iter = 2 lbfgs_tolerance_grad = -1 lbfgs_tolerance_change = 1e-3 lbfgs_lr = 1.0 lbfgs_n_epochs = 100 lbfgs_tol = 1e-3 em_max_iter = 200 Qe_reg_param_ga = None Qe_reg_param_em = 1e-5 .. GENERATED FROM PYTHON SOURCE LINES 52-54 Provide initial conditions -------------------------- .. GENERATED FROM PYTHON SOURCE LINES 54-71 .. code-block:: default pos_x0 = 0.0 pos_y0 = 0.0 vel_x0 = 0.0 vel_y0 = 0.0 ace_x0 = 0.0 ace_y0 = 0.0 sigma_a0 = 1.0 sigma_x0 = 1.0 sigma_y0 = 1.0 sqrt_diag_V0_value = 0.1 if math.isnan(pos_x0): pos_x0 = y[0, 0] if math.isnan(pos_y0): pos_y0 = y[1, 0] .. GENERATED FROM PYTHON SOURCE LINES 72-74 Get mouse positions ------------------- .. GENERATED FROM PYTHON SOURCE LINES 74-82 .. code-block:: default data_filename = "http://www.gatsby.ucl.ac.uk/~rapela/svGPFA/data/positions_session003_start0.00_end15548.27.csv" data = pd.read_csv(data_filename) data = data.iloc[start_position:start_position+number_positions,:] y = np.transpose(data[["x", "y"]].to_numpy()) date_times = pd.to_datetime(data["time"]) dt = (date_times.iloc[1]-date_times.iloc[0]).total_seconds() .. GENERATED FROM PYTHON SOURCE LINES 83-85 Build the matrices of the CWPA model ------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 85-118 .. code-block:: default B, _, Qe, Z, _ = ssm.tracking.utils.getLDSmatricesForKinematics_np( dt=dt, sigma_a=np.nan, pos_x_R_std=np.nan, pos_y_R_std=np.nan) m0 = np.array([pos_x0, vel_x0, ace_x0, pos_y0, vel_y0, ace_y0], dtype=np.double) vars_to_estimate = {} if skip_estimation_sigma_a: vars_to_estimate["sigma_a"] = False else: vars_to_estimate["sigma_a"] = True if skip_estimation_R: vars_to_estimate["pos_x_R_std"] = False vars_to_estimate["pos_y_R_std"] = False vars_to_estimate["R"] = False else: vars_to_estimate["pos_x_R_std"] = True vars_to_estimate["pos_y_R_std"] = True vars_to_estimate["R"] = True if skip_estimation_m0: vars_to_estimate["m0"] = False else: vars_to_estimate["m0"] = True if skip_estimation_V0: vars_to_estimate["sqrt_diag_V0"] = False vars_to_estimate["V0"] = False else: vars_to_estimate["sqrt_diag_V0"] = True vars_to_estimate["V0"] = True .. GENERATED FROM PYTHON SOURCE LINES 119-121 Perform gradient ascent optimization ------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 121-145 .. code-block:: default sqrt_diag_R_torch = torch.DoubleTensor([sigma_x0, sigma_y0]) m0_torch = torch.from_numpy(m0.copy()) sqrt_diag_V0_torch = torch.DoubleTensor([sqrt_diag_V0_value for i in range(len(m0))]) if Qe_reg_param_ga is not None: Qe_regularized_ga = Qe + Qe_reg_param_ga * np.eye(Qe.shape[0]) else: Qe_regularized_ga = Qe y_torch = torch.from_numpy(y.astype(np.double)) B_torch = torch.from_numpy(B.astype(np.double)) Qe_regularized_ga_torch = torch.from_numpy(Qe_regularized_ga.astype(np.double)) Z_torch = torch.from_numpy(Z.astype(np.double)) optim_res_ga = ssm.learning.torch_lbfgs_optimize_SS_tracking_diagV0( y=y_torch, B=B_torch, Qe=Qe_regularized_ga_torch, Z=Z_torch, sigma_a0=sigma_a0, pos_x_R_std0=sigma_x0, pos_y_R_std0=sigma_y0, m0_0=m0_torch, sqrt_diag_V0_0=sqrt_diag_V0_torch, max_iter=lbfgs_max_iter, lr=lbfgs_lr, vars_to_estimate=vars_to_estimate, tolerance_grad=lbfgs_tolerance_grad, tolerance_change=lbfgs_tolerance_change, n_epochs=lbfgs_n_epochs, tol=lbfgs_tol) print("gradient ascent: " + optim_res_ga["termination_info"]) .. rst-class:: sphx-glr-script-out .. code-block:: none -------------------------------------------------------------------------------- startup likelihood: -3948969.481708237 ll=-3948969.481708237, sigma_a=1.0, pos_x_R_std=1.0, pos_y_R_std=1.0, m0=tensor([0., 0., 0., 0., 0., 0.], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000], dtype=torch.float64, requires_grad=True) ll=-737534.2923452, sigma_a=1.0313226103069837, pos_x_R_std=1.0455456579842255, pos_y_R_std=1.2001321980975206, m0=tensor([2.3505e-04, 5.9404e-05, 8.0036e-06, 4.9398e-04, 1.2692e-04, 1.7218e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.2250, 0.1080, 0.1001, 0.6519, 0.1364, 0.1007], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 0 likelihood: -737534.2923452 sigma_a: tensor([1.0313], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.0455], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.2001], dtype=torch.float64, requires_grad=True) m0: tensor([2.3505e-04, 5.9404e-05, 8.0036e-06, 4.9398e-04, 1.2692e-04, 1.7218e-05], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([0.2250, 0.1080, 0.1001, 0.6519, 0.1364, 0.1007], dtype=torch.float64, requires_grad=True) ll=-737534.2923452, sigma_a=1.0313226103069837, pos_x_R_std=1.0455456579842255, pos_y_R_std=1.2001321980975206, m0=tensor([2.3505e-04, 5.9404e-05, 8.0036e-06, 4.9398e-04, 1.2692e-04, 1.7218e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.2250, 0.1080, 0.1001, 0.6519, 0.1364, 0.1007], dtype=torch.float64, requires_grad=True) ll=-548307.3861159257, sigma_a=1.0342589150317776, pos_x_R_std=1.0624168702824324, pos_y_R_std=1.2054903396696848, m0=tensor([3.8851e-04, 9.8342e-05, 1.3279e-05, 5.6221e-04, 1.4673e-04, 2.0178e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.3233, 0.1111, 0.1002, 0.7067, 0.1377, 0.1007], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 1 likelihood: -548307.3861159257 sigma_a: tensor([1.0343], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.0624], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.2055], dtype=torch.float64, requires_grad=True) m0: tensor([3.8851e-04, 9.8342e-05, 1.3279e-05, 5.6221e-04, 1.4673e-04, 2.0178e-05], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([0.3233, 0.1111, 0.1002, 0.7067, 0.1377, 0.1007], dtype=torch.float64, requires_grad=True) ll=-548307.3861159257, sigma_a=1.0342589150317776, pos_x_R_std=1.0624168702824324, pos_y_R_std=1.2054903396696848, m0=tensor([3.8851e-04, 9.8342e-05, 1.3279e-05, 5.6221e-04, 1.4673e-04, 2.0178e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.3233, 0.1111, 0.1002, 0.7067, 0.1377, 0.1007], dtype=torch.float64, requires_grad=True) ll=-352989.1772245695, sigma_a=1.035773016132182, pos_x_R_std=1.0693196267332854, pos_y_R_std=1.2054940650509616, m0=tensor([6.9201e-04, 1.7613e-04, 2.3952e-05, 7.6842e-04, 2.0977e-04, 2.9846e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.4829, 0.1125, 0.1002, 0.8413, 0.1387, 0.1007], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 2 likelihood: -352989.1772245695 sigma_a: tensor([1.0358], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.0693], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.2055], dtype=torch.float64, requires_grad=True) m0: tensor([6.9201e-04, 1.7613e-04, 2.3952e-05, 7.6842e-04, 2.0977e-04, 2.9846e-05], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([0.4829, 0.1125, 0.1002, 0.8413, 0.1387, 0.1007], dtype=torch.float64, requires_grad=True) ll=-352989.1772245695, sigma_a=1.035773016132182, pos_x_R_std=1.0693196267332854, pos_y_R_std=1.2054940650509616, m0=tensor([6.9201e-04, 1.7613e-04, 2.3952e-05, 7.6842e-04, 2.0977e-04, 2.9846e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.4829, 0.1125, 0.1002, 0.8413, 0.1387, 0.1007], dtype=torch.float64, requires_grad=True) ll=-239770.89278952047, sigma_a=1.0345912248607383, pos_x_R_std=1.0649549949989956, pos_y_R_std=1.192186139945691, m0=tensor([1.0877e-03, 2.7817e-04, 3.7957e-05, 1.1019e-03, 3.1841e-04, 4.6838e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.6352, 0.1120, 0.1002, 1.0107, 0.1377, 0.1007], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 3 likelihood: -239770.89278952047 sigma_a: tensor([1.0346], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.0650], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.1922], dtype=torch.float64, requires_grad=True) m0: tensor([1.0877e-03, 2.7817e-04, 3.7957e-05, 1.1019e-03, 3.1841e-04, 4.6838e-05], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([0.6352, 0.1120, 0.1002, 1.0107, 0.1377, 0.1007], dtype=torch.float64, requires_grad=True) ll=-239770.89278952047, sigma_a=1.0345912248607383, pos_x_R_std=1.0649549949989956, pos_y_R_std=1.192186139945691, m0=tensor([1.0877e-03, 2.7817e-04, 3.7957e-05, 1.1019e-03, 3.1841e-04, 4.6838e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.6352, 0.1120, 0.1002, 1.0107, 0.1377, 0.1007], dtype=torch.float64, requires_grad=True) ll=-158792.43454294148, sigma_a=1.031266253437804, pos_x_R_std=1.0530834420544053, pos_y_R_std=1.1642081151418975, m0=tensor([1.7347e-03, 4.4600e-04, 6.0884e-05, 1.7048e-03, 5.3310e-04, 8.0950e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.8187, 0.1101, 0.1002, 1.2433, 0.1347, 0.1006], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 4 likelihood: -158792.43454294148 sigma_a: tensor([1.0313], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.0531], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.1642], dtype=torch.float64, requires_grad=True) m0: tensor([1.7347e-03, 4.4600e-04, 6.0884e-05, 1.7048e-03, 5.3310e-04, 8.0950e-05], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([0.8187, 0.1101, 0.1002, 1.2433, 0.1347, 0.1006], dtype=torch.float64, requires_grad=True) ll=-158792.43454294148, sigma_a=1.031266253437804, pos_x_R_std=1.0530834420544053, pos_y_R_std=1.1642081151418975, m0=tensor([1.7347e-03, 4.4600e-04, 6.0884e-05, 1.7048e-03, 5.3310e-04, 8.0950e-05], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.8187, 0.1101, 0.1002, 1.2433, 0.1347, 0.1006], dtype=torch.float64, requires_grad=True) ll=-107003.36378827522, sigma_a=1.0265649438188955, pos_x_R_std=1.0345559508769064, pos_y_R_std=1.1272516034940505, m0=tensor([2.7165e-03, 7.0298e-04, 9.5754e-05, 2.6644e-03, 9.2083e-04, 1.4355e-04], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.0267, 0.1071, 0.1001, 1.5230, 0.1301, 0.1005], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 5 likelihood: -107003.36378827522 sigma_a: tensor([1.0266], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.0346], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.1273], dtype=torch.float64, requires_grad=True) m0: tensor([2.7165e-03, 7.0298e-04, 9.5754e-05, 2.6644e-03, 9.2083e-04, 1.4355e-04], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([1.0267, 0.1071, 0.1001, 1.5230, 0.1301, 0.1005], dtype=torch.float64, requires_grad=True) ll=-107003.36378827522, sigma_a=1.0265649438188955, pos_x_R_std=1.0345559508769064, pos_y_R_std=1.1272516034940505, m0=tensor([2.7165e-03, 7.0298e-04, 9.5754e-05, 2.6644e-03, 9.2083e-04, 1.4355e-04], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.0267, 0.1071, 0.1001, 1.5230, 0.1301, 0.1005], dtype=torch.float64, requires_grad=True) ll=-72519.50427712481, sigma_a=1.0212614793416899, pos_x_R_std=1.0076850908683614, pos_y_R_std=1.0897621584560877, m0=tensor([0.0043, 0.0011, 0.0002, 0.0042, 0.0017, 0.0003], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.2768, 0.1026, 0.1000, 1.8649, 0.1247, 0.1005], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 6 likelihood: -72519.50427712481 sigma_a: tensor([1.0213], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.0077], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.0898], dtype=torch.float64, requires_grad=True) m0: tensor([0.0043, 0.0011, 0.0002, 0.0042, 0.0017, 0.0003], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([1.2768, 0.1026, 0.1000, 1.8649, 0.1247, 0.1005], dtype=torch.float64, requires_grad=True) ll=-72519.50427712481, sigma_a=1.0212614793416899, pos_x_R_std=1.0076850908683614, pos_y_R_std=1.0897621584560877, m0=tensor([0.0043, 0.0011, 0.0002, 0.0042, 0.0017, 0.0003], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.2768, 0.1026, 0.1000, 1.8649, 0.1247, 0.1005], dtype=torch.float64, requires_grad=True) ll=-49686.43647528592, sigma_a=1.0172690512410587, pos_x_R_std=0.970620315721213, pos_y_R_std=1.0760647714463025, m0=tensor([0.0067, 0.0018, 0.0002, 0.0066, 0.0031, 0.0005], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.5801, 0.0963, 0.0999, 2.2741, 0.1195, 0.1004], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 7 likelihood: -49686.43647528592 sigma_a: tensor([1.0173], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.9706], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.0761], dtype=torch.float64, requires_grad=True) m0: tensor([0.0067, 0.0018, 0.0002, 0.0066, 0.0031, 0.0005], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([1.5801, 0.0963, 0.0999, 2.2741, 0.1195, 0.1004], dtype=torch.float64, requires_grad=True) ll=-49686.43647528592, sigma_a=1.0172690512410587, pos_x_R_std=0.970620315721213, pos_y_R_std=1.0760647714463025, m0=tensor([0.0067, 0.0018, 0.0002, 0.0066, 0.0031, 0.0005], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.5801, 0.0963, 0.0999, 2.2741, 0.1195, 0.1004], dtype=torch.float64, requires_grad=True) ll=-34046.61543713145, sigma_a=1.0181891658869535, pos_x_R_std=0.9199677343259305, pos_y_R_std=1.1362722752597483, m0=tensor([0.0104, 0.0028, 0.0004, 0.0104, 0.0060, 0.0010], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.9596, 0.0879, 0.0998, 2.7658, 0.1162, 0.1005], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 8 likelihood: -34046.61543713145 sigma_a: tensor([1.0182], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.9200], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.1363], dtype=torch.float64, requires_grad=True) m0: tensor([0.0104, 0.0028, 0.0004, 0.0104, 0.0060, 0.0010], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([1.9596, 0.0879, 0.0998, 2.7658, 0.1162, 0.1005], dtype=torch.float64, requires_grad=True) ll=-34046.61543713145, sigma_a=1.0181891658869535, pos_x_R_std=0.9199677343259305, pos_y_R_std=1.1362722752597483, m0=tensor([0.0104, 0.0028, 0.0004, 0.0104, 0.0060, 0.0010], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([1.9596, 0.0879, 0.0998, 2.7658, 0.1162, 0.1005], dtype=torch.float64, requires_grad=True) ll=-23031.088729876134, sigma_a=1.0289880255994346, pos_x_R_std=0.8522517630782999, pos_y_R_std=1.3241334500984403, m0=tensor([0.0161, 0.0045, 0.0006, 0.0165, 0.0114, 0.0020], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([2.4398, 0.0769, 0.0996, 3.3618, 0.1148, 0.1009], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 9 likelihood: -23031.088729876134 sigma_a: tensor([1.0290], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.8523], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.3241], dtype=torch.float64, requires_grad=True) m0: tensor([0.0161, 0.0045, 0.0006, 0.0165, 0.0114, 0.0020], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([2.4398, 0.0769, 0.0996, 3.3618, 0.1148, 0.1009], dtype=torch.float64, requires_grad=True) ll=-23031.088729876134, sigma_a=1.0289880255994346, pos_x_R_std=0.8522517630782999, pos_y_R_std=1.3241334500984403, m0=tensor([0.0161, 0.0045, 0.0006, 0.0165, 0.0114, 0.0020], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([2.4398, 0.0769, 0.0996, 3.3618, 0.1148, 0.1009], dtype=torch.float64, requires_grad=True) ll=-15520.287365153943, sigma_a=1.0547304947761345, pos_x_R_std=0.7667390521558166, pos_y_R_std=1.612859393851679, m0=tensor([0.0246, 0.0073, 0.0009, 0.0258, 0.0207, 0.0038], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([3.0220, 0.0637, 0.0994, 4.0879, 0.1103, 0.1015], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 10 likelihood: -15520.287365153943 sigma_a: tensor([1.0547], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.7667], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.6129], dtype=torch.float64, requires_grad=True) m0: tensor([0.0246, 0.0073, 0.0009, 0.0258, 0.0207, 0.0038], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([3.0220, 0.0637, 0.0994, 4.0879, 0.1103, 0.1015], dtype=torch.float64, requires_grad=True) ll=-15520.287365153943, sigma_a=1.0547304947761345, pos_x_R_std=0.7667390521558166, pos_y_R_std=1.612859393851679, m0=tensor([0.0246, 0.0073, 0.0009, 0.0258, 0.0207, 0.0038], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([3.0220, 0.0637, 0.0994, 4.0879, 0.1103, 0.1015], dtype=torch.float64, requires_grad=True) ll=-10480.990514234054, sigma_a=1.1073379890009312, pos_x_R_std=0.6593231709435864, pos_y_R_std=1.9662619243109694, m0=tensor([0.0372, 0.0122, 0.0016, 0.0401, 0.0357, 0.0070], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([3.7193, 0.0482, 0.0991, 4.9804, 0.0970, 0.1026], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 11 likelihood: -10480.990514234054 sigma_a: tensor([1.1073], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.6593], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.9663], dtype=torch.float64, requires_grad=True) m0: tensor([0.0372, 0.0122, 0.0016, 0.0401, 0.0357, 0.0070], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([3.7193, 0.0482, 0.0991, 4.9804, 0.0970, 0.1026], dtype=torch.float64, requires_grad=True) ll=-10480.990514234054, sigma_a=1.1073379890009312, pos_x_R_std=0.6593231709435864, pos_y_R_std=1.9662619243109694, m0=tensor([0.0372, 0.0122, 0.0016, 0.0401, 0.0357, 0.0070], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([3.7193, 0.0482, 0.0991, 4.9804, 0.0970, 0.1026], dtype=torch.float64, requires_grad=True) ll=-7092.864655124649, sigma_a=1.2083100085861398, pos_x_R_std=0.5245917198425245, pos_y_R_std=2.3969283197644358, m0=tensor([0.0560, 0.0219, 0.0028, 0.0619, 0.0596, 0.0125], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([4.5623, 0.0303, 0.0989, 6.0756, 0.0667, 0.1041], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 12 likelihood: -7092.864655124649 sigma_a: tensor([1.2083], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.5246], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([2.3969], dtype=torch.float64, requires_grad=True) m0: tensor([0.0560, 0.0219, 0.0028, 0.0619, 0.0596, 0.0125], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([4.5623, 0.0303, 0.0989, 6.0756, 0.0667, 0.1041], dtype=torch.float64, requires_grad=True) ll=-7092.864655124649, sigma_a=1.2083100085861398, pos_x_R_std=0.5245917198425245, pos_y_R_std=2.3969283197644358, m0=tensor([0.0560, 0.0219, 0.0028, 0.0619, 0.0596, 0.0125], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([4.5623, 0.0303, 0.0989, 6.0756, 0.0667, 0.1041], dtype=torch.float64, requires_grad=True) ll=-4821.532436545909, sigma_a=1.3897246451006247, pos_x_R_std=0.38354405197546165, pos_y_R_std=2.917592575540884, m0=tensor([0.0840, 0.0441, 0.0056, 0.0948, 0.0973, 0.0217], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([5.5806e+00, 9.6251e-03, 9.8725e-02, 7.4155e+00, 3.0666e-03, 1.0619e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 13 likelihood: -4821.532436545909 sigma_a: tensor([1.3897], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.3835], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([2.9176], dtype=torch.float64, requires_grad=True) m0: tensor([0.0840, 0.0441, 0.0056, 0.0948, 0.0973, 0.0217], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([5.5806e+00, 9.6251e-03, 9.8725e-02, 7.4155e+00, 3.0666e-03, 1.0619e-01], dtype=torch.float64, requires_grad=True) ll=-4821.532436545909, sigma_a=1.3897246451006247, pos_x_R_std=0.38354405197546165, pos_y_R_std=2.917592575540884, m0=tensor([0.0840, 0.0441, 0.0056, 0.0948, 0.0973, 0.0217], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([5.5806e+00, 9.6251e-03, 9.8725e-02, 7.4155e+00, 3.0666e-03, 1.0619e-01], dtype=torch.float64, requires_grad=True) ll=-3289.4863324444264, sigma_a=1.7098249459797807, pos_x_R_std=0.4374580016467312, pos_y_R_std=3.5428260154639197, m0=tensor([0.1260, 0.0996, 0.0129, 0.1445, 0.1553, 0.0363], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 6.7971, -0.0195, 0.0991, 9.0645, -0.1258, 0.1084], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 14 likelihood: -3289.4863324444264 sigma_a: tensor([1.7098], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.4375], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([3.5428], dtype=torch.float64, requires_grad=True) m0: tensor([0.1260, 0.0996, 0.0129, 0.1445, 0.1553, 0.0363], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 6.7971, -0.0195, 0.0991, 9.0645, -0.1258, 0.1084], dtype=torch.float64, requires_grad=True) ll=-3289.4863324444264, sigma_a=1.7098249459797807, pos_x_R_std=0.4374580016467312, pos_y_R_std=3.5428260154639197, m0=tensor([0.1260, 0.0996, 0.0129, 0.1445, 0.1553, 0.0363], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 6.7971, -0.0195, 0.0991, 9.0645, -0.1258, 0.1084], dtype=torch.float64, requires_grad=True) ll=-2268.3489998370387, sigma_a=2.1054395181701606, pos_x_R_std=0.2379194880953605, pos_y_R_std=4.2955681882363175, m0=tensor([0.1893, 0.1866, 0.0242, 0.2190, 0.2419, 0.0580], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 8.3168, -0.0735, 0.0995, 11.0794, -0.3686, 0.1102], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 15 likelihood: -2268.3489998370387 sigma_a: tensor([2.1054], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.2379], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([4.2956], dtype=torch.float64, requires_grad=True) m0: tensor([0.1893, 0.1866, 0.0242, 0.2190, 0.2419, 0.0580], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 8.3168, -0.0735, 0.0995, 11.0794, -0.3686, 0.1102], dtype=torch.float64, requires_grad=True) ll=-2268.3489998370387, sigma_a=2.1054395181701606, pos_x_R_std=0.2379194880953605, pos_y_R_std=4.2955681882363175, m0=tensor([0.1893, 0.1866, 0.0242, 0.2190, 0.2419, 0.0580], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 8.3168, -0.0735, 0.0995, 11.0794, -0.3686, 0.1102], dtype=torch.float64, requires_grad=True) ll=-1420.0848234925124, sigma_a=3.0291029023271507, pos_x_R_std=2.471600800500445, pos_y_R_std=5.611907394212267, m0=tensor([0.3166, 0.4308, 0.0547, 0.3693, 0.4151, 0.1008], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([10.8086, -0.2959, 0.1011, 14.8299, -0.9118, 0.1125], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 16 likelihood: -1420.0848234925124 sigma_a: tensor([3.0291], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([2.4716], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([5.6119], dtype=torch.float64, requires_grad=True) m0: tensor([0.3166, 0.4308, 0.0547, 0.3693, 0.4151, 0.1008], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([10.8086, -0.2959, 0.1011, 14.8299, -0.9118, 0.1125], dtype=torch.float64, requires_grad=True) ll=-1420.0848234925124, sigma_a=3.0291029023271507, pos_x_R_std=2.471600800500445, pos_y_R_std=5.611907394212267, m0=tensor([0.3166, 0.4308, 0.0547, 0.3693, 0.4151, 0.1008], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([10.8086, -0.2959, 0.1011, 14.8299, -0.9118, 0.1125], dtype=torch.float64, requires_grad=True) ll=-1147.9501184695964, sigma_a=3.399261351111765, pos_x_R_std=2.797126297817509, pos_y_R_std=6.3372240583281405, m0=tensor([0.4144, 0.5330, 0.0673, 0.4808, 0.5405, 0.1305], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([12.3911, -0.3975, 0.1013, 17.0117, -1.3473, 0.1129], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 17 likelihood: -1147.9501184695964 sigma_a: tensor([3.3993], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([2.7971], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([6.3372], dtype=torch.float64, requires_grad=True) m0: tensor([0.4144, 0.5330, 0.0673, 0.4808, 0.5405, 0.1305], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([12.3911, -0.3975, 0.1013, 17.0117, -1.3473, 0.1129], dtype=torch.float64, requires_grad=True) ll=-1147.9501184695964, sigma_a=3.399261351111765, pos_x_R_std=2.797126297817509, pos_y_R_std=6.3372240583281405, m0=tensor([0.4144, 0.5330, 0.0673, 0.4808, 0.5405, 0.1305], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([12.3911, -0.3975, 0.1013, 17.0117, -1.3473, 0.1129], dtype=torch.float64, requires_grad=True) ll=-842.9216932287975, sigma_a=4.091913074964331, pos_x_R_std=3.1989828912614096, pos_y_R_std=7.523208401105215, m0=tensor([0.6478, 0.7656, 0.0955, 0.7454, 0.8368, 0.1987], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([15.4074, -0.6133, 0.1020, 21.2251, -2.4562, 0.1129], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 18 likelihood: -842.9216932287975 sigma_a: tensor([4.0919], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([3.1990], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([7.5232], dtype=torch.float64, requires_grad=True) m0: tensor([0.6478, 0.7656, 0.0955, 0.7454, 0.8368, 0.1987], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([15.4074, -0.6133, 0.1020, 21.2251, -2.4562, 0.1129], dtype=torch.float64, requires_grad=True) ll=-842.9216932287975, sigma_a=4.091913074964331, pos_x_R_std=3.1989828912614096, pos_y_R_std=7.523208401105215, m0=tensor([0.6478, 0.7656, 0.0955, 0.7454, 0.8368, 0.1987], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([15.4074, -0.6133, 0.1020, 21.2251, -2.4562, 0.1129], dtype=torch.float64, requires_grad=True) ll=-669.453373295344, sigma_a=4.773398117788069, pos_x_R_std=3.2254498589611473, pos_y_R_std=8.451973966071769, m0=tensor([0.9552, 1.0758, 0.1320, 1.0904, 1.2192, 0.2808], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([18.5136, -0.8797, 0.1034, 25.6192, -3.9935, 0.1117], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 19 likelihood: -669.453373295344 sigma_a: tensor([4.7734], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([3.2254], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([8.4520], dtype=torch.float64, requires_grad=True) m0: tensor([0.9552, 1.0758, 0.1320, 1.0904, 1.2192, 0.2808], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([18.5136, -0.8797, 0.1034, 25.6192, -3.9935, 0.1117], dtype=torch.float64, requires_grad=True) ll=-669.453373295344, sigma_a=4.773398117788069, pos_x_R_std=3.2254498589611473, pos_y_R_std=8.451973966071769, m0=tensor([0.9552, 1.0758, 0.1320, 1.0904, 1.2192, 0.2808], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([18.5136, -0.8797, 0.1034, 25.6192, -3.9935, 0.1117], dtype=torch.float64, requires_grad=True) ll=-526.6241053511125, sigma_a=5.705504727173416, pos_x_R_std=2.3137885463557737, pos_y_R_std=9.076165043268128, m0=tensor([1.4641, 1.6546, 0.1977, 1.6545, 1.8270, 0.3928], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([22.5971, -1.3441, 0.1071, 31.4619, -6.5419, 0.1088], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 20 likelihood: -526.6241053511125 sigma_a: tensor([5.7055], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([2.3138], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([9.0762], dtype=torch.float64, requires_grad=True) m0: tensor([1.4641, 1.6546, 0.1977, 1.6545, 1.8270, 0.3928], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([22.5971, -1.3441, 0.1071, 31.4619, -6.5419, 0.1088], dtype=torch.float64, requires_grad=True) ll=-526.6241053511125, sigma_a=5.705504727173416, pos_x_R_std=2.3137885463557737, pos_y_R_std=9.076165043268128, m0=tensor([1.4641, 1.6546, 0.1977, 1.6545, 1.8270, 0.3928], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([22.5971, -1.3441, 0.1071, 31.4619, -6.5419, 0.1088], dtype=torch.float64, requires_grad=True) ll=-419.6738967914765, sigma_a=8.206360081964778, pos_x_R_std=-2.3527725535406283, pos_y_R_std=9.271186031228853, m0=tensor([2.4730, 3.1835, 0.3691, 2.7662, 2.9941, 0.5662], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 29.7736, -2.5397, 0.1201, 41.5404, -11.4699, 0.1018], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 21 likelihood: -419.6738967914765 sigma_a: tensor([8.2064], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([-2.3528], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([9.2712], dtype=torch.float64, requires_grad=True) m0: tensor([2.4730, 3.1835, 0.3691, 2.7662, 2.9941, 0.5662], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 29.7736, -2.5397, 0.1201, 41.5404, -11.4699, 0.1018], dtype=torch.float64, requires_grad=True) ll=-419.6738967914765, sigma_a=8.206360081964778, pos_x_R_std=-2.3527725535406283, pos_y_R_std=9.271186031228853, m0=tensor([2.4730, 3.1835, 0.3691, 2.7662, 2.9941, 0.5662], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 29.7736, -2.5397, 0.1201, 41.5404, -11.4699, 0.1018], dtype=torch.float64, requires_grad=True) ll=-394.7914366586798, sigma_a=8.07425986017163, pos_x_R_std=-1.580002106665833, pos_y_R_std=9.253905387018673, m0=tensor([2.5687, 3.1780, 0.3672, 2.8699, 3.1020, 0.5785], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 30.3950, -2.5052, 0.1188, 42.6305, -11.8724, 0.1018], dtype=torch.float64, requires_grad=True) ll=-378.0699668520697, sigma_a=7.479148360993492, pos_x_R_std=1.9013287565051207, pos_y_R_std=9.176056084851815, m0=tensor([3.0000, 3.1529, 0.3584, 3.3370, 3.5881, 0.6341], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 33.1945, -2.3498, 0.1129, 47.5413, -13.6855, 0.1017], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 22 likelihood: -378.0699668520697 sigma_a: tensor([7.4791], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([1.9013], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([9.1761], dtype=torch.float64, requires_grad=True) m0: tensor([3.0000, 3.1529, 0.3584, 3.3370, 3.5881, 0.6341], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 33.1945, -2.3498, 0.1129, 47.5413, -13.6855, 0.1017], dtype=torch.float64, requires_grad=True) ll=-378.0699668520697, sigma_a=7.479148360993492, pos_x_R_std=1.9013287565051207, pos_y_R_std=9.176056084851815, m0=tensor([3.0000, 3.1529, 0.3584, 3.3370, 3.5881, 0.6341], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 33.1945, -2.3498, 0.1129, 47.5413, -13.6855, 0.1017], dtype=torch.float64, requires_grad=True) ll=-10249.015952388172, sigma_a=8.424559732631094, pos_x_R_std=0.0035997831962235605, pos_y_R_std=9.176767173887171, m0=tensor([3.3256, 3.6909, 0.4187, 3.6954, 3.9642, 0.6873], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 35.4868, -2.7734, 0.1179, 50.7121, -15.2745, 0.0993], dtype=torch.float64, requires_grad=True) ll=-322.2408296054813, sigma_a=8.108612531533383, pos_x_R_std=0.637802179683593, pos_y_R_std=9.176529534910962, m0=tensor([3.2168, 3.5111, 0.3986, 3.5756, 3.8385, 0.6695], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 34.7207, -2.6318, 0.1162, 49.6525, -14.7435, 0.1001], dtype=torch.float64, requires_grad=True) ll=-292.8137022646379, sigma_a=8.318425519194994, pos_x_R_std=0.21664352049351907, pos_y_R_std=9.17668734527741, m0=tensor([3.2891, 3.6305, 0.4119, 3.6551, 3.9220, 0.6813], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 35.2294, -2.7258, 0.1173, 50.3562, -15.0961, 0.0996], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 23 likelihood: -292.8137022646379 sigma_a: tensor([8.3184], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.2166], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([9.1767], dtype=torch.float64, requires_grad=True) m0: tensor([3.2891, 3.6305, 0.4119, 3.6551, 3.9220, 0.6813], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 35.2294, -2.7258, 0.1173, 50.3562, -15.0961, 0.0996], dtype=torch.float64, requires_grad=True) ll=-292.8137022646379, sigma_a=8.318425519194994, pos_x_R_std=0.21664352049351907, pos_y_R_std=9.17668734527741, m0=tensor([3.2891, 3.6305, 0.4119, 3.6551, 3.9220, 0.6813], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 35.2294, -2.7258, 0.1173, 50.3562, -15.0961, 0.0996], dtype=torch.float64, requires_grad=True) ll=-353.54930497692925, sigma_a=11.079947521958482, pos_x_R_std=-2.8609590164243173, pos_y_R_std=8.897212542001895, m0=tensor([4.2782, 4.8020, 0.5627, 4.7387, 5.0565, 0.8280], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 41.9839, -3.5720, 0.1267, 60.2272, -19.7491, 0.0940], dtype=torch.float64, requires_grad=True) ll=-287.41476612194015, sigma_a=8.729654141556932, pos_x_R_std=-0.241653842398331, pos_y_R_std=9.135069701738502, m0=tensor([3.4364, 3.8050, 0.4344, 3.8165, 4.0909, 0.7032], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 36.2353, -2.8518, 0.1187, 51.8261, -15.7890, 0.0988], dtype=torch.float64, requires_grad=True) ll=-301.34764190185894, sigma_a=8.62261649632766, pos_x_R_std=-0.12236479520853462, pos_y_R_std=9.145902251619903, m0=tensor([3.3980, 3.7595, 0.4285, 3.7745, 4.0469, 0.6975], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 35.9735, -2.8190, 0.1184, 51.4435, -15.6087, 0.0990], dtype=torch.float64, requires_grad=True) ll=-287.1516954426813, sigma_a=8.690740591685477, pos_x_R_std=-0.19828629374761592, pos_y_R_std=9.139007876659502, m0=tensor([3.4224, 3.7884, 0.4323, 3.8012, 4.0749, 0.7011], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 36.1401, -2.8399, 0.1186, 51.6870, -15.7235, 0.0988], dtype=torch.float64, requires_grad=True) ll=-286.9580313447161, sigma_a=8.704812948503571, pos_x_R_std=-0.2139693558417938, pos_y_R_std=9.13758370942512, m0=tensor([3.4275, 3.7944, 0.4330, 3.8067, 4.0807, 0.7018], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 36.1745, -2.8442, 0.1186, 51.7373, -15.7472, 0.0988], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 24 likelihood: -286.9580313447161 sigma_a: tensor([8.7048], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([-0.2140], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([9.1376], dtype=torch.float64, requires_grad=True) m0: tensor([3.4275, 3.7944, 0.4330, 3.8067, 4.0807, 0.7018], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 36.1745, -2.8442, 0.1186, 51.7373, -15.7472, 0.0988], dtype=torch.float64, requires_grad=True) ll=-286.9580313447161, sigma_a=8.704812948503571, pos_x_R_std=-0.2139693558417938, pos_y_R_std=9.13758370942512, m0=tensor([3.4275, 3.7944, 0.4330, 3.8067, 4.0807, 0.7018], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 36.1745, -2.8442, 0.1186, 51.7373, -15.7472, 0.0988], dtype=torch.float64, requires_grad=True) ll=-277.2848435961711, sigma_a=9.856742774105811, pos_x_R_std=-0.11377958252227706, pos_y_R_std=9.049005823123409, m0=tensor([4.2243, 4.4768, 0.5142, 4.6778, 4.9926, 0.8205], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 41.6129, -3.2745, 0.1210, 60.0883, -19.4053, 0.0955], dtype=torch.float64, requires_grad=True) ll=-273.01236225331905, sigma_a=9.498497597670278, pos_x_R_std=-0.14493816720526453, pos_y_R_std=9.076553161338957, m0=tensor([3.9765, 4.2646, 0.4890, 4.4069, 4.7090, 0.7836], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 39.9216, -3.1407, 0.1202, 57.4912, -18.2676, 0.0965], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 25 likelihood: -273.01236225331905 sigma_a: tensor([9.4985], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([-0.1449], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([9.0766], dtype=torch.float64, requires_grad=True) m0: tensor([3.9765, 4.2646, 0.4890, 4.4069, 4.7090, 0.7836], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 39.9216, -3.1407, 0.1202, 57.4912, -18.2676, 0.0965], dtype=torch.float64, requires_grad=True) ll=-273.01236225331905, sigma_a=9.498497597670278, pos_x_R_std=-0.14493816720526453, pos_y_R_std=9.076553161338957, m0=tensor([3.9765, 4.2646, 0.4890, 4.4069, 4.7090, 0.7836], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 39.9216, -3.1407, 0.1202, 57.4912, -18.2676, 0.0965], dtype=torch.float64, requires_grad=True) ll=-232.29140859555304, sigma_a=45.76207160943872, pos_x_R_std=-1.0678707739922504, pos_y_R_std=6.183814837522444, m0=tensor([27.9218, 25.1685, 2.9904, 30.5857, 32.1141, 4.3469], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.0334e+02, -1.6452e+01, 1.9790e-01, 3.0782e+02, -1.2833e+02, -5.9530e-03], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 26 likelihood: -232.29140859555304 sigma_a: tensor([45.7621], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([-1.0679], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([6.1838], dtype=torch.float64, requires_grad=True) m0: tensor([27.9218, 25.1685, 2.9904, 30.5857, 32.1141, 4.3469], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.0334e+02, -1.6452e+01, 1.9790e-01, 3.0782e+02, -1.2833e+02, -5.9530e-03], dtype=torch.float64, requires_grad=True) ll=-232.29140859555304, sigma_a=45.76207160943872, pos_x_R_std=-1.0678707739922504, pos_y_R_std=6.183814837522444, m0=tensor([27.9218, 25.1685, 2.9904, 30.5857, 32.1141, 4.3469], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.0334e+02, -1.6452e+01, 1.9790e-01, 3.0782e+02, -1.2833e+02, -5.9530e-03], dtype=torch.float64, requires_grad=True) ll=-230.95045449243585, sigma_a=45.410933808764845, pos_x_R_std=-1.0446533408115102, pos_y_R_std=6.102408601152707, m0=tensor([27.6899, 24.9597, 2.9658, 30.3318, 31.8475, 4.3104], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.0170e+02, -1.6316e+01, 1.9709e-01, 3.0536e+02, -1.2726e+02, -4.9107e-03], dtype=torch.float64, requires_grad=True) ll=-224.65891104742084, sigma_a=43.82905801672904, pos_x_R_std=-0.9400588043322757, pos_y_R_std=5.735673506307042, m0=tensor([26.6454, 24.0191, 2.8548, 29.1880, 30.6462, 4.1460], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.9434e+02, -1.5703e+01, 1.9345e-01, 2.9429e+02, -1.2241e+02, -2.1530e-04], dtype=torch.float64, requires_grad=True) ll=-178.88960040929936, sigma_a=35.12258747057533, pos_x_R_std=-0.3643819681799938, pos_y_R_std=3.7172038403635037, m0=tensor([20.8965, 18.8416, 2.2438, 22.8928, 24.0344, 3.2410], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.5380e+02, -1.2332e+01, 1.7344e-01, 2.3332e+02, -9.5739e+01, 2.5628e-02], dtype=torch.float64, requires_grad=True) ll=-308.7803081074337, sigma_a=-12.798623507040684, pos_x_R_std=2.804196042155922, pos_y_R_std=-7.392637995181446, m0=tensor([-10.7459, -9.6556, -1.1190, -11.7566, -12.3576, -1.7399], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-6.9325e+01, 6.2208e+00, 6.3277e-02, -1.0222e+02, 5.1071e+01, 1.6787e-01], dtype=torch.float64, requires_grad=True) ll=-161.856606950695, sigma_a=27.07072870462095, pos_x_R_std=0.16801156085771402, pos_y_R_std=1.8504964774447397, m0=tensor([15.5798, 14.0534, 1.6788, 17.0709, 17.9197, 2.4041], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1631e+02, -9.2150e+00, 1.5493e-01, 1.7694e+02, -7.1072e+01, 4.9527e-02], dtype=torch.float64, requires_grad=True) ll=-1042.4650396768468, sigma_a=29.445438093760686, pos_x_R_std=0.010994411293587428, pos_y_R_std=2.401038610677753, m0=tensor([17.1479, 15.4656, 1.8454, 18.7879, 19.7231, 2.6509], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.2737e+02, -1.0134e+01, 1.6039e-01, 1.9357e+02, -7.8347e+01, 4.2479e-02], dtype=torch.float64, requires_grad=True) ll=-178.48279623970024, sigma_a=28.576752562380456, pos_x_R_std=0.06843239786521342, pos_y_R_std=2.1996463949641596, m0=tensor([16.5743, 14.9490, 1.7845, 18.1598, 19.0634, 2.5606], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.2332e+02, -9.7981e+00, 1.5839e-01, 1.8749e+02, -7.5686e+01, 4.5057e-02], dtype=torch.float64, requires_grad=True) ll=-160.72298901172053, sigma_a=27.74727820892625, pos_x_R_std=0.12327771881756282, pos_y_R_std=2.007344725504646, m0=tensor([16.0266, 14.4557, 1.7263, 17.5601, 18.4335, 2.4744], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1946e+02, -9.4770e+00, 1.5648e-01, 1.8168e+02, -7.3145e+01, 4.7519e-02], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 27 likelihood: -160.72298901172053 sigma_a: tensor([27.7473], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.1233], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([2.0073], dtype=torch.float64, requires_grad=True) m0: tensor([16.0266, 14.4557, 1.7263, 17.5601, 18.4335, 2.4744], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.1946e+02, -9.4770e+00, 1.5648e-01, 1.8168e+02, -7.3145e+01, 4.7519e-02], dtype=torch.float64, requires_grad=True) ll=-160.72298901172053, sigma_a=27.74727820892625, pos_x_R_std=0.12327771881756282, pos_y_R_std=2.007344725504646, m0=tensor([16.0266, 14.4557, 1.7263, 17.5601, 18.4335, 2.4744], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1946e+02, -9.4770e+00, 1.5648e-01, 1.8168e+02, -7.3145e+01, 4.7519e-02], dtype=torch.float64, requires_grad=True) ll=-412.31021516342486, sigma_a=-111.85475831568615, pos_x_R_std=10.309414230378485, pos_y_R_std=-36.85376674254815, m0=tensor([-76.2868, -69.0836, -8.1094, -83.5502, -87.8126, -12.1776], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-5.3459e+02, 4.5080e+01, -1.6835e-01, -7.9928e+02, 3.5576e+02, 4.6552e-01], dtype=torch.float64, requires_grad=True) ll=-85616.51273369906, sigma_a=2.0056478592220426, pos_x_R_std=2.001529406972545, pos_y_R_std=-5.158369982481728, m0=tensor([-0.9954, -0.9483, -0.0873, -1.0839, -1.1576, -0.2273], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-1.1410, 0.5829, 0.0966, 0.7989, 5.9416, 0.1246], dtype=torch.float64, requires_grad=True) ll=-340.62430955743326, sigma_a=11.783720261128126, pos_x_R_std=1.2880671768697507, pos_y_R_std=-2.4364414847827005, m0=tensor([5.4705, 4.9030, 0.6016, 5.9981, 6.2842, 0.7990], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 44.6700, -3.2384, 0.1193, 69.5080, -24.0996, 0.0953], dtype=torch.float64, requires_grad=True) ll=-164.44670268876433, sigma_a=26.677610789215112, pos_x_R_std=0.20132656846472255, pos_y_R_std=1.709580696655323, m0=tensor([15.3192, 13.8156, 1.6509, 16.7853, 17.6194, 2.3622], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1445e+02, -9.0590e+00, 1.5399e-01, 1.7417e+02, -6.9858e+01, 5.0722e-02], dtype=torch.float64, requires_grad=True) ll=-160.38094014825506, sigma_a=27.532475419466998, pos_x_R_std=0.13895091808432466, pos_y_R_std=1.9475499305816295, m0=tensor([15.8845, 14.3272, 1.7111, 17.4045, 18.2700, 2.4519], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1845e+02, -9.3930e+00, 1.5598e-01, 1.8017e+02, -7.2485e+01, 4.8163e-02], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 28 likelihood: -160.38094014825506 sigma_a: tensor([27.5325], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.1390], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.9475], dtype=torch.float64, requires_grad=True) m0: tensor([15.8845, 14.3272, 1.7111, 17.4045, 18.2700, 2.4519], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.1845e+02, -9.3930e+00, 1.5598e-01, 1.8017e+02, -7.2485e+01, 4.8163e-02], dtype=torch.float64, requires_grad=True) ll=-160.38094014825506, sigma_a=27.532475419466998, pos_x_R_std=0.13895091808432466, pos_y_R_std=1.9475499305816295, m0=tensor([15.8845, 14.3272, 1.7111, 17.4045, 18.2700, 2.4519], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1845e+02, -9.3930e+00, 1.5598e-01, 1.8017e+02, -7.2485e+01, 4.8163e-02], dtype=torch.float64, requires_grad=True) ll=-150.22891217808984, sigma_a=29.5698747454269, pos_x_R_std=0.13483231718720412, pos_y_R_std=1.4301073203138195, m0=tensor([17.2245, 15.4737, 1.8498, 18.8678, 19.7984, 2.6436], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.2740e+02, -1.0110e+01, 1.6014e-01, 1.9403e+02, -7.8608e+01, 4.2573e-02], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 29 likelihood: -150.22891217808984 sigma_a: tensor([29.5699], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.1348], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([1.4301], dtype=torch.float64, requires_grad=True) m0: tensor([17.2245, 15.4737, 1.8498, 18.8678, 19.7984, 2.6436], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.2740e+02, -1.0110e+01, 1.6014e-01, 1.9403e+02, -7.8608e+01, 4.2573e-02], dtype=torch.float64, requires_grad=True) ll=-150.22891217808984, sigma_a=29.5698747454269, pos_x_R_std=0.13483231718720412, pos_y_R_std=1.4301073203138195, m0=tensor([17.2245, 15.4737, 1.8498, 18.8678, 19.7984, 2.6436], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.2740e+02, -1.0110e+01, 1.6014e-01, 1.9403e+02, -7.8608e+01, 4.2573e-02], dtype=torch.float64, requires_grad=True) ll=-128.56331202099318, sigma_a=47.91344450724506, pos_x_R_std=0.08457435945717082, pos_y_R_std=-2.0479485583659245, m0=tensor([29.0934, 25.6369, 3.0784, 31.8308, 33.3415, 4.3469], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.0695e+02, -1.6500e+01, 1.9701e-01, 3.1674e+02, -1.3290e+02, -7.6213e-03], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 30 likelihood: -128.56331202099318 sigma_a: tensor([47.9134], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0846], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([-2.0479], dtype=torch.float64, requires_grad=True) m0: tensor([29.0934, 25.6369, 3.0784, 31.8308, 33.3415, 4.3469], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.0695e+02, -1.6500e+01, 1.9701e-01, 3.1674e+02, -1.3290e+02, -7.6213e-03], dtype=torch.float64, requires_grad=True) ll=-128.56331202099318, sigma_a=47.91344450724506, pos_x_R_std=0.08457435945717082, pos_y_R_std=-2.0479485583659245, m0=tensor([29.0934, 25.6369, 3.0784, 31.8308, 33.3415, 4.3469], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.0695e+02, -1.6500e+01, 1.9701e-01, 3.1674e+02, -1.3290e+02, -7.6213e-03], dtype=torch.float64, requires_grad=True) ll=-123.0320870742048, sigma_a=42.53172095314036, pos_x_R_std=0.12147851202649429, pos_y_R_std=-0.9173407591729674, m0=tensor([25.5922, 22.6375, 2.7158, 28.0072, 29.3479, 3.8459], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.8354e+02, -1.4619e+01, 1.8612e-01, 2.8058e+02, -1.1689e+02, 7.1597e-03], dtype=torch.float64, requires_grad=True) ll=-120.55422824591385, sigma_a=44.19777802025397, pos_x_R_std=0.11005384043646704, pos_y_R_std=-1.2673507689852532, m0=tensor([26.6761, 23.5661, 2.8281, 29.1909, 30.5842, 4.0010], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.9079e+02, -1.5201e+01, 1.8949e-01, 2.9178e+02, -1.2185e+02, 2.5839e-03], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 31 likelihood: -120.55422824591385 sigma_a: tensor([44.1978], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.1101], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([-1.2674], dtype=torch.float64, requires_grad=True) m0: tensor([26.6761, 23.5661, 2.8281, 29.1909, 30.5842, 4.0010], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.9079e+02, -1.5201e+01, 1.8949e-01, 2.9178e+02, -1.2185e+02, 2.5839e-03], dtype=torch.float64, requires_grad=True) ll=-120.55422824591385, sigma_a=44.19777802025397, pos_x_R_std=0.11005384043646704, pos_y_R_std=-1.2673507689852532, m0=tensor([26.6761, 23.5661, 2.8281, 29.1909, 30.5842, 4.0010], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.9079e+02, -1.5201e+01, 1.8949e-01, 2.9178e+02, -1.2185e+02, 2.5839e-03], dtype=torch.float64, requires_grad=True) ll=-124.97495164899436, sigma_a=43.361429607294816, pos_x_R_std=0.10331130687354928, pos_y_R_std=-0.7826698674308084, m0=tensor([26.1215, 23.1048, 2.7714, 28.5866, 29.9560, 3.9276], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.8724e+02, -1.4923e+01, 1.8787e-01, 2.8618e+02, -1.1934e+02, 4.8147e-03], dtype=torch.float64, requires_grad=True) ll=-120.13707669876071, sigma_a=43.91930369596836, pos_x_R_std=0.10780881617527044, pos_y_R_std=-1.1059692389157907, m0=tensor([26.4915, 23.4125, 2.8092, 28.9897, 30.3750, 3.9766], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.8961e+02, -1.5108e+01, 1.8895e-01, 2.8991e+02, -1.2101e+02, 3.3266e-03], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 32 likelihood: -120.13707669876071 sigma_a: tensor([43.9193], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.1078], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([-1.1060], dtype=torch.float64, requires_grad=True) m0: tensor([26.4915, 23.4125, 2.8092, 28.9897, 30.3750, 3.9766], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.8961e+02, -1.5108e+01, 1.8895e-01, 2.8991e+02, -1.2101e+02, 3.3266e-03], dtype=torch.float64, requires_grad=True) ll=-120.13707669876071, sigma_a=43.91930369596836, pos_x_R_std=0.10780881617527044, pos_y_R_std=-1.1059692389157907, m0=tensor([26.4915, 23.4125, 2.8092, 28.9897, 30.3750, 3.9766], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.8961e+02, -1.5108e+01, 1.8895e-01, 2.8991e+02, -1.2101e+02, 3.3266e-03], dtype=torch.float64, requires_grad=True) ll=-118.17218549398713, sigma_a=45.5920377934972, pos_x_R_std=0.1050403906530122, pos_y_R_std=-1.1127352995430793, m0=tensor([27.5537, 24.3319, 2.9197, 30.1511, 31.5913, 4.1344], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.9689e+02, -1.5698e+01, 1.9233e-01, 3.0102e+02, -1.2590e+02, -1.2716e-03], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 33 likelihood: -118.17218549398713 sigma_a: tensor([45.5920], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.1050], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([-1.1127], dtype=torch.float64, requires_grad=True) m0: tensor([27.5537, 24.3319, 2.9197, 30.1511, 31.5913, 4.1344], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.9689e+02, -1.5698e+01, 1.9233e-01, 3.0102e+02, -1.2590e+02, -1.2716e-03], dtype=torch.float64, requires_grad=True) ll=-118.17218549398713, sigma_a=45.5920377934972, pos_x_R_std=0.1050403906530122, pos_y_R_std=-1.1127352995430793, m0=tensor([27.5537, 24.3319, 2.9197, 30.1511, 31.5913, 4.1344], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.9689e+02, -1.5698e+01, 1.9233e-01, 3.0102e+02, -1.2590e+02, -1.2716e-03], dtype=torch.float64, requires_grad=True) ll=-104.33533277223155, sigma_a=63.09503315804713, pos_x_R_std=0.08659647616457454, pos_y_R_std=-0.8984552915078041, m0=tensor([38.6450, 33.9389, 4.0741, 42.2783, 44.2941, 5.7879], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.7305e+02, -2.1869e+01, 2.2766e-01, 4.1711e+02, -1.7694e+02, -4.9377e-02], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 34 likelihood: -104.33533277223155 sigma_a: tensor([63.0950], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0866], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([-0.8985], dtype=torch.float64, requires_grad=True) m0: tensor([38.6450, 33.9389, 4.0741, 42.2783, 44.2941, 5.7879], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.7305e+02, -2.1869e+01, 2.2766e-01, 4.1711e+02, -1.7694e+02, -4.9377e-02], dtype=torch.float64, requires_grad=True) ll=-104.33533277223155, sigma_a=63.09503315804713, pos_x_R_std=0.08659647616457454, pos_y_R_std=-0.8984552915078041, m0=tensor([38.6450, 33.9389, 4.0741, 42.2783, 44.2941, 5.7879], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.7305e+02, -2.1869e+01, 2.2766e-01, 4.1711e+02, -1.7694e+02, -4.9377e-02], dtype=torch.float64, requires_grad=True) ll=-95.62930362325272, sigma_a=79.82655176770538, pos_x_R_std=0.09277195281666355, pos_y_R_std=-0.7874243721436601, m0=tensor([49.2385, 43.1059, 5.1761, 53.8607, 56.4254, 7.3648], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 3.4574e+02, -2.7755e+01, 2.6133e-01, 5.2795e+02, -2.2567e+02, -9.5278e-02], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 35 likelihood: -95.62930362325272 sigma_a: tensor([79.8266], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0928], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([-0.7874], dtype=torch.float64, requires_grad=True) m0: tensor([49.2385, 43.1059, 5.1761, 53.8607, 56.4254, 7.3648], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 3.4574e+02, -2.7755e+01, 2.6133e-01, 5.2795e+02, -2.2567e+02, -9.5278e-02], dtype=torch.float64, requires_grad=True) ll=-95.62930362325272, sigma_a=79.82655176770538, pos_x_R_std=0.09277195281666355, pos_y_R_std=-0.7874243721436601, m0=tensor([49.2385, 43.1059, 5.1761, 53.8607, 56.4254, 7.3648], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 3.4574e+02, -2.7755e+01, 2.6133e-01, 5.2795e+02, -2.2567e+02, -9.5278e-02], dtype=torch.float64, requires_grad=True) ll=-87.79560465369661, sigma_a=132.14156166338873, pos_x_R_std=0.07975240598198552, pos_y_R_std=-0.08773900228658738, m0=tensor([82.3594, 71.7865, 8.6229, 90.0752, 94.3596, 12.3026], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 5.7320e+02, -4.6181e+01, 3.6676e-01, 8.7464e+02, -3.7808e+02, -2.3893e-01], dtype=torch.float64, requires_grad=True) ll=-78.59551191328201, sigma_a=114.89521203002332, pos_x_R_std=0.08404447545165718, pos_y_R_std=-0.318399735142558, m0=tensor([71.4407, 62.3315, 7.4866, 78.1366, 81.8541, 10.6748], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 4.9821e+02, -4.0107e+01, 3.3200e-01, 7.6034e+02, -3.2784e+02, -1.9157e-01], dtype=torch.float64, requires_grad=True) ll=-75.5173950925675, sigma_a=124.90968261949745, pos_x_R_std=0.08155219132279722, pos_y_R_std=-0.18446152940980615, m0=tensor([77.7809, 67.8217, 8.1464, 85.0690, 89.1157, 11.6200], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 5.4176e+02, -4.3634e+01, 3.5218e-01, 8.2671e+02, -3.5701e+02, -2.1907e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 36 likelihood: -75.5173950925675 sigma_a: tensor([124.9097], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0816], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([-0.1845], dtype=torch.float64, requires_grad=True) m0: tensor([77.7809, 67.8217, 8.1464, 85.0690, 89.1157, 11.6200], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 5.4176e+02, -4.3634e+01, 3.5218e-01, 8.2671e+02, -3.5701e+02, -2.1907e-01], dtype=torch.float64, requires_grad=True) ll=-75.5173950925675, sigma_a=124.90968261949745, pos_x_R_std=0.08155219132279722, pos_y_R_std=-0.18446152940980615, m0=tensor([77.7809, 67.8217, 8.1464, 85.0690, 89.1157, 11.6200], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 5.4176e+02, -4.3634e+01, 3.5218e-01, 8.2671e+02, -3.5701e+02, -2.1907e-01], dtype=torch.float64, requires_grad=True) ll=-55.761944226306, sigma_a=229.18311955796196, pos_x_R_std=0.04988448296175818, pos_y_R_std=0.27735963327432794, m0=tensor([143.8545, 125.0056, 15.0206, 157.3102, 164.7782, 21.4532], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 9.9503e+02, -8.0339e+01, 5.6227e-01, 1.5179e+03, -6.6097e+02, -5.0532e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 37 likelihood: -55.761944226306 sigma_a: tensor([229.1831], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0499], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.2774], dtype=torch.float64, requires_grad=True) m0: tensor([143.8545, 125.0056, 15.0206, 157.3102, 164.7782, 21.4532], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 9.9503e+02, -8.0339e+01, 5.6227e-01, 1.5179e+03, -6.6097e+02, -5.0532e-01], dtype=torch.float64, requires_grad=True) ll=-55.761944226306, sigma_a=229.18311955796196, pos_x_R_std=0.04988448296175818, pos_y_R_std=0.27735963327432794, m0=tensor([143.8545, 125.0056, 15.0206, 157.3102, 164.7782, 21.4532], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 9.9503e+02, -8.0339e+01, 5.6227e-01, 1.5179e+03, -6.6097e+02, -5.0532e-01], dtype=torch.float64, requires_grad=True) ll=-57.1130647333998, sigma_a=206.3852928619817, pos_x_R_std=0.06952602120669439, pos_y_R_std=-0.30048063622517024, m0=tensor([129.4286, 112.5009, 13.5186, 141.5358, 148.2522, 19.2972], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 8.9581e+02, -7.2296e+01, 5.1625e-01, 1.3668e+03, -5.9457e+02, -4.4265e-01], dtype=torch.float64, requires_grad=True) ll=-238.95221768454132, sigma_a=218.707131625971, pos_x_R_std=0.05891010283174217, pos_y_R_std=0.011832231446920286, m0=tensor([137.2255, 119.2595, 14.3304, 150.0616, 157.1842, 20.4625], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 9.4943e+02, -7.6643e+01, 5.4112e-01, 1.4485e+03, -6.3046e+02, -4.7652e-01], dtype=torch.float64, requires_grad=True) ll=-46.13312259302397, sigma_a=222.42277339687192, pos_x_R_std=0.05570888014850713, pos_y_R_std=0.10600995596833532, m0=tensor([139.5767, 121.2976, 14.5752, 152.6325, 159.8777, 20.8139], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 9.6561e+02, -7.7954e+01, 5.4862e-01, 1.4731e+03, -6.4128e+02, -4.8674e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 38 likelihood: -46.13312259302397 sigma_a: tensor([222.4228], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0557], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.1060], dtype=torch.float64, requires_grad=True) m0: tensor([139.5767, 121.2976, 14.5752, 152.6325, 159.8777, 20.8139], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 9.6561e+02, -7.7954e+01, 5.4862e-01, 1.4731e+03, -6.4128e+02, -4.8674e-01], dtype=torch.float64, requires_grad=True) ll=-46.13312259302397, sigma_a=222.42277339687192, pos_x_R_std=0.05570888014850713, pos_y_R_std=0.10600995596833532, m0=tensor([139.5767, 121.2976, 14.5752, 152.6325, 159.8777, 20.8139], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 9.6561e+02, -7.7954e+01, 5.4862e-01, 1.4731e+03, -6.4128e+02, -4.8674e-01], dtype=torch.float64, requires_grad=True) ll=-45.77948959116562, sigma_a=244.56228800562798, pos_x_R_std=0.07828247797814959, pos_y_R_std=0.1863680596281236, m0=tensor([153.5895, 133.4178, 16.0326, 167.9532, 175.9239, 22.8984], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.0617e+03, -8.5735e+01, 5.9311e-01, 1.6197e+03, -7.0574e+02, -5.4742e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 39 likelihood: -45.77948959116562 sigma_a: tensor([244.5623], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0783], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.1864], dtype=torch.float64, requires_grad=True) m0: tensor([153.5895, 133.4178, 16.0326, 167.9532, 175.9239, 22.8984], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.0617e+03, -8.5735e+01, 5.9311e-01, 1.6197e+03, -7.0574e+02, -5.4742e-01], dtype=torch.float64, requires_grad=True) ll=-45.77948959116562, sigma_a=244.56228800562798, pos_x_R_std=0.07828247797814959, pos_y_R_std=0.1863680596281236, m0=tensor([153.5895, 133.4178, 16.0326, 167.9532, 175.9239, 22.8984], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.0617e+03, -8.5735e+01, 5.9311e-01, 1.6197e+03, -7.0574e+02, -5.4742e-01], dtype=torch.float64, requires_grad=True) ll=-41.08393330605275, sigma_a=257.8123300506661, pos_x_R_std=0.07441222964122243, pos_y_R_std=0.13327077032141918, m0=tensor([161.9918, 140.6856, 16.9066, 177.1394, 185.5440, 24.1468], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1193e+03, -9.0396e+01, 6.1980e-01, 1.7076e+03, -7.4439e+02, -5.8379e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 40 likelihood: -41.08393330605275 sigma_a: tensor([257.8123], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0744], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.1333], dtype=torch.float64, requires_grad=True) m0: tensor([161.9918, 140.6856, 16.9066, 177.1394, 185.5440, 24.1468], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.1193e+03, -9.0396e+01, 6.1980e-01, 1.7076e+03, -7.4439e+02, -5.8379e-01], dtype=torch.float64, requires_grad=True) ll=-41.08393330605275, sigma_a=257.8123300506661, pos_x_R_std=0.07441222964122243, pos_y_R_std=0.13327077032141918, m0=tensor([161.9918, 140.6856, 16.9066, 177.1394, 185.5440, 24.1468], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1193e+03, -9.0396e+01, 6.1980e-01, 1.7076e+03, -7.4439e+02, -5.8379e-01], dtype=torch.float64, requires_grad=True) ll=-214.28630203764487, sigma_a=308.9968149989999, pos_x_R_std=0.057501200348976385, pos_y_R_std=0.009745853616064226, m0=tensor([194.4460, 168.7612, 20.2824, 212.6215, 222.7033, 28.9702], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.3418e+03, -1.0840e+02, 7.2289e-01, 2.0469e+03, -8.9365e+02, -7.2427e-01], dtype=torch.float64, requires_grad=True) ll=-42.33751995177726, sigma_a=290.0794154659185, pos_x_R_std=0.06375138920913558, pos_y_R_std=0.055399731035453426, m0=tensor([182.4511, 158.3847, 19.0347, 199.5076, 208.9695, 27.1875], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.2595e+03, -1.0175e+02, 6.8479e-01, 1.9215e+03, -8.3849e+02, -6.7235e-01], dtype=torch.float64, requires_grad=True) ll=-38.94198460474341, sigma_a=275.6126780408463, pos_x_R_std=0.06853110752818437, pos_y_R_std=0.09031270392624456, m0=tensor([173.2783, 150.4494, 18.0806, 189.4789, 198.4668, 25.8242], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1967e+03, -9.6658e+01, 6.5565e-01, 1.8256e+03, -7.9630e+02, -6.3264e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 41 likelihood: -38.94198460474341 sigma_a: tensor([275.6127], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0685], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0903], dtype=torch.float64, requires_grad=True) m0: tensor([173.2783, 150.4494, 18.0806, 189.4789, 198.4668, 25.8242], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.1967e+03, -9.6658e+01, 6.5565e-01, 1.8256e+03, -7.9630e+02, -6.3264e-01], dtype=torch.float64, requires_grad=True) ll=-38.94198460474341, sigma_a=275.6126780408463, pos_x_R_std=0.06853110752818437, pos_y_R_std=0.09031270392624456, m0=tensor([173.2783, 150.4494, 18.0806, 189.4789, 198.4668, 25.8242], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.1967e+03, -9.6658e+01, 6.5565e-01, 1.8256e+03, -7.9630e+02, -6.3264e-01], dtype=torch.float64, requires_grad=True) ll=-36.26931118120439, sigma_a=302.12441355943184, pos_x_R_std=0.05723856362196606, pos_y_R_std=0.08488019941425716, m0=tensor([190.0865, 164.9926, 19.8291, 207.8556, 217.7126, 28.3234], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.3119e+03, -1.0599e+02, 7.0907e-01, 2.0014e+03, -8.7361e+02, -7.0542e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 42 likelihood: -36.26931118120439 sigma_a: tensor([302.1244], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0572], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0849], dtype=torch.float64, requires_grad=True) m0: tensor([190.0865, 164.9926, 19.8291, 207.8556, 217.7126, 28.3234], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.3119e+03, -1.0599e+02, 7.0907e-01, 2.0014e+03, -8.7361e+02, -7.0542e-01], dtype=torch.float64, requires_grad=True) ll=-36.26931118120439, sigma_a=302.12441355943184, pos_x_R_std=0.05723856362196606, pos_y_R_std=0.08488019941425716, m0=tensor([190.0865, 164.9926, 19.8291, 207.8556, 217.7126, 28.3234], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.3119e+03, -1.0599e+02, 7.0907e-01, 2.0014e+03, -8.7361e+02, -7.0542e-01], dtype=torch.float64, requires_grad=True) ll=-39.26596785168318, sigma_a=451.63838707322543, pos_x_R_std=0.01715199089509003, pos_y_R_std=0.037591109652872164, m0=tensor([284.8641, 246.9922, 29.6881, 311.4773, 326.2350, 42.4152], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.9617e+03, -1.5860e+02, 1.0102e+00, 2.9926e+03, -1.3096e+03, -1.1158e+00], dtype=torch.float64, requires_grad=True) ll=-31.257233392815955, sigma_a=390.24615743675486, pos_x_R_std=0.03361201814438529, pos_y_R_std=0.05700857672769899, m0=tensor([245.9473, 213.3222, 25.6399, 268.9290, 281.6744, 36.6289], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.6949e+03, -1.3700e+02, 8.8656e-01, 2.5856e+03, -1.1306e+03, -9.4728e-01], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 43 likelihood: -31.257233392815955 sigma_a: tensor([390.2462], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0336], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0570], dtype=torch.float64, requires_grad=True) m0: tensor([245.9473, 213.3222, 25.6399, 268.9290, 281.6744, 36.6289], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 1.6949e+03, -1.3700e+02, 8.8656e-01, 2.5856e+03, -1.1306e+03, -9.4728e-01], dtype=torch.float64, requires_grad=True) ll=-31.257233392815955, sigma_a=390.24615743675486, pos_x_R_std=0.03361201814438529, pos_y_R_std=0.05700857672769899, m0=tensor([245.9473, 213.3222, 25.6399, 268.9290, 281.6744, 36.6289], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 1.6949e+03, -1.3700e+02, 8.8656e-01, 2.5856e+03, -1.1306e+03, -9.4728e-01], dtype=torch.float64, requires_grad=True) ll=-26.754694892115552, sigma_a=487.2457348712854, pos_x_R_std=0.03134052692130207, pos_y_R_std=0.06291893481448813, m0=tensor([307.4198, 266.5029, 32.0342, 336.1379, 352.0624, 45.7690], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.1164e+03, -1.7112e+02, 1.0818e+00, 3.2285e+03, -1.4133e+03, -1.2134e+00], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 44 likelihood: -26.754694892115552 sigma_a: tensor([487.2457], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0313], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0629], dtype=torch.float64, requires_grad=True) m0: tensor([307.4198, 266.5029, 32.0342, 336.1379, 352.0624, 45.7690], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.1164e+03, -1.7112e+02, 1.0818e+00, 3.2285e+03, -1.4133e+03, -1.2134e+00], dtype=torch.float64, requires_grad=True) ll=-26.754694892115552, sigma_a=487.2457348712854, pos_x_R_std=0.03134052692130207, pos_y_R_std=0.06291893481448813, m0=tensor([307.4198, 266.5029, 32.0342, 336.1379, 352.0624, 45.7690], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.1164e+03, -1.7112e+02, 1.0818e+00, 3.2285e+03, -1.4133e+03, -1.2134e+00], dtype=torch.float64, requires_grad=True) ll=-29.907157979945016, sigma_a=574.2794743734719, pos_x_R_std=0.031996967015306896, pos_y_R_std=0.033106720589132194, m0=tensor([362.5771, 314.2183, 37.7715, 396.4421, 415.2187, 53.9694], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.4945e+03, -2.0173e+02, 1.2571e+00, 3.8054e+03, -1.6670e+03, -1.4522e+00], dtype=torch.float64, requires_grad=True) ll=-25.98703100358798, sigma_a=525.8315172324711, pos_x_R_std=0.03163155491510429, pos_y_R_std=0.049701904081530496, m0=tensor([331.8734, 287.6572, 34.5778, 362.8734, 380.0623, 49.4046], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.2840e+03, -1.8469e+02, 1.1595e+00, 3.4843e+03, -1.5258e+03, -1.3193e+00], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 45 likelihood: -25.98703100358798 sigma_a: tensor([525.8315], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0316], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0497], dtype=torch.float64, requires_grad=True) m0: tensor([331.8734, 287.6572, 34.5778, 362.8734, 380.0623, 49.4046], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.2840e+03, -1.8469e+02, 1.1595e+00, 3.4843e+03, -1.5258e+03, -1.3193e+00], dtype=torch.float64, requires_grad=True) ll=-25.98703100358798, sigma_a=525.8315172324711, pos_x_R_std=0.03163155491510429, pos_y_R_std=0.049701904081530496, m0=tensor([331.8734, 287.6572, 34.5778, 362.8734, 380.0623, 49.4046], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.2840e+03, -1.8469e+02, 1.1595e+00, 3.4843e+03, -1.5258e+03, -1.3193e+00], dtype=torch.float64, requires_grad=True) ll=-25.74660611312993, sigma_a=541.3702650874316, pos_x_R_std=0.03273641171585284, pos_y_R_std=0.05361745732584272, m0=tensor([341.7199, 296.1752, 35.6020, 373.6387, 391.3368, 50.8687], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.3515e+03, -1.9015e+02, 1.1908e+00, 3.5872e+03, -1.5711e+03, -1.3619e+00], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 46 likelihood: -25.74660611312993 sigma_a: tensor([541.3703], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0327], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0536], dtype=torch.float64, requires_grad=True) m0: tensor([341.7199, 296.1752, 35.6020, 373.6387, 391.3368, 50.8687], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.3515e+03, -1.9015e+02, 1.1908e+00, 3.5872e+03, -1.5711e+03, -1.3619e+00], dtype=torch.float64, requires_grad=True) ll=-25.74660611312993, sigma_a=541.3702650874316, pos_x_R_std=0.03273641171585284, pos_y_R_std=0.05361745732584272, m0=tensor([341.7199, 296.1752, 35.6020, 373.6387, 391.3368, 50.8687], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.3515e+03, -1.9015e+02, 1.1908e+00, 3.5872e+03, -1.5711e+03, -1.3619e+00], dtype=torch.float64, requires_grad=True) ll=-25.693122787594945, sigma_a=565.5208481607478, pos_x_R_std=0.030261452563859335, pos_y_R_std=0.05255148050280519, m0=tensor([357.0263, 309.4173, 37.1941, 390.3734, 408.8631, 53.1445], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.4565e+03, -1.9865e+02, 1.2394e+00, 3.7473e+03, -1.6415e+03, -1.4282e+00], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 47 likelihood: -25.693122787594945 sigma_a: tensor([565.5208], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0303], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0526], dtype=torch.float64, requires_grad=True) m0: tensor([357.0263, 309.4173, 37.1941, 390.3734, 408.8631, 53.1445], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.4565e+03, -1.9865e+02, 1.2394e+00, 3.7473e+03, -1.6415e+03, -1.4282e+00], dtype=torch.float64, requires_grad=True) ll=-25.693122787594945, sigma_a=565.5208481607478, pos_x_R_std=0.030261452563859335, pos_y_R_std=0.05255148050280519, m0=tensor([357.0263, 309.4173, 37.1941, 390.3734, 408.8631, 53.1445], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.4565e+03, -1.9865e+02, 1.2394e+00, 3.7473e+03, -1.6415e+03, -1.4282e+00], dtype=torch.float64, requires_grad=True) ll=-25.684974135536745, sigma_a=564.296417589708, pos_x_R_std=0.03134897970936324, pos_y_R_std=0.052301325936117395, m0=tensor([356.2497, 308.7452, 37.1133, 389.5243, 407.9739, 53.0290], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.4512e+03, -1.9822e+02, 1.2370e+00, 3.7392e+03, -1.6379e+03, -1.4248e+00], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 48 likelihood: -25.684974135536745 sigma_a: tensor([564.2964], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0313], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0523], dtype=torch.float64, requires_grad=True) m0: tensor([356.2497, 308.7452, 37.1133, 389.5243, 407.9739, 53.0290], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.4512e+03, -1.9822e+02, 1.2370e+00, 3.7392e+03, -1.6379e+03, -1.4248e+00], dtype=torch.float64, requires_grad=True) ll=-25.684974135536745, sigma_a=564.296417589708, pos_x_R_std=0.03134897970936324, pos_y_R_std=0.052301325936117395, m0=tensor([356.2497, 308.7452, 37.1133, 389.5243, 407.9739, 53.0290], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.4512e+03, -1.9822e+02, 1.2370e+00, 3.7392e+03, -1.6379e+03, -1.4248e+00], dtype=torch.float64, requires_grad=True) ll=-25.684440364580713, sigma_a=563.581055913467, pos_x_R_std=0.03119064976734833, pos_y_R_std=0.05243528783872346, m0=tensor([355.7964, 308.3531, 37.0662, 389.0287, 407.4548, 52.9616], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 2.4481e+03, -1.9797e+02, 1.2355e+00, 3.7345e+03, -1.6358e+03, -1.4229e+00], dtype=torch.float64, requires_grad=True) -------------------------------------------------------------------------------- epoch: 49 likelihood: -25.684440364580713 sigma_a: tensor([563.5811], dtype=torch.float64, requires_grad=True) pos_x_R_std: tensor([0.0312], dtype=torch.float64, requires_grad=True) pos_y_R_std: tensor([0.0524], dtype=torch.float64, requires_grad=True) m0: tensor([355.7964, 308.3531, 37.0662, 389.0287, 407.4548, 52.9616], dtype=torch.float64, requires_grad=True) sqrt_diag_V0: tensor([ 2.4481e+03, -1.9797e+02, 1.2355e+00, 3.7345e+03, -1.6358e+03, -1.4229e+00], dtype=torch.float64, requires_grad=True) gradient ascent: success: converged .. GENERATED FROM PYTHON SOURCE LINES 146-148 Perform EM optimization ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 148-178 .. code-block:: default sqrt_diag_R = np.array([sigma_x0, sigma_y0]) R_0 = np.diag(sqrt_diag_R) m0_0 = m0 sqrt_diag_V0 = np.array([sqrt_diag_V0_value for i in range(len(m0))]) V0_0 = np.diag(sqrt_diag_V0) times = np.arange(0, y.shape[1]*dt, dt) not_nan_indices_y0 = set(np.where(np.logical_not(np.isnan(y[0, :])))[0]) not_nan_indices_y1 = set(np.where(np.logical_not(np.isnan(y[1, :])))[0]) not_nan_indices = np.array(sorted(not_nan_indices_y0.union(not_nan_indices_y1))) y_no_nan = y[:, not_nan_indices] t_no_nan = times[not_nan_indices] y_interpolated = np.empty_like(y) tck, u = scipy.interpolate.splprep([y_no_nan[0, :], y_no_nan[1, :]], s=0, u=t_no_nan) y_interpolated[0, :], y_interpolated[1, :] = scipy.interpolate.splev(times, tck) if Qe_reg_param_em is not None: Qe_regularized_em = Qe + Qe_reg_param_em * np.eye(Qe.shape[0]) else: Qe_regularized_em = Qe optim_res_em = ssm.learning.em_SS_tracking( y=y_interpolated, B=B, sigma_a0=sigma_a0, Qe=Qe_regularized_em, Z=Z, R_0=R_0, m0_0=m0_0, V0_0=V0_0, vars_to_estimate=vars_to_estimate, max_iter=em_max_iter) print("EM: " + optim_res_em["termination_info"]) .. rst-class:: sphx-glr-script-out .. code-block:: none LogLike[0000]=-1381440.889848 sigma_a=1.000000 R: [[1. 0.] [0. 1.]] m0: [0. 0. 0. 0. 0. 0.] V0: [[0.1 0. 0. 0. 0. 0. ] [0. 0.1 0. 0. 0. 0. ] [0. 0. 0.1 0. 0. 0. ] [0. 0. 0. 0.1 0. 0. ] [0. 0. 0. 0. 0.1 0. ] [0. 0. 0. 0. 0. 0.1]] LogLike[0001]=-468.983233 sigma_a=15.498759 R: [[ 2632.08266461 5451.9175971 ] [ 5451.9175971 11390.85041816]] m0: [186.25895255 44.30023579 5.43377754 390.73095854 97.33426239 12.20335086] V0: [[ 0.03201735 -0.0160773 -0.00197267 0. 0. 0. ] [-0.0160773 0.08961045 -0.00251776 0. 0. 0. ] [-0.00197267 -0.00251776 0.09901842 0. 0. 0. ] [ 0. 0. 0. 0.03201735 -0.0160773 -0.00197267] [ 0. 0. 0. -0.0160773 0.08961045 -0.00251776] [ 0. 0. 0. -0.00197267 -0.00251776 0.09901842]] LogLike[0002]=-465.238407 sigma_a=15.680700 R: [[ 3931.02599865 8087.00016226] [ 8087.00016226 16717.67074932]] m0: [186.30939419 44.25647504 5.42942818 390.72029221 97.36680557 12.21036756] V0: [[ 3.14196609e-02 -1.60555858e-02 -1.95092312e-03 2.85630337e-04 -1.11251674e-05 -1.07340039e-05] [-1.60555858e-02 8.95247919e-02 -2.53259505e-03 -1.11251674e-05 3.91920300e-05 6.19251534e-06] [-1.95092312e-03 -2.53259505e-03 9.90134505e-02 -1.07340039e-05 6.19251534e-06 1.90506789e-06] [ 2.85630337e-04 -1.11251674e-05 -1.07340039e-05 3.18785397e-02 -1.60734589e-02 -1.96816781e-03] [-1.11251674e-05 3.91920300e-05 6.19251534e-06 -1.60734589e-02 8.95877558e-02 -2.52264648e-03] [-1.07340039e-05 6.19251534e-06 1.90506789e-06 -1.96816781e-03 -2.52264648e-03 9.90165111e-02]] LogLike[0003]=-464.700912 sigma_a=15.844083 R: [[ 4127.30830729 8446.86793093] [ 8446.86793093 17361.82965518]] m0: [186.35057479 44.20508546 5.42437781 390.70975568 97.40016282 12.21648708] V0: [[ 3.07646560e-02 -1.60195772e-02 -1.92526815e-03 6.02221600e-04 -2.90919212e-05 -2.33900055e-05] [-1.60195772e-02 8.94324639e-02 -2.54830518e-03 -2.90932889e-05 8.25021060e-05 1.31102542e-05] [-1.92526815e-03 -2.54830518e-03 9.90083395e-02 -2.33901902e-05 1.31102093e-05 4.02132530e-06] [ 6.02221600e-04 -2.90932889e-05 -2.33901902e-05 3.17239049e-02 -1.60658546e-02 -1.96251683e-03] [-2.90919212e-05 8.25021060e-05 1.31102093e-05 -1.60658546e-02 8.95639054e-02 -2.52741913e-03] [-2.33900055e-05 1.31102542e-05 4.02132530e-06 -1.96251683e-03 -2.52741913e-03 9.90147459e-02]] LogLike[0004]=-464.405936 sigma_a=16.034073 R: [[ 4156.95679327 8480.16247418] [ 8480.16247418 17370.86599603]] m0: [186.38421558 44.15133524 5.41940448 390.70242506 97.43451268 12.2224564 ] V0: [[ 3.01107018e-02 -1.59776466e-02 -1.89879389e-03 9.20212038e-04 -5.00269693e-05 -3.65116131e-05] [-1.59776466e-02 8.93379127e-02 -2.56427284e-03 -5.00319421e-05 1.27190134e-04 2.02172933e-05] [-1.89879389e-03 -2.56427284e-03 9.90032238e-02 -3.65122971e-05 2.02171287e-05 6.16473993e-06] [ 9.20212038e-04 -5.00319421e-05 -3.65122971e-05 3.15677248e-02 -1.60567139e-02 -1.95658577e-03] [-5.00269693e-05 1.27190134e-04 2.02171287e-05 -1.60567139e-02 8.95393679e-02 -2.53225255e-03] [-3.65116131e-05 2.02172933e-05 6.16473993e-06 -1.95658577e-03 -2.53225255e-03 9.90129879e-02]] LogLike[0005]=-464.178903 sigma_a=16.246166 R: [[ 4161.13530659 8471.54219664] [ 8471.54219664 17316.38112822]] m0: [186.41297246 44.09666302 5.41457071 390.69740069 97.46935796 12.22834372] V0: [[ 2.94759506e-02 -1.59327029e-02 -1.87249568e-03 1.22998362e-03 -7.25046314e-05 -4.95931245e-05] [-1.59327029e-02 8.92428064e-02 -2.58023866e-03 -7.25155292e-05 1.72309964e-04 2.73521808e-05] [-1.87249568e-03 -2.58023866e-03 9.89981692e-02 -4.95946386e-05 2.73518181e-05 8.28802166e-06] [ 1.22998362e-03 -7.25155292e-05 -4.95946386e-05 3.14149979e-02 -1.60467762e-02 -1.95064783e-03] [-7.25046314e-05 1.72309964e-04 2.73518181e-05 -1.60467762e-02 8.95145629e-02 -2.53710239e-03] [-4.95931245e-05 2.73521808e-05 8.28802166e-06 -1.95064783e-03 -2.53710239e-03 9.90112408e-02]] LogLike[0006]=-463.971029 sigma_a=16.475060 R: [[ 4157.79978866 8453.45580889] [ 8453.45580889 17255.20549193]] m0: [186.43878492 44.04164087 5.40985743 390.69379617 97.50440883 12.23416345] V0: [[ 2.88668099e-02 -1.58860618e-02 -1.84676664e-03 1.52793210e-03 -9.58615394e-05 -6.24249084e-05] [-1.58860618e-02 8.91479237e-02 -2.59607835e-03 -9.58805279e-05 2.17421042e-04 3.44432327e-05] [-1.84676664e-03 -2.59607835e-03 9.89932066e-02 -6.24275626e-05 3.44425978e-05 1.03721834e-05] [ 1.52793210e-03 -9.58805279e-05 -6.24275626e-05 3.12677082e-02 -1.60363792e-02 -1.94481541e-03] [-9.58615394e-05 2.17421042e-04 3.44425978e-05 -1.60363792e-02 8.94897252e-02 -2.54193284e-03] [-6.24249084e-05 3.44432327e-05 1.03721834e-05 -1.94481541e-03 -2.54193284e-03 9.90095134e-02]] LogLike[0007]=-463.766151 sigma_a=16.717430 R: [[ 4150.73051557 8431.06598563] [ 8431.06598563 17192.32501313]] m0: [186.46284517 43.9865448 5.40524396 390.69104453 97.53951127 12.23992343] V0: [[ 2.82851640e-02 -1.58384037e-02 -1.82176597e-03 1.81285856e-03 -1.19749936e-04 -7.49181474e-05] [-1.58384037e-02 8.90536694e-02 -2.61172731e-03 -1.19779010e-04 2.62292036e-04 4.14534568e-05] [-1.82176597e-03 -2.61172731e-03 9.89883509e-02 -7.49222266e-05 4.14524809e-05 1.24082941e-05] [ 1.81285856e-03 -1.19779010e-04 -7.49222266e-05 3.11265764e-02 -1.60257005e-02 -1.93913728e-03] [-1.19749936e-04 2.62292036e-04 4.14524809e-05 -1.60257005e-02 8.94649839e-02 -2.54672324e-03] [-7.49181474e-05 4.14534568e-05 1.24082941e-05 -1.93913728e-03 -2.54672324e-03 9.90078109e-02]] LogLike[0008]=-463.558715 sigma_a=16.971457 R: [[ 4141.44504759 8406.00113622] [ 8406.00113622 17127.9100135 ]] m0: [186.48584935 43.9315167 5.40071597 390.68881144 97.57458315 12.2456286 ] V0: [[ 2.77308696e-02 -1.57900966e-02 -1.79754422e-03 2.08467459e-03 -1.43980546e-04 -8.70416935e-05] [-1.57900966e-02 8.89602602e-02 -2.62715143e-03 -1.44021588e-04 3.06798165e-04 4.83632262e-05] [-1.79754422e-03 -2.62715143e-03 9.89836087e-02 -8.70474651e-05 4.83618436e-05 1.43922627e-05] [ 2.08467459e-03 -1.44021588e-04 -8.70474651e-05 3.09917230e-02 -1.60148371e-02 -1.93363169e-03] [-1.43980546e-04 3.06798165e-04 4.83618436e-05 -1.60148371e-02 8.94404098e-02 -2.55146218e-03] [-8.70416935e-05 4.83632262e-05 1.43922627e-05 -1.93363169e-03 -2.55146218e-03 9.90061360e-02]] LogLike[0009]=-463.346620 sigma_a=17.236287 R: [[ 4130.74384063 8379.05545626] [ 8379.05545626 17061.79226008]] m0: [186.50819714 43.87663062 5.39626424 390.68690427 97.60958034 12.25128189] V0: [[ 2.72028668e-02 -1.57413425e-02 -1.77409938e-03 2.34380951e-03 -1.68448984e-04 -9.87928615e-05] [-1.57413425e-02 8.88678118e-02 -2.64233301e-03 -1.68503840e-04 3.50872438e-04 5.51622826e-05] [-1.77409938e-03 -2.64233301e-03 9.89789822e-02 -9.88005857e-05 5.51604287e-05 1.63225249e-05] [ 2.34380951e-03 -1.68503840e-04 -9.88005857e-05 3.08629816e-02 -1.60038425e-02 -1.92830161e-03] [-1.68448984e-04 3.50872438e-04 5.51604287e-05 -1.60038425e-02 8.94160415e-02 -2.55614345e-03] [-9.87928615e-05 5.51622826e-05 1.63225249e-05 -1.92830161e-03 -2.55614345e-03 9.90044898e-02]] LogLike[0010]=-463.128863 sigma_a=17.511664 R: [[ 4119.07412571 8350.66124578] [ 8350.66124578 16993.83455194]] m0: [186.5301179 43.82192448 5.39188281 390.68521231 97.64447925 12.25688492] V0: [[ 2.66997143e-02 -1.56922529e-02 -1.75140443e-03 2.59091874e-03 -1.93097705e-04 -1.10182800e-04] [-1.56922529e-02 8.87763837e-02 -2.65726338e-03 -1.93168256e-04 3.94480005e-04 6.18455541e-05] [-1.75140443e-03 -2.65726338e-03 9.89744715e-02 -1.10192739e-04 6.18431623e-05 1.81988553e-05] [ 2.59091874e-03 -1.93168256e-04 -1.10192739e-04 3.07400558e-02 -1.59927460e-02 -1.92314246e-03] [-1.93097705e-04 3.94480005e-04 6.18431623e-05 -1.59927460e-02 8.93918997e-02 -2.56076376e-03] [-1.10182800e-04 6.18455541e-05 1.81988553e-05 -1.92314246e-03 -2.56076376e-03 9.90028728e-02]] LogLike[0011]=-462.904770 sigma_a=17.797707 R: [[ 4106.68709182 8321.05456487] [ 8321.05456487 16923.93342248]] m0: [186.55174481 43.76741632 5.38756768 390.68367119 97.6792676 12.2624385 ] V0: [[ 2.62198517e-02 -1.56428882e-02 -1.72942129e-03 2.82673833e-03 -2.17895661e-04 -1.21229000e-04] [-1.56428882e-02 8.86860038e-02 -2.67193890e-03 -2.17983890e-04 4.37604214e-04 6.84108893e-05] [-1.72942129e-03 -2.67193890e-03 9.89700752e-02 -1.21241429e-04 6.84078894e-05 2.00217349e-05] [ 2.82673833e-03 -2.17983890e-04 -1.21241429e-04 3.06225989e-02 -1.59815634e-02 -1.91814607e-03] [-2.17895661e-04 4.37604214e-04 6.84078894e-05 -1.59815634e-02 8.93679948e-02 -2.56532143e-03] [-1.21229000e-04 6.84108893e-05 2.00217349e-05 -1.91814607e-03 -2.56532143e-03 9.90012850e-02]] LogLike[0012]=-462.673736 sigma_a=18.094781 R: [[ 4093.72266268 8290.35994279] [ 8290.35994279 16852.00294612]] m0: [186.57315717 43.71311293 5.38331593 390.68224258 97.71393953 12.26794297] V0: [[ 2.57617266e-02 -1.55932792e-02 -1.70810783e-03 3.05201312e-03 -2.42827230e-04 -1.31951455e-04] [-1.55932792e-02 8.85966818e-02 -2.68635886e-03 -2.42935281e-04 4.80238946e-04 7.48578143e-05] [-1.70810783e-03 -2.68635886e-03 9.89657918e-02 -1.31966667e-04 7.48541299e-05 2.17920119e-05] [ 3.05201312e-03 -2.42935281e-04 -1.31966667e-04 3.05102535e-02 -1.59703020e-02 -1.91330278e-03] [-2.42827230e-04 4.80238946e-04 7.48541299e-05 -1.59703020e-02 8.93443312e-02 -2.56981571e-03] [-1.31951455e-04 7.48578143e-05 2.17920119e-05 -1.91330278e-03 -2.56981571e-03 9.89997263e-02]] LogLike[0013]=-462.435138 sigma_a=18.403423 R: [[ 4080.2573965 8258.63797457] [ 8258.63797457 16777.96511047]] m0: [186.59440427 43.65901446 5.37912524 390.68090261 97.74849289 12.27339834] V0: [[ 2.53238533e-02 -1.55434385e-02 -1.68742145e-03 3.26746260e-03 -2.67886133e-04 -1.42370703e-04] [-1.55434385e-02 8.85084166e-02 -2.70052424e-03 -2.68016368e-04 5.22384353e-04 8.11868444e-05] [-1.68742145e-03 -2.70052424e-03 9.89616192e-02 -1.42389019e-04 8.11823913e-05 2.35107182e-05] [ 3.26746260e-03 -2.68016368e-04 -1.42389019e-04 3.04026712e-02 -1.59589648e-02 -1.90860251e-03] [-2.67886133e-04 5.22384353e-04 8.11823913e-05 -1.59589648e-02 8.93209100e-02 -2.57424638e-03] [-1.42370703e-04 8.11868444e-05 2.35107182e-05 -1.90860251e-03 -2.57424638e-03 9.89981964e-02]] LogLike[0014]=-462.188301 sigma_a=18.724302 R: [[ 4066.33193832 8225.91284974] [ 8225.91284974 16701.74557266]] m0: [186.6155187 43.60511697 5.37499361 390.67963548 97.78292777 12.27880447] V0: [[ 2.49048369e-02 -1.54933676e-02 -1.66732074e-03 3.47376605e-03 -2.93072072e-04 -1.52506876e-04] [-1.54933676e-02 8.84212000e-02 -2.71443706e-03 -2.93227123e-04 5.64044507e-04 8.73991043e-05] [-1.66732074e-03 -2.71443706e-03 9.89575552e-02 -1.52528649e-04 8.73937883e-05 2.51789725e-05] [ 3.47376605e-03 -2.93227123e-04 -1.52528649e-04 3.02995209e-02 -1.59475510e-02 -1.90403527e-03] [-2.93072072e-04 5.64044507e-04 8.73937883e-05 -1.59475510e-02 8.92977300e-02 -2.57861355e-03] [-1.52506876e-04 8.73991043e-05 2.51789725e-05 -1.90403527e-03 -2.57861355e-03 9.89966949e-02]] LogLike[0015]=-461.932480 sigma_a=19.058200 R: [[ 4051.96666841 8192.18803867] [ 8192.18803867 16623.27130977]] m0: [186.6365239 43.55141381 5.37091916 390.67842976 97.81724565 12.28416107] V0: [[ 2.45033805e-02 -1.54430598e-02 -1.64776625e-03 3.67155754e-03 -3.18388872e-04 -1.62379262e-04] [-1.54430598e-02 8.83350195e-02 -2.72810001e-03 -3.18571700e-04 6.05226096e-04 9.34961180e-05] [-1.64776625e-03 -2.72810001e-03 9.89535977e-02 -1.62404885e-04 9.34898331e-05 2.67979295e-05] [ 3.67155754e-03 -3.18571700e-04 -1.62404885e-04 3.02004927e-02 -1.59360581e-02 -1.89959141e-03] [-3.18388872e-04 6.05226096e-04 9.34898331e-05 -1.59360581e-02 8.92747887e-02 -2.58291750e-03] [-1.62379262e-04 9.34961180e-05 2.67979295e-05 -1.89959141e-03 -2.58291750e-03 9.89952216e-02]] LogLike[0016]=-461.666843 sigma_a=19.406002 R: [[ 4037.17088986 8157.45574234] [ 8157.45574234 16542.47028379]] m0: [186.65743839 43.49789636 5.36690007 390.67727636 97.851449 12.28946777] V0: [[ 2.41182828e-02 -1.53925024e-02 -1.62872073e-03 3.86142583e-03 -3.43843486e-04 -1.72006147e-04] [-1.53925024e-02 8.82498590e-02 -2.74151629e-03 -3.44057438e-04 6.45937740e-04 9.94796973e-05] [-1.62872073e-03 -2.74151629e-03 9.89497445e-02 -1.72036061e-04 9.94723232e-05 2.83687545e-05] [ 3.86142583e-03 -3.44057438e-04 -1.72036061e-04 3.01052976e-02 -1.59244817e-02 -1.89526170e-03] [-3.43843486e-04 6.45937740e-04 9.94723232e-05 -1.59244817e-02 8.92520826e-02 -2.58715865e-03] [-1.72006147e-04 9.94796973e-05 2.83687545e-05 -1.89526170e-03 -2.58715865e-03 9.89937761e-02]] LogLike[0017]=-461.390466 sigma_a=19.768697 R: [[ 4021.94817817 8121.70243556] [ 8121.70243556 16459.27144599]] m0: [186.67827819 43.44455443 5.36293448 390.67616735 97.88554098 12.29472413] V0: [[ 2.37484332e-02 -1.53416779e-02 -1.61014908e-03 4.04391665e-03 -3.69445482e-04 -1.81404805e-04] [-1.53416779e-02 8.81656995e-02 -2.75468944e-03 -3.69694355e-04 6.86189647e-04 1.05351884e-04] [-1.61014908e-03 -2.75468944e-03 9.89459935e-02 -1.81439504e-04 1.05343284e-04 2.98926129e-05] [ 4.04391665e-03 -3.69694355e-04 -1.81439504e-04 3.00136674e-02 -1.59128160e-02 -1.89103739e-03] [-3.69445482e-04 6.86189647e-04 1.05343284e-04 -1.59128160e-02 8.92296075e-02 -2.59133750e-03] [-1.81404805e-04 1.05351884e-04 2.98926129e-05 -1.89103739e-03 -2.59133750e-03 9.89923582e-02]] LogLike[0018]=-461.102313 sigma_a=20.147386 R: [[ 4006.29968796 8084.91247355] [ 8084.91247355 16373.60551277]] m0: [186.69905827 43.39137635 5.35902048 390.67509524 97.91952541 12.29992964] V0: [[ 2.33928038e-02 -1.52905639e-02 -1.59201827e-03 4.21953604e-03 -3.95206821e-04 -1.90591547e-04] [-1.52905639e-02 8.80825196e-02 -2.76762340e-03 -3.95494935e-04 7.25993483e-04 1.11114927e-04] [-1.59201827e-03 -2.76762340e-03 9.89423426e-02 -1.90631588e-04 1.11104946e-04 3.13706667e-05] [ 4.21953604e-03 -3.95494935e-04 -1.90631588e-04 2.99253528e-02 -1.59010540e-02 -1.88691014e-03] [-3.95206821e-04 7.25993483e-04 1.11104946e-04 -1.59010540e-02 8.92073582e-02 -2.59545463e-03] [-1.90591547e-04 1.11114927e-04 3.13706667e-05 -1.88691014e-03 -2.59545463e-03 9.89909675e-02]] LogLike[0019]=-460.801224 sigma_a=20.543285 R: [[ 3990.22622698 8047.07044281] [ 8047.07044281 16285.40584495]] m0: [186.71979339 43.33834912 5.35515604 390.67405257 97.95340665 12.30508373] V0: [[ 2.30504421e-02 -1.52391337e-02 -1.57429710e-03 4.38875395e-03 -4.21141816e-04 -1.99581819e-04] [-1.52391337e-02 8.80002950e-02 -2.78032238e-03 -4.21474092e-04 7.65362366e-04 1.16771278e-04] [-1.57429710e-03 -2.78032238e-03 9.89387898e-02 -1.99627829e-04 1.16759738e-04 3.28040763e-05] [ 4.38875395e-03 -4.21474092e-04 -1.99627829e-04 2.98401216e-02 -1.58891872e-02 -1.88287200e-03] [-4.21141816e-04 7.65362366e-04 1.16759738e-04 -1.58891872e-02 8.91853290e-02 -2.59951069e-03] [-1.99581819e-04 1.16771278e-04 3.28040763e-05 -1.88287200e-03 -2.59951069e-03 9.89896039e-02]] LogLike[0020]=-460.485894 sigma_a=20.957739 R: [[ 3973.72971625 8008.16297455] [ 8008.16297455 16194.60974831]] m0: [186.74049871 43.2854583 5.35133904 390.67303158 97.98718964 12.31018579] V0: [[ 2.27204634e-02 -1.51873559e-02 -1.55695601e-03 4.55200797e-03 -4.47267209e-04 -2.08390305e-04] [-1.51873559e-02 8.79189989e-02 -2.79279099e-03 -4.47649261e-04 8.04310954e-04 1.22323598e-04] [-1.55695601e-03 -2.79279099e-03 9.89353330e-02 -2.08442994e-04 1.22310296e-04 3.41940046e-05] [ 4.55200797e-03 -4.47649261e-04 -2.08442994e-04 2.97577572e-02 -1.58772059e-02 -1.87891534e-03] [-4.47267209e-04 8.04310954e-04 1.22310296e-04 -1.58772059e-02 8.91635135e-02 -2.60350638e-03] [-2.08390305e-04 1.22323598e-04 3.41940046e-05 -1.87891534e-03 -2.60350638e-03 9.89882670e-02]] LogLike[0021]=-460.154855 sigma_a=21.392239 R: [[ 3956.81430447 7968.18025328] [ 7968.18025328 16101.16000932]] m0: [186.76119015 43.23268792 5.34756722 390.67202405 98.02087993 12.31523513] V0: [[ 2.24020433e-02 -1.51351942e-02 -1.53996682e-03 4.70970685e-03 -4.73602332e-04 -2.17031046e-04] [-1.51351942e-02 8.78386012e-02 -2.80503418e-03 -4.74040574e-04 8.42855585e-04 1.27774779e-04] [-1.53996682e-03 -2.80503418e-03 9.89319702e-02 -2.17091212e-04 1.27759483e-04 3.55416223e-05] [ 4.70970685e-03 -4.74040574e-04 -2.17091212e-04 2.96780566e-02 -1.58650989e-02 -1.87503281e-03] [-4.73602332e-04 8.42855585e-04 1.27759483e-04 -1.58650989e-02 8.91419045e-02 -2.60744249e-03] [-2.17031046e-04 1.27774779e-04 3.55416223e-05 -1.87503281e-03 -2.60744249e-03 9.89869567e-02]] LogLike[0022]=-459.806452 sigma_a=21.848433 R: [[ 3939.48735512 7927.11749146] [ 7927.11749146 16005.00685242]] m0: [186.78188476 43.18002042 5.34383815 390.67102106 98.0544837 12.32023103] V0: [[ 2.20944116e-02 -1.50826069e-02 -1.52330254e-03 4.86223378e-03 -5.00169340e-04 -2.25517546e-04] [-1.50826069e-02 8.77590683e-02 -2.81705734e-03 -5.00671109e-04 8.81014482e-04 1.33127969e-04] [-1.52330254e-03 -2.81705734e-03 9.89286995e-02 -2.25586094e-04 1.33110411e-04 3.68481151e-05] [ 4.86223378e-03 -5.00671109e-04 -2.25586094e-04 2.96008290e-02 -1.58528535e-02 -1.87121726e-03] [-5.00169340e-04 8.81014482e-04 1.33110411e-04 -1.58528535e-02 8.91204938e-02 -2.61131987e-03] [-2.25517546e-04 1.33127969e-04 3.68481151e-05 -1.87121726e-03 -2.61131987e-03 9.89856727e-02]] LogLike[0023]=-459.438812 sigma_a=22.328151 R: [[ 3921.76033194 7884.97631494] [ 7884.97631494 15906.10998228]] m0: [186.802601 43.12743645 5.34014923 390.67001289 98.08800789 12.32517271] V0: [[ 2.17968452e-02 -1.50295465e-02 -1.50693710e-03 5.00994957e-03 -5.26993508e-04 -2.33862896e-04] [-1.50295465e-02 8.76803631e-02 -2.82886633e-03 -5.27567211e-04 9.18807994e-04 1.38386600e-04] [-1.50693710e-03 -2.82886633e-03 9.89255187e-02 -2.33940846e-04 1.38366475e-04 3.81146908e-05] [ 5.00994957e-03 -5.27567211e-04 -2.33940846e-04 2.95258939e-02 -1.58404550e-02 -1.86746170e-03] [-5.26993508e-04 9.18807994e-04 1.38366475e-04 -1.58404550e-02 8.90992721e-02 -2.61513950e-03] [-2.33862896e-04 1.38386600e-04 3.81146908e-05 -1.86746170e-03 -2.61513950e-03 9.89844149e-02]] LogLike[0024]=-459.049815 sigma_a=22.833419 R: [[ 3903.64977528 7841.76640495] [ 7841.76640495 15804.44132384]] m0: [186.82335899 43.07491469 5.33649767 390.66898878 98.1214602 12.33005933] V0: [[ 2.15086633e-02 -1.49759589e-02 -1.49084519e-03 5.15319547e-03 -5.54103594e-04 -2.42079877e-04] [-1.49759589e-02 8.76024437e-02 -2.84046756e-03 -5.54758878e-04 9.56258904e-04 1.43554432e-04] [-1.49084519e-03 -2.84046756e-03 9.89224260e-02 -2.42168388e-04 1.43531387e-04 3.93425883e-05] [ 5.15319547e-03 -5.54758878e-04 -2.42168388e-04 2.94530801e-02 -1.58278869e-02 -1.86375921e-03] [-5.54103594e-04 9.56258904e-04 1.43531387e-04 -1.58278869e-02 8.90782291e-02 -2.61890245e-03] [-2.42079877e-04 1.43554432e-04 3.93425883e-05 -1.86375921e-03 -2.61890245e-03 9.89831831e-02]] LogLike[0025]=-458.637059 sigma_a=23.366488 R: [[ 3885.17828771 7797.50719764] [ 7797.50719764 15699.98798792]] m0: [186.8441809 43.0224317 5.33288045 390.66793682 98.15484926 12.33489001] V0: [[ 2.12292216e-02 -1.49217826e-02 -1.47500200e-03 5.29229599e-03 -5.81532258e-04 -2.50181078e-04] [-1.49217826e-02 8.75252631e-02 -2.85186804e-03 -5.82280216e-04 9.93392770e-04 1.48635595e-04] [-1.47500200e-03 -2.85186804e-03 9.89194191e-02 -2.50281462e-04 1.48609224e-04 4.05330860e-05] [ 5.29229599e-03 -5.82280216e-04 -2.50281462e-04 2.93822238e-02 -1.58151304e-02 -1.86010291e-03] [-5.81532258e-04 9.93392770e-04 1.48609224e-04 -1.58151304e-02 8.90573531e-02 -2.62260992e-03] [-2.50181078e-04 1.48635595e-04 4.05330860e-05 -1.86010291e-03 -2.62260992e-03 9.89819771e-02]] LogLike[0026]=-458.197818 sigma_a=23.929857 R: [[ 3866.37559948 7752.22977717] [ 7752.22977717 15592.75572913]] m0: [186.8650912 42.96996167 5.32929432 390.66684368 98.18818468 12.33966382] V0: [[ 2.09579074e-02 -1.48669480e-02 -1.45938304e-03 5.42756139e-03 -6.09316563e-04 -2.58179009e-04] [-1.48669480e-02 8.74487688e-02 -2.86307550e-03 -6.10169976e-04 1.03023834e-03 1.53634642e-04] [-1.45938304e-03 -2.86307550e-03 9.89164961e-02 -2.58292755e-04 1.53604476e-04 4.16875122e-05] [ 5.42756139e-03 -6.10169976e-04 -2.58292755e-04 2.93131675e-02 -1.58021642e-02 -1.85648587e-03] [-6.09316563e-04 1.03023834e-03 1.53604476e-04 -1.58021642e-02 8.90366308e-02 -2.62626329e-03] [-2.58179009e-04 1.53634642e-04 4.16875122e-05 -1.85648587e-03 -2.62626329e-03 9.89807968e-02]] LogLike[0027]=-457.728996 sigma_a=24.526303 R: [[ 3847.27969596 7705.97893051] [ 7705.97893051 15482.77284587]] m0: [186.88611703 42.91747625 5.32573577 390.66569445 98.22147721 12.34437978] V0: [[ 2.06941351e-02 -1.48113764e-02 -1.44396391e-03 5.55929015e-03 -6.37498538e-04 -2.66086209e-04] [-1.48113764e-02 8.73729013e-02 -2.87409850e-03 -6.38472170e-04 1.06682805e-03 1.58556609e-04] [-1.44396391e-03 -2.87409850e-03 9.89136546e-02 -2.66215013e-04 1.58522103e-04 4.28072552e-05] [ 5.55929015e-03 -6.38472170e-04 -2.66215013e-04 2.92457587e-02 -1.57889642e-02 -1.85290107e-03] [-6.37498538e-04 1.06682805e-03 1.58522103e-04 -1.57889642e-02 8.90160469e-02 -2.62986410e-03] [-2.66086209e-04 1.58556609e-04 4.28072552e-05 -1.85290107e-03 -2.62986410e-03 9.89796421e-02]] LogLike[0028]=-457.227082 sigma_a=25.158906 R: [[ 3827.93801813 7658.81540741] [ 7658.81540741 15370.09465856]] m0: [186.90728863 42.86494424 5.322201 390.66447236 98.25473881 12.34903691] V0: [[ 2.04373416e-02 -1.47549785e-02 -1.42872013e-03 5.68777128e-03 -6.66125830e-04 -2.73915366e-04] [-1.47549785e-02 8.72975935e-02 -2.88494650e-03 -6.67236777e-04 1.10319853e-03 1.63407083e-04] [-1.42872013e-03 -2.88494650e-03 9.89108924e-02 -2.74061158e-04 1.63367605e-04 4.38937749e-05] [ 5.68777128e-03 -6.67236777e-04 -2.74061158e-04 2.91798487e-02 -1.57755028e-02 -1.84934129e-03] [-6.66125830e-04 1.10319853e-03 1.63367605e-04 -1.57755028e-02 8.89955843e-02 -2.63341413e-03] [-2.73915366e-04 1.63407083e-04 4.38937749e-05 -1.84934129e-03 -2.63341413e-03 9.89785128e-02]] LogLike[0029]=-456.688097 sigma_a=25.831086 R: [[ 3808.40864962 7610.81823873] [ 7610.81823873 15254.80833024]] m0: [186.9286397 42.81233145 5.31868592 390.66315854 98.28798285 12.35363417] V0: [[ 2.01869829e-02 -1.46976533e-02 -1.41362690e-03 5.81328655e-03 -6.95252427e-04 -2.81679426e-04] [-1.46976533e-02 8.72227694e-02 -2.89563006e-03 -6.96520540e-04 1.13939130e-03 1.68192279e-04] [-1.41362690e-03 -2.89563006e-03 9.89082071e-02 -2.81844410e-04 1.68147094e-04 4.49486149e-05] [ 5.81328655e-03 -6.96520540e-04 -2.81844410e-04 2.91152912e-02 -1.57617488e-02 -1.84579909e-03] [-6.95252427e-04 1.13939130e-03 1.68147094e-04 -1.57617488e-02 8.89752231e-02 -2.63691543e-03] [-2.81679426e-04 1.68192279e-04 4.49486149e-05 -1.84579909e-03 -2.63691543e-03 9.89774087e-02]] LogLike[0030]=-456.107540 sigma_a=26.546624 R: [[ 3788.76146359 7562.0871058 ] [ 7562.0871058 15137.03811279]] m0: [186.95020787 42.75960043 5.31518609 390.66173169 98.3212242 12.35817053] V0: [[ 1.99425294e-02 -1.46392871e-02 -1.39865891e-03 5.93611258e-03 -7.24939462e-04 -2.89391714e-04] [-1.46392871e-02 8.71483427e-02 -2.90616094e-03 -7.26387859e-04 1.17545345e-03 1.72919129e-04] [-1.39865891e-03 -2.90616094e-03 9.89055962e-02 -2.89578412e-04 1.72867375e-04 4.59734151e-05] [ 5.93611258e-03 -7.26387859e-04 -2.89578412e-04 2.90519412e-02 -1.57476669e-02 -1.84226672e-03] [-7.24939462e-04 1.17545345e-03 1.72867375e-04 -1.57476669e-02 8.89549408e-02 -2.64037034e-03] [-2.89391714e-04 1.72919129e-04 4.59734151e-05 -1.84226672e-03 -2.64037034e-03 9.89763296e-02]] LogLike[0031]=-455.480344 sigma_a=27.309699 R: [[ 3769.07910891 7512.74457758] [ 7512.74457758 15016.95076587]] m0: [186.97203518 42.70671028 5.31169677 390.66016784 98.3544794 12.36264496] V0: [[ 1.97034630e-02 -1.45797511e-02 -1.38379020e-03 6.05652289e-03 -7.55256083e-04 -2.97066041e-04] [-1.45797511e-02 8.70742155e-02 -2.91655226e-03 -7.56911769e-04 1.21143849e-03 1.77595367e-04] [-1.38379020e-03 -2.91655226e-03 9.89030569e-02 -2.97277343e-04 1.77536035e-04 4.69699245e-05] [ 6.05652289e-03 -7.56911769e-04 -2.97277343e-04 2.89896541e-02 -1.57332168e-02 -1.83873603e-03] [-7.55256083e-04 1.21143849e-03 1.77536035e-04 -1.57332168e-02 8.89347113e-02 -2.64378159e-03] [-2.97066041e-04 1.77595367e-04 4.69699245e-05 -1.83873603e-03 -2.64378159e-03 9.89752754e-02]] LogLike[0032]=-454.800838 sigma_a=28.124907 R: [[ 3749.45768449 7462.93798244] [ 7462.93798244 14894.76081134]] m0: [186.99416844 42.6536165 5.30821285 390.65843995 98.38776679 12.36705647] V0: [[ 1.94692741e-02 -1.45189009e-02 -1.36899392e-03 6.17478971e-03 -7.86280379e-04 -3.04716812e-04] [-1.45189009e-02 8.70002765e-02 -2.92681872e-03 -7.88174990e-04 1.24740728e-03 1.82229642e-04] [-1.36899392e-03 -2.92681872e-03 9.89005865e-02 -3.04956040e-04 1.82161544e-04 4.79400138e-05] [ 6.17478971e-03 -7.88174990e-04 -3.04956040e-04 2.89282840e-02 -1.57183530e-02 -1.83519844e-03] [-7.86280379e-04 1.24740728e-03 1.82161544e-04 -1.57183530e-02 8.89145049e-02 -2.64715231e-03] [-3.04716812e-04 1.82229642e-04 4.79400138e-05 -1.83519844e-03 -2.64715231e-03 9.89742459e-02]] LogLike[0033]=-454.062715 sigma_a=28.997277 R: [[ 3730.00697287 7412.8407434 ] [ 7412.8407434 14770.7354323 ]] m0: [187.01665973 42.60027089 5.30472891 390.65651768 98.42110662 12.37140411] V0: [[ 1.92394584e-02 -1.44565740e-02 -1.35424226e-03 6.29118571e-03 -8.18100325e-04 -3.12359123e-04] [-1.44565740e-02 8.69263991e-02 -2.93697673e-03 -8.20271028e-04 1.28342908e-03 1.86831613e-04] [-1.35424226e-03 -2.93697673e-03 9.88981819e-02 -3.12630097e-04 1.86753349e-04 4.88856879e-05] [ 6.29118571e-03 -8.20271028e-04 -3.12630097e-04 2.88676834e-02 -1.57030238e-02 -1.83164483e-03] [-8.18100325e-04 1.28342908e-03 1.86753349e-04 -1.57030238e-02 8.88942873e-02 -2.65048614e-03] [-3.12359123e-04 1.86831613e-04 4.88856879e-05 -1.83164483e-03 -2.65048614e-03 9.89732407e-02]] LogLike[0034]=-453.259041 sigma_a=29.932281 R: [[ 3710.84994247 7362.6526707 ] [ 7362.6526707 14645.1981106 ]] m0: [187.03956669 42.54662152 5.30123917 390.65436707 98.45452118 12.37568701] V0: [[ 1.90135150e-02 -1.43925889e-02 -1.33950630e-03 6.40598535e-03 -8.50814703e-04 -3.20008834e-04] [-1.43925889e-02 8.68524396e-02 -2.94704464e-03 -8.53305280e-04 1.31958261e-03 1.91412066e-04] [-1.33950630e-03 -2.94704464e-03 9.88958397e-02 -3.20315960e-04 1.91321977e-04 4.98090954e-05] [ 6.40598535e-03 -8.53305280e-04 -3.20315960e-04 2.88077014e-02 -1.56871711e-02 -1.82806553e-03] [-8.50814703e-04 1.31958261e-03 1.91321977e-04 -1.56871711e-02 8.88740191e-02 -2.65378730e-03] [-3.20008834e-04 1.91412066e-04 4.98090954e-05 -1.82806553e-03 -2.65378730e-03 9.89722595e-02]] LogLike[0035]=-452.382272 sigma_a=30.935824 R: [[ 3692.12129342 7312.5988207 ] [ 7312.5988207 14518.53027377]] m0: [187.06295275 42.49261277 5.2977376 390.65195026 98.48803491 12.37990445] V0: [[ 1.87909449e-02 -1.43267435e-02 -1.32475601e-03 6.51946606e-03 -8.84533957e-04 -3.27682617e-04] [-1.43267435e-02 8.67782346e-02 -2.95704285e-03 -8.87396101e-04 1.35595737e-03 1.95983013e-04] [-1.32475601e-03 -2.95704285e-03 9.88935564e-02 -3.28030981e-04 1.95879130e-04 5.07125366e-05] [ 6.51946606e-03 -8.87396101e-04 -3.28030981e-04 2.87481838e-02 -1.56707293e-02 -1.82445024e-03] [-8.84533957e-04 1.35595737e-03 1.95879130e-04 -1.56707293e-02 8.88536550e-02 -2.65706064e-03] [-3.27682617e-04 1.95983013e-04 5.07125366e-05 -1.82445024e-03 -2.65706064e-03 9.89713019e-02]] LogLike[0036]=-451.424323 sigma_a=32.014232 R: [[ 3673.96480471 7262.9264473 ] [ 7262.9264473 14391.1699342 ]] m0: [187.08688731 42.43818545 5.29421788 390.64922535 98.52167446 12.38405583] V0: [[ 1.85712504e-02 -1.42588143e-02 -1.30996029e-03 6.63190879e-03 -9.19380918e-04 -3.35397971e-04] [-1.42588143e-02 8.67035992e-02 -2.96699405e-03 -9.22675769e-04 1.39265486e-03 2.00557790e-04] [-1.30996029e-03 -2.96699405e-03 9.88913282e-02 -3.35793447e-04 2.00437775e-04 5.15984665e-05] [ 6.63190879e-03 -9.22675769e-04 -3.35793447e-04 2.86889719e-02 -1.56536247e-02 -1.82078802e-03] [-9.19380918e-04 1.39265486e-03 2.00437775e-04 -1.56536247e-02 8.88331434e-02 -2.66031176e-03] [-3.35397971e-04 2.00557790e-04 5.15984665e-05 -1.82078802e-03 -2.66031176e-03 9.89703675e-02]] LogLike[0037]=-450.376641 sigma_a=33.174216 R: [[ 3656.52917384 7213.89933787] [ 7213.89933787 14263.60561578]] m0: [187.11144577 42.38327691 5.2906735 390.64614616 98.5554688 12.38814081] V0: [[ 1.83539356e-02 -1.41885559e-02 -1.29508706e-03 6.74359830e-03 -9.55491360e-04 -3.43173186e-04] [-1.41885559e-02 8.66283243e-02 -2.97692329e-03 -9.59291321e-04 1.42979000e-03 2.05151144e-04] [-1.29508706e-03 -2.97692329e-03 9.88891510e-02 -3.43622560e-04 2.05012209e-04 5.24694926e-05] [ 6.74359830e-03 -9.59291321e-04 -3.43622560e-04 2.86299025e-02 -1.56357756e-02 -1.81706731e-03] [-9.55491360e-04 1.42979000e-03 2.05012209e-04 -1.56357756e-02 8.88124248e-02 -2.66354704e-03] [-3.43173186e-04 2.05151144e-04 5.24694926e-05 -1.81706731e-03 -2.66354704e-03 9.89694555e-02]] LogLike[0038]=-449.230292 sigma_a=34.422847 R: [[ 3639.96231427 7165.7892628 ] [ 7165.7892628 14136.36452147]] m0: [187.13670955 42.32782106 5.2870978 390.64266212 98.58944942 12.39215933] V0: [[ 1.81385067e-02 -1.41157002e-02 -1.28010351e-03 6.85482280e-03 -9.93014387e-04 -3.51027271e-04] [-1.41157002e-02 8.65521742e-02 -2.98685809e-03 -9.97405238e-04 1.46749260e-03 2.09779303e-04] [-1.28010351e-03 -2.98685809e-03 9.88870206e-02 -3.51538376e-04 2.09618126e-04 5.33283665e-05] [ 6.85482280e-03 -9.97405238e-04 -3.51538376e-04 2.85708074e-02 -1.56170909e-02 -1.81327592e-03] [-9.93014387e-04 1.46749260e-03 2.09618126e-04 -1.56170909e-02 8.87914315e-02 -2.66677380e-03] [-3.51027271e-04 2.09779303e-04 5.33283665e-05 -1.81327592e-03 -2.66677380e-03 9.89685653e-02]] LogLike[0039]=-447.976004 sigma_a=35.767534 R: [[ 3624.4041785 7118.86434065] [ 7118.86434065 14009.99381921]] m0: [187.16276642 42.27174809 5.28348394 390.63871794 98.62365062 12.39611173] V0: [[ 1.79244744e-02 -1.40399568e-02 -1.26497634e-03 6.96587342e-03 -1.03211275e-03 -3.58979840e-04] [-1.40399568e-02 8.64748835e-02 -2.99682857e-03 -1.03719610e-03 1.50590902e-03 2.14460035e-04] [-1.26497634e-03 -2.99682857e-03 9.88849324e-02 -3.59561702e-04 2.14272653e-04 5.41779700e-05] [ 6.96587342e-03 -1.03719610e-03 -3.59561702e-04 2.85115142e-02 -1.55974702e-02 -1.80940109e-03] [-1.03211275e-03 1.50590902e-03 2.14272653e-04 -1.55974702e-02 8.87700864e-02 -2.67000030e-03] [-3.58979840e-04 2.14460035e-04 5.41779700e-05 -1.80940109e-03 -2.67000030e-03 9.89676961e-02]] LogLike[0040]=-446.604100 sigma_a=37.216038 R: [[ 3609.97832197 7073.37432077] [ 7073.37432077 13885.0342143 ]] m0: [187.18971124 42.21498373 5.2798249 390.63425307 98.65811019 12.39999889] V0: [[ 1.77113545e-02 -1.39610125e-02 -1.24967202e-03 7.07704361e-03 -1.07296330e-03 -3.67051000e-04] [-1.39610125e-02 8.63961538e-02 -3.00686748e-03 -1.07885938e-03 1.54520420e-03 2.19212718e-04] [-1.24967202e-03 -3.00686748e-03 9.88828815e-02 -3.67713996e-04 2.18994410e-04 5.50212964e-05] [ 7.07704361e-03 -1.07885938e-03 -3.67713996e-04 2.84518456e-02 -1.55768032e-02 -1.80542956e-03] [-1.07296330e-03 1.54520420e-03 2.18994410e-04 -1.55768032e-02 8.87483016e-02 -2.67323588e-03] [-3.67051000e-04 2.19212718e-04 5.50212964e-05 -1.80542956e-03 -2.67323588e-03 9.89668469e-02]] LogLike[0041]=-445.104271 sigma_a=38.776542 R: [[ 3596.78288426 7029.53376776] [ 7029.53376776 13761.98718143]] m0: [187.21764772 42.15744749 5.2761133 390.62920064 98.69287037 12.4038224 ] V0: [[ 1.74986686e-02 -1.38785295e-02 -1.23415698e-03 7.18862905e-03 -1.11575796e-03 -3.75261280e-04] [-1.38785295e-02 8.63156489e-02 -3.01701041e-03 -1.12260885e-03 1.58556418e-03 2.24058446e-04] [-1.23415698e-03 -3.01701041e-03 9.88808631e-02 -3.76017298e-04 2.23803588e-04 5.58614319e-05] [ 7.18862905e-03 -1.12260885e-03 -3.76017298e-04 2.83916199e-02 -1.55549687e-02 -1.80134757e-03] [-1.11575796e-03 1.58556418e-03 2.23803588e-04 -1.55549687e-02 8.87259770e-02 -2.67649105e-03] [-3.75261280e-04 2.24058446e-04 5.58614319e-05 -1.80134757e-03 -2.67649105e-03 9.89660164e-02]] LogLike[0042]=-443.465120 sigma_a=40.457797 R: [[ 3584.88156167 6987.50416067] [ 6987.50416067 13641.2778543 ]] m0: [187.2466916 42.09904986 5.27234113 390.6234856 98.72797964 12.40758481] V0: [[ 1.72859416e-02 -1.37921426e-02 -1.21839758e-03 7.30092880e-03 -1.16070574e-03 -3.83631649e-04] [-1.37921426e-02 8.62329891e-02 -3.02729606e-03 -1.16867902e-03 1.62719973e-03 2.29020202e-04] [-1.21839758e-03 -3.02729606e-03 9.88788719e-02 -3.84494259e-04 2.28722107e-04 5.67015410e-05] [ 7.30092880e-03 -1.16867902e-03 -3.84494259e-04 2.83306500e-02 -1.55318333e-02 -1.79714088e-03] [-1.16070574e-03 1.62719973e-03 2.28722107e-04 -1.55318333e-02 8.87029981e-02 -2.67977764e-03] [-3.83631649e-04 2.29020202e-04 5.67015410e-05 -1.79714088e-03 -2.67977764e-03 9.89652034e-02]] LogLike[0043]=-441.673497 sigma_a=42.269344 R: [[ 3574.29531063 6947.37672325] [ 6947.37672325 13523.21815402]] m0: [187.27697556 42.03968788 5.2684993 390.61702189 98.76349503 12.41129 ] V0: [[ 1.70726968e-02 -1.37014520e-02 -1.20235981e-03 7.41424810e-03 -1.20803625e-03 -3.92183690e-04] [-1.37014520e-02 8.61477412e-02 -3.03776674e-03 -1.21732924e-03 1.67035126e-03 2.34123160e-04] [-1.20235981e-03 -3.03776674e-03 9.88769027e-02 -3.93168317e-04 2.33773881e-04 5.75448619e-05] [ 7.41424810e-03 -1.21732924e-03 -3.93168317e-04 2.82687418e-02 -1.55072490e-02 -1.79279465e-03] [-1.20803625e-03 1.67035126e-03 2.33773881e-04 -1.55072490e-02 8.86792332e-02 -2.68310899e-03] [-3.92183690e-04 2.34123160e-04 5.75448619e-05 -1.79279465e-03 -2.68310899e-03 9.89644063e-02]] LogLike[0044]=-439.713711 sigma_a=44.221777 R: [[ 3564.99514796 6909.15763568] [ 6909.15763568 13407.975461 ]] m0: [187.30865579 41.97923942 5.26457707 390.60970862 98.7994854 12.41494348] V0: [[ 1.68584472e-02 -1.36060143e-02 -1.18600868e-03 7.52890295e-03 -1.25800515e-03 -4.00939930e-04] [-1.36060143e-02 8.60594074e-02 -3.04846909e-03 -1.26884981e-03 1.71529563e-03 2.39395127e-04] [-1.18600868e-03 -3.04846909e-03 9.88749498e-02 -4.02064032e-04 2.38985229e-04 5.83947139e-05] [ 7.52890295e-03 -1.26884981e-03 -4.02064032e-04 2.82056913e-02 -1.54810497e-02 -1.78829326e-03] [-1.25800515e-03 1.71529563e-03 2.38985229e-04 -1.54810497e-02 8.86545298e-02 -2.68650022e-03] [-4.00939930e-04 2.39395127e-04 5.83947139e-05 -1.78829326e-03 -2.68650022e-03 9.89636235e-02]] LogLike[0045]=-437.566806 sigma_a=46.326995 R: [[ 3556.89595897 6872.75684295] [ 6872.75684295 13295.55196527]] m0: [187.34191975 41.91755627 5.26056137 390.60142546 98.83603514 12.41855283] V0: [[ 1.66426855e-02 -1.35053289e-02 -1.16930746e-03 7.64522589e-03 -1.31090117e-03 -4.09924255e-04] [-1.35053289e-02 8.59674091e-02 -3.05945509e-03 -1.32357002e-03 1.76235487e-03 2.44867119e-04] [-1.16930746e-03 -3.05945509e-03 9.88730075e-02 -4.11207517e-04 2.44385403e-04 5.92545117e-05] [ 7.64522589e-03 -1.32357002e-03 -4.11207517e-04 2.81412817e-02 -1.54530473e-02 -1.78362007e-03] [-1.31090117e-03 1.76235487e-03 2.44385403e-04 -1.54530473e-02 8.86287091e-02 -2.68996856e-03] [-4.09924255e-04 2.44867119e-04 5.92545117e-05 -1.78362007e-03 -2.68996856e-03 9.89628529e-02]] LogLike[0046]=-435.210236 sigma_a=48.598328 R: [[ 3549.85087705 6837.98106922] [ 6837.98106922 13185.77847282]] m0: [187.37699361 41.85445698 5.25643619 390.59202798 98.87324817 12.42212808] V0: [[ 1.64248755e-02 -1.33988241e-02 -1.15221709e-03 7.76357094e-03 -1.36705392e-03 -4.19162252e-04] [-1.33988241e-02 8.58710685e-02 -3.07078299e-03 -1.38186694e-03 1.81190645e-03 2.50573982e-04] [-1.15221709e-03 -3.07078299e-03 9.88710698e-02 -4.20626778e-04 2.50007146e-04 6.01277733e-05] [ 7.76357094e-03 -1.38186694e-03 -4.20626778e-04 2.80752798e-02 -1.54230263e-02 -1.77875721e-03] [-1.36705392e-03 1.81190645e-03 2.50007146e-04 -1.54230263e-02 8.86015610e-02 -2.69353373e-03] [-4.19162252e-04 2.50573982e-04 6.01277733e-05 -1.77875721e-03 -2.69353373e-03 9.89620926e-02]] LogLike[0047]=-432.618314 sigma_a=51.050415 R: [[ 3543.64575229 6804.53099418] [ 6804.53099418 13078.32375534]] m0: [187.41414739 41.78972091 5.25218221 390.58134412 98.91125127 12.4256819 ] V0: [[ 1.62044510e-02 -1.32858461e-02 -1.13469632e-03 7.88431446e-03 -1.42684024e-03 -4.28681171e-04] [-1.32858461e-02 8.57695901e-02 -3.08251806e-03 -1.44417307e-03 1.86439403e-03 2.56554874e-04] [-1.13469632e-03 -3.08251806e-03 9.88691304e-02 -4.30351673e-04 2.55887112e-04 6.10180950e-05] [ 7.88431446e-03 -1.44417307e-03 -4.30351673e-04 2.80074357e-02 -1.53907394e-02 -1.77368557e-03] [-1.42684024e-03 1.86439403e-03 2.55887112e-04 -1.53907394e-02 8.85728374e-02 -2.69721818e-03] [-4.28681171e-04 2.56554874e-04 6.10180950e-05 -1.77368557e-03 -2.69721818e-03 9.89613400e-02]] LogLike[0048]=-429.763855 sigma_a=53.698728 R: [[ 3537.99376384 6772.00225581] [ 6772.00225581 12972.71721129]] m0: [187.45369493 41.72308528 5.2477769 390.56917341 98.9501959 12.42922964] V0: [[ 1.59808293e-02 -1.31656561e-02 -1.11670316e-03 8.00784764e-03 -1.49068582e-03 -4.38509105e-04] [-1.31656561e-02 8.56620454e-02 -3.09473241e-03 -1.51097895e-03 1.92033616e-03 2.62853287e-04] [-1.11670316e-03 -3.09473241e-03 9.88671831e-02 -4.40413040e-04 2.62065817e-04 6.19290558e-05] [ 8.00784764e-03 -1.51097895e-03 -4.40413040e-04 2.79374863e-02 -1.53559062e-02 -1.76838527e-03] [-1.49068582e-03 1.92033616e-03 2.62065817e-04 -1.53559062e-02 8.85422474e-02 -2.70104712e-03] [-4.38509105e-04 2.62853287e-04 6.19290558e-05 -1.76838527e-03 -2.70104712e-03 9.89605927e-02]] LogLike[0049]=-426.621363 sigma_a=56.558651 R: [[ 3532.53161656 6739.89065147] [ 6739.89065147 12868.38032427]] m0: [187.49598538 41.65424762 5.24319537 390.55529104 98.99025711 12.43278883] V0: [[ 1.57534468e-02 -1.30374453e-02 -1.09819861e-03 8.13455533e-03 -1.55905757e-03 -4.48672930e-04] [-1.30374453e-02 8.55473692e-02 -3.10750329e-03 -1.58282593e-03 1.98032933e-03 2.69516187e-04] [-1.09819861e-03 -3.10750329e-03 9.88652222e-02 -4.50840526e-04 2.68586738e-04 6.28640077e-05] [ 8.13455533e-03 -1.58282593e-03 -4.50840526e-04 2.78651683e-02 -1.53182166e-02 -1.76283685e-03] [-1.55905757e-03 1.98032933e-03 2.68586738e-04 -1.53182166e-02 8.85094550e-02 -2.70504812e-03] [-4.48672930e-04 2.69516187e-04 6.28640077e-05 -1.76283685e-03 -2.70504812e-03 9.89598481e-02]] LogLike[0050]=-423.171960 sigma_a=59.644116 R: [[ 3526.82056659 6707.60328488] [ 6707.60328488 12764.66162719]] m0: [187.54138325 41.58287632 5.23841223 390.53945879 99.03162797 12.43637825] V0: [[ 1.55218236e-02 -1.29003755e-02 -1.07915300e-03 8.26477700e-03 -1.63244151e-03 -4.59194628e-04] [-1.29003755e-02 8.54243758e-02 -3.12090891e-03 -1.66028315e-03 2.04503967e-03 2.76591760e-04] [-1.07915300e-03 -3.12090891e-03 9.88632427e-02 -4.61658689e-04 2.75494059e-04 6.38257151e-05] [ 8.26477700e-03 -1.66028315e-03 -4.61658689e-04 2.77902414e-02 -1.52773435e-02 -1.75702351e-03] [-1.63244151e-03 2.04503967e-03 2.75494059e-04 -1.52773435e-02 8.84740835e-02 -2.70924982e-03] [-4.59194628e-04 2.76591760e-04 6.38257151e-05 -1.75702351e-03 -2.70924982e-03 9.89591038e-02]] LogLike[0051]=-419.410273 sigma_a=62.965754 R: [[ 3520.35674966 6674.47851394] [ 6674.47851394 12660.8705648 ]] m0: [187.59023448 41.50863214 5.23340459 390.52144482 99.07450784 12.44001619] V0: [[ 1.52856582e-02 -1.27536550e-02 -1.05955540e-03 8.39874713e-03 -1.71130079e-03 -4.70085789e-04] [-1.27536550e-02 8.52918088e-02 -3.13502110e-03 -1.74390219e-03 2.11517605e-03 2.84125218e-04] [-1.05955540e-03 -3.13502110e-03 9.88612418e-02 -4.72881144e-04 2.82828590e-04 6.48158242e-05] [ 8.39874713e-03 -1.74390219e-03 -4.72881144e-04 2.77125251e-02 -1.52329672e-02 -1.75093453e-03] [-1.71130079e-03 2.11517605e-03 2.82828590e-04 -1.52329672e-02 8.84357299e-02 -2.71367966e-03] [-4.70085789e-04 2.84125218e-04 6.48158242e-05 -1.75093453e-03 -2.71367966e-03 9.89583581e-02]] LogLike[0052]=-415.353410 sigma_a=66.528417 R: [[ 3512.59592189 6639.81943549] [ 6639.81943549 12556.3113405 ]] m0: [187.64281686 41.43120434 5.22815646 390.50105377 99.11908238 12.44371792] V0: [[ 1.50449535e-02 -1.25966595e-02 -1.03942566e-03 8.53651489e-03 -1.79600826e-03 -4.81340380e-04] [-1.25966595e-02 8.51484416e-02 -3.14989390e-03 -1.83414286e-03 2.19143497e-03 2.92152188e-04] [-1.03942566e-03 -3.14989390e-03 9.88592196e-02 -4.84502829e-04 2.90621413e-04 6.58341746e-05] [ 8.53651489e-03 -1.83414286e-03 -4.84502829e-04 2.76319504e-02 -1.51848174e-02 -1.74456985e-03] [-1.79600826e-03 2.19143497e-03 2.90621413e-04 -1.51848174e-02 8.83939950e-02 -2.71836019e-03] [-4.81340380e-04 2.92152188e-04 6.58341746e-05 -1.74456985e-03 -2.71836019e-03 9.89576098e-02]] LogLike[0053]=-411.051079 sigma_a=70.327857 R: [[ 3502.99665552 6602.94631651] [ 6602.94631651 12450.32434924]] m0: [187.69927448 41.3503657 5.22266426 390.47816777 99.16549269 12.44749236] V0: [[ 1.48001673e-02 -1.24291061e-02 -1.01882833e-03 8.67784601e-03 -1.88674869e-03 -4.92926208e-04] [-1.24291061e-02 8.49932498e-02 -3.16554770e-03 -1.93126396e-03 2.27440525e-03 3.00689296e-04] [-1.01882833e-03 -3.16554770e-03 9.88571805e-02 -4.96490847e-04 2.98884965e-04 6.68780062e-05] [ 8.67784601e-03 -1.93126396e-03 -4.96490847e-04 2.75486228e-02 -1.51327338e-02 -1.73794568e-03] [-1.88674869e-03 2.27440525e-03 2.98884965e-04 -1.51327338e-02 8.83485360e-02 -2.72330374e-03] [-4.92926208e-04 3.00689296e-04 6.68780062e-05 -1.73794568e-03 -2.72330374e-03 9.89568593e-02]] LogLike[0054]=-406.592439 sigma_a=74.346851 R: [[ 3491.08299737 6563.27194044] [ 6563.27194044 12342.34757594]] m0: [187.75954057 41.26604829 5.21694308 390.45279607 99.21379281 12.45133826] V0: [[ 1.45523702e-02 -1.22512792e-02 -9.97886402e-04 8.82211805e-03 -1.98339211e-03 -5.04776328e-04] [-1.22512792e-02 8.48256685e-02 -3.18194955e-03 -2.03518002e-03 2.36442446e-03 3.09722314e-04] [-9.97886402e-04 -3.18194955e-03 9.88551345e-02 -5.08775241e-04 3.07601952e-04 6.79412031e-05] [ 8.82211805e-03 -2.03518002e-03 -5.08775241e-04 2.74628920e-02 -1.50767472e-02 -1.73110025e-03] [-1.98339211e-03 2.36442446e-03 3.07601952e-04 -1.50767472e-02 8.82991471e-02 -2.72850579e-03] [-5.04776328e-04 3.09722314e-04 6.79412031e-05 -1.73110025e-03 -2.72850579e-03 9.89561090e-02]] LogLike[0055]=-402.101167 sigma_a=78.552355 R: [[ 3476.51972404 6520.39513685] [ 6520.39513685 12232.00490742]] m0: [187.82326714 41.17842953 5.21103173 390.42512194 99.26390103 12.45524085] V0: [[ 1.43033580e-02 -1.20642629e-02 -9.76789348e-04 8.96824202e-03 -2.08536178e-03 -5.16783535e-04] [-1.20642629e-02 8.46459111e-02 -3.19899428e-03 -2.14530954e-03 2.46140084e-03 3.19194419e-04] [-9.76789348e-04 -3.19899428e-03 9.88530977e-02 -5.21243078e-04 3.16714597e-04 6.90138856e-05] [ 8.96824202e-03 -2.14530954e-03 -5.21243078e-04 2.73754058e-02 -1.50171646e-02 -1.72409772e-03] [-2.08536178e-03 2.46140084e-03 3.16714597e-04 -1.50171646e-02 8.82458583e-02 -2.73393821e-03] [-5.16783535e-04 3.19194419e-04 6.90138856e-05 -1.72409772e-03 -2.73393821e-03 9.89553632e-02]] LogLike[0056]=-397.713031 sigma_a=82.896387 R: [[ 3459.17865959 6474.18750846] [ 6474.18750846 12119.20371672]] m0: [187.88979881 41.08799447 5.20499329 390.39552358 99.31556416 12.45917107] V0: [[ 1.40556200e-02 -1.18700696e-02 -9.55786539e-04 9.11467347e-03 -2.19156022e-03 -5.28803371e-04] [-1.18700696e-02 8.44552326e-02 -3.21649647e-03 -2.26048511e-03 2.56466383e-03 3.29000587e-04] [-9.55786539e-04 -3.21649647e-03 9.88510916e-02 -5.33741576e-04 3.26119934e-04 7.00828444e-05] [ 9.11467347e-03 -2.26048511e-03 -5.33741576e-04 2.72871097e-02 -1.49546216e-02 -1.71702694e-03] [-2.19156022e-03 2.56466383e-03 3.26119934e-04 -1.49546216e-02 8.81890211e-02 -2.73954598e-03] [-5.28803371e-04 3.29000587e-04 7.00828444e-05 -1.71702694e-03 -2.73954598e-03 9.89546284e-02]] LogLike[0057]=-393.544135 sigma_a=87.322054 R: [[ 3439.16398004 6424.83048853] [ 6424.83048853 12004.19318288]] m0: [187.95822992 40.99552374 5.1989083 390.36454353 99.36836268 12.46308895] V0: [[ 1.38120697e-02 -1.16715151e-02 -9.35158988e-04 9.25957160e-03 -2.30043478e-03 -5.40669982e-04] [-1.16715151e-02 8.42559534e-02 -3.23420553e-03 -2.37901751e-03 2.67294643e-03 3.38995364e-04] [-9.35158988e-04 -3.23420553e-03 9.88491400e-02 -5.46094902e-04 3.35677872e-04 7.11331522e-05] [ 9.25957160e-03 -2.37901751e-03 -5.46094902e-04 2.71991531e-02 -1.48900489e-02 -1.70999204e-03] [-2.30043478e-03 2.67294643e-03 3.35677872e-04 -1.48900489e-02 8.81293202e-02 -2.74525119e-03] [-5.40669982e-04 3.38995364e-04 7.11331522e-05 -1.70999204e-03 -2.74525119e-03 9.89539121e-02]] LogLike[0058]=-389.668981 sigma_a=91.772449 R: [[ 3416.77730095 6372.77229238] [ 6372.77229238 11887.53425012]] m0: [188.02754498 40.90198397 5.19286167 390.33280485 99.42177017 12.46695089] V0: [[ 1.35755805e-02 -1.14718057e-02 -9.15175145e-04 9.40108567e-03 -2.41020445e-03 -5.52221881e-04] [-1.14718057e-02 8.40511350e-02 -3.25184472e-03 -2.49894131e-03 2.78456057e-03 3.49015108e-04] [-9.15175145e-04 -3.25184472e-03 9.88472649e-02 -5.58131460e-04 3.45232732e-04 7.21505888e-05] [ 9.40108567e-03 -2.49894131e-03 -5.58131460e-04 2.71127142e-02 -1.48245376e-02 -1.70309656e-03] [-2.41020445e-03 2.78456057e-03 3.45232732e-04 -1.48245376e-02 8.80676796e-02 -2.75096521e-03] [-5.52221881e-04 3.49015108e-04 7.21505888e-05 -1.70309656e-03 -2.75096521e-03 9.89532217e-02]] LogLike[0059]=-386.118856 sigma_a=96.197782 R: [[ 3392.43902973 6318.62234862] [ 6318.62234862 11769.98597574]] m0: [188.09678319 40.80836373 5.18692888 390.3009093 99.4752437 12.47071752] V0: [[ 1.33485266e-02 -1.12740086e-02 -8.96050142e-04 9.53764573e-03 -2.51915370e-03 -5.63326416e-04] [-1.12740086e-02 8.38440349e-02 -3.26915757e-03 -2.61834072e-03 2.89769731e-03 3.58905301e-04] [-8.96050142e-04 -3.26915757e-03 9.88454835e-02 -5.69709850e-04 3.54639136e-04 7.31238159e-05] [ 9.53764573e-03 -2.61834072e-03 -5.69709850e-04 2.70288164e-02 -1.47591586e-02 -1.69642787e-03] [-2.51915370e-03 2.89769731e-03 3.54639136e-04 -1.47591586e-02 8.80050973e-02 -2.75660375e-03] [-5.63326416e-04 3.58905301e-04 7.31238159e-05 -1.69642787e-03 -2.75660375e-03 9.89525628e-02]] LogLike[0060]=-382.893954 sigma_a=100.558065 R: [[ 3366.60743945 6263.04007792] [ 6263.04007792 11652.37442956]] m0: [188.16515332 40.71553512 5.18116763 390.2693634 99.52830089 12.47435873] V0: [[ 1.31325325e-02 -1.10806551e-02 -8.77925836e-04 9.66813020e-03 -2.62585547e-03 -5.73892521e-04] [-1.10806551e-02 8.36376123e-02 -3.28594030e-03 -2.73560211e-03 3.01070174e-03 3.68540319e-04] [-8.77925836e-04 -3.28594030e-03 9.88438059e-02 -5.80732456e-04 3.63780334e-04 7.40454063e-05] [ 9.66813020e-03 -2.73560211e-03 -5.80732456e-04 2.69482171e-02 -1.46948208e-02 -1.69004856e-03] [-2.62585547e-03 3.01070174e-03 3.63780334e-04 -1.46948208e-02 8.79424927e-02 -2.76209791e-03] [-5.73892521e-04 3.68540319e-04 7.40454063e-05 -1.69004856e-03 -2.76209791e-03 9.89519392e-02]] LogLike[0061]=-379.977523 sigma_a=104.822100 R: [[ 3339.72614156 6206.66325345] [ 6206.66325345 11535.50712626]] m0: [188.23207111 40.62418734 5.17561634 390.23855125 99.58055783 12.47785478] V0: [[ 1.29284674e-02 -1.08935893e-02 -8.60873222e-04 9.79188206e-03 -2.72925919e-03 -5.83870365e-04] [-1.08935893e-02 8.34342613e-02 -3.30205212e-03 -2.84951913e-03 3.12222489e-03 3.77830451e-04] [-8.60873222e-04 -3.30205212e-03 9.88422359e-02 -5.91145199e-04 3.72574308e-04 7.49116848e-05] [ 9.79188206e-03 -2.84951913e-03 -5.91145199e-04 2.68713899e-02 -1.46322104e-02 -1.68399591e-03] [-2.72925919e-03 3.12222489e-03 3.72574308e-04 -1.46322104e-02 8.78806203e-02 -2.76739827e-03] [-5.83870365e-04 3.77830451e-04 7.49116848e-05 -1.68399591e-03 -2.76739827e-03 9.89513525e-02]] LogLike[0062]=-377.346022 sigma_a=108.964903 R: [[ 3312.20339148 6150.08191055] [ 6150.08191055 11420.14526304]] m0: [188.29714034 40.53482377 5.17029703 390.20874273 99.63173186 12.48119484] V0: [[ 1.27365878e-02 -1.07140074e-02 -8.44907336e-04 9.90863033e-03 -2.82867241e-03 -5.93243364e-04] [-1.07140074e-02 8.32357573e-02 -3.31740932e-03 -2.95927890e-03 3.23125675e-03 3.86719296e-04] [-8.44907336e-04 -3.31740932e-03 9.88407728e-02 -6.00929204e-04 3.80970833e-04 7.57219231e-05] [ 9.90863033e-03 -2.95927890e-03 -6.00929204e-04 2.67985671e-02 -1.45717963e-02 -1.67828628e-03] [-2.82867241e-03 3.23125675e-03 3.80970833e-04 -1.45717963e-02 8.78200498e-02 -2.77247362e-03] [-5.93243364e-04 3.86719296e-04 7.57219231e-05 -1.67828628e-03 -2.77247362e-03 9.89508026e-02]] LogLike[0063]=-374.974810 sigma_a=112.965143 R: [[ 3284.41116632 6093.84008914] [ 6093.84008914 11307.01098879]] m0: [188.36011182 40.44779064 5.16521968 390.18011643 99.68162663 12.48437474] V0: [[ 1.25567164e-02 -1.05425826e-02 -8.30004615e-04 1.00183842e-02 -2.92369407e-03 -6.02018421e-04] [-1.05425826e-02 8.30433251e-02 -3.33197287e-03 -3.06439252e-03 3.33709172e-03 3.95177036e-04] [-8.30004615e-04 -3.33197287e-03 9.88394123e-02 -6.10090539e-04 3.88944797e-04 7.64774184e-05] [ 1.00183842e-02 -3.06439252e-03 -6.10090539e-04 2.67298013e-02 -1.45138668e-02 -1.67292088e-03] [-2.92369407e-03 3.33709172e-03 3.88944797e-04 -1.45138668e-02 8.77611830e-02 -2.77730735e-03] [-6.02018421e-04 3.95177036e-04 7.64774184e-05 -1.67292088e-03 -2.77730735e-03 9.89502884e-02]] LogLike[0064]=-372.840731 sigma_a=116.803273 R: [[ 3256.69178168 6038.44720386] [ 6038.44720386 11196.80486521]] m0: [188.4208425 40.36331237 5.16038604 390.15278358 99.73011318 12.48739477] V0: [[ 1.23883988e-02 -1.03796009e-02 -8.16117473e-04 1.01213386e-02 -3.01414072e-03 -6.10217513e-04] [-1.03796009e-02 8.28577453e-02 -3.34573584e-03 -3.16461587e-03 3.43927155e-03 4.03193340e-04] [-8.16117473e-04 -3.34573584e-03 9.88381485e-02 -6.18651359e-04 3.96489299e-04 7.71807227e-05] [ 1.01213386e-02 -3.16461587e-03 -6.18651359e-04 2.66650232e-02 -1.44585733e-02 -1.66789085e-03] [-3.01414072e-03 3.43927155e-03 3.96489299e-04 -1.44585733e-02 8.77042849e-02 -2.78189365e-03] [-6.10217513e-04 4.03193340e-04 7.71807227e-05 -1.66789085e-03 -2.78189365e-03 9.89498081e-02]] LogLike[0065]=-370.922936 sigma_a=120.460525 R: [[ 3229.36468645 5984.38816106] [ 5984.38816106 11090.21857843]] m0: [188.47926361 40.2815219 5.15579257 390.12680714 99.77711353 12.49025817] V0: [[ 1.22310176e-02 -1.02250741e-02 -8.03184855e-04 1.02178029e-02 -3.09998409e-03 -6.17871507e-04] [-1.02250741e-02 8.26794567e-02 -3.35871313e-03 -3.25988147e-03 3.53752957e-03 4.10771467e-04] [-8.03184855e-04 -3.35871313e-03 9.88369746e-02 -6.26643367e-04 4.03609986e-04 7.78350875e-05] [ 1.02178029e-02 -3.25988147e-03 -6.26643367e-04 2.66040847e-02 -1.44059671e-02 -1.66318110e-03] [-3.09998409e-03 3.53752957e-03 4.03609986e-04 -1.44059671e-02 8.76495132e-02 -2.78623425e-03] [-6.17871507e-04 4.10771467e-04 7.78350875e-05 -1.66318110e-03 -2.78623425e-03 9.89493595e-02]] LogLike[0066]=-369.202798 sigma_a=123.918680 R: [[ 3202.73008698 5932.12737467] [ 5932.12737467 10987.93635118]] m0: [188.53535853 40.20248286 5.15143234 390.10221519 99.82258857 12.49297004] V0: [[ 1.20838700e-02 -1.00788255e-02 -7.91139234e-04 1.03081516e-02 -3.18130379e-03 -6.25015967e-04] [-1.00788255e-02 8.25086405e-02 -3.37093397e-03 -3.35024640e-03 3.63174462e-03 4.17923852e-04] [-7.91139234e-04 -3.37093397e-03 9.88358832e-02 -6.34103370e-04 4.10320846e-04 7.84440946e-05] [ 1.03081516e-02 -3.35024640e-03 -6.34103370e-04 2.65467900e-02 -1.43560285e-02 -1.65877294e-03] [-3.18130379e-03 3.63174462e-03 4.10320846e-04 -1.43560285e-02 8.75969442e-02 -2.79033601e-03] [-6.25015967e-04 4.17923852e-04 7.84440946e-05 -1.65877294e-03 -2.79033601e-03 9.89489401e-02]] LogLike[0067]=-367.663421 sigma_a=127.160486 R: [[ 3177.06844665 5882.10546197] [ 5882.10546197 10890.62370086]] m0: [188.58914844 40.12620485 5.14729628 390.07900987 99.8665298 12.49553669] V0: [[ 1.19462179e-02 -9.94055109e-03 -7.79911052e-04 1.03927928e-02 -3.25825316e-03 -6.31688448e-04] [-9.94055109e-03 8.23452845e-02 -3.38243659e-03 -3.43585436e-03 3.72190534e-03 4.24668960e-04] [-7.79911052e-04 -3.38243659e-03 9.88348672e-02 -6.41070393e-04 4.16641235e-04 7.90114215e-05] [ 1.03927928e-02 -3.43585436e-03 -6.41070393e-04 2.64929164e-02 -1.43086878e-02 -1.65464589e-03] [-3.25825316e-03 3.72190534e-03 4.16641235e-04 -1.43086878e-02 8.75465921e-02 -2.79420920e-03] [-6.31688448e-04 4.24668960e-04 7.90114215e-05 -1.65464589e-03 -2.79420920e-03 9.89485476e-02]] LogLike[0068]=-366.289048 sigma_a=130.170518 R: [[ 3152.63618852 5834.72937582] [ 5834.72937582 10798.90512785]] m0: [188.64068322 40.05265371 5.14337387 390.05717311 99.90895362 12.49796522] V0: [[ 1.18173184e-02 -9.80986252e-03 -7.69431443e-04 1.04721465e-02 -3.33103511e-03 -6.37926783e-04] [-9.80986252e-03 8.21892324e-02 -3.39326459e-03 -3.51690843e-03 3.80808300e-03 4.31029074e-04] [-7.69431443e-04 -3.39326459e-03 9.88339196e-02 -6.47583850e-04 4.22593800e-04 7.95406973e-05] [ 1.04721465e-02 -3.51690843e-03 -6.47583850e-04 2.64422283e-02 -1.42638408e-02 -1.65077877e-03] [-3.33103511e-03 3.80808300e-03 4.22593800e-04 -1.42638408e-02 8.74984239e-02 -2.79786624e-03] [-6.37926783e-04 4.31029074e-04 7.95406973e-05 -1.65077877e-03 -2.79786624e-03 9.89481796e-02]] LogLike[0069]=-365.064572 sigma_a=132.936232 R: [[ 3129.65865567 5790.35789591] [ 5790.35789591 10713.33417107]] m0: [188.69003526 39.98175903 5.13965365 390.03667061 99.94989719 12.50026332] V0: [[ 1.16964441e-02 -9.68631831e-03 -7.59633904e-04 1.05466316e-02 -3.39988444e-03 -6.43767995e-04] [-9.68631831e-03 8.20402212e-02 -3.40346435e-03 -3.59365108e-03 3.89041037e-03 4.37028709e-04] [-7.59633904e-04 -3.40346435e-03 9.88330337e-02 -6.53682375e-04 4.28203008e-04 8.00354142e-05] [ 1.05466316e-02 -3.59365108e-03 -6.53682375e-04 2.63944858e-02 -1.42213598e-02 -1.64715049e-03] [-3.39988444e-03 3.89041037e-03 4.28203008e-04 -1.42213598e-02 8.74523717e-02 -2.80132085e-03] [-6.43767995e-04 4.37028709e-04 8.00354142e-05 -1.64715049e-03 -2.80132085e-03 9.89478338e-02]] LogLike[0070]=-363.975239 sigma_a=135.448951 R: [[ 3108.32214871 5749.28574678] [ 5749.28574678 10634.36164739]] m0: [188.73729473 39.91342059 5.13612355 390.01745493 99.98941481 12.502439 ] V0: [[ 1.15828953e-02 -9.56944707e-03 -7.50455321e-04 1.06166559e-02 -3.46505465e-03 -6.49247598e-04] [-9.56944707e-03 8.18979116e-02 -3.41308320e-03 -3.66634900e-03 3.96906470e-03 4.42693448e-04] [-7.50455321e-04 -3.41308320e-03 9.88322034e-02 -6.59403062e-04 4.33494075e-04 8.04988740e-05] [ 1.06166559e-02 -3.66634900e-03 -6.59403062e-04 2.63494521e-02 -1.41811024e-02 -1.64374053e-03] [-3.46505465e-03 3.96906470e-03 4.33494075e-04 -1.41811024e-02 8.74083416e-02 -2.80458737e-03] [-6.49247598e-04 4.42693448e-04 8.04988740e-05 -1.64374053e-03 -2.80458737e-03 9.89475081e-02]] LogLike[0071]=-363.006603 sigma_a=137.704555 R: [[ 3088.76698708 5711.72983196] [ 5711.72983196 10562.30829047]] m0: [188.78256523 39.8475145 5.13277121 389.99946843 100.02757446 12.50450049] V0: [[ 1.14760094e-02 -9.45876565e-03 -7.41836622e-04 1.06826102e-02 -3.52680746e-03 -6.54399129e-04] [-9.45876565e-03 8.17619137e-02 -3.42216801e-03 -3.73528103e-03 4.04425334e-03 4.48049049e-04] [-7.41836622e-04 -3.42216801e-03 9.88314230e-02 -6.64780960e-04 4.38492154e-04 8.09341550e-05] [ 1.06826102e-02 -3.73528103e-03 -6.64780960e-04 2.63068977e-02 -1.41429186e-02 -1.64052930e-03] [-3.52680746e-03 4.04425334e-03 4.38492154e-04 -1.41429186e-02 8.73662223e-02 -2.80768025e-03] [-6.54399129e-04 4.48049049e-04 8.09341550e-05 -1.64052930e-03 -2.80768025e-03 9.89472004e-02]] LogLike[0072]=-362.144651 sigma_a=139.703724 R: [[ 3071.08331438 5677.8206719 ] [ 5677.8206719 10497.3473035 ]] m0: [188.82595962 39.78389957 5.12958419 389.98264611 100.06445422 12.50645603] V0: [[ 1.13751666e-02 -9.35379381e-03 -7.33723169e-04 1.07448632e-02 -3.58540446e-03 -6.59253832e-04] [-9.35379381e-03 8.16318078e-02 -3.43076413e-03 -3.80072833e-03 4.11620159e-03 4.53120750e-04] [-7.33723169e-04 -3.43076413e-03 9.88306873e-02 -6.69848737e-04 4.43221717e-04 8.13440927e-05] [ 1.07448632e-02 -3.80072833e-03 -6.69848737e-04 2.62666041e-02 -1.41066559e-02 -1.63749843e-03] [-3.58540446e-03 4.11620159e-03 4.43221717e-04 -1.41066559e-02 8.73258919e-02 -2.81061371e-03] [-6.59253832e-04 4.53120750e-04 8.13440927e-05 -1.63749843e-03 -2.81061371e-03 9.89469089e-02]] LogLike[0073]=-361.376052 sigma_a=141.451757 R: [[ 3055.31076584 5647.60105118] [ 5647.60105118 10439.50040282]] m0: [188.86759579 39.72242363 5.12655027 389.96691839 100.10013863 12.50831375] V0: [[ 1.12797939e-02 -9.25406564e-03 -7.26064968e-04 1.08037578e-02 -3.64110037e-03 -6.63840476e-04] [-9.25406564e-03 8.15071632e-02 -3.43891459e-03 -3.86296656e-03 4.18514231e-03 4.57932744e-04] [-7.26064968e-04 -3.43891459e-03 9.88299916e-02 -6.74636473e-04 4.47706089e-04 8.17312719e-05] [ 1.08037578e-02 -3.86296656e-03 -6.74636473e-04 2.62283667e-02 -1.40721644e-02 -1.63463085e-03] [-3.64110037e-03 4.18514231e-03 4.47706089e-04 -1.40721644e-02 8.72872234e-02 -2.81340136e-03] [-6.63840476e-04 4.57932744e-04 8.17312719e-05 -1.63463085e-03 -2.81340136e-03 9.89466319e-02]] LogLike[0074]=-360.688402 sigma_a=142.957991 R: [[ 3041.44175651 5621.03139861] [ 5621.03139861 10388.64640588]] m0: [188.90759287 39.66292941 5.12365767 389.95221375 100.13471538 12.51008148] V0: [[ 1.11893674e-02 -9.15913837e-03 -7.18816726e-04 1.08596096e-02 -3.69413804e-03 -6.68185259e-04] [-9.15913837e-03 8.13875526e-02 -3.44665961e-03 -3.92225986e-03 4.25130764e-03 4.62507794e-04] [-7.18816726e-04 -3.44665961e-03 9.88293316e-02 -6.79171562e-04 4.51967113e-04 8.20980260e-05] [ 1.08596096e-02 -3.92225986e-03 -6.79171562e-04 2.61919959e-02 -1.40392997e-02 -1.63191095e-03] [-3.69413804e-03 4.25130764e-03 4.51967113e-04 -1.40392997e-02 8.72500899e-02 -2.81605602e-03] [-6.68185259e-04 4.62507794e-04 8.20980260e-05 -1.63191095e-03 -2.81605602e-03 9.89463679e-02]] LogLike[0075]=-360.070431 sigma_a=144.235016 R: [[ 3029.42788972 5598.00096925] [ 5598.00096925 10344.5406235 ]] m0: [188.94606793 39.60525972 5.12089521 389.93846101 100.16827231 12.51176666] V0: [[ 1.11034130e-02 -9.06859834e-03 -7.11937759e-04 1.09127055e-02 -3.74474486e-03 -6.72311818e-04] [-9.06859834e-03 8.12725634e-02 -3.45403616e-03 -3.97885651e-03 4.31492248e-03 4.66866987e-04] [-7.11937759e-04 -3.45403616e-03 9.88287035e-02 -6.83478716e-04 4.56024950e-04 8.24464436e-05] [ 1.09127055e-02 -3.97885651e-03 -6.83478716e-04 2.61573190e-02 -1.40079260e-02 -1.62932459e-03] [-3.74474486e-03 4.31492248e-03 4.56024950e-04 -1.40079260e-02 8.72143680e-02 -2.81858961e-03] [-6.72311818e-04 4.66866987e-04 8.24464436e-05 -1.62932459e-03 -2.81858961e-03 9.89461156e-02]] LogLike[0076]=-359.512120 sigma_a=145.297782 R: [[ 3019.18805052 5578.34218217] [ 5578.34218217 10306.84017401]] m0: [188.98313339 39.54926157 5.11825246 389.92559116 100.20089511 12.51337627] V0: [[ 1.10215048e-02 -8.98206459e-03 -7.05391801e-04 1.09633042e-02 -3.79313062e-03 -6.76241297e-04] [-8.98206459e-03 8.11618056e-02 -3.46107792e-03 -4.03298633e-03 4.37620002e-03 4.71029605e-04] [-7.05391801e-04 -3.46107792e-03 9.88281041e-02 -6.87580040e-04 4.59897979e-04 8.27783814e-05] [ 1.09633042e-02 -4.03298633e-03 -6.87580040e-04 2.61241792e-02 -1.39779172e-02 -1.62685907e-03] [-3.79313062e-03 4.37620002e-03 4.59897979e-04 -1.39779172e-02 8.71799404e-02 -2.82101299e-03] [-6.76241297e-04 4.71029605e-04 8.27783814e-05 -1.62685907e-03 -2.82101299e-03 9.89458736e-02]] LogLike[0077]=-359.004730 sigma_a=146.162747 R: [[ 3010.61709705 5561.84611609] [ 5561.84611609 10275.1315432 ]] m0: [189.01889524 39.49478922 5.11571981 389.91353859 100.23266551 12.51491672] V0: [[ 1.09432633e-02 -8.89919036e-03 -6.99146725e-04 1.10116369e-02 -3.83948657e-03 -6.79992489e-04] [-8.89919036e-03 8.10549165e-02 -3.46781523e-03 -4.08485931e-03 4.43533879e-03 4.75013094e-04] [-6.99146725e-04 -3.46781523e-03 9.88275303e-02 -6.91495178e-04 4.63602786e-04 8.30954797e-05] [ 1.10116369e-02 -4.08485931e-03 -6.91495178e-04 2.60924363e-02 -1.39491580e-02 -1.62450306e-03] [-3.83948657e-03 4.43533879e-03 4.63602786e-04 -1.39491580e-02 8.71466979e-02 -2.82333603e-03] [-6.79992489e-04 4.75013094e-04 8.30954797e-05 -1.62450306e-03 -2.82333603e-03 9.89456409e-02]] LogLike[0078]=-358.540760 sigma_a=146.847113 R: [[ 3003.59404616 5548.27713134] [ 5548.27713134 10248.9566492 ]] m0: [189.05345188 39.44170616 5.11328848 389.90224206 100.26366015 12.5163939 ] V0: [[ 1.08683521e-02 -8.81966295e-03 -6.93174204e-04 1.10579090e-02 -3.88398535e-03 -6.83582005e-04] [-8.81966295e-03 8.09515636e-02 -3.47427520e-03 -4.13466542e-03 4.49252119e-03 4.78833104e-04] [-6.93174204e-04 -3.47427520e-03 9.88269796e-02 -6.95241489e-04 4.67154218e-04 8.33991816e-05] [ 1.10579090e-02 -4.13466542e-03 -6.95241489e-04 2.60619653e-02 -1.39215439e-02 -1.62224653e-03] [-3.88398535e-03 4.49252119e-03 4.67154218e-04 -1.39215439e-02 8.71145399e-02 -2.82556757e-03] [-6.83582005e-04 4.78833104e-04 8.33991816e-05 -1.62224653e-03 -2.82556757e-03 9.89454166e-02]] LogLike[0079]=-358.113852 sigma_a=147.368201 R: [[ 2997.98921318 5537.38562424] [ 5537.38562424 10227.83558147]] m0: [189.08689358 39.38988626 5.11095057 389.89164506 100.29394993 12.51781312] V0: [[ 1.07964742e-02 -8.74320241e-03 -6.87449356e-04 1.11023024e-02 -3.92678170e-03 -6.87024467e-04] [-8.74320241e-03 8.08514449e-02 -3.48048195e-03 -4.18257517e-03 4.54791299e-03 4.82503587e-04] [-6.87449356e-04 -3.48048195e-03 9.88264497e-02 -6.98834254e-04 4.70565489e-04 8.36907520e-05] [ 1.11023024e-02 -4.18257517e-03 -6.98834254e-04 2.60326553e-02 -1.38949811e-02 -1.62008065e-03] [-3.92678170e-03 4.54791299e-03 4.70565489e-04 -1.38949811e-02 8.70833749e-02 -2.82771550e-03] [-6.87024467e-04 4.82503587e-04 8.36907520e-05 -1.62008065e-03 -2.82771550e-03 9.89451998e-02]] LogLike[0080]=-357.718672 sigma_a=147.742979 R: [[ 2993.67001727 5528.91837972] [ 5528.91837972 10211.28502432]] m0: [189.11930245 39.33921418 5.10869893 389.88169602 100.32359971 12.51917916] V0: [[ 1.07273685e-02 -8.66955944e-03 -6.81950374e-04 1.11449773e-02 -3.96801346e-03 -6.90332715e-04] [-8.66955944e-03 8.07542884e-02 -3.48645681e-03 -4.22874068e-03 4.60166364e-03 4.86036918e-04] [-6.81950374e-04 -3.48645681e-03 9.88259387e-02 -7.02286887e-04 4.73848303e-04 8.39712976e-05] [ 1.11449773e-02 -4.22874068e-03 -7.02286887e-04 2.60044086e-02 -1.38693857e-02 -1.61799763e-03] [-3.96801346e-03 4.60166364e-03 4.73848303e-04 -1.38693857e-02 8.70531203e-02 -2.82978682e-03] [-6.90332715e-04 4.86036918e-04 8.39712976e-05 -1.61799763e-03 -2.82978682e-03 9.89449898e-02]] LogLike[0081]=-357.350777 sigma_a=147.987708 R: [[ 2990.50536927 5522.62636475] [ 5522.62636475 10198.83206535]] m0: [189.15075262 39.28958535 5.10652721 389.87234822 100.35266832 12.52049631] V0: [[ 1.06608055e-02 -8.59851293e-03 -6.76658176e-04 1.11860745e-02 -4.00780301e-03 -6.93518001e-04] [-8.59851293e-03 8.06598506e-02 -3.49221863e-03 -4.27329713e-03 4.65390707e-03 4.89444041e-04] [-6.76658176e-04 -3.49221863e-03 9.88254448e-02 -7.05611144e-04 4.77012994e-04 8.42417853e-05] [ 1.11860745e-02 -4.27329713e-03 -7.05611144e-04 2.59771391e-02 -1.38446832e-02 -1.61599065e-03] [-4.00780301e-03 4.65390707e-03 4.77012994e-04 -1.38446832e-02 8.70237021e-02 -2.83178771e-03] [-6.93518001e-04 4.89444041e-04 8.42417853e-05 -1.61599065e-03 -2.83178771e-03 9.89447860e-02]] LogLike[0082]=-357.006483 sigma_a=148.117719 R: [[ 2988.3688385 5518.27031923] [ 5518.27031923 10190.02403565]] m0: [189.18131071 39.24090551 5.10442973 389.86355957 100.38120881 12.52176839] V0: [[ 1.05965840e-02 -8.52986723e-03 -6.71556070e-04 1.12257179e-02 -4.04625866e-03 -6.96590181e-04] [-8.52986723e-03 8.05679144e-02 -3.49778399e-03 -4.31636430e-03 4.70476275e-03 4.92734609e-04] [-6.71556070e-04 -3.49778399e-03 9.88249665e-02 -7.08817317e-04 4.80068673e-04 8.45030596e-05] [ 1.12257179e-02 -4.31636430e-03 -7.08817317e-04 2.59507709e-02 -1.38208077e-02 -1.61405372e-03] [-4.04625866e-03 4.70476275e-03 4.80068673e-04 -1.38208077e-02 8.69950542e-02 -2.83372364e-03] [-6.96590181e-04 4.92734609e-04 8.45030596e-05 -1.61405372e-03 -2.83372364e-03 9.89445877e-02]] LogLike[0083]=-356.682746 sigma_a=148.147271 R: [[ 2987.14073811 5515.6243949 ] [ 5515.6243949 10184.4348313 ]] m0: [189.21103638 39.19309015 5.10240145 389.8552923 100.40926876 12.52299879] V0: [[ 1.05345274e-02 -8.46344946e-03 -6.66629452e-04 1.12640160e-02 -4.08347617e-03 -6.99557886e-04] [-8.46344946e-03 8.04782869e-02 -3.50316749e-03 -4.35804813e-03 4.75433694e-03 4.95917133e-04] [-6.66629452e-04 -3.50316749e-03 9.88245025e-02 -7.11914422e-04 4.83023362e-04 8.47558584e-05] [ 1.12640160e-02 -4.35804813e-03 -7.11914422e-04 2.59252376e-02 -1.37977006e-02 -1.61218161e-03] [-4.08347617e-03 4.75433694e-03 4.83023362e-04 -1.37977006e-02 8.69671179e-02 -2.83559943e-03] [-6.99557886e-04 4.95917133e-04 8.47558584e-05 -1.61218161e-03 -2.83559943e-03 9.89443946e-02]] LogLike[0084]=-356.377049 sigma_a=148.089484 R: [[ 2986.70940575 5514.47834713] [ 5514.47834713 10181.66863497]] m0: [189.23998297 39.14606374 5.10043788 389.84751261 100.43689068 12.52419052] V0: [[ 1.04744808e-02 -8.39910693e-03 -6.61865531e-04 1.13010643e-02 -4.11954013e-03 -7.02428682e-04] [-8.39910693e-03 8.03907966e-02 -3.50838200e-03 -4.39844226e-03 4.80272399e-03 4.98999113e-04] [-6.61865531e-04 -3.50838200e-03 9.88240517e-02 -7.14910361e-04 4.85884127e-04 8.50008270e-05] [ 1.13010643e-02 -4.39844226e-03 -7.14910361e-04 2.59004806e-02 -1.37753103e-02 -1.61036970e-03] [-4.11954013e-03 4.80272399e-03 4.85884127e-04 -1.37753103e-02 8.69398409e-02 -2.83741932e-03] [-7.02428682e-04 4.98999113e-04 8.50008270e-05 -1.61036970e-03 -2.83741932e-03 9.89442061e-02]] LogLike[0085]=-356.087316 sigma_a=147.956321 R: [[ 2986.97185411 5514.63859486] [ 5514.63859486 10181.36160788]] m0: [189.26819811 39.09975894 5.09853505 389.84019028 100.46411246 12.52534626] V0: [[ 1.04163080e-02 -8.33670472e-03 -6.57253087e-04 1.13369469e-02 -4.15452530e-03 -7.05209213e-04] [-8.33670472e-03 8.03052918e-02 -3.51343883e-03 -4.43762953e-03 4.85000755e-03 5.01987163e-04] [-6.57253087e-04 -3.51343883e-03 9.88236130e-02 -7.17812068e-04 4.88657194e-04 8.52385309e-05] [ 1.13369469e-02 -4.43762953e-03 -7.17812068e-04 2.58764484e-02 -1.37535911e-02 -1.60861395e-03] [-4.15452530e-03 4.85000755e-03 4.88657194e-04 -1.37535911e-02 8.69131770e-02 -2.83918705e-03] [-7.05209213e-04 5.01987163e-04 8.52385309e-05 -1.60861395e-03 -2.83918705e-03 9.89440219e-02]] LogLike[0086]=-355.811824 sigma_a=147.758608 R: [[ 2987.8339636 5515.92846151] [ 5515.92846151 10183.18211905]] m0: [189.29572434 39.05411586 5.09668939 389.83329832 100.4909678 12.52646838] V0: [[ 1.03598889e-02 -8.27612346e-03 -6.52782256e-04 1.13717377e-02 -4.18849783e-03 -7.07905319e-04] [-8.27612346e-03 8.02216379e-02 -3.51834799e-03 -4.47568324e-03 4.89626182e-03 5.04887123e-04] [-6.52782256e-04 -3.51834799e-03 9.88231856e-02 -7.20625643e-04 4.91348061e-04 8.54694663e-05] [ 1.13717377e-02 -4.47568324e-03 -7.20625643e-04 2.58530957e-02 -1.37325025e-02 -1.60691081e-03] [-4.18849783e-03 4.89626182e-03 4.91348061e-04 -1.37325025e-02 8.68870851e-02 -2.84090592e-03] [-7.07905319e-04 5.04887123e-04 8.54694663e-05 -1.60691081e-03 -2.84090592e-03 9.89438417e-02]] LogLike[0087]=-355.549142 sigma_a=147.506083 R: [[ 2989.21038645 5518.18790457] [ 5518.18790457 10186.83007073]] m0: [189.3225997 39.00908124 5.09489775 389.82681257 100.51748663 12.52755899] V0: [[ 1.03051178e-02 -8.21725736e-03 -6.48444345e-04 1.14055019e-02 -4.22151635e-03 -7.10522150e-04] [-8.21725736e-03 8.01397155e-02 -3.52311831e-03 -4.51266840e-03 4.94155261e-03 5.07704164e-04] [-6.48444345e-04 -3.52311831e-03 9.88227687e-02 -7.23356462e-04 4.93961588e-04 8.56940697e-05] [ 1.14055019e-02 -4.51266840e-03 -7.23356462e-04 2.58303823e-02 -1.37120086e-02 -1.60525713e-03] [-4.22151635e-03 4.94155261e-03 4.93961588e-04 -1.37120086e-02 8.68615287e-02 -2.84257886e-03] [-7.10522150e-04 5.07704164e-04 8.56940697e-05 -1.60525713e-03 -2.84257886e-03 9.89436652e-02]] LogLike[0088]=-355.298076 sigma_a=147.207452 R: [[ 2991.02419617 5521.27279239] [ 5521.27279239 10192.03542073]] m0: [189.34885825 38.9646078 5.09315732 389.82071145 100.54369554 12.52861996] V0: [[ 1.02519008e-02 -8.16001247e-03 -6.44231670e-04 1.14382976e-02 -4.25363296e-03 -7.13064263e-04] [-8.16001247e-03 8.00594185e-02 -3.52775763e-03 -4.54864276e-03 4.98593838e-03 5.10442869e-04] [-6.44231670e-04 -3.52775763e-03 9.88223616e-02 -7.26009281e-04 4.96502087e-04 8.59127266e-05] [ 1.14382976e-02 -4.54864276e-03 -7.26009281e-04 2.58082727e-02 -1.36920775e-02 -1.60365014e-03] [-4.25363296e-03 4.98593838e-03 4.96502087e-04 -1.36920775e-02 8.68364755e-02 -2.84420846e-03] [-7.13064263e-04 5.10442869e-04 8.59127266e-05 -1.60365014e-03 -2.84420846e-03 9.89434921e-02]] LogLike[0089]=-355.057621 sigma_a=146.870462 R: [[ 2993.20645991 5525.0540528 ] [ 5525.0540528 10198.55649437]] m0: [189.37453057 38.92065352 5.09146555 389.81497555 100.56961811 12.52965298] V0: [[ 1.02001546e-02 -8.10430506e-03 -6.40137412e-04 1.14701760e-02 -4.28489406e-03 -7.15535698e-04] [-8.10430506e-03 7.99806526e-02 -3.53227291e-03 -4.58365782e-03 5.02947113e-03 5.13107319e-04] [-6.40137412e-04 -3.53227291e-03 9.88219637e-02 -7.28588317e-04 4.98973390e-04 8.61257782e-05] [ 1.14701760e-02 -4.58365782e-03 -7.28588317e-04 2.57867353e-02 -1.36726807e-02 -1.60208738e-03] [-4.28489406e-03 5.02947113e-03 4.98973390e-04 -1.36726807e-02 8.68118966e-02 -2.84579702e-03] [-7.15535698e-04 5.13107319e-04 8.61257782e-05 -1.60208738e-03 -2.84579702e-03 9.89433223e-02]] LogLike[0090]=-354.826927 sigma_a=146.501973 R: [[ 2995.69568564 5529.4166019 ] [ 5529.4166019 10206.17791217]] m0: [189.3996442 38.87718109 5.08982019 389.80958746 100.59527526 12.53065952] V0: [[ 1.01498052e-02 -8.05006027e-03 -6.36155502e-04 1.15011829e-02 -4.31534114e-03 -7.17940054e-04] [-8.05006027e-03 7.99033339e-02 -3.53667037e-03 -4.61775962e-03 5.07219722e-03 5.15701156e-04] [-6.36155502e-04 -3.53667037e-03 9.88215746e-02 -7.31097327e-04 5.01378921e-04 8.63335275e-05] [ 1.15011829e-02 -4.61775962e-03 -7.31097327e-04 2.57657421e-02 -1.36537929e-02 -1.60056664e-03] [-4.31534114e-03 5.07219722e-03 5.01378921e-04 -1.36537929e-02 8.67877664e-02 -2.84734660e-03] [-7.17940054e-04 5.15701156e-04 8.63335275e-05 -1.60056664e-03 -2.84734660e-03 9.89431554e-02]] LogLike[0091]=-354.605269 sigma_a=146.108040 R: [[ 2998.43727574 5534.25829402] [ 5534.25829402 10214.70857385]] m0: [189.42422399 38.83415733 5.08821917 389.80453144 100.62068558 12.53164093] V0: [[ 1.01007860e-02 -7.99721095e-03 -6.32280511e-04 1.15313595e-02 -4.34501142e-03 -7.20280550e-04] [-7.99721095e-03 7.98273871e-02 -3.54095557e-03 -4.65098947e-03 5.11415811e-03 5.18227644e-04] [-6.32280511e-04 -3.54095557e-03 9.88211936e-02 -7.33539670e-04 5.03721746e-04 8.65362449e-05] [ 1.15313595e-02 -4.65098947e-03 -7.33539670e-04 2.57452677e-02 -1.36353911e-02 -1.59908595e-03] [-4.34501142e-03 5.11415811e-03 5.03721746e-04 -1.36353911e-02 8.67640620e-02 -2.84885903e-03] [-7.20280550e-04 5.18227644e-04 8.65362449e-05 -1.59908595e-03 -2.84885903e-03 9.89429913e-02]] LogLike[0092]=-354.392023 sigma_a=145.693973 R: [[ 3001.38294011 5539.48880427] [ 5539.48880427 10223.97953173]] m0: [189.44829249 38.79155272 5.08666063 389.79979327 100.64586559 12.53259842] V0: [[ 1.00530372e-02 -7.94569657e-03 -6.28507563e-04 1.15607426e-02 -4.37393847e-03 -7.22560077e-04] [-7.94569657e-03 7.97527450e-02 -3.54513353e-03 -4.68338464e-03 5.15539093e-03 5.20689723e-04] [-6.28507563e-04 -3.54513353e-03 9.88208204e-02 -7.35918362e-04 5.06004625e-04 8.67341727e-05] [ 1.15607426e-02 -4.68338464e-03 -7.35918362e-04 2.57252894e-02 -1.36174548e-02 -1.59764356e-03] [-4.37393847e-03 5.15539093e-03 5.06004625e-04 -1.36174548e-02 8.67407628e-02 -2.85033597e-03] [-7.22560077e-04 5.20689723e-04 8.67341727e-05 -1.59764356e-03 -2.85033597e-03 9.89428300e-02]] LogLike[0093]=-354.186647 sigma_a=145.264423 R: [[ 3004.49017486 5545.02863819] [ 5545.02863819 10233.84211022]] m0: [189.47187024 38.74934097 5.0851429 389.79536002 100.67082999 12.53353307] V0: [[ 1.00065050e-02 -7.89546234e-03 -6.24832257e-04 1.15893655e-02 -4.40215264e-03 -7.24781243e-04] [-7.89546234e-03 7.96793471e-02 -3.54920876e-03 -4.71497885e-03 5.19592911e-03 5.23090050e-04] [-6.24832257e-04 -3.54920876e-03 9.88204546e-02 -7.38236127e-04 5.08230053e-04 8.69275289e-05] [ 1.15893655e-02 -4.71497885e-03 -7.38236127e-04 2.57057869e-02 -1.35999653e-02 -1.59623786e-03] [-4.40215264e-03 5.19592911e-03 5.08230053e-04 -1.35999653e-02 8.67178503e-02 -2.85177892e-03] [-7.24781243e-04 5.23090050e-04 8.69275289e-05 -1.59623786e-03 -2.85177892e-03 9.89426711e-02]] LogLike[0094]=-353.988666 sigma_a=144.823437 R: [[ 3007.72167496 5550.80802454] [ 5550.80802454 10244.16581866]] m0: [189.494976 38.70749862 5.08366442 389.79121988 100.69559187 12.53444585] V0: [[ 9.96114031e-03 -7.84645841e-03 -6.21250602e-04 1.16172585e-02 -4.42968155e-03 -7.26946415e-04] [-7.84645841e-03 7.96071386e-02 -3.55318539e-03 -4.74580280e-03 5.23580282e-03 5.25431041e-04] [-6.21250602e-04 -3.55318539e-03 9.88200959e-02 -7.40495432e-04 5.10400296e-04 8.71165104e-05] [ 1.16172585e-02 -4.74580280e-03 -7.40495432e-04 2.56867413e-02 -1.35829056e-02 -1.59486740e-03] [-4.42968155e-03 5.23580282e-03 5.10400296e-04 -1.35829056e-02 8.66953077e-02 -2.85318923e-03] [-7.26946415e-04 5.25431041e-04 8.71165104e-05 -1.59486740e-03 -2.85318923e-03 9.89425147e-02]] LogLike[0095]=-353.797660 sigma_a=144.374529 R: [[ 3011.04486208 5556.7660245 ] [ 5556.7660245 10254.83667108]] m0: [189.51762702 38.66600468 5.08222377 389.78736203 100.7201629 12.53533767] V0: [[ 9.91689844e-03 -7.79863923e-03 -6.17758959e-04 1.16444491e-02 -4.45655047e-03 -7.29057750e-04] [-7.79863923e-03 7.95360701e-02 -3.55706715e-03 -4.77588454e-03 5.27503941e-03 5.27714901e-04] [-6.17758959e-04 -3.55706715e-03 9.88197440e-02 -7.42698530e-04 5.12517423e-04 8.73012964e-05] [ 1.16444491e-02 -4.77588454e-03 -7.42698530e-04 2.56681359e-02 -1.35662603e-02 -1.59353085e-03] [-4.45655047e-03 5.27503941e-03 5.12517423e-04 -1.35662603e-02 8.66731200e-02 -2.85456814e-03] [-7.29057750e-04 5.27714901e-04 8.73012964e-05 -1.59353085e-03 -2.85456814e-03 9.89423606e-02]] LogLike[0096]=-353.613253 sigma_a=143.920730 R: [[ 3014.43138823 5562.84959951] [ 5562.84959951 10265.75543587]] m0: [189.53983922 38.62484036 5.08081963 389.78377649 100.74455355 12.53620935] V0: [[ 9.87373839e-03 -7.75196294e-03 -6.14353993e-04 1.16709628e-02 -4.48278259e-03 -7.31117224e-04] [-7.75196294e-03 7.94660966e-02 -3.56085749e-03 -4.80524990e-03 5.31366378e-03 5.29943658e-04] [-6.14353993e-04 -3.56085749e-03 9.88193985e-02 -7.44847482e-04 5.14583336e-04 8.74820502e-05] [ 1.16709628e-02 -4.80524990e-03 -7.44847482e-04 2.56499548e-02 -1.35500150e-02 -1.59222701e-03] [-4.48278259e-03 5.31366378e-03 5.14583336e-04 -1.35500150e-02 8.66512733e-02 -2.85591678e-03] [-7.31117224e-04 5.29943658e-04 8.74820502e-05 -1.59222701e-03 -2.85591678e-03 9.89422087e-02]] LogLike[0097]=-353.435106 sigma_a=143.464644 R: [[ 3017.85669981 5569.01279341] [ 5569.01279341 10276.83610123]] m0: [189.56162738 38.58398881 5.07945079 389.78045405 100.76877318 12.53706162] V0: [[ 9.83162237e-03 -7.70639085e-03 -6.11032633e-04 1.16968228e-02 -4.50839934e-03 -7.33126659e-04] [-7.70639085e-03 7.93971767e-02 -3.56455958e-03 -4.83392270e-03 5.35169872e-03 5.32119178e-04] [-6.11032633e-04 -3.56455958e-03 9.88190592e-02 -7.46944189e-04 5.16599787e-04 8.76589219e-05] [ 1.16968228e-02 -4.83392270e-03 -7.46944189e-04 2.56321839e-02 -1.35341565e-02 -1.59095475e-03] [-4.50839934e-03 5.35169872e-03 5.16599787e-04 -1.35341565e-02 8.66297551e-02 -2.85723620e-03] [-7.33126659e-04 5.32119178e-04 8.76589219e-05 -1.59095475e-03 -2.85723620e-03 9.89420588e-02]] LogLike[0098]=-353.262911 sigma_a=143.008498 R: [[ 3021.29965571 5575.2160163 ] [ 5575.2160163 10288.0045325 ]] m0: [189.58300525 38.54343481 5.07811612 389.77738612 100.7928302 12.5378952 ] V0: [[ 9.79051533e-03 -7.66188707e-03 -6.07792032e-04 1.17220509e-02 -4.53342062e-03 -7.35087741e-04] [-7.66188707e-03 7.93292727e-02 -3.56817635e-03 -4.86192510e-03 5.38916514e-03 5.34243199e-04] [-6.07792032e-04 -3.56817635e-03 9.88187259e-02 -7.48990410e-04 5.18568400e-04 8.78320498e-05] [ 1.17220509e-02 -4.86192510e-03 -7.48990410e-04 2.56148096e-02 -1.35186726e-02 -1.58971304e-03] [-4.53342062e-03 5.38916514e-03 5.18568400e-04 -1.35186726e-02 8.66085537e-02 -2.85852736e-03] [-7.35087741e-04 5.34243199e-04 8.78320498e-05 -1.58971304e-03 -2.85852736e-03 9.89419110e-02]] LogLike[0099]=-353.096386 sigma_a=142.554182 R: [[ 3024.74212501 5581.42529278] [ 5581.42529278 10299.19706717]] m0: [189.60398572 38.50316466 5.07681454 389.77456469 100.8167322 12.53871071] V0: [[ 9.75038462e-03 -7.61841809e-03 -6.04629544e-04 1.17466671e-02 -4.55786501e-03 -7.37002041e-04] [-7.61841809e-03 7.92623497e-02 -3.57171056e-03 -4.88927779e-03 5.42608236e-03 5.36317336e-04] [-6.04629544e-04 -3.57171056e-03 9.88183983e-02 -7.50987783e-04 5.20490693e-04 8.80015622e-05] [ 1.17466671e-02 -4.88927779e-03 -7.50987783e-04 2.55978195e-02 -1.35035518e-02 -1.58850090e-03] [-4.55786501e-03 5.42608236e-03 5.20490693e-04 -1.35035518e-02 8.65876584e-02 -2.85979117e-03] [-7.37002041e-04 5.36317336e-04 8.80015622e-05 -1.58850090e-03 -2.85979117e-03 9.89417651e-02]] LogLike[0100]=-352.935271 sigma_a=142.103291 R: [[ 3028.16867057 5587.61166995] [ 5587.61166995 10310.35940751]] m0: [189.62458092 38.46316593 5.07554506 389.77198222 100.84048604 12.53950875] V0: [[ 9.71119970e-03 -7.57595247e-03 -6.01542689e-04 1.17706904e-02 -4.58174992e-03 -7.38871025e-04] [-7.57595247e-03 7.91963753e-02 -3.57516475e-03 -4.91600017e-03 5.46246832e-03 5.38343107e-04] [-6.01542689e-04 -3.57516475e-03 9.88180763e-02 -7.52937841e-04 5.22368084e-04 8.81675786e-05] [ 1.17706904e-02 -4.91600017e-03 -7.52937841e-04 2.55812020e-02 -1.34887834e-02 -1.58731742e-03] [-4.58174992e-03 5.46246832e-03 5.22368084e-04 -1.34887834e-02 8.65670594e-02 -2.86102846e-03] [-7.38871025e-04 5.38343107e-04 8.81675786e-05 -1.58731742e-03 -2.86102846e-03 9.89416211e-02]] LogLike[0101]=-352.779324 sigma_a=141.657156 R: [[ 3031.56623151 5593.75062489] [ 5593.75062489 10321.44551454]] m0: [189.6448023 38.42342733 5.07430672 389.76963163 100.86409793 12.54028988] V0: [[ 9.67293180e-03 -7.53446062e-03 -5.98529140e-04 1.17941387e-02 -4.60509178e-03 -7.40696077e-04] [-7.53446062e-03 7.91313194e-02 -3.57854135e-03 -4.94211055e-03 5.49833970e-03 5.40321939e-04] [-5.98529140e-04 -3.57854135e-03 9.88177597e-02 -7.54842024e-04 5.24201911e-04 8.83302110e-05] [ 1.17941387e-02 -4.94211055e-03 -7.54842024e-04 2.55649461e-02 -1.34743571e-02 -1.58616177e-03] [-4.60509178e-03 5.49833970e-03 5.24201911e-04 -1.34743571e-02 8.65467474e-02 -2.86224004e-03] [-7.40696077e-04 5.40321939e-04 8.83302110e-05 -1.58616177e-03 -2.86224004e-03 9.89414789e-02]] LogLike[0102]=-352.628319 sigma_a=141.216885 R: [[ 3034.92386293 5599.82157846] [ 5599.82157846 10332.41670015]] m0: [189.66466071 38.38393856 5.07309863 389.76750619 100.88757355 12.54105463] V0: [[ 9.63555377e-03 -7.49391449e-03 -5.95586697e-04 1.18170286e-02 -4.62790612e-03 -7.42478499e-04] [-7.49391449e-03 7.90671537e-02 -3.58184262e-03 -4.96762626e-03 5.53371214e-03 5.42255184e-04] [-5.95586697e-04 -3.58184262e-03 9.88174482e-02 -7.56701692e-04 5.25993438e-04 8.84895644e-05] [ 1.18170286e-02 -4.96762626e-03 -7.56701692e-04 2.55490413e-02 -1.34602634e-02 -1.58503311e-03] [-4.62790612e-03 5.53371214e-03 5.25993438e-04 -1.34602634e-02 8.65267136e-02 -2.86342664e-03] [-7.42478499e-04 5.42255184e-04 8.84895644e-05 -1.58503311e-03 -2.86342664e-03 9.89413384e-02]] LogLike[0103]=-352.482044 sigma_a=140.783381 R: [[ 3038.23244526 5605.80735428] [ 5605.80735428 10343.24061959]] m0: [189.68416651 38.34469017 5.07191993 389.76559952 100.91091807 12.54180349] V0: [[ 9.59903984e-03 -7.45428747e-03 -5.92713278e-04 1.18393761e-02 -4.65020772e-03 -7.44219527e-04] [-7.45428747e-03 7.90038518e-02 -3.58507074e-03 -4.99256381e-03 5.56860037e-03 5.44144125e-04] [-5.92713278e-04 -3.58507074e-03 9.88171418e-02 -7.58518136e-04 5.27743866e-04 8.86457384e-05] [ 1.18393761e-02 -4.99256381e-03 -7.58518136e-04 2.55334778e-02 -1.34464931e-02 -1.58393070e-03] [-4.65020772e-03 5.56860037e-03 5.27743866e-04 -1.34464931e-02 8.65069501e-02 -2.86458897e-03] [-7.44219527e-04 5.44144125e-04 8.86457384e-05 -1.58393070e-03 -2.86458897e-03 9.89411995e-02]] LogLike[0104]=-352.340300 sigma_a=140.357377 R: [[ 3041.48451232 5611.69385714] [ 5611.69385714 10353.89067037]] m0: [189.70332956 38.30567351 5.0707698 389.76390551 100.93413623 12.54253693] V0: [[ 9.56336546e-03 -7.41555412e-03 -5.89906898e-04 1.18611965e-02 -4.67201066e-03 -7.45920339e-04] [-7.41555412e-03 7.89413885e-02 -3.58822776e-03 -5.01693892e-03 5.60301826e-03 5.45989986e-04] [-5.89906898e-04 -3.58822776e-03 9.88168402e-02 -7.60292584e-04 5.29454338e-04 8.87988270e-05] [ 1.18611965e-02 -5.01693892e-03 -7.60292584e-04 2.55182462e-02 -1.34330375e-02 -1.58285379e-03] [-4.67201066e-03 5.60301826e-03 5.29454338e-04 -1.34330375e-02 8.64874490e-02 -2.86572773e-03] [-7.45920339e-04 5.45989986e-04 8.87988270e-05 -1.58285379e-03 -2.86572773e-03 9.89410624e-02]] LogLike[0105]=-352.202898 sigma_a=139.939450 R: [[ 3044.67399279 5617.46959254] [ 5617.46959254 10364.34509891]] m0: [189.72215933 38.26688055 5.06964745 389.76241834 100.95723243 12.54325539] V0: [[ 9.52850717e-03 -7.37769015e-03 -5.87165663e-04 1.18825043e-02 -4.69332845e-03 -7.47582058e-04] [-7.37769015e-03 7.88797402e-02 -3.59131565e-03 -5.04076668e-03 5.63697900e-03 5.47793937e-04] [-5.87165663e-04 -3.59131565e-03 9.88165433e-02 -7.62026208e-04 5.31125949e-04 8.89489200e-05] [ 1.18825043e-02 -5.04076668e-03 -7.62026208e-04 2.55033373e-02 -1.34198882e-02 -1.58180169e-03] [-4.69332845e-03 5.63697900e-03 5.31125949e-04 -1.34198882e-02 8.64682030e-02 -2.86684354e-03] [-7.47582058e-04 5.47793937e-04 8.89489200e-05 -1.58180169e-03 -2.86684354e-03 9.89409267e-02]] LogLike[0106]=-352.069660 sigma_a=139.530047 R: [[ 3047.79607978 5623.12542281] [ 5623.12542281 10374.58654497]] m0: [189.7406649 38.2283039 5.06855213 389.76113241 100.98021069 12.5439593 ] V0: [[ 9.49444250e-03 -7.34067218e-03 -5.84487759e-04 1.19033134e-02 -4.71417404e-03 -7.49205762e-04] [-7.34067218e-03 7.88188844e-02 -3.59433629e-03 -5.06406159e-03 5.67049513e-03 5.49557104e-04] [-5.84487759e-04 -3.59433629e-03 9.88162510e-02 -7.63720133e-04 5.32759749e-04 8.90961030e-05] [ 1.19033134e-02 -5.06406159e-03 -7.63720133e-04 2.54887427e-02 -1.34070371e-02 -1.58077372e-03] [-4.71417404e-03 5.67049513e-03 5.32759749e-04 -1.34070371e-02 8.64492053e-02 -2.86793703e-03] [-7.49205762e-04 5.49557104e-04 8.90961030e-05 -1.58077372e-03 -2.86793703e-03 9.89407926e-02]] LogLike[0107]=-351.940417 sigma_a=139.129501 R: [[ 3050.8470484 5628.65422795] [ 5628.65422795 10384.60141079]] m0: [189.75885505 38.18993665 5.06748311 389.76004232 101.00307479 12.54464907] V0: [[ 9.46114983e-03 -7.30447775e-03 -5.81871443e-04 1.19236373e-02 -4.73455990e-03 -7.50792486e-04] [-7.30447775e-03 7.87587994e-02 -3.59729150e-03 -5.08683760e-03 5.70357862e-03 5.51280568e-04] [-5.81871443e-04 -3.59729150e-03 9.88159632e-02 -7.65375438e-04 5.34356748e-04 8.92404579e-05] [ 1.19236373e-02 -5.08683760e-03 -7.65375438e-04 2.54744539e-02 -1.33944765e-02 -1.57976924e-03] [-4.73455990e-03 5.70357862e-03 5.34356748e-04 -1.33944765e-02 8.64304491e-02 -2.86900879e-03] [-7.50792486e-04 5.51280568e-04 8.92404579e-05 -1.57976924e-03 -2.86900879e-03 9.89406600e-02]] LogLike[0108]=-351.815005 sigma_a=138.738049 R: [[ 3053.8241068 5634.05062852] [ 5634.05062852 10394.37934549]] m0: [189.77673821 38.15177236 5.06643969 389.75914288 101.02582822 12.54532508] V0: [[ 9.42860834e-03 -7.26908515e-03 -5.79315038e-04 1.19434888e-02 -4.75449809e-03 -7.52343227e-04] [-7.26908515e-03 7.86994648e-02 -3.60018303e-03 -5.10910822e-03 5.73624095e-03 5.52965373e-04] [-5.79315038e-04 -3.60018303e-03 9.88156796e-02 -7.66993166e-04 5.35917921e-04 8.93820634e-05] [ 1.19434888e-02 -5.10910822e-03 -7.66993166e-04 2.54604629e-02 -1.33821988e-02 -1.57878762e-03] [-4.75449809e-03 5.73624095e-03 5.35917921e-04 -1.33821988e-02 8.64119280e-02 -2.87005940e-03] [-7.52343227e-04 5.52965373e-04 8.93820634e-05 -1.57878762e-03 -2.87005940e-03 9.89405288e-02]] LogLike[0109]=-351.693272 sigma_a=138.355840 R: [[ 3056.72525204 5639.31071761] [ 5639.31071761 10403.91274698]] m0: [189.79432257 38.11380497 5.06542119 389.75842906 101.04847429 12.54598771] V0: [[ 9.39679791e-03 -7.23447340e-03 -5.76816924e-04 1.19628805e-02 -4.77400026e-03 -7.53858948e-04] [-7.23447340e-03 7.86408606e-02 -3.60301256e-03 -5.13088648e-03 5.76849314e-03 5.54612530e-04] [-5.76816924e-04 -3.60301256e-03 9.88154002e-02 -7.68574319e-04 5.37444209e-04 8.95209954e-05] [ 1.19628805e-02 -5.13088648e-03 -7.68574319e-04 2.54467619e-02 -1.33701969e-02 -1.57782827e-03] [-4.77400026e-03 5.76849314e-03 5.37444209e-04 -1.33701969e-02 8.63936361e-02 -2.87108941e-03] [-7.53858948e-04 5.54612530e-04 8.95209954e-05 -1.57782827e-03 -2.87108941e-03 9.89403990e-02]] LogLike[0110]=-351.575071 sigma_a=137.982952 R: [[ 3059.5492181 5644.43196331] [ 5644.43196331 10413.19657861]] m0: [189.81161604 38.07602882 5.06442695 389.757896 101.07101607 12.54663733] V0: [[ 9.36569911e-03 -7.20062219e-03 -5.74375537e-04 1.19818244e-02 -4.79307770e-03 -7.55340580e-04] [-7.20062219e-03 7.85829678e-02 -3.60578172e-03 -5.15218505e-03 5.80034583e-03 5.56223014e-04] [-5.74375537e-04 -3.60578172e-03 9.88151249e-02 -7.70119870e-04 5.38936523e-04 8.96573269e-05] [ 1.19818244e-02 -5.15218505e-03 -7.70119870e-04 2.54333434e-02 -1.33584636e-02 -1.57689060e-03] [-4.79307770e-03 5.80034583e-03 5.38936523e-04 -1.33584636e-02 8.63755673e-02 -2.87209936e-03] [-7.55340580e-04 5.56223014e-04 8.96573269e-05 -1.57689060e-03 -2.87209936e-03 9.89402706e-02]] LogLike[0111]=-351.460260 sigma_a=137.619407 R: [[ 3062.29531343 5649.41290736] [ 5649.41290736 10422.22781044]] m0: [189.82862629 38.03843851 5.06345632 389.75753896 101.09345648 12.54727427] V0: [[ 9.33529309e-03 -7.16751180e-03 -5.71989361e-04 1.20003324e-02 -4.81174135e-03 -7.56789023e-04] [-7.16751180e-03 7.85257680e-02 -3.60849210e-03 -5.17301620e-03 5.83180925e-03 5.57797776e-04] [-5.71989361e-04 -3.60849210e-03 9.88148536e-02 -7.71630759e-04 5.40395749e-04 8.97911284e-05] [ 1.20003324e-02 -5.17301620e-03 -7.71630759e-04 2.54202001e-02 -1.33469921e-02 -1.57597404e-03] [-4.81174135e-03 5.83180925e-03 5.40395749e-04 -1.33469921e-02 8.63577160e-02 -2.87308977e-03] [-7.56789023e-04 5.57797776e-04 8.97911284e-05 -1.57597404e-03 -2.87308977e-03 9.89401434e-02]] LogLike[0112]=-351.348707 sigma_a=137.265170 R: [[ 3064.96334012 5654.25301488] [ 5654.25301488 10431.00513947]] m0: [189.84536077 38.00102896 5.0625087 389.75735338 101.11579828 12.54789889] V0: [[ 9.30556157e-03 -7.13512309e-03 -5.69656927e-04 1.20184157e-02 -4.83000186e-03 -7.58205153e-04] [-7.13512309e-03 7.84692436e-02 -3.61114524e-03 -5.19339189e-03 5.86289331e-03 5.59337735e-04] [-5.69656927e-04 -3.61114524e-03 9.88145861e-02 -7.73107900e-04 5.41822743e-04 8.99224684e-05] [ 1.20184157e-02 -5.19339189e-03 -7.73107900e-04 2.54073248e-02 -1.33357758e-02 -1.57507805e-03] [-4.83000186e-03 5.86289331e-03 5.41822743e-04 -1.33357758e-02 8.63400766e-02 -2.87406114e-03] [-7.58205153e-04 5.59337735e-04 8.99224684e-05 -1.57507805e-03 -2.87406114e-03 9.89400176e-02]] LogLike[0113]=-351.240282 sigma_a=136.920164 R: [[ 3067.55355514 5658.95260148] [ 5658.95260148 10439.52885308]] m0: [189.86182673 37.96379533 5.06158347 389.7573348 101.1380441 12.54851149] V0: [[ 9.27648682e-03 -7.10343744e-03 -5.67376810e-04 1.20360853e-02 -4.84786958e-03 -7.59589819e-04] [-7.10343744e-03 7.84133772e-02 -3.61374262e-03 -5.21332372e-03 5.89360762e-03 5.60843788e-04] [-5.67376810e-04 -3.61374262e-03 9.88143223e-02 -7.74552178e-04 5.43218340e-04 9.00514129e-05] [ 1.20360853e-02 -5.21332372e-03 -7.74552178e-04 2.53947107e-02 -1.33248081e-02 -1.57420209e-03] [-4.84786958e-03 5.89360762e-03 5.43218340e-04 -1.33248081e-02 8.63226439e-02 -2.87501396e-03] [-7.59589819e-04 5.60843788e-04 9.00514129e-05 -1.57420209e-03 -2.87501396e-03 9.89398929e-02]] LogLike[0114]=-351.134863 sigma_a=136.584278 R: [[ 3070.06656854 5663.51264453] [ 5663.51264453 10447.80047881]] m0: [189.87803119 37.92673299 5.06068005 389.7574789 101.16019644 12.54911241] V0: [[ 9.24805159e-03 -7.07243675e-03 -5.65147626e-04 1.20533522e-02 -4.86535457e-03 -7.60943846e-04] [-7.07243675e-03 7.83581522e-02 -3.61628570e-03 -5.23282301e-03 5.92396149e-03 5.62316805e-04] [-5.65147626e-04 -3.61628570e-03 9.88140622e-02 -7.75964455e-04 5.44583350e-04 9.01780261e-05] [ 1.20533522e-02 -5.23282301e-03 -7.75964455e-04 2.53823511e-02 -1.33140828e-02 -1.57334564e-03] [-4.86535457e-03 5.92396149e-03 5.44583350e-04 -1.33140828e-02 8.63054126e-02 -2.87594872e-03] [-7.60943846e-04 5.62316805e-04 9.01780261e-05 -1.57334564e-03 -2.87594872e-03 9.89397695e-02]] LogLike[0115]=-351.032332 sigma_a=136.257371 R: [[ 3072.5032775 5667.93466064] [ 5667.93466064 10455.82255671]] m0: [189.89398102 37.88983753 5.05979787 389.75778144 101.18225769 12.54970195] V0: [[ 9.22023913e-03 -7.04210338e-03 -5.62968030e-04 1.20702266e-02 -4.88246665e-03 -7.62268034e-04] [-7.04210338e-03 7.83035525e-02 -3.61877588e-03 -5.25190078e-03 5.95396396e-03 5.63757634e-04] [-5.62968030e-04 -3.61877588e-03 9.88138056e-02 -7.77345568e-04 5.45918563e-04 9.03023704e-05] [ 1.20702266e-02 -5.25190078e-03 -7.77345568e-04 2.53702394e-02 -1.33035938e-02 -1.57250820e-03] [-4.88246665e-03 5.95396396e-03 5.45918563e-04 -1.33035938e-02 8.62883778e-02 -2.87686586e-03] [-7.62268034e-04 5.63757634e-04 9.03023704e-05 -1.57250820e-03 -2.87686586e-03 9.89396473e-02]] LogLike[0116]=-350.932577 sigma_a=135.939278 R: [[ 3074.86484343 5672.22066247] [ 5672.22066247 10463.59855808]] m0: [189.90968287 37.85310471 5.05893638 389.75823835 101.20423015 12.55028041] V0: [[ 9.19303310e-03 -7.01242017e-03 -5.60836713e-04 1.20867189e-02 -4.89921536e-03 -7.63563166e-04] [-7.01242017e-03 7.82495623e-02 -3.62121454e-03 -5.27056777e-03 5.98362382e-03 5.65167102e-04] [-5.60836713e-04 -3.62121454e-03 9.88135524e-02 -7.78696331e-04 5.47224747e-04 9.04245062e-05] [ 1.20867189e-02 -5.27056777e-03 -7.78696331e-04 2.53583693e-02 -1.32933349e-02 -1.57168927e-03] [-4.89921536e-03 5.98362382e-03 5.47224747e-04 -1.32933349e-02 8.62715344e-02 -2.87776586e-03] [-7.63563166e-04 5.65167102e-04 9.04245062e-05 -1.57168927e-03 -2.87776586e-03 9.89395262e-02]] LogLike[0117]=-350.835489 sigma_a=135.629812 R: [[ 3077.15262449 5676.37303362] [ 5676.37303362 10471.1326534 ]] m0: [189.92514322 37.81653046 5.05809503 389.75884561 101.22611602 12.55084808] V0: [[ 9.16641766e-03 -6.98337038e-03 -5.58752402e-04 1.21028388e-02 -4.91561003e-03 -7.64830001e-04] [-6.98337038e-03 7.81961663e-02 -3.62360301e-03 -5.28883444e-03 6.01294962e-03 5.66546013e-04] [-5.58752402e-04 -3.62360301e-03 9.88133026e-02 -7.80017538e-04 5.48502651e-04 9.05444924e-05] [ 1.21028388e-02 -5.28883444e-03 -7.80017538e-04 2.53467345e-02 -1.32833005e-02 -1.57088837e-03] [-4.91561003e-03 6.01294962e-03 5.48502651e-04 -1.32833005e-02 8.62548779e-02 -2.87864914e-03] [-7.64830001e-04 5.66546013e-04 9.05444924e-05 -1.57088837e-03 -2.87864914e-03 9.89394062e-02]] LogLike[0118]=-350.740964 sigma_a=135.328776 R: [[ 3079.3681451 5680.39447173] [ 5680.39447173 10478.42960609]] m0: [189.94036841 37.78011086 5.0572733 389.75959934 101.24791743 12.55140524] V0: [[ 9.14037732e-03 -6.95493771e-03 -5.56713860e-04 1.21185961e-02 -4.93165972e-03 -7.66069278e-04] [-6.95493771e-03 7.81433496e-02 -3.62594260e-03 -5.30671102e-03 6.04194967e-03 5.67895152e-04] [-5.56713860e-04 -3.62594260e-03 9.88130560e-02 -7.81309961e-04 5.49753004e-04 9.06623862e-05] [ 1.21185961e-02 -5.30671102e-03 -7.81309961e-04 2.53353290e-02 -1.32734847e-02 -1.57010505e-03] [-4.93165972e-03 6.04194967e-03 5.49753004e-04 -1.32734847e-02 8.62384034e-02 -2.87951614e-03] [-7.66069278e-04 5.67895152e-04 9.06623862e-05 -1.57010505e-03 -2.87951614e-03 9.89392873e-02]] LogLike[0119]=-350.648905 sigma_a=135.035958 R: [[ 3081.5130505 5684.28790406] [ 5684.28790406 10485.49461576]] m0: [189.95536458 37.74384214 5.05647068 389.76049574 101.26963642 12.55195216] V0: [[ 9.11489705e-03 -6.92710627e-03 -5.54719880e-04 1.21340002e-02 -4.94737328e-03 -7.67281717e-04] [-6.92710627e-03 7.80910979e-02 -3.62823456e-03 -5.32420745e-03 6.07063210e-03 5.69215283e-04] [-5.54719880e-04 -3.62823456e-03 9.88128126e-02 -7.82574351e-04 5.50976516e-04 9.07782431e-05] [ 1.21340002e-02 -5.32420745e-03 -7.82574351e-04 2.53241468e-02 -1.32638821e-02 -1.56933884e-03] [-4.94737328e-03 6.07063210e-03 5.50976516e-04 -1.32638821e-02 8.62221066e-02 -2.88036727e-03] [-7.67281717e-04 5.69215283e-04 9.07782431e-05 -1.56933884e-03 -2.88036727e-03 9.89391694e-02]] LogLike[0120]=-350.559216 sigma_a=134.751138 R: [[ 3083.58908896 5688.05645413] [ 5688.05645413 10492.33325566]] m0: [189.97013772 37.70772062 5.05568666 389.76153113 101.29127498 12.55248912] V0: [[ 9.08996220e-03 -6.89986056e-03 -5.52769289e-04 1.21490601e-02 -4.96275935e-03 -7.68468020e-04] [-6.89986056e-03 7.80393969e-02 -3.63048015e-03 -5.34133346e-03 6.09900477e-03 5.70507152e-04] [-5.52769289e-04 -3.63048015e-03 9.88125723e-02 -7.83811440e-04 5.52173881e-04 9.08921173e-05] [ 1.21490601e-02 -5.34133346e-03 -7.83811440e-04 2.53131824e-02 -1.32544872e-02 -1.56858930e-03] [-4.96275935e-03 6.09900477e-03 5.52173881e-04 -1.32544872e-02 8.62059830e-02 -2.88120295e-03] [-7.68468020e-04 5.70507152e-04 9.08921173e-05 -1.56858930e-03 -2.88120295e-03 9.89390525e-02]] LogLike[0121]=-350.471806 sigma_a=134.474093 R: [[ 3085.59808344 5691.70338903] [ 5691.70338903 10498.95137466]] m0: [189.98469367 37.67174279 5.05492078 389.76270189 101.31283501 12.55301637] V0: [[ 9.06555848e-03 -6.87318549e-03 -5.50860945e-04 1.21637850e-02 -4.97782633e-03 -7.69628869e-04] [-6.87318549e-03 7.79882329e-02 -3.63268055e-03 -5.35809850e-03 6.12707540e-03 5.71771485e-04] [-5.50860945e-04 -3.63268055e-03 9.88123349e-02 -7.85021943e-04 5.53345773e-04 9.10040614e-05] [ 1.21637850e-02 -5.35809850e-03 -7.85021943e-04 2.53024300e-02 -1.32452946e-02 -1.56785600e-03] [-4.97782633e-03 6.12707540e-03 5.53345773e-04 -1.32452946e-02 8.61900283e-02 -2.88202356e-03] [-7.69628869e-04 5.71771485e-04 9.10040614e-05 -1.56785600e-03 -2.88202356e-03 9.89389367e-02]] LogLike[0122]=-350.386587 sigma_a=134.204591 R: [[ 3087.54189671 5695.23205462] [ 5695.23205462 10505.35497684]] m0: [189.99903811 37.63590521 5.05417254 389.76400452 101.33431839 12.55353417] V0: [[ 9.04167199e-03 -6.84706635e-03 -5.48993736e-04 1.21781833e-02 -4.99258242e-03 -7.70764930e-04] [-6.84706635e-03 7.79375926e-02 -3.63483694e-03 -5.37451183e-03 6.15485148e-03 5.73008991e-04] [-5.48993736e-04 -3.63483694e-03 9.88121005e-02 -7.86206552e-04 5.54492850e-04 9.11141267e-05] [ 1.21781833e-02 -5.37451183e-03 -7.86206552e-04 2.52918841e-02 -1.32362994e-02 -1.56713852e-03] [-4.99258242e-03 6.15485148e-03 5.54492850e-04 -1.32362994e-02 8.61742384e-02 -2.88282950e-03] [-7.70764930e-04 5.73008991e-04 9.11141267e-05 -1.56713852e-03 -2.88282950e-03 9.89388218e-02]] LogLike[0123]=-350.303476 sigma_a=133.942403 R: [[ 3089.42243848 5698.64588836] [ 5698.64588836 10511.55024457]] m0: [190.01317657 37.60020457 5.05344149 389.76543559 101.3557269 12.55404276] V0: [[ 9.01828920e-03 -6.82148877e-03 -5.47166578e-04 1.21922638e-02 -5.00703562e-03 -7.71876851e-04] [-6.82148877e-03 7.78874630e-02 -3.63695047e-03 -5.39058245e-03 6.18234031e-03 5.74220359e-04] [-5.47166578e-04 -3.63695047e-03 9.88118689e-02 -7.87365945e-04 5.55615752e-04 9.12223630e-05] [ 1.21922638e-02 -5.39058245e-03 -7.87365945e-04 2.52815396e-02 -1.32274963e-02 -1.56643645e-03] [-5.00703562e-03 6.18234031e-03 5.55615752e-04 -1.32274963e-02 8.61586092e-02 -2.88362115e-03] [-7.71876851e-04 5.74220359e-04 9.12223630e-05 -1.56643645e-03 -2.88362115e-03 9.89387078e-02]] LogLike[0124]=-350.222394 sigma_a=133.687298 R: [[ 3091.24162545 5701.94834516] [ 5701.94834516 10517.54340099]] m0: [190.02711446 37.56463764 5.05272718 389.76699179 101.3770623 12.55454239] V0: [[ 8.99539694e-03 -6.79643880e-03 -5.45378418e-04 1.22060345e-02 -5.02119371e-03 -7.72965262e-04] [-6.79643880e-03 7.78378312e-02 -3.63902225e-03 -5.40631913e-03 6.20954903e-03 5.75406262e-04] [-5.45378418e-04 -3.63902225e-03 9.88116401e-02 -7.88500781e-04 5.56715104e-04 9.13288190e-05] [ 1.22060345e-02 -5.40631913e-03 -7.88500781e-04 2.52713910e-02 -1.32188805e-02 -1.56574939e-03] [-5.02119371e-03 6.20954903e-03 5.56715104e-04 -1.32188805e-02 8.61431366e-02 -2.88439887e-03] [-7.72965262e-04 5.75406262e-04 9.13288190e-05 -1.56574939e-03 -2.88439887e-03 9.89385948e-02]] LogLike[0125]=-350.143263 sigma_a=133.439050 R: [[ 3093.00137838 5705.14289177] [ 5705.14289177 10523.34069911]] m0: [190.040857 37.5292013 5.05202918 389.76866986 101.39832629 12.55503329] V0: [[ 8.97298235e-03 -6.77190279e-03 -5.43628231e-04 1.22195035e-02 -5.03506430e-03 -7.74030778e-04] [-6.77190279e-03 7.77886850e-02 -3.64105335e-03 -5.42173045e-03 6.23648457e-03 5.76567356e-04] [-5.43628231e-04 -3.64105335e-03 9.88114139e-02 -7.89611701e-04 5.57791515e-04 9.14335417e-05] [ 1.22195035e-02 -5.42173045e-03 -7.89611701e-04 2.52614336e-02 -1.32104473e-02 -1.56507694e-03] [-5.03506430e-03 6.23648457e-03 5.57791515e-04 -1.32104473e-02 8.61278169e-02 -2.88516302e-03] [-7.74030778e-04 5.76567356e-04 9.14335417e-05 -1.56507694e-03 -2.88516302e-03 9.89384826e-02]] LogLike[0126]=-350.066009 sigma_a=133.197426 R: [[ 3094.70359692 5708.23296 ] [ 5708.23296 10528.94833495]] m0: [190.05440931 37.49389249 5.05134706 389.77046664 101.41952053 12.55551567] V0: [[ 8.95103296e-03 -6.74786750e-03 -5.41915017e-04 1.22326788e-02 -5.04865478e-03 -7.75073997e-04] [-6.74786750e-03 7.77400122e-02 -3.64304484e-03 -5.43682474e-03 6.26315372e-03 5.77704280e-04] [-5.41915017e-04 -3.64304484e-03 9.88111904e-02 -7.90699329e-04 5.58845577e-04 9.15365772e-05] [ 1.22326788e-02 -5.43682474e-03 -7.90699329e-04 2.52516622e-02 -1.32021919e-02 -1.56441874e-03] [-5.04865478e-03 6.26315372e-03 5.58845577e-04 -1.32021919e-02 8.61126462e-02 -2.88591395e-03] [-7.75073997e-04 5.77704280e-04 9.15365772e-05 -1.56441874e-03 -2.88591395e-03 9.89383714e-02]] LogLike[0127]=-349.990562 sigma_a=132.962206 R: [[ 3096.35019301 5711.22200807] [ 5711.22200807 10534.37256028]] m0: [190.06777637 37.45870826 5.05068041 389.77237906 101.44064662 12.55598978] V0: [[ 8.92953659e-03 -6.72431999e-03 -5.40237806e-04 1.22455678e-02 -5.06197236e-03 -7.76095501e-04] [-6.72431999e-03 7.76918012e-02 -3.64499774e-03 -5.45161014e-03 6.28956306e-03 5.78817655e-04] [-5.40237806e-04 -3.64499774e-03 9.88109694e-02 -7.91764274e-04 5.59877867e-04 9.16379701e-05] [ 1.22455678e-02 -5.45161014e-03 -7.91764274e-04 2.52420720e-02 -1.31941098e-02 -1.56377441e-03] [-5.06197236e-03 6.28956306e-03 5.59877867e-04 -1.31941098e-02 8.60976209e-02 -2.88665200e-03] [-7.76095501e-04 5.78817655e-04 9.16379701e-05 -1.56377441e-03 -2.88665200e-03 9.89382609e-02]] LogLike[0128]=-349.916855 sigma_a=132.733167 R: [[ 3097.94303229 5714.11341235] [ 5714.11341235 10539.6194824 ]] m0: [190.080963 37.42364572 5.05002883 389.77440412 101.46170614 12.55645582] V0: [[ 8.90848142e-03 -6.70124767e-03 -5.38595652e-04 1.22581780e-02 -5.07502406e-03 -7.77095857e-04] [-6.70124767e-03 7.76440403e-02 -3.64691305e-03 -5.46609456e-03 6.31571903e-03 5.79908088e-04] [-5.38595652e-04 -3.64691305e-03 9.88107508e-02 -7.92807127e-04 5.60888947e-04 9.17377640e-05] [ 1.22581780e-02 -5.46609456e-03 -7.92807127e-04 2.52326585e-02 -1.31861965e-02 -1.56314359e-03] [-5.07502406e-03 6.31571903e-03 5.60888947e-04 -1.31861965e-02 8.60827373e-02 -2.88737750e-03] [-7.77095857e-04 5.79908088e-04 9.17377640e-05 -1.56314359e-03 -2.88737750e-03 9.89381513e-02]] LogLike[0129]=-349.844822 sigma_a=132.510097 R: [[ 3099.48395309 5716.91050209] [ 5716.91050209 10544.69512782]] m0: [190.09397392 37.38870208 5.04939193 389.77653889 101.4827006 12.556914 ] V0: [[ 8.88785592e-03 -6.67863828e-03 -5.36987635e-04 1.22705167e-02 -5.08781672e-03 -7.78075617e-04] [-6.67863828e-03 7.75967185e-02 -3.64879175e-03 -5.48028573e-03 6.34162790e-03 5.80976168e-04] [-5.36987635e-04 -3.64879175e-03 9.88105347e-02 -7.93828464e-04 5.61879365e-04 9.18360012e-05] [ 1.22705167e-02 -5.48028573e-03 -7.93828464e-04 2.52234171e-02 -1.31784479e-02 -1.56252593e-03] [-5.08781672e-03 6.34162790e-03 5.61879365e-04 -1.31784479e-02 8.60679921e-02 -2.88809079e-03] [-7.78075617e-04 5.80976168e-04 9.18360012e-05 -1.56252593e-03 -2.88809079e-03 9.89380425e-02]] LogLike[0130]=-349.774401 sigma_a=132.292784 R: [[ 3100.97474494 5719.61651964] [ 5719.61651964 10549.60536852]] m0: [190.1068137 37.35387461 5.04876932 389.77878055 101.5036315 12.55736452] V0: [[ 8.86764889e-03 -6.65647991e-03 -5.35412862e-04 1.22825909e-02 -5.10035699e-03 -7.79035316e-04] [-6.65647991e-03 7.75498249e-02 -3.65063478e-03 -5.49419115e-03 6.36729577e-03 5.82022470e-04] [-5.35412862e-04 -3.65063478e-03 9.88103209e-02 -7.94828845e-04 5.62849652e-04 9.19327229e-05] [ 1.22825909e-02 -5.49419115e-03 -7.94828845e-04 2.52143433e-02 -1.31708595e-02 -1.56192109e-03] [-5.10035699e-03 6.36729577e-03 5.62849652e-04 -1.31708595e-02 8.60533817e-02 -2.88879216e-03] [-7.79035316e-04 5.82022470e-04 9.19327229e-05 -1.56192109e-03 -2.88879216e-03 9.89379344e-02]] LogLike[0131]=-349.705533 sigma_a=132.081022 R: [[ 3102.41717277 5722.23466485] [ 5722.23466485 10554.3560035 ]] m0: [190.11948681 37.31916065 5.04816064 389.78112632 101.52450028 12.5578076 ] V0: [[ 8.84784943e-03 -6.63476092e-03 -5.33870462e-04 1.22944075e-02 -5.11265137e-03 -7.79975477e-04] [-6.63476092e-03 7.75033489e-02 -3.65244307e-03 -5.50781814e-03 6.39272861e-03 5.83047554e-04] [-5.33870462e-04 -3.65244307e-03 9.88101094e-02 -7.95808816e-04 5.63800327e-04 9.20279691e-05] [ 1.22944075e-02 -5.50781814e-03 -7.95808816e-04 2.52054328e-02 -1.31634275e-02 -1.56132874e-03] [-5.11265137e-03 6.39272861e-03 5.63800327e-04 -1.31634275e-02 8.60389028e-02 -2.88948192e-03] [-7.79975477e-04 5.83047554e-04 9.20279691e-05 -1.56132874e-03 -2.88948192e-03 9.89378271e-02]] LogLike[0132]=-349.638161 sigma_a=131.874614 R: [[ 3103.81295359 5724.76805193] [ 5724.76805193 10558.95267881]] m0: [190.13199756 37.2845576 5.04756553 389.78357352 101.54530836 12.55824341] V0: [[ 8.82844693e-03 -6.61347002e-03 -5.32359590e-04 1.23059730e-02 -5.12470615e-03 -7.80896608e-04] [-6.61347002e-03 7.74572801e-02 -3.65421751e-03 -5.52117383e-03 6.41793220e-03 5.84051962e-04] [-5.32359590e-04 -3.65421751e-03 9.88099001e-02 -7.96768907e-04 5.64731895e-04 9.21217786e-05] [ 1.23059730e-02 -5.52117383e-03 -7.96768907e-04 2.51966814e-02 -1.31561477e-02 -1.56074857e-03] [-5.12470615e-03 6.41793220e-03 5.64731895e-04 -1.31561477e-02 8.60245522e-02 -2.89016039e-03] [-7.80896608e-04 5.84051962e-04 9.21217786e-05 -1.56074857e-03 -2.89016039e-03 9.89377205e-02]] LogLike[0133]=-349.572229 sigma_a=131.673364 R: [[ 3105.16374249 5727.2196834 ] [ 5727.2196834 10563.40083931]] m0: [190.14435018 37.25006296 5.04698364 389.78611953 101.56605711 12.55867214] V0: [[ 8.80943109e-03 -6.59259621e-03 -5.30879422e-04 1.23172940e-02 -5.13652747e-03 -7.81799202e-04] [-6.59259621e-03 7.74116085e-02 -3.65595897e-03 -5.53426515e-03 6.44291220e-03 5.85036226e-04] [-5.30879422e-04 -3.65595897e-03 9.88096929e-02 -7.97709633e-04 5.65644846e-04 9.22141895e-05] [ 1.23172940e-02 -5.53426515e-03 -7.97709633e-04 2.51880851e-02 -1.31490162e-02 -1.56018024e-03] [-5.13652747e-03 6.44291220e-03 5.65644846e-04 -1.31490162e-02 8.60103267e-02 -2.89082783e-03] [-7.81799202e-04 5.85036226e-04 9.22141895e-05 -1.56018024e-03 -2.89082783e-03 9.89376147e-02]] LogLike[0134]=-349.507685 sigma_a=131.477089 R: [[ 3106.47117106 5729.5925209 ] [ 5729.5925209 10567.70585889]] m0: [190.15654875 37.21567425 5.04641463 389.78876181 101.58674787 12.55909399] V0: [[ 8.79079187e-03 -6.57212880e-03 -5.29429160e-04 1.23283769e-02 -5.14812132e-03 -7.82683740e-04] [-6.57212880e-03 7.73663243e-02 -3.65766831e-03 -5.54709883e-03 6.46767411e-03 5.86000859e-04] [-5.29429160e-04 -3.65766831e-03 9.88094879e-02 -7.98631497e-04 5.66539658e-04 9.23052385e-05] [ 1.23283769e-02 -5.54709883e-03 -7.98631497e-04 2.51796398e-02 -1.31420294e-02 -1.55962347e-03] [-5.14812132e-03 6.46767411e-03 5.66539658e-04 -1.31420294e-02 8.59962233e-02 -2.89148455e-03] [-7.82683740e-04 5.86000859e-04 9.23052385e-05 -1.55962347e-03 -2.89148455e-03 9.89375095e-02]] LogLike[0135]=-349.444479 sigma_a=131.285601 R: [[ 3107.73678564 5731.88937105] [ 5731.88937105 10571.87282977]] m0: [190.16859727 37.18138909 5.04585817 389.79149788 101.60738196 12.55950912] V0: [[ 8.77251955e-03 -6.55205737e-03 -5.28008027e-04 1.23392276e-02 -5.15949350e-03 -7.83550689e-04] [-6.55205737e-03 7.73214179e-02 -3.65934635e-03 -5.55968146e-03 6.49222328e-03 5.86946363e-04] [-5.28008027e-04 -3.65934635e-03 9.88092849e-02 -7.99534985e-04 5.67416794e-04 9.23949613e-05] [ 1.23392276e-02 -5.55968146e-03 -7.99534985e-04 2.51713417e-02 -1.31351836e-02 -1.55907794e-03] [-5.15949350e-03 6.49222328e-03 5.67416794e-04 -1.31351836e-02 8.59822390e-02 -2.89213081e-03] [-7.83550689e-04 5.86946363e-04 9.23949613e-05 -1.55907794e-03 -2.89213081e-03 9.89374050e-02]] LogLike[0136]=-349.382563 sigma_a=131.098730 R: [[ 3108.96213268 5734.11304285] [ 5734.11304285 10575.90685255]] m0: [190.18049959 37.14720515 5.04531394 389.79432534 101.62796064 12.55991771] V0: [[ 8.75460463e-03 -6.53237181e-03 -5.26615267e-04 1.23498522e-02 -5.17064966e-03 -7.84400501e-04] [-6.53237181e-03 7.72768802e-02 -3.66099389e-03 -5.57201941e-03 6.51656493e-03 5.87873226e-04] [-5.26615267e-04 -3.66099389e-03 9.88090839e-02 -8.00420572e-04 5.68276707e-04 9.24833927e-05] [ 1.23498522e-02 -5.57201941e-03 -8.00420572e-04 2.51631870e-02 -1.31284751e-02 -1.55854339e-03] [-5.17064966e-03 6.51656493e-03 5.68276707e-04 -1.31284751e-02 8.59683708e-02 -2.89276687e-03] [-7.84400501e-04 5.87873226e-04 9.24833927e-05 -1.55854339e-03 -2.89276687e-03 9.89373012e-02]] LogLike[0137]=-349.321889 sigma_a=130.916305 R: [[ 3110.14866578 5736.26617604] [ 5736.26617604 10579.81271938]] m0: [190.19225949 37.11312015 5.04478162 389.79724184 101.64848516 12.56031993] V0: [[ 8.73703791e-03 -6.51306227e-03 -5.25250147e-04 1.23602565e-02 -5.18159529e-03 -7.85233620e-04] [-6.51306227e-03 7.72327020e-02 -3.66261171e-03 -5.58411889e-03 6.54070413e-03 5.88781921e-04] [-5.25250147e-04 -3.66261171e-03 9.88088848e-02 -8.01288719e-04 5.69119835e-04 9.25705666e-05] [ 1.23602565e-02 -5.58411889e-03 -8.01288719e-04 2.51551721e-02 -1.31219005e-02 -1.55801951e-03] [-5.18159529e-03 6.54070413e-03 5.69119835e-04 -1.31219005e-02 8.59546160e-02 -2.89339301e-03] [-7.85233620e-04 5.88781921e-04 9.25705666e-05 -1.55801951e-03 -2.89339301e-03 9.89371980e-02]] LogLike[0138]=-349.262414 sigma_a=130.738162 R: [[ 3111.29780104 5738.35134311] [ 5738.35134311 10583.59510192]] m0: [190.20388061 37.07913189 5.04426092 389.8002451 101.66895674 12.56071595] V0: [[ 8.71981044e-03 -6.49411918e-03 -5.23911953e-04 1.23704461e-02 -5.19233576e-03 -7.86050473e-04] [-6.49411918e-03 7.71888745e-02 -3.66420057e-03 -5.59598594e-03 6.56464582e-03 5.89672910e-04] [-5.23911953e-04 -3.66420057e-03 9.88086876e-02 -8.02139872e-04 5.69946605e-04 9.26565158e-05] [ 1.23704461e-02 -5.59598594e-03 -8.02139872e-04 2.51472935e-02 -1.31154563e-02 -1.55750604e-03] [-5.19233576e-03 6.56464582e-03 5.69946605e-04 -1.31154563e-02 8.59409717e-02 -2.89400946e-03] [-7.86050473e-04 5.89672910e-04 9.26565158e-05 -1.55750604e-03 -2.89400946e-03 9.89370954e-02]] LogLike[0139]=-349.204094 sigma_a=130.564145 R: [[ 3112.41091322 5740.37104212] [ 5740.37104212 10587.25853789]] m0: [190.21536651 37.0452382 5.04375155 389.80333293 101.68937655 12.56110591] V0: [[ 8.70291352e-03 -6.47553325e-03 -5.22599992e-04 1.23804264e-02 -5.20287624e-03 -7.86851477e-04] [-6.47553325e-03 7.71453892e-02 -3.66576122e-03 -5.60762644e-03 6.58839483e-03 5.90546639e-04] [-5.22599992e-04 -3.66576122e-03 9.88084922e-02 -8.02974469e-04 5.70757432e-04 9.27412722e-05] [ 1.23804264e-02 -5.60762644e-03 -8.02974469e-04 2.51395476e-02 -1.31091394e-02 -1.55700273e-03] [-5.20287624e-03 6.58839483e-03 5.70757432e-04 -1.31091394e-02 8.59274352e-02 -2.89461649e-03] [-7.86851477e-04 5.90546639e-04 9.27412722e-05 -1.55700273e-03 -2.89461649e-03 9.89369934e-02]] LogLike[0140]=-349.146890 sigma_a=130.394100 R: [[ 3113.48931381 5742.32765613] [ 5742.32765613 10590.80735611]] m0: [190.22672063 37.01143698 5.04325321 389.80650316 101.70974574 12.56148998] V0: [[ 8.68633870e-03 -6.45729542e-03 -5.21313592e-04 1.23902029e-02 -5.21322180e-03 -7.87637037e-04] [-6.45729542e-03 7.71022379e-02 -3.66729436e-03 -5.61904611e-03 6.61195581e-03 5.91403546e-04] [-5.21313592e-04 -3.66729436e-03 9.88082986e-02 -8.03792931e-04 5.71552719e-04 9.28248669e-05] [ 1.23902029e-02 -5.61904611e-03 -8.03792931e-04 2.51319311e-02 -1.31029465e-02 -1.55650929e-03] [-5.21322180e-03 6.61195581e-03 5.71552719e-04 -1.31029465e-02 8.59140040e-02 -2.89521432e-03] [-7.87637037e-04 5.91403546e-04 9.28248669e-05 -1.55650929e-03 -2.89521432e-03 9.89368920e-02]] LogLike[0141]=-349.090760 sigma_a=130.227884 R: [[ 3114.53428282 5744.22351175] [ 5744.22351175 10594.24578425]] m0: [190.23794634 36.97772618 5.04276563 389.80975372 101.73006543 12.56186831] V0: [[ 8.67007778e-03 -6.43939690e-03 -5.20052098e-04 1.23997807e-02 -5.22337735e-03 -7.88407546e-04] [-6.43939690e-03 7.70594122e-02 -3.66880069e-03 -5.63025048e-03 6.63533335e-03 5.92244053e-04] [-5.20052098e-04 -3.66880069e-03 9.88081067e-02 -8.04595670e-04 5.72332858e-04 9.29073299e-05] [ 1.23997807e-02 -5.63025048e-03 -8.04595670e-04 2.51244407e-02 -1.30968745e-02 -1.55602550e-03] [-5.22337735e-03 6.63533335e-03 5.72332858e-04 -1.30968745e-02 8.59006755e-02 -2.89580318e-03] [-7.88407546e-04 5.92244053e-04 9.29073299e-05 -1.55602550e-03 -2.89580318e-03 9.89367912e-02]] LogLike[0142]=-349.035669 sigma_a=130.065353 R: [[ 3115.54703647 5746.06081948] [ 5746.06081948 10597.57783867]] m0: [190.24904687 36.94410382 5.04228854 389.81308255 101.75033673 12.56224104] V0: [[ 8.65412277e-03 -6.42182916e-03 -5.18814875e-04 1.24091648e-02 -5.23334766e-03 -7.89163388e-04] [-6.42182916e-03 7.70169045e-02 -3.67028089e-03 -5.64124496e-03 6.65853185e-03 5.93068572e-04] [-5.18814875e-04 -3.67028089e-03 9.88079166e-02 -8.05383084e-04 5.73098230e-04 9.29886905e-05] [ 1.24091648e-02 -5.64124496e-03 -8.05383084e-04 2.51170733e-02 -1.30909204e-02 -1.55555110e-03] [-5.23334766e-03 6.65853185e-03 5.73098230e-04 -1.30909204e-02 8.58874472e-02 -2.89638332e-03] [-7.89163388e-04 5.93068572e-04 9.29886905e-05 -1.55555110e-03 -2.89638332e-03 9.89366910e-02]] LogLike[0143]=-348.981579 sigma_a=129.906374 R: [[ 3116.5287616 5747.84173701] [ 5747.84173701 10600.80744103]] m0: [190.26002541 36.91056794 5.04182167 389.81648771 101.77056069 12.56260831] V0: [[ 8.63846592e-03 -6.40458389e-03 -5.17601307e-04 1.24183600e-02 -5.24313737e-03 -7.89904932e-04] [-6.40458389e-03 7.69747069e-02 -3.67173562e-03 -5.65203480e-03 6.68155564e-03 5.93877503e-04] [-5.17601307e-04 -3.67173562e-03 9.88077280e-02 -8.06155563e-04 5.73849206e-04 9.30689772e-05] [ 1.24183600e-02 -5.65203480e-03 -8.06155563e-04 2.51098258e-02 -1.30850812e-02 -1.55508586e-03] [-5.24313737e-03 6.68155564e-03 5.73849206e-04 -1.30850812e-02 8.58743167e-02 -2.89695493e-03] [-7.89904932e-04 5.93877503e-04 9.30689772e-05 -1.55508586e-03 -2.89695493e-03 9.89365913e-02]] LogLike[0144]=-348.928455 sigma_a=129.750815 R: [[ 3117.48058642 5749.56831537] [ 5749.56831537 10603.93831881]] m0: [190.27088502 36.87711666 5.04136479 389.81996726 101.79073836 12.56297027] V0: [[ 8.62309971e-03 -6.38765302e-03 -5.16410793e-04 1.24273712e-02 -5.25275100e-03 -7.90632541e-04] [-6.38765302e-03 7.69328121e-02 -3.67316551e-03 -5.66262510e-03 6.70440891e-03 5.94671235e-04] [-5.16410793e-04 -3.67316551e-03 9.88075411e-02 -8.06913481e-04 5.74586145e-04 9.31482176e-05] [ 1.24273712e-02 -5.66262510e-03 -8.06913481e-04 2.51026951e-02 -1.30793541e-02 -1.55462955e-03] [-5.25275100e-03 6.70440891e-03 5.74586145e-04 -1.30793541e-02 8.58612816e-02 -2.89751825e-03] [-7.90632541e-04 5.94671235e-04 9.31482176e-05 -1.55462955e-03 -2.89751825e-03 9.89364921e-02]] LogLike[0145]=-348.876265 sigma_a=129.598555 R: [[ 3118.40360709 5751.24254768] [ 5751.24254768 10606.97409508]] m0: [190.28162869 36.84374813 5.04091763 389.82351934 101.81087074 12.56332704] V0: [[ 8.60801683e-03 -6.37102871e-03 -5.15242753e-04 1.24362029e-02 -5.26219292e-03 -7.91346564e-04] [-6.37102871e-03 7.68912129e-02 -3.67457120e-03 -5.67302082e-03 6.72709574e-03 5.95450145e-04] [-5.15242753e-04 -3.67457120e-03 9.88073557e-02 -8.07657207e-04 5.75309396e-04 9.32264386e-05] [ 1.24362029e-02 -5.67302082e-03 -8.07657207e-04 2.50956784e-02 -1.30737363e-02 -1.55418194e-03] [-5.26219292e-03 6.72709574e-03 5.75309396e-04 -1.30737363e-02 8.58483398e-02 -2.89807347e-03] [-7.91346564e-04 5.95450145e-04 9.32264386e-05 -1.55418194e-03 -2.89807347e-03 9.89363934e-02]] LogLike[0146]=-348.824977 sigma_a=129.449471 R: [[ 3119.29885771 5752.86631391] [ 5752.86631391 10609.91818652]] m0: [190.29225931 36.81046056 5.04047996 389.82714215 101.83095882 12.56367876] V0: [[ 8.59321017e-03 -6.35470334e-03 -5.14096621e-04 1.24448597e-02 -5.27146739e-03 -7.92047342e-04] [-6.35470334e-03 7.68499020e-02 -3.67595327e-03 -5.68322676e-03 6.74962010e-03 5.96214600e-04] [-5.14096621e-04 -3.67595327e-03 9.88071719e-02 -8.08387095e-04 5.76019299e-04 9.33036662e-05] [ 1.24448597e-02 -5.68322676e-03 -8.08387095e-04 2.50887729e-02 -1.30682250e-02 -1.55374282e-03] [-5.27146739e-03 6.74962010e-03 5.76019299e-04 -1.30682250e-02 8.58354888e-02 -2.89862079e-03] [-7.92047342e-04 5.96214600e-04 9.33036662e-05 -1.55374282e-03 -2.89862079e-03 9.89362952e-02]] LogLike[0147]=-348.774558 sigma_a=129.303450 R: [[ 3120.16734555 5754.44144568] [ 5754.44144568 10612.77392282]] m0: [190.3027797 36.7772522 5.04005154 389.83083392 101.85100357 12.56402555] V0: [[ 8.57867283e-03 -6.33866952e-03 -5.12971847e-04 1.24533458e-02 -5.28057855e-03 -7.92735206e-04] [-6.33866952e-03 7.68088727e-02 -3.67731232e-03 -5.69324762e-03 6.77198585e-03 5.96964958e-04] [-5.12971847e-04 -3.67731232e-03 9.88069895e-02 -8.09103491e-04 5.76716185e-04 9.33799257e-05] [ 1.24533458e-02 -5.69324762e-03 -8.09103491e-04 2.50819757e-02 -1.30628178e-02 -1.55331198e-03] [-5.28057855e-03 6.77198585e-03 5.76716185e-04 -1.30628178e-02 8.58227267e-02 -2.89916042e-03] [-7.92735206e-04 5.96964958e-04 9.33799257e-05 -1.55331198e-03 -2.89916042e-03 9.89361975e-02]] LogLike[0148]=-348.724981 sigma_a=129.160379 R: [[ 3121.01003882 5755.96970366] [ 5755.96970366 10615.54450487]] m0: [190.3131926 36.74412135 5.03963216 389.83459295 101.87100591 12.56436753] V0: [[ 8.56439811e-03 -6.32292003e-03 -5.11867900e-04 1.24616655e-02 -5.28953043e-03 -7.93410477e-04] [-6.32292003e-03 7.67681183e-02 -3.67864891e-03 -5.70308795e-03 6.79419674e-03 5.97701563e-04] [-5.11867900e-04 -3.67864891e-03 9.88068086e-02 -8.09806731e-04 5.77400373e-04 9.34552417e-05] [ 1.24616655e-02 -5.70308795e-03 -8.09806731e-04 2.50752842e-02 -1.30575120e-02 -1.55288922e-03] [-5.28953043e-03 6.79419674e-03 5.77400373e-04 -1.30575120e-02 8.58100512e-02 -2.89969254e-03] [-7.93410477e-04 5.97701563e-04 9.34552417e-05 -1.55288922e-03 -2.89969254e-03 9.89361003e-02]] LogLike[0149]=-348.676216 sigma_a=129.020150 R: [[ 3121.82787053 5757.45278472] [ 5757.45278472 10618.23301784]] m0: [190.32350066 36.71106634 5.03922158 389.83841757 101.89096677 12.56470483] V0: [[ 8.55037951e-03 -6.30744791e-03 -5.10784260e-04 1.24698229e-02 -5.29832694e-03 -7.94073467e-04] [-6.30744791e-03 7.67276324e-02 -3.67996361e-03 -5.71275215e-03 6.81625643e-03 5.98424754e-04] [-5.10784260e-04 -3.67996361e-03 9.88066291e-02 -8.10497139e-04 5.78072175e-04 9.35296382e-05] [ 1.24698229e-02 -5.71275215e-03 -8.10497139e-04 2.50686959e-02 -1.30523051e-02 -1.55247432e-03] [-5.29832694e-03 6.81625643e-03 5.78072175e-04 -1.30523051e-02 8.57974604e-02 -2.90021734e-03] [-7.94073467e-04 5.98424754e-04 9.35296382e-05 -1.55247432e-03 -2.90021734e-03 9.89360035e-02]] LogLike[0150]=-348.628236 sigma_a=128.882666 R: [[ 3122.62175048 5758.89234382] [ 5758.89234382 10620.84247149]] m0: [190.33370646 36.67808556 5.03881961 389.84230618 101.91088703 12.56503757] V0: [[ 8.53661069e-03 -6.29224636e-03 -5.09720426e-04 1.24778221e-02 -5.30697186e-03 -7.94724479e-04] [-6.29224636e-03 7.66874085e-02 -3.68125694e-03 -5.72224455e-03 6.83816846e-03 5.99134858e-04] [-5.09720426e-04 -3.68125694e-03 9.88064509e-02 -8.11175034e-04 5.78731894e-04 9.36031383e-05] [ 1.24778221e-02 -5.72224455e-03 -8.11175034e-04 2.50622081e-02 -1.30471948e-02 -1.55206711e-03] [-5.30697186e-03 6.83816846e-03 5.78731894e-04 -1.30471948e-02 8.57849522e-02 -2.90073499e-03] [-7.94724479e-04 5.99134858e-04 9.36031383e-05 -1.55206711e-03 -2.90073499e-03 9.89359072e-02]] LogLike[0151]=-348.581015 sigma_a=128.747828 R: [[ 3123.39249624 5760.28986688] [ 5760.28986688 10623.37556556]] m0: [190.34381251 36.64517744 5.03842602 389.8462572 101.93076756 12.56536585] V0: [[ 8.52308553e-03 -6.27730878e-03 -5.08675909e-04 1.24856668e-02 -5.31546890e-03 -7.95363808e-04] [-6.27730878e-03 7.66474406e-02 -3.68252943e-03 -5.73156929e-03 6.85993630e-03 5.99832192e-04] [-5.08675909e-04 -3.68252943e-03 9.88062741e-02 -8.11840722e-04 5.79379824e-04 9.36757646e-05] [ 1.24856668e-02 -5.73156929e-03 -8.11840722e-04 2.50558185e-02 -1.30421786e-02 -1.55166739e-03] [-5.31546890e-03 6.85993630e-03 5.79379824e-04 -1.30421786e-02 8.57725248e-02 -2.90124568e-03] [-7.95363808e-04 5.99832192e-04 9.36757646e-05 -1.55166739e-03 -2.90124568e-03 9.89358113e-02]] LogLike[0152]=-348.534528 sigma_a=128.615540 R: [[ 3124.14094561 5761.64687798] [ 5761.64687798 10625.83507186]] m0: [190.35382124 36.61234045 5.03804063 389.85026912 101.95060919 12.56568978] V0: [[ 8.50979805e-03 -6.26262876e-03 -5.07650235e-04 1.24933608e-02 -5.32382162e-03 -7.95991739e-04] [-6.26262876e-03 7.66077226e-02 -3.68378158e-03 -5.74073046e-03 6.88156329e-03 6.00517065e-04] [-5.07650235e-04 -3.68378158e-03 9.88060985e-02 -8.12494502e-04 5.80016249e-04 9.37475389e-05] [ 1.24933608e-02 -5.74073046e-03 -8.12494502e-04 2.50495246e-02 -1.30372544e-02 -1.55127499e-03] [-5.32382162e-03 6.88156329e-03 5.80016249e-04 -1.30372544e-02 8.57601762e-02 -2.90174956e-03] [-7.95991739e-04 6.00517065e-04 9.37475389e-05 -1.55127499e-03 -2.90174956e-03 9.89357159e-02]] LogLike[0153]=-348.488750 sigma_a=128.485712 R: [[ 3124.86788343 5762.96480441] [ 5762.96480441 10628.22358529]] m0: [190.36373501 36.5795731 5.03766323 389.85434044 101.97041276 12.56600947] V0: [[ 8.49674247e-03 -6.24820007e-03 -5.06642943e-04 1.25009078e-02 -5.33203353e-03 -7.96608551e-04] [-6.24820007e-03 7.65682489e-02 -3.68501390e-03 -5.74973198e-03 6.90305271e-03 6.01189777e-04] [-5.06642943e-04 -3.68501390e-03 9.88059242e-02 -8.13136665e-04 5.80641449e-04 9.38184826e-05] [ 1.25009078e-02 -5.74973198e-03 -8.13136665e-04 2.50433241e-02 -1.30324199e-02 -1.55088971e-03] [-5.33203353e-03 6.90305271e-03 5.80641449e-04 -1.30324199e-02 8.57479046e-02 -2.90224682e-03] [-7.96608551e-04 6.01189777e-04 9.38184826e-05 -1.55088971e-03 -2.90224682e-03 9.89356208e-02]] LogLike[0154]=-348.443658 sigma_a=128.358260 R: [[ 3125.57408148 5764.24505017] [ 5764.24505017 10630.54365934]] m0: [190.37355612 36.54687393 5.03729364 389.85846975 101.99017906 12.56632503] V0: [[ 8.48391316e-03 -6.23401667e-03 -5.05653586e-04 1.25083114e-02 -5.34010800e-03 -7.97214512e-04] [-6.23401667e-03 7.65290137e-02 -3.68622685e-03 -5.75857769e-03 6.92440772e-03 6.01850620e-04] [-5.05653586e-04 -3.68622685e-03 9.88057512e-02 -8.13767492e-04 5.81255692e-04 9.38886163e-05] [ 1.25083114e-02 -5.75857769e-03 -8.13767492e-04 2.50372149e-02 -1.30276729e-02 -1.55051140e-03] [-5.34010800e-03 6.92440772e-03 5.81255692e-04 -1.30276729e-02 8.57357082e-02 -2.90273760e-03] [-7.97214512e-04 6.01850620e-04 9.38886163e-05 -1.55051140e-03 -2.90273760e-03 9.89355262e-02]] LogLike[0155]=-348.399230 sigma_a=128.233099 R: [[ 3126.26024759 5765.48890212] [ 5765.48890212 10632.79763288]] m0: [190.38328681 36.51424153 5.03693167 389.86265563 102.00990886 12.56663656] V0: [[ 8.47130464e-03 -6.22007267e-03 -5.04681730e-04 1.25155750e-02 -5.34804832e-03 -7.97809886e-04] [-6.22007267e-03 7.64900115e-02 -3.68742091e-03 -5.76727131e-03 6.94563143e-03 6.02499878e-04] [-5.04681730e-04 -3.68742091e-03 9.88055793e-02 -8.14387257e-04 5.81859241e-04 9.39579600e-05] [ 1.25155750e-02 -5.76727131e-03 -8.14387257e-04 2.50311946e-02 -1.30230115e-02 -1.55013989e-03] [-5.34804832e-03 6.94563143e-03 5.81859241e-04 -1.30230115e-02 8.57235853e-02 -2.90322207e-03] [-7.97809886e-04 6.02499878e-04 9.39579600e-05 -1.55013989e-03 -2.90322207e-03 9.89354319e-02]] LogLike[0156]=-348.355444 sigma_a=128.110150 R: [[ 3126.92708264 5766.69763505] [ 5766.69763505 10634.98782391]] m0: [190.39292923 36.48167452 5.03657715 389.86689674 102.02960294 12.56694414] V0: [[ 8.45891162e-03 -6.20636237e-03 -5.03726952e-04 1.25227020e-02 -5.35585769e-03 -7.98394927e-04] [-6.20636237e-03 7.64512370e-02 -3.68859652e-03 -5.77581646e-03 6.96672683e-03 6.03137825e-04] [-5.03726952e-04 -3.68859652e-03 9.88054086e-02 -8.14996227e-04 5.82452349e-04 9.40265334e-05] [ 1.25227020e-02 -5.77581646e-03 -8.14996227e-04 2.50252612e-02 -1.30184334e-02 -1.54977501e-03] [-5.35585769e-03 6.96672683e-03 5.82452349e-04 -1.30184334e-02 8.57115342e-02 -2.90370037e-03] [-7.98394927e-04 6.03137825e-04 9.40265334e-05 -1.54977501e-03 -2.90370037e-03 9.89353381e-02]] LogLike[0157]=-348.312279 sigma_a=127.989335 R: [[ 3127.57525224 5767.87245939] [ 5767.87245939 10637.11643306]] m0: [190.40248549 36.44917157 5.0362299 389.87119175 102.04926202 12.56724789] V0: [[ 8.44672893e-03 -6.19288020e-03 -5.02788842e-04 1.25296955e-02 -5.36353922e-03 -7.98969882e-04] [-6.19288020e-03 7.64126851e-02 -3.68975413e-03 -5.78421666e-03 6.98769685e-03 6.03764729e-04] [-5.02788842e-04 -3.68975413e-03 9.88052391e-02 -8.15594658e-04 5.83035265e-04 9.40943552e-05] [ 1.25296955e-02 -5.78421666e-03 -8.15594658e-04 2.50194126e-02 -1.30139369e-02 -1.54941660e-03] [-5.36353922e-03 6.98769685e-03 5.83035265e-04 -1.30139369e-02 8.56995533e-02 -2.90417265e-03] [-7.98969882e-04 6.03764729e-04 9.40943552e-05 -1.54941660e-03 -2.90417265e-03 9.89352446e-02]] LogLike[0158]=-348.269715 sigma_a=127.870583 R: [[ 3128.20541266 5769.01456901] [ 5769.01456901 10639.18563168]] m0: [190.41195764 36.41673137 5.03588975 389.87553938 102.06888682 12.56754788] V0: [[ 8.43475156e-03 -6.17962078e-03 -5.01867003e-04 1.25365588e-02 -5.37109592e-03 -7.99534991e-04] [-6.17962078e-03 7.63743506e-02 -3.69089415e-03 -5.79247533e-03 7.00854432e-03 6.04380850e-04] [-5.01867003e-04 -3.69089415e-03 9.88050707e-02 -8.16182804e-04 5.83608229e-04 9.41614439e-05] [ 1.25365588e-02 -5.79247533e-03 -8.16182804e-04 2.50136469e-02 -1.30095199e-02 -1.54906452e-03] [-5.37109592e-03 7.00854432e-03 5.83608229e-04 -1.30095199e-02 8.56876410e-02 -2.90463905e-03] [-7.99534991e-04 6.04380850e-04 9.41614439e-05 -1.54906452e-03 -2.90463905e-03 9.89351514e-02]] LogLike[0159]=-348.227734 sigma_a=127.753821 R: [[ 3128.81816807 5770.12506241] [ 5770.12506241 10641.19741638]] m0: [190.42134766 36.38435264 5.03555653 389.87993839 102.08847806 12.56784422] V0: [[ 8.42297464e-03 -6.16657887e-03 -5.00961048e-04 1.25432950e-02 -5.37853073e-03 -8.00090489e-04] [-6.16657887e-03 7.63362287e-02 -3.69201701e-03 -5.80059578e-03 7.02927201e-03 6.04986441e-04] [-5.00961048e-04 -3.69201701e-03 9.88049033e-02 -8.16760907e-04 5.84171473e-04 9.42278175e-05] [ 1.25432950e-02 -5.80059578e-03 -8.16760907e-04 2.50079620e-02 -1.30051806e-02 -1.54871861e-03] [-5.37853073e-03 7.02927201e-03 5.84171473e-04 -1.30051806e-02 8.56757957e-02 -2.90509972e-03] [-8.00090489e-04 6.04986441e-04 9.42278175e-05 -1.54871861e-03 -2.90509972e-03 9.89350586e-02]] LogLike[0160]=-348.186316 sigma_a=127.638983 R: [[ 3129.41412308 5771.20503941] [ 5771.20503941 10643.15378742]] m0: [190.43065749 36.35203416 5.0352301 389.88438757 102.10803641 12.56813698] V0: [[ 8.41139345e-03 -6.15374936e-03 -5.00070600e-04 1.25499071e-02 -5.38584650e-03 -8.00636602e-04] [-6.15374936e-03 7.62983146e-02 -3.69312311e-03 -5.80858125e-03 7.04988260e-03 6.05581748e-04] [-5.00070600e-04 -3.69312311e-03 9.88047370e-02 -8.17329205e-04 5.84725225e-04 9.42934932e-05] [ 1.25499071e-02 -5.80858125e-03 -8.17329205e-04 2.50023561e-02 -1.30009171e-02 -1.54837873e-03] [-5.38584650e-03 7.04988260e-03 5.84725225e-04 -1.30009171e-02 8.56640160e-02 -2.90555479e-03] [-8.00636602e-04 6.05581748e-04 9.42934932e-05 -1.54837873e-03 -2.90555479e-03 9.89349662e-02]] LogLike[0161]=-348.145444 sigma_a=127.526001 R: [[ 3129.99383901 5772.2555207 ] [ 5772.2555207 10645.05660013]] m0: [190.43988899 36.31977471 5.0349103 389.88888574 102.12756253 12.56842626] V0: [[ 8.40000337e-03 -6.14112730e-03 -4.99195294e-04 1.25563979e-02 -5.39304600e-03 -8.01173551e-04] [-6.14112730e-03 7.62606036e-02 -3.69421285e-03 -5.81643488e-03 7.07037872e-03 6.06167008e-04] [-4.99195294e-04 -3.69421285e-03 9.88045717e-02 -8.17887927e-04 5.85269707e-04 9.43584879e-05] [ 1.25563979e-02 -5.81643488e-03 -8.17887927e-04 2.49968272e-02 -1.29967278e-02 -1.54804473e-03] [-5.39304600e-03 7.07037872e-03 5.85269707e-04 -1.29967278e-02 8.56523003e-02 -2.90600438e-03] [-8.01173551e-04 6.06167008e-04 9.43584879e-05 -1.54804473e-03 -2.90600438e-03 9.89348740e-02]] LogLike[0162]=-348.105101 sigma_a=127.414817 R: [[ 3130.55786971 5773.27751363] [ 5773.27751363 10646.90768635]] m0: [190.44904399 36.28757312 5.03459696 389.89343176 102.14705708 12.56871213] V0: [[ 8.38879996e-03 -6.12870789e-03 -4.98334777e-04 1.25627703e-02 -5.40013193e-03 -8.01701550e-04] [-6.12870789e-03 7.62230912e-02 -3.69528659e-03 -5.82415972e-03 7.09076290e-03 6.06742455e-04] [-4.98334777e-04 -3.69528659e-03 9.88044074e-02 -8.18437299e-04 5.85805131e-04 9.44228182e-05] [ 1.25627703e-02 -5.82415972e-03 -8.18437299e-04 2.49913738e-02 -1.29926109e-02 -1.54771649e-03] [-5.40013193e-03 7.09076290e-03 5.85805131e-04 -1.29926109e-02 8.56406472e-02 -2.90644864e-03] [-8.01701550e-04 6.06742455e-04 9.44228182e-05 -1.54771649e-03 -2.90644864e-03 9.89347822e-02]] LogLike[0163]=-348.065270 sigma_a=127.305366 R: [[ 3131.10672927 5774.27195285] [ 5774.27195285 10648.70874479]] m0: [190.45812426 36.25542826 5.03428996 389.89802452 102.16652068 12.56899467] V0: [[ 8.37777886e-03 -6.11648644e-03 -4.97488702e-04 1.25690270e-02 -5.40710690e-03 -8.02220806e-04] [-6.11648644e-03 7.61857731e-02 -3.69634471e-03 -5.83175872e-03 7.11103762e-03 6.07308313e-04] [-4.97488702e-04 -3.69634471e-03 9.88042441e-02 -8.18977537e-04 5.86331707e-04 9.44864998e-05] [ 1.25690270e-02 -5.83175872e-03 -8.18977537e-04 2.49859939e-02 -1.29885648e-02 -1.54739387e-03] [-5.40710690e-03 7.11103762e-03 5.86331707e-04 -1.29885648e-02 8.56290555e-02 -2.90688767e-03] [-8.02220806e-04 6.07308313e-04 9.44864998e-05 -1.54739387e-03 -2.90688767e-03 9.89346907e-02]] LogLike[0164]=-348.025936 sigma_a=127.197595 R: [[ 3131.64093722 5775.23978351] [ 5775.23978351 10650.46149447]] m0: [190.46713153 36.223339 5.03398915 389.90266293 102.18595395 12.56927397] V0: [[ 8.36693586e-03 -6.10445840e-03 -4.96656736e-04 1.25751707e-02 -5.41397346e-03 -8.02731523e-04] [-6.10445840e-03 7.61486449e-02 -3.69738757e-03 -5.83923479e-03 7.13120528e-03 6.07864802e-04] [-4.96656736e-04 -3.69738757e-03 9.88040818e-02 -8.19508852e-04 5.86849636e-04 9.45495483e-05] [ 1.25751707e-02 -5.83923479e-03 -8.19508852e-04 2.49806859e-02 -1.29845877e-02 -1.54707673e-03] [-5.41397346e-03 7.13120528e-03 5.86849636e-04 -1.29845877e-02 8.56175236e-02 -2.90732161e-03] [-8.02731523e-04 6.07864802e-04 9.45495483e-05 -1.54707673e-03 -2.90732161e-03 9.89345996e-02]] LogLike[0165]=-347.987082 sigma_a=127.091445 R: [[ 3132.16095826 5776.18185013] [ 5776.18185013 10652.16746977]] m0: [190.47606746 36.19130427 5.03369439 389.90734595 102.20535749 12.5695501 ] V0: [[ 8.35626685e-03 -6.09261936e-03 -4.95838551e-04 1.25812040e-02 -5.42073409e-03 -8.03233898e-04] [-6.09261936e-03 7.61117025e-02 -3.69841552e-03 -5.84659072e-03 7.15126822e-03 6.08412135e-04] [-4.95838551e-04 -3.69841552e-03 9.88039203e-02 -8.20031451e-04 5.87359117e-04 9.46119789e-05] [ 1.25812040e-02 -5.84659072e-03 -8.20031451e-04 2.49754482e-02 -1.29806783e-02 -1.54676496e-03] [-5.42073409e-03 7.15126822e-03 5.87359117e-04 -1.29806783e-02 8.56060503e-02 -2.90775057e-03] [-8.03233898e-04 6.08412135e-04 9.46119789e-05 -1.54676496e-03 -2.90775057e-03 9.89345087e-02]] LogLike[0166]=-347.948694 sigma_a=126.986866 R: [[ 3132.66727623 5777.09903301] [ 5777.09903301 10653.82827188]] m0: [190.48493368 36.15932301 5.03340554 389.91207256 102.22473187 12.56982312] V0: [[ 8.34576786e-03 -6.08096502e-03 -4.95033833e-04 1.25871294e-02 -5.42739119e-03 -8.03728121e-04] [-6.08096502e-03 7.60749419e-02 -3.69942890e-03 -5.85382923e-03 7.17122872e-03 6.08950519e-04] [-4.95033833e-04 -3.69942890e-03 9.88037598e-02 -8.20545534e-04 5.87860340e-04 9.46738060e-05] [ 1.25871294e-02 -5.85382923e-03 -8.20545534e-04 2.49702791e-02 -1.29768348e-02 -1.54645843e-03] [-5.42739119e-03 7.17122872e-03 5.87860340e-04 -1.29768348e-02 8.55946343e-02 -2.90817467e-03] [-8.03728121e-04 6.08950519e-04 9.46738060e-05 -1.54645843e-03 -2.90817467e-03 9.89344181e-02]] LogLike[0167]=-347.910759 sigma_a=126.883806 R: [[ 3133.16031808 5777.99210798] [ 5777.99210798 10655.44531018]] m0: [190.49373177 36.1273942 5.03312248 389.91684177 102.24407768 12.57009313] V0: [[ 8.33543500e-03 -6.06949118e-03 -4.94242273e-04 1.25929493e-02 -5.43394710e-03 -8.04214378e-04] [-6.06949118e-03 7.60383591e-02 -3.70042805e-03 -5.86095299e-03 7.19108900e-03 6.09480156e-04] [-4.94242273e-04 -3.70042805e-03 9.88036002e-02 -8.21051293e-04 5.88353492e-04 9.47350440e-05] [ 1.25929493e-02 -5.86095299e-03 -8.21051293e-04 2.49651772e-02 -1.29730559e-02 -1.54615702e-03] [-5.43394710e-03 7.19108900e-03 5.88353492e-04 -1.29730559e-02 8.55832744e-02 -2.90859400e-03] [-8.04214378e-04 6.09480156e-04 9.47350440e-05 -1.54615702e-03 -2.90859400e-03 9.89343278e-02]] LogLike[0168]=-347.873262 sigma_a=126.782213 R: [[ 3133.64051803 5778.86186467] [ 5778.86186467 10657.02002026]] m0: [190.50246328 36.09551683 5.03284508 389.9216526 102.26339545 12.57036017] V0: [[ 8.32526451e-03 -6.05819380e-03 -4.93463572e-04 1.25986662e-02 -5.44040410e-03 -8.04692851e-04] [-6.05819380e-03 7.60019503e-02 -3.70141327e-03 -5.86796456e-03 7.21085121e-03 6.10001242e-04] [-4.93463572e-04 -3.70141327e-03 9.88034414e-02 -8.21548919e-04 5.88838753e-04 9.47957067e-05] [ 1.25986662e-02 -5.86796456e-03 -8.21548919e-04 2.49601408e-02 -1.29693401e-02 -1.54586062e-03] [-5.44040410e-03 7.21085121e-03 5.88838753e-04 -1.29693401e-02 8.55719694e-02 -2.90900869e-03] [-8.04692851e-04 6.10001242e-04 9.47957067e-05 -1.54586062e-03 -2.90900869e-03 9.89342377e-02]] LogLike[0169]=-347.836190 sigma_a=126.682042 R: [[ 3134.10830646 5779.70908609] [ 5779.70908609 10658.55382626]] m0: [190.51112969 36.06368993 5.03257322 389.92650412 102.28268574 12.57062433] V0: [[ 8.31525272e-03 -6.04706890e-03 -4.92697439e-04 1.26042824e-02 -5.44676441e-03 -8.05163714e-04] [-6.04706890e-03 7.59657117e-02 -3.70238489e-03 -5.87486646e-03 7.23051746e-03 6.10513967e-04] [-4.92697439e-04 -3.70238489e-03 9.88032835e-02 -8.22038594e-04 5.89316300e-04 9.48558075e-05] [ 1.26042824e-02 -5.87486646e-03 -8.22038594e-04 2.49551686e-02 -1.29656859e-02 -1.54556911e-03] [-5.44676441e-03 7.23051746e-03 5.89316300e-04 -1.29656859e-02 8.55607180e-02 -2.90941884e-03] [-8.05163714e-04 6.10513967e-04 9.48558075e-05 -1.54556911e-03 -2.90941884e-03 9.89341480e-02]] LogLike[0170]=-347.799530 sigma_a=126.583248 R: [[ 3134.56406938 5780.53447377] [ 5780.53447377 10660.04800281]] m0: [190.51973245 36.03191255 5.03230677 389.93139542 102.30194905 12.57088567] V0: [[ 8.30539607e-03 -6.03611266e-03 -4.91943592e-04 1.26098001e-02 -5.45303017e-03 -8.05627140e-04] [-6.03611266e-03 7.59296398e-02 -3.70334320e-03 -5.88166112e-03 7.25008977e-03 6.11018517e-04] [-4.91943592e-04 -3.70334320e-03 9.88031263e-02 -8.22520498e-04 5.89786304e-04 9.49153596e-05] [ 1.26098001e-02 -5.88166112e-03 -8.22520498e-04 2.49502591e-02 -1.29620921e-02 -1.54528239e-03] [-5.45303017e-03 7.25008977e-03 5.89786304e-04 -1.29620921e-02 8.55495192e-02 -2.90982455e-03] [-8.05627140e-04 6.11018517e-04 9.49153596e-05 -1.54528239e-03 -2.90982455e-03 9.89340585e-02]] LogLike[0171]=-347.763270 sigma_a=126.485785 R: [[ 3135.00818938 5781.33872329] [ 5781.33872329 10661.50381425]] m0: [190.52827299 36.00018377 5.03204563 389.93632561 102.32118592 12.57114425] V0: [[ 8.29569111e-03 -6.02532133e-03 -4.91201756e-04 1.26152214e-02 -5.45920348e-03 -8.06083293e-04] [-6.02532133e-03 7.58937310e-02 -3.70428851e-03 -5.88835093e-03 7.26957014e-03 6.11515071e-04] [-4.91201756e-04 -3.70428851e-03 9.88029700e-02 -8.22994804e-04 5.90248932e-04 9.49743757e-05] [ 1.26152214e-02 -5.88835093e-03 -8.22994804e-04 2.49454109e-02 -1.29585572e-02 -1.54500036e-03] [-5.45920348e-03 7.26957014e-03 5.90248932e-04 -1.29585572e-02 8.55383718e-02 -2.91022592e-03] [-8.06083293e-04 6.11515071e-04 9.49743757e-05 -1.54500036e-03 -2.91022592e-03 9.89339692e-02]] LogLike[0172]=-347.727398 sigma_a=126.389612 R: [[ 3135.44104258 5782.12251871] [ 5782.12251871 10662.92250429]] m0: [190.53675267 35.96850269 5.03178968 389.94129382 102.34039682 12.57140013] V0: [[ 8.28613445e-03 -6.01469128e-03 -4.90471663e-04 1.26205487e-02 -5.46528638e-03 -8.06532336e-04] [-6.01469128e-03 7.58579819e-02 -3.70522110e-03 -5.89493818e-03 7.28896050e-03 6.12003807e-04] [-4.90471663e-04 -3.70522110e-03 9.88028145e-02 -8.23461681e-04 5.90704344e-04 9.50328681e-05] [ 1.26205487e-02 -5.89493818e-03 -8.23461681e-04 2.49406226e-02 -1.29550801e-02 -1.54472289e-03] [-5.46528638e-03 7.28896050e-03 5.90704344e-04 -1.29550801e-02 8.55272748e-02 -2.91062305e-03] [-8.06532336e-04 6.12003807e-04 9.50328681e-05 -1.54472289e-03 -2.91062305e-03 9.89338802e-02]] LogLike[0173]=-347.691903 sigma_a=126.294692 R: [[ 3135.86298 5782.88649812] [ 5782.88649812 10664.30523256]] m0: [190.54517283 35.93686842 5.0315388 389.94629922 102.35958225 12.57165338] V0: [[ 8.27672282e-03 -6.00421895e-03 -4.89753054e-04 1.26257838e-02 -5.47128085e-03 -8.06974427e-04] [-6.00421895e-03 7.58223891e-02 -3.70614125e-03 -5.90142513e-03 7.30826274e-03 6.12484893e-04] [-4.89753054e-04 -3.70614125e-03 9.88026597e-02 -8.23921292e-04 5.91152700e-04 9.50908489e-05] [ 1.26257838e-02 -5.90142513e-03 -8.23921292e-04 2.49358930e-02 -1.29516593e-02 -1.54444991e-03] [-5.47128085e-03 7.30826274e-03 5.91152700e-04 -1.29516593e-02 8.55162270e-02 -2.91101602e-03] [-8.06974427e-04 6.12484893e-04 9.50908489e-05 -1.54444991e-03 -2.91101602e-03 9.89337914e-02]] LogLike[0174]=-347.656774 sigma_a=126.200982 R: [[ 3136.27431817 5783.63123632] [ 5783.63123632 10665.65304252]] m0: [190.55353477 35.90528013 5.0312929 389.95134099 102.37874269 12.57190405] V0: [[ 8.26745304e-03 -5.99390092e-03 -4.89045675e-04 1.26309289e-02 -5.47718882e-03 -8.07409717e-04] [-5.99390092e-03 7.57869493e-02 -3.70704923e-03 -5.90781396e-03 7.32747869e-03 6.12958496e-04] [-4.89045675e-04 -3.70704923e-03 9.88025056e-02 -8.24373799e-04 5.91594152e-04 9.51483299e-05] [ 1.26309289e-02 -5.90781396e-03 -8.24373799e-04 2.49312207e-02 -1.29482938e-02 -1.54418130e-03] [-5.47718882e-03 7.32747869e-03 5.91594152e-04 -1.29482938e-02 8.55052275e-02 -2.91140494e-03] [-8.07409717e-04 6.12958496e-04 9.51483299e-05 -1.54418130e-03 -2.91140494e-03 9.89337029e-02]] LogLike[0175]=-347.622000 sigma_a=126.108445 R: [[ 3136.67540649 5784.35736903] [ 5784.35736903 10666.96709063]] m0: [190.56183976 35.87373697 5.03105186 389.95641833 102.39787858 12.5721522 ] V0: [[ 8.25832200e-03 -5.98373382e-03 -4.88349280e-04 1.26359858e-02 -5.48301216e-03 -8.07838356e-04] [-5.98373382e-03 7.57516595e-02 -3.70794531e-03 -5.91410680e-03 7.34661013e-03 6.13424778e-04] [-4.88349280e-04 -3.70794531e-03 9.88023523e-02 -8.24819356e-04 5.92028849e-04 9.52053224e-05] [ 1.26359858e-02 -5.91410680e-03 -8.24819356e-04 2.49266045e-02 -1.29449822e-02 -1.54391698e-03] [-5.48301216e-03 7.34661013e-03 5.92028849e-04 -1.29449822e-02 8.54942753e-02 -2.91178988e-03] [-8.07838356e-04 6.13424778e-04 9.52053224e-05 -1.54391698e-03 -2.91178988e-03 9.89336146e-02]] LogLike[0176]=-347.587570 sigma_a=126.017046 R: [[ 3137.06655749 5785.06546433] [ 5785.06546433 10668.2484091 ]] m0: [190.57008903 35.84223813 5.03081559 389.96153046 102.41699039 12.57239789] V0: [[ 8.24932669e-03 -5.97371440e-03 -4.87663631e-04 1.26409566e-02 -5.48875271e-03 -8.08260488e-04] [-5.97371440e-03 7.57165165e-02 -3.70882975e-03 -5.92030571e-03 7.36565879e-03 6.13883895e-04] [-4.87663631e-04 -3.70882975e-03 9.88021997e-02 -8.25258115e-04 5.92456937e-04 9.52618375e-05] [ 1.26409566e-02 -5.92030571e-03 -8.25258115e-04 2.49220433e-02 -1.29417235e-02 -1.54365685e-03] [-5.48875271e-03 7.36565879e-03 5.92456937e-04 -1.29417235e-02 8.54833694e-02 -2.91217095e-03] [-8.08260488e-04 6.13883895e-04 9.52618375e-05 -1.54365685e-03 -2.91217095e-03 9.89335265e-02]] LogLike[0177]=-347.553475 sigma_a=125.926749 R: [[ 3137.44807811 5785.75608023] [ 5785.75608023 10669.49801213]] m0: [190.57828377 35.81078283 5.03058399 389.96667664 102.43607854 12.57264117] V0: [[ 8.24046417e-03 -5.96383949e-03 -4.86988493e-04 1.26458431e-02 -5.49441223e-03 -8.08676255e-04] [-5.96383949e-03 7.56815174e-02 -3.70970279e-03 -5.92641272e-03 7.38462638e-03 6.14336002e-04] [-4.86988493e-04 -3.70970279e-03 9.88020478e-02 -8.25690223e-04 5.92878557e-04 9.53178860e-05] [ 1.26458431e-02 -5.92641272e-03 -8.25690223e-04 2.49175357e-02 -1.29385165e-02 -1.54340082e-03] [-5.49441223e-03 7.38462638e-03 5.92878557e-04 -1.29385165e-02 8.54725088e-02 -2.91254821e-03] [-8.08676255e-04 6.14336002e-04 9.53178860e-05 -1.54340082e-03 -2.91254821e-03 9.89334386e-02]] LogLike[0178]=-347.519706 sigma_a=125.837523 R: [[ 3137.8202557 5786.42973889] [ 5786.42973889 10670.71684833]] m0: [190.58642516 35.77937029 5.03035697 389.97185614 102.45514347 12.5728821 ] V0: [[ 8.23173157e-03 -5.95410598e-03 -4.86323641e-04 1.26506470e-02 -5.49999247e-03 -8.09085793e-04] [-5.95410598e-03 7.56466591e-02 -3.71056468e-03 -5.93242978e-03 7.40351454e-03 6.14781246e-04] [-4.86323641e-04 -3.71056468e-03 9.88018965e-02 -8.26115824e-04 5.93293847e-04 9.53734784e-05] [ 1.26506470e-02 -5.93242978e-03 -8.26115824e-04 2.49130807e-02 -1.29353601e-02 -1.54314880e-03] [-5.49999247e-03 7.40351454e-03 5.93293847e-04 -1.29353601e-02 8.54616926e-02 -2.91292176e-03] [-8.09085793e-04 6.14781246e-04 9.53734784e-05 -1.54314880e-03 -2.91292176e-03 9.89333510e-02]] LogLike[0179]=-347.486253 sigma_a=125.749333 R: [[ 3138.18336385 5787.08693737] [ 5787.08693737 10671.90582051]] m0: [190.59451432 35.74799977 5.03013442 389.97706822 102.47418558 12.57312072] V0: [[ 8.22312613e-03 -5.94451089e-03 -4.85668854e-04 1.26553702e-02 -5.50549510e-03 -8.09489237e-04] [-5.94451089e-03 7.56119389e-02 -3.71141565e-03 -5.93835880e-03 7.42232486e-03 6.15219773e-04] [-4.85668854e-04 -3.71141565e-03 9.88017459e-02 -8.26535055e-04 5.93702939e-04 9.54286250e-05] [ 1.26553702e-02 -5.93835880e-03 -8.26535055e-04 2.49086772e-02 -1.29322533e-02 -1.54290071e-03] [-5.50549510e-03 7.42232486e-03 5.93702939e-04 -1.29322533e-02 8.54509199e-02 -2.91329167e-03] [-8.09489237e-04 6.15219773e-04 9.54286250e-05 -1.54290071e-03 -2.91329167e-03 9.89332635e-02]] LogLike[0180]=-347.453106 sigma_a=125.662150 R: [[ 3138.53768134 5787.72818259] [ 5787.72818259 10673.06585011]] m0: [190.60255237 35.71667053 5.02991626 389.98231221 102.49320529 12.57335709] V0: [[ 8.21464513e-03 -5.93505128e-03 -4.85023917e-04 1.26600143e-02 -5.51092176e-03 -8.09886716e-04] [-5.93505128e-03 7.55773540e-02 -3.71225595e-03 -5.94420165e-03 7.44105892e-03 6.15651724e-04] [-4.85023917e-04 -3.71225595e-03 9.88015960e-02 -8.26948055e-04 5.94105966e-04 9.54833356e-05] [ 1.26600143e-02 -5.94420165e-03 -8.26948055e-04 2.49043240e-02 -1.29291949e-02 -1.54265647e-03] [-5.51092176e-03 7.44105892e-03 5.94105966e-04 -1.29291949e-02 8.54401898e-02 -2.91365803e-03] [-8.09886716e-04 6.15651724e-04 9.54833356e-05 -1.54265647e-03 -2.91365803e-03 9.89331762e-02]] LogLike[0181]=-347.420258 sigma_a=125.575942 R: [[ 3138.88347104 5788.35395234] [ 5788.35395234 10674.19780533]] m0: [190.61054037 35.68538187 5.0297024 389.98758743 102.51220298 12.57359126] V0: [[ 8.20628594e-03 -5.92572430e-03 -4.84388621e-04 1.26645810e-02 -5.51627406e-03 -8.10278356e-04] [-5.92572430e-03 7.55429016e-02 -3.71308579e-03 -5.94996013e-03 7.45971824e-03 6.16077237e-04] [-4.84388621e-04 -3.71308579e-03 9.88014466e-02 -8.27354953e-04 5.94503053e-04 9.55376200e-05] [ 1.26645810e-02 -5.94996013e-03 -8.27354953e-04 2.49000202e-02 -1.29261841e-02 -1.54241599e-03] [-5.51627406e-03 7.45971824e-03 5.94503053e-04 -1.29261841e-02 8.54295016e-02 -2.91402090e-03] [-8.10278356e-04 6.16077237e-04 9.55376200e-05 -1.54241599e-03 -2.91402090e-03 9.89330892e-02]] LogLike[0182]=-347.387700 sigma_a=125.490682 R: [[ 3139.22098969 5788.96471331] [ 5788.96471331 10675.30253431]] m0: [190.61847938 35.65413308 5.02949276 389.99289321 102.53117905 12.57382327] V0: [[ 8.19804599e-03 -5.91652717e-03 -4.83762763e-04 1.26690720e-02 -5.52155354e-03 -8.10664281e-04] [-5.91652717e-03 7.55085792e-02 -3.71390539e-03 -5.95563601e-03 7.47830429e-03 6.16496444e-04] [-4.83762763e-04 -3.71390539e-03 9.88012979e-02 -8.27755880e-04 5.94894323e-04 9.55914877e-05] [ 1.26690720e-02 -5.95563601e-03 -8.27755880e-04 2.48957646e-02 -1.29232197e-02 -1.54217920e-03] [-5.52155354e-03 7.47830429e-03 5.94894323e-04 -1.29232197e-02 8.54188543e-02 -2.91438036e-03] [-8.10664281e-04 6.16496444e-04 9.55914877e-05 -1.54217920e-03 -2.91438036e-03 9.89330023e-02]] LogLike[0183]=-347.355424 sigma_a=125.406340 R: [[ 3139.55046649 5789.56088167] [ 5789.56088167 10676.38079248]] m0: [190.6263704 35.6229235 5.02928725 389.99822891 102.55013387 12.57405317] V0: [[ 8.18992279e-03 -5.90745719e-03 -4.83146145e-04 1.26734888e-02 -5.52676172e-03 -8.11044610e-04] [-5.90745719e-03 7.54743841e-02 -3.71471497e-03 -5.96123101e-03 7.49681853e-03 6.16909478e-04] [-4.83146145e-04 -3.71471497e-03 9.88011498e-02 -8.28150961e-04 5.95279899e-04 9.56449477e-05] [ 1.26734888e-02 -5.96123101e-03 -8.28150961e-04 2.48915563e-02 -1.29203008e-02 -1.54194602e-03] [-5.52676172e-03 7.49681853e-03 5.95279899e-04 -1.29203008e-02 8.54082471e-02 -2.91473649e-03] [-8.11044610e-04 6.16909478e-04 9.56449477e-05 -1.54194602e-03 -2.91473649e-03 9.89329156e-02]] LogLike[0184]=-347.323422 sigma_a=125.322891 R: [[ 3139.87215209 5790.14291338] [ 5790.14291338 10677.433409 ]] m0: [190.63421445 35.59175246 5.02908579 390.00359392 102.5690678 12.574281 ] V0: [[ 8.18191389e-03 -5.89851173e-03 -4.82538573e-04 1.26778330e-02 -5.53190008e-03 -8.11419461e-04] [-5.89851173e-03 7.54403140e-02 -3.71551473e-03 -5.96674679e-03 7.51526236e-03 6.17316463e-04] [-4.82538573e-04 -3.71551473e-03 9.88010022e-02 -8.28540317e-04 5.95659895e-04 9.56980091e-05] [ 1.26778330e-02 -5.96674679e-03 -8.28540317e-04 2.48873942e-02 -1.29174265e-02 -1.54171637e-03] [-5.53190008e-03 7.51526236e-03 5.95659895e-04 -1.29174265e-02 8.53976793e-02 -2.91508935e-03] [-8.11419461e-04 6.17316463e-04 9.56980091e-05 -1.54171637e-03 -2.91508935e-03 9.89328291e-02]] LogLike[0185]=-347.291687 sigma_a=125.240310 R: [[ 3140.18626477 5790.71120485] [ 5790.71120485 10678.46110364]] m0: [190.64201247 35.56061931 5.02888829 390.00898761 102.58798121 12.57450681] V0: [[ 8.17401693e-03 -5.88968822e-03 -4.81939860e-04 1.26821061e-02 -5.53697006e-03 -8.11788947e-04] [-5.88968822e-03 7.54063662e-02 -3.71630488e-03 -5.97218499e-03 7.53363715e-03 6.17717525e-04] [-4.81939860e-04 -3.71630488e-03 9.88008553e-02 -8.28924067e-04 5.96034428e-04 9.57506806e-05] [ 1.26821061e-02 -5.97218499e-03 -8.28924067e-04 2.48832775e-02 -1.29145959e-02 -1.54149018e-03] [-5.53697006e-03 7.53363715e-03 5.96034428e-04 -1.29145959e-02 8.53871500e-02 -2.91543901e-03] [-8.11788947e-04 6.17717525e-04 9.57506806e-05 -1.54149018e-03 -2.91543901e-03 9.89327428e-02]] LogLike[0186]=-347.260211 sigma_a=125.158571 R: [[ 3140.49300878 5791.26612687] [ 5791.26612687 10679.46454926]] m0: [190.64976543 35.52952344 5.02869469 390.01440941 102.60687443 12.57473065] V0: [[ 8.16622961e-03 -5.88098417e-03 -4.81349823e-04 1.26863095e-02 -5.54197304e-03 -8.12153179e-04] [-5.88098417e-03 7.53725386e-02 -3.71708561e-03 -5.97754719e-03 7.55194424e-03 6.18112784e-04] [-4.81349823e-04 -3.71708561e-03 9.88007088e-02 -8.29302328e-04 5.96403607e-04 9.58029707e-05] [ 1.26863095e-02 -5.97754719e-03 -8.29302328e-04 2.48792052e-02 -1.29118081e-02 -1.54126739e-03] [-5.54197304e-03 7.55194424e-03 5.96403607e-04 -1.29118081e-02 8.53766586e-02 -2.91578554e-03] [-8.12153179e-04 6.18112784e-04 9.58029707e-05 -1.54126739e-03 -2.91578554e-03 9.89326566e-02]] LogLike[0187]=-347.228988 sigma_a=125.077648 R: [[ 3140.79260745 5791.8080856 ] [ 5791.8080856 10680.44448432]] m0: [190.65747422 35.49846423 5.02850491 390.01985872 102.6257478 12.57495255] V0: [[ 8.15854967e-03 -5.87239714e-03 -4.80768283e-04 1.26904447e-02 -5.54691041e-03 -8.12512265e-04] [-5.87239714e-03 7.53388287e-02 -3.71785712e-03 -5.98283495e-03 7.57018494e-03 6.18502357e-04] [-4.80768283e-04 -3.71785712e-03 9.88005630e-02 -8.29675211e-04 5.96767542e-04 9.58548875e-05] [ 1.26904447e-02 -5.98283495e-03 -8.29675211e-04 2.48751765e-02 -1.29090621e-02 -1.54104791e-03] [-5.54691041e-03 7.57018494e-03 5.96767542e-04 -1.29090621e-02 8.53662043e-02 -2.91612900e-03] [-8.12512265e-04 6.18502357e-04 9.58548875e-05 -1.54104791e-03 -2.91612900e-03 9.89325706e-02]] LogLike[0188]=-347.198010 sigma_a=124.997521 R: [[ 3141.08526863 5792.33745878] [ 5792.33745878 10681.4015952 ]] m0: [190.66513975 35.46744108 5.02831888 390.02533499 102.64460167 12.57517255] V0: [[ 8.15097492e-03 -5.86392476e-03 -4.80195068e-04 1.26945130e-02 -5.55178348e-03 -8.12866309e-04] [-5.86392476e-03 7.53052343e-02 -3.71861959e-03 -5.98804976e-03 7.58836050e-03 6.18886358e-04] [-4.80195068e-04 -3.71861959e-03 9.88004176e-02 -8.30042827e-04 5.97126337e-04 9.59064393e-05] [ 1.26945130e-02 -5.98804976e-03 -8.30042827e-04 2.48711903e-02 -1.29063573e-02 -1.54083170e-03] [-5.55178348e-03 7.58836050e-03 5.97126337e-04 -1.29063573e-02 8.53557863e-02 -2.91646946e-03] [-8.12866309e-04 6.18886358e-04 9.59064393e-05 -1.54083170e-03 -2.91646946e-03 9.89324848e-02]] LogLike[0189]=-347.167271 sigma_a=124.918168 R: [[ 3141.37116992 5792.85456864] [ 5792.85456864 10682.33646627]] m0: [190.67276288 35.43645342 5.02813651 390.03083766 102.66343634 12.5753907 ] V0: [[ 8.14350324e-03 -5.85556471e-03 -4.79630007e-04 1.26985159e-02 -5.55659355e-03 -8.13215415e-04] [-5.85556471e-03 7.52717532e-02 -3.71937321e-03 -5.99319310e-03 7.60647218e-03 6.19264899e-04] [-4.79630007e-04 -3.71937321e-03 9.88002728e-02 -8.30405283e-04 5.97480096e-04 9.59576338e-05] [ 1.26985159e-02 -5.99319310e-03 -8.30405283e-04 2.48672458e-02 -1.29036927e-02 -1.54061867e-03] [-5.55659355e-03 7.60647218e-03 5.97480096e-04 -1.29036927e-02 8.53454041e-02 -2.91680697e-03] [-8.13215415e-04 6.19264899e-04 9.59576338e-05 -1.54061867e-03 -2.91680697e-03 9.89323991e-02]] LogLike[0190]=-347.136764 sigma_a=124.839566 R: [[ 3141.65050213 5793.35976188] [ 5793.35976188 10683.24972728]] m0: [190.68034447 35.40550067 5.02795775 390.03636621 102.68225214 12.57560703] V0: [[ 8.13613253e-03 -5.84731475e-03 -4.79072936e-04 1.27024546e-02 -5.56134189e-03 -8.13559683e-04] [-5.84731475e-03 7.52383833e-02 -3.72011815e-03 -5.99826640e-03 7.62452118e-03 6.19638089e-04] [-4.79072936e-04 -3.72011815e-03 9.88001285e-02 -8.30762683e-04 5.97828919e-04 9.60084786e-05] [ 1.27024546e-02 -5.99826640e-03 -8.30762683e-04 2.48633423e-02 -1.29010675e-02 -1.54040877e-03] [-5.56134189e-03 7.62452118e-03 5.97828919e-04 -1.29010675e-02 8.53350568e-02 -2.91714160e-03] [-8.13559683e-04 6.19638089e-04 9.60084786e-05 -1.54040877e-03 -2.91714160e-03 9.89323135e-02]] LogLike[0191]=-347.106483 sigma_a=124.761693 R: [[ 3141.9234508 5793.85337563] [ 5793.85337563 10684.14199069]] m0: [190.68788534 35.37458229 5.02778253 390.04192012 102.70104937 12.57582158] V0: [[ 8.12886077e-03 -5.83917268e-03 -4.78523695e-04 1.27063303e-02 -5.56602973e-03 -8.13899209e-04] [-5.83917268e-03 7.52051225e-02 -3.72085459e-03 -6.00327105e-03 7.64250866e-03 6.20006033e-04] [-4.78523695e-04 -3.72085459e-03 9.87999846e-02 -8.31115129e-04 5.98172904e-04 9.60589814e-05] [ 1.27063303e-02 -6.00327105e-03 -8.31115129e-04 2.48594789e-02 -1.28984809e-02 -1.54020193e-03] [-5.56602973e-03 7.64250866e-03 5.98172904e-04 -1.28984809e-02 8.53247440e-02 -2.91747341e-03] [-8.13899209e-04 6.20006033e-04 9.60589814e-05 -1.54020193e-03 -2.91747341e-03 9.89322282e-02]] LogLike[0192]=-347.076423 sigma_a=124.684530 R: [[ 3142.19019684 5794.33573867] [ 5794.33573867 10685.01385387]] m0: [190.69538631 35.34369773 5.02761078 390.04749886 102.71982833 12.57603438] V0: [[ 8.12168599e-03 -5.83113636e-03 -4.77982126e-04 1.27101445e-02 -5.57065825e-03 -8.14234088e-04] [-5.83113636e-03 7.51719687e-02 -3.72158270e-03 -6.00820843e-03 7.66043579e-03 6.20368834e-04] [-4.77982126e-04 -3.72158270e-03 9.87998413e-02 -8.31462719e-04 5.98512145e-04 9.61091492e-05] [ 1.27101445e-02 -6.00820843e-03 -8.31462719e-04 2.48556548e-02 -1.28959323e-02 -1.53999809e-03] [-5.57065825e-03 7.66043579e-03 5.98512145e-04 -1.28959323e-02 8.53144648e-02 -2.91780245e-03] [-8.14234088e-04 6.20368834e-04 9.61091492e-05 -1.53999809e-03 -2.91780245e-03 9.89321429e-02]] LogLike[0193]=-347.046578 sigma_a=124.608059 R: [[ 3142.45090731 5794.80715442] [ 5794.80715442 10685.86586762]] m0: [190.70284814 35.31284647 5.02744243 390.05310195 102.73858932 12.57624549] V0: [[ 8.11460626e-03 -5.82320370e-03 -4.77448077e-04 1.27138982e-02 -5.57522865e-03 -8.14564413e-04] [-5.82320370e-03 7.51389200e-02 -3.72230263e-03 -6.01307984e-03 7.67830368e-03 6.20726595e-04] [-4.77448077e-04 -3.72230263e-03 9.87996984e-02 -8.31805551e-04 5.98846736e-04 9.61589892e-05] [ 1.27138982e-02 -6.01307984e-03 -8.31805551e-04 2.48518693e-02 -1.28934207e-02 -1.53979720e-03] [-5.57522865e-03 7.67830368e-03 5.98846736e-04 -1.28934207e-02 8.53042187e-02 -2.91812878e-03] [-8.14564413e-04 6.20726595e-04 9.61589892e-05 -1.53979720e-03 -2.91812878e-03 9.89320578e-02]] LogLike[0194]=-347.016941 sigma_a=124.532257 R: [[ 3142.70573907 5795.26790758] [ 5795.26790758 10686.69854859]] m0: [190.71027162 35.282028 5.02727742 390.05872891 102.75733261 12.57645492] V0: [[ 8.10761970e-03 -5.81537266e-03 -4.76921400e-04 1.27175927e-02 -5.57974205e-03 -8.14890275e-04] [-5.81537266e-03 7.51059743e-02 -3.72301456e-03 -6.01788659e-03 7.69611343e-03 6.21079411e-04] [-4.76921400e-04 -3.72301456e-03 9.87995560e-02 -8.32143718e-04 5.99176768e-04 9.62085084e-05] [ 1.27175927e-02 -6.01788659e-03 -8.32143718e-04 2.48481215e-02 -1.28909456e-02 -1.53959920e-03] [-5.57974205e-03 7.69611343e-03 5.99176768e-04 -1.28909456e-02 8.52940051e-02 -2.91845245e-03] [-8.14890275e-04 6.21079411e-04 9.62085084e-05 -1.53959920e-03 -2.91845245e-03 9.89319729e-02]] LogLike[0195]=-346.987508 sigma_a=124.457109 R: [[ 3142.95486646 5795.71831522] [ 5795.71831522 10687.51247331]] m0: [190.71765749 35.25124181 5.02711569 390.06437926 102.77605848 12.57666271] V0: [[ 8.10072448e-03 -5.80764127e-03 -4.76401949e-04 1.27212292e-02 -5.58419956e-03 -8.15211760e-04] [-5.80764127e-03 7.50731300e-02 -3.72371864e-03 -6.02262994e-03 7.71386609e-03 6.21427380e-04] [-4.76401949e-04 -3.72371864e-03 9.87994140e-02 -8.32477313e-04 5.99502328e-04 9.62577134e-05] [ 1.27212292e-02 -6.02262994e-03 -8.32477313e-04 2.48444107e-02 -1.28885062e-02 -1.53940402e-03] [-5.58419956e-03 7.71386609e-03 5.99502328e-04 -1.28885062e-02 8.52838233e-02 -2.91877352e-03] [-8.15211760e-04 6.21427380e-04 9.62577134e-05 -1.53940402e-03 -2.91877352e-03 9.89318880e-02]] LogLike[0196]=-346.958273 sigma_a=124.382596 R: [[ 3143.19843765 5796.15864635] [ 5796.15864635 10688.30812996]] m0: [190.72500647 35.22048742 5.02695719 390.07005255 102.79476721 12.5768689 ] V0: [[ 8.09391881e-03 -5.80000758e-03 -4.75889582e-04 1.27248087e-02 -5.58860228e-03 -8.15528955e-04] [-5.80000758e-03 7.50403849e-02 -3.72441501e-03 -6.02731112e-03 7.73156271e-03 6.21770594e-04] [-4.75889582e-04 -3.72441501e-03 9.87992725e-02 -8.32806425e-04 5.99823502e-04 9.63066108e-05] [ 1.27248087e-02 -6.02731112e-03 -8.32806425e-04 2.48407363e-02 -1.28861018e-02 -1.53921162e-03] [-5.58860228e-03 7.73156271e-03 5.99823502e-04 -1.28861018e-02 8.52736728e-02 -2.91909203e-03] [-8.15528955e-04 6.21770594e-04 9.63066108e-05 -1.53921162e-03 -2.91909203e-03 9.89318033e-02]] LogLike[0197]=-346.929231 sigma_a=124.308702 R: [[ 3143.43660254 5796.58917324] [ 5796.58917324 10689.08601298]] m0: [190.73231928 35.18976435 5.02680185 390.07574832 102.81345906 12.57707351] V0: [[ 8.08720096e-03 -5.79246971e-03 -4.75384163e-04 1.27283324e-02 -5.59295126e-03 -8.15841943e-04] [-5.79246971e-03 7.50077375e-02 -3.72510384e-03 -6.03193133e-03 7.74920430e-03 6.22109144e-04] [-4.75384163e-04 -3.72510384e-03 9.87991313e-02 -8.33131142e-04 6.00140375e-04 9.63552071e-05] [ 1.27283324e-02 -6.03193133e-03 -8.33131142e-04 2.48370975e-02 -1.28837318e-02 -1.53902194e-03] [-5.59295126e-03 7.74920430e-03 6.00140375e-04 -1.28837318e-02 8.52635531e-02 -2.91940804e-03] [-8.15841943e-04 6.22109144e-04 9.63552071e-05 -1.53902194e-03 -2.91940804e-03 9.89317188e-02]] LogLike[0198]=-346.900378 sigma_a=124.235410 R: [[ 3143.66949541 5797.01013953] [ 5797.01013953 10689.84656422]] m0: [190.73959662 35.15907213 5.02664961 390.08146614 102.83213429 12.57727659] V0: [[ 8.08056922e-03 -5.78502582e-03 -4.74885555e-04 1.27318013e-02 -5.59724753e-03 -8.16150806e-04] [-5.78502582e-03 7.49751859e-02 -3.72578526e-03 -6.03649174e-03 7.76679184e-03 6.22443120e-04] [-4.74885555e-04 -3.72578526e-03 9.87989907e-02 -8.33451548e-04 6.00453029e-04 9.64035084e-05] [ 1.27318013e-02 -6.03649174e-03 -8.33451548e-04 2.48334936e-02 -1.28813954e-02 -1.53883494e-03] [-5.59724753e-03 7.76679184e-03 6.00453029e-04 -1.28813954e-02 8.52534634e-02 -2.91972159e-03] [-8.16150806e-04 6.22443120e-04 9.64035084e-05 -1.53883494e-03 -2.91972159e-03 9.89316343e-02]] LogLike[0199]=-346.871708 sigma_a=124.162705 R: [[ 3143.89725994 5797.42180627] [ 5797.42180627 10690.59025791]] m0: [190.74683915 35.12841032 5.02650043 390.08720559 102.85079316 12.57747815] V0: [[ 8.07402195e-03 -5.77767411e-03 -4.74393629e-04 1.27352166e-02 -5.60149211e-03 -8.16455624e-04] [-5.77767411e-03 7.49427285e-02 -3.72645942e-03 -6.04099350e-03 7.78432632e-03 6.22772608e-04] [-4.74393629e-04 -3.72645942e-03 9.87988504e-02 -8.33767726e-04 6.00761544e-04 9.64515210e-05] [ 1.27352166e-02 -6.04099350e-03 -8.33767726e-04 2.48299240e-02 -1.28790921e-02 -1.53865055e-03] [-5.60149211e-03 7.78432632e-03 6.00761544e-04 -1.28790921e-02 8.52434035e-02 -2.92003274e-03] [-8.16455624e-04 6.22772608e-04 9.64515210e-05 -1.53865055e-03 -2.92003274e-03 9.89315500e-02]] EM: success: reached maximum number of iterations .. GENERATED FROM PYTHON SOURCE LINES 179-181 Plot convergence ---------------- .. GENERATED FROM PYTHON SOURCE LINES 181-193 .. code-block:: default fig = go.Figure() trace = go.Scatter(x=optim_res_ga["elapsed_time"], y=optim_res_ga["log_like"], name="Gradient ascent", mode="lines+markers") fig.add_trace(trace) trace = go.Scatter(x=optim_res_em["elapsed_time"], y=optim_res_em["log_like"], name="EM", mode="lines+markers") fig.add_trace(trace) fig.update_layout(xaxis_title="Elapsed Time (sec)", yaxis_title="Log Likelihood") fig .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 194-196 Perform smoothing with optimized parameters ------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 198-200 Gradient ascent ~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 202-206 Perform batch filtering ####################### View source code of `ssm.inference.filterLDS_SS_withMissingValues_np `_ .. GENERATED FROM PYTHON SOURCE LINES 206-216 .. code-block:: default Q_ga = optim_res_ga["estimates"]["sigma_a"].item()**2*Qe m0_ga = optim_res_ga["estimates"]["m0"].numpy() V0_ga = np.diag(optim_res_ga["estimates"]["sqrt_diag_V0"].numpy()**2) R_ga = np.diag([optim_res_ga["estimates"]["pos_x_R_std"].item()**2, optim_res_ga["estimates"]["pos_y_R_std"].item()**2]) filterRes_ga = ssm.inference.filterLDS_SS_withMissingValues_np( y=y, B=B, Q=Q_ga, m0=m0_ga, V0=V0_ga, Z=Z, R=R_ga) .. GENERATED FROM PYTHON SOURCE LINES 217-221 Perform batch smoothing ####################### View source code of `ssm.inference.smoothLDS_SS `_ .. GENERATED FROM PYTHON SOURCE LINES 221-226 .. code-block:: default smoothRes_ga = ssm.inference.smoothLDS_SS( B=B, xnn=filterRes_ga["xnn"], Pnn=filterRes_ga["Pnn"], xnn1=filterRes_ga["xnn1"], Pnn1=filterRes_ga["Pnn1"], m0=m0_ga, V0=V0_ga) .. GENERATED FROM PYTHON SOURCE LINES 227-229 EM ~~ .. GENERATED FROM PYTHON SOURCE LINES 231-235 Perform batch filtering ####################### View source code of `ssm.inference.filterLDS_SS_withMissingValues_np `_ .. GENERATED FROM PYTHON SOURCE LINES 235-244 .. code-block:: default Q_em = optim_res_em["estimates"]["sigma_a"].item()**2*Qe m0_em = optim_res_em["estimates"]["m0"] V0_em = optim_res_em["estimates"]["V0"] R_em = optim_res_em["estimates"]["R"] filterRes_em = ssm.inference.filterLDS_SS_withMissingValues_np( y=y, B=B, Q=Q_em, m0=m0_em, V0=V0_em, Z=Z, R=R_em) .. GENERATED FROM PYTHON SOURCE LINES 245-249 Perform batch smoothing ####################### View source code of `ssm.inference.smoothLDS_SS `_ .. GENERATED FROM PYTHON SOURCE LINES 249-254 .. code-block:: default smoothRes_em = ssm.inference.smoothLDS_SS( B=B, xnn=filterRes_em["xnn"], Pnn=filterRes_em["Pnn"], xnn1=filterRes_em["xnn1"], Pnn1=filterRes_em["Pnn1"], m0=m0_em, V0=V0_em) .. GENERATED FROM PYTHON SOURCE LINES 255-257 Plot smoothing results ---------------------- .. GENERATED FROM PYTHON SOURCE LINES 259-261 Define function for plotting ############################ .. GENERATED FROM PYTHON SOURCE LINES 261-401 .. code-block:: default def get_fig_kinematics_vs_time( time, measured_x, measured_y, finite_diff_x, finite_diff_y, ga_mean_x, ga_mean_y, ga_ci_x_upper, ga_ci_y_upper, ga_ci_x_lower, ga_ci_y_lower, em_mean_x, em_mean_y, em_ci_x_upper, em_ci_y_upper, em_ci_x_lower, em_ci_y_lower, cb_alpha, color_true, color_measured, color_finite_diff, color_ga_pattern, color_em_pattern, xlabel, ylabel): fig = go.Figure() if measured_x is not None: trace_mes_x = go.Scatter( x=time, y=measured_x, mode="markers", marker={"color": color_measured}, name="measured x", showlegend=True, ) fig.add_trace(trace_mes_x) if measured_y is not None: trace_mes_y = go.Scatter( x=time, y=measured_y, mode="markers", marker={"color": color_measured}, name="measured y", showlegend=True, ) fig.add_trace(trace_mes_y) if finite_diff_x is not None: trace_fd_x = go.Scatter( x=time, y=finite_diff_x, mode="markers", marker={"color": color_finite_diff}, name="finite difference x", showlegend=True, ) fig.add_trace(trace_fd_x) if finite_diff_y is not None: trace_fd_y = go.Scatter( x=time, y=finite_diff_y, mode="markers", marker={"color": color_finite_diff}, name="finite difference y", showlegend=True, ) fig.add_trace(trace_fd_y) trace_ga_x = go.Scatter( x=time, y=ga_mean_x, mode="markers", marker={"color": color_ga_pattern.format(1.0)}, name="grad. ascent x", showlegend=True, legendgroup="ga_x", ) fig.add_trace(trace_ga_x) trace_ga_x_cb = go.Scatter( x=np.concatenate([time, time[::-1]]), y=np.concatenate([ga_ci_x_upper, ga_ci_x_lower[::-1]]), fill="toself", fillcolor=color_ga_pattern.format(cb_alpha), line=dict(color=color_ga_pattern.format(0.0)), showlegend=False, legendgroup="ga_x", ) fig.add_trace(trace_ga_x_cb) trace_ga_y = go.Scatter( x=time, y=ga_mean_y, mode="markers", marker={"color": color_ga_pattern.format(1.0)}, name="grad. ascent y", showlegend=True, legendgroup="ga_y", ) fig.add_trace(trace_ga_y) trace_ga_y_cb = go.Scatter( x=np.concatenate([time, time[::-1]]), y=np.concatenate([ga_ci_y_upper, ga_ci_y_lower[::-1]]), fill="toself", fillcolor=color_ga_pattern.format(cb_alpha), line=dict(color=color_ga_pattern.format(0.0)), showlegend=False, legendgroup="ga_y", ) fig.add_trace(trace_ga_y_cb) trace_em_x = go.Scatter( x=time, y=em_mean_x, mode="markers", marker={"color": color_em_pattern.format(1.0)}, name="EM x", showlegend=True, legendgroup="em_x", ) fig.add_trace(trace_em_x) trace_em_x_cb = go.Scatter( x=np.concatenate([time, time[::-1]]), y=np.concatenate([em_ci_x_upper, em_ci_x_lower[::-1]]), fill="toself", fillcolor=color_em_pattern.format(cb_alpha), line=dict(color=color_em_pattern.format(0.0)), showlegend=False, legendgroup="em_x", ) fig.add_trace(trace_em_x_cb) trace_em_y = go.Scatter( x=time, y=em_mean_y, mode="markers", marker={"color": color_em_pattern.format(1.0)}, name="EM y", showlegend=True, legendgroup="em_y", ) fig.add_trace(trace_em_y) trace_em_y_cb = go.Scatter( x=np.concatenate([time, time[::-1]]), y=np.concatenate([em_ci_y_upper, em_ci_y_lower[::-1]]), fill="toself", fillcolor=color_em_pattern.format(cb_alpha), line=dict(color=color_em_pattern.format(0.0)), showlegend=False, legendgroup="em_y", ) fig.add_trace(trace_em_y_cb) fig.update_layout(xaxis_title=xlabel, yaxis_title=ylabel, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', ) return fig .. GENERATED FROM PYTHON SOURCE LINES 402-404 Set variables for plotting ########################## .. GENERATED FROM PYTHON SOURCE LINES 404-420 .. code-block:: default N = y.shape[1] time = np.arange(0, N*dt, dt) smoothed_means_ga = smoothRes_ga["xnN"] smoothed_covs_ga = smoothRes_ga["PnN"] smoothed_std_x_y_ga = np.sqrt(np.diagonal(a=smoothed_covs_ga, axis1=0, axis2=1)) smoothed_means_em = smoothRes_em["xnN"] smoothed_covs_em = smoothRes_em["PnN"] smoothed_std_x_y_em = np.sqrt(np.diagonal(a=smoothed_covs_em, axis1=0, axis2=1)) color_true = "blue" color_measured = "black" color_finite_diff = "blue" color_ga_pattern = "rgba(255,0,0,{:f})" color_em_pattern = "rgba(255,165,0,{:f})" cb_alpha = 0.3 .. GENERATED FROM PYTHON SOURCE LINES 421-423 Gradient ascent ~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 425-427 Plot true, measured and smoothed positions (with 95% confidence band) ##################################################################### .. GENERATED FROM PYTHON SOURCE LINES 427-471 .. code-block:: default measured_x = y[0, :] measured_y = y[1, :] finite_diff_x = None finite_diff_y = None smoothed_mean_x_ga = smoothed_means_ga[0, 0, :] smoothed_mean_y_ga = smoothed_means_ga[3, 0, :] smoothed_mean_x_em = smoothed_means_em[0, 0, :] smoothed_mean_y_em = smoothed_means_em[3, 0, :] smoothed_ci_x_upper_ga = smoothed_mean_x_ga + 1.96*smoothed_std_x_y_ga[:, 0] smoothed_ci_x_lower_ga = smoothed_mean_x_ga - 1.96*smoothed_std_x_y_ga[:, 0] smoothed_ci_y_upper_ga = smoothed_mean_y_ga + 1.96*smoothed_std_x_y_ga[:, 3] smoothed_ci_y_lower_ga = smoothed_mean_y_ga - 1.96*smoothed_std_x_y_ga[:, 3] smoothed_ci_x_upper_em = smoothed_mean_x_em + 1.96*smoothed_std_x_y_em[:, 0] smoothed_ci_x_lower_em = smoothed_mean_x_em - 1.96*smoothed_std_x_y_em[:, 0] smoothed_ci_y_upper_em = smoothed_mean_y_em + 1.96*smoothed_std_x_y_em[:, 3] smoothed_ci_y_lower_em = smoothed_mean_y_em - 1.96*smoothed_std_x_y_em[:, 3] fig = get_fig_kinematics_vs_time( time=time, measured_x=measured_x, measured_y=measured_y, finite_diff_x=finite_diff_x, finite_diff_y=finite_diff_y, ga_mean_x=smoothed_mean_x_ga, ga_mean_y=smoothed_mean_y_ga, ga_ci_x_upper=smoothed_ci_x_upper_ga, ga_ci_y_upper=smoothed_ci_y_upper_ga, ga_ci_x_lower=smoothed_ci_x_lower_ga, ga_ci_y_lower=smoothed_ci_y_lower_ga, em_mean_x=smoothed_mean_x_em, em_mean_y=smoothed_mean_y_em, em_ci_x_upper=smoothed_ci_x_upper_em, em_ci_y_upper=smoothed_ci_y_upper_em, em_ci_x_lower=smoothed_ci_x_lower_em, em_ci_y_lower=smoothed_ci_y_lower_em, cb_alpha=cb_alpha, color_true=color_true, color_measured=color_measured, color_finite_diff=color_finite_diff, color_ga_pattern=color_ga_pattern, color_em_pattern=color_em_pattern, xlabel="Time (sec)", ylabel="Position (pixels)") # fig_filename_pattern = "../../figures/smoothed_pos.{:s}" # fig.write_image(fig_filename_pattern.format("png")) # fig.write_html(fig_filename_pattern.format("html")) fig .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 472-474 Plot true and smoothed velocities (with 95% confidence band) ############################################################ .. GENERATED FROM PYTHON SOURCE LINES 474-518 .. code-block:: default measured_x = None measured_y = None finite_diff_x = np.diff(y[0, :])/dt finite_diff_y = np.diff(y[1, :])/dt smoothed_mean_x_ga = smoothed_means_ga[1, 0, :] smoothed_mean_y_ga = smoothed_means_ga[4, 0, :] smoothed_mean_x_em = smoothed_means_em[1, 0, :] smoothed_mean_y_em = smoothed_means_em[4, 0, :] smoothed_ci_x_upper_ga = smoothed_mean_x_ga + 1.96*smoothed_std_x_y_ga[:, 1] smoothed_ci_x_lower_ga = smoothed_mean_x_ga - 1.96*smoothed_std_x_y_ga[:, 1] smoothed_ci_y_upper_ga= smoothed_mean_y_ga + 1.96*smoothed_std_x_y_ga[:, 4] smoothed_ci_y_lower_ga = smoothed_mean_y_ga - 1.96*smoothed_std_x_y_ga[:, 4] smoothed_ci_x_upper_em = smoothed_mean_x_em + 1.96*smoothed_std_x_y_em[:, 1] smoothed_ci_x_lower_em = smoothed_mean_x_em - 1.96*smoothed_std_x_y_em[:, 1] smoothed_ci_y_upper_em= smoothed_mean_y_em + 1.96*smoothed_std_x_y_em[:, 4] smoothed_ci_y_lower_em = smoothed_mean_y_em - 1.96*smoothed_std_x_y_em[:, 4] fig = get_fig_kinematics_vs_time( time=time, measured_x=measured_x, measured_y=measured_y, finite_diff_x=finite_diff_x, finite_diff_y=finite_diff_y, ga_mean_x=smoothed_mean_x_ga, ga_mean_y=smoothed_mean_y_ga, ga_ci_x_upper=smoothed_ci_x_upper_ga, ga_ci_y_upper=smoothed_ci_y_upper_ga, ga_ci_x_lower=smoothed_ci_x_lower_ga, ga_ci_y_lower=smoothed_ci_y_lower_ga, em_mean_x=smoothed_mean_x_em, em_mean_y=smoothed_mean_y_em, em_ci_x_upper=smoothed_ci_x_upper_em, em_ci_y_upper=smoothed_ci_y_upper_em, em_ci_x_lower=smoothed_ci_x_lower_em, em_ci_y_lower=smoothed_ci_y_lower_em, cb_alpha=cb_alpha, color_true=color_true, color_measured=color_measured, color_finite_diff=color_finite_diff, color_ga_pattern=color_ga_pattern, color_em_pattern=color_em_pattern, xlabel="Time (sec)", ylabel="Velocity (pixels/sec)") # fig_filename_pattern = "../../figures/smoothed_vel.{:s}" # fig.write_image(fig_filename_pattern.format("png")) # fig.write_html(fig_filename_pattern.format("html")) fig .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 519-521 Plot true and smoothed accelerations (with 95% confidence band) ############################################################### .. GENERATED FROM PYTHON SOURCE LINES 521-565 .. code-block:: default measured_x = None measured_y = None finite_diff_x = np.diff(np.diff(y[0, :]))/dt**2 finite_diff_y = np.diff(np.diff(y[1, :]))/dt**2 smoothed_mean_x_ga = smoothed_means_ga[2, 0, :] smoothed_mean_y_ga = smoothed_means_ga[5, 0, :] smoothed_mean_x_em = smoothed_means_em[2, 0, :] smoothed_mean_y_em = smoothed_means_em[5, 0, :] smoothed_ci_x_upper_ga = smoothed_mean_x_ga + 1.96*smoothed_std_x_y_ga[:, 2] smoothed_ci_x_lower_ga = smoothed_mean_x_ga - 1.96*smoothed_std_x_y_ga[:, 2] smoothed_ci_y_upper_ga = smoothed_mean_y_ga + 1.96*smoothed_std_x_y_ga[:, 5] smoothed_ci_y_lower_ga = smoothed_mean_y_ga - 1.96*smoothed_std_x_y_ga[:, 5] smoothed_ci_x_upper_em = smoothed_mean_x_em + 1.96*smoothed_std_x_y_em[:, 2] smoothed_ci_x_lower_em = smoothed_mean_x_em - 1.96*smoothed_std_x_y_em[:, 2] smoothed_ci_y_upper_em = smoothed_mean_y_em + 1.96*smoothed_std_x_y_em[:, 5] smoothed_ci_y_lower_em = smoothed_mean_y_em - 1.96*smoothed_std_x_y_em[:, 5] fig = get_fig_kinematics_vs_time( time=time, measured_x=measured_x, measured_y=measured_y, finite_diff_x=finite_diff_x, finite_diff_y=finite_diff_y, ga_mean_x=smoothed_mean_x_ga, ga_mean_y=smoothed_mean_y_ga, ga_ci_x_upper=smoothed_ci_x_upper_ga, ga_ci_y_upper=smoothed_ci_y_upper_ga, ga_ci_x_lower=smoothed_ci_x_lower_ga, ga_ci_y_lower=smoothed_ci_y_lower_ga, em_mean_x=smoothed_mean_x_em, em_mean_y=smoothed_mean_y_em, em_ci_x_upper=smoothed_ci_x_upper_em, em_ci_y_upper=smoothed_ci_y_upper_em, em_ci_x_lower=smoothed_ci_x_lower_em, em_ci_y_lower=smoothed_ci_y_lower_em, cb_alpha=cb_alpha, color_true=color_true, color_measured=color_measured, color_finite_diff=color_finite_diff, color_ga_pattern=color_ga_pattern, color_em_pattern=color_em_pattern, xlabel="Time (sec)", ylabel="Acceleration (pixels/sec^2)") # fig_filename_pattern = "../../figures/smoothed_acc.{:s}" # fig.write_image(fig_filename_pattern.format("png")) # fig.write_html(fig_filename_pattern.format("html")) fig .. raw:: html


.. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 9.448 seconds) .. _sphx_glr_download_auto_examples_kinematics_plotEMvsGAcomparisonForagingMouse.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/joacorapela/lds_python/gh-pages?filepath=notebooks/auto_examples/kinematics/plotEMvsGAcomparisonForagingMouse.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plotEMvsGAcomparisonForagingMouse.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plotEMvsGAcomparisonForagingMouse.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_